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Chitin is an important component of the fungal cell wall with a family of chitin synthases mediating its synthesis . Here , we report on the genetic characterization of the full suite of seven chitin synthases ( MaChsI-VII ) identified in the insect pathogenic fungus , Metarhizium acridum . Aberrant distribution of chitin was most evident in targeted gene knockouts of MaChsV and MaChsVII . Mutants of MaChsI , MaChsIII , MaChsIV showed delayed conidial germination , whereas ΔMaChsII and ΔMaChsV mutants germinated more rapidly when compared to the wild-type parent . All MaChs genes impacted conidial yield , but differentially affected stress tolerances . Inactivation of MaChsIII , MaChsV , MaChsVII resulted in cell wall fragility , and ΔMaChsV and ΔMaChsVII mutants showed high sensitivity to Congo red and calcofluor white , suggesting that the three genes are required for cell wall integrity . In addition , ΔMaChsIII and ΔMaChsVII mutants showed the highest sensitivities to heat and UV-B stress . Three of seven chitin synthase genes , MaChsIII , MaChsV , MaChsVII , were found to contribute to fungal virulence . Compared with the wild-type strain , ΔMaChsIII and ΔMaChsV mutants were reduced in virulence by topical inoculation , while the ΔMaChsVII mutant showed more severe virulence defects . Inactivation of MaChsIII , MaChsV , or MaChsVII impaired appressorium formation , affected growth of in insecta produced hyphal bodies , and altered the surface properties of conidia and hyphal bodies , resulting in defects in the ability of the mutant strains to evade insect immune responses . These data provide important links between the physiology of the cell wall and the ability of the fungus to parasitize insects and reveal differential functional consequences of the chitin synthase family in M . acridum growth , stress tolerances , cell wall integrity and virulence .
The fungal cell wall is a dynamic and flexible organelle that modulates the interaction of the organism with its environment and , in the case of pathogens , acts as a critical recognition and evasion interface with host defenses [1] . Chitin , a homopolymer of β- ( 1/4 ) –linked N-acetylglucosamine , is a basic component of the fungal cell wall , and its synthesis required for normal hyphal growth and spore production [2] . Chitin fibrils and cross-linked proteins help shape and maintain the overall mechanical strength of the fungal cell , contributing to the environmental survival and virulence in pathogenic fungi [3–6] . Fungal chitin biosynthesis is catalyzed by a family of chitin synthases grouped into seven classes ( I to VII ) based on amino acid homology , with variation seen in the number of chitin synthase ( Chs ) genes seen in different fungi [2 , 7] . In the budding yeast Saccharomyces cerevisiae , three Chs genes Chs1 ( class I ) , Chs2 ( class II ) and Chs3 ( class IV ) are present [7] , whereas Chs III , V , VI and VII are found only in filamentous fungi [8] . Filamentous fungi often contain 6–10 Chs genes , e . g . the plant pathogen Ustilago maydis contains eight Chs genes ( Chs1-Chs7 and Mcs1 ) in its genome [9] . In S . cerevisiae , the three Chs genes have distinct functional roles in cell wall expansion , septum formation , and budding [10 , 11] . Chs1 ( class I ) is responsible for cell wall repair and regeneration during division as mother and daughter cells separate . Chs2 ( class II ) synthesizes chitin that is localized to the primary septum during its formation [12] . Chs3 ( class IV ) catalyzes the synthesis of the chitin ring found at the base of an emerging bud . Chs3 ( class IV ) appears to be the most catalytically active enzyme in yeast , involved in most chitin synthesis , whereas Chs1 ( class I ) and Chs2 ( class II ) synthesize relatively small amounts of chitin [13] . In the filamentous fungus , Aspergillus nidulans , deletion of ChsA ( class II ) , ChsC ( class III ) , or ChsD ( class IV ) individually did not lead to any obvious phenotypes , whereas deletion of ChsB ( class I ) resulted in defects in hyphal growth [14–16] . Variations in the functions of Chs homologues belonging to the same class in different fungi had also been reported . In the plant pathogen , Ustilago maydis , inactivation of Chs1 ( class III ) had no obvious effects on either growth or virulence [9] . However , in the rice blast fungus , Magnaporthe oryzae , inactivation of the homologous Chs1 ( class III ) resulted in significantly reduced virulence towards plant hosts [6 , 17] . In the human pathogen , A . fumigatus , the homologous ChsG ( class III ) mutant showed no defects in virulence , hypha morphology , or sensitivity to cell wall disturbing agents [18] . These data indicate that important functional divergences between homologous family members in different fungi appear to have occurred . Thus , in A . fumigatus , of eight Chs genes ( ChsA-F , csmA and csmB ) that have been identified , two MMD ( myosin motor domain ) -containing chitin synthase genes , csmA ( class V ) and csmB ( class VII ) have been shown to play important roles in fungal virulence [18–20] . In U maydis , Chs7 ( class IV ) , mcs1 ( class V ) and Chs6 ( class VII ) have been shown to impact fungal virulence , whereas the nature of the infection process affected by different Chs mutants has been shown to differ [9 , 21 , 22] . In M . oryzae , three of the seven chitin synthase genes identified have been implicated in contributing to fungal virulence , two of which belong to different classes than the Chs genes involved in U . maydis infection [6 , 23 , 24] . Little , however , is known about the function of Chs genes in other filamentous fungi , especially beyond plant and animal pathogens . Insect pathogenic fungi are emerging as a novel model system that can be used to examine unique aspects of fungal development and virulence . Genome sequences of species of the Beauveria and Metarhizium genera are available [25 , 26] , along with molecular methods for genetic manipulation , and , especially compared to animal pathogens , insect hosts are inexpensive and can readily be obtained in large quantities for experimental use . Infection of insect hosts by entomogenous fungi involves conidial ( spore ) attachment and germination on the host ( epi ) cuticle , followed by formation of exoskeleton penetrating structures ( e . g . appressoria ) [27 , 28] . The penetrating hyphae reach the insect hemocoel where they undergo a dimorphic transition to the production of yeast-like hyphal bodies that disseminate throughout the hemolymph , having evolved the ability to overcome/evade insect immune defenses [29 , 30] . The fungus then grows outwards from within the insect body , ultimately killing the host and sporulating on the cadaver [31] . Successful infection depends on the ability of the fungus to overcome host antimicrobial ( antifungal ) responses . The insect innate immune system can recognize fungal-specific components of the cell wall [32–34] and/or sense various fungal virulence factors [34–36] . In response to fungal infections , several host immune defenses can be activated including , ( i ) melanization , mediated by the prophenoloxidase pathway [37] , ( ii ) cellular immune defenses , such as the phagocytosis mediated by hemocytes [38] and nodulation [39] , and ( iii ) humoral immune defenses , that include production of antimicrobial peptides ( AMPs ) via the action of the Toll and/or immune deficiency ( Imd ) pathways [40 , 41] . Whereas most Beauveria and Metarhizium species are broad host range cosmopolitan pathogens , M . acridum displays a narrower host range towards acridids ( grasshoppers and locusts ) [26] . Here , we report on the systematic genetic dissection of the seven-member chitin synthase family ( MaChsI-VII ) in M . acridum via characterization of target single knockout mutants of each gene . Our findings demonstrate that individual MaChs genes played differential roles in fungal growth , stress responses , cell wall integrity and virulence . These data provide important links between chitin involved in fungal membrane and cell wall structure and hyphal growth , appressorium development , and fungal dimorphic transition , impacting the ability of the fungus to respond to abiotic stress as well as to successfully infect and parasitize insect hosts .
Sequence analysis showed that the M . acridum genome includes seven predicted chitin synthase genes [26] . A phylogenetic tree using functionally characterized Chs proteins from other fungi was constructed using the neighbor-joining method as detailed in the Materials and methods section . The chitin synthase genes in M . acridum were named as MaChsI-VII in accordance with their placement among orthologs ( S1 Fig ) . All the M . acridum chitin synthase genes had multiple transmembrane ( TM ) domains apart from the active site chitin synthase domain . MaChsIV and MaChsV had one cytochrome b5-like heme/steroid-binding domain ( Cyt-b5 ) upstream from Chs domain , and MaChsV and MaChsVII contained myosin motor domains ( MMD ) at their N-termini ( Fig 1A ) . The expression levels of the MaChs genes were examined by real time reverse transcription PCR ( qRT-PCR ) in conidia , hyphae , infection structures ( appressoria ) , and in cells produced inside the host during infection ( hyphal body ) . In conidia , MaChsIII and MaChsIV were the most highly expressed , followed by MaChsVII > MaChsI and MaChsV , with little to no expression of MaChsII or MaChsVI seen ( Fig 1B ) . In hyphae , MaChsVII showed the highest relative expression > MaChsIV > MaChsIII > MaChsII > MaChsI , with little expression of MaChsVI and no expression of MaChsV seen ( Fig 1C ) . During appressorium formation ( at both 8 and 20 h , p . i . ) , MaChsIII and MaChsVII were the most highly expressed followed by MaChsV then MaChsIV > MaChsI > MaChsII > MaChsVI ( Fig 1D and 1E ) . In contrast , MaChsVII showed the highest relative expression in hyphal bodies > MaChsIII > MaChsI > MaChsIV and MaChsV , with little expression of either MaChsII or MaChsVI seen in these cells ( Fig 1F ) . In order to probe the biological functions of the M . acridum Chs gene family , targeted gene disruption mutants as well as complemented strains were constructed via homologous recombination as detailed in the Materials and methods section . Gene disruption vectors were constructed to replace genomic target regions with a 900 bp cassette of phosphinothricin resistance marker ( bar ) . Complemented strains were made via ectopic insertion with promoter regions . Initial screening of putative transformants was performed by PCR followed by verification using Southern blotting ( S2–S8 Figs ) . Unless otherwise noted , complemented strains of each respective MaChs gene were identical to wild type in phenotypes examined . On 1/4 strength Sabouraud dextrose agar ( 1/4 SDAY ) medium , no growth difference was observed for the ΔMaChsI , ΔMaChsII , ΔMaChsIV and ΔMaChsVI mutants as compared to the wild-type strain ( S9 Fig ) . In contrast , the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants displayed aberrant colony phenotypes and grew markedly slower than the wild type and the corresponding complemented strains ( Fig 2 ) . The ΔMaChsIII mutant produced a compact colony with limited radial growth . This mutant showed no appreciable additional phenotypes on media supplemented with SDS , sorbitol , high salt ( NaCl ) , H2O2 , or calcofluor white , with only moderate impairment of growth seen in the presence of Congo red . Similarly , minor phenotypes were seen for the ΔMaChsV and ΔMaChsVII strains undermost conditions , with the exception of severe sensitivity to Congo red and calcofluor white . No significant phenotypes were seen for ΔMaChsI , II , IV , VI mutants under similar conditions . Only a small effect was seen in the overall vegetative growth of the ΔMaChsVII strain grown in liquid 1/4 SDY for 3 d , with no significant differences seen for any of the other mutants ( S10 Fig ) . In order to probe any effects on hyphal morphology , hyphae were examined microscopically after staining for chitin using calcofluor white . A number of distinct aberrant phenotypes in different Chs mutants were noted ( Fig 3A ) . Hyphae from the ΔMaChsII mutant were more elongated than wild-type cells , especially at the apical cell regions , with greater chitin deposition seen at hyphal tips , the latter a phenomenon seen to a lesser extent in the MaChsI mutant ( arrows in Fig 3A ) . Although overall hyphal morphology for the ΔMaChsV mutant appeared unaffected , enhanced chitin accumulation was seen at septae with dramatically increased chitin accumulation seen at the hyphal tips ( arrows in Fig 3A ) . The MaChsVII mutant showed swelling or ballooning of intercalary cells , with distortions and chitin accumulation ( mainly at tips , arrows in Fig 3A ) . Little to no noticeable changes in calcofluor white staining were seen for the MaChsIII , IV , or VI mutants . In addition , measurement of hyphal elongation rates on 1/4 SDAY confirmed that the elongation ( growth ) for the ΔMaChsII mutant was significantly faster ( P < 0 . 05 ) than wild type , while no significant differences were found between the wild type and other MaChs mutants ( Fig 3B ) . The time required for 50% of the conidia to germinate ( GT50 ) of the ΔMaChsI , ΔMaChsIII , ΔMaChsIV and ΔMaChsVII mutants were significantly longer ( P < 0 . 05 , i . e . slower germination ) than the wild type . In contrast , the GT50 of the ΔMaChsII and ΔMaChsV mutants were significantly shorter ( P < 0 . 05 ) , i . e . they germinated faster than the wild type ( Fig 4A and S11 Fig ) . Quantification of conidial yield revealed that all MaChs mutants produced significantly ( P < 0 . 05 ) less conidia than the wild type , with the most notable reductions in spore production seen in the ΔMaChsIII , ΔMaChsIV , ΔMaChsV and ΔMaChsVII mutants , with approximately 60% less conidia produced by these latter strains than the wild-type strain after 15 d of incubation . These results indicated that chitin synthesis plays an important role in fungal conidiation ( Fig 4B ) . The germination rates of the mutant strains after exposure to UV-B radiation or incubation at 45°C were also examined as a function of exposure time . The results showed that the germination rates of all strains decreased with increasing of treatment time . For heat shock ( 45°C ) , the exposure time required for 50% inhibition of germination ( IT50 ) of the wild type was significantly higher ( P < 0 . 05 ) than that of the ΔMaChsIII , ΔMaChsV , ΔMaChsVI and ΔMaChsVII mutants , while lower than that for the ΔMaChsII mutant ( Fig 4C ) . These results indicated that inactivation of MaChsIII , MaChsV , MaChsVI and MaChsVII increased fungal sensitivity to heat , while disruption of MaChsII decreased fungal sensitivity to heat . With respect to exposure to UV-B irradiation , the IT50 of the wild type was significantly higher than that of the ΔMaChsIII and ΔMaChsVII mutants , while lower than that of the ΔMaChsIV mutant , with no significant differences seen for the other MaChs mutants ( Fig 4D ) . These results indicated that inactivation of MaChsIII and MaChsVII increased fungal sensitivity to UV-B treatment , while disruption of MaChsIV decreased fungal sensitivity to UV-B treatment . To investigate the contributions of individual M . acridum chitin synthases on cell wall structure , the ability of conidia derived from the various mutant strains to resist distortion during application of a centrifugal force was investigated as detailed in the Materials and methods section , the results revealed that ~30% to 50% of the conidia derived from the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants were distorted or broken , whereas ~90% of the conidia of the wild type and remaining mutant ( ΔMaChsI , II , IV , VI ) strains were still intact ( Fig 5A ) . In addition , examination of the cell walls of conidia by TEM revealed significant changes in cell wall thickness , with 1 . 63- ( P < 0 . 01 ) , 2 . 58- ( P < 0 . 01 ) , 2 . 40- ( P < 0 . 01 ) , 4 . 48- ( P < 0 . 01 ) and 2 . 28- ( P < 0 . 01 ) fold decreases in cell wall thickness seen for the ΔMaChsII , ΔMaChsIII , ΔMaChsIV , ΔMaChsV and ΔMaChsVII mutants , respectively , as compared to the wild type and each respective complemented strain ( Fig 5B and 5C ) . In order to examine the extent to which altered chitin synthesis in any of the mutants affect fungal cell wall carbohydrate components , the overall chitin , β-1 , 3-glucan , and mannoprotein contents in the cell walls of all of the strains were determined . Loss of either the MaChsI , MaChsII , or MaChsIV chitin synthases did not significantly alter chitin , β-1 , 3-glucan , or mannoprotein levels in the cells as compared to the wild type parent . In contrast , significant changes in these cell wall components were seen in mutants of the MaChsIII , V , VI , and VII genes . Chitin levels were reduced by 37% in the ΔMaChsVI mutant compared to the wild type ( Fig 5D ) , whereas both chitin and β-1 , 3-glucan levels in the ΔMaChsV and ΔMaChsVII mutants cell walls were significantly higher than those of wild-type cells ( Fig 5E ) . Total mannoprotein content was reduced slightly ( ~10% ) in the ΔMaChsVI mutant but was increased ( ~20% ) in the ΔMaChsIII mutant as compared to wild type levels ( S12 Fig ) . To determine contributions of the MaChs genes in fungal virulence , two types of bioassays were employed , namely , ( 1 ) topical inoculation requiring cuticle penetration and representing the natural route of parasitic infection , and ( 2 ) intrahaemocoel injection of conidia , thus bypassing the cuticle penetration stage , but requiring the fungus to retain the ability to evade host hemolymph defenses . In both topical inoculation and intrahaemocoel injection bioassays , no significant changes in virulence as measured by the mean lethal time to kill ( LT50 ) were seen for the ΔMaChsI , ΔMaChsII , ΔMaChsIV , or ΔMaChsVI mutants ( S13 Fig ) . However , the ΔMaChsIII and ΔMaChsV mutants exhibited significantly ( P < 0 . 05 ) reduced virulence in topical inoculation assays ( Fig 6A and 6B ) . Interestingly , disruption of MaChsIII had no effect when the conidia were directly injected into the host hemocoel , however for the MaChsV mutant a small , but significant ( P < 0 . 05 ) increase in fungal virulence was seen in these assays ( Fig 6C and 6D ) . Unlike the other MaChs mutants , the ΔMaChsVII mutant was essentially non-pathogenic when applied topically ( Fig 6A ) and significantly reduced ( ~81% , P < 0 . 01 ) in virulence by intrahaemocoel injection ( Fig 6C and 6D ) . These data demonstrate that MaChsIII and MaChsV are both involved in fungal virulence during the normal infection via cuticle penetration , and MaChsVII play important roles in mediating virulence in M . acridum . To further probe potential mechanisms underlying the reduced virulence seen in the ΔMaChsIII and ΔMaChsV mutants , and the near loss of pathogenicity in ΔMaChsVII mutant seen in topical inoculations of these mutants , conidial surface hydrophobicity , conidial germination and appressorium formation on locust hind wings were examined . Conidial hydrophobicity assays revealed significant ( P < 0 . 01 ) reductions in spore hydrophobicity for the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants as compared to the wild type , with little to no changes seen for the other mutants ( Fig 7A and S14 Fig ) . The GT50 on locust wings of ΔMaChsIII and ΔMaChsVII mutants was significantly higher than the wild type , while the GT50 of the ΔMaChsV mutant was lower than the wild type ( Fig 7B ) . Measurement of appressorium formation on locust wings revealed defects in the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants as compared to the wild type and corresponding complemented strains . The percentage of appressoria forming from germinated conidia after 48 h of incubation was ~53 , 61 , and 15% for the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants , respectively , whereas greater than 70% of the germinated wild-type conidia formed appressoria under identical conditions ( Fig 7C ) . In addition to decreased appressorium formation , various morphological defects could be seen in MaChs mutants . Distortions , including smaller and deformed structures were seen for ΔMaChsIII appressoria , and irregular formations , i . e . a single conidium forming multiple appressoria , cell-expansion and abnormal conidial shapes , and narrower appressoria were seen for in ΔMaChsV mutant appressoria ( Fig 7D ) . Finally , ΔMaChsVII mutant formed aberrant and longer germ tubes as compared to wild type ( Fig 7D , in all cases complemented mutants were similar to wild type ) . Moreover , the turgor pressure of appressoria from ΔMaChsV and ΔMaChsVII mutants was significantly reduced ( P < 0 . 05 ) compared to that of wild type ( Fig 7E ) . The relative expression of Pr1A in conidia of the ΔMaChsIII , ΔMaChsV , ΔMaChsVII mutants were significantly lower than in wild type ( P < 0 . 01 ) . In appressoria , lower Pr1A expression in ΔMaChsVII mutant and higher Pr1A expression in the ΔMaChsV mutant were found compared to wild type ( P < 0 . 01; Fig 7F ) . In order to examine whether any of the chitin synthases affected fungal growth in insecta , the production of hyphal bodies during infection was examined . After topical inoculation , no significant differences were found in the concentration of fungal hyphal bodies between the different fungal strains tested 3 d post-inoculation as determined by quantification of fungal DNA ( P > 0 . 05; Fig 8A ) . At 5 d post-inoculation , however , significant reductions in total fungal concentrations were observed for the ΔMaChsV or ΔMaChsVII mutants as compared to the wild type ( P < 0 . 05; Fig 8A and 8B ) . In intrahemocoel injection assays , treatment with the ΔMaChsVII mutant resulted in significantly lower fungal growth 3 and 5 d post-injection as compared to the wild type parent ( P < 0 . 05; Fig 8A and 8B ) , whereas higher total fungal growth was seen for the ΔMaChsV mutant 5 d post-injection ( P < 0 . 05; Fig 8A and 8B , note complemented mutants were similar to wild type in fungal proliferation in the locust in all experiments tested ) . In addition , growth of fungal cells cultured in in vitro on locust hemolymph without hemocytes was assessed by real time PCR ( qPCR ) . These data indicated that growth of ΔMaChsV was significantly faster than that of wild type at 3 d after incubation , while the growth of ΔMaChsVII was significantly slower than that of the wild type ( P < 0 . 05; Fig 8C ) . As indicated previously all complemented mutants were essentially identical to the wild type and mutants not indicated were also similar to wild type . In order to probe whether the MaChs genes contributed to the ability of the fungus to evade specific immune defenses three aspects of the host immune reaction were examined; ( 1 ) the expression of Defensin and Attacin , Toll- and/or Imd-activating antimicrobial peptide genes , in the insect fat bodies , ( 2 ) nodule formation was quantified after fungal infection , and ( 3 ) phenoloxidase ( PO ) activity was quantified in the insect hemolymph . At 24 h after post-injection of fungal conidia , Defensin expression was elevated ( ~2–3 fold ) to similar levels by the wild type and ΔMaChsIII and ΔMaChsV mutants as compared to mock treated controls , however , Defensin levels were induced ~5–6 fold above the wild type levels by the ΔMaChsVII mutant ( Fig 9A ) . Under similar conditions , no changes in Attacin expression was seen ( Fig 9A ) . In topical bioassays , a slightly different pattern of Defensin and Attacin expression was seen . At 24 h post-topical inoculation , higher expression of both Defensin and Attacin were detected in locusts infected with the ΔMaChsV and ΔMaChsVII mutants compared to control , whereas no significant change was found in those infected by the wild type and ΔMaChsIII strains ( Fig 9B ) . At 30 h post-topical inoculation , the expression of Defensin in locusts infected by fungal strains was higher than that of control , and with Defensin expression in locusts infected with the ΔMaChsIII mutant the highest . However , at this time point , no significant changes of the Attacin expression was found among the locusts infected by the different fungal strains and control , with exception of a slight but significant ( P < 0 . 01 ) , lower expression of the gene in locusts infected by the ΔMaChsVII mutant ( Fig 9B ) . Examination of nodule formation after infection revealed a wide distribution of nodules formed on the inner body walls of the insect after injection of fungal conidia ( Fig 10A ) . Quantification of the number of nodules formed indicated an increase after infection by the ΔMaChsVII mutant ( 173 ± 8 nodules/locust ) as compared to the wild type ( 137 ± 10 nodules/locust ) on the ventral diaphragm ( P < 0 . 01; Fig 10B ) . The ΔMaChsV showed a slight reduction in nodule formation that was , however , not significant ( P > 0 . 05 ) . Quantification of host phenoloxidase ( PO ) activity revealed elevated PO activity for the ΔMaChsV and ΔMaChsVII mutants , 8 h post-intrahemocoel injection , however , only the former was significant ( P < 0 . 05 ) at this time point ( Fig 10C ) . By 12 h post-inoculation , PO activity for both mutants ( ΔMaChsV and ΔMaChsVII ) were significantly increased ( P < 0 . 01 for ΔMaChsV and P < 0 . 05 for ΔMaChsVII ) , above wild type . No significant changes in nodulation or PO activity were seen for the ΔMaChsIII mutant . In order to determine examine alterations in cell surface carbohydrates that could potentially contribute to the virulence phenotypes of the MaChs mutants , the surface carbohydrates of those displaying altered virulence , were examined using a series of fluorescently labeled lectins and antibodies . These included probing using wheat germ agglutinin ( WGA ) , Concanavilin A ( ConA ) , a β-1 , 3-glucan antibody , and an α-1 , 3-glucan antibody . Microscopic and flow cytometry analyses revealed that conidia from the ΔMaChsV and ΔMaChsVII mutants decreased WGA and ConA binding , as well as a slight decrease in staining with the α-1 , 3-glucan , but no changes in binding of the β-1 , 3-glucan antibody as compared to conidia derived from the wild-type strain ( Fig 11A and 11B ) . Examination of the surface carbohydrates of the fungal hyphal bodies isolated from infected insects indicated increased WGA reactivity towards ΔMaChsIII and ΔMaChsVII hyphal bodies , with decreased ConA binding as compared to the wild type ( Fig 11C ) . In addition , hyphal bodies derived from the ΔMaChsV mutant showed increased fluorescence intensity when stained with the β-1 , 3-glucan antibody ( Fig 11C ) .
Chitin , a biopolymer consisting of β-1 , 4-linked N-acetylglucosamine , is an important component of the fungal cell wall and the exoskeletons of insects but is not found in the cellular membranes of plants or animals [42] . Chitin synthases play important roles in fungal cell wall stability , yeast growth , filamentous hyphal development and fungal pathogenicity [17 , 43] . To date , chitin synthases have been examined in only a few fungal species and have not been systemically studied in insect pathogenic fungi . In this study , all seven chitin synthase genes identified in the locust-specific entomopathogenic fungus , M . acridum , were characterized . The MaChs mutant phenotypes have been summarized ( S2 Table ) . Results revealed that the seven chitin synthase genes of M . acridum perform important functions in growth and development , stress tolerances , as well as in cell wall integrity and virulence . All of the MaChs genes with the exception of MaChsVI were found to contribute to the ability of fungal conidia to germinate . Disruption of MaChsIII , MaChsV , MaChsVI and MaChsVII also affected fungal vegetative growth , and all of the MaChs genes were found to positively contribute to the production of conidia . All of the MaChs genes with the exception of MaChsI were involved in fungal stress tolerances . MaChsIII , MaChsV and MaChVII were involved in the fungal cell wall integrity and the ability of conidia to withstand biophysical pressure . MaChsIII , MaChsV and MaChVII also contributed to fungal virulence . Deletion of MaChsIII , MaChsV , or MaChsVII led to defects in fungal appressorium formation and/or impaired the ability of the fungus to undergo a dimorphic transition allowing it to grow in the insect hemolymph . Loss of MaChsIII , MaChsV , or MaChsVII also led to enhanced ability of the insect immune system to recognize and respond to the invading fungus , presumably due to alterations of conidial surface structures including changes in cell wall carbohydrates: This was supported by lectin and antibody binding experiments data that also suggest that β-glucans are exposed in hyphal bodies of ΔMaChsV . The fungal cell wall acts as the barrier protecting the cell from external stress and maintains the shape of the cell [44] . In many fungal species , chitin can contribute more than 50% of the dry weight of the cell wall [45] and is thought to be essential for fungal cell wall integrity and cell wall rigidity . In addition , chitin is found at the septal membranes that separate cells in other filamentous fungal multicellular structures . Consequently , the enzymes responsible for chitin synthesis , i . e . chitin synthases , can be expected to play critical roles in the maintenance of fungal cell wall integrity . However , the observation of multiple chitin synthase genes in all fungi , suggests that some may have specialized roles in cell wall architecture . In M . oryzae , the Chs2 ( class II ) mutant had increased sensitivity to cell wall disrupting agents [6] . In Botrytis cinerea , disruption of Bcchs1 ( class II ) resulted in cell wall weakening [46] . In C . graminicola , the class VII CHS gene contributes to cell wall integrity of conidia and vegetative hyphae [47] . In this study , conidia of the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants became more fragile than those of the wild type . Imaging of the cell wall by TEM showed that ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants had much thinner cell walls than wild-type cells . In addition , disruption of MaChsIII , MaChsV and MaChsVII led to significant changes in cell wall composition compared with other strains . These results demonstrated that MaChsIII , MaChsV and MaChVII are involved in the fungal cell wall integrity . The thinner cell walls and significant changes of cell wall composition in ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants may result in the cell wall become more fragile . Disruption of cell wall biosynthetic genes or treatments with cell wall perturbing agents often results in compensatory alterations in the cell wall , such as enhancing the synthesis of the cell wall polymers , in an attempt to maintain cellular integrity [2] . Here , we measured the levels of the major carbohydrate constituents of the cell walls of mutants of the Chs genes . Levels of β-1 , 3-glucan in the ΔMaChsIII , ΔMaChsV , and ΔMaChsVII mutants and chitin levels in the ΔMaChsV and ΔMaChsVII mutants significantly higher than those of the wild-type strain . It is possible that the cell wall integrity defects in the ΔMaChsIII , ΔMaChsV , and ΔMaChsVII mutants resulted in compensatory effects in which the cell increased cell wall constituents in an attempt to maintain cellular membrane integrity . Fungal cells containing higher chitin levels are usually less resistant to cell wall disturbing agents such as calcofluor white , whereas cells with lower chitin levels are more resistant to cell wall disturbing agents [48 , 49] . In Aspergillus fumigatus , the ΔChsA/C/B/G and ΔChsG mutants were able to withstand high concentrations calcofluor white or Congo red , becoming hyper-resistant to cell wall disturbing agents [18] . In this study , increased fungal cell wall chitin levels seen in the ΔMaChsV and ΔMaChsVII mutants resulted in greater sensitivity to calcofluor white than the wild type parent . Conidial yield , stress tolerances , and virulence are very important parameters for entomopathogenic fungi with respect to biological control applications of the fungus [50–52] . Previous works in other filamentous fungi have shown that chitin synthesis impacts fungal conidiation . In A . nidulans , ChsA ( class I ) , ChsC ( class III ) and ChsD ( class IV ) are involved in the regulation of conidiation [14 , 53 , 54] . In A . fumigatus , the quadruple ΔChsA/C/B/G deletion mutant formed abnormal vesicles and showed a drastic reduction in conidiation . The two MMD ( myosin motor domain ) -containing chitin synthase genes CsmA ( ChsV ) and CsmB ( ChsVII ) also play important roles in conidiogenesis [18] . In Magnaporthe oryzae , Chs1 ( class III ) mutant produced pear-shaped , single-celled conidia , while other Chs mutants formed three-celled pyriform conidia with normal morphology . The conidiation in Chs2 ( class II ) and Chs6 ( class V ) mutants were decreased over 5-fold compared with their parental strain [6] . In this study , similar results were found in that all the MaChs mutants exhibited defects in conidiation , especially the ΔMaChsIII , ΔMaChsIV , ΔMaChsV and ΔMaChsVII mutants displayed significant reductions in conidial yield ( ~60% ) . This observation is consistent with the MaChsIII , MaChsIV and MaChsVII showing higher expression levels than the other MaChs genes during the conidia formation stage . As potential pest biological control agent , tolerances to abiotic stress including heat and UV-B irradiation are critical for entomopathogenic fungi to survive in various environments [28 , 55] . Our data showed that MaChs genes with the exception of MaChsI are involved in fungal stress tolerances . The ΔMaChsIII , ΔMaChsV , ΔMaChsVI , and ΔMaChsVII mutants displayed significantly increased sensitivity to heat shock , and the ΔMaChsIII and ΔMaChsVII mutants were more sensitive to UV-B radiation . In addition , the ΔMaChsII and ΔMaChsIV mutants showed decreased sensitivity to heat shock and UV-B radiation , respectively . Changes in the chitin content in the cell wall of various fungi have been shown to result in defects in appressorium formation and hyphal growth , as well as increased susceptibility to host immune defenses [6 , 9 , 18 , 20] . In M . oryzae , the Chs1 ( class III ) and Chs7 ( class VI ) genes play important roles in appressorium formation . The ΔChs1 ( class III ) and ΔChs7 ( class VI ) mutants were reduced in virulence [6] . The Chs6 ( class V ) of M . oryzae plays critical roles in penetration and development invasive hyphae in plant cells , and deletion of Chs6 , resulted in avirulence [6] . Our data show that disruption of the MaChsI , MaChsII , MaChsIV , or MaChsVI did not affect fungal virulence , whereas inactivation of MaChsIII or MaChsV significantly reduced fungal virulence and deletion of MaChsVII essentially resulted in loss of pathogenicity . The ΔMaChsIII , ΔMaChsV , and ΔMaChsVII mutants showed defects in appressorium formation , structures which play a critical role in the initiation of fungal infection of the insect [56] . The appressorium morphology of the ΔMaChsV and ΔMaChsVII mutants was aberrant , likely impacting the ability of these structures to penetrate the insect cuticle . This observation is consistent with the observation that the MaChsIII , MaChsV , and MaChsVII genes were more highly expressed in the appressorium formation stage as compared to the other MaChs genes . The appressoria of fungal pathogen need very rigid cell walls to control the high osmotic pressure in the stage of infection [43] . In this study , the ΔMaChsV and ΔMaChsVII mutants exhibited lower appressorial turgor pressure as compared to the wild type . Previous research has shown that weakening of the cell wall can lead to reduced fungal virulence [4 , 57] . Here , deletion of the MaChsIII , MaChsV , or MaChsVII genes resulted in fragility of the cell wall of M . acridum . After reaching the insect hemocoel , the penetrating hyphae undergo a dimorphic transition producing hyphal bodies that spread and grow within the insect [29 , 30] . Hyphal body growth of the ΔMaChsVII mutant was reduced , consistent with its decreased virulence in both topical and intrahemocoel injection assays . In contrast , the hyphal body growth for the ΔMaChsV mutant was increased in the insect hemocoel . Intriguingly , for this mutant , virulence was decreased in topical bioassays , but was slightly higher than the wild type in injection assays . One explanation for this observation is that disruption of MaChsV reduced the formation and/or turgor pressure of appressoria , resulting in impaired ability of the fungus to penetrate the cuticle . Thus , this mutant , appears to have a specific deficiency in penetration ( as seen for several other genes , e . g . a bifunctional catalase-peroxidase [50] ) , but once the cuticle is bypassed can grow quickly ( as indicated by the hyphal body proliferation data ) . Humoral immunity is considered the dominant immune response of insects [58] , and includes the Toll and Imd signal pathways [59] . Attacin and Defensin are antimicrobial peptides present in locusts that are regulated by the Imd and Toll pathways [60–63] . Our data indicate that when compared to wild type , the ΔMaChsV and ΔMaChsVII mutants were unable to block certain aspects of the insect humoral immune response . After topical inoculation , higher expression levels of both Attacin and Defensin in locusts infected by the ΔMaChsV and ΔMaChsVII mutants suggest activation of Imd and Toll pathways , that can at least partially account for the decreased virulence seen for these mutants . In injection assays , only Defensin appeared up-regulated in response to the ΔMaChsVII mutant . Not too surprisingly , these data suggest that the mode of infection can affect the ability of the Toll/Imd pathways to recognize and/or respond to the fungal pathogen . It should be borne in mind that topical application represents that “natural” route of infection and would represent the likely ability of the host to respond to the pathogen . In addition , an increase in the number of nodules was seen in infections with the ΔMaChsVII mutant indicated that the insect cellular immune response was also active and hence provides additional explanation for the reduced virulence seen for this mutant . The systemic immune response is triggered not only by the cell wall compounds , but also by the enzymatic activity of fungal proteases [34–36] . The expression levels of the Pr1A protease gene in conidia and appressoria revealed lower expression in the chitin synthase mutants as compared to the wild type , with exception of appressoria derived from the ΔMaChsV mutant . On the cell wall , fungal-specific components are ideal targets recognized by the host innate immune system [64 , 65] . Deletion of the MaChsIII , MaChsV , or MaChsVII genes resulted in altered cell surface carbohydrates potentially leading to increased detection in the insect’s immune system . Cell wall components , including hydrophobins and collagen-like protein , can shield the fungus from host recognition [66–68] . Conidial surface hydrophobicity was decreased in the ΔMaChsIII , ΔMaChsV , and ΔMaChsVII mutants . Since some cell wall components are fungal-genera specific [64] , they are ideal targets for recognition by the host innate immune system . In this respect , most mannans are recognized by many membrane-bound receptors that include the mannose receptor , DC-SIGN or SIGN-R1 , galectin 3 , Dectin-2 , TLR4 , and/or Mincle [69–71] . Cell wall mannan exposure in conidia from the ΔMaChsV and ΔMaChsVII mutants was increased , while mannan exposure was decreased in hyphal bodies of these mutants . The presence of α-1 , 3-glucan is known to mask certain fungal pathogens from host immune recognition [72] . Fluorescence detection and flow cytometry data showed that the conidia of ΔMaChsV and ΔMaChsVII mutants had decreased α-1 , 3-glucan , while no obvious differences of this polymer were found among the hyphal bodies from all fungal strains . Thus , although the ability to detect the original infectious cells ( conidia ) may remain , the hyphal bodies produced appear to be still capable of shielding themselves ( at least partly ) . β-1 , 3-glucan is recognized by insect host immune system and can activate the host Toll pathway [34] . In this work , the increased β-1 , 3-glucan antibody binding , and hence presumably increased β-1 , 3-glucan exposure were seen in hyphal bodies derived from the ΔMaChsV mutant ( no significant changes were found on conidia from all fungal strains ) . This result is puzzling , as this strain displays increased virulence in injection assays and increased hyphal body proliferation . At present we cannot account for this observation , with the possibility that other compensating factors may be able to overcome any potential negative consequences of the potential for increased immune activation by these cells . Chitin can also stimulate different host immune pathways [2 , 73 , 74] . An increase in chitin exposure was seen for conidia of the ΔMaChsV and ΔMaChsVII mutants and in hyphal bodies of the ΔMaChsIII and ΔMaChsVII mutants , consistent ( but not necessarily accounting for ) the decreased virulence of these mutants . Overall , the changes of surface carbohydrates in conidia and hyphal bodies of the ΔMaChsIII , ΔMaChsV and ΔMaChsVII mutants may lead to the enhanced insect immune responses , with the increased virulence of the ΔMaChsV mutant in injection assays , the outlier finding . Alterations in surface carbohydrates in hyphal bodies as opposed to conidia has been previously observed [29 , 30] , with our data indicating that chitin synthases contribute to this process . In summary , members of chitin synthase family in M . acridum , a model entomopathogenic fungus , were systematically characterized . As a specialized insect pathogen , M . acridum possess distinct mechanisms for infection of their locust hosts [60 , 75] . Our results indicate that different chitin synthase genes of M . acridum play diverse roles in growth , stress tolerances , as well as in cell wall integrity and virulence . MaChsIII , MaChsV , and MaChsVII , were found to contribute to fungal virulence . Disruption of MaChsIII , MaChsV and MaChsVII leads to defects in fungal appressorium formation and blocks the ability of the fungus to inhibit insect immune recognition/activation to fungus that could in part be due to increased exposure of recognition signals on the surface of conidia and/or hyphal bodies . These data provide new insights into the multifunctional roles of chitin synthases in fungal development and virulence .
M . acridum CQMa102 ( China General Microbiological Culture Collection Center , CGMCC , No . 0877 ) , and all constructed strains were routinely cultured in 1/4 SDAY ( 1% dextrose , 0 . 25% mycological peptone , 0 . 5% yeast extract and 2% agar , w/v ) plates for 3–15 days at 28°C for conidial production [76] , as well as on potato dextrose agar ( PDA ) ( Solarbio , Beijing , China ) or Czapek-dox agar ( CZA , 3% sucrose , 0 . 3% NaNO3 , 0 . 1% K2HPO4 , 0 . 05% KCl , 0 . 05% MgSO4 and 0 . 001% FeSO4 plus 1 . 5% agar ) as needed . Escherichia coli DH5α and Agrobacterium tumefaciens AGL-1 were used for DNA manipulations and transformations and routinely grown in Luria-Bertani ( LB ) broth . Accession numbers for the seven M . acridum chitin synthases are as follows; MaChsI ( XP_007814354 . 1 ) , MaChsII ( XP_007814507 . 1 ) , MaChsIII ( XP_007810158 . 1 ) , MaChsIV ( XP_007813853 . 1 ) , MaChsV ( XP_007814979 . 1 ) , MaChsVI ( XP_007814966 . 1 ) and MaChsVII ( XP_007814978 . 1 ) . The BLAST program was used to select the fungal Chs protein on the NCBI website ( www . ncbi . nlm . nih . gov ) . Phylogenetic dendrograms were constructed for the Chs protein sequences retrieved from the genomes of M . oryzae ( Mo ) , Candida albicans ( Ca ) , S . cerevisiae ( Sc ) , U . maydis ( Um ) , A . fumigatus ( Af ) and M . acridum ( Ma ) by neighbor-joining method using MEGA ( ver . 7 . 0 ) ( http://www . megasoftware . net ) with a bootstrap test of 1 , 000 replicates . Construction of targeted gene disruption vectors for each of the 7 M . acridum Chs genes followed a similar plan . Briefly , 5′ and 3′ flanking sequences of each gene were cloned into pK2-PB [77] to create pK2-PB-MaChs-5′-bar-3′ vectors ( the bar gene flanked by the cloned sequences ) . The 5′ and 3′ flanking sequences of each MaChs gene was amplified from M . acridum genomic DNA by PCR using primer pairs listed in S1 Table . Restriction cloning sites and primers were used for PCR verification as given for each gene ( S1 Table ) . Final vectors were confirmed by sequencing and the gene disruption vector was mobilized into A . tumefaciens AGL-1 for transformation in M . acridum . Fungal genomic DNA was prepared using Fungal DNA Kit ( Omega , Beijing , China ) . Complementation vectors for each gene were constructed using the pK2-sur platform conferring resistance to chlorimuron ethyl [78] . For each Chs gene , the entire ORF ( 1 . 5–2 . 5 kb ) as well as promoter ( ~2 kb ) and terminator ( ~1 kb ) sequences were amplified by PCR using M . acridum genomic DNA as the template and primer pairs as given ( S1 Table ) . The resulting PCR products were digested with EcoRV/BamHI , and inserted into the pK2-sur . Vectors were checked by sequencing and the resultant plasmids were transformed into A . tumefaciens AGL-1 for transformation into each respective Chs mutant strain for complementation . Fungal transformation using A . tumefaciens was performed as described previously [79] . The MaChs disruption mutants were initially selected CZA supplemented with 500 μg ml-1 glufosinate ammonium resistance . Initial screens of putative MaChs-disruption transformants were performed using PCR using primer pairs MaChs-VF/Pt-R2 and MaChs-VR/Bar-F2 as listed for each gene in S1 Table . Complemented transformants were initially selected on CZA containing 20 μg ml-1 chlorimuron ethyl and confirmed by PCR using the primer pair of Sur-F/Sur-R ( S1 Table ) . Southern blotting was used to verify the integrity of select disruption and complemented mutant strains . The expression levels of the MaChs genes were analyzed by qRT-PCR . Total RNA was isolated from M . acridum conidia , hyphae , appressoria and hyphal bodies . Conidia were harvested from 1/4 SDAY plates after 15 d of growth . Hyphae were harvested from 1/4 SDAY plates after 18 h of growth . Appressoria were collected from wings of Locusta migratoria manilensis at 8 h , 20 h and 30 h post-inculation . Briefly , 5 locust wings were immersed in 2 ml of 1×108 conidia ml-1 conidial suspension for 10 min and rinsed with sterile water , and the entire sample used for total RNA extraction . Hyphal bodies were collected from infected insect hemolymph at 5 d after topical inoculation as described [77] . Total RNA was extracted using an RNeasy Mini Kit ( QIAGEN , Hilden , Germany ) . cDNA was synthesized from 1 μg of DNaseI-treated total RNA with oligo-dT primer in 20 μl PrimeScript RT Master Mix ( TaKaRa , Dalian , China ) . Real-time PCR was performed using the SYBR-Green PCR Master Mix kit ( Bio-Rad , USA ) in the iCycler PCR System ( Bio-Rad , USA ) . The relative transcript levels of each gene were quantified using the 2−ΔΔCT method [80] . The M . acridum gpdh ( GenBank accession number: EFY84384 ) , a gene encoding glyceraldehyde 3-phosphate dehydrogenase , was used as an internal control . All PCR amplifications were conducted in triplicate and the entire experiment repeated with a biological duplicate sample . Fungal growth assays examining stress sensitivity were performed as follows: conidia from 15 d 1/4 SDAY plates were harvested and suspended in doubled-distilled H2O and final concentration of conidial suspension was adjusted to 1×106 conidia ml-1 . Aliquots of conidial suspensions ( 2 μl , 1×106 conidia ml-1 ) were spotted with a micropipette onto the center of various plates including 1/4 SDAY amended with Congo red ( CR; 500 μg ml-1 ) , calcofluor white ( CFW; 50 μg ml-1 ) , H2O2 ( 6 mmol l-1 ) , SDS ( 0 . 1 g l-1 ) , sorbitol ( 1 mol l-1 ) and NaCl ( 0 . 5 mol l-1 ) . Plates were incubated at 28 oC for 5–7 d and colony diameters quantified [77] . Three replicate plates were used for each condition/experiment and the entire experiment repeated with 1–2 independent batches of conidia . Conidial yield was estimated on fungal colonies grown in 24-well microtiter plates . Each well contained 1 ml 1/4 SDAY and was inoculated with 2 μl conidial suspension ( 1×106 conidia ml-1 ) and incubated for 15 d at 28 oC . Conidia were harvested from wells by flooding with 3 ml water containing 0 . 5% Tween 20 and the subsequent cells in the suspension quantified by counting using haemocytometer . For conidial germination assays an aliquot of 100 μl of 1×107 conidia ml-1 was spread on 1/4 SDAY and incubated at 28 oC . Conidial germination was examined microscopically every 2 . 5 h after staining with CFW buffer [81] . Cells were photographed using a fluorescence microscope ( Olympus BX51 , Tokyo , Japan ) . Three replicate wells were used for each fungal strain and the entire experiment repeated using a separate batch of fungal conidia . Fungal hyphal elongation rates were measured using an inverted microscope ( Nikon Ti-E , Tokyo , Japan ) . Images were captured at 2 h intervals from 10 h to 18 h after inoculation , and analyzed with the NIS-Elements BR3 . 2 software . Fungal tolerances to heat shock and ultraviolet radiation ( UV-B ) were tested as described [82] . Briefly , a 100-μl aliquot of conidial suspension at a concentration of 5×106 conidia ml-1 was transferred to sterile eppendorf tubes and immediately placed in a water bath at 45 oC for 2 , 4 , 6 and 8 h . After exposure , a 20-μl aliquot was spread evenly on PDA agar . Plates were incubated at 28 oC for 24 h . With respect to ultraviolet radiation ( UV-B ) tolerances , fungal cells were spread evenly onto 1/4 SDAY plates . The plates were immediately exposed to irradiances of 1350 mW m-2 for 2 , 4 , 6 and 8 h , respectively . After irradiation , the plates were incubated in darkness at 28 oC for 24 h . The germination rates were calculated via microscopic observation . Samples were analyzed by transmission electron microscopy ( TEM ) as described [83] . Briefly , conidia were collected from 1/4 SDAY plates 28°C for 15 days and washed three times with phosphate buffer solution ( PBS , pH 7 . 4 ) . Following centrifugation , conidia were fixed with 4% glutaraldehyde at 4°C overnight . Fixed samples were washed with 0 . 1 M PBS buffer three times and fixed with 2% osmium tetroxide in 0 . 1 M PBS for 2 h at room temperature , followed by dehydration in the gradients of 50–90% ethanol and 100% acetone . Samples were embedded in resin and ultrathin sectioned . Sections were stained with uranyl acetate and lead citrate and observed on the TEM ( Hitachi H-7500 , Tokyo , Japan ) . From TEM images , the cell wall thickness was measured from 3 to 5 ultrathin sections of conidia using the Nis-elements BR3 . 2 software ( Nikon ) . Fungal strains were grown in 50 ml 1/4 SDY at 28 oC for 2 days before harvesting of the growing hyphae by centrifugation ( 6 , 000 × g , 3 min ) . Cell were washed three times with 30 ml of 2% SDS and total chitin content was determined by acid hydrolysis of the fungal cell wall as described [84] . Total β-1 , 3-glucan content was determined by degradation of the alkali-insoluble fraction of the cell wall in reaction mixtures containing 1 mg ml-1 zymolyase 100T as described previously [83] . Mannoproteins were extracted with 1 M NaOH at 100°C from cell walls and measured using Folin’s reagent and bovine serum albumin as the standard [85] . All experiments were repeated three times with independent batches of growing cells . To test the fungal cell wall integrity , 500 μl of freshly harvested conidial suspensions ( 1×107 conidia ml-1 ) in 1 . 5 ml eppendorf tubes were centrifuged at 12 , 000 × g for 5 min . After centrifugation , the concentration ( C1 ) of intact conidia was determined using a hemocytometer . Conidia with normal shape were considered as intact conidia . The fragility was calculated using the following equation: ( 1×107–C1 ) /1×107 . Conidial hydrophobicity was measured using the microbial adhesion to hydrocarbons assay as described with slight modifications [86] . Briefly , conidia were harvested from 1/4 SDAY after 15 d and washed into reaction buffer ( 0 . 2 g MgSO4 , 1 . 8 g urea , 7 . 26 g KH2PO4 , 22 . 2 g K2PO4 per L , pH 7 . 1 ) . Conidial suspensions were adjusted to 1×107 conidia ml-1 and 3 ml dispensed into 5 ml tubes . To each tube , 300 μl hexadecane was added , the sample was mixed thoroughly on a vortex mixer ( three times for 30 s ) and then allowed to equilibrate at room temperature for 15 min after which the organic ( hexadecane ) layer removed . To remove any residual hexadecane , the tubes were cooled to 4°C and any solidified hexadecane remaining removed . The concentration ( C2 ) of conidial in the aqueous phase was determined by counting . The hydrophobic index was calculated using the following equation: ( 1×107 _C2 ) /1×107 . Fungal conidia were harvested from 1/4 SDAY after 15 d of growth . Hyphal bodies were collected from the locust hemolymph 4 d post-injection with 5 μl of conidial suspension ( 1×107 conidia ml-1 ) as described [30] . All the samples were washed 3 times with 0 . 01 M PBS buffer and immunofluorescent labeling and detection of α-1 , 3-glucan and β-1 , 3-glucan in fungal cell walls were performed using IgM clone MOPC-104E and Alexa Fluor 488 goat antimouse IgM ( Invitrogen ) , and β-1 , 3-glucan using β-1 , 3-glucan-specific antibody , Alexa Fluor 594 goat antimouse IgG anti-body ( Invitrogen ) as described previously [30] . Fungal surface carbohydrates containing β-1 , 4 N-acetylglucosamine or mannose were visualized using conjugated wheat germ agglutinin ( WGA ) Texas Red @-X conjugates ( Vector Laboratories , Burlingame , CA , USA ) or fluorescein-labeled Concanavalin A ( ConA ) ( Vector Laboratories , Burlingame , CA , USA ) , respectively , and as described previously [30] . Fluorescent signals of conidia were quantified by BD FACSCalibur with an argon laser , with the excitation wavelength set at 488 nm ( Ex: 488 nm ) and the emitted light detector at 530 nm ( Em: 530±15 nm ) , adjusted to a fixed channel using standard Brite Beads prior to determining fluorescence of α-1 , 3-glucan and mannose . And with the excitation wavelength set at 488 nm ( Ex: 488 nm ) and the emitted light detector at 630 nm ( Em: 630±15 nm ) to quantify fluorescence of β-1 , 3-glucan and chitin . Samples were diluted to 4×104 conidia ml-1 , and briefly mixed on a vortex mixer before introduction to sheath fluid . Data acquisition and manipulation were performed with BD CellQuest Pro and FACS Express v3 , and fluorescence was measured for 24 , 000 conidia . Experiments were performed on at least three independent batches of conidia . The expression levels of the locust Attacin ( GenBank accession number: AB757753 ) and Defensin ( GenBank accession number: KU516094 ) antimicrobial peptide gene , were analyzed by qRT-PCR . Total RNA was isolated from dissected L . migratoria manilensis fat bodies after topical infection with M . acridum ( 1×108 conidia ml-1 ) after 24 and 30 h , or injection with M . acridum ( 1×107 conidia ml-1 ) after 24 h post-treatment . Total RNA extraction , cDNA synthesis , and qRT-PCR were conducted as described earlier . The L . migratoria manilensis β-actin gene ( GenBank accession number: KC118986 ) was used as an internal control . All experiments were performed in triplicate . To determine the phenoloxidase ( PO ) activity in the locust hemolymph , samples was harvested from L . migratoria manilensis after topical infection with M . acridum ( 1×108 conidia ml-1 ) 8 and 12 h post-inoculation by cutting off of the proleg and collecting hemolymph droplets on ice . Immediately after collection 100 μl of hemolymph was added to 1 ml PBS buffer ( 50 mM , pH 6 . 5 ) . The mixture was then centrifuged at 3 , 099 × g for 10 min at 4°C in order to remove cells and debris . PO activity was measured using a TriStar multimode microplate reader LB941 ( Berthold , Bad Wildbad , Germany ) . Protein quantification was performed using the Folin-Phenol Protein Quantification Kit ( Dingguo , Beijing , China ) . One unit of PO activity was defined as ΔA490 = 0 . 001 after 60 minutes , similar to previously described [87 , 88] . The nodules transformation was performed with 30 L . migratoria manilensis fifth-instar nymphs after 12 h which injected with 5 μl aqueous suspension containing 1×10 8 conidia ml-1 into the hemocoel as described previously [89] . A mid-dorsal cut was made along the full length of the body . The gut and fat bodies were removed to expose the inner ventral surface and nodules were counted routinely in all abdominal segments under a dissecting microscope . The number of nodules was calculated as previously described with some modifications [90] . Nodule size was factored in as follow; single nodules were tabulated for sizes ranging from 50 to 90 μm , with nodules > 100 μm considered as two nodules . All experiments were repeated three times . Fifth-instar nymphs of L . migratoria manilensis ( Meyen ) were used for bioassays as described previously [77] . Conidial germination and appressorium formation were examined on locust hind wings using a previously described method [91] . Appressorial turgor pressure was assayed using a previously described method [92] . For the fungal growth rates in insect hemolymph in vivo , conidial suspensions from different fungal strains were injected ( 5 μl of 2×106 conidia ml-1 aqueous conidial suspensions ) into the locust hemocoel cavity through the third abdominal segment or topically inoculated ( 3 μl of 2×107 conidia ml-1 paraffin oil conidial suspensions ) onto pronotums of locusts . Treated locusts were reared at 28°C with a 16:8 h ( light–dark ) photoperiod and bled at day 3 and day 5 after inoculation . Three cohorts of 10 treated locusts were bled ( 30 μl blood per locust ) for genomic DNA extraction . Hyphal bodies in vivo were observed and photographed under a microscope by bleeding infected locusts 5 d post inoculation . For the fungal growth rates in insect haemolymph in vitro , 10 μl of conidial suspension ( 1×106 conidia ml-1 ) was inoculated into a 2 ml microcentrifuge tube which containing 500 μl fresh locust hemolymph , from which host cells were removed by centrifugation at 30 × g for 10 min at 4°C . Samples were stand at 28°C on a rotary shaker at 250 rpm for 3 days . Genomic DNA from samples was extracted at 48 h or 24 h intervals as described above . The concentration of fungal genomic DNA was examined by qPCR using primer pair of ITS-F/ITS-R ( S1 Table ) to determine the fungal growth rate . All datasets were analyzed with SPSS 16 . 0 program ( IBM , Armonk , NY , USA ) . The mean 50% lethality time ( LT50 ) and mean 50% inhibition time ( IT50 ) were estimated using the Data Processing System program [93] . Shapiro-Wilk test and Levene's test were used for testing the normality and homogeneity of variances , respectively . When the data distributed normally , one-way analysis of variance ( ANOVA ) followed by Tukey’s test was selected , or t tests was selected . Tukey’s honestly significant difference test was used to separate means at α = 0 . 05 or 0 . 01 . All experiments were repeated at least three times . | The fungal cell wall is a dynamic and flexible organelle that modulates the interaction of the pathogen with its host and acts as a critical recognition and evasion interface with host defenses . Chitin is a hallmark constituent of the fungal cell wall and all fungi contain multiple chitin synthase ( Chs ) genes . However , systematic characterization of chitin synthase genes has not yet been reported in entomopathogenic fungi . By using the insect pathogen Metarhizium acridum as a model , we performed a systematic genetic analysis of the seven member Chs family ( ChsI-VII ) in the insect pathogenic fungus . Construction of strains bearing targeted single gene mutations revealed differential contributions of specific Chs genes to growth , cell wall integrity , and stress responses . In addition , we revealed that three chitin synthase genes MaChsIII , MaChsV and MaChsVII were shown to be important for fungal appressorium formation and evasion of insect cellular and/or humoral defenses , promoting the fungal dimorphic transition to the production of hyphal bodies that occurs within hosts , and ultimately to virulence . These data provide new insights into the roles of Chs genes and chitin as critical components affecting fungal membrane structure and function . | [
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"structures... | 2019 | Members of chitin synthase family in Metarhizium acridum differentially affect fungal growth, stress tolerances, cell wall integrity and virulence |
Polyploidy is increasingly seen as a driver of both evolutionary innovation and ecological success . One source of polyploid organisms’ successes may be their origins in the merging and mixing of genomes from two different species ( e . g . , allopolyploidy ) . Using POInT ( the Polyploid Orthology Inference Tool ) , we model the resolution of three allopolyploidy events , one from the bakers’ yeast ( Saccharomyces cerevisiae ) , one from the thale cress ( Arabidopsis thaliana ) and one from grasses including Sorghum bicolor . Analyzing a total of 21 genomes , we assign to every gene a probability for having come from each parental subgenome ( i . e . , derived from the diploid progenitor species ) , yielding orthologous segments across all genomes . Our model detects statistically robust evidence for the existence of biased fractionation in all three lineages , whereby genes from one of the two subgenomes were more likely to be lost than those from the other subgenome . We further find that a driver of this pattern of biased losses is the co-retention of genes from the same parental genome that share functional interactions . The pattern of biased fractionation after the Arabidopsis and grass allopolyploid events was surprisingly constant in time , with the same parental genome favored throughout the lineages’ history . In strong contrast , the yeast allopolyploid event shows evidence of biased fractionation only immediately after the event , with balanced gene losses more recently . The rapid loss of functionally associated genes from a single subgenome is difficult to reconcile with the action of genetic drift and suggests that selection may favor the removal of specific duplicates . Coupled to the evidence for continuing , functionally-associated biased fractionation after the A . thaliana At-α event , we suggest that , after allopolyploidy , there are functional conflicts between interacting genes encoded in different subgenomes that are ultimately resolved through preferential duplicate loss .
Polyploidy events ( also known as whole-genome duplications or WGDs ) are widespread across the eukaryotic tree of life [1] and have long interested geneticists and evolutionary biologists for reasons varying from the nature of interspecific crosses to the organismal effects of changes in gene copy number to the origins of novel functions in evolution [2–5] . Recent work has associated genome duplications with evolutionary innovations [6–9] and with shifts in net diversification rates [10–13] . Understanding how polyploidy contributes to these biologically important processes requires coming to grips with three key patterns in the evolution of polyploid genomes . The first is the rapid loss of genetic redundancy after polyploidy . Most WGD-created duplicate genes , termed “ohnologs” [14] , do not survive: their losses start very soon after WGD [15–17] and may be governed epigenetically in this period [18] . The net result of such losses can be dramatic: only 551 of an estimated 5000 duplicate gene pairs produced by the WGD in yeast survive in the Saccharomyces cerevisiae genome [19] . Nonetheless , the footprint of WGD is clear in the extant patterns of double-conserved synteny [DCS; 20 , 21]: homologs of genes from a single genomic region in an non-polyploid relative will be split between two regions in the polyploid genomes ( upper and lower blocks of Fig 1 ) . The second key trend is that , despite the rapidity of these duplicate losses , they are nonrandom , with certain functional classes of genes being overly frequent among surviving ohnologs and other being overly rare . In both yeasts and angiosperms , genes involved in DNA repair and those targeted to the organelles were rapidly returned to single copy after WGD [22 , 23] . On the other hand , genes coding for transcription factors , ribosomal proteins and kinases were over-retained in duplicate after independent WGD events across a phylogenetically wide range of organisms from amoebae and plants to vertebrates and yeasts [24–28] . The force underlying these convergent patterns of loss/retention is most likely selection to maintain dosage balance among interacting gene products [29] . The dosage balance hypothesis explains a variety of observations about the evolution of both polyploid and non-polyploid genomes , including the pattern of post-WGD duplicate retentions [28 , 30–34] and the tendency of these same gene families not to undergo single gene duplications , where balance would be perturbed [26 , 35 , 36] . Similarly , genes in central network positions or whose products are parts of protein complexes are likely to show dosage phenotypes [37] and are over-retained after WGD [22 , 38] . The third and final trend in post-WGD evolution is that when genes are lost , they are apparently not always lost equally from the paired DCS regions . This pattern of biased fractionation has been observed across a range of WGD events , primarily in angiosperms [39–41] but also from other taxa [42] . The most plausible current hypothesis for why biased fractionation occurs is that the events in question were allopolyploidies [39 , 43] . In the alternative case of autopolyploidy , the paired genomic regions created by polyploidy are identical , and we know of no mechanism by which these identical regions could be stably marked over evolutionary time so as to differ strongly in their duplicate retention patterns . However , the converse is not true: the absence of biased fractionation cannot be taken as evidence for autopolyploidy . If the genomes that merged were from closely related taxa , bias is not necessarily expected . As for the genetic mechanism behind the bias in ohnolog losses , biases in gene expression between the two subgenomes in recent allopolyploids appear to be common [44 , 45] and the chromosomal regions with lowered expression also appear more prone to ohnolog loss [41 , 46] , leading to the suggestion that biased fractionation might result from a tendency for the ohnolog with lower expression to be less likely to show a fitness defect when lost . One potential source of these initial differences in expression might then be the difference in transposon load between the subgenomes of an allopolyploid , with the transposon-rich genome facing greater silencing and hence higher rates of gene loss [41 , 43] . A difficulty that arises in the analysis of biased fractionation ( BF ) is that there has been a degree of circularity in its detection . Because rearrangements occur after WGD events , the duplicated regions in a paleopolyploid genome , which are identified by shared gene order or synteny , will be separated from each other by breakpoints . Within each syntenic block , the identification of the homeologous region with more retained genes is straight forward . However , when comparing a single polyploid genome to a diploid outgroup , it is difficult to formally refute the possibility that the parent-of-origin of the highly retained subgenome in one block might be the same as that of the lowly retained subgenome in another [but see; 42] . This difficulty in fact motivates the phylogenetic approach to studying polyploidy that we use below . There are also other potential factors that might be involved in driving BF that remain to be investigated . For instance , the convergent pattern of rapid losses in gene coding for the DNA repair enzymes [22 , 23] suggests that there may be incompatibilities between the versions of these genes contributed by the two allopolyploid parents . If such incompatibilities were common , they could contribute to BF by favoring retention from a single subgenome once the symmetry of a particular genetic module has been broken by the first loss . Using POInT , the Polyploid Orthology Inference Tool , we analyzed the resolution of three WGD events , one in yeasts [20] , one in the grasses [the ρ event; 47 , 48] and the most recent event ( At-α ) in Arabidopsis thaliana and its relatives . Using POInT’s synteny-based estimates of post-WGD gene losses , we show that BF was a genome-wide evolutionary pattern after the At-α and ρ WGD events and persisted over long periods . In contrast , in yeasts we find evidence for BF only in a very short time interval post-WGD . In Arabidopsis , we also find that there is preferential co-retention of genes from the same subgenome whose products interact , as opposed to interactions involving proteins from different parents . Collectively , these results suggest that biased fractionation is at least in part a relic of conflicts between the paralogous genes contributed by the two parents at the time of the allopolyploidy .
Our previous POInT analyses in yeast were based on human curated datasets [19 , 49] . We do not have such inferences for either the At–α or the grass ρ event . Instead , using experience from previous projects [40 , 50] , we developed a new pipeline for inferring the paralogous genomic regions created by a WGD in the genomes sharing that event . We then merged these regions of DCS [20 , 21] across all polyploid genomes and sought an ancestral gene order that minimized the number of synteny breaks . Fig 1 shows examples of such DCS blocks for At-α . The goal of the pipeline is to find a common set of DCS blocks shared by the genomes of the six Brassicaceae species that possess At-α: Arabidopsis thaliana [51] , Arabidopsis lyrata [52] , Capsella rubella [53] , Shrenkiella parvula [54] , formerly known as Thellungiella parvula or erroneously as Thellungiella halophila [55] , Eutrema salsugineum [56] , and Aethionema arabicum [57] and for the four grasses with ρ: Brachypodium distachyon [58] , Oropetium thomaeum [59] , Setaria italica [60] and Sorghum bicolor [61] . To do so , we used outgroup genomes that lacked the WGD in question . For the At-α event , we used the draft genome of the outgroup plant Cleome violacea , which split from the six taxa studied prior to that event [11]: it likewise lacks the WGD found in other taxa in the Cleomaceae [9] . The C . violacea genome is available from the CoGe comparative genomics portal ( https://genomevolution . org/coge/ ) under accession number 23822 . For the grass ρ event , we used the genome of the pineapple Ananas comosus as an outgroup [62] . CoGe accession numbers for all plant genomes used are listed in S1 Data . The product of a WGD is a set of duplicated genes in a genome that each originate from a single ancestral gene . Here , the C . violacea and pineapple genomes give us an estimate of these ancestral loci , and we seek to place either one ( e . g . , a duplicate loss has happened ) or two genes ( the ohnologs survive ) from the duplicated genome in a “pillar” with each such ancestral gene ( see Fig 1 ) . Genome annotation files for these 12 plant genomes were obtained from CoGe [63] . With these data in hand , the inference of the shared DCS blocks that serve as POInT’s input is a three step process: 1 ) a homology search of each polyploid genome against the diploid outgroup , 2 ) inference of species-specific DCS blocks and 3 ) inference of a common set of DCS blocks across all genomes along with an estimate of their ancestral order at the time of the polyploidy . Step 1: Homology search . For At-α , we used a fast homology search program based on the SeqAn package [64 , 65] to identify pairs of homologous genes , one from a genome with At-α and one from C . violacea . We defined a pair of genes as being homologous for the purposes of DCS inference if their protein sequences: 1 ) share two 7 amino acid residue exact matches , 2 ) have the shorter sequence having 80% of the length of the longer , and 3 ) show 70% amino acid identity overall . Because of the greater evolutionary distances involved in the grass ρ event , we used a slower but more sensitive BLAST-based search , employing our tool GenomeHistory to do so [66 , 67] . In this case , we required a maximal BLAST E-value of 10−8 to identify matches between the four duplicated grasses and pineapple: we then used the same 70% identity and 80% aligned length cutoffs as used with At-α to select homologs . Step 2: Genome-specific DCS inference . Sequence homology alone is insufficient to identify the DCS blocks given the angiosperms’ history of nested polyploidy [1] . Instead , for the second step of the pipeline , we used gene order information ( synteny ) to identify which of the potentially many homologs in each polyploid genome are the WGD-produced ohnologs . We frame this problem as follows . First , we define a set A of n DCS blocks that consists of ancestral pillars Ai such that Ai ∈ A|1 ≤ i ≤ n . Each pillar is linked to a unique gene from C . violacea or pineapple and has elements Ai ( p1 ) and Ai ( p2 ) , which represent the potential homologous genes created by WGD . Each pillar Ai also has associated a set of genes {h1…hh} from the polyploid genome that are homologous to the pillar’s ancestral gene . A maximum of two of these homologs can be assigned to Ai ( p1 ) and Ai ( p2 ) . We next define O ( A1…An ) to be the order of the pillars in A for our analysis . Hence , AO ( i ) represents the ith pillar in this ordering . For a given AO ( i ) ( pk ) |1 ≤ k ≤ 2 , define AO ( i+j ) ( pk ) such that j = min ( x; i+1≤x≤n ) where AO ( i+x ) ( pk ) ≠ ∅: in other words , i+j is the next pillar after i in O ( A1…An ) with an assigned gene for parental genome k . We define the score s of such a combination of homolog assignments and pillar orders: s=∑i=1n∑k=1210|AO ( i ) ( pk ) andAO ( i+j ) ( pk ) areneighborsotherwise ( 1 ) In other words , the score is the sum of the number of positions in O ( A1…An ) where the genes in each pillar are the genomic neighbors of the genes in the next non-empty position . We cannot simply use the pillar order seen in the outgroup , because neither C . violacea nor pineapple is the true ancestor of the WGD events in question: both have evolved independently for many millions of years . Instead we must optimize O ( A1…An ) . Note that , throughout this pipeline , neighbor is understood to exclude any genes that are not part of the current analysis set . For instance , a gene in Arabidopsis thaliana with no identified C . violacea homolog is ignored in the neighbor computation because it could never appear in an ancestral pillar . By the same logic , any position for which AO ( i ) ( pk ) and AO ( i+j ) ( pk ) are not neighbors is defined as a synteny break , and , if this situation is true for both k = 1 and k = 2 , we refer to position i as having a double synteny break . To infer the combination of the homolog assignments Ai ( pk ) | 1 ≤ i ≤ n , 1 ≤ k ≤ 2 and the ordering O ( A1…An ) , we used simulated annealing [68 , 69] . This algorithm proposes random changes to either O ( A1…An ) or to the Ai ( pk ) assignments with the goal of maximizing s , which recomputed after each such change . We used the extant C . violacea and pineapple gene orders as our initial orders and made increasingly long runs until longer run times no longer produced meaningfully higher values of s . A . thaliana and its relatives share a history of WGD [26]: prior to the WGD-α event modeled here there was another WGD , termed WGD-β , which is shared with C . violacea . One might wonder if our simulated annealing algorithm has mistaken synteny blocks surviving from WGD-β for the more recent products of WGD-α . We suspect that any such errors are quite rare for two reasons . First , C . violacea also experienced WGD-β and hence also possesses the corresponding synteny blocks , meaning that they are accounted for in the inputs to our simulated annealing routines . Second , we only considered homology relationships between genes in C . violacea and in A . thaliana , A . lyrata , C . rubella , S . parvula and E . salsugineum with nonsynonymous divergence ( Ka ) less than 0 . 1 and between C . violacea and A . arabicum with Ka≤0 . 2 . As a result , between 41% and 45% of the genes from C . violacea have only a single homolog identified in the other 6 genomes and hence cannot represent ambiguous surviving blocks from WGD-β in C . violacea . Hence , it is difficult to see how ancestral WGD-β blocks would have infiltrated our inferences in significant numbers . Step 3: Inferring a global ancestral ordering for POInT analyses . Using the four/six individually optimized set of ancestral pillars ( for ρ and At-α , respectively ) with assigned genes ( the Ai ( pk ) values for each genome ) , we extracted , for each genome , only ancestral pillars for which each gene in the pillar had synteny support ( i . e . , each gene was a neighbor of at least one other gene in that pillar set ) . Using the outgroup gene from each ancestral pillar as an index , we then merged all of these inferences . Because we required that at least one gene from each genome be in each pillar , the effect of this merging was to limit our analyses to a set of m = 7243 and = 3091 ancestral pillars for At-α and ρ , respectively . However , those pillars have shared syntenic support across all genomes . The optimal ancestral order for each extant genome differs , so once the ancestral pillars were assembled , we inferred a globally-optimal ancestral order O ( AG1 . . AGm ) , again using simulated annealing . The optimality criterion here was to maximize the number of neighbor relationships , but in this case the Ai ( pk ) assignments were held constant and only O ( AG1 . . AGm ) was changed . To assess the influence of the ancestral ordering on POInT’s estimates , we fit the WGD-bf model ( Fig 2B ) to both the initial C . violacea order and to the 10 inferences of O ( AG1 . . AGm ) with the largest simulated annealing scores , using the order with the highest likelihood for further analyses ( S1 Table ) . We similarly used the ancestral ordering of highest likelihood for our ρ analyses . We have previously described POInT [22 , 70] , which fits a Markov model to duplicate loci created by WGD . The model has four states ( Fig 2B ) , namely U ( undifferentiated duplicated genes ) , F ( fixed duplicate genes ) and S1 and S2 ( the single copy states ) : it is a generalization of a model proposed by Lewis [71] . Note that once the genes of each post-WGD genome have been assembled into ancestral pillars using the simulated annealing approach above , the sequences of the genes of the post-WGD genomes are never used again: all of POInT’s inferences are based on shared DCS information . Since our prior work , we have completely re-written POInT to allow for user-defined evolutionary models , computing the resulting transition probabilities by exponentiating the user-supplied instantaneous rate matrix [72] . Using this new version of POInT , we fit five models to our four datasets ( two from At-α and one each from the yeast and grass WGD events , Fig 2 ) . We used likelihood ratio tests to assess whether more complex models better fit the loss data than did simpler models [73] . POInT’s focus on WGD has advantages over applying more general gene birth-death models to polyploid species [74 , 75] . POInT models the process of duplicate loss and retention jointly across all genomes and along a phylogeny . Hence , the probability of a particular model state at a given ancestral locus is conditioned on all other loci and all other genomes . This conditioning is performed by analogy to the linkage analysis model of Lander and Green [76] using the hidden-Markov approach of Felsenstein and Churchill [77] . The states the Markov model considers are the set of 2n possible orthology relationships between the 2n different loci ( e . g . , 2 duplicated loci in each of n genomes ) . The likelihood of site i+1 having orthology state j given that site i has that orthology assignment is ( 1-θ ) , where θ is a small constant estimated from data ( 0 . 0004≤θ≤0 . 0081 across these analyses ) . In cases where there is a double break in gene order in a particular genome , θ = 0 . 5 . From this model structure , we can infer orthologous chromosomal regions produced by WGD between the genomes studied , along with confidence estimates in these inferences ( Fig 1 ) . The previous version of POInT did not distinguish between states S1 and S2 . The result was degeneracy in the inferences of orthologous regions . In other words , assigning the first member of each DCS pair to subgenome 1 and the second to subgenome 2 produced orthology assignment 111111 across the six genomes , which was identical in likelihood to assignment 222222 . ( The computation is completely analogous for the other two WGD events studied . ) Effectively , this degeneracy corresponds to flipping the upper and lower panels of Fig 1 , because each of the 2n possible orthology assignments has an equivalent assignment with all 1s converted to 2s and vice versa . To model the process of BF , we relaxed this assumption by introducing parameter ε ( Fig 2B ) . This parameter makes losses to state S2 potentially less common than to S1 . If BF is present in the data , the maximum likelihood estimate of ε will be less than 1 . 0 , and the likelihood of orthology assignment 111111 will no longer be the same as 222222 . We can then use the POInT model to estimate the posterior probability of the subgenome assignments ( the numbers shown above every column in Fig 1 ) at every pillar . For convenience we refer to the resulting two regions as deriving from allopolyploid parents 1 and 2 [43] , respectively , defining parent 1 as containing genes in state S1 ( e . g . , it is potentially less fractionated ) , similar to Thomas et al . , [39] . In previous work in yeast [15 , 22 , 70] , we found evidence for “convergent” gene losses that were phylogenetically independent and yet more often from the same subgenome than could be explained by chance . We modeled these events by adding two duplicated converging states to our model , C1 and C2 . Gene losses from C1 were always to S1 and similarly for C2 . We fit versions of this model both with ( 0≤ ε≤1 . 0 ) and without ( ε = 1 ) BF to our yeast , grass and At-α data: while these models improved the fit relative to the WGD-bf model used here , we present our results in terms of the WGD-bf model because both model classes give similar parameter estimates ( S2 Table ) , and the more complex models do not add insight for the questions considered here . Because we analyzed only four genomes sharing the grass ρ event , it was possible to use POInT to test all 15 possible rooted phylogenetic trees for these taxa to assess the dependence of our inferences on the inferred phylogeny . We present our results in terms of the optimal tree , but the global parameter estimates for the WGD-bf model were very similar for all topologies ( 0 . 061≤γ≤0 . 067; 0 . 719≤ε≤0 . 739; 0 . 0061≤θ≤0068; Fig 2 ) . We asked whether genes surviving from one or the other of the subgenomes showed patterns of interconnection in the networks of Arabidopsis thaliana . We use the BioGrid database [78] to extract known protein-protein interactions [79] . We tested for paucity of interactions between the products of genes from different subgenomes with a randomization approach . We thus compared the number of interactions between gene products from alternative subgenomes in the actual data to this value computed after 1000 randomizations of the subgenome assignments . To assess the degree to which our conclusions were potentially affected by errors in the assignment of genes to subgenomes , we conducted our tests at a range of confidences in subgenome assignment ( Fig 3 ) . We used the Gene List Analysis tool from the PANTHER classification system [80] to perform statistical overrepresentation tests to find over/under-represented Gene Ontology ( GO ) terms associated with biological processes , molecular functions , or cellular components . The input of our analysis consists of two sets of genes: the target list to analyze , and a reference list . The expected number of genes for a GO term in the target list was calculated based on the number of genes with that term in the reference list: binomial statistics for each GO term associated with genes in the target list were then computed from these expectations [81] . We first performed an overrepresentation test for 4 , 086 single copy genes from both subgenomes against the reference set of 4 , 152 surviving duplicated genes . The over/under represented GO terms in the analysis were filtered with a threshold P-value ≤ 0 . 01 after Bonferroni correction , and only terms with a fold-enrichment larger than 1 . 5 ( overrepresented ) or smaller than 0 . 67 ( underrepresented ) are reported . We next compared 2 , 552 single copy genes from subgenome 1 ( dominant ) relative to the terms for the 1 , 534 genes from subgenome 2 ( more fractionated ) with a similar approach . To compensate for the smaller number of terms found to be enriched in this second analysis , we used an FDR-corrected P-value of 0 . 05 as a threshold . Full lists of all significantly enriched terms for any comparison with associated GO identifiers are given as S3–S5 Tables .
Using POInT , we analyzed the resolution of three phylogenetically widely-spaced polyploidy events: the WGD in the ancestor of Saccharomyces cerevisiae and relatives [20 , 82] , the ρ event found in the ancestor of the grasses [47 , 48] and the At-α event shared by the model plant Arabidopsis thaliana and its relatives [26 , 83] . Previous work has suggested that all of these WGDs were allopolyploid events [43 , 82] , meaning the duplicated regions in the extant polyploid genomes ( hereafter subgenomes ) derive from parental genomes from differing species . Whatever their origins , however , these subgenomes produced by polyploidy are now distinct due to their individual histories of gene loss . In order to assign the extant genes to one of the two subgenomes , we applied new duplicate resolution models that distinguished between a less fractionated genome ( more surviving genes ) and the more fractionated genome [fewer surviving genes; 39 , 43] . As previously described [15 , 22 , 70] , we used ohnologs from the Yeast Genome Order Browser project and an inferred ancestral genome order as POInT’s inputs for the yeast analyses [19 , 49] . For the At-α and ρ events , no such data exist , so we developed a new pipeline that uses sequence homology and shared gene order ( synteny ) to assign genes from the polyploid genomes to a “pseudo-ancestral” gene from the extant outgroups Cleome violacea ( for At-α ) and pineapple ( for ρ ) . First , we used simulated annealing to assign genes from each of the polyploid genomes to double-conserved synteny ( DCS ) blocks . These assignments were made forcing pairs of regions in the polyploid genomes to possess one or two homologous genes to one gene from a single region in outgroup genome: the simulated annealing algorithm then sought such assignments that maximized the shared gene order ( see Methods for additional details ) . We then merged these single-genome inferences into a set of 7243 and 3091 ( for At–α and ρ , respectively ) ancestral gene pillars , each consisting of at least one gene from every genome that shared synteny with at least one other gene ( see Fig 1 ) . We then again used simulated annealing to optimize our estimate of ancestral genome order of these loci by maximizing the synteny among the pillars . Fig 1 gives an example of the estimates made by POInT based on these inferred pillars: from the inferred pillar order , POInT is able to estimate the probability associated with assigning each genome segment from each species to either of the two subgenomes ( numbers above the columns in that figure ) . Using these data , we tested the hypothesis that biased fractionation ( BF ) was observed after the three WGD , explored its temporal characteristics and sought to associate it with functional properties of the genes in question . As mentioned , it is not guaranteed that two genomic regions each showing a higher retention rate than their homeologous partners necessarily originate from the same parental subgenome ( the circularity problem in measuring BF ) . We used the high-synteny subset of the At-α data to assess the degree of this problem . From it , we produced a visual representation of the set of ancestral synteny blocks POInT was using for its inferences . In Fig 4B , we show how often 5 , 4 , or 3 genomes agree from pillar to pillar in their subgenome assignments . Notably , when only 3 of 6 genomes are required to agree at high probability , the model infers a relatively small number of ancestral syntenic blocks , consistent with a set of ancestral chromosomes prior to At-α . Moreover , these blocks are identifiable without the assumption of BF ( e . g . , they are also inferable from the WGD-f model , Fig 4B ) and , at least for most of the larger blocks , give estimates of BF similar to the dataset as a whole ( Fig 4C ) . Hence , it is clear that biased fractionation is not an artifact of synteny-block inference . Similar diagrams for the full At-α dataset , the ρ dataset and yeast are given in S2 Fig . Using data from BioGrid [78 , 79] , we asked whether protein-protein interactions between the products of A . thaliana single-copy genes from alternate subgenomes were rarer than would be expected by chance . Across a large range of subgenome confidence estimates from POInT , there were fewer such “crossing” interactions than expected ( Fig 3A ) , and the frequency of such interactions decreases as our confidence in the subgenome assignments increases ( Fig 3B ) . Similar analyses were not performed for the ρ and yeast WGD events due to the lack of large-scale interaction data and the lack of substantial fractionation , respectively . As seen in previous analyses [23 , 25 , 26 , 30] , the surviving At-α ohnologs are enriched or depleted for a number of GO ontology categories ( Fig 5 and S3 Fig ) . We had anticipated that those categories that were depleted for ohnolog pairs might represent a set of single-copy genes drawn preferentially from the dominant subgenome . However , such was not the case: even at a quite liberal FDR-corrected significant threshold ( P≤0 . 05 ) , there are relatively few GO terms significantly differentially retained between the single copy genes of the two subgenomes . Moreover , these terms do not overlap with the ohnolog-depleted terms: instead the single copy genes operating in the endoplasmic reticulum more often derive from the less-fractionated subgenome ( Fig 5 ) . Similarly , genes involved in the cell cycle and circadian rhythm are preferentially drawn from the more fractionated subgenome and those for developmental genes in phloem or xylem from the less-fractionated subgenome ( S3 Fig ) .
There is considerable and accumulating evidence for the actions of biased fractionation ( BF ) after WGD in angiosperms [39–42] and strong suggestions that allopolyploidy is more likely to produce such biases than autopolyploidy [43] . Nonetheless , there remains at least a theoretical danger that analyses of BF that consider only a single polyploid genome at a time [often by comparison to a diploid outgroup; 40 , 41 , 46 , 86 , 87] could mistake the random variation in preservation in small synteny blocks for biases in fractionation . The results presented here refute this concern , and indicate that , at a minimum , BF acts consistently across regions at the chromosome scale . Our confidence in this conclusion is driven by the concordance of multiple lines of evidence as to the presence and strength of BF . At a methodological level , POInT integrates across multiple genomes , such that lineage-specific synteny breaks are passed through using data from genomes without such breaks ( subject to limitations in genome assemblies and in the degree of shared history in the genomes ) . This approach dramatically increases synteny block size ( see Fig 4 ) . Moreover , POInT employs a very strict and transparent definition of synteny: only genomic neighbors are considered to be in synteny , meaning that POInT employs no parameters such as a window size that need to be tuned by the user and that could confound inferences . POInT also employs a robust modeling framework similar to those used in sequence evolution studies [88] and allows for explicit statistical tests for the presence of BF . Using this framework , we have shown very strong statistical support for BF after two independent WGD events: At-α and the grass ρ event , with a ratio of single copy genes from the less and more fractionated subgenomes somewhere between 3:2 and 5:4 , in line with previous estimates [39] . This modeling approach has the further advantage of avoiding the circularity in block estimation: POInT infers parental genome assignments on the basis of shared gene losses , a point we have exploited previously [22 , 89–91] . As a result , POInT effectively recovers the same shared parental genome assignments under a model without biased fractionation ( red/purple blocks in Fig 4 ) as it does under the BF model . Moreover , the simultaneous consideration of multiple genomes allows us to assess if the evidence for BF is consistent across those genomes: our loss estimates for each branch of the phylogeny all show BF of roughly similar magnitude , despite the fact that losses on the different tip branches of the phylogeny in Fig 1 are necessarily independent ( an estimate of the BF ratio is given under each branch of the lower tree in that Figure ) . Finally , the absence of evidence for BF on most branches of the post-WGD yeast phylogeny [which was recently conclusively found to be an allopolyploidy; 82] illustrates that POInT is fully capable of rejecting the hypothesis of BF when evidence for it is weak ( or temporally variable in this case ) . One might argue instead that BF favored some chromosomes from one parental genome and some from another . However , this position is inconsistent with the results of our interaction data and GO term analyses , since such interactions more often occur between products of genes from the same subgenome than between products of genes encoded on different subgenomes , and genes assigned to the same subgenome show consistency in low-level GO term associations . Likewise , there is a good accordance between the estimates of the strength of BF in three of the four largest synteny blocks of Fig 4 and the overall estimate: were BF a chromosome-by-chromosome phenomenon , it is difficult to understand why its strength would be so consistent across blocks . While POInT represents a significant improvement over analyses of single polyploid genomes , there are always limitations to any modeling framework . From a practical point of view , our inferences are limited by the quality of the genomic data used as inputs: the more fragmented these genome assemblies , the less power POInT has to infer parental genomes of origin . The inference of DCS blocks by simulated annealing is a costly and computationally difficult problem , and while our scoring functions are reasonable , they may not be the optimal method for inferring ancestral genome orders [49] . As mentioned in the Methods section , there is also a potential for older polyploidies that are shared by the outgroup genome to mislead our scaffolding , although we do not believe this problem was significant here . Finally , POInT itself is imperfect in how it treats uncertainty in parental genome assignments: the error parameter θ estimates the degree to which the input data fails to conform to POInT’s underlying model . While our results above appear to be robust to these various sources of error , future studies of polyploid genomes with improved approaches could give more refined estimates of parental genomes of origin and fine-scale temporal patterns of post-polyploidy gene losses . Having reaffirmed that BF is a robustly detectable phenomenon in the evolution of polyploid genomes , it is reasonable to try to better understand its origins . In this vein , several of our observations , which arise from POInT’s unique capacity to probe polyploidy phylogenetically , serve to again suggest a link between BF and the hypothesized effects of allopolyploidy . The association of genes that physically interact with the same parental genome is one example of such an observation . Another is the conclusion that , after the At-α and ρ events , the strength of BF was uniform in time , but in yeast , BF was associated only with the very earliest stages of WGD resolution . We have previously found that a very particular group of genes , involved in DNA repair and mitochondrial function , were returned to single copy immediately after the yeast WGD [22] . Given the biases in those losses found here , it appears likely that BF in yeast was a result of selection for the removal of some ohnolog copies in order to prevent the mixing of genes for these two functions from the two diploid progenitor species . It is likely that the DNA repair enzymes and nuclear-encoded proteins targeted to the mitochondria have co-evolved separately in each parental genome ( and that only one of the two parents contributed a mitochondrial genome to the hybridization ) . If true , these hypotheses would suggest that BF in yeast resulted from selection to maintain co-adapted genes after hybridization . Because these losses , in addition to being biased towards one subgenome and a limited set of functions , occurred very rapidly after the WGD event [15] , it is difficult to believe they occurred through purely neutral processes: the proposal by De Smet et al . , [23] that forces such as dominant negative interactions may have driven selection to favor certain losses seems increasingly plausible . These results also reinforce a point we have made several times before: one’s understanding of the forces acting on a polyploid genome may depend on when in its history you look [22 , 34 , 92] . Our analyses are compatible with differences in gene expression driving BF [41 , 93] . However , the BF process does not appear to be solely a product of expression: the presence of co-evolved modules in the two parental genomes also apparently plays a role . Not only do we see a strong bias in the retention of DNA repair enzymes and mitochondrially-targeted proteins in yeast , but we also see a relative absence of protein-protein interactions between proteins encoded by different subgenomes in A . thaliana . This hypothesis would also explain our previous observation that both ribosomal proteins and histones underwent post-WGD gene conversions in yeasts [89 , 90] , as gene conversion represents a second mechanism for resolving parent-of-origin conflicts induced by polyploidy . Returning to our point about the timing of post-WGD events , we propose that the process of BF and the selection that retains some ohnologs to preserve dosage balance are linked . In this view , some genetic modules [a vague but still useful concept; 94] do not tolerate being duplicated and are quickly returned to single-copy [23] . Others remain duplicated as predicted by the DBH [3 , 30] . However , these duplications are not necessarily stable over long timescales [22 , 34]: any incompatibilities between the subgenomes will favor one subgenome when duplicates are in the end lost . The origins of these conflicts most likely arise through co-evolution between genes in individual genomes [95] . From our GO analyses , it appears that the effects of this co-evolution decay quickly as one moves away from directly interacting genes: hence many biological processes have “mixed and matched” set of genes from the two subgenomes . The three WGD events considered here cannot completely resolve these questions: the yeast WGD mostly lacks prolonged BF , while the early events after At-α and ρ are difficult to identify because of the long shared post-WGD branch . In the future , we will perform similar analyses with the recent Brassica hexaploidy to further refine our understanding of post-WGD functional evolution . So doing will not only improve our understanding of polyploidy but also of the nature of the functional links and the degree of co-evolution inherent in the interacting macromolecules that make up the cell . | Genome duplications/polyploidies can transiently double an organism’s gene content . However , this doubled condition is unstable and descendants of polyploid founders rapidly lose many of their duplicate genes . Here , we describe a phylogenomic pipeline that allows us to trace this history of gene loss across a set of modern genomes that all descend from the same three polyploidy events ( two in flowering plants and one in yeasts ) . Polyploidy often occurs by the hybridization of related but not identical lineages , and previous studies in single polyploid genomes have identified a tendency for one lineage to lose fewer genes after polyploidy than the other ( known as biased fractionation ) . However , single genome studies can be misled into inferring biased fractionation by incorrectly assuming that short regions of shared gene order are derived from the same parental genome . By phylogenetically modeling the resolution of these three polyploidy events across 21 genomes ( 10 plants and 11 yeasts ) , we confirm the existence of biased fractionation in plants and provide new evidence for it in yeasts ( where it occurred only for a short interval post-polyploidy ) . We also show that genes from alternative parental genomes tend to encode products that do not physically interact , suggesting that selection to maintain function in co-adapted complexes helped to drive this bias in loss patterns . | [
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"mole... | 2018 | Preferential retention of genes from one parental genome after polyploidy illustrates the nature and scope of the genomic conflicts induced by hybridization |
Zika virus ( ZIKV ) is a mosquito-transmitted virus that can cause severe defects in an infected fetus . ZIKV is also transmitted by sexual contact , although the relative importance of sexual transmission is unclear . To better understand the role of sexual transmission in ZIKV pathogenesis , a nonhuman primate ( NHP ) model of vaginal transmission was developed . ZIKV was readily transmitted to mature cycling female rhesus macaque ( RM ) by vaginal inoculation with 104–106 plaque-forming units ( PFU ) . However , there was variability in susceptibility between the individual RM with 1–>8 vaginal inoculations required to establish infection . After treatment with Depoprovera , a widely used contraceptive progestin , two RM that initially resisted 8 vaginal ZIKV inoculations became infected after one ZIKV inoculation . Thus , Depoprovera seemed to enhance susceptibility to vaginal ZIKV transmission . Unexpectedly , the kinetics of virus replication and dissemination after intravaginal ZIKV inoculation were markedly different from RM infected with ZIKV by subcutaneous ( SQ ) virus inoculation . Several groups have reported that after SQ ZIKV inoculation vRNA is rapidly detected in blood plasma with vRNA less common in urine and saliva and only rarely detected in female reproductive tract ( FRT ) secretions . In contrast , in vaginally inoculated RM , plasma vRNA is delayed for several days and ZIKV replication in , and vRNA shedding from , the FRT was found in all 6 animals . Further , after intravaginal transmission ZIKV RNA shedding from FRT secretions was detected before or simultaneously with plasma vRNA , and persisted for at least as long . Thus , ZIKV replication in the FRT was independent of , and often preceded virus replication in the tissues contributing to plasma vRNA . These results support the conclusion that ZIKV preferentially replicates in the FRT after vaginal transmission , but not after SQ transmission , and raise the possibility that there is enhanced fetal infection and pathology after vaginal ZIKV transmission compared to a mosquito transmitted ZIKV .
Zika virus ( ZIKV ) was first isolated in the Zika forest of Uganda in 1947 ( 21 , 22 , 30 ) and the first descriptions of human disease were reported a few years later ( 2 , 53 ) . ZIKV has a positive-sense RNA genome and belongs to the genus Flavivirus , which also includes dengue virus ( DENV ) , Yellow Fever virus , Japanese encephalitis virus , and West Nile virus ( WNV ) ( 30 ) . In approximately 20% of infected humans , ZIKV causes a febrile illness that can include rash , arthralgia and conjunctivitis . In addition , ZIKV has been associated with the development of microcephaly and lissencephaly and ocular lesions in infants born to women who acquired the infection during early pregnancy . In adults , ZIKV infection has also been associated with Guillan-Barré syndrome and other neurological complications including hearing loss and tinnitus . Although ZIKV is a mosquito-transmitted virus , sexual transmission of ZIKV in humans has been documented in several settings [1–13] . After returning to the U . S . from Africa , a man infected his partner [2] and male-to-female [14] , male-to-male [5] and female-to-male [10] sexual transmission of ZIKV have been reported in travelers returning to the U . S . from ZIKV positive regions in the Americas . ZIKV was isolated from semen during the ZIKV outbreak in French Polynesia in 2013 [4] and infectious virus has been isolated from semen up to 24 days after the onset of symptoms [9] . Further , ZIKV RNA has been detected in semen up to 6 months after onset of symptoms [15 , 16] and in the semen of a vasectomized man up to 96 days after onset of symptoms [6]; however , the infectivity and transmission potential of persistent ZIKV RNA in semen is not known . Of significant concern , a case of male-to-female sexual transmission of ZIKV from an asymptomatic male traveler to a woman with no travel history has been reported [8] . This case suggests that transmission via semen is possible even if a man has minimal or no symptoms . In 2007 , an Asian lineage ZIKV outbreak from mosquito transmission was reported in Yap Island with 185 clinical cases and an estimated 5000 infections ( 75% of the population ) in just 3 months [17 , 18] . Six years later ( in 2013 ) , another ZIKV outbreak involving 28 , 000 infected people was reported approximately 5000 miles away in French Polynesia ( FPY ) [19] . The ZIKV strain in the FPY outbreak had 99 . 9% nucleotide and amino acid identities with the Asian ZIKV strain in the Yap Island outbreak [17 , 19 , 20] , suggesting that the virus in French Polynesia outbreak was imported from Yap Island . Given the distance between the two locations it is unlikely that mosquitoes introduced ZIKV into FPY; it is more likely that an infected person imported ZIKV to FPY . ZIKV subsequently spread from FPY to other Pacific Islands , and by 2014 imported cases and cases of autochthonous transmission were reported in New Caledonia , Easter Island and the Cook Islands [21 , 22] . The nucleotide sequence of the ZIKV strain in all these outbreaks was 99 . 9% identical to the ZIKV strain in the Yap Island and FPY outbreaks . In March 2015 , the first cases of autochthonous transmitted ZIKV were reported in Bahia , Brazil with a ZIKV strain that was 99 . 9% identical ( nucleotide and aa sequences ) to the ZIKV strain in the Yap Island and FPY outbreaks [23 , 24] . Based on this chain of events and the similarity of the ZIKV strains involved , it is generally accepted that ZIKV moved from the Pacific Islands to South America . ZIKV mosquito vectors are endemic in the Pacific Islands and Brazil [25 , 26] and ZIKV is readily transmitted between humans by sexual activity [1–13] . Thus , it is likely that one or more infected individuals imported ZIKV over considerble distances to these widely separated islands and countries and then served as reservoir hosts for mosquito transmission , or transmitted ZIKV by sex , to naïve persons . The World Health Organization declared the ZIKV pandemic a public health emergency on February 1 , 2016 , and in November 2016 , WHO declared Zika virus endemic in the Americas . As of May 2017 , more than 5 , 109 cases of ZIKV infection have been reported in the United States , excluding those in Puerto Rico , Virgin Islands and Guam . Most infections are in travelers returning from affected areas , but 266 ZIKV infections were acquired in the continental US . Of these US acquired infections , 221 infections ( 83% ) were transmitted through mosquito bites in Florida and Texas , while 45 infections ( 17% ) were sexually transmitted [27] . It is now estimated that 1 . 6 million people are , or have been , infected with ZIKV in the Americas . Despite these observations , the frequency and efficiency of sexual ZIKV transmission is unclear . To better understand the biology of ZIKV sexual transmission , we developed a RM model of vaginal ZIKV transmission .
The captive-bred mature ( > 5year old ) parous , cycling female rhesus macaques ( Macaca mulatta ) used in this study were from the California National Primate Research Center . All animals were negative for antibodies to WNV , HIV-2 , SIV , type-D retrovirus , and simian T cell lymphotropic virus type 1 at the time the study was initiated . The animals were housed in accordance with the recommendations of the Association for Assessment and Accreditation of Laboratory Animal Care International Standards and with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The Institutional Animal Use and Care Committee of the University of California , Davis , approved these experiments ( Protocol # 19471 ) . When immobilization was necessary , the animals were injected intramuscularly with 10 mg/kg of ketamine HCl ( Parke-Davis , Morris Plains N . J . ) . All efforts were made to minimize suffering . Details of animal welfare and steps taken to ameliorate suffering were in accordance with the recommendations of the Weatherall report , "The use of non-human primates in research" . Animals were housed in an air-conditioned facility with an ambient temperature of 21–25°C , a relative humidity of 40%-60% and a 12 h light/dark cycle . Animals were individually housed in suspended stainless steel wire-bottomed cages and provided with a commercial primate diet . Fresh fruit was provided once daily and water was freely available at all times . A variety of environmental enrichment strategies were employed including housing of animals in pairs , providing toys to manipulate and playing entertainment videos in the animal rooms . In addition , the animals were observed twice daily and any signs of disease or discomfort were reported to the veterinary staff for evaluation . The menstrual cycles were assessed on the basis of menstrual bleeding , with the first day of menses designated day 0 of the cycle . For sample collection , animals were anesthetized with 10 mg/kg ketamine HCl ( Park-Davis , Morris Plains , NJ , USA ) or 0 . 7mg/kg tiletamine HCl and zolazepan ( Telazol , Fort Dodge Animal Health , Fort Dodge , IA ) injected intramuscularly . The animals were sacrificed by intravenous administration of barbiturates . Plasma from a ZIKV infected blood donor was used to produce the ZIKV stock for these studies . The donated blood was collected at the Hematology and Transfusion Center , Hospital of Clinics , Universidade Estadual de Campinas-UNICAMP , Campinas , SP , Brazil , and after it was found to positive for ZIKV by RT-PCR , the fresh frozen plasma was released for research . However , no donor personal identification information accompanied the sample and thus , the donor is anonymous [28] and IRB approval was not needed to isolate virus from the sample . We produced a high titer ZIKV stock from the plasma of a Brazilian blood donor [28] by short-term culture on Vero cells ( ATCC , Manassas , VA ) . The plasma was an aliquot of the same plasma sample from which strain Zika virus/H . sapiens-tc/BRA/2015/Brazil_SPH2015 was isolated [28] . The ZIKV stock contained approximately 107 PFU/ml of infectious virus when titrated by Vero cell plaque assay and approximately 6x109 vRNA copies/ml by the Taqman RT-PCR described below . The atraumatic virus inoculation procedure consisted of inserting a 1 CC needless tuberculin syringe containing 1 ml of the ZIKV stock into the vagina until the tip touched the cervix . Then the syringe was gently withdrawn while the viral inoculum was expelled . This procedure was repeated weekly until an animal was plasma ZIKV RNA+ on 2 consecutive time points ( Fig 1 ) . Animals that remained uninfected after 8 vaginal ZIKV inoculations were treated with Depoprovera using published protocols [29] that have been used to enhance vaginal SIV transmission in RM . Briefly , 4 weeks before , and on the day of , challenge with ZIKV , 30 mg of Depo-Provera [29] was administered by intramuscular injection . The Zika virus inoculum was sequenced in duplicate using a method adapted from Quick et . al . [30] . Briefly , viral RNA was isolated from 1 ml of cell culture supernatant using the Maxwell 16 Total Viral Nucleic Acid Purification kit . Approximately 1 . 4x105 viral RNA templates were converted into cDNA using the SuperScript IV Reverse Transcriptase enzyme . The cDNA was then split into two multi-plex PCR reactions using the PCR primers described in Quick et . al with the Q5 High-Fidelity DNA Polymerase enzyme . PCR products were then tagged with the Illumina TruSeq Nano HT kit and sequenced with a 2 x 300 kit on an Illumina MiSeq . Fastq reads were analyzed using a series of custom scripts generated in Python , as follows . First , up to 1000 reads spanning each of 35 amplicons were extracted from the data set . Extracted reads were then mapped to the Zika reference for PRVABC59 and Zika virus ( strain Zika virus/H . sapiens-tc/BRA/2015/Brazil_SPH2015 ) . Variant nucleotides were then called using SNPeff , using a 5% cutoff . The output . vcf and . bam files could be interrogated in Geneious and differences between the inocula and reference strains could be determined . Blood was collected from the femoral vein by venipuncture 3–4 times a week , on the day of ZIKV inoculation and , 2 , 4 , and often 6 , days later . Urine samples were collected from pans placed under the animals’ cages on the days that blood samples were collected . Cervicovaginal lavages ( CVL ) were also collected on the days blood samples were collected by vigorously infusing 1–2 ml of sterile PBS into the vaginal canal and aspirating as much of the instilled volume as possible . Care was taken to insure that the cervical mucus was included in the lavage fluid and that no trauma to the mucosa occurred during the procedure . One half of the CVL sample was snap frozen on dry ice and stored at −80°C until analysis . The remainder was spun and the resulting cell pellet was used RNA isolation . The supernatant was treated with 10× Protease Inhibitor ( Roche/Sigma Aldrich , St Louis Mo ) and subsequently used for cytokine and chemokine quantitation . The lavage for sample collection and the preparation procedure resulted in at least a 10-fold dilution of the cervicovaginal secretions . RNA was isolated from 1 ml urine , EDTA blood plasma , or CVL by QIAamp UltraSens Virus Kit ( Qiagen , Redwood City CA ) following the manufacturer’s protocol . Genital tract tissues ( vulva , vagina , cervix , uterus , ovary ) and genital lymph nodes ( inguinal , obturator and iliac lymph nodes ) , gut tissues ( duodenum , jejunum , ileum , colon and mesenteric lymph nodes ) , oral tissues ( lip/cheek pouch , tonsil , tongue , parotid salivary gland ) distal lymphoid tissues ( axillary , bronchial lymph nodes and spleen ) , urinary tract ( bladder , kidney ) , CNS ( Frontal cortex , temporal lobe , eye ) , cerebrospinal fluid ( CSF ) and blood were collected at the time of necropsy and analyzed for ZIKV RNA levels . Tissues were stored in RNAlater ( Ambion , Austin , TX ) and kept at -20°C until preparation of RNA . Tissues stored at -20°C thawed and removed from RNAlater were diced with a razor blade in a sterile petri dish as small as possible . Tissues fragments were then placed into 2 . 0ml screw cap Sarstedt tubes with 1 x 7mm stainless beads ( Qiagen , Redwood City CA ) added per tube with 600ul RLT Buffer and shaken 5 mins in bead beater to homogenize . The homogenates were processed using the Qiagen RNeasy Mini Kit ( Qiagen , Redwood City CA ) to extract total RNA with optional DNase treatment on column per manufacturer’s instructions . Skin and fibrous tissues were treated with additional proteinase K digestion as described in Appendix C of the kit handbook . Brain samples required 5ul of Reagent DX to prevent excessive foaming . We used monolayers of Vero cells ( ATCC , Manassas , VA ) to isolate infectious virus from selected tissue samples collected from the ZIKV-inoculated animals at necropsy . Briefly , up to 107 , tissue mononuclear cells isolated from tissues were added to a confluent monolayer of Vero cells in 6-well plates ( Costar Inc . , Cambridge , MA ) for tissues yielding <106 cells ) , or T25 flasks ( Costar Inc . ) for tissues yielding > 106 cells . The co-cultures were incubated at 37°C and culture supernatants were harvested at 2 , 4 and 7 days after initiation . The supernatants were assayed for the presence of ZIKV RNA by qRT-PCR ( described below ) . A sample was considered to be positive for infectious virus if the vRNA levels steadily increased in supernatants of the corresponding co-culture . No effort was made to titer the levels of infectious virus in samples . For urine , plasma , and CVL samples , 25ul of eluted RNA was converted to cDNA with Superscript III ( Thermo Fisher Scientific , Waltham , MA ) using random primers in a 60ul reaction and quantified in quadruplicate by qPCR on an Applied Biosystems QuantStudio 6 Flex Real-Time PCR System using 2x Universal Taqman Master Mix ( Thermo Fisher Scientific , Waltham , MA ) with published primers and probe that target the ZIKV E glycoprotein from Lanciotti et al [17] ( forward 5’-CGYTGCCCAACACAAGG-3’ , reverse 5’-CACYAAYGTTCTTTTGCABACAT-3’ , and probe 5’-6fam AGCCTACCTTGAYAAGCARTCAGACACYCAA-BHQ1-3’ ) . All RNA samples were tested in 4 replicate PCR reactions carried out in 96-well optical plates ( Applied Biosystems , Foster City , CA ) . All PCR reactions included primers and probes for GAPDH to detect problems with the assay or RNA isolation and all plates contained several wells that held only 25μl nuclease free water to detect contamination . Standard curves for the ZIKV E glycoprotein primers and probe assay were generated on every plate by making 10-fold dilutions of a purified 444bp E glycoprotein PCR fragment starting at a known concentration . The 444bp PCR fragment was generated for this purpose by PCR amplification from ZIKV stock cDNA using primers z_F: 5’-CATACAGCATCAGGTGCATAGGAG-3’ , z_R: 5’-AGCCATGAACTGACAGCATTATCC-3’ with Phusion HotStart II DNA Polymerase ( Thermo Fisher Scientific , Waltham , MA ) . The fragment was purified with QIAquick PCR Purification Kit ( Qiagen , Redwood City CA ) and the concentration calculated using the average of 6 independent spectrophotometer readings ( Nanodrop/\ , Thermo Fisher Scientific , Waltham , MA ) . Five 96-well plates of individually serially diluted standard curves with concentrations ranging from 107 copies/well to 1 copy/well were run to generate the line equation used to analyze all qPCR assays . For each 96 well plate , 11 wells of each dilution were run including positive and no-template controls . When the dilution of this fragment is done correctly , we generate 7–10 positive wells out of 10 at the 10-copy range and 2–3 positive wells out of 10 in the single copy range . Thus , the assay can detect a single copy of ZIKV env cDNA per well . To determine the sensitivity of the assay in actual samples , 10-fold serial dilutions of vRNA from the ZIKV stock were added to plasma or RNA extracted from a mesenteric LN collected from a ZIKV negative RM . The assay was negative when 10–15 copies of ZIKV RNA were added to the cDNA synthesis reaction , which results in about 1 vRNA copy in each well . However , 6 of 6 wells were positive when 100–150 copies of ZIKV RNA were added to the cDNA synthesis reaction , which is equivalent to 10–13 copies of vRNA in each well . There was no amplification of ZIKV E glycoprotein sequences from the RNA isolated from any plasma or tissue samples from ZIKV negative animals . Thus , we estimate that the limit of quantitation in this ZIKV E glycoprotein PCR assay is 120 vRNA copies/ml of CVL , plasma or urine . While in tissue samples , the limit of quantitation is 33 vRNA copies/ug of total tissue RNA analyzed . Viral load data from plasma , urine , and CVL are expressed as vRNA copies/ml . Viral load data from tissues are expressed as vRNA copies/ug total RNA . A commercial ELISA kit was used to test for the presence of ZIKV-specific-antibodies in plasma and CVL of inoculated animals . The NHP Zika virus serology test kit ( XpressBio , Frederick MD ) uses a Ugandan ZIKV NS1 protein as the capture antigen . There is about 97 . 5% amino acid identity between the Ugandan virus and contemporary circulating Asian ZIKV virus strains in the NS1 region . The kit was used as directed by the manufacture to test plasma samples . CVL samples , processed as described above , were diluted 1:1 and 1:2 and tested with the same kit . Twenty-nine cytokines , chemokines and growth factors were measured in plasma and CVL samples using the Monkey Cytokine Magnetic 29-Plex Panel for the Luminex ( Invitrogen , Carlsbad CA ) according to the manufacturer's instructions . The analytes measured included IL-1β , IL-1RA , IL-2 , IL-6 , IFN-γ , IL-12 , CCL3 , CCL5 , CCL11 , CXCL8 , CXCL9 , CXCL10 , CXCL11 , and MIF . EDTA-plasma samples were diluted up to four fold with assay diluent and CVL samples were diluted up to 4 fold with a 1:1 mixture of PBS and assay diluent . Samples were incubated with antibody-coupled beads for 2 hours at room temperature , followed by incubation with a biotinylated detection antibody for 1 hour and streptavidin-phycoerythrin for 30 minutes . Each sample was assayed in duplicate , and cytokine standards supplied by the manufacturer were run on each plate . Multianalyte profiling was performed using a Luminex-100 system , and data were analyzed using Miliplex analyst software , version 5 . 1 ( Millipore/Fisher Scientific , Waltham , MA ) . The median level of each analyte in a sample is reported . For these analytes , the sensitivity of the assay ranges from 0 . 5–20 pg/ml plasma according to the manufacturer . GraphPad Prism version 5 for Apple OSX10 . 4 ( GraphPad Software , San Diego California USA ) and Macintosh computers ( Apple Inc . , Cupertino CA ) were used for statistical analysis and graphing the data . Zika virus strain PRVABC59; genbank accession number KU501215 Zika virus strain/H . sapiens-tc/BRA/2015/Brazil_SPH2015; genbank accession number KU321639 . 1
We produced a high titer ( 107 PFU/ml/6x109 vRNA copies/ml ) ZIKV stock by culturing the plasma of a Brazilian blood donor [28] on Vero cells . The isolate was confirmed by next generation sequencing to be an Asian-lineage ZIKV . We mapped the sequences to the Zika-PRVABC59; genbank accession number KU501215 and Zika virus/H . sapiens-tc/BRA/2015/Brazil_SPH2015; genbank accession number KU321639 . 1 . We found that nucleotide and AA sequence of the major variant in our Zika virus stock was identical to the Brazilian KU321639 . 1 reference sequence but had 35 positions with fixed nucleotide differences compared to the Puerto Rican KU501215 reference sequence . There were two other minor variants present in the stock at a frequency of between 5–10%; the defining nucleotide differences were not in a location of repeated nucleotides . Thus , our ZIKV stock is essentially clonal as it contains only a few infrequent variations from a single ZIKV sequence . It has been reported that the dose of WNV or Dengue virus in an infected mosquito bite ranges from 104−106 PFU [31 , 32] . While the level of infectious ZIKV in semen is unknown , ZIKV RNA levels of 10 7–10 8 vRNA copies/ ml semen have been reported [4 , 33] . As one of the purposes of our study was to define the dose of ZIKV required for vaginal transmission , we chose to use a similar range of ZIKV doses for vaginal inoculation of RM . Thus , two animals were vaginally inoculated weekly with104 PFU ( 6x106 vRNA copies ) , two animals were inoculated with 105 PFU ( 6 x 107 vRNA copies ) and two animals were inoculated with 106 PFU ( 6 x 108 vRNA copies ) . There was a 7-day interval between each vaginal inoculation ( Fig 1 ) . Two RM became infected ( plasma ZIKV RNA+ ) after 1 vaginal inoculation with ZIKV ( Fig 1 ) . One ( 37812 ) of these 2 RM was exposed to a moderate virus dose ( 105 PFU/6 x 107 vRNA copies ) in the luteal phase of the cycle ( approx . cycle day 21 ) ( Fig 2B ) and the other ( 37072 ) to a low dose ( 104 PFU/6 x 106 vRNA copies ) of ZIKV in the peri-ovulatory phase of the cycle ( approx . cycle day 15 ) ( Fig 2A ) . A third RM ( 37828 ) became infected after 2 high dose ( 106 PFU/6x108 vRNA copies ) vaginal ZIKV inoculations with transmission occurring after the 2nd inoculation in peri-ovulatory phase of the cycle ( approx . cycle day 14 ) ( Fig 2C ) , and another RM ( 40125 ) after 5 vaginal inoculations with a high dose ( 106 PFU/6 x 108 vRNA copies ) of ZIKV with transmission occurring after the 2nd inoculation in follicular phase of the cycle ( approx . cycle day 7 ) ( Fig 2D ) . Finally , after 8 weekly vaginal ZIKV inoculations , one low dose RM ( 36813 ) and one moderate dose RM ( 39933 ) remained uninfected . Both of these animals were treated with Depoprovera and 30 days later they were re-inoculated vaginally with the same dose of ZIKV they were previously inoculated with 8 times without transmission . After Depoprovera treatment , both of these RM became infected after 1 vaginal ZIKV inoculation ( Figs 1 and 3 ) . Thus , RM were readily infected with ZIKV after vaginal inoculation with a concentration of ZIKV within the range that is found in human semen [4 , 33] . In all 4 Depoprovera-naive RM , plasma ZIKV RNA was first detected at 4 or 6 days post-inoculation ( PI ) , reached peak levels at 6–10 days PI and was undetectable by 9–14 days PI . The mean duration of virema was 8 . 2 days ( Fig 2A–2D ) . ZIKV RNA levels in CVL and urine were also determined . In 3 of 4 Depoprovera-naive RM , a blip of vRNA was detected in CVL 24–48 hours after vaginal inoculation , and then vRNA became undetectable ( Fig 2A , 2C and 2D ) . In 2 of these RM , vRNA reappeared in CVL before plasma vRNA was detectable ( Fig 2C and 2D ) . In the fourth RM , high and sustained levels of ZIKV RNA were found in CVL beginning at 3 days PI , prior to detection of plasma vRNA ( Fig 2B ) . Among all 4 Depoprovera-naive RM , CVL ZIKV RNA was detected at 2–6 days PI , peaked at 2–9 days PI and was undetectable by 12–21 days PI . The mean duration of ZIKV RNA shedding in CVL was 8 . 1 days ( Fig 2A–2D ) . In 2 of 4 RM , a blip of vRNA was detected in urine within 24–48 hours post-inoculation ( PI ) ( Fig 2B and 2C ) , with vRNA reappearing in urine before plasma vRNA was detectable in 1 of these 2 RM ( Fig 2C ) . In the other 2 RM , high and sustained levels of ZIKV RNA were found in urine beginning at 9–12 days PI , long after detection of plasma vRNA . Among all 4 RM , urine ZIKV RNA was detected by 1–11 days PI , peak levels occurred at 7–14 days PI and vRNA was undetectable in urine by 9–32 days PI , ( mean duration of urine ZIKV RNA shedding: 6 days ) ( Fig 2A–2D ) . The detailed virology of the 2 RM that resisted systemic infection , despite 8 vaginal ZIKV inoculations spanning 2 menstrual cycles , until they were treated with Depoprovera is shown in Fig 3 . On day 57 PI , 8 days after the last ZIKV vaginal inoculation on Day 49 PI and before DepoProvera treatment , vRNA was detected in one urine sample , but not plasma or CVL , of RM 36813 ( Fig 3B ) . However following Depoprovera treatment , vRNA was present in CVL 2 days after the vaginal ZIKV rechallenge on day 100 , while plasma vRNA was detected 2 days later on day 102 ( Fig 3B ) . In the other Depo-treated RM , ( 39933 ) ZIKV RNA was first detected in plasma , CVL and urine on day 102 , 4 days after vaginal ZIKV inoculation ( Fig 3A ) . We used a Luminex-based bead array assay to assess changes in the levels on cytokines and chemokines in the plasma and CVL in the 4 DepoProvera naive RM ( Fig 4 ) . All 4 RM ( 37072 , 40125 , 37182 , 38728 ) had clear increases in the level of macrophage inhibitory factor ( MIF ) in plasma . In addition 3 of 4 RM had increased plasma levels of L-1RA ( 37072 , 40125 , 37182 ) , and CCL5 ( RANTES ) ( 37072 , 40125 , 38728 ) , and 2 of 4 RM ( 37072 , 40125 ) had increased plasma levels of CCL11 ( Eotaxin ) , CXCL10 ( IP-10 ) and CXC11 ( I-TAC ) ( Fig 4 ) . The plasma levels of these mediators both increased and decreased after infection , but the highest levels of an analyte were generally found in plasma samples collected the day after peak vRNA levels and the lowest levels of most analytes were found in plasma samples with low vRNA levels ( Fig 4 ) . The pattern of changes in MIF levels were unique in that they increased prior to , or just after , initial detection of plasma vRNA; were lowest at the peak in plasma vRNA levels; and , in 3 of 4 RM ( 37072 , 40125 , 38728 ) , increased to their highest level days after the peak in plasma vRNA ( Fig 4 ) . The effect of vaginal ZIKV transmission on cytokine and chemokine levels in CVL was more dramatic and was detectable prior to changes in plasma levels of these analytes ( Fig 4 ) . All 4 RM had clear changes in the levels of IL-1b , IL-1RA , IL-6 and macrophage inhibitory factor ( MIF ) in CVL ( Fig 4 ) . In addition , 3 of 4 RM ( 40125 , 37182 , 38728 ) had increased levels of CXCL8 ( IL-8 ) , and 2 of 4 RM ( 37072 , 40125 ) had increased levels of CCL5 ( RANTES ) and CCL11 . The levels of these mediators in CVL both increased and decreased after infection , but the highest levels of most analytes were generally found in CVL samples with high vRNA levels and the lowest levels of most analytes were found in CVL samples with low vRNA levels ( Fig 4 ) . In 3 animals ( 37072 , 37182 , 38728 ) , IL-1Ra levels increased on the first day vRNA was detected in CVL and remained elevated until vRNA levels dropped ( Fig 4 ) . Of note , the levels of IL-1b and IL-1Ra were 10–100 fold higher in CVL than plasma ( Fig 4 ) , despite the dilution that occurs when CVL samples are collected . CVL sample collection began on Day 0 , just prior to the first ZIKV inoculation , and thus for the animals ( 37072 , 38728 ) that became infected after 1 ZIKV inoculation there was a single pre-infection sample , for the animal ( 38728 ) infected after 2 inoculations there were 1 week of pre-infection samples and for the animal ( 40125 ) infected after 5 inoculations 4 weeks of pre-infection samples are available . The cytokine levels in the pre-infection CVL of the latter 2 animals were relatively stable , except in the CVL samples collected during menses ( day -18 to -14 ) from 40125 , in which many of the analytes were elevated ( Fig 4 ) . We used a commercial ELISA assay to assess the levels of ZIKV-specific antibodies in plasma and CVL of the ZIKV inoculated animals . For all 6 RM infected with ZIKV after vaginal inoculation ( Fig 1 ) , paired plasma and CVL samples collected weekly from the day of first ZIKV inoculation to necropsy were tested . Of the 4 RM that became infected without DepoProvera treatment , ZIKV-specific antibodies were detected in plasma of one ( 37072 ) 7 days after vaginal ZIKV transmission and 14 days after vaginal ZIKV transmission in the other 3 RM ( 37812 , 38728 , 40125 ) ( Fig 2 ) . ZIKV-specific antibodies were never detected in the CVL samples of any of these 4 animals ( Fig 2s ) . The 2 RM that remained ZIKV negative after 8 vaginal inoculations but then became infected after DepoProvera treatment and 1 additional vaginal inoculation , remained anti-ZIKV plasma antibody negative from the day of the first ZIKV inoculation until necropsy in the acute stage of infection ( Fig 3 ) . To better understand the tissue tropism of ZIKV , we determined vRNA levels in tissues of all 6 RM infected with ZIKV by vaginal inoculation ( Fig 5 ) . The 2 RM treated with Depoprovera prior to infection ( Fig 1 ) were necropsied at 4 and 8 days after vaginal ZIKV inoculation , when vRNA was detectable in plasma and CVL . At 4 days PI ( 39933 ) , ZIKV RNA was present at low to moderate levels in the urinary tract , FRT and draining lymph nodes . vRNA was also detected in distal lymph nodes and spleen ( Fig 5 ) . At 8 days PI ( 36813 ) , vRNA levels were 100–1000 fold higher in all tissues , with the highest levels in salivary glands and lymphoid tissues . ZIKV RNA was also detected in the central nervous system ( CNS ) at this early stage of infection ( Fig 5 ) . In addition , infectious ZIKV was isolated from 1 of 6 vRNA+ lymphoid tissues at 4 days PI ( 39933 ) ; while at 8 days PI ( 36813 ) ZIKV was isolated from 6 of 6 lymphoid tissues tested ( Table 1 ) . The remaining 4 RM were necropsied between 30 and 35 days PI , about 2 weeks after vRNA was last detectable in plasma . At this stage , the RM were plasma ZIKV RNA negative and anti-ZIKV IgG positive . However , ZIKV RNA was detected at low to moderate levels in all lymphoid tissues tested from all 4 RM . In addition , low level ZIKV RNA was detected in the CNS ( temporal lobe of brain ) of one RM and the FRT ( uterus ) of a second RM ( Fig 5 ) . Zika RNA is also detected in tissues , including the brain and male and female reproductive tissues , during early and late stages of infection after SQ ZIKV inoculation of RM [34–36] . However , we were not able to recover infectious ZIKV from tissues of any of these 4 animals ( Table 1 ) . Thus , the significance of the ZIKV RNA that persists in tissues of RM long after it is cleared from plasma is unclear .
Given the severe disease ZIKV can cause in a developing fetus [37] , the risk of transmission to women during pregnancy is of particular concern . Despite documented cases of ZIKV sexual transmission [1–13] , the frequency and efficiency of sexual ZIKV transmission is unclear . Two modeling studies of ZIKV transmission dynamics in the recent outbreak in the Americas estimated that sexual transmission contributed between 3–45% to the overall basic reproduction number ( R0 ) of ZIKV in a population [38] [39] . Obviously , this very wide range indicates that there is still considerable uncertainty about the significance of sexual transmission ZIKV in propagating and maintaining the virus in human populations [38] [39] . To better understand the potential for sexual transmission of ZIKV , a NHP model of vaginal transmission is needed . Macaques were experimentally infected with mouse-brain passaged ZIKV in the 1950s , however , until recently there were no published reports describing the biology of ZIKV infection in nonhuman primates . Since early 2016 , animal models of human ZIKV have been developed using Type-1 IFN-antibody treated mice , Type-1 IFNR knockout mice [40–46] and RM [35 , 36 , 47–49] . To date , the reported non-human primate ( NHP ) studies have used intravenous ( IV ) or SQ routes of ZIKV inoculation to infect RM [35 , 36 , 47 , 48] . The data reported here demonstrate that ZIKV can be readily transmitted to mature cycling female RM by vaginal inoculation . Perhaps , the most striking finding in this study is that the kinetics of virus replication and dissemination in RM after intravaginal ZIKV inoculation are markedly different than after SQ virus inoculation [34 , 36 , 49] . After SQ inoculation of RM with Asian lineage ZIKV , vRNA is detected in blood plasma as early as 1 d after infection and subsequently in both the urine and saliva [36 , 49] . The appearance of vRNA in urine and saliva is delayed and blunted when compared to plasma and ZIKV RNA was detected only infrequently in CVL of RM after SQ inoculation [36 , 49] . As in SQ inoculated RM , ZIKV shedding from the FRT is rare in ZIKV-infected women [50] the majority of whom were presumably infected by mosquito bite . In SQ inoculated RM , viral RNA is cleared from plasma and urine by day 10 , but remains detectable in saliva and semen for more than 3 weeks [36] . In marked contrast , plasma vRNA is delayed by several days , and virus shedding from the FRT occurred , in all RM inoculated with ZIKV intravaginally ( Fig 2 ) . Of note , ZIKV is found in the FRT of a subset of infected women [51–53] , and it is tempting to speculate that in these cases the virus was sexually acquired . In addition to the delay in plasma vRNA in ZIKV vaginally inoculated RM compared to SQ infected RM , virus dissemination to tissues was slower and stepwise in the vaginally inoculated animals . Four days after vaginal inoculation , ZIKV RNA was present at low to moderate levels in the urinary tract , FRT , draining lymph nodes distal lymph nodes , spleen . However , at 8 days PI , vRNA levels were 100–1000 fold higher in all tissues , with the highest levels in salivary glands and lymphoid tissues indicating that the virus was still disseminating more than 1 week after infection . At 30 and 35 days PI , the vaginally infected RM were plasma ZIKV RNA negative but had low to moderate ZIKV RNA levels in all lymphoid tissues tested . In addition , low level ZIKV RNA was detected in the uterus of one of these 4 RM ( Fig 5 ) . Similarly , 7 days after SQ ZIKV inoculation high levels of ZIKV RNA were found in numerous tissues , including the brain and reproductive tract; and ZIKV RNA persisted through day 35 PI in neuronal , lymphoid and joint/muscle tissues [34 , 36] . However , while infectious ZIKV was isolated from multiple tissues at day 7 PI , infectious virus was not found in tissues collected at 28 days PI [34] . Thus , although ZIKV RNA seems to persist in target tissues for a considerable period after it is cleared from the blood , it remains to be seen if this persistent RNA contributes to pathogenesis or can serve as a reservoir for infectious virus . In the RM infected by vaginal ZIKV inoculation , the levels ZIKV RNA in CVL was similar to plasma vRNA levels . Given the 10–100 fold dilution of cervicovaginal secretions that occurs during the CVL collection process , vRNA levels in CVL were at least equal to , and often higher than , plasma vRNA levels ( Fig 2 ) . Thus the FRT is able to support a high level of ZIKV replication . The timing of ZIKV shedding in CVL also demonstrated that virus replication in the FRT was independent of systemic replication . Often ZIKV RNA was detected in CVL before it appeared in plasma and ZIKV RNA could also be found in CVL after virus had been cleared from plasma . This suggests that the virus being shed in CVL is from local replication in the FRT that is independent of virus replication in other tissues . The presence of ZIKV in the FRT after its disappearance from blood and urine samples has also been documented in women [51 , 52] , which suggests that the ZIKV preferentially replicates in the FRT of RM and women that acquire the infection through sex or vaginal inoculation . There was substantial variability between the individual RM in susceptibility to infection after vaginal ZIKV inoculation in this study . It has been reported that the stage of the menstrual cycle at vaginal inoculation effects susceptibility to infection with SHIV and SIV in RM [54 , 55]Sodora . In these reports , susceptibility to viral infection was highest in menses and the luteal phase of the cycle [56] . In the current study , of the 4 ZIKV+ animals infected without Depo-Provera treatment , 37072 was infected in peri-ovulatory phase of the cycle ( approx . cycle day 15 ) ; 38728 was infected in peri-ovulatory phase of the cycle ( approx . cycle day 14 ) ; 37812 was infected in early luteal phase of the cycle ( approx . cycle day 21 ) ; and 40125 was infected in follicular phase of the cycle ( approx . cycle day 7 ) ( Fig 2 ) . Thus , there is no evidence that the stage of the menstrual cycle at exposure explains the variability vaginal ZIKV transmission in these 4 monkeys , however this initial observation needs to be confirmed in larger studies . Depo-Provera , a brand of the injectable hormonal contraceptive depot-medroxyprogesterone acetate ( DMPA ) , is the most widely used injectable contraceptive in the world . We chose to test the effects of Depoprovera on vaginal ZIKV transmission because DMPA treatment enhances infectivity of viruses in various rodent and nonhuman primate models of female genital tract infection [57–61] . In fact , progesterone treatment is needed to infect mice with ZIKV by vaginal inoculation [46] . In addition , some observational studies identified DMPA as a significant risk factor for acquisition of HIV and other sexually transmitted infections ( STI ) in women , while other studies failed to detect this association [62–65] . Our observation that , after Depoprovera treatment , both of the RM that initially resisted vaginal ZIKV transmission became infected with one vaginal ZIKV inoculation is consistent with the conclusion that Depoprovera enhanced susceptibility to vaginal ZIKV transmission . Caution is warranted in interpreting our study however as only 2 animals were treated with Depo-Provera in the study . Several mechanisms have been proposed to explain enhanced STI acquisition with Depo-Provera including mucosal epithelium thinning , enhanced tissue inflammation , suppressed cell-mediated immune responses , and altered vaginal microbiota . However , none of these putative biological mechanisms are experimentally proven [66 , 67] . It was recently reported that Depoprovera use in women is associated with increased hemoglobin , immune activation markers ( HBD , HBB , IL36G ) , and decreased epithelial repair proteins ( TFF3 , F11R ) in reproductive tract secretions [68] . Further , in mice Depo-Provera reduced expression of the desmosomal cadherin desmoglein-1α in the genital epithelium , enhanced inflammatory cells numbers in genital tissue by increasing mucosal epithelial permeability , and increased susceptibility to HSV-2 infection [69] . The results of both of these recent studies suggest that Depo-provera mediated increases in mucosal permeability facilitate endogenous vaginal microbiota invasion and tissue inflammation by breaking down the epithelial barrier . Thus the most likely explanation for enhanced vaginal ZIKV virus transmission in the Depo-Provera treated animals is that increased permeability of the vaginal mucosa allowed the virus inoculum to access more target cells in the lamina propria . Although our ZIKV inoculum was delivered to monkeys as cell-free virions suspended in tissue culture fluid , women are exposed to ZIKV virions in semen , which may affect virus transmission . Seminal plasma ( SP ) has a basic pH that neutralizes the acidic pH of the vagina , thus seminal plasma may limit the inactivation of ZIKV deposited into the vagina . In fact , it has been shown that SP boosts SIV and HIV-1 infection in vitro and semen amyloid proteins contribute to this activity [70–74] . However , the significance of the in-vitro observations is unclear , as the addition of semen , SP or semen amyloid proteins does not dramatically enhance vaginal SIV transmission [75] . However , it has been reported that SP marginally increases vaginal SIV transmission if low-dose viral inoculums are used [76 , 77] . In addition , human and macaque seminal plasma are complex biologic fluids that vary substantially in chemical composition between individuals , and between individual ejaculates making it impossible to replicate experiments without using an aliquot of the semen sample used in the original experiment . Due to the limited volume , it is not possible to use the same human or macaque seminal plasma material for more than a few experimental vaginal inoculations . These technical factors make it impractical to use seminal plasma in animal experiments modeling vaginal virus transmission if reproducible results are desired . To insure the reproducibility of the results in the studies reported here , we did not include seminal plasma in the inoculum . RM infected by SQ inoculation with ZIKV during the first trimester of pregnancy have persistent plasma vRNA , leading to the hypothesis that the fetus or placenta may be the source of persistent virus replication in the immune suppressed pregnant female [49] . This conclusion is consistent with a report that the placental/fetal tissues from 24 of 44 women suspected of being infected with Zika virus during pregnancy were positive for ZIKV RNA by RT-PCR . [78] . However the results reported here , and previous results in RM [34] and women [51 , 52] , demonstrate that ZIKV RNA persists in the FRT and lymphoid tissues in non-pregnant RM and these tissues are another possible source of persistent plasma vRNA in pregnant animals . Although we detected anti-ZIKV IgG antibodies in plasma of all 4 animals infected for more than 10 days with ZIKV . We did not detect anti-ZIKV IgG antibodies in CVL of any animals . This is unexpected as antiviral antibodies are routinely found in CVL of RM and women [79–82] . It is possible that this result is due to a technical issue with the commercial ELISA kit we used . We are in the process of developing ELISA assays to measure anti-ZIKV IgG subclass and IgA antibody responses and these assays will clarify whether anti ZIKV antibody responses that were undetectable by the commercial assay are present in the CVL . Our inability to detect a plasma antibody response in the 2 animals inoculated 8 times with ZIKV is consistent the lack plasma vRNA in the animals and confirms that they remained uninfected despite the repeated ZIKV exposures . Apparently , in the absence of infection , the amount of ZIKV antigen in the inoculum is insufficient to elicit a systemic antibody response when placed on the mucosal surfaces of the FRT . The systemic cytokine response is minimal after SQ ZIKV inoculation of RM [34] , and it was suggested that the low levels of cytokine activation in vivo may be the result ZIKV inhibiting the innate immune pathways that direct synthesis and secretion of pro-inflammatory cytokines [34] . However , we found evidence that vaginal ZIKV transmission and subsequent systemic infection results in an acute inflammatory response characterized by increases in pro-inflammatory cytokines and chemokines in CVL and , to a lesser degree , plasma . Further , after vaginal ZIKV transmission , the inflammatory response in the FRT corresponded temporally to periods of local ZIKV replication . Thus , peak levels of ZIKV shedding/replication in the FRT were often associated with increased levels of pro-inflammatory cytokines ( IL-1b , IL-6 ) , anti-inflammatory mediators ( IL-1RA ) and a subset of chemokines in CVL . These changes are consistent with an acute antiviral inflammatory response to local ZIKV replication and viral mediated tissue damage in the FRT . However , the pattern and levels of the inflammatory mediators were very different in the blood and CVL . MIF and IL1Ra were elevated in both plasma and CVL of 3 of 4 RM . While IL-6 were elevated in CVL , but not in plasma , ( Fig 4 ) all animals and CXCL8 was elevated in CVL but not plasma of 3 of 4 animals . In addition , given the 10-fold dilution of secretions that occurs during the collection of CVL , the concentration of all these inflammatory mediators was generally higher in CVL than plasma . Thus , after vaginal ZIKV transmission , there was an obvious local and systemic inflammatory response that was delayed and enhanced compared to that reported in SQ inoculated RM [34] . This finding suggests that the pathogenesis of ZIKV disease can vary with the route of transmission . Taken together , the distinct timing and nature of the inflammatory response in the FRT compared to blood and the unique pattern of virology in the FRT , is consistent with the conclusion that ZIKV replication in the FRT is independent of replication in the systemic compartment . The pattern of inflammation in the FRT and systemic compartments also provides considerable insight into ZIKV pathogenesis . MIF , the only cytokine that was elevated in both plasma and CVL samples of all 4 animals . DENV infection induces MIF production and secretion and secreted MIF can enhance DENV replication and increase vascular leakage through autophagy [83] . Thus MIF may contribute to inflammation and hemostatic abnormality during DENV infection [84] and there is a correlation between MIF serum levels and disease severity in dengue patients [85] . The high concentrations of IL-1b and CXCL8 in the CVL after ZIKV infection suggest that enhanced neutrophil recruitment is a major response to ZIKV replication in the FRT [86–88] . Recruitment of neutrophils i requires the upregulation and release of IL-1β [89 , 90] and IL-1 also markedly prolongs the lifespan and stimulates the effector function of neutrophils and macrophages [91] . IL1Ra was elevated in the plasma of 3 of 4 RM and the CVL of all RM . Of note , the levels of IL-1Ra were 10–100 fold higher in CVL than plasma ( Fig 4 ) . IL-1Ra competes with IL-1 for binding to the IL-1 receptor , blocking IL-1–induced pro-inflammatory signaling , and thus , may affect viral pathogenicity . Elevated levels of IL-1Ra have been described in humans with a number of viral infectious diseases [92–94] , but the role of IL-1Ra in viral pathogenesis is unclear . Changes in the levels of IL-6 were found in the CVL of all 4 animals tested . Although , IL-6 is considered a marker of inflammation L-6 levels do not necessarily correlate with the levels of other inflammatory cytokines and IL-6 directly affects the adaptive antiviral immune response . IL-6 affects differentiation of CD4 T cells [95] and can also modulate aspects of the innate immune response to viral infection [96–98] . The findings that ZIKV shedding in CVL is not related to plasma vRNA levels and that a local inflammatory response develops in the FRT that is distinct from the systemic response is consistent with the conclusion that ZIKV replicates , and persists , in the FRT independent of the systemic ZIKV infection . This conclusion is also supported by observation that after vaginal ZIKV inoculation of IFNR+/+ mice , ZIKV replicates in the FRT but not in systemic tissues [43] . Thus , data from both the NHP and mice models of vaginal ZIKV transmission support the conclusion that , after vaginal ZIKV transmission the virus preferentially replicates in the FRT independent of replication levels in other tissues . The unusual tropism of ZIKV for the FRT raises the possibility of additional unexpected effects of vaginal ZIKV transmission , including the potential for enhanced fetal infection and pathology . In addition , it remains to be shown that a vaccine that protects animal models from mosquito transmitted ZIKV can protect against vaginal ZIKV transmission . | Zika virus was introduced to Brazil in 2015 and it rapidly spread to all of tropical America . Although Zika virus infection is usually mild in adults , it can cause severe birth defects in the developing fetus that makes it critical to prevent ZIKV infection in women who are pregnant or who could become pregnant . Although Zika virus is transmitted primarily by mosquito bite , it can also be transmitted by sex . To understand the role of sexual transmission in Zika virus disease , we inoculated rhesus monkeys intravaginally with the virus and monitored virus in blood and reproductive tract secretions . ZIKV was detected in the female reproductive tract before it was detected in plasma and replication levels in the female reproductive tract did not reflect ZIKV levels in other parts of the body . Thus ZIKV prefers the reproductive tract after vaginal transmission suggesting that fetal disease could be more common or severe after vaginal ZIKV transmission compared to a mosquito transmitted ZIKV infection . | [
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"d... | 2017 | Zika virus preferentially replicates in the female reproductive tract after vaginal inoculation of rhesus macaques |
Bacteria–host interactions are dynamic processes , and understanding transcriptional responses that directly or indirectly regulate the expression of genes involved in initial infection stages would illuminate the molecular events that result in host colonization . We used oligonucleotide microarrays to monitor ( in vitro ) differential gene expression in group A streptococci during pharyngeal cell adherence , the first overt infection stage . We present neighbor clustering , a new computational method for further analyzing bacterial microarray data that combines two informative characteristics of bacterial genes that share common function or regulation: ( 1 ) similar gene expression profiles ( i . e . , co-expression ) ; and ( 2 ) physical proximity of genes on the chromosome . This method identifies statistically significant clusters of co-expressed gene neighbors that potentially share common function or regulation by coupling statistically analyzed gene expression profiles with the chromosomal position of genes . We applied this method to our own data and to those of others , and we show that it identified a greater number of differentially expressed genes , facilitating the reconstruction of more multimeric proteins and complete metabolic pathways than would have been possible without its application . We assessed the biological significance of two identified genes by assaying deletion mutants for adherence in vitro and show that neighbor clustering indeed provides biologically relevant data . Neighbor clustering provides a more comprehensive view of the molecular responses of streptococci during pharyngeal cell adherence .
Microarray technology is now commonly used to reveal genome-wide transcriptional changes in bacterial pathogens during interactions with the host . Several factors , however , limit the power of such analyses , including inadequate statistical analysis and insufficient sample replication , both of which do not account for experimental variability , and often result in arbitrary thresholds for significance [1 , 2] . In addition , unknown bacterial genes can confound the interpretation of expression profiles , restricting many microarray studies to the differential expression of well-characterized genes . Several methods are available to organize gene expression profiles and to assist in extracting functional or regulatory gene information from microarray datasets . Clustering algorithms group genes by similarities in expression patterns , based on the assumption that co-expressed genes share common function or regulation [3 , 4]; however , clustering solely by co-expression patterns may not reveal a considerable amount of information contained in array data . These methods often: ( 1 ) produce unreliable data by missing known gene members of biological pathways; ( 2 ) fail to distinguish truly related gene clusters from coincidental groupings; and ( 3 ) identify clusters containing only unknown genes that may lack either common function or regulation , a considerable limitation for genomes containing a large percentage of undefined genes [1 , 2] . Because no tools exist to interpret unknown gene clusters or to assess their significance and completeness , a significant portion of bacterial expression profiles are not interpretable using current clustering methods . We introduce neighbor clustering as a new tool for analyzing bacterial microarray data that addresses some of these limitations by incorporating the physical position of genes on the bacterial chromosome into the analysis of expression data . Information about gene function and regulation is stored intrinsically in the bacterial genome structure , as genes with common function or regulation tend to be physically proximate on the chromosome and often linked as operons [5 , 6] . We incorporated these positional data into a series of neighbor clustering algorithms , named GenomeCrawler , that identifies groupings of potentially related genes from array data by combining two informative characteristics of bacterial genes that share common function or regulation [3–6]: ( 1 ) similar gene expression profiles ( i . e . , co-expression ) ; and ( 2 ) physical proximity of genes on the chromosome . The algorithms also recalculate the statistical significance of each gene as a member of a particular cluster , as well as the significance of each resulting grouping as a whole , to ensure accuracy of cluster assignments . This process ultimately identifies significant clusters of co-expressed gene neighbors that likely share common function or regulation . We used this approach to analyze microarray expression data from group A streptococci ( Streptococcus pyogenes ) during adherence to human pharyngeal cells , the first overt infection step [7] . The ability of all bacterial pathogens to infect the human host depends upon coordinated regulation of diverse gene sets that are required for survival in host environments . Although recent microarray studies have highlighted the molecular responses of streptococci in relevant host conditions [8–10] , characterizing differentially expressed loci during pharyngeal cell adherence is critical for understanding the molecular basis for host colonization . Studies from our laboratory [11 , 12] and others [13] have demonstrated that in vitro association with pharyngeal cells results in streptococcal phage induction and the increased expression of phage-encoded virulence factors . Although the mechanisms mediating these responses are not known , the results of these studies indicate that streptococci sense and , on a transcriptional level , respond to various signals and cues in the pharyngeal cell environment . We undertook the present study to understand and to assess more accurately the genome-wide transcriptional responses of streptococci during one of the earliest recognized stages of infection , namely adherence to human pharyngeal cells . We compared data generated before and after neighbor clustering to show that this method provides a more comprehensive view of transcription by: ( 1 ) identifying more differentially expressed genes than even traditional , rigorous statistical analyses; ( 2 ) reconstructing intact biological pathways that statistical significance analysis could not reconstruct; and ( 3 ) providing preliminary insight and clues about the function or regulation of uncharacterized genes by associating their co-expression with physically proximate , functionally defined genes .
We developed spotted oligonucleotide arrays of the S . pyogenes SF370 ( an M1 serotype ) genome [14] and compared the transcriptomes of streptococci that adhere to Detroit 562 human pharyngeal cells to non-adherent ( “associated” ) streptococci within the same experiment . Adherence assays were performed as described [15] with modifications to minimize eukaryotic cell disruption . We replicated experiments independently and used dye-swaps to incorporate biological and technical variation [16 , 17] . Following filtering and normalization [18 , 19] , we analyzed data from four biological replicates [16] with robust summary statistics [20] , Bayesian statistics [21 , 22] , and permutation algorithms [19] to identify genes differentially expressed with significance during pharyngeal cell adherence . This analysis identified 79 genes ( 4% of the genome ) exhibiting statistically significant fold changes in expression ( PF value < 0 . 05 ) during adherence from 1 , 769 open reading frames represented on the array ( Table 1 ) . We refer to such genes as “differentially expressed . ” We present the entire dataset from all experiments as Table S1 . Genes demonstrating upregulation ( n = 45 ) and downregulation ( n = 34 ) included virulence factors , prophage-encoded transcripts , metabolic genes , and transcriptional regulators ( Table 2 ) . Undefined or hypothetical genes comprised 27% of differentially expressed genes ( n = 21; 11 chromosomally encoded genes , ten phage-encoded genes ) . We conducted TaqMan ( qRT-PCR ) analysis [23] of 11 differentially expressed genes to validate selected microarray hybridization results ( see Table S2 for genes and primer–probe sequences ) . Five genes chosen for validation demonstrated statistically significant fold changes in expression by microarray analysis ( PF value < 0 . 05; two upregulated , three downregulated ) . The remaining six genes ( four upregulated , two downregulated ) did not have significant PF values , but were statistically significant as members of particular neighbor clusters in subsequent analyses ( PE < 0 . 05 ) as detailed in later sections ) . We averaged the data to generate a value for each gene , creating a set of 11 paired values from quantitative real-time ( qRT ) -PCR and microarray analyses ( Table S3 ) . Results of standard linear regression analysis demonstrated a strong positive correlation ( r = 0 . 9 ) between data obtained using the different techniques ( see Figure S1 ) . Streptococci elaborate several factors implicated in infection , including surface-exposed adhesins and secreted toxigenic proteins ( reviewed in [7 , 14 , 24] ) . The initial statistical analysis identified four differentially expressed virulence genes ( Tables 1 and 2 ) . Genes encoding streptolysin O ( slo or spy0167 ) and the SpeB protease ( spy2039 ) were downregulated , while genes encoding pyrogenic exotoxin H ( speH or spy1008 ) and a putative fibronectin-binding protein ( spy0130 ) were upregulated . We verified the differential expression of spy2039 and spy0130 by qRT-PCR . The downregulation of virulence loci during presumably inappropriate stages of infection was not surprising . Streptolysin O is a cytotoxin that damages human tissue and increases host cell cytotoxicity [7 , 25] . The resulting cellular damage , particularly to polymorphonuclear leukocytes [26] , decreases internalization and subsequent intracellular killing of streptococci [27] . Based on its downregulation during adherence , we infer that slo was transcribed during pre-adherence associations , perhaps , as previously reported , to protect streptococci from phagocytic killing in vivo [27] . However , once adhered , our data suggest that streptococci downregulate production of this cytotoxin , presumably to prevent further host tissue destruction that could interfere with adherence . SpeB ( encoded by spy2039 ) is a multifunctional cysteine protease implicated in numerous infection strategies [28 , 29] . Although few studies have examined gene expression patterns during adherence , SpeB production ( as detected by Western blot analysis ) decreases during co-culture with human peripheral blood mononuclear cells [30] and in a mouse infection model [31] . When SpeB expression is limited , several streptococcal proteins necessary for adherence remain intact [24 , 32 , 33]; thus , decreased SpeB production ( as indicated here ) may promote pharyngeal cell attachment . Furthermore , SpeB abolishes internalization ( following adherence ) of certain streptococcal strains by epithelial cells ( including Detroit 562 cells ) , a process mediated in part by the fibronectin-binding protein F [34 , 35] . We observed significant upregulation of the gene spy0130 , encoding a protein recently found to be associated with the production of surface-exposed pili on strain SF370 [36] . The protein shares 60% sequence similarity to protein F , suggesting that it may coordinate a similar internalization mechanism or may be involved directly in adherence ( discussed later in detail ) . SpeB downregulation also coincides with increased expression of pyrogenic exotoxins [33 , 37] that reportedly increase streptococcal survival in vivo . We observed that the exotoxin-encoding speH gene [38] was upregulated . Taken together , our results agree with previous reports on SpeB production during host cell interactions , suggesting that decreased expression may promote streptococcal adherence ( by preventing proteolytic degradation of key virulence factors or adhesins ) , enhance internalization ( perhaps through a fibronectin-mediated pathway ) , and increase survival ( through increased pyrogenic exotoxin production , discussed below ) . SF370 contains one inducible prophage ( 370 . 1 ) and three defective prophages ( 370 . 2 , 370 . 3 , and 370 . 4 ) that produce no infectious phage [39] . We identified 11 differentially expressed phage 370 . 2 genes , suggesting that this defective phage is not transcriptionally silent ( Table 1 ) . The speH gene ( spy1008 ) was induced , and the remaining genes , hypothetically involved in replication and regulation [39] , were downregulated . The speH gene encodes a mitogenic exotoxin [38] reportedly induced during polymorphonuclear leukocyte phagocytosis [8] but not implicated previously in adherence . Increased expression of speH during pharyngeal cell adherence suggests that the SpeH exotoxin is either necessary for adherence , or is a component of a downstream infection process . Adherence-mediated upregulation of speH is likely not the result of phage induction , as the remaining phage 370 . 2 genes identified in our analysis were downregulated . To determine if SpeH plays a direct role in the adherence process , we created a deletion mutant in strain SF370 ( SF370ΔspeH ) , which was confirmed by PCR ( unpublished data ) and RT-PCR ( Figure 1A ) and tested in vitro for adherence to human pharyngeal cells . We observed no significant difference in adherence between the wild-type ( SF370 ) and mutant strains ( Figure 1B ) , indicating that SpeH is not involved directly in attachment to the pharyngeal cell . The significant upregulation of the speH gene during adherence suggests that the gene product may function instead during a subsequent stage of infection . We identified a number of genes encoding proteins involved in housekeeping processes ( such as carbohydrate and coenzyme metabolism ) that were differentially expressed , indicating a shift in metabolic processes due to host cell adherence ( Tables 1 and 2 ) . For example , genes encoding proteins involved in folate biosynthesis [40] were upregulated , suggesting that certain cofactors that may be necessary during adherence were unavailable . Also upregulated were genes encoding subunits of the F0F1 ATPase [41] ( discussed in more detail later ) , which may indicate an acid stress response to maintain cytoplasmic pH or a need to generate ATP in response to increased energy requirements . We also identified the adherence-mediated upregulation of four transcriptional regulators ( Table 1 ) , suggestive of an adaptive response to host cell contact that is dynamic and complex . For example , RopB ( encoded by spy2042 ) , a member of the Rgg family of response regulators , interacts with a number of regulatory networks throughout the streptococcal genome ( e . g . , mga , csrRS , sagA , and fasBCA ) , affecting the transcription of numerous proteins , virulence factors , and two-component regulatory systems [42 , 43] . Although the delineation of genes influenced by RopB ( or any identified transcriptional regulator ) is beyond the scope of this study , our initial analysis did identify the upregulation of a two-component regulatory system , encoded by spy1236–1237 . The functions of these particular loci are not yet known , and their adherence-mediated upregulation represents new targets in the study of regulators that function during host cell contact . Our initial analysis revealed the differential expression of a wide range of functionally diverse genes and provided insight into the adaptive response of streptococci to host cell contact . However , despite a rigorous statistical approach , this analysis , like many previous microarray studies , identified the differential expression of a large number of unknown genes ( n = 21 ) and a number of incomplete biological pathways ( e . g . , F0F1 ATPase [41] and folate biosynthesis [40] ) by failing to detect the differential expression of a number of known gene pathway members ( Table 1 ) . To overcome these limitations and to extract more functional information from the array dataset ( including more complete biological pathways ) , we developed the neighbor clustering algorithms to combine the physical position of genes on the streptococcal chromosome with gene expression data . Neighbor clustering was designed to identify expanded groupings of potentially related genes from our array data by incorporating two reliable predictors of genes that share common function or regulation , namely physical proximity and similar expression profiles [5 , 6] . We implemented this approach by developing an algorithm with dynamic windowing ( GenomeCrawler ) that sequentially stepped through the microarray data and identified clusters of adjacent genes exhibiting similar fold changes in expression . Because the genome contains many possible clusters , we restricted the algorithm's search space to identify only spatially related clusters . GenomeCrawler applied a separate permutation algorithm , using the sum of each gene's t-statistics to calculate adjusted P values ( PK ) for each cluster , which corresponded to the probability of assembling a cluster by chance . Significance was assigned to clusters with PK < 0 . 05 , and the resulting groupings are listed in Table 3 . Because individual genes could be members of many different significant clusters , GenomeCrawler then applied a distinct permutation algorithm to calculate the probability ( PC ) that a gene was clustered coincidentally . Calculation of PC values relies on Bayes' Theorem , in which the probability of a gene's log2-fold change ( PF value ) is combined with the cluster probability itself ( PK value ) . We stress that PC reflects the significance of a gene based on its cluster context rather than a recapitulation of PF . This ensures a strong dependency between PF and PC , preventing a gene with a relatively low log2-fold change from being scored as significant simply because it is clustered with a gene with a highly significant PF value . Finally , GenomeCrawler calculated the overall significance of differentially expressed genes ( PE values ) by integrating differential expression probabilities ( PF ) and cluster context probabilities ( PC ) . We developed a plotting application ( GenomeSpyer ) that represents the chromosome as a linear molecule to visualize GenomeCrawler output , with genes displayed on the x-axis and their log2-fold change magnitudes on the y-axis . Applications and all datasets are available for download at http://www . rockefeller . edu/vaf/streparray . php . We visually inspected the resulting clusters and disqualified those that violated our neighbor cluster definition ( see Methods for details ) . All output prior to cluster disqualifications is included for comparison ( see Table S4 ) . Of the 309 qualifying clusters ( Table S5 ) , 197 ( 63 . 8% ) were composed entirely of known , functionally defined genes; however , 26 ( 13% ) of these were incorrectly assembled , as they contained known genes that are functionally unrelated . Because we did not incorporate functional annotations of genes into the algorithms ( i . e . , to keep the analysis “blind” ) , we anticipated the possibility that some groupings could be assembled incorrectly despite the statistical framework for assigning clusters . Of the remaining 283 ( 91 . 6% ) groupings , a number of differently sized clusters contained the same gene ( Table S5 ) . We report such clusters first by highest significance ( lowest PK value ) , then by largest number of genes . Thus , if clusters containing a particular gene were of equal significance , we report the cluster with the most gene members . This method identified 47 significant clusters containing 173 differentially expressed genes ( listed in Table 3 and visualized in Figures 2 and S2–S4 ) , a considerably larger group than could have been compiled using only the initial 79 significant genes . A total of 56 of the original 79 significant genes became components of significant clusters , whereas 23 remained unclustered . We subdivided all clusters into three qualitative types based on the functional annotation of gene members . We present examples of Type I and II clusters: Type I clusters ( n = 25 ) contained only functionally defined and functionally related genes ( as reported in published studies ) , such as biological pathways components ( Figures 2B and S2 ) ; Type II clusters ( n = 20 ) included both known and unknown genes ( Figures 2C and S3 ) . Type III clusters ( n = 2 ) were composed entirely of unknown genes ( Figures 2D and S4 ) , and are not discussed in detail . We measured the performance of our algorithm by examining whether it identified gene groupings known to be functionally related ( Type I clusters ) . Only four ( 16% ) of 25 Type I clusters ( spy0080–0081 , spy1236–1237 , spy1707–1711 , spy2041–2042 ) could have been identified in entirety by significance analysis because all clustered genes exhibited significant differential expression ( PF value < 0 . 05 ) . A total of 11 ( 52 . 4% ) of the remaining 21 clusters would not have been identified in their entirety without GenomeCrawler because we initially identified significant fold-changes in only a subset of genes necessary to encode particular pathways or loci; this is intuitively unreasonable if all genes are essential for functionality . GenomeCrawler expanded these clusters to contain more genes that encode intact loci ( Table 3 ) . For example , we initially identified ( Table 1 ) the significant upregulation of three of the five known gene members of the folate biosynthetic pathway [40] ( spy1096–1100 ) , but GenomeCrawler identified a significant cluster containing all five genes ( Table 3 and Figure 2B ) . We obtained a similar result for the eight-gene operon encoding the F0F1-type proton translocating ATPase [41] ( spy0754–0761 ) . The initial significance analysis identified only four atp genes ( Table 1 ) , but neighbor clustering identified a significant cluster containing all eight genes necessary to encode a functional ATPase ( Table 3 ) . Each of the 11 neighbor clusters that could have been only partially identified by our initial analysis alone gained gene members after application of the algorithms and became more complete sets of functionally related genes than initially identified ( Table 3 ) . These clusters encompass various metabolic processes , including purine biosynthesis ( spy0025–0028 ) , lactose metabolism ( spy1916–1923 ) , fatty acid biosynthesis ( spy1743–1747 ) , lipoteichoic acid synthesis ( spy1308–1312 ) , and sugar phosphotransferase transport ( spy1058–1060 ) [14] , suggesting that specific changes occur in the streptococcal metabolic program as the bacteria adhere to human pharyngeal cells in vitro . Notably , the remaining ten Type I clusters were composed entirely of genes that individually were not significant; however , after applying our algorithms , the combined contribution of each gene resulted in a significant cluster . For example , the nine-gene operon that spans genes spy0738–0746 encodes streptolysin S , a potent cytolytic toxin that promotes internalization and host tissue dissemination [25 , 44] . Though the differential expression of the individual genes was not significant following our initial statistical analysis , GenomeCrawler identified a significant downregulated cluster containing all nine genes ( Table 3 ) . Adherence-induced downregulation of streptolysin S is consistent with its previously determined role in host cell internalization [25]; however , without neighbor clustering , expression of this operon was not evident immediately . Although individual gene members of Type I clusters may not be statistically significant as a result of technical variability within experiments [17] , the genetic structure of certain Type I operons may provide an alternative explanation . For example , the streptolysin operon encodes an internal terminator downstream of the sagA gene ( the first gene in the operon ) , which modulates the abundance of particular mRNA species ( e . g . , sagA mRNA versus the polycistronic message for all nine genes ) under different environmental conditions [45] . If transcription is internally disrupted by such a terminator , the abundance of the sagA transcript may be much greater than the polycistronic message; such disproportionate transcript levels would affect log2-fold change values and impact the statistical significance of individual genes within these types of clusters . Thus , in addition to helping resolve clusters that would not be easily recognized because of experimental technical variability , the neighbor clustering method may help to resolve operons with such internal terminators and regulators . These results demonstrate that neighbor clustering effectively reconstructed a number of complete pathways and loci from processed array data . Importantly , because functional gene data are not incorporated into its algorithms , GenomeCrawler is not biased toward identifying “expected” clusters . Curating the dataset following its application may make the algorithms less user-friendly; however , the elimination of such bias is essential for this type of analysis . Based on the Type I cluster results , we speculated that genes contained in Type II clusters might be related by function or regulation . Type II groupings contain a combination of both known and unknown gene members and could provide preliminary clues about the function of unknown genes within a particular cluster by associating their expression with neighboring genes of known and defined function . Alternatively , co-expression of genes results from common regulation , and Type II associations may suggest shared regulatory mechanisms for clustered genes . We note , however , that despite the statistical framework with which groupings are assigned , experimental evidence is necessary to confirm functional or regulatory relatedness . We do not suggest simply assigning either based on cluster membership; rather , cluster associations may provide some preliminary functional or regulatory clues for gene members . A total of 18 ( 90% ) of 20 Type II clusters ( Table 3 and Figure S3 ) may not have been identified without neighbor clustering: eight ( 44 . 4% ) of 18 gained additional gene members; the remaining ten comprised genes that demonstrated significant differential expression only after applying GenomeCrawler . Only two clusters ( spy0127–0130 and spy1701–1704 ) could have been identified without neighbor clustering; however , a number of these genes were initially annotated as hypothetical proteins , so a potential relationship between the gene members may not have been readily apparent . The upregulated spy0127–0130 cluster is part of a larger genomic region known as FCT ( for fibronectin- and collagen-binding proteins and T antigen–encoding loci ) , which spans spy0123–0136 in the SF370 genome and encodes surface proteins and transcriptional regulators [46] . A search of both the PFAM database [47] ( http://pfam . wustl . edu ) and sortase database ( http://www . doe-mbi . ucla . edu/Services/Sortase ) predicted that spy0129 encodes a sortase enzyme , which are transpeptidases that cleave protein substrates at conserved C-terminal motifs ( often LPXTG ) and then anchor these proteins to the bacterial cell wall [48 , 49] . Recently , it was reported that the four genes spanning spy0127–0130 encode , and are responsible for , the formation of surface-localized , trypsin-resistant pili that induce protective immunity against a lethal dose of group A streptococci in a mouse model of infection [36] . This same report provided the first experimental evidence supporting the sortase prediction , indicating that the gene product of spy0129 is responsible for the cell-wall sorting of the proteins encoded by both spy0128 ( annotated as a Cpa homolog [50] ) and spy0130 ( annotated as a protein F homolog [14] ) . Furthermore , the spy0128-encoded protein is the structural backbone of the pili , and the gene product of spy0130 may be involved in stabilizing the structure [36] . Together with the identification of this cluster by GenomeCrawler , these results prompted us to study this cluster and the contributions of the gene products to pharyngeal cell adherence . We determined experimentally that cluster spy0127–0130 is an operon , verifying both related function and regulation of the gene members . Reverse transcription of SF370 RNA , with primer combinations that spanned all four genes , produced cDNA fragments of sizes that could only result from a polycistronic mRNA template ( Figure 3 ) . In silico sequence inspection identified a single putative promoter sequence upstream of spy0127 ( see Table S6 ) . Although GenomeCrawler is not an operon-identifying algorithm , these results show that it could ( 1 ) identify this commonly regulated gene cluster and ( 2 ) define the cluster boundaries , excluding other proximate genes , such as an additional sortase-encoding gene , spy0135 . We created a spy0129 deletion mutant in strain SF370 ( SF370Δspy0129 ) to determine if genes contained within the spy0127–0130 cluster were directly involved in adherence to pharyngeal cells . We posited that a deletion in the spy0129 sortase gene may have the greatest overall effect on the production and processing of the gene products of this cluster , since both the spy0128 and spy0130 gene products do not localize to the cell-wall surface in the absence of the sortase enzyme [36] . Allelic replacement created two putative deletion mutants; however , RT-PCR analysis ( Figure 4A ) revealed that only one such clone ( SF370Δspy0129 . 2 ) was a true knock-out for the spy0129 gene and useful for further study . Because the gene cluster is also an operon , expression of the downstream gene spy0130 , encoding the protein F homolog/pilus protein , was also eliminated in this mutant ( Figure 4A ) . In vitro pharyngeal cell adherence assays revealed that the SF370Δspy0129 . 2 mutant was approximately 66% less adherent than the parental control strain , SF370 ( Figure 4B; p = 0 . 03 as determined by the Student's t-test ) . These results suggest that either the spy0130 gene product is involved directly in adherence , or that due to the elimination of the sortase , the pili , which may function in their entirety as adhesins , were not assembled on the surface of the mutant . Because the spy0129 gene product is not expected to be found on the streptococcal surface ( i . e . , it lacks a cell-wall anchoring motif ) , it is not likely to be involved directly in adherence . We are working to produce an in-frame deletion of spy0128 and a spy0130 single knock-out mutant to delineate the contribution of each individual clustered gene product to adherence . These results show that neighbor clustering is able to identify biologically relevant gene clusters . This attribute may be particularly important for datasets in which the relationship between clustered genes is not obvious , and may facilitate the organization of larger datasets into more manageable packages . Another cluster , spy1725–1719 , contained six genes that together ( though not individually ) exhibited significant downregulation . The genes spy1724 , spy1722 , spy1721 , and spy1719 share transcriptional order and predicted function with homologs in the nusA-infB protein biosynthesis operon of Bacillus subtilis and Escherichia coli [51] . We examined the spy1725 and spy1723 gene products ( annotated as hypothetical proteins [14] ) for similarities with known proteins that might indicate a role for these gene products in protein biosynthesis . BlastP analysis aligned the spy1725 gene product , which has homologs in all sequenced streptococcal genomes , with the SP14 . 3 protein from S . pneumoniae [52] ( 80% sequence similarity; 67% identity ) . Based on structural characterization , SP14 . 3 is a predicted RNA-binding protein . The spy1723 gene product has similar domain structure to the YlxR protein of S . pneumoniae , an RNA-binding protein implicated in transcription termination [53] . These results indicate that both genes likely encode RNA-binding proteins , in agreement with their functionally defined cluster members . Although domain and homology searches yielded the functional predictions , their membership within a protein biosynthetic cluster provided the initial indication of common function or regulation . Although neighbor clustering is not an operon-predicting method , we wanted to identify additional putative operons among the groupings since neighbor clusters by definition share certain operon characteristics ( tandemly arranged genes , separated by <300 bp , with similar expression patterns ) . Although operon-modeling methods exist [54 , 55] , we inspected clusters in silico for upstream regulatory elements and identified 17 candidates , including clusters such as streptolysin S that have been previously confirmed as operons [56]; the spy0127–0130 grouping , which was confirmed as an operon in this study; and others that have yet to be verified ( Table S6 ) . Experimental confirmation of each candidate is beyond the scope of this study , but Northern blot and RT-PCR analyses could provide such information . We applied the statistical analysis and the GenomeCrawler algorithms to data from a recently published streptococcal microarray study that is relevant for comparison to our own data ( same streptococcal strain , similar array platform ) [57] . In this study , the transciptomes of S . pyogenes strain SF370 and an isogenic mutant deficient for the Mga regulon were compared during exponential growth in culture broth . The Mga regulator is a growth-phase mediator of a number of surface-exposed molecules and secreted proteins involved in colonization and immune evasion during infection [58] . Although the authors of that study did not provide a statistical analysis of their data , we compared the published results for the magnitude and direction of fold-changes for each gene reported in this study with those obtained from our initial significance analysis of this dataset ( presented as Table S7 ) . A total of 256 genes reported in this study were also detected by our analysis , and the magnitude and log2-fold change were found to be in agreement for 81% of the genes . We suspect that this discrepancy results from different normalization methods used , or from different methods that were applied to analyze the ratio of signal intensities between sample and control ( i . e . , we analyzed the ratios of the median rather than the ratios of the mean [57] ) . Although the published report did not include statistical analysis of the data , we note that the statistical analysis that we performed identified four genes with significant log2-fold changes in expression ( PF < 0 . 05; Table S8 ) . We applied the GenomeCrawler algorithms to the statistically analyzed dataset , which identified an expanded group of genes ( 107 versus four ) contained within 36 statistically significant clusters ( PK < 0 . 05; Table S9 ) . These groupings included clusters of genes that have been shown previously in streptococci to be functionally related , indicating that the algorithms were performing as expected . Two of the identified upregulated clusters ( spy2009–2010 and spy2039–2040 ) encoding the well-studied virulence factors , C5a peptidase and SpeB , respectively , showed consistently large log2-fold changes of the genes across replicates [57] . GenomeCrawler confirmed these results by identifying both groupings as statistically significant neighbor clusters . GenomeCrawler also identified a number of clusters that contained genes known to share common function or regulation; however , they were not as apparent in the dataset without its application . For example , the algorithm identified a significant neighbor cluster spanning spy0711–0712 . This grouping encodes two known virulence factors , pyrogenic exotoxin SpeC and the MF2 DNase , previously shown to be commonly regulated as an operon [11] . The algorithm also identified other neighbor clusters containing genes known to be functionally related , including spy0098–0100 ( encoding the β and β′ subunits of DNA-dependent RNA polymerase ) , spy2159–2160 ( encoding the 50S ribosomal subunit proteins L32 and L33 ) , and spy0741–0746 ( six of the nine streptolysin S–encoding genes ) [14] . Although the analysis of this previously published dataset did not reveal as many intact biological pathways as were identified from the pharyngeal cell adherence data , the inclusion of more replicates in the analysis to increase statistical power could resolve such loci . However , these results provided further supporting evidence that the GenomeCrawler algorithms can identify ( 1 ) a larger group of genes than a rigorous statistical analysis alone and ( 2 ) biologically relevant groupings in other microarray datasets , even if they contain fewer replicates than presented in our study . Although GenomeCrawler improves bacterial array analyses , it has limitations: it cannot identify regulons comprising genes dispersed throughout the genome by virtue of its design , it does not specifically interrogate single-gene operons , and it only applies to genomes with available and accurate experimental information ( expression data and gene annotations ) . We recognize that incorporating intergenic distance and transcription direction into the algorithms would reduce processing time . Adding available clusters of orthologous groups ( COG ) information into a downstream processing step could decrease errors by minimizing clustering of unrelated genes . Nonetheless , neighbor clustering provided a more comprehensive view of the transcriptome of group A streptococci during adherence to human pharyngeal cells , a critical step in the infection program of this organism . We found that even a rigorous statistical analysis of well-replicated microarray data produced a dataset that was somewhat limited , although certainly more informative than assigning arbitrary thresholds for significance . As described in other microarray reports , we had initially identified a number of incomplete biological pathways in which we did not detect the differential expression of a number of known pathway members . Neighbor clustering was able to extend the results by identifying more differentially expressed genes and reconstructing more intact biological pathways . Neighbor clustering , despite the statistical framework with which it assigns groupings , would be valuable to microarray data analysis only if it produced biologically relevant data . Although biological testing of every identified gene or cluster is unrealistic , we provided evidence , through the creation and testing of isogenic deletion mutants and through the identification of clusters of known , functionally related genes from a published streptococcal array study , that the algorithms produce results that are pertinent to the biology of streptococci . This may be of particular importance for data in which the relationship between clustered genes is not obvious , and may facilitate the organization of larger datasets into more meaningful packages . It is also possible that GenomeCrawler ( in its current form ) could be used to interrogate intergenic portions of the genome ( such as those encoding small noncoding RNAs or sRNAs ) , if probes representing such regions were included on the microarray , and experimental conditions were designed to promote their differential expression . Finally , because of the common architecture of bacterial chromosomes , the neighbor clustering algorithms may be applicable to microarray datasets from other prokaryotes .
Sense strand oligonucleotides ( primarily 55-mers ) , representing the 1 , 769 open reading frames in the genome of S . pyogenes strain SF370 ( M1 serotype ) [14] were designed and produced by Illumina ( http://www . illumina . com ) . Oligonucleotides were spotted using a Biorobotics Tas II 6100 arrayer ( http://biorobotics . org ) onto Corning UltraGAPS ( gamma amino propyl silane–coated ) slides ( Corning Life Sciences , http://www . corning . com/lifesciences ) , and slides were post-processed and blocked according to the manufacturer's instructions . Each oligonucleotide was spotted four times in a well-spaced configuration to generate in-slide replicates . For adherence assays , S . pyogenes strain SF370 ( kindly provided by J . Ferretti , University of Oklahoma Health Sciences Center , Oklahoma City , Oklahoma , United States ) was grown to late log-phase ( OD600 = 0 . 7 ) in Todd Hewitt broth ( BD Biosciences , http://www . bdbiosciences . com ) containing 0 . 2% yeast extract ( THY; BD Biosciences ) . Bacterial cells were washed in 0 . 1 M phosphate-buffered saline ( PBS; pH 7 . 4 ) , resuspended in minimal essential medium ( MEM; Invitrogen , http://www . invitrogen . com ) , and incubated for 1 h at 37 °C . Glycerol ( 20% vol/vol ) was added , and cultures were flash frozen in liquid N2 and stored at −80 °C . To minimize culture-to-culture variability , these stock cultures were used for all subsequent adherence and association experiments . Assays on streptococcal adherence to human pharyngeal cell line Detroit 562 were performed as described previously [15] with the following modifications . Streptococcal stock cultures were pre-incubated at 37 °C for 1 h , and 2-ml aliquots ( 2 × 108 CFUs ) were added to confluent monolayers of Detroit 562 cells grown in wells of six-well tissue culture plates ( 1 × 107 cells/well ) . Co-cultures were incubated for 2 . 5 h at 37 °C , and the monolayers were then washed with PBS to recover associated ( nonadherent ) streptococci . Pharyngeal cells were treated with 0 . 005% trypsin–0 . 004% EDTA for 15 min at 37 °C to desorb adherent streptococci ( 90% recovery ) without disrupting the eukaryotic monolayer . Trypsin treatment does not affect gene expression in adherent streptococci compared with associated bacterial control . The monolayers were washed with PBS to recover bacteria detached by the trypsin treatment . Recovered streptococci were washed twice in PBS and lysed with the amidase enzyme lysin [59] . Lysin was added to the bacterial samples ( 2 U/108 CFUs ) and incubated for 15 min at room temperature , which in preliminary experiments was determined to be optimum for complete streptococcal lysis . RNA was isolated immediately after lysis with a modified phenol-chloroform protocol as described previously [60] . RNA was digested with DNase I ( Invitrogen ) , and RNA quality was assessed with the Nucleic Acid Bioanalyzer 2100 ( Agilent Technologies , http://www . agilent . com ) . DNase-treated streptococcal RNA ( 5 μg ) was reverse-transcribed using the Atlas Glass Fluorescent Labeling kit ( BD Biosciences Clontech , http://www . bdbiosciences . com/clontech ) . Random hexamers ( Invitrogen ) primed the reverse transcription reaction that incorporated a 5- ( 3-aminoallyl ) -dUTP into the first synthesized cDNA strand . cDNAs from associated streptococci and from adherent streptococci were indirectly labeled with the N-hydroxysuccinimide activated fluorescent dyes cyanine 3 ( Cy3 ) and cyanine 5 ( Cy5 ) , respectively , as outlined in the Atlas kit . Labeled cDNA samples were purified following Atlas kit instructions . Four biological replicate experiments incorporating dye swaps [17] were performed to account for both biological and technical variability . Labeled cDNA samples were hybridized to the arrays in SlideHyb hybridization buffer ( Ambion , http://www . ambion . com ) for 16 h at 55 °C using a GeneTAC hybridization station ( Genomic Solutions , http://www . genomicsolutions . com ) . Slides were washed twice in 0 . 1 × SSC , dried , and then scanned with a Scanarray 4000 scanner ( GSI Lumonics , http://www . gsilumonics . com ) at 10 μm per pixel resolution . The resulting images were processed using the GenePix Pro program ( version 4 . 0; Axon Instruments , http://www . axon . com ) . Following image analysis , low-level processing of microarray data included probe and array quality filtering to remove probes that were saturated , that displayed a low signal-to-noise ratio , and/or that produced signal in only one dye channel . Lowess standardization [19] was performed , and robust summary statistics were applied to the standardized log2-fold change data for outlier control ( Huber M-estimator and unbiased MAD estimator ) [20] . A Bayesian-derived regularized t-test was implemented with the Cyber-T program for control of variance artifacts associated with low sample size [20–22] . Calculation of the p-value of the log2-fold change for each gene ( PF ) uses the Westfall–Young stepdown permutation algorithm [18 , 19] for multiplicity adjustment in place of the Bonferroni correction typically implemented in Cyber-T . Although more computationally intensive , we chose Westfall–Young over the Bonferroni correction because: ( 1 ) Bonferroni assumes independence between tests and since genes can be regulated in conjunction with one another , we preferred to avoid the assumption of independence; ( 2 ) Westfall–Young , which is based on permutation , calculates p-values ( from t-test statistics ) based on the actual distribution of the data itself , and no assumption of independence is required; and ( 3 ) the power of coupling a permutation algorithm with a t-test is that one can take advantage of the sensitivity associated with a t-test , while using the distribution-free nature of a randomization test . We used the t-test statistics and PF values generated in this analysis ( referred to throughout the text as initial statistical significance analysis ) to rank genes [61] undergoing statistically significant changes in expression ( PF < 0 . 05 ) during adherence to pharyngeal cells compared with the associated control ( Table 1 ) . Datasets resulting from each processing step are available for download at http://www . rockefeller . edu/vaf/streparray . php . We performed real-time qRT-PCR analysis ( TaqMan ) on 11 different genes to verify the fold-change in gene expression estimated by microarray analysis . Five of these genes exhibited statistically significant fold-changes in expression ( PF < 0 . 05 ) during adherence ( two demonstrated increased expression , and three demonstrated decreased expression ) , and the remaining six selected genes were scored as statistically significant only when included in a significant neighbor cluster ( PE < 0 . 05 ) . The list of genes , as well as the oligonucleotide primers and fluorogenic ( TaqMan ) probes designed using Primer Express Software ( Applied Biosystems , http://www . appliedbiosystems . com ) and purchased from Sigma-Genosys ( http://www . sigmaaldrich . com ) , are provided in Table S2 . Each of the 11 genes , as well as spy0929 ( endogenous reference/control gene ) , was amplified in its entirety from SF370 genomic DNA by PCR and cloned into pCR-TOPO plasmids ( Invitrogen ) . spy0929 was chosen as control due to equivalent expression between adherent and associated SF370 cultures . We used a two-step RT-PCR procedure to reverse transcribe RNA samples from two biological replicate SF370 cultures ( two adherent and two associated ) , which were prepared as those for microarray analysis . Using SuperScript II First Strand Synthesis System for RT-PCR ( Invitrogen ) , DNase I-treated RNA preparations ( 2 μg each ) were separately converted to cDNA preparations with 50 ng random hexamers ( Invitrogen; 45 °C , 50 min , 20 μl reactions ) according to manufacturer instructions . RNA samples were reverse-transcribed in separate reactions , and no pooling of samples occurred . Control reactions without reverse transcriptase were included to confirm that genomic DNA was not present . TaqMan analysis was performed ( in duplicate ) with an ABI Prism 7900 sequence detection system ( Applied Biosystems ) using Platinum Quantitative PCR SuperMix-UDG ( Invitrogen ) ( according to manufacturer instructions ) and primer–probe pairs listed in Table S2 . No-template negative controls were included . Cycling conditions , optimized with plasmid standards , were as follows: 50 °C for 2 min and 95 °C for 2 min , followed by 45 cycles at 60 °C for 45 s . We constructed standard curves for threshold cycle ( CT ) versus copy number for each gene with known concentrations of plasmid DNA standards ( 10-fold dilutions ranging from 108 copies to ten copies ) that were subjected to the same reaction and cycling conditions and included on each reaction plate . Results were normalized with CT values for the control , spy0929 . We averaged data from duplicate reactions to produce a single value for each gene and log2-transformed the fold difference in the number of cDNA molecules present in adherent streptococcal samples relative to associated streptococcal samples . This created a dataset of 11 paired values from RT-PCR and microarray analyses for each gene . We performed linear regression analysis and regressed qRT-PCR data on the microarray data . The sequences of forward ( F ) and reverse ( R ) primers for each of the four genes contained within the spy0127–0130 neighbor cluster are provided in Table S2 . RT–PCR generation of amplicons was performed with the SuperScript III One-Step RT–PCR system with Platinum Taq DNA polymerase ( Invitrogen ) in reaction mixtures ( 50 μl ) containing 0 . 2 μM of each gene-specific forward and reverse primers and 0 . 1 μg of DNase-treated , purified total RNA from late-log phase cultures ( OD = 0 . 7 ) of strain SF370 . All remaining components were added as per manufacturer specifications . We included control reactions , in which Taq DNA polymerase was substituted for the reverse transcription enzyme mixture , to confirm that genomic DNA was not present in the RNA preparations . RNA was converted to cDNA ( 50 °C for 30 min ) , which was then PCR amplified in the same tube ( 45 cycles of the following conditions: 94 °C for 15 s , 52 °C for 30 s , and 68 °C for 2 min ) . Resulting DNA fragments were separated on 1% agarose gels in TAE buffer and visualized by ethidium bromide staining . The strategy for allelic replacement of speH and spy0129 genes was followed as previously described [62] . Briefly , upstream and downstream DNA regions flanking both genes were separately amplified using the primer sets listed in Table S2 . PCR products were treated with the appropriate restriction enzymes ( New England Biolabs , http://www . neb . com ) and used according to manufacturer instructions . Fragments were gel-purified ( Qiaex II Gel Extraction Kit; Qiagen , http://www . qiagen . com ) , and the respective upstream and downstream regions for either speH or spy0129 were ligated together into the allelic replacement vector pFW15 [63] , creating plasmids pFW15-speH and pFW15-spy0129 . To construct deletion mutants of the speH and spy0129 genes , the vectors were separately electroporated into S . pyogenes SF370 [62] , and transformants were selected on proteose peptone blood agar supplemented with erythromycin ( 300 μg/ml ) . Allelic replacement was confirmed by both PCR and RT-PCR analyses of total RNA extracted ( as described above ) from both mid-logarithmic ( OD = 0 . 4 ) and stationary phase ( OD = 1 ) bacterial cultures using gene-specific primers . Total RNA from strain SF370 served as control . The resulting strains , SF370ΔspeH and SF370Δspy0129 , lacked the speH and spy0129 genes , respectively . We tested late-logarithmic phase SF370ΔspeH and SF370Δspy0129 mutants in an in vitro assay for adherence to Detroit 562 pharyngeal cells as previously described [15] to determine if either the speH or spy0129 gene product was involved directly in the adherence of strain SF370 to pharyngeal cells . The parental strain SF370 served as control . We provide a general explanation of the principles of neighbor clustering followed by a more detailed explanation of the algorithms . Due to the large number of all putative clusters in the SF370 genome ( ~10500 ) , we restricted our search space to clusters that are spatially related . During the assignment of neighbor clusters , we did not associate genes with functional annotations to prevent biasing the formation of clusters toward those that were “expected . ” The GenomeCrawler algorithm , written in the statistical language R ( http://www . R-project . org ) , steps through the expression data and identifies adjacent gene groupings that exhibit similar expression fold changes . The algorithm varies window size and applies a gap penalty for including in a cluster those genes that we did not observe experimentally to be present or genes that did not exhibit differential expression between sample and control . GenomeCrawler calculates statistical significance of all putative resulting neighbor clusters ( PK value ) , using a permutation algorithm with the sum of the t-test statistics ( generated by Cyber-T ) from each gene within a given cluster as the metric for comparison . We then inspected the output visually and disqualified groupings that violate the neighbor cluster definition based on established guidelines for functionally coupled gene pairs: genes occur on the same DNA strand and adjacent genes are separated by ≤300 bp [5] . We further restricted qualifying clusters to contain genes with a uniform direction of differential expression ( i . e . , all upregulated or all downregulated ) . Visual inspection is necessary because we have not yet had success at incorporating these specific parameters into the algorithms . To emphasize the importance of such inspections , we included the output prior to disqualifications for comparison ( Table S4 ) . We disqualified the following groupings: 491 contained genes located on different DNA strands; 127 contained adjacent genes separated by greater than 300 bp; and 24 contained genes that did not exhibit a uniform direction of expression . Since a specified gene could be a member of many different clusters , the cluster that generated the lowest PK value ≤ 0 . 05 and met all of the defined conditions of a neighbor cluster ( as detailed in the text ) is the one that we reported . The GenomeSpyer algorithm , also written in R , provides a method to view the GenomeCrawler output and to visualize clusters and their respective gene members . The GenomeSpyer plots of all datasets derived from this study can be found as Figures S2–S4 . Conceptually , PF reflects the physical change in gene expression between sample and control , whereas PC reflects the significance of a gene in the context of a cluster and is based on combined information about genome structure ( i . e . , genome position ) and activity ( i . e . , measured changes in expression ) . PC reflects the cluster context and is not merely a recapitulation of the effect related by PF for an individual gene , because on its own the PF of a single gene is not sufficient to generate an informative PC ( i . e . , PC << 1 ) . Validation of this point is found in the details of the algorithms implemented for calculating PF and PC . The overall statistical significance of a specified gene , g , in regard to change in expression between sample and control is referred to as PE , and this probability is calculated as the product of two probabilities: PF , the p-value associated with observing the log2-fold change for the given gene , and PC , the p-value associated with the same given gene being a member of a specified cluster of genes . We treated this new probability as the posterior in Bayes' Theorem [22] and used the respective prior , likelihood , and cluster probabilities for its calculation . Calculation of the prior and likelihood used essentially the same algorithm for determining the cluster PK value above , with the arguments of the prior and likelihood defining the respective set of t-test statistics to sum . PE ( , tg , K ) , called the expression P-value and referred to as the PE value , is equal to the product of two probabilities , PF ( ) and PC ( tg | K ) , calculated with distinct permutation resampling algorithms ( Equation 1 ) : PF ( ) is the p-value associated with the log2-fold change in expression of a given gene ( referred to as the PF value ) . Its calculation uses the Westfall–Young stepdown permutation algorithm [19] , where is the average log2-fold change of a specific gene and the basis set is the log2-fold change of a gene , Mi , a , in which i is an element of the genes of the observable transcriptome and a is an element of the set of microarrays . The metric for comparison is tg , a Bayesian-derived regularized t-test statistic of the log2-fold change for the given gene [21] . PC ( tg | K ) is the p-value that corresponds to the probability that a specific gene is a member of its assigned cluster . Calculation of PC ( tg | K ) also uses t , but rather than a metric for comparison , it is the basis set for resampling composed of ti in which i is an element of the genes of the annotated genome . The metric for comparison is the sum of the elements of the set K = {tj : j ∈ J} in which J = {j: j ∈ {genes of the specified cluster}} . Since a gene can have membership in multiple clusters , our approach uses a dynamic windowing algorithm to sequentially search the genome for spatial clusters . The cluster that generates the lowest PE ( , tg , K ) for the specified gene determines the reported value of PE ( , tg , K ) . Calculation of PC ( tg | K ) relies on Bayes' Theorem ( Equation 2 ) in which PC ( tg ) is the prior , PC ( K | tg ) is the likelihood , and PC ( K ) is the probability associated with the cluster . All of the right-hand side probabilities are readily calculated using the following general equation: in which Λ ⊆ K and Λ = {tj′ : j′ ∈ Γ} in which Γ ⊆ J and represents a set of genes defined in the parenthesis of Equation 2 . Since PC ( K ) is the measure for the statistical significance of the specified cluster in our analysis , for this probability Γ = J . For the prior , Γ = {j′: j′ = g , ∃ ! g ∈ J} , whereas for the likelihood , Γ = {j′: j′ ∈ J , j′ ≠ g , ∃ ! g ∈ J ) . B is the total number of iterations of permutation resampling performed , with ( b ) representing a resampled value of the bth iteration . The indicator function I ( · ) equals 1 when the condition in parentheses is satisfied , and 0 when it is not . We define the relationship between PF ( ) and PC ( tg | K ) , as both use tg for their respective calculations . For tg ≅ max ti , PF ( ) → 0 and 0 < PC ( tg | K ) ≤ 1 . Therefore , the analysis ensures that even the most significant gene with respect to PF ( ) can theoretically have PC ( tg | K ) = 1 . For example , when a gene is a member of a cluster in which the other members are insignificant on a genome scale , PC ( tg | K ) ≅ 1 , since PC ( K | tg ) ≅ 1 and PC ( tg ) ≅ PC ( K ) . Conversely , for tg ≅ min ti , PF ( ) ≅ 1 and PC ≅ 1 , since PC ( tg ) ≅ 1 and PC ( K | tg ) ≅ P ( K ) . Here , there is a strong dependency between PF ( ) and PC ( tg | K ) . This prevents a gene with a relatively low tg value from being scored as significant due to a pure circumstantial association with a gene of PF ( ) → 0 . Hence , this analysis exhibits the required dynamic relationship between PF ( ) and PC ( tg | K ) and , more important , is consistent with the criterion that , on its own , a gene with a low PF ( ) should not generate an informative PC ( tg | K ) ( i . e . , PC ( tg | K ) << 1 ) . PC ( tg | K ) , therefore , reflects a group context derived from a cluster of genes and is not merely the recapitulation of the PF ( ) of an individual gene . The published SF370 genome does not contain promoter annotations , so we examined the entire genome in 100 , 000-bp segments ( available for download at ftp://ftp . genome . ou . edu/pub/strep ) and used the Vector NTI advance 9 . 0 sequence analysis suite ( Invitrogen ) to identify sequences that were similar ( 75% similarity threshold ) to consensus streptococcal promoter sequences [64] . We cross-referenced the clusters containing a single upstream putative promoter sequence with a list of rho-independent terminator sequences , previously identified in the SF370 genome by TransTerm ( www . tigr . org/software/transterm . html ) . We analyzed recently published microarray data from S . pyogenes strain SF370 [57] in the same manner as the adherence data presented in this study to assess the overall reliability of our analytical methods . We applied the initial statistical package to assess the differential expression of individual genes , followed by the GenomeCrawler algorithms . We compared the results of this analysis , when applicable , to the published analysis of the array data . For MIAME ( Minimum Information About a Microarray Experiment ) compliance , all microarray datasets ( pre- and post-processing ) have been deposited in the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/geo ) and given accession number GSE7620 . Software to implement the GenomeCrawler and GenomeSpyer algorithms , as well as all corresponding datasets , are available for download at http://www . rockefeller . edu/vaf/streparray . php .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for S . pyogenes strain SF730 ( serotype M1 ) is AE004092 . The National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/geo ) accession number for all microarray datasets in this paper is GSE 7620 . | Microarray technology is commonly used to reveal genome-wide transcriptional changes in bacterial pathogens during interactions with the host . Clustering algorithms , which group genes with similar expression patterns , facilitate microarray data organization and are based on assumptions that co-expressed genes share common function or regulation; however , clustering solely by co-expression may not reveal all of the information contained in bacterial array data . We introduce neighbor clustering , a new tool for analyzing bacterial gene expression profiles , which distinguishes itself from other programs by incorporating details unique to the architecture of bacterial chromosomes into the analysis . Neighbor clustering combines two informative characteristics of bacterial genes that share common function or regulation— ( 1 ) similar expression profiles and ( 2 ) physical proximity on the chromosome—and extracts statistically significant clusters of gene neighbors that are potentially related by function or regulation . We present the analysis of microarray data from group A streptococci during adherence to human pharyngeal cells , the first overt infection step . We show that neighbor clustering identifies more differentially expressed genes than rigorous statistical analyses alone , and can provide functional clues about unknown genes . We extended the analysis to include a previously published streptococcal array study to demonstrate the applicability of the method . | [
"Abstract",
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"Results/Discussion",
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"genetics",
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] | 2007 | Novel Algorithms Reveal Streptococcal Transcriptomes and Clues about Undefined Genes |
Mechanisms of adaptation to environmental changes in osmolarity are fundamental for cellular and organismal survival . Here we identify a novel osmotic stress resistance pathway in Caenorhabditis elegans ( C . elegans ) , which is dependent on the metabolic master regulator 5’-AMP-activated protein kinase ( AMPK ) and its negative regulator Folliculin ( FLCN ) . FLCN-1 is the nematode ortholog of the tumor suppressor FLCN , responsible for the Birt-Hogg-Dubé ( BHD ) tumor syndrome . We show that flcn-1 mutants exhibit increased resistance to hyperosmotic stress via constitutive AMPK-dependent accumulation of glycogen reserves . Upon hyperosmotic stress exposure , glycogen stores are rapidly degraded , leading to a significant accumulation of the organic osmolyte glycerol through transcriptional upregulation of glycerol-3-phosphate dehydrogenase enzymes ( gpdh-1 and gpdh-2 ) . Importantly , the hyperosmotic stress resistance in flcn-1 mutant and wild-type animals is strongly suppressed by loss of AMPK , glycogen synthase , glycogen phosphorylase , or simultaneous loss of gpdh-1 and gpdh-2 enzymes . Our studies show for the first time that animals normally exhibit AMPK-dependent glycogen stores , which can be utilized for rapid adaptation to either energy stress or hyperosmotic stress . Importantly , we show that glycogen accumulates in kidneys from mice lacking FLCN and in renal tumors from a BHD patient . Our findings suggest a dual role for glycogen , acting as a reservoir for energy supply and osmolyte production , and both processes might be supporting tumorigenesis .
Water is a fundamental molecule for life and the ability of an organism to adapt to changes in water content is essential to ensure survival . Hyperosmotic stress promotes water efflux , causing cellular shrinkage , protein and DNA damage , cell cycle arrest and cell death . All living organisms encounter hyperosmotic environments [1 , 2] . In humans , both renal and non renal tissues are exposed to hyperosmotic stress , a condition that is regarded as a major cause for many chronic and fatal human diseases including diabetes , inflammatory bowel disease , hypernatremia , dry eye syndrome , and cancer [1] . Cells/tissues/organisms have evolved adaptive strategies to cope with threatening hyperosmotic environments [1 , 2] . Among adaptive strategies , the synthesis of compatible organic osmolytes , which keeps cellular osmotic pressure equal to that of the external environment , is widely used by all organisms [3] . In yeast and C . elegans , hyperosmotic stress triggers glycerol production via transcriptional upregulation of glycerol-3-phosphate dehydrogenase-1 ( gpdh-1 ) , a rate-limiting enzyme in glycerol synthesis [4 , 5] . Moreover , several osmotic stress resistance mutants of divergent signaling pathways exhibit a constitutive transcriptional upregulation of gpdh-1 , leading to increased glycerol content [6–10] . Here we define a novel hyperosmotic stress resistance pathway mediated by the 5' AMP-activated protein kinase ( AMPK ) , a key regulator of cellular energy balance [11] , which is chronically inactivated by the worm ortholog of the renal tumor suppressor Folliculin ( FLCN-1 ) . In humans , FLCN is a tumor suppressor gene responsible for the BHD disease , an autosomal dominantly-inherited syndrome associated with increased susceptibility to the development of several cancerous and non cancerous lesions including kidney cancer , pulmonary , renal , pancreatic and hepatic cysts and skin fibrofolliculomas [12–25] . FLCN has been shown to bind AMPK via the scaffold FLCN-interacting proteins FNIP1 and FNIP2 [26 , 27] . We have recently demonstrated that FLCN negatively regulates AMPK signaling in the nematode C . elegans and in mammalian cells [28 , 29] . Moreover , loss of FLCN increased ATP levels via heightened flux of glycolysis , oxidative phosphorylation , and autophagy , which resulted in an AMPK-dependent resistance to several metabolic stresses in C . elegans and mammalian cells [28 , 29] . Here we identify a pathway involved in the physiological response to hyperosmotic stress resistance in C . elegans mediated by FLCN-1 and AMPK . We demonstrate that glycogen is an essential reservoir that is used upon acute hyperosmotic stress to generate glycerol and promote fast and efficient adaptation to prevent water loss and ensure survival . We show that in flcn-1 ( ok975 ) mutant animals , this phenotype is significantly enhanced , due to the robust AMPK-mediated accumulation of glycogen , which is rapidly converted to the osmolyte glycerol upon salt stress . Our results also suggest that the FLCN/AMPK pathway might be an evolutionarily conserved key regulator of glycogen metabolism and stress resistance .
Since we have previously observed that loss of flcn-1 in C . elegans increases AMPK-dependent resistance to energy stresses including oxidative stress , heat , and anoxia [28] , we asked whether it would also increase resistance to hyperosmotic stress . We measured the survival of wt and flcn-1 ( ok975 ) animals ( S1A Fig ) on plates supplemented with 400mM and 500mM NaCl . Loss of flcn-1 conferred a significant increase in resistance to hyperosmotic stress ( Fig 1A and 1B and S1 Table ) . Although NaCl treatment severely reduced the survival of both wt and flcn-1 ( ok975 ) animals as compared to untreated animals ( Figs 1A , 1B and S1B ) , the mean survival of flcn-1 ( ok975 ) animals increased by ~2 and ~3 fold upon treatment with 400mM and 500mM NaCl respectively , as compared to wt animals ( Fig 1A–1C ) . Moreover , we did not observe a significant difference in lifespan between untreated wt and flcn-1 ( ok975 ) animals , as reported previously [28] ( S1B Fig and S1 Table ) . Importantly , NaCl treatment led to shrinkage and paralysis in both wt and flcn-1 ( ok975 ) animals . However , flcn-1 ( ok975 ) mutant nematodes recover significantly faster than wt animals after 2 hours of NaCl treatment suggesting that the mechanism of adaptation to salt is more robust upon loss flcn-1 ( Fig 1D ) . We also observed a significantly greater number of wt animals with more than 30% reduction of body size as compared to flcn-1 suggesting that loss of flcn-1 activates pathways that favor body size recovery after hyperosmotic stress ( Fig 1E ) . Importantly , the hyperosmotic stress resistance phenotype can be rescued by transgenic re-expression of C . elegans flcn-1 ( S1 Table and Figs 1F and S1A ) . In addition , we used Agilent whole genome C . elegans microarrays to determine transcriptional profile differences between wt and flcn-1 ( ok975 ) mutant animals [30] . Key genes that were differentially expressed were validated by qRT-PCR ( S1C Fig ) . We compared our data to published transcriptional profiles and found a significant overlap between genes upregulated in untreated flcn-1 ( ok975 ) animals versus genes upregulated in wt animals treated with NaCl or osmotic stress resistant strains including osm-7 and osm-11 [8] ( S1D , S1E and S1F Fig and S2 , S3 and S4 Tables ) . Altogether , these data suggest that flcn-1 is involved in a mechanism of regulating the resistance to hyperosmotic stress . To determine how loss of flcn-1 increases resistance to hyperosmotic stress , we assessed the morphological differences between wt and flcn-1 ( ok975 ) using electron microscopy with or without NaCl treatment . Interestingly , we observed an increase in the size and number of glycogen stores in adult ( Fig 2Ai and 2Aii ) and L4 ( S2Ai , S2Aii , S2Ci , and S2Cii Fig ) flcn-1 ( ok975 ) mutant worms as compared to wt . Specifically , our transmission electron data indicate a strong accumulation of glycogen in the hypodermis , muscle , and intestine of flcn-1 ( ok975 ) animals as compared to wt ( S2C Fig ) . Glycogen has been previously shown to accumulate in these tissues in C . elegans [31] . Importantly , glycogen stores were barely detectable in wt and flcn-1 ( ok975 ) animals after NaCl treatment , suggesting that glycogen degradation is used to protect the animals from hyperosmotic stress ( Fig 2Aiii , 2Aiv ) . Furthermore , we found that the prominent accumulation and salt stress-dependent degradation of glycogen in flcn-1 ( ok975 ) adult animals occurs in the hypodermis ( Figs 2A , S2A and S2C ) . We validated and quantified the increase in glycogen levels conferred by loss of flcn-1 using iodine staining which has been previously shown to specifically stain glycogen in C . elegans [32–34] ( Fig 2B and 2C ) . In accordance with the electron microscopy results , glycogen levels were significantly increased in untreated flcn-1 ( ok975 ) animals as compared to wt , and NaCl treatment severely reduced glycogen content in both wt and flcn-1 ( ok975 ) animals ( Fig 2B and 2C ) . We then asked whether glycogen is used to protect wt and flcn-1 ( ok975 ) animals from damage during hyperosmotic stress . Glycogen synthase ( gsy-1 ) is responsible for the synthesis of glycogen from UDP-glucose molecules and glycogen phosphorylase ( pygl-1 ) catalyzes glycogen breakdown to form glucose-1-phosphate [35] . Importantly , the inhibition of glycogen synthesis or degradation using RNAi against gsy-1 and pygl-1 respectively , strongly reduced the survival in both wt and flcn-1 ( ok975 ) animals to an equal level , suggesting that the accumulation of glycogen and its degradation are both required for the resistance of wt and flcn-1 ( ok975 ) mutant animals to hyperosmotic stress ( Fig 2D and 2E and S1 Table ) . Additionally , transcript levels of gsy-1 and pygl-1 with or without 2 hours of 400mM NaCl stress remained unchanged suggesting allosteric regulation of glycogen metabolism ( Fig 2F ) . Altogether , these results demonstrate that the accumulation of glycogen stores and the degradation of glycogen are essential to survive hyperosmotic stress in wt and flcn-1 ( ok975 ) mutant animals . Since we have previously reported that the flcn-1-dependent resistance to energy stresses requires aak-2 , the worm ortholog of the AMPKα subunit , we wondered whether the hyperosmotic stress resistance phenotype conferred by loss of flcn-1 is also mediated by AMPK [28] . AMPK is activated by hyperosmotic stress in mammalian systems [36] and its deletion confers sensitivity to NaCl stress in yeast [37] . C . elegans nematodes have two catalytic α subunits aak-1 and aak-2 . Loss of aak-2 was shown to mediate lifespan extension and resistance to various stresses including oxidative stress , anoxia , nutrient deprivation , and dietary restriction [38–42] . To determine whether AMPK is involved in the increased resistance of flcn-1 ( ok975 ) animals to stress , we crossed aak-2 ( ok524 and gt33 ) [39 , 43] and aak-1 ( tm1944 ) [43] loss of function mutants with flcn-1 ( ok975 ) animals . Interestingly , loss of aak-2 ( ok524 and gt33 ) or aak-1 ( tm1044 ) alone conferred stress sensitivity but did not fully suppress the increased survival to hyperosmotic stress conferred by loss of flcn-1 ( Fig 3A , 3B and 3C and S1 Table ) . To control for compensatory effects , we generated the flcn-1 ( ok975 ) ; aak-1 ( tm1944 ) ; aak-2 ( ok524 ) triple mutant and compared its survival under high salt conditions to aak-1 ( tm1944 ) ; aak-2 ( ok524 ) double mutant animals . Simultaneous loss of aak-1 and aak-2 completely abolished the increased osmotic stress resistance upon loss flcn-1 demonstrating that this phenotype requires both AMPK catalytic subunits ( Fig 3D and S1 Table ) . AMPK has been shown to regulate glycogen metabolism in different organisms [44–56] . In fact , acute activation of AMPK leads to glycogen degradation [44–47] , while chronic AMPK activation results in glycogen accumulation [48–50] . Since we observed an increased constitutive phosphorylation of AMPK upon loss of flcn-1 in nematodes and mammalian cells [28 , 29] , we hypothesized that the chronic AMPK activation in flcn-1 ( ok975 ) mutants may lead to increased glycogen levels . We determined glycogen levels in aak-1 ( tm1944 ) ; aak-2 ( ok524 ) animals compared to flcn-1 ( ok975 ) ; aak-1 ( tm1944 ) ; aak-2 ( ok524 ) triple mutant worms and found that loss of AMPK strongly reduced glycogen levels in both strains ( Fig 3E and 3F ) . This suggests that the chronic AMPK activation in flcn-1 animals is leading to glycogen accumulation . Interestingly , the survival and glycogen accumulation in aak-1 ( tm1944 ) ; aak-2 ( ok524 ) mutant animals was also severely reduced as compared to wt ( Fig 3E and 3F ) , suggesting an important role for AMPK in maintaining glycogen stores , which are used for hyperosmotic stress resistance . Autophagy is a biological survival process through which cellular components and damaged organelles are degraded to produce energy upon starvation [57] . We reported previously that autophagy was essential for the energy stress resistance of flcn-1 ( ok975 ) mutant animals [28] . Therefore , we asked whether autophagy plays a role in osmotic stress resistance . Interestingly , atg-18 ( gk378 ) mutant animals were hypersensitive to high salt concentrations suggesting that autophagy is a process involved in the resistance to hyperosmotic stress . However , loss of flcn-1 significantly increased the resistance of atg-18 ( gk378 ) animals suggesting that flcn-1-dependent hyperosmotic stress resistance does not require autophagy , which is different from what we observed before during energy stress [28] ( S3 Fig and S1 Table ) . Degradation of glycogen polymers leads to the formation of glucose-1-phosphate which is converted to glucose-6-phosphate , an important metabolite used in multiple pathways including glycolysis , pentose phosphate pathway , and glycerol production ( Fig 4A ) [35] . We hypothesized that glycogen degradation may lead to heightened glycerol levels that could protect the animals from hyperosmotic stress . To address this , we measured the mRNA levels of gpdh-1 and gpdh-2 . Interestingly , we observed a significant 2-fold increase in gpdh-1 but not gpdh-2 at unstressed conditions in flcn-1 ( ok975 ) mutant animals compared to wt , which was consistent with our microarray results ( Fig 4B and 4C and S2 Table ) . Strikingly , after 2 hour treatment with 400mM NaCl , we detected a strong induction of gpdh-1 and gpdh-2 mRNA levels in wt and flcn-1 ( ok975 ) mutant animals , which was significantly enhanced in the latter ( Fig 4B and 4C ) . Accordingly , flcn-1 ( ok975 ) mutant animals exhibit higher glycerol content at basal level as compared to wt animals which was further increased upon NaCl treatment ( Fig 4D ) . To determine the importance of glycerol in the protection against hyperosmotic stress , we inhibited gpdh-1 and gpdh-2 using RNAi and using mutant strains . Importantly , treatment of flcn-1 ( ok975 ) animals with RNAi against either gpdh-1 or gpdh-2 alone did not fully suppress the increased resistance of flcn-1 ( ok975 ) animals to hyperosmotic stress ( S4A and S4B Fig ) . We then compared the resistance of flcn-1 ( ok975 ) ; gpdh-1 ( kb24 ) ; gpdh-2 ( kb33 ) triple mutant animals to gpdh-1 ( kb24 ) ; gpdh-2 ( kb33 ) mutant nematodes . Simultaneous loss of gpdh-1 and gpdh-2 strongly reduced the survival of flcn-1 ( ok975 ) mutant animals demonstrating an important role for the osmolyte glycerol in the survival of flcn-1 ( ok975 ) and wt animals ( Fig 4E and S1 Table ) . Altogether , these data suggest that upon hyperosmotic stress glycogen stores are metabolized into the osmolyte glycerol via enhanced transcriptional upregulation of gpdh enzymes . This glycerol mediated osmo-protective phenotype is significantly enhanced upon loss of flcn-1 in nematodes . HOG/p38/PMK-1 MAP kinase signaling is widely known to control adaptation to hypertonic stresses in multiple organisms [4 , 9 , 58] . As expected , pmk-1 ( km25 ) mutant worms were highly sensitive to osmotic stress . However , loss of pmk-1 in flcn-1 ( ok975 ) mutant animals reduced but did not fully suppress the increased resistance conferred by flcn-1 depletion ( S5A Fig and S1 Table ) . Supporting this result , the expression of gpdh-1 is ~2-fold higher in flcn-1 ( ok975 ) ; pmk-1 ( km25 ) mutant animals as compared to pmk-1 ( km25 ) alone ( S5B Fig ) . Altogether , this suggests that pmk-1 is not involved in the transcriptional upregulation of gpdh-1 upon loss of flcn-1 and that it acts in parallel to flcn-1 and aak-1/2 . Glycogen is linked to the progression and the aggressiveness of multiple cancer types in humans [59 , 60] . To determine whether loss of FLCN also leads to the accumulation of glycogen in mammalian systems , we used the Flcnflox/flox/Pax8-Cre mouse model where Flcn is specifically deleted in the kidney and determined glycogen content using Periodic-Acid-Schiff ( PAS ) staining . The Flcnflox/flox/Pax8-Cre mouse was generated by mating Pax8-Cre mice with the Flcn flox/flox C57BL/6 mice . By six months of age , all mice developed visible macroscopic lesions confirmed as cysts that later developed into tumors . Strikingly , kidneys from Flcnflox/flox/Pax8-Cre mice accumulated higher glycogen levels as compared to normal kidneys from Flcnflox/flox mouse littermates ( Figs 5A and S6A ) . Our data show a stronger glycogen accumulation in the kidney cortex , which is due to the fact that Pax8 is expressed in the epithelial cells of the proximal and distal renal tubules , loops of Henle , collecting ducts and the parietal epithelial cells of Bowman’s capsule [61] . Importantly , PAS staining of tumors from BHD patients also indicate a strong accumulation of glycogen as compared to adjacent unaffected kidneys ( Figs 5B and S6B ) . We also compared the expression level of glycogen biosynthesis and degradation genes in 3 different subtypes of kidney cancer , kidney renal papillary cell carcinoma ( KIRP ) , kidney renal clear cell carcinoma ( KIRC ) , and kidney chromophobe ( KICH ) tumors . Strikingly , we observed a significant upregulation of genes involved in the synthesis and degradation of glycogen ( Fig 5C and S5 Table ) . We also observed that the expression of 46% of these genes are negatively correlated with FLCN expression ( Fig 5D ) . Overall , our data indicate that the accumulation of glycogen upon loss of FLCN is be conserved from nematodes to mammals , and that it might play a role in tumorigenesis .
A common mechanism to survive osmotic stress is the synthesis of compatible osmolytes [3] . In yeast and in C . elegans , the rapid accumulation of glycerol after hyperosmotic stress has been demonstrated [4 , 5] . However , it is not clear what fuels glycerol production upon acute hyperosmotic stress . Here we show that animals have evolved an interesting strategy to maintain glycogen stores , which can serve as fuel for glycerol production to ensure survival to acute hyperosmotic stress ( Fig 6 ) . While storage of soluble glucose molecules in cells would lead to osmotic stress , the storage of glucose in the form of insoluble glycogen polymers ensures osmotic homeostasis . Importantly , our data uncover that glycogen stores have a dual role: they can serve as a reservoir for production of energy or osmolytes . Indeed , pretreatment of wt and flcn-1 ( ok975 ) animals with oxidative and energy stressor paraquat , depletes glycogen stores rapidly and suppresses survival upon treatment with 400mM NaCl ( S2A and S2B Fig ) . The regulation of glycogen metabolism by AMPK has long been a paradox [44–50] . Acute activation of AMPK , by in vitro short term treatment of the AMP mimetic drug 5-Aminoimidazole-4-Carboxamide Riboside ( AICAR ) , leads to the phosphorylation and inhibition of glycogen synthase , which favors glycogen degradation for supply of short term energy [44–47] . However , chronic AMPK activation induced by a long term AICAR treatment or by genetic manipulation of AMPK regulatory subunits , results in glycogen accumulation via glucose-6-phosphate-dependent allosteric activation of glycogen synthase , which bypasses the inhibitory effect of the AMPK-mediated phosphorylation [48–50] . In agreement , constitutive AMPK activation through transgenic expression of activating mutations in the γ2 and γ3 subunits in mice and pigs leads to substantial glycogen accumulation in cardiac and skeletal muscles [36 , 50–53 , 55 , 56] . In light of these results , our data indicate that chronic AMPK activation upon loss of flcn-1 leads to glycogen accumulation . Similarly to what has been shown in yeast [54] , we demonstrate that AMPK-deficient strains exhibit reduced glycogen content as compared to wt . We further show that the accumulation of glycogen in wt and flcn-1 ( ok975 ) mutant animals depends on AMPK . Based on the data presented here together with our recently published reports [28 , 29] , we propose that FLCN is a key regulatory component of AMPK . Flcn muscle-specific knockout mice and Fnip1 knockout mice exhibited increased glycogen accumulation in muscles and liver , respectively [62 , 63] . Here we show that loss of FLCN leads to glycogen accumulation in kidneys of mice and in the tumors of BHD patients , suggesting that this pathway is evolutionarily conserved . In agreement with the important role for glycogen in organismal survival to stress , glycogen can be used by tumor cells to survive harsh microenvironments such as hypoxia [59 , 64] . In fact , glycogen accumulates in many cancer types [64] and inhibition of its degradation led to induction of apoptosis and impaired in vivo growth of tumor xenografts [59] . Importantly , our data might impinge on a novel role for glycogen in tumorigenesis . In addition to its critical role as an energy supplier , we speculate that glycogen degradation might lead to higher osmolyte levels to help survive hyperosmotic tumor microenvironments . In fact , we found that taurine and sorbitol synthesis genes , CSAD and AKR1B1 respectively , are upregulated in many kidney tumors ( S5 Table ) . Supporting this idea , recent evidence shed light on an important role of the nuclear factor of activated T cells 5 ( NFAT5 ) , a major transcription factor that regulates osmotic stress resistance genes , in promoting tumorigenesis and metastasis of several cancer types [2 , 65–67] . In summary , we speculate that the increased glycogen stores in tumors might lead to extended survival of cells under hyperosmotic stress , which could ultimately lead to neoplastic transformation by accumulation of DNA damage [1 , 2] .
C . elegans strains were obtained from the Caenorhabditis Genetics Center ( S6 Table ) . Nematodes were maintained and synchronized using standard culture methods [68] . The RNAi feeding experiments were performed as described in [69] , and bacteria transformed with empty vector were used as control . For all RNAi experiments , phenotypes were scored with the F1 generation . To measure osmotic stress resistance , synchronized 1 day adult worms were transferred to high concentration NaCl plates . Survival was measured daily . Worms that responded by movement to touch with the platinum wire were considered as alive . To measure the percentage of animals that recovered after hyperosmotic shock , 1 day adult animals were transferred to high NaCl plates . Animals shrink and paralyse shortly after exposure to NaCl . After 2 hours , animals that were able to move their entire body forward or backward in response to touch with a platinum wire were considered as “recovered” . Paralyzed animals often look straight and are unable to move . Synchronized young adult nematodes were harvested and total RNA was extracted with Trizol . Reverse transcription and qRT-PCRs were performed as previously described [28] . Transcripts were normalized to cdc-42 . Synchronized young adult wt and flcn-1 ( ok975 ) animals were harvested and RNA was extracted using Trizol and purified using Qiagen RNeasy columns . Total RNA samples were then hybridized onto Agilent gene chips . Fold change values are calculated using the mean of both data sets . The overlapping genes between flcn-1 ( ok975 ) mutant animals and the specified conditions and strains [8] were performed using the “compare two lists” online tool at http://www . nemates . org/MA/progs/Compare . html . The significance of the overlap and enrichment scores were determined via hypergeometric distribution method using http://nemates . org/MA/progs/overlap_stats . html . The number of genes in the C . elegans genome was considered to be19 , 735 . Synchronized 1 day adult nematodes were transferred to 400mM NaCl plates for 16 hours . Recovering animals were picked and transferred for TEM . Immersion fixation and embedding was performed according to [70] . Thin sections were cut on an RMC Powertome XL ( Boeckler Instruments ) using a diamond knife ( DDK ) and collected on Pioloform-coated copper slot grids . Grids were post-stained with 4% uranyl acetate and lead citrate and viewed using a Philips CM10 electron microscope ( FEI ) equipped with a Morada digital camera ( Olympus ) and iTEM software ( Olympus SIS ) . Synchronized young adult animals were transferred to agarose pads . For comparisons between strains , different conditions were transferred to the same agarose pad and were exposed to iodine vapor for 30 seconds . Animals were rapidly imaged individually . Quantification of the intensity of the staining was performed using ImageJ software . For human normal kidney and BHD tumor samples , slides were rehydrated after deparaffination and treated with 1% periodic acid for 10 minutes . Periodic acid was washed off with H2O and slides were then incubated in Schiff reagent for 20 min . Slides were then rinsed with H2O , counterstained with hematoxylin and embedded in entellan . Images were taken as described in [71] . Synchronized L4/young adult animals exposed or not to 400mM NaCl for 2 hours and were harvested and washed with M9 buffer adjusted to match plate salinity . Pellets were flash frozen in liquid nitrogen . Extraction was performed according to [5] . Briefly , frozen pellets were ground using a cold mortar and pestle on dry ice . The worm powder was then resuspended in 1N perchloric acid , and solutions were transferred to 15ml conical tubes and kept on ice for 1 hour . The lysate was then centrifuged and the supernatant was neutralized with 5N KOH containing 61 . 5mM K2HPO4 and 38 . 5mM KH2PO4 . Glycerol levels were determined using a glycerol determination kit ( R-Biopharm , Marshall , MI ) . Pellets were solubilized in 0 . 1N NaOH and protein content was determined using BCA . Glycerol levels were normalized to protein content . TCGA data including 91 kidney chromophobe gene expression RNASeq ( IlluminaHiSeq ) , 604 kidney renal clear cell carcinoma gene expression RNASeq ( IlluminaHiSeq ) , and 258 kidney renal papillary cell carcinoma gene expression RNASeq ( IlluminaHiSeq ) , were extracted from cancer Genomics Browser ( https://genome-cancer . ucsc . edu/proj/site/hgHeatmap ) . For expression analysis , data were expressed as median fold change and the Mann-Whitney test was used to calculate the p-values between normal and tumor samples . P-values less than 0 . 05 were considered to be statistically significant . For correlation analysis TCGA expression data ( same as expression analysis ) were used to calculate the Pearson correlation coefficient , and generate a heat map , using R software 3 . 1 . 1 ( http://www . r-project . org/ ) . P-values less than 0 . 05 were considered to be statistically significant . Data are expressed as means ±SEM . Statistical analyses for all data were performed by student's t-test , using Excel ( Microsoft , Albuquerque , NM , USA ) . For hyperosmotic stress survival curve comparisons we used the Log-rank Mantel Cox test using GraphPad software . Statistical significance is indicated in figures ( * P<0 . 05 , **P<0 . 01 , ***P<0 . 001 ) or included in the supplemental tables . | The ability of an organism to adapt to sudden changes in environmental osmolarity is critical to ensure growth , propagation , and survival . The synthesis of organic osmolytes is a common adaptive strategy to survive hyperosmotic stress . However , it was not well understood , which biosynthetic pathways and storage strategies were used by organisms to rapidly generate osmolytes upon acute hyperosmotic stress . Here , we demonstrate that glycogen is an essential reservoir that is used upon acute hyperosmotic stress to generate the organic osmolyte glycerol promoting fast and efficient protection . Importantly , we show that this pathway is regulated by FLCN-1 , an ortholog of the human tumor suppressor Folliculin responsible for the Birt-Hogg-Dubé cancer syndrome , and by AMPK , the master regulator of energy homeostasis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | FLCN and AMPK Confer Resistance to Hyperosmotic Stress via Remodeling of Glycogen Stores |
Rift Valley Fever virus ( RVFV ) is an enzootic virus that causes extensive morbidity and mortality in domestic ruminants in Africa , and it has shown the potential to invade other areas such as the Arabian Peninsula . Here , we develop methods for linking mathematical models to real-world data that could be used for continent-scale risk assessment given adequate data on local host and vector populations . We have applied the methods to a well-studied agricultural region of California with 1 million dairy cattle , abundant and competent mosquito vectors , and a permissive climate that has enabled consistent transmission of West Nile virus and historically other arboviruses . Our results suggest that RVFV outbreaks could occur from February–November , but would progress slowly during winter–early spring or early fall and be limited spatially to areas with early increases in vector abundance . Risk was greatest in summer , when the areas at risk broadened to include most of the dairy farms in the study region , indicating the potential for considerable economic losses if an introduction were to occur . To assess the threat that RVFV poses to North America , including what-if scenarios for introduction and control strategies , models such as this one should be an integral part of the process; however , modeling must be paralleled by efforts to address the numerous remaining gaps in data and knowledge for this system .
Rift Valley fever virus ( RVFV; viral family Bunyaviridae , genus Phlebovirus ) is a pathogen that causes febrile illness in domestic ruminants ( sheep , cattle , and goats ) and humans throughout Africa and parts of the Arabian Peninsula [1]–[3] that may be transmitted by several genera of mosquitoes [3]–[6] . Outbreaks often result in heavy economic costs through loss of livestock , especially when associated with an incursion into a new area [7] , [8] . Although never detected in the Western Hemisphere , RVFV is a threat to human and livestock health in North America and is included on select agent lists of the U . S . Department of Health and Human Services and the U . S . Department of Agriculture [9] . Mosquito species found to be vectors of RVFV with varying degrees of efficiency in laboratory settings [6] , [10] are known to be present throughout much of the U . S . [11] , but other aspects of potential transmission cycles remain inadequately studied . To properly assess and mitigate the risk posed by a RVFV invasion , methods are needed to identify areas that are most likely to support transmission , the time periods when transmission is expected to pose a risk , and whether an introduced virus could become established . To date , such questions have been addressed by only a few analytic methods , including expert elicitation [12] , basic GIS overlays of humans and vectors with a hypothetical host [13] , and pathways analysis [14]–[16] . Process-based mathematical models provide a useful platform to coalesce disparate data , make logical assumptions concerning data gaps , and evaluate a range of potential scenarios . Gaff et al . developed a dynamical model for RVFV [17] that included livestock hosts and two genera of mosquitoes , Aedes and Culex , that respectively were or were not capable vertically transmitting RVFV . This model's structure has been extended in several important ways to 1 ) accommodate spatial structure through host or vector movements [18] , [19] , 2 ) assess potential control methods [20] , [21] , or 3 ) include humans [22] or asymptomatic livestock hosts [23] . These models have resulted in important advances in modeling RVFV , but their application is limited by the lack of appropriate data to inform parameters , many of which have been recycled between models , defined arbitrarily , or borrowed from literature on other arboviruses that may not apply for RVFV . In the current study , we apply a unique and generalizable approach that links real-world data with the mathematical models , utilizing broadly available national-scale data where possible . To illustrate the methodology , we consider results for the southern Central Valley in California , an area with large , well-documented host and vector populations . We present two model-derived transmission metrics that quantify expectations for typical ( , [24] ) and maximal ( , [25] ) transmission from an initial disease-free state , and we map these metrics according to the spatial pattern of vector abundance associated with various land uses . We also show how these metrics change in time as a function of temperature , thereby enabling the assessment of seasonal transmission risk . We also highlight several critical data gaps that must be addressed .
California's Great Central Valley extends for 700 km north to south through the center of the state and is home to extensive and varied agricultural lands that include irrigated crops , livestock operations , natural or restored wetlands , and urbanized areas . In this study , we considered the southern half of the Central Valley ( Figure 1 ) , which contains very high densities of livestock ( primarily dairy cows , with 1 million cattle within the study area ) interspersed with managed wetlands and multicrop agriculture that can produce large populations of competent vectors ( e . g . , Culex tarsalis ) . Deer were rare on the valley floor and generally were restricted to surrounding higher-elevation foothills and mountains , and sheep typically were moved into the valley for grazing only during the cooler months of the year when transmission was expected to be minimal . The area is likely to be climatologically permissive for RVFV transmission as this is the warmest part of the valley and supports consistently high transmission of West Nile virus [26] and , previously , other arboviruses [27] . For the model , appropriate spatial dimensions were needed for patches that would represent the heterogeneity in land cover and host and vector densities at a fine enough scale that populations could be assumed to be well-mixed , given described ranges of vector movement [28] . To achieve this , we defined a uniform grid of km squares ( 25 km2 ) that covered the study area , and all model input variables were scaled to this grid . All model outputs were calculated by grid cell for each day of the year . Process-based , dynamical mathematical models of virus transmission are built from knowledge of the interactions among virus , hosts , and vectors . In the case of RVFV in North America , such issues are uncertain . In the current study we extend previous work [17] , [41] to construct a mathematical model of RVFV ( Figure 4 ) , with the following assumptions regarding the anticipated epidemiology of RVFV in California . Temperature dependence of the vectors' extrinsic incubation rate ( i . e . , the inverse of the extrinsic incubation period , EIP ) and the gonotrophic cycle length ( gonotrophic period , GP ) were modeled based on published data as follows . We digitally extracted data points for temperatures at which a median EIP could be estimated from Figure 3 of [42] ( 26 and 33C ) and Figure 1 of [43] ( 17 and 28C ) . Logistic curves were fitted to the proportion of mosquitoes with disseminated infections over time for experiments with Ae . fowleri and Ae . taeniorhynchus . To our knowledge , these studies are the only published experiments that explore the temperature-vector competence relationship for RVFV . From the resulting model functions , we estimated the EIP as the median time to disseminated infection of RVFV in the mosquito . Carrying out a linear regression on the rate as a function of temperature resulted in the model: EIP = ( 0 . 007084 × temperature−0 . 103820 ) −1 . The relationship between EIP and temperature also has been studied for Culex mosquitoes [42] , [44] , but heterogeneity among experiments and a paucity of comparable data points precluded construction of an analogous EIP model for this genus . Therefore , we modeled extrinsic incubation in Culex and Aedes by the same function . The gonotrophic period ( i . e . , the number of days between bloodmeals ) was modeled as GP = 2+ ( −0 . 066+0 . 018 × temperature ) −1 , using a published linear regression equation for the ovarian maturation rate [45] plus 2 days for oviposition and locating a bloodmeal host . The environmental carrying capacity could not be explicitly measured , and for both vectors , it was approximated daily using the vector abundance for the following day based on the typical abundance time series described above . This resulted in the desired inflation and deflation of the density-dependent birth rates in proportion to the rate of population growth or shrinkage , respectively , with a corresponding inverse impact on death rates . We implemented the full model using a set of ordinary differential equations ( mathematical details appear in the appendix ) . Using the methods described in van den Driessche and Watmough [46] , we derived an expression for the basic reproduction ratio , , which represents the average number of secondary infections that arise from a single infectious individual ( vector or host ) introduced into a completely susceptible population[24] , [47] , so that when , there are insufficient new cases per case for propagation and the pathogen cannot persist in the population . When , the pathogen is efficiently transmitted and becomes enzootic; elevated values indicate that transmission is more intense and that stochastic fadeout of the pathogen is less likely . For complex models of vectorborne infections , it has been demonstrated that outbreaks are possible for under certain circumstances [48] , [49] . Because the model incorporates both vertical and horizontal transmission , was written as the sum of the values for each mode of transmission determined separately , [17] , [18] , [50] . Details of the computation and a sensitivity analysis of the model appear in the Mathematical Appendix . In addition to , we computed a recently described , metric , , that quantifies the reactivity , or epidemicity , of the system [25] . represents the maximum number of new infections produced by an infective individual at a disease free equilibrium , and like , epidemicity is a threshold quantity; when , transient epidemics ( i . e . , outbreaks that may eventually fade out ) are possible regardless of the average system behavior predicted by . When , transmission , even for brief time periods , is not expected . Evaluating from our model allows us to investigate the potential for RVFV outbreaks in areas and times when suggests that efficient transmission is not possible . and are both functions of the model parameters shown in Mathematical Appendix Table S1 in Text S1 . Stochastic sampling from biologically relevant ranges of parameters was used to assess the sensitivity of and to the model parameters . The ranges for each parameter are presented in Mathematical Appendix Table S3 in Text S1 . and , the vector carrying capacities , were computed from observed data as described above . Likewise , the EIP and vector GP values were functions of temperature . We assumed a uniform distribution for each parameter across ranges shown in Mathematical Appendix Table S3 in Text S1 . The ranges of all the other parameters are from the references shown in Mathematical Appendix Table S1 in Text S1 . Our model includes uncertain variables , so sets of sampled parameter values were generated by Latin hypercube sampling following the suggestion of Matala [51] that an such that should suffice for the number of stochastic samples of complete parameter sets . Partial rank correlation coefficients ( PRCC ) were computed across ranges of parameters described in Mathematical Appendix Table S3 in Text S1 to assess the significance of each parameter with respect to and . Spatial analysis of temperatures , land cover , and host and vector abundance was carried out using R version 2 . 15 [52] , ArcGIS 10 . 0 ( ESRI , Redlands , CA , USA ) , and PostgreSQL 9 . 0 ( http://www . postgresql . org ) databases with added spatial capabilities of PostGIS ( http://postgis . refractions . net ) . All code for mathematical modeling was written in version 2 . 15 [52] .
The seasonal patterns for the basic reproductive ratio for RVFV ( ; Figure 5 ) indicated that the risk for sustained transmission increased rapidly by May , with exceeding 1 in the areas with both cattle and field crops ( Figure 5 ) . Initially , these areas at risk consisted of a small number of grid cells in the center of the study area and a single cell near the southernmost end of the valley that could serve as early foci for transmission , and these areas remained at higher risk than other areas through the summer . Risk was greatest overall from late June–September , when a much broader area was at risk for sustained transmission that included grains and field crops and covered Tulare County , the core of California's dairy industry . In all areas , values 1 from October–April indicate that introductions from late fall–early spring would be unlikely to become established and that persistence of RVFV through winter may depend on mechanisms for long-term maintenance between epidemics ( e . g . , vertical transmission in vectors ) . Our estimates of epidemicity for RVFV , , were much higher than the average expectations of ( max = 95 . 3 and 3 . 2 , respectively ) and indicated that transient outbreaks could occur over a broader spatio-temporal window than that circumscribed by alone ( Figure 6 ) , although the relative seasonal patterns for the two metrics were strongly correlated ( ) . values 1 indicate that transmission was possible from February–November in agricultural areas , although the number of cases probably would remain small for introductions during February–April or November . The highest transmission potential ( Figure 6 ) occurred in places and times where cattle were present and vectors reached high densities . Specifically , values were greatest in areas dominated by field crops and grains , which generally had the highest combined concentrations of Culex vectors and cattle in the study area , although the latter had little impact on epidemicity . Risk of RVFV transmission in urban areas was somewhat lower , with the highest level expected during spring , associated with the typical peak in Culex abundance at that time , followed by a slow decline in both and vector abundance through the end of the summer . Orchards and vineyards ( trees/grapes ) and grasslands had low risk for epidemics due to their low vector abundance , and fallow or barren habitats had no risk for outbreaks , even when cattle were present . In all areas , was closely linked to the abundance and carrying capacity of vectors , and was greatest during spring and summer when the abundance of vectors , primarily Culex , was highest . For the scenarios under study , other parameters had little impact on , resulting in negligible variation around the average seasonal pattern for each land use class ( Figure 6 ) . Seasonally-flooded wetlands were not included in our analysis because the relatively small fraction of the total study area that they occupied ( Figure 1 ) did not include dairy cattle . However , these areas deserve special consideration because they were the only areas where both Aedes and Culex reached high abundance ( Figure 3 ) , making them a reasonable analogue to the dambo habitats where RVFV is enzootic in east Africa , although the timing of their flooding is linked to human water management rather than rainfall . If dairy cattle or other competent hosts were present , both horizontal and vertical transmission would be possible , presenting strong potential for both transient epidemics and establishment . To evaluate this possibility , we calculated and for the wetlands in the study area ( Figure 1 ) , with the addition of 1 , 000 cattle to each grid cell to simulate the effect of a moderate-sized dairy adjacent to the wetlands ( median dairy size for the study area1 , 140 cattle ) . Transmission potential was higher than for other land uses and reflected the abundance of Culex vectors as before . There were two annual peaks of 186 on June 14 and 489 on September 30 for and 2 . 84 on June 27 and 4 . 37 on September 27 for . Sensitivity analysis revealed that both and were particularly responsive to the abundance ( ) , carrying capacity ( ) , and vector competence ( ) of mosquito vectors . All of these relationships were positive , with the exception that had a negative correlation with mosquito abundance due to the density dependence of birth ( and correspondingly , death ) rates such that higher numbers of vectors resulted in smaller values for the ratio , which also limited . was much more sensitive than to variation in temperature that affected RVFV extrinsic incubation and vector biting rates , which explained its broader range of values within each land use class ( Figure 5 , lower panel ) . The abundance of hosts had little effect on either transmission metric . Complete results for the sensitivity analysis are presented in Mathematical Appendix Table S3 in Text S1 .
We have developed novel , generalizable methods to link mathematical models for RVFV with broad-scale spatio-temporal data for realistic landscapes . These methods could be useful for prioritizing when and where to focus control strategies ( e . g . , vector control or cattle vaccination ) during an invasion . Many gaps in both data and knowledge remain , but this is an important step toward understanding the potential seasonal transmission cycles of RVFV and other vectorborne pathogens that may invade temperate North America . | Rift Valley fever virus is a pathogen enzootic to sub-Saharan Africa , with epidemic transmission occurring sporadically between mosquitoes and mammals , notably livestock . The virus is regarded as a global threat to agriculture and human health because it has proven capable of expanding its range into western and northern Africa , Madagascar , and the Arabian Peninsula , and a recent study has shown that mosquitoes in North America are capable of transmitting the virus . Here , we used a set of mathematical equations to formulate a logical representation of potential transmission mechanisms , and we informed the model with real-world data and generalizable methods to define spatial and temporal variation in mosquito and host abundance . We applied these methods in California's warm , agricultural Central Valley , an area with a history of mosquito-borne virus transmission and a hub of California's dairy industry . Model-derived transmission estimates indicated broad potential for transient epidemics that could result in economic losses in livestock in all but the coldest winter months , but the greatest risk for intense , sustained transmission occurred during the summer when both vector abundance and temperatures were highest . We also highlight critical gaps in the data available to inform models for Rift Valley fever virus . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Data-Driven Modeling to Assess Receptivity for Rift Valley Fever Virus |
Monoamines , such as 5-HT and tyramine ( TA ) , paralyze both free-living and parasitic nematodes when applied exogenously and serotonergic agonists have been used to clear Haemonchus contortus infections in vivo . Since nematode cell lines are not available and animal screening options are limited , we have developed a screening platform to identify monoamine receptor agonists . Key receptors were expressed heterologously in chimeric , genetically-engineered Caenorhabditis elegans , at sites likely to yield robust phenotypes upon agonist stimulation . This approach potentially preserves the unique pharmacologies of the receptors , while including nematode-specific accessory proteins and the nematode cuticle . Importantly , the sensitivity of monoamine-dependent paralysis could be increased dramatically by hypotonic incubation or the use of bus mutants with increased cuticular permeabilities . We have demonstrated that the monoamine-dependent inhibition of key interneurons , cholinergic motor neurons or body wall muscle inhibited locomotion and caused paralysis . Specifically , 5-HT paralyzed C . elegans 5-HT receptor null animals expressing either nematode , insect or human orthologues of a key Gαo-coupled 5-HT1-like receptor in the cholinergic motor neurons . Importantly , 8-OH-DPAT and PAPP , 5-HT receptor agonists , differentially paralyzed the transgenic animals , with 8-OH-DPAT paralyzing mutant animals expressing the human receptor at concentrations well below those affecting its C . elegans or insect orthologues . Similarly , 5-HT and TA paralyzed C . elegans 5-HT or TA receptor null animals , respectively , expressing either C . elegans or H . contortus 5-HT or TA-gated Cl- channels in either C . elegans cholinergic motor neurons or body wall muscles . Together , these data suggest that this heterologous , ectopic expression screening approach will be useful for the identification of agonists for key monoamine receptors from parasites and could have broad application for the identification of ligands for a host of potential anthelmintic targets .
Nematode infections cause significant morbidity and contribute significantly to a loss of Disability Adjusted Life Years ( DALYs ) [1–4] . For example , soil-transmitted nematodes , including Necator americanus , Trichuris trichuris and Ascaris lumbricoides infect nearly 2 billion worldwide and are a source of disease in over 400 million children [5] . More importantly , in many cases , such as filarial infection , effective chemotherapy is still not available [6] . Parasitic nematodes also have a devastating economic impact in agricultural settings that , at least secondarily , contributes significantly to a decline in human welfare , especially in areas where good nutrition is already compromised . For example , parasitic nematodes infect livestock and major crops ( corn and soybeans ) and cause billions in economic losses yearly in the US alone [7] . Importantly , most commercially available anthelmintics have become increasingly ineffective because of growing resistance ( benzimidazoles , levamisole and , most recently , ivermectin ) and most nematicides ( DCBP ( 1 , 2-dibromo-3-chloropropane ) , methyl bromide ) , to control plant nematodes , have been banned by the EPA because of human toxicity [8–13] . New drugs , new drug targets and new , more effective screening protocols are desperately needed in all settings . Most anthelmintics in use today act as agonists at key receptors and cause paralysis by interfering with muscle contraction and/or locomotion [14–17] . Since receptor “activation” is essential for anthelmintic activity , receptor knockout is not necessarily the “gold standard” for target validation; in fact knockout may not be lethal . Five molecular targets have been used for drug discovery , two nicotinic cholinergic receptor subunits ( tetrahydropyrimidines/imidathiazoles and amino-acetonitriles ) , glutamate-/GABA-gated Cl- channels ( macrocyclic lactones and piperazine , respectively ) and Ca++-gated K+ channels ( emodepside ) [14–17] . Importantly , each of these anthelmintics is active in the free-living nematode , Caenorhabditis elegans and our understanding of their modes of action has , in large part , resulted from our ability to genetically manipulate their putative targets in receptive C . elegans mutant backgrounds [18–21] . Importantly , the identification of new targets has been limited by the lack of useful information about the identity , function and localization of the additional receptors regulating muscle contraction and locomotion . In addition to identifying new targets , we also need new screening protocols that preserve the unique pharmacologies of the receptors from the different parasites and maintain a nematode-specific context that includes the cuticle and appropriate accessory proteins , especially given that no nematode cells lines are available and that the parasites themselves are extremely difficult and expensive to culture . In the present study , we have developed a heterologous , ectopic over-expression approach to provide a unique nematode screening platform for selective agonist identification , exploiting the unique experimental advantages of the C . elegans model system . Previously , we and others have demonstrated that exogenous monoamines , such as serotonin ( 5-HT ) , dopamine ( DA ) and tyramine ( TA ) , each paralyze C . elegans and , where examined , parasitic nematodes [22–33] . In each case , the key C . elegans receptors mediating this locomotory inhibition have been identified and functionally localized , with each operating at a different level within the locomotory circuit: 5-HT in a few key interneurons , including the two AIB interneurons , DA in the cholinergic motor neurons and TA in head muscle and additional interneurons associated with locomotory decision-making [24 , 28 , 30] . We have previously constructed quintuple 5-HT receptor null C . elegans ( 5-HT quint ) that do not express any previously identified 5-HT receptors and do not respond to exogenous 5-HT , to identify essential roles for the Gαo-coupled 5-HT1-like SER-4 and the unique 5-HT-gated Cl- channel , MOD-1 in 5-HT-dependent locomotory paralysis [23 , 24] . Importantly , SER-4 agonists appear to function as anthelmintics in vivo and have been used to clear Haemonchus contortus infections from gerbils [34 , 35] . In the present study , we ectopically expressed SER-4 and MOD-1 orthologues from parasitic nematodes , insects and humans in either the cholinergic motor neurons or body wall muscles of quintuple C . elegans 5-HT receptor null animals that lack all known C . elegans 5-HT receptors , on the assumption that agonist-dependent receptor activation at these sites will cause robust phenotypes that can be readily adapted for agonist screening . For example , the activation of a ligand-gated Cl- channel in body wall muscles would be predicted to hyperpolarize the muscle and significantly inhibit locomotion , while the activation of a Cl- channel or Gαo-coupled GPCR on the cholinergic motor neurons would significantly inhibit acetylcholine ( ACh ) release from the motor neurons and inhibit both muscle contraction and thus , locomotion . Importantly , as noted below , both hypotheses have proven to be correct .
bus-8 ( e2968 ) , bus-16 ( e2802 ) and bus-17 ( e2800 ) were obtained from Caenorhabditis Genetics Center ( CGC ) . ser-5 ( tm2654 ) ;ser-4 ( ok512 ) ;mod-1 ( ok103 ) ;ser-7 ( tm1325 ) ser-1 ( ok345 ) ( 5-HT quint ) , ser-5 ( tm2654 ) ;mod-1 ( ok103 ) ;ser-7 ( tm1325 ) ser-1 ( ok345 ) ( SER-4 quad ) and lgc-55 ( tm2913 ) ;tyra-3 ( ok325 ) tyra-2 ( tm1846 ) ser-2 ( pk1357 ) ( TA quad ) were generated as described previously [23 , 24] . All strains were maintained on NGM plates with OP50 at 16°C . The cDNA clone of Drosophila melanogaster 5-HT1A ( RE57708 ) was ordered from the Drosophila Genomics Resource Center ( DGRC ) , the cDNA clone Human HTR1A ( MGC: 167873; clone ID: 9020250 ) from GE Healthcare Dharmacon Inc . and cDNA clones of Haemonchus contortus ( Hco ) lgc-55 and mod-1 orthologues were kindly provided by Dr . Sean Forrester [33 , 36] . The unc-17ß promoter , RM#621p , was obtained from Dr . James Rand . The integrated AIB::HisCl1 in N2 ( cx15457 ) animals were a kind gift from Dr . Cornelia Bargmann [37] . Serotonin ( 5-HT ) ( H7752-25G ) , tyramine ( TA ) ( T2879-25G ) , 8-OH DPAT ( H141-25MG ) , sumatriptan succinate ( S1198-10MG ) , PAPP ( S009-25MG ) and histamine dihydrochloride ( H7250-5G ) were purchased from Sigma Life Sciences . Stock solutions ( 50 mM ) of 5-HT , TA , 8-OH-DPAT , sumatriptan and histamine were made up in distilled water , PAPP in 100% ethanol . The constituent of for nematode growth media ( NGM ) , potassium phosphate monobasic ( KH2PO4; P285-3 ) , sodium chloride ( NaCl; S271-3 ) , calcium chloride dehydrate ( CaCl2 . 2H2O; C79-500 ) , magnesium sulfate heptahydrate ( MgSO4 . 7H2O; BP213-1 ) , tryptone ( BP1421-2 ) and agar ( DF0812071 ) were purchased from Thermo Fisher Scientific Inc . , cholesterol ( C3045-5G ) purchased from Sigma Life Science . All transgenic constructs were created by overlap fusion PCR [38] . All transgenes contain a GFP marker ( with unc-54 3′-UTR ) at the 3’-end . Primers used are listed in S1 Text . PCR products from multiple reactions were pooled and co-injected with coelomocyte-RFP screening marker into the appropriate null backgrounds [39] . Once generated , transgenic animals are frozen in liquid nitrogen and thawed fresh weekly for assay . Multiple transgenic lines from each construct were examined . Fresh agar plates ( without NaCl , KH2PO4 , MgSO4 , CaCl2 , tryptone and cholesterol ) containing 5-HT , TA , PAPP , sumatriptan or 8-OH DPAT at desired concentrations were made daily . For assays involving bus mutants , fresh NGM agar plates ( with NaCl , KH2PO4 , MgSO4 , CaCl2 , tryptone and cholesterol ) containing 5-HT were used for all assays . For assays with AIB::HisCl1 ( cx15457 ) animals , freshly poured NGM agar or agar only plates containing 10 mM and 2 mM histamine were used . NGM agar plates were prepared as described in WormBook [40] . For all paralysis assays , well-fed , transgenic young adults expressing RFP screening markers were picked 2 hrs prior to assay and maintained on NGM plates with E . coli OP50 . For assay , 10 animals are transferred to assay plates ( agar only for all assays and NGM agar for assays with bus mutants ) containing the appropriate drug and motility was assessed at intervals of 5 min for 30 min . Experiments with sumatriptan were carried out for 60 min , with motility assessed every 5 min . All assays were conducted in the absence of food , i . e . OP50 . Animals that moved less than 1 body bend/20 s were counted as paralyzed . Each transgenic line was assayed at least 3 times with 10 animals/assay for each agonist concentration . Data is presented as % paralyzed ± SE over drug exposure time ( min ) . Dose-response curves and EC50s were then generated using a variable slope nonlinear regression model with GraphPad Prism 6 software . Drug concentrations were log10-transformed prior to analysis . The accession numbers of the proteins involved in our study are C . elegans SER-4 ( accession no . NP_497452 ) , C . elegans LGC-55 ( accession no . NP_507870 ) , C . elegans MOD-1 ( accession no . CCD72364 ) , D . melanogaster 5-HT1A ( accession no . NM_166322 . 2 ) , D . melanogaster HisCl1 ( accession no . Q9VGI0 ) , human HTR1A ( accession no . BC136263 ) , H . contortus LGC-55 ( accession no . ACZ57924 . 1 ) and H . contortus MOD-1 ( accession no . ADM53350 . 1 ) .
The monoamines , 5-HT , DA and TA each dramatically inhibit locomotion in C . elegans when applied exogenously at concentrations high enough to overcome the permeability barrier of the nematode cuticle , ultimately resulting in paralysis [24 , 25 , 27 , 30] . Using the C . elegans model , the receptors involved in monoamine-dependent locomotory inhibition have been identified and localized [22–30] . Interestingly , the key receptors involved in 5-HT , DA and TA inhibition each function at a different level in the locomotory circuit with 5-HT-dependent paralysis requiring the expression of the Gαo-coupled , 5-HT1-like receptor , SER-4 , and the 5-HT-gated Cl- channel , MOD-1 in a limited number of interneurons , including the two AIBs [24 , 25] . Unfortunately , since nematode cell lines are not available and the maintenance of parasitic nematodes outside their hosts is problematic , screening platforms for anti-nematodal activity have been limited and do not usually incorporate the nematode cuticle or potentially important nematode accessory proteins . The present study was designed to develop a screening platform for nematode monoamine receptor agonists in “chimeric” genetically-engineered C . elegans by heterologously expressing 5-HT and TA receptors at sites likely to yield robust phenotypes upon agonist stimulation . Previously , many investigators have rescued a range of behaviors in C . elegans null animals with the expression of proteins from the parasites , validating this approach [41–43] . We chose to examine locomotion as an endpoint for heterologous , ectopic expression , as the neurons and circuits modulating locomotion in C . elegans and parasitic nematodes appear to be conserved , can be readily assessed by established screening assays , and have always been the primary target for the majority of existing anthelmintics . Specifically , we expressed 1 ) Gαo-coupled , 5-HT1-like receptors , or 5-HT/ TA-gated Cl- channels in the cholinergic motor neurons of C . elegans mutants lacking any 5-HT or TA receptors , respectively on the assumption that robust agonist-dependent Gαo signaling or potential hyperpolarization , respectively , would dramatically inhibit ACh release and locomotion and 2 ) 5-HT or TA-gated Cl- channels in body muscle of C . elegans mutants lacking any 5-HT or TA receptors , respectively , on the assumption that agonist-dependent muscle hyperpolarization would cause paralysis . The role of the C . elegans 5-HT1-like receptor , SER-4 , in 5-HT-dependent paralysis is well documented [23–25 , 44] . Indeed , the utility of the H . contortus SER-4 orthologue , 5-HT1HC as an anthelmintic target has been validated previously both in vivo and in vitro [34 , 35] . Locomotion in C . elegans has been assessed previously using a number of different assays , many of which can be readily adapted for screening [45–50] . For example , automated thrashing assays allow thousands of compounds to be easily screened per day [48] . Monoamine-dependent locomotory inhibition and paralysis has been quantified on agar plates ( sinusoidal body bends ) and in liquid medium ( C-shaped “swimming” ) , containing either M9 buffer or water [22 , 24 , 25 , 27 , 29 , 30] . The permeability of the C . elegans cuticle appears to vary depending on incubation conditions , with much less 5-HT apparently required in water , than in salt-containing media ( M9 ) , possibly because of an increased cuticular permeability under hypotonic conditions [25] . Previously , we assayed locomotion under standard C . elegans culture conditions on NGM agar plates . Under these conditions , 15 mM 5-HT initiated a rapid paralysis in wild type animals , and ser-5;mod-1;ser-7 ser-1 quadruple null ( SER-4 quad ) animals [24 , 44] . As predicted , 5-HT had no effect on locomotion in 5-HT quint animals that lack all previously identified 5-HT receptors ( Fig 1A and 1B ) [24] . This 5-HT-dependent paralysis was not the classical spastic paralysis associated with cholinergic agonists , such as levamisole , or the flaccid paralysis associated with glutamatergic agonists , such as ivermectin , but instead appeared to result more from “locomotory confusion , ” with animals unable to effectively integrate conflicting sensory inputs to initiate and sustain forward/backward locomotion . The C . elegans cuticle appears to be more impermeable than those of some of the parasitic nematodes [51–53] . Therefore , since the concentration of 5-HT required for maximal paralysis was quite high ( 15 mM ) in these short term assays , presumably to overcome cuticular permeability , we re-assayed these animals under hypotonic conditions on agar plates without salt ( non-NGM ) ( Fig 1C and 1D ) . Attempts to repeat published data from others on 5-HT paralysis in water were unsuccessful , as majority of the animals burst soon ( within 5 min ) after exposure to water [25] . However , in a hypotonic environment ( agar alone without NGM ) , much lower concentrations of 5-HT were required for inhibition of wild type animals , with 1 mM 5-HT yielding 50% paralysis after 10 min exposure ( EC50 about 0 . 4 mM ) ( Fig 1C and 1D ) . In addition to hypotonic incubation , we also examined 5-HT-dependent paralysis in a number of C . elegans mutants that exhibit increased cuticular permeability . For example , the Hodgkin group previously identified a series of bus mutants that exhibit increased cuticular permeability that have been hypothesized to be excellent vehicles for small molecule screening [54] . Indeed , as noted in Fig 1E and 1F , many of the bus mutants are hypersensitive to 5-HT-dependent paralysis , even under isotonic assay conditions ( on NGM agar plates ) . For example , bus-17 mutants are acutely paralyzed after 10 min on 5-HT with an EC50 of about 0 . 24 mM , which is substantially lower than that observed in wild-type animals incubated under the same conditions ( EC50 = 11 . 5 mM ) ( Fig 1F ) . These results suggest that these mutants might be useful for agonist identification , especially when only limited amounts of compound are available . Indeed , it may even be possible to select mutants that exhibit cuticular permeabilities that mimic those of individual parasites . Unfortunately , these mutants are also sensitive to hypotonicity and burst under the hypotonic conditions used in the present study , so that they could not be used in combination with hypotonicity to further increase sensitivity . Therefore , unless specified , hypotonic conditions were used to assay the transgenic animals described below . A ser-4::gfp transgene is expressed in a limited number of neurons , including the AIBs [25] . Therefore , SER-4::GFP was specifically expressed in either the AIB interneurons ( Pnpr-9 ) or ectopically , in the cholinergic motor neurons ( Punc-17β ) of the 5-HT quint . Expression was confirmed by GFP fluorescence ( Fig 2A ) . As predicted , 5-HT quint animals expressing SER-4 in either the AIBs or cholinergic motor neurons were rapidly paralyzed by 5-HT ( Fig 2B ) . Interestingly , on 5-HT , although 5-HT quint animals expressing SER-4 in the AIBs alone moved only infrequently , they initiated backward locomotion for a short distance when prodded with a blunt platinum wire at the tail , suggesting that they were probably unable to process conflicting locomotory signals , as hypothesized above . In contrast , animals expressing SER-4 in the cholinergic motor neurons were fully paralyzed and did not move when prodded . To demonstrate the utility of this screening approach , the Drosophila 5-HT1 orthologue ( 5HT1A ) or the human 5-HT-1A receptor ( HTR1A ) were also expressed specifically in the cholinergic motor neurons ( Punc-17β ) of 5-HT quint animals . Locomotion in animals from both transgenic lines was dramatically inhibited by exogenous 5-HT , demonstrating that the receptors were functionally expressed ( Fig 3A ) . To demonstrate the specificity of these chimeric C . elegans for agonist identification , we examined the effect of 8-hydroxy-2- ( di-n-propylamino ) tetralin ( 8-OH-DPAT ) , a subtype-selective agonist for the human 5-HT1A receptor , sumatriptan succinate , a selective mammalian 5-HT1B/D agonist , and p-amino-phenethyl-m-trifluoromethylphenyl piperazine ( PAPP ) . As predicted , 8-OH-DPAT rapidly paralyzed the 5-HT quint animals expressing the human 5-HT1A receptor ( Fig 3B ) . In contrast , 8-OH-DPAT , even at 2 mM , had no effect on locomotion 5-HT quint animals expressing either Drosophila or C . elegans 5-HT1 receptor orthologues , suggesting the conservation of ligand-receptor specificity in chimeric C . elegans ( Fig 3B ) . Sumatriptan , at low concentrations , is a selective mammalian 5-HT1B/D agonist , and , indeed in the present study , sumatriptan was much less effective than 8-OH-DPAT in initiating paralysis [55] . For example , 0 . 5 mM sumatriptan had no effect on locomotion in either wild type or transgenic animals expressing 5-HT1A receptor orthologues in cholinergic motor neurons and , even at higher concentrations , failed to fully paralyze animals expressing the human 5-HT1A receptor . In addition , although animals expressing the human 5-HT1A receptor responded to increased sumatriptan concentrations more rapidly , these locomotory effects were transient and reduced dramatically after 25 min , presumably due to receptor desensitization ( Fig 3C ) . In contrast , paralysis increased with prolonged sumatriptan exposure in animals expressing either the C . elegans or Drosophila receptors , demonstrating kinetic differences between the orthologous receptors . PAPP , a high affinity agonist for the H . contortus 5-HT1-like receptor , paralyzes H . contortus L3s in vitro and clears experimental H . contortus infections from gerbils [34 , 35] . As predicted , PAPP initiated a rapid paralysis in wild type animals ( EC50 = 0 . 37 mM ) and , even more rapidly , in 5-HT quint animals expressing the C . elegans SER-4 in the cholinergic motor neurons ( EC50 = 0 . 17 mM ) , supporting the previous identification of PAPP as a 5-HT1-like receptor agonist ( Fig 4A and 4B ) . In contrast , and somewhat surprisingly , at higher concentrations ( ≥0 . 5 mM ) , PAPP also paralyzed 5-HT quint animals ( EC50 = 0 . 68 mM ) that were unaffected by 5-HT , suggesting that , in addition to acting as a 5-HT1-like receptor ( SER-4 ) agonist , PAPP also acted at second target ( s ) ( Fig 4A and 4B ) . Since exogenous TA and DA also paralyze C . elegans , we surmised that , at higher concentrations , PAPP might be activating additional monoamine receptors . DA-dependent paralysis requires the expression of the Gαo-coupled DA receptor , DOP-3 in the cholinergic motor neurons [26] . Therefore , dop-3 expression was knocked down in the 5-HT quint animals using dop-3 RNAi driven by the dop-3 promoter . As noted in Fig 4C , dop-3 RNAi knockdown in this background significantly reduced PAPP-dependent paralysis , suggesting that DOP-3 is a secondary PAPP target . Screening is in progress to identify additional target ( s ) . Together , these data highlight the utility of this approach in preliminary drug screening and suggest that it may also be useful for the identification of nematode-specific agonists . Nematodes also express a unique family of monoamine-gated Cl- channels that appear to be highly conserved within the phylum , including the C . elegans 5-HT- and TA-gated Cl- channels , MOD-1 and LGC-55 , that play key roles in 5-HT- and TA-dependent muscle paralysis , respectively . The C . elegans MOD-1 and its H . contortus orthologue were expressed directly in either cholinergic motor neurons ( Punc-17β ) or body wall muscles ( Pmyo-3 ) of 5-HT quint animals and 5-HT-dependent paralysis was assayed as described above . Muscle expression was confirmed by GFP fluorescence ( Fig 5A ) . As previously noted , 5-HT had no effect on locomotion in 5-HT quint animals , but rapidly paralyzed the 5-HT quint animals expressing either the C . elegans MOD-1 in the cholinergic motor neurons or the H . contortus ( Hco ) MOD-1 orthologue in cholinergic motor neurons or body wall muscle , with EC50s of about 0 . 3 mM , 0 . 2 mM and 0 . 2 mM , respectively ( Fig 5B and 5C ) . Interestingly , 5-HT-dependent paralysis was more rapid in the transgenic animals expressing MOD-1 orthologues in the cholinergic motor neurons than in wild type animals . Similarly , LGC-55 was expressed in the body wall muscles ( Pmyo-3 ) or its H . contortus orthologue in the cholinergic motor neurons ( Punc-17β ) of lgc-55;tyra-3 tyra-2 ser-2 quadruple TA receptor null ( TA quad ) animals . TA quad animals lack all previously identified TA receptors and fail to respond to TA in a range of behavioral assays , including locomotion . As predicted , TA had no effect on locomotion in the TA quad animals , but significantly inhibited locomotion in TA quad animals expressing either C . elegans LGC-55 in body wall muscles or H . contortus ( Hco ) LGC-55 orthologue in cholinergic motor neurons , each with EC50 of about 0 . 1 mM ( Fig 5D and 5E ) . Together , these data suggest that monoaminergic activation of these Cl- channels hyperpolarizes either the cholinergic motor neurons or body wall muscles and inhibits muscle contraction , as well as highlighting the utility of chimeric C . elegans as a functional expression platform to identify ligand-gated Cl- channels agonists for use as anthelmintics . Our results suggest that inhibiting AIB signaling by the expression of a Gαo-coupled 5-HT receptor in the AIBs of the 5-HT quint can cause paralysis ( Fig 2B ) . Similarly , the AIB-specific expression ( Pinx-1 ) of the 5-HT-gated Cl- channel , MOD-1 can also cause paralysis ( Fig 6A ) . In contrast , ablation of the AIBs does not cause paralysis [56 , 57] . Interestingly , the activation of a Drosophila histamine-gated Cl- channel ( HisCl1 ) expressed ectopically in the AIBs ( cx15457 ) with 2 mM exogenous histamine ( His ) caused AIB hyperpolarization and locomotory phenotypes , but not paralysis [37] . In contrast , increasing the histamine concentration to 10 mM caused paralysis that persisted for up to 24 hrs in the presence of histamine [37] . Similarly , in the present study , 2 mM histamine did not cause paralysis in wild type animals or in transgenic animals expressing HisCl1 in the AIBs ( cx15457 ) on NGM plates ( Fig 6B ) . However , 2 mM histamine caused significance paralysis under the modified hypotonic assay conditions used in the present study or when the histamine concentration was raised to 10 mM on NGM plates ( Fig 6B and 6C ) . Since the ablation of the AIBs does not cause paralysis , these results support our previous hypothesis that the partial inhibition of AIB signaling by partial hyperpolarization or the activation of Gαo signaling causes an imbalance in the locomotory circuit that results in a state of decision-making “confusion , ” an inability to execute and sustain unidirectional movement and ultimately , in cessation of locomotion ( paralysis ) . Theoretically , any ligand that selectively unbalances AIB signaling has the potential to yield a similar locomotory phenotype and its target a potential site for anthelmintic development .
The monoamines , 5-HT , DA and TA each dramatically inhibit locomotion in C . elegans when applied exogenously at concentrations high enough to overcome the permeability barrier of the nematode cuticle , ultimately resulting in paralysis [24 , 25 , 27 , 30] . In addition , monoamine-dependent locomotory paralysis is also observed in many parasitic nematodes , including Ascaris suum and Heterodera glycines [31 , 32] . Using the C . elegans model , the receptors involved in monoamine-dependent locomotory inhibition have been identified and localized [22–30] . Interestingly , the key receptors involved in 5-HT , DA and TA inhibition each function at a different level in the locomotory circuit [24 , 28 , 30] . For example , 5-HT-dependent paralysis in C . elegans involves the expression of the Gαo-coupled , 5-HT1-like receptor , SER-4 , and the 5-HT-gated Cl- channel , MOD-1 in a limited number of interneurons , including the two AIBs [25] . Importantly , 5-HT1-like agonists appear to have anti-nematodal activity in vivo [34 , 35] . Indeed , the results of the present study suggest that partial inhibition of the AIBs by activation of an endogenously expressed Gαo-coupled 5-HT1-like receptor or 5-HT-gated Cl- channel , or a heterologously expressed histamine-gated Cl- channel , interferes with AIB signaling , causes “locomotory confusion” and ultimately paralysis . Interestingly , animals with ablated AIBs are still motile and move efficiently , although their rates of spontaneous reversal are dramatically altered , suggesting either that this partial inhibition differentially affects AIBs signaling to cause locomotory paralysis or that the ablated animals have compensated for the loss of the AIBs [56 , 57] . The present study provides further support to the use of “chimeric” C . elegans , created by the heterologous , ectopic expression of potential key drug targets from parasitic nematodes , for use as a platform for agonist identification and potential anthelmintic screening . Although the present is focused on inhibitory monoamine GPCRs and monoamine-gated ion channels , it can potentially be expanded to any signaling molecules for which the appropriate mutant backgrounds can be prepared . Specific promoters are available for C . elegans muscles and most neurons; alternatively , specific promoters to other neurons can be generated using a Cre-Lox approach [58] . This screening system has the potential to combine the individual pharmacologies of the receptors from different parasitic nematodes with the environment and accessory proteins necessary for functional expression . This becomes especially important because nematode-specific cells lines are not available and the expression of nematode receptors in mammalian cells is quite variable and can require a host of additional modifications , including temperature shock to achieve expression [59 , 60] . In fact , few studies have compared receptor pharmacologies in vivo with those of the nematode receptors heterologously expressed in mammalian cells . The functional reconstitution of nematode receptors in heterologous systems ( Xenopus oocytes , etc . ) often requires additional accessory proteins and/ or subunits that might not have been identified previously , hindering the further development of potential drug targets [61–64] . Not only do transgenic C . elegans provide a promiscuous expression platform for distantly-related receptors: these ectopically-expressed receptors are functional and appear to maintain their ligand-receptor specificity , as highlighted above where only the transgenic animals expressing the human receptor were paralyzed by 8-OH-DPAT . The identification of DOP-3 as a secondary target in PAPP-dependent paralysis also validates the utility and convenience of transgenic C . elegans as a platform for drug target identification and potential anthelmintic screening . Although the current study uses transgenic animals expressing the desired receptor as an extra-chromosomal array , stable lines can be readily constructed if required [65] . This screening platform also includes the nematode cuticle , a potential barrier to the entry of any anthelmintic , as well as a wide array of ABC transporters involved in drug efflux and resistance [66] . The cuticle is made up of six layers , the epicuticle , external cortical , internal cortical , medial , fiber and basal , as well as a carbohydrate-rich surface coat external to the epicuticle [67] . The lipid-rich epicuticle layer might be the key barrier to externally-applied drugs , especially water-soluble molecules ( 5-HT , TA , 8OH-DPAT etc . ) and the reason for the high concentration required to cause paralysis under isotonic environment , i . e . on NGM agar plates [52 , 67 , 68] . As mentioned , although C . elegans cuticle appears to be more impermeable than those of some parasitic nematodes , the permeability of the C . elegans cuticle can be manipulated by modifying incubation conditions and the availability of various mutant backgrounds . By incubating the animals in a salt-free , hypotonic environment , 5-HT paralyzes wild-type animals with an EC50 of about 0 . 5 mM , in contrast with an EC50 of about 12 mM on isotonic NGM agar plates . In addition , a number of C . elegans mutations that appear to have increase cuticular permeability may also be useful for enhancing small molecule screening against an array of medically-important targets , including those involved in locomotory paralysis [54 , 69] . For example , many of the bus ( bacterially swollen ) mutations appear to alter the cuticle and increase permeability [54] . Indeed , as shown in Fig 1E and 1F , it might be possible to select specific cuticle mutants with permeabilities that mimic those of individual parasitic nematodes , providing a mean to bypass complicated and expensive process of culturing live parasites , at least during preliminary stages of agonist screening . In fact , C . elegans has been used in the past for large-scale small molecule screens and chemical genomics and predictive models for drug accumulation and bioactivity have been developed that may be used to bias preliminary screening [70 , 71] . This ability to alter cuticular permeability will certainly be useful for agonist and potential anthelmintic identification , but in the case of the monoamines examined , relatively high concentrations of ligand are still required and , ultimately , any potential agonists identified using this approach will have to be validated in the target of choice . In summary , this study has identified two key AIB interneurons that play a role in 5-HT-dependent paralysis and suggests that partial inhibition of signaling from the neurons has the potential to cause “locomotory confusion , ” and paralysis . In addition , these studies have demonstrated and validated the utility of these “chimeric” C . elegans as a platform for agonist identification and potential anthelmintic screening . | Monoamines , such as serotonin ( 5-HT ) and tyramine ( TA ) , paralyze both free-living and parasitic nematodes when applied exogenously . Since nematode cell lines are not available and animal screening options are limited , we have developed a screening platform to identify monoamine receptor agonists that involves the heterologous expression of key receptors from parasitic nematodes in chimeric , genetically-engineered mutant C . elegans , at sites likely to yield robust phenotypes upon agonist stimulation . Specifically , we have demonstrated that agonist dependent activation of Gαo-coupled 5-HT receptors or monoamine-gated Cl- channels in key interneurons , cholinergic motor neurons or body wall muscle inhibited locomotion and caused paralysis . This approach includes nematode-specific accessory proteins and the nematode cuticle , and appears to preserve the unique pharmacologies of the individual receptors . Together these data highlight the utility of these transgenic C . elegans for agonist identification and their potential for anthelmintic screening . | [
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Statistical books can provide deep insights into statistics and software . There are , however , many resources available to the practitioner . Book reviews have the capacity to function as a critical mechanism for the learner to assess the merits of engaging in part , in full , or at all with a book . The “ten simple rules” format , pioneered in computational biology , was applied here to writing effective book reviews for statistics because of the wide breadth of offerings in this domain , including topical introductions , computational solutions , and theory . Learning by doing is a popular paradigm in statistics and computation , but there is still a niche for books in the pedagogy of self-taught and instruction-based learning . Primarily , these rules ensure that book reviews function as a form of short syntheses to inform and guide readers in deciding to use a specific book relative to other options for resolving statistical challenges .
Extensive resources now support the statistical programmer and analyst . The learner , reader , and general problem solver is thus faced with a choice of how to learn what is needed [1 , 2] . This brief synthesis is not intended to be a comment or criticism on the pedagogy associated with successfully acquiring statistical and coding expertise , but there is evidence suggesting that up to 80% of coders do not read books to learn how to code [6] . This seems like an unfortunate statistic , but the philosophy of “learning statistics by doing statistics” is not without merit and can be a viable approach to both introductory and expert learners alike [4] . Nonetheless , R , Python , SAS , and MATLAB/C++ are quite literally deep languages that need to be mastered . Fluency in a written or spoken language conveys reason and semantics [5]; statistical reasoning [4] with a corresponding representation of the associated mathematics [3] can likely be secured by both doing and reading [7] . Different problems and topics can also require the statistical programmer to embrace a diversity of resources to illuminate a solution , and the depth required must be defined by the prior knowledge of an individual and nature of the challenge . Many statistical texts can be a significant time commitment , and open electronic resources are abundant . The decision to read a statistical programming book is not necessarily trivial . Short syntheses , i . e . , a review , of the relative merits of a specific resource can provide a critical decision tool to the potential reader . The “ten simple simple rules” paper format was pioneered by Philip Bourne in PLOS Computational Biology [14] , and it has proliferated to nearly 100 papers , all functioning as a succinct , unique form of synthesis in itself [8] . Sometimes extensive resources are summarized that support how to describe a focused process or get a task done in many domains of the scientific endeavor [11] . Of these “ten simple rules” papers , there have been three that address the review process , including how to be an effective referee [9] , how to write a literature review [12] , and how to write a reply paper [10] . Many of these rules certainly support improvements in how to write a review of statistical books and should be consulted . Yet , book reviews in the Journal of Statistical Software , e . g . , strongly suggest that the importance of this topic warrants specific treatment because these reviews can serve many functions from descriptive summary to critical analysis to a launchpad for the importance of a statistical test , approach , program , language , and/or package . All are important functions that advance statistics , but at least some of the rules here can enhance their capacity to assess merit and need for the end practitioner . ( Appropriately ) defend books . Write reviews . Use reviews . Book reviews that effectively support the decision process for better statistical reasoning are needed . These rules promote this paradigm shift .
The book title is an excellent starting point for the reader to assess whether this is the resource for her but not the only mechanism . The book cover or sleeve synopsis and publisher description can also fail to capture the whole story , and some statistical treatises , both introductory and advanced , necessarily invoke related principles and topics [13] . As the objective expert of that specific text , an introduction to the necessity , scope , depth , and breadth of the topic in general can inform the reader on the challenges and solutions , including types of data or domains of inquiry that this field examines . Place the work within the span of the literature with a brief explanation of the area in which it is embedded . The goal of the first rule is therefore to ensure that the reader is in right place—conceptually , at least . Most technical book reviews state the level of expertise required by the reader . This is a critical form of synthesis that should be mentioned , even in brief , in a book review for statistics . The most typical categories range from introductory to advanced , with relatively higher-level offering described by “graduate student” and beyond as the reader . If the text is a blend of theory and practice with significant programming , the review should further explain the relative expertise needed for each and whether both dimensions are aligned in the assumed relative audience . Book reviews can also take the opportunity here to frame this assessment by the expertise of the referee ( i . e . , it is sometimes useful to know if the book reviewer is a statistician , a programmer , or a domain-specific end-user ) or by the intended use of the text , such as primer , guide , in-depth treatise , or textbook appropriate for instruction at a given level . If more than one edition exists , it is useful to describe the revisions to the more recent version of a book . Professional and teaching textbooks can be relatively expensive , and a critical assessment of value can provide instructors with the merits associated with potentially higher costs to students of purchasing a newer text . At minimum , a list of additions will facilitate a more informed choice for the reader and instructor , and mention of case studies , updates to code and data sets , and addition of supplements are all important criteria for the choice to learn or seek solutions from a specific edition . Organization of the content matters to all learning [15] , and content provides context [16] . The structure of statistical and programming texts can vary significantly . The length and complexity of chapters , use of headings and subsections within chapters , and end-of-chapter summaries are not always needed but often do no harm . Case studies , appendices , data sets , and location of supplements and supporting materials should be described . Contemporary texts in statistics are often a hybrid of print and electronic resource materials , and description of the extent that a text functions in this capacity can influence the decision by the reader based on her preferred modality of learning and the rationale for exploring this topic . This is also a good place to mention the different formats of the book ( if available in print and online ) . As the reviewer , use parsimony and caution in deciding what level of detail to describe for the structural elements of a book—focus only on those elements that promoted the most effective learning . These are the results , so to speak , similar to a conventional scientific publication or study report . The description should be brief , topological , and highlight the most substantive elements of the book . This component of the book review need not be unduly critical but should provide an overview of the what the text offers . Some reviews take this description of what is done to also highlight what is done best and list the most valuable chapters to the reader . This can be a useful guide to the potential reader and a means to assess expectations from the book as a whole . If there are data sets or case studies that are revisited throughout the book or across multiple chapters , the extent that the chapters connect to one another can also be summarized . Mention whether the content of the book is serialized or if chapters can be read piecemeal . Readability is an intuitive concept . It is the ease that one can comprehend writing [17 , 18] . Complexity in syntax , vocabulary , and sentence structure should be described in a review of a statistical book . A technical book need not be a technical challenge to read . More broadly , appeal , style , and interest are important to all but the most committed readers , and it is reasonable to assume that a book on statistics provide some sense of enthusiasm for the topic , compel the reader to think deeply , and engage one with the challenges explored . Composition is critical , particularly in long-form writing endeavors . Within the R statistics community , there are now over 11 , 000 packages that extend the base language archived on https://cran . r-project . org . SAS Procs and libraries in Python and MATLAB are also extensive . Some statistical texts are associated with not only a single statistical program or language but with a single package or library . A review of a statistical book should thus describe the specificity of the book , explain the extent that the book relies on single solution sets , or conversely contrasts alternatives in different languages , applications , packages , and/or libraries , and frame the programming ( if provided ) to general statistical theory and reasoning . At times , this can be self-evident if the title of the book includes mention of the programming language or software , but the breadth of the statistics and case studies illustrated is typically not evident without review of the book . If the book is not tied to a specific computation tool in any form , then the reviewer should mention that this is the case and state that the concepts described can be applied and transferred broadly from the book . Compare and contrast . There is a wealth of both short- and long-form documentation available for many open coding languages used in statistics and data wrangling . There is also an extensive opportunity to seek specific solutions through numerous forums such as Stack Overflow ( https://stackoverflow . com/questions/tagged/statistics ) , Cross Validated ( https://stats . stackexchange . com ) , and Stack Exchange Mathematics ( https://math . stackexchange . com ) . Online tutorials , blogs , carpentries , massive online open courses ( MOOCs ) , and webinars often provide useful , and at times , deep-learning opportunities . A book review will certainly not comprehensively list all these options and compare and contrast to the principal subject text discussed , but if there is a significant alternative to consider , it should be mentioned . Finally , there are also other books . The reviewer should explicitly state the extent that she is contrasting to other resources , and due diligence by the reviewer suggests at the minimum a mention of the relative novelty and niche of the text in question . Reading a book is a relationship . The content , style , and perspective of the author ( s ) becomes a shared , internalized form of knowledge in a good book . As the reviewer , it is legitimate and useful to others to mention the extent that one enjoyed the text , connected with the writing and concepts , or struggled with certain elements ( i . e . , comment on the quality of the relationship with the book ) . A review should also mention the time that the reader should set aside to read and/or fully digest the content . If the “summarize content” rule proposed above did not mention the standout , best chapters , this is an excellent spot to describe the chapters that provided the most for your buck and should not be skipped . This is also an ideal opportunity to consider describing whether this is was a read-the-book-straight-through or piecemeal technical read for critical needs . In general , it is best to be decisive in writing reviews [9] . Evaluate the capacity that the book delivers on its stated goal . Accept that you are part of the review process and likely have your own , specific purpose in reading this text . Admit this in the review by articulating the need , success of text , and decision ( or not ) to use the described tools , framework , or theory . Being specific and listing criteria point-by-point is useful to editors , authors , and readers [9] . Similar to the peer review process for papers , be balanced , fair , and professionally critical by mentioning both strengths and weaknesses from your perspective . Do your best to reveal implicit biases in your review .
Reading , writing , and statistics . By putting oneself on the hook for a book to take notes and annotate or further synthesize these efforts and provide a review profoundly changes how one approaches a statistical and programming text [19 , 20] . Higher education in the sciences and statistics has largely done away with book reviews and/or reports , but application and dissemination of critical thinking in statistics in the form of reviews is a learning opportunity . Capitalize on this process , particularly when using a text to solve a problem and write a review . Reviewing is a both a collegial and educational service that includes oneself as the beneficiary . The rules proposed herein for writing a book review for statistics and increasingly for the associated coding or implementation of statistics and data do not mean to imply that reading texts in this domain is a burden . On the contrary , the gratification of immersion in the structured reasoning inherent in these fields is a powerful form of literacy that merits discussion by people , for people . Recommendation algorithms certainly influence many aspects of human behavior , and a book review is a reminder to take a moment and savor the story . | Book reviews are a useful tool to inform learners in statistics and computational biology . As an ecologist , I teach biostatistics and use many resources in the analysis and coding of research data . In-depth texts can provide a critical resource , but well-written reviews can faciliate the decision to use a specific book . | [
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... | 2019 | Ten simple rules for writing statistical book reviews |
A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection . This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues . In order to better understand the pathophysiologic mechanisms involved , we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP ( cecal ligation and puncture ) -induced sepsis in rats . This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response . Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling . Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets . Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung , consequently contributing to tissue damage and mortality . Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis . Furthermore , the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats . Simulations identified a sub-population ( about of the treated population ) that benefited from blood purification . Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils , contributing to improved outcome . The model ensemble presented herein provides a platform for generating and testing hypotheses in silico , as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection , a problem of growing magnitude in humans .
Sepsis is defined as infection accompanied by signs of systemic inflammation , such as fever , tachycardia , tachypnea , or an abnormal white blood cell count [1] . The complex pathophysiological interaction network of sepsis and its systemic nature involve many inflammatory mediators including a number of cell types , tissues and organs , making it difficult to fully understand the exact mechanisms contributing to its high mortality and morbidity despite recent progress in underlying molecular mechanisms . Furthermore , a highly variable clinical presentation significantly hampers timely diagnosis and treatment of patients with severe sepsis and adds another layer of complexity . [2] , [3] . Adequate recruitment of neutrophils to sites of infection is one of the early and important features of a successful immune response . Mounting evidence suggests that severe sepsis is characterized by impaired neutrophil migration to the primary infected site and deleterious accumulation of neutrophils in distant , yet uninfected organs , resulting in organ dysfunction and death [4] , [5] . Neutrophil migration to a site of bacterial infection occurs through a highly coordinated sequence of stages . Circulating quiescent neutrophils are first primed by interacting with inflammatory mediators that have entered the circulation as a consequence of a large local production by dedicated tissue macrophages at the site of infection and subsequent spill over of these mediators in tissue capillaries . Primed neutrophils express integrins , surface molecules that can interact with similarly activated capillary endothelial cells , resulting in rolling and activation . Activated neutrophils adhere to endothelial cells followed by transmigration into tissue [6] , [7] . This delicate coordination is achieved through paracrine cell-cell communication , effected by chemokines and cytokines , which when present in large quantities , results in distant endocrine effects such as systemic manifestations of inflammation , distant organ endothelial activation , and overwhelming activation of neutrophils , all contributing to dysregulated neutrophil trafficking [5] , [6] . Quiescent , primed , and activated neutrophils carry distinct surface molecules which can be experimentally identified , and which also play important functional roles such that specific signatures of neutrophil receptors can quantify the stage and appropriateness of the systemic inflammatory response . For example , excessive neutrophil activation leads to sequestration in distant organs and promotes tissue damage by release of reactive oxygen and nitrogen species nefarious to healthy cells [8] , [9] . To better understand the pathophysiologic mechanisms involved , we developed a population-based computational framework that incorporates distinct neutrophil kinetic responses in a compartmentalized model of CLP ( cecal ligation and puncture ) -induced sepsis in rats . Experimentally constrained model ensembles were generated to represent a heterogeneous population , data uncertainty and other unexplained sources of variability . We previously showed that uncertain deterministic ensembles collectively exhibited population-like behavior and suggested deterministic ensembles could be a coarse-grained strategy to model population heterogeneity [10] . We explored population heterogeneity using multivariate comparative analyses of the parameter ensembles from different phenotypes ( survivors and non-survivors ) and identified mechanisms that may play an important role in the expression of such phenotypes . The primary motivation of this work is the experimental observation that one form of extracorporeal blood purification , known as hemoadsorption ( HA ) , was found to be beneficial in animal models of sepsis , including endotoxic shock [11] and CLP [12] . HA is a non-specific immunomodulatory intervention that successfully removes circulatory molecular effectors [11] . Recent evidence suggests that it may also directly impact neutrophil behavior , either by direct adsorption to the filter , or indirectly by altering immune signaling . HA is observed to decrease lung accumulation of neutrophils and improve outcome [13] , but the underlying mechanisms remain elusive . The mathematical model constructed herein provides a physiologic rationale that explains such experimental observations and constitutes an in silico platform for generating and testing immunomodulatory interventions for sepsis . Presumably , insight as to dominant mechanisms at work would guide the rational engineering of improved HA devices resulting in an enhanced impact on outcome .
We developed a compartmentalized , coarse-grained phenomenological model of the inflammatory response to an invading pathogen in the specific context of CLP-induced sepsis in rats . Model parameters sets were optimized to reproduce the time courses of mean plasma measurements from a cohort of septic rats , while insuring that some basic heuristic behaviors of the system in accord with published literature were maintained [14] . Because of population variability and other sources of uncertainty , we generated population-based ensemble models ( survivor and non-survivor populations ) which describe distinct distributions of parameter sets consistent with their experimental observations and heuristics . These ensembles were statistically generated using Markov-Chain Monte Carlo ( MCMC ) sampling of their posterior parameter distributions . Convergence diagnostics was applied to support that the sampling process had reached equilibrium . Prediction uncertainties in the model states were quantified over the resulting ensemble . Simulation of the model ensembles successfully reproduced experimental observations and desired heuristic behaviors , and suggests that systemic activation of circulatory neutrophils impair their migration to primarily infected tissue , while promoting sequestration in lung tissue favoring local damage and presumably , mortality . Statistical analysis of the model ensembles obtained for separate populations provided useful insights as to key pathologic mechanisms associated with mortality in sepsis . We next simulated a hypothetical blood purification intervention on the calibrated model ensemble . Simulations suggest that this therapy might improve targeting of primed neutrophils to the primary site of infection while interfering with lung sequestration of activated neutrophils , but that there is also a potential for harm in animal with poorly responsive immune systems . We hypothesized that dysregulated neutrophil trafficking in severe sepsis may contribute to mortality [15] . We therefore developed a model of the acute innate response to an infectious challenge , with special emphasis on neutrophil trafficking and phenotypic variation . The network components and interactions were assembled based on qualitative domain knowledge of the acute inflammatory response , including multiple phenotypes of neutrophils and major effectors in three compartments: blood , peritoneum , and lung ( Figure 1 ) . To capture impaired recruitment of neutrophils , one of the key pathophysiologic features in severe sepsis , coarse-grained mechanisms influencing neutrophil migration were included in the model . In the blood compartment , neutrophils can be characterized as belonging to one of three phenotypes: resting , primed , and systemically activated . While primed blood neutrophils migrate to the site of infection and become activated locally in tissue , blood neutrophils activated in the circulation have an impaired ability to migrate to infected tissue because they possess fewer essential chemokine receptors . We chose the lung as a preferred site for the accumulation of activated blood neutrophils due to the long and narrow microvascular bed and in accord with experimental data [16] . As a result , systemically activated blood neutrophils are easily sequestered in lung capillaries . Sequestered activated neutrophils can then migrate into the lung tissue when lung vascular endothelium becomes activated by systemically circulating inflammatory mediators . The network of interactions included in the model includes 19 variables and 57 parameters . Although some parameter values were available from literature , most of them represent lumpedprocesses and therefore not directly available from published experimental studies . Experimental data were collected from CLP-induced sepsis in rats ( ) , consisting of eight longitudinal measurements of key cytokines and damage-related markers in blood ( see Materials and Methods ) . Seven rats ( ) survived to seven days ( the survivor population ) , while the remaining animals died between two and five days after CLP ( the non-survivor population ) . In addition to the experimental data , we used the two qualitative constraints to define survival for in silico septic rats: at the end of the simulation time ( 200 hours ) , both the following constraints should be satisfied: ( 1 ) the number of bacteria ( ) is less than which was set to ( CFU/ml ) , ( 2 ) the value of systemic inflammation ( ) is less than 0 . 5 ( see Materials and Methods ) . Otherwise rats were considered non-survival . To explore the ability of the model to synthesize the inflammatory responses of both the survivor and the non-survivor populations , we estimated a model ensemble separately for each population without changing model structure or initial conditions . In other words , it is assumed that the experimental settings are identical for both populations and the differences in the inflammatory responses of the two populations can be adequately represented by model parameterization . Ensemble methods have been developed to approach ill-posed inverse problems in fields as diverse as systems biology , weather forecasting , and nuclear reaction modeling [17]–[20] . Furthermore , experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information [10] . The multiple starting points for the construction of the model ensemble were constructed as initial parameter sets that produced simulations reasonably close to experimental data , while exhibiting behaviors compatible with heuristic domain knowledge ( summarized in Table 1 , 2 ) . To ensure consistent observation mappings for both the survivor and non-survivor populations , only the 34 model parameters in Table 1 among the total parameters were allowed to be sampled by MCMC chains and the other parameters were kept in their baseline distribution . Five million parameter sets for each population ( survivor and non-survivors ) were sampled from five MCMC chains initiated from different starting points randomly chosen from the baseline parameter distribution ( Figure 2 ) . To estimate whether stationarity had been achieved in the MCMC chains , we preformed Gelman-Rubin diagnostics , computing the potential scale reduction factor ( PSRF ) for each parameter [21] . The Gelman-Rubin diagnostics tests whether parallel chains converge to the same posterior distribution . PSRF is defined as the square root of the ratio of the between-chain variance and the within-chain variance . A large PSRF indicates that the between chain variance is substantially greater than the within-chain variance , so that more samples are needed . Approximate convergence was diagnosed as the PSRFs of all parameters were close to one . The proposed model reproduced quantitative dynamic features observed in CLP-induced septic rats , verifying the description of the model reflects the inflammatory responses in CLP-induced septic rats ( Figure 3 ) . Instead of exploring explicit interactions among observation variables due to the lack of kinetic and causal information , each experimental observation was nonlinearly mapped from its high level coarse-grained state variable ( see Materials and Methods ) . These mapping functions , which include 16 parameters ( Table 2 ) , should be consistent in both survivor and non-survivor populations and estimated in a way that minimizes the sum of cost functions for both populations . Some observation points are out of the – quantiles , e . g . the early time points of Lsel and ALT in 3 , suggesting our proposed structure may be too simple to capture finer dynamic details of the biological process . In particular , HMGB1 , CRT , and ALT are collectively used to constrain the damage state in the model . This coarse-grained approach does not allow the independent dynamic mapping of three observables from the single damage state . However , the general trends were well captured by the model , suggesting that our phenomenological description could be used to explore the general inflammatory response to CLP . Impaired neutrophil migration to infected tissue and deleterious neutrophil accumulation in lung were predicted in the simulation of the non-survivor population . It should be emphasized that the model prediction of dysregulated neutrophil trafficking is an emergent property of the model since no neutrophil data were used to constrain model behavior ( Figure 3: panels Nl , Ns , and Nt ) . In other words , the proposed mechanism of action about functionally heterogeneous neutrophil populations was consistent with experimental evidence not used in model training . Although blood purification using HA results in important survival benefits in animal models of sepsis [11] , [13] , [25] , our early attempts at understanding mechanisms through modeling suggested that cytokine removal alone was inadequate to explain the experimental findings [26] , [27] . We confirmed this experimentally by scaling down cytokine removal below that which resulted in acute changes in circulating mediators and still found reduced organ injury and improved survival [12] . We then extended the model to include elimination of these inflammatory effector from the circulatory compartment by a hypothetical HA device . We hypothesized that the HA device adsorbs activated neutrophils ( ) as well as pro- and anti-inflammatory mediators ( PI and AI ) from the circulation . To emulate reported experimental work [12] , we simulated four hours of treatment starting at 18 hrs after CLP using a random set of 10000 parameter vectors sampled from the non-survivor ensemble . Output profiles were classified into survivor and non-survivor populations based on a 7-day value of . Figure 6 compares the non-survivor ensemble ( shams ) to survivors and non-survivor sub-ensembles obtained after treating shams with HA . About of the treated population ( n = 1768 ) survived . We observe that enhanced neutrophil migration to the tissue ( increased ) and reduced sequestered and lung neutrophils ( decreased and ) were all present in the survivor population after treatment , contributing to the improved outcome . These prediction results are consistent with our experimental observations [12] , supporting that the proposed model could be used to guide future experiments and as a computational framework for generating hypotheses . To investigate which factors play an important role in successful HA treatment in a population that would otherwise die , we analyzed the two sub-ensembles of survivor and non-survivor after HA treatment in the same manner as the previous section ( see Figure 7 ) . We observed that systemic pro-inflammation related parameters are involved in most of the parameter pairs whose partial correlations changed significantly between the two sub-ensembles ( Table 4 ) . In particular , partial correlations involving parameter 3 ( PI inhibition by AI ) disappeared in the HA non-survivor population , suggesting a robust anti-inflammatory response is a critical factor for treatment success . Interestingly , when HA treatment was applied to a sample of the survivor ensemble , ( ) died according to our criteria , suggesting that there also exists a sub-population of survivors for whom treatment is actually harmful . Further analysis of univariate differences in parameter distribution between survivor and the harmed sub-population identified that non-survivors had larger PI inhibition by AI ( parameter 3 , ) and faster PI decay rates ( parameter 4 , PI decay , ) .
We have developed a coarse-grained phenomenological model of the inflammatory response to CLP-induced sepsis in rats that centers on dynamic interactions of distinct neutrophil phenotypes and fundamental effectors . The model simulations and reproduction of experimental data support our main hypothesis that systemic inflammation leads to heterogeneous circulating neutrophil subsets which contribute to differential fates of septic animals . The emergent properties observed in the in silico non-survivor population that systemically activated neutrophils lose their chemotactic ability to the infectious focus and instead become trapped in narrow lung capillaries comply with biological domain knowledge [8] , [28] . Given that the experimental sepsis model was performed with a high consistency in a standardized manner to minimize extrinsic noises [29] , the heterogeneity of sepsis severity can be assumed to originate from intrinsic ( genetic and epigenetic ) differences in rats . In order to investigate the underlying differences , we identified ensemble models for two distinct severity populations , survivor and non-survivor . The network structure and initial conditions of the model were assume to be identical in all populations . Therefore , the heterogeneity in populations is represented by differences in parameter distributions within ensemble . This population-based ensemble approach allows us not only to assess parametric uncertainty , but also to characterize differences in parameters between distinct populations . Multivariate analyses of the population ensembles suggest that balanced regulation of the pro-/anti-inflammations and coordinated neutrophil functions play important roles in survival . The non-survivors are characterized by a loss of dynamic features in survival-associated regulation of inflammatory responses . First , delayed anti-inflammatory response and damage resolution contribute to mortality . Second , prompt neutrophil priming and migration to the infectious focus before uncontrollable systemic inflammation develops are critical for survival . These in silico findings underline the importance of timely diagnosis and treatment of sepsis in clinical practice . With the extremely complex nature of sepsis in mind , determining the precise inflammatory status can be very useful to start timely and specific treatment . Patients with hyper-responsive inflammatory states will benefit from limiting their inflammation , whereas others with hypo-responsive states would be better treated by boosting inflammation [1] . However , assessing the precise inflammatory status still poses a significant challenge . Plasma levels of cytokines may not be sufficient to define accurately the inflammatory state because pro- and anti-inflammatory mediators increase simultaneously in septic patients and animals [30] . In addition , the dynamic changes of cytokine levels in severely septic patients are not clearly consistent with the course of sepsis [31] . Our simulation work therefore supports the notion that an evaluation of cellular function would be a better method than measuring soluble mediators alone to define the precise inflammatory response and should be targeted clinically [32] . Among the effector cells in the septic response , neutrophils are critical elements of the innate immune response to the infection . Recent studies have reported several molecular mechanisms for dysregulated neutrophil trafficking over the whole spectrum of neutrophil migration . The systemic activation of TLR4 results in down-regulation of neutrophil rolling on endothelial cell surface and migration to the tissue [6] , [33] . Patients with a deficiency of leukocyte adhesion molecules show easier bacterial infection and sepsis development [34] . Gaseous molecules such as nitric oxide ( NO ) and peroxynitrite ( ) downregulate neutrophil migration by reducing leukocyte adhesion and migration [35] , [36] . Cunha et al showed that excessive production of NO during sepsis induced by Toll-like activation reduces the expression of the chemokine receptor CXCR2 in circulating neutrophils and contributes to the impairment of neutrophil migration [37] . These results provide experimental evidence that altered neutrophil phenotypes in the circulation contribute to the pathogenesis of sepsis and its mortality . More precisely then , our study suggests that a phenotypic characterization of circulatory neutrophils would be an effective way to determine the inflammatory status and guide future therapeutic strategies . Changes in the relative proportion of neutrophil phenotypes and their absolute numbers , as measured in the circulation , could constitute an effective early marker of disease progression or the therapeutic effect of an intervention on infection control and downstream organ dysfunction . In view of the multiple factors modulating neutrophil functions , using multiple markers to quantify the differential expression of neutrophil receptors looks promising [8] , [9] , [38] . Considering the highly heterogeneous population of patients with sepsis , identifying a sub-population of patients that is most likely to benefit from a specific intervention is potentially of great benefit in the design of interventional trials or bedside therapeutic decisions . Mathematical modeling can be a useful framework toward this goal . Our model simulations coupled to a hypothetic HA device model generated a subset of animals that survived after HA treatment and their survival features were characterized by their parametric description . In order to make this in silico work meaningful for translation , the parametric descriptions should be translated into measurable biological or physiological phenotypes , that is predictive biomarkers . The key idea is that population dependent parameter distributions reflect the heterogeneity of the treatment efficacy . A distribution of physiologically interpretable model parameters and states inferred from the patient information may be able to serve as early-stage markers for identifying a sub-population that can be benefited from a certain treatment option . Further experimental investigations are warranted to validate our computational findings . Furthermore , the experimental and clinical relevance of our analysis on HA treatment simulations are limited by a simplistic HA device model which was not calibrated by experimental data . We recently developed a more realistic HA device model calibrated by in vitro experimental data [39] . Coupling to a calibrated HA device model and rigorous analysis of the effects of HA treatment are currently underway . In conclusion , the ensemble models constructed herein in order to explore heterogeneity in distinct sepsis severity populations provided useful insights as to key pathologic mechanisms associated with mortality in sepsis . The population-based ensemble approach can be extended to explore critical mechanistic differences between different pathologies within a same context of disease . One could therefore apply the method to investigate differences in chronic/acute , old/young , races , or any other “natural” groups . The population-based computational framework holds promise as a tool for integrating domain knowledge and experimental data into a quantitative assessment of population dynamics .
The CLP-induced sepsis experimental protocol is a recommended proxy for human sepsis , where the infection spreads beyond a local focus , resulting in systemic symptoms , septic shock and a high mortality [29] , [40] . The experiments were designed to evaluate long-term ( one week ) survival in a model of sepsis that resulted in a mortality rate similar to that observed clinically . Following approval by the Animal Care and Use Committee of the University of Pittsburgh , the CLP procedure was modified ( 25% ligated length of cecum and 20-gauge needle , two-puncture ) in rats to induce less lethal sepsis compared to that which we have described previously [25] . Plasma cytokines ( tumor necrosis factor ( TNF ) , interleukin ( IL ) -1β , IL-6 and IL-10 ) , high mobility group box1 ( HMGB1 ) , creatinine ( CRT ) and alanine aminotransferase ( ALT ) were measured from blood samples from 23 rats at 18 , 22 , 48 , 72 , 120 , 144 , and 168 h after CLP . Each cytokine measurement data was natural log transformed and normalized by its maximum value across all time points for all animals . Other measurements were also normalized by their maximum values . Seven rats out of the total population survived up to 7 days , being considered as the survivor population; the remaining 16 animals comprised the non-survivor population . The network components and interactions of the model were compiled from available information in the literature and the general domain knowledge about the acute inflammatory response . Ordinary differential equations ( ODEs ) governing the phenomenological signaling interactions in the network were formulated based on the standardized steps presented in [41] , [42] . We wished to represent the qualitative interactions in the system as a Boolean model and then transform the logic operations into a system of ODEs . We chose HillCube ODEs as continuous homologues of the Boolean interactions , in which model parameters were regularized as one of three types: Hill coefficient ( ) , half maximal activation constant ( ) , or time constant ( ) . Exceptions were made for model parameters that should be constrained by mass action kinetics . The five parameter sets randomly sampled from the baseline parameter distribution in Table 1 were used as starting points to generate Markov Chain Monte Carlo samples based on the Metropolis Algorithm [45] , [46] . A uniform ( non-informative ) prior distribution over the range specified by the lower and upper bounds of parameters was chosen . The prior ranges were made very wide to include all plausible values . For example , the prior ranges for threshold parameters were set to and the ones for time constants were set to . The goal is then to draw samples in the accessible parameter space from the posterior target distribution , which was taken to be proportional to the likelihood , the probability that our model with parameters would generate the observed data . The difference between the measured and simulated value of species at time was quantified by the cost function , or the weighted sum of squared residuals , ( 23 ) with and denoting the mean and standard deviation of the measured value of species at time and denoting its simulated value . Assuming that the measurement noises represent Gaussian random measurement errors , the target distribution is as follows . ( 24 ) which is an analogy to a Bolzmann distribution with energy and temperature . The temperature was used to tune the rate of acceptance of candidate parameter sets as the Markov chain is constructed , where the acceptance ratio increases with increasing temperatures . The proposal density that generates a new candidate set of parameters using current values ( ) was chosen to be a normal distribution centered at the current point , . The parameter was also used to tune the acceptance ratio of candidate samples with smaller values increasing the ratio . We tuned and to get a reasonable convergence with the acceptance ratio being around 0 . 25 [47] . The numerical integration of the systems of ODEs described above was implemented by using the SUNDIALS package ( https://computation . llnl . gov/casc/sundials/main . html ) . All other algorithms used for this work were implemented in the MATLAB version 7 . 10 . 0 . 499 ( The MathWorks ) . The source code used in generating results is available at http://code . google . com/p/source-code-sepsis-model/ . The stiff directions in the parameter space can be identified by the principal component analysis of the Hessian matrix [17] . Instead of using the computationally expensive Hessian matrix , we used the inverse of the correlation matrix of the parameter ensemble , with the understanding that in a maximum likelihood estimation setting the covariance matrix of the parameters can be approximated by the Hessian matrix of the likelihood function [48] . An eigenvalue decomposition of allowed us to obtain the information about stiff ( large eigenvalue ) directions in the parameter space . The inverse of the correlation matrix can also be used to extract valuable information in multivariate data [23] . The basic formulas for computing the multiple correlation coefficients and the partial correlation coefficients are as follows . The diagonal elements of , , are related to the multiple correlation between the parameter and all other parameters . ( 25 ) The partial correlation between the parameter and the parameter controlling all other variables can be calculated as ( 26 ) | The pathophysiology of sepsis is complex and our mechanistic understanding remains incomplete . Mathematical models of the inflammatory response have been providing intellectual frameworks to reason about the complexity of sepsis . Due to an incompletely understood system along with very limited data , our approach focuses on building simplified , falsifiable and predictive models , and offers a means to quantify parametric uncertainty . Based on the construct that deterministic ensemble models exhibit population-like behavior , we developed a population-based computational framework that incorporates dysregulated neutrophil hyperactivity as a cellular dysfunction in septic processes . We hypothesize that probability distributions of physiological parameters conditional on population observations can characterize the range of possible physiologic responses in a population . Comparing the parameter ensembles from different phenotypes reveals some factors that play an important role in the expression of such phenotypes , such as sepsis survival . This framework can serve as an effective tool to gain insight into the pathophysiology of severe sepsis and generate testable hypotheses that guide future experiments . Our approach holds promise as a tool for integrating domain knowledge and experimental data into a quantitative assessment of population dynamics . | [
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] | 2012 | Ensemble Models of Neutrophil Trafficking in Severe Sepsis |
The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters . On the other hand , the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters . We use the Manifold Boundary Approximation Method ( MBAM ) as a tool for deriving simple phenomenological models from complicated mechanistic models . The resulting models are not black boxes , but remain expressed in terms of the microscopic parameters . In this way , we explicitly connect the macroscopic and microscopic descriptions , characterize the equivalence class of distinct systems exhibiting the same range of collective behavior , and identify the combinations of components that function as tunable control knobs for the behavior . We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway . From a 48 parameter mechanistic model , the system can be effectively described by a single adaptation parameter τ characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates , Michaelis-Menten constants , and biochemical concentrations . The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters . The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior .
Complexity is a ubiquitous feature of biological systems . It is both the origin of the richness of biological phenomena and a major hurdle to advancing a mechanistic understanding of that behavior . Mathematical models , formulated as differential equations of biochemical kinetics for example , supply many tools for improving our understanding of complex biological systems . Systems biology is largely concerned with identifying mechanistic explanations for how complex biological behaviors arise [1–3] . However , mathematical models are never a complete representation of a biological ( or physical or chemical ) system . Indeed , one of the advantages to mathematical modeling is the ability to apply simplifying approximations and abstractions that provide insights into which components ( or collection of components ) of the system are ultimately responsible for a particular behavior [4] . A mathematical model , therefore , reflects the judicious distillation of the essence of the complex biological system into a more manageable representation . A good mathematical representation , while not complete , will be both complex enough to convey the essence of the real system and sufficiently simple to reveal useful mechanistic insights that enable the prediction of the system behavior under new experimental conditions , i . e . , “as simple as possible , but not simpler . ” Biological research has collected a wealth of knowledge about gene regulatory networks , epigenetic controls , and biochemical reactions from which systems-level behavior derives . While this enterprise is not complete , it is sufficient in many cases to motivate models that are reasonably accurate surrogates of the real system . Exhaustive pathway maps are nearly overwhelming in their complexity [5] . Such models are often very complex , reflecting both the wealth of information available and the intricacies of the underlying mechanisms . This complexity is manifested , for example , in the high-order dynamics of the model , the number of interacting heterogeneous components , or the nontrivial topology of the network structure . These models typically have a large number of parameters that are unknown and which are left to be inferred from data . The problem of parameter estimation has consequently received considerable attention in the systems biology community . Over-parameterized models are often “sloppy , ” i . e . , leading to extremely ill-posed inference problems when fitting to data [6–12] . Identifiability analysis is useful for determining which parameters’ values can be estimated from data [13–16] , and optimal experimental design methods judiciously choose experiments that can most efficiently produce accurate parameter estimates [15 , 17–25] . This enterprise is in many respects the natural continuation of the program of cataloging the complex web of gene regulatory networks and protein signaling cascades . Unknown parameters represent a gap in our knowledge of a specific biological system that ought to be filled . The present work looks to answer an orthogonal question . A parameterized model can be interpreted as class of potential biological systems . Different parameter values correspond to distinct members of this class that have a related structure but differ in the microscopic specifics , i . e . , parameter values . For example , parameter values may vary depending on cell-type , developmental stage , species , or many other factors . Rather than estimate all the parameters for specific biology systems , we seek a characterization of the biologically relevant behavior for all systems in the model class . Because parameter inference problems are ill-posed there are many members of the model class that exhibit identical systems-level behavior . We therefore expect that a minimal model with many fewer parameters exists that reproduces the same behaviors as the family of biological systems . In other words , we would like to characterize the class of microscopic models with indistinguishable macroscopic behavior . In addition , we would like to identify which combination of microscopic components controls the collective behavior . Our approach to this problem is a non-local parameter reduction method known as the Manifold Boundary Approximation Method ( MBAM ) [12 , 16 , 26] . Model reduction is an active area of research and there are many techniques available . Common methods involve exploiting a separation of scales [27–29] , clustering/lumping similar components into modules [30–32] , or other methods to computationally construct a simple model with similar behavior [33 , 34] . Many methods have been developed by the control and chemical kinetics communities focused on dynamical systems [27–29 , 34 , 35] . Systems biology has been a popular proving ground for new methods [33 , 36–39] . Most model reduction methods suffer from two problems that make them unsuitable for the present work . First , many techniques , particularly automatic methods , produce “black box” approximations that are not immediately connected to the complicated , mechanistic model . In contrast , MBAM connects the microscopic to the macroscopic through a series of limiting approximation that provide clear connections between the macroscopic control parameters and the microscopic components from which they are derived . Second , most methods make “local” approximations , in the sense that they find computationally efficient approximations to a single behavior . However , we seek a ( semi- ) “global” approximation that can reproduce the entire behavior space of a model class . This is a challenging problem; brute force exploration of the parameter space is impossible because of its high-dimensionality . MBAM solves this problem by using manifold boundaries in behavior space as approximate models [26] . Manifold boundaries are topological features and therefore characterize the global behavior space [16] . Finding a minimal , “distilled” version of a complicated model has many practical applications . It identifies the system’s control knobs that could effectuate a change in the system’s behavior , reducing the search space for effective control methods . It highlights the “design principles” underlying the system and inspires approaches for engineering synthetic systems . Finally , it leads to conceptual insights into the system behavior that deepen the understanding of “why it works . ” In this paper we show that the well-known Michaelis-Menten approximation is a simple case of the MBAM . We then use this method to derive minimal models of adaptation discovered by Ma et al . [40] and a more complex mechanical model of EGFR signaling due to Brown et al [7] . Our primary result is that adaptation can be characterized by a single dimensionless parameter , τ , the ratio of the activation and recovery time scales of the system . We express these time scales as nonlinear expressions of the microscopic , mechanical parameters . Any adaptive system can be easily characterized by its value of τ from simple measurements . We discuss the advantages and limitations of this approach . We also consider more profound implications for modeling and understanding complexity in biology and how it relates to similar questions in the physical sciences .
Many biological reactions take the form of an enzyme catalyzed reaction in which an enzyme and a substrate combine reversibly to form an intermediate complex which can then disassociate as the enzyme and a product: E + S ⇌ C → E + P . These reactions can be modeled using the law of mass action as: d d t [ E ] = - k f [ E ] [ S ] + k r [ C ] + k c [ C ] ( 1 ) d d t [ S ] = - k f [ E ] [ S ] + k r [ C ] ( 2 ) d d t [ C ] = k f [ E ] [ S ] - k r [ C ] - k c [ C ] ( 3 ) d d t [ P ] = k c [ C ] . ( 4 ) These equations have two conservation laws E 0 = [ E ] + [ C ] ( 5 ) S 0 = [ S ] + [ C ] + [ P ] , ( 6 ) so that the system in Eqs ( 1 ) – ( 4 ) has only two independent differential equations . We take the initial conditions of the enzyme and substrate to be E0 and S0 respectively and those of the intermediate complex and final product to be zero . Consider the scenario in which E0 and S0 are fixed to 0 . 25 and 1 respectively and the three rate constants kf , kr , and kc are allowed to vary . In Fig 1 ( top ) we see many of the possible time series for the fractional concentration of the final product . If we take as QoIs , the fractional concentration of product at times t = 5 , 10 , 15 , then Fig 1 ( bottom ) shows the corresponding model manifold . Because the model has three parameters , the model manifold is a three dimensional volume . The two colors ( red and green ) are two faces that enclose this volume and correspond to two possible reduced models that we consider shortly . Notice that the model manifold , in this case a three-dimensional volume , is highly anisotropic . There is clearly a dominant , long axis , a second thinner axis , and a third axis that is much thinner still . MBAM exploits this low effective dimensionality in order to construct a model with an equivalent range of behavior with fewer parameters . We now consider the phenomenon of adaptation . More specifically , we consider the problem of “adaptation to the mean of the signal” which is the ability of a system to reset itself after an initial response to a stimulus as illustrated in Fig 5 [45] . Throughout this work , we follow the problem statement in reference [40]: A system is given a step-function stimulus at time t = 0 and the response is observed . In this section we consider two minimal topologies exhibiting adaptation due to Ma et al . [40] . We then consider a more complete mechanistic description of EGFR signaling [7] , a real system known to exhibit adaptation . We will identify the EGFR pathway as being equivalent to one of the two minimal adaptive topologies . Finally , we will show that each of these adaptive systems can be represented by a single parameter model . We note that it is possible to choose inputs other than a single step function . In fact , different adaptive systems are known to respond differently to different types of inputs [46 , 47] . We here restrict ourselves to single step inputs as those the conditions described in references [7 , 40] and because it is the most natural context for defining adaptation . If responses to other inputs are biologically relevant and controlled by different microscopic parameters , other choices for QoIs could be considered . We now consider a model of EGFR signaling due to Brown et al . [7] that has been used extensively as a prototypical “sloppy model” for purposes of sensitivity analysis [6 , 7 , 9] and experimental design [21 , 23] . The model describes the system response to two external stimuli , extra-cellular EGF and NGF hormones . The differing responses to these stimuli ultimately determine the differentiated cell type . The authors applied the MBAM to this model in reference [26] where the quantities of interest were taken to be the experimental conditions of the original analysis . From the original 48 parameter model , a 12 parameter model was constructed that could fit all of the data in the original experiments . In the current context the model is interesting because the level of ERK activity ( the final protein in the signaling cascade ) exhibits adaptation behavior in response to EGF stimulus but long-term sustained ERK activity in response to NGF . We therefore seek a hybrid mechanistic/phenomenological description of this dual response . This requires a different set of QoIs from those in reference [26] . We here consider how the reduced model varies as the quantities of interest change . We will see that by systematically coarsening the QoIs , we can bridge the mechanistic and phenomenological descriptions of the system and gain a deeper understanding for the relationship between the structure of the model’s components and the resulting phenomenology . Specifically , we consider the effect of four successive coarsening of the QoIs . First , we preserve the predictions of all species in the model under the same experimental conditions as reference [7] and deduce an 18 parameter model . Next , we consider only those species experimentally observed in reference [7] , in which case we recover the 12 parameter model of reference [26] . Third , we consider only the response of ERK activity to EGF and NGF stimulus , reducing the model further to 6 parameters . Finally , we consider only the response of ERK to an EGF stimulus and recover a four parameter model exhibiting a minimal negative feedback loop topology characterizing the system’s adaptation and spanning the same phenomenological degrees of freedom in Fig 5 . Fig 9 shows the FIM eigenvalues for the entire reduction process . The initial reduction process from 48 to 18 parameters is summarized in Fig 9 ( top left ) . The initial 48 parameter model exhibits the characteristic “sloppy model” eigenvalue spectrum in which the eigenvalues are logarithmically spaced over many orders of magnitude [6–9] . Observe that each iteration of MBAM removes the smallest FIM eigenvalue from the model while the remaining eigenvalues are approximately unchanged . Thus , the resulting approximate model is not sloppy; the eigenvalues cover fewer than four orders of magnitude . At this point the remaining parameter combinations are precisely those phenomenological parameters necessary to explain the important features of the QoIs; further reductions would sacrifice statistically significant model flexibility . We can also consider the effect of the reduction process on the model’s network structure as summarized in Fig 10 . Observe the condensation of the network between the top left and right panels in Fig 10 . Many of the nodes in the network exhibit similar behavior; the reduction naturally clusters these nodes and highlights the emergent , effective topology governing the system . Using this 18 parameter model as a starting point , we next coarsen the QoIs by ignoring those species for which experimental data was not available in reference [7] . The remaining observed species are Ras , Raf1 , Rap1 , B-Raf , Mek1/2 , and Erk1/2 . The eigenvalues of the 18 parameter model in top right panel of Fig 9 therefore correspond to the same parameters as those in the top left of the same Figure . This is the eigenvalue spectrum that would have resulted if the 18 parameter model had been fit to the original data . Notice that three eigenvalues are now zero ( numerical zero ∼10−16 ) . These correspond to the three remaining parameter of the EGF/PI3K/Akt cascade for which there were no observations in reference [7] . The data allow no predictions for these unobserved species . Two other eigenvalues are dramatically smaller after coarsening the QoIs ( λ ∼ 10 - 4 ) . One parameter corresponds to the relative activity level of P90/Rsk ( exactly analogous to the limit leading to Eq ( 27 ) ) . The other parameter is the unbinding rate of NGF from NGFR . The dramatic decrease in these eigenvalues upon coarsening the QoIs indicate that these QoIs contain practically no information about these parameters . These parameters are therefore irrelevant for explaining the system behavior . Additionally , one other parameter can be removed which lumps MEK and ERK as a single dynamical variable . These approximations are further reflected in the condensed network ( Fig 10 bottom center ) . Model predictions that depend strongly on these parameters could not be constrained by the original data . The activity level of ERK is the quantity of primary biological interest in this model as it signals to the nucleus the presence of extra-cellular EGF or NGF and ultimately determines cell fate . Therefore , we next consider only the level of ERK activity in response to EGF and NGF stimuli ( Fig 9 bottom left and Fig 10 bottom center ) . These QoIs can be explained by a six parameter model . Of these six parameters , two are associated with the C3G cascade which is only activated by NGF stimulation . Coarsening the QoIs to only include an EGF stimulus therefore reduces the model to four parameters ( Fig 9 bottom right ) and a minimal negative feedback loop ( Fig 10 bottom right ) analogous to that in Fig 6 ( left ) . In Fig 11 we illustrate the sensitivities of the ERK adaptation curve to each of the four coarse-grained parameters . The sensitivities of parameters 1 and 4 are very similar in that they both increase the over-all level of ERK activity through the time series . Unlike parameter 4 , parameter 1 is also characterized by a narrowing of the response peak . It is interesting to compare these sensitivities with those in Fig 7 . Parameters 2 in both models have the same functional effect , controlling the turnover point for the adaptation . Similarly , parameters 4 in both models control the over scale of the time series . In contrast , parameters 1 and 3 in the minimal EGFR model have a different functional role from parameters 1 and 3 in the simple negative feedback loop above . However , by tuning an appropriate combination of parameters 1 and 3 in the minimal EGFR model , it is possible to control only the final steady state of the model without affecting the transient peak , directly analogous to parameter 3 in Fig 7 . Likewise , another combination can be chosen to be functionally equivalent to parameter 1 in Fig 7 . Although the mechanism by which these degrees of freedom are controlled are different in the two models , they ultimately span the same four degrees of freedom summarized in Fig 5 . We have seen that all three adaptation models can be simplified to four phenomenological parameters . These four parameters span the same four degrees of freedom illustrated in Fig 5 . The four parameter models can fit artificial adaptation data generated from the full models , and the systematic errors due to approximations in the model are indistinguishable from the artificial noise . However , removing more parameters results in statistically significant errors when the models are fit to data . That is , further simplifications result in observable systematic errors . However , it is possible to remove additional parameters and still preserve the qualitative behavior of the system . For example , by increasing error bars for the QoIs , additional parameters can be removed . The resulting models still exhibit adaptation , but are unable to fit the exact curvature of the true model’s time series . In general applications , the level of granularity in the final model will be driven by many factors , and it may be preferable to consider several models of varying levels of complexity . We illustrate this for the adaptation models considered above . In all three cases , the qualitative adaptation behavior can be approximated by models with two parameters . Although these minimal models are not quantitatively accurate they provide insight into the governing mechanisms . The equations governing the two parameter negative feedback model are d d t [ C ] = k A C I Θ ( 1 - C ) - B ˜ [ C ] ( 37 ) d d t B ˜ = k C B k B C K C B K B C [ C ] . ( 38 ) Those governing the two parameter incoherent feed forward loop model are d d t [ C ] = k A C I Θ ( 1 - C ) - B ˜ [ C ] ( 39 ) d d t B ˜ = k A B k B C K B C I . ( 40 ) In both cases B ˜ = [ B ] ( k B C / K B C ) . The only difference between these two adaptation mechanisms is how in the stimulus information is transmitted to the buffer node , either indirectly through the adaptive node C in the case of negative feedback , or directly from the input in the case of feed forward . In both models , one parameter defines the time unit of the system . In particular , the models are invariant to the transformation t → αt , kAC → kAC/α , kCB → kCB/α , kBC → kBC/α , kAB → kAB/α . By choosing units in which kAC = 1 , i . e . , the initial slope of the rising portion of the curve , the models are reduced to a single parameter . The lone remaining parameter controls the time scale for recovery from the initial inputs . Adaptation can therefore be universally characterized by the dimensionless ratio τ of these two scales: τ N F B L B = k A C K C B K B C k C B k B C ( 41 ) τ I F F L P = k A C K B C k A B k B C . ( 42 ) The time series for various values of τ are given in Fig 12 for both mechanisms . While the curves are similar , notice that negative feedback loop generally achieves better sensitivity , i . e . , height of the peak in response to the input . The incoherent feed forward loop , in contrast , achieves better precision ( i . e . , final steady state closer to zero ) after the initial transient has faded . Fig 13 shows the time to achieve a maximal response and the value of the maximal response for various values of τ for the two mechanisms . In going from the four phenomenological parameters in Fig 5 to the single parameter τ , the models have lost some flexibility . It is important to remember that the sensitivities in Figs 7 , 8 and 11 are based on a local analysis . An actual adaptive system can vary its parameters to make small adjustments to all four phenomenological degrees freedom . However , the primary adaptation response is characterized by the value of τ as in Fig 12 . Notice that the phenomenological interpretation of τ does not correspond directly to any one of the four phenomenological parameters in Fig 5 . From Fig 12 we see that increasing τ corresponds to an increase in parameters ϕ1 , … , ϕ4 . This correlation is common to both mechanisms and indicates a universality in the types of adaptation curves that can be constructed in nature . There will be small small variations from these universal curves from system to system that represent fine-tuning of less important parameter combinations . The equations governing the two parameter EGFR model are d d t [ Erk ] = θ 1 [ EGF ] - P 90 ˜ [ Erk ] ( 43 ) d d t P 90 ˜ = θ 2 [ Erk ] , ( 44 ) which are identical to those governing the negative feedback loop . The phenomenological parameters have expressions in terms of the structural parameters: θ1= ( KmRasGapKmdErkKmdMekKmdRaf1KEGFKRasToRaf1KSoskpMekCytoplasmickpRaf1KmEGFKmpMekCytoplasmicKmpRaf1kRasGapkdErkkdMekkdRaf ) × ( Mek SosPP2A2Raf1PPtase RasGap ) ( 45 ) θ2= ( kdSoskpP90RskKmdSosKmpP90Rsk ) ( P90Rsk ) ( 46 ) P90˜=KKmpP90RskkpP90RskP90Rsk , ( 47 ) with values θ1 ≈ 1 . 558 and θ2 ≈ 0 . 977 . The dimensionless parameter characterizing the EGFR system for the rat model from reference [7] is therefore τEGF ≈ 1 . 6 .
The control mechanisms underlying adaptation in both the negative feedback and incoherent feed-forward loops has been discussed extensively in the literature , particularly in reference [40] . It is therefore interesting and instructive to consider these analyses in light of the minimal models derived above . First , consider the steady state values for the four-parameter negative feedback loop in Eqs ( 28 ) – ( 30 ) : A * = 1 ( 48 ) B * = K F B k A C k C B k B C F B k F B K C B K B C ( 49 ) C * = F B k F B k A C K C B K B C K F B k C B k B C . ( 50 ) Of particular interest is the case of “perfect adaptation” in which node C returns very nearly to its pre-input value ( zero in this case ) . Precision refers to the discrepancy between the final steady state of node C and the its pre-input value . Eq ( 50 ) identifies a combination of parameters that control this system behavior . Note , that one way to accomplish this is for the parameter KCB to become very small , consistent with one of the findings of reference [40] . At first , this result appears to contradict the limit ( kCB , KCB ) →∞ was used in deriving the equations for the negative feedback loop . However , this limit should not be interpreted to mean that kCB and KCB are really large in the full model . Rather , it means that the model predictions do not require these parameters to be finite so long as the ratio kCB/KCB has the appropriate value . In a real system KCB will certainly be finite and decreasing its value will affect the the system behavior . The effect decreasing KCB has on the outputs of the full model is preserved in the reduced system through the ratio kCB/KCB . Eq ( 50 ) also predicts that large values of KFB are preferable for improved precision . Interestingly , reference [40] found that KFB was often small . These results are not necessarily in contradiction . Eq ( 50 ) allows for high precision with small KFB provided other parameter compensate accordingly . Reference [40] reports on a global search over all parameter space , i . e . , allowing other parameter values to float as well . However , holding all other parameters fixed , precision can be improved by increasing KFB , a result that we confirm numerically . In reference [40] , the mechanism of the incoherent feed-forward loop was explained as an “anticipation” by directly monitoring the input node A . This was confirmed by demonstrating a proportionality between the steady state values of node A and node B so that “Node B will negatively regulate C in proportion to the degree of pathway input” [40] . This result can be seen readily in the reduced model in Eqs ( 34 ) – ( 36 ) for the entire dynamics . Assuming a constant input ( as we have done ) , the equations for A and B can be integrated exactly to give ( for times before saturation ) A = k I A I t ( 51 ) B = ( 1 / 2 ) k A B k I A I t 2 = ( 1 / 2 ) k A B t A . ( 52 ) Both the negative feedback and incoherent feed-forward loops share a more general integral control mechanism . For the simple three node models , the topology of these networks is preserved by the reduction process so that previous analyses specific to the topology still apply to the simplified models [40] . In many cases of practical importance , however , the relevant control mechanism is embedded in a large network with many more than three nodes that has many potential control mechanisms . Consider , for example , the full network of in Fig 10 ( top left ) that contains both extended negative feedback and incoherent feed-forward loops as well as many other interconnections . In such a case , it is desirable to condense the network into a minimal mechanistic model in order to identify the relevant control mechanism . This is what is done by the MBAM . Strikingly , this relatively complicated network was reduced to exactly the same functional form as minimal negative feedback topology . We have presented the Manifold Boundary Approximation Method specialized to the context of differential equation models of biochemical kinetics . We have shown that MBAM is capable of deriving simple phenomenological models of system behavior directly from a microscopic , mechanistic description . Because it was derived directly from the microscopic , the resulting simplified model is not a black box but provides real insights into how the microscopic mechanisms govern the emergent system behavior . MBAM connects the microscopic to the macroscopic through a series of limiting approximations that are automatically identified and rigorously justified in a specific context defined by the Quantities of Interest ( QoI ) . The parameters of the reduced model are therefore given as ( often nonlinear ) expressions of microscopic parameters that are exactly the identifiable combinations relative to the specific QoIs . It therefore becomes possible to identify how microscopic perturbations , such as gene mutations , over-expression , or knockout , will alter the macroscopic phenomenological parameters . Selecting appropriate QoIs is an important component of the MBAM; however , the results are usually robust to many changes in the QoIs . The question of how the MBAM results are dependent on the QoIs has begun to be explored in reference [16] . Changing the QoIs will change the Fisher Information and by extension the geometric properties of the manifold . First , consider changes to the QoIs such as changing which time points are considered or the time dependence of the inputs . These changes effectively “stretch” or “compress” portions of the manifold , i . e . , transform the model in a differentiable way–transformations known as diffeomorphisms . Because the boundaries of the model manifold are singularities of the FIM , the relationship among the boundaries are invariant to these diffeomorphisms . In other words , the boundaries are a feature of the differential topology of the family of manifolds generated by varying the QoIs . MBAM is therefore robust to changes in the QoIs because it is identifying a topologically invariant feature of the parameter space . MBAM uses geometric operations ( e . g . , geodesics ) find these topological invariants , so that the QoIs are incidental to the process , but the details of the QoIs are not critical to the final result . More drastic changes to the QoIs , such as changing which chemical species are observed , are not necessarily differentiable changes to the model manifold . Indeed , we have seen for the case of the Brown et al . model , that observing fewer species had a dramatic effect on the final reduced model as summarized in Fig 10 . Other cases are considered in reference [16] where it is observed that changing the QoIs can lead to folding/unfolding of the manifold or even a “manifold collapse” along some dimensions . By systematically coarsening the QoIs , we have seen how the microscopic mechanism can be connected to the simple effective description . In many cases it may not be obvious which QoIs should be chosen . Drastically different choices in QoIs will lead to different reduced models . While MBAM cannot say which choice is correct , it does provide way to systematically study the implications of different choices and generate testable hypotheses about how some intermediate behaviors may or may not influence larger-scale phenomena such as phenotype . MBAM requires a model that is a more-or-less complete microscopic description as a starting point . Of course , any real model is never complete in the reductionist sense . However , microscopic models that can be used effectively with MBAM have made approximations that do not affect the important dynamics of the system . For example , the Brown et al . model is already a dramatic simplification over a comprehensive pathway map [5] . In many cases , however , little to nothing is known about the microscopic mechanisms . Although beyond the scope of this paper , we speculate that MBAM could be used to reverse engineer mechanisms when the microscopic model is unknown . It is instructive to compare the MBAM with another common approach to parameter identification in complex biological models . Many parameter values are often fixed based on educated guesses found for example from in-vitro experiments . The small number of remaining parameters are fit to data . If there are only a few effective degrees of freedom in the model , this procedure will succeed if the remaining parameters have components along the stiff direction of the complete model . While this procedure will reduce the number of fitting parameters in the model , the model is not made conceptually simpler . Furthermore , it is difficult to know a priori how many or which parameters to fix and which to fit . After fixing several parameters , the remaining degrees of freedom in the model are generally misaligned with the true long axes of the model manifold . The restricted model will therefore not encompass the full range of possible model behavior of the original model . In other words , this procedure gives a local approximate model . For different regimes in the model’s parameter space , it will be necessary to fix a different set of parameters . In contrast , the MBAM is a semi-global approximation scheme . Boundaries are a global , topological feature of a manifold [16] . By construction , the parameters of an MBAM simplified model are aligned with the true principal axes of the original model manifold and naturally follow its curvature . The MBAM approximation will generally be valid over a much broader range of the original parameter space than a model in which a handful of parameters are fixed . Furthermore , the boundaries represent structurally simplified approximate models that lead to conceptual insights about collective behavior while retaining an explicit connection to the microscopic mechanisms . The key insight that enables this semi-global approximation scheme is an empirically observed correlation between local information , i . e . , the eigenvalues of the FIM , and the global structure of the manifold , i . e . , manifold widths [48 , 49] . This observation allows the geodesic to find a path to the nearest model boundary using the eigenvalues of the FIM calculated at some point in the interior . In order for this to work , it is generally necessary for the parameters to be dimensionless and in the natural units of the QoIs . This is the reason we recommend using log-transformed parameters ( see Materials and Methods section ) . In our experience , the procedure of identifying limits from a single geodesic generally works; however , it is not fool-proof . On some occasions , the geodesic may encounter a region of high-curvature and bend away from the desired boundary and become lost–analogous to a spaceship experiencing a gravitational slingshot around a planet . In these cases , it will be necessary to guide the method by hand . In our experience , calculating a few geodesics starting from either nearby points or oriented along different directions in the sloppiest subspace ( i . e . two or three eigendirections with smallest eigenvalues ) will eventually identify the desired limit . For most models , the curvature has been demonstrated to be small , so this is a rare occurrence . We encountered it twice in our reduction of the EGFR model and once in our reduction of the adaptation models . Because MBAM is a nonlinear approximation , it is involves considerably more computational expense than other local approximations . Fortunately , as mentioned above , the correspondence between FIM eigenvalues , manifold widths , and the existence of boundaries greatly reduces the computational cost associated with finding a semi-global approximation . Here , we have applied the method to a model of 48 parameters and 15 independent differential equations . However , we estimate that the method could be reasonably applied to models with several hundred parameters given standard simulation methods common in systems biology . “Phenomenological” and “mechanistic” are two adjectives often used to describe models as well as general modeling philosophies . These two approaches reflect a dichotomy that pervades nearly all scientific disciplines between top-down , phenomenological models and bottom-up , mechanistic models [12 , 50–55] . Both approaches have relative strengths and weaknesses . Phenomenological models reflect the relative simplicity of the collective behavior , automatically including the appropriate number of parameters to avoid over-fitting but lacking mechanistic explanations . Phenomenological models exploit correlations among observed data to make predictions about statistically similar experiments . In contrast mechanistic models are constructed to reflect causal relationships among components . These models are often complex and consequently susceptible to over-explaining behavior or over-fitting data . Because they model causal relationships , mechanistic models have a type of a priori information about the system behavior . Mechanistic descriptions are therefore an important ingredient for enabling new engineering and control applications that directly manipulate microscopic components . A precise delineation between “mechanistic” and “phenomenological” modeling is difficult to define . Here , we take the difference between phenomenological and mechanistic models to be the model interpretation with respect to physical reality ( in the reductionist sense ) . For example , the EGFR model summarized in Fig 10 ( top left ) is mechanistic because the modeler claims that there really is a biochemical agent known as Ras , for example , that really does respond to mSos and really does influence Raf1 and PI3K . In contrast , consider the phenomenological models derived from time series data by Daniels and Nemenman [54 , 55] . In this case , the S-systems that make up the model components are not claimed to correspond to any real microscopic components . The models derived in this work have properties of both phenomenological and mechanistic models . The original EGFR model of Brown et al . is mechanistic , but what about the minimal , condensed , negative feedback loop of Fig 10 ( bottom right ) ? We claim that this mechanism reflects the reality of the collective biological system . Similarly , we interpret the components of this minimal model as representing real biological components . In some sense , the parameter τ is phenomenological; it can be easily determined from experimental data without regard to microscopic mechanisms . However , because the expression for τ was derived incrementally from a mechanistic description , expressions such as Eqs ( 41 ) – ( 42 ) and Eqs ( 45 ) – ( 47 ) explicitly identify the mechanisms that control its value . In principle it would be possible to use these expressions to predict the value of τ from the microscopic parameter values . This is an important conceptual advance because it bridges the high-level phenomenological description and the low-level mechanisms . Indeed , these expressions identify which information about the microscopic components are necessary to predict a macroscopic behavior or conversely , which information about microscopic mechanisms can be inferred from systems-level observations . Expressions directly relating microscopic and phenomenological parameters allows one to easily predict the effect on phenomenology ( i . e . , τ ) in response to changes in any of the microscopic parameters ( such as gene-knockout , over-expression , etc . ) without the need to directly explore the large microscopic parameter space . Compressing parameter space in this way reduces the potential for over-fitting and over-explaining system behavior and significantly simplifies the ensuing statistical analysis . In many cases of interest , mechanistic explanations are elusive . Although we have not explored the possibility here , we believe the current approach may be useful in these situations as well . For example , given several candidate mechanistic models , understanding how each mechanistic would hypothetically explain a system-level behavior could be useful in motivating experiments to distinguish among competing hypotheses by providing insights into competing theories . Complexity in biological modeling is often contrasted with the apparent simplicity of models from the physical sciences . Indeed , many of the seminal examples of physics models are surprisingly simple and have very few parameters . Consider for example , the diffusion equation that is typically characterized by a single parameter [11] . Furthermore , the forms for many of the simple , phenomenological models of physics were guessed long before the microscopic mechanisms were understood . In contrast , the immense complexity of biological models often give rise to arguments that biology demands a new approach to mathematical modeling and that analogies drawn from physics are not likely to be useful for guiding computational biology . In many cases the justification for simple models in physics can be traced to either a small parameter or the symmetries of the underlying physical interactions . That these symmetries are not present in living systems gives credence to this perspective . Despite the complexity of the underlying mechanisms , biological systems , like physical systems , often exhibit relativity simple collective behavior , especially when only a few QoIs are considered at a time . Adaptation , for example , is a common biological function that , as we have seen , could be modeled by a simple function with just one parameter . This situation is not unlike the diffusion equation from physics . In both cases , a simple macroscopic form can be expressed , independent of the microscopic details , with a few parameters that are easily inferable from data . The stability of macroscopic behaviors to microscopic perturbations leads to the concept of a universality . Universality has been used with great success in physics by mapping the behavior of many different systems into a relatively small number of universality classes . Once the appropriate universality class has been identified , a simple , computationally tractable model can be used to calculate all universal physical quantities . For example , the critical exponents of many different fluids can be predicted almost exactly by the Ising model , a toy model of ferromagnetism . It does not matter that the Ising model is not a mechanistically accurate model of fluids because it is in the same universality class . There has been considerable speculation about the extent to which universality may or may not prove useful in biology or other complex systems . Here we consider one such argument that is particularly relevant in the context of the manifold boundary approximation . One source of complexity in biology arises when attempting to predict how the simple collective behavior will be altered by microscopic perturbations , such as mutating genes or applying protein-targeting drugs , or how a desired collective behavior could be engineered from microscopic components . Indeed , this is a much more challenging question that is not easily answered by phenomenological models without mechanistic information . However , this problem is not unique to biology . In physics , for example , the Ising model does not predict the critical temperature and pressure of a fluid , only the properties at the critical point . Similarly , macroscopic , phenomenological models of material strength do not give any insights into how to engineer stronger alloys . Phenomenological models have limited predictive power for experiments that manipulate microscopic control knobs . As experimental and engineering efforts in physics , biology , and other scientific fields have advanced to the realm of the microscopic , these simple macroscopic theories need to be explicitly connected to their microscopic mechanisms . How does one systematically identify the microscopic parameter combinations that control the non-universal behavior of a system ? It is true that the types of questions advanced by both modern physics and biology demand new approaches to modeling beyond what has been “unreasonably successful” historically in the physical sciences [56] . Indeed , the challenges faced by biological and physical modeling are shared by many disciplines across the sciences . How do microscopic mechanisms govern collective behavior and how can that behavior be controlled and engineered ? Simple , phenomenological models can play an important role in answering these questions since they distill the essence of the system behavior . What is often missing , however , is an explicit connection between the phenomenology and the mechanistic description . The manifold boundary approximation method is a step toward providing such a bridge in a general way . It is our hope that similar analysis can lead to a likewise comprehensive picture of other complex processes both in physics , biology , and elsewhere .
The Manifold Boundary Approximation Method ( MBAM ) is a model reduction scheme described in reference [26] . As the name suggests , it is based on a geometric interpretation of information theory ( known as information geometry [48 , 49 , 57–61] ) that is applicable to a wide range of model types . In this section we give a more algorithmic description and presentation specialized to the types of models common in systems biology , i . e . , those that are formulated as differential equations of chemical kinetics that would be fit to data by least squares . Notably , this excludes stochastic differential equations . In principle , the MBAM formalism can be applied to SDEs , but we do not address that question here . Throughout this section , we refer to the relevant information geometric objects ( manifold , metric , geodesics , etc . ) and provide external references for completeness . However , the reader can ignore these technicalities if desired and implement the method as summarized here . We assume the existence of a model of a biological system with many parameters θ that can be evaluated to make predictions . Examples of possible predictions include the concentrations of specific chemical species at specific times in response to specific stimuli . Approximations inherently disregard pieces of the model , so it is necessary to decide the objective of the model , i . e . , which model behaviors the approximation should preserve . Therefore , from the many possible predictions , the modeler selects a subset that we refer to as Quantities of Interest ( QoI ) . We denote these by r m ( θ ) = y m ( θ ) σ m ( 53 ) where m is an index that enumerates the QoIs , ym ( θ ) denotes the prediction of the model for the corresponding QoI evaluated at parameters θ , and σm represents the tolerance with which the QoI should be preserved . The QoI is analogous to a data point ym ( θ ) with experimental uncertainty σm . In practice , the QoIs will often include predictions for which experimental data is available . The data will then be used to calibrate the reduced model . However , QoIs may also include predictions for which data is unavailable but for which the modeler would nevertheless like to make predictions . Alternatively , QoIs may include a very small subset of possible predictions as we have done here for the case of EGFR signaling . The underlying idea of the MBAM is that rm ( θ ) can be interpreted as a vector in ℝM , where M is the number of QoIs . If the model contains N parameters , then this vector sweeps out an N-dimensional hyper-surface embedded in ℝM . This hyper-surface is known as the model manifold and denoted by M . For biological systems such as we consider here ( in addition to models from many other fields ) , the model manifold is bounded . Furthermore , the model manifold has many cross sections that are very thin . Consequently , M often has an effective dimensionality that is much less than N . Our goal is to construct a low dimensional approximation to the model manifold by finding the boundaries of M . The procedure for doing this can be summarized as a four step algorithm . First , from an estimate of the parameters θ0 calculate the matrix g μ ν = ∑ m ∂ r m ∂ θ μ ∂ r m ∂ θ ν . ( 54 ) This matrix is the Fisher Information Matrix ( FIM ) of the model and corresponds to the Riemannian metric on M . Calculating the eigenvalues of this matrix reveal the “sloppiness” of the corresponding parameter inference problem . The eigenvectors with small eigenvalues correspond to the parameter combinations that have negligible effect on the QoIs . We denote the direction of the smallest eigenvector by v0 . The second step is to calculate a parameterized path through parameter space θ ( τ ) corresponding to the geodesic originating with parameters θ0 and direction v0 . This is found by numerical solving a differential equation: d 2 d τ 2 θ μ = ∑ ν , m ( g - 1 ) μ ν ∂ r m ∂ θ ν A ( v ) m ( 55 ) where A ( v ) is the directional second derivative: A m ( v ) = ∑ μ ν d θ μ d τ d θ ν d τ ∂ 2 r m ∂ θ μ ∂ θ ν . ( 56 ) ( As an aside , in order to avoid unnecessary complications for the uninitiated , we have not used many of the standard differential geometric conventions , including the Einstein summation convention or the use of raised and lowered indices to denote contravariant and covariant vector components . ) It is possible to estimate Am ( v ) efficiently using finite differences A m ( v ) ≈ r ( θ + h v ) + r ( θ - h v ) - 2 r ( θ ) h 2 , ( 57 ) where v = d θ d τ . The solution to Eq ( 55 ) is a parameterized curve through the parameter space . Along this curve , the modeler monitors the eigenvalues of the FIM ( Eq ( 54 ) ) . A boundary of the model manifold is identified by the smallest eigenvalue of gμν approaching zero . When the smallest eigenvalue becomes much less than the next smallest , then the corresponding direction will reveal a limiting approximation in the model . This leads to step three . The approximation will typically correspond to one or more parameters approaching zero or infinity in a coordinated way . The goal is to identify this limit and analytically evaluate it in the model . This is done explicitly for several models in this manuscript . The result of the process is a new model with one less parameter than the previous . We denote the new vector of parameters by ϕ and the QoIs for this approximate model by y ˜ m ( ϕ ) / σ m Finally , the values of the parameters ϕ in the approximate model are calibrated to the parameters θ0 by minimizing the sum of square distance between min ϕ ∑ m y m ( θ 0 ) - y ˜ m ( ϕ ) σ m 2 . ( 58 ) This four-step procedure is iterated , removing one parameter at a time , until the model becomes sufficiently simple . A python script that can be used for calculating geodesics is available on github [62] . The procedure just described requires a few comments , particularly as it applies to biological systems . First , the MBAM requires a parameter estimate as a starting point θ0 , which usually cannot be estimated accurately . Although an accurate estimate of θ0 might be elusive , it has been shown that the resulting reduced model is largely independent to these uncertainties . Indeed , one purpose of the MBAM is to remove the unconstrained parameters from the model . The reason for this is seen by considering a geometric argument given in reference [26] . Huge variations in parameter values can result when fitting to data , but these variations all lie within the same statistical confidence region , which means they map to nearby points on the model manifold . Starting from any points within this confidence region will identify the same sequence of boundaries as the true parameters . For most systems biology models , the microscopic parameters are restricted to positive values ( reaction rates , Michaelis-Menten constants , Hill coefficients , and initial concentrations ) . In order to guarantee positivity , we assume that these parameters have been log-transformed in the model , i . e . , θ = log k , where k are the reaction rates , etc . This serves the dual purpose of non-dimensionalizing the parameters , that is important for the initial eigendirection of the FIM to point to the narrowest width of the M . MBAM is a semi-global approximation method that is enabled by a correspondence between local information ( FIM ) and global structure ( boundaries ) . This correspondence is less likely to hold if the parameters are not log-transformed . We use the term semi-global to denote something between purely local and fully global . For the case of the enzyme-catalyzed reaction in Fig 1 , the MBAM approximation is a global approximation; the Michaelis-Menten model is capable of well-approximating the full range of behavior of the mass-action kinetics . However , one could imagine , a more complicated model manifold with several narrow “arms” extending from a central location ( something like a star ) . Beginning from a point in one of the arms of the manifold , the MBAM will likely only approximate the behavior along of the principal axis of that arm . Because of this possibility , we describe MBAM as semi-global . With the exception of the enzyme-substrate reaction ( Fig 1 ) , it is unknown whether the approximations given in this paper are global or semi-global . This is due to the intrinsic difficulties in both exploring and characterizing high-dimensional spaces . | Dynamic systems biology models typically involve many kinetic parameters that reflect the complexity of the constituent components . This mechanistic complexity is usually in contrast to relatively simple collective behavior exhibited by the system . We use a semi-global parameter reduction method known as the Manifold Boundary Approximation Method to construct simple phenomenological models of the behavior directly from complex models of the underlying mechanisms . We show that the well-known Michaelis-Menten approximation is a special case of this approach . We apply the method to several complex models exhibiting adaptation and show that they can all be characterized by a single parameter that we denote by τ . The scenario is similar to modeling complex systems in physics in which a large number of microscopically distinct systems are mapped onto relatively simple universality classes characterized by a small number of parameters . By generalizing this approach to dynamical systems biology models , we hope to identify the high-level governing principles that control system behavior and identify their mechanistic control knobs . | [
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... | 2016 | Bridging Mechanistic and Phenomenological Models of Complex Biological Systems |
Pathogen-pattern-recognition by Toll-like receptors ( TLRs ) and pathogen clearance after immune complex formation via engagement with Fc receptors ( FcRs ) represent central mechanisms that trigger the immune and inflammatory responses . In the present study , a linkage between TLR4 and FcγR was evaluated in vitro and in vivo . Most strikingly , in vitro activation of phagocytes by IgG immune complexes ( IgGIC ) resulted in an association of TLR4 with FcγRIII ( CD16 ) based on co-immunoprecipitation analyses . Neutrophils and macrophages from TLR4 mutant ( mut ) mice were unresponsive to either lipopolysaccharide ( LPS ) or IgGIC in vitro , as determined by cytokine production . This phenomenon was accompanied by the inability to phosphorylate tyrosine residues within immunoreceptor tyrosine-based activation motifs ( ITAMs ) of the FcRγ-subunit . To transfer these findings in vivo , two different models of acute lung injury ( ALI ) induced by intratracheal administration of either LPS or IgGIC were employed . As expected , LPS-induced ALI was abolished in TLR4 mut and TLR4−/− mice . Unexpectedly , TLR4 mut and TLR4−/− mice were also resistant to development of ALI following IgGIC deposition in the lungs . In conclusion , our findings suggest that TLR4 and FcγRIII pathways are structurally and functionally connected at the receptor level and that TLR4 is indispensable for FcγRIII signaling via FcRγ-subunit activation .
The immune system is traditionally divided into innate and adaptive entities . Adaptive immunity is organized around T cells and B cells and requires a process of maturation and clonal selection of lymphocytes . In contrast , innate immunity can be immediately activated during the onset of infection in order to control replication of pathogenic microbes and bring about their clearance from tissues or blood . As an important aspect of innate immunity , pattern-recognition receptors ( PRRs ) collectively recognize lipid , carbohydrate , peptide , and nucleic-acid structures of invading microorganisms [1] . PRRs comprise the toll-like receptor family ( TLR ) , which consists of at least 12 different evolutionarily conserved membrane proteins that trigger innate immune responses [2] . Initially identified in 1997 , TLR4 represents the most thoroughly investigated TLR [3] . TLR4 is essential for responses to bacterial lipopolysaccharide ( LPS ) , a well-known pathogen-associated molecular pattern ( PAMP ) [3] , [4] . Besides LPS , various endogenous ligands , such as hyaluronan and high mobility group box 1 protein ( HMGB1 ) , appear to engage TLR4 [5] , [6] . After binding of LPS to the TLR4/MD-2/CD14 receptor complex , activation of the intracellular signaling pathway is initiated , ultimately leading to NF-κB activation and its translocation to the nucleus , resulting in subsequent cytokine/chemokine production and release [7] . As part of the adaptive immune system , antibodies of high affinity binding specifically recognize and neutralize intruding pathogens or their products . After antibody binding to antigen , the Fc domain of immunoglobulin ( Ig ) is recognized by Fc receptors ( FcRs ) which are predominantly expressed on immune and inflammatory cells and thereby link antibody-mediated ( humoral ) immune responses to cellular effector functions [8] , [9] . Specific FcRs exist for all classes of immunoglobulins . Binding of IgGs to FcγRs on phagocytes triggers a wide variety of cellular functions including phagocytosis , release of inflammatory mediators , and clearance of immune complexes [8] . FcγRs specifically bind IgG and are divided into four subclasses . FcγRI ( CD64 ) , FcγRIII ( CD16 ) , and FcγRIV are activating receptors , while FcγRII ( CD32 ) mediates inhibitory functions . The cellular response is determined by the balance between activating ( ITAM , immunoreceptor tyrosine-based activation motif ) and inhibitory ( ITIM , immunoreceptor tyrosine-based inhibitory motif ) signals [10] , [11] , [12] , [13] . Despite extensive research in the past , the highly complex regulation of innate and adaptive immunity and their interactions are still poorly understood . It has been suggested that adaptive immune responses are controlled by innate immune recognition and vice versa [14] , [15] , [16] . In particular , TLRs and FcγRs are considered to be important regulators of immune responses [13] , [17] . Recently , evidence has emerged that there is indirect interaction between TLR4 and FcγR pathways . TLR4 has been shown to up-regulate FcγR expression in experimental immune complex arthritis; inhibition of TLR4 resulted in attenuation of in vivo cytokine release in models of glomerulonephritis and rheumatoid arthritis [18] , [19] , [20] . In the present study , we addressed the question as to whether there is a direct link between TLR4 and FcγR pathways in vitro and in vivo .
In the past , the investigation of TLR4 faced the problem of LPS contamination , which imposed considerable restrictions on the interpretation of data [5] . Therefore , the LPS concentration was determined in reagents used for lung injury induction by deposition of IgG immune complexes ( IgGIC ) , such as DPBS , anti-BSA IgG and BSA , although none of these reagents had been prepared using bacterial ( E . coli ) systems . Using Limulus Amebocyte Lysate Kinetic-QCL assay , LPS levels were not detectable ( <5×10−3 units/ml ) in any of the reagents ( data not shown ) , suggesting that in vitro stimulation by IgGIC is based upon a genuine agonist effect that is not due to LPS contamination . In addition to determination of LPS contamination ( see above ) , DPBS , anti-BSA IgG and BSA were subjected to endotoxin removal by solid-phase polymyxin . Using the polymyxin-treated reagents , immune complexes were generated and then applied in in vitro experiments or the reagents were administered in mice for the formation of immune complexes in vivo . Furthermore , commercially available , preformed peroxidase/anti-peroxidase immune complexes ( PAP IgGIC ) were used at the same concentration in order to confirm the results using BSA IgGIC or polymyxin-treated BSA IgGIC . The results of both , polymyxin-treated BSA IgGIC and PAP IgGIC , are presented in the corresponding figures . In summary , using different in vitro and in vivo approaches , it is highly unlikely that any of the effects following IgGIC stimulation in the present study are based on LPS contamination of the reagents . In order to assess whether crosstalk between TLR4 and FcγR might occur at the receptor level , neutrophils ( PMNs ) and macrophages from wild-type ( Wt ) mice were incubated in vitro with IgGIC , LPS , or the combination of the two . After incubation , cell lysates were immunoprecipitated ( IP ) with anti-TLR4 and then analyzed for FcγRII/III by immunoblotting ( IB ) . As shown in Figure 1A , B , immunoprecipitated TLR4 was associated with FcγR after cell exposure to IgGIC . Inversely , LPS incubation did not result in an association of both receptors as indicated by the absence of bands for FcγR , whereas the combination of LPS+IgGIC seemed to enhance the signal for FcγR co-immunoprecipitated by anti-TLR4 IgG ( Figure 1A , B ) . The band for FcγR under the conditions described above indicated a protein mass of 55 kDa , in accord with the reported molecular weight for FcγRIII [21] , [22] . In contrast , there was no band at the 40 kDa position ( data not shown ) , the molecular weight of FcγRII , which is also recognized by the anti-FcγR antibody ( mAb , clone 2 . 4G2 ) used for Western blot analyses [23] , [24] . In accord with Figure 1A , B , reverse direction immunoprecipitation using FcγRIII antibody followed TLR4 Western blots revealed bands at around 90 kDa , consistent with the reported molecular weight of TLR4 ( Figure 1C , D ) . However , under these conditions bands also occurred after stimulation of phagocytes with LPS ( Figure 1C , D ) , which may suggest that FcγRIII and TLR4 heterodimerize upon LPS stimulation , although to a lesser extent as compared to IgGIC treated cells . When PMNs and macrophages from FcγRIII−/− mice were exposed to the same in vitro conditions ( IgGIC , LPS , LPS+IgGIC ) , the band for FcγRIII failed to appear , confirming its specificity ( Figure 1E , F ) . In order to examine whether the interaction between TLR4 and FcγRIII was specific for these two receptors or whether there also might be multimerization with other TLRs or Fc receptors , lysates from Wt phagocytic cells under various conditions ( see above ) were subjected to immunoprecipitation with anti-TLR6 or anti-CD23 ( anti-FcεRII ) , followed by Western blots for FcγRIII or TLR4 , respectively ( Figure 1G–J ) . In both combinations , specific bands for either FcγRIII ( after immunoprecipitation with anti-TLR6; Figure 1G , H ) or TLR4 ( immunoprecipitation of cell lysates with anti-TLR6; Figure 1I , J ) failed to appear , whereas the strong bands in the lower panels ( loading controls ) demonstrate that immunoprecipitation of the samples worked properly . In addition , macrophages from Wt mice were incubated with polymyxin-treated BSA IgGIC and PAP IgGIC , followed by immunoprecipitation with anti-TLR4 and Western blotting with anti-FcγRIII . As shown in Figure 1K , receptor heterodimerization occurred under these conditions as well , confirming the results shown in Figure 1A , B . In summary , these findings indicate that association of TLR4 and FcγRIII occurs following activation of phagocytes with IgGIC and/or LPS and that this receptor association is a specific phenomenon for FcγRIII and TLR4 . Elicited peritoneal neutrophils ( PMNs ) and macrophages were obtained from Wt and TLR4 mut mice . The cells were incubated in vitro with IgGIC or LPS . Subsequently , supernatant fluids were collected and evaluated by ELISA for intereukin-6 ( IL-6 ) and tumour necrosis factor alpha ( TNFα ) levels ( Figure 2 ) . PMNs from Wt mice showed significant release of IL-6 and TNFα after exposure to either IgGIC or LPS . In the case of TLR4 mut PMNs , cytokine responses to IgGIC or LPS were lost ( Figure 2A–D ) . When peritoneal macrophages were employed in the same protocol , similar results were found ( Figure 2E , F ) . There was a 4-fold increase in IL-6 after exposure of Wt macrophages to LPS , and a 3-fold increase in IL-6 after IgGIC exposure ( Figure 2E ) . Likewise , there was a robust release of TNFα by Wt macrophages into supernatant fluids after stimulation with IgGIC or LPS . When TLR4 mut macrophages were used under the same conditions , IL-6 and TNFα responses to IgGIC or LPS were greatly abolished ( Figure 2E , F ) . Similar results were found when macrophages were incubated with polymyxin-treated BSA IgGIC or PAP IgGIC indicating that the results are reproducible and not based on LPS contamination of the reagents ( Figure 2E , F ) . Thus , the lack of a functional TLR4 is associated with the in vitro inability of PMNs and macrophages to respond to LPS or IgGIC . In order to assess if the impaired response of TLR4 mut cells observed in vitro might be due to a general impairment of the inflammatory response , peritoneal PMNs and macrophages from Wt and TLR4 mut mice were exposed to opsonized zymosan particles as well as to Pam3Cys , which is a specific ligand for TLR2 [25] , [26] , [27] . As displayed in Figure S1 , Wt cells showed a significant increase of IL-6 ( Figure S1A , C , E , G ) and TNFα ( Figure S1B , D , F , H ) release when incubated in vitro with Pam3Cys or opsonized zymosan particles . In contrast to the findings described above ( incubation with LPS or IgGIC ) , PMNs ( Figure S1A–D ) and macrophages ( Figure S1E–H ) from TLR4 mut mice showed full responses for IL-6 and TNFα when incubated with opsonized zymosan particles or Pam3Cys . These data indicate that the ability to produce cytokines in response to non-TLR4 agonists is intact in TLR4 mut cells and that the impairment of the inflammatory response to LPS and IgGIC is specific for the non-functional TLR4 protein . In another set of experiments , cells from FcγRIII-deficient mice were tested for responsiveness to LPS . Peritoneal PMNs and macrophages from Wt and FcγRIII−/− were incubated with LPS and opsonized zymosan ( as a positive control ) under the same conditions described above and supernatant fluids were analyzed for IL-6 and TNFα levels by ELISA . As shown in Figure 3 , phagocytes from FcγRIII+/+ and FcγRIII−/− mice robustly produced cytokines when incubated with LPS , opsonized zymosan or IgGIC . There was no difference in cytokine secretion between the FcγRIII+/+ and FcγRIII−/− cells , except for LPS-induced TNFα release by FcγRIII−/− PMNs , which was lower as compared to FcγRIII+/+ PMNs , but significantly elevated above baseline levels . As expected , FcγRIII+/+ macrophages robustly released IL-6 and TNFα into supernatant fluids when stimulated with IgGIC , whereas macrophages from FcγRIII−/− mice were unresponsive to IgGIC ( Figure 3C , D ) . These results suggest that FcγRIII-deficient phagocytes can respond to LPS and that FcγRIII is not required for direct TLR4 signaling , while FcγRIII is essential for the mediation of IgGIC-induced responses . After binding of LPS , TLR4 engages intracellular signaling pathways via the adaptor molecules MyD88 and TRIF [27] . In the case of FcγR-immune-complex interaction , intracellular pathways are activated by tyrosine phosphorylation of the FcRγ-subunit ITAM region [8] , [28] . This subunit is known to be the common adaptor of FcγRI , FcγRIII and FcεRI [29] , [30] , the first two being essential for development of IgGIC induced acute lung injury [31] . In order to evaluate the mechanism behind the impaired response of TLR4 mut cells to IgGIC , tyrosine phosphorylation of the FcRγ-subunit was investigated in vitro . When peritoneal PMNs ( Figure 4A ) or macrophages ( Figure 4B ) from Wt mice were exposed to IgGIC , rapid tyrosine phosphorylation ( PY ) of the FcRγ-subunit occurred over the first 30 min , as indicated by robust bands in the Western blots . In striking contrast , phosphorylation of the FcRγ-subunit failed to occur when TLR4 mut cells were used . Here , the intensity of the bands was comparable to those in non-stimulated cells ( Figure 4A , B ) . When LPS was used as a stimulus ( Figure 4C , D ) , slight phosphorylation of the FcRγ-subunit occurred in Wt cells ( but not in TLR4 mut cells ) , indicating that TLR4 has little ability to activate the FcRγ-subunit as an intracellular signaling event ( Figure 4C , D ) . Furthermore , the above mentioned results were confirmed in macrophages by using polymyxin-treated BSA IgGIC for stimulation under the same conditions in order to exclude LPS contamination of the reagents ( Figure 4E ) . Collectively , these data suggest that the integrity of TLR4 seems to be required for a proper function of FcγR activation via phosphorylation of the FcRγ-subunit , further suggesting communication between the TLR4 and FcγR signaling pathways . Using the LPS and IgGIC models of ALI , Wt , TLR4 mut , TLR4+/+ and TLR4−/− mice were evaluated for responses following lung deposition of IgGIC or LPS . While FcγRs play a key role in the IgG immune complex ( IgGIC ) model of ALI [31] , [32] , TLR4 is critical for the development of lung injury in the LPS model [33] , [34] , [35] . As indicated in Figure 5A , LPS-induced lung injury , as defined by the permeability index ( leak of plasma albumin into the extravascular lung compartment ) , showed a 4-fold increase in Wt mice ( compared to controls , ctrl ) and remained at the control level in LPS-challenged TLR4 mut mice . In the case of IgGIC ( Figure 5B ) , the permeability index rose 5-fold above control ( basal ) levels in Wt mice . However , TLR4 mut mice unexpectedly showed no evidence of injury after deposition of IgGIC ( Figure 5B ) . TLR4−/− mice behaved similar to TLR4 mut mice in terms of lung injury , with virtually no lung injury in response to deposition of either LPS or IgGIC ( Figure 5A , B ) . When IL-6 levels were measured in bronchoalveolar lavage ( BAL ) fluids , LPS and IgGIC induced high levels of IL-6 in Wt mice and very low levels in TLR4 mut mice ( Figure 5C ) . Similar patterns were found for TNFα levels ( Figure 5D ) . Similarly , induction of ALI by intrapulmonary deposition of polymyxin-treated BSA IgGIC in Wt and TLR4 mut mice ( Figure 5E ) revealed no difference to the results displayed in Figure 5B; when polymyxin-treated reagents were administered for intrapulmonary IgGIC formation lung permeability rose 3 . 5 fold in Wt mice whereas mice TLR4 mut mice did not show a significant increase . Thus , these findings support the conclusion that lung injury induction by IgGICs is not linked to contamination of the reagents with endotoxin . In addition , reagents that were used for the formation of IgGIC were administered separately in vivo at the same concentration as they were used in combination for intrapulmonary IgGIC deposition ( Figure 5F ) . When BSA was injected intravenously , followed by intratracheal PBS injection lung permeability was not different from control mice . Similarly , intratracheal injection of anti-BSA and subsequent intravenous DPBS injection ( containing a trace amount of I125-labelled BSA ) did not result in increased lung permeability . In striking contrast , the combination of anti-BSA ( i . t . ) and by BSA ( i . v . ) injection lead to the development of acute lung injury , as also shown in Figure 5B and 5E . These data indicate that the development of lung injury in the IgG model is dependent on the in vivo formation of immune complexes and may not be explained by putative LPS contamination of the reagents since their separate , independent administration failed to increase lung permeability . Finally , IgGIC lung injury was induced in FcR γ-subunit-deficient mice , which do not express FcγRI and FcγRIII on the surface of PMNs and macrophages [36] . In contrast to Wt mice ( FcR γ-subunit+/+ ) , FcR γ-subunit−/− mice did not develop acute lung injury after intrapulmonary IgGIC deposition , as determined by lung permeability ( Figure 5G ) . These findings suggest that the IgGIC-induced lung injury using anti-BSA and BSA is strictly dependent on the FcγR-mediated signalling , and not on LPS-induced activation of TLR4 . However , the caveat remains that there is always a concern about LPS contamination in the context of sensitive assays and in vivo responses . In particular , the possibility that LPS was present at concentrations below the detection limit of the available assays , which would not result in any in vivo ( and in vitro ) responses alone , but would be responsible for putative synergistic effects and an augmentation of IgGIC-induced inflammatory responses cannot be entirely excluded . It is well established that engagement of FcγRIII with IgGIC as well as activation of the complement system with generation of C5a and its interaction with C5aR play crucial roles in the pathogenesis of IgGIC-induced ALI [31] , [37] , [38] . Therefore , elicited peritoneal PMNs were evaluated by flow cytometry for surface expression of FcγRII/III and C5aR protein . As shown in Figure 6A , F , the levels of each receptor on the surface of PMNs were the same in Wt versus TLR4 mut cells . The original flow cytometry data of FcγRII/III expression on Wt and TLR4 mut PMNs are displayed in Figure 6B , C . In addition , the total content of FcγRIII and FcRγ-subunit in cell lysates from Wt and TLR4 mut PMN ( Figure 6D ) and macrophages ( Figure 6E ) were analyzed by Western blotting . In accordance with the flow cytometry results ( Figure 6A , B ) , unstimulated phagocytes from both mouse strains expressed the same levels of FcγRII/III and FcRγ-subunit . The analysis for the house keeping protein GAPDH ( lower bands ) indicates equal loading of the cell lysates . Thus , the inability of TLR4 mut mice to respond to IgGIC or LPS is not associated with reduced surface content of FcγR protein on PMNs , consistent with the findings that there is cross-talk between FcγR and TLR4 signaling pathways such that downstream production of IL-6 and TNFα upon IgGIC stimulation requires participation of both pathways . Collectively , these data indicate that TLR4 is required for proper FcγRIII functions .
The mechanisms by which the recognition of pathogens leads to host responses are inadequately understood . The modulation of immune responses is inter alia mediated by cell surface receptors that are associated with signaling molecules that contain ITAMs ( immunoreceptor tyrosine-based activation motifs ) , TREMs ( triggering receptors expressed on myeloid cells ) and OSCARs ( human osteoclast-associated receptors ) [1] . Intracellular signaling after TLR4 activation is mediated through the adaptor proteins , MyD88 and TRIF , whereas FcγRI and FcγRIII both contain the FcRγ-subunit , which is phosphorylated at tyrosine residues by Src and Syk kinases upon FcγR activation [28] , [30] , [39] , [40] . Interestingly , ligation of FcRγ-subunit containing FcRs results in inhibition of IL-12 production by monocytes in response to TLR ligands [41] . The specificity of IL-12 downregulation appears to be based on inhibition at the transcription level [41] . Moreover , TLRs are considered to control activation of acquired immunity [14] , supporting the hypothesis for an instructive role of innate immunity in adaptive immune responses [15] . In the present study , we describe that TLR4 and FcγRIII associate , possibly by heterodimerization , following stimulation with IgGIC in vitro ( Figure 1 ) . Binding of IgGICs to the extracellular domain of FcγRs causes clustering of these receptors , followed by phosphorylation of tyrosine residues within the ITAM region , and subsequent activation of intracellular signaling cascades [28] , [30] , [40] . TLR signaling is initiated by dimerization of TLRs , which can form homo- or heterodimers [42] . Previously , it has been suggested that TLR4 co-associates with FcγRIII after activation of human monocytes [43] . Based on our findings , it is possible that TLR4 and FcγRIII multimerize into clusters following stimulation by LPS or IgGIC , a mechanism known as capping [44] , which is required for engagement of intracellular signaling pathways . TLR4 may represent the central component for such signaling or “docking platforms” [45] and interconnect intracellular signaling pathways via association to adaptor proteins . As demonstrated in the present study , dysfunction of TLR4 results in impaired signaling in FcγRIII pathways ( Figure 4 ) . The mutation that is responsible for the endotoxin tolerance of C3H/HeJ mice has recently been demonstrated to cause suppressed tyrosine phosphorylation by Src tyrosine kinases ( Lyn ) in the toll-IL-1 resistance ( TIR ) domain of TLR4 , resulting in signaling-incompetence [45] . Altered or suppressed TLR4 tyrosine phosphorylation correlated with impaired MyD88 association and suppressed IRAK-1 activation [45] . In addition , our data suggest that this mutation in the TLR gene not only hinders phosphorylation of its own TIR domain but also blocks the tyrosine phosphorylation of the ITAM-containing FcRγ-subunit , the consequence of which ultimately leads to impaired signaling after engagement of FcγRIII . In the LPS model of acute lung injury , TLR4 mut or TLR4−/− mice were , as expected , highly protected from the development of tissue damage in the LPS-induced model of acute lung injury ( Figure 5 ) . It is well established that mice with mutation in the TLR4 gene or genetic deficiency of TLR4 are non-responsive to LPS [4] , including LPS-mediated lung injury [33] , [34] , [35] . In the present study , TLR4 mut and TLR4−/− mouse strains unexpectedly also showed greatly attenuated susceptibility to IgGIC-induced lung injury ( Figure 5 ) . For this model , it is known that , besides complement activation , FcγRs are critical for initiation and development of IgGIC alveolitis [31] , [32] , particularly through engagement and activation of ITAM-containing FcγRs ( FcγRI and FcγRIII ) [31] . In accordance , mice with targeted disruption of the FcRγ-subunit showed an impaired inflammatory response in the reverse passive Arthus reaction [46] . In our study , TLR4 mut mice not only were resistant to lung injury , but also failed to locally release cytokines in vivo after intrapulmonary IgGIC deposition , as indicated by baseline levels of IL-6 and TNFα in BAL fluids ( Figure 5 ) . In companion experiments , in vitro exposure of TLR4 mut phagocytes to IgGIC resulted in complete suppression of proinflammatory cytokines ( TNFα , IL-6 ) in comparison to phagocytes from Wt mice ( Figure 2 ) . Furthermore , TLR4 mut cells showed impaired tyrosine phosphorylation of the FcRγ-subunit when exposed to IgGIC , in striking contrast to Wt cells ( Figure 4 ) . The fact that TLR4 mut PMNs and macrophages responded with cytokine release when incubated with opsonized zymosan particles or with Pam3Cys ( Figure 3 ) indicates that 1 . ) the mutation in the TLR4 gene does not lead to a global impairment of the cellular inflammatory/immune response and 2 . ) the intracellular signaling pathways are intact since other TLRs ( such as TLR2 and TLR6 ) , which share common pathways , could be activated in vitro . On the other hand , phagocytes from FcγRIII-deficient mice are fully responsive to LPS ( Figure 3 ) , suggesting that TLR4 signaling does not depend on the functional integrity of FcγRIII , whereas TLR4 is required for FcγRIII signaling . Especially in the field of immunology , there is an increasing number of reports describing effects of receptor interactions . Examples include a previous study suggesting cross-talk between IFN-gamma and IFN-alpha receptors with signaling pathways [47] . In brief , signalling by IFN-gamma was shown to depend on the IFN-alpha/beta receptor components . A more recent publication describes that signalling triggered by NKG2D and DAP10 is coupled to the interleukin 15 receptor signalling pathway , suggesting that coupling of activating receptors to other receptor systems may regulate cell type-specific signaling events [48] . In the case of innate immunity , it has been proposed several times that there is a link between TLR4 and the complement system , especially to the C5a signalling pathway , which can negatively regulate TLR4-induced responses [49] , [50] . Under physiological conditions , receptor interactions and cross-talk between signalling pathways might represent important regulatory mechanisms of the immune system to provide distinct but fine-tuned responses . In the case of TLR4 and FcγRIII , cross-talk may provide an optimal and rapid response against invading microorganisms by mediating an interplay between adaptive and innate immunity . However , in certain conditions , such as systemic inflammation ( sepsis ) or autoimmune diseases that are characterized by a loss of inhibitory action or uncontrolled activation of signalling pathways , a loss of control over otherwise carefully orchestrated receptor interactions can become instruments of harm . Taken together , the present findings strongly suggest that ( i ) there is a direct link between TLR4 and FcγR pathways , ( ii ) phosphorylation of tyrosine residues in the ITAM-containing FcRγ-subunit requires the presence and integrity of TLR4 during cellular activation after binding of IgGICs to FcγRs , and ( iii ) presence of IgGICs results in an association between TLR4 and FcγRIII ( CD16 ) on phagocytic cells . These data imply that innate and adaptive immunity are closely connected at the receptor level and post receptor signaling pathways , which might have ramifications for a variety of inflammatory conditions , such as IgGIC-mediated autoimmune diseases ( rheumatoid arthritis or glomerulonephritis ) , ischemia/perfusion injury , trauma or systemic inflammation ( sepsis ) , etc .
Adult male ( 22–25 g ) specific pathogen-free C3H/OuJ ( Wt ) and C3H/HeJ ( TLR4 mut ) mice with a missense mutation in the TLR4-gene were used in these studies [4] . In addition , lung injury was employed in mice lacking the genes for TLR4 ( TLR4−/−; C57BL/10ScCr ) and the corresponding wild-type mice ( TLR4+/+; C57BL/ScSn ) [4] . In some in vitro experiments , cells from FcγRIII-deficient ( FcγRIII−/−; B6 . 129P2-Fcgr3tm1Sjv/J ) , FcR γ-subunit-deficient ( FcRγ-subunit−/−; B6 . 129P2-Fcer1gtm1RavN12 ) and appropriate Wt mice ( C57BL/6 ) were used [51] . All studies were performed in accordance with the University of Michigan Committee on Use and Care of Animals . Mouse peritoneal leukocytes were harvested 5 h ( PMNs ) or 5 days ( macrophages ) after intraperitoneal injection of thioglycolate into untreated Wt and TLR4 mut mice by peritoneal lavage with PBS . 3×106 cells / sample were incubated in HBSS for up to 4 h at 37°C in the presence of LPS ( 20 ng/ml; serotype O111:B4; Sigma , St . Louis , MO ) , BSA IgG immune complexes ( IgGIC , 100 µg/ml; MP Biomedicals ) , polymyxin-treated BSA IgG immune complexes ( p . -t . BSA IC , 100 µg/ml ) , peroxidase/anti-peroxidase IgG immune complexes ( PAP IC , 100 µg/ml; MP Biomedicals ) , opsonized zymosan particles ( 300 µg/ml; Sigma ) or Pam3Cys ( 1 µg/ml; InvivoGen ) . After incubation , supernatant fluids were collected for assessment of cytokines by ELISA and pellets were lysed with RIPA buffer ( Upstate ) for immunoprecipitation analyses . After incubation of peritoneal PMNs or macrophages with either IgG immune complexes ( 100 µg/ml; prepared as described elsewhere [52] or LPS ( 20 ng/ml ) for 5 to 30 min , supernatant fluids were removed and pellets were lysed with 1X RIPA buffer containing Vanedate and protease inhibitors ( Roche Diagnostics ) . Protein concentrations were determined in cell lysates using BCA protein assay ( Pierce ) . Equal protein amounts of supernatants were then incubated overnight with preblocked protein A and G beads ( Santa Cruz ) in the presence of anti-FcRγ-subunit IgG ( Upstate ) or anti-TLR4 IgG ( Santa Cruz ) , respectively . Reverse direction immunoprecipitation included anti-FcγRIII IgG ( Santa Cruz ) . After centrifugation , pellets were resuspended in Laemmli sample buffer ( Biorad ) followed by boiling of the samples . After a final spin step , supernatant fluids were electrophoretically separated under reducing conditions in SDS-PAGE and transferred onto PVDF membrane . The membrane was blocked in 5% bovine milk in TBST and then probed for TLR4 or FcγRIII using polyclonal anti-TLR4 IgG ( 1 µg/ml , Santa Cruz ) or monoclonal anti-FcγRII/III IgG ( 1 µg/ml; clone 2 . 4G2; BD Pharmingen ) . Alternatively , membranes containing the samples co-immunoprecipitated with anti-FcRγ-subunit IgG were incubated with anti-phospho-tyrosine monoclonal antibody ( 1 µg/ml; clone 4G10 , Upstate ) . As secondary antibodies , HRP-conjugated donkey anti-goat IgG ( 1∶80 , 000; Jackson Immunoresearch ) , HRP-conjugated goat anti-rat IgG ( 1∶10 , 000; Amersham ) HRP-conjugated donkey anti-rabbit IgG ( 1∶10 , 000; Amersham ) and HRP-conjugated sheep anti-mouse IgG ( 1∶20 , 000; Amersham ) were added and the blot was developed using ECL-procedure ( Amersham ) . For measurement of IL-6 and TNFα in BAL fluids and supernatant fluids after in vitro incubation of mouse PMNs and macrophages , commercially available ELISA-kits ( “Duo set” , R&D Systems ) were used according to the manufacturer's protocol . To induce IgGIC lung injury , tracheae of mice were surgically exposed and 125 µg rabbit anti-BSA IgG ( MP Biomedicals ) was administered using a 30 gauge needle ( volume of 42 µl/mouse ) followed by intravenous injection of BSA ( 500 µg; Sigma ) . For determination of the permeability index as a quantitative marker for vascular leakage , 125I-labelled bovine serum albumin ( 1 µCi 125I-BSA/mouse ) was injected intravenously . After the development of acute lung injury , the pulmonary vasculature was flushed with 2 . 0 ml PBS . The amount of lung radioactivity was then measured as a ratio of radioactivity present in 100 µl blood recovered from the inferior vena cava at the time of animal euthanasia and that in lung . For bronchoalveolar lavage retrieval , lung injury was performed as described above , but without the intravenous injection of 125I-BSA . The airways were flushed with 0 . 8 ml ice cold PBS using a blunt 20 gauge needle and BAL fluids were recovered for further studies . 50 µg LPS from E . coli ( serotype O111:B4; Sigma ) were given intratracheally ( volume of 42 µl/mouse ) . When lung permeability was measured , a trace amount of 125I-BSA was injected intravenously , as described above . The permeability index was determined and BAL fluids were collected as described for the IgGIC model . Reagents other than LPS , such as DPBS , BSA , anti-BSA IgG that were used for the in vivo and in vitro experiments were tested for LPS-contamination . For quantification of LPS content , samples were conducted in Limulus Amebocyte Lysate Kinetic-QCL assay ( Cambrex ) according to the manufacturer's protocol and as described elsewhere [53] . In addition , reagents used for immune complex formation ( DPBS , BSA , anti-BSA IgG ) were subjected to endotoxin removal ( Pierce ) prior to induction of lung injury or preparation of immune complexes used stimulation of phagocytes in vitro . Flow cytometric analysis was conducted after whole blood collection of untreated wild-type and TLR4 mut mice in a citrate-containing syringe . Rabbit anti-mouse C5aR serum ( 1∶10 dilution; Lampire ) was incubated with mouse whole blood . Non-specific rabbit serum ( Jackson Immunoresearch ) was added to control samples in equal amounts . For detection of FcγR on PMNs , mouse whole blood was either incubated with 1 µg monoclonal anti-FcγRII/III IgG ( clone 2 . 4G2; BD Pharmingen ) or with the appropriate isotype IgG control ( Jackson Immunoresearch ) . After washing , cells were suspended in Phycoerythrin ( PE ) -labeled anti-rabbit IgG ( Invitrogen ) diluted 1∶200 in staining buffer and incubated at room temperature for 45 min . Erythrocytes were lysed by addition of 1× FACS lysing solution ( BD Pharmingen ) for 10 min . After washing , the leukocytes were resuspended in a 1%-paraformaldehyde fixing solution and analyzed on a flow cytometer ( BD Pharmingen ) . All values were expressed as mean±SEM . Data sets were analyzed by one-way analysis of variance ( ANOVA ) ; differences in mean values among experimental groups were then compared using Tukey multiple comparison test . Results were considered statistically significant when P<0 . 05 . | The immune system is traditionally divided into innate and adaptive entities . Pattern-recognition receptors ( PRRs ) collectively recognize molecular structures of invading microorganisms , followed by initiation of immune responses . PRRs comprise the toll-like receptor ( TLR ) family , including TLR4 , which is essential for responses to bacterial lipopolysaccharide ( LPS ) . As part of the adaptive immune system , Fc receptors ( FcRs ) on immune cells recognize antigen–antibody complexes and link antibody-mediated immune responses to cellular effector functions . Here , we describe cross-talk between the pathogen-recognition-receptor toll-like receptor 4 ( TLR4 ) and receptors for IgG immune complexes ( IgGIC ) , Fcγ receptors ( FcγRs ) . We found that TLR4 is involved in FcγRIII ( CD16 ) signaling and that heterodimerization of TLR4 and FcγRIII occurs in the presence of IgGIC but not LPS . Consequently , dysfunctional TLR4 signaling results in unresponsiveness of immune cells in vitro to both LPS and IgGIC , resulting in absence of acute lung injury after intratracheal administration of LPS or intrapulmonary immune complex deposition . In summary , we describe that TLR4 and FcγRIII pathways are structurally and functionally connected . These findings provide new insights of the interplay between innate and adaptive immunity , which closely interact with each other at the receptor level and post receptor signaling pathways . | [
"Abstract",
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"Results",
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] | [
"immunology/cellular",
"microbiology",
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] | 2009 | Cross-Talk between TLR4 and FcγReceptorIII (CD16) Pathways |
Glioblastoma multiforme ( GBM ) is the most common and lethal brain tumor in humans . Recent studies revealed that patterns of microRNA ( miRNA ) expression in GBM tissue samples are different from those in normal brain tissues , suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM . However , little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action . To address this issue , in this study , we systematically explored the main regulation format ( feed-forward loops , FFLs ) consisting of miRNAs , transcription factors ( TFs ) and their impacting GBM-related genes , and developed a computational approach to construct a miRNA-TF regulatory network . First , we compiled GBM-related miRNAs , GBM-related genes , and known human TFs . We then identified 1 , 128 3-node FFLs and 805 4-node FFLs with statistical significance . By merging these FFLs together , we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network . Then , from the network , we extracted a composite GBM-specific regulatory network . To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components , we specifically examined a Notch signaling pathway subnetwork . Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs ( miR-124 , miR-137 , miR-219-5p , miR-34a , miR-9 , and miR-92b ) might play important roles in GBM , including some results that are supported by previous studies . In this study , we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM , providing a valuable resource for further understanding the complex regulatory mechanisms in GBM . The observation of critical miRNAs in the Notch signaling pathway , with partial verification from previous studies , demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and , potentially , other cancers .
Glioblastoma multiforme ( GBM ) is the most common and lethal primary brain tumor in humans and is classified as a grade IV astrocytoma by the World Health Organization ( WHO ) [1] . The tumor is characterized by rapid growth , a high degree of invasiveness , and strong resistance to radiation and chemotherapy [2] . To illuminate its complex characteristics , an understanding of the underlying genetics is critical . During the last decade , numerous genetic studies , including microRNA ( miRNA ) and mRNA expression profiling , somatic mutation , copy number variation and methylation studies performed by the Cancer Genome Atlas ( TCGA ) project , and genome-wide association studies ( GWAS ) by other groups , have substantially contributed to the comprehensive profiling of GBM [3]–[6] . In addition to confirming previous findings , such as TP53 mutation , NF1 deletion or mutation , and EGFR amplification , these results included several new genetic discoveries such as frequent mutations of the IDH1 and IDH2 genes in secondary GBM [3] . Most importantly , these studies support the idea that many of the current risk factors are likely coordinated at the biological pathway or network level rather than at an individual molecular level [6] . Several studies have interrogated networks in the context of gene expression profiles and/or protein interactions to identify novel critical genes and core pathways for GBM , which provides us with new insights into the mechanisms of the disease pathology [7]–[10] . Another important type of biological network , a miRNA-transcription factor ( TF ) regulatory network , acts as a functional unit in the regulation of cell fate in many cell types and systems , including cancer [11] , [12] , but this type of network has not yet been systematically investigated in GBM . In recent years , an increasing number of miRNAs have been identified and linked to cancer [13] , [14] . miRNAs are small ( ∼22 nucleotides ) non-coding RNAs that mainly regulate gene expression at the post-transcriptional level in animals [14] . They are involved in cellular development , differentiation , proliferation , apoptosis and tumorigenesis [15] , [16] . Similar to other types of cancer , patterns of differential miRNA expression versus normal tissues have been identified for GBM [17]–[19] . For example , several studies consistently confirmed the overexpression of miR-21 in GBM [20]–[24] , and several miRNAs are weakly expressed compared with the normal brain , including miR-124 , miR-7 , and miR-128 [18] , [24] . In addition to traditional low-throughput studies , the TCGA project assessed the expression of 534 miRNAs in 240 tumor tissue samples and 10 normal tissue samples . The results have been used to establish GBM subclasses [25] , identify miRNA expression signatures to predict GBM patient survival [17] , and identify important miRNAs in GBM [26] . These and other studies have made it clear that miRNAs play important roles in GBM , and it appears increasingly likely that miRNAs will be clinically useful as biomarkers and/or therapeutic targets for brain tumors and other cancers [19] . Despite a number of miRNAs reported to be dysregulated in GBM , little is known about which miRNAs play critical roles in the pathology of GBM and their relevant targets [27] . To address these questions , we hypothesized that an investigation of miRNAs in the context of the regulatory transcriptional and post-transcriptional networks will provide a far more comprehensive view of their functional roles in GBM . TFs regulate gene expression by translating cis-regulatory codes into specific gene-regulatory events [28] . Since TFs and miRNAs are both categorized as gene-regulatory molecules and share a common regulatory logic [29] , they are capable of cooperatively regulating the same gene: TFs regulate a gene's transcription in the gene's promoter region , while miRNAs regulate a gene's post-transcription in the gene's 3′ untranslated region ( UTR ) . At the network level , it has been demonstrated that the regulation of transcription by TFs and post-transcriptional regulation by miRNAs are tightly coupled [30] , [31] . Moreover , the examination of regulatory networks showed that TFs , miRNAs and genes form a combination of transcriptional/post-transcriptional feed-forward loops ( FFLs ) , which comprise over-represented motifs in the mammalian regulatory network [30] , [31] . Therefore , the analysis of mixed FFLs in a cellular system has emerged as a powerful tool to understand specific biological events , such as the control of cell fate in many cell types and systems [11] . In a regulatory network , a typical mixed FFL motif contains three components: TF , miRNA and gene . This mixed FFL motif is defined as a 3-node FFL . Considering co-expressed genes may have similar regulation patterns [32] , [33] , i . e . , genes regulated by the same TF and the same miRNA , we hypothesized that inclusion of co-expressed genes in FFL analysis would have more power to detect disease-specific regulatory modules . Accordingly , we extended the 3-node FFL model to a 4-node FFL model , which might complement to the former . Here , we pursued a regulatory network-based approach for a comprehensive investigation of gene regulation patterns in GBM . This method can be used to identify network modules containing known GBM-related miRNAs and genes . It can also be used to reveal new components for core pathways . Among GBM candidate genes , we identified the potential targets of TFs and GBM-related miRNAs . These datasets and their regulations were used to construct a comprehensive GBM-specific miRNA-TF mediated regulatory network . Furthermore , we constructed the subnetwork from one well-known core pathway in GBM , the Notch signaling pathway , and identified miRNA components involved in it . Based on the network topological analysis and functional analysis , we identified six functionally critical miRNAs in this pathway . Among them , four have been implicated in GBM by previous work . These results demonstrated that the comprehensive GBM-specific miRNA-TF mediated regulatory network contains valuable information for GBM investigators to identify critical miRNAs and their targets for further experimental design , providing further understanding of the regulatory mechanisms of GBM .
One major purpose of this study was to develop an integrative framework for the construction of a comprehensive regulatory network for GBM . This network consisted of feed-forward regulation among three components: GBM-related genes , GBM-related miRNAs and known human TFs . GBM-related genes and miRNAs with evidence of involvement in the pathology of GBM were collected and curated from public databases and literature . For GBM-related genes , we restricted our analyses to the 415 genes with mutation evidence in previous studies ( Table S1 and Text S1 ) . For GBM-related miRNAs , we collected 124 mature miRNAs that were reported to be dysregulated in studies assessing miRNA expression only in GBM tissue samples or cell lines . Human TFs were extracted from TRANSFAC Professional ( release 2011 . 4 ) [34] , a manually curated database of eukaryotic TFs , their genomic binding sites and DNA binding profiles . There are five types of regulatory relationships: TF regulation of gene expression ( TF-gene ) or miRNA expression ( TF-miRNA ) , miRNA repression of gene expression ( miRNA-gene ) or TF expression ( miRNA-TF ) , and gene-gene coexpression ( gene-gene ) . Each of these regulatory relationships was predicted using computational approaches ( Table 1 ) . Considering the disadvantage of these reverse engineering methods , we applied stringent parameters in prediction to obtain high confidence regulations . To integrate these regulations into a miRNA-TF regulatory network , we only included FFLs with significant miRNA-TF pairs , pinpointed by the hypergeometric test , that potentially cooperate in regulating the same targets . Based on the combinatory regulatory network , we performed further analyses of the network topological properties and functional associations to identify critical miRNAs ( see Figure 1 for the framework and the Materials and Methods for details ) . It is necessary to point out that , in this computational framework , a novel FFL model ( 4-node model ) was developed for the construction of the regulatory network . To illustrate that the framework has a promising application in cancer investigation , in this study , we focused on the GBM regulatory network and identified the miRNA components for the Notch signaling pathway . The analyses illustrated the framework is promising for further identification of critical miRNAs in the pathology of cancer . Table 1 summarizes the five types of potential regulatory relationships mentioned above and their related methods . We provide more details below . FFLs have been demonstrated as one of the most common types of transcriptional network motifs [42] . Typically , a FFL consists of three components: a miRNA , a TF , and a joint target , which is defined as a 3-node FFL . In this study , we expanded the 3-node FFL model to a 4-node FFL model to explore more regulatory modules . Figure 2A shows the detailed relationships in these FFLs . According to the regulatory relationship between two regulators ( TF and miRNA ) in each FFL , we classified FFLs into 3 types: TF-FFL , miRNA-FFL and composite FFL ( Figure 2 ) . Specific to the 3-node FFLs , the TF-FFL model includes TF regulation of a miRNA and a gene , and it also includes miRNA repression of a target gene . The miRNA-FFL model includes miRNA repression of both a target gene and a targeted TF , as well as TF regulation of a target gene . The composite-FFL model includes TF regulation of both a miRNA and a target gene , as well as miRNA repression of the TF gene and the target gene . The three types of FFLs are exclusive to each other . For 4-node FFLs , the design is similar to the 3-node FFL model , but each TF or miRNA may regulate both co-expressed genes . Furthermore , we merged those FFLs with the same TF-miRNA regulation . Thus , the merged FFLs composed of a known TF , a mature miRNA , and a list of GBM-related genes or a list of GBM co-regulated gene pairs ( Figure S1 ) . Table 2 summarizes the number of nodes and links in the 3-node and 4-node FFLs . After converging the significant 3-node and 4-node FFLs identified in the previous subsection , we constructed a miRNA-TF mediated regulatory network for GBM , the major biological output of our computational analysis . The resultant network contained a total of 4 , 354 edges and 408 unique nodes ( Table S7 ) . Among the 4 , 354 edges , 1 , 033 belonged to miRNA-gene pairs , 550 to miRNA-TF pairs , 1 , 863 to TF-gene pairs , 804 to TF-miRNA pairs , and 104 to gene-gene pairs . Among the 408 nodes , 176 belonged to GBM-related genes , 99 to GBM-related miRNAs and 142 to human TFs . Among GBM-related genes and TFs in this regulatory network , 9 genes overlapped ( ARNT , FLI1 , FOXO3 , FOXO4 , GATA3 , SMAD4 , STAT3 , TCF12 , and ZEB1 ) . Although the network only recruited 176 ( 43 . 46% ) of the 415 GBM-related genes and 99 ( 79 . 84% ) of the 124 GBM-related miRNAs , given the uncertainty of associations between candidate genes and the disease , we regarded it as a representation of the regulatory network in GBM . To provide a general view of this regulatory network , we calculated degrees ( connectivity ) and their distribution , which are basic topological network measures [47] . In this complicated network , degree values of genes , miRNAs and TFs ranged from 2 to 66 , 2 to 77 , and 2 to 123 , respectively . The average degrees of genes , miRNAs and TFs were 18 . 70 , 24 . 11 , and 23 . 80 , respectively . The degree distribution for genes , miRNAs and TFs were strongly right-skewed , indicating that most nodes had a low degree , while only a small portion of nodes had a high degree ( Figure S5 ) . Therefore , we observed only a few miRNAs , GBM-related genes and TFs exhibited a high degree in the network . In the context of this regulatory network , these molecules act as hubs that might play important roles in GBM . Hubs are highly connected nodes in a network , suggesting critical roles in maintaining the overall connectivity of the network [47] . Consistently , hubs in the PPI network are more likely to be essential genes [48] , [49] . Using the hub definition method proposed by Yu et al . [50] , we determined the degree cutoff values 38 , 49 , and 71 for genes , miRNAs and TF hubs , respectively . Accordingly , we identified 15 hub genes ( FOXO3 , SMAD4 , TCF12 , BCL11A , PDGFRA , KLF4 , NRAS , SOX11 , CACNA1E , ELAVL2 , PIK3R1 , RPS6KA3 , SLC9A2 , CYLD , and PTCH1 ) , 4 hub miRNAs ( miR-9 , let-7i , miR-495 and miR-130a ) and 6 hub TFs ( TEAD1 , SP1 , MZF1 , NEUROD1 , GATA1 , and TCF7 ) . Among them , genes PIK3R1 and PDGFRA had been reported to have high mutation frequencies in 91 GBM samples ( 9% and 13% , respectively ) , and are involved in the RTK/PI3K signaling pathway , a core GBM pathway [6] . In the above FFL analyses , we noticed that composite 3-node and 4-node FFLs recruited the most GBM-related genes in each category ( 49 . 67% and 72 . 73% , respectively ) , which indicated that composite-FFLs could play important roles in regulating GBM candidate genes . Therefore , we converged these composite-FFLs and generated a regulatory subnetwork that only included composite-FFLs . The resulting subnetwork included 457 edges and 101 GBM-related genes , which accounted for 57 . 38% of GBM-related genes ( 176 ) in the GBM-specific miRNA-TF mediated regulatory network and were regulated by only 26 GBM-related miRNAs ( 24 . 24% ) and 24 TFs ( 16 . 90% ) . We defined this subnetwork as the composite miRNA-TF regulatory network in GBM; it could provide a main framework for the regulatory systems involved in GBM ( Figure 3A ) . In this regulatory network , the distribution of all nodes was again strongly right-skewed; that is , only a few nodes had high degree in the network ( Figure 3B ) . Using the same method to define hubs , we identified four hub genes ( NRP1 , FOXO3 , SMAD4 , and TNFRSF1B ) , six hub miRNAs ( miR-495 , miR-9 , miR-137 , miR-30d , miR-181c , and miR-30e ) , and three hub TFs ( TEAD1 , SP1 , and ZBTB7A ) . Previously , Zhang et al . [51] proposed that a higher-order network structure is a frequently observed motif in integrated mRNA-protein networks . In our regulatory network , we also found several miRNAs and TFs involved in higher-order subnetworks . For instance , we identified three higher-order composite subnetworks . The first one ( Figure 3C ) included one hub TF ( SP1 ) and one hub miRNA ( miR-137 ) , which together regulated 10 genes . The second composite subnetwork included one TF , one hub miRNA , and 6 genes ( Figure 3D ) . The third one included one hub TF , two hub miRNAs , and 12 genes ( Figure 3E ) . We further examined enriched pathways in these 101 GBM-related genes involved in the GBM composite regulatory network . This further examination was important , as biological pathways that are statistically enriched in a set of disease genes may provide important cellular process information for our understanding of the molecular pathology of the disease . For the 101 genes , we identified 39 pathways that were significantly enriched ( adjusted P-value<0 . 01 ) ( Table 3 ) . Among these 39 pathways , 10 ( 25 . 6% ) were directly related to cancer , including glioma and GBM . Several are well-known core pathways involved in GBM , such as PTEN signaling , PI3K/AKT signaling and Notch signaling . To demonstrate that the GBM-specific miRNA-TF mediated regulatory network is useful to identify miRNA components for core pathways , we took a convergent strategy to narrow down the candidate list . We first generated subnetworks for core pathways in GBM and then performed network characteristic analyses , including degree and degree distribution , hub , network modularity , to identify key components . Aside from degree of the node and degree distribution and hub definition mentioned before , the most frequently used approach for biological network analysis is to cluster or partition the whole network into subcomponents , i . e . , modularity . Previous studies have revealed that highly connected groups of proteins tend to participate in the same biological process or complex [52] . In this study , we selected the Notch signaling pathway as an example to illustrate that the network is a useful resource for hypothesis generation and that our computational framework is promising . The Notch signaling pathway strongly influences stem cell maintenance , development and cell fate [53] . Growing evidence indicates it plays a key role in cancer , including gliomas [54] , [55] . According to pathway information recorded in the KEGG database [56] and Ingenuity Canonical Pathways ( http://www . ingenuity . com/ ) , there were five genes in the GBM miRNA-TF mediated regulatory network that belonged to the Notch pathway: EP300 , NOTCH1 , NOTCH2 , FURIN , and JAG1 . We generated a subnetwork for these 5 genes by merging the FFLs that included at least one of these five genes ( Figure 4A ) . We defined it as the GBM Notch-specific miRNA-TF regulatory network , which included 222 edges , 17 GBM-related genes , 32 GBM-related miRNAs and 31 TFs . These 32 miRNAs might be involved in the Notch signaling pathway , providing a potential pool for further experimental determination of miRNAs involved in this pathway ( Table S8 ) . We noticed that there was no 4-node FFL involved in the GBM Notch-specific regulatory network . To identify the critical candidates from the above 32 miRNAs , we further evaluated their importance based on network topological and functional analyses . The degree distribution of all nodes in this subnetwork was also strongly right-skewed . Using the same method to identify the hubs above , we identified four GBM hub genes ( NOTCH1 , FURIN , NOTCH2 , and EP300 ) , four hub miRNAs ( miR-9 , miR-92b , miR-137 and miR-295-5p ) and four hub TFs ( EP300 , SP1 , TEAD1 , and TBX5 ) . Thus , the network global property analysis indicated that these four hub miRNAs might play important roles in the Notch signaling pathway . To investigate other miRNAs in the GBM Notch-related miRNA-TF regulatory network , we used the software CFinder [57] to identify tightly connected subnetworks . CFinder is a popular network analysis tool for examination of nodes' distributions in networks and communities . We obtained four communities in the Notch regulatory network . The first one ( Figure S6A ) included 15 GBM-related genes , 14 GBM-related miRNAs and 18 TFs . Since the subnetwork included the most GBM-related genes ( 88 . 2% ) involved in the GBM Notch related regulatory network , we called this subnetwork the gene-centered subnetwork . The second community ( Figure S6B ) includes two GBM-related genes , 17 GBM-related miRNAs and 15 TFs . Since most of the nodes in this subnetwork are regulators , we defined it as the regulator-centered subnetwork . The third one includes one GBM-related gene , two miRNAs , and three TFs ( Figure S6C ) ; the last one includes one GBM-related gene , one miRNA and one TF ( Figure S6D ) . Considering that the last two subnetworks had one common GBM-related gene , JAG1 , and both were located in the center of the Notch-specific network , we merged these subnetworks together and defined it as a centered subnetwork ( Figure 4 ) . Consequently , three Notch-specific subnetworks were identified ( Figure 4B , 4C , and 4D ) . The centered subnetwork included 8 nodes , none of which belonged to the hubs we identified above . When the centered subnetwork was removed , the connection between the other two subnetworks was lost ( Figure S7 ) . To further examine this feature , we removed the nodes directly linked to the centered subnetwork; most parts of the Notch regulatory network were loosely connected except among GBM-related genes ( Figure S8 ) . These local network analyses showed that the centered subnetwork could serve as a bridge subnetwork and play an important role in the development of GBM . To further examine the role of the centered subnetwork , we used a GO enrichment analysis to identify biological processes associated with the three subnetworks . The gene-centered subnetwork mainly corresponded to the development processes . The centered subnetwork corresponded to regulation of biological processes and developmental processes . The regulator-centered subnetwork corresponded to regulation of biological processes and metabolic processes . These functional association analyses revealed that the centered subnetwork could play the central role in this subnetwork . Based on the important role of this centered subnetwork in the Notch-specific pathway , and two miRNAs , miR-124 and miR-34a , which have direct connections with two other subnetworks , we proposed that these two miRNAs might play important roles in the Notch signaling pathway involved in GBM . In summary , based on the network topological analysis of the GBM Notch regulatory network and its subnetworks , we identified 32 human miRNAs that might be involved in the Notch signaling pathway , and six of them ( miR-124 , miR-137 , miR-219-5p , miR-34a , miR-9 , and miR-92b ) might play important roles in this pathway .
In this study , we explored the combinatory regulation of miRNAs and TFs that have an impact on genes involved in the pathology of GBM . We developed a computational framework to construct and analyze a regulatory network for complex diseases . Our framework started with a compilation of numerous data sources to identify disease candidate genes and miRNAs and then inferred regulatory relationships using a large panel of computational tools . Based on these relationships , we focused on 3-node FFLs and 4-node FFLs to generate a GBM-specific regulatory network . This unique computational framework illustrated that it is indeed possible to process multiple types of data ( e . g . , mutation data , gene expression data , and knowledgebase ) by combining a large collection of methods to identify potential miRNAs in complex diseases . A significant concern regarding the computational approaches used in this study is controlling false positives from both public databases and prediction results caused by computational tools . In our framework , to minimize the effect of these false positives , we first performed a comprehensive compilation from multiple data sources to identify genes and miRNAs relevant to GBM . Next , we chose the most popular databases and software to conduct the prediction . Finally , we applied stringent parameters in the prediction of TF-gene/miRNA , miRNA-gene/TF , and gene-gene relationships . For TF-gene/miRNA and miRNA-gene/TF , we further required conservation among multiple mammalian genomes . Thus , our framework could potentially detect the most important regulatory relationships and might be applied to other complex diseases for the purpose of deciphering their regulatory systems and identifying critical miRNAs . Compared to high-throughput and low-throughput experimental methods that have been used to discover and profile miRNAs , our computational framework could complement them and facilitate the discovery of critical miRNAs in the pathology of disorders . As much more regulatory data is expected to be released in the near future , such as ChIP-Seq ( chromatin immunoprecipitation sequencing ) , RNA-Seq ( transcriptome sequencing ) and GRO-Seq ( global run-on sequencing ) , this framework could be improved with the integration of high-throughput data by filtering out interactions in low confidence . One important output of this comprehensive study is the GBM-specific miRNA-TF combinatory regulatory network . The regulatory network was massive and complex , presenting us with another challenging task: finding the tactic to decipher this huge network to mine the important regulatory components . Recently , pathway analysis has been reported as a useful approach to investigate the pathology of complex diseases [6] , [58] . Specifically in our work , our strategy was to apportion the large regulatory network and extract relatively small but functionally critical subnetworks for pathways that have been previously implicated in the corresponding disease . We then performed network topology analyses and investigated modularity to identify critical miRNAs in these small subnetworks . To demonstrate this strategy , we used the Notch signaling pathway as an example and found six critical miRNAs in the pathway in GBM ( Figure 4 ) . Among them , miR-34a has already been shown in an independent study led by one of the authors in 2009 ( B . P . ) to be down-regulated in GBM , target Notch family members , and cause differentiation in GBM stem-like cells [59] , [60] . Additional studies have shown that this miRNA has been involved in the Notch pathway in other cancers such as medulloblastoma [61] , pancreatic cancer [62] and carcinoma [63] . Moreover , miR-124 and miR-137 have functioned in a tumor-suppressive fashion in GBM and caused differentiation when re-expressed in GBM cells [24] . miR-9 has also been strongly linked to GBM subtypes in a recent analysis [25] . Interestingly , miR-124 has been reported to be involved in the Notch signaling pathway during Ciona intestinalis neuronal development [64] . The evidence from these studies suggests the effectiveness of our approach . Further experimental validation of these miRNAs is warranted . Among the six miRNAs , the most noteworthy one is miR-34a . It regulates a number of target proteins that are involved in cell cycle , apoptosis , differentiation and cellular development [65] . In the independent study mentioned above , led by one of the authors ( B . P . ) , the effects of miR-34a on MET , NOTCH1 , NOTCH2 , CDK6 , and PDGFRA expression in brain tumor cells and stem cells were tested . The results showed that miR-34a suppressed brain tumor growth by targeting MET and Notch [66] . To check if these results exist in our predicted regulatory network , we further extracted miR-34a FFLs and merged them to form a miR-34a-specific regulatory network ( Figure S9 ) . Among 15 miR-34a targets , 8 ( NOTCH2 , MET , PDGFRA , JAG1 , MYCN , BCL2 , DCX , and CACNA1E ) belonged to GMB-related genes and 7 ( FOSB , FOSL1 , NFE2L1 , NR4A2 , SMAD4 , TCF12 , and YY1 ) belonged to human TFs . Among the 8 GBM-related genes , NOTCH2 and MET have been reported in our previous study to be targeted by miR-34a , while PDGFRA was not [66] . JAG1 has been reported to be targeted by the miRNA in the regulation of human monocyte-derived dendritic cell differentiation [67] . MYCN has been reported to be targeted by miR-34a in neuroblastoma cells [68] , [69] and somatic cell reprogramming [70] . BCL2 has been reported to be targeted by the same miRNA in neuroblastoma cells [69] . All 7 targeted TFs were significantly involved in the transcription of DNA according to Biology Function Analysis in IPA ( Ingenuity Pathway Analysis ) ( Fisher's exact test , P-value = 8 . 75×10−9 ) as expected . Among them , YY1 has been reported to be directly targeted by miR-34a in neuroblastoma cells [71] . Taken together , miR-34a is likely not only regulates GBM-related genes directly but also regulates the TFs for gene expression through transcriptional mechanism . This assertion needs further experimental confirmation . While our analyses , especially of miR-34a and its targets , support the utility of our regulatory network framework , it still needs to be improved . Most GBM-related genes have not been confirmed to be causal , the human TF and miRNA binding profiles are neither complete nor error- or bias-free , and reverse engineering software has its own weaknesses . This work represents the first application of a 4-node FFL as a regulatory motif in complex disease . Although there have been several genome-wide studies applying integrative regulation of TFs and miRNAs [30] , [72] , [73] , none have considered gene coexpression profiles in an FFL model . The 4-node FFL model contains four components: one miRNA , one TF , and two co-expressed genes related to GBM ( Figure 2 ) . There are four types of possible regulations between the co-expressed genes and the TF and miRNA , making the regulatory network more informative and tolerant ( Figure S10 ) . Compared with 3-node FFLs , the main impact of 4-node FFLs is the recruitment of more GBM-related genes and regulatory relationships into the regulatory network ( Table 2 , Figure S3 , and Figure S4 ) . We found that 4-node FFLs tended to regulate the genes that might belong to the same biological processes , the same protein family , or be located in the same cellular components ( Figure S2 ) . Additionally , among the 20 GBM-related genes involved in the miR-34a-specific regulatory network , 3 were in the 3-node FFLs and 4-node FFLs , 11 from 4-node FFLs , and 6 from 3-node FFLs . This observation indicated that the recruitment of GBM-related genes in miR-34a network was greatly improved by applying the 4-node FFLs . In summary , our comparison of the 4-node and 3-node FFLs and the performance in the recruitment of GBM-related genes by the 3-node FFLs and 4-node FFLs to the miR-34a-specific regulatory network indicate that both are useful models , and they may complement each other in a regulatory network analysis . Another interesting observation in this study is composite-FFLs , in which TF and miRNA regulate each other . The regulation between a TF and a miRNA has been defined as a TF↔miRNA feedback loop [38] . In our study , we observed 40 TF↔miRNA feedback loops in 3-node FFLs and 24 TF↔miRNA feedback loops in 4-node FFLs . Among the two sets of feedback loops , there were 19 loops in common between two sets , resulting in 45 unique feedback loops in the whole regulatory network for GBM . Compared to the 759 unique TF-miRNA regulatory relationships and the 505 miRNA-TF regulatory relationships in the regulatory network , the TF↔miRNA regulatory relationships were rarely observed . This low frequency is consistent with previous reports involving a pure transcriptional regulatory network [42] . However , interestingly , these TF↔miRNA feedback loops regulate 101 GBM-related genes , accounting for 57 . 38% of the GBM-related genes ( 176 ) in the GBM miRNA-TF mediated regulatory network . This observation indicated that composite-FFLs are more effective in unveiling the regulatory systems underlying the complex disease .
To collect genes involved in the pathology of GBM , we compiled GBM-related genes from six sources , which included multiple types of variations with experimental evidence , such as point mutation , gene fusion , structure rearrangement , and copy number variation . These sources included the Catalogue Of Somatic Mutations In Cancer ( COSMIC , version 51 ) [74] , the Online Mendelian Inheritance in Man ( OMIM ) [75] , The Cancer Genome Atlas ( TCGA ) [6] , and the Genetic Association Database ( GAD ) [76] , as well as one recently published integrative genomic analysis of GBM [39] and two genome-wide association studies [4] , [5] ( Text S1 ) . We mapped these genes to Entrez gene symbols and ultimately obtained 415 unique genes . To collect a set of dysregulated miRNAs in GBM , we conducted a comprehensive literature search to identify studies that directly assess miRNA dysregulation in GBM patients' cell lines or tissues . We first searched the miR2Disease [77] , PhenomiR [78] and HMDD [79] databases for relevant articles using the keyword “glioblastoma” and PubMed using the keywords “glioblastoma AND microRNA . ” Then , we manually checked each title and abstract for relevance and reviewed the full text if the abstract indicated that the article reported associations between miRNA expression and GBM . As a result , we included 24 papers that directly assessed miRNA expression in GBM samples or cell lines . From these papers , we retrieved 134 miRNAs with up/down-regulated information , which were mapped to 124 unique mature miRNAs based on human miRNAs from miRBase [80] . Currently , several online databases that predict binding sites and target genes of individual miRNAs are available , such as PicTar [81] , TargetScan [35] , [82] , and miRanda [83] . Among them , TargetScan has demonstrated the best performance compared to other miRNA target prediction software [84] , [85] . Therefore , we extracted the miRNA-gene pairs between GBM-related miRNAs and GBM-related genes from the TargetScan server ( version 5 . 2 , February 2011 ) [35] . We required that miRNA-target interactions be evolutionarily conserved in four species ( human , mouse , rat and dog ) and have a total context score higher than −0 . 30 [86] . The score quantitatively measures the overall target efficacy [84] , [85] . To obtain the posttranscriptional repression of miRNAs on TFs , we first retrieved 428 TFs that have human genes as targets from the TRANSFAC Professional database ( release 2011 . 4 ) [34] and used the same procedure to obtain the relationships between miRNAs and TFs . To predict the regulatory relationship between TF and gene/miRNA , we first downloaded the defined promoter region ( −1500/+500 around TSS ) of 415 GBM-related genes or 134 GBM-related miRNAs from the UCSC Table Browser [87] . Then , we performed a binding sites search using the Match™ software that is integrated in TRANSFAC Professional ( release 2011 . 4 ) [36] . For the purpose of this study , we used pre-calculated cut-offs to minimize false positive matches ( minFP ) and create a high-quality matrix . To restrict the search , we required a core score of 1 . 00 , a matrix score of 0 . 95 , and TFs that only belong to the human genome . To further reduce false positive prediction , we required the predicted pairs to be conserved among humans , mice and rats [73] . Recently , Verhaak et al . [39] integrated the gene expression data from 200 GBM and two normal brain samples examined by three gene expression microarray platforms ( Affymetrix HuEx array , Affymetrix U133A array , and Agilent 244 K array ) into a single , unified data set of 11 , 861 genes using a factor analysis model . Then , they filtered the unified genes down to 1 , 740 genes with consistent but highly variable expression across the platforms using several filters to eliminate unreliably measured genes . We directly applied the resulting data to identify co-regulated genes . Among the 415 GBM-related genes we collected , 120 were included in the 1 , 740 genes . We estimated co-regulated relationships among these genes via the ARACNE software , which implemented the mutual information ( MI ) theory to identify transcriptional interactions between genes [40] . We used a high significance threshold for MI values with a P-value of 1 . 0×10−7 to sort out possible false positive and true negative data . To remove indirect regulatory relationship , we employed a data process inequality ( DPI ) tolerance of 0 . 15 according to the recommendation by Margolin et al . [88] . To identify TF and miRNA pairs that cooperatively regulate the same target genes , we calculated a P-value using a cumulative hypergeometric test [43] based on the common targets of any pair of miRNAs and TFs as in the following function:where is the number of genes targeted by a given miRNA , is the number of genes regulated by a given TF , and Total is the number of common genes between all human genes targeted by human miRNAs and all human genes regulated by all human TFs . We further used the false discovery rate ( FDR ) to adjust for multiple testing [89] , and only those pairs with a corrected P-value less than 0 . 05 were chosen as significant pairs of regulators . To quantify functional similarity , we calculated GO semantic similarity scores for the GO terms for each pair of the co-regulated genes using the R GOSemSim package [44] . For each of the three GO categories ( BP: biological process , MF: molecular function , and CC: cellular component ) , the semantic similarity scores were computed for all gene pairs in the 3-node and 4-node FFLs . A gene pair was compiled from any two genes targeted by the same miRNA-TF pairs . To evaluate the statistical significance of the functional similarity of co-targeted genes in FFLs , we randomly selected the same number of genes in 3-node or 4-node FFLs from the 20 , 441 Entrez protein-coding genes with GO annotations , and calculated their GO similarities . We repeated this process 1 , 000 times . We performed a Kolmogorov-Smirnov test ( KS-test ) to examine whether the GO similarity of all the gene pairs from the FFLs is significantly greater than that of randomly selected pairs . In this work , we constructed three major networks . The first network was the GBM-specific miRNA-TF mediated gene regulatory network , which was generated by converging all significant 3-node and 4-node FFLs . The second one was the GBM composite regulatory network generated by merging only those significant 3-node and 4-node composite-FFLs . The third one was the subnetwork for the Notch signaling pathway . We first collected the genes belonging to the Notch pathway from the KEGG and Ingenuity systems and merged those FFLs that included at least one Notch pathway gene to generate a Notch-specific regulatory network in GBM . Considering the complexity of regulatory networks and our goal of distilling critical elements , we simplified the network analysis by disregarding the direction of the edges . We computed nodes' degrees and their distributions in order to assess network characteristics . The degree of a node , the network's most elementary characteristic , is measured by the number of links of the node in the network . If the degree distribution of one network follows a power law , the network would have only a small portion of nodes with a large number of links ( i . e . , hubs ) [47] . To determine the hubs in our network , we applied the method proposed by Yu et al . [50] to draw a degree distribution for each node in the network . For local network analysis , we used the software CFinder ( version 2 . 0 . 5 ) [57] to generate tightly connected sub-networks from the pathway network , and we then visualized them using Cytoscape ( version 2 . 8 ) [90] . To identify pathways overrepresented in GBM-related genes from the GBM composite regulatory network , we performed a pathway enrichment analysis using the Core Analysis Tool in Ingenuity Pathway Analyses ( IPA ) from Ingenuity Systems [68] . Given a list of genes , a right-tailed Fisher's exact test was performed for the enrichment of these genes based on its hand-curated canonical pathway database . To control the error rate in the analysis results , IPA also provided a corrected P-value based on the Benjamini-Hochberg method [89] . GO and KEGG enrichment of the subnetworks was analyzed using WebGestalt [46] . | Several recent studies have implicated the critical role of microRNAs ( miRNAs ) in the pathogenesis of glioblastoma ( GBM ) , the most common and lethal brain tumor in humans , suggesting that miRNAs may be clinically useful as biomarkers for brain tumors and other cancers . However , to date , the regulatory mechanisms of miRNAs in GBM are unclear . In this study , we have systematically constructed miRNA and transcription factor ( TF ) mediated regulatory networks specific to GBM . To demonstrate that the GBM-specific regulatory network contains functional modules that may composite of critical miRNA components , we extracted a subnetwork including GBM-related genes involved in the Notch signaling pathway . Through network topological and functional analyses of the Notch signaling pathway subnetwork , several critical miRNAs have been identified , some of which have been reinforced by previous studies . This study not only provides novel miRNAs for further experimental design but also develops a novel computational framework to construct a miRNA-TF combinatory regulatory network for a specific disease . | [
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] | 2012 | Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma |
Rabies was known to humans as a disease thousands of years ago . In America , insectivorous bats are natural reservoirs of rabies virus . The bat species Tadarida brasiliensis and Lasiurus cinereus , with their respective , host-specific rabies virus variants AgV4 and AgV6 , are the principal rabies reservoirs in Chile . However , little is known about the roles of bat species in the ecology and geographic distribution of the virus . This contribution aims to address a series of questions regarding the ecology of rabies transmission in Chile . Analyzing records from 1985–2011 at the Instituto de Salud Pública de Chile ( ISP ) and using ecological niche modeling , we address these questions to help in understanding rabies-bat ecological dynamics in South America . We found ecological niche identity between both hosts and both viral variants , indicating that niches of all actors in the system are undifferentiated , although the viruses do not necessarily occupy the full geographic distributions of their hosts . Bat species and rabies viruses share similar niches , and our models had significant predictive power even across unsampled regions; results thus suggest that outbreaks may occur under consistent , stable , and predictable circumstances .
Rabies was known to humans as a disease as of about ∼4000 years ago [1] . Although important advances have been made in immunization and diagnosis , rabies is still considered a neglected disease [2] . Rabies is a zoonosis: indeed , although all mammals studied to date are susceptible to infection , major reservoirs that maintain and transmit the virus in the long term are limited to Carnivora and Chiroptera [2] . Rabies virus ( RABV ) is a neurotropic RNA virus ( family Rhabdoviridae , genus Lyssavirus ) , including at least 14 species [3] . In the Americas , with generally good control of rabid canines , bats are the main reservoirs of RABV [4] . Rabies transmission from non-hematophagous bats ( mainly insectivores ) to humans is considered an increasing risk in urban and economically developed areas of Latin America [5] , while dog rabies has decreased dramatically in frequency , now occurring only in specific areas of Latin America [6] , [7] . Viral “strains” are defined as virus populations maintained by a particular reservoir host in a defined geographic region that can be distinguished from other strains based on molecular and antigenic characteristics [8] . RABV lineages generally show specificity to particular bat hosts [9]–[11] . Antigenic typing depends on use of monoclonal antibodies; their power depends on numbers of monoclonal antibodies that bind consistently to antigenic sites that are conserved in a viral strain [8] , [12] . Antigenic characterization is used widely in rabies surveillance in Latin America [9] , showing differences among viruses in different host species and geographic locations [13] . Tadarida brasiliensis , an important reservoir of rabies in urban areas , maintains antigenic variant AgV9 in North America , but AgV4 in South America [14] . Lasiurus cinereus differs , carrying AgV6 across its entire geographic distribution [15] . Viral specificity to these two host species has been confirmed with molecular analyses [9] , [10] , [13] . These bat species presently constitute the principal rabies reservoirs in Chile [16] , [17] , but little is known about roles of different hosts in their ecology and distribution . T . brasiliensis inhabits sites with other species , roosting in colonies over long periods; owing to anthropogenic perturbation , this species is that which has seen greatest negative population effects in Chile [18] . In contrast , L . cinereus avoids urban areas , roost solitarily , and shows seasonal migrations [19] . Both species have broad geographic distributions across the Americas . Previous such geographic and environmental analyses of rabies lineages have focused on RABV in terrestrial mammal hosts in North America , and documented that rabies in raccoons ( Procyon lotor ) is associated with low wetlands coverage , low elevation , low-intensity residential land use , and absence of major roads , and that rivers act as natural barriers [20] , [21] . Several studies have explored features of host-virus relationships of bat-borne rabies , based on molecular genetic analyses [22]–[25] . However , in these key studies , inferences about geographic pattern were made based on points on an empty map , without reference to environmental drives . Hence , landscape- and niche-based approaches could offer a valuable complement to conclusions generated in molecular genetic studies , evaluating effects of environment and landscape on rabies host and virus distributions , but such methods must be explored and validated first . To test these approaches , we address a series of questions regarding rabies transmission ecology in Chile . ( i ) Do rabies lineages have coarse-grained ecological “signatures” ( i . e . , Grinnellian niches ) that can be characterized robustly ? ( ii ) Do macro-ecological and macro-geographic linkages exist among viruses and hosts ? Finally , ( iii ) do different bat-borne rabies lineages have distinct ecological signatures ? Answering these questions will help to illuminate details of virus-host dynamics in bat rabies transmission cycles in South America .
Delimitation of the geographic area of analysis is a crucial issue in generating robust niche models , with significant effects on model results [29] . The study area must be established a priori based on ( 1 ) the dispersal potential of the species involved , ( 2 ) the sampling available by which to characterize distributions , and ( 3 ) the objectives of the study [29] . We delimited our study area to the area between −28 . 0° and −43 . 5°s latitude in Chile , corresponding both to the enzootic area in recent decades [16] and to the area sampled by the Chilean Ministerio de Salud ( Ministry of Health; Fig . 1 ) . Another crucial aspect in niche model development is the set of environmental variables used to characterize the environmental space in which the species is distributed [30] . We used information on land-surface reflectance from remote sensing , in light of its high information content , fine spatial resolution , and minimal need for interpolation and inference [31] . Environmental variation can be summarized using multiple seasonal values of the Normalized Difference Vegetation Index ( NDVI ) , which has values correlating strongly with photosynthetic mass and primary productivity [31] , [32] . Numerous previous studies have shown the importance of such vegetation indices as indicators of ecological and geographic dimensions [31] , including in development of robust ecological niche models [33] , [34] . We used NDVI images available as monthly maximum raster data layers for 1992 , 1993 , and 1995 , which correspond to the middle years of the study period , at a spatial resolution of 0 . 01°×0 . 01°; to standardize these variables and reduce dimensionality , we generated principal components across all of the monthly data sets using ArcGIS 9 . 3 ( ESRI , Redlands , CA , USA ) . Principal components analysis used the original NDVI layers to generate 27 new , uncorrelated components: we used the first 10 components in model development ( i . e . , the initial 10 axes that best characterized the major dimensions of the cloud of points ) , as they explained 99 . 99% of overall variance . To characterize spatial patterns of bat-rabies occurrence across Chile , we only digitized bat surveillance data from the Instituto de Salud Pública de Chile ( ISP ) , for 1985–2011 , corresponding to the major enzootic period for bat rabies in Chile ( Fig . 1 ) . Host mammal occurrences were obtained from both active and passive surveillance programs , with hosts tested for rabies and identified at ISP . Coordinates of bat occurrences ( both species , regardless of rabies status ) were derived from geographic centroids of municipalities , as they were submitted by municipal agencies for testing . Further occurrences were obtained through data mediated by the Global Biodiversity Information Facility ( GBIF; see Acknowledgments for full list of institutions ) , with georeferencing derived from original data records . Virus occurrences were obtained in the form more precise georeferences derived from postal addresses of sites of origin of rabies-positive bats of both species , although the vast majority ( 78% ) came from Tadarida . These cases were diagnosed by ISP using direct inmunofluorescence ( IFD ) , to confirm virus presence , and monoclonal antibodies to identify virus variants [35] . To calibrate niche models , we used a maximum entropy algorithm , considering its predictive power and broad acceptance in the scientific community [36] . The algorithm uses the information theory concept of maximum entropy to optimize estimates of suitability across complex environmental spaces . The maximum entropy approach seeks to estimate the probability of suitability through finding the probability distribution closest to uniform , subject to certain restrictions; in our case , the restrictions are environmental conditions associated with known occurrences of the species in question [37] . In Chile active surveillance is initiated after a positive bat is reported from passive surveillance . ISP samples originated from passive surveillance [16] , [17] associated with human settlements , without anything close to uniform geographic coverage . We incorporated sampling bias across the study area in model calibration because spatial and environmental biases in data collection can cause biases in model results [38] . Maxent can use a sampling bias distribution ( σ in Phillips et al . , 2009 ) to establish areas from which to focus extraction of background data with which to calibrate models [38] . We thus developed a sampling bias surface for T . brasiliensis based on all of the passive surveillance data , using overall numbers of samples submitted to ISP per municipality ( municipalities with no samples set to no data , and thus excluded from background sampling ) , regardless of rabies-positive status , on the final raster , we added 1 to all pixels to avoid zero values , according to Maxent requirements . This surface appropriately characterized the sampling that underlies the virus-positive records that drove calibration of the niche models . We calibrated models with and without this bias file to assess the degree to which sampling effort affects results . We calibrated models using Maxent version 3 . 3 . 3 . k . Specific options were a bootstrap subsampling with 1000 replicates , random seed , and the median of replicates as output . We converted raw Maxent output to binary maps considering an error rate of E = 10% among occurrence points , and thus used the highest threshold that included 90% of training presence points [26] , a modification of the least training presence threshold idea [39] . The error rate ( E ) is the proportion of the occurrence data expected to place the species erroneously under inappropriate conditions , as a consequence of incorrect species identifications , errors in georeferencing , and errors in environmental data , among other factors , and is estimated via exploration and error-checking of the occurrence data [40] . We visualized ecological niche models in environmental spaces based on plots of NDVI values in winter and summer from across the study area , comparing this environmental ‘background’ with corresponding values associated with known occurrences of bat species and rabies variant . Niche models must be evaluated to validate their predictive power , before any use or interpretation [26] . We evaluated the predictive ability of models for T . brasiliensis; however , sample sizes for L . cinereus were too small and too clumped spatially to permit detailed evaluations . Two different spatial subsetting schemes were explored , taking advantage of the roughly linear shape of Chile . First , we subset data latitudinally by quintiles of frequency , dividing occurrences into five subsets , and using subsets 1 , 3 , and 5 for model calibration and subsets 2 and 4 for evaluation [26] . Second , we divided the study area into five equal-width latitudinal bands , again using subsets 1 , 3 , and 5 for model calibration and 2 and 4 for evaluation . In the first scheme , subsets had equal sample sizes , whereas in the second scheme , subsets had similar areal dimensions ( Fig . S1 for supporting information ) . For evaluating models , we avoided traditional receiver operating characteristic ( ROC ) area under the curve ( AUC ) approaches , considering that AUC tests require presence and absence data for proper implementation [41] , and in light of recent critiques [40] , [41] . Rather , models were first evaluated using areas and points predicted as suitable and unsuitable after thresholding ( based on E = 10% ) using a cumulative binomial probability distribution [26] . Second , models ( without thresholding ) were evaluated using partial ROC approaches [42] , [43] , evaluating the predictive ability of niche models considering only omission errors and proportional areas predicted as suitable , and only over a range of omission errors deemed acceptable in light of error characteristics of the input data ( here again we used E = 10% , and thus allowed up to 10% omission in our partial ROC calculations ) . In partial ROC , the area under the observed line of model performance is related to the area under the line of random expectations , and a ratio is calculated . Bootstrap manipulations ( 1000 total ) , in which 50% of evaluation data are resampled with replacement and AUC ratios recalculated , are used to test the hypothesis that model performance is better than random expectations . When ≥95% of bootstrap-replicate AUC ratios were >1 , we rejected the null hypothesis of performance no better than random expectations [42] . Partial ROC software is available for free download in http://kuscholarworks . ku . edu/dspace/handle/1808/10059 Finally , to compare niche models between virus strains and bat species , we used niche identity tests to determine whether two niche models are indistinguishable from one other [44] . Identity tests have the advantage of restricting comparisons to the same set of points , a feature that is particularly relevant for our occurrence data , which did not come randomly from across the entire landscape . We calculated observed Hellinger's modified ( I ) and Schoener's ( D ) distances between niche models ( thresholded using minimum training presence approaches ) , and compared them to a null distribution of comparable distances derived from 1000 replicate random subdivisions of the overall pool of occurrence data between the two species , maintaining observed sample sizes . We used ENMTools ( version 1 . 3; http://enmtools . com ) for these comparisons [45] . We evaluated whether niche characteristics were identical between rabies lineages ( AgV6 versus AgV4 ) , between the host species and associated viruses , and between the two host species . In all comparisons , our critical value was the 5th percentile of similarity ( i . e . , low end ) , as we were seeking evidence of niche differentiation [45] .
In all , 26 , 323 bat samples from active and passive surveillance were submitted to ISP during 1985–2011 , a data set that was captured digitally as part of this study . However , many records corresponded to the same county centroids , such that sample sizes were nowhere near the number of samples: in all , to model hosts , we found 70 unique occurrences for L . cinereus ( 9% from GBIF; 91% from ISP ) and 238 for T . brasiliensis ( 3% from GBIF and 97% from ISP ) . For rabies samples , we obtained 910 unique coordinates for rabies AgV4 ( bat rabies-positive associated with T . brasiliensis ) and 52 for rabies AgV6 ( associated with Lasiurus spp . ; Fig . 1 ) ; sample sizes are larger in this case because georeferencing was to street addresses , rather than just to county centroid . Sampling intensity for T . brasiliensis varied 0–1178 samples submitted per municipality ( Fig . 2 ) , while that for L . cinereus varied 0–164; with only 64 of the 301 counties in the study area submitting L . cinereus samples . Niche models , whether considering sampling bias or not , all performed significantly better than random expectations , with partial ROC AUC ratios associated with our niche models were >1 ( Fig . 3 ) . However , considering that models controlling for sampling bias generated predictions with smaller suitable areas , we prefer to use these models in further steps . For example , quintile subsetting considering sampling bias had less area predicted ( 35 . 2% of the study area ) than comparable models without considering sampling bias ( 38 . 0% of the study area ) . Bias control also resulted in lower variance in AUC ratios in the partial ROC analyses ( Fig . 3 ) . With this general confirmation of predictive power , we proceeded to build ecological niche models for each species ( Fig . 4 ) for interpretation . None of the six identity tests comparing niches between the two host species , between each host species and its associated virus linage , and between the two virus lineages , was able to reject the null hypothesis of niche “identity” ( Table 1 ) . Figure 5 shows the latter comparison graphically: observed similarity fell well above the critical value in all comparisons . In sum , at least across central Chile , the two bat species and their associated viruses share very similar ecological niches , at least in the coarse-grained environmental dimensions explored in this study . The two bat species had broad distributions in environmental space ( Fig . 6 ) . Rabies infections were found across the great bulk of the environmental distribution of each of the hosts . However , both hosts appear to avoid areas presenting extremely low NDVI values in summer and winter , corresponding to the high Andes regions .
In Chile , rabies has been reported as far back as 1879 [46] . All data have been centralized in the Sección de Rabia , Instituto de Salud Pública , since 1929 [17] . Via effective monitoring , mass dog vaccination , elimination of biting stray dogs , improvement of diagnosis quality , and post-exposure vaccination in humans , urban canine rabies was eradicated as of about 1990 [47] , [48] . However , over the same period , the zoonotic cycle , wherein the main reservoirs are bats , has been increasing in importance [16] . Hence , in Chile , reports suggest rabies in a process of re-emergence in the wildlife cycle [16] , [17] , [49] . Our large-scale data set , broad latitudinal gradient , and dramatic diversity of landscapes and biomes across the study area allowed a robust test and validation in the use of niche modeling in understanding the spatial epidemiology of bat-related rabies , as required when modeling diseases [50] . Answering our first question , it was possible to characterize ecological niches of rabies viruses and their hosts consistently and with good predictive power . In the broadest sense , niche models for the two bat species confirmed the obvious: the high Andes Mountains in the east and the Pacific Ocean in the west are natural barriers [18] , while the Atacama Desert to the north and cold regions in the south delimitated our study region naturally [29] . With this definition of relevant areas , we derived clear predictions of the geographic distribution of both bat species ( Fig . 4 ) , wherein T . brasiliensis may be somewhat more limited in its use of cold and high zones in the Andes and the northern deserts than L . cinereus ( Fig . 4 ) . The broad suitable areas for both species corroborate the ecological plasticity known in bats [51] and migratory behavior reported in the northern hemisphere for both T . brasiliensis and L . cinereus . Niche models provided a first view of rabies distributions in geographic and environmental spaces [27] . Our ecological niche models for rabies lineages using fine-resolution satellite imagery identified putative potential areas of rabies distribution , albeit under stable characterizations of environments averaged across several years of conditions; clearly , more dynamic characterizations of rabies distributions merit future evaluation . Although we assembled large data sets that are reasonably comprehensive for Chile , we hasten to point out potential gaps and failings in our data and analysis . A first such caution is that of the uneven spatial and environmental distribution of rabies in Chile: although samples were submitted from across the county , rabies locations were mainly from passive surveillance , producing three clusters of rabies cases in the main cities of central Chile ( Santiago , Valparaiso , Concepción; Fig . 1 ) , biases that we took into account in our analyses . Using the bias file helped to reduce variance in model performance , allowing clearer discrimination of performance between models ( Fig . 3 ) . We used sampling bias summaries for T . brasiliensis to consider the availability , quantity , and quality of data available for this species; for Lasiurus , parallel data were not available in sufficient quantity , reflecting the relative rarity of sample submissions for that species . Incorporating information on sampling intensity in niche modeling for public health applications is an issue that merits further exploration , particularly considering that the more biased the data are , the more benefit that derives from use of sampling bias surfaces . Our improvements in model performance with bias surfaces were analogous to previous results in biodiversity studies [38] . As result , our models provide at least a preliminary assessment of risk in several areas that currently represent gaps in surveillance [52] . Ecological niche models have seen detailed performance testing in challenges centered on estimating niches and predicting species' distributions , showing impressive success even in spite of spatial sampling biases ( e . g . , sampling along roads ) [53] , [54] . Problems arise when sampling is biased with respect to environments , however , since models based on such sampling will be effectively blinded to potential for occurrence in unsampled environments [53] , [55] . An additional source of potential problems is the precision of georeferencing that was possible for these data , considering that reports of disease occurrence may simply provide the patient's address , but not necessarily the site of infection , which is more relevant in spatial epidemiology [56] . In this study , such problems introduce a basement level of spatial accuracy in model predictions , such that finest-resolution phenomena may not be “visible” in results . In relation to our second question , it is important to note that , although viruses and hosts share ecological niche characteristics , the virus does not necessarily occupy the full host distribution ( Fig . 4 ) ; the geographic bias , however , at least within our study area , appears to be without consistent environmental correlates . Our methodology corroborates the rabies-bat relationship that has heretofore gone untested at landscape scales , and our results suggest that niche modeling offers a useful tool for mapping disease occurrences and potential for occurrence in public health [27] . With respect to our third question , niche identity tests between hosts and viral variants indicated that niches of all actors in the Chilean bat-rabies system are similar in environmental requirements; that is , we were unable to reject the null hypothesis that niche models of host species are not different from niches of associated virus strains , and indeed that the two host species and the two virus strains do not differ from one another either . Currently , little is known about the ecology and transmission of rabies virus among bats , but phylogenetic evidence gives strong indications of host specificity [9] , [13] . In this sense , not only do rabies virus variants appear to track the ecology of their respective hosts , but also the pairs of viruses and hosts do not differ from one another . A recent report offers some corroboration of this assumption via molecular analysis: a rabies strain specific to Lasiurus spp . bats was found in T . brasiliensis in Chile [13] , which indicates cross-species spillover transmission of virus lineages in taxonomically distant bat species under natural conditions . These results support the idea that rabies viruses may infect hosts without environmental bias ( see [44] , for parallel results ) . Restating , the bat species and rabies lineages evaluated appear to share very similar portions of environmental space , even if this result is not manifested as complete overlap in geographic space ( Fig . 4 ) , perhaps because different geographic distributions do not necessarily reflect niche differences [28] . This result allows a view into how rabies host ecology influences virus biology , and suggests that taxonomic differences in hosts or viruses do not necessarily translate into ecological differences . Our results and those of similar studies [51] , [57] may help to clarify the ecology of bat rabies lineages in other hosts and geographic regions . Potential distribution maps of hosts and their viruses can be an important tool by which to understand potential transmission areas for rabies , although these approaches remain little explored [51] . Bat-borne rabies has seen some events of cross-species transmission in zoonotic cycles in Chile , with AgV 4 ( related to T . brasiliensis ) found in Lasiurus spp . and AgV 6 ( related to Lasiurus spp . ) found in T . brasiliensis [10] , [13] . Accidental hosts have also been reported in recent years: for instance , mortality of dogs , cats , farm animals , and a human caused by rabies related to T . brasiliensis [10] , [13] . Via this scenario , control of stray dogs and feral cats as well as vaccination campaigns must be implemented with priority in those areas where host and virus distribution match ( Fig . 4 ) . In conclusion , one should take care to avoid the logical , scale-related error that can be termed the “Beale fallacy . ” Beale et al . [58] , analyzed distributions of European birds with respect to climate , and concluded that their distributions were not limited by climate . While this conclusion was , to some degree true , it was completely dependent on the particular context of Western Europe and relatively broadly-distributed bird species; a parallel analysis in a different context found abundant climatic determination of ranges [59] . In this sense , our conclusion about no niche difference among our bat species and rabies lineages must be considered as context-dependent [59]: analyses over broader regions may well detect clear and significant differences . Our results show two viral lineages as sharing similar environmental signatures with two bat host species , regardless of antigenic characteristics , known associations , and phylogenetic position . Recent years have seen important advances in molecular dimensions of studies of rabies , but few have explored how regional landscapes affect ( or not ) distributions and dynamics of rabies in zoonotic cycles [20] , [21] . In light of the results reported herein , the spatial epidemiology and ecology of zoonotic bat rabies should see further exploration . | The situation of rabies in America has been changing: rabies in dogs has decreased considerably , but bats are increasingly documented as natural reservoirs of other rabies variants . A significant gap exists in understanding of bat-borne rabies in Latin America . We identified bat species known to be connected with enzootic rabies with different antigenic variants in Chile , and compiled large-scale data sets by which to test for ecological niche differences among virus lineages and bat hosts . Our results begin to characterize important ecological factors affecting rabies distribution; modeling rabies in Chile allows comparisons across different latitudes and diverse landscapes . We found that rabies virus strains are found in similar environments , regardless of the bat host involved . This research improves understanding of bat-borne rabies dynamics , and important step towards preventing and controlling this and other emergent diseases linked to bats . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Ecology and Geography of Transmission of Two Bat-Borne Rabies Lineages in Chile |
The capacity of infected cells to undergo apoptosis upon insult with a pathogen is an ancient innate immune defense mechanism . Consequently , the ability of persisting , intracellular pathogens such as the human pathogen Mycobacterium tuberculosis ( Mtb ) to inhibit infection-induced apoptosis of macrophages is important for virulence . The nuoG gene of Mtb , which encodes the NuoG subunit of the type I NADH dehydrogenase , NDH-1 , is important in Mtb-mediated inhibition of host macrophage apoptosis , but the molecular mechanism of this host pathogen interaction remains elusive . Here we show that the apoptogenic phenotype of MtbΔnuoG was significantly reduced in human macrophages treated with caspase-3 and -8 inhibitors , TNF-α-neutralizing antibodies , and also after infection of murine TNF−/− macrophages . Interestingly , incubation of macrophages with inhibitors of reactive oxygen species ( ROS ) reduced not only the apoptosis induced by the nuoG mutant , but also its capacity to increase macrophage TNF-α secretion . The MtbΔnuoG phagosomes showed increased ROS levels compared to Mtb phagosomes in primary murine and human alveolar macrophages . The increase in MtbΔnuoG induced ROS and apoptosis was abolished in NOX-2 deficient ( gp91−/− ) macrophages . These results suggest that Mtb , via a NuoG-dependent mechanism , can neutralize NOX2-derived ROS in order to inhibit TNF-α-mediated host cell apoptosis . Consistently , an Mtb mutant deficient in secreted catalase induced increases in phagosomal ROS and host cell apoptosis , both of which were dependent upon macrophage NOX-2 activity . In conclusion , these results serendipitously reveal a novel connection between NOX2 activity , phagosomal ROS , and TNF-α signaling during infection-induced apoptosis in macrophages . Furthermore , our study reveals a novel function of NOX2 activity in innate immunity beyond the initial respiratory burst , which is the sensing of persistent intracellular pathogens and subsequent induction of host cell apoptosis as a second line of defense .
The phagocytic NADPH-oxidase ( NOX2-complex or phox ) resides on phagosomes and has been shown to be involved in microcidal activity in phagocytes . NOX2 is the original member of the NOX family of reactive oxygen species ( ROS ) -generating NADPH oxidases , which now includes NOX1-NOX5 , DUOX1 and DUOX2 [1] , [2] . The multicomponent NOX2 complex consists of two transmembrane proteins , gp91phox and gp22 phox , and three cytosolic components , p40 phox , p47 phox and p67 phox [1] , [2] . Additionally , the cytosolic GTPase Rac has to be recruited in order to form a fully active NOX2 complex [1] . The gp91phox subunit , which is constitutively associated with gp22 phox , is a transmembrane redox chain that generates phagosomal superoxide by transferring electrons from cytosolic NADPH to phagosomal oxygen [1] . NOX2-generated superoxide can then be converted into a multitude of microcidal oxidants , including hydrogen peroxide and hypochlorous acid , which are important components of the bactericidal activity of the macrophage phagosome [3] . However , NOX2 activity seems to serve a different function in phagosomes of dendritic cells , where it is important for efficient crosspresentation of antigens [4] . The significance of the NOX2-complex for innate immune response is illustrated by the development of chronic granulomatous disease ( CGD ) in human subjects that have genetic defects in components of the complex . CGD is characterized by greatly increased susceptibility to fungal and bacterial infections [5] . Correspondingly , mice deficient in the NOX2 subunits are much more susceptible to infections with bacterial pathogens such as Salmonella typhimurium for example [3] , [5] . Not surprisingly , some pathogens have developed strategies to counter the NOX2 response by either inhibiting NOX2 assembly on the phagosome , as is the case for S . typhimurium [3] and Helicobacter pylori [6] , or reducing steady-state levels of NOX2 components as illustrated by Anaplasma phagocytophila [7] or Ehrlichia chaffeensis [8] ( for review [9] ) . Programmed cell death ( PCD ) , or apoptosis , plays an important role in the innate immune response ( IR ) against pathogens , a defense strategy that is evolutionarily conserved and extends even into the plant world[10] . Inhibition of host cell apoptosis has been extensively studied and there are numerous examples of viral proteins directly interfering with host cell apoptosis signaling[11] . Furthermore , an increasing number of protozoal pathogens have been shown to manipulate PCD signaling of infected host cells[12] . Finally , prokaryotic pathogens such as Chlamydia , Legionella , Rickettsia , and Mycobacterium among others have the capacity to inhibit host cell apoptosis signaling [13] , [14] . Mycobacterium tuberculosis ( Mtb ) is an extremely successful human pathogen that manipulates host cells via multiple pathways in order to achieve survival[15] , [16] , [17] . The inhibition of host cell apoptosis by Mtb has been implicated as a potential virulence mechanism[18] . Indeed , an inverse correlation between the virulence of a mycobacterial species and their capacity to induce apoptosis of primary human alveolar macrophages was demonstrated[19] . Cells infected with virulent Mtb have also been shown to be more resistant to various apoptosis stimuli when compared to uninfected or avirulent strains of Mtb[18] . For example , Mtb-infected macrophages secrete soluble TNF-α-receptor in order to inhibit TNF-α-mediated host cell apoptosis induction [20] . Mtb-infection reduces the cell surface expression of Fas receptors , resulting in the resistance of the host cells to Fas-ligand induce cell death[21] . Infection with Mtb also induces the upregulation of the anti-apoptosis gene mcl-1 , which confers resistance of cells to apoptosis induction via the host cell mitochondria[22] . Finally , it has recently been shown that Mtb can manipulate the surface of infected macrophages in order to favor a necrotic , rather than apoptotic , cell death outcome[23] . In macrophages infected with virulent MtbH37Rv , but not avirulent MtbH37Ra , the amino-terminal domain of annexin-1 is removed by proteolysis , preventing completion of the apoptotic envelop [24] . Similarly , cells infected with Mtb are less likely to induce host cell membrane repair , which is important for the induction of apoptosis and supports the induction of necrotic cells death and the subsequent dissemination of bacteria[24] , [25] . While there is substantial evidence supporting the ability of Mtb to inhibit host cell apoptosis , a causal link between apoptosis inhibition and virulence of Mtb had not been established due to the lack of defined pro-apoptosis mutants . We have recently performed a “gain-of-function” genetic screen and identified three independent regions in the genome of Mtb that contain anti-apoptosis genes[26] . The deletion of one of the identified genes , the nuoG gene of Mtb , which encodes one of the 14 subunits of the type I NADH dehydrogenase ( NDH-1 ) , increased infection-induced apoptosis of macrophages and significantly reduced bacterial virulence in mice . These findings support a direct causal relationship between virulence of pathogenic mycobacteria and their ability to inhibit macrophage apoptosis[26] . Our findings are consistent with the identification of another anti-apoptosis gene ( superoxide dismutase A ) that plays an important role in the virulence of Mtb[27] . Finally , a third gene ( Protein kinase E ) with anti-apoptosis capacity has recently been described , but the impact of the deletion of this gene on bacterial virulence has not been established[28] . Altogether , the identification of multiple anti-apoptosis genes suggests that Mtb utilizes several strategies to inhibit the apoptotic response of the host cell; however the molecular mechanisms of these interactions have not been investigated . The present study describes the investigation of the molecular mechanisms by which NuoG of Mtb inhibits host cell apoptosis . The use of TNF-α-neutralizing antibodies and specific caspase inhibitors on human macrophage cell lines , as well as the infection of bone-marrow derived macrophages ( BMDM ) of wild-type and TNF−/− mice demonstrated that NuoG is involved in inhibiting an extrinsic , TNF-α-dependent , apoptosis pathway . Furthermore , the pro-apoptotic phenotype of the nuoG mutant was abolished in the presence of both ROS scavengers and in the absence of a functional NOX2 system as demonstrated in BMDM and primary human alveolar macrophages . Altogether , our results reveal a novel function of the NOX2 system in helping the host macrophage in sensing persistent intracellular mycobacteria via increased phagosomal ROS levels and the subsequent induction of host cell apoptosis . This may constitute a second line of defense of the macrophage and it is intriguing to speculate that this NOX2 mediated apoptosis induction is equally important in the defense against other intracellular pathogens .
We previously demonstrated that a ΔnuoG mutant of Mtb induced more apoptosis in host cells than wild type bacteria [26] . In order to analyze the mechanism of the NuoG/NDH-1 mediated host cell apoptosis inhibition , we first determined the involvement of caspases in the pro-apoptotic phenotype of MtbΔnuoG using specific caspase inhibitors . PMA-differentiated THP-1 cells were pre-treated with Caspase-3 inhibitor ( C3I ) or a chemical analog with no inhibitor activity ( C3I-A ) at 20 µM for 3 h before infection , during infection , and after infection . Cells were either left uninfected , or were infected with Mtb or MtbΔnuoG . After five days cells were harvested and stained for genomic DNA degradation using the fluorescent Terminal deoxynucleotidyl transferase dUTP Nick End Labeling ( TUNEL ) assay . The percentage of TUNEL positive cells was determined by flow cytometry analysis . This analysis revealed that the uninfected population contained low percentage of apoptotic cells ( 2 . 3±0 . 3% ) , Mtb infection slightly increased this amount to 11±0 . 6% . As expected from previously published results [26] , cells infected with the nuoG mutant showed a very significant increase in apoptosis ( 67 . 5±7 . 0% ) . Interestingly , treatment of THP-1 cells with the C3I reduced the percentage of ΔnuoG induced apoptosis to 8 . 3±1 . 2% , whereas the C3I-A had no significant effect on apoptosis induction ( 65 . 3±9 . 7% ) ( Figure 1A ) . The C3I did not have an effect on the low level of Mtb-induced apoptosis , as 10 . 0±1 . 0% of C3I-treated Mtb-infected cells were TUNEL positive , suggesting that Mtb may be inducing low levels of apoptosis via a caspase independent mechanism . In order to determine if the nuoG mutant induces apoptosis via the extrinsic ( i . e . , death receptor mediated ) , or the intrinsic ( i . e . , mitochondrial ) pathway [29] , cells were treated with Caspase-8 and Caspase-9 inhibitors , respectively . The experimental conditions and analysis were identical to the previous experiment with the exception that cells were harvested 3 days post infection . Analysis of TUNEL staining after this shorter time period resulted in similar rates of apoptosis for Mtb infected and uninfected cells ( 2 . 1±0 . 1% and 1 . 1±0 . 1% , respectively ) . Treatment of these populations with either C8I or C9I had no effect . The nuoG mutant induced apoptosis in about 34 . 7±0 . 7% of cells , which was not significantly affected by the addition of C9I ( 33 . 2±0 . 6 ) , but was significantly reduced by the addition of C8I to levels similar to uninfected cells ( 1 . 2±0 . 2% ) ( Figure 1B ) . These results indicated that the nuoG mutant induced host macrophage apoptosis via an extrinsic , caspase-8 dependent signaling pathway . TNF-α is of major importance for a successful host defense against mycobacterial infections , and has also been implicated in the apoptosis response to mycobacterial infection by the macrophage [30] , [31] . Since TNF-α receptor signaling can result in cellular apoptosis , we tested whether autocrine TNF-α production and signaling were involved in apoptosis of MtbΔnuoG infected THP-1 cells . We first determined if infection with MtbΔnuoG resulted in an increase of secreted TNF-α . Supernatants from infected THP-1 ( Figure 2A ) and BMDM cells ( Figure 2B ) were collected 3 days post infection and levels of TNF-α were measured by ELISA . In both systems , MtbΔnuoG infected cells secreted significantly more TNF-α than those infected with wild type ( 30 pg/ml to 2 . 1 ng/ml for Mtb and MtbΔnuoG in THP-1 cells; 0 . 2 ng/ml to 1 ng/ml for Mtb and MtbΔnuoG in murine cells ) . Having established the presence of higher levels of TNF-α , we next evaluated the effect of TNF-α signaling on the pro-apoptotic phenotype of MtbΔnuoG . This was first addressed by addition of human TNF-α-specific , neutralizing antibodies ( 5 µg/ml ) to the culture media of THP-1 cells during and after infection . The anti-TNF-α-antibody significantly inhibited macrophage apoptosis induced by MtbΔnuoG infection , as the percentage of apoptotic cells was reduced from 62 . 3±9 . 6% to 7 . 2±1 . 96% after addition of antibody ( Figure 2C ) . PCD in uninfected or Mtb infected cells was not significantly affected by the addition of antibodies ( Figure 2C ) . The involvement of TNF-α in the pro-apoptotic phenotype of the nuoG mutant was further analyzed by utilizing BMDM from TNF-α−/− mice . The nuoG mutant induced apoptosis in 28±2 . 3% of wild type C57B/6 ( B6 ) cells , as compared to 5 . 8±1 . 8% of Mtb infected cells ( Figure 2D ) . In contrast , the pro-apoptotic phenotype of the nuoG mutant was partially complemented in TNF−/− BMDM , resulting in levels of apoptosis of 13 . 3±1 . 9% , where as Mtb infected cells were not significantly different at 4 . 3±1 . 6% . Overall , these experiments confirmed the involvement of TNF-α in the pro-apoptosis phenotype of the nuoG mutant . Reactive oxygen species ( ROS ) are involved in shifting the balance of TNF-α-R1 mediated signaling from anti-apoptotic to pro-apoptotic [32] , [33] . We investigated the role of ROS in MtbΔnuoG induced apoptosis by utilizing a general ROS scavenger ( the antioxidant glutathione ) and an oxidase inhibitor ( diphenylene iodonium or DPI ) during infections of THP-1 cells [1] . THP-1 cells were incubated with 15 mM glutathione or 10 µM DPI 3 hours prior to and throughout infection with Mtb and MtbΔnuoG . Untreated cells infected with the nuoG mutant induced apoptosis in about 40 . 95±3 . 8% in the population , as compared to 1 . 3±0 . 4% in uninfected , and 3 . 1±0 . 2% in Mtb infected cells ( Figure 3A ) . The presence of DPI and glutathione reduced apoptosis induced by the mutant to 6 . 6±0 . 4% and 3 . 3±0 . 2% of cells , respectively ( Figure 3A ) . Thus , both of these agents greatly suppressed apoptosis induced by MtbΔnuoG in THP-1 cells , a finding consistent with a strong dependence of the apoptotic death response on ROS accumulation ( Figure 3A ) [33] . These inhibitors can also potentially affect cellular NO levels but we determined , using the Griess assay , that THP-1 cells produce no significant increase in NO after infection with the bacteria ( Figure S2 ) . Increased ROS levels in the cytosol can also lead to increased gene transcription of an array of genes involved in oxidative stress and immunity , including TNF-α [32] . For that reason , the TNF-α levels in the supernatant of infected THP-1 cells were analyzed after 3 and 5 days by ELISA . Insignificant amounts of TNF-α were detected in supernatants of uninfected cells , and only low concentrations of TNF-α ( below 50 pg/ml ) were detected in supernatants of cells infected with Mtb or the complemented nuoG mutants strains ( Figure 3B ) . In contrast , the nuoG mutant increased secretion of TNF-α by a factor of 10 to 0 . 5–0 . 6 ng/ml . This increase was partially reduced to about 0 . 1–0 . 2 ng/ml by treatment of the cells with DPI , and almost completely reduced by the addition of glutathione ( 0 . 02–0 . 03 pg/ml ) ( Figure 3B ) . Thus , the increase of intracellular ROS induced by infection of cells with the nuoG mutant is required for the increase in TNF-α secretion by infected cells . Next , we addressed the question of the subcellular origin of ROS during MtbΔnuoG infection . The mitochondrial respiratory chain complex I is an important generator of cellular ROS that is shared by all cells types and might be at the origin of mitochondrial-induced host cell apoptosis . However , the NADPH oxidases are also potent inducers of cellular and extracellular ROS . In macrophages , the phagocyte NADPH oxidase , NOX2 , is recruited to phagosomes and generates the production of superoxide in the lumen of the phagosome . These superoxides and their derivates are thought to be important for the killing of ingested bacteria , although their role in pathogenesis is not completely understood . In order to address the importance of NOX2 in the pro-apoptotic phenotype of the nuoG mutant , we utilized BMDM derived from mice deficient in NOX2 activity due to the deletion of the major transmembrane subunit of the NOX2 complex , gp91phox ( gp91−/− ) . The nuoG mutant induced significantly more apoptosis than Mtb in macrophages of wild type C57Bl/6 mice , 28 . 6±3 . 4% versus 8 . 8±1 . 6% , respectively ( Figure 3C ) . Importantly , this increase was abolished when gp91−/− BMDM were used as host cells , since only 5 . 7±1 . 1% of MtbΔnuoG infected cells were apoptotic compared to 4 . 1±1 . 1% of Mtb-infected cells . Therefore , the presence of functional NOX2 is required for the pro-apoptotic phenotype of the nuoG mutant of Mtb . Interestingly , the absence of NOX2 in infected primary macrophages did not result in the reduction of TNF-α secretion as nuoG infected cells secreted more TNF-α ( day 3: 1 . 7±0 . 3 ng/ml; day 5: 1 . 1±0 . 2 ng/ml ) than those infected with Mtb ( day 3: 0 . 5±0 . 2 ng/ml; day 5: 0 . 2±0 . 1 ng/ml ) . If the ROS responsible for the pro-apoptotic phenotype of the nuoG mutant originate from NOX2 , then macrophages infected with MtbΔnuoG should have higher intracellular levels of ROS than those infected with Mtb . In order to address this hypothesis , two dyes for detection of ROS were used: DCFDA , which is more sensitive to H2O2 , and DHE , which is more sensitive to O2- . Macrophages were infected and after 24 h the amount of ROS was detected in uninfected , Mtb and MtbΔnuoG infected cells using flow cytometry analysis . Mtb infection induced only slightly elevated levels ROS as detected either by DCFDA or DHE since the histogram overlays closely with that of uninfected cells ( Figure 4A–4C ) . Conversely , both dyes detected a significant increase in ROS levels after infection of wild type cells with the nuoG mutant as depicted by the positive shift in fluorescence ( Figure 4A–4C ) . The pro-apoptotic phenotype of the nuoG mutant was also observed under these conditions ( Figure S1A ) . Importantly , this increase in ROS staining was abolished in gp91−/− BMDM , thus clearly indicating that ROS are being generated by the NOX2 complex ( Figure 4A–4C ) . In order to directly observe ROS localization on a subcellular level , macrophages were infected with DiI-labeled mycobacteria ( Figure 4D ) , stained with DCFDA , fixed , and analyzed by fluorescence microscopy . Only in the MtbΔnuoG infected macrophages were phagosomes stained with the ROS sensor DCFDA , whereas phagosomes of Mtb-infected macrophages remained DCFDA negative ( Figure 4E ) . This data also revealed that the DiI fluorescence is quenched in the presence of ROS and thus the bacterial staining is lost during infection with MtbΔnuoG , but not during infection with Mtb ( Figure 4E ) . Other dyes were used for external labeling of bacteria with similar results ( Data not shown ) . These results not only confirmed the flow cytometry analysis in which an increase of ROS signal was detected only after infection of macrophages with the MtbΔnuoG mutant ( Figure 4A ) , but furthermore localized this increase of ROS to the host cell phagosome ( Figure 4E ) . Nitric oxide ( NO ) can also oxidize DCFDA to induce fluorescence[34] . However , BMDMS infected with ΔnuoG did not produce significantly more NO ( 0 . 97±0 . 3 µM ) than those infected with wild type Mtb ( 0 . 82±0 . 09 µM ) and both values were only very marginally elevated compared to uninfected cells ( 0 . 45±0 . 04 µM ) . In contrast , IFNγ-activated macrophages infected with non-pathogenic Mycobacterium smegmatis induced very significant increases in NO levels ( 6 . 77±0 . 41 µM at MOI 3 and 13 . 25±0 . 30 µM at MOI 10 ) ( Figure 4F ) . The overall NO production in the human THP-1 cells was low , even after IFNγ activation ( Figure S2 ) . Thus , the visualized increase of DCFDA fluorescence is likely due to oxidation by ROS . In order to analyze if the ROS-dependent mechanism of apoptosis induction upon infection with the ΔnuoG is conserved in human cells , primary alveolar macrophages were used as host cells . Due to the source of the cells , only a limited number of cells were available , and therefore the apoptosis assay was adapted to be performed on slides which were analyzed by fluorescence microscopy . For each donor triplicate wells were infected with Mtb , MtbΔnuoG , or were left uninfected . Cells were stained with TUNEL assay 3 days post infection ( Figure 5A ) . Approximately 500 cells were counted on each slide in blinded fashion and the number of TUNEL positive cells was recorded ( Figure 5B ) . Approximately 7 . 9±2 . 2% of uninfected macrophages were apoptotic , a percentage which was not significantly different from that of Mtb infected cells ( 8 . 5±1 . 7% ) . In contrast , there was roughly a 3fold increase in the percentage of apoptotic macrophages infected with MtbΔnuoG ( 26 . 9±3 . 3% ) . These results were pooled from five different donors , indicating that the phenotype of NuoG-mediated apoptosis inhibition is consistently conserved among different human subjects . The dependence of this pro-apoptotic phenotype on the generation of intracellular ROS was analyzed in two different donors using the inhibitor DPI as described above . Approximately 5 times as many human cells infected with the nuoG mutant underwent apoptosis as compared to those infected with Mtb ( 21 . 9±2 . 4% and 4 . 5±0 . 8% respectively ) . However , this difference between the two strains was abolished by the treatment of cells with the inhibitor DPI , as about 8 . 7±2 . 3% of Mtb and 8 . 1±0 . 3% of ΔnuoG infected cells were apoptotic under these conditions ( Figure 5C ) . These data strongly suggests that in primary human alveolar macrophages , as in murine BMDM , the NOX2 complex is critical for the pro-apoptotic phenotype of the nuoG mutant . Lastly , the intracellular ROS levels in Mtb or MtbΔnuoG infected cells were analyzed using DCFDA staining . The percentage of infected cells containing one or more ROS-positive phagosomes was quantified from two donors . The amount of cells containing ROS-positive Mtb phagosomes was similar from both donors ( 19 . 1±2 . 9% and 21 . 8±1 . 1% ) . However , these percentages were increased at least 3 fold in MtbΔnuoG infected cells to be 69 . 3±1 . 9% and 69 . 5±7 . 4 for the two donors ( Figure 6A ) . Also of note , cells infected with MtbΔnuoG contained many more ROS-positive phagosomes than those infected with Mtb ( Figure 6B and data not shown ) . Since the pro-apoptotic phenotype of MtbΔnuoG is dependent upon the accumulation of ROS in the phagosome , we hypothesized that neutralization or countering of phagosomal ROS may be a general mechanism of inhibition of apoptosis . If this hypothesis was true , other known ROS neutralizing proteins could potentially play a role in inhibition of PCD in host cells . M . tuberculosis contains several enzymes involved in the neutralization of ROS including a secreted Mg , Fe superoxide dismutase ( SodA ) , an outer membrane bound Cu , Zn superoxide dismutase ( SodC ) , and a secreted catalase ( KatG ) . Interestingly , a previous report established the involvement of SodA in the inhibition of apoptosis [27] . To determine if SodC or KatG could likewise affect cell death pathways , THP-1 cells were infected with sodC and katG deletion mutants and stained with TUNEL after 3 days . MtbΔsodC did not induce more apoptosis than the wild type Mtb ( strain Erdman ) ( Figure S3 ) , possibly due to the redundant presence of secreted SodA . However , MtbΔkatG induced significantly more apoptosis than Mtb , both at day 3 ( 63±5 . 1% and 23±3 . 2% , respectively ) ( Figure 7A ) and at day 1 ( Figure S1B ) post infection . Similar to cells infected with ΔnuoG bacteria , MtbΔkatG infected cells secreted 50-fold more TNF-α ( 0 . 5 ng/ml ) than those infected wild type bacteria ( 16 pg/ml ) ( Figure 7B ) . Infection of murine macrophages with the katG knockout also resulted in the increase of NOX2-dependent phagosomal ROS ( Figure 7C–7E ) . These results are consistent with the data obtained from the MtbΔnuoG analysis and reinforce the hypothesis that the NOX2-mediated accumulation of phagosomal ROS can lead to induction of host cell apoptosis .
The search for anti-apoptosis genes in the genome of M . tuberculosis led to the identification of nuoG as being important in host cell apoptosis inhibition and bacterial virulence [26] . Here we describe that primary human alveolar macrophages and murine BMDMs infected with the nuoG mutant responded with a NOX2-mediated increase in phagosomal ROS , which was essential to its pro-apoptotic phenotype when compared to wild-type Mtb-infected cells . The presence of TNF-α was necessary but not sufficient for the nuoG mediated apoptosis induction . Furthermore , the infection with the nuoG mutant led to an increase in TNF-α secretion in human and murine macrophages . It is to our knowledge the first time that a direct connection between phagocytosis of a pathogen , NOX2-generated phagosomal ROS levels , and TNF-α-mediated apoptosis signaling has been demonstrated in infected macrophages . TNF-α receptor 1 ( TNF-R1 ) mediated signaling has either pro-survival or pro-apoptotic consequences [32] . The ligation of TNFR-1 results in either activation of NF-κB , leading to survival of the cell , or activation of the c-Jun N-terminal kinase ( JNK ) , which entails an apoptotic response [32] , [35] . A major determinant in the outcome of TNF-α-mediated cell signaling is the concentration of cytosolic ROS [36] . High ROS levels lead to oxidation and inactivation of the MAP Kinase Phosphatases ( MKPs ) , which in their active form inhibit JNK activity . Without active MKP , TNF-α signaling leads to prolonged activation of JNK and subsequent cell death [33] . We have clearly demonstrated that intracellular ROS levels are important for the apoptosis phenotypes of the nuoG and katG deletion mutants of Mtb ( Figure 3+7 ) . It will be of interest to determine if the increased phagosomal ROS during mutant Mtb-infection leads to the oxidation of MKPs , or if other components are involved to modify TNF-α signaling outcome . How the increase in phagosomal ROS actually affects cytosolic host cell signaling components is not a trivial question . NOX2-complex generated superoxide is impermeable to lipid bilayer of the phagosome[37] . In contrast , hydrogen peroxide is highly permeable and might thus quickly diffuse into the host cell cytosol to oxidize susceptible cysteines in signaling proteins . Indeed , the JNK phosphatases MKP-1 , MKP-3 , MKP-5 and MKP-7 all share a phosphatase domain that contains a cysteine which is oxidized upon increase of H2O2 to inactivated the phosphatase and thus lead to increased JNK activity[33] . Nevertheless , H2O2 diffuses rapidly and so it would be surprising that we could detect such a strong accumulation of ROS in the phagosome of infected cells ( Figures 4D , 6B and 7E ) . An alternative hypothesis is that the increase in phagosomal ROS leads to a change in the signaling of receptors in the phagosomal membrane . Cell surface receptors such as TLRs , MARCO and TNF-R1 are phagocytosed together with bacteria in macrophages and the content of the phagosome influences outcome of receptor signaling [38] , [39] , [40] , [41] . The highly oxidative environment of the phagosome containing mutant Mtb compared to wild-type Mtb , may lead to the modification of the ligand/receptor-interactions which could affect the outcome of the signals transmitted by the receptors . The signaling difference observed between non-oxidized and oxidized LDL may serve as an example of how the oxidative modification of a ligand affects receptor signaling [42] . Infection of macrophages with either the katG mutant or the nuoG deletion mutant of Mtb increased the amount of secreted TNF-α ( Figures 2A , 2B and 7B ) . Infection of macrophages with wild-type Mtb induces a low basal level of TNF-α secretion which is induced by transcriptional upregulation of TNF-α mRNA expression[43] . The Mtb mutant mediated increase in TNF-α secretion in human THP-1 cells was inhibited by glutathione and DPI but was not affected in murine BMDMs derived from NOX2-deficient mice ( Figures 3B and 3D ) . This would suggest that there are functional differences between the human macrophage-like cell line THP-1 and murine BMDMs . Alternatively , the TNF- α induction might be mediated via ROS generated in mitochondria which would be inhibited by glutathione and DPI but not by deletion of NOX2 . How the nuoG mutant infection leads to an increase in TNF-α secretion by macrophages needs to be investigated in more detail but activation of transcription factors such as ATF-2 , Elk-1 and c-Jun upon JNK activation have been reported and would lead to an increase in TNF-α gene transcription[35] . The respiratory burst associated with Mtb infection has been shown to rapidly induce a MAP kinase cascade and NF-κB activation in a NOX2-dependent manner during very early time points ( <1 hr post infection ) [44] . However , our data suggests that ROS signaling may also play a role at later stages of infection as NOX2-derived ROS are necessary for induction of apoptosis several days post infection ( Figure 3 ) . Comparing the effects of Mtb and the nuoG mutant should prove to be a useful model for elucidating the interactions of NOX2-generated phagosomal ROS levels on the host cell apoptosis signaling cascade after prolonged infection . The specific mechanism by which NuoG inhibits ROS accumulation in the phagosome remains to be determined . However , one potential mechanism could be via the direct neutralization of NOX2 generated superoxides , since they are able to oxidize iron-sulphur ( [Fe-S] ) clusters with extremely high efficiency[37] . The Mtb NuoG protein contains four [Fe-S] clusters which could directly compete for NOX2 generated superoxides . Nevertheless , this model would require NuoG to enter the lumen of the phagosome , and to date there is no evidence that NuoG is being secreted by Mtb . NuoG does not have a signal peptide and structural analyses of other bacterial NDH-1 systems predict NuoG to be in the cytosol of the bacteria [45] . Furthermore , we have previously failed to detect secretion of a NuoG-phoA fusion protein[26] and in the current manuscript we also did not observe secretion of a myc-tagged NuoG protein into culture filtrate ( Figure S4 ) . These results are significant as they suggest that it is not NuoG by itself that is important for phagosomal superoxide neutralization , but that it is potentially the enzymatic activity of the NDH-1 complex that it is required . In order to address this question experimentally , deletion mutants of the NuoL and NuoM subunits of NDH-1 will be created . In homology with other prokaryotic NDH-1 complexes the L and M subunits are proposed to be important in translocation of protons across the membrane during the dehydrogenase activity of NDH-1 and thus their deletion should abolish the enzymatic activity of the NDH-1 complex[45] . If the hypothesis that the enzymatic activity of the NDH-1 complex is important for NOX2 neutralization is valid , then these deletion mutants should have a similar phenotype to the nuoG mutant in regard to ROS and apoptosis increases in host macrophages . In the light of our results it is tempting to hypothesize that the NDH-1-encoding nuo-operon in M . tuberculosis might have acquired a different function when compared to other prokaryotes . Accordingly , regulation of the Mtb nuo-operon is opposite to that in E . coli . In Mtb , gene expression of the nuo-operon is down-regulated under hypoxic conditions in vitro and at late stage infections in the lungs of mice , whereas it is upregulated under these conditions in E . coli [46] . Interestingly , it is the type II dehydrogenase complex , NDH-2 ( ndh , ndhA ) , of Mtb that is upregulated under hypoxic , nonreplicating conditions[46] . Under these conditions NDH-2 is crucial for maintaining a minimal PMF which is essential for survival[47] , suggesting a possible alternative role for the Mtb NDH-1 system . The nuo-operon is under positive control by the two-component system PhoPR [48] , which is important for virulence of Mtb and is one of the targets for attenuating mutations in Mtb H37Ra[49] , [50] . The phoP mutant fails to replicate in macrophages and infected mouse organs; however bacteria are able to survive in a state of nonreplicating persistence , suggesting that the dormancy regulon is not affected by the phoP mutation and that the PhoPR system is important for early steps of Mtb infection[51] . This is consistent with a role of the NDH-1 complex during the replicative phase of Mtb infections when the host cell NOX2 system is the most active . The NOX2 complex has been investigated and demonstrated to be of great importance for innate immune defense against a variety of pathogens[5] . In order for bacterial or protozoal pathogens to survive inside the macrophage they must have developed strategies to overcome NOX2 activity . One approach is to directly inhibit NOX2 activity by either perturbing the recruitment of the subunits to the phagosome[3] , [52] or by decreasing the steady state levels of NOX2 complex subunits[7] , [8] . A novel mechanism employed by Helicobacter pylori is to misdirect the assembly of functional NOX2 complex away from the membrane of phagosome to the plasma membrane , so that superoxides are being released into the extracellular space instead of the phagosomal lumen[6] . Furthermore , a common strategy used by several pathogenic bacteria , including M . tuberculosis , is the enzymatic detoxification of NOX2 generated superoxides via the secretion of enzymes such as superoxide dismutases and catalases[53] . In the case of Mtb , the secretion of large amounts of SodA and KatG may account for the relative insensitivity of the bacteria to bactericidal effects of NOX2 produced superoxides[54] . If our discovery that the NuoG-mediated neutralization of NOX2 activity is important for inhibition of host cell apoptosis is of general importance , one would predict that any mutant deficient in inhibition NOX2 activity should have a pro-apoptotic phenotype . There are few defined mutants for any pathogen described that are deficient in neutralizing NOX2 activity , and could thus be used to confirm or disprove this hypothesis . In the present study , we interrogated a Mtb deletion mutant of the only catalase in the Mtb genome ( katG ) and demonstrated that it had a similar phenotype to the nuoG mutant of Mtb in regard to an increase in phagosomal ROS and host cell apoptosis induction , both of which were dependent upon functional NOX2 ( Figure 7 ) . Interestingly , the katG mutant has been described as being attenuated and the attenuation was dependent on the presence of functional NOX2 complex in the host[55] . Furthermore , inhibition of SodA secretion by Mtb achieved either via deletion of secA2 or via inhibiting sodA transcription also leads to a pro-apoptotic phenotype of the bacteria[27] . This increase in apoptosis is likely to be due to increases in phagosomal ROS levels and dependent upon host cell NOX2 activity , although that has not been investigated to date . Mycobacterium tuberculosis also contains the membrane bound superoxide dismutase SodC . We have found that the deletion of sodC does not result in a pro-apoptotic phenotype , likely due to the presence of secreted SodA ( Figure S3 ) . However , this deletion does render the mutant more susceptible to the bacteriacidal effects of ROS [56] . It is possible that the deletion of nuoG from Mtb may also create a bacterial strain that is more vulnerable to ROS mediated killing , in which case the pro-apoptotic phenotype of ΔnuoG may be due to decreased fitness of the mutant . However , the nuoG deletion mutant was not more susceptible to superoxides being added directly to bacteria using the hypoxanthine/xanthine oxidase system ( Figure S5 ) . Therefore , the increase in phagosomal ROS may be affecting apoptosis signaling rather than direct bacterial killing . The identification of both SodA and KatG as anti-apoptotic proteins indicate that for Mtb , mutants deficient in countering host cell NOX2 activity are generally pro-apoptotic . It will be interesting to know if this mechanism can be extended to other pathogens such as Leishmania donovani , which is able to inhibit host cell NOX2 recruitment to the phagosome . This hypothesis is testable as a mutant deficient in producing the surface glycolipid lipophosphoglycan has lost the capacity to inhibit NOX2 recruitment [52] . Other pathogens , such as Listeria monocytogenes , may evade NOX2 activity by escaping from the phagosome into the cytosol . This is clearly a successful approach to evading the detrimental effects of increased proteolytic activity associated with phagosome maturation . Nevertheless , in the light of our results it is tempting to speculate that this strategy also helps to evade the NOX2-mediated apoptosis induction . It will interesting to test this hypothesis using bacterial mutants that fail to escape the phagosome such as the Listeriolysin O mutant of Listeria monocytogenes . In conclusion , the investigation of the pro-apoptotic phenotype of a mutant of Mtb deficient in functional NDH-1 complex serendipitously revealed a novel important function of host cell NOX2 complex in macrophages . Our results demonstrate that continuous NOX2 activity will ultimately lead to host macrophage apoptosis induction . The classical respiratory burst is transient , since this generates sufficient amounts ROS to kill susceptible bacteria and thus reduce NOX2 activity . However , infection of macrophages with persistent pathogens , who have adapted to the macrophage as a survival niche and are able to survive this initial ROS burst , would thus potentially lead to continuous NOX2 activity . The results presented in the current manuscript enable us to formulate the following hypothesis: successful intracellular pathogens need strategies to inhibit prolonged activation of NOX2 and/or neutralize the generated superoxides since this will otherwise be sensed by the host cell and will lead to host cell apoptosis . This hypothesis expands the function of NOX2 from the previously described ROS generation for bactericidal activity , to postulate that the host cell macrophages use the NOX2 complex as a mechanism to detect persisting intracellular pathogens .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board of the University of Maryland . All patients provided written informed consent for the collection of samples and subsequent analysis . All animals were handled in strict accordance with protocols approved by the Institutional Animal Care & Use Committee of the University of Maryland ( protocol #R-09-35 ) . C57/B6 and GP91 knockout mice were obtained from Jackson laboratories ( www . jaxmice . jax . org ) . Caspase specific inhibitors and analogs were purchased from Calbiochem ( www . emdbiosciences . com ) . Neutralizing anti human-TNF antibody ( #500-M26 ) , the biotinylated detection antibody ( 500-P31Abt ) were purchased from Peprotech Inc ( www . peprotech . com ) . Recombinant human and murine TNF-α , and anti murine-TNF-α antibodies were purchased from BD Pharmingen ( www . bdbiosciences . com ) . CM-DCFDA , DHE , and Vybrant® DiI cell-labeling solution were purchased from Invitrogen ( www . invitrogen . com ) . All other reagents unless otherwise noted were purchased from Sigma ( www . sigma . com ) . M . tuberculosis H37RV ( ATCC 25618 ) was obtained from the American Type Culture Collection ( www . atcc . org ) , MtbΔkatG was obtained from TARGET ( http://webhost . nts . jhu . edu/target/Default . aspx ) , and MtbΔnuoG has been previously described [26] . GFP expressing Mtb and ΔnuoG were created by transfecting the GFP-pmV261 plasmid into competent cells by electroporation as previously described [26] . All mycobacteria , excluding ΔkatG , were grown in 7H9 media supplemented with 0 . 5%glycerol , 0 . 5% Tween-80 , and 10% ADS . ΔKatG was grown in the same media supplemented with ADC in place of ADS . For selective media , 50 µg/ml Hygromycin or 25 µg/ml Kanamycin were added . Human myelomonocytic cell line THP1 ( ATCC TIB-202 ) was cultured in RPMI ( ATCC ) supplemented with 10% heat inactivated FCS ( Hyclone ) and differentiated using 20 ng/ml phorbol myristate acetate ( PMA ) ( Sigma ) as described [26] . Bacteria were grown to an OD600 ranging from 0 . 5 to 0 . 8 and the culture was allowed to settle for 10 minutes . Infections were carried out at a multiplicity of infection ( MOI ) of 5∶1 ( 5 bacilli to 1 cell ) for 4 hours in infection media containing 10% human serum ( Sigma ) and 10% non heat inactivated FCS . After 4 hours , extracellular bacteria were removed by 2 washes with phosphate buffered saline ( PBS ) and the cells were incubated in chase media containing 100 µg/ml of gentamicin ( Invitrogen ) . Cells were assayed for apoptosis by TUNEL staining 3 or 5 days post infection as detailed in the figure legends . The protocol to obtain normal human bronchoalveolar lavage fluid ( BALF ) was pre-approved by the IRB of the University of Maryland-Baltimore ( H-23204 ) . Normal , asymptomatic , non-smoking volunteers between the ages of 18 and 50 were anesthetized with topical and endobronchial lidocaine , and clinically standard fiberoptic bronchoscopy ( FOB ) was performed in the endoscopy suite at the University of Maryland Hospital . BALF was obtained using 200 mL of sterile normal saline infused in an identical manner into the right middle lobe of each subject , yielding 75–125 mL of BALF . 10–15 mL of BALF was filtered through sterile gauze to remove mucous , and the alveolar macrophages were washed 3 times with PBS before being used in experiments . Cells were resuspended in warm RPMI with 10% heat inactivated FCS , seeded on 8 well slides , and allowed to rest for 1–3 days . Infection was carried out as described above . Bone marrow macrophages were derived from the femur and tibia of C57B/6 and knockout mice and differentiated in DMEM media containing 20% L-929 supernatant . Murine cells were infected as described above using 10% FCS and 5–10% L929 supernatant in the infection and chase media . L929 supernatant was included in order to protect against cytokine withdrawal induced apoptosis . For experiments using caspase inhibitors or analog ( 20 µM ) , antioxidants ( 15 mM glutathione ) , and oxidase inhibitor ( 10 µM diphenylene iodonium , DPI ) , the cells were incubated with the reagents during infection and chase period . In experiments using TNF-α neutralizing antibody ( #500-M26 , Peprotech ) the antibody was included only in the chase medium at a concentration of 5 µg/ml . The TUNEL assay was preformed to reveal apoptosis-induced DNA fragmentation in tissue culture , primary human , or murine cells using the “In Situ Cell Death Detection Kit-Fluorescein or –TMR Red” ( Roche Applied Sciences at roche . com ) . The assay was carried out as described by the manufacturer and the percentage of stained cells was analyzed using flow cytometry or quantification via fluorescent microscopy . Reactive oxygen species in primary murine bone marrow cells and alveolar macrophages were detected at 24 hrs or 3 days post infection respectively using the ROS sensitive dyes 5- ( and-6 ) -chloromethyl-2′ , 7′-dichlorodihydrofluorescein diacetate , acetyl ester ( CM-DCFDA ) and dihydroethidium ( DHE ) ( Invitrogen ) . Bone marrow cells were deprived of L929 supernatant 16 hrs prior to infection and maintained in L929 free media without phenol red for the length of the experiment . Human alveolar macrophages were maintained in normal growth , infection , and chase media . In some cases bacteria were labeled with lipophilic red dye Vybrant-DiI ( invitrogen ) . Bacteria were incubated in 7H9 media containing 5 µl/ml of DiI for 30 minutes , washed twice with PBS with 0 . 05%tween , and then used for infection as normal . Post infection , murine or alveolar macrophages were washed once in HBSS and then incubated in 10 µM DCFDA for 30 minutes or 2 µM DHE for 15 minutes . Cells were washed 3 times with HBSS , fixed with 4% paraformaldehyde , and analyzed by either flow cytometry or fluorescence microscopy . Nitrite ( NO ) concentrations in supernatants from C57/B6 BMDMs were quantified via the Griess assay according to the manufacturer's protocol . In brief , supernatants were collected from macrophagess 3 days post infection with Mtb or ΔnuoG . Supernatants from macrophages primed for 16 hrs with IFNγ ( 100 units/ml ) , and infected with heat-killed M . smegmatis ( MOI of 5 and 20 ) , were used as positive controls . 150 µl of sample was mixed with 20 µl of Greiss reagent and 130 µl water , and the mixture was incubated at 30°C for 30 minutes before measuring absorbance at 548 nm . Nitrite concentrations were calculated from a standard curve with sodium nitrite as the reference . ELISA was performed with the supernatants of bone marrow derived macrophages or THP-1 cells infected for 3 or 5 days and treated with or without glutathione or DPI as described above . For detection of human TNF-α , the ELISA-plates were coated with 2 µg/ml capture antibody ( 500-M26 , Peprotech ) for 2 hours at 37°C . 100 µl of cell supernatant was used for the reaction and recombinant human TNF-α ( 554618 , BD Pharmingen ) diluted in infection medium was used as a standard . TNF-α was detected using the secondary biotinylated anti human-TNF-α detection antibody at 200 ng/ml ( 500-P31Abt , Peprotech Inc ) , Streptavidin-alkaline phosphatase at 1 µg/ml ( Zymed ) , and phosphatase substrate at 1 mg/ml ( Sigma ) . The plate was read at an absorbance of 405 nm . Murine TNF-α ELISAs were preformed as above using recombinant mouse TNF-α standard , the capture antibody rat anti murine-TNF-α at 8 µg/ml , and the biotinylated detection antibody rat anti mouse-TNF-α antibody at 1 µg/ml ( Catalog numbers 554589 , 551225 , 554415 respectively , BD Pharmingen ) . Statistical analyses were performed on three independent experiments ( ANOVA with Tukey post-test ) unless otherwise noted in the figure legends . Significance indications are as follows: * , 0 . 01<p<0 . 05; ** , 0 . 001<p<0 . 01; *** , p<0 . 001 . Graphs and in –text citations are a representation of the population mean and standard error of mean . Percentages of DCFDA or DHE positive cells found in the sample and not the control ( Figure 4 and Figure 7 ) were calculated by subtracting the histogram of uninfected cells from experimental histograms using Overton cumulative histogram subtraction ( FlowJo version 8 . 8 . 6 DMV ) . Differences were compared via ANOVA . | Mycobacterium tuberculosis , the causative agent of tuberculosis , is highly adapted to survive in macrophages of its human host . Host cell suicide is an ancient host cell defense mechanism employed by organisms to wall off invading pathogens . M . tuberculosis manipulates infected cells to inhibit host cell death but the molecular mechanism of this interaction has not been elucidated . Here we describe that M . tuberculosis uses an enzyme complex ( NDH-1 ) usually needed for energy generation in order to neutralize the NOX-2 enzyme-mediated production of toxic oxygen radicals ( ROS ) by the host cell . We demonstrate that an M . tuberculosis mutant deficient in NDH-1 accumulates ROS inside the macrophage which induces the secretion of an inflammatory cytokine ( TNF-α ) and subsequent host cell death . The increase of ROS is dependent upon functional NOX-2 , since host cells missing a NOX-2 component do not undergo cell death upon infection with the mutant . We propose that a novel function of the host cell NOX-2 complex is to allow sensing of intracellular pathogens by the host cell in order to commit suicide and thus limit bacterial survival . | [
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"di... | 2010 | The Type I NADH Dehydrogenase of Mycobacterium tuberculosis Counters Phagosomal NOX2 Activity to Inhibit TNF-α-Mediated Host Cell Apoptosis |
The goal of the global lymphatic filariasis ( LF ) program is to eliminate the disease as a public health problem by the year 2020 . The WHO mapping protocol that is used to identify endemic areas in need of mass drug administration ( MDA ) uses convenience-based sampling . This rapid mapping has allowed the global program to dramatically scale up treatment , but as the program approaches its elimination goal , it is important to ensure that all endemic areas have been identified and have received MDA . In low transmission settings , the WHO mapping protocol for LF mapping has several limitations . To correctly identify the LF endemicity of woredas , a new confirmatory mapping tool was developed to test older school children for circulating filarial antigen ( CFA ) in settings where it is uncertain . Ethiopia is the first country to implement this new tool . In this paper , we present the Ethiopian experience of implementing the new confirmatory mapping tool and discuss the implications of the results for the LF program in Ethiopia and globally . Confirmatory LF mapping was conducted in 1 , 191 schools in 45 woredas , the implementation unit in Ethiopia , in the regions of Tigray , Amhara , Oromia , SNNP , Afar and Harari , where the results of previous mapping for LF using the current WHO protocol indicated that LF endemicity was uncertain . Within each woreda schools were selected using either cluster or systematic sampling . From selected schools , a total of 18 , 254 children were tested for circulating filarial antigen ( CFA ) using the immuno-chromatographic test ( ICT ) . Of the 18 , 254 children in 45 woredas who participated in the survey , 28 ( 0 . 16% ) in 9 woredas tested CFA positive . According to the confirmatory mapping threshold , which is ≥2% CFA in children 9–14 years of age , only 3 woredas out of the total 45 had more CFA positive results than the threshold and thus were confirmed to be endemic; the remaining 42 woredas were declared non-endemic . These results drastically decreased the estimated total population living in LF-endemic woredas in Ethiopia and in need of MDA by 49 . 1% , from 11 , 580 , 010 to 5 , 893 , 309 . This study demonstrated that the new confirmatory mapping tool for LF can benefit national LF programs by generating information that not only can confirm where LF is endemic , but also can save time and resources by preventing MDA where there is no evidence of ongoing LF transmission .
Lymphatic Filariasis ( LF ) is a neglected tropical disease ( NTD ) , known to disproportionally cause disability among people with low socioeconomic status living in endemic areas [1] . Globally , about 1 . 4 billion people are at risk of LF infection[2] . Though the disease is endemic in 73 countries , 80% of the total number of people at risk live in only 10 countries [3] . LF is caused by three types of nematode parasites: Wuchereria bancrofti , Brugia malayi and Brugia timori[4] . More than 90% of human LF is caused by Wuchereria bancrofti , nine percent is caused by Brugia malayi in southeast and eastern Asia , and the remaining 1% is caused by Brugia timori in the Pacific region [5 , 6] . In Africa , Wuchereria bancrofti is the only known cause of LF[7] . Preventive chemotherapy ( PC ) for LF is a single dose of albendazole ( ALB ) , given in combination with either diethylcarbamazine ( DEC ) or , in countries where onchocerciasis is co-endemic , ivermectin ( IVM ) [5 , 8] . In at risk area the eligible population is treated through annual mass drug administration ( MDA ) . Five years of MDA with combination PC can result in a 99% reduction in microfilaria prevalence[9] . The World Health Organization ( WHO ) has established a global goal of eliminating LF by 2020 using a strategy of MDA with PC for eligible individuals living in endemic areas . From 2008–2013 , Ethiopia was mapped for LF using the current WHO mapping protocol for LF . The protocol uses two-stage cluster sampling and convenience sampling of adults to determine LF endemicity and treatment need at the woreda level . First , two communities per woreda are purposively selected using information obtained from woreda health bureau . In each selected community , a convenience sample of 100 individuals of age greater than 15 years , with equal sex proportion , are selected from a random starting point . Each individual is tested for circulating filarial antigen using immunochromatographic test ( ICT ) . A woredas is classified as endemic and in need of MDA when the prevalence of CFA is 1% or greater [10] . The Faculty of Medicine at Addis Ababa University conducted the first LF mapping survey from 2008 to 2012 in 112 woredas using the current WHO mapping protocol [11] . An additional 4 woredas were surveyed in 2012 . In 2013 , the Ethiopian Public Health Institute ( EPHI ) and partners surveyed an additional 658 woredas , again using the WHO mapping protocol . As a result of these surveys 774 woredas , 93% of the total number of woredas in the country , were mapped for LF by 2013 [12] . This mapping indicated that LF was endemic and potentially endemic in 112 woredas , with approximately 11 million people at risk as shown in Fig 1 [11 , 12] . According to WHO guidelines , if the prevalence of microfilaremia or CFA is 1% in either of the two sites , the woreda is classified as endemic and MDA is required [10] . However , 45 of the 112 woredas had only one CFA positive individual among the 200 tested , which is less than the 1% threshold . Such few positive cases indicate that these woredas are likely areas with very low or no transmission of LF and raised a question of whether MDA was warranted [12] . To address this concern of uncertain endemicity , a new LF confirmatory mapping tool was developed by global LF experts and approved during the 2014 meeting of the WHO’s Regional Program Review Group for Africa ( RPRG ) [13] for piloting in Ethiopia and Tanzania . The new confirmatory mapping tool was designed to be more precise than the current WHO LF mapping protocol . In contrast to the current WHO protocol , the confirmatory mapping tool aims to be more geographically representative at the woreda level and tests children , not adults . Unlike the Transmission Assessment Surveys in young children that are used to make treatment stopping decisions for LF , this confirmatory mapping methodology tests older children who have a longer period of potential exposure to LF infection . The new confirmatory methodology set a threshold for confirming LF endemicity and initiating MDA of >2% CFA in older children based on the null hypothesis that the average prevalence of antigenemia in older school children is >2% . To operationalize this threshold for programmatic use , a critical cutoff was established such that the upper 1-sided 95% confidence interval does not exceed 2% . Using this cutoff value , MDA is not warranted if the total number of children testing positive for CFA in a district is less than or equal to the critical cutoff . However , when the number of children testing positive for CFA is greater than the critical cutoff , the woreda is considered endemic and requiring MDA . Based on the number of primary schools in a woreda , either a cluster or systematic element sampling strategy is used . In woredas with 40 or more schools , cluster sampling is used . Thirty primary schools are randomly selected from a complete list of schools in the woreda using probability proportional to estimated size sampling ( PPS ) based on the total school enrollment rate , target sample size per school and assuming a 15% absentee rate . In each selected school , students with parental or guardian consent are sampled from grades in which the majority of children are between the age of 9 and 14 . In woredas with fewer than 40 schools , systematic element sampling is used , and all schools are selected for a total sample size of 320 children in the targeted grades . The task Force for Global Health built an electronic platform , Survey Sample Builder , which automated the sampling selection . The complete sampling design and statistical calculations of the confirmatory mapping methodology are described in Gass et al . [14] . In this paper , we present the Ethiopian experience of implementing the LF confirmatory mapping tool for the first time in 45 woredas of uncertain endemicity . We describe the logistics and feasibility of implementing this new mapping strategy and discuss the impact of the results on Ethiopia’s national LF program .
The confirmatory mapping protocol was piloted in 1 , 191 schools in 45 woredas where the results of 2013 mapping using the current WHO protocol indicated that LF endemicity was uncertain . Of the 45 woredas , 4 were in Tigray , 13 in Amhara , 19 in Oromia , 7 in SNNP , one in Afar and one in Harari regions . Cluster sampling was performed in 27 large woredas , while systematic sampling was conducted in the remaining 18 woredas . In the 27 woredas where cluster sampling was used ( ≥40 schools ) , the target sample size was 480 students per woreda . Within each school , students were selected according to a defined sampling interval based on the total number of students in the targeted grades in the school . When a selected school had fewer students in the targeted grades ( according to the enrollment figures ) , the school was merged with another nearby school . In the 18 woredas where systematic sampling was used ( <40 schools ) , the target sample size was 320 students per woreda . In this case , all schools in the woreda were visited , and students in the targeted grades were included after accounting for the expected absentee rate . The sampling interval was similar in each school . With the exception of a few woredas , sampling took place in all initially selected schools ( S1 Table ) . Of the 1 , 227 schools selected , data collection was conducted in 97% ( n = 1 , 191 ) of them . In 3% ( n = 36 ) of the schools , data collection could not be conducted due to security issues and scarcity of water which had closed the schools during the time of data collection . The school enrollment rate obtained from Ethiopian Federal Ministry of Education was very close to the actual school enrollment rate in 33 of the total 45 woredas . In advance of the survey , the national team at EPHI contacted the regional health bureaus with an official letter requesting health professionals to participate as survey team members . Sixty-one persons ( 42 laboratory technicians and 19 nurses ) from the regions where the survey was conducted ( Tigray , Oromia , Amhara , SNNPR , Afar and Harari ) were identified and sent to EPHI in Addis Ababa to participate in training on the sampling methodology and sample collection and processing . The three-day training was composed of theoretical and practical sessions . The theoretical session addressed how to communicate the confirmatory mapping strategy to community and school leaders and how they , in turn , can coordinate and mobilize students . The practical experience addressed how to take blood from the consenting students , how to operate the Immunochromatographic Test ( ICT ) and how to use smart phones to collect information . On the last day of the training , all team members visited primary school in Addis Ababa to practice the confirmatory mapping tool . Following the training , teams composed of 2 laboratory technicians and one nurse per team were formed and deployed to the selected woredas . When the central teams reached the woredas , a local guide , teacher and school director were selected to join the team . Upon arriving at a selected school , the purpose of the study was explained to the school director . After the director’s permission was obtained , school information and global positioning system ( GPS ) coordinates were collected . With the help of the school director , the students were gathered at central point in the school compound . The purpose of the study was explained to the students in their local language using a poster showing the clinical manifestations of LF . Questions from students , teachers and the director were addressed by the field team . Parental consent forms were distributed among the targeted grade students , with a request to bring them back the next day . This activity was repeated in at least two schools in a single day , based on the distance from one school to the next . The following day , students in the targeted grades with signed consent forms were lined up and randomly selected based on the sampling interval provided to that specific school by the Survey Sample Builder . Each selected student in the targeted grades , including those aged older than 14 years , participated in the study . The selected children were assigned a unique ID using barcode labels and basic demographic information from each was collected . All data collection was done in the selected primary schools . Approximately 100 μl of whole blood was taken by finger prick from each selected child . The ICT card required 100ul of whole blood , which was collected directly from the finger using a calibrated capillary tube and added to the sample pad of the ICT card according to the WHO guidelines[10] . Cold chain was maintained to transport the ICT cards to the study sites . The ICT tests were conducted in the schools at the time of data collection , and the results were read after ten minutes . All positive ICT tests were immediately followed up by a repeat ICT test to confirm the result , which required an additional finger prick . Similarly , when the test result was invalid or indeterminate , additional blood was collected from the child to repeat the ICT test . If the child did not consent to the second blood draw the test result was recorded as invalid . The sample collection and testing continued until all selected children were tested , regardless of the number of positive results . The pilot study was conducted in two phases . Phase I was conducted from December 2014 to January 2015 , while Phase II took place from December 2015 to March 2016 . The study was designed to allow field teams to complete sampling in two schools per day . While this goal was met in most schools , in some schools , the survey took 2 days or more . On average , data collection in each woreda took 32 days to complete , including weekends during which no data collection could take place . The total survey took three months to complete due to delays in data collection in a few woredas due to logistical issues such as difficulties accessing remote communities and temporary insecurity in the woredas . Data was collected electronically , using Smart Phones [15] . EPHI's local server in Addis Ababa was used to retrieve data from the field and encoded later for analysis . Data verification , cleaning and descriptive analysis was done by the EPHI team and the NTD Support Center at the Task Force for Global Health using Excel and STATA statistical software . GIS software ( ArcMap 10 . 5 , ESRI ) was used for mapping . This study obtained ethical approval from the Ethiopian Public Health Institute Scientific Ethical Review Committee ( EPHI_6 . 13/58 ) . Only children who came with signed consent of their parents/guardians were included in the study . All children who tested positive were referred to the nearest health center for treatment . In woredas in which the number of positives was more than the determined threshold , the recommendation was for the entire population to be treated with ivermectin and albendazole by MDA .
A total of 18 , 254 children in 1 , 191 schools were tested for CFA . Females composed 45 . 8% ( n = 8 , 363 ) of the total study participants . Of the total children in the targeted grades who are included in this survey , 92 . 9% ( n = 16 , 958 ) were between the age 9 and 14 , 7 . 09% ( n = 1 , 294 ) were older than 14 years and 0 . 01% ( n = 2 ) were aged 8 years . The mean age was 12 . 6 years , with 8 and 25 years the minimum and maximum ages respectively . The fewest woredas were selected in the Harari region ( n = 1 , 1 . 1% of the total sample size ) , and the most woredas from Oromia region ( n = 19 , 38 . 3% of the sample size ) . Most of the children ( 91% ) had lived in the study area for more than ten years . Only 161 ( 0 . 88% ) lived in their current location for fewer than five years . In 36 of the 45 woredas surveyed , no children tested positive for CFA by ICT . In the 9 remaining woredas there were 28 CFA-positive results ( 0 . 15% positivity overall ) . The number of children testing CFA-positive ranged from one to ten per woreda , with 9 ( 32 . 1% ) in two woredas in Amhara , 6 ( 21 . 4% ) in four woredas in Oromia , 10 ( 35 . 7% ) in one woreda in SNNP and 3 ( 10 . 7% ) in two woredas of the Tigray region . Thirteen of the 28 positive children ( 46 . 4% ) were female ( Table 1 ) . The average age of the positive children was 12 . 6 ( range: 10–14 ) . None of the CFA-positive children was from schools where more than five percent of the children lived in the area for five years or less . All of the CFA-positive children had lived in the current location for 10 and more years . In 3 of the woredas: Semada , Tach Gaynt and Debub Ari , the number of CFA-positive results exceeded the maximum threshold set by the confirmatory mapping tool . As a result , these woredas were declared ‘endemic’ and in need of MDA . In the remaining six woredas where CFA-positive children were found , the CFA-positive number was less than the maximum threshold set by the confirmatory mapping tool . These woredas , together with the 36 woredas that had no positive results , were classified as ‘non-endemic’ and therefore not in need of MDA ( Fig 2 ) . Regarding ICT test performance , 22 tests were “invalid” ( 0 . 1% ) , and one test was recorded as “indeterminate” . The main reason cited for the invalid tests was the lack of a control line and the child was not willing to allow the second blood draw for re-testing . There was one instance of a child having an initial “weak positive” result by ICT that , upon repeat testing , was found to be negative .
This manuscript describes the piloting of a new LF confirmatory mapping tool that could help NTD programs make treatment decisions in areas where the traditional WHO mapping protocol for LF has yielded uncertain results . In 42 of the 45 woredas of uncertain endemicity , the results of the confirmatory mapping tool indicate that LF is non-endemic and no MDA is necessary . In the other 3 woredas , LF was confirmed to be endemic , and these woredas will need to initiate MDA . Only one of these 3 woredas , South-Ari , that was found to be endemic using the confirmatory mapping tool is surrounded by woredas identified as endemic in 2013 by the WHO protocol ( Benatsemay , Selemago , and Teltele ) . The other 2 woredas found to be endemic by the confirmatory mapping tool: Simada and Tach-Gayint , are not geographically adjacent to any other woredas determined to be endemic in 2013 , but they are adjacent to each other . During the 2008 to 2013 mapping of LF the total number of woredas confirmed to be endemic or potentially endemic , including the 45 woredas with uncertain transmission , was 112[12] . The present study shrank the number of LF-endemic woredas from 112 to 70 in Ethiopia ( Figs 1 and 2 ) , dramatically decreasing the estimated number of people at risk for LF transmission and requiring MDA from 11 , 580 , 010 [12] to 5 , 893 , 309 . Reducing the total number of woredas requiring MDA from 112 to 70 has major resource and logistic implications for the national LF program in Ethiopia . The cost required to implement 5 years of MDA , including the monitoring and evaluation requirements , is much greater than the cost of conducting the confirmatory mapping tool to confirm whether or not the woreda is truly endemic . The approximate cost of implementing the confirmatory mapping tool in one woreda is $7 , 910; a detailed description of the cost effectiveness of this survey is presented by Gass et . al . [14] . The new confirmatory mapping tool addresses several of the weakness of the standard WHO LF mapping protocol that are exacerbated in low-prevalence settings . These include restricting the age group to a population that is more likely to be indicative of recent transmission ( e . g . 9–14 year olds ) and expanding the number of sites sampled per woreda [14] . This latter improvement was particularly important to Ethiopia . In the current WHO mapping protocol for LF , two sites are selected based primarily on the presence of lymphedema patients . However , in Ethiopia , podoconiosis , another major cause of lymphedema , is prevalent and could be mistaken for LF-related morbidity [16] . The rationale behind the confirmatory mapping tool is similar to that of the LF transmission assessment survey ( TAS ) , in that both employ cluster sampling of children in schools and use a 2% threshold for decision-making [10] . The two surveys differ , however , in that the sample size of the confirmatory mapping tool is approximately a fourth the size required for the TAS . As a result , the confirmatory mapping tool has less power than the TAS and , consequently , is more likely to recommend MDA in areas where the true prevalence may be below 2% . This feature was determined to be acceptable for mapping , where elimination is the ultimate goal , because it biases programs in favor of starting MDA . Our experience demonstrates that the confirmatory mapping tool is beneficial to the national program . The survey was conducted in collaboration with both regional health and education bureaus , which greatly facilitated the survey in the selected woredas and schools . The average time to complete data collection in a single woreda was 32 days , inclusive of weekends . The implementation took longer than originally anticipated due to the need for parental consent forms and the time required to travel between some of the remote school sites . Where written parental consent is not required , we expect the total time required to be much less . Road access between schools was another challenge that caused increased number of days per woreda . If the sample size per school increased up to 50 , which is similar to the sample size in the transmission assessment survey ( TAS ) [10] , the number of schools to be visited in the woreda will be decreased . This has the advantage of decreasing the total number of days spent to complete data collection in a single woreda . Conducting the survey in schools made the data collection simple by reducing the effort required to find the target population and facilitating probability sampling . A challenging aspect of this confirmatory mapping tool was the need for accurate school enrollment figures in advance in order to select the study sites and develop the sampling intervals . In Ethiopia , this information was obtained from the Federal Ministry of Education and , though enrollment estimation was sufficiently accurate in many woredas , there were some schools in which the enrollment figures were vastly different from the actual number of students present on the day of the study . Such discrepancies affected the accuracy of the sampling interval and resulted in sample sizes that differed dramatically from the target of 16 children per school . This issue was further compounded by an absentee rate that varied widely across the woredas . In areas with political instability or areas where there was no water [17] , the absentee rate was much higher than the 15% rate assumed during the sample size calculations . In contrast to this , there were also some schools with absentee rates less than 5% . Despite these logistical challenges , the required sample size was obtained from most of the schools . An additional challenging aspect of this survey was getting a consent from the child for the second blood draw , as the tests needed to be repeated if the result was either invalid or positive during the first blood draw . Some children were unwilling to allow a second blood draw and the test results were considered as invalid . This might lead to recording a positive result as invalid . Fortunately , the second test was conducted for all positive children in the study , so there was no risk of reporting false positive cases . From the point of view of donors and the NTD control program of Ethiopia , the confirmatory mapping tool could be considered to be a wise investment because it reduced the need for an expensive MDA program lasting five years in the 42 ruled out woredas . | Lymphatic filariasis ( LF ) is a mosquito-borne parasitic disease , caused by 3 nematode parasites , Wuchereria bancrofti , Brugia malayi and Brugia timori . The aim of the Global Program to Eliminate LF ( GPELF ) is to interrupt LF transmission through mass drug administration ( MDA ) by 2020 and to alleviate the suffering of affected people . Mapping is the first programmatic step to determining areas of LF endemicity and establishing a national program . Ethiopia was believed to be endemic for LF , but until recently the distribution of LF in the country was unknown . From 2008–2013 , mapping for LF was conducted using the current WHO protocol , and 112 woredas were identified as endemic or possibly endemic . In 45 of these 112 woredas , only a single CFA positive result was found ( <1% prevalence ) , which called into question the stutus of transmission and need for MDA . To help resolve this uncertainty , a new confirmatory mapping tool was designed and tested in Ethiopia . The new mapping tool was piloted in the 45 woredas with uncertain LF transmission from the 2008–2013 mapping ( S1 Table ) . This mapping confirmed that only 3 of the 45 woredas were endemic , which decreased estimated total population at risk of LF and in need of MDA from 11 , 580 , 010 to 5 , 893 , 309 . | [
"Abstract",
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"filariasis"... | 2018 | Results of a confirmatory mapping tool for Lymphatic filariasis endemicity classification in areas where transmission was uncertain in Ethiopia |
Transstadial transmission of tick-borne rickettsiae has been well documented . Few studies , however , have evaluated the role of transovarial transmission of tick-borne rickettsiae , particularly in nature within the host-vector ecosystem . This cross-sectional study aimed to understand the role of transovarial transmission of tick-borne rickettsiae among feeding ticks at different life stages . Tick eggs laid by engorged wild-caught adult female ticks were pooled and tested for Rickettsia spp . and Anaplasma/Ehrlichia spp . using molecular techniques , while adult fed ticks were tested individually . Additionally , larval and nymphal ticks were collected in the wild from small mammals , pooled and tested for Rickettsia spp . and Anaplasma/Ehrlichia spp . There were 38 fed adult and 618 larvae/nymphs ( 60 pools total ) Dermacentor spp . ticks collected from livestock and rodents . All individual adult ticks and tick pools were positive for Rickettsia spp . While none of the larvae/nymphs were positive for Anaplasma/Ehrlichia spp . , two adult fed ticks were positive . Rickettsia spp . DNA was detected in 91% ( 30/33 ) of the pooled eggs tested , and one pool of eggs tested positive for Anaplasma/Ehrlichia spp . Sequencing data revealed Rickettsia spp . shared ≥99% identity with R . raoultii ompA . Anaplasma/Ehrlichia spp . shared ≥89% identity with A . ovis 16S ribosomal RNA . This study identified potential transovarial transmission of Rickettsia spp . and Anaplasma spp . among D . nuttalli ticks . Additional studies are needed to further assess the proportion of transovarial transmission occurring in nature to better understand the burden and disease ecology of tick-borne rickettsiae in Mongolia .
While significant effort has been directed to study tick-borne rickettsiae , they continue to be a global public health threat . Mongolia is a country known for its rich nomadic and pastoral culture , with populations of people who work very closely with their livestock in environments that are often densely populated with ticks . Additionally , ecotourism is a rapidly growing industry in Mongolia , placing international visitors at risk of exposure to tick-borne rickettsiae [1 , 2] . This public health challenge is further complicated by a limited knowledge and understanding of tick and tick-borne rickettsiae ecology within Mongolia [2–4] . The mobility of ticks is restricted to questing and travelling via feeding on animals and humans [5] . Tick-borne rickettsiae typically undergo transstadial transmission before being vectored by a competent tick host . However , depending on the tick species and the type of tick-borne rickettsiae , transovarial transmission may also occur [6] . Research related to transovarial transmission has been particularly limited within the Asian and Eurasian regions of the world . Several tick-borne rickettsiae surveillance and case studies have been conducted throughout China , Russia and Mongolia , which have tested humans [7–10] , livestock [11–15] , wildlife [16–19] , and ticks [20–28] . However , most of these studies focused exclusively on ticks in their adult life stage , either fed or unfed . Few studies have examined the larval and nymphal stages of ticks in the Eurasian environment . Larval and nymphal life stages of ticks are of special interest in regard to exposure risk , as their small size can lead to less readily detectable feeding on human hosts [29–31] . Tick-borne rickettsiae of most concern in the Asian and Eurasian regions of the world are Rickettsia spp . [21 , 24] , Anaplasma spp . [12 , 14 , 16 , 25 , 27 , 32] , and Ehrlichia spp . [17 , 25] . These rickettsiae have been associated with small mammal reservoirs [6 , 17 , 19] . Collectively , the objectives of this study were to further investigate the life cycle of tick-borne rickettsiae in locally occurring ticks; to examine the propensity of certain tick-borne rickettsiae to undergo potential transovarial transmission; and to evaluate the infection prevalence of tick-borne rickettsiae infections from early life stage ticks throughout the Northern Mongolia region .
Handling procedures for livestock were conducted by trained veterinary staff prior to this study during animal care and were in accordance with the Mongolian Institute of Veterinary Medicine , Ulaanbaatar , Mongolia . Verbal consent was obtained from livestock owners at time of tick collection . Female adult fed ticks were collected from livestock at time of veterinary care of livestock and kept alive at room temperature in storage containers at the Laboratory of Arachno-Entomology and Protozoolgy , Institute of Veterinary Medicine in Ulaanbaatar , Mongolia . Moist cotton was placed near the ventilation of the containers to replicate environmental humidity conditions . Once female ticks laid eggs ( between 2–7 days of incubation ) , both adult ticks and eggs were stored separately at -80°C until DNA extraction was performed . The whole egg clutch was pooled and tested from each adult female tick . Mass of egg clutches ranged from 10 to 410 milligrams . Eggs and adult ticks were briefly rinsed with 70% ethanol in sterile 1 mL vials to remove contamination and then air dried on a sterile dish in preparation for processing [33 , 34] . Trapping and handling procedures for small mammals were approved by the Duke University Institutional Animal Care and Use Committee ( #A086-16-04 ) in accordance with the Mongolian Institute of Veterinary Medicine , Ulaanbaatar , Mongolia . At each location , live Tomahawk and Sherman traps were placed near holes where there were signs of recent small mammal habitation . All captured small mammals were sedated with ketamine ( 50 mg/Kg ) and inspected for ticks . Ticks were stored in 70% ethanol at room temperature . Specimens were taxonomically identified to genus for larvae and nymphs and species for adults by a trained entomologist . Ticks were air dried and pooled based on life stage ( larvae range 1–15; nymphs range 1–5 ) , small mammal host , sampling location , and tick genus . Pools ( n = 60 ) were stored at -20°C in new sterile 1 mL vials before genomic DNA was extracted . All ticks and eggs were ground using a sterile pre-chilled mortar and pestle with 500 μL of sterile PBS and 50 mg sterile sand for friction [35] . Contents were then centrifuged in a 1 . 5 mL vial at 9 , 500 g for 5 minutes . Supernatant was pipetted from the sand deposit , inserted into a new vial and stored at -20°C . Genomic DNA was extracted from tick supernatant using TIANamp Genomic DNA Kit ( Tiangen Biotech ( Beijing ) Co . , LTD , Beijing , China ) and tested for molecular detection of Rickettsia spp . targeting the citrate synthase gene ( gltA ) [36] and the outer-membrane protein gene ( ompA ) [37] , as previously described ( Table 1 ) . For the molecular detection of Anaplasma spp . and Ehrlichia spp . , the 16S rRNA gene [17] was targeted as previously described ( Table 1 ) . Gel electrophoresis was used to evaluate amplified products using 1% ( w/v ) agarose gels stained with ethidium bromide at 120 V . Gels were analyzed using the Gel Doc EZ System ( Bio-Rad , Hercules , California ) with ultra-violet illumination . A representative subset of positive amplicons were selected and directly sequenced using Sanger sequencing ( Eton Biosciences , Inc . , NC , USA ) . Sequencing results were then compared against the NCBI nucleotide database using the Standard Nucleotide BLAST application ( http://www . ncbi . nlm . nih . gov/BLAST/ ) for species identification . Rickettsia spp . gltA and ompA sequences were used as confirmation of amplified Rickettsia spp . samples and Anaplasma/Ehrlichia spp . 16S rRNA sequences were used as confirmation of amplified Anaplasma spp . Anaplasma/Ehrlichia spp . , and Rickettsia spp . sequences were structured for phylogenetic relatedness using Molecular Evolutionary Genetics Analysis ( MEGA ) software , version 7 . 0 . Engorged tick infection status was compared to corresponding oviposited egg infection status for PCR-positive Rickettsia spp . and Anaplasma/Ehrlichia spp . samples , as well as sequence data . Transovarial transmission was considered to have occurred when the corresponding female tick and egg mass were both PCR positive . Statistical analyses , including two-way frequencies with measures of association , were conducted using STATA 14 . 1 ( StataCorp , College Station , TX ) .
All individual adult ticks and larval/nymphal tick pools were PCR-positive for Rickettsia spp . Subsequent PCR testing of paired eggs resulted in 91% ( 30/33 ) PCR positive among tick egg pools for Rickettsia spp . Sequencing data revealed Rickettsia spp . shared ≥99% identity with R . raoultii ompA ( Accession numbers MH234455 and MH234456 ) shown in the phylogenetic analysis ( Fig 4 ) . A majority ( 23/32 ) of gltA sequences shared ≥99% identity with R . raoultii ( Accession numbers MH208721 and MH208722 ) shown in the phylogenetic analysis ( Fig 5 ) , however 9/32 sequences were considered inconclusive , falling between 84%-95% identity with R . raoultii . Of the 38 engorged adult ticks collected , two ticks ( 5% ) were PCR-positive for Anaplasma/Ehrlichia spp . , while none of the larval/nymphal pools were PCR-positive for Anaplasma/Ehrlichia spp . Additionally , one pool of eggs laid by an Anaplasma/Ehrlichia spp . -positive engorged adult female tick , was also found to be PCR-positive for Anaplasma/Ehrlichia spp . All PCR-positive Anaplasma/Ehrlichia spp . ticks and the positive egg clutch were further examined using a sequencing approach to identify the infecting rickettsial species . Sequencing results indicated that the Anaplasma/Ehrlichia spp . positive egg clutch and corresponding engorged adult female tick shared 99% identity ( accession number MG461482 ) and the other engorged adult female tick shared 89% ( accession number MG461483 ) identity with the A . ovis 16S ribosomal RNA gene . Both Anaplasma spp . sequences are shown in the phylogenetic analysis ( Fig 6 ) .
Like many tick pool studies , it is difficult to determine the exact prevalence of disease using this approach . Due to the nature of the maximum likelihood estimation calculation , the proportion of infected ticks with maximum likelihood of being Rickettsia spp . infected within tick pools cannot be calculated if 100% of sample pools are positive [59] . Additionally , due to the pooling of tick eggs , this study was unable to determine a more precise proportion of transovarial transmission from an infected female tick to at least one progeny . Though data suggest that transovarial transmission for R . raoultii did occur , we were unable to determine how many progeny were infected . Additionally , by only screening infection status of egg mass , we are unable to discuss if infected larvae will hatch . Furthermore , Rickettsia spp . PCR primers have been shown to cross-react with Anaplasma spp . and Ehrlichia spp . However , this study also utilized a general screening assay for Anaplasma/Ehrlichia spp . and confirmation by sequencing , which allowed for greater confidence in the Rickettsia spp . PCR assay . The indication that D . nuttalli ticks can serve as reservoirs for R . raoultii may warrants additional evaluation of transovarial and transstadial transmission of R . raoultii . Studies should focus on assessing tick eggs , either in smaller egg pools or individually , to determine the proportion of transovarial transmission as well as transstadial transmission for R . raoultii in eggs entering larval life stage , and larvae entering nymphal stages in the natural foci of Mongolia . Additionally , the testing of larvae from animal hosts and the environment should be further examined , preferably testing individual ticks instead of tick pools . Also , this report has identified a potentially novel transovarial transmission of A . ovis . Further investigation would be needed to determine the efficiency and prevalence of transovarial transmission of this rickettsiae . | In this study , we evaluate the probability or likelihood that tick-borne rickettsiae might be transmitted vertically from wild engorged adult female ticks collected throughout the Northern region of Mongolia during the summer of 2016 . While significant effort has been directed to study tick-borne rickettsiae , this public health challenge is complicated by the limited knowledge and understanding of tick and tick-borne rickettsiae ecology within Mongolia . Tick-borne rickettsiae of concern to humans and animals in this region of the world are Rickettsia spp . , Anaplasma spp . , and Ehrlichia spp . Using molecular techniques , we detected rickettsiae among all Dermacentor spp . tick life stages and demonstrated potential vertical transmission of Rickettsia spp . , and Anaplasma spp . among wild engorged adult female Dermacentor nuttalli ticks . We believe our findings provide important information regarding the ecology of key rickettsiae associated with tick-borne disease in Mongolia . | [
"Abstract",
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"phylogenet... | 2018 | Evidence for transovarial transmission of tick-borne rickettsiae circulating in Northern Mongolia |
Prevention of tissue damages at the site of Leishmania major inoculation can be achieved if the BALB/c mice are systemically given L . major antigen ( LmAg ) -loaded bone marrow-derived dendritic cells ( DC ) that had been exposed to CpG-containing oligodeoxynucleotides ( CpG ODN ) . As previous studies allowed establishing that interleukin-4 ( IL-4 ) is involved in the redirection of the immune response towards a type 1 profile , we were interested in further exploring the role of IL-4 . Thus , wild-type ( wt ) BALB/c mice or DC-specific IL-4 receptor alpha ( IL-4Rα ) -deficient ( CD11ccreIL-4Rα−/lox ) BALB/c mice were given either wt or IL-4Rα-deficient LmAg-loaded bone marrow-derived DC exposed or not to CpG ODN prior to inoculation of 2×105 stationary-phase L . major promastigotes into the BALB/c footpad . The results provide evidence that IL4/IL-4Rα-mediated signaling in the vaccinating DC is required to prevent tissue damage at the site of L . major inoculation , as properly conditioned wt DC but not IL-4Rα-deficient DC were able to confer resistance . Furthermore , uncontrolled L . major population size expansion was observed in the footpad and the footpad draining lymph nodes of CD11ccreIL-4Rα−/lox mice immunized with CpG ODN-exposed LmAg-loaded IL-4Rα-deficient DC , indicating the influence of IL-4Rα-mediated signaling in host DC to control parasite replication . In addition , no footpad damage occurred in BALB/c mice that were systemically immunized with LmAg-loaded wt DC doubly exposed to CpG ODN and recombinant IL-4 . We discuss these findings and suggest that the IL4/IL4Rα signaling pathway could be a key pathway to trigger when designing vaccines aimed to prevent damaging processes in tissues hosting intracellular microorganisms .
Leishmania spp . infection in experimental mouse models provided insights into the polarization of immune responses against intracellular parasites , resulting either in self-healing local inflammation ( e . g . , C57BL/6 ) or severe and fatal leishmaniasis ( e . g . , BALB/c ) [1] , [2] . BALB/c mice fail to develop a protective interferon ( IFN ) -γ-mediated T helper ( Th ) 1 response to Leishmania infection [3] , [4] , but show a disease-promoting IL-4-driven Th2 response [5] , [6] . Dendritic cells ( DC ) are migratory antigen-presenting cells ( APC ) which are highly specialized in uptake , processing and presentation of pathogen-derived antigens via major histocompatibility complex ( MHC ) molecules to T cells [7] , resulting in the cytokine-regulated differentiation into Th1 or Th2 cells . Activated DC are characterized by high levels of MHC class II , CD80 and CD86 molecules [8] , [9] . The ability of DC to release IL-12 in response to microbial stimuli is considered to be pivotal for the induction of Th1 responses [10] , [11] . We previously demonstrated that the Toll-like receptor ( TLR ) 9 ligand CpG oligodeoxynucleotides ( ODN ) is a potent inducer of DC-derived IL-12 , thus enabling DC to mediate complete and long-lasting immunity to experimental leishmaniasis . Prophylactic immunization with CpG ODN-activated and Leishmania major antigen ( LmAg ) -loaded BMDC one week or even 16 weeks prior to challenge has been shown to confer protection against L . major and , furthermore , these cured mice resist a secondary challenge 10 weeks after primary infection showing no sign of disease up to 20 weeks after rechallenge [12] . Interestingly , protection was not dependent on IL-12 secretion by the immunizing DC , as BALB/c mice treated with LmAg-loaded IL-12p35−/− or IL-12p40−/− DC were resistant against L . major infection , but the availability of recipient IL-12 was essential for the initiation of a protective immune response by DC , as neutralization of IL-12 during T cell priming diminished the protective effect of the vaccine [12] . The elaboration of DC-mediated vaccination strategies in animal models can be used as a tool to enhance the knowledge of the complex parasite-host interactions resulting in immunity against intracellular pathogens . It is well established that the main inducer of a Th2 response in Leishmania-susceptible BALB/c mice is IL-4 [13] . On the other hand , it has been shown that IL-4 has the ability to instruct a Th1 response and resistance against L . major in BALB/c mice . The presence of IL-4 during the initial phase of DC activation results in an increased IL-12-driven Th1 response [14] . To investigate the functional role of IL-4-mediated signaling during Leishmania infection , various knock-out mice have been generated . IL-4-deficient ( IL-4−/− ) [15] , as well as IL-4 receptor alpha ( IL-4Rα ) -deficient BALB/c mice [16] are resistant to infection with L . major . Cell-specific IL-4Rα-deficient ( IL-4Rα−/− ) mice have been generated to investigate the impact of IL-4Rα-mediated signaling on various cell types during Leishmania infection . CD4+ T cell-specific LckcreIL-4Rα−/lox BALB/c mice show a resistant phenotype [17] , whereas DC-specific CD11ccreIL-4Rα−/lox BALB/c mice are hyper-susceptible ( Hurdayal et al . , manuscript in preparation ) . Hyper-susceptibility in CD11ccreIL-4Rα−/lox mice is characterized by increased footpad swelling , the development of severe necrotic lesions and high parasite dissemination into organs , demonstrating that IL-4Rα signaling in DC is a necessity to control severe Leishmania infection . In the present study , IL-4Rα−/− bone marrow-derived DC ( BMDC ) from IL-4Rα−/− BALB/c mice were used to investigate the effect of IL-4Rα-mediated signaling in DC used as vaccine carrier in L . major-susceptible mice . The results demonstrate that IL-4Rα signaling in BMDC plays an important role in the vaccine-mediated induction of protective immunity against L . major infection .
All mice were kept under specific pathogen-free conditions . Mice experiments were performed in strict accordance with the German Animal Welfare Act 2006 ( TierSchG ) and the animal protocol was approved by the government of Lower Franconia ( permission no . 55 . 2-2531 . 01-16/09 ) and by the Animal Research Ethics Committee of the University of Cape Town ( license no . 009/042 ) . Sex- and age-matched wild-type ( wt ) BALB/c ( Charles River Breeding Laboratories , Sulzfeld , Germany ) and CD11ccreIL-4Rα−/lox BALB/c mice ( Hurdayal et al . , manuscript in preparation ) were 6–8 weeks old at the onset of the experiments . IL-4Rα−/− BALB/c [18] mice were kindly provided by Gottfried Alber ( University of Leipzig , Germany ) . The virulent L . major isolate ( MHOM/IL/81/FE/BNI ) was maintained by continuous passage in BALB/c mice . Amastigotes were isolated from lesions as previously described [19] . Promastigotes were grown in vitro in blood-agar cultures . For the preparation of LmAg , stationary-phase promastigotes were subjected to three cycles of rapid freezing and thawing and diluted to a final concentration of 1×109 ml−1 in phosphate-buffered saline ( PBS ) . DC were generated from bone marrow progenitors as described previously [20] . Briefly , isolated bone marrow cells from 6–8 week-old female BALB/c or IL-4Rα−/− mice were cultured in RPMI 1640 medium ( Invitrogen , Karlsruhe , Germany ) in the presence of 200 U ml−1 recombinant mouse granulocyte-macrophage colony-stimulating factor ( GM-CSF; PeproTech , London , United Kingdom ) . Fresh medium supplemented with GM-CSF was added to the culture on days 3 and 6 . After 10 days , non-adherent cells were harvested and used for further experiments . These cells were shown to have the typical myeloid DC morphology [12] . BMDC were incubated for 4 hours in the presence of either 25 µg ml−1 CpG ODN 1668 ( 5′-TCCATGACGTTCCTGATGCT-3′ , Qiagen Operon , Cologne , Germany ) or 20 ng ml−1 recombinant mouse IL-4 ( rIL-4; BD Biosciences , Heidelberg , Germany ) , or a combination of both , prior to the addition of LmAg for 18 hours . Thereafter , the BMDC were washed and resuspended at 5×106 ml−1 in PBS . BALB/c and CD11ccreIL-4Rα−/lox mice were treated with 5×105 BMDC intravenously ( i . v . ) into the tail vein . Control mice were treated with PBS . One week post vaccination the mice were infected subcutaneously into the right hind footpad with 2×105 stationary-phase L . major promastigotes in a final volume of 30 µl in PBS . The course of infection was monitored weekly by measuring the increase in footpad size of the infected versus the noninfected footpad . One . 3 or 6 weeks post infection , mice were sacrificed and single cell suspensions from the infected footpads as well as the draining popliteal lymph nodes were obtained . The parasite burden was determined by limiting dilution assays as described previously [21] . 5×106 lymphocytes were cultured in the presence of LmAg ( parasite-to-cell ratio 30∶1 ) or left untreated for 72 hours . The levels of IL-4 , IL-12p70 and IFN-γ in the culture supernatants were determined by sandwich ELISA using Ab pairs purchased from BD Biosciences according to the manufacturer's instructions . Values are given as mean ± SD and significant differences were determined using Student's t test ( GraphPad Prism version 5 , San Diego , CA , USA ) .
It has been shown that the Th2 key cytokine IL-4 can induce protective Th1-mediated immunity in L . major-susceptible BALB/c mice , as characterized by the secretion of high levels of DC-derived IL-12 [14] . In order to investigate whether IL-4Rα signaling in DC used as vaccine carrier is required to induce protection against leishmaniasis , BMDC were generated from IL-4Rα-deficient BALB/c mice or wt BALB/c mice . The BMDC were activated with the TLR 9 ligand CpG ODN and pulsed with LmAg prior to i . v . injection into naive BALB/c mice . Immunized BALB/c or control mice were challenged with L . major one week after vaccination , and the course of disease was monitored weekly . In accordance with our previous study [12] , mice immunized with CpG ODN-activated and LmAg-pulsed wt BMDC were able to control leishmaniasis . However , a significant progression of L . major infection was observed in mice immunized with CpG ODN-activated and LmAg-pulsed BMDC generated from IL-4Rα-deficient donors ( Figure 1A ) . Even though these mice were able to restrict footpad swelling during the first three weeks , an uncontrolled lesion development was observed in the advanced phase of infection . Unprotected control mice showed a progressive course of disease with massive footpad swelling and development of necrotic lesions , which was not observed in mice immunized with conditioned IL-4Rα-deficient BMDC . The lack of necrotic lesions in these mice can most likely be explained by the delayed course of disease . To determine whether the clinical outcome corresponds with the control of parasite replication , we analyzed the parasite burden in the infected footpads and the draining popliteal lymph nodes . The results revealed a significant correlation between parasite numbers and clinical outcome . The parasite burden at the site of infection was reduced about 105-fold and within the draining lymph node about 102-fold in protected mice ( wt DC/CpG/LmAg ) compared to unprotected mice . BALB/c mice immunized with IL-4Rα-deficient BMDC developed severe and progressive leishmaniasis , even though the footpad swelling did not reach the levels of PBS-treated mice . The parasite burden at the lesion site was not affected , but a 10-fold reduction of parasite burden was observed in the infected PLN . Equivalent results were obtained 3 weeks post infection ( data not shown ) . Together , these findings demonstrate that IL-4Rα-deficient BMDC were unable to induce parasite clearance in the host organism . The above results showed the importance of IL-4Rα-mediated instruction of DC used as vaccine carrier . To investigate whether DC of the host also require IL-4Rα signaling during vaccination , we immunized and infected DC-specific IL-4Rα−/− BALB/c mice ( CD11ccreIL-4Rα−/lox ) ( Figure 1B ) . Wt DC loaded only with LmAg or CpG had no protective effect ( data not shown ) , as expected . The treatment of mice with conditioned wt BMDC induced protection independent of whether DC of the host organism are IL-4 responders ( Figure 1A ) or not ( Figure 1B ) . In contrast , immunization with conditioned IL-4Rα−/− BMDC was not capable to induce the control of infection , as indicated by uncontrolled lesion development and parasite burden at the site of infection . We observed controlled footpad swelling in mice treated with PBS or BMDC until 3 weeks post infection ( Figure 1B ) . Delayed lesion development was accompanied by reduced parasite burden and high IFN-γ response by LmAg-stimulated draining LN cells 3 weeks post infection , but a Th1-biased immunity was not established during the onset of infection ( data not shown ) . In a complete IL-4Rα-deficient system ( neither vaccine carrier nor recipient DC are IL-4 responders ) , an uncontrolled parasite replication in the infected lymph nodes ( 102-fold increase compared to PBS group ) was observed . This observation is in contrast to the 10-fold reduction of parasite burden within wt BALB/c mice immunized with IL-4Rα-deficient BMDC ( Figure 1A ) , indicating that the inhibition of IL-4Rα signaling on host DC is detrimental and leads to increased dissemination of parasites into lymph nodes . These results indicate the importance of IL-4Rα-mediated instruction of DC used as vaccine carrier to mediate protection against leishmaniasis . To confirm the hypothesis that IL-4Rα signaling is critical for the ability of DC to induce resistance against leishmaniasis and to address the possible combinations of how to activate BMDC used in our vaccination strategy , we used BMDC generated from wt BALB/c mice and stimulated these BMDC with either rIL-4 or CpG ODN alone or a combination of both prior to loading with LmAg . These differently treated BMDC were injected into wt ( Figure 2A ) or CD11ccreIL-4Rα−/lox mice ( Figure 2B ) one week prior to infection with L . major . The course of lesion development was monitored weekly and the parasite burden at the site of infection and the draining lymph node was analyzed . The results show that in the absence of CpG ODN stimulation , rIL-4-treated and LmAg-pulsed BMDC did not have the potential to induce protective immunity with regard to the footpad swelling and the parasite burden in the infected lymph nodes ( 102-fold increase compared to positive control ) and footpad ( 104-fold increase compared to positive control ) in wt ( Fig . 2A ) or CD11ccreIL-4Rα−/lox ( Fig . 2B ) mice . We did not observe differences in the course of disease in mice immunized with CpG ODN-activated BMDC generated in the presence or absence of rIL-4 . Both groups of mice were clinically protected as indicated by controlled footpad swelling and parasite burden in the examined tissues . The results show that additional stimulation of IL-4-responsive BMDC with rIL-4 during vaccine generation seems not to be essential to mediate immunity to L . major , but that the boosting effect of additional rIL-4 ( see below , Figure 5A ) depends on properly activated BMDC . Host-derived DC migrate to the site of infection , take up and process antigens , which are then loaded onto MHC class I or II molecules . Thus activated , the DC differentiate into mature DC and initiate the immune response while migrating to the local draining lymph nodes , where they cross-talk with other cells of the immune system [22] , [23] . For this reason , we analyzed the activation and maturation status of CD11c+ cells in the lesion-draining lymph nodes with regard to MHC class II and CD80 expression . Wt or CD11ccreIL-4Rα−/lox mice that had been immunized with properly conditioned IL-4Rα-deficient BMDC were less capable of inducing high levels of activated and mature DC in the draining lymph node ( Figure 3 ) . Clinically protected mice ( immunized with wt DC/CpG/LmAg , grey bars , and wt DC/rIL-4/CpG/LmAg , lined bars ) showed significantly higher percentages of CD11c+CD80+ and CD11c+MHCII+ cells compared to mice that had been immunized with IL-4Rα−/−DC/CpG/LmAg ( black bars ) . These results were indicated in BALB/c mice as early as one week post infection ( data not shown ) . These results demonstrate that upon immunization with IL-4-responsive BMDC higher percentages of activated and mature recipient DC are observed in lymph nodes of clinically protected mice . We analyzed the secretion of IL-4 , IL-12 and IFN-γ by CD11c+ and CD4+ cells in the lymph nodes draining the lesions . Intracellular FACS staining of PMA/ionomycin-stimulated lymphocytes revealed that clinically protected mice have higher levels of Th1 cytokines and low levels of IL-4 in CD11c+ and CD4+ cells ( Figure 4A–D ) . Both types of vaccinating DC ( conditioned wt BMDC in the presence or absence of rIL-4 ) led to a higher IL-12 secretion by CD11c+ cells in the infected lymph nodes compared to unprotected mice ( Figure 4C ) . CD11c+ cells of protected mice also displayed lower levels of IL-4 ( Figure 4D ) . In comparison to wt BALB/c mice , CD11c+ cells of CD11ccreIL-4Rα−/lox mice showed per se lower levels of IL-12 and higher levels of IL-4 . CD4+ cells secreted lower amounts of IL-4 and higher amounts of IFN-γ in protected mice , compared to unprotected control mice or mice immunized with conditioned IL-4Rα-deficient BMDC ( Figure 4A and B ) . Mice immunized with IL-4Rα-deficient BMDC controlled to a certain level the IL-4 secretion by CD4+ cells ( Figure 4B ) , but failed to control IL-4 secretion by CD11c+ cells ( Figure 4D ) and failed to induce high levels of Th1 cytokines by CD11c+ ( Figure 4C ) or CD4+ cells ( Figure 4A ) . IL-4 secretion by CD4+ cells of CD11ccreIL-4Rα−/lox mice is also increased compared to wt BALB/c mice ( Figure 4B ) . These results demonstrate that IL-4Rα signaling in BMDC used as vaccine carrier enables host DC to secrete high levels of protective IL-12 and , thus , to control IL-4 secretion . This was already indicated at 3 weeks post infection ( data not shown ) . Protection against leishmaniasis is associated with a Th1 immune response characterized by high levels of IL-12 and low levels of IL-4 . To analyze the potential of BMDC-based vaccines to mediate a L . major-stimulated Th1 response , total lymphocytes of all groups were collected six weeks post infection and stimulated for 72 hours with LmAg . Subsequently , the cytokine levels of IL-4 , IL-12 and IFN-γ were measured by ELISA . LmAg stimulation of lymph node cells from protected BALB/c mice caused the secretion of high levels of IL-12 and IFN-γ and low levels of IL-4 , whereas mice immunized with IL-4Rα−/− DC showed a reversed cytokine pattern ( Figure 5A ) . These results were already indicated at one week post infection ( data not shown ) . Interestingly , the additional stimulation of properly activated IL-4-responsive BMDC with rIL-4 resulted in elevated levels of IL-12 upon L . major infection in vivo in BALB/c mice . Low levels of IL-4 were observed in CD11ccreIL-4Rα−/lox mice independent of the presence or absence of IL-4Rα on DC used for immunization , as in contrast to BALB/c mice , also conditioned IL-4Rα−/− BMDC were able to inhibit the release of IL-4 upon L . major stimulation ( Figure 5B ) . In line with the results obtained with BALB/c mice , increased levels of IFN-γ were observed in mice immunized with properly conditioned wt BMDC , resulting in protection against L . major . Immunization with conditioned IL-4Rα−/− BMDC was unable to induce the production of IL-12 and IFN-γ , even though the L . major-stimulated IL-4 secretion was controlled . Elevated levels of IL-12 were only observed in wt BALB/c mice upon immunization with properly activated and additionally rIL-4-stimulated BMDC , hence indicating the role of IL-4-responding host DC in the induction of IL-12 release upon L . major infection . As already shown in Figure 4 , wt BALB/c mice secrete higher levels of Th1 cytokines , whereas CD11ccreIL-4Rα−/lox mice secrete higher levels of IL-4 . A complete IL-4Rα-deficient set-up ( vaccine and host ) showed that IL-4Rα-mediated instruction of DC is important to enhance protection against leishmaniasis , as IL-4Rα-deficient DC were not capable of mediating resistance in CD11ccreIL-4Rα−/lox mice .
In the present study , we investigated the importance of IL-4Rα triggering during DC-mediated vaccination against the protozoan parasite L . major . The results show that complete protection against otherwise lethal leishmaniasis required immunization of BALB/c mice with IL-4-responsive BMDC , while IL-4Rα−/− BMDC failed to induce the restriction of lesion development . Even though the footpad swelling was restricted during the first three weeks of infection , a progressive course of disease with development of severe and necrotic lesions was observed at later stages of infection . In vitro studies showed that IL-4Rα-deficient BMDC secrete lower amounts of IL-12 and higher amounts of IL-10 upon stimulation with CpG ODN and LmAg compared to wt BMDC , which is most probably the reason for the failure of immunization with IL-4Rα-deficient BMDC , as no differences were observed regarding the activation status of wt or IL-4Rα-deficient BMDC ( data not shown ) . Importantly , the levels of Leishmania-stimulated IL-12 , the most potent inducer of immunity to L . major [24] , [25] , were significantly increased in the lymph nodes of wt BALB/c mice immunized with properly conditioned wt BMDC that had been additionally activated with rIL-4 . Elevated levels of IL-12 upon stimulation with LmAg were only observed in IL-4 responder recipients ( wt BALB/c ) , but not in CD11ccreIL-4Rα−/lox mice , demonstrating that IL-4 instruction of host DC is required to induce elevated levels of IL-12 during L . major infection . BMDC activated with rIL-4 alone were not able to mediate protection against L . major , but induced elevated Leishmania-stimulated IL-4 levels in vivo ( data not shown ) , skewing CD4+ T cells towards a Th2 cell phenotype and promoting susceptibility in BALB/c mice . At the site of infection , neutrophils instruct DC recruitment and activation , leading to Th1 cell activation and immunity to microbial infection [26] . Our results extend these findings by showing that upon immunization with IL-4-responsive DC , higher percentages of activated and mature recipient DC are observed in the lymph nodes draining the site of infection . In contrast , a less pronounced increase of mature DC is found upon immunization with IL-4 non-responder DC . The Th1/Th2 paradigm of experimental leishmaniasis is associated with IL-12- and IFN-γ-mediated resistance or IL-4-mediated susceptibility to L . major infection [2] . It is commonly accepted that IL-4 is the hallmark cytokine mediating the differentiation of naïve Th0 cells into the Th2 phenotype . However , the point that an IL-4-mediated Th2 response renders mice necessarily susceptible has never been proven for visceral leishmaniasis [27] . Furthermore , several data revealed a Th1-promoting effect of IL-4 which is capable to prime for bioactive IL-12 . For example , treating human peripheral blood mononuclear cells ( PBMC ) with IL-4 enhanced their IL-12 response to lipopolysaccharide or Staphylococcus aureus [28] , IL-12 production by human monocytes during interaction with T cells was increased upon IL-4 stimulation [29] and IL-4 provided a negative feedback causing murine as well as human DC to produce IL-12 [30] . IL-4 was furthermore reported to be required for the induction of protective Th1 cell responses to fungal infections , such as Candida albicans [31] . A protective role of IL-4 has also been shown for L . major infection in susceptible BALB/c mice [14] . It is important to note that the resistance-promoting role of IL-4 was only achieved when IL-4 was strictly present during the initial activation of DC upon infection . The presence of IL-4 during T cell priming resulted in the development of Th2 cells , which even rendered resistant TCR Vβ4-deficient BALB/c mice susceptible to leishmaniasis . IL-4 acting on DC induced the generation of a protective Th1 immune response against leishmaniasis in BALB/c mice [14] . Furthermore , it has been demonstrated that endogenous IL-4 is necessary for effective drug therapy with sodium stibogluconate against visceral leishmaniasis in BALB/c mice , as IL-4-deficient mice responded poorly to this treatment and showed increased parasite burdens in infected tissues [32] . Another example for IL-4-promoted healing has been documented in BALB/c mice vaccinated with a liposomal formulation against L . donovani , where an initially vaccine-induced mixed Th1/Th2 response , characterized by high levels of IFN-γ and IL-4 , instructed an efficient Th1-mediated resistance [33] . Our data are consistent with these studies , showing that IL-4Rα signaling is important to enable DC to induce a protective immune response in the recipient mice , hallmarked by high levels of L . major-induced IL-12 production in the lymph nodes of infected IL-4 responder mice . Elevated IL-4 levels during the late phase of L . major infection in resistant C57BL/6 mice were associated with the maintenance of an existing protection [34] , whereas susceptible BALB/c mice showed elevated IL-4 levels only during the early phase of infection [35] . These findings suggested a role of IL-4 in sustaining protection during the chronic phase of leishmaniasis . The results of the present study indicate a direct link between IL-4Rα triggering of BMDC used for immunization and the induction of elevated levels of IL-12 upon L . major infection in BALB/c mice , which mediated complete protection against otherwise lethal leishmaniasis . Resistance in leishmaniasis has been reported to depend on DC-derived IL-12 [11] , the inhibition of Leishmania-specific IL-4-secretion by Vβ4Vα8 CD4+ T cells and the induction of a Th1-dominated immune response in vivo [36] . Another aspect to give consideration to is that immunizing CD11ccreIL-4Rα−/lox mice with IL-4Rα−/− DC resulted in progressive leishmaniasis , showing the importance of IL-4Rα signaling not only in the immunizing DC but also in the host DC . This complete IL-4Rα-deficient DC set-up caused uncontrolled parasite dissemination into the draining lymph node , indicating that the inhibition of IL-4Rα signaling in host DC is detrimental and leads to increased dissemination of parasites into organs . In general , CD11ccreIL-4Rα−/lox mice secrete elevated levels of IL-4 and decreased Th1 cytokines compared to wt BALB/c mice and the effect of increased IL-12 secretion upon immunization with additionally rIL-4-stimulated wt BMDC was not observed in these mice , showing the important role of IL-4Rα signaling in host DC for IL-12 production during L . major infection . Immunized CD11ccreIL-4Rα−/lox mice showed controlled IL-4 levels independent of the type of vaccine , while IL-4Rα-deficient BMDC failed to induce a protective Th1 cytokine profile , resulting in a nonprotective Th2 immune response . These results showed that IL-4Rα signaling in the DC vaccine carrier is more critical than the IL-4 responsiveness of host DC . The present study enhances our understanding of the role of IL-4Rα signaling in DC during cell-mediated vaccination against an intracellular pathogen by showing that triggering this receptor is essential to confer protection . Vaccination strategies against Th2-related diseases , such as allergies or parasitic infections , should not only concentrate on inhibiting anti-inflammatory Th2 responses by inducing a strong Th1 phenotype , but need to consider the proinflammatory effect of IL-4 as adjuvant on the vaccine efficiency . An important aspect to be considered is that IL-4 as well as IL-13 can signal through the common IL-4Rα chain . In vitro results showed that wt BMDC stimulated with CpG ODN and IL-4 , but not IL-13 , induced the secretion of elevated IL-12 levels compared to CpG ODN stimulated wt BMDC , and that additional stimulation with IL-4 or IL-13 failed to induce elevated levels of IL-12 secretion by IL-4Rα-deficient BMDC ( data not shown ) . These in vitro results strongly suggest that the elevated secretion of DC-derived IL-12 is induced by IL-4 instruction of DC and not by IL-13 instruction ( see also Hurdayal et al . , manuscript in preparation ) . The results of the present study underline the importance of IL-4 signaling during vaccine design , as IL-4Rα signaling in the DC vaccine carrier is more important than IL-4Rα signaling in the host DC . Our results document the crucial role of IL-4Rα signaling in DC-based vaccination against leishmaniasis by promoting a protective Th1 immune response . | Cutaneous leishmaniasis is endemic in tropical and subtropical regions of the world . Effective vaccination strategies are urgently needed because of the emergence of drug-resistant parasites and severe side effects of chemotherapy . We previously established a DC-based vaccination strategy to induce complete and long-lasting immunity to experimental leishmaniasis using Leishmania major antigen-loaded and CpG oligodeoxynucleotide-activated DC as a vaccine carrier . In the present study we investigated the role of IL-4Rα-mediated instruction of the vaccinating DC and the host DC during induction of protection against leishmaniasis . The results demonstrate that IL-4Rα signaling in DC used as vaccine carrier plays an important role in induction of protective immunity against L . major infection , as only mice vaccinated with IL-4 responder DC are able to trigger effective Th1-mediated immunity . The immunity is hallmarked by high levels of L . major-induced bioactive IL-12 production in the lymph nodes of vaccinated mice . Together , these findings suggest that IL-4 is a strong adjuvant to induce Th1-biased immunity against leishmaniasis and possibly other infections with intracellular pathogens , indicating that IL-4 needs to be considered in the development of efficient cell-mediated vaccination strategies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine"
] | 2012 | Dendritic Cell-Mediated Vaccination Relies on Interleukin-4 Receptor Signaling to Avoid Tissue Damage after Leishmania major Infection of BALB/c Mice |
Toll-like receptor 3 ( TLR3 ) and cytosolic RIG-I-like helicases ( RIG-I and MDA5 ) sense viral RNAs and activate innate immune signaling pathways that induce expression of interferon ( IFN ) through specific adaptor proteins , TIR domain-containing adaptor inducing interferon-β ( TRIF ) , and mitochondrial antiviral signaling protein ( MAVS ) , respectively . Previously , we demonstrated that hepatitis A virus ( HAV ) , a unique hepatotropic human picornavirus , disrupts RIG-I/MDA5 signaling by targeting MAVS for cleavage by 3ABC , a precursor of the sole HAV protease , 3Cpro , that is derived by auto-processing of the P3 ( 3ABCD ) segment of the viral polyprotein . Here , we show that HAV also disrupts TLR3 signaling , inhibiting poly ( I:C ) -stimulated dimerization of IFN regulatory factor 3 ( IRF-3 ) , IRF-3 translocation to the nucleus , and IFN-β promoter activation , by targeting TRIF for degradation by a distinct 3ABCD processing intermediate , the 3CD protease-polymerase precursor . TRIF is proteolytically cleaved by 3CD , but not by the mature 3Cpro protease or the 3ABC precursor that degrades MAVS . 3CD-mediated degradation of TRIF depends on both the cysteine protease activity of 3Cpro and downstream 3Dpol sequence , but not 3Dpol polymerase activity . Cleavage occurs at two non-canonical 3Cpro recognition sequences in TRIF , and involves a hierarchical process in which primary cleavage at Gln-554 is a prerequisite for scission at Gln-190 . The results of mutational studies indicate that 3Dpol sequence modulates the substrate specificity of the upstream 3Cpro protease when fused to it in cis in 3CD , allowing 3CD to target cleavage sites not normally recognized by 3Cpro . HAV thus disrupts both RIG-I/MDA5 and TLR3 signaling pathways through cleavage of essential adaptor proteins by two distinct protease precursors derived from the common 3ABCD polyprotein processing intermediate .
Hepatitis A virus ( HAV ) [1] and hepatitis C virus ( HCV ) [2] are positive-strand RNA viruses that cause hepatitis in humans . Despite important differences in virion structure , they share similar genome structures and many aspects of their replication strategies . Both viruses demonstrate strong tropism for the hepatocyte , and replicate their RNA genomes in replicase complexes contained within cytoplasmic vesicles . Both produce double-stranded RNA ( dsRNA ) , a potent pathogen-associated molecular pattern ( PAMP ) recognized by innate immune sensors , as replication intermediates . Thus , both HAV and HCV face similar challenges posed by the innate immune system early in the course of hepatic infection . However , HAV and HCV infections have dramatically different outcomes . HAV never causes chronic hepatitis while HCV does so in the majority of those it infects . Prolonged shedding of HAV has been reported in premature infants [3] , but long-term persistent infection has never been documented . This contrasts sharply with HCV , which persists for decades in the majority of those infected [2] , [4] . Although factors controlling HCV infection outcome are poorly understood , T cell responses are critical [reviewed in 2] . T cells also appear to be important for HAV clearance [5] , [6] . In both cases , the vigor and breadth of the virus-specific T response is likely to be profoundly influenced by early interferon ( IFN ) and other cytokine responses evoked by innate antiviral response pathways . How HCV both induces and disrupts signaling initiated by retinoic acid-inducible gene I ( RIG-I ) and Toll-like receptor 3 ( TLR3 ) has been studied in depth . Proteolytic cleavage of mitochondrial antiviral signaling protein ( MAVS , also known as IPS-1 , VISA or Cardif ) and TIR domain-containing adaptor inducing IFN-β ( TRIF , also known as TICAM-1 ) by the NS3/4A serine protease of HCV effectively blocks the activation of IFN-regulatory factor 3 ( IRF-3 ) and nuclear factor-κB ( NF-κB ) induced by RIG-I and TLR3 , respectively [7] , [8] , [9] . Much less is known about how HAV stimulates or antagonizes these innate signaling pathways . Both clinical and experimental observations indicate that there is extensive replication of HAV within the liver prior to the onset of hepatic inflammation 3–4 weeks after infection [10] . This lengthy , clinically silent incubation period suggests that HAV either blocks or otherwise fails to induce innate immune responses to dsRNA in the early stages of the infection . Consistent with this , HAV , like HCV , disrupts virus-induced signaling initiated by RIG-I-like receptors ( RLRs , RIG-I and melanoma differentiation associated gene 5 , MDA-5 ) [11] , [12] . Our previous work shows that it does this by targeting MAVS for proteolysis by a precursor of its 3Cpro cysteine protease , 3ABC [12] . Cleavage requires both the protease activity of 3Cpro and a transmembrane domain in 3A that directs 3ABC to the mitochondrial outer membrane where MAVS is localized [12] . The shared capacity of the HAV 3ABC and HCV NS3/4A proteases to cleave MAVS and disrupt signaling from RLRs suggests that MAVS-dependent signaling is critical to antiviral defense in the liver ( as it is in other tissues ) , but also indicates that NS3/4A cleavage of MAVS is not primarily responsible for the unique ability of HCV to establish persistent infections . In addition to RLRs , TLR3 is functionally expressed in primary human hepatocytes [13] . During HCV infection , signaling initiated by TLR3 recognition of dsRNA is blocked by NS3/4A cleavage of the adaptor protein , TRIF [7] , [13] . This led us to ask whether HAV also antagonizes TLR3 signaling . We show here that HAV strongly inhibits TLR3 signaling by also targeting TRIF for degradation . We demonstrate that TRIF is proteolytically cleaved by a distinct intermediate in the polyprotein processing cascade , the viral 3CD protease-polymerase . Cleavage requires expression of the cysteine protease activity of 3Cpro fused in cis to 3Dpol sequence . Mutational studies reveal an unexpected role of the 3Dpol domain in modulating the substrate specificity of 3Cpro such that it is able to achieve scission of non-canonical 3Cpro cleavage sites within TRIF . The role played by the polymerase sequence in innate immune evasion represents a remarkable and unique mechanism of viral adaptation to the intrahepatic environment , and provides a second major evasive strategy by which HAV can escape innate immunity .
Although hepatocytes express TLR3 , Huh7 hepatoma cells , which are permissive for replication of cell culture-adapted HAV , are defective in TLR3 signaling [14] , [15] . We therefore studied the impact of HAV infection on TLR3 signaling in Huh7 cells in which signaling was functionally reconstituted by retroviral transduction of TLR3 expression [13] . Previous studies of these cells include extensive control experiments showing that the activation of IRF-3 by extracellular poly- ( I:C ) occurs specifically through TLR3 signaling [13] . Control cells used in here included Huh7 cells transduced in parallel with a TIR-domain TLR3 deletion mutant or empty vector . Stimulation of Huh7-TLR3 cells with extracellular poly ( I:C ) , a synthetic dsRNA analog , induced transcriptional activation of the IFN-β promoter and expression of the interferon-stimulated gene ( ISG ) ISG15 , neither of which were observed in Huh7-ΔTIR or Huh7-Vector cells ( Fig . 1A , B ) . However , prior infection of Huh7-TLR3 cells with HM175/18f , a cell culture-adapted HAV variant [16] , strongly inhibited both responses ( Fig . 1A , B ) . The expression levels of TLR3 and its ΔTIR mutant were not affected by HAV ( Fig . 1B ) , suggesting that HAV does not disrupt TLR3 signaling by reducing TLR3 abundance . HAV inhibition of TLR3 signaling was also observed in Huh7 . 5-TLR3 cells , TLR3-reconstituted Huh-7 . 5 cells that are deficient in RIG-I signaling ( Fig . S1A in Text S1 ) [13] . The IFN-β promoter is activated by overexpression of the TLR3 adaptor protein , TRIF , or downstream kinases , TBK-1 or IKKε , that phosphorylate IRF-3 [17] . In HAV-infected cells , however , its activation by TRIF was reduced by about 50% , while there was no reduction in its stimulation by IKKε ( Fig . 1C ) . This result is similar to that reported previously by Fensterl et al . [11] . We also observed a marked reduction in the abundance of IRF-3 dimers in HAV-infected Huh7-TLR3 cells stimulated with extracellular poly- ( I:C ) ( Fig . 1D ) . In addition , confocal microscopy revealed that IRF-3 did not undergo nuclear translocation upon poly- ( I:C ) stimulation of HAV-infected Huh7-TLR3 cells ( Fig . 1E ) , while this occurred uniformly in uninfected cells ( Fig . S1B in Text S1 , right panel ) . Importantly , HAV infection itself induced neither IRF-3 dimerization ( Fig . 1D ) nor nuclear translocation ( Fig . S1B in Text S1 , left panel ) , indicating an absence of IRF-3 activation . Collectively , these results indicate that HAV infection disrupts the signal transduction pathway from TLR3 prior to the kinases responsible for IRF-3 activation . Consistent with a defect in signaling at this level , we found the abundance of endogenous TRIF was substantially reduced in HAV-infected Huh7 or Huh7 . 5-TLR3 cells ( Fig . 2A ) . In addition , we could not detect TRIF in a stable Huh7 cell line harboring an autonomously replicating subgenomic HAV replicon ( HAV-Bla cells ) [18] , while TRIF expression was restored after eliminating the replicon by IFN treatment ( Bla-C cells , Fig . 2B ) . Thus , HAV infection disrupts TLR3 signaling by substantially decreasing the expression of TRIF . To determine whether a specific HAV protein or polyprotein processing intermediate was responsible for the reduction in TRIF abundance and , as a result , the inhibition of TLR3 signaling , we over-expressed individual proteins in Huh7-TLR3 cells , assessing the impact on poly ( I:C ) -induced , TLR3-dependent activation of the IFN-β promoter . While ectopic expression of the P1-2A structural proteins ( P1-2A , VP0 , VP3 , VP1-2A ) and 2B or 2C had little impact on TLR3 signaling , the processing intermediate 3ABCD and its 3CD protease-polymerase derivative strongly blocked promoter activation ( Fig . 3A , top panel ) . An intermediate degree of suppression was observed with 2BC and 3Cpro expression . The effect of 2BC on signaling may be related to its capacity to induce intracellular membrane rearrangement [19] , and was not studied further . The common presence of the 3Cpro cysteine protease domain in 3ABCD , 3CD and 3Cpro suggested it may play a role in disrupting TLR3 signaling . However , 3Cpro alone was significantly less inhibitory than either 3CD or 3ABCD ( Fig . 3A , top panel , p<0 . 002 by Student's t test ) , despite being expressed in much greater abundance ( Fig . 3A , lower panel ) . Since 3ABCD is the precursor of 3CD , its inhibitory effect on TLR3 signaling could be due entirely to 3CD . 3ABC , which is also derived from 3ABCD and targets MAVS for cleavage [12] , had no effect . Consistent with these results , the co-expression of 3CD , but not 3Cpro or 3Dpol , or a combination of these two proteins , resulted in a marked reduction of ectopically expressed TRIF in HEK 293FT cells ( in which endogenous TRIF expression is negligible ) ( Fig . 3B ) . The reduced abundance of full-length TRIF in cells expressing 3CD was accompanied by the appearance of two TRIF fragments with apparent molecular masses of 75 and 55 kDa that were detected by an antibody recognizing residues surrounding Ser-219 ( Fig . 3B , open triangles ) . Since full-length TRIF has a mass of ∼90-kDa , these are likely to be overlapping degradation products . An antibody to residues 4–31 of TRIF identified an additional fragment with an apparent mass of 20 kDa , likely derived from the N-terminus of TRIF ( not shown ) . The accumulation of at least three different fragments suggests that 3CD causes multiple scission events within TRIF . 3CD-mediated cleavage of TRIF was not dependent upon the cell culture-adaptive mutations present in the 3CD sequence of the HM175/18f virus [16] used in these studies , as it was also observed with ectopically expressed , wild-type 3CD ( Fig . S3 in Text S1 ) . Consistent with these observations , 3CD overexpression resulted in a marked reduction in poly-I:C-stimulated IFN-β and PRD-II ( NF-κB-responsive ) promoter activity in HeLa cells ( Fig . 3C ) . Thus , 3CD effectively antagonizes an endogenous TLR3 pathway , as well as the reconstituted pathway in Huh7-TLR3 cells . Since co-expression of 3Cpro and 3Dpol did not result in detectable TRIF cleavage ( Fig . 3B ) , efficient scission appears to require expression of the protease and polymerase domains in cis . 3CD is known to be a catalytically active precursor of 3Cpro [20] , thus 3CD could directly cleave TRIF . To test this hypothesis , we expressed a 3CD mutant with an Ala substitution of the active-site nucleophile , Cys-172 ( Fig . 4A ) . This lacked any capacity to cleave ectopically expressed TRIF ( Fig . 4B ) , confirming that the protease activity of 3CD is responsible . In contrast , a mutant in which the conserved GDD motif required for RNA-dependent RNA polymerase activity was ablated ( 3CD-GAA ) remained capable of cleaving TRIF . The 3CD-GAA mutant also inhibited poly ( I:C ) -induced activation of the IFN-β promoter in Huh7-TLR3 cells , whereas the proteinase-deficient 3CD-C172A mutant did not ( Fig . 4C ) . Collectively , the results shown in Figs . 3 and 4 indicate that TRIF cleavage results from the 3Cpro protease acting in cis with 3Dpol . To assess whether TRIF is cleaved by a 3Cpro–3Dpol complex forming after auto-processing of 3CD , we constructed 3CD mutants with modifications at the 3C–3D junction that accelerate or retard autoprocessing . The 3C–3D junction is comprised of a primary 3Cpro cleavage site , IESQ↓R , and an alternative cleavage site , EFTQ↓C , separated by 9 residues ( Fig . S2A in Text S1 ) . In one mutant , 3CD-QQRR , both cleavage sites were abolished by Gln-to-Arg mutations such that expression resulted only in the 3CD precursor ( Fig . S2B in Text S1 ) . In the other , 3CD-LWG , the primary cleavage site was optimized to LWSQ↓G , making it identical to the efficiently cleaved 2C–3A junction ( Fig . S2A in Text S1 ) . Expression of 3CD-LWG led to less 3CD precursor , and a greater abundance of mature 3Cpro due to enhanced 3C/3D processing ( Fig . S2B in Text S1 ) . While the 3CD-QQRR mutant remained capable of cleaving TRIF , the hyper-processing 3CD-LWG mutant did not ( Fig . S2C in Text S1 ) . We conclude that the cleavage of TRIF results from the cysteine protease activity of the unprocessed 3CD sequence . TRIF is cleaved by cellular caspases at residues D281 and D289 under conditions favoring apoptosis , including over-expression of TRIF [21] . This generates a 38-kDa fragment that is distinct from those generated by 3CD ( Fig . S4A in Text S1 ) . Moreover , a D281E-D289E ( DDEE ) TRIF mutant resistant to caspase cleavage [21] remained subject to cleavage by 3CD ( Fig . S4A in Text S1 ) . Thus , TRIF is not degraded indirectly by cellular caspases when 3CD is expressed . While a broadly active caspase inhibitor , z-VAD-fmk , partially inhibited the 3CD-mediated cleavage of TRIF ( Fig . S4B in Text S1 ) , z-VAD-fmk is known to inhibit cellular proteases other than caspases that , like 3Cpro , contain cysteine nucleophiles [22] . To show that TRIF is cleaved directly by the viral protease , we attempted to purify GST-3CD and GST-3Cpro fusion proteins produced in E . coli . The GST-3CD fusion product formed an insoluble pellet upon extraction , precluding its purification . This likely reflects the extreme insolubility of 3Dpol , which has hindered previous efforts to purify the HAV polymerase [23] . We were able to produce purified GST-3Cpro . In cell-free cleavage assays , this demonstrated a limited ability to process [35S]-labeled TRIF prepared by in vitro translation ( Fig . S5 in Text S1 ) , but did produce the expected ∼75- , ∼55- , ∼27- , and ∼18-kDa cleavage products when incubated with full-length TRIF , or fragments representing amino acids 1–372 or 373–712 of TRIF , in vitro ( Fig . S5B in Text S1 ) . Taken collectively with the data shown in Fig . S4 in Text S1 , these results confirm that TRIF cleavage is caused by the HAV protease directly , and not by indirect activation of a caspase or other cellular protease . The incomplete proteolysis of TRIF observed in the cell-free cleavage reactions is consistent with the partial inhibition of IFN-β promoter activation by poly- ( I:C ) we observed following high-level expression of 3Cpro in Fig . 3A . Thus , while 3Cpro is capable of cleaving TRIF , its capacity to do so is much less than 3CD . We next examined the sequence of human TRIF for potential 3Cpro cleavage sites . Previous studies of 3Cpro substrate specificity have documented a preference for Gln at the P1 position and a consensus sequence ( L , V , I ) X ( S , T ) Q↓X where X is any amino acid [24] . The 3ABC cleavage site in MAVS , LASQ↓V , fits this consensus perfectly [12] . In contrast , TRIF does not contain any consensus 3Cpro cleavage sites , although several sites are partial fits that could serve as non-canonical cleavage sites with consensus P1 and P2 resides . We focused on two clusters of such sites ( Fig . 4D ) that could potentially generate cleavage fragments of appropriate size ( 75 , 55 , and 20 kDa , see above ) . We constructed a series of mutants in which the invariant Gln at each potential P1 position was substituted with Arg , and examined their cleavage by 3CD . 3CD cleavage was not affected by Q211R , Q581R-Q583R , or Q612R mutations ( Fig . 4E , left ) , excluding these as 3CD cleavage sites . In contrast , a Q190R mutation blocked the cleavage event that generates the 55 kDa but not the 75 kDa fragment , while Q552R-Q554R mutations completely abolished 3CD cleavage of TRIF ( Fig . 4E , left ) . We then constructed individual Q552R and Q554R mutants , and showed the loss of cleavage in Q552R-Q554R was due to Q554R ( Fig . 4E , right ) . These results establish Q190 and Q554 as 3CD cleavage sites within TRIF , and clarify the identities of the observed TRIF cleavage fragments . The 75 kDa fragment results from cleavage at Q554 and corresponds to aa 1–554 of TRIF . This fragment is further cleaved at Q190 , giving rise to the 55-kDa and 20-kDa fragments that correspond to aa 191–554 and 1–190 , respectively . The different effects of the Q190R and Q554R mutations on 3CD cleavage of TRIF indicate that 3CD cleaves TRIF in an ordered process . The fact that cleavage at Q190 cannot proceed when cleavage at Q554 is blocked ( Fig . 4E ) suggests that the cleavage at Q554 is a prerequisite to cleavage at Q190 . We thus propose a “two-step” model for the 3CD cleavage of TRIF , in which primary cleavage at Q554 site induces a conformational change that exposes the Q190 site , allowing the second cleavage to occur ( Fig . 4F ) . The N-terminal region of TRIF contains three TRAF6-binding motifs that are important for activation of the transcription factors NF-κB and IRF-3 in TLR3 signaling [25] , [26] . Q190 is located between the first and second of these TRAF6-binding motifs , while the Q554 cleavage site is located between the TIR domain and RHIM motif ( Fig . 4D , right ) , both important for transcriptional activation of IFN-β [27] . Cleavage at these residues could yield fragments with reduced signaling ability , or potentially dominant negative activity against signal transduction . We thus ectopically expressed the predicted , individual 3CD-generated TRIF fragments: N-190 ( aa 1–190 ) , N-554 ( aa 1–554 ) , M-364 ( aa 191–554 ) and C-158 ( aa 555–712 ) ( Fig . S6A in Text S1 ) , and examined their abilities to activate IFN-β and NF-κB-specific ( PRD-II ) promoters in luciferase reporter assays . While N-190 and C-158 were incapable of activating either promoter , overexpression of N-554 and M-364 stimulated both the IFN-β and PRD-II promoters ( Fig . S6B in Text S1 , left and right , respectively ) . When co-expressed with wild-type TRIF at a 1∶1 ratio , these fragments did not demonstrate any dominant negative effects ( Fig . S6C in Text S1 ) . Other evidence suggests they do not transduce signals from TLR3 ( data not shown ) . We next addressed the question of why TRIF is cleaved by 3CD but very inefficiently or not at all by 3Cpro . The ability of 3ABC to cleave MAVS , while 3Cpro cannot , is related to its unique mitochondrial targeting [12] . To determine if differences in intracellular localization could similarly account for the unique activity of 3CD , we compared the cellular localization of 3Cpro and 3ABCD by confocal microscopy . When expressed ectopically with an N-terminal Flag tag , 3Cpro was diffusely present throughout the cytoplasm , while Flag-3ABC , included as a control , demonstrated prominent mitochondrial localization , as reported previously [12] ( Fig . 5A ) . In contrast , 3ABCD , expressed with a C-terminal V5 tag , was present at much lower abundance and with a perinuclear , ER-like distribution . Both 3CD and 3ABCD are known to be subject to ubiquitin-mediated proteolysis [28] , potentially explaining the low abundance of 3ABCD-V5 , much of which is likely processed to 3CD or 3Dpol . Confocal microscopy of cells ectopically expressing both TRIF and proteolytically-inactive C172A mutants of 3Cpro and 3CD revealed no evidence for specific co-localization of these proteins ( Fig . 5B ) . Thus , the unique ability of 3CD to cleave TRIF is not due to its localization to a TRIF-rich compartment . The noncanonical nature of the 3CD cleavage sites in TRIF , DWSQ190 and EQSQ554 , in which the P4 position is occupied by an amino acid residue with an acidic ( Asp or Glu ) rather than hydrophobic side chain ( Leu , Ile or Val ) ( Fig . 5C ) , could explain why TRIF is not efficiently cleaved by 3Cpro . The fact that they are nonetheless cleaved by 3CD suggests that the substrate specificity of 3CD may differ from that of 3Cpro in tolerating or possibly preferring an acidic residue at the P4 position . To assess this potential difference in substrate specificity , we altered the P4 positions within the non-canonical cleavage sites in TRIF , substituting the acidic P4 residues in each with Leu , thereby generating consensus 3Cpro sites ( TRIF-D187L and TRIF-E551L , respectively , Fig . 5C ) . When expressed ectopically , the D187L mutant , now carrying a LWSQ cleavage sequence , was readily processed by both 3Cpro and 3CD , yielding a novel fragment with an apparent molecular mass of 70 kDa ( Fig . 5D , lane 5 , 6 ) . This 70 kDa fragment co-migrated in SDS-PAGE with the TRIF C-522 fragment corresponding to aa 191–712 ( Fig . 5D , compare lane 5 vs . 14 ) , confirming that cleavage had occurred at Q190 . As expected , this fragment was not further cleaved by 3Cpro , but was further processed by 3CD at Q554 , generating the same 55 kDa fragment produced from wild-type TRIF by 3CD ( Fig . 5D , lane 3 vs . 6 ) . On the other hand , there was no difference in the processing of the wild-type and the E551L mutant TRIF , the latter of which was only marginally cleaved by 3Cpro despite carrying a canonical LQSQ sequence ( Fig . 5D , lane 7 vs . 8 ) . Nonetheless , when both cleavage sites were changed to a 3Cpro consensus , the resulting double mutant ( TRIF-DELL ) was readily cleaved by both 3Cpro and 3CD ( Fig . 5D , lanes 11 and 12 ) . This produced a 70 kDa fragment similar to that observed with the D187L mutant , suggesting that the order of cleavage had been altered to occur first at Q190 ( Fig . 5E ) . Additional processing led to the 55 kDa fragment , although there was less of this product produced by 3Cpro than 3CD . Collectively , these results demonstrate that 3CD differs in its substrate specificity from 3Cpro , tolerating an acidic residue at the P4 position while 3Cpro does not , and that this accounts , at least in part , for its ability to cleave TRIF . The ability of HAV to antagonize TLR3 signaling is likely to have evolved because antiviral responses evoked by TLR3 act in someway to restrict infection . To test this hypothesis , we assessed viral replication by a variety of methods over a range of multiplicity of infection . Immunoblots demonstrated that viral protein abundance ( 3Cpro ) was reduced in Huh7 . 5-TLR3 cells infected at an m . o . i . of 3 , compared to cells expressing empty vector or TLR3-ΔTIR ( Fig . 1B , lane 4 vs . lanes 2 or 6 ) . Similarly , the fluorescence intensity of HAV antigen was noticeably less in infected Huh7 . 5-TLR3 cells compared with the control ΔTIR cells ( Fig . 6A ) , although the proportion of cells expressing HAV antigen was not reduced 5 days after infection at an m . o . i . of 1 . HAV antigen-specific ELISA assays also showed that Huh7 . 5-TLR3 cells ( infected at an m . o . i . of 0 . 05 ) produced less than 50% of the amount of assembled HAV capsid antigen produced by cells expressing empty vector , or a TLR3 mutant incapable of binding dsRNA ( TLR3-H539E ) [13] ( Fig . 6B ) . Infectious virus yields were also reduced ( by 30–60% ) in Huh7 . 5-TLR3 cells infected at low m . o . i . ( Fig . 6C ) . Somewhat different results were obtained in one-step growth assays done at an m . o . i . of 5 . 0 . HAV replicates very slowly compared to other picornaviruses , with an infectious cycle of approximately 24 hrs evident in such assays ( Fig . 6D ) . Somewhat surprisingly , we observed no differences in the kinetic of intracellular infectious virus accumulation between Huh7 . 5-TLR3 vs . H539E cells , up to 24 hrs after inoculation of the cells under one-step growth conditions ( Fig . 6D , left ) . Subsequent to this time point , however , less virus was produced in cells expressing functional TLR3 . This restriction on virus replication was also reflected in slightly lower ( about half log10 ) yields in extracellular infectious virus released from the cells ( Fig . 6D ) . Collectively , these data suggest that TLR3 signaling imposes a modest restriction on HAV infection , particularly at low m . o . i . , and after the first round of viral RNA replication . This is reminiscent of the effects of TLR3 expression on low vs . high m . o . i . HCV infections that we have observed in previous studies [13] . To confirm these findings , we sought evidence of a gain in permissiveness for HAV infection in PH5CH8 cells in which TLR3 signaling was impaired by RNAi-mediated depletion of TRIF . PH5CH8 cells are T-antigen transformed adult human hepatocytes that possess robust TLR3 and RLR signaling [14] and are generally nonpermissive for HAV . TRIF was depleted by lentiviral transduction of a TRIF-specific short-hairpin RNA ( shRNA ) ( Fig . 6E ) , eliminating IFN-β promoter activation by extracellular poly-I:C ( Fig . 6F ) . The cells were infected with HAV at an m . o . i . of 1 , and examined 8 days later by immunofluorescence microscopy for viral antigen expression . This was rarely observed in PH5CH8 cells transduced with a non-targeting control shRNA , but detected in ∼2% of the TRIF depleted cells ( Fig . 6G ) . Similar results were obtained in cells transduced with a TLR3-specific shRNA ( data not shown ) . Thus , reconstitution of TLR3 signaling in Huh7 . 5 cells results in a modest inhibition of HAV infection , while the ablation of TLR3 signaling in PH5CH8 cells provides a significant replication advantage to HAV . In both cases , these effects are of relatively small magnitude , likely reflecting the presence of redundant innate antiviral defense mechanisms , including responses generated by RLRs or protein kinase R . The abrogation of pro-inflammatory signals , the effects of which cannot be deduced from in vitro experiments , may represent a more substantial advantage to the virus in vivo in HAV-infected persons .
Here , we show that HAV disrupts TLR3 signaling by targeting the essential adaptor protein TRIF for degradation by the 3CD protease-polymerase processing intermediate . The ability of poly-I:C to stimulate the IFN-β promoter or induce the expression of ISGs when added to media was markedly attenuated in HAV-infected Huh7 hepatoma cells in which TLR3 expression had been reconstituted by retroviral gene transduction ( Fig . 1A and B ) . This disruption of TLR3 signaling was associated with a loss of detectable TRIF ( Fig . 2 ) , and could be recapitulated by ectopic expression of 3ABCD or 3CD in both Huh7-TLR3 cells and HeLa cells which possess an endogenous TLR3 signaling pathway ( Fig . 3 ) . The loss of TRIF expression was linked to the cysteine protease activity residing within the 3C sequence of 3CD , which we demonstrate cleaves TRIF sequentially at two noncanonical 3Cpro cleavage sites ( Fig . 4B and E ) . Additional studies suggested that this is due to the ability of the 3D sequence in 3CD to alter the substrate specificity of the protease such that it better accommodates the acidic residues present at the P4 position of cleavage sites in TRIF ( Fig . 5D ) . These observations add to our understanding of the pathogenesis of HAV , a significant human pathogen that has received scant attention in recent years . In previous work , we demonstrated that HAV also antagonizes the induction of IFN responses by the cytosolic RLR pattern recognition receptors , RIG-I and MDA-5 , by inducing proteolysis of the adaptor protein MAVS [12] ( Fig . 7 ) . As we report here with poly-I:C-induced TLR3 signaling , we found that ectopically expressed 3ABCD was capable of disrupting Sendai virus-induced RIG-I signaling . 3ABCD results from secondary processing of the HAV polyprotein at the P2-P3 junction , and is itself subject to further processing via two distinct pathways , one leading to production of 3ABC and the other to 3CD ( Fig . 7 ) . Both intermediates contain the catalytically active 3Cpro cysteine protease domain , but they have distinct cellular localization and substrate specificities ( Fig . 5 ) . 3ABC , due to the presence of a mitochondrial targeting transmembrane domain in 3A , localizes to the mitochondrial membrane where it cleaves MAVS ( Fig . 5A ) . In contrast , 3CD appears to be localized primarily to the perinuclear ER , and its ability to cleave TRIF is dependent upon its unique substrate specificity rather than its intracellular localization ( Figs . 5B and 5D ) . Our results reveal an unexpected role of the 3D sequence in modulating the substrate specificity of 3CD . 3Cpro cleavage sites within the HAV polyprotein , as well as MAVS , contain a hydrophobic amino acid ( Leu , Ile , or Val ) at the P4 position [12] , [24] that fits into the hydrophobic S4 binding pocket within the crystal structure of 3Cpro [29] , [30] . In contrast , both 3CD cleavage sites within TRIF contain an acidic amino acid residue ( Asp-190 and Glu-551 ) at the P4 position ( Fig . 5C ) , and therefore do not conform to the canonical cleavage sequence . A previous study showed that a peptide substrate with a Glu substitution ( underlined ) at the P4 position , Ac-EERTQ↓SFS-NH2 , which is similar to the TRIF cleavage site EQSQ554 , was not cleaved by 3Cpro [31] . Our data suggest that 3CD possesses a unique substrate specificity that allows it to recognize and hydrolyze cleavage sites within TRIF that are otherwise relatively resistant to 3Cpro . In support of this notion , we showed that changing the non-canonical cleavage site at Gln-190 of TRIF to a canonical 3Cpro cleavage sequence resulted in efficient 3Cpro proteolysis and a reversal of the order of cleavage at the two sites in TRIF ( . 5D ) . A similar change at the Gln-554 site did not make it fully permissive for 3Cpro cleavage , however , suggesting that there are other differences in the substrate specificities of 3Cpro and 3CD . Our data indicate that the change in substrate specificity of 3CD is conferred in cis by the 3D sequence ( Fig . 3B ) , although the structural basis for this remains to be determined . In addition to their differentiated roles in evading innate immune responses , 3ABC and 3CD are likely to have specialized roles in the viral life cycle . 3ABC is a stable intermediate that is important in processing of the P1-2A segment of the polyprotein required for assembly of the viral capsid [32] . 3CD , based on studies with other picornaviruses , may play a role in the uridylyation of the protein primer of RNA synthesis , 3B ( VPg ) [33] . The multiple functions of these viral proteins reflect a strategy used by picornaviruses to create processing intermediates that are functionally distinct from their mature products [34] , [35] , [36] . The dual targeting of RLR and TLR3 signaling by HAV 3ABCD processing intermediates is reminiscent of the HCV NS3/4A protease , which disrupts both RIG-I and TLR3 signaling pathways by proteolytically cleaving the same signaling adaptor proteins , MAVS and TRIF , respectively [7] , [8] , [9] . 3Cpro and NS3/4A are both chymotrypsin-like proteases with double β-barrel folds [30] , [37] , but they are not closely related phylogenetically . The HAV 3Cpro protease has a cysteine nucleophile in its active site , while NS3/4A has a serine . These viral proteases have very different substrate specificities , and they cleave MAVS and TRIF at distinctly different sites [7 , 8 , 9 , 12 , and Fig . 3] . The fact that both of these hepatotropic viruses express proteases targeting these two critical adaptor molecules is thus a remarkable example of convergent evolution . It also speaks strongly to the importance of these signaling pathways in the control of RNA viruses in the liver . However , since HAV infection is always successfully controlled by the host ( except in rare cases of fulminant disease ) , these data indicate that the disruption of RLR and TLR3-mediated antiviral defenses is not sufficient for a virus to establish the longterm persistence that typifies most HCV infections . HCV must possess additional immune evasion strategies to account for its unique capacity to establish chronic infections . We demonstrated a minimal gain of permissiveness for HAV replication in hepatocyte-derived cells in which TLR3 or TRIF expression was depleted ( Fig . 6G ) , and a reduction in viral antigen expression in hepatoma cells with active TLR3 signaling ( Fig . 6B ) . However , these effects were modest , potentially reflecting very efficient control of TLR3 signaling by 3CD in infected cells such that TLR3 has little impact on viral replication . Alternatively , it may be that the primary advantage gained by HAV in antagonizing TLR3 signaling is impaired production of proinflammatory cytokines and reduced inflammation associated with the infection . TLR3 signaling is critically important to murine host defense against coxsackievirus B , another picornavirus [38] , and it is plausible that the disruption of TLR3 signaling has significance beyond impairing the type I IFN response . The subversion of both RLR and TLR3 signaling likely contributes to the relatively lengthy , clinically silent incubation period that precedes acute liver injury in hepatitis A . This period is characterized by robust viral replication within the liver and shedding of virus in feces , which reaches a maximum at the onset of hepatic inflammation [10] , [39] . The absence of a type I IFN response in acute infectious hepatitis was hinted at in clinical studies done almost 40 years ago [40] . Consistent with this , we recently documented a paucity of type I IFN-dependent ISG expression ( e . g . , IFIT-1 , ISG15 ) within the liver of HAV-infected chimpanzees during the first weeks of infection despite high viral RNA copy numbers [41] . The cleavage of MAVS and TRIF by 3ABC and 3CD , respectively , provides a partial mechanistic explanation for this . By dealing a double blow to two major cellular antiviral response pathways , HAV appears able to block somatic cell expression of IFN-α/β , thus facilitating its replication . Yet to be explained is how it evades recognition by plasmacytoid dendritic cells ( pDCs ) , which may play a significant role in sensing HCV infection in the liver and generating the strong intrahepatic ISG responses that are often observed in acute and chronic hepatitis C [42] , [43] .
HEK 293FT cells , Huh7 , Huh-7 . 5 and Bla-C cells [12] were cultured in DMEM with 8% FBS . Huh7-TLR3 , Huh-7 . 5-TLR3 and related control cells [13] , and HAV-Bla subgenomic replicon cells were cultured in the same medium supplemented with blasticidin [12] . The cell culture-adapted HAV strain HM175/18f [16] was amplified in Huh7 cells; on fetal rhesus kidney FRhK4 cells . pCDNA6-TRIF [7] and pCMV-HA vectors expressing N-terminally HA-tagged HAV proteins derived from HM-175/18f virus [12] have been described previously . Similar pCMV-HA vectors expressing the wild-type HM175 3Cpro and 3CD proteins were constructed by amplification of the corresponding sequences from pHAV/8y ( Suzanne Emerson , NIAID ) . Truncations of TRIF were generated by PCR mutagenesis , and mutations in TRIF and 3CD constructed by site-directed mutagenesis ( Strategene ) . Other plasmids were obtained from the following sources: pIFN-β-Luc ( Rongtuan Lin , McGill University ) , pPRD-II-Luc ( Michael Gale , University of Washington ) , pCMV-β-gal ( Clontech ) , pRL-CMV ( Promega ) , pEF-Bos-TRIF ( Kate Fitzgerald , University of Massachusetts ) and pCDNA3-Flag-IKKε ( Tom Maniatis , Harvard University ) . Antibodies used in these studies included: anti-TLR3 ( Ilkka Julkunen , National Institute for Health and Welfare , Finland ) , anti-TRIF S219 ( Cell Signaling Technology ) , anti-TRIF aa 4–31 ( Alexia ) , anti-IRF-3 sc-9082 ( Santa Cruz ) , monoclonal anti-HAV K2-4F2 , K3-4C8 ( Commonwealth Serum Laboratories , Victoria , Australia ) , and 6A5 ( John Hughes , Merck , Sharp & Dohme ) , anti-HAV 2A ( David Sangar , Wellcome Biotech ) , anti-3Cpro ( Verena Gauss-Muller , University of Lübeck ) , anti-ISG15 ( Santa Cruz ) , and anti-HA and anti-Actin ( Sigma ) . Rabbit anti-TRIF antibody S537-2 was obtained by immunization of rabbits with recombinant TRIF protein [7] . For protein expression , cells were transfected with plasmid DNA using Lipofectamine 2000 ( Invitrogen ) and lysates prepared 20 hrs later using 1% NP-40 lysis buffer . HAV-infected cells ( m . o . i . = 3 ) were cultured for 4 days prior to transfection . For luciferase reporter assays , expression and/or Luc reporter plasmids were transfected into cells ( seeded in triplicate in 96-well format ) with an internal β-galactosidase ( pCMV-β-gal ) or Renilla luciferase ( pRL-CMV , Promega ) transfection control . At 20 hours posttransfection , when indicated , poly ( I:C ) ( Sigma ) was added to the medium and cells incubated for additional 6 hours . Cells were lysed in Reporter Lysis Buffer ( Promega ) and equal quantity of lysate used for luciferase and β-galactosidase assays ( Promega ) . In experiments using the Renilla luciferase control , cells were lysed in Passive Lysis Buffer ( Promega ) and tested by Dual-Luciferase assay ( Promega ) . For each sample , the luciferase activity was normalized to the β-galactosidase or renilla luciferase activity . In the case of poly ( I:C ) stimulation , results were presented as fold induction compared to unstimulated cells . Statistical analysis was performed using two-tailed Student's t test . HAV antigen-specific ELISA was carried out using a post-convalescent human antibody for capture [44] , and a murine monoclonal antibody ( K24F2 ) for detection . Absorption at 450 nm was determined on a Synergy ( Biotek , Inc ) plate reader . The infrared fluorescent immunofocus assay ( IR-FIFA ) for infectious HAV was done using FRhK-4 cells as previously described [12] . For detection of HAV antigen by immunofluorescence microscopy , cells were fixed with 4% PFA for 25 min , labeled with murine mAb 6A5 and after extensive washing incubated with goat anti-mouse IgG Alexa-594 conjugate ( Invitrogen ) . The 3Cpro coding sequence from HAV strain HM175/18f was cloned into bacterial expression vector pGEX-4T3 ( GE Life Sciences ) , fused in-frame with an N-terminal GST tag . For protein expression , an overnight culture of E . coli strain BL21 ( DE ) ( Novagen ) containing the expression construct was diluted 10-fold and cultured at 37°C for 2 hrs . Expression was induced by addition of 0 . 1 mM IPTG and continued culture at 25°C for 3 hrs . Bacterial cells were harvested and lysed in BugBuster solution ( EMD Biosciences ) containing 37 . 5 U/ml Benzonase , 15 KU/ml recombinant lysozyme and 2 mM DTT . GST-3Cpro fusion protein was purified from the bacterial lysate by affinity chromatography using the GST MicroSpin Purification Module ( GE Life Sciences ) according to the manufacturer's instructions . Myc-TRIF and its truncated forms were synthesized in vitro and labeled with [35S]-Met/Cys using T7 Coupled Transcription/Translation System ( TNT , Promega ) according to the manufacturer's instructions . Cleavage assays were performed in a 10-µl mixture containing 1 µl TNT product and 0 . 5 µM purified GST-3Cpro in a buffer containing 50 mM Tris-HCl pH 8 . 0 , 2 . 5 mM EDTA and 2 mM DTT . Similarly purified GST was used as a negative control at the same concentration . Reactions were carried out overnight at 3°C and stopped by addition of an equal volume of 2X SDS sample buffer . Cleavage products were analyzed by SDS-PAGE followed by autoradiography . For detection of endogenous TRIF , 1 mg of cell lysate was immunoprecipitated with 1 µg of rabbit anti-TRIF antibody S537-2 [7] , followed by immunoblotting with anti-TRIF S219 . HA-3CD was detected with a similar method using anti-HA for immunoprecipitation and anti-3Cpro for immunoblotting . Huh7-TLR3 cells were mock-infected or infected with HAV at m . o . i . = 5 and cultured for 5 days , then stimulated by the addition of poly- ( I:C ) ( 50 µg/ml ) to the medium for 2 hours and lysed with 1% NP-40 lysis buffer . Cell lysates ( 10 µg ) were mixed with deoxycholate ( DOC ) sample buffer ( final concentration 1% DOC ) and separated by Tris-Glycine/1% DOC native PAGE . IRF-3 monomer and dimer were detected by immunoblotting with rabbit anti-IRF-3 sc-9082 . Ectopically expressed Flag-tagged TRIF , 3Cpro and 3ABC , HA-tagged 3Cpro and 3CD , and V5-tagged 3ABCD were imaged as described previously [12] . For visualization of poly ( I:C ) -induced changes in IRF-3 localization , Huh7-TLR3 cells were grown on chamber slides , infected at low m . o . i . and cultured for 5 days . Cells were then mock-stimulated or stimulated with poly- ( I:C ) ( 50 µg/ml ) added to the medium for 2 hours , and fixed with 4% paraformaldehyde . After permeabilization with 0 . 25% Triton X-100 , the cell monolayer was incubated with rabbit anti-IRF-3 sc-9082 and murine anti-HAV K3-4C8 , followed by secondary antibodies goat anti-rabbit Alexa Fluor 488 and goat anti-mouse Alexa Fluor 594 ( Invitrogen ) . Nuclei were counterstained stained with DAPI . Images were collected using a Leica DMIRB Inverted Microscope in the Michael Hooker Microscopy Facility . HAV strain HM175/18f ( including individual HAV proteins ) , M59808; wild-type HAV strain HM175 , M14707 . 1; TLR3 , NP_003256; ISG15 , NP_005092; TRIF ( TICAM-1 ) , NP_891549; IKKe , NP_054721; IRF-3 , NP_001562; MDA5 , NP_071451; RIG-I , O95786; MAVS ( IPS-1 , Cardif , VISA ) , Q7Z434; GAPDH , P04406; Actin , P60709 . | While viruses that target the liver often cause lengthy infections with considerable morbidity , there is limited understanding of how they evade host responses . We have studied hepatitis A virus ( HAV ) , an important cause of acute hepatitis in humans . Although HAV infection typically results in hepatic inflammation , there is no disease in the liver during the first weeks of infection despite robust virus replication . This suggests that HAV either fails to stimulate or efficiently evades recognition by host innate immune sensors . Our prior work showed HAV disrupts RIG-I/MDA5 signaling by targeting MAVS , an essential adaptor protein , for degradation by 3ABC , a precursor of the only HAV protease , 3Cpro . Here , we show here that a distinct viral processing intermediate , the 3CD protease-polymerase , disrupts TLR3 signaling by degrading its adaptor protein , TRIF . HAV has evolved a novel strategy to target two different host adaptor proteins with a single protease , using its 3Dpol RNA polymerase to modify the substrate specificity of its 3Cpro protease when fused to it in the 3CD precursor , thus allowing it to target non-canonical 3Cpro recognition sequences in TRIF . This remarkable example of viral adaptation allows the virus to target two different host adaptor proteins with a single viral protease . | [
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"immunolog... | 2011 | Disruption of TLR3 Signaling Due to Cleavage of TRIF by the Hepatitis A Virus Protease-Polymerase Processing Intermediate, 3CD |
Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases . Here we describe a new statistical method , genomeDCA , which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures . We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae ( pneumococcus ) and Streptococcus pyogenes ( group A Streptococcus ) . For pneumococcus we identified 5 , 199 putative epistatic interactions between 1 , 936 sites . Over three-quarters of the links were between sites within the pbp2x , pbp1a and pbp2b genes , the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics . A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase , changes to which underlie trimethoprim resistance . Distinct from these antibiotic resistance genes , a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions , while another distinct component included genes associated with virulence . The group A Streptococcus ( GAS ) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome . Despite this , we were able to pinpoint two RNA pseudouridine synthases , which were each strongly linked to a separate set of loci across the chromosome , representing biologically plausible targets of co-selection . The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs , potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions , or genes whose product activities contribute to the same phenotype . This discovery approach greatly enhances the future potential of epistasis analysis for systems biology , and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work .
The study of co-evolution in recombining populations of bacteria has been limited by the scale and polymorphisms present in population samples for which whole genome sequences are available . Even the most recent population genomic studies of bacterial pathogens have been constrained in this respect , such as focusing on a particular genotype[1–3] , biasing sampling towards particular clinical outcomes[4–6] , or surveying organisms in which limited genetic diversity and strong linkage disequilibrium ( LD ) mask the signals of shared selection pressures[7 , 8] . For whole genome-scale modeling of co-evolution , sampling should preferentially span the entirety of a diverse , recombining species in an unbiased manner . The first organism satisfying all the above-mentioned desiderata is Streptococcus pneumoniae ( the pneumococcus ) , for which over 3 , 000 genome sequences from a well-defined , limited study population were recently published[9] . As the pneumococcus is an obligate nasopharyngeal commensal and pathogen , the bacterial population was evenly sampled through a structured survey of the hosts . The diverse multi-strain population structure , coupled with the naturally transformable nature of S . pneumoniae , results in low LD across the genome . Hence this set of pneumococci can be considered as an ideal set for detecting genes that evolve under shared selection pressure . In contrast , we also investigate how well a genome-wide analysis of co-evolution can perform when the sampling is done from the opposite end of the genetic variation spectrum by considering 3 , 442 genomes of the M1 lineage of S . pyogenes , which has contributed significantly to the global epidemic of group A Streptococcus infections during the past three decades[3] . In this case recent expansion from a single progenitor has generated a clonal population that has experienced a minimal amount of homologous recombination . Analyzing sets of co-evolving polymorphisms is a powerful means of identifying sites that interact directly , through protein-protein contacts , and indirectly , through epistatic interactions that affect the same phenotype . The former type of selection pressure has previously been studied on the scale of individual proteins . It has been known for more than 20 years that the correlations of amino acids in two columns in a multiple sequence alignment ( MSA ) , contain exploitable information and provide a non-trivial predictor of protein tertiary structure spatial proximity[10 , 11] . Detection of co-evolving mutations in genomes is in the statistical sense analogous to this structural prediction problem , as both phenomena can arise as a consequence of joint selection pressures . The latest advances in computational structural biology have shown that by changing the modeling framework from correlations to high-dimensional model learning one can improve protein contact predictions significantly , an approach generally referred to as direct coupling analysis ( DCA ) [12–15] . Furthermore , including considerations of epistatic interactions between sites has recently been shown to significantly improve the mapping between genotype and phenotype for a beta lactamase protein[16] . Co-evolving sites do not necessarily directly interact . Rather , changes at distinct sites may represent selection for a particular phenotype determined by multiple polymorphic loci . However , the complexity of the possible set of interactions has mostly limited previous analyses of epistasis to viral datasets of limited diversity; nevertheless , these studies have shown epistasis to be an important factor in evolution . An application of a phylogenetically-informed method to influenza subtypes H1N1 and H3N2 identified patterns of substitutions associated with the emergence of resistance to oseltamivir[17] , and many sites were found to be undergoing coordinated evolution within the hepatitis C virus[18] . However , the non-linear expansion in the number of interactions as the genome length and diversity increase has hampered the application of such methods to the study of bacterial populations . In recent work , pairwise statistical correlation analysis was demonstrated to successfully reveal certain types of co-evolutionary patterns across the genome for 51 Vibrio parahaemolyticus isolates[19] . While this approach appears promising given the way in which linkage disequilibrium is handled in it , pairwise analyses of association are in general subject to Simpson’s paradox which may cause difficulties in separating direct and indirect links between variables[20–22] . Furthermore , the necessity of correcting for a quadratically increasing number of multiple hypothesis tests reduces the statistical power to detect the true positive associations . A model-based approach to estimating the strength of co-evolution between genome sites is therefore preferential to correlation based analysis , as has been clearly demonstrated earlier in the context of protein evolution[12–15] . Here we demonstrate a new method for the identification of statistically significantly co-evolving polymorphisms from bacterial genome sequence alignments named genomeDCA ( freely available at https://github . com/mskwark/genomeDCA ) . By considering the evolution of polymorphic sites simultaneously and using the inference tools for regularized statistical model learning one avoids both the problems that drive high levels of false positives and negatives when the number of pairwise interactions grows . The method introduced here offers a powerful complementary approach to traditional GWAS analyses[23] for the explorative discovery of polymorphisms potentially underlying phenotypes that have an unknown genetic basis in the emerging era of massive population sequencing for bacteria .
Analysis of coupled loci for the pneumococcus is derived from a whole genome alignment generated from short-read data for 3 , 156 systematically-sampled pneumococcal isolates [9] each aligned to the reference sequence of S . pneumoniae ATCC 700669 [24] . Filtering this alignment for biallelic loci at which the minor allele frequency was >1% and >85% of isolates had a base called identified 81 , 560 polymorphic sites . Of these , 88 . 3% were within protein coding sequences ( CDSs ) , a slight enrichment relative to the 87 . 2% of the S . pneumoniae ATCC 700669 reference sequence annotated as CDSs . Following the genome-wide linkage analysis ( see Methods ) , estimates of association strength were retained from 102 , 551 couplings ( S1 Table ) . A Gumbel distribution fitted to this sample ( Fig 1; μ = 0 . 096 and β = 0 . 028 ) significantly diverged from the empirical data above a coupling strength of 0 . 129 . The 5 , 199 couplings ( S2 Table ) exceeding this threshold were considered as putative epistatic interactions; these affected 1 , 936 sites , 89 . 0% of which were within CDSs . As closely proximal sites were excluded from this analysis , these coupled sites had a mean separation of 587 . 4 kb , with only two sites separated by less than five kilobases ( Fig 2 ) . Hence these associations are unlikely to be artifacts of genetic linkage . The putative epistatic interactions identified by genomeDCA were used to generate a network ( Fig 3 ) . The nodes , each corresponding to a CDS , were colored according to function and scaled according to the number of epistatic links with which they were associated . The edges were weighted according to the number of interactions between CDS pairs . Most of the annotated functional categories were represented in the network , with the notable exception of mobile genetic element genes . This absence was almost entirely the consequence of the lack of informative sites in these regions of the genome , owing to their high variability across the population . By contrast , the functional category most over-represented in the dataset was surface-associated proteins . Although previous work has suggested immune selection might drive epistasis between antigens [25] , in fact this enrichment was entirely the consequence of selection for antibiotic resistance . Of the 4 , 617 links represented in this network , 3 , 578 ( 77 . 5% ) involved 175 sites found in one of three genes encoding penicillin-binding proteins ( PBPs; Fig 3A ) : SPN23F03080 ( pbp2x ) , SPN23F03410 ( pbp1a ) and SPN23F16740 ( pbp2b ) . These PBPs have been experimentally demonstrated to be the major determinants of resistance to beta lactams [26] , with changes to each individual protein reducing its affinity for beta lactam antibiotics while retaining its the ability to bind its natural substrates[27 , 28] . This link between genotype and phenotype was also identified by a genome-wide association study ( GWAS ) using this dataset[29] . Of the 858 sites found to be significantly associated with beta lactam resistance by this GWAS , 403 were within these three coding sequences for PBPs; of these , 216 met the criteria to be analysed in this study . These corresponded to 161 of the 175 sites identified within the same genes by this analysis , representing a highly significant overlap with those sites significantly associated with beta lactam resistance by the GWAS ( Fisher exact test , OR = 110 . 2 , 95% confidence interval = 58 . 85–221 . 83 , p < 2 . 2x10-16 ) . Hence the couplings between sites in these genes represent a set of changes that distinguish pneumococci with differing sensitivities to beta lactam antibiotics . This was in line with the distribution of these alleles across the population ( S1 Fig ) , with particular alleles at the coupled sites found in multiple penicillin-insensitive lineages . This also confirmed that none of the identified associations correlated with the expansion of a single clone . Changes in these genes are strongly epistatic , as alterations of all three PBPs are necessary for pneumococci to develop non-susceptibility to a broad range of beta lactam antibiotics . Consequently , the emergence of multidrug-resistant pneumococci is associated with genetic changes that alter all three of these enzymes over very short evolutionary timescales [30–32] . Another factor that might underlie both the concerted changes at all three loci , rather than a gradual emergence of resistance , as well as the non-uniform distribution of coupled sites across these genes ( Fig 4 ) is the potential for alterations in only one protein disrupting direct protein-protein interactions . As these proteins perform similar functions on the same substrate , and are all co-localised to the cell membrane , it has been hypothesised that they function as constituents of a multi-enzyme complex[33] . Evidence from co-immunoprecipitation and crosslinking experimental work has supported this idea[34 , 35] and hence the distribution of coupled sites between the PBPs was investigated in greater detail . Couplings were identified between all three PBPs , although almost 95% involved pbp2x . This might reflect that pbp2x has to be altered for resistance to both penicillins and cephalosporins , whereas modifications of pbp1a are important primarily for cephalosporin resistance [36] , and modifications to pbp2b are important primarily for penicillin resistance [37] . Alternatively , if these proteins do interact , these data suggest pbp2x would be central to any potential protein-protein interactions . The coupled sites are distributed broadly across pbp2x , with the exception of the PBP dimerization domain , despite the identification of sites within this region by GWAS . Co-evolving sites related to pbp1a are more narrowly distributed and involve stronger interactions with pbp2x than with pbp2b ( Fig 4 , S2 Table ) . The links between the proteins PBP1A and PBP2X are distributed between two domains and a further structural analysis ( Fig 5 , S3 Table ) showed how the identified positions in pbp1a with the strongest couplings were located in strand β-4 and the loop connecting strands β-3 and β-4 , near the transpeptidase active site , but not overlapping with the conserved catalytic residues . The counterpositions in pbp2x showed a spatially less focused pattern with linked positions near the active site , including the typically conserved active site residues Ser395 and Asn397 , as well as links to positions not in direct spatial proximity of the active site , but structurally linked to that region through helical secondary structure elements . Alterations in the active site surroundings can interfere with inhibitor binding with only minor effect to catalytic function as was evident for substitutions in the identified β-3/β-4 loop region residues 574–577 which were strongly linked to beta-lactam resistance[38] . Resistant strain pbp1a likewise showed amino acid substitutions at the identified positions 583 and 585 in strand β-4 in comparison to a beta-lactam susceptible strain[38] ( resistant strain PDB code 2V2F ) . Position 580 at the N-terminal end of β-4 ( typically a proline ) in pbp1a was linked to position 363 in pbp2x , which is part of an ionic interaction ( Glu363 to Arg372 ) present in all current crystal structures of S . pneumoniae pbp2x ( see Methods for details ) and may play a structural role by stabilising the α-2/α-4 loop region proximal to the pbp2x active site . Residues at PBP2X positions 401 , 404 , 412 and 413 are all buried within the protein , but are connected to active site residues Asn397 and Ser395 via helix α-5 . It is possible that these positions are implicated in active-site shaping as well . For pbp2b , structural mapping of the top ranking co-evolved sites revealed two major groupings: positions in the α-2/α-4 loop region that , similarly to the pbp1a case , partially cover the active site , and positions that , similarly to the pbp2x side of pbp2x –pbp1a couplings , were spatially more distant but structurally linked to the active site . As in pbp1a , observed flexibility in the α-2/α-4 loop region proximal to the active site points to a potential role in antibiotic resistance of this structural feature[39] . The PBPs are confined to a single network component that contains ten other proteins . Seven of these are found in close proximity to the three PBPs , and likely represent sequences altered when resistance-associated alleles of the PBP genes were acquired through transformation events , which often span tens of kilobases [30 , 32] . However , it is also possible these could play a role in ‘compensating’ for deleterious side-effects of the changes in the PBP proteins . One of these CDSs proximal to a penicillin-binding protein gene is mraY , directly downstream of pbp2x and encoding a phospho-N-acetylmuramoyl-pentapeptide-transferase also involved in cell wall biogenesis ( S2 Table , Fig 3 ) . It was previously predicted that mutations in this transferase associated with beta lactam resistance could represent compensatory changes ameliorating the costs of evolving beta lactam resistance[29] . Another CDS , gpsB , is shortly upstream of pbp1a and encodes a paralogue of DivIVA that also plays an important role in peptidoglycan metabolism [40] . The three proteins in the network component that were not proximal to a PBP-encoding CDS were dyr ( also known as folA or dhfR ) , encoding dihydrofolate reductase , and three nearby genes ( S2 Table , Fig 3 ) . Mutations in the dyr gene cause resistance to trimethoprim [41] . The earlier GWAS study[29] found a significant association between both dyr and folP with beta lactam resistance , despite no functional link to such a phenotype , nor any likely reason why they would directly interact with PBPs . Hence the detected interaction between dyr and the pbp genes is most likely explained by the co-selection for resistances that have accumulated in the same genetic background , resulting in the multi-drug resistant genotypes observed to have emerged over recent decades[42] . To identify other functional roles that might underlie the distinct sets of couplings represented in Fig 3 , a gene ontology ( GO ) analysis was performed for each network component containing more than two nodes . This identified five significant signals , including that for penicillin-binding associated with the previously described component ( GO:0008658 , Fisher’s exact test , OR = 337 . 3 , 95% confidence interval = 25 . 40–15878 . 4 , p = 0 . 00048 after Benjamini-Hochberg correction ) . However , the strongest association was that of the largest network component , containing 384 CDSs ( Fig 3B ) , with ATP binding activity ( GO: 0005524 , Fisher’s exact test , OR = 2 . 89 , 95% confidence interval = 2 . 01–4 . 18 , p = 8 . 75x10-7 ) . Other GO terms significantly associated with this component were GO:0005737 , corresponding to cytosolic localisation ( Fisher exact test , OR = 3 . 44 , 95% confidence interval = 2 . 10–5 . 62 , p = 0 . 0001 after Benjamini-Hochberg correction ) , and GO:0016021 , corresponding to integral membrane proteins ( Fisher exact test , OR = 2 . 32 , 95% confidence interval = 1 . 45–3 . 66 , p = 0 . 043 after Benjamini-Hochberg correction ) , which have cytosolic segments , despite being surface associated . These associations partly reflect the preponderance of cytosolic ATP-hydrolysing tRNA synthetases , of which enzymes for the processing of eleven amino acids were present among these CDSs , and membrane-associated ATP-hydrolysing ABC transporters . The most highly connected node in the component , linking to 22 other CDSs , was another ATPase . SPN23F11420 , encoded the Smc protein , is critical in organizing the chromosome and forms the basis of a multi-protein complex in both prokaryotes and eukaryotes [43] . Hence this large diverse set of coupled CDSs included many components of the essential cytosolic machinery , the interactions of which are critical to the basic functioning of the cell . The fourth significant enrichment of GO terms also involved tRNAs ( GO:0000049—tRNA binding; Fisher exact test , OR = 869 . 1 , 95% confidence interval = 36 . 83–4 . 50x1015 , p = 0 . 00048 after Benjamini-Hochberg correction ) , which applied to a component containing three nodes ( Fig 3C ) . One corresponded to pheS , a phenylalanyl tRNA synthetase , while the other was SPN23F19340 , annotated as encoding a tRNA binding protein of unknown function . Attempting to identify a more specific functional prediction using the CDD database[44] we found this protein possessed a “tRNA_bind_bactPheRS” domain , specifically involved in processing phenylalanyl-tRNAs , and only otherwise found in PheT , which directly interacts with PheS in the phenylalanyl-tRNA synthetase . Hence this coupling may represent a previously unexpected direct protein-protein interaction . The CDS directly downstream of pheS , encoding a putative membrane-associated nuclease ( SPN23F05260 ) , was coupled to a tRNA methyltransferase adjacent to pspA in a separate network component ( Fig 3D ) . The pspA gene , encoding a surface-associated protein involved in pathogenesis and immune evasion , was itself present in the same network component , and engaged in some of the strongest coupling interactions in the dataset . These linked to the divIVA , encoding a cell morphogenesis regulator [40] , and three CDSs upstream of ply , encoding the major pneumococcal toxin pneumolysin[45] which , like pspA , is critically important in pneumococcal virulence and upregulated during infection[46] . These three ply-associated CDSs ( SPN23F19480-SPN23F19500 ) encode proteins likely to play a role in localising or transporting pneumolysin from the cytosol into the cell wall[45] . Hence these coupling links could be the consequence of these virulence proteins engaging in interactions at the surface of the cell . Interactions were also detected between CDSs and non-CDS sequence . The 213 non-CDS coupled sites were enriched in non-coding RNAs ( Fisher exact test , OR = 1 . 92 , 95% confidence interval = 0 . 963–3 . 48 , p = 0 . 041 ) , suggesting they may represent functional links between RNA and proteins . However , the non-coding RNAs involved were riboswitches in 5’ untranslated regions . When a bipartite network was constructed that displayed couplings between CDSs and upstream non-CDS regions ( S2 Fig ) , the network components mirrored those in Fig 3 . This suggested the links represented sequence proximal , and therefore linked , to CDSs that were coupled , rather than epistatic interactions involving direct protein-DNA interactions . Correspondingly , neither DNA binding ( GO: 0003677 ) nor RNA binding ( GO:0003723 ) were enriched in this network . Similarly , there was no enrichment of coupled sites in non-coding regions between divergently transcribed CDSs , which should be enriched for regulatory elements ( Fisher test , OR = 0 . 48 , 95% confidence interval = 0 . 283–0 . 757 , p = 0 . 00074 ) . The whole genome alignment used in this analysis consists of short-read data for 3 , 442 isolates belonging to the contemporary M1 lineage of S . pyogenes , which has contributed significantly to the global epidemic of group A Streptococcus infections during the past three decades[3] . Filtering this alignment for polymorphic sites at which the minor allele frequency was >1% resulted in 324 SNP loci ( coupling estimates in S4 Table ) . To investigate the effect of including SNPs with more rare minor alleles we also performed an analysis without such MAF based filtering . However , uninformative polymorphic sites where the minor allele was private to a single genome only were still excluded in this analysis , leaving 5078 SNPs out of the total of 12400 SNPs present in the alignment ( coupling estimates in S6 Table , http://dx . doi . org/10 . 5061/dryad . gd14g ) . Similar to the pneumococcus analysis , we employed the Gumbel model to determine a threshold for significant couplings ( Fig 6 ) . For the two sets of loci there are 5952 and 1840 couplings exceeding the thresholds 0 . 014 and 0 . 023 , respectively . Inclusion of loci with rare minor alleles ( MAF < 1% ) did not impact rankings of the strongest couplings found in the smaller locus set . None of the loci with the rare minor alleles were among the 2000 top ranking couplings , while 5 . 3% of couplings ranking between 2000–3000 involved any such loci . However , their fraction was approximately 30% among couplings ranking between 3000–5000 , which indicates that relatively rare minor alleles may also lead to moderate levels of coupling signals when they are tightly linked as is the case for M1 data . Nevertheless , the vast majority of the loci with MAF < 1% were included in couplings very close to zero ( S5 Fig ) . Since the M1 lineage stems from a recent expansion from a single progenitor cell , there has been on an evolutionary timescale relatively little opportunity for recombination to disrupt the clonal frame resulting from this genome-wide population sweep , consequently the SNP loci are relatively tightly linked across the whole chromosome . To better enable an inspection of the biological meaning of the significant 5952 couplings , we ranked them using successively the following three criteria: 1 ) size of the coupling coefficient ( rounded off to two decimal points ) , 2 ) percentage of isolates where both SNP loci involved in a coupling had the minor allele , 3 ) minimum of the average genome-wide Hamming distances of isolates carrying the minor alleles at the two loci , respectively . Large values of the latter two criteria emphasize cases where both minor alleles at a strongly coupled pair of loci are simultaneously widely distributed across the population , i . e . maximize their phylogenetic spread . The loci included in the twenty highest ranking couplings are visualized together with the maximum likelihood phylogeny in Fig 7 . The overall pattern of couplings between S . pyogenes genes lacked a pattern akin to the very strongly linked penicillin-binding proteins observed in S . pneumoniae . However , there were some similarities evident in comparison of the results obtained from the two population genome data sets . Of the twenty coupling interactions ranked most highly according to the above stated criteria , eight involved two RNA pseudouridine synthases , despite only eight such enzymes being annotated in the chromosome . The 16S rRNA U516 pseudouridylate synthase gene rsuA ( M5005_Spy1092 ) accounted for five couplings , one of which linked to a CDS ( M5005_Spy0101 ) directly orthologous with the tRNA binding protein identified in the analysis of S . pneumoniae ( SPN23F19340 ) . Other linked genes involved in nucleotide metabolism were the prsA2 ribose-phosphate pyrophosphokinase ( M5005_Spy0018 ) and the GTP-binding transcription factor typA ( M5005_Spy1255 ) . The tRNA pseudouridine synthase gene truB accounted for a further three couplings , one of which was to the Cas1 nuclease-encoding CDS M5005_Spy1286 , part of the RNA-dependent S . pyogenes CRISPR2 system . These polymorphisms were homoplasic , as indicated by the phylogeny in Fig 7 , demonstrating the pairings have arisen on multiple occasions , and do not simply reflect common descent . The smaller dimensionality of the M1 data set allowed us to systematically vary the level of population structure correction in the pseudolikelihood inference and study its effect on the estimated couplings . Since the inference was computationally expensive for the pneumococcus data , only the default level of correction ( 0 . 90 ) was used for all the resampling based analyses . S6 Fig shows that the ranking of the few thousand top couplings is stable across threshold levels 0 . 75–0 . 90 , whereas the lower thresholds pull more strongly some of the uncorrected couplings ranked between 4000–10000 towards the lowest ranking ( around 50000 ) . In contrast , the threshold 0 . 95 appears to be too high since the ranking becomes less stable and some of the weaker signals are given too much emphasis .
Natural selection continuously shapes genetic variation in bacterial populations , acting to purge sequence variation that is deleterious and to maintain variation that is beneficial . Laboratory experiments provide the gold-standard method for establishing underlying mechanisms among observed variable sites . However , such experiments also necessitate the definition of a measurable phenotype , which may be a daunting task for many complex traits relevant for survival and proliferation of bacterial strains . The exponentially increasing size of the genome sequence databases provide a valuable resource for generation of hypotheses for experimental work . In eukaryotes , GWAS methods have been used for more than a decade to probe for DNA variation that is non-randomly allied with phenotypic differences indicative of a possible causal genotype-phenotype relationship [47 , 48] . In bacteria , use of GWAS for this purpose is of much more recent origin but has been demonstrated to hold a considerable promise in the light of more densely sampled populations[23 , 29 , 49–52] . However , GWAS is not the only way in which wealth of bacterial sequence information has been proposed to be used to gauge which genes could potentially be targets of positive selection and to generate hypotheses for experimental work . For example , Li et al . screened genome sequences of closely related pairs of isolates in a densely sampled pneumococcal population which would differ at particular genes of interest to provide candidate targets for phenotypic tests[53] . By leveraging from the most recent advances in computational protein structure prediction and statistical machine learning , we have been able to introduce a method that promises to complement the popular GWAS approach for understanding how polymorphisms affect phenotypic variation . This work identified many different coupled sites across the genome , which network analysis revealed to define separate clusters of genes involved in resistance , virulence , and core cell functions . Our study represents the first attempt to use statistical modeling to fully exploit large-scale bacterial population genomics to identify patterns of co-evolution in sequence variation . As illustrated by the genomic data from the contemporary M1 lineage , when a population has experienced a very limited amount of homologous recombination , it will be more challenging to separate couplings related to LD from those that are under shared co-evolutionary pressure . Our further analysis ranking pairs of SNP loci by the degree of phylogenetic spread of the minor alleles and their matching at both loci in addition to the strength of the coupling , illustrates that it may be possible to deduce biologically relevant signals even under such circumstances . Further research on different ways to post-process the coupling estimates to more precisely reflect different evolutionary scenarios is therefore warranted . Our approach provides a comprehensive genome-wide view of epistasis , which is both quantitatively and qualitatively different from studies of coupled evolution between functionally-related genes or proteins . Results applying to single pathways or functions , such as the penicillin-binding proteins , likely reflect strong selection for functionally important interactions . Many such interactions are likely to exist throughout the pneumococcal genome , particularly related to central and secondary metabolism , but will not be detected in this study for two reasons . Firstly , the operonic organisation of coding sequences means such sites will often be in linkage disequilibrium with one another , and therefore excluded from this analysis on the basis of proximity within the chromosome . Secondly , strong selection against mismatched alleles at interacting sites would be expected to limit the maintenance of diversity at these sites , meaning minor allele frequencies above the threshold required in this study will not be attained . This genomeDCA method instead highlights interactions important in shaping the evolution of a population , such as the diversification of the pneumococcal penicillin-binding proteins . These unlinked genes have been under strong selection to decrease their affinity for beta lactams in some isolates , yet still co-existing with sensitive isolates , resulting in the genetic variation necessary for both coupling analyses and GWAS . The biological interpretation of the many other coupled sites should account for the possibility of being driven by the avoidance of deleterious non-productive interactions , necessary for the maintenance of binding specificity between interacting proteins . These can only be detected through a whole genome approach , providing a previously unattainable insight on those interactions that cannot be anticipated from a priori functional analyses . Such non-productive interactions are , by definition , more numerous than the specific interactions between functionally related proteins . Furthermore , each individual interaction is likely to be under weaker selection compared with specific functional interactions , and therefore detectable levels of variation are more likely to be maintained at the relevant polymorphic sites across the population . Therefore not all couplings detected in this work will represent functional links; some will instead provide a new type of data about the ‘noise’ inherent in an imperfectly specific complex biological system . This type of interaction is very difficult to detect in a high-throughput manner by any other methodology , and may well play a previously underappreciated role in bacterial evolution . Importantly , as we have demonstrated , it is not necessary to specify the relevant phenotypes a priori for this approach to work successfully . Therefore a single analysis with this method may simultaneously reveal co-selected sites for many different traits , and provide an indication of their relative influence on evolutionary patterns , as illustrated by the results on the pneumococcus . The selective pressure for at least some of the population to develop beta lactam insensitivity clearly has a substantially greater influence on the co-evolution of unlinked sites than any other loci in this population . Since our approach is widely applicable to data generated by bacterial population genomic studies , it has considerable potential to identify important targets for subsequent experimental work designed to gain system level understanding about the evolution and function of bacteria .
The 3 , 156 genomes of the pneumococcus used in our study are obtained from the study by Chewapreecha et al[9] , including 71 additional genomes not presented in their original article . A list of the accession numbers of all the 3 , 156 genomes is provided in the Supplementary materials ( S5 Table ) and the multiple sequence alignment is available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . gd14g . We used a genome alignment produced as in Chewapreecha et al[9] of the total length 2 , 221 , 305 bp , in which 388 , 755 SNP loci were present , out of which 134 , 037 loci had a minor allele frequency ( MAF ) of at least 0 . 01 . Out of these we selected 81 , 560 loci that had coverage of at least 84 . 1% , i . e . not more than 500 genomes with a gap/unresolved base pair at the considered sequence position . As the analysis focused solely on the biallelic loci , the observed nucleotides have been replaced , so that the entire alignment was composed of only three letters ( two representing observed allele ( major/minor ) and one gap/unobserved allele ) . This was done in the interest of reducing the number of parameters of learnt models approximately 3-fold . The 3 , 442 genomes of the S . pyogenes were obtained from the study by Nasser et al . [3] , representing the post-resurgence epidemic strains of the original article , that is , the contemporary lineage . The multiple sequence alignment is available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . gd14g . We used the genome alignment produced in Nasser et al . with the total length of 1 , 838 , 562 bp in which 12400 SNP loci were present for the post-epidemic strains . Maximum likelihood phylogeny was estimated with RAxML[54] using the 12400 SNPs and the GTR+Gamma model with 100 bootstraps . The same filtering criteria were used as for the pneumococcus , which resulted in 324 SNP loci being included in the genomeDCA analysis . To visualize the phylogeny together with the allele distributions observed at a subset of the SNP loci , we used the Phandango software available at http://jameshadfield . github . io/phandango/ . In addition , analysis was also performed without MAF based filtering , such that only SNP loci where the minor allele was private to a single genome were excluded . This analysis included 5078 out of the 12400 SNPs present in the alignment . Direct Coupling Analysis ( DCA ) was introduced as a method to predict residue-residue contacts in protein structures from multiple sequence alignments ( MSAs ) of many homologous proteins[12 , 55] . A general review about the use of DCA in protein contact prediction has been recently made by de Juan et al . [56] The essence of DCA is to use the data to learn a probabilistic model in an exponential family referred to as a Potts model , and to use model parameters to characterize the data . Potts models are generalizations of the well-known Ising model[57] , where each variable can take q different values ( q = 2 for the Ising model ) ; for the contact prediction problem q equals 21 corresponding to the twenty normally occurring amino acids and a gap state in an MSA . Potts models and Ising models contain linear ( one-variable ) and quadratic ( two-variable ) terms; in DCA the quadratic terms , also referred to as couplings or interactions , are used to characterize the data . For completeness we give a mathematical definition of Potts models in Supplementary Information , where we also give further details on how these models are parametrized . In this work we use the MSA built on loci of variation in the genome sequences , filtered such that at each locus in one sequence we only have a major allele , a minor allele , or the locus can be missing . A sequence is thus coded as a string of symbols major/minor/gap , and we use the table of such strings to learn a Potts model with q = 3 states . A coupling between a pair of loci can be parametrized by a 3x3 parameter matrix . The parametric dimensionality consequently grows by the number of loci squared , i . e . is here on the order of 1010 for the pneumococcus data , whereas the number of observations is on the order of 103 . Models are therefore regularized as discussed in Ekeberg et al[58] . A central aspect of the DCA procedure is that only a set of strongest predicted interactions are retained[12 , 55] . Indeed , as the number of parameters of these models in practical use is much larger than the sample size , a selection is necessary . For the contact prediction problem the number of retained predictions has often been taken to be about the length of the protein[14 , 59] . Here we have determined instead a threshold for significant interactions using the statistical theory of extreme value distributions , as explained below . The final central aspect of DCA is the actual learning procedure , or inference . Exact solution of maximum likelihood ( ML ) inference of general Ising and Potts models is a computationally hard problem and not feasible for the instances of interest; in statistics such likelihood functions are often referred to as intractable . Several approximate learning schemes have therefore been developed[12 , 55 , 58 , 60–64] . DCA for protein contact prediction has also been combined with other methods[59 , 65] . We have in this work used pseudolikelihood inference as described below . Deriving the meaning of inferred Potts model parameters in a highly under-sampled situation is not a simple mathematical problem , and no agreement has so far been reached in the DCA literature[66 , 67] . For instance , the numerical values of inferred parameters depend on the regularization , such that large regularization yields small inferred parameter values . As in most DCA studies we have in this work relied on the empirical observation , discussed e . g . in Ekeberg et al . [62] , that the order of the largest inferred parameters is only weakly dependent on regularization . The subsequent analysis is then based on the identity of these largest predictions , while the corresponding numerical values are not used further . For a recent theoretical discussion of the performance of DCA for contact prediction compared to faithfully reconstructing the full probability distribution over sequences , see Jacquin et al . [68] Pseudolikelihood was originally introduced in the early 1970’s to enable estimation of parameters in spatial statistical models with intractable likelihood functions[69 , 70] . This inference technique has experienced a strong revival in the recent years for high-dimensional applications where the number of possible model parameters greatly exceeds the number of observations , known as the ‘small n , large p’ problem[71] . In particular , pseudolikelihood provides consistent estimators of the model parameters unlike the competing variational inference methods[72] . The outcome of the inference is a set of matrices Jij describing the interactions between loci i and j . We score these numbers by their Frobenius norms |Jij| , which we refer to as interaction strengths , but without the Average Product Correction ( APC ) which has become the most common choice in the protein contact prediction problem . The APC approach is not suitable for the interaction parameter matrices in the current application since they are not sparse in the statistical sense as is the case with residue interaction matrices . The pseudolikelihood method allows an efficient correction for population structure by the reweighting scheme used in plmDCA for an MSA in protein analysis[58] , which ensures that highly similar sequences are not artificially inflating the support for direct dependence between alleles . We used the default reweighting scheme in plmDCA with a 0 . 9 similarity threshold over the variable alignment positions , except for the 5078 locus M1 data where the minimum similarity between any pair of sequences was 98% and threshold 0 . 95 was used . The number of parameters in our model for the pneumococcus data is far larger than has hereto been considered in analogous studies . Also , neighboring loci are most often in linkage disequilibrium ( LD ) which has a confounding biological effect on their interaction . For both these reasons , we have chosen to dissect the pneumococcal core chromosome into approximately 1 , 500 non-overlapping segments and apply pseudolikelihood inference on subsets of the loci , chosen randomly from each genomic window of average size of 1500 nt . These particular choices were motivated by attempting to achieve a balance between minimizing the effect of LD and the need to keep the number of SNP loci included in any single instance of the pseudolikelihood inference limited enough such that the computation time would remain reasonable . For each such sample we learned about 106 parameters , scored them as described above and saved only the 3000 largest interaction parameters to reduce memory consumption . As seen from the results , the vast majority of the stored maximal couplings were relatively small , indicating that sufficiently many values were included from each analysis . For each sample the inference took approximately 190 minutes and used approximately 2GB of memory on a single core of a standard Intel Core i5 processor . The whole re-sampling and model fitting procedure was repeated 38 , 000 times to ensure stable inference about the parameters . However , sequential examination of the results from the estimation runs showed that the list of top scoring couplings did not change markedly after a few thousand iterations , indicating that substantially fewer iterations had been sufficient for practical purposes . Interaction estimates were averaged for any pairs of sites that occurred multiple times among the saved parameters , which resulted in 102 , 551 pairs of sites with non-negligible coupling coefficients from the aggregated re-sampling results ( S1 Table ) . This set is approximately five orders of magnitude smaller than the set of all possible interactions for the 81 , 560 considered loci . The whole pneumococcus analysis workflow is illustrated in S4 Fig . Software implementing both the resampling procedure ( Python ) and the parameter inference ( Matlab ) is made freely available at https://github . com/mskwark/genomeDCA . The smaller number of variable sites in the S . pyogenes alignment made it possible to fit the model simultaneously to all considered SNP loci and save the couplings exhaustively without resampling . The analysis of the smaller set of 324 loci took approximately 2 minutes and used 370 MB of memory , whereas the set of 5078 loci took approximately 11 hours and used 5GB of memory . The couplings for the smaller set are listed in S4 Table ( 52326 entries ) and for the larger set in S6 Table ( approximately 13M entries ) , which is available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . gd14g . To select a list of highest scoring interactions among the 102 , 551 estimates for the pneumococcus which are unlikely a result of neutral and sampling variation in the studied population , we employed the statistical theory of extreme value distributions [73] . Since in each resampling step the largest 3000 parameters were saved , these can under a null model of random interactions between loci be considered as a sample from an extreme value distribution , such as the Gumbel distribution . We fitted a Gumbel distribution to the distribution of the estimated parameters using least squares minimization between the fitted distribution and the empirical rank distribution of the coefficients located between 25% and 75% quantiles . Fig 1A shows the fitted distribution which has a remarkably good fit to the vast majority ( 95% ) of the coefficients . To select a threshold for a significant deviation from the null model we identified the first value for which the predicted curve was more than six standard deviations ( SD ) away from the empirical distributions ( Fig 1B ) . The SD was estimated using the deviances for the 50 , 000 smallest coefficients . The 5199 couplings exceeding the threshold 0 . 129 are listed in S2 Table and in addition the 500 strongest couplings in S3 Table . The latter were used in the structural plots for the PBPs . The same Gumbel model fitting procedure was also employed to define the significance thresholds for the S . pyogenes data , except that we excluded all couplings below 0 . 01 for the larger locus set where the vast majority of the nearly 13M couplings in total were very close to zero ( S5 Table ) . This resulted in 7514 couplings being included in the Gumbel analysis . The empirical distributions and the fitted Gumbel models are shown in Fig 6 . A resampling-based analysis of haplotypes generated randomly from a population by merging alleles sampled from the marginal allele frequency distribution of each SNP locus showed that couplings as large as those exceeding the threshold chosen for the coefficients in the original data were never encountered ( S3 Fig ) . In the analysis we used 5000 replicates of the haplotype re-sampling based on the same chromosomal windows as in the analysis of the original data . Hence , our approach was concluded to maintain a strict control of false positive interactions for unlinked loci stemming from population sampling variation . Networks were displayed and analysed using Cytoscape [74] . GO terms were inferred from applying Interpro scan [75] and CD-search [44] to the S . pneumoniae ATCC 700669 genome [EMBL accession: FM211187] . These were matched to network components , and a Fisher exact test used to test for enrichment of 139 instances of GO terms that featured in a network component twice or more , relative to the CDSs that contained sites analyzed in this study , but not found to include a significantly coupled loci . The p values were corrected for multiple testing using the method of Benjamini and Hochberg [76] . Crystal structures of S . pneumoniae PBPs with the following IDs: 2C5W ( pbp1a ) , 2WAF ( pbp2b ) , 2ZC3 ( pbp2x ) were retrieved from the Protein Data Bank[77] ( www . rcsb . org; accession date January 8 , 2016 ) and visualized in The PyMOL Molecular Graphics System , Version 1 . 8 Schrödinger , LLC . Inferred co-evolving sites were visualized using the Circos software[78] . | Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms . Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data , suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces . We introduce the first model-based method for genome-wide epistasis analysis and use two of the largest available bacterial population genome data sets on Streptococcus pneumoniae ( the pneumococcus ) and Streptococcus pyogenes ( group A Streptococcus ) to demonstrate its potential for biological discovery . Our approach reveals interacting networks of resistance , virulence and core machinery genes in the pneumococcus , which highlights putative candidates for novel drug targets . We also discover a number of plausible targets of co-selection in S . pyogenes linked to RNA pseudouridine synthases . Our method significantly enhances the future potential of epistasis analysis for systems biology , and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work . | [
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"cal... | 2017 | Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis |
Bacteria form multicellular communities known as biofilms that cause two thirds of all infections and demonstrate a 10 to 1000 fold increase in adaptive resistance to conventional antibiotics . Currently , there are no approved drugs that specifically target bacterial biofilms . Here we identified a potent anti-biofilm peptide 1018 that worked by blocking ( p ) ppGpp , an important signal in biofilm development . At concentrations that did not affect planktonic growth , peptide treatment completely prevented biofilm formation and led to the eradication of mature biofilms in representative strains of both Gram-negative and Gram-positive bacterial pathogens including Pseudomonas aeruginosa , Escherichia coli , Acinetobacter baumannii , Klebsiella pneumoniae , methicillin resistant Staphylococcus aureus , Salmonella Typhimurium and Burkholderia cenocepacia . Low levels of the peptide led to biofilm dispersal , while higher doses triggered biofilm cell death . We hypothesized that the peptide acted to inhibit a common stress response in target species , and that the stringent response , mediating ( p ) ppGpp synthesis through the enzymes RelA and SpoT , was targeted . Consistent with this , increasing ( p ) ppGpp synthesis by addition of serine hydroxamate or over-expression of relA led to reduced susceptibility to the peptide . Furthermore , relA and spoT mutations blocking production of ( p ) ppGpp replicated the effects of the peptide , leading to a reduction of biofilm formation in the four tested target species . Also , eliminating ( p ) ppGpp expression after two days of biofilm growth by removal of arabinose from a strain expressing relA behind an arabinose-inducible promoter , reciprocated the effect of peptide added at the same time , leading to loss of biofilm . NMR and chromatography studies showed that the peptide acted on cells to cause degradation of ( p ) ppGpp within 30 minutes , and in vitro directly interacted with ppGpp . We thus propose that 1018 targets ( p ) ppGpp and marks it for degradation in cells . Targeting ( p ) ppGpp represents a new approach against biofilm-related drug resistance .
Biofilms are structured multicellular communities of microorganisms associated with surfaces . They have been widely studied , in part because they cause at least 65% of all human infections , being particularly prevalent in device-related infections , on body surfaces ( skin and soft tissue , lung , bladder , endocarditis , etc . ) and in chronic infections [1] , [2] . They represent a major health problem worldwide due to their resistance to host defence mechanisms and to conventional antimicrobials , which generally target free-swimming ( planktonic ) bacteria [1] , [2] . Hence , there is an urgent need to identify compounds that effectively clear biofilm-related infections . Bacteria are known to respond to stressful environmental conditions ( such as starvation ) by activating the stringent response ( SR ) [3] . As a consequence , the cell synthesizes two small signaling nucleotides , guanosine 5′-diphosphate 3′-diphosphate ( ppGpp ) and guanosine 5′-triphosphate 3′-diphosphate ( pppGpp ) , collectively denoted ( p ) ppGpp [3] . These serve as a second messenger response that is induced by a variety of stress conditions , is highly conserved in both Gram-negative and Gram-positive species [3] , [4] , regulates the expression of a plethora of genes [3] , and is known to play a role in biofilm formation in certain species [5]–[11] , although some variability has been observed [6] , [7] , [9] , [12] . Synthetic cationic peptides , derived from natural peptides such as the human cathelicidin LL-37 and the bovine peptide indolicidin [13] , have been recently identified as biofilm inhibitory compounds [14] . Anti-biofilm peptides are similar to cationic antimicrobial peptides ( which are active against planktonic bacteria ) , comprising both cationic and hydrophobic amino acids [14] , but have substantially different structure-activity relationships . Thus , we previously identified peptides with good anti-biofilm but virtually no activity vs . planktonic bacteria ( i . e . , very high MIC values ) [14] , and vice versa . Moreover , certain anti-biofilm peptides are active against biofilms formed by Burkholderia cenocepacia [14] , a pathogen that is completely resistant to all antimicrobial peptides in the planktonic state . The broad-spectrum activity of anti-biofilm peptides [14] suggests that they target a biofilm-specific process common amongst bacteria . Given the above , we hypothesized that anti-biofilm peptides exerted their activity by blocking a widespread stress response that contributes to biofilm development , and that this was indeed the stringent response mediated through ( p ) ppGpp . Here , we have identified a peptide that has very broad spectrum activity against many of the most antibiotic-resistant species of concern in human medicine and provide evidence it acts to promote ( p ) ppGpp degradation .
While screening for peptides with anti-biofilm activity , we identified the previously unknown ability of the immunomodulatory peptide IDR ( innate defense regulator ) -1018 ( VRLIVAVRIWRR-NH2; abbreviated here as 1018 ) [15] to specifically target and kill biofilm cells ( Fig . 1 ) , at much lower concentrations than previously described peptides [14] . At concentrations that had no effect on planktonic growth ( Table 1 ) , this peptide was able to potently prevent biofilm formation ( Fig . 1 , middle panels ) and eradicate preformed ( 2-day old ) biofilms ( Fig . 1 , right hand panels ) formed by diverse species of Gram-negative bacteria and the Gram-positive bacterium Staphylococcus aureus . We investigated the role of 1018 in biofilm cell dispersion and killing of P . aeruginosa PA14 2-day old biofilms . At very low concentrations ( 0 . 8 µg/ml ) , the peptide increased live cell dispersion from existing biofilms by ∼4-fold after 23 h of treatment ( Fig . 2 ) , resulting in an average of 8 . 2±6 . 6% residual biofilm biovolume compared to the untreated controls ( P<0 . 05 ) . Only 26±7 . 4% of the cells that remained attached within the flow cell chambers were killed by treatment with 0 . 8 µg/ml 1018 . Conversely , higher concentrations of peptide ( 10 µg/ml ) did not trigger live biofilm cell dispersal ( Fig . 2 ) , and most of the cells remaining bound to the surface were dead , as judged by uptake of the normally impermeant stain propidium iodide ( 67±7 . 7% red cells compared to 2 . 5±1 . 0% in the untreated controls; P<0 . 05 ) . In experiments with pre-grown , 2-day old biofilms , only E . coli 0157 , S . enterica 14028S and B . cenocepacia samples consistently had attached cells after peptide treatment ( Fig . 1 , right hand panels ) . However , the remaining cell population was mostly dead in the case of E . coli 0157 as , on average , there was a significant increase in dead cell number in treated samples ( 54 . 4±3 . 1% ) compared to untreated samples ( 1 . 5±0 . 9%; P<0 . 05 ) . On the other hand , the peptide caused more substantial dispersal but lesser cell death in S . enterica 14028S and B . cenocepacia biofilms . Salmonella biofilms treated with 1018 had 29 . 2±19 . 0% dead cells as opposed to just 0 . 54±0 . 69% in samples without peptide ( P<0 . 05 ) . B . cenocepacia biofilms exhibited no significant increase in cell death ( 7 . 9±3 . 4% cf . 3 . 8±2 . 4% in the untreated controls ) . The basis for the broad-spectrum activity of peptide 1018 was investigated . Previous studies , based on transcriptomic and biochemical investigations , have suggested that peptides LL-37 [13] and 1037 [14] act against Pseudomonas by modestly inhibiting attachment and quorum sensing as well as promoting twitching motility . However , although we could show that 1018 had similar modest effects on these processes , it was difficult to rationalize these mechanisms with the observed broad-spectrum activity , since these processes vary substantially within the above-described target species . Thus we considered that there might be a common mechanism and hypothesized that the peptide acted to inhibit a common stress response in target species , namely the so-called stringent response , mediating ( p ) ppGpp synthesis through the enzymes RelA and SpoT . Overproducing the potential target of a given drug is a well-established method for identifying drug targets . Here we overproduced ( p ) ppGpp by addition of serine hydroxamate ( SHX; a structural analogue of L-serine that induces the stringent response by inhibiting charging of seryl-tRNA synthetase [16] ) , and by IPTG induction of the cloned relA gene , and observed resistance against peptide 1018 ( Fig . 3A , B ) . First we performed checkerboard microtiter plate assays , using established methods [17] , to analyze the interaction between SHX addition at time 0 and 1018 treatment in more detail . Minor modifications were made to previously described methodology [17] to quantify adherent biofilm biomass ( as opposed to planktonic bacterial growth ) using the crystal violet assay [14] . The crystal violet-stained biofilm was resuspended using 70% ethanol and quantified using a spectrophotometer at 595 nm . Three independent experiments were performed and statistical significance was determined using Student's t test . At concentrations of SHX ( 10 µM ) that did not affect P . aeruginosa PAO1 planktonic growth ( which required 250 µM SHX to inhibit growth , Fig . S1C ) , we observed increased biofilm formation by nearly 2-fold ( to 188±0 . 3% cf . the SHX untreated control; P<0 . 05 ) . In these cells the minimal biofilm inhibitory concentration ( MBIC ) went from 10 µg/ml ( Table 1 ) to 80 µg/ml of 1018 ( leading to a reduction to 8 . 9±0 . 02% biofilm volume cf . the peptide untreated control; P<0 . 05; no difference was observed at 40 µg/ml of the peptide , which led to 93 . 2±0 . 04% biofilm formation cf . the peptide untreated control ) . These results clearly showed that peptide resistance was not due to slow growth . At 320 µM SHX , whereby biofilm production was increased nearly 4-fold ( to 395±0 . 4% cf . the SHX untreated control; P<0 . 01 ) , 160 µg/ml 1018 was required to fully inhibit biofilm formation ( reduced to 4 . 7±0 . 002% cf . peptide untreated control; P<0 . 01 ) . Thus the amount of peptide required to inhibit biofilms depended on the concentration of SHX , and therefore on the levels of ( p ) ppGpp , since increasing the levels of SHX resulted in peptide resistance unless a higher dose of peptide was used . While SHX by itself clearly resulted in an increase in biofilm development over the 18 to 24 h of the assay , the peptide was present in these studies even before biofilms began to develop . These results were confirmed and extended using flow cell methods . Overproduction of ( p ) ppGpp by SHX treatment of P . aeruginosa and S . aureus wild-type strains led to peptide resistance ( Fig . 3A ) . The fold-change in biovolume of P . aeruginosa PAO1 biofilms treated with 20 µg/ml 1018 was 0 . 095±0 . 03 ( P<0 . 05 ) compared to untreated controls . However , adding SHX restored the ability to form biofilms in the presence of the peptide ( 2 . 6±0 . 5 fold-increase compared to untreated samples; P<0 . 05 ) . Similarly , the biovolume of 1018-treated S . aureus HG001 biofilms was only 0 . 6% that of untreated samples , which was complemented when adding SHX ( 1 . 65±0 . 6 fold-change compared to untreated samples; P<0 . 05 ) . Similar results were obtained by genetic means whereby peptide resistance was increased by overproduction of ( p ) ppGpp in an E . coli strain overexpressing the cloned relA gene under the control of an IPTG-inducible promoter ( Fig . 3B ) . Previous studies have reported that mutants influencing ( p ) ppGpp production are biofilm-deficient but not always completely defective , occasionally forming monolayers of attached cells or extremely-deficient biofilms ( as opposed to well-structured biofilms ) [7]–[11] . To confirm that there was a correlation between the production of ( p ) ppGpp and biofilm production under the experimental conditions reported here , biofilm formation of ( p ) ppGpp-deficient mutants was compared to their respective wild-type strains in Gram-negative Pseudomonas aeruginosa , Salmonella enterica serovar Typhimurium , and Escherichia coli and the Gram-positive bacterium Staphylococcus aureus ( Fig . 4 , Fig . S1A ) . Cells unable to synthesize ( p ) ppGpp showed a substantial decrease in their ability to adhere tightly to the plastic surface of flow cell chambers and were unable to develop structured biofilms , although they formed residual aggregates ( Fig . 4 , Fig . S1A ) . Genetic complementation of the genes responsible for ( p ) ppGpp synthesis in P . aeruginosa relA spoT and S . aureus rsh mutants restored the full ability to form biofilms ( Fig . 4 ) . In the un-complemented mutants ( Fig . 4 , Fig . S1A ) , residual ( p ) ppGpp-deficient mutant cells appeared to be in the planktonic state as opposed to adhering to the surface , and often were dead or division-inhibited ( demonstrating filaments ) ( Fig . S1B ) . These poorly-attached cells could be cleared by increasing the flow rate . This might explain in part the variability in the defect in biofilm formation in mutants defective in ( p ) ppGpp production ( i . e . due to flow rate , or other factors such as the age of the biofilms , temperature and media utilized here , which differed compared to previous reports; 6 , 7 , 9 , 12 ) . To further demonstrate the role of ( p ) ppGpp in biofilm development and maintenance , we introduced the relA gene under the control of an araC promoter into P . aeruginosa PAO1ΔrelAspoT such that it expressed relA upon arabinose induction . Biofilms of this strain that were expressing relA due to the introduction of arabinose into the flow medium during the 3-day experiment , were able to form well-structured biofilms ( Fig . 5A ) . However , when induction of relA was stopped at day 2 ( i . e . for the last 24 h of the experiment by removal of arabinose from the flow medium ) , analogous to delayed treatment by peptide 1018 , pre-formed biofilms were dispersed ( Fig . 5A ) . Indeed , we performed viable cell counts of dispersed cells from these biofilms and found that repressing relA expression after 2 days of continuous induction led to biofilm dispersion ( Fig . 5B ) , while continued induction of relA for the 3 days of the experiment resulted in significantly reduced dispersal levels ( Fig . 5B ) that were similar to that of the wild-type strain ( data not shown ) . These results clearly highlighted the roles that relA-dependent ( p ) ppGpp production play both in biofilm formation and in biofilm maintenance , as well as the consequences of blocking ( p ) ppGpp synthesis . The role of ( p ) ppGpp in the anti-biofilm mechanism of peptide 1018 was further assessed in multiple species . Direct measurement of the cellular levels of ( p ) ppGpp by thin layer chromatography ( TLC ) revealed that cells from multiple bacterial species treated with 5 µg/ml of peptide 1018 did not accumulate ( p ) ppGpp ( Fig . 6A and Fig . S4B ) . In contrast , the conventional cationic antibiotics colistin , polymyxin B and tobramycin were unable to prevent ( p ) ppGpp accumulation ( Fig . S3 , left panel; indeed the latter two actually increased ppGpp ) or cause degradation of accumulated ( p ) ppGpp ( Fig . S3 , right panel ) , thus demonstrating that these cationic antibiotics did not utilize a similar mechanism to that of peptide 1018 . The peptide was able to interact directly with ppGpp as demonstrated by co-precipitation ( Fig . 7A ) and TLC of residual ppGpp ( Fig . 6 , Fig . S4B ) and by nuclear magnetic resonance spectrometry ( NMR ) of the complexed molecules ( Fig . 7B–D ) . These studies further showed that peptide 1018 preferentially bound to ppGpp compared to other nucleotides such as GTP ( Fig . 7A , Fig . S5B ) . We then investigated the mechanism by which the peptide 1018-ppGpp interaction led to the loss of the ppGpp signal in cells . One possibility was that the peptide sequestered the nucleotide , forming a peptide-ppGpp complex , which prevented ppGpp detection in our TLC and NMR assays . However , we observed that formic acid ( used to extract nucleotides in both TLC and NMR experiments ) led to a disruption of the peptide-ppGpp complex while maintaining ppGpp in its intact form ( Fig . S5A , right panel ) , so this explanation seems unlikely as no ppGpp was visible on TLC and NMR after formic acid treatment ( Fig . 6 , Fig . 7D , Fig . S5A ) . Alternatively , peptide treatment could lead to ppGpp degradation . In agreement with this second possibility , TLC and NMR analysis of in vivo experiments showed that the addition of peptide led to the rapid degradation of ( p ) ppGpp within cells that had pre-accumulated these nucleotides ( Fig . 6B , Fig . 7D ) . In the TLC experiments , ( p ) ppGpp synthesis was induced in P . aeruginosa PAO1 cultures by SHX for 3 h , after which 20 µg/ml of the peptide was added and the fate of ( p ) ppGpp was monitored over time , revealing elimination within only 30 min ( Fig . 6B ) . Likewise , after treatment with SHX to induce ( p ) ppGpp synthesis in P . aeruginosa PAO1 cells and then treatment with peptide for 1 h followed by extraction of nucleotides and NMR , the ppGpp peak observed in untreated cells was dramatically reduced ( Fig . 7D ) . Taken together , these results indicate that peptide 1018 directly and specifically interacts with ( p ) ppGpp and triggers its degradation , thus preventing its signaling effects within the cell ( e . g . its role in biofilm development and maintenance ) .
The results described here indicate that an anti-biofilm peptide can exhibit potent broad-spectrum activity due to its ability to depress ( p ) ppGpp levels in live bacterial cells . Previous studies have suggested that the stringent response might be involved in biofilm formation [5]–[11] , although some controversy exists as to whether this may be due to the particular experimental conditions used , particularly flow rate and age of the biofilms examined . The stringent response is induced in reaction to bacterial stresses such as amino-acid starvation , fatty acid , iron or nutritional limitation , heat shock , and other stress conditions . It is signaled by the alarmone ( p ) ppGpp , and modulates transcription of up to one third of all genes in the cell . Conceptually it is designed to divert resources away from growth and division and toward metabolism in order to promote survival . Its specific role in biofilm formation is not known but it may be involved in initiating and/or perpetuating biofilm development . In addition , the results presented here indicate that it may actually suppress the tendency of biofilms to disperse ( Fig . 5 ) , and even promote viability in adhered cells ( Fig . S1B ) . Consistent with its effects on biofilms , in vivo studies have shown that ( p ) ppGpp-deficient mutants are easily cleared by the host , unable to establish chronic infections , incapable of long-term survival and overall more susceptible to exogenous stresses than their parent strains [18] , [19] . Interestingly , various classes of antibiotics are known to induce ( p ) ppGpp synthesis [20-23; Fig . S3] , which in turn leads to antibiotic adaptive resistance [12] , [24] . Importantly , recent efforts have identified molecules that target the stringent response [25] . Here we have demonstrated that peptide 1018 , which triggers the degradation of the ( p ) ppGpp signal within the cell , acts as a broad-spectrum biofilm inhibitor . Given the importance of biofilms in human medicine , constituting at least 65% of all infections , and increasing antibiotic resistance which in biofilms is adaptive and broad spectrum , such a peptide offers considerable potential in the fight against the burgeoning resistance to antibiotics . Peptides with biofilm inhibitory activities have been previously identified [13] , [14] , [26] . However , the mechanism of action by which these peptides selectively target and kill biofilm cells of both Gram-negative and Gram-positive bacteria was previously postulated to involve changes in motility , adherence and quorum sensing [13] , [14] , which are all species-specific and thus do not satisfactorily explain the action of peptide 1018 against a broad range of pathogens . Here we provide evidence that an anti-biofilm peptide , 1018 , potently inhibited biofilm formation and eradicated existing mature biofilms in a broad-spectrum manner , through a direct interaction with ppGpp , which led to its degradation in live bacterial cells . The mechanism of action involves direct contact between peptide 1018 and ( p ) ppGpp so the peptide must be able to cross the cell membranes to reach the cytoplasm . Previous studies have demonstrated that amphipathic cationic peptides , like 1018 , have the characteristics of so-called cell penetrating peptides that are able to freely translocate across membranes [27] . The peptide had at least three effects on biofilms , which might reflect the role of ( p ) ppGpp in cells . First when added prior to initiation of biofilms it prevented biofilm formation , second it specifically led to cell death in biofilms at concentrations that were not lethal for planktonic ( free-swimming ) cells ( Table 1; Figs . 1 and 2 ) , and third it promoted biofilm dispersal even in maturing ( 2-day old ) biofilms ( Fig . 2 ) , effects that were in fact reciprocated in mutants unable to accumulate ( p ) ppGpp ( Fig . 5 ) . We suggest that the ability to cause cell death in biofilms might have been due to inhibition of cell wall biosynthesis and triggering of murine hydrolases , a known tendency for antimicrobial peptides [28] . Critically we propose that this is due in part to the impact of the stringent response in bacteria growing in the biofilm state , since stringent response is known to influence susceptibility to cell wall specific antibiotics , presumably through effects on cell wall synthesis [24] , while the lack of ppGpp leads to cell death through the Slt soluble lytic transglycosylase [29] . Consistent with this , both peptide-treated biofilms and ( p ) ppGpp deficient mutants grown in flow cells demonstrated increased bacterial cell filamentation ( Fig . S1B ) and cell lysis/death ( Figs . 1 , 2 , S1B and Fig . 5 ) . Previous studies have demonstrated that the structure-activity relationships of anti-biofilm peptides vary substantially from antimicrobial peptides [14] despite certain common features ( being amphipathic molecules with excess cationic and hydrophobic amino acids ) . Consistent with this , 1018 was able to potently inhibit Burkholderia cenocepacia biofilms , despite the fact that this species is completely resistant to all antimicrobial peptides . Thus we have an opportunity to now develop more active peptides that have even more potent anti-biofilm activity . Indeed we have recently started to isolate such peptides and obtained preliminary evidence that they also act by inhibiting the stringent response .
Strains utilized included wild-type strains of Pseudomonas aeruginosa PAO1 , strain H103 , and PA14 and clinical isolates E . coli O157 , Salmonella enterica serovar Typhimurium ( clinical isolate 14028S ) , Staphylococcus aureus MRSA ( clinical isolate #SAP0017 ) , Klebsiella pneumoniae ATTC 13883 ( a colistin-heteroresistant reference strain from American Type Culture Collection , Rockville , MD ) , Acinetobacter baumannii SENTRY C8 ( a polymyxin B resistant blood clinical isolate from the U . S . A . obtained through the SENTRY surveillance system ) and Burkholderia cenocepacia genomovar IIIa ( Vancouver Children's Hospital clinical isolate 4813 ) . P . aeruginosa PAO1 ( p ) ppGpp mutant ΔrelAspoT [ ( ΔrelA ( Δ181-2019 ) ΔspoT ( Δ200-1948 ) ] and its complemented strain ΔrelAspoT+SR were a kind gift from D . Nguyen [12] . S . aureus parent strain HG001 , its ( p ) ppGpp mutant HG001 rshsyn ( Δ942–950 nt ) and the strain complemented with full length rsh were kindly provided by T . Geiger [30] . S . enterica serovar Typhimurium parent strain SL1344 and its ( p ) ppGpp mutant SL1344 ΔrelAspoT ( ΔrelA71::kan rpsL ΔspoT281::cat ) were provided by K . Tedin [31] . Escherichia coli parent strain ( MG1655 ) , E . coli ΔrelAspoT [ΔrelA:: kan ( Δ209-2302 ) ΔspoT:: cat ( Δ700-2355 ) ] deletion insertion mutant and E . coli relA+ ( p ) ppGpp positive control ptac::relA ( pALS10 ) were also provided by D . Nguyen and obtained as previously described [32] , [33] . For the expression of relA in the P . aeruginosa mutant strain PAO1ΔrelAspoT , the pHERD20T plasmid carrying an arabinose-inducible promoter was used [34] . A 3 . 2 kb DNA fragment containing the relA gene was amplified with primers relAF ( 5'-GCTAGGATGCCTGCGTAATC-3' ) and relAR ( 5'-GAGATCGCCATCGAGGAATA-3' ) and cloned into a TOPO Zero-Blunt cloning vector ( Invitrogen ) and then into the pHERD20T vector . This construct was then electroporated into electrocompetent P . aeruginosa PAO1ΔrelAspoT cells . Positives clones carrying the plasmid were selected on LB plus 500 µg/ml of carbenicillin , and relA overexpression upon induction was confirmed by RT-qPCR . In all experiments 0 . 01% arabinose was used to induce the promoter . Peptide 1018 ( VRLIVAVRIWRR-NH2 ) used in this study was synthesized by CPC Scientific using solid-phase 9-fluorenylmethoxy carbonyl ( Fmoc ) chemistry and purified to a purity of ∼95% using reverse-phase high-performance liquid chromatography ( HPLC ) . Peptide mass was confirmed by mass spectrometry . The medium used was generally BM2 minimal medium ( 62 mM potassium phosphate buffer , pH 7 . 0 , 7 mM [ ( NH4 ) 2SO4 , 2 mM MgSO4 , 10 µM FeSO4] containing 0 . 4% ( wt/vol ) glucose as a carbon source , except for Staphylococcus aureus HG001 wild-type for which BM2 glucose+0 . 5% casamino acids ( CAA ) was used , and Salmonella enterica SL1344 that was grown in Luria Broth . Escherichia coli MG1655 was grown in BM2+0 . 1% CAA . The broth microdilution method with minor modifications for cationic peptides [35] was used for measuring the MIC of peptide 1018 . Minimal biofilm inhibitory concentrations leading to 50% decrease in biofilm growth ( MBIC50 ) were obtained using 96-well plate assays and crystal violet staining of adherent biofilms as previously described [14] . The minimal peptide concentrations that completely inhibited biofilm formation ( MBIC100 ) were obtained using flow cells at different input concentrations of peptide . Biofilms were grown in flow chambers with channel dimensions of 1×4×40 mm , as previously described for 72 h at 37°C [14] in the absence or presence of the desired concentration of peptide 1018 . Flow cell chambers were inoculated by injecting 400 µl of an overnight culture diluted to an OD600 of 0 . 05 . After inoculation , chambers were left without flow for 2 h to enable initial adherence , after which the medium ( with or without sub-inhibitory concentrations of 1018 ) was pumped through the system at a constant rate of 2 . 4 ml/h . In all cases , after 3 days of growth the flow rate ( 90 rpm ) was increased so as to limit the amount of planktonic and loosely-attached cells within the flow cell chamber . All media used ( see above ) in flow cell assays supported the planktonic growth of the bacterial species tested , as determined by growth curves ( e . g . Fig . S1A ) . Except where otherwise specified , the concentrations of peptide 1018 used were 10 µg/ml for Pseudomonas aeruginosa , Escherichia coli 0157 , Acinetobacter baumannii SENTRY C8 and Burkholderia cenocepacia genomovar IIIa 4813 , 20 µg/ml for Salmonella enterica sv . Typhimurium 14028S experiments , 2 µg/ml for Klebsiella pneumoniae ATTC13883 , and 2 . 5 µg/ml for methicillin resistant Staphylococcus aureus MRSA #SAP0017 . The different concentrations used correspond to the MBIC100 of the peptide against the different bacterial species as shown in Table 1 . For inhibition studies , peptide was added to the flow-through medium immediately after the initial adherence phase , and maintained for 3 days . For treatment of existing biofilms , bacteria were allowed to develop into structured 2-day old biofilms prior to peptide treatment by addition into the flow cell flow-through medium for the following 24 h . Biofilm cells were stained using the LIVE/DEAD BacLight Bacterial Viability kit ( Molecular Probes , Eugene , OR ) or Syto-9 alone prior to microscopy experiments . A ratio of Syto-9 ( green fluorescence , live cells ) to propidium iodide ( PI ) ( red fluorescence , dead cells ) of 1∶5 was used . Microscopy was done using a confocal laser scanning microscope ( Olympus , Fluoview FV1000 ) and three-dimensional reconstructions were generated using the Imaris software package ( Bitplane AG ) . Quantification of the overall biofilm biovolume ( µm3 ) and the percentage of live and dead cell volume were performed using Imaris software as previously described [7] , [36] . Experiments were performed at least in triplicate . In experiments looking at the effect of enhanced ( p ) ppGpp production of biofilm susceptibility to peptides , E . coli strain MG1655 expressing relA from a lac promoter on plasmid pALSl0 [32] was grown in flow cells for 3 days in BM2+0 . 1% CAA containing 20 µg/ml of 1018 and in the presence or absence of 100 µM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . P . aeruginosa PAO1 and S . aureus HG001 strains were grown as described above but in the presence or absence of serine hydroxamate ( SHX ) . Cell counts of live dispersed bacteria from flow cell biofilms were performed using strain P . aeruginosa PA14 grown in BM2 minimal glucose medium . P . aeruginosa PA14 biofilms were grown in the flow cell system for 2 days as described above and treated with 0 . 8 µg/ml or 10 µg/ml of peptide 1018 . To count the dispersed viable cells , 1 . 5 ml of the output flow was collected at the designated times ( time 0 and after 3 and 23 h ) and serially diluted 10-fold . One hundred-µl portions from these serial dilutions were then plated onto LB agar plates . Plates were incubated at 37°C overnight , and colony counts were performed to obtain total CFU/ml at each time point . Bacteria were grown overnight in modified MOPS minimal medium containing 0 . 4% glucose , 2 mM phosphate ( KH2PO4 ) , and 0 . 2% CAA . The cells were then diluted 1∶20 in the same MOPS minimal medium except containing 0 . 4 mM phosphate ( KH2PO4 ) and 500 µM SHX to induce ( p ) ppGpp synthesis , in the presence or absence of peptide 1018 , colistin , tobramycin or polymyxin B and cells were labelled with 10 µCi/ml 32P for 3 h . For experiments evaluating the ability of the peptide ( or different antibiotics ) to directly lead to degradation of ( p ) ppGpp , the cells were induced with SHX and allowed to synthesize ( p ) ppGpp ( for 3 h ) prior to peptide treatment . Samples were then extracted with frozen 13 M formic acid by three cycles of freeze-thaw . Aliquots ( 7 . 5 µl ) of the supernatants were applied to 20×20 cm PEI cellulose TLC plates , resolved with 1 . 5 M KH2PO4 ( pH 3 . 4 ) for 4 h . After chromatography , nucleotides were visualized by autoradiography and quantified with a MolecularImager FX PhosphorImager and Quantity One software ( Bio-Rad ) . Unlabeled GTP was spotted on the plates as markers and visualized after chromatography by UV light-induced fluorescence . The ability of 1018 to co-precipitate with nucleotides with varying phosphate content was examined as described previously by Hilpert et al . [37] . Peptide 1018 was mixed separately with ppGpp , GTP , GDP , ATP , ADP or NaH2PO4 in 50 mM Tris buffer at pH 7 . 4 . ppGpp was purchased from TriLink BioTechnologies and all other nucleotides were purchased from Sigma . Nucleotide concentrations ranging from 0 . 25 to 0 . 008 mM were mixed in a microtiter plate with 0 . 25 nmoles of 1018 in a final volume of 100 µl per well . Samples were also prepared containing only 1018 or the nucleotide of interest at each concentration . The co-precipitation of 1018 with a nucleotide resulted in an increase in turbidity , which was quantified by measuring the A620 using a Powerwave X 340 microplate reader ( BioTek Instruments Inc . , Winooski , VT , USA ) . 31P-NMR spectroscopy was used to evaluate the binding of 1018 to ppGpp and GTP . Samples containing 0 . 5 mM GTP or ppGpp were prepared in buffer ( 10 mM Tris , 50 mM NaCl , pH 7 . 4 ) and 1 mM phosphate was added as an internal chemical shift reference as well as for quantification . Separate samples containing 0 . 5 mM 1018 mixed with each nucleotide were prepared in the same way . Additional samples of 0 . 5 mM ppGpp , GTP and ATP with 1 mM phosphate were also prepared in 6 . 5 M formic acid for comparison to the samples prepared from the P . aeruginosa PAO1 extracts ( see below ) . All samples contained 10% D2O . Each NMR sample was briefly centrifuged on a benchtop centrifuge ( ∼30 s ) to pellet any precipitate that formed and the supernatant liquid was used as the NMR sample . 31P spectra were acquired at 25 °C on a Bruker Avance 500 MHz spectrometer , operating at a 31P frequency of 202 . 272 MHz . A single pulse experiment , with a 90 degree pulse of 20 µs was used , on a BB 500 probe . 4096 scans were acquired for the pure nucleotide samples while 12288 scans were accumulated for samples that contained peptide . Spectra were processed with an exponential window and line broadening of 50 Hz . To evaluate the differential binding of 1018 to ppGpp or GTP , samples containing an equimolar mixture of ppGpp and GTP ( both at 0 . 5 mM ) were prepared in Tris buffer . Peptide 1018 was added to separate nucleotide mixtures to achieve final peptide concentrations of 0 . 25 , 0 . 5 , 0 . 75 and 1 mM . The samples were again centrifuged to pellet the precipitate and the resulting supernatant was used in the NMR experiments . The experiments were performed as above , but the spectra were processed with a shifted sine bell window only . The phosphorous peak signal intensity resulting from unique chemical shift peaks from either ppGpp or GTP was determined at every concentration of 1018 tested . To examine the effect of 1018 on ppGpp levels in vivo , 3×20 ml cultures of PAO1 ( in BM2 media with 0 . 5% casamino acids ) were grown overnight at 37°C in the presence of SHX ( 500 µM ) to induce the production of ppGpp . Following overnight incubation , 1018 was added to a final concentration of 20 µg/ml and the sample was grown for an additional hour at 37°C . For comparison , a separate culture was prepared with no 1018 added . The PAO1 cells were harvested by centrifugation for 20 min at ∼2000×g in a Beckman Coulter Allegra 6 centrifuge . All three bacterial pellets were resuspended in a total of 400 µl H2O . To prepare the NMR sample , 400 µl of the bacteria suspension was added to 500 µl of 13 M formic acid and 100 µl of D2O . The sample was subjected to three rounds of freezing and thawing using liquid nitrogen and a room temperature water bath . The sample was centrifuged at 4°C and 14000 rpm in a microcentrifuge and 500 µl of the resulting supernatant was used as the NMR sample . Spectra were acquired as described for the pure nucleotide samples but with an accumulation of 24576 scans . | Bacteria colonize most environments , including the host by forming biofilms , which are extremely ( adaptively ) resistant to conventional antibiotics . Biofilms cause at least 65% of all human infections , being particularly prevalent in device-related infections , infections on body surfaces and in chronic infections . Currently there is a severe problem with antibiotic-resistant organisms , given the explosion of antibiotic resistance whereby our entire arsenal of antibiotics is gradually losing effectiveness , combined with the paucity of truly novel compounds under development or entering the clinic . Thus the even greater resistance of biofilms adds to the major concerns being expressed by physicians and medical authorities . Consequently , there is an urgent need for new strategies to treat biofilm infections and we demonstrate in the present study an approach , based on the inhibition of ( p ) ppGpp by a small peptide , that eradicates biofilms formed by four of the so-called ESKAPE pathogens , identified by the Infectious Diseases Society of America as the most recalcitrant and resistant organisms in our society . The strategy presented here represents a significant advance in the search for new agents that specifically target bacterial biofilms . | [
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] | 2014 | Broad-Spectrum Anti-biofilm Peptide That Targets a Cellular Stress Response |
We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines . Four traits from the male inflorescence ( tassel ) and three traits from the female inflorescence ( ear ) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population . Inflorescence loci have larger effects than flowering and leaf loci , and ear effects are larger than tassel effects . Ear trait models also have lower predictive ability than tassel , flowering , or leaf trait models . Pleiotropic loci were identified that control elongation of ear and tassel , consistent with their common developmental origin . For these pleiotropic loci , the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved . This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms . Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time , since the traits that changed most during the relatively recent domestication of maize have the largest effects .
The genetic architecture of a complex trait is defined by the number , effect size , frequency , and gene action of the quantitative trait loci ( QTL ) that affect it . A comparison of studies from flies , mice , and humans shows that genetic architecture is remarkably consistent among these species , with many loci of small additive effect [1] . Distributions of QTL effect sizes are strikingly similar among different classes of mouse traits including behavior , biochemistry , immunology , and metabolism [2] . Similar results have been obtained in maize for flowering time , leaf morphology , and disease resistance traits [3]–[5] . Despite many well-powered genome-wide association studies ( GWAS ) of height variation in humans , no single polymorphism explaining even 1% of the variance in adult height has been found [6]–[9] . Fisher [10] provides a simple theoretical justification for these observations . For a well-adapted organism close to its fitness optimum , only small effects can increase fitness . Orr [11] showed that regardless of the distance from the fitness optimum , the expected distribution of effect sizes progressively fixed during adaptation is exponential , with a small number of large-effect loci fixed first , followed by progressively larger numbers of loci with smaller effects becoming fixed . The genetic architecture of intraspecific variation consists of many loci with small effects because loci with larger effects tend to be only briefly polymorphic . A few traits exposed to strong , recent selection show distinct genetic architectures not characterized by many loci of small additive effect . For inbred dogs , three loci explain 38% of the variance in body weight among diverse breeds [12] , and a single nucleotide polymorphism ( SNP ) at the IGF2 locus in pigs explains 15–30% of the variance in muscle mass [13] . In a cross between chicken populations recurrently-selected for high and low body weight , an epistatic network of four major loci explains 45% of the difference between parents [14] . Independent populations of anadromous stickleback fish that became trapped in freshwater lakes subsequently lost their armor plating through mutational changes at a single major locus [15] . The Fisher-Orr model predicts segregation of such large effects between populations exposed to divergent selective pressures , but not within a population exposed to directional selection . Mating system also appears to influence genetic architecture . Flowering time QTL effects are much larger in the inbreeding species Arabidopsis thaliana than in maize , an outcrosser [16] . Inbreeding might allow isolated populations to fix large-effect mutations in response to divergent selective pressures , as the dog , chicken , and fish examples suggest . However , mating system differences cannot account for differences in genetic architecture between traits within an organism . Plant and animal domesticates provide opportunities to compare genetic architecture between selected and unselected traits in populations exposed to the same demographic effects [17] , [18] . Maize ( Zea mays ssp . mays ) was domesticated from teosinte ( Zea mays ssp . parviglumis ) 5 , 000 to 10 , 000 years ago in southwest Mexico [19] . Beadle [20] suggested that 4–5 recessive mutations underlie maize domestication as one out of every five hundred F2 progeny from a maize-teosinte cross appear maize-like . Two of these mutations have been identified: teosinte branched1 ( tb1 ) causes an increase in apical dominance and reduction of lateral branching and teosinte glume architecture ( tga1 ) causes “release” of the nutritious grain from bony , enclosing glumes [21] , [22] . Other remarkable changes that occurred during maize domestication have yet to be fully explained . Maize is a monoecious plant with an apical male inflorescence , the tassel , and an axillary female inflorescence , the ear ( Figure 1 ) . Maize and teosinte tassels are relatively similar , but dissimilarity between maize and teosinte ears fueled historical controversy about whether one could have evolved from the other [23] , [24] until molecular data provided irrefutable evidence that maize evolved from teosinte [19] . Teosinte “ears” are small , occupy the lateral positions of a primary lateral branch , and have two rows of kernels . Maize ears are large , occupy the apical position of a primary lateral branch , and have from eight to over twenty rows of kernels . Although maize tassels are clearly different from teosinte tassels , the maize ear stands out as a monument of morphological evolution under human selection . The maize tassel and ear , despite their differences , share a common developmental origin and are nearly indistinguishable from each other during early development . Tassel and ear become distinct through the formation of long branch primordia and the abortion of female floral organs in the tassel , and through the abortion of male floral organs in the ear . Several mutant phenotypes support a close developmental relationship between tassel and ear . Branches are usually only found in the tassel , but a number of mutations produce branched ear phenotypes [25] . Tasselseed phenotypes are characterized by failure to abort female development in the tassel , and can be induced by mutation or epigenetic change [25] , [26] . Because the underlying genetic control of maize tassel and ear development is so similar , human selection for ear morphology may have indirectly changed the morphology of the tassel as well . In this study we compare the genetic architecture of thirteen maize morphological traits , including seven inflorescence traits reported here and three leaf and three flowering traits reported previously [3] , [4] . The four tassel and three ear traits were measured over eight environments in the maize nested association mapping ( NAM ) population , a set of 4892 recombinant inbred lines ( RILs ) derived from 26 biparental families that capture much of the genetic diversity of maize [27] . These RILs are ∼97% homozygous , show little evidence for segregation distortion or inter-chromosomal linkage disequilibrium ( LD ) , and have been genotyped with 836 markers for an average of one marker every ∼1 . 3 cM [28] . Two methods were used to detect QTL: linkage mapping across the 26 families ( joint linkage ) and a GWAS approach that incorporates polymorphism data from 1 . 6 million maize SNPs [4] , [29] . The NAM population has been recently studied for flowering , leaf , and disease-resistance traits [3]–[5] , revealing genetic architectures characterized by many loci of small additive effect . Maize inflorescence traits have distinct genetic architectures characterized by larger QTL effect sizes . Increased effect size in maize inflorescences is caused by many hundreds of polymorphisms with larger effects and a deficiency of small-effect inflorescence polymorphisms . Ear traits have the largest effects and also show lower model predictive abilities . The close developmental relationship between male and female maize inflorescences allows us to infer from our results that genetic architecture may vary independently of genetic control , providing new evidence for how selection affects the genetic architecture of complex traits .
The four tassel traits and three ear traits measured are shown in Figure 1 and in Table 1 . Traits measured in units of counts , branch number ( BN ) and ear row number ( ERN ) , were not normally distributed and required box-cox transformation . Traits measured in units of length , tassel length ( TL ) , spike length ( SL ) , length of the branch zone ( BZ ) , cob length ( CL ) , and cob diameter ( CD ) , were normally distributed . Broad-sense heritabilities ranged from 0 . 87–0 . 93 , within the range of heritabilities reported previously for flowering and leaf traits . Correlations between phenotypes from temperate and tropical growing environments were high for all inflorescence traits , so a single best linear unbiased predictor was calculated for each trait over all locations . We mapped loci controlling maize inflorescence variation using joint linkage and GWAS analyses in the NAM population of ∼5000 RILs as described previously [3]–[5] . Table 2 presents the major differences between these analyses , and full results are presented in Tables S1 and S2 . In brief , the joint linkage analysis used 836 markers , whereas the GWAS analysis incorporated genetic information from over 1 . 6 million SNPs genotyped in the 27 parental lines . Joint linkage QTL were fit as marker-by-family terms , meaning that 26 separate effects were fit for each QTL [3] , whereas GWAS SNPs are biallelic . A single joint linkage model was developed for the entire genome , whereas GWAS models were fit for each chromosome separately . For the GWAS analysis , a subsampling procedure was used to assign a resample model inclusion probability ( RMIP ) value for each SNP ranging from 0 to 1 , representing the percentage of subsamples in which that SNP was selected [30] . High correlations were observed between trait heritabilities , the number of joint linkage QTL detected , and the number of GWAS SNPs detected across the seven inflorescence traits ( Table 1 ) . Full results for joint linkage and GWAS analyses are presented in Tables S1 and S2 . GWAS analysis confirms the presence of all QTL detected by joint linkage analysis , and often splits a multiallelic QTL into two or more biallelic loci . The specific families assigned to carry a given QTL are often different between the two analyses . We compared effects between ear , tassel , leaf , and flowering traits , and found that ear effects are largest and flowering effects are smallest in both joint linkage and GWAS analyses ( Figure 2 ) . Joint linkage analysis produces many more small effects than GWAS analysis as an artifact of the model fitting process , which assigns a separate effect to all 26 families at each QTL . Since most QTL are not present in all families , many of these effects are near zero . To compare effects among traits , the absolute value of each effect was scaled by the total heritable variation for that trait . Total heritable variation was calculated as the standard deviation of the trait BLUPs among a set of 282 diverse maize lines ( this set includes the 27 parental lines ) multiplied by our broad-sense heritability estimate for that trait ( Table 1 ) . Using the standard deviation of the trait BLUPs among just the 27 parental lines gave very similar results . Trait heritabilities were not included in an initial scaling process , leading to a modest correlation between heritability and median effect size ( r2 = 0 . 127 for joint linkage and r2 = 0 . 233 for GWAS ) . Scaling by the total heritable variation reduced this correlation considerably ( r2 = 0 . 045 for joint linkage and r2 = 0 . 075 for GWAS , Figures S1 and S2 ) . QTL number varied from 26 to 40 among inflorescence , flowering , and leaf traits for joint linkage analysis . To control for variation in QTL number , we refit a model containing the 26 most significant QTL for all 13 traits and recalculated the effects . Results presented are for recalculated effects . This process did not change the magnitude of differences in effects among trait categories . Our QTL effects are biased by the reference design of the NAM population , in which 26 diverse inbreds were each crossed to a common parent . Since the common parent is the reference point from which all other effects are judged , traits for which the common parent is an outlier , such as ear row number , will have inflated QTL effects in the NAM population . To correct for this bias , we inferred and present results of the full 26×26 matrix of QTL effects between all parental lines rather than using the 26×1 vector of observed QTL effects relative to the common parent ( see Materials and Methods ) . We also regressed median QTL effects on the deviations of the common parent from the mean of the 27 parental lines and found little correlation ( r2 = 0 . 067 for joint linkage and r2 = 0 . 043 for GWAS; Figures S1 and S2 ) , even though ear row number had the largest deviation and the largest effects . To compare GWAS SNP effect sizes among traits , we calculated the absolute value of each median SNP effect across all subsamples in which that SNP was selected , and scaled this value by the total heritable variation . GWAS SNP number is correlated with heritability ( Table 1 ) , so we selected a fixed number of SNPs for each trait , ordered by decreasing RMIP value . Results presented at the bottom of Figure 2 include top 200 SNPs for each trait , and including the top 50 , 100 , or 500 SNPs yielded very similar results ( Figure S3 ) . Inflorescence traits have larger effects than flowering or leaf traits across a range of QTL and SNP frequencies ( Figure 3; Kolmogorov-Smirnov test p<10−16 for joint linkage and GWAS ) , and ear traits have larger effects than tassel effects ( Kolmogorov-Smirnov test p = 0 . 004 for joint linkage and p<10−15 for GWAS ) . The few large-effect flowering QTL are contributed by anthesis-silking interval ( ASI; Figure S4 ) , and not days to anthesis ( DA ) or days to silking ( DS ) . Low-frequency GWAS SNPs present in four or fewer families account for nearly all loci with scaled effects above 0 . 15 ( Figure 3 ) . Several high-frequency , large-effect SNPs for ear row number are exceptions . The GWAS SNPs with the largest effects are found at low frequency and have low Resample Model Inclusion Probability ( RMIP ) values , although there is no overall correlation between frequency and RMIP ( Figure S5 ) . In contrast , joint linkage effect sizes show no relationship with frequency . Joint linkage results span a larger range of effect sizes than GWAS results , which likely reflects stacking of linked QTL with effects in the same direction . We assessed the predictive value of our GWAS models for each trait by summing the effects of SNPs with RMIP values of 0 . 05 or greater , weighted by their RMIP values , and calculating predicted values for the 27 parents and the 4892 RILs , with and without the inclusion of a family term ( Figure 4 ) . Ear trait models had lower model predictive abilities than all other traits except anthesis-silking interval . Inclusion of the family term always improved predictive ability , and predictive ability was generally higher for parents than for RILs . Joint linkage and GWAS analyses yield similar estimates of pleiotropy among 13 diverse maize morphological traits ( Figure 5 ) . Pleiotropy was assessed from joint linkage results by fitting the QTL for each trait to every other trait and correlating the resulting vectors of effects across the 26 families ( Table S3 , [3] ) . If a QTL has large positive or large negative effects for two traits in many of the same families , the effect vectors will be significantly correlated and pleiotropy will be inferred . For GWAS results , pleiotropy was assessed by averaging SNP effects for each trait in each family , weighted by their RMIP values , in sliding windows across the genome ( see Materials and Methods and Table S4 ) . Pleiotropy is observed between developmentally related traits across male and female inflorescences: cob length shows positive pleiotropy with spike length and with tassel length . Pleiotropy is also observed between elongation of vegetative and reproductive organs: leaf length shows positive pleiotropy with cob length , tassel length , spike length , and branch zone length . In addition we observed very strong pleiotropy between days to anthesis and days to silking , and moderate pleiotropy between both leaf length and leaf width with flowering traits . This pattern of pleiotropy has been observed previously using joint linkage results [3] , [4] and is corroborated here using GWAS . Since ear QTL have larger effects , we reasoned that the subset of QTL for other traits that show evidence of pleiotropy with ear traits might also have larger effects . To address this hypothesis , all pleiotropic GWAS SNPs were grouped according to whether they showed pleiotropy within or between trait categories ( tassel , ear , and flowering/leaf; Figure 6 ) . In general there are no differences in QTL effects between types of pleiotropic QTL within a trait category: pleiotropic tassel QTL have similarly-sized effects regardless of whether they are pleiotropic with ear , flowering/leaf , or other tassel QTL . The same pattern is observed for pleiotropic flowering and leaf QTL . The one exception is that ear QTL pleiotropic with flowering/leaf QTL appear slightly smaller than ear QTL pleiotropic with other ear QTL ( Kolmogorov-Smirnov test p = 0 . 005 ) . When there is shared genetic control between ear traits and other traits , ear effects are larger than effects for other traits . Similarly , when there is shared genetic control between flowering/leaf traits and other traits , flowering/leaf effects are smaller than effects for other traits . Non-pleiotropic QTL are not displayed in Figure 6 but have significantly smaller effects than pleiotropic QTL , suggesting that our power to detect pleiotropy may be greater for QTL with larger effects . Induced and spontaneous mutations in many maize genes cause dramatic inflorescence phenotypes ( Table 3 ) . We considered these genes to be candidates for our maize inflorescence QTL , and tested them for enriched proximity to our GWAS SNPs for maize inflorescence traits . Two of the genes responsible for changes in inflorescence morphology during maize domestication have also been identified: teosinte glume architecture ( tga1 ) encodes a squamosa-binding-protein ( SBP ) -domain transcription factor [22] and teosinte branched1 ( tb1 ) encodes a TCP-domain protein [21] . For this reason , annotated SBP-domain and TCP-domain genes in the maize genome were also considered to be candidates and tested for enriched proximity to our GWAS SNPs for maize inflorescence traits . To test for enrichment , we considered only the ten GWAS SNPs with the highest RMIP values for each trait , both to minimize the number of tests and because we assumed that these high-RMIP SNPs would be closely linked to their causal polymorphisms . For each of the three sets of candidates ( 26 genes identified using induced or spontaneous mutations , 17 SBP genes , and 24 TCP genes ) , we calculated the genetic distance to the nearest GWAS SNP for each gene and compared these results to a null distribution estimated from 1000 sets of the same number of random genes . For instance , the null distribution for SBP genes was estimated from 1000 sets of 17 random genes . Cloned maize inflorescence mutants showed slight enrichment for proximity to tassel length and spike length loci: GWAS SNPs for both these traits fell within 1 cM of the fea2 and td1 loci ( Figure S6-top ) . SBP-domain genes showed enrichment for proximity to GWAS SNPs for branch number and branch zone length ( Figure S6-middle ) . Overall , three SBP domain genes are implicated in tassel branching , at 4 Mb on chromosome 2 , 205 Mb on chromosome 4 , and 139 Mb on chromosome 10 ( AGP version1 coordinates ) . The first of these genes corresponds to liguleless1 , which lies near a high-RMIP SNP for leaf angle as reported by Tian et al . [4] . SBP genes have no overall enrichment for proximity to GWAS SNPs for leaf angle , however . TCP-domain genes show no significant enrichment for proximity to GWAS SNPs for any trait ( Figure S6-bottom ) . Only the enrichment between SBP-domain genes and branch number survives a Bonferonni correction . Two well-characterized SBP-domain genes were included in our candidate list ( tga1 and tsh4 ) , but are not associated with variation in branch number .
Low-frequency SNPs found in four families or fewer account for most of the largest GWAS effects ( Figure 3 ) . Lack of power likely accounts for both the failure to detect small effect GWAS SNPs at low frequency and the greater proportion of intermediate-frequency GWAS SNPs relative to the null distribution ( see [4] Fig . 4 ) . Lack of power does not help explain the over-representation of large-effect SNPs at low frequency , however . Causal variants at low and high frequencies are more likely matched by random SNPs . A causal variant present in one or 25 of the 26 families has just 26 possible incidence patterns , whereas a causal variant present in 13 families has over 10 million possible incidence patterns . Our dataset of 1 . 6 million SNPs is too small to tag all causal variants , and we are far less likely to tag intermediate-frequency than low- or high-frequency variants . We observe large-effect SNPs at low frequency but not at high frequency , however . One explanation is linkage: linked variants with effects in the same direction will more often be combined into a single “synthetic” effect if they are present at low frequency . Low-frequency SNPs with very large effects also have low RMIP values ( Figure S5 ) , which supports this explanation: rare recombinant individuals allow separation of linked synthetic loci , but are sampled only intermittently . Because all GWAS SNPS with effects over 0 . 3 standard deviations in this study are found in a single family , we hypothesize that they result from linked QTL . These large effects explain a small proportion of the total phenotypic variation because their frequencies are low . Larger QTL effects may reflect either larger effects of individual causal variants or greater linkage disequilibrium between causal variants with effects in the same direction . The latter phenomenon is expected to be most prevalent for SNPs found in a single family . However , the difference in magnitude between inflorescence and flowering/leaf effects holds true across the entire range of SNP frequencies ( Figure 3 ) , suggesting that individual inflorescence variants have larger effects than individual flowering or leaf variants . Also noteworthy is the deficiency of small effects for inflorescence variants , which cannot feasibly be due to linkage . Since many inflorescence traits are pleiotropic with flowering and leaf traits , we assume that many of the same polymorphisms underlie these QTL for different traits . Even in instances of shared genetic control , however , inflorescence effects are larger than flowering/leaf effects , and ear effects are larger than tassel effects ( Figure 6 ) . This does not support the scenario that inflorescence polymorphisms are unique , consisting for example of more frame-shifts , premature stop codons , or nonsynonymous substitutions . Rather , these results suggest that the maize inflorescence , and the maize ear in particular , is more labile . Three flowering traits show a disjunct distribution of effect sizes , with days to anthesis ( DA ) and days to silking ( DS ) effects much smaller than anthesis-silking interval ( ASI ) effects ( Figure S4 ) . Stabilizing selection over millions of years may have purged Zea populations of large-effect variants for DA and DS due to the fitness cost of flowering too early or late relative to the rest of the population . In contrast , ASI may be a much “younger” trait specific to the apically-dominant architecture of the maize plant . Our scaling procedures may also have inflated effects for ASI . The development and maintenance of inbred lines by self-fertilization strongly selects for synchronous male and female flowering ( ASI values close to zero ) , reducing the total heritable variation in ASI and increasing our scaled ASI QTL effects . The utility of GWAS studies is contingent on their ability to predict phenotypes . In this study we show that simple additive models containing several hundred SNPs explain over 50% of the phenotypic variation in a set of 4892 RILs for most of the 13 maize morphological traits ( Figure 4 ) . SNP number in these models could probably be reduced considerably without sacrificing predictive ability by removing SNPs in high linkage disequilibrium with each other [5] . Additive model predictions are least accurate for the ear traits ( cob length ( CL ) , cob diameter ( CD ) , ear row number ( ERN ) ) , and the flowering trait anthesis-silking interval ( ASI ) . To investigate the nature of this apparent non-additivity , we focus on models without a family term ( Figure 4-top ) that rely solely on GWAS SNPs to explain phenotypic differences within and between families . Most traits show ∼10% greater predictive ability in the parents than in the RILs , but for cob length this difference is dramatic ( ∼30% ) . We observe the opposite situation for cob diameter and ear row number: predictive ability is higher in the RILs than in the parents . Here we interpret these observations in terms of interaction effects . For cob length , additive effects detected in the RILs accurately predict parental phenotypes , so we infer that interaction effects are equally likely to enhance or mask a given QTL ( their mean effect is close to zero ) and they must be common enough to account for a ∼20% drop in predictive ability in the RILs . For cob diameter and ear row number , additive effects detected in the RILs do not predict parental phenotypes , so we infer that parental phenotypes are caused by more complex interaction effects that are seldom recapitulated in the RILs and have little influence on additive effect sizes . We observe several pleiotropic relationships consistent with previous developmental genetic work . Negative pleiotropy between spike length ( SL ) and branch number ( BN ) indicates a trade-off between the two , consistent with the finding that a given meristem in the maize inflorescence acquires the fate of either a long indeterminate branch or a short indeterminate spikelet pair [31] . Knowledge of shared developmental networks , not only between ears and tassels but also between the elongation of vegetative and reproductive structures , can help inform the choice of candidate genes . The QTL with pleiotropic effects on leaf length , tassel length , and cob length may involve genes that function in cell elongation throughout the plant , rather than inflorescence-specific developmental genes . In a biparental family , close linkage of genes cannot be distinguished from pleiotropic effects of a single gene . Assessment of pleiotropy in NAM is made possible by testing correlations between vectors of QTL or SNP effects across the 26 families of RILs . This analysis will only detect pleiotropy when the same polymorphism or haplotype is consistently associated with phenotypic effects on different traits . Another less stringent definition of pleiotropy would allow a single gene to control variation in different traits through different polymorphisms . A possible example of this type of pleiotropy is the liguleless1 ( lg1 ) locus , which is associated with variation in both leaf angle [4] and tassel branching ( this study ) . lg1 encodes an SBP-domain transcription factor . The association of lg1 with leaf angle is supported by its mutant phenotype [32] , and the association of SBP-domain transcription factors with branching is supported by our results and by studies in rice [33] , [34] . Effect estimates for lg1-linked leaf angle and branch number QTL in NAM are not correlated , suggesting that different polymorphisms may be responsible for the effects of lg1 on leaf and tassel traits . Since structural mutations in a gene are more likely to have effects wherever the gene is expressed , lg1-linked variants for leaf angle and branch number might be cis-regulatory variants operating independently of each other in specific tissues [35] . Pleiotropy of this type cannot be distinguished from linkage in our analyses . Only a small degree of overlap is observed between the location of cloned maize inflorescence development genes and SNPs significant for inflorescence traits ( Table 3 , Figure S6A ) . Overlap between SBP-domain genes and loci for tassel branch number shows that our analysis has the power to detect such overlap where it does exist ( Figure S6B ) . Most cloned maize inflorescence genes involve loss-of-function alleles generated by transposon or chemical mutagenesis that have obvious phenotypes in mutant screens . Such screens generally cannot uncover mutations for which there is genetic redundancy . Purifying selection may be relaxed for genes with redundant functions , allowing them to accumulate more mutations that change gene function than non-redundant genes . If this is true , then mutagenesis studies may be somewhat biased against the discovery of loci controlling natural variation . The genetic architecture observed for maize inflorescence traits is novel . Very large effect sizes for a few major loci are commonly observed in plant and animal domesticates , including maize-teosinte segregants [36] , divergently-selected dog breeds [12] and chicken populations [14] . Fish populations subjected to habitat change [15] demonstrate that these unusual genetic architectures may be caused by natural as well as human selective pressure . These observations are consistent with theoretical predictions of an exponential distribution of effect sizes underlying adaptation [11] . In each case , the number of large-effect QTL is very few , because large effects quickly move a trait close to its fitness optimum . In contrast , the genetic architecture of inflorescence traits within domesticated Zea is characterized by a shift in the entire distribution of effect sizes , with many more effects of intermediate size and many fewer small effects . Although unusual genetic architectures observed in domesticates are sometimes attributed to human preference for novelty , which may preserve unadaptive , large-effect mutations [12] , it is difficult to explain how such a preference for novelty could account for the deficiency of small-effect inflorescence QTL . Maize domestication released cryptic genetic variation for inflorescence traits [37] . For example , ear row number is invariant in teosinte but varies widely in maize , indicating that all genetic variants for ear row number in maize must be cryptic genetic variants in teosinte . Maize inflorescence QTL may have more large effects and fewer small effects because more of them are caused by newly-released cryptic variants . The distribution of effects for cryptic variation could differ from that of old , standing variation for two reasons . First , large effects become fixed or purged more rapidly than small effects [38] . Second , large-effects could become smaller through the gradual accumulation of buffering mutations [39] . This is the canalization hypothesis: organisms evolve robustness to genetic and environmental perturbation . Since the maize ear is a relatively recent creation , it has accumulated the least genetic buffering . These scenarios differ in their prediction of the distribution of effects of new mutations: either large-effect mutations keep arising transiently , or the canalized phenotype becomes resilient to large-effect mutations . Maize ear and tassel traits have distinct genetic architectures even though they have shared genetic control: pleiotropic loci with effects on both tassel and ear show larger effects on the ear . This is the expected pattern if these pleiotropic loci had phenotypic effects in male but not female inflorescences in teosinte . Following maize domestication , they would act as newly-released cryptic variants in maize ears but not tassels . Maize domestication moved the ear from an axillary position to an apical position in the primary branch , which may have brought it under the control of an apical dominance network [40] . The process of domesticating maize from teosinte transformed plant architecture . The long lateral branch of teosinte with multiple , axillary , two-rowed female inflorescences was reshaped into a short , unbranched structure bearing a single , apical , multi-rowed ear . We present evidence that this process also transformed genetic architecture , creating a state of increased genetic lability in the maize ear that humans have cleverly exploited . Because only a few thousand generations have elapsed since the maize ear was created , ear traits still show a larger range of effect sizes than tassel , flowering , and leaf traits , for which maize and teosinte are phenotypically much more similar . Future advancements in medicine and agriculture will benefit from an improved understanding of the forces that shape the genetic architecture of complex traits . The most rigorous study to date comparing the genetic architecture of traits within a species [2] examined 97 traits in mice and found little variation in effect size ( see [1] Figure 2 ) . These traits were predominantly fitness-related and may have stabilized over many millions of years . By comparing a suite of maize morphological traits that have experienced very different selective pressures over the last 5 , 000 years , we show that effect sizes are inversely proportional to trait stability and that genetic architecture may vary even when there are common underlying genes . We suggest that most large-effect maize ear QTL represent cryptic genetic variants released by the fixation of large-effect domestication mutations . The release of cryptic variation by directional selection might help explain the seemingly inexhaustible genetic variation in long-term selection experiments [41] . Because transgenesis can have large effects , it may also unveil cryptic variants , suggesting that interaction between natural and transgenic variation could impact phenotypes and selection schemes for a variety of domesticated and agricultural organisms .
The creation of the NAM population of RIL families has been described previously [3] , [28] . Environments , field design , traits . Another publicly-available maize RIL family , the intermated B73-by-Mo17 ( IBM ) family , was also included in our analyses for a total of 4892 RILs from 26 biparental families with B73 as a common parent , and a total of 27 parents . All inflorescence traits were measured in eight environments , including Aurora , NY , Clayton , NC , Urbana , IL , Homestead , FL , and Ponce , PR in 2006 , and Aurora , NY in 2007 . Tassel traits were additionally measured in Columbia , MO in 2006 and Urbana , IL in 2007 . Ear traits were additionally measured in Clayton , NC in 2007 and Aurora , NY in 2008 . In each location , each family was represented by 220 rows: 200 rows of RILs and 10 rows of each parent . Data from some RILs was later discarded to bring the total RIL number to 4892 [28] . Trait transformations were performed using the boxcox function in R with lambda ranging from −10 to +10 in increments of 0 . 1 , where lambda values of 0 and 1 are equivalent to log and linear transformations , respectively . Branch number and ear row number traits had maximum likelihood values of lambda of 0 . 3 and 0 . 4 respectively . Box-cox transformed values of these traits were used to calculate BLUPs . BLUPs were calculated in SAS using PROC MIXED and a model with location , set ( location ) , family , family*location , and entry ( family ) as random effects . The genotypic dataset consisted of 836 markers , representing the subset of 1106 markers that could be placed unambiguously on the physical map , scored on 4892 RILs . Missing data , consisting primarily of markers that were non-informative in particular families , were imputed as previously described [4] . Joint linkage models were obtained in SAS using the stepwise selection procedure in PROC GLMSELECT . The family term was forced into the model , and each of the 836 possible marker-by-family terms was made available for inclusion . Significance levels for entry and exit of model terms were determined by permutation: phenotypic data were permuted against the genotypic data separately within each family , all 836 marker-by-family terms were tested , and the lowest resulting p-value was recorded for each permutation . 1000 permutations were performed , and alpha was set at . 05 . Missing SNP data from the maize HapMap project [29] were imputed as previously described [4] . For non-recombinant RIL marker intervals , SNP values of 0 ( common parent allele ) and 1 ( alternate allele ) were assigned according to the parental genotype . For recombinant RIL marker intervals , SNP values between 0 and 1 were assigned based on the physical position of the SNP within the interval and assuming a linear relationship between physical and genetic distance . Projection was also tested assuming a linear relationship between “genespace” and genetic distance , but this had very little effect on the results . GWAS models were fit for each chromosome separately . The phenotypes for each chromosome consisted of residuals from a joint linkage model excluding both the family covariate and all QTL on the chromosome under consideration . GWAS genotypes were obtained by scoring 1 . 6 million SNPs in the 27 parental lines and then “projecting” these genotypes into the progeny RILs . We employed a subsampling procedure wherein 80% of the RILs from each family were sampled without replacement , and forward regression was used to fit SNPs in the presence of the family term using permutation-derived significance thresholds [4] . This process was repeated 100 times to obtain a resample model inclusion probability ( RMIP ) value for each SNP ranging from 0 to 1 , which represents the percentage of samples in which that SNP was selected . Only SNPs with RMIP values greater than or equal to 0 . 05 were used for further analysis . QTL effects for each trait were divided by the standard deviation of BLUP values across a set of 282 diverse maize lines that included the 27 parental lines , and multiplied by the broad-sense heritability estimate for that trait . Since a minimum of 26 QTL were detected for each trait , a 26-QTL model was refit for each trait and used to determine effect sizes . This experiment used a reference design ( 26 inbred lines were each crossed to a common parent ) , meaning that QTL effect sizes are potentially biased for traits for which the common parent is an outlier . To circumvent this problem , for each QTL we calculated the predicted effects of all pairwise matings between the 26 parents ( eg: for two parents with effects of +1 and −1 relative to the common parent , the predicted QTL effect size in this family is 2 ) , yielding a total of 325 ( 26 choose 2 ) effect sizes for each QTL , or a total of 6825 qtl effects per trait . Pleiotropy between pairs of traits in the joint linkage analysis was evaluated as described previously [3] . Briefly , the QTL model for each trait was applied to every other trait , and correlations between effect estimates were used to detect significant pleiotropic QTL . For each QTL in each pairwise trait comparison , the Pearson correlation coefficient ( r ) between the two effect vectors of length 26 is significant at p<0 . 01 if r exceeds 0 . 495 ( two tailed t distribution , 24 d . f . ) . The percentage of shared QTL between two traits is the sum of two fractions: the fraction of significant correlations when the model for trait 1 is applied to trait 2 , and vice versa . Pleiotropy between pairs of traits in GWAS analysis has not been reported previously . First , the effects of all GWAS SNPs for each trait in each family were weighted by their RMIP values and averaged in sliding windows across the genome , in order to derive a vector of effect estimates for each trait in each window . Results presented here used a 5 cM window size and a 2 . 5 cM step , but similar results were obtained for larger and smaller windows . Second , for each pair of traits , only windows where the sum of RMIP values for each trait fell above a threshold ( RMIP = 0 . 10 for the results presented ) were considered . Finally , significance of Pearson correlation coefficients between effect estimates was calculated as for joint linkage analysis . We considered only the top ten GWAS SNPs for each trait , ordered by decreasing RMIP value , on the assumption that these more robustly-selected SNPs should be more closely linked to the causal variants . To test for significant enrichment , the number of high-RMIP SNPs for a given trait that fell within 0 . 5 , 1 , and 2 cM of candidates was compared with a null distribution obtained by selecting an equivalent number of random genes ( eg: 17 random genes for comparison to 17 SBP candidates ) , calculating their proximity to trait SNPs , and repeating this process 1000 times . Selection of random positions rather than random genes represents a far less stringent test , since genes are clustered in the maize genome . | Genetic architecture is of broad interest in evolutionary biology , plant and animal breeding , and medicine , because it influences both the response to selection and the success of trait mapping . Results from the most rigorously studied genetic systems suggest a similar genetic architecture across all species and traits studied , with many loci of small effect . A few strongly selected traits in domesticated organisms show unusual genetic architecture , for reasons that are unclear . We compare maize inflorescence , flowering , and leaf traits and show that inflorescence traits have distinct genetic architectures characterized by larger effects . Female inflorescences ( ears ) have larger effects than male inflorescences ( tassels ) even though the two structures have similar developmental origins . Analysis of pleiotropic loci shows that these larger effects are not inherent features of the underlying polymorphisms . Rather , maize inflorescences appear to be exceptionally labile , with female inflorescences more labile than male inflorescences . These results support the canalization hypothesis , which predicts that rapidly changing traits will have larger effects . We suggest that maize inflorescence traits , and ear traits in particular , have larger effects than flowering or leaf traits as a result of strong directional selection during maize domestication . | [
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"agriculture"
] | 2011 | Distinct Genetic Architectures for Male and Female Inflorescence Traits of Maize |
Acidocalcisomes are acidic organelles present in a diverse range of organisms from bacteria to human cells . In this study acidocalcisomes were purified from the model organism Trypanosoma brucei , and their protein composition was determined by mass spectrometry . The results , along with those that we previously reported , show that acidocalcisomes are rich in pumps and transporters , involved in phosphate and cation homeostasis , and calcium signaling . We validated the acidocalcisome localization of seven new , putative , acidocalcisome proteins ( phosphate transporter , vacuolar H+-ATPase subunits a and d , vacuolar iron transporter , zinc transporter , polyamine transporter , and acid phosphatase ) , confirmed the presence of six previously characterized acidocalcisome proteins , and validated the localization of five novel proteins to different subcellular compartments by expressing them fused to epitope tags in their endogenous loci or by immunofluorescence microscopy with specific antibodies . Knockdown of several newly identified acidocalcisome proteins by RNA interference ( RNAi ) revealed that they are essential for the survival of the parasites . These results provide a comprehensive insight into the unique composition of acidocalcisomes of T . brucei , an important eukaryotic pathogen , and direct evidence that acidocalcisomes are especially adapted for the accumulation of polyphosphate .
Acidocalcisomes were originally observed in bacteria and unicellular eukaryotes and named metachromatic [1] or volutin [2] granules . Later , when polymers of orthophosphate called polyphosphate ( polyP ) were identified at high levels within these organelles , acidocalcisomes were also called polyphosphate granules [3] . The length of polyP varies from as few as three to as many as thousands of residues [4] . The discovery of a diverse array of transporters established that acidocalcisomes are real organelles present from bacteria to human cells [5] . Acidocalcisomes have been well described in some species of bacteria [6] , [7] , trypanosomatids [8]–[10] , apicomplexan parasites [11]–[13] , fungi [14] , [15] , algae [16] , [17] , insect eggs [18] , [19] , sea urchin eggs [20] , and chicken eggs [21] . Additionally , these organelles are also present in mammalian cells such as human platelets [22] and mast cells and basophils [23] , where they belong to the group of organelles known as lysosome-related organelles ( LROs ) . However , the name acidocalcisome was first used to describe these organelles in trypanosomatids [8] , [9] , and acidocalcisomes have been most extensively studied in these organisms . Trypanosoma brucei belongs to a group of organisms responsible for human African trypanosomiasis ( sleeping sickness ) , and nagana , a cattle disease in Africa . The two best-studied life stages of T . brucei are the procyclic forms ( PCF ) , which grow in the intestine of the tse tse fly vector , and the bloodstream forms ( BSF ) , which replicate in the blood of the mammalian host . Both stages can be grown in the laboratory and possess acidocalcisomes , although these are more abundant in the PCF [24] . Knowledge of the protein composition of acidocalcisomes will facilitate understanding of the physiological roles of these organelles . Among the proteins localized to acidocalcisomes of T . brucei so far is the vacuolar proton pyrophosphatase ( TbVP1 ) , which has been used as an acidocalcisome marker for subcellular fractionation studies [24] . In this work , we used iodixanol gradient centrifugation to obtain TbVP1-enriched fractions and examine the acidocalcisome proteome . We validated localization and essentiality of a selected group of proteins by in situ epitope tagging and immunofluorescence assays with specific antibodies , and RNA interference ( RNAi ) experiments , respectively . The results support the important role of these organelles in phosphate and cation homeostasis , and calcium signaling .
We identified a total of 580 proteins ( 1% false discovery rate , protein probabilities >0 . 95 ) from fraction 5 of the first ( ACCS1 ) and second ( ACCS2 ) experiments . The ACCS1 and ACCS2 datasets included 520 and 340 protein identifications , respectively ( proteins are reported in S1 Table; peptides in S2 Table ) . When variants of similar proteins are indistinguishable from peptide data , the ProteinProphet [30] algorithm utilized by the ProteoIQ software treats these identifications as a single protein ( a protein “group” ) . For example , two virtually identical isoforms ( Tb927 . 4 . 4380 and Tb927 . 8 . 7980 ) of vacuolar-H+-pyrophosphatase ( TbVP1 ) are present in T . brucei and vary in only 6 of 826 residues . Peptides from these proteins were unequivocally identified in our acidocalcisome datasets , and we report them as a single identification . In these instances , one or both of the proteins may be present . Two hundred nineteen are annotated as “hypothetical” in the T . brucei genome , and five were not represented in proteomic data available in TriTrypDB . org ( downloaded May 28 , 2014 ) . Of the five with no prior mass spectrometry evidence , three were annotated as hypothetical . The remaining two proteins for which we provide novel expression evidence are annotated as frame-shift pseudogenes for a retrotransposon hot spot protein and a variant surface glycoprotein . Approximately 21% ( 120 ) of our 580 proteins have predicted transmembrane domains ( S3 Table ) , consistent with estimates of representation in other organisms [31] . Of 40 identifications ( 6 . 9% of total ) , with predicted signal peptides , 22 also possessed putative transmembrane domains . Annotated proteins in our proteomic dataset span a broad range of metabolic groups . Transport-related proteins accounted for ∼15% . Among these were transporters and pumps , vacuolar-H+-pyrophosphatase , an acidocalcisomal marker , was identified in our dataset . Other well-represented metabolic groups in our dataset were energy metabolism ( ∼14% ) , protein , lipid , carbohydrate , and nucleic acid metabolism ( ∼36% ) , and cell structure and organization ( ∼18% ) . Subcellular localizations of each protein were predicted ( S4 Table ) using a series of algorithms ( pTARGET , targetP , WoLF-PSORT , and SLP-LOCAL ) . Both plant and non-plant-optimized predictions were performed as a means of comparison , but we report here non-plant , targeting predictions . Approximately 20% of our identifications are nuclear , 17% are cytosolic , and ∼9% are mitochondrial . Plasma membrane and secretory predictions represent ∼5% and 1% , respectively . Table 2 shows proteins with known localization to acidocalcisomes of T . brucei and those established in this work ( see below ) and other proteins that we selected for localization studies . Table 2 indicates which of these markers were not present in our proteomic datasets ( labeled with asterisks ) . Of the proteins identified by proteomic analysis of the subcellular fractions , we selected several proteins , some previously tested , for further validation ( Table 2 ) . Additionally , we selected other targets for validation based on properties that could justify acidocalcisome localization ( Table 2 ) . The acidocalcisomes in trypanosomatids serve as large acidic calcium stores [5] , [32] , and a number of proteins in these organelles can mediate Ca2+ signaling in the cell . The localization of the inositol 1 , 4 , 5-trisphosphate receptor ( IP3R ) in trypanosomatids has been controversial , but endogenous tagging of the IP3R of T . brucei with a 3× HA epitope tag demonstrated specific localization to the acidocalcisomes in this species [27] . The IP3R-HA did not co-localize with TbBiP , an ER marker [33] with a clear reticular labeling . Proteomic analysis of acidocalcisome fractions ( unpublished ) and contractile vacuole complex fractions [34] of T . cruzi also supported the presence of IP3R in these organelles . These results corroborate the punctate vacuolar localization in T . cruzi reported for TcIP3R by other authors [35] . These authors suggested an endoplasmic reticulum ( ER ) localization of TcIP3R , but no clear co-localization with TbBiP antibodies was presented [35] . To confirm the acidocalcisome localization of TbIP3R , we generated an antibody against the IP3 binding region of TbIP3R . Immunofluorescence analysis using this antibody confirmed the acidocalcisome localization , as determined by co-localization with antibodies against TbVP1 in T . brucei ( Fig . 2A ) . Western blot analysis confirmed specificity of these antibodies ( Fig . 2B and S3B Figure ) . The acidocalcisome localization of the vacuolar Ca2+-ATPase ( TbPMC1 , Tb927 . 8 . 1180 ) [36] was also confirmed in our proteomic analysis ( Table 2 ) . Peptides from other Ca2+-ATPases ( Tb927 . 3 . 3400 , annotated as sarcoplasmic-endoplasmic reticulum-type Ca2+-ATPase; and Tb927 . 8 . 1160 , annotated as vacuolar-type Ca2+-ATPase ) were also detected ( S3 Table ) , although they probably indicate similarity of peptides from different ATPases or contamination with other subcellular membrane fractions . The vacuolar transporter chaperone complex ( VTC complex ) is involved in polyP synthesis in yeast [37] and trypanosomes [38] , [39] . Homologues of the yeast proteins ( Vtc1p to Vtc4p ) are present in the genomes of trypanosomatids , apicomplexan , fungi , and algae but absent in mammalian cells . GFP-tagged T . brucei vacuolar transporter chaperone 1 ( TbVtc1 ) localized to acidocalcisomes and the ER , although ER localization was attributed to an artifact of protein overexpression [40] . Although we did not detect peptides for this protein in the acidocalcisome proteome , we re-examined its localization and avoided pitfalls of overexpression and abnormal distribution by expressing 3× HA-tagged TbVtc1 in its endogenous locus under wild-type regulation . TbVtc1 perfectly co-localized with TbVP1 to acidocalcisomes ( Fig . 3A ) . TbVtc4 , which was positively identified in the acidocalcisome proteome ( S1 Table ) , also co-localized to acidocalcisomes with TbVP1 ( Fig . 3B ) , as reported previously [38] . Western blot analyzes confirmed the expression of the tagged proteins ( Fig . 3E and 3F ) . A putative phosphate transporter ( TcPho1 , TcCLB . 508831 . 60 ) in T . cruzi , which was originally annotated as a sodium/sulphate symporter , localizes to the contractile vacuole and intracellular membranes of epimastigotes of T . cruzi [34] . The product of the T . brucei homologue ( TbPho91 , Tb927 . 11 . 11160 ) co-localized with TbVP1 in acidocalcisomes ( Fig . 3C ) . Expression of the tagged protein was confirmed by western blot analysis ( Fig . 3G ) . Previous work [28] has indicated the presence of a vacuolar soluble pyrophosphatase in acidocalcisomes of T . brucei ( TbVSP , Tb927 . 11 . 7060 and Tb927 . 11 . 7080 ) . Although peptides corresponding to this protein were not identified in the proteome , antibodies against this protein reacted with a band of ∼50 kDa corresponding to the apparent molecular mass of the protein in the acidocalcisome fraction ( S3C Figure , arrow ) . We also investigated the localization of a putative acid phosphatase ( Tb927 . 10 . 7020; TbAP ) , which was present in our acidocalcisome fractions ( S1 Table ) . The presence of an acid phosphatase activity in T . rangeli acidocalcisomes was detected by cytochemical methods [41] , and early work in T . brucei rhodesiense also localized an acid phosphatase activity to lysosome-like vesicles that probably correspond to acidocalcisomes [42] . We found that TbAP co-localized with TbVP1 to acidocalcisomes ( Fig . 3D ) . Western blot analysis confirmed the expression of the tagged protein ( Fig . 3H ) . Proton pumps maintain a low pH inside acidocalcisomes . We identified both TbVP1 and vacuolar proton ATPase ( V-H+-ATPase ) in our proteomic analysis ( Table 2 ) . Early physiological studies using bafilomycin A1 , a specific inhibitor of V-H+-ATPase [43] , demonstrated V-H+-ATPase activity in permeabilized T . brucei PCF trypanosomes [8] . This finding was later confirmed in experiments with intact cells [44] and isolated acidocalcisomes [24] . All putative subunits of this pump are present in the T . brucei genome ( TriTrypDB . org , S5 Table ) , and two of the subunits , the putative H+-translocating subunit a ( TbVAa ) and the putative H+ transporting subunit d ( TbVAd ) , were found in our acidocalcisome proteomic analysis ( Table 2 ) . We tagged subunits a , and d with a 3× HA tag and found excellent co-localization with TbVP1 ( Fig . 4A , and S4A Figure ) . Additional punctate staining of the a and d subunits that did not co-localize with TbVP1 could correspond to labeling of the Golgi complex and endocytic pathway , where the V-H+-ATPase also localizes in most eukaryotic cells . In agreement with that additional localization , we found that part of the antibody reaction against these subunits co-localizes with the Golgi marker Golgi reassembly and stacking protein ( TbGRASP ) [45] ( Fig . 4B and S4B Figure ) and with the lysosomal markers cathepsin L ( TbCATL ) , a luminal lysosomal cysteine peptidase , and p67 , a lysosomal membrane glycoprotein [46] ( Figs . 4C and 4D , and S4C Figure and S4D Figure , respectively ) . Western blot analyses confirmed the expression of these proteins ( Fig . 4E and S4E Figure ) . Several acidocalcisome proteins of other trypanosomatids or with potential localization to acidocalcisomes were also investigated . Since iron has been detected in acidocalcisomes of T . cruzi [47] , Phytomonas spp . [48] , [49] , and Leishmania amazonensis [50] , we tagged a hypothetical protein ( Tb927 . 3 . 800 ) with similarity to vacuolar iron transporters ( VIT ) . This protein co-localized with TbVP1 ( Fig . 5A ) , and western blot analysis of PCF trypanosome lysates showed a single band using anti-HA antibodies ( Fig . 5C ) . We also tagged a putative metal-ion ( zinc ) transporter ( Tb927 . 4 . 4960 ) as an homologue in T . cruzi ( TcCLB . 511439 . 50 ) occurs in acidocalcisomes [51] . Fig . 5B shows that HA-tagged Tb927 . 4 . 4960 co-localized with TbVP1 , and western blot analyses ( Fig . 5D ) confirmed its expression . Several proteins enriched in the acidocalcisome proteome possess transmembrane domains ( TMD ) , and some have homologues present in acidocalcisomes of other species . For example , Tb927 . 10 . 3640 has six predicted TMD and is annotated in TriTrypDB . org as a hypothetical protein . The C terminus was tagged with a 3× HA tag using homologous recombination with the endogenous locus . Surprisingly , the protein showed nuclear membrane localization ( S5A Figure ) , and western blot analysis identified a single band of ∼35 kDa ( predicted molecular mass , 32 kDa , S5C Figure ) . Interestingly , this protein was previously identified in a nuclear proteome of T . brucei [52] An ABC transporter was identified in the acidocalcisomes of Cyanidoschyzon merolae [17] and Tb927 . 11 . 540 , listed as a putative ABC transporter with six predicted TMD , was enriched in the T . brucei acidocalcisome proteome ( S1 Table ) . However , antibodies against HA co-localized with MitoTracker in the mitochondrion of PCF ( S5B Figure ) , and western blot analysis showed a strong band of ∼75 kDa compatible with the predicted molecular mass of 76 kDa . A second band at ∼60 kDa , may be due to cleavage of a mitochondrial targeting signal of 97 amino acids ( S5D Figure ) . Tb927 . 8 . 1870 is a Golgi/lysosome glycoprotein 1 ( TbGLP1 ) reported to localize in the Golgi complex , multivesicular lysosomes , and in unidentified small vesicles [53] . As we detected localization of other acidocalcisome proteins in Golgi and lysosomes ( Fig . 4 and S4 Figure ) we tagged the C terminus of TbGLP1 with 3× HA . The small vesicles previously described [53] are apparently not the acidocalcisomes as TbGLP1 does not co-localize with TbVP1 ( S6A Figure ) . Consistent with this , antibodies against HA co-localized with TbGRASP ( S6B Figure ) , TbCATL ( S6C Figure ) and p67 ( S6D Figure ) . Western blot analysis showed a band of ∼90 kDa , close to the apparent molecular mass of the native protein [53] ( S6E Figure ) . Acidocalcisomes are rich in basic amino acids , and potentially polyamines to balance anionic charges of polyphosphate , as occurs in the yeast vacuole [54] . We investigated the localization of HA-tagged putative polyamine transporters TbPOT1 ( Tb927 . 9 . 10340 ) and TbPOT2 ( Tb927 . 11 . 6680 ) . TbPOT1 partially co-localizes with acidocalcisomes ( S7A Figure ) , and with lysosomes ( S7B–C Figure ) . TbPOT2 , in contrast , did not co-localize with Golgi complex ( S8A Figure ) and showed an exclusive lysosomal localization ( S8B–C Figure ) . Western blot analyses confirmed the expression of the tagged proteins ( S7E Figure and S8E Figure , respectively ) . Biochemical evidence for the presence of a Na+/H+ exchanger in acidocalcisomes of different trypanosomatids [55] including T . brucei PCF [56] , [57] has been presented . We therefore investigated the localization of Tb927 . 11 . 840 . 1 , which has 15 predicted TMD and is annotated as a putative cation/proton antiporter in TriTrypDB . org , and as a potential Na+/H+ exchanger in TransportDB . Interestingly , HA-tagged TbFTP localizes to the distal tip of the flagellum of PCF , and does not co-localize with acidocalcisomes ( S7D Figure ) . Western blot analysis identified one band absent in wild type cells ( S7F Figure ) . Few proteins , among them adenylyl cyclases [58] , a calpain-like protein TbCALP . 1 . 3 [59] , the kinesin motor Kif13-2 [60] , an unknown antigen , and the flagellar protein FLAM8 [61] , have previously been reported to exhibit localization to the flagellar tip of T . brucei . In addition , a cation channel does occur in the distal tip of the flagellum T . cruzi [62] and the presence of channels and exchangers at this localization may be compatible with the proposed role of the flagellum as an environmental sensor . We have reported before that a number of genes encoding acidocalcisome proteins such as TbVP1 [29] , TbPMC1 [36] , TbIP3R [27] , TbVtc1 [40] , TbVtc4 [38] , [39] , TbVSP [28] , and AP-3 β and δ subunits [63] are essential for the growth of BSF and/or PCF trypanosomes ( Table 2 ) . We therefore selected some of the newly identified acidocalcisome proteins to investigate their requirement for growth . Knockdown of TbVAa or TbVAd by induction of double-stranded RNA resulted in growth defects in both BSF and PCF trypanosomes ( Fig . 6A , 6B , and 6D , and 6E , respectively ) , with an 81±4% and 69±3% reduction in the number of cells , respectively . Northern blots ( analysis performed with ImageJ software ) showed that mRNA was down-regulated by 73–96% after 2 and 4 d of RNAi in BSF and PCF trypanosomes , respectively ( Fig . 6C and 6F ) . Knockdown of TbVIT1 in both BSF and PCF trypanosomes ( Fig . 6G and 6H ) resulted in growth defects with a 44±6% and 41±3% reduction in the number of cells after 2 and 4 d of tetracycline addition to BSF and PCF trypanosomes , respectively . Knockdown of TbZnT only weakly affected the growth of PCF trypanosomes ( Fig . 6J and 6K ) . Northern blot analyses showed that the mRNA was downregulated in all cases ( Fig . 6I and 6L ) .
We report here the proteomic analysis of subcellular fractions enriched in acidocalcisomes from T . brucei . These fractions are enriched in proteins previously demonstrated to localize to acidocalcisomes like TbVP1 [24] , TbPMC1 [36] , TbVtc4 [38] , and TbIP3R [27] . Our protocol yields fractions well resolved from organelle markers for mitochondria ( succinate cytochrome c reductase , TbVDAC ) , glycosomes ( hexokinase , TbPPDK ) and lysosomes ( α-mannosidase , Tbp67 ) . We made 580 identifications in fractions highly enriched in TbVP1 activity . Membrane proteins are challenging for proteomic analysis , but our dataset includes a relatively high representation of membrane proteins ( 21% in fraction 5 ) . A published plasma membrane proteome of T . brucei contains a lower proportion of membrane proteins ( 16 . 1% of 1 , 536 proteins , [64] , suggesting that our fractionation successfully enriched proteins with potential , membrane-related functions . Additionally , our proteomic analysis confirmed expression of five proteins previously undetected in whole cell analyses of T . brucei ( data from TriTrypDB . org , accessed May 28 , 2014 ) . This confirms the relevance of subcellular proteomics as a method of choice for the identification of larger numbers of proteins than whole cell proteomics [51] . Subcellular fractionation only partially purifies cellular components from contaminants . This contamination is due in part to the abundance of some proteins , the adhesive properties of others , and also because there are junctions that connect organelles with each other [65] . In this regard we previously discussed [66] the close association of acidocalcisomes with mitochondria of trypanosomes [67] , an association that is important for Ca2+ signaling , and could explain the contamination of our fractions with mitochondrial membrane proteins . It is therefore essential that mass spectrometric analysis be validated with in vivo expression of tagged proteins . Only few studies to date [34] , [51] , [61] , [68] have implemented such a method to verify proteomes of trypanosomatid parasites . To validate our dataset , we expressed a number of proteins in the acidocalcisome proteome as HA-fusion proteins . We complemented this set of proteins with selected proteins with known localizations to the acidocalcisomes in other species , and with proteins that could potentially be present in the acidocalcisomes on the basis of our knowledge of the organelle . Interestingly , several proteins previously localized to acidocalcisomes were absent in our dataset . These notable absences from our dataset suggest very low expression levels . The proteins we localized to the acidocalcisomes ( Fig . 7 ) belong to three groups: proteins involved in Ca2+ signaling , phosphate homeostasis , and membrane transport . The acidocalcisome localization of the IP3R [27] was confirmed using antibodies against the IP3 binding region of the receptor , which recognized a band of 345 kDa that corresponds to the apparent molecular mass of the receptor ( 343 kDa ) . The antibody marked an additional band at ∼80 kDa that likely corresponds to a hydrolysis product , as this band is very weak in immunoblots of total cell lysates . Although TbIP3R in T . cruzi was suggested to localize to the ER [35] , the IFA results from T . cruzi were not convincing given that endogenously tagged T . brucei IP3R localizes to acidocalcisomes [27] . Further work is necessary to confirm this localization in other trypanosomatids . The identification of a mechanism for Ca2+ uptake ( TbPMC1 ) and Ca2+ release ( TbIP3R ) in acidocalcisomes underscore the relevance of these organelles in Ca2+ signaling . The acidocalcisome localization of two components of the VTC complex involved in synthesis of polyP [38] , [40] was confirmed , and the excellent co-localization of TbVtc1 and TbVtc4 with TbVP1 in acidocalcisomes suggest that previously reported localization of TbVtc1 in the ER [40] was the consequence of its overexpression from an exogenous locus . A phosphate transporter ( TbPho91 ) annotated as sulfate/sodium symporter , and encoding for a putative Saccharomyces cerevisiae Pho91p orthologue ( S9A Figure ) was localized to the acidocalcisomes . Pho91p , is localized to the vacuole and proposed to be involved in exporting Pi from the vacuole to the cytosol [69] . The orthologue identified in T . cruzi ( TcCLB . 508831 . 60 ) shares 65% amino acid identity to TbPho91 , and has been localized to the contractile vacuole and other membranes of that parasite [34] . The ORF of TbPho91 encodes a predicted , 728 amino acid protein with an apparent molecular weight of 81 kDa , nine transmembrane domains , an N-terminal regulatory SPX domain and an anion-permease domain that is also present in other anion transporters . The recognized polypeptide had an apparent molecular mass of ∼70 kDa and , since T . brucei Pho91 possesses ten transmembrane domains , a size discrepancy between the expected ( 99 kDa ) and the observed molecular mass could be attributed to the usual anomalous migration of hydrophobic proteins on SDS gels [70] . If TbPho91 functions as its orthologue in S . cerevisiae [69] , it could be involved in the release of Pi from the acidocalcisomes . The acid phosphatase ( TbAP ) is the first soluble enzyme identified at the molecular level in acidocalcisomes of trypanosomatids . The gene ( Tb927 . 10 . 7020 ) encodes a 50 kDa protein that has a signal peptide and belongs to the histidine phosphatase superfamily ( TriTrypDB . org ) . Catalytic activity in the superfamily centers on phosphorylation and dephosphorylation of a histidine residue that follows the first β-strand of the protein . A conserved Arg-His-Gly ( RHG ) triad has been proposed to contain the phosphorylated histidine [71] and is conserved in TbAP . The LTXXG motif in the region between β1 and β2 is also conserved [71] . It is interesting to note that some acid phosphatases , like the tartrate-resistant or purple acid phosphatase ( S9B Figure ) have exopolyphosphatase activity [72] and further work will be needed to investigate whether the exopolyphosphatase activity detected in acidocalcisomes [73] is due to this enzyme . The presence of a V-H+-ATPase activity was one of the defining properties that led to the identification of acidocalcisomes in trypanosomes [8] , [9] . The enzyme activity was later localized to acidocalcisomes of different unicellular eukaryotes [5] , but this is the first work studying the localization of the enzyme using epitope-tagged subunits . V-H+-ATPases are multisubunit proton pumps composed of two subcomplexes . The peripheral V1 complex consists of eight subunits ( A to H ) and is responsible for ATP hydrolysis , whereas the membrane-integral V0 complex ( a , c , c′ , c″ , d , and e subunits ) is responsible for proton translocation from the cytosol into the lumen of endomembrane compartments [74] . Epitope tagging of two membrane integral V0 complex subunits ( a and d ) identified the localization of this multisubunit complex to acidocalcisomes , lysosomes , and Golgi complex . This is in contrast with T . cruzi in which a P-type H+-ATPase is involved in acidification of the endocytic pathway [75] . As occurs with most organisms studied to date , the enzyme is essential for parasite growth and survival . It is also quite interesting that there is some heterogeneity in TbVP1 stain compared to some of these markers , which may well suggest that there is more than one class of compartment or at least differential compositions . This could indicate either functional differences or maturation/degradation of these compartments . Two new metal ion transporters were identified . Tb927 . 3 . 800 is an orthologue to the vacuolar iron transporter ( VIT1 ) originally described in Arabidopsis thaliana [76] and to the yeast Ca2+-sensitive cross-complementer 1 ( CCC1 ) [77] ( S9C Figure ) . These transporters are localized to the plant and yeast vacuole , respectively , and have been involved in iron and manganese sequestration into the vacuoles . The present of an iron transporter is in agreement with the detection of iron in acidocalcisomes of different species [78] . Tb9274 . 4960 is a member of the cation diffusion facilitator ( CDF ) family [79] , which includes mammalian zinc transporters such as ZnT4 [80] , S . cerevisiae ZRC1 [81] , A . thaliana metal tolerance protein 1 ( AtMTP1 ) [82] , and Escherichia coli YiiP ( EcYiiP ) [83] ( S10A Figure ) . These transporters function as antiporters of Zn , Cd , Co and/or Ni with protons . All known CDF domains proteins contain 6 TMD and share characteristic motifs , such as a CDF family-specific signature sequence at the start of the second membrane-spanning helix ( TM2 ) , and a long C-terminus [82] . The presence of this zinc transporter is in agreement with the abundant presence of zinc in the acidocalcisomes , as detected by X-ray microanalyses of different prokaryotes and eukaryotes [5] , [78] . We also report the localization of some proteins not previously investigated , such as a mitochondrial ABC transporter ( Tb927 . 11 . 540 ) ( TbABCT ) , a flagellar cation/proton antiporter ( Tb927 . 11 . 840 . 1 ) or flagellar tip protein ( TbFTP ) , a nuclear periphery protein ( Tb927 . 10 . 3640 ) ( TbNP ) , a lysosome/acidocalcisome putative polyamine transporter ( Tb927 . 9 . 10340 ) ( TbPOT1 ) ( S10B Figure ) , and a lysosomal putative polyamine transporter ( Tb927 . 11 . 6680 ) ( TbPOT2 ) . We also confirmed the Golgi and lysosomal localization of TbGLP1 [53] . Finally , we report the requirement for growth of two subunits of the V-H+-ATPase ( TbVAa and TbVAd ) , and of an orthologue of a vacuolar iron transporter ( TbVIT1 ) in both PCF and BSF trypanosomes , supporting the role of acidocalcisomes in parasite growth and survival . The identification of novel acidocalcisome proteins provides useful insights into the biogenesis of these organelles . A common feature of all the acidocalcisome proteins validated by endogenous expression with HA-tags in this study is the presence of one or more tyrosine-based , sorting signals with the YXXØ ( Ø corresponds to an hydrophobic amino acid ) consensus motif ( see S7 Table ) . The μ subunits of at least four of the adaptor protein ( AP ) complexes bind to this motif [84] . In this regard , AP-3 is required for the biogenesis of the acidocalcisomes [63] . All of the proteins we validated by expression also possess generic N-glycosylation motifs , phosphothreonine modules binding FHA domains with large aliphatic amino acids at the pT+3 position as well as casein kinase 2 ( CK2 ) , glycogen synthase kinase β ( GSK3β ) and NEK2 ( never in mitosis ( NimA ) -related kinases 2 ) phosphorylation sites ( see S7 Table ) . A variety of kinases such as GSK3β localize to the Golgi and regulate post-Golgi membrane trafficking [85] . These findings will help guiding future studies on the biogenesis of these organelles . In summary , in addition to validate the expression at the protein level of a number of important genes and identify the localization of proteins not previously studied , we identified several new acidocalcisome proteins using a strategy complementing subcellular proteomics and bioinformatics with their localization using in situ epitope-tagged proteins or specific antibodies , and RNAi for functional validation . Four of these proteins are newly identified acidocalcisome proteins , and their identification will facilitate further studies to elucidate the roles of this organelle in T . brucei physiology .
Mice experiments in this work followed a reviewed and approved protocol by the Institutional Animal Care and Use Committee ( IACUC ) . Animal protocols followed the US Government principles for the Utilization and Care of Vertebrate animals . The University of Georgia IACUC approved the animal protocol ( Protocol number A2012-3-010 ) . T . brucei PCF trypanosomes ( wild type and 29-13 strains ) and BSF ( single marker ( SM ) strains ) were used . PCF 29-13 ( T7RNAP NEO TETR HYG ) co-expressing T7 RNA polymerase and Tet repressor were a gift from Dr . George A . M . Cross ( Rockefeller University , NY ) and were grown in SDM-79 medium [86] , supplemented with hemin ( 7 . 5 µg/mL ) and 10% heat-inactivated fetal bovine serum , and at 27°C in the presence of G418 ( 15 µg/ml ) and hygromycin ( 50 µg/ml ) to maintain the integrated genes for T7 RNA polymerase and tetracycline repressor , respectively [87] . BSF trypanosomes ( single marker strain ) were also a gift from Dr . G . A . M . Cross and were grown at 37°C in HMI-9 medium [88] supplemented with 10% fetal bovine serum ( FBS ) , 10% serum plus ( JRH Biosciences , Inc . ) , and 2 . 5 µg/ml G418 . TRIzol reagent , Taq polymerase , Magic Marker protein standards , BenchMark protein ladder , Mito-Tracker Red , and Alexa-conjugated secondary antibodies were purchased from Life Technologies ( Carlsbad , CA ) . The expression vector pET32 EK/Lic was purchased from Novagen ( Madison , WI ) . E . coli OverExpression C43 ( DE3 ) strain was purchased from Lucigen ( Middleton , WI ) . [α-32P]dCTP ( 3 , 000 Ci mmol−1 ) was from Perkin Elmer ( Waltham , Massachusetts ) . Rabbit antibodies against T . brucei vacuolar H+-pyrophosphatase ( TbVP1 ) [29] were a gift from Dr . Norbert Bakalara ( Ecole Nationale Supérieure de Chimie de Montpellier , Montpellier , France ) . Mouse monoclonal antibody against HA ( purified HA . 11 clone 16B12 ) was purchased from Covance Inc . ( Princeton , NJ ) . Rat monoclonal antibody against HA ( clone 3F10 ) and Complete , EDTA-free protease inhibitor cocktail tablets were purchased from Roche Applied Science ( Indianapolis , IN ) . The pMOTag4H vector [89] was a gift from Dr . Thomas Seebeck ( University of Bern , Bern , Switzerland ) . The p2T7Ti vector [90] was a gift from Dr . John Donelson ( University of Iowa , Iowa City , IA ) . Antibody against GRASP [45] was a gift Dr . Graham Warren ( Max F . Perutz Laboratories , Vienna , Austria ) , and antibodies against p67 and TbCATL [46] were a gift from Dr . James Bangs ( University of Wisconsin , Madison , WI ) . Rabbit polyclonal antibody against TbVDAC was a gift from Dr . Minu Chadhuri ( Meharry Medical College , TN ) . Anti T . brucei pyruvate , phosphate dikinase ( PPDK ) -producing mouse hybridoma culture supernatant was a gift from Dr . Frédéric Bringaud ( University of Bordeaux , France ) . The enhanced chemiluminescence ( ECL ) detection kit was purchased from Amersham Biosciences ( GE Healthcare Life Sciences , Piscataway , NJ ) , and Pierce ECL Western blotting substrate was from Thermo Fisher Scientific Inc . ( Rockford , IL ) . The Bradford protein assay reagent , Precision Plus Protein WesternC pack , 4–15% polyacrylamide Ready gels , Zeta-Probe GT Genomic Testing blotting and nitrocellulose membranes were from Bio-Rad ( Hercules , CA ) . AMAXA Human T-cell Nucleofector kit was purchased from Lonza ( Koln , Germany ) . Prime-a Gene Labeling System was from Promega ( Madison , WI ) . QIAquick gel extraction kit and MinElute PCR purification kit , Ni-NTA agarose , and Protein G Agarose Resins were from Qiagen ( Valencia , CA ) . The primers were purchased from Integrated DNA Technologies ( Coralville , IA ) . All other reagents of analytical grade were from Sigma ( St . Louis , MO ) . Fractions enriched in acidocalcisomes were isolated and purified using two iodixanol gradient centrifugations ( S1 Figure ) . PCF trypanosomes ( 3–4 g wet weight ) were washed twice with Buffer A ( 116 mM NaCl , 5 . 4 mM KCl , 0 . 8 mM MgSO4 , 50 mM Hepes , pH 7 . 2 ) with 5 . 5 mM glucose . The parasites were washed once in cold isolation buffer ( 125 mM sucrose , 50 mM KCl , 4 mM MgCl2 , 0 . 5 mM EDTA , 20 mM Hepes , 3 mM dithiothreitol ( DTT ) supplied with Complete , EDTA-free , protease inhibitor cocktail ( Roche ) prior to lysis with silicon carbide in isolation buffer . Silicon carbide and cell debris were eliminated by a series of low speed centrifugations ( 100 g for 5 min , 300 g for 10 min , and 1 , 200 g for 10 min ) . The supernatant was centrifuged at 15 , 000 g for 10 min , and the pellet was resuspended in 1 ml isolation buffer and applied to the 34% step of a discontinuous gradient with 4 ml steps of 20 , 24 , 28 , 34 , 37 and 40% iodixanol ( diluted in isolation buffer ) . The gradient was centrifuged at 50 , 000 g in a Beckman JS-24 . 38 rotor for 60 min at 4°C , and fractions were collected from the top . The pellet was resuspended in 700 µl isolation buffer and applied to the 27% step of another discontinuous gradient of iodixanol , with 1 . 4 ml of isolation buffer containing 10% w/v sucrose over-layered on the top and 1 ml steps of 27 , 62 and 80% iodixanol , which were diluted from 90% w/v iodixanol with isolation buffer . To prepare 90% w/v iodixanol , 60% w/v iodixanol solution ( Optiprep ) was dried completely at 70°C and resuspended with isolation buffer . After the second gradient centrifugation at 50 , 000 g for 60 min at 4°C , fractions were collected from the top , washed twice with isolation buffer by centrifugation at 20 , 000 g for 15 min at 4°C , and analyzed by various organelle marker enzyme assays . The protein concentration was quantified by Bradford assay using a SpectraMax Microplate Reader . After washing fraction 5 , containing the highest vacuolar-H+-pyrophosphatase ( PPase ) activity ( Fig . 1A ) , it was resuspended in 200-µl isolation buffer . Aliquots of the purified acidocalcisome suspension were separated on 4–15% SDS-PAGE gels and stained with Coomassie brilliant blue , immunoblotted with several acidocalcisome markers , precipitated for electron microscopy , or used for proteomic analysis . Chromatograms of protein bands in the SDS-PAGE gels were obtained after background subtraction using ImageJ ( National Institute of Health , Bethesda , MD ) . Gel lanes were washed twice in ddH2O for 15 min and cut into 10 equal slices . Proteins were reduced in a 10 mM dithiothreitol ( DTT ) /100 mM ammonium bicarbonate solution at 65°C for 1 h and carboxyamidomethylated with 55 mM iodoacetamide/100 mM ammonium bicarbonate for 1 h at room temperature in the dark . Enzymatic digestion was performed with porcine trypsin ( 1∶50 , Promega , Madison , WI ) at 37°C overnight . Tryptic peptides were extracted two times with 100 µl of 50% acetonitrile/0 . 1% formic acid . Combined extracts were evaporated to dryness and stored at −20°C until mass spectrometry analysis . Peptides were resuspended in 20 µl of 2% acetonitrile/0 . 1% formic acid . Data was acquired using an Agilent 1100 Capillary LC system ( Palo Alto , CA ) with a 0 . 2×150 mm Halo Peptide ES-C18 capillary column packed with 2 . 7 µm diameter superficially porous particles ( Advanced Materials Technology , Inc . , Wilmington , DE ) . On-line MS detection used the Thermo-Fisher LTQ ion trap ( San Jose , CA ) with a Michrom ( Michrom Bioresources , Auburn , CA ) captive spray interface . Sample analysis utilized the LTQ divert valve fitted with an EXP Stem Trap 2 . 6 µL cartridge packed with Halo Peptide ES-C18 2 . 7 µm diameter superficially porous particles ( Optimize Technologies , Oregon City , OR ) . Sample injection volume was 8 µl . Gradient conditions increased the concentration of mobile phase B from 6% to 75% B over 90 min . Mobile phase A consisted of 99 . 9% water , 0 . 1% formic acid and 10 mM ammonium formate . Mobile phase B contained 80% acetonitrile , 0 . 1% formic acid and 10 mM ammonium formate . Mobile phases used formic acid , ammonium formate and acetonitrile from Sigma-Aldrich ( St . Louis , MO ) . Raw tandem mass spectra were converted to mzXML files , then into mascot generic files ( MGF ) via the Trans-Proteomic Pipeline ( Seattle Proteome Center , Seattle , WA ) . MGF files were searched using Mascot ( Matrix Scientific Inc , Boston , MA ) against separate target and decoy databases obtained from the National Center for Biotechnology Information ( NCBI ) . The target database contained all T . brucei protein sequences and the decoy database contained the reversed sequences from the target database . Mascot settings were as follows: tryptic enzymatic cleavages allowing for up to 2 missed cleavages , peptide tolerance of 1000 parts-per-million , fragment ion tolerance of 0 . 6 Da , fixed modification due to carboxyamidomethylation of cysteine ( +57 Da ) , and variable modifications of oxidation of methionine ( +16 Da ) and deamidation of asparagine or glutamine ( +0 . 98 Da ) . Mascot files were loaded into ProteoIQ ( NuSep , Bogart , GA ) , where a 1% false discovery rate and a 0 . 9 peptide probability were applied for confirmation of protein identifications . The ProteinProphet algorithm utilized by ProteoIQ software combines hit proteins with degenerate peptide fingerprints into a single identification ( a protein “group” ) and generates a group probability . In these cases , one or more of the individual proteins may actually be present in the sample . Subcellular fractionation protocols enrich samples for target organelles but produce somewhat heterogeneous preparations containing material from other cell compartments that are readily detected by exquisitely sensitive tools such as mass spectrometry . To identify likely contaminants from non-acidocalcisomal compartments in our proteomic dataset , we used a series of subcellular prediction algorithms: TargetP 1 . 1 [91] , pTARGET [92] , SLP-LOCAL [93] , and WoLF-PSORT [94] . Data from each of these algorithms was processed using Perl scripts and a MySQL database to screen for proteins with prediction confidence thresholds of 80% . Final consensus predictions of subcellular localization for individual protein hits were assigned when two or more algorithms agreed . In the event when the mass spectrometry data identified a protein group with more than one member , consensus predictions for individual proteins were combined into a group consensus prediction when predictions between at least two individual proteins agreed . The membrane topology and presence of signal peptides and was predicted using the following tools: SignalP3 [95] , TMHMM2 . 0c [96] , HMMTOP2 . 1 [97] and PolyPhobius [98] , [99] ( accessed May 28 , 2014 ) . In addition , we also used published data for annotated proteins to validate our data . Pyrophosphatase ( PPase ) activity ( acidocalcisome marker ) was assayed by measuring phosphate ( Pi ) release using the malachite green assay [100] with some modifications . Briefly , reactions contained 130 mM KCl , 2 mM MgCl2 , 10 mM Hepes , pH 7 . 2 , 100 µM PPi , 0 . 5 µg of gradient fraction with or without 40 µM aminomethylenediphosphonate ( AMDP ) . After incubation at 30°C for 10 min , the reaction was stopped by the addition of an equal volume of freshly prepared mixture of three parts of 0 . 045% malachite and one part of 4 . 2% ammonium molybdate . The absorbance ( A ) at 660 nm was read using the microplate reader . The amount of Pi released was determined by comparison with a standard curve . AMDP was used to distinguish between vacuolar ( sensitive ) and soluble ( insensitive ) PPase activities . The specific activity of TbVP1 was defined as µmol Pi released/min×mg of protein . Succinate-cytochrome C reductase activity ( mitochondria marker ) was assayed as described previously [101] , using 3 mM succinate ( pH 7 . 2 ) as the substrate and following the reaction containing 0 . 1 mM cytochrome C ( Cyt C ) , 0 . 3 mM KCN , 40 mM Hepes pH 7 . 5 , and 10 µl of gradient fraction at 30°C at 550-540 nm in the microplate reader . Hexokinase ( glycosome marker ) was assayed as described previously [102] . The reaction mixtures ( 100 µl ) contained 10 mM D-glucose , 0 . 6 mM ATP , 0 . 6 mM NADP+ , 10 mM MgCl2 , 2 . 5 units/ml glucose-6-phosphate dehydrogenase , and 50 mM potassium Hepes , pH 7 . 8 . The oxidation of NADP was monitored at 30°C in the microplate reader at 340–430 nm . Alpha-mannosidase activity ( lysosome marker ) was assayed using p-nitrophenyl-α-D-mannopyranoside ( pNP-Man ) as substrate as described previously [103] . The reaction mixtures contained 200 mM sodium acetate buffer ( pH 4 . 6 ) , 0 . 6 mM pNP-Man and 10 µl of gradient fraction in a total volume of 100 µl . The mixture was incubated at 30°C for 30 min , and the reaction was terminated by the addition of 160 µl of 1 M Na2CO3 . Two hundred microliter of the final mixture was transferred to a microtitre plate and read at 405 nm using the micro plate reader . 1 unit of activity corresponds to the hydrolysis of 1 µmol of substrate/min at 30°C . The α-mannosidase activity was expressed as µmol/min×mg protein . Aliquots ( 25 µl ) of the 15 , 000×g pellet fraction , the pellet of the first gradient and fraction 5 of the second gradient ( Fig . 1 and S1 Figure ) were precipitated by centrifugation at 20 , 000 g for 15 min at 4°C . The pellets were fixed in 2 . 5% glutaraldehyde and 4% paraformaldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) at room temperature for 1 h . The supernatants were carefully replaced with fresh fixative without disturbing the pellets and then stored 4°C . Samples were processed for transmission electron microscopy at the Electron Microscopy Laboratory at the University of Georgia College of Veterinary Medicine . The one-step epitope-tagging protocol reported by Oberholzer et al . [89] was used to produce 14 C-terminal HA-tagging cassettes ( TriTrypDB gene ID numbers listed in Table 2 ) for transfection of T . brucei PCF trypanosomes . In brief , the PCR forward and reverse primers included terminal 100–120 nucleotides of each ORF before its stop codon and the reverse complement of the first 100–120 nucleotides of the 3′UTR , respectively , followed in frame by the 21–26 nucleotides of the backbone sequences of pMOTag vector series [89] . The HA-tagging cassettes containing a hygromycin resistant gene as a selection marker were generated for cell transfection by PCR using pMOTag4H as template with the corresponding PCR primers of the gene . To knockdown the expression of the TbVAa , TbVAd , TbVIT , or TbZnT genes ( TriTrypDB gene ID numbers listed in Table 2 ) by double-stranded RNA expression , the inducible T7 RNA polymerase-based protein expression system and the p2T7Ti vector with dual-inducible T7 promoters were employed . A cDNA fragment ( ranging from 566 to 757 bp ) of the genes targeted to nucleotides ( TbVAa: 310–876 , TbVAd: 364–1121 , TbVIT: 125–755 , TbZnT: 620–1241 ) of the open reading frames ( ORFs ) was amplified using the forward and reverse primers listed in S6 Table , digested with restriction enzymes ( BamHI and HindIII ) , and cloned into p2T7Ti vector . The recombinant constructs were confirmed by sequencing at the DNA Analysis Facility at Yale University ( New Heaven , CT ) , NotI-linearized , and purified with QIAGEN's DNA purification kit for cell transfections . Mid-log phase PCF ( ∼5×106 cells/ml ) were harvested by centrifugation at 1 , 000 g for 7 min , washed with Cytomix buffer ( 2 mM EGTA , 3 mM MgCl2 , 120 mM KCl , 0 . 5% glucose , 0 . 15 mM CaCl2 , 0 . 1 mg/ml BSA , 10 mM K2HPO4/KH2PO4 , 1 mM hypoxanthine , 25 mM Hepes , pH 7 . 6 ) and resuspended in 0 . 45 ml of the same buffer at a cell density of 2 . 5×107 cells/ml . The washed cells were mixed with 50 µl of NotI-linearized plasmid DNA or purified PCR products ( 10 µg ) in a 0 . 4-cm electroporation cuvette and subjected to two pulses from a Bio-Rad Gene Pulser electroporator set at 1 . 5 kV and 25 µF . The stable transformants were obtained in SDM-79 medium supplemented with 15% FBS plus appropriate antibiotics ( 5 µg/ml phleomycin , 50 µg/ml hygromycin and 15 µg/ml G418 ) . For the BSF , 10 µg of NotI-linearized plasmid DNA ( <10 µl ) were used per 4×107 mid-log phase cells in 100 µl AMAXA Human T-cell Nucleofector solution . Electroporation was performed using 2 mm gap cuvettes with program X-001 of the AMAXA Nucleofector . Following each transfection , stable transformants were selected and cloned by limiting dilution in HMI-9 medium containing 15% FBS with appropriate antibiotics ( 2 . 5 µg/ml phleomycin and 2 . 5 µg/ml G418 ) in 24-well plates . Antibiotic-resistant clones were further characterized as described below . The correct epitope-tagging of the target genes was confirmed by PCR followed by sequencing and Western blot analyses . RNAi was induced with 1 µg/ml fresh tetracycline when the cells were at a density of 2×106 PCF or 1×105 BSF/ml . The cDNA fragment of TbIP3R encoding a putative IP3 binding domain ( amino acids 329–804 ) [27] was amplified by PCR using primers TbIP3BD-F and TbIP3BD-R ( S6 Table ) and cloned in frame into the expression vector pET32 EK/Lic ( Novagen ) to generate pET32 ( TbIP3R-BD ) . The correct plasmid pET32 ( TbIP3R-BD ) was confirmed by sequencing and then transformed into E . coli OverExpress C43 ( DE3 ) strain ( Lucigen , WI ) . His-tagged TbIP3R-BD fusion protein was affinity purified with Ni-NTA agarose ( Qiagen ) based on the manufacturer's protocol . The purified protein was used to immunize mice and polyclonal antibodies were purified from anti-serum with Protein G Agarose Resins ( Qiagen ) . When Mitotracker Red CMXRos ( Invitrogen ) was used , live cells were labeled for 30 min with the red-fluorescent dye at 50 nM in trypanosome culture medium . PCF trypanosomes were washed with PBS and then fixed with 4% paraformaldehyde in PBS at room temperature for 1 h . The fixed parasites were washed twice with PBS , allowed to adhere to poly-L-lysine-coated coverslips , and permeabilized with 0 . 3% Triton X-100/PBS for 3 min for PCF . After blocking with PBS containing 3% BSA , 1% fish gelatin , 50 mM NH4Cl and 5% goat serum for 1 h , trypanosomes were stained in 3% BSA/PBS with the polyclonal rabbit antibody against TbVP1 ( 1∶500 ) , mouse polyclonal antibody against TbIP3R-BD ( 1∶100 ) , purified HA . 11 clone 16B12 mouse monoclonal antibody against HA ( 1∶50 ) , rat monoclonal antibody against HA ( 1∶100 ) ( Roche ) , rabbit anti-GRASP antibody ( 1∶100 ) , mouse anti-p67 monoclonal antibody ( 1∶200 ) , rabbit anti-trypanopain ( TbCATL ) antibody ( 1∶600 ) for 1 h . After thoroughly washing with PBS containing 3% BSA , cells were incubated with Alexa 488-conjugated goat anti-mouse or anti-rat antibody , and Alexa 546-conjugated goat anti-rabbit or anti-mouse antibody at 1∶1 , 000 for 1 h . The cells were counterstained with DAPI before mounting with Gold ProLong Gold antifade reagent ( Molecular Probes ) . Differential interference contrast ( DIC ) and fluorescent optical images were captured using an Olympus IX-71 inverted fluorescence microscope with a Photometrix CoolSnapHQ CCD camera driven by DeltaVision software ( Applied Precision , Seattle , WA ) . Images were deconvolved for 15 cycles using Softwarx deconvolution software . Pearson's correlation coefficients ( PCC ) were calculated using the Softwarx software by measuring the images of whole cells or specific cell-staining regions . The cells were harvested and washed twice in PBS . The washed cells or aliquots of purified acidocalcisome suspension were lysed with RIPA buffer ( 150 mM NaCl , 20 mM Tris/HCl , pH 7 . 5 , 1 mM EDTA , 1% SDS , and 0 . 1% Triton X-100 ) containing protease inhibitor tablet ( Roche ) in ice for 1 h . The protein concentration was determined by using Pierce BCA protein assay kit with the microplate reader . The total cell lysates were mixed with 2× Laemmli sample buffer ( BioRad ) at 1∶1 ratio ( volume/volume ) and directly loaded . The separated proteins were transferred onto nitrocellulose membranes using a Bio-Rad transblot apparatus . The membranes were blocked with 10% non-fat milk in PBS containing 0 . 5% Tween-20 ( PBS-T ) at 4°C overnight . The blots were incubated with rabbit antibodies against TbVP1 ( 1∶5 , 000 ) , rabbit antibodies against TbVDAC ( 1∶2 , 000 ) , mouse antibodies against TbPPDK ( 1∶200 ) , mouse antibodies against Tbp67 ( 1∶3 , 000 ) , rabbit antibodies against TcVSP ( 1∶5 , 000 ) , mouse antibodies against TbIP3R ( 1∶1 , 000 ) , mouse antibodies against HA ( 1∶1 , 000 ) , or mouse antibodies against tubulin ( 1∶20 , 000 ) for 1 h . After five washings with PBS-T , the blots were incubated with horseradish peroxidase conjugated anti-mouse or anti-rabbit IgG ( H+L ) antibody at a dilution of 1∶20 , 000 for 1 h . After washing five times with PBS-T , the immunoblots were visualized using Pierce ECL Western blotting substrate according to the manufacturer's instructions . Total RNA was isolated with TRIzol reagent and treated with DNA-free following the manufacturer's instructions . RNA samples ( 10 µg/lane ) were fractionated on 1% agarose/formaldehyde gels , transferred to Zeta-Probe nylon membranes by capillary action , and fixed onto the membranes by baking at 80°C for 1 h . The probes for TbVAa , TbVAd , TbVIT and TbZnT were generated by PCR using the same set of primers ( S6 Table ) from the corresponding RNAi constructs in p2T7Ti as described above and labeled with [α-32P]-dCTP using a Prime-a-Gene Labeling System according to the manufacturer's protocol . The [α-32P]-dCTP-labeled probe of Tb-β-tubulin gene ( GeneDB Tb927 . 1 . 2390 ) was generated from T . brucei genomic DNA by PCR using gene-specific primers TbTubb-F and TbTubb-R ( S6 Table ) . RNA-bound membranes were hybridized with the 32P-labeled probes in 0 . 5 M Na2HPO4 , pH 7 . 4 and 7% SDS at 65°C overnight with agitation . After hybridization , the membranes were washed twice for 10 min each at 68°C with 1× SSC and 0 . 1% SDS and twice for 30 min at 65°C with 0 . 1× SSC and 0 . 1% SDS . Northern blots were visualized by autoradiography , and quantified by using ImageJ ( National Institute of Health , Bethesda , MD ) . | Acidocalcisomes are acidic organelles conserved from bacteria to human cells that are rich in polyphosphate , a polymer of orthophosphate units linked by high-energy phospho-anyhidride bonds . We found here that acidocalcisomes from Trypanosoma brucei , belonging to the group of organisms that produces African sleeping sickness and nagana , are rich in pumps , channels , and transporters involved in phosphate and cation homeostasis , and calcium signaling . Proteomic analysis of acidocalcisome fractions and expression of genes with epitope tags validated the presence of a number of novel transporters , and RNA interference demonstrated the essentiality of these organelles . | [
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] | 2014 | Proteomic Analysis of the Acidocalcisome, an Organelle Conserved from Bacteria to Human Cells |
Inflammasomes are multimeric protein complexes that respond to infection by recruitment and activation of the Caspase-1 ( CASP1 ) protease . Activated CASP1 initiates immune defense by processing inflammatory cytokines and by causing a rapid and lytic cell death called pyroptosis . Inflammasome formation is orchestrated by members of the nucleotide-binding domain and leucine-rich repeat ( NLR ) or AIM2-like receptor ( ALR ) protein families . Certain NLRs and ALRs have been shown to function as direct receptors for specific microbial ligands , such as flagellin or DNA , but the molecular mechanism responsible for activation of most NLRs is still poorly understood . Here we determine the mechanism of activation of the NLRP1B inflammasome in mice . NLRP1B , and its ortholog in rats , is activated by the lethal factor ( LF ) protease that is a key virulence factor secreted by Bacillus anthracis , the causative agent of anthrax . LF was recently shown to cleave mouse and rat NLRP1 directly . However , it is unclear if cleavage is sufficient for NLRP1 activation . Indeed , other LF-induced cellular events have been suggested to play a role in NLRP1B activation . Surprisingly , we show that direct cleavage of NLRP1B is sufficient to induce inflammasome activation in the absence of LF . Our results therefore rule out the need for other LF-dependent cellular effects in activation of NLRP1B . We therefore propose that NLRP1 functions primarily as a sensor of protease activity and thus could conceivably detect a broader spectrum of pathogens than just B . anthracis . By adding proteolytic cleavage to the previously established ligand-receptor mechanism of NLR activation , our results illustrate the remarkable flexibility with which the NLR architecture can be deployed for the purpose of pathogen-detection and host defense .
Recognition of pathogens is an essential first step in the initiation of protective host immune responses . Recognition of pathogens has been shown to be mediated by several families of germ-line encoded receptors that include the Toll-like receptors ( TLRs ) , Nucleotide-binding domain and Leucine-rich Repeat containing proteins ( NLRs ) , and RIG-I-like receptors ( RLRs ) [1] . Most TLRs , NLRs , and RLRs for which activation mechanisms have been defined appear to function as “pattern recognition receptors” [2] that directly bind to molecular structures called pathogen-associated molecular patterns ( PAMPs ) that are broadly conserved among many microbes . In addition to detection of PAMPs , it has been previously proposed that the innate immune system might also respond to ‘Patterns of Pathogenesis’ , the virulence-associated activities that pathogens utilize to invade or manipulate their hosts [3] . Detection of pathogen-associated activities might be a beneficial innate immune strategy , complementary to PAMP recognition , as it could allow the innate immune system to discriminate pathogenic from non-pathogenic microbes , and scale responses appropriately , despite the fact that pathogenic and non-pathogenic microbes often share the same PAMPs . However , few instances of a molecular mechanism by which a pathogen-encoded activity could be detected have been described in mammals . For example , a previous study showed how pathogen-induced inhibition of protein synthesis by Legionella pneumophila could be detected , leading to a specific cytokine response [4] , [5] . Disruption of the actin cytoskeletal signaling by bacterial toxins was also found to lead to a protective innate immune response [6] , [7] Overall , however , there is still considerable uncertainty as to whether or how ‘patterns of pathogenesis’ are sensed by the innate immune system . Anthrax lethal toxin ( LeTx ) is a critical virulence factor secreted by Bacillus anthracis . LeTx is composed of two proteins: protective antigen ( PA ) and lethal factor ( LF ) . PA binds to anthrax toxin receptors on host cells , and subsequently translocates the zinc-metalloprotease , LF , into the cytosol . The canonical proteolytic substrates of LF are mitogen-activated protein kinase kinases ( MAPKKs ) 1–4 and 6–7 [8] , [9] . Cleavage by LF inactivates MAPKKs and results in the disruption of signaling pathways involved in host defense [10] , [11] . Macrophages from certain strains of mice and rats respond to LeTx by undergoing a rapid and lytic form of Caspase-1 ( CASP1 ) -dependent cell death called pyroptosis [12]–[16] . The ability to undergo pyroptosis in response to LeTx was genetically mapped to the Nlrp1b gene in mice [13] , and subsequently to the orthologous Nlrp1 gene in rats [16] . Importantly , mice harboring an allele of Nlrp1b that is responsive to LeTx are protected from challenge with B . anthracis spores [17] , [18] . This protection correlates with enhanced production of IL-1β , recruitment of neutrophils to the site infection , and decreased bacterial counts , and these processes depend on expression of the interleukin-1 receptor [17] , [18] . Despite the importance of NLRP1B in host defense against B . anthracis , the mechanism of NLRP1B activation by LF remains unclear . NLRP1B belongs to the NLR family of innate immune sensors [19]–[21] . Several NLRs , including NLRP1 , have been found to assemble into oligomeric complexes , called ‘inflammasomes’ [22] , in response to a variety of infectious or noxious stimuli [21] . The primary function of inflammasomes appears to be to form a platform for activation of inflammatory caspase proteases , most notably CASP1 , but the molecular mechanism by which NLRs are activated is poorly understood [21] . Although NLRP1 proteins contain NBD and LRR domains , as do all other NLRs , the domain organization of NLRP1 differs from other NLRs in two respects . First , NLRP1 proteins contain a C-terminal Caspase Activation and Recruitment Domain ( CARD ) , whereas the CARDs in other NLRs are usually N-terminal . The second unique feature of NLRP1 proteins is that they contain an unusual domain called the ‘function-to-find’ ( FIIND ) domain [19] . The FIIND is located between the LRRs and the C-terminal CARD , and was recently shown to undergo an auto-proteolytic processing event that results in the C-terminal CARD being cleaved from the rest of the NLRP1 protein [23] . It is believed that the N- and C-terminal auto-processed fragments of mature NLRP1B remain associated with each other despite cleavage of the polypeptide chain [24] . The FIIND auto-processing event occurs constitutively , prior to NLRP1B activation by LF , but for reasons that remain unclear , is required for the ability of NLRP1 to activate CASP1 [24] , [25] . Several inflammasomes have been suggested to be activated upon direct binding to specific bacterial ligands . For example , another NLR-family member , NAIP5 , assembles into an inflammasome upon binding to flagellin , whereas the related NAIP2 inflammasome assembles upon binding to the inner rod proteins from a variety of bacterial type III secretion systems [26] , [27] . A direct receptor-ligand model also applies to the ALR-family AIM2 inflammasome , which is activated upon direct binding to microbial DNA [28]–[31] . In contrast , certain inflammasomes , notably the NLRP3 inflammasome , are believed not to bind directly to bacterial ligands , but have instead been proposed to respond to virally encoded ion channels [32] , bacterial toxins , or other cellular stresses , via indirect mechanisms [21] . However , the molecular basis for how these stresses are sensed by NLRP3 remains unclear . By contrast , the molecular basis for indirect pathogen recognition by plant NLRs has been well-established [33] , [34] . For example , the plant NLR RPS2 has been shown to be maintained in an inactive state by its association with RIN4 , a host protein that is targeted for degradation by a bacterial protease [35] , [36] . RPS2 thus detects the activity of a bacterial protease indirectly by monitoring or ‘guarding’ the integrity of the protease substrate . Direct proteolytic cleavage of a plant NLR by a pathogen-encoded protease has not been described . NLRP1B responds to the protease activity of LF , as catalytically inactive forms of LF do not activate NLRP1 [37] , [38] . This suggests that NLRP1 does not recognize LF via simple receptor-ligand binding , such as that occurs with the NAIP or AIM2 inflammasomes . Boyden and Dietrich initially hypothesized that LF could cleave and activate NLRP1B [13] , but evidence for this simple model of NLRP1B activation was not provided . In fact , several groups have demonstrated that the activity of the proteasome is specifically required for this inflammasome and not for other inflammasomes such as the NAIP5/NLRC4 inflammasome [37] , [39] . In addition , inhibitors of the N-end rule degradation pathway block NLRP1B activation but do not affect the ability of LF to cleave MAPKKs [40] . In contrast to a model in which NLRP1B is activated upon cleavage by LF , these observations suggest that LF might activate NLRP1B by cleaving and destabilizing a negative regulator of NLRP1B . This ‘indirect’ model resembles the activation of the certain NLRs , e . g . , RPS2 , in plants . Recently , however , it was shown that the NLRP1 proteins from Fischer rats and BALB/c mice can be directly cleaved near their N-termini by LF [41] , [42] . Mutation of the cleavage site in rat NLRP1 rendered NLRP1 resistant to cleavage by LF and also prevented NLRP1 activation in response to LF . These results suggest that cleavage of rat NLRP1 by LF is essential for NLRP1 activation , but it is difficult to rule out the possibility that mutation of the cleavage site disrupted the fold of NLRP1 , or rendered NLRP1 non-functional for other reasons . In addition , the site at which LF cleaves rat NLRP1 is not conserved in the mouse [42] , and moreover , the functional effects of mutating the mouse cleavage site have not been assessed . Lastly , and most importantly , existing studies have not ruled out the involvement of other LF-dependent cellular events in NLRP1B activation , as cleavage of NLRP1 was not shown to be sufficient for its activation . Here , we present data that suggest that murine NLRP1B requires LF-dependent cleavage for its activation . We further demonstrate that cleavage is sufficient for NLRP1B inflammasome activation in the absence of LF , which rules out a requirement for cleavage of other LF substrates in activation of NLRP1B . Our results provide evidence for a simple direct mechanism by which an innate immune sensor detects a pathogen-encoded activity . In addition , our results open the possibility that NLRP1 could function as a direct cytosolic sensor of other pathogen-derived proteases . More broadly , by adding direct proteolytic cleavage to the existing ligand-receptor models for NLR activation , our results also illustrate the remarkable adaptability of the NLR architecture to function as pathogen-detectors in host defense .
The N-termini of mouse NLRP1B and rat NLRP1 were recently reported to be cleaved by LF [41] , [42] . Interestingly , these proteins do not exhibit much similarity in the region surrounding the cleavage site ( Fig . 1A ) , whereas the rest of the protein is highly conserved between mice and rats ( 37% amino acid identity from position 1–54 vs . 70% identity from residue 55 to the C-terminus ) . The N-terminal fragment produced by LF is under 10 kDa and appears to be unstable , making it difficult to detect by conventional western blotting techniques in cell lysates . Thus , to confirm that mouse NLRP1B is cleaved by LF , we augmented the mass of the putative N-terminal fragment by 29 kDa by fusing full-length NLRP1B to enhanced green fluorescent protein ( EGFP ) and a hemagglutinin ( HA ) affinity-tag . We transfected this construct into HEK 293T cells and then treated the cells with LeTx . As reported previously , NLRP1B constitutively but only partially auto-processes its FIIND domain in untreated cells , resulting in a loss of 29 kDa from the C-terminus , and producing a doublet of 140 kDa and 169 kDa that we will refer to as the ‘processed’ and ‘unprocessed’ forms of NLRP1B , respectively ( Fig . 1B and 1D ) [24] . After LeTx addition , an N-terminal fragment smaller than 37 kDa , but larger than EGFP-HA alone ( 29 kDa ) , begins to accumulate inside cells . To distinguish LF-dependent cleavage from auto-processing of the FIIND domain , we will refer to the LF-dependent fragments as ‘cleaved’ ( as opposed to ‘processed’ ) NLRP1B ( Fig . 1D ) . With kinetics corresponding to the appearance of the cleaved N-terminal fragment , the amount of the detectable uncleaved NLRP1B decreased over time , consistent with removal of the N-terminal tag . The LF-dependent cleavage of NLRP1B is not complete even after 6 hours , and thus occurs much more slowly than the LF-dependent cleavage of the MAP kinase kinase MEK2 , a canonical LF substrate , which appears complete within 2 hours ( Fig . 1B ) . To test if LF cleaves NLRP1B directly , without the GFP fusion , we expressed an HA-tagged NLRP1B in 293T cells , immunoprecipitated NLRP1B , and then treated the purified protein with recombinant LF in vitro . In the sample treated with LF , a fragment smaller than 10 kDa is produced ( Fig . 1C ) , suggesting that LF can cleave mouse NLRP1B directly near the N-terminus , confirming recent findings [42] . Even though the cleavage site in rat NLRP1 is not well-conserved in mouse NLRP1B ( Fig . 1A ) , two sequences can be found in the N-terminus of mouse NLRP1B that partially fit the previously established consensus specificity of LF [42] ( Fig . S1A ) . For clarity , we refer to the putative site nearest to the N-terminus ( cleavage after K38 ) as site-1 , and the C-terminal site ( cleavage after K44 ) as site-2 ( Fig . 1D and S1A ) . These two sites were also identified as putative LF cleavage sites in a recent study [42] , but their functional importance was not addressed . We attempted to generate cleavage resistant ( CR ) forms of NLRP1B by mutating each site . We made a variety of amino acid substitutions at site-1 ( CR1A-D ) and site-2 ( CR2A-C ) ( Fig . S1A ) , utilizing residues that have previously been used to render MKK3 and MKK6 cleavage resistant [43] , or residues not found in LF consensus sites [44] , [45] . These mutants were transfected into 293T cells , and cells were then treated with LeTx and assayed for cleavage . Mutation of cleavage site-2 produced a cleavage-resistant form , despite the fact that site-1 is intact in this mutant ( Fig . 2A S1A–C ) . By contrast , mutation of cleavage site-1 had little or no effect on NLRP1B cleavage ( Fig . S1A–C ) . When Casp1 and Il1b cDNA expression vectors were cotransfected into this same 293T system , only CR2A and CR2B were defective for induction of IL-1β processing into p17 above the basal processing induced by CASP1 and NLRP1B prior to stimulation ( Fig . S1B–C ) . Thus , while confirming the previous finding that both site-1 and site-2 of mouse NLRP1B can be cleaved by LF [42] , these results suggest that site-2 is the predominant LF target within NLRP1B in cells . We tested the ability of the CR2A NLRP1B mutant to form an inflammasome capable of promoting pyroptosis . In these experiments , we used immortalized macrophages from a C57BL/6 ( B6 ) mouse , because the endogenous B6 allele of NLRP1B is not responsive to LeTx . As expected , immortalized B6 macrophages transduced with a retroviral construct expressing the wild-type 129S1 allele of NLRP1B became sensitive to LeTx and underwent pyroptosis , as assessed by release of cytosolic lactate dehydrogenase ( LDH ) into the supernatant ( Fig . 2B ) . By contrast , transduction of B6 macrophages with the CR2A NLRP1B mutant did not confer any measurable sensitivity to LeTx over the same time period . This difference in responsiveness is not due to differences in expression of the NLRP1B alleles ( Fig . S2A ) . B6 cells harbor a functional NAIP5 inflammasome; thus , as a further control , the NLRP1B-transduced cells can be tested for inflammasome responses to the cytosolic presence of flagellin . We therefore delivered flagellin to the cytosol , via the protective antigen translocation channel used by lethal factor , as a fusion to the translocation signal in LF ( dubbed ‘FlaTox’ ) [46] ( Fig . 2B ) . Cells transduced with wild-type and CR2A NLRP1B were equally susceptible to FlaTox , indicating that they expressed functionally equivalent levels of anthrax toxin receptor , CASP1 , and downstream effectors required for pyroptosis . These data demonstrate that the ability of mouse NLRP1B to respond to LF correlates with the ability of LF to cleave NLRP1B at its N-terminus . The ability of LF to cleave and activate NLRP1B has only been tested in the presence of PA , since PA is typically required in order to deliver LF to the cytosol . It is therefore unclear if PA is only necessary for the translocation of LF in to the cytosol , or if it is also required for NLRP1B activation . We decided to test the ability of LF expression to induce pyroptosis and cytokine secretion in B6 and 129 immortalized macrophage-like cell lines with a Tet-On inducible vector . We transduced these cell lines with a lentiviral Tet-On GFP or LF expression vector and then treated the transduced cells with doxycycline to induce GFP or LF expression . LF expression was able to consistently induce pyroptosis in 129 ( NLRP1B LeTx-responsive ) cells but not B6 ( NLRP1B LeTx-nonresponsive ) cells ( Fig . S5A ) . Further addition of PA had no additional effect on pyroptosis induction . Similar results were obtained when IL-1β production was used to monitor NLRP1B activation ( Fig . S5B ) . These results show that the cytosolic presence of LF is sufficient to activate NLRP1B and that additional putative signals provided by PA pore formation are not required . Together the above results suggest that mouse NLRP1B requires direct cleavage in order to be activated by LF , but it is difficult to rule out the formal possibility that the CR2A mutant is misfolded or is otherwise non-functional for reasons unrelated to its resistance to cleavage by LF . Moreover , the above experiments did not address whether cleavage alone is sufficient for activation of NLRP1B . For example , LF may have other substrates that must be cleaved in addition to NLRP1B , or LF itself could provide a ligand-like signal for the cleaved NLRP1B receptor . To address these possibilities , we replaced the predicted LF cleavage sites-1 and -2 in NLRP1B with a Tobacco Etch Virus ( TEV ) NIa protease cleavage-site ( Fig . S1A ) . TEV protease was selected because it has no known endogenous substrates in mouse or human cells . We transfected 293T cells with plasmids expressing the wild-type and TEV-site forms of NLRP1B , along with plasmids encoding CASP1 , pro-IL-1β , and either LF or TEV protease . As expected , wild-type NLRP1B was cleaved only in the presence of LF , and this coincided with the generation of mature IL-1β ( Fig . 3A ) . Importantly , NLRP1B harboring a target sequence for TEV protease in place of the LF target sequence at site-2 ( TEV-site2 NLRP1B ) was cleaved efficiently by TEV protease , and this cleavage was sufficient to promote IL-1β processing . Consistent with the relatively low sequence specificity of LF , the TEV-site2 NLRP1B protein was also cleaved by LF , but this cleavage was inefficient as most of the NLRP1B remained uncleaved , and IL-1β was not efficiently processed . Cleavage of TEV-site1 also was sufficient to induce IL-1β processing , but this occurred upon expression of either LF or TEV proteases . Furthermore , the TEV-induced cleavage at site-1 produced a fragment of NLRP1B that was smaller than the fragment produced by LF expression ( Fig . 3A and S1D ) . Consistent with the mutagenesis experiments shown in Fig . 2 , this observation may indicate that LF prefers to cleave at site-2 , which is still present in the TEV-site1 NLRP1B protein . Taken together , these results suggests that cleavage of NLRP1B at either site-1 or site-2 is sufficient to induce inflammasome activation independently of other LF-dependent cellular effects . We also confirmed that cleavage of NLRP1B is sufficient to induce pyroptosis in macrophages cell lines . We transduced immortalized B6 macrophages with two different retroviral vectors , one expressing GFP-NLRP1B and the other expressing TEV protease with an IRES-Thy1 . 1 expression marker . If cleavage is sufficient to activate NLRP1B , it is expected that only cells expressing both components would undergo pyroptosis and therefore be underrepresented in the live population of cells . We analyzed the percentage of cells that contained both retroviruses by measuring THY1 . 1 surface-expression and GFP fluorescence by flow cytometry . As expected , an underrepresentation of the THY1 . 1 and GFP double-positive population was specifically seen in cells transduced with TEV and TEVsite2-NLRP1B , while the frequency of THY1 . 1+ cells was similar in cells that where negative for both forms of NLRP1B ( Fig . 3B ) . To further confirm that cleavage of NLRP1B is sufficient for inflammasome activation , we transduced RAW 264 . 7 cells with retroviral vectors encoding various NLRP1B alleles , as well as a lentiviral Tet-On vector that inducibly expresses GFP , TEV-protease or LF after exposure of cells to doxycycline . In this system , TEV expression induced high levels of LDH release only for cells expressing TEVsite2-NLRP1B . As expected , since RAW cells express an endogenous functional allele of NLRP1B , LF induced pyroptotic lysis of cells expressing either wild-type or TEVsite2-NLRP1B ( Fig . 3C ) . The percent LDH release was generally consistent with the percentage of cells that expressed both constructs ( Fig . S2B ) . These data demonstrate that cleavage of NLRP1B is sufficient to activate this inflammasome in macrophages and cause pyroptosis . We next tested whether the N-terminal fragment generated by cleavage of NLRP1B by LF at site-2 must be present along with the corresponding C-terminal fragment . We generated a construct to express a ‘pre-cleaved’ C-terminal fragment by deleting residues 1–44 of full-length NLRP1B and replacing amino acid 45 ( leucine ) with an initiator methionine . The resulting C-terminal fragment contains all known functional domains of NLRP1B ( Fig . 1D ) . In the 293T cell system , high levels of spontaneous IL-1β cleavage was observed upon expression of the precleaved NLRP1B . The activity of pre-cleaved NLRP1B was comparable to that of a ΔLRR mutant , a form of NLRP1B that is known to be constitutively active ( Fig . 3D and S3C ) [47] . When the N-terminal fragment ( amino acids 1–44 ) was coexpressed with the precleaved C-terminal fragment , no change in the amount of IL-1β processing was observed ( Fig . S3B ) , suggesting it is neither necessary nor inhibitory when expressed in trans . For all of these experiments , the differences in the amount of IL-1β cleavage were not explained by differences in expression of NLRP1B ( Fig . S3A–C ) . NLRP1B inflammasome activation can be blocked by proteasome inhibitors , an effect that is observed with multiple inhibitors and is specific to the NLRP1B inflammasome and not the NAIP/NLRC4 inflammasome [37] , [39] . The mechanism by which proteasome inhibitors affect NLRP1B inflammasome activation is not currently known . Therefore , we tested whether the proteasome inhibitor MG132 blocked NLRP1B cleavage . In the 293T system , an equivalent amount of cleaved of NLRP1B occurred in the presence of MG132 and its vehicle ( Fig . 4A ) , suggesting NLRP1B cleavage is not the step at which MG132 interferes with NLRP1B activation ( Fig . S4A ) . The FIIND of NLRP1B contains a ZU-5/UPA-like domain that can auto-process , and this auto-processing is required for NLRP1B activation [23] , [24] . We tested if auto-processing at the FIIND region is prerequisite for N-terminal cleavage by LF . We tested the FIIND mutant S984A , which cannot auto-process , and found it to be indistinguishably sensitive to LeTx cleavage as wild-type NLRP1B ( Fig . 4B ) . Thus FIIND auto-processing appears to be required for a downstream step in NLRP1B activation ( Fig . S4B ) , and is not required for NLRP1B to be sensitive to LeTx cleavage .
Activation of the NLRP1B inflammasome by LeTx is an important resistance mechanism during Bacillus anthracis infections in mice [17] , [18] . However the question of how NLRP1B senses the protease activity of LF remains unresolved . Here we investigated the molecular mechanism by which the protease activity of B . anthracis lethal toxin could be detected by NLRP1B . Our studies provide a clear molecular mechanism for how a pathogen-encoded activity ( or ‘pattern of pathogenesis’ [3] ) can be sensed by the innate immune system . Two recent studies by Moayeri and colleagues provided a considerable advance in our understanding of NLRP1B activation by LeTx [41] , [42] . These two studies showed that both rat and mouse NLRP1 can be cleaved near the N-terminus by LF , and that mutation of the cleavage site abolished responsiveness of rat NLRP1 to LF . While these findings strongly suggest that direct cleavage of rat NLRP1 could be its mechanism of activation , the functional role of cleavage of mouse NLRP1B was not addressed , and it is also possible that mutation of the cleavage site blocked activation of rat NLRP1 by affecting the folding or assembly of NLRP1 . Most significantly , the question of whether cleavage of NLRP1B was sufficient for its activation has not been addressed . This question is especially important to address because LT has been shown to have complex effects on cells , including disruption of MAP kinase signaling [43] , [48] , [49] , that could conceivably play a role in NLRP1B activation . Moreover , several other cellular functions , such as proteasome activity and N-end rule degradation pathways , have been implicated in NLRP1B activation [37] , [39] , [40] . Therefore , to demonstrate that cleavage of NLRP1B is sufficient to induce inflammasome activation , we engineered an allele of NLRP1B that could be activated by the heterologous TEV protease . This protease is not known to have endogenous substrates in mouse or human cells , so is likely to exert its effects solely via direct cleavage of the engineered NLRP1B protein . Indeed , the TEV protease did not activate NLRP1B unless NLRP1B contained a site that could be cleaved by TEV ( Fig . 3A ) . Recent data have suggested that mouse NLRP1B can be cleaved at two distinct sites by LF [42] , but our cleavage site mutants and TEV-site forms of the receptor are most consistent with site-2 ( cleavage between residues 44–45 ) being the predominant cleavage site . Interestingly , this site coincides with the same amino acid position as the LF cleavage site in rat NLRP1 , even though the sequences of the two sites are not conserved ( Fig . 1A ) . The low degree of target sequence specificity exhibited by LF may have allowed the sequence of the cleavage site in NLRP1 to diverge without losing responsiveness to LF . The position within NLRP1 at which LF cleaves may be determined in part by interactions between LF and regions of NLRP1 outside of the cleavage site . Indeed , similar non-cleavage-site interactions appear to control the specificity of LF for its other known substrates , the MAPKKs [9] , [10] . The divergence of the amino acid sequence of the N-terminus of NLRP1B is interesting given the high degree of conservation in the rest of the protein . This divergence may be due to random drift of a structurally unconstrained domain , or alternatively , the divergence may reflect evolutionary pressure for NLRP1 to be recognized by other pathogen-encoded proteases . Notably , our data suggest that cleavage outside of the primary LF target site ( e . g . , at site-1 ) can also activate NLRP1B ( Fig . 3A ) , although it is unclear if LF can cleave and activate NLRP1B at this position . In addition , our data suggest that cleavage of NLRP1B does not necessarily have to be complete to be sufficient to permit inflammasome assembly and CASP1 activation . Taken together , these observations suggest that NLRP1B could be responsive to proteases from other pathogens even if these proteases cleave NLRP1B at different sites with low efficiency . Indeed , countless pathogens , including bacteria , viruses and parasites , depend on cytosolically-localized proteases for virulence [50]–[53] . Therefore the presence of cytosolic proteases could be considered a ‘pattern of pathogenesis’ [3] , that could be detected by NLRP1 proteins to allow the innate immune system to discriminate pathogenic and harmless microbes . The divergence of rat and mouse NLRP1 may thus reflect evolution under the selective pressure imposed by distinct sets of pathogens in the two different rodents species . It will be interesting to determine if other proteases can activate rodent NLRP1s . The detection of protease activity by NLRP1B represents a fundamentally distinct mode of pathogen recognition in vertebrates as compared to the classic mode of direct recognition of PAMPs observed with most innate immune receptors of the TLR , NLR and RLR families . The N-terminus of NLRP1B appears to function to detect LF activity in a manner analogous to the ‘decoy’ model [54] , which has been previously proposed to explain detection of certain pathogen effectors by plant NLRs . The proteolytic mechanism by which NLRP1B is activated represents one of the few examples in mammals in which a molecular mechanism has been established for how an innate immune sensor can respond to a pathogen-encoded activity . It is currently unknown how cleavage of the N-terminus results in structural changes that lead to NLRP1B activation . A simple model is that the N-terminus of NLRP1B mediates an auto-inhibitory intramolecular interaction , perhaps via an interaction with the LRR domain , which is known also to be required for auto-inhibition of NLRP1B ( Fig . 3D ) [47] . An alternative model that is not excluded by our data is that the removal of the original N-terminus allows the neo-N-terminus to provide a positive signal to activate NLRP1B . More complicated models involving interactions with other proteins can also be envisaged . We did not observe an inhibitory role of the N-terminal fragment when expressed in trans ( Fig . S3 ) . This suggests that the N-terminus is necessary to maintain NLRP1B in a conformation that is inactive , but can only do so when the N-terminus is covalently attached to the rest of NLRP1B . In addition to proteolytic cleavage by LF , additional layers of NLRP1B regulation appear to exist . For example , FIIND auto-processing is required for NLRP1B activity , for reasons that remain poorly understood [24] . Since we found that FIIND auto-processing mutants are still cleaved by LF , the role of FIIND auto-processing appears not to be to render NLRP1B susceptible to cleavage by LF . Further complexities in NLRP1B activation are also suggested by the observation that proteasome and N-end rule pathway inhibitors appear to specifically prevent NLRP1B-dependent CASP1 activation [37] , [39] , [40] . Even though previous studies have shown that proteasome inhibitors do not block cleavage of MAPKK by LF [37] , [39] , we tested if proteasome inhibition might affect cleavage of NLRP1B , which appears to be a less optimal substrate than the MAPKKs . However , we observed no effect of the proteasome inhibitor MG132 on the ability of LF to cleave NLRP1B . Thus it remains unclear how this inhibitor specifically blocks the NLRP1B inflammasome and not other inflammasomes . Models that attempt to explain the mechanism of NLRP1B are further complicated by other unique features of NLRP1B . For example , ATP binding to the NBD of NLRP1B is not necessary for inflammasome activation , and mutants of NLRP1B that are unable to bind ATP are actually constitutively active [55] . This is contrary to what is known for other mammalian NLRs , where ATP binding appears to be required for oligomerization and downstream signaling [21] . Furthermore , gross truncations of NLRP1B can also lead to constitutively active forms of NLRP1B that contain only the very C-terminal CARD and a portion of the FIIND [47] . Thus , other disturbances , by proteolysis or by other means , to the overall structure of NLRP1B could lead to loss of the conformation that mediates auto-inhibition . In general , the molecular conformational changes that occur in NLRs as they transition from an inactive to an active state are poorly understood . Thus , our studies of NLRP1B provide an important point of comparison that helps us to develop a broader understanding of the NLR class of innate immune sensors and the mechanisms of their activation .
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 . The protocol was approved by the Animal Care and Use Committee at the University of California , Berkeley ( MAUP #: R301-0313BRC ) . HA-NLRP1B was amplified from a Nlrp1b ( DQ117584 . 1 ) cDNA ( gift of E . Boyden and B . Dietrich , Harvard Medical School ) with primers 1–2 and cloned into pCMSCV-IRES-hCD4 using the XhoI and NotI restriction sites ( Fig . S5 ) . A construct for expression a GFP-HA-NLRP1B fusion was created by amplifying HA-NLRP1B with primers 3–4 and cloning the resulting PCR product into MSCV downstream of and in-frame with GFP using NotI and SalI sites . TEV expression constructs were created by amplifying a His6-TEV ORF with the primers 7–9 and 8–9 into pCMSCV-IRES-Thy1 . 1 and pFG12-rtTA-IRES-Thy1 . 1 , respectively . LF was similarly cloned into the same vectors as TEV , but with the primers 10–11 and 12–13 using a template provided by Bryan Krantz ( UC Berkeley ) . Mutagenesis of Nlrp1b was performed using Quickchange ( Stratagene/Agilent ) , but modified by substituting the Pfu polymerase for PrimeSTAR HS ( TAKARA/Clonetech ) . The primers used are listed in Fig . S6 . HEK293T ( ATCC ) cells were grown in complete media ( DMEM , 10%FBS , 100 U/ml Penicillin , 100 µg/ml Streptomycin , and supplemented with 2 mM L-glutamine ) . RAW 264 . 7 and immortalized B6 macrophages were grown in complete media ( RPMI 1640 , 10%FBS , 100 U/ml Penicillin , 100 µg/ml Streptomycin ) . HEK 293T cells were seeded the day prior to transfection at a density of 1 . 5×105 cells/well in a 24-well plate with complete media . DNA complexes were made with Lipofectamine 2000 ( Invitrogen ) according to manufactures instructions and overlaid on cells for 24–36 hours . Cells were lysed in RIPA buffer supplemented with 1 mM PMSF and 1×X Complete Protease Inhibitor Cocktail ( Roche ) . Lysates were spun down at max speed at 4C for 20 min and supernatants were mixed with 6× Laemmli sample buffer . To detect full length NLRP1B , lysates were incubated at room temperature prior to SDS-PAGE . To analyze all other proteins , including the N-terminally cleaved form of NLRP1B , samples were boiled for 10 min prior to separation . SDS-PAGE was performed with Novex BisTris gel system according to manufacturer instructions ( Invitrogen ) . Separated proteins were transferred on to Immobilon-FL PVDF membranes . Membranes were blocked with Odyssey blocking buffer ( Licor ) . The following antibodies were used for the following antigens: HA mAB 3F10 ( Roche ) , MEK-2 SC-13115 ( Santa Cruz ) , Beta Actin SC-4778 ( Santa Cruz ) , IL-1B AF-401-NA ( R&D systems ) . Secondary antibodies anti-rat , mouse and goat were all conjugated to Alexa Flour-680 ( Invitrogen ) . Transfected cells were lysed in a non-denaturing buffer ( 1% NP-40 , 137 mM NaCl , 2 mM EDTA , 20 mM Tris pH 8 supplemented with protease inhibitors ) . Cleared lysates were bound to EZview Red Anti-HA Affinity Gel ( Sigma ) washed four times with lysis buffer , once in LF cleavage buffer ( 10 mM NaCl , 5 uM ZnSO4 , 10 mM HEPES pH 7 . 4 ) , and resuspended back into cleavage buffer . One microgram of recombinant LF was added to immunoprecipitated NLRP1B and incubated at 37°C for 2 hours , and analyzed by western blotting as described above . Macrophages were seeded one day prior to treatment in a 96well plate at 5×104 cell/well in RPMI media without phenol red . The next day cells were treated with LeTx 1 µg/ml , FlaTox 1 µg/ml [46] , or doxycycline at 5 µg/ml in ethanol for the indicated time , and spun down at 400×g . For IL-1β release cells were pretreated/cotreated with 1 µg/ml of Pam3CSK4 . Supernatants were removed and assayed for LDH and IL-1β release as described previously [56] . | Recognition of pathogens by the innate immune system is necessary for initiating an appropriate immune response . The innate immune system must distinguish pathogens from abundant harmless microbes present within the host and the environment , and scale the response appropriately . It has been proposed that the host can respond specifically to pathogens by monitoring common virulence-associated activities , previously termed “patterns of pathogenesis , ” that are used by pathogens to survive and replicate within their hosts . For example , pathogens can manipulate host functions by delivering toxins into host cells . In response , the host encodes dedicated cytosolic sensors to detect these toxins , but the molecular basis for how the sensors recognize the toxins is poorly understood . Here we define the molecular mechanism by which a mouse sensor , NLRP1B , directly recognizes the activity of a bacterial toxin , lethal factor . Lethal factor is a protease secreted by Bacillus anthracis , the causative agent of anthrax . We show that anthrax lethal factor cleaves NLRP1B and this cleavage event is both necessary and sufficient for the activation of this sensor . Our findings raise the possibility that NLRP1B could sense the activity of other proteases encoded by diverse pathogens . | [
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] | 2013 | Direct Proteolytic Cleavage of NLRP1B Is Necessary and Sufficient for Inflammasome Activation by Anthrax Lethal Factor |
Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities . Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is , therefore , of utmost importance . There are several non-invasive cardiovascular state biomarkers based on the pulse ( pressure ) wave propagation properties , but their major determinants are not fully understood . In the current study we aimed to provide a framework to precisely dissect the information available in non-invasively recorded pulse wave in hemodialysis patients . Radial pressure wave profiles were recorded before , during and after two independent hemodialysis sessions in 35 anuric prevalent hemodialysis patients and once in a group of 32 healthy volunteers . Each recording was used to estimate six subject-specific parameters of pulse wave propagation model . Pressure profiles were also analyzed using SphygmoCor software ( AtCor Medical , Australia ) to derive values of already established biomarkers , i . e . augmentation index and sub-endocardial viability ratio ( SEVR ) . Data preprocessing using propensity score matching allowed to compare hemodialysis and healthy groups . Augmentation index remained on average stable at 142 ± 28% during dialysis and had similar values in both considered groups . SEVR , whose pre-dialytic value was on average lower by 12% compared to healthy participants , was improved by hemodialysis , with post-dialytic values indistinguishable from those in healthy population ( p-value > 0 . 2 ) . The model , however , identified that the patients on hemodialysis had significantly increased stiffness of both large and small arteries compared to healthy counterparts ( > 60% before dialysis with p-value < 0 . 05 or borderline ) and that it was only transiently decreased during hemodialysis session . Additionally , correlation-based clustering revealed that augmentation index reflects the shape of heart ejection profile and SEVR is associated with stiffness of larger arteries . Patient-specific pulse wave propagation modeling coupled with radial pressure profile recording correctly identified increased arterial stiffness in hemodialysis patients , while regular pulse wave analysis based biomarkers failed to show significant differences . Further model testing in larger populations and investigating other biomarkers are needed to confirm these findings .
Chronic kidney disease ( CKD ) is associated with a significant increase in cardiovascular disease ( CVD ) incidence , with almost two-fold increase of CVD prevalence in elderly CKD patients in US [1] . At the same time , death due to the cardiovascular diseases , with congestive heart failure and peripheral arterial disease being the two most common , is the single largest cause of attributable mortality in the prevalent and incident dialysis patients [1] . Thus , the development of sensitive and non-invasive cardiovascular diagnostic methods is of great importance for patients with more severe forms of chronic kidney disease , as they could be used to design better treatment . High incidence of cardiovascular diseases in dialysis patients is related , among others , to high prevalence of vascular calcification caused by abnormal mineral metabolism that yields increased arterial wall stiffness [2–4] . Pulse wave velocity ( PWV ) measurement is the gold-standard method for the assessment of arterial stiffness [5 , 6] with higher PWV values indicating less elastic vessels . Most of the commercial devices dedicated to PWV estimation rely on simultaneous gating of electrocardiograph with pressure waveform recordings in two separate peripheral vessels ( typically carotid and femoral ) [7 , 8] . However , this electromechanical approach can bring a large discomfort to a patient [9] , takes a substantial amount of time to perform the measurement , and relies on external measurements of distances between the pulse wave recording sites what is only a coarse grain approximation of the true artery lengths . Pulse wave analysis ( PWA ) is another and much simpler to perform non-invasive technique that provides various cardiovascular system state indices based on a peripheral pulse ( pressure ) wave recording [10 , 11]; see Fig 1 . The underlying assumption of the PWA method is that there exists a generalized transfer function that allows to reconstruct the aortic pulse waveform from the peripheral pressure recording for any individual from general population [10 , 11] . However , despite some studies showing validity of this assumption [12–14] , there are still significant concerns about the robustness of aortic wave reconstruction [15–19] . Moreover , it is not completely clear what the major determinants of PWA-derived indices are as the waveform can be attributed to those cardiovascular properties that determine the wave propagation and reflection , such as , among other , arterial geometry , elasticity of the vessel wall , systemic resistance , heart frequency , cardiac output [20] . Nevertheless , one of the PWA-derived indices , i . e . augmentation index ( AI = PP/ ( PP-AP ) x100%; see Fig 1 ) that represents the augmentation of central pressure height that is being introduced by the reflected waves [21] , has been clearly associated with aging and cardiovascular risk [22–25] . Here , in a detailed study of pulse wave propagation properties using our previously published model [20] , we aimed to check whether the cardiovascular risk factors for hemodialysis patients , such as arterial stiffness , can be derived from a single , quick and non-invasive peripheral wave recording and thus , overcome the PWA and PWV limitations . Moreover , we try to decipher what are the major determinants of PWA-derived indices and how they relate to the predicted pulse wave velocity .
The study was approved by the Bioethical Committee at the Medical University of Lublin ( Poland ) and informed consent has been obtained from all patients . Two standard hemodialysis ( HD ) sessions ( duration 240 . 2 ± 13 . 4 min ) were monitored in 35 anuric , prevalent hemodialysis patients ( dialysis vintage 9 . 1 ± 8 . 9 years ) . Basic patients’ characteristics are shown in Table 1 . At the time of the study none of the patients had diagnosed cardiovascular disease . All patients underwent their regular treatment with ultrafiltration volume set to achieve patient-specific post-dialytic dry weight ( ultrafiltration rate of 11 ± 3 . 6 ml/min ) . The average flow of blood and dialysis fluid in extracorporeal circuit was 287 . 3 ± 47 . 4 ml/min ( range 180 − 380 ml/min ) and 500 ml/min , respectively . All patients had arteriovenous fistulas . We enrolled an additional group of 32 healthy volunteers ( control group ) in order to investigate the differences between HD and general population . The only enrollment criterion for this group was lack of diagnosed cardiovascular disease . Basic volunteers characteristics are shown in Table 1 . The data about 20 out of those 32 healthy individuals have been published previously in [20] . Participants were enrolled from June 2014 through February 2018 . In order to compare measurements and model predictions between the HD and control groups we preprocessed the data using age and gender based propensity score matching ( PSM ) technique [26] . This approach allowed us to select the subgroups for which there were no statistically significant differences when comparing basic characteristics; compare last two columns in Table 1 . Pulse wave shape in radial artery was recorded using applanation tonometry ( SphygmoCor , AtCor Medical , Australia ) once in healthy individuals and about 15 minutes before start , after start , before end , and after end of two hemodialysis sessions performed after 3- and 2-day interdialytic intervals; see Fig 2A . All measurements were made in at least duplicate and the recording with higher quality ( defined and calculated by SphygmoCor software as ‘operator index’ ) was chosen . Measurements with insufficient quality ( ’operator index’ ≤ 74 ) were excluded in accordance to the user manual . All recordings were performed by one trained clinician with measurement performed in the non-fistula arm in the case of hemodialysis patients . The radial pulse wave was calibrated to the blood pressure measured oscillometrically at the brachial artery ( Omron M3 , Omron Healthcare , Kyoto , Japan ) . We used SphygmoCor software together with its built-in transfer function to derive systolic ( SP ) and diastolic ( DP ) pressures in ascending aorta , together with the augmented pressure ( AP ) which is the additional pressure added by the reflected wave to the forward pressure wave . We have extracted also augmentation index ( AI = ( SP-DP ) / ( SP-DP-AP ) x100% ) and ejection duration ( ED ) being the time from the start of the pulse to the end of systole . Information about ejection duration is used by the SphygmoCor software to calculate sub-endocardial viability ratio ( SEVR ) which serves as an estimate for the adequacy of myocardial blood flow . We use our previously published model [20] that allows to simulate the blood flow in a bifurcating binary tree of fifty-five larger systemic arteries in which individual vessels are axisymmetric elastic cylinders tapering along their length . The model describes spatiotemporal changes in the pressure and blood flow , under the assumption that each vessel is impermeable and blood is an incompressible fluid with given density and viscosity . The model is formulated as a large system of partial differential equations coupled to a series of outflow conditions expressed using ordinary differential equations . Here , as in [20] , we use the model to estimate six patient-specific parameters: k1 and k3 being hyperparameters describing stiffness of small and large arteries , respectively; cardiac output ( CO ) ; moment of the heart ejection peak ( τ ) ; and scaling constants of resistances ( SR ) and compliances ( SC ) of capillary beds ( Fig 2B ) . Equation describing stiffness of an artery ( F ) has the following form F ( x ) = 4 3 ( k 1 exp ( - k 2 r 0 ( x ) ) + k 3 ) , where k2 = 22 . 53 and r0 ( x ) is the assumed vessel diameter at the reference pressure ( 97 mmHg ) and distance x from the vessel’s inlet [20 , 27] . Therefore , larger vessels’ ( r0 > > 1 ) stiffness is governed primarily by parameter k3 whereas for smaller vessels ( r0 < < 1 ) stiffness F is related to k1 + k3 . Certain amount of the inter-patient variability in r0 is introduced through scaling of the nominal arterial tree definition according to the patients’ height; see [20] for further details . Parameters SC and SR are used to respectively scale nominal compliances and resistances in the Windkessel models imposed at each terminal end of the modeled arterial tree . The goal of the parameter estimation procedure was to find model parameters for which model-predicted radial pressure waveforms correspond the best to those recorded using applanation tonometry; see [20] for the details and other fixed parameters values . Most importantly , after calibrating the model parameters using patient-specific radial pressure waveform recording we can easily compute patient-specific pulse wave velocity between any two given points within the modeled arterial tree . The data are presented as mean ± standard deviation ( SD ) and statistical significance was set at the level of p-value = 0 . 05 , unless otherwise indicated . Statistical dependence between variables was tested using Spearman’s correlation coefficient . Changes in model- and SphygmoCor-derived parameters related to dialysis were investigated by Wittkowski test followed by multiple pairwise comparison analysis based on adjusted Scheffe’s procedure . Wittkowski test is a Friedman-type statistics for consistent multiple comparisons for unbalanced designs with missing data [28] . Due to insufficient radial wave recording quality , there were 25 missing records ( among 280 ) in the HD group database . The Wilcoxon rank-sum test with normal approximation was used to compare continuous factors between hemodialysis and healthy subjects groups . Pearson’s Chi-squared test with Yates’ continuity correction was used to compare categorical factors .
We have previously shown that the model is able to reproduce clinically measured radial waveforms in healthy individuals [20] . After applying the same data fitting procedure as in [20] we obtained also an excellent agreement between measured and simulated radial pressure profiles in hemodialysis patients , compare Fig 3A and 3B . Average relative error between simulated and recorded pressure profile was about 4% and did not depend on the measurement moment , Fig 3C . The average values of model-estimated patient-specific parameters were: k3 = 14 . 51 ± 9 . 03 x105g/ ( s2 cm ) , k1 = 2 . 28 ± 1 . 73 x107g/ ( s2 cm ) , CO = 3 . 56 ± 0 . 8 l/min , τ = 101 . 08 ± 20 . 78 ms , SC = 13 . 57 ± 4 . 34 , and SR = 1 . 34 ± 0 . 56 . In line with our previous study [20] we found that women had on average lower stroke volume ( SV ) than men ( SV = CO/HR; 51 . 3 ± 16 . 4 vs . 54 . 9 ± 15 ml; p-value = 0 . 02 ) . In the hemodialysis group , however , we have also found gender differences in the time to the heart ejection peak ( τ; 92 . 3 ± 16 . 2 vs . 108 ± 21 . 4 ms for males and females , respectively; p-value < 0 . 001 ) and resistances of capillary beds ( SR; 1 . 51 ± 0 . 56 vs . 1 . 22 ± 0 . 54 for males and females , respectively; p-value < 0 . 001 ) . Hemodialysis had a significant effect on the model-estimated parameter describing stiffness of large arteries ( k3 ) with the most pronounced decrease from 16 . 44 ± 8 . 73 to 9 . 92 ± 6 . 81 x105g/ ( s2 cm ) when comparing average values before and just before the end of HD session performed after 3-day interdialytic break; compare Fig 4A . This drop in arterial stiffness was directly related to the change of model-predicted pulse wave velocity calculated from the aortic arch to the femoral artery which decreased in the same period from 9 . 97 ± 2 . 67 to 7 . 7 ± 2 . 46 m/s ( p-value = 0 . 021 ) . Interestingly , those changes were only transient as the pre- and post-dialytic stiffness was not statistically different for both considered interdialytic break lengths . Statistical testing revealed also that hemodialysis has an impact on the model-estimated stroke volume and time to the heart ejection peak ( τ ) , but the multiple comparisons testing was not able to provide clear answer about the direction of those hemodialysis induced changes; compare Fig 4B and 4C . From the boxplots , however , it seems that value of both of those parameters decrease during hemodialysis . Despite the significant differences in the pre-dialytic values of augmented pressure ( AP ) for both considered interdialytic break lengths , augmentation index ( AI ) calculated by the SphygmoCor device was not significantly different between propensity score matched groups , with the average value of 135 . 79 ± % and 142 ± 28% for healthy subjects and hemodialysis patients , respectively; compare Fig 5 . Sub-endocardial viability ratio ( SEVR ) , whose pre-dialytic value was on average lower by 12% compared to healthy subjects , was improved by hemodialysis , with post-dialytic values indistinguishable from those in control group ( p-value >0 . 2 ) . Systolic and diastolic aortic blood pressures derived by SphygmoCor showed only minor differences between considered groups with only two moments for which the differences were statistically significant . Patients just before the end and just after hemodialysis had shorter SphygmoCor-calculated ejection duration , but it seems that this change may be directly related to the increase in the heart rate , at least for the measurements performed after 2-day interdialytic break; compare the first and the fourth row from the bottom in Fig 5 . The model , however , clearly identified that the individuals on hemodialysis had significantly increased stiffness of both large and small arteries compared to the healthy counterparts ( > 60% difference before dialysis with p-value < 0 . 05 or borderline ) and that the stiffness was only transiently decreased during hemodialysis session; see the fourth and the fifth row from top in Fig 5 . Those differences are reflected in significantly larger model-predicted velocities of the pulse waves traveling from the ascending aorta to the femoral artery; see the first row in Fig 5 . The model identified also that the patients on hemodialysis have increased compliances of the capillary beds when compared to the healthy individuals , what probably counteracts the blood pressure increase caused by the larger arterial stiffness , because the model-predicted cardiac output remains unchanged . In order to detect the major determinants of the standard pulse wave analysis indices , i . e . augmentation index , augmented pressure , and sub-endocardial viability ratio , we calculated their correlation coefficients with the model-estimated parameters and basic clinical characteristics . In line with our previous study [20] , correlation based hierarchical clustering revealed that both the augmented pressure and the augmentation index are related to the heart ejection profile , i . e . model-derived peak heart ejection moment and SphygmoCor-derived ejection duration , rather than to the stiffness of arteries , see Fig 6 . The strongest positive correlation of SphygmoCor-derived augmentation index , in addition to the obvious dependence on augmented pressure , was detected when comparing its value with the parameter describing the shape of heart ejection profile ( τ; average R = 0 . 71; maximal p-value < 0 . 001 ) , i . e . the steeper is the ejection profile the smaller is the augmentation index . More closely related to the stiffness of large arteries ( parameter k3 ) was SphygmoCor-derived SEVR , i . e . it correlated negatively with k3 value . Interestingly , the correlation analysis revealed that the major determinant of both peripheral and aortic peak pressures is , in addition to the arterial stiffness , the resistance of the capillary beds , see top-left cluster in Fig 6 . We found that the above relationships are similar for healthy individuals , see S1 Fig . In the case of healthy individuals , however , SEVR correlated with the model-estimated PWV rather than the stiffness of larger arteries and augmentation index was also negatively correlated with the stiffness of small arteries S1 Fig .
Information about the patient’s cardiovascular system state can be nowadays obtained non-invasively by analyzing pulse waveform recordings from peripheral arteries . Pulse wave analysis method relies on the reconstruction of the aortic blood pressure waveform from the peripheral measurement using generalized transfer function , an approach that has been validated but also questioned by some studies [16 , 29–33] . It is also not completely clear what the major determinants of PWA-derived indices are as the waveform can be attributed to multiple cardiovascular properties that determine the wave propagation and reflection properties [20] . Nevertheless , the clinical benefits of using PWA to assess the cardiovascular risk and the impact of therapeutic intervention on the central blood pressure have been clearly shown in clinical trials [34 , 35] . Simultaneous gating of the electrocardiographs with peripheral pressure recordings at two distinct sites allows to estimate pulse wave velocity ( PWV ) which is a validated biomarker for arterial stiffness [7] . However , PWV measurements are much more complicated to perform , rely on external inaccurate measurements of distances between the pulse wave recording sites , and can bring a large discomfort to a patient [9] . Here , we propose the framework in a modern spirit of highly personalized medicine that can bridge the gap between those two methods as it can provide , among others , the information about arterial stiffness separately for large and small arteries from a single peripheral waveform measurement . Proposed framework correctly identified increased arterial stiffness in hemodialysis patients , with its transient decrease during the treatment session . Those changes in the arterial stiffness translate to hemodialysis induced decrease in model-predicted pulse wave velocity , the effect that has been observed in other clinical studies [36] . Detected hemodialysis-induced changes in some of the framework-derived and standard PWA-derived indices can be directly related to physical changes in blood circulation during treatment session . Namely , initiation of hemodialysis session results in decreased pressure and workload for the heart as the blood volume circulating in the body is reduced by the blood volume required to fill the tubing together with dialyzer itself and because there is an additional ( artificial ) blood pump for extracorporeal circulation . Further reduction of circulating blood volume during dialysis session due to the removal of excess fluid yields further improvement of heart workload , with the best conditions before the end of the session ( assuming the stability of circulatory system and no hypotensive episodes ) . The final phase of dialysis—the return of the blood from the extracorporeal circuit to the body circulation and switching off the blood pump—has a reverse effect on the heart and the pulse wave: the changes are towards pre-dialytic conditions . Our goal at the study onset was also to check what is the influence of the applanation tonometry done only in the arm without the fistula . The investigator that was performing the measurements did initially try to capture the radial waveform at the arm with the arteriovenous ( AV ) fistula , but unfortunately , due to a weak pulse , it was impossible in most cases to obtain recording with sufficient quality . One of the possible limitations of our study could be the existence of AV fistula in hemodialysis patients which may influence pulse wave-derived parameters , both from the standard PWA method and when using the proposed model . This is because the AV fistula , with the blood flows that may exceed 600 ml/min , substantially alters the normal systemic blood flow and results in the increased cardiac output [37] . To estimate the potential effect of the AV presence on the obtained results we introduced the AV fistula into the model following the work by Huberts et al . [38 , 39]; see S1 File for details . Comparison of the simulated aortic and radial pressure waveforms before and after introduction of arteriovenous fistula showed that AV creation results in decreased systemic pressure without substantial changes in the pulse wave shape; see S2A and S2B Fig . Most importantly , this decrease in systemic pressure was obtained for clinically relevant flows through AV fistula , see S2C Fig . Further investigations of the radial-to-aortic transfer function showed that introduction of AV fistula has a little effect on its shape , see S3 Fig , indicating that PWA method is not affected by the presence of vascular access . To check whether it is valid to use the model without fistula for hemodialysis patients , as it has been done in this work , we performed model fitting to the profiles simulated using the model with AV fistula . Results of the parameter estimation showed that using the model without fistula introduces small approximation error , see S4 Fig , and can result in underestimation of small artery stiffness ( parameter k1 ) and scaling of terminal resistances ( parameter SR ) , see S5 Fig . Therefore , because we show increased arterial stiffness , our initial assumption to use the model without fistula , in order to keep the number of patient- and group-specific parameters to minimum , does not corrupt the results of our study . Another possible limitation of our study is that the parameter estimating procedure simultaneously adjusts only six patient-specific parameters when trying to minimize the discrepancy between the model-predicted and measured peripheral artery waveform . Of course there are other parameters in the model that are currently fixed at the literature values and thus , are not patient-specific . This is because there is only a limited amount of information present in the peripheral pressure profile recording what does not allow us to robustly estimate all of the model parameters nor the exact shape of the heart ejection profile . Recorded pressure profiles have multiple characteristic points that differ significantly between the subjects ( see five randomly selected recorded pressure profiles in S6 Fig ) allowing us to identify a specific subset of parameters chosen in the current work . Of course we cannot guarantee the uniqueness of those selected parameters , but for each subject we tried to extensively search the whole possible parameter space by starting first with heuristic particle swarm optimization ( PSO ) algorithm coupled with a gradient based procedure; see [20] for further details . In conclusion , we showed that patient-specific pulse wave propagation modeling coupled with radial pressure recording can correctly identify increased arterial stiffness in HD patients , while regular PWA-based biomarkers failed to show significant differences . However , further model testing in larger populations and investigating other biomarkers is needed to confirm these findings . It is worth mentioning , that PWA is being nowadays routinely performed in many hospitals and thus , our framework can shed a new light on those existing datasets . | There are more than 2 million people receiving hemodialysis ( HD ) treatment worldwide . Cardiovascular disease is the most common cause of death in those patients . There are several non-invasive methods to assess if a person from general population has a high risk for developing cardiovascular disease , but it is unclear whether they are useful in hemodialysis patients . Here we assessed the ability of patient-specific pulse wave propagation modeling to correctly identify high cardiovascular risk factors in hemodialysis patients . We performed pulse wave analysis ( PWA ) in patients on hemodialysis and in healthy subjects . Recorded peripheral pressure profiles were simultaneously used to inform subject-specific mathematical model of pulse wave propagation . We found that standard PWA-derived biomarkers failed to clearly show the differences between hemodialysis patients and healthy subjects . However , proposed mathematical model of pulse wave propagation identified significantly increased arterial stiffness in HD patients and provided also the major determinants of PWA-derived biomarkers . Our study suggests that current pulse wave analysis based biomarkers can be insufficient to accurately diagnose hemodialysis patients . Proposed patient-specific pulse wave propagation modeling framework may be a new tool to assess the cardiovascular risk in both general and hemodialysis populations . | [
"Abstract",
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"waves"... | 2018 | Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients |
Periodontitis is an inflammatory disease of the supporting structures of the teeth caused by , among other pathogens , Prevotella intermedia . Many strains of P . intermedia are resistant to killing by the human complement system , which is present at up to 70% of serum concentration in gingival crevicular fluid . Incubation of human serum with recombinant cysteine protease of P . intermedia ( interpain A ) resulted in a drastic decrease in bactericidal activity of the serum . Furthermore , a clinical strain 59 expressing interpain A was more serum-resistant than another clinical strain 57 , which did not express interpain A , as determined by Western blotting . Moreover , in the presence of the cysteine protease inhibitor E64 , the killing of strain 59 by human serum was enhanced . Importantly , we found that the majority of P . intermedia strains isolated from chronic and aggressive periodontitis carry and express the interpain A gene . The protective effect of interpain A against serum bactericidal activity was found to be attributable to its ability to inhibit all three complement pathways through the efficient degradation of the α-chain of C3—the major complement factor common to all three pathways . P . intermedia has been known to co-aggregate with P . gingivalis , which produce gingipains to efficiently degrade complement factors . Here , interpain A was found to have a synergistic effect with gingipains on complement degradation . In addition , interpain A was able to activate the C1 complex in serum , causing deposition of C1q on inert and bacterial surfaces , which may be important at initial stages of infection when local inflammatory reaction may be beneficial for a pathogen . Taken together , the newly characterized interpain A proteinase appears to be an important virulence factor of P . intermedia .
Periodontitis is an inflammatory condition with an infective etiology that leads to loss of tooth support . Prevotella intermedia is a major bacterial periodontal pathogen in humans together with Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans [1] . P . intermedia is often recovered from subgingival plaque in patients suffering from acute necrotising gingivitis , pregnancy gingivitis and chronic periodontitis [2] . Recently , P . intermedia was reported to be found in 14% of adult population in Finland and there was association between the carriage of this species and the number of teeth with deepened periodontal pockets [3] . P . intermedia was also frequently isolated from root canal infections [4] . Periodontitis is one of the most common diseases affecting humans and is primarily the result of colonization of the subgingival surfaces of teeth by bacteria . The complex interaction between these bacteria harboring many virulence factors and the host's immune response results in localized chronic inflammation and subsequent destruction of the supporting structures of the tooth . Proteinases are crucial virulence factors produced by many periodontal pathogens , which can cause the degradation of host proteins for essential nutrients but they can also protect the bacteria from the host's defenses such as the complement system [5] , [6] . Complement is a major arm of the innate immune defense system and its main function is to recognize and destroy microorganisms [7] . The three pathways of human complement ensure that virtually any non-host surface is recognized as hostile . The classical pathway is usually mediated by binding of the C1 complex ( composed of recognition molecule C1q and two proteinases C1s and C1r ) to invading pathogens either directly or via immunoglobulins . The lectin pathway is able to recognize , via mannose-binding lectin ( MBL ) , polysaccharide molecules normally present only on microbial surfaces . Finally , complement can also be activated through the alternative pathway , which is not so much an activation pathway but as a failure to appropriately regulate the constant low-level spontaneous activation of C3 ( constantly initiated due to inherent instability of this protein ) . All three pathways lead to opsonisation of the pathogen with C3b ( activated form of complement factor C3 ) , which enhances phagocytosis by phagocytes . Furthermore , anaphylatoxins C5a and C3a are released as byproducts to attract phagocytes to the site of infection . Finally , the end result of the complement cascade is formation of the membrane attack complex and bacterial cell lysis . Host cells protect themselves from bystander damage following complement activation through the expression of membrane-bound or recruitment of soluble endogenous complement inhibitors . Complement deficiencies are very rare but it has been observed that partial C4 gene deficiencies are more frequent in patients with severe chronic periodontitis [8] . A patient with aggressive periodontitis and severe edema , localized to the free gingival tissues was reported to be deficient in C1-inhibitor [9] . Furthermore , the highest salivary levels of C3 were measured in periodontally healthy subjects while low levels were often found in edentulous and chronic periodontitis patients [10] . It has been demonstrated that heat inactivation of NHS ( i . e . inactivation of complement ) significantly reduced opsonic activity for P . intermedia in vitro [11] suggesting that complement is important for host defense against this pathogen . Previous studies have shown that P . intermedia was opsonized by the alternative pathway in the absence of the classical pathway , probably in response to the endotoxin [12] , however , kinetic studies revealed that opsonisation proceeded at significantly faster rates when the classical pathway was intact [11] . Interestingly , the alternative pathway contributed to the killing of serum sensitive strains while the classical pathway was primarily responsible for killing of strains with intermediate sensitivity [13] . Therefore , it appears that complement is able to recognize P . intermedia via several sensory molecules . However , it appears that P . intermedia is able to override to some extent the complement defenses and to establish chronic infections in the oral cavity . Every successful human pathogen must develop means to circumvent complement . Many bacteria are able to capture human complement inhibitors such as C4b-binding protein and factor H thereby inhibiting complement and avoiding opsonisation and lysis [14]–[16] . Herpes viruses , on the other hand , produce their own homologues of complement inhibitors [17] . Furthermore , many bacteria use proteinases to incapacitate components of the complement system . For example , most strains of P . gingivalis are resistant to bacteriolytic activity of human serum [13] , [18] and the gingipain proteinases have been implicated as the major factor providing protection against complement in serum [5] , [19]–[22] . For a number of Prevotella subspecies and strains , including P . intermedia , the level of proteolytic activity for clinical strains was significantly higher than that recorded for commensal strains isolated from healthy mouths [23] . This , we hypothesize , may provide P . intermedia with serum protection . We have identified three cysteine proteinases in the genome of P . intermedia that appeared to be homologues of SpeB protein of Streptococcus pyogenes [24] . Recently , the first of these genes coding for interpain A ( InpA; locus PIN0048 ) was studied in more detail and its 3D structure was determined [25] . Based on similarity of primary and tertiary structures to known proteinases , InpA is now classified into clan CA , family C10 and registered in the peptidase database MEROPS ( [26]; http://merops . sanger . ac . uk ) . InpA is a secreted protein composed of 868 amino acid residues including a 44-residue signal peptide , a pro-domain ( Ala1-Asn111 ) , a catalytic domain ( Val112-Pro359 ) and a 465-residue C-terminal extension arranged in domains with putative regulatory and secretory functions . However , the specific target ( s ) and function of InpA have yet to be characterized . In the present study , we have examined in detail the effect of InpA on the human complement system and found that this proteinase targets mainly the C3 component , thereby inhibiting all three complement pathways simultaneously .
In order to estimate what fraction of P . intermedia strains found in periodontitis carry the inpA gene , detection by PCR was used on subgingival plaque samples obtained from 24 and 58 patients with chronic and aggressive periodontitis , respectively . We have validated specificity of the PCR assay by investigating 25 samples that were negative for P . intermedia but rich in other periodontal pathogens . No positive signal was obtained in any of the tested Prevotella-negative samples showing that the assay is specific for Prevotella inpA gene ( data not shown ) . P . intermedia was detected in 33% and 57% of plaque samples from chronic and aggressive periodontitis , respectively ( Table 1 ) . The majority of P . intermedia-positive samples also yielded positive results regarding the inpA gene implying that InpA fulfils some important physiological function . Similarly , we found that the majority of cultivated P . intermedia strains from various sources also express InpA at the protein level as shown by Western blotting analysis of culture supernatants ( Figure 1A ) . The upper band recognized by the specific antibody corresponds to an unprocessed form of InpA while the lower bands are products of autocatalytic processing [25] . Western blotting of lysates of bacterial cells did not yield a signal implying that InpA is mainly secreted by the bacteria and does not associate in large amounts with cell wall in the strains tested ( data not shown ) . Furthermore , we have detected InpA protein in gingival crevicular fluid samples collected from four chronic periodontitis patients characterized with regard to pocket depth and bleeding-on-probing . The samples were analyzed for the P . intermedia load using qPCR and subjected to Western blotting analysis . We detected InpA in various forms in samples obtained from patients with significant load of P . intermedia but not from those negative for this pathogen ( Figure 1B ) . The 90 kDa form is the unprocessed full length protein , the 76 kDa and the 40 kDa proteins are processed on the N-terminus and the C-terminus , respectively , while the 28 kDa form is the mature , fully-processed protein . These molecular weights are calculated based on amino acid composition . The 40 kDa form runs in fact as 45 kDa protein ( 28 kDa form as 32 kDa protein ) upon separation on 12% SDS-PAGE gel . In order to quantitatively assess the effect of purified InpA on the bactericidal activity of human serum , we used an E . coli DH5α model system whereby cells were incubated with normal human serum ( NHS ) pretreated with various concentrations of InpA or its inactive mutant ( InpAC154A ) and surviving cells enumerated by colony counting . InpA was found to be able to destroy the bactericidal activity of human serum in a dose-dependent manner and rescued E . coli that are otherwise very sensitive to killing by NHS ( Figure 1C ) . Moreover , P . intermedia strains have been known to vary significantly in their ability to resist killing by NHS [13] , hence , various strains were investigated to see if there was a relationship between the serum resistance of a given strain and its InpA expression level . By Western blotting , P . intermedia strain 59 producing a large amount of InpA was found to have a 100% survival rate in 20% NHS while only 78% of the strain 57 with non-detectable InpA production survived ( Figure 1A and 1D ) . Furthermore , addition of a cysteine proteinase inhibitor E64 to NHS decreased the ability of P . intermedia strain 59 to survive while it did not affect the killing of strain 57 or E . coli ( Figure 1D ) . Taken together , the results obtained with both purified InpA and P . intermedia strains showed that InpA compromised the bactericidal activity of human serum . In order to understand in detail how InpA destroys the bactericidal activity of NHS , i . e . complement , the enzyme was incubated at various concentrations with human serum and hemolytic assays were used to assess activity of the classical and alternative pathways of complement in the pre-treated sera . InpA was found to be an efficient inhibitor of both pathways , whereas the inactive mutant InpAC154A did not show any inhibition ( Figure 2A and 2B ) . InpA was able to inhibit the classical pathway by 80% when present at high nanomolar concentrations ( 0 . 5 µM ) while the alternative pathway was inhibited by 80% at 1 . 5 µM concentration . It should be noted , however , that 10% serum was used for the alternative pathway hemolytic assay versus 1 . 25% for the classical pathway . These concentrations were chosen on a basis of the initial titration and represent conditions in which each assay was most sensitive . The alternative pathway is known to require high concentrations of serum in order to function properly in contrast to the classical pathway that is rapidly activated even at fractions of percent of NHS . Taken together , it appears that InpA is approximately equally able to destroy activity of both the classical and alternative pathways . Each complement pathway is composed of several factors activated in a consecutive manner . In order to assess which complement factor ( s ) were affected by InpA , a microtiter plate-based assay in which complement activation was initiated by various ligands depending on the pathway analyzed was used and the deposition of successive complement factors was then detected with specific antibodies . In the case of the classical pathway , complement activation was initiated by immunoglobulin deposition . We found that depositions of C1 and C4 from 2% serum were not affected by InpA ( Figure 3A and 3B ) . However , C3 was found to be sensitive to InpA and deposition of C3b from NHS was abolished at 2 µM InpA ( Figure 3C ) . The inactive InpAC154A mutant had no effect on activation and deposition of C3b at any concentration tested . Accordingly , deposition of C9 that appears in the cascade after C3 was inhibited at similar concentrations as C3 indicating that the inhibitory effect on deposition of C9 was due to degradation of C3 ( Figure 3D ) . For assessment of the lectin pathway , we used plates bound with mannan carbohydrate . In this case , InpA did not affect the binding of MBL , which is the initiator of the pathway ( Figure 4A ) and weakly inhibited deposition of C4b ( Figure 4B ) . However , similar to the classical pathway , InpA strongly inhibited the deposition of C3b and C9 while the InpAC154A mutant had no effect ( Figure 4C and 4D ) . The alternative pathway was activated by immobilized zymosan and InpA was found to be able to inhibit deposition of C3b and C9 with a similar efficiency as previously found for the other two pathways ( Figure 5A and 5B ) . Taken together , all three pathways were sensitive to InpA and its main target appeared to be C3 , which is the key protein for all pathways of the complement system . NHS contains several proteinase inhibitors that could potentially inhibit the activity of InpA . However , we found that the InpA activity measured with fluorogenic substrate was not affected by NHS when NHS was present at concentrations up to 30% ( Figure 5C ) . In order to assess the sites cleaved by InpA , in complement factors , purified C3 and structurally related C4 were incubated with InpA at various molar ratios . The proteins were then separated by SDS-PAGE and visualized using silver staining ( Figure 6A and 6B ) . C3 is composed of covalently linked α- and β-chains while C4 contains α- , β- and γ-chains . For both proteins , InpA first attacks the α-chain while the β-chain is relatively resistant ( Figure 6A–6D ) ; which is similar to what we have previously observed for gingipains [5] . The InpAC154A mutant did not cause any degradation of C3 or C4 ( Figure 6A and 6B ) . Interestingly , similar concentrations of InpA were required for the degradation of purified C3 and C4 , whereas in the presence of NHS , InpA preferentially inactivates C3 ( Figures 3 and 4 ) . To determine sites of proteolysis by InpA , C3 and C4 were treated with InpA and degradation products were separated by SDS-PAGE electrophoresis . The proteins were transferred to PVDF membrane , visualized with Coomassie ( Figure 6E ) and selected bands were subjected to N-terminal sequencing . Interestingly , cleavage of the C3 polypeptide chain at the site resulting in the N-terminal sequence SNLDEDIIA generated the exact sequence of an anaphylatoxin fragment C3a . Similarly , the cleavage of C4 producing the N-terminal sequence ALEILQEE generated the exact sequence of C4a . Sequence 2 ( SPMYSII ) corresponds to the N-terminus of the C3 β-chain . When degradation of C3 and C4 was assessed at a set concentration of InpA for increasing incubation times , C4 was degraded at a faster rate than C3 ( Figure 7A ) . To determine the kinetic parameters of degradation of C3b by InpA , surface plasmon resonance was employed . When the inactive InpAC154A proteinase was injected over immobilized C3b , no change in signal was detected ( data not shown ) . However , upon injection of InpA , there was a rapid decrease in the signal measured in resonance units ( RU ) corresponding to degradation of C3b . The initial rates of proteolysis at each concentration of InpA were obtained from the initial slopes in the sensorgrams ( Figure 7B ) . In this system , 1000 RU corresponds to a mass shift of 1 ng/mm2 . The analysis demonstrated that 3 µM InpA degrades C3b at an initial rate of 7 pg/s ( Figure 7B inset ) . The kinetic parameters of C3 and C4 degradation by InpA were also determined by fitting initial rates of degradation of α-chains of C3 and C4 into Michaelis-Menten equation . A constant amount of InpA was incubated with increasing concentrations of C3 and C4 and the initial rate of proteolysis at various substrate concentrations was estimated from the decrease of intensity of scanned bands corresponding to α-chains of C3 and C4 as resolved by SDS-PAGE . Using this approach , Km and kcat for C4 degradation was determined to be 4 . 3+/−0 . 8 µM and 0 . 026+/−0 . 005 s−1 , respectively ( Figure 7D ) . Unfortunately , a reasonably accurate measurement of the kinetic constants for C3 was not possible since there was no visible saturation of the initial rate of C3 degradation up to 2 mg/mL ( 10 µM ) of substrate , hence , the Km could only be estimated as greater than 20 µM ( Figure 7C ) . We have observed previously that gingipains did not degrade C1 but instead were able to cause C1 deposition on surfaces that would not normally activate C1 [5] . In order to assess if this was also the case for InpA , human serum was incubated with InpA in the absence of any immobilized C1 activator and we observed that it did cause deposition of C1q on the empty microtiter plates blocked with BSA ( Figure 8A ) . In the absence of InpA or in the presence of its inactive mutant , the deposition of C1q from serum was negligible as expected . In addition , InpA was also found to be able to cause deposition of C1q on bacterial surfaces . To this end , Prevotella nigrescens was incubated with NHS containing InpA at different concentrations and the deposition of C1q was measured using flow cytometry . We found that addition of InpA to NHS caused an increase in deposition of C1q on the surface of Prevotella that mimicked results obtained using microtiter plates ( Figure 8B ) . Taken together , our results show that InpA is able to cause deposition of active C1 complex on normally non-activating surfaces such as BSA coated plastic or bacteria . We did not observe degradation of C1q during incubation with InpA , neither when InpA was added to NHS nor when it was incubated with purified C1q ( data not shown ) . Since InpA and gingipains are often present simultaneously at the sites of infection colonized with P . intermedia and P . gingivalis , we assessed how they acted on complement when present together . To this end , InpA and the three gingipains ( HRgpA and RgpB are arginine-specific gingipains while Kgp is lysine-specific ) were pre-incubated with 4% NHS at concentrations chosen to affect the activity of the lectin pathway by only 10–30% . The deposition of C3b was assessed and we found that the proteinases acted synergistically since the deposition of C3b in combinations of InpA and the gingipains was lower than predicted if the effects of the proteinases were added separately ( Figure 9 ) . For example , InpA alone decreased the deposition of C3b by 30% at the concentration used , while Kgp yielded only 25% decrease . When used together at the same concentrations , InpA and Kgp decreased C3b deposition by 85% instead of 55% that would be expected if these proteinases had only additive effects . When all three gingipains were added together with InpA , the deposition of C3b was inhibited by 93% .
Factors governing P . intermedia infection are poorly studied when compared to other periodontal pathogens such as P . gingivalis . However , it is becoming apparent that all successful human bacterial pathogens must develop strategies to circumvent the complement system [15] . Microorganisms in gingival sulcus are immersed in serum-derived tissue exudate—gingival crevicular fluid , which is similar in composition to human serum . Since complement components are present in gingival crevicular fluid at up to 70% of serum concentration [27] and in vivo there is high level of complement activation in gingival fluid of patients with periodontitis [28] , [29] , successful evasion of the complement system is paramount for the survival of P . intermedia in the periodontal pocket . One such strategy of defense against complement developed by P . intermedia appears to depend on the production of InpA , which we now show , is able to degrade complement factor C3 , which is the central molecule of the whole complement system . Importantly , the majority of P . intermedia strains isolated from aggressive and chronic periodontitis carry and express the inpA gene . The proteolytic activities of oral bacteria are thought to play important roles in the etiology of periodontitis and dental abscesses . These proteinases may contribute to tissue destruction , increase availability of nutrients and impair host defense by degrading immunoglobulins and components of the complement system . Proteinases of P . intermedia display trypsin-like and dipeptidylpeptidase activities [30] and also have the properties of cysteine proteinases [31]–[33] . They have also been reported to be capable of degrading immunoglobulins , particularly IgG [34] , [35] , fibronectin [36] and host proteinase inhibitors [37] . The degradation of immunoglobulins was mediated mainly by cysteine proteinase ( s ) [35] . Now we can add C3 to this list . Importantly , inhibition of C3 function occurred even when InpA was incubated with whole NHS showing that C3 will be specifically degraded even in the presence of all other plasma proteins ( Figures 3–5 ) . This is not the case for C4 , which was degraded efficiently when purified proteins were used but its function was only weakly affected in the presence of whole serum . According to kinetic parameters determined with purified proteins , C4 should be a far better target for InpA than C3 in serum . Despite that C4 in serum seems to be resistant to proteolytic inactivation by InpA . To explain this discrepancy , we speculate that C4 may interact with other protein ( s ) in serum , which hinders InpA access to a cleavage site . Alternatively , the α-chain of C4 may also be susceptible to proteolysis in serum but the cleaved protein is still a functional source of C4b . The latter explanation is supported by the observation that in contrast to C3 , proteolysis of C4 is more limited ( Figure 6 ) . Such phenomenon has previously been observed for α2-macroglobulin , which remained functionally active after cleavage with gingipains [38] . Importantly , it is clear that InpA will affect C3 in a way that it can no longer propagate the complement cascades; which should be of direct benefit to InpA producing P . intermedia . Interestingly , InpA showed a preference for the α-chain of C3 and C4 , similar to what we have previously observed for gingipains . At low concentrations , gingipains were able to activate complement factors C3 , C4 and C5 as they preferentially target the α-chains of these proteins to cause the release of anaphylatoxins C3a and C5a as well as the activated forms C3b , C4b and C5b . Similarly , N-terminal sequencing of C3 and C4 fragments generated by InpA revealed that InpA will also release C3a and C4a . At higher concentrations , gingipains simply degrade these three complement factors , particularly C3 , into smaller fragments so that they can no longer propagate the complement cascade [19] , [39] . Yet again , we observe a similar phenomenon for InpA in case of C3 . Also similar to gingipains , InpA was able to cause the deposition of C1 from serum onto inert surfaces without the need for a specific C1 activator; which may lead to local inflammation . However , whereas this effect could be recreated in vitro using purified C1 for gingipains [5] , InpA required serum to be present for this to occur ( data not shown ) . Thus , it appears that InpA may require a third protein to induce C1 deposition from serum . Consequently , an intricate strategy emerges: periodontal bacteria at low concentrations appear to cause non-specific activation of C1 and to generate C5a and C3a fragments—chemotactic factors for neutrophils . This may lead to a low grade inflammation that provides access to nutrients for bacterial growth and colonization . At higher concentrations of bacteria and proteinases , the complement system becomes incapacitated by multiple cleavages of critical proteins within the cascade . P . intermedia can be highly resistant to complement and survive at very high serum concentrations but there are significant differences between various strains with regard to sensitivity to killing by complement [13] . In this study , we have shown that there is a correlation between the presence of InpA and serum resistance of P . intermedia . Using E . coli as a sensitive model to detect bactericidal activity of human serum , we have found that they were able to survive when supplemented with low micromolar concentrations of InpA in the presence of 2% NHS . In contrast , cells exposed to NHS alone or to NHS containing the inactive interpain mutant showed total loss of viability at this serum concentration . This clearly shows that purified InpA is very efficient at destroying bactericidal activity of NHS . Further , the cysteine proteinase inhibitor E64 diminished serum resistance of P . intermedia strains . It is plausible that P . intermedia , in similarity to other bacterial pathogens , has several strategies for evasion of killing by complement . P . gingivalis employs not only proteinases for defense from complement [5] but it also produces a surface anionic polysaccharide , the presence of which strongly correlates with exceptional serum resistance of these bacteria [40] . This bacterium also attenuates the effects of complement by capturing human complement inhibitor C4b-binding protein [16] . In this study , we have found that P . intermedia was able to retain some of its ability to resist killing even when incubated with serum containing the broad-spectrum inhibitor E64 . However , InpA is a secreted protein and we do not expect large amounts of it being present in our bactericidal assay that has been performed within 1 . 5 h of culturing . In vivo , the bacteria will have the opportunity to secrete much more interpain into its pericellular environment . Our current methodology does not allow for truly quantitative analysis of the InpA content in gingival crevicular fluid . However , we can estimate from our Western blotting analysis that 20 µL of crevicular fluid contained at least 0 . 1 µg of InpA . Taking into account at least 20-fold dilution of crevicular fluid upon collection , the concentration of InpA in the two positive samples analyzed must be greater than 100 µg/mL . This corresponds to approximately 4 µM of fully processed InpA , implying that the concentration of InpA is high enough for inhibition of the complement system as described to occur in vivo . Our experiments also showed that InpA will aid survival of bystander bacterial species , thus , creating a favorable condition for the establishment of a common ecosystem that would be a beneficial habitat for all participating species . P . intermedia , together with Streptococcus gordonii may be considered to be the early colonizers of tooth surfaces , thereby promoting secondary colonization of pathogenic organisms such as P . gingivalis by providing attachment sites , growth substrates and reduced oxygen concentration locally [41] , [42] . P . intermedia belongs to the “orange complex” , which encompasses bacterial species bridging between healthy state and advanced periodontitis . Thus , degradation of C3 by InpA in synergy with gingipains of P . gingivalis will complement the host immune evasion strategy of subgingival microbiota . Importantly , Prevotella species readily acquire resistance towards antibiotics [43] and deeper knowledge of how infection and serum resistance occur will be crucial for the development of alternative treatments to periodontal disease .
Purified complement proteins were purchased from Complement Technology . InpA as well as its inactive mutant InpAC154A ( the catalytic cysteine was replaced by alanine ) were expressed as His-tagged recombinant proteins in Escherichia coli and purified by affinity chromatography on Fast Flow Ni-NTA Sepharose ( Qiagen ) followed by anion exchange chromatography ( MonoQ , GE Healthcare ) as described previously [25] . The amount of active enzyme in wild-type InpA preparation was determined by active site titration using inhibitor E64 ( Sigma ) . Briefly , recombinant protein was activated at 37°C for 15 min in 0 . 1 M Tris-HCl , 5 mM EDTA , pH 7 . 5 freshly supplemented with 2 mM DTT and then preincubated with increasing concentrations of E64 for 37 min at room temperature . Residual enzyme activity was determined by measurement of fluorescence ( λex = 380 nm and λem = 460 nm ) of AMC released from Boc-Val-Leu-Lys-AMC ( PeptaNova ) added to the reaction mixture at 250 µM final concentration and using the microplate spectrofluorimeter SpectraMax Gemini EM ( Molecular Devices ) . The concentration of active InpA was calculated from the amount of inhibitor needed for total inactivation of the proteinase . The final preparations of wild type InpA and InpAC154A were assayed for possible contamination with lipopolysaccharide using Limulus test ( Hycult Biotechnology ) and found to contain 7 and 1 ng/mL lipopolysaccharide , respectively . Arginine-specific ( HRgpA and RgpB ) and lysine-specific ( Kgp ) gingipains were purified from the P . gingivalis HG66 strain culture fluid as described previously [5] . Before using in any assay , InpA and InpAC154A were preactivated for 15 min by incubation in a buffer specific for the particular assay supplemented with 2 mM DTT . InpA was activated by 15 min incubation in 0 . 1 M Tris·HCl , pH 7 . 6 , 5 mM EDTA , 2 mM DTT at 37°C . InpA was mixed with increasing concentrations of NHS and incubated for 30 min at 37°C . Control samples without serum and with E64 were prepared simultaneously . After incubation , the substrate Boc-Val-Leu-Lys-AMC was added to all samples , rendering final volume 200 µL and final concentrations of 16 . 8 nM InpA , 0–30% NHS , 100 µM E64 and 5% DMSO . Substrate hydrolysis was monitored as AMC release . Activity was determined as the initial velocity of the reaction and expressed in relative fluorescence units ( RFU ) /s . Results from triplicates were plotted using GraphPad Prism software and calculated as relative activity compared to an uninhibited control . For detection of P . intermedia in clinical samples , subgingival plaque samples were obtained from patients with severe periodontitis ( aggressive periodontitis ( n = 24 ) , chronic periodontitis ( n = 58 ) ) . Two paper points were inserted in each pocket for 20 s and DNA was subsequently extracted using the Genomic Mini system ( A&A Biotechnology ) according to the manufacturer's recommendations . PCR was carried out using primers: Pi-1: TTT GTT GGG GAG TAA AGC GGG and Pi-2: TCA ACA TCT CTG TAT CCT GCG T [44] . Presence of the inpA gene was determined using PCR with the following primers that were designed based on Oral Pathogen Sequence Database ( gene pPI0032; http://www . oralgen . lanl . gov ) : pPI-1: GAA GGA CAA CTA CAG CGG AAA; pPI-2: TCC TTT CGT TAG TTC GCT GA . Some of the samples were cultivated on Schaedler agar and Schaedler agar supplemented with 7 . 5 mg/L vancomycin . Colonies typical for P . intermedia were then subcultivated yielding strains 57 , 59 , 120 , 106 , BGH10 , BGH30 , H13 and their identification was confirmed by PCR exactly as described previously [45] . P . intermedia OMZ 248 [46] , was kindly provided by Dr . Frandsen ( Department of Oral Biology , Royal Dental College , Faculty of Health Sciences , University of Aarhus , Denmark ) . For the experiments conducted in this study , all P . intermedia strains were grown on blood-enriched tryptic soy broth ( TSB ) agar plates at 37°C in an anaerobic chamber ( Concept 400 , Biotrace ) with an atmosphere of 90% N2 , 5% CO2 and 5% H2 . Escherichia coli laboratory strain DH5α ( Invitrogen ) and Escherichia coli clinical strain were grown on standard Luria-Bertani ( LB ) agar plates or in LB broth . Prevotella nigrescens ( ATCC 25261 ) was grown on BBL Columbia II agar containing 8 . 5% horse blood , 0 . 04% L-cysteine HCl , 5 mg/mL hemin and 2 mg/mL vitamin K1 . Bacterial strains used in this study are listed in Table 2 . Crevicular washes were obtained using a previously described method from 4 patients with chronic periodontitis . For analysis of P . intermedia presence , DNA was extracted from 5 µL of crevicular fluid using the High Pure PCR Template Preparation Kit ( Roche ) according to the manufacturer's recommendations . Real-time PCR was carried out using a RotorGene 2000 ( Corbett Research ) . Primers specific for 16S rDNA from P . intermedia were designed as described by [44] . PCR amplification was carried out as described earlier [47] . Determination of InpA in gingival crevicular fluid samples was performed by Western blotting analysis using rabbit polyclonal Ab against 40 kDa ( without C-terminal profragment ) form of InpA raised in rabbits by standard immunization with purified recombinant InpAC154A . Strain E . coli DH5α was cultured in LB broth until exponential growth phase . Cells were harvested , washed once in GVB++ ( 5 mM veronal buffer pH 7 . 3 , 140 mM NaCl , 0 . 1% gelatin , 1 mM MgCl2 and 0 . 15 mM CaCl2 ) and adjusted to an optical density at 600 nm of 0 . 5 . NHS was prepared from blood taken from six healthy volunteers and pooled . NHS was diluted in GVB++ to a concentration of 2% and incubated with various concentrations of preactivated InpA or InpAC154A for 15 min at RT . Thereafter , 104 bacteria cells were added and incubated with serum supplemented with InpA for 20 min at 37°C in a total volume of 60 µl . After incubation , aliquots were removed , diluted serially and spread onto LB agar plates . Heat inactivated serum ( 56°C , 30 min ) was used as a negative control . Plates were incubated for 12 h in 37°C after which colonies were counted and percentages of the surviving bacteria were calculated . P . intermedia from four-day old agar plate culture were harvested and washed once in GVB++ and adjusted to an optical density at 600 nm of 0 . 6 . Thereafter , 2×104 bacteria were mixed with 20% NHS diluted in GVB++ and incubated anaerobically for 1 . 5 h at 37°C in total volume of 110 µl . The aliquots were removed , diluted serially and spread onto TSB plates . Plates were incubated for 4 days at 37°C in an anaerobic chamber after which colonies were counted and percentages of the surviving bacteria were calculated . E . coli were treated in a similar manner except for that 40% NHS was used . All incubations were performed aerobically and the bacteria were spread on LB agar plates for counting colonies after overnight incubation . P . intermedia strains OMZ 248 , 59 , 57 , 120 , 106 , BGH 10 , BGH 30 , H13 and ATCC 25611 were cultured in the Schaedler liquid medium at 37°C in an anaerobic chamber for 5 days . Aliquots of cell culture media adjusted to an optical density at 600 nm of 2 . 0 were separated under reducing conditions by SDS-PAGE electrophoresis using 12% gel . The proteins were transferred onto PVDF membrane using semi-dry blotting system . After blocking with 50 mM Tris-HCl , 150 mM NaCl , 2 mM CaCl2 , 0 . 1% Tween 20 and 3% fish gelatin , pH 8 . 0 , InpA was visualized using an anti-InpA polyclonal antibody ( 1∶500 dilution ) followed by goat anti-rabbit Abs conjugated to HRP and developed using enhanced chemiluminescence ( ECL ) . The signals were collected using CCD camera ( LAS3000 , Fujifilm ) . To assess activity of the classical pathway , sheep erythrocytes were washed three times with DGVB++ buffer ( 2 . 5 mM veronal buffer pH 7 . 3 , 70 mM NaCl , 140 mM glucose , 0 . 1% gelatin , 1 mM MgCl2 and 0 . 15 mM CaCl2 ) . The cells were incubated with a complement-fixing antibody ( amboceptor; Boehringverke; diluted 1∶3000 in DGVB++ buffer ) at a concentration of 109 cells/mL for 20 min at 37°C . After two washes with DGVB++ , 5×108 cells/mL were incubated for 1 h at 37°C with 1 . 25% NHS diluted in DGVB++ buffer ( total volume 200 µl ) . Before incubation with erythrocytes , NHS was pre-incubated with various concentrations of preactivated InpA or InpAC154A for 15 min at RT . The buffer used for activation of InpA did not interfere with the hemolytic assay or erythrocytes ( data not shown ) . The samples were centrifuged and the amount of lysed erythrocytes was determined by spectrophotometric measurement of the amount of released hemoglobin ( 405 nm ) . To assess activity of the alternative pathway , rabbit erythrocytes were washed three times with Mg++EGTA buffer ( 2 . 5 mM veronal buffer , containing 70 mM NaCl , 140 mM glucose , 0 . 1% gelatin , 7 mM MgCl2 , 10 mM EGTA , pH 7 . 3 ) . Erythrocytes at a concentration of 5×108 cells/mL were then incubated for 1 . 5 h at 37°C with 10% NHS diluted in Mg++ EGTA buffer ( total volume 200 µl ) . NHS used was pre-treated with various concentrations of preactivated InpA or InpAC154A for 15 min at RT . The samples were centrifuged and the amount of lysed erythrocytes was determined spectrophotometrically . Microtiter plates ( Maxisorp; Nunc ) were incubated overnight at 4°C with 50 µl of a solution containing 2 µg/mL human aggregated IgG ( Immuno ) , 100 µg/mL mannan ( Sigma , M-7504 ) or 20 µg/mL zymosan ( Sigma , Z-4250 ) in 75 mM sodium carbonate ( pH 9 . 6 ) . Between each step of the procedure , the plates were washed four times with 50 mM Tris-HCl , 150 mM NaCl , and 0 . 1% Tween 20 ( pH 7 . 5 ) . The wells were blocked with 1% BSA ( Sigma ) in PBS for 2 h at RT . NHS was diluted in GVB++ buffer and used at a concentration of 2% for C3b , C4b , C1q ( classical pathway ) , 4% for C3b , C4b , MBL ( lectin pathway ) , 6% for C3 ( alternative pathway ) and 10% for C9 ( all three pathways ) . These concentrations were chosen on the basis of initial titrations . NHS was mixed with various concentrations of preactivated InpA or InpAC154A and incubated in the wells of microtiter plates for 45 min at 37°C for C9 and MBL and 20 min at 37°C for C3b and C4b in case of the alternative and the lectin pathways . For the classical pathway , NHS was incubated with preactivated InpA or InpAC154A for 15 min at RT in eppendorf tubes and the enzyme was inhibited by addition of 20 µM E-64 ( Calbiochem ) to avoid degradation of IgM deposited on plates . Immediately after addition of inhibitor , NHS was incubated in microtiter plates for 45 min at 37°C for C9 and C1q and 20 min at 37°C for C3b and C4b . The inhibitor itself did not affect activation of complement at the concentration used ( data not shown ) . Complement activation was assessed by detecting deposited complement factors using rabbit anti-C1q , anti-C4b , anti-C3d polyclonal antibodies ( pAbs , DakoCytomation ) goat anti-C9 pAb ( Complement Technology ) and goat anti-MBL ( R&D ) diluted in the blocking buffer . Bound antibodies were detected with HRP-labeled anti-rabbit or anti-goat secondary pAb ( DakoCytomation ) . Bound HRP-labelled pAb were detected with 1 , 2-phenylenediamine dihydrochloride ( OPD ) -tablets ( DakoCytomation ) and the absorbance was measured at 490 nm . To assess deposition of purified C1q on microtiter plates without any complement activator , plates were blocked with 1% BSA in PBS for 2 h at RT . NHS was diluted in GVB++ buffer to 4% and mixed with various concentration of interpain A . Plates were incubated for 45 min at 37°C with shaking and the deposited C1q was detected with specific antibodies . P . nigrescens ATCC 25261 from two-day old agar plate cultures were harvested , washed twice in GVB++ buffer and adjusted to an optical density at 600 nm of 1 . 0 . NHS was diluted in GVB++ to a concentration of 5% , mixed with 6×105 cells and incubated with various concentrations of preactivated InpA or InpAC154A for 30 min at 37°C . Thereafter , the cells were washed twice in the binding buffer ( 10 mM HEPES , 140 mM NaCl , 5 mM KCl , 1 mM MgCl2 , 2 mM CaCl2 , pH 7 . 2 ) . C1q deposition was assessed by incubation of the cells with rabbit anti-human C1q FITC-conjugated polyclonal antibodies ( DakoCytomation , diluted in the binding buffer 1∶100 ) for 1 h . The cells were washed twice in the binding buffer and finally resuspended in flow cytometry buffer ( 50 mM HEPES , 100 mM NaCl , 30 mM NaN3 , 1% BSA; pH 7 . 4 ) . Flow cytometry analysis was performed using FACS Calibur ( Beckton Dickinson ) . C4 and C3 ( 0 . 8 µM each ) were incubated with InpA at concentrations ranging from 50 nM to 1250 nM . Incubations were carried out in 0 . 2 M Tris-HCl , pH 7 . 4 , containing 0 . 1 M NaCl , 5 mM CaCl2 and 2 mM DTT for 30 min at 37°C . For the time course experiment , C4 and C3 ( 0 . 8 µM each ) were incubated with 640 nM InpA for 5 , 10 , 20 , 30 , 45 , 60 and 75 min . The proteins were separated by SDS-PAGE electrophoresis using standard Laemmli procedure and 12% gels . Prior to electrophoresis the samples were boiled for 5 min at 95°C in a sample loading buffer containing 25 mM DTT and 4% SDS . After separation , the gels were stained with silver salts to visualize the separated proteins and quantified by densitometry using ImageGauge ( FujiFilm , Tokyo , Japan ) . To determine sites of cleavage by InpA , 10 µg of C3 and C4 were incubated with 500 nM preactivated InpA for 2 h at 37°C and the proteins were separated by 12% SDS-PAGE under reducing condition . The proteins were then transferred to PVDF membranes ( Pall ) and stained using Coomassie Blue . Bands of interest were excised and analyzed by automated Edman degradation in an Applied Biosystems PROCISE 494 HT sequencer with on-line phenylthiohydantion HPLC analysis using a 140 C Microgradient System from Applied Biosystems , operated according to the manufacturer's recommendations . The analysis was performed according to a previously published protocol [48] . Human C3b was diluted in 10 mM Na-acetate pH 4 . 0 to a concentration of 30 µg/mL and immobilized on chip CM5 to a level of 3000 RU using amino coupling kit ( Biacore ) and Biacore 2000 . Interpain A was pre-activated by 15 min activation at 37°C in the running buffer ( 10 mM HEPES , 150 mM NaCl , 1 mM MgCl2 , 0 . 15 mM CaCl2 , 0 . 005% Tween 20 , 0 . 2 mM DTT; pH 7 . 4 ) with 2 mM DTT and diluted in the running buffer in a concentration range 0 . 25–6 µM . Interpain A was then injected at the flow rate of 5 µl/min at 37°C over the immobilized C3met and its activity was quantified as decrease in RU on the sensorgram and analyzed using Biaevaluation software ( Biacore ) . Several concentrations of C3 ( 1 . 2–7 . 2 µM ) and C4 ( 0 . 2–4 . 8 µM ) diluted in DGVB++ were incubated with 110 nM or 40 nM of preactivated InpA , respectively . The incubation time was 4 h and 20 min for C3 and C4 , respectively . In parallel , the same concentrations of C3 and C4 were incubated without enzyme . Proteins were separated under reducing conditions by SDS PAGE using 12% gel , stained with Coomassie and the gels were scanned followed by densitometry determination of α-chains of C3 and C4 ( ImageGauge ) . Intensity of α-chain bands in the presence of InpA was compared to corresponding controls and expressed as the amount of substrate remaining . Initial velocity of the reaction at each concentration was calculated as amount of substrate consumed within one second and fitted by nonlinear regression into the Michaelis-Menten equation V = ( kcat*[E]t*[S] ) / ( [S]+Km ) using GraphPad Prism . Values Km and kcat were obtained as regression curve parameters . Similar values were obtained from two independent experiments . The ethical board of Lund University has approved collection of sera from healthy human volunteers . The ethical committee of Jena University approved collection of periodontal plaques and crevicular fluid . Informed consent was obtained from patients and the investigation was performed according to principles of the Declaration of Helsinki . Student's t-test was used to calculate the p values in order to estimate if the observed differences between experimental results were statistically significant . | Prevotella intermedia is one of the bacterial pathogens that has been implicated in causing periodontitis—an endemic inflammatory disease of the supporting structures of the teeth . The complement system is an important part of host innate immunity and is able to directly kill invading bacteria . To become successful pathogens , many strains of P . intermedia developed mechanisms making them very resistant to killing by complement . We found that a cysteine protease , interpain A , that is produced by many clinical strains of P . intermedia was able to destroy the bacterial killing activity of human serum . A strain of P . intermedia that produces interpain A was found to be more resistant to complement than the one lacking interpain A , and the resistance of the interpain A–producing strain could be diminished by a specific inhibitor of cysteine proteases . We attributed the protective effect of interpain A to its ability to inhibit the complement system through the efficient degradation of C3—a major complement protein that is common to all three pathways of complement activation . Understanding the mechanism governing pathogen resistance to complement may help us to design novel therapeutic strategies to prevent or treat an important bacterial disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/bacterial",
"infections",
"immunology/innate",
"immunity"
] | 2009 | Interpain A, a Cysteine Proteinase from Prevotella intermedia, Inhibits Complement by Degrading Complement Factor C3 |
Palmitoylation involves the reversible posttranslational addition of palmitate to cysteines and promotes membrane binding and subcellular localization . Recent advancements in the detection and identification of palmitoylated proteins have led to multiple palmitoylation proteomics studies but these datasets are contained within large supplemental tables , making downstream analysis and data mining time-consuming and difficult . Consequently , we curated the data from 15 palmitoylation proteomics studies into one compendium containing 1 , 838 genes encoding palmitoylated proteins; representing approximately 10% of the genome . Enrichment analysis revealed highly significant enrichments for Gene Ontology biological processes , pathway maps , and process networks related to the nervous system . Strikingly , 41% of synaptic genes encode a palmitoylated protein in the compendium . The top disease associations included cancers and diseases and disorders of the nervous system , with Schizophrenia , HD , and pancreatic ductal carcinoma among the top five , suggesting that aberrant palmitoylation may play a pivotal role in the balance of cell death and survival . This compendium provides a much-needed resource for cell biologists and the palmitoylation field , providing new perspectives for cancer and neurodegeneration .
S-Acylation ( commonly referred to as palmitoylation ) involves the reversible post-translational addition of long-chain fatty acids , typically palmitate , to cysteine residues of both peripheral and integral membrane proteins by palmitoyl acyltransferases ( PATs; Fig 1A ) [1 , 2] . Palmitoylation increases the hydrophobicity of a protein and thereby promotes membrane binding , regulates subcellular localization and protein stability , induces tilting of transmembrane domains , and modulates protein-protein interactions [3] . While the fatty acid moiety is typically associated with membrane association , palmitoylation has also been shown to regulate the active cysteines of enzymes [4] . In mammals , palmitoylation is mediated by 23 DHHC-domain containing PATs [5–8] . While palmitoylation can be highly dynamic in some proteins due to its reversibility , many proteins have been found to be stably palmitoylated and retain their palmitate . Dynamic depalmitoylation is mediated by acyl protein thioesterases in the cytosol [9 , 10] . Therefore , the reversible nature of palmitoylation , which is analogous to that of phosphorylation , can add another layer of regulation to promote “on/off” states of membrane association or activity . Alterations in PAT activity or palmitoylation of specific proteins have been implicated in a number of diseases , including cancer [11–15] , diabetes [16 , 17] , Schizophrenia [18–20] , X-linked mental retardation [21 , 22] , and neurodegeneration , including Alzheimer disease [23–25] , Huntington disease [8 , 26] , and Amyotrophic Lateral Sclerosis ( AD , HD , and ALS , respectively ) [27 , 28] . Palmitoylated proteins previously implicated in neurodegeneration include APP [25] , BACE1 , APH1 , nicastrin , HTT [29] , and SOD1 [27 , 28] . Recently , a number of studies have focused on determining the “palmitoylome” in diverse cell types to determine the role of palmitoylation in various processes including cancer , immunity , and synaptic function . Sixteen mammalian palmitoylation proteomics studies have been described to date in rat , mouse , and human cells , including endothelial , immune , and neuronal cells , as well as mouse brain tissue ( Table 1 ) . Three assays were used to detect palmitoylation in these studies: acyl-biotin exchange ( ABE ) [30 , 31] , acyl resin-assisted capture ( Acyl-RAC ) [32] , and bioorthogonal labeling assays [33 , 34] ( Fig 1B and 1C ) . The two former assays exploit the reversibility of palmitoylation and the reactivity of cysteines to replace the palmitate moiety with biotin for affinity purification and mass spectrometry ( MS ) analysis . The latter assay uses long-chain fatty acid analogs , similar to radioactive labeling with iodinated or tritiated palmitate that , can be chemically ligated to biotin . The majority of the data generated by these studies have been overlooked since they are contained within large supplemental tables where proteins are described with different types of identifiers , making downstream analyses and data mining of the combination of datasets time consuming and inaccessible to many researchers without bioinformatics expertise . Searching the supplemental data of these studies to determine if a protein of interest may be palmitoylated is a tedious and time consuming task . Therefore , we curated the palmitoylated proteins identified in these proteomics studies into a consolidated non-redundant searchable list . This compendium provides a valuable resource for those working in the field of palmitoylation and for the wider research community that may be interested in the post-translational regulation of a given protein of interest . In addition , enrichment analysis of this compendium provides the first unbiased approach to understanding the role of palmitoylation in cell biology and disease .
15 mammalian proteomic studies ( Table 1 ) were compiled into a single compendium , in which palmitoylated proteins were identified in one of three species ( human , mouse , and rat; S1 Table ) . The consolidated mammalian palmitoylation compendium , or palmitoylome , comprises 1 , 838 genes ( S1 Table ) . Strikingly , this revealed that nearly 10% of the genes in the genome encode a proteoform that is palmitoylated in human , mouse , or rat , which is much greater than previously predicted or revealed by any individual palmitoyl proteomics study [14 , 27 , 32 , 33 , 35–45] . In order to determine if there was any bias towards a particular method or biological sample used in the studies included in the compendium , a hierarchical clustering of the 15 palmitoylomes ( Fig 2A ) was performed using the pvclust package in the statistical software program R [46] . This revealed that there was no apparent clustering based on biological sample or method used , suggesting no technical or biological biases in the data . Indeed , the only statistically significant cluster by resampling involved studies from Martin et al . 2012 and Li et al . 2012 ( bootstrap value = 1 . 0 ) , which is not surprising as these proteomics experiments were published in the same year from the same laboratory [41 , 42] . We then investigated the statistical significance of enrichments of GO biological processes [47] , metabolic and process networks , pathway maps , and disease-associations in the mammalian palmitoylome ( S2–S10 Tables ) . Protein annotations were considered significantly enriched at a false discovery rate ( FDR; multiple hypothesis testing corrected p-value ) below 0 . 001 and a fold-enrichment ( FE; the ratio of the proportion of palmitoylated proteins with a given annotation over the proportion of proteins with the annotation in the background dataset ) greater or equal to 2 . Not surprisingly , the top 15 enriched GO biological process annotations were primarily related to protein localization and trafficking ( Fig 3 and S10 Table ) . Enrichment in localization confirms the validity of this approach . Palmitoylation also appeared to be enriched with proteins involved in cell metabolism , which is surprising , as only a small number of proteins involved in metabolism have been shown to be palmitoylated . Interestingly , 36% ( 662 ) of the proteins in the compendium are annotated in UniProt as transmembrane proteins whereas only 6% are annotated as peripheral membrane proteins [48] . This suggests that for a large portion of proteins , palmitoylation may play an alternate role other than simply targeting to membranes , such as regulating trafficking , or modulating protein confirmation , protein-protein interactions , or function . The enrichment analyses also revealed a potential role for palmitoylation in the nervous system , particularly at the synapse . The top MetaCore pathway map annotation was “synaptic vesicle fusion and recycling in nerve terminals” ( FDR = 3 . 11x10-6 and FE = 5 . 83 , S8 Table and Fig 4A ) suggesting an important role for palmitoylation in neurophysiological processes at the synapse . To investigate further whether palmitoylation is indeed enriched at the synapse , the palmitoylome was compared to the SynSysNet list of 1 , 028 manually annotated list of genes encoding a synaptic protein [49] . There was a highly significant enrichment of palmitoylated genes in the synaptic gene list versus background ( p-value ( p ) = 2 . 22x10-16; 95% CI = 5 . 22–6 . 85 fold; Fig 4B ) , with 419 of the 1 , 028 ( 41%; S11 Table ) synaptic genes found in the palmitoylation compendium . Overall , this suggests that palmitoylation may play an important role at the synapse and that dysregulation of palmitoylation at the synapse may have detrimental effects . To confirm that the enrichment of palmitoylated proteins in the synaptic gene list and of synaptic proteins in the palmitoylome is not due to a large portion of the compendium being from a neuronal source , the overlap between those proteins identified from neuronal sources was compared to those identified in non-neuronal sources ( Fig 4B ) . More than 75% ( 1 , 386 ) of the proteins in the compendium were identified in a non-neuronal source with 52% being identified only in a non-neuronal source ( 966 ) . Only 25% ( 452 ) of the genes in the compendium were identified only in a neuronal study . Finally , the enrichment analysis revealed that a large number of MetaCore biomarker-based disease annotations were significantly associated with nervous system diseases , such as “Schizophrenia” ( FDR = 6 . 39x10-28 and FE = 2 . 61 ) , “Huntington disease” ( FDR = 6 . 09x10-8 and FE = 2 . 46 ) , and “Amyotrophic Lateral Sclerosis” ( FDR = 9 . 59x10-7 and FE = 2 . 57 ) . Cancer annotations , such as “Pancreatic ductal carcinoma” ( FDR = 4 . 37x10-9 and FE = 2 . 12 ) and “Neuroblastoma” ( FDR = 1 . 47x10-8 and FE = 2 . 00 ) were also enriched ( Fig 5A ) . When all significantly associated MetaCore disease annotations were broadly classified as diseases of the nervous system , cancers , infections , anemias , gastrointestinal diseases , or other , 14 of the 40 significantly associated MetaCore disease annotations were disease of the nervous system and 14 were cancers ( 35% each ) ( Fig 5B and S7 Table ) . The enrichment analyses revealed a potential role for palmitoylation in the nervous system , particularly at the synapse , and in diseases and disorders of the nervous system . Thus we sought to determine whether there were any disease-causing mutations of known or putatively palmitoylated cysteines in genes that cause diseases and disorders of the nervous system . Of the palmitoylated proteins associated with disease phenotypes , superoxide dismutase 1 ( SOD1 ) , commonly mutated in hereditary ALS [50] , was detected in two studies ( S1 Table ) and is known to be palmitoylated at cysteine 6 [27 , 28] . A dominant missense mutation at cysteine 6 of SOD1 was associated with rapid progression in a family with ALS and resulted in a 75% loss of SOD1 activity [51] , suggesting that loss of SOD1 palmitoylation may be detrimental ( Table 2 ) . Niemann-Pick C1 ( NPC1 ) was also found to be palmitoylated in four studies ( S1 Table ) . Mutations in NPC1 cause Niemann-Pick disease type C , in which progressive neurological symptoms , including dementia , dystonia , and ataxia , are hallmarks [52] . Two disease-causing mutations in NPC1 involve substitutions of cysteine residues within a di-cysteine motif [53] ( Table 2 ) . Such motifs are often palmitoylated [54] . A number of other disease-causing mutations in NPC1 involve other cysteine residues [53] that are predicted to be palmitoylated ( Table 2 ) . Of note , NPC1 has also been implicated in AD [52] . Finally , leucine-rich glioma-inactivated protein 1 ( LGI1 ) was detected as palmitoylated in one study . LGI1 cysteine mutations have been associated with autosomal dominant lateral temporal lobe epilepsy [55] . These cysteines are located in a cysteine-rich region likely to contain palmitoylation sites ( Table 2 ) [54] .
Herein we present a compendium of palmitoylated proteins curated from 15 previously published proteomics studies aimed at identifying palmitoylated proteins . This compendium is the first curated list of palmitoylated proteins and thus fills the need for such a resource not only in the palmitoylation field but also for the wider research community that may be interested in the post-translational regulation of a given protein . This resource makes proteomics data accessible to researchers attempting to determine if their protein of interest may be palmitoylated . The functional enrichment analysis of the palmitoylated proteins in the compendium is particularly important as it reveals that palmitoylation plays a greater role than previously thought in the nervous system , particularly at the synapse , and in diseases and disorders of the nervous system . This is the first time that the enrichment of palmitoylation in the nervous system has been shown using an unbiased approach . In addition , the significant enrichment of palmitoylated proteins in the synaptic proteome demonstrates that the highly dynamic characteristic of palmitoylation is likely an important regulator of the synapse , where rapid signaling at the membrane is required . Of particular note , the compendium revealed that 1 , 838 human genes , or approximately 10% of human genes , encode a proteoform that is palmitoylated . This proportion was surprisingly high as the individual proteomics studies suggested that palmitoylation is much less common as approximately 200–500 proteins were identified in any given study [14 , 27 , 32 , 33 , 35–45] . The high percentage of genes encoding palmitoylated proteins , in light of the reversibility of this post-translational modification , suggests that palmitoylation acts similarly to phosphorylation for regulation of protein function and localization for a large number of proteins . Like phosphorylation and kinases , this may explain why alterations in palmitoylation are implicated in certain diseases where the regulation of palmitoylation is altered resulting in mislocalization of proteins . For example , alterations in the dynamic nature of RAS palmitoylation and its membrane localization have been implicated in cancers and targeting RAS palmitoylation has been suggested as a potential chemotherapeutic approach [56 , 57] . This list of 1 , 838 genes is likely an under-ascertainment , as some proteins may not be fully solubilized during cell lysis and many proteins are not amenable to the repeated protein precipitation/purification steps required for these proteomics studies . Proteins that are of low abundance to begin with or that contain no proteotypic peptides , i . e . peptides that are likely to be detected by MS to identify a protein , would also be difficult to ascertain in any one of these proteomics studies . Additionally , proteins whose palmitoylated proteoforms make up only a very small portion of the total protein population may not be easy to detect in these types of studies . For example , Wan et al . identified glutathione synthase ( GS ) and carbonic anhydrase II ( CAII ) as palmitoylated by MS and confirmed that they are palmitoylated using low throughput methods but showed that less than 10% of the protein population of each was palmitoylated [40] . These two proteins were only identified in this one study . Also , some proteins may have an isoform that is expressed in one specific tissue or cell type and not in any other , such as CDC42 , which is palmitoylated in the brain and prenylated in other tissues [45] . The reversible nature of palmitoylation may also make it difficult to detect some proteins in one individual study . The power of this meta-analysis comes from the curation of data from many sources into a single list . The three methods used in these studies to detect palmitoylation ( Fig 1 ) have their various strengths and weaknesses . The bioorthogonal labeling methods are very sensitive but only detect those proteins that are palmitoylated during the limited metabolic labeling period . Thus they detect palmitoylation of proteins that are dynamically palmitoylated or are newly synthesized and palmitoylated during the metabolic labeling time but they do not detect palmitoylation of proteins that are stably palmitoylated and have long half-lives . False positives can arise from the incorporation of the lipid analogue into other lipid modifications other than S-acylation , such as N-palmitoylation , O-palmitoylation , and N-myristoylation , particularly following β-oxidation of the lipid analogue with longer labeling periods . In contrast , the ABE and Acyl-RAC assays detect proteins that are stably palmitoylated and have long half-lives , as they assay the entire population of palmitoylated proteins at a given time . However , the ABE and Acyl-RAC assays are more prone to false positives as hydrolysis of the thioester bond of other cysteine modifications , such as nitrosylation and glutathionylation , or by reduction of disulphide bonds can lead to labeling and false detection . It is due to these above-mentioned caveats in palmitoylation proteomics studies that the compendium presented here should be used as a starting-off resource for further studies to determine if a protein of interest that appears in the compendium is indeed palmitoylated . The palmitoylation status of any protein in the compendium should be confirmed using multiple low-throughput methods with the appropriate controls . However , proteins identified in more than one study , particularly those identified using more than one method are more likely to truly be palmitoylated . Also , proteins identified in bioorthogonal labeling assays that used hydroxylamine treatment and non-clickable palmitate negative controls ( Martin et al 2009 , Martin et al 2012 , and Li et al 2012 ) are also more likely to be palmitoylated [33 , 41 , 42] . These studies involve the identification of a protein in two different experiments; one using a non-clickable palmitate and the other using hydroxylamine treatment as negative controls and thus are annotated in a separate “methodology” column in the compendium titled “Bioorthogonal labeling ( stearic: 17-ODYA ) + HAM” . The advantage to using hydroxylamine as a negative control is that it does not eliminate N- and O-acylation modifications thus revealing these types of false positives . However , those proteins that were only identified in one study are still likely to have a proteoform that is palmitoylated . Indeed , a number of proteins that were only identified in a single proteomics study , including DHHC17 [58] , DHHC12 [59] , PKC epsilon [60] , thioredoxin [61] , mitochondrial HMG-CoA synthase [4] , and β4-integrin [62] have been previously confirmed to be palmitoylated in low-throughput studies . In the Wilson et al . study , various lipid analogues were used , including myristate , palmitate , and stearate [38] . All of the data for this study were included in the compendium as some proteins may be preferentially S-acylated in a fatty acid length dependent manner . Indeed , the PAT DHHC3's activity has been shown to greatly reduce with acyl-CoAs with chains longer than 16-carbons [59] . Of note , only four proteins ( FMNL , CHP1 , HPCL1 , CANB1 ) were detected using myristate analogues that were not detected using palmitate or stearate or detected in another study . This suggests that most of the proteins detected with myristate in Wilson et al are either S-acylated and not myristoylated or are dually acylated proteins that are N-terminally myristoylated and S-acylated elsewhere . This is not surprising , since myristate requires a secondary membrane-binding signal , which typically consists of palmitoylation [63] . In addition , the two N-myristoyltransferases ( NMTs ) that are responsible for catalyzing N-myristoylation of proteins are highly specific for myristate and do not tolerate longer fatty acids well [63] . Therefore , proteins that were detected using myristate and longer fatty acids are likely to be S-acylated . Consequently , all the proteins detected by Wilson et al were included in the compendium . This includes the four proteins not detected in other studies as they may have alternative proteoforms that may be S-acylated . This allows for a more complete and agnostic list that is easy to access and interpret . Previously , based on the identification of a few palmitoylated proteins , palmitoylation was predicted to be important for synaptic signaling in neurons [64] . Now , for the first time , we demonstrate using an unbiased conservative statistical approach that palmitoylation plays a broad role in synaptic signaling and , consequently , in many diseases and disorders of the nervous system . The fact that the compendium is enriched for synaptic signaling pathways and diseases and disorders of the nervous system was surprising since only three of the 15 proteomics studies included the use of neuronal cells or tissues and 52% of the genes in the compendium were identified only in non-neuronal studies . Despite this , synaptic proteins are significantly enriched for palmitoylated proteins ( 41% ) . This significant enrichment of palmitoylated proteins in the synaptic proteome demonstrates that palmitoylation may be an important regulator at the synapse . Alterations of palmitoylation may have detrimental effects specifically at the synapse and this may explain why palmitoylation is enriched for so many diseases of the nervous system . Indeed , we identified a number of cysteine mutations in putative or known palmitoylation sites in a number of diseases and disorders of the nervous system ( Table 2 ) , which provide a few examples where loss of palmitoylation of a residue of a protein in humans may lead to disease . The compendium gene list was also enriched for association with cancers ( Fig 5B ) . Palmitoylation has been previously linked to cancer [11–15] , but this is the first time this has been shown using a conservative meta-analysis approach instead of low throughput methods . The fact that the top two enriched classes of diseases are diseases and disorders of the nervous system and cancers is intriguing as they can be considered as two ends of a pendulum of cell growth and death . Neurodegenerative diseases , in particular , involve cell death , whereas cancers involve over-proliferation of cells . In fact , patients with neurodegenerative diseases , particularly HD , AD and Parkinson disease , have a lower incidence of cancers and those who have had cancer have a lower incidence of AD and Parkinson disease [65–67] . The association of palmitoylation with both nervous system diseases and cancers suggests that aberrant palmitoylation may lead to cell death or uncontrolled cell growth depending on the proteins involved . For example , loss of activity of the PAT DHHC17 ( also known as Huntingtin interacting protein 14 [HIP14] ) has been implicated in HD [58 , 68] whereas overexpression of DHHC17 may lead to cancer [69] . In addition , the inhibition of palmitoylation of oncogenic proteins such as RAS has been suggested as an avenue for development of chemotherapeutic drugs [56 , 57] . An interesting role of palmitoylation that came out of the functional enrichment analysis performed here was the association with cell metabolic processes . The role of palmitoylation in cell metabolism may provide a connection between these two types of diseases as metabolic disturbances play a large role in both cancers and diseases and disorders of the nervous system [70 , 71] . In fact , increased risk of cancer has been linked to both dietary fat intake and increased intracellular levels of palmitate synthesized de novo by fatty acid synthase [72 , 73] . Consequently , a high fat diet or increases in cellular palmitate may alter palmitoylation or lead to increased palmitoylation of many signaling cascades , like ongenic Ras , and potentially promote the cancerous state . In addition , as S-acylation primarily uses palmitate because it is the most abundant lipid in the cell [1 , 2] , it is worth considering that a change in lipid bioavailability due to diet could alter the types of lipids used for S-acylation . This could dramatically affect protein interactions with membranes . Finally , as it has been shown that metabolic proteins may be inhibited by palmitoylation at their actives sites [4] , it is possible many proteins may be regulated by palmitate availability . In contrast , loss of palmitoylation by dietary uptake or metabolism or dysregulated pathways in neurodegeneration may lead to cell death . It would be very interesting to know if simply treating with fatty acids could ameliorate some palmitoylation defects in neurodegenerative diseases . Indeed , recent evidence has shown that dietary lipids have a beneficial effect in the treatment of Schizophrenia [74] , which was shown to be enriched in our study . Our meta-analysis suggests that aberrant palmitoylation plays a role in many nervous system diseases and it provides a myriad of putative targets for the treatment of diseases of the nervous system including ALS , Schizophrenia , and HD .
To date , 16 palmitoyl proteomics studies have been performed with three different species: human , mouse , and rat . The data from all but one of these palmitoyl proteomics studies were included . Ren et al . was excluded since multiple errors were detected regarding the protein identifiers reported in the supplemental data [75] . With the goal of building a unique consolidated list of palmitoylated proteins , the different identifiers , database accession numbers , and names of the palmitoylated proteins in each dataset of these 15 studies were extracted . The human palmitoylation studies of Dowal et al . [37] , Forrester et al . [32] , and Wei et al . [35] reported UniProt entry names; Martin et al . [33] reported Ensembl Gene IDs; while those of Ivaldi et al . [36] , Marin et al . [27] , Wilson et al . [38] , and Yang et al . [39] reported gene names and descriptions . The dataset from Zhang et al . [14] reported NCBI RefSeq Accession Numbers [76] . In the mouse studies , Li et al . [42] , Martin et al . [41] , Wan et al . [40] , and Yount et al . [77] reported gene names and descriptions of palmitoylated proteins , while Merrick et al . [43] reported NCBI protein GI numbers and descriptions . Finally , the rat study of Kang et al . [45] reported gene names and descriptions of palmitoylated proteins . “SUSD3_HUMAN” was manually removed from the dataset of Dowal et al . , as it was labeled as disproved . Also , rows 182–183 from S1A Table and rows 188–198 from S2A Table from Wilson et al . were excluded as recommended in that paper , since the signal from these proteins was enriched in the negative controls [38] . In order to consolidate the different lists of palmitoylated proteins described above , the UniProt databases Swiss-Prot and TrEMBL ( release-2014_01 ) [48] and the associated entry name-mappings for human , mouse , and rat were downloaded on February 19 , 2014 . The UniProt databases were processed to extract the entry name , gene name , full name , Entrez Gene ID , and available synonyms of each protein . Using their respective means of identification , palmitoylated proteins from all studies were matched with the corresponding entry names in the UniProt database . If a protein was matched to a database entry in both TrEMBL and Swiss-Prot , the one from the Swiss-Prot database was given priority , since Swiss-Prot is manually annotated and reviewed and therefore of higher quality . On average , 97 . 67% of the proteins of a study were matched to the UniProt database entry names , with the minimum being 90 . 1% for the study from Merrick et al . Hence , this compendium of palmitoylated proteins was built from the studies mentioned above in human , mouse , and rat by reporting their gene names and corresponding UniProt entry names and Entrez Gene IDs [78] . The methods of detection of palmitoylated proteins typically do not always allow the differentiation between different protein isoforms , but do permit mapping to a single gene . Therefore gene identifiers were used to curate the compendium . Palmitoylated proteins in the consolidated list that were not reported in all three species had their homologs inferred from the UniProt database based on their UniProt entry names and gene names . The resulting complete consolidated compendium of palmitoylated proteins from the published proteomics studies is reported in S1 Table . When a protein was reported in a given study it is annotated in the compendium under the appropriate study column with a “1” . When a protein was identified in a given study using a particular type of assay it is also annotated in the compendium under the appropriate method column with a “1” . The total number of studies each protein was identified in was annotated under the “Number of Studies Observed in” column . Importantly , these annotations were made on a per study basis not based on the number of times each study identified a given protein . Hierarchical clustering of the consolidated study list was performed using a binary array in which each study was represented by a vector of length equal to the total number of palmitoylated genes . A value of 1 was entered in the vector if a palmitoylated protein was observed in the corresponding study and 0 if it was not . The complete array of study vectors were clustered and plotted with the pvclust package in R [46] , using average linkage and binary distance . Bootstrap values were calculated from 5000 samplings . The MetaCore software package version 6 . 19 build 65960 ( Thomson Reuters ) was used to assess the statistical significance of the enrichments of disease-associations , pathway maps , process networks , Gene Ontology ( GO ) biological processes , and metabolic networks [47] in our compendium of human palmitoylated proteins ( MetaCore output files are provided as S2 Table , S3 Table , S4 Table , S5 Table , and S6 Table , respectively ) . Since the palmitoylated proteins in our dataset were identified using proteomics experiments involving MS , the MetaCore enrichment analysis was performed using the union of two recent human proteome MS-based datasets as background [79 , 80] . The 32 palmitoylated proteins from our consolidated list that were not present in this background dataset were appended to it . Entrez Gene IDs were obtained for all proteins in this background dataset . This resulted in a background dataset of 17 , 858 Entrez Gene IDs . Using this dataset as background in lieu of the entire set of human genes has the advantage that biases introduced from the identification of palmitoylated proteins through MS will be considered in the MetaCore enrichment analysis . For the MetaCore enrichment analysis , protein annotations among palmitoylated proteins that obtained a FDR below 0 . 001 and a FE greater or equal to 2 were deemed statistically significantly enriched ( S7–S10 Tables ) . The FE , a ratio of the proportion of palmitoylated proteins within a given annotation over the proportion of proteins with the annotation in the background dataset , was used to perform a conservative enrichment analysis and avoid the inclusion of very broad and largely uninformative annotations in our results , which may obtain significant FDRs using the MetaCore analysis . The conservative FE threshold of 2 was based on previously used thresholds in the literature [81–83] . Results from the enrichment analysis for disease-associations , pathway maps , process networks , and GO biological processes are reported in S7 , S8 , S9 , and S10 Tables , respectively . Only one metabolic network annotation , “Lipid metabolism , fatty acid beta-oxidation” was significantly enriched ( FDR = 1 . 45x10-4 and FE = 6 . 02 ) in the complete list of palmitoylated proteins . The 40 significantly enriched disease associations were broadly classified as diseases of the nervous system , cancers , infections , anemias , gastrointestinal diseases , or other ( S7 Table and Fig 5B ) . Some repetitive disease annotations in S7 Table were not reported . Entrez gene names from the compendium were compared to the Entrez gene names in the manually annotated and updated SynSysNet list of 1 , 028 synaptic genes ( downloaded on January 13 , 2015 ) [49] . A Fisher’s exact test was used to assess the statistical significance of the enrichment of palmitoylated genes in the synaptic genes dataset ( 419 in 1 , 028 ) to that in the background proteome dataset ( 1 , 838 in 17 , 858 ) and to compare the converse enrichment of synaptic genes in the compendium ( 419 in 1 , 838 ) to that in the background dataset ( 996 in 17 , 858 ) . All genes from the compendium that are associated with diseases of the nervous system were extracted using the OMIM morbid map [84] . OMIM entries corresponding to these genes were then obtained and searched for disease-causing cysteine mutations that were annotated in UniProt , reported in the literature as palmitoylated , or predicted to be palmitoylated by CSS-Palm 3 . 0 [85] . | Protein localization is essential for mediating protein function within the cellular context . Mislocalization of proteins can offset cellular balance , influencing whether a cell lives or dies . Many proteins are directed to cellular membranes through the addition of fats , or lipidation . In particular , palmitoylation involves the reversible addition of the fatty acid palmitate to cysteines . Its reversibility makes it a unique form of lipidation allowing its dynamic regulation . Recent advancements in fast , sensitive , non-radioactive methods to detect palmitoylation have led to an explosion in the identification of palmitoylated proteins through proteomics studies . However , the data is hidden in large supplemental tables in various formats . Thus , we curated a list of palmitoylated proteins revealing that approximately 10 percent of the human genome encodes for a proteoform that is palmitoylated . Computational analysis confirmed that palmitoylation is involved in protein localization and indicated a new role in metabolism . Importantly , we found that palmitoylation was enriched at neuronal synapses and in disorders of the nervous system , including Schizophrenia and Huntington disease . Interestingly , palmitoylation was equally enriched in cancers . Consequently , we suggest that palmitoylation plays a critical role in cell fate and our compendium provides a plethora of targets for neurodegeneration and cancer . | [
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] | [] | 2015 | Curation of the Mammalian Palmitoylome Indicates a Pivotal Role for Palmitoylation in Diseases and Disorders of the Nervous System and Cancers |
Chagas disease ( Trypanosoma cruzi infection ) is the leading cause of non-ischemic dilated cardiomyopathy in Latin America . Texas , particularly the southern region , has compounding factors that could contribute to T . cruzi transmission; however , epidemiologic studies are lacking . The aim of this study was to ascertain the prevalence of T . cruzi in three different mammalian species ( coyotes , stray domestic dogs , and humans ) and vectors ( Triatoma species ) to understand the burden of Chagas disease among sylvatic , peridomestic , and domestic cycles . To determine prevalence of infection , we tested sera from coyotes , stray domestic dogs housed in public shelters , and residents participating in related research studies and found 8% , 3 . 8% , and 0 . 36% positive for T . cruzi , respectively . PCR was used to determine the prevalence of T . cruzi DNA in vectors collected in peridomestic locations in the region , with 56 . 5% testing positive for the parasite , further confirming risk of transmission in the region . Our findings contribute to the growing body of evidence for autochthonous Chagas disease transmission in south Texas . Considering this region has a population of 1 . 3 million , and up to 30% of T . cruzi infected individuals developing severe cardiac disease , it is imperative that we identify high risk groups for surveillance and treatment purposes .
Chagas disease ( Trypanosoma cruzi infection ) can cause fatal cardiomyopathy in up to 30% of infected people [1] . Transmission to mammals occurs via vector , oral , congenital , and/or transfusion/transplantation routes [2] . The triatomine vector , or “kissing bug , ” serves as the predominate mode of transmission , particularly in established sylvatic and/or domestic transmission cycles [3] . Over 100 different wildlife mammalian species are competent reservoirs of disease and have been implicated in propagation of sylvatic transmission cycles in nature [4] . Canines , in particular , are important components of peridomestic transmission , resulting in a bridge between sylvatic and domestic transmission cycles [5–7] . Finally , human infections can occur when vectors establish nests inside or near the home , and vectors feed on both humans and domesticated animals [7 , 8] . Disease prevalence is highest in impoverished regions of endemic countries due to a plethora of societal factors , including substandard living conditions that result in increased exposure to vectors [9] . While the southern United States is not traditionally considered an endemic area , recent evidence has implicated the establishment of vector transmission cycles , particularly in Texas [10 , 11] . Historical evidence of T . cruzi infected vectors and mammalian reservoirs date back to the early 1900s [12] . While the first documented locally acquired human case was published in Corpus Christi , Texas in 1955 , the south Texas region , including the Rio Grande Valley , has been the subject of investigation by public health authorities dating back to the 1940s [12] . South Texas has compounding factors that could contribute to this area being a high-risk region for transmission . Within the state , sylvatic transmission cycles have been reported with seven different vector species and 27 sylvatic mammalian reservoirs [10] . The potential for sylvatic spillover to humans in this region has been implicated from increased outdoor exposure and interactions in rural environments [13] . In addition , colonias ( primarily Hispanic communities ) in this region of Texas have unprecedented poverty rates and living conditions that allow for easy access for vectors to enter and colonize homes , which might place residents at an increased risk of domestic transmission [5 , 14] . Despite this compounding evidence of increased potential for Chagas disease in the region , epidemiologic assessments are lacking . The aim of our current assessment was to ascertain the prevalence of T . cruzi in three different mammalian species ( coyotes , stray domestic dogs , and humans ) and vectors ( Triatoma species ) to understand the disease burden attributable to Chagas disease among sylvatic , peridomestic , and domestic cycles .
Texas Department of State Health Services in the lower Rio Grande Valley originally collected terminal samples of coyote sera as part of their rabies control programs in 2005–2006 , and secondary aliquots from these specimens were shared for T . cruzi testing for the purposes of this study . Canine sera collection and Chagas disease testing were approved by the University of Texas Health Science Center Animal Welfare Committee ( AWC-07-147 and AWC-03-029 ) . For the human seroprevalence aspects of our study , the original Cameron County Hispanic Cohort study was reviewed and approved by the University of Texas Health Science Center at Houston Committee for the Protection of Human Subjects ( HSC-SPH-03-007B ) , and Chagas disease testing on coded samples was approved under Baylor College of Medicine Institutional Review Board ( H-32192 ) . We conducted a retrospective analysis of previously collected sera from coyotes , stray domestic dogs housed in public shelters , and residents participating in related research studies . With regards to the coyote specimens , secondary aliquots from specimens noted above were shared by the Texas Department of State Health Services for T . cruzi testing . For domestic dog specimens , sera were collected in 2007 and 2009 from juvenile ( less than 6 months of age and over 8 weeks of age based on tooth development ) stray dogs housed in public shelters at one of two locations ( Brownsville in Cameron County and Edinburg in Hidalgo County ) . The rationale for collecting samples from dogs under 6 months of age was to identify new , acute cases of infection so that incidence , as opposed to prevalence , could be determined . We purposefully excluded puppies under 8 weeks of age to eliminate issues related to the possible transfer of Chagas-positive maternal antibodies . Investigators from the University of Texas Health Science Center at Houston , School of Public Health , Brownsville Regional Campus , collected sera from an established cohort living in Cameron County , TX . The participants were recruited from randomly selected households between 2005 and 2008 as a means of assessing the general health of residents along the US-Mexico border . Potential participants were not excluded based on race/ethnicity , with all race/ethnicities eligible for study inclusion . Data from the original health questionnaire and echocardiograms performed by the Cameron County Cohort ( CCC ) study were available for descriptive analysis [15] . From 2012 to 2013 , we received 115 Triatomine insects that were collected in peridomestic areas by citizens across 6 counties in south Texas . Insect specimens were shipped , typically live , to The University of Texas Rio Grande Valley for further processing . PCR testing was performed in collaboration with Baylor College of Medicine Laboratory for Vector-Borne and Zoonotic Diseases . Serum samples were thawed and analyzed using Chagas Stat-Pak and DPP assays ( Chembio Diagnostic Systems , Inc , Medford , NY ) . These rapid immunochromatographic assays test for antibodies against T . cruzi . These highly sensitive and specific assays were designed for feasibility in field-testing of both human and canine blood [6 , 16–18] . Tests were examined visually and scored as negative or positive , following manufacturer’s directions . A positive sample was defined as being positive on both assays . Negative samples included those that were positive on only one diagnostic but negative on the second diagnostic . Any equivocal samples were retested for further clarification . Due to the samples being retrospectively tested without potential for prospective clinical intervention and the exploratory nature of the project , additional confirmation testing with alternate diagnostics was not performed . For T . cruzi testing and taxonomic species identification of Triatoma insects , the posterior third of the insects’ abdomen was homogenized with a 5 mm stainless steel bead in AL buffer ( Qiagen , Valencia , CA ) in TissueLyser II ( Retsch , Haan , Germany ) for 3 min at 25 Hz . Following manufacturer’s instructions , DNA was then extracted using DNeasy Blood & Tissue kit ( Qiagen , Valencia , CA ) . T . cruzi DNA detection and insect-specific mitochondrial 16S DNA for speciation were performed using PCR and sequencing as previously described [8 , 19] . Descriptive statistics were used to identify prevalence infection rates with 95% confidence interval ( CI ) and stratified by pertinent variables . For domestic dogs , positive infection was translated to incidence since all dogs would have acquired infection in the first 6 months of life . Statistical analysis was performed using STATA v12 ( College Station , TX ) . Spatial analysis was performed using MapInfo Professional v11 . 5 ( Stamford , CT ) .
Coyote samples collected in the Rio Grande Valley had an overall seroprevalence rate of 8% ( 16 out of 199; 95% CI = 4 . 2% to 11 . 8% ) ( Table 1 ) . Sampled coyotes were evenly distributed by gender ( 45% female ) and all but one were adults . There was no difference in seropositivity by year of sampling . Interestingly , seroprevalence varied with regards to county of collection , with the highest seroprevalence identified in Zapata County ( 16%; 10/64 ) , followed by Jim Hogg County ( 14%; 3/22 ) , Dimmit County ( 10%; 2/20 ) , and Webb County ( 1%; 1/83 ) ( Fig 1 ) . No positive coyotes were identified in Cameron , Hidalgo , Starr , or Wallacy counties , although sample sizes from each of these counties were low ( range 1 to 4 , total tested = 10 ) . Samples collected from juvenile domestic dogs from neighboring Hidalgo and Cameron counties had an overall serologic incidence of 3 . 8% ( 8 out of 209 samples; 95% CI = 1 . 2% to 6 . 4% ) . We found a pronounced increase ( 4 . 4 fold ) in Chagas incidence when comparing sampling in 2007 to 2009 ( Fisher’s exact test , p-value = 0 . 04 , 95% CI = 1 . 1 to 18 . 0 ) , with 2% ( 3/152 ) of dogs positive in 2007 versus 9% ( 5/57 ) found positive in 2009 . Of 841 human sera samples tested from participants in the CCC , 3 individuals ( 0 . 4%; 95% CI = 0% to 0 . 8% ) tested positive on both Stat-Pak and DPP assays . Limited residential history , medical histories and socioeconomic variables were reported as listed below . The precise origin and duration of their infection is unknown . CCC Participant 1 was a 76-year-old female born in Canary , Texas ( now known as Livingston , Texas ) with a 52-year residential history in Brownsville , Texas . Case-patient 1 reported no current employment with an annual disability-benefit income of $3 , 336 . Her medical history included diabetes , stroke , and hypertension . Case-patient 1’s mother was born in Texas while her father was born in central Mexico ( Guanajuato ) . No data regarding any abnormal cardiac findings were available for this case-patient . On follow-up , participant’s husband reported that the participant had died recently with an apparent cause of death reported as leukemia . CCC Participant 2 was a 45-year-old male born in San Luis Potosi , San Luis Potosi , Mexico with a 6-year residential history in Brownsville , Texas . In addition , he reported a prior 6-year residential history ( while attending school ) in the Brownsville , Texas border town of Matamoros , Tamaulipas , Mexico . Case-patient 2 was employed at the time of enrollment , reporting an annual income of $12 , 000 . His past medical and social histories included diabetes and smoking . Both parents were born in north-central Mexico ( San Luis Potosi ) . An echocardiogram performed on this participant showed normal left ventricular and right ventricular systolic function , mild concentric left ventricular hypertrophy , grade 1 left ventricular diastolic dysfunction , and no significant valvular abnormalities . The participant reported no symptoms related to any type of infection , and no additional cardiac evaluations were performed . CCC Participant 3 was a 63-year-old male born in Matamoros , Tamaulipas , Mexico with a 22-year history of living in Brownsville , Texas . Case-patient 3 was retired with a prior occupational history in agriculture ( occupational duration unknown ) and a current annual income of $10 , 248 . His medical history was negative for pre-existing conditions or co-morbidities . Case-patient 3’s parents were born in northern Mexico ( Nuevo León ) . An echocardiogram performed at the same time as the original blood collection demonstrated normal biventricular systolic function , mild concentric left ventricular hypertrophy , grade 1 left ventricular diastolic dysfunction , and no significant valvular abnormalities . Similarly , the participant reported no symptoms , and no additional cardiac evaluations were performed . Finally , to determine the likelihood of infection in vectors in the region , PCR was performed on 115 insects ( Triatoma species ) collected around homes across 6 counties of south Texas . We found 65 ( 56 . 5% ) positive for T . cruzi DNA , with prevalence ranked by county as follows: Brooks County ( 84%; 21/25 ) , Hidalgo County ( 60%; 6/10 ) , Jim Wells County ( 50%; 12/24 ) , Kleberg County ( 47%; 22/47 ) , Dimmit County ( 33%; 2/6 ) , and Cameron County ( 0%; 0/1 ) ; 2 positive insects did not have a georeference provided . The most common insect collected was Triatoma gerstaeckeri ( 96 . 5% of insects; 62/111 T . cruzi positive ) , followed by T . lecticularia ( 2 . 6% of insects; 2/3 T . cruzi positive ) and T . sanguisuga ( 0 . 9% of insects; 1/1 T . cruzi positive ) .
Chagas disease transmission has been identified along the Texas-Mexico border dating back to the 1970s [20 , 21] . Our current study is the first to assess the infection status of vectors and seroprevalence among mammalian and human populations all living in the same geographic region of south Texas . Seroprevalence was highest among the sylvatic adult coyote reservoir ( 8% ) , moderate among peridomestic juvenile dogs in community shelters ( 3 . 8% ) , and lowest among local residents ( 0 . 36% ) , with one of the three positive CCC participants having a life-long history of living in Texas . In addition to finding evidence of infection in canines and humans , we found a high percentage ( 56 . 5% ) of vectors carrying the parasite , further solidifying the risk of Chagas disease transmission in the region . Prior case reports have suggested the potential for domestic transmission along the eastern side of the Texas-Mexico border [5 , 20] , and now our larger regional assessment confirms this risk . Compounding evidence of poverty , substandard housing , rural residential exposure to sylvatic animals , and high infection prevalence of multiple species all can contribute to an increased risk of Chagas disease transmission to local residents [10 , 14 , 22] . Coyotes ( Canis latrans ) are den dwelling animals native to North America . Habitat preferences include caves and natural holes , or abandoned domestic structures such as drainage pipes , vacant homesteads and railroad tracks [23] . Similarly , triatomine vectors prefer natural or domestic habitats , living in large numbers within dens that provide constant access to a host meal source [3] . Our finding of 8% seroprevalence among coyote populations in the Rio Grande Valley is slightly lower than a prior study in 1978 which found a 12 . 8% ( 20 out of 156 ) prevalence of infection [20] . A second study published in 1984 found a 14% seroprevalence rate in coyotes from across Texas; however , none of the eastern Rio Grande Valley counties were included in this sampling [24] . Tennessee , Georgia , and Virginia are other southern states with known T . cruzi positive coyote populations [25–27] . Comparable to our study , these more recent studies found seroprevalence rates between 7–10% , suggesting that infection rates might be decreasing with time or current diagnostic tests have better sensitivity-specificity . Dog ( Canis lupus familiaris ) populations in the United States can be feral or domesticated; however , both groups can serve as bridge hosts for transferring Chagas disease between sylvatic environments and humans . Dogs serve as important sentinel for disease surveillance purposes as their infection rates can be early predictors of transmission risk to humans , especially considering dogs develop clinical cardiac disease quicker than humans [5 , 21 , 28–30] . Using public health veterinary shelters as a sampling venue is a convenient methodology to capture feral , community-owned , and domesticated dog populations . The shelter dogs in our study of the Rio Grande Valley had a seroprevalence of 3 . 8% , which is considerably lower than other published infection prevalence estimates among shelter dog populations from across the state . Over 48 different dog breeds in Texas have demonstrated natural infection with T . cruzi , with prevalence estimates ranging from 8 . 8–20 . 3% [31 , 32] . In the greater Brownsville , Texas area , infection prevalence of shelter dogs has ranged from 7 . 5% in 2003 to 6 . 7% in 2014 [5 , 32] . While our prevalence is slightly lower than other studies , the reason is most likely related to our decision to sample dogs that were under 6 months of age , allowing us to estimate incidence related to recent vector-borne or congenitally-acquired infection . By estimating incidence , we can better understand the annual contribution of disease transmission in this geographic area . The epidemiology and seroprevalence of human infection in the southern United States is largely unknown . Even in endemic areas , human seroprevalence is typically lower than sylvatic and domestic animals due to multiple factors , including increased mammalian-vector habitat exposure , mammalian predilection for oral ingestion of the triatomine vector , and varying defecation behaviors of different triatomine species [3 , 30 , 33] . While sylvatic transmission cycles between wildlife and vectors have been established in the southern United States , we are still in our infancy of understanding disease burden and transmission source in infected populations . A prior study conducted in 1977 found a seroprevalence of 2 . 4% ( 12 out of 500 ) among eastern Rio Grande Valley residents [20] , which is a sharp contrast to our finding of 0 . 4% ( 3 out of 841 ) . Our study sampling included random selection of participants , while their study biased their results by recruiting patients at Texas Chest Hospital in Harlingen . It is likely our sampling methodologies influenced the varying rates , especially as other historical random-selection population studies reported 0 . 01–0 . 9% seroprevalence [12] . Despite our selection methodology differences , both Burkholder et al . ’s study and ours included long-time residents of the Rio Grande Valley , with one positive participant in our study very likely acquiring the infection in Texas . Based on our findings of a seroprevalence estimate of 0 . 4% , and considering a population of 1 . 3 million for the Rio Grande Valley , we can estimate that ~4 , 600 people in this region are currently infected with Chagas , with ~1 , 300 at risk for developing Chagas-related cardiomyopathy . If this estimate is accurate , then the burden of Chagas disease in the Rio Grande Valley is 23 times higher than what we had previously estimated based on our findings of 1 out of 6 , 500 ( 0 . 02% ) blood donors in Texas testing positive for the disease [34] . Future studies should aim to further clarify the true disease burden and rate of autochthonous transmission in the Rio Grande Valley , an area with documented sylvatic and domestic T . cruzi transmission [5] . Our study had a few important limitations notable for discussion . The current World Health Organization guidelines require a minimum of two positive results on different antibody-based assays for diagnostic confirmation [35] . While we used two different assays , neither are currently FDA approved in the United States; however , Stat-Pak rapid immunochromatographic assay has demonstrated efficacy in all three populations of mammals in multiple studies [6 , 16–18 , 27] . For the purposes of this retrospective study we felt confident in the test results , especially as they were relatively consistent with other published literature . In addition to our finding of a high rate of infection ( 56 . 5% ) among local vector species , other studies have also confirmed high rates of infection ( 51–82% ) in Triatomine vectors throughout Texas [7 , 8 , 10] . Provided the retrospective nature of our study , the obvious lack of travel history in these coyote and dog populations , and the establishment of known T . cruzi positive vector populations in our study , we would argue that these are true infections acquired via local vector-sylvatic mammal transmission cycles . Another possible limitation , due to our retrospective sampling of frozen sera collected 8–10 years prior , is the potential for antibody decay resulting in a lower prevalence rate . Handling of the specimens included freezing aliquots to -80°C immediately following collection , constant monitoring of freezer temperature , and adhering to discipline standards during the serum thawing process in an effort to maintain sample preservation . Finally , we cannot rule-out the potential for cross-reaction with leishmaniasis . Rare reports of cutaneous leishmaniasis have been reported in the state [36]; however , none of our three Chagas-positive study participants presented with skin ulcers , lowering the potential for cross-reaction . In conclusion , we contribute to the growing body of evidence for autochthonous Chagas disease transmission among mammals in south Texas . Coyotes , shelter dogs , and vectors in this region continue to demonstrate high infection rates of T . cruzi . Random sampling of residents also revealed a higher than expected disease burden that had previously been undiagnosed , with one human patient suspected of having locally acquired the disease . With up to 30% of infected individuals developing a potentially fatal cardiac disease , it is imperative that we identify and treat patients before irreversible clinical manifestations have occurred . Future prospective studies are necessary to elucidate and validate the disease burden in the Rio Grande Valley . | In this study , we contribute to the growing body of evidence for autochthonous Chagas disease transmission in south Texas along the US-Mexico border . We found that coyotes , shelter dogs , and vectors in this region demonstrated high infection rates of T . cruzi . Random sampling of residents also revealed a higher than expected disease burden that had previously been undiagnosed . With up to 30% of infected individuals developing potentially fatal cardiac disease , it is imperative that we identify and treat patients before irreversible clinical manifestations have occurred . Future prospective studies are necessary to elucidate and validate the disease burden in this area . | [
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"mammals",... | 2016 | One Health Interactions of Chagas Disease Vectors, Canid Hosts, and Human Residents along the Texas-Mexico Border |
Listeria monocytogenes is an environmental saprophyte and facultative intracellular bacterial pathogen with a well-defined life-cycle that involves escape from a phagosome , rapid cytosolic growth , and ActA-dependent cell-to-cell spread , all of which are dependent on the master transcriptional regulator PrfA . The environmental cues that lead to temporal and spatial control of L . monocytogenes virulence gene expression are poorly understood . In this study , we took advantage of the robust up-regulation of ActA that occurs intracellularly and expressed Cre recombinase from the actA promoter and 5’ untranslated region in a strain in which loxP sites flanked essential genes , so that activation of actA led to bacterial death . Upon screening for transposon mutants that survived intracellularly , six genes were identified as necessary for ActA expression . Strikingly , most of the genes , including gshF , spxA1 , yjbH , and ohrA , are predicted to play important roles in bacterial redox regulation . The mutants identified in the genetic selection fell into three broad categories: ( 1 ) those that failed to reach the cytosolic compartment; ( 2 ) mutants that entered the cytosol , but failed to activate the master virulence regulator PrfA; and ( 3 ) mutants that entered the cytosol and activated transcription of actA , but failed to synthesize it . The identification of mutants defective in vacuolar escape suggests that up-regulation of ActA occurs in the host cytosol and not the vacuole . Moreover , these results provide evidence for two non-redundant cytosolic cues; the first results in allosteric activation of PrfA via increased glutathione levels and transcriptional activation of actA while the second results in translational activation of actA and requires yjbH . Although the precise host cues have not yet been identified , we suggest that intracellular redox stress occurs as a consequence of both host and pathogen remodeling their metabolism upon infection .
Intracellular pathogens such as Plasmodium spp . , Mycobacterium tuberculosis , Salmonella enterica , Trypanosoma cruzi , and Leishmania spp . are responsible for an overwhelming amount of morbidity and mortality worldwide . Successful dissemination of many of these pathogens requires complex life cycles that involve survival and replication in environmental or vector niches . To propagate within their hosts , these pathogens establish a variety of unique intracellular niches that are essential for their pathogenesis [1] . Although there is considerable understanding of how intracellular pathogens manipulate host cell biology to promote their pathogenesis , less is known about the precise mechanisms by which these pathogens sense their host cell . Such an understanding may lead to targets for therapeutic intervention . In this study we used Listeria monocytogenes as a model system for understanding virulence gene regulation of a facultative intracellular bacterium that transitions from extracellular to intracellular growth . L . monocytogenes is a ubiquitous environmental saprophyte capable of causing severe disease as a foodborne pathogen [2] . L . monocytogenes is also a model system for studying bacterial adaptation to the host [3] . The bacterial virulence program is coordinated with a life cycle that begins upon entry into a mammalian cell either by phagocytosis or bacteria-mediated internalization . To commence intracellular growth , L . monocytogenes must first escape from the hostile phagosomal environment by the expression and secretion of a cholesterol-dependent cytolysin , listeriolysin O ( LLO ) that mediates destruction of the phagosome [4] . Upon entry into the cytosol , L . monocytogenes grows rapidly and expresses an essential determinant of pathogenesis , ActA , an abundant surface protein that mediates host actin polymerization [5 , 6] . Appropriate regulation of LLO and ActA is critical for L . monocytogenes pathogenesis and transcriptionally coordinated by the master virulence regulator PrfA [7] . PrfA is a cAMP receptor protein ( Crp ) family transcriptional regulator that is absolutely essential for L . monocytogenes virulence gene expression and pathogenesis [8] . PrfA-mediated gene expression is regulated by PrfA abundance , affinity for target promoters , and activation via cofactor binding [9] . PrfA levels are controlled by three promoters . The most proximal promoter contains a site of negative regulation , while the most distal is a PrfA-dependent read-through transcript that is essential for appropriately high levels of intracellular gene expression [10–12] . PrfA binds a palindromic DNA sequence ( PrfA-box ) and deviations from a consensus sequence result in lower affinity DNA-PrfA interactions [13] . The affinity of PrfA for DNA determines the degree of transcriptional activation prior to PrfA allosteric activation [14] . For example , the gene encoding LLO ( hly ) has a high affinity PrfA-box and consequently is expressed even during growth in broth when PrfA is not activated . In contrast , the actA promoter contains a lower affinity PrfA box and is not expressed during growth in broth [15 , 16] . Upon entry into the host cell cytosol , PrfA is over-expressed and is activated by a two-step process: first , binding of PrfA to DNA requires reduction of the four PrfA cysteine residues while full transcriptional activation of PrfA requires allosteric binding to glutathione [17] . The requirement for glutathione can be bypassed by mutations that lock PrfA in its active conformation ( PrfA* ) [18] . Strains with PrfA* mutations constitutively express PrfA-activated genes and consequently have growth defects extracellularly , demonstrating the importance of regulating virulence gene expression [19 , 20] . However , even PrfA* strains grown in broth fail to synthesize the amount of ActA observed intracellularly , which is likely attributable to translational control localized to the 5’ untranslated region ( 5’ UTR ) [21] . Despite these findings of exquisite gene regulation , little is known about trans-acting factors that affect expression of PrfA or PrfA-activated genes . In a previous study , a genetic system was designed to select for L . monocytogenes mutants that failed to express ActA intracellularly [17] . This screen led to the identification of L . monocytogenes glutathione synthase ( GshF ) and glutathione , a tripeptide antioxidant , as the allosteric activator of PrfA . In this study we sought to further understand the host cues that are recognized by intracellular pathogens during infection . We returned to the forward genetic selection and exhaustively screened for additional mutants that failed to express sufficient ActA intracellularly . This selection identified genes required at each stage of the intracellular lifecycle , including: vacuolar escape , PrfA activation , and cell-to-cell spread . These data suggest a model of compartmentalized gene expression , furthering our understanding of the L . monocytogenes virulence program .
The goal of this study was to identify genes involved in regulation of a principle virulence determinant in L . monocytogenes , ActA . A bacterial strain was constructed that failed to replicate upon activation of the actA gene , which is specifically up-regulated during cytosolic growth and is essential for pathogenesis . This ‘suicide’ strain harbored loxP sites in the chromosome flanking the origin of replication ( ori ) and several essential genes . Codon-optimized cre recombinase was expressed from the actA promoter ( Fig 1A ) . The suicide strain grew like wild type in rich media but was unrecoverable after infection of bone marrow-derived macrophages ( BMMs ) . A himar1 transposon library was then constructed in the suicide strain background and used to infect BMMs . When bacteria were isolated at five hours post-infection ( p . i . ) nearly all mutants harbored transposon insertions in cre , the actA promoter driving cre expression ( actA1p ) , loxP sites , and gshF , encoding glutathione synthase . To identify additional genes required during infection , colonies were isolated at three and four hours p . i , generating a library of 1 , 090 transposon mutants from an initial inoculum of >1 million bacteria . Colony PCR excluded strains with transposon insertions in cre and gshF , resulting in a collection of ~700 strains ( Fig 1A ) . Transposon mutants in the suicide background were screened individually for survival in BMMs , narrowing the list to 300 mutants . Six transposon insertions were identified in hly and nine insertions in prfA , emphasizing that cytosolic access and PrfA are absolutely required for actA activation and subsequent cre expression . Saturation of the screen was further demonstrated after identification of 11 insertions in the actA promoter driving cre and 31 insertions in the loxP sites ( which are each only 34 nucleotides ) . The remaining transposon mutations were transduced into a wild type background and analyzed in a plaque assay , a highly sensitive measure of cell-to-cell spread , which is completely dependent on actA expression [22] . Using a threshold of 85% , 12 mutants were identified that formed plaques significantly smaller than wild type in L2 murine fibroblasts ( Fig 2A and Table 1 ) . With one exception , the transposon insertions were in open reading frames and likely resulted in loss-of-function mutations . The transposon in the promoter of lmo2191 ( spxA1 ) , a gene predicted to be essential in L . monocytogenes [23] , resulted in a 10-fold decrease in spxA1 expression when the bacteria were grown in broth , essentially resulting in a knock-down strain ( S1 Fig ) . Attempts to make an in-frame deletion of spxA1 using conventional methods were unsuccessful , consistent with a previous report [23] . As the goal of this selection was to identify mutations that affect ActA expression in vivo , we measured ActA abundance during infection of BMMs . Four hours post-infection , cells were lysed and ActA and the constitutively expressed P60 protein were analyzed by immunoblot . Nine strains were found to express less ActA than wild type after normalizing to P60 abundance ( Fig 2B ) . The work-flow of this selection used cre expression from the actA promoter and plaque area as a criterion for inclusion in the core set of twelve mutants analyzed here . It was therefore unexpected that three mutants ( lmo0441::Tn , lmo0443::Tn , and citC::Tn ) did not display a defect in ActA abundance during intracellular growth . We hypothesize that these mutations may disrupt elements of bacterial physiology critical to appropriate Cre activity or normal growth . The twelve mutants isolated by the genetic selection were identified based on in vitro assays for virulence . While these assays are correlated to in vivo outcomes , the importance of these genes to L . monocytogenes pathogenesis was confirmed in a murine model of infection . Intravenous infection of mice revealed that four of the mutants displayed no virulence defect ( lmo0441::Tn , rsbX::Tn , lmo2107::Tn , and gtcA::Tn ) while the remaining eight mutants were significantly attenuated ( Fig 2C ) . It was surprising that four mutants exhibited impaired plaque formation yet were fully virulent; it is possible that these four mutants are impaired in other aspects of pathogenesis not reflected by changes in CFU during these infection conditions . To determine if the plaque defects in these mutants were due to cell-specific defects evident only in the L2 murine fibroblasts used for plaque assays , cell-to-cell spread defects were also analyzed in TIB-73 cells , a murine hepatocyte cell line ( Table 1 ) . We observed consistent phenotypes between the plaque defects in TIB-73 cells and L2 cells with the exception of citC::Tn , P-spxA1::Tn , and ohrA::Tn . However , these mutants were significantly attenuated during infection and thus it was unclear why they did not display a plaque defect in TIB-73 cells . The specificity of the transposon insertion in seven of the eight attenuated strains was confirmed by expressing the disrupted gene in trans and complementing the plaque defect ( S2 Fig ) . Attempts to complement the pplA::Tn plaque defect were unsuccessful . However , pplA mutants are difficult to complement and the mutant we identified exhibited phenotypes consistent with published ΔpplA defects [24] . Other reports have identified genes necessary for virulence of L . monocytogenes by comparing changes in gene expression in vivo [25–27] . In our analysis , only gshF was differentially transcribed between host cells and rich media ( Fig 2D ) . It remains to be investigated if the activity of these genes is regulated post-transcriptionally in response to the host . In this study we focused on the following genes that were required for actA expression and pathogenesis ( Fig 1B ) . yjbH ( lmo0964 ) encodes a putative thioredoxin similar to YjbH in Bacillus subtilis ( 57% amino acid similarity ) [28] . A transposon in L . monocytogenes yjbH was previously identified in a screen for mutants defective in LLO production in vitro and was found to be attenuated in a competitive infection model [29] . spxA1 ( lmo2191 ) encodes an ArsC family transcriptional regulator similar to the disulfide stress regulator Spx conserved in Firmicutes ( 83% amino acid identity to B . subtilis Spx ) [30] . The difference in nomenclature is due to the presence of a paralogous gene in L . monocytogenes ( lmo2426 or spxA2 ) that is 59% identical to B . subtilis Spx while B . subtilis encodes only a single spx . In B . subtilis and Staphylococcus aureus YjbH post-translationally regulates Spx [28 , 31] , although it is not known if this function is conserved in L . monocytogenes . lmo2199 encodes a hypothetical protein with a peroxiredoxin domain and is part of the organic hydroperoxide resistance ( Ohr ) protein subfamily . It is co-transcribed with lmo2200 , encoding a MarR family transcriptional regulator which was not required for virulence , suggesting that Lmo2200 may act as a transcriptional repressor [26] . In B . subtilis homologs of Lmo2199 and Lmo2200 are named OhrA ( 63% amino acid similarity ) and OhrR ( 68% ) , respectively , and we have adopted this nomenclature for consistency [32] . arpJ ( lmo2250 ) encodes a predicted amino acid ABC transporter permease that was originally identified in a screen for genes with increased intracellular expression [25] . However , the data presented here did not show an increase in arpJ expression during infection of BMMs . This may be explained by the different growth media and cell types used in the two studies . It is also possible that arpJ is autoregulated , as the previous study analyzed arpJ expression in an arpJ transposon mutant . pplA ( lmo2637 ) encodes a lipoprotein whose secretion is increased in a PrfA* mutant [33] . The signal sequence of this lipoprotein is processed into a secreted peptide , which is required for vacuolar escape from non-phagocytic cells [24] . Finally , gshF ( lmo2770 ) encodes the only glutathione synthase in L . monocytogenes [34] . Glutathione has been demonstrated to be an allosteric activator of PrfA and therefore gshF mutants are severely attenuated in vivo due to insufficient virulence gene expression [17] . Given the role of glutathione in activating PrfA , we hypothesized that suppressor mutations of ΔgshF might illuminate alternative pathways for PrfA activation , potentially involving other genes identified . Accordingly , we screened for mutations that increased the virulence of a ΔgshF mutant . Mice were serially infected with a high-inoculum of ΔgshF , livers were harvested at 72 hours p . i . , homogenized , and diluted to inoculate naive mice . After four successive infections bacteria isolated from infected livers were analyzed by plaque assay . This approach previously identified a mutation in prfA that constitutively activates the protein ( G145S ) , known as PrfA* , completely bypassing the requirement for glutathione during infection [17] . The ΔgshF PrfA* suppressor forms 100% plaque; therefore , for these experiments we selected bacteria that formed intermediate-sized plaques , which were then subjected to genome sequencing . Two suppressor mutants were isolated and found to encode a G>A mutation 58 nucleotides 5’ of the prfA start codon ( Fig 3A ) . This mutation lies within a previously identified site of negative regulation of prfA , the so-called “P2 promoter” ( prfA2p , Fig 3A ) and deletion of the -35 region of this promoter ( ΔP2 mutant ) results in a 10-20-fold up-regulation of the prfA1p-dependent prfA transcript [11] . We hypothesized that the prfA -58 G>A mutation also inactivated the P2 promoter and resulted in greater PrfA abundance . Indeed , the ΔP2 gshF::Tn double mutant and the ΔgshF prfA -58 G>A suppressor mutants all formed plaques approximately 60% the size of wild type ( Fig 3B ) . These results did not directly implicate any of the other genes identified in our genetic selection , however these findings did highlight the impact of both PrfA abundance and activation during infection . PrfA expression is controlled by a feed-forward loop in which activated PrfA drives its own transcription [12] . Strains expressing ΔP2 or PrfA* decouple PrfA abundance and activation whereby ΔP2 increases PrfA abundance but still relies on glutathione for PrfA activation; PrfA* increases both the amount and activity of PrfA , independent of glutathione . We next sought to determine if the other mutants identified in the screen affected PrfA abundance or activation by transducing each into L . monocytogenes ΔP2 and PrfA* backgrounds and measuring the plaque size in each background ( Fig 3C ) . Based on these analyses , mutants fell into three categories . The first category ( yjbH::Tn , P-spxA1::Tn , ohrA::Tn , and arpJ::Tn ) was unaffected by alterations in PrfA expression or activity , indicating that these genes were required down-stream of PrfA . In the second category was gshF::Tn , which was partially rescued by ΔP2 and completely rescued by PrfA* , consistent with the demonstrated role for glutathione as the allosteric activator of PrfA . The third category describes pplA::Tn , which formed 100% plaques in both the ΔP2 and PrfA* backgrounds . These data suggested that the pplA mutant was capable of activating PrfA ( because it was rescued by ΔP2 ) but was deficient in expression of PrfA-dependent genes required early during infection before cytosolic access and glutathione-mediated activation of PrfA . A principle difference between early and late PrfA-dependent genes is that expression of early genes are less dependent on PrfA activation by glutathione [35] . The two early genes are hly ( encoding LLO ) and plcA , which share a high-affinity PrfA-box and are transcribed by unactivated PrfA [35 , 36] . The ΔP2 mutation results in increased transcription of early genes but does not affect late gene expression , whereas PrfA* increases transcription of both early and late genes . We hypothesized that strains rescued by ΔP2 are specifically deficient in early gene expression . Accordingly , we analyzed early gene expression ( LLO production ) in broth for each mutant . Several of the mutants were found to secrete less LLO than wild type ( Fig 4A ) . To determine if the defect in LLO production led to impaired phagosomal escape and thus a plaque defect , these mutations were transduced into a Δhly mutant over-expressing hly from a constitutive HyPer promoter ( pH-hly strain ) [37 , 38] . In this background , efficiency of vacuolar escape should be equivalent in all strains , and indeed , equal LLO secretion was confirmed in broth . Constitutive expression of hly rescued the plaque defects of three mutants: P-spxA1::Tn , ohrA::Tn , and pplA::Tn ( Fig 4B ) . Interestingly , there was discordance between LLO production in broth and the defect in plaque formation one might predict from an LLO deficiency . For this reason , measuring LLO production in broth may be revealing aspects of bacterial physiology unrelated to LLO production in vivo . The above results suggested that mutations in P-spxA1 , ohrA , and pplA resulted in aberrant LLO secretion and/or that these mutants were unable to survive in the harsh environment of the vacuole . Constitutive expression of hly would likely overcome either defect . We attempted to segregate these two possibilities by analyzing sensitivity to vacuolar conditions , including reactive oxygen species which L . monocytogenes must adapt to in order to survive [39 , 40] . The response of each mutant to peroxide , disulfide stress , and organic hydroperoxide was analyzed by measuring their sensitivity to hydrogen peroxide ( H2O2 ) , diamide , and cumene hydroperoxide ( CHP ) , respectively . Knock-down of spxA1 and disruption of ohrA or gshF significantly increased the sensitivity of L . monocytogenes to both peroxide and disulfide stress ( Fig 4C ) . In accordance with its annotation and the published role of ohrA in B . subtilis [32] , the ohrA::Tn mutant was significantly more susceptible to CHP ( Fig 4C ) . As these results suggested a role for redox control of virulence genes , we tested the hypothesis that host reactive oxygen or nitrogen species may be sensed by the bacteria during infection to activate actA . However , growth of the suicide mutant was not rescued in BMMs lacking inducible nitric oxide synthase ( NOS2-/- ) or NADPH oxidase ( NOX2-/- ) ( S3 Fig ) . Therefore , L . monocytogenes may activate virulence genes in response to multiple redundant host cues or depend on yet unidentified host pathways . Constitutive production of hly restored the majority of the plaque defect for P-spxA1::Tn and ohrA::Tn , however , it did not restore the plaque to 100% of the parent strain ( Fig 4B ) . We hypothesized that these mutants might also be impaired in the ability to grow in the host cytosol , independently from virulence gene expression . All of the mutants identified in the screen grew similarly to wild type in BMMs with the exception of P-spxA1::Tn and ohrA::Tn ( Fig 4D ) . In fact , P-spxA1::Tn and ohrA::Tn were also the only mutants that exhibited growth defects in rich media ( Fig 4E ) . These pleiotropic growth defects and sensitivity to redox stress are likely why pH-hly was only partially able to complement the plaque defect of these mutants ( Fig 4B ) . Previous work clearly demonstrated that glutathione was essential for transcriptional activation of virulence genes [17] . In order to assess which factors might be independent of glutathione-dependent transcriptional activation , we combined each transposon with an in-frame ΔgshF mutation . The only mutation not epistatic to gshF was yjbH::Tn , which produced an additive plaque defect ( Fig 5A ) . Further , yjbH::Tn was not rescued by constitutive activation of hly ( Fig 4B ) or prfA ( Fig 3C ) . Together , these data suggested that yjbH was required for actA expression post-transcriptionally . Indeed , transcript levels of actA were identical in BMMs infected with wild type or the ΔyjbH mutant ( Fig 5B ) . It is intriguing that arpJ::Tn was epistatic to gshF , yet not rescued by constitutive activation of PrfA , indicating that arpJ may contribute to glutathione-dependent transcriptional activation of actA through an unknown mechanism . The actA gene is preceded by 149 nucleotides of untranslated mRNA ( Fig 5C ) which is important for sufficient ActA expression [21] . A strain was constructed in which ActA was expressed independent of PrfA by expressing the entire actA transcript ( including the 5’ UTR ) under the control of the constitutive HyPer promoter in a strain deleted for actA ( pH-actA Strain , Fig 5D ) . ActA protein abundance was then analyzed by immunoblot . In this background , ActA abundance was equivalent among all strains when the bacteria were grown in broth ( Fig 5E ) . However , during infection of BMMs , disruption of yjbH resulted in significant impairment in ActA abundance ( Fig 5F ) , indicating a failure to translationally activate actA . Given that disrupting yjbH rescued the death of the suicide strain in which cre was expressed under actA1p and the 5' UTR , these data indicate a genetic interaction between yjbH and the 5’ UTR of actA . To further support this genetic interaction we engineered a fluorescent strain of L . monocytogenes in which rfp was expressed under the actA1p promoter and 5' UTR ( actA1p-rfp , Fig 5G ) . During infection of BMMs the ΔyjbH actA1p-rfp strain exhibited significantly less fluorescence than wild type actA1p-rfp ( Fig 5H ) . Unfortunately , we were unable to interrogate the effect of a yjbH mutation on ActA abundance in the absence of its 5’ UTR due to an inability to detect ActA when the 5’ UTR was deleted , consistent with this region being critical for ActA expression [21] . A drawback to pH-actA is that although ActA is over-expressed in broth , this strain still elaborates much less ActA in vivo and fails to form a plaque ( Fig 5E and 5F ) . To analyze the role of translational activation during infection , the actA gene and 5’ UTR were moved to a neutral locus within the L . monocytogenes chromosome [41] . In this strain , actA was expressed only from the PrfA-dependent actA1p proximal promoter , eliminating read-through transcription from the distal actA2p promoter ( Fig 5C ) . This strain was called actA1p and was only mildly impaired in plaque formation and virulence ( Fig 5I and 5J ) . However , actA1p yjbH::Tn was unable to form a plaque ( Fig 5I ) . The importance of actA translational activation was further underscored by a 3-log defect for actA1p yjbH::Tn in the livers of infected mice ( Fig 5J ) . These data revealed a critical role for yjbH in actA activation that was less apparent in the wild type background due to redundant PrfA-dependent promoters .
In this study , rather than search for novel virulence factors or genes up-regulated in vivo , we screened for genes required for activation of an essential determinant of L . monocytogenes pathogenesis ( ActA ) that is up-regulated over 200-fold during intracellular growth . Mutants identified in the genetic selection fell into three broad categories: ( 1 ) those that failed to reach the cytosolic compartment; ( 2 ) mutants that entered the cytosol , but failed to activate the master virulence transcriptional regulator PrfA; and ( 3 ) mutants that entered the cytosol and activated transcription of actA , but failed to synthesize it ( Fig 6 ) . This approach highlighted how expression of virulence factors is spatially and temporally compartmentalized via regulation of transcription and translation during infection . One of the most striking findings of this study was that the majority of genes identified in the selection encode proteins predicted to control bacterial redox regulation , suggesting that redox changes represent one of the biological cues sensed by L . monocytogenes to regulate its virulence program . Redox stress during infection can arise from endogenous by-products of bacterial metabolism and exogenously derived factors generated by the host . However , it remains to be discovered whether the redox stress that may trigger virulence gene expression is produced by the host , the bacteria , or both . YjbH , Spx , OhrA , and GshF have defined roles in maintaining redox homeostasis in the presence of disulfide and organic peroxide stresses in Firmicutes . In B . subtilis OhrA is a peroxiredoxin required during organic hydroperoxide stress [32] . In S . aureus and B . subtilis YjbH interacts with Spx to regulate the abundance and activity of Spx [28 , 31] . Specifically , YjbH-bound Spx is recognized by the ClpXP protease and is degraded so that Spx concentrations are low under steady-state conditions [42 , 43] . During disulfide stress the YjbH:Spx interaction is disrupted by intramolecular disulfide bonds in both proteins that result in reduced proteolysis of Spx . B . subtilis Spx represses transcription of 176 genes and activates transcription of 106 genes [44] , the majority of which are required to adapt to redox stress , including genes for production of the low-molecular weight ( LMW ) thiol utilized by B . subtilis , bacillithiol [45] . L . monocytogenes spxA1 cannot be deleted and its regulon has not yet been characterized [23] . Similarly , in Streptococcus pneumoniae simultaneous deletion of both spxA1 and spxA2 paralogues is lethal [46] , supporting the notion that the Spx regulon ( s ) may contain essential genes in some Firmicutes . Mutants exhibiting the most severe virulence phenotypes contained insertions in gshF , which encodes the sole L . monocytogenes glutathione synthase [34] . Glutathione is a tripeptide LMW thiol antioxidant present at millimolar concentrations that contributes to maintaining a reducing environment in both bacterial and host cells [47] . Not surprisingly , L . monocytogenes ΔgshF mutants are more sensitive to redox stressors such as hydrogen peroxide and diamide and are 200-fold less virulent in mice , indicating that bacterially-derived glutathione is essential for pathogenesis [17] . However , ΔgshF mutants are fully virulent in L . monocytogenes harboring prfA* mutations that lock PrfA in its constitutively active conformation . Therefore , the primary role of GshF-derived glutathione during infection is to activate virulence gene expression via PrfA activation , although we cannot rule out a contribution of imported host-derived glutathione [17] . Indeed , host-derived glutathione activates virulence gene expression in Burkholderia pseudomallei [48] . In the case of L . monocytogenes , gshF is transcriptionally up-regulated 10-fold during intracellular growth , suggesting the existence of an unidentified cue , likely redox-related , that stimulates glutathione production . The identification of many redox-related bacterial factors in this genetic selection led to our working model that specific redox changes during infection are sensed by the bacteria as a mechanism to identify their intracellular location and activate virulence genes appropriately . Redox stress during infection could arise from host-derived antimicrobial factors . For example , the host generates antibacterial factors that assault invading pathogens with redox stresses , including: reactive oxygen species ( ROS ) , reactive electrophilic species ( RES ) such as methylglyoxal , and reactive nitrogen species ( RNS ) such as nitric oxide and peroxynitrite [40 , 49 , 50] . Interestingly , these redox stresses from the host are spatially compartmentalized . RNS and ROS are produced in the phagosome and once in the host cytosol , L . monocytogenes is confronted with RES and mitochondrial-derived ROS [40 , 51] . It is possible that the bacterial response to the redox stressors is also compartmentalized , requiring specific factors in the vacuole ( such as spxA1 and ohrA ) and host cytosol ( such as yjbH ) . Eliminating host nitric oxide synthase ( NOS2 ) or NADPH oxidase did not rescue growth of the suicide mutant ( S3 Fig ) . NOS2-generated nitric oxide is required for efficient L . monocytogenes cell-to-cell spread during infection , although this is due to the nitric oxide-mediated delay of phagolysosome maturation and not a direct effect on the bacteria [52] . Together , these data suggest that a combination of host factors are likely required to activate actA during infection . Alternatively , the source of redox stress may come from bacterial metabolism via ROS generated from incomplete reduction of oxygen during aerobic respiration [53] . Carbon source and phosphate abundance also affect the production of ROS and methylglyoxal [54 , 55] . PrfA activity has been demonstrated to be sensitive to available carbon sources [2] . Growth on plant-derived beta-glucoside sugars in the environment , such as cellobiose , represses PrfA activation , whereas growth on host-derived sugars such as glucose-1-phosphate stimulates PrfA-dependent gene expression [9 , 56 , 57] . Therefore , entry of L . monocytogenes into the host cytosol results in a remodeling of carbon metabolism that may be linked to virulence gene regulation . Glycerol is the principle carbon source used by L . monocytogenes intracellularly and growth on glycerol is a well-described stimulant of methylglyoxal production [58–61] . In B . subtilis , methylglyoxal stress stimulates the Spx regulon and production of bacillithiol , a low molecular weight thiol used by B . subtilis to detoxify methylglyoxal [62] . Thus , the 10-fold increase in gshF transcript levels in L . monocytogenes may correspond to increased methylglyoxal production during infection , which would further link metabolism of an alternative carbon source to virulence . Coupling of metabolism to virulence gene regulation may allow the system to remain OFF in the environment while remaining poised to turn ON upon entering a host . Considering our finding of multiple redox factors that are required for proper virulence gene expression , we speculate that changes in carbon metabolism could alter the endogenous levels of ROS and RES produced , thus affecting PrfA activation and leading to the “sugar-mediated repression” observed previously [9] . Appropriate up-regulation of actA at the translational level is understood to require its 5’ UTR , although the mechanism remains unknown [21] . The data reported here further emphasize the sensitivity of actA translation to the environment in which L . monocytogenes is growing . In broth , the PrfA* strain elaborated 2 . 4% the amount of ActA protein as compared to constitutively expressed actA ( Fig 5E ) , and increased 200-fold during infection ( Fig 5F ) , despite the fact that transcript levels of actA are equivalent in both growth conditions [17] . These data emphasize the importance of the translational control of this virulence factor . Importantly , yjbH was required for the increased abundance of ActA protein during infection . In wild type L . monocytogenes , multiple PrfA-dependent promoters may compensate for loss of translational activation; however , when actA was isolated under its most proximal promoter , disruption of yjbH resulted in an attenuation of over 3-logs in the livers of infected animals ( Fig 5J ) . It seems unlikely that the thioredoxin YjbH activates translation of actA via direct binding to the 5’ UTR . However , YjbH may indirectly activate translation via interaction with another factor ( s ) or modulation of a small-molecule signal produced by the host . PrfA-dependent transcription and activation are regulated redundantly at multiple levels , including: a temperature-sensitive riboswitch [63] , allosteric activation by glutathione [17] , multiple read-through transcripts [10 , 64] , positive and negative promoter elements [11 , 65] , and yet to be fully characterized translational control . The complexity of actA activation is likely the result of selective pressure to respond appropriately to host-derived cues . This study investigated the virulence defects associated with failure to up-regulate virulence genes; however , over-production or inappropriate regulation of virulence factors extracellularly also results in a competitive disadvantage for L . monocytogenes [19 , 20] . How L . monocytogenes and other intracellular pathogens regulate virulence gene expression is central to understanding their pathogenesis . Results reported here suggest that redox cues are a mechanism by which intracellular pathogens recognize the host and represents an exciting new area of further investigation .
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 protocols were reviewed and approved by the Animal Care and Use Committee at the University of California , Berkeley ( AUP-2016-05-8811 ) . All L . monocytogenes strains are a derivative of wild type 10403S [67 , 68] and were cultivated in Brain Heart Infusion ( BHI , Difco ) , shaking at 37°C unless otherwise stated . All E . coli strains were cultivated shaking in LB ( Miller ) at 37°C . Antibiotics ( purchased from Sigma ) were used at the following concentrations: carbenicillin ( 100 μg/mL ) , streptomycin ( 200 μg/mL ) , chloramphenicol ( 7 . 5 μg/mL for L . monocytogenes and 10 μg/mL for E . coli ) , erythromycin ( 1 μg/mL ) , and tetracycline ( 2 μg/mL ) . All E . coli strains are listed in Table 2 and all L . monocytogenes strains are listed in Table 3 . Bacterial broth growth curves were performed as previously described [69] . The suicide strain was a gift from Peter Lauer and Bill Hanson ( Aduro Biotech ) ; details of its construction are reported elsewhere [17] . Briefly , loxP sites were inserted on either side of the origin of replication by allelic exchange into a ΔactAΔinlB strain of L monocytogenes . A transcriptional fusion of cre with actA that included the actA1p promoter , 5’ UTR , and ribosomal binding site of actA , was inserted adjacent to a loxP site . Knock-in of pPL2 derivative plasmids was performed by standard methods [41] . Briefly , constructed pPL2 plasmids were transformed into chemically competent SM10 E . coli , selecting on chloramphenicol . Donor SM10 and recipient L . monocytogenes were mixed at a 1:1 ratio on a non-selective BHI plate at 37°C for 4–24 hours , then trans-conjugation was selected for by plating bacteria on BHI containing streptomycin plus either chloramphenicol ( pPL2 ) , erythromycin ( pPL2e ) , or tetracycline ( pPL2t ) . Single colonies were re-streaked for purifying selection onto BHI containing the same antibiotics as used after trans-conjugation . In-frame deletions of genes was accomplished by allelic exchange using pKSV7-oriT and conventional methods [64] . Briefly , the constructed knock-out plasmid was transformed into SM10 E . coli , recovered on LB containing carbenicillin , and trans-conjugated into L . monocytogenes by mixing the donor SM10 and recipient L . monocytogenes at a 1:1 ratio on a non-selective BHI plate for 4–24 hours at 30°C , the permissive temperature for pKSV7-oriT to replicate in Gram-positive organisms . Trans-conjugation was selected on BHI containing both streptomycin and chloramphenicol at 30°C . After isolation of a single colony of L . monocytogenes containing pKSV7-oriT at 30°C , bacteria were grown at 42°C on BHI agar containing both streptomycin and chloramphenicol to select for chromosomal integration . Colonies were re-streaked onto selective media at 42°C two additional times for purifying selection and integrated pKSV7-oriT . This strain was then serially passaged at 30°C to enrich for excision and loss of pKSV7-oriT . Mutants that lost pKSV7-oriT were identified by sensitivity to chloramphenicol using indirect patch-plating methods . Finally , allelic exchange was confirmed by PCR and , when necessary , Sanger DNA sequencing . Preparation of electro-competent L . monocytogenes and himar1 transposon mutagenesis were performed as previously described [29] , generating a transposon mutant library that was not fully characterized previously [17] . Transposon junctions were mapped as previously described [71] . The position of each himar1 transposon refers to to the distance of the insertion site , 3’ of the first nucleotide of each gene . Transposons were mapped to the 10403S genome , however , for continuity of nomenclature the EGD-e loci names have been used . For reference: lmo0441 ( LMRG_00133 ) , lmo0443 ( LMRG_00135 ) , rsbX is lmo0896 ( LMRG_02320 ) , yjbH is lmo0964 ( LMRG_02063 ) , citC is lmo1566 ( LMRG_01401 ) , lmo2107 is ( LMRG_01261 ) , spxA1 is lmo2191 ( LMRG_01641 ) , ohrA is lmo2199 ( LMRG_01633 ) , arpJ is lmo2250 ( LMRG_01581 ) , gtcA is lmo2549 ( LMRG_01698 ) , pplA is lmo2637 ( LMRG_02182 ) , gshF is lmo2770 ( LMRG_01925 ) . Transposons in the chromosome were introduced into different genetic backgrounds by generalized transduction using the phage U153 , as previously described [29 , 75] . Briefly , a transducing lysate was generated by lysogenizing approximately 109 CFU of L . monocytogenes transposon donor with approximately 107 PFU of phage in 3–4 mL of 0 . 7% LB Agar containing MgSO4 and CaCl2 ( 10 mM each ) on LB agar and incubated overnight at 30°C . Phage was soaked out of the agar by incubating with 5 mL of TM buffer ( 10 mM Tris , pH 7 . 5 and 10 mM MgSO4 ) for 8–24 hours and these recovered phage stocks were filter sterilized . With the newly generated transducing lysate , 108 L . monocytogenes recipients were lysogenized with 107 PFU of lysate , incubated at 30°C for 30 min in LB containing MgSO4 and CaCl2 ( 10 mM each ) , and then plated on selective BHI agar at 37°C . When transducing the himar1 transposon using erythromycin selection , colonies appeared after two days . These colonies were purified by re-streaking transductants for single colonies and verified by sequencing the transposon junction . U153 phage stocks were propagated using L . monocytogenes strain SLCC-5762 . Knock-in plasmids were constructed as previously described using primers listed in Table 4 and reagents are from New England Biolabs , unless otherwise specified [71] . Briefly , vectors for complementing yjbH and spxA1 were constructed by amplifying each gene along with its predicted native promoters using a reverse primer that appended a DNA sequence encoding a six histidine affinity tag at the C-terminus . These DNA fragments and pPL2 [41] were then digested with KpnI and BamHI and ligated using Quick Ligase , according to manufacturer’s instructions . The arpJ and ohrA complement vectors were constructed by amplifying their entire predicted operon and predicted native promoter ( arpJ: LMRG_01581-LMRG_01580 , ohrA: LMRG_01632-LMRG_01633 ) without addition of affinity tags . The DNA fragment was combined with linearized pPL2t harboring a transcriptional terminator [71] and assembled using In-Fusion Cloning ( Clontech ) or Gibson Assembly Ultra ( Synthetic Genomics ) . The pPL2t . Phyper-actA vector was constructed by amplifying both 5’ UTR and CDS of actA ( LMRG_02626 ) , and combining the DNA fragment with linearized pPL2t harboring a modified Pspac-hy ( Phyper ) [38] sequence: “aattgtgagcgctcacaattttgcaaaaagttgttgactttatctacaaggtgtggcataatgtgtGTAATTGTGAGCGCTCACAATT” , inserted via gBLOCK ( IDT ) , and a transcriptional terminator for assembly using In-Fusion Cloning ( Clontech ) . The pKSV7-oriT-ΔyjbH vector was constructed according to methods previously described [71] . Briefly , the vector was constructed by sequentially amplifying ~1 kb of homology flanking the yjbH coding region using primers in Table 3 . These two fragments were joined by sequence overlap extension PCR , which included the coding region for the first six and last six amino acids of YjbH . The final PCR fragment and pKSV7-oriT were digested with KpnI and PstI ( rSAP was also included for the vector ) and ligated using Quick Ligase . The ligation product was transformed into XL1 Blue E . coli and transformants were screened by PCR for the presence of the insert , followed by Sanger sequencing confirmation . The plaque assay was carried out by conventional methods [22 , 76] . Briefly , L2 fibroblasts ( generated previously from L929 cells [77] and provided as a generous gift from Susan Weiss in 1988 , as detailed in Sun et al . [22] ) or TIB-73 hepatocytes ( ATCC TIB-73 ) were maintained in high-glucose DMEM medium plus 10% FBS ( Hyclone ) , 2 mM L-glutamine ( Gibco ) , and 1 mM sodium pyruvate ( Gibco ) . Cells were plated at 1 . 2 x 106 cells per well in a six-well dish and infected the next day at an MOI of 300 with L . monocytogenes grown overnight at 30°C , stationary . The infection was allowed to proceed for one hour before the wells were washed twice with PBS and 3 mL of medium plus 0 . 7% agarose and 10 μg/mL gentamicin was overlaid . At 48 hours post-infection the plaques were stained with 2 mL of medium plus 0 . 7% agarose , 10 μg/mL gentamicin , and 25 μL/mL neutral red ( Sigma ) . The plaques were then imaged at 72 hours post-infection . Plaque area was quantified using ImageJ software [78] . Each experiment represented an average of the area of at least five plaques per strain as a proportion to wild type plaques in that experiment . Data are representative of at least three experiments . Macrophage growth curves were performed as previously described [72 , 79] . Briefly , bone marrow-derived macrophages ( BMMs ) were derived from bone marrow of C57BL/6 mice purchased from The Jackson Laboratory and were cultivated/differentiated in high-glucose DMEM medium containing CSF ( from mouse CSF-1-producing 3T3 cells ) , 20% FBS ( Hyclone ) , 2 mM L-glutamine ( Gibco ) , 1 mM sodium pyruvate ( Gibco ) , and 14 mM 2-mercaptoethanol ( BME , Gibco ) . BMMs were derived as previously described and plated in 60 mm non-TC treated dishes that contained 14 TC-treated coverslips at 3 x 106 cells per dish . These dishes were then infected at an MOI of 0 . 1 for 30 minutes , washed twice with PBS prior to replacing media , and gentamicin was added at 50 μg/mL one hour post-infection . Three coverslips were removed from each dish at 0 . 5 , 2 , 5 , and 8 hours post-infection and added to 5 mL of sterile water . Coverslips were rigorously mixed prior to plating on LB agar . Each graph is representative of three experiments and each data point represents the average of three coverslips . To analyze virulence , female CD-1 mice were infected intravenously ( i . v . ) via the tail vein using 200 μL of sterile PBS containing 105 CFU of each L . monocytogenes strain as previously described [80] . The infection was allowed to progress for 48 hours , at which point animals were euthanized and the spleens and livers were harvested . Organs were homogenized in 0 . 1% NP-40 and serial dilutions were plated on LB agar containing streptomycin . Graphs represent pooled data from at least two experiments of greater than four mice each . Groups were statistically compared using a heteroscedastic Student’s t-test . In vivo suppressors were identified similarly to previously described methods [17] . Briefly , CD-1 mice were infected i . v . with 1 x 107 CFU of ΔgshF for 72 hours and the livers were harvested , homogenized , and 100 μL was inoculated into broth . Naïve mice were then infected with these liver homogenate cultures . After four successive infections bacteria isolated from infected livers were analyzed via plaque assay and two strains with intermediate plaque phenotype were selected for genome sequencing . Genomic DNA was isolated from L . monocytogenes using the MasterPure Gram-Positive DNA Purification Kit ( Epicentre ) according to the manufacturer's instructions . Genome sequencing and DNA library preparation was performed as previously described [71] at the Vincent J . Coates Genomics Sequencing Laboratory at UC Berkeley . Data was assembled and aligned to the 10403S reference genome ( GenBank: GCA_000168695 . 2 ) demonstrating >50x coverage . SNP/InDel/structural variation was determined as compared to the ΔgshF parent strain using CLC Genomics Workbench ( CLC bio ) . All immunoblotting was performed as previously described [17] . Briefly , for bacteria grown in broth , overnight cultures were diluted 1:10 into BHI , incubated for five hours at 37°C , shaking , then the bacteria were separated from the supernatant by centrifugation . For secreted proteins , the supernatant was treated with 10% v/v TCA for one hour on ice to precipitate all proteins . The protein pellet was washed twice with ice- cold acetone , followed by vacuum drying . The proteins were dissolved in LDS buffer ( Invitrogen ) containing 5% BME using a volume that normalized for OD600 of harvested bacteria , boiled for 20 minutes , and separated by SDS-PAGE . For surface associated proteins , bacteria were suspended in 150 μL of LDS buffer containing 5% BME , boiled for 20 minutes , and proteins separated by SDS-PAGE . Immunoblots of bacteria grown intracellularly within infected BMMs used 12-well dishes with BMMs at a density of 106 cells per well and infected with an MOI of 10 . One hour post-infection the cells were washed and media containing gentamicin ( 50 μg/mL ) was added . Four hours post-infection the cells were washed twice with PBS and harvested in 150 μL LDS buffer containing 5% BME . The samples were then boiled and separated by SDS-PAGE . Primary antibodies were each used at a dilution of 1:5 , 000 , including: rabbit polyclonal antibody against the N-terminus of ActA [81] , rabbit polyclonal antibody against LLO , and a mouse monoclonal antibody against P60 ( Adipogen ) . P60 is a constitutively expressed bacterial protein used as a loading control [82] . All immunoblots were visualized and quantified using Odyssey Imager and appropriate secondary antibodies from the manufacturer according the manufacturer’s instructions . Transcript analysis in broth was performed as previously described [83] . Briefly , bacteria were grown overnight in BHI and subcultured 1:100 into 25 mL BHI . Bacteria were harvested at an OD600 = 1 . 0 . Transcript analysis during infection was analyzed as previously described [17] . Briefly , BMMs were plated at a density of 3 x 107 cells in 150 mm TC-treated dishes and infected with an MOI of 10 . One hour post-infection the cells were washed and media containing gentamicin ( 50 μg/mL ) was added . Four hours post-infection the cells were washed with PBS and lysed in 5 mL of 0 . 1% NP-40 . After collecting the lysate , the dishes were then washed in RNAprotect Bacteria Reagent ( Qiagen ) , which was combined with the lysate . Bacteria were isolated by centrifugation . Bacteria harvested from either broth or BMMs were lysed in phenol:chloroform containing 1% SDS by vortexing with 0 . 1 mm diameter silica/zirconium beads ( BioSpec Products Inc . ) . Nucleic acids were precipitated from the aqueous fraction overnight at -80°C in ethanol containing 150 mM sodium acetate ( pH 5 . 2 ) . Precipitated nucleic acids were washed with ethanol and treated with TURBO DNase per manufacturer’s specifications ( Life Technologies Corporation ) . RNA was again precipitated overnight and then washed in ethanol . RT-PCR was performed with iScript Reverse Transcriptase ( Bio-Rad ) and quantitative PCR ( qPCR ) of resulting cDNA was performed with KAPA SYBR Fast ( Kapa Biosystems ) . Primers used for qPCR are listed in Table 4 . Disk diffusions were performed similarly to previously described methods [84] . Briefly , approximately 3 x 107 CFU from overnight cultures of bacteria were immobilized using 4 mL of molten ( 55°C ) top-agar ( 0 . 8% NaCl and 0 . 8% bacto-agar ) spread evenly on tryptic soy agar plates . After the agar cooled , Whatman paper disks soaked in 5 μL of 5% hydrogen peroxide , 1 M diamide solution , or 80% cumene hydroperoxide solution were placed on top of the bacteria-agar . The zone of inhibition was measured after 18–20 hours of incubation at 37°C . BMMs were differentiated and cultivated as described for BMM growth curves . Cells were plated at 5 x 105 cells per well in a 24-well dish in media without antibiotics . The following day BMMs were infected at an MOI of 5 with L . monocytogenes mutants that had been incubated at 30°C without shaking . After 30 minutes cells were washed once with PBS and fresh media containing gentamicin ( 50 μg/mL ) was added . Six hours post infection media was removed from each well , the cells were washed with 1 mL of PBS , and 0 . 5 mL of PBS was replaced for each well . RFP fluorescence was measured using a plate reader ( Infinite M1000 PRO , TECAN ) with 555 nm excitation , 584 nm emission , and 5 nm band filters . Each well was interrogated 64 times on an 8 X 8 grid and the edge reads were excluded . Data were normalized by subtracting baseline fluorescence of wild type ( without RFP ) infected cells and plotting data as a percentage of wild type expressing actA1p-rfp . Each experiment represents three infected wells per L . monocytogenes genotype and data are representative of three pooled independent experiments . | Upon recognition of the host , bacterial pathogens activate a genetic virulence program to establish their replicative niche . In this study , we selected for mutants in the model intracellular pathogen Listeria monocytogenes that did not up-regulate virulence factors during infection . The screen identified genes involved in sensing the host cell and suggests a model in which expression of virulence factors is spatially and temporally compartmentalized via regulation of transcription and translation . Specifically , results described here indicate two non-redundant host cytosolic cues are sensed by the bacterium in order to activate its virulence program . Future research will illuminate the exact molecular identity of these cytosolic signals . However , the majority of the genes identified are part of the bacterial redox stress response , suggesting that redox changes represent one of the biological cues sensed by L . monocytogenes to regulate its virulence program . | [
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"gen... | 2016 | An In Vivo Selection Identifies Listeria monocytogenes Genes Required to Sense the Intracellular Environment and Activate Virulence Factor Expression |
Urbanization is one of the major drivers of dengue epidemics globally . In Kenya , an intriguing pattern of urban dengue virus epidemics has been documented in which recurrent epidemics are reported from the coastal city of Mombasa , whereas no outbreaks occur in the two major inland cities of Kisumu and Nairobi . In an attempt to understand the entomological risk factors underlying the observed urban dengue epidemic pattern in Kenya , we evaluated vector density , human feeding patterns , vector genetics , and prevailing environmental temperature to establish how these may interact with one another to shape the disease transmission pattern . We determined that ( i ) Nairobi and Kisumu had lower vector density and human blood indices , respectively , than Mombasa , ( ii ) vector competence for dengue-2 virus was comparable among Ae . aegypti populations from the three cities , with no discernible association between susceptibility and vector cytochrome c oxidase subunit 1 gene variation , and ( iii ) vector competence was temperature-dependent . Our study suggests that lower temperature and Ae . aegypti vector density in Nairobi may be responsible for the absence of dengue outbreaks in the capital city , whereas differences in feeding behavior , but not vector competence , temperature , or vector density , contribute in part to the observed recurrent dengue epidemics in coastal Mombasa compared to Kisumu .
Dengue virus ( DENV ) is a global public health threat with epidemics mostly reported in urban and semi-urban areas [1 , 2] . Dengue virus consists of four related serotypes ( DENV-1-4 ) , belonging to the genus Flavivirus ( Family: Flaviviridae ) [3] . The most recent epidemics in Africa have predominantly been reported in East African countries , with DENV-2 responsible for the highest number of epidemics [4 , 5] . Dengue epidemics have been linked to urbanization , globalization , climate change , and the broad distributional range of the primary vector , Aedes aegypti [6–9] . There are a number of factors such as temperature , vector bionomics ( survival , density , feeding frequency/behavior ) , extrinsic incubation period ( EIP ) , and vector competence that can affect DENV transmission [10–17] . Whilst determination of individual factors is valuable , they are rarely studied in parallel , yet their combined effects may be critical to fully understanding the complex interrelationships influencing DENV transmission risk . Studies investigating the various dengue risk factors in parallel are lacking in many endemic areas , including Kenya . In the last decade , dengue has re-emerged as one on the most important vector-borne diseases in Kenya , with recurrent urban outbreaks occurring in coastal areas , particularly in and around the city of Mombasa [18–20] . In contrast , no outbreaks have been reported in the other major cities of Kisumu and Nairobi , in spite of population movement between cities . Previous entomological studies in Kenya have examined risk of DENV transmission by studying vector density/abundance and vector competence data separately [10–12 , 20] . However , the observed differential dengue outbreak pattern in these urban areas remains unexplained . In this study , different risk parameters related to the DENV vector in the three major cities of Kenya; Mombasa , Kisumu , and Nairobi were studied in parallel to gain a better understanding on their potential influence on the observed differential outbreak patterns . We hypothesized that 1 ) the Ae . aegypti vector density differs between these three cities , 2 ) the ability of Ae . aegypti to transmit DENV-2 ( vector competence ) differs between populations from these cities and may be influenced by temperature , 3 ) the Ae . aegypti populations in the three cities differed in their anthropophilic behavior , and 4 ) there is an underlying population genetic component to DENV-2 susceptibility of Ae . aegypti in each city . An improved understanding of the factors responsible for differences in dengue outbreak risk is key to informing targeted interventions and preventing future outbreaks in the urban areas of Kenya .
Scientific and ethical approval was obtained from Kenya Medical Research Institute Scientific and Ethics Review Unit ( KEMRI-SERU ) ( Project Number SERU 2787 ) . We sought permission from household heads through oral informed consent to allow their residences to be surveyed for mosquitoes . The animal use component was reviewed and approved by the KEMRI Animal care and use committee ( KEMRI ACUC ) ( approval number KEMRI/ACUC/ 03 . 03 . 14 ) through the KEMRI-SERU review process . The KEMRI ACUC ensures adherence to national guidelines on the care and use of animals in research and education in Kenya enforced by National Commission for Science , Technology and Innovation ( NACOSTI ) . The Institute has a foreign assurance identification number F16-00211 ( A5879-01 ) from the Office of Laboratory Animal Welfare under the Public Health Service and commits to the International Guiding Principles for Biomedical Research Involving Animals . This study was carried out in three major cities of Kenya; Mombasa ( dengue endemic , average monthly temperature 27–31°C ) , Kisumu , and Nairobi ( no dengue outbreak reports , average monthly temperatures of 28–30°C and 22–28°C , respectively ) . Mombasa ( 4°03'S 39°40'E , population 1 . 2 million people ) is Kenya’s second largest city and is located on the coast . Apart from being a major tourist site , Mombasa is also an important port city . Inland Nairobi ( 01°17'S36°48'E , population 3 . 1 million people ) is the capital city of Kenya . Kisumu ( 0°03′S34°45′E , population >400 , 000 ) is located on the shores of Lake Victoria and is the third largest city in Kenya . All three cities are characterized by the presence of a national/international airport and thus serve as local , regional , and international transport hubs . These cities serve as main gateways to East Africa , and due to the ease of interconnectivity , we would expect periodic generation of dengue epidemics in all three cities resulting from either importation of infectious cases or infected vectors into these cities from within/outside the country [8] . This , however , has not been the case in Kenya , and epidemics ( of DENV-1 , DENV-2 , and DENV-3 ) remain primarily limited to the city of Mombasa , with DENV-1 and DENV-2 responsible for the highest number of cases . All three cities experience three seasons; the long-rain ( April-June ) , the short-rain ( October-December ) , and the dry ( January-March and July-September ) seasons . Outbreaks that have occurred in Mombasa have mostly been reported during the long-rain season [20] . In Mombasa , we specifically selected sites from around the city ( Rabai-Kilifi ) based on previous history of DENV-2 circulation [19] . In Kisumu and Nairobi , selection of sites was partly informed by logistical constraints , such as ease of access to homes . The study sites were Kanyakwar , Kajulu , and Nyalenda B in Kisumu and Githogoro in Nairobi ( Fig 1 ) . Host seeking Ae . aegypti mosquitoes were collected from Mombasa , Kisumu , and Nairobi during the long-rain ( April-June ) , short-rain ( October-December ) , and dry ( January-March and July-September ) seasons , during 2014–2016 . The seasons were defined by the average amount of rainfall two weeks prior to mosquito sampling . The data was obtained from the Kenya Meteorological Department and the values were 12 . 4 , 10 . 8 and 8 . 3 mm during the long-rains , 5 . 5 , 4 . 0 and 7 . 3 mm during the short-rains and 0 , 0 . 3 and 0 mm during the dry season in Mombasa , Kisumu and Nairobi , respectively . Briefly , BG-Sentinel traps ( BioQuip Products , Rancho Dominiguez , CA , USA ) were baited with carbon dioxide in the form of dry ice . In each city , 12 traps were set up in vegetation close to human habitation at our selected sites at 7 am and retrieved at 6 pm on the same day [11] . This was done for five consecutive days in each season in each city , translating to 180 traps per city ( 60 traps per season ) . Alongside the BG trapping , Ae . aegypti mosquitoes were collected indoors ( sitting room , bedroom , and kitchen ) using a battery powered Prokopack aspirator ( BioQuip Products , Rancho Dominguez , CA , USA ) from 150 houses per city ( 50 per season ) . Aspiration was done between 11am and 3pm , and lasted about 20 minutes per house . Mosquitoes were morphologically identified using taxonomic keys [21–23] at the International Centre of Insect Physiology and Ecology ( icipe ) as previously described [11] . The number of female Ae . aegypti mosquitoes collected was recorded and used to estimate the vector density . This was done by dividing the total number of female Ae . aegypti mosquitoes collected by the number of traps for each city . Similarly , the blood-fed Ae . aegypti mosquitoes trapped were used to perform a blood meal analysis . In addition to mosquito collection conducted using the BG-Sentinel traps , we also attempted to collect blood-fed mosquitoes using a prokopack aspirator indoors , outdoors , and on the nearby vegetation from 150 houses per city ( 50 per season ) . As no blood-fed Ae . aegypti mosquito were collected using the aspirator approach [11] , blood meal analyses were performed on wild-caught , blood-fed Ae . aegypti mosquitoes collected using BG-Sentinel traps , only . The abdomen of individual mosquitoes was cut using a scalpel , sterilized with 70% ethanol between specimens to prevent cross contamination of samples . Genomic DNA was extracted from whole blood contained in individual mosquito abdomens using the DNeasy blood and tissue Kit ( Qiagen , GmbH-Hilden , Germany ) as per the manufacturer’s recommendation . The extracted DNA was used as a template for amplification of a 500 bp fragment of the mitochondrial 12S rRNA gene ( S1 Protocol ) , a target used for mammalian blood meal identification [24] . Amplicons were individually purified using ExoSap PCR purification kit ( USB Corp . , Cleveland , OH ) . Unidirectional sequencing ( forward strand ) was outsourced to a commercial company ( Inqaba biotec , Pretoria , South Africa ) . Sequences were evaluated through BLAST nucleotide searches against the Genbank database ( www . ncbi . nlm . nih . gov/blast ) in order to identify the closest sequence matches ( threshold > 98% ) and infer the species identity of the blood meal . To increase the sample size of blood fed mosquitoes , an additional sampling was done using the BG-Sentinel traps for 7 consecutive days ( 12 traps per day ) during the long-rain season ( April-June ) in 2018 , for the three cities . These mosquitoes , which were used for host blood meal determination alone , were processed for blood meal identification in the same manner as the blood-fed mosquitoes collected during 2014–2016 . Aedes aegypti density ( total number of female Ae . aegypti per trap ) was estimated and the difference between the cities compared using a t-test . Recovery of virus from the mosquito’s body and not legs confirmed that the mosquito had a non-disseminated infection limited to the midgut . Recovery of virus in the body and legs was considered as a disseminated infection [25] . Mosquitoes with positive saliva were considered competent in transmitting DENV-2 . The overall dissemination and transmission rates at each temperature were compared for the different cities using Fisher’s Exact test . Human blood feeding rates were compared between the cities using Chi-Square test . All analyses were performed in R version 3 . 3 . 1 [29] at α = 0 . 05 level of significance .
Based on the total number of female Ae . aegypti mosquitoes collected using the BG-Sentinel traps from each of the three cities ( n = 1 , 432 , n = 1 , 686 , and n = 661 in Mombasa , Kisumu , and Nairobi respectively ) , the estimated vector density per trap was comparable in Mombasa and Kisumu , 8 . 0 and 9 . 4 Ae . aegypti/trap ( T-test , p = 0 . 186 ) , with each being ~ 2-fold higher than in Nairobi , 3 . 7 Ae . aegypti/trap ( T-test , p < 0 . 001 ) ( Table 1 ) . The total number of female Ae . aegypti collected indoors using the prokopack aspirator was quite low ( n = 5 , n = 1 , and n = 0 for Mombasa , Kisumu , and Nairobi respectively ) , and these data were not considered in the estimation of vector density . To test if Ae . aeypti mosquitoes from Mombasa , Kisumu , and Nairobi were able to transmit DENV-2 , a total of 505 mosquitoes were exposed to an infectious blood meal with average titers of 106 . 9–7 . 1 PFU/ml and evaluated at the minimum/maximum temperatures of the three study cities . Although there was no significant difference in dissemination or transmission rates between mosquitoes from each of the three cities , both dissemination and transmission rates increased with an increase in holding temperature ( Table 2 ) . For mosquitoes held at 22 , 28 , or 31°C , dissemination rates were 2/140 ( 1% ) , 25/182 ( 14% ) , and 27/183 ( 15% ) , respectively . The dissemination rates for mosquitoes held at either 28 or 31°C were significantly higher than the dissemination rate for mosquitoes held at 22°C ( Fisher’s exact test , p < 0 . 0001 ) . For mosquitoes held at 22 , 28 , or 31°C , transmission rates were 1/140 ( 1% ) , 2/179 ( 1% ) , and 9/179 ( 5% ) , respectively . Similarly , mosquitoes held at 31°C had a significantly higher transmission rate ( Fisher’s exact test , p = 0 . 048 ) than those held at 22°C , and a higher transmission rate that approached significance ( Fisher’s exact test , p = 0 . 061 ) when compared to those held at 28°C . Of relevance is that with the exception of two mosquitoes from Mombasa , none of the mosquitoes held at 22°C developed a disseminated infection , and only one of the two ( 1/140 for all those tested at 22°C ) transmitted the virus . At 22°C , no viral dissemination was observed in any of the populations at 7 days post-exposure . However , by day 14 , viral dissemination and transmission were observed exclusively in those mosquitoes originating from Mombasa , but at rates that did not differ significantly from the other two locations . Further , while no virus transmission was observed in the Nairobi mosquito population at 28°C , virus transmission was observed for the Mombasa and Kisumu populations by day 14 . At 31°C virus transmission was observed in all three populations by day 7 ( S2 Table ) . When examining the host blood meal sources in 102 Ae . aegypti mosquitoes from Mombasa ( n = 48 ) , Kisumu ( n = 34 ) , and Nairobi ( n = 20 ) , we identified 13 different host blood meal sources ( Fig 2 , S1 Table ) . While a significant difference was observed in Ae . aegypti human feeding between Mombasa and Kisumu ( χ2 = 4 . 67 , df = 1 , p = 0 . 03 ) , the differences between Mombasa and Nairobi ( χ2 = 1 . 94 , df = 1 , p = 0 . 16 ) , and between Kisumu and Nairobi ( χ2 = 0 . 0001 , df = 1 , p = 1 ) , were not significant . This translated to a human blood index of 0 . 4 , 0 . 1 and 0 . 2 in Mombasa , Kisumu and Nairobi , respectively . Based on phylogenetic analysis of Ae . aegypti samples that were both susceptible and non-susceptible to DENV-2 from all three cities , we identified three Ae . aegypti lineages; lineage 1 within which the domestic form ( Ae . aegypti aegypti -Genbank Accession No . AF390098 and MF194022 ) clustered , lineage 2 containing the forest form ( Ae . aegypti formosus—Genbank Accession No . AY056597 ) , and lineage 3 which clusters within a well-supported Ae . aegypti clade ( Fig 3 ) . DENV-2 susceptible and non-susceptible Ae . aegypti were fairly represented in all three lineages ( Fig 3 ) .
In Kenya , during the past decade , urban dengue outbreaks remain limited to Mombasa , but not Kisumu and Nairobi [18–20] . Besides urbanization being a risk factor for the emergence of dengue , our study showed that Ae . aegypti density , feeding pattern , and prevailing environmental temperatures were important contributing factors that can differentially drive the emergence of dengue . For dengue to emerge in an area , the various risk factors must align , creating a connecting interface between the virus , the arthropod vectors , and the susceptible human population . Differences in vector competence would be the expected explanation for the outbreaks in Mombasa , but not Kisumu and Nairobi . However , we found that populations of Ae . aegypti from all three cities had a similar vector competence for DENV-2 . This suggested that differences in vector competence between the various mosquito populations does not appear to be the explanation for the differences in outbreaks . Overall , the DENV-2 transmission rates were generally low in all three populations . This may be explained by the fact that we used the capillary tube method to estimate DENV-2 transmission . Methods collecting mosquito saliva may underestimate virus transmission [30] . Similarly , exposing mosquitoes to virus via a membrane feeder tends to produce a lower infection rate than feeding them on a viremic host [31 , 32] . However , there are currently no suitable animal models to estimate DENV transmission [33] . Because the methods of virus exposure and transmission determination were the same for all three mosquito populations , our transmission rate estimates would not be significantly affected should transmission rates increase under field conditions . As a limitation , our study only focused on DENV-2 , one of the most prevalent serotype , and further studies on the other DENV serotypes ( DENV-1 and -3 ) circulating in Kenya [18 , 34 , 35] are needed . Similarly , environment temperature is a critical factor for the ability of Ae . aegypti to transmit DENV , with transmission rates significantly reduced at lower temperatures [17 , 36] . In addition , lower temperatures are known to reduce vector feeding/biting frequency [14] and can significantly reduce vector density/human-vector contact , consequently lowering the risk of DENV transmission by Ae . aegypti mosquitoes in an area [37] . This may explain the lack of dengue outbreaks in Nairobi . However , the temperatures in Mombasa and Kisumu , 27/31°C , and 28/30°C , respectively , are nearly identical . Therefore , temperature cannot explain the lack of dengue outbreaks in Kisumu . Another possible explanation for the lack of dengue in Kisumu and Nairobi could be the density of Ae . aegypti populations . Although the Ae . aegypti density in Nairobi , was about half of that observed in Kisumu and Mombasa , and might also be one of the reasons for the lack of dengue in Nairobi , the density of Ae . aegypti was similar in Mombasa and Kisumu , so Ae . aegypti density would not explain the lack of outbreaks in Kisumu . The absence of epidemics in Kisumu , but their presence in Mombasa , must therefore be linked to factors other than vector competence , temperature , and Ae . aegypti density . Differences in feeding behavior of the Ae . aegypti from the three locations , as determined from blood meal analysis , showed that the Ae . aegypti population from Mombasa was more anthropophilic than the population from Kisumu ( Fig 2 ) . The higher anthropophily observed in Mombasa compared to Kisumu is consistent with the observed dengue epidemics reported in Mombasa and the coastal area of Kenya at large [18–20] . Higher human blood feeding has also been reported in dengue endemic areas connoting the importance of Ae . aegypti feeding behavior in the emergence of dengue [38 , 39] . However , it is worth noting that the observed proportion of Ae . aegypti feeding on humans ( 35% ) in the dengue endemic area of Mombasa was far less than the proportion recorded in other dengue endemic areas ( Thailand and Puerto Rico ) where Ae . aegypti feeding occurs almost exclusively on humans ( 80–100% ) [38–40] . Feeding preference in Ae . aegypti mosquitoes has an underlying genetic basis [41 , 42] , with the Ae . aegypti aegypti subspecies reportedly being more anthropophilic , whereas the sister taxon , Ae . aegypti formosus , is more zoophilic [42] . Thus , the low human blood feeding rates observed for the Ae . aegypti population in Kisumu may be indicative of a more zoophilic vector population composition , possibly explaining why the city is less affected by dengue . We further observed that the Ae . aegypti population in Mombasa was not significantly more anthropophilic than that from Nairobi , suggesting that lower Ae . aegypti density and lower temperatures in Nairobi ( Table 1 and Table 2 ) , rather than mosquito feeding pattern , explained the absence of dengue from this city . To fully understand the Ae . aegypti feeding pattern , additional studies incorporating larger sample sizes are required . Although Ae . aegypti has been reported to feed less on bovine [38] , we observed about 17% feeding on cattle in both Mombasa and Kisumu ( Fig 2 ) . This can potentially be exploited in dengue , and possibly chikungunya , control by diverting Ae . aegypti feeding away from humans to insecticide-treated cows , as has been suggested for Anopheles mosquitoes in malaria control [43] . As a limitation , data on the density of the different host types in the study areas were not available and should be considered in future studies in order to obtain better estimates of feeding preference . Phylogenetic analysis of DENV-2 susceptible and non-susceptible Ae . aegypti mosquitoes suggested that DENV-2 susceptibility did not vary on the basis of the Ae . aegypti subspecies/lineages present in these cities , as both the susceptible and non-susceptible Ae aegypti were fairly represented in all three mitochondrial lineages ( Fig 3 ) . As a limitation , the susceptible mosquitoes corresponded to mosquitoes that were shown to be infected with DENV . However , not all infected mosquitoes eventually disseminate virus , and even fewer successfully transmit virus by bite . Thus , further studies investigating the genetic basis of vector competence should consider mosquito populations with at least a disseminated virus infection . In conclusion , our study indicated that the current concentration of dengue in coastal Mombasa , and absence of outbreaks from Kisumu , appeared to be due to differences in Ae . aegypti feeding patterns rather than differences in vector competence or environmental temperature . However , lower vector density and environmental temperature appeared to be contributory factors to the current absence of dengue outbreaks in Nairobi . Risk factors such as mosquito density , environmental temperature , vector competence , host feeding pattern , and vector genetics , when interpreted individually , may not sufficiently inform risk of transmission of DENV and must therefore be evaluated collectively . Although the risk of DENV transmission is high in Mombasa , and low in Kisumu and Nairobi , continued monitoring of DENV transmission risk and vector surveillance , as well as monitoring of contributing behavioral and environmental factors , are needed to improve early warning and pre-emptive action . | Dengue is a viral disease of global public health significance owing to rapid spread and increasing disease burden . Urbanization is an important risk factor for dengue emergence . In Kenya , repeated outbreaks of the disease have occurred in the urban areas of Mombasa but not in Nairobi and Kisumu , despite the presence of susceptible human hosts and the primary vector , Aedes aegypti throughout these areas . We set out to determine whether this trend is related to variations in biological parameters of the vector , Ae . aegypti between these areas . Our findings show that ( i ) Ae . aegypti had lower density and human blood feeding ability in Nairobi and Kisumu , respectively , compared to Mombasa , ( ii ) the ability of Ae . aegypti populations from the three cities to transmit dengue-2 virus was comparable with no observed association between susceptibility and vector genetic variation , and ( iii ) vector competence was temperature-dependent . Based on this , it appears that higher temperature and Ae . aegypti vector density explains the higher risk of dengue virus transmission in Mombasa , compared to Nairobi , whereas differences in feeding behavior of Ae . aegypti may be responsible for the lower risk at Kisumu . | [
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... | 2019 | Entomological assessment of dengue virus transmission risk in three urban areas of Kenya |
Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks . These data provide information on possible transmission events which can be used for further epidemiologic analyses , such as identification of risk factors for infectivity and transmission . However , the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission , case observation , within-host pathogen dynamics and mutation . To properly resolve transmission events , these processes need to be taken into account . Recent years have seen much progress in theory and method development , but existing applications make simplifying assumptions that often break up the dependency between the four processes , or are tailored to specific datasets with matching model assumptions and code . To obtain a method with wider applicability , we have developed a novel approach to reconstruct transmission trees with sequence data . Our approach combines elementary models for transmission , case observation , within-host pathogen dynamics , and mutation , under the assumption that the outbreak is over and all cases have been observed . We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution , taking account of all unobserved processes at once . This allows for efficient sampling of transmission trees from the posterior distribution , and robust estimation of consensus transmission trees . We implemented the proposed method in a new R package phybreak . The method performs well in tests of both new and published simulated data . We apply the model to five datasets on densely sampled infectious disease outbreaks , covering a wide range of epidemiological settings . Using only sampling times and sequences as data , our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees .
As sequencing technology becomes easier and cheaper , detailed outbreak investigation increasingly involves the use of pathogen sequences from host samples [1] . These sequences can be used for studies ranging from virulence or resistance related to particular genes [1 , 2] , to the interaction of epidemiological , immunological and evolutionary processes on the scale of populations [3 , 4] . If most or all hosts in an outbreak are sampled , it is also possible to use differences in nucleotides , i . e . single-nucleotide polymorphisms ( SNPs ) , to resolve transmission clusters , individual transmission events , or complete transmission trees . With that information it becomes possible to identify high risk contacts and superspreaders , as well as characteristics of hosts or contacts that are associated with infectiousness and transmission [5 , 6] . Much progress has been made in recent years in theory and model development , but existing methods either include assumptions that do not take the full uncertainty in the evolutionary process into account [7 , 8] , are designed for specific datasets , with fit-for-purpose code for data analysis [9–11] , or make limiting assumptions about the relation between sampling times and infectivity [12] . An easily accessible method without these restrictions and with the flexibility to cover a wide range of infections is currently lacking , and would bring analysis of outbreak sequence data within reach of a much broader community . The interest in easily applicable methods for sequence data analysis in outbreak settings is demonstrated by the community’s widespread use of the Outbreaker package in R [8 , 13–15] . However , the model in Outbreaker assumes that mutations occur at the time of transmission , which does not take the pathogen’s in-host population dynamics into account , nor the fact that mutations occur within hosts . The publications by Didelot et al [7] and Ypma et al [11] revealed that within-host evolution is crucial to relate sequence data to transmission trees , as is illustrated in Fig 1A: there are four unobserved processes , i . e . the time between subsequent infections , the time between infection and sampling , the pathogen dynamics within hosts , and mutation . The difference in sequences between host 2 and infector 1 result from all of these processes . As a result , a host’s sample can have different SNPs from his infector’s ( Fig 1B: hosts 1 and 2 ) ; a host can even be sampled earlier than his infector with fewer SNPs ( Fig 1B: hosts 1 and 3 ) . Several recently published methods do allow mutations to occur within the host , but make other assumptions not fully reflecting the above-described process , such as using a phenomenological model for pairwise genetic distances [16] , presence of a single dominant strain in which mutations can accumulate [9 , 17] , or absence of a clearly defined infection time [18] . To take the complete process into account , Didelot et al [7] and Numminen et al [10] took a two-step approach: first , phylogenetic trees were built , and second , these trees were used to infer transmission trees . Didelot et al [7] used the software BEAST [19 , 20] to make a timed phylogenetic tree , and used a Bayesian MCMC method to colour the branches such that changes in colour represent transmission events . Numminen et al [10] took the most parsimonious tree topology , and accounted for unobserved hosts by a sampling model ( which is an additional complication ) . This two-step approach is likely to work better if the phylogenetic tree is properly resolved ( unique sequences with many SNPs ) , but less so if there is uncertainty in the phylogenetic tree . However , also in that case construction of the phylogenetic tree is done without taking into account that only lineages in the same host can coalesce , and that these go through transmission bottlenecks during the whole outbreak . That is likely to result in incorrect branch lengths and consequently incorrect infection times . Hall et al [12] implemented a method in BEAST for simultaneous inference of transmission and phylogenetic trees . BEAST allows for much flexibility when it comes to phylogeny and population dynamics reconstruction ( for which it was originally developed [19 , 20] ) , e . g . by allowing variation in mutation rates between sites in the genome , between lineages , and in time . However , datasets of fully observed outbreaks often do not contain sufficient information for reliable inference: they typically cover only a few months up to at most several years ( as in Didelot et al [7] , with tuberculosis ) and do not contain many SNPs ( usually of the same order of magnitude as the number of samples ) . A more important limitation is that the transmission model implemented in BEAST is rather specific: it allows for transmission only during an infectious period informed by positive and negative samples , during which infectiousness is assumed to be constant . This may put prior constraints on the topology and order of events in the transmission and phylogenetic trees , which is undesirable if the primary aim is to reconstruct the transmission tree with little or no prior information about when hosts were infectious . Previously , Ypma et al [11] had also developed a method for simultaneous inference of transmission and phylogenetic trees , albeit with rather specific assumptions on the within-host pathogen dynamics and the time and order of transmission events , and with no available implementation . However , their view on the phylogenetic and transmission trees was quite different . Instead of a phylogenetic tree with transmission events , they regarded it as a hierarchical tree . The top level is the transmission tree , with hosts having infected other hosts according to an epidemiological transmission model . The lower level consists of phylogenetic “mini-trees” within each host . A mini-tree describes the within-host micro-evolution . It is rooted at the infection time and has tips at transmission and sampling events; in its simplest form it is only a single branch from infection to sampling . The complete phylogenetic tree then consists of all these mini-trees , connected through the transmission tree . That description allowed them to develop new MCMC updating steps , some changing the transmission tree , some the phylogenetic mini-trees . We built further on that principle to reconstruct the transmission trees of outbreaks , in a new model and estimation method . The method requires data on pathogen sequences and sampling times . The model includes all four underlying stochastic processes ( Fig 1A ) , each described in a flexible and generic way , such that we avoid putting unnecessary prior constraints on the order of unobserved events ( Fig 1B ) . This allows for application of the method to a wide range of infectious diseases , including new emerging infections where we have little quantitative information on the infection cycle . The method is implemented in R , in a package called phybreak . We illustrate the performance of the method by applying it to both new and previously published simulated datasets . We demonstrate the range of applicability by applying the model to five datasets on densely sampled infectious disease outbreaks , covering a wide range of epidemiological settings .
The method infers infection times and infectors of all cases in an outbreak . The data consist of sampling times and sequences of all cases , where some of the sequences may be non-informative if no sequence is available . Using simple models for transmission , sampling , within-host dynamics and mutation , samples are taken from the posterior distributions of model parameters and transmission and phylogenetic trees , by a Markov-Chain Monte Carlo ( MCMC ) method . The main novelty of our method lies in the proposal steps for the phylogenetic and transmission trees that are used to generate the MCMC chain . It makes use of the hierarchical tree perspective , in which the phylogenetic tree is described as a collection of phylogenetic mini-trees ( one for each host ) , connected through the transmission tree ( see Methods for details ) . The posterior samples are summarized by medians and credible intervals for parameters and infection times , and by consensus transmission trees . Consensus transmission trees are based on the posterior support for infectors of each host , defined as the proportion of posterior trees in which a particular infector infects a host . The Edmonds’ consensus tree takes for each host the infector with highest support , and uses Edmonds’ algorithm to resolve cycle and multiple index cases [21] , whereas the Maximum Parent Credibility ( MPC ) tree is the one tree among the posterior trees with maximum product of supports [12] . The models and parameters used for inference are as follows: We generated 25 new simulated datasets of 50 cases with the above model , which we modified by taking a population of 86 individuals and a basic reproduction number R0 = 1 . 5 ( instead of an infinite population with R0 = 1 ) . Parameters were aG = aS = 10 , mG = mS = r = 1 , μ = 10−4 and sequence length 104 , resulting in 1 genome-wide mutation per mean generation interval of one year . Table 1 shows some summary measures on performance of the method ( see S1 Results for additional measures and results for more simulations ) . A 5 , 000 cycle burn-in followed by sampling a single chain of 25 , 000 MCMC cycles took about 30 minutes on a 2 . 6 GHz CPU ( Linux ) . Four sets of results are shown , all with an uninformative prior for μ: one with all parameters other than μ fixed at their correct value , and three with uninformative priors for mG and r , and different levels of prior knowledge on mS: informative with correct mean , uninformative , and informative with incorrect mean . The top of the table shows effective sample sizes ( ESSs ) for all parameters and for the infection times to evaluate mixing of continuous parameter samples . The path-distance approximate topological ESS [23] was calculated to assess phylogenetic tree mixing . To evaluate mixing across and within chains of infectors per host , we tested for differences between the chains and for dependency within the chains by Fisher’s exact tests: the proportion of accepted tests ( P > 0 . 05 ) is a measure of mixing . The MCMC mixing is generally good for tree inference and model parameters , as most ESSs are above 200 and an expected 95% of Fisher’s tests is accepted; the only exceptions being mS with an uninformative prior . The bottom part of Table 1 shows the results on tree inference . Infection times ( using all MCMC samples ) are well recovered if the mean sampling interval does not have a strong incorrect prior: coverage of 95% credible intervals is good , and medians may only be slightly positively biased ( later than true infection time ) if uninformative priors are used . For this simulation scenario , consensus transmission trees contained almost 70% ( 35 out of 50 ) correct infectors , as determined by counting infectors and resolving multiple index cases and cycles in the tree ( Edmonds’ method [21] ) and slightly fewer when choosing the maximum parent credibility ( MPC ) tree [12] among the 50 , 000 posterior trees . Infectors with high support are more likely correct: 84% ( 28 out of 33 ) are correct if the support is above 50% , and 96% ( 15 . 2 out of 15 . 8 ) are correct if the support is above 80% . These numbers are similar in smaller outbreaks ( S1 Results ) . If sampling and generation interval distributions are wider , the sampling times contain less information on the order of infection , which reduces the accuracy of transmission tree reconstruction ( S1 Results ) . Using prior information on the mean sampling interval did not improve on this , but if prior information is incorrect , fewer hosts have a strongly supported infector , which makes inference more uncertain . In conclusion , the method is fast and efficient if applied to simulated data fitting the model . In that case , no informative priors are needed for transmission tree inference , though correct estimation of the infection time is aided by some information . For comparison , we analysed the same datasets with the Outbreaker package in R [8] , which uses the assumption of mutation at transmission , and with the TransPhylo package [7 , 24] , which requires input of a phylogenetic tree that we created in BEAST v2 [19] with a constant population coalescent model and Jukes-Cantor substitution model . Both Outbreaker and TransPhylo require input of a generation and sampling interval distribution , for which we supplied the distributions used to simulate the data . Thus , the results are best compared to the results of the reference scenario of our model ( Table 1 , left-most column ) . The numbers of correctly identified infectors ( Edmonds’ consensus tree [21] ) were smaller with both alternative methods: in the 25 outbreaks of Table 1 ( 50 cases , aG = aS = 10 ) , Outbreaker identifies on average 27 . 5 out of 50 infectors correctly , TransPhylo 32 . 2 , and phybreak 34 . 9 . Also in smaller outbreaks or with different generation and sampling interval distributions , phybreak identified 8–22% more infectors correctly ( S1 Results ) . We also analysed the simulated results with 20% of the cases removed from the dataset , to assess performance if outbreaks are not completely observed . Table 2 shows the results with reference ( parameters fixed and correct ) and uninformative analyses , in comparison with the reference scenario and all data observed . With some of the cases removed , some of the remaining cases did not have their infector in the dataset anymore; these cases are referred to as orphans in Table 2 . Infection time estimation was less accurate , with only 85% of credible interval containing the correct value , and more infection times estimated too early in the outbreak . Surprisingly , this was not only the case with orphans , for which this may have been expected with their infector not present in the data . It turns out that infectors are correctly identified about 20% less accurately , for all threshold levels of support . However , when correcting for presence of the infector in the data , infectors are identified with the same accuracy as in the complete dataset . We also checked how frequently the identified infector of orphans was in fact an earlier ancestor in the transmission tree , i . e . the infector’s infector in most cases . It turned out that ancestors were often identified as infector , but not as accurately as the true infector identification in complete datasets ( Table 2 ) . We applied the method to previously published outbreak simulations [12] . Briefly , a spatial susceptible-exposed-infectious-recovered ( SEIR ) model was simulated in a population of 50 farms , with a latent period ( exposed ) of two days and a random infectious period with mean 10 days and standard deviation 1 day , at the end of which the farm was sampled . Two mutation rates were used with an HKY substitution model , either Slow Clock or Fast Clock , equivalent to 1 or 50 genome-wide mutations per generation interval of one week , respectively . Table 3 shows performance of the method with naïve and informative prior information on the sampling interval distribution ( see S1 Results for uninformative ) . Effective sample sizes of parameters and phylogenetic trees are a bit smaller than with analysis of the new simulations , but in most cases still good for infection times , whereas sampling of infectors was excellent . The low variance of the sampling interval distribution caused some problems in efficient sampling of mS because of its high correlation with the associated infection times , but it also caused problems in the burn-in phase if inference starts with parameter values far from their actual values ( not shown ) . This was especially the case in the uninformative Slow Clock analyses , resulting in unreliable estimates of the mean sampling interval and infection times ( S1 Results ) . With the Fast Clock analyses there were no such problems , as long as the full set of proposal paths in the MCMC chain was used ( see Methods for details ) . Posterior median mutation rates are slightly higher than used for simulation , which could be due to different rates for transition and transversion in the simulation model [12] . Consensus trees with uninformative and informative settings were almost as good as in the original publication [12] , in which spatial data were used and in which it was known that there was a latent period and that hosts could not transmit after sampling . In the Slow Clock simulations about 62% of infectors were correct , and in the Fast Clock simulations about 92% . Infection times were also well recovered in most cases , but not in the uninformative Slow Clock analysis ( S1 Results ) . In the naïve analyses , the Slow Clock consensus trees were only slightly less good ( but not the infection times ) , whereas the Fast Clock consensus trees became much worse , with only 65% of infectors correct . In conclusion , the method performs well if applied to data simulated with a very different model . Good prior information on the sampling interval can significantly improve both MCMC mixing and transmission tree inference , especially if the genetic data contain many SNPs . We finally applied the method to five published datasets on outbreaks of Mycobacterium tuberculosis ( Mtb , [7] ) , Methicillin-resistant Staphylococcus aureus ( MRSA , [25] ) , Foot-and-mouth disease ( FMD2001 and FMD2007 , [9 , 11 , 26 , 27] ) , and H7N7 avian influenza ( H7N7 , [12 , 28–30] ) . The results for the four smaller datasets are shown in Table 4 , which shows that mixing of the MCMC chains was generally good . Fig 2 shows the Edmond’s consensus trees ( full details in S1 Results ) , with each host’s estimated infection time and most likely infector , colour coded to indicate posterior support . Fig 3 shows one sampled tree for each dataset ( from the posterior set of 50 , 000 ) , matching the MPC consensus tree topology . The Mtb data were analysed with naïve prior information , which resulted in a median sampling interval of 419 days ( similar to estimated incubation times [31] ) , a median generation interval of 107 days , and a mutation rate equivalent to 0 . 3–1 . 3 mutations per genome per year , as estimated before [32 , 33] . The Edmonds’ consensus transmission tree ( Fig 2A ) shows low support for most infectors , which is a reflection of the low number of SNPs , but also of the long sampling interval relative to the generation interval , which makes the sampling time less informative of the order of infection . However , the same index case K02 and three clusters as identified in Didelot et al [7] are distinguished: one starting with K22 , one with K35 , and the remaining cases starting with K16 or infected by the index case . The main difference compared to the original analysis lies in the shape of the phylogenetic tree and the estimated infection times ( Fig 3A ) . Whereas the topology is very similar , the timing of the branching events is different: in the original tree , internal branches decrease in length when going from root to tips . That shape is consistent with a coalescent tree based on a single panmictic population but also reflects the fact that three mutations separate the two clades after the root node , whereas the posterior median genome-wide mutation rate is estimated at 0 . 48 per year ( mutation rate × sequence length ) . By taking into account the fact that coalescent events take place within individual hosts , our analysis shows branch lengths that are more balanced in length across the tree . Importantly , this results in a more recent dating of root of the tree: early 2008 ( Fig 3A ) instead of early 2004 [7] . The MRSA data were analysed with an informative prior for the mean sampling interval mS and a shape parameter aS based on data on the intervals between hospitalisation and the first positive sample . The estimated mutation rate is similar to literature estimates [34 , 35] , but the posterior median mS of 31 days is considerably higher than the prior mean of 15 days ( Table 4 ) . This may be explained by the two health-care workers ( HCW_A and HCW_B ) , which have very long posterior sampling intervals that were not part of the data informing the prior ( Edmonds’ consensus tree , Fig 2B ) . In contrast with the original analysis , we now identify a transmission tree rather than only a phylogenetic tree , resulting in the observation that the two health-care workers may not have infected any patient in spite of their long infection-to-sampling interval . Almost all transmission events with low support ( <20% ) involved unsequenced hosts . Two of them were identified as possible infector ( P5 and P7 ) , in the initial stage of the outbreak , when only few samples were sequenced . This indicates that some unsequenced hosts may have played a role in transmission , but that it is not clear which . Finally , a major difference between our results and those in the original publication is the shape of the phylogenetic tree and the dating of the tree root: around 1st January ( Fig 3B ) instead of 1st September the year before [25] . Analysis of the FMD2001 and FMD2007 datasets resulted in posterior sampling intervals with means of 14 and 10 days , respectively , close to the 8 . 5 days estimated from epidemic data [36] . Generation intervals were about the same ( Table 4 ) . Both datasets contained more SNPs than the Mtb and MRSA data , with unique sequences for each host and higher mutation rates , similar to published rates in FMD outbreak clusters [37] . This resulted in equal Edmonds’ and MPC consensus transmission trees , and much higher support for most infectors ( Figs 2C , 2D , 3C and 3D ) . Our transmission tree is almost identical to the one from Ypma et al [11] , who used a closely related method but did not allow for transmission after sampling . When comparing to the analysis of these data by Morelli et al [9] , the topologies of the phylogenetic trees ( Fig 3C and 3D ) match the topologies of the genetic networks ( Fig S18 in [9] ) , but the transmission trees are quite different . The main differences are that in the FMD2001 outbreak , they identify farm B as the infector of C , E , K , L , O , and P; and in the FMD2007 outbreak , they have IP4b infecting IP3b , IP3c , IP6b , IP7 , and IP8 . Differences are likely the result of their use of the spatial data [9] . Use of additional data is expected to improve inference , although their estimates of infection-to-sampling intervals ( about 30 days ) were unrealistically long . The H7N7 dataset was analysed with the sequences of the three genes HA , NA , and PB2 separately , and combined; with informative priors for both the mean sampling and mean generation intervals . Five parallel chains were run , and mixing was generally good ( Table 5 ) ; it took about 7 hours on a 2 . 6GHz CPU to obtain 25 , 000 unthinned samples in a single chain . Analysis of the three genes combined resulted in a posterior median mS of 8 . 4 days , slightly longer than the 7 days on which the informative prior was based [38] , and longer than in the separate analyses . The mean generation time was slightly shorter than the prior mean: 4 . 6 days with all genes . We also calculated the parsimony scores of the posterior sampled trees , defined as the minimum numbers of mutations on the trees that can explain the sequence data [39] , and compared these with the numbers of SNPs in the data ( Table 5 ) . It appeared that with the genes separately analysed , parsimony scores were 6–13% higher than the numbers of SNPs , indicating some homoplasy in the phylogenetic trees ( this was not seen with any of the other datasets ) . The analysis of all genes together resulted in parsimony scores of 18% higher than the number of SNPs . The estimated mutation rates are among the highest estimates for Avian Influenza Virus , as already noted before in earlier analyses of the same data [28 , 40] . Fig 4 shows the Edmonds’ consensus tree in generations of infected premises , indicating locations and inferred infection days ( full details in S1 Results ) . Without the use of location data , there is a large Limburg cluster , a Central cluster including two sampled Limburg cases , and a small Limburg cluster of three cases with an exceptionally long generation time ( asterisk in Fig 4 ) . A closer look at the sequences makes clear that the first of these cases ( L24/34 ) has 3 SNPs different from assigned infector G4/11 , and 4 SNPs different from cases in the large Limburg cluster . Using geographic data as in earlier analyses [12 , 30] will probably place these cases within that cluster .
We developed a new method to reconstruct outbreaks of infectious diseases with pathogen sequence data from all cases in an outbreak . Our aim was to have an easily accessible and widely applicable method . For ease of access , we developed efficient MCMC updating steps which we implemented in a new R package , phybreak . We tested the method on newly simulated data , previously published simulated data , and published datasets . Our model is fast: 25 , 000 iterations took roughly 30 minutes with the Mtb and MRSA datasets of about 30 hosts , and 7 hours with the full three-genes H7N7 dataset in 241 hosts . Two chains with 50 , 000 posterior samples proved sufficient ( measured by ESS ) for tree inference ( infectors and infection times ) and most model parameters with most simulated and published data . The package contains functions to enter the data , to run the MCMC chain , and to analyse the output , e . g . by making consensus trees and plotting these ( as in Figs 2 and 3 ) . Analysis of simulated datasets showed that the sampling times play an important role in transmission tree reconstruction . Firstly , the use of prior information on the sampling interval distribution ( shape parameter as well as mean ) greatly improves mixing of the MCMC chain ( Tables 1 and 3 ) . Secondly , the use of ( correct ) prior information on the sampling interval distribution can significantly improve infection time estimation as well as transmission tree reconstruction ( Table 3 ) . Thirdly , the extent to which sampling times are correlated with infection times determines how well the method is capable of reconstructing transmission trees , which appears from the fact that outbreaks are less well reconstructed with wider sampling interval distributions ( Table 1 vs S1 Results ) and the low support for the posterior infectors in the Mtb analysis , where sampling intervals were much longer than generation intervals . Therefore , it is advisable to use prior information on sampling intervals in the analysis ( if available ) , and also to base conclusions not only on the summary transmission tree , but also on the posterior support of links in that tree . We tested the method on five published datasets , with outbreaks of viral and bacterial infections , and in diverse settings of open and closed populations , in human and veterinary context . The method performed well on all datasets in terms of MCMC chain mixing and tree reconstruction . With naive priors on mean sampling intervals and mutation rates , we obtained estimates that were all very accurate when compared to literature , and the inferred transmission trees seemed as good , or even better when considering estimated infection times . With two datasets ( MRSA and H7N7 ) we included some prior information on sampling and/or generation intervals , which mainly affected the inferred infection times , but not so much the transmission trees . It is possible that not all cases have been observed in these outbreaks , especially in the Mtb and MRSA outbreaks , an assumption nevertheless made by our model . If not too many cases are missing , the analyses of simulations show that this does not disturb identification of infector-host pairs that are in the data . It will only limitedly affect identification of transmission clusters , because if a host’s true infector is not in the data , the true infector’s infector is often selected as the most likely infector . Only some of the infection times may have been estimated too early . For wide applicability , we kept the underlying model simple without putting prior constraints on the order of unobserved events such as infection and coalescence times . Four submodels with only one or two parameters each were used for sampling , transmission , within-host pathogen dynamics , and nucleotide substitution . The sampling model , a gamma distribution for the interval between infection and sampling , has a direct link to inferred infection times , and is the model for which it is most likely that prior information is available from epidemiological data in the same or other outbreaks . We used simulated data to study the effect of uninformative or incorrect prior information on shape parameter aS and mean mS . It appears that an incorrect aS or an incorrect informative prior for mS does reduce accuracy of inferred infection times . However , consensus trees are hardly affected , at least if the number of SNPs is in the order of the number of hosts as we saw in the actual datasets ( Table 1 and Table 2 Slow Clock ) . Only the precision of consensus trees is reduced , i . e . there are fewer inferred infectors with high support . Results with the Fast Clock simulations did show a significant reduction in consensus tree accuracy . In that case , there are so many SNPs that the phylogenetic tree topology and times of coalescent nodes are almost fixed; then , too much variance in sampling intervals ( low aS ) results in many incorrect placements of infection events on that tree . Possibly , with so many SNPs it could be more efficient to first make the phylogenetic tree , and then use that tree to infer transmission events [7 , 10] , but it is questionable whether genome-wide mutation rates are ever so high that this becomes a real issue [41] . The submodel for transmission is relevant for transmission tree inference in describing the times between subsequent infection events . Transmission is modelled as a homogeneous branching process , implicitly assuming that there was a small outbreak in a large population , with a reproduction number ( mean number of secondary cases per primary case ) of 1 . If all , or almost all , infectors are in the data , the generation interval distribution reflects the course of infectiousness , separating the cases in time along the tree . This interpretation may be obscured with many unobserved cases , as in the absence of the actual infector , the method often identifies an earlier ancestor in the transmission tree as infector ( Table 2 ) . Apart from not taking heterogeneity across hosts into account ( an extension we wish to leave for future development , see below ) , the current model also neglects the possibility that susceptibles can have contact with several infecteds in a smaller population or more structured contact network . That could be modelled by a force of infection , which would more realistically describe contraction of the generation interval during the peak of the outbreak , and provide estimates for relevant quantities such as reproduction ratios [6] . However , it requires information about uninfected susceptibles in the same population and a more complicated transmission model , which is a significant disadvantage when it comes to general applicability , one of our primary aims . More importantly , for transmission tree inference it does not seem to be a problem: the analyses of the published simulations were almost as accurate as in the original publication [12] , and these simulations were in very small populations with almost all hosts infected . The role of the within-host model is to integrate over all possible phylogenetic mini-trees and mutation events within the hosts , and through that , to obtain a posterior distribution of all transmission trees consistent with the ( genetic ) data . For this , we used a coalescence model based on a linearly growing within-host population , combined with a Jukes-Cantor substitution model . These models contain each only one parameter , but we think that—as long as only few mutations occur in each host , as in our own simulations , the published Slow Clock simulations , and most datasets—for most applications more complex models are not needed for the following reasons . First , the gross structure of the phylogenetic tree topology and branch lengths result from transmission and sampling models , and only the finer within-host details are determined by the within-host model . With only few mutations within each host , precise mini-tree inference is not possible , and for our aim of inferring transmission trees , unnecessary . Second , and confirming this imprecise mini-tree inference , most tree proposal steps include simulation of the within-host phylogenetic mini-trees , resulting in good mixing of transmission and phylogenetic tree topologies . The fact that proposing from the prior distribution works so well indicates that the sequence data do not contain much information on within-host branch lengths . Third , if there are few SNPs , the posterior contains almost only phylogenetic trees with fewest mutations ( maximum parsimony ) . It is therefore not likely that tree inference will improve with more general substitution models . Fourth , inference of transmission trees and infection times appears not to be biased if the underlying simulation model was more realistic ( Table 2 ) . If data do contain many SNPs , as in the Fast Clock simulations , more detailed and realistic models for within-host pathogen growth and nucleotide substitution do probably improve inference , especially on the phylogenetic tree . Nonetheless , even then our method was still capable of correctly inferring the infection times and transmission trees with almost the same accuracy as in the original publication . With two exceptions , the parsimony scores of posterior tree samples were always equal to the number of SNPs in the datasets ( the minimum possible ) . The first exception is the set of Fast Clock published simulations , which had so many SNPs that many of the same mutations had occurred in parallel . The second exception is the H7N7 dataset . In that case , the analyses of the three genes separately resulted in parsimony scores with 6–12 ( 6%-13% ) more mutations than the number of SNPs , whereas the analysis of all genes together resulted in a parsimony score of 313 ( median ) to explain only 257 SNPs , a surplus of 56 mutations ( 18% ) . The results for separate genes could indicate positive selection , confirming the analysis by Bataille et al [28] , who even identified specific mutations that had occurred multiple times . The even higher discrepancy for the combined analysis is suggestive of reassortment events , also recognised by Bataille et al [28] . The proposed method and implementation opens perspectives for further extending the methodology to reconstruct phylogenetic and transmission trees from pathogen sequence data . One possible set of extensions arises from changes to the models embedded in our method , to include additional aspects of outbreak dynamics . For instance , the generation time distribution ( infectiousness curve ) could be made dependent on the sampling interval , which may be relevant for the MRSA outbreak analysis in which the two health-care workers may have transmitted the bacterium until late after infection . This dependence is implicit in methods in which transmission is modelled more mechanistically ( e . g . [11 , 12 , 16] ) , but we chose not to do that to keep the model more generic . Another important extension would be to relax the assumption of a complete bottleneck at transmission; the bottleneck may be larger in reality [42 , 43] and it has previously been relaxed by looking at transmission pairs [44] or modelling it as separate transmission events [18] , but not yet in a timed transmission tree . In our model , this would mean that a host can carry multiple phylogenetic mini-trees , rooted at the same infection time to the same infector . A third extension would be to include the possibility of reassortment of genes within a host , primarily motivated by the results of the H7N7 analysis . This may be done by modelling the coalescent process within hosts , the phylogenetic mini-trees , differently for different genes , but constrained by a single transmission tree . Finally , it would be possible to allow for multiple index cases , which may play a role in open populations with possible re-introductions ( as in the MRSA setting ) , or when only a subset of a large epidemic is analysed ( the FMD2001 dataset ) . This is implemented in models using genetic models based on pairwise genetic distances [8 , 16] and with a model assuming coalescence at transmission [45] , but is considered a major challenge with a within-host coalescent model [46] . Multiple index cases could also reflect unobserved hosts in the outbreak itself , recently addressed by Didelot et al [24] in their two-step approach of first inferring a phylogenetic and then a transmission tree . A second type of extension would stem from incorporating additional data . An example is the use of data that make particular transmissions more or less likely , such as contact tracing data , or censoring times for infection times per host or transmission times between sets of hosts , motivated by the MRSA dataset in which admission and discharge days are known for each patient . Sampling of infection times and infectors could be constrained by these additional data ( as in [12 , 30] ) and could then become less dependent on the sampling times and sampling interval distribution , as in the current implementation . Another example is the use of spatial data in combination with a spatial transmission kernel , so that the likelihood of infectors includes a distance-dependency , a possible extension motivated by the FMD and H7N7 analyses ( as in [9 , 30] ) . A third example is the use of host characteristics to model infectivity as a function of covariates . With the MRSA data , it would then be possible to test for increased infectivity of the health-care workers , or to test for differences in transmissibility in the three wards . In general , the use of additional host data would make dealing with hosts for which a sequence is not available less problematic: the method currently can include these hosts , but without additional data their role is unclear and they are often placed at the end of transmission chains in consensus trees ( Fig 2B , Fig 3 ) .
We developed our model for fully observed outbreaks of size n hosts . Data consist of the sampling times S and DNA sequences G , which means that for each host i we know the time of sampling or diagnosis Si and the sequence Gi associated with the sampling time . Hosts without available sequence are given a sequence with noninformative nucleotides ( only ‘n’ ) . We illustrate the method with the following five datasets from earlier publications ( all in S1 Data ) : The model describes the spread of an infectious pathogen in a population through contact transmission , the dynamics of the pathogen within the infected hosts , and mutation in the DNA or RNA of that pathogen . Furthermore , it describes how these dynamics are observed through sampling of pathogens in infected hosts . We infer the transmission tree and parameters describing the relevant processes by a Bayesian analysis , using Markov-Chain Monte Carlo ( MCMC ) to obtain samples from the posterior distributions of model parameters and transmission trees ( infectors and infection times of all cases ) . We first introduce the models and likelihood functions; then we explain how we update the transmission trees and parameters in the MCMC chain . The posterior distribution is given by Pr ( I , M , P , θ|S , G ) ∝Pr ( S , G|I , M , P , θ ) ⋅Pr ( I , M , P , θ ) . ( 1 ) Eq ( 1 ) is the probability for the unobserved infection times I , infectors M , phylogenetic tree P , and model parameters θ , given the data ( sampling times and sequences ) . The posterior probability can be split up into separate likelihood terms representing the four processes , times a prior probability for the parameters ( see S1 Methods ) : Pr ( I , M , P , θ|S , G ) ∝Pr ( G|P , θ ) ⋅Pr ( P|S , I , M , θ ) ⋅Pr ( S|I , θ ) ⋅Pr ( I , M|θ ) ⋅Pr ( θ ) . ( 2 ) We now introduce the four models , the associated likelihoods , and prior distributions for associated parameters . We use Bayesian statistics to infer transmission trees and estimate the model parameters from the data , and MCMC methods to obtain samples from the posterior distribution . The procedure is implemented as a package in R ( phybreak ) , which can be downloaded from GitHub ( www . github . com/donkeyshot/phybreak ) and is available on CRAN ( cran . r-project . org/web/packages/phybreak/index . html ) . The package also contains functions to simulate data , and to summarize the MCMC output . The main novelty of our method lies in the proposal steps for the phylogenetic and transmission trees , used to generate the MCMC chain . It makes use of the hierarchical tree perspective , in which the phylogenetic tree is described as a collection of phylogenetic mini-trees ( one for each host ) , connected through the transmission tree . Most proposals are done by taking one host , changing its position in the transmission tree , and simulating the phylogenetic mini-trees in the hosts involved in that change . In a second type of proposal , the transmission tree is changed while keeping the phylogenetic tree intact . A third type of proposal only resimulates the within-host mini-tree topology , keeping the transmission tree and coalescent times intact . Initialization of the MCMC chain requires initial values for the six model parameters ( aG , mG , aS , mS , r , and μ ) . The transmission tree is initialized by generating an infection time for each host ( sampling day minus random sampling interval ) . The first infected host is the index case , and for the remaining hosts an infector is randomly chosen , weighed by the density of the generation time distribution . Finally , the phylogenetic mini-trees in each host are simulated according to the coalescent model and combined with one another to create a complete phylogenetic tree . Each MCMC iteration cycle starts with updates of the transmission and phylogenetic trees , followed by updates of the model parameters . To start with the latter , the parameters mS and mG are directly sampled from their posterior distribution given the current infection times and transmission tree ( Gibbs update ) . This is done by sampling the rate parameters bS and bG , which were given conjugate prior distributions ( see above ) . If TS=∑Si−Ii is the sum of n sampling intervals in the tree , a0 , S and b0 , S are the shape and rate of the prior distribution for bS , then a new posterior value is drawn as bS∼Γ ( shape=a0 , S+aSn , rate=b0 , S+TS ) , ( 9 ) from which mS is calculated as aS/bS . Posterior values for mG are drawn from a similar distribution , with TG=∑Ii−IMi the sum of n– 1 generation intervals . The parameters r and μ are updated by Metropolis-Hastings sampling; proposals r’ and μ’ are generated from lognormal distributions LN ( r , σr ) and LN ( μ , σμ ) , i . e . with current values as mean . The standard deviations are calculated based on the expected variance of the target distributions , given the outbreak size for σr , and number of SNPs for σμ ( see S1 Methods ) . In a single proposal path K , only a new topology of the phylogenetic mini-tree of focal host i is proposed; the coalescent times are kept unchanged . We took three approaches to evaluate our method: analysis of newly simulated data , analysis of published simulated data [12] , and analysis of published observed data . When not specified , the following parameter settings and priors were used: shape parameters for sampling and generation interval distributions aS = aG = 3 , uninformative priors for mean sampling and generation intervals with μS = μG = 1 and σS = σG = ∞ , and a prior for within-host growth parameter r with ar = br = 3 . The prior for log ( μ ) ( mutation rate ) is always uniform . Analyses were done by two MCMC chains , in each taking 25 , 000 samples ( 25 , 000 MCMC cycles ) . Burn-ins were different: 5000 MCMC cycles for the newly simulated data , 25 , 000 for the published simulated data [12] , and 5000 for the observed data . With the H7N7 data , five MCMC chains were run , with a burn-in of 5000 samples , followed by 25 , 000 samples . Burn-in lengths of simulated data were based on visual inspection of convergence for two datasets , and then choosing a burn-in period at least 10 times longer than necessary for all the other simulations , followed by comparing ESS and infector sampling in the parallel chains . The Slow clock published simulations had not all converged in the uninformative analysis ( S1 Results ) . For the published data , all chains were inspected visually to confirm convergence . | It is becoming easier and cheaper to obtain ( whole genome ) sequences of pathogen samples during outbreaks of infectious diseases . If all hosts during an outbreak are sampled , and these samples are sequenced , the small differences between the sequences ( single nucleotide polymorphisms , SNPs ) give information on the transmission tree , i . e . who infected whom , and when . However , correctly inferring this tree is not straightforward , because SNPs arise from unobserved processes including infection events , as well as pathogen growth and mutation within the hosts . Several methods have been developed in recent years , but often for specific applications or with limiting assumptions , so that they are not easily applied to new settings and datasets . We have developed a new model and method to infer transmission trees without putting prior limiting constraints on the order of unobserved events . The method is easily accessible in an R package implementation . We show that the method performs well on new and previously published simulated data . We illustrate applicability to a wide range of infectious diseases and settings by analysing five published datasets on densely sampled infectious disease outbreaks , confirming or improving the original results . | [
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"da... | 2017 | Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks |
Dengue virus serotype 4 ( DENV 4 ) has had a relatively low prevalence worldwide for decades; however , likely due to data paucity , no study has investigated the epidemiology and evolutionary dynamics of DENV 4 genotype I ( DENV 4-I ) . This study aims to understand the diversity , epidemiology and dynamics of DENV 4-I . We collected 404 full length DENV4-1 envelope ( E ) gene sequences from 14 countries using two sources: Yunnan Province in China ( 15 strains during 2013–2016 ) and GenBank ( 489 strains up to 2018-01-11 ) . Conducting phylogenetic and phylogeographical analyses , we estimated the virus spread , population dynamics , and selection pressures using different statistical analysis methods ( substitution saturation , likelihood mapping , Bayesian coalescent inference , and maximum likelihood estimation ) . Our results show that during the last 60 years ( 1956–2016 ) , DENV 4-I was present in mainland and maritime Southeast Asia , the Indian subcontinent , the southern provinces of China , parts of Brazil and Australia . The recent spread of DENV 4-I likely originated in the Philippines and later spread to Thailand . From Thailand , it spread to adjacent countries and eventually the Indian subcontinent . Apparently diverging around years 1957 , 1963 , 1976 and 1990 , the different Clades ( Clade I-V ) were defined . The mean overall evolution rate of DENV 4-I was 9 . 74 ( 95% HPD: 8 . 68–10 . 82 ) × 10−4 nucleotide substitutions/site/year . The most recent common ancestor for DENV 4-I traces back to 1956 . While the demographic history of DENV 4-I fluctuated , peaks appeared around 1982 and 2006 . While purifying selection dominated the majority of E-gene evolution of DENV 4-I , positive selection characterized Clade III ( Vietnam ) . DENV 4-I evolved in situ in Southeast Asia and the Indian subcontinent . Thailand and Indian acted as the main and secondary virus distribution hubs globally and regionally . Our phylogenetic analysis highlights the need for strengthened regional cooperation on surveillance and sharing of sample sequences to improve global dengue control and cross-border transmission prevention efforts .
Dengue is a mosquito-borne viral infectious disease . Although the geographical origin of dengue is still under some debate , the recent global expansion has been attributed to environmental changes , unprecedented population growth , uncontrolled urbanization , spread of the mosquito vectors , and host population movements [1] . Currently , dengue is endemic in more than 100 countries in much of the globe’s tropical and subtropical areas , being reported predominantly in Southeast Asia , the Americas , and the Western Pacific , and less frequently in Africa and Eastern Mediterranean WHO regions [2] . The prevalence of dengue has increased 74 . 7% between 2006 and 2016 [3] . While dengue infections are most often asymptomatic , a recent study has estimated a global total of 58 . 4 ( 95% CI: 24–122 ) million symptomatic dengue cases occur annually costing some US$8 . 9 ( 95% CI: 3 . 7–19 . 7 ) billion [4] . The four antigenically distant serotypes comprising dengue virus are categorized as DENV 1 , -2 , -3 , and -4 . Each serotype is classified into different genotypes based on complete E gene sequences [5] . The four serotypes were first identified at different times and locations , and diffused globally at different rates . Although clearly circulating before isolation techniques enabled the viruses’ discovery and characterization , DENV 1 was first reported in 1943 in French Polynesia and Japan , DENV 2 in 1944 in Papua New Guinea and Indonesia , DENV 3 and DENV 4 both in 1953 in the Philippines and Thailand [6] . A study mapping the global spread of DENV 1–4 over the 70-year history ( 1943–2013 ) , indicated DENV 1 was reported most often , followed by DENV 2 , DENV 3 , and DENV 4 [7] . Although , DENV 4 was the first dengue serotype to diverge in phylogenetic analyses of the genus Flavivirus [8] , it spread the slowest geographically [7] . Similar to other serotypes , DENV 4 can also cause severe dengue including dengue haemorrhagic fever ( DHF ) . An epidemiological study in Thailand from 1973 to 1999 revealed that despite the proportionately lower prevalence of DENV 4 , it was responsible for 10% of all DHF cases in Children [9] . Among DENV 4 , there are four genotypes ( I , II , III and Sylvatic ) . The dengue cases from DENV 4 genotype I ( DENV 4-I ) have increased in recent years . In 2013 , a large DENV outbreak occurred in central Vietnam with a total of 204 , 661 clinical cases reported , of which 48 . 9% were DENV 4-I cases [10] . The same year , DENV 1-I , DENV 2-I and DENV 4-I caused a large outbreak with 20 , 255 cases including 84 deaths in Myanmar [11] . In Sri Lanka , the dominant virus in the 2012 epidemic was DENV 1 , but DENV 4-I infections were also commonly observed [12] . While all four serotypes have been detected , a 2015 study showed that dengue in China remains primarily an imported disease with DENV 1 most frequently found in samples [13] . However , since 2013 several strains of DENV 4-I have been detected in Yunnan Province , China . Among the recorded DENV 4-I strains , both imported and autochthonous cases were found . The dengue viruses DENV 1–4 are typically prevalent in tropical and subtropical regions globally . However , the spatial distribution of different genotypes is not uniform , e . g . , some genotypes exist only in specific parts of Asia and others are more cosmopolitan . While the distinct distribution patterns of different genotypes remain enigmatic , mapping of the genotypes’ distribution can generate hypotheses on their spatial pattern and support policies on dengue prevention and control effort . In the past , efforts have been made to infer the dispersal of DENV 1–4 and to elucidate the evolution of their diffusion patterns [14–17] . However , so far no studies investigated globally the spatial distribution of the single genotype , DENV 4-I , its diversity , and its temporal evolution . This may be due to the limited number of recorded cases and the distribution of DENV 4-I worldwide . In this study , we used available GenBank data in addition to Chinese data sources to make the first attempt to more comprehensively understand the spatial and evolutionary patterns of DENV 4-I . Leveraging full envelope gene sequences in our analysis , we sought to investigate the origin and spreading routes of this less-studied , rare but deadly virus , in order to contribute important information for future dengue prevention and control efforts around the globe .
Ethical approval for the study was obtained from the Chinese Center for Disease Control and Prevention Ethical Committee ( No . 201214 ) . Dengue viruses detected in Yunnan Province were recovered from serum samples of suspected dengue patients visiting hospital from 2013 to 2016 . The envelope ( E ) genes of isolates were sequenced as described previously [18] . These have been assigned GenBank accession numbers ( HM893690-HM893699 , MG601754 , KJ470764 , KJ470765 , KX262920 , KX262923 ) . The sequences of Yunnan were compared with published sequences by using the nucleotide blast program in the NCBI . All the sequences of human DENV 4-I with full length E-gene ( 1 , 485 nucleotides ) were downloaded with the accession number , collection date and country/region ( as of January 11th , 2018 ) . All the sequences were aligned using MAFFT [19] . We chose only one sequence to represent sequences with 100% matching identity by location and time . Recombinants detected based on the analyses of RDP3 program [20] were also excluded . Ultimately , 404 sequences of DENV 4-I obtained from 14 countries were included for analyses in this study ( S1 Table ) . The phylogenetic signal of the aligned DENV 4-I was evaluated by plotting the observed number of transitions and transversions against genetic distance for the n ( n-1 ) /2 pairwise comparisons in an alignment of n taxa using DAMBE [21] . It is expected that transitions and transversions increase linearly with the genetic distance , with transitions being more frequent than transversions . In the Supplementary Materials , S1 Fig shows that no substitution saturation was detected , indicating phylogeny reconstruction was appropriate . In likelihood mapping analysis , groups of four sequences ( quartets ) were evaluated using the maximum likelihood approach . For each quartet , the three possible unrooted tree topologies were weighted . The likelihood weights were then plotted into a triangular surface . The fully resolved tree topologies were plotted in the three corners , which indicated the presence of a tree-like phylogenetic signal , and the unresolved quartets , indicating a star-like signal were shown in the central region of the triangle [22] . Likelihood mapping was performed using the TREE-PUZZLE program [23] , by analyzing 10 , 000 random quartets . S1 Fig showed tree-like area accounted for 96 . 4% , which further suggested that the data were reliable for phylogenetic inference . Rates of nucleotide substitution per site per year and time to The Most Recent Common Ancestor ( TMRCA ) were estimated using Bayesian Markov Chain Monte Carlo ( MCMC ) and implemented using the BEAST v1 . 8 . 2 software package [24] . The best-fit model of nucleotide substitutions was determined using Bayesian Information Criteria ( BIC ) as implemented in jModelTest [25] . The calibration point was the “year” that each strain was isolated . Statistical simulations were performed under strict or relaxed ( uncorrelated exponential and lognormal ) clock model , with the Bayesian Skyride Coalescent Tree Prior [26] . To determine the best-fit combination , we have applied Posterior-simulation Akaike Information Criterion through MCMC ( AICM ) [27] , Bayes Factor ( BF ) [28] , Harmonic Mean ( HM ) [29] , and Path Sampling ( PS ) and Stepping-stone Sampling ( SS ) [30] model selection methods . The results showed that the best fitting model was the combination of uncorrelated relaxed exponential clock model and the Bayesian Skyride Coalescent model ( S2 Table ) . Statistical uncertainties in parameter values were given by the 95% Highest Probability Density ( HPD ) intervals . All chains were run sufficiently long to achieve convergence ( the effective sample size of continuous parameters greater than 200 ) after burn-in , as checked using TRACER v1 . 5 ( http://tree . bio . ed . ac . uk/software/tracer/ ) . The programs TreeAnnotator v1 . 8 . 2 in the BEAST v1 . 8 . 2 software package and Figtree ( http://tree . bio . ed . ac . uk/software/Figtree/ ) were used to summarize the posterior tree distribution and to visualize the annotated Maximum Clade Credibility ( MCC ) tree , respectively . Based on the MCC tree , we identified five Clades using visual judgement and comparison among all the countries/regions that reported DENV 4-I . Using the definition of a minimum of three sequences of monophyletic origin , DENV 4-I were labelled with Clade I to V ( the largest five ) where every Clade included as many strains as possible . The spatial diffusion of DENV 4-I was estimated using the Bayesian Markov chain Monte Carlo ( MCMC ) statistical framework implemented in the BEAST v1 . 8 . 2 package . The phylogeographical diffusion process was identified using the Bayesian Stochastic Variable Search Selection ( BSVSS ) . Effective population size dynamics were estimated using the Bayesian Skyride Coalescent statistical approach . Open source data from http://tapiquen-sig . jimdo . com ( Carlos Efraín Porto Tapiquén . Orogénesis Soluciones Geográficas . Porlamar , Venezuela , 2015 ) were used in this study for the results shown in Figs 1 & 4 with help of ArcGIS 10 . 2 and Adobe Illustrator . We used a variety of computational methods to explore the selection pressures . A Maximum Likelihood ( ML ) method was used to examine selection pressures [31] . In the analysis , the non-synonymous to synonymous rate ratio ( ω = dN/dS ) was determined codon-by-codon using various models of codon substitution . These models differ in how ω ratios are allowed to vary along the sequence . Four models of codon substitution were conducted in the study: M1a ( ω < = 1; nearly neutral ) , M2a ( ω < = 1 and ω > 1; positive selection ) , M7 ( beta; a discrete distribution with 10 site classes to model values of ω between 0 and 1 ) and M8 ( beta and ω > 1 ) . M1a is nested with M2a , and M7 is nested with M8 . Models that are nested are compared statistically using a Likelihood Ratio Test ( LRT ) . Positive selection can be inferred when a group of codons with a ω ratio > 1 is identified and the likelihood of the codon substitution model in question is significantly higher ( p < 0 . 05 ) than that of a nested model that does not take positive selection into account . Lastly , using Bayes Empirical Bayes ( BEB ) methods , posterior probabilities were calculated to identify sites under positive selection ( posterior Bayesian probability ( Pp ) > 95% ) . All the analyses were performed by using CODEML from the PAML v4 . 9 package [32] . Evolution rate , effective population size dynamics , divergence time and selection pressure were estimated based on two different types of datasets: ( i ) all sequences of DENV 4-I and ( ii ) those from different Clades . The spatial diffusion was estimated based on all sequences of DENV 4-I . In order to minimize oversampling of Thailand and Vietnam during the spatial diffusion analysis , we down-sampled dataset for sensitive analysis . The down-sampled dataset included 50 sequences at random , from each Thailand and Vietnam and all the available sequenced strains from other countries/regions , therefore making the sample size 207 sequences .
Fig 1 shows that DENV 4-I were detected in Mainland Southeast Asia and the adjacent provinces of China , Maritime Southeast Asia , the Indian subcontinent , Brazil and Australia . Specifically , the recorded samples revealed that DEVN 4-I was mainly observed in Mainland Southeast Asia , especially Thailand and Vietnam . Collection of DENV 4-I covered a period of 60 years . The first strain of DENV 4-I was detected in 1956 in the Philippines , where it transmitted exclusively for some 20 years , according to known reporting and sequencing records . Over the two decades following 1976 , most detected strains of DENV 4-I were found to be circulating in Thailand , while a few strains were discovered in other four countries . Detected in a total of 14 countries , DENV 4-I continued to diffuse to more areas around the globe between 1996 and 2016 . Fig 2 shows the evolution and spread of DENV 4-I over time . During the last 60 years , great geographical and genetic diversity has occurred . This is especially prominent during the last two decades after DENV 4-I became more prevalent as shown in the genetic record . Fig 3 shows the Maximum Clade Credibility ( MCC ) tree . It indicates that the recent spread of DENV 4-I most likely originated from the Philippines with 0 . 98 posterior location probability . Viruses evolved in the Philippines and then spread across the sea to Thailand , Cambodia , Australia and China at different times . Thailand played the dominate role in spreading the viruses , gradually spreading virus to the Indian subcontinent , Myanmar , Cambodia , Singapore , Indonesia and China . These viruses diverged around 1957 , 1963 , 1976 and 1990 , and shaped different Clades ( Clade I to V ) . Since the introduction from Thailand , DENV 4-I has evolved in the Indian subcontinent ( Clade IV ) , Myanmar ( Clade II ) and Cambodia ( Clade I and III ) , respectively . Strains obtained in Vietnam correspond to Clade III , which evolved notably in situ for three decades after introduction from Cambodia . In the Indian subcontinent ( Clade IV ) , dengue virus first apparently arrived in Sri Lanka in the 1960s from Thailand and then spread onto India in the early 1970s . India then became an epicenter for transmission and spread virus to Pakistan and back to Sri Lanka in the 2000s . Fig 4 shows the detailed spatial diffusion of DENV 4-I as summarized from the MCC tree . The result of the down-sampled dataset showed that the phylogenetic topology and spatial spreading patterns were equivalent with those from full dataset ( S2 Fig ) . Fig 5A illustrates the demographic history of DENV 4-I . A fluctuation was observed over the 60 years with an approximately “M” shape . The two highest plateaus were around 1982 and 2006 with a width about 6-years , while the lowest point was around 1996 . Analysis of the Clade I dataset revealed , as Fig 5B exhibits , that the effective population size increased approximately linearly from 1992 to 2004 , and then decreased slowly from 2004 to 2013 . The effective population size of Clade II , as Fig 5C conveys , increased slowly for the first two decades and then much more sharply ( 2007–2013 ) , before a rapid recent decrease ( 2013–2016 ) . Data from the Clade III in Fig 5D shows that there were two peaks ( ~2000 and ~2011 ) over nearly four decades of demographic analysis . The effective population size of Clade IV over the same period decreased slowly and then stayed constant with the inflexion lying around 1990 , as Fig 5E depicts . In Fig 5F , one can see the long demographic history pattern of Clade V ( 1956–2013 ) is mirrored by Clade IV , but with the inflexion point much earlier ( ~1965 ) . Table 1 shows the evolution rate and the divergence time of the different Clades’ datasets . The overall evolution rate was 9 . 74 × 10−4 ( 95% HPD: 8 . 68 × 10−4–10 . 82 × 10−4 ) nucleotide substitutions/site/year and TMRCA of DENV 4-I was 1956 ( 95% HPD: 1955–1956 ) ( year ) . The mean evolution rate of different Clades was comparable , with the smallest being 8 . 66 × 10−4 nucleotide substitutions/site/year in Clade V and the largest being 11 . 3 × 10−4 nucleotide substitutions/site/year in Clade II . TMRCA of Clade I to V was 1991 , 1990 , 1977 , 1971 and 1962 , respectively . In the Supplementary Material , S3 Table shows the summary of positive selection analysis performed on different datasets . M1a vs M2a test and M7 vs M8 test indicated consistently that there was no positive selection across overall DENV 4-I , Clade I , II , IV and V datasets . In Clade III dataset , no positive selection was indicated by M1a vs M2a test , however , M7 vs M8 test inferred a weak positive selection ( 5 . 6% of codons with ω = 1 . 193 ) with amino acid site 95 Pp > 0 . 95 .
In this study , we have investigated the global patterns of DENV 4-I dissemination—its spatial and temporal distribution . This is the most extensive molecular epidemiological study of DENV 4 genotype I to date to our knowledge . Our results indicate that recent spread of DENV 4-I originated in maritime Southeast Asia , probably from the Philippines , from where it spread to mainland Southeast Asia , and then on to the Indian subcontinent . Thailand acted as a distribution hub for spreading the virus regionally and globally . Within the India subcontinent , India was the distribution center for spreading the virus . We found that there is no uniform spreading pattern among genotypes . In addition , purifying selection was still the dominant acting force on E gene to shape the evolution , but weak positive selection existed in dengue viruses detected in Vietnam . This work is a first step towards increased understanding of the underlying mechanisms governing the spread of DENV 4-I virus . Our study suggests that surveillance could be enhanced to better leverage next generation sequencing for informing dengue control practices . Regional cooperation should be strengthened to determine and communicate information on the genotype-specific spreading pathways , to explore the related underlying mechanisms , and ultimately to better coordinate dengue control efforts globally . | Dengue virus ( DENV ) can be classified into four serotypes , DENV 1 , 2 , 3 and 4 . Although DENV 4 is the first dengue serotype to diverge in phylogenetic analyses of the genus Flavivirus , this serotype occurs at a low prevalence worldwide and spreads the least rapidly . Similar to other serotypes , DENV 4 can also cause severe dengue ( SD ) disease manifestations , such as dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . To date , no study has investigated the epidemiology and dynamics of DENV 4 genotype I comprehensively . In this study , we seek to address this gap . Our study shows that the distribution of DENV 4-I is mainly restricted to Southeast Asia and the Indian subcontinent . The most recent spread of DENV 4-I likely originated from Southeast Asia–initially circulating in the Philippines , then Thailand and later on the Indian subcontinent . Viruses evolved in situ in Southeast Asia and the Indian subcontinent , respectively . Although DENV 4-I occasionally spread elsewhere , this genotype did not become widely established . The overall evolution rate of DENV 4-I was comparable with that of DENV 2–4 . The nucleotide sequences indicates that the demographic history of DENV 4-I fluctuated with peaks apparent during parts of the 1980s and 2000s . Although a weak positive selection existed in Clade III -predominately in Vietnam , purifying selection dominated the E-gene evolution of DENV 4-I . | [
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"location... | 2019 | The evolutionary dynamics of DENV 4 genotype I over a 60-year period |
In dairy cattle , the widespread use of artificial insemination has resulted in increased selection intensity , which has led to spectacular increase in productivity . However , cow fertility has concomitantly severely declined . It is generally assumed that this reduction is primarily due to the negative energy balance of high-producing cows at the peak of lactation . We herein describe the fine-mapping of a major fertility QTL in Nordic Red cattle , and identify a 660-kb deletion encompassing four genes as the causative variant . We show that the deletion is a recessive embryonically lethal mutation . This probably results from the loss of RNASEH2B , which is known to cause embryonic death in mice . Despite its dramatic effect on fertility , 13% , 23% and 32% of the animals carry the deletion in Danish , Swedish and Finnish Red Cattle , respectively . To explain this , we searched for favorable effects on other traits and found that the deletion has strong positive effects on milk yield . This study demonstrates that embryonic lethal mutations account for a non-negligible fraction of the decline in fertility of domestic cattle , and that associated positive effects on milk yield may account for part of the negative genetic correlation . Our study adds to the evidence that structural variants contribute to animal phenotypic variation , and that balancing selection might be more common in livestock species than previously appreciated .
Widespread application of artificial insemination , combined with the use of the animal model ( exploiting kinship inferred from pedigree and/or genome-wide SNP data ) to accurately predict breeding values , has led to spectacular increases in the productivity of livestock . As an example , average milk yield per lactation has nearly doubled in US Holstein cows between 1960 ( ∼6 , 300 kgs ) and 2000 ( ∼11 , 800 kgs ) , and more than half of this progress was genetic [1] . Milk yield and composition have moderate heritability ( 20–40% ) , and – with the exception of a handful of genes with detectable effects including DGAT1 [2]- their genetic architecture is quasi-infinitesimal ( e . g . , [3] , [4] ) . During the same period , cow fertility has declined severely in most countries . In the same US dairy cattle population , the number of days between calving and first estrus has increased from 126 to 169 between 1976 and 1999 [5] . Lucy [6] reports that , between 1970 to 2000 , the number of inseminations required to obtain a pregnancy increased from 1 . 8 to 3 . 0 , and that the interval between successive calvings increased from 13 . 5 to 14 . 9 months in US Holstein . Between 1972 and 1996 , the conception rate for the first insemination reportedly dropped from 62% to 34% [7] . Fertility traits in cattle have low heritability , ranging from 1 to 10% ( e . g . , [8] ) . Fertility is negatively correlated with milk yield and composition . For example , the genetic correlation between milk yield and interval between calving and first insemination is 0 . 43 ( e . g . , [8] ) . It is generally assumed that the reduction in fertility is due to the negative energy balance of high-producing cows at the peak of lactation ( e . g . , [6] ) . The genetic architecture of fertility is consequently assumed to be primarily quasi-infinitesimal as well ( e . g . , [4] ) . It was recently observed that the typical human carries >100 loss-of-function ( LoF ) variants [9] . Epidemiological evidence indicates that a handful of these might be highly deleterious in homozygotes , including by causing embryonic death [10] . It was recently shown that the majority of conceptuses that are homozygous for LoF mutations in the bovine SLC35A3 and FANCI genes causing complex vertebral malformation and brachyspina , respectively , die before birth [11] , [12] . The main economic impact of these genetic defects might thus result from their effect on fertility rather than from calf mortality per se . The observation of significant depletions in autozygosity for specific haplotypes suggests that several other embryonically lethal mutations ( EL ) are segregating at intermediate frequencies in livestock populations and jointly account for a non-negligible proportion of insemination failures [13]–[15] . We herein report the positional cloning of a quantitative trait locus ( QTL ) with major effect on cow fertility . We show ( i ) that the causative mutation is a 660-Kb deletion that encompasses four genes on bovine chromosome 12 ( Bos taurus – BTA12 ) , ( ii ) that it affects fertility by causing early embryonic death of homozygous conceptuses , and ( iii ) that it is maintained at high frequency in Nordic Red breeds because of its association with positive effects on milk yield and composition . Our results thus add to the evidence that the spread of recessive embryonic lethal variants account for at least part of the decline in fertility observed in cattle . This is at least the seventh example in livestock where an allele that is deleterious at the homozygous state is maintained at high frequency in the population because of the selective advantage it confers to heterozygotes .
To map QTL influencing cow fertility , we performed a genome wide association study ( GWAS ) using a cohort comprising 4 , 072 Holstein-Friesian , 1 , 177 Jersey , 894 Danish Red , 1 , 714 Swedish Red , and 2 , 242 Finnish Ayrshire progeny-tested bulls . The 10 , 099 bulls were genotyped using the 50K Bovine Array ( Illumina , San Diego , CA ) . The phenotypes for this initial scan were the bull's predicted breeding values ( EBV ) for an index combining the different fertility traits ( number of inseminations in heifers and cows ( AISH and AISC ) , interval between calving and first insemination ( ICF ) and interval between first and last insemination in heifers and cows ( IFLH and IFLC ) ) . Association analysis was conducted using all animals on a SNP-by-SNP basis ( assuming an additive model ) , yet accounting for familial relationships and population stratification by including a random sire effect and four principal components . We obtained 14 genome-wide significant QTL , of which one on chromosome 12 was the strongest ( p<10−20; Figure 1A ) . We repeated the analysis of BTA12 by breed , and this indicated that the QTL was mainly segregating in the Finnish Ayrshire and Swedish Red , but was not detectable in Holstein-Friesian , Danish Red and Jerseys ( Figure 1B ) . A QTL influencing fertility has previously been reported at approximately the same position in Finnish Ayrshire [16] , [17] and Norwegian Red [18] . We repeated the association analysis using a previously described haplotype-based method including an animal model [19] , in Finnish Ayrshire and Swedish Red . Haplotyping was done jointly across all Nordic red breeds , while the association analysis was conducted separately within each breed . We now analyzed fertility traits individually , including number of inseminations for cows/heifers ( AISC & AISH ) , interval between calving and first insemination ( ICF ) , interval between first and last insemination for cows/heifers ( IFLC & IFLH ) , non-return ( to heat after insemination ) rate at 56 days for cows/heifers ( NRRC & NRRH ) , and heat strength ( HS ) . Genome-wide significant signals were obtained in both breeds at the expected map position for all tested traits except ICF and HS ( Figure S1 ) . It is worth noting that ICF and HS are related to oestrus while all other traits are related to pregnancy success . One of the 40 fitted ancestral haplotypes ( hereafter called haplotype A27 – see Figure S2 ) , shared across breeds , was shown to have a pronounced negative effect on all fertility traits affected by the QTL ( Figure S1A–G ) . Closer examination of the SNPs in Finnish Ayrshire in the immediate vicinity of the association peak identified five markers that departed very significantly from Hardy-Weinberg equilibrium ( p-values ranging from 10−66 to 10−161 ) as a result of excess homozygosity ( Figure S3A ) . The same markers were also characterized by an inflation of Mendelian parent-offspring incompatibilities ( Figure S3B ) . Both findings suggested the occurrence of a chromosomal deletion . To test this hypothesis , we first took advantage of available SNP genotypes obtained with the 770K HD bovine array ( Illumina ) for 243 Finnish Ayrshire ( including 82 animals carrying haplotype A27 ) to search for structural variation . The animals carrying haplotype A27 were shown to present both reduced total signal intensity ( referred to as “Log R ratio” ) and complete homozygosity for 174 consecutive SNPs spanning positions 20 , 101 , 696 to 20 , 755 , 193 , confirming the deletion hypothesis ( Figure 2A ) . The same deletion was previously reported in a multi-breed CNV scan , in which it was only observed in Norwegian Reds [20] . We then took advantage of whole genome next generation sequencing ( NGS ) information available for 30 Red Danish and 18 Finnish Ayrshire bulls including respectively one and six individuals carrying haplotype A27 . In all carrier animals , the occurrence of a deletion was obvious from ( i ) the approximately halved sequence depth from positions 20 . 10 to 20 . 76 Mb and ( ii ) the incongruent mapping of paired-ends separated by approximately 660 Kb ( Figure 2B & Figure S4 ) . In addition to the paired reads bridging the breakpoint , detailed analysis of individual sequences identified several split reads that sized the deletion at exactly 662 , 463 bp ( position 20 , 100 , 649 to 20 , 763 , 116 bp ) ( Figure S5A ) . Proximal and distal breakpoints mapped to non-homologous LINE repeats ( L1ME1 and L1BT respectively ) and are characterized by 2-bp microhomology , while the deletion event was accompanied by a one bp insertion ( Figure S2B ) . The breakpoint was confirmed by PCR amplification , using a forward primer in a unique sequence upstream of the L1ME1 repeat and a reverse primer within the L1BT element . The expected 281 bp PCR product was obtained from carriers , but not from homozygous wild-type controls . In comparison , an amplicon positioned within the deletion yielded the expected 318 bp product in the four animals ( Figure S5B ) . Sanger sequencing of the 281 bp deletion-specific amplicon confirmed the NGS results ( Figure 2B ) . Examination of the annotation of the orthologous region in mammals suggests that the deletion encompasses three protein-encoding genes ( RNASEH2B , GUCY1B2 and 3 out of 4 exons of FAM124A ) , one gene with uncertain coding potential ( DLEU7 ) and two non-coding RNA genes ( DLEU7-AS1 and LINC00371 ) ( Figure 2B ) . Whole-genome RNA-Seq reads available from the cortex of a 60 days post-fertilization bovine embryo , supported the organization and coding potential of the three protein coding genes , revealed reads mapping to the putative DLEU7 gene , but no reads corresponding to the two putative non-coding RNA genes ( Figure 2B ) . The 660-Kb deletion spans five SNPs interrogated by the 50K Bovine array . As the deletion might have compromised the phasing accuracy and hence the mapping accuracy , we first rephased SNP data after exclusion of the five corresponding SNPs and repeated the haplotype-based analysis described above . We obtained a chromosome-wide significant signal immediately adjacent to the 660-Kb deletion . It was entirely driven by one of the 40 newly fitted ancestral haplotypes ( hereafter called haplotype B28 – see Figure S2 ) , which had strong negative effect on fertility . Indeed , adding B28 genotype to the model completely annihilated the QTL signal ( Figure 3A–B and Figure S6A–G ) . As expected , haplotype states B28 and A27 were closely related in the immediate vicinity of the deletion ( Figure S2 ) . Carriers of the B28 haplotype had a frequency of ∼32% in Finnish Ayrshire , ∼23% in Swedish Red and ∼13% in Danish Red ( yet were absent in Holstein-Friesian and Jerseys ) . We then exploited signal intensity , obligate homozygosity and parentage conflicts for the corresponding markers to confidently genotype 2 , 139 Finnish Ayrshire , 1 , 221 Swedish Red and 1 , 096 Danish Red sires for the deletion ( see Text S1 ) . Linkage disequilibrium ( r2 ) between the deletion and haplotype B28 was 0 . 96 , indicating that haplotype B28 tagged the deletion nearly perfectly ( see Text S1 ) . Taken together , these findings indicate that the 660-Kb deletion is most likely the causative variant underlying the fertility QTL . The complete ablation of four genes , including RNASEH2B known to cause embryonic lethality when knocked-out in the mouse [21] , [22] , suggested that the 660-Kb deletion might affect fertility by causing embryonic death in homozygotes . This would also be compatible with the fact that the QTL affects the interval between first and successful insemination , number of inseminations and non-return rate ( all related to pregnancy success ) , but not the interval from calving to first insemination and heat strength ( related to oestrus ) . The EL hypothesis makes two predictions: ( i ) there should be no homozygotes for the 660-Kb deletion amongst live animals , and ( ii ) the fertility problems should be restricted to matings between carrier sires and carrier dams . To test the first prediction we first examined the signal intensities for the five SNPs ( 50K array ) within the deletion in 3 , 095 Finnish Ayrshires and 1 , 312 Swedish Red . The lowest average logRR value was −1 . 09 , which is within the range of values expected for heterozygotes . As much lower values are expected for homozygotes , we can confidently conclude that none of these were present in the analyzed sample . To test the second prediction , we compiled the rate of reproductive failure established by the fact that the cows returned in oestrus 35 , 56 , 100 and 150 days after insemination for matings sorted by genotype ( sire and maternal grand-sire ) for the 660-Kb deletion: ( i ) non-carrier ( NC ) sire X daughter of NC maternal grand-sire , ( ii ) NC sire X daughter of carrier ( C ) maternal grand-sire , ( iii ) C sire X daughter of NC maternal grand-sire , and ( iv ) C sire X daughter of C maternal grand-sire . The corresponding rates were estimated using a mixed model that included parity and month of insemination as fixed effects , and maternal grand-sire as random effect . The expected proportions of conceptuses that are homozygous for the 660-Kb deletion are , respectively , ( i ) 0 , ( ii ) 0 , ( iii ) 0 . 25p , and ( iv ) for the four different matings . In these , p corresponds to the frequency of the 660-Kb deletion in the corresponding population . Assuming that the background rate of reproductive failure equals f , the extra rate of reproductive failure is expected to be ( i ) 0 , ( ii ) 0 , ( iii ) 0 . 25 ( 1-f ) p and ( iv ) , if all embryos that are homozygous for the 660-Kb deletion have died at the corresponding developmental stage . Figure 4 shows the observed versus expected extra rates of reproductive failure in the four mating types , assuming that f corresponds to the weighted average of the failure rate ( at the corresponding days post-insemination ) for mating types ( i ) and ( ii ) , and p to the weighted average of the frequency of the deletion in the Nordic Red breeds included in the analysis . As expected , we observed a highly significant extra rate of reproductive failure ranging from ∼2% ( p<10−29 ) at 35 days post-insemination to ∼5% ( p<10−154 ) at 150 days post-insemination in mating type ( iv ) . Comparing this extra rate with theoretical expectation computed as described above , indicates that 20% homozygous embryos have died before 35 days post-insemination and 79% before 150 days post-insemination . As we have demonstrated above that homozygosity for the 660 Kb deletion is fully lethal , this finding implies that a remaining 20% of homozygous embryos die between 150 days post-insemination and parturition . Mating type ( iii ) is expected to yield a proportion 0 . 25p of homozygous embryos as the sire is known to be carrier and the dam may have inherited the deletion from her ungenotyped mother ( i . e . the maternal grand-dam ) . Performing the same comparison between observed and expected extra rate of reproductive failure in this mating type , yielded comparable estimates of embryonic death of homozygous embryos of 25% at 35 days and 88% at 150 days . In conclusion , increased rate of reproductive failure according to parental genotype supports the hypothesis that the 660Kb deletion is EL , causing fetal death between one and >5 months of gestation . The carrier frequencies observed in the three Nordic Red breeds ( 32% , 23% and 13% ) are intriguingly high given the highly deleterious effect of the deletion . We reasoned that this might be due to a positive , direct or indirect effect of the deletion on desirable traits . We tested this hypothesis by scanning chromosome 12 for QTL influencing milk yield and composition using the same haplotype-based approach . We observed chromosome-wide significant QTL on milk , fat and protein yield in the three Nordic Red breeds ( joint analysis ) , maximizing in the immediate vicinity of the 660-Kb deletion ( Figure 5A ) . All QTLs were entirely driven by the strong positive effect of haplotype B28 , previously shown to be associated with decreased fertility ( Figure 5B ) . Indeed , including the B28 genotype as a fixed effect in the model annihilated the QTL effects on milk and fat yield , except for a small residual effect on protein ( Figure 5A ) . Taken together , these findings suggest that the 660-Kb deletion is maintained at moderate to high frequency in Nordic Red breeds despite its deleterious effect on fertility because of its positive ( direct or indirect ) effect on milk yield and composition . The same pleiotropic effect on fertility and milk traits of a BTA12 QTL was previously reported in Norwegian Red [18] .
We herein demonstrate that a QTL with major effect on fertility in Nordic Red cattle is due to the segregation of a 660-Kb deletion on chromosome 12 that is lethal in homozygous embryos . It demonstrates , somewhat counter-intuitively , that discernible Mendelian entities account for part of the inherited variation for this highly complex and lowly heritable trait . Our work adds to the evidence that EL are at least in part responsible for the increase in insemination failure that is a growing concern in highly selected cattle populations . It is becoming apparent that , as human , domestic animals carry several deleterious alleles . In man , highly deleterious alleles are typically rare , and hence homozygosity for such variants is exceptional in the absence of consanguinity . In domestic animals , however , and as a result of intense selection and reduction in effective population sizes , a yet unidentified number of EL may be segregating at low to moderate frequencies in most populations . Assuming ( i ) that ∼25% of the increase in insemination failure is due to recessive EL and ( ii ) random mating , this could correspond for instance to 7 . 5 embryonic lethal equivalents segregating at 10% frequency , ∼30 embryonic lethal equivalents segregating at 5% frequency , or ∼750 embryonic lethal equivalents segregating at 1% frequency . Fertility traits in livestock are typically modeled as being determined by ( additive ) breeding values of sire and dam . In fact , embryonic mortality caused by recessive EL alleles is determined by the non-additive genotype of the conceptus . The corresponding source of variation is only poorly captured by the additive parental breeding values , particularly when the population frequency of the corresponding EL is low . Accounting for the inbreeding coefficient of the conceptus may provide better estimates of the parental breeding values for fertility . It is noteworthy that the dam's fertility is often considered to be subject to inbreeding depression . This corresponds to the inbreeding coefficient of the dam , which is distinct from the inbreeding coefficient of the offspring . In this work , we have used a traditional phenotype-driven forward genetic approach to map and subsequently dissect the QTL . Given the nature of the phenotype ( difficult to observe embryonic lethality ) , a genotype-driven reverse genetic approach might be equally if not more appropriate . One way to achieve this is to search for significant , local depletions in autozygosity for haplotypes that are assumed to be associated with EL alleles . This approach has been successfully applied in the Holstein , Brown Swiss and Jersey breeds [13] , [14] . It is noteworthy that several SNPs on both sides of the 660-Kb deletion were in significant Hardy-Weinberg disequilibrium as a result of excess heterozygosity ( Figure S3 ) . Moreover , there was a significant depletion in homozygosity for the B28 haplotype in Danish ( p<1 . 616e-06 ) , Swedish ( p<1 . 570e-27 ) and Finnish ( p<4 . 769e-52 ) Red cattle , indicating retrospectively that this approach could have been effective ( particularly when combined with our Hidden Markov Model-based haplotyping method [19] ) in detecting the corresponding deletion . However , to work this approach requires near complete LD ( r2∼1 ) between the EL and the haplotype upon which it appears ( which is unlikely always to be the case ) , as well as a very large study population ( which is unlikely always to be available ) . We have recently proposed an alternative approach that might obviate some of these limitations [11] . In this approach , genome-wide NGS data obtained on a representative sample of moderate size of the population of interest are mined for predicted LoF variants . Candidate LoF variants , segregating at intermediate or even low frequency , are then genotyped on a much larger sample to test for Hardy-Weinberg disequilibrium ( absence of homozygotes ) and association with reduced fertility , two features expected for genuine EL variants . Until now attention has primarily focused on frame-shift , stop-gain , splice-site , and - to a lesser extent – highly disruptive missense variants . The present study indicates that the search for structural variants in NGS data , particularly deletions , might also be worth the effort . In the specific case of the 660-Kb deletion uncovered in this work , we show that its high frequency is not only due to random drift , but also to the associated effect on milk yield and composition . This is at least the seventh example of balancing selection maintaining a deleterious allele at high frequency in livestock . Other examples include the R615C mutation in the porcine RYR1 gene increasing muscle mass in heterozygotes yet causing the Porcine Stress and Pale Soft Exudative Meat Syndromes in homozygotes [23] , MSTN LoF variants increasing muscle mass in heterozygotes yet causing dystocia in homozygotes [24] , variants in BMP15 and GDF9 ( members of the TGFβ family ) increasing prolificity in heterozygous females yet causing infertility in homozygous ewes ( e . g . , [25] , [26] ) , the V700E mutation in the ovine FGFR3 gene increasing size in heterozygotes yet causing Spider Lamb in homozygotes [27]–[29] , MRC2 LoF variants increasing muscle mass in heterozygotes yet causing Crooked Tailed Syndrome in homozygotes [30] , [31] , and a LINE insertion in the porcine SPEF2 gene that causes infertility in boars yet increases fertility in sows [32] . In all these examples , available evidence indicates that the same mutation and target gene underlie both the desirable effect in heterozygotes and undesirable effect in homozygotes . In the present case , we believe it most likely ( because of its size ) that the 660-Kb deletion also causes both embryonic lethality and favorable effect on milk yield and composition . However , we cannot exclude that the milk effect is due to a variant distinct from , yet in high linkage disequilibrium with the deletion . The residual effect on protein yield observed after correction for the deletion genotype tends to support the latter hypothesis . Which target genes are responsible for the antagonistic effects on fertility and milk production remains to be determined . RNASEH2B is a strong candidate causative gene for the embryonic lethality as knocking it out causes embryonic death in the mouse [21] , [22] . RNASEH2B ( ribonuclease H2 , subunit B ) codes for the non catalytic subunit of RNase H2 , an endonuclease that specifically degrades the RNA of RNA:DNA hybrids and participates in DNA replication . RNASEH2B loss-of-function mutations cause Aicardi-Goutieres syndrome type 2 in humans ( AGS2 , OMIM 610181 ) . It remains possible , however , that one or the two other coding genes included in the deletion ( GUCY1B2 and FAM124A ) or even DLEU7 and the two non-coding RNA genes ( DLEU7-AS1; LINC00371 ) contribute to the embryonic lethality as well . GUCY1B2 ( guanylate cyclase 1 , soluble , beta 2 ) codes for the widely expressed beta sub-unit of a nitric oxide-sensitive guanylyl cyclase of poorly defined function . Although apparently pseudogenized in humans , GUCY1B2 is highly conserved in vertebrates including bovine . FAM124A codes for a protein conserved across vertebrates yet of unknown function . Human DLEU7 is predicted to code for a “low quality protein” which , however , is poorly conserved in other mammals . Its function , as well as those of DLEU7-AS1 and LINC00371 , remain unknown . We can also not exclude the possibility that the deletion perturbs the expression of genes lying outside of it , and that this also affects embryonic development . Whether the effect on milk yield and composition is due to altered expression of one of the four genes in the deletion and/or one or more genes outside of the deletion remains unknown . It is worthwhile noting , however , that FAM124A is strongly expressed in myoepithelial cells of mammary gland [33] . Initial suspicion that a deletion might underlie the QTL came from the observation of significant deviation from Hardy-Weinberg equilibrium and inflation of parentage conflicts for a set of clustered SNPs . In most other studies that we are conducting , stringent quality control measures would have eliminated the corresponding markers prior to GWAS . While this would not have precluded the identification of the QTL , it would probably have hampered the discovery of the deletion . It thus seems advisable to at least verify whether SNPs that do not pass such stringent QC-tests are randomly scattered across the genome rather than clustered . The latter might be indicative of structural variants that deserve further analysis .
All animals were genotyped using the BovineSNP50 beadchip ( Illumina , San Diego , CA ) , which assayed 54 , 001 SNP markers , at Aarhus University and GenoScan A/S , Denmark . Genomic DNA was extracted from whole blood or semen . The Illumina Infinium II multi-sample assay protocol was followed to prepare SNP chips for scanning using the iScan imaging system . Analysis was performed using Beadstudio software ( version 3 . 1 ) . SNP positions within a chromosome were designated according to the Bos taurus genome UMD3 . 1 assembly [34] . The quality parameters used for selection of SNPs in the study were minimum call rates of 90% for individuals and 95% for loci . Marker loci with minor allele frequencies ( MAFs ) below 5% were excluded for SNP-by-SNP association analysis . The minimum acceptable Beadstudio Gencall ( GC ) score ( see http://res . illumina . com/documents/products/technotes/technote_infinium_genotyping_data_analysis . pdf for more details ) was 0 . 60 for individual typing , and individuals with average GC scores below 0 . 65 were excluded . After quality control the whole genome map reduced to 37 , 123 common SNPs across the five breeds analyzed . For the breed-wise analysis on BTA12 , 1166 SNPs passed the filtering for call rates . After filtering for MAF >0 . 05 there were 1095 , 983 , 1125 , 1107 , 1093 SNPs for Holstein , Jersey , Danish Red , Swedish Red and Finnish Red cattle respectively . In the second phasing stage , 17 markers presenting more than 10 parentage conflicts were removed from the analysis . In addition , 243 Finnish Red bulls were genotyped on the Bovine HD Genotyping BeadChip ( Illumina , San Diego , CA ) with 725 , 293 SNPs mapping on autosomes . SNP positions were designated according to the Bos taurus genome UMD 3 . 1 . We used phenotypic data from three Nordic cattle breeds ( Holstein , Jersey and Nordic Red ) . Fertility traits analyzed in this study and number of individuals with records are described in Text S2 . For details regarding the phenotypes recorded and models used in routine breeding value prediction , see http://www . nordicebv . info . A whole genome scan was performed to test for the presence of fertility QTL in a multi-breed data set comprised of Holstein , Jersey , Red dairy cattle of Denmark , Sweden and Finland . The effect of each SNP was estimated by successively fitting the following linear mixed modelwhere y is the vector of estimated breeding values ( EBVs ) of the bulls for the fertility index , μ is the overall mean , b is the vector of breed effects , P is the matrix of the four top principal components ( estimated as in [35] from the genome wide markers ) , c is the vector of effects of the principal components , m is the vector of additively coded SNP genotypes , s is the allele substitution effect of the SNP , u is the vector of random sire effects assumed to be , where is the additive genetic variance and As is the additive genetic relationship among the sires of the bulls derived from the pedigree , and e is the vector of random individual error term assumed to be . The allele substitution effect s was estimated by AI-REML implemented in DMU [36] and its significance was estimated using a t-test . The genome-wide significance threshold corresponding to a familywise error rate of 0 . 05 , was set at p<1e-6 after correction for multiple testing using a Bonferroni correction for 50 , 000 independent tests . Genotypes were first phased and clustered into ancestral haplotypes with the PHASEBOOK software package [19] . The SNPs were first phased utilizing information from pedigree with LINKPHASE [19] , and then using LD with DAGPHASE [19] and Beagle [37] . The phased genotypes were then clustered into 40 ancestral haplotypes using HIDDENPHASE [19] . A haplotype-based association was then carried out using these ancestral haplotypes at each SNP position on BTA12 . The presence of a QTL was tested using the following linear mixed model . Where y are the de-regressed proofs ( e . g . , [38] ) for the fertility traits , μ is the overall mean , P is the matrix of the four top principal components estimated from the genome wide markers , c is the vector of effects of principal components , h is a vector of 40 random ancestral haplotype effects with variance assumed to be , I is an identity matrix , u is the vector of individual polygenic effects with variance assumed to be , A is the additive relationship matrix estimated from the pedigree , and e is the vector of individual error terms with variance assumed to be , W is the diagonal matrix containing weights derived from the reliabilities ( r2 ) of the de-regressed EBVs ( ) . The variances , and were estimated using AI-REML implemented in DMU software package [36] , and the presence of a QTL was tested using a Likelihood Ratio Test ( distributed as a chi-square distribution with 1 df ) . The chromosome-wide significance threshold corresponding to a familywise error rate of 0 . 05 was set at p<0 . 00005 after correction for multiple testing using a Bonferroni correction for 1 , 000 independent tests . A linear mixed model was applied to test the effect of mating type on the rate of reproductive failure established by the fact that the cows returns in oestrus at 35 , 56 , 100 and 150 days after insemination . Four classes of matings were defined according to 660-kb deletion genotype: ( i ) non-carrier ( NC ) sire X daughter of NC maternal grand-sire , ( ii ) non-carrier ( NC ) sire X daughter of carrier ( C ) maternal grand-sire , ( iii ) C sire X daughter of NC maternal grand-sire , and ( iv ) C sire X daughter of C maternal grand-sire . Only genotyped bulls for which the genotype of the 660-kb deletion could be predicted based on the haplotype B28 were used . A total of 3 , 157 , 753 inseminations were analyzed ( 1 , 936 , 585 , 590 , 806 , 443 , 464 and 186 , 898 for mating types i , ii , iii and iv , respectively ) . The average rate of insemination failure was 0 . 278 , 0 . 396 , 0 . 475 and 0 . 493 at , respectively , 35 , 56 , 100 and 150 days after insemination . The fitted mixed model included parity and month of insemination ( by year ) as fixed effect , and maternal grand-sire as random effect:where y is a vector indicating return to oestrus 35 , 56 , 100 or 150 days after insemination ( 0 in case of success and 1 in case of failure ) , p is the vector of effects of parity , t is the vector of effects of month and year of insemination , m is the vector of effects of mating type , u is the random sire effect assumed to be ; where is the additive genetic relationship among the sires of the dams derived from the pedigree , and e is the vector of random individual error terms assumed to be In a population where the deletion has a frequency p , the proportion of carriers is 2p . In mating types ( i ) and ( ii ) the probability that both parents are carriers is null ( since the sires are non-carriers ) . In class ( iii ) and ( iv ) the maternal grand-dam has probability 2p to be carrier . As a result in class ( iii ) the dam has a probability p to be carrier . Finally , in class ( iv ) the maternal grand-sire is a carrier and has 0 . 5 chance to transmit the deletion whereas the maternal grand-dam has a probability p to transmit the deletion to the dam . As a consequence , the dam has probability to be carrier ( since dams cannot be homozygotes for the deletion ) . The expected proportion of conceptuses that are homozygous for the 660-Kb deletion is equal to 0 . 25 multiplied by the probability that both parents are carriers , corresponding respectively to ( i ) 0 , ( ii ) 0 , ( iii ) 0 . 25p , and ( iv ) for the four different mating types . DNA samples were extracted at Aarhus University ( Foulum ) from semen samples using standard procedures . Sequencing was done using Illumina sequencers at Beijing Genomics Institute ( Shenzhen , China ) . Sequencing was shotgun paired-end sequencing with a read length of 91 base pairs . Fastq data were converted from Illumina to Sanger quality encoding using a patched version of MAQ [39] . They were aligned to the UMD3 . 1 assembly of the cattle genome [34] using BWA [40] version 0 . 6 . 2 . They were converted to raw BAM files using samtools [41] . Quality scores were re-calibrated using the Genome Analysis Toolkit version 1 . 6 [42] following the Human 1000 Genome guidelines incorporating information from dbSNP version 133 [43] . Sequences were realigned around insertion/deletions using the Genome Analysis Toolkit version 1 . 6 . Variants were called using the Genome Analysis Toolkit version 1 . 6 . Genomic DNA from 18 Finnish Ayrshire bulls was extracted and purified according to standard protocols . Sample preparation , cluster generation and sequencing were performed according to the manufacturer's protocols ( Illumina Paired-End Cluster Generation kit ( version 4 ) ) . Briefly , two paired-end libraries were prepared and sequenced on a HiSeq2000 ( Illumina , San Diego , California , USA ) . Genomic DNA was sheared by nebulization , ligated with Illumina's PE adaptors , and fragments approximately 300 and 800 bases in length were gel purified followed by PCR amplification and column purification . Purity and yield were checked using a 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , California , USA ) and yields were additionally measured using a Qubit ( Invitrogen , Carlsbad , California , USA ) . Fastq files were generated using the Illumina data analysis workflow software Casava versions 1 . 7 and 1 . 8 , and base qualities of a subset of reads from each sequencing lane were visually inspected using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . Each bull was sequenced to an approximate coverage of 20× . We used the UMD3 . 1 assembly of the cow genome as a reference sequence for mapping ( http://stothard . afns . ualberta . ca/1000_bull_genomes/reference_for_mapping/umd_3_1_reference_1000_bull_genomes . fa . gz ) . Sequencing reads were aligned to the cow reference genome using BWA ( 0 . 5 . 9-r16; [40] ) with default parameters . Merging of BAM files and duplicate filtering was performed using Picard ( version 1 . 67; http://picard . sourceforge . net ) . Filtering for mapping quality was done during variant detection with the Genome Analysis Toolkit version 2 ( http://www . broadinstitute . org/gatk/; [42] ) including indel realignment and base score recalibration . Genomic DNA was extracted from frozen sperm straws of two carrier and two homozygous wild-type sires using the MagAttract Mini M48 Kit ( Qiagen ) . PCR amplification was carried out with the Phusion Hi-Fidelity PCR Kit ( New England BioLabs , Ipswich ) in a 20 µl volume of 1× Phusion buffer , 3% of DMSO , 0 . 5 mM dNTP , 0 . 5 µM primer mix ( forward primer: 5′-CGA ATT CTA TTT CTG AAA GGG GAA A-3′ and reverse primer: 5′-TTT GTC TTA CAT ATT GCG GCA CTC-3′ ) and 20 ng of genomic DNA . The cycling conditions were the following: ( i ) an initial denaturation of 98°C for 30 sec , ( ii ) 10 cycles of 10 sec denaturation ( 98°C ) , 30 sec hybridization ( 70°C with 1°C decrease at each cycle ) , 30 sec elongation ( 72°C ) , ( iii ) 25 cycles of 10 sec denaturation ( 98°C ) , 30 sec hybridization ( 60°C ) , 30 sec elongation ( 72°C ) and a final 7 min elongation ( 72°C ) . PCR products were separated a on a 1 . 5% agarose gel , purified and directly sequenced using the Big Dye terminator cycle sequencing kit ( Applied Biosystems , Foster City , CA ) . Electrophoresis of sequencing reactions was performed on an ABI PRISM 3730 DNA analyzer ( PE Applied Biosystems , Foster City , CA ) . Sequence traces were visualized using the CodonCode Aligner 4 . 1 software ( LI-COR , Inc . ) . A 318 bp control amplification , with a primer pair within the deletion ( forward primer: 5′-AGC TGC TTC TCG GAA GGG AC-3′ and reverse primer: 5′-CAG GAG TAC GCT ACT AAC AC-3′ ) , was performed in parallel using standard PCR conditions . | We report the identification of a large deletion encompassing four genes and the demonstration of its negative effect on fertility in Nordic Red dairy cattle . We show that this deletion is recessively lethal ( homozygous embryos die ) and therefore , when carrier cows are mated to carrier bulls , there is a high risk of embryonic mortality . As a result , chances of insemination failure are higher for such matings . Surprisingly , despite its negative effect , the deletion is frequent in Nordic Red cattle . We show that this high frequency may be a consequence of the fact that the deletion is associated with increased milk production and therefore selected for . Due to increased levels of inbreeding resulting from the widespread use of artificial insemination , such recessive lethal alleles may account for a non-negligible fraction of the reduction in fertility observed in cattle . | [
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"production"
] | 2014 | A 660-Kb Deletion with Antagonistic Effects on Fertility and Milk Production Segregates at High Frequency in Nordic Red Cattle: Additional Evidence for the Common Occurrence of Balancing Selection in Livestock |
There has been considerable interest from the fields of biology , economics , psychology , and ecology about how decision costs decrease the value of rewarding outcomes . For example , formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences , as in animal species populating different habitats , or normal and clinical human populations . Strikingly , it remains largely unclear how humans evaluate rewards when these are tied to energetic costs , despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort ( e . g . , depression ) . One common assumption is that effort discounts reward in a similar way to delay . Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting . We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs ( Experiment 1 ) . We then additionally characterized the profile of effort discounting free of model assumptions ( Experiment 2 ) . Contrary to previous reports , in both experiments effort costs devalued reward in a manner opposite to delay , with small devaluations for lower efforts , and progressively larger devaluations for higher effort-levels ( concave shape ) . Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model , with the largest reward devaluations occurring at shorter delays . In contrast , an altogether different relationship was observed for effort-choices , which were best described by a model of inverse sigmoidal shape that is initially concave . Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting . This enables accurate modelling of cost-benefit decisions , a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity .
Many of the choices that humans and animals make every day depend on cost-benefit analyses . There has been considerable interest from the fields of biology , economics , psychology and behavioral ecology about how decision costs—in particular the delay and uncertainty associated with a rewarding outcome—decrease the subjective value of the expected outcome [1–9] . For example , there is now ample evidence that rewards are hyperbolically discounted by delays [10–12] . This implies that an additional delay in receiving reward has a large effect when added to a short delay , but a small effect when added to a long delay . Similarly , the probability with which reward is received is subjectively distorted such that small probabilities are over-weighted and large probabilities under-weighted [1 , 13] . Understanding how different decision costs influence human reward discounting has obvious implications for economic decisions , and for clinical disorders , such as anxiety and impulsivity disorders , depression , gambling , and addiction [14–21] , in which deficits in cost-benefit decision-making are hallmark features of the disease . Intriguingly , how physical effort influences our choices has been studied far less than other decision costs , and the underlying discounting effect of effort on behavior remains unclear . This is particularly surprising given the recent interest in the neural and neurochemical mechanisms involved in effort-based choice [22–31] , and the fact that a diminished willingness to exert effort is a key signature of several clinical disorders such as apathy , abulia , negative-symptom schizophrenia and depression [32–36] . The most commonly applied models of effort discounting assume that effort costs decrease reward value either linearly [31 , 37] or hyperbolically [29 , 38 , 39] , and thus in the same way as delay ( although see [40] ) . However , in the few cases where effort discounting has been examined , a limited number of effort levels were used , contrary to the continuum of effort-based choices that humans and animals experience every day , and the assumption that effort discounting resembles delay discounting has not been formally tested against alternative models . Indeed , several theoretical arguments suggest that individuals may incorporate effort costs into their decisions in a different way to delay costs . First , the perceived sense of effort increases by a small amount for small efforts , which are easy to execute and energetically inconsequential , but more steeply when efforts become exhausting . More specifically , there is a reduced discriminative ability at lower efforts , and it has been suggested that perceived effort increases as a power function of the required force [41 , 42] . This would imply an initially concave discounting curve , rather than a hyperbolic function . Second , the willingness to travel further or to wait for rewards differs between animal species [6] , providing some indication that the willingness to exert effort or to wait for rewards may not correlate between species or individuals . Finally , from a psychological perspective , it has been argued that the two costs differ fundamentally with regard to what the cost is ascribed to: effort costs are ascribed to actions , whereas delay costs ( when not caused by movement times ) are ascribed to outcomes [43 , but see 44 , 45] . These dissociable psychological constructs are supported by both neurophysiological and neuropsychological data: lesions of anterior cingulate cortex ( ACC ) —an area implicated in decision-making and action selection which connects strongly to the motor system—cause deficits in effort-based , but not delay-based , choice behavior , while lesions to the orbitofrontal cortex ( OFC ) —an area implicated in reward processing but that does not connect with the motor system—cause deficits in delay-based , but not effort-based , decision-making [46] . The hyperbolic effect of delay discounting has been used extensively as a model for self-control and provides a powerful tool to quantify decision-making differences in the normal and clinical populations ( e . g . , impulsivity , addiction ) , and even as a predictor of financial mismanagement [47–56] . Establishing a formal model that describes how effort discounts rewards may provide an analogous tool for quantifying behavioral changes in clinical disorders associated with motivational disturbances including reduced physical activity ( i . e . , apathy , depression , fatigue , abulia ) [9] , but may also help to understand how reinforcement can influence effortful neurorehabilitation regimes following brain injury . With this aim , we directly compared how effort and delay costs discount the subjective value of rewards . Based on the evidence above , we hypothesized that value discounting of effortful options would be dissociable from that of delayed options . To address this , we directly compared the performance of competing behavioral models on choices involving effort and delay costs ( Experiment 1 ) . In a second step , we determined how effort costs affect reward valuation free of any assumptions about the discounting model ( Experiment 2 ) . Data from both experiments supported our hypothesis that reward devaluations caused by energetic costs differ from reward devaluations caused by temporal delays , and therefore cannot be described using the same convex hyperbolic model . Instead , we found that effort discounting is best described by a model of inverse sigmoidal shape that is initially concave , rather than convex as previously proposed .
In Experiment 1 , we sought to directly dissociate between effort and delay discounting . To this end , choice stimuli were optimized to distinguish between competing models and involved continuous levels of effort . Choices were made between two options associated with varying reward magnitudes and efforts . This required participants to evaluate and compare the subjective values of both offers ( Fig . 1A ) . Physical efforts involved exerting a force grip on a custom-made grip device , and force levels were adjusted to the maximum voluntary contraction ( MVC ) of each participant . To avoid fatigue , only a subset of randomly chosen trials required participants to exert effort ( realized trials ) , and only the gains accumulated on these trials contributed to participants’ payment . On these trials participants were required to produce a power grip with the force level of the chosen option for a fixed duration of 12s . Thus , effort costs were unconfounded from delay costs . In a separate session , the same participants performed an identical choice task but with rewards involving delay ( 0–75 weeks ) , unconfounded by physical effort . A single pseudo-randomly chosen trial was paid out in the delay task , whereas money was accumulated from all grip trials during the effort task , as explained above . Overall , participants ( n = 23 ) chose the higher effort option on 54 ± 5% ( mean ± SEM ) of trials , and the more delayed option on 52 ± 3% of trials . This demonstrates that effort and delay were both factored into the choices , and to a similar extent . A logistic regression of participant’s choices furthermore showed that costs and magnitudes both influenced choices ( S1B Fig . ) . Moreover , the pattern of choice response times reflected an integration of costs and benefits ( S1 Text and S1 Fig . ) . Participants were successful in producing the 12s-grip on 97 . 32±1 . 2% of realized effort trials ( criterion: maintaining the grip force indicated by the bar-height for at least 80% of the 12s duration; Fig . 2A , C ) . This result shows that required force levels had been adequately adjusted to each individual’s maximum grip force . The average reaction time ( RT ) to reach the required force level was 727 ±105ms , determined here as the time spent above the required force level , but exceeding it by at most 20% ( see Materials and Methods ) . The average time spent above the required level was 94 . 95±0 . 5% ( 11 . 39s of 12s ) . The variability of the produced force output scaled with the force level ( six equally sized bins of effort levels: F ( 5 , 85 ) = 112 . 61 , p<0 . 001 , ɳp2 = 0 . 87; Fig . 2E ) , the produced forces correlated with required force levels ( r = 0 . 96 , p<0 . 001; Fig . 2G ) , and fatigue did not affect choice behavior ( see below ) . Our main aim was to directly compare choice behavior when rewards were tied to two different types of decision costs , physical effort versus temporal delay . To this end , we initially compared two behavioral models of subjective discounted value , and contrasted their performance for choices on the effort and delay task . The first was the hyperbolic model widely accepted as the best characterization of delay discounting behavior [8 , 10–12] , but which has also been suggested for effort discounting [29] ( Fig . 3A ) . The second was a sigmoidal model with an initially concave shape and a flexible turning point ( see Materials and Methods ) . Such a model can accommodate discounting behaviour in which effort increases at low effort levels have a smaller effect on value than increases at higher effort levels . Such a behaviour would be consistent with studies showing that the perceived sense of effort increases as a power function of the exerted force level , with a reduced sensitivity to lower compared to higher efforts [41 , 42] . To strengthen our claim that effort discounting is concave and dissociable from delay discounting , we also performed a comparison of a larger set of models . This comparison included three one-parameter models previously suggested for effort discounting ( hyperbolic [29]; linear [37]; quadratic [40] ) , and a two-parameter power function , with the latter two sharing the initially concave nature with the inverse sigmoidal model ( see S2A–B Fig . ) . For each model , participants’ choices were fitted using a Bayesian estimation procedure . Comparison between the resulting fits was conducted using Bayesian model comparison ( see Materials and Methods ) . The resulting exceedance probability ( xp ) indicates the likelihood of each model to be the most frequently occurring model in the population , and the mean of the posterior distribution ( mp ) provides an estimate of the frequency with which the model appears in the population . As expected , the hyperbolic model best explained participants’ choice behavior in the delay task ( xp = 0 . 99 , mp = 0 . 80; Fig . 3B ) . By contrast , the sigmoidal model outperformed the hyperbolic model in the effort task ( xp = 1 , mp = 0 . 96; Fig . 3B ) . We note that our choice set was optimized to distinguish hyperbolic from quadratic/concave shapes ( see Materials and Methods ) . Therefore the quadratic model suggested previously [40] and which is also concave , also provided a good explanation of effort-based choices , and explained our data substantially better than the hyperbolic model ( S2 Fig . ) . The same held true for a two-parameter power function with flexible curvature and exponent ( see S2 Fig . , C ) : it performed almost equally well as the sigmoidal model ( for a comparison of all five models , see S2 Fig . , B ) . Crucially , all three concave models ( quadratic , two-parameter power , sigmoidal ) clearly outperformed the hyperbolic model ( all xp>0 . 99 , mp>0 . 85 ) . This reinforces our main result that effort discounting is best characterized by concave , rather than convex , models . Note that the quadratic and two-parameter power functions do not asymptote when efforts become increasingly high . By contrast , the sigmoidal model has an asymptote which may be a critical difference between these models . We argue that it is a plausible feature for an effort discounting model to have an asymptote because very high effort levels that cannot be reached are likely to become non-discriminable and have a similar impact on value . However , we note that this precise feature of the model remains to be tested more systematically in future studies , especially with regards to the values it converges to . Formally , the quadratic and two-parameter power functions are additive models . This means that their native definition can be reduced to the form V = M+f ( C ) . By contrast , the sigmoidal model proposed here is a multiplicative model and of the form V = M*f ( C ) . Forthcoming investigations will need to determine whether , or under which conditions , effort discounting is of additive or multiplicative nature . For example , using a fixed value for the effortful option and a variable value for the non-effortful option would enable one to distinguish between the two alternatives . Because the sigmoidal model can also describe shapes that have an early turning point ( and therefore take a concave shape only for the initial small part of the curve , but are otherwise convex within the range between [0 , 1] ) , we directly compared the fits obtained from this model for both the delay and effort task . This revealed that the same model generated opposite discounting shapes: the majority of participants had predominantly convex shapes for delay discounting ( turning point < 0 . 3 in 17 out of 23 participants ) , but initially concave shapes for effort discounting ( turning point > 0 . 5 in 17 out of 23 participants; Figs . 3C and S2D ) . The percentage of choices correctly predicted by the two models were 84 . 0 ± 1 . 3% for the winning sigmoidal model ( vs . 76 . 6 ± 1 . 6% for the hyperbolic model ) for the effort task , and 71 . 0 ± 1 . 8% for the winning hyperbolic model ( vs . 70 . 9 ± 1 . 9% for the sigmoidal model ) for delay , respectively ( S3A Fig . ; Table 1 ) . The sigmoidal model explained a larger percentage of choices , compared to the hyperbolic model , in 18 of 23 participants for the effort task , and the hyperbolic model in 6 of 23 participants for the delay task ( at least equally many choices: 22 vs 12 of 23 , respectively; Table 1 ) . The percentage of choices predicted by both models is shown in S3 Fig . The sigmoidal model predicted significantly more choices than the hyperbolic model for effort-choices ( t ( 22 ) = 4 . 69 , p<0 . 001 , Cohen’s d = 1 . 17 ) , but the two models did not differ for delay-choices ( p = 0 . 8 ) . This is importantly not our measure for determining the winning model . Only the formal model comparison presented above took into account additional model parameters . Fig . 3C shows the individual and average fits of the winning models for both tasks . The average model parameters were k = 10 . 49±4 . 28 and p = 0 . 70±0 . 09 for the sigmoidal model for effort , and k = 4 . 86±2 . 20 for the hyperbolic model for delay ( see S1 Table ) . Overall , Experiment 1 confirmed that effort discounting is best described using an initially concave curve in cases when both options involve efforts , and it established that humans assign subjective value to reward differently when choices involve effort versus delay costs . To examine whether individuals’ discounting behavior was similar across the two types of costs , we tested for a correlation of the estimated model-parameters between the two tasks . We performed this test for each of the parameters of the respective winning models , but also included comparisons of the same model ( hyperbolic , sigmoid ) applied to both cost types . None of these tests showed any evidence for a relationship between effort and delay discounting ( all p>0 . 23 , abs ( r ) <0 . 26 ) , indicating that participants did not exhibit similar ( or opposing ) tendencies to discount reward when it was associated with effort versus delay costs . The full table of p-values and correlation coefficients is reported in Table 2 . It is possible that effort discounting operates in units of utility , rather than units of reward magnitude ( here pounds and pence ) . The utility function is typically concave for the average human participant [57 , 58] . Therefore , to ensure that the shape of effort discounting would be unchanged when using utility instead of raw magnitude , we repeated the model fits using a generic utility parameter ( see Materials and Methods ) . This revealed no qualitative change in the shape of the discount curve ( Fig . 3E ) . Similarly , when all models were fitted using utility instead of magnitude , this did not change the conclusions from the model comparisons reported above ( xp = 1 , mp = 0 . 96 for sigmoidal model , compared to hyperbolic , for effort , and xp = 0 . 99 , mp = 0 . 81 for the hyperbolic model , compared to sigmoidal , for delay as in Fig . 3B ) . Our Bayesian Model Comparison accounts for model complexity by using the Kullback-Leibler divergence between prior and posterior densities over parameters . These divergences tend to be larger as the number of parameters increases , thus automatically penalizing larger models . But , for example , if the marginal distribution over one of the model parameters does not change as a result of model fitting , a penalty will not be paid for it . To clarify this issue , we decomposed the log model evidences into accuracy and complexity terms so as to be clear about what is driving each of the model comparisons ( Materials and Methods; see S2 Table ) . This showed that the additional complexity of the sigmoidal model was more than compensated for by an increase in accuracy . By contrast , for delay the hyperbolic model had a higher accuracy than the sigmoidal model , despite having one less parameter . The force exerted during effort production trials slightly exceeded the required force and became more variable with increasing effort levels ( Fig . 2 , G-H ) . We conducted a control analysis to test whether this non-linear variability increase in the produced force could explain the concave shape of discounting observed for effort costs . Bayesian parameter estimation and model comparison were performed as before , but using the force level produced on a given trial instead of the required target force . Because many trials did not require an effort production , we fitted a quadratic model to the force measured in all effort production trials for every participant ( Fig . 2 , G-H ) . This subject-specific fit was used to predict the ‘produced force’ for each trial . None of our results changed as a result of using the ( predicted ) produced , rather than required , force level: as before , model comparison showed that the sigmoidal model best accounted for the data , and the resulting shape of discounting was still concave ( see S3 Table; S4 Fig . ) . Thus , effort discounting is concave independent of whether participant’s evaluation relies on the required or the predicted produced effort cost . We furthermore assessed whether our results could have been influenced by fatigue . To this end , we distinguished two types of fatigue: trial-by-trial fatigue , which may reduce choices of the higher-reward/higher-cost ( HRHC ) option in trials immediately following a 12s-grip ( or potentially only a hard 12s grip ) , and accumulating fatigue , which may lead to gradually decreasing HRHC choices as the experiment progresses . The percentage of HRHC choices did not differ on post-effort versus post-no-effort trials ( p = 0 . 84; see S4 Table ) . Trials following hard as opposed to easy effort trials also did not differ in the percentage of HRHC choices ( median split separates trials at a force of 0 . 35% MVC: neasy = 30 . 6±2 . 4 , nhard = 27 . 7±2 . 4; p = 0 . 95 ) . Finally , the percentage of HRHC options did not differ between the first and second half of the experiment ( 53±5% and 56±5% HRHC choices; p = 0 . 23 ) , indicating that participants did not become more effort-averse as the experiment progressed . The percentage of choices correctly predicted by the winning sigmoidal model differed by about 7% from those predicted by the hyperbolic model , indicating that a majority of choices could be explained by both models . Critically , the magnitude of such a difference in explanatory power cannot be taken as a direct measure of how much better a model can , in general , explain certain types of choices . Whether a choice can be better predicted by one model compared to another not only depends on the model , but also on the choice in question . To illustrate this point , we compared the percentage of choices correctly predicted by the hyperbolic and sigmoidal model on a subset of our choice stimuli , namely 36 choices where both options were in the lower cost range or both options were in the higher range of cost levels ( S5B Fig . ) . These choices should be particularly good at distinguishing between the two models . Indeed , while the percentage of predicted choices on the entire stimulus set was 84 . 0 ± 1 . 3% vs . 76 . 6 ± 1 . 6% ( for effort ) for the sigmoidal and hyperbolic model ( difference of 7 . 4% ) , the difference in predictive power was >12% on this subset of choices , specifically 81 . 9 ± 2 . 0% versus 69 . 4 ± 2 . 6% , respectively . A strength of the present study was that choice stimuli were kept constant for two tasks and all participants , but as a consequence , the stimuli did not maximally distinguish between competing models in every participant . We note that the differences in predictive performance of the present models are nevertheless comparable , or exceed that of established models ( e . g . , hyperbolic versus exponential model for delay discounting [59]; an improved volatility learning model over a standard reinforcement learning model; see [60] ) . Collectively , this demonstrates that the sigmoidal model is superior to the hyperbolic model because it consistently predicts more choices across subjects . Another observation was that participants were less consistent in their choices in the delay task , and therefore both models compared here overall predicted fewer choices correctly in this task , compared to the effort task . One of the reasons for this difference in consistency could be that our choice display which used the height of a bar to present decision costs was more appropriate for representing efforts than for representing temporal delays . To test this hypothesis , a subset of participants ( 14 out of 23 ) completed the delay task again , using identical stimuli , except that delays were displayed using words ( ‘6 weeks’ ) instead of horizontal bars . This drastically increased the percentage of correctly predicted choices . S3B Fig . shows the percentage of predicted choices for this subset of participants in the original and modified task versions ( delay as bar: 71 . 9 ± 2 . 3%; delay in words: 85 . 5 ± 1 . 9% ) . Importantly , in either task version , the hyperbolic model outperformed the sigmoidal model ( Bayesian model comparison for delay task involving words: xp = 0 . 98; mp = 0 . 75 ) , consistent with a large literature on delay-based choices . Experiment 2 aimed at establishing how rewards are devalued when requiring physical effort using a model-free approach . Participants made choices between an option for which reward magnitude and effort level varied from trial to trial , and a ‘default’ option with constant reward magnitude that never required effort exertion ( block1: 40 pence , block2: £2; Fig . 1B ) . The advantage of keeping one option constant was that stimuli only varied along two dimensions ( one effort and one reward magnitude ) . As a result , choice preferences could be easily visualized ( Fig . 4A , C ) and indifference points determined , free of any assumptions about the precise discounting function . This allowed us to validate our main conclusion from Experiment 1 that effort discounting is initially concave . Physical efforts again involved exerting a 12s-force grip adjusted to the MVC of each participant . For each of the six levels of effort , the reward magnitude of the variable option was adjusted on a trial-by-trial basis , based on the participants’ choices , using a hidden staircase procedure ( median magnitude range of the variable option: block1: 25±3 to 81±38 pence; block2: 187±5 to 293±23 pence ) . The shape of effort-discounting was inferred from the point of subjective indifference at which participants assigned equal value to the option involving effort and the default option ( see Materials and Methods ) . This procedure was free of any assumptions about the shape of discounting . We also determined how effort-discounting scales with different levels of reward magnitude by comparing discounting behavior between the 40p and £2 block . As in Experiment 1 , only a subset of randomly chosen trials required participants to exert effort , and only the gains accumulated on these trials contributed to participants’ payment . On these trials participants were required to produce a 12s power grip with the force level of the chosen option . Alternatively , when participants had chosen the default option , they had to wait for 12s without producing any force . This ensured that effort costs were unconfounded from delay costs . On average , 15 . 8 trials ( 8 . 2% ) required effort production and participants ( n = 14 ) successfully reached the criterion ( pressing the grip-device with at least the force indicated by the bar-height for at least 80% of the time ) on 97 . 2±1 . 1% of these effort-trials ( Fig . 2B , D ) . This shows that the required force levels had been adequately adjusted to each individual’s maximum grip force . The average RT to reach the required force level was 793±53ms , and the average time spent above the required level was 91±0 . 4% ( 10 . 92 of 12s ) . The variability of the produced force output increased with increasing force levels ( main effect of force level: F ( 5 , 25 ) = 168 . 86 , p<0 . 001 , ɳp2 = 0 . 97; Fig . 2F ) , and exerted and required force levels were highly correlated ( r = 0 . 98 , p<0 . 001; Fig . 2H ) . Overall , fatigue did not affect choice behavior ( see below ) . Fig . 4A shows the choices from three participants with the shallowest , median , and steepest discounting respectively , separately for each of the six levels of effort , and for the 40p and £2 blocks ( see Fig . 4B and Materials and Methods for calculation of indifference points ) . Here the reward magnitude of the varying option is depicted as percentage of the default option , and thus in a multiplicative fashion . The resulting points of subjective indifference are therefore a measure of relative subjective value . An alternative way to depict these choices is in an additive fashion , by plotting the difference between the default and varying reward magnitude ( see Fig . 4C ) . Multiplicative and additive representations , as well as the untransformed indifference points for all participants are shown in Fig . 4C . As explained above for Experiment 1 , it is yet unclear whether effort discounting is multiplicative or additive in nature . Therefore , either transformation could be valid . But importantly our main conclusion of initially concave effort discounting does not depend on the type of transformation . For the sake of clarity , the next paragraph focuses on the multiplicatively inferred subjective values . Comparison of the multiplicatively inferred indifference points across effort levels revealed that the majority of participants ( 10 out of 14 ) exhibited concave ( or ‘parabolic’ , see [40] ) discounting , which is characterized by initially small decreases in value followed by steeper reward devaluations at higher effort levels; a curve was classified as concave when at least three out of five consecutive indifference points lay above the line connecting ( 0 , 1 ) with the sixth indifference point ( Fig . 4C; 3 . 64±0 . 20 of 5 inferred indifference points lay above this line for multiplicatively inferred subjective values ) . On average , this analysis revealed a concave shape for effort discounting ( Fig . 4C ) both for the lower and higher default reward magnitudes . However , the discounting rate scaled with the absolute level of reward ( ‘magnitude effect’ ) . Effort discounting was shallower when more reward was at stake ( ANOVA of multiplicatively inferred indifference points for default magnitude ( 2 ) x effort level ( 6 ) : all main effects and interactions p<0 . 001: default magnitude: F ( 1 , 13 ) = 38 . 35 , ɳp2 = 0 . 75; effort level: F ( 5 , 65 ) = 37 . 12 , ɳp2 = 0 . 74; magnitude x effort level: F ( 5 , 65 ) = 19 . 13 , ɳp2 = 0 . 60; Fig . 4C ) . This shows that effort discounting depends on the offer value , a well-known effect for delay discounting [59 , 61–64] . Using alternative representations of indifference points ( raw or additive ) the above conclusions remained the same: the change in value was smallest at lower effort levels , implying an initially concave discount curve , and discounting was shallower for a larger default reward magnitude . Because there were overall fewer effort trials , fatigue should have contributed less to choices in Experiment 2 compared to Experiment 1 . Indeed , choice behavior was not affected by trial-by-trial fatigue . The percentage of HRHC choices did not differ between post-effort trials ( i . e . , the trials immediately following a trial with realized effort production of 12s ) , and trials following no-effort trials , matched in magnitude and effort difference ( 100 , 000 permutations of random subsets of the same number of trials; p = 0 . 10 ) . If anything , there were descriptively more HRHC choices following effort trials ( 64±3% versus 58±2% ) , suggesting fatigue did not bias choices in a systematic way . Similarly , there was no difference in the percentage of HRHC choices on trials following an easy compared to a hard realized effort ( median split; p = 0 . 69 ) ; these trials did not differ in their average magnitude or effort difference . The experimental design aimed to minimize effects of accumulating fatigue , by realizing efforts on a subset of unpredictable trials only . Moreover , ITIs following realized effort trials were longer and thus minimized carry-over between trials . When comparing the first ( block 1 ) and the second ( block 2 ) half of the experiment , participants actually became more likely to choose HRHC options , but this is trivially explained by the change in the default reward magnitude from 40p to 2£ ( t ( 13 ) = 3 . 02 , p = 0 . 01 , Cohen’s d = 1 . 68; Fig . 4C ) . This outcome also is not compatible with accumulating fatigue .
First , humans seem to be able to easily tolerate a range of proportionally low energetic costs . In other words , physical efforts perceived as requiring small amounts of energy ( e . g . , taking the escalator or walking up a small number of stairs ) will hardly decrease the value of reward . Only once these costs exceed a level that is subjectively perceived as ‘tiring’ or ‘hard’ ( e . g . , walking up several flights of stairs ) do individuals start to discount rewards more noticeably . Thus , the impact of effort on reward valuation should become stronger with increasing effort levels , consistent with the initially concave discounting shape observed in the majority of our participants . This choice behavior is the opposite of delay discounting behavior , where additional delays have the strongest effect when added to small , compared to large , delays . More generally , data from two tasks in which effort costs were unconfounded from delay costs provide robust evidence against the notion that effort discounting is best explained by a hyperbolic function as previously suggested [29 , 38 , 39] . By using a robust Bayesian modeling approach ( Experiment 1 ) , and by directly measuring participants’ indifference points for different effort levels ( Experiment 2 ) , our results instead indicate that effort discounting is best characterized by a sigmoidal two-parameter model that allows initially concave discounting shapes . Other work has suggested or implicitly used a linear model of effort discounting [31 , 37] , and , more recently , a quadratic function [40] . It is notable that a close look at some of the published data suggests a discount function of the concave shape proposed here ( e . g . , compare Fig . 2 in [31] , and Fig . 1 in [65] ) . Critically , we note that previous studies did not directly compare the performance of the hyperbolic or linear model to any alternative models , and did not dissociate choices involving delay and effort costs . Therefore , the question as to whether effort and delay are discounted in similar ways remained unresolved . The sigmoidal model proposed here provides a superior fit to effort-based choices compared to any of the other models , but there are also several theoretical arguments that justify its choice . A discount function that is concave for achievable effort levels is consistent with work showing that the subjective ‘sense of effort’ increases as a power function of the exerted force level [41 , 42] , and it may directly relate to the underlying physiology . For example the rate coding of muscle units changes little at low force levels , but increases more with increasing effort levels [66 , 67] . More work is required to test for such relationships and to determine which aspects of effort discounting relate to physiology or inter-individual personality differences . We found that simpler functions fulfilling the concave property of our model , such as a quadratic function with only one parameter , also provide good fits to effort-based choices [40] . Importantly , the concave nature of effort discounting cannot be accounted for by the concave shape of the utility function . When using utility instead of reward magnitude for fitting the model , the concave shape of the effort discounting function is preserved . The second property of our model is that it entails a turning point after which discounting becomes progressively less steep . When we represent our data multiplicatively ( Fig . 4 ) , there is evidence that about half the participants experience such a turning point before 100% MVC ( and others may do at higher levels exceeding 100% MVC not tested here ) , but it is important to note that our task was explicitly designed to distinguish concave from convex shapes . With the benefit of hindsight a design with additional power to determine the tail of the discounting function with confidence would have allowed us to address this issue . Nevertheless , the sigmoidal model outperforms a two-parameter power function with similar flexibility in the lower range of efforts , which suggests that the turning point may be a critical feature . Here we intentionally stayed in the range of efforts a participant can produce with 100% certainty to avoid any confounds with risk . Future work should focus on higher effort levels and potentially even go beyond 100% MVC to clarify this issue . While 100% MVC is clearly the maximum a participant can produce , efforts could be made harder without involving risk , for example by involving both hands or legs . Another point worth noting is that the model we propose here converges to zero . We speculate that with increasingly high effort levels , there may be a point where participants would be willing to pay money to avoid having to exert effort , or would reject choices with exceedingly high costs if given the option , which would imply a negative value . Addressing this question would require a different experimental design . For example , one could use a fixed value for the effortful option and a variable value for the non-effortful option , which would also allow distinguishing additive from multiplicative discounting . Though negative values are not currently incorporated in our model , we speculate that an improved effort discounting model that can accommodate negative effort values will still entail a turning point , rather than converging to –∞ in the way a quadratic function does . The remaining question then is whether , or under which conditions , this may occur . The implication of an asymptote to a constant value would be that all efforts which are unattainable ( i . e . too large to be realized ) affect value in a similar way . We hope that future work will address these remaining questions . In order to avoid confounding influences between effort and delay , we manipulated effort such that higher levels required a stronger muscle force over a fixed period of time . This type of effort stands in contrast to persistent efforts , which require repeated execution of the same movement ( walking up a staircase; pressing a lever ) , and which are commonly used in rodent experiments [68–71] . It is conceivable that our characterization of the discount curve is specific to situations where effort and muscle force are in direct relation with each other ( e . g . , deciding to hit the break at a yellow light , lifting a water container etc . ) . The persistent efforts required on fixed-ratio schedules share some features with the force-related effort used here in that the harder conditions may lead to exhaustion and temporary muscle fatigue . However , persistent efforts also share a critical feature with delay discounting in that more difficult efforts ( e . g . more lever presses ) , require additional time to be completed . This causes an additional delay to reward delivery , which of course complicates the interpretation of how effort and delay independently influence choice . It is an intriguing hypothesis that the linear discounting of efforts suggested in the context of fixed-ratio schedules [37] may be caused by an interaction of simultaneously occurring discounting processes , namely the convex discounting of delays , and the concave discounting of force-related effort put forward here . Our second finding shows that the steepness of effort discounting relates to the overall range of rewards at stake . Our hypothesis was that the same energetic requirement will affect larger rewards less than smaller rewards , because we might be willing to work harder for a better outcome . This ‘magnitude effect’ is known for delay costs , where different discount rates best explain choices in different reward ranges [59 , 61–64] . Importantly , it describes a relative rather than an absolute decrease in value . Our data provides support for this hypothesis also for effort costs , as reflected in a stronger decrease in value for smaller reward ranges , and a shallower discount curve for larger reward ranges . Critically , in our two experiments effort discounting was consistently concave , and the reward range affected the scale but not the shape of discounting . This implies that the model established here as the best model for effort discounting may generalize to different ranges of magnitude than the ones used here . A large body of literature similarly shows that the range of delays tested ( from seconds to days to years ) has an impact on the discount rate . However , the hyperbolic nature of temporal discounting is remarkably consistent across all time scales [72–77] . Whether this generalization also holds true for a broad spectrum of effort ranges and the sigmoidal model for effort discounting proposed here remains to be tested . In any case , since the fitted parameters depend on the range of reward magnitudes used , separate fits need to be obtained for ranges of rewards in different orders of magnitude , which could be seen as a limitation of both models . The behavioral dissociation between effort and delay discounting described here is consistent with the notion that these two decision costs may be supported by different neural networks . Although effort and delays are commonly referred to as decision costs , one attractive proposal is that while effort is a decision cost that is ascribed to a particular action , delay is one of the parameters ( like risk , uncertainty ) that can be ascribed to—and discount—the value of an outcome ( [43] , but see [44 , 45] for how delays can relate to actions ) . Consistent with this idea , we are not aware of any evidence to suggest that delay- and effort-discounting behaviors correlate , and indeed there is no evidence for a relationship between the individual discount tendencies for effort and delay costs in our data . This means , a person who is willing to wait for reward does not necessarily also accept high levels of energy expenditure to secure reward and vice versa; in line with this , these abilities have developed independently in different species [6] . Furthermore , lesions of ACC impair effort-guided choices , but leave intact those guided by delay , while lesions of OFC have the opposite effects , suggesting that ( at least partially ) distinct networks of brain regions support these two functions [46 , 78] . This evidence is consistent with the deficits seen in patients , where dysfunction of ACC-related circuits causes symptoms such as apathy [32] , while damage to OFC-related circuits causes impulsivity and disinhibition [46 , 79–81] . The ACC may be particularly crucial for guiding effort-based choices because neurons encode both the effort cost and reward payoff of an option as well as the selected action [25 , 82–84] , and also the number of actions required to obtain reward [85] . There is also some evidence that different neurotransmitters are critical in assessing delay- versus effort-related costs [86 , 87] , and that dopamine manipulations have differential effects on these two behaviors [69] . Our data and modeling approach provide an important step forward in understanding how effort influences the decision making process . The behavioral model proposed for effort discounting here may provide a powerful diagnostic tool for quantifying motivational disturbances evident in clinical disorders ( i . e . , apathy , depression , fatigue , abulia ) , and may thus be critical in understanding the basis of several common psychiatric and neurological conditions [9 , 88] . The motivational disturbances experienced in some of these conditions can profoundly affect one’s personal and professional life , and cause limitations in terms of life function , interaction with the environment , and responsiveness to treatment [26 , 89 , 90] . Understanding how reinforcement influences the willingness to exert effort may also be of relevance for neurorehabilitation following brain injury which is commonly based on effortful and repeated physical exercise . In summary , as well as contributing to the understanding of clinical disorders involving motivational disturbances , the formal mathematical framework provided here will provide a powerful tool for the neural and behavioral study of effort-based choice .
This study was ethically approved by the UCL Research Ethics committee , and all participants gave written informed consent . Twenty-eight ( Experiment 1: age range: 22–35 years , mean age 26 years , 20 female , 8 male ) and fifteen ( Experiment 2: age range: 22–35 years , mean age 26 years , 11 female , 4 male , including the same fifteen as in Experiment 1 ) right-handed volunteers with no history of neurological or psychiatric disorder , and with normal or corrected-to-normal vision , participated in this study . Five participants from Experiment 1 and one participant from Experiment 2 were excluded from the analysis ( see below ) . Inclusion in Experiment 2 was a self-selection process; every participant from Experiment 1 was invited to take part and all those available completed Experiment 2 . The choice stimuli were optimized for two different purposes in the two Experiments . In Experiment 1 , choice options were identical for every individual and for the effort and delay versions of the task . They were chosen such that they would maximally contribute to differentiating between three competing behavioral models , e . g . so that an agent with hyperbolic discounting would make different choices to someone exhibiting linear or concave discounting behavior ( for each of the three models , we modelled nine agents , spanning from shallow to steep , and selected stimuli that contributed in at least two simulated agents to distinguish at least two of the models ) . Second , the stimuli in Experiment 1 covered the entire range of magnitudes and costs , and magnitude and cost differences ( S5A Fig . ) , and were paired such that different levels of ( the smaller ) magnitude were paired with a wide range of cost differences . We did not include trivial choices where a small cost difference would be paired with a large magnitude difference because we expected participants would be entirely magnitude driven on such a trial and it would therefore be uninformative with regards to their discount behavior . We also kept the number of trials with two high-costs options small because we did not want to force ‘effort-averse’ participants into having to exert a high force . In Experiment 2 , by contrast , choice options were adjusted based on individual participant’s preferences in order to obtain points of subjective indifference . Therefore , the number of effort levels had to be restricted in this Experiment . The grippers were custom-made and consisted of two force transducers ( FSG15N1A , Honeywell , NJ , USA ) placed between two moulded plastic bars [92] . A continuous recording of the differential voltage signal , proportional to the exerted force , was acquired , fed into a signal conditioner ( CED 1902 , Cambridge Electronic Design , Cambridge , UK ) , digitized ( CED 1401 , Cambridge Electronic Design , Cambridge , UK ) and fed into the computer running the stimulus presentation using Cogent ( http://www . vislab . ucl . ac . uk/cogent_graphics . php ) . This enabled us , during effort trials , to give online feedback reflecting the exerted force using the thermometer display . The range of forces and the duration of the grip on effort trials ( 12s ) had been determined in pilot experiments . Our aim was to use force levels that are factored into the choice process , but that are still in the range of possible forces for a given participant . This seemed most like the type of choices we make in real-world scenarios . Pilot experiments had shown that durations significantly shorter than 12s meant that participants often ignored the effort because they were merely interested in earning money . Importantly , the duration of 12s was fixed and unrelated to the force level , thus enabling us to study effort unconfounded from temporal costs . The force traces obtained from realized trials were analyzed to establish the time it took participants to reach the required force level , the time ( out of 12s ) that they spent at the required level , and the mean and variance of the produced force output ( Fig . 2 ) . First , every time point was classified as above the required level or below the required level [alternatively: within +0 . 2 of the required level] and the time to reach the criterion was defined as the first time at which the effort level was reached , provided the 10 subsequent time points ( 170ms ) also exceeded the required level . For Experiment 1 , it was more appropriate to report the time at which they had reached the required level , but were at most 20% above the required level ( also for 170ms ) because a grip force of 0 . 35 was exerted at the time of response when the 12s grip started , implying that the required force level was already exceeded at time zero for lower forces , which would result in a misleading RT of 0ms ( see Fig . 2C ) . Force traces were then grouped into six bins ( equally-sized between [0 , 1] in Experiment 1 , and pre-defined as [15 25 35 50 75 100] in Experiment 2 ) to display average force time courses ( Fig . 2C , D ) , and the variance of the force output across the 12s interval was measured and averaged across all trials in the same bin ( Fig . 2E , F ) . Finally , we tested for a correlation between the required and produced force levels using the mean force produced between 2–10s from every effort trial ( Fig . 2G , H ) . In the effort experiments participants were paid the sum of the rewards accumulated on all effort trials ( Experiment 1: average: £21 . 40; range: £15 . 70-£25 . 75; Experiment 2: average: £35 . 62; block1: £6 . 04; block2: £29 . 59 ) . In the delay experiment , they were paid out one choice from a pseudo-randomly selected trial and the money was transferred via bank transfer , pre-dated to the corresponding delay ( average: £21 . 60 in 8 weeks ) . This procedure was chosen for several reasons . First , while paying out one randomly selected option is an established procedure for delay-based choices , we believe efforts would not have had a strong impact on choice behavior if we had kept them abstract and only realized one at the end of the experiment . Participants knew that effort levels were within the range they could achieve , and the majority would have chosen the HRHC option in all trials to ensure a larger reward outcome , knowing only one effort needed to be produced at the end . Second , the evaluation of effort levels depends heavily on the bodily state of a person , and the occasional experience of an effort meant that its evaluation was more real . Given the different number of realized trials in the effort and delay sessions of Experiment 1 , we chose different ranges of reward ( pence and pounds , respectively ) and thus approximately matched the overall reward sum . Extensive piloting had shown that larger reward ranges would mostly produce choices of the HRHC option for effort ( and vice versa , smaller rewards for delay would have led to choices of the immediate option on most trials ) . Of course , to match the reward range , we could have instead increased the difficulty of the required efforts , but given practical and time constraints , it seemed reasonable to adjust the reward range so that both types of costs would influence choices to a similar extent which we achieved ( 54% and 52% HRHC choices in effort versus delay ) . Our main analyses regarded participant’s choice behavior , but a detailed analysis of response times and a logistic regression of choices were also performed ( see S1 Text; S1 Fig . ) . We compared several models to formally characterize effort discounting behavior . For all models , the free parameters ( k , β ) or ( k , p , β ) , respectively , were fitted using the Variational Laplace algorithm [93 , 94] . This is a Bayesian estimation method which incorporates Gaussian priors over model parameters and uses a Gaussian approximation to the posterior density . The parameters of the posterior are iteratively updated using an adaptive step size , gradient ascent approach . Importantly , the algorithm also provides the free energy F , which is an approximation to the model evidence . The model evidence is the probability of obtaining the observed choice data , given the model . This free energy approach yields better model scores than does the Akaike Information Criterion ( AIC ) or Bayesian Information Criterion ( BIC ) [95] . The model evidence can be decomposed into an accuracy and a complexity term . The accuracy term reflects the model fit to the current data sample , whereas the complexity term penalizes models that have unlikely parameter values . The combination of the two terms provides a Bayes optimal estimate of how good a model is and is the standard criterion used for model comparison in the Bayesian statistics literature [96 , 97] . Other metrics for model assessment include the use of out-of-sample model fit using for example cross–validation . There is a high degree of correlation between findings from cross-validation and Bayesian model comparison ( see for example [98] ) . In Experiment 1 , the log model evidences averaged over subjects for the effort data were −95 . 3 for the sigmoidal model and-130 . 6 for the hyperbolic model , providing a difference of 35 . 3 in favor of the sigmoidal model . As differences of log model evidence greater than 5 provide ‘very strong’ evidence for a hypothesis [99 , 100] it is clear that the sigmoidal model is superior . Breaking down the log model evidences into contributions from accuracy and complexity [99] we found that the sigmoidal model had an accuracy of −85 . 6 and a complexity of 9 . 7 whereas the hyperbolic model had an accuracy of −124 . 8 and a complexity of 5 . 8 ( see S2 Table ) . Note that log model evidence = accuracy—complexity ( e . g . , -95 . 3 = -85 . 6–9 . 7 ) . Thus , the sigmoidal model was more complex but this additional complexity was more than compensated for by an increase in accuracy . For the delay data , the log model evidences averaged over subjects were −159 . 2 for the sigmoidal model and −156 . 4 for the hyperbolic model , providing a difference of 2 . 8 in favor of the hyperbolic model . Breaking down the log model evidences into contributions from accuracy and complexity we found that the sigmoidal model had an accuracy of −153 . 6 and a complexity of 5 . 7 whereas the hyperbolic model had an accuracy of −151 . 4 and a complexity of 5 . 0 ( see S2 Table ) . Thus , the models were of similar complexity but the hyperbolic model more accurately described the data . Readers more familiar with model selection criteria such as AIC and BIC may be surprised here as these latter criteria have model complexity terms that scale in proportion to the number of parameters . Thus models with more parameters are always more ‘complex’ . However , the Bayesian model evidence penalizes models in proportion to how far the posterior is from the prior ( as quantified by the KL-divergence ) . Thus , in the limit of the posterior equaling the prior , our beliefs about model parameters will not change and the penalty will be zero . This property renders the Bayesian model evidence a better model comparison criterion than AIC or BIC [95] . To maximize our chances to find global , rather than local maxima using the gradient ascent algorithm , parameter estimation was repeated over a grid of initialization values . The grids contained eight initializations per parameter , spanning the relevant parameter range . The optimal set of parameters , i . e . , that obtained from the initialization that resulted in the maximal free energy , is reported in this manuscript . The above computation of the model evidence is dependent on the choice of prior . To ensure robustness of our findings , the above estimation was therefore repeated with the prior covariances set to be an order of magnitude larger and smaller , respectively . Changing the prior variances in this way had no effect on any of our conclusions . We can therefore be confident our conclusions are driven by the behavioral data rather than the prior beliefs . The free energy F obtained for each model and participant was then used to perform a formal Bayesian Model Comparison ( BMC ) at the group level [101 , 102] . These group level inferences provide an estimate of the frequency with which a model occurs in the population from which the participants are drawn . For the ith model , ri is the frequency with which it appears in the population . The BMC approach uses the table of model evidence values ( subjects x models; see S2 Table ) to estimate a posterior distribution over ri . The mean of this posterior distribution , mp ( i ) , is our best estimate of ri . We can additionally ask about the probability that model i occurs the most frequently . This is known as the exceedance probability , xp ( i ) . BMC at the group level , also known as random effects model [101 , 102] , has become a standard statistical test in the fields of neuroimaging and behavioral modeling ( [101] cited > 250 times ) . As described above , the central quantity of interest is ri , the frequency with which model i is used in the population from which the subjects are drawn . Given a table of model evidence values ( see S2 Table ) an algorithm [101 , 102] can be derived for computing a posterior distribution over ri , from which subsequent inferences can be made . Intuitively , this is based on two quantities ( 1 ) the proportion of subjects in the sample group that favor model i and ( 2 ) the degree to which the models are favored . As we will see in the results section r^1=0 . 80 for the delay task ( 80% subjects in the population use the hyperbolic model ) and r^2=0 . 96 for the effort task ( 96% subjects use the sigmoidal model; see Materials and Methods and Results ) . Our main analysis asked whether the hyperbolic , linear , quadratic or sigmoidal models were better at describing participants’ choices . An important feature of BMC is that it can compare models with different numbers of parameters in an unbiased manner ( see above ) . Experiment 1 was optimized to establish a direct dissociation between delay and effort-based choices , but was less sensitive to slight changes in model shapes . Our main analysis was therefore a comparison between the sigmoidal model developed above ( initially concave shape but convex after a turning point ) and the hyperbolic model proposed for delay discounting [10–12] . In a second step , to confirm that the concave feature of the model constitutes a critical improvement for modelling effort discounting , we performed pairwise comparisons between the hyperbolic model and the two other concave alternatives ( quadratic and two-parameter power function ) . Finally , we included all models in the same model comparison to check which discount function best described effort discounting in our task . Rather than using reward magnitudes on a scale of pounds and pence , in economic choice contexts the utility is often used as a more subjective measure of the experience of a monetary reward . To test whether the shape of discounting would change when utility was used instead of reward magnitude , the magnitude M was replaced by Mα in all models described above . Because our stimuli were not optimized for fitting this additional parameter , we used a generic utility value of α = 0 . 8 . This value provides an estimate for the average utility parameter of healthy human participants [57 , 58] and if anything slightly overestimates the curvature of the utility function . Therefore , it provides a conservative test of the effect of using utility on the resulting discounting function . Parameter estimation and model comparisons were repeated for all models using this generic measure of utility . The advantage of keeping one option constant in Experiment 2 was that stimuli only varied along two dimensions ( one effort and one reward magnitude ) . As a result , choice preferences could be easily visualized ( Fig . 4A , C ) and indifference points determined , free of any assumptions about the precise discounting function . By contrast , Experiment 1 allowed us to test a wide range of effort and magnitude differences ( S5A Fig . ) , and allowed for a more robust characterization of choice preferences ( because stimuli varied along four dimensions ) . Because understanding choice preferences in Experiment 1 relied on the use of behavioral models , we now conducted a virtually model-free analysis of effort discounting . To this end , we determined points of subjective indifference based on participants’ choices . The sixteen choices performed for each of the six levels of effort were plotted as a function of the magnitude of the alternative offer ( Fig . 4B ) . A simple sigmoid was then fitted using equation ( 1 ) below , and the indifference point was defined as the reward magnitude ( on x ) at which the sigmoid crossed y = 0 . 5 , which corresponds to α; β is the slope . This procedure is illustrated in Fig . 4B . | One of the main functions of the brain is to select sequences of actions that lead to rewarding outcomes ( e . g . , food ) . However , such rewards are often not readily available; instead physical effort may be required to obtain them , or their arrival may be delayed . The ability to integrate the costs and benefits of potential courses of action is severely impaired in several common disorders , such as depression and schizophrenia . Mathematical models can describe how individuals depreciate rewards based on the costs associated with them . For example , models of how a reward loses appeal with increasing temporal delays can provide individual impulsivity scores , and can serve as a predictor of financial mismanagement . To date , there is no established model to describe accurately how humans depreciate rewards when obtaining them requires physical effort . This is surprising given the prevalence of disorders related to a diminished willingness to exert effort . Here we derive a biologically plausible mathematical model that can describe how healthy humans make decisions tied to physical efforts . We show that effort and delay influence reward valuation in different ways , contrary to common assumptions . Our model will be important for characterizing decision-making deficits in clinical disorders characterized by behavioral inactivity . | [
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] | [] | 2015 | Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation |
Transmission of drug-resistant pathogens presents an almost-universal challenge for fighting infectious diseases . Transmitted drug resistance mutations ( TDRM ) can persist in the absence of drugs for considerable time . It is generally believed that differential TDRM-persistence is caused , at least partially , by variations in TDRM-fitness-costs . However , in vivo epidemiological evidence for the impact of fitness costs on TDRM-persistence is rare . Here , we studied the persistence of TDRM in HIV-1 using longitudinally-sampled nucleotide sequences from the Swiss-HIV-Cohort-Study ( SHCS ) . All treatment-naïve individuals with TDRM at baseline were included . Persistence of TDRM was quantified via reversion rates ( RR ) determined with interval-censored survival models . Fitness costs of TDRM were estimated in the genetic background in which they occurred using a previously published and validated machine-learning algorithm ( based on in vitro replicative capacities ) and were included in the survival models as explanatory variables . In 857 sequential samples from 168 treatment-naïve patients , 17 TDRM were analyzed . RR varied substantially and ranged from 174 . 0/100-person-years;CI=[51 . 4 , 588 . 8] ( for 184V ) to 2 . 7/100-person-years;[0 . 7 , 10 . 9] ( for 215D ) . RR increased significantly with fitness cost ( increase by 1 . 6[1 . 3 , 2 . 0] per standard deviation of fitness costs ) . When subdividing fitness costs into the average fitness cost of a given mutation and the deviation from the average fitness cost of a mutation in a given genetic background , we found that both components were significantly associated with reversion-rates . Our results show that the substantial variations of TDRM persistence in the absence of drugs are associated with fitness-cost differences both among mutations and among different genetic backgrounds for the same mutation .
Drug-resistant pathogens represent one of the major public health and clinical challenges in infectious diseases ( http://www . who . int/drugresistance/en/ ) . It is an almost universal observation that as soon as a chemotherapeutic agent against a given pathogen is introduced , resistant pathogen strains emerge , which reduce the clinical benefits conferred by that agent . One crucial obstacle in curbing drug resistance is that once it has emerged it often persists even in the absence of drug pressure . The central concept here is pathogen fitness: whereas the resistant pathogen has a very strong advantage over the sensitive one in the presence of drug pressure , its disadvantages in the absence of treatment are typically weaker and can be compensated by other mechanisms such as compensatory mutations or selection at linked loci . Despite this key role of pathogen fitness for a conceptual understanding of the spread and persistence of drug resistance , real-world epidemiological examples documenting its role are rare . An ideal opportunity to assess this role of fitness is provided by the dynamics of antiretroviral resistance in HIV-1 . In the case of HIV , combinations of modern anti-retroviral treatment ( ART ) have successfully reduced the morbidity and mortality of HIV-1 infected individuals [1] . Though drug resistance prevalence has been shown to decrease or to stabilize in various industrialized countries due to successful ART , it still remains a major concern jeopardizing treatment success [2 , 3] . Transmission of a drug-resistant virus has been observed in most countries where ART is available [4–10] . After transmission , viruses with transmitted drug resistance mutations ( TDRM ) persist either as the dominant species or as minority variants , which are difficult to detect by population sequencing techniques [11–17] . Consequently , patients harboring TDRM have a higher chance to fail their first-line therapy [12 , 18–20] . Several studies have illustrated that the persistence time of individual TDRM in the absence of drug pressure exhibits substantial variance [11 , 13 , 15 , 17 , 21 , 22] . Persistence times have been suggested to be associated with fitness costs [18] , which are typically measured as the reduction of replicative capacity of the virus caused by a given mutation [21] . It is generally assumed that transmitted drug-resistant viruses revert more rapidly to wild-type viruses if the fitness is reduced to a larger extent by the TDRM ( high fitness cost ) because then reversion of TDRM confers correspondingly high fitness gains [23] . Several studies have measured the fitness of some specific TDRM using phenotypic replicative capacity assays [6 , 17 , 21] . However , evidence for the impact of such fitness costs on the dynamics of TDRM at an in vivo and epidemiological level is largely lacking . Here , we aimed to determine the persistence times of TDRM in an epidemiological approach in vivo and to determine whether these persistence times depend on the fitness costs of TDRM .
The SHCS is a prospective , nationwide , clinic-based study including a biobank . The SHCS is very representative of the HIV epidemiology in Switzerland; it includes at least 53% of all HIV cases ever diagnosed in Switzerland , 72% of all patients receiving ART , and 69% of the nationwide registered AIDS cases [24 , 25] . Since 1996 , the SHCS includes approximately 85% of the newly diagnosed HIV infected individuals in Switzerland . This number was obtained when we compared the estimated numbers of newly diagnosed HIV cases published by the Swiss Federal Office of Public Health to the numbers of patients enrolled in the SHCS annually since 1996 . Genotypic resistance data stem from routine clinical testing and from systematic retrospective sequencing before routine genotyping was introduced ( over 11000 sequences were retrospectively generated ) . Genotyping is performed by four laboratories in Switzerland authorized by the Federal Office of Public Health . All laboratories perform population-based sequencing of the full protease gene and at least codons 28–225 of the reverse transcriptase gene using commercial assays such as Viroseq Vs . 1 PE Biosystems; Virsoseq Vs . 2 , Abbott AG; VircoTYPE HIV-1 Assay , Virco Lab or in-house methods [4] and has participated in the yearly quality control evaluation by the Agence Nationale de la Recherche du SIDA ( ANRS ) since 2002 . All sequences are stored the SHCS drug-resistance database using SmartGene’s Integrated Dababase Network System ( SmartGene , Zug , Switzerland , IDNS version 3 . 6 . 3 ) [12] . For details on the sequencing procedure , see [12] . To increase coverage , we have systematically selected all treatment-naïve individuals carrying TDRM and retrieved their sequential plasma samples before therapy from the SHCS biobank . For this study we considered genotypic resistance test ( GRT ) performed for a patient when being treatment-naïve . All sequential GRTs were included for individuals having ≥ 2 GRTs and harboring TDRM at baseline before ever starting any antiretroviral therapy . TDRM was defined according to the WHO surveillance list of transmitted HIV drug resistance [11] . We studied mutations to the major three drug classes: nucleoside and nucleotide analogue reverse transcriptase inhibitors ( NRTIs ) , protease inhibitors ( PIs ) , and nonnucleoside reverse transcriptase inhibitors ( NNRTIs ) . Additionally , we excluded 17 potential super-infections based on phylogenetic distance and the lack of phylogenetic clustering . Finally , since TDRM in HIV-1 CTL epitopes can disrupt binding to the HLA allele and such CTL-escape may essentially influence the reversion dynamics , we screened the list of optimal HIV-1 CTL epitopes ( according to the Los Alamos HIV database , http://www . hiv . lanl . gov/content/immunology/pdf/2013/optimal_ctl_article . pdf ) for epitopes containing TDRM and excluded from our analysis those mutations that disrupted binding to the epitope according to NetMHCcons ( http://www . cbs . dtu . dk/services/NetMHCcons/ ) . The SHCS , enrolling HIV-infected adults aged ≥ 16 years old , has been approved by ethics committees of all participating institutions . The data collection was anonymous and written informed consent was obtained from all participants [24] . Our goal was to assess systematically the persistence of TDRM in the absence of drug pressure . In particular we considered the persistence across different mutations and viral genetic backgrounds ( for a given mutation occurring in a given virus , the viral genetic background is given by the entire amino acid sequence in which this mutation is observed ) . To allow inter-patient comparisons we included TDRM that were present in at least five individuals at baseline . We quantified the persistence via calculating reversion rates of individual TDRMs . Reversion of a TDRM was defined as an event at which a TDRM becomes undetectable by population sequencing assays . In other words , a TDRM has reversed when the HIV variant carrying that TDRM has decreased to the level below the detection limit of population sequencing assays ( ∼20–30% [26] ) . Therefore , reversion is not necessarily always to wild type . We fitted our data with an interval-censored survival model using exponential waiting times . We chose an interval-censored model because the data did not allow to determine the exact time point of reversion; instead a GRT not detecting a given resistance mutation preceded by a GRT with that mutation informs that the reversion event must have occurred in the time interval between those two tests . Our results were expressed with 95% CI and two-sided p-values with p<0·05 being statistically significant . We analyzed our data with Stata 13 . 1 SE ( StataCorp , Texas , USA ) . We estimated fitness costs based on a previously published approach to predict HIV replicative fitness from amino acid sequences [27] . This approach uses a machine-learning algorithm ( ridge regression ) trained on >70000 data points , each consisting of a pol-amino-acid sequence and an in vitro replicative capacity . Specifically , the algorithm predicts replicative capacity ( pRC ) from an amino acid sequence by a quadratic fitness model of the form pRC ( x ) =∑ijMijxixj where xi denotes the presence ( 1 ) or absence ( 0 ) of a given mutation i and Mij the epistatic effects ( i<j ) and the main effects ( i = j ) characterizing the fitness landscape . These coefficients were derived in [27] by fitting the model to the >70000 data points . Since the number of parameters of the above model exceeds the number of data points , this model was fitted using an approach based on ridge regression . In essence , in this approach the data set was split into a “training” , “training-test” , and “true-test” data set . Then assuming a given penalty weight for model parameters , the model parameters are determined such that for the “training” data set , the sum of squared residuals plus the sum of squares of parameters times the penalty weight are minimized . In this specific case the approach was modified to a generalized linear ridge regression to take the non-normal error structure into account . The model was evaluated on the “test-training” data set , and the penalty weight was determined such that the predictive power on the “test-training” test was optimized . This final model was then evaluated on the “true-test” data set ( which was used neither in deriving the model parameters nor in determining the penalty weight ) . Details on the method and validation on in vitro and clinical data can be found in [27] and [28] . Using this model , we estimated the fitness cost of a mutation in a given genetic background as follows . If A denotes the partial pol-amino-acid sequence ( first 404 amino acid used in the reference [27] ) with a given resistance mutation m and A’ the same amino acid sequence but with the mutation reverted to its wild-type allele , then the fitness cost of the mutation m in the background A can be estimated as c ( m , A ) =pRC ( A ) −pRC ( A′ ) . A negative fitness cost was set to zero . The impact of this fitness cost was assessed in univariable and multivariable versions of the interval-censored model . The multivariable models were adjusted for whether a given TDRM was present as a mixture with another amino acid at this position . Specifically , this was considered to be the case if the nucleotide sequence coding for this mutation contained at least one ambiguous nucleotide that affects the amino acid encoded .
From 7920 treatment-naïve patients enrolled in the SHCS from May 1995 to February 2013 , we could identify 987 sequential GRTs from 197 patients , who had ≥ 2 GRT while being treatment-naïve and presented with ≥ 1 TDRM at baseline . See S1 Table for all types and numbers of mutations and reversions observed from these 197 patients . The criterion that a given mutation must have been present in at least 5 individuals at baseline reduced the number of sequential GRTs and patients to 857 and 168 , respectively . From our studied population most individuals were male ( 80% ) , white ( 87 . 5% ) , and infected with subtype-B viruses ( 81 . 5%; Table 1 ) . The median ( IQR ) number of GRT performed per person was 7 ( 4 , 11 ) and the median ( IQR ) of test interval was 193 ( 170 , 243 ) days . Baseline CD4 count was relatively high ( 494 [347 , 656] ) , suggesting that patients were tested relatively early on after infection . 60 . 1% of patients had a single mutation detected at their first GRT . Detailed patient characteristics were shown in Table 1 . In total , 21 TDRM were analyzed . One mutation ( 190A of NNRTI ) was excluded because we observed no reversion at all from the studied patients and three mutations ( 101E , 181C , 210W ) were further excluded because they were located in the HLA epitopes ( see Methods ) . Thus we could obtain reversion rates for 17 TDRM ( Fig . 1 ) . Among them , 10 were mutations associated with resistance to NRTI , 6 to PI , and 1 to NNRTI . The quantified linear reversion rate showed that persistence time varied strongly among mutations . Among three drug classes , NRTI mutations showed the largest variability . Both the fastest and the slowest reversion rates , 174 . 0/100-person-years [confidence interval = 51 . 4 , 588 . 8] from 184V and 2 . 7/100-person-years [0 . 7 , 10 . 9] from 215D , respectively , belonged to this drug class . We found that reversion rates were associated significantly with the predicted fitness costs of resistance mutations ( Fig . 2 ) . Specifically , the survival analysis with predicted fitness cost as an explanatory variable yielded that reversion rates increased by a factor 1 . 6[1 . 3 , 2 . 0] ( p<0 . 001 ) if fitness is increased by one standard deviation . Thus predicted fitness has a considerable and highly significant impact on reversion rates . Since this analysis included different fitness costs of mutations , each in at least five patients , the observed effect of fitness can be caused by two mechanisms: On the one hand , by overall differences in costs among mutations ( “main effects” ) and , on the other hand , by different costs of the same mutation in different backgrounds ( “epistatic effects” ) . In order to distinguish between these two effects , we further analyzed the data with two alternative approaches: In the first approach , we still used predicted fitness cost as the explanatory variable but adjusted for the identity of the resistance mutation ( i . e . the type of resistance mutation was included as a categorical variable ) . In this approach , the estimated effect of fitness corresponded to the impact of fitness within a given type of mutation . Since this approach introduced 17 variables for 264 data points and 62 events ( and hence carries the risk of over-parameterization ) , we considered an alternative second approach , which only included two parameters . Specifically , we divided fitness cost into two components: the mean fitness cost of a mutation ( across backgrounds ) and the residual fitness cost , which is given as the difference between the predicted fitness cost in a given background and the mean fitness cost . In the first approach , reversion rate was increased by a factor 1 . 8[1 . 1 , 3 . 1] ( p<0 . 001 ) if fitness cost was increased by one standard deviation ( after adjusting for type of mutation ) . In the second approach , both mean fitness cost and residual fitness cost increased the reversion rate significantly by a factor 1 . 7[1 . 3 , 2 . 1] ( p<0 . 001 ) and 1 . 4[1 . 1 , 1 . 8] ( p = 0 . 007 ) per standard deviation , respectively . Thus our models predict that a typical difference in fitness cost among resistance mutations ( i . e . one standard deviation of the fitness costs observed in our data set ) , causes a 40%-80% increase in the rate with which resistance mutations revert . Moreover , both approaches showed that both types of fitness cost ( different overall costs of drug resistance mutations , and different costs in different backgrounds ) are associated with higher reversion rates . These multivariable models also showed that , as can be expected , reversion occurs much faster if a given TDRM is present as a mixture ( see Methods , Table 2 ) .
In this study we investigated the differential persistence behaviors of TDRM in the absence of drug pressure and analyzed the association of the reversion rate with the predicted fitness cost of a given mutation . We used an interval-censored survival model to quantify the reversion rate of each mutation that was at least harbored by five individuals at baseline . We observed that the reversion rate of individual mutations varied substantially . Moreover , the reversion rates were significantly associated with the differential fitness costs of the TDRM: We showed that both the fitness-cost differences among mutations and among viral genetic backgrounds for the same mutation contributed to the variation in reversion rates . Thus , the novelty of this study is that we compared in total 17 TDRM from patients in a single cohort and could associate the persistence times with fitness costs of mutations predicted by a machine-learning model . An additional strength of this study is the high frequency and the number of resistance tests performed per patient . Our results were consistent with most studies showing that M184V disappeared rapidly [15 , 21 , 29] whereas most thymidine analogue associated mutations ( TAMs: 41L , 67N , 70R , 215Y , 219Q ) disappeared at a slower rate [21 , 29 , 30] with the exception of 70R and 215Y . It is known however that 215Y has a high impact on fitness [21] and is rapidly replaced by intermediate 215S or atypical variants 215C/D [31] . Additionally , the fitness cost of 70R was shown to be higher when combined with other mutations in vitro [21 , 24] . This could explain the observed high reversion rate of 70R regardless of its low fitness cost because in our data set 7 from 11 patients harboring 70R had at least one other mutation . Our data showed that most TDRM to PI reverted more rapidly , compared to NRTI mutations . From a more general perspective our findings have important implications for understanding the epidemic spread of drug-resistant pathogens . One of the general problems with drug resistance is that it can be quickly selected by drug pressure , but upon transmission it reverts only slowly if at all in the absence of drug pressure [32] . The intuition behind this is that drugs cause an enormous reduction in the replicative capacity of wild-type virus and hence lead to a strong relative fitness benefit for resistant mutants . By contrast , the fitness cost in the absence of drugs is typically weak . Our results highlight the large variability in reversion rates and the central role of fitness cost in governing the speed of reversion in the in vivo setting within the SHCS . In particular , they show that the genetic background of a resistance mutation substantially modulates the fitness cost and thereby the reversion rate of the mutation . This implies heritable variation in the fitness cost of resistance and thereby the danger that such fitness costs are reduced by evolutionary selection , i . e . mutations in genetic backgrounds causing lower fitness cost will have larger chances to spread to other patients and hence may dominate the population in the long run . Assessing the impact of the genetic background on reversion rates is central for understanding the spread of antimicrobial resistance in general . For example , theoretical models and in vitro evidence suggest a crucial role of compensatory mutations in boosting antibiotic resistance for a broad range of bacterial pathogens [33] . However , real-world epidemiological evidence for an impact of the genetic backgrounds found in natural pathogen populations on reversion of resistance in patients is largely lacking . In this context our approach offers a proof of principle for using machine learning approaches to bridge the gap between epidemiological data on resistance reversion and in vitro fitness measurements and thereby to address this crucial issue . In the context of HIV epidemiology in Switzerland , such a scenario of mutation evolution can be probably prevented by the good surveillance and the early treatment of HIV-infected individuals , implying that resistant strains have only limited opportunity to cause new infections and hence to select backgrounds with lower fitness cost . By contrast , this scenario is a very real danger in settings with poorer surveillance and hence ampler opportunities for resistant viruses to spread . In those settings evolution might indeed successfully act on the variation of fitness costs and lead in the long term to resistant viruses with a low fitness cost . Previous work [18] has assessed fitness costs of some antiretroviral resistance mutations in vitro by site directed mutations ( SDM ) . Since these studies did not consider the impact of different genetic backgrounds , we can only compare the average fitness cost of a mutation determined by our method with the fitness costs determined by SDM . This comparison reveals a good qualitative but not perfect agreement to our estimates with SDM data ( as summarized in [18] ) . Estimates were available in both data sets for the RT mutations 184V , 70R , 41L , 103N , and 215Y; in agreement with [18] we found a high fitness cost for 184V ( 1 . 8 standard deviations above mean fitness cost = +1 . 8s . d ) and a moderate fitness cost for 70R , 41L , and 103N ( +0 . 58 s . d . , −0 . 16 s . d . , and +0 . 48 s . d . , respectively ) . In agreement with [18] we also found moderate fitness costs for 210W and 181C ( −0 . 85 s . d . and −0 . 69 s . d . respectively ) , which were excluded from our analysis because they lie in HLA epitopes and disrupt binding . The main discrepancy was found for 215Y , where our methods predicted low fitness costs ( −0 . 86 s . d . ) in contrast to the SDM data [18] . The fact that reversion rates are high for this mutation indicates that our estimator has underestimated the real fitness cost of this mutation . This failure may be also related to the complexity of the mutational pathways at this position , which may have been oversimplified by our approach ( in which we do not distinguish which amino acid a TDRM reverts to ) . This deviation is also not surprising since the computational predictor underlying our approach is not perfect ( 42% of deviance in in vitro fitness were explained in [27] ) . Overall this comparison thus validates our method but also reveals that there is potential for improvement and hence our approach should be best viewed as a proof of principle of using machine-learning approaches in conjunction with in vitro fitness measurements to assess reversion of TDRM in vivo . This assessment of the fitness predictor is confirmed by considering the quality of fit of the different models summarized in Fig . 2: Starting from an interval-censored survival model without explanatory variables , adding the information of whether a given TDRM is present as a mixture reduces the model deviance by 22% . Adding TDRM-fitness as an explanatory variable reduces the model’s deviance by a further 9% . If we separate fitness cost into the mean fitness cost of a given mutation type and the corresponding residual fitness cost ( as in Fig . 2 ) , this 9% results from a 6% of deviance-reduction explained by the mean fitness cost and 3% by the residual fitness cost . This indicates an important role of fitness for TDRM reversion; especially given that , firstly , the fitness predictor used here is not perfect ( it explains 42% of deviance of in vitro replicative capacity [27] ) and that , secondly , being a mixture implies that a nucleotide has already started to revert and hence the corresponding variable represents a very strong determinant of reversion . Finally , these numbers suggest that the differential fitness-costs of the same mutation in different genetic backgrounds contribute half as much to the population-level variability in reversion than different fitness-costs of different mutations . Given the well-described and strong differences in reversion rates across mutation types this therefore implies an important role of the genetic background . However , these fractions of deviance explained by our predicted fitness costs imply that reversion rates also depend on other factors not captured by in vitro replicative capacity . This includes interactions between host-viral factors such as HLA escape . Even though we excluded TDRMs known to mediate CTL escape ( see Methods ) , it is likely that this does not encompass all such escape mutations or more generally all mutations that affect the interaction of a virus with a given patient’s immune system . Our study had several limitations . One of the limitations of this study was the lack of information before the first GRT was performed . More specifically , we could not determine how long a TDRM had already persisted before the first GRT . We studied the reversion of TDRM from the baseline GRT instead of the infection date of a patient because an exact infection date was not known for most of the patients and because GRTs at infection time are typically not available . This approach increased the sample size considerably in exchange for missing some TDRM that had reverted before the first GRT was performed . This could explain why K65R or T215F , which are known to revert rapidly , were not identified in our study . The fast reverting TDRM such as M184V were either missed or detected right after the infection by GRT , thus the estimated reversion rates were not altered to a large extent and only the sample size may be lower . Another limitation was that around 40% ( 67 / 168 ) of patients carried > 1 TDRM at baseline . Although combinations of mutations could modulate the fitness costs substantially [21] , causing that a given mutation has varying fitness costs when having different genetic backgrounds , the number of mutations detected at the first GRT was not found to be associated with the reversion of TDRM [29] . Additionally we adjusted for different genetic backgrounds including the residual fitness costs in our model and still found positive associations of reversion rates with average fitness costs . In conclusion , our study demonstrated that TDRM showed substantial variation in reversion rates , which were positively associated with the fitness costs these mutations had in their genetic background . | The evolution of resistance is a universal challenge in antimicrobial chemotherapy . A key driver of resistance is that drug resistance mutations often persist even in the absence of drugs and despite the fact that resistance mutations are often associated with reduced pathogen replication ( “fitness costs” ) . Such persistence may occur because fitness costs are low , especially if they are compensated by additional mutations in their “genetic background” . Here we assessed the role of fitness-cost and the genetic background for resistance in a real-world epidemiological setting by studying the persistence behavior of transmitted antiretroviral resistance mutations of HIV . This persistence behavior was associated with the predicted fitness cost of a given resistance mutation in the particular genetic background in which it occurred . We found that persistence behavior varied strongly across both mutation types and genetic backgrounds and that persistence was significantly associated with predicted fitness costs . In particular we found that even mutations of the same type tended to persist longer if they occurred in a genetic background where they caused weak fitness costs . Overall our results underline the variability of persistence behavior as well as the important role of fitness costs and the genetic background in the evolution of antimicrobial resistance . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Persistence of Transmitted HIV-1 Drug Resistance Mutations Associated with Fitness Costs and Viral Genetic Backgrounds |
To identify genetic contributions to type 2 diabetes ( T2D ) and related glycemic traits ( fasting glucose , fasting insulin , and HbA1c ) , we conducted genome-wide association analyses ( GWAS ) in up to 7 , 178 Chinese subjects from nine provinces in the China Health and Nutrition Survey ( CHNS ) . We examined patterns of population structure within CHNS and found that allele frequencies differed across provinces , consistent with genetic drift and population substructure . We further validated 32 previously described T2D- and glycemic trait-loci , including G6PC2 and SIX3-SIX2 associated with fasting glucose . At G6PC2 , we replicated a known fasting glucose-associated variant ( rs34177044 ) and identified a second signal ( rs2232326 ) , a low-frequency ( 4% ) , probably damaging missense variant ( S324P ) . A variant within the lead fasting glucose-associated signal at SIX3-SIX2 co-localized with pancreatic islet expression quantitative trait loci ( eQTL ) for SIX3 , SIX2 , and three noncoding transcripts . To identify variants functionally responsible for the fasting glucose association at SIX3-SIX2 , we tested five candidate variants for allelic differences in regulatory function . The rs12712928-C allele , associated with higher fasting glucose and lower transcript expression level , showed lower transcriptional activity in reporter assays and increased binding to GABP compared to the rs12712928-G , suggesting that rs12712928-C contributes to elevated fasting glucose levels by disrupting an islet enhancer , resulting in reduced gene expression . Taken together , these analyses identified multiple loci associated with glycemic traits across China , and suggest a regulatory mechanism at the SIX3-SIX2 fasting glucose GWAS locus .
Type 2 diabetes ( T2D ) is a chronic disease affecting over 422 million people worldwide[1] with over 30% of cases occurring in East Asian populations [2] . Large-scale genome-wide association studies ( GWAS ) have identified >100 loci associated with T2D and >80 loci associated with fasting glucose , fasting insulin , and glycated hemoglobin ( HbA1c ) , many of which have also been implicated in T2D susceptibility [3–6] . While the largest GWAS of glycemic traits and T2D to date have been performed in populations of predominantly European ancestry [3 , 6–9] , other studies have identified glycemic trait and T2D associations in East Asian individuals [5 , 10 , 11] . As glycemic trait profiles , allele frequencies , and environmental contributions differ between populations , continued investigation of genetic factors can discover additional loci influencing inter-individual variation in fasting glucose , fasting insulin , and HbA1c levels and T2D . A new resource for genetic analyses , the China Health and Nutrition Survey ( CHNS ) is an ongoing , household-based , longitudinal survey aimed at examining economic , sociological , demographic , and health questions in a diverse Chinese population [12] . Using a multistage random-cluster design and stratified probability sampling to select counties and cities , data were collected from 228 communities across nine provinces ( Guangxi , Guizhou , Heilongjiang , Henan , Hubei , Hunan , Jiangsu , Liaoning , and Shandong ) that constituted 44% of China’s population as of the 2009 census . In addition to nearly 30 years of longitudinal survey data collected during 9 survey rounds from 1989–2011 , quantitative biomarker measurements and DNA are available on 8 , 403 subjects in the CHNS . Individual GWAS loci can harbor multiple association signals . More than one association signal has been reported at G6PC2 and PCSK1 for fasting glucose and at KCNQ1 , ANKRD55 , CDKN2A/B , DGKB , HNF4A , and CCND2 for T2D [5] . Imputation reference panels generated from large sample sizes can facilitate identification of additional signals . For non-European populations , the 1000 Genomes Phase 3 reference panel is currently the most comprehensive , containing information for more than 88 million variants in >2 , 500 individuals from 26 diverse populations [13] . Identification of additional association signals at trait-associated loci could explain additional heritability and provide further insights into the biology between the locus and the trait or disease . GWAS have been an efficient method for studying genetic factors influencing biological mechanisms underlying glycemic traits and T2D , but for many of the identified loci , the underlying gene ( s ) , direction of effect , and disease mechanism are largely unknown [14] . For variants located in non-coding regions of the genome , bioinformatic datasets can be used to annotate and predict regulatory variants , target genes , and direction of effect [15–18] , and these variants can be tested for allelic differences in regulatory activity with in vitro laboratory assays [19 , 20] . For example , among previous functional studies of variants associated with fasting glucose at the G6PC2-ABCB11 locus , two variants in the promoter were shown to affect G6PC2 expression levels by altering FOXA2 binding , and two variants located in the third intron of G6PC2 were shown to affect G6PC2 splicing [21–23] . However , the majority of the glycemic trait-associated variants have not been examined . To further clarify the genetic contributions to normal variation in glycemic traits in a multi-provincial Chinese population , we performed a GWAS of fasting glucose , insulin , and HbA1c levels and T2D in subjects from the CHNS , using genetic data imputed to 1000 Genomes Phase 3 in up to 7 , 178 subjects [12] . We examined the population substructure within the CHNS and evaluated candidate functional regulatory variants at one locus using annotation and in vitro laboratory assays .
To evaluate population substructure among 8 , 403 CHNS subjects with genotype data available , we constructed principal components ( PCs ) using a subset of variants ( MAF > 0 . 05; pairwise LD r2 <0 . 02 in a sliding window of 50 variants ) . Compared to HapMap 3 populations , the CHNS participants clustered closely with the Han Chinese in Beijing ( CHB ) , the Han Chinese in Denver ( CHD ) , and the Japanese in Tokyo ( JPT ) populations , with greater diversity than any of these populations ( S1A Fig ) . A comparison to only the East Asian samples showed more clearly that the distribution of the CHNS extends beyond that of the CHB , CHD , and JPT samples ( S1B Fig ) . Within the CHNS , the subjects showed two axes of diversity ( Fig 1 , S2 Fig ) . PC1 , which explained 4 . 2% of the variance , appeared to cluster by province , while PC2 , explaining 0 . 6% of the variance , showed diversity among subjects within the Guangxi and Guizhou provinces in southern China . PC2 could be partially explained by differences in self-reported ethnicity , particularly among subjects from the Guizhou province , as PC2 appeared to characterize the Miao and Buyi ethnic groups ( S3 Fig ) . To account for population substructure in subsequent association analyses with glycemic traits , we included PC1 as a covariate and performed analyses using an efficient mixed model approach that accounts for sample structure between individuals [24] . We performed genome-wide association analyses of fasting glucose and fasting insulin levels in up to 8 , 045 , 193 genotyped and imputed variants ( MAF >0 . 01 ) from 5 , 786 non-diabetic individuals in the CHNS who provided fasting blood samples ( S1 Table , S4 Fig ) . We also performed a genome-wide association analysis of HbA1c in 7 , 178 nondiabetic individuals who provided fasting or non-fasting samples . In addition , 5 , 731 unrelated subjects were used to assess variant association with T2D status , including 748 cases and 4 , 983 controls . For each trait , we also searched for additional signals by conditioning on the lead variants ( reciprocal conditional analyses ) . Overall , a majority of CHNS subjects were female ( 54% ) with a normal BMI ( mean = 23 . 2 kg/m2 ) , and subjects with T2D were older ( cases: 59 . 7 years; controls: 51 . 2 years ) with a higher BMI , higher fasting glucose levels , and higher fasting insulin levels ( S1 Table ) . Analysis of fasting glucose confirmed eight loci previously identified in East Asian and European samples ( G6PC2 , SIX3-SIX2 , PROX1 , ABCB11 , GCK , KANK1 , GLIS3 , and TCF7L2; S2 Table ) , two of which achieved genome-wide significance ( rs34177044 , near G6PC2 , P = 6 . 9 x 10−12 , Fig 2; rs895636 , near SIX3-SIX2 , P = 2 . 3 x 10−8 , Table 1 , Fig 3A , S5 Fig ) [11 , 25–27] . At these two loci , we used stepwise conditional analyses to identify additional association signals at a locus-wide threshold of P <1 x 10−5 . Conditional analysis including rs34177044 at the G6PC2 locus revealed a second signal ( rs2232326 , MAF = 0 . 04 , Punconditioned = 1 . 8 x 10−9 , Pconditioned = 2 . 0 x 10−6 , Fig 2 ) . When conditioning only on rs2232326 , rs34177044 was attenuated but remained significantly associated with fasting glucose ( Pconditioned = 7 . 0 x 10−9 ) ; the attenuation suggests the two signals are distinct yet not fully independent . Haplotype analyses ( S3 Table ) and regression models containing an interaction term with both variants ( P = 0 . 69 ) do not suggest a haplotype effect between the two signals , providing further evidence that the two signals are separate . While conditional analyses could be influenced by the moderate imputation quality of rs34177044 ( r2 = 0 . 70 ) in CHNS , genotypes from the 1000 Genomes project show that the minor allele of rs2232326 is only inherited with the major allele of rs34177044 ( East Asian LD r2 = 0 . 04 , D’ = 1 . 0 ) . No additional association signals were identified at the SIX3-SIX2 locus ( S5 Fig ) . The lead variant in the second signal at G6PC2 ( rs2232326 ) is a missense variant ( S324P ) . Amino acid 324 is located in a helix spanning the cell membrane [28] , and the substitution of a proline for a serine in the middle of a helix may add kinks to the protein [29] . In addition , both SIFT and PolyPhen [30] predict this variant to be “probably damaging” , suggesting that it may affect function of the G6PC2 protein . Based on data from 1000 Genomes Phase 3 , rs2232326 is rare in all ancestry populations ( MAF: African , 0 . 2%; Admixed American , 0 . 3%; European , 0 . 3%; South Asian , 0 . 3% ) except in East Asians ( MAF 5% ) , and it has few ( Admixed American , rs34102076; East Asian , rs139014876 ) , to no ( African , European , and South Asian ) proxy variants ( LD r2>0 . 80 ) . This variant contributed to a significant G6PC2 gene-based association with glucose in Europeans [31] and other protein-coding variants within G6PC2 have been individually associated with fasting glucose levels ( e . g . rs492594 , rs138726309 , rs2232323 ) [21] . In the CHNS , rs492594 was nominally associated with fasting glucose levels ( P = 0 . 002 ) ; other previously described coding variants were either monomorphic or did not pass imputation quality control thresholds in CHNS ( S4 Table ) . We examined whether the strength of fasting glucose associations at SIX3-SIX2 and G6PC2 varied by province ( Table 2 ) . At rs895636 ( SIX3-SIX2 ) , the minor allele frequencies ( MAF ) differed by as much as 0 . 12 between provinces . Most of the provinces in which individuals have a relatively lower minor allele frequency ( 0 . 35–0 . 38 ) showed a stronger association between the variant and fasting glucose levels than similarly sized samples of individuals with higher MAF of 0 . 43–0 . 47 . The opposite pattern was observed at rs2232326 ( G6PC2 ) , for which the province in which the largest MAF ( 0 . 09 ) showed the strongest association with fasting glucose levels . The allele associated with higher levels of fasting glucose trended from less frequent in the northern provinces ( MAF = 0 . 02 ) to more frequent in the southern provinces ( MAF = 0 . 09 ) . Although allele frequencies between provinces were not statistically different , observed allele frequency differences are consistent with genetic drift and the observed population substructure ( Fig 1 ) [32] , demonstrating that study samples from across China have differing power to detect specific associations . Analysis of fasting insulin validated ( P <0 . 05 ) two loci previously reported in European and Hispanic samples ( PPARG and LOC284930; S5 Table ) [33 , 34] and did not reveal any genome-wide significant loci ( Table 1 ) . The most significant association was identified near CNTN6 ( rs13078376 , P = 3 . 22 x 10−7; Supplementary Materials , S6 Fig ) . Previous studies have demonstrated genome-wide significant associations between variants approximately 1 Mb upstream of the CNTN6 gene and both fasting insulin ( rs9841287 ) [33] and HbA1c levels ( rs892295 ) [35] , although the rs13078376 is not a proxy for either of the two previously reported variants ( East Asian LD r2<0 . 01 ) . Data from the CHNS strengthen the evidence for these nominally significant loci near CNTN6 . Analysis of HbA1c validated ( P <0 . 05 ) nine loci previously identified in East Asians and Europeans ( FN3KRP , MYO9B , PIEZO1 , ANK1 , GCK , SPTA1 , HBS1L , MTNR1B , and ABCB11; S6 Table ) [10 , 36 , 37] , and did not reveal any genome-wide significant loci . The most significant variant was located within an intron of FN3KRP ( rs9895455 , P = 3 . 5 x 10−7; Supplementary Materials , S7 Fig ) . rs9895455 is in high LD ( East Asian , r2 = 0 . 99 ) with a variant previously reported to be associated with HbA1c in East Asians ( rs1046875 ) [10] . Three additional variants in high LD with rs9895455 have previously demonstrated moderate associations in Europeans with both HbA1c ( P = 4 . 1x 10−7 ) and modified Stumvoll insulin sensitivity index ( P = 0 . 02 ) [38] . While power to detect associations is limited , data from the CHNS provide further support for these established loci . Association analyses for T2D validated ( P <0 . 05 ) sixteen loci previously identified in East Asians and/or Europeans ( POU5F1/TCF19 , SLC30A8 , CUBN , MIR17HG , TMEM18 , GLP2R , GIPR , MC4R , BCL2L11 , PAX4 , IGF2BP2 , PRC1 , KCNQ1 , CDKN2A/B , TLE4 , and PAM/PPIP5K2; S7 Table ) [4 , 26 , 39–41] , and did not reveal any genome-wide significant loci . Of the validated loci , the most significant are at SLC30A8 ( rs3802177 , P = 3 . 0 x 10−4 ) and POU5F1/TCF19 ( rs2073721 , P = 2 . 3 x 10−4 ) . The three most significant variants not described previously were located near RTN4RL1 ( rs62069176 , P = 2 . 7 x 10−7 ) , SOCS6 , ( rs2581685 , P = 2 . 6 x 10−6 ) , and ARID1B ( rs6557473 , P = 3 . 3 x 10−6 ) ( S8 Table , S8 Fig ) . Of these , prior suggestive associations ( P<0 . 05 ) have been detected previously between rs6557473 at ARID1B and both fasting insulin and T2D in Europeans [9 , 42 , 43] . The CHNS data provide suggestive evidence for these loci . To aid in the identification of candidate genes at the strongest association signals , we examined whether any of the variants associated with glycemic traits are also associated with expression of nearby transcripts in pancreatic islets , blood , subcutaneous adipose , or tissues from GTEx ( S9 Table ) [44–46] . These expression quantitative trait locus ( eQTL ) datasets were generated predominantly from European ancestry donors . GWAS and eQTL signals more clearly coincide when the GWAS variant and the variant most strongly associated with expression level of the corresponding transcript exhibit high pairwise LD ( r2>0 . 80 ) . To allow for differences in LD patterns across ancestries in the GWAS and eQTL datasets , we considered GWAS and eQTL signals to be possibly coincident at a less stringent threshold for pairwise LD values ( r2>0 . 60 , East Asian 1000G Phase 3 ) . The association signal for fasting glucose in East Asians at the SIX3-SIX2 locus contained fifteen variants meeting this criterion ( lead GWAS variant rs895636 and fourteen variants with East Asian LD r2≥0 . 60 ) , and the association signal for islet SIX3 expression in Europeans contained 14 variants ( lead variant and 13 variants with European LD r2≥0 . 60 ) ( Fig 3B , S9 Table ) . One variant , rs12712928 , exhibited high LD ( r2>0 . 80 ) with both the lead GWAS and eQTL variants ( Fig 3C ) . rs12712928-C showed strong association with higher fasting glucose ( P = 3 . 4 x 10−8 ) , similar to the lead fasting glucose GWAS variant ( rs895636 , P = 2 . 3 x 10−8 ) , and strong association with lower SIX3 expression level in pancreatic islets ( P = 4 . 7 x 10−8 ) , similar to the lead SIX3 eQTL variant ( rs12712929 , P = 1 . 7 x 10−8 ) . In addition to SIX3 , rs12712928-C was strongly associated with lower expression level of SIX2 ( P = 1 . 4 x 10−4 ) , SIX3-AS1 ( P = 4 . 8 x 10−6 ) , and two other long non-coding transcripts ( S9 Table ) [47] . Assuming the fasting glucose GWAS and islet eQTL signals are shared across ancestries , then the strongest candidate variant that may be responsible for both associations is rs12712928 . To establish a set of candidate functional variants at the SIX3-SIX2 locus , we used regulatory chromatin marks ( open chromatin and histone states ) to predict which variants may affect the transcription of nearby genes . Of 19 candidate variants at the SIX3-SIX2 locus ( including the lead GWAS variant rs895636 , the lead islet eQTL variant rs12712929 , variants in EAS LD r2>0 . 60 with rs895636 , and variants in EUR LD r2>0 . 6 with rs12712929 ) , only five variants ( rs10192373 , rs10168523 , rs12712928 , rs12712929 , and rs748947 ) overlap pancreatic islet active enhancer and pancreatic islet open chromatin ( DNase or FAIRE ) marks , as well as predicted transcription factor binding motifs ( S9 Fig ) . All five of these variants have EAS LD r2 0 . 66–0 . 87 with lead GWAS variant rs895636 . These data suggest that these five variants are the strongest candidates to affect transcription of the gene ( s ) at this locus . To evaluate the allelic differences in enhancer activity of the five candidate functional variants , we conducted transcriptional reporter assays in MIN6 mouse insulinoma cells . We tested 4–6 independent constructs corresponding to each allele or haplotype for a 312-bp DNA region located 18 kb downstream of SIX3 and 37 kb from the 3’ end of SIX2 spanning rs10192373 , rs10168523 , rs12712928 , rs12712929 ( tested as a haplotype ) and for a 365-bp region located 20 kb from the 3’ end of SIX3 spanning rs748947 ( S9 Fig ) . While the rs748947 construct showed no enhancer or allele-specific activity ( S10 Fig ) , the haplotype construct had haplotype-differences in enhancer activity in both orientations ( Fig 4A ) . This 4-variant construct containing the fasting glucose-increasing alleles ( rs10192373-A , rs10168523-G , rs12712928-C , and rs12712929-T ) showed significantly decreased enhancer activity of ≥ 1 . 4-fold in magnitude ( forward , P = 0 . 0008; reverse , P = 0 . 0001 ) compared to the haplotype containing the non-risk alleles . To determine whether rs12712928 could account for the allele-specific effects , we used site-directed mutagenesis to create two additional haplotype constructs . Haplotype constructs containing rs12712928-C exhibited a 1 . 5-fold decrease in enhancer activity compared to the haplotype constructs containing rs12712928-G ( Fig 4B ) . Taken together , these data show that rs12712928 exhibits allelic differences in transcriptional enhancer activity and suggest it functions within a cis-regulatory element at the SIX3-SIX2 fasting glucose-associated locus . We next asked whether alleles of rs12712928 or the other three variants differentially affect DNA binding to nuclear proteins . A DNA-protein complex specific to the rs1272928-C allele was observed using electrophoretic mobility shift assays ( EMSA ) with MIN6 nuclear lysate ( S10 Table , S11–S12 Figs ) . Competition with excess unlabeled C-allele probe more efficiently competed away allele-specific bands than excess unlabeled G-allele probe , providing further support for allele-specificity of the protein-DNA complexes ( Fig 4C ) . Based on these results , we hypothesized that rs12712928-C is located in a binding site for a transcriptional regulatory complex that may be disrupted by the rs12712928-G allele . The sequence containing rs12712928-C is predicted to include a consensus core-binding motif for several transcription factors , and a ChIP-seq peak for CTCF also overlaps this region [48–50] . To identify transcription factor ( s ) binding to rs12712928 , we used a DNA-affinity capture assay . A protein band showing allele-specific binding to the C allele was identified as the alpha subunit of GABP using MALDI TOF/TOF mass spectrometry . In EMSA supershift assays using antibodies to GABP-α , we observed a supershift of the allele-specific band ( Fig 4C ) , suggesting that GAPB may act as a repressor to reduce enhancer activity at this locus ( Fig 4D ) . We used a similar approach to identify potentially functional variants at the G6PC2 locus . The first signal is comprised of two intergenic variants ( GWAS index variant and one variant in LD r2 >0 . 80 ) . GWAS variant , rs34177044 , is ~3 . 2 kb upstream from the transcription start site of G6PC2 and does not overlap any predicted open chromatin marks . rs1402837 ( LD r2 = 0 . 97 ) is located 646 bp upstream of the G6PC2 transcription start site and 187 bp and 208 bp upstream , respectively , of other promoter variants previously shown to exhibit allelic differences on transcriptional activity , rs573225 and rs2232316 ( S13 Fig ) [51] . rs1402837 also overlaps open chromatin marks , suggesting rs1402837 may play a regulatory role in fasting glucose levels in the context of other G6PC2 promoter variants .
In this study of genetic associations with T2D and related glycemic traits in Chinese individuals from 9 provinces in the CHNS , we observed associations with fasting glucose at SIX3-SIX2 and G6PC2 , including a coding variant representing an additional signal at G6PC2 . We also showed that the SIX3-SIX2 fasting glucose locus colocalizes with eQTL associations for SIX3 , SIX2 , SIX3-AS1 , RP11-89K21 . 1 , and AC012354 . 6 in pancreatic islets , and we showed evidence that rs12712828 functions as a regulatory variant at the SIX3-SIX2 fasting glucose locus . Genetic associations in CHNS also supported ( P<0 . 05 ) previously reported associations at 6 , 2 , 9 , and 16 loci with fasting glucose , fasting insulin , HbA1c , and T2D , respectively . The moderate sample size of CHNS prohibited us from identifying additional associations . Hundreds of genes contribute to the heritability of complex traits [52] . As more GWAS and genome-wide meta-analyses are conducted across genetically diverse populations , identification of additional association signals and loci will help to explain the levels of heritability . The CHNS adds to the growing number of population-based cohorts available for the study of metabolic traits . With its multi-provincial study design , the CHNS includes subjects of differing ethnicities , from both urban and rural areas across China . Additionally , linkage of the genotype data with biomarkers and decades of longitudinal phenotype data ( e . g . nutrition , health outcomes , environment ) will allow environmental and societal contributions to trait or disease outcomes to be evaluated . Alterations in regulatory elements or the coding sequence of G6PC2 can impact levels of fasting plasma glucose . G6PC2 encodes an enzyme belonging to the glucose-6-phosphatase catalytic subunit family responsible for the terminal step in gluconeogenic and glyconeogenic pathways that lead to the release of glucose into the bloodstream [53] . Several previous studies have identified >1 fasting glucose association signals at this locus in populations of European and African ancestries , two of which include nonsynonymous coding variants [25 , 26 , 31 , 33 , 54–57] . We identified two distinct signals at G6PC2 associated with fasting plasma glucose levels . Variants within the primary CHNS association signal have been associated with fasting glucose in East Asian populations previously [11] . The lead variant in the second signal at G6PC2 ( rs2232326 ) is a missense variant ( S324P ) . We were unable to assess evidence of association with other coding variants in G6PC2 as the variants were either monomorphic in CHNS or did not pass quality control thresholds . To date , the association between variants near SIX3 and glycemic traits remains specific to East Asian populations . rs895636 was reported as the lead variant associated with fasting plasma glucose in a GWAS of >17 , 000 Korean and Japanese subjects ( P = 9 . 9x10-13 ) and in a separate GWAS meta-analysis of up to 46 , 085 East Asians ( P = 2 . 5x10-13 ) [27] . However , in Europeans , nominal to no association has been observed between rs895636 and fasting glucose ( P = 0 . 002 , n>96 , 000 ) [33] , HbA1c ( P = 0 . 05 , n>46 , 000 ) [36] , fasting insulin ( P = 0 . 73 , n>96 , 000 ) [33] , and T2D ( P = 0 . 41 , n>120 , 000 ) [3] . Allele frequency is a possible explanation for ancestry differences . In East Asians , the MAF of rs895636 is 0 . 42 while the MAF in Europeans is only 0 . 16 . Larger sample sizes of European-ancestry individuals may be needed to identify the association between variants at the SIX3-SIX2 locus and glycemic traits . Other genetic and environmental factors may also be playing a role in the fasting glucose association at SIX3-SIX2 in East Asian populations that are not present in other populations . We provide compelling evidence that rs12712928 is a regulatory variant at the SIX3-SIX2 fasting glucose locus . The rs895636-T allele is associated with increased fasting glucose levels and decreased SIX3 , SIX2 , and other transcript expression in pancreatic islets [47] . rs12712928 is in high LD ( East Asian and European r2>0 . 87 ) with both rs895636 and the lead pancreatic islet eQTL variant , rs12712929 , and was the strongest candidate for an effect of regulatory function based on its location in a putative islet enhancer element . Compared to rs12712928-G , the allele rs12712928-C demonstrated decreased transcriptional activity , as well as allele-specific binding to the alpha subunit of GABP , which suggests that at least GABP , and possibly other transcription factors , bind to the C allele and repress expression of SIX3 and SIX2 . rs12712928 may be responsible for the GWAS signal , or given that some GWAS signals are affected by multiple functional variants [58 , 59] , other variants at this locus may also contribute to variation in fasting glucose . SIX3 is a strong candidate for a target gene at the SIX3-SIX2 fasting glucose locus . Highly expressed in pancreatic islets [44] , SIX3 encodes sine oculis homeobox-like protein 3 , a transcription factor that localizes to the nucleus of adult beta cells to regulate insulin production and secretion . Decreased expression of SIX3 results in the misregulation ( i . e . decreased levels ) of insulin [60] , which promotes the uptake of glucose into fat , liver , and skeletal muscle cells , thus lowering blood glucose levels [61] . Consistent with the effects of SIX3 in mice , the risk allele rs12712928-C is associated with both decreased expression of SIX3 and increased levels of fasting glucose . In CHNS , rs12712928-C was also moderately associated with decreased fasting insulin levels ( P = 7 . 6x10-5 ) and an increased risk for T2D ( P = 0 . 03 ) . However , in the islet eQTL data , rs12712928 was also associated with expression level of SIX2 , SIX3-AS1 , RP11-89K21 . 1 , and AC012354 . 6 . SIX2 is also believed to play a role in the regulation of islet beta cell functions such as insulin output [60]; however , less is known about its biologic function compared to SIX3 . Additionally , the roles of SIX3-AS1 , RP11-89K21 . 1 , and AC012354 . 6 are not well characterized . One or more of these transcripts could be a target gene underlying the association signal and contributing to the biological effect on fasting glucose . In conclusion , this study confirmed many previously identified loci associated with T2D and related glycemic traits and validated a recently described G6PC2 missense variant associated with fasting glucose . We report a functional variant at the SIX3-SIX2 locus , rs12712928 , and provide evidence of a potential mechanism by which this variant affects expression of at least SIX3 , leading to decreased levels of fasting glucose . Our use of a denser reference panel of >8 million variants in a diverse Chinese population allowed us to conduct higher resolution genetic analyses than reported previously . Further functional analyses of the variants identified in this study is the next step to confirm which variants and genes are affected . Replication of the moderately-significant associations would be useful to better understand the genetic architecture of glycemic traits .
The China Health and Nutrition Survey ( CHNS ) is a nationwide , longitudinal survey aimed at examining economic , sociological , demographic , and health questions in a Chinese population . Details of subject selection and study design have been described elsewhere [12] . Briefly , a stratified probability sample with a multistage , random cluster design was used to select counties and cities within 9 diverse provinces ( Guangxi , Guizhou , Heilongjiang , Henan , Hubei , Hunan , Jiangsu , Liaoning , and Shandong ) , stratified by income and urbanicity using State Statistical Office definitions . A total of 4 , 560 households from 228 communities were then randomly selected from within each stratum . Health data was collected during nine rounds of surveys from 1989–2011 ( 1989 , 1991 , 1993 , 1997 , 2000 , 2004 , 2006 , 2009 , and 2011 ) . The 2009 survey was the first to collect fasting blood samples . The CHNS was approved by the Institutional Review Boards at the University of North Carolina at Chapel Hill ( #07–1963 , #05–2369 ) , the Chinese National Human Genome Center at Shanghai ( #2017–01 ) , and the Institute of Nutrition and Food Safety at the China Centers for Disease Control ( #201524–1 ) . All participants provided written informed consent . The present analysis was limited to subjects who participated in the 2009 survey round and for whom blood biomarker traits were available ( n = 9 , 551 ) . For the glucose and insulin analyses , subjects were only included if their blood sample was obtained after an overnight fast ( n = 6 , 779 ) . Subjects were excluded from a particular analysis if their biomarker trait value exceeded 4 standard deviations beyond the group mean or have type 1 diabetes . Fasting blood samples were not required for the HbA1c or T2D analysis . Additionally , one member of each first-degree relative pair was randomly removed for analyses of T2D , as current software to analyze associations with binary traits do not control for the high number of related individuals with CHNS . Following an overnight fast , a blood sample ( 12 mL ) was collected by venipuncture . Glucose and insulin were measured in the central laboratory of the China-Japan Friendship Hospital . Detailed descriptions of the laboratory procedures for measuring glucose ( GOD-PAP method; Randox Laboratories Ltd , UK ) , and insulin ( radioimmunology in a gamma counter , XH-6020 analyzer; North Institute of Bio-Tech , China ) , levels have been described previously [62] . HbA1c was measured in the central laboratory of the China-Japan Friendship Hospital [Guizhou and Hunan , HLC method , HLC-723 G7 ( machine ) , Tosoh , Japan ( reagent ) . Theory: Boronic Acid Afinity HPLC] or in the field [Guangxi and Henan: HPLC method , Primus Ultra 2 ( PDQA1c ) , Primus , USA; Heilongjiang , Hubei , Jiangsu , and Liaoning: HLP method , Bio-Rad ( D10 ) , Bio-Rad , USA; Shandong: HLC method , HLC-723 G7 , Tosoh , Japan . Theory: Ion exchange HPLC] . Different methods and machines were calibrated with the same quality control products made in Bio-Rad , USA . Participants were classified as having T2D if they were at least 18 years old and met at least one of the following criteria: 1 ) HbA1c ≥6 . 5% , 2 ) fasting blood glucose ≥126 mg/dL , 3 ) received the diagnosis from a physician after the age of 20 ( self-report ) , or 4 ) reported taking diabetes medication ( self-report ) ( S1B Table ) . DNA samples were extracted and genotyped at the Chinese National Human Genome Center , Shanghai , China . Genotyping was performed with the Illumina HumanCoreExome chip using the standard protocol recommended by the manufacturer . Genotyping was attempted on the 10 , 131 unique samples , 316 duplicates , and 1 set of triplicates ( total n = 10 , 449 ) . 1 , 513 samples were unable to be genotyped due to inadequate DNA concentrations ( <10 ng/uL or OD 260/280 outside the 1 . 5–2 . 0 range ) , and an additional 69 samples were excluded for poor quality . Using KING , we identified 7 pairs of samples that were unintentionally duplicated; one sample from each pair was excluded . We used PLINK v . 1 . 9 to compare genotype heterozygosity on the X chromosome to self-reported gender and excluded 129 mismatched samples and six samples with apparent XXY or XXXY genotypes . The CHNS data contained 4 sets of identical twins; 1 subject was randomly excluded from each twin pair . Finally , based on the principal component analysis described below , we excluded two samples that were outliers from HapMap samples of Han Chinese in Beijing , China ( CHB ) , Chinese in Metropolitan Denver , Colorado ( CHD ) , and Japanese in Tokyo , Japan ( JPT ) . After exclusions , 8 , 403 samples were successfully genotyped and passed all genotyping quality control . We applied variant quality control checks in PLINK v . 1 . 9 on the 8 , 403 remaining CHNS samples that were successfully genotyped . Of the initial 538 , 448 variants , we discarded 4 , 306 variants due to call rate <95% and/or deviation from Hardy-Weinberg equilibrium ( P <10−6 ) . Of the remaining 534 , 143 variants , 193 , 236 ( 36 . 2% ) were monomorphic and 340 , 906 ( 63 . 8% ) were polymorphic . Because of the household-based study design of CHNS , we expected the CHNS to include many first- and second-degree relatives . Using KING [63] , we calculated kinship coefficients for all pairwise relationships and identified 3 , 681 first-degree relative pairs and 1 , 567 second-degree relative pairs . We performed genotype imputation of the autosomal chromosomes of 8 , 403 samples with the 1000 Genomes Project Phase 3 v5 reference panel [13 , 64] using the Michigan Imputation Server [65] . We used Eagle2 [66] for pre-phasing , followed by imputation with Minimac3 software . We also imputed the X chromosome using with the 1000 Genomes Project Phase 3 v5 reference panel . We imputed male ( n = 3 , 927 ) and female ( n = 4 , 476 ) samples separately , using Mach for pre-phasing and Minimac2 for imputation . Imputation yielded data for 47 , 095 , 001 variants , and the 534 , 143 directly genotyped variants were also assigned imputed genotypes . We removed variants with an imputation r2 <0 . 30 ( 35 , 615 , 501 variants ) or a MAF <0 . 01 ( 37 , 891 , 969 variants ) as additional quality control procedures . In total , we tested 8 , 045 , 193 variants for association with fasting glucose , insulin , and HbA1c levels and T2D . We constructed principal components ( PCs ) to capture population substructure among the CHNS subjects . We identified a set of 55 , 601 independent variants with MAF > 0 . 05 and pairwise LD r2 <0 . 02 in a sliding window of 50 variants and used the variants to construct PCs in 8 , 403 CHNS subjects ( Fig 1; S1–S3 Figs ) . The set of 55 , 601 variants was trimmed to match a list of 47 , 032 variants that were also available in HapMap Phase III samples . Individuals from CHNS and HapMap III were plotted based on the first two eigenvectors produced by the PC analysis ( S1 Fig ) . We tested for the association between each of the first 10 PCs and each of the phenotypic traits to identify PCs associated at P<0 . 05; the first PC was included as a covariate in the regression models . Fasting glucose , fasting insulin , HbA1c , and T2D were adjusted for age , age2 , BMI , gender , and PC1 . Residuals were then inverse normal-transformed to satisfy model assumptions of normality . Efficient mixed model associations ( EMMAX ) accounting for population structure and relatedness were performed using EPACTs v . 3 . 1 . 0 [24] . Because EMMAX was designed for analysis of linear traits , GWA analyses for T2D were performed using the Firth bias-corrected logistic regression likelihood ratio test implemented in EPACTs [67] . For all analyses , genotype was modeled as an additive effect , with the genotype dosage values used as the primary predictor of interest . Due to the correlation of the glycemic traits , we used a genome-wide significance threshold of P <5 x 10−8 to define a single result as genome-wide significant , as used in previous association studies of this scale and high trait correlation [68] . A conservative experiment-wide Bonferroni-corrected P-value for four could be considered as P<1 . 25x10-8 . We created regional association plots using LocusZoom [69] with LD estimates generated from the CHNS subjects . All variant positions correspond to build hg19 . At loci that exhibited evidence of genome-wide significant association ( P <5 x 10−8 ) , we identified additional association signals using conditional analysis . We added the most strongly associated variant into the regression model as a covariate and tested all remaining regional variants ( +/- 1 Mb from the initial lead GWA variant at each locus ) for association . Since we were focusing on a much narrower region of variants during the conditional analyses , we set a less stringent locus-significance threshold of P <1 x 10−5 based on ~5 , 000 variants in a 2 Mb region . We performed sequential conditional analyses until the strongest variant no longer met the P-value threshold . We used summary data available in the Type 2 Diabetes Knowledge Portal [70] to explore associations between the newly identified loci and other metabolic traits and outcomes . Association summary statistics available ( last assessed June 23 , 2017 ) included coronary artery disease from CARDIoGRAM [71]; kidney-related traits from CKDGen [72]; T2D from DIAGRAM , GoT2D , BioMe AMP , CAMP , and SIGMA [3 , 43 , 73–75]; BMI and waist-hip-ratio from GIANT [76 , 77]; and glycemic traits from MAGIC [26 , 36 , 78 , 79] . Additionally , we used data available from the ICP-GWAS ( systolic and diastolic blood pressure ) [80] and the AGEN adiponectin GWAS [81] . To identify variants in high LD ( r2>0 . 80 ) with the lead variants , we used LDlink with all East Asian sample populations from the 1000 Genomes Project as the reference [82] . We used ENCODE [15] , ChromHMM [83] , and Human Epigenome Atlas [84] data available through the UCSC Genome Browser to determine which of the candidate variants in each association signal overlapped open-chromatin peaks , ChromHMM [83] chromatin states , and chromatin-immunoprecipitation sequencing ( ChIP-seq ) peaks of histone modifications H3K4me1 , H3K4me3 , and H3K27ac , and transcription factors in pancreatic islets and the pancreas . We searched the following publicly available eQTL databases to identify cis-eQTLs at the observed loci: GTEx v7 [85] , the University of Chicago eQTL browser [86] , the Islet eQTL Explorer ( http://theparkerlab . org/tools/isleteqtl/ ) [47] , and the Blood eQTL Browser [45] . We also searched for cis-eQTLs in subcutaneous adipose tissue from the METSIM study [87] . All eQTL data sources used a false discovery rate ( FDR ) <5% for identifying cis-eQTLs , with the exception of the METSIM study , which used an FDR <1% . MIN6 mouse insulinoma cells[88] were cultured in DMEM ( Sigma ) supplemented with 10% FBS , 1 mM sodium pyruvate , and 0 . 1 mM beta-mercaptoethanol . The cell cultures were maintained at 37°C with 5% CO2 . To measure variant allelic differences in enhancer activity at the SIX3-SIX2 locus , we designed oligonucleotide primers ( S10 Table ) with KpnI and XhoI restriction sites , and amplified the 312-bp DNA region ( GRCh37/hg19 –chr2: 45 , 191 , 902–45 , 192 , 213 ) around: rs10192373 , rs10168523 , rs12712928 , rs12712929 ( tested as a haplotype ) . Separately , we amplified a 365-bp region ( GRCh37/hg19 –chr2:45 , 192 , 357–45 , 192 , 721 ) around rs748947 . The 312-bp haplotype construct was altered to create a missing haplotype for rs12712928 using the QuickChange site directed mutagenesis kit ( Stratagene ) . As previously described [19] , we ligated amplified DNA from individuals homozygous for each allele into the multiple cloning site of the luciferase reporter vector pGL4 . 23 ( Promega ) in both orientations with respect to the genome . Isolated clones were sequenced for genotype and fidelity . 2x105 MIN6 cells were seeded per well , and grown to 90% confluence in 24-well plates . We co-transfected five independent luciferase constructs and Renilla control reporter vector ( phRL-TK , Promega ) using Lipofectamine 2000 ( Life Technologies ) and incubated for another 48 hours . 48 hours post-transfection , the cells were lysed with Passive Lysis Buffer ( Promega ) . Luciferase activity was measured using the Dual-luciferase Reporter Assay System ( Promega ) per manufacturer instructions and as previously described [19] . Nuclear cell protein was extracted from MIN6 cells using the NE-PER nuclear extraction kit ( Thermo Scientific ) . 17 bp oligonucleotide probes were designed centered on each variant: rs10192373 , rs10168523 , rs12712928 , and rs12712929 ( S10 Table ) . The annealed double-stranded oligonucleotide biotin labeled and unlabeled probes for both alleles were generated as previously described [19] . To conduct EMSAs , we used the LightShift Chemiluminescent EMSA Kit ( ThermoFisher Scientific ) and followed the manufacturer’s recommendations . Briefly , a 20 μl binding reaction consisting of 6 μg nuclear extract , 1X binding buffer , 50 ng/μL poly ( dI-dC ) , and 200 fmol of labeled probe was incubated at room temperature for 25 minutes . For competition reactions , 25-fold excess of unlabeled probe for either allele were incubated for 15 min prior to the addition of 200 fmol labeled probe and incubated for an additional 25 minutes . For supershift assays , 6 μg of polyclonal GABP-α antibody ( sc28312X; Santa Cruz Biotechnology ) was added to the binding reactions and incubated for 25 minutes prior to the addition of 200 fmol labeled probe . The reaction was further incubated for an additional 25 minutes . Protein-probe complexes were resolved on non-denaturing PAGE on 6% DNA retardation gels ( Thermo Scientific ) , transferred to Biodyne B nylon membranes ( PALL Life Sciences ) , cross-linked on a UV-light cross linker ( Stratagene ) , and detected by chemiluminescence . EMSAs were carried out on second independent day and yielded comparable results . To identify factors in the protein complex binding rs12712928 , we conducted a DNA affinity capture assay as previously described [19] . Briefly , the 450 μL binding reactions consisted of 300 μg of pre-cleared , dialyzed MIN6 nuclear extract , 1X binding buffer , 50 ng/μL poly ( dI-dC ) , and 40 pmol of biotin-labeled probe for either rs12712928 allele ( same as EMSA probes ) or a scrambled control . Binding reactions were incubated at room temperature for 30 min on a rotator , and then 100 μL of streptavidin-magnet Dynabeads were added to the reaction and incubated for an additional 20 minutes . Beads were washed and bound DNA-proteins were eluted in 1X reducing sample buffer . Proteins were separated on NuPAGE denaturing gel and allelic differences in protein bands was visualized with Coomassie G-250 staining . The UNC Michael Hooker Proteomics Center used a Sciex 5800 MALDI-TOF/TOF mass spectrometer to identify the proteins in the excised protein bands . | Type 2 diabetes risk and levels of glucose , insulin , and HbA1c are heritable traits correlated with risk of other diseases and mortality . To identify genetic variants associated with these traits , we studied up to 7 , 178 men and women from nine provinces in China . We found established variants that could affect fasting glucose located in or near genes named G6PC2 and SIX3 . One of the variants at G6PC2 changes the protein sequence and is predicted to affect how the protein functions . Variants located near SIX3 that are associated with levels of glucose are also associated with levels of expression of the genes SIX3 and SIX2 in pancreatic islets . These variants are located in a genomic region predicted to enhance gene expression . We used laboratory assays to show that alleles at one variant , rs12712928 , demonstrate significant differences in transcriptional activity , suggesting that this variant influences levels of the SIX3 and SIX2 genes in islets , ultimately increasing glucose levels . | [
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"diab... | 2018 | Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey |
Acquisition of a single copy , large virulence plasmid , pINV , led to the emergence of Shigella spp . from Escherichia coli . The plasmid encodes a Type III secretion system ( T3SS ) on a 30 kb pathogenicity island ( PAI ) , and is maintained in a bacterial population through a series of toxin:antitoxin ( TA ) systems which mediate post-segregational killing ( PSK ) . The T3SS imposes a significant cost on the bacterium , and strains which have lost the plasmid and/or genes encoding the T3SS grow faster than wild-type strains in the laboratory , and fail to bind the indicator dye Congo Red ( CR ) . Our aim was to define the molecular events in Shigella flexneri that cause loss of Type III secretion ( T3S ) , and to examine whether TA systems exert positional effects on pINV . During growth at 37°C , we found that deletions of regions of the plasmid including the PAI lead to the emergence of CR-negative colonies; deletions occur through intra-molecular recombination events between insertion sequences ( ISs ) flanking the PAI . Furthermore , by repositioning MvpAT ( which belongs to the VapBC family of TA systems ) near the PAI , we demonstrate that the location of this TA system alters the rearrangements that lead to loss of T3S , indicating that MvpAT acts both globally ( by reducing loss of pINV through PSK ) as well as locally ( by preventing loss of adjacent sequences ) . During growth at environmental temperatures , we show for the first time that pINV spontaneously integrates into different sites in the chromosome , and this is mediated by inter-molecular events involving IS1294 . Integration leads to reduced PAI gene expression and impaired secretion through the T3SS , while excision of pINV from the chromosome restores T3SS function . Therefore , pINV integration provides a reversible mechanism for Shigella to circumvent the metabolic burden imposed by pINV . Intra- and inter-molecular events between ISs , which are abundant in Shigella spp . , mediate plasticity of S . flexneri pINV .
The genus Shigella is a major cause of diarrhoeal disease worldwide , and is responsible for around 188 million cases and 600 , 000 deaths each year [1 , 2] . Most infections occur in low income countries where contaminated water and inadequate sanitation promote the transmission of the bacterium [2 , 3] . Shigella is a human-specific pathogen that is divided into four species: Shigella dysenteriae , Shigella flexneri , Shigella sonnei and Shigella boydii [3] . Although the prevalence of each species depends on the geographic region , S . flexneri remains the leading cause of endemic shigellosis worldwide [2] . The four species of Shigella have emerged from Escherichia coli following the acquisition of a large plasmid , pINV , a 213 kb element that is essential for virulence [4] . pINV is a single copy , non-conjugative element that consists of a patchwork of pathogenesis-associated and plasmid maintenance genes , separated by regions of repeated sequences such as insertion sequence ( IS ) elements [5] . Indeed , ISs are highly abundant in S . flexneri , and account for 53% of pINV-encoded genes and 6 . 7% of all chromosomal sequence [5 , 6] . Genes present on pINV enable the bacterium to invade intestinal epithelial cells , escape into the host cell cytosol , undergo cell-to-cell spread , and induce pyroptosis in macrophages [3 , 7 , 8] . Most of the virulence genes on pINV are located in a 30 kb pathogenicity island ( PAI ) , which encodes components of a Type III Secretion System ( T3SS ) , a molecular syringe that delivers bacterial effector proteins into the host cytoplasm [5 , 9] , with most secreted effectors also encoded by genes in the PAI . Expression of the T3SS is highly regulated and responds to specific environmental cues such as temperature [10] , pH [11] , osmolarity [10] , oxygen [12] , and iron concentrations [13] . Temperature is a key signal for Shigella [14] , as it distinguishes between free-living and host-associated environments . The Shigella T3SS is activated at temperatures found in the gastrointestinal tract [14]; a rise in temperature to 37°C relieves H-NS repression of the pINV-encoded regulator VirF [15] . In turn , VirF activates the expression of another regulator , VirB , which is encoded on the PAI and controls expression of genes for the T3SS and its effectors [16] . For any single copy plasmid , its replication must be matched with the division of the chromosome , and active partitioning systems are needed to ensure that each daughter cell receives a copy of the plasmid on division . Furthermore plasmids can be maintained in bacterial populations through post-segregational killing ( PSK ) mechanisms , typically consisting of toxin:antitoxin ( TA ) systems which eliminate cells lacking a plasmid after division . S . flexneri pINV possesses specific systems to prevent plasmid loss . To date , two partitioning systems , ParAB and StbAB , have been identified by sequence analysis [5] , and three functional TA systems , MvpAT , CcdAB and GmvAT have been characterised in more detail [17–19] . Type II TA systems , such those found on pINV , are composed of genes encoding a toxic protein and a protein antidote . In general , the toxin is more stable than the antitoxin , with the antitoxin specifically degraded by proteases belonging to Lon or Clp families [20] . Typically , the antitoxin is produced at higher levels so that once degraded , it is rapidly replenished [21] . In the presence of the plasmid , the antitoxin counteracts the activity of the toxin , preventing cell death . However , once the plasmid is lost , the relatively unstable antitoxin is degraded and no longer replaced , leaving the toxin free to arrest cell growth , resulting in PSK [22] . MvpAT is the most characterized TA system on pINV and belongs to the VapBC family of TA systems [17–19] . The toxin MvpT is a site-specific endonuclease that stalls translation by cleaving tRNAfMET [23] . MvpAT is essential for pINV maintenance at 37°C , while GmvAT confers pINV stability at environmental temperatures [19] . However , aside from the influence of temperature , the need for multiple TA systems on pINV and other plasmids remains unclear . Colonies of virulent S . flexneri expressing a T3SS bind Congo red ( CR ) when grown on solid media containing this dye , giving rise to a CR+ phenotype [24] . As the T3SS is specifically expressed at 37°C [10 , 25] , binding to CR is only evident for colonies cultured at this temperature . In the laboratory , S . flexneri can spontaneously lose expression of its T3SS , resulting in white , avirulent colonies ( CR- phenotype ) [26] . Of note , expression of the T3SS represents a high metabolic burden for Shigella , evident from the higher growth rate of CR- strains compared with CR+ bacteria at 37°C [26 , 27] . Although CR binding is widely used to distinguish between virulent and non-virulent Shigella [14] , the molecular events that lead to S . flexneri becoming CR- have not been characterised , although examples of segregational instability ( i . e . loss of the entire plasmid ) , or structural instability ( i . e . undefined deletions and/or rearrangements of pINV ) have been described [14 , 26–28] . Our aim was to define the genetic events that underlie the plasticity of pINV leading to the appearance of CR- colonies during growth of S . flexneri at host and environmental temperatures . The majority of events occurring at 37°C result from structural instability of pINV , resulting in loss of the T3SS following intra-molecular events between ISs flanking the PAI . We also show that the TA system mvpAT not only contributes to segregational stability of pINV as described previously [19 , 29] , but also exerts local effects and prevents the loss of adjacent sequences . During growth at 21°C , we show for the first time that pINV spontaneously integrates into the chromosome . Chromosomal integration of S . flexneri pINV has been described previously [30] , but only following exposure of bacteria with curing agents , or introduction of an incompatible plasmid . Furthermore , we observed that spontaneous integration occurs by inter-molecular events between copies of IS1294 present on pINV and the chromosome , and leads to reduced expression of the T3SS regulatory cascade ( virF and virB ) ; CR+ revertants in which the plasmid had excised were recovered from strains with pINV integration . Therefore , integration provides a reversible mechanism for expression of the T3SS . Our findings highlight the importance of ISs in remodelling of S . flexneri pINV , and provide a framework for understanding changes in plasmid behaviour that influence the evolution and maintenance of virulence in this important human pathogen .
To examine the impact of MvpAT on the nature of pINV instability in S . flexneri , we introduced a point mutation into mvpT ( mvpTD7A ) to abolish its activity while leaving mvpAT in its original position on pINV [23] with cat marker downstream of mvpAT for selection; the strain was designated native mvpATD7A , with “native” referring to its location on pINV . We also constructed an isogenic strain , native mvpWT , with the resistance marker in the same location , but downstream of a wild-type copy of mvpAT . The emergence of CR- colonies from S . flexneri M90T , native mvpD7A and native mvpWT was assessed by measuring the proportion of CR- colonies after approximately 50 generations of growth at 37°C , the temperature of the human intestinal tract . There was no significant difference in the number of CR- bacteria arising from native mvpWT and wild-type S . flexneri M90T , demonstrating that introduction of the cat cassette has no impact on the rate of emergence of CR- bacteria ( Fig 1A ) . Furthermore , consistent with previous work [19 , 26] , inactivation of mvpAT resulted in a significant increase in the proportion of CR- colonies after 50 generations , which reached almost 90% in native mvpD7A; the average loss of CR binding did not exceed 60% of colonies of M90T or native mvpWT following the same number of generations ( native mvpWT vs . native mvpD7A , p <0 . 001 , Fig 1A ) . Next we assessed whether MvpAT has any effect on the nature of pINV instability . Eight CR- colonies arising from native mvpWT or native mvpD7A were isolated on six separate occasions ( i . e . a total of 48 colonies ) , and examined by PCR for the presence of virB , virF , and the ori on pINV , and hns as a chromosomal control ( Fig 1B ) . Loss of either virB and virF is sufficient to render Shigella CR- [26] , and virB is located within the T3SS PAI [5] . Amplification of the replication origin ( ori ) was used to monitor the presence of pINV . We found that virB was the only gene not amplified by multiplex PCR from 92% of CR- colonies emerging from S . flexneri M90T ( Fig 1B ) . A similar result was obtained for native mvpWT , demonstrating that the presence of cat downstream of mvpAT does not affect the nature of emerging CR- colonies ( loss of virB in S . flexneri M90T vs . native mvpWT , p >0 . 9999 ) . However , the profile of CR- bacteria arising from native mvpD7A was distinct from S . flexneri M90T and native mvpWT; loss of the entire plasmid was the major cause of loss of CR binding in native mvpD7A , accounting 92% of CR- colonies ( Fig 1B; native mvpD7A vs . native mvpWT , p <0 . 0001 ) ; loss of virB alone ( as detected by multiplex PCR ) was associated with only 8% of CR- colonies in native mvpD7A . Taken together , these data confirm that mvpAT is fundamental for plasmid stability at 37°C [19 , 29] and contributes to the maintenance of the entire plasmid , consistent with its role in PSK . Inactivation of MvpAT by introducing a non-toxic allele of mvpT leads a dramatic increase in the CR- phenotype , mainly due loss of pINV ( i . e . segregational instability ) . However , in contrast to a previous study of a different S . flexneri strain [26] , we found that in wild-type M90T S . flexneri CR- bacteria mostly arise following deletions involving the PAI rather than loss of the entire plasmid . To further characterise the molecular events responsible for the emergence of CR- S . flexneri , we performed whole genome sequencing of 20 CR- colonies independently derived from native mvpWT at 37°C ( Fig 1C ) ; plasmid sequences were aligned with BLAST Ring Image Generator with pINV from S . flexneri M90T as the reference [9] . Results confirmed that CR binding is mainly lost at 37°C following loss of the T3SS PAI and not loss of pINV ( Fig 1C ) . Out of 20 CR- colonies , 17 lacked sequences unique to the PAI , with the remaining three displaying an intact plasmid sequence ( Fig 1C ) ; the regions of homology within the PAI in the 17 strains correspond to sequences also present elsewhere on pINV or on the chromosome , such as IS elements [5] , so represent an alignment artefact . To define the sequences mediating loss of the PAI , we performed PCR to amplify regions flanking the deletions ( Fig 2 and S3 Table for primer sequences ) , and analysed the products by restriction enzyme digestion and sequencing . We identified four events leading to loss of the PAI , each involving a pair of homologous ISs ( Fig 2A ) . The most frequent deletion ( Variant 1 ) occurred in 11 out of 17 CR- colonies and involved two copies of ISSfl4 , spanning positions 87 , 663–90 , 277 bp and 164 , 461–167 , 075 bp of pINV [9] . The second most frequent deletion event ( Variant 2 ) occurred in four of colonies and included two copies of IS1294 , located at positions 58 , 970–60 , 658 bp and 134 , 268–135 , 956 bp . Variant 3 and 4 each accounted for a single CR- colony and employed copies of IS600 ( 83 , 866–85 , 129 bp and 132 , 311–133 , 574 bp ) and IS1294 ( 48 , 065–49 , 417 bp and 134 , 268–135 , 956 bp ) , respectively . Taken together these results demonstrate that ISs on pINV are “hot spots” for recombination , resulting in deletion of different regions of the plasmid that include the PAI ( S1 Table ) . The reason why S . flexneri pINV has three functional TA systems is unclear [19] . As we found that distinct regions on pINV can be lost by IS-mediated recombination , we hypothesised that the presence of multiple TA systems could be due to a positional effect of these elements on plasmid dynamics . To assess whether the position of mvpAT influences plasmid stability , we deleted mvpAT from pINV and introduced either a wild-type or a non-functional version ( mvpD7A ) of mvpAT adjacent to the T3SS PAI , at nt . 100 , 792 of pINV [9] , generating ectopic mvpWT and ectopic mvpD7A , respectively ( Fig 3A ) . Similar to native mvpD7A , when the non-functional allele was introduced into the ectopic site , the inactivity of MvpAT led to a large population of CR- bacteria emerging after 50 generations ( ~ 80% of the total population , Fig 3B , ectopic mvpWT vs . ectopic mvpD7A , p <0 . 0001 ) , most of which resulted from loss of the entire plasmid . This demonstrates that MvpAT contributes to retention of pINV , irrespective of its position on the plasmid . However , there was a dramatic reduction in the number of CR- bacteria emerging from ectopic mvpWT compared with native mvpWT at 37°C ( Fig 3B , p <0 . 0001 ) . Indeed , over approximately 50 generations , the proportion of CR- colonies in ectopic mvpWT did not exceed 5% , compared with ~ 50% for native mvpWT ( Fig 3B ) . Furthermore , there was a striking change in the events leading to CR- colonies when mvpAT is positioned next to the PAI . By multiplex PCR , loss of virB represented a significantly lower proportion of CR- colonies emerging from ectopic mvpWT compared with native mvpWT ( Fig 3C , p <0 . 0001 ) , and accounted for only 8% of CR- colonies emerging from ectopic mvpWT . Instead , loss of virF was the most frequent event , accounting for 71% of CR- derivatives ( ectopic mvpWT vs . native mvpWT , p<0 . 0001 ) . However , differences in the proportion of genetic changes leading to loss of CR binding observed for native mvpWT and ectopic mvpWT need to be considered in the context of the rate of emergence of CR- colonies in these strains . The absolute values for the loss of virF in ectopic mvpWT are comparable to those observed for native mvpWT ( 3 . 55% and 2% of all colonies , respectively ) . Thus , the position of mvp on pINV affects loss of virB but not loss of virF , demonstrating that mvp influences structural plasmid stability by acting to retain nearby sequences . We also examined the emergence of CR- colonies at 21°C , the temperature the bacterium faces in the external environment during host-to-host transmission . Bacteria were grown in liquid media at 21°C for approximately 50 generations , then aliquots were plated on solid media containing CR and incubated at 37°C , as the CR binding is visible at 37°C but not at 21°C . We found previously that , although there is no difference in the stability of pINV in S . flexneri at 37°C and at 21°C [19] , a large population of CR- bacteria emerges at the higher temperature from wild-type S . flexneri ( Fig 1 ) because of the growth advantage of CR- colonies at 37°C . As expected , when S . flexneri M90T was grown at 21°C , fewer CR- colonies emerged after 50 generations than at 37°C , and accounted for approximately 2% of the total population ( Fig 4A ) ; again results for native mvpWT were indistinguishable from S . flexneri M90T , confirming that the presence of the cat cassette does not affect plasmid stability ( S1 Fig ) . Furthermore , there was no significant difference in the number of CR- bacteria emerging from ectopic mvpWT and native mvpWT ( p = 0 . 6603 ) . However , there was a slight but significant increase in the proportion of CR- colonies emerging from strains with the inactive mvpD7A allele compared with control strains ( Fig 4A , native mvpWT vs . native mvpD7A , p = 0 . 0449; ectopic mvpWT vs . ectopic mvpD7A , p = 0 . 0397 ) , consistent with our previous findings [19] that MvpAT possesses some residual activity at environmental temperatures . Next , we examined the nature of CR- bacteria emerging at 21°C by multiplex PCR ( Fig 4B ) . Similar to 37°C , loss of virB still prevailed in native mvpWT , and accounted for 80% of CR- colonies . Furthermore , the majority of CR- bacteria derived from native mvpD7A and ectopic mvpD7A had lost pINV , accounting for 73% and 55% of CR- colonies , respectively ( Fig 4B; native mvpWT vs . native mvpD7A , p <0 . 0001; ectopic mvpWT vs . ectopic mvpD7A , p = 0 . 0454 ) , demonstrating that MvpAT prevents plasmid loss at 21°C as well as 37°C . However , we found that a considerable number of CR- colonies emerging at 21°C from strains from wild-type mvpAT retained virB , virF , and ori ( Fig 4B ) . More than 25% and 50% of all CR- colonies derived from native mvpWT and ectopic mvpWT , respectively , harboured all three pINV genes showing that alternative mechanisms might be responsible for the emergence of CR- bacteria at 21°C . Illumina sequencing of nine CR- colonies that emerged independently at 21°C from native mvpWT and ectopic mvpWT confirmed that these strains harboured an intact plasmid with no detectable single nucleotide polymorphisms ( S2 Fig ) . To identify the mechanisms by which CR- bacteria emerge at 21°C , we analysed the nine CR- colonies with intact pINV sequence which arose from native mvpWT ( Fig 4B ) . It has previously been shown that pINV of S . flexneri M90T can integrate into the host chromosome [30] , but only following exposure of bacteria to curing agents , or selecting for acquisition of a plasmid with an ori which is incompatible with pINV . Therefore , we hypothesised that plasmid integration could occur spontaneously , so analysed plasmid DNA extracted from the nine CR- isolates . Results demonstrate that a distinct band for pINV was missing in two out of nine CR- strains ( Fig 5A , isolates 4 and 6 ) . As previous Illumina sequence analysis confirmed that neither strain had lost any pINV sequence ( S2 Fig ) , isolates 4 and 6 were subjected to PacBio sequencing . In both strains , we found an intact copy of pINV integrated into the chromosome via different copies of IS1294 ( Fig 5B and S3 Fig ) ; there are six copies of IS1294 on the plasmid and three copies on the chromosome . Integration in strain 4 involved the chromosomal copy of IS1294 at position 1 , 615 , 894–1 , 617 , 578 bp [31] and pINV IS1294 at 58 , 970–60 , 658 bp [9] ( i . e . IS1294pINV4 and IS1294Ch . 1 , respectively , S3 Fig ) ; for isolate 6 , chromosomal IS1294 at position 1 , 872 , 287–1 , 874 , 073 bp [31] and plasmid IS1294 at position 205 , 186–206 , 599 bp are involved ( IS1294pINV1 and IS1294Ch . 3 , respectively , S3 Fig ) [9] . Furthermore , we investigated whether integration of pINV is a reversible event . We examined isolate 4 and 6 for the emergence of CR+ colonies , as phenotypic evidence for plasmid excision . The isolates were grown overnight in liquid media at 37°C and plated to solid media containing CR . CR+ colonies emerged from both isolates ( Fig 6A ) , and we analysed plasmid DNA from two independent CR+ revertants ( Fig 6B ) . Results confirm that pINV not only integrates into the host chromosome , but can also subsequently excise , restoring the CR+ phenotype ( Fig 6A ) . Next we examined the activity of the T3SS in isolates 4 and 6 , and their corresponding revertants . Bacteria were grown to exponential phase at 37°C in liquid media and exposed to CR to induce secretion through the T3SS [32] . Silver staining of secreted proteins demonstrates that both isolates 4 and 6 fail to secrete the T3SS effectors , IpaA , IpaB , IpaC , IpaD and IpgD ( Fig 6C ) . In contrast , the revertants secrete T3SS effectors at levels similar to S . flexneri M90T ( Fig 6C ) , indicating that integration and excision of pINV provides a reversible mechanism that controls T3SS activity . To determine the mechanisms underlying the lack of secretion through the T3SS in the pINV-integrated isolates , we examined mRNA levels of ipaB , virB and virF in strains 4 and 6 , and wild-type S . flexneri M90T . mRNA levels were measured by qRT-PCR in bacteria during exponential growth at 37°C , and results were normalized to the expression of the chromosomal gene , polA ( Fig 7 ) . Consistent with the secretion assays , mRNA levels of ipaB were significantly lower in the two strains with plasmid integration ( Fig 7A , M90T vs . strain 4 or strain 6 , p <0 . 0001 ) . A similar statistically significant trend was shown for mRNA levels of virB ( Fig 7B , M90T vs . strain 4 or strain 6 , p <0 . 0001 ) and virF ( Fig 7C , M90T vs . strain 4 p <0 . 01; M90T vs . strain 6 , p <0 . 001 ) , suggesting that the observed down-regulation of the T3SS PAI in plasmid-integrated isolates is correlated with reduced expression of virB and virF .
For many pathogenic bacteria , large plasmids are critical for their virulence and/or the spread of antimicrobial resistance [33–36] . While plasmids confer beneficial traits to bacteria in certain circumstances , they often impose a considerable metabolic cost on the host cell so have evolved dedicated mechanisms to ensure their maintenance within a bacterial population . Genetic plasticity is a fundamental feature of many large plasmids , and facilitates rearrangements that allow appropriate gene expression and the acquisition of novel traits such as antibiotic resistance , virulence or metabolic capabilities [37] , facilitating adaptation to new ecological niches . S . sonnei pINV is an example of how genetic events contribute to the evolution of the plasmid , reflecting bacterial adaptation to new lifestyles [19] . For example , S . sonnei pINV has lost two TA systems and a partitioning system , and acquired an O-antigen gene cluster [19 , 38] , during its transition to a species undergoing predominant host-to-host transmission [19 , 39] . Here we analysed the genetic changes associated with plasticity of pINV . The dynamic nature of S . flexneri pINV is a feature shared with plasmids in other Shigella spp . , which contain a high proportion of ISs; ISs represent approximately 53% of the ORFs on pINV [5] . These elements have shaped the evolution of Shigella by acting as substrates for recombination , mediating inversions , translocations , insertions and deletions [37] . On the chromosome , similar processes have led to loss of co-linearity of genomes of four Shigella species , and gene loss associated with enhanced virulence [40 , 41] . Nonetheless , the T3SS PAI and most other pINV encoded molecular effectors have been maintained in all Shigella spp . , suggesting that selection pressure has preserved these sequences in disease-causing isolates . We demonstrate that ISs influence the architecture of Shigella pINV and mediate a series of deletions that result in the loss of PAI-associated virulence genes and CR binding during growth in the laboratory . Others have found that the most frequent cause of loss of the CR+ phenotype during extended growth of S . flexneri 2a at 37°C is loss of the entire plasmid [26] . In contrast , we show that the predominant event leading to loss of CR binding in S . flexneri 5a M90T is deletion of the T3SS PAI ( Fig 1 ) , consistent with other studies [27 , 28 , 42] . This discrepancy could be explained by the different MvpAs in the strains; for example , MvpA in M90T has a single amino acid difference ( Glu70 instead of His70 ) compared with the corresponding protein in the S . flexneri 2a strain used in previous work [26 , 43] . However , the consequences of this difference in MvpAT are unknown . We found that ISSfl4 is the most common IS involved in PAI deletion ( Fig 2 ) , even though there are only two copies of this IS on pINV . The plasmid harbours multiple copies of other ISs which occupy a far greater proportion of the plasmid sequence than ISSfl4 . Therefore , the reason why this pair of ISs is prone to recombination is not clear . The frequency of recombination between pairs of ISs depends on several factors including the extent and length of homology , the coverage of sequence and local DNA topology ( S1 Table ) . In fact , the percentage of coverage between the ISs involved in the less frequent deletions is lower than for the pair of ISs implicated in Variant 1 strains , which have the commonest rearrangement ( S1 Table ) . Similar to previous work [17 , 19 , 29] , we found that MvpAT plays a critical role in segregational stability of pINV . Inactivation of this TA system led to increased loss of pINV at 37°C . We also demonstrated that the location of mvpAT is critical in governing plasmid dynamics at a local level; repositioning mvpAT to near the PAI dramatically reduced the loss of this region ( Fig 3 ) . The likely reason for this is ‘post-recombinational killing’ whereby recombination between flanking ISs causes loss of mvpAT and cell death in a manner analogous to PSK . To our knowledge , this is the first description of TA loci exerting local effects on plasmids , although they can prevent large-scale deletions of adjacent sequences on bacterial chromosomes [44 , 45] . The localised effects of MvpAT and other TA systems might provide an explanation for the number and distribution of TA systems on pINV . Of note , mvpAT is located close to the ori of pINV in all Shigella species . Following growth at 21°C , we identified CR- strains emerging from S . flexneri that retained all plasmid genes . By analysis of this population of strains , we demonstrate that pINV can spontaneously integrate into the Shigella chromosome . Previous reports of chromosomal integration of Shigella pINV and the related plasmid from enteroinvasive E . coli have only followed exposing bacteria to curing agents ( such as rifampicin ) or by introducing other plasmids with pINV-incompatible replicons [30] . Following these artificial treatments , pINV usually integrated into metB , leading to methionine auxotrophy . In contrast , we found that chromosomal and plasmid copies of IS1294 mediate intermolecular recombination , resulting in integration of pINV at different sites in the chromosome . At both chromosomal sites , integration led to loss of CR binding and prevented secretion through the T3SS ( Fig 6B ) . Comparing pINV-integrated strains with the wild-type strain , integration was associated with reduced levels of mRNA for virF and virB , the two transcription factors involved in the activation cascade of T3SS PAI expression , and ipaB , which encodes a T3SS-secreted protein . We also demonstrate that integration is reversible and excision occurs during bacterial growth at 37°C ( Fig 5 ) . The mechanisms responsible for reduced T3SS activity following pINV integration are unknown . H-NS is a transcriptional repressor of the genes in the PAI , which binds to the promoters of virF and virB in a temperature-dependent manner due to alterations in DNA topology [15] . When integration of pINV from S . flexneri and enteroinvasive E . coli was forced and occurred at metB , H-NS repression of the virB promoter was enhanced , probably due to alteration in the topology of pINV DNA when in the chromosome [46] . However , our qRT-PCR results showed that spontaneous integration via IS1294 also affects virF expression ( Fig 7C ) . The E . coli and Salmonella chromosomes are organised into topologically distinct regions , containing structured DNA macrodomains [47–49] . As the architecture of the chromosome is not homogenous and other ISs could also mediate integration , the exact site and orientation of pINV integration are likely to influence gene expression [50] . Further experiments are underway to define the precise mechanisms modulating changes in T3SS expression following spontaneous integration of pINV through ISs . Other examples of the integration of large plasmids in pathogens include the Salmonella typhimurium virulence plasmid [51] . Following plasmid integration , Salmonella becomes susceptible to complement-mediated killing probably through down-regulation of gene expression . Serum resistance can be restored by introduction of an autonomous plasmid harbouring a copy of rsk , a plasmid gene responsible for resistance to complement [51] . Our findings define the molecular rearrangements that affect S . flexneri pINV , and highlight the importance of IS elements in processes that are fundamental for plasmid remodelling and evolution . We found that intramolecular events between ISs on pINV lead to the emergence of CR- bacteria lacking the T3SS PAI during growth of S . flexneri . CR- bacteria have a significant growth advantage in rich media at 37°C compared with CR+ bacteria , and quickly proliferate and dominate a bacterial population [27] . While the loss of a T3SS could hinder survival of Shigella within the intestine , the fitness advantage upon deletion of the PAI could prove advantageous for Shigella outside the host . Indeed , a significant proportion of Shigella isolated from aquatic environments have lost key virulence genes on pINV [52] , consistent with IS-mediated events . However , deletion of the PAI T3SS and loss of virulence , is a one-way process , that can only be reversed by acquisition of a new plasmid . Although S . flexneri pINV has an origin of transfer , the plasmid is incapable of self-mobilisation [53] . Therefore , IS mediated deletions causing PAI loss are likely to be deleterious in the longer term as they will render the bacterium non-invasive . In contrast , pINV integration follows an inter-molecular event that is completely reversible . We recovered virulent revertants in which pINV had excised and the T3SS was functional . Therefore , plasmid integration via ISs offers S . flexneri an alternative strategy to maintain the plasmid within dividing bacteria while circumventing the fitness costs imposed by expression of the T3SS . pINV integration/excision leads to bi-stable expression of virulence genes by Shigella , which provides a mechanism for the avoidance of host responses against immunogenic T3SS components [54] , and for phenotypic heterogeneity without genetic loss which would promote the evolutionary stability of virulent Shigella .
Bacterial strains used for this study are shown in S2 Table . S . flexneri was grown in Tryptic Soy Broth ( TSB; Sigma Aldrich , St . Louis , USA ) or on Tryptic Soy solid media containing 1 . 5% ( w/v ) agar ( Oxoid , Basingstoke , UK ) ( TSA ) . Chloramphenicol was used as appropriate at a final concentration of 20 μg/mL . Congo red ( Sigma Aldrich , St . Louis , USA ) was added to TSA media at a final concentration of 0 . 01% w/v to make CR-TSA plates . DNA constructs were ligated into pUC19 using the NEBuilder HiFi master mix ( New England Biolabs , NEB , Ipswich , MA ) , and PCR products were generated using primers shown in S3 Table . Resulting plasmids were transformed into E . coli DH5α and linear DNA constructs were amplified by PCR using plasmids as the template . Lambda Red recombination [55 , 56] was used to introduce changes into pINV in S . flexneri M90T . Approximately 1 kb of homologous flanking sequence was used to allow integration into the plasmid . For strains native mvpATD7A and ectopic mvpATD7A , a point mutation was introduced into mvpAT by site-directed mutagenesis ( S3 Table ) . Mutations were then transduced into S . flexneri using P1vir [19] . In ectopic mvpATWT and ectopic mvpATD7A , the native mvpAT locus was deleted and either the wild-type or the mutated mvpAT were positioned between virB and ipaJ at nt . 100 , 792 on pINV [9] . S . flexneri was grown on CR-TSA plates overnight at the selected temperature to obtain single colonies . Three CR+ colonies from each strain were re-suspended in 5mL TSB and incubated at 30°C , 180 r . p . m . ; there is no detectable growth rate difference between CR+ and CR- bacteria at this temperature [19] , so CR- bacteria do not outcompete CR+ bacteria . After this initial overnight growth , cultures were grown for approximately 50 generations at either 37°C or 21°C by sub-culture . Samples were diluted in PBS and plated onto CR-TSA , and incubated overnight at 37°C before CR+ and CR- colonies were counted . Multiplex PCR was performed with primers ( S3 Table ) to amplify virF , virB , mvpAT , and ori to generate products of distinct sizes; hns , a chromosomal gene , was included as a control ( S4 Fig ) . Reactions included Taq polymerase ( Sigma Aldrich , St . Louis , USA ) with an annealing temperature of 51 . 2°C and extension time of 1 . 5 min . Eight CR- colonies emerging on six independent occasions were analysed by multiplex PCR for each strain . Statistical analysis of results was performed using two-way ANOVA with Tukey’s post test comparison , evaluating loss of a single locus alone or in pairwise comparison with other loci . For Illumina sequencing , genomic DNA was purified from bacteria grown overnight in 5 mL TSB using Charge Switch gDNA Mini Bacteria Kit ( Thermo Fisher Scientific , MA , USA ) . Colonies were isolated after plating at 37°C or 21°C and grown at 37°C for 16 hrs or 21°C for 24 hrs , respectively , before DNA isolation . DNA was sequenced at The Wellcome Trust Centre for Human Genetics at the University of Oxford . Sequence data were analysed using Snap Gene for identifying single nucleotide polymorphisms and deletions , while BLAST Ring Image Generator ( BRIG ) was employed to align plasmid sequences [57] . For both approaches , S . flexneri M90T was used as the reference . For PacBio sequencing [58] , DNA was recovered from a loop of colonies using Wizard Genomic DNA Purification kit ( Promega , WI , USA ) . Sequencing was performed at The Earlham Institute , Norwich . Isolates were plated on CR-TSA from 15% glycerol stocks , and incubated at 37°C overnight . One colony from each isolate was streaked again onto CR-TSA and grown as above . A loop of each solid culture was then processed for pINV extraction as previously described [59] . DNA samples were run on 0 . 7% agarose gel at 1 V/cm voltage for approximately 16 hrs . To activate secretion through the T3SS , bacteria were grown overnight at 37°C in TSB at 180 r . p . m . , then sub-cultured to an OD600 of 0 . 05 into 10mL fresh TSB grown in the same conditions until OD600 ~ 1 was reached . Cultures were centrifuged and pellets were resuspended in PBS to an OD600 ~ 5 . T3SS secretion was induced by adding Congo red ( final concentration , 0 . 02% w/v ) followed by incubation at 37°C for 15 minutes . Bacteria were pelleted and supernatants containing secreted proteins were boiled in an equal volume of 2x SDS-PAGE loading buffer ( 0 . 1M Tris-HCl ph 6 . 8 , 1 . 5% SDS , 20% glycerol , 25mM EDTA , 2% β-mercaptoethanol , 150 μg/mL bromophenol blue ) at a 1:1 dilution before loaded on 10% SDS-PAGE gels . After electrophoresis , gels were silver stained using the SilverXpress Kit ( Invitrogen LC6100 ) following manufacter’s protocol . Bacteria were grown at 37°C overnight in TSB then sub-cultured to OD600 = 0 . 05 into 25mL fresh TSB and grown at 37°C until the OD600 reached ~ 1 . A 20mL aliquot of each culture medium was pelleted , resuspended in 460μL Resuspension Solution ( 200μL 20% glucose , 200μL Tris 25mM pH7 . 6 10mM EDTA and 60 μL 0 . 5M EDTA ) and lysed by using Lysing Matrix B Tubes ( MP Biomedicals ) in presence of 500μL acid phenol . RNA was then purified by TRIzol-chloroform extraction , precipitated with isopropanol and washed in 75% ethanol . RNA samples were subjected to two DNase I treatments ( TURBO DNase; Ambion ) , each followed by further phenol-chloroform purification . RNA quality was checked by gel electrophoresis and NanoDrop analysis . First-strand cDNA was synthesized from 5μg total RNA by using polA- , ipaB- , virB- and virF-specific primers with a unique 5’ tag sequences not present in S . flexneri M90T ( S3 Table ) . Reverse transcription was performed in presence of actinomycin and samples were then treated with RNAse H ( Life Technologies ) at 37°C for 20 minutes . After purification , cDNA was analysed by quantitative real-time PCR with Power SYBR green PCR master mix ( Applied Biosystems ) using a gene-specific forward primer and a tag-specific reverse primer to ensure strand-specific cDNA amplification . StepOnePlus real-time PCR system was used to monitor the reaction . Results represent the average of four biological replicates and were normalised to polA cDNA levels by using the 2-ΔΔCt method [60] . Values are shown in relation to wild type M90T 2-ΔCt levels , which are indicated as 1 . Therefore , values less than 1 indicate decreased transcription levels of the target genes in the analysed samples . Statistical analysis was performed using one-way ANOVA with Tukey’s multiple comparison test . | Shigella flexneri is the leading cause of bacillary dysentery worldwide . Key to its virulence is a large 210 kb single copy plasmid , pINV , which encodes a Type III Secretion System ( T3SS ) on a 30 kb pathogenicity island ( PAI ) . When S . flexneri is grown on solid media containing Congo red ( CR ) , virulent , T3SS-expressing colonies appear red ( CR+ ) . Colonies of bacteria are white and large ( CR- ) if they lose T3SS expression; thus , the T3SS imposes a significant metabolic burden on S . flexneri . Within the laboratory , spontaneous emergence of CR- colonies is observed , but the molecular events responsible have not been defined . We characterised CR- bacteria that arise during growth at 37°C and 21°C , and demonstrate that recombination between insertion sequences ( ISs ) on pINV results in loss of the PAI . Furthermore , we demonstrate that MvpAT , a member of the VapBC family of toxin:antitoxin systems encoded on pINV , is responsible for both plasmid maintenance through post-segregational killing , and retention of adjacent sequences . We show for the first time that ISs on the plasmid and chromosome mediate inter-molecular recombination events , resulting in spontaneous and reversible integration of pINV into the chromosome; following integration , T3SS expression is down-regulated . Therefore , integration/excision results in phenotypic heterogeneity that provides a bet-hedging strategy for Shigella to circumvent the metabolic burden associated with retaining virulence genes . | [
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"analysis... | 2017 | Genetic plasticity of the Shigella virulence plasmid is mediated by intra- and inter-molecular events between insertion sequences |
The unfolded protein response ( UPR ) is activated to sustain cell survival by reducing misfolded protein accumulation in the endoplasmic reticulum ( ER ) . The UPR also promotes programmed cell death ( PCD ) when the ER stress is severe; however , the underlying molecular mechanisms are less understood , especially in plants . Previously , two membrane-associated transcriptions factors ( MTFs ) , bZIP28 and bZIP60 , were identified as the key regulators for cell survival in the plant ER stress response . Here , we report the identification of another MTF , NAC089 , as an important PCD regulator in Arabidopsis ( Arabidopsis thaliana ) plants . NAC089 relocates from the ER membrane to the nucleus under ER stress conditions . Inducible expression of a truncated form of NAC089 , in which the transmembrane domain is deleted , induces PCD with increased caspase 3/7-like activity and DNA fragmentation . Knock-down NAC089 in Arabidopsis confers ER stress tolerance and impairs ER-stress-induced caspase-like activity . Transcriptional regulation analysis and ChIP-qPCR reveal that NAC089 plays important role in regulating downstream genes involved in PCD , such as NAC094 , MC5 and BAG6 . Furthermore , NAC089 is up-regulated by ER stress , which is directly controlled by bZIP28 and bZIP60 . These results show that nuclear relocation of NAC089 promotes ER-stress-induced PCD , and both pro-survival and pro-death signals are elicited by bZIP28 and bZIP60 during plant ER stress response .
In eukaryotic cells , ER is a major site for the production of secreted , plasma membrane and organelle proteins . Cells have evolved a sophisticated quality control system to ensure the accuracy of protein folding through optimizing the protein-folding machinery and ER-associated degradation ( ERAD ) [1] , [2] , [3] . To coordinate protein-folding capacity with protein-folding demand , a collection of phylogenetically conserved signaling pathways , termed the UPR , senses the accumulation of misfolded proteins in the ER and sustains homeostatic balance according to the protein folding needs which change constantly depending on different developmental programs and/or environmental conditions [1] , [4] , [5] . Three arms of UPR signaling pathways , namely inositol requiring enzyme 1 ( IRE1 ) , double-stranded RNA-activated protein kinase ( PKR ) like ER kinase ( PERK ) , and activating transcription factor 6 ( ATF6 ) , were identified in mammalian cells that have the ability to promote cell survival by reducing misfolded protein accumulation in the ER . IRE1 is a key component in the most conserved branch , which acts by splicing messenger RNA encoding transcription factor Hac1p in yeast or XBP1 in mammalian cell , respectively [6] , [7] , [8] . Recently , the equivalent pathways were discovered in plants ( e . g . the IRE1-bZIP60 pathway in Arabidopsis ) , which also play important roles in heat stress response , as well as in plant immune response [9] , [10] , [11] , [12] , [13] , [14] . PERK is an ER-localized kinase and its activation upon ER stress leads to the attenuation of bulk protein translation in metazoan cells [15] . ATF6 is an ER membrane-associated bZIP transcription factor; its activation requires ER-to-Golgi translocation and regulated intramembrane proteolysis ( RIP ) [16] . Although the plant PERK ortholog has not yet been reported , the ER membrane-associated Arabidopsis bZIP28 was found to be the functional homolog of mammalian ATF6 , which is activated in a manner similar to ATF6 [17] , [18] , [19] , [20] , [21] . Severe or chronic ER stress can also lead to PCD , a process that kills unwanted cells under ER stress conditions to protect other cells [22] . In contrast to what is known about how UPR protects cells , less is known about the mechanisms that link UPR to PCD , especially in plants [23] . In mammalian cells , IRE1 can trigger PCD by activating the Jun amino-terminal kinase ( JNK ) pathway [24] . Phosphorylation of JNK leads to the activation of pro-death protein BIM and inhibition of anti-death protein BCL-2 [25] . Mammalian IRE1 also binds to BAX and BAK , two cell-death-inducing proteins involved in the mitochondrial cell death pathway [26] . The activation of mammalian IRE1 is able to cause rapid decay of selected microRNAs ( miRs -17 , -34a , -96 , and -125b ) that normally repress translation of caspase-2 mRNA , and thus sharply elevates protein levels of this initiator protease in the mitochondrial cell death pathway [27] . The ER stress-induced mammalian bZIP transcription factor CHOP is one of the major players that induces PCD , most probably through suppression of the pro-survival protein BCL-2 and up-regulation of ERO1α to further perturb the cellular redox state [28] . CHOP is a downstream target of all three aforementioned UPR signaling pathways in mammals [4] . Recently , transcriptional induction through ATF4 and CHOP was shown to increase protein synthesis leading to oxidative stress and PCD [29] . Orthologs of mammalian JNK , BAX , BAK , CHOP and ATF4 are not found in the Arabidopsis genome [30] . However , ER stress-induced PCD is reported in plants with the hallmark of DNA segmentation , and the conserved BAX inhibitor-1 ( BI-1 ) plays important roles in suppression of ER stress-induced PCD in Arabidopsis plant [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] . When animal cells are subjected to severe ER stress , IRE1 loses its specificity and begins to degrade mRNAs in a process called regulated IRE1-dependent decay ( RIDD ) [39] . IRE1 in Arabidopsis also has similar function in the RIDD process in the UPR for degradation of mRNA encoding proteins in the secretory pathway to decrease the amount of proteins entering the ER [40] . Different from the animal system , knock-outs of both IRE1s in Arabidopsis impairs UPR and enhances PCD upon ER stress , indicating that RIDD may play a negative role in PCD in plants [40] . Despite the emerging evidence on ER stress-mediated PCD in plants , the underlying molecular mechanisms of PCD in plant UPR is still largely unknown . In soybean plants , prolonged ER stress and osmotic stress synergistically activate N-rich proteins ( NRPs ) to induce the expression of NAC6/NAC30 to regulate PCD together with NAC081 [33] , [41] , [42] , however , the link from ER stress-sensing machinery to these NRPs is still missing . Here we show that NAC089 plays important roles in regulating ER-stress-induced PCD in the model plant Arabidopsis . NAC089 relocates from the ER membrane to the nucleus during ER stress response . Inducible expression of a truncated form of NAC089 promotes PCD with increased caspase 3/7-like activity and DNA fragmentation . Down-regulation of NAC089 confers ER stress tolerance and impairs ER-stress-induced caspase 3/7-like activity . Several UPR downstream genes including the PCD regulators MC5 , BAG6 and NAC094 are shown to be regulated by NAC089 under ER stress condition . NAC089 itself is also up-regulated by ER stress , which is directly controlled by bZIP28 and bZIP60 . Therefore , NAC089 is an important PCD regulator in plant UPR , linking ER stress-sensing to downstream PCD regulators during ER stress response in plants .
An ER-stress-related NAC ( for NAM , ATAF , and CUC ) transcription factor NAC089 ( also known as ANAC089 ) was identified from our previous microarray analysis [43] . Its expression was up-regulated rapidly by ER stress inducers tunicamycin ( TM ) and dithiothreitol ( DTT ) ( Figure 1A and Figure S1 ) . Knock-out of either bZIP28 or bZIP60 partially suppressed , while knock-outs of both bZIP28 and bZIP60 in zip28zip60 double mutant completely abolished the up-regulation of NAC089 under ER stress condition ( Figure 1B ) . Previously two ER stress responsive cis-elements UPRE and ERSE-I were identified as the binding sites of bZIP28 and bZIP60 [21] , [43] , [44] . We searched the NAC089 promoter region and found that one copy of UPRE and one copy of ERSE-I like ( one mismatch ) cis-elements are present over the segment [−95 , −49] relative to the TSS site of NAC089 . To assess the activation of NAC089 promoter by bZIP28 and bZIP60 , an effector-reporter dual-luciferase transient assay was set up . The NAC089 promoter fragment containing the aforementioned cis-elements was fused to the firefly luciferase reporter and tested in Arabidopsis leaf protoplasts . As expected , the reporter was activated by either TM or DTT treatment ( Figure 1C ) . Using this assay system , co-expression of either bZIP28D or bZIP60S dramatically enhanced the firefly luciferase reporter activity ( Figure 1D ) . To demonstrate the direct binding of bZIP28 or bZIP60 to the NAC089 promoter , electrophoretic mobility shift assays ( EMSAs ) were performed with the biotin-labeled NAC089 promoter DNA . When the truncated form of either bZIP28 or bZIP60 was incubated with the biotin-labeled DNA probe , a band shift was observed reflecting the formation of the respective complex . To show the binding specificity , excess un-labeled probe was added and shown to be an effective competitor for the formation of each complex . On the contrary , the un-labeled mutated UPRE probe could not compete with the binding ( Figure 1E–F ) . Through further mutation analysis , it was found that neither bZIP28 nor bZIP60 binds to the ERSE-I like cis-element presented in the probe ( Figure S2 ) . Thus , the expression of NAC089 is up-regulated by ER stress , which is directly controlled by both bZIP28 and bZIP60 through the UPRE cis-element . NAC089 is predicted to be a membrane-associated transcription factor [45] with the N-terminal DNA-binding domain facing the cytoplasm ( Figure 2A ) . It has transcriptional activation activity and forms homodimers [46] ( Figure S3 ) . To confirm the membrane association of NAC089 and also to investigate the possible membrane-to-nucleus translocation of NAC089 in response to ER stress , 4X MYC tag was fused to NAC089 at the N-terminus and the fusion protein was expressed in Arabidopsis plants . Total proteins were extracted from transgenic seedlings and MYC-NAC089 was detected with the western blotting analysis . Without TM or DTT treatment , one prominent band reacted with the anti-MYC antibody . After TM or DTT treatment for 6 hr , one excess band with smaller molecular weight was induced , with a similar migration rate to the truncated form NAC089D , in which the C-terminal 24 amino acids of NAC089 were replaced with the 4X MYC tag ( Figure 2B ) . To track the movement of NAC089 in response to ER stress , mGFP-NAC089 was expressed in Arabidopsis and observed under confocal laser scanning microscopy . In the mock ( H2O ) treatment , most of the mGFP-NAC089 signals were observed in the ER ( Figure 2C–D ) ; after either TM or DTT treatment for 6 hr , the fluorescence signals were largely found in the nuclei of the Arabidopsis leaf protoplasts and root cells ( Figure 2C–D ) . The nuclear relocation of mGFP-NAC089 was not observed in the root cells when the transgenic plants were treated with TM ( 5 µg/ml ) for short period of time ( e . g . 2 hr ) . The ER-to-nucleus movement was also confirmed in the protein fractionation studies ( Figure S4 ) . Taken together , NAC089 is an ER membrane-associated protein and it relocates from the ER membrane to the nucleus in response to ER stress . To investigate the biological function of NAC089 in the ER stress response , we created partial loss-of-function mutants by RNA interference ( RNAi ) and chimeric repressor silencing technology ( CRES-T ) . NAC089 knock-down plants ( RNAi089 ) grew as well as the wild-type ( wt ) control under normal growth condition , but they were more tolerant to ER stress than the wt ( Figure 3A–B , Figure S5 ) . More greenish big ( G-B ) plants and less yellowish small ( Y-S ) plants were observed in the RNAi089 plants than that in the wt under ER stress conditions ( Figure 3B ) . In CRES-T system , fusion of an EAR-motif repression domain to a transcription factor converts an activator into a repressor , which results in partial loss function of the transcription factor [47] . We replaced the C-terminal hydrophobic tail of NAC089 with the EAR-motif , and expressed the chimerical fusion protein NAC089D-EAR in Arabidopsis with NAC089's native promoter . NAC089D-EAR expression did not affect seedling development under normal growth condition , but also conferred ER stress tolerance in plants ( Figure S6A–D ) . ER stress should be built-up in plants which had been grown on solid growth medium with low concentrations of tunicamycin for a long period of time , as ascertained by the up-regulation of UPR marker genes in the wild-type plants ( Figure S6E ) . All together , we concluded that partial loss-of-function of NAC089 in Arabidopsis increases chronic ER stress tolerance . To gain insight into mechanisms by which NAC089 operates , we conditionally expressed a MYC-tagged truncated form of NAC089 ( NAC089D-MYC ) with the beta-estradiol ( BE ) inducible system [48] ( Figure S7 ) . This truncated form co-migrates with the ER stress-induced nuclear form of MYC-NAC089 , has the transcriptional activation activity and localizes in the nucleus as mentioned above . The wt control and NAC089D-MYC expressing plants ( XVE089D ) were transferred to growth medium supplemented with or without BE . There was no obvious difference between the wt and XVE089D plants on the growth medium without BE ( Figure 4A ) . However , when BE was included in the growth medium , root growth of the XVE089D plants was inhibited , and chlorotic leaves were observed , representing a typical PCD phenotype ( Figure 4B–C ) . Cysteine-dependent aspartate-directed proteases ( caspases ) are the key regulators of PCD in animals , of which caspase-3 is the crucial executioner of PCD and recognize tetra-peptide motif DEVD [49] . Although the ortholog of animal caspase-3 is absent in plants , caspase 3/7-like activity has been reported in many examples involved in plant development and adaptation to environmental stresses [50] . To investigate whether the NAC089D-MYC-induced PCD was associated with the caspase-like activity , we performed caspase-3/7 activity assays using the same tetra-peptide substrate as previously reported [32] , [33] , [41] . It was found that the expression of NAC089D-MYC considerably induced caspase 3/7-like activity in the XVE089D plants ( Figure 4D ) . The caspase 3/7 activity was also checked in the wt control and NAC089 RNAi plants ( line RNAi089-25 ) . ER stress gradually induced caspase 3/7-like activity in both the wt and RNAi089-25 plants in response to chronic ER stress . However , the caspase 3/7-like activity in the RNAi089-25 plants was about half of that in the wt plants after 3 days of TM treatment ( Figure 4E ) , suggesting that NAC089-regulated caspase 3/7-like activity is stress severity-dependent . It is possible that other pathways also regulate such caspase 3/7-like activity . Loss of cell viability , accumulation of H2O2 and rupture of plasma membrane are often associated with PCD [30] . To assess cell viability , roots of XVE089D plants were stained with fluorescein diacetate ( FDA ) , which is a substrate for many endogenous esterases . NAC089D-MYC expression dramatically reduced the endogenous esterase activities ( Figure 4F–G ) . Further 3 , 3′-diaminobenzidine ( DAB ) staining demonstrated that H2O2 was accumulated in the roots when NAC089D-MYC was induced ( Figure 4H–I ) . Propidium iodide ( PI ) binds to DNA , but it is often used to stain plant cell wall or plasma membrane ( Figure 4J ) because it is membrane impermeant . When NAC089D-MYC was induced , PI signals were observed in both the cytoplasm and nucleus ( Figure 4K ) , indicating that expression of NAC089D-MYC reduced the rigidity of cell membrane . Another characteristic of PCD is the morphological changes in the nucleus which could be revealed by 4 , 6′-diamidino-2-phenylindole ( DAPI ) staining . Intact and round nuclei were found in most of the XVE089D root cells without BE treatment ( Figure 4L ) . In contrast , nuclei with stretches and speckles were observed in the XVE089D root cells when the plants were treated with BE ( Figure 4M–N ) . Cleavage of genomic DNA at internucleosomal sites by endogenous nucleases is always associated with PCD and terminal deoxynucleotidyl transferase-mediated dUTP nick and labeling ( TUNEL ) assay is frequently used to label the fragmentation of nuclear DNA in situ [31] . Compared to the low level of background green fluorescence in normal-grown roots ( Figure 4O ) , strong TUNEL-positive signals were observed in the XVE089D root cells when the plants were treated with BE ( Figure 4P–Q ) . BE treatment had no obvious effect on the aforementioned histochemical staining in the wt control plants ( Figure S8 ) . These results suggest that NAC089 has the ability to promote PCD in plants . PCD is a genetically controlled process that plays important roles in plant development and responses to abiotic stress or pathogens [51] , [52] . Many PCD regulators that have been well characterized in humans , worms and flies are absent from the Arabidopsis genome , indicating that plants may use different regulators to execute PCD [52] , [53] . To understand how NAC089 regulates plant PCD , we performed microarray ( Agilent 4X44K ) experiments with the BE inducible gene expression system [48] . BE treatment did not affect much of the gene expression in the wt plants as reported by other colleagues [54] ( Figure S9 ) , but up-regulated 1363 probes ( fold change >2 , P<0 . 01 ) in the NAC089D-MYC expressing plants ( XVE089D-13 ) ( Dataset S1 ) . Gene ontology ( GO ) analysis revealed that the most significant GO term among the NAC089D-MYC-regulated genes is the transcription factor activity ( Figure S10 ) , indicating that NAC089 is an important transcriptional regulator . To validate the microarray expression data , 23 genes were selected from the microarray data and their expression was examined by qRT-PCR in two NAC089D-MYC expressing lines . It was found that the expression of these genes was highly induced in both transgenic lines , especially in line XVE089D-13 ( Figure 5 ) . Among the 23 selected genes , 13 of them were up-regulated more than two fold by the prolonged ER stress , especially after 12 hr TM treatment ( Figure S11 ) . The up-regulation of these 13 genes by ER stress was also checked in the wt and NAC089 knock-down plants ( line RNAi089-25 ) . Previously , the BAX inhibitor 1 ( BI-1 ) was reported to be an important modulator of ER stress-mediated PCD in Arabidopsis [31] . The transcription factor WRKY33 , which is required for resistance to necrotrophic pathogens , plays critical roles in autophagy [55] . These two PCD markers along with other cell survival UPR markers were also included in the expression study . It was found that the up-regulation of 6 NAC089D-MYC-regulated genes ( i . e . AT1G69325 , encoding remorin-like protein; AT1G79330 , encoding metacaspase MC5; AT2G46240 , encoding BCL-2-associated athanogene BAG6; AT3G52350 , encoding unknown protein; AT4G30880 , encoding lipid transfer protein; and AT5G39820 , encoding transcription factor NAC094 ) and autophagy-related gene WRKY33 was impaired in the RNAi089-25 plants under ER stress condition comparing to the wt control ( Figure 6A–B ) . These results indicate that NAC089 plays critical roles in regulating these ER-stress-induced genes including several PCD-related genes . ER stress also up-regulates several cell survival UPR marker genes and BI-1 in both the wt and RNAi089-25 plants ( Figure 6B ) , suggesting that NAC089 plays minor role in regulating the expression of these genes . In order to know whether NAC089 directly regulates these downstream genes , chromatin immunoprecipitation ( ChIP ) experiments were carried out with NAC089D-MYC plants ( line XVE089D-13 ) using anti-MYC antibody . It was found that NAC089D-MYC was enriched significantly with fold change greater than 2 at the promoter regions of 7 genes ( i . e . AT1G65240 , AT1G71390 , AT1G79330 , AT2G46240 , AT4G30880 , AT5G39820 and AT5G40010 ) ( Figure 7 ) , indicating that these genes might be the direct targets of NAC089 . Among the 7 NAC089 possible targets , the up-regulation of 3 genes ( i . e . AT1G65240 , encoding aspartyl protease; AT1G71390 , encoding receptor-like protein RLP11 and AT5G40010 , encoding AAA ATPase 1 ) by ER stress was not suppressed in the NAC089 knock-down mutants ( Figure 6A ) , suggesting that other factors may also up-regulate these genes under ER stress condition . We concluded that NAC089 has the ability to regulate some of the UPR downstream genes , including the PCD regulatory genes MC5 , BAG6 and NAC094 , and the autophagy regulatory gene WRKY33 . The function of other NAC089 downstream genes in ER stress-induced PCD needs to be investigated in the future .
The unmitigated ER stress is believed to induce PCD in animals [3] , as well as in plants [31] , [56] . Given that PCD components are not highly conserved between animals and plants [30] , [57] , our knowledge on ER stress-induced PCD in plants is very limited [58] . Previously , BI-1 and IRE1 were reported to be the negative regulators of PCD in plants [31] , [40] . In the current study , a membrane-associated transcription factor NAC089 was identified as an important transcriptional regulator of plant PCD under ER stress condition based on the following evidences: 1 ) NAC089 is up-regulated by UPR regulators bZIP28 and bZIP60 under ER stress condition; 2 ) NAC089 relocates from the ER membrane to the nucleus in response to ER stress; 3 ) Inducible expression of the truncated form of NAC089 induces PCD; 4 ) Partial loss-of-function of NAC089 confers resistance to chronic ER stress with reduced caspase 3/7-like activity; 5 ) NAC089 has transcriptional activity and binds to the promoter of many downstream targets; 6 ) Knock-down NAC089 suppresses the ER-stress-induced expression of several PCD regulators . As in animals , plant development and adaptations to environmental stresses are intimately connected to PCD [59] , [60] . In mammals , PCD is controlled predominately through functionally conserved proteins such as CED9/BCL-2 and BAX , but such genes have not been identified in plants [52] . Interestingly , the heterotrimeric G protein signaling was reported to be involved in ER stress-associated PCD . Null mutants of G beta subunit ( AGB1 ) were more resistant to ER stress than either the wt plants or null mutants of G alpha subunit , but the underlying molecular mechanism was not known yet [61] . On the contrary , Chen and Brandizzi recently reported that the null AGB1 mutants were more sensitive to ER stress [62] . The function of AGB1 in ER stresses response needs to be further clarified . Caspases are cysteine-aspartic proteases that play essential roles in PCD in animals [4] . Plant caspase homologs are not found so far , and the metacaspases were demonstrated to have similar function in plant PCD [53] , [63] , [64] . Caspase-like activity has been detected in plant PCD associated with xylem formation and adaptations to heavy metal stress , pathogen infection , as well as exposure to ultraviolent-C [50] , [65] , [66] , [67] , [68] . In the current study , chronic ER stress induced caspase 3/7-like activity , and such induction was impaired in the NAC089 knock-down plants . Several NAC089 downstream targets including some known PCD regulators were also indentified in the current study . Among them , one metacaspase ( MC5 ) and several other proteases were induced by ER stress , which was suppressed in the NAC089 knock-down plants . BAG ( BCL2-associated athanogene ) family proteins were originally identified as the anti-cell-death protein in mammals [69] . Among the seven animal BAG homologs found in Arabidopsis [70] , overexpression of BAG6 induced PCD in yeast and plants [71] , indicating that BAG6 is a pro-death protein in plants . In the current study , BAG6 was induced by ER stress in the Arabidopsis wt plants , which was impaired in the NAC089 knock-down plants . Previously , the soybean NAC transcription factor NAC6/NAC30 was shown to induce caspase 3-like activity and promote extensive DNA fragmentation when it was overexpressed in soybean protoplasts [33] . Here in the current study , we found that one of the direct targets of NAC089 , NAC094 , is the close-related homolog of soybean NAC6/NAC30 in Arabidopsis . ER stress-induced expression of NAC094 was greatly suppressed in the NAC089 knock-down plants . These results support that NAC089 controls the expression of several PCD-related downstream genes in Arabidopsis under ER stress condition . Interestingly , the autophagy-related gene WRKY33 [55]was also up-regulated by ER stress , which was dependent on NAC089 . Other NAC089 downstream genes such as genes encoding protease and nuclease were also identified in the current study . The identification of NAC089 as a PCD regulator provides more opportunities for further understanding new molecular components involved in plant PCD , especially under ER stress condition . NAC089 is regulated by ER stress at both transcriptional and post-translational levels . At the transcriptional level , NAC089 is up-regulated by ER stress , which is directly controlled by bZIP28 and bZIP60 , two important regulators in plant UPR [9] , [18] , [44] , [72] . At the protein level , NAC089 is an ER membrane-associated transcription factor ( MTF ) and it relocates from the ER to the nucleus under ER stress condition . Interestingly , bZIP28 and bZIP60 are also ER MTFs . bZIP28 is activated through regulated proteolysis . In response to ER stress , bZIP28 relocates from the ER to the Golgi where it is cleaved by two Golgi-resident proteases S1P and S2P , and the C-terminal lumen-facing domain is thought to be responsible for the sensing of ER stress [17] , [18] , [19] , [72] , [73] . The activation mechanism for bZIP60 is unconventional , and the activation of bZIP60 is dependent on the ER membrane-localized IRE1 proteins . Under ER stress conditions , bZIP60 mRNA is spliced by IRE1 , which results in an open reading frame ( ORF ) shift and elimination of the transmembrane domain [9] , [11] , [12] . The N-terminal part of yeast IRE1 is inserted into the ER lumen and plays important role in direct sensing the unfolded proteins in the ER in yeast [74] . Recently , at least 13 MTFs in NAC family are found in Arabidopsis , of which some are activated during development and adaptations to environmental stresses [45] , [75] , [76] , [77] , [78] . However , the activation mechanisms of these NAC MTFs are still largely unknown . The NAC089 mRNA does not have the predicted double stem-loop structure that has been shown to be very important for IRE1 splicing [9] . Furthermore , there is no alternative spliced transcript of NAC089 observed in the ER stressed wt seedlings ( Figure S12 ) , suggesting that NAC089 might be activated in a manner different from bZIP60 . The C-terminal ER lumen facing tail of NAC089 is very short and does not have the canonical S1P cutting site , which implicates that NAC089 might not be proteolytically processed in the same way as bZIP28 . We did not include protease inhibitors in the NAC089 activation experiments because most of the protease inhibitors are not permeable to live plant cells . Further investigation of the activation mechanism of NAC089 will improve our understanding of MTFs in plants . Surprisingly , one rare nucleotide polymorphism caused by natural variation in the Arabidopsis Cvi ecotype results in premature stop and constitutive nuclear localization of NAC089 , in which the C-terminus ( 114 AA ) including the hydrophobic tail is not translated . Although Cvi ecotype is much more sensitive to fructose than Ler ecotype , expressing the Cvi NAC089 suppresses fructose sensitivity in Ler seedlings [79] . The truncated form ( 114 AA deletions ) of NAC089 was found in the nucleus , however , the activation or nuclear relocation of NAC089 in response to fructose treatment is not reported , and how the truncated form of NAC089 represses fructose signaling is not clear . Since the deletion occurred in Cvi was found neither in over 100 Arabidopsis accessions nor in the Arabidopsis Genome 1001 sequence collections , the biological function of NAC089 in fructose signaling other than in the Cvi ecotype is elusive [79] . Recently , it was reported that fructose feeding induced ER stress in mice [80] . It would be interesting to determine whether the high concentration of fructose could also induce ER stress in Arabidopsis plant . Recently , NAC089 was reported to be involved in redox regulation [81] . Besides its effect on redox status , DTT also inhibits disulphide bond formation and therefore promotes protein misfolding . However , TM is a more specific ER stress inducer because of its specific effect on blocking protein N-glycosylation in the ER . In the current study , both TM and DTT treatments were employed to demonstrate the specific role of NAC089 in ER stress response . Our current study has also advanced the understanding of the function of NAC089 in ER-stress-induced plant PCD and the underlying molecular mechanisms . How cells make the cell fate selection between life and death remains enigmatic . In human cells , ER stress activates all the three arms of UPR pathways; each branch has different effect on cell survival or cell death , but attenuation of each branch is different . Switch between cell survival and cell death outputs lies in part in the duration of individual branch activity , which guides the cell toward survival or demise [82] . In plants , except the PERK pathway , the bZIP28 and bZIP60 branches of signaling pathway have been previously discovered to regulate downstream genes involved in promoting cell survival [83] . Knock-outs of both branches in the zip28zip60 double mutant causes high sensitivity to ER stresses and accelerated PCD under prolonged ER stress condition [72] . IRE1A and IRE1B redundantly control the activation of bZIP60 and RIDD; knock-outs of both IRE1s in Arabidopsis also promotes PCD while knock-out of single bZIP60 gene has no PCD phenotype [40] . The ER stress-induced up-regulation of NAC089 is dependent on both bZIP28 and bZIP60 . Different from the zip28zip60 mutant , knock-down NAC089 confers ER stress tolerance and over-expression of the truncated form of NAC089 promotes PCD . These results may not necessarily be controversial . Firstly , bZIP28 and bZIP60 regulate many survival genes whose expressions are almost completely abolished in the zip28zip60 mutant [72] . Lacking the expression of survival genes in the zip28zip60 mutant may lead to the accelerated PCD . Secondly , NAC089 has substantial constitutive expression and the NAC089 pathway may still operate for PCD in the zip28zip60 mutant even without further NAC089 up-regulation . Thirdly , it is possible to have other PCD pathways turned on to execute PCD in the zip28zip60 mutant . Recently , heterotrimeric G protein signaling [61] , vacuolar processing enzyme ( VPE ) -triggered cell death [42] , [84] and IRE1-mediated autophagy [85] pathways were reported to be involved in the ER stress-induced PCD in plants . A hypothetical working model has been emerged from the current study ( Figure 8 ) . When Arabidopsis cells are confronted with ER stress , both bZIP28 and bZIP60 pathways are activated to mitigate the stress by up-regulation of genes involved in protein folding or ERAD to improve survival [1] , [83] . The activated bZIP60 also induces its own transcription [44] and another transcription factor NAC103 [86] to amplify the cell survival signal . The ER-localized IRE1 protects cell through a process called RIDD to reduce the protein folding demand [40] . In the meantime , besides the constitutive high expression of NAC089 under normal condition , both bZIP28 and bZIP60 up-regulate the expression of NAC089 under ER stress condition . Up-regulation of NAC089 mRNA may increase the protein level of the membrane-associated NAC089 precursor . However , nuclear relocation of NAC089 is tightly controlled , in which bZIP28 and bZIP60 may play negative roles . When the ER stress is severe , NAC089 is activated and relocates from the ER to the nucleus , inducing the expression of PCD regulators to promote cell death . It is possible that the stress intensity and/or duration of ER stress might determine the signaling output and the final cell fate . Further understanding on how cells balance the cell survival and cell death effects in UPR is of great fascination .
All Arabidopsis ( Arabidopsis thaliana ) wild-type , T-DNA mutants and transgenic plants in the current study were in the Columbia ( Col-0 ) ecotype background . The double mutant zip28zip60 was made as previously reported [72] . Methods for plant growth were described previously [43] . For short time treatment , different concentrations of TM ( 5 µg/ml ) , DTT ( 2 mM ) or BE ( 10 µM ) were added in the half-strength MS medium unless mentioned in the text . For long time treatment , 10 µM BE or various low concentrations of TM were supplied in the solid growth medium . Root length was measured and emergence rate of true leaves was calculated . Total chlorophylls were extracted from seedlings with 80% ( v/v ) acetone at 4°C overnight and measured from A663 and A646 readings for each sample [87] . All the data in the paper were subjected to Student's t-test or two-way ANOVA ( analysis of variance ) analysis . The coding sequence of NAC089 was amplified with PCR and inserted into pSKM36 after digestion with AscI and SpeI restriction enzymes to produce the vector Pro35S:NAC089 . Modified green fluorescence protein ( mGFP ) tag and 4X MYC tag were amplified and inserted into Pro35S:NAC089 at AscI site to generate the Pro35S:mGFP-NAC089 and Pro35S:MYC-NAC089 constructs , respectively . For RNAi construct preparation , part of the NAC089 gene sequence covering cDNA 651–1150 was inserted into pHANNIBAL in both sense and antisense orientations separated by an intron sequence . The entire RNAi cassette was cut with NotI and inserted into pART27 to make the RNAi expression vector . To express the dominant negative fusion protein ProNAC089:NAC089D-EAR , the sequence encoding EAR motif ( QDLDLELRLGFA ) was synthesized and firstly inserted into pCAMBIA1300; about 1 kb upstream sequence of NAC089 and sequence encoding the truncated form of NAC089 ( NAC089D , aa 1–316 ) were amplified and subsequently inserted . To generate the conditionally overexpression construct [48] , nucleotides encoding NAC089D was amplified and inserted into pER10M . For dual luciferase activity assay , fragment of the NAC089 promoter ( −98 bp to −46 bp relative to the TSS site ) was synthesized and inserted into pGreen0800-II after the 35S minimal promoter was introduced . Constructs expressing bZIP28D and bZIP60S were made as described [9] , [18] . For protein expression in E . coli , the respective sequence of bZIP18D ( aa 1–321 ) or bZIP60T ( aa 87–217 ) was amplified and inserted into pET28 and pET32 , respectively . All the primers were listed in Table S1 and error-free clones were introduced into plants by either transient expression or stable transformation . Total protein was extracted from plants with extraction buffer described previously [17] . Membrane fraction and nuclear fraction were prepared with sucrose gradient centrifuge according to the standard protocol [88] . Proteins were resolved on 8–10% SDS-PAGE gels and visualized by western blotting using antibodies against c-MYC ( Santa Cruz Biotechnology ) , nuclear protein marker histone H3 ( Abcam ) and ER protein marker BiP ( Santa Cruz Biotechnology ) . Caspase-like activity was measured with luminescent assays based on DEVD short peptides with Caspase-Glo 3/7 Assay Kit ( Promega ) . Both caspase 3 and caspase 7 recognize the same DEVD substrate . Briefly , seedlings were harvested after various treatments and total proteins were extracted with liquid nitrogen in a buffer containing 100 mM sodium acetate , pH 5 . 5 , 100 mM NaCl , 1 mM EDTA , and 5 mM DTT . To measure caspase-3/7 activity , 30 µl caspase-3/7 luminogenic substrate ( Z-DEVD-aminoluciferin ) was added to 50 µg protein extracts and incubated at 22°C for 1 hr protected from light . The luminescence of each sample was measured with the Synergy 2 Multi-Mode Microplate Reader ( BioTek ) . For dual-luciferase activity assays [89] , Arabidopsis leaf protoplasts were isolated from 4-week-old soil-grown seedlings and transfected according to a standard protocol [43] with various reporter constructs or cotranstransfected with different effectors . Firefly and renilla luciferase were quantified with Dual-Luciferase Reporter Assay Kit ( Promega ) according to the manufacturer's instructions in the Synergy 2 Multi-Mode Microplate Reader ( BioTek ) . For FDA and DAB staining , seedlings were stained with 2 . 5 µg/ml FDA ( Sigma-Aldrich ) in phosphate-buffered saline for 10 min or 1 mg/ml DAB ( pH 5 . 5 , Sigma-Aldrich ) for 2 hr at room temperature , immersed into boiled ethanol for 10 min according to the standard protocol [31] . For PI and DAPI staining , samples were stained using DAPI ( Sigma-Aldrich ) at 1 µg/ml in 0 . 1% ( v/v ) Triton X-100 for 10 min or PI ( Sigma-Aldrich ) at 10 µg/ml for 1 min , and washed twice with water . For in situ TUNEL staining , roots were stained in microcentrifuge tubes ( 1 . 5 ml ) using the in situ cell death detection kit ( Takara ) according to the manufacturer' instructions . Except DAB staining , which was observed under differential interference contrast ( DIC ) microscopy , other staining , BiFC and subcellular localization of mGFP-NAC089 were visualize with laser confocal fluorescence microscopy ( Zeiss LSM A710 ) . ChIP was performed according to the standard protocols . Briefly about 3 g of 2-week-old XVE089D transgenic seedlings were treated with either 10 µM BE or DMSO ( solvent control ) for 16 hr and fixed with 1 . 0% formaldehyde for 10 min subsequently . Antibodies against c-MYC ( Santa Cruz Biotechnology ) and GST ( IgG control , Abmart ) were used for immunoprecipitation . Protein-A-agarose beads were blocked with salmon sperm DNA and used to pull down the protein-DNA complex . Equal amounts of starting plant material and the ChIP products were used for quantitative PCR . Primers were selected in the promoter regions of each selected gene . DNA levels were calculated relative to TA3 ( AT1G37110 ) using a comparative threshold cycle method . The ChIP experiments were performed 3 times with biological replications and similar results were obtained . For microarray analysis or qRT-PCR , the wt control , XVE089D and NAC089 RNAi plants were grown vertically on agar plates for one week and then transferred to 1/2 MS liquid supplied with 10 µM BE or DMSO or TM for a period of time as noted . Total RNA was extracted and purified according to the manufacturer's instructions [43] . Agilent Arabidopsis gene chips ( 4X44K ) were used to compare the gene expression profiles with three independent replications . P-values were calculated and used to select the genes that are up-regulated by NAC089D-MYC ( cut-off: P<0 . 01 , fold change >2 ) . Microarray data from this article can be found in ArrayExpress under the accession number E-MTAB-1377 . Quantitative PCR and RT-PCR were routinely conducted [43] and all the primers are listed in Table S1 . GO analysis was performed with AgriGO ( http://bioinfo . cau . edu . cn/agriGO/analysis . php ) . EMSA was performed using a LightShift Chemiluminescent EMSA Kit ( Pierce ) , according to the manufacturer's protocols [43] . Briefly , each 20 µL binding reaction contained 2 µl binding buffer , 0 . 3 µl Poly ( dI-dC ) , 4 µg purified protein , 0 . 83 µmol biotin-labeled probe or certain amount of unlabeled probe as the competitor . The pNAC089 wt or pNAC089M1-M3 probes were created by annealing together complementing oligonucleotides and biotinylated with a labeling kit ( Pierce ) . His-tagged bZIP28D or Trx-His-tagged bZIP60T proteins were expressed in E . coli strain BL21 and purified with Ni-NTA agarose beads ( Qiagen ) . The binding reactions were allowed to incubate on ice for 1 hr and run on a 5% polyacrylamide mini-gel ( 37 . 5∶1 acrylamide-bisacrylamide in 0 . 5× Tris-Borate-EDTA ( TBE ) containing 3% glycerol ) . The complex was transferred to a membrane and developed according to a standard protocol . | Protein folding is fundamentally important for development and responses to environmental stresses in eukaryotes . When excess misfolded proteins are accumulated in the endoplasmic reticulum ( ER ) , the unfolded protein response ( UPR ) is triggered to promote cell survival through optimizing protein folding , and also promote programmed cell death ( PCD ) when the stress is severe . However , the link from ER-stress-sensing to PCD is largely unknown . Here , we report the identification of one membrane-associated transcription factor NAC089 as an important regulator of ER stress-induced PCD in plants . We have established a previously unrecognized molecular connection between ER stress sensors and PCD regulators . We have shown that organelle-to-organelle translocation of a transcription factor is important for its function in transcriptional regulation . Our results have provided novel insights into the molecular mechanisms of PCD in plants , especially under ER stress conditions . | [
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... | 2014 | The Membrane-Associated Transcription Factor NAC089 Controls ER-Stress-Induced Programmed Cell Death in Plants |
Meiotic cytokinesis influences the fertility and ploidy of gametes . However , limited information is available on the genetic control of meiotic cytokinesis in plants . Here , we identified a rice mutant with low male fertility , defective callose in meiosis 1 ( dcm1 ) . The pollen grains of dcm1 are proved to be defective in exine formation . Meiotic cytokinesis is disrupted in dcm1 , resulting in disordered spindle orientation during meiosis II and formation of pollen grains with varied size and DNA content . We demonstrated that meiotic cytokinesis defect in dcm1 is caused by prematurely dissolution of callosic plates . Furthermore , peripheral callose surrounding the dcm1 pollen mother cells ( PMCs ) also disappeared untimely around pachytene . The DCM1 protein contains five tandem CCCH motifs and interacts with nuclear poly ( A ) binding proteins ( PABNs ) in nuclear speckles . The expression profiles of genes related to callose synthesis and degradation are significantly modified in dcm1 . Together , we propose that DCM1 plays an essential role in male meiotic cytokinesis by preserving callose from prematurely dissolution in rice .
Cytokinesis is the process by which the two daughter nuclei resulting from nuclear division are physically separated by the establishment of a cell plate and/or cell wall . In animal and yeast dividing cells , an actomyosin ring contracts centripetally to separate the daughter cells [1] . In higher plants , however , cytokinesis involves the formation of a cell plate through the fusion of vesicles at its centrifugally expanding periphery [2 , 3] . Meiosis is a specialized type of cell division consisting of one round of DNA replication and two rounds of nuclear division [4] . In plant male meiosis , two different types of cell plate formation , namely successive and simultaneous cytokinesis , are documented . Each caryokinesis is directly followed by a cytokinetic event in successive cytokinesis ( typically in monocots ) , while the simultaneous cytokinesis occurs only when both nuclear division are finalized ( typically in dicots ) [5] . Callose , a β-1 , 3-glucan polymer with β-1 , 6-branches , plays important roles in response to biotic and abiotic stresses , as well as in a variety of developmental processes , especially in cell plate formation and reproductive development in plants [6 , 7] . During mitosis , callose is deposited at the cell plate during the tubular network stage and is later replaced by cellulose [8] . Mutation of MASSUE/AtGSL8 in Arabidopsis , which encodes a putative callose synthase , leads to seedling lethality and a striking cytokinesis-defective phenotype [9 , 10] . Moreover , enlarged tetrad 2 , which harbors a splice site mutation of AtGSL8 , undergoes premeiotic endomitosis due to cytokinetic defects in flowers [11] . These findings indicate that callose is required for plant mitotic cytokinesis . For pollen mother cells ( PMCs ) , callose is placed both at the division site as well as at the outer cell wall . The peripheral callose may prevent PMCs fusion and cohesion , and appear to participate in the formation of the primexine by providing a mold for pollen exine construction during microsporogenesis [12] . Callose synthase 5 ( CalS5 ) , also known as glucan synthase-like 2 ( AtGSL2 ) , is responsible for the synthesis of callose deposited at the primary cell wall of PMCs , tetrads and microspores , and is essential for exine formation of pollen wall in Arabidopsis [12 , 13] . Mutation of OsGSL5 , the rice homolog of AtGSL2 , also results in defective callose deposition and abnormal pollen exine structure formation [14] . AtGSL1 and AtGSL5 , two closely related genes in Arabidopsis , are required for synthesis of the interstitial callose that normally separates microspores . The enlarged pollen grains and multi-nucleate microspores in gsl1-1/+ gsl5-2/gsl5-3 suggest that callose is required for meiotic cytokinesis [15] . In Arabidopsis mpk4 , which has a male-specific meiotic cytokinesis defect , PMCs fail to form normal intersporal callose walls after meiosis , and thus cannot complete meiotic cytokinesis [16] . Moreover , TETRASPORE/STUD , a kinesin required for male meiotic cytokinesis in Arabidopsis [17 , 18] , was reported to function upstream of MPK4 in a putative cascade pathway . CCCH zinc finger proteins are characterized by a zinc finger motif consisting of three cysteines and one histidine coordinated by a zinc cation and have been identified in Arabidopsis , rice [19] and other plant species [20–22] . Recent studies have revealed that CCCH proteins participate in the regulation of many developmental processes and environmental responses [23 , 24] . Although the molecular functions have not been fully characterized , multiple lines of evidence suggest that proteins with CCCH motifs can regulate gene expression through modulation of RNA metabolism . For example , OsTZF1 binds to U-rich sequences in the 3’UTR of two potential target mRNAs in vitro [25] , and AtTZF1 can trigger the degradation of AU-rich elements ( ARE ) -containing mRNA in vivo [26] . Here , we report a novel protein , DCM1 ( Defective Callose in Meiosis 1 ) , is required for meiotic cytokinesis in rice . The meiotic cytokinesis defect is caused by the premature dissolution of callosic plates during both telophase I and telophase II . DCM1 interacts with two nuclear poly ( A ) binding proteins , which are members of the polyadenylation factor family , independently of the conserved tandem CCCH domain . Our study provides insight into the mechanism underlying meiotic cytokinesis in monocots .
We isolated the defective callose in meiosis 1 ( dcm1 ) mutant in a screen of our rice mutant libraries with reduced fertility . The dcm1 mutant exhibited no defects in vegetative growth but was nearly sterile with panicles occasionally bearing a few seeds ( Fig 1A ) . When stained with I2-KI , most pollen grains ( 85 . 1% , n = 766 ) were less stained or even shrunken compared with wild-type pollens , indicating the dramatically reduced viability of male gametes . The viability of dcm1 female gametes was evaluated by pollinating its flowers with wild-type pollen grains . The resulting normal seed setting rate indicated that female fertility was not affected in dcm1 ( S1 Table ) . To investigate the cellular defects of dcm1 , we examined transverse sections of both wild-type and mutant anthers at different developmental stages ( Fig 1B ) . During early meiosis , four layers of anther somatic cells ( epidermis , endothecium , middle layer and tapetum ) enclosed the anther locule , where PMCs contacted with the tapetal layer . After meiosis , the PMCs gave rise to microspores . Among these stages , no significant differences were observed between wild-type and dcm1 anthers . During the bicellular pollen stage , wild-type pollens were vacuolated with an increased volume and tapetal cells were deeply stained with toluidine blue . By contrast , tapetal cells of dcm1 were larger than normal with reduced staining and started to degenerate at this stage . During the mature pollen stage , all wild-type pollen grains were round and the inner layers of the anther degenerated . However , besides round pollen grains , shrunken empty pollen grains ( 75% , n = 56 ) were observed in dcm1 . In addition , the middle layer , which was not visible in mature wild-type anther , was swollen in the mutant . To obtain a more detailed understanding of the abnormalities of the dcm1 pollen grains , mature pollen was visualized using transmission electron microscopy ( TEM ) ( Fig 1C ) . Ubisch bodies , specialized structures forming along the inner surface of the tapetum , seemed to be normal in dcm1 . The wild-type pollen grains had completed pollen exine deposition and exhibited distinctive sublayers , including sexine and nexine . However , only one layer , which seemed to be uniform in component , was observed in dcm1 pollen grains ( n = 17 ) , indicating that the formation of pollen exine was disrupted in dcm1 . The size of the pollen grains of dcm1 seemed to be nonuniform ( Fig 1A and Fig 1B ) . To confirm this , we observed the mature pollen grains of wild type and dcm1 using scanning electron microscope ( SEM ) . The wild-type pollen grains were round and uniform in size ( n = 182 ) . However , the pollen grains of the mutant varied in size , with some pollen grains ( 41 . 7% , n = 206 ) obviously larger than the wild type ( Fig 2A ) . As the pollen size is always correlated with its ploidy [27] , the nuclear DNA of pollen grains was stained with 4’ , 6-diamino-phenylindole ( DAPI ) ( Fig 2B ) . In the 238 checked pollen grains of dcm1 , 153 normal-sized dcm1 pollen grains ( 64 . 3% ) had a wild-type-like nuclei configuration and staining intensity . However , 78 enlarged pollen grains ( 32 . 8% ) with more intensively stained and enlarged nuclei were observed in dcm1 , suggesting increased gametophytic DNA content in the mutant . Doubled nuclei were also observed in seven larger pollen grains ( 2 . 9% ) among the 238 checked pollen grains of dcm1 . PMCs give rise to a group of four haploid spores through meiosis . Each uninucleate microspore undergoes an asymmetric mitotic division to produce the vegetative cell and the generative cell . The generative nucleus completes the second mitotic division , developing into trinucleate pollen . To clarify the time when the defect of dcm1 occurred , before or after the microspore stage , we observed the microspores of both wild type and dcm1 ( S1 Fig ) . Most microspores ( 98% , n = 200 ) of wild type were uniform in size , with the diameter ranging from 17 μm to 23 μm . However , the sizes of microspores in dcm1 were variable ( n = 277 ) , ranging from 15 μm to 47 μm . We then checked the meiotic products of wild type and dcm1 by acetocarmine dyeing ( Fig 2C ) . In wild-type tetrad , four microspores were held together in a tetragonal shape . However , the arrangement of meiotic products in dcm1 varied . In 95 checked PMCs of dcm1 , 18 PMCs ( 18 . 9% ) divided into two daughter cells and each cell contained two nuclei . 43 PMCs ( 45 . 3% ) divided into three daughter cells with one nucleus in each small cell and two nuclei in the big one . In addition , four daughter cells , each of which had one nucleus ( 14 . 7% ) , and one daughter cell with four nuclei ( 21 . 1% ) were also observed in dcm1 . Spindle organization during telophase II was investigated by performing immunolocalization with α-tubulin antibody ( Fig 2C ) . In wild-type PMCs , two sets of spindles were roughly parallel to each other , leading to the formation of four well separated poles at telophase II . However , most checked PMCs ( n = 67 ) in dcm1 had fused spindles ( 13 . 4% ) , tripolar spindles ( 59 . 7% ) or linear spindles ( 7 . 5% ) . Among them , spindles in seven dcm1 PMCs ( 10 . 4% ) linked every two nuclei when they were adjacent to each other , which was reminiscent of the spindle arrangement during simultaneous meiotic cytokinesis . The abnormal spindle arrangements observed in dcm1 during telophase II indicated that meiotic cytokinesis might be defective , leaving the spindle physically unseparated and free to move . As a monocot , rice undergoes successive cytokinesis during meiosis , in which the dyad is generated after meiosis I and the tetrad is formed after meiosis II . In the wild-type PMCs , homologous chromosomes segregated and were pulled to opposite poles during meiosis I ( Fig 3 ) . A cell plate was established that insulated the two chromatin groups and cytoplasm at telophase I . The cell plate persisted through metaphase II when the chromosomes aligned at the equatorial plate and anaphase II when the chromatids were pulled toward opposite poles . Two new cell plates , both perpendicular to the first one , were formed during telophase II and tetrads were then produced . The meiotic stages from metaphase I to anaphase I in dcm1 were roughly the same as those in wild type ( Fig 3 ) . However , no cell plate was formed in most PMCs ( 94 . 4% , n = 72 ) during telophase I . The two equatorial plates and spindles were not parallel to each other in dcm1 during metaphase II . During anaphase II , the separating sister chromatids were pulled in varied orientations . Without the cell plate , meiosis II took place in a single cell . We also observed defects in the second meiotic cytokinesis in the mutant , which gave rise to microspores with four nuclei . Previous studies have demonstrated that cell plate formation in successive-type PMCs follows a similar pattern as somatic cytokinesis , in which callose plays an essential roles [5 , 9 , 10] . So , we monitored callosic plate formation in wild-type and dcm1 PMCs by combined DAPI and aniline blue staining . Aniline blue binds to callose and emits fluorescence under ultraviolet light . In wild-type anaphase I PMCs , callose was absent between the two separating chromosome mass . When the chromosomes reached the opposite poles during early telophase I , only weak callose signals appeared in the center of the PMCs ( Fig 4A ) . As chromosomes decondensed into chromatin during telophase I , a callosic plate was formed . At the end of meiosis , two additional callosic plates were formed and four chromatin masses were separated . In dcm1 , aberrance was first observed at telophase I , which corresponds to the time when the callosic plate was formed in wild-type PMCs . Although intact callosic plates were occasionally observed in a few cells ( 3 . 6% , n = 138 ) , incomplete callosic plates with an intact periphery and a broken inner plane were always observed ( Fig 4A ) . During meiosis in rice , callosic plate formation occurs in an outward direction ( S2 Fig ) [28] , indicating that the callosic plates with broken inner planes in dcm1 were in the process of dissolution , not formation . After the complete dissolution of the inner plane , only a callose ring was observed between the chromatin groups . This callose ring , which was presented as two dot-like callose signals in anther transverse sections ( 88 . 2% , n = 51 ) , was closely associated with the periphery of the parental PMCs ( Fig 4B ) . Callosic plates with broken inner planes and callose rings were also observed during telophase II , accompanied by a disordered distribution of nuclei . In wild type PMCs , callosic plates persisted to the tetrad stage . Our observations suggested that prematurely dissolution of callosic plates occurred during both telophase I and telophase II in dcm1 . During rice meiosis , callose is deposited both at the cell plate and around the PMCs . To determine whether the callose surrounding the PMCs was also disrupted in the mutant , we compared the callose pattern in wild type and dcm1 by staining anther transverse sections ( Fig 4B ) . At preleptotene stage , the very beginning of meiosis , callose was detectable neither in wild type nor in dcm1 . In wild-type PMCs , callose first appeared in the center of the locule at leptotene and extended to form an intact callose wall at pachytene . However , despite the normal callose deposition at leptotene , no callose signal around dcm1 PMCs ( n = 72 ) was observed at pachytene and later meiotic stages . These observations indicated that peripheral callose is also under untimely dissolution in dcm1 PMCs . Immunogold assay using antibody to β-1 , 3-glucan showed similar callose defect in dcm1 ( Fig 5 ) . In wild-type dyad , callose signals were observed both in cell plate and cell wall ( Fig 5A ) . In the PMCs of dcm1 during corresponding stage , cell plate was not formed and callose located in cell wall was almost completely lost ( Fig 5B and 5C ) . Corresponding to the dot-like callose signals in semi-thin transverse sections ( Fig 4B ) , immunogold labeling particles were only observed in the region of cell wall to which the hypothetical cell plate attached in dcm1 . The progenies of a dcm1 heterozygous plant segregated for fertile and sterile phenotypes in a ratio of approximately 3:1 ( 154:48 , χ2 = 0 . 165 , P>0 . 05 ) , indicating that the mutant phenotype was caused by a single recessive nuclear gene mutation . Through map-based cloning ( S3 Fig ) and next-generation sequencing , a nucleotide deletion in the first exon of LOC4341610 was found . We knocked out this gene in wild-type plants using a CRISPR/CAS9 gene editing approach . The resulting transgenic lines displayed defective meiotic cytokinesis , mimicking the phenotype of dcm1 ( S4 Fig ) . This confirmed that the mutant phenotype is indeed caused by LOC4341610 dysfunction . We obtained a 6807 bp full-length cDNA of DCM1 by performing rapid amplification of cDNA ends ( RACE ) ( Fig 6A ) . The open reading frame ( ORF ) is 6207 bp in length , and the deduced protein contains 2068 amino acids . The DCM1 protein contains five tandem CCCH type zinc finger motifs at its C terminus ( Fig 6B ) . Phylogenetic analysis showed that DCM1 is more closely related to its plant homologs , which constitute an isolated branch in the phylogenetic tree ( Fig 6C ) . Multiple sequence alignments of the tandem CCCH domain from different species revealed that this domain is conserved among different kingdoms ( Fig 6D ) . The expression profile of DCM1 in different organs was determined by RT-PCR analysis . DCM1 transcripts were detected in all tested organs of wild type and young panicle of dcm1 ( Fig 7A ) . In transgenic plants carrying the pDCM1::GUS construct , GUS signals were detectable in the anther ( Fig 7B ) . To more precisely determine the spatial and temporal patterns of DCM1 expression in the anthers , we performed RNA in situ hybridization on wild-type anther sections . No DCM1 expression was detected in sporogenous cells in the anther during the premeiotic stage ( Fig 7C ) . At the stage when callose began to deposit around the PMCs , DCM1 was specifically expressed in PMCs that adhered to each other . DCM1 expression was also observed in the tapetum during later stages . To identify potential interacting partners of DCM1 , we screened a rice anther cDNA library using the yeast two-hybrid system . The full-length DCM1 protein can autonomously activate the reporter gene in the absence of a prey protein and thus cannot be used as bait . Therefore , we split the DCM1 protein into two parts and tested their autoactivation activity separately . We found that the N terminus ( 1–1293 ) is responsible for autoactivation ( Fig 8A ) . Therefore , the C terminus of DCM1 from the amino acid 1294 to the end was used as the bait . From this screen , we identified the two nuclear poly ( A ) binding proteins ( OsPABNs ) in rice and named them OsPABN1 ( LOC_Os02g52140 ) and OsPABN2 ( LOC_Os06g11620 ) ( 32 . 7% of sequenced positive colonies ) , respectively ( Fig 8B ) . OsPABN1 and OsPABN2 are homologs of human poly ( A ) binding protein nuclear 1 ( PABPN1 ) and members of the polyadenylation factor family , which are required for mRNA polyadenylation . OsPABN1 and OsPABN2 share high sequence similarity ( 77% identity ) and both contain an RNA recognition motif ( RRM ) in the central region . The clone with shortest sequence identified in this screen corresponds to a peptide ranging from the 85th amino acid to the C terminal end of OsPABN1 , indicating that the coiled-coil domain is not necessary for its interaction with DCM1 . We conducted yeast two-hybrid assays to verify these interactions . Transformants with DCM1-C and OsPABN1 , OsPABN2 grew on QDO/X/A media , confirming the interactions between them ( Fig 8C ) . To clarify whether the tandem CCCH domain of DCM1 is required for its interaction with the OsPABNs , we split the DCM1-C into three parts , DCM1-D ( 1294–1563 ) , DCM1-E ( 1557–1772 ) , and DCM1-F ( 1773–2068 ) and tested their interactions with OsPABNs . The results showed that DCM1-D interacts with OsPABNs , while DCM1-E and DCM1-F , which contain the coiled-coil domain and the tandem CCCH domain , respectively , do not . We also verified the interaction between DCM1 and the OsPABNs using the bimolecular fluorescence complementation ( BiFC ) assay ( Fig 8E ) . Interactions between DCM1 and the two OsPABNs reconstituted the cyan fluorescent protein ( CFP ) in rice protoplasts and the CFP signal was observed in nuclear speckles . Moreover , OsPABN1 and OsPABN2 interact with themselves and between each other ( Fig 8D and 8E ) . The BiFC results indicate that these interactions also take place primarily in the nuclear speckles . Based on the observation of prematurely dissolution of callose in the dcm1 PMCs and DCM1 might be involved in mRNA metabolism , we examined the expression profiles of genes involved in callose metabolism in rice anthers during meiosis ( Fig 9 ) . There are ten predicted callose synthase genes ( OsGSL1-OsGSL10 ) in the rice genome [29] . Among them , only OsGSL5 and OsGSL8 have been evaluated for their respective functions in male fertility and ovary expansion [14 , 30] . Compared with wild type , the expression level of OsGSL2 and OsGSL10 increased by 1 . 49- and 1 . 97-fold , respectively , in dcm1 ( Fig 9A ) . However , the expression level of OsGSL3 and OsGSL9 in dcm1 decreased to 58 . 2% and 72 . 6% of that in wild type , respectively . OsGSL2 and OsGSL3 are closely related to AtGSL1 and AtGSL5 , which synthesize the interstitial callose during meiosis in Arabidopsis [15] . No significant changes in expression level between wild type and dcm1 were observed for the other OsGSL genes . Osg1 encodes a rice β-1 , 3-glucanase that was reported to degrade callose during pollen development [31] . The expression level of this gene in dcm1 increased to 1 . 30-fold of that in wild type ( Fig 9B ) . The A6 gene , which encodes an O-Glycosyl hydrolases family 17 protein , and its positive regulator AtMYB80 were reported to affect callose dissolution in Arabidopsis [32 , 33] . The expression patterns of their homologs in rice , OsA6 and OsMYB80 , were examined . In the mutant , the expression levels of OsA6 increased to 1 . 28-fold and OsMYB80 increased to 2 . 89-fold of wild type . These results suggested that callose degradation process might be altered in dcm1 . No significant difference in the expression of UGP1 , an UDP-glucose pyrophosphorylase gene that is essential for callose deposition [34] , was observed between wild type and dcm1 . We also evaluated the expression level of genes related to exine formation ( Fig 9C ) . OsABCG15 encodes an ATP-binding cassette transporter protein that is essential for pollen exine development in rice [35] . The expression level of OsABCG15 in dcm1 reduced to 13 . 3% of that in wild type . A fatty acyl carrier protein reductase , DPW , plays a role in the formation of regular exine [36] . The expression level of DPW in the mutant reduced to 29 . 6% of that in wild type . These results suggested that exine development process is affected in dcm1 . We also compared the expression level of genes related to meiosis . However , no significant differences in the expression of these genes , including OsSPO11-1 , OsCOM1 , OsRAD17 , OsDMC1 , OsRAD51 , OsMER3 and OsREC8 , were observed between wild type and dcm1 ( S5 Fig ) .
The deposition of callose during mitotic and meiotic cytokinesis in plants has been meticulously analyzed by electron microscopy [39 , 40] . It is possible that the membrane network serves as a trigger for the induction of callose accumulation , since it only appears directly at the cell plate and not in the secretory vesicles en route to the division plane [41] . Samuels et al . ( 1995 ) postulated that callose helps to mechanically stabilize the early delicate membrane networks of forming cell plates in tobacco , and to create a rapid spreading force that widens the tubules and converts them into plate-like structures . However , Thiele et al . ( 2009 ) revealed that the cell-plate membrane compartment forms and expands , seemingly as far as the parental wall , prior to the appearance of callose . Based on characterization of the gsl8 mutant in Arabidopsis , they speculated that callose might be required for inserting the nascent crosswall at the division site during mitosis . Callose is only a transient structure during somatic cytokinesis that is replaced by cellulose during the final step . However , this is not the case in successive-type meiotic cytokinesis , which is typically observed in monocots PMCs . Callosic plates are formed after both the first and the second caryokinesis events during meiosis . The callosic plate is preserved when the first cell division is completed and persists through the second cell division . Whether other polysaccharides , such as cellulose and pectin , are deposit on the cell plate during meiotic cytokinesis is still uncertain . It is possible that the callosic plate is the only physical barrier insulating the cytoplasm during meiotic cytokinesis . The most prominent feature of DCM1 is the five tandem CCCH domain at its C-terminus , which is conserved between different kingdoms . The homolog of DCM1 in Arabidopsis was named SOP1 ( SUPPRESSOR OF PAS2 1 ) , the mutation of which suppressed the developmental defects of a splicing-defective allele of PASTICCINO2 [42] . Red5 , the homolog of DCM1 in fission yeast , is essential for efficient elimination of specific meiotic mRNAs during vegetative growth and the proper splicing of meiotic genes [43–45] , indicating that DCM1 might be involved in mRNA processing or elimination . OsPABN1 and OsPABN2 , the two nuclear poly A binding proteins in rice , were identified as interacting partners of DCM1 . In fission yeast , Red5 was co-immunoprecipitated with Pab2 , the orthologue of OsPABN1 and OsPABN2 [43 , 46] . In addition , dZC3H3 , the homolog of DCM1 in flies , also associates with PABP2 [47] . Recently , the direct interaction between Red5 and Pab2 was detected in a proteome-wide interactome study [48] . Therefore , the interactions between proteins containing five tandem CCCH domain and nuclear poly ( A ) binding proteins might be conserved in fungi , animals and plants . Studies of DCM1 homologs in the other species mentioned above indicate that they are involved in nuclear exosome functions , and RNA degradation by the exosome is the main pathway for the removal of unwanted RNA in all kingdoms [49] . Moreover , studies in fission yeast have demonstrated that Pab2 and the nuclear exosome subunit , Rrp6 , are the main factors involved in a polyadenylation-dependent pre-mRNA degradation pathway , and inefficient splicing is important to dictate susceptibility to this process [50] . The retained PAS2 transcript in sop1 harbors a nucleotide substitution before a GC splicing site that mimics a pre-mRNA with low splicing efficiency [42] , indicating that the tandem CCCH zinc finger protein and the poly ( A ) binding protein could be involved in conserved biological processes across species . During meiosis , callose metabolism must be precisely controlled to ensure the completion of meiotic cytokinesis by its deposition and release of microspores by its dissolution . In dcm1 , callose around PMCs was deposited normally during leptotene and disappeared during pachytene ( Fig 4B ) . Similarly , prematurely dissolution of callosic plates occurred during telophase I and telophase II in the mutant . It seems that the dynamic changes in callose ( deposition and dissolution ) in dcm1 reflect the antagonism between callose synthesis and degradation . We speculate that the defective callose metabolism in dcm1 is caused by unwanted RNAs that are eliminated in the presence of DCM1 . In Arabidopsis , the expression of some callose-related genes is affected by the mutation of CALLOSE DEFECTIVE MICROSPORE1 ( CDM1 ) , a gene encoding a tandem CCCH-type zinc finger protein [51] . CalS5 , the Arabidopsis gene responsible for callose deposition surrounding the PMCs , tetrads and microspores , is regulated in a post-transcription manner [52] . We checked the expression level , splicing and polyadenylation site of OsGSL5 gene and no significant differences were found between dcm1 and wild type . Besides Osg1 , whose function was demonstrated by RNA interference assay [31] , few callase genes ( encoding β-1 , 3-glucanase ) have been functionally characterized in rice meiosis . Thus , further studies to identify the mRNA targets of DCM1 will help to uncover the mechanisms by which DCM1 regulates callose metabolism during rice meiosis .
The dcm1 mutant was isolated from a collection of mutants induced by 60Co-Ƴ-ray irradiation on indica rice ( Oryza sativa ) variety , Guangluai 4 . The variety Guangluai 4 was used as the wild type in all experiments . All plants were grown in the paddy field . When a line segregated 1:3 for sterile and fertile plants , the fertile plants was selected to cross with the japonica rice variety Wuyunjing 8 . Using the sterile plants that segregated in the F2 , F3 and F4 population ( 299 plants in total ) , the mutated gene was mapped to a 138 . 5kb interval between markers C6-25 . 86 and C6-26 . 003 . Based on the MSU Rice Genome Annotation Project Database and Resource ( http://rice . plantbiology . msu . edu/ ) , there are 23 predicted genes in this region and 1bp deletion in the gene LOC4341610 was detected between the mutant and wild type by sequencing . Indel ( insertion-deletion ) markers used for mapping were designed based on the sequence differences between indica variety 9311 and japonica variety Nipponbare according to the data published ( http://www . ncbi . nlm . nih . gov ) . Primers used were listed in the supporting information ( S2 Table ) . The CRISPR/Cas9 gene editing approach in rice was performed according to a protocol described previously [53] . Target sequences ( TTGGATTTGACTTTGCTCTT ) were selected according to the following criterions: ( 1 ) Sequence located in the exon . ( 2 ) Followed by the PAM ( NGG ) . ( 3 ) Be unique among the genome to avoid multiple knock out sites . Designed primer were incubated at 100°C for 5 min and subcloned into vector pC1300-Cas9-1gRNA with a hygromycin resistance marker . The resulting constructs were transformed into Agrobacterium tumefaciens EHA105 and then into calli of Yandao 8 , a japonica variety . T0 plants were genotyped to verify whether the editing site was modified . Target sequences used in this study were listed in the supporting information ( S2 Table ) . Total RNA extraction was conducted using the TRIzol reagent ( Invitrogen ) , as described by the supplier , from rice young panicle . Reverse transcription was performed using SuperScript III First-Strand Synthesis System for RT-PCR kit ( Invitrogen ) . The cDNA was amplified using the 5’and 3’-Full RACE kits ( TaKaRa ) . PCR using primers CDS-F and CDS-R was performed to amplify the open reading frame . The sequences were sequenced and then spliced together to obtain the full-length cDNA . Primers mentioned above were listed in the supporting information ( S2 Table ) . Real-time PCR analysis was performed using the Bio-Rad CFX96 real-time PCR instrument and Hieff qPCR SYBR Green Master Mix ( No Rox Plus ) ( YEASEN ) . All PCR experiments were conducted using 40 cycles of 95°C for 10 s , 60°C for 30 s . All reactions were performed in triplicate , with Ubiqutin as the normalized reference gene for all comparisons . The primers for qRT-PCR were listed in S2 Table . A 2 . 4Kb upstream region of the DCM1 gene was amplified and cloned into pCAMBIA1301 . The construct was introduced into the japonica rice variety Nipponbare . Spikelets at different developmental stages from transgenic lines were incubated with X-Gluc ( 5-bromo-4-chloro-3-indolyl-β-D-glucuronide ) solution . The spikelets were cleared in 75% ( v/v ) ethanol and photographed with a stereoscope . For in situ hybridization , sense and antisense probes were synthesized with T7 RNA polymerase using the digoxigenin RNA labeling kit ( Roche ) . Tissue fixation and hybridization were performed as previously described [54] . Primers used were listed in S2 Table . Rice anthers at various developmental stages were fixed in 4% paraformaldehyde overnight . The samples were washed in PBS , dehydrated in a graded ethanol series , and embedded in Technovit 7100 resin ( Heraeus Kulzer ) . Microtome sections ( 4μm thick ) were stained with 0 . 25% toluidine blue to stain the cells and 0 . 1% aniline blue to stain the callose , respectively . The slides were photographed with an Olympus BX51 microscope and a digital camera . DAPI staining of mature pollen was performed according to Liu [55] . Young panicles were fixed in Carnoy Solution for at least 24 h at room temperature . Anthers at the proper developmental stage were then squashed in acetocarmine . Meiotic chromosome preparation and immunofluorescence analysis were performed as previously described [56] . Aniline blue ( 0 . 1% in PBS ) was applied to slides with DAPI to co-stain the callose and DNA . Images were captured under a Zeiss A2 fluorescence microscope with a micro CCD camera . Immunogold assay was conducted using antibody to β-1 , 3-glucan at dilution of 1:100 ( Biosupplies ) . The secondary antibody is goat anti-mouse IgG-15 nm gold ( Abcam ) at dilution of 1:20 . Total RNA was extracted from wild-type anthers during meiosis . Highly purified and intact mRNA was isolated using mRNA Purification Kit ( Life technology , NO . 61006 ) . cDNA library construction was performed using Make Your Own Mate & Plate Library System ( Clontech NO . 630490 ) according to the manufacturer’s instructions . The Y2H assay was conducted with Yeast Transformation System 2 ( Clontech NO . 630439 ) . The yeast strain Y2HGOLD was used for library screening and Y2H assays . All primers used were listed in S2 Table . The BiFC assay was performed as previously described [57] . The full-length coding sequences of DCM1 , OsPABN1 and OsPABN2 were subcloned into vector pSCYNE ( R ) and pSCYCE ( R ) , respectively . The plasmid pairs were co-transfected into protoplasts of young rice seedling by 40% PEG ( polyethylene glycol ) . Transfected protoplasts were incubated in the dark at 28°C overnight , and observed using a laser scanning confocal microscopy . Primers used were listed in S2 Table . | Meiosis comprises two successive cell divisions after a single S phase , generating four haploid products . Meiotic caryokinesis ( nuclear division ) has been extensively studied in many organisms , while mechanisms underlying meiotic cytokinesis remain elusive . Here , we identified a novel CCCH-tandem zinc finger protein DCM1 that prevent the premature dissolution of callose both around the PMCs and at the dividing site ( callosic plates ) . Loss of the callosic plates disrupts the meiotic cytokinesis , leading to the random distribution of spindles during meiosis II and aberrant meiotic products . DCM1 interacts with the two rice poly ( A ) binding proteins , independently of the conserved CCCH domain . Moreover , DCM1 coordinates the expression profiles of genes related to callose synthesis and degradation . We suspect monocots and dicots may adopt distinct meiotic cytokinesis patterns during male gamete generation . | [
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... | 2018 | The zinc finger protein DCM1 is required for male meiotic cytokinesis by preserving callose in rice |
Variants in the growth factor receptor-bound protein 10 ( GRB10 ) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes ( T2D ) if inherited from the father , but inexplicably reduced fasting glucose when inherited from the mother . GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues . GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion , which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion . Together , these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis . The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father .
Type 2 diabetes ( T2D ) is a multifactorial polygenic disease , in which genes interact with environmental and genetic factors to unmask the disease . To date , genome-wide association studies ( GWAS ) have identified more than 65 variants increasing risk of T2D [1]–[8] . Many of the identified variants seem to influence the capacity of β-cells to cope with increased insulin demands imposed by insulin resistance [9] , [10] . Despite this , no GWAS to date has explored the extent to which common genetic variants influence dynamic measures of insulin secretion and action . Therefore , we have here performed the first large-scale meta-analysis for glucose-stimulated insulin secretion ( GSIS ) during an oral glucose tolerance test ( OGTT ) . Association analyses included a GWAS-based discovery stage and a replication stage using a custom-designed iSelect CardioMetabochip array containing 93 , 896 SNPs overlapping with the discovery GWAS imputed from the HapMap2 reference panel in up to 10 , 831 non-diabetic individuals . We identified variants in the growth factor receptor-bound protein 10 ( GRB10 ) gene ( NCBI Gene ID: 2887 ) to be associated with impaired β-cell function at a genome-wide significant level ( p<5×10−8 ) . Since GRB10 has been shown to have parent-of-origin specific effects on expression in various tissues [11] , [12] , we investigated the transmission patterns of the risk alleles and their effects on insulin and glucose levels during an OGTT , risk for T2D , expression of GRB10 and methylation status in human pancreatic islets , as well as evaluated the effects on islet function through disruption of GRB10 in human pancreatic islets .
We conducted a meta-analysis for dynamic measures of insulin response to glucose during an OGTT using GWAS , CardioMetabochip and de novo genotyping . The combined analysis in up to 26 , 037 non-diabetic individuals provided genome-wide significant association ( p<5×10−8 ) with insulin secretion measured as corrected insulin response ( CIR ) to glucose at 30 min during an OGTT for the locus within the GRB10 gene ( lead SNP rs933360 located in intron 2 ) ( Figure 1A , B ) , and at 7 previously reported T2D and glycemic trait variants , including MTNR1B , HHEX/IDE/KIF11 , CDKAL1 , GIPR/QPCTL , C2CD4A ( NLF1 ) , GCK and ANK1 ( Table 1 , Table S2A , Figure S1 , S2 ) . These associations remained virtually unchanged when CIR was adjusted for insulin sensitivity ( disposition index ) ( Table S2A ) . In addition , nominal ( p<0 . 05 ) association with reduced insulin response to glucose ( CIR ) during the OGTT was seen for the risk alleles in 24 out of 65 reported T2D loci [4] , as well as for 20 glucose and 7 insulin loci out of 53 reported being associated with these traits [6] . Notably , the risk alleles in 5 of them ( ANKRD55 , GRB14 , PPP1R3B , IRS1 and ARAP1 ( fasting glucose variant ) ) were associated with higher insulin response to glucose ( p<0 . 05 ) ( Table S2B ) . In line with a previous report [6] , we confirmed the association of the index GRB10 SNP rs933360 with fasting glucose levels ( N = 24 , 608 , β = −0 . 016 , p = 0 . 007 ) ( Figure S3 ) . However , we did not observe a significant effect of SNP rs2237457 , previously associated with glucose concentrations and risk for T2D in an Amish population [13] . Since GRB10 is differentially expressed when transmitted from mothers and fathers , we explored whether the GRB10 variant would have sex-specific effects on insulin and glucose levels . The sex-stratified analysis showed a greater insulin-reducing effect of the GRB10 variant in women ( CIR: N = 6 , 202; β = −0 . 110±0 . 019; p = 1 . 52×10−8 ) than in men ( CIR: N = 15 , 192; β = −0 . 038±0 . 012; p = 0 . 0012; sex heterogeneity p = 0 . 0016 ) ( Figure 1C , D ) . Given the complex parent-of-origin imprinting pattern described for GRB10 , we next turned to families to explore in detail the inheritance patterns of identified variants and their potential effect on risk of T2D and insulin/glucose levels . Using a cohort of 2 , 322 parents-offspring trios with 4 , 182 individuals from Finland and Sweden , we first performed a transmission disequilibrium test , taking into account the parental phenotype being either T2D or not , to increase power ( parenTDT ) [14] . We also used gene-dropping permutation to control for stratification and the dependence of related individuals [14] , or restricting the analysis to independent trios , including only the youngest affected offspring ( N = 1 , 055; 182 with T2D ) and oldest unaffected offspring ( N = 1 , 019; 873 unaffected ) . We observed an increased transmission of the A-allele of rs933360 from parents to diabetic offspring ( p = 0 . 0063 ) ( Table S3A-I ) , particularly from diabetic fathers ( p = 0 . 049 ) ( Table S3A-V ) , and the G-allele was preferentially transmitted from non-diabetic parents to non-diabetic offspring ( p = 0 . 026 ) ( Table S3A-II ) . In accordance with these findings , when simply counting transmission of the A- and G-alleles , we observed an increased transmission of the major A-allele of rs933360 from a diabetic parent to a diabetic offspring ( Chi-square p = 0 . 017 ) ( Table S3A-V ) . This effect was even stronger when we relaxed the definition of hyperglycemia to include IFG and IGT in addition to T2D ( p = 0 . 006 ) ( Table S3A-VI ) . We observed a similar pattern of an increased transmission of the T-allele of rs6943153 ( LD with rs933360; D′ = 0 . 99 , r2 = 0 . 82 ) to a hyperglycemic offspring ( IFG/IGT/T2D ) ( p = 0 . 0045 ) ( Table S3A-III ) . This latter association was confirmed using the Family Based Association Test ( FBAT ) [15] ( p = 0 . 035 ) , which can accommodate any type of genetic model and family construction . Consistent with previous findings [6] , we confirmed the association of the SNP rs6943153 with fasting glucose levels ( 1 , 083 nuclear families , p = 0 . 02 ) ( Table S3A-VII ) . To explore whether GRB10 rs933360 would show a stronger effect on insulin secretion when inherited from either parent , we examined its effect on GSIS in 3 , 117 non-diabetic individuals from parents-offspring trios from Finland and Sweden [16] and USA [13] . In these families , the maternally transmitted A-allele of rs933360 was associated with reduced GSIS ( CIR β = −0 . 127 , p = 0 . 014; Ins30adjBMI β = −0 . 125 , p = 0 . 005; Ins30 β = −0 . 112 , p = 0 . 014; AUCIns β = −0 . 095 , p = 0 . 016; AUCIns/AUCGluc β = 0 . 107 , p = 0 . 005 ) ( Figure 2A , B , Table S3B ) . No significant effect was observed for the paternally transmitted A-allele on GSIS . Surprisingly , the maternally transmitted A-allele was associated with reduced rather than elevated fasting glucose levels ( β = −0 . 139 , p = 0 . 0009 ) . In contrast , the paternally transmitted A-allele was associated with elevated glucose levels ( β = 0 . 102 , p = 0 . 002 ) ( Figure 2C , D , Table S3B ) . Thereby , the A-allele of rs933360 exerted virtually opposite effects on glucose metabolism if transmitted from the father than the mother . It is very likely that the association with risk or protection from T2D would be missed or diluted in any traditional association study , which does not take the transmission pattern into account . In support of this , we did not observe any association between the SNP rs933360 and T2D in 16 , 715 non-diabetic individuals , of whom 2 , 637 developed T2D during a mean 25-year follow-up period ( Table S4 ) [17] . Also , in the recent DIAGRAM+ meta-analysis , none of the evaluated GRB10 SNPs were associated with T2D [4] . A potential explanation for the paradoxical reduction in glucose levels despite reduced insulin secretion could be that the variant also enhances insulin sensitivity or reduces glucagon levels . In fact , the maternally transmitted A-allele was associated with enhanced , whereas the paternally transmitted A-allele was associated with decreased insulin sensitivity as measured by ISI during the OGTT ( p<0 . 05 for difference between parental alleles ) . Although we could not observe any significant effect of rs933360 on fasting or 2 hr glucagon levels in a Finnish cohort with glucagon data available ( Table S5B ) , we identified several GRB10 SNPs from the same haplotype block which were in weak LD with rs933360 and nominally ( p<0 . 05 ) associated with fasting and 2 hr glucagon levels in the DGI GWAS ( Table S6 ) . Unfortunately , there was no glucagon data available for the trios . GRB10 protein was detected in human α- , β- and δ-cells by immunofluorescence ( Figure 3A ) . We observed strong expression of GRB10 mRNA in total human islets , with no significant difference between islets from normoglycemic and hyperglycemic individuals ( Figure S4A ) , or between carriers of different GRB10 genotypes ( Figure S4B ) . While we did not observe any correlation between the amount of GRB10 and INS ( insulin ) mRNA , nor between GRB10 mRNA and in vitro GSIS , there was an inverse correlation between GRB10 and GCG ( glucagon ) mRNA in human pancreatic islets ( all donors: rho = −0 . 267 , p = 0 . 017; normoglycemic: rho = −0 . 228 , p = 0 . 10; hyperglycemic: rho = −0 . 651 , p = 0 . 00003 ) , suggesting that higher GRB10 expression is associated with lower glucagon ( Figure S4C ) . Although there was no effect of rs933360 on total GRB10 mRNA expression in human islets , we cannot exclude that the variant could influence splicing or methylation , especially as 3 different transcriptional start sites ( UN1 , UN1a and UN2 ) and tissue-specific expression have been described for the GRB10 gene ( Figure 3b , S5 ) [12] . We tested for allelic imbalance , i . e . deviation from the expected equal expression of both alleles . For this purpose , we used the SNP rs1800504 ( A→G ) located in exon 3 as a reporter SNP , as it is the nearest coding variant located 16 kb from the index SNP rs933360 ( D′ = 1 , r2 = 0 . 5 ) . This reporter SNP indicated a clear allelic imbalance with A- to G-allele ratios ranging from 35% to 75% in pancreatic islets ( Table S7 ) . We therefore examined whether the observed allelic imbalance could be related to specific transcripts arising from different promoters . Transcripts containing exon UN2 were monoallelically expressed from either the A- or G-allele , indicating imprinting of the promoter giving rise to the UN2 transcripts ( pUN2 ) from one parent . Until now , paternally expressed ( i . e . maternally imprinted ) UN2 transcripts have only been observed in the brain [12] . This differential imprinting was recurring in all tissues analyzed ( Figure S6 ) . Our findings extend this expression pattern to human islets . In contrast , transcripts containing exon UN1 showed great variation ( from 50% to 80% ) , but were mainly expressed from the other allele than those containing exon UN2 ( Figure 3B , C ) , in line with the maternally expressed/paternally imprinted transcripts observed in placental trophoblasts [12] . It can be hypothesized that these SNPs might regulate usage of alternative promoters and thereby influence the expression of GRB10 . The opposite effects of maternally and paternally inherited rs933360 allele could then be attributed to different effects of rs933360 on the promoters pUN2 and pUN1 , e . g . especially if promoter preferences differ strongly between α- and β-cells . Although allelic imbalance is an attractive model to explain differences in expression of different GRB10 transcripts , as well as the observed differences in effects of the A-allele on risk of diabetes in the offspring when transmitted from father or from mother , the above data can only point at this possibility , as we did not have enough human islets for this kind of analysis . We therefore also tested for allelic imbalance using an alternative method , i . e . by comparing data from exome and RNA sequencing . We found that another coding variant , SNP rs11555134 , in the GRB10 gene was associated with allelic imbalance in 8 human pancreatic islet samples ( p<0 . 05 , Fisher's exact test ) . Since GRB10 is imprinted and methylated in humans and rodents in a tissue-specific fashion [12] , [18] , [19] , we studied whether GRB10 would be methylated in human islets or in DNA from human peripheral blood lymphocytes ( PBL ) and whether the degree of the DNA methylation would correlate with gene expression in human islets . We found tissue-specific differences in DNA methylation of GRB10 in human islets compared to PBL and the degree of methylation in the region analyzed with the Sequenom MassARRAY EpiTYPER ranged from 56 . 9% to 99 . 8% ( Figure 3D ) . Although we did not observe any significant effect of rs933360 on the degree of DNA methylation in human islets ( Figure S7A ) , there was a nominal association between rs933360 and DNA methylation in PBL in the region analyzed using EpiTYPER ( p = 0 . 07 ) ( Figure S7B ) . Moreover , we observed an inverse correlation between DNA methylation at a CpG site located 31 . 7 kb downstream of rs933360 and GRB10 mRNA expression ( N = 81 , rho = −0 . 335 , p = 0 . 002 ) ( Figure 3E ) in human islets , particularly in islets from diabetic donors ( N = 24 , rho = −0 . 656 , p = 0 . 001 ) , and at a CpG site 8 , 196 bp downstream from rs933360 ( N = 66 , rho = −0 . 23 , p = 0 . 047 ) ( Figure S7C ) , suggesting that decreased methylation in this region is associated with increased GRB10 mRNA . Given that we observed differences in insulin sensitivity when the risk A-allele of SNP rs933360 was inherited from the mother compared to the father , and that GRB10 is an inhibitor of insulin signaling , we also explored whether the SNP would influence expression of the GRB10 gene in human skeletal muscle and adipose tissue [20] , [21] . We observed that carriers of the A-allele had decreased GRB10 mRNA level in muscle ( N = 38 , β = −0 . 405 , p = 0 . 013 ) and adipose tissue ( N = 1 , 375 , β = −0 . 038 , p = 0 . 005 ) . To gain insight into the mechanisms by which GRB10 influences pancreatic β- and α-cell function , we disrupted Grb10 expression in rat insulinoma INS-1 cells by siRNA and in human islets by shRNA achieved by lentiviral transfection . There was a clear reduction in GSIS after siRNA-disruption of Grb10 in the INS-1 cell line lacking glucagon ( Figure 4A ) . In human pancreatic islets , decreased GRB10 expression resulted in a reduction of both insulin and glucagon secretion and expression ( Figure 4B , C ) . In addition , GRB10 knock-down was also associated with a decrease in forskolin- and K+-stimulated glucagon secretion ( Figure S8A ) . Grb10 has been reported to both increase [22] and decrease [23] apoptosis in islets , in addition to its effects on insulin signaling [22] . Disruption of GRB10 was associated with a significant reduction in the number of viable human pancreatic islets , as assessed by an MTS technique , supporting recent data in mice using shRNA to disrupt Grb10 in pancreatic islets [22] ( Figure S8C ) .
The present study provides mechanistic insights into the role of GRB10 in the regulation of islet function and glucose metabolism in man . First , the A-allele of the GRB10 rs933360 variant was associated with different effects on insulin secretion , sensitivity and glucose concentrations , if transmitted from fathers or mothers . Second , disruption of GRB10 in human islets resulted in a reduction of both insulin and glucagon secretion , the latter of which can provide an additional explanation for the reduction in glucose levels despite reduced insulin levels . Third , metabolic effects of GRB10 on glucose homeostasis involved tissue-specific methylation and parental imprinting . GRB10 is an adaptor protein , which interacts with a number of receptor tyrosine kinases and signaling molecules and serves as a down-regulator of insulin receptor activity [24] . GRB10 participates in phosphorylation and activation of the mTORC1 protein , which is a central regulator of cellular metabolism , growth and survival [25] . Studies in mice have shown an abundant expression of Grb10 in brain , fat , muscle and heart , and with the highest expression in pancreas [26] . An interesting observation in the present study was that the common variant rs933360 in the GRB10 gene was associated with reduced GSIS , enhanced insulin sensitivity and reduced glucose levels when inherited from the mother . On the contrary , the paternally transmitted allele was associated with elevated glucose level and increased risk for T2D . These findings might partially explain the modest or lack of effects seen in several studies for variants in the GRB10 gene on T2D risk [27] . The effect on insulin sensitivity is indirectly supported by the view that carriers of the A-allele showed decreased expression of GRB10 in skeletal muscle and adipose tissue . Another potential explanation could be the effect of GRB10 on glucagon secretion . Unfortunately , due to lack of samples for glucagon measurements , we were not able to test this directly in the trios . To further explore whether GRB10 influences glucagon secretion/expression and thereby mechanisms by which GRB10 might influence glucose metabolism , we disrupted GBR10 in human pancreatic islets . While Grb10 knock-down in the rat insulinoma cell line INS-1 lacking glucagon resulted in marked reduction of insulin , GRB10 knock-down in human pancreatic islets resulted in reduction in both insulin and glucagon secretion and expression . The effect on glucagon seen in human islets is in line with data from mice , showing reduced glucagon levels after Grb10 disruption using shRNA [22] . Since rs933360 is located in intron 2 of the GRB10 gene , one could envision an effect on gene expression . This could be a possible mechanism , as monoallelic and isoform-specific expression of GRB10 has been reported in fetal brain , skeletal muscle and in the placental trophoblasts [11] , [12] . Unfortunately , we could not observe any effect of the SNP on expression of the GRB10 transcript in islets , nor could we examine parent-of-origin effects in the islets from human cadaver donors , as we lacked information on the parents . However , using two different strategies we could demonstrate allelic imbalance for SNPs in the GRB10 gene , leaving this possibility open . In line with earlier findings , we also identified three transcripts arising from alternative GBR10 promoters , with the paternally expressed UN2 isoform being imprinted from one parent in islets . However , there is sufficient amount of the maternally expressed UN1a isoform in islets to allow an effect of the maternally transmitted allele , which could contribute to the stronger effect of the maternal allele as described above . Genetic imprinting is usually a consequence of increased DNA methylation . In line with previous reports [11] , [12] , we observed tissue-specific methylation of GRB10 , showing highest degree of methylation in human pancreatic islets , which was associated with decreased expression of GRB10 . Although this relationship was not influenced by the SNP in human pancreatic islets , there was a tendency for an effect of the SNP on degree of methylation in PBL . Our study has some limitations . Despite large sample size for measurements of insulin secretion , we did neither have samples with measurements of glucagon in the trios , nor did we have parental information for islet donors . Also , tissue limitations prevented us from directly exploring methylation patterns and imprinting in trios . Finally , although this is the largest meta-analysis for insulin secretion to date , we discovered only one novel locus influencing GSIS . While this could be a consequence of limited power , another plausible explanation is that many homeostatic mechanisms , as well as parent-of-origin specific effects , would dilute and neutralize such effects . In conclusion , our data demonstrate a complex genetic regulation of GRB10 function in human islets with different effects of paternally and maternally transmitted alleles . Together , these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis . The data also emphasize the need in genetic studies to consider whether disease risk alleles are inherited from the mother or the father .
All studies were approved by local research ethics committees , and all participants gave informed consent . All procedures in human islets were approved by the ethics committees at the Uppsala and Lund Universities and informed consent obtained by appropriate measures from donors or their relatives . Association analyses of insulin secretion and action traits were performed within 11 cohorts participating in the Meta-Analysis of Glucose- and Insulin-related traits Consortium ( MAGIC ) in a total of up to 10 , 831 individuals . In the discovery stage 1 , we performed a meta-analysis of 6 GWASs ( Diabetes Genetics Initiative ( DGI ) , Amish Family Diabetes Study , Sorbs , Helsinki Birth Cohort Study ( HBCS ) , French Obese Adults , and Relationship between Insulin Sensitivity and Cardiovascular disease Study ( RISC ) ) for glucose-stimulated insulin secretion ( GSIS ) during an oral glucose-tolerance test ( OGTT ) at 3 time points ( fasting , 30 min , 120 min ) for primary traits measured as ( 1 ) insulin response to glucose after the first 30 min estimated as corrected insulin response ( CIR ) , and ( 2 ) overall insulin response to glucose estimated as area under the curve ( AUC ) for insulin over a total AUC for glucose ( AUCIns/AUCGluc ) in up to 5 , 318 non-diabetic individuals ( Table S1A ) . As none of the traits gave genome-wide significant association , we selected the top 50 independent signals from both primary and secondary traits ( see “Phenotype Definition” below ) after LD pruning ( r2<0 . 2 ) . Signals prioritized for replication were ranked by the number of associations observed at primary traits and/or secondary traits , association p-value and number of times the signal was observed across the traits ( more than 2 ) . We selected 14 SNPs for replication genotyping and follow-up analyses , out of which 3 loci were based on biological relevance: GRB10 [13] ( rs933360 , discovery p-value ( CIR ) = 5 . 09×10−6 ) , UCN3 ( rs11253130 , discovery p-value ( Ins30adjBMI ) = 9 . 46×10−7 ) and INADL ( rs2476186 , discovery p-value ( AUCIns/AUCGluc ) = 1 . 88×10−6 ) . Replication stage 2A de novo genotyping was undertaken in five population-based studies ( Botnia-PPP , ULSAM , METSIM , BPS and Haguenau; only GWAS index SNP rs933360 in the latter three; max N = 15 , 273 ) ( Table S1A , S2A ) . Replication stage 2B in silico was undertaken using an iSelect CardioMetabochip array ( CM ) ( Illumina , San Diego , CA , USA ) to genotype data in 5 independent population-based studies ( Botnia-PPP , ULSAM , Ely , DR's Extra and METSIM ) including up to 5 , 513 individuals ( Table S1A ) . The GWAS/CM ( stage 1 and stage 2B ) data including 93 , 896 SNPs were pooled together with the de novo genotyping results from stage 2A for non-overlapping individuals . In this meta-analysis , we defined all independent ( r2<0 . 2 ) genome-wide significant ( p-value<5×10−8 ) association signals for insulin secretion traits at 8 genomic loci ( Table 1 ) . The primary insulin secretion and action indices were: ( i ) Corrected Insulin Response ( CIR ) = ( 100× insulin at 30 min ) / ( glucose at 30 min× ( glucose at 30 min–3 . 89 ) ) , and ( ii ) ratio of the area under the curve ( AUC ) for AUC insulin/AUC glucose ( AUCIns/AUCGluc , mU/mmol ) calculated using the trapezium rule [28] . Insulin sensitivity index ( ISI ) = 10 , 000/√ ( fasting plasma glucose ( mg/dl ) ×fasting insulin×mean glucose during OGTT ( mg/dl ) ×mean insulin during OGTT ) . Secondary insulin secretion and action indices during OGTT were: ( i ) disposition index ( DI ) = CIR×ISI; ( ii ) insulin at 30 min ( Ins30 ) ; ( iii ) incremental insulin at 30 min ( Increm30 ) = insulin 30 min – fasting insulin; ( iv ) insulin response to glucose during the first 30 min adjusted for BMI ( Ins30adjBMI ) = insulin at 30 min/ ( glucose at 30 min×BMI ) ; ( v ) area under the curve ( AUC ) of insulin levels during OGTT ( AUCIns , mU*min/l ) . Individuals with missing data on any of the three time points included in the AUC calculation were excluded . Linear regression models were used for association of phenotypes ( z-score residuals of insulin secretion and action traits ) with genotypes coded additively . Discovery ( stage 1 ) GWAS analyses were carried out using a statistical tool that was able to account for genotype uncertainty , SNPTEST [29] , or by using allele dosages in the linear regression model in MACH2QTL [30] , [31] , probABEL [32] , corrected for residual inflation of the test statistics using the genomic control method [33] . The meta-analyses of effect sizes were performed with the fixed-effect inverse-variance method using GWAMA [34] . The GC correction was applied only once to cohort-specific results before including them into the meta-analyses . Sex-differentiated analyses were performed using GWAMA , with an assumed heterogeneity p-value of <0 . 05 . Effect sizes for glucose levels were estimated using a fixed-effect model using the metaphor package for R version 2 . 14 . 2 ( http://www . r-project . org/ ) . The Trios from Finland and Sweden , Amish Family Diabetes Study and Kuopio Offspring Study ( Table S1A , S3A , B ) consisted of a father , a mother and an offspring . Genotype phase was determined using Merlin and then analyzed using Solar , which uses the kinship matrix to account for family . Meta-analysis on insulin secretion and action and glucose levels during OGTT ( as earlier described ) was performed using a fixed-effect model . Analyses were performed on IBM SPSS Statistics 20 . 0 ( IBM Corp . , Chicago , IL , USA ) , R version 2 . 14 . 2 with the metaphor package ( http://www . r-project . org/ ) and MMAP ( MMAP: mixed models analysis for pedigrees and populations , http://edn . som . umaryland . edu/mmap/index . php ) . The Transmission Disequilibrium Test ( TDT ) used to compare frequencies of transmission of the two alleles from heterozygote parents to an affected offspring was performed using PLINK ( http://pngu . mgh . harvard . edu/purcell/plink/ ) [35] ( Table S3A ) . The deviations from Mendelian transmissions were assessed and the power of the test was enhanced by incorporating information from phenotypically discordant parents ( ParenTDT ) [14] . To confirm the association , another independent test was performed , which can accommodate any type of genetic model and family construction , i . e . the Family Based Association Test ( FBAT ) [15] . Quantitative traits related to glucose metabolism and insulin secretion were assessed in non-diabetic individuals using qTDT and parent-of-origin effect tests . IBD estimates were calculated using Merlin . Permutations were performed using QFAM ( PLINK ) . OGTT values were natural log-transformed and adjusted for BMI . The Botnia Prevalence , Prediction and Prevention of diabetes ( Botnia-PPP ) study is a population-based study from the Botnia region of Western Finland and has previously been described [36] . For this study , we selected 4 , 641 non-diabetic individuals above the age of 18 . Linear regression analysis assuming an additive genetic risk model was performed to evaluate genotype-phenotype association . Hyperglycemic individuals were identified based on previous diagnosis , fasting plasma glucose levels of 5 . 5–6 . 9 mmol/l and 2 hr plasma glucose levels of 7 . 8–11 . 1 mmol/l ( Table S5 ) . A subgroup of 203 men with IGT at screening visit selected from the MPP study participated 20 years later in more extensive metabolic studies , including a new OGTT , a euglycemic-hyperinsulinemic clamp combined with indirect calorimetry and infusion of [3-3H]glucose [37] , [38] . The men were similar in age , but had varying degrees of glucose tolerance; 69 were in the normal range , 52 had IFG and/or IGT , and 82 had T2D . T2D patients were either treated with diet alone ( 42% ) or with oral hypoglycemic agents , which were withheld the day before the test . Microarray expression data were analyzed as previously described [20] . The Malmö Preventive Project ( MPP ) is a large population-based prospective study from the city of Malmö , Sweden , and has previously been described [17] . For this study , we selected 16 , 715 non-diabetic subjects , of whom 2 , 637 developed T2D during a 24 . 1 year mean follow-up period . The odds ratio for risk of developing T2D was calculated using logistic regression analysis assuming an additive genetic risk model . The analysis was adjusted for age , sex , BMI , participation period and an interaction term ( participation period x sex ) [10] . IBM SPSS Statistics 20 . 0 ( IBM Corp . ) was used for the statistical analysis . Islets from cadaver donors were provided by the Nordic Islet Transplantation Program ( www . nordicislets . org ) by the courtesy of Prof . Olle Korsgren , Uppsala University , Sweden . The microarray experiments ( Human Gene 1 . 0 ST whole transcript ) were performed on islets isolated from 81 normoglycemic ( mean±SEM; age 56 . 3±1 . 3 yrs , BMI 25 . 7±0 . 4 kg/m2 , HbA1c 5 . 5±0 . 04% ) and 46 hyperglycemic ( age 60 . 3±1 . 2 yrs , BMI 27 . 8±0 . 6 kg/m2 , HbA1c 6 . 6±0 . 1% ) islet donors . RNA products were fragmented and hybridized to the GeneChip Human HG U 133A Array ( Affymetrix , Santa Clara , CA , USA ) [39] . Statistical analyses of expression data were performed using two-tailed Spearman's T-test . Immunocytochemistry with antibodies for GRB10 ( K20 ) 18 ( code sc-1026 , Santa Cruz Biotech . Inc . , CA , USA ) , insulin , glucagon and somatostatin was performed on human pancreatic sections as previously described [40] . RNA from 48 human pancreatic islets donors ( 24 with HbA1c <5 . 5% and 24 donors with HbA1c >6 . 0% ) was isolated and purified using miRNeasy kit ( Qiagen , Hilden , Germany ) . As quality thresholds for RNA samples , we demanded RIN values >8 , 28S/18S ratio >1 . 5 and the absorbance ratios 260/280 >1 . 8 and 260/230 >1 . Sample preparation for sequencing reactions was performed using TrueSeq sample prep kit ( Illumina ) . Fragmentation was performed using inbuilt fragmentation in the sample prep kit to obtain fragments of approximately 300 bp in length . Sequencing was performed on the Illumina HiSeq 2000 platform . Obtained sequenced reads were transformed into . qseq files using the Illumina pipeline . Alignment of the reads was performed using the TopHat short read aligner ( tophat . cbcb . umd . edu ) . Cufflinks ( cufflinks . cbcb . umd . edu ) was used for splice variant calling . Analytical RT-PCRs were performed on cDNA from human islet , visceral fat , subcutaneous fat , liver and muscle in 15 µl reactions using 7 . 5 µl AmpliTaq Gold PCR Master Mix ( Applied Biosystems , Foster City , CA , USA ) supplemented with 0 . 75 µl DMSO and 1 . 5 µmol/l of forward and reverse primers . The following primers were used in various combinations: UN1fw: CAAACGCCTGCCTGACGACTG , UN1Afw: GCCCGGGACAGTCTTGAGC , UN2fw: GGCGCACACGCAGCGAC , UN3fw: ACCACCTACATCAGAGCTGACTGCC , 1bfw: CCTGGGCTACCCTCTGCTTC , 3fw: GCCTGTACTCGGCCTGCAGC , 9fw: GCCCCTACAGACCACGGGCT , 11fw: GCTGTCCCCGTTCTCGACGC , 3rv: ATGTGCACAGGCTGGGAGCG , 7rv: CTGGCTGTCATGTCTGCT , 11rv: CTGCTGAGGGATTCGGT , 16rv: GGATGCAGTGGTGCTTGA , the names referring to the target exon . PCR reactions were carried out with 53°C annealing temperature and over 50 cycles . Products were analyzed on 2% agarose . Prior to sequencing , 2 . 5 µl PCR product was treated with 0 . 5 µl ExoSAP-IT ( USB , Cleveland , OH , USA ) at 37°C for 30 min followed by deactivation at 80°C for 15 min . Subsequently , 1 µl was sequenced in both directions using BigDye 3 . 1 according to the manufacturer's protocol ( Applied Biosystems ) . The sequence reactions were purified and analyzed by GATC Biotech AG ( Konstanz , Germany ) . Allelic imbalance measurements were performed by RT-PCR using the reverse primer 3rv and either of the forward primers UN1afw , UN1fw , UN2fw , UN3fw , 1bfw or 3fw in samples heterozygous for the common SNP rs1800504 ( GRB10 exon3 ) . The individual contribution from each allele was measured at the position of rs1800504 using Sanger sequence traces and the software Mutation Surveyor ( Soft Genetics , PA , USA ) . Allelic imbalance in GRB10 was also detected by a different method: After extensive quality and coverage filtering , we did a Fisher exact test for comparing the ratio of reference/alternative alleles in the exome sequencing vs . RNA-seq for each sample . Exome sequencing was performed using the Illumina exome sequencing protocols ( TruSeq DNA sample preparation Kit v2 ) . Sequenom's MassARRAY EpiTYPER protocol was applied to measure DNA methylation ( Sequenom , San Diego , CA , USA ) in human islets of 96 donors and peripheral blood lymphocytes ( PBL ) of 6 diabetic offspring trios ( 18 individuals ) . EpiDesigner was used for assay design at GRB10 and the primer sequences were the following; forward: aggaagagagGGGAAAGGGTGTTAAATTGTTTATG , reverse: cagtaatacgactcactatagggagaaggctTTTTAAACCCCTCAAATTCAAAAAT . 500 ng genomic DNA was bisulfite-treated with the EZ DNA Methylation kit ( Zymo Research , Orange , CA , USA ) . The spectra were analyzed and the methylation ratios were obtained by the EpiTYPER software v . 1 . 0 . 1 . Global methylation analyses were performed on DNA extracted from PBL on the Illumina Infinium 450 Bead Chip and the chips were scanned on an Illumina iScan as per protocol ( Illumina ) . 1 µg of DNA was bisulfite-treated according to protocol ( EZ DNA Methylation Kit , Zymo research ) . For analysis , the Genome Studio Methylation Module of the Genome studio Genome Browser was used , which facilitates integration of the SNP and CpG location data ( NCBI build 37 ) . Methylation status was assessed after normalization to internal controls and background subtraction and expressed as β . The β values for the CpG sites were mapped to the gene and plotted to give an overview of methylation status for the region of interest ( Figure S7D ) . Human pancreatic β-cell viability assay was performed using a CellTiter 96 AQueous One Solution Cell Proliferation Assay Reagent ( Promega , Stockholm , Sweden ) according to the manufacturer's instructions . The actual performance is based on the spectrophotometric detection of a colored formazan product converted from a 3- ( 4 , 5-dimethylthiazol-2-yl ) -5- ( 3-carboxymethoxyphenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium ( MTS ) compound by NADPH or NADH via metabolically active cells . | In this paper , we report the first large genome-wide association study in man for glucose-stimulated insulin secretion ( GSIS ) indices during an oral glucose tolerance test . We identify seven genetic loci and provide effects on GSIS for all previously reported glycemic traits and obesity genetic loci in a large-scale sample . We observe paradoxical effects of genetic variants in the growth factor receptor-bound protein 10 ( GRB10 ) gene yielding both reduced GSIS and reduced fasting plasma glucose concentrations , specifically showing a parent-of-origin effect of GRB10 on lower fasting plasma glucose and enhanced insulin sensitivity for maternal and elevated glucose and decreased insulin sensitivity for paternal transmissions of the risk allele . We also observe tissue-specific differences in DNA methylation and allelic imbalance in expression of GRB10 in human pancreatic islets . We further disrupt GRB10 by shRNA in human islets , showing reduction of both insulin and glucagon expression and secretion . In conclusion , we provide evidence for complex regulation of GRB10 in human islets . Our data suggest that tissue-specific methylation and imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis . The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father . | [
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"geno... | 2014 | A Central Role for GRB10 in Regulation of Islet Function in Man |
The mechanisms whereby guanine nucleotide exchange factors ( GEFs ) coordinate their subcellular targeting to their activation of small GTPases remain poorly understood . Here we analyzed how membranes control the efficiency of human BRAG2 , an ArfGEF involved in receptor endocytosis , Wnt signaling , and tumor invasion . The crystal structure of an Arf1–BRAG2 complex that mimics a membrane-bound intermediate revealed an atypical PH domain that is constitutively anchored to the catalytic Sec7 domain and interacts with Arf . Combined with the quantitative analysis of BRAG2 exchange activity reconstituted on membranes , we find that this PH domain potentiates nucleotide exchange by about 2 , 000-fold by cumulative conformational and membrane-targeting contributions . Furthermore , it restricts BRAG2 activity to negatively charged membranes without phosphoinositide specificity , using a positively charged surface peripheral to but excluding the canonical lipid-binding pocket . This suggests a model of BRAG2 regulation along the early endosomal pathway that expands the repertoire of GEF regulatory mechanisms . Notably , it departs from the auto-inhibitory and feedback loop paradigm emerging from studies of SOS and cytohesins . It also uncovers a novel mechanism of unspecific lipid-sensing by PH domains that may allow sustained binding to maturating membranes .
Arf GTPases are pivotal regulators of most aspects of intracellular membrane traffic ( reviewed in [1] ) . They are activated by guanine nucleotide exchange factors ( ArfGEFs ) that share a conserved Sec7 domain , which stimulates GDP/GTP exchange . Arf GTPases and their GEFs establish intimate interactions with membranes ( reviewed in [2] ) . Arf GTPases feature an allosteric mechanism by which their guanine nucleotide-binding site communicates with their membrane-binding myristoylated N-terminal helix [3] , which is harnessed by the Sec7 domain to ensure that their active form is bound to membranes [3] , [4] . However , Arf GTPases , notably the most abundant Arf1 isoform , which is found on most membranes of the endocytosis and exocytosis pathways , have little if any membrane specificity on their own . ArfGEFs are therefore predicted to carry elements that restrict their activation of Arf proteins to specific subcellular membranes . Cytohesins are the only ArfGEFs in which such elements have been characterized [5] , [6] , while the physicochemical and/or curvature properties of membranes that are recognized by other ArfGEF families remain unknown . BRAG family ArfGEFS ( also called IQSec ) , which are present only in higher organisms , are pivotal regulators of myoblast fusion [7] , Wnt signaling [8] , and receptor endocytosis [9]–[11] and promote invasive phenotypes in cancer [8] , [11]–[13] . Members of this family carry a calmodulin-binding IQ motif in their N-terminus , a Sec7 nucleotide exchange domain followed by a PH domain and a predicted coiled-coil in their C-terminus ( reviewed in [14] ) . BRAG2 ( also called GEP100 or IQSec1 ) , the most studied of the three mammalian members , promotes the endocytosis of β1 integrins [9] , [10] and of the AMPA receptor in neurons [15] , whereas its depletion resulted in increased E-cadherin expression at the cell surface [11] , [16] . BRAG2 is responsible for invasive phenotypes in various tumors , notably in breast tumors and lung adenocarcinoma where it binds to tyrosine kinases of the epidermal growth factor receptor ( EGFR ) family [12] and in melanoma where it is necessary for invasion and metastasis mediated by the Wnt/β-catenin pathway [8] . Current evidence regarding the specificity and the regulation of BRAG2 is fragmentary and somewhat conflicting . BRAG2 has been described as an Arf6-specific GEF in vitro and in transfected cells [9] , [10] , [12] , [17] , but also shown to be able to use Arf1 [18] or Arf5 [19] as substrates . It was also proposed to be insensitive to phospholipids [17] , or to be specific of phosphatidylinositol 4 , 5 bisphosphate ( PI ( 4 , 5 ) P2 ) [10] . A unique feature that has been put forward is its possible regulation by direct interactions with receptors [12] , [13] , [15] , the mechanism of which is unknown . Understanding the molecular mechanisms whereby guanine nucleotide exchange factors ( GEFs ) coordinate their GDP/GTP exchange activities with their targeting to specific intracellular membranes is a major issue in small GTPases biology ( reviewed in [2] ) . Pivotal insight can be gained by reconstituting the activity of GEFs on membranes and capturing them in structures that mimic their soluble and membrane-bound conformations . Such combined studies remain difficult and have been done only for the RasGEF SOS [20] , [21] . These pioneering studies and recent investigations of ArfGEFS of the cytohesin [5] , [6] and BIG families [22] and of DH-PH containing RhoGEFs of the Lbc family [23] , [24] lead to an emerging paradigm in which GEFs are regulated by auto-inhibition combined with a positive feedback loop mediated by freshly produced GTP-bound GTPases . In this schema , the switch from auto-inhibition to full exchange activity is supported by large conformational changes that concurrently optimize nucleotide exchange efficiency and interactions with membranes . Although various other GEFs have been shown to comply with one or another of these mechanisms , notably in the family of DH-PH containing RhoGEFs ( reviewed in [2] ) , the extent to which this scenario can be generalized remains an open issue . In this study , we investigated the regulatory modalities of BRAG2 on membranes by combined structural and biochemical assays . We find that BRAG2 is regulated by a mechanism that departs considerably from those previously described for other GEFs and involves an atypical PH domain with unprecedented lipid-sensing properties .
BRAG2 proteins carry a Sec7-PH tandem remotely related to that of cytohesins , which are dual Arf1 and Arf6 GEFs [25] , [26] and are auto-inhibited by their PH domain in solution [5] . We assessed whether any of these characteristics applies to BRAG2 by measuring its nucleotide exchange activity in solution by tryptophan fluorescence kinetics using recombinant proteins purified to homogeneity ( Figure S1A ) . Arf1 and Arf6 were truncated of their N-terminal helix , which allows them to by-pass the requirement for membranes to be fully activated ( reviewed in [27] ) . BRAG2 constructs encompassing the Sec7 and PH domains and proximal downstream residues ( BRAG2Sec7-PH , residues 390–763 or 390–811 , numbering according to the short isoform BRAG2a [9] ) were highly active in solution on both Arf isoforms ( kcat/Km values in Table 1 ) , suggesting that BRAG2 is not auto-inhibited by its PH domain . We confirmed that BRAG2 has the hallmarks of an Arf1–GEF by showing that a mutant in which the catalytic glutamate was replaced by a lysine ( BRAG2Sec7-PH/E498K ) traps Arf1–GDP in an early intermediate of the exchange reaction ( Figure S1B ) and that removal of GDP yields the subsequent nucleotide-free Arf/ArfGEF intermediate ( Figure S1C ) . This allowed us to solve the crystal structure of the Arf1–GDP/BRAG2Sec7-PH/E498K complex in two crystal forms ( Figure 1A , crystallographic statistics in Table S1 ) . The structure of the complex is similar in the two space groups , but is of better overall quality for the P2 crystal form , which will therefore be used for all subsequent analysis . The structure reveals that the PH domain of BRAG2 has various unanticipated features , although its fold is similar to those of PH domains of known structures ( reviewed in [28] ) . First , instead of forming an isolated domain , the PH domain is expanded by the linker that bridges the Sec7 and PH domains ( residues 592–627 ) , which forms a small subdomain rather than an unstructured tether ( Figure 1A and 1B ) . This subdomain packs against strands β1 , β2 , and β3 of the PH domain and stabilizes loop β3–β4 away from the pocket that binds phosphoinositides in other PH domains . The interface between the linker and the PH domain ( 1 , 200 Å2 buried surface area ) is largely hydrophobic and contains residues that are highly conserved in the BRAG family ( Figures S2A and S3A ) , indicating that the linker and the PH domain behave as a single domain . Next , this expanded PH domain establishes a large intramolecular contact with the N-terminus of the Sec7 domain remote from the Arf-binding site ( Figures 1B , S2A , and S3B ) . This contact encompasses the C-terminal helix of the PH domain and proximal downstream residues , which do not form a homodimeric coiled-coil contrary to prediction [9] . Accordingly , BRAG2Sec7-PH behaved as a monomer in solution ( Figure S1B and S1C ) . The interface buries a surface area of 1 , 800 Å2 , suggesting that it is a constitutive rather than a regulatory intramolecular interaction . To assess whether this interaction exists in unbound BRAG2 , we analyzed the conformation of BRAG2 in solution by synchrotron small-angle X-ray scattering ( SAXS ) . The SAXS curve calculated from the structure of BRAG2Sec7-PH extracted from the crystalline complex agreed well with the experimental SAXS curve of unbound BRAG2 in solution ( Figure 1C ) . These observations , together with the fact that BRAG2 is not auto-inhibited , suggest that the predominant conformation of unbound BRAG2Sec7-PH is similar to that seen in the crystalline Arf1–BRAG2 complex . Accordingly , the expanded PH domain is not auto-inhibitory and does not move away to activate Arf proteins . Given the structural conservation of the Sec7 domain , we surmise that its N-terminus may serve an as yet underestimated purpose in scaffolding intramolecular interactions in other ArfGEF families , which may explain why mutations in this region impaired plant Golgi ArfGEFs functions [29] . Finally , the PH domain of BRAG2 displays a striking sequence difference with phosphoinositide-specific PH domains: Glu639 in strand β1 replaces a highly conserved lysine in the canonical lipid-binding pocket ( as reviewed in [28] ) ( Figures 1D and S2B ) . This lysine is critical for PI ( 4 , 5 ) P2 recognition , as exemplified in cytohesins where its mutation to an alanine abolished the GEF activity on membranes [6] . The glutamate in BRAG2 would thus be predicted to generate repulsive interactions that impair PI ( 4 , 5 ) P2 binding . We analyzed the binding of BRAG2 to PI ( 4 , 5 ) P2-containing liposomes by a flotation assay , which was preferred over a co-sedimentation assay for its ability to accurately separate liposome-bound proteins from insoluble misfolded proteins . We observed significant binding to liposomes containing PS as the sole negatively charged lipid ( Figure S1D ) and near complete binding with liposomes containing PS and PI ( 4 , 5 ) P2 whether or not complemented with cholesterol , a major component that distinguishes the plasma membrane from other cellular membranes ( Figures S1D and 1E ) . Binding was dependent on both the expanded PH domain and on negatively charged lipids , as no binding was detected with the Sec7 domain alone ( residues 390–594 ) or with uncharged lipids ( Figure 1E ) . Thus , the atypical glutamate does not prevent the PH domain of BRAG2 from binding to membranes . The crystal structure of the Arf1–GDP/BRAG2Sec7-PH complex captured the relative arrangement between Arf1 , the catalytic Sec7 domain , and the PH domain in the course of the exchange reaction . First , it shows that Arf1 forms edge contacts with the PH domain . The interface involves the switch 1 of Arf1 and the Sec7-PH linker subdomain and is loosely packed ( 250–450 Å2 buried surface area , Figures 1A , 1B , S1E , S2A , and S3C ) . To analyze whether this contact contributes to the efficiency of the exchange reaction , we compared the exchange rates of BRAG2Sec7 and BRAG2Sec7-PH in solution . BRAG2Sec7-PH was 10 times more active than BRAG2Sec7 towards Arf1 , and 4 times more active towards Arf6 ( kcat/Km values in Table 1 , Figure 2A and 2B ) . Thus , the conformation of the Sec7-PH linker as a small domain rather than as an extended tether allows the enlarged PH domain to potentiate the exchange reaction , a contribution that we therefore call “conformational . ” The loose packing of the Arf/PH domain contact probably allows for the rotation of Arf towards the catalytic site that occurs as the exchange reaction proceeds [4] , [30] and for the subsequent release of Arf–GTP . Next , the structure shows that BRAG2-bound Arf1–GDP has undergone a two-residue shift of the interswitch , a conformational change that has been shown to occur prior to GDP dissociation [4] and to secure active Arf proteins to membranes ( [30] , reviewed in [3] ) , suggesting that the complex mimics a membrane-bound intermediate of the exchange reaction . The intramolecular interaction between the Sec7 domain and the enlarged PH domain constrains the relative orientations of Arf and the PH domain , thereby aligning the membrane-binding N-terminus of Arf1 and the PH domain on the same side of the complex ( Figure 1A ) . They could thus bind to membranes simultaneously , potentially contributing to BRAG2 efficiency . This was analyzed by reconstituting the exchange reaction on liposomes ( Figure 2C and 2D ) using highly pure myristoylated Arf1 and Arf6 ( Figure S1A ) . The efficiency of BRAG2Sec7-PH towards Arf1 on liposomes was increased by 160-fold compared to its efficiency in solution ( kcat/Km values in Table 1 ) . Liposomes did not increase the exchange efficiency of BRAG2Sec7 ( Table 1 ) , indicating that the effect requires the PH domain . BRAG2Sec7-PH also strongly activated myrArf6 in the presence of liposomes , although with unusual kinetics that could not be analyzed by a single exponential fit and were analyzed using initial velocities ( Vi ) ( Figures 2D , S4A and S4B ) . Vi values were linear as a function of BRAG2 concentration and were in the same range as those found for Arf1 ( Figure S4C ) , indicating that membranes potentiate the efficiency of BRAG2Sec7-PH towards Arf1 and Arf6 to the same extent . Altogether , these observations reveal that membranes strongly potentiate the efficiency of BRAG2 , and that this effect depends on the unconventional PH domain . Regulation of ArfGEFs on membranes by a positive feedback loop mediated by freshly produced Arf–GTP has been put forward for plasma membrane cytohesins [6] and Golgi BIG [22] . Feedback loops can be highlighted in vitro by preloading liposomes with increasing amounts of Arf–GTP prior to measuring nucleotide exchange rates . A positive feedback loop would then be detected by an increase of the exchange rates , while a decrease would indicate a negative feedback loop . The exchange rates of BRAG2Sec7-PH towards myrArf1 were unaffected when increasing amounts of myrArf6–GTP were pre-loaded on liposomes ( Figure 2E ) . Thus , BRAG2 is not regulated by a feedback loop , unlike cytohesins and BIG . Most phosphatidylinositides ( PIs ) ( reviewed in [28] ) as well as phosphatidylserine ( PS ) [31] can be recognized by specific PH domains . Since the unusual glutamate located in the lipid pocket of the PH domain did not preclude BRAG2 from binding to PI ( 4 , 5 ) P2-containing liposomes or from activating Arf proteins on these liposomes , we investigated whether it could serve as a sentry to exclude other PIs . We took advantage of the sensitivity of the nucleotide exchange kinetics assay to compare the seven major PIs ( PI ( 3 ) P , PI ( 4 ) P , PI ( 5 ) P , PI ( 3 , 4 ) P2 , PI ( 3 , 5 ) P2 , PI ( 4 , 5 ) P2 , and PI ( 3 , 4 , 5 ) P3 ) . Surprisingly , none of these phosphoinositides significantly increased or decreased the nucleotide exchange rate of BRAG2Sec7-PH towards myrArf1 taking PI ( 4 , 5 ) P2-containing liposomes as a reference ( maximum 2-fold ) ( Figure 3A ) . A nucleotide exchange rate in the same range was achieved when PS ( 10–30% ) was the sole negatively charged lipid added to liposomes . In contrast , the activity of BRAG2Sec7-PH was weak and remained in the same range as that of BRAG2Sec7 with liposomes devoid of negatively charged lipids . These data indicate that the PH domain of BRAG2 is sensitive to negatively charged membranes but does not discriminate between the different PIs . Notably , it is not specific for PI ( 4 , 5 ) P2 unlike previously suggested [10] . Consistently , we did not detect binding of IP3 , the soluble headgroup of PI ( 4 , 5 ) P2 , to BRAG2 Sec7-PH as measured by isothermal calorimetry , unlike what would have been expected for a tight specific interaction . These observations suggest that the PH domain of BRAG2 may not use its canonical lipid-binding pocket to recognize negatively charged lipids . We analyzed the contribution of this pocket by mutating Arg654 , a highly conserved residue located at the bottom of this pocket where it binds PI phosphates in PI-specific PH domains ( Figures 3B and S2B ) . The R654E charge reversal mutation had no effect of nucleotide exchange efficiency on membranes containing PS and PI ( 4 , 5 ) P2 ( kcat/Km values in Table 1 ) , supporting the hypothesis that the pocket is not involved in membrane recognition . To analyze whether Glu639 is the sole residue responsible for the lack of phosphoinositide specificity and/or recognition , we analyzed the exchange rates of BRAG2Sec7-PH constructs carrying the E639A or E639K mutations in the presence of liposomes containing each of the different PIs ( Figure 3C ) . Neither of the mutations had a marked effect on nucleotide exchange ( maximum 2-fold decrease ) and they had no effect when assayed in the presence of liposomes containing PS as the sole negatively charged lipid or containing PI ( 4 , 5 ) P2 . Notably , the E639K mutation did not restore phosphatidylinositide specificity but slightly inhibited nucleotide exchange . These data indicate that the atypical glutamate is not the only feature responsible for the lack of specificity of BRAG2 for PIs . The periphery of the canonical lipid-binding pocket in BRAG2 is enriched in positively charged residues ( Figure 3B ) , resulting in a highly positive electrostatic potential ( Figure 3D ) . The linker subdomain contributes to organizing this positively charged patch by stabilizing the loop β3–β4 , which contains several conserved lysines , away from the pocket ( Figures 1A and S2B ) . We propose that BRAG2 uses this positively charged surface to establish nonspecific electrostatic interactions with the phosphates of PS and PIs , rather than recognizes specifically any of them by the canonical pocket . ArfGEFs of the cytohesin family are regulated by a positive feedback loop mediated by their PH domain , which switches from auto-inhibition of the Sec7 domain in solution [5] to an activating role on membranes by coincident binding to PI ( 4 , 5 ) P2 or PI ( 3 , 4 , 5 ) P3 phosphoinositides [32] and to GTP-bound Arf proteins [6] . Cytohesins and BRAG ArfGEFs have a closely related organization encompassing a Sec7 and PH domain in tandem , which would predict that they have similar regulatory modes . At odds with this prediction , our study reveals that BRAG2 is not auto-inhibited by its PH domain , is not regulated by a feedback loop , and does not respond to specific phosphoinositides . We find that unanticipated differences between the structures of cytohesins and BRAG explain their diverging mechanisms . First , elements proximal to the PH domain that insert into the Sec7 active site to mediate auto-inhibition in cytohesins [5] have a different structure in BRAG2 , where they support a constitutively active conformation instead . Notably the unusually long C-terminal helix of the PH domain is kinked in cytohesins , and hence would conflict with the N-terminus of the Sec7 domain in BRAG2 ( Figure 4A ) , whereas it is straight in BRAG2 and would not be autoinhibitory in cytohesins ( Figure 4B ) . Next , the Sec7-PH linker in BRAG2 , by behaving as a subdomain that enlarges the PH domain ( Figure 1A and 1B ) , shields the surface of the PH domain predicted to bind Arf–GTP in cytohesins ( Y290 and I303 corresponding to V664 and S683 in BRAG2 ) [6] , [26] and hence makes it unavailable for feedback regulation . This also implies that cytohesins cannot adopt the same active conformation as BRAG2 , which would not be compatible with their binding of Arf–GTP . Finally , differences in sequence and conformation in and near the canonical lipid-binding pocket of the PH domain explain why cytohesins recognize PI ( 4 , 5 ) P2 or PI ( 3 , 4 , 5 ) P3 phosphoinositides specifically , while BRAG2 recognizes negatively charged membranes nonspecifically without using its pocket ( Figure 1D and S2B ) . Notably , stabilization of the long β3–β4 loop of the PH domain by the linker in BRAG2 organizes a positively charged surface that accounts well for its unspecific avidity for negatively charged lipids ( Figure 3B ) . Thus , localized differences between these related ArfGEFs add up to yield considerably different regulatory regimes , which could not be predicted from their overall domain homologies alone .
Understanding how small GTPases and their regulators depend on their lipid environment for their activity and specificity is a major issue in small GTPases biology that remains poorly understood . In this study , we combined structural analysis and nucleotide exchange reconstituted on liposomes to analyze how the ArfGEF activity of endosomal and cancer-involved BRAG2 is regulated on membranes . Our data reveal that the structure of BRAG2 constrains the relative orientations of its catalytic Sec7 domain , of its atypical membrane-binding PH domain , and of Arf such as to optimize them concurrently for membrane recruitment and for nucleotide exchange . The PH domain plays a pivotal role in modulating BRAG2 nucleotide exchange efficiency by integrating two separable components . On the one hand , its extension by the Sec7-PH linker allows it to form a loose interaction with Arf GTPases , thus providing a conformational contribution to the exchange efficiency of BRAG2Sec7-PH by about one order of magnitude compared to the Sec7 domain alone in the absence of membranes . On the other hand , it increases the exchange efficiency of BRAG2Sec7-PH by about two orders of magnitude by a dual membrane-controlled spatial contribution comprised of ( 1 ) an atypical interaction with negatively charged membranes outside the canonical lipid-binding pocket ( Figure 3B ) and ( 2 ) an intramolecular interaction with the Sec7 domain that increases the probability of a catalytically productive encounter between Arf and BRAG by aligning their lipid-binding regions ( Figure 1A ) . Remarkably , the conformational and spatial contributions are cumulative , resulting in a 2 , 000-fold increase of nucleotide exchange efficiency between BRAG2Sec7 in solution and BRAG2Sec7-PH on membranes ( Table 1 ) . Other members of the BRAG ArfGEF subfamily are highly homologous to members of the BRAG2 subgroup in the regions involved in lipid binding and nucleotide exchange . Notably , residues involved in intramolecular linker/PH and PH/Sec7 interactions ( Figure S2A ) and positively charged residues at the periphery of the canonical lipid-binding pocket ( ) are highly conserved in the entire subfamily . The only significant difference is a 11-residue insert in the BRAG2 linker , which is highly flexible in our structures and does not carry positively charged residues , making it unlikely that is has a major conformational or lipid-binding contributions . We therefore propose that the regulatory modalities of other BRAG members are similar to those of BRAG2 . The modalities of this large potentiation of the intrinsic activity of a GEF domain by a noncatalytic domain depart from the emerging paradigm of up-regulation of Ras , Arf , and Rho GEFs by auto-inhibition release via positive feedback loops [5] , [6] , [20]–[24] . The mechanism of BRAG2 thus reveals that not all GEFs comply to the feedback regulatory paradigm and expands the repertoire of mechanisms that should be considered in future studies of GEFs . Although it is known that many PH domains do not bind PIs with high specificity ( reviewed in [28] ) , the PH domain of BRAG2 is , to the best of our knowledge , the first PH domain shown to use nonspecific recognition of negatively charged membranes to quantitatively control a biochemical activity . An important issue arising is thus why BRAG2 activity would depend on the unspecific recognition of PS and PI-containing membranes . Different PIs in combination with PS constitute major signposts of plasma and endocytic membranes ( reviewed in [33] , [34] ) . PI ( 4 , 5 ) P2 , PI ( 3 , 4 , 5 ) P3 , as well as PI ( 4 ) P to some extent [35] contribute to define plasma membrane identity , while PI ( 3 ) P [36] and PI ( 5 ) P [37] are preferentially found on early endosomes . On the other hand , PS is the predominant anionic lipid at the plasma membrane and a major lipid in early endosomal membranes where it contributes to target or maintain proteins , but it is poorly abundant on late endosomes and on Golgi membranes [38] . This suggests an appealing model in which the PH domain of BRAG2 would be tailored for dual and/or sustained interaction with both plasma and early endosomal membranes . This could allow BRAG2 to activate Arf proteins at the plasma membrane where receptors nearing endocytosis are located , and to remain active on maturating membranes entering the receptor endocytic pathway ( Figure 5A ) . Divergences in regulation between cytohesin and BRAG ArfGEFs highlighted in this study may thus reflect their adaptation to distinct functional needs . Autoinhibition and PI specificity of cytohesins would allow them to be temporally and spatially restricted by phosphoinositide signals at the plasma membrane ( Figure 5B ) . BRAG , in contrast , would be suited for sustained activity on membranes undergoing phospholipid maturation along the receptor endocytosis pathway ( Figure 5A ) . Future work will be needed to analyze whether the efficient regulatory mechanism of BRAG2 relies either on autoregulatory features mediated by N-terminal elements of BRAG2 and/or on direct interaction with receptors . The dual specificity of cytohesins and BRAG2 for Arf1 and Arf6 could also fulfill different functional needs . While in cytohesins it may amplify an initial Arf signal , in BRAG2 it could reflect the sequential and/or simultaneous activation of different Arf isoforms . This could explain why , while BRAG2 has been consistently shown to activate Arf6 , its depletion and that of Arf6 have opposite effects on endocytosis of β1 integrins [9] , or that both Arf1 and Arf6 regulate the Wnt/β-catenin pathway [39] , a pathway that was recently demonstrated to require BRAG2 [8] . The robust structural and biochemical characterization of BRAG2 regulation reported in our study should now be valuable for future investigations of the coordination between trafficking pathways and receptor endocytosis and signaling in normal and cancer cells .
PCR products encoding human BRAG2Sec7 ( residues 390–594 ) or BRAG2Sec7-PH ( residues 390–763 or 390–811 ) were cloned into the pProEX-HTb vector ( Invitrogen ) as a fusion with a N-terminal 6-His tag followed by a tobacco etch virus ( TEV ) protease cleavage site . BRAG2 mutants were generated with the QuikChange II XL kit ( Stratagene ) . All constructs were confirmed by sequencing . All BRAG2 constructs were expressed in E . coli BL21 Gold strain at 37°C with 3 h of induction with IPTG ( 0 . 5 mM ) . Seleno-methionine ( SeMet ) BRAG2Sec7-PH/E498K was incorporated as described in [40] . Cells were disrupted by sonication in buffer A ( 20 mM phosphate buffer pH 7 . 4 , 10 mM imidazole , 500 mM NaCl , and 5 mM β-mercaptoethanol ) completed with 0 . 5 mg/ml of lysozyme and a protease inhibitor cocktail . Cleared lysates were loaded on nickel-nitrilotriacetic acid ( Ni-NTA ) affinity chromatography ( HisTrap FF , GE Healthcare ) equilibrated with buffer A , eluted with a 10–500 mM linear imidazole gradient , and when indicated , cleaved with the TEV protease ( 1∶10 w/w ) overnight at 4°C and reloaded on a HisTrap column . For BRAG2Sec7-PH/E498K , an additional step of ion exchange chromatography was performed on a MonoS column ( GE Healthcare ) . Purification of all BRAG2 constructs was polished by gel filtration on a Superdex 75 XK 16/90 ( GE Healthcare ) equilibrated with 20 mM HEPES pH 7 . 4 , 5 mM β-mercaptoethanol , and 100–500 mM NaCl . Human Δ17Arf1 and Δ13Arf6 were expressed and purified as described in [41] and [42] and loaded with GDP prior to kinetics experiments . Nucleotide content was assessed by thermal denaturation followed by ion exchange chromatography . Myristoylation of full-length Arf1 was done by co-expression with yeast N-myristoyl transferase and purified as described in [43] . Myristoylation of full-length Arf6 carrying a C-terminal 6-His tag was done in vitro with recombinant human N-myristoyltransferase [44] . SDS-PAGE gels of proteins used in this study are shown in Figure S1A . The Δ17Arf1–GDP/BRAG2Sec7-PH/E498K complex was obtained by incubation in 20 mM HEPES pH 7 . 4 , 100 mM NaCl , 1 mM MgCl2 , 5 mM β-mercaptoethanol , and 2 mM EDTA . The nucleotide-free complex was obtained by incubating Δ17Arf1–GDP and BRAG2Sec7-PH ( 2∶1 ratio ) with 1 U/mg of alkaline phosphatase ( Sigma ) in 20 mM HEPES pH 7 . 4 , 150 mM NaCl , 4 mM β-mercaptoethanol overnight at 4°C . Both complexes were purified by size exclusion chromatography on a Superdex75 10/300 column ( GE Healthcare ) equilibrated with their incubation buffer , supplemented with 5 mM EDTA for the nucleotide-free complex . All lipids were from Avanti Polar Lipids , and NBD-PE was from Invitrogen . Liposomes were prepared as described [6] in 50 mM HEPES pH 7 . 4 , 120 mM potassium acetate buffer , and freshly extruded through a 200 nm filter ( Whatman ) . Liposome flotation assays were performed as described in [45] . Briefly , 1 µM of protein was incubated with liposomes ( 1 mM total lipids ) for 5 min at room temperature in 50 mM HEPES pH 7 . 4 buffer containing 120 mM potassium acetate , 1 mM MgCl2 , and 1 mM DTT ( HKM buffer ) . The solution was brought to 30% sucrose , overlaid with two layers of HKM containing 25% , and no sucrose then submitted to centrifugation at 240 , 000 g in a TLS55 swing rotor ( Beckman ) for 1 h at 20°C . Liposome-bound proteins ( top fraction ) and unbound proteins ( bottom fraction ) were collected manually and analyzed by SDS-PAGE after SYPRO Orange ( Invitrogen ) staining using a Fuji LAS-3000 fluorescence imaging system . All experiments were done in triplicate . Nucleotide exchange kinetics were monitored by tryptophan fluorescence with excitation and emission wavelengths of 292 nm and 340 nm on a Cary Eclipse fluorimeter ( Varian ) under stirring . All experiments were carried out at 37°C by the successive addition of Arf , BRAG2 , and finally 100 µM GTP to initiate nucleotide exchange . Exchange assays without liposomes were performed in 50 mM HEPES pH 7 . 4 , 50 mM NaCl , 2 mM MgCl2 , 2 mM β-mercaptoethanol , using 1 µM Arf and BRAG2 constructs ( 0–0 . 4 µM range ) for catalytic efficiency ( kcat/Km ) determinations . Exchange assays with liposomes were done with 100 µM pre-warmed liposomes in 50 mM HEPES pH 7 . 4 , 120 mM potassium acetate , 1 mM MgCl2 , 1 mM DTT with 0 . 4 µM myrArf and BRAG2 constructs ( 0–1 nM range ) for kcat/Km determinations , or a fixed concentration of 1 nM for single exchange rates ( kobs ) determination . Except for myrArf6 activation , kobs were determined from a monoexponential fit taking into account the linear drift of fluorescence due to photobleaching . kcat/Km were obtained following a Michaelis-Menten formalism as described in [41] from:where kspont is the spontaneous nucleotide exchange rate constant . All experiments were done at least in triplicate . myrArf6 activation kinetics could not be analyzed by a single exponential fit . This unusual behavior was observed whether the exchange reaction was monitored by tryptophan fluorescence , which measures Arf conformational change upon nucleotide exchange ( Figure S4A ) , or by mantGTP fluorescence , which measures nucleotide exchange directly ( unpublished data ) . This behavior was seen with other Arf6–GEFs ( [46] , our unpublished results ) , but was not observed with BRAG2Sec7 in the presence of liposomes or with BRAG2Sec7–PH in solution , and was not due to undesirable liposome aggregation due to Arf6 or to BRAG2 ( Figure S4B ) . This behavior was also independent of the concentration of myrArf6 used in the assay , thus ruling out a saturation effect ( unpublished data ) . We surmise that it is due to the fact that Arf6 releases GDP spontaneously much faster than Arf1 ( [47] , compare also Figure 2A to 2B and 2C to 2D ) , resulting in a fraction of membrane-bound nucleotide-free myrArf6–GDP that undergoes fast activation . To circumvent this feature , Arf6 exchange kinetics were analyzed using initial velocities ( Vi ) , which were plotted as a function of BRAG2 concentration . Liposomes ( 150 µM ) were loaded with increasing amounts of myrArf6–GTP before 1 nM BRAG2Sec7-PH , 100 µM GTP , and 0 . 4 µM myrArf1–GDP were added in sequence . The exchange rate of myrArf1 was determined by fitting the fluorescence change of the second part of the reaction to a single exponential . The BRAG2Sec7-PH/E498K/Δ17Arf1–GDP complex was concentrated to about 1 . 5 mg/ml for crystallization . Crystals were obtained either with Se-Met BRAG2 with the 6-His tag cleaved , and with native BRAG2 carrying the tag ( native crystals ) . Se-Met crystals grew in 0 . 15 M ammonium sulfate , 0 . 1 M MES pH 6 , and 16% PEG 4000 , and native crystals in 0 . 15 M ammonium sulfate , 0 . 1 M NaH2PO4/Na2HPO4 pH 6 , and 13% PEG 4000 . Crystals were transferred to the reservoir solution adjusted at 17% PEG 4000 and supplemented with 20% PEG 400 and flash frozen in liquid nitrogen . Diffraction data were collected at beamline PROXIMA1 ( SOLEIL Synchrotron , Gif-sur-Yvette , France ) at 0 . 98 Å wavelength for the native crystals , and at the f′ maximum of the selenium edge ( 0 . 979 Å ) for the Se-Met crystals . Intensities were integrated and scaled with XDS [48] for the Se-Met crystals and integrated with imosflm [49] and scaled with scala for the native crystal . The native crystals belong to space group C2 and contain two complexes related by translational non-crystallographic symmetry ( TNCS ) in the asymmetric unit , and the Se-Met crystals belong to space group P2 and contain four complexes related by TNCS in the asymmetric unit . The selenium anomalous signal from the Se-Met crystals did not allow for phasing . Alternatively , the structure of the C2 native crystal was solved by molecular replacement with the program AMORE [50] , using Δ17Arf1–GDP from the Δ17Arf1–GDP/ARNO complex ( PDB entry 1R8S , [4] ) , the Sec7 domain from the Δ17Arf1–GDP/ARNO ( PDB entry 1R8S ) from which sequence differences were modeled as alanines , and the PH domain of BRAG2 ( unpublished PDB entry 3QWM ) as search models . The solution was found using TNCS with data between 15 and 4 . 5 Å . A similar strategy using TNCS and data between 45 and 3 . 5 Å was used to solve the P2 crystal form . Rigid body refinement was done with Phaser [51] . Refinement was carried out with Phenix [52] and autoBUSTER [53] , in alternation with graphical building using Coot [54] . The bound nucleotide is GDP-3′P , a GDP derivative produced by E . coli under stress conditions that commonly substitutes for GDP in other small GTPases structures without impairing their structures ( PDB entries 2HXS , 2ZJ6 , 1R8Q , 1MR3 ) . The conformation of Arf1 and its position relative to the Sec7 domain of BRAG2 are also similar to those previously observed for Arf1–GDP in complex with the Sec7 domain of ARNO carrying the E/K mutation [4] , indicating that it is not due to GDP-3′-P . Crystallographic statistics and details of the refinement procedure are given in Table S1 . Coordinates have been deposited with the Protein Data Bank with accession code 4C0A . The electrostatic potential was calculated from the crystallographic coordinates of BRAG2 with PDB2PQR [55] . Contour levels were expressed as multiples of dimentionless unit kT/e , where k is the Boltzmann's constant , T is the temperature , and e is the charge of an electron , and were displayed with PYMOL . SAXS experiments were conducted on beamline SWING ( SOLEIL Synchrotron , Gif-sur-Yvette , France ) essentially as described in [56] . The histidine tag of BRAG2Sec7-PH was cleaved for SAXS data collection , as unstructured tags add noise to SAXS experiments . The protein sample was injected into a size-exclusion column and eluted directly into the SAXS flow-through capillary cell . Data were analyzed with Foxtrot ( SOLEIL software group and SWING beamline ) and the ATSAS software suite ( EMBL , Hamburg , www . embl-hamburg . de/biosaxs/software . html ) . Scattered intensity from the atomic coordinates of the crystallographic structure was calculated using CRYSOL . The fit of the calculated intensity to the experimental intensity was assessed as described in [56] . | Understanding the molecular mechanisms that allow guanine exchange factor proteins ( GEFs ) to coordinate their GDP/GTP exchange activity with their being targeted to specific intracellular membranes is an important issue . In this study , we solved the crystal structure of the ArfGEF BRAG2 , an endosomal protein that is involved in invasive phenotypes in various tumors , in a complex with the small GTPase Arf1 . We show that the pleckstrin homology ( PH ) domain of BRAG2 atypically does not auto-inhibit its Sec7 domain ( as has been seen in ArfGEFs belonging to the cytohesin family ) , but instead potentiates nucleotide exchange 10-fold in solution and up to 2 , 000-fold in the presence of liposomes . This stimulatory effect requires negatively charged membranes , and does not involve a preference of the PH domain for specific phosphoinositides or the use of its canonical lipid-binding pocket . This uncovers a regulatory mechanism in which the PH domain controls GEF efficiency by concurrently optimizing membrane recruitment and nucleotide exchange . | [
"Abstract",
"Introduction",
"Results",
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"and",
"Methods"
] | [] | 2013 | Integrated Conformational and Lipid-Sensing Regulation of Endosomal ArfGEF BRAG2 |
Species-dependent variation in proteins that aid or limit virus replication determines the ability of lentiviruses to jump between host species . Identifying and overcoming these differences facilitates the development of animal models for HIV-1 , including models based on chimeric SIVs that express HIV-1 envelope ( Env ) glycoproteins , ( SHIVs ) and simian-tropic HIV-1 ( stHIV ) strains . Here , we demonstrate that the inherently poor ability of most HIV-1 Env proteins to use macaque CD4 as a receptor is improved during adaptation by virus passage in macaques . We identify a single amino acid , A281 , in HIV-1 Env that consistently changes during adaptation in macaques and affects the ability of HIV-1 Env to use macaque CD4 . Importantly , mutations at A281 do not markedly affect HIV-1 Env neutralization properties . Our findings should facilitate the design of HIV-1 Env proteins for use in non-human primate models and thus expedite the development of clinically relevant reagents for testing interventions against HIV-1 .
Adaptation of primate lentiviruses to new hosts requires the acquisition of both resistance to species-specific inhibitors and ability to optimally use key host factors critical for virus replication [1] . Understanding how host protein variation drives lentivirus adaptation provides important insights into the evolutionary history of lentiviruses and it can guide efforts to generate improved animal models for AIDS by expanding primate lentivirus host range . Non-human primates are commonly used to model human HIV-1 infection . Rhesus and pigtail macaques ( Macaca mulatta and Macaca nemestrina , respectively ) are not natural hosts for primate lentiviruses and they can develop AIDS-like disease when infected with certain simian immunodeficiency viruses ( SIV ) derived from the SIVSM/SIVMAC lineage . Moreover , chimeric SIVMAC-based viruses that encode HIV-1 envelope ( Env ) glycoproteins allow the evaluation of interventions directed against the HIV-1 Env protein rather than its genetically and antigenically distinct SIVMAC counterpart ( reviewed in [1] ) . For example , approaches that aim to elicit broadly neutralizing antibodies ( bnabs ) [2–5] by vaccination , or to use bnabs and antibody-like molecules as treatment and prevention agents depend on the use of SHIV models for in vivo evaluation [6–15] . SHIV development has been difficult , and the paucity of available SHIVs with robust transmissibility and consistent pathogenicity fails to represent the clinically relevant diversity of circulating HIV-1 strains . Recent efforts have focused on the development of SHIVs incorporating Env proteins derived from transmitted/founder ( T/F ) HIV-1 , viruses that have successfully established initial infections in humans [16 , 17] . Compared to most SHIVs used previously , such reagents might represent more relevant targets at which prophylactic and treatment interventions would be aimed [18–21] . Our own studies have described the generation and in vivo selection of SHIVs encoding clade B T/F HIV-1 Env proteins , designated SHIV1054 and SHIVAD081 , that are pathogenic in rhesus macaques and lead to AIDS-like disease in a subset of infected rhesus macaques without requiring animal-to-animal passage [22] . Nevertheless , pathogenicity of these SHIVs is not consistent . Indeed , most SHIVs , have not given persistent high level viral replication , authentic HIV-like pathogenesis and progression to AIDS in macaques . Derivation of pathogenic isolates from T/F-SHIVs has required animal-to-animal passage [18–21] . In addition to SHIVs , animal models based on HIV-1 strains , rather than SIV , are also being developed by generating minimally modified simian-tropic HIV-1 strains , designated stHIVs , that can replicate in pigtail macaques [23 , 24] . Recently , by employing serial animal-to animal passage , we derived an stHIV that can consistently cause AIDS-like disease in pigtail macaques subjected to transient CD8+ cell depletion during acute infection [25] . Although restriction factor evasion was critical for the development of stHIV , adaptation of HIV-1 Env to pigtail macaques also likely played an important role in the development of this model , similar to HIV-1 Env adaptation in rhesus macaques in the context of SHIVs . The goal of the present study was to identify properties of HIV-1 Env proteins that determine their ability to support virus replication in macaques and whether these properties can be exploited to guide development of additional SHIV or stHIV strains . Our experiments show that most HIV-1 Env proteins have reduced ability to use macaque CD4 compared to human CD4 , in agreement with other published studies [21 , 26] and further demonstrate that selection by SHIV or stHIV by adaptation in macaques can improve this ability . Notably , we identify a single amino-acid change , A281T/V , within the CD4 binding site that improves the ability of HIV-1 Env proteins to use macaque CD4 and enables SHIV replication in macaque T-cells . Although previous studies have uncovered other individual residues in HIV-1 Env proteins that can improve macaque CD4 use [21 , 27 , 28] , only the A281T/V change identified herein occurs repeatedly during HIV-1 Env adaptation in macaques . Importantly , unlike some of the other mutations , the A281T/V does not have major effects on Env protein neutralization properties . These findings identify a host range determinant in HIV-1 and should facilitate the expansion of the clinically relevant viral repertoire for use in animal models .
We previously demonstrated that in vivo competition following inoculation of macaques with pools of viruses allows the identification of unadapted HIV-1 Env proteins that confer high levels of replication in the context of a SHIV [22] . Although these SHIVs caused AIDS-like disease in some rhesus macaques , progression to disease was inconsistent . We selected one such SHIV , SHIV1054 , and employed serial animal-to-animal passage to improve its pathogenicity ( Fig 1A ) . Blood obtained at 66 weeks post-inoculation ( p . i . ) from a SHIV1054-infected rhesus macaque , here designated P1A , that eventually succumbed to AIDS [22] was used to inoculate two naïve rhesus macaques , P2A and P2B ( Fig 1A ) . Both P2 macaques developed high levels of acute viremia ( Fig 1B ) and peripheral CD4+ T cell levels declined markedly in both animals following inoculation , partially recovered in P2A and eventually declined further ( S1A Fig ) . A progressive decline to AIDS defining levels of CD4+ T cells ( S1A Fig ) occurred in P2A which succumbed to AIDS-like disease at 44 weeks p . i . ( Fig 1A and 1B ) . Blood from P2A obtained at 21 weeks p . i . was used to initiate a third passage of SHIV1054 in two macaques and both P3 animals exhibited more rapid progression to disease , requiring euthanasia at weeks 11 and 28 weeks p . i . ( Fig 1B ) . Peripheral CD4+ T cells declined dramatically in both infected animals upon inoculation and did not recover ( S1A Fig ) . To understand the nature of in vivo adaptation that occurred during SHIV1054 passage , single-genome sequence analysis of Env-coding sequences was performed using plasma samples obtained at various time points p . i . from all infected animals and , as expected , revealed progressive accumulation of sequence changes ( Fig 1C ) . To assess the potential functional consequences of these changes , representative Env clones derived from P1 macaques at weeks 21 and 66 p . i . and P2 and P3 macaques at week 1 p . i . , were amplified by PCR and re-introduced into the parental SHIV backbone ( Fig 1C ) . Adapted Env sequences were named according to passage number and time of blood draw . For example “P2 ( Aw1 ) a” would refer to an individual sequence recovered at passage 2 , animal A at 1 week post infection , designated clone ‘a’ . We next determined the ability of the cloned Env sequences derived from macaques to support viral replication in vitro . In huPBMC , SHIV1054 and derivatives encoding most of the adapted clones replicated efficiently , though for a few adapted clones from P1 and P3 replication was somewhat delayed or attenuated ( Fig 2 ) . In contrast , large differences in replication were observed in rhesus macaque cells ( Fig 2 ) . In rhPBMC , SHIV1054 and three SHIVs expressing adapted Env clones from P1 and P3 barely replicated , however , the majority of SHIVs expressing adapted Env clones replicated markedly more efficiently than the parental SHIV1054 ( Fig 2 ) . These differences were less pronounced in 221 cells , an immortalized rhesus macaque T-cell line [29] . The parental SHIV1054 replicated efficiently is 221 cells as did the majority of SHIVs expressing adapted Env proteins ( Fig 2 ) . SHIVs with impaired replication in rhPBMC also exhibited defects in replication in 221 cells to various degrees; replication of SHIV-P1 ( Aw21 ) a and SHIV-P3 ( Aw1 ) b was extremely low in 221 cells , whereas SHIV-P1 ( Bw21 ) d and SHIV-P1 ( Bw21 ) e replicated more efficiently in 221 cells than rhPBMC but were nonetheless delayed ( peak virus levels reached at 9 and 12 days p . i . respectively ) compared to most viruses ( peak virus levels reached at day 7 p . i . ) ( Fig 2 ) . SHIV-P3 ( Aw1 ) e also replicated more efficiently in 221 cells compared to rhPBMC but the delay in replication in 221 was more prominent than SHIV-P1 ( Bw21 ) d and SHIV- P1 ( Bw21 ) e ( Fig 2 ) . We also tested replication of a subset of the SHIVs in CD8 depleted rhesus macaque cells ( S1B Fig ) . Critically , the rank order of replication efficiency for SHIV1054 and derivatives mirrored that obtained in 221 cells , demonstrating that this cell line maintains species-specific differences that impact SHIV replication efficiency . However , like CD4 enriched primary T-cells , 221 cells are more permissive than rhPBMC . Altogether , the data showed that , with the exception of a few clones , most adapted Env sequences conferred improved SHIV replication compared to the parental 1054 Env protein , specifically in macaque cells . Replication of SHIVs in macaque cells could be potentially limited by the inability of HIV-1 Env proteins to engage macaque CD4 . Indeed , a prior study has shown that a single amino-acid ( aa ) difference at position 39 of macaque CD4 decreases HIV-1 receptor function compared to human CD4 [26] . CD4 is well conserved among individual humans , with only one common nonsynonymous single nucleotide polymorphism ( SNP ) [30] , but analysis of CD4 polymorphism in macaques has been limited , often using few animals or partial sequence analysis [26 , 31] . We analyzed CD4 cDNA sequences from 17 rhesus and 14 pigtail macaques and found no less than twelve variants encoding distinct protein sequences ( that included all previously reported macaque CD4 sequences ) . Two different variants were clearly predominant in rhesus macaques and pigtailed macaques ( S2A Fig ) and were thus designated rhCD4 and pgtCD4 ( Fig 3A ) , though we note that the rhCD4 variant was also found in four heterozygous pigtail macaques . In pigtail macaques , 7/14 animals were homozygous for pgtCD4 , one was heterozygous for pgtCD4 and rhCD4 and 6 animals expressed either pgtCD4 or rhCD4 along with one of four minor variants . In rhesus macaques , 7/12 animals were homozygous for rhCD4 , while 5 expressed rhCD4 and one minor variant . Of note , variation within individual animals in CD4 has been previously observed in sooty mangabeys and African green monkeys [31] . In macaques , the minor variants differed from rhCD4 and pgtCD4 at one or two amino acid positions , mainly toward the C-terminus of the protein ( S2A Fig ) and some also had synonymous changes ( Genbank accession numbers MF769792-804 ) . All macaque CD4 variants differed from human CD4 at aa 39 ( Fig 3A ) . Although the biological significance of CD4 polymorphism in macaques is unknown and warrants further examination , for the purposes of this study we focused our attention on the two major variants , rhCD4 and pgtCD4 , that were represented in all animals . To determine the ability of these CD4 variants to function as receptors for HIV-1 Env , we generated reporter cell lines , designated Helios cells , based on HeLa cells that express the nanoluciferase gene under the control of the HIV-2 LTR ( that responds to both HIV-1 and SIVMAC ) . Subsequently , Helios derivatives were engineered to stably express huCCR5 and either huCD4 , rhCD4 or pgtCD4 ( S2C Fig ) . Identical , high levels of CD4 were present in Helios cells expressing each of the CD4 variants ( S2B Fig ) . Viruses were titrated on all cell lines and results expressed as the relative infectivity of each virus on rh/pgt-CD4-Helios compared to huCD4-Helios cells . We note , that 221 cells express a minor variant that differed from the major rhCD4 variant used in Helios at two amino acid positions; I144L and A324T . SIVMAC infectivity was similar in cells expressing huCD4 , rhCD4 or pgtCD4 , ( ratio approaching 1 ) but the parental , unadapted SHIV1054 was less infectious on rhCD4-Helios and pgt-CD4-Helios compared to huCD4-Helios ( Fig 3B ) . In contrast , the relative infectivity of the majority of adapted SHIV1054 Env variants on rhCD4-/pgtCD4-Helios was higher than the parental SHIV1054 ( Fig 3B ) , suggesting that in vivo adaptation resulted in a more efficient use of macaque CD4 receptor variants . Unlike most SHIVs expressing adapted clones , SHIV-P3 ( Aw1 ) b was impaired in its ability to infect rh/pgtCD4-Helios ( Fig 3B ) correlating with its inability to replicate efficiently in macaque cells ( Fig 2 ) . Additionally , the overall infectivity of SHIVs expressing Env proteins P1 ( Aw21 ) a , and P3 ( Bw1 ) e was low in all Helios cells ( S3 Fig and compared with Fig 3B ) again correlating with delayed or decreased levels of replication in both human and macaque cells ( Fig 2 ) . Despite these exceptions , these data show that , in general , adaptation of HIV-1 Env in macaques improves their ability to use macaque CD4 proteins . To identify changes that were responsible for improved macaque CD4 use by adapted Env proteins , we compared the sequences of adapted SHIV1054 clones with the parental SHIV1054 Env . Although many changes accumulated in the SHIV1054 Env coding sequence during in vivo passage , only a single change , A281T , in the CD4 binding site was common to the majority of clones ( Fig 4A ) . This change was first detected at week 17 p . i . in animal P1A ( Fig 4A ) . However , due to the limited number of sequences analyzed from the early time points we cannot exclude the possibility that this change appeared earlier . Notably , the A281T change was present in all Env clones sequenced at week 35 p . i . and subsequent time points ( Fig 4A ) . We compared the in vivo adaptation of 1054 Env to the adaptation of a different HIV-1 Env sequence , AD8 , that we previously passaged in pigtail macaques in the context of stHIV [25] . We compared the parental AD8 sequence with twenty adapted AD8 Env coding sequences obtained from the first pigtailed macaque animal to succumb to HIV-1-induced AIDS ( at passage 4 ( P4 ) ) . Although multiple mutations had accumulated in individual clones , all had a change at position A281: 11 clones had A281T , 8 clones had A281V and 1 clone had A281I . Env coding sequences from clones obtained from subsequent passages ( P5 and P6 ) almost exclusively contained V at position 281 [25] . The A281 mutation was the only change in the CD4-binding site common to all our pigtail macaque-adapted AD8 sequences , ( represented here by a functional Env clone AD8-P4 ( Fig 4B ) ) , as well as the adapted 1054 Env clones ( Fig 4A ) . Notably , an A281T mutation also occurred in an independent adaptation of the AD8 Env in rhesus macaques in the context of a SHIV , [32 , 33] , represented by AD8-EO ( Fig 4B ) . Given that A281T/V mutations occurred in all three independent adaptation experiments , we inspected additional published sequences and found that an A281V change also occurred during the macaque adaptation of several other SHIVs , specifically SHIVSF162P3 [34] , SHIVKB9 ( derived from SHIV89 . 6 ) [35] , SHIVSF33A2 [36] and clade C SHIVC109P4 ( [18] and Cecilia Cheng-Mayer personal communication ) ( Fig 4B ) . ( SIV divergence in this region precluded identification of the corresponding residue in SIV Env proteins . ) These findings suggested a role for residue 281 in the adaptation of HIV-1 Env proteins in macaques . Inspection of the monomeric HIV-1 gp120-huCD4 crystal structure [37] revealed that A281 is positioned at the gp120-CD4 interface ( Fig 4C ) . Remarkably , this position in HIV-1 gp120 is proximal to CD4 aa 39 that confers poor HIV-1 receptor function on macaque CD4 proteins [26] . Therefore , variants at position 281 are optimally positioned to directly and specifically affect the interaction between HIV-1 Env and macaque CD4 . To determine the effects of mutations at residue A281 on the ability to use macaque CD4 , we introduced A281T into the parental SHIV1054 . Unlike most HIV-1 Env proteins , the parental SHIV1054 was able to use macaque CD4 variants with modest efficiency ( ~2 . 5–4 fold less efficiently than huCD4 ) . Introduction of the A281T change further improved this ability ( Fig 5A ) to a degree comparable to the in vivo adapted SHIV1054–derived Env proteins ( Fig 3B ) . As SHIV1054 replicated efficiently in huPBMC and 221 cells the A281T mutation did not appreciably impact this ability , however , in rhPBMC where SHIV1054 is attenuated , the A281T change caused a small increase in replication efficiency ( Fig 5B ) . To determine the effects of the A281 change in the context of the AD8 Env , we generated SHIVs expressing the parental AD8 Env and introduced the A281T or V mutations therein . We also generated a SHIV expressing the adapted AD8 Env from our passage 4 stHIV clone , AD8-P4 . As was the case with SHIV1054 , SHIVAD8 infectivity on rh/pgtCD4-Helios cells was reduced only modestly ( ~3-fold ) compared to huCD4-Helios ( Fig 5C ) . Nevertheless , introduction of the single amino acid change at position A281 further decreased these differences ( Fig 5C ) . Notably , the effects of the A281T/V mutations on use of macaque CD4 were comparable to that of in vivo adaptation in AD8-P4 . Although these effects were modest , we note that AD8 , like 1054 , is unusual amongst HIV-1 Env proteins in that it can use macaque CD4 variants quite efficiently ( see below ) . Although the A281 mutations in AD8 Env did not affect replication in huPBMC or 221 cells , they noticeably improved replication in rhPBMC ( Fig 5D ) . Like 221 cells [29] , human and macaque PBMC , Helios cells express high levels of CD4 ( S2B and S2C Fig ) . However , CD4 density on macrophages in humans is relatively low [38] and consequently macrophage-tropic viruses have adapted to low levels of CD4 [38–40] . Interestingly , AD8 is a macrophage tropic Env and maintains macrophage tropism in pigtail macaques [23] . We reasoned that the ability to use to low levels of CD4 , as is the case for macrophage tropic HIV-1 Env proteins , could enhance their ability to use CD4 molecules that are normally not used efficiently , such as macaque CD4 . Therefore , we introduced the N283T mutation into our SHIVAD8 , a change known to specifically disrupt the ability of AD8 Env to infect macrophages and render it T-cell tropic [41] . The N283T mutation diminished SHIVAD8 infectivity on Helios cells expressing rhCD4 or pgtCD4 by ~5- to 10- fold but had no effect in cells expressing huCD4 ( Fig 5C ) . Consequently , while all SHIVAD8 derivatives replicated equivalently in huPBMC , the N283T mutation abolished replication specifically in 221 and rhPBMC ( Fig 5D ) . Notably , the A281T mutation was able to partially restore the infectivity of AD8-N283T on rhCD4 or pgtCD4 variants and enabled low levels of replication in 221 cells ( Fig 5C and 5D ) . The effects of the A281T mutations on AD8-N283T were not sufficient to improve replication in rhPBMC . Taken together these data demonstrate a correlation between the ability of SHIVs to use macaque CD4 ( a property influenced by Env residue 281 ) and replication efficiency in rhesus macaque cells in vitro . To determine whether A281 mutations could generally enhance the ability SHIVs to use macaque CD4 , we introduced the A281V change into 7 randomly selected members of a library of SHIVs expressing T/F HIV-1 Env proteins [22] . Introduction of the A281V mutation did not affect envelope protein expression or incorporation into virions ( S4A Fig ) . All the unmodified HIV-1 Env proteins exhibited poor infectivity on Helios cells expressing macaque CD4 variants compared to huCD4-Helios ( Fig 6A ) , markedly lower than 1054 and AD8 Env proteins ( Fig 5A and 5C ) . In all seven cases , the A281V change enhanced infectivity in cells expressing rh/pgtCD4 , with effect sizes ranging from 2 . 5-fold to 10-fold for rhCD4 and 3-fold to 17-fold for pgtCD4 ( Fig 6A ) . Moreover , the A281V change had dramatic consequences in the ability of SHIVs to replicate in macaque cells . All SHIVs replicated with comparable efficiency in human cells , however , with one exception ( SHIVCH040 ) none replicated in macaque 221 cells ( Fig 6B ) . Introduction of the A281V mutation did not affect replication in human cells but it conferred the ability to replicate in macaque 221 cells in most SHIVs ( Fig 6B ) . In four of those SHIVs , SHIV1051C , SHIV1051TD , SHIV62357 and SHIVCH040 , the effects were striking . In the remaining 2 SHIVs the A281V change resulted in only marginal improvements ( SHIVTT31 ) or failed to improve replication efficiency in 221 cells ( SHIVTT29 ) ( Fig 6B ) . Interestingly , in these two SHIVs , the A281V mutation had the smallest effect on their ability to use rhCD4 ( Fig 6A ) . Additionally , only the two SHIVS , SHIV1051TD and SHIVCH040 , for which the A281V substitution had the biggest effects on replication in 221 gained the ability to replicate in rhPBMC ( S4B Fig ) . These results suggest that HIV-1 Env proteins are required to achieve a certain threshold ability to use macaque CD4 in order to replicate robustly in macaque cells and demonstrate an important role for residue 281 in expanding the tropism of HIV-1 Env proteins to include macaque CD4 . Although optimal macaque CD4 use is critical for efficient SHIV replication in vivo , for utility in their intended applications , it is also important that SHIV neutralization properties reflect those of parental HIV-1 strains circulating in humans [42] . Therefore , we determined the effects of the A281T/V mutation on SHIV neutralization tier classification ( a representation of overall neutralization sensitivity ) [43] and sensitivity/resistance to individual antibodies . The introduction of the A281T mutation in 1054 did not alter the neutralization tier ( S5 Fig ) nor the sensitivity to soluble CD4 or the majority of antibodies tested ( Fig 7 ) . As might be expected , neutralization sensitivity to individual antibodies was altered for some of the adapted 1054 clones ( Fig 7 ) , that could potentially reflect selection attributable to particular immune responses in the animal from which they were derived . Furthermore , two clones P1 ( Bw21 ) e and P3 ( Bw1 ) e had acquired a substantially more neutralization sensitive tier classification compared to the parental Env protein ( Fig 7 and S5 Fig ) . However , the majority of adapted clones maintained the neutralization resistant tier 3 phenotype of SHIV1054 ( Fig 7 and S5 Fig ) . Introduction of individual mutations A281T/V , N283T and A281T/N283T in SHIVAD8 did not substantially affect overall Env neutralization properties ( Fig 7 and S5 Fig ) . SHIVAD8-P4 sensitivity to certain antibodies , such as IgGb12 , 2G12 and 2F5 was different from the parental SHIVAD8 ( Fig 7 ) , a finding that is not unexpected given that this Env clone was derived following extensive in vivo adaptation . Nevertheless , this Env maintained the parental AD8 neutralization resistant tier 1B phenotype ( S5 Fig ) and bnAb neutralization properties ( Fig 7 ) . Similarly , the additional seven SHIVs maintained their resistant phenotype upon introduction of the A281V mutations ( Fig 7 and S5 Fig ) . Importantly , the introduction of mutation ( s ) at position 281 had little or no effect on the sensitivity of HIV-1 Env proteins AD8 , SC31 , TT29 TT31 , 1051C and 1051TD to PG9 and/or PG16 , antibodies that target conformational epitopes ( Fig 7 ) . This finding further supports the notion that A281V/T mutations do not affect overall Env conformation and thus do not alter Env sensitivity to various antibodies nor the overall neutralization phenotype .
Here , we demonstrate that a single aa , 281 , in the CD4 binding site of HIV-1 Env is a determinant of the narrow tropism exhibited by HIV-1 . We demonstrate that in vivo adaptation in macaques commonly results in a mutation at aa 281 that enhances the ability of HIV-1 Env to use macaque CD4 , thereby improving replication in macaque cells without changing overall Env conformation or neutralization properties . Previous studies have reported that other individual HIV-1 Env residues can affect macaque CD4 use . Specifically , two aa changes , A204E and G312V , acquired during adaptation of a clade A SHIV in pigtail macaque cells in vitro significantly enhanced macaque CD4 use [27 , 28] . These mutations also cause resistance to PG9 and PG16 , antibodies known to recognize quaternary gp120 epitopes , while enhancing sensitivity to other antibodies . This finding suggests that A204E and G312V induce the adoption of a more ‘open’ Env protein conformation and increase both interaction with CD4 and overall neutralization sensitivity . Indeed , these residues are located distal to the CD4 binding site in the structure of the gp120 trimer [44] ( Fig 8 ) . It is possible that A204E/G312V changes arose in vitro because they were selected in the absence of immune pressure that might otherwise constrain Env proteins to neutralization resistant conformations . Interestingly , we did not observe changes at positions 204 or 312 in the SHIV sequences identified during in vivo adaptation , but did find G312 in about 30% of stHIVAD8 Env clones we obtained at necropsy from the P4 animal that succumbed to AIDS [25] . However , G312V was lost in subsequent stHIVAD8 passages and was not present in the AD8-P4 Env clone used in these studies . Crucially , the in vivo adapted 1054 and AD8 Env proteins maintained the overall neutralization properties of the parental protein , with some exceptions ( Fig 7 ) . In contrast to results from in vitro selection experiments , we did not observe correlation between resistance to antibodies recognizing quaternary epitopes or acquisition of a neutralization sensitive phenotype ( indicating adoption of a more ‘open’ conformation ) with improved macaque CD4 use . Thus , HIV-1 Env proteins can apparently adapt to macaque CD4 in multiple ways with variable consequences , and it is clearly possible to derive viruses that can use macaque CD4 effectively yet maintain authentic neutralization properties . The atypical inherent ability of some unadapted HIV-1 Env proteins to use macaque CD4 , exemplified in 1054 and AD8 , may underlie their comparative success in macaques and may be an important factor in determining why viruses expressing 1054 or AD8 Env proteins were selected during competition experiments in vivo [22 , 45] . While , in contrast to 1054 and AD8 , most HIV-1 Env proteins cannot use macaque CD4 efficiently , introduction of a single aa change , A281V , substantially improved replication in macaque cells in 5 out of 7 divergent clade B HIV-1 Env proteins ( Fig 6 ) . Residue 281 has previously been implicated in influencing HIV-1 Env protein neutralization sensitivity/resistance [46] . However , our data suggest that A281T/V directly affects the ability of HIV-1 Env proteins to specifically use macaque CD4 ( Figs 5A and 6A ) without large effects on neutralization sensitivity ( Fig 7 ) . It is also evident , that encoding T/V at position 281 is not always sufficient for improving replication in macaque cells . For example , the replication of SHIVTT31 and SHIVTT29 was not dramatically improved by the A281V mutation ( Fig 6B ) . Of note , the A281V mutation was obtained during in vitro passage of the lab adapted NL4 . 3 Envelope protein in cells expressing common marmoset CD4 and CXCR4 and shown to be associated with increased binding of gp120 to marmoset CD4 that , like macaque CD4 , has I at position 39 [47] . Recent studies have identified aa 375 as another determinant of macaque CD4 use in HIV-1 Env proteins [21] . Substitution of S375 with a mixture of the amino acids M/T/H/Y/W in four divergent Env proteins results in improved macaque CD4 usage and replication in macaque cells in vitro and in macaques in vivo . The preferred 375 residue selected following in vivo replication of mutant pools of SHIVs varied depending on the HIV-1 Env used [21] . Although residue 375 is not in the classical CD4 binding site [37] , a long aa side chain at this position has the potential to affect interactions with Phe 43 on CD4 [21] ( Fig 8 ) . Of note , and in striking contrast to the consistent selection of A281 Env mutations upon serial passage in vivo , we have not observed mutations at position 375 in any of our in vivo passaged envelope proteins nor have mutations at this position been reported in serial passage experiments by other labs , despite the clear in vivo replicative advantages conferred by in vitro engineered mutations at this site . It will be interesting to determine the combined effects of A281 and S375 mutations . Our data also demonstrate the impact of other HIV-1 Env residues on use of macaque CD4 . Specifically , a single amino acid mutation , N283T , abolished the ability of SHIVAD8 to infect macaque CD4-expressing cells and replicate in macaque cells without affecting replication in human PBMC ( Fig 5C and 5D ) . N283T is a key determinant of macrophage tropism in the AD8 Env [41] . Therefore , these data suggest a link between the ability of an Env protein to use low levels of CD4 ( e . g . huCD4 on macrophages ) and the ability to engage non-optimal CD4 variants ( macaque CD4 on indicator/macaque T-cells ) . They also highlight the complexity of interactions between Env and CD4 that are dependent upon multiple residues making optimal contacts between the two proteins . Indeed , a recent study has revealed that a single CD4 molecule engages two gp120 monomers in the HIV-1 Env trimer through a novel CD4 binding site ( CD4-BS2 ) on one of the monomers . This second binding site involves interactions between gp120 residues E/D62 , E64 , H66 and K207 and CD4 residues K22 and D63 and to a lesser extent K21 [48] . The gp120 CD4-BS2 site is highly conserved between HIV-1 group M Env proteins ( and thus in all Env proteins used in this study ) and we did not observe any changes therein during the in vivo adaptation of HIV-1 Env proteins in macaques ( in our studies and other published sequences ) . Furthermore , CD4 residues involved in contacts with this second binding site in gp120 are conserved between human and macaque variants ( Fig 3A ) . Even though adjacent CD4 residues S23 and I24 differ between human and macaque proteins , these differences do not affect the ability of macaque CD4 to act as a receptor for HIV-1 Env proteins [26] . Nevertheless , given the complexity of the gp120-CD4 interactions , it is rather remarkable that multiple divergent Env proteins have adopted the same solution to overcome their inability to use macaque CD4 efficiently through mutations at residue A281 . Indeed , of all the changes described above , A281T/V is the only one to be consistently identified in divergent HIV-1 Env proteins adapted in macaques in vivo . In addition to the SHIV adaptations referenced in Fig 4 , two clade C SHIVs , SHIV1157i [49] and SHIVCHN19 [50] that replicate in rhesus and rhesus/pigtail macaques respectively , have Env proteins that encode a V at position 281 . Finally , in a recent SHIV competition experiment with viruses expressing 7 divergent clade C Env proteins , the winner encoded T281 [19] . However , two SHIVs , SHIVHxBc2P3 . 2 [51] and SHIVDH12 [52] maintained A281 following adaptation in macaques suggesting that alternate infrequently used strategies to improve macaque CD4 use exist . Notably , both these SHIVs use CXCR4 rather than CCR5 and SHIVHxBc2P3 . 2 is based on the laboratory adapted HIV-1 HXB2c Env , that is highly neutralization sensitive and likely has an ‘open’ conformation , that should facilitate macaque CD4 use . Yet , in an independent adaptation experiment in rhesus and pigtail macaques SHIVHxB acquired the A281T/V mutation in one animal [53] . We have previously demonstrated that overcoming species-specific restriction factors that determine HIV-1 host range is key to generating HIV-1 strains that can replicate in a new host [23 , 25 , 45] . It is evident that CD4 variation can present an additional barrier that Env proteins must negotiate in order for HIV-1 and SHIV strains to replicate optimally in macaques . We have identified a single aa that can contribute to a blueprint for manipulating HIV-1 Env proteins in a highly targeted way so that they can use macaque CD4 effectively . Such Env proteins should be better suited for the development of SHIVs that accurately represent HIV-1 strains circulating in humans as models for testing Env-directed prophylactic and therapeutic interventions against HIV-1 .
Ten Indian origin rhesus macaques ( Macaca mulatta; five females , five males , weighing 5–12 kg at study initiation ) were used in accordance with protocols approved by the IACUC of the National Cancer Institute . For procedures requiring chemical immobilization and sedation , different anesthetics were used at the discretion of the attending veterinarian according to the IACUC approved protocol . For phlebotomy to obtain blood samples ( within IACUC approved volume limits ) anesthetics included ketamine ( 5–25 mg/kg , IM ) , telazol ( 4–10 mg/kg , IM ) , midazolam ( 200–500 μg/kg , IM ) , medetomidine ( 20–50 μg/kg , IM ) , and/or dexmedetomidine ( 7 . 5–15 μg/kg , IM ) . For euthanasia according to endpoints specified in the IACUC approved protocol , animals were initially sedated with ketamine ( 10–25 mg/kg , IM ) or telazol ( 4–10 mg/kg , IM ) followed by lethal overdose of sodium pentobarbital ( >75 mg/kg , IV ) to effect . For serial passages from infected to naïve macaques , whole blood from donor animals was drawn into acid citrate dextrose ( ACD ) Vacutainer tubes ( BD ) immediately prior to i . v . infusion into the recipients ( 10ml per recipient animal ) . Plasma for viral RNA ( vRNA ) quantification and viral genomic sequence analysis and peripheral blood mononuclear cells ( PBMCs ) for flow cytometry assays were prepared from whole blood collected in EDTA anticoagulated Vacutainer tubes ( BD ) . Plasma was separated from whole blood by centrifugation , recentrifuged to eliminate cells , platelets and debris , then aliquoted and then stored at -80°C until analysis . Plasma viral load determinations were performed as described previously [21] . A limiting dilution , single-genome amplification PCR approach was used so that only one amplifiable molecule , encompassing the entire env gene , was present in each reaction . Reverse transcription of RNA to single-stranded cDNA was performed using SuperScript III reverse transcriptase ( Invitrogen ) according to manufacturer’s recommendations and a gene specific primer: SIVEnvR1 5’-TGT AAT AAA TCC CTT CCA GTC CCC CC-3’ . The env gene was then amplified using a 1st round PCR buffer supplemented with of 2 mM MgCl2 , 0 . 2 mM of each deoxynucleoside triphosphate , 0 . 2 μM of each primer , and 0 . 025 U/μl Platinum Taq polymerase ( Invitrogen ) in a 20-μl reaction . First round PCR was performed with sense primer SIVEnvF1 5’-CCT CCC CCT CCA GGA CTA GC-3’ and antisense primer SIVEnvR1 under the following conditions: 1 cycle of 94°C for 2 min , 35 cycles at 94°C for 15 sec , 55°C for 30 sec , and 72°C for 4 min , followed by a final extension of 72°C for 10 min . Nested PCR was performed with primers SIVEnvF2 5’-TAT AAT AGA CAT GGA GAC ACC CTT GAG GGA GC-3’ and SIVEnvR2 5’-ATG AGA CAT RTC TAT TGC CAA TTT GTA-3’ under the same conditions used for first round PCR , but for a total of 45 cycles . Correctly sized amplicons were sequenced directly using inner PCR primers and 6 additional HIV-1 specific primers using Big Dye Terminator technology ( Applied Biosystems ) . To confirm that PCR amplification was from a single template , chromatograms were manually examined for multiple peaks , indicative of the presence of amplicons resulting from PCR-generated recombination events , Taq polymerase errors or multiple variant templates in a single reaction . Env coding sequences were amplified from selected SGA reactions using PCR and degenerate primers and introduced , using AgeI–XhoI , into a SHIV-KB9 plasmid backbone as previously described [22] . The sequence of some clones therefore differed from the SGA sequences in 1–5 amino acid positions generally clustering at the C-terminus ( cytoplasmic tail ) depicted in Fig 1C . Specifically , the following clones contained the indicated amino acid differences compared to their SGA counterparts: P1 ( Aw21 ) a-E186G/R456G/F721I/T723P , P1 ( Aw66 ) b-L721F , P1 ( Aw66 ) c-F721V/T723S , P1 ( Bw21 ) d-F721V/T723S , P1 ( Bw21 ) e- N448I/K500E/F721I/T723P , P1 ( Bw21 ) f-F210L/F721V/T723P , P2 ( Aw1 ) a-L721V/ T723P , P3 ( Aw1 ) a-N160K/F717L/F721I/T723P , P3 ( Aw1 ) b-S481R/ F721I/T723P , P3 ( Aw1 ) c-L721V/T723P , P3 ( Aw1 ) d-M86I/N553D/F721I/T723S , P3 ( Bw1 ) e-K683E/F717L/L721V/T723P/K734E . To determine the coding sequences for CD4 proteins expressed by macaques , total RNA was extracted from 17 rhesus and 14 pigtail PBMC samples using TRIZOL ( GibcoBRL ) and cDNA synthesized using the SuperScript III RT kit ( Invitrogen ) . CD4 coding sequences were amplified using primers based on an available macaque CD4 sequence ( GenBank accession number D63347 . 1 ) . Two or three independent PCR reactions were performed for each PBMC RNA sample and PCR products were introduced into the pCR-Blunt vector ( Invitrogen ) and analyzed by sequencing . SHIV proviral plasmids encoding T/F HIV-1 Env proteins have been described previously [22] . Single amino acid changes in the HIV-1 Env coding sequences were introduced by overlap extension PCR . Adapted Env coding sequences were derived from plasma of SHIV-infected animals ( see single-genome amplification methods ) . The SIV used in this study is SIVMAC239 . AD8 Env coding sequences were amplified by PCR from the unmodified HIV-1AD8 or an HIV-1 3’ genome half obtained from a P4-infected pigtail macaque using SGA [23] . All Env fragments were inserted using AgeI–XhoI into a SHIV-KB9 plasmid backbone following the same cloning strategy used to generate the parental T/F-SHIVs [22] . All virus stocks were produced by transient transfection of 293T cells using PEI ( Polysciences ) . Indicator HeLa cell lines , designated Helios , were generated using a self-inactivating retroviral vector that transduces an HIV-2 LTR-nanoluciferase-2A-GFP reporter cassette and a puromycin resistance gene . Following selection with puromycin single cell clones were derived by limiting dilution . A single cell clone with the highest difference in luciferase expression between infected and uninfected cells was selected and transduced with a retroviral vector expressing huCCR5 and hygromycin ( LHCX , Clontech ) . Following selection with hygromycin and sorting of populations of cells with high levels of CCR5 expression , single cell clones were derived using limiting dilution and one such clone with uniform levels of CCR5 expression was selected for subsequent experiments ( S2B Fig ) . huCD4 , rhCD4 and pgtCD4 were introduced into Helios-R5 cells by retroviral gene transfer using LNCX2-based vectors ( Clontech ) . Following G418 selection , cell populations expressing high levels of each CD4 variant were selected by sorting ( S1C Fig ) . Rhesus macaque 221 T-cells [29] were grown in RPMI medium with 20% FCS , 90 U/ml IL-2 ( Peprotech ) and gentamicin . Human peripheral blood mononuclear cells ( PBMC ) were isolated from leukopaks obtained from the New York Blood Center . Human and rhesus PBMC were isolated from blood by Ficoll-Paque gradient centrifugation . PBMC were activated with 5 μg/ml phytohemaglutinin ( PHA-P; Sigma ) and 20 U/ml IL-2 ( Peprotech ) for 48 h and then grown in the presence of 20 U/ml IL-2 in RPMI medium supplemented with 10% FCS . CD4+ T cells were enriched from rhPBMC by negative selection ( CD4 T Cell Isolation Kit , Miltenyi ) . RhCD4+ T cells were activated with aCD2 , /aCD3/aCD28 beads ( T Cell Activation Kit , Miltenyi ) per the manufacturer’s instructions for 72 hours and cultured with 100U/mL IL-2 in RPMI medium supplemented with 10% FBS ( RPMI Complete ) at a density of 2-3x106 cells/ml . After 72 hours , activation beads were removed and cells were maintained in RPMI Complete with 100U/mL IL-2 for the duration of the experiment . 293T and HeLa cells were obtained from American Type Culture Collection ( ATCC ) . Cells , ( Helios , 1x106 ) detached with 0 . 5 mM EDTA in PBS or hu/rhPBMC ( 1x106 ) , were stained in 100μl FACS buffer ( 2% FCS in PBS ) with anti-CD4 ( clone OKT4 ) conjugated with Alexa700 ( Biolegend , 1/100 ) and anti-CD195 ( CCR5 ) conjugated with PE ( BD Pharmingen 550632 , 1/20 ) for 30 min on ice . Fluorescence was measured using a LSRII ( Becton Dickenson ) and data analyzed using the FlowJo Software . For whole blood cell counts in samples from infected animals , antibodies and reagents were obtained from BDBiosciences , unless indicated otherwise , and data analysis was performed using FCS Express ( De Novo Software ) . Absolute cell counting was performed on EDTA-anticoagulated whole blood , as previously described [54 , 55] , using the following immunophenotyping panel: CD45 fluorescein isothiocyanate ( FITC ) ( DO58-1283 ) , CD3 phycoerythrin ( PE ) ( SP34-2 ) , CD4 allophycocyanin ( APC ) ( L200 ) , CD14 APC-Cy7 ( M5E2; BioLegend ) , CD8α PE-Cy7 ( SK1 ) , and CD20 Pacific Blue ( 2H7; Bio-Legend ) . The samples were lysed , and approximately 50 , 000 CD45+ CD3+ cells were acquired for each sample to calculate cell counts , using a BD FACSVerse flow cytometer equipped with a volumetric flow sensor . Individual SHIV stocks produced in 293T were normalized for RT and 100pg RT of each virus was used per 1x105 freshly activated hu/rhPBMC . For 221 cells 50pg RT of each virus was used per 1x105 cells . 16h post infection , cells were washed and supernatants collected over 14 days . The amount of virus was quantified using a one-step SYBR Green I based PCR RT assay [56] . Briefly , 5μl cell culture supernatant was incubated with 5μl 2x lysis buffer ( 0 . 25% Triton X-100 , 50mM KCl , 100mM TrisHCl pH7 . 4 , 40% glycerol ) for 10 min at RT . The lysate was diluted 10x in 1x Core buffer: 5 mM ( NH4 ) 2SO4 , 20 mM KCl and 20 mM Tris–HCl pH 8 . 3 . 10μl of the sample were then mixed with 10μl of 2x reaction buffer: 5mM ( NH4 ) 2SO4 , 20mM KCl and 20mM Tris–Cl ( pH8 . 3 ) , 10mM MgCl2 , 0 . 2mg/ml BSA , 1x dilution of SYBR Green I ( Life technologies S-7563 ) , 400μM dNTPs , 1μM forward primer ( TCCTGCTCAACTTCCTGTCGAG ) , 1μM reverse primer ( CACAGGTCAAACCTCCTAGGAATG ) , 0 . 0002U/ml MS2 RNA ( USBiological Life Sciences ) . RT reaction conditions were: 42°C for 20 min , 95°C for 2 min , followed by 40 cycles of 95°C for 5 sec , 60°C for 5 sec , 72°C for 15 sec and 80°C for 10 seconds measured using the StepOnePlus Real time PCR system ( Life technologies ) . Each replication assay was repeated between 4 to 10 times and a representative experiment is shown . The RT assay was also used to quantify RT levels in viral stocks . Replications in purified rhesus CD4+ T cells were performed by infecting 1x106 activated RhCD4+ T cells at a nominal MOI of 0 . 02 in a total volume of 1mL . Infections were spinoculated at 800xg for 2 hours at room temperature , then incubated at 37C for 2 hours . At the end of this incubation period , cultures were washed 3 times to remove excess virus . Culture supernatants were collected over 14–17 days . Viral p27 protein was quantified by ELISA ( Advanced Bioscience Laboratories ) . To determine efficiency of use of human and macaque CD4 , Helios cells were seeded at 7x103 cells/well in 96-well plates the day prior to infection . 3-fold dilutions of each virus stock were used for inoculation . 48 hours post-infection the percentage of infected cells was quantified by measuring luciferase expression using the Nano-Glo Luciferase assay ( Promega ) . Statistical significance was calculated using the Fisher’s exact test to compare the infectivity values ( Relative Light Units from two independent experiments ) on huCD4-Helios and rh/pgtCD4-Helios of SHIVAD8 and each of the SHIVAD8 mutants . Neutralizing titers against SHIVs were determined in TZM-bl cells , as previously described [57] . Neutralizing titers against SHIVs were determined in TZM-bl cells , as previously described [57] . Briefly , neutralizing antibody activity was measured in 96-well culture plates by using Tat-regulated luciferase ( Luc ) reporter gene expression to quantify reductions in virus infection in TZM-bl cells . TZM-bl cells were obtained from the NIH AIDS Research and Reference Reagent Program , as contributed by John Kappes and Xiaoyun Wu . Test samples were diluted over a range of 1:20 to 1:43740 in cell culture medium and pre-incubated with different SHIVs ( ~150 , 000 relative light unit equivalents ) for 1 hr at 37°C before addition of cells . Following a 48 hr incubation , cells were lysed and Luc activity determined using a microtiter plate luminometer and BriteLite Plus Reagent ( Perkin Elmer ) . Neutralization titers are the sample dilution ( for serum ) or antibody concentration ( for sCD4 , purified IgG preparations and monoclonal antibodies ) at which relative luminescence units ( RLU ) were reduced by 50% compared to RLU in virus control wells after subtraction of background RLU in cell control wells . Serum samples were heat-inactivated at 56°C for 1 hr prior to assay . 293T cells seeded at 106 cell/well in 6-well plates were transfected with 3μg of the indicated SHIV expression plasmid . 48hrs post-transfection the supernatant was harvested , filtered and virions purified by ultracentrifugation through a 20% sucrose cushion . Lysed virions and cells were separated by electrophoresis on NuPage 4–12% Bis-Tris gels ( Novex ) and transferred onto nitrocellulose membranes ( GE Healthcare ) that were probed with anti-gp120 HIV-1 goat antibody ( 12-62605-1 , American Research Products ) and an anti-SIV capsid mouse antibody ( 55-2F12 , NIH AIDS Reagents Program ) . Genebank accesion numbers for the 1054 adapted Env coding sequences are MF775894-MF776025 and for and rh/pgt-CD4 coding sequences MF769792-MF769804 . Previously reported macaque CD4 coding sequences are: M31134 , D63346 , D63347 , X73325 , X73326 , NM_001042662 . Ten Indian origin rhesus macaques ( Macaca mulatta; five females , five males , weighing 5–12 kg at study initiation ) were housed at the National Institutes of Health and cared for in accordance with American Association for the Accreditation of Laboratory Animal Care ( AAALAC ) standards in an AAALAC-accredited facility ( Animal Welfare Assurance number A3081-01 ) . The study was conducted under protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute ( Protocols AVP-013 and AVP-040 ) and adhered to the standards of the NIH Guide for the Care and Use of Laboratory Animals ( National Research Council . 2011 . Guide for the care and use of laboratory animals , 8th ed . National Academies Press , Washington , DC ) . At the start of the study , all animals were free of cercopithecine herpesvirus 1 , simian immunodeficiency virus ( SIV ) , simian type-D retrovirus , and simian T lymphotropic virus type 1 . Animals with Mamu- alleles B*08 and B*17 were excluded from these studies . Animals on AVP-013 were used only as naïve donors of PBMC for in vitro experiments and amenable animals were socially housed with oversight from facility behavioral management staff . AVP-040 had an IACUC-approved exemption from social housing based on scientific justification and were housed in adjacent individual primate cages allowing interactions but no direct contact . Primary enclosures consisted of stainless steel primate caging provided by a commercial vendor . Animal body weights and cage dimensions were regularly monitored . Overall dimensions of primary enclosures ( floor area and height ) met the specifications of The Guide for the Care and Use of Laboratory Animals , and the Animal Welfare Regulations ( AWR's ) . Further , all primary enclosures were sanitized every 14 days at a minimum , in compliance with AWRs . Secondary enclosures ( room level ) met specifications . All animals were housed under controlled conditions of humidity , temperature and light ( 12-hour light/12-hour dark cycles ) . Animals were fed commercial monkey chow , twice daily , with supplemental fruit or other produce at least three times per week . Filtered water was available ad libitum . Animals were observed at least twice daily by trained personnel , including behavioral assessments . Environmental enrichment included provision of species appropriate manipulatives , and foraging opportunities , as well music and video watching opportunities multiple times per week . Human PBMC were purified from anonymous leukopaks obtained from the New York Blood Center . | Understanding the interactions between viruses and their hosts allows manipulation of primate lentiviruses and the generation of better animal models for HIV/AIDS . Species-dependent differences in cellular proteins that play key roles in virus replication , such as the primary HIV-1 receptor CD4 , can limit virus tropism . Our data reveal how adaptation in macaques improves the ability of HIV-1 envelope glycoproteins to use macaque CD4 . Moreover , we identify a single amino acid in the HIV-1 envelope glycoprotein CD4 binding site that improves macaque CD4 use by most HIV-1 envelope proteins tested and allows viruses expressing these proteins to replicate efficiently in macaque cells without compromising their sensitivity to various antibodies . These findings should facilitate the development and preclinical evaluation of HIV-1 Env directed prophylactic and therapeutic interventions . | [
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"immun... | 2017 | A single gp120 residue can affect HIV-1 tropism in macaques |
All animals live in intimate association with microorganisms that profoundly influence their health and development , yet the traits that allow microorganisms to establish and maintain host associations are not well understood . To date , most investigations aimed at identifying traits required for host association have focused on intrahost niches . Consequently , little is known about the relative contribution of extrahost factors such as environmental growth and survival and immigration into hosts from the external environment , as promoters of host association . To address this , we developed a tractable experimental evolution system that investigates both intra- and extrahost factors contributing to bacterial adaptation to the vertebrate gut . We passaged replicate lines of a zebrafish bacterial isolate , Aeromonas veronii , through populations of germ-free larval zebrafish ( Danio rerio ) , each time using gut-associated Aeromonas populations to inoculate the aquatic environment of the next zebrafish population . We observed rapid increased adaptation to the host in all replicate lines . The initial adaptations present in early-evolved isolates did not increase intrahost fitness but rather enhanced both immigration from the environment and interhost transmission . Only in later-evolved isolates did we find evidence for intrahost-specific adaptations , as demonstrated by comparing their competitive fitness in the host genotype to which they evolved to that in a different genotype . Our results show how selection for bacterial transmission between hosts and their environment can shape bacterial-host association . This work illuminates the nature of selective forces present in host–microbe systems and reveals specific mechanisms of increased host association . Furthermore , our findings demonstrate that the entire host–microbe–environment system must be considered when identifying microbial traits that contribute to host adaptation .
Animals are intimately associated with highly complex and dynamic communities of microbes that inhabit virtually every surface of their bodies . An explosion of research in recent decades has revealed that both the microbiota and the hosts they colonize have profound influences on the physiology and evolution of one another [1 , 2] . In vertebrates , it is especially evident that host–microbiota interactions within the gastrointestinal tract are fundamental to host health and development [3] . Wide-ranging surveys of the earth’s microbial communities have shown that host-associated microbial communities are distinct from non-host-associated communities , suggesting that there are unique properties of host–microbe systems that drive microbiome assembly [4] . Elucidating the microbial traits that allow host colonization and support host–microbe association could shed light on the ecology of host-associated microbes across the entire symbiotic spectrum , from mutualism ( e . g . , beneficial microbes ) to parasitism ( e . g . , pathogens ) . Knowledge of these traits may also provide insights into the development of microbiota-driven diseases and ultimately into innovative ways to treat or prevent them . Several different approaches have been used in past studies to identify traits that mediate microbial association with vertebrate hosts , including forward genetic screens ( e . g . , transposon mutagenesis ) [5–8] , functional genomics ( e . g . , transcriptomics ) [9 , 10] , and approaches from comparative genomics [11–13] . However , these approaches all have substantial limitations; for example , comparative genomics approaches have limited ability to distinguish between differences arising from adaptation to the host and those accumulated over evolutionary time in the non-host environment . Here , we use experimental evolution , which allows for the observation of evolution in real time under controlled laboratory conditions , to identify traits that mediate host association . This approach has the advantage that changes in traits can be directly attributed to the selection imposed by the experimental conditions , and there is no inherent constraint on the spectrum of mutations or traits that can be selected . Furthermore , this approach focuses on a phenotypic-level characterization of the system; adaptive traits may be acquired through many different genetic mechanisms , but their shared phenotypes reveal the selective pressures that drive their evolution . Historically , experimental evolution has been a powerful strategy to study how microorganisms adapt to in vitro environments [14 , 15] . In recent years , this approach has been expanded to in vivo studies of host–microbe interactions , although it has been underutilized , relative to other approaches , for such questions [16] . Previous work has focused primarily on experimentally evolving microbes to the in vivo environment of a single host [17–21] . However , hosts and their associated microbes do not exist in isolation but rather within a broader ecological framework , and host association likely involves not just intrahost factors but also extrahost factors such as immigration into hosts from the external environment , host-to-host transmission , and extrahost growth and survival [22] . Indeed , previous work implicating the importance of dispersal and transmission in shaping host-associated microbial communities indicates that microbes adapt to more than just the within-host environment [23–29] . The zebrafish host–microbe model system is well suited to studying both intra- and extrahost factors simultaneously [30] . To perform precise , manipulative studies , there are established gnotobiotic protocols for generating germ-free ( GF ) zebrafish that can be colonized with defined microbial constituents [31] . Bacterial colonization of larval zebrafish occurs initially via the fish culture medium , which serves as a conduit supporting bacterial transmission throughout the system . Furthermore , tracking this transmission between the intra- and extrahost bacterial populations , as well as from host to host , is feasible . For example , previous work has demonstrated that gut populations can undergo dramatic expulsion events and that environmental populations contribute to repopulation of the gut [32] . Using this powerful model system , we conducted an evolution experiment by serially passaging the bacterium Aeromonas ZOR0001 through GF larval zebrafish hosts , each time specifically using gut-associated populations as inoculum for the subsequent passage . We found that host adaptation occurred quickly and reproducibly across replicate lines . Phenotypic characterization of the early adaptations ( i . e . , those found in early-evolved isolates ) showed that they were not specific to the intrahost environment but rather they enhanced initial colonization , specifically immigration . Finally , we show that later adaptations confer host specialization by comparing the fitness of “early-” and “further-evolved” isolates in the host genotype in which they evolved to that of a different host genotype . Our results demonstrate that the intrahost environment does not always play the dominant role in selection of host-associated traits but rather the entire colonization cycle ( immigration , intrahost growth and survival , emigration , and extrahost growth and survival; illustrated in Fig 1A ) must be considered when identifying traits that confer host association .
Cryo-archived evolved population samples from passages 4 , 8 , 13/14 , 18 , and 22 were streaked for isolation on rich media , and freezer stocks of colony-purified isolates were made . These isolates were assayed for adaptation by competing them against a non-mutator , differentially tagged ( green fluorescent protein [GFP] ) Aer01 reference strain . For the competitions , ancestral or evolved isolates were mixed with the reference strain and inoculated into the EM of individual flasks of GF fish . Competitions were conducted under the same conditions as the evolution passages , for which strains were competed for 3 days ( from 4 to 7 dpf ) , and all aspects of the colonization cycle are included . At 7 dpf , the fish were dissected and the guts plated to enumerate in vivo abundances of each strain via fluorescence microscopy . Competitive indices ( CIs ) were calculated by dividing the strain ratio ( competitor:reference ) at the end of the competition by the starting ratio ( Fig 2 ) . Adaptation occurred very quickly ( by passage 4 ) in all three replicate lines , with greater than 10-fold average CIs for the passage 4 isolate from all three lines . This increase in competitive fitness is maintained or increased ( approximately 100- to 1 , 000-fold averages ) across isolates from later passages . The increased competitive fitness of the evolved isolates is not due to a general growth advantage , as there are no competitive advantages in vitro , in rich media ( S5 Fig ) . To determine how the initial adaptations observed in the passage 4 isolates improved fitness , we next investigated the stage of the colonization cycle impacted . To do this , we used an inoculation approach that would isolate where competition takes place , thereby distinguishing fitness specific to the within-host environment from that of extrahost factors . Microgavaging is a technique whereby a blunt needle is used to deliver substances—in this case , bacterial cultures—directly into the gut via the oral route [44] . Competition mixtures of ancestral or passage 4 evolved isolates and reference strain ( as above ) were inoculated directly into the guts of GF larval fish . Concurrently , the same inocula were introduced into the flasks of GF fish as in the previous competition experiments . For all three lines , the competitive advantage of flask-inoculated evolved isolates was significantly diminished when the strains were introduced directly into the gut ( Fig 3A–3C ) . This loss of competitive advantage was apparent at both 0 . 5 hours and 5 hours post gavage ( Fig 3A–3C ) . The matched CFU/gut data for the same fish show that Aer01 was gavaged at sufficiently low densities ( approximately 5 × 102 CFU/gut ) to allow for >10-fold increase in abundance during the 5-hour colonization ( Fig 3D ) . If the CIs had increased between 0 . 5 and 5 hours post gavage , this would have indicated either a higher growth rate of the evolved strains or a better capacity to persist or survive within the gut environment . Since there were no differences in CIs between these time points , neither of these traits can be ascribed to the evolved lines . Instead , the competitive advantage of the evolved isolates is apparent when they are inoculated into the flasks ( Fig 3A–3C ) , with median CIs of 11 , 4 , and 98 for evolved isolates from lines 1 , 2 , and 3 , respectively . This suggests that the competitive advantage of these isolates is specific to initial colonization events , prior to reaching the gut environment . We wondered whether the competitive advantage was due to the ability to transmit from the mouth into the gut . To investigate this , we gavaged competition mixtures into the mouths of the fish rather than into the gut and then again measured the gut CIs after 5 hours . As with the gut gavages , the evolved strains when introduced into the mouth showed little to no significant advantage over the ancestral strain ( Fig 3A–3C ) . In addition , the variation in these data is high , implying there is a variable—and at times tight—bottleneck imposed between the mouth and the gut such that in some fish one tag or the other dominates the Aeromonas gut population . Combined , these data suggest that the competitive advantage of the early-evolved isolates is driven by initial colonization of the host from the environment . To further quantify differences between passage 4 evolved isolates and the ancestral strain , we conducted migration rate experiments . Here , fish were mono-associated ( inoculated into the flask ) with either the ancestor or a passage 4 evolved isolate . Every 45 minutes , groups of 8–10 fish were dissected and the guts plated to determine ( 1 ) the fraction of fish colonized at each time point ( Fig 4A ) and ( 2 ) the Aeromonas abundance ( Fig 4B ) . All three evolved isolates were present in a higher proportion of fish at each time point than the ancestor , with close to 100% colonization by the evolved isolates even at the earliest time points , whereas the ancestor was not present in 100% of the fish even at the final sampling ( about 5 hours post inoculation ) . Abundance of the evolved isolates in the guts was also higher than the ancestral strain , with , on average , approximately 10-fold higher CFU/gut at the first time point compared to the ancestor . These data do not reflect a difference in growth or survival in the flask EM , since abundance remained relatively constant for all strains throughout the experiment ( S6 Fig ) . In addition to assessing bacterial colonization of zebrafish directly from the flask medium , we wanted to test if the increased migration phenotype of the evolved isolates could affect fish-to-fish colonization . To do this , we mono-associated fish with either the ancestor or the passage 4 isolate from line 3 , washed and added two “donor” fish to a flask of GF “recipient” fish , and dissected and plated the recipient fish 12 hours later . Indeed , we found that the abundance of the evolved isolate was significantly higher than the ancestor in their respective recipient fish ( S7 Fig ) . We next developed a mathematical stochastic colonization and growth model ( see Materials and methods for details ) with which we could use the measured distribution of bacterial abundances across fish from the migration rate experiments to estimate the migration and growth rates of each strain . We consider a model in which migration into a fish by an individual bacterium is a stochastic event , with some probability per unit time . The statistics of the colonizers at any time are therefore governed by a Poisson distribution characterized by a mean time to entry , τ . Following entry into the host , the descendants of a colonizer obey logistic growth ( initially roughly exponential , constrained by a finite carrying capacity , K ) , characterized by some growth rate r . We fit the experimental data ( i . e . , each set of CFU measurements at a particular measurement time t ) to the above model of stochastic colonization and growth , determining the best-fit parameters τ and r . The datasets from each measurement time give independent estimates of τ and r; the average and standard error give the final estimates of mean entry time , growth rate , and their uncertainty for each bacterial strain ( Table 1 ) . We found that the characteristic time required to enter a larval zebrafish , τ , is approximately 2-fold smaller for the evolved isolates compared to the ancestor . Furthermore , the model output did not show significant differences in in vivo growth rates among these strains . In an effort to identify the physiological mechanism underlying increased migration into the host , we investigated the motility behavior of the ancestral and evolved isolates . The role of motility and chemotaxis in host associations is well recognized [5 , 45–48] , especially for pathogenic bacteria [49 , 50] . Furthermore , the importance of motility and chemotaxis in the zebrafish–microbe symbiosis has been previously reported [5 , 46] . How these traits increase host colonization has not been characterized extensively , but they could play roles in increasing the rate of microbe–host contact . We conducted low-agar swim plate assays using rich media ( tryptic soy broth [TSB] ) or media made from fish-conditioned EM ( FC-EM ) , which more closely replicates the conditions experienced by Aer01 when colonizing fish . Motility in the rich media plates was determined via standard protocol , by measuring the diameter of the motility zone . In rich media , population progression away from the site of inoculation is facilitated by growth , motility , and chemotaxis . We observed no significant differences in diameters of the motility zones between the ancestor and evolved isolates , indicating no motility advantage in rich media ( Fig 5A ) . FC-EM was obtained by filter-sterilizing EM from flasks of hatched 5–6 dpf GF zebrafish larvae . It is essentially a nutrient-poor , dilute salt solution containing only limited fish-derived nutrients ( mucus and other secreted or sloughed fish compounds ) , and therefore , the contribution of growth to progression through this media should be minimal . Cell density in FC-EM swim plates is too low to visually measure a motility zone . Hence , assessment of Aer01’s ability to swim in the FC-EM plates was accomplished by sampling an agar plug at a standardized distance away from the site of inoculation ( Fig 5B ) . The plates were inoculated from rich media liquid cultures; then , a plug was sampled 8 hours and 24 hours post inoculation , homogenized , and plated to enumerate Aer01 cells . In FC-EM swim plates , a significantly higher number of cells were present in the agar plug at the 8 hour postinoculation sampling for each of the evolved isolate populations , compared to the ancestor ( Fig 5C ) . Replicate FC-EM swim plates sampled at 24 hours show that the ancestor did eventually migrate to the standardized distance given sufficient time ( Fig 5D ) . Differences in progression of populations through the media in swim plate assays can be attributed to either chemotaxis ( i . e . , the ability to sense and respond to gradients of chemical attractants and/or repellants ) or capacity for motility ( e . g . , presence of functional flagellar machinery , differences in cellular swim speeds , etc . ) . To distinguish between these and more precisely characterize the advantage of the evolved isolates , we used microscopy to directly track and measure cellular swimming characteristics of Aer01 ancestral and evolved populations . Ancestral and evolved isolates were incubated in FC-EM for several hours to acclimate and then were imaged . In these experiments , we were specifically testing for differences in general motility , as there are no chemical gradients and therefore no influence of chemotactic behavior . For each strain , 8 movies were recorded ( from two independent replicate cultures ) , which were analyzed with custom tracking software to measure the velocity of thousands of cells each . The swim speed data are plotted in Fig 5E . Each dot represents the mean swim speed across the entire population of cells tracked in an individual movie . The ancestor had a median swim speed of 11 . 5 μm/second , whereas the evolved isolates from lines 1 , 2 , and 3 were about 2-fold faster , on average , with median swim speeds of 26 . 5 , 27 , and 24 . 7 μm/second , respectively ( Fig 5E ) . When we determined the motile fraction of the population from each movie ( i . e . the proportion of the cells tracked with >5 μm/second swim speeds ) , two of the three evolved isolates also had significantly higher motile fractions compared to the ancestor , with the ancestor populations displaying a median motile fraction of 0 . 13 , whereas the medians for evolved isolates from lines 1 and 2 were 0 . 44 and 0 . 33 , respectively ( Fig 5F ) . Taken together , these data demonstrate that the evolved isolates have a hypermotile phenotype in this nutrient-poor medium , the same condition that Aer01 experiences in a fish flask . The experiments described above were focused specifically on investigating the adaptation mechanisms of the earlier-evolved ( passage 4 ) isolates . Based on the progressive increase in CIs seen in further-evolved isolates ( Fig 2 ) , we hypothesized that those strains carried adaptations in addition to the increased immigration phenotype seen in passage 4 isolates . We first verified that further-evolved isolates ( passage 18 ) have the increased-immigration phenotype ( S8 Fig; S2 Table ) . The two passage 18 isolates tested from two of the independent lines both had increased migration rates , of a similar magnitude as the passage 4 isolates , compared to the ancestor . This is consistent with the model that they are of the same lineage as the passage 4 isolates , carrying additional adaptive mutations , rather than being independent lineages with more beneficial gain-of-fitness mutations . We then hypothesized that the further-evolved isolates have adaptations specific to the intrahost environment . Identification of a host genotype in which evolved isolates compete differently compared to the WT host would support our hypothesis that the evolved isolates are adapted to the specific conditions within the WT host . To test this , we compared CIs when competed in WT hosts ( the same host genotype used for the evolution experiment ) to a different host genotype , an myd88—mutant . MyD88 is a protein adaptor for innate immune receptors , including the Toll-like receptors; hence , mutants are immunodeficient [23 , 51] . Use of this alternate host genotype is based on the rationale that host immune functions in the vertebrate gut potentially alter , either directly or indirectly , the gut environment [52 , 53] . Indeed , we have shown previously that myd88 mutant larval zebrafish intestines differ from WT intestines in their paucity of neutrophils , mucus-secreting goblet cells , and proliferating epithelial cells [36 , 51 , 54] . We first verified that there were no overt differences in Aeromonas colonization of the myd88—fish by showing that ancestral Aer01 competed equally in both WT and myd88—hosts ( S9 Fig ) . We then measured the CIs of early- and further-evolved isolates in both host genotypes . As in WT hosts , early-evolved isolates had increased CIs in myd88—hosts . The increased migration rate of early-evolved isolates was recapitulated in the myd88—host , confirming that the mechanism of this competitive advantage is the same as in WT fish ( S10 Fig ) . For all three lines , the CIs of the passage 18 isolates in WT hosts are significantly higher than the passage 4 isolates in WT hosts ( Fig 6 ) . If this advantage was not specific to the host in vivo environment , it would be expected that the CIs of the passage 18 isolates in myd88—hosts would also be higher compared to the level observed in the passage 4 isolates in myd88—hosts . However , there were no significant differences in CIs in myd88—hosts between the passage 4 and passage 18 isolates . Combined , these data support that further-evolved isolates have additional adaptations specific to the intrahost environment , because they confer increased fitness in the WT host but do not provide fitness advantages in a different host genotype .
In addition to playing a role in the initial establishment of the gut community , enhanced immigration could amplify the competitive fitness of the evolved isolates in intrahost populations over time . It has been shown previously that Aer01 goes through major stochastic population collapse events approximately every 24 hours , with decreases in abundance of more than 90% in as little as 30 minutes [32] . These collapse events result in Aer01 expulsion from the fish vent via gut motility and peristaltic activity . Recovery of the gut population from these events can occur from the residual population within the gut , but external Aer01 can also contribute to repopulation [32] . Indeed , we previously established that there is mixing of extrahost with intrahost populations over time ( S3 Fig ) . Therefore , it is likely that our evolved , hypermigratory Aer01 strains have the potential to out-compete ancestral Aer01 for available open niche space during the repopulation phase that follows each collapse event . This amplification could explain how increased migration evolved so quickly within this system , because it would effectively ratchet up the strength of selection for the immigration phenotype . Moreover , these neutral processes ( stochastic collapse events ) combined with the deterministic processes ( repopulation/regrowth dynamics ) may account for the high variability we observe within our competition data . This combination of collapse and regrowth events is not unique to Aer01; these types of dynamics have been reported for other host-associated microbiome constituents—for example , from the human skin [55] and intestinal tract [56] . Whether these cyclic growth dynamics impart a selective pressure for transmission traits and contribute to the evolution of these communities is an open question . Another way in which the immigration phenotype could impact colonization dynamics within this system is through host-to-host transmission . Our data showing that an evolved isolate had higher abundance than the ancestor in GF recipient fish when introduced from mono-associated donor fish support this hypothesis ( S7 Fig ) . Recent research has demonstrated a role of transmission and dispersal in shaping the gut communities of many animals [23 , 26 , 57–60] . For example , Burns and colleagues showed that interhost dispersal is a key driver of gut community assembly in zebrafish hosts and can even override the selective forces imposed by the host immune system [23] . This body of literature suggests that selection of traits that improve transmission or dispersal among hosts could be a dominant force in the evolution of host-associated microbes . The work presented here provides support for this hypothesis and has important implications for how we think about the ways in which bacterial symbionts initiate and hone relationships with their hosts . In the future , it will be important to add back complexity to this distilled model system—for example , by increasing microbiota diversity—and determine if the relative importance of immigration is evident . Such studies could reveal trade-offs between colonization of the host and microbe–microbe competitive fitness . Enhanced transmission is predicted to alter a number of important aspects of host–microbe ecology . For example , it has been proposed that trade-offs exist between transmission and virulence [61] and that mode of transmission influences virulence evolution [62] . However , we do not have any evidence for Aer01 virulence in zebrafish . Previous work has shown that Aer01 , in mono-association , does not elicit a host immune response above what is measured in conventional fish and can even attenuate the host response to hyper-immunostimulatory species [38] . Of note , we did not observe any indications of pathogenicity in evolved , hypertransmissible Aer01 . In order to more thoroughly characterize the phenotype of our evolved isolates , we developed a mathematical model to estimate and compare the migration and in vivo growth rates of our ancestral and evolved isolates . Our mathematical model links observed population statistics to essential processes of bacterial colonization , treated stochastically , followed by logistic growth , treated deterministically . We note , however , that additional processes are present and could be elaborated in future models if warranted by relevant data . Bacterial growth , death , and expulsion from the gut [32] are inherently stochastic , and future stochastic models could quantitatively link these processes to measurable population statistics , either through brute force computation or potentially by making use of recently developed analytic tools [63] . Additional processes neglected in this work include the transition time between lag phase and exponential growth upon entering the gut environment ( itself stochastic ) and interindividual variation in growth rates . Building on the present model to explore how these factors can alter observable features of bacterial abundances will likely lead to further insights . To further investigate a cellular mechanism to explain the immigration phenotype , we tested the capacity for motility across strains both in classical low-agar swim plate assays and by directly measuring swim velocities of individual cells . Both assays show that the evolved isolates have hypermotile phenotypes compared to the ancestor . It is unclear how this would translate into increased host immigration , but it may act by increasing the likelihood that the evolved isolate will encounter a host . Indeed , once in the host mouth , this phenotype does not promote competitive fitness , since we demonstrated that starting competitions from the host mouth ( via mouth gavage ) did not confer a competitive advantage ( Fig 3 ) . The importance of motility in zebrafish–microbe symbiosis has been previously reported [5 , 23 , 46] . For example , Burns and colleagues used ancestral trait reconstruction to infer that genetic pathways associated with motility and chemotaxis were enriched in microbial communities present in the guts of cohoused zebrafish ( relative to those in solitary-housed zebrafish ) , suggesting that motility and chemotaxis may be important to dispersal-adapted gut communities [23] . Stephens and colleagues conducted a transposon-mediated mutational screen to identify genetic determinants for host colonization using two zebrafish gut isolates from different genera , Vibrio and Aeromonas ( although a different species than Aer01 ) [5] . For both species , mutations in chemotaxis and motility genes decreased host-colonization propensity . It was not determined , however , how the impaired motility affected colonization dynamics . Rawls and colleagues previously showed that flagellar motility is important for host colonization of the zebrafish by a human opportunistic pathogen , Pseudomonas aeruginosa , and is required for interaction with the host immune system [46] . The role of motility in other aquatic host–microbe systems has been described as well , such as the Vibrio–squid symbiosis [47] . Additionally , chemotactic motility was shown to be important for maximum virulence of a pathogenic Vibrio anguillarum strain in rainbow trout , and this was shown to be dependent on a natural route of transmission , as motility mutants directly injected into fish did not have decreased virulence [64] . In each of these examples , the stage of colonization impacted by loss of motility functions was not fully resolved . To our knowledge , our study is the first to clearly demonstrate an association between bacterial motility and immigration into the zebrafish host . This new model system was designed with the specific goal of studying bacterial host association in the context of an entire ecological framework , incorporating multiple hosts and extra- and intrahost microbial populations . Although this aquatic model system may not be an intuitive surrogate for understanding terrestrial host–microbe interactions , it is relevant to such systems in many ways . For example , like zebrafish , all animals begin life with very few to no resident microbes . Therefore , there is an initial establishment phase during which immigration of microbes into the naïve host occurs . Likewise , there is growing awareness that individual host microbiomes can be considered as part of a larger metacommunity , connected to the microbiomes of different hosts via transmission and dispersal [22 , 65] . Compelling evidence for this is the growing body of research showing that cohabiting animals such as mice [66 , 67] and humans ( e . g . , [58 , 68] ) have more similar gut microbiomes than those not cohoused . Furthermore , although the mechanism of immigration may be different in different systems ( e . g . , swimming through the EM in our system versus transmitting between human hosts by attachment to a fomite ) , the trait ( increased immigration ) is the same . These points illustrate that the intra- and extrahost factors for host association that we consider in our model are likely broadly applicable to other systems . We achieved the foremost goal of this study , which was to identify bacterial traits that confer host association via phenotypic characterization of the evolved isolates . Determination of the genetic basis of these adaptations would provide an even deeper understanding of the underlying biological mechanisms at play . A concession of using a mutator strain is that the increased number of mutations could make identification of adaptive ones difficult . In our evolved genomes , initial sequencing and preliminary analysis of passage 4 evolved isolates resulted in an average of 40 mutations per genome , with no obvious adaptive mutation candidates . An important next step is to repeat this study with non-mutator Aer01 , in which there will be much fewer mutations , and therefore the adaptive mutations would presumably be easier to identify . This would allow more in-depth investigation of the genetic and physiological mechanisms of host association and adaptation . Using a highly tractable model system , we were able to demonstrate that an enhanced extrahost process , immigration from the external environment , can increase host association by a bacterium . This finding challenges conventional assumptions about the primary importance of the intrahost environment in shaping host–microbe interactions . Furthermore , it supports a newly emerging paradigm that recognizes that ecological processes such as transmission may play substantive roles in microbiome assembly and dynamics .
All experiments with zebrafish were done in accordance with protocols approved by the University of Oregon Institutional Animal Care and Use Committee ( IACUC ) ; the animal protocol number for this work is 15–98 [69] . All experiments involving zebrafish were conducted following standard protocols and procedures approved by the University of Oregon Institutional Care and Use Committee . For the evolution passaging and bacterial competitions , WT ( AB × Tu strain ) fish were used . Competition experiments were also conducted using myd88 mutant zebrafish , which were previously generated via CRISPR-Cas9 system and verified to have the expected phenotype of an myd88 KO mutant [23] . Fish were maintained as previously described [69] . Fish were not fed in any of the experiments described here . GF derivation of fish embryos followed protocols previously described [31] . Generally , fish were inoculated with bacterial cultures at 4 dpf . At 7 dpf , fish were euthanized with tricaine ( Western Chemical ) , following approved procedures , and mounted in sterile 4% methylcellulose solution ( Fisher ) , and the intestines were removed by dissection ( described in [70] ) and used for enumeration of colonizing bacteria or as inoculum for GF fish . The bacterial strain used in this study is the zebrafish isolate Aer01 ( A . veronii , PRJNA205571 ) previously described [33] . Fluorescently tagged ( dTomato or superfolder GFP ) variants of this strain were generated using a Tn7 transposon-based system as previously described [32 , 34] . This method results in the integration of a cassette containing the dTomato/GFP gene and a gentamycin resistance gene in the chromosome at a specific target location . Subsequently , mutS deletion mutants ( unmarked , clean deletions ) were made in the fluorescently tagged genetic background strains following an allelic exchange system [34] . Strains were always grown at 30 °C , with shaking for liquid cultures . The mutator phenotype of the mutS KO mutants was verified via fluctuation assay [71] . Briefly , overnight TSB ( BD , Sparks , MD , United States ) cultures of the WT , ΔmutS , ΔmutS , attTn7::dTomato , and ΔmutS , attTn7::sfGFP strains were diluted to 10−7 in TSB and then split into 2 ml aliquots in replicate tubes ( WT- 20; ΔmutS- 20; ΔmutS , attTn7::dTomato- 5 , and ΔmutS , attTn7::sfGFP—5 ) , and cultures were grown at 30 °C with shaking overnight . Cultures were spread-plated ( 100 μl ) on either tryptic soy agar ( TSA ) or TSA supplemented with 12 μg/ml rifampicin ( RPI ) and grew at 30 °C overnight for CFU/ml determination . Mutation rates were calculated using FALCOR ( http://www . mitochondria . org/protocols/FALCOR . html [72] ) with the Lea-Coulson Method of the Median ( MODIFIED ) option . No overt differences in fitness were detected between WT and tagged mutator strains in vitro or in vivo . Overnight rich media cultures of untagged and dTomato-tagged Aer01 ( non-mutator ) were pelleted , washed with sterile EM , and mixed ( either 1:100 or 1:300 , tagged:untagged ) . These mixed cultures were used as inoculum for flasks of ( 10–15 ) 4 dpf larval fish to a starting density of approximately 106 CFU/ml . After 24-hour colonization , at 5 dpf , the fish were dissected and the guts transferred into 1 . 6 ml tubes containing 500 μl sterile EM and approximately 100 μl of 0 . 5 mm zirconium oxide beads ( Next Advance , Averill Park , NY , US ) and were then homogenized using a bullet blender tissue homogenizer ( Next Advance , Averill Park , NY , US ) for 30 seconds at power 4 . One hundred microliters of the homogenate was spread-plated on TSA plates , incubated overnight , and screened by fluorescence microscopy for the presence of dTomato-tagged Aer01 colonies . The experiment was repeated twice , on independent days , with two replicate flasks for each ratio on each day . We used a binomial sampling model to estimate a bottleneck size for the first 24 hours of colonization , using the cumulative distribution function ( CDF ) to determine the number of cells sampled that would result in the probability of colonization observed . The CDF gives the probability of not finding a “success”—in this case , a tagged cell—given a number of trials ( the number of cells sampled in 24 hours ) , with a given success rate ( 1:100 or 1:300 ) . One minus this probability is our observed frequency of colonized fish . We can then solve for the number of trials that yield the observed colonization rate . This analysis yielded an average bottleneck estimate of 193 . 7 cells sampled per day , with a standard error of 140 . 0 . At 4 dpf , GF larval zebrafish were mono-associated with untagged Aer01 ( non-mutator ) by inoculating to approximately 106 CFU/ml . At 5 dpf , dTomato-tagged Aer01 was added to the flask to the same approximately 106 CFU/ml density . At 6 dpf ( 24 hours ) and 7 dpf ( 48 hours ) , fish were dissected and the guts homogenized and plated as described above . The ratios of untagged ( founder ) to tagged ( invader ) were determined by fluorescence microscopy of the colonies . At 4 dpf , flasks of GF larval zebrafish were mono-associated with either the ancestral strain or the passage 4 ( line 3 ) evolved isolate . At 6 dpf , two mono-associated fish ( “donors” ) were washed with sterile EM and then transferred into flasks containing 13 GF larval zebrafish that were 6 dpf ( “recipients” ) . Twelve hours later , all fish in the flasks were dissected and the guts homogenized and plated to determine the colonization level of each strain in the recipient fish . Evolution passaging was initiated using an equal mixture of ΔmutS , attTn7::dTomato and ΔmutS , attTn7::sfGFP strains as a way of tracking gain-of-fitness lineages throughout passaging by monitoring changes in ratios of tagged populations . In all three replicate lines , the ΔmutS , attTn7::sfGFP populations were undetectable by passage 3 , suggesting either a fitness defect in this genome or that the emergence of gain-of-fitness mutants arose in the ΔmutS , attTn7::dTomato lineage in all three lines by chance . The first passage was inoculated by pelleting 1 ml TSB overnight cultures of ΔmutS , attTn7::dTomato and ΔmutS , attTn7::sfGFP strains , resuspending them in 1 ml sterile EM , mixing them 1:1 , and then adding them to replicate flasks of 4 dpf GF WT fish ( 10–15 larval fish , 15 ml EM ) to a final volume of 106 CFU/ml . Inoculated fish were incubated according to IACUC protocol . At 7 dpf , fish were euthanized with tricane , and the intestines were removed by dissection ( described in [70] ) . All guts from a flask of fish were combined into a single 1 . 6 ml tube containing 500 μl sterile EM and approximately 100 μl of 0 . 5 mm zirconium oxide beads ( Next Advance , Averill Park , NY , US ) and were then homogenized using a bullet blender tissue homogenizer ( Next Advance , Averill Park , NY , US ) for 30 seconds at power 4 . Aeromonas populations were monitored by dilution-plating a small aliquot ( 20 μl ) of the combined gut sample and an aliquot of the flask EM on TSA plates . Half ( approximately 250 μl ) of the homogenate was mixed with 250 μl of sterile 50% glycerol and then stored at −80 °C . The remaining homogenate was stored at 4 °C for 4 days and then used as inocula for the subsequent flasks of 4 dpf GF fish . For subsequent inoculations , all of the 4 °C sample ( about 200 μl ) was added to the next flask of fish ( resulting in approximately 104 CFU/ml at the beginning of the passage ) . This process was continued for 22 passages total , for all three lines . From freezer stocks of whole populations from selected evolution passages ( namely , 4 , 8 , 14 [13 , line 1] , 18 , and 22 ) , cells were streaked out onto TSA plates for isolation and then incubated at 30 °C for 1 day . Isolated colonies were then picked from the plates into 5 ml TSB cultures and allowed to grow shaking at 30 °C for about 6 hours; then , 25% glycerol freezer stocks were made and stored at −80 °C . For in vivo bacterial competitions , to assess fitness , selected strains were grown overnight in TSB from freezer stocks . One milliliter of the overnight cultures was pelleted ( 8 , 700 rcf , 2 minutes ) and then resuspended in 1 ml sterile EM . Competing strains were mixed at about 1:10 ( competitor:reference ) and then added to flasks of 4 dpf GF fish ( either WT or myd88 mutant zebrafish ) at about 106 CFU/ml . For all competitions , ancestor ( ΔmutS , attTn7::dTomato ) or evolved isolates were competed against the WT ancestral strain , Aer01 attTn7::sfGFP . At 7 dpf , fish guts were dissected as described above , and each gut was transferred into a 1 . 6 ml tube containing 500 μl sterile EM and about 100 μl of bullet beads and then bullet blended as described above . Blended samples were then diluted appropriately in sterile EM , spread-plated on TSA plates , and incubated at 30 °C for 1–2 days , and the colonies were counted . Strains were differentiated by fluorescence microscopy . The limit of detection is 5 CFU/gut . For in vitro competitions , selected strains were grown overnight in TSB from freezer stocks . Competing strains were mixed at about 1:4 ( competitor:reference ) ; ancestor ( ΔmutS , attTn7::dTomato ) or evolved isolates were competed against the WT ancestral strain , Aer01 attTn7::sfGFP . Mixed cultures were then used to inoculate 5 ml TSB cultures ( 1:100 back dilution ) . Cultures were incubated at 30 °C for 24 hours and then diluted and spread-plated on TSA for enumeration via fluorescence microscopy . Data are combined from three independent replicate experiments . Generally , the gavage protocol was followed as previously described [44] , with the following modifications . Briefly , gavage needles were made by pulling 3 . 5-inch ( Drummond #3-000-203 GIX ) capillaries , microforging ( DMF1000 , World Precision Instruments ) them to an internal diameter of approximately 30 μm , and polishing the ends . Inoculum for gavaging was prepared by pelleting 1 ml of TSB overnight cultures ( 8 , 700 rcf , 2 minutes ) , resuspending them in 1 ml sterile EM , and mixing competing strains at about 1:2 ( competitor:reference ) ; ancestor ( ΔmutS , attTn7::dTomato ) or evolved isolates were competed against the differentially tagged non-mutator ancestral strain , Aer01 attTn7::sfGFP . Culture mixes were then diluted 1:30 in sterile EM for gut gavage ( no dilution for mouth gavage ) . Prepared inocula were incubated at room temperature until gavaging and flask inoculation ( approximately 30–60 minutes ) , allowing time for acclimation to the EM . GF 5 or 6 dpf WT fish were euthanized ( 120 μg/ml tricaine ) and transferred to 3% methylcellulose-coated gavage mold ( 4% agar ) . Culture mixes were loaded into gavage needles , and 4 . 6 nl of culture mix was gavaged directly into the lumen of the gut or the mouth of individual fish using a Nanoject II ( Drummond Scientific Company ) on the “slow” setting . After gavage , fish were rinsed in sterile EM and then transferred into sterile EM . Immediately after gavaging , flasks of GF fish were inoculated at 106 CFU/ml with the same inocula used for gavaging . Approximately 5 hours post gavage , fish were euthanized and dissected , and the guts were plated as described above . Selected isolates were grown overnight in TSB , at 30 °C , with shaking . Overnight cultures were diluted to 10−4 in EM , subcultured 1:100 in FC-EM , and grown for an additional 24 hours at 30 °C , with shaking . FC-EM was obtained by filter-sterilizing ( 0 . 2 μm ) flask EM from flasks of hatched 5–6 dpf GF zebrafish larvae . Flasks of 5 or 6 dpf GF zebrafish were combined and then inoculated with FC-EM cultures to yield approximately 104 CFU/ml . The fish were then split into replicate flasks containing 10 fish and 10 ml inoculated flask EM . An EM sample was taken and plated immediately to quantify CFU/ml of inoculating strain . Subsequently , about every 45 minutes ( for up to about 350 minutes ) , all of the fish in a replicate flask were dissected and the guts transferred into 1 . 6 ml tubes containing 200 μl sterile EM and approximately 100 μl bullet beads . The guts were homogenized as described above and all 200 μl spread-plated on TSA plates . Flask EM samples were also plated to enumerate bacterial CFU/ml . After 24–48 hours incubation , colonies on plates were counted . The distribution of bacterial abundances across fish allowed us to estimate the migration and growth rates of each strain . We considered a model in which migration into a fish by an individual bacterium is a stochastic event , with some probability per unit time . The statistics of the colonizers at any time are therefore governed by a Poisson distribution characterized by a mean time to entry , τ . Following entry into the host , the descendants of a colonizer obey logistic growth ( initially roughly exponential , constrained by a finite carrying capacity , K ) , characterized by some growth rate r . For the migration rate experiments shown here , the population size is orders of magnitude less than the carrying capacity , leaving r as the only relevant growth parameter . For each fish gut examined by dissection and plating , we measured the bacterial abundance , N ( CFUs ) . For N from any individual fish , one cannot separately determine τ and r; the same final population can be reached , for example , by rapid colonization followed by slow growth ( low τ , high r ) or by slow colonization followed by fast growth ( high τ , low r ) . The distribution of N across multiple fish , however , constrains both τ and r . Roughly , high τ and high r gives distributions with large variances and many uncolonized fish ( N = 0 ) , being dominated by the stochasticity of rare migration events , whereas low τ and low r gives distributions with smaller variance ( S11 Fig ) . We fit the experimental data ( i . e . , each set of CFU measurements at a particular measurement time t ) to the above model of stochastic colonization and growth , determining the best-fit parameters τ and r . This is done by varying τ and r across a range of possible values , simulating for each τ and r the model outcome for many ( 10 , 000 ) replicates , and calculating the likelihood that the simulated data match the set of measurements . This gives both the maximum-likelihood parameter values and a measure of the likelihood distribution across the full parameter range . For most datasets ( 41 of 50 ) , the likelihood p ( τ , r | CFU ) shows a sharp peak ( S12 Fig ) . For the remainder , it does not , indicating either limitations of this model ( e . g . , the neglect of exit from the gut ) or experimental error or contamination; these datasets were manually discarded . The datasets from each measurement time give independent estimates of τ and r; the average and standard error give the final estimates of mean entry time , growth rate , and their uncertainty for each bacterial strain ( Table 1 ) . Rich media swim plates were made using TSB containing 0 . 2% agar ( VWR Life Science AMRESCO Agarose ) . FC-EM swim plates were made by heat-dissolving 100 mg agar in 20 ml sterile EM and then cooling slightly and adding 30 ml sterile FC-EM ( 0 . 2% agar ) . Media ( 20 ml ) were added to 100 mm petri dishes , cooled for 2 hours , and then inoculated . For inoculation , TSB overnight cultures of selected strains were pelleted ( 8 , 700 rcf , 2 minutes ) and resuspended in one-tenth of the culture volume , and 3 μl was used to inoculate plates . A template for inoculation was made to facilitate insulation and sampling ( S6 Fig ) . Plates were incubated at 30 °C and then sampled and measured at 8 hours ( TSB and FC-EM ) , 24 hours ( FC-EM ) , and 48 hours ( FC-EM ) . To assess motility in TSB swim plates , the diameter of the swim zone was measured in centimeters . To determine motility in FC-EM swim plates , in which cell density was too low to visualize , we sampled agar plugs from the plates at a defined distance from the point of inoculation ( Fig 5 ) using x-tracta Gel Extractor tool ( Promega , Madison , WI , US ) . The agar plug was transferred into a 1 . 6 ml tube containing 500 ml sterile EM and approximately 100 μl 0 . 5 mm zirconium oxide beads ( Next Advance , Averill Park , NY , US ) , which were then homogenized using a bullet blender tissue homogenizer ( Next Advance , Averill Park , NY , US ) for 30 seconds at power 4 . Homogenized samples were diluted and spread-plated onto TSA plates and incubated at 30 °C overnight , and colonies were counted to enumerate the number of bacterial cells that migrated to the site of sampling . Overnight cultures ( TSB ) of the ancestral and evolved isolates were washed with sterile EM; then , 50 μl was added to 1 ml sterile FC-EM , and the cultures were incubated at 30 °C for approximately 4 hours to recover and acclimate . The culture was placed in a glass cuvette and imaged in a custom-built light sheet fluorescence microscope , as previously described [73] . The light sheet optically sections bulk samples; thus , the motility of the imaged bacteria is unconstrained by surfaces . Movies in a single optical plane were captured at 30 frames per second for a duration of 20 seconds , with excitation light provided by a 561 nm solid-state laser ( Coherent Sapphire 20 mW; all strains express dTomato fluorescent protein ) . Images were analyzed using standard particle-tracking techniques , with fine localization of bacterial positions determined by a radial symmetry–based algorithm [74] , the source code for which is available here: http://pages . uoregon . edu/raghu/particle_tracking . html . For each strain , four movies were recorded from each of two biologically independent replicate cultures ( eight total; seven for the ancestor ) . An average of 6 , 000 ( minimum of 2 , 300 ) bacteria were tracked in each movie . In Fig 5 , each data point represents average bacterial swimming velocity ( μm/sec ) across motile cells ( velocity > 5 μm/sec ) in an individual movie ( Fig 5E ) or the fraction of the total cells that were considered motile ( velocities > 5 μm/sec ) in an individual movie ( Fig 5F ) . | Animals live in a world teeming with microbes; from this pool , unique subsets of microbes colonize animal tissues . We know very little about the selection pressures that contribute to a microbe’s ability to be host associated ( i . e . , to colonize or otherwise exist within a host ) . We used the zebrafish as a model animal host to investigate how a bacterium , Aeromonas veronii , increases its ability to be host associated . Our selection regime consisted of serially exposing larval zebrafish to A . veronii , each time selecting for gut-associated A . veronii populations . We found that the primary adaptive strategy of the bacterium was to increase immigration from the environment into the host . Selection to conditions specifically within the host only arose later in the experiment . The conditions within a host are often assumed to provide the primary selection for host association . Our results show that previously underappreciated aspects of host–microbe systems ( e . g . , immigration from the external environment ) can play important roles in the establishment and maintenance of host–microbe relationships and that host–microbe interactions occur in a broader ecological context not generally captured in experimental models . | [
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"e... | 2018 | Experimental bacterial adaptation to the zebrafish gut reveals a primary role for immigration |
Monitoring of insect vector populations with respect to their susceptibility to one or more insecticides is a crucial element of the strategies used for the control of arthropod-borne diseases . This management task can nowadays be achieved more efficiently when assisted by IT ( Information Technology ) tools , ranging from modern integrated databases to GIS ( Geographic Information System ) . Here we describe an application ontology that we developed de novo , and a specially designed database that , based on this ontology , can be used for the purpose of controlling mosquitoes and , thus , the diseases that they transmit . The ontology , named MIRO for Mosquito Insecticide Resistance Ontology , developed using the OBO-Edit software , describes all pertinent aspects of insecticide resistance , including specific methodology and mode of action . MIRO , then , forms the basis for the design and development of a dedicated database , IRbase , constructed using open source software , which can be used to retrieve data on mosquito populations in a temporally and spatially separate way , as well as to map the output using a Google Earth interface . The dependency of the database on the MIRO allows for a rational and efficient hierarchical search possibility . The fact that the MIRO complies with the rules set forward by the OBO ( Open Biomedical Ontologies ) Foundry introduces cross-referencing with other biomedical ontologies and , thus , both MIRO and IRbase are suitable as parts of future comprehensive surveillance tools and decision support systems that will be used for the control of vector-borne diseases . MIRO is downloadable from and IRbase is accessible at VectorBase , the NIAID-sponsored open access database for arthropod vectors of disease .
Diseases transmitted by arthropod vectors and , in particular , mosquitoes pose an immense load on global health , with malaria alone being responsible for more than 46 , 000 , 000 DALYs ( Disease-adjusted Life Years ) ; pertinent calculations are based solely on official , yet largely incomplete statistical estimates [1] , and the global burden of falciparum malaria is nowadays estimated by some to be lower than originally thought [2] . Nevertheless , given the fact that arthropod-borne diseases affect mostly the populations of tropical regions , these huge numbers directly imply that their control is a conditio sine qua non for the socio-economic development of many of the poor areas of the world . Control of disease , then , directly entails the control of the arthropod vector populations and , most prominently among them , mosquitoes . Of course , economic development itself is one of the key players in the control of vector-borne diseases , unfortunately leading to an argument of a spiral form [3] . However , since the original recognition of the causes of malaria and other tropical diseases , campaigns aiming at eradicating vector-borne diseases included environmental management [4] , indoor residual spraying ( IRS ) with the widespread use of DDT ( Dichloro-Diphenyl-Trichloroethane ) or other insecticides [5] , [6] , as well as the use of impregnated nets ( Insecticide-Treated Nets , ITN [7]; and Long-Lasting Insecticide-treated Nets , LLIN [8] ) . These approaches , combined with extensive use of drugs , soon led to the disappearance of the disease from most non-tropical areas of the world and notably Europe [9] . In spite of the initial wide successes achieved in the temperate zones , eradication of vector-borne diseases proved to be elusive in the tropics . Moreover , the failure of vaccine development for vector-borne diseases , with the exception of the relatively early production of a vaccine directed against yellow fever [10] , complicated the strategies aimed at controlling these diseases . Perhaps , most prominent among several problems that were faced by the national and international public health agencies were the occurrences of resistance relating to both parasites becoming resistant to anti-parasitic drugs [11] and mosquitoes to insecticides [12] . The gradual development of insecticide resistance against all classes of insecticides used today soon after their introduction [13] , which was exacerbated by the use of such chemicals in agriculture [14] , is considered by some to be presently the most important impediment in the successful control of vector-borne diseases . Resistance to one or more insecticides used in vector control can have a crucial impact on the management of arthropod vector-borne diseases . In the case of ITN and LLIN measures [15] , [16] , monitoring of insecticide resistance needs to become a key component for the efficient usage of control strategies [17] . Although overall data on pesticide resistance have been collected over a long period of time [18]–[20] , these often remain inaccessible to public health workers around the world for a variety of reasons . One of them is the lack of a central database tool that would gather , store and exploit such data . Although pertinent studies are often published in refereed journals , their accessibility is limited by the use of restrictions , such as expensive subscription , something that is of extreme importance to scientists from disease-endemic countries , namely the very ones who urgently need to access these data . With this in mind we decided to develop IT tools that could offer solutions to some of the problems and most importantly to help monitor the occurrence of insecticide resistance in vector populations; we decided to first focus on mosquitoes as these represent the most important vectors of disease . Rather than only expanding the simple repository of insecticide resistance studies that we had previously developed [21] , we decided to completely restructure the database and support it by a dedicated ontology ( or controlled vocabulary ) . This type of tool , which among others helps standardize terminology in a computer-comprehensible form , has already proved its immense potential in cases such as , most prominently , the Gene Ontology ( GO ) project [22] . Both the ontology ( hereafter called MIRO for Mosquito Insecticide Resistance Ontology ) and the novel , enhanced database ( called IRbase ) are freely accessible to the research community through their incorporation in VectorBase [23] , [24] .
A Dell PowerEdge 850 , with a dual core Intel Pentium D CPU running at 3 GHz , 3 GB of RAM , and 150 GB of hard disk storage was used for the development of IRbase . The operating system used is CentOS 4 . 5 and the web service is handled by the Apache server . Both MySQL and PostgresQL database servers were used for data storage . Webpage scripts and command line scripts are written in PHP . For PHP development we have been using the Zend Development Environment ( ZDE ) . The OBO-edit software package [25] was used for the development of the MIRO . To display the locations of the collection sites the Google Maps API and maps are used . Geographic data are exchanged between the applications using the Keyhole Markup Language ( KML ) , a data schema for annotating and visualizing two or three dimensional maps . All coordinates are based on the World Geodetic System ( WGS ) 84 projection standard . Data are entered through the online AJAX web interface , which is ontology based . Alternatively , submitters may send in their data in Open Office ( ods ) , Excel ( xls ) , comma separated values ( csv ) , or tab separated values ( tsv ) files , which are processed and imported into the database using PHP scripts . The MIRO can be accessed and browsed at the URL http://www . vectorbase . org/Search/CVSearch/ and its latest version can be downloaded from http://anobase . vectorbase . org/miro/miro_release . obo; it is also available through the OBO-Foundry at http://obofoundry . org/cgi-bin/detail . cgi ? mosquito_insecticide_resistance; the home page for the IRbase is at the URL http://anobase . vectorbase . org/ir/ . Both MIRO and IRbase are freely accessible . To access all necessary files for a local usage of IRbase the authors should be contacted by e-mail ( louis@imbb . forth . gr ) .
For the construction of the MIRO we followed the rules established by the OBO Foundry [26] in order to establish maximum interoperability in the future . This implied the use , to some extent , of already established ontologies , rather than the de novo development of new ones , such as the geographical component ( see below ) . This decision obviously restricted the usage of relations linking terms to those allowed by the OBO Foundry rules and thus only is_a , part_of and agent_in are used throughout [27] . We are convinced , though , that this choice increases cross-ontology coordination and makes the tools developed more amenable to integration in a suite of malaria decision tools that are being developed . The next choice we were faced with was the one of whether this ontology should follow the ontological scaffold and the rules and conventions described for the Basic Formal Ontology [28] . This ontological arrangement is already used for a variety of biomedical ontologies , including anatomical ontologies of disease vectors , notably mosquitoes and ticks [29] . Although the obvious advantages of a BFO-based ontology such as , for example , the ease of expansion that is based on its modularity cannot be easily discarded , we decided to initially design the MIRO on a more “traditional” scheme that would make it easily recognizable by users who are not proficient in ontologies . The single reason for this is to be able to provide the insecticide resistance community with a module that can be easily incorporated into other IT tools currently being devised . Nevertheless we are in the process of transporting the MIRO into a BFO-based format in order to be able to integrate that version in future constructs that would potentially require such a layout . MIRO is based on five top-level classes that actually form independent sub-ontologies ( see Figure 1 ) ; four of them , “biological material” , “insecticidal substance” , “method” and “resistance” , were developed de novo by us explicitly for the MIRO . In Figure 1 ( left part ) the ontology's terms are shown in a depth of two levels with the exception of the fifth class , the “gazetteer” . This class represents a full importation of the Gazetteer ( GAZ ) , a controlled vocabulary following ontological rules that describes named geographical locations ( http://darwin . nerc-oxford . ac . uk/gc_wiki/index . php/GAZ_Project ) . GAZ is a community-based project of the EnvO Consortium for describing instances of organism environments and biological samples , supporting consistent annotation of locations and environments . The Gazetteer describes places and place names and the relations between them . Here , GAZ is basically used to describe the locations of sampling . Although it is a fully integrated component of MIRO , due to its size GAZ is not incorporated as such in our ontology , but it is automatically loaded through the Internet every time that one opens the MIRO using the OBOedit software . At this moment the MIRO contains 4 , 291 terms excluding , of course , the GAZ component that contains more than one hundred and fifty thousand geographical names from all over the world; more than 99% of the MIRO terms have full definitions . It should be noted that terms are not fixed and more are being added as these become necessary . Based on feedback from the malaria entomology research community it was decided several years ago to include in AnoBase , the Anopheles database [21] , a section on insecticide resistance; this tool was later transferred to and included in VectorBase [23] , [24] after this comprehensive genome database was established . The section consisted only of a series of manually-curated , already published studies; its role , therefore , was mostly to make data available to the community in a fashion that would be independent of the need for a library , rather than a use as an on-line epidemiological tool . The new IRbase in contrast is meant to serve as an expanding repository of associated data , which can be searched in a detailed fashion , thus providing immediately applicable information . Furthermore , IRbase now covers vectors of more diseases than the previous database that was only restricted to malaria . These are the reasons for designing a relational schema de novo ( see Figure 5 ) . It was our intention to design a schema that would easily enable both the addition of novel tables and the incorporation of IRbase into a larger and more complicated entity , which could be expanded later to encompass additional items linked to the control of vector-borne diseases . The nine distinct tables can be distinguished in two major categories: While two of them ( cv_term ) handles all MIRO terms , including GAZ , and their relationships , the remaining are there to handle , mostly , ontology-independent items . These include , most prominently , description of the study in terms of details of the collection site , the mosquito population sampled ( including collection dates , etc . ) and the assay ( s ) performed . The “household” table is presently not in use by IRbase , but it has been included by request as it could be needed by decision support systems currently under development for Dengue and malaria [32] . The schema allows for a high degree of interoperability due to the enhanced usage of the ontology component , and it enhances the two distinctive features of IRbase , i . e . the two interactive components , search and curator's tool , both of which are accessible through a simplified web interface . In addition to the completely new architecture of the database and to the fact that the software used is free and open source , IRbase has some key characteristic features: i ) The data are stored in the database using MIRO terms wherever possible; ii ) the Gaz geographic ontology is used for storing location data and the output can be viewed using maps; iii ) extensive use of Ajax ( Previously AJAX: Asynchonous JAvascript XML ) is made in order to minimize network traffic and improve look and feel [33] . Moreover IRbase was built around basic entities: 1 . “Study data” - a storage space for the data pertaining to an individual “study” . The “study” could be an entire study , previously published or not , on an entire population or parts thereof , pertaining to one or more insecticides; the pertinent data would include the “owner” of the particular data , time it was carried out , the publication record when available , etc . 2 . “Collection site” - common names of the collection site ( s ) , their alias ( es ) and , most importantly , the geographic coordinates . Should these not be available through the submitter of the data , the IRbase curators will assign values based on available information and feedback . For those names that already exist in Gaz the Gaz ID is also stored . The alias is an ID that the submitter can use for faster data entry: the collection site needs to be defined once and from thereon the alias can be used to identify that particular site . Collection sites that have no Gaz ID are exported and sent to the curators of that ontology for ID assignment . 3 . “Insect collections” - this area holds information such as the species name , the collection date , the catch method , the sex , food state etc . of the specimens ( field collected or lab bred ) that were subsequently used to test resistance . 4 . “Assay data” - The actual data expressing the findings and referring to the methods used , the conditions ( insecticide concentration ) and the results of an assay , etc . We described here a set of IT tools to be used for the analysis of insecticide resistance in wild populations of insect disease vectors and in particular mosquitoes . The concept of intimately linking a dedicated database to a specific application ontology describing the field offers the advantage that the database can later be easily expanded to include additional items and offer further tools . This fact , in our case , can form the overall foundation or one of the pillars of a comprehensive tool , which could be used to globally monitor insecticide resistance; this would form the basis for a global decision support system for malaria and/or other vector-borne diseases . A database on insecticide resistance , the Arthropod Pesticide Resistance Database ( APRD ) , can already be found in the world wide web ( http://www . pesticideresistance . org/ ) . APRD covers a large variety of arthropods , but its philosophy is different from the one of IRbase . It provides reports of instances of occurrence of resistance , without any precision as to the exact location and the actual data . Although useful as a general indicator of resistance , especially in the domain of agriculture , the lack of geographic accuracy , combined with the lack of a map interface makes this database less suitable as a tool that could be used either by itself , or in combination to a modern , IT-based decision support system . Such decision support systems are considered to be a prerequisite for the efficient control of insect vector populations . Many potential components of such systems have been described ( see for example [32] , [34]–[35] ) , especially components that are based on GIS . Our tool has for the moment the capacity to depict data of insecticide resistance on a map provided the geographic coordinates have been incorporated in the data collection . Since many of the data that will populate IRbase are old , some of the coordinates will have to be input manually; once this has been the case , it will be possible to link all available information to maps based on , and retrieved from Google Earth . The MIRO/IRbase set of tools is presently focused completely on insecticide resistance linked to mosquitoes of medical importance . The open source policy linked to the MIRO , an ontology that abides with the OBO Foundry rules , makes it easy to further develop these tools in order to later include data of agricultural interest as well , should an interested party turn up . In that sense one should also consider the fact that development of resistance detected in disease vectors can often be traced back to the often-improper use of insecticides in agriculture ( see [36] for a discussion of that problem ) . We are currently in the process of populating IRbase with both data from the literature and data that are being collected from the field . This is done in collaboration with the international community in the frame of large consortia ( e . g . African Network on Vector Research , Innovative Vector Control Consortium , WHO/Gates Foundation Vector Biology and Control Project , etc . ) , as well as on the basis of smaller individual research networks . We hope that , this way , IRbase will soon be established as the global repository for data insecticide resistance . | It is a historical fact that a successful campaign against vector populations is one of the prerequisites for effectively fighting and eventually eradicating arthropod-borne diseases , be that in an epidemic or , even more so , in endemic cases . Based mostly on the use of insecticides and environmental management , vector control is now increasingly hampered by the occurrence of insecticide resistance that manifests itself , and spreads rapidly , briefly after the introduction of a ( novel ) chemical substance . We make use here of a specially built ontology , MIRO , to drive a new database , IRbase , dedicated to storing data on the occurrence of insecticide resistance in mosquito populations worldwide . The ontological approach to the design of databases offers the great advantage that these can be searched in an efficient way . Moreover , it also provides for an increased interoperability of present and future epidemiological tools . IRbase is now being populated by both older data from the literature and data recently collected from field . | [
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] | 2009 | MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors |
Although many of the regulators of actin assembly are known , we do not understand how these components act together to organize cell shape and movement . To address this question , we analyzed the spatial dynamics of a key actin regulator—the Scar/WAVE complex—which plays an important role in regulating cell shape in both metazoans and plants . We have recently discovered that the Hem-1/Nap1 component of the Scar/WAVE complex localizes to propagating waves that appear to organize the leading edge of a motile immune cell , the human neutrophil . Actin is both an output and input to the Scar/WAVE complex: the complex stimulates actin assembly , and actin polymer is also required to remove the complex from the membrane . These reciprocal interactions appear to generate propagated waves of actin nucleation that exhibit many of the properties of morphogenesis in motile cells , such as the ability of cells to flow around barriers and the intricate spatial organization of protrusion at the leading edge . We propose that cell motility results from the collective behavior of multiple self-organizing waves .
Rac and its downstream effector Scar/WAVE are central regulators of cell shape and movement [1–4] . Rac binds WAVE indirectly via a multiprotein complex [5] . This complex contains the WAVE protein , which activates the actin nucleating Arp2/3 complex , and four other proteins that may regulate or localize the complex . The Hem-1 component ( homologous to Nap1 ) of the leukocyte WAVE2 complex is required for actin polymerization and proper leading edge morphology in neutrophils [6] , and its homologues play important roles in regulating cell shape or movement for Dictyostelium [7] , plants [8] , worms [9] , insects [10–12] , and mammals [1 , 13 , 14] . Although many of the regulators of actin assembly are known , understanding how they act together to generate cell morphology is a much more difficult problem . Spatial and dynamic information will be key to understanding their overall function . We show here that the leading edge of neutrophils contains moving waves of Hem-1 ( including complexes which contain WAVE2 ) , whose collective behavior corresponds to the morphology of the leading edge . These waves are not formed by lateral movement of individual proteins but , like action potentials , are the result of propagated cycles of activation and inhibition . These waves reveal a far more complex and dynamic interaction between inducers of actin nucleation and the cytoskeleton than is represented in current models of cell motility .
To analyze spatial and temporal dynamics of signaling and morphogenesis , we and others have used confocal or wide-field microscopy of fluorescently tagged proteins . During polarization and migration , some proteins localize to the leading edge , others to the trailing edge , and some appear uniform [15–21] . We have analyzed some of the same components by using total internal reflection fluorescence ( TIRF ) microscopy , which , with its considerably higher resolution in the vertical axis , is especially useful for visualizing events at the plasma membrane . By confocal microscopy , we found that Hem-1-GFP , which assembles into the WAVE complex and other multiprotein complexes , exhibits a nearly uniform leading-edge accumulation [6] . In contrast , Hem-1-YFP visualized by TIRF exhibits much more complex local patterns ( Figure 1 ) . Hem-1 initially accumulates on the membrane as foci , which burst into outwardly propagating waves ( Figure 1A and Videos S1 and S2 . Note that here and throughout the rest of this manuscript , the supplemental videos capture the dynamics much more clearly than a few individual frames ) . In polarized cells , Hem-1 waves are concentrated near the leading edge ( Figure 1A , arrow ) but are not confined to the cell periphery ( Figure 1A and 1B and Video S5 ) . The morphology ( Figure 1B ) and speed ( Figure 1C ) of leading edge advance is highly correlated with the most peripheral Hem-1 waves . The complex structure of the leading edge is composed of many discrete waves ( Figure 1A–1C ) . Two lines of evidence suggest that these waves are not an artifact of the narrow depth of field of TIRF . First , in TIRF microscopy , proteins that are expressed uniformly throughout the cell membrane ( such as the C5a receptor ) show no wavelike behavior and appear uniform ( Figure 1D ) , indicating that Hem-1 waves do not represent portions of the plasma membrane coming in and out of the very thin TIRF illumination field . Second , with very careful critical focusing , the Hem-1 waves can be visualized satisfactorily by confocal microscopy , which has a depth of field about 5–10 times larger than that of TIRF ( Video S6 ) . Hem-1 is observed in regions of the plasma membrane other than the ventral surface of the cell , but we do not have the signal-to-noise ratio necessary to determine whether Hem-1 is wavelike in these regions [6] . In neutrophils polarized by exposure to a chemotactic signal , Hem-1 waves are nonuniformly distributed and have different dynamics in different regions of the cell . These features suggest an underlying regulator of Hem-1 waves that itself would be spatially polarized . The velocity of the Hem-1 waves varies reproducibly with the distance from the leading edge ( 5 μm/min at the leading edge versus 3 . 6 μm/min at 5 μm from the leading edge [Figure 1E , first panel]; also note from Figure 1C , lower panel , that individual waves increase velocity as they approach the leading edge ) . There is also a strong gradient in Hem-1 wave lifetime that is inversely dependent on distance to the leading edge ( 34 s at 1 μm from the leading edge versus 5 s at 5 μm from the leading edge [Figure 1E , second panel] ) . Acute stimulation with chemotactic peptide first produces a uniform field of Hem-1 spots . These break symmetry , polarize to one end of the cell ( Figure 1F , arrow ) , and begin generating Hem-1 waves at the leading edge ( Figure 1F , Video S7 ) . What could account for the polarized distributions , velocities , and lifetimes of Hem-1 waves in moving cells ? Active Rac is a likely candidate , because it directly binds the WAVE2 complex ( thereby indirectly binding Hem-1 ) , and the WAVE2 complex is required for Rac-induced actin assembly [1] . To visualize the spatial dynamics of Rac activation ( Rac-GTP ) in motile cells , two basic approaches have been used in the past . The first relies on fluorescence resonance energy transfer between a fluorescently tagged Rac GTPase and a tagged effector that binds activated Rac ( Pak GTPase binding domain [GBD] ) [22 , 23] . Because Rac is untagged , it is not possible to visualize activation of endogenous Rac with this approach . A complementary approach relies on fluorescently-tagged Pak-GBD–green fluorescent protein ( GFP ) to visualize activation of endogenous Rac , but this technique has only been successfully applied to fixed permeabilized cells [24] . With the significantly increased signal-to-noise of TIRF microscopy , we are now able to visualize the activation of endogenous Rac in living cells using Pak-GBD–yellow fluorescent protein ( YFP ) ( Figure 2A and 2B . Note that previous experiments indicate that this probe is more sensitive to Rac-GTP levels than Cdc42-GTP levels in HL-60 cells [24] ) . In contrast to the discrete waves of Hem-1 , cells exhibit more homogeneous zones of Rac activation ( Figure 2A and 2B , Videos S9 and S10 ) . The spatial distribution of Rac activation generally overlaps with the portion of the cell containing Hem-1 waves for cells acutely stimulated with chemoattractant ( compare Figure 2A with Figure 1F ) . Rac activation also correlates with Hem-1 wave distribution during cell migration ( compare Figure 2B with Figure 1A and 1B ) . However , the zones of Rac activation are homogeneous , whereas Hem-1 forms sharp wavefronts , suggesting that Hem-1 does not simply mirror the presence of active Rac on the membrane , which instead may provide a permissive condition for Hem-1 waves to form . What is the spatial distribution of the downstream effectors of Hem-1 waves ? The WAVE2 component of Hem-1 complexes is thought to act through the Arp2/3 complex to induce actin assembly . WAVE directly activates the Arp2/3 complex [25] , and WAVE complex mutants phenocopy loss of the Arp2/3 complex in a variety of systems [9 , 26–29] . In contrast to Hem-1 , the Arp2/3 complex ( Figure 2C and Video S10 ) and total actin ( Figure 2D ) fill the leading edge of chemoattractant-stimulated neutrophils without discrete gaps , possibly because Arp2/3 complex ( which incorporates into polymerized actin ) and actin polymer represent a history of actin assembly versus the current sites of actin nucleation . Do propagated Hem-1 waves represent sequential rounds of protein recruitment and release from the soluble pool , or do they represent the movement of Hem-1 proteins in the plane of the membrane , powered by actin polymerization , motor proteins , or some other process ? To distinguish between these very different mechanisms , we photobleached a segment of an advancing wave ( Figure 3A–3C ) . Lack of recovery after photobleaching and maintenance of the bleached spot as the rest of the wave moves outward would indicate that a wave is composed of a nonexchangeable pool of Hem-1 moving in the plane of the membrane . In contrast , the photobleached Hem-1 gap very rapidly recovers before there is appreciable forward progress of the wave , suggesting that the recruited Hem-1 protein dynamically equilibrates with the cytosolic pool and that the bulk of wave movement is generated by recruiting more Hem-1 from the cytosol ( Figure 3A and 3C , Video S11 ) . Recruited Hem-1 only transiently associates with the membrane ( half life t1/2 = 6 . 4 s , Figure 3C , second panel ) . Hem-1 recruitment occurs with high probability adjacent to existing membrane-bound Hem-1 ( 99% ± 1% of newly-observed Hem-1 is observed within 0 . 2 μm of existing Hem-1 foci and 0 . 15% ± . 1% is observed greater than 0 . 2 μm away . The distribution that would be expected for random Hem-1 association with the membrane would be 50 . 3% ± 14% within 0 . 2 μm of existing Hem-1 foci and 48 . 5% ± 13% more than 0 . 2 μm from existing Hem-1 foci ) . Actin polymer is required for the rapid equilibration of Hem-1 with the cytoplasmic pool . When latrunculin is added to depolymerize filamentous actin , the photobleached Hem-1 spot is stable for at least 10 min , after which the cells detach from the substrate ( Figure 3B ) . Collectively , these data make two important points . First , Hem-1 waves move by sequential rounds of protein recruitment , not by a significant amount of movement of individual Hem-1 complexes in the plane of the membrane . Second , the observed rapid recycling of recruited Hem-1 depends on polymeric actin . Particularly in cells with a single strong peripheral Hem-1 wave and multiple interior waves , a gap between waves can be easily observed , presumably reflecting an inhibitory period between waves ( Figure 3D , Video S12 ) . When other biological waves collide , they annihilate , because the region in the wake of each wave is transiently inhibitory to further activation [30 , 31] . In a similar fashion , colliding Hem-1 waves also annihilate ( Figure 3E , Video S13 ) . Annihilation of colliding waves could potentially account for the observations that peripheral waves are more continuous than internal waves and that waves tend to propagate toward the cell periphery ( Figures 1 and 3 ) . What downstream products of Hem-1 complex recruitment might act to inhibit further Hem-1 recruitment between waves ? Actin polymer is a likely candidate , because actin nucleation is a known output of the WAVE2 complex [1] , and polymeric actin fills the leading edge ( Figure 2D ) . To test a role for actin as an inhibitor of further Hem-1 complex recruitment , we exposed cells to drugs that either stabilize or destabilize the actin cytoskeleton . In contrast to fibroblasts , which require actin polymers and adhesion for signal transduction downstream of Rac [32] , neutrophils require neither adhesion nor actin polymer for activating Rac effectors [6] , making perturbations of the actin cytoskeleton less confounding for neutrophils . In untreated cells , waves propagate with a characteristic velocity of 3 . 93 ± 0 . 15 μm/min and persist with average lifetime of 23 . 52 ±- 2 . 24 s , ( Figure 4A and 4B , Video S14 ) . Depolymerization of actin with latrunculin increases wave lifetime 20-fold to 488 ± 13 . 6 s and increases the intensity of membrane-associated Hem-1 by more than an order of magnitude , suggesting that actin polymer drives Hem-1 off of the membrane ( Figure 4B and 4C , Videos S15 and S16 ) . These data are consistent with the lack of exchange of membrane-associated Hem-1 following actin depolymerization ( Figure 3B and 3C ) . Latrunculin reduces wave motility 10-fold to 0 . 41 ± 0 . 02 μm/min ( Figure 4A and 4B ) ; the residual motion is nondirectional and most likely represents random diffusion . In contrast , jasplakinolide , which stabilizes actin polymers against depolymerization , produces a phenotype that is consistent with an enlarged field of inhibition between waves ( Figure 4A and 4D , Video S17 ) . In the presence of jasplakinolide , the wave closest to the leading edge initially persists upon actin stabilization ( Figure 4A , green arrow ) , but interior waves are extinguished ( Figure 4A , 40 s time point , and Figure 4D ) . The stabilization of the actin cytoskeleton produced by jasplakinolide decreases the velocity ( 2 . 04 ± 0 . 11 μm /min ) , intensity , and lifetime ( 11 ± 1 . 27 s ) of the remaining Hem-1 waves ( Figure 4B and 4C ) . These data suggest that actin polymer is required for Hem-1 wave movement as well as the removal of Hem-1 from the plasma membrane . As cells migrate , Hem-1 waves propagate with a characteristic velocity and lifetime toward the cell periphery ( Figure 1E ) . We never observe the Hem-1 waves to stall for more than a few seconds except when actin is depolymerized . We believe this is because a standing wave of Hem-1 on the membrane would be destabilized by locally generated actin polymers ( Figures 3 and 4 ) . However , stalling can be forced to occur when a cell encounters a physical barrier that prevents further membrane protrusion . In such a condition , the Hem-1 waves stall and are extinguished ( Figure 5 , Video S18 ) . When this happens , other regions of the cell that are not in contact with the external barrier continue to propagate Hem-1 waves and expand in size . The effect of this behavior is that the cell has effectively restricted actin polymerization and leading edge organization to domains compatible with productive movement , potentially accounting for contact inhibition of movement . As a result , rather than be stymied by physical barriers , cells tend to flow around them . The integration of signaling and actin polymerization in Hem-1 waves ensures that the cytoskeleton is not simply a passive readout of the cell polarity but rather has local dynamic properties of its own that contribute to the overall behavior and give it some of its most life-like properties .
Based on the observed in vivo dynamics of Hem-1 , we propose a model for Hem-1 waves that is similar to the circuitry of other biological waves , such as action potentials ( Figure 6A , first panel ) . Such biological waves are based on autoactivation and delayed inhibition . Our photobleaching experiments ( Figure 3A–3C ) suggest that Hem-1 waves result from successive rounds of Hem-1 recruitment and release . Consistent with this idea , Hem-1 recruitment is observed with high probability adjacent to existing Hem-1 membrane distributions . Several pieces of evidence suggest that the actin polymerization generated downstream of Hem-1 complexes is required to remove Hem-1 from the membrane ( potentially forming the autoinhibitory portion of the cycle ) . First , Hem-1 waves normally have characteristic lifetime and intensity , and actin depolymerization increases both more than an order of magnitude ( Figure 4B and 4C ) . Second , Hem-1 normally cycles on and off the membrane , and actin depolymerization stops this flux ( Figure 3A–3C ) . Third , there is a gap observed between Hem-1 waves ( Figure 3D ) , and stabilization of the actin cytoskeleton increases the length of this gap ( Figure 4D ) . Fourth , stabilization of the actin cytoskeleton decreases the velocity , intensity , and lifetime of Hem-1 waves ( Figure 4B and 4C ) . Using mathematical simulation , we asked whether a simple circuit involving auto-activation and transient inhibition could generate Hem-1 waves with the properties we observed ( Figure 6 , Video S19–S21 ) . These simulations ( done under a chosen set of parameters , not experimentally confirmed , and generally not experimentally accessible at this time ) recapitulate most of the basic behaviors of Hem-1 waves ( Figure 6B and 6C ) . They indicate that Hem-1 wave movement as well as Hem-1 removal can be explained simply by the inhibitory role of actin polymers for Hem-1 membrane association . In the simulations , actin depolymerization not only increases Hem-1 wave intensity and lifetime but also stops wave movement due to depletion of cytosolic Hem-1 ( Figure 6D , Video S21 ) . Consistent with this , we observe biochemically that the majority of Hem-1 translocates to the plasma membrane in response to latrunculin treatment ( unpublished data ) . The propagated waves occur over a range of parameters where actin polymers are metastable , as they are in the cell ( Figure S1 ) . However , additional components almost certainly contribute to wave organization , and specific experimental parameters will need to be evaluated . Yet this basic circuit indicates that these propagated waves may be based on simple and general properties of the system . Oscillatory and wavelike patterns for both actin polymers and the Arp2/3 complex have been observed in Dictyostelium and in other systems [33–36] . Furthermore , wavelike protrusive activity has been noted from Drosophila cells to mammalian cells [37] . These activities exhibit different organization from the Hem-1 waves , although they may be related to them . Conceptually , however , there is a strong distinction between actin and the Arp2/3 complex , which are incorporated into the cytoskeleton and represent the physical substrate that generates protrusion , and the Hem-1 waves that represent a moving inductive field that patterns this substrate . When actin is depolymerized , it and the Arp2/3 complex dissociate from the cortex , whereas Hem-1 continues to accumulate at the membrane . Yet the behavior of the Hem-1 waves is closely tied to the actin substrate , and this , in turn , produces a moving front of actin nucleation . These data suggest a different view of cell motility as neither a global process at the level of whole cell organization nor a biasing of local actin polymerization events organized at the molecular level . Rather , cell movement may result from the collective behavior of multiple self-organizing waves , an idea consistent with the morphological pattern of leukocyte movement during chemotaxis [38] . The wavelike organization of protrusion has been observed in cells from flies to mammals [37] , and the WAVE complex plays a conserved role in morphogenesis throughout plants and metazoans [6–14] . We propose that these Hem-1/Nap1 waves may also occur in many cell types and serve as a conserved subcircuit for cellular morphogenesis and movement .
The following reagents were used: Latrunculin ( Calbiochem; http://www . emdbiosciences . com/html/CBC/home . html ) , jasplakinolide ( Calbiochem ) , formyl met-leu-phe ( fMLP; Sigma; http://www . sigmaaldrich . com ) , low endotoxin human serum albumin ( Sigma ) , and human fibronectin ( Sigma ) . HL-60 cell stably expressing Hem-1-YFP , C5AR-GFP , and actin-YFP were generated as previously described [6 , 39 , 40] . Arp3-GFP cells were generated by retroviral-based infection of HL-60 cells using the pLNCX vector and GPG packaging cell lines [41] . Stable cell lines were obtained after neomycin selection . Cells were cultured and differentiated as previously described [39] . Cells were plated on coverslips precoated with 0 . 2 mg/ml human fibronectin , and cells were stimulated with 20 nM fMLP in modified Hank's buffered saline solution ( mHBSS ) containing 0 . 2% human serum albumin . For drug treatments , cells were either simultaneously treated with drug and stimulated with fMLP or pretreated with drugs prior to exposure to fMLP . All experiments were performed at room temperature . TIRF microscopy images were acquired on a Nikon TE2000E2 Inverted microscope equipped with a 1 . 45 numerical aperture ( NA ) 60× and 100× PlanApo TIRF objectives and an electron microscopy charge coupled device ( EM-CCD ) ( Hamamatsu; http://www . hamamatsu . com/ or Roper 512 II; http://www . roperscientific . com/ ) . Typical imaging conditions for most experiments used 100 ms exposures every 1–2 s with 20 mW 488 nm or 514 nm laser lines attenuated 8–64-fold with neutral density filters , and near maximal multiplication on the intensified CCDs to minimize phototoxicity and photobleaching ( these cells are very sensitive to phototoxicity ) . For some experiments , a laser-based autofocus system ( Perfect Focus , Nikon; http://www . nikonusa . com ) was used to minimize sample drift . Metamorph or Nikon Elements were used for image acquisition . ImageJ was used to generate kymographs and pseudocolored images . All image analysis was performed using Matlab 7 . 0 . 4 ( http://www . mathworks . com ) and the Image Processing Toolbox 5 . 0 . 2 . Sequential TIRF images of a single cell expressing fluorescent Hem-1 were acquired and then the image set was processed in the following manner . First , the images were segmented with a user-defined threshold to separate out the background from the regions where Hem-1 particles were expressed . Second , the displacement of each particle between consecutive image frames was computed using a correlation-based approach [42] . The displacement measurements were refined by quadratic interpolation to achieve subpixel resolution ( accuracy ∼ 0 . 01 μm ) . Third , the displacements were used to follow the trajectory of each particle and to determine particle lifetime . Fourth , because the particle trajectory varied linearly with time , the average velocity of each particle during its lifetime was measured with curve fitting . Fifth , the position of the cell's leading edge in each image frame was manually determined , and the distance between the leading edge and each particle was computed . The mobility of Hem-1 was investigated using fluorescence recovery after photobleaching ( FRAP ) . The Hem-1 fluorescence in a small region ( ∼1–2 μm in radius or a diffraction-limited spot for some experiments ) at the leading edge of a chemoattractant-stimulated cell was photobleached and allowed to recover for up to 60 s . The dependence of Hem-1 mobility on actin dynamics was also investigated by photobleaching Hem-1 expressing cells that were first activated with 20 nM fMLP and then treated with 10 μM latrunculin B . The halftime of Hem-1 recovery ( i . e . , the time from the bleach to the time where the fluorescence intensity reaches half of the final recovered intensity ) was determined by plotting the recovery of relative fluorescence within the bleached region as a function of time and fitting the data with an exponential function [43] . Simulations of the Hem-1/actin circuit were performed using the Ordinary Differential Equations stochastic simulator in Matlab . Details of the code and variation of parameters used can be found in Protocol S1 . Many of the interactions and rate constants required for an exact model of the Hem-1 waves are unknown . Hence , we chose a modeling strategy that accomplished the following: ( 1 ) directly incorporates the phenomenology experimentally observed behaviors , ( 2 ) favors the interpretability of a small number of equations and interactions over the flexibility of more complex models , and ( 3 ) will allow future refinement as more network details are clarified . The model assumes Hem-1 and actin are present either on a membrane ( represented as a grid or pixels ) or in a cytosolic pool . These are represented at a pixel and time ( x , t ) as: A is the proportion of polymerized actin at each pixel and ( 1 − A ) is the proportion of unpolymerized actin at each pixel; H is the Hem-1 density at each pixel and HC is the total cytosolic pool of Hem-1 . The model of Hem-1–actin interaction described in the main text is: Each term is described below . Note that * indicates the mathematical convolution operation , that is: . We modeled molecular interaction at one spatial scale ( Figure 6A ) . Short range Hem-1 autoactivation is modeled by convolving Hem-1 with a Gaussian kernel GSA of small ( measured in units of pixels ) variance: HACT = HC ( H * GSA ) . This term is positive when there is both available cytosolic Hem-1 and “near-by” Hem-1 ( for most simulations Hem-1 at adjacent pixel ) . Putting this together , in the equations above: A ( x , t ) = 0 initially , but is allowed to nucleate with probability proportionate to HACT . Upon nucleation , actin concentration is set to a small number , and driven positive at each pixel proportionately to the amount of Hem-1 and remaining available unpolymerized actin . We assume that if Hem-1 ever falls below a ( small ) threshold , actin concentration is reset to zero until the next nucleation event ( Figure 6A ) . Hem-1 is increased proportionately to the amount of free actin and HACT and decreased proportionately to the amount of polymerized actin and Hem-1 . Thus , if actin is fully polymerized in the pixel , the Hem-1 concentration can no longer increase . Conversely , if actin is fully depolymerized at a pixel , the Hem-1 concentration will monotonically increase . | Many cells guide their movement in response to external cues . This ability is required for single-celled organisms to hunt and mate , enables innate immune cells to seek and destroy pathogens , and is also essential for the development of multicellular organisms . Misregulation of cell migration is intimately involved in atherosclerosis and in cancer metastasis . Although many of the regulators of cell migration are known , we do not understand how these components act together to organize cell shape and movement . We used advanced light microscopy to follow the distribution of a key regulator of cell migration in living cells . We focus on a protein called Hem-1 , which is part of a large multimolecular protein complex that regulates cell shape in animals and plants . We found that Hem-1 exhibits complex cycles of activation and inhibition to generate waves of propagating Hem-1 and actin assembly that are similar in mechanism to grass fires or the action potentials used in neuronal signaling . These waves potentially explain many of the complex behaviors of motile cells such as the ability of cells to flow around barriers and the intricate spatial organization of protrusion at the front of moving cells . | [
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] | 2007 | An Actin-Based Wave Generator Organizes Cell Motility |
The extraordinary phenotypic diversity of dog breeds has been sculpted by a unique population history accompanied by selection for novel and desirable traits . Here we perform a comprehensive analysis using multiple test statistics to identify regions under selection in 509 dogs from 46 diverse breeds using a newly developed high-density genotyping array consisting of >170 , 000 evenly spaced SNPs . We first identify 44 genomic regions exhibiting extreme differentiation across multiple breeds . Genetic variation in these regions correlates with variation in several phenotypic traits that vary between breeds , and we identify novel associations with both morphological and behavioral traits . We next scan the genome for signatures of selective sweeps in single breeds , characterized by long regions of reduced heterozygosity and fixation of extended haplotypes . These scans identify hundreds of regions , including 22 blocks of homozygosity longer than one megabase in certain breeds . Candidate selection loci are strongly enriched for developmental genes . We chose one highly differentiated region , associated with body size and ear morphology , and characterized it using high-throughput sequencing to provide a list of variants that may directly affect these traits . This study provides a catalogue of genomic regions showing extreme reduction in genetic variation or population differentiation in dogs , including many linked to phenotypic variation . The many blocks of reduced haplotype diversity observed across the genome in dog breeds are the result of both selection and genetic drift , but extended blocks of homozygosity on a megabase scale appear to be best explained by selection . Further elucidation of the variants under selection will help to uncover the genetic basis of complex traits and disease .
There are more than 400 breeds of domestic dog , which exhibit characteristic variation in morphology , physiology and behavior . This astonishing phenotypic diversity has been molded by two main phases of evolution: 1 ) the initial domestication from wolves more than 15 , 000 years ago , where dogs became adapted to life in closer proximity to humans and 2 ) the formation of distinct breeds in the last few hundred years , where humans chose small groups of dogs from the gene pool and strongly selected for novel and desirable traits [1] , [2] . A by-product of these processes has been that many dog breeds suffer from a high incidence of inherited disorders [3] , [4] . Its unique population history makes the dog an ideal model organism for mapping the genetic basis of phenotypic traits due to extensive linkage disequilibrium ( LD ) and a reduction in haplotype diversity due to genetic drift in isolated populations [3]-[5] . Another major advantage of the canine model is that much of the variation in morphological characteristics in dogs appears to be governed by a relatively small number of genetic variants with large effect [6] . This is likely because novel variants with large effects are preserved by artificial selection . This is in strong contrast to humans where morphological variation in traits such as height appears to be controlled by hundreds of loci with small effects , which have proven extremely difficult to catalogue [7] . Identifying the targets of artificial selection in dog breeds is therefore an extremely promising approach for identifying genetic variants involved in phenotypic variation , which could greatly facilitate the identification of similar variants and novel molecular pathways in humans . Several loci have now been identified that control variation in morphological traits between dog breeds . In some cases , variation in a trait occurs within a breed , and long blocks of LD can be used to identify the locus responsible using genome wide association studies ( GWAS ) . Using this approach loci involved in traits including size ( IGF1 ) [8] , coat type ( RSPO2 , FGF5 , KRT71 ) [9] and coat color ( MITF , CBD103 ) [10] , [11] were identified in single breeds , and it was shown that variation in these loci is also correlated with phenotypic variation between breeds . An alternative approach , when a particular trait is shared by several breeds , is to perform across-breed GWAS . In general , levels of LD decay much faster between breeds , and this reduces the power to detect association [11] . However , selection acts to fix long haplotypes bearing the causative variant , thus increasing levels of LD between breeds in regions under selection . Jones et al . [12] used a sparse marker set and across-breed GWAS to identify correlations with a number of morphological traits , such as size , height , and shape of ears , snout and limbs , which was further refined by Boyko et al . [6] using 80 dog breeds and ∼61 , 000 SNPs . Across-breed GWAS have also been used to identify an FGF4 retrogene associated with chondrodysplasic breeds [13] and the THBS2 locus associated with brachycephalic breeds [14] . Genomic regions with a high degree of genetic differentiation between breeds are also indicative of selection . A large proportion of SNPs with high FST between dog breeds are found in loci associated with phenotypic traits such as size , ear morphology and coat color [6] . Akey et al . [15] scanned patterns of variation in 10 dog breeds and ∼21 , 000 SNPs using a 1 Mb sliding windows to identify larger regions with elevated FST in particular breeds . This scan identified many regions likely to be under selection in one or more of the breeds in their dataset . Notably , a highly differentiated interval in Shar-Pei on chromosome 13 contains the HAS2 gene and is likely associated with the wrinkled skin phenotype of this breed [15] , [16] . Although a large number of loci under selection have now been identified , the genetic basis of much of the phenotypic variation in dog breeds and particularly behavioral traits remains unexplained . One drawback of previous studies is the use of SNP arrays with relatively low coverage of the genome . With the development of a new high-density array it is now possible to examine the dog genome at much higher resolution , allowing a comprehensive characterization of regions under selection . Genetic variants under selection in dogs can be loosely divided into two categories: 1 ) those that control variation in common traits such as size and ear carriage , which segregate across many breeds [6] , [8] and 2 ) those that encode rare traits that present in one or a few breeds , such as brachycephaly , chondrodysplasia and skin wrinkling [13] , [14] , [16] . Here we implement a variety of approaches to identify both these types of loci . In cases where a common trait has been identified , it is possible to search for genotype-phenotype correlations . We attempt to identify both behavioral and morphological traits that vary between breeds using across-breed GWAS . We also use FST statistics to identify additional SNPs that have high variability in frequency between breeds . These methods identify known loci and indicate new regions that may be involved in common trait variation . The action of selection can potentially be identified by examining patterns of variation in individual breeds in order to detect the characteristic signature of selective sweeps . This signature is characterized by the presence of long haplotypes , a skew in allele frequency , reduced heterozygosity , and elevated population differentiation . A large number of statistical methods have been developed to detect sweeps based on these different patterns [17]-[22] . The formation of dog breeds occurred during an extremely brief evolutionary time , and likely involved rapid fixation of haplotypes under strong artificial selection . Under this scenario , simulations suggest that statistics based on FST and differences in heterozygosity are likely to be most powerful . [23] . Furthermore , dog breeds are known to be characterized by extensive LD and limited haplotype diversity , including long blocks of homozygosity , which reflect the action of population bottlenecks and selective breeding . This suggests that tests based on allele frequency spectrum and haplotype length will be of limited applicability , as many genomic regions are essentially devoid of genetic variation . We therefore base our approach to identify selective sweeps on pairwise comparisons of both FST and heterozygosity between breeds . The presence of long blocks of homozygosity in the dog genome [1] , [11] is likely to reflect the action of both selection and genetic drift . We therefore conduct extensive coalescent simulations in order to distinguish between these processes . These simulations incorporate a realistic model of dog population history under neutrality to provide null distributions to compare with the real data . We also conduct a comprehensive characterization of SNP variation in a 3 Mb region encompassing several loci with extreme population differentiation that are associated with at least two morphological traits .
Our first goal was to develop a high-density , high-accuracy mapping array with uniform SNP coverage across the whole genome . Since the SNP map from the canine genome project , although containing >2 . 8 million SNPs at fairly even coverage , still contained gaps , we first performed targeted resequencing within 1 , 555 regions that lie within intervals >40 kb containing no known SNPs in unique sequence . We performed Roche NimbleGen array capture to enrich these regions followed by sequencing using the Illumina Genome Analyzer on 4 pools containing multiple samples of a single dog breed ( Irish Wolfhounds , West Highland White Terrier , Belgian Shepherds and Shar-Pei ) and one pool of wolf samples . In total , we discovered 4 , 353 additional high-quality SNPs using this method . We selected SNPs from this improved map to form the “CanineHD” array panel . We generated an initial panel of 174 , 943 SNPs that were included on the array of which 173 , 622 ( 99 . 2% ) give reliable data . These loci are distributed with a mean spacing of 13 kb and only 21 gaps larger than 200 kb . Loci with unreliable SNP calls , potentially due to copy number polymorphism , were not included in the analysis . In total , 172 , 115 are validated for SNP genotyping and 1 , 547 are used only for probe intensity analyses . This is a significant improvement compared with the largest previously existing array , which has 49 , 663 well performing SNPs , with a mean spacing of 47 kb and 1 , 688 gaps larger than 200 kb . Figure S1 shows the distribution of SNPs in 100 kb windows across the genome . The improvement in coverage is particularly striking on the X chromosome , where >75% of 100 kb windows contain no SNPs on the previous array , but <5% of windows do not contain SNPs on the CanineHD array . Of all the SNPs on the array , 0 . 9% are novel SNPs discovered by the targeted resequencing experiment . The remaining SNPs have been previously described: 65 . 1% of them were present in a comparison of the boxer reference genome with a previously sequenced poodle , 21 . 7% were present in alignments of low coverage sequencing reads from various dog breeds to the boxer reference genome , 25 . 4% were present within the boxer reference and 1 . 2% were present in alignments of wolf and/or coyote sequencings with the reference boxer genome . There is therefore a bias in the way that SNPs were ascertained: all of them were identified in a comparison involving the boxer reference assembly . However this has not had a great impact on the number of SNPs polymorphic in different breeds ( see below ) . The array was initially evaluated using 450 samples from 26 breeds termed the “Gentrain” dataset . Within this dataset , average call rates were 99 . 8% and reproducibility and Mendelian consistency were both >99 . 9% . A subset of 24 samples generated by whole genome amplification ( WGA ) of 12 blood and 12 cheek swab samples produced slightly lower call rates ( blood-WGA 99 . 3%; buccal-WGA 98 . 9% ) . Probe intensities from the array can also be used to analyze copy number polymorphisms , although this is not evaluated here . To perform a broader analysis of canine breed relationships and selective sweeps , we constructed a larger dataset consisting of unrelated samples from the Gentrain dataset , and unrelated control dogs genotyped for disease gene mapping studies from multiple breeds as part of the LUPA consortium . This dataset , which we refer to here as the “full LUPA genotype dataset” consists of 509 dogs from 46 diverse breeds and 15 wolves , genotyped on the CanineHD array . These include 156 dogs from 13 breeds derived from LUPA control dogs and 353 dogs from 33 breeds from the Gentrain dataset ( See Table S1 for full details ) . A subset of this dataset , referred to here as the “reduced LUPA genotype dataset” is made up of all the samples in the 30 breeds ( plus wolf ) with more than 10 samples in the full dataset ( 471 samples in total ) . Table 1 shows patterns of polymorphism in the reduced LUPA genotype dataset . In total , 157 , 393 SNPs on the array were polymorphic ( 90% of SNPs on the array ) . A mean of 119 , 615 SNPs ( 69% ) were polymorphic within a single dog breed . Hence although there is a bias in the way that SNPs were ascertained , there is a substantial amount of variation within all breeds surveyed . On average 39 SNPs were polymorphic only in one breed , although this figure shows large variation between breeds . A subset of 1 , 471 SNPs were variable in wolves but not within any dog breed . However , most of these SNPs were originally discovered by comparisons of sequences from different dog breeds , which suggests that they are also variable between ( but not within ) dog breeds . We used the CanineHD array to investigate breed relationships by constructing a neighbor-joining tree [24] of raw genetic distances in the full LUPA genotype dataset ( Figure 1 ) . Three main features are obvious: 1 ) Dogs from the same breed almost invariably cluster together . This reflects the notion that modern breeds are essentially closed gene pools that originated via population bottlenecks . 2 ) Little structure is obvious in the internal branches that distinguish breeds . This is consistent with the suggestion that all modern dog breeds arose from a common population within a short period of time and that only a very small proportion of genetic variation divides dog breeds into subgroups . 3 ) The internal branches leading to boxer and wolf are longer than those leading to other breeds . The long boxer branch can be explained by the fact that a large proportion of the SNPs were assayed by comparing boxer with other breeds , which implies that the dataset is enriched for SNPs that differ between boxer and other breeds . The longer wolf branch probably reflects more distant relatedness . Some breeds show a tendency to group together in the tree , such as breeds of retrievers , spaniels , setters , and terriers . However , the length of the internal branches leading to these clusters is only a small fraction of the average total length of branches in these clusters , which indicates that genetic variation in dogs is much more severely affected by breed creating bottlenecks than it is by historical origins of various breeds , although detailed analysis of these data has power to reveal their historical origins [25] . The most obvious clustering of breeds is exhibited by two wolf hybrids: Sarloos and Czechoslovakian wolf dog , which exhibit a closer relationship to the wolf than other breeds as predicted by their known origin [26] . The German shepherd also clusters with this group , although this is likely to be a result of its close relationship with the Czechoslovakian wolf dog , rather than with wolf . The tree is consistent with previous studies and supports the accuracy and reliability of the array . Although the long boxer branch likely reflects SNP ascertainment bias on the array , the tree reflects extensive polymorphism both within and between breeds . This suggests the SNP ascertainment scheme is not problematic and that the array is well suited for both within and across breed gene mapping . We performed coalescent simulations modeling the ascertainment bias , sample size , and inferred recombination rate in the true dataset ( see Materials and Methods ) in order to predict the expected patterns of genetic diversity that we expect to observe within and between breeds in the absence of selection . The bottleneck population sizes at breed creation used in the simulations are presented in Table S2 . The decay of LD in the simulated data closely matches the real decay in LD ( Figure S2 ) . To identify genetic variation associated with common traits that vary among breeds , we performed across-breed GWAS using the full LUPA genotype dataset . A list of traits and their variation between breeds is in Table S3 . Each sample was given a value corresponding to the standardized breed phenotype for the trait under study . We performed quantitative association studies for size and personality traits whereas other traits were binary coded . For each GWAS , we assayed genome-wide significance by permuting the phenotype of each breed , assigning each dog of the same breed with identical phenotype values . The true significance of genotype-phenotype correlation at each SNP was compared with the maximum permuted value of all SNPs across the array in order to estimate genome-wide significance ( see Materials and Methods ) . This permutation procedure corrects for the extreme population substructure present in dog breeds . Using this method we were able to replicate several known associations . We first performed a GWAS comparing 4 breeds with furnishings ( a coat type with moustache and eyebrows [9] ) compared to 42 without them . Genome-wide significant associations were observed at 3 SNPs distributed located between 10 . 42 - 11 . 68 Mb on chromosome 13 . The most strongly associated SNP is at 11 , 678 , 731 ( Pgenome<0 . 001 ) , 44 kb from the causative SNP previously identified in RSPO2 [9] . We next scanned the genome for associations with size , using weight in kilograms as a proxy ( data taken from [8]; see Table S3 ) . The most strongly associated SNP was located on chromosome 15 at 44 , 242 , 609 ( Pgenome = 0 . 004 ) , which is within the IGF1 gene , previously implicated in size variation [8] . Genome-wide significant associations ( Pgenome<0 . 05 ) were observed at 7 SNPs within an interval between 44 . 23 - 44 . 44 Mb . In addition , we observed an association within a previously defined region on chr10 ( 11 , 169 , 956 bp; Pgenome = 0 . 036 ) . The SNP at chr10:11 , 169 , 956 is about 500kb upstream of HMGA2 , which has been established to be associated with body size variation in other species [27]–[29] . The frequency of the SNP ( chr15:44 , 242 , 609 ) most strongly associated to size shows a steady decline according to the size of the breed . However , the differences in allele frequency at the SNP chr10:11 , 169 , 956 are more striking , as one allele appears at very low frequencies in all breeds apart from a number of very small breeds ( Yorkshire Terrier , Border Terrier , Jack Russell Terrier , Schipperke ) , where it is at or close to fixation ( Figure S3 ) . Hence , there appears to be relatively continuous variation in frequency in a variant affecting IGF1 between breeds , whereas a variant upstream of HMGA2 appears to have been fixed in a subset of small breeds but shows little variation in allele frequencies in other breeds . Dog breeds show extreme variation in ear morphology ranging from pricked ears to low hanging dropped ears . We performed a GWAS using 12 breeds with pricked ears and 15 breeds with dropped ears . Within an interval between 10 . 27 - 11 . 79 Mb , 23 SNPs had genome-wide significant associations ( Pgenome<0 . 05; Figure 2 ) . The most strongly associated SNP was chr10: 11 , 072 , 007 ( Pgenome<0 . 001 ) , which lies between the HGMA2 and MSRB3 genes . This region has been associated with ear type and body size in previous studies [6] , [12] . Using the CanineHD array , we are able to type SNPs at a much higher density in the associated region . There is also large variation between dog breeds in degree of tail curl . We classified breeds in our dataset into 11 with curly tails and 7 with straight tails and performed a GWAS . Six SNPs on chromosome 1 were most significantly associated within an interval 96 . 26 - 96 . 96 Mb ( Pgenome<0 . 05; Figure 2 ) , which are downstream of RCL1 and upstream of JAK2 ( Figure S4 ) . This region has not been previously associated with tail curl . We performed GWAS to search for variants that affect breed differences in behavior . We first performed a GWAS by comparing 18 bold and 19 non-bold breeds using phenotypic definitions from ref . [12] . Highly significant associations were found at two SNPs on chromosome 10 , 11 , 440 , 860 ( Pgenome<0 . 001 ) and 10 , 804 , 969 ( Pgenome = 0 . 006 ) , in the same region associated with both drop ear and size . Variation within this region is therefore associated with at least two morphological and one behavioral trait , which may be correlated . The region contains several genes including WIF1 , HMGA2 , GNS , and MSRB3 ( see Figure S5 ) . However , the most significant associations for each trait appear to occur in different places . The SNPs most associated with drop ear and size occur 98 kb apart between the MSRB3 and HMGA2 genes , with the drop ear association closer to MSRB3 , whereas the top boldness association occurs within an intron of HMGA2 , 271 kb 3′ of the size association . There is however a strong correlation between the bold and non-bold breed classifications and the drop ear and size classifications . All prick eared and small dogs were classified as bold in the dataset , whereas all drop eared dogs were classified as non-bold , with the exception of Bernese Mountain Dog ( see Table S3 ) . Breed averages for five personality traits measured objectively under controlled conditions were obtained from the Swedish Kennel Club . The traits are defined as sociability , curiosity , playfulness , chase-proneness and aggressiveness [30] and have been shown to be consistent among multiple tests of the same dog [31] . We performed quantitative GWAS using the breed-average trait values presented in Table S3 . We observed significant associations at a number of SNPs for the trait sociability , which measures a dog's attitude toward unknown people ( Figure 2 ) . No SNPs reached genome-wide significance , but a large number of SNPs on the X chromosome also showed strong association . In order to accurately measure genome-wide significance in the sex chromosomes compared to autosomes we removed male dogs from the analysis . This analysis identified 10 SNPs with genome-wide significant associations ( Pgenome<0 . 05 ) in the interval 106 . 03–106 . 61 Mb on the X chromosome ( see Figure S6 ) . This region was also identified in a previous study [6] to be highly differentiated between breeds and correlated with body size and skull shape . Across breed GWAS is a powerful approach for identifying genotype-phenotype relationship for traits shared among breeds . The variants identified by this approach , by definition , have large variation in allele frequencies between breeds . However , there may be many more such SNPs that have been subjected to similar selective pressures for common traits between breeds where the trait is not identified . In order to find such loci , we identified SNPs that exhibit high levels of differentiation between dog breeds using the FST statistics calculated for the >173 , 000 SNPs in the reduced LUPA genotype dataset . A total of 240 SNPs have a FST>0 . 55 and overall minor allele frequency >0 . 15 in the reduced dataset containing breeds with at least 10 samples . These cut offs are identical to those used by Boyko et al . [6] and are chosen for comparison . In the simulated data , no SNPs pass this cut off ( p<0 . 0001; χ2 test ) . We then generated a list of highly differentiated regions , by merging all SNPs in this list within 500kb of each other into single regions , resulting in 44 regions containing between 1 and 94 SNPs with elevated FST . Regions with two or more SNPs are presented in Table 2 and the complete list is presented in Table S4 . Figure 2 presents a value for each SNP ( used for illustration purposes only ) that we term “pairwise fixation index” to highlight differences in allele frequencies between breeds . This is defined as pq , where p is the number of breeds where allele A is fixed or close to fixation ( frequency >0 . 95 ) and q is the number of breeds where allele B is fixed or close to fixation ( frequency >0 . 95 ) . In total 53 , 944 out of 154 , 034 variable SNPs have a pq value > 0 , indicating that they are fixed for different alleles in at least 2 breeds . The regions of high FST correspond strongly to loci where trait associations have been reported . In particular , 8 of the 9 regions comprised of more than 3 high-FST SNPs overlap known trait-associated regions , and it is likely that most or all of the remaining regions with high FST show a correlation with an as yet undefined trait . Three of these regions were not previously reported in a study based on a less dense array [6] including a region on chromosome 7 ( 27 . 99 - 28 . 15 Mb ) containing five highly differentiated SNPs that encompasses the DMD gene . The locations of all regions are marked in Figure 3 , which presents a comprehensive map of regions that are likely to contain major loci influencing phenotypic variation between dog breeds . Three regions longer than 1Mb are identified by this measure , likely signifying regions under strong selection in many breeds . These consist of a 2 . 6 Mb region on chromosome X that associates with body size , skull shape and sociability , a 2 . 0 Mb region on chromosome 10 that associates with drop ear , size and boldness and a 2 . 1 Mb region on chromosome X associated with limb and tail length ( see also [6] ) . Other loci identified include three loci involved in coat type ( RSPO2 , FGF5 , KRT71 ) [9] . In particular the RSPO2 gene associated with furnishings is found within an extended 0 . 6 Mb region . The MITF and ASIP ( Agouti ) genes known to be involved in coat color in dogs [11] are also identified . The region on chromosome 1 identified here as associated with curly tail and previously associated with snout ratio [6] is associated with 4 SNPs with high FST across 50 kb . Other genes of note identified are LCORL , known to associate with human height [27] , [29] , KITLG , associated with coat color in other species [32] and several genes with key developmental roles , such as sonic hedgehog ( SHH ) involved in patterning in the early embryo , msh homeobox 1 ( MSX1 ) , involved in embyrogenesis and bone morphogenic protein 1 ( BMP1 ) involved in bone development . Rare selective sweeps corresponding to regions of the genome under selection in only one or a small number of breeds in our dataset cannot be detected by across-breed GWAS due to lack of power . They also have a weak effect on FST values at single SNPs across all breeds compared to regions under selection in many breeds . In order to identify such rare sweeps , we scanned patterns of variation in the reduced LUPA genotype dataset to identify extended regions where haplotypes had become fixed in one or more breeds , leading to a local reduction in genetic variation and increase in population differentiation . We analyzed 150 kb sliding windows , overlapping by 25 kb in each breed compared with other breeds using two statistics . The first statistic , Si , is calculated by summing regional deviations in levels of relative heterozygosity across the genome between two breeds compared to the genomic average and summing across all pairwise comparisons . Relative heterozygosity is defined as the number of SNPs segregating in a genomic window in one breed divided by the number of SNPs segregating in that window in two breeds under comparison . Hence , regions with low Si in a breed contain few segregating SNPs compared to other breeds . The second statistic , di , was implemented by Akey et al . [15] , and is based on pairwise FST values normalized for a given breed relative to the genome-wide average , summed across all pairwise combinations involving the given breed . Regions of high di in a particular breed exhibit a large difference in allele frequencies compared with other breeds . We first identified windows with Si or di values in an extreme 1% tail of their respective distributions ( the bottom 1% for Si and top 1% for di ) . Overlapping windows were then collapsed into larger regions ( see Materials and Methods ) . These regions represent a map of blocks of reduced heterozygosity or elevated population differentiation in each breed . We repeated our analysis of di and Si on the simulated data ( see above ) . For both statistics , the average length of regions identified was similar in real versus simulated datasets . However , there was a strong excess of regions >250 kb in the real compared with simulated datasets , which likely reflects regions influenced by selection . In order to distinguish regions generated by genetic drift compared with those generated by selective sweeps we first estimated a marginal p-value for each block , equal to the proportion of simulated blocks with longer lengths in the same breed . We then adjusted these p-values using a 5% False Discovery Rate ( FDR; see Materials and Methods and ref . [33] ) . In total 524 high confidence putative sweeps ( an average of 17 per breed ) were identified using the Si statistic , with a mean size of 475 kb . However , none of the regions identified by the di statistic remained significant after FDR correction . Figure S7 shows the distribution of significant Si regions in the dog genome . Full lists of regions identified by the Si and di analyses including the marginal and FDR corrected p-values are presented in Table S5 and summary statistics of these regions are presented in Table S6 . These regions are also available as a UCSC annotation dataset ( see Materials and Methods for URLs ) . The UCSC browser offers a graphical display of Si and di regions as well as di values for all SNPs analyzed [34] . Table S7 shows the overlap between these regions and those identified in previous studies ( refs . [6] and [15] ) . The Si test identifies blocks of the genome where one breed has little or no variation consistent with fixation of a long haplotype by a selective sweep . On average , only 19 . 9% of SNPs have segregating variants in these regions in the breed where they are identified compared with the genome average of 74 . 5% . Among the 524 putative sweeps are several loci already implicated in breed-defining characters . Notably , a 590 kb region of low Si overlapping the FGF4 retrogene on chromosome 18 associated with chondrodysplasia in Dachshunds . A 1 . 4 Mb region of low Si overlapping the HAS2 gene implicated in skin wrinkling [16] is observed in Shar-Pei . Regions in the vicinity of the RSPO2 locus implicated with furnishings are observed in 2 breeds , which both have furnishings ( Yorkshire Terrier and Standard Poodle ) . However , many variants implicated in phenotypic variation between breeds are not strongly associated with regions of reduced Si . No putative sweeps overlapping the IGF1 locus are identified in small breeds using this statistic . This is likely to be because there appears to be continuous variation in allele frequency at this locus between breeds rather than complete fixation of certain haplotypes in several breeds ( see Figure S3 ) . Table 3 shows the top 20 longest regions of significantly reduced Si . It should be noted that two pairs of putative sweep regions occur at contiguous locations in the same breed ( no . 2 and 12 in Beagle and no . 5 and 6 in Irish Wolfhound ) , which could potentially represent single selective sweeps . The longest region we identified is 3 . 1 Mb long ( chr22: 5 . 3–8 . 4 Mb ) in beagles . This region overlaps 3 other putative sweeps within the top 20 in other breeds ( Gordon Setter , Rottweiler , and Newfoundland ) whereas no other regions in the top 20 are overlapping . As this and other regions with strongest evidence for sweeps are long and contain many genes , it is not possible to identify the locus under selection in a single sweep . However , it is interesting to note that they contain genes associated with disease in humans and dogs including epilepsy ( KCNQ5 ) , cancer ( NPM1 , FGR ) , and autoimmune disease ( IL6 ) . A long sweep on chromosome 30 in Golden retrievers spans the RYR1 gene , involved in the skeletal muscle calcium release channel and implicated in canine malignant hyperthermia by linkage analysis [35] . We also identified a number of genes involved in spermatogenesis and fertilization ( SPAG1 , FNDC3A , CLGN ) which is a category often enriched in genes under positive selection in other species [36] . In cases where multiple breeds are affected by selection acting on the same variant , it may be possible to narrow an interval containing the causative mutation by identifying a core region of identity by state ( IBS ) between all breeds where haplotypes are shared , most likely reflecting common ancestry . We searched our dataset for regions with significant drops in Si that overlapped between different breeds . We then identified the maximal region where the same haplotype was fixed in all breeds identified . For many significant long regions we were able to identify shorter regions of IBS . The regions shared by 3 or more breeds are shown in Table 4 and a full list is in Table S5 . As a validation of this method , we identified a 187 kb region where an identical haplotype is fixed among the 3 breeds with furnishings where we identified a sweep spanning the previously defined causative indel ( region 14 ) . Hence this method is able to identify interval containing the causative mutation in shared region of identity by descent . The inferred selective sweep shared by the most breeds in this analysis was a 485 kb haplotype on chromosome 22 ( 5 . 4–5 . 9 Mb ) shared by 8 breeds ( Beagle , Border Terrier , English Bulldog , Gordon Setter , Irish Wolfhound , Newfoundland , Rottweiler , Weimaraner ) . This region contains 2 genes: FNDC3A , fibronectin type III domain containing 3A [37] , which is involved in spermatogenesis and also expressed in odontoblasts indicating a role in odontogenesis , and CYSLTR2 cysteinyl leukotriene receptor 2 , a member of the superfamily of G protein-coupled receptors . A 402 kb haplotype on chromosome 37 ( 3 . 5–3 . 8 Mb ) is shared among 7 breeds ( Bernese Mountain Dog , Beagle , Border Terrier , Doberman , Elkhound , Finnish Spitz , Golden Retriever ) . This haplotype contains 7 genes including the MSTN ( myostatin ) gene . This gene is associated with double muscling in cattle [38] and in a similar phenotype observed in whippets [39] . It is therefore plausible that this region has been a target of selection in multiple dog breeds in order to modify muscle mass . A 354 kb haplotype on chromosome X is fixed in 5 breeds ( 101 . 6–102 . 0 Mb ) and contains only one gene: UBE2I , an ubiquitin-conjugating enzyme . This enzyme has been shown to interact with MITF , involved in coat color , and is suggested to be a key regulator of melanocyte differentiation [40] although it also has a number of other features . There are many extremely differentiated regions although none of them passed the 5% FDR correction for length ( see Table S5 for full list ) . Variation in Si and di statistics in the 10 longest regions identified by the Si test is presented in Figure S8 . This comparison of the di and Si tests reveals that the increases in di often occur within a more restricted region of a large block of fixed haplotype from the Si tests , indicating that they represent regions where an otherwise rare ancestral sub-haplotype has been fixed in a certain breed . It therefore appears that many regions detected by di and Si tests are complementary . Among the top 20 longest putative sweeps identified by the di statistic ( Table 5 ) are 3 overlapping sweeps that also overlap the common sweep containing FNDC3A and CYSLTR2 identified by the Si test . We also identify putative sweeps in 4 breeds overlapping the region associated with drop ear , size and boldness among the top 20 di sweeps . Two putative sweeps in this list overlap a region on chromosome 13 ( 3 . 3–5 . 2 Mb ) , which is also identified by the Si test . One gene of note in this region is VPS13B , which may have an important role in development and is associated with Cohen syndrome , which has an effect on development of many parts of the body [41] . The second longest putative sweep identified by Si on chromosome 26 is also identified in two of the top 20 longest di regions . We performed an additional validation of our results using a third statistic , XP-EHH , which identifies regions where a long haplotype has reached fixation , or is close to fixation in one breed compared with other breeds [18] . We calculated the mean XP-EHH for all of the regions identified by the Si and di tests . For the regions constructed from the top 1% of di ( 6404 regions ) and Si ( 7618 regions ) statistics , mean XP-EHH was -0 . 94 and -1 . 13 respectively across all breeds compared with a genome average of zero . This difference is consistent across all 30 breeds and is highly significant ( binomial test: P<10−9 ) . This confirms that regions identified by the Si and di tests are associated with unusually long haplotypes at or near fixation in the breeds under selection compared with other breeds . We analyzed genes closest to all singleton SNPs with high FST for enrichment in gene ontology ( GO ) categories . The six most significantly overrepresented GO categories were all involved in development . 11 of the 22 genes were found in the “developmental processes category” ( P = 0 . 00036 ) and tissue , system , organ , anatomical structure and multicellular organismal development were all significantly overrepresented ( P<0 . 0007 ) . These highly differentiated SNPs therefore highlight a number of regions involved in development that are likely to have been modified by artificial selection and contribute to the high diversity of dog breeds . We next analyzed gene content of all of the regions constructed from the top 1% of di and Si distributions that pass the marginal p-value <0 . 05 for each breed . We only considered regions containing a single gene , in order to enrich the analysis for true targets of selection , although this list is still expected to contain false positives . There were 119 di regions and 272 Si regions containing one gene only ( 29 genes shared ) . We performed GO analysis using human genes with 1∶1 human-dog orthologous relationship . As longer genes are over-represented within long genomic segments containing only one gene , we compared these candidate selection genes to a background dataset with similar length ( see Materials and Methods ) . A total of 40 GO categories were significantly enriched in the Si analysis and 6 in the di analysis ( Table 6 ) . Developmental processes , central nervous system , organ development and pigmentation pathways are significantly enriched in Si regions whereas cell communication and signal transduction are the most represented in di regions . These differences in enriched GO categories could potentially reflect differences in the form of selection detected by the two statistics . A large number of genes detected by the Si analysis are significantly over-represented in several GO categories , which may reflect pleiotropic effects . A total of 23 of the genes belong to at least 10 enriched GO categories . As an example , one candidate selection gene the thyroid stimulating hormone receptor ( TSHR ) is involved in 25 enriched GO categories , including central nervous system and regulation of nucleotide biosynthetic process . This gene is suggested to have an essential role in photoperiod control of reproduction in vertebrates , in organ development and in metabolic regulation and has been recently been implicated as an important domestication gene in chicken [42] . The two larger biological processes over-represented by di regions are ‘cell communication’ and ‘signal transduction’ , which are represented by 16 and 15 genes , respectively . A region on chromosome 3 with strong statistical support contains the gene for insulin-like growth factor receptor1 ( IGF1R ) , also detected by Si statistics . This is a strong candidate gene in relation to selection for growth , a phenotype that has been strongly selected in dog . Another example is ANGPT1 , which plays roles in vascular development and angiogenesis and contributes to blood vessel maturation and stability . This gene has been identified in a set of positively-selected genes in human Tibetan populations for which selection may have occurred to allow for more efficient oxygen utilization [43] . The presence of TSHR and ANGPT1 in enriched GO categories may suggest that these pathways are commonly involved in recent adaptation . The region containing the most highly differentiated SNPs identified by the single-SNP FST analysis is 9 . 8 – 11 . 8 Mb on chromosome 10 . Variation in this region was also found to correlate with multiple traits: drop ear , size and boldness . As boldness shows a strong correlation with the other traits , we focused on analyzing the contribution of variants in this region to drop ear and size . We first analyzed the variation in allele frequencies of the SNPs most associated with size and drop ear across breeds scored for these traits . Size was measured as the breed average in kg , and drop ear was scored on a scale of 1-5 ( Figure 4A ) . The SNP most associated with ear type ( chr10: 11 , 072 , 007 ) showed correlation with this trait , but little association with size . Allele frequencies display continuous variation between breeds . In contrast , the minor allele at the SNP most associated with size ( chr10: 11 , 169 , 956 ) was not present in most breeds , but close to fixation in a subset of small breeds ( Chihuahua , Yorkshire Terrier , Border Terrier , Schipperke , Jack Russell Terrier ) . All of these breeds were also fixed for the prick ear allele at the ear type SNP . Based on this analysis , we hypothesize that combinations of two alleles at these two SNP loci result in three main haplotypes affecting ear type and body size segregate among dogs ( Figure 4B ) . The small size-pricked ear combination is present in the small ( non-chondrodysplasic ) breeds mentioned . All other breeds genotyped possess the large-prick or large size-drop ear haplotype , and the small size-drop ear combination is not observed in our dataset . In order to identify variants potentially responsible for these traits , we comprehensively characterized variation in a genomic segment encompassing this region ( chr10: 9 . 5 Mb – 12 . 5 Mb ) using Roche NimbleGen hybrid capture and sequencing using an Illumina Genome Analyzer . We choose 3 breeds with the dropped ear phenotype ( Lagotto Romagnolo , Leonberger , and Bernese Mountain Dog ) and 3 with the pricked ear phenotype ( Chinese Crested , Schipperke , and Finnish Spitz ) . Two of the pricked ear breeds are small , with breed average <6 kg: Chinese Crested and Schipperke . We sequenced each breed independently , using a pool of 5 dogs from each breed . On average 8 million reads per pool were produced , of which 48% mapped to the 3 Mb region on chr10 . In total , 61% of this region was mapped by at least one read . In the 68% of the region defined as non-repetitive , reads mapped to 98% of bases , at an average coverage depth of 114x . By comparison with the reference sequence , we identified fixed differences and polymorphic sites within each breed . Differences in the pattern of polymorphism between dropped and pricked eared dogs are clearly apparent , and drop eared breeds exhibit a lower level of variation on average compared with prick eared dogs , which is mainly restricted to a ∼2 Mb region between 9 . 5 Mb and 11 . 5 Mb ( Figure 5 ) . We next identified SNPs in this region that were completely fixed for different alleles in dropped and pricked eared breeds . These SNPs are distributed unevenly across the region , and a peak in the number of such fixed SNPs occurs around 11 . 3-11 . 5 Mb . In total 287 SNPs or small indels were completely fixed for different alleles in the drop ear compared with pricked ear breeds . Twenty-five of these SNPs reside in regions that show evidence for sequence conservation and are therefore candidates for being the causative mutation ( Table S8 ) . Of the 6 breeds , only Chinese Crested was completely fixed for the small size allele at chr10: 11 , 169 , 956 in our dataset . We therefore identified SNPs fixed for different alleles in Chinese Crested compared with all other breeds except Schipperke ( this breed was excluded because it is small but was not completely fixed for the size-associated SNP from the GWAS ) . In total 297 SNPs or small indels were completely fixed for different alleles in these two groups . Of these , 17 were in conserved regions and are therefore candidates for affecting size ( Table S9 ) .
Here we present a comprehensive catalogue of genomic regions that are candidates for being affected by artificial selection in dogs using the densest panel of SNPs to date . We focus on two main types of variant: 1 ) common variants that affect variation in a trait in many breeds and 2 ) rare variants that have undergone selective sweeps in one or a few breeds . For the first category , we identify loci where variation correlates with morphological traits such as body size and tail curl , and behavioral traits such as sociability and boldness . We also identify several loci with evidence for a high degree of population differentiation between breeds , for which the connection with phenotypic traits in dogs is not known , but that are known to associate with traits such as pigmentation and body size . To identify loci in the second category , we searched for regions with reduced heterozygosity and high population differentiation , characteristic of selective sweeps . This analysis identified loci known to be associated with breed-defining characteristics such as chondrodysplasia , skin wrinkling , and furnishings . In addition , we identify several extended regions with reduced heterozygosity > 1 Mb consistent with recent selective sweeps in one or more breeds , including striking examples such as a region containing the FNDC3A and CYSLTR2 genes , and a region containing the MSTN ( myostatin ) gene that both bear the signal of selection in multiple breeds . The candidate selection loci we identified are strongly enriched for genes involved in developmental and metabolic processes . In general , the GO terms we find to be significantly enriched are different from analyses of selection in natural populations , in which genes commonly targeted by positive selection include those involved in immunity and defense , olfaction and responses to external stimuli [36] . These results are consistent with the idea that artificial selection in domestic animals target different functional categories than natural selection . This result contrasts with that of Akey et al . [15] who found genes involved in immunity and defense to be overrepresented among their candidate selection regions . Artificial selection on dog breeds coincided with breed creation bottlenecks leading to genetically distinct breeds fixed for novel traits [1] , [3] , [4] . Hence a large proportion of phenotypic and genetic variation is apportioned between but not within breeds . It is notable that 35% of polymorphic SNPs we analyzed are fixed or almost fixed for alternative alleles in two or more breeds . This is in sharp contrast to the differences between human populations , where only 78 near-fixed differences , that are all strong candidates for being under selection , were observed between four populations among 15 million SNPs identified using whole-genome resequencing [44] . The strong influence of genetic drift on genetic variation in dog breeds has also led to random fixation of long haplotypes and it is estimated that on average ∼25% of the genome lies within a homozygous block >100kb in an average breed . This suggests that functional genetic variation has also been affected by genetic drift . This background of fixation of haplotypes by drift makes it extremely difficult to distinguish the signal of a selective sweep from background variation , and they may often be indistinguishable . We performed coalescent modeling using realistic estimates of recombination and demographic parameters in order to compare the length distributions of genomic segments identified by our analyses with those expected under neutrality . These simulations are by necessity an approximation of the actual evolutionary and demographic forces that shaped patterns of genetic variation in dog breeds . In particular , we do not model selection , which may reduce effective population size . Secondly , we assume a simplified demographic model , involving a single domestication bottleneck , and simultaneous breed creation . The true history of dog evolution is likely to be more complex than this , with some breeds showing closer relatedness than others . Nevertheless , long segments identified by the Si and di that pass the 5% FDR cut off are strong candidates for selective sweeps , and contain a number of regions already associated with phenotypic traits . Simulations indicate that large segments of reduced heterozygosity and elevated FST are expected under neutrality but longer segments of reduced heterozygosity , particularly those longer than 1 Mb , are not expected to occur due to drift alone and hence are more likely to reflect selection . In general we expect segments of reduced heterozygosity to contain causative variants under selection , however , in some cases we observe large blocks of reduced heterozygosity that appear to be broken up into adjacent regions separated by more variable regions . This pattern may reflect heterogeneity in ancestral haplotypes , which makes it difficult to pinpoint the focus of selection . Smaller blocks of elevated di often occur within extended regions of reduced heterozygosity . These probably reflect the fixation of variants that are otherwise rare in the dog population due to hitchhiking on the selected haplotype . However , most variants that are fixed by hitchhiking during a selective sweep are likely to be already common in the population , and therefore will not have a big effect on the di value of a region . This leads to stochasticity in the di statistic , which may explain the fact that even the longest di segments still do not pass a 5% FDR . When even denser surveys of SNP variation ( e . g . from whole genome sequencing ) are available , a more promising approach could be to identify selective sweeps using reductions in heterozygosity , and identify potential causative variants within these sweeps by their elevated FST ( see e . g . [45] ) . In addition to aiding in the dissection of the genetic components of phenotypic variation in dog breeds , we anticipate that our fine-scale map of genomic regions of extreme population differentiation and fixation of extended haplotypes will find utility for identification of disease causing variants . Firstly , regardless of whether they are caused by selection or drift , regions with reduced heterozygosity in a particular breed are problematic to interrogate with GWAS and may harbor disease-causing variants that are not tagged on a SNP array . Secondly , genetic variants responsible for breed characteristics may have pleiotropic effects that increase incidence of disease in that breed . Thirdly , disease-causing mutations may have risen in frequency in regions under selection by genetic hitchhiking on haplotypes bearing variants under artificial selection . These considerations suggest that our candidate selection regions warrant additional scrutiny in disease mapping studies . An example of the second effect has recently been highlighted in the Shar Pei breed , where strong artificial selection for genetic variants that likely affect expression of the HAS2 gene is responsible for both the characteristic wrinkled skin of the breed and an increased predisposition to periodic fever syndrome [16] . Our analysis of single-SNP FST across breeds identified a number of extended genomic regions of extreme population differentiation between dog breeds , which harbor variants responsible for commonly varying traits between dog breeds . Genetic variation in some of these regions correlates with multiple traits that vary between dog breeds , in some cases including both morphological and behavioral differences . There are several possible reasons for these multiple associations . One possibility is that these regions harbor multiple variants that each has an effect on different traits . Alternatively the associations could be the result of single mutations with pleiotropic effects that affect multiple traits . It is also possible that traits may correlate with each other for other reasons . For example , there may have been coordinated selection for more than one trait in a subset of breeds , or a subset of breeds may share a trait simply by chance . We have comprehensively surveyed genetic variation in a region of extreme population differentiation on chromosome 10 , where genetic variation correlates with body size , drop ears and boldness . As boldness shows strong correspondence with drop ears it is unclear whether this trait is affected by an independent variant in this region . A more detailed analyses of the allele frequencies of SNPs associated with body size and drop ears is consistent with a hypothesis that these traits are controlled by two linked SNPs , which in combination produce three observed haplotypes associated with distinct phenotypes . It is therefore possible that additional regions of extreme population differentiation also harbor multiple variants affecting different traits . Careful genetic dissection of each region is necessary to identify all functional variants and the traits they affect . As extensive LD is found in these regions , it is difficult to determine how many functional variants are present and their precise location . Such analysis would therefore be aided by the use of multiple breeds or populations with less extensive LD in order to narrow down the associated intervals . In its most extreme form , a selective sweep is characterized by the rapid fixation of a new mutation under selection along with linked genetic variants ( a hard sweep ) . However , less extreme selective episodes ( soft sweeps ) , such as incomplete selective sweeps or selection on standing variation may also be common [46] , [47] . It has been argued that polygenic adaptation , where subtle changes in allele frequencies occur at many loci , is the dominant form of phenotypic evolution in natural populations [48] . This type of evolution is likely when variation in a trait of interest is controlled by a large number of loci with small effect , which is now known to be the case with a number of highly heritable quantitative metabolic and morphological traits in humans . A long-term selection experiment in Drosophila melanogaster also uncovers evidence for this kind of adaptation [49] . Artificial selection in dogs appears to have caused genetic variants with much larger phenotypic effects to segregate at high frequencies , resulting in the simplification of the genetic architecture of phenotypic variation . In some cases , breed-defining characteristics such as chondrodysplasia , skin wrinkling and brachycephaly are likely to result from hard sweeps at breed creation . However , many variants with large phenotypic effects appear to show continuous variation between breeds that correlates with particular traits , including genetic variants that associate with body size in the IGF1 locus on chromosome 15 and with drop ear on chromosome 10 , suggesting that selection by attenuation of allele frequencies is also common . Hence , although hard sweeps are likely to be a more common form of selection in domestic compared with wild species , it is likely that more minor changes in allele frequencies across many loci also contribute to phenotypic evolution . The huge phenotypic diversity present in dogs raises the question as to whether levels of functional genetic variation in the ancestral dog population were elevated , adding to the raw material that artificial selection could act on . Relatively higher levels of replacement amino acid changes are found in dogs compared with wolves , possibly indicating a relaxation of selective constraint [50] , [51] . There are also a large number of loci in the dog genome polymorphic for the active SINEC_Cf elements [52] , which may also contribute to functional genetic variation , although it is not known whether functional variation due to these elements is increased in dogs compared with wolves . It has also been suggested ( and disputed ) that the dog genome has a high intrinsic mutation rate [53] , [54] . There is also great interest in looking for “domestication genes” by identifying loci under selection in domestic species compared to wild ancestors . Investigation of these processes that occurred in the ancestral dog population requires detailed comparisons of patterns of genetic variation in dogs and wolves . As the majority ( >98% ) of SNPs on the CanineHD array were discovered by comparisons of dog breeds , they are biased against fixed differences between dogs and wolves and wolf-specific SNPs . Additional SNP discovery in wolves is therefore necessary to unravel the evolutionary processes involved in early dog domestication . Whole genome resequencing of both dogs and wolves will be important for a more detailed understanding of these processes . It is likely that artificial selection in dogs ( and other domestic animals ) has led to the proliferation of mutations with large effects . This has contributed to the success of the dog as a model for genetic dissection of phenotypic traits . Such variants are likely to be maladaptive in the wild , and may also increase susceptibility to disease . Hence examining regions under selection in breeds may aid in identification of genetic risk factors affecting susceptibility to disease . Studying the extreme variation in forms produced by artificial selection also gives us a window into studying the effects of selection in natural populations , as first realized by Darwin [55] . Understanding the effects of selection on the genomes of domestic animals should give us insight into understanding its effects on nondomestic species , including our own .
Blood samples were taken from dogs by trained veterinarians according to relevant national and international guidelines . We scanned the existing list of 2 . 8 million high quality SNPs and identified 1 , 555 regions >40 kb ( gaps ) with no known SNPs in non-repetitive DNA ( 588 of these are on chromosome X ) . Gaps >100 kb were divided into a series of shorter ones resulting in a set of 2 , 375 genomic segments with no known SNPs of average length 50kb . We designed a Roche NimbleGen sequence capture array containing probes matching on average 2 . 1 kb within each segment , giving a total of 5 Mb . This array was used to enrich pools of DNA from Belgian Shepherds , Irish Wolfhounds , West Highland White Terrier , Shar-Pei and wolves . The samples were then sequenced using an Illumina Genome Analyzer and aligned to the CanFam2 dog reference sequence using MAQ . We identified 4 , 353 novel SNPs ( 973 on chromosome X ) . After updating the canine SNP map with these variants the number of gaps >40 kb was reduced to 714 ( 392 on chrX ) . We selected SNPs from initial list of 2 . 8 million augmented by the resequencing to be included in the Illumina CanineHD array . We selected SNPs by scanning the genome using non-overlapping windows of length 11 , 500 bp ( this length was calculated to return the desired number of SNPs ) . Every SNP in each window was scored and ranked according to a number of different criteria in order to maximize quality , coverage of the genome and a number of other factors according to a scoring criteria ( Table S10 ) . The main criteria considered for each SNP were Illumina design score , presence on a list of SNPs known to be informative for studies of canid phylogeny and presence on lists of previous dog SNP arrays ( Affymetrix and Illumina ) . SNPs in repetitive DNA or those that required two bead types on the Illumina array were disfavored . We also included 13 Y chromosome specific SNPs presented in ref [56] . The resultant list was analyzed to identify possible duplicates or incompatibilities between primers . The problematic SNPs were removed , and the final SNP list was edited manually to produce a list of 200k bead types by removing SNPs with the smallest distance to other SNPs . Genotyping was performed by Illumina Inc . , USA ( Gentrain dataset ) and Centre National de Genotypage , France ( LUPA dataset ) . All data is available at: http://dogs . genouest . org/SWEEP . dir/Supplemental . html . For each trait we performed a GWAS with plink ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) , using a breed permutation procedure to determine genome-wide significance implemented using a perl script . Each sample within a breed was first assigned a phenotype corresponding to the breed-specific value of a trait . Traits were either coded as dichotomous or quantitative depending on how they were measured ( see below ) . An association study was performed for each trait followed by a permutation procedure , where the phenotypes of each breed were randomized , always assigning an identical phenotype value to each sample within the same breed . For each GWAS , 1000 permutations were performed , and the real significance values at each SNP were compared to the maximum permuted values across all SNPs in order to calculate genomewide significance . We used the full LUPA dataset of 46 breeds to perform breed GWAS . The phenotypic values used are shown in Table S2 . Personality traits and size were considered as quantitative traits . Other traits were considered dichotomous , and breeds were divided as follows ( breed abbreviations in Table 1 ) : We phased the genotypes in the reduced dataset containing 471 dogs from breeds with 10 or more samples using fastPHASE [57] version 1 with the default parameters . We analyzed each breed and chromosome separately , dividing the X chromosome into the pseudo-autosomal region ( PAR ) and nonrecombining portion . Missing genotypes were imputed by the software , and we subsequently removed all SNPs that were not polymorphic or had less than a 100% call rate in all dog samples . In total , 19 , 176 invariant SNPs were removed: 14 , 309 on the autosomes and PAR , and 1027 on the nonrecombining X chromosome . An additional 3 , 840 SNPs were removed due to poor call rate . This dataset was used for subsequent selection scans and coalescent modeling analyses . The Si statistic is a measure of the proportion of SNPs that are variable in a region in a particular breed relative to all other breeds . We first divided the genome into 150 kb sliding windows , overlapping by 25 kb . Each window contained on average 10 SNPs; windows with less than 5 SNPs were not retained in the analysis . The same sliding window coordinates were used for the Si and di analyses . Given a pair of breeds i and j and a given genomic window , we define relative heterozygosity as:where hi is the number of polymorphic SNPs in breed i and hj is the number of polymorphic SNPs in breed j in a given genomic window . Si for a given genomic window in breed i is then calculated as:where E[θij] is the expected value of θij , calculated by comparing all of the SNPs between breed i and j , and sd[θij] is the standard deviation of all sliding windows . The Si statistic was calculated in this manner for all predefined 150 kb sliding windows across the genome , for all 30 breeds in the dataset . The Si statistic was calculated separately for the autosomal regions ( including PAR ) and the nonrecombining portion of the X chromosome , and was calculated in exactly the same way outlined above for the coalescent simulated data . Using the same dataset , we calculated FST for each pairwise breed combination . To identify regions with elevated FST calculated the di statistic for each SNP ( Akey et al ) , which is a standardized measure of pairwise FST values involving breed i and all other breeds:where E[FSTij] and sd[FSTij] represent respectively , the expected value and the standard deviation of FST between breed i and j computed from all SNPs . For each breed , di values were calculated for the 150 kb windows used for the Si analysis . We retained , for each breed , windows with an average di within the top 1% of all di values . For each breed , we retained the top 1% of windows in each breed based on both Si and di statistics . Overlapping windows were then combined to create a set of larger regions for each statistic . We applied this method to both the real and simulated data ( see below ) after which we compared the distribution of lengths . We then computed a marginal p-value for each region as the proportion of regions defined from the simulated dataset of the same breed that were longer . Finally we corrected the p-values using the Benjamini-Hochberg FDR method [33] . The UCSC graphical display of regions identified by the Si and di statistics as well as di values for all SNPs from the CanineHD array are available at the following URL: http://dogs . genouest . org/SWEEP . dir/Supplemental . html The aim of this analysis was to identify putative regions of Identity By Descent ( IBD ) within haplotypes inferred to be involved in selective sweeps in multiple breeds in order to narrow down the boundaries of putative sweep regions . This is based on the assumption that the selected variant was present on an ancestral haplotype prior to breed creation and is shared by multiple breeds . We first identified core regions that overlapped Si sweeps ( at the 5% FDR ) and were completely homozygous in each breed . Once these fixed regions were defined they were then grouped into clusters of overlapping physical locations between breeds . Where possible , we then identified the region of maximal overlap between all homozygous regions in all of the breeds in a cluster that had been fixed for identical haplotypes . In order to calculate XP-EHH for SNPs in our dataset , we first removed SNPs with a minor allele frequency < 5% in the entire dataset . We calculated the EHH statistic between all SNP pairs across all breeds in the whole dataset . We retained SNP pairs with EHH between 0 . 03 and 0 . 05 for the XP-EHH analysis . We calculated normalized log XP-EHH scores between these SNP pairs from iHS scores as described by [18] . However , instead of comparing iHS score between pairs of populations , we compared iHS scores in a given breed and SNP pair to the average of iHS scores in all other breeds . The normalization step was performed for each chromosome in each breed separately . In order to confirm the presence of extended haplotypes in putative sweep regions , we averaged XP-EHH scores across these regions in each breed compared to the genomewide average . We performed whole genome simulations under a realistic demographic model , using variable regional recombination rates as inferred from the original data . The simulation process consisted of three main steps: ( 1 ) recombination rate inference , ( 2 ) breed bottleneck modeling and ( 3 ) main simulations . We selected human orthologs with a 1∶1 human-dog orthologous relationship to perform GO analyses . Biomart version 0 . 8 ( Ensembl v . 62 ) was used to collect orthologous human protein-coding genes . WebGestalt [62] , a web-based gene set analysis toolkit , was used to retrieve GO terms associated with human ensembl gene stable IDs . A hypergeometric test computed the statistical significance of over-representations of GO terms that were compared to a background list of genes selected to control for possible gene length bias as observed in the selected gene set . The background set was composed of human genes selected using biomart with 1∶1 human-dog orthologous relationship , longer than 100 kb and with a mean size of 230 kb , similar to the tested set . GO biological processes that were significantly over-represented ( p<0 . 05 ) were considered . We selected a 3 Mb region on chromosome 10 ( 9 . 5-12-5 Mb ) for resequencing in 6 breeds . We first prepared pools of DNA containing 5 samples from each of the breeds ( Chinese Crested , Lagotto , Schipperke , Finnish Spitz , Leonberger and Bernese Mountain Dog ) . We next performed sequence capture using a Roche NimbleGen array containing probes designed to hybridize to this region . This was followed by sequencing using the Illumina Genome Analyzer . Reads were mapped to the dog genome reference sequence using bwa ( http://bio-bwa . sourceforge . net/ ) followed by SNP calling using samtools ( http://samtools . sourceforge . net/ ) . Mapping and SNP calling was done independently for each breed and custom scripts were used to identify SNPs with certain patterns of segregation . SNPs in conserved elements were identified relative to those defined by the dog genome analysis based on human-dog-mouse-rat alignments , and on identification of phastcons elements within mammals based on alignments of 44 vertebrates , converted from human to dog coordinates by LiftOver ( http://genome . ucsc . edu/ ) . | There are hundreds of dog breeds that exhibit massive differences in appearance and behavior sculpted by tightly controlled selective breeding . This large-scale natural experiment has provided an ideal resource that geneticists can use to search for genetic variants that control these differences . With this goal , we developed a high-density array that surveys variable sites at more than 170 , 000 positions in the dog genome and used it to analyze genetic variation in 46 breeds . We identify 44 chromosomal regions that are extremely variable between breeds and are likely to control many of the traits that vary between them , including curly tails and sociality . Many other regions also bear the signature of strong artificial selection . We characterize one such region , known to associate with body size and ear type , in detail using “next-generation” sequencing technology to identify candidate mutations that may control these traits . Our results suggest that artificial selection has targeted genes involved in development and metabolism and that it may have increased the incidence of disease in dog breeds . Knowledge of these regions will be of great importance for uncovering the genetic basis of variation between dog breeds and for finding mutations that cause disease . | [
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"... | 2011 | Identification of Genomic Regions Associated with Phenotypic Variation between Dog Breeds using Selection Mapping |
Schistosoma mansoni is the major causative agent of schistosomiasis . The parasite takes advantage of host signals to complete its development in the human body . Tumor necrosis factor-alpha ( TNF-α ) is a human cytokine involved in skin inflammatory responses , and although its effect on the adult parasite's metabolism and egg-laying process has been previously described , a comprehensive assessment of the TNF-α pathway and its downstream molecular effects is lacking . In the present work we describe a possible TNF-α receptor ( TNFR ) homolog gene in S . mansoni ( SmTNFR ) . SmTNFR encodes a complete receptor sequence composed of 599 amino acids , and contains four cysteine-rich domains as described for TNFR members . Real-time RT-PCR experiments revealed that SmTNFR highest expression level is in cercariae , 3 . 5 ( ±0 . 7 ) times higher than in adult worms . Downstream members of the known human TNF-α pathway were identified by an in silico analysis , revealing a possible TNF-α signaling pathway in the parasite . In order to simulate parasite's exposure to human cytokine during penetration of the skin , schistosomula were exposed to human TNF-α just 3 h after cercariae-to-schistosomula in vitro transformation , and large-scale gene expression measurements were performed with microarrays . A total of 548 genes with significantly altered expression were detected , when compared to control parasites . In addition , treatment of adult worms with TNF-α caused a significantly altered expression of 1857 genes . Interestingly , the set of genes altered in adults is different from that of schistosomula , with 58 genes in common , representing 3% of altered genes in adults and 11% in 3 h-old early schistosomula . We describe the possible molecular elements and targets involved in human TNF-α effect on S . mansoni , highlighting the mechanism by which recently transformed schistosomula may sense and respond to this host mediator at the site of cercarial penetration into the skin .
Schistosomiasis is a public health problem in many developing countries , and Schistosoma mansoni is one of the most widespread species of the causative trematode parasite , affecting 200 million people in the world . Parasite eggs laid in the hepatic portal vasculature are the principal cause of morbidity , and the resulting pathology may prove fatal [1] . The complex parasite life cycle in the vertebrate host begins when cercariae penetrate the human host skin , transforms into schistosomula and begin their life as endoparasites . To continue their development schistosomula take advantage of signals from the host; co-evolution has allowed the schistosome parasite to sense and respond to host mediators [2] . One of the important responses of human host at the site of cercariae penetration in the skin is TNF-α ( Tumor necrosis factor alpha ) cytokine production in the early steps of the inflammatory process , along with the synthesis of other cytokines such as IL-1 , IL-8 , TGF-β , IFN-γ [3] , [4] . Human cytokines such as IL-7 and TGF-β have been described as host signals that interfere with the metabolism , gene expression and development of schistosomes [2] , [5] . The effects of TNF-α on S . mansoni metabolism , development and egg-laying process have been described a long time ago [6]–[8]; Amiri et al reported that TNF-α induces liver granulomas and egg-laying of parasites in vivo [6]; additionally , Cheever et al [8] observed that egg laying and fecundity were delayed when SCID immuno deficient schistosome-infected mice were studied , suggesting the possibility that the delay in fecundity is due to a delay in tissue produced TNF-α in SCID mice [8]; nevertheless , their data provided little evidence that TNF-α alone can reconstitute early fecundity in this infected-mouse model [8] . Conversely , Haseeb et al showed that in S . mansoni females the egg-laying process is decreased and tyrosine up-take is increased in the presence of TNF-α [7] . There is controversial evidence of TNF-α influence on the parasite's metabolism , and the molecular mechanisms of its action have never been explored . The TNF receptor ( TNFR ) superfamily comprises membrane bound or soluble receptors which interact with one or more specific ligands . Currently more than 40 members of the TNFR superfamily have been identified in humans [9] . The TNF-like receptors are transmembrane proteins characterized by extra-cellular Cysteine-Rich domains ( CRD ) that are the hallmark of the TNFR superfamily . These pseudo-repeats are defined by intrachain disulphides , typically having highly conserved cysteine residues within the receptor chains . Significant variation in the number of CRDs exists among the receptor family members , with the most common structure bearing four CRDs and other members having from one up to six CRDs [9]–[11] . TNFR superfamily can be divided into three groups: ( i ) the death receptors , which mediate cell death through their cytoplasmic Death Domain , with TNFR1 as the typical member; ( ii ) the non-death receptors , which signal mostly through one or more of the TNF receptor-associated factors ( TRAFs ) , TNFR2 being the typical member; and ( iii ) decoy receptors which bind TNF-related apoptosis-inducing ligands but may prevent ligand-mediated apoptosis through a number of different mechanisms [12] . Until this moment , no member of the TNFR superfamily has ever been described in S . mansoni . Recent advances on schistosome genomics have led to considerable progress in understanding of the complex molecular mechanisms controlling the life cycle of this helminth parasite . Publication of large-scale sequence databases of both S . mansoni and S . japonicum transcriptomes in 2003 provided the first large repository of schistosome genes and brought insights into several aspects of schistosome biology [13] , [14] . A large-scale joint effort to sequence the S . mansoni genome is under way [15] , [16] and a draft of the assembled sequences is accessible at the project website . One of the post-genomic approaches that have been explored in schistosomes is the use of microarrays to perform large-scale studies of gene expression at different stages of the life cycle [17]–[20] and gender associated expression profiles [21]–[25] . However , no studies have looked at the effect of hormones or cytokines on large-scale gene expression in the parasite . In the present work we describe the homolog to human TNF-α receptor and the possible TNF-α downstream signaling pathway elements in S . mansoni; we also evaluate the in vitro effect of human TNF-α on the gene expression profile of S . mansoni adult parasites along with the effect of TNF-α on schistosomula just 3 hours after cercariae-to-schistosomula in vitro transformation .
Total RNA ( 1 µg ) was extracted from BH strain adult worms using Trizol reagent ( Invitrogen , Life Technologies Inc . , Carlsbad , CA , USA ) and then treated with DNAse I ( QIAGEN , Hilden , Germany ) . Total RNA was used for reverse transcription with SuperScript III First Strand Synthesis SuperMix ( Invitrogen ) , using 50 ng of random hexamers according to manufacturer's instructions . The PCR step was performed with Advantage II polymerase ( Clontech , Mountain View , CA , USA ) with buffer supplied by the manufacturer , 2 µl of reverse transcription reaction and 200 nM of each primer using the following program: 95°C ( 1min ) ; 40 cycles of 95°C ( 30 s ) , 49 to 57°C ( 30 s ) , depending on the primer pair combination used , and 68°C ( 5min ) ; final extension of 68°C ( 5min ) . Primers used are listed in Table S1 , part A . Rapid Amplification of cDNA Ends ( RACE ) 3′kit ( Invitrogen ) and RACE 5′kit ( Invitrogen ) were used according to manufacturer's instructions . The PCR step was performed as described above . Products from all PCR experiments were cloned into pGem-T vector ( Promega , Madison , WI , USA ) transformed into E . coli and stocked in TB-ampicillin . Selected clones were sequenced and the sequences were analyzed and assembled using PhredPhrap program [26] . The resulting SmTNFR sequence has been deposited in GenBank with accession number GQ222226 . Total RNA ( 3 µg ) from each sample was treated with DNAse I ( QIAGEN ) and purified using RNeasy Mini kit ( QIAGEN ) according to the manufacturer's instructions . 1 . 5 µg of total RNA were used as template to perform reverse transcription with random hexamer primers , 200 units of SuperScript III ( Invitrogen ) reverse transcriptase for 10 min at 25°C , 50 min at 50°C followed by 5 min at 85°C . 1 µl of Rnase H was added to the reaction and incubated for 20 min at 37°C . A parallel negative control reaction was carried out with addition of all components except reverse transcriptase . Each resulting cDNA sample was assayed by real-time PCR in triplicate reactions using gene-specific primers that were designed with the Primer Express program ( V2 . 0 ) with default parameters ( Applied Biosystems , Life Technologies Inc . , Carlsbad , CA , USA ) . Reactions were carried out with SybrGreen PCR core reagent ( Applied Biosystems ) for 40 cycles in a volume of 20 µl and according to the manufacturer's instructions using the GenAmp5700 sequence detector ( Applied Biosystems ) . Tubulin was used as internal standard gene in expression measurements among parasite's life cycle stages . Gene specific primers used are described in Table S1 , part A . The results were analyzed by comparative CT method [27] , and the statistical significance among expression changes in the expression level was calculated using ANOVA followed by Tukey Range Test [28] . Human protein sequences of TNF-α signal transduction pathway elements were used as queries to perform a search in the S . mansoni genome ( downloaded from the Sanger Institute website; ftp://ftp . sanger . ac . uk/pub/pathogens/Schistosoma/mansoni/genome ) using the locally installed copy of the genome sequence and the tBLASTn algorithm . A similar search with Blastp was performed against gene predictions at Schisto GeneDB website ( available at http://www . genedb . org/genedb/smansoni ) . S . mansoni ESTs available at GenBank were assembled into EST contigs with Cap3 [29] ( contig sequences are available in a FASTA format in supplementary File S1 ) and were searched with Human protein sequences of TNF-α signal transduction pathway elements as queries , using tBLASTn algorithm . The S . mansoni contig sequences obtained or their translated sequences were queried back to the GenBank protein dataset with BLASTx or BLASTp in order to confirm their identities with the respective element used for the initial search . Sequences with BLASTx or BLASTp best hit e-value<10−5 were considered as putative S . mansoni homologs of TNF-α signaling elements . For SmTNFR translated protein , a signal peptide was predicted with SignalP algorithm [30] and transmembrane helix domains were predicted with the TMHMM-2 . 0 algorithm [31] available at http://www . cbs . dtu . dk/services/TMHMM-2 . 0/ . Conserved protein domains were identified using PFAM algorithm [32] ( available at http://pfam . sanger . ac . uk/ ) , Interpro algorithms [33] ( available at http://www . ebi . ac . uk/Tools/InterProScan/ ) and SMART database [34] ( available at http://smart . embl-heidelberg . de/ ) . Sequences corresponding to the TNFR conserved domain ( cd00185 ) from 17 different species were aligned along with SmTNFR sequence using ClustalX . Sequence alignment was imported to MEGA 4 . 0 [35] and the evolutionary history was inferred using the Neighbor-Joining method . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test ( 100 replicates ) was calculated . Evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site . All positions containing gaps and missing data were eliminated from the dataset ( Complete deletion option ) . There were a total of 96 positions in the final dataset . Accession numbers for the sequences used in the alignment are: AAX43474 , NP_035741 . 2 , XP_522334 . 2 , NP_777099 . 1 , AAA40465 . 1 , NP_001025950 . 1 , EDL29730 . 1 , NP_001035580 . 1 , NP_001057 . 1 , XP_548191 . 2 , NP_001095948 . 1 , AAD17943 . 1 , P18519 . 1 , XP_001339291 . 2 , XP_002224391 . 1 , XM_001625712 . 1 , FF473364 . 1 . Schistosomula were obtained by mechanical transformation as described by Basch et al [36] , transformed schistosomula were incubated during 3 h ( 37°C , 5% CO2 ) in 11 ml M-169 medium containing 10% bovine fetal serum . Then 20 ng/ml human TNF-α ( Sigma; stock solution dissolved at 100 µg/ml in 10 mM Tris-Cl pH 8 . 0 ) were added to the cultures according to [7] and incubated during 1 h ( 37°C , 5% CO2 ) . Negative control schistosomula were incubated in parallel in 11 ml M-169 containing 10% bovine fetal serum with the vehicle ( 2 . 2 µL 10mM Tris-Cl pH8 . 0 ) . Three independent biological replicas of schistosomula treated with human TNF-α during 1 h with respective control were obtained ( in a total of 6 samples ) . Adult parasites were obtained from portal vein perfusion of hamsters 7–8 weeks after infection . After perfusion the worms were washed in PBS and only paired worms were incubated during 1 h , and 24 h ( 37°C , 5% CO2 ) in 30 ml RPMI containing 10% bovine fetal serum with 20 ng/ml human TNF-α ( Sigma , stock solution dissolved as before ) in parallel with negative control worms ( incubated in 30 ml RPMI containing 10% bovine fetal serum with the vehicle , 6 µL 10mM Tris-Cl pH8 . 0 ) . Three independent biological replicas were obtained for each experimental time with respective controls ( in a total of 12 samples ) . Infected hamsters were maintained at Instituto Adolfo Lutz that approved the study , and the experimental procedures were conducted adhering to the institution's guidelines for animal husbandry . Total RNA was extracted using Trizol reagent ( Invitrogen ) according to the manufacturer's instructions . After Trizol extraction RNAs were treated with DNAse I ( QIAGEN ) and subsequently purified using RNA easy mini kit ( QIAGEN ) . The integrity of RNA samples was evaluated using microfluidic electrophoresis in the Bioanalyzer equipment ( Agilent Technologies , Santa Clara , CA , USA ) . The oligoarray platform used in this work was previously described by Verjovski-Almeida et al [37] for the experiments using adult worm samples . A new platform , 4×44k , containing the same probe set , but a different control set was used for the experiments with 3-hour old early schistosomula . For each of the two platforms , the control set is the one recommended by Agilent , according to the probe disposition on the array . The platforms contain the same set of 44000 oligonucleotide probes that were designed by our group to represent all S . mansoni gene fragments available in the public GenBank database; slides were manufactured for us by Agilent Technologies . The microarray platform design along with gene annotation names was deposited at NCBI gene expression omnibus ( GEO ) under accession numbers GPL4791 ( 1×44K ) and GPL8606 ( 4×44K ) . MIAME compliant data were deposited under accession numbers GSE16261 ( adults ) and GSE16260 ( schistosomula ) . Table S1 , part B contains the oligoarray probe numbers , the corresponding ESTs contig represented by each probe , the predicted protein Smp_xxxxxx number when available ( see annotation method below ) , and the putative human homolog ( E-value <1e-20 ) Accession number when available ( see below ) . Either 230 ng from each RNA sample of schistosomula treated for 1 h and their respective control or 300 ng from each RNA from paired adult worms treated for 1 h and 24 h with human TNF-α and their respective negative controls were used for labeling with the Linear RNA amplification and labeling kit ( Agilent Technologies ) according to manufacturer's instructions . Each sample was separately labeled with either Cy3 or Cy5 and 825 ng cRNA from each amplification were used for hybridization; in experiments with schistosomula a control sample was combined with a treated sample and hybridized; in experiments with adult worm samples Self-Self hybridizations were performed [38] . Washing and scanning procedures were according to the manufacturer's instructions using GenePix 4000B scanner ( Molecular Devices , Sunnyvale , CA , USA ) . Data was extracted using Feature Extraction software ( Agilent Technologies ) . Low intensity data points were filtered out according to Feature Extraction software criteria , which essentially determine those points that are significantly below the average background signal of the array . Total intensity data from each experiment were normalized by Quantiles Normalization Method [39] , excluding positive and negative external controls . For both experiments ( treated schistosomula and treated adult paired worms ) Significance Analysis of Microarray ( SAM ) was used as the statistical test to identify differentially expressed genes [40] . For schistosomula , we performed a z-score transformation of normalized data was performed [41] and SAM two class tests were applied; genes were considered as significantly differentially expressed at q-value ≤0 . 05 . For adult worms , we calculated a virtual Log2 ratio between intensities of TNF-α Treated/Control , for each gene . With these ratios , we used two different approaches for SAM statistical analyses . SAM one-class approach was used to identify genes with sustained changes in their expression levels along the entire time period of observation ( 1 and 24 h ) ; SAM two-class approach was used to identify genes with transient changes in their expression levels . In both cases , genes were considered differentially expressed at q-value ≤0 . 05 . Hierarchical clustering of selected genes was generated using Spotfire Decision Site software ( TIBCO Software Inc . , Palo Alto , CA , USA ) . For a gene that was represented in the array by multiple probes , we picked a single representative probe by selecting the probe with the highest absolute value of the Log2 ratio between intensities of TNF-α Treated/Control . Gene ontology terms were assigned to genes represented in the array as described by Verjovski-Almeida et al [37] . GO terms enrichment was calculated using Ontologizer program [42] . The p-values were adjusted for multiple comparisons using the Benjamini-Hochberg method . Ingenuity Pathway Analysis software was used for identifying significantly enriched gene networks among the differentially expressed S . mansoni genes . For this purpose , S . mansoni genes encoding putative homologues to human proteins were identified in the following way . A search of each S . mansoni ESTs contig against the gene prediction data set on GeneDB was performed ( cutoff = >90% identity and over 20% coverage of the contig ) and a total of 15491 contigs could be associated to Smp_xxxxxx predictions . The 15491probes , representing each of these contigs , correspond to 39% of the 39343 probes on the oligoarray; they comprise a set of 7480 unique Smp entries . Next , the sequence of each of these Smp predictions was aligned to the GenBank human proteins dataset ( using BLASTp ) , with a BLASTp cutoff e-value ≤1×10−20; a total of 10036 out of the 15491 probes on the oligoarray that represent S . mansoni predicted proteins were associated to human proteins ( Table S1 , part B ) . This set of probes represents 3736 unique human protein-coding genes . The GI number of each human protein putative homolog was associated to the correspondent S . mansoni probe ( see Table S1 , part B ) and the expression data was up-loaded to Ingenuity Pathway Analysis System version 7 . 6 .
Human TNF-α receptor sequence NP_001057 . 1 , tumor necrosis factor receptor 2 precursor [Homo sapiens] was used as query to perform an in silico tBLASTn search in the S . mansoni genomic DNA sequence downloaded from Welcome Trust Sanger Institute ftp website . With this approach we found genomic scaffold Smp_scaff000357 that contains a 150 bp sequence ( from position 38758 to 38907 ) with 42% identity and 54% similarity to human tumor necrosis factor receptor 2 precursor ( NP_001057 . 1 ) from amino acid positions Arg-141 to Ala-189 . Upon close inspection and a cross reference to protein sequence predictions at Schisto GeneDB website ( http://www . genedb . org/genedb/smansoni/ ) , a gene prediction ( Smp_168070 ) with a partial sequence was found in scaffold Smp_scaff000357 ( see scheme in Figure 1 ) , annotated as “tumor necrosis factor receptor related” . This was the only TNF-α receptor sequence found in the entire genome . The 5′-end of this prediction was incomplete and did not contain sequence encoding all extracellular-domain conserved elements of the TNF receptor superfamily . Additionally , its last exon was not correctly predicted as we will show below . In order to obtain the complete TNF-α receptor sequence in S . mansoni we designed three different experiments using total RNA or mRNA as template: ( i ) cloning the 5′end by 5′-Rapid Amplification of cDNA Ends ( 5′-RACE ) extension ( primers used are identified in Figure 1 as arrows 1 and 2 ) , complemented by PCR reactions with a reverse primer based on the sequence of 5′-RACE extension ( primers 3 and 4 ) and a forward primer based on upstream genome sequence in relation to the 5′end of the RACE-extended message ( primers 5 to 7 ) ; ( ii ) cloning the central region based on the predicted Smp_168070 gene sequence and the 5′-RACE extended sequence ( primers 8 and 9 ) ; and ( iii ) extending the 3′end by performing 3′-RACE experiments ( primers 10 to 12 plus adaptor reverse primer supplied with the RACE kit ) . Table S1 , part A contains all primers used in these experiments . The assembled cDNA sequence obtained from all the above experiments resulted in a 1967 bp transcript that was named SmTNFR ( Figure 1 ) . The full-length transcript matches perfectly ( with exception of bases 1954 and 1958 ) to the genome sequence in scaffolds Smp_scaff001950 and Smp_scaff000357 . Based on the alignment regions we concluded that the SmTNFR transcript is composed of 7 exons . The first exon has 408 bp and aligns with genome scaffold Smp_scaff001950 ( between coordinates 84375 to 84782 in the minus strand; therefore , for clarity this scaffold is pictured in Figure 1 as the reverse-complement ) . The second exon has 207 bp ( from base 409 to 615 of assembled cDNA ) and aligns with genome scaffold Smp_scaff000357 ( between coordinates 38695 to 38901 in the plus strand ) . The third exon has 143 bp ( 616 to758 ) and it aligns with the same genomic scaffold between coordinates 39254 to 39396 . Exon number four is 206 bp in length ( base 759 to 964 of assembled cDNA ) and matches the genome sequence between coordinates 41864 to 42069 ) ; exon 5 has 441 bp ( base 965 to 1405 of assembled cDNA ) that matches with genomic coordinates 60952 to 61392; exon 6 has 102 bp ( 1406 to 1507 bp ) and aligns to the genomic scaffold from 61778 to 61879; finally the last and longest exon number 7 has 460 bp and aligns to the genomic scaffold from 62996 to 63467 . Donor and acceptor splicing sites are the canonical GT/AG bases for all intron-exon junctions . Curiously , the genomic sequence between coordinates 63139 and 63202 , corresponding to a region within exon 7 , is composed of 11 copies of a three-base simple sequence repeat ( with sequence AAT ) . Upon aligning the assembled cDNA to genome scaffold Smp_scaff000357 a gap was detected from 63139 to 63150 , which in the genome sequence corresponds to 4 out of the 11 AAT repeats . Consequently , we concluded that our cDNA sequence lacks 4 of the 11 AAT repeats , being characteristic of the parasite BH strain used in the laboratory , which is different from the Puerto Rican strain used for genome sequencing ( http://www . tigr . org/tdb/e2k1/sma1/intro . shtml ) . In addition , cDNA bases 1954 and 1958 at the very 3′-end of our cloned cDNA are the only two bases that disagree with the genomic sequence: in the cDNA they are A's that come from the poly-dT-adaptor reverse primer used for the 3′-RACE and in the genome we found T and G in the respective positions . As discussed later , an internal priming of the message has probably occurred during reverse-transcription at the cDNA synthesis step . The translated sequence encodes a complete TNFR protein with 599 amino acids , predicted by ORF finder , shown in the scheme of Figure 1 . A second Met-22 is present in the SmTNFR translated sequence , 21 amino acids downstream of the predicted ORF start ( marked by a dotted vertical line in the purple block , Figure 1 ) ; in case that translation eventually starts there , the receptor would be shorter , with 578 amino acids in length . A signal peptide was predicted with SignalP 3 . 0 [30] . For the longer sequence , using the Neural Networks ( SignalP-NN ) algorithm a signal peptide was predicted within the first 43 amino acids of SmTNFR ( max S-score = 0 . 918; D-score = 0 . 436 ) . The SignalP Hidden Markov Model ( SignalP-HMM ) was not able to find a signal peptide motif . For the shorter sequence , SignalP-NN predicted a signal peptide within the first 22 amino acids ( max S-score = 0 . 971; D-score = 0 . 826 ) ; SignalP-HMM predicted the same signal peptide ( p = 0 . 980 ) , within the first 22 amino acids . In all cases , the predicted signal peptide cleavage site is between Ala and Gly residues within the VIA-GPL sequence motif; in the longer sequence the most likely cleavage site is between positions Ala-43 and Gly-44 ( SignalP-NN cleavage site C-score = 0 . 521 ) . In the alternative shorter sequence , the same cleavage site would correspond to positions Ala-22 and Gly-23 in a re-numbered sequence ( SignalP-NN cleavage site C-score = 0 . 923; SignalP-HMM cleavage site probability p = 0 . 967 ) . The signal peptide is indicated in purple in the scheme of Figure 1 . Using the TMHMM-2 . 0 algorithm , a transmembrane helix domain was predicted within the signal peptide domain ( see yellow hatched box , in the scheme of Figure 1 ) from amino acids Val-27 to Ile-47 ( max membrane probability p = 0 . 754 ) , thus defining a predicted extracellular N-terminal domain that extends from Gly-44 to Asn-304 in the mature protein , following signal peptide cleavage . A second transmembrane helix domain ( yellow box , in the scheme of Figure 1 ) was predicted from amino acids Gln-305 to Tyr-327 ( max membrane probability p = 0 . 945 ) , using the TMHMM-2 . 0 algorithm . Four TNFR/NGFR domains ( Cystein-Rich Domains , CRD ) ( green boxes in the scheme of Figure 1 ) were predicted in the extracellular region; these domains were found by PROSITE ( PS50050 , TNFR_NGFR_2 cysteine-rich region domain ) between amino acids Cys-103 to Cys-142 ( score = 10 . 108 ) , Pro-144 to Cys-188 ( score = 11 . 670 ) , Gln-189 to Cys-227 ( score = 10 . 943 ) and Ser-229 to Cys-269 ( score = 11 . 371 ) . The intracellular region of the predicted protein encoded by SmTNFR is composed of the C-terminal 272 amino acids extending from Lys-328 to Asn-599 ( see scheme in Figure 1 ) ; no conserved protein domain could be identified in this region , suggesting that SmTNFR belongs to the group of TNFR-superfamily members such as TNFR2 that typically lack the C-terminal Death-Domain motif . SmTNFR highest similarity to a human TNF-family member is with Tumor necrosis factor receptor superfamily , member 5 isoform 1 precursor , usually called human CD40 ( P25942 . 1 , TNR5_HUMAN ) , a non-death domain receptor , with E-value = 4×10−9 , 51% similarity and 32% identity over 57% of the Homo sapiens protein sequence; highest sequence similarity was present at the 5′-end of the gene , which encodes the conserved ligand-binding N-terminal extracellular domain . According to TreeFam [43] HMMer prediction , SmTNFR belongs to the “Tumor necrosis factor receptor superfamily” ( TF331157 ) with score 233 . 8 . Analysis with Conserved Domain Database tools [44] showed that SmTNFR has a complete Tumor Necrosis Factor Receptor ( TNFR ) conserved domain ( cd00185 ) ( E-value = 5×10−12 ) . A multi-sequence alignment of this conserved domain , including invertebrate and vertebrate TNFR-family members , is shown in Figure 2A , where it is seen that the complete conserved cystein-rich TNFR motif is present in SmTNFR . A gene family tree is shown in Figure 2B . It can be seen that SmTNFR is placed at a basal position in relation to a branch that includes the vertebrate TNFR1 , TNFR2 and CD40 clades , and was separate from the cluster of vertebrate and invertebrate NGFR-family members . Real-time RT-PCR experiments were performed to study the expression level of SmTNFR among life cycle stages . Three independent biological replicas from five different life cycle stages were used ( eggs , miracidia , cercariae , 7 day-old schistosomula and adult worms ) . The highest expression of SmTNFR was detected in cercariae ( Figure 3 ) , with 3 . 5 ( ±0 . 7 ) times higher levels than in adult worms , which exhibited the second highest expression . Eggs , miracidia and 7-day old schistosomula had the lowest expression levels of SmTNFR ( Figure 3 ) . Human TNF-α receptors interact with cytoplasmic proteins that activate a signaling cascade composed of multiple protein kinases . In order to evaluate if the TNF-α signal transduction pathway is conserved in S . mansoni , we performed an in silico analysis using human protein sequences described as involved in the canonical TNF-α signaling pathway [45] , aiming to find conserved signaling elements between S . mansoni and human . We found S . mansoni genes potentially encoding 9 signaling elements that participate in the human TNF-α signaling pathway that is activated by TNFR2 ( the TNFR group that does not contain the intracellular C-terminal Death Domain ) . Figure 4 shows a schematic representation of TNF-α signal transduction pathway conserved elements in S . mansoni . Table S2 contains specific alignment and similarity parameters for each component , including ESTs contigs , gene predictions , mRNAs that encode sequences with similarity to the human proteins and the S . mansoni genomic scaffolds where these elements are located . Human TNFR2-activated pathway involves a conserved signaling cascade composed of protein kinases ( Figure 4 ) . The human receptor interacts with TRAF2 that will activate MAPKKK , which in turn activates JNKK; the latter activates JNK that will phosphorylate and activate c-JUN transcription factor in the nucleus . Homologs of all these elements were found in S . mansoni ( Figure 4 ) . Phosphorylated c-JUN promotes transcription of target genes in the TNF-α signaling pathway . We also found a possible ortholog to A20-like protein ( A20-L , Traf-Binding Domain-Containing Protein; TRABID ) , a protein that interacts with TRAF , along with orthologs to c-IAP ( cellular inhibitor of apoptosis protein 1/2 ) , and to caspase-3 and caspase-8 , which are related to apoptosis in human cells , especially when activated by signaling through TNFR1 . We note that human MAPKKK is the most difficult element in the pathway for which to predict an ortholog in S . mansoni because it refers to two similar kinases , namely MEKK1 ( also known as MAPKKK1 ) and MEKK5 ( also known as ASK1 or MAPKKK5 ) , and there are still other very similar MAP kinases [46] . Human MAPKKK5 best hit against S . mansoni gene prediction is Smp_162800 . 1 , annotated as “protein kinase” , with E-value = 3×10−156 , 36% identity , 56% similarity , and 64% coverage of the human MAPKKK5 . The best human hit of Smp_162800 . 1 against GenBank is MAPKKK15 , with E-value = 1×10−160 , 39% identity , 59% similarity , and 64% coverage , while MAPKKK5 is only the second best human hit against GenBank . BLAST search of GenBank using contig C911313 . 1 gives the same MAPKKK15 and MAPKKK5 hits , although with lower scores than for the full-length predicted S . mansoni protein ( Table S2 ) . We propose that the MAPKKK homolog found in S . mansoni could activate JNKK because of the overall context of other signaling elements . In order to find the possible target genes of SmTNFR signaling pathway we performed microarray experiments with S . mansoni parasites treated in vitro with human TNF-α . In the first set of experiments , cercariae were in vitro transformed into schistosomula by mechanical removal of their tails [36] , and incubated for three hours at 37°C for recovery . Human TNF-α ( 20 ng/ml ) was added to these recently transformed schistosomula , in an attempt to mimic the exposure to TNF-α upon penetration of the human skin . We chose to use the same TNF-α concentration that was previously used in the literature [7] for in vitro experiments with adult parasites . The 3-hour old early schistosomula were incubated during 1 h with TNF-α; in parallel a negative control was maintained in the absence of cytokine . Three independent replicate experiments were performed . RNA was extracted from TNF-α treated and non-treated early schistosomula , and processed for microarray hybridization . A set of 755 probes was identified with a statistically significant ( q-value <0 . 05 ) differential expression between TNF-α treated and control early schistosomula ( Figure 5 ) . These probes represent 548 unique genes; among them , 309 were induced and 239 were repressed in presence of human TNF-α . Table S3 shows the number of differentially expressed genes for this 1h-treatment , according to the three annotation categories: S . mansoni genes with orthologs in other species , S . mansoni genes with orthologs only in S . japonicum and in no other species , and S . mansoni genes with no similarity in GenBank ( NoMatch ) . Table S4 contains the list of differentially expressed genes . S . mansoni genes represented in the microarray were automatically annotated with gene ontology ( GO ) terms [47] . The Ontologizer program [48] was used for highlighting gene ontologies that were significantly over-represented in the set of genes differentially expressed between 1h-treated and control parasites ( Table 1 ) . Categories involved in the translation process such as ribosomal proteins and proteins from the pyruvate dehydrogenase complex were identified as over-represented in genes with higher expression in TNF-α 1h-treated schistosomula . In an opposite group , i . e . genes with higher expression in TNF-α non-treated schistosomula ( control parasites ) , enrichment of GO category involved in the molecular function “nucleotide binding” ( GO: GO:0000166 ) was observed . Table S5 shows the names of the differentially expressed genes found in each category . In order to identify the possible networks of interaction among the significant differentially expressed genes we used the Ingenuity Pathway Analysis software . The most significantly enriched ( p = 10−29 ) network comprising differentially expressed genes in 1h-treated schistosomula is shown in Table 2 . Genes belonging to this enriched network are involved with the following functions: Gene Expression regulation ( p-value<2 . 7×10−2 ) , Cellular growth and proliferation ( p-value <2 . 4×10−2 ) , Cell cycle ( p-value<2 . 5×10−2 ) and Cellular development ( p-value <2 . 5×10−2 ) . In a second set of experiments , paired adult worms freshly recovered from portal perfusion of infected hamsters were incubated with human TNF-α for 1 , 6 or 24 h . Two different patterns of expression were detected: a transient change and a sustained change in expression throughout the entire time of exposure to TNF-α . A set of 1594 probes that represent 1365 unique genes revealed statistically significant ( q-value ≤0 . 05 ) transient changes in expression . Among them , 821 genes were induced by TNF-α in 1 h and repressed after 24 h of treatment , and 544 have the opposite pattern ( Figure 6A ) . Table S6 shows the number of differentially expressed genes according to the three annotation categories: S . mansoni genes with orthologs in other species , S . mansoni genes with orthologs only in S . japonicum and in no other species , and S . mansoni genes with no similarity in GenBank ( NoMatch ) . Table S7 contains the list of differentially expressed genes that showed transient changes . A search for enriched Gene Ontology ( GO ) terms associated to the transiently affected genes was performed . Significantly enriched GO terms were found in the sets of genes induced by TNF-α in 1 h ( Table 3 ) . Genes with induced expression in 1 h of treatment belong to categories related to translation . Table S8 identifies the genes found in these categories . Using Ingenuity Pathway Analysis we identified that the most significantly enriched ( p = 10−59 ) network with transient changes in expression in adult worms is comprised of genes related to these functions: cellular assembly and organization ( p-value<2 . 2×10−2 ) , RNA post-transcription modification ( p-value<1 . 1×10−2 ) , Protein synthesis ( p-value<1 . 03×10−2 ) , Cell morphology ( p-value<4 . 6×10−2 ) , Gene Expression ( p-value<3 . 2×10−2 ) , Cell cycle ( p-value<4 . 1×10−2 ) . Table 4 lists the genes involved in these functions and Figure S1 shows a schematic representation of network interactions . A sustained pattern of expression change in adult worms throughout the 24 h of observation was detected for another set of genes , when treated with human TNF-α ( Figure 6B ) . A total of 626 probes that represent 492 genes had a sustained change in expression; among them , 336 were induced and 155 repressed by TNF-α . Table S9 shows the number of differentially expressed genes according to the three different annotation categories: S . mansoni genes with orthologs in other species , S . mansoni genes with orthologs only in S . japonicum and in no other species , and S . mansoni genes with no similarity in GenBank ( NoMatch ) . Table S10 contains the list of genes with sustained changes in their expression levels . Among the set of repressed genes an enriched GO category related to “nucleotide metabolic process” ( GO: 0000166 ) was enriched ( Table 3 ) . The list of genes found in this category is shown in Table S8 . Using Ingenuity Pathway Analysis we identified that the most significantly enriched ( p = 10−29 ) network with sustained changes in expression throughout the 24 h of treatment in adult worms is comprised of genes related to these functions: molecular transport ( p-value<1 . 3×10−2 ) , Nervous System Development ( p-value<1 . 23×10−2 ) , Tissue morphology ( p-value<3 . 8 ×10−2 ) , Nucleic Acid metabolism ( p-value<1 . 1×10−2 ) , lipid metabolism ( p-value<1 . 36 ×10−2 ) , Cellular assembly and organization ( p-value<1 . 0 ×10−2 ) , Cell morphology ( p-value<1 . 2 ×10−2 ) and Cell signaling ( p-value<1 . 3 ×10−2 ) . Table 5 lists the genes involved in the above functions . Figure 7 shows a schematic representation of network interactions for the genes with a sustained pattern of expression change in adult worms throughout the 24 h of observation that belong to the most significantly enriched ( p = 10−29 ) network . It is interesting to note that one of the nodes in the network is centered at TNF-α . No evidence is found for a S . mansoni TNF-α gene in the transcriptome or genome . The identified altered network indicates that in adult S . mansoni the downstream targets of added human TNF-α are analogous to the known target genes affected by this cytokine in humans . By comparing the lists of differentially expressed genes in early schistosomula and in paired adult worms , we found only 73 probes , representing 58 genes in common . Table S11 shows these 58 genes and their expression pattern in each experiment . The very small fraction of differentially expressed genes affected in common in both early schistosomula and adult worms ( 11% and 3% , respectively ) indicates that S . mansoni TNF-α response is stage specific; probably some additional elements might have emerged and have been incorporated into the signal transduction pathway during host-parasite co-evolution , causing the activation of different stage-specific target genes .
Our results indicate that the SmTNFR identified in the present work is a possible S . mansoni homolog to human TNFR . The possible conserved elements of a complete TNF-α signal transduction pathway were in silico identified in S . mansoni . In addition , our microarray results show different sets of target genes that are transcriptionally activated by TNF-α signaling in the two different developmental stages studied . SmTNFR identified in the present work is the only detectable member of the TNF-receptor superfamily in the S . mansoni genome . A phylogeny tree analysis suggested that SmTNFR is orthologous to vertebrate TNFRs . Considering the tree topology , SmTNFR represents an ancestral protein that diverged before the formation of TNFR1 , TNFR2 and CD40 families in vertebrates , but after the divergence to NGFRs and other TNF-receptors . Interestingly , due to its dissimilar distribution of introns [9] it has been previously hypothesized that the NGFR gene has been formed early in the evolution of TNFR superfamily . Naismith et al . [49] developed a classification method for TNFR-family extracellular cysteine-reach domains ( CRDs ) based on the cysteine repeats: each CRD domain contains modules named according to their type ( A , B or C module ) and the number of disulphide bridges ( 1 , 2 or 3 ) . The four CRD domains of NGFR contain four A1 modules and four B2 modules that are combined in a manner similar to the first two CRD domains of TNFR2 ( Figure 8 ) . Regarding the intracellular region , human NGFR and TNFR1have a C-terminal Death-Domain motif , whereas SmTNFR intracellular region lacks the conserved Death-Domain , indicating that SmTNFR is a non-death receptor similar to human TNFR2 ( Figure 8 ) . The presence of Death-Domain motif in NGFR orthologs is also observed in invertebrate protein sequences such as in Branchiostoma floridae ( XP_002224391 . 1 ) . The absence of NGFR homolog with a Death-Domain in S . mansoni and in other invertebrates such as Drosophila melanogaster ( accession numbers: BAC01264 . 1 and AAM484141 ) and Caenorhabditis elegans ( accession numbers: AAP82639 , AAB36865 , NP_001024870 and NP_001024869 ) , but its presence in invertebrates of the Deuterostomia groups suggests that this family of receptors may have been lost in Protostomes . Interestingly , the cnidarian Nematostella vectensis displays a TNFR with a Death-Domain ( XP_001629570 . 1 ) ; the available sequence is partial , lacking a portion of the N-terminal extracellular region of the protein containing the TNFR conserved domain ( cd00185 ) , which prevented us from adding this sequence to our multiple alignment . However , analysis of the domain fragment suggests that this protein forms a monophyletic group with the other N . vectensis TNFR and is not closely related to any NGFR ( data not shown ) . Overall , data suggests that multiple events of acquisition or loss of Death-Domains by TNFR/NGFR receptors must have occurred during their evolution . Human NGFR does not interact with bona fide TNFR ligands and its only known interactions are to dimeric ligands of the neurotrophin family [50] . In contrast , the CD40 , TNFR1 and TNFR2 receptors included in our phylogenetic analysis are able to bind bona fide trimeric TNF ligands . The protein region chosen for our evolutionary analysis includes the A1-B2-A1 modules previously shown in this family of receptors to be responsible for ligand interaction [49] , [51] . Therefore , it is possible to infer that SmTNFR must be capable of binding bona fide TNF ligands . We were unable to find any obvious gene homologs encoding TNF-like ligands in the S . mansoni genome . The presence of a single TNF-superfamily receptor in S . mansoni , along with the observed effect of human TNF-α on the expression of a very broad range of parasite genes with a stage-specific pattern , is a strong suggestion that host TNF-α as well as other TNF-like ligands may bind to SmTNFR and activate a canonical signal transduction pathway in the parasite . This hypothesis is further supported by the observation that human TNF- α treatment of S . mansoni adult worms has induced a sustained change in expression of genes that encode proteins belonging to a significantly enriched network that comprises homologs to known human TNF-α interactors ( Figure 7 ) . In this respect , we postulate that the minimal downstream elements of the TNF-α signaling pathway are present in the parasite ( see below ) . Further experiments , involving SmTNFR heterologous expression and direct ligand binding assays are warranted to confirm this hypothesis . The 5′- and 3′-end sequences of SmTNFR differ from the predicted Smp_168070 gene . The 5′-end sequence was obtained by a combination of 5′-RACE experiment and PCRs with cDNA template and primers based on upstream genomic sequence . This approach revealed SmTNFR first exon that contains the start codon and a signal peptide , typical of this type of membrane proteins . The 3′-end sequence was obtained through 3′-RACE experiments; the resulting sequence is different from gene prediction Smp_168070 because in our cDNA the second to last exon is longer than the predicted and we found that this was the last exon ( Figure 1 ) . Therefore , the last exon of Smp_168070 ( that matches the same genomic scaffold but in a downstream region: 64497 to 64519 ) had been wrongly predicted . Probably SmTNFR cDNA 3′-end is longer than we have obtained here , because the poly-dT-Adaptor primer used in the cDNA synthesis step of the 3′-RACE experiment has annealed to an A-rich region of the message . This can be determined by observing the genomic sequence at the segment to which the cDNA 3′-end is aligned . This genomic sequence contains 12 A's out of 14 bases from 63453 to 63467 . The eleven copies of the simple sequence repeat ( SSR ) detected in genome scaffold Smp_scaff000357 were not observed in the cDNA sequence; rather , only 7 repeats were present in the cDNA . The seven AAT repeats occur inside the protein-coding region of the message and will encode asparagine residues in the protein . Strand slippage at trinucleotide repeat sequences and expansion in the number of repeats has been proposed to occur when DNA polymerase encounters difficulty while replicating a repeat [52] and may result in diverse human diseases . In bacteria , fungi and in kinetoplastids such as Trypanosoma brucei the SSR expansion is described as a mechanism that generates functional diversity [53] . The observed difference between the S . mansoni strain sequenced by the genome project and the strain used in this work , with the resulting change in the number of asparagine residues at the intracellular protein domain , could alter the receptor's affinity to downstream signaling elements . An interesting point of the present work is the in silico identification of TNF-α signal transduction pathway elements in S . mansoni . We were able to identify nine conserved elements comprising a complete TNFR2-activated pathway ( Figure 4 ) [54] . Noteworthy is the fact that none of the downstream elements that interact with the Death-Domain in the canonical vertebrate TNFR1-activated or Neurotrophin/NGF-activated pathways were identified in S . mansoni ( Figure 4 ) . Thus , similarly to the vertebrate TNFR2 , it is possible that SmTNFR intracellular region interacts with TRAF ( Figure 4 ) . Interestingly , the JNK pathway has been shown in D . melonagaster to be triggered by Eiger TNF ligand [55]; JNK pathway elements are present in S . mansoni , suggesting that Schistosomes may have a response to TNF ligands analogous to that observed in Drosophila . The c-JUN transcription factor ( also known as AP1 ) is likely to be the downstream effector of the pathway , acting on promoter regions of target genes to activate their transcription . Other unknown signaling elements may integrate the signaling pathway , such as different protein kinases and transcription factors . Further experiments are necessary to confirm the postulated interaction of each element with upstream and downstream proteins . An important point to be considered in S . mansoni TNF-α signaling pathway is the presence of Caspases 3 and 8 and C-IAP protein . Classically , caspases 3 and 8 are involved in the apoptosis process induced by TNF-α signaling and are inhibited by c-IAP . No direct evidence exists so far in the literature for the apoptosis process in schistosomes . The present findings open a new perspective for S . mansoni biology that warrants further investigation . When cercariae penetrate the human skin , macrophages start secreting TNF-α in that tissue [4] . We propose that the highest expression level of SmTNFR observed in cercariae reflects an evolutionary adaptation; during the penetration process , cercariae will be exposed to host TNF-α in the skin and this could act to promote parasite survival and development , in a similar manner as described in adult worms by Davies et al [56] . Finally , this is the first work that evaluated the effect of a human cytokine such as TNF-α on the expression profile of S . mansoni using oligonucleotide arrays comprised of 44000 elements [37] , probing the entire set of known S . mansoni gene fragments . We reasoned that the exposure of 3-hour old recently transformed schistosomula to human TNF-α would somewhat mimic the conditions at the skin during penetration . In fact , about 3-4 h after exposure to the host skin , schistosomula start invading the epidermis where they will have contact with human TNF-α [4] . In this respect , it is noteworthy that genes involved in mRNA translation and protein synthesis were found among the enriched GO categories in TNF-α treated schistosomula . High metabolic rates are probably present , as the expression of pyruvate dehydrogenase complex and the glucose transporter gene ( C800471 . 1 ) were increased ( Table S4 ) . Two proteins related to cell cycle control were induced in early schistosomula treated with human TNF-α: cell division cycle 23 ( C800690 . 1 ) and cell-cycle and apoptosis regulatory protein 1 ( C917134 . 1 ) ( Table S4 ) . In humans , TNF-α signaling resulting from TNFR2 activation is described to induce cell proliferation through anti-apoptotic mechanisms [54] . It is noteworthy that a number of receptors were induced in early schistosomula by human TNF-α , such as Activin receptor ( C811502 . 1 ) , Olfactory receptor ( C903953 . 1 ) , Retinoic acid receptor RXR ( Contig C910708 . 1 ) , a putative seven-transmembrane receptor ( C809780 . 1 ) and nicotinic acetylcholine receptor ( C812523 . 1 ) ( Table S4 ) . These TNF-α induced receptors may increase parasite sensitivity to host stimuli . A most interesting finding was the up-regulation of TRAF expression ( C905513 . 1 ) induced in early schistosomula by human TNF-α ( Table S4 ) . Cercariae have the highest levels of SmTNFR mRNA; cercariae-to-schistosomula early transformed parasites exposed to TNF-α apparently signal a positive feed-back regulation of expression of TRAF , a putative SmTNFR partner . The target genes activated by human TNF-α in paired adult worms have shown two distinct expression patterns: genes with transient changes ( that are induced in 1h and then repressed in 24hs or the opposite pattern ) and genes with sustained changes in their expression levels ( induced or repressed by TNF-α , independently of parasite's exposure time ) . Among the genes induced in 1 h by TNF-α , we highlight those that are categorized in the “RNA binding” and “RNA processing” GO terms; they encode proteins present in ribonucleoprotein complexes , such as Ribosomes ( Table S8 ) . AUT1 gene ( C918331 . 1 ) had a significantly induced expression in 1 h of treatment . AUT 1 is described in Drosophila as essential for development and autophagy process [57] ( Table S7 ) . Among TNF-α target genes with transient expression it is worth mentioning a set of genes related to egg laying ( Table S7 ) . Curiously , Egg shell protein ( C910370 . 1 ) and P40 egg shell protein ( C806383 . 1 ) had an induced expression at 1 h treatment and were repressed at 24 h . On the contrary , p48 egg shell protein ( contig JAP04349 . S ) and Egg protein ( C804676 . 1 ) along with gynecophoral canal protein ( C810707 . 1 ) had an opposite transient expression change , being repressed at 1 h and induced at 24 h treatment with TNF- α . Amiri et al [6] reported an increase in egg laying induced by human TNF-α , whereas Haseeb et al [7] described a decrease in egg production upon short time exposure to TNF-α ( 1 , 3 and 6 h ) . Our results revealed that there is a complex pattern of regulation of the egg laying related genes upon TNF-α treatment that deserves additional characterization . A particularly interesting finding is the sustained increase of expression in adults ( 1 h and 24 h treatment ) of MAPKKK5 ( contig C902728 , probe Q2_P09086 ) , a kinase member of the TNF-α signal transduction pathway ( Table S10 ) . MAPKKK5 kinase protein acts at the initial steps of the signaling process by phosphorylation and activation of other target kinases; the increase of MAPKKK5 expression suggests that as soon as 1 h after TNF-α exposure the signaling pathway is activated not only by phosphorylation but also by an increase in the number of MAPKKK5 molecules present in the cell . In conclusion , the present work reports an important signaling element in schistosomes , namely SmTNFR , thus giving a molecular perspective to the effects of human TNF-α on S . mansoni . The possible receptor is described along with all the elements of a conserved TNF-α signaling pathway together with a set of probable target genes activated by TNF-α in the parasite . The work extends the complexity of signal transduction biology of S . mansoni . | Schistosoma mansoni is the major causative agent of schistosomiasis in the Americas . This parasite takes advantage of host signaling molecules such as cytokines and hormones to complete its development inside the host . Tumor necrosis factor-alpha ( TNF-α ) is one of the most important host cytokines involved in the inflammatory response . When cercariae , the infective stage , penetrates the human skin the release of TNF-α is started . In this work the authors describe the complete sequence of a possible TNF-α receptor in S . mansoni and detect that the receptor is most highly expressed in cercariae among all life cycle stages . Aiming to mimic the situation at the site of skin penetration , cercariae were mechanically transformed in vitro into schistosomula and exposed to human TNF-α . Exposure of early-developing schistosomula to the human hormone caused a large-scale change in the expression of parasite genes . Exposure of adult worms to human TNF-α caused gene expression changes as well , and the set of parasite altered genes in the adult parasite was different from that of schistosomula . This work increases the number of known signaling pathways of the parasite , and opens new perspectives into understanding the molecular components of TNF-α response as well as into possibly interfering with parasite–host interaction . | [
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] | 2009 | Identification of the Schistosoma mansoni TNF-Alpha Receptor Gene and the Effect of Human TNF-Alpha on the Parasite Gene Expression Profile |
In recent years , it has become increasingly apparent that antisense transcription plays an important role in the regulation of gene expression . The circadian clock is no exception: an antisense transcript of the mammalian core-clock gene PERIOD2 ( PER2 ) , which we shall refer to as Per2AS RNA , oscillates with a circadian period and a nearly 12 h phase shift from the peak expression of Per2 mRNA . In this paper , we ask whether Per2AS plays a regulatory role in the mammalian circadian clock by studying in silico the potential effects of interactions between Per2 and Per2AS RNAs on circadian rhythms . Based on the antiphasic expression pattern , we consider two hypotheses about how Per2 and Per2AS mutually interfere with each other's expression . In our pre-transcriptional model , the transcription of Per2AS RNA from the non-coding strand represses the transcription of Per2 mRNA from the coding strand and vice versa . In our post-transcriptional model , Per2 and Per2AS transcripts form a double-stranded RNA duplex , which is rapidly degraded . To study these two possible mechanisms , we have added terms describing our alternative hypotheses to a published mathematical model of the molecular regulatory network of the mammalian circadian clock . Our pre-transcriptional model predicts that transcriptional interference between Per2 and Per2AS can generate alternative modes of circadian oscillations , which we characterize in terms of the amplitude and phase of oscillation of core clock genes . In our post-transcriptional model , Per2/Per2AS duplex formation dampens the circadian rhythm . In a model that combines pre- and post-transcriptional controls , the period , amplitude and phase of circadian proteins exhibit non-monotonic dependencies on the rate of expression of Per2AS . All three models provide potential explanations of the observed antiphasic , circadian oscillations of Per2 and Per2AS RNAs . They make discordant predictions that can be tested experimentally in order to distinguish among these alternative hypotheses .
Messenger RNAs , which encode proteins , are transcribed in the 5'-to-3' direction from one strand ( the sense strand ) of a structural gene , under the control of an upstream promoter region . For some genes , an ‘antisense’ RNA molecule is transcribed from the opposite strand , driven by an alternative promoter which often lies in an intron of the sense transcript [1 , 2] . Antisense transcripts are rarely translated into proteins; their primary effects are in regulating the expression of a ‘target’ transcript [3–6] . Because of their complementary sequences , the natural target of an antisense transcript is typically its sense counterpart and vice versa . Interactions between these transcripts are possible not only post-transcriptionally [7–9] but also during the transcription process [10–12] . Difficulties in simultaneously transcribing RNAs from both strands of the same genomic locus , termed transcriptional interference , can mutually repress the expression of both sense and antisense transcripts [13] . Recently Koike et al . [14] reported that an antisense transcript of PER2 , a key core-clock gene , displays oscillatory dynamics . The maximum level of the antisense transcript , Per2AS , was about 5% of Per2’s maximum level , and the two transcripts were expressed in antiphase , i . e . , the peak of Per2AS expression was displaced about 12 h from the peak of Per2 mRNA . From previous studies of the regulation of gene expression by antisense transcripts in other organisms , it is known that antisense expression can effectively control expression of sense mRNAs; for example , by a tunable , bistable switch [13 , 15 , 16] . To date the potential regulatory roles of antisense transcripts in a system with oscillatory dynamics have not been studied systematically . Therefore , a natural question is to what extent the rhythms in the mammalian circadian clock can be affected by Per2AS expression . In this work , we study , by numerical simulation and bifurcation analysis [17] , the effects of sense-antisense interactions in a mathematical model of the mammalian circadian network proposed by Relogio et al . [18] . Relogio’s model is based on two , synergistic feedback loops: the classic , negative feedback loop involving CLOCK/BMAL1 and PER/CRYPTOCHROME ( CRY ) , and the alternative , mixed feedback loop involving BMAL1 , REV-ERB ( REV ) and ROR . We supplement Relogio’s model with an additional , double-negative feedback loop between Per2 and Per2AS RNA species . ( Simulations of the original Relogio model agree with many previously reported experimental observations [2 , 19–21] , and we are careful to retain these successful features of the published model . ) By incorporating new terms and variables into Relogio’s model , we study , in silico , the effects of two different hypotheses concerning Per2-Per2AS interactions . In our first model , called the pre-transcriptional model , we assume that Per2 and Per2AS mutually repress each other’s production during the process of transcription . This hypothesis is motivated by recent observations of circadian rhythmicity in Neurospora , where it was shown that sense and antisense transcripts of the FREQUENCY ( FRQ ) gene control the circadian rhythm by transcriptional interference [22] . Our second model , the post-transcriptional model , is based on the assumption that fully transcribed Per2 and Per2AS form double-stranded duplex RNAs , which are degraded by RNases , similar to siRNA- or miRNA-mediated RNA degradation mechanisms [23] . After considering these two models separately , we study a third model that combines pre- and post-transcriptional interactions . In our simulations of these three modified Relogio-models , the dynamics of Per2 and Per2AS are consistent with the fundamental observation of Koike et al . that the RNAs oscillate with ~24 h period and in antiphase to each other . Our pre-transcriptional model shows that the interference of Per2AS on the transcription of Per2 and vice versa can generate new modes of oscillations ( both circadian and non-circadian ) in the network , because of the way the double-negative feedback loop between Per2 and Per2AS interacts with the synergistic feedback loops in the original Relogio model . In contrast , the post-transcriptional model shows that circadian rhythms can be destroyed by Per2AS overexpression , because duplex formation rapidly suppresses the expression of Per2 mRNA . A characteristic feature of the pre- and post-transcriptional models is that the period of the oscillation is sensitive to the interactions of Per2 and Per2AS . The combined pre/post-transcriptional model shows that if Per2AS is involved in two different levels of Per2 regulation , then the period of the oscillation , as a function of Per2AS overexpression , can be restricted to a narrow interval .
Fig 1A presents a schematic diagram of the circadian clock network in mammalian cells , as originally proposed by Relogio et al . [18] . CLOCK/BMAL1 up-regulates the expression of the core clock genes , PER , CRY , REV , and ROR . Newly synthesized PER and CRY proteins form multimeric complexes in the cytoplasm , and these complexes enter the nucleus , in both phosphorylated and unphosphorylated forms of PER . The PER/CRY complex inhibits CLOCK/BMAL1-activated transcription , by creating a delayed negative-feedback loop in the transcription-translation process . The PER/CRY complex is degraded during the night , releasing its inhibitory effect on CLOCK/BMAL1 , to allow a fresh restart of the transcription processes [18] . ROR and REV proteins in the nucleus bind to the promoter region of the BMAL1 gene , thereby modulating the expression of Bmal1 mRNA . ROR is an activator and REV an inhibitor of BMAL1 expression [24] . Previously , ROR and REV genes were often considered as auxiliary elements in the network , whose primary roles were to fine-tune the expression of BMAL1 and add robustness to the rhythmic dynamics [25 , 26] . However , in the model of Relogio et al . , the effects of REV and ROR on BMAL1 expression form independent loops that can generate sustained oscillations autonomously , even if the PER and CRY genes are expressed constitutively . Some experimental evidence suggests that the feedback loops through REV and ROR are critical for maintaining circadian oscillations; for instance , when REV or ROR is overexpressed or both REV-ERBα and REV-ERBβ are knocked-out , circadian rhythmicity can be lost [18 , 19 , 27] . From the schematic diagram in Fig 1A , Relogio et al . derived a system of ordinary differential equations ( ODEs ) that represent the temporal dynamics of these circadian genes and proteins . Other groups have presented alternative mathematical models of mammalian circadian rhythms [28–31] , but the Relogio model is most fitting for our purposes in this paper . In contrast to other models that focus on the negative feedback loop , in which PER/CRY inhibits CLOCK/BMAL1 , the Relogio model considers the mammalian circadian clock as a network of synergistic and interlocked feedback loops whereby , in addition to PER/CRY inhibition of CLOCK/BMAL1 , REV and ROR control the expression of BMAL1 , as inhibitor and activator , respectively ( see Fig 1A ) . The Relogio model [18] consists of 19 ODEs with 76 parameters ( rate constants for the constituent biochemical reactions in the network ) . With an appropriate choice of these parameter values , the model generates simulations in agreement with many well-established experimental properties of circadian rhythms in mammalian cells . For this reason , we have chosen the Relogio model for studying the effects of Per2 sense-antisense interactions . Our strategy is to incorporate into the model new variables and reaction rates that represent potential interactions of sense-antisense RNAs ( Per2 and Per2AS ) , while keeping the modified model as close as possible to the original Relogio ODEs , and keeping the parameter values as close as possible to the ‘wild-type’ ( WT ) values in reference [18] . Previously , it was shown by Xue et al . in Neurospora crassa [22] that coupled transcription of the key circadian gene FRQ and its antisense partner QRF directly modulates the circadian rhythm , as a consequence of mutually inhibitory interactions between frq and qrf RNAs . Following this lead , we hypothesize that the interactions of Per2 and Per2AS may also modulate circadian rhythmicity in mammalian cells , by forming a double-negative feedback loop . In Fig 1A , we indicate the mutually inhibitory interactions between Per and PerAS RNAs by the red lines in a small blue box . At present , there are no experimental data about the exact molecular mechanisms by which Per2 and Per2AS interact in the circadian network . Therefore , our strategy is to propose reasonable hypotheses for the interaction and to study the consequences of these interactions in silico . We propose two simple , feasible mechanisms for sense-antisense interactions , which function either before or after the transcriptional process is complete ( Fig 1B ) . Our aim is not to prove that one or other of these hypotheses is correct , but rather to study the potential effects of sense-antisense interactions on circadian rhythms of the core-clock network , in terms of modulating the period , amplitude , and phases of oscillations . Numerical simulations were carried out in Mathematica , and bifurcation diagrams were calculated using AUTO [17] . In some circumstances , parameter values in the models were fitted to experimental data using the ensemble method [32] described in Suppl . S4 Text .
The differential equations of the pre-transcriptional model ( i . e . , Relogio’s differential equations supplemented with Eqs ( 1A ) and ( 1B ) ) are provided in Suppl . S2 Text . The parameter values proposed by Relogio et al . [18] are listed in Suppl . S1 Table , where they are called ‘WT’ values . Suppl . S1 Table also lists proposed values for the parameters μ , λ , KS and KAS that characterize the mutual interference between Per2 and Per2AS . In the post-transcriptional model ( the Relogio model modified by Eq ( 2 ) ) , we assume that the physical interaction ( duplex formation ) between sense-antisense transcripts causes mutual degradation of both RNAs . In this case , the amount of Per2AS in a cell is especially important , and this amount is determined by the parameter λ0 , which represents constitutive transcription of Per2AS from both the endogenous PER2AS sequence and from exogenous Per2AS sequences carried on a plasmid . In Fig 7 we show how the period , amplitudes and phases of the rhythm depend on the value of λ0 . In these simulations , unless otherwise specified , all parameters of the Relogio model are fixed at their WT values , and the additional parameters in Eq ( 2 ) are fixed at kassn = 0 . 1 , kdiss = 0 . 1 , and ddup = 0 . 1 . We assume that the contribution to λ0 from the endogenous gene is small ( say , 0 . 1 < λ0 < 1 ) compared to the contribution due to plasmid copies of Per2AS sequences ( say , λ0 > 1 ) . At λ0 = 0 . 2 ( representative of endogenous synthesis only ) , the period of the oscillations in the post-transcriptional model is ~23 . 5 h , the maximum level of Per2AS is about 5% of the maximum level of Per2 , and Per2 and Per2AS oscillate out-of-phase , i . e . |ϕPer2 − ϕPer2AS| ≈ 12 h ( see Suppl . S7 Fig ) . In other words , at these parameter values , the post-transcriptional model exhibits oscillations that fit reasonably well the time-courses of Per2 and Per2AS oscillations observed by Koike et al . , shown by the black circles in Fig 2 . Fig 7 shows how the properties of circadian rhythms change in the post-transcriptional model with increasing rates of synthesis of exogenous Per2AS ( parameter λ0 ) . The period of oscillations increases modestly with increasing λ0 ( Fig 7A ) . The amplitude of Per2 oscillations drops with increasing λ0 , because of the duplex formation , whereas the amplitudes of oscillation of other core-clock genes increase ( presumably CLOCK/BMAL1 is less strongly repressed by PER/CRY ) ( Fig 7B ) . With Per2AS phase set at 0 hours , we plot in Fig 7C the changes in the phases of oscillation of core clock genes . Blue and black lines in Fig 7C show that the phases of Per2 and Cry mRNAs are most sensitive to the increase of Per2AS level . In Suppl . S8A Fig we plot a two-parameter bifurcation diagram on the parameter plane ( λ0 , kassn ) . Although the oscillatory domain is very large in this diagram , the region where the post-transcriptional model oscillates with circadian properties is restricted; the black symbols mark the region where following conditions are fulfilled: 23h<T<25h , ( AmaxPer2−AminPer2 ) >0 . 5 , 11h<|ϕPer2−ϕPer2AS|<13h . ( 4 ) In Suppl . S8B Fig we plot a two-parameter bifurcation diagram on the parameter plane ( ddup , kassn ) , while fixing λ0 = 10 and kdiss = 0 . 1 . The non-oscillatory domain in the middle of the diagram separates a region of circadian oscillations ( 22 h < T < 25 h ) at the bottom of the diagram from a region of slow oscillations ( T > 50 h ) at the top . The region of this diagram where conditions in Eq ( 4 ) are fulfilled ( marked by small red symbols ) is quite restricted: 0 . 08 < ddup < 0 . 27 and 0 < kassn < 0 . 2 . The blue symbols in Suppl . S8B Fig mark the region where the first and second conditions of Eq ( 4 ) are fulfilled , but the oscillations of Per2 and Per2AS are not strictly antiphasic , i . e . , 9 h < |ϕPer2 − ϕPer2AS| < 15 h . In Suppl . S9 Fig , we plot the time-courses of oscillations at three locations in Suppl . S8B Fig . Suppl . S9A–S9C Fig show the case: kassn = 1 , ddup = 0 . 1 for two values of λ0 . When λ0 = 1 , the dynamics of Per2 is reminiscent of WT dynamics in the Relogio model , but when λ0 = 10 , the amplitude of Per2 oscillations has become very small . For the case kassn = 1 , ddup = 0 . 2 ( Suppl . S9D–S9F Fig ) , the amplitudes of oscillations at λ0 = 10 are larger , but the waveform has become distinctly non-harmonic . For the case kassn = 5 , ddup = 0 . 2 ( Suppl . S9G–S9I Fig ) , the amplitudes of oscillations at λ0 = 10 are quite large , the waveforms are very non-haromonic , and the period ( ~30 h ) is non-circadian . Our explorations of the post-transcriptional model show that it can be parameterized to fit the observations in Koike et al . [14] , but the range of suitable parameter values is restricted . If any of the parameters ddup , kassn , or kdiss in Eq ( 2 ) deviate too much from the preferred values , the oscillations may no longer fulfill the requirements in Eq ( 4 ) . Especially if kdiss > kassn , the peak amplitudes of Per2 and Per2AS become quickly non-antiphasic . Therefore , we conclude that the formation of Per2/Per2AS duplex RNA tends to destroy circadian rhythms over a wide range of values of the parameters ddup , kassn , and kdiss . Eq ( 3 ) details how we modified the Relogio model to include both pre- and post-transcriptional interactions of Per2 and Per2AS . Fig 8 shows how the period , amplitude , and phases of circadian oscillations change with increasing λ1 for fixed λ0 = 0 , μ = 1 and kassn = 0 . 1 . The combined model is consistent with circadian , antiphasic oscillations of Per2 and Per2AS ( see Suppl . S10 Fig ) . Unlike simulations of the pre- or post-transcriptional model , shown in Figs 4 and 7 , the period , amplitude and phases of oscillation in the combined model are distinctly non-monotonic in dependence on λ1 . On Fig 9 we continue the limit cycle oscillations of period T = 23 . 5 h on the parameter plane ( μ , λ ) for three different values of the rate constant for duplex formation , kassn . Notice that , compared to the case kassn = 0 ( i . e . , no duplex formation ) , the locus of 23 . 5-hour rhythms does not change much for kassn = 0 . 05 , but it is radically different for kassn = 0 . 1 , intersecting the line μ = 1 twice , at λ ≈ 1 and λ ≈ 25 . Therefore , as Figs 8 and 9 show , the combined pre/post-transcriptional model can restrict the period of oscillations within tighter bounds of μ . The reason is that , unlike the pre- or post-transcriptional model for which Per2-Per2AS interactions directly modulate only a single process of gene regulation , in the combined model two different gene-regulatory processes are simultaneously modulated . As a result , due presumably to counter-balancing effects , the period of oscillations can be restricted to a narrow interval .
A better understanding of the molecular mechanisms underlying mammalian circadian rhythms will undoubtedly inform our efforts to improve human health and deal with modern societal problems such as shiftwork and jetlag . However , the inventory of genes and genetic interactions in the mammalian circadian-clock network is still incomplete . Important players may be yet unknown or under-appreciated [34 , 35] . For example , recent experimental data about oscillations of an antisense RNA transcript in the circadian rhythm in mouse liver [14 , 36 , 37] suggest a possible antagonistic relationship between a core-clock mRNA , Per2 , and its natural antisense partner , Per2AS . Because antisense transcripts can be fundamental regulators of gene expression , the interactions between Per2 and Per2AS may be important factors for controlling circadian rhythms [1] . To date , the molecular mechanisms of Per2-Per2AS interactions are unknown . In this work , we propose two realistic mechanisms for these interactions and study their effects in silico by incorporating Per2-Per2AS interactions into a well-documented mathematical model [18] of mammalian circadian rhythms . In the first hypothesis , Per2 mRNA molecules interfere with the transcription of Per2AS molecules and vice versa . In the second hypothesis , mature Per2 and Per2AS molecules form double-stranded RNA duplexes , which are rapidly degraded by RNases . Simulations and analysis of our pre-transcriptional model ( the first hypothesis ) show that mutual transcriptional interference can generate emergent oscillations in the clock network . That is to say , Per2-Per2AS interactions can generate new modes of circadian oscillations not seen in the original model [18] . For example ( Fig 5; purple curve ) , our model predicts that Per2AS overexpression restores circadian rhythms to ROR-overexpressing cells by rebalancing the positive and negative interactions exerted on BMAL1 expression by ROR and REV ( Fig 1 ) . According to our post-transcriptional model ( the second hypothesis ) , circadian oscillations are expected to be eradicated by an increasing rate of Per2AS expression , which is to be expected if Per2AS forms unstable duplex molecules with Per2 mRNA . For both the pre- and post-transcriptional models and for a combined pre/post-model , we have computed how the period of oscillation and the amplitudes and phases of core clock gene oscillations will vary with the rate of synthesis of Per2AS transcripts ( see Figs 4 , 7 and 8 ) . By altering the rate of expression of Per2AS transcripts , these predicted dependencies of period , amplitudes , and phases can be tested experimentally . Comparison between such experimental results and mathematical predictions can evaluate the accuracy and predictive power of the three alternative models of sense-antisense interactions . In this way , experimental interrogation , in combination with mathematical simulations , can shed light on the mechanisms of sense-antisense interactions in the mammalian circadian rhythm , and a more realistic mathematical model can be developed . Of the three models we have studied ( pre- , post- , and combined pre/post-transcriptional models ) , the pre-transcriptional model is the most likely , in our opinion , because it provides the most robust account of the observed , circadian , antiphasic oscillations of Per2 and Per2AS RNAs [14] , in the context of all the other experimental data that went into the development and parameterization of the circadian-rhythm model of Relogio et al . [18] . Furthermore , the pre-transcriptional model makes the counterintuitive prediction that Per2AS overexpression can restore circadian rhythms to cells that are overexpressing ROR . This striking prediction of the model can be tested in a suitably designed mutant strain of mouse liver cells that overexpress both Per2AS RNA and Ror mRNA . Our study of sense-antisense interactions has been made in the context of a specific mathematical model of mammalian circadian rhythms [18] , but we suspect that our results are generic , in the sense that similar results will be found if our hypotheses are tested in different models of the circadian clock [29–31 , 38] . As an example , we studied the effects of Per2 and Per2AS interactions in the Mirsky et al . model [30] of mammalian circadian rhythms . The three main differences between the Relogio and Mirsky models are that ( a ) Mirsky’s model includes paralogs of Per and Cry ( i . e . , Per1 and Per2 , Cry1 and Cry2 ) , ( b ) the two models make different assumptions about how PER/CRY interferes with CLOCK/BMAL-induced gene expression , and ( c ) Rev and Ror play less prominent roles in the generation of rhythmic dynamics in Mirsky’s model relative to Relogio’s model . Suppl . S11 Fig shows how period , amplitudes , and phases of oscillations change in the Mirsky et al . model [30] with increasing rate of Per2AS transcription . Notice the similarity between Fig 4 and Suppl . S11 Fig , despite the fact that Mirsky’s model distinguishes between Per1 and Per2 transcripts and proteins . Suppl . S12A Fig shows that Per1 oscillations are indirectly affected by the double negative feedback interactions of Per2 and Per2AS , but the amplitude changes of Per1 and Per2 are uncorrelated . Suppl . S12B Fig shows that Cry1 and Cry2 oscillations also respond to Per2AS interference , and that their amplitudes are anti-correlated with each other . The generic effects of Per2-Per2AS interactions in different models are due , presumably , to generic , network-level consequences of a double-negative feedback loop embedded in the delayed negative-feedback that generates circadian rhythms . Obviously , depending on the choice of a base model , of the mathematical representations of our hypotheses , and of parameter values , a rich repertoire of interesting dynamics are possible in a mathematical model involving many feedback loops that can generate independent oscillations [39–41] . For example , in a recent paper El-Athman et al . [42] have combined the Relogio-2011 model of the mammalian circadian clock with a model of mammalian cell-cycle controls and shown that knocking out the tumor suppressors that bridge the two systems induces notable phase shifts in the expression of circadian clock genes . Interesting research directions in the future would be a ) whether these phase shifts can be controlled by antisense transcripts of Per2 , and b ) whether the positive regulation of the tumor protein p53 by Per2 , as reported by Gotoh et al . [43] , can induce predictable amplitude and phase modulations in the oscillations of cell cycle elements . Finally , we hope that the modeling results reported here , suggesting that Per2-Per2AS interactions may have profound effects on circadian rhythmicity , may stimulate new experiments about the roles of this sense-antisense pair of RNAs in the mammalian circadian-clock network . | A better understanding of the molecular mechanisms underlying circadian rhythms will undoubtedly improve the treatment of human health problems related to circadian dysrhythmias . However , the inventory of genes and genetic interactions in the circadian clock is still incomplete . Important players may yet be unknown or under-appreciated . For example , in mouse liver , the core clock gene PER2 is transcribed into both a Per2 mRNA molecule ( a ‘sense’ transcript ) and an antisense RNA transcript ( Per2AS ) . Because it is important to know how interactions between Per2 and Per2AS may affect circadian gene expression , we have carried out a mathematical modeling study of two possible mechanisms for these interactions . In the pre-transcriptional model , Per2 mRNA interferes with the transcription of Per2AS RNA and vice versa . In the post-transcriptional model , Per2 and Per2AS molecules form double-stranded RNA duplexes , which are rapidly degraded by RNases . We find that the pre-transcriptional model gives a more robust account of the circadian , antiphasic oscillations of Per2 and Per2AS transcripts in mouse liver . The model makes an unexpected prediction that co-overexpression of the ROR gene and Per2AS sequences can generate a new mode of circadian oscillations not seen in contemporary models of circadian rhythms and not yet looked for experimentally . | [
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... | 2018 | Modeling the interactions of sense and antisense Period transcripts in the mammalian circadian clock network |
An unresolved question in herpesvirus biology is why some herpesviruses contain more than one lytic origin of replication ( oriLyt ) . Using murine gammaherpesvirus 68 ( MHV-68 ) as model virus containing two oriLyts , we demonstrate that loss of either of the two oriLyts was well tolerated in some situations but not in others both in vitro and in vivo . This was related to the cell type , the organ or the route of inoculation . Depending on the cell type , different cellular proteins , for example Hexim1 and Rbbp4 , were found to be associated with oriLyt DNA . Overexpression or downregulation of these proteins differentially affected the growth of mutants lacking either the left or the right oriLyt . Thus , multiple oriLyts are required to ensure optimal fitness in different cell types and tissues .
Herpesviruses show two stages in their life cycle: lytic replication and latency . Lytic DNA replication is initiated at a defined site on the viral genome , the lytic origin of replication ( oriLyt ) . While some herpesviruses , for example human cytomegalovirus ( HCMV ) , have a single oriLyt , others have multiple oriLyts [1] . Why some herpesviruses need more than one oriLyt is not known [2] . Across different herpesvirus family members , oriLyts may vary in size and in complexity , but are usually characterized by the presence of binding sites for transcription factors and repeat sequences [3] . "Trans"-acting factors , usually multi-protein complexes , are necessary for efficient oriLyt-dependent DNA replication . During the lytic cycle , a multi-protein complex is formed at the oriLyt and initiates the replication process [4] . This complex is composed of viral proteins , which first form a pre-replication complex that is then recruited to the oriLyt , binds to it and subsequently becomes the replication initiation complex . The viral proteins are conserved among the various herpesviruses , and are often referred to as the six core replication proteins: DNA polymerase , processivity factor , helicase , primase , primase-associated factor and ssDNA binding protein . In addition to these factors , each herpesvirus needs at least one origin-binding protein , e . g . Zta for Epstein-Barr virus ( EBV ) and Rta and bZIP for Kaposi's sarcoma-associated herpesvirus ( KSHV ) [5] . Besides viral proteins , cellular proteins are also involved in the complex . While the identity of viral proteins is relatively well established , not all of the cellular proteins are known [6] . For KSHV , Topoisomerase I and II , for example , have been shown to interact with the oriLyt [6] . Using depletion of Topoisomerase I and II by shRNA-mediated "gene silencing" or by chemical inhibition , lytic replication of KSHV could be significantly inhibited [7] . The two known human gammaherpesviruses ( γHV ) EBV and KSHV belong to those herpesviruses that have more than one oriLyt , namely two [8 , 9] . The prototypic γ1-herpesvirus EBV is associated with lymphomas and nasopharyngeal carcinoma [10] . KSHV , a γ2-herpesvirus , is associated with lymphoproliferative disorders and Kaposi’s sarcoma [11] . In Kaposi's sarcoma lesions , most of the endothelial-derived spindle cells are latently infected with KSHV . In some cells , however , there is also spontaneous lytic replication which might contribute to viral spread and thereby to the preservation of the pool of latently infected cells [12] . In addition , soluble factors are produced during lytic replication which promote tumorigenesis by paracrine mechanisms [13 , 14] . Consistent with these findings is the observation that treatment with ganciclovir which inhibits lytic replication limited the development of Kaposi's sarcoma [15] . It was therefore postulated that for KSHV , lytic replication and continuous re-infection of naive cells are of great importance for tumorigenesis [6] . To gain insight into why γHV like KSHV need two oriLyts may thus not only lead to a better understanding of oriLyt-dependent lytic replication in general but might also aid in the development of new avenues for interference with herpesvirus lytic replication and disease development . Although there are suitable cell culture systems to study EBV and KSHV lytic replication , they are rather inefficient when compared to other viruses . Murine gammaherpesvirus 68 ( MHV-68 ) is also a member of the γHV and closely related to KSHV and EBV [16] . MHV-68 replicates well in tissue culture and infection of mice serves as a small animal model to investigate γHV pathogenesis [17] . Thus , it is a good model to study oriLyt-dependent lytic replication in vitro and in vivo . Importantly , since MHV-68 also contains two oriLyts [4 , 18 , 19] , it represents a suitable model to approach the question why γHV need more than one oriLyt . In the present study , we investigate the role of the oriLyts in the context of a γHV infection using MHV-68 mutants lacking either a functional left or right oriLyt . We find that in vitro , the efficiency of replication of the oriLyt mutants is dependent on the cell type . In vivo , we observe differences amongst the mutants with regard to acute lytic replication , latent viral load and reactivation capacity . Identification of oriLyt-bound cellular proteins reveals that , depending on the cell line , a different repertoire of proteins interacts with the respective oriLyt or the associated replication complex . Overexpression or downregulation of such proteins differentially affects the growth of mutants lacking either the left or the right oriLyt . Taken together , our data suggest that the presence of multiple oriLyts enables γHV to efficiently establish infection in different cell or tissue types and during different phases of the viral life cycle .
Throughout the infection process in vivo , MHV-68 encounters various cell types in various tissues . This might pose specific challenges to the capacity of an oriLyt in these tissues and cells . In order to model this situation in vitro , we first analyzed the growth of mutants lacking the left or the right oriLyt ( Fig A in S1 Text ) in thirteen cell lines of different cell type and origin . In some cell lines , for example in the smooth muscle cell line MOVAS ( Fig 1A ) , both oriLyt mutants attained similar titers as the parental virus . However , in other cell lines like MHEC and SVEC4-10 endothelial cells , both mutants showed a replication deficit compared to parental virus ( Fig 1B and 1C ) . Interestingly , cell lines were also found in which only one but not the other mutant was impaired in lytic growth , indicating that the oriLyts are of varying importance in these cell lines . For example , the Δright oriLyt mutant attained titers comparable to parental virus in the epithelial cell line TCMK-1 , whereas the Δleft oriLyt mutant showed a replication deficit of more than one order of magnitude in this cell line ( Fig 1D ) . In contrast , in the alveolar macrophage cell line MH-S and in the mesenchymal stromal cell line CS16 , only the Δright oriLyt mutant was impaired in lytic replication while the Δleft oriLyt mutant was not ( Fig 1E and 1F ) . Table A in S1 Text summarizes the results for lytic growth of the mutants in comparison to parental virus in all tested cell lines . To demonstrate that the phenotypes observed with the oriLyt mutants were due to the deletion of the oriLyt and not to rearrangements outside of the mutated region or side effects of the deletion on neighbouring genes , ectopic revertants were constructed . In these revertants , the oriLyt-sequence has been re-inserted at an ectopic position while the original deletion is still present ( Fig A in S1 Text ) . Thus , a complete reversion of the mutant phenotype clearly indicates that it was not due to disruption of other structures or to side effects on neighboring genes . We selected a cell line ( MHEC ) in which both oriLyt mutants displayed reduced growth , and tested the respective ectopic revertants . A shown in Fig B in S1 Text , both ectopic revertants showed a complete reversion of the phenotype of their respective mutant . Our results demonstrate that differences exist between the two oriLyt mutants regarding their capacity for lytic growth in specific cell lines . To analyze whether the observed growth differences between the two oriLyt mutants are only an in vitro phenomenon , or whether the two oriLyts might have different functions also in vivo , C57BL/6 mice were inoculated intranasally ( i . n . ) . Following i . n . inoculation , there is an acute phase of lytic virus replication which involves alveolar epithelial cells [17] . Virus titers were determined in lung homogenates by plaque assay at an early time point ( day 3 p . i . ) and at a time point when peak viral titers are usually reached ( day 6 p . i . ) . No differences in viral titers were detected at day 3 after infection ( Fig 2A ) . Consistent with results published previously by our group , viral titers in the lungs of mice infected with the Δright oriLyt mutant were significantly lower at day 6 p . i . when compared to parental virus ( Fig 2A; [20] ) . Likewise , viral titers in mice infected with the Δleft oriLyt mutant were reduced; however , the titers were significantly higher than after infection with the Δright oriLyt mutant . Viral titers after infection with the ectopic revertants , both for the right oriLyt ( Fig 2B ) and for the left oriLyt ( Fig 2C ) , were not significantly reduced when compared to parental virus . This indicated that the phenotypes observed with the oriLyt mutants were indeed due to the deletion of the oriLyt and not to rearrangements outside of the mutated region or side effects of the deletion on neighboring genes . Taken together , our results show that deletions of the right or the left oriLyt also differentially affect lytic replication in vivo . To analyze the oriLyt mutants during the latent phase of infection , C57BL/6 mice were i . n . inoculated . After i . n . inoculation , the spleen is a major site of latently infected cells [17] . Thus , ex vivo reactivation of latently infected splenocytes and the viral genomic load in the spleen were determined 17 days after infection ( early latency ) . At this stage , the majority of cells in the spleen harbouring MHV-68 are B cells [17] . Significantly fewer splenocytes reactivated from mice infected with the oriLyt mutants compared to those infected with parental virus , the number of reactivating splenocytes being lowest in mice infected with the Δright oriLyt mutant ( Fig 3A ) . The frequency of reactivating splenocytes was 1 in 9849 for the parental virus . This number was significantly lower in mice infected with the Δright oriLyt mutant ( 1 in 250649; P = 0 . 0021 versus parental virus ) and in mice infected with the Δleft oriLyt mutant ( 1 in 53122; P = 0 . 004 versus parental virus ) . In all experiments , the frequency of reactivating splenocytes from mice infected with the Δright oriLyt mutant was found to be significantly lower than in mice infected with the Δleft oriLyt mutant ( P = 0 . 0145 ) . Splenocytes from mice infected with the ectopic revertants showed a frequency of reactivation similar to splenocytes from mice infected with parental virus ( Fig 3B ) . The viral genomic load in the spleens of infected mice was determined by real-time PCR . A significantly lower viral copy number was found in both groups infected with oriLyt mutant viruses when compared to mice infected with parental virus ( Fig 3C ) . The copy numbers between the group infected with the Δright oriLyt mutant and the group infected with the Δleft oriLyt mutant were comparable . Thus , the reduced reactivation frequency of the Δleft oriLyt mutant might solely be due to the lower viral copy number while for the Δright oriLyt mutant , a defect in reactivation itself seems to be present additionally . Importantly , the viral copy numbers after infection with the ectopic revertants were not significantly reduced compared to the copy numbers after infection with parental virus , neither for the right oriLyt ( Fig 3D ) nor for the left oriLyt ( Fig 3E ) . We also tested ex vivo reactivation and viral genomic load at day 42 post infection ( late latency ) . Here , no reactivation could be detected and the viral genomic loads were comparable between the group with parental virus and the two groups with mutant viruses ( 8 . 3 ± 1 . 6 , 10 . 1 ± 1 . 8 and 14 . 6 ± 4 . 4 copies gB/1000 copies L8 [means ± SEM; n = 6] for parental virus , Δright oriLyt mutant and Δleft oriLyt mutant , respectively ) . Our results regarding viral latency after i . n . inoculation demonstrate that both the deletion of the right or the left oriLyt affect latency establishment and the capacity to reactivate from early latency ( day 17 post infection ) . Deletion of the right oriLyt had a stronger effect than deletion of the left oriLyt . The defect in latency establishment and reactivation of the oriLyt mutants might be the result of inefficient acute replication ( Fig 2A ) but could also be due to effects on latency establishment and reactivation itself . Although we cannot formally rule out the first option , we favor the second alternative for the following reasons: i ) vaccination studies with MHV-68 suggest that efficient acute infection is not a mandatory step for latency establishment [21 , 22] , and ii ) the level of latently infected cells in the spleen is largely independent of the dose used for inoculation—in case of i . n . inoculation over a range from 4 x 101 to 4 x 105 PFU [23] . In our study , the titers of the oriLyt mutants during acute infection were within this range and were approx . 20-fold lower when compared to parental virus ( Fig 2A ) . The route of inoculation determines , at least to a certain extent , which cell types are initially infected and how viruses subsequently spread . Consequently , the requirements for oriLyts might differ between various routes of inoculation . Another site of MHV-68 latency is the peritoneum , where macrophages are the major cell type harbouring latent MHV-68 [17] . Thus , we also investigated latency establishment and reactivation from latency in peritoneal exudate cells ( PECs ) after intraperitoneal ( i . p . ) inoculation . C57BL/6 mice were inoculated i . p . , and ex vivo reactivation and viral genomic load of latently infected PECs were determined 17 days p . i . The frequency of reactivating cells was lower in both groups infected with mutant virus compared to parental virus ( Fig 4A and 4B; 1 in 34114 for parental virus , 1 in 145053 for the Δright oriLyt mutant , and 1 in 198144 for the Δleft oriLyt mutant ) . The viral copy number found in PECs of mice infected with the Δright oriLyt mutant was reduced only by trend but did not reach statistical significance ( Fig 4C ) . In contrast , a significantly lower viral copy number was found in PECs of mice infected with the Δleft oriLyt mutant when compared to mice infected with parental virus ( Fig 4D ) . Ex vivo reactivation and viral copy numbers after infection with the ectopic revertants were not significantly reduced when compared to parental virus ( Fig 4A–4D ) . Thus , our results regarding viral latency after i . p . inoculation demonstrate that the deletion of the right or the left oriLyt influences latency establishment in PECs , and that differences regarding the need for two oriLyts exist depending on the compartment and the route of inoculation . Table B in S1 Text provides a schematic summary of all results obtained in vivo , indicating that , as in vitro , specific differences between the two oriLyt mutants also exist in vivo . Since we observed that the loss of one oriLyt can obviously be completely compensated by the other oriLyt in some but not in all cell types , we hypothesized that two oriLyts are necessary to guarantee optimal fitness throughout the viral life cycle in all cell types which are encountered during the course of infection . One way to achieve this goal might be by differential interaction of either oriLyt with cell-type specific factors which might impose either activating or inhibitory effects . To test this hypothesis , we chose to investigate the right oriLyt . To identify proteins that might interact with the right oriLyt in one cell line but not in the other , we selected TCMK-1 , a cell line in which we had observed reduced growth of the Δleft oriLyt mutant ( contains only the right oriLyt ) , and as control NIH 3T3 , a cell line in which the Δleft oriLyt mutant grew like parental virus ( Table A in S1 Text ) . Proteins bound to the right oriLyt were identified by a modification of the DNA-affinity purification method described previously by Wang et al . [6] . Using this method , 193 proteins could be identified which were exclusively detected in extracts from TCMK-1 cells and 37 proteins in extracts from NIH 3T3 cells ( Table 1 ) . Two candidate proteins , Hexim1 , found in TCMK-1 cells only , and Rbbp4 , found in NIH 3T3 cells only , were chosen for further investigation ( Fig C in S1 Text and Table C in S1 Text ) . A list of all proteins which were identified by nanoHPLC-ESI-MS/MS is provided in S1 Table . Since the MS data were suggestive of cell line specificity , we wanted to confirm the association of Hexim1 and Rbbp4 with the right oriLyt by additional approaches . First , formaldehyde cross-linking chromatin immunoprecipitation ( ChIP ) assays were performed . Chromatin from TCMK-1 and NIH 3T3 cells , respectively , infected with MHV-68 , was immunoprecipitated with specific antibodies against Hexim1 or Rbbp4 . Isotype matched antibodies were used as a control . Quantification of the precipitated protein-bound DNA by qPCR showed that the DNA of the right oriLyt could be enriched by precipitation with a Hexim1- or a Rbbp4-specific antibody while DNA of the left oriLyt or DNA of an unrelated genomic region ( ORF23 ) , that was used as a negative control , could not be enriched ( Fig 5A and 5B ) . Second , Western Blots for Hexim1 and Rbbp4 with samples from both TCMK-1 and NIH 3T3 cells after DNA-affinity purification were performed ( Fig D in S1 Text ) . Hexim1 was only detected in samples from TCMK-1 cells purified with the specific oriLyt DNA but not in NIH 3T3 cells , whereas Rbbp4 was only found in DNA-affinity purified samples from NIH 3T3 cells . As a control , Topoisomerase I could be detected in samples from both cell lines . No bands were found in any sample purified with unspecific control DNA . Hexim1 was found to be associated with the DNA of the right oriLyt in TCMK-1 cells but not in NIH 3T3 cells . Since the mutant containing only the right oriLyt showed reduced growth in TCMK-1 cells but not in NIH 3T3 cells , we hypothesized that in TCMK-1 cells , Hexim1 exerts an inhibitory effect on lytic replication originating at the right oriLyt . This inhibitory effect is normally compensated by the left oriLyt but this is not the case when the left oriLyt is absent . Consequently , downregulation of Hexim1 in TCMK-1 cells should reverse the growth deficit of the mutant containing only the right oriLyt , while forced overexpression of Hexim1 in NIH 3T3 cells might result in a growth deficit of this mutant . To prove these hypotheses , we first upregulated Hexim1 in NIH 3T3 cells by treatment with the chemical compound Hexamethylene bis-acetamide ( HMBA ) which is known to induce expression of Hexim1 [24 , 25] . Two different concentrations of HMBA were used , and upregulation of Hexim1 was confirmed by RT-PCR and Western Blot ( Fig 6A and 6B ) . Multistep growth curves were performed with HMBA-treated cells and the growth of parental virus and the mutant containing only the right oriLyt was tested in parallel with or without HMBA-treatment . The viral titers of the mutant containing only the right oriLyt were comparable to parental virus in untreated cells , but treatment with HMBA ( both at 5 or 10 mM ) resulted in viral titers of about one order of magnitude lower than in cells infected with parental virus ( Fig 7A–7C ) , indicating that Hexim1 in fact reduces virus replication originating at the right oriLyt . This inhibitory effect was specific for the right oriLyt since treatment with 10 mM HMBA did not significantly affect the growth of the mutant containing only the left oriLyt ( Fig E in S1 Text ) . In a second set of experiments , expression of Hexim1 was downregulated in TCMK-1 cells . For this purpose , stable cell lines expressing shRNAs specific for Hexim1 were generated . Downregulation was confirmed on mRNA and protein level ( Fig 6C and 6D ) . Two cell lines with the most prominent reduction of Hexim1 expression were chosen and multistep growth curves were performed . Again , lytic growth of the parental virus and the mutant containing only the right oriLyt was tested in parallel in the same cell lines . As a control , a cell line stably expressing a scrambled shRNA was used . Consistent with our previous results ( Fig 1D ) , the mutant containing only the right oriLyt showed reduced growth in the control cell line expressing a scrambled shRNA when compared to the parental virus . However , in the cell lines expressing shRNAs specific for Hexim1 , no or only a marginal reduction in lytic virus growth of the mutant containing only the right oriLyt was observed when compared to parental virus ( Fig 7D ) . We conclude from these results that Hexim1 inhibits lytic replication originating at the right oriLyt but this is normally compensated by the presence of the left oriLyt . Rbbp4 was found to be associated with the DNA of the right oriLyt in NIH 3T3 cells but not in TCMK-1 cells ( Table 1 ) . The mutant containing only the right oriLyt showed reduced growth in TCMK-1 cells but not in NIH 3T3 cells ( Table A in S1 Text ) . Thus , we hypothesized that Rbbp4 supports lytic replication originating at the right oriLyt . Consequently , forced overexpression of Rbbp4 in TCMK-1 cells should , at least partially , release the growth deficit of the mutant containing only the right oriLyt also in this cell line . To prove this hypothesis , we inserted an Rbbp4 expression cassette in the mutant containing only the right oriLyt , resulting in the recombinant virus Δleft oriLyt-Rbbp4 . Thus , infection with this mutant should lead to the expression of Rbbp4 in infected cells . This was confirmed by Western Blot ( Fig F in S1 Text ) . Multistep growth curves were performed and the growth of parental virus , Δleft oriLyt mutant ( containing only the right oriLyt ) and Δleft oriLyt-Rbbp4 virus was tested in parallel . Expression of Rbbp4 by the Δleft oriLyt-Rbbp4 virus partially reversed the growth deficit of the Δleft oriLyt mutant ( Fig 8A ) , indicating that Rbbp4 in fact supports virus replication originating at the right oriLyt . In contrast to the mutant containing only the right oriLyt , the mutant containing only the left oriLyt showed reduced growth in NIH 3T3 cells but not in TCMK-1 cells ( Table A in S1 Text ) . Thus , we hypothesized that Rbbp4 exerts an inhibitory effect on lytic replication originating at the left oriLyt . If so , forced overexpression of Rbbp4 should inhibit lytic replication of the mutant containing only the left oriLyt even further . Moreover , neither the inhibitory nor the supporting effect of Rbbp4 overexpression should become apparent in the parental wildtype virus since both effects are compensated by the simultaneous presence of both the right and the left oriLyt . To prove these hypotheses , we also inserted the Rbbp4 expression cassette both in the mutant containing only the left oriLyt and in the parental virus , resulting in the recombinant viruses Δright oriLyt-Rbbp4 and parental virus-Rbbp4 , respectively . Again , infection with these recombinant viruses resulted in expression of Rbbp4 in infected cells which was confirmed by Western Blot ( Fig F in S1 Text ) . Consistent with our hypothesis , overexpression of Rbbp4 exerted an inhibitory effect on the mutant containing only the left oriLyt , notably to a very strong extent . In fact , it was not possible to reconstitute considerable amounts of infectious virus after transfection of cells with the respective BAC DNA ( Fig 8B , left panel , and Fig 8C ) . Since there was such a strong effect , we attempted to exclude any unwanted side effects which might have occurred during the construction of the recombinant virus Δright oriLyt-Rbbp4 . To this end , we re-inserted the right oriLyt into the Δright oriLyt-Rbbp4 virus , resulting in the recombinant virus Rev Δright oriLyt-Rbbp4 . Transfection of the respective BAC DNA readily resulted in virus reconstitution and production of infectious virus ( Fig 8B , right panel , and Fig 8C ) , thus clearly demonstrating the specificity of the Rbbp4 inhibitory effect on the left oriLyt . To get a first insight whether the observed inhibitory effect of Rbbp4 on the left oriLyt occurs at the level of DNA replication , we performed a modified plasmid replication assay using the BAC plasmid of the Δright oriLyt mutant ( containing only the left oriLyt ) . To this end , we co-transfected the BAC plasmid DNA with either an Rbbp4-expression plasmid or an appropriate control plasmid ( expressing gfp ) , and measured the replication of the BAC plasmid DNA by real-time PCR eight hours after transfection . Co-transfection with the Rbbp4-expression plasmid significantly inhibited the replication of the BAC plasmid DNA , when compared to the co-transfection with the control plasmid ( Fig 8D ) . Finally , consistent with our hypothesis , the growth of the parental virus was not affected by overexpression of Rbbp4 ( Fig 8E ) . We conclude from these results that Rbbp4 may exert specific effects on each oriLyt and that wildtype virus is able to circumvent potential negative effects by the presence of more than one oriLyt .
Several herpesviruses including EBV and KSHV have two oriLyts but the need for more than one oriLyt has never been defined [26] . In clinical EBV isolates , usually both oriLyts are found but there are also laboratory strains , such as B95-8 , which have only one oriLyt and still can replicate lytically , at least in vitro [8] . The standard assay for studying oriLyt-dependent replication is the so called "transient in vitro plasmid replication assay" . In this assay , a plasmid carrying the oriLyt sequence is transfected into cells and the trans-factors needed are provided by co-transfection or co-infection . Then , the replication ability of the plasmid is determined [9] . This assay which examines the role of oriLyts as isolated DNA sequences cloned into a plasmid provides important information but says little about the role of oriLyts in the context of an infection [27] . Therefore , recombinant BACs are increasingly used to investigate the function of the oriLyts . Using an EBV BAC , for example , it was shown that the BZLF1 oriLyt binding sites ( ZRE ) are important but not essential for lytic replication [27] . The analysis of recombinant KSHV BACs revealed that KSHV can replicate lytically in vitro with only one oriLyt . Interestingly , deletion of the right oriLyt had no influence on replication , whereas deletion of the left oriLyt led to the loss of replicative capacity [28] . In previous work , using recombinant MHV-68 generated by BAC-technology , we could show that the absence of one oriLyt was well tolerated regarding in vitro replication in NIH 3T3 cells [18] whereas in vivo , a significant reduction of viral fitness was observed [20] . Viruses lacking both oriLyts could not replicate at all [18] . Here , using recombinant , BAC-based MHV-68 , we systematically addressed the question why some herpesviruses need more than one oriLyt . Our central working hypothesis was that the presence of two oriLyts provides optimal fitness to efficiently establish infection in different cell or tissue types and during different phases of the viral life cycle by enabling interaction with cell-type specific cellular proteins which might impose either activating or inhibitory effects . Indeed , loss of either of the two oriLyts was well tolerated in some cell types but not in others in vitro . Similarly , in vivo , loss of either of the two oriLyts resulted in different effects on the viral life cycle dependent on the organ and the route of inoculation . For example , both oriLyt mutants showed reduced lytic replication in vitro in the alveolar epithelial cell line LA-4 ( Table A in S1 Text ) , with a tendency of a stronger reduction of the Δright oriLyt mutant . The same phenotype was observed during lytic replication in vivo in the lung ( Fig 2 ) which mainly takes place in alveolar epithelial cells . Thus , both the in vitro and the in vivo data were consistent and in support of our hypothesis . Identification of oriLyt-bound cellular proteins by DNA-affinity purification and mass spectrometry revealed that , depending on the cell line , a different repertoire of proteins seemed to interact with the respective oriLyt or the associated replication complex . We selected two of the differentially oriLyt-interacting cellular proteins , Hexim1 and Rbbp4 , for further investigation . In a recently performed extensive mutagenesis screen in haploid human cells , Hexim1 was identified as a non-critical gene while Rbbp4 was found to be a core essential gene that is required for cell survival [29] . In the absence of the left oriLyt , Hexim1 exerted an inhibitory effect on lytic replication in TCMK-1 but not in NIH 3T3 cells . Hexim1 is an inhibitor of positive transcription elongation factor b ( P-TEFb ) which plays a key role in regulation of RNA polymerase II elongation [30] . P-TEFb consists of cyclin-dependent kinase 9 ( cdk9 ) and cyclin 1 [30] . In conjunction with 7SK non-coding RNA , Hexim1 inhibits the kinase activity of cdk9 [30] . Interestingly , it has been shown that inhibition of cdk9 by overexpression of Hexim1 resulted in decreased viral yields during herpes simplex virus 1 infection [31] . We demonstrated that upregulation of Hexim1 in NIH 3T3 cells , a cell line where it was not found to be associated with the right oriLyt , inhibited virus replication of the mutant lacking the left oriLyt . Since Hexim1 is present in extracts of NIH 3T3 cells but could not be enriched by DNA affinity purification with DNA of the right oriLyt ( Fig D in S1 Text ) , we assume that Hexim1 does not directly bind to the DNA of the right oriLyt but rather via one or more additional interaction partners which might be present in TCMK-1 but not in NIH 3T3 cells . It is currently not clear how forced overexpression of Hexim1 can exert the observed effect on lytic replication at the right oriLyt . One possible explanation might be that , by the high abundance of Hexim1 after HMBA-induced upregulation , the normally strict need for an additional interaction partner is circumvented . Alternatively , HMBA might also induce or up-regulate potential interaction partners . Another explanation might be that the effect is mediated by an additional protein form of Hexim1 which we detected after treatment of NIH 3T3 cells with HMBA ( Fig 6 ) . The identity and function of this form has not been described yet . Downregulation of Hexim1 in TCMK-1 , a cell line where it was found to be associated with the right oriLyt , enhanced replication of the mutant lacking the left oriLyt . Thus , overexpression and downregulation of Hexim1 either reduced or enhanced , depending on the cell line , the replication of the mutant lacking the left oriLyt , indicating that Hexim1 is a rate limiting cellular protein in a situation where only one oriLyt is present . However , the virus can overcome this hurdle by the presence of two oriLyts , indicating that two oriLyts are of advantage for optimal virus fitness . How Hexim1 is functioning in the context of oriLyt-dependent DNA replication is not known at the moment but we can envisage several scenarios: i ) Hexim1 has additional , so far unknown functions in DNA-replication . ii ) By its interference with transcription , it might inhibit the generation of various RNA molecules which might aid in lytic DNA replication . iii ) Since it can bind RNA , it might simply sequester RNAs important for lytic DNA replication . The latter two possibilities are particularly attractive since it has been shown that a GC-rich oriLyt transcript is an important component of the EBV oriLyt [32] . Similarly , a tight coupling of DNA replication and transcription was shown for KSHV: oriLyt-dependent DNA replication was inhibited when RNA transcription was prematurely terminated [33 , 34] . The cellular protein Rbbp4 supported lytic replication originating at the right oriLyt while it strongly inhibited lytic replication originating at the left oriLyt . We could show that the inhibitory effect of Rbbp4 on the left oriLyt also occurs at the level of DNA replication , however , this does not rule out additional effects on other steps of the viral life cycle . While we could demonstrate an interaction of Rbbp4 with the right oriLyt , we did not find a direct interaction of Rbbp4 with the left oriLyt . Thus , it is currently not clear how forced overexpression of Rbbp4 can exert the observed inhibitory effect on lytic replication at the left oriLyt . Nevertheless , it seems that Rbbp4 , like Hexim1 , is a rate limiting cellular protein in situations where only one oriLyt is present . Again , the virus can overcome potential negative effects by the presence of two oriLyts . Rbbp4 , also known as RbAp48 or NURF55 , is a component of several chromatin-related complexes , for example NuRD ( nucleosome remodeling histone deacetylase complex ) and CAF-1 ( chromatin assembly factor-1 ) [35] . The function of CAF-1 is known to be tightly associated with DNA replication [35] . Interestingly , Rbbp4 , most likely as a component of a functional NuRD complex , has been shown to be required for efficient replication of human cytomegalovirus ( HCMV ) [36] . Thus , it is easily conceivable that Rbbp4 might be involved in oriLyt-dependent DNA replication . Taken together , our data suggest that the presence of multiple oriLyts enables γHV to efficiently deal with the variety of conditions which they encounter during the viral life cycle , for example by facilitating interaction with cell-type specific proteins .
All cell lines used in this study were cultured under standard conditions and have been described before [37–39] or are commercially available ( American Type Culture Collection [ATCC] or Leibniz Institute DSMZ—German Collection of Microorganisms and Cell Cultures [DSMZ] ) . More details are provided in the Supplementary Information . For this study , we constructed eight recombinant MHV-68 ( schematically depicted in Fig A in S1 Text ) : 1 ) The mutant virus Δleft oriLyt , lacking the essential part of the left oriLyt [19] . 2 ) A revertant of the Δleft oriLyt mutant with an ectopic insertion of the left oriLyt ( ER Δleft oriLyt ) . 3 ) The mutant virus Δright oriLyt with a deletion of the essential part of the right oriLyt . This mutant has been described previously [18] . 4 ) A revertant of the Δright oriLyt mutant with an ectopic insertion of the right oriLyt ( ER Δright oriLyt ) . 5 ) Three recombinant MHV-68 expressing Rbbp4 from an intergenic expression cassette: Parental virus-Rbbp4 , Δleft oriLyt-Rbbp4 and Δright oriLyt-Rbbp4 . 6 ) A revertant of the Δright oriLyt-Rbbp4 . Details on the construction of all recombinant MHV-68 used in this study can be found in the Supplementary Information . To reconstitute recombinant MHV-68 , BHK-21 cells were transfected with 2 μg of BAC MHV-68 DNA using X-treme GENE HP DNA Transfection Reagent ( Roche , Mannheim , Germany ) . When cells showed a total cytopathic effect ( CPE ) , an aliquot of the supernatant was used to infect Ref-Cre cells carrying the Cre recombinase to remove the BAC-cassette including the GFP sequence . Removal of the BAC-cassette was done for all viruses to be used in vivo . BAC-cassette free viruses were identified by a limiting dilution assay on BHK-21 cells performed in a 96 well plate . All recombinant viruses were grown and titrated on BHK-21 cells as previously described [37] , and were characterized by restriction enzyme and Southern blot analysis and by sequencing across the mutated regions . To test in vitro growth of the virus mutants , cells of different type and origin were infected with a MOI of 0 . 01 ( or 1 for MH-S macrophages ) for one hour . After removing the inoculum , cells were incubated with fresh medium at 37°C and 5% CO2 until the supernatants together with the cells were harvested at different time points after infection . Virus titers were determined by plaque assay . Female C57BL/6 mice ( 6–8 weeks old ) were purchased from Charles River Laboratories ( Sulzfeld , Germany ) and housed in individually ventilated cages ( IVC ) during the MHV-68 infection period . To characterize the recombinant MHV-68 in vivo , mice were inoculated i . n . with 1x 103 PFU or i . p . with 1x 104 PFU . Prior to i . n . inoculation , mice were anesthetized with ketamine and xylazine . To determine virus titers , organs were harvested at the indicated time points after infection and homogenized by using the FASTPREP-24 instrument ( MP Biomedicals , Heidelberg , Germany ) . After two times freezing and thawing the homogenates , plaque assays were performed with 10-fold dilutions of the supernatants on BHK-21 cells . For determination of spleen weight , frequency of virus reactivation and genomic load , spleens were harvested at the indicated time points after infection . All animal experiments were in compliance with the German Animal Welfare Act ( German Federal Law §8 Abs . 1 TierSchG ) , and the protocol was approved by the local Animal Care and Use Committee ( District Government of Upper Bavaria; permit number 124/08 ) . To determine the frequency of cells carrying virus reactivating from latency , threefold dilutions of splenocytes or PECs were plated onto NIH 3T3 cells as described previously [40] . Frequencies of reactivating cells were calculated on the basis of the Poisson distribution by determining the cell number at which 63 . 2% of the wells scored positive for CPE . Viral load in splenocytes or PECs of infected mice was determined by quantitative real-time PCR using the ABI 7300 Real Time PCR System ( Applied Biosystems , Foster City , CA ) as described previously [20] . Proteins bound to the right oriLyt were purified using a modified method described earlier by Wang et al . [6] . A biotinylated DNA fragment spanning the minimal region of the right oriLyt plus a few extra nucleotides on both sides ( genome coordinates 100 . 018–102 . 031 ) was amplified by PCR with MHV-68 DNA as a template . The primers for the right oriLyt were ori_right . for ( 5′-AGCGAGGGAGCGGGCTGC-3′ ) and ori_right_Bio . rev ( 5′-biotin-CCTACGTCATCAAGCAGCGACG-3′ ) . An unspecific PCR-product amplified from the Kanamycin resistance cassette of the plasmid pCP15 was used as a control . The primers for this control were pCP15Kan_Bio . for ( 5′-biotin-CCAGGGTTTTCCCAGTCACGACGT-3′ ) and pCP15Kan . rev ( 5′-CACAGGAAACAGCTATGACCATGA-3′ ) . The resultant biotinylated PCR fragments were diluted in buffer 1 ( 20 mM HEPES , pH 7 . 9 , 20% glycerol , 0 . 2 mM EDTA , 1 mM dithiothreitol ( DTT ) , 0 . 05% NP-40 , 15 mM MgCl2 , 75 μg/ml salmon sperm DNA ) and mixed with nuclear extracts prepared from cells infected with recombinant MHV-68 at an MOI of 0 . 1 for 48h . In each reaction mixture , 2/3 volume of biotinylated PCR-product was mixed with 1/3 volume of the nuclear extract in buffer 2 ( 20 mM HEPES , pH 7 . 9 , 25% glycerol , 0 . 2 mM EDTA , 0 . 42 M NaCl , 1 mM DTT , 0 . 05% NP-40 , and a protease inhibitor tablet ) and incubated for 10 min at room temperature . Streptavidin MicroBeads ( Miltenyi Biotec , Bergisch Gladbach , Germany ) were added to each sample and incubated at room temperature for 5 min . The labeled samples were applied to a MACS column placed in a MACS separator and the column was washed three times in D150 buffer ( 20 mM HEPES , pH 7 . 9 , 20% glycerol , 0 . 2 mM EDTA , 150 mM KCl , 1 mM DTT , 0 . 05% NP-40 ) and three times in D300 buffer ( same as D150 buffer , except the KCl concentration was increased to 300mM ) . The bound material was pre-incubated with 25μl buffer D500 ( KCl 500 mM ) for 10 min at room temperature and then eluted with 50μl buffer D500 . DNA affinity-purified proteins were resolved on 10% Mini-PROTEAN TGX gels ( BioRad , Munich , Germany ) and stained with FireSilver staining kit ( Proteome Factory AG , Berlin , Germany ) . The protein bands were excised and subjected to mass spectrometric analyses . Protein identification using nanoHPLC-ESI-MS/MS was performed by Proteome Factory ( Proteome Factory AG , Berlin , Germany ) . Details can be found in the Supplementary Information . ChIP was performed according to standard protocols and is described in detail in the Supplementary Information . Overexpression of Hexim1 was accomplished by treatment with hexamethylene bis-acetamide ( HMBA ) which induces Hexim1 expression [24 , 25] . 5mM or 10mM HMBA ( Sigma-Aldrich , Seelze , Germany ) was added to NIH 3T3 cells for the indicated time periods . For downregulation of Hexim1 , four different TCMK-1/shRNA cell lines were established . Each cell line was stably transfected with a plasmid vector expressing shRNA specific for Hexim1 ( Genecopoeia , Rockville , MD ) . The 19mer sequences specific for Hexim1 were: shRNA1: 5’-GTTGTCCATGAAGAGCATA-3’; shRNA2: 5’-TTAAGCGGAGCTATAAGGT-3’; shRNA3: 5’-GTTTGCCTACCTTGGTAAG-3’; shRNA4: 5’-TGCAGCTATTCTCAATCTC-3’ . As a control , shRNA with a scrambled sequence was used ( Genecopoeia , Rockville , MD ) . Cells stably expressing shRNA were selected by using puromycin at a concentration of 5 μg/ml . Up- or downregulation of Hexim1 was determined by RT-PCR and Western Blot . Overexpression of Rbbp4 was accomplished by insertion of a Rbbp4 expression cassette in the respective viruses , and overexpression of Rbbp4 was determined by Western Blot of infected cells . Overexpression/downregulation of selected proteins or presence of proteins in DNA-affinity purified samples was analyzed by Western Blot . For analysis of protein expression , cells were treated as specified and lysed in 2x Lämmli buffer . DNA affinity purified samples were prepared as described and mixed with an equal amount of 2x Lämmli buffer . The samples were separated by SDS-Page gel electrophoresis and transferred to nitrocellulose membrane . The membrane was blocked in 5% skim milk ( for Rbbp4 , Topoisomerase I , and GAPDH ) or 5% skim milk plus 0 , 5% BSA ( for Hexim1 ) for 1 hour at room temperature and then incubated with primary antibody in blocking solution at 4°C overnight . Primary antibodies used in this paper were: rabbit anti-Hexim1 ( Abcam , Cambridge , UK; dilution 1:2000 ) , rabbit anti-Rbbp4 ( Novus Biologicals , Cambridge , UK; 1:10000 ) , rabbit anti-Topoisomerase I ( Abcam , Cambridge , UK; dilution 1:10000 ) , and rabbit anti-GAPDH ( Cell Signaling , Boston , MA; 1:2000 ) . The membrane was washed and then incubated with horseradish peroxidase-conjugated donkey-anti-rabbit IgG ( Amersham/GE Healthcare , Freiburg , Germany; 1:5000 ) for 1 hour at room temperature . The signal was detected by an enhanced chemiluminescence system ( Pierce/Thermo Scientific , Rockford , IL ) on X-ray film . mRNA was isolated from cell lines expressing shRNAs or HMBA-stimulated cells using the RNeasy MiniKit ( Qiagen , Hilden , Germany ) . Genomic DNA was removed with the TURBO DNA-free Kit ( Ambion/Life Technologies , Darmstadt , Germany ) , and RNA was reverse-transcribed using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) . The cDNA was analyzed for the expression of Hexim1 and the ribosomal Protein L8 by real time quantitative PCR using the Taqman SYBR green PCR master mix ( Applied Biosystems , Foster City , CA ) . For Hexim1 , a commercially available primer set was used ( Qiagen , Hilden , Germany ) . The following PCR primer set was used for L8: forward 5’-CAG TGA ATA TCG GCA ATG TTT TG-3’; reverse 5’-TTC ACT CGA GTC TTC TTG GTC TC-3’ . The fold change in expression of each target mRNA relative to L8 was calculated based on the threshold cycle ( Ct ) as 2-Δ ( ΔCt ) , where ΔCt = CtHexim-CtL8 and Δ ( ΔCt ) = ΔCttreated-ΔCtcontrol . BHK-21 cells were co-transfected with 200ng BAC plasmid DNA of the Δright oriLyt mutant and 600ng of an Rbbp4-expression plasmid ( OriGene Technologies , Rockville , MD , USA ) using X-treme GENE HP DNA Transfection Reagent ( Roche , Mannheim , Germany ) . Co-transfection of BAC plasmid DNA with a GFP-expression plasmid ( OriGene Technologies , Rockville , MD , USA ) was used as a control . Cells were harvested 2 hours ( = input DNA ) and 8 hours ( = input + replicated DNA ) after transfection . DNA was isolated and the BAC plasmid DNA copy number in the transfected cells was determined by quantitative real-time PCR using the ABI 7300 Real Time PCR System ( Applied Biosystems , Foster City , CA ) as described previously [20] . Datasets were tested for Gaussian distribution before statistical analysis by D’Agostino-Pearson omnibus K2 normality test using the GraphPad Prism software , vs5 ( GraphPad Software , Inc . , San Diego , CA , USA ) . Datasets that passed the normality test were analyzed by Student’s t-test . All other datasets were analyzed by Mann-Whitney U test . Results with a p-value < 0 . 05 were considered significant . | Herpesviruses show two stages in their life cycle: lytic replication and latency . Lytic DNA replication is initiated at a defined site on the viral genome , the so-called lytic origin of replication ( oriLyt ) . While some herpesviruses have a single oriLyt , others have multiple oriLyts . Why some herpesviruses need more than one oriLyt is not known . This study demonstrates that the presence of multiple oriLyts enables gammaherpesviruses to efficiently establish infection in different cell or tissue types and during different phases of the viral life cycle . Depending on the cell type , different cellular proteins were found to be associated with oriLyt DNA , and overexpression or downregulation of these proteins differentially affected the growth of viruses containing only a single oriLyt . Thus , multiple oriLyts ensure optimal viral fitness in different cell types and tissues . | [
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"an... | 2016 | Multiple Lytic Origins of Replication Are Required for Optimal Gammaherpesvirus Fitness In Vitro and In Vivo |
Mini-chromosome maintenance ( MCM ) 2–9 proteins are related helicases . The first six , MCM2–7 , are essential for DNA replication in all eukaryotes . In contrast , MCM8 is not always conserved in eukaryotes but is present in Arabidopsis thaliana . MCM8 is required for 95% of meiotic crossovers ( COs ) in Drosophila and is essential for meiosis completion in mouse , prompting us to study this gene in Arabidopsis meiosis . Three allelic Atmcm8 mutants showed a limited level of chromosome fragmentation at meiosis . This defect was dependent on programmed meiotic double-strand break ( DSB ) formation , revealing a role for AtMCM8 in meiotic DSB repair . In contrast , CO formation was not affected , as shown both genetically and cytologically . The Atmcm8 DSB repair defect was greatly amplified in the absence of the DMC1 recombinase or in mutants affected in DMC1 dynamics ( sds , asy1 ) . The Atmcm8 fragmentation defect was also amplified in plants heterozygous for a mutation in either recombinase , DMC1 or RAD51 . Finally , in the context of absence of homologous chromosomes ( i . e . haploid ) , mutation of AtMCM8 also provoked a low level of chromosome fragmentation . This fragmentation was amplified by the absence of DMC1 showing that both MCM8 and DMC1 can promote repair on the sister chromatid in Arabidopsis haploids . Altogether , this establishes a role for AtMCM8 in meiotic DSB repair , in parallel to DMC1 . We propose that MCM8 is involved with RAD51 in a backup pathway that repairs meiotic DSB without giving CO when the major pathway , which relies on DMC1 , fails .
Meiosis is a process that occurs in the germlines of sexually reproducing organisms . Two successive rounds of chromosome segregation ( meiosis I and II ) follow a single round of DNA replication ( S phase ) . The resulting four cells each contain half the genetic content of the pre-meiotic mother cell . The genetic complement of these gametes is a mosaic of the paternal and maternal DNA due to meiotic recombination that occurs between S phase and the first meiotic division [1] . Meiotic recombination begins with programmed DSBs that are dependent on SPO11 and multiple cofactors , including PRD1 in plants [2] , [3] . DSBs are subsequently resected to yield 3′ overhangs that invade the homologous chromosome . At this step , two recombinases co-operate to achieve efficient strand exchange with the homolog , RAD51 and DMC1 [4] . RAD51 is a recombinase involved both at mitosis and meiosis while DMC1 is specific to meiosis . Importantly , it has been recently shown in S . cerevisiae that only the strand exchange activity of DMC1 , and not of RAD51 , is required for meiotic crossover formation [5] . RAD51 appears thus to be an accessory factor of DMC1 for meiotic homologous crossover formation , but may also serve as a backup to repair breaks when DMC1 fails [5] . In Arabidopsis thaliana , RAD51 is indispensable for repair of meiotic DSBs as shown by the extensive meiotic chromosome fragmentation which occurs at meiosis in Atrad51 mutants [6] , [7] . AtDMC1 is required for CO formation but not meiotic DSB repair . Indeed , in Atdmc1 mutant , meiotic DSBs are repaired in a AtRAD51-dependent manner which does not promote chromosome pairing and does not yield COs between homologs , likely using the sister chromatid as a template [7] , [8] . In addition , consistent with a role of RAD51 in helping DMC1 in wild type , the number of DMC1 foci is severely decreased in a Atrad51 mutant [7] , [9] , while RAD51 foci are unaffected in Atdmc1 [9] . Thus two meiotic functions of RAD51 emerge , helping DMC1 to promote COs and promoting DSB repair on the sister without DMC1 . Two other Arabidopsis mutants , sds and asy1 , have phenotypes reminiscent of Atdmc1 , repairing breaks using AtRAD51 but exhibiting major homologous chromosome pairing defects and making no or few COs [10]–[12] . Both sds and asy1 show localization defects of AtDMC1 but not of AtRAD51 , suggesting that they work with DMC1 to promote interhomolog recombination [12] , [13] . Based on its amino acid sequence , SDS is a cyclin-like protein and ASY1 is a HORMA domain protein making it the likely functional homologue of S . cerevisiae Hop1 . DSB repair events form intermediates that are resolved as either crossovers ( COs ) or non-crossovers ( NCOs ) ( gene conversion ) . COs are required for accurate segregation of chromosomes during meiosis I and can arise from at least two independent pathways known as class I and class II COs . These two pathways coexist in budding yeast , mammals and Arabidopsis [1] , [14]–[17] . Class I COs are subject to a phenomenon known as interference , whereby the occurrence of a CO significantly reduces the probability of a CO occurring at an adjacent locus , in a distance dependent manner . This pathway is dependent on the ZMM proteins ( defined as ZIP1 , ZIP2/SHOC1 , ZIP3 , ZIP4 , MSH4 , MSH5 , MER3 ) and , in most eukaryotes , is responsible for the majority of COs during meiosis . Class II COs , that do not display interference , require MUS81 [1] , [14]–[17] . Here we addressed the meiotic function of MCM8 . MCM8 is a member of the eight MCM family proteins ( MCM2–9 ) , that all share a well conserved helicase domain . Together MCM2–7 , as a hexamer , form a well characterized DNA helicase , which is essential for replication in all eukaryotes [18] . In contrast , MCM8–9 is not present in all eukaryotes [19] , being notably missing in S . cerevisiae , S . pombe and C . elegans , but existing in vertebrates and plants . A study in Xenopus showed that MCM8 functions during DNA replication at the elongation stage but it is not required for replication licensing . The Xenopus MCM8 protein is the only MCM8 representative for which helicase activity has been demonstrated in vitro [20] . MCM8 is also involved in , but not essential for the assembly of the pre-replicative complex in human [21] . Very recently , MCM8 and MCM9 has been shown to be involved in homologous recombination-mediated DNA repair in mouse and chicken somatic cells [22] , [23] . MCM8 has also been shown to be involved in meiosis . In the fruit fly ( Drosophila melanogaster ) , in which MCM9 has not been identified , MCM8 ( also known as REC ) is required for 95% of meiotic COs . In contrast to COs , the frequency of NCOs increases in the absence of Dmrec [24] . Finally , a very recent study pointed out a role for MCM8 , but not MCM9 , in meiotic recombination in mouse [22] . Indeed meiocytes in the mouse mcm8 mutant accumulate DMC1 foci , display synapsis defects and go into apoptosis , consistent with a defect in meiotic DSB repair . The meiotic function of MCM8 has been analyzed only in Drosophila and mouse , with contrasting conclusions . This raises the question of the conservation of this function in eukaryotes . The aim of the present study was to further explore the meiotic function of MCM8 by deciphering its role in the model plant Arabidopsis .
Phylogenetic analyses of the MCM family [19] , [24] , showed that the Arabidopsis genome contains one clear homolog for each MCM2–9 , At3g09660 being the MCM8 homolog . We sequenced the At3g09660 CDS using RT-PCR on mRNA from Arabidopsis inflorescences . Because of some differences in splicing sites , the At3g09660 CDS slightly differed from the predicted sequence found in the genebank ( NM_111800 ) , measured 2 , 406 bp and contained 17 exons ( Figure 1 ) ( genebank BankIt1577803 MCM8 KC109786 ) . We nonetheless confirmed by reciprocal BLAST analysis and multiple protein alignment that At3g09660 encodes the Arabidopsis MCM8 homolog ( Figure S1 and [24] ) . We identified three T-DNA insertions from the public collections within the AtMCM8 gene: Atmcm8-1 , Atmcm8-2 and Atmcm8-3 ( Figure 1 ) . Plants homozygous for the insertions showed normal vegetative growth but reduced fertility as shown by Alexander staining of pollen ( Figure 2 ) . This phenotype ( and others described below ) was detected only in homozygotes of each mutant . Moreover seed counts showed that Atmcm8-1 has significantly less seeds than wild type ( 44 . 8±5 . 2 ( n = 41 ) compared to 52 . 4±5 . 8 ( n = 77 ) , Z test p<10−13 ) . Allelism tests showed that the meiotic defects observed ( see below ) were due to the insertions in Atmcm8 . To investigate if this reduction in fertility was linked to a meiotic defect , we analyzed meiotic progression by DAPI ( 4′ , 6-diamidino-2-phenylindole ) staining of meiotic chromosome spreads in all three mutant alleles . In wild type meiosis ( Figure 3A–3E ) , chromosomes condense at leptotene . Then , synapsis is initiated at zygotene until its completion in pachytene when the two homologous chromosomes are connected along their entire length by a proteinous structure called the synaptonemal complex [25] ( Figure 3A and Figure 4A ) . Desynapsis occurs at diplotene and further condensation of the chromosomes occurs . Five bivalents continue to condense and become visible at diakinesis . At metaphase I , the five bivalents align on the metaphase I plate ( Figure 3B ) . At anaphase I homologous chromosomes segregate to opposite poles ( Figure 3C ) . At telophase I the two groups of five recombinant chromosomes begin to decondense . At prometaphase II chromosomes recondense and align on the two metaphase II plates ( Figure 3D ) . At anaphase II each of the ten chromosomes segregate their two sister chromatids to opposite poles resulting in four balanced groups of five chromatids ( Figure 3E ) . In all three Atmcm8 alleles , meiosis appeared to progress normally from leptotene through to pachytene ( Figure 3F ) where chromosomes condensed , aligned and fully synapsed like wild type . The completion of synapsis in Atmcm8 was confirmed by immunolabelling meiotic chromosomes with antibodies against ASY1 and AtZYP1 ( Figure 4B ) , that are components of the axial elements and of the transverse filament of the synaptonemal complex , respectively [26] , [27] . Chromosomes desynapsed normally during diplotene and we observed five bivalents as condensation progressed during diakinesis , revealing the presence of chiasmata ( the cytological manifestation of CO ) . At metaphase I , five bivalents were systematically observed in all mutant alleles , showing that at least one CO is formed per pair of homologous chromosomes ( Figure 3G ) . Anaphase I proceeded , however chromosome fragmentation was observed in all three Atmcm8 alleles ( Figure 3H–3K ) , with 1 to 10 chromosome fragments detected in 60 to 80% of the cells ( Figure 5 ) . Chromosomes aligned on the metaphase II plate , with fragments dispersed throughout the cell ( Figure 3L ) . Anaphase II proceeded but additional chromosome fragments appeared ( Figure 3M–3O ) . This fragmentation persists at telophase II . We also observed fragmentation in female meiosis showing that Atmcm8 mutation also affects female meiosis ( data not shown ) . In Atspo11-2 and Atprd1 , no meiotic DSBs are formed and therefore recombination does not occur [3] , [28] . Thus at metaphase I , ten univalents are observed and segregate randomly ( Figure 6A–6B and 6E–6F ) . To test whether the chromosome fragmentation seen in Atmcm8 mutants are dependent on DSB formation or not , we introduced the Atspo11-2 and Atprd1 mutations independently into Atmcm8 . At meiosis , we observed ten univalents at metaphase I in the Atmcm8/Atspo11-2 or Atmcm8/Atprd1 and , importantly , the chromosome fragmentation was abolished ( Figure 6C–6D and 6G–6H , Figure 5 ) . Therefore , the fragmentation defect of Atmcm8 is dependent on AtSPO11-2 and AtPRD1 . Thus , AtMCM8 is required for efficient repair of the DSBs that initiate meiotic recombination . We then tested if the Atmcm8 fragmentation phenotype is dependent on the presence of any of the known pathways of CO formation , using epistasis tests . We used Atmsh4 and Atzip4 that are both required for class I CO formation and Atmus81 that is required for class II CO formation . In the Atmcm8/Atmsh4 , Atmcm8/Atzip4 , Atmcm8/Atmus81 double mutants and the Atmcm8/Atmsh4/Atmus81 triple mutant , we still observed a chromosome fragmentation defect as in the Atmcm8 single mutant ( Figure 5 and Figure 7 , data not shown for Atmcm8/Atzip4 ) . Thus the Atmcm8 fragmentation phenotype is independent of MSH4 , ZIP4 and MUS81 . In Atmcm8 and Atmcm8/Atmus81 we invariably observed five bivalents at metaphase I , suggesting that the formation of class I COs , which account for most of the CO in wild type , is not grossly affected by the Atmcm8 mutation . This was further supported by counts of AtMLH1 foci , a marker of class I COs at late prophase of meiosis I [29] , [30] ( Figure S2 ) , that revealed no significant differences between wild type ( 10 . 1±1 . 4 per cell; n = 81 ) and the Atmcm8 mutant ( 10 . 3±1 . 9; n = 86 ( Z p = 0 . 55 ) ) . In Atmcm8/Atmsh4 ( Figure 6 ) , the residual number of bivalents at metaphase I was unchanged compared to the single Atmsh4 mutant ( 1 . 5±1; n = 91 vs 1 . 3±1 . 1; n = 91 ( Z p = 0 . 94 ) ) , strongly suggesting that class II CO formation is not affected neither by Atmcm8 mutation . We then measured recombination frequency and crossover interference genetically in Atmcm8 . This was achieved using tetrad analysis ( Fluorescent-Tagged Lines , FTL ) which is a visual pollen assay allowing the measurement of multiple COs simultaneously with access to all four chromatids from the same meiosis [31] . Two different sets of adjacent intervals on chromosome 5 have been analyzed , ( I5aI5b and I5cI5d ) , representing four intervals in total . We did not detect any difference in recombination frequency between the Atmcm8 and wild type for any of these intervals ( Table 1 , Genetic Distance ) , consistent with the cytological data . Also , interference , that affects the distribution of crossovers , was unchanged compared to wild type for both sets of adjacent intervals ( Table 1 , Interference Ratio ) . Taken together these data suggest that AtMCM8 is not involved in CO formation . This contrasts from the observation that the absence of MCM8 reduces COs frequency by 95% in Drosophila [24] . Mei9/Rad1 is another gene required for the formation of more than 90% of the COs in Drosophila [32] . Given the major difference in MCM8 function between Arabidopsis and Drosophila , we tested the role of AtRAD1 [33]–[35] in crossover formation in Arabidopsis . Cytological analysis showed that the single Atrad1 mutant has no obvious defect in CO formation . We then analyzed if AtRAD1 has a minor effect . To achieve this , we constructed a shoc1/Atrad1 double mutant and a Atmus81/shoc1/Atrad1 triple mutant to be able to detect a weak reduction in CO formation , in a sensitive context where there are no class I and class II COs . However , this triple mutant was not different from Atmus81/shoc1 ( 0 . 99±0 . 84 ( n = 74 ) versus 1 . 15±1 . 28 ( n = 75 ) , Z p = 0 . 36 ) and neither was shoc1/Atrad1 different from shoc1 ( 1 . 47±1 . 07 ( n = 51 ) versus 1 . 56±0 . 86 ( n = 32 ) , Z p = 0 . 67 ) . These genes , MCM8 and MEI9/RAD1 , are essential for CO formation in Drosophila but not in Arabidopsis showing divergent functions . However , contrary to RAD1 , MCM8 has conserved a meiotic function in Arabidopsis . DMC1 is involved at the strand invasion stage of meiotic recombination and Atdmc1 mutants fail to synapse and to make COs ( Figure 8A–8B , 8G–8H ) . However , DSBs are repaired in Atdmc1 , in an AtRAD51-dependent manner , without CO formation , suggesting that the DSBs are repaired on sister chromatids in these mutants [8] , [12] . In the Atmcm8/Atdmc1 double mutant , from metaphase I to the end of the meiosis we observed extensive chromosome fragmentation in all cells , which was much more intense than in the single Atmcm8 mutant ( compare Figure 8C–8D to Figure 3I–3K and see quantification in Figure 5 ) . Consistently , the Atmcm8/Atdmc1 double mutant was completely sterile whereas Atmcm8 has moderate fertility reduction and Atdmc1 produce some residual seeds [8] , [12] ( Table 2 ) . Mutating SPO11-2 in this Atmcm8/Atdmc1 double mutant abolished the chromosome fragmentation ( Figure 8E–8F , Figure 5 ) , demonstrating that MCM8 and DMC1 act in parallel pathways of meiotic DSB repair . Furthermore in the Atmcm8 mutant context , we observed a more drastic meiotic chromosome fragmentation in plants heterozygous for DMC1 ( Atmcm8−/−AtDMC1+/− ) than wild type for DMC1 ( Atmcm8−/−AtDMC1+/+ ) ( compare Figure 8I–8J to Figure 3I–3K , quantification on Figure 5 ) , accompanied by a strong reduction of fertility ( Table 2 ) . However , the fragmentation observed in Atmcm8−/−AtDMC1+/− was less dramatic than in the double mutant ( Atmcm8−/−Atdmc1−/− ) ( Figure 5 ) , which is also supported by the fertility levels ( Table 2 ) . This is despite the AtDMC1 mutation being recessive ( in an AtMCM8+/+ or AtMCM8+/− context ) . Thus , in the absence of Atmcm8 , the mutation of one of the two copies of DMC1 was enough to enhance fragmentation , which is even more drastic when both DMC1 alleles are disrupted . Therefore we tested the relationship of AtMCM8 with ASY1 and SDS , two proteins that are required for normal DMC1 localization [3] , [13] . In the sds and asy1 single mutants , COs are greatly reduced ( Figure 9E–9F ) [10] , [11] . In the Atmcm8/asy1 and Atmcm8/sds double mutant , we observed chromosome fragmentation from anaphase I onwards , which was much greater than that seen in the Atmcm8−/− single mutant ( compare Figure 9G–9H with Figure 3I–3K , quantification on Figure 5 ) . Thus , mutation of SDS or ASY1 amplified the fragmentation phenotype of Atmcm8 . Finally , both the single Atrad51 mutant and the double Atmcm8/Atrad51 mutant show intense chromosome fragmentation ( Figure 10 ) . Interestingly , while AtRAD51+/− does not show chromosome fragmentation , Atmcm8−/−/AtRAD51+/− showed more chromosome fragmentation that Atmcm8 ( Figure 10 , Figure 5 ) . Thus , in the absence of Atmcm8 , the mutation of one of the two copies of RAD51 was enough to enhance fragmentation . Given the relationship between DMC1 functional gene copy number and the degree of Atmcm8-dependent fragmentation , we looked at DMC1 behavior in Atmcm8 . No significant difference in DMC1 foci shape or number was observed in Atmcm8−/− compared to wild type ( Table 2 ) . Similarly , we did not detect any differences in number or shape of DMC1 foci in Atmcm8−/−AtDMC1+/− or Atmcm8−/−AtRAD51+/− compared to either wild type or Atmcm8−/− ( Figure S3 , Table 2 ) . In the Atmcm8 Atrad51 double mutant , we observed a marked decrease of DMC1 foci number , which was however similar to what was previously observed in a single Atrad51 mutant [7] ( Table 2 ) . It is intriguing that Atmcm8−/−AtDMC1+/− and Atmcm8−/−AtRAD51+/− exhibit a more drastic meiotic defect than Atmcm8−/−AtDMC1+/+ , while DMC1 foci number and shape appear similar . However , it is possible that immunolocalization fails to detect subtle differences in DMC1 protein quantity or dynamics . Next we explored the functional relationship between MCM8 and DMC1 , in haploid plants , where homologous chromosomes are not present . Thus , the only template available for meiotic DSB repair is the sister chromatid . Meiotic chromosome spreads , in a wild-type haploid , showed that the five chromosomes were intact and segregated randomly at anaphase I [36] ( Figure 11A–11B ) , suggesting that DSBs are efficiently repaired . The haploid Atmcm8 mutant had a limited fragmentation defect ( Figure 11C–11D ) , similar to the defect in the diploid Atmcm8 mutant ( Figure 5 for quantification ) . The Atdmc1 haploid had no fragmentation ( Figure 11C–11F ) . In clear contrast , in the double Atmcm8/Atdmc1 haploid , we observed extensive meiotic chromosome fragmentation ( Figure 11G–11H , see Figure 5 for quantification ) . This shows that in a haploid context , DSB repair is efficient in wild type and Atdmc1 , only slightly affected in Atmcm8 , but ineffective in the Atmcm8/Atdmc1 double mutant . This suggests that in the absence of a homologous template , AtMCM8 and AtDMC1 catalyze DSB repair on the sister chromatid in a redundant manner .
Arabidopsis MCM8 is required for effective meiotic DSB repair as all Atmcm8 mutant alleles had a clear , albeit limited , chromosome fragmentation defect at meiosis . The fragmentation is dependent on meiotic DSB formation as it disappears when AtSPO11-2 or AtPRD1 is absent . However , in contrast to Drosophila rec ( mcm8 ) mutants , genetic and cytological data strongly support that CO formation is not affected by AtMCM8 mutation: ( 1 ) In the absence of AtMSH4 or AtZIP4 ( class I COs ) or AtMUS81 ( class II COs ) fragmentation still occurred and the number of bivalents was unchanged . ( 2 ) MLH1 foci numbers , a marker of class I COs , were unchanged in Atmcm8 . ( 3 ) The genetic analysis using FTLs revealed no difference in terms of genetic distance and the strength of interference . These data showed that AtMCM8 acts in a pathway which repairs a subset of meiotic DSB and does not lead to CO formation . A striking finding was that AtMCM8 becomes crucial when the DMC1 pathway was affected . Indeed , we observed a drastic amplification of the Atmcm8 mutant chromosome fragmentation defect when one of the two allelic copies of DMC1 was mutated , which was even more drastic when both DMC1 copies were mutated . This extensive fragmentation defect reflects a failure of DSB repair , as it is abolished by SPO11-2 mutation . Further , this extensive fragmentation was consistently confirmed in the absence of AtMCM8 and SDS , or AtMCM8 and ASY1 . SDS and ASY1 are essential for AtDMC1 loading/stability [12] , [37] . Extensive fragmentation was also observed when one copy of RAD51 was mutated in the Atmcm8 mutant . A function of RAD51 as a cofactor of DMC1 has been recently identified in yeast [5] , and consistently DMC1 foci number is drastically reduced in the Arabidopsis rad51 mutant [7] , [9] . We thus propose that two pathways of DSB repair coexist , one dependent on AtMCM8 and the other one on AtDMC1 . In the absence of AtDMC1 , efficient DSB repair occurs without CO formation . This repair depends on AtRAD51 [7] , [8] , [12] and on AtMCM8 ( this study ) . Such RAD51-mediated , DMC1-independent , repair also exists in S . cerevisiae but is normally inhibited by RAD51 regulators [38]–[42] . Consequently , we suggest that , in the Atdmc1 context , AtMCM8 and AtRAD51 can co-operate to repair DSBs using the sister as a template . In addition to this function , AtRAD51 is required for the AtDMC1-dependent pathway ( possibly as an accessory factor for the DMC1 strand-exchange activity as shown in yeast [5] ) as repair is completely defective in the single Atrad51 mutant [6] , like in the double Atmcm8/Atdmc1 mutant . The fact that the fragmentation defect is limited in the single Atmcm8 mutant , suggests that the AtMCM8/AtRAD51 pathway would be essential for a limited number of events in wild type , when DMC1 fails . The repair events promoted by AtMCM8 are likely not intended to become a CO , as CO formation was not affected in Atmcm8 , leaving sister chromatid repair or NCOs as the only other known possibilities . The absence of synapsis in Atdmc1 [7] , [8] , in which the AtMCM8/AtRAD51 pathway must be active , favors the hypothesis of sister chromatid repair . In contrast , the DMC1 pathway promotes CO formation . However , DMC1 foci in wild type , outnumber COs by approximately 25 to 1 [7] , [43] . This suggests that repair of many DSBs catalyzed by DMC1 do not become CO , but NCO ( that involve the homologous chromosome ) or sister chromatid exchange ( SCE ) . In Arabidopsis , the genome-wide frequency of NCOs and SCEs is currently unknown . We favor the hypothesis that DMC1 promotes NCOs , as DMC1 promotes synapsis . However , it should be noted that DMC1 is also able to promote SCE , notably in the haploid mcm8 context . Indeed , only the simultaneous mutation of AtDMC1 and AtMCM8 in haploids led to extensive chromosome fragmentation . The capacity of DMC1 to promote inter-sister repair was previously shown in other mutant background in both Arabidopsis [9] and yeast [44] . In summary we suggest that two pathways of DSB repair exist in wild type meiosis: The first pathway relies on the strand exchange activity of DMC1 , and is also promoted by ASY1 , SDS and RAD51 as a co-factor of DMC1 [5] . This pathway generates the COs , but also NCOs and SCEs in a ratio that remains to be determined . The second pathway of the model , which may be viewed as a backup pathway in case of failure of DMC1 , relies on the strand exchange activity of RAD51 and the helicase activity of MCM8 , and uses the sister chromatid as a template . The function of MCM8 appears to differ markedly in Arabidopsis and in Drosophila . Interestingly , DMC1 and MCM8 appear to be partially redundant in Arabidopsis while the Drosophila genome seems devoid of a DMC1 homolog [45] . Thus CO formation in Drosophila appears to rely on a RAD51/MCM8 pathway , which has only a minor role in wild type meiotic DSB repair in Arabidopsis . The CO pathways appear to differ considerably in the two species , mainly using ZMMs in Arabidopsis but not RAD1 , and the reverse in Drosophila , i . e . RAD1 but not ZMMs ( that are absent from the Drosophila genome ) . Drosophila appears to be unique , as in distant species like S . cerevisiae , mammals and C . elegans CO formation depends mainly on ZMM . Adding to the complexity , MCM8 exists in mammals but not in S . cerevisiae and C . elegans [19] , [24] . In mouse , MCM8 mutation leads to a meiotic arrest , with defects in homologous synapsis and over-accumulation of DMC1 foci before apoptosis , suggestive of defects in DSB repair [22] . We would like to suggest that these defects may compatible with MCM8 being required for a backup pathway in the case of failure of DMC1 to repair breaks , like in Arabidopsis . The lack of the backup pathway may lead to the accumulation of DMC1 foci , and a failure to repair a subset of breaks , triggering apoptosis ( it is noteworthy that DSB repair defects do not trigger meiotic arrest or apoptosis in Arabidopsis ) . This illustrates the variety of mechanisms that arose in the course of evolution to fulfill the conserved outcome of meiotic DSB repair and CO formation . In conclusion , our data reveals the meiotic function of MCM8 in Arabidopsis . Cytological and genetic analyses showed that AtMCM8 is involved in DSB repair but it is not a determinant for CO formation . This study identified a new pathway of meiotic DSB repair independent of AtDMC1 .
A . thaliana accession Columbia ( Col-0 ) was the wild type reference . Atmcm8-1 ( Salk_032764 , N532764 ) , Atmcm8-2 ( Salk_104007 , N604007 ) Atmcm8-3 ( Salk_099327 , N599327 ) were obtained from the collection of T-DNA mutants at the Salk Institute Genomic Analysis Laboratory ( SIGnAL , http://signal . salk . edu/cgi-bin/tdnaexpress ) [46] via NASC ( http://nasc . nott . ac . uk/ ) . Other mutants used in this study were Atspo11-2 ( Gabi_749C12 , N359272 ) [47] , Atprd1 ( Salk_024703 , N524703 ) [3] , Atdmc1-3 ( Sail_170_F08 , N871769 ) [48] , Atrad51 ( Atrad51-1 ) [6] , asy1-4 ( Salk_046272 , N546272 ) , sds-2 ( Sail_129_F09 , N806294 ) [12] , Atzip4-2 ( Salk_068052 , N568052 ) [43] , Atmsh4 ( Salk_136296 , N636296 ) [49] , mus81-2 ( Salk_107515 , N607515 ) , mus81-3 ( Salk_002761 , N502761 ) [50] , [51] , and shoc1-1 ( Salk_057589 , N557589 ) . rad1-1 ( uvh1-1 ) has a EMS ( ethyl methanesulfonate ) mutation [33] , [34] and was provided by C . White . Plants were cultivated in greenhouse or growth chamber with a 16 h/day and 8 h/night photoperiod , at 20°C and 70% humidity . Allelism tests were performed by crossing Atmcm8-1+/− with Atmcm8-2+/− and selecting F1 plants hemizygous for both alleles and likewise for Atmcm8-2+/− with Atmcm8-3+/− . Double mutants were obtained by crossing heterozygous plants for each mutation and selfing the double heterozygous F1 plants . Atmcm8/Atmhs4/Atmus81 triple mutant was identified by crossing Atmcm8/Atmsh4 double heterozygous with Atmus81 single mutant . As Atmsh4 and Atmus81 are linked , a plant heterozygous for Atmcm8/Atmsh4 was self-fertilized and homozygous for Atmus81 to identify the triple mutant in the offspring . Haploid Atmcm8 and Atmcm8/Atdmc1 were obtained by crossing a heterozygous plant for Atmcm8 or Atmcm8/Atdmc1 mutations as male and the GEM line as female [36] , [52] . In F1 , haploid plants of the desired genotype were selected . Plants of interest were selected by PCR genotyping using diagnostic primer sets . The three AtMCM8 insertions were genotyped by PCR using following primer combinations to amplify genomic DNA flanking the T-DNA insertions . Atmcm8-1: left borders ( LB ) with LBsalk2 ( 5′-GCTTTCTTCCCTTCCTTTCTC-3′ ) /N532764L ( 5′-AGCGCCATTAGCAAAATGTC-3′ ) or with LBsalk2/N532764U ( 5′-GCAGCTTCATTCTGCAAGTG-3′ ) . Wild type allele with N532764U/N532764L . Atmcm8-2 LB with LBsalk2/N604007L ( 5′- TCACTACAGCAACGGTGAGC -3′ ) , right border ( RB ) with RBsalk1 ( 5′-TCA GAG CAG CCG ATT GTC-3′ ) /N604007U ( 5′-GCTGATGGAAGACCTTGTGG-3′ ) . Wild type allele with N604007U/N604007L . Atmcm8-3 LB with LBsalk2/N599327L ( 5′-TGGTGTGGAATCAGCAGATG-3′ ) or with Lbsalk2/N599327U ( 5′-TGTGTCTCTGTTGCAAAGGC-3′ ) . Wild type allele with N599327U/N599327L . T-DNA right and left borders were analyzed by sequencing PCR products . AtSPO11-2 wild type allele was amplified using primers 749C12U ( 5′-GAGCGAGAATTTTTGGTTGG-3′ ) and 749C12L ( 5′- CCACAAGGTCAATTCTTCAAC-3′ ) and mutant allele using N524703L and LBgabi1 ( 5′-CCCATTTGGACGTGAATGTAGACAC-3′ ) . AtPRD1 wild type allele was amplified using primers N524703U ( 5′-AAGTCTGCCCATGGTCACGATTCTCTCTG-3′ ) and N524703L ( 5′-GCCTGCTCAAAGGGTCCAGC-3′ ) and mutant allele using N524703L and LbSalk2 . AtDMC1 wild type allele was amplified using primers N871769U ( 5′- TTTTTAATTGTTTACAGAGGAAATCAG-3′ ) and N871769L ( 5′-TCCACTCGGAATAAAGCAATG-3′ ) and mutant allele using N871769L and Lb3sail ( 5′-TAGCATCTGAATTTCATAACCAATCTCGATACAC-3′ ) . AtRAD51 wild type allele was amplified using primers RAD51-1U ( 5′-ATGCCAAGGTTGACAAGATTG-3′ ) and RAD51-1L ( 5′- CTCCCCTTCCAGAGAAATCTG -3′ ) and mutant allele using RAD51-1U and LBgabi1 ( 5′-CCCATTTGGACGTGAATGTAGACAC-3′ ) . We amplified SDS wild type allele using primers N806294U ( 5′-CTGCTCCCTGATTACAAGCAG-3′ ) and N806294L ( 5′-CTTAACGCATTCAGGCAACTC-3′ ) and mutant allele using N806294U and Lb3sail . AtMSH4 wild type allele was amplified using primers N636296U ( 5′-CTTCTTGCAGGTTGTGTTTG-3′ ) and N636296L ( 5′-GCCAGCTGTTTTTGTTGTC-3′ ) and mutant allele using N636296L and LbSalk2 . AtMUS81A wild type allele was amplified for Salk_107515 using primers N607515U ( 5′-CATGCTGACAGTTGAAGGTC-3′ ) and N607515L ( 5′-CCTCAAACGTTTCTCCAAAT-3′ ) and mutant allele using N607515L and LbSalk2 . AtMUS81A wild type allele was amplified for Salk_002176 using primers N502176U ( 5′-CACATACGTTTTTGGTTCCC-3′ ) and N502176L ( 5′-AGTGTCCAAGTCCTGCTTTC-3′ ) and mutant allele using N607515L and LbSalk2 . AtZIP4 wild type allele was amplified using primers N568052U ( 5′-TCCTTCCCACACCTTGACCC-3′ ) and N568052L ( 5′-GACTGCTGGAGCAGAAACT-3′ ) and mutant allele using N568052L and LbSalk2 . ASY1 wild type allele was amplified using primers N546272U ( 5′-TCTATGTTTGTTACGCGTTAATCAG-3′ ) and N546272L ( 5′-AGGTGGCTCGTAATCTGGTGGCTGC-3′ ) and mutant allele using N546272L and LbSalk2 . SHOC1 wild type allele was amplified using primers N557589U ( 5′-TTACCGGAGTTTGAAAACCG-3′ ) and N557589L ( 5′-GGCAAAGACTTGAAGGCATC-3′ ) and mutant allele using N557589L and LbSalk2 . AtRAD1 was amplified using primers o629 ( 5′-CTGGTGAAGAACATTTGGTAG-3′ ) and o630 ( 5′-CTCTTATGGCTGCTGCGTCTTC-3′ ) . Polymorphism between wild type and mutant alleles was revealed with Dde1 digestion . FTL lines were obtained from G . P . Copenhaver . For this study , we used two couple of adjacent intervals: I5aI5b and I5cI5d [31] . The procedure to create plants of interest and to collect data was described in [31] , [53] . Statical analysis was performed as described in [31] . Alexander staining for pollen viability was performed as described [54] . The protocol described by [55] was used to observe the female meiosis and the protocol described by [29] for male meiotic spreads . Immunolocalization of AtMLH1 was made as described by [29] . Immunolocalization of AtZYP1 and AtDMC1 was performed according to [56] with the modifications described in [43] . The anti-ASY1 polyclonal [56] and anti-ZYP1 polyclonal [49] antibodies were used at a dilution of 1∶250 . The anti-MLH1 antibody [29] was used at a dilution of 1∶200 . The anti-DMC1 antibody was described in [43] and the purified serum was used at 1∶20 . For male meiotic spreads , observations were made with a Leica DM RXA2 epifluorescence microscope using an oil PL APO 100X/1 . 40 objective ( Leica ) . Photographs were taken using a CoolSNAP HQ ( Roper Scientific ) camera driven by Open-LAB 4 . 0 . 4 software ( Improvision ) . For immunocytology and FTLs analyzes , observations were made using a Zeiss Axio Imager2 microscope . We analyzed FTLs using the automatic slide-scanner function of the ZEISS AxioObserver DIC FISH Apotome and its workbench . Photographs were taken using an AxioCam MRm ( Zeiss ) camera driven by Open-LAB 4 . 0 . 4 software AxioVision 4 . 8 . All pictures were processed with AdobePhotoshop 7 . 0 ( Adobe Systems Inc . ) . | Species that reproduce sexually have two copies of each chromosome , inherited from their father and mother . During a special cell division called meiosis , these two chromosomes are mixed by homologous recombination to give genetically unique chromosomes that will be transmitted to the next generation . This recombination process is initiated by DNA breaks that must be repaired efficiently to maintain fertility . Using the model plant Arabidopsis thaliana we revealed here that the gene AtMCM8 is required to repair a subset of these DNA breaks . However MCM8 appears to not be required for recombination with the homologous chromosome . Instead MCM8 appears to be involved in a safety system that operates to repair DNA breaks that have not been used for homologous recombination . Interestingly the equivalent gene also has an essential meiotic function in the fly and the mouse . However the three species require MCM8 for different aspects of meiosis . | [
"Abstract",
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] | 2013 | MCM8 Is Required for a Pathway of Meiotic Double-Strand Break Repair Independent of DMC1 in Arabidopsis thaliana |
Glycosphingolipids are key elements of cellular membranes , thereby , controlling a variety of cellular functions . Accumulation of the simple glycosphingolipid glucosylceramide results in life-threatening lipid storage-diseases or in male infertility . How glucosylceramide regulates cellular processes is ill defined . Here , we reveal that glucosylceramide accumulation in GBA2 knockout-mice alters cytoskeletal dynamics due to a more ordered lipid organization in the plasma membrane . In dermal fibroblasts , accumulation of glucosylceramide augments actin polymerization and promotes microtubules persistence , resulting in a higher number of filopodia and lamellipodia and longer microtubules . Similar cytoskeletal defects were observed in male germ and Sertoli cells from GBA2 knockout-mice . In particular , the organization of F-actin structures in the ectoplasmic specialization and microtubules in the sperm manchette is affected . Thus , glucosylceramide regulates cytoskeletal dynamics , providing mechanistic insights into how glucosylceramide controls signaling pathways not only during sperm development , but also in other cell types .
Spermatogenesis occurs in the seminiferous tubules of the testis . Defects in sperm development often result in male infertility . The beta-glucosidase GBA2 plays an important role in sperm development [1] . GBA2 knockout-mice are subfertile , because sperm display severe morphological defects: heads are round rather than sickle-shaped , mitochondria are misaligned along the sperm flagellum , and the acrosome , needed to penetrate the egg coat , is lacking [1] . This phenotype is called globozoospermia [2] . GBA2 degrades the glycosphingolipid glucosylceramide ( GlcCer ) to glucose and ceramide . Accumulation of GlcCer in GBA2 knockout-mice has been proposed to underlie the defects in spermatogenesis leading to globozoospermia [1] . However , the underlying mechanism is not known . Several knockout-mouse models display globozoospermia . In some models , vesicle fusion leading to acrosome formation is impaired [3–10] . The acrosome is a large , Golgi-derived vesicle that is tethered to the nuclear envelope [11] . The acrosome is formed in round and elongated spermatids [12 , 13] through budding of vesicles from the trans-Golgi network ( TGN ) . These vesicles are transported to the nuclear envelope , where they fuse to form a single acrosomal vesicle [12 , 13] . However , other globozoospermia-related proteins are not involved in vesicular transport , but rather in acrosomal anchoring to the nuclear envelope or condensation of the sperm nucleus [14–16] . During spermiogenesis , spermatids undergo dramatic morphological changes , which occur while the cells are transported across the seminiferous epithelium into the lumen . The transport depends on the close interaction between developing sperm and Sertoli cells [17 , 18] . Actin bundles emanating from Sertoli cells into the ectoplasmic specialization ( ES ) , a testis-specific adherens junction , undergo extensive re-organization while they break-down and reassemble to transport the developing sperm to the lumen [19 , 20] . A podosome-like structure , the so-called tubulobulbar complex , forms between spermatids and Sertoli cells; it internalizes intact junctions during sperm development and positions the developing sperm cell during the transit through the seminiferous tubules [21 , 22] . A bundle of filamentous actin ( F-actin ) , which emanates from Sertoli cells , embraces each tubulobulbar complex; this interaction connects the endoplasmic reticulum ( ER ) of Sertoli cells to the tubulobulbar complex of spermatids [17] . Furthermore , the spermatid manchette , a microtubule-based structure that is transiently formed also contributes to shaping of the sperm head [23] . The manchette consists of a perinuclear microtubule ring . During spermatid elongation , this ring constricts to decrease the diameter of the elongating spermatid head [23] . Here , we demonstrate that cytoskeletal dynamics controlling sperm-head shaping and acrosome formation are affected by accumulation of GlcCer in GBA2 knockout-mice , which results in globozoospermia and , thereby , male infertility .
To investigate the role of GBA2 during spermatogenesis , we analyzed GBA2 expression in the testis ( Fig . 1A ) . Although the main defect in GBA2 knockout-mice occurs in sperm , GBA2 was only weakly if at all expressed in sperm ( Fig . 1B ) . In fact , also mass spectrometry failed to detect peptides derived from GBA2 in mouse sperm . However , three lines of evidence demonstrate that GBA2 is expressed in Sertoli cells . First , immunofluorescent labeling of testis sections demonstrated co-localization of GBA2 and beta-tubulin III , a Sertoli cell marker ( Fig . 1C ) . Second , Western blot-analysis of Sertoli cells , isolated from P7 old males , when the seminiferous epithelia consist exclusively of Sertoli cells and type A spermatogonia [24] , showed that GBA2 is expressed in Sertoli cells ( Fig . 1D ) . Finally , mass spectrometry analysis identified four and two unique GBA2 peptides in testis and Sertoli cells , respectively ( S1 Fig ) . In summary , GBA2 is predominantly expressed in Sertoli cells . In the mammalian testis , sperm are continuously produced throughout the reproductive period . Although spermatogenesis is precisely controlled , only the first spermatogenic wave is synchronized and , therefore , allows assigning the expression of a protein to the respective developmental stage . Thus , we analyzed the time course of GBA2 expression and activity during the first spermatogenic wave ( P7: pre-puberty , P21: early puberty ) and in adult testis ( > 22 weeks ) . GBA2 followed the expression pattern of beta-tubulin III during the first spermatogenic wave ( Fig . 1E ) . When normalizing the expression of GBA2 to beta-tubulin III , GBA2 expression increased from P7 to P21 and decreased again from P21 to adult ( Fig . 1F , G ) . In parallel , GBA2 activity increased from P7 to P21 and decreased from P21 to adult ( Fig . 1H ) , suggesting that GBA2 plays an important role during the first wave of spermatogenesis . The fertility defect of GBA2 knockout-mice has been attributed to the accumulation of GlcCer in testis [1] . Germ and Sertoli cells contain a plethora of different glycosphingolipids ( GSLs ) [25] . Whereas germ cells contain glycosphingolipids ( fucosylated GSLs , GSLs ) , ceramides ( Cer ) , and sphingomyelings ( Spm ) with polyenoic very long-chain fatty acids , Sertoli cells predominantly harbor sphingolipids with saturated long-chain fatty acids [25] . We analyzed the amount of neutral sphingolipids in testis , Sertoli cells ( P7 ) , and sperm from wild-type and GBA2 knockout-mice by mass spectrometry and distinguished between sphingoid bases ( long-chain bases , LCB; C18 ) , ceramides ( different chain length , saturated and unsaturated ) , hexosylceramides ( HexCer; different chain length , saturated and unsaturated ) , and sphingomyelin ( different chain length , saturated and unsaturated ) . In testis from GBA2 knockout-mice , total HexCer levels were dramatically increased , whereas total levels of LCB , Cer , and Spm were not affected ( Fig . 2A ) . Using TLC ( thin-layer chromatography ) , it has already been shown that in brain , only glucosylceramide , but not galactosylceramide accumulates in GBA2 knockout-mice [1] . Thus , the increase in HexCer levels can be attributed to an increase in GlcCer levels . Sertoli cells at P7 did not show a change in any of the sphingolipids ( Fig . 2B ) , whereas in sperm , similar to testis , the GlcCer levels were increased ( Fig . 2C ) . In both testis and sperm , C16 GlcCer levels were most dramatically increased , but also C18 , C20 , C22 , and C24 GlcCer levels were elevated ( Fig . 2D , E ) . In sperm , levels of long-chain GlcCer were similar between wild-type and GBA2 knockout-mice , whereas in testis , the C28 GlcCer levels were increased , indicating an accumulation in Sertoli cells ( Fig . 2F ) . Thus , GlcCer accumulates in testis and sperm from adult GBA2 knockout-mice ( ˃ 22 weeks ) , but not in Sertoli cells at P7 . Sperm from GBA2 knockout-mice display globozoospermia [1]: the heads are round rather than sickle-shaped and the acrosome is malformed ( Fig . 3A ) . However , the underlying molecular mechanisms are not known . The cytoskeleton plays a major role during spermatogenesis , in particular in regulating the interaction between Sertoli and germ cells at the ES and in shaping the sperm head [20 , 23 , 26 , 27] . Using fluorescence microscopy , we investigated the distribution of the actin and microtubule cytoskeleton in the testis of adult wild-type and GBA2 knockout-mice ( Fig . 3B ) : in wild-type testis , F-actin hoops in the ES displayed a sickle-shape alignment around sperm heads , whereas in GBA2 knockout-mice , F-actin accumulated in the ES and was misaligned around the round sperm heads ( Fig . 3B ) . However , the microtubule network appeared rather normal ( Fig . 3B ) . To analyze the cytoskeletal structures on a single cell level , we isolated Sertoli and germ cells from adult wild-type and GBA2 knockout-testis . In isolated cells , both the microtubule and F-actin network appeared more extensive in Sertoli cells from GBA2 knockout-mice with increased microtubule bundling and protrusive F-actin structures ( Fig . 3C ) . Together , our results show that cytoskeletal defects occur in particular in Sertoli cells , where GBA2 is predominantly expressed . In testis , GBA2 expression and activity increased from P7 to P21 . To correlate the expression and activity of GBA2 in wild-type mice with the cytoskeletal defects in GBA2 knockout-mice , we determined the developmental time course of the cytoskeletal defects . At P7 , Sertoli cells from GBA2 knockout-mice did not show any obvious defect in the F-actin or microtubule structures ( Fig . 3D ) , underlining the results from the lipidomics and indicating that P7 might be a too early time point during spermatogenesis for defects to occur . To follow the first spermatogenic wave in more detail , we analyzed the organization of the cytoskeleton in the testis at different stages until P34 , when the first wave terminates . Until P21 , no difference in the microtubule or actin cytoskeleton was observed between wild-type and GBA2 knockout-mice ( Fig . 4A , B ) . However , at P23 , the alignment of F-actin in GBA2 knockout-testis started to change and at P34 , the F-actin structures around the sperm heads were misaligned and sperm organization inside the testis lumen was disturbed ( Fig . 4C , D ) . Thus , the occurrence of cytoskeletal defects in GBA2 knockout-mice correlates with the time course of GBA2 expression and activity in wild-type mice . Although GBA2 is predominantly expressed in Sertoli cells , GlcCer also accumulates in sperm from GBA2 knockout-mice ( Fig . 2C ) . Thus , we hypothesized that the sperm cytoskeleton is also affected by accumulation of GlcCer . Abnormal sperm-head morphology has been linked to defects in the microtubule manchette [28–31] . The manchette is a microtubule structure that appears during the elongation process of spermatids [29 , 32 , 33] , which first surrounds the proximal tip of the nucleus , gradually migrates towards the caudal end , and creates the force that is needed to shape the sperm head [34] . To analyze the formation of the microtubule manchette in wild-type and GBA2 knockout-spermatids , we monitored its formation in single , isolated germ cells ( Fig . 4E ) . The microtubule manchette of wild-type spermatids was symmetric and conical-shaped , whereas the manchette of knockout spermatids was much more elongated . The manchette length of GBA2 knockout-spermatids was about twofold longer compared to wild-type spermatids ( Fig . 4F;-/-: 10 . 9 ± 1 . 0 μm and +/+: 5 . 3 ± 0 . 5 μm , respectively ) . Thus , accumulation of GlcCer in the absence of GBA2 affects both the microtubule cytoskeleton of Sertoli cells and developing sperm . During sperm development , the cytoskeleton plays a crucial role in forming the acrosome [35] . Acrosome formation and the appearance of the sperm manchette start at the same time [34] . Immunofluorescent analysis of GBA2 knockout-sperm revealed that the formation of the acrosome is disrupted ( Fig . 3A ) [1] . We therefore analyzed the acrosome formation during the first spermatogenic wave ( Fig . 5 ) . The acrosome is derived from vesicles emanating from the TGN [12 , 13] and its formation can be divided into different phases: Golgi phase , cap phase , and acrosomal phase . In the Golgi phase , vesicles from the TGN fuse at one pole of the nucleus to form a single large vesicle that is attached to the nucleus . In the cap phase , the vesicle flattens over the nuclear membrane , forming a cap-like structure around the spermatid head . In the acrosome phase , the cap-like structures stretches out to form the final acrosome [36] . During the Golgi phase ( P21 ) , lectin staining revealed that vesicles in wild-type spermatids were completely fused and polarized , whereas in GBA2 knockout-spermatids , vesicles were still dispursed throughout the cell and not fused at one end of the cell ( Fig . 5 ) . This defect was even more prominent in the cap ( P23 ) and the acrosome phase ( P34 , Fig . 5 ) . The acrosomal cap at P23 was incomplete and at P34 , only a few GBA2 knockout-spermatids showed an acrosomal-like structure . Furthermore , the nuclear morphology was altered , leaving sperm with round heads at P34 ( Fig . 5 ) . Thus , the cytoskeletal defects in developing sperm and Sertoli cells and the defect in acrosome formation occur in parallel in GBA2 knockout-mice . Germ and Sertoli cells from adult testis are difficult to culture and to manipulate . In contrast , primary fibroblasts are easy to isolate and to maintain in culture . To gain a more mechanistic insight into how accumulation of GlcCer affects cytoskeletal dynamics , we analyzed dermal fibroblasts from adult wild-type and GBA2 knockout-mice as a model system . Wild-type fibroblasts express GBA2 ( Fig . 6A ) and GBA2 knockout-fibroblasts accumulate GlCer in the absence of GBA2 ( Fig . 6B ) . Indeed , the F-actin and microtubule network appeared strikingly different in GBA2 knockout compared to wild-type fibroblasts , which resulted in a dramatic change in cell morphology—knockout cells were more filopodia-like with extensive protrusions emanating from the plasma membrane ( Fig . 6C ) . However , when maintained in culture , dermal fibroblasts are morphologically heterogeneous , which makes it difficult to analyze cytoskeletal structures in detail . Thus , we seeded fibroblasts on chips that were coated with specific patterns ( crossbow , disc , Y-shape , dumb-bell ) to force the cells into a given shape . Wild-type fibroblasts fully adhered to the given fibronectin pattern , whereas GBA2 knockout-fibroblasts did not completely align with a given pattern and showed extensive F-actin structures protruding from the cell membrane ( Fig . 6D ) . This was particularly evident for the crossbow and disc shape ( Fig . 6D ) . Cells mainly contain three different F-actin structures: filopodia , lamellipodia , and stress fibers . We compared the different actin structures between wild-type and GBA2 knockout-fibroblasts on the crossbow shape and observed that GBA2 knockout-fibroblasts were more prone to develop filopodia and lamellipodia compared to wild-type cells , while stress fiber formation appeared normal ( Fig . 6D , E ) . Furthermore , the average number of lamellipodia per cell was significantly increased in GBA2 knockout compared to wild-type fibroblasts ( 3 . 35 ± 0 . 58 vs 1 . 98 ± 0 . 29 , respectively; Fig . 6E ) . Actin dynamics are prominently regulated by three members of the Rho family of small GTPases [37] . The formation of filopodia is controlled by Cdc42 , whereas lamellipodia formation depends on the activity of Rac1 and stress fiber formation on the activity of RhoA [38–42] . First , we analyzed the expression of Cdc42 , Rac1 , and RhoA on the mRNA level using qRT-PCR . The expression of RhoA was similar in wild-type and GBA2 knockout-fibroblasts , whereas the expression of both Rac1 and Cdc42 was slightly increased ( 1 . 22 ± 0 . 11 and 1 . 24 ± 0 . 15 , respectively; Fig . 6F ) . However , this was not reflected on the protein level: neither the expression level of Rac1 nor of Cdc42 was different between wild-type and GBA2 knockout-fibroblasts ( Fig . 6G , H ) . Thus , a change in the expression level of these key proteins controlling actin dynamics does not underlie the cytoskeletal defects observed in GBA2 knockout-fibroblasts . Lamellipodia and filopodia formation is induced by actin polymerization [41] . To study the impact of GlcCer accumulation on actin polymerization , we determined the G- and F-actin content and calculated the F-actin/G-actin ratio as a read-out for actin polymerization . The F-actin/G-actin ratio was significantly higher in GBA2 knockout-fibroblasts compared to wild-type fibroblasts , indicating that actin polymerization was augmented in GBA2 knockout-fibroblasts ( Fig . 6I ) . Although actin turnover in testis from adult GBA2 knockout-mice was similar to wild-type mice , we could clearly show that F-actin structures in the ES are augmented ( Fig . 6J , 3B ) . This is probably due to the heterogeneity of cells in adult testis , which makes it difficult to detect a change in F-/G-actin in a particular subcellular structure like the ES . Taken together , our results reveal that accumulation of GlcCer induces actin polymerization without changing the expression of RhoA , Rac1 , or Cdc42 . An increase in the length of the manchette in GBA2 knockout-spermatids indicates that microtubules persist longer . Thus , we analyzed microtubule dynamics in wild-type and GBA2 knockout-fibroblasts . Cells were transfected with EB3-cherry , a fluorescent probe that binds to the plus-ends of growing microtubules and allows following microtubule assembly using live-cell imaging ( Fig . 6K ) [43] . Growing plus-ends appear as shooting comets , which allows determining the microtubule growth rate and persistence ( Fig . 6L , M ) . Microtubule growth rate was not different between genotypes , but microtubules in GBA2 knockout-fibroblasts persisted significantly longer compared to wild-type fibroblasts ( Fig . 6L , M ) . This is in line with the finding that microtubules in the manchette from GBA2 knockout-spermatids appear longer ( Fig . 4E , F ) . In fibroblasts , microtubule polymerization can induce actin polymerization and , together , promote cell migration [44] . Thus , we analyzed the migration of wild-type and GBA2 knockout-fibroblasts in a wound-healing assay . GBA2 knockout-fibroblasts migrated faster than wild-type cells , in particular 2 to 6 h after starting the assay ( Fig . 7A , B ) . Thus , loss of GBA2 followed by an accumulation of GlcCer promotes microtubule and actin polymerization , which in turn changes cellular behavior . To analyze whether the cellular defects observed in GBA2 knockout-fibroblasts are solely due to the loss of GBA2 and , thereby , accumulation of GlcCer , we independently assessed the contribution of GBA2 in controlling cytoskeletal dynamics by incubating wild-type fibroblasts with the GBA2 inhibitor NB-DNJ [45] . Treatment of fibroblasts for 48 h with 2 μM NB-DNJ abolished GBA2 activity ( Fig . 8A ) . In turn , GlcCer accumulated ( Fig . 8B ) . Similar to GBA2 knockout-fibroblasts , wild-type fibroblasts treated with NB-DNJ showed a striking difference in the organization of the F-actin cytoskeleton ( Fig . 8C ) . We also analyzed the migration of cells treated with NB-DNJ . Similar to GBA2 knockout-fibroblasts , fibroblasts treated with NB-DNJ migrated faster than non-treated cells ( Fig . 8D ) . Thus , inhibition of GBA2 has a similar effect on cytoskeletal dynamics and cellular behavior as genetically ablating GBA2 . The cell membrane is a lipid bilayer , in which proteins are incorporated . The lipid composition of the membrane determines its characteristics and determines the function of proteins at the membrane . GlcCer is incorporated into the inner leaflet of the plasma membrane [46] . We hypothesized that accumulation of GlcCer increases the amount of GlcCer in the plasma membrane and that this controls the function of proteins at the membrane . Thus , we analyzed lipid stacking in intact plasma membranes isolated from wild-type and GBA2 knockout-fibroblasts . Giant plasma-membrane vesicles ( GPMV ) were isolated from fibroblasts by chemical vesiculation [47] and analyzed using laurdan fluorescent labeling . Laurdan is a membrane dye that reports the extent of water penetration into the bilayer surface due to the dipolar relaxation effect [48] . Water penetration can be directly correlated with lipid packaging and membrane fluidity [49] . Thus , laurdan fluorescence reports lipid packaging in the membrane . For highly ordered membranes , laurdan emission peaks at around 440 nm , whereas it is shifted to longer wavelengths in relatively disordered membranes [47] . GPMVs from wild-type and GBA2 knockout-fibroblasts were loaded with laurdan and the emission spectrum was measured ( Fig . 9 ) . The emission peak for wild-type fibroblasts was centered at around 460 nm , whereas the emission peak for GBA2 knockout-fibroblasts was shifted to shorter wavelengths at around 440 nm . This demonstrates that lipid packaging in the plasma membrane form GBA2 knockout-fibroblasts is more ordered than in wild-type fibroblasts . To quantify the shift , we calculated the generalized polarization ( GP ) index . The GP index in GBA2 knockout-fibroblasts was significantly higher than in wild-type fibroblasts ( 0 . 15 ± 0 . 06 vs . 0 . 24 ± 0 . 03 , respectively; Fig . 9 ) . We performed similar experiments with wild-type fibroblasts treated with 2 μM NB-DNJ for 48 h . The emission peak for GPMVs isolated from treated cells was also shifted to shorter wavelength with a GP index of 0 . 26 ± 0 . 07 ( Fig . 9 ) . In summary , our data indicate that accumulation of GlcCer in the absence of GBA2 leads to a more ordered lipid packaging in the plasma membrane . This in turn seems to control the activity of proteins incorporated in or associated with the plasma membrane , resulting in an increase in actin and microtubule polymerization . In vivo , this defect is most prominent in the testis , resulting in malformation of the acrosome and the sperm head and , thereby , in male infertility .
Spermatogenesis is a complex process that relies on the interaction between germ and Sertoli cells . Our study reveals that the glycosphingolipid GlcCer regulates cytoskeletal dynamics in germ and Sertoli cells . Accumulation of GlcCer due to the lack of GBA2 activity induces actin polymerization and microtubule persistence , resulting in globozoospermia and male infertility . The role of GlcCer in controlling cytoskeletal dynamics has not been established so far . However , it has been shown that ceramide , in particular C16 ceramide , promotes actin polymerization in mouse embryonic stem cells ( ESCs ) through PKC ( protein kinase C ) activation and FAK ( focal adhesion kinase ) /paxillin-dependent N-WASP/Cdc42/Arp2/3 complex formation [50] . This , in turn , increases cell migration . Ceramide serves as a building block for glycosphingolipids with GlcCer being one of them . Thus , the cellular changes observed after increasing C16 ceramide levels could also be due to an increase in GlcCer levels . Indeed , in GBA2 knockout-fibroblasts , we observed an increase in actin polymerization—similar to what has been described for ESCs treated with C16 ceramide [50] . Regulation of actin polymerization by lipids has also been shown for phospholipids using PIP2-containing vesicles [51] . In particular , PIP2 controls actin dynamics in highly polarized cells like T cells and neurons [52–54] . Here , PIP2 accumulates in membrane microdomains , where it directly regulates protein function and recruits proteins to form signaling complexes at the plasma membrane . Not only phospholipids , but also glycosphingolipids accumulate in microdomains , where they act as signaling domains involved in recognition processes [55] . GlcCer is a structural element in the inner leaflet of the plasma membrane , but can also be incorporated into the outer leaflet of the plasma membrane . It has been suggested that GlcCer at the cytosolic surface of the plasma membrane forms signaling domains by recruiting specific proteins , thereby , controlling e . g . vesicle budding [46] . Using the environment-sensitive dye laurdan in GPMVs , we demonstrated that accumulation of GlcCer results in a more ordered lipid packaging of the plasma membrane . Future studies will reveal whether this occurs in a localized or uniform manner . A change in lipid composition and/or lipid packaging of the membrane might also directly control protein function . For the EGF receptor ( epidermal growth factor receptor ) , it has been demonstrated that the ganglioside GM3 , surrounding the receptor in the membrane , regulates the allosteric transition from an inactive to an active EGF receptor dimer even in the absence of the ligand [56] . Furthermore , it has been shown that GlcCer controls the activity of a V-type ATPase in melanocytes [57] . Our results suggest that GlcCer accumulation in the plasma membrane controls the function of proteins like Cdc42 , thereby , regulating cytoskeletal dynamics . The role of GlcCer during spermatogenesis has been enigmatic so far . Our results suggest that GBA2 is predominantly expressed in Sertoli cells . However , more experiments are needed to clarify the physiological function of GBA2 during sperm development . In particular , Sertoli-cell specific knockout-mice will allow to unravel the role of GBA2 in Sertoli cells . In addition , analyzing the expression of GBA2 in different cell types during germ cell development will help to further understand GBA2 function in the testis . Our results demonstrate that the GBA2 substrate GlcCer accumulates in both , Sertoli cells and sperm in GBA2 knockout-mice . Similar to our observations in dermal fibroblasts , cytoskeletal structures were also altered in Sertoli cells and sperm . Sertoli cells are polarized cells that are connected to germ cells via the ES at the apical end [20 , 27] . This allows to exchange proteins and lipids between these two cell types [58] . Thus , GlcCer could accumulate in germ cells even though GBA2 is not expressed there ( at least not in detectable amounts ) . Furthermore , one function of Sertoli cells is to ensure a sufficient availability of lipids required for proliferation of millions of germ cells per day [58] . One way for the Sertoli cells to provide lipids is by recycling the lipid content of the residual body from spermatids , which is phagocytosed during spermatogenesis [58] . GBA2 could play a major role in this recycling process and accumulation of GlcCer in Sertoli cells would be further augmented . Accumulation of GlcCer in the testis in GBA2 knockout-mice occurs after P7 . This is in line with our finding that GBA2 expression and activity in wild-type testis increases after P7 during sperm development . We hypothesize that GlcCer accumulates in particular at the junction between germ and Sertoli cells , similar to what has been shown for PIP2 in other polarized cells like T cells or neurons , where PIP2 forms microdomains at the immunological synapse or at the growth cone of the axon [52–54] . In GBA2 knockout-testis , F-actin hoops in the ES and the microtubule manchette in spermatids were majorly disrupted . F-actin hoops were misaligned around the developing sperm head and the microtubule manchette was more elongated . Both structures have been shown to be crucial for shaping the sperm head and forming the acrosome [35] . Interestingly , in GBA2 knockout-fibroblasts , actin polymerization was augmented and microtubule persistence was increased . These results indicate that lack of GBA2 and the concomitant accumulation of GlcCer affect similar signaling pathways in different cell types , suggesting that the regulation of cytoskeletal dynamics by GlcCer is a general mechanism . Glycosphingolipids not only play a role in the plasma membrane , but also in membranes of organelles . In the Golgi and endosomes , glycosphingolipids have been shown to form microdomains that segregate membrane proteins and drive their sorting [59] . Furthermore , it has been proposed that GlcCer controls vesicle budding from the TGN [46] . The acrosome is formed by fusion of proacrosomal vesicles from the TGN . Thus , accumulation of GlcCer in sperm might disturb acrosome formation by inhibiting vesicle budding from the TGN and vesicle fusion . Indeed , vesicle fusion in GBA2 knockout-mice was disturbed during the cap and acrosome phase of acrosome formation , suggesting that GlcCer controls acrosome formation by regulating vesicle fusion . In summary , our study reveals that GlcCer is a key regulator of cytoskeletal dynamics . This is particularly important during sperm development , opening up new avenues in understanding the molecular mechanisms underlying male infertility .
All animal experiments were in accordance with the relevant national and international guidelines and regulations . Animal procedures were approved by the local authorities ( LANUV NRW ) . The generation of GBA2 knockout-mice has been described elsewhere [1] . Sperm were isolated by incision of the cauda epididymis in modified TYH medium containing 138 mM NaCl , 4 . 8 mM KCl , 2 mM CaCl2 , 1 . 2 mM KH2PO4 , 1 mM MgSO4 , 5 . 6 mM glucose , 0 . 5 mM sodium pyruvate , 10 mM L-lactate , pH 7 . 4 . After 15 min swim out at 37°C and 5% CO2 , sperm were counted . All subsequent experiments were performed at room temperature , unless otherwise stated . For isolation of germ cells , testes were decapsulated and incubated in 1 ml Hank’s Balanced Salt Solution ( HBSS ) ( 20 mM HEPES , 137 mM NaCl , 5 . 4 mM KCl , 0 . 3 mM Na2HPO4 , 0 . 4 mM KH2PO4 , 1 . 2 mM MgSO4 , 1 . 3 mM CaCl2 , 6 . 6 mM sodium pyruvate , 0 . 05% lactate , 5 . 6 mM glucose , pH 7 . 2 ) containing 0 . 5 mg/ml collagenase type IA ( Sigma ) for 30 min at 32°C . The dissociated interstitial cells were removed by two washing steps with HBSS . The seminiferous tubules were then incubated in 1 ml HBSS containing 0 . 5 mg/ml Trypsin type XIII ( Sigma ) and 1 μg/ml DNase I ( Applichem ) for 10 min at 32°C . Cell aggregates were sheared gently with a Pasteur pipette . The dispersed seminiferous cells were washed twice by centrifugation at 200 x g for 5 min at room temperature . The final cell pellet was re-suspended in HBSS and filtered through a Nylon mesh ( 40 μm mesh ) . Seminiferous tubules were isolated from testes of 7 days old mice ( P7 ) by removal of the tunica albuginea . The tubules were treated with 1 mg/ml collagenase ( Sigma ) at 37°C in a shaker for 8 min . The digestion was stopped by addition of DMEM/GlutaMax medium ( Invitrogen ) containing 10% FCS ( Biochrom ) . The cell suspension was centrifuged at 400 x g for 8 min , the pellet was re-suspended in medium containing 0 . 5 mg/ml trypsin and 0 . 22 mg/ml EDTA ( Sigma ) , and incubated in a shaker at 37°C for 5 min . The reaction was stopped by adding medium . The cell suspension was then treated with 1 μg/ml DNase I ( Applichem ) in a shaker at 37°C for 5 min . The cells were centrifuged at 600 x g for 10 min , and re-suspended in medium containing 70 U/ml penicillin , 70 μg/ml streptomycin , 100 mM sodium pyruvate , and 200 mM L-glutamine ( all Life technologies ) . Cells were seeded at a density of 5 x 104 cells/5 cm cell culture plate ( Greiner bio-one ) and used on the 5th day for experiments . The purity of the cell population isolated using this protocol has been analyzed using immunocytochemistry using markers for Sertoli cells ( beta-tubulin III , Sox9 ) —the preparation contains ca . 80% Sertoli cells . Dermal fibroblasts were isolated from mouse tails using collagenase digestion . Tail pieces were incubated in DMEM/GlutaMax containing 10% FCS , 100 mM sodium pyruvate , 200 mM L-glutamine , 70 I . U . /ml penicillin , 70 μg/ml streptomycin , 0 . 1 mg/ml collagenase ( Sigma ) for 3 h , 37°C , and 5% CO2 . After digestion , the supernatant was centrifuged for 5 min , 600 x g at room temperature . The cell pellet was re-suspended in DMEM medium , cells were plated on cell culture plates , and cultured at 37°C , 5% CO2 . After 24 h , the medium was changed . Primary antibodies: 4A12 ( rat , ICC: 1:20 ) , 2F8 ( rat , WB: 1:50 ) , pcGBA2 ( rb , ICC/IHC: 1:2 , 000 ) [1 , 45] , calnexin ( Sigma #C4731 , WB: 1:20 , 000 ) , beta-tubulin-CY3 ( Sigma #C4585 , ICC: 1:200 ) , beta-tubulin ( Sigma T4026 , WB: 1:1 , 000 ) , HA ( Roche #11867431001 , WB: 1:10 , 000 ) , beta-tubulin III ( HISS Diagnostics , MMS-435P , ICC: 1:500 , WB: 1:1 , 000 ) . Secondary antibodies: WB: IRDye680 and IRDye800 antibodies ( LI-COR , 1:20 , 000 ) ; ICC: fluorescently-labeled antibodies ( Dianova , 1:500 ) . Dyes: Alexa Fluor 488 Phalloidin ( Molecular Probes , #A12379 , ICC: 1:500 ) , MitoTracker ( Molecular Probes , M22426 , ICC: 0 . 5 μM ) , peanut lectin ( Sigma , #7381 , ICC: 1:100 ) , DAPI ( Molecular Probes , D1306 , ICC: 1:10 , 000 ) . Testes were fixed overnight with 4% paraformaldehyde/PBS , cryo-protected in 10 and 30% sucrose , and afterwards embedded in TissueTec ( Sakura Finetek ) . Sperm were immobilized on microscope slides and fixed for 10 min . To block unspecific binding sites , frozen sections and sperm were incubated for 1 h with blocking buffer ( 0 . 5% Triton-X 100 and 5% ChemiBLOCKER ( Millipore ) in 0 . 1 M phosphate buffer , pH 7 . 4 ) . Fibroblasts , Sertoli , and germ cells were fixed for 10 min . Primary antibodies were diluted in blocking buffer and incubated overnight . Fluorescent secondary antibodies were diluted in blocking buffer containing 0 . 5 μg/μl DAPI ( Invitrogen ) and pictures were taken on a confocal microscope ( Olympus FV1000 ) . For the analysis of cytoskeletal structures in dermal fibroblasts , cells were seeded on multi-pattern fibronectin-coated CYTOO chips ( #10–900–13–06 , CYTOO Cell Architects ) . All steps were performed at 4°C in the presence of mammalian protease inhibitor cocktail ( Sigma Aldrich ) . Tissues or cells were homogenized in a 10-fold surplus ( v/w ) of hypotonic buffer ( 10 mM HEPES , 0 . 5 mM EDTA , pH 7 . 4 ) by using an Ultra-turrax ( IKA ) and three pulses ( 20 s each ) of sonification ( Branson sonifier ) . The suspension ( total lysate ) was centrifuged for 20 min at 1 , 000 × g . The supernatant ( PNS , post-nuclear supernatant ) was used for activity assays or Western-blot analysis . The assay has been performed as described previously [45] . Briefly , cleavage of 4-methylumbelliferyl ( MU ) -beta-D-glucopyranoside ( Sigma Aldrich ) was monitored in real-time in a Fluostar Omega reader ( BMG labtech ) at 29°C using the filter pair 355 nm/460 nm for excitation and emission , respectively . The assays were performed in 384-well plates ( Greiner ) in the plate mode . Per well , 25 μl of lysate containing 20 μg of total protein were used . To discriminate between GBA1 and GBA2 activity , 30 μM CBE ( Conduritol B epoxide , Sigma Aldrich ) , an inhibitor for GBA1 , or 10 μM NB-DNJ ( N-butyldeoxynojirimycin , Sigma Aldrich ) , an inhibitor for GBA2 , were included . The pH of the protein lysates and the 4-MU-beta-D-glucopyranoside solution were adjusted by diluting with McIlvaine buffer . The assay was initiated by adding 5 μl of 4-MU-beta-D-glucopyranoside ( 10 mM ) resulting in a final concentration of 1 . 67 mM . The hydrolysis of 4-MU-beta-D-glucopyranoside was monitored and recorded as a change of relative fluorescence units ( rfu ) per minute . Each analysis was performed as a quadruplicate in parallel . Per genotype , tissues or cells from three animals were analyzed if not otherwise stated . All samples were heated for 5 min at 95°C prior to separation on SDS-PAGE . For Western-blot analysis , proteins were transferred onto PVDF membranes and probed with antibodies by using the Odyssey Imaging System ( LI-COR ) . 1x106 mouse fibroblasts were re-suspended in 100 μl of transfection buffer ( Neon transfection system , Life technologies ) and 4 μg of plasmid DNA was added . Using a microporator mini ( Digital Bio Technology , MP-100 ) , 10 μl of the cell suspension were subjected to two pulses ( 20 ms each ) of 1000 V and afterwards transferred to Poly-L-Lysine-coated glass-bottom dishes ( Mat Tek , #P35G-1 . 5–20-C ) . A total of 30 μl of cells were electroporated . The cells were allowed to grow overnight at 37°C and 5% CO2 in medium . Cells were imaged 24 h after transfection using the DeltaVision Core microscope ( Applied Precision , Inc . ) . Images were acquired every 3 s for 200–500 ms over 5 min and image analysis was done using Metamorph ( version 7 . 0 , Molecular Devices Corporation ) using the track-points function . Microtubule tracks were followed from the first frame an EB3-labeled microtubule plus-tip appeared until the last frame ( when the plus-tip was no longer visible ) . Data for velocity ( microtubule growth-rate ) and distance ( microtubule persistence ) were calculated . At least 10 microtubule tracks were followed in each cell , and at least 7 cells per cell line and genotype were analyzed . For GeLCMS , proteins of Sertoli cells , testis , and sperm were separated on SDS gels and stained with Coomassie . Per lane , 14–17 gel slices were excised , proteins were in-gel digested with trypsin ( Promega ) , peptides were separated in a 90 or 180 min gradient by a nanoAcquity LC System equipped with a HSS T3 analytical column ( 1 . 8 μm particle , 75 μm x 150 mm ) ( Waters ) , and analyzed by ESI-LC-MS/MS using an LTQ Orbitrap Elite mass spectrometer ( Thermo Scientific ) . All database searches were performed using SEQUEST as well as MS Amanda ( Mechtler lab , Vienna , Austria ) algorithm , embedded in Proteome Discoverer ( Rev . 1 . 4 , Thermo Electron 2008–2011 ) , with a NCBI protein database ( mouse , accession number NP_766280 . 2 , accessed June 13 , 2013 ) . Only fully tryptic peptides with up to two missed cleavages were accepted . Oxidation of methionine was permitted as variable modification . The mass tolerance for precursor ions was set to 10 ppm; the mass tolerance for fragment ions was set to 0 . 4 amu . To filter the results , a peptide FDR threshold of 0 . 01 ( q-value ) according to Percolator was set in Proteome Discoverer; two peptides per protein and peptides with search result rank 1 were required . For lipid extraction , dermal fibroblasts from GBA2 wild-type ( +/+ ) and knockout-mice ( -/- ) were grown until confluency , washed once in PBS , and harvested using trypsin/EDTA in medium . Cells were pelleted for 7 min at 700 x g and room temperature . Afterwards , cells were lysed in 1 ml bi-destilled water with three pulses ( 30 s each ) of sonification ( Branson sonifier ) . Lipids were extracted for 24 h at 37°C in chloroform/methanol/water ( 10/5/1 , v/v/v ) . For a better analysis of glucosylceramide , glycerophospholipids were degraded by alkaline hydrolysis with 125 mM sodium hydroxide for 2 h at 37°C . After neutralization with acetic acid , lipid extracts were desalted by reversed-phase chromatography and separated into acidic and neutral glycosphingolipids as described previously [60 , 61] . For separation of neutral lipids by thin layer chromatography ( TLC ) , lipids derived from an extract of 1–1 . 5 mg of total protein were applied to prewashed thin layer Silica Gel 60 ( Merck , Darmstadt , Germany ) and chromatograms were developed and quantified as described previously [61] . Sperm cells , testis , and Sertoli cells were frozen in liquid nitrogen and ground to a fine powder using the Precellys24 tissue homogenizer ( PeqLab ) . Lipids were extracted and fractionated using solid-phase-extraction on silica columns [62] . Long chain bases ( LCB ) , ceramides ( Cer ) , and hexosylceramides ( HexCer ) were eluted with acetone/2-propanol ( 9/1 , v/v ) and sphingomyelin ( Spm ) was eluted with methanol . The purified sphingolipids were analyzed via direct infusion nanospray mass-spectrometry using an Agilent 6530 Accurate-Mass Q-TOF LC/MS device [62] . Sphingolipids were quantified after collision-induced dissociation by scanning for specific fragment ions: LCB , neutral loss of 18 . 0106; Cer and HexCer , precursor ion scanning for m/z 266 . 2842 ( d18:0 ) , m/z 264 . 2686 ( d18:1 ) and m/z 262 . 2493 ( d18:2 ) ; Spm , precursor ion scanning for m/z 184 . 0739 . Internal standards were added for each sphingolipid class [63] . The assay was performed according to the manufacturer’s protocol ( #BK037 , Cytoskeleton ) . In brief , cells were lysed in a detergent-based lysis buffer , which solubilizes G-actin but also stabilizes and maintains F-actin . As a control , protein lysates were treated with an F-actin polymerizing solution for 15 min at 37°C . Equal volumes of each samples were subjected to ultracentrifugation ( 100 , 000 x g , 1 h ) to separate F-actin from G-actin . F-actin was maintained in the pellet fraction , whereas G-actin was maintained in the supernatant . The pellet was dissolved in F-actin depolymerizing buffer and incubated on ice for 1 h . Samples were run on a SDS-PAGE and analyzed by Western blot . Ratios of F-actin/G-actin for each genotype were calculated as percentage of the control . Mouse fibroblasts were plated on Cytoo chips ( Cytoo Cell Architects , # 10–900–13–06 ) placed in a 35 mm cell culture plate . Cells were fixed with 4% paraformaldehyde and labeled with Alexa Fluor 488 Phalloidin and DAPI . Images were taken using an Olympus FV1000 confocal microscope . Filopodia ( slender actin-protrusions ) and lamellipodia ( wave-like actin extensions ) structures were counted . Silicone cell culture-inserts ( Ibidi ) with a defined cell-free gap ( width = 500 μm ) were placed in 35 mm cell culture dishes . 4 x 104 cells were transferred into each of the culture inserts and incubated at 37°C , 5% CO2 for 2 h . Afterwards , inserts were removed and cells were washed with PBS . Fresh medium was added and a phase contrast image was taken ( t = 0 h ) using the Nikon eclipse ( TE 2000-S ) microscope . An image of the same region was taken every 2 hours ( t = 2 , 4 , 6 , 8 h ) . The area of the cell-free gap was measured using ImageJ ( version 1 . 46m ) and the speed of migration was calculated . Giant plasma-membrane vesicles ( GPMVs ) were isolated as described elsewhere [47] . In brief , dermal fibroblasts were incubated with GPMV buffer ( 10 mM HEPES , 150 mM NaCl , 2 mM CaCl2 , pH 7 . 4 ) containing 2 mM NEM for 1–2 h at 37°C , 5% CO2 . The supernatant was centrifuged for 10 min at 2000 x g and room temperature to pellet cell debris and intact cells . The resulting supernatant was subjected to high-speed centrifugation for 1 h at 20 , 000 x g and 4°C to pellet the vesicles . The pellet was re-suspended in GPMV buffer . Measurements were performed in a quartz cuvette using the FluoroMax-4 Spectrofluorometer . The emission spectrum was recorded from 400 to 500 nm at 385 nm excitation to detect the lipid resonance-peak at 425 nm . All samples were normalized to the lipid resonance-peak for the GPMV buffer . GPMVs were labeled with 5 μM laurdan ( 6-Dodecanoyl-2-Dimethylaminonaphthalene , Molecular Probes , #D250 ) for 20 min at 23°C . Measurements were performed at 350 nm excitation and fluorescence emission was recorded from 400 to 600 nm . All measurements were done at 23°C . The GP value was calculated according to the following equation: GP=∑420460Ix-∑470510Ix∑420460Ix+∑470510Ix | During mammalian spermatogenesis , sperm with a head and a tail are formed from a round cell . This process is tightly regulated and involves the close interaction of somatic Sertoli cells and germ cells . Accumulation of the glycosphingolipid glucosylceramide in the absence of the beta-glucosidase GBA2 has been proposed to disturb sperm development , leading to morphological defects . However , the underlying mechanism is not known . Here , we demonstrate that accumulation of glucosylceramide in GBA2 knockout-mice controls the dynamics of the actin and microtubule cytoskeleton , which are crucial for sperm development . In particular , cytoskeletal structures at the interface between Sertoli and germ cells are disorganized , leading to malformation of the sperm head and a defect in acrosome formation . In summary , we provide mechanistic insights into how glucosylceramide controls cellular signaling and dysregulation of this essential glycosphingolipid leads to male infertility . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [] | 2015 | Accumulation of Glucosylceramide in the Absence of the Beta-Glucosidase GBA2 Alters Cytoskeletal Dynamics |
The envelope glycoprotein ( Env ) of the Human Immunodeficiency Virus Type-1 ( HIV-1 ) is a critical determinant of viral infectivity , tropism and is the main target for humoral immunity; however , little is known about the cellular machinery that directs Env trafficking and its incorporation into nascent virions . Here we identify the mammalian retromer complex as a novel and important cellular factor regulating Env trafficking . Retromer mediates endosomal sorting and is most closely associated with endosome-to-Golgi transport . Consistent with this function , inactivating retromer using RNAi targeting the cargo selective trimer complex inhibited retrograde trafficking of endocytosed Env to the Golgi . Notably , in HIV-1 infected cells , inactivating retromer modulated plasma membrane expression of Env , along with Env incorporation into virions and particle infectivity . Mutagenesis studies coupled with coimmunoprecipitations revealed that retromer-mediated trafficking requires the Env cytoplasmic tail that we show binds directly to retromer components Vps35 and Vps26 . Taken together these results provide novel insight into regulation of HIV-1 Env trafficking and infectious HIV-1 morphogenesis and show for the first time a role for retromer in the late-steps of viral replication and assembly of a virus .
The Human Immunodeficiency Virus type-1 ( HIV-1 ) assembles at the plasma membrane of virus-infected cells from which nascent particles are released by a process of budding . Efficient virus assembly therefore requires correct spatial and temporal trafficking of viral proteins and necessitates critical interactions between viral and cellular cofactors . The envelope glycoprotein ( Env ) of primate lentiviruses including Human Immunodeficiency Virus type-1 ( HIV-1 ) is a key determinant of viral infectivity , facilitating attachment of virions to the surface of susceptible cells , triggering fusion of the viral and cellular membranes and determining the site of infectious virus assembly at the plasma membrane [1] , [2] , [3] , [4] . Moreover because Env is expressed on the surface of infected cells and is the only viral protein exposed on the virion , it is also the major target for neutralizing antibody responses; however the mechanism of Env trafficking in HIV-1 infected cells and how it is incorporated into viral particles is poorly understood . HIV-1 Env consists of approximately 856 amino acids and is synthesized in the endoplasmic reticulum as a 160 kDa precursor ( gp160 ) . During passage through the secretory pathway , Env undergoes cleavage by the Golgi-localized protease furin [5] to produce two subunits that remain non-covalently associated: the receptor binding surface subunit gp120 and a transmembrane subunit gp41 , which are collectively referred to as Env . Gp120 contains the binding sites for the receptor ( CD4 ) and coreceptor ( CCR5 or CXCR4 ) expressed on the surface of susceptible cells and thus determines viral tropism . The gp41 subunit contains an ectodomain , a transmembrane domain , and a cytoplasmic domain that mediates intracellular trafficking , interaction with HIV-1 Gag and incorporation of Env into virions ( reviewed in [1] ) . Notably , the cytoplasmic tail ( CT ) of Env is long in lentiviruses ( around 150 amino acids ) by contrast to other retroviruses whose EnvCT is considerably shorter ( approximately 50 amino acids ) ( reviewed in [6] ) . Conservation of a long CT in HIV-1 suggests the presence of regions , many still undefined , which are critical for efficient viral replication . Indeed , truncation of the cytoplasmic tail of Env has been shown to alter Env intracellular trafficking and to profoundly reduce the infectivity of HIV-1 in many cell types , including CD4 T cells that are the main targets for HIV-1 replication in vivo [7] , [8] . Following exit from the ER , HIV-1 Env traverses the Golgi complex to the trans-Golgi network ( TGN ) from where it is trafficked via the secretory pathway to the plasma membrane . Once at the cell surface , Env either interacts with membrane-associated HIV-1 Gag and gets incorporated into viral particles , or alternatively is rapidly endocytosed [9] . Two motifs in the cytoplasmic tail Env can promote efficient internalization: the highly-conserved and well-defined membrane proximal YSPL ( YxxL ) motif at position 712 in HIV-1 [10] , [11] , [12] and a C-terminal dileucine sequence [13] , either of which can act autonomously to mediate clathrin-dependent endocytosis . The fact that the endocytic motifs are highly-conserved across HIV-1 strains and related simian lentiviruses suggests that efficient internalization of Env from the plasma membrane plays an important role in the viral life-cycle . However , once within the endosomal pathway the intracellular itinerary of Env and how trafficking is regulated remains unclear . Retromer is a cellular protein complex and member of the endosomal sorting machinery that is conserved from yeast [14] to humans [15] , [16] , [17] , [18] and plays an essential role in endosomal sorting of a select group of physiologically important cargo proteins . Retromer operates in eukaryotes by recognizing and sorting cargos in maturing endosomes into nascent endosomal tubules , and is most closely associated with retrograde transport back to the Golgi complex [17] , [18] , [19] , [20] . Increasing evidence also points to a role for retromer in the transport of some cargo proteins from endosomes to the plasma membrane [19] , [21] , [22] . Mammalian retromer is a heteropentameric complex comprising a cargo-selective trimer of Vps26-Vps29-Vps35 and a membrane-bending complex consisting of two sorting nexins ( SNX1 or SNX2 with SNX5 or SNX6 ) ( reviewed in [19] , [20] , [23] ) . Selection of cargo proteins for retromer-mediated trafficking is by direct binding to Vps35 or Vps26 [17] , [24] , [25] , [26] , [27] , [28] and depleting either ablates retromer function . One of the best characterized cargos for retromer sorting is the cation-independent mannose 6-phosphate receptor ( CIMPR ) , a membrane protein whose primary function is to transport newly synthesized acid hydrolases from the TGN to endosomes for eventual delivery to lysosomes . Following dissociation of their cargos in endosomes , CIMPRs are recycled via retrograde transport to the TGN in a retromer-dependent manner , thus avoiding lysosomal degradation [17] , [18] , [25] . In the absence of retromer , CIMPRs fail to retrieve to the TGN and are missorted to the plasma membrane and/or degraded in lysosomes [17] , [18] , [25] . A number of other proteins are also trafficked in mammalian cells in a retromer-dependent manner [26] , [29] , [30] , [31] , [32] and collectively these studies have placed the retromer complex at the center of endosomal sorting . Based on the known role of retromer in endosomal sorting and the fact that HIV-1 Env traverses the endosomal network in HIV-1 infected cells , we hypothesized that retromer may be implicit in Env trafficking . Here we report that the mammalian retromer complex is an important cellular cofactor regulating the intracellular transport of Env . We found that inhibiting retromer function relocalized Env in both HIV-1 infected and Env expressing cells , resulting in increased plasma membrane expression , greater incorporation into virions and failure of endocytosed protein to retrieve to the Golgi . Notably , this was dependent on the gp41 cytoplasmic tail of Env that we show binds directly to retromer . These findings identify retromer as a novel modulator of HIV-1 Env transport and infectious HIV-1 assembly and describe for the first time a role for the mammalian retromer complex in assembly of a virus .
To investigate whether the mammalian retromer complex plays a role in HIV-1 replication , sequential rounds of siRNA transfection were used to deplete HeLa TZM-bl cells of Vps26A , an essential component of the retromer complex . Vps26A is the predominant isoform of Vps26 in HeLa cells , constituting >90% of the total Vps26 [33] and depletion of Vps26A ablates retromer function by abrogating the function of the cargo selective trimer complex [17] , [18] , [20] , [25] . Western blotting confirmed that endogenous Vps26A ( hereafter referred to as Vps26 ) was readily detected in untreated HeLa cells and in cells transfected with control siRNA , but was almost completely depleted after two rounds of transfection with siRNA targeting Vps26 ( 96% and 91% reduction in Vps26 at 200 nM and 20 nM respectively ) ( Figure 1A ) . As expected , further titration of the siRNA to 2 nM and 1 nM gave less efficient depletion of Vps26 ( Figure 1A ) therefore higher concentrations of siRNA were used in all subsequent experiments . In agreement with previous reports [17] , [18] , [25] , we observed that Vps26 knockdown redistributed a proportion of intracellular CIMPR to early endosomes and increased colocalization with EEA1 , thereby confirming phenotypic loss of function ( R value for CIMPR/EEA1 = 0 . 34+/−0 . 02 in untreated cells and 0 . 44+/−0 . 02 in Vps26 knockdown cells , p = 0 . 007 ) ( Figure S1 ) . To test the consequences of inactivating retromer for HIV-1 replication , HeLa TZM-bl cells ( that express the HIV-1 entry receptors CD4 , CXCR4 and CCR5 and contain an HIV-1-Tat inducible luciferase reporter gene ) were transfected with siRNA targeting Vps26 and infected with the replication competent HIV-1 strain NL4 . 3 immediately after the second knockdown . Twenty-four hours later luciferase activity was measured to quantify virus infection . This short time interval meant that we were measuring the early steps of infection . No difference in reporter gene expression was detected between control and Vps26-depleted cells , demonstrating that Vps26 knockdown did not inhibit viral entry or the early steps of HIV-1 infection ( Figure S2A ) . Furthermore , western blotting of cell lysates showed that HIV-1 infected cells ( >95% of cells HIV-1 Gag+ ) and uninfected cells expressed similar levels of Vps26 and Vps35 protein , demonstrating that HIV-1 infection alone does not modulate retromer expression ( Figure S2B and C ) . To determine whether retromer knockdown affected HIV-1 assembly and budding , virus-containing supernatants were harvested from HIV-1 infected cells at 48 h post-infection , purified by ultracentrifugation and equal volumes of purified virions were subjected to SDS-PAGE and western blotting for viral proteins ( Figure 1B ) . Probing membranes with antisera specific for HIV-1 Gag revealed similar levels of Gag p24 in viral supernatants harvested from control and Vps26 siRNA treated cells ( p>0 . 05 quantified from four independent experiments ) . Similar levels of Gag precursor p55 were also present in cell lysates ( p>0 . 05 ) showing that inactivating retromer had no effect on Gag expression , processing or virus budding . Strikingly , when the same blots were probed with antisera against HIV-1 Env we observed clear increase in the amount of Env gp120 and gp41 incorporated into virions produced by Vps26 KD cells . Quantification of western blots from three independent experiments showed that Vps26 KD consistently resulted in a 2−3 fold increase in the amount of Env gp120 in viral particles relative to Gag p24 . To determine whether the increased Env content correlated with increased infectivity , viral supernatants were used to infect fresh HeLa TZM-bl cells and infection was measured by luciferase assay . Consistent with the increased Env incorporation , virions produced by Vps26KD cells displayed a 2−3 fold increase in particle infectivity , confirming that the extra gp120 packaged in virions was functional ( Figure 1C ) . Env is incorporated into nascent particles as the viral cores bud through the plasma membrane . We therefore reasoned that the greater Env content of virions might reflect increased plasma membrane expression on cells depleted of Vps26 . To test this , virus-infected cells were stained on ice with an anti-Env antibody to label only cell surface exposed protein and analyzed by flow cytometry . Figures 1D and E show that Vps26 KD cells expressed 3 fold more Env on the cell surface when compared to untreated or control siRNA treated cells . Western blotting of total cell lysates prepared from HIV-1 infected cells treated with Vps26 or control siRNA revealed no statistically significant difference in the relative abundance of Env gp160 ( gp160:gp120 ratio , p = 0 . 08 from 4 independent experiments ) and no difference in the gp160 to Gag p55 ratio ( p = 0 . 1 ) ( Figure 1F ) . From these data we conclude that changes to Env synthesis or proteolytic processing of gp160 are unlikely to account for the increased Env content of virions . Taken together these data suggest a specific role for retromer in HIV-1 Env trafficking and its subsequent incorporation into nascent viral particles . To further investigate retromer-dependent Env sorting , we performed immunofluorescence microscopy to examine the steady-state localization of Env in virus-infected cells . Cells infected with HIV-1 were fixed , permeabilized and stained for Env ( mAb 2G12 ) , the Golgi ( giantin ) and the retromer component Vps26 . Intracellular staining revealed that a proportion of the total immunoreactive Env colocalized with the retromer component Vps26 in HIV-1 infected cells ( R value for Env/Vps26 = 0 . 34+/−0 . 03 ) ( Figure 2A ) . This was most clearly seen when examining peripheral Env + cytoplasmic foci . As expected , we also observed that in untreated cells the majority of intracellular Env was concentrated in a perinuclear region and colocalized with the Golgi marker giantin ( R value for Env/giantin = 0 . 52+/−0 . 02 ) . That we observed a partial colocalization of Env and retromer is indicative of the dynamic nature of Env trafficking through the endosomal network . Vps26 knockdown did not induce any apparent accumulation of Env in early endosomes or Lamp1 + lysosomes ( Figure S3 ) , indicating that Env was not sequestered into intracellular compartments , but rather ( as the flow cytometry data suggest ) that surface expression was increased after Vps26 knockdown . In agreement with the data showing no effect of Vps26 KD on HIV-1 Gag budding or processing ( Figure 1 ) , a punctate cytoplasmic pattern of Gag staining was observed with little or no overlap with Vps26 ( Figure S4 ) . Because HIV-1 Env is a transmembrane protein , the cytoplasmic tail projects through cellular membranes into the cytosol and is available to interact with components of the cellular trafficking and endosomal sorting machinery , such as retromer that resides on the cytosolic face of endosomal membranes [34] , [35] . The cytoplasmic tail of the gp41 subunit of HIV-1 Env ( gp41CT ) is relatively long , spanning residues 706HxB2 to 856HxB2 ( corresponding to 704NL4 . 3 to 854NL4 . 3 ) ( Figure 3A ) . To investigate whether retromer-mediated Env trafficking is dependent on the gp41CT , we used a mutant of the HIV-1 strain NL4 . 3 that contains a 144 amino acid deletion in the cytoplasmic tail , leaving only the first 6 residues proximal to the transmembrane domain [36] , [37] ( referred to herein as NL4 . 3 Δ144 ) ( Figure 3A ) . This virus retains infectivity in many commonly used cell lines [7] , [8] , [13] , [37] including HeLa TZM-bl cells . In direct contrast to what was observed using WT virus containing the complete gp41CT ( Figure 1 and Figure 3C ) , infecting cells depleted of Vps26 with NL4 . 3 Δ144 did not result in an increase in Env expression at the cell surface or alter the amount of gp120 and gp41 incorporated into virions ( Figures 3B and 3C ) . These data show that deletion of the Env gp41CT renders HIV-1 completely insensitive to retromer depletion , suggesting that the cytoplasmic tail of Env is a critical determinant of retromer-dependent sorting . It should be noted that consistent with previous reports , untreated cells infected with NL4 . 3 Δ144 expressed higher levels of Env on plasma membrane compared to cells infected with WT virus , owing to deletion of endocytic signals in the cytoplasmic tail ( Figure 3C ) . To further define the role of the Env gp41CT and to interrogate retromer-dependent trafficking of Env in the absence of other viral proteins , we generated reporter constructs in which the ecto and transmembrane domains of CD8 ( that contain no known trafficking motifs ) were cloned upstream of the Env gp41CT to generate chimeric CD8-gp41 reporter proteins . Constructs were made that contained the entire HIV-1 Env gp41CT ( CD8-gp41-CT ) or the CIMPR cytoplasmic tail as positive control . Stable cell lines were generated and expression validated by western blotting ( Figure S5 ) . Immunofluorescence staining of cells stably expressing reporter constructs revealed that the full-length gp41CT ( CD8-gp41CT ) colocalized with vesicular Vps26 ( R value = 0 . 3+/−0 . 03 ) ( Figure 4A ) , showing that the cytoplasmic tail of Env is alone sufficient for trafficking of Env through the retromer-containing compartment . The mammalian retromer complex is associated with retrograde transport of a select group of cargo proteins from endosomes to the Golgi complex [19] , but because most steady-state Env is present within the Golgi complex ( Figure 2 ) [13] , [38] specific endosome-to-Golgi recycling can be difficult to discern when staining steady-state intracellular protein . Especially so when there is newly synthesized protein moving through the Golgi . Therefore to determine whether retromer mediates endosomal sorting and Golgi retrieval of Env , we performed an antibody-feeding assay to specifically follow trafficking of CD8-gp41CT after endocytosis from the plasma membrane . A similar approach has been taken to show the involvement of retromer in the retrieval of a CD8-CIMPR reporter [18] and to identify a sorting motif in the CIMPR tail [25] . Surface staining for CD8 and flow cytometry analysis confirmed that CD8-gp41CT was expressed at the plasma membrane ( MFI = 150 . 9+/−20 . 3 compared to 10 . 8+/−0 . 5 for untransfected cells ) . HeLa cells expressing gp41CT constructs were incubated on ice with anti-CD8 in order to label surface exposed protein , washed extensively and incubated at 37°C to allow endocytic uptake and intracellular trafficking . We confirmed that non-specific fluid-phase uptake of antibody was undetectable using cells that did not express CD8 fusion proteins ( Figure S6 ) . Figure 4B shows that following incubation at 37°C , endocytosed CD8-gp41CT protein relocalized from a generally peripheral and punctate cytoplasmic distribution at 10 min , to a more defined perinuclear region at 120 min and showed increased colocalization with the Golgi marker giantin , indicative of gp41CT-mediated endosome-to-Golgi retrieval ( CD8/giantin R value at 10 min = 0 . 26+/−0 . 02 and at 120 min = 0 . 39+/−0 . 03 ) . Notably , in cells depleted of Vps26 the endocytosed protein displayed a more vesicular intracellular staining pattern , with weaker perinuclear localization and giantin co-staining ( R value at 120 min = 0 . 29+/−0 . 02 , p = 0 . 03 ) . The same results were obtained when RNAi was also used to deplete HeLa cells of Vps35 that also abrogates retromer function [17] . As expected we found that steady-state CD8-gp41CT colocalized with Vps35 in untreated cells ( Figure 4C ) . Importantly , Vps35 KD impaired endosome-to-Golgi retrieval of endocytosed CD8-gp41CT , in exact agreement with what we observed following Vps26 KD ( Figure 4D , CD8/giantin R value at 120 min for CTRL = 0 . 42+/−0 . 01; Vps35 siRNA = 0 . 33+/−0 . 03 , p = 0 . 001 ) . Taken together , these data show that two independent routes to retromer inactivation result in impaired endosome-to-Golgi retrieval of Env , providing compelling evidence for a specific role for retromer in retrograde trafficking of Env . Having shown that retromer mediates Golgi recycling in a gp41CT-dependent manner , we next sought to determine whether the gp41CT physically interacts with retromer using native coimmunoprecipitation ( co-IP ) . Lysates prepared from HeLa cells stably expressing CD8-gp41CT , CD8-CIMPR protein or untransfected controls were incubated with anti-CD8 coated beads and immunoprecipitated proteins were analyzed by western blotting for Vps35 and Vps26 ( Figure 5A ) . As expected , the CD8-CIMPR included as a positive control readily coimmunoprecipitated both Vps35 and Vps26 ( Figure 5A ) [18] , [25] . Importantly , the CD8-gp41CT protein was also able coimmunoprecipitate both Vps35 and Vps26 indicative of an interaction between the EnvCT and the retromer complex . Mass spectrometry analysis of bands excised from coomassie stained gels confirmed the presence of Vps26 in samples immunoprecipitated from both CD8-CIMPR and CD8-gp41CT expressing cells , but not in untransfected cells ( Figure S7 ) . In order to determine if binding of the gp41CT to the retromer complex was direct , GST pulldown assays were performed . Bacterially expressed GST-gp41CT was purified and incubated with a bacterially expressed and purified FLAG-tagged retromer complex , which consists of the cargo selective trimer components 3xFLAG-Vps26-3x-FLAG-Vps29-3x-FLAG-Vps35-His6 [26] . GST only was used as negative control and GST-CIMPR as a positive control . Western blotting for retromer components using antibodies specific for Vps26 and Vps35 showed that gp41CT specifically immunoprecipitated retromer in a dose-dependent manner , as did the GST-CIMPR , while GST alone did not pulldown retromer ( Figure 5B ) . Immunoreactive Vps26 and Vps35 were also detected in the lanes loaded with the purified retromer complex only . Taken together , these results show that the cytoplasmic tail of Env physically interacts with retromer and that binding is direct and does not require additional cellular factors . To further define the role of the gp41CT in retromer-dependent Env trafficking , endocytic uptake and Golgi retrieval assays were performed using constructs in which the C terminal 50 or 100 amino acids of gp41 were deleted ( CD8-gp41-L805* and CD8-gp41-L753* respectively ) ( Figure S5 ) . Figure 6A shows that although both the CD8-gp41-L805* and CD8-gp41-L753* proteins were readily detected after 10 minutes of endocytic uptake at 37°C , they showed markedly reduced immunofluorescence staining after 120 minutes and failed to retrieve to the Golgi complex ( gp41-L805* and giantin R value at 120 min = 0 . 21+/−0 . 02; gp41-L753* and giantin R value at 120 min = 0 . 20+/−0 . 04 ) . Colocalization of all CD8-gp41 proteins with intracellular Vps26 was readily detected after 10 minutes incubation at 37°C ( R values for CD8 and Vps26 for: gp41CT = 0 . 48+/−0 . 1; gp41-L805* = 0 . 35+/−0 . 04; gp41-L753* = 0 . 46+/−0 . 07 ) ( Figure 6B ) , demonstrating that failure to recycle to the Golgi was not due to a block in endocytic uptake of these truncation mutants . This is in agreement with published data showing that cargos that can no longer interact with retromer may still colocalize with Vps26-containing intracellular compartments [25] . Having shown that truncated gp41CT mutants were impaired in recycling to the Golgi after endocytic uptake , we sought to determine if these proteins were no longer able bind retromer . Whereas the CIMPR and gp41CT were both able to pull-down retromer components Vps26 and Vps35 by co-IP , CD8-gp41-L753* containing the largest C- terminal deletion repeatedly failed to co-IP either Vps35 or Vps26 ( Figure 6C ) . Barely detectable bands corresponding to Vps35 and Vps26 were seen in immunoprecipitates from cells expressing the CD8-gp41-L805* . We observed that the truncated gp41CT proteins that failed to bind retromer and did not retrieve to the Golgi complex also showed a gradual loss of immunofluorescence staining , suggestive of reduced protein stability . To investigate this , cycloheximide was used to arrest protein synthesis and degradation of existing protein was followed over time by western blotting ( Figure 6D ) . As previously described , the CD8-CIMPR migrates as a doublet during SDS-PAGE [25] . In agreement with previous reports [25] we observed that the higher molecular weight CIMPR band remained relatively stable over time in the presence of cycloheximide ( 95% remaining at 6 h when normalized to 0 h = 100% ) . Notably , similar results were seen for the full length CD8-gp41CT that also migrated as a doublet , with the higher molecular weight band appearing stable and readily detected up to 6 h after cycloheximide treatment ( 99% of protein remaining at 6 h ) . By contrast CD8-gp41-L805* showed more rapid degradation of the higher molecular weight protein ( 68% remaining at 6 h ) . The short CD8-gp41-L753* protein ( that showed the most marked loss of immunofluorescence staining after extended incubation at 37°C ) migrated only as the lower molecular weight species that was rapidly lost in all samples . These data reveal that CD8-gp41CT proteins that fail to bind to retromer ( by co-IP ) and do not recycle to the Golgi appear to be less stable . It has previously been reported that two adjacent internal regions within the EnvCT may be involved in trafficking Env back to the Golgi complex [39] and influence Env incorporation into virions [40] . To test whether these regions were implicit in retromer-dependent Env sorting , site-directed mutagenesis was used to independently delete these sequences from the CD8-gp41CT protein . The first mutant ( termed Δis1 ) has an internal deletion that removes a 14 amino acid stretch V747-S760 ( inclusive of S760 ) . The second mutant ( termed Δis2 ) has an internal deletion that removes 23 amino acids from L761-L783 ( inclusive of L783 ) . Both mutants remove amino acids immediately downstream of the CD8-gp41-L753* truncation . Figure 7A and B show that deletion of either is1 or is2 resulted in loss of binding of the EnvCT to retromer and failure to coimmunoprecipitate Vps26 and Vps35 . Moreover , both mutants failed to retrieve to the Golgi following endocytosis from the plasma membrane ( R values for CD8 and giantin at 120 min: gp41CT = 0 . 44+/−0 . 05; Δis1 = 0 . 1+/−0 . 06; Δis2 = 0 . 08+/−0 . 07 ) . CD4 T cells are the main targets for HIV-1 replication . To investigate the effect of retromer depletion in these cells we generated Jurkat T cell lines stably expressing lentiviral shRNA against Vps26 or a non-targeting control sequence . Western blotting identified one hairpin ( designated sh-4 ) that reduced Vps26 expression in T cells by 67% ( Figure 8A ) . When infected with HIV-1 , cells expressing sh-4 were found to express 50% more Env at the cell surface compared to cells that were transfected with a non-targeting shRNA control ( Figure 8B ) . Furthermore , analysis of particle infectivity revealed that virions produced from Vps26-depleted Jurkat cells were 50% more infectious on a per particle basis ( Figure 8C ) and that there was no effect on HIV-1 Gag budding ( Figure 8C ) . Altered Env incorporation into virions was also evident in Vps26 KD cells ( Figure 8D ) . Immunofluorescence staining showed that Env also partially colocalized with Vps26 in a perinuclear region of HIV-1 infected Jurkat cells ( Env/Vps26 R value = 0 . 5+/−0 . 05 ) ( Figure 8E ) ; however the comparatively small cytoplasm of T cells makes colocalization analysis considerably more difficult than HeLa cells . Thus , although the efficiency of Vps26 knockdown in T cells was more modest than can be achieved in HeLa cells , the effect on Env was proportional and supports a role for retromer in Env trafficking in T cells .
The assembly of HIV-1 virions takes place at the plasma membrane of infected cells during which Env is incorporated into nascent particles as the viral capsid buds through the cell membrane . Although Env is critical for viral infectivity , the cellular factors regulating Env transport in HIV-1 infected cells and thus its incorporation into nascent virions are ill defined . Most steady-state Env is localized to the Golgi complex , not only because Env traverses the Golgi en route to the plasma membrane , but also because Env that is not incorporated into virions can be recycled back to the Golgi following endocytosis [10] , [11] , [12] , [13] . Based on the known cellular function of retromer in endosomal sorting and Golgi retrieval , we hypothesized that the mammalian retromer complex may play a key role in HIV-1 Env trafficking . Here we have tested this and coupled RNAi with functional virology , trafficking assays and coimmunoprecipitations and find that retromer regulates intracellular trafficking of Env and promotes retrograde endosome-to-Golgi transport , in a manner that is dependent on a direct interaction between the Env cytoplasmic tail and the retromer complex . Perturbing retromer-mediated trafficking of Env , using either RNAi or deleting domains within the EnvCT resulted in failure of endocytosed Env to retrieve to the Golgi complex . In HIV-1 infected cells , subsequent missorting of Env following retromer depletion altered the localization of Env , which in turn impacted on plasma membrane expression and incorporation of Env into nascent viral particles . Taken together , these results identify retromer as a novel cellular factor regulating HIV-1 Env trafficking and infectious virus assembly . Once cargo proteins are endocytosed from the plasma membrane and reach the endosome , they may either continue along the endosomal maturation pathway and be delivered to lysosomes where they are degraded , or be directed into recycling compartments for trafficking back to the plasma membrane , or be sorted back to the Golgi complex . Retromer is believed to function by sorting a select group of cargo proteins into tubular endosomal membranes and is most-closely associated with the pathway of endosome-to-Golgi transport [19] , [33] . Although recent work has revealed retromer also operates in transporting some cargo proteins from endosomes to the plasma membrane [19] , [21] , [22] , our data suggest that in the case of HIV-1 Env , retromer functions to transport Env via the endosome-to-Golgi route since ablating retromer function did not reduce Env surface expression . Our results demonstrating retromer-dependent Golgi retrieval of endocytosed Env are consistent with previous studies that have implicated other cellular factors that intersect the Golgi retrieval pathway in HIV-1 replication , including Rab9 and its interactors p40 and PIKfyve , as well as TIP47 [38] , [41] , [42] , although the function of TIP47 in endosome-to-Golgi transport and its contribution to Env trafficking remains unclear [43] , [44] , [45] . Furthermore , the clathrin adaptor AP-1 that can promote transport of proteins between endosomes and the Golgi also binds to the cytoplasmic tail of Env [10] , [46] , [47] . The association of Env with multiple cellular factors that are implicit in endosomal sorting and retrograde transport suggests that this pathway may play an important role during HIV-1 replication , possibly by protecting endocytosed protein from degradation by bypassing trafficking to lysosomes; limiting Env exposure to immune surveillance and humoral immunity by regulating cell surface expression and virion incorporation [48]; or by providing repeated exposure of Env to necessary Golgi modifications . In addition to blocking endosome-to-Golgi transport , we found that inactivating retromer in HIV-1 infected cells was associated with increased cell surface expression of Env and greater gp120 incorporation into nascent virions , a phenotype that we confirmed was not related to altered gp160 processing or Gag-mediated budding , the latter in agreement with another study [49] . Notably , others have also reported mislocalization and increased surface expression of cellular cargos that are trafficked by retromer after Vps26 or Vps35 depletion [18] , [25] . Precisely how Env gets relocalized to the plasma membrane in HIV-1 infected cells in the absence of retromer-mediated trafficking is presently unclear since multiple cellular factors may be implicit . However , based on our results we believe that the most likely explanation is that inhibiting retromer-dependent endosome-to-Golgi redirects Env into an alternative trafficking pathway from endosomes towards the plasma membrane , in effect increasing outward Env transport . In support of this we found no evidence of impaired endocytosis of Env in retromer-depleted cells , suggesting that it is altered outward transport , rather than defective internalization that is responsible for increasing Env cell surface expression . The well-established connection between endocytic uptake of proteins and subsequent endosomal sorting leading to plasma membrane recycling makes this scenario likely . Precisely what cellular machinery are involved and how they operate , and whether endosomal recycling compartments that have been recently implicated in Env transport [50] contribute to this warrants future investigation . Interestingly , while our data identifying the retromer complex as new cellular factor interacting with the EnvCT are novel , it is noteworthy that previous proteomics analysis has found Vps35 to be one of many cellular proteins incorporated into viral particles [51] . We have depleted the major isoform Vps26A . Thus it is formally possible that the other isoform Vps26B that localizes to the cytoplasm and plasma membrane , rather than endosomes , may contribute in some way to infectious HIV-1 assembly [52] , [53] , [54] . Like other viruses , HIV-1 must strike a balance between efficient replication and immune evasion . One way this can be achieved is by controlling exposure of viral proteins to immune surveillance . It is easy to envisage how removing Env ( that is not incorporated into virions ) from the plasma membrane and recovering it back to the Golgi for subsequent recycling might confer a replicative advantage to the virus . By contrast , it could be argued that limiting Env surface expression and incorporation into virions would be disadvantageous to the virus , not least because we found that virions released from Vps26 depleted cells were more infectious by virtue of increased Env incorporation . Importantly , it is known that increasing surface expression of Env by mutating endocytic motifs within the EnvCT impairs in vivo replication of simian viruses in animal models , presumably increasing exposure of Env to antiviral immunity [48] . Since only 8−14 trimeric Env spikes are present on HIV-1 virions and as few as four appear to be needed to mediate attachment and entry of virions to target cells [55] , increasing Env cell surface expression and incorporation into virions may not necessarily confer a significant advantage under conditions where infection is already efficient and the virus must strike a balance between robust replication and immune evasion . Given that regulation of Env trafficking to and from the plasma membrane is critical for infectious virus assembly and spread , future studies aimed at identifying the cellular factors controlling Env trafficking and dissecting the molecular details of virus-host interactions is clearly important , not only to better understand how HIV-1 hijacks the host cell machinery , but potentially to inform efforts to manipulate these processes to better expose the virus to antiviral immune responses . The list of cargo proteins that are trafficked in a retromer-dependent manner is expanding and includes a diverse range of proteins implicated in many cellular processes [19] . We now add the HIV-1 Env glycoprotein to this family of retromer cargos . Importantly this is the first viral structural protein that has been shown to interact with retromer and to be trafficked in a retromer-dependent manner . It seems likely that other viruses may be similarly dependent on retromer for viral protein sorting and replication . While this manuscript was in preparation , Lipovsky et al . identified retromer as a cellular factor implicit in papillomavirus entry [56] . While we found no evidence for retromer playing a role in the initial steps of HIV-1 infection of cells , we provide evidence for retromer during infectious HIV-1 assembly by modulating Env trafficking and virion incorporation , and thus describe the first report of a role for retromer in assembly of a virus . It will clearly be informative to further to investigate the role of other cellular proteins associated with retromer-mediated trafficking , endosomal sorting and recycling in lentiviral replication . In light of the important role that Env plays in lentiviral pathogenesis and anti-viral immunity , illuminating the molecular mechanisms of Env trafficking is timely .
HeLa and HEK293T cells were originally from the ATCC ( American Type Culture Collection ) . HeLa TZM-bl cells were obtained from the Center for AIDS Reagents , National Institutes of Biological Standard and Control , UK ( CFAR , NIBSC ) and donated by J . Kappes , X . Wu and Tranzyme Inc . Adherent cells were maintained in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with streptomycin ( 100 µg/ml ) , penicillin ( 100 U/ml ) and 10% fetal calf serum ( FCS , Invitrogen ) . The CD4+/CXCR4+ T cell line Jurkat CE6 . 1 and derivative Jurkat line 1G5 ( obtained through the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH: from Dr . Estuardo Aguilar-Cordova and Dr . John Belmont ) were maintained in RPMI 1640 supplemented with streptomycin ( 100 µg/ml ) , penicillin ( 100 U/ml ) and 10% fetal calf serum ( Invitrogen ) . The HIV-1 clone pNL4 . 3 was produced by Dr Malcolm Martin and obtained from the NIH AIDS Reagent and Reference Program ( NIAID , NIH , USA ) . The CTdel-144 mutant ( referred to herein a Δ144 ) was a kind gift from Dr Eric Freed ( National Cancer Institute , Frederick , USA ) [36] , [57] . Stocks of infectious virus were made by transfecting 293T cells using Fugene 6 ( Promega ) and infectious viral titer measured on HeLa TZM-bl reporter cells using the Bright-Glo Luciferase assay kit ( Promega ) . The CMS28 retroviral expression vector , a derivative of MIGR1 , was used in which the BglII/XhoI/HpaI/EcoI polylinker had been replaced with EcoRI/NotI/Xhol ( a gift from M . Malim King's College London ) . The CD8-CIMPR fusion construct generated by M . Seaman [18] was subcloned into CMS28 and was a gift from S . Neil ( King's College London ) . The CD8-gp41 fusion constructs containing the ecto and transmembrane domains of CD8 fused to the cytoplasmic tail of HIV-1 gp41 were generated as follows . CD8 DNA was PCR amplified from the CD8-CIMPR using forward ( 5′-ATAGAATTCATGGCCTTACCAGTGA-3′ ) and reverse ( 5′-ATAGCGGCCGCGGTGATAACCAGT-3′ ) primers containing an EcoRI and NotI restriction site respectively . CD8 PCR products were ligated into CMS28 to generate CMS28-CD8 . The full-length gp41 cytoplasmic tail was PCR amplified from the HIV-1 molecular clone pNL4 . 3 using a forward primer ( 5′-AAGCGGCCGCAAATAGAGTTAGGCAG-3′ ) containing a NotI restriction site and a reverse primer ( 5′-ATCTCGAGTTATAGCAAAATCCTTTCCAA-3′ ) containing a XhoI restriction site . The gp41 forward primer was designed such that all six amino acids upstream of the YSPL endocytosis motif in the gp41 cytoplasmic tail were included in addition to an alanine triplet encoded by the primer , maintaining this YSPL motif within the required distance from the plasma membrane for endocytosis [58] . For PCR amplification of the truncated gp41 cytoplasmic tails the reverse primers ( 5′-ATCTCGAGTTATAGTTCCTGACTCCAATACTG-3′ or 5′-ATCTCGAGTTAAAGTGCTAAGGATCCGTTCA-3′ ) containing a XhoI site and 3′ stop codon were used , terminating immediately after L805 and L753 in NL4 . 3 respectively ( corresponding to L807 and L755 in HxB2 ) . HIV-1 gp41 PCR products were ligated into CMS28-CD8 to generate 3 constructs: CD8-gp41CT , CD8-gp41L805* and CD8-gp41L753* . To generate ΔIS1 and ΔIS2 , site-directed mutagenesis was used to delete residues V747-S760 ( inclusive ) or L761-L783 ( inclusive ) from the CD8-gp41CT plasmid . All constructs were verified by sequencing and stable expression in HeLa cells was achieved using puromycin selection and confirmed by western blotting and flow cytometry . Adherent cells were seeded at a density of 3×105 per well and transfected with Dharmacon Smartpool targeting human Vps26A , Vps35 or a non-targeting control siRNA using Oligofectamine based on the method of Seaman [18] . Unless otherwise stated , 200 nM of siRNA was used . Twenty-four hours later the cells were reseeded and the following day an identical second transfection was performed . Forty-eight hours later the cells were harvested and Vps26 and Vps35 knockdown were determined by western blotting . HeLa TZM-bl cells were infected with pNL4 . 3 or pNL4 . 3-Δ144 virus at an MOI of 0 . 1 immediately after the second knockdown . Excess virus was removed by washing and cells incubated at 37°C for 48 h prior to analysis . RNAi knockdown of Vps26 in T cells was performed by generating 4 individual oligonucleotide hairpins based on the Dharmacon Smartpool target sequences . shRNA hairpins were cloned individually into the HIV-1 vector pCSRQ , a modified version of pSIREN RetroQ [59] ( a gift from G . Towers , UCL ) and co-transfected into 293T cells with the VSV-G envelope plasmid . Virions were used to infect T cells and stable cell lines selected with puromycin . siRNA transfected HeLa cells or shRNA expressing Jurkat T cells were infected with HIV-1 and incubated for 48 h . Virus was harvested from supernatants by filtration and equal volumes were titrated on Jurkat 1G5 cells and infectivity measured by luciferase assay ( Bright-Glo Promega ) . HIV-1 Gag p24 ELISA was performed as described [60] . For biochemical analysis by western blotting , virus-containing supernatants were ultracentrifuged through a 25% sucrose cushion and pelleted virus was resuspended in PBS and stored at -80°C . Virus was lysed in SDS-PAGE loading buffer and analyzed by SDS-PAGE and western blotting . Cells were pelleted , washed in cold PBS and lysed in RIPA buffer on ice for 10 min . Soluble proteins were collected following centrifugation at 15 000×g for 10 min at 4°C and the protein concentration was determined using a BCA Protein Assay Reagent Kit ( Pierce , USA ) . Twenty µg of cell lysate and an equal volume of purified virus were separated by SDS-PAGE and analyzed by western blotting using the following primary antibodies: rabbit antisera raised against HIV-1 Gag ( donated by Dr G . Reid and obtained from the CFAR ) ; rabbit antisera raised against gp120 ( donated by Dr S . Ranjbar and obtained from the CFAR ) ; human anti HIV-1 gp41 Mab 246-D ( donated by Dr S . Zollar-Pazner and Dr M . Gorny , obtained from the CFAR ) ; rabbit anti-Vps26 ( Abcam ) ; rabbit anti-Vps35 ( Abcam ) ; rabbit anti-actin ( Sigma ) ; mouse anti-tubulin ( DM1A , Sigma ) ; anti-CD8 ( sc-7188 , Santa Cruz ) . Primary antibodies were detected with goat anti-rabbit or anti-mouse HRP ( DAKO ) and visualized by ECL ( GE Healthcare ) . To arrest protein synthesis cells were washed and incubated in media containing 100 µg/ml cycloheximide or carrier control for up to 6 h . Cells were detached with EDTA , lysed in RIPA buffer and analyzed by SDS-PAGE and Western blotting . Cells were washed in cold FACS wash buffer ( FWB: PBS with 1% FCS and 0 . 01% sodium azide ) and incubated on ice for 1 h with 20 µg/ml of the anti-gp120 mAb 2G12 ( Polymun ) to detect cell surface expressed HIV-1 Env or a mAb specific for CD8 ( UCHT-4 , Abcam ) . Cells were subsequently washed in cold FWB , fixed in 4% formaldehyde and incubated with anti-human or anti-mouse IgG-phycoerythrin for 30 min . Acquisition and analysis was performed using a Becton Dickinson FACS Calibur . Adherent cells were detached with 2 mM EDTA in PBS and reseeded onto glass coverslips 24 h prior to staining . Cells were fixed in 4% formaldehyde in PBS-1% BSA and permeabilized either in 0 . 1% Triton X-100/5% FCS for 20 min at RT or cold 100% methanol for 5 minutes . Intracellular staining used the following primary antibodies: rabbit anti-Vps26 serum ( Abcam ) , mouse anti-EEA1 mAb ( clone 14 , BD Biosciences ) , mouse anti-Lamp1 ascites ( H4A3 , Developmental Studies Hydridoma Bank , University of Iowa ) , mouse anti-cation independent mannose 6 phosphate receptor mAb ( 2G11 , Abcam ) , mouse anti-CD8 mAb ( UCHT-4 , Abcam ) and mouse anti-giantin mAb ( 9B6 , Abcam ) . HIV-1 Gag was detected with rabbit antisera against Gag p17 and p24 ( donated by G . Reid and obtained from the CFAR , NIBSC ) and HIV-1 Env with the human mab 2G12 ( Polymun ) . Primary antibodies were detected with either FITC , TRITC , Cy5 ( Jackson Immunoresearch ) or Alexa-conjugated ( Invitrogen ) anti-mouse , anti-human or anti-rabbit secondary antibodies that were tested for an absence of inter-species reactivity . Anti-mouse isotype-specific secondary antibodies were also used that were tested for an absence of inter-isotype reactivity ( Invitrogen ) . Coverslips were mounted with ProLong antifade mounting solution containing DAPI ( Invitrogen ) and cells were imaged through a 63×1 . 4 NA oil immersion lens with an inverted Olympus IX71 microscope ( DeltaVision ELITE Image Restoration Microscope , Applied Precision ) and a CoolSNAP HQ2 camera . Images were acquired as serial 0 . 2 µm sections through the entire volume of the cell and deconvolved with softWoRx 5 . 0 . Processing was performed using Huygens Professional version 4 . 0 and Adobe Photoshop C3 . Quantification of colocalization was performed using the DeltaVision softWoRx image acquisition and analysis software . Z stacks of 0 . 2 µm sections were acquired for each fluorescence channel through the entire volume of the cell and images deconvolved using softWoRx . To quantify the degree of colocalization , the Pearson correlation coefficient ( R ) values were calculated using the softWoRx colocalization module . To do this , a colocalized image is generated using the two selected channels . Scatter plots are plotted showing the pixel-by-pixel intensity of the two fluorescent channels and the R value is calculated by dividing the covariances of each channel by their standard deviations . Colocalization analysis was performed on a single xy slice through the middle of the cell ( determined by DAPI staining of the nucleus ) . At least twenty cells selected at random from a total of 3 independent experiments were analysed . R values reported are the mean and SEM . For immunofluorescence staining of CD8-fusion proteins endocytosed from the plasma membrane an antibody-feeding method was used . To label plasma membrane Env or CD8 cells were cooled on ice , washed in ice cold DMEM and incubated with 1 µg/ml anti-CD8 mAb for 30 minutes on ice . Excess antibody was removed by washing with ice cold DMEM and the cells were incubated at 37°C for up to 120 minutes to allow antibody uptake . Cells were then fixed , permeabilized and stained with primary antibodies for cellular markers , followed by secondary antibodies as described above . Native coimmunoprecipitations of CD8-CIMPR and CD8-gp41CT proteins was performed essentially as described previously [25] . The method was adapted to use 9×106 untransfected HeLa cells and HeLa cells expressing CD8-gp41 proteins and 4 . 5×106 cells expressing the CI-MPR . Lysates were precleared by addition of 1 . 5 mg Protein G Dynabeads ( Life Technologies ) for 60 min at 4°C . Lysates were then incubated with 1 . 5 mg Protein G Dynabeads precoupled with 5 µg mouse anti-CD8 ( clone UCHT4 ) for 90 min at 4°C . Samples were washed 3 times with lysis buffer [25] , eluted in SDS sample buffer and analyzed by SDS-PAGE and western blotting using rabbit anti-Vps35 , rabbit anti-Vps26 and rabbit anti-CD8 ( Santa Cruz , sc-7188 ) . GST and GST-CIMPR-CT were a gift from M . Tabuchi and F . Kishsi ( Kagawa University , Japan ) [26] . To construct the GST-gp41CT , restriction enzyme sites in GST-CIMPR-CT were replaced using site-directed mutagenesis , the CIMPR-CT was removed and the gp41CT inserted and verified by sequencing . GST-fusion proteins were expressed in Rosetta-gami 2 ( DE3 ) pLysS cells ( Novagen ) and purified as previously described [26] with modifications . Briefly , GST and GST-CIMPR-CT were induced with IPTG according to the method of Tabuchi et al . , [26] and GST-gp41-CT protein according to Wyss et al . , [12] . Following induction , cells were pelleted , subjected to five freeze/thaw cycles with liquid nitrogen , resuspended in buffer A ( 0 . 5% Triton X-100 in PBS containing 1 mM phenylmethylsulfonyl fluoride ( PMSF ) and 1 mg/mL lysozyme ) , and disrupted by sonication . Lysates were cleared by centrifugation and GST-tagged proteins were purified using glutathione-Sepharose 4B beads ( GE Healthcare ) overnight at 4°C with rotation . Beads were then washed and proteins eluted with buffer G ( 20 mM reduced glutathione , 0 . 1% v/v Triton X-100 , 50 mM Tris-HCl , pH 9 . 0 ) . GST-gp41-CT protein was subjected to an additional concentration and purification step using 50% v/v of saturated ( NH4 ) 2SO4 . All purified GST and GST-fusion proteins were dialysed overnight in PBS and protein concentrations determined using a BCA protein assay kit ( Pierce ) . FLAG-tagged retromer complex ( a gift from M . Tabuchi and F . Kishsi ( Kagawa University , Japan ) was expressed and purified as described , with minor modifications [26] . Briefly , Rosetta-gami 2 ( DE3 ) pLysS containing the pET23d-3 × FLAG-Retromer construct were induced with IPTG [26] and cell pellets resuspended in buffer B ( 50 mM NaH2PO4 , 300 mM NaCl , 1 mM imidazole , pH 8 . 0 , containing complete EDTA-free protease inhibitor cocktail [Roche Molecular Biochemicals] , 1 mM PMSF and 1 mg/mL lysozyme ) . Cells were freeze-thawed using liquid nitrogen , sonicated , and lysates were cleared by centrifugation before being applied to a Ni-NTA agarose column ( Qiagen ) . Columns were then washed ( 50 mM NaH2PO4 , 300 mM NaCl , 5 mM imidazole , pH 8 . 0 ) and proteins eluted ( 50 mM NaH2PO4 , 300 mM NaCl , 250 mM imidazole , pH 8 . 0 ) . Eluted 3 × FLAG-Retromer complex was dialysed overnight in PBS and protein concentration determined using a BCA protein assay kit ( Pierce ) . GST pulldowns were performed as described [26] except that proteins were eluted at 4°C with rotation in modified buffer G ( 20 mM glutathione , 0 . 1% v/v Triton X-100 , 50 mM Tris-HCl , pH 9 . 0 ) . Samples were analyzed by SDS-PAGE and western blotting using anti-Vps35 and anti-Vps26 as described above . Immunoprecipitated samples were separated by SDS-PAGE and gel bands corresponding to the known size of retromer components were excised , digested with trypsin and peptides analyzed mass spectrometry using an Orbitrap XL ( Thermo Electron , Bremen , Germany ) and identified using Mascot v2 . 4 ( Matrix Science ) and SwissProt databases . Statistical significance was calculated either using the parametric Anova test for multiple comparisons with Bonferroni correction or parametric student's t test . Significance was assumed when p<0 . 05 . | Virus assembly necessitates the hijacking of the host cell machinery in order for new infectious viral particles to be constructed and disseminate . The envelope glycoprotein ( Env ) of HIV is a critical determinant of viral infectivity and is also a major target for antiviral immune responses . The long cytoplasmic tail of HIV Env plays an essential role in the assembly of infectious virions and limiting exposure of Env to the immune system , but the cellular machinery that transports HIV Env in virus-infected cells remain poorly understood . Here we have identified the mammalian retromer complex involved in endosomal sorting as a novel cellular factor regulating Env trafficking in virus-infected cells . We show that inactivating retromer alters Env localization , cell surface expression and incorporation into virions and that retromer binds directly to the Env cytoplasmic tail to perform these functions . This study defines an important pathway of Env transport and describes for the first time a role for this highly conserved cellular complex in assembly of a virus . | [
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"structure"
] | 2014 | Retromer Regulates HIV-1 Envelope Glycoprotein Trafficking and Incorporation into Virions |
During blood stage Plasmodium falciparum infection , merozoites invade uninfected erythrocytes via a complex , multistep process involving a series of distinct receptor-ligand binding events . Understanding each element in this process increases the potential to block the parasite’s life cycle via drugs or vaccines . To investigate specific receptor-ligand interactions , they were systematically blocked using a combination of genetic deletion , enzymatic receptor cleavage and inhibition of binding via antibodies , peptides and small molecules , and the resulting temporal changes in invasion and morphological effects on erythrocytes were filmed using live cell imaging . Analysis of the videos have shown receptor-ligand interactions occur in the following sequence with the following cellular morphologies; 1 ) an early heparin-blockable interaction which weakly deforms the erythrocyte , 2 ) EBA and PfRh ligands which strongly deform the erythrocyte , a process dependant on the merozoite’s actin-myosin motor , 3 ) a PfRh5-basigin binding step which results in a pore or opening between parasite and host through which it appears small molecules and possibly invasion components can flow and 4 ) an AMA1–RON2 interaction that mediates tight junction formation , which acts as an anchor point for internalization . In addition to enhancing general knowledge of apicomplexan biology , this work provides a rational basis to combine sequentially acting merozoite vaccine candidates in a single multi-receptor-blocking vaccine .
Malaria is caused by protozoan Plasmodium parasites and Plasmodium falciparum ( Pf ) is the most pathogenic of the five species known to infect humans , accounting for the majority of mortality from malaria . Recent clinical trials involving a pre-erythrocytic Pf vaccine , known as RTS , S , demonstrate partial efficacy [1 , 2] , however , there remains a need to explore other vaccine options , especially those which have the potential of controlling blood stage infection . To prevent malaria caused by blood stage infection , pre-erythrocytic vaccines need to be capable of preventing virtually all parasites from exiting the liver to infect the blood . To date this has not been achieved , so pre-erythrocytic vaccines should therefore be paired with a blood stage vaccine to eliminate breakthrough parasites , thereby providing better protection from both clinical malaria and more severe sequelae . Vaccines targeting merozoites , the stage of the parasite that infects erythrocytes , have long shown promise , but their development has been hampered by limited functional knowledge of the molecular targets . In particular , while many receptor-ligand associations have been characterised , their distinct functions and relative contributions to invasion are not well established [3] . To improve our understanding of merozoite invasion , we filmed invasion of Pf merozoites and analysed the kinetics and morphology of its distinct steps [4 , 5] . We categorised these into three stages; pre-invasion , internalisation and echinocytosis , as was first described in P . knowlesi ( Dvorak et al . , 1975 ) . The approximately 10 second pre-invasion step , is characterised by dramatic deformation of the target erythrocyte . Internalisation then ensues and 20–60 seconds later the newly infected erythrocyte takes on a stellate appearance , a phenomenon known as echinocytosis . The erythrocyte remains like this for 5–10 minutes before returning to its pre-invasion biconcave shape . The morphology and kinetics of these invasion steps are remarkably conserved across evolutionarily divergent Plasmodium species [4 , 5 , 6] . Despite its formidable technical challenges [7] , live cell microscopy is a powerful tool for examining the behaviour of parasites and can reveal much about pathogenesis . Most studies of pharmacological or biological ( i . e . antibodies ) growth inhibitors of Pf consist of adding the inhibitor to parasite culture and measuring the parasitemia after a few days . This approach often provides little data on whether the inhibitor blocks growth , egress or invasion , and how quickly this occurs . While the effects of invasion inhibitors have been examined in great detail using fluorescent antibody probes or electron microscopy , it has usually been done with fixed cells , and therefore provides only a snapshot of a single moment in time during a rapid and highly dynamic process . To complement the considerable body of work on merozoite invasion using traditional microscopy methods with fixed cells , we have used live cell microscopy to provide an unprecedented examination of the process of invasion through systematically blocking ligands known to be involved . Here we have given new definition to these interactions , elucidating the resulting cellular morphology and temporal sequence . A number of merozoite invasion ligands have been described . One group of these , merozoite surface proteins ( MSPs ) , form a major component of the merozoite surface coat [3 , 8] . While there are many MSPs , the GPI-anchored merozoite surface protein 1 ( MSP1 ) is both the largest , a dimer of >500kDa , and the most abundant [8 , 9 , 10 , 11] . Although the function of MSP1 remains unclear , the binding of exogenous heparin sulphate to MSP1 blocks invasion , suggesting it interacts with an as yet unknown erythrocyte receptor [12] . Another group of ligands involved in invasion are the ‘alternative-pathway’ ligands , so-called because the individual ligands appear to be functionally redundant . It is most likely , however , that these proteins have slight variation in their roles and work together with a combination of overlapping function and cooperation . In Pf , these ligands comprise the erythrocyte binding antigens ( EBA-175 , EBA-140 , EBA-181 and EBL1 ) , and the reticulocyte-binding like homologs ( PfRh2a , PfRh2b and PfRh4 ) ( reviewed in [13 , 14] ) . It appears that two PfRh proteins , PfRh1 and PfRh5 , have distinct functions which are different from the others [15 , 16] . PfRh1 likely has a role immediately upstream of the alternative-pathway ligands , in signalling the release of micronemes containing EBA-175 [16] , a process which is dependent on calcium [17] . While the alternative-pathway EBA and PfRh ligands bind to different erythrocyte receptors , studies with gene knockout parasites , in combination with mutant erythrocytes deficient in particular receptors , have shown that increased expression of some EBAs and PfRhs can functionally compensate for the lack of another [18 , 19 , 20] . This redundancy has most likely evolved to counter erythrocyte receptor polymorphism , although varying the expression of these ligands may also help the parasite circumvent the host’s antibody responses [21] . The other PfRh protein known to have a distinct function is PfRh5 . Both the essentiality of PfRh5 [22] , and the erythrocyte protein which PfRh5 interacts with , basigin [15 , 23] , have been recently identified . Unlike other alternative ligands , PfRh5 lacks a transmembrane anchor , localises to the tight junction and appears anchored to the merozoite via the Ripr protein [15 , 24] . The PfRh5–basigin interaction , and hence invasion , can be blocked by antibodies to either protein but the precise role of this interaction in the process of invasion is unknown [15 , 22 , 25 , 26] . Once the merozoite binds to the erythrocyte , it reorientates its apical end onto the host cell surface and forms a connective ring between it and the erythrocyte . This ring is called the ‘tight junction’ or ‘moving junction’ , and the merozoite passes through it to enter the erythrocyte powered by the parasite’s actin-myosin motor [27 , 28 , 29] . An important component of the tight junction is AMA1 , a type 1 transmembrane protein , secreted onto the merozoite surface during egress from the old host cell . AMA1 binds to RON2 , a member of the RON complex , which is translocated from the merozoite into the erythrocyte surface [27 , 30 , 31 , 32] . RON2 has an exposed loop that acts as the AMA1 binding site and in this way Plasmodium parasites encode their own ligand and host-embedded receptor . [29 , 33] . Both a synthetic peptide called R1 [34 , 35] , and a peptide derived from the RON2 exposed loop , can block native RON2 binding by competing for the RON2-interacting groove of AMA1 and thus inhibit invasion [29 , 32 , 33 , 36] . Video microscopy of parasites treated with R1 , or which have had their ama1 gene deleted , shows that merozoites still bind to and deform erythrocytes , but they cannot properly invade and fail to progress to a ring stage [32 , 37 , 38] . This appears to be due to a defective tight junction that fails , either in assisting internalization , or in resealing of the host membrane behind the merozoite , in the few cases where internalization does occur [38] . At least a dozen receptor-ligand interactions are known to play roles in the invasion of erythrocytes by Pf merozoites and these are summarised in Fig . 1A . Here we have inhibited the following five receptor-ligand interactions using; 1 ) heparin sulphate to inhibit MSP142 binding to unknown erythrocyte glycoproteins , 2 ) treatment with neuraminidase ( NM ) to remove sialic acids on glycophorin A ( GYPA ) and thus prevent binding of EBAs , 3 ) genetic deletion of EBA175 and NM treatment in addition to complement receptor 1 ( CR1 ) fragments to block PfRh4 binding to CR1 , 4 ) anti-PfRh5 and anti-basigin IgGs to block the binding of PfRh5 to basigin , and 5 ) R1 and RON2 peptides to inhibit AMA1–RON2 interactions . To study these invasion pathways we have used live cell imaging while specifically ablating these interactions to characterise the role played by each receptor-ligand event in the morphological and physiological events that typify invasion . In addition to revealing the order of these events , we show that after initial contact , vigorous deformation associates with successful invasion . This deformation is mediated by the parasite’s actin-myosin motor and alternative-pathway receptor-ligand interactions . We further define the role of PfRh5 , and provide evidence that an open connection forms between the apical tip of the parasite and the host cell immediately prior to invasion . We hypothesize that this open connection is mediated by PfRh5 to basigin binding and may act as a conduit for invasion proteins which establish the tight junction .
As a prelude to these invasion inhibition studies we first determined whether the four Pf strains , 3D7 , D10 , W2mef and ΔEBA175 ( W2mef with its EBA175 gene deleted ) , used here , had similar invasion kinetics under permissive conditions . We began by examining the proportion of contacts between merozoites and erythrocytes that culminated in successful invasion . This method was chosen rather than total invasions per schizont rupture since it would remove some of the elements of chance such as differences in the density of target erythrocytes surrounding rupturing schizonts or the directions in which merozoites happened to be released . Putative brief contacts between merozoites and erythrocytes that produced no definitive deformation or adhesion period were discounted . For 3D7 , D10 and W2mef , we observed that approximately half of parasite-host contacts progressed to invasion ( Fig . 1B , S1 Table ) . In contrast , only 25% of the ΔEBA175 contacts resulted in invasion , however , this difference only reached statistical significance relative to 3D7 and D10 . Next , in a parallel approach , we counted the number of erythrocytes contacted before invasion occurred including the invaded erythrocyte . Both unique and total contacts ( where the same erythrocyte may be contacted more than once ) were counted ( Fig . 1C ) . This indicated that 3D7 was the most efficient , since it contacted fewer erythrocytes before invading than did other strains ( p≤0 . 04 for all comparisons ) , with 85% of 3D7 merozoites invading the first erythrocyte they encountered . On the other hand , ΔEBA175 was the least efficient tending to contact 2 erythrocytes on average before invasion although this was only significant relative to 3D7 and W2mef ( Fig . 1C , S1 Table ) . Because ΔEBA175 does eventually invade , after more contacts , the number of invasions per rupture was comparable across all strains ( Fig . 1D ) . Multiple schizont ruptures ( n = 15–23 ) were observed for each strain to minimize discrepancies from rupture to rupture . We also determined the periods of pre-invasion ( primary contact , deformation and resting [5] ) , internalisation and the time to the commencement of echinocytosis and noted that , with the exception of 3D7 pre-invasion as compared to D10 ( p = 0 . 025 ) , there were no significant differences between the four strains tested ( Fig . 1E-G ) . The mean lengths of pre-invasion ranged from 9–13 seconds , followed by 10–11 seconds for merozoite internalisation , which was the most tightly regulated phase of invasion with the narrowest range ( Fig . 1E , F , S1 Table ) . From the completion of merozoite internalization to the beginning of erythrocyte echinocytosis , average times ranged from 31–38 seconds ( Fig . 1G ) . Erythrocyte echinocytosis occurred following most , but not all , invasions ( 3D7: 76 . 7%; D10: 72 . 7%; W2mef: 91 . 9%; ΔEBA175: 83 . 8% ) . Examples of invasions for each strain are shown in S1–S4 Videos . As shown , the four strains used here have very similar invasion kinetics making it probable that the role of an invasion protein studied in one strain is conserved across all strains . To further dissect the initial contact period we noted the degree to which merozoites deformed individual erythrocytes . Deformation is a complex occurrence that is difficult to quantify because it varies in intensity and time , lasting from a fraction of a second to several seconds and can occur in multiple waves . A simplified four-point deformation scale ( 0 , 1 , 2 and 3 ) was therefore devised , based on the most extreme degree of deformation achieved ( Fig . 2A , B , and S1 , S5–S7 Videos ) . When assessed using this scale there were no differences between the four parasite strains when all contacts were taken into account ( Fig . 2C , D ) . However , there was a significant difference when comparing the deformation scores caused by merozoites which were invaders vs . non-invaders . The majority of merozoites that invaded deformed strongly ( scores 2 and 3 ) , while the majority of merozoites which did not invade deformed weakly or not at all ( scores 0 and 1 ) ( Fig . 2C , D ) . Thus , the degree of deformation correlated with the success of subsequent invasion . To investigate the underlying causes of deformation we treated purified schizonts with cytochalasin D ( cytD ) , an inhibitor of actin polymerisation , to block the merozoite’s actin-myosin invasion motor . The cytD not only blocked invasion as expected but also inhibited deformation with 88 . 4% of treated merozoites deforming their erythrocytes weakly or not at all ( Def score 0–1 ) , and only 11 . 6% deforming their erythrocytes strongly ( W2m cytD , Lane 3 vs . 5 , Fig . 3A , B , S2 Table , S8 Video ) . This data suggests that a functioning actin-myosin motor is required for deformation of the erythrocyte . To determine if there was any contribution made by the erythrocyte’s actin , we added untreated schizonts to erythrocytes treated with cytD and observed no differences as compared to untreated erythrocytes ( S2 Table ) . It has previously been shown that heparin is capable of inhibiting an early step in invasion [12] . To quantify the effects we performed video microscopy of D10 parasite invasion in the presence of heparin and observed a 17-fold reduction in the number of invasions per schizont rupture ( from 1 . 7 to 0 . 1 , D10 vs . Heparin or Lane 2 vs . 6 , Fig . 3A , S2 Table ) . In addition , heparin markedly reduced the capacity of merozoites to deform erythrocytes following contact and deformation scores of greater than 0 were rarely observed ( Lane 6 , Fig . 3B , S5 Video ) . In spite of dramatically reduced invasion and deformation , heparin treated merozoites maintained contact with erythrocytes for extended periods . Thus , a heparin-blocked protein is likely not the initial protein binding merozoites to erythrocytes , but is involved in an early binding event that mediates weak deformation , and blocking with heparin prevents both this weak deformation and progression to stronger levels of deformation . While heparin binds to several parasite proteins , the most obvious known candidate is the MSP142 fragment of MSP1 , which is already present on the merozoite surface at the time of egress and is thought to play a role early in invasion [12] . We hypothesize , therefore , that MSP1 may be responsible for mediating weak deformation of the erythrocyte , allowing progression to stronger deformation and subsequent invasion , and that blocking MSP1 via heparin prevents both this weak deformation and the progression to stronger deformation via downstream receptors . To examine the effect of blocking the alternative-pathway we used W2mef , a parasite strain that preferentially expresses EBA175 over other alternative-pathway ligands , and relies on it during invasion . The target erythrocytes were treated with NM to cleave sialic acid from erythrocyte glycoproteins and thus inhibit the EBA proteins from binding . With this block in place the invasion rate declined 9 fold from 2 . 6 to 0 . 3 invasions per rupture ( W2m vs . W2m NM or Lane 3 vs . 7 , Fig . 3A , S2 Table ) . While the majority of merozoites weakly deformed their erythrocytes , scoring 1 , 2 . 4% of merozoite deformed more strongly , scoring 2 ( Lane 7 , Fig . 3B , C ) . We further tested invasion in alternative pathway null conditions by filming ΔEBA175 merozoites which primarily use PfRh4 , invading NM treated erythrocytes in the presence of soluble CR1 [39] . Under these alternative-pathway null conditions , the invasion rate declined 9 fold from 2 . 6 to 0 . 3 invasions per rupture , which is in line with the degree of inhibition previously published ( W2m vs Δ175 , NM , CR1 or Lane 4 vs . 8 , Fig . 3A , S2 Table , S6 Video [20] ) . In terms of invasions per erythrocyte contact , this represents a decline from 22% to 4% ( S2 Table ) . As the inhibited ΔEBA175 merozoites failed to deform strongly and the ratios of deformations scoring 0 and 1 are similar to W2mef under NM treatment , this suggests PfRh4 and EBA175 are performing comparable roles ( Lane 7 vs . 8 , Fig . 3B ) . Overall the data indicate that EBA/PfRh protein interactions most likely cause the strong deformation observed in the pre-invasion stage . To examine the role of PfRh5 binding to basigin we inhibited the interaction using rabbit anti-PfRh5 polyclonal IgG , which reduced invasion of 3D7 by ~90% [25] . For all parasite strains tested , a total of 42 schizont ruptures were filmed in the presence of anti-PfRh5 IgG with no successful invasions ( 3D7 vs 3D7 α-Rh5 or Lane 1 vs . 10 , D10 vs D10 α-Rh5 or Lane 2 vs . 11 and W2m vs W2m α-Rh5 or Lane 3 vs . 12 , Fig . 3A , S2 Table ) . Despite this , and in sharp contrast to the receptor-ligand events described above , the merozoites were able to bind and vigorously deform the erythrocytes to a similar degree as normal parasites ( Lane 1–3 vs . 10–12 , Fig . 3B , C , S9 Video ) . To determine if inhibitory anti-basigin antibodies would produce a similar effect to anti-PfRh5 antibodies , we observed invasions in the presence of the anti-basigin mAb MEM-M6/6 [22] . Similar to blocking with anti-PfRh5 , in four schizont ruptures , no invasions were filmed compared to an average of 1 . 7 invasions per rupture without antibody ( S2 Table ) . Likewise , although invasion was blocked , deformation with anti-basigin treatment was similar to that seen with anti-PfRh5 treatment , and deformation in both conditions was comparable to no treatment ( Fig . 3C , S7 Video ) . This indicates that PfRh5 has a distinctive role from the other PfRhs and the EBAs , that appears to be downstream as the PfRh5-blocked merozoites can progress beyond weak to strong erythrocyte deformation . This putative role is further explored and substantiated by experiments shown below . With PfRh5 appearing to function downstream of the EBA/PfRh ligands we next examined a step we hypothesized to be even further downstream , the AMA1 to RON complex interaction that forms the tight junction [29 , 30 , 32 , 33 , 36] . We filmed invading merozoites in the presence of RON2 peptide , which has been shown to block invasion with approximately 99% efficiency [36 , 40] . In our studies , treatment with RON2 peptide reduced the invasion rate of D10 merozoites approximately 8 fold , from 1 . 7 to 0 . 2 invasions per rupture ( D10 vs RON2 or Lane 2 vs . 13 , Fig . 3A , S2 Table , S10 Video ) . Some of the peptide treated merozoites appeared to embed themselves into the erythrocyte surface , however , they failed to transform into intracellular rings indicating invasion was unsuccessful . Deformation scores were similar to those of untreated parasites ( Lane 2 vs . 13 , Fig . 3B , C ) . Although the invasion-blocking effects of RON2 peptide appeared similar to that previously described for R1 peptide [40] we repeated this analysis here ( Lane 14 , Fig . 3A , S2 Table ) . We found the ratio of deformation scores in R1 peptide was similar to untreated parasites ( Lane 1 vs . 14 , Fig . 3B , C , S11 Video ) . Our data are consistent with AMA1 having a major function downstream of the EBA/PfRh’s , namely at the tight junction . Apart from merozoite invasion and deformation we also assessed whether the erythrocytes underwent echinocytosis after being contacted following the various inhibitory treatments . Merozoite contacts in heparin or following inhibition of the alternative pathways did not result in erythrocyte echinocytosis which is unsurprising since they were blocked quite early in the invasion sequence ( Fig . 3C ) . Interestingly , cytD treated merozoites , despite being unable to deform and invade erythrocytes , were able to reorientate and trigger echinocytosis in the absence of invasion . A comparison between untreated and cytD treated W2mef parasites , revealed that per schizont rupture , there was no difference in the number of resulting invasions or echinocytes , respectively ( Fig . 4A ) . However , cytD treated merozoites were less efficient at triggering echinocytosis in the first cell they contacted and tended to detach and contact additional erythrocytes before triggering echinocytosis in one of them ( Fig . 4B ) . However , once an erythrocyte was selected , cytD-treated parasites caused echinocytosis within a similar time period as untreated parasites , irrespective of invasion ( Fig . 4C ) . This indicates that the parasite’s actin-myosin motor is not required for reorientation and echinocytosis , but rather might be important for rapid host cell selection though deformation which possibly helps embed the merozoite in the erythrocyte surface , leading to subsequent downstream events . In cultures treated to block the PfRh5-basigin interaction , vigorous deformation did not result in echinocytosis ( Fig . 3C , S7 and S9 Videos ) . When we blocked invasion by preventing the AMA1-RON2 tight junction interaction with RON2 or R1 peptides , echinocytosis occurred ( Fig . 3C , S10 and S11 Videos ) . This places the AMA1-RON2 interaction downstream of PfRh5-basigin and suggests that echinocytosis is not caused by tight junction formation but rather an event upstream , possibly the PfRh5-basigin interaction . Rhoptry release from apically reorientated merozoites has previously been shown to occur even when the AMA1-RON2 interaction is blocked [27] . We thus hypothesize that the PfRh5-basigin interaction is responsible for triggering rhoptry release from apically reorientated merozoites , which in turn is responsible for stimulating echinocytosis , an event that can be separated from invasion . If erythrocyte echinocytosis is triggered by rhoptry release or some other perturbation to the erythrocyte membrane , what specific factor ( s ) is ( are ) causing echinocytosis ? It had previously been hypothesised that entry of Ca2+ into the erythrocyte during invasion may elicit echinocytic shape changes by direct effects of elevated intracellular Ca2+ concentration [4] possibly by acting on the cytoskeletal mesh[41] . Since erythrocytes do not store Ca2+ , an obvious Ca2+ source was the growth media and live cell imaging of invasion was therefore performed in Ca2+ free media to determine if echinocytosis still occurred . Prior to this , invasion assays were carried out with mature schizont cultures exposed to calcium containing RPMI and DMEM media and Ca2+ free DMEM media with or without EGTA over a 90 minute invasion window ( S1A Fig . ) . These experiments indicated invasion was reduced several fold in Ca2+ free media in agreement with previous studies [42 , 43 , 44] . We then performed live cell imaging in Ca2+ free DMEM with EGTA and found a 13 fold decrease in the average number of invasions per schizont rupture relative to RPMI ( S2 Table ) . Interestingly , without external Ca2+ the merozoites were still able to attach to and deform their target cells normally , but there was no echinocytosis , and thus the inhibition of invasion in Ca2+ free media occurred at the same point as it did when the Rh5-basigin interaction was blocked ( Fig . 3 , S2 Table , S1 Fig . , S12 Video ) . Having established that external Ca2+ was needed for invasion , we sought to determine if the external Ca2+ was entering the erythrocyte during invasion by visualising a potential flux with live cell imaging . We did this by purifying mature schizonts and adding them to erythrocytes labelled with the membrane permeable calcium-sensitive dye Fluo-4 AM . The majority of Ca2+ signals were punctate and confined to the invasion site ( Fig . 5A , S13 Video ) . The punctate Ca2+ signal was detected 112 times in 248 invasions ( 45 . 2% ) . While there was approximately a half-second time lapse between brightfield and Fluo-4 images , allowing slight movement , it appeared that the punctate Ca2+ signal was located at the apical end of the merozoite ( Fig . 5A ) . The Ca2+ signal first appeared near the end of deformation , on average 3 . 5 seconds prior to the initiation of invasion , and continued for an average of 11 . 3 seconds ( Fig . 5B , C ) . Since rhoptry release into the erythrocyte surface has to precede invasion , the Ca2+ signal was being observed at around the same time we would expect the rhoptries to discharge . Occasionally , simultaneous to or immediately after the punctate Ca2+ signal , we observed a strong Ca2+ flux spreading into the erythrocyte from the invasion site prior to the start of echinocytosis ( 5 . 2% of invasions ) ( S14 Video ) . There were also occasional instances where a strong Ca2+ flux in the erythrocyte occurred during echinocytosis ( 3 . 4% of invasions ) . The intensity and magnitude of the signal suggested a large influx of Ca2+ was possibly coming from the media . The punctate Fluo-4 fluxes could indicate that Ca2+ is a component of the rhoptry contents and that we are observing the ion being discharged into the host cell . The highly punctate and often confined nature of the signal could also indicate that Fluo-4 is entering the rhoptry compartment from the erythrocyte due to an opening forming in the erythrocyte membrane during rhoptry discharge ( Fig . 5D ) . The punctate Ca2+ signal was also strongly associated with echinocytosis and invasion , with the Ca2+ signal observed in 59 . 2% of the cases where echinocytosis occurred . In addition , in 94 . 7% of cases where a Ca2+ signal was observed , echinocytosis followed , strongly linking the Ca2+ signal , and putative rhoptry discharge , to echinocytosis ( Fig . 5E ) . However , the question remains; is it the Ca2+ or is it other components being discharged from the rhoptries such as proteins and lipids that cause echinocytosis ? To investigate this further we performed live cell imaging of merozoites invading erythrocytes treated with BAPTA-AM to chelate rhoptry Ca2+ or even external Ca2+ leaking into the erythrocyte during invasion . Relative to untreated erythrocytes , no inhibition of deformation , invasion or echinocytosis was observed ( S2 Table , S1B Fig . ) . The timing of all events leading up to invasion was also normal ( S1C Fig . ) . Collectively our observations indicate that Ca2+ is probably not required for echinocytosis , and we hypothesize that upon rhoptry discharge other rhoptry contents enter the erythrocyte membrane and trigger echinocytosis independent of invasion ( Figs 5 and 6 ) . In the absence of being able to directly visualise rhoptries discharging their contents we decided to use the punctate Ca2+ signal as marker for the release of these organelles and explore their putative role in triggering echinocytosis . It follows that if invasion was blocked under conditions where putative rhoptry Ca2+ signals were still observable , that echinocytosis should occur in the majority of cases . Conversely , when the rhoptry discharges were blocked , and no Ca2+ signals were observed , no echinocytosis should take place . The first invasion blocking condition tested where echinocytosis still occurred was with cytD-treated parasites . In this condition , apical orientation without deformation occurs , followed by tight junction formation but no erythrocyte internalization . In these parasites , we observed a punctate Ca2+ signal and where the alignment of merozoite and erythrocyte was side on , the Ca2+ signal localised to the region where the parasite and host made contact ( Fig . 5A , right panel set ) . In 75% of cases where there was a Ca2+ signal , echinocytosis occurred ( Fig . 5D ) , again reinforcing the link between the Ca2+ signal , echinocytosis and probable rhoptry release . Similarly when we blocked with R1 , which allows rhoptry release and echinocytosis , but not tight-junction formation or invasion , we observed the punctate Ca2+ signal which appeared at the same the time after merozoite contact as without R1 , with the average signal starting at 16 . 3 seconds after contact in Fluo-4 alone and at 12 . 6 seconds after contact for Fluo-4 with R1 . These R1 Ca2+ signals were observed in 39% of contacts that triggered echinocytosis ( 28 echinocytosis events and 11 Ca2+ signals , S11 Video ) . This further supports the use of the punctate Ca2+ signal as an indicator of rhoptry discharge which leads to echinocytosis . Next , we attempted to verify that blockage of rhoptry release and subsequent echinocytosis would coincide with no observable punctate Ca2+ signals . Live imaging was performed on untreated schizonts with Fluo-4 AM treated erythrocytes in Ca2+ free media with EGTA , and of 11 schizont ruptures with 127 merozoite contacts we observed no Ca2+ signals ( S15 Video , S2 Table ) . Next we imaged untreated schizonts with Fluo-4 AM treated erythrocytes in normal media containing anti-basigin IgG at 20 , 10 , and 2 . 5 μg/mL . In 20 μg/mL , from 22 schizont ruptures we observed no echinocytosis and 3 punctate Ca2+ signals in 128 merozoites that failed to invade their target erythrocytes but remained attached until the end of filming . In 10 μg/mL , from 19 ruptures we observed 2 incidents of echinocytosis ( 2 . 5% ) and 9 punctate Ca2+ signals in 81 merozoites that failed to invade their target erythrocytes but remained attached until the end of filming . In 2 . 5 μg/mL , in 16 ruptures we observed 5 incidents of echinocytosis ( 5 . 9% ) and 21 punctate Ca2+ signals in 85 merozoites that failed to invade their target erythrocytes but remained attached until the end of filming . This corresponds to punctate Ca2+ signals in 2 . 3% , 11 . 1% , and 24 . 7% of cases , respectively , and in comparison to 45 . 2% with no block , we see a dose-related increase in punctate Ca2+ signals with a decrease in anti-basigin antibody .
This study represents the first attempt to comprehensively overlay known receptor-ligand interactions with the morphological effects on erythrocytes and physiological events that occur in the seconds preceding , during and immediately after merozoite invasion of erythrocytes . By linking receptors to morphological effects on erythrocytes , as well as physiological and kinetic features , we show the probable order of sequentially acting receptor-ligand interactions leading up to invasion . In order , these are a heparin-blocked interaction ( possibly MSP1 binding an unknown erythrocyte receptor [12] ) , the alternative-pathway PfRh/EBA ligands binding a range of known and unknown host receptors , PfRh5 ligand binding the erythrocyte basigin receptor and finally the AMA1 ligand binding to another parasite protein , RON2 , that has been translocated into the erythrocyte membrane ( Fig . 6 ) [22 , 30 , 39] . To investigate the functional order of invasion ligands , three parasite strains and one mutant derivative were characterized . Strain 3D7 was chosen because inhibition of invasion in this strain has previously been characterized with a variety of laboratory reagents including R1 peptide . Recently , the D10 strain has gained favour because its 48-hour cell cycle permits ease of use relative to other lines . To study EBA/PfRh function , the EBA175 dominant line , W2mef , was chosen since this ligand is easily blocked by NM treatment . A derivative of this line , the ΔEBA175 mutant was also assayed because its switch to dominant use of the CR1-dependent PfRh4 ligand can be blocked with soluble CR1 subunits . Before commencing our study of inhibition of invasion it was important to ensure the lines invaded with similar frequencies and kinetics , which they did . The exception was in the number of invasions per erythrocyte contact , where merozoites lacking EBA175 were only half as efficient as other strains tested . ΔEBA175 parasites also interacted with more erythrocytes prior to invasion than did other strains , thus overall invasion rates were comparable . In most parasites EBA175 is a dominant ligand with about a million copies of its receptor , glycophorin A , available for binding ( reviewed in [45] ) . In contrast , there are only about a thousand molecules per erythrocyte of CR1 , the receptor for PfRh4 and the dominant ligand in ΔEBA175 parasites . The relatively low number of CR1 molecules could result in lower avidity , with the parasites detaching more readily , both preventing invasion and allowing merozoites which are still viable to interact with other erythrocytes . Thus , both the reduction in efficiency of invasions per contact in ΔEBA175 parasites , and the increased number of erythrocytes contacted prior to invasion could be due to the abundance of the respective erythrocyte receptors . The difference in receptor abundance , however , does not translate into different growth rates between the ΔEBA175 mutant and its W2mef parent strain , both in our data presented here and in unpublished growth rate data ( Lopatiki and Cowman , see Acknowledgments ) . Of the receptor-ligand interactions that we inhibited , MSP1 and the EBAs/PfRhs reduced the ability of merozoites to deform their target erythrocytes and invade . In addition , cytD treatment of parasites , but not erythrocytes , inhibited deformation and invasion , indicating that function of the actin-myosin motor is also required to deform the erythrocyte during apical reorientation prior to the motor’s role in host cell internalization . MSP142 is a target of heparin [12] , which almost completely ablated deformation . This block of progression to stronger deformation suggests both that MSP142 binding is responsible for weak deformation and that this leads to stronger deformation , reorientation and invasion . Although heparin is known to bind to other invasion ligands [46] , because it inhibits an early step in invasion , MSP142 could be the main invasion blocking target of heparin [12] . Specific inhibition of MSP1 using an antibody could help validate its role in early deformation , but unfortunately , the only known MSP1 invasion inhibitory antibody blocks surface shedding and therefore would be expected to function further downstream as the merozoite passes through the tight junction [47] . Heparin did not stop merozoites binding to erythrocytes suggesting other ligands are responsible for primary attachment . The weak deformation ( scoring 1 ) mediated by MSP1 could be passive in nature , as GPI-anchored proteins such as MSP1 , have fluid movement in membranes and , as they aggregate at the site of merozoite contact , could form a depression in the erythrocyte membrane . This “cap” of aggregated GPI-anchored ligands could hold the merozoite to the erythrocyte membrane but still allow the merozoite to rotate and move until the EBAs and PfRhs bind their respective partners . Blocking EBA175 and PfRh4 strongly inhibited both deformation and invasion . The observation that both EBA175 and PfRh4 function similarly , supports a long held view that these and most of the other EBA/PfRh ligands have redundant functions [48] . This , however , likely does not apply to all of these ligands since PfRh1 binding was recently shown to trigger parasite calcium signalling and EBA175 release [16] , and a separate role for PfRh5 , late in invasion , has been indicated by data presented here . Strong deformation ( scoring 2 and 3 ) that culminated in apical reorientation could be produced passively by a gradient of high affinity EBAs/PfRhs emanating from the merozoite apex or could be actively powered by the parasite’s actin-myosin motor interacting with the cytoplasmic tails of the EBAs/PfRhs . To discriminate between these we blocked the actin-myosin motor with cytD and the merozoites no longer deformed strongly , suggesting a role for the motor in powering deformation in addition to its role in internalization . Unexpectedly , the cytD treated merozoites could reorientate their apical ends onto the erythrocyte surface , release their rhoptries and presumably form a tight junction and trigger echinocytosis in the same time frame as untreated parasites . This indicates a gradient of EBA and PfRh proteins are passively responsible for reorientation and so what then is the function of strong deformation ? We observed in cytD treated merozoites that they contacted more erythrocytes before finally selecting one with which to form a tight junction than did untreated parasites . Could the actin-myosin powered movement of EBA and PfRh molecules be causing deformation to help to embed the merozoite into the erythrocyte surface so it is less easily detached and more rapidly commits to invasion ? Within an in vivo setting , strong binding , embedding into the host cell , and rapid host cell selection could be important to out-pace host immune responses and the shear forces associated with circulation . Whilst attempting to determine the sequence of invasion events , some key new findings have emerged . We show that successful invasion correlates with preceding strong deformation suggesting that vigorous surface contacts might have a function in promoting invasion or triggering downstream signalling required for subsequent invasion steps . Variability in the time and amplitude of deformation may correspond to where the merozoite makes initial contact with its target erythrocyte . For example , if the merozoite contacts near its apical end , then shorter and shallower deformation may occur prior to apical reorientation than if the merozoite makes contact with its basal end . To validate this would require the apical end to be visibly discernible from the rest of the merozoite during reorientation , but unfortunately we could not distinguish this using brightfield illumination alone . The apical end was , however , evident with Ca2+ imaging after reorientation where an apical Ca2+ signal was observed . When Fluo-4 AM dye was loaded into erythrocytes and added to unstained parasites , we observed a Fluo-4 flux at the apical end of the merozoite immediately prior to invasion . As erythrocytes have no known Ca2+ stores and the parasites were not labelled with Fluo-4 AM , this suggests that a Ca2+-containing organelle in the parasite is coming into contact with contents of the erythrocyte or that Ca2+ from the media is entering possibly via a pore . Previous observations indicate that rhoptry release still occurs when the AMA1–RON2 interaction is blocked [27] and here we show that when the AMA1–RON2 interaction is inhibited , the Ca2+ signal in the parasite still occurs . Further , when the parasite’s actin-myosin motor was blocked with cytD , the Ca2+ signal was still observed . Presence of the Ca2+ signal associates strongly with echinocytosis in untreated parasites as well as cytD and RON2–treated parasites . Together , this implicates that the rhoptries release factors that result in permeabilization of the host cell , Ca2+ entry and the triggering of echinocytosis . That Ca2+ entry might be triggering echinocytosis by changing the cytoskeletal mesh was tested by performing invasions in BAPTA-AM treated erythrocytes to chelate the ion upon entry . Chelation of Ca2+ had little effect upon any aspect of invasion apart from slightly decreasing the time to echinocytosis contrary to expectations . Since the secretion of lipid-rich rhoptry contents have been observed to penetrate the host membrane and form whorls within its cytoplasm , even when invasion is blocked with cytD [49 , 50] , these lipids could be responsible for echinocytosis . It has been proposed that materials which insert into the outer leaflet of a cell membrane will cause outward bending , leading to crenation of the cell [51] . The released rhoptry lipids would first make contact with the outer leaflet of the erythrocyte before penetrating as whorls into the cytoplasm . The excess material in the outer leaflet could cause transient crenation and echinocytosis of the host cell before flippases correct the asymmetry and return the echinocyte to its biconcave shape after several minutes , which is what we observe [38] . In RON2 and R1 blocked invasions we often observe that echinocytes fail to quickly return to their pre-invasion shape , even after 30 minutes which was the length of the filming period . It has been noted previously that in the presence of R1 peptide , rhoptry material was distributed over the outside of the erythrocyte membrane rather the penetrating it , since it was not confined within the boundary of the tight junction [27] . The deposition of the rhoptry’s full contents into the outer leaflet of the erythrocyte membrane could trigger great imbalance in bilayer components , which is why it could take longer for the erythrocyte to recover its normal shape . One of our most interesting new findings was that when PfRh5–basigin was blocked , or when invasion was inhibited by absence of extracellular calcium , incidences of echinocytosis virtually stopped , suggesting that the rhoptries were not released . The fact that an apical Ca2+ signal was rarely observed in Fluo-4 labelled erythrocytes when the PfRh5-basigin interaction was blocked corroborates this and suggests that PfRh5–basigin binding mediates a pre-tight junction in a calcium-dependant manner that triggers rhoptry release . Calcium does not appear to be required for PfRh5 binding to basigin [52] and it may be required for some upstream or downstream function . As external calcium is required for proteases namely PfSUB2 , that shed the merozoite surface coat [53] [54] , we did not initially suspect rhoptry release would be affected . Discharge of the rhoptries releases the RON complex that then crosses to the erythrocyte surface in order to form a tight junction with AMA1 to allow subsequent invasion of the erythrocyte . The Fluo-4 signal observed prior to and during invasion could be due to release of Ca2+ from the rhoptries and/or diffusion of erythrocyte Fluo-4 into the rhoptry neck region via a pore . Alternatively , media Ca2+ could be entering the erythrocyte at the invasion site . To discriminate between these , we tested for Fluo-4 signals in Ca2+ free media with the expectation that if a signal was observed it must be derived from the merozoite , or if absent , then the Ca2+ must be from media . The result however proved to be inconclusive for although no Fluo-4 signal was observed there appeared to be no release of rhoptries ( because there was no echinocytosis ) that would have provided a putative pore for entry of Ca2+ into the erythrocyte . Recently it was reported that P . berghei parasites with their ama1 gene deleted grow at about a third the rate of wildtype parasites and can still penetrate their target erythrocytes and form a parasitophorous vacuole membrane ( PVM ) [55] . We concur with the latter because we have observed here that a few RON2 peptide treated merozoites still penetrated their host cells . Previously , we also observed penetration in AMA1 knock down Pf merozoites , however in both cases we did not observe merozoites transforming into rings after internalization , and there was a delay in the recovery of normal erythrocyte shape after echinocytosis [38] . We suspect , therefore , that there was a defect in erythrocyte resealing at the invasion site , which could be one of the additional functions of the AMA1-RON2 tight junction . Normal PVM formation in P . berghei Δama1 parasites is compatible with our own observations and those of others , namely that AMA1 function is not needed for the release of rhoptry contents to form the PVM [27 , 55] . Live cell imaging can reveal much about parasite behaviour and pathogenesis . Our work here sheds light on the specific roles and order of receptor-ligand interactions leading up to invasion , expanding our knowledge of the biology of Plasmodium . This work also lays the foundation for the development of a sequential-step invasion-blocking vaccine , which might provide a significant biological advantage by targeting proteins functioning at multiple sequential steps of invasion , rather than targeting multiple proteins that function at the same step .
Pf strains 3D7 , D10 PfM3’ [56] , W2mef and W2mefΔEBA175 were maintained in continuous culture as per [57] . Where indicated , parasites were also cultured in Dulbecco’s Modified Eagle Medium ( DMEM , Gibco® ) supplemented with L-glutamine ( Sigma ) and Albumax II ( Invitrogen ) . Parasites were synchronised using sorbitol and heparin treatments as described previously [58] . Highly synchronous parasite cultures at 4% hematocrit were diluted to 0 . 16% in RPMI media or washed in Ca2+ free DMEM+EGTA media ( see below ) and 2 mL of this was allowed to settle to produce a monolayer onto a 35 mm Fluorodish ( World Precision Instruments ) . Custom dishes holding smaller 25 or 50 μL volumes were used for experiments containing invasion inhibitory antibodies . All live-cell experiments were performed at 37°C on a Zeiss AxioObserver Z1 fluorescence microscope equipped with humidified gas chamber ( 90% N2 , 1% O2 , and 5% CO2 ) . Late stage schizonts were observed until they looked ready to rupture ( described in [7] ) and time-lapse videos were recorded with a AxioCam MRm camera usually at 4 frames per second . ImageJ and Prism ( Graphpad ) were used to perform image and statistical analyses . For data sets with normal distribution ( Figs 1E-G , 4C , and S14C ) an unpaired t test was used . For data sets without normal distribution ( Figs 1B-D , 3A , 4A&B , S6 and S14A&B ) the Mann-Whitney test was used . For comparison of deformation scores between groups ( Figs 2 , 3B&C and 5E ) a Chi-square analysis was performed . A value of p≤0 . 05 was used as the determinant of statistical significance for all tests . To score the degree of merozoite deformation of the erythrocyte surface we initially attempted to develop software to automate the process but were unable to . Instead , we resorted to scoring deformation by eye using a simplified deformation score and multiple trained scorers . We defined contact between the merozoite and erythrocyte as interactions where the erythrocyte and merozoite maintained physical contact with each other for two frames ( 0 . 25 seconds ) or longer . The vast majority of contacts which we observed lasted considerably longer than 0 . 25 seconds . Interactions where the merozoite and erythrocyte appeared to touch for a single frame were not counted unless there was evident deformation , indicating clear contact , however these were very rare events . A deformation score of: 0 = sustained contact but no deformation , 1 = weak deformation at the point of contact , 2 = strong deformation with the erythrocyte membrane extending up sides of the embedded merozoite , and effects of deformation no longer strictly local to the merozoite , and 3 = extreme deformation with the deeply embedded merozoite partially covered by the erythrocyte membrane and extremely strong deformation distant from the merozoite to the point of distortion of erythrocyte borders ( Fig . 2A , B ) . Invasion-blocking reagents were used as follows; Heparin 100 μg/ml ( Sigma ) , 100 μg/mL R1 peptide ( Mimotopes ) [35] , 10 μg/mL RON2 peptide ( LifeTein LLC ) [36] , 1 μg/mL cytochalasin D ( Sigma ) , 10 μg/mL anti-basigin mAb MEM-M6/6 [22] , and 10 mg/mL rabbit anti-PfRh5 [25] . Calcium within erythrocytes was chelated using BAPTA-AM ( Sigma ) . The stock BAPTA-AM prepared in DMSO was added to erythrocytes at 0 . 16% hematocrit to achieve the final concentration of 50 μg/mL . The treated erythrocytes were incubated at 37° for 30 min , and then were washed three times . To inhibit merozoite invasion in W2mef ΔEBA175 parasites 0 . 2U/mL neuriminidase ( Sigma ) was added directly to the parasitized erythrocytes for 30 mins at 37°C and 10 μg of CR1 CCP1–3 was added prior to microscopy [20 , 39] . Control invasion assays were performed in complete RPMI or DMEM media ( Life Technologies ) . For Ca2+ free experiments Ca2+ free DMEM was used ( Life Technologies ) , both in its standard form and modified with 2 . 5 mM EGTA since Ca2+ free RPMI was not commercially available . The media were supplemented with 25 mM HEPES and dialysed calcium free Albumax II to 0 . 5% and prior to use all the media were supplemented with 0 . 2% NaHCO3 . The invasion experiments were conducted with two transfected 3D7 parasites lines expressing secreted or exported Nanoluciferase [59] . Late stage magnet purified schizonts ( ~2 × 108 ) were split into four equal portions and each was washed in 1000 volumes of either RPMI , DMEM , Ca2+ free DMEM or Ca2+ free DMEM+EGTA . In parallel , uninfected erythrocytes were washed the same media types and then each batch of parasites and erythrocytes of the same media type were mixed together to final hematocrit of 2% . Each of the fractions was then split into two and heparin ( Sigma ) at a final concentration of 100 μg/mL was added to one half of the parasites to block invasion ( Boyle et al 2010 ) . The parasites were then incubated at 37°C for 90 mins to permit some invasion to occur . The infected erythrocytes were then pelleted at 3000g and a small amount of media retained to measure luciferase activity to ensure that egress and Nanoluciferase release had occurred . The parasites treated with 5% sorbitol to lyse the remaining schizonts and merozoites . The sorbitol was then replaced with complete RPMI media and the new ring stage parasites were grown in triplicate 100 μL cultures in a 96 well plate for 24 and 72 hrs to amplify the invasion signal . To assay parasite growth at 2 , 24 and 72 hrs post invasion , 5 μL of the culture was lysed in 50 μL of water containing 1/1000 NanoGlo substrate ( Promega ) . Relative light units ( RLU ) were measured in a FLUOstar Omega Luminometer ( BMG Labtech ) for 2s with the gain set to the brightest well and adjusted to 10% below saturation . To derive the invasion signal , the RLU from the heparin blocked invasion samples were deduced from their corresponding heparin free samples . Fluo-4 AM ( Life Technologies ) was added to a final concentration of 10 μM to erythrocytes diluted in incomplete RPMI with no albumax or serum at 2% hematacrit . The erythrocytes were incubated at 37° for 1 hr and washed 3x in 10 volumes in complete RPMI or Ca2+ free DMEM+EGTA before adding to a 35 mm Fluorodish as described above . Magnet purified late schizont stage parasites was added to the dish . The parasites were imaged with low power brightfield conditions ( 2–3 volts ) until rupture occurred whereupon alternative brightfield and fluorescence images with the GFP filter set were recorded . To limit photodamage each 35 mm dish was only imaged four times , once per quarter , and the 50 μL dishes were prepared fresh for each movie made . | The development of an effective malaria vaccine is a world health priority and would be a critical step toward the control and eventual elimination of this disease . In addition , new pharmacological solutions are necessary as Plasmodium falciparum , the deadliest of the malaria-causing parasites , has developed resistance to every drug currently approved for treatment . Understanding the interactions required for the parasite to invade its erythrocyte host , as well as being valuable to our basic knowledge of parasite biology , is important for the development of drug-based therapies and vaccines . In this study we have , for the first time , filmed P . falciparum parasites invading erythrocytes while systematically blocking several specific interactions between the parasite and the erythrocyte . We have shown there is a sequential progression of specific interactions that occur in at least four distinct steps leading up to invasion . Previous vaccine attempts have targeted one or two of these steps , however , if a single vaccine were designed to block interactions at all four steps , the combined effect might so reduce invasion that parasite growth and disease progression would be arrested . A better understanding of each interaction during invasion , their role and order , can also inform the development of new anti-malarial drugs . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Revealing the Sequence and Resulting Cellular Morphology of Receptor-Ligand Interactions during Plasmodium falciparum Invasion of Erythrocytes |
Most tumors arise from epithelial tissues , such as mammary glands and lobules , and their initiation is associated with the disruption of a finely defined epithelial architecture . Progression from intraductal to invasive tumors is related to genetic mutations that occur at a subcellular level but manifest themselves as functional and morphological changes at the cellular and tissue scales , respectively . Elevated proliferation and loss of epithelial polarization are the two most noticeable changes in cell phenotypes during this process . As a result , many three-dimensional cultures of tumorigenic clones show highly aberrant morphologies when compared to regular epithelial monolayers enclosing the hollow lumen ( acini ) . In order to shed light on phenotypic changes associated with tumor cells , we applied the bio-mechanical IBCell model of normal epithelial morphogenesis quantitatively matched to data acquired from the non-tumorigenic human mammary cell line , MCF10A . We then used a high-throughput simulation study to reveal how modifications in model parameters influence changes in the simulated architecture . Three parameters have been considered in our study , which define cell sensitivity to proliferative , apoptotic and cell-ECM adhesive cues . By mapping experimental morphologies of four MCF10A-derived cell lines carrying different oncogenic mutations onto the model parameter space , we identified changes in cellular processes potentially underlying structural modifications of these mutants . As a case study , we focused on MCF10A cells expressing an oncogenic mutant HER2-YVMA to quantitatively assess changes in cell doubling time , cell apoptotic rate , and cell sensitivity to ECM accumulation when compared to the parental non-tumorigenic cell line . By mapping in vitro mutant morphologies onto in silico ones we have generated a means of linking the morphological and molecular scales via computational modeling . Thus , IBCell in combination with 3D acini cultures can form a computational/experimental platform for suggesting the relationship between the histopathology of neoplastic lesions and their underlying molecular defects .
The environment in which tumor cells are growing in vivo can be very complex , and may include distinct stromal cells , normal or aberrant vasculature , inhomogeneous concentrations of nutrients , proteases or growth factors , gradients in interstitial pressure or non-uniform alignment and cross-linking of various fibrous proteins forming the extracellular matrix ( ECM ) . Since the cells are exposed to these various and often contradictory microenvironmental cues , and moreover , they can actively participate in remodeling of the physical structure and chemical composition of the stroma , it is difficult to predict tumor progression and response to treatments . The change in cell phenotypic state ( i . e . , the initiation of cell proliferation or death , cell epithelial polarization or epithelial-mesenchymal transition ) depends not only on cell intrinsic sensitivity to extrinsic cues from the surrounding microenvironment , but also on cell robustness and adaptability to microenvironmental conditions . Several in vivo techniques have been used to investigate interactions between individual cells and to test cell responses to various extrinsic cues in more controlled conditions . In particular in the three-dimensional ( 3D ) culture systems cells display many features characteristic of their in vivo growth , but not observed when these cells are cultured in two-dimensional monolayers . Ideally , one would like to be able to make an initial assessment about the possible molecular changes or underlying mutations by examining the morphology of the multicellular structures grown from mutated or tumorigenic cells . Therefore , we have developed a computational model , IBCell ( Immersed Boundary model of a Cell [1] , [2] ) that allows us to simulate the development of multicellular structures by focusing on cell mechano-biology and the interactions between individual cells and their microenvironment . IBCell is a general computational framework that has been previously used to model different tumor related phenomena , such as growing multiclonal colonies [3] , various patterns of ductal carcinoma in situ [4] , and formation of invasive cell cohorts [5] . The advantage of the IBCell model over other cell-based modeling approaches in which cells are represented either as point particles or as deformable cells composed of fixed size grid sites [6] , [7] , [8] , [9] lies in the fact that the cells in our model are fully deformable . Cell geometry in IBCell is neither predefined nor grid-determined , but can vary dynamically due to interactions between individual cells . Moreover , the plasticity of cell shape is accompanied by dynamical changes in cell sensors/receptors configuration . Thus two neighboring cells or two phenotypically identical cells may have slightly different distributions of specific cell surface receptors leading to a natural cell-to-cell heterogeneity , which is similar to what actually happens in real cells . Therefore , IBCell model simulations represent more faithfully the emerging multicellular morphologies ( such as multilayered structures [2] , [10] , epithelial acini [11] , [12] , or ductal carcinoma in situ [4] ) than other computational models in which cells are modeled as points , squares , hexagons or spheres [6] , [8] , [9] , [13] , [14] . This makes the outcomes of IBCell simulations more easily comparable with experimental morphologies in both qualitative ( shape ) and quantitative ( cell numbers ) ways . The rest of this paper is organized as follows . We first summarize how IBCell has been adjusted to model the formation of epithelial acini by focusing on three cellular processes: cell proliferation , cell apoptotic death and ECM-density dependent inhibition of cell growth . Then we show how the model can be tuned , so the emerging acinar structures match qualitatively and quantitatively the experimental data collected from 3D culture of a non-tumorigenic breast cell line MCF10A [15] . We then use this MCF10A-tuned model to explore the 3D parameter subspace ( the IBCell morphochart ) corresponding to different non-stabilizing acinar mutants and compare these simulated structures with the experimentally observed morphologies of MCF10A-HER2 mutants . In addition , we analyze more closely the IBCell simulation reproducing the MCF10A-HER2-YVMA [16] mutant morphology to quantitatively assess the changes in model parameters when compared to the simulation reproducing the wild-type MCF10A acinus . This allows us to formulate hypotheses about the phenotypic changes that have occurred in the mutated cells and to potentially guide further experimentation .
The generic IBCell model has been adjusted to represent interactions between individual cells that collectively lead to the formation of hollow multicellular structures ( acini ) composed of a shell of epithelial cells enclosing a hollow lumen . As our experimental prototype , we used human epithelial breast cells , MCF10A , and followed their development in a 3D Matrigel culture as described in [15] , [17] , [18] . In particular , our goal was to capture several stages of MCF10A acini formation starting from a single cell and including self-organization into a multicellular cluster , emergence of inner and outer cell subpopulations , epithelial polarization of all outer cells as well as death of the inner cells and lumen formation . An important feature of this model is the use of cell surface receptors/sensors to define cell interactions and communication with the surrounding environment . The following five kinds of receptors/sensors are considered here: ( 1 ) cell-ECM receptors that are activated if the concentration of ECM in their vicinity exceeds a prescribed density threshold ( similarly to actions of integrins [19] ) ; ( 2 ) cell-cell adhesive receptors that define an adhesive link between two distinct cells located sufficiently close to one another ( similarly to cadherins [20] ) ; ( 3 ) cell apical markers [21] that emerge in an outer polarized cell by disassembling all existing cell-cell adhesive links with inner cells; ( 4 ) cell death receptors [22] that are created in an inner cell upon its detachment from the polarized cell or from another dying cell; ( 5 ) cell growth sensors that are used by the cell to sense free space in their vicinity that is necessary for the initiation and progression of cell growth – this is the assumed default state of all sensors when they are not engaged in the other processes listed above . The phenotypic state of each cell , i . e . , its growth , death , senescence or epithelial polarization , is modulated by the percentage of receptors/sensors recruited to a particular process . For example , the host cell can grow only if it can sense sufficient space in its vicinity , which is defined by a percentage of growth sensors located on its surface . If this ratio is small because of an excess of cell-cell or cell-ECM adhesion receptors , the host cell is considered to be in contact inhibition . Similarly , the process of cell apoptotic death is initiated when the percentage of cell death receptors reaches a prescribed level . Thus by varying these sensor/receptor thresholds we can specify whether the cell is more sensitive or more resistant to a specific life process , such as cell death , growth , attachment to the ECM or contact inhibition . Certain assumptions of IBCell have proven to be necessary to generate the hollow monolayered structures [11] , [12] , but may be challenged experimentally in order to falsify model predictions . We assumed that in the developing acinar structures the orientation of dividing cells need to be quite tightly controlled . In our model , all outer cells divide orthogonal to their basal membrane domains ( a symmetric division producing two basal daughter cells ) in the initial stage of acinar formation , but the mode of cell division is switched in the later stages to asymmetric division that results in emergence of one basal and one luminal daughter cell . This assumption could be verified by systematic inspection of growing cells within the multicellular structure ( for instance by using the live-cell confocal microscopy ) in order to determine the axes of cell division at the various stages of acinar development . We have previously shown in [12] that improper cell divisions may lead to irregular , degenerate morphologies . We also assumed that stabilization of acinar structures is due to secretion and accumulation of ECM proteins along the membranes of all outer cells , which eventually leads to their growth arrest . Experimentally , one could either inhibit secretion of ECM proteins ( i . e . , laminin , collagen , elastin and/or fibronectin ) , or modify cell ECM receptors ( i . e . , integrins ) response , and investigate whether the MCF10A cells will still form stable growth arrested structures . We discuss later in this paper how the changes in cell response to ECM cues leads to the emergence of invasive-like mutants . More details about the IBCell model can be found in [11] , [12] . See also the Methods section for model equations . We used an integrative approach ( Fig . 1 ) combining in vitro experiments , confocal image analysis and quantification , and high throughput simulation studies to better understand the phenotypic and molecular changes in mutated cells in comparison to their parental non-tumorigenic cell line , MCF10A . The multicellular acini grown in the 3D Matrigel culture were collected every four days for 20 days and stained with nuclear marker , DAPI , and antibodies against cleaved caspase-3 and Ki-67 , for apoptosis and proliferation , respectively ( Fig . 1a ) . The confocal images of central acinar cross sections ( Fig . 1b ) were segmented ( Fig . 1c ) and cellular nuclei delineated ( Fig . 1d ) using the BioSig bioinformatics software ( see Methods ) . Intensities of red ( for caspase-3 ) and green ( for Ki67 ) wavelengths ( Fig . 1f ) were determined in BioSig , and the proliferative and apoptotic events were reconstructed ( Fig . 1e ) in all considered samples . This allowed for determination of the counts of growing , dying and the total number of cells in all collected samples ( Fig . 1g ) . The previously developed model of epithelial morphogenesis , IBCell [11] , [12] , has been used to simulate the formation of a generic epithelial acinar structure ( as described in [11] ) , and subsequently tuned with experimental data for MCF10A . The tuning process requires construction of the search tree ( Fig . 1d ) that discriminates between model parameters showing promise to generate the desired structure as well as cell counts comparable with experimental data at each considered time point ( see Methods ) and those that lead to false multicellular structures ( see Methods for a description and Fig . S1 for examples ) . The resulting simulated morphologies from the MCF10A-tuned IBCell model together with the evolution of growing , dying and the total number of cells matching the experimental data are shown in Figs . 1i–1j . By systematically varying model parameters in IBCell simulations we can obtain a spectrum of different final acinar morphologies ( a morphochart ) . A typical chart constructed for parameters defining cell sensitivity to proliferating and apoptotic cues is shown in Fig . 1k . In this morphochart the normal hollow acini are produced by cells resistant to contact inhibition , but sensitive to apoptotic cues ( lower left region ) ; partially or fully filled acini arise from cells that are resistant to death signals ( right region ) ; and irregularly shaped acini emerge from cells that enter a growth arrest phase due to their sensitivity to contact inhibition ( top region ) . By varying multiple model parameters one can create a multidimensional space of final acinar morphologies and group them according to their architecture . Fig . 1l shows four color-coded regions containing normal ( red ) , filled ( blue ) , irregular ( yellow ) and non-stabilized ( green ) acinar structures generated by IBCell . For more details on the subspace of normal and non-stabilized acini see Fig . 2 and Fig . 3 , respectively . We have previously shown [11] , [12] that our IBCell model can simulate the development of normal acini starting from a single cell and ending with a structure composed of one layer of tightly packed epithelial cells enclosing the hollow lumen . However , our aim here is not only at qualitative ( morphological ) agreement with experimental data , but we also want to quantitatively recapitulate the dynamics of the emergent structures . Therefore , the generic model of epithelial morphogenesis has been tuned ( as described in Methods ) with quantitative experimental data from 3D cultures of MCF10A cells . For the tuning process we used counts of proliferating and apoptotic cells , together with information on the times and locations of these events ( i . e . , initially the proliferating cells are detectable in the whole cluster; at the later stages the growing cells are mostly confined to the outer layer; in contrast the dying cells are only located inside the cluster ) . The third model parameter that we used , corresponds to the ECM density and may be adjusted to fit experimental data on the density of ECM proteins , such as collagen , elastin , fibronectin or laminin . As a result of this tuning process we identified a set of model parameters that reproduced an acinar morphology in good quantitative agreement with the experimental cellular baseline ( Fig . 1i–1j and video S1 ) . This combination of model parameters was then utilized as the initial seed for a suite of simulations that examined the outcome of varying all three thresholds systematically to inspect the whole 3D parameter space ( Fig . 2 ) . A broad region in this parameter space can be identified ( indicated by light red color ) that comprise only of hollow acini of various areas , cell counts and luminal sizes ( inserts in Fig . 2i–2v and Fig . S2 ) . A smaller subregion ( indicated by dark red color ) corresponds to acini that agree quantitatively with the experimental data from the MCF10A cell line , i . e . , the counts of viable , proliferating and apoptotic cells . Interestingly , the tuned region highlights that there is a degree of variability in the cellular traits and appears to contain two distinct acinar morphologies with slightly different cell counts and evolution curves that both fall within the ranges of MCF10A experimental measurements ( Fig . 2i–2ii ) . The area outside the indicated regions contains multicellular morphologies that do not represent normal acinar structures , i . e . , they are either distorted , or not hollow , or multilayered and not stabilized ( yellow , blue and green regions in Fig . 1l , respectively; see also Fig . 3 and [11] , [12] , [23] for other examples ) . The light-red region specifies the range of acinar plasticity defined here as morphological variations in acinar structure arising as a result of developmental dynamics . Several epithelial systems have been successfully grown in 3D cultures showing variations in acinar sizes both within and across different cells lines ( i . e . , averaged diameters of prostate acini can reach: 140 . 8±31 . 0µm , and 149 . 8±24 . 3µm [24] , canine kidney acini: 80–90µm [21] , breast acini: 67 . 2±16 . 5µm and 93 . 9±19 . 5µm [15] ) . In our model such differences in acinar sizes , shapes and cell counts depend on the time at which the acinar structures become growth arrested and stabilized . We hypothesized that extracellular matrix produced by cells acts as an inhibitory mechanism on proliferation . That is , as the density of the matrix increases in the cell vicinity the sensors in contact with the ECM are converted to ECM sensors , thus decreasing the number of growth sensors and inhibiting cell growth . This could be considered biologically equivalent to the process of “matrix assembly” . Therefore , for low values of the ECM threshold the acini stabilize very early reaching only a few cells with a minimal inner lumen ( Fig . 2v ) . With the increasing ECM threshold the acini grow larger and need more time and a higher density of accumulated ECM to become stabilized ( compare inserts in Fig . 2iv and Fig . 2iii ) . Biologically , contact inhibition resembles this kind of behavior , and it is generally observed in 2D tissue culture ( i . e . , confluence ) . In a 3D setting , contact inhibition is likely to be more closely related to epithelial polarization , whereby plasma membranes become committed to basolateral and apical domains . Our model recapitulates this mechanism , however , further experiments are needed to confirm which ECM proteins may play a major role in this inhibitory mechanism for a given cell line . The dark-red region defines robustness of MCF10A-comparable acinar structures , i . e . , their capability to retain the architecture of the hollow one-layered acini , and to remain in quantitative agreement with experimental data when cell sensors thresholds are varied . Interestingly , the model is less sensitive to death signals , with changes in the death threshold from 7 . 5% to 17 . 5% of all cell membrane sensors still resulting in a completely hollow luminal space . Similar effects have been observed experimentally when anti-apoptotic proteins Bcl-2 or Bcl-XL were overexpressed in the MCF10A cells [17] causing cells to become more resistant to the initiation of the apoptotic process , but still resulting in formation of the hollow lumen . There is no quantitative data available to assess the relative differences between wild-type MCF10A cells and MCF10A-Bcl-2 or MCF10A-Bcl-XL cells in terms of their sensitivity to apoptotic cues , but these experiments show a trend similar to that seen in our simulations . The dark-red region of MCF10A-comparable acini in Fig . 2 contains two slightly different morphologies and corresponding evolution curves , both falling within the ranges of experimental measurements . These morphologies are robust with respect to slight variations in the cell proliferation threshold ( changes of 15–20% , and 22 . 5–25% of all cell membrane sensors for ECM threshold of 12 . 5 and 15 , respectively ) . Again , such variability in acinar sizes , shapes and cell counts is observed experimentally for MCF10A cells ( Fig . 1g and [15] ) . By tuning the generic model with the experimental data we identified parameter vectors for which the model generates MCF10A-like structures . However , when the model parameters are chosen outside this range , the resulting structures are morphologically different from hollowed and monolayered acini . By inspecting the whole MCF10A-tuned morphochart we can identify regions in which our computational model produces altered morphologies . Of particular interest to us is the region of non-stabilized mutant structures ( shown in green in Fig . 1l and in Fig . 3 ) , as these morphologies represent the potentially invasive mutants of the non-tumorigenic epithelial baseline MCF10A . Out of the five different structures that emerged from our simulations , four can be qualitatively matched with morphologies from experimental mutants of MCF10A cells . We describe below the morphological similarities between experimental and simulated structures . It must be stressed , however , that further experiments are needed in order to validate or falsify model predictions by comparing model parameter vectors to experimental measurements . A non-polarized structure ( Fig . 3i ) showing no cell growth resembles the MCF10A-HER2 mutant ( Fig . 3I ) that at day 24 consists of a mass of cells with no detectable staining for proliferation or apoptosis . This simulation was run with low thresholds for both cell growth and cell ECM receptors , and a high threshold for cell death receptors ( lower left corner of the mutant parameter space in Fig . 3 ) . This suggests that both cell proliferation and apoptosis were upregulated in comparison to the parental non-tumorigenic cell line , but since the ECM threshold was downregulated all cells became growth-arrested either due to cell-cell adhesion ( inner cells ) or cell-ECM adhesion ( outer cells ) . A non-stabilized structure with growing cells observed only on the outer rim ( Fig . 3ii ) is similar to the MCF10A-HER2-Bcl2 mutant ( Fig . 3II ) that at day 24 forms a solid cluster of cells with a few outer cells stained with Ki67 nuclear marker showing proliferating events . Matching computational morphologies were obtained when all three thresholds were chosen to be high , such that no cell death was detectable , and all inner cells were in contact inhibition , thus non-growing , but most outer cells have not reached the growth arrested state and therefore continued to proliferate ( region ii in the parameter space in Fig . 3 ) . A disorganized acinus with multiple cells growing throughout the cell cluster , and with sporadic apoptotic events ( Fig . 3iii ) resembles the MCF10A-HER2-YVMA mutant ( Fig . 3III ) that at day 24 forms an irregular mass of cells with numerous proliferative cells located both inside the cluster and on its outer rim . This simulation was run with high thresholds of ECM and apoptotic receptors ( both upregulated in comparison to the parental cell line ) , but a low threshold of growth sensors resulting in frequent proliferations even in the center of the cluster due to diminished contact inhibition response ( region iii in Fig . 3 ) . A non-stabilized structure with multiple proliferating and apoptotic events ( Fig . 3iv ) is similar to the morphology of the MCF10A-HER2-E7 mutant ( Fig . 3IV ) . In this case the proliferative and apoptotic thresholds were set to a lower level , resulting in numerous growing and dying cells in the simulated acinus . However , the ECM threshold was set high , such that the emerging structures did not reach the growth arrested state by day 24 in culture ( right upper corner of the mutant region in Fig . 3 ) . A non-polarized but hollow acinus with an outer rim containing multiple growing cells ( Fig . 3v ) was simulated by choosing lower values for all three cell receptors thresholds , resulting in a hollow inner lumen with a constantly growing rim of outer cells ( right lower corner of the mutant region in Fig . 3 ) . However , to our knowledge there is no experimental data matching this simulated morphology of a MCF10A mutant . These distinct acinar architectures simulated by our model and indicated in Fig . 3 and Fig . S3 arise for different combinations of all three receptor thresholds defining cell sensitivity to proliferative , apoptotic and ECM signals . By mapping various experimental morphologies of MCF10A genetic mutants onto the non-stabilized region of the MCF10A-tuned morphochart we can estimate the relative changes in the proliferation , death and ECM sensitivities between the mutated and the parental cell lines , i . e . , we can indicate whether a certain cellular process is up- or down-regulated in comparison to the parental cell line . These back estimated values effectively link genetic mutations to cell life processes via the generated multicellular morphology and can be used to guide further experimentation . To illustrate how the IBCell morphocharts can be employed to shed light on phenotypic differences between normal and mutated cells , we used the MCF10A-tuned model as a starting point and adjusted its parameters ( following the tuning procedure described in the Methods section and the search tree depicted in Fig . 1h ) to quantitatively match the experimental data collected from MCF10A-Her2-YVMA ( called YVMA thereafter ) cells carrying a constitutively active HER2 mutant . This allowed us to identify which aspects of the non-tumorigenic baseline needed to be changed in the computational model in order to simulate both the morphology of this specific mutant cell line and its developmental dynamics . Typical cross sections from the YVMA experimental data collected over a period of 24 days are shown in the top row of Fig . 4a , whereas the bottom row shows corresponding computational outcomes . The counts of proliferating ( green ) , apoptotic ( red ) and the total number of cells ( blue ) from the experimental YVMAs ( quantified using BioSig , see Methods ) are shown in Fig . 4b and from the simulated structures in Fig . 4c . The table in Fig . 4d lists the set of model features that were set differently in simulations reproducing the MCF10A-like hollow acini and the non-polarized and non-stabilized multicellular clusters typical of YVMA mutants . We assumed that both cell types , MCF10A and YVMA , have similar diameters of about 20µm . This was estimated from confocal images of the early developmental stages when both cell lines formed solid spheres of cells with no apoptotic events present . The average areas of the central cross sections through MCF10A clusters were measured to be 3490±810µm2 at day 4 , and 7640±1550µm2 at day 8 with the average cell counts of 11 . 5±2 and 25 . 8±6 . 3 , respectively; whereas the average areas of YVMA sections were 6180±1280µm2 at day 4 and 11450±2770µm2 at day 8 with cell counts of 20 . 2±4 . 2 and 40 . 7±10 . 7 , respectively . In both cases the average cell diameter was about 19 . 5µm . This clearly highlights that the YVMA mutants grow larger and contain more cells than MCF10A acini . To achieve the matched computational results presented in Fig . 4 we first needed to match the number of YVMA cells at day 4 , which are two times larger than the MCF10A samples from the same day . We identified model parameters that directly influence the duration of cell growth leading to the desired increase in cell number . As a result the effective doubling time in our simulations has been changed from 47 . 7±9 . 4 hours for MCF10A cells to 33 . 3±6 . 8 hours for YVMA cells . This is consistent with the reported MCF10A population doubling time of 48 hours [25] , and with the 2- and 4-fold increase in YVMA cell number after 4 and 16 days , respectively , when compared to the parental MCF10A cells ( see [16] as well as Fig . 1g and Fig . 4a ) . To achieve the desired cell counts and distributions of proliferating cells that match the YVMA experimental data at later stages we needed to release the constraints of cell contact inhibition and remove the restriction blocking the inner cells' receptors from sensing free luminal space . We have previously shown that removing these constraints may lead to complete repopulation of the empty lumen [12] . Also , the final morphologies of the YVMA mutant are much larger than the MCF10A acini , so to reach comparable sizes in our simulations we needed to increase the ECM sensors threshold to prevent or delay acini stabilization . This modification can be interpreted as making the computational YVMA cells less sensitive to ECM binding or ECM contact inhibition . Confocal images of YVMA clusters show very limited staining for caspase-3 ( apoptotic marker ) and no lumen formation even at later stages in contrast to MCF10A acini that became completely hollow at day 20 . In fact , in both experimental systems the quantitative data did not show any significant apoptotic cell death ( 1–2 cells on average at days 12–20 ) . This may be due to the fact that the apoptotic staining of caspase-3 is detectable only for a few hours and only at the later stages of cell apoptotic death , whereas the experimental data was collected every 4 days . Alternatively , other forms of cell death , such as autophagy [26] or entosis [27] may be responsible for clearing the MCF10A lumen . To avoid formation of the luminal space we set the threshold for death sensors significantly higher in the YVMA simulations in comparison to MCF10A ( Fig . 4d ) , and apoptotic cells were then seen to emerge sporadically ( video S2 ) . With these parameters our simulated results for both the total number of cells and the number of growing cells fall within the range of experimental measurements ( Fig . 4c ) , and the distributions of proliferating and apoptotic cells also match the experimental samples ( Fig . 4a ) . The MCF10A-HER2-YVMA tuned simulation of the IBCell model led to identification of changes in cell doubling time , lower sensitivity to contact inhibition , modifications in ECM-dependent inhibition of cell proliferation and luminal space promotion of cell growth , that together resulted in the emergence of YVMA-like morphologies that quantitatively agreed with our experimental data . These model predictions ( Fig . 4d ) may be further investigated experimentally in order to confirm or rule out our findings .
In this paper we presented an integrative approach combining in vitro experiments , confocal image analysis and quantification , and high throughput simulation studies to understand the relationship between phenotypic and molecular changes in certain mutated cells when compared with their parental non-tumorigenic cell line . We used the previously developed IBCell model of epithelial morphogenesis to simulate the formation of a generic acinar structure composed of a shell of tightly packed epithelial cells enclosing the hollow lumen . Subsequently , we tuned this model with quantitative experimental data collected from several samples of MCF10A cells grown in three-dimensional cultures . This allowed us to identify starting parameters that served as a seed for constructing a 3D morphochart , i . e . , a collection of final morphologies produced by IBCell when the model parameters were varied systematically around these baseline values . Next we used this IBCell morphochart to identify regions in which our computational model produced structures that quantitatively agreed with MCF10A data , as well as those that were morphologically different from the hollow monolayered acini . We then mapped morphologies of four MCF10A mutants onto the IBCell morphochart to identify the cell phenotypic changes in terms of three cellular processes: proliferation , apoptosis , and ECM-dependent growth inhibition , potentially underlying these mutants . Finally , we examined more closely one specific mutant , i . e . , MCF10A-HER2-YVMA , and adjusted MCF10A-tuned model to quantitatively match the YVMA data . This led us to identify the up- and down-regulated cellular processes responsible for observed qualitative and quantitative changes . These computational findings need to be examined experimentally , however , this is beyond the scope of this paper . It is often a major experimental challenge to acquire certain quantitative data from 3D culture systems , particularly at the cellular scale . Our IBCell model has allowed us to acquire a range of quantitative measurements in terms of both individual cellular phenotypes and the whole cluster morphology . Analysis of these measurements revealed that the time of cell growth depends on what fraction of cell surface is exposed to external medium and is not in adhesive contact with other cells . Therefore , the first few cells of the acinus ( that have only a few neighbors to adhere to ) grow much faster than the cells at the later stages that have a well-developed adhesive neighborhood contributing to their growth arrest . Though the molecular details of contact inhibition are not entirely understood , it is believed that as the number of adherens junctions on the plasma membrane increases , proliferation decreases proportionally . In a 3D setting , contact inhibition is likely more closely related to epithelial polarization , whereby plasma membranes become committed to basolateral and apical domains . Our model recapitulates this mechanism . The IBCell model naturally links multicellular , cellular and molecular scales by allowing us to directly compare mutant and normal cell lines in terms of their phenotypic and morphological changes . Thus enabling a computational investigation of the impact that different cell phenotypes can have on the emerging multicellular structures they produce both as an end point and dynamically as they develop . This may also suggest ways to experimentally investigate the underlying molecular mechanisms . In the current implementation of IBCell we considered cell sensitivity to proliferative and apoptotic signals , cell contact inhibition and ECM-dependent inhibition of cell proliferation . However , other phenotypic characteristics can also be taken into consideration , such as the orientation of cell division , cell motility , response to metabolic factors or to various anti-cancer drugs . IBCell can easily integrate multiple cellular traits measured independently in different experimental settings . By using high throughput computational simulations and multidimensional IBCell-morphocharts we can map multiple cellular traits to their morphogenetic outcomes and identify combinations of model parameters that define subregions in IBCell-morphocharts corresponding to experimentally observed morphologies , and thus determine the common ranges of individually measurable traits for each morphological structure . It is worth to indicate that multiple , differently networked mechanisms , implemented in different ways can give rise to essentially the same phenomena ( e . g . multicellular structures of distinct types ) , thus it is important to validate model findings with wet-lab experiments . By developing a method that maps mutant morphologies onto simulated ones we have generated a means of linking the morphological and molecular scales via computational modeling . High throughput simulation studies , with the systematically generated model parameter space can point to the altered cell-cell and cell-microenvironment interactions , as well as to changes in cell intrinsic sensitivity to the extrinsic cues . This in turn may guide further experimentation in order to dissect the underlying molecular mechanisms . This procedure of mapping changes in epithelial morphology to cellular phenotypes and to the underlying cancer mutations can also enable quantitative transformations of molecular to pathological findings and vice versa . IBCell in combination with 3D acini cultures can form a new computational/experimental platform for suggesting the link between histopathology of neoplastic lesions and underlying molecular defects . These computationally mapped values effectively link genetic mutations to cellular traits and can be used to guide further experimentation and to identify relationships between mutations and early cancer tissue lesions .
A human mammary non-tumorigenic epithelial cell line MCF10A and its four mutants expressing HER2 , HER2-Bcl2 ( HER2 and Bcl2 ) , HER2-E7 ( HER2 and HPV E7 ) , HER2-YVMA ( HER2 mutant containing a G776YVMA insertion in exon 20 ) , respectively , were grown in 3D cultures by seeding on Growth Factor Reduced Matrigel ( BD Biosciences , San Jose , CA ) in 8-well chamber slides . The developed multicellular acini were collected every four days for 20 days for the parent cell line MCF10A and MCF10A-HER2-YVMA , and at end of day 24th for HER2 , HER2-Bcl2 , HER2-E7 and HER2-YVMA . All samples were stained with nuclear marker , DAPI , and with antibodies against cleaved caspase-3 and Ki-67 . Confocal analyses were performed with an inverted Zeiss LSM-510 confocal microscopy system ( Zeiss , Germany ) . The images of central acinar cross sections were subsequently used to count the numbers of viable ( DAPI-positive ) , proliferating ( Ki67-positive ) and apoptotic ( caspase-3-positive ) cells . This analysis was performed using the BioSig software ( LBNL National Laboratory , Berkeley CA ) . BioSig is a bioinformatics framework of integrated image acquisition , annotation , and hierarchical image abstraction to create a database that registers localization and intensity information about multiple targets along with positional references and morphological features [28] , [29] . It was originally developed at the Lawrence Berkeley National Laboratory ( LBNL ) and is accessible through the worldwide web . The first step of this method includes transfer of raw , high-resolution images to the online BioSig bioinformatics repository . Using the nuclear marker DAPI as a guide for individual cells present in each acinar structure , images were segmented using a radial voting function [30] . Briefly , this technique includes constraining the solution to provide seeds corresponding to the nuclear regions of all cells , which are calculated based on fluorescence intensity and geometric constraints . Once these seeds are established , Voronoi tessellation provides the local neighborhood where each nucleus resides; this locality is then further partitioned based on its intensity distribution using level sets methods [31] . Additionally , Voronoi tessellation enables quantification of signal within and outside the nuclear regions . As a result , quantification of fluorescence signal ( e . g . , caspase-3 , Ki67 ) becomes more accurate . These types of analyses are intended to capture inherent heterogeneity in cellular responses and to remove bias associated with user interactions . The IBCell model is based on the Immersed Boundary Method [32] , a fluid-dynamics framework suitable to model interactions between deformable elastic bodies ( such as eukaryotic cells ) and the viscous incompressible fluid ( such as cell cytoplasm or the extracellular matrix ) . The cell structure consists of an elastic plasma membrane modeled as a network of linear springs that defines cell shape and encloses the fluid providing cell mass ( Fig . 5c ) . These individual cells can interact with other cells and with the environment via a set of discrete membrane receptors/sensors located on the cell boundary ( Fig . 5a ) . These sensors can be engaged in adhesion either with one of the neighboring cells or with the extracellular matrix , or can be used to sense the presence of other cells or the ECM in cell local vicinity . The host cell can initiate certain cell life processes , such as proliferation , division , apoptotic death or epithelial polarization , based on its membrane receptors/sensors configuration ( a distribution of growth , death , apical , cell-cell and cell-ECM adhesion sensors , Fig . 5a ) . More precisely , cell growth is modeled by placing point sources and sinks around the cell boundary to model transport of fluid through the cell membrane ( Fig . 5d ) , and once the cell area is doubled the contractile ring is formed by introducing springs between opposite points on the cell boundary that upon contraction split the cell into two daughters ( Fig . 5e ) . Cell-cell adhesion is modelled by introducing a short liner spring between adhesive receptors on two neighboring cells ( Fig . 5f ) . Cell epithelial polarity is acquired by developing three distinct membrane domains: basal , defined by cell membrane sensors contacting the external media; lateral , defined by cell sensors being in contact with other cells; and apical , facing the hollow lumen ( Fig . 5b ) . Cell apoptotic death is modelled by placing point sinks and sources along the membrane of the whole cell to release fluid from the cell interior to the extracellular space . The IBCell model is fully deterministic ( i . e . , given the same starting parameters , such as numbers of cell membrane sensors and values of receptor thresholds ) , the model will produce the same morphological output . The IBCell model is governed by the following set of equations . ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) In this system , Eq . ( 1 ) is the Navier-Stokes equation of a viscous incompressible fluid defined on the Cartesian grid , where is the fluid velocity , is the fluid pressure , is the fluid viscosity , is the fluid density , is the local fluid expansion , and is the external force density . Eq . ( 2 ) is the law of mass balance . Interactions between the fluid and the material points on cell boundaries ( where is a material point index ) and at point sources and sinks placed in the cell local microenvironment are defined in Eqs . ( 3 ) – ( 5 ) . Here , the force density defined on cell boundaries , and the sources and sinks defined in the cell microenvironment are applied to the fluid using the two dimensional Dirac delta function , while all material boundary points are carried along with the fluid . The boundary forces arise from elastic properties of cell boundaries , from cell-cell adhesion and from contractile forces splitting a cell during its division . The sources and sinks are chosen such that they balance around each cell separately . The kinetics of ECM proteins is defined along the cell boundaries and includes: constant secretion ( at a rate ) along the cells' basal domains and its decay ( at a rate ) around all cells' boundaries . More details about model implementation and computational complexity can be found in [2] , [11] . Overall , each simulation reproducing a single acinar morphology takes about 20–40 hours of computational time on a standard single processor Mac Pro desktop computer . The IBCell tuning process is based on fitting the simulated acinar structures and cell counts to experimental data and measurements collected at certain time points . This is done by constructing the search tree ( Fig . 1h and Fig . S1 ) that discriminates between these model parameters that can generate the desired structure and cell counts at the consecutive time points , and those that lead to false morphologies . For example , in order to attain the desired number of cells at day 4 , one of the following parameters may be modified: i ) cell-cell adhesion may be reduced resulting in the increased number of growth receptors and subsequently in larger amounts of water pumped into the host cell; thus the total cell area will be doubled faster and the total number of cells will increase faster; ii ) cell maturation time ( i . e . , time used by the host cell to rest after the mitotic division is completed ) may be shortened; iii ) cell doubling time may be reduced by choosing a larger fluid source strength for all growing cells; iv ) the growth receptor threshold may be reduced that will require a smaller percentage of cell membrane receptors to be acuired to trigger the process of cell growth ( for more details on implementation of cell life processes in IBCell see [2] , [11] ) . The simulated results are then compared qualitatively and quantitatively to experimental data and these branches of the search tree which do not match with experiments are neglected in further analysis . In the case presented in Fig . 1h both peripheral branches ( at the top and bottom of the search tree ) will be cut as the number of cells in both branches is significantly different at day 4 than in the experimental data ( compare Fig . 1a day 4 , and Fig . 1h ) . It is worth noting that in the initial phase the most significant model parameters are those that result in comparable cell proliferation , whereas the parameters influencing cell apoptosis ( f . e . the threshold for death sensors , time delay needed for cell cytoskeleton to collapse , time delay in cell-cell adhesion disassembly ) , cell polarization and acinar stabilization ( f . e . a threshold for cell-ECM adhesion , distance for cell-cell adhesion links assembly and disassembly , time dalay for the apical sensors emergence ) are more important in matching the simulated and experimental data in the later stages of acinar development . A more detailed exapmle of constructing the parameter tree including the matching and false morphologies is presented in Figure S1 . | The majority of tumors arise in epithelial tissues that form monolayers of tightly packed cells enclosing the inner ductal or lobular cavities . Epithelial tumors ( carcinomas ) are associated with a disruption of epithelial architecture , such as filling of the inner lumen in the early stages of cancer , or the distortion of the ductal structure and spreading to the surrounding stroma in the subsequent invasive stages of tumor . Non-tumorigenic epithelial cells grown in 3D in vitro cultures form regular monolayered spheroids with hollow lumen ( acini , Fig . 1a ) resembling the architecture of normal epithelial cysts . In contrast , tumor cells taken from patients' biopsies and grown in 3D culture acquire various morphologies , often loosing the epithelial-like architecture . How these molecular defects produce such abnormal morphologies remains an open issue . We propose here to use the bio-mechanical model of epithelial morphogenesis , IBCell , to quantitatively investigate the phenotypical changes that the epithelial cells need to obtain in order to produce the aberrant morphologies observable experimentally and clinically . IBCell in combination with 3D acini cultures can form a computational/experimental platform for suggesting the link between histopathology of early tumors and underlying molecular defects . | [
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"computati... | 2010 | Linking Changes in Epithelial Morphogenesis to Cancer Mutations Using Computational Modeling |
Cellular restriction factors , which render cells intrinsically resistant to viruses , potentially impose genetic barriers to cross-species transmission and emergence of viral pathogens in nature . One such factor is APOBEC3G . To overcome APOBEC3G-mediated restriction , many lentiviruses encode Vif , a protein that targets APOBEC3G for degradation . As with many restriction factor genes , primate APOBEC3G displays strong signatures of positive selection . This is interpreted as evidence that the primate APOBEC3G locus reflects a long-term evolutionary “arms-race” between retroviruses and their primate hosts . Here , we provide direct evidence that APOBEC3G has functioned as a barrier to cross-species transmission , selecting for viral resistance during emergence of the AIDS-causing pathogen SIVmac in captive colonies of Asian macaques in the 1970s . Specifically , we found that rhesus macaques have multiple , functionally distinct APOBEC3G alleles , and that emergence of SIVmac and simian AIDS required adaptation of the virus to evade APOBEC3G-mediated restriction . Our evidence includes the first comparative analysis of APOBEC3G polymorphism and function in both a reservoir and recipient host species ( sooty mangabeys and rhesus macaques , respectively ) , and identification of adaptations unique to Vif proteins of the SIVmac lineage that specifically antagonize rhesus APOBEC3G alleles . By demonstrating that interspecies variation in a known restriction factor selected for viral counter-adaptations in the context of a documented case of cross-species transmission , our results lend strong support to the evolutionary “arms-race” hypothesis . Importantly , our study confirms that APOBEC3G divergence can be a critical determinant of interspecies transmission and emergence of primate lentiviruses , including viruses with the potential to infect and spread in human populations .
In addition to the human immunodeficiency viruses ( HIV-1 M , N , O and P and HIV-2 A–H ) , there are more than forty lentiviruses endemic to African old world primates [1] , [2] . The distribution of these viruses among modern primates is consistent with a complex history of cross-species transmissions between different host lineages [2] , [3] . Well-documented examples include the emergence of both HIV-1 and HIV-2 in humans in the mid-to-late 20th century , and emergence of pathogenic SIVmac in captive Asian macaques in the 1970s [2] , [4] , [5] , [6] , [7] , [8] . The exact time and circumstances under which SIVsm initially jumped from sooty mangabeys into rhesus macaques to give rise to SIVmac are unknown , but it is very likely that transmission may have resulted from experimental interventions involving the transfusion of material from one species into another [9] . Despite the extraordinary attention the primate lentiviruses have since received from the AIDS research community , very little is known about the impact of host genetic variation on the transmission of these viruses between different primate species , or the degree to which successful emergence of lentiviral pathogens requires adaptation to overcome genetic divergence between reservoir species and newly emergent hosts . Cellular restriction factors are host factors that render the host cell resistant to viral infection ( also referred to as intrinsic immunity ) [10] , [11] . For many restriction factor genes , the rate of fixation of nonsynonymous changes often exceeds that expected by genetic drift alone , consistent with evolution under strong positive selection , and it is generally assumed that viral pathogens are the source of selective pressure driving evolution of such genes [12] , [13] , [14] . As a consequence of positive selection , even phylogenetically similar species are likely to differ in terms of restriction factor functionality . Thus , interspecies differences in restriction factor loci could serve as genetic barriers to cross-species transmission and emergence of viruses . One way to test this hypothesis is to focus on known transmission events and ask whether specific restrictions played a role . Viruses that successfully overcome species-level barriers imposed by restriction should harbor adaptive changes ( relative to viruses in the established host ) that confer resistance to restriction in the new host . Thus , identification of changes which correlate with host-switching events , and which demonstrably overcome restriction ( s ) imposed by the new host , constitute direct evidence that a specific host restriction factor can act as determinant of cross-species transmission and emergence of viral pathogens . For example , we previously reported that the host restriction factor TRIM5 can suppress viral replication in vivo , exerting selective pressure during the initial stages of cross-species transmission [15] . More specifically , we demonstrated that allelic variation in TRIM5 influences the outcome of SIVsm infection in rhesus macaques , and that emergence of SIVsm ( as pathogenic SIVmac ) required adaptations to overcome the genetic barrier imposed by the most common variant of rhesus macaque TRIM5 . The cellular restriction factor APOBEC3G ( apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3G ) inhibits retroviruses by incorporating into budding virions and inducing hypermutation of the viral cDNA during reverse transcription [16] , [17] , [18] . APOBEC3G ( A3G ) is one of seven members of the APOBEC3 gene cluster found in primates [19] , [20] . A3G is expressed in hematopoietic cell populations , including T cells and myeloid cells , all of which are targets for lentiviral infection [21] . Most lentiviruses counter the antiviral activity of A3G by means of an accessory protein called Vif ( viral infectivity factor ) . Vif functions by bridging A3G proteins with a cellular E3 ubiquitin ligase complex , thus marking A3G for proteasomal degradation [22] . Typically , the interaction between A3G homologs and the Vif proteins of different lentiviruses are species-specific , indicative of Vif-specific adaptation to A3G of the native host [23] , [24] , [25] . The emergence of SIVmac , and subsequent outbreaks of AIDS in captive macaque colonies in the 1970s , was a striking parallel to the emergence of HIV and outbreaks of AIDS in humans around the same time [5] , [26] , [27] . To examine the importance of A3G as a determinant of cross-species transmission and emergence of primate lentiviruses , we asked whether A3G-mediated restriction influenced the successful transmission of SIVsm from its native host , the sooty mangabey ( Cercocebus atys ) , into colonies of Asian rhesus macaques ( Macaca mulatta ) . We found that the A3G coding sequences of sooty mangabeys and rhesus macaques have several fixed , nonsynonymous differences . Moreover , we found that rhesus A3G ( rhA3G ) has an unusual polymorphism in the N-terminal domain , in which a highly conserved tyrosine is replaced by a two-amino acid insertion ( either Y59/LR59–60 or Y59/LL59–60 ) . Structural modeling of the insertion indicates that it is in spatial proximity to residue 128 , a previously identified determinant of human and African green monkey A3G sensitivity to the Vif proteins of HIV-1 and SIVagm [24] , [28] , [29] . The model suggests that these two positions ( 60 and 128 ) may define a common Vif-interaction surface on A3G that is exploited by lentiviral Vif proteins . While we found that Vif from SIVsm can degrade the more ancestral rhA3GY allele , it was unable to induce degradation of rhA3GLR . In contrast , we found that the Vif proteins of multiple , independent SIVmac isolates induced degradation of all three rhesus alleles ( rhA3GLR , rhA3GLL and rhA3GY ) . Furthermore , we identified a single 17Gly-to-17Glu adaptation common to the Vif proteins of multiple SIVmac isolates that confers the ability to degrade rhA3GLR and rhA3GLL . This same adaptation occurred at least twice , once during emergence of SIVmac , and again during intentional adaptation of SIVsm to rhesus macaques by experimental passage in vivo ( SIVsmE543 ) [30] . Convergent evolution , at a specific Vif residue in at least two independent pathogenic SIVmac viruses , clearly indicates that A3G influenced the emergence of pathogenic SIV and simian AIDS in U . S . macaques . Thus , our results demonstrate that APOBEC3G-mediated restriction initially served as an active barrier to full emergence of SIVsm ( as SIVmac ) in rhesus macaques , selecting for adaptations rendering the virus insensitive to restriction ( and therefore better adapted to the macaque host ) .
To investigate the possible role of A3G as a genetic barrier to transmission of SIVsm from its natural host into rhesus macaques , we first surveyed cDNA samples from multiple individual sooty mangabeys and rhesus macaques for polymorphisms in the A3G coding sequence . We identified seven nonsynonymous single-nucleotide polymorphisms ( nsSNPs ) in rhesus macaque A3G ( rhA3G ) and four nsSNPs in sooty mangabey A3G ( smA3G ) ( Figure 1A , see also Figure S1 ) . Changes were only chosen when they were detected in at least three independent clones . With one exception ( 144D/G ) , the nsSNPs clustered either in the first active domain or the second pseudoactive domain of A3G . In addition to single amino-acid substitutions ( 59Y/L , 73Q/H/R , 77E/K , 144D/G , 353C/R and 356 C/R ) , rhesus macaques also have an insertion at position 60 ( L or R ) . The four substitutions in sooty mangabeys included two in the first active domain ( 76L/R and 111K/E ) and two in the second pseudoactive domain ( 370D/A and 376R/Q ) . It is notable that polymorphisms in both species are clustered in the same two regions of the protein , and that almost all substitutions in both regions resulted in a difference in charge ( either a charge switch or gain/loss of a charge ) . It is striking that polymorphism at some of these sites occur in multiple species; for example , one of the polymorphic sites found in rhesus macaque ( residue 77 ) and one found in sooty mangabey ( residue 110 ) are both polymorphic in African green monkeys [31] . Similarly , position 130 is polymorphic in both rhesus macaques ( either asparagine or aspartic acid ) and African green monkeys ( either aspartic acid or histidine ) [32] . The clustering of nonsynonymous polymorphisms in the first active domain , including polymorphisms at sites that are polymorphic in more than one species , is consistent with a region undergoing extensive , adaptive evolution in response to positive selection . We sought to determine whether any of these variants had functional consequences with respect to restriction of SIVsm and SIVmac . We tested all of the rhesus macaque A3G alleles ( rh1-rh6 ) and sooty mangabey A3G alleles ( sm1-sm6 ) for incorporation into SIVmac239ΔVif virions and for the ability to restrict SIVmac239ΔVif ( Figure S2 ) . We found that all twelve alleles ( six sooty mangabey and six rhesus macaque ) are incorporated into budding virions in the absence of Vif , and all twelve were able to restrict infectivity . Thus , the differences in protein sequence that distinguish the alleles do not result in substantial defects in overall A3G function . We then asked whether variation in rhesus macaque APOBEC3G results in differences in resistance or sensitivity to degradation by lentiviral Vif proteins . Specifically , we tested the Vif proteins of two viruses , a primary sooty mangabey isolate ( SIVsmE041 ) and a pathogenic , macaque-adapted isolate ( SIVmac239 ) , for the ability to induce degradation of each of the rhA3G alleles ( Figure 1B ) . Of the six different rhA3G alleles tested , only one ( allele rh6 ) was clearly sensitive to degradation by Vif-SIVsmE041 , although one other allele ( rh2 ) was weakly sensitive to degradation in the presence of Vif-SIVsmE041 . In contrast , all six rhesus macaque alleles ( rh1-rh6 ) were clearly degraded by Vif-SIVmac239 . Both Vif-SIVsmE041 and Vif-SIVmac239 induced degradation of all six sooty mangabey A3G alleles ( Figure 1B and Figure S3 ) . All of the rhesus A3G alleles that resist Vif-SIVsmE041-mediated degradation have a Tyr-to-Leu substitution at position 59 , followed by either an Arg insertion ( alleles rh1 , rh3 , rh4 , and rh5 ) , or a Leu insertion ( allele rh2 ) . In contrast , the rh6 allele and all six of the sooty mangabey alleles retain a highly conserved Tyr at position 59 , and lack any additional insertions . Importantly , the Y/LR and Y/LL substitutions are the only differences that distinguish all of the Vif-SIVsmE041-resistant A3G proteins from all of the sensitive alleles , leading us to hypothesize that these substitution are responsible for the observed differences in sensitivity to degradation by Vif-SIVsmE041 . To test this hypothesis , we used site-directed mutagenesis to change one of the resistant alleles ( rh1 ) at position 59/60 , either substituting the Arg for an Leu ( LR → LL ) or removing the insertion and reverting position 59 to the ancestral/conserved Tyr ( LR → Y ) . Both mutations ( R60L and LR59/60Y ) rendered rhA3G sensitive to Vif-SIVsmE041 degradation , but did not alter sensitivity to Vif-SIVmac239-mediated degradation ( Figure 2A ) . Notably , the tyrosine at position 59 is highly conserved , and is found at the homologous position in most other primate A3G proteins , whereas the additional insertions ( LR and LL ) are unique to Asian macaques ( Figure 2B ) . We therefore generated A3G expression constructs representing three additional primate species , including pigtail macaque ( Macaca nemestrina ) , African green monkey ( Clorocebus aethiops ) and human ( Homo sapiens ) , and tested these for sensitivity or resistance to degradation by Vif-SIVsmE041 and Vif-SIVmac239 ( Figure 2C ) . All three A3G orthologs retain the ancestrally conserved Tyr59 , and in accordance with our earlier observations , all three of these were degraded by expression of Vif-SIVsmE041 and Vif-SIVmac239 . Finally , we asked whether the Y/LR substitution had a direct effect on the Vif-A3G interaction , using co-immunoprecipitation ( Figure 2D ) . We compared binding of rhA3GLR and rhA3GY to both Vif-SIVmac239 and Vif-SIVsmE041 . While Vif-SIVmac239 bound equally well to both rhA3GLR and rhA3GY , there was a dramatic reduction in binding of Vif-SIVsmE041 to rhA3GLR relative to rhA3GY . This result suggests that the Y/LR substitution exerts a direct effect on the Vif-A3G interaction . Compton and Emerman previously described a N/D polymorphism at position 130 in rhA3G [32] . Out of 219 rhesus macaques that we screened , almost half carried an asparagine at position 130 ( animals with the 130N/N or 130N/D genotypes ) ( Figure S4B ) , and alleles rh1-rh6 all have an asparagine at position 130 . Strikingly , of 72 animals that were homozygous for the 60LR allele ( LR/LR genotype ) , only three had an aspartic acid at position 130 ( LR/LR + N/D genotype ) ( Figure S4C ) . There were no LR/LR + D/D homozygous animals . In contrast , 69 animals were of the 60LR/LR 130N/N genotype ( 95 . 8% ) . Thus , despite the high frequency of both 60LR and 130D , the combined 60LR+130D haplotype was present at very low frequency ( Figure S4C ) . Because the 60LR + 130D haplotype appears to be rare in our cohort , we decided to test the functional consequences of the residue at position 130 in the context of alleles differing at position 59/60 . To do this , we constructed a series of double mutants by site directed mutagenesis ( Figure S4A ) . We found that rhA3GLR , rhA3GLL and rhA3GY remained sensitive to Vif-SIVmac239 regardless of the presence of an asparagine or aspartic acid at position 130 . Likewise , degradation of rhA3GLL and rhA3GY by Vif-SIVsmE041 was also unaffected by the substitution at position 130 . Surprisingly , however , introduction of an asparagine at position 130 rendered rhA3GLR sensitive to degradation by Vif-SIVsmE041 . Given the rarity of the 60LR+130D haplotype , it is tempting to speculate that the low frequency of this allele reflects the history of SIV outbreaks in captive macaque colonies . However , proving or disproving this possibility is complicated by the use of selective breeding procedures in U . S . primate research centers and a current lack of information regarding allele frequencies in natural macaque populations . To determine the respective frequencies of the rhA3GLR , rhA3GLL and rhA3GY alleles , we used archived genomic DNA samples to genotype 219 captive-bred rhesus macaques ( Table 1 ) . The allelic frequency of rhA3GLR was an impressive 48 . 9% , followed by rhA3GY ( 24 . 7% ) and rhA3GLL ( 26 . 5% ) . Likewise , homozygote genotype frequencies were the lowest for rhA3GY/Y and rhA3GLL/LL ( 8 . 7% and 14 . 6% , respectively ) and highest for rhA3GLR/LR ( 32 . 9% ) . Using BLAST and sequence alignments , we identified the presence of the LR insertion in at least one additional species of Asian macaques , Macaca fasicularis ( crab-eating macaque ) ( see also [33] ) . We confirmed this finding by genotyping genomic DNA from 17 crab-eating macaques , and found that this species also harbors the Y/LR polymorphism at positions 59–60 ( genotype frequencies in this small sampling were: 23% LR/LR; 42% Y/Y; 35% Y/LR ) . Thus , the A3GLR allele likely dates back at least as far as the common ancestor of rhesus macaques and crab-eating macaques , giving a minimum age of approximately 1 . 2–2 . 5 million years ago [34] . So far , all other primate A3G sequences in the public sequence databases have the ancestral Tyr at position 59 , except for Colobinae spp . which have a Ser at that position [32] . Interestingly , Compton and Emerman also describe a three amino-acid insertion in Colobinae A3G four residues downstream ( positions 64–66 ) that affects sensitivity to degradation by Vif from SIVagm . Sab in tissue culture . We next sought to identify adaptations in Vif-SIVmac239 that conferred the ability to degrade rhA3GLR . Previous studies reported that sequences in the N-terminal part of Vif proteins are important for determining the species-specificity of Vif interactions with A3G [35] , [36] , [37] , [38] . Because there are multiple differences in the N-terminal regions of the Vif-SIVsmE041 and Vif-SIVmac239 proteins , we first constructed a chimeric Vif protein in which the N-terminal 56 residues of Vif-SIVsmE041 were replaced with the homologous portion of Vif-SIVmac239 ( Figure 3A ) . As with Vif-SIVmac239 , expression of the chimeric Vif protein was also able to induce degradation of all three rhA3G alleles ( Figure 3B ) , indicating that adaptative changes to overcome rhA3G-mediated restriction might be found within the first 56 amino acids of the Vif protein . We tested a series of additional Vif proteins derived from SIV isolates of several different host species , including rhesus macaque- , pig-tailed macaque- , cynomolgus macaque- and sooty mangabey-derived isolates . These included Vif from SIVsmE543 , a virus isolated after experimental passage of SIVsm through two rhesus macaques [27] , [30]; Vif from SIVmac142 , which was isolated from a rhesus macaque infected unintentionally in utero [39]; Vif from SIVmne027 , which was isolated from a pig-tailed macaque [27]; and Vif from SIVmfa186 , a virus isolated from the lymph node of an SIV+ cynomolgus macaque ( in vivo passage history unknown ) [40] , [41] . All four SIV Vifs ( Vif-SIVsmE543 , Vif-SIVmac142 , Vif-SIVmne and SIVmfa186 ) were able to induce degradation of all three rhA3G variants ( Figure 3C ) . In contrast , and as shown previously , Vif-SIVsmE041 tested in parallel induced degradation of rhA3GY and rhA3GLL , but not of rhA3GLR . To rule out the possibility that Vif-SIVsmE041 is unique among SIVsm viruses , we also tested the Vif protein of a second primary SIVsm isolate ( SIVsmCFU212 , GenBank Accesssion #JX860407 ) [42] , as well as the Vif proteins of two SIVsm isolates that were each derived by infection of single pig-tailed macaques with primary SIVsm ( SIVsmPBj and SIVsm-PG ) [27] [43] . As with Vif-SIVsmE041 , the Vif-SIVsmCFU212 , Vif-SIVsmPBj and Vif-SIVsmPG proteins all failed to induce degradation of rhA3GLR but were able to induce degradation of rhA3GY ( Figure S6 ) . By inspecting an alignment of the first 56 amino acids of Vif from multiple SIVmac and SIVsm isolates , we identified two residues ( positions 17 and 21 ) that were conserved amongst all SIVmac strains and SIVsmE543 yet differed from the Vif proteins of SIVsm viruses ( Figure 3D and Figure S5 ) . We therefore substituted the individual residues at positions 17 and 21 in Vif-SIVmac239 with the corresponding residues of Vif-SIVsmE041 , thereby changing the negatively-charged glutamic acid to an uncharged glycine ( E17G ) or the positively-charged arginine to a positively charged lysine ( R21K ) , and asked whether either substitution had an effect on the ability of Vif-SIVmac239 to degrade rhA3GLR . We tested both mutants for the ability to induce degradation of rhA3GLR , rhA3GLL and rhA3GY by co-transfection ( Figure 4A ) . Expression of mutant Vif-SIVmac239 ( E17G ) , in which the negatively charged amino acid was changed to an uncharged amino acid , failed to induce degradation of rhA3GLR , but was nonetheless functionally intact , as evidenced by the ability to degrade both the rhA3GLL and rhA3GY proteins . We also altered the Vif of rhesus-macaque isolate SIVsmE543 at the same position , again changing the glutamic acid to a glycine , and tested the mutant against the three rhA3G alleles and a sooty mangabey allele ( Figure 4B ) . As with Vif-SIVmac , as a consequence of changing this one amino acid , the Vif-SIVsmE543 protein was unable to induce degradation of rhA3GLR , yet retained the ability to induce degradation of rhA3GLL , rhA3GY and smA3G . To confirm that this loss of Vif function ( the ability to degrade A3G ) was due to the amino acid at position 17 , we also performed the converse experiment , altering Vif-SIVsmE041 by mutation of residue 17 from a glycine to a glutamic acid , and testing the mutant against the three rhA3G alleles ( Figure 4B ) . We observed that the Vif-SIVsmE041 ( G17E ) mutant gained the ability to degrade the rhA3GLR allele , while also retaining the ability to induce degradation of the other two rhA3G alleles ( as well as sooty mangabey A3G ) . To further confirm the importance of the residue at position 17 , we looked at additional Vif sequences from multiple , previously described SIVsm strains [42] ( including SIVsmCFU212 , SIVsmPBj and SIVsm-PG ) and found that they all had a glycine at position 17 ( Figure S5 and Figure S6 ) . Likewise , HIV-2 Vif and Vif-SIVstm ( an SIV from stump tailed macaques , or Macaca arctoides ) , both of which represent independent cross-species transmissions originating from SIVsm , also have a glycine at position 17 . Thus , Gly17 is well conserved among SIVsm lineages , whereas Glu17 is very likely to have been evolutionarily derived in conjunction with emergence of SIVmac in rhesus macaques , and independently during experimental adaptation of SIVsmE543 to rhesus macaques . This observation , coupled with our experimental results , strongly suggests that the G17E substitution found in viruses of the SIVmac lineage represents adaptation to overcome the interspecies genetic barrier imposed by rhA3GLR alleles . We next tested the effects of Vif-SIVsmE041 , Vif-SIVmac239 and the Vif-SIVmac239 ( E17G ) mutant in an infectivity assay . To this end , we trans-complemented an SIVmac239 Vif deletion mutant ( SIVmac239ΔVif ) by co-expression of Vif-SIVsmE041 , Vif-SIVmac239 or the Vif-SIVmac239 ( E17G ) mutant , and asked whether these rescued viral infectivity in the presence of different rhA3G alleles . Specifically , complementation with the Vif-SIVmac239 ( E17G ) mutant or with Vif-SIVsmE041 resulted in reduced infectivity relative to virus complemented with wild type Vif-SIVmac239 in the presence of rhA3GLR ( p-value<0 . 0001 for both Vif-SIVmac239 ( E17G ) vs . Vif-SIVmac239 and Vif-SIVsmE041 vs . Vif-SIVmac239 ) . There was no significant difference between the Vif-SIVmac239 ( E17G ) and Vif-SIVsmE041 complemented viruses , relative to rhA3GLR ( Figure 5A ) . In contrast , both proteins were nearly as effective as wild type Vif-SIVmac239 in the presence of the other rhesus alleles ( rhA3GLL and rhA3GY ) . Importantly , the observed activity of the different Vif proteins against the rhA3G alleles mirrored the results obtained from degradation experiments ( compare Figure 5A and Figure 4 ) . In parallel , we analyzed the levels of encapsidated rhA3G in the same virion preparations used for the infectivity assays , by immunoblotting and densitometry ( Figure 5B ) . In accordance with the degradation and infectivity experiments , we observed reduced incorporation of rhA3GLR in Vif-SIVmac239 complemented virions relative to virions complemented with Vif-SIVsmE041 ( 3 . 4-fold increase relative to p27 ) or Vif-SIVmac239 ( E17G ) ( 4-fold increase relative to p27 ) . Incorporation of rhA3GY in the presence of any of the three Vif proteins was essentially undetectable . Similar to the results of degradation experiments ( Figure 1B and Figure 3B ) , there were low but detectable levels of rhA3GLL incorporation into Vif-SIVsmE041-complemented and Vif-SIVmac239 ( E17G ) -complemented virions , and even very low levels of rhA3GLL incorporation into Vif-SIVmac239 complemented virions . Thus , it is possible that rhA3GLL also confers some resistance to Vif , albeit significantly less than rhA3GLR .
New viral pathogens arise when viruses succeed in jumping from natural reservoirs into new host populations , resulting in emerging infectious diseases such as AIDS , SARS and pandemic influenza [6] , [44] , [45] , [46] . The importance of ecological factors in viral emergence is well documented , whereas the significance of host genetic differences as barriers to emergence is still not clear [47] . In particular , it is believed that cellular antiviral restriction factors can act as blockades to cross-species transmission and viral emergence , yet very few studies have examined this possibility in vivo . The emergence of SIVmac and outbreaks of simian AIDS in outbred colonies of macaques in the 1970s provides an unusual opportunity to examine the impact of specific , host-encoded restriction factors in the context of an emerging pathogen . Focusing on SIVsm and SIVmac has multiple advantages for understanding the genetics of lentiviral emergence: both the natural reservoir host and the emergent new host are known ( sooty mangabeys and rhesus macaques , respectively ) ; archived samples and cloned , primary viral isolates are readily available; and most importantly , several key host-encoded restriction factors that target primate lentiviruses are known and have been well-characterized at the molecular level [48] , [49] , [50] , [51] . Cellular restriction factors that target replication of primate lentiviruses include A3G [17] , TRIM5α [52] , BST2/Tetherin [53] , [54] , [55] and SAMHD1 [56] , [57] . Here , we demonstrate that spread of SIVsm in rhesus macaques and emergence of pathogenic SIVmac and simian AIDS required viral adaptation to overcome restriction by rhesus macaque A3G . In particular , we found that A3G is polymorphic in rhesus macaques , and that the most frequent rhesus A3G alleles have complex substitutions in the N-terminal domain ( Y59/LR59–60 and Y59/LL59–60 ) . We found that Vif from SIVsm , a virus that jumped species at least twice ( to emerge as SIVmac in macaques and as HIV-2 in humans ) does not degrade the most common rhesus allele ( rhA3GLR ) , whereas SIVmac Vif degrades all three classes of rhesus alleles ( rhA3GLR , rhA3GLL and rhA3GY ) . So far , two important determinants ( amino acids 128 and 130 ) in A3G have been shown to play a role in conferring species-specific sensitivity to Vif [24] , [25] , [28] , [29] , [31] , [32] , [35] suggesting that these residues might be part of a Vif-binding domain . At present , there are no structural data for the N-terminal active domain of A3G . To visualize how the rhesus 59Y/59LL60/59LR60 polymorphism might also affect interactions with Vif proteins , we modeled the N-terminal sequence of rhA3G onto the recently published human A3C crystal structure [58] , [59] ( Figure 6 ) . Unlike A3G , A3C has only one active domain; however , both the sequence and predicted secondary structure of A3C are similar to the N-terminal active domain of A3G , such that the structure is a useful approximation [60] . Using this hypothetical model of the N-terminal active site of rhA3GLR , we compared the predicted positions of residues which are known or thought to influence species-specific interactions of A3G and Vif , including Arg60 ( identified in this study ) , Lys128 [24] , [28] and Asp/Asn130 [32] . The model suggests that Arg60 and Lys128 are probably located on the same face of A3G , within approximately 20 Å of each other . Because variation at both sites affects Vif-mediated degradation , the simplest explanation is that residues 60 and 128 are part of a single Vif-binding surface on A3G ( this does not exclude the possibility that different Vif homologs have evolved to make different contacts with the same surface ) . Similarly , an insertion at position 64–66 in Colobinae A3G ( indicated in blue spheres in Figure 6 ) can also affect degradation by Vif proteins of primate lentiviruses [32] . It would therefore appear that these three documented determinants of species-specific antiviral activity might occur on a single face of the A3G protein . This observation offers indirect but compelling evidence that divergent lentiviral Vif proteins have exploited the same or closely overlapping binding sites on primate A3G . The adaptation of SIVmac to rhesus macaques included a G17E substitution in Vif . Vif proteins from all SIVmac isolates tested degraded rhA3GLR and all carried the G17E change , including Vif from the independently derived SIVsmE543 - a clear case of convergent evolution in a viral accessory protein . Our results establish that interspecies variation in cellular restriction factor A3G can pose a selective barrier to emergence of AIDS-causing viruses , and just as adaptation of Vif contributed to the emergence of pathogenic SIVmac , Vif plasticity could have contributed to the modern distribution of simian immunodeficiency viruses among their respective primate reservoirs . The means by which SIVsm was initially transmitted to rhesus macaques in US primate research centers is not known , although indirect evidence suggests it was the unintentional result of experimental exchange of infected material between the two species in the 1960s or early 1970s [9] . Thus , the original transmission event ( s ) may not recapitulate the kinds of mechanisms that are thought to typically give rise to interhost transmission of primate retroviruses in nature , such as biting , fighting with , or preying on , viremic individuals . Nevertheless , replication of primary isolates of SIVsmm in rhesus macaques is highly variable and typically much lower relative to SIVmac isolates , such that the emergence of SIVmac is very likely to have required adaptations to overcome genetic divergence between the reservoir ( sooty mangabey ) and spillover ( rhesus macaque ) hosts [15] , [61] , [62] Our data indicate that adaptation to overcome restriction by a specific subset of rhesus macaque A3G alleles played a key role in spread of SIVsm in captive colonies of rhesus macaques , ultimately contributing to its emergence as pathogenic SIVmac . At the time of initial cross-species exposure , SIVsm was most likely sensitive to rhA3G alleles with an LR at position 59/60 ( and perhaps to a lesser extent , alleles with an LL at position 59/60 ) , but not to alleles with a Y at position 59 . It is possible that animals bearing rhA3GY alleles served as evolutionary “stepping-stones” , by allowing for higher levels of replication and higher probability of continued transmission and spread of SIVsm in the captive rhesus macaque population . The G17E mutation in Vif , whether it coincided with the initial cross-species transmission or appeared subsequently , would have allowed the virus to spread to a larger percentage of the macaque population ( e . g . , those bearing restrictive A3GLR alleles ) , fostering further adaptation to the new host . It is tempting to speculate that the current frequency of rhA3GLR alleles in U . S . macaque colonies was directly influenced by the emergence of SIVmac and outbreaks of simian AIDS in the 1970s and early 1980s . However , the high frequency of rhA3GLR in macaque colonies should also reflect the influence of founder effects , depending on the initial frequency of the allele in the founder populations that were used to establish and maintain breeding colonies at the National Primate Research Centers ( NPRC ) . Because selective breeding programs are used to intentionally maintain diversity in the NPRC colonies , deviations from Hardy-Weinberg equilibrium cannot reliably be used to estimate the relative contributions of selection and founder effects . In order to study the possible influence of SIV emergence on host A3G allele frequencies , it may be necessary to recover archived genomic DNA samples from that era , if possible , and to compare historical allele frequencies of captive macaques to allele frequencies of wild macaques in Asia . At least one study describes analysis of A3G polymorphism in a cohort of SIV-infected macaques , and reports a weak , possible association between rhA3G variation and viral replication [63] . Importantly , the virus in that retrospective study was SIVmac239 , a virus that is already well-adapted to the rhesus macaque host ( and has been used extensively as a model for preclinical AIDS research for almost 30 years ) [39] , [64] , [65] , [66] , [67] . Thus , the modest correlation reported by Weiler et al . is consistent with our observation that Vif-SIVmac can induce degradation of all three allelic forms of rhA3G , and should be largely unaffected by the A3G genotype . We have also reported that variation in another restriction factor , TRIM5 , correlates with large differences in viremia in rhesus macaques infected with SIVsmE543 , and that TRIM5-mediated suppression selects for emergence of resistant SIVsmm variants [15] . As with A3G and SIVmac239 , the impact of rhesus TRIM5 on replication of established SIVmac isolates is minimal [63] , [68] . Thus , our data and previously published studies of both A3G and TRIM5 in macaques support the hypothesis that restriction has its greatest biological impact as a modulator of interspecies transmission and emergence of viral pathogens . Evidence of positive selection in primate restriction factor loci may therefore reflect recurrent or continuous lineage-specific exposures to viruses of other species , with repeated transmission events and outbreaks of disease driving fixation of resistance alleles . This could occur in situations where different host populations come into physical contact - for example , as the result of fighting over territory or when one species routinely preys on another . Whether restriction factors continue to play a role after a pathogen becomes established in the new host is not as clear , and will require further study .
Human Embryonic Kidney 293T/17 ( HEK293T/17 ) cells were obtained from American Type Culture Collection ( Manassas , VA ) . HeLa Human cervical carcinoma ( TZM-bl ) cells were obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: TZM-bl from Dr . John C . Kappes , Dr . Xiaoyun Wu and Tranzyme Inc . [69] , [70] , [71] , [72] . Both cell lines were grown in Dulbecco's modified Eagles medium ( DMEM ) containing 10% fetal bovine serum ( FBS ) ( Invitrogen; Carlsbad , CA ) HEK293T/17 cells were seeded at a density of 6×105 cells per well in a 6-well plate or at 2×106 in a 60 mm dish or a T25 flask . Cells were co-transfected with appropriate amounts of the indicated plasmids using either the GenJet transfection system ( SignaGen; Ijamsville , MD ) or the Lipofectamine Plus reagent ( Invitrogen; Carlsbad , CA ) following the manufacturer's recommendations and harvested 48–72 hours post transfection . Cells were lysed in M-PER reagent ( Pierce Biotechnology , Rockford , IL ) or Pierce IP-lysis Buffer ( Thermo Fisher Scientific , Waltham , MA ) , mixed with an equal volume of 2× laemmli sample buffer ( Sigma , St . Louis , MO ) and solubilized by boiling for 10 min at 99°C . Protein was separated by SDS/PAGE and tagged proteins were detected with either mouse monoclonal anti-V5 antibody ( Invitrogen; Carlsbad , CA ) or mouse monoclonal anti-HA antibody ( Covance; Dedham , MA ) using dilutions recommended by the manufacturer . β-actin was detected with mouse monoclonal beta actin-HRP antibody at a concentration of 1∶1000 ( Abcam Inc . , Cambridge , MA ) . Virus was harvested by centrifugation ( see “Encapsidation of Virus” section ) . Lysed , replication-competent virus was detected with monoclonal SIVmac anti-p27 Antibody ( NIH AIDS Reagent Program ) [73] at a dilution of 1∶2000 . FLAG-tagged Vif expression plasmids ( 100 ng ) were co-transfected with HA-tagged A3G expression plasmid ( 900 ng ) and 1000 ng GST-expressing filler plasmid in 293T cells in a 6-well format ( 2 µg DNA total ) . Cells were lysed two days post transfection in a mild lysis buffer ( 0 . 5% triton X-100 in 1× PBS supplemented with EDTA-free protease inhibitor cocktail , Roche ) on ice and the cell lysates were cleared by centrifuging at 14 , 000×g for 10 minutes at 4°C . Cleared lysates were incubated with EZ-View anti-HA beads ( Sigma ) at 4°C for two hours . Beads were washed 4 times in cold mild lysis buffer , followed by 4 additional washes in cold stringent lysis buffer ( 1% triton , 0 . 1% SDS , 500 mM NaCl in PBS ) . Proteins were eluted from the beads by boiling in LDS loading buffer ( Sigma ) . Proteins were analyzed by western blot for Vif ( FLAG ) , A3G ( HA ) and tubulin . Full-length SIV viruses were produced in HEK293T/17 cells by transfection using the GenJet transfection system ( SignaGen; Ijamsville , MD ) . SIVmac239ΔVif [74] plasmids were co-transfected with 4 µg pcDNA3 . 1-Vif . HA ( or and empty vector control ) and 3 µg pcDNA3 . 2-A3G . V5 . Culture supernatants containing virions were harvested three days post infection and used for infectivity assays on TZM-Bl cells and for immunoblotting . TZM-bl cells were seeded at a concentration of 1×104 cells per well in 96-well plates and infected with equal volumes of replication-competent virus . Two independent experiments were conducted , and all infections were performed in triplicate . Three days post infection expression of luciferase was analyzed using the Britelite Plus Ultra-High Sensitivity Luminescence Reporter Gene Assay System ( PerkinElmer Inc; Waltham , MA ) and a Victor X5 Multilabel Plate Reader ( PerkinElmer Inc; Waltham , MA ) . Results were analyzed by one-way ANOVA and Tukey's multiple comparisons test , using PRISM 6 . 0b ( GraphPad Software , Inc . , La Jolla , CA . ) . For the APOBEC3G clones , RNA was extracted from rhesus macaque , sooty mangabey , African green monkey and pigtail macaque B-cells and reverse transcribed to synthesize cDNA using the one-step RT-PCR kit ( Invitrogen; Carlsbad , CA ) and cloned into the pENTR directional TOPO cloning vector ( Invitrogen; Carlsbad , CA ) according to manufacturer's manual for further sequence analysis . Sequences for independent rhesus macaque alleles ( rh1-rh6 ) , sooty mangabey alleles ( sm1-sm6 ) and one pigtail allele ( ptA3G ) were submitted to Genbank ( rh1-rh6: KF020481-KF020486 , sm1-sm6: KF020487- KF020492 , ptA3G: KF169801 ) . For expression in mammalian cells , the protein coding fragments were transferred into the pcDNA3 . 2/V5-DEST cloning vector ( Invitrogen , Carlsbad , CA ) using the Gateway LR Clonase II Enzyme Mix ( Invitrogen , Carlsbad , CA ) . For Vif-expressing plasmids , vif coding sequences were synthesized ( GeneArt ) and cloned into pcDNA3 . 1 ( Invitrogen , Carlsbad , CA ) using PCR to add restriction sites 5′HindIII and 3′NotI as well as adding a C-terminal HA-tag . Vif gene sequences used can be found in GenBank: SIVmac ( M33262 ) , SIVsmE041 ( HM059825 ) , SIVmac142 ( Y00277 ) , SIVmne027 ( U79412 ) , SIVmfa186 ( KF030930 ) , SIVsmE543 ( U72748 ) . The mutant constructs were obtained by site-directed mutagenesis using PfuTurbo Hotstart Polymerase ( Agilent Technologies , Santa Clara , CA ) and were confirmed by sequence analysis . Constructs for co-immunoprecipitation ( rhA3GLR and rhA3GY ) were subcloned into ptr600 plasmids [75] . The A3G constructs were C-terminally labeled with a 3×HA tag by standard overlap PCR . Vif-SIVmac239 and Vif-SIVsm were cloned into the pcrv expression plasmid [76] . The Vif constructs were C-terminally FLAG-tagged by standard overlap PCR . Correct cloned inserts were confirmed by sequencing . Retrospective analysis revealed that a pathogenic SIV was freely circulating in crab-eating macaques ( Macaca fascicularis ) housed at the New England Primate Research Center ( NEPRC ) [41] . An isolate of this virus from animal MF186-76 was obtained from frozen PBMCs , which were co-culture with H9 cells in 1987 [41] . A vial containing cell supernatants from this co-culture was obtained ( a gift from R . C . Desrosiers ) . Viral RNA was isolated using a High Pure Viral RNA Kit ( Roche USA , Indianapolis , IN ) . Specific cDNA products corresponding to the Vif coding sequence were amplified using the SuperScript™ III One-Step RT-PCR System with Platinum® Taq High Fidelity kit ( Invitrogen , Carlsbad CA ) using following the primers: 647-F AGGGGAGGAATAGGGGATATGACTC and SME041-envR1 R- CACTTAATAGCAAGAGCGCGATAAG . The PCR fragment was then cloned using the TOPO TA cloning kit ( Invitrogen: Carlsbad , CA ) and sequenced . Rhesus macaque and sooty mangabey RNA samples were used for initial screens for polymorphisms in APOBEC3G by RT-PCR of the entire A3G coding sequence , cloning , and sequencing , as described above ( see section entitled “Plasmids” ) . Additional , allele-specific typing was performed by extraction of genomic DNA followed by targeted PCR and direct sequencing . Specifically , APOBEC3G genotypes of rhesus macaques ( Macaca mulatta ) and cynomolgus macaques ( Macaca fascicularis ) ( also known as crab-eating macaques ) were determined by isolation of genomic DNA from PBMCs using QIAamp DNA Blood Mini Kit ( Qiagen; Valencia , CA ) and PCR using 100 ng gDNA , Taq polymerase and primers ( APOBEC3G-1aF: 5′-GAG GAA AGG AGC TTC AGT GGC AAG A-3′ , APOBEC3G-1aR: 5′-GGA GGC CTC AAG AGG GTA AGC AG-3′ ) with following PCR conditions: 94°C – 1 min , 94°C – 15 sec , 59°C – 30 sec , 68°C – 1 min for a total of 30 cycles , 68°C – 10 min ) followed by direct sequencing of a PCR fragment ( APOBEC3G-1aF: 5′-GAG GAA AGG AGC TTC AGT GGC AAG A-3′ , APOBEC3G-1aR: 5′-GGA GGC CTC AAG AGG GTA AGC AG-3′ ) . PCR fragments were sequenced by Retrogen ( San Diego , CA ) and data were analyzed with the Codoncode software ( Codoncode Corp; Dedham , MA ) . Two milliliters of the harvested virus-containing supernatants were used to concentrate virus by ultracentrifugation through 20% sucrose at 35 , 000 rpm for 75 min at 4°C . The virions were then lysed with 100 µl 2× leammli sample buffer ( Sigma , St . Louis , MO ) and boiled at 99°C for 10 min . Incorporated A3G and p27 was detected by an immunoblot assay . A model of the N-terminal active site of residues 1–195 of the rhA3GLR allele was geminated with high confidence using the Protein Homology/analogy Recognition Engine V 2 . 0 server ( PHYRE2 ) ( http://www . sbg . bio . ic . ac . uk/phyre2/html/page . cgi ? id=index ) [59] . Rhesus macaque alleles: rh1-rh6: KF020481- Rh6:KF020486 Sooty mangabey alleles: sm1-sm6: KF020487- KF020492 Pig tailed macaque: KF169801 Vif-SIVmfa186: KF030930 | APOBEC3G is a host factor that can inhibit replication of primate lentiviruses , including HIV-1 , HIV-2 , and the related simian immunodeficiency viruses ( SIVs ) of African primates . As a consequence , primate lentiviruses encode a protein , called Vif , which can induce degradation of APOBEC3G . Given its antiviral role , APOBEC3G may be an important genetic barrier to interspecies jumping of primate lentiviruses . To study this possibility , we asked whether APOBEC3G affected transmission of SIV from sooty mangabeys ( SIVsm ) to rhesus macaques and subsequent emergence of pathogenic SIVmac in the 1970s . We found that APOBEC3G of sooty mangabeys and rhesus macaques have divergent protein sequences , and that the Vif proteins of SIVsm ( Vif-SIVsm ) cannot counteract rhesus macaque APOBEC3G . We mapped Vif-SIVsm resistance to a specific substitution in the N-terminal domain of rhesus APOBEC3G , in which a highly conserved tyrosine is replaced by leucine-arginine ( Y→LR ) . We also identified a viral counter-adaptation , found in the Vif proteins of all SIVmac strains , which specifically confers the ability to antagonize APOBEC3G of rhesus macaques . This change was most likely selected during adaptation of SIV to its new host . Together , these results demonstrate that APOBEC3G can serve as a critical genetic determinant of interspecies transmission of primate immunodeficiency viruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | APOBEC3G Polymorphism as a Selective Barrier to Cross-Species Transmission and Emergence of Pathogenic SIV and AIDS in a Primate Host |
Staphylococcus aureus is a pyogenic abscess-forming facultative pathogenic microorganism expressing a large set of virulence-associated factors . Among these , secreted proteins with binding capacity to plasma proteins ( e . g . fibrinogen binding proteins Eap and Emp ) and prothrombin activators such as Coagulase ( Coa ) and vWbp are involved in abscess formation . By using a three-dimensional collagen gel ( 3D-CoG ) supplemented with fibrinogen ( Fib ) we studied the growth behavior of S . aureus strain Newman and a set of mutants as well as their interaction with mouse neutrophils by real-time confocal microscopy . In 3D-CoG/Fib , S . aureus forms microcolonies which are surrounded by an inner pseudocapsule and an extended outer dense microcolony-associated meshwork ( MAM ) containing fibrin . Coa is involved in formation of the pseudocapsule whereas MAM formation depends on vWbp . Moreover , agr-dependent dispersal of late stage microcolonies could be observed . Furthermore , we demonstrate that the pseudocapsule and the MAM act as mechanical barriers against neutrophils attracted to the microcolony . The thrombin inhibitor argatroban is able to prevent formation of both pseudocapsule and MAM and supports access of neutrophils to staphylococci . Taken together , this model can simulate specific stages of S . aureus abscess formation by temporal dissection of bacterial growth and recruitment of immune cells . It can complement established animal infection models in the development of new treatment options .
Staphylococcus aureus is a common human colonizer of skin and nasopharynx . Under conditions of impaired immune defense S . aureus carriers are at increased risk to develop severe infections ranging from localized soft tissue to invasive infections such as endocarditis , metastatic infections of joints , kidneys and lungs with progression to sepsis [1] . Treatment of staphylococcal infections has been further complicated by the massive development of antibiotic resistances in recent years [2] . Adherence to host epithelium is critical to colonization in the carrier stage as well as to invasion and metastatic dissemination . In regard of this complex host-pathogen interaction S . aureus has evolved a highly adaptive and versatile strategy to survive and replicate in beneficial as well as in hostile environments . S . aureus is equipped with a large set of fine-tuned virulence-associated genes of which gene products can be roughly classified into several groups , among those are adhesins/invasins ( which are mainly involved in the interaction with extracellular matrix ( ECM ) proteins ) , pore-forming toxins , superantigens and immune evasion factors [3] . The adhesin/invasin comprises a subgroup of cell wall anchored proteins , termed MSCRAMMs ( Microbial Surface Components Recognizing Adhesive Matrix Molecules ) and a subgroup of SERAMs ( Secretable Expanded Repertoire Adhesive Molecules ) which are released but mainly surface-associated proteins [4] , [5] . The MSCRAMM subgroup includes fibronectin binding proteins ( FnbpA , FnbpB ) , fibrinogen/fibrin binding proteins such as the clumping factor A and B ( ClfA , ClfB ) , the collagen binding protein ( Cna ) and Staphylococcus protein A ( Spa ) , which binds immunoglobulin G ( IgG ) and von Willebrand factor ( vWF ) [4] , [6] . The SERAM subgroup also includes fibrinogen/fibronectin binding proteins such as the extracellular adherence protein ( Eap ) and the extracellular matrix binding protein ( Emp ) [5] , [7] , [8] but also prothrombin-activating proteins such as coagulase ( Coa ) and von Willebrand factor binding protein ( vWbp ) [9] , [10] . The latter are able to activate prothrombin in a non-proteolytic manner , opposed to physiological prothrombin activation . The resulting Coa- or vWbp-prothrombin complex converts soluble fibrinogen into insoluble fibrin fibers [9] , [11] . At a first glance MSCRAMMs and SERAMs may be of redundant function in the context of colonization and infection . On the other hand there must be a selective pressure for maintenance of virulence-associated genes with apparent redundant functions , suggesting different roles in the complex life style of S . aureus . The virulon of S . aureus is orchestrated by different global regulatory systems such as Agr , Sar and Sae , all of which sense environmental changes [12] . The Sae regulatory system ( S . aureus exoprotein expression ) seems not to affect the Agr and Sar systems and controls the expression of genes encoding hemolysins ( hla and hlb ) and several MSCRAMMs and SERAMs [13] . To investigate the contribution of these virulence-associated factors to disease initiation and progression , various in vitro and in vivo infection models have been established . Recently , the molecular mechanisms of S . aureus Newman abscess formation in the mouse infection model could be elucidated by using defined mutants deficient in production of e . g . Coa , vWbp , Eap and Emp [14] , [15] . It could be demonstrated that mature abscesses in the kidney are composed of a core structure of the staphylococcal abscess community ( SAC ) which is enclosed by a pseudocapsule of fibrin deposits and a layer of neutrophils in the periphery . This supports a novel concept in abscess formation by pointing to an exploitation of host clotting machinery by staphylococcal virulence factors in order to establish a protective niche for the pathogen [16] . However , it is difficult to assess the role of single MSCRAMM or SERAM proteins during infection in vivo because of the complexity of the infection process and coevolution-driven host specificity of S . aureus ( e . g . species specificity of coagulase and human-specific hemoglobin utilization [17] , [18] ) . In this study , we focused on growth behavior of S . aureus strain Newman in a three dimensional collagen gel setup ( 3D-CoG ) and the interaction with polymorphonuclear leukocytes ( PMNs or neutrophils ) . The 3D-CoG is a suitable matrix to study neutrophil migration and microbial growth under tissue-like conditions [19]–[21] . The 3D-CoG can be modified by adding purified ECM or plasma proteins of interest in order to study the dynamics of bacterial growth and interaction with these proteins as well as their role in supporting or inhibiting bacteria-neutrophil interaction . We used this tissue-like system to study the formation of a fibrin capsule/meshwork surrounding S . aureus clusters by using defined mutants of strain Newman but also other clinical isolates . In a second step we analyzed the implications of these barrier-like structures for the infiltration of neutrophils and for the subsequent degradation of bacterial microcolonies .
In a first approach to establish a collagen gel-system ( 3D-CoG ) for studying staphylococci-neutrophil interaction we analyzed the growth behavior of strain Newman . When growing S . aureus Newman for 16 h in RPMI 1640 medium without agitation , single bacterial cells gave rise to irregularly shaped bacterial clusters of variable size ( Fig . 1A ) . In the next step we mixed monodispersed staphylococci with neutralized collagen type I solutions , followed by incubation at 37°C . Gelling occurred within 45 min and resulted in a rigid matrix which was then overlaid with medium . Staphylococci replicated and formed clusters similar to those observed in RPMI 1640 without collagen after 16 h ( Fig . 1B ) . The 3D-CoG meshwork was not degraded by staphylococci even after days ( data not shown ) . Moreover , we could not observe specific interaction of staphylococci with collagen fibers by confocal microscopy . Thus , a 3D-CoG system is suitable for studying growth of S . aureus in a rigid matrix by microscopy . In order to supply a more tissue-like environment we added fibrinogen corresponding to normal serum concentration ( 3 mg/ml ) to the growth medium ( 3D-CoG/Fib ) . This led to dramatic changes in the growth behavior of staphylococci ( Fig . 1C ) : Firstly , single staphylococci gave rise to discrete microcolonies of uniform size after 16 h of growth which were surrounded by a spherical pseudocapsule ( about 35 µm in diameter and 1–3 µm in thickness , Fig . 2A ) . The encapsulated microcolonies consisted of densely packed staphylococci and appeared to be free of collagen fibers . Secondly , the pseudocapsules were embedded into an outer dense microcolony-associated meshwork ( MAM ) surrounding the microcolonies ( approximately 150 µm in diameter , Fig . 2A ) . Both of these concentric structures , the inner pseudocapsule and the outer MAM , were only formed in the presence of fibrinogen . The observation that these structures - in contrast to collagen fibers - were rapidly degraded after the addition of plasmin ( 8 µg/ml ) suggests that they are at least in part composed of fibrin ( Fig . 2B and Video S1 ) . As the observed pseudocapsule and the MAM appeared to consist of fibrinogen/fibrin components , we assumed an involvement of secreted proteins belonging to the SERAM family [5] . Most , if not all of these genes have been shown to be transcriptionally activated by the saeRS two-component system [13] , [22] . Therefore , the Newman sae mutant with severe repression of SERAM-encoding genes ( Newman-29 , [13] ) was analyzed for its growth behavior in 3D-CoG/Fib ( Fig . 1D and 2A ) . Both pseudocapsule and MAM formation were completely abrogated; the growth phenotype resembled cluster formation of strain Newman in 3D-CoG without fibrinogen . Of note , cell wall- anchored fibrinogen binding proteins ClfA and ClfB are not affected in the sae mutant [23] . Thus , we reasoned that SERAM family members activated by the saeRS two-component system could be involved in the formation of these putatively fibrin-based structures . Immunostaining of Emp and Coa revealed their localization on or within the pseudocapsule ( Fig . 3 ) . In order to elucidate the pseudocapsule and MAM formation process , we analyzed a set of Newman mutants in coa , vWbp , eap , and emp . Emp and Eap production were confirmed by SDS surface extracts ( Fig . S1 ) , Coa and vWbp were detected in culture supernatants and confirmed by MALDI-TOF ( Fig . S1 ) . A coa mutant was still able to form pseudocapsules and MAM , although the pseudocapsules and the enclosed microcolonies were considerably more irregularly shaped than those of the parental Newman strain ( Fig . 1E , 2A and 3C ) . Obviously , Coa was partially involved in the formation of the pseudocapsule . The vWbp emp double mutant was able to form a pseudocapsule similar to that of the parent strain ( Fig . 1F and 2A ) . However , the microcolonies were completely devoid of the MAM . Ectopic complementation studies with a set of plasmids ( encoding vWbp or emp or both ) showed that this phenotype was solely dependent on vWbp ( Fig . S2 ) . The increased MAM diameter compared to the parental strain could be explained by the increased secretion of vWbp due to the multicopy ectopic complementation ( compare Fig . S1 ) . Interestingly , the eap mutant and an ica mutant were phenotypically indiscernible from the parent strain when grown in suspension or in 3D-CoG/Fib ( Fig . 2A and data not shown ) . Taken together , S . aureus Newman microcolonies grown in a 3D-CoG matrix in the presence of fibrinogen were surrounded by two distinguishable concentric structures: an inner pseudocapsule and an outer dense microcolony-associated meshwork which we termed MAM . It has been reported that strain Newman is dysregulated in SERAM-production because of an amino acid exchange in SaeS , the sensor kinase of the saeRS two-component system , compared to other S . aureus strains [13] . Therefore , we also assessed several clinical S . aureus isolates for their growth behavior ( Fig . 4 ) , among these MSSA strains freshly isolated from several patients ( blood , sputum , abscesses ) and two MRSA reference strains ( USA300 FPR3757 [24] and ST239-CC8 [25] ) . Strikingly , pseudocapsule formation was found in all of these strains , although there was a broad spectrum of microcolony size variations . Furthermore , the pseudocapsules of some strains were less regularly shaped in comparison to strain Newman . We also found MAM-like structures in 5 of 11 isolates , the size and regularity of which were also strain-dependent ( e . g . for USA300 compare Fig . 2A and 4E ) . Some strains forming much larger microcolonies than Newman caused complete solidification of the medium supernatant within the first 16 h which was observed with strain Newman only after about 20–40h . We could show that at later time points ( >20–40 h ) single S . aureus Newman microcolonies transited from relative growth arrest to massive growth and dispersal ( Fig . 5 ) . This event was accompanied by degradation of both the pseudocapsule and the MAM , probably by releasing a soluble fibrin-specific protease as the underlying collagen matrix was not degraded . This factor also degraded the fibrin structures of nearby colonies without inducing growth immediately ( data not shown and compare Video S2 ) . Similar to strain Newman , fibrin degradation was observed with USA300 microcolonies ( Fig . 5C and Video S2 ) . Comparison of Newman wildtype with the agr mutant revealed the involvement of the agr system in microcolony dispersal ( Fig . 5B ) . Moreover , addition of the fibrinolysis inhibitors aprotinin or tranexamic acid [26] to the growth medium suppressed this behavior completely , even up to 6d after inoculation , while formation of pseudocapsule and MAM was unaffected ( Fig . S3 ) . Bacterial pathogens shield themselves from humoral and cellular factors of the host defense system by formation of exopolysaccharide or proteinaceous capsules . Therefore , we assessed if the S . aureus pseudocapsule and the MAM also affect the interaction of neutrophils with staphylococcal microcolonies . For this purpose we established a novel method for applying neutrophils to our 3D-CoG system . Lys-EGFP mice [27] were used , in which mature neutrophils express eGFP under the control of the lysM promoter . Native spleen explants of lys-EGFP mice were cut into thin slices ( 300 µm ) using a vibrating blade microtome . These slices were then layered upon the surface of the 3D-CoG and fluorescent neutrophils presumably originating from the red pulp migrated into the collagen matrix ( see also Fig . 6 ) . This technique of direct transfer of neutrophils from splenic tissue to 3D-CoG avoids cell manipulation required for neutrophil isolation from blood or bone marrow which may affect neutrophil behavior . By this approach we could show that almost exclusively eGFP-positive cells migrated into the 3D-CoG . These cells were identified as neutrophils by immunostaining of the specific marker Ly-6G ( Fig . S4 ) . Non-fluorescent cells inside the 3D-CoG were only rarely observed . In order to analyze the effects of pseudocapsule and MAM on neutrophils , staphylococci were pre-grown in 3D-CoG/Fib for 16–17 h and then challenged with murine neutrophils . In the absence of fibrinogen ( 3D-CoG ) , neutrophils migrated towards and invaded bacterial clusters of strain Newman , followed by immediate phagocytosis of staphylococci ( Fig . 7A ) . A high ratio of these neutrophils lost their fluorescence and their nuclei became stainable by Sytox Blue , a cytoplasmic membrane impermeable fluorescent dye , indicating necrotic neutrophils . In contrast to that , neutrophils were not able to approach and contact strain Newman microcolonies grown in 3D-CoG/Fib ( Fig . 7C , D and Video S3 ) . In order to visualize the dynamics of neutrophil migration , all single time frames of this image sequence were projected onto one another , producing a time projection ( Fig . 7E ) . The microcolonies exhibited a halo ( 101+/−46 µm ) free of neutrophils during the observation period of ≥3 h after neutrophil challenge . The Newman sae mutant microcolony which neither formed pseudocapsule nor MAM in 3D-CoG/Fib , was immediately invaded by neutrophils ( Fig . 7B , Video S4 and Fig . S5 ) , similarly to that of strain Newman in the absence of fibrinogen ( Fig . 7A ) . The microcolonies of eap and coa mutant strains grown in 3D-CoG/Fib appeared to be protected from neutrophils similarly as strain Newman ( Fig . 7I ) . In contrast to this , microcolonies of the Newman vWbp emp double mutant did not exhibit such a neutrophil-free halo , instead the neutrophils were able to reach the pseudocapsule surrounding the microcolonies ( Fig . 7F–I and Video S5 ) . Thus , the presence of the neutrophil-free halo correlated with the presence of the vWbp-dependent MAM surrounding bacterial microcolonies . Obviously , the MAM functioned as a mechanical barrier inhibiting neutrophil immigration into this zone . A possible artifact resulting from the combination of murine neutrophils with human fibrinogen could be ruled out by reproducing the migration restriction for human neutrophils isolated from peripheral blood ( data not shown ) . Taken together , vWbp-producing staphylococci grown in 3D-CoG/Fib produce a MAM surrounding the microcolonies which inhibits the migration of neutrophils , thus acting as a mechanical barrier . In order to assess whether the pseudocapsule also contributed to shielding from neutrophils , we used the Newman vWbp emp mutant strain which was unable of MAM formation but still produced a pseudocapsule ( Fig . 7F–I ) . As shown in Fig . 8A , immigrating neutrophils were not able to directly contact staphylococci of the microcolony . Instead , they were kept at a short distance , as can be seen from the narrow gap between neutrophils and the staphylococcal fluorescence signal in single z sections ( see also Video S6 ) . The dimension of this gap roughly equaled the dimension of the pseudocapsule ( compare Fig . 2A and 7I ) . From this we suggest that the pseudocapsule acts as a second mechanical barrier sequestering the bacterial microcolonies from phagocytic cell attack . However , after about 4 h microcolony-associated neutrophils started to penetrate the pseudocapsule and to take up staphylococci . Typically , invasion of the pseudocapsule started with a localized invasive event , that is , only a single or few neutrophils squeezed through a narrow hole in the pseudocapsule . Once in direct contact with staphylococci , they immediately started phagocytosis ( Fig . 8B and Video S6 ) . This initial breakthrough of neutrophils resulted in recruitment of a wave of neutrophils and disruption of the entire colony . With some microcolonies we observed that initially a larger fragment of the pseudocapsule ruptured , allowing access of a number of neutrophils to the staphylococci . This led to a rapid dispersal of staphylococci from the ruptured pseudocapsule , while massive phagocytosis was observed ( Fig . 8C and Video S7 ) . Interestingly , phagocytosis of staphylococci after pseudocapsule rupture was frequently associated with neutrophil cell lysis/necrosis ( Fig . 8E–H and Video S8 ) . This was in agreement with neutrophil/microcolony-interaction of strain Newman grown in 3D-CoG ( Fig . 7A ) . In the very rare case that strain Newman wildtype or coa mutant microcolonies were encountered by neutrophils which had penetrated the disrupted MAM , similar events were observed ( data not shown ) . Such mechanical injury can be occasionally observed when overlaying the 3D-CoG with a spleen slice . In the case of the coa mutant strain , the more irregularly shaped pseudocapsule retained residual barrier function ( Video S9 ) . Taken together , the pseudocapsule acts as a second mechanical barrier protecting staphylococcal microcolonies from neutrophil attack . As shown above , formation of both a pseudocapsule and a MAM is not a unique capacity of strain Newman as it is also observed with other clinical isolates . Upon challenge with neutrophils , some isolates producing MAM were protected from neutrophil encounter ( Video S10 and S11 ) . Microcolonies of strains producing a prominent pseudocapsule but no visible MAM exhibited protection from initial neutrophil encounter but were subsequently invaded eventually , similar to the Newman vwbp emp mutant ( Video S12–S14 ) . USA300 microcolonies were more accessible to neutrophils compared to strain Newman , probably due to a weaker MAM barrier function . However , upon first contact with neutrophils , barrier function similar to other clinical isolates was obvious ( Fig . 8D and Video S15 ) . Thus , clinical isolates were able to exploit similar strategies as strain Newman in 3D-CoG/Fib to protect themselves from neutrophils . Several synthetic thrombin protease inhibitors in clinical use have been shown to inhibit S . aureus coagulase activity ( dabigatran [28]; argatroban [29] , [30] ) , but inhibition of vWbp-mediated clotting has not been addressed yet . Therefore , we investigated whether argatroban could interfere with Coa-mediated pseudocapsule and vWbp-dependent MAM formation . By supplementing the growth medium ( 3D-CoG/Fib ) with 10 nM argatroban , MAM formation was affected ( Fig . 9A ) . Marked impairment of pseudocapsule formation required about 50 nM argatroban . These results demonstrate that argatroban is able to prevent the formation of the outer and the inner barrier generated by staphylococci grown in 3D-CoG/Fib . In consequence , microcolonies became prone to neutrophil attack: at 10 nM argatroban , 75% of the microcolonies were protected from neutrophil attack by the MAM , at 50 nM only 16% retained this barrier function , at 100 nM all microcolonies were accessible to neutrophils ( Fig . 9B ) . Pseudocapsule function was diminished in the same argatroban concentration-dependent manner . Thus , argatroban inhibits both pseudocapsule and MAM formation , probably by inhibiting the proteolytic activity of Coa- and vWbp-activated prothrombin . This inhibitory effect supports the disruption of microcolonies by neutrophils .
Here we report for the first time on growth behavior of S . aureus in a 3D-CoG setup supplemented with fibrinogen as a surrogate of host tissue environment . When S . aureus Newman is cultivated in liquid cell culture medium RPMI 1640 without agitation , bacterial clusters of variable size are formed . This is independent from the gene products of ica , eap , emp , vWbp or coa . Similar growth behavior of S . aureus was observed in 3D-CoG . The bacterial clusters were somewhat more compact due to spatial restriction by collagen fibers . Definite attachment of single staphylococci to collagen fibers was not observed . This can be explained by the fact that strain Newman lacks the collagen binding adhesin CNA [31] . From this we suggest that the 3D-CoG serves as an almost inert fibrillar collagen meshwork for strain Newman . Consequently the 3D-CoG can be used as a migration substrate for neutrophils when studying staphylococci-neutrophil interactions . A characteristic feature of S . aureus is its capability to convert fibrinogen into fibrin by activating prothrombin via the secreted proteins Coa and vWbp . Therefore we added human fibrinogen to our assay ( 3D-CoG/Fib ) . Under these conditions , strain Newman forms regular microcolonies which are surrounded by two concentric structures: an inner spherical pseudocapsule and an outer dense microcolony-associated meshwork ( MAM ) . Both obviously contain fibrin as they can be degraded by plasmin . This architecture resembles that of staphylococcal abscess communities ( SAC ) in experimental murine infection described by Cheng et al . [14] , [15] and Sawai et al . [32] . We could show that the staphylococcal clotting factors Coa and vWbp are required for the formation of these structures . Firstly , Coa was detected in association with the pseudocapsule by immunostaining . This suggests that Coa is retained and accumulated in the vicinity of the pseudocapsule and activates prothrombin . Furthermore , we demonstrated that a coa mutant strain forms more irregularly shaped pseudocapsules . This seems to influence the shape of the microcolony . Secondly , formation of the MAM is not affected in a coa mutant but completely abolished in a vWbp mutant . From this , we conclude that vWbp , and not Coa , is the clotting factor involved in MAM formation . Assuming that the coa mutant is producing only vWbp as clotting factor , we can conclude that vWbp is also able to partially take over Coa function by supporting partial formation of the pseudocapsule . From these results we propose the following model ( Fig . 10 ) : single S . aureus cells give rise to microcolonies during growth in 3D-CoG/Fib . Staphylococcal clotting factors Coa and vWbp mediate conversion of soluble fibrinogen to insoluble fibrin . This leads to fibrin deposition in the vicinity of the microcolony , resulting in two independent structures: an inner pseudocapsule and an outer MAM . Eap and Emp are apparently not required for the formation of these microscopically visible structures , though Emp localized on or within the pseudocapsule in a similar manner to Coa . It is of note that both Coa and vWbp have no direct proteolytic activity for fibrinogen conversion but they hijack the host clotting machinery by activating prothrombin independently of the coagulation cascade [9] , [11] . Traces of prothrombin , plasminogen and vWF can be present in commercially available fibrinogen prepared from human plasma . Obviously , even these prothrombin traces appear to be sufficient for the observed clotting activity of S . aureus in 3D-CoG . However , during our experiments we encountered a fibrinogen batch which did not induce the described encapsulation . In that case addition of 2–5 µg/ml prothrombin to 3D-CoG/Fib restored the phenotype ( data not shown ) . Both Coa and vWbp are members of the bifunctional zymogen activator and adhesion protein ( ZAAP ) family: besides their prothrombin-binding and -activating function , they possess binding properties for other host proteins such as fibrinogen and vWF [10] . Moreover , vWF is capable of binding collagen fibers [33] . It has been discussed that such binding activities might be responsible for localized coagulation activity by acting as a homing device to direct these proteins to a certain spatial context after release from staphylococci . Our results support this hypothesis by spatial dissection of the clotting activity due to the formation of two discrete capsule-like structures . This is further corroborated by a recent study in a mouse infection model which reports that Coa is localized to a pseudocapsule enclosing the staphylococci , whereas vWbp was also found more distant in the abscesses [15] . Therefore we suggest that Coa activity might be restricted to a narrow zone of the microcolony interface , probably by binding to a released staphylococcal protein or via a host-derived bridging molecule . From this localization it might exert a short-distance effect on fibrinogen conversion , resulting in pseudocapsule formation . In contrast , vWbp acts preferentially in the periphery of the microcolony , mediating the formation of the MAM ( long-distance effect ) . These conclusions are supported by the growth behavior of the saeRS-mutant which is impaired in secretion of Coa and vWbp ( [34] , [35] , compare Fig . S1 ) and thus is unable to form the MAM or the pseudocapsule . Another important virulence mechanism of S . aureus is its ability to degrade fibrin clots which play a role in sequestering infected foci from healthy tissue in abscesses . This is suggested to lead to dissemination of staphylococci into deeper and more remote tissue . After extended periods of growth ( 20–40 h ) , we detected single microcolonies which exhibited massive growth and subsequent dispersal probably after disintegration of the pseudocapsule and MAM . S . aureus is able to induce fibrin degradation by activation of host plasminogen by staphylokinase [36] . Staphylokinase secretion is positively regulated by the agr system [37] and its production peaks in stationary phase [38] . We showed that the observed occasional dispersal of microcolonies after incubation for extended periods of time is affected in an agr mutant and therefore suggest that staphylokinase might play a role . However , staphylokinase requires plasminogen for conversion into plasmin which in turn degrades fibrin . Commercially available fibrinogen purified from human plasma is known to contain traces of plasminogen . The inhibition of fibrinolysis by the serine protease inhibitor aprotinin or by the specific plasmin inhibitor tranexamic acid further corroborates the involvement of plasmin-staphylokinase . Of note , both compounds did not affect pseudocapsule or MAM formation . Only recently , the concept of S . aureus hijacking the host clotting machinery to establish a protective niche has gained support from experimental mouse infection [14] , [15] . In corroboration of this concept , we asked whether the observed two discrete fibrin capsule-like structures mediate any barrier function for immigrating phagocytes and whether pseudocapsule and MAM function differently in regard of phagocyte invasion . Neutrophils are considered to be the first line of cellular defense of localized infections . Moreover , their predominance in abscesses formed by staphylococci has been shown [14] . Here , we have established for the first time an in vitro approach to study neutrophil-staphylococci interactions in 3D-CoG/Fib by using native spleen slices as a source of neutrophils . This approach has advantages in comparison to the application of neutrophils isolated from blood or bone marrow: Firstly , the neutrophils are not affected by various steps during the isolation procedure . Moreover , their inherent ability of amoeboid migration in 3D-CoG serves as a coarse passive phenotypical isolation step . Secondly , spleen slices provide a steady supply of native murine neutrophils throughout the experiments by acting as “neutrophil-soaked sponges” . Compared to overlaying the 3D-CoG with blood only or neutrophils in solution , we observed more consistent neutrophil migration into the 3D-CoG ( data not shown ) . This might be explained by facilitating the entry of neutrophils into the pre-formed 3D-CoG by supplying a direct and close tissue/3D-CoG contact instead of a liquid/3D-CoG interface . However , this model can be used with isolated neutrophils from other sources as well ( e . g . neutrophils isolated from peripheral blood , dHL-60 cell line ) . Thirdly , neutrophils from various transgenic or knock out mouse strains can be used in this system in order to assess the impact of gene deletions on specific host-pathogen interactions . Such studies are currently conducted in our laboratory . As shown previously , neutrophils migrate randomly in 3D-CoG [39] . In this study we observed unrestricted neutrophil migration towards S . aureus microcolonies pre-grown in 3D-CoG , followed by high rates of phagocytosis . However , in the presence of fibrinogen , we observed that the vWbp-mediated MAM probably acted as a mechanical barrier and prevented neutrophil migration towards the staphylococcal microcolony . In addition to mechanical restraints imposed onto neutrophils , it is also conceivable that released S . aureus proteins bind to the fibrin meshwork and interfere with neutrophil signaling pathways involved in chemotaxis . This remains to be elucidated . Strikingly , the pseudocapsule turned out to be a second safety barrier against neutrophil attack . Only after accumulation of a higher number of neutrophils at the interface of the microcolony we observed selected neutrophils being able to penetrate . This might be due to a bacteria-mediated dispersal mechanism degrading the pseudocapsule or more likely due to direct degradation of the pseudocapsule by released neutrophil proteases . These neutrophils obviously gained entry to the microcolony by squeezing through small holes . The penetration of such a “pioneer neutrophil” elicited a massive attraction and invasion of neutrophils to the interior of the microcolony . Whether pioneer neutrophils release chemokines or whether destruction of the pseudocapsule leads to the release of staphylococcal chemoattractants remains to be elucidated . Interestingly , we observed a high rate of neutrophil cell lysis/necrosis after direct contact of neutrophils with staphylococci regardless of whether microcolonies were previously surrounded by a pseudocapsule or not . Probably neutrophils are killed after phagocytosis and oxidative burst or by toxic substances released by staphylococci . It is conceivable that staphylococci reached stationary phase after pseudocapsule formation and start toxin production , resulting in “caged toxins” . By using mutants affected in toxin production ( e . g . PSMs or α-hemolysin ) , it should be possible to unravel the mechanism of this staphylococci-induced neutrophil cell death after pseudocapsule rupture . Of note , it has been reported recently that CA-MRSA strains induce a form of programmed necrosis [40] . Taken together , both the pseudocapsule and the MAM exert a strong barrier function for neutrophils and protect staphylococci against phagocytosis . However , compared to the rapid phagocytosis of staphylococci after barrier breakdown , this suggests that other reported anti-phagocytosis activities , e . g . ClfA-mediated phagocytosis inhibition [41] , are minimal compared to this massive phagocytosis inhibition . Various studies have shown that strain Newman differs from other S . aureus model strains , mainly due to a missense mutation in saeS resulting in derepression of gene expression [13] . In order to check , whether the results obtained with strain Newman are unique , we compared several clinical S . aureus isolates and found similar characteristics . Presence of a pseudocapsule appears to be a very common phenomenon , while formation of MAM showed strong variation among the analyzed isolates . This is in line with the reported pseudocapsule of strain USA300 in the mouse infection model [14] . Several thrombin inhibitors have been reported to inhibit Coa-mediated activation of prothrombin [28] , [30] . We wondered if these agents have an impact on the growth phenotypes of S . aureus in 3D-CoG/Fib and on the barrier function for neutrophils . Indeed , argatroban , a thrombin inhibitor in clinical use , prevented pseudocapsule and MAM formation in a concentration-dependent manner and by this enhanced neutrophil access to staphylococci . This result provides us with an attractive therapeutic option in combating staphylococcal infections: By hijacking host machinery and relying on this mechanism for virulence , S . aureus offers a specific host-derived target . This target is well characterized , not least for its importance in coagulation-associated diseases . Early access of neutrophils to staphylococci can be expected to counteract this virulence advantage gained by usurping host machinery . These results may lead to new strategies in treatment of staphylococcal infections by using protease inhibitors in combination with antibiotics . Furthermore , it should be mentioned that a species specificity for various clotting-related S . aureus proteins has been shown: staphylokinase shows high activity towards human but only limited activity towards murine plasminogen [38] , coagulase activates bovine and rabbit prothrombin only weakly compared to human prothrombin [17] , and recently it has been shown , that certain S . aureus strains carry species-specific vWbp alleles [42] . Thus , the presented 3D-CoG infection model can be useful in the study of such host specificities by supplementing the system with further plasma proteins , as well as with additional cell types , e . g . mesenchymal cells . Nevertheless , it has to be stated that this is a reductionstic in vitro model lacking the complexity of an in vivo infection model . However , it will be helpful in improving the design of animal infection experiments and to complement the interpretation of in vivo results . Taken together , the 3D-CoG model in combination with native spleen slices is a suitable in vitro infection model to study both in vivo-like growth characteristics and the resulting phagocyte-microbe interactions . It opens a broad field of applications by complementing established animal infection models for the development of new treatment options for infections .
All experimental procedures involving animals were performed according to the “German Animal Protection Act” ( TierSchG ) and approved by the regional authorities of the city of Munich ( KVR-I/221 , TA077/10 ) . Human serum was pooled from voluntary donors at the Max von Pettenkofer-Institute , LMU Munich , Germany , according to approval by the ethics commission of the Medical Faculty of the LMU Munich . Written , informed consent was provided by the volunteers . Clinical S . aureus isolates were obtained as discarded de-identified isolates from the clinical microbiological laboratory of the University Hospital of Munich . The strains and plasmids used in this study are listed in Table 1 and Table 2 . Staphylococci were routinely cultured under constant agitation in LB or Basic medium ( BM; 1% peptone , 0 . 5% yeast extract , 0 . 1% glucose , 0 . 5%NaCl , 0 . 1% K2HPO4 ) supplemented with antibiotics if the strains carry resistance cassettes ( 50 µg/ml kanamycin , 10 µg/ml erythromycin , 5 µg/ml tetracycline , 10 µg/ml chloramphenicol , 100 µg/ml ampicillin ) . RPMI 1640 medium ( No Phenol Red , Invitrogen ) was used as a growth medium for 3D-CoG . The vWbp gene and the emp gene were amplified from chromosomal DNA of strain Newman by PCR ( High Fidelity PCR Enzyme Mix , Fermentas ) using the primer pairs vWbp-f-EcoRI/vWbp-r-SalI ( 1 ) , vWbp-f-EcoRI/vwb-r-BamHI ( 2 ) and emp-f-BamHI/emp-r-PstI ( 3 ) , respectively ( Table S1 ) . The PCR products ( 1 ) and ( 3 ) were digested with EcoRI/SalI and BamHI/PstI , respectively , and ligated into pSK236 isolated from DH5α digested in the same way , resulting in the plasmids pvWbp and pEmp . In order to produce pvWbpEmp , the PCR product from ( 2 ) was cloned into pSK236 via EcoRI/BamHI , followed by cloning of the PCR product from ( 3 ) into the resulting plasmid . Ligation products were transformed into electrocompetent DH5α . Transformants were screened for growth with ampicillin . The resulting plasmids were isolated from DH5α and transformed into electrocompetent RN4220 , isolated again and then transformed into electrocompetent Newman wildtype or vWbp emp mutant strain . Presence of Emp on the S . aureus cell surface was detected as described previously with some modifications [43]: S . aureus was cultivated in 10 ml of BM medium for 18 h . Differences in optical density at 600 nm ( OD600 ) were adjusted by addition of BM medium . Cells were harvested by centrifugation at 4 , 000 x g for 10 min . The pellets were resuspended in 300 µl of 2% sodium dodecyl sulfate ( SDS , Sigma ) and incubated at 95°C for 5 min shaking at 750 rpm . The supernatant was then isolated in two sequential centrifugation steps at 10 , 000 x g for 5 min and stored at −80°C . Equal amounts were mixed with sample buffer and proteins were separated by 12% SDS-polyacrylamide gel electrophoresis ( PAGE ) . For analysis of secreted proteins , overnight cultures were diluted 1∶25 in 5 ml of fresh LB medium . After 3 h of growth , secreted proteins were precipitated from 3 . 6 ml culture supernatants with 10% trichloroacetic acid for 1h on ice . The pellets were washed three times in −20°C aceton ( 12 . 000 x g , 10 min , 4°C ) , followed by a final washing step in H2O . The pellet was dried and resuspended in sample buffer . Equal amounts of supernatant according to the OD600 before harvesting were separated by 10% SDS-PAGE . Collagen gels ( 3D-CoG ) were generated as described previously [21] and incubated without agitation . Staphylococci were grown in LB medium with agitation before inoculation into 3D-CoG and subsequently suspended in liquid collagen solution ( about 2×104/ml ) . Liquid collagen solution consisted of 1 . 78 mg/ml bovine type I collagen ( Purecol , Advanced Biomatrix ) in RPMI 1640 medium adjusted to pH 7 . 4 . 10 µl of this solution were dispersed on the bottom of a microscopic dish ( 9 . 4×10 . 7 mm , µ-slide 8 well , ibidi ) . The samples were allowed to polymerize for 45 min ( 37°C; 5% CO2 ) . 3D-CoGs were overlaid with 150 µl medium: RPMI 1640 or RPMI 1640 supplemented with 3 mg/ml fibrinogen from human plasma ( Calbiochem ) ( 3D-CoG/Fib ) . For protease inhibition experiments , the plasmin inhibitors aprotinin and tranexamic acid or the thrombin inhibitor argatroban were obtained from Santa Cruz and added directly to the growth medium . For neutrophil challenge of staphylococcal microcolonies in 3D-CoG , native spleens from 8–12 weeks old heterozygous lys-EGFP C57BL/6 mice [27] were used . Lys-EGFP mice were bred and maintained at the Max von Pettenkofer-Institute in isolated ventilated cages ( Tecniplast ) under SPF conditions . All animal work was conducted according to the relevant national and international guidelines . Mice were sacrificed by CO2 asphyxiation and spleens were harvested and cut into 300 µm slices with a vibrating blade microtome ( Leica ) at 4°C . Supernatants from 3D-CoG samples were removed and then 3D-CoG were overlaid with spleen slices . To immobilize the slices on the 3D-CoG , a drop of 4% NuSieve GTG agarose ( Lonza ) was applied . After solidification , 150 µl of RPMI 1640 were added . Sytox Blue for staining of cells with corrupted membranes was used at 1 µM according to the manufacturer's protocol ( Invitrogen ) . The manual process of overlaying 3D-CoG with tissue slices can partially result in compression or injury of some areas of the collagen gel . To compensate for artifacts , only microcolonies in a lateral distance of about 200 µm to the tissue slice-collagen gel interface were analyzed after verifying that the collagen gel in this area was not compressed or injured . This was achieved by visualization of the 3D-CoG structure with confocal reflection microscopy . The samples were incubated at 37°C/5% CO2 in a cell culture incubator or in the microscope incubation chamber . Confocal laser scanning microscopy ( CLSM ) was performed on a Leica SP5 microscope ( Leica , Germany ) equipped with an incubation chamber ( The Cube & The Box , Life Imaging Services , Switzerland ) . Images were acquired with a 63x oil immersion objective , a 40x oil immersion objective or a 10x objective . Image acquisition , processing and quantification were performed with LAS AF software ( Leica , Germany ) . Confocal reflection contrast microscopy was used to visualize unstained collagen and fibrin fibers as described previously [44] . The dimensions of z projections from xyz stacks are mentioned in the figure legends . Time projections of xyzt series were performed by projecting all single xy frames onto a single one . All steps were performed in a volume of 150 µl in microscopic dishes ( µ-slide 8 well , ibidi ) at room temperature . CoG were fixed with 4% paraformaldehyde for 20 min at room temperature and then washed three times ( PBS , 5 min each ) . Samples were blocked for 1 h in blocking buffer ( PBS with 3% bovine serum albumin and 5% human serum from donors ) for Coa- or Emp-staining or with 5% goat serum in TBS-T for Ly-6G staining . Primary antibody was added to the samples ( 1∶100 rabbit anti-Coa , 1∶333 rabbit anti-Emp; both were kindly provided by O . Schneewind; 1∶100 anti-Ly-6G , BD Biosciences ) and incubated for 1 . 5 h , followed by three washing steps ( PBS or TBS-T , 5 min each ) . Secondary antibody ( 1∶200 Alexa Fluor 555 goat anti-rabbit or Alexa Fluor 647 goat anti-rat , Invitrogen ) was diluted in blocking buffer , added to the samples and incubated for 1 h in the dark , followed by three washing steps ( PBS , 5 min each ) . 1 µg/ml DAPI ( Sigma-Aldrich ) was used for staining of DNA . Cell surface of staphylococci was stained with 5 µg/ml FITC-labeled lectin from T . vulgaris , specific for N-Acetyl-glucosamine ( Sigma-Aldrich ) . Additional stainings were performed in parallel to incubation with secondary antibody . Statistical significance calculations were performed by Student's unpaired t-test . The GenBank ( http://www . ncbi . nlm . nih . gov/genbank/ ) accession number for genes discussed in this paper are: Coa ( 5330026 ) , vWbp ( 5331820 ) , Emp ( 5330439 ) . | Staphylococcus aureus is one of the most frequent pathogens causing divers localized and metastatic abscess-forming infections . Here we studied the role of the staphylocoagulases Coa and vWbp in the formation of microcolony-associated fibrin structures . By using a three-dimensional collagen gel ( 3D-CoG ) supplemented with human fibrinogen as a growth environment for staphylococci and as a neutrophil migration matrix , we were able to demonstrate that Coa is involved in producing a fibrin-containing pseudocapsule wrapping the staphylococcal microcolony whereas vWbp is required for establishing an extended outer fibrin meshwork . The pseudocapsule and the outer meshwork hinder neutrophils from attacking the staphylococci . Addition of the thrombin inhibitor argatroban prevents conversion of fibrinogen to fibrin and thus abolishes barrier formation . This in vitro model provides us with new options to study formation as well as prevention of staphylococcal abscesses under tissue-like conditions . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
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"Methods"
] | [
"immunity",
"biology",
"microbiology",
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"pathogens"
] | 2012 | Two Distinct Coagulase-Dependent Barriers Protect Staphylococcus aureus from Neutrophils in a Three Dimensional in vitro Infection Model |
The Bicoid morphogen gradient directs the patterning of cell fates along the anterior-posterior axis of the syncytial Drosophila embryo and serves as a paradigm of morphogen-mediated patterning . The simplest models of gradient formation rely on constant protein synthesis and diffusion from anteriorly localized source mRNA , coupled with uniform protein degradation . However , currently such models cannot account for all known gradient characteristics . Recent work has proposed that bicoid mRNA spatial distribution is sufficient to produce the observed protein gradient , minimizing the role of protein transport . Here , we adapt a novel method of fluorescent in situ hybridization to quantify the global spatio-temporal dynamics of bicoid mRNA particles . We determine that >90% of all bicoid mRNA is continuously present within the anterior 20% of the embryo . bicoid mRNA distribution along the body axis remains nearly unchanged despite dynamic mRNA translocation from the embryo core to the cortex . To evaluate the impact of mRNA distribution on protein gradient dynamics , we provide detailed quantitative measurements of nuclear Bicoid levels during the formation of the protein gradient . We find that gradient establishment begins 45 minutes after fertilization and that the gradient requires about 50 minutes to reach peak levels . In numerical simulations of gradient formation , we find that incorporating the actual bicoid mRNA distribution yields a closer prediction of the observed protein dynamics compared to modeling protein production from a point source at the anterior pole . We conclude that the spatial distribution of bicoid mRNA contributes to , but cannot account for , protein gradient formation , and therefore that protein movement , either active or passive , is required for gradient formation .
Accurate development of metazoan embryos requires precise production , reception , and interpretation of patterning cues along appropriate spatial axes on realistic timescales . Many embryonic patterning events utilize graded spatial distributions of patterning molecules , or morphogens , whose activities rely fundamentally on molecular interactions susceptible to environmental fluctuations and stochasticity in gene expression [1] . Despite the widespread occurrence of morphogen-mediated tissue patterning , in most cases little or no quantitative data exist regarding the dynamics of gradient establishment or the spatio-temporal regulation of morphogen production . The wealth of molecular and genetic tools available in Drosophila melanogaster offers an optimal context in which to study the basis of developmental accuracy in embryonic patterning by morphogens . In the Drosophila embryo , anterior-posterior ( AP ) axial patterning originates with maternal cues deposited into the developing egg [2] . Among these cues is the transcription factor Bicoid ( Bcd ) , the mRNA of which localizes at the anterior cortex of the oocyte [3]–[6] . Translation of bcd mRNA is believed to commence upon fertilization , after which the embryo undergoes 13 rapid nuclear mitotic cycles ( n . c . ) without cytokinesis . By the start of interphase 14 about 2 h after egg deposition ( AED ) , the embryo consists of a syncytial blastoderm layer of about 6 , 000 nuclei at the cortical surface , surrounding the interior core of yolk and vitellogenic nuclei . During the blastoderm stage Bcd protein distributes along the AP axis as an exponentially decaying gradient [7]–[9] , and nuclear Bcd activates target genes in a dosage-dependent manner ( reviewed in [10] ) . Recently , quantitative analysis of living embryos expressing Bcd-GFP revealed that the protein gradient remains nearly unchanged subsequent to the arrival of nuclei to the cortex at n . c . 10 ( about 85 min AED ) , and therefore that the nuclear gradient achieves stability within only about 80 min at 25°C [9] . Moreover , nuclear gradients at n . c . 14 exhibit remarkable reproducibility between embryos: along the AP axis , similarly positioned nuclei in different embryos contain Bcd-GFP concentrations that differ by only about 10% [9] . In principle , this level of precision would be sufficient for nuclei to distinguish their AP positions with an error of only a single nuclear diameter [11] . These observations raise fundamental questions regarding the underlying cell-biologic mechanisms responsible for the rapid , precise establishment of nearly equivalent gradients in essentially every embryo . One such question concerns the dependence of protein gradient formation on the strength , localization , and dynamics of the underlying bcd mRNA . Females carrying altered genetic dosages of bcd produce gradients of altered amplitude , resulting in mispatterned embryos [12]–[14] . Moreover , the packaging of bcd mRNAs into discrete ribonucleoprotein ( RNP ) complexes , and their subsequent localization to the anterior oocyte cortex , requires a suite of maternal factors ( reviewed in [15]–[17] ) . Ooctyes lacking these factors produce embryos with distorted protein gradients resulting from bcd mRNA translation at inappropriately posterior locations [12] . The reliable formation of the Bcd protein gradient must depend , at least in part , on the spatial distribution and the number of bcd mRNA molecules . While prior work has utilized in situ hybridization to document the spatial distribution of bcd mRNA during embryogenesis [3]–[5] , [18] , [19] , no work has yet determined bcd mRNA particle numbers or examined their global localization in the developing embryo . A second question regards the biophysical processes affecting the Bcd protein , such as its transport and degradation ( reviewed in [20] ) . The observed exponentially decaying nuclear steady state Bcd protein distribution is consistent with analytical models of gradient formation via constant anterior protein synthesis , coupled with diffusion and uniform degradation throughout the embryo ( the SDD model ) . Within this model , the effective diffusion constant of the Bcd protein D and the protein lifetime τ determine the dynamics of gradient formation . A relatively short τ would allow the gradient to reach an equilibrium distribution ( i . e . when protein degradation is matched by new synthesis ) within the available developmental time of ∼2 h . Conversely , a relatively long protein lifetime will prohibit the achievement of such an equilibrated state within that time . Likewise , larger or smaller values of D will increase or decrease , respectively , the predicted distance a protein can move away from its source mRNA . A direct measurement of D for cytoplasmic Bcd-GFP at n . c . 13 [9] yielded a substantially slower diffusion constant than for comparably sized , biologically inert molecules [21] , and about an order of magnitude too low to account for the rapid achievement of a steady state gradient with the appropriate length constant . More recent work has suggested the presence of multiple populations of Bcd-GFP , where a fraction of Bcd-GFP may diffuse rapidly enough to establish stability in the time available [22] , [23] . Slow diffusivity , however , would be consistent with an alternate model in which the protein gradient arises from graded bcd mRNA distribution [19] . Low values of D coupled with rapid protein degradation would minimize protein movement away from source molecules , so that the local mRNA amount would predominately determine protein concentration along the AP axis . Under these conditions , sufficiently mobile mRNA would greatly impact the dynamics of gradient formation , and protein diffusion contributes only minimally . However , at present it is difficult to evaluate the legitimacy of these models , or to determine what values of D and τ , if any , might accurately describe the dynamics of Bcd gradient formation , because no quantitative data exist which span the first 80–90 min of development . To address these questions , we developed two novel quantitative measurement approaches . First , we adapted a method of fluorescent in situ hybridization to the Drosophila embryo employing fluorescently labeled DNA oligonucleotides [24] , [25] . This method allowed us to identify individual bcd mRNA particles and determine their positions and intensities . We found that bcd mRNA particles undergo movement into the core of the embryo , away from their initial site of localization at the ooctye cortex , by the end of the third cleavage division ( within 30 min AED ) . Subsequently , bcd mRNA is relocalized to the egg cortex during cortical nuclear migration ( mitoses 6 to 9 , 55 to 75 min AED ) , during which bcd particles tend to dissociate without bcd mRNA degradation . Despite the dynamic behavior of bcd particles , at all times >90% of mRNA is localized within the anterior 20% of the embryo , a distribution which is insufficiently extended to produce the observed protein gradient in the absence of large-scale protein redistribution along the anterior-posterior axis . Second , we measured nuclear Bcd-GFP levels during presyncytial stages using GFP fluorescence in fixed embryos . We discovered that Bcd-GFP does not begin accumulating in nuclei until interphase 6 , about 45 min AED , reaching peak levels 50 min later in n . c . 11 and 12 . Unexpectedly , in fixed samples we found that the nuclear gradient declines between n . c . 13 and n . c . 14 , a result which differs from prior analysis of living embryos . We present evidence that attributes this difference in part to delayed fluorescence maturation of GFP in living embryos . Finally , to examine whether the bcd mRNA distribution impacts protein gradient formation , we incorporated its observed spatio-temporal dynamics into numerical simulations of protein production and movement . We found that models utilizing the actual mRNA distribution result in improved predictions of protein gradient dynamics , compared to models employing a single point source at the anterior pole . Therefore , we conclude that although the spatially extended mRNA localization contributes to the protein gradient , mRNA localization alone cannot account for protein gradient dynamics . Our results demonstrate that protein movement , whether active or passive , is a necessary component of Bcd protein gradient formation .
To examine bcd mRNA distribution in fixed embryos , we adapted a fluorescent in situ hybridization ( FISH ) protocol [24] using a set of 48 20-mer DNA oligonucleotides complementary to the bcd open reading frame ( Table S1 ) directly conjugated to AlexaFluor fluorophores [25] . This approach has been used previously to detect single mRNA molecules in mammalian cell culture and C . elegans embryos [25] . By employing directly labeled oligonucleotides as probes , we bypassed the use of antibodies and enzymes for detection [26]–[28] , and thus minimized nonspecific background , nonlinear signal response , variable tissue penetration by reagents , or other potential difficulties associated with quantification of conventional FISH signal [29] . Using standard laser scanning confocal microscopy , we can readily detect the anterior localization of bcd mRNA at low magnification ( Figure S1 ) and distinguish individual bcd RNP complexes in high resolution images ( Figures 1 and 2 ) . To obtain the complete three-dimensional ( 3d ) structure of bcd mRNA distribution , we generated high resolution confocal stacks at high magnification , such that a spatial unit voxel represents a volume of 75 nm×75 nm×420 nm . These stacks span the entire left or right half of each embryo from the midsagittal plane to the embryo surface , representative of the entire embryo due to left-right symmetry . Figures 1 and 2 show typical images of embryos labeled during interphase 4 and interphase 11 , respectively . Corresponding 3d stacks are provided in Movies S1 and S2 . Images taken at the anterior of labeled embryos reveal readily identifiable individual bcd RNP particles above background noise ( Figures 1A–E and 2A–D ) , whereas the embryo posterior is essentially devoid of bcd mRNA particles ( Figures 1F–G and 2E–F ) . Individual particles appear on multiple neighboring z-slices ( Figure 1H ) , owing to the objective's spatially extended point spread function ( PSF ) . To obtain counts of discrete bcd mRNA particles , their localization , and fluorescence intensities , we designed custom image analysis software to determine the spatial positions , radii , and fluorescence intensities of bcd mRNA particles ( see Materials and Methods ) . As illustrated in Figure S2 , the analysis algorithm discriminates between overlapping particles which densely populate the anterior and readily detects dim particles . A large majority of detected bcd particles are circular in shape ( Figure 3A ) , confirming that the algorithm resolves closely neighboring particles . The particles have a spatial extent of about 3 pixels on average , corresponding to a physical distance of ∼200 nm ( Figure 3B ) . This is identical to the PSF in the confocal slices ( Figure S3 ) , indicating that the particles are smaller than the diffraction limit of our microscopy . The extended PSF dictates that each mRNA particle must be detected simultaneously on consecutive z-slices , and the algorithm uses this property to discriminate true particles from local background noise ( see Materials and Methods ) . We identified an optimal threshold for distinguishing between candidate particles and random fluorescence fluctuations by examining posterior stacks which contain few particles ( see Materials and Methods and Figure S4 ) . As additional controls , we examined background fluorescence in cleavage stage embryos processed without any probes ( Figure S5A–C ) or exposed to probes against the purely zygotically expressed gene giant ( Figure S5D–F ) . Both show a low level of fluorescent signal , but no detected particles . These controls allow us to exclude falsely identified particles . bcd particles coalesce in vivo during bcd mRNA anterior localization in late oogenesis , and subsequently disperse upon egg activation [18] , [30] , [31] , suggesting that bcd RNPs may remain at least partially intact in fertilized embryos . The fluorescent intensities of particles observed by our method span about a 10-fold range ( Figure 3C ) . This broad distribution suggests the presence of multiple bcd mRNAs in a single detected particle , consistent with previous biochemical and fluorescence microscopy observations [32]–[34] . bcd mRNA is not degraded until the onset of cellularization at n . c . 14 [3] . Therefore , it is highly likely that the broad intensity distribution reflects variation in the number of RNAs per particle . Fluorescence variability is reflected in the axial ( z ) diameter of particles: we observe that bright particles occupy up to 5 z-slices , whereas dim particles are detectable above noise level in fewer slices ( Figure 1H ) . Our ability to distinguish discrete particles affords an unprecedented quantitative view of mRNA particles and allows us to separate their dynamics from the ultimate dynamics of Bcd protein . To quantify the spatial distributions of bcd mRNA , we examined embryos ranging from n . c . 3 to n . c . 14 . During all stages of development , bcd particles are found in a graded distribution peaking at around 7% egg length ( EL , as measured from the anterior pole ) and leveling off at nearly zero particle density by 40% EL ( Figure S6 ) . Confocal images taken at both early cleavage ( Figure 1 ) and blastoderm ( n . c . 11 , Figure 2 , and n . c . 13 , Figure S7 ) stages reveal a higher density of bcd particles in anterior compared to posterior regions . Moreover , anteriorly localized particles are brighter than those toward the posterior ( Figure 3D ) . The higher density of brighter anterior particles results in a sharp , steep gradient of total bcd mRNA distribution within the anterior third of the embryo ( Figure 3E ) . At all times , 90% or more of total bcd mRNA is found within the anterior 20% of the embryo ( Figure 3F ) , as previously reported [35] . The quantitative difference between Figures 3E and S4A arises from both the increased number and intensity of anterior particles . Although the majority of mRNA particles are detected in the anterior 20% , we nevertheless observe faint discrete particles scattered along the entire AP axis . During both cleavage and blastoderm , very few or no particles are detected in the posterior embryo core ( Figures 1F–G , 2E–F , S7E–F ) . Conversely , a small number of particles exhibiting weak fluorescence sparsely populate the posterior surface ( Figures 1I–K , 2G–L , S7I–L ) . A handful of particles can be detected even at almost 100% EL near the posterior pole ( Figure 2K–L ) . Thus , we can document even the small fraction of particles that either fail to localize during oogenesis or which are transported an unusually large distance away from the anterior cortex after egg activation . To our knowledge , bcd RNP complexes have not been detected in the posterior of wild-type embryos . These observations demonstrate the exquisite sensitivity of our method to the presence of dimly fluorescent bcd mRNA particles . However , we emphasize that the particles found in the entire posterior 60% of the embryo constitute less than 1% of the total bcd mRNA ( Figure 3F ) . These particles are uniformly dimly fluorescent ( Figure 3D ) and are found at a sparse density which does not change significantly during development . Translation of bcd mRNA is repressed by Nanos activity [36] , silencing posteriorly localized bcd mRNA . Based on these observations , the fraction of bcd mRNA present in the posterior 60% of the embryo likely contributes only negligibly to protein gradient formation . Previous studies have demonstrated that egg activation triggers release of bcd mRNA from its initial tight localization at the anterior egg cortex , resulting in a posterior dispersion of bcd mRNA within 25 min at 25°C [30] . This corresponds to the interphase of n . c . 3 ( Figure S8 ) , by which time bcd mRNA has already reached its most posterior extent , where it remains from n . c . 4 through 6 ( Figure 3E and 3F ) . The AP particle distribution at n . c . 4 therefore results from this early release of bcd mRNA from the cortex . Prior to and during n . c . 6 , 97% of all bcd mRNA is found in the anterior 20% ( Figure 3F ) , with the remainder forming a gradient which drops to nearly zero before 40% EL . In midsagittal planes at n . c . 4 , regions near the embryo surface contain fewer particles than the center of the embryo ( compare upper and lower panels in Figure 1B–D ) . Consistent with this , confocal planes taken near the embryo surface contain fewer particles compared to the same AP position at the midsagittal plane ( compare Figure 1A and 1I ) . Therefore , much of bcd mRNA is not near the cortex but is found in the embryo interior ( Figures 1A , S1A–B , S8 ) , as observed previously [5] . In low magnification images , we often observe mRNA localization in a wedge or cone-shaped distribution jutting in toward the interior core of the embryo in midsagittal planes ( Figures S8 , S9A–D ) , suggesting the presence of uncharacterized structures along which bcd mRNA particles might translocate upon egg activation . The observation that particles do not progress further into the posterior after n . c . 3 supports the idea that bcd mRNA is tethered to underlying cytoskeletal structure ( s ) throughout embryogenesis [32] and is not free to diffuse . We observed a marked change in bcd mRNA spatial localization after interphase 6 . As the nuclei undergo expansion toward the axial poles beginning at the sixth mitosis and continuing at n . c . 7 , bcd mRNA moves ahead of the expanding nuclei ( Figures 4A , S1C–E ) . bcd mRNA progresses to the cortex during nuclear cortical migration between n . c . 8 to 10 , such that in midsagittal planes at n . c . 11 , bcd mRNA is highly enriched near the embryo cortex , with the majority of fluorescence found within about 25 µm of the embryo surface ( Figures 2A–G , 4A ) . This enrichment is also apparent in z-slices collected at the cortical nuclear layer ( Figures 2G–L , 4B , S7G–L ) . Compared to earlier cycles , bright bcd particles are now present on the surface at a greater distance from the anterior pole ( compare Figure 1I to Figure 2G ) . Thus , with the nuclei penetrating the cloud of bcd mRNA during their cortical migration , by the start of the syncytial blastoderm stage , bcd mRNA fluorescence resembles a cup covering the anterior end of the embryo ( Figure S9E–H ) , which remains in place through n . c . 14 ( Figures 3E , 3F , S1F–G , and S6 ) . Concomitant with bcd particle translocation from core to cortex , we observe a mild posterior shift of the bcd mRNA distribution along the AP axis by the onset of the blastoderm stage at n . c . 10 ( Figures 3E , 3F , and S6 ) , as previously observed [5] . Embryos at all blastoderm stages appear similar , with bcd mRNA particles surrounding the nuclei on both the basal and apical surfaces ( compare n . c . 11 in Figure 2 and n . c . 13 in Figure S7 ) . Despite dynamic cortical relocalization , more than 90% of bcd is found in the anterior 20% of the embryo ( Figure 3F ) , with the remaining 10% falling to zero particle density by 40% EL . Concomitant with the particle relocalization , the distribution of fluorescence intensities shifts from bright to dim spot intensities ( Figure 3C ) , and the number of detected particles tends to increase with developmental age from about 70 , 000 per embryo in cycles prior to n . c . 7 , to about 110 , 000 particles in blastodermal stages ( Figure 4C ) . Despite these changes , the total fluorescent signal from bcd mRNA remains constant across all developmental times through early n . c . 14 ( Figure 4D ) , arguing that our method is capable of detecting the constant maternal bcd mRNA pool . Given previous work indicating the presence of multiple bcd mRNA molecules per particle [32]–[34] , these observations indicate that such particles disassemble during their relocalization to the cortex and suggest that mRNA degradation likely plays little role in particle dissolution . bcd RNA is degraded with other maternal mRNAs at the midblastula transition during n . c . 14; prior to this time , no degradation of bcd mRNA occurs [4] , [37] . To contrast bcd mRNA particle disassembly with mRNA degradation , we documented the loss of bcd mRNA during n . c . 14 ( Figure 5 ) , which spans about 1 h at 25°C . During this time , wholesale zygotic gene expression is activated , maternal mRNAs are degraded , and cellular membranes form between the cortically positioned nuclei . We gauged the approximate age of fixed embryos in n . c . 14 on the presence of the cellularization front and the morphology of nuclei which elongate during cellularization . Immediately following the 13th mitosis , embryos exhibit particles with intensity and spatial distributions similar to the earlier blastoderm stages ( Figure 5A , 5D , 5G ) . During the next ∼10 min , the cell surface protrudes above each nucleus and membrane invagination begins [38] , [39] , during which we observe a dramatic reduction in overall fluorescence and a sharp decrease in the number of particles and fluorescence of particles in all regions of the embryo ( Figure 5B , 5E , 5H , 5J–L ) . As nuclei undergo elongation , bcd mRNA is almost completely lost , and we detect only a handful of dim particles in the anterior , usually at the cortical or lateral surfaces between nuclei ( Figure 5F , 5L ) . At this time , the total particle count has dropped to essentially zero ( see the final data point on x-axis in Figure 4D ) . Thus , degradation of bcd can be readily distinguished from the separate earlier event of particle dissolution . In summary , we have observed that bcd mRNPs behave dynamically in the embryo: after fertilization , bcd mRNA establishes a wedge-shaped distribution , which is subsequently reformed into a cup-shaped geometry during n . c . 6–10 ( Figure S9 ) , followed by mRNA decay at the onset of cellularization . Despite these dynamic rearrangements , more than 99% of all mRNA particles occupy the anterior 40% of the egg at all times . These findings demonstrate that an exponentially graded protein distribution , which is detectable as far as the posterior 85% of the egg [8] , [9] , [40] , can be established only if the protein moves away from the anterior source . Understanding the degree to which a graded mRNA source influences the formation of the Bcd protein gradient requires a comparison of mRNA and protein dynamics . Live imaging of transgenic embryos suggests that nuclear Bcd-GFP levels approach a nearly stable concentration gradient during the 10th interphase , approximately 80–90 min AED [9] . Gradient formation must occur prior to this time; however , no previous study has quantified Bcd distributions in living cleavage stage embryos , due to challenges to optical measurements created by the greater opacity and autofluorescence of presyncytial embryos . To minimize these complications , we devised a specific fixation protocol ( see Materials and Methods ) , which preserves GFP fluorescence of Bcd-GFP expressing embryos , allowing us to detect and measure nuclear Bcd-GFP with conventional confocal microscopy ( Figure 6 ) . The earliest time at which we can detect nuclear Bcd-GFP is about 45 min AED , when Bcd-GFP begins to accumulate in the most anteriorly positioned nuclei during the first 2 min of the sixth interphase ( Figure 6A ) , coinciding with the expansion of nuclei into the cloud of bcd mRNA and with the onset of bcd mRNA relocalization . We detect Bcd-GFP fluorescence above background autofluorescence in nuclei positioned within the anterior third of the embryo when the nuclei extend from about 25% to 60% EL ( Figure 6A ) . In contrast , during interphase 5 nuclear Bcd-GFP appeared no greater than background ( Figure S10; n = 12 embryos ) , showing that Bcd-GFP has not accumulated to high enough levels for visualization in nuclei . Tissue autofluorescence may preclude detection of lower levels of nuclear localized Bcd-GFP; therefore , Bcd-GFP accumulation begins during n . c . 6 at the latest . Delayed nuclear accumulation would be consistent with previous work suggesting that the translation rate of bcd may be relatively low during the first hour of development before polyadenylation of bcd mRNA [41] . Between the sixth mitosis and interphase 10 , nuclei migrate toward the egg cortex as an expanding , elliptical shell , while continuing to accumulate Bcd-GFP ( Figure 6A–D ) . A gradient of nuclear Bcd-GFP can be readily distinguished within the anterior third of the embryo from n . c . 7 and 8 ( Figure 6B , C ) . By n . c . 9 , Bcd-GFP is apparent in nuclei throughout the anterior half of the embryo ( Figure 6D ) , demonstrating the continued heightening of Bcd-GFP levels along the AP axis . The nuclei arrive at the egg cortex and form the syncytial blastoderm upon completing the ninth mitosis . From n . c . 10 onward , the Bcd-GFP gradient is evident in single confocal slices of the midsagittal plane ( Figure 6E–I ) , reminiscent of images of Bcd-GFP obtained previously by live imaging [9] . To quantify the Bcd gradient at these early stages , we extracted nuclear fluorescence intensities from embryos expressing both Bcd-GFP and Histone-RFP . The latter provides a nominally uniform fluorescence signal in all nuclei and serves as a reference to normalize Bcd-GFP values at different optical depths in the sample ( see Materials and Methods ) . Corrected nuclear Bcd-GFP gradients between n . c . 6 and n . c . 11 show that at all positions along the AP axis , nuclear Bcd-GFP rises continuously ( Figure 7A and 7C ) . The shape of the gradient , however , does not change ( see slopes on log-linear inset to Figure 7A ) , whereas its amplitude continues to rise and increases by a factor of ∼3 over the course of n . c . 6–11 . The embryo-wide increase in Bcd-GFP is abruptly halted along the entire AP axis at n . c . 11–12 , by which time nuclei have attained Bcd concentrations >90% of their maximal values . Therefore , the nuclear gradient forms over approximately 50 min between n . c . 6 and n . c . 11 . During n . c . 11 and 12 nuclear Bcd-GFP concentration remains within ∼95% of its maximum along the whole AP extent . After n . c . 12 , nuclear concentrations drop , starting at the anterior; by n . c . 13 , values in the anterior 20% of the embryo are approximately 80%–90% of their maxima at n . c . 12 . At the beginning of n . c . 14 , all positions along the AP axis decay to around 70%–85% of the maximum ( Figure 8D ) . Despite this decrease in nuclear Bcd , the total amount of Bcd protein still rises through n . c . 13 ( Figure S11 ) [9] , consistent with continued protein production until mRNA degradation . These observations of gradient dynamics differ sharply from the bcd mRNA distribution , which at no time resembles an exponentially decreasing gradient reaching to >75% EL , such as observed for Bcd-GFP from n . c . 9 onward . Bcd-GFP is visible in nuclei positioned at 50% EL and beyond from n . c . 8 onward , yet at these positions we observe essentially no mRNA particles . Instead , the protein gradient can only be accounted for if protein moves towards the posterior from anteriorly localized mRNA . By live imaging , nuclear Bcd-GFP levels appear relatively stable after the 10th mitosis [9] , in contrast to our observations in fixed tissue . Additionally , we observe that the decay length of fixed gradients is approximately 0 . 15 EL ( Figure 8A inset ) , compared to a decay length of around 0 . 2 EL in live embryos [9] . These differences do not result from the use of different microscopy methods ( Figure S12 ) and therefore must arise from the fixation procedure . To determine the effect of fixation on Bcd-GFP fluorescence , we imaged living Bcd-GFP and Histone-RFP embryos at blastoderm stages , then fixed the embryos within 3 min of live imaging , and subsequently re-imaged under the same microscopy settings ( Figure 8A–D ) . We found that fixation increases the overall brightness by about 3-fold ( Figure 8E–F ) , possibly as a result of increased transparency of fixed material mounted in a glycerol-based medium . However , rescaling live or fixed Bcd-GFP gradients by a factor of 3 reveals qualitatively different gradient shapes in the anterior 25% of the egg , wherein fixed gradients exhibit an additional increase in relative intensity ( Figure 8G ) . This remaining difference could be accounted for if newly synthesized Bcd-GFP is not immediately visible but becomes fluorescent after fixation . This phenomenon can be attributed to the well-known process of GFP maturation ( [42] and refs therein ) . To test whether delayed maturation can affect the appearance of Bcd gradients detected by fluorescent protein fusions , we imaged transgenic embryos expressing protein fusions of Bcd with either the rapidly maturing GFP derivative Venus [43] or the more slowly maturing mRFP [44] . We found that after 3-fold rescaling , live and fixed Bcd-Venus gradients exhibited similar appearances , consistent with the rapid attainment of fluorescence by Venus ( Figures 8H , S13A–C ) . In contrast , rescaled live Bcd-mRFP gradients showed poor correspondence with fixed gradients along the majority of the AP axis ( Figures 8H , S13D–F ) . This result supports the view that delayed maturation can alter the appearance of the Bcd protein gradient . In addition , by incorporating delayed maturation into a simulation of gradient formation ( see below ) , we could qualitatively recapture the stable nuclear gradients observed in live embryos at late blastoderm ( Figure S14 ) . We note that previous work has determined that gradients of equivalent shape are observed upon quantification of GFP fluorescence and anti-GFP immunostaining in the same embryo [45] . Moreover , the distribution of Bcd protein detected by anti-Bcd antibodies does not differ between wild-type and embryos in which Bcd-GFP is the only source of Bcd protein [8] . A maturation-based explanation supports the idea of protein movement away from anterior mRNA . With delayed maturation , immature fluorescent protein synthesized at the anterior would tend to attain fluorescence while en route toward the posterior . As a result , in a live embryo , the fraction of immature eGFP would be higher in the anterior than the posterior , and allowing additional time for eGFP to attain fluorescence would then disproportionately increase anterior fluorescence compared to posterior , as we observe by fixation . Diffusion represents a simple plausible mechanism of protein transport away from the anterior mRNA source , and diffusion-based models such as the SDD model provide a straightforward mathematical framework for describing gradient establishment . Previous modeling efforts have treated the site of synthesis either as an anteriorly localized point source [9] , [46]–[49] or as conjectured anterior domains [11] , [50]–[53] . Having characterized the actual distribution of bcd RNA , we asked whether that distribution is a necessary component of gradient establishment by performing numerical reaction-diffusion simulations of gradient formation . We compared our measured nuclear gradients to protein distributions predicted to arise from either a realistic mRNA distribution or an anterior point source . We find that the combination of a non-monotonic time course for nuclear gradient amplitude ( Figure 7D ) and a relatively stable length constant between n . c . 8 and 14 ( Figure 7B ) precludes a classical SDD-type model in which the biophysical parameters of translation rate S , degradation rate τ , and diffusion D are kept constant at all times . Therefore , to reproduce the measured nuclear concentrations , our simulations utilized an “extended SDD” model which allowed some subset of these parameters to change in time ( see Materials and Methods ) . Using the measured bcd mRNA distribution for the source , we found excellent fits between predicted and observed nuclear protein gradients from n . c . 7 to n . c . 14 ( Figure 9A–B ) , as determined by a χ2-error measure ( Figure S15 ) . Conversely , if the model assumes a point-source geometry at the anterior of the embryo , an equivalent search through the parameter space of extended SDD models fails to adequately reproduce our observed Bcd protein data ( Figure 9C ) . In particular , at early time points , the point source causes anterior protein levels to rise inappropriately high to attain a good overall fit . Additionally , the disperse source allows the model to better predict the late decrease in gradient amplitude . Indeed , estimates of total protein based on available measurements suggest that total Bcd decreases between n . c . 13 and n . c . 14 despite the doubling of nuclei ( Figure S11 ) . Simultaneously with the global decrease in protein levels , we see a degradation of disperse mRNA . Our simulation can account for the observed drop in protein distribution only if we use the realistic mRNA geometry during this decay period , rather than a point source ( Figure 9B , C ) . These numeric results demonstrate that realistic mRNA distributions significantly outperform the point source geometry in reproducing the observed protein gradient dynamics .
Our method enabled us to quantify the global bcd mRNA particle distribution as a function of developmental time . The detection of dim particles even in the far posterior of the embryo demonstrates the improved sensitivity of our protocol . In addition , the high degree of spatial resolution allows us to document the characteristics of individual particles over developmental time . The method is sufficiently sensitive that we can successfully discriminate mRNA degradation at n . c . 14 from particle dissolution by the beginning of blastoderm stages . Until n . c . 7 , we see intensity distributions clearly separated from the noise peak ( Figure S4 ) , yielding robust estimates of particle counts with very low deviations ( ∼10% ) across embryos ( Figure 4C ) . As the particles disassemble after n . c . 10 , the weakest particles ( possibly single bcd mRNA molecules ) move closer to the detection threshold , which could , in principle , lead to a significant number of missed particles . Therefore our counting results for late embryos should be strictly interpreted as lower bounds on the actual number of particles in late embryos . However , the fact that the total particle light is approximately conserved ( Figure 4D ) , and that we can clearly identify an explicit mRNA degradation event during n . c . 14 , strongly argue for the possibility that even in later embryos we are in fact recovering most of the particles and their cumulative intensity . The biological mechanism ( s ) mediating mRNA particle movement during embryogenesis are as yet unknown . Previous work has shown that the reorganization of the cortical actin cytoskeleton upon egg activation allows the dispersion of bcd away from the oocyte cortex [30] . Whether endogenous bcd particles continue to localize with other cytoskeletal elements , such as microtubules , during embryogenesis is unknown , although in vitro synthesized mRNA containing the bcd 3′ UTR localization signal associates with microtubules when injected into embryos [32] . Redistribution of bcd RNP binding sites on microtubules might provide a method of scaling mRNA spatial distribution with different egg sizes . Such scaling could then potentially influence the observed scaling of the protein gradient [21] . It is interesting that bcd particles containing more mRNAs tend to be localized closer to the anterior than weakly fluorescent particles . Possibly , brighter bcd RNP complexes possess greater numbers of protein components required for establishment and/or maintenance of anterior localization , such as Staufen [5] . A greater content of Staufen ( or other factors ) would then lead to a greater probability of maintenance at the anterior . If so , then the slight posterior shift we observe may be linked to particle dissolution: as particles dissociate during development , they lose some fraction of localization components and tend to move posteriorly . We observed weakly fluorescent particles in the posterior at all nuclear cycles . Such particles likely represent bcd RNA which failed to localize to the anterior during oogenesis , although we cannot formally rule out that such RNAs were in fact initially correctly localized and became posterior later . The fraction of bcd mRNA in the posterior 60% is near zero and thus contributes only negligibly to the protein gradient . Nevertheless , the embryo possesses a failsafe mechanism of suppressing bcd translation via Nanos activity in the posterior [36] . Such a failsafe might buffer the effects of environmental or genetic conditions that result in mislocalization of bcd . Our observations of bcd mRNA are similar to the originally observed , steeply graded mRNA distributions of St . Johnston et al . [5] . In contrast , our results do not fully agree with a recent study which suggested that posteriorly directed bcd mRNA movement at the cortex accounts for the gradient of Bcd protein [19] . Our analysis is partially consistent with their findings: during early cleavages , bcd mRNA on the embryo surface does not extend far beyond ∼5% EL ( Figure 2I–J ) , whereas surface particles are relatively dense even at 15% EL during syncytial stages ( Figure 3A , 3C , 3G ) . However , the latter change is the result of bulk particle movement from the interior to the embryo surface . Such movement would not have been detected in the study by Spirov et al . [19] , which did not succeed in labeling internally localized bcd mRNA in either early cleavage stage embryos or unfertilized eggs ( Figures 2A–C , 6A–C of Spirov et al . 2009 [19] ) . This is reflected in the apparent increase in total mRNA fluorescence observed in that study between n . c . 3 and 10 , a behavior impossible for strictly maternally supplied transcripts , but consistent with the arrival of previously undetected internal bcd mRNA at the embryo cortex . Since the amount of mRNA falls to nearly zero too close to the anterior to account for the presence of Bcd-GFP in nuclei at nearly 70% EL , we conclude that the spatial distributions of bcd mRNA and protein are at no point in time rescaled versions of each other . As we note above , Bcd-GFP is evident in nuclei positioned at 50% EL and beyond beginning at n . c . 8 , yet at these positions , the mRNA concentration is essentially zero . Similarly , Bcd-GFP is retained in yolk nuclei after bcd mRNA has relocalized cortically ( Figures 6E–I , S16 ) . Furthermore , if we suppose that Bcd is unstable so that the local concentration of bcd mRNA dictates protein concentration ( as proposed recently [19] ) , then the amount of mRNA at , e . g . , 20% EL during n . c . 7 should be the same as the amount of mRNA at 50% EL at n . c . 11 , given that nuclear Bcd-GFP concentrations are approximately the same in those locations at those times; however , this is not what we observe . Our observations present a revised description of protein gradient dynamics in which along the entire AP axis , nuclear Bcd concentrations reach their maximum values by n . c . 11 . Previous work cited the difficulties of a simple SDD-based model of gradient formation given the apparent slow diffusion coefficient D of Bcd-GFP ( ∼0 . 3 µm2/s ) as measured in the blastoderm cytoplasm by photobleaching [9] . More recent measurements by fluorescence correlation spectroscopy have estimated D≈7 µm2/s , which in principle would be rapid enough to establish a stable gradient prior to n . c . 9 [22] , [23] . The observations presented here , however , indicate that nuclear Bcd-GFP continues evolving beyond that point in a non-monotonic manner . Taken together with recent work suggesting that nuclei function as passive absorbers of local Bcd [40] , these results would appear to preclude a classical SDD model with constant parameters in which the Bcd distribution reaches a stable state . Simple extensions of SDD provide the mathematical framework within which to model protein movements from disperse sources . While such minimally extended models do not explicitly include the role of nuclei and non-diffusive modes of protein transport that might play an important role [51] , they nevertheless allowed us to discover parameter sets that successfully predict the observed protein gradient when incorporating the observed , disperse mRNA sources; in contrast , the best fit parameters utilizing only an anterior point source do not match data as well . This result can be understood by noting that while the protein gradient beyond the AP extent of its mRNA source is not influenced much by the geometry of the source , the same does not hold for the anterior part of protein gradient that overlaps with mRNA . We note that our measurements are limited to nuclei and that we do not explicitly account for cytoplasmic Bcd-GFP . Using currently available estimates [9] , it is likely that total nuclear and cytoplasmic Bcd-GFP continue rising through n . c . 13 ( Figure S11 ) . We also note that n . c . 13 and 14 exhibit extended interphases , during which nuclear diameter increases and Bcd-GFP concentrations fall as a result [9] . These changes in Bcd levels both during and between interphases pose a challenge to understanding the transcriptional readout of Bcd concentrations . The well-documented dependence of Bcd target gene expression on Bcd levels implies that the readout process must be insensitive to such global changes in nuclear Bcd concentration . The nature of the underlying readout process , and how it achieves the observed reproducibility and accuracy of transcriptional regulation , will be the subject of future experiments .
OregonR embryos were fixed as described [26] in 10% paraformaldehyde and stored in methanol . We performed fluorescent in situ hybridization as described [25] , using AlexaFluor 514-conjugated probes complementary to the bcd open reading frame diluted in a solution of 35% formamide , 2× SCC , 0 . 1% Tween-20 . Embryos were mounted in Aqua-Poly/Mount ( Polysciences ) . For high resolution imaging , we used a 100× HCX PL APO CS NA 1 . 46 oil immersion objective on a Leica SP5 laser-scanning confocal microscope with 514 nm excitation wavelength at 2048×2048 pixels ( linear dimension of 75 . 7 nm ) and z-spacing of 420 nm , and obtained one anterior and one posterior stack of each embryo . Low resolution images of the midsagittal plane were taken with a 20× HC PL APO NA 0 . 7 oil immersion objective at 1024×500 pixels . To correct for drift of the microscope stage in the xy-plane , we first realigned the z-slices by starting at the midsaggital plane ( z = 0 ) and shifted successive slices such that the pixel-wise cross-correlation in raw intensity between slice z and z+1 was maximized . To normalize for changes in optical medium with depth z , we selected 105 small image patches from each posterior z-slice , computed the mean and variance in raw intensity from each patch , and fit a linear relation between the two; if noise in the image were purely uncorrelated Poisson shot noise , the slope would yield a conversion factor between the raw microscope signal and the photon counts . We then normalized every z-slice by this factor , which changed slowly and systematically with z . Neither realignment nor normalization crucially affect our reported results . We next filtered each z-slice by a balanced circular difference-of-Gaussians filter with center radius size of 1 . 5 pixels and surround size 2 . 5 pixels , roughly matched to the size of single bcd mRNA particle , and found the local maxima in the filtered images that exceed the detection threshold . These are candidate particles . If the detection threshold is too low , the number of candidate particles in the posterior stack rapidly increases due to false detections of noise and autofluorescence , as shown in Figure S4 ( red line ) . We pick for the threshold a value just above this rapid increase and find that after normalization , a single threshold value can be chosen for all embryos . Each candidate particle is then processed in turn: starting with the brightest unprocessed candidate C in the z-stack , successive nearby z-slices are examined for other candidates that appear at the same x-y location due to the axially extended PSF of the microscope ( Figure S3 ) ; these candidates are considered as shadows of C , not independent particles . By identifying candidates and their shadows on successive planes , we find that the average light intensity profile of the mRNA particle in the axial ( z ) direction is approximately Gaussian , with decay length of about 1 slice . Given our detection and microscopy settings , we therefore expect to observe 1–2 shadows for each true mRNA particle . Next , we proceed by fitting the light intensity of each candidate particle as a 2D elliptical Gaussian profile plus constant background ( Figure S2 ) . In detail , we clip 9×9 pixel patches centered on the candidate and fit a light intensity model I ( x , y ) = offset+amplitude * Gaussian ( x−xc , y−yc , rx , ry , theta ) , where xc and yc are the light intensity center coordinates , rx and ry are orthogonal ellipse axes , with rx inclined by angle theta to the horizontal . If within the 9×9 patch other candidates are identified , we clip out a larger patch and simultaneously fit all nearby candidates to properly resolve their radii and amplitudes ( Figure S2 ) . Each candidate particle in the list has thus been concisely described by parameters ( xc , yc , rx , ry , offset , amplitude , number of shadows ) . Since the threshold is set as low as possible to capture all mRNA signal , the candidates might include false positive detections . To eliminate these , we require that ( i ) rx , ry be above 0 . 7 and below 2 . 5 pixels , ( ii ) the amplitude to exceed the background offset , and ( iii ) the number of shadows be at least 1 . Detection threshold and number of shadows are the most stringent criteria ( Figure S4 , blue and cyan lines ) , while ( i ) , ( ii ) are far less restrictive . Candidates passing these criteria are considered as mRNA particles , which exhibit stereotyped , circular shape ( Figure 3A–B ) ; their intensity is proportional to amplitude * rx * ry . We find a very good linear correspondence between the intensity and difference-of-Gaussians filter value for each particle , and the results are robust to the choice of either of these intensity measures as the thresholded variable . Figures 3C and 4C–D report the statistics of particle counts and intensities ( distributions are normalized to unit area ) . To extract the spatial distributions , we must translate the xc , yc location of each particle detected at 100× into the projection along AP axis . Using image manipulation , we register the 20× and 100× images of midsaggital plane and extract automatically the coordinates of the AP axis , of length L , from 20× images . This allows us to compute for each particle its x/L relative coordinate , and to construct distributions in Figure 3D–F . In 4A we similarly compute the density of mRNA particles per unit area in midsaggital plane in thin slices parallel to embryo surface at depth d/L . Embryos from yw egfp-bcd; His2Av-mRFP1; bcdE1 females ( obtained by crossing [9] , [54] ) were fixed in 6 . 7% paraformaldehyde and the vitelline membrane removed by hand . DAPI-stained embryos were mounted in Aqua-Poly/Mount and imaged using a 20× HC PL APO Gly 0 . 7 NA objective on a Leica SP5 laser-scanning microscope with excitation wavelengths of 405 nm ( DAPI ) , 488 nm ( eGFP ) , and 561 nm ( mRFP ) . Image stacks were obtained at 2048×2048 pixels ( linear dimension 445 nm ) with z interval of 3 . 5 µm spanning approximately 75% of embryo z-thickness . Custom software employed smoothened DAPI and RFP images to identify nuclei by thresholding in individual z slices , as well as embryo edges [11] , resulting in 3d nuclear masks . For embryos at blastoderm stages , a watershed was applied to separate closely spaced objects . 3d masks were checked in parallel with GFP , RFP , and DAPI images to ensure detected objects consisted only of nuclei; inappropriate objects were discarded and each independent set of connected pixels was designated a nucleus . The mean GFP and RFP pixel intensities and centroid position for each nucleus were calculated . Histone-GFP and Histone-RFP mean nuclear intensities attenuate at similar rate as function of depth beneath the embryo surface . Assuming that all nuclei contain the same DNA content and therefore the same concentration of Histone-RFP , we used the attenuation in mean RFP intensity to apply a corrective factor to nuclei ( as performed previously using anti-Histone antibodies [55] ) . We calculated autofluorescent background by comparing to OregonR embryos , observing that nuclei at 85%–95% EL in Bcd-GFP expressing embryos contain the same mean GFP intensity as fixed OregonR embryos imaged under the same conditions . In all plots except Figure 8E–F , we show attenuation corrected , background subtracted mean nuclear Bcd-GFP values . For the experiment in Figure 8 and Figure S13 , after live imaging , embryos were immediately transferred to fixative , processed , and imaged as described above . Embryos were handled individually to ensure we could match live and fixed images to the same embryo . Live and fixed images were taken under identical conditions . We performed numerical reaction-diffusion simulations of the extended SDD model on a 3d 10 µm grid . We adopted the embryo geometry from the BDTNP map [56] , which represented our solution domain with no-flux boundary conditions . In the interior , we solved ( 1 ) where is the concentration of Bcd protein , and D , τ , and ρ are ( nominally time-dependent ) diffusion , degradation , and synthesis rates . The source ρ was taken to be a collection of measured bcd mRNA particle intensities; the positions of the particles were readjusted to undo the axial compression caused by the imaging protocol . We kept the source geometry unchanged for nuclear cycles for which we do not have explicit measurements . The total source strength was kept constant according to the observed constant total intensity with developmental time ( Figure 4D ) . We extracted the temporal snapshots after time increments of 8 min each for n . c . 1 to 9 , 9 min for n . c . 10 , 10 min for n . c . 11 , 12 min for n . c . 12 , and 20 min for n . c . 13 and 14 . The obtained concentration profiles were projected along the AP axis . As the units of measurement are arbitrary , the simulation gradients were rescaled with a single scale parameter to match as closely as possible the measurements for n . c . 7–12; the goodness of fit was defined as ( 2 ) Thus , models that have small match the data better . We then asked how closely we can reproduce the observed gradients ( without any assumption about the gradient equilibration and without any reference to previously measured D and τ parameters ) within the classic SDD model , where D , τ are constant and ρ is given by the measurement , assuming continuous translation from the source . As expected from the non-monotonic temporal dependence of the gradient amplitude reported in Figure 7D , no combination of parameters for the classic SDD model fits the data well . The most straightforward extension of the SDD model is to allow some subset of translation rate , degradation rate τ , and diffusion constant D parameters to vary in time . To guide our exploration of such extended SDD models , we first noted that protein gradients at different times are rescaled versions of each other , i . e . the length scale is approximately constant from n . c . 8 onwards ( relative change is less than 10% from n . c . 8 till n . c . 14 ) , while the gradient amplitude increases by ∼3-fold from n . c . 8 to the time it reaches its peak in n . c . 12 , and decreases to about 3/4 of the value in n . c . 14 . Simultaneously satisfying both constraints , i . e . preserving the decay length and properly scaling the gradient amplitude as a function of time , is challenging in the framework of such extended SDD models . Decrease in gradient amplitude at later times can be generated in models in which the source turns off at n . c . 12 , but if diffusion still remains active , the gradients are very flat at low x/L , inconsistent with the data . Models in which the decrease in gradient amplitude after n . c . 12 is generated by turning on degradation later yield gradients that match the data reasonably at low x/L , but do not preserve the exponential profile with a conserved decay constant across developmental time; even models in which degradation is present during the whole developmental time , but can accelerate to a higher value in blastoderm stages , do not fit the data well . Furthermore , to account for low gradient amplitudes at n . c . 7 and 8 , the source needs to be turned on late , i . e . for most of the models around n . c . 5 or 6 . With these observations in mind we carried out an exhaustive search for D , τ , and the start and end of bcd mRNA translation , to find the set of parameters minimizing error ( Figure S15 ) ; the source term is set to 0 before the start and after the end of translation . A well-fitting model could only be found if additionally the diffusion constant was allowed to change during developmental time . The best match to our data is obtained for models where ( i ) the source is active only during n . c . 6–12 , ( ii ) degradation is essentially constant with τ∼100–140 min , and , ( iii ) the diffusion constant ( initially set to D∼3 µm2/s ) decreases sharply to 0 at n . c . 11–12 ( Figure 9C–D ) ( a decrease to a small value , e . g . 0 . 3 µm2/s , instead of 0 yields an even better fit ) . The of this model is approximately one half of the classic SDD with realistic source . To assess the effects of the source geometry on the protein gradients , we replaced our measured source by a point source localized at the anterior pole , and repeated the parameter search by varying D , τ , and the start and stop times for translation . The best fit in this scenario deviates substantially from the measured gradients ( Figure 9D ) , mostly because simulations tend to overestimate the steepness of the gradient close to x/L = 0 . The of this model is 2 times larger than the best extended SDD model with a realistic source . To assess the effects of GFP maturation ( Figure S14 ) , in equation ( 1 ) represented immature Bcd-GFP and we used an additional reaction-diffusion equation for the mature species without the source ( ρ ) term , but including a conversion term from the immature to mature species with rate k . We emphasize that we employ these simulations for the express purpose of exploring the potential of disparate mRNA geometries to impact protein gradient formation . Our simulations represent coarse-grained descriptions of all processes that might impact protein production , stability , and diffusion . Multiple events could influence protein movement , such as advective transport , nuclear import/export dynamics , and/or changes in numbers or affinities of Bcd binding sites [9] , [48] , [51] , [53] , [57] , [58] . As such , our model does not distinguish between these , and thus it does not explicitly support the notion that Bcd movement occurs only via thermally driven random molecular motion . Likewise , within our model , protein production and stability are bound by two assumptions: first , that translation occurs from particles in proportion to their measured fluorescence intensities; and second , that nuclear Bcd-GFP gradients at all times reflect rescaled versions of the underlying cytoplasmic gradient [9] , [40] . Because we observe nuclear accumulation of Bcd-GFP beginning at n . c . 6 , it is unsurprising that we obtained better fits in simulations where protein production does not begin immediately upon fertilization ( Figure S15 ) . However , absent additional data between n . c . 1 and 5 , we cannot advocate that translation of bcd mRNA necessarily begins at a later time point , although this would be consistent with previous proposals [41] . Similarly , at late blastoderm , simulating an apparent gradient decay while preserving the gradient shape requires that both production and effective diffusion decrease ( see Materials and Methods , Figure S15 ) . However , we do not conclude that either production or diffusion actually slow over time , as our model does not explicitly incorporate any mechanisms that could in principle affect the distribution of protein among nuclei ( e . g . , the geometric increase in the number of nuclei , Bcd trapping within local nuclear neighborhoods [51] , [53] , or the possibility that the nuclear-cytoplasmic ratio of Bcd in an equilibrated nucleus could change over time ) . | The Bicoid protein gradient plays a crucial role in determining the anterior body pattern of Drosophila embryos . This gradient is the classic example of morphogen-mediated patterning of a developing metazoan and serves as a major topic for mathematical modeling . Accurate modeling of the gradient requires a detailed account of the underlying bicoid mRNA distribution . The classic model holds that mRNA protein gradient arises via protein diffusion from mRNA localized at the anterior of the developing egg . In contrast , recent proposals suggest that an mRNA gradient generates the protein gradient without protein movement . In this study , we introduce a novel mRNA quantification method for Drosophila embryos , which allows us to visualize each individual mRNA particle accurately in whole embryos . We demonstrate that all but a few mRNA particles are confined to the anterior 20% of the egg , and consequently that the protein must move in order to establish a gradient . We further report that the mRNA distribution is highly dynamic during the time of protein synthesis . In numerical simulations , we show that incorporating realistic spatial locations of the individual source mRNA molecules throughout the developmental period is necessary to accurately model the experimentally observed protein gradient dynamics . | [
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"... | 2011 | The Formation of the Bicoid Morphogen Gradient Requires Protein Movement from Anteriorly Localized mRNA |
KLF3 is a Krüppel family zinc finger transcription factor with widespread tissue expression and no previously known role in heart development . In a screen for dominant mutations affecting cardiovascular function in N-ethyl-N-nitrosourea ( ENU ) mutagenized mice , we identified a missense mutation in the Klf3 gene that caused aortic valvular stenosis and partially penetrant perinatal lethality in heterozygotes . All homozygotes died as embryos . In the first of three zinc fingers , a point mutation changed a highly conserved histidine at amino acid 275 to arginine ( Klf3H275R ) . This change impaired binding of the mutant protein to KLF3's canonical DNA binding sequence . Heterozygous Klf3H275R mutants that died as neonates had marked biventricular cardiac hypertrophy with diminished cardiac chambers . Adult survivors exhibited hypotension , cardiac hypertrophy with enlarged cardiac chambers , and aortic valvular stenosis . A dominant negative effect on protein function was inferred by the similarity in phenotype between heterozygous Klf3H275R mutants and homozygous Klf3 null mice . However , the existence of divergent traits suggested the involvement of additional interactions . We conclude that KLF3 plays diverse and important roles in cardiovascular development and function in mice , and that amino acid 275 is critical for normal KLF3 protein function . Future exploration of the KLF3 pathway provides a new avenue for investigating causative factors contributing to cardiovascular disorders in humans .
Congenital heart defects are the most common congenital malformations in humans affecting 1–2% of live births [1] and 18% of stillbirths [2] . Causative mutations have been identified in families with inherited congenital heart defects [3] but in most cases remain unknown [2] . A strong genetic role is nevertheless likely given high heritability scores , for example >0 . 7 for left-sided congenital heart defects [4] , [5] , [6] . To discover new genes important in cardiovascular development , we measured aortic blood velocity in an ultrasound screen undertaken to assess left ventricular outflow function , in the offspring of N-ethyl-N-nitrosourea ( ENU ) mutagenized male mice [7] . One mutant had very high aortic blood velocities due to aortic valvular stenosis and this trait was heritable . Additional abnormalities in cardiovascular development and function were found in subsequent phenotyping of this mutant mouse line . A dominant point mutation in the region encoding the DNA binding domain of Klf3 was found by linkage analysis and gene sequencing . KLF3 is a zinc finger transcription factor that has discrete regions of expression that are widely distributed among embryonic and adult tissues in mice [8] , [9] . KLF3 functions predominantly as a gene repressor [10] although it also has activator functions [11] . KLF3 had hitherto no described role in heart or vascular development or function . In prior work , homozygous deletion of the region encoding the Klf3 zinc finger DNA binding domain caused partially penetrant perinatal lethality in mice and significant abnormalities in adiposity [12] , B cell development [13] , and erythroid maturation [14] whereas cardiovascular defects were not reported . However , embryonic lethality [12] occurred at a stage of development consistent with death due to cardiovascular dysfunction [15] . Furthermore , several Klfs are expressed in cardiomyocytes and vascular smooth muscle cells [16] including Klf3 ( current study and [17] ) . KLF3 is enriched at promoters of several muscle-specific genes including muscle creatine kinase ( MCK ) where it interacts with Serum Response Factor to act as a transcriptional activator [11] . Thus , despite known molecular mechanisms whereby KLF3 may alter cardiac or vascular development or function at a cellular level , a cardiovascular phenotype remained unidentified . Herein we report the characterization of a new ENU-induced mouse mutant . Results reveal important and novel roles for KLF3 in cardiovascular development and function . Strong similarities in phenotype with homozygous Klf3 gene trap mice , where KLF3 is largely eliminated , suggest a predominantly dominant negative effect of the point mutant protein . However , intriguingly , the existence of divergent traits suggests the involvement of additional interactions . At the molecular level , the point mutation illuminates the critical importance of a highly conserved residue in the DNA binding domain of KLF3 . The discoveries reported here provide impetus for exploring the KLF3 pathway to discover new causative factors contributing to cardiovascular disorders in humans .
In a screen of 1770 adult heterozygous offspring from ENU mutagenized C57BL/6J male mice crossed with wild-type ( WT ) C3H/HeJ females , we identified a mutant mouse with an aortic blood velocity >7 standard deviations ( SD ) above the mean . Using a cut-off aortic blood velocity of 150 cm/s ( i . e . >3 SD above the mean of all animals ) , we found that the trait ( Figure 1A ) was heritable when mutants were bred to BALB/cJ females although only 10% ( 17 of 165 ) had the trait . Nevertheless linkage analysis localized the mutation to chromosome 5 between 4 . 9 and 75 . 6 Mb ( Figure S1A ) . The LOD score exceeded 4 in this interval ( Figure S1B ) whereas it was <2 . 5 elsewhere in the genome ( not shown ) . The incidence of the trait was higher on a C57BL/6J ( B6 ) background ( 136 of 584; 23% ) so we performed fine mapping by crossing affected animals with B6–Chr 5 A/J consomic mice ( incidence of trait was 40 of 183; 22% ) . We narrowed the interval to a 12 . 6 Mb region on chromosome 5 ( Figure S1B ) , which contained 35 genes ( Table S1 ) . Genomic sequencing of 7 candidates ( Table S1 ) revealed only one point mutation predicted to affect the protein product ( Figure 1B ) . The mutation in exon 5 of Klf3 ( Krüppel-like factor 3 ) ( Figure 1C ) changed a histidine residue ( CAC ) at amino acid 275 to arginine ( CGC ) ( KLF3H275R ) ( Figure 1B ) . This histidine is conserved across species ( www . ncbi . nlm . nih . gov/homologene ) and across all but one of the 22 Sp/Klf family members [18] . It is the central of 3 amino acids predicted to make contact with DNA in the DNA binding region of the first of three zinc finger domains in KLF3 [18] , [19] . We predicted that mutation at this site would be highly likely to affect the DNA binding function of KLF3 and thereby its function in transcriptional control . The Klf3H275R line was subsequently maintained by breeding with B6 mice . High peak aortic blood velocity in adult heterozygous Klf3 point mutants ( Klf3H275R/+ ) was caused by aortic valvular stenosis as shown by augmented valvular gradients in blood velocity ( Figure 2A ) and blood pressure in Klf3H275R/+ mice ( Figure 2B , C ) , and by abnormal valve morphology detected by gross dissection ( not shown ) , histopathology ( Figure 3A ) , and scanning electron microscopy ( Figure 3B ) . Aortic valves were tricuspid although bicuspid valves were occasionally observed . The leaflets were thickened , often partially fused , and sometimes exhibited blebs or small hematomas ( Figure 3A ) . When genotype was used to identify mutants , most Klf3H275R/+ mice had peak velocities >150 cm/s ( 20 of 31 or 65% ) in contrast with WT littermates ( 0 of 43 or 0% ) ( Figure 3D ) . Males and females were similarly affected . Significant aortic valve regurgitation was not observed . In humans , aortic valvular stenosis is often associated with post-stenotic aortic dilatation [20] . We therefore measured diastolic diameter of the ascending aorta in vivo and found a significant 27% post-stenotic enlargement in male and female Klf3H275R/+ mice ( 1 . 95±0 . 07 vs . 1 . 53±0 . 08 mm in males ( n = 5 ) and 1 . 70±0 . 09 vs . 1 . 34±0 . 03 mm in females ( n = 4 ) ; P<0 . 01 ) ( Figure 3C ) . We next examined blood velocities through the other heart valves . Peak blood velocity was ∼30% higher in the main pulmonary artery , and at the atrioventricular valves during early ventricular filling ( E-wave ) in Klf3H275R/+ mice ( Table 1 ) . E-wave fusion with the atrial filling wave ( A-wave ) occurred significantly more often in the left or right filling waveforms in Klf3H275R/+ ( 9 of 20 ) than WT littermates ( 0 of 20 ) . There were no abnormalities in peak inflow velocities during atrial contraction ( i . e . A-wave ) or in heart rate ( Table 1 ) , and no evidence of significant valve regurgitation in Doppler waveforms ( not shown ) . No structural abnormalities in the pulmonary valves ( Figure S2A , B ) or the atrioventricular valves ( not shown ) were detected in adults by gross or histopathology examination . Thus , the mutation appeared to predominantly impact the aortic semilunar valve . At weaning , we observed 100% lethality of homozygous offspring and only ∼50% of the anticipated Klf3H275R/+ pups from Klf3H275R/+ intercross breeding ( Table S2 ) . At E14 . 5–16 . 5 , significant lethality of homozygotes , but not heterozygotes , was observed ( Table S2 ) . We used histology to examine embryonic heart structure of Klf3H275R embryos at E12 . 5 ( i . e . before the age of lethality ) and at E14 . 5 in heterozygotes and in the few surviving homozygous embryos . At E12 . 5 ( Figure 4A ) and E14 . 5 ( Figure 4B ) , homozygous embryos exhibited a thinned and disorganized ventricular myocardium and septum suggesting cardiac failure as the cause of their later demise . At E14 . 5 , ventricular and atrial septation defects were also observed ( Figure 4B ) . In contrast , heterozygotes at E12 . 5 had apparently normal cardiac anatomy ( Figure 4A ) and at E14 . 5 showed disorganization and thickening of the septal myocardium ( Figure 4B , C ) . Some had atrial septation defects similar to homozygotes ( Figure 4B ) and enlarged atrioventricular cushion tissue that may have obstructed flow ( Figure 4C ) . At birth , heterozygous neonates had abnormally thickened myocardial walls by magnetic resonance imaging ( MRI ) ( Figure 5A ) and aortic valve leaflets by histology ( Figure 5B ) and by optical projection tomography ( Figure 5C ) . No abnormalities in placental weight or histology were detected at E12 . 5 and E14 . 5 ( not shown ) so placental dysfunction was unlikely to play a causative role ( e . g . as in [21] ) . To better define the age of lethality in heterozygotes , we delivered 3 litters of Klf3H275R/+ crossed with B6 mice by caesarean section at term ( E18 . 5 ) . Three Klf3H275R/+ embryos had recently died in utero and 2 Klf3H275R/+ died within 30 min with only occasional breathing ( Table S2 ) . Klf3H275R/+ pups that survived for up to 2 h were significantly smaller ( 1 . 02±0 . 02 g ( n = 9 ) ) than WT littermates ( 1 . 13±0 . 02 g ( n = 10 ) ; P = 0 . 002 ) . We next allowed 5 litters of Klf3H275R/+ crossed with B6 mice to deliver naturally at term . One day after birth , cardiac hypertrophy in Klf3H275R/+ pups was significant in surviving pups ( 7 . 5±0 . 2 mg/g body weight ( n = 9 ) vs . WT 6 . 5±0 . 1 mg/g ( n = 23 ) ; P <0 . 001 ) whereas it was striking in dead or dying pups on day 1 ( 15±2 mg/g ( n = 3 ) ; P<0 . 001 ) . Imaging showed that pups that died within 1 d of delivery had markedly diminished ventricular lumens , markedly thickened ventricular and septal myocardia , and aortic valve leaflets that were short and thick ( Figure 5A , B , C ) . Thus , heterozygous Klf3H275R/+ pups that had the most pronounced ventricular hypertrophy apparently died in the perinatal period . Aortic blood velocity was not higher in Klf3H275R/+ pups at day 1 of age ( 49±4 cm/s; n = 9 ) relative to WT littermates ( 48±4 cm/s; n = 21 ) . In Klf3H275R/+ pups assessed on day 1 and again at 8 wk; 6 of 7 developed high velocities ( >2 SD ) by 8 wk ( Figure 5D ) . Peak aortic blood velocity did not increase further between 9 wk and 1 y ( n = 15; not shown ) . Thus , the Klf3 mutation may alter prenatal aortic valve development but the development of sufficient stenosis to elevate aortic blood velocity is a postnatal event occurring by 8 wk in Klf3H275R/+ mice . Heterozygous mice that survived the perinatal period survived into adulthood . However the number of adults that died before 60 wk was significantly increased; 21% of Klf3H275R/+ mice with high aortic blood velocities at 8 wk died under 60 wk of age ( 41 of 195 ) vs . 4% of WT cage-mates ( 2 of 54 ) ( P<0 . 0001 ) . Premature death in adulthood was associated with a rapid deterioration in health and all 4 mice found moribund exhibited marked cardiac enlargement ( e . g . Figure S3; heart weight 0 . 367±0 . 040 g ( n = 3 ) vs . 0 . 159±0 . 008 g ( n = 3 ) WT cage mates ) . In the Klf3H275R/+ group as a whole at ∼20 wk , cardiac enlargement was less pronounced and lung weight was not elevated ( Table S3 ) . Results are consistent with premature death caused by heart failure . We anticipated that intraventricular pressures would be elevated due to aortic valvular stenosis in surviving adult Klf3H275R/+ mutants and that this would lead to concentric ventricular hypertrophy ( i . e . increased wall thickness ) secondary to increased afterload . While the hearts were hypertrophic ( i . e . the heart to body weight ratio was significantly elevated; Table S3 ) , hypertrophy was not concentric because there was no increase in wall thickness in mutant adults ( Table 1 ) . Furthermore , the heart to body weight ratio did not correlate with aortic blood velocity ( r2 = 0 . 17; P = 0 . 1; n = 13 ) or with the transvalvular pressure gradient ( r2 = 0 . 06; P = 0 . 5; n = 7 ) in adult Klf3H275R/+ mice . Indeed , left ventricular systolic blood pressure was not significantly elevated when directly measured in isoflurane-anesthetized mice ( Figure 2C ) . Instead , we found that surviving adult Klf3H275R/+ mice had eccentric hypertrophy ( i . e . increased chamber dimensions ) . Thus , other prominent cardiovascular abnormalities were caused by the Klf3H275R allele; abnormalities not due to aortic valve defects . Echocardiography on Klf3H275R/+ mice was therefore performed to discover other effects of this mutation on adult cardiac function . We found that the diastolic volume of the left ventricle was significantly increased , and cardiac output was nearly doubled despite their smaller body weight ( Table 1 ) . The left and right atrial areas measured by ultrasound were also significantly increased in Klf3H275R/+ hearts ( Table 1 ) , as was the heart to body weight ratio ( Table S3 ) . Left ventricular wall thicknesses and heart rate were unchanged ( Table 1 ) . Klf3H275R/+ mice had improved systolic function as suggested by a significant increase in ejection fraction ( Table 1 ) and improved diastolic filling as suggested by a 30% increase in the early filling ( E-wave ) velocity for left and right atrioventricular filling ( Table 1 ) . The high E-wave may explain its more frequent fusion with the A-wave in Klf3H275R/+ mice ( Table 1 ) . Improved systolic and diastolic function was also supported by a change in the Tei Index ( Table 1 ) , a global indicator of cardiac function [22] . The change in the Tei Index occurred due to significantly shorter isovolumetric contraction and relaxation times , and a significantly longer ejection time ( Table 1 ) . Other than eccentric hypertrophy , there was no abnormality in histological structure of the heart , or atrial or ventricular myocardium detected by light microscopy ( Figure S4A ) or electron microscopy although myocardial contraction bands were more frequent in Klf3H275R/+ mice ( Figure S4B ) . The Klf3H275R/+ mutation caused a doubling of cardiac output ( Table 1 ) . Physiological increases in cardiac output can be evoked by increased metabolic rate or by reduced oxygen carrying capacity of the blood . Both would tend to increase tissue requirements for perfusion . We therefore measured oxygen consumption but found that it was only 15% higher in Klf3H275R/+ mice ( 3112±28 ml h−1 kg−1 vs . 2709±22 ml h−1 kg−1 in WT littermates , P = 0 . 03; n = 4 males per group averaged over 24 h ) and thus was insufficient to explain the doubling in cardiac output in Klf3H275R/+ mice . Similarly , although Klf3H275R/+ mice were slightly anemic with a significant 10% reduction in RBC count ( Figure S5 ) and haemoglobin concentration ( not shown ) , reduced oxygen carrying capacity of the blood was insufficient to explain the large increase in cardiac output in these mutants . High cardiac output can also be induced physiologically by low total peripheral vascular resistance . This mechanism was implicated by the significant reduction in arterial blood pressure in Klf3H275R/+ mice both when awake measured by tail cuff ( 98±2 mmHg vs . 108±1 mmHg in WT littermates at 9–12 wk; n = 10/genotype ) and in the ascending aorta of anesthetised mice using a catheter-tip pressure transducer ( Figure 2C ) . Heart rate did not significantly differ by genotype whether measured awake ( 677±13 min−1 Klf3H275R/+ vs . 636±21 min−1 in WT; n = 10/genotype ) or under anesthesia ( Table 1 ) . We found that low blood pressure was not caused by low plasma volume . Indeed , plasma volume measured by Evan's Blue dilution was 34% greater in Klf3H275R/+ ( 44±3 ml/kg vs . 33±2 ml/kg in WT; P = 0 . 015; n = 6 per group ) . We were unable to find peripheral vascular malformations by color Doppler echocardiography , gross dissection , MRI or histology that could explain the low total peripheral vascular resistance . These results suggest that the Klf3H275R/+ mutation caused low peripheral vascular resistance by increasing peripheral vascularity and/or vascular calibre . The resulting low arterial pressure resulted in intraventricular pressures that were not elevated despite aortic valvular stenosis and this likely explains the absence of left ventricular wall thickening in adult Klf3H275R/+ mutants . Thus the Klf3H275 mutation caused other prominent cardiovascular abnormalities in addition to defects in aortic valve development . A defect in adipogenesis leading to reduced body weight and fat mass was the primary phenotype reported for homozygous mice with targeted deletion of the KLF3 zinc finger DNA binding domain [12] . An in vitro role for KLF3 in adipocyte differentiation was also found . In young adult Klf3H275R/+ mutants in our study , percent body fat was reduced by 14% ( P<0 . 04; n = 13/genotype ) and body weight by 8% ( P<0 . 001; n = 13/genotype ) in surviving Klf3H275R/+ mice at 10–11 wk . At 18–25 wk , the relative weight of the superficial abdominal fat pad was reduced by 43% , which contrasted with increased relative weights of the heart , spleen , kidney , and brain ( Table S3 ) . Relative lung and liver weights were not affected ( Table S3 ) . The similarity in body weight and fat mass phenotype between Klf3H275R/+ and Klf3 DNA binding domain deletion mutants [12] suggested that Klf3H275R is a loss of function allele . However , divergent traits ( below ) suggest that the interaction is more complex . Klf3 mRNA ( Figure 6A ) and protein ( Figure 6B ) were expressed at wild-type levels in homozygous and heterozygous Klf3H275R embryos at E12 . 5 . H275R protein exhibited reduced binding to KLF3's canonical CACCC binding region of the β-globin gene promoter both using recombinant bacterial GST-Klf3 zinc finger 1–3 protein ( Figure 6C ) or full length KLF3 protein expressed in COS cells ( Figure 6D ) , but did not interfere with the ability of WT KLF3 to bind to DNA ( Figure S6 ) . In vivo , the H275R protein significantly opposed the repression of Lgals3 , a gene that is normally repressed by KLF3 [14] , in Klf3H275R homozygous embryos at E12 . 5 ( Figure S7 ) . However , expression of other known KLF3 targets including Klf8 , Crip1 , and Pqlc3 ( Crossley M et al . unpublished ) was unaffected ( not shown ) . Because a role for KLF3 in cardiovascular development and function was hitherto unknown , we asked whether reduced KLF3 function could cause abnormal cardiovascular development in zebrafish embryos by using anti-klf3 morpholinos to inhibit translation of klf3 transcript . At 48 hour post-fertilization ( hpf ) , embryos appeared to be developing normally suggesting that initial differentiation and morphogenesis of the heart proceeded normally . However by 72 hpf , 65% of klf3 knockdown embryos exhibited cardiac edema indicative of cardiovascular dysfunction ( i . e . 49 of 59 , and 27 of 58 in 2 replicate experiments ) and some hearts were visibly dysmorphic and did not properly loop ( Figure 7 ) . Of those with edema , blood flow was visible in the embryonic vasculature in 60% of embryos at 72 hpf ( i . e . 22 of 49 , and 8 of 27 in the 2 replicates ) with no occlusion of the outflow tract evident ( data not shown ) . This supports cardiac dysfunction as the primary cause of cardiac edema and heart defects . Results were consistent over 6 replicate experiments ( 50–100 embryos per experiment ) . Injection of the mismatch control morpholino had no effect on the developing heart . To further validate that altered KLF3 function was the cause of the cardiovascular defects observed in Klf3H275R mutants , we generated mutant mice from two embryonic stem cell lines with gene trap vectors inserted near the start of the Klf3 gene ( XS and CH; Figure 8A ) . These insertions largely eliminated Klf3 mRNA ( Figure 8B ) and KLF3 protein ( Figure 8C ) expression in homozygotes . If Klf3H275R was a simple loss of function allele , then these gene trap mutants would be anticipated to exhibit perinatal lethality and cardiovascular defects similar to Klf3H275R mutant mice . At weaning , heterozygosity did not affect survival in Klf3 gene trap mutants . This contrasted with ∼50% of Klf3H275R heterozygotes dying in the perinatal period . However , there was significant lethality prior to weaning age in homozygous gene trap mutants compared to WT littermates ( Table S4 ) . This was reported previously for homozygous mutants lacking Klf3's DNA binding region where ∼half those anticipated were found at weaning whereas the anticipated ratio was observed at E14 . 5 [12] ) . We found that some homozygous gene trap mutants survived to adulthood whereas homozygosity of the point mutation was always embryonic lethal . To evaluate the effect of Klf3 gene trap mutations on the aortic valve , we measured ascending aortic blood velocity in homozygous adults . We found that aortic velocity was elevated in a significantly greater proportion of surviving homozygotes of both gene trap lines ( Figure 9A ) . Heterozygotes were not significantly affected ( not shown ) . Homozygote gene trap mutants often exhibited thickened aortic valve leaflets by gross morphology ( 5 of 7 mutants vs . 0 of 6 WT littermates ) ( e . g . Figure 9B ) . Thus , homozygous Klf3 gene trap mutations caused stenotic , malformed aortic semilunar valves that resembled those of the heterozygous point mutant mice ( Klf3H275R/+ ) . Like adult Klf3H275R heterozygotes , homozygous gene trap mutants also had other diverse cardiac defects . They had significantly higher cardiac output ( Table 1 ) with no change in left ventricular wall thickness or heart rate ( Table 1 ) and , in homozygous CH mice , significantly higher left ventricular end diastolic volume ( Table 1 ) , consistent with a phenotype of left ventricular eccentric hypertrophy . Homozygous CH mice also had significantly reduced arterial pressure ( −26 mmHg ) ( Figure 9C ) , which was similar to Klf3H275R heterozygotes ( −10 mmHg ) ( Figure 2C ) . Also like adult Klf3H275R heterozygotes , homozygous gene trap mutants had significantly reduced body weights , enlarged hearts , and decreased abdominal fat pad weights ( Table S5 ) . Homozygous Klf3 gene trap mutants exhibited a pronounced right ventricular trait not observed in Klf3H275R/+ mice . They often had marked pathological enlargement of the right ventricle ( 8 of 15; e . g . Figure 9D ) and an abnormal leftward septal deviation in early diastole ( Movie S1 and S2 ) . Many also exhibited abnormally thickened pulmonary valve leaflets ( Figure S2C , D ) , and pulmonary valve regurgitation ( 13 of 15 ) that was often associated with tricuspid valve regurgitation ( 7 of 15 ) ( Figure 9E ) . Lung weight was significantly increased ( Table S5 ) consistent with pulmonary congestion . No septal defects were detected . It is noteworthy that valve closure and septal deviation abnormalities could be secondary to a primary right ventricular enlargement defect . There were also differences in the haematological phenotypes of the point mutant and gene trap mutants . All 3 mutant lines exhibited greater variation in red cell volume ( %RDW; Figure S5 ) and a larger number of reticulocytes ( immature RBC ) in blood smears suggesting a defect in erythrocyte production , structure , and/or elimination . However , only Klf3H275R heterozygotes were anaemic and had increased RBC cell size , and only homozygous gene trap mutants had increased white blood cell counts ( WBC ) ( Figure S5 ) . In Klf3H275R/+ mice , slight anaemia was deemed insufficient to explain the large increase in cardiac output in these mutants . This is supported by the finding that homozygous gene trap mutants had increased cardiac output ( Table 1 ) but no significant change in RBC count ( Figure S5 ) or haemoglobin concentration ( not shown ) . Staining for LacZ generated by the XS Klf3 gene trap vector indicated strong Klf3 expression in the E10 . 5–12 . 5 embryonic aorta and cardiac outflow tract ( where heart valve primordia form ) ( Figure 10A–D ) . LacZ staining in the embryonic myocardium was diffuse and punctate ( not shown ) . LacZ staining was also observed in the adult atrial and ventricular myocardium , heart valves , and endothelial and vascular smooth muscle of the vasculature ( Figure 10E–L , Figure S8 ) . Thus local alterations in KLF3 transcriptional activity in cardiovascular cells may directly cause aortic valvular stenosis , hypotension , and abnormal myocardial growth . Strong LacZ staining was observed at other discrete albeit widespread sites within the embryo ( e . g . Figure 10A , C ) as observed previously by ISH in embryos [8] , [23] and by qRT-PCR in adult tissues [9] so indirect hormonal or neural mechanisms may also play a role in cardiovascular abnormalities in Klf3 mutants . Gene expression was evaluated in Klf3H275R and CH homozygotes , Klf3H275R heterozygotes , and WT by microarray ( Figure S9 ) . To increase the likelihood of revealing immediate downstream targets of KLF3 , we used mRNA isolated from whole embryos collected at E12 . 5 d of gestation . At this stage , Klf3H275R homozygosity was not yet lethal , and Klf3 LacZ expression was widespread ( e . g . Figure 10A ) . When comparing Klf3H275R homozygotes , heterozygotes , or both groups combined versus WT , no genes were identified as significantly changed using a false discovery rate threshold of 0 . 1 . For CH homozygotes vs . WT , 18 genes were changed significantly and 2 were changed >2-fold; Klf3 ( 0 . 07× ) and Hsd3b6 ( 0 . 23× ) ( Table S6 ) . Overall , CH homozygous embryos had 16 genes differentially expressed when compared to Klf3H275R homozygotes ( including 4 genes changed by >2-fold; Klf3 ( 0 . 07× ) , Glmap4 ( 2 . 07× ) , Snora31 ( 2 . 20× ) , and Fah ( 2 . 31× ) ) and 185 genes were differentially expressed when compared to Klf3H275R heterozygotes ( including 5 genes changed by >2-fold; Klf3 ( 0 . 07× ) , Hsd3b6 ( 0 . 38× ) , Mir1948 ( 0 . 48× ) , Snora31 ( 2 . 04× ) , and Slc6a16 ( 2 . 24× ) ) ( Table S6 ) . These differences in embryonic gene expression profiles may explain phenotype differences between heterozygous point mutant and homozygous gene trap lines that were observed later in development . We then used qRT-PCR to validate the large down regulation of Hsd3b6 expression found in E12 . 5 embryos by microarray . Expression was significantly reduced in Klf3H275R heterozygotes ( 0 . 45× ) , and in Klf3H275R homozygotes ( 0 . 27× ) vs . WT ( Figure S10A ) whereas a similar trend in CH homozygotes was not statistically significant possibly due to the smaller sample size ( Figure S10B ) . We also evaluated Lilra6 by qRT-PCR; among genes with apparent up-regulation by visual inspection of the heat map ( Figure S9 ) , Lilra6 was the most consistently high in Klf3 mutant embryos ( 8 of 11 ) and low in WT ( 4 of 4 ) . Lilra6 was significantly elevated in Klf3H275R heterozygotes ( 2 . 4× ) , and in Klf3H275R homozygotes ( 13 . 8× ) ( Figure S10C ) whereas the much smaller increasing trend in CH homozygotes ( 2 . 2× ) was not statistically significant ( Figure S10D ) . These results show that the heterozygous and homozygous presence of the point mutant protein can down or up regulate normal gene expression in E12 . 5 embryos , and that effects may differ from that caused by reduced expression of the native protein ( i . e . in CH homozygotes ) .
Herein we report the discovery of novel and important roles for Klf3 in cardiovascular development and function . Although KLF3 was identified and cloned nearly 20 years ago [8] and Klf3 knock out mice have been studied [12] , KLF3's role in cardiovascular biology had not been revealed until the current unbiased genome-wide ENU screen , in which prominent abnormalities were discovered in heterozygous Klf3H275R point mutants . We found abnormalities in embryonic gene expression during organogenesis and in heart morphology at E12 . 5 , increased perinatal lethality associated with marked biventricular myocardial hypertrophy and aortic valve leaflet thickening , and adult survivors exhibited hypotension , aortic valvular stenosis , aortic dilatation , and myocardial hypertrophy with increased chamber size . KLF3 in vitro is enriched at promoters of muscle-specific genes [11] , and in vivo is expressed by multiple cell types integral to the cardiovascular system including cardiomyocytes , heart valves , and vascular endothelial and smooth muscle cells based on LacZ expression patterns reported here . Cardiovascular abnormalities may therefore result directly from abnormal KLF3 function in cardiac and/or vascular cells . Abnormal renal or brain regulatory mechanisms may also contribute to hypotension given that Klf3 is expressed at these sites as well ( e . g . figure 9 A , B; ref #10 ) . Elucidating the likely multifactorial roles of KLF3 in cardiovascular development and function will require temporal- and cell-type specific control of Klf3 expression in future studies . A role for KLF3 in cardiac valve development was detected in our ENU mutagenesis screen , in which high aortic blood velocity revealed aortic semilunar valve stenosis in heterozygous adults with Klf3H275R point mutations . In contrast , other cardiac valves were relatively unaffected although they similarly expressed Klf3 based on LacZ staining . Valve specificity may arise due to differences in developmental mechanisms in semilunar versus atrioventricular valves [24] . For example cells derived from the secondary heart field [25] and cardiac neural crest [26] selectively contribute only to semilunar valve formation . Homozygous gene trap mutants also exhibited a high incidence of aortic valvular stenosis in adults but , in contrast with point mutants , the pulmonary semilunar valve leaflets sometimes appeared thickened histologically and often failed to close sufficiently to prevent regurgitation . In gene trap mutants , it is possible that pulmonary valve defects were secondary to a primary chamber enlargement defect of the right ventricle , a trait that was also never observed in heterozygous Klf3H275R point mutants . Although aortic valve cusps were thickened in late gestation in Klf3H275R/+ embryos , aortic valvular stenosis sufficient to elevate aortic blood velocity developed only after birth when the normal separation , elongation , and thinning of the valve leaflets occurs [27] , [28] . This suggests KLF3 plays a particularly critical role in these later events in aortic valve maturation . This finding is especially interesting given the paucity of knowledge about the genetic regulation of aortic semilunar valve development and the prevalence of aortic valve defects in humans [24] . Additional phenotypic characterization revealed broader roles for KLF3 in cardiovascular development and function , roles that were independent of its role in aortic valve development . In adults , high cardiac output and cardiac hypertrophy due to chamber enlargement ( i . e . eccentric growth ) with no change in left ventricular wall thickness was paradoxical given aortic valvular stenosis in adult Klf3 point mutant and gene trap mutants . This was explained by the surprising observation that stenosis did not elevate intraventricular systolic blood pressure in Klf3H275R/+ hearts relative to WT . Instead , normal intraventricular pressure , low arterial blood pressure , and augmented blood volume in adult Klf3H275R/+ were likely caused by low systemic vascular resistance , resulting in a hyperdynamic circulation and the high cardiac output that we observed . Intriguingly , hypotension in a wide variety of other mouse models does not elicit cardiac hypertrophy and/or an increase in cardiac output . Examples include transgenic mice with overexpression of eNOS ( −18 mmHg and no change in heart weight to body weight ratio ) [29] , Rgs5-deficiency ( >−20 mmHg and no increase in left ventricular inner diameter in diastole ) [30] , and overexpression of atrial natriuretic factor ( −24 mmHg and no change in cardiac output ) [31] . One exception is homozygous deletion of sarcomeric mitochondrial creatine kinase ( Ckmt2 ) where hypotension , left ventricular hypertrophy and high cardiac output can occur depending on genetic background [32] , [33] . Interestingly , KLF3 acts as a transcriptional activator of muscle creatine kinase [11] and interacts with promoters of several other muscle-specific genes but whether it influences Ckmt2 expression is not known . Thus , although a hyperdynamic circulation may cause eccentric cardiac growth , a direct myocardial hypertrophic mechanism may also be involved . In the embryo at E12 . 5 , prominent Klf3 LacZ expression was present in the ventricular outflow tract and vascular endothelium whereas LacZ expression in the myocardium was diffuse and punctate . It is therefore possible that the thin myocardium in Klf3H275R homozygote embryos was secondary to a vascular defect , which caused low peripheral vascular resistance and low intracardiac pressures . A thin myocardium also occurs by E12 . 5 in mouse embryos with endothelial-specific Klf2 gene deletion [34] . In that model , phenylephrine ( a vasoconstrictor ) reduced lethality at E14 . 5 in mutant mouse embryos , and in zebrafish embryos injected with anti-klf2 morpholino [34] . However , when we injected zebrafish with anti-klf3 morpholino , phenylephrine failed to significantly reduce lethality ( not shown ) . Also we observed cardiac septal defects whereas none were reported in Klf2 endothelial-specific knockout mouse embryos [34] . Thus , at E12 . 5 , cardiac defects were likely caused by the direct myocardial effects of Klf3H275R protein although contribution from vascular defects cannot be ruled out . Later in gestation , LacZ staining appeared to become more prominent in the heart and heart valves , ventricular wall thicknesses and heart weights were increased , ventricular chamber sizes were diminished , and aortic valves appeared abnormally thickened . Pressure loading of the heart due to outflow tract obstruction may have contributed to cardiac hypertrophy , which was dramatic in mutant embryos that died in the perinatal period . However , in surviving newborns , aortic blood velocities were not significantly elevated suggesting minimal aortic valvular stenosis . Nevertheless , left ventricles were significantly hypertrophied . These data suggest that at least a component of the perinatal hypertrophic cardiac phenotype was a direct effect of Klf3H275R on cardiac development and growth . When we compared the phenotype of the H275R mutation with the loss of function gene trap mutations in Klf3 , we found that there were considerable similarities , but also marked differences , between the heterozygous point mutants and the homozygous gene trap mutants . Similarities may be explained by the point mutation acting as a dominant negative so that it interfered with wild-type KLF3 function . However , the more severe embryonic lethality shown by the homozygous point mutants than the homozygous gene trap mutants , suggests that the point mutant form of KLF3 may also disrupt other proteins and pathways . It is not uncommon for point mutants to display phenotypes that are more robust and/or different than null mutants for the same gene [35] because point mutations can cause not only reduced gene function , but also enhanced or abnormal gene function , and/or generate proteins with dominant negative activity . Some phenotype differences between Klf3H275R mutants and Klf3 gene trap mutants may be due to differences in genetic background; phenotyping of Klf3H275R was largely on a B6 background whereas Klf3 gene trap mutants were on a mixed 129/B6 background . However , genetic background within the Klf3H275R strain cannot account for the more severe phenotype in homozygotes ( lethal by ∼E14 . 5–16 . 5 ) than heterozygotes ( ∼50% survived to adulthood ) . Thus results are inconsistent with a simple dominant negative effect of the mutant protein . This is also supported by our finding that the mutant Klf3H275R protein exerted a dominant negative action on one of KLF3's gene targets ( Lgals3 ) while sparing other normal targets . In this case , it is not clear how the point mutant protein would disable the native protein to exert a dominant negative effect because KLFs are not known to dimerize [36] . Furthermore , the point mutant protein did not interfere with the ability of WT KLF3 to bind to KLF3's canonical CACCC DNA binding region in vitro . Nevertheless , in vivo , the point mutant protein may have a greater affinity for rate-limiting co-factor binding proteins thereby competitively inhibiting the activity of native protein . In addition , alterations in the mutant protein's DNA binding specificity may cause the mutant protein to transcriptionally regulate additional genes not normally regulated by KLF3 ( i . e . a gain of function effect ) . A similarly complicated interaction caused by a single amino acid change in the second zinc finger of Klf1 ( E339D ) has also been found in mutant mice [36] , [37] , and the homologous mutation in humans causes human disease [38] . Similar to our case , the Klf1 mutation changed the central of 3 amino acids predicted to contact DNA in the zinc finger of a KLF protein . That mutation resulted in a mutant KLF1 protein that failed to bind and transactivate a subset of KLF1's downstream targets in a manner dependent on the target gene's DNA binding sequence [36] . While an association between the Klf3H275R mutation and human disease is not currently known , autosomal dominant point mutations , like H275R , are among the most common causes of human genetic disorders . With Klf1 as precedent , the current work provides strong impetus for searching for mutations in Klf3 in humans with cardiovascular dysfunction . In conclusion , we have discovered important and hitherto unknown roles for Klf3 in cardiovascular development and function . We have also revealed the critical importance of amino acid 275 in the DNA binding region of the first of three zinc fingers , for normal DNA binding and transcriptional gene regulation , and its importance in cardiovascular development . This histidine residue is conserved in KLF3 across species from zebrafish to humans , and in all but one of 22 Sp/Klf family members [18] , [28] ( http://www . ncbi . nlm . nih . gov/sites/entrez ? cmd=Retrieve&db=homologene&dopt=MultipleAlignment&list_uids=7 ) . Thus the identification of the critical importance of this histidine residue may have broad implications; it may apply across species , across the Sp/Klf family , and across other C2H2 zinc finger ( C2H2-ZNF ) proteins , one of the largest and most complex gene super-families with hundreds of members in the human and mouse genome [29] . Involvement of this histidine residue within the zinc finger domain in human disease warrants further investigation .
All experimental procedures received approval from the Animal Care Committee of Mount Sinai Hospital and were conducted in accordance with the guidelines of the Canadian Council on Animal Care . When male C57BL/6J mice ( www . jax . org ) regained fertility after ENU treatment ( 85 mg/kg i . p . 1/wk for 3 wk ) , they were bred to C3H/HeJ females ( www . jax . org ) . Offspring were screened at 8–10 wk for high blood velocity in the ascending thoracic aorta . A male with an aortic blood velocity >7 SD above the mean of previous offspring was identified . It was bred to BALB/c females to test for heritability . PCR amplification of individual microsatellite markers using fluorescently tagged primers ( IDT , Coralville , IA ) was performed on extracted tail DNA from offspring . Labeled products were multiplexed and analyzed on a BaseStation automated sequencer ( MJ Research , Waltham , MA ) to identify alleles from the mutagenized strain ( C57BL/6J ) . Linkage analysis on BALB/c localized the causative mutation to chromosome 5 ( Figure S1 ) . Phenotype penetrance was greater on C57BL/6J so mice were then bred to the consomic chromosome substitution strain , C57BL/6J-Chr 5A/J/NaJ ( www . jax . org , Stock Number 004383 ) and linkage analysis continued on chromosome 5 from the A/J strain . SNP markers unique to C57BL/6J and A/J strains were used to refine the linkage interval . Genomic sequencing of candidate genes revealed a point mutation in exon 5 of Klf3 ( Krüppel-like factor 3 ) that changed a histidine residue ( CAC ) at amino acid 275 to arginine ( CGC ) ( KLF3H275R ) . The mouse line has been named Klf3m1Jrt . For genotyping the Klf3H275R point mutant mice , we distinguished the mutant Klf3 DNA from the endogenous wild type DNA using the base pair change from an A to a G , which introduced a novel BstUII restriction enzyme site ( Table S7 ) . The zebrafish klf3 ( Accession Number NM_131859 ) sequence was verified and targeted . A morpholino spanning the translation start site of the zebrafish klf3 gene ( sequence: 5′- agcatggctgcttccagtggaattt – 3′ ) was designed and ordered from Gene Tools ( Philomath , Oregon ) . Following morpholino titration to determine the optimal concentration , 7 ng of morpholino was injected per embryo at the 1-cell stage using standard techniques . The myl7:EGFPtwu34 transgenic line was used to image the developing heart . Embryonic stem cell lines XS0187 ( XS ) ( MGI:4331780 ) and CH0516 ( CH ) ( MGI:3872001 ) from the Sanger Institute Gene Trap Resource were obtained from the NIH/NCRR-sponsored Mutant Mouse Regional Resource Center at UC Davis . The location of the gene trap insertion was determined by long range PCR and confirmed by sequencing . 5′ RACE analysis ( Sanger Institute Gene Trap Resource ) placed the gene trap insertion cassette in Klf3 in intron 2 for XS and in intron 1 for CH . Primers for long-range PCR were designed at regular intervals spanning the preceding intronic sequence for the 5′ primer and a common sequence at the beginning of the B-geo fusion gene was used for the 3′ primer . PCR with each of the long-range primer sets was then undertaken systemically to narrow down the interval of the insertion site as determined by the production of a ∼1–2 . 5 kbp fragment . The resulting PCR product was purified and sent to The Centre for Applied Genomics ( The Hospital for Sick Children , Toronto , Canada ) for sequencing . To determine the exact site of insertion , sequencing results were compared to the intronic sequence of the Klf3 gene and the gene trap vector using the NCBI BLAST programme . In XS , the gene trap cassette inserted 4105 bp downstream of the 5′ end of intron 2 ( 4 , 843 bp in total ) ( Figure 8A ) . In CH , the gene trap cassette inserted 3578 bp downstream from the start of intron 1 ( 12 , 943 bp in total ) ( Figure 8A ) . Primers were designed for genotyping mutants such that PCR and gel electrophoresis of genomic DNA yielded different sized products for the endogenous WT allele and for the allele with the gene trap insertion ( Table S7 ) . The mouse lines B6;129-Klf3Gt ( XS0187 ) Wtsi/Cmhd ( XS ) and B6;129-Klf3Gt ( CH0516 ) Wtsi/Cmhd ( CH ) were derived from embryonic stem cells by the Transgenic Core at the Toronto Centre for Phenogenomics . Significant differences ( P<0 . 05 ) were tested using Student's t-test or , if normality failed , a Mann-Whitney Rank Sum Test . Proportions were tested using Chi Square or Fisher's Exact Test . Multiple groups were tested by 2-way or 3-way ANOVA as appropriate , and if significant , then a multiple comparison test was performed . If sex was not a significant factor , then combined data are shown ( mean ± SE ) . Total RNA was extracted from whole embryos ( E12 . 5 ) for the following genotypes: wild type ( n = 4 ) , Klf3H275R/+ ( n = 4 ) , Klf3H275R/H275R ( n = 3 ) and Klf3 CH homozygous ( n = 4 ) . RNA was prepared and hybridized to Affymetrix GeneChIP 2 . 0 ST arrays ( Affymetrix , Santa Clara , CA ) by the Ramaciotti Centre ( University of New South Wales , Australia ) as previously described [14] . Data were normalized using the robust multiarray average ( RMA ) algorithm and analyzed using Partek Genomic Suite version 6 . 6 ( Partek Inc . , St Louis , MO ) . Genes that showed greater than 1 . 5-fold deregulation of expression in Klf3H275R/H275R samples relative to wild type were used to construct the heat map ( Figure S9 ) . Microarray data were deposited in the Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/projects/geo ) under accession number GSE43908 . | Cardiac defects are among the most common malformations in humans . Most causative genetic mutations remain unknown . To discover new causative genes important in cardiovascular development and function , we examined 1770 mice with randomly mutated genes and found a mutant with aortic valvular stenosis , and increased risk of fetal and neonatal death . Using linkage analysis and sequencing , we identified a protein-altering point mutation in the gene regulatory protein KLF3 . Mice that survived into adulthood with one mutant copy of the Klf3 gene had low arterial blood pressure , enlarged hearts , and increased mortality due to heart failure . When both copies of the Klf3 gene was mutant , then embryos had heart defects , and all died before birth . KLF3 had no previously known role in heart development so to confirm these findings , we ( 1 ) knocked down klf3 expression in zebrafish embryos and ( 2 ) examined mice with a mutation that effectively eliminated the KLF3 protein . In both cases , cardiovascular dysfunction was observed . In conclusion , we have discovered that KLF3 plays diverse and important roles in cardiovascular development and function in mice . Future exploration of the KLF3 pathway provides a new avenue for investigating causative factors contributing to cardiovascular disorders in humans . | [
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] | 2013 | ENU-induced Mutation in the DNA-binding Domain of KLF3 Reveals Important Roles for KLF3 in Cardiovascular Development and Function in Mice |
The RAD51 protein plays a key role in the homology-directed repair of DNA double-strand breaks and is important for maintaining genome stability . Here we report on a novel human RAD51 variant found in an aggressive and therapy-refractive breast carcinoma . Expression of the RAD51 G151D variant in human breast epithelial cells increases the levels of homology-directed repair . Expression of RAD51 G151D in cells also promotes high levels of chromosomal aberrations and sister chromatid exchanges . In vitro , the purified RAD51 G151D protein directly and significantly enhances DNA strand exchange activity in the presence of RPA . In concordance with this result , co-incubation of G151D with BRCA2 resulted in a much higher level of strand-exchange activity compared to WT RAD51 . Strikingly , the RAD51 G151D variant confers resistance to multiple DNA damaging agents , including ionizing radiation , mitomycin C , and doxorubicin . Our findings demonstrate that the RAD51 G151D somatic variant has a novel hyper-recombination phenotype and suggest that this property of the protein is important for the repair of DNA damage , leading to drug resistance .
Human RAD51 is a RecA-like recombinase required for HDR ( homology-directed repair ) of DSBs ( double-strand breaks ) , forming helical nucleoprotein filaments on DNA in an ATP-dependent manner and catalyzing strand exchange between homologous sequences . RAD51 is an essential protein for genome maintenance with roles in both HDR and replication fork maintenance [1–4] . Both germline and somatic mutations in HDR genes are clearly involved in the initiation and progression of cancer [5–12] . Highlighting this link between HDR and carcinogenesis is the incredibly high lifetime risk for cancer sustained by carriers of BRCA1 and BRCA2 mutations [13–18] . Since RAD51 is a vital component of HDR , it has been hypothesized that missense mutations that significantly alter its function or regulation would be highly deleterious and therefore likely not tolerated in cells . However , there is a dearth of data reporting the cellular effects of cancer-associated RAD51 variants . Currently , the association between cancer incidence and/or progression and RAD51 is strictly correlative . Previous studies have shown elevated RAD51 expression levels in prostate cancer , invasive breast cancer and small cell lung cancers [19–22]; however , decreased RAD51 expression levels in breast tumors and breast cancer cell lines have also been reported [23] . Naturally occurring single nucleotide polymorphisms ( SNPs ) of RAD51 have been identified in the population in association with cancer . RAD51 G135C is a naturally occurring variant in the 5’ untranslated region of RAD51 that was shown to increase breast cancer incidence in BRCA2 mutation carriers and gastric cancer . Mutation of G to C at position 135 increases the promoter activity of RAD51 thereby elevating RAD51 expression levels , one possible mechanism underpinning the contribution of this mutation to cancer susceptibility . Nonetheless , additional studies are needed to elucidate the link between RAD51 G135C and cancer etiology . Another germline variant , RAD51 R150Q , was identified in a study conducted in Japanese hereditary breast cancer patients [24] , yet association with disease incidence was not definitive . More recently , a dominant-negative RAD51 mutation , T131P , was identified in a Fanconi anemia-like patient [25] . Cells expressing RAD51 T131P were found to be proficient in HR but defective in DNA interstrand cross-link repair ( ICL ) [25] . These data , in combination with other studies , exemplify the function of RAD51 during DNA replication and maintenance of replication fork stability in addition to its established function in HDR of DSBs [25–29] . Clearly , proper RAD51 function is important for multiple cellular processes and crucial for maintaining genome stability . Therefore , RAD51 SNPs identified in the general population may yield clues to better understand both RAD51 function and how dysfunction may lead to an increased risk of cancer and/or acquired resistance to standard of care treatments . In this study , we investigated the cellular effects of a heterozygous somatic tumor variant , RAD51 G151D , we identified in 1 out 32 breast tumors analyzed by DNA sequencing . Previously , we demonstrated that the RAD51 G151D protein possesses altered biochemical and biophysical properties [30] . Located on the outer surface , the G151D mutation confers a net electronegative charge propagated throughout the RAD51 filament likely affecting biomechanical properties and protein-protein interactions [30] . In fact , the G151D mutation appears to decrease filament stiffness , which , in combination with the altered surface charge , dramatically increases the electrophoretic mobility of both RAD51-ssDNA and RAD51-dsDNA filaments [30] . Significantly , RAD51 G151D forms mixed filaments with WT RAD51 on DNA and exhibits intermediate physical properties [30] . These findings suggest mixed filament formation is possible both within the heterozygous patient cells as well as in our human cell models . Collectively , the data indicate that mixed filaments are likely to give rise to profound biochemical and biological phenotypes . RAD51 G151D was identified in an African-American woman with early-onset infiltrating ductal adenocarcinoma . After failed radiation and chemotherapy treatment at the primary site , the patient developed metastatic disease only a year after the initial diagnosis . Attempts to eradicate the sites of metastasis using radiation and chemotherapy were unsuccessful . Given the potential linkage of RAD51 with chemo-resistance and genome instability [31–35] , we proposed that the refractory and aggressive characteristic of the tumor was induced by expression of the RAD51 G151D variant . We provide evidence that expression of RAD51 G151D in both non-transformed and transformed human cells results in a hyper-recombination phenotype leading to increased HDR of DSBs and increased resistance to DNA damaging agents . We also demonstrate that the RAD51 G151D protein itself possesses enhanced DNA strand exchange activity , possibly uncovering novel regulatory mechanisms of RAD51 .
To determine the HDR efficiency of RAD51 G151D activity at DSBs , we generated MCF-7 DR-GFP cells with stable and equivalent expression of RAD51 WT or G151D ( Fig 1A ) and performed the DR-GFP reporter assay [36] . In this assay , expression of the rare-cutting endonuclease I-SceI results in a chromosomal DSB at an integrated I-SceI recognition site in a gene encoding for GFP ( SceGFP ) . Repair of the I-SceI-induced double strand break via HDR , using a truncated GFP repeat ( iGFP ) downstream as a template , results in restoration of a functional GFP gene subsequently measured by flow cytometry . As shown in Fig 1B , expression of RAD51 G151D results in a significant increase in GFP positive cells compared to either exogenously expressed RAD51 WT or the parental MCF-7 DR-GFP cells . Therefore , expression of RAD51 G151D , but not RAD51 WT expression , results in a significant increase in HDR of an I-SceI-induced DNA DSB . Although HDR of an I-SceI-induced DNA DSB restores GFP expression , repair by other pathways such as non-homologous end-joining ( NHEJ ) or single-strand annealing ( SSA ) can occur . Repair by HDR , NHEJ or SSA each result in loss of the I-SceI recognition site , therefore I-SceI site loss can be used to query overall DSB repair [36] . RAD51 G151D expressing cells exhibited slightly higher levels of I-SceI site loss compared to RAD51 WT expressing cells ( Fig 1C ) , supporting the hyper-recombinant activity of RAD51 G151D . The fairly equivalent levels of I-SceI site loss in RAD51 WT and G151D expressing cells indicate a lack of significant effect of RAD51 G151D on DSB repair by non-homologous end joining ( NHEJ ) and single-strand annealing ( SSA ) . However , dividing the percentage of GFP positive cells by the percentage of I-SceI site loss demonstrates increased HDR in RAD51 G151D expressing cells as compared to WT expressing cells ( Fig 1D ) [36] . We confirmed increased HDR levels associated with G151D expression in another human cell line , MCF10A , an immortalized non-transformed breast epithelial cell line ( Fig 1E ) using a luciferase-based HDR assay ( schematic in Fig 1F ) [37] . Similar to the DR-GFP assay , an I-SceI site has been integrated into the open reading frame of a coding sequence for luciferase thereby disrupting expression . Downstream is another coding sequence for luciferase that lacks a promoter , and therefore expression is prevented . Repair of an I-SceI induced DSB by HDR using the promoter-less luciferase sequence downstream as a template for repair restores luciferase expression . MCF10A cells expressing RAD51 WT or G151D were nucleofected with no DNA ( negative control ) , the luciferase reporter construct alone ( labeled LUX ) ( control for background ) , or the luciferase reporter construct and an I-SceI construct ( labeled I-SceI ) . Restoration of luciferase expression was measured by the production of light upon catalysis of the substrate luciferin . As shown in Fig 1G , MCF10A cells expressing RAD51 G151D demonstrated a significant increase in luciferase expression ( measured by luminescence units ) as compared to WT expressing cells , indicative of a significant increase in HDR of the I-SceI induced DSB . Collectively , these data demonstrate expression of RAD51 G151D significantly increases HDR of a nuclease-induced DNA DSB suggesting that it confers a hyper-recombination phenotype . As an outcome of HDR , elevated sister chromatid exchanges ( SCEs ) can be indicative of increased levels of HDR as well as increased illegitimate or inappropriately regulated HDR [38] . Therefore , we quantified the number of SCEs in MCF10A cells with stable and equivalent expression of RAD51 WT or G151D ( Fig 1E ) . As shown in Fig 2A , MCF10A cells expressing RAD51 G151D exhibit increased levels of SCEs per nucleus as compared to WT expressing cells . To further emphasize the differences observed , cells expressing RAD51 G151D had 72% of nuclei with >10 SCEs in contrast to WT expressing cells with only 4% of nuclei with >10 SCEs . Representative images of stained metaphase spreads from RAD51 WT or G151D expressing cells are shown in Fig 2B and 2C , respectively . In combination with the HDR reporter results , our data suggest that cells expressing G151D have a hyper-recombinant phenotype . RAD51 forms discrete nuclear foci in response to endogenous and exogenous DNA damage and these foci are considered sites of HDR repair of damage [39] . Previous studies have reported increased RAD51 foci in hyper-recombination models [40–43] , therefore we investigated spontaneous and damage-induced RAD51 focus formation in RAD51 WT and G151D expressing MCF10A cells by immunofluorescence . MCF10A cells expressing G151D exhibited higher levels of damage-induced RAD51 foci at 4 and 8 hours post-IR exposure ( Fig 2D and 2E ) , and spontaneous RAD51 foci ( Fig 2F and 2G ) , as compared to WT-expressing cells . Additionally , there is an increase in the percentage of cells in S-phase in both damaged ( after IR-exposure ) and undamaged RAD51 G151D expressing cells as compared to WT expressing cells ( S2 Fig ) . Accumulation of cells in S-phase and increased RAD51 foci in damaged and undamaged cells supports the hyper-recombination phenotype induced by RAD51 G151D . To visualize DSB repair after exposure to IR , we monitored gamma-H2AX ( γ-H2AX ) and p53 binding protein 1 ( 53BP1 ) foci using immunofluorescence . Phosphorylation of H2AX occurs rapidly when DSBs are present; the resultant γ-H2AX product forms foci at sites of DSBs , participating in the recruitment of DNA repair proteins to the break site . Similarly , 53BP1 rapidly forms discreet foci upon IR exposure at sites of DSBs , as compared to the diffuse localization demonstrated in undamaged cells . Therefore , both γ-H2AX and 53BP1 are surrogate markers for DSBs . MCF10A cells expressing RAD51 G151D had significantly fewer γ-H2AX foci compared to WT cells at 4 and 8 hours post IR exposure ( Fig 3A and 3B ) as well as significantly fewer 53BP1 foci at 4 hours post IR ( Fig 3C and 3D ) . Although γ-H2AX and 53BP1 are well-established marker for DSBs , we also provide direct physical evidence for DSB repair by utilizing the neutral comet assay to compare the kinetic resolution of breaks by RAD51 WT and G151D expressing cells . The neutral comet assay utilizes single cell gel electrophoresis to detect DSBs by evaluating the DNA tail produced after voltage is applied to lysed cells embedded in agarose . MCF10A cells expressing RAD51 G151D had significantly shorter DNA “comet” tails at 4 and 8 hours post IR exposure as compared to WT expressing cells , indicative of fewer DSBs ( Fig 3E and 3F ) . Notably , analysis of untreated cells with the neutral comet assay shows that neither WT nor G151D expressing cells have large numbers of DSBs ( S3 Fig ) , even though elevated levels of spontaneous γH2AX foci are observed in G151D expressing cells . Thus , few DSBs are present in untreated WT or G151D expressing cells , but the γH2AX results indicate that perhaps some type of altered chromatin structure is present in G151D expressing cells . Collectively , these data indicate enhanced repair of IR-induced DSBs by RAD51 G151D expressing cells . Enhanced repair via HDR is likely to confer resistance to DNA damaging agents that result in DSBs . From a translational perspective , the tumor from which RAD51 G151D was identified turned out to be largely refractory to a range of therapeutic interventions including ionizing radiation ( IR ) and mitomycin C ( MMC ) . Therefore , we characterized the response of MCF10A cells expressing either RAD51 WT or RAD51 G151D to IR , MMC , and doxorubicin . IR can lead to both DNA SSBs and DSBs through the formation of hydroxyl radicals or by direct ionization . MMC is an alkylating agent that forms interstrand DNA cross-links leading to inhibition of DNA replication and collapse of replication forks thereby resulting in the formation of DSBs [44] . Cells expressing RAD51 G151D exhibit increased clonogenic survival upon exposure to IR and MMC as compared to WT expressing cells ( Fig 4A and 4B , respectively ) . There is no observable difference in sensitivity to IR or MMC between MCF10A empty vector expressing cells and WT expressing cells ( Fig 4A and 4B , respectively ) . To rule out cell line specific effects , we generated MCF-7 cells with stable and equivalent expression of RAD51 WT or G151D ( see Fig 5D for a western blot demonstrating expression levels of the exogenous proteins ) . Analogous to the increased resistance observed in MCF10A RAD51 G151D expressing cells , MCF-7 cells expressing RAD51 G151D exhibited increased resistance to IR as compared to RAD51 WT expressing cells in a clonogenic survival assay ( S4 Fig ) . These results demonstrate that expression of RAD51 G151D in both non-transformed and transformed human cells results in enhanced resistance to damaging agents . Doxorubicin intercalates into DNA and inhibits topoisomerase II leading to the formation of both SSBs and DSBs . Clonogenic survival assays in MCF10A cells were difficult to interpret due to the highly cytotoxic and cytostatic nature of this compound , and therefore , the MTT [3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide] assay was employed to differentiate the response of G151D compared to WT cells . The MTT assay measures the conversion by metabolically active cells of water soluble MTT to formazan , an insoluble purple precipitate , and therefore assays for cell viability [45–47] . As shown in Fig 4C , cells expressing RAD51 G151D exhibited increased survival in response to the cytotoxic effects of doxorubicin as compared to WT expressing cells . Collectively , these data demonstrate that expression of RAD51 G151D in two independent cell lines , MCF10A and MCF-7 , increases resistance to DNA damaging agents as compared to RAD51 WT expressing cells . The data thus far indicate that expression of RAD51 G151D resulted in increased HDR in vivo . However , it remained unclear whether the fidelity of HDR is affected by RAD51 G151D expression . To address this question , we prepared metaphase spreads from early passage MCF10A cells expressing RAD51 WT or G151D and scored chromosomal aberrations . As shown in Fig 5A , MCF10A cells expressing RAD51 G151D had significantly higher numbers of fragments , fusions ( as indicated by # in Fig 5C ) and breaks ( as indicate by * in Fig 5C ) . Increased spontaneous chromosomal rearrangements in RAD51 G151D expressing cells suggests faulty , error-prone repair . Chromosomal rearrangements can contribute to genomic instability , potentially contributing to therapeutic resistance and tumor progression . The patient from whom this mutation was identified developed aggressive metastatic disease , therefore we postulated that RAD51 G151D may have contributed to the acquisition of the invasive and eventual metastatic behavior of the tumor cells . To address this hypothesis , we measured changes in cell invasion using a transwell system . MCF-7 cells expressing RAD51 WT or G151D ( Fig 5D ) were plated in a Boyden chamber with a bottom filter that is lined with matrigel , which mimics the extracellular matrix composition of the basement membrane . The chamber was then placed into a 6-well plate with FBS-containing media , acting as a chemo-attractant to stimulate chemotaxis . After 48 hours , cells that invaded through the matrigel were quantified . MCF-7 cells expressing RAD51 G151D exhibited significantly increased invasive potential as compared to MCF-7 cells expressing WT RAD51 ( Fig 5E ) . Taken together , these data indicate expression of RAD51 G151D increased chromosomal aberrations as well as the invasive potential of MCF-7 cells . These results indicate that in addition to inducing a hyper-recombination phenotype , RAD51 G151D expression may be promoting illegitimate or error-prone HDR with mutational consequences . The mechanism by which RAD51 G151D induces a hyper-recombination phenotype was pursued with particular interest since the mutation is not located in regions vital to RAD51 recombinase function , including the single- and double-stranded DNA binding surfaces ( loops L1 and L2 ) or Walker A and B motifs . The location of the mutation in a loop on the outer surface of the RAD51 monomer and RAD51 filament indicated the potential for changes in protein-protein contact . We initially hypothesized that RAD51 G151D may possess altered affinity for pro-recombination mediators such as BRCA2 or PALB2 resulting in increased loading efficiency of RAD51 G151D onto ssDNA . However , as shown in S5 Fig , both RAD51 WT and G151D exhibited comparable binding affinities for BRCA2 and PALB2 . Therefore , we decided to take a closer look at DNA strand exchange efficiency . DNA strand exchange measures the ability of RAD51 to nucleate on an ssDNA substrate and catalyze the subsequent invasion and pairing to a homologous duplex donor DNA . We employed an oligonucleotide strand exchange assay previously developed to monitor the mediator activity of BRCA2 ( see schematic in Fig 6A ) [48] . We purified both the WT and the mutant G151D RAD51 proteins and a direct comparison of the two by protein titration revealed comparable activities ( see S6 Fig , WT and G151D protein titration in absence of RPA ) . RPA poses a blockade to RAD51-mediated DNA strand exchange when oligonucleotide substrates are utilized enabling the detection of mediator activity by proteins such as BRCA2 . Surprisingly , by incubating a 3’ tailed DNA substrate with RPA first , the G151D protein promoted significant strand exchange activity under conditions that effectively block activity of the WT protein ( compare lanes 3 , 4 and 5 to 10 , 11 , and 12 , respectively , in Fig 6B and 6C ) . In concordance with this result , co-incubation of G151D with BRCA2 resulted in a much higher level of activity compared to WT RAD51 ( Fig 6D and 6E ) . Enhanced DNA strand exchange in the presence of RPA provides a plausible mechanistic framework for the hyper-recombination phenotype observed in cells expressing the G151D variant . However , in our cell models , the G151D variant is expressed in the presence of endogenous RAD51 potentially mimicking the heterozygous state present within the patient’s tumor . We previously demonstrated that RAD51 WT and G151D directly interact in a yeast two-hybrid system and presumably assemble into mixed filaments in a biochemically reconstituted EMSA analysis [30] . Taken together , it is reasonable to assume that heterogeneous filaments , comprised of endogenous RAD51 and exogenous G151D , are capable of forming in cells expressing G151D . We measured DNA strand exchange activity using a mixture of WT and G151D purified proteins in various ratios using the same experimental protocol as described above . As previously shown , WT and G151D alone ( in the absence of RPA ) exhibited similar DNA strand exchange activities ( Fig 6F , lanes 2 & 9 ) , whereas G151D stimulated higher levels of strand exchange than WT in the presence of RPA ( Fig 6F , compare lanes 3 & 10 ) . Surprisingly , at all ratios tested , G151D mixed with WT increased DNA strand exchange to similar or slightly higher levels than G151D alone in the presence of RPA ( Fig 6F and 6G ) . Even at 25% G151D to 75% WT , DNA strand exchange trended higher than G151D alone ( Fig 6F lanes 4 & 13 , Fig 6G ) . These data indicate that G151D incorporation into the RAD51 filament at equimolar or lower concentrations relative to WT improves the quality or stability of the overall filament leading to enhanced DNA strand exchange activity . Together , the mixed filament data demonstrated a range of G151D expression levels was sufficient to drive elevated DNA strand exchange activity indicative of the biochemical mechanism underlying the hyper-recombination phenotype observed in cells . In order to investigate differences in filament flexibility or stiffness , we performed single molecule fluorescence resonance energy transfer ( smFRET ) ( see [49] for a detailed smFRET methodology ) . Although we observed no difference in the CD spectra of RAD51 WT and G151D , which measures changes in folding of the RAD51 protein monomer [30] , it is possible that the G151D mutation could propagate subtle structural changes throughout the filament , altering the physical properties of the filament . RAD51 WT and G151D filaments on ssDNA both exhibited a zigzag pattern that is characteristic of RAD51-DNA filaments , as seen by electron microscopy ( EM ) [30] . RAD51 G151D , however , qualitatively formed filaments with a more smooth coiled appearance as compared to RAD51 WT filaments indicating G151D filaments may have structural differences compared to WT filaments [30] . smFRET measures the relative fluorescence intensity of a donor fluorophore ( green ) , placed in the ssDNA and an acceptor fluorophore ( red ) , present in the duplex DNA . When the donor and acceptor fluorophore are within close proximity , the donor fluorophore transfers its energy to the acceptor fluorophore , and therefore , only the acceptor fluorophore is detected ( red channel ) ; this is termed a high FRET state with a FRET efficiency ( EFRET ) approaching 1 . 0 ( Fig 7A ) . When the ssDNA is maximally extended , the distance between the donor and acceptor fluorophores is increased , and therefore , only the donor fluorophore will be detected ( green channel ) , resulting in a low FRET state with an EFRET approaching 0 ( Fig 7A and 7B ) . The relative fluorescence of the donor and acceptor fluorophore is directly proportional to the distance between them . Therefore , changes in RAD51 filament properties on the ssDNA tail will be reflected in changes in the distance between the donor and acceptor fluorophores ( measured by changes in the relative fluorescence , and corresponding EFRET ) . The addition of 400 nM of RAD51 WT or G151D in the presence of 2 nM ATP decreases the EFRET , indicating efficient filament formation on the ssDNA tail ( Fig 7B ) . However , there are clearly 2 peaks in the RAD51 WT and a broader EFRET distribution ( Fig 7B ) . In contrast , RAD51 G151D exhibits only 1 peak with a very narrow EFRET distribution ( Fig 7B ) . These data suggest that RAD51 G151D forms a different , more stable , filament species than RAD51 WT . To determine the efficiency of RPA displacement of RAD51 G151D as compared to RAD51 WT , we pre-loaded the ssDNA tail with 20 nM RPA and performed smFRET analysis with 400 nM RAD51 WT or G151D and 2 nM ATP ( Fig 7C ) . As shown in Fig 7D , RPA binding alone to the ssDNA tail results in an intermediate FRET state . Surprisingly , RAD51 WT more effectively displaced RPA from the ssDNA tail as seen by the predominant shift of the intermediate EFRET peak when RPA is bound to a low EFRET peak upon RAD51 WT filament formation ( Fig 7D ) . In contrast , a prominent intermediate EFRET peak persisted after the addition of RAD51 G151D to the RPA-bound smFRET substrate , indicating that a higher proportion of RPA remained bound to the ssDNA ( Fig 7D ) . These results suggest that the enhanced DNA strand exchange activity cannot be attributed to increased efficiency in RPA displacement but perhaps RAD51 G151D may be better at promoting the strand invasion and pairing reaction than WT . To test this hypothesis , we measured the strand invasion and pairing reaction by smFRET , using a donor labeled ssDNA substrate that has sequence homology with a short section of surface tethered duplex DNA substrate containing the acceptor fluorophore ( Fig 8A ) . The acceptor fluorophore is positioned in the duplex DNA such that upon strand invasion and pairing between the homologous sequences by the RAD51-bound ssDNA , the donor and acceptor fluorophores are within close proximity resulting in fluorescence detectable by the acceptor fluorophore only ( red channel ) ( Fig 8A; representative smFRET trajectory shown in Fig 8B ) . As shown in Fig 8C , RAD51 G151D exhibited increased strand exchange efficiency as compared to RAD51 WT . As a control , strand exchange was measured using a non-homologous ssDNA substrate containing the donor fluorophore . Interestingly , RAD51 G151D also exhibited increased strand exchange activity with the non-homologous ssDNA substrate compared to RAD51 WT ( Fig 8C ) , providing additional evidence that RAD51 G151D may promote error-prone or illegitimate HDR ( as suggested by data in Figs 2 and 5 ) . Collectively , these data indicate that RAD51 G151D possesses significantly higher DNA strand exchange efficiency compared to RAD51 WT . The hyper-recombinant activity of RAD51 G151D , observed both in vivo and in vitro herein , would suggest that RAD51 G151D expression might have an effect on replication fork maintenance . Therefore , we performed the DNA fiber assay and measured replication tract length . MCF10A cells expressing RAD51 WT or G151D were pulsed with iododeoxyuridine ( IdU ) for 20 min followed by treatment with 0 . 5 mM hydroxyurea ( HU ) , IR ( 8 GY ) or untreated then pulsed with chlorodeoxyuridine ( CIdU ) for 20 min ( Fig 9A ) . IdU and CIdU are analogs of thymidine and therefore can get incorporated into newly synthesized DNA . Incorporated IdU or CIdU into newly synthesized DNA can be detected using IdU- or CIdU-specific primary antibodies , producing a red tract ( IdU ) followed by a green tract ( CIdU ) ( Fig 9A ) . As shown in Fig 9B , RAD51 G151D expression significantly increases replication tract length in untreated and in HU treated cells as compared to cells expressing RAD51 WT . However , upon induction of DSBs by IR , there is no significant difference in tract lengths of G151D-expressing cells compared to WT expressing cells ( Fig 9B ) . These data indicate that RAD51 G151D may bind more extensively to ssDNA present during replication , thereby increasing replication tract length .
The DNA strand exchange activity of RAD51 G151D was higher than WT in both the oligonucleotide DNA strand exchange assay and the smFRET analysis , providing a mechanistic explanation for the hyper-recombination phenotype observed in cellular assays , including increased RAD51 foci , increased HDR , and enhanced DSB repair . Gain-of-function mutations in yeast Rad51 , predominantly in the L2 loop in Rad51 , were previously shown to decrease sensitivity to IR in rad55 and rad57 mutant yeast strains [50] . The Rad51 paralogs , Rad55 and Rad57 , are recombination mediators that have been shown to stimulate DNA strand exchange by promoting Rad51 nucleation onto RPA-bound ssDNA [51] . Similar to the enhanced DNA strand exchange activity exhibited by RAD51 G151D , the Rad51 I345T gain-of-function mutant also increased DNA strand exchange activity [50 , 52] . Whereas the hyper-recombinant activity of Rad51 I345T was attributed to increased binding affinity for single- and double-stranded DNA , we detected no difference in DNA binding affinities between RAD51 G151D and RAD51 WT ( S7 Fig ) [30] . Increased pairing and strand exchange reaction was observed using both the oligonucleotide DNA strand exchange activity assay and smFRET analysis , which utilize short ssDNA substrates ( <126bp ) . In contrast , there was no difference in strand exchange activity between RAD51 WT and G151D using the ϕX174 virion DNA which measures strand exchange activity using a >5kb ssDNA as a substrate [30] . Furthermore , G151D expression increased replication tract length in untreated and HU-treated cells , suggesting that RAD51 G151D may be stabilizing elongating replication forks by binding to ssDNA at the fork . In combination our data suggest that G151D may not require the extensive strand resection or ssDNA sequence needed by WT RAD51 to associate with DNA and engage in strand exchange activities . Increased filament stability is another possible contributing factor to the hyper-recombinant activity of RAD51 G151D . We previously demonstrated a 6-fold decrease in catalytic efficiency of ATP hydrolysis by RAD51 G151D compared to WT [30] . The decreased catalytic turnover of the RAD51 G151D variant may provide a more stable ATP-bound active filament . The smFRET analysis of filament flexibility and stiffness may provide experimental evidence of a more stable filament formed by RAD51 G151D . Clearly , WT and G151D form different filament species on the ssDNA tail , as seen by the different peak shapes and EFRET distributions produced by the respective proteins . In conjunction with the measured decrease in ATP hydrolysis , it is conceivable that G151D may form a more stable filament . A more stable RAD51 filament could promote the formation of double Holliday junctions ( dHJs ) resulting in increased crossover events ( increased SCEs ) with mutagenic outcomes ( increased chromosomal aberrations ) . HDR is a cell cycle regulated process utilizing multiple mechanisms at each step to prevent inappropriate engagement or erroneous recruitment of HDR proteins to the site of DNA damage [53 , 54] . Inappropriate engagement of HDR during G1 , utilization of imprecise templates ( e . g . homologous chromosome or repetitive sequences ) , and improper dissolution of Holliday junction intermediates can lead to mutations , loss-of-heterozygosity , and other genomic abnormalities [35 , 55–57] . For instance , disruption of the Bloom ( BLM ) and Werner ( WRN ) genes , RecQ helicases that function to suppress inappropriate recombination , leads to increased chromosomal rearrangements and most notably , increased sister chromatid exchanges ( SCEs ) [3 , 40 , 58–60] . In a similar manner , we demonstrate that expression of RAD51 G151D in human cells generates chromosomal aberrations and SCEs . Given the parallel phenotypes , as well as the increased strand exchange activity using a non-homologous substrate in the smFRET analyses , perhaps RAD51 G151D initiates HDR at inappropriate times during the cell cycle or facilitates strand invasion into inappropriate substrates . Glycine 151 is located on the outer surface of both the RAD51 monomer as well as the filament; therefore the G151D mutation may alter the surface properties of RAD51-DNA filaments [30] . Changes at the surface of the RAD51 monomer and filament could alter interactions with proteins that regulate and/or enhance HDR . RAD51 G151D mediated DNA strand exchange was further enhanced by the presence of BRCA2 , therefore it is possible that altered interactions with other recombination mediators could affect subsequent strand invasion , homology search stringency , or eventual displacement of RAD51 to facilitate DNA polymerase extension of the 3’ invading strand . RAD51 G151D contributes significantly to increased genomic instability in both non-transformed and transformed cells . In this report we demonstrate increased SCEs in cells expressing RAD51 G151D . Human mitotic cells preferentially process HDR intermediates in favor of non-crossover ( NCO ) events to prevent removal of genetic information in the form of loss-of-heterozygosity ( LOH ) [61–63] . Therefore , spontaneous SCEs are indicative of increased crossover ( CO ) events as a consequence of de-regulated HDR . It has previously been shown in yeast that lack Sgs1 ( the yeast BLM ortholog ) , a vital component of the BTR complex that processes dHJs via dissolution giving rise to NCO recombinants only , have increased SCEs [62 , 64] . In the absence of proteins necessary for branch migration and dissolution of dHJs , cells can utilize an alternative HJ processing mechanism giving rise to both NCO and CO recombinants , increasing the levels of SCEs . Based on its genetic and biochemical attributes , perhaps G151D assembles filaments during the post-synaptic phase of SDSA resulting in re-formation of the D-loop and biased repair towards DSBR resulting in increased CO events . We propose that RAD51 G151D contributes significantly to the refractory phenotype of cancer cells , by conferring resistance to therapeutics as a result of its hyper-recombinant phenotype . We have shown that expression of RAD51 G151D in two independent human cell models confers increased resistance to DNA damaging agents . The patient from whom the G151D mutation was identified failed to respond to a number of therapeutics , including IR , MMC and doxorubicin . In total , our data lead us to conclude that RAD51 G151D expression directly contributed to the therapeutic resistance of the primary and metastatic tumors in the patient . Previous work has demonstrated multiple mechanisms by which tumor cells can acquire multidrug resistance [5 , 65–69] . Overexpression of RAD51 has been associated with radio- and chemo-resistance [33 , 34 , 70 , 71] . However , in this study , we provide the first evidence that RAD51-induced hyper-recombination is a mechanism of drug resistance in both normal mammary epithelial cells and a breast cancer cell line . Importantly , based on the mixing experiments of WT with G151D , and the resultant change in DNA strand exchange activity , we predict that low levels of G151D expression in a tumor would result in a hyper-recombination phenotype and the potential for therapeutic resistance . These findings have important clinical significance should the G151D variant be used as a guide for therapeutic intervention in the future . Genetic diversity and adaptation in cancer cells is driven , in part , by loss-of-heterozygosity and copy number variation , which generate populations of cells with increased proliferative capacity , resistance to DNA damaging agents , and invasive and metastatic properties . We propose that RAD51 G151D increases the levels of cellular genomic instability and resistance to mainline DNA damaging agent therapy , driving the clinically aggressive disease exhibited by this patient . Utilization of the RAD51 G151D mutation as clinical guide for predicting therapeutic response and disease progression could have a significant impact on the survival outcome of cancer patients harboring this somatic tumor variant . Furthermore , our results provide support for future studies to identify RAD51 mutations and other HDR-associated variants associated with disease progression and therapy resistance . We anticipate that further detailed mechanistic insights into the role of HDR factors in cancer will lead to novel pharmaceutical targets and improved clinical outcomes for those patients with refractory tumors and metastatic disease .
The RAD51 G151D variant was generated as previously described [6] . The RAD51 WT and RAD51 G151D sequences were then amplified by PCR and cloned into the NotI , BamHI sites in the pRVY-Tet retroviral vector . The pCBAIsceI expression vector was a kind gift from the Jasin laboratory . MCF10A cells , an immortalized , non-transformed mammary epithelial cell line , were obtained from ATCC . MCF10A cells were maintained in DMEM/F12 medium ( Corning , Cellgro ) supplemented with 5% horse serum ( HyClone ) , 1% penicillin-streptomycin ( Gibco ) , epidermal growth factor ( EGF ) ( 20 ng/mL , Peprotech ) , hydrocortisone ( 0 . 5 μg/mL , Sigma-Aldrich ) , insulin ( 10 μg/mL , Invitrogen ) , Cholera Toxin ( 100 ng/mL , Sigma-Aldrich ) . GP2-293 cells ( Clonetech ) , a retroviral packaging cell line , MCF-7 DRGFP cells ( a kind gift from the Jasin lab ) , and U2OS-DRGFP cells ( a kind gift from the Bindra lab ) were maintained in DMEM ( Corning , Cellgro ) 10% fetal bovine serum ( FBS ) ( Invitrogen ) supplemented with 1% penicillin-streptomycin . All cells were maintained at 37°C in a humidified 5% CO2 incubator . Pools of MCF10A , MCF-7 , MCF-7 DRGFP , and U2OS-DRGFP cells with stable expression of RAD51 WT or RAD51 G151D were generated as previously described [72] , using a TET-OFF inducible expression vector . All cells were maintained in 2 ug/mL doxycyline during selection with 200 ug/mL hygromycin . Once selection was completed , doxycycline was removed from the media to induce expression and cells were maintained in 15 ug/mL hygromycin . All experiments were performed using cells at or under passage 5 ( post-removal of doxycycline from the media ) . Synchronized cells were collected in cell lysis buffer ( 150 mM NaCl , 50 mM Tris pH 7 . 8 , 1% NP-40 , 0 . 25% sodium deoxycholate ) then centrifuged at 10 , 000xg for 10 minutes . Samples were mixed with 6x loading buffer ( 375 mM Tris-HCl , 9% SDS , 50% glycerol , bromophenol blue ) , separated on a 10% SDS-PAGE gel and transferred to polyvinylidene difluoride ( PVDF-FL ) membranes ( Millipore , Billerica , MA , USA ) . The membrane was blocked in Odyssey blocking buffer ( PBS ) ( Millipore ) for 1hr at room temperature ( RT ) with gentle shaking . The membrane was then probed rabbit polyclonal RAD51 primary antibody ( 1/300 ) ( Santa Cruz Biotechnology , H-92 ) and mouse monoclonal tubulin primary antibody ( 1/10000 ) ( Abcam , DM1A ) overnight at 4°C with gentle shaking . After 3 washes in PBS/0 . 1% Tween , the membrane was probed with IRDye 800CW goat anti-mouse IgG ( H+L ) ( 1/20 , 000 ) and IRDye 680RD goat anti-rabbit IgG ( H+L ) ( 1/20 , 000 ) for 1hr at RT . The membrane was washed with PBS/0 . 1% Tween and visualized using the Odyssey CLx Infrared Imaging System . Expression levels were quantified using Image Studio Software version 2 . 1 . 10 . MCF-7 DR-GFP cells expressing RAD51 WT or RAD51 G151D were plated at 2x106 cells per well in a 6-well plate . The next day , the cells were nucleofected using the Amaxa Cell Line Nucleofector Kit V ( Lonza ) as per the manufacturers instructions with either no DNA , 2 μg of pmaxGFP , or 1–4 μg of the I-SceI expression vector pCBASce [73] . The percentage of GFP-positive cells was quantified by flow cytometry 72 hours post-nucleofection on a Becton Dickinson FACSAria LSRII analytical cytometer and using FlowJo x software version 10 . 0 . 7v2 . Genomic DNA was isolated 5 days after nucleofection in order to determine the percentage of I-SceI site loss for cells nucleofected with no DNA or the I-SceI expression vector . To amplify the DR-GFP sequence surround the I-SceI site , PCR reactions with a final volume of 50 μL included: 1 μg of genomic DNA as a template , dNTPs ( 1 mM ) , MgSO4 ( 1 mM ) , Pfx amplification buffer ( 1x ) , PCRx Enhancer solution ( 1x ) , Platinum Pfx DNA Polymerase ( Invitrogen ) and primers at 0 . 2 μM . The primer sequences were DR-GFP-RS-F 5’ CGTGCTGGTTATTGTGCTGTCTCA and DR-GFP-RS-R 5’ TGCTGCTTCATGTGGTCGGGGTAG . Amplification was performed as previously described [36] using a BioRad C1000 Thermal Cycler . Following amplification , the PCR products were purified using a Qiagen PCR purification kit as per the manufacturers instructions . The purified PCR products were digested with 10 units of I-SceI ( NEB ) for 20 hours , separated on a 2 . 5% agarose gel containing SYBR Safe DNA gel stain ( Invitrogen ) and visualized using the BioRad VersaDoc Imaging System . Signals of the +I-SceI and–I-SceI bands were quantified using Quantity One software version 4 . 6 . 5 . The percent HDR was calculated by dividing the percent GFP+ cells by percent I-SceI site loss . The HDR luciferase assay was performed as previously described [37] . Briefly , a reporter gene ( gWiz . Lux-5’-3’Luc ) was constructed from the parental vector gWiz Luciferase ( Genlantis ) . An I-SceI site was created in the luciferase ORF that completely disrupts luciferase activity . The second luciferase ORF inserted downstream lacks a promoter but can be utilized as a donor in HDR of the first luciferase ORF upon generation of a DSB by expression of the I-SceI nuclease . The pSce-MJ mammalian I-SceI expression vector was a kind gift from Dr . Fen Xia . To perform the assay , cells were seeded into 6-well plates at 5x105 cells/well . 24 hours later , cells were nucleofected with 500 ng of gWiz . Lux-5’-3’Luc vector and 500 ng of the I-SceI expression vector using Amaxa Cell Line Nucleofector Kit L ( program T020 ) . As negative and positive controls respectively , gWiz . Lux-5’-3’Luc or gWiz . Lux vector were transfected alone . Cells were harvested 24 and 48 hours post-nucleofection . Luminescence was measured using an integration time of 5 seconds with 40 μL of the lysate plus 100 μL of luciferin substrate ( One-Glo luciferase assay , Promega ) . Luciferase values were measured as independent triplicates in each experiment . The data presented is the average of two independent experiments . The sister chromatid exchange assay was performed as previously described [74 , 75] with minor changes . Briefly , 24 hours after cells were plated , 20 μM bromodeoxyuridine ( BrDu ) ( Sigma ) was added to the plates for 72 hours . After 0 . 1 μg/mL of Colcemid ( Invitrogen ) was added to the plates for 3 hours , the cells were harvested by trypsinization and metaphase spreads were prepared as previously described [76] . The subsequent slides were dried for 3 days , rehydrated in 1xPBS then incubated with 25 μg/mL Hoechst 33258 ( Sigma ) for 20 minutes . Slides were mounted in equal volumes of 0 . 1 M Na2HPO4 and 0 . 1 M KH2PO4 ( pH 6 . 8 ) , sealed with rubber cement then placed under one 100-W lamp ( Reveal; 100 W; 1 , 352 Lumens; A-19 Shape; General Electric ) that was placed at a distance of 20 cm from the surface of the slides for 25 minutes . Slides were incubated in pre-warmed 1X SSC ( 20X SSC: 3 M NaCl , 300 mM sodium citrate ) for 1hr at 50°C , washed in water then stained in 5% KaryoMax Giemsa ( 6 . 0 g Azur II Eosin and 1 . 6 g Azur II per liter in glycerol/methanol in equal volumes of 0 . 004 M Na2HPO4 and 0 . 004 M KH2PO4 ( pH 6 . 8 ) ( Invitrogen ) for 20 minutes . Slides were washed in water , dried and mounted in Permount mounting media . Spreads were imaged under a 100x objective using an Olympus BX50 Light Microscope with QImaging Retiga 2000R digital camera and software . Metaphase spreads were prepared and chromosomal aberrations were analyzed as described [76] . For ionizing radiation ( IR ) sensitivity and mitomycin C ( MMC ) , cells seeded at various low densities were exposed to 0 , 2 , 4 , 6 , 8 GY of IR ( X-irradiation ) or 0 , 0 . 6 , 0 . 8 , 1 . 0 or 1 . 2 uM of MMC for 4 hours then rinsed twice with PBS . The media was replaced with DMEM/F12 complete growth media and the cells were incubated for 10–12 days before being washed with 1xPBS and stained with crystal violet ( 0 . 5% crystal violet in 80% methanol ) . Colonies with more than 50 cells were scored by eye . For both IR and MMC experiments , plating efficiency was calculated by dividing the number of colonies counted by the number of cells plated; clonogenic survival was determined by dividing the plating efficiency of treated cells by the plating efficiency of untreated cells . The data is representative of 4 independent experiments . To measure doxorubicin sensitivity , 103 cells were plated per well in triplicate in a 96 well plate . The following day , the cells were treated with 0 , 100 , 200 , 400 or 600 nM for 1 hour after which the media was replaced with DMEM/F12 complete media . Cell viability was measured 72 hours post-treatment using the Vibrant MTT Cell proliferation assay kit ( Invitrogen ) as per the manufacturers instructions . Absorbance was measured using a BIO-TEK Synergy HT micro-titer plate reader . Percent cell death was calculated by dividing the absorbance of treated cells by the absorbance of untreated cells , subtracting that value from 1 then multiplying by 100 . The data is representative of 4 independent experiments . MCF10A RAD51 WT or G151D expressing cells plated in 8 well chamber slides ( Millipore ) were exposed to 0 or 8GY of IR ( X-rays ) then allowed to recover for 0 , 2 , 4 , 8 or 24 hours post-exposure . Cells were washed 2 times with PBS then fixed ( 4% paraformaldehyde , 0 . 02% TritonX-100 ) for 15 minutes at room temperature . Cells were rinsed with PBS then incubated with blocking/permeabilization solution ( 10% normal goat serum , 0 . 5% TritonX-100 ) for 1 hour at with gentle shaking . The blocking/permeabilization solution was then replaced with blocking/permeabilization solution containing diluted primary antibody as follows; rabbit polyclonal RAD51: 1/100 ( Santa Cruz , H-92 ) and mouse monoclonal anti-phospho-Histone H2A . X ( Ser139 ) : 1/200 ( Millipore , clone JBW301 ) and incubated overnight at 4°C with gentle shaking . The next day , the cells were washed with PBS/0 . 5% TritonX-100 followed by 2 washes with PBS . Cells were then incubated with AlexaFlour 488 goat anti-mouse IgG ( H+L ) antibody ( 1/1000 ) ( Invitrogen ) and AlexaFlour 647 goat anti-rabbit IgG ( H+L ) antibody ( 1/1000 ) ( Invitrogen ) diluted in blocking/permeabilization solution for 1 hour with gentle shaking . The cells were washed with PBS/0 . 5% TritonX-100 followed by 2 washes with PBS then slides were mounted in Prolong Gold Antifade reagent with DAPI ( Invitrogen ) . Cells were imaged using a Zeiss LSM 510 META confocal scanning laser microscope . For spontaneous foci formation , 1243 nuclei of MCF10A RAD51 WT expressing cells and 1168 nuclei of MCF10A RAD51 G151D expressing cells were counted . For RAD51 and gamma H2A . X foci formation post-IR exposure , at least 1500 nuclei were counted for both cell lines . The Trevigen CometAssay was performed in order to measure double strand DNA breaks after exposure to ionizing radiation as per the manufacturers instructions . Briefly , 5 x 105 cells were plated in 60 mm tissue culture dishes . The next day , the cells were exposed to 8GY of ionizing radiation ( X-rays ) or untreated and harvested by scraping at 0 , 4 and 8 hours post-exposure . Cells were washed in cold PBS ( without Ca++/Mg++ ) . Cells were combined with molten agarose at 37°C and 50 ul was spread evenly across the circular sample area on the provided slides . Slides were placed at 4°C for 20 minutes then immersed in cold lysis buffer containing 10% DMSO overnight at 4°C . The next day , slides were immersed in neutral electrophoresis buffer for 30 minutes then aligned in an electrophoresis tank at 4°C , equidistance from the electrodes , filled with neutral electrophoresis buffer no more than 0 . 6 cm above the slides and 14 volts were applied for 45 minutes . Slides were then immersed in DNA precipitation buffer for 30 minutes at room temperature , followed be 70% ethanol for 30 minutes then dried at 37°C for at least 15 minutes . Slides were stained with DAPI and visualized under a 20x objective by epifluorescence microscopy using an Olympus BX50 Microscope with QImaging Retiga 2000R digital camera and software . Data analysis was performed using OpenComet software plugin for ImageJ 1 . 45s ( NIH , USA ) . At least 50 nuclei were counted per experimental group . Treated and untreated cells were fixed in 1% paraformaldehyde in PBS with 5mM EDTA on ice . Cells were then permeabilized in 70% ethanol for at least 30 minutes at -20°C then rehydrated in 1% BSA/0 . 1% Triton X-100 in PBS for 30 minutes at 4°C . Rehydrated cells were resuspended in propidium iodide ( PI ) /RNase staining buffer ( BD pharmingen ) and filtered through a 44μM pore filter . Labeled cells were analyzed on a MACS VYB flow cytometer using 535nm excitation and PI fluorescence detection at 617nm . Percentages of cells in G0/G1 , S , and G2/M were calculated using ModFit LT Version 4 . 1 . 7 ( Verity Software House , Topsham ME ) . The cell invasion assay ( Millipore ) was performed as per the manufacturer’s instructions . Briefly , the ECM layer of the insert was rehydrated and placed in the wells containing DMEM 10% FBS . MCF-7 cells expressing RAD51 WT or G151D were plated at 5x105 cells per insert in serum-free DMEM . 48 hours after plating , cells that had not invaded were removed using a cotton-tipped swab . Cells that had invaded to the bottom of the transwell were stained in cell stain solution then washed in water . The relative invasiveness of the cells was measured by dissolving the stained cells in 10% acetic acid and performing a colometric reading ( OD ) at 560nm . All DNA substrates were obtained PAGE purified from IDT . The following oligonucleotides were utilized: RJ-167-mer ( 5’-CTG CTT TAT CAA GAT AAT TTT TCG ACT CAT CAG AAA TAT CCG TTT CCT ATA TTT ATT CCT ATT ATG TTT TAT TCA TTT ACT TAT TCT TTA TGT TCA TTT TTT ATA TCC TTT ACT TTA TTT TCT CTG TTT ATT CAT TTA CTT ATT TTG TAT TA TCC TTA TCT TAT TTA-3’ ) , RJ-PHIX-42-1 ( 5’-CGG ATA TTT CTG ATG AGT CGA AAA ATT ATC TTG ATA AAG CAG-3’ ) , RJ-Oligo1 ( 5’-TAA TAC AAA ATA AGT AAA TGA ATA AAC AGA GAA AAT AAA G-3’ ) , and RJ-Oligo2 ( 5’-CTT TAT TTT CTC TGT TTA TTC ATT TAC TTA TTT TGT ATT A-3’ ) . The 3’ tail DNA substrate was generated by annealing RJ-167-mer to RJ-PHIX-42-1 at a 1:1 molar ratio . The dsDNA donor was generated by first radiolabeling RJ-Oligo1 with 32P ( T4 Polynucleotide Kinase ) on the 5’-end and annealing it to RJ-Oligo2 at a 1:1 molar ratio . The assay buffer contained: 25 mM TrisOAc ( pH 7 . 5 ) , 1 mM MgCl2 , 2 mM CaCl2 , 0 . 1 μg/μL BSA , 2 mM ATP , and 1 mM DTT . All pre-incubations and reactions were at 37°C . The protein and DNA substrates were used at the following concentrations: RAD51 ( 0 . 3 μM ) , 3’ tail DNA ( 4 nM molecules ) and dsDNA ( 4 nM molecules ) . RPA and BRCA2 proteins were used at the concentrations indicated in the figure ( unless otherwise noted ) . The 3’ tail DNA was incubated first with RPA for 5 minutes , followed by the addition of RAD51 for 5 minutes ( or BRCA2 and RAD51 where indicated ) , and finally , the radiolabeled donor dsDNA was added for 30 minutes . Where proteins were omitted , storage buffer was substituted . In the case where RAD51 protein was titrated , the RPA protein was omitted . The reaction was terminated with Proteinase K/0 . 5% SDS for 10 minutes . The reactions were loaded on a 6% polyacrylamide gel in TAE buffer and electrophoresis was at 60 V for 80 minutes . The gel was then dried onto DE81 paper and exposed to a PhosphorImager screen overnight . The screen was scanned on a Molecular Dynamics Storm 840 PhosphorImager and bands quantified using ImageQuant software . The percentage of DNA strand exchange product was calculated as labeled product divided by total labeled input DNA in each lane . All reactions were performed at room temperature in a buffer composed of 50 mM Tris-HCL , pH 8 . 0 , 1 mM MgCl 2 , 2 mM CaCl 2 , 0 . 1 mg/mL BSA and an oxygen scavenging system ( 1 mg/ml glucose oxidase , 0 . 4% ( w/v ) D-glucose , 0 . 02 mg/ml catalase , and 2 mM Trolox ) [1] . smFRET assays were preformed according to previously described protocols [77 , 78] . In brief , 50 pM– 300 pM DNA ( Supplementary Table 1 ) was tethered to a PEG-coated quartz surface through biotin-neutravidin linkage followed by the addition of proteins . Data were recorded and analyzed by scripts written in IDL , which extracted corresponding single-molecule donor and acceptor spots into single-molecule trajectories . FRET efficiency ( EFRET ) was calculated as the ratio between the acceptor intensity and the sum of the acceptor and donor intensities . Programs written in Matlab were used to view and analyze FRET trajectories . Histograms were generated using a sample size of over 75 individual molecular trajectories . Cells were grown in appropriate media until 30–40% confluent . Cells were pulsed with 25 μM ( final concentration ) of 5-Iodo-2’-deoxyuridine ( IdU ) ( Sigma ) for 20 minutes at 37°C . Cells were washed with PBS then treated with 0 . 5 mM hydroxyurea ( HU ) ( Sigma ) for 2hrs , exposed to 8GY IR or untreated . Cells were then pulsed with 250 μM ( final concentration ) of 5-Chloro-2’-deoxy-uridine ( CIdU ) for 20 minutes at 37°C . Cells were harvested , spotted onto microscope slides then lysed in fiber lysis solution ( 50 mM EDTA , 0 . 5% SDS , 200 mM Tris-HCl , pH 7 . 5 in ddH2O ) for 2 minutes . Slides were then tilted at a 15° angle to allow DNA fibers to spread on slide then allowed to air-dry . Slides were fixed in 75% methanol/25% acetic acid then placed in 2 . 5 M HCl for 3 hours . Slides were washed in ddH2O then blocked in 5% BSA . IdU-incorporated replication tracts were labeled using a mouse anti-BrdU primary antibody ( 1/400 ) ( BD Biosciences ) and CIdU-incorporated replication tracts were labeled using rat anti-BrdU primary antibody ( 1:25 ) ( Abcam ) for 2 hrs at RT . Slides were washed then labeled with goat anti-rat Alexa Flour 488 ( Invitrogen ) secondary antibody ( 1/200 ) and goat anti-mouse Texas Red ( Santa Cruz ) secondary antibody ( 1/200 ) for 2 . 5 hrs at RT . Slides were mounted in Prolong Gold antifade mounting media ( Invitrogen ) and visualized under a 60x objective by epifluorescence microscopy using an Olympus BX50 Microscope with QImaging Retiga 2000R digital camera and software . Data analysis was performed using ImageJ v . 2 . 0 . 0 ( NIH , USA ) . At least 100 elongating replication structures were measured per cell line/per experimental group for replication tract length . The data are representative of 3 independent experiments . ***p<0 . 001; **p<0 . 01 . Amylose pull-down assays were performed by transiently transfecting 1 μg of the indicated constructs into 5x105 293TD cells/well seeded in 6-well plates using TurboFect ( Thermo Fisher Scientific ) . 36 hours post-transfection , the cells were harvested in 500 μL of buffer ‘BB’: ( 50 mM HEPES [pH = 7 . 5] , 250 mM NaCl , 1% Igepal CA-630 , 1 mM MgCl2 , 1 mM DTT , 250 Units/mL Benzonase ( EMD Millipore ) , and 1X EDTA-free protease inhibitor cocktail ( Roche ) . Cell lysates were batch bound to 20 μL of amylose resin for 2 hours . The bound proteins were washed two times with buffer ‘B’: 50 mM HEPES ( pH 7 . 5 ) , 0 . 5% Igepal CA-630 , 0 . 1% Triton X-100 , 0 . 5 mM EDTA , and 1 mM DTT containing 1M NaCl followed by two washes in buffer B containing 250 mM NaCl . Purified RAD51 at the concentrations indicated in the figure were then added and incubated at 37°C for 30 minutes . The protein complexes were then washed again three times with buffer B containing 250 mM NaCl followed by elution in 20 μL of 10 mM maltose . Loading sample buffer was added , samples were heated at 54°C for 4 minutes , and loaded onto a 4–15% gradient SDS-polyacrylamide gel ( Bio-Rad TGX Stain-Free gel ) . The gel was run for 2 hours at 100 Volts . The proteins were visualized by staining with SyproOrange ( Invitrogen ) and quantified using ImageQuant software on a Storm 860 PhosphorImager ( Molecular Dynamics ) . RAD51 non-specific binding to amylose beads was negligible . Oligonucleotide substrates were obtained PAGE purified from IDT . To generate the 3’ tail DNA substrate , RJ-167-mer was radiolabeled with 32P at the 5’-end using T4 Polynucleotide Kinase ( NEB ) and then annealed at a 1:1 molar ratio to RJ-PHIX-42-1 . ssDNA was the radiolabeled RJ-167-mer alone and the dsDNA substrate was RJ-Oligo1 labeled at the 5’-end by 32P ( T4 Polynucleotide Kinase ) and annealed to RJ-Oligo2 at 1:1 molar ratio . RAD51 ( at the indicated concentrations ) was incubated with 0 . 2 nM ( molecules ) of the radiolabeled DNA substrate for 30 min at 37°C in DNA strand exchange buffer ( 25 mM TrisOAc [pH 7 . 5] , 1 mM MgCl2 , 2 mM CaCl2 , 0 . 1 μg/μL BSA , 2 mM ATP , and 1 mM DTT ) . The reactions were resolved by electrophoresis on a 6% polyacrylamide gel in TAE ( 40 mM Tris-acetate [pH 7 . 5] , 0 . 5 mM EDTA ) buffer for 90 minutes at 80 V in the cold room ( 4°C ) . The gel was then dried onto DE81 paper and exposed to a PhosphorImager screen overnight . The screen was scanned on a Molecular Dynamics Storm 840 PhosphorImager and bands quantified using ImageQuant software . The percentage of protein-DNA complexes was calculated as the free radiolabeled DNA remaining in a given lane relative to the protein-free lane , which defined the value of 0% complex ( 100% free DNA ) . | Therapeutic resistance is a major hurdle for the treatment and eradication of cancer . Furthermore , the development of therapeutic resistance significantly decreases patient survival and negatively impacts the quality of life of patients battling cancer . Cancer cells utilize a number of previously described mechanisms in order to overcome sensitivity to cancer therapeutics , including overexpression of RAD51 . However , in this study we report a novel gain-of-function heterozygous somatic variant , RAD51 G151D , identified in a highly refractory and aggressive breast adenocarcinoma . RAD51 G151D induces a hyper-recombination phenotype in human cells resulting in increased resistance to therapeutics via enhanced HDR of DSBs . We further demonstrate enhanced DNA strand exchange activity in the presence of RPA , providing a possible mechanism for the hyper-recombination phenotype observed in cells . Our study presents a novel hyper-recombinant RAD51 tumor-associated variant ( RAD51 G151D ) , providing the first evidence that links altered RAD51 function with therapeutic resistance as well as a novel genetic marker to identify patients at high risk for aggressive and refractory disease . | [
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... | 2016 | The Tumor-Associated Variant RAD51 G151D Induces a Hyper-Recombination Phenotype |
Dengue virus ( DENV ) infection can range in severity from mild dengue fever ( DF ) to severe dengue hemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) . Changes in host gene expression , temporally through the progression of DENV infection , especially during the early days , remains poorly characterized . Early diagnostic markers for DHF are also lacking . In this study , we investigated host gene expression in a cohort of DENV-infected subjects clinically diagnosed as DF ( n = 51 ) and DHF ( n = 13 ) from Maracay , Venezuela . Blood specimens were collected daily from these subjects from enrollment to early defervescence and at one convalescent time-point . Using convalescent expression levels as baseline , two distinct groups of genes were identified: the “early” group , which included genes associated with innate immunity , type I interferon , cytokine-mediated signaling , chemotaxis , and complement activity peaked at day 0–1 and declined on day 3–4; the second “late” group , comprised of genes associated with cell cycle , emerged from day 4 and peaked at day 5–6 . The up-regulation of innate immune response genes coincided with the down-regulation of genes associated with viral replication during day 0–3 . Furthermore , DHF patients had lower expression of genes associated with antigen processing and presentation , MHC class II receptor , NK and T cell activities , compared to that of DF patients . These results suggested that the innate and adaptive immunity during the early days of the disease are vital in suppressing DENV replication and in affecting outcome of disease severity . Gene signatures of DHF were identified as early as day 1 . Our study reveals a broad and dynamic picture of host responses in DENV infected subjects . Host response to DENV infection can now be understood as two distinct phases with unique transcriptional markers . The DHF signatures identified during day 1–3 may have applications in developing early molecular diagnostics for DHF .
Dengue viruses ( DENV ) are arthropod-borne single stranded RNA viruses of the family Flaviviridae , genus Flavivirus . There are 4 closely related , but antigenically distinct serotypes , DENV-1 , -2 , -3 , and -4 . DENVs are endemic in more than 100 tropical and subtropical countries of the world . Presently , no specific therapies or vaccines are available to treat the diseases or to prevent DENV transmission [1] , [2] . Typically , febrile illnesses begin 5–7 days from the initial DENV infection by an infected mosquito . The virus propagates in the human host and the viremia titer in the host's peripheral blood peaks during the early days ( first 2–3 days ) of acute illness , which is then followed by a steep decline [3] . Illnesses caused by DENV infection include undifferentiated dengue fever ( DF ) , dengue hemorrhagic fever ( DHF ) , and dengue shock syndrome ( DSS ) [4] , [5] . According to the WHO's 1997 guidelines [6] , [7] , DF is an acute febrile illness with two or more manifestations of headache , retro-orbital pain , myalgia , arthralgia , rashes , etc . Symptoms of DF can last for 2–7 days . A DHF case is diagnosed by persistent high fever , hemorrhage tendency , signs of plasma leakage ( ≥20% increase in hemoconcentration or hypoprotemia , pleural effusion or ascites ) , and thrombocytopenia ( platelet counts ≤100 , 000/mm3 ) . DHF is further classified into 4 grades according to the severity of bleeding and plasma leakage . DSS refers to DHF grades III and IV , which , if not diagnosed and treated in a timely manner , can lead to death [6]–[9] . The pathogenic feature of severe dengue disease is a transient increase in vesicular permeability , resulting in plasma leakage . DENV-infection rigorously activates the host innate immune system , which contributes to early anti-viral defense , but may concomitantly also contribute to the development of plasma leakage [3] , [10] . Analyses of host genome-transcript patterns have revealed multiple gene expression profiles associated with dengue infection: NF-κB-initiated immune responses [11] , type I interferon ( IFN ) , ubiquitin proteasome pathway [11] , cell cycle and endoplasmic reticulum ( ER ) [12] , genes involved in T and B cell activation , surface marker expression , immunoglobulin , complement activation , and inmate immunity [12]–[14] . Some of these genes are differentially expressed in DF vs . DHF or DSS patients [12]–[14] . These studies demonstrated a powerful way for exploring the roles of host immune responses to DENV infection , as well as identifying disease severity markers using peripheral blood mononuclear cells ( PBMCs ) without purifying the immune cell subsets or manipulating the cells . In this study , the dynamics of gene expression were analyzed in cohorts of clinically defined DF and DHF subjects . The study established an evolving picture of the host immune response throughout the entire progression of dengue disease , from as early as fever onset to early defervescence . In addition , the study identified key genomic signatures that might be useful for the future development of early diagnosis of severe dengue disease .
All of the procedures were conducted in accordance with the ethical standards of the Naval Medical Research Center Institutional Review Board and with the Helsinki Declaration of 1975 , as revised in 1983 . Prior to participating in the study , an informed written consent was obtained from each participant or from their parents or legal guardians . The Naval Medical Research Center Institutional Review Board , in compliance with all Federal regulations governing the protection of human subjects , approved the study protocol ( NMRCD . 2005 . 0007 ) . This study was also approved by the Comité de Bioética del Instituto de Investigaciones Biomédicas de la Universidad de Carabobo ( CBIIB-UC ) , in Maracay , Venezuela . Approximately 2500 residents in the city of Maracay , Venezuela , participated in an active surveillance program for DENV transmission . The subjects were monitored for febrile illness through visits or phone calls three times a week . The subjects who were five years of age and older , with a fever duration of less than 120 hours and with two or more of the following symptoms: myalgia , athralgia , leucopenia , rash , headache , lymphadenopathy , nausea , vomiting , positive tourniquet test , thrombocytopenia and hepatomegaly ( World Health Organization , Dengue hemorrhagic fever: diagnosis , treatment , prevention and control . 2nd ed . Geneva: WHO; 1997 ) , were enrolled in this study by nurses and physicians of the field team from the Febrile Surveillance Cohort , and by site physicians from local outpatient clinics ( Ambulatorio 23 de Enero , Ambulatorio la Candelaria , Ambulatorio Efrain Abad Y Ambulatorio del Norte ) , and from two metropolitan reference hospitals ( Hospital Central de Maracay and Hospital Ivss Jose Maria Carabaño Tosta ) . Blood samples were obtained by standard venipuncture procedures in a Vacutainer® collection tube with anticoagulant . An initial blood sample was taken upon enrollment for DENV serotype determination using Taqman-based RT-PCR . Only if DENV infection was confirmed by RT-PCR , then serial blood samples were collected at 24 , 48 and 72 hours following the initial sample , and one to two samples within 0–72 hours post-fever defervescence and one sample at ≥20 days ( convalescent period ) following the initial sample . Viremia levels were measured using quantitative RT-PCR at enrollment and at 24 , 48 and 72-hour specimens . Clinical symptoms were monitored and recorded at every visit . Separation of plasma and PBMCs was performed by gradient centrifugation over Histopaque-Ficoll ( Sigma , St . Louis , MO ) . The plasma and PBMCs were stored at −70°C . For the confirmed DENV cases , serum IgM was measured from the samples at acute phase and convalescent phase using enzyme-linked immunosorbant assays ( ELISA ) [15] , [16] . The primary infection was determined using IgM serology: elevated IgM titers ( ≥1∶100 ) without detectable PRNT in the acute sample , and elevated IgM levels in the convalescent sample . To determine the prevalence and cumulative incidences of DENV infections , PRNT titers were measured in the sera obtained from acute specimens [17] as previously described [16] , [18] . Complete blood cell counts were performed for each blood sample collected using the QBC automated system according to the manufacturer's instructions ( Becton-Dickinson 1996 ) . The QBC STAR measures 9 important CBC hematological parameters: hematocrit , hemoglobin , MCHC ( mean corpuscular hemoglobin concentration ) , platelet count , white blood cell count , granulocyte count and percentage , and lymphocyte/monocyte count and percentage . Viral RNA was prepared from 140 µL sera using QIAamp ViralRNA Mini Kits according to the manufacturer's instructions ( Qiagen Inc . , Valencia , CA , USA ) . Briefly , the TaqMan® One-Step RT-PCR Master Mix Reagents ( PN:4309169 , Applied Biosystems ) were prepared in the following manner: final concentration of primers , 1 µM; probes , 0 . 22 µM in a final reaction volume of 25 µl [19] . Thermocycling was set to 50°C for 30 min and 95°C for 10 min , followed by 45 cycles of 95°C for 15 sec and 60°C for 1 min . The PCR reactions were performed in a 7500 Real-Time PCR System ( Applied Biosystems ) . DENV serotype-specific RT TaqMan PCR was performed on the acute samples using a protocol previously described by Laue et al: a standard curve for each DENV serotype was developed using four DENV representative strains ( one for each serotype ) . The starting concentration followed a ten-fold dilution of each serotype: serotype 1 ( 16007 ) = 2 . 3×106 PFU/ml; serotype 2 ( 16681 ) = 2 . 5×107 PFU/ml; serotype 3 ( IQD1728 ) = 4 . 8×105 PFU/ml and serotype 4 ( 1036 ) = 4 . 6×105 PFU/ml . Extraction of cellular RNA , assessment of the integrity and quantity of the extracted RNA , and subsequent processing of the RNA for gene array were performed using the Agilent 2100 Bioanalyzer system ( Agilent Technologies , Santa Clara , CA ) as previously described [20] , [21] . Two chip types were used in the study: Affymetrix HG-U133 plus 2 and HG-Focus . The HG-U133 plus 2 contained 54 , 675 probe sets , where each set consisted of 11 25-mer probes . The HG-Focus gene chip is consisted of 8 , 793 probe sets to assess 8 , 500 transcripts encoding 8 , 400 full length and fully annotated genes [20] , [21] . The 8793 probe sets of the HG-Focus are a subset of the probe sets from HG-U133plus2 . Gene chips with a scaling factor >50 on the dChip software ( 2005 version ) were eliminated from further analysis [21] , [22] . Gene chips were also examined using the QA/QC functions built into the Partek software according to the Company's User Manual . Affymetrix CEL files were imported into Partek Genomics Suite Version 6 . 6 ( Partek ) . The gene chips that failed according to the QC metrics and appeared as outliers using principal component analysis ( PCA ) were eliminated for further analysis . The CEL files were normalized at the probe level using the Robust Multi-chip Average ( RMA ) method built into the Partek Genomic Suite software . RMA was used to normalize the microarray data , which leveraged the gene expression assessments made on Affymetrix gene chips . For each probe set , the technique generated an estimated value for the probe and chip effects , which resulted in an overall pattern of probe set values observed in the data set . RMA consisted of 3 calculation applications that address background correction , normalization of the quantiles , and median polish summarization . The gene expression data were expressed as log2 values . The data sets from the two gene chip platforms were normalized and analyzed independently . Differentially expressed genes were analyzed using analysis of variance ( ANOVA ) using the Partek software . Multi-way ANOVA was chosen based on the number of factors contributing to data variation . The following factors were taken into consideration as variation factors: subject # , scan date ( date that the chip image was taken ) , infecting serotype , illness day ( days of illness when the sample was taken ) , and disease severity ( samples associated with the DF or DHF category ) . Genes that were considered significant only by chance were defined by the false discovery rate ( FDR≥5% ) and were excluded from further analysis . In this study , the genes with a fold change of >2 or <−2 and a p-value plus a FDR <5% were considered significant . A heat map and PCA were used to visualize the most informative trends by showing the predominant gradients in the data set . Analysis of the gene biological functions and pathways were performed using the pathway analysis modules in the Partek Genomic Suite 6 . Two analysis modules were used: gene ontology ( GO ) [23] and KEGG pathway [24] . The gene ontology/biological process level-5 ( GOTERM_BP_5 ) [25] was used for the GO analysis . The Database for Annotation , Visualization and Integrated Discovery ( DAVID ) , an online program for microarray data mining developed by NIAID was used as a second tool for gene function and pathway analysis . Class prediction was performed using the Partek Model Selection tool in the Partek software . Using this software , nested cross-validations were used to select an optimal classifier and to estimate the accuracy of the optimal classifier when multiple classifiers were considered for a single problem . For the 2-level cross-validation , an “inner” cross-validation was performed to select predictor variables and optimal model parameters , and an “outer” cross-validation was used to produce overall accuracy estimates for the classifier . Following the 2-level cross-validation method , the 1-Level Cross-Validation was used to evaluate multiple models and to select the best model to deploy . In this study , the 2-level nested cross validation process used ANOVA or Shrinking Centroid to filter the data . Multiple groups of variables with specified sizes were evaluated . The best classification model was determined by running the following classifiers: K-nearest neighbors with 1–25 neighbors , Nearest Centroid , Discriminant Analysis , and Support Vector Machine .
From 2006 to 2010 , approximately 300 febrile individuals in Maracay , Venezuela , who presented themselves at participating clinics and hospitals or were identified by community-based monitoring , with signs and symptoms consistent with dengue disease , met the study enrollment criteria . These individuals were subsequently enrolled into the study . Approximately 130 subjects were confirmed by PCR of DENV infection and were categorized as DF and DHF patients according to the WHO 1997 dengue disease classification guidelines . DHF patients were recognized based on fever , bleeding , thrombocytopenia ( platelet counts ≤100 , 000 ) and signs of plasma leakage . Subjects who had ≥3 serially collected samples , 1 at each of the febrile , defervescent and convalescent stages were selected for the study , thus we had 51 DF and 13 DHF subjects providing a sum of more than 200 samples for the study . The demographics of the study participants and their clinical , immunological and hematological characteristics are summarized in Table 1 . The timing of the collection , serotype , and severity , related to the samples are presented in Table 2 . To assess the statistical difference between DF and DHF , hypothesis testing of 2 independent samples with unknown variances was performed for mean values , and hypothesis testing for 2 sample proportions was performed for percentages . As shown in Table 1 , most of the DHF cases met all 4 classification criteria . The percentages of lymphocytes and neutrophils in the peripheral blood of DF and DHF patients were plotted in Figure S1 . A total of 166 specimens from 47 DF and 3 DHF subjects were used to study the dynamics of host responses using the HG-focus gene chip platform ( Table 2 ) . To analyze the dynamics of the host response in DENV-infected individuals , samples obtained at different illness days were grouped together into stages ( G ) . Group sizes ( # of specimens ) were shown by G and also by serotype or severity ( Table 2 ) . As illustrated in Figure 1a , G0 was the day of fever onset , G1–G5 corresponded to 1–5 days from fever onset , G6 was day 6–10 , and G7 was the convalescent time point day ≥20 . As shown in Figure 1a , each G had ≥19 samples except G0 and G1 . Using levels of gene expression at G7 as the baseline , significantly up- or downregulated genes were detected at each G using 5-way ANOVA ( Subject # , scan date , severity , stage , and serotype as 5 factors ) . The number of significant genes in each G from G0–G6 was 360 ( G0 ) , 320 ( G1 ) , 92 ( G2 ) , 136 ( G3 ) , 122 ( G4 ) , 198 ( G5 ) , and 152 ( G6 ) , respectively . Combined , these genes consisted of a total of 615 genes ( 7 . 0% of 8793 probe sets on the FG-focus platform ) representing the total up- or down regulated genes activated throughout the entire illness period . Visualizing the expression patterns of the 615 in all of the samples at all time points using PCA , we observed that gene expression patterns gradually shifted by stages from G0 to G6 ( Figure 1b ) . Within this gradual shift , G0 , G1 , G2 , and G3 formed a cluster that was clearly distinguishable from that of G5 and G6 . The samples from G4 bridged between the two clusters . To determine whether the change of gene expression , especially the two distinguishable clusters , was modulated by the presence or absence of fever , we performed PCA on only the defervescent samples . As shown in Figure 1c , the majority of the G4–G6 defervescent samples clustered together , while 4/5 G2–3 and some G4 defervescent samples separated ( Figure 1c ) , suggesting that defervescent samples from a shorter febrile duration ( 2–3 days ) maintained gene expression patterns that were more similar to those of early febrile samples . Similarly , when PCA analysis was performed only on the febrile samples ( Figure 1d ) , it was clear that all of the 5 G5–6 samples , together with a few of the G4 febrile samples separated from the rest of G0–G4 febrile samples , and maintained a pattern more similar to that of late defervescent samples . Based on the 2 distinguishable clusters shown in Figure 1b , we regrouped the samples into early acute ( G0–G3 ) , late acute ( G4–G6 ) and convalescent phases ( G7 ) . A second 4-way ANOVA was performed by comparing the gene expression in the early acute vs . convalescent and late acute vs . convalescent phases . We identified 223 and 140 significant genes that were differentially expressed in the early acute and late acute phase , respectively ( Figure 1e , Table S1 and S4 ) . Only 25 genes ( ∼8% ) , as shown by a Venn Diagram ( Figure 1e ) , were shared between the early and late phase , indicating persistent expression of these genes; whereas the majority ( ∼92% ) of the genes ( 198+115 = 313 ) were uniquely expressed in either the early or late phase ( gene list shown in Table S7 ) . These phase-unique markers showed a better separation of the 3 phases ( Figure 1f ) : early acute , late acute and convalescent . To validate our observations , we performed a similar analysis for the samples assayed using another chip type: HG-U133plus2 ( sample information shown in Table 2 ) , and similar results were obtained ( Figure S2a–c ) . We further separated the DF and DHF samples and analyzed them separately . The gradual change in gene expression over time and the clustering of G1–G3 and G4–G6 were both observed in the DF and DHF groups ( Figure S3 ) , suggesting that the time of sampling contributed most significantly to gene expression variation . To understand the functions of the differentially expressed genes identified by ANOVA , gene ontology ( GO ) and Kegg pathway analysis was used to annotate the functional profiles . The entire list of GO categories in the early and late acute phases with a pathway p-value <0 . 01 and with more than 3 involved genes are shown in Tables S2 , S3 , S5 , and S6 . Listed in Table 3 are the most significant pathways with the highest enrichment scores as detected using the HG-focus gene chip platform . As shown in Table 3 , genes upregulated in G0–G3 were related to the innate immune pathways , type-I interferon-mediated signaling , cytokine-mediated signaling , response to virus , chemotaxis , and inflammatory responses . Gene down-regulated in G0–G3 were related to gene transcription and translation , cellular protein metabolic processes , structural constituent of ribosome , viral transcription , and viral infectious cycle . Genes upregulated in G4–G6 were related to mitotic cell cycle , cell division , mitosis , DNA replication , chromosome , spindle organization , phosphatidylinositol-mediated signaling . Strikingly , there was minor overlap between early acute and late acute phases in terms of gene functionality . The GO analysis using genes expressed on HG-U133plus2 showed same GO pathways as those on HG-focus , reemphasizing the reproducibility of the data ( Table S8 ) . Furthermore , two of the top Kegg pathways representing the early acute and late acute phases were the Systemic Lupus Erythematosus ( SLE ) ( Figure S4 ) and Cell Cycle ( not shown ) pathways , respectively . SLE is an autoimmune disease associated with type III hypersensitivity . It is triggered by the precipitation of antibody immune complexes to cells and tissues , causing complement activation , immune cell activation , and inflammation , and results in tissue and organ damage . Genes encoding complement components C2 , C1q and C1r and genes associated with antigen processing and presentation , cytokine-cytokine signaling , T cell receptor signaling , cell adhesion , complement coagulation , and all those marked with a red star , were present in G0–G3 . For the classification of dengue disease phases , 2-level cross-validation using all of the data on the HG focus chips returned a Normalized Accuracy Estimate = 88% . The 1-level cross-validation returned a top model which used 65 variables and yielded a Normalized Correction Rate of 91% ( Figure 2a ) . These results showed that a set of 65 variables classified early acute , late acute and convalescent phases with an accuracy of 91% ( Figure 2b ) . Among these 65 genes , 23 and 27 variables were unique gene signatures for the early acute and late acute phase , respectively , whereas 15 variables were expressed in both the early and late acute phases . We also performed classification analysis with samples on the HG-U133plus2 platform and found that 55 out of these 65 phase classifiers were also ( Table S9 ) found on this platform . The dynamics of the expression of these 65 markers from G0–G7 were analyzed and shown in Figure 3 . Markers belonging to the early acute phase peaked at G0–G1 and gradually declined to baseline around G4–G5; whereas markers unique for the late acute phase emerged at G4 and peaked at G5–G6 . These results showed two sequential waves of genes representing two host response periods: an innate response period followed by a cell mitotic cycle period . The cross-point of the two waves was at G4 . We further analyzed the data on the HG-U133plus2 chips for gene expression related to DF vs . DHF . Classification analysis was first performed using all of the genes on the HG-U133plus2 chip for samples in G0–G3 . A total of 140 genes ( Table S10 ) classified DF vs . DHF with a correction rate of 86% using the classification methods of K-nearest Neighbor with Euclidean distance and 3 neighbors . The fold changes of the 140 genes between DF and DHF were >2 or <2 with p ( FDR ) <0 . 05 by ANOVA analysis ( Table S10 ) . Among these 140 genes , 79 genes ( 59% ) showed higher levels of expression in the DF samples compared to the DHF samples; whereas 61 genes ( 41% ) showed higher levels in the DHF samples compared to the DF samples ( Table S10 ) . Antigen processing and presentation of the peptide or polysaccharide antigen via MHC class II , MHC class II protein complex , interferon-gamma-mediated signaling pathway , T cell receptor signaling pathway , MHC class II receptor activity , and T cell co-stimulation were among the top functional bio-pathways with a higher level expression in DF specimens compared to DHF specimens ( Table 4 ) . Since our ultimate goal was to discover markers for the early diagnosis of DHF , we sought markers expressed differently between DF and DHF throughout G1 to G3 , but not the ones different in only one or two stages . The median level of gene expression for each of the 140 genes in G1 , G2 and G3 for the DF and DHF group , respectively , was analyzed . There were 56 ( out of 140 ) genes met this criterion with a fold change of >2 or <−2 throughout G1 to G3 compared to G7 . Ranking of the 56 genes revealed 7 genes ( Table 5 ) that differentiated DF from DHF with a 96% accuracy . PCA using these 7 genes clearly segregated the DF from the DHF samples ( Figure 4a ) . The dynamic expression of these 7 genes was shown in Figure 4b .
The classification of DF and DHF cases played a key role in this study . We followed the 1997 WHO guidelines: persistent high fever , hemorrhagic manifestations ( spontaneous bleeding ) , thrombocytopenia ( platelet counts ≤100000/mm3 ) , and signs of plasma leakage ( ≥20% increase in hemoconcentration , pleural effusion or ascites ) . Most of our DHF cases met all 4 criteria . Other differences characterizing DHF from DF included a prolonged fever duration ( 5 . 2 vs . 4 . 3 days ) , higher hospitalization rate , higher secondary infection rate , and a trend of higher viremia titers , which were all consistent with our current knowledge [9] , [26] , [27] . In all patients , there was a decrease in lymphocytes but an increase in neutrophils within the first 1–3 days of illness compared to the convalescent baseline ( G7 ) . Changes in the lymphocytes and neutrophils were significantly more pronounced in the DHF compared to the DF cases . Potts et al attempted to use hematological measures for diagnosis of severe dengue illness [28] . They found that higher counts of neutrophils and lower counts of white blood cells within 3 days from the onset of illness predisposed people at a higher risk of DHF . Mechanistically , early stage T cell apoptosis [29] , [30] demonstrated by Green et al . may account for the lower lymphocyte counts . T cell apoptosis may also account for the lack of functional CD8+ T cell cytokine production [31] , and lack of T cell proliferation at the febrile period of dengue illness [32] . Taken together , these data support the role of cellular immunity in the defense against dengue disease severity , and provided some biological insights on our gene array results , which are later discussed . Neutrophils are one of the first cells to migrate to infection sites; they play an important role in the control of various bacterial and viral infections though phagocytosis and cytokine/chemokine production [33] . Neutrophils release an array of cytokines and chemokines to impact the functions of other cells of the innate as well as the acquired immune system [34] , [35] . The changes of neutrophil counts in early stages may provide mechanistic insights for our gene array study , as the chemokine and inflammatory cytokine responses are the top functional gene pathways in early acute phase . We observed a gradual evolution in the gene expression patterns over time from G0 to G6 with a more significant change at G4 , resulting in the separation of the early acute ( G0–G3 ) and late acute phases ( G4–G6 ) . This was independent of disease severity and was incompletely associated with fever status . The early acute and late acute phases were represented by two waves of functionally distinct gene clusters: the innate immunity followed by cell cycle . Strikingly , only 10% genes were shared between the two phases , and >90% of the genes were unique for either the early or late phase . A number of published dengue gene array studies [11] , [12] have highlighted the importance of sampling time and addressed the difference with respect to timing . However , there is not a uniformed and clear method in the timing of the samples . Some studies used ≤72 hours to define the early phase , while others treated samples from various days as one group . Furthermore , due to the limitation of their sample bank ( most of the studies had samples from day 3–6 ) , the studies did not show an evolving picture of gene expression on a day-to-day basis , and were mostly unable to capture both waves of gene expression [11] , [13] , [36] , [37] . Their findings highlighted a specified period of dengue disease . To the best of our knowledge , we are the first study to present the host response to dengue infection as an entirety . Nevertheless , when we examined our results and those of others by breaking down to separate phases , we found that our results and those of others were mutually supportive . Hoang et al . had used samples collected within 72 hours of the illness from Vietnam , and had identified major functional pathways , response to virus , immune response , innate immune response and inflammatory responses , which were nearly identical to those found in our study . Fink et al . conducted a study on dengue febrile subjects in Singapore [11] . Approximately 50% of their 32 reported innate immunity genes , including TNF , IP-10 ( CXCL10 ) , I-TAC ( CXCL11 ) , Stat 1 , OAS1 , OAS2 , OAS3 , OASL , IFIH1 , IFI44 , UBE2L6 , UPS18 , HERC5 , ISG15 , PSMB9 , MxA ( Mx1 ) were also identified during the same period of disease G0–G3 in our study . More interestingly , IP-10 ( CXCL10 ) gene was one of the most upregulated genes demonstrated in both ours and Fink's study . In addition , Simmons et al . used samples from day 3–6 with a mean length of illness of 4 . 6 days from hospitalized patients in Ho Chi Minh City , Vietnam [12]; Loke et al . studied gene expression using samples within 3–6 days of illness from children in Nicaragua . The presence of genes related to cell cycle , protein metabolism and nucleic acid metabolism in these two studies confirmed our observation of the late acute phase . Our results suggested that future studies should consider carefully the time of sample collection since host responses at early acute and late acute phases showed little in common . The switch in major biological processes from innate immunity to cell mitotic cycle at G4 occurred at G4 . It appeared that this switch coincided with a decrease in viremia . As shown in Table 1 , viremia decreased to low levels in most dengue cases at around G3–G4 . Genes related to the viral replication cycle , RNA synthesis and RNA transcription were downregulated in G0–G3 , which coincided with the upregulation of innate immune responses during this phase . It is known that anti-viral inflammatory cytokines and soluble factors , IL-1 , -6 , -8 and -10 or TNFα and γ , MIP-1α and γ or VEGF [38]–[42] , and the nitrogen and oxygen species [43] , were present in patient sera . Our study suggested a vital role of type I IFN responses , anti-viral responses , as well as chemokines , chemotaxis and complement activation in suppressing viral replication . Apoptosis of DENV-infected cells has been demonstrated in vitro or in vivo in almost every single type of cells infected by DENV . Studies using PBMCs from dengue patients show that T cells undergo activation and apoptosis during the acute illness phase when viremia was present . Impaired CD8+ T cell activity [31] , decreased circulation of CD4+ and CD8+ T cell counts , impaired T cell proliferation [30] , [32] , and increased T cell apoptosis , were all demonstrated during the acute phase . Restoration of T cell activities , T cell counts , and cytokine production was detected from day 5 onward [29]–[32] , which coincided with the absence of viremia in vivo . In our study , the patients also showed a decrease in lymphocytes during G0–G3 , which subsequently returned to above baseline levels . Although they were not the top categories , apoptotic pathways , including the activation of pro-apoptotic gene products , response to unfolded proteins , activation of caspase activity , release of cytochrome C from the mitochondria , and induction of apoptosis by extracellular signals were upregulated during the early acute phase , supporting previous in vitro and in vivo observations on cell apoptosis during early days of infection . The subsequent upregulation of genes related to DNA synthesis and the mitotic cell cycle in G4–G6 suggests a period of immune cell recovery , which may begin when viremia has significantly decreased . Our previous proteomics study revealed several serum biomarkers that predicted DHF . One of these markers was the complement component C4a [44] . Although direct expression of C4a gene was not detected , its upstream complement components , C1q and C2 , and other factors such as HF1 , BF1 , CD59 and SERPING1 were detected in the early acute phase . The complement system can be activated by a classical immune complex ( IC ) -dependent pathway , an alternative pathway and a lectin pathway . Activation of the complement system restricts viral or bacterial infection , but it also promotes strong inflammatory responses . Complement C1q- or C3 have been shown to eliminate ADE [45] . In dengue patients , the peak presence of C3a and C5a coincided with the onset of shock and leakage . In addition , the levels of C3a correlated well with disease severity [46] . By capturing genes in the complement pathways , our study highlighted the involvement of the complement system in the early acute phase . Since most DHF cases are secondary infectious cases , the involvement of the complement system in dengue disease severity requires further investigation . We found a set of 140 genes that distinguished DF from DHF with 86% accuracy . Genes expressed more abundantly in DF were associated with antigen processing and presentation , such as the MHC class II protein complex , interferon-gamma-mediated signaling , T cell receptor signaling , and T cell co-stimulation pathways . Seeking gene markers for severe dengue disease has been an exclusive goal in nearly every gene array study conducted . For those studies which used samples collected from day 3–6 , their results differed from our findings [13] . Popper et al . recently performed a second gene array study in Nicaragua , where they investigated gene expression in a day-to-day manner [37] . They showed that on fever day 3 , lower levels of IFN-stimulated gene transcripts were associated with the development of DSS . The results from this study showed some consistency with our findings . Nascimento et al . conducted a gene array study on a well-characterized dengue cohort from Recife , Brazil [47] . Their results also showed that at early stages of DENV infection , the genes involved in the effector functions of innate immunity were weaker in DHF patients . Furthermore , Devignot et al . showed that genes related to T and NK cell responses were decreased and genes related to anti-inflammatory and repair/remodeling were increased in DSS patients in a study in Cambodia [48] . Overall , the results generated from our study and from those previously reported illustrate an association of IFN-γ and T cell immunity with lower risk of DHF . In our study , the majority ( 61 . 5% ) of DHF cases were caused by DENV-2 . In contrast , 50% DF cases was due to infection with DENV-1 . We were unable to identify differences in the gene expression pattern between any of the two serotypes ( data not shown ) . Thus , different serotypes may not be the main cause underlying the differential gene expression patterns associated with DF or DHF . The association of cellular immunity with DF , but not DHF , strengthened the protective role of cellular immunity against the severity of dengue disease . Since most of the DHF cases were secondary infections , theoretically , cellular immunity should be more rigorous due to the presence of T cell memory . It is known that DENV primarily infects monocytes , macrophages and dendritic cells . These cells are antigen-presenting cells responsible for antigen processing and presentation . Apoptosis caused by DENV infection of these cells may account for the decrease in lymphocytes in the peripheral blood during the early acute phase of the disease and may explain the weakened gene expression of cellular immunity in DHF patients . For future diagnostic purposes , we selected 7 genes that were able to differentiate between severe and non-severe dengue disease with a high accuracy ( 96% ) . These genes were selected on the basis of their consistent expression throughout the early acute days ( G1 to G3 ) . The CD244 gene encodes a cell surface receptor expressed on natural killer ( NK ) cells ( and some T cells ) that mediate non-major histocompatibility complex ( MHC ) restricted killing . The interaction between NK-cells and target cells via this receptor was thought to modulate NK-cell cytolytic activity . The SMAD5 gene encoded protein is involved in TGF-β signaling , which results in an inhibition of the proliferation of hematopoietic progenitor cells . In addition , CD244 and SMAD5 genes were both downregulated in DHF subjects . The CACNA2D3 gene encodes a member of the alpha-2/delta subunit family , a protein involved in the voltage-dependent calcium channel complex . To the best of our knowledge , this study was the first study to present a systemic analysis of the full dynamics of the host response in dengue clinical subjects . This study was completed using two chip platforms and obtained highly consistent results between the two platforms . The gene microarray analysis was supported by clinical observations , comprehensive hematological test results , as well as viremia and immunology data . This study also provided solid data to highlight the importance of the timing in the collection of the clinical samples . This study also strengthened the role of IFN-γ and T cell immunity in the defense against DHF . Our results will advance the understanding of the DENV-mediated disease progression , which will provide enormous support for future clinical research , diagnostics and vaccine development . GSE43777 | The clinical outcome of DENV infection in humans can be DF or the more severe DHF and DSS . The individual's previous DENV exposure history , infecting serotypes , and host genetics are thought to be contributing factors to dengue disease severity . Our study contributed to the current dengue research field in the following ways: 1 ) Our study reveals the dynamics of host gene expression over each day post onset of symptoms . The gene transcription patterns enabled classification of dengue disease into 2 subtle phases: early acute and late acute . 2 ) The study identified gene markers differentiating severe dengue cases from non-severe cases with >90% accuracy . Taken together , our study offers insight into host responses in DENV-infected subjects and these results may be valuable for the future development of diagnostic tools for disease severity . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [
"genome",
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] | 2013 | Sequential Waves of Gene Expression in Patients with Clinically Defined Dengue Illnesses Reveal Subtle Disease Phases and Predict Disease Severity |
Leptospira is a highly heterogeneous bacterial genus that can be divided into three evolutionary lineages and >300 serovars . The causative agents of leptospirosis are responsible of an emerging zoonotic disease worldwide . To advance our understanding of the biodiversity of Leptospira strains at the global level , we evaluated the performance of whole-genome sequencing ( WGS ) as a genus-wide strain classification and genotyping tool . Herein we propose a set of 545 highly conserved loci as a core genome MLST ( cgMLST ) genotyping scheme applicable to the entire Leptospira genus , including non-pathogenic species . Evaluation of cgMLST genotyping was undertaken with 509 genomes , including 327 newly sequenced genomes , from diverse species , sources and geographical locations . Phylogenetic analysis showed that cgMLST defines species , clades , subclades , clonal groups and cgMLST sequence types ( cgST ) , with high precision and robustness to missing data . Novel Leptospira species , including a novel subclade named S2 ( saprophytes 2 ) , were identified . We defined clonal groups ( CG ) optimally using a single-linkage clustering threshold of 40 allelic mismatches . While some CGs such as L . interrogans CG6 ( serogroup Icterohaemorrhagiae ) are globally distributed , others are geographically restricted . cgMLST was congruent with classical MLST schemes , but had greatly improved resolution and broader applicability . Single nucleotide polymorphisms within single cgST groups was limited to <30 SNPs , underlining a potential role for cgMLST in epidemiological surveillance . Finally , cgMLST allowed identification of serogroups and closely related serovars . In conclusion , the proposed cgMLST strategy allows high-resolution genotyping of Leptospira isolates across the phylogenetic breadth of the genus . The unified genomic taxonomy of Leptospira strains , available publicly at http://bigsdb . pasteur . fr/leptospira , will facilitate global harmonization of Leptospira genotyping , strain emergence follow-up and novel collaborative studies of the epidemiology and evolution of this emerging pathogen .
Spirochetes constitute an evolutionarily and morphologically unique group of bacteria [1] . Pathogenic members of this phylum are the causative agents of several important diseases including leptospirosis , an emerging zoonotic disease with more than one million severe cases and 60 , 000 deaths every year worldwide , mostly in the tropical countries [2] . Pathogenic Leptospira species can cause a wide range of diseases in human , ranging from mild flu-like symptoms to severe complications , such as Weil's disease and pulmonary hemorrhagic syndrome , in which the case fatality rate can reach 40% [3] . Leptospirosis is expected to become more prominent worldwide due to climate change and the growing urban population living in slums . In addition , infections with pathogenic species can lead to major economic losses in livestock , as animal infections include e . g . , abortion and loss of milk production [4] . The high public health and economic importance of Leptospira calls for better control of the infections the bacteria cause to both humans and animals . However , the control of Leptospira transmission is challenging for several reasons . First , the life cycle of pathogenic Leptospira is complex . Pathogenic leptospires are excreted through the urine of a wide range of animals including rodents which are asymptomatic reservoirs and livestock . Transmission to susceptible hosts usually occurs through contact with water contaminated with the urine of infected animals [5] . Therefore , multiple environmental sources of exposures , linked to multiple animal species , must be considered as possibilities . Further complicated matters , the genus Leptospira is genetically highly heterogeneous and knowledge of its biodiversity remains largely incomplete . Taxonomically , the genus is currently subdivided into 35 species [6] . These species are ordered into three major evolutionary clades named according to their virulence status: pathogens , intermediates and saprophytes [1] . The agents of leptospirosis belong to two subclades , the pathogens ( 13 species ) and the intermediates ( 11 species ) . The pathogenic species are responsible of the most severe infections in both human and animals , yet we know little about which component of the spirochete are critical for virulence . The species of the intermediates subclade are widely distributed in the environment [6–10] and they may be responsible for mild infections in both human and animals [11–19] . Intermediates possess most of the virulence factors found in the pathogens [1 , 20] . In turn , the saprophytes form a single clade containing eleven species that are regarded as non-pathogenic environmental bacteria [1] . Saprophytes are relatively fast-growing in vitro when compared to the pathogens and lack the virulence factors described in infectious strains [1] . Classification into the three main clades has been typically performed using housekeeping and 16S rRNA genes sequencing [20] . Yet another barrier against leptospirosis control is the difficulty in isolating and cultivating Leptospira , which hinders optimal diagnostics of infections as well as laboratory identification , and hampers the constitution and maintenance of strain culture collections that are needed for microbiological studies and diagnostic or vaccine development purposes . Finally , there is a lack of efficient strain typing methods that would allow tracking Leptospira strains ( i ) from their environmental or animal sources to their infected hosts and ( ii ) as they spread across time and space . Serotyping , which relies on the use of specific monoclonal antibodies , has led to the distinction of >300 serovars based on the structural heterogeneity of the surface-exposed lipopolysaccharides ( LPS ) . This method has demonstrated an association of serovars with some animal reservoir hosts [21] , even though the mechanisms that have allowed the adaptation of pathogenic Leptospira to various hosts are still unknown . However , serovar identification is currently performed by only two reference laboratories worldwide and is fastidious and time-consuming [22] . Furthermore , correlation between serotypes and genomic background is not always accurate , as the LPS biosynthetic locus ( rfb ) can be horizontally transferred between Leptospira species [23–25] . Molecular typing methods include pulsed-field gel electrophoresis ( PFGE ) [26 , 27] , and multilocus variable-number tandem-repeat analysis ( MLVA ) [28] , but both methods have important practical limitations . Thus , PFGE [26] is not widely used and laborious , and only the most common serovars are typeable . More recently , multilocus sequence typing ( MLST ) was developed [29–31] , but unfortunately three distinct MLST schemes have been proposed and applied to distinct collections of isolates , resulting in fragmentation of Leptospira epidemiological knowledge . Further , given the heterogeneity of Leptospira , the above methods are not universally applicable to all clades and species . In particular , MLST schemes are mainly focused on pathogens . As a consequence , current knowledge on the biodiversity and epidemiology of Leptospira is limited , and there is a critical need for a consensus Leptospira genotyping method that would be inclusive for its entire biodiversity , would facilitate fine-level strain discrimination for epidemiological purposes , and would reach high standardization allowing comparison of data from laboratories globally . Whole-genome sequencing ( WGS ) has emerged as a powerful tool for bacterial strain classification and epidemiological typing [32] . The core genome MLST ( cgMLST ) approach , which extends the MLST concepts to the core genome , was demonstrated to be a useful high-resolution typing method in other bacterial species [33–36] . Taking advantage of the unique strain collection of the Reference Center for Leptospirosis in charge of the leptospirosis surveillance in mainland France and French overseas territories , our objectives were ( i ) First , to define based on genomic sequencing , the phylogenetic diversity of Leptospira , and its links with ecology and geography . In particular , our purpose was to shed light on the saprophyte and intermediate clusters , which have been scarcely studied thus far , and to include potentially novel species in this analysis . ( ii ) Second , we aimed to devise a genomic sequence-based genotyping method that is simultaneously universally applicable across the entire Leptospira genus and highly discriminatory at the strain level , and to propose a genomic taxonomy of Leptospira strains .
We sequenced 327 genomes from the collection of the National Reference Centre for Leptospirosis ( Institut Pasteur , Paris , France ) , which is a globally representative strain collection of isolates from environmental , animal , and human samples gathered in the last 50 years . All strains and genome sequences used here are listed in S1 Table . Leptospira strains were grown at 30°C in liquid Ellinghausen , McCullough , Johnson and Harris ( EMJH ) medium . Species identification and serovar typing were performed at the National Reference Centre for Leptospirosis ( Institut Pasteur , Paris , France ) as previously described [37–39] . Collection of the strains was conducted according to the Declaration of Helsinki . A written informed consent from patients was not required as the study was conducted as part of routine surveillance of the national reference center and no additional clinical specimens were collected for the purpose of the study . Cultures originating from human samples were anonymized . Approval for bacterial isolation from soil and water was not required as the study was conducted as part of investigations into leptospirosis outbreaks . For New Caledonia , approval for bacterial isolation from the natural environment was obtained from the South Province ( reference 1689–2017 ) and North Province ( reference 60912-2002-2017 ) . Bacterial genomic DNA was purified using MagNA Pure 96 Instrument ( Roche ) . Next-generation sequencing was performed by the Mutualized Platform for Microbiology ( P2M ) at Institut Pasteur , using the Nextera XT DNA Library Preparation kit ( Illumina ) , the NextSeq 500 sequencing systems ( Illumina ) , and the CLC Genomics Workbench 9 software ( Qiagen ) for analysis . Draft genomes with 50x minimum coverage , a total size < 5 . 3 Mb , and a minimum N50 of 10 , 000 nt were used for subsequent analysis . All raw reads generated and/ or contig sequences were submitted to NCBI under the project number PRJEB29877 and are available under genome accession numbers ERR3047203 to ERR3047514 . We also downloaded 182 assembled genome sequences from the NCBI and PATRIC ( www . patricbrc . org ) databases , including reference strains of previously described species [20] and representative isolates for each clade ( S1 Table ) . To determine a core gene set , 103 high-quality genome sequences of Leptospira covering the whole diversity of the Leptospira genus , i . e . , representative isolates from the three clusters ( 50% from the pathogens , 12% from the intermediates , and 38% from the saprophytes ) were selected ( S1 Table ) ; 50% of the genomes were downloaded from NCBI , the others were sequenced as described above . From this set we inferred the genus core genome using the CoreGeneBuilder pipeline [40] and L . interrogans serovar Copenhageni strain Fiocruz L1-130 ( GCF_000007685 ) as a reference . The pipeline’s first step relies on the eCAMBer software [41] , which consists of a de novo annotation of the genomes ( except the reference ) using Prodigal [42] and the harmonization of the positions of the stop and start codons . In the next step , the core genome is inferred with a bidirectional best hits ( BBH ) approach as previously described by Touchon et al . [43] . We used CoreGeneBuilder default settings except for the synteny parameters ( options–R and–S ) both of which were set to 1 . A gene was considered as part of the core genome if found in at least 90% of our genomes . Genes were not requested to be present in all genomes , as this stringent definition of a core genome would have resulted in too few genes given the diversity of Leptospira . Instead , the set of genes defined using the relaxed requirement of 90% presence can be viewed as a “soft core genome” . This resulted in an initial core genome containing 764 genes . We then filtered out some genes based on the following criteria . ( i ) First , we removed potential paralogs . Indeed , the presence of paralogs inside a typing scheme can lead to ambiguities , as a candidate gene might be attributable to two different core gene loci . To detect those potential paralogs , we compared each allele of each locus against all the alleles of all the other loci using the software BLAT [44] . If a single hit was found between two different loci ( more than 70% protein identity between two alleles ) , we removed both . ( ii ) Second , we also removed genes that belong to one of the 3 existing Leptospira MLST schemes [29 , 45 , 46] and the ribosomal genes , so that they can be analyzed independently . ( iii ) Third , we also removed loci whose length varies too much among alleles , which is useful in reduceing ambiguities during the genotyping process . We aligned the protein sequences and removed those for which the alignment contained more than 10% of gaps ( total number of gaps compared to the total number of characters ) . ( iv ) We removed loci containing ambiguous characters . ( v ) Finally , to avoid redundancy in the information contained within the cgMLST scheme , we removed loci that were overlapping in the reference genome using the definition of Prodigal [42]: a minimum of 60 bp of overlap if genes are on the same strand , and of 200 bp if genes are on different strands . The analysis resulted in the selection of 545 core genes listed in S2 Table and this cgMLST scheme was then used to analyze the presence of genes and to call alleles in 509 genomes ( S1 Table ) , including the 103 genomes used for core genome definition . The allele and profiles definitions of the Leptospira cgMLST scheme were made publicly available through an Internet-accessible genotyping platform at https://bigsdb . pasteur . fr/leptospira/ . To derive a phylogenetic tree based on cgMLST gene loci , the allelic sequences of each locus were extracted and aligned as protein sequences using MAFFT v7 [47] . The concatenation of all loci yielded to a supermatrix of characters . IQ-TREE v1 . 5 . 4 [48] was used to infer a phylogenetic tree from this supermatrix of characters with an LG+G evolutionary model . Branch supports were assessed with both bootstrap ( 1 , 000 replicates ) and aLRT-SH methods [49] . All trees were drawn using the iTOL webserver [50] . To evaluate classical MLST against the newly defined cgMLST scheme , all available Leptospira STs were downloaded from the Oxford University MLST database at https://pubmlst . org/leptospira/ [51] which comprises schemes 1 , 2 , and 3 developped by Boonsilp et al . [45] , Varni et al . [46] and Ahmed et al . [29] , respectively ( S1 Table ) . MLST alleles derived from our WGS data were compared to the MLST database to determine the ST of our genome assemblies . Simpson index of discrimination and Wallace or Rand indices of concordance among partitions were computed using the web site http://www . comparingpartitions . info [52 , 53] .
A total of 327 Leptospira isolates were sequenced , covering the diversity of the Leptospira genus . A complementary set of 182 genome sequences of Leptospira strains , mostly reference strains from the Leptospira Genome project [20] , was downloaded from GenBank and PATRIC ( S1 Table ) . The total set of 509 genomes contained representatives of most Leptospira species currently described . The clusters of pathogens , intermediates and saprophytes were represented by 402 , 31 , and 76 genomes , respectively . Geographically , the dataset was highly diverse: strains were isolated from different geographical areas ( Africa: 19 , East Asia: 17 , Caribbean: 13 , Central America: 7 , Europe: 73 , Indian Ocean: 123 , Middle East: 4 , North America: 24 , Oceania: 11 , Pacific Ocean: 14 , South America: 101 , Southeast Asia: 97 ) . The ecological sources of the strains were also diverse: 111 were from the environment , 226 were from humans , while the remaining isolates were from various animal hosts , such as rodents , cows , dogs , and pigs ( S1 Table ) . The strains corresponded to 42 species including 15 novel species isolated from the environment in Japan , Mayotte , France , Malaysia , Algeria , and New Caledonia [54] . There were 26 serogroups and 73 serovars in the dataset ( S1 Table ) . The strains selected for this study are therefore highly diverse geographically , ecologically and taxonomically . The general features of the 509 genomes are reported in S1 Table and summarized in S1 Fig . Genomic assembly sizes ranged from 3 , 450 , 639 to 5 , 267 , 227 base pairs . Pathogens had a heterogeneous genome size , which was larger on average than the genome size of intermediates , which in turn had a larger genome than saprophytes ( p < 0 . 001 for both comparisons ) . The genomic assemblies of pathogens were more fragmented ( average contig number , 222 ) than those of the two other clusters ( 52 and 47 for the saprophytes and intermediates , respectively ) , which may reflect the high number of mobile elements in the pathogens [55] . The guanine+cytosine content ( G+C% ) of genomes was higher in the intermediates ( 42 . 39% ) than in the saprophytes ( 38 . 27% , p < 1e-7 ) and in the pathogens ( 38 . 83% , p < 1e-7 ) . Saprophytes were more homogeneous in their G+C% content than the two other clusters ( S1 Fig ) . To define the phylogenetic diversity of the dataset , 545 selected genes ( see Methods , section cgMLST definition ) were translated , aligned and then concatenated ( S2 Table ) . The resulting phylogenetic tree is shown in S2 Fig . ANI analysis [54] revealed 42 species defined using the 95% ANI cutoff [56 , 57] , including 15 novel species for which a formal description was proposed elsewhere [54] . The phylogenetic tree with representatives of each species ( Fig 1 ) is consistent with previous data [1] showing two major clades , the “saprophytes” containing species isolated in the natural environment and not responsible for infections and “pathogens” containing all the species responsible for infections in both humans and animals , plus environmental species for which the virulence status is not clearly established . This latter clade is further subdivided in two subclades that we named P1 ( formerly described as the pathogen group ) and P2 ( formerly described as the intermediate group ) . Note that two strains previously assigned to the saprophytes ( strains 201400974 and E30 isolated from the natural environment in Algeria and Japan , respectively ) were clearly distinct from the other saprophytes and represent new species , named L . ilyithenensis and L . kobayashii , of a novel subclade within the clade of saprophytes . We named this new subclade S2 for convenience , in comparison to S1 which is constituted by species formerly described as the saprophyte group [54] . The basal position of the saprophyte clade with respect to P1 and P2 subclades is concordant with previous studies [58 , 59] . The mean genetic distances among the three main subclades S1 , P1 and P2 ( S3 Fig ) ranged between 0 . 33 substitutions per site ( pathogens P1- intermediates P2 ) and 0 . 47 substitutions per site ( intermediates P2- saprophytes S1 ) , underlining the fact that these subclades are separated by large evolutionary distances . In contrast , mean intra-subclade genetic distances were 0 . 13 ( saprophytes S1 ) , 0 . 12 ( pathogens P1 ) and 0 . 17 substitutions per site ( intermediates P2 ) , reflecting the higher heterogeneity and deeper phylogenetic branching of the intermediates P2 subclade . The distance between the new subclade S2 and saprophytes S1 was 0 . 29 , showing that it lies close the P1-P2 inter-subclade distance . We found that all species were monophyletic ( S2 and S4 Fig ) . Furthermore , as expected , the intra-species distances were much lower than the inter-species . For example , L . borgpetersenii isolates formed a tight cluster with a maximum genetic divergence of 0 . 179 substitutions per site . Similarly , L . interrogans isolates showed high genetic relatedness , with a maximum distance of 0 . 033 . This is remarkable given that both species are distributed worldwide ( Fig 2 ) . L . mayottensis , which is confined to the islands of Mayotte and Madagascar , showed a level of diversity of 0 . 008 . The phylogenetic analysis ( Fig 1 ) revealed some structuration and led us to recognize several subgroups of species within subclades . Regarding the subclade P1 , species L . interrogans , L . noguchi and L . kirschneri clustered into one subgroup ( P1-1 ) , whereas L . borgpeterseni , L . alexanderi , L . weilii , L . mayottensis , and L . santarosai formed a second subgroup ( P1-2 ) . Two other subgroups are constituted by L . alstonii ( P1-3 ) and L . kmetyi , L . barantonii from New Caledonia [60] and L . dzianensis isolated from the environment in Mayotte ( P1-4 ) . Finally , subgroup P1-5 comprised L . adleri , L . putramalaysiae from the environment in Malaysia and L . typperaryensis . These subgroups are consistent with previous studies [20 , 59 , 61 , 62] . To improve resolution , separate trees were constructed for the saprophytes S1 and the intermediates P2 ( S4 Fig ) , showing the high level of genetic diversity among environmental isolates . The saprophytes were grouped into two subgroups . Subgroup 1 ( S1-1 ) comprised L . vanthielli , L . brenneri , L . wolbachii; two new species: L . perdikensis and L . congkakensis from Malaysia; L . meyeri , L . harrisiae and the new species L . mtsangambouenesis and L . bandrabouensis isolated from Mayotte . Subgroup 2 ( S1-2 ) comprised L . biflexa and three new species , L . bouyouniensis , L . kemamanensis , and L . jelokensis , isolated from Mayotte and Malaysia; and L . levetti and the new species L . ellinghausenii isolated from soil in Japan . Among the intermediates P2 , three subgroups were recognizable: subgroup P2-1 with L . fainei , L . broomi , and L . inadai; subgroup P2-2 with L . wolffii; and subgroup P2-3 with L . venezuelensis , L . licerasiae , L . saintgironsiae and four new species , named L . dzoumogneensis , L . johnsonii , L . selangorensis , and L . sarikeiensis , isolated from soils in Malaysia , Japan , and Mayotte ( Figs 1 , S2 and S4 ) . A scheme for classifying Leptospira strains is proposed in S3 Table . The phylogenetic structuration reflects a strong contrast between inter- and intra-species distances , which makes it possible to assign isolates at the species level based on their genome sequence-derived phylogenetic position . This led us to re-identify some isolates . For example , strain GWTS assigned to pathogen L . alstonii based on the 16S rRNA and secY genes [63 , 64] did not cluster with the L . alstonii reference strain and formed a distinct branch in our phylogenetic tree ( S2 Fig ) . Based on ANI values with representative species , including new species described in this study , it represents a new pathogenic species that we named L . tipperaryensis ( S1 Table ) [54] . Similarly , strains of serovar Rushan were previously identified as belonging to L . noguchi [65] but were phylogenetically clustered with L . alstonii ( Figs 1 and S2 ) and had ANI values of 99 . 29% compared with the type strain of L . alstonii . These strains therefore appear to be new members of L . alstonii . Interestingly , the L . alstonii reference strain , of serovar Sichuan , was isolated from a frog [66] , as were the strains from serovar Rushan , suggesting a tropism of this species for frogs . Species of the saprophytes and intermediates subclades were represented by few strains . In contrast , some species of pathogens subclade P1 were represented by multiple isolates ( e . g . , 160 for L . interrogans , 76 for L . borgpetersenii , 52 for L . kirschneri , 27 for L . santarosai , 27 for L . noguchi and 23 for L . mayottensis ) . Based on the present sample of Leptospira genomes , the geographic distribution of these species showed clear differences ( Fig 2 ) . L . interrogans , L . borgpetersenii and L . kirschneri were found in all world regions , even though L . kirschneri appeared more rarely in Asian and American samples than in Europe and Mayotte . In contrast , in our dataset , L . santarosai was only sampled from the American continent and the Caribbean islands and L . noguchi was found predominantly in the Americas and rarely in Asia . So far , L . mayottensis has been only isolated from the Indian Ocean islands ( Fig 2 ) . We analyzed in more details the geographic distribution of the diversity of L . interrogans , the most common Leptospira species from human infections around the world , and which was the most represented in our dataset . S5 Fig presents a phylogenetic tree of the 152 L . interrogans isolates for which the geographic source was known; these were from 32 countries in all world regions . The data reveal extensive geographical spread of L . interrogans sublineages . Although some sublineages were sampled in a single world region ( e . g . , the sublineage containing serovars Szwajizak , Wewak , and Hawain originated in Oceania ) , it is clear that most sublineages are geographically widespread ( S5 Fig ) . This is true even for genetically homogenous subgroups , which have limited phylogenetic depth and have therefore emerged recently . These data demonstrate the rapid spread of L . interrogans sublineages over large geographic distances . To develop a standardized subtyping strategy for Leptospira , we analyzed genome sequences using a gene-by-gene approach [34] , based on the 545 genes that were highly conserved across the genus ( S1 and S2 Tables ) . We define this set of gene loci as a core genome MLST ( cgMLST ) scheme [33 , 34] for Leptospira; note that due to occasional absence of a few genes in some genomes , strictly speaking this set of genes is a ‘soft core genome’ . The majority of cgMLST genes ( 527 loci per isolate on average , 96 . 7% ) were called successfully ( i . e . , an allele was defined ) , including in the saprophytes S1 and intermediates P2 ( S1 Table ) . The number of successfully called alleles per isolate ranged from 436 to 545 depending on the gene ( S1 Table ) . Hence , this cgMLST scheme allows genotyping of all Leptospira genomes , with only a few missing data points . For high-resolution subtyping , we defined cgMLST sequence types ( cgST ) as groups of cgMLST allelic profiles that are identical at all loci except for missing data , which are ignored in pairwise comparisons of allelic profiles ( S1 Table ) . Considering the 509 genomes , there were 463 distinct profiles ( defined by their cgST identifier , S1 Table ) , i . e . , most genomes could be identified by a unique allelic profile . The discriminatory power of cgST classification ( Simpson’s index ) was 99 . 9% , much higher than that of MLST: for genomes that were typeable by cgMLST and the three MLST schemes [29 , 45 , 46] , the Simpson indices of discrimination were 0 . 999 ( confidence interval: 0 . 998–1 . 000 ) , 0 . 793 ( 0 . 735–0 . 851 ) , 0 . 787 ( 0 . 730–0 . 845 ) and 0 . 787 ( 0 . 730–0 . 845 ) for cgST , MLST1 , MLST2 and MLST3 , respectively . Hence , as expected , the use of 545 genes instead of 7 cgMLST largely improves our ability to distinguish among Leptospira isolates . To assess the reproducibility and stability of cgST subtyping , sequencing replicates were performed for three isolates: L . licerasiae strain VAR010 , L . meyeri strain Veldrat , and L . interrogans strain L495 . The two replicates of the same isolate shared the same cgST , indicating high reproducibility of cgST classification . We next analyzed a culture‐attenuated strain of L . interrogans serovar Lai that had accumulated mutations ( insertions , deletions , and single-nucleotide variations ) in 101 genes after serial in vitro passages over several years [67] . The derived strain ( cgST20 ) was clearly distinct from the virulent parental strain ( cgST23 , differing by 15 loci ) . Nevertheless , these subcultures were grouped together in the phylogenetic tree ( S2 Fig ) . Similarly , a virulence-attenuated isolate of L . interrogans serovar Manilae passaged 67 times was sequenced [68] and compared with the corresponding parental virulent culture . The cgMLST analysis classified the 2 cultures as cgST31 and cgST32 , differing by only 2 alleles out of 545 genes . These results illustrate the high resolutive power of cgMLST , which can distinguish genomes of isolates that evolved in-vitro over several generations . To evaluate the genetic diversity among isolates classified into the same cgST ( or groups of cgSTs differing only by missing data in some isolates ) , we analyzed the three most numerous ones ( highlighted with colors in S1 Table , column cgST ) using a whole-genome single nucleotide polymorphisms ( SNP ) approach . First , cgST128 and its related cgST123 and cgST308 comprised eight L . borgpetersenii isolates from Mayotte . These differed by a maximum of 16 SNPs , and five isolates had only up to 2 SNPs among themselves . Second , cgST262 and related cgSTs ( cgST130 , cgST321 and cgST396 ) comprised 11 isolates , also of L . borgpetersenii from Mayotte . These isolates differed among themselves by up to 23 SNPs . Finally , cgST482 and related cgST484 comprised seven L . interrogans isolates from cows in Uruguay; all these isolates were identical ( no SNP ) except one , which differed by only three SNPs from the others . These results show that isolates sharing the same cgST , or cgSTs that are identical except for missing data , are very closely related also based on whole-genome SNPs , and include levels of whole-genome SNPs that are compatible with the isolates being part of recent chains of transmissions [69 , 70] . To define groups of Leptospira strains based on cgMLST , we first explored the distribution of pairwise distances among all cgMLST profiles ( S6 Fig ) . We also evaluated the quality of clustering , using the Silhouette index [71] , resulting from the use of all possible threshold values ( from 1 to 544 ) in single-linkage clustering ( S7 Fig ) , revealing a plateau of maximal clustering quality between 40 and 300 allelic mismatches . Based on the above analyses , a threshold of 40 allelic differences was chosen as the cut-off value to define clonal groups ( CG ) . In other words , a CG is defined as a group of cgMLST allelic profiles differing by no more than 40 allelic mismatches , out of 545 gene loci , from at least one other member of the group . This definition resulted in the identification of 237 CGs ( S1 Table ) . To evaluate this choice as compared to alternative thresholds , we compared using the adjusted rand coefficient [72] the partitions ( i . e . , groups of isolates classified into the same CG ) obtained using thresholds of 20 , 30 , 50 , 60 , 150 , 200 and 300 mismatches ( S7 Fig ) . Interestingly , confidence intervals overlapped with those of threshold 40 within a wide range of possible cutoff values ( 20 to 150 ) . Hence , a choice of alternative thresholds in that range would have a limited impact on the resulting clusters . Finally , the effect of missing data ( uncalled cgMLST alleles ) on the clustering results was evaluated in-silico by introducing increasing amounts of missing data and assessing the resulting clusters of isolates as compared to their initial cluster ( S8 Fig ) . This simulation showed that cluster assignment is robust to even high amounts of missing data ( affecting up to 400 loci out of 545 ) . The clusters created at the 40-mismatch level represent a potentially useful genome-based taxonomy of Leptospira strains . To evaluate this classification system in comparison with previous Leptospira strain classifications , we first compared them to the 6- or 7-gene MLST classifications currently in use [29 , 45 , 46] . The three MLST classifications ( S1 Table ) were mapped onto the phylogenetic tree and their concordance with cgMLST was analyzed ( Fig 3 ) . A total of 260 , 106 , and 143 Leptospira STs are currently defined for MLST schemes 1 , 2 , and 3 , respectively ( April 2018; https://pubmlst . org/leptospira/ ) . These MLST schemes were developed for strain typing of the main pathogenic Leptospira species but not for the saprophytes and intermediates [29 , 30 , 45 , 46 , 73] . As expected , saprophytes and most intermediates were not typeable by the three classical MLST schemes , whereas by design , all strains were typeable by cgMLST ( Fig 3 ) . Therefore , the typeability of the proposed cgMLST scheme appears greatly enhanced as compared with classical MLST . We also assessed the concordance among assignments produced by the three MLST schemes and the cgMLST clustering into CGs , using Sankey diagrams ( S9 Fig ) and adjusted Rand and Wallace coefficients [72] . The adjusted Rand index of concordance of MLST with cgMLST was 0 . 86 , 0 . 89 and 0 . 89 for MLST1 [45] , MLST2 [46] and MLST3 [29] , respectively . Wallace indices are not symmetrical , and thus produce two values: one for the comparison of MLST versus cgMLST clustering ( i . e . , how well MLST identity predicts CG identity ) , and one for the reciprocal comparison . The results were 0 . 86 and 0 . 86 for MLST1 , 0 . 82 and 0 . 97 for MLST2 , and 0 . 83 and 0 . 96 for MLST3 . Hence , the CG accurately predicts with high accuracy the STs of MLST2 and MLST3 . Only 4 , 1 and 2 cgMLST clusters matched more than one MLST ST for scheme 1 , 2 and 3 , respectively ( S9 Fig ) . Reciprocally , 26 , 9 and 13 STs for MLST1 , MLST2 and MLST3 , respectively , were subdivided into more than one CG . In other words , despite accepting 40 mismatches within members of the groups , CG classification is still more discriminatory than each of the classical MLST systems . Note that although the low- and high-passage strains ( see above ) of L . interrogans serovar Lai and L . interrogans serovar Manilae were distinguishable at the level of cgST subtypes , they were classified into the same CG ( CG16 and CG23 , respectively ) , consistent with their recent evolutionary link . To provide access to the cgMLST allele and profiles nomenclature , allowing for comparison and sharing of typing results among laboratories worldwide , a database was set-up and was made publicly accessible online ( https://bigsdb . pasteur . fr/leptospira/ ) . This database is based on the software framework Bacterial Isolate Genome Sequence Database ( BIGSdb ) [33 , 34 , 74] . The distribution of serovars and serogroups along the phylogeny showed that most serogroups had a polyphyletic distribution . The fact that phylogenies can be in disagreement with serotyping was previously reported , and some serovars or related serovars from a same serogroup may belong to different species [21] . Thus , isolates from the same serogroup can be distributed in different species or sublineages within species . For example , L . interrogans strains of serogroup Australis or of serogroup Pyrogenes did not all cluster together in the phylogenetic tree ( S2 Fig ) . We investigated the correspondence of cgMLST groups with serovars . Serogroups ( sg ) were usually sub-divided into several CGs ( S1 Table ) . For example , the 29 isolates of sg Australis were subdivided into 14 CGs , the 42 isolates of sg Grippotyphosa fell into 16 CGs , and the 20 isolates of sg Pyrogenes were grouped into 12 CGs . At the serovar level , highly related strains belonged to the same clonal group ( S1 Table ) . This was the case for the 19 isolates from serovars Copenhageni and Icterohaemorrhagiae , which were clustered together in CG6 , and for serovars Ratnupura and Vanderhoedeni ( CG185 , L . kirschneri sg Grippotyphosa ) and Bajan and Barbudensis ( CG179 , L . noguchii sg Australis ) . However , some serovars were genetically more heterogeneous and were themselves sub-divided into different cgMLST clonal groups ( e . g . L . kirschneri and L . interrogans sv Grippotyphosa: 6 CGs; L . interrogans sv Lai , 3 CGs; L . interrogans sv Pyrogenes: 8 CGs ) ( S1 Table ) . Therefore , cgMLST groups represent a useful classification system that is genome sequence-based and is complementary to serogroup and serovar classification , which are based on surface antigens . To explore the links between cgMLST classifications and the epidemiology of Leptospira strains , we first analyzed the correspondence of cgMLST groups with hosts . It is well established that serovars are usually associated with a specific animal reservoir; for example , rats usually carry serovars of the Icterohaemorrhagiae serogroup; and serovar Canicola is associated with dogs [21] . Here , the most frequent cgMLST clonal groups of subclade P1 contained isolates obtained from both human and animals ( except in Mayotte where few isolates have been isolated from animals ) . Thus , isolates of L . interrogans sg Pyrogenes ( CG23 ) , L . borgpetersenii sg Ballum ( CG15 ) and L . borgpetersenii sg Javanica ( CG25 ) , associated with human leptospirosis , were clustered by cgMLST with rodent isolates , suggesting that these serogroups are maintained in rodents and that these animals represent reservoirs of human infections ( S1 Table ) . Similarly , CG19 corresponding to serovar Sejroe comprised human and cattle isolates ( S1 Table ) . Some CGs were found in an even larger range of hosts . For example , the 37 isolates belonging to CG5 ( serovar Pomona ) were obtained from humans , dogs and cows from seven countries . Likewise , CG28 contained isolates from dogs , rodents , pigs , and humans , indicating that some CGs or serotypes are not always restricted to specific hosts and may have a more generalist ecology . The environmental strains from our study were usually not grouped with animal or human isolates , as they formed distincts CGs . We next analyzed 90 clinical isolates collected in the island of Mayotte ( Indian Ocean ) over a period of 10 years ( 2007–2017 ) . cgMLST separated them into 10 CGs , which were highly congruent with their serotypes and species ( S1 Table ) . Serogroup Mini was predominant ( 60% ) and subdivided into five CGs , which agreed with their species assignments ( CG63 , CG83 and CG84 for L . kirschneri , CG78 for L . borgpetersenii , and CG79 for L . mayottensis ) . The most frequent CG was CG78 , corresponding to 39 isolates , which were distributed into 25 cgSTs and were isolated over the 10-year period . Isolates belonging to L . mayottensis were sub-divided into two CGs , CG79 ( n = 7 , 5 cgSTs ) and CG82 ( n = 16 , 14 cgSTs ) ( S1 Table ) . These two groups were previously recognized by PFGE , MLST and serotyping [37 , 75] . Isolates from the island of Mayotte belonged to cgMLST groups that were not found in other world regions , consistent with the unique epidemiology of leptospirosis in this insular ecosystem [37 , 39] . In contrast , multiple CGs were observed in different geographical locations around the world ( S1 Table ) . The wide geographic distribution of CGs indicates that geographic spread of Leptospira strains is faster than their genetic evolution into distinct CGs . We next analyzed the geographic distribution of the high-resolution cgMLST types ( cgST ) . One of the most represented cgSTs ( cgST482 ) in our dataset is constituted by L . interrogans serovar Pomona strains ( n = 6 ) isolated from cattle in Uruguay [76] ( S1 Table ) . Although five out of six of these strains have been isolated from the same farm and were undistinguishable by SNP analysis , one isolate from another region of the country differed from the group of five isolates by 3 SNPs . This shows that cgST classification could possibly inform on the epidemiological links among Leptospira isolates .
Until now , a consensus approach to characterize and compare Leptospira isolates has been lacking , limiting our understanding of the biology and epidemiology of strains within this important genus and impeding progress in establishing appropriate control and prevention measures . Advanced knowledge on the diversity and distribution of Leptospira strains is also essential for the design and evaluation of the efficacy of new vaccines and diagnostic tools . This study lays a foundation for a comprehensive understanding of the biodiversity of Leptospira and for the epidemiological surveillance of medically important Leptospira pathogens . The availability of high-throughput sequencing technologies and the reduction of their costs makes genome sequencing a viable option as the new gold standard for Leptospira genotyping and taxonomy . Recently , 14 new species were identified based on genomic comparisons and a high degree of biodiversity of Leptospira species in soils and water was recently uncovered [6 , 17 , 75] . Besides , there is growing evidence that “intermediate” species are responsible for mild infections in humans [6 , 8 , 11–17 , 19 , 77 , 78] . Novel genotyping methods should therefore encompass the entire genus , including both potentially pathogenic and non-pathogenic strains , in order to provide universal Leptospira strain characterization systems . The classical MLST schemes were developped using six or seven genes with a focus on pathogenic Leptospira species [29 , 45 , 73] . More recently , a new MLST scheme was proposed and applied to a wider collection of strains , including a few intermediate species [46 , 62] . However , none of these MLST methods enables the inclusion of all major Leptospira lineages , including saprophytic strains . Here we sought to develop a cgMLST strategy , which is an extension of conventional MLST at genome scale [34] . Our comparative genome analyses resulted in the identification of 764 genus-wide core genes , including 545 that were deemed suitable for use in cgMLST genotyping . This is in accordance with previous estimates of 700 to 1 , 000 Leptopira core genes [6 , 20 , 59] . Importantly , our cgMLST scheme was developed using genomes representing the entire breadth of the phylogenetic diversity of the genus and was validated using Leptospira strains from diverse sources and geographical locations . The cgMLST scheme was used to construct amino-acid sequence-based phylogenetic trees that were consistent with previous work and current species designations . In addition , this work revealed the existence of novel Leptospira species isolated from soils and water across a wide geographic range ( Algeria , Mayotte , Japan , New Caledonia and Malaysia ) , including species from the new subclade S2 that is phylogenetically related to the previously known saprophytes S1 . This work confirms the high diversity of Leptospira species in the natural environment [6 , 60] , and the novel taxa were described more formally elsewhere [54] . Further , cgMLST-based phylogenetic analysis provides high-level resolution , allowing discrimination among closely related species and strains . Much like classical MLST data , cgMLST data can be used to devise a classification of isolates using the single linkage algorithm [79] . Here we defined clonal groups based on cgMLST with a 40 allelic mismatches cut-off value . In order to optimize discrimination among groups , this threshold was chosen as the smallest threshold within the range of thresholds that maximized the quality of clustering . We demonstrated the robustness of CG classification to missing data and to threshold choice , and therefore propose that CG identifiers will become a practical and highly stable genomic taxonomy system for Leptospira strains . However , it must be underlined that clonal groups are broad classification categories that are of limited use for transmission studies , as illustrated by the wide geographical and temporal distribution of isolates from single clonal groups . Isolates belonging to the same clonal group always belonged to the same serogroup . Conversely , strains of a given serogroup can fall into phylogenetically unrelated clonal groups , suggesting that some Leptospira serogroups are derived from multiple independent ancestors . Further , strains belonging to the same serovar were not always clustered together by cgMLST , indicating that serovars can also be polyphyletic . In contrast , genetically related serovars were sometimes conflated by cgMLST clustering . These observations underline the complementarity of cgMLST clonal groups with previous classifications based on serotyping . cgMLST allows assigning Leptospira isolates both at the species and serogroup levels , and in most cases at the serovar level as well . With the increasing description of novel species and the continuous recording of strain diversity within species by surveillance networks and microbiology laboratories , a precise understanding of the biodiversity of Leptospira strains is needed . cgMLST might represent a useful standard for classification and nomenclature , and would advantageously replace the current classical MLST nomenclatures , which are incomplete , and the serotyping nomenclature , which is complex and does not always reflects phylogenetic relationships , as is the case for other pathogens [80] . Although many CGs were found in distinct geographic regions , the island of Mayotte was a notable exception in that its CGs were endemic . The lack of dissemination of CGs from Mayotte , or of colonization of Mayotte by cosmopolitan CGs such as those of Icterohaemorrhagiae , illustrates the unique ecosystem of this island [81] . However , whether the distribution of species or CGs reported here reflects strong endemicity , or is due to currently limited sampling , will be subject of future studies . As an example of our sampling limitations , L . santarosai is not only found in America as shown in Fig 2 but also in Taiwan where this species is the most frequently encountered species in patients [82] . Isolation of additional strains from both humans and animals will also be required to evaluate whether or not environmental strains belonging to subclades P1 and P2 have the ability to cause infections . We propose a high-resolution classification of Leptospira strains into cgSTs , which correspond to groups of isolates with total sequence identity at the 545 cgMLST genes , with a tolerance of missing data . We showed that this level of discrimination is able to distinguish among in-vitro evolved cultures . Due to the occurrence of missing data , the cgMLST profiles of some isolates can match several distinct cgSTs . Isolates with identical cgST or belonging to groups of related cgSTs ( defined as matching single isolates’ profiles ) were shown to differ at the whole-genome scale by less than ~30 SNPs . This level of divergence is indicative that they share a very recent common ancestor and might be part of an ongoing transmission chain [69 , 70] , even though genomic epidemiology applications to Leptospira remain to be evaluated taking into account its specific mutation rate and transmission dynamics . L . interrogans and L . borgpetersenii are ubiquitous pathogenic species . This is probably due to the fact that rodents are major reservoir hosts for these species [45] . Thus , L . interrogans strains belonging to serovars Copenhageni and Icterohaemorrhagiae share the same CG regardless of their geographic origin . This limited genetic diversity and broad geographic distribution ( S5 Fig ) is consistent with recent evolution/expansion following extensive migration of rodents , the main reservoir of serovars Copenhageni and Icterohaemorrhagiae , and multiple introductions due to modern global transport , in particular long-range , ship-based travel and trade . Due to this rapid geographic diffusion , little phylogeographic signal was present in the dataset , rendering challenging the reconstruction of the geographic origins of L . interrogans and its sublineages with confidence . By contrast , species such as L . noguchi , L . kirschneri , and L . mayottensis are not associated with rats and are largely confined in specific geographical areas . The pathogen L . mayottensis may have been introduced into Mayotte from Madagascar via the tenrec , a small terrestrial mammal [83] . This work provides a framework for the definition of Leptospira clades , subclades , subgroups , species , as well as strains at two levels of resolution ( S3 Table ) . The possibility for laboratories around the world to identify the same strains using a unified nomenclature and a centralised genotyping database will facilitate the sharing and dissemination of knowledge on circulating Leptospira genotypes , worldwide . The cgMLST scheme will also enable early detection of new genotypes being introduced into locations where they are not usually found . The links between genotypes and their pathogenic potential and virulence will be an important subject for future studies . For yet unknown reasons , a limited number of Leptospira serovars are much more likely to cause severe disease than others [84–87] . The role of phages , plasmids , and horizontal transfer in the acquisition of virulence factors also remains to be determined . The molecular basis of host specificity is also largely unknown . Future dedicated studies will be needed to characterize the gene content of subclades , species and strains , and their association with the clinical presentation and outcome of Leptospira infections . | Leptospirosis , caused by pathogenic Leptospira strains , is an emerging bacterial zoonotic disease mostly affecting humans in tropical countries . Despite its public health importance , little is known about the strains that are circulating worldwide due to the lack of a universal common language on strain types . In this work we describe a new strain genotyping and classification system that is highly standardized , thus facilitating global collaboration , and that can discriminate all members of the Leptospira genus at high resolution . We then examine the genetic diversity of Leptospira strains from different origins . This study provides a framework for optimizing diagnostic methods and epidemiological surveillance of leptospirosis . | [
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"p... | 2019 | Genus-wide Leptospira core genome multilocus sequence typing for strain taxonomy and global surveillance |
Ebola virus disease is a highly virulent and transmissible disease . The largest recorded fatality from Ebola virus disease epidemic is ongoing in a few countries in West Africa , and this poses a health risk to the entire population of the world because arresting the transmission has been challenging . Vaccination is considered a key intervention that is capable of arresting further spread of the disease and preventing future outbreak . However , no vaccine has yet been approved for public use , although various recombinant vaccines are undergoing trials and approval for public use is imminent . Therefore , this study aimed to determine the acceptability of and willingness-to-pay for Ebola virus vaccine by the public . The study was a community-based cross-sectional qualitative and quantitative interventional study conducted in two communities , each in two states in Nigeria . An interviewer-administered questionnaire was used to collect information on respondents’ knowledge of the Ebola virus , the ways to prevent the disease , and their preventive practices , as well as their acceptability of and willingness-to-pay for a hypothetical vaccine against Ebola virus disease . The association between acceptability of the vaccine and other independent variables were evaluated using multivariate regression analysis . Ebola virus disease was considered to be a very serious disease by 38 . 5% of the 582 respondents ( 224/582 ) , prior to receiving health education on Ebola virus and its vaccine . Eighty percent ( 80% ) accepted to be vaccinated with Ebola vaccine . However , among those that accepted to be vaccinated , most would only accept after observing the outcome on others who have received the vaccine . More than 87 . 5% was willing to pay for the vaccine , although 55 . 2% was of the opinion that the vaccine should be provided free of charge . The level of acceptability of Ebola virus vaccine among respondents was impressive ( though conditional ) , as well as their willingness to pay for it if the vaccine is not publicly funded . In order to achieve a high uptake of the vaccine , information and education on the vaccine should be extensively shared with the public prior to the introduction of the vaccine , and the vaccine should be provided free of charge by government .
Ebola virus disease ( EVD ) is caused by Ebola virus ( EBV ) , a highly virulent and infectious virus that infects humans and non-human primates . EVD is transmitted through human-to-human contact [1 , 2] and has up to 70% case fatality rate [3] . The current outbreak of EVD in six West African countries; Sierra Leone , Liberia , Guinea , Senegal , Mali , Nigeria [4] and reported cases in developed countries [5] have infected about 20 , 416 persons and caused 8 , 483 deaths [6 , 7] as at January 13 , 2015 . The only available control strategy is strict personal and environmental hygiene , since no drug [8] has been approved for the treatment and no vaccine has been approved for its prophylaxis . The implementation of adequate hygiene; ( avoiding contact with body fluids from an infected person or contact with items handled by an Ebola-infected patient , regular hand washing with soap and water and use of sanitizer ) in West Africa is a challenge [9 , 10] principally due to poverty with existing low standard of living; lack of access to clean water , inadequate sanitation and overcrowded housing . Also inadequate health system in these countries lead to lack of or delayed case identification , inadequate supportive case management , contact tracing and surveillance which aid the spread of the disease . In view of the above limitations , effective prophylaxis through the introduction of Ebola virus vaccine ( EVV ) is urgently needed . On August 28 2014 , the National Institute of Health ( NIH ) , USA announced that the first testing of EVV on humans by the National Institute of Allergy and Infectious Disease ( NIAID ) and GlaxoSmithKline ( GSK ) was imminent . The EVV , is a viral-vector-based recombinant vaccine in which genes encoding protein of Ebola virus is inserted into the genome of another virus ( not Ebola virus ) , recombinant replication-deficient Chimpanzee-derived adenovirus 3 or cAd 3 ) [11] which when injected will generate both cellular and humoral immunity in the recipients . If approved for usage , the countries that have reported EBV cases may be among the first to benefit . Whenever a new vaccine is introduced , it has to contend with public acceptability . Although , previous studies have reported favorable attitudes towards newly introduced vaccines [12–14] it would be an over assumption to conclude that introduction of the EVV will be welcomed with the same attitude and uncritical acceptance [15] . The existing poor knowledge on the vaccine by the uninformed masses [12] , and the misconception of the possible risk of contracting an illness through a vaccine: as was attributed to oral polio vaccine [16 , 17] , may dissuade majority from accepting the EVV with resultant low uptake of the vaccine , through propaganda [18 , 19] . Therefore , health program managers have to be proactive in identify early factors that can either facilitate or militate against the effective implementation of EVV program . Issues such as: the acceptance of the vaccine , making the decision to be vaccinated [20 , 21] and the willingness-to-pay ( WTP ) for EVV if not publicly funded need to be sorted out . The issue of willingness-to-pay for a newly introduced vaccine is paramount in Nigeria , since some vaccines that have previously been approved for public use are yet to be introduced in the National Programme on Immunization which is funded by the government . Therefore the aim of this study in Nigeria ( West Africa ) is to determine the public acceptability and willingness-to-pay for EVV . The outcome of this study will contribute to the strategic plan for a successful EVV implementation .
The study was conducted in two sites: an EBV low risk community ( Umuahia ) , Abia State , Southeast and EBV high-risk community ( Ajah ) in Lagos State , Southwest , of Nigeria . The study took place from August to September 2014 during the period of Ebola outbreak in Nigeria , and data collection was completed before the Monday 20th October 2014 when the World Health Organization ( WHO ) certified Nigeria free of EBV . The distance between the two communities is about 600 kilometers [22] ( Fig 1 ) . The first case of EVD in Nigeria was reported in Lagos in Ikoyi-Obalende Local Council Development Area ( LCDA ) about 22 kilometers from Ilaje community in Eti-Osa East LCDA , both administrative areas within Eti-Osa Local Government Area ( LGA ) . By the time the WHO certified Nigeria free of EBV a total of 20 cases were reported out of which 8 deaths occurred . Among the reported cases and deaths , 19 cases occurred in Lagos State , out of which 7 deaths were recorded . Throughout the period , there was no report of EVD in Abia State . Umuahia-North and Eti-Osa LGAs have populations of 359 , 230 [23] and 983 , 515 [24] respectively . The population density of Umuahia and Eti-Osa LGAs were 450 persons/km2 and 20 , 000 persons /km2 respectively . Ugba ward is one of the 12 wards in Umuahia-North and Ilaje is a community within Ward A of Eti-Osa East LCDA , one of the four council’s areas controlled by Eti-Osa LGA . The two communities are mixtures of both urban and rural areas . It was a community-based cross-sectional qualitative and quantitative interventional study conducted in two communities , each in two states in Nigeria . A stratified random sampling was used to select Ugba and Ilaje wards from a sample frame of 12 and 20 wards from Umuahia-North and Eti-Osa LGA respectively . A systematic random sampling was used to select the households from the house numbering done by the National Primary Health Care Development Agency . Households were selected , beginning with a house randomly selected and subsequent sampling was in alternate of four houses until the stipulated number was obtained . The minimum sample size of 260 for each study site was calculated using a power of 80% , 95% confidence level and based on the vaccine acceptability rate of 81 . 3% as reported by Williams et al [25] . The household heads participated in the study; if the head of the household was not around during the visit , the spouse was interviewed . The Health Research and Ethics Committee of the University of Nigeria Teaching Hospital ( UNTH ) Enugu , gave ethical approval for this study . The committee approved the use of only verbal consent from each respondent , the reason was to reduce contact between the researchers and their multiple respondents which would occur through exchange of writing materials . This was precautionary due to the EBV threat during the period of the study . Although a prior information sheet was given to the identified households seeking their consent to be part of the study , there was no signing of the counterpart consent sheet attached to the questionnaire . The questionnaire was pre-tested in a community that was not involved in the final study . Few questions were modified to clear ambiguity and some translated words were changed to convey appropriate meaning . Also provisions were made to record comments made by the respondents , since it was realized that there were a lot of valuable information that were not originally included in the initial questionnaire design . The pre-tested interviewer-administered questionnaire was used to collect information on socio-demographic characteristics , respondent’s knowledge on EVD , preventive practices , attitude towards EVV , their knowledge and acceptability of EVV , and their WTP for EVV from the head of the family ( S1 Text; sample of the questionnaire ) . Also information on the medium through which they first heard about Ebola virus disease outbreak was collected . One respondent per household , was interviewed . The preferred respondent was the head of the family , and in a situation where the father/husband was not available , his spouse was interviewed . If neither the head of the house nor the spouse was available to be interviewed , a second visit was rescheduled . The respondents’ acceptability and WTP were assessed pre and post health education on Ebola virus and its potential vaccine . They were informed that EVD is caused by Ebola virus ( EBV ) which is highly infectious and is transmitted through human-to-human contact [1 , 2] . That the virus has a very short incubation period and a victim manifests the disease within a very short time of exposure to the virus . The illness has 70% case fatality rate [3] and for a person to be protected by the vaccine , he/she has to receive the vaccine before exposure or not later than five days from the time of exposure . The EVV will be neither an inactivated vaccine which has been found to be unsuccessful with Ebola virus , nor live-attenuated vaccine which is generally considered too dangerous in the case of Ebola virus . The EVV will be a viral-vector-based recombinant vaccine in which genes encoding protein of Ebola virus will be inserted into the genome of another virus ( not Ebola virus ) , a recombinant replication-deficient adenovirus ( Ads ) or attenuated vesicular stomatitis viruses ( VSVs ) , which are known to cause no serious side effects or disease in human [26] The Ebola virus genes encoded proteins are recognized by the immune system and stimulate immune response against the disease but do not cause Ebola virus disease . The inequality in WTP for EVV was done using the SES of the households [27 , 28] which was generated based on functional household asset they owned [29 , 30] . The household expenditure consumables were used to estimate the household income . The Principal Component Analysis ( PCA ) was used to create a continuous SES quartiles based on household asset owned and expenditure on food [31] . It is easier to elicit monetary information , like income [32 , 33] with this approach . The estimation was in Nigerian naira ( N ) and converted at the rate of 1 United States dollars ( USD ) to N170 . 00 . Respondents were asked to recall the first medium through which they got to know about EVD , and multiple options were not allowed . This was to determine the effective channel of disseminating information to the wider population . They were also asked to state their perception of the severity of the disease when they first heard about of EVD and this was to indirectly assess the content and impact of the first information on the public awareness on the disease . Respondents were asked questions to elicit their knowledge on ways EBV could be contracted , ways to prevent Ebola virus infection , and their preventive practices . They were also tested on their awareness of any vaccine against EVD . The respondents were allowed to give their reply to the questions without pre-empting them with answer options . There were five possible correct preventive measures , as well as preventive practices [34] . A normative value of 1 or 0 was given for correct and wrong responses respectively for the questions . For their knowledge on how EBV could be prevented , a score of 1 was given for any correct response . The lowest and highest possible scores for preventive knowledge were 0 and 5 respectively . The respective total scores were grouped into “very adequate” if 5 , “adequate” if 3–4 and “inadequate” if 0–2 . Adequacy means that the respondent scored at least three on knowledge of prevention of Ebola virus infection . On the evaluation of practice , personal hygiene which has two major components was assessed . Nigeria had only 20 cases of EBV and 8 deaths . Therefore most of the respondents had neither seen a person suffering from EVD nor seen someone die from it . The respondents were asked whether they knew of any EVV . Those that responded stated that: a ) there was a vaccine but not approved for use or any related response , were categorized as “correct” and those that stated that: b ) there was a vaccine available to combat EBV , or any related response were categorized as “wrong” . To assess the respondents’ acceptability of EVV , a hypothetical cAd3 [17] was described to them . They were informed that the vaccine would be safe and may cause little or no adverse events and lacks the potential of causing the disease . Their willingness to be vaccinated was elicited . Those that accepted to be vaccinated were asked their preferred time to receive the vaccine . A 5-point Likert scale scoring system ranging from 1 = “very unwilling” , 2 = “unwilling” , 3 = “not sure’ , 4 = “willing” and 5 = “very willing” was used to rate their level of acceptability of EVV . The respondents who replied either “1’ , ”2” or “3” were categorized as unwilling , while those whose responses were either “4” or “5” were grouped as willing . The WTP for the hypothetical EVV was evaluated only among those who accepted to be vaccinated . Their WTP for the vaccine was determined using the contingent valuation method . The highest amount that they were willing to pay for the vaccine was sought after . Since the vaccine is yet to be deployed to the market , no market price is yet available . The contingent valuation method ( CVM ) , is a survey-based approach to elicit monetary valuation of products of healthcare [35 , 36] by individuals’ using bidding game approach ( BGM ) . This is best suited for exploring individual preferences for goods and services with no known market price , as in this case where there is no market price for EVV . The respondents were presented with a scenario describing the hypothetical EVV , as an effective injectable vaccine , with no risk of getting infected with Ebola virus through the vaccine and should be given preferably before exposure to Ebola virus or at most within five days of exposure . Since no market price was available for the vaccine , the respondents were asked an open ended question: “How much will you be willing to pay for the Ebola virus vaccine ? ” After the initial response , they were allowed one more option to either increase or reduce the initially stated amount by a second question: “If due to inflation or other uncertainties , the cost for the vaccine is higher than what you have just stated , what is the maximum amount you are very certain to pay bearing in mind that your entire household ( both adults and children ) may have to receive the vaccine at about the same period ? ” The effect of cost of vaccine on acceptance rate was elicited by comparing the willingness to vaccinate before knowledge on cost and thereafter . Any other important comments made were documented and analyzed as direct speech . Certain variables were dichotomized into binary variables: educational status into primary education or less and secondary education and above , acceptability into before and after knowledge on WTP . The households were categorized into those with household size of 1–3 and those of 4 and above . The two major components of practice care hygiene , were each scored 0 . 5 and a total score of 1 = adequacy and 0 . 5 = inadequacy . Association of other variables with acceptances of EVV was tested using univariate and multivariate analysis . Two-by-two table was created to test for statistical significance and p-value of <0 . 05 was taken to be statistically significant . The continuous socio-economic status ( SES ) index was generated using Principal components analysis ( PCA ) based on combination of household assets owned and mean weekly expenditure on food items . The SES index was categorized into four equal quartiles ( Q ) and these four groups were: the poorest ( Q1 ) , very poor ( Q2 ) , poor ( Q3 ) , and least poor ( Q4 ) . The correlation between SES and WTP was measured using SES groups .
The media through which most of the respondents first learnt about EVD were television ( 32 . 4% , 178/582 ) and radio ( 27 . 1% , 150/582 ) . No one heard it from either church/mosque ( 0 . 0% ) or hospital ( 0 . 0% ) ( Fig 3 ) . Ninety five percent of respondents stated that EBV can be transmitted by contact , Table 1 . Majority ( 73% , 425/582 ) mentioned washing of hands as a preventive measure . Only 38 . 5% ( 224/582 ) appreciated the seriousness of the disease when they heard of the EVD for the first time . Hand washing ( 66 . 7% , 388/582 ) was the commonly adopted preventive measure while 12 . 3% ( 72/582 ) took no precautions . The respondents in Ilaje and Ugba that acknowledged that there was an EVV were 2 . 7% ( 8/293 ) and 41 . 0% ( 119/289 ) respectively ( p = 0 . 0001 ) ( Table 2 ) . Prior to health information on EVV , 80 . 0% ( 234/293 ) and 79 . 5% ( 230/289 ) of the respondents in Ilaje and Ugba respectively would accept EVV ( p = 0 . 93 ) . After information on EVV , 86 . 3% ( 253/293 ) ( Ilaje ) and 82 . 1% ( 237/289 ) ( Ugba ) were willing to be vaccinated . Among those that accepted to vaccinate once EVV is available in Ilaje and Ugba were 79 . 8% ( 202/253 ) and 47 . 7% ( 113/237 ) respectively , while 12 . 2% ( 31/253 ) and 51 . 5% ( 122/237 ) in Ilaje and Ugba would like to receive the vaccine later , after observing the effect on those that received it ( p = 0 . 0001 ) . The respondents that were unwilling to vaccinate in Ilaje and Ugba were 6 . 9% ( 16/233 ) and 37 . 8% ( 90/237 ) respectively ( p = 0 . 0001 ) . Among those willing to vaccinate , 91 . 2% ( 212/233 ) and 87 . 5% ( 207/237 ) in Ilaje and Ugba respectively were willing to pay for EVV ( p = 0 . 2 ) . The very poor and the least poor were willing to pay the least amount of money for the vaccine , while the poorest and the poor were willing to pay a higher amount for the EVV . Households with household size of 4–5 in number were willing to pay the highest amount of money for the EVV ( S2 Table ) . Some of the respondents who were not willing to pay for the EVV as well as some of those who were willing were of the opinion that government should provide the vaccine at no cost to the recipients ( commented by 55 . 2% ) . Respondents stated that “Government should pay for it and make it free . ” “It is among the duties of the government to protect the citizens and providing this vaccine should be one way to do that” . “I don’t think it is right to expect people to pay for a vaccine that will protect them from a disease they do not have any control on how it can be contracted . ” Others suggested that government should coerce people to receive the vaccine . “EBV disease is a public health problem” and “Government should persuade everybody to receive the vaccine , and the only way they can do that is to provide it free of charge” . “It should be made compulsory and enforced . If it would be enforced , you cannot ask people to pay for it . ” Other common suggestions on how to avoid or minimize out-of pocket payment by the people ( reported by 21 . 0% ) were: “National Health Insurance Scheme ( NHIS ) should cover the cost . ” “The vaccine bill should be incorporated into the costs of GSM phone bills… . ” Univariate and multivariate analyses demonstrated that educational status strongly correlated with acceptance of EVV ( Table 3 ) . The lower the educational status , the more likely they are to accept the vaccine ( 95% CI: 0 . 20–0 . 74 , p-value = 0 . 001 ) . It was also shown that giving health education on EVV improved its acceptability and the difference found to be statistically significant ( 95% CI: 0 . 54–1 . 01 , p-value = 0 . 046 )
The level of acceptability of Ebola virus vaccine among respondents was high , although majority confirmed that they would not hastily receive the vaccine until they observe the effect on others . Nonetheless , they was willingness to pay for the vaccine whenever they are to receive it should the vaccine not be publicly funded . However , it is recommended that for high uptake to be achieved , the vaccine introduction should be preceded with wide public health education , counselling and persuasion , and government should endeavor to provide the vaccine free of charge . | Ebola virus disease ( EVD ) is highly virulent and transmissible . The transmission is mostly by direct contact with an infected person or indirectly through contact with material contaminated with the secretions or body fluids of an infected person . Currently there is no vaccine or drug for EVD . Maintaining good personal and environmental hygiene remains the only control strategy , and its implementation was a challenge in West Africa countries . Ebola virus vaccine ( EVV ) is being developed and may soon be deployed; thus a need to evaluate factors that can improve or discourage the uptake of the vaccine when it becomes approved for public administration . This study highlights the acceptability and willingness-to-pay for EVV . Majority of the respondents were willing to accept the vaccine and pay for it if it is not publicly funded . Of interest was that among those that accepted to be vaccinated , most would only accept to do so after they had observed the outcome on others that had received the vaccine . There is need for early dissemination of correct information and education on EVV to the populace so as to prevent any misinformation and misperception about the vaccine . This will improve universal coverage with the vaccine when deployed . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Acceptability and Willingness-to-Pay for a Hypothetical Ebola Virus Vaccine in Nigeria |
Bacteria use quorum sensing ( QS ) for cell-cell communication to carry out group behaviors . This intercellular signaling process relies on cell density-dependent production and detection of chemical signals called autoinducers ( AIs ) . Vibrio cholerae , the causative agent of cholera , detects two AIs , CAI-1 and AI-2 , with two histidine kinases , CqsS and LuxQ , respectively , to control biofilm formation and virulence factor production . At low cell density , these two signal receptors function in parallel to activate the key regulator LuxO , which is essential for virulence of this pathogen . At high cell density , binding of AIs to their respective receptors leads to deactivation of LuxO and repression of virulence factor production . However , mutants lacking CqsS and LuxQ maintain a normal LuxO activation level and remain virulent , suggesting that LuxO is activated by additional , unidentified signaling pathways . Here we show that two other histidine kinases , CqsR ( formerly known as VC1831 ) and VpsS , act upstream in the central QS circuit of V . cholerae to activate LuxO . V . cholerae strains expressing any one of these four receptors are QS proficient and capable of colonizing animal hosts . In contrast , mutants lacking all four receptors are phenotypically identical to LuxO-defective mutants . Importantly , these four functionally redundant receptors act together to prevent premature induction of a QS response caused by signal perturbations . We suggest that the V . cholerae QS circuit is composed of quadruple sensory inputs and has evolved to be refractory to sporadic AI level perturbations .
Bacteria produce and detect multiple classes of chemical signals called autoinducers to monitor local population density and species complexity . This cell-to-cell communication process , called Quorum Sensing ( QS ) , allows groups of bacteria to synchronize population-wide gene expression and effectively carry out collective behaviors that are presumably ineffective if performed by a single bacterial cell acting alone . Disruption of the QS signal transduction cascade leads to uncoordinated gene expression and renders many pathogenic bacteria avirulent [1–5] . Vibrio cholerae , the etiological agent of the diarrheal disease cholera , uses QS to regulate virulence factor production , biofilm formation , Type VI secretion , and competence development , all of which are important for survival and adaptation inside and outside of its human hosts [6–17] . Two parallel QS signaling systems that function via phosphorelay-type regulatory pathways have been identified in V . cholerae [6] . The CqsA/CqsS system , which produces and detects CAI-1 ( S-3-hydroxytridecan-4-one ) as a QS signal , is present in many Vibrio species and is believed to be used for intra-genus communication [18–23] . The LuxS/LuxPQ system , which produces and detects AI-2 ( S-TMHF-borate ) as a QS signal , is present in many bacterial species and is believed to be used for inter-species signaling [6 , 24–27] . In environments where the concentrations of these two autoinducers are below their detection threshold , such as at low cell density ( LCD ) , CqsS and LuxQ function as kinases . They hydrolyze ATP and shuttle the phosphoryl group , via a histidine-phosphotransfer protein LuxU , to the key response regulator , LuxO . Phosphorylated LuxO ( LuxO~P ) activates transcription of the genes encoding four regulatory sRNAs , called Qrr1-4 [28] . Aided by the RNA chaperone Hfq , Qrr1-4 activate the translation of the AphA regulator and inhibit the translation of the HapR regulator ( Fig 1A ) [6 , 28–30] . At high cell density ( HCD ) , when autoinducers accumulate to high levels , the kinase activities of CqsS and LuxQ are inhibited by binding of their cognate signals . As a consequence , phosphate flow is reversed , leading to dephosphorylation and deactivation of LuxO . Transcription of qrr1-4 terminates and , hence , HapR , but not AphA , is produced ( Fig 1B ) [6 , 28–30] . Reciprocal production of AphA and HapR at LCD and HCD is central to the switch from individual to group behaviors in Vibrio species [29] . Together these two transcriptional regulators regulate the expression levels of over 100 genes [7 , 29] . Although CqsS and LuxQ both contribute to LuxO activation ( Fig 1 ) , strikingly , mutants missing both receptors are phenotypically identical to the wild-type and remain virulent [6] . Thus , additional unknown signaling pathways are predicted to activate LuxO [6 , 31] . A clue to the identity of a potential LuxO-activation pathway came from a study in which overexpression of a hybrid histidine kinase VpsS leads to a LuxO-dependent up-regulation of the biofilm biosynthetic gene vpsL in V . cholerae , suggesting that VpsS could regulate LuxO activity to control biofilm formation [32] . Moreover , the purified receiver domains of VpsS and another hybrid histidine kinase VC1831 are capable of effectively accepting the phosphoryl group from phosphorylated LuxU in vitro [32] . However , it is unclear if VpsS and VC1831 , together with CqsS and LuxQ , function as phosphoryl group donors to activate LuxO and control QS inside V . cholerae cells . Furthermore , it is also unknown if , and to what extent , each of these four histidine kinase receptors is individually contributing to the global control of the QS response in V . cholerae . The role of VpsS and VC1831 in V . cholerae pathogenesis also has not been investigated . Here we report the connections between VpsS and CqsR and QS in V . cholerae ( VC1831 is renamed as CqsR hereafter based on its role as Cholera Quorum Sensing Receptor ) . VpsS and CqsR function in parallel with CqsS and LuxQ and act upstream of LuxO in the V . cholerae QS signal transduction pathway . Indeed V . cholerae is capable of QS when any single one of these four receptors is present . Furthermore , in addition to CAI-1 and AI-2 , additional stimuli whose levels presumably vary depending on cell density , are perceived by VpsS and CqsR to modulate QS . Finally , multiple functionally redundant receptors that control a signal regulator ( i . e . , LuxO ) enable a QS response that is insensitive to perturbations in the cognate sensory cues .
Previous studies established that LuxO , the key response regulator in the V . cholerae QS system , is activated by phosphorylation at the conserved Asp61 by CqsS and LuxQ at LCD ( Fig 1 ) [33] . V . cholerae mutants lacking LuxO are unable to express Qrr1-4 sRNAs; as a result , they fail to express AphA and instead produce HapR at all population densities [6 , 28] . Therefore , ΔluxO mutants are highly attenuated in colonization of animal hosts [6 , 7] . Surprisingly , V . cholerae mutants lacking both CqsS and LuxQ are phenotypically identical to the wild-type and colonize animal hosts effectively . Thus , LuxO appears to be activated by additional mechanisms [6] . Alternatively , these results could be interpreted to mean that unphosphorylated LuxO , but not phosphorylated LuxO , is required for host colonization and there is no additional source of activation . If the former model is correct , a V . cholerae luxOD61A mutant , expressing a form of LuxO that is incapable of being phosphorylated , should be defective in colonizing animal hosts . Indeed , both ΔluxO and luxOD61A mutant cells were out-competed by the wild-type in an infant mouse colonization model , however , there was a 10-fold difference in the CIs observed for these two mutants . In many cases , we did not detect any ΔluxO and luxOD61A mutants inside the animal hosts ( Fig 2 ) . Furthermore , V . cholerae cells lacking all 4 Qrr sRNAs , the only known targets of activated LuxO , were defective in host colonization ( Fig 2 ) . Together , these in vivo data indicate that phosphorylated LuxO and the downstream Qrr sRNAs are required for V . cholerae host colonization , and further suggest that phosphorylation by CqsS and LuxQ are not the only sources of LuxO activation . To further explore the pathway for LuxO activation , we focused on the protein that interacts with LuxO in the V . cholerae QS circuit . Only a single histidine phosphotransfer ( HPT ) protein , LuxU , is known to interact with LuxO and link to LuxO activation ( [6] , Fig 1 ) . Unexpectedly , V . cholerae mutants lacking LuxU were shown to be active in QS gene regulation and colonize animal hosts effectively [6] . These findings seem to contradict the apparent role of active LuxO in V . cholerae pathogenicity regulation ( Fig 2 ) . Alternatively , LuxO could be activated by interacting with other HPT proteins . However , LuxU was found to be required for V . cholerae virulence in two independent genome-scale transposon mutant analyses [34 , 35] . To resolve these conflicting results , we revisited the role of LuxU in V . cholerae QS control and pathogenicity regulation . We constructed a new ΔluxU mutant ( WN3557 ) and compared its QS response to that of the ΔluxU mutant ( WN3045 ) previously reported [6] . We used the heterologous Vibrio harveyi luxCDABE luciferase operon to measure QS-dependent gene regulation , because expression of this operon is activated by HapR , whose level is inversely proportional to the amount of activated LuxO inside the cell ( [6] , Fig 1 ) . Therefore , if LuxU is the major HPT protein that is essential for LuxO activation , mutants lacking LuxU would express a high level of luciferase and be bright . We found that bioluminescence production ( shown as specific light production versus cell density ) was very different between these two ΔluxU strains . The newly-constructed ΔluxU mutant cells were constitutively bright , indicating that LuxO is inactive and that HapR is constantly produced at all population densities ( Fig 3A ) . In contrast , the original ΔluxU mutant cells were 10- to 100-fold darker , depending on the cell density at which they were sampled , indicating that less HapR is produced in the original ΔluxU mutant ( Fig 3A ) . To understand these differences , we sequenced the luxOU locus of these ΔluxU strains . Using published V . cholerae genome sequences as a reference , we identified a missense mutation in luxO of the original ΔluxU strain , resulting in a change from glycine to serine at amino acid residue 333 of LuxO . In contrast , no mutation was identified in the luxOU locus of the new ΔluxU strain . To explain the difference in phenotype between the two strains , we hypothesized that LuxOG333S mimics the active form of LuxO such that it does not require LuxU for activation . To test this hypothesis , plasmids expressing luxO+ or luxOG333S were introduced into the new ΔluxU strain , and HapR-dependent bioluminescence from the resulting strains was measured . We found that extra copies of luxO+ did not alter the specific bioluminescence production of the new ΔluxU mutant and the strain remained constitutively bright , similar to the empty plasmid control ( Fig 3B ) . However , when luxOG333S was expressed in the new ΔluxU mutant , the resulting strain was >50-fold darker than the other two strains ( Fig 3B ) . These results indicate that the luxOG333S mutation is dominant to the luxO+ allele and is epistatic to the ΔluxU mutation . Although the activation mechanism is unclear , we suggest that LuxOG333S mimics an active form of LuxO , bypassing the requirement of LuxU in QS signal transduction in the original ΔluxU strain . Consistent with the idea that LuxU is the key HPT protein in QS control , the new ΔluxU mutant cells were out-competed by the wild-type in the infant mouse colonization model , while the original ΔluxU cells were not ( Fig 3C ) . After confirming the importance of LuxU in LuxO activation , we hypothesized that , similar to CqsS and LuxQ , histidine kinases that employ LuxU as an intermediate phosphorelay partner are able to activate LuxO . The isolated receiver domains of two hybrid histidine kinases , VpsS and CqsR ( VC1831 ) , are capable of interacting with and effectively removing the phosphoryl group from phosphorylated LuxU in vitro [32] . Thus , we reasoned that full length VpsS and CqsR , when active , could phosphorylate LuxO via LuxU and contribute to its activation in V . cholerae cells . To explore these ideas , we measured HapR-dependent bioluminescence in mutants missing one or more of these histidine kinases . As previously shown [6] , wild-type V . cholerae cells produce a U-shaped bioluminescence profile , representing the change in LuxO activity and HapR levels at different cell-densities ( Fig 4A ) . At HCD ( OD600 >1 ) , V . cholerae produced a high level of HapR-dependent bioluminescence , indicating that LuxO activity is low in this condition ( Fig 4A ) . When these HCD cells were diluted in fresh medium , specific luciferase activity was high initially since the enzyme had not been turned over from the overnight culture . However , when these diluted cells started to grow , HapR-dependent bioluminescence decreased due to activation of LuxO and repression of HapR production . Light production reached a minimum at OD600 ~0 . 5 . Afterwards , HapR-dependent bioluminescence increased and reached a maximum at OD600 >1 ( Fig 4A ) . Using the same assay , we found that cells missing both CqsS and LuxQ displayed a HapR-dependent bioluminescence profile indistinguishable from that of the wild-type , indicating that LuxO activation is still controlled by a cell density-dependent mechanism in the absence of these two known QS receptors ( Fig 4A ) . Likewise , V . cholerae cells missing both VpsS and CqsR also displayed a HapR-dependent bioluminescence profile similar to that of the wild-type and the ΔcqsS ΔluxQ double QS receptor mutant ( Fig 4A ) . We constructed four different triple receptor mutants expressing only one of the four possible QS receptors and found that their HapR-dependent bioluminescence profiles were different from each other and from the wild-type ( Fig 4B ) . Although each triple receptor mutant still displayed a U-shaped HapR-dependent bioluminescence profile , switching from low to high light production from LCD to HCD , these mutants all produced more light than the wild-type at LCD ( Fig 4B ) . Mutant cells with only LuxQ showed the largest difference ( ~100-fold ) in relative light production between LCD and HCD , while mutant cells with only CqsR showed the smallest difference ( ~10-fold ) . The temporal dynamics of the response in each triple receptor mutant were also distinct . Mutant cells expressing only CqsS switched from low to high light production at the lowest cell density ( OD600 ~0 . 05 ) , while the other three mutant strains switched at around the same cell density ( OD600 ~0 . 5 ) ( Fig 4B ) . Thus , our results indicate that CqsS , LuxQ , VpsS , and CqsR can each independently activate LuxO , but the influence of each receptor on the overall QS response is not identical . We then measured the HapR-dependent bioluminescence in a ΔluxQ ΔcqsS ΔvpsS ΔcqsR quadruple receptor mutant and found that the profile was identical to the new ΔluxU mutant ( Figs 3A and 4B ) , indicating that very little , if any , active LuxO is present . To determine if VpsS and CqsR both act upstream to activate LuxO , we introduced the luxOD61E mutation , which renders LuxO constitutively active by mimicking the phosphorylated form of the protein [8 , 36] , into the quadruple receptor mutant . We predicted that the luxOD61E allele would override the effect of the loss of all four histidine kinases . Indeed , we found that the ΔluxQ ΔcqsS ΔvpsS ΔcqsR luxOD61E strain was constitutively dark , similar to the luxOD61E mutant ( S1 Fig ) . Likewise , LuxO activity could be partially restored when cqsS , luxQ , vpsS , or cqsR was individually overexpressed episomally in the quadruple receptor mutant ( S2 Fig ) . Additionally , we used a qrr4-lux transcriptional fusion to study the contribution of CqsS , LuxQ , VpsS , and CqsR and found that each receptor alone was sufficient to support Qrr4 expression to different degrees at LCD ( S3 Fig ) , while the quadruple receptor , ΔluxO , and new ΔluxU mutants all expressed very little , if any , Qrr4 . As expected , the luxOD61E mutation restored Qrr4 expression in the quadruple receptor mutant ( S3 Fig ) . Previously , CsrA was proposed to enhance LuxO~P activity [31] . VpsS and CqsR could exert their regulatory effects on LuxO by modulating the activity of CsrA . However , a csrA::Tn5 insertion mutation that had been identified before was not sufficient to abolish Qrr4 production in the ΔcqsS ΔluxQ mutant ( S4 Fig ) , arguing against the possibility that VpsS and CqsR signal through CsrA . The above studies show that CqsS , LuxQ , VpsS , and CqsR each independently contributes to part of the QS response in V . cholerae growing under laboratory conditions . To determine the minimal requirement of LuxO activation through these histidine kinases that is sufficient for V . cholerae infection of animal hosts , we tested double , triple , and quadruple receptor mutants using an infant mouse colonization model . We found that the two double receptor mutants ( ΔluxQ ΔcqsS and ΔvpsS ΔcqsR ) and the four triple receptor mutants all colonized the small intestine effectively ( Fig 4C ) . In contrast , the quadruple receptor mutant was highly defective in animal colonization ( Fig 4C ) . While a slight advantage in host colonization ( ~2 fold ) was observed for the luxOD61E mutants in the wild-type genetic background , the luxOD61E mutation was epistatic to the ΔluxQ ΔcqsS ΔvpsS ΔcqsR mutations and restored the colonization defects ( >10 , 000-fold ) of the quadruple receptor mutants ( Fig 4C ) . Thus , even though these four receptors contribute to the control of the V . cholerae QS response to different extents under laboratory conditions , any one of the receptors appears to be sufficient to promote LuxO activation enough to support colonization of mice . It is curious that V . cholerae integrates four parallel sensory inputs to activate a common response regulator LuxO , even though a single receptor is sufficient for a QS response ( Fig 4B and 4C ) . We hypothesized that by integrating multiple signals , LuxO activation and the downstream QS response is less sensitive to perturbations from any one of the sensory inputs . To test this idea , we first determined that 2μM of synthetic CAI-1 was sufficient to induce a premature QS response in the triple receptor mutant expressing only CqsS ( S5 Fig ) . Then , we measured HapR-dependent bioluminescence in the presence of surplus CAI-1 ( 20 μM ) in the wild-type and different receptor mutants . Consistent with our prediction , we found that extra CAI-1 did not significantly alter the HapR-dependent bioluminescence profiles of the wild-type or any single receptor mutant missing LuxQ or VpsS or CqsR ( Fig 5A–5D ) . We likewise found that extra CAI-1 did not significantly increase light production in strains expressing CqsS and LuxQ ( ΔvpsS ΔcqsR ) ( Fig 5E ) , but addition of CAI-1 slightly , yet reproducibly , increased light production in strains expressing CqsS and VpsS ( ΔluxQ ΔcqsR ) ( Fig 5F ) , indicating that inhibition of CqsS kinase activity is compensated for by LuxQ and partially by VpsS ( Fig 5E and 5F ) . In contrast , surplus CAI-1 caused the strains expressing CqsS and CqsR ( ΔluxQ ΔvpsS ) to produce light constitutively , indicating that CqsR is not sufficient to compensate for the loss of CqsS kinase activity ( Fig 5G ) . Finally , as expected , strains expressing CqsS alone ( ΔluxQ ΔcqsR ΔvpsS ) constantly produced light in the presence of surplus CAI-1 , as no compensating kinase activity is present ( Fig 5H ) . Thus , functionally redundant receptors , particularly LuxQ and VpsS , render V . cholerae cells insensitive to surplus CAI-1 . These combined results are consistent with the idea that multiple parallel sensory inputs controlling a single LuxO protein are important for resisting perturbations in signal inputs to maintain the robustness of the V . cholerae QS system . To achieve QS regulation , the activities of VpsS and CqsR must be controlled by a cell density dependent mechanism . We reasoned that , similar to CqsS and LuxPQ , the autokinase activities of VpsS and CqsR could both be inhibited by binding to specific molecules that accumulate during cell growth ( Fig 1 ) . Therefore , we studied the effects of addition of cell-free spent medium harvested from V . cholerae HCD cultures on Qrr sRNA expression in the two triple receptor mutants expressing either VpsS or CqsR with a qrr4-lux reporter . To ensure that any observed regulatory effect from the spent medium was not due to nutrient deprivation after bacterial growth , we replenished any missing ingredients by reconditioning the spent medium ( 80% v/v ) with 20% ( v/v ) of 5× LB . As expected , when the strains were grown in fresh medium , Qrr4 expression levels were high at LCD and low at HCD ( Fig 6A–6D ) . In contrast , addition of reconditioned spent culture medium decreased LCD Qrr4 production in both strains ( Fig 6A and 6B ) . Qrr4 expression was also repressed by reconditioned spent culture medium harvested from a ΔcqsA ΔluxS double synthase mutant that cannot make CAI-1 and AI-2 ( Fig 6C and 6D ) , indicating that the signals sensed by VpsS and CqsR are different from the two canonical autoinducers . Addition of reconditioned spent culture medium did not alter the growth rates of these two strains ( S6 Fig ) . Moreover , reconditioned spent medium harvested from LCD ( OD600 ~ 0 . 5 ) V . cholerae did not alter Qrr4 expression in these two strains ( S7 Fig ) . These combined results suggest that additional molecules other than CAI-1 and AI-2 are made and secreted by V . cholerae to regulate VpsS and CqsR kinase activities and ultimately control its QS response .
In the current study , we show that the in vitro and in vivo behaviors of the ΔluxU mutant are essentially identical to the ΔluxO mutant , therefore suggesting that all the LuxO-activation inputs , including VpsS and CqsR , must shuttle through LuxU to activate LuxO . Indeed , if VpsS and CqsR do not signal through LuxU to activate LuxO , mutants lacking LuxU would behave like mutants lacking CqsS and LuxQ . However , we demonstrate in multiple assays that this was not the case . Therefore , we propose that LuxO , the key QS regulator , is activated by four independent histidine kinase receptors CqsS , LuxQ , VpsS , and CqsR through HPT protein LuxU to control the QS response in V . cholerae ( Fig 1 ) . Our new model provides additional insights into the V . cholerae QS signal transduction pathway and explains why V . cholerae mutants missing the canonical QS receptors CqsS and LuxQ remain proficient in controlling cell density-dependent genes [6] . The influence on LuxO activation of each of the four histidine kinases is not identical; LuxQ is the strongest and CqsR is the weakest activator of LuxO ( Fig 4B ) . Similarly , Yildiz and coworkers previously showed that overexpression of LuxQ and VpsS , but not CqsR and CqsS , increases vpsL expression and biofilm formation through a LuxO-dependent mechanism [32] . Surprisingly , our results are in contrast to previous studies of autoinducer synthase mutants in which the QS response is affected more by a ΔcqsA mutation than a ΔluxS mutation , arguing that CqsS has a stronger impact than LuxQ on V . cholerae QS [6 , 36] . However , it should be noted that our current study was performed under conditions in which autoinducers are produced by V . cholerae cells at their native levels . Thus , the accumulation rate of each cognate signal in the culture and the signal sensitivity of each receptor could influence the contribution of each receptor to QS control . Unlike the case in laboratory cultures , CqsS , LuxQ , CqsR , or VpsS alone is sufficient to activate LuxO enough for V . cholerae to effectively colonize the mouse small intestine ( Fig 4C ) . That is , the loss of three histidine kinase activities has little effect on V . cholerae colonization of animal hosts , a trait that is strongly dependent on LuxO activation . Perhaps the overall kinase activities of these receptors are substantially stronger in V . cholerae growing inside an animal than in bacteria growing under laboratory conditions . Additionally , the level of the cognate signals for these receptors could be altered in the host environment such that each receptor maintains a longer period of activation . Alternatively , the level of LuxO activation required to repress HapR-dependent bioluminescence and activate biofilm formation under laboratory conditions is higher than the level required for expression of virulence genes in animal hosts , and that could also explain the difference observed between the in vitro and in vivo phenotypes . It is interesting that differential contribution from multiple receptor inputs is observed in other microbial signaling pathways . For instance , the sporulation pathway of Bacillus subtilis is controlled by five histidine kinases , KinA-E [37–40] . These five receptors participate in the phosphorylation of Spo0F , which in turn activates the key response regulator Spo0A . Although all of these kinases can activate Spo0A , only KinA and KinB can activate Spo0A to a level high enough to trigger sporulation , while KinC and KinD kinases are only capable of initiating entry into stationary phase [40 , 41] . Thus , this “many-to-one” configuration has evolved independently in multiple bacterial signal transduction pathways to maintain a specific input-output relationship depending on particular environmental parameters [42] . It is not uncommon for bacterial species to possess multiple QS systems for cell-cell communication . These systems can be wired in different configurations to accomplish specific biological goals [43 , 44] . We show here that by using four different receptors in parallel to control the overall QS response , the V . cholerae QS circuit is built to resist perturbations in external conditions ( Fig 5 ) . This circuit architecture could be especially important to maintain synchronous expression of QS genes in the population , and to prevent premature commitment to HCD gene expression . This set up could also be useful for filtering out signal noise caused by analogous molecules present in the environment . It should be noted that a high level of CAI-1 was tested for the sensitivity of the system and V . cholerae likely will not encounter CAI-1 alone without other autoinducers . However , previous studies showed that molecules with structures drastically different from CAI-1 could inhibit CqsS activity [45] , suggesting possibilities for decoy molecules acting alone on a single QS receptor . Such circuitry has been proposed to function as a “coincidence detector” in other QS systems [18 , 46] . However , whether the V . cholerae QS circuit is used for coincidence detection requires further investigation . Indeed , we suspect that not all genes in the V . cholerae QS regulon display the same regulatory pattern as the HapR-dependent bioluminescence operon , and we predict that a subset of V . cholerae QS genes could be more sensitive to perturbations . For instance , it has been shown that addition of CAI-1 alone is able to resuscitate viable but non-culturable ( VNBC ) V . cholerae [47] . Although these VNBC cells are physiologically distinct from cells cultured under laboratory conditions , these results suggest that a single autoinducer input can trigger differential gene expression in certain V . cholerae cell types . The other advantage of using multiple sensory systems is to allow QS bacteria to decipher distinctive information contained within each specific signal . For instance , V . harveyi detects three autoinducers HAI-1 , CAI-1 , and AI-2 , using LuxN , CqsS , and LuxPQ , respectively , to control its QS response . These circuits are proposed to be used for intra-species , intra-genus , and inter-species communication , respectively [18 , 21 , 48] . Intriguingly , both VpsS and CqsR are predicted to be capable of detecting small chemical molecules . VpsS is predicted to be cytoplasmic , as it lacks any obvious membrane spanning domain . However , vpsV , a gene upstream of vpsS , could encode the signal-sensing partner [32] . VpsV carries a FIST domain , which could bind small ligands [49] . CqsR , in contrast , is predicted to be membrane-bound and possess a periplasmic CACHE domain , which is often found in receptors that detect amino acids and other molecules [50–52] . Thus , we speculate that the signals detected by VpsS and CqsR are chemical in nature and contain information that is absent from CAI-1 and AI-2 . Although VpsS and CqsR are found predominantly in Vibrio species , it remains to be determined if their cognate signals are used for enumeration of species composition or as cell density proxies .
All V . cholerae strains used in this study were derived from C6706str2 , a streptomycin-resistant isolate of C6706 ( O1 El Tor ) [53] . E . coli S17-1 λpir was used as hosts for plasmids . All strains used in this study are described in S1 Table . V . cholerae and E . coli cultures were grown with aeration in Luria-Bertani ( LB ) broth at 30°C and 37°C , respectively . Unless specified , media was supplemented with streptomycin ( Sm , 100 μg/ml ) , tetracycline ( Tet , 5 μg/ml ) , ampicillin ( Amp , 100 μg/ml ) , kanamycin ( Kan , 100 μg/ml ) , chloramphenicol ( Cm , 10 μg/ml ) and polymyxin B ( Pb , 50 U/ml ) when appropriate . All DNA manipulations were performed using standard procedures . High-fidelity PCR was performed using Phusion DNA polymerase ( New England Biolabs ) . Taq DNA polymerase was used for routine screenings . Oligonucleotide sequences used for PCR , site-directed mutagenesis , and sequencing reactions will be provided upon request . Deletions and point mutations were introduced into the V . cholerae genome by allelic exchange using the suicide vector pKAS32 [54] . Mutations carried in vector pKAS32 from E . coli donors were introduced into the V . cholerae genome by conjugation on LB plates . Transconjugants were selected for by plating on Pb/Amp plates . Subsequent recombinants were selected on LB/Sm ( 5000 μg/ml ) plates , followed by single colony isolation on LB/Sm ( 5000 μg/ml ) plates . Mutant strains carrying the desired mutations were screened and confirmed by PCR . All mutant strains were confirmed by sequencing at the Tufts University Core Facility . V . cholerae bacterial cultures were grown aerobically for 16 hr in LB/Sm at 30°C . Mutant strains were then mixed equally with the wild-type ΔlacZ strain and approximately 106 colony forming units ( CFU ) were fed orally to 3- to 5-day-old CD-1 mice ( Charles River Laboratories ) . Prior to infection , infant mice were housed with ample food and water for at least 24 hr and monitored in accordance with the regulations of the Department of Laboratory Animal Medicine at Tufts University School of Medicine . Infected infant mice were sacrificed 24 hr post inoculation and their small intestines were harvested and homogenized . V . cholerae colonization in the small intestine was measured by plating serial dilutions of intestinal homogenate on LB/Sm/X-Gal plates and enumerating bacterial colonies the next day . Competitive index ( CI ) was calculated as the ratio of output to input of the mutant strain relative to the wild-type . A minimum of eight infected animals were used to calculate CI . V . cholerae colonization of the small intestine is presented as a single data point per mouse and data are graphed with the median . If the mutant strains were below the level of detection , it was assumed that there was 1 mutant CFU present at the next lowest dilution of the wild-type sample ( indicated by open symbols in the figures ) . V . cholerae strains carrying cosmid pBB1 [6] , which harbors the heterologous V . harveyi luxCDABE operon , were first streaked on LB/Tet plates . Individual colonies were then grown aerobically for 16 hr at 30°C in LB/Tet . Cultures were further diluted 1:200 in 20 ml of LB/Tet and grown at 30°C with aeration . OD600 ( 1 ml of culture ) and light production ( 0 . 1 ml of culture ) were measured every 45–60 min for at least 10 hr using a Thermo Scientific Evolution 201 UV-Visible Spectrophotometer and a BioTek Synergy HT Plate Reader , respectively . Light production per cell was calculated from dividing light production by OD600 . For the assays that determined the effects of surplus CAI-1 , a 100 mM CAI-1 stock dissolved in DMSO was diluted to 20 μM in fresh media , DMSO was used as a negative control . For the assays that determined the effects of LuxO overexpression , IPTG was added to the cultures at 100 μM . Spent culture medium was prepared from wild-type V . cholerae or the ΔcqsA ΔluxS double synthase mutant . These two strains were grown in LB at 30°C aerobically to HCD ( OD600 >4 ) . Cells were removed by centrifugation and the supernatants were filtered through a 0 . 2 μm filter . Filtered cell-free spent culture medium ( 80% , v/v ) was reconditioned by adding back 20% ( v/v ) 5× LB . As a control , fresh medium was prepared by adding 1× LB ( 80% , v/v ) to 20% ( v/v ) 5× LB . V . cholerae mutants expressing only vpsS or cqsR ( vc1831 ) and carrying pBK1003 ( Pqrr4-lux ) [55] were inoculated ( 1:1000 dilution ) into these two media conditions in triplicate and grown in a 96-well microplate at 30°C with aeration . OD600 and light production were measured every 30 min for at least 10 hr using a BioTek Synergy HT Plate Reader . Light production per cell was calculated from dividing light production by OD600 . All animal experiments were done in accordance with NIH guidelines , the Animal Welfare Act , and US federal law . The infant mouse colonization experimental protocol B2013-03 was approved by Tufts University School of Medicine's Institutional Animal Care and Use Committee . The mice were housed in a centralized and AAALAC-accredited research animal facility that is fully staffed with trained husbandry , technical and veterinary personnel . CqsS Q9KM66 LuxP Q9KLK6 LuxQ Q9KLK7 VpsS Q9KS16 CqsR Q9KR16 LuxO Q9KT84 LuxU Q9KT83 AphA H9L4T0 HapR B2CKP3 LuxS Q9KUG4 CqsA Q9KM65 CsrA Q9KUH3 | Quorum-sensing ( QS ) is a microbial cell-cell communication process that allows bacteria to function as a collective group . Many pathogens , including Vibrio cholerae , the causative agent of cholera , depend on QS to regulate important cellular processes that are essential for survival and adaptation inside and outside of their hosts . Since its discovery , the V . cholerae QS system has served as a model to understand how bacterial pathogens employ QS for temporal control of virulence factor production . Yet , after a decade of research , our understanding of the V . cholerae QS system is still incomplete . Here we re-define the QS network architecture of this important pathogen . We show that two novel sensory inputs function in parallel with the two canonical QS pathways to regulate V . cholerae virulence gene expression . Moreover , our study illustrates a strategy that bacteria employ to maintain QS system robustness . By perceiving multiple parallel sensory inputs , the V . cholerae QS network is structured to be highly resistant to signal perturbations , therefore preventing premature commitment to QS . Our study provides new insights into how bacterial pathogens integrate multiple sensory signals to elicit robust and coordinated QS responses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Quadruple Quorum-Sensing Inputs Control Vibrio cholerae Virulence and Maintain System Robustness |
It is currently believed that the atlas of existing protein structures is faithfully represented in the Protein Data Bank . However , whether this atlas covers the full universe of all possible protein structures is still a highly debated issue . By using a sophisticated numerical approach , we performed an exhaustive exploration of the conformational space of a 60 amino acid polypeptide chain described with an accurate all-atom interaction potential . We generated a database of around 30 , 000 compact folds with at least of secondary structure corresponding to local minima of the potential energy . This ensemble plausibly represents the universe of protein folds of similar length; indeed , all the known folds are represented in the set with good accuracy . However , we discover that the known folds form a rather small subset , which cannot be reproduced by choosing random structures in the database . Rather , natural and possible folds differ by the contact order , on average significantly smaller in the former . This suggests the presence of an evolutionary bias , possibly related to kinetic accessibility , towards structures with shorter loops between contacting residues . Beside their conceptual relevance , the new structures open a range of practical applications such as the development of accurate structure prediction strategies , the optimization of force fields , and the identification and design of novel folds .
The total number of distinct protein folds which have been experimentally solved is very small compared to the amount of genome-wide protein sequences [1] , [2] . Indeed , folds are evolutionarily more conserved than sequences and the same fold can house proteins performing different biological functions [3] , [4] . Thus a fundamental question concerns the extension of the library of protein folds: are the observed structures a small fraction of the whole fold universe ? If so , then is it because evolution has not yet run enough to explore it or rather because a selection principle is on which has slowed down/stopped the search for alternatives ? Addressing these issues on the basis of the principles of physics and chemistry is a question of fundamental importance , currently at the center of intense investigation . Several properties of the folding process have been shown to depend more on the fold topology than on the specificity of the aminoacids [5]–[10] . For a few proteins , native backbone geometries were shown to be closely mimicked by local energy minima of poly-alanine chains [11] . More recently , a unified approach to the origin of protein folds was proposed in which the inherent anisotropy of a chain molecule , the geometrical and energetic constraints placed by hydrogen bonds , steric hindrance and hydrophobicity yield a free energy landscape with minima resembling protein structures [12]–[14] . One of the predictions is that a limited library of folds exists . Along the same lines , based on a coarse grained model , Zhang et al proposed [15] that there is a one-to-one correspondence between the Protein Data Bank ( PDB ) library and the structures that one can obtain with a homopolymer from the requirement of “having compact arrangements of hydrogen-bonded , secondary structure elements and nothing more” [15] . A different scenario has been proposed in ref . [16] where , by using structure prediction method based on an idealized secondary structure lattice representation they argued that the space of possible folds might be larger than the space of natural folds . Recent advances in supercomputing power and sampling methods [17] , [18] allow us addressing these issues by accurate atomistic simulations . We here describe the results of a molecular dynamics simulation of a 60 amino acids polypeptide chain performed with an accurate all-atom interaction potential and a setup specifically designed in order to extensively explore the configuration space . The length of 60 was chosen because it represents the limit of what can be simulated with our computational resources . Natural proteins are on average much longer than 60 amino acid , but several autonomously folded domains of this size exist [19] , making the comparison between simulation and nature meaningful . In the simulation we visit practically all the folds observed in nature for proteins of comparable length . However , at variance with what found in [15] , we find that natural folds are only a small fraction of the structures that are explored . Many of the structures found in our simulation resemble real proteins ( in terms of secondary content , stability and compactness ) but have not been observed in nature . This finding immediately rises a question on the nature and meaning of these novel folds: why are they not exploited in real proteins ? Do natural folds have something “special” or have they simply been selected randomly ?
By using a state-of-the art enhanced sampling technique [18] , we simulate a 60 amino acid polyvaline ( VAL60 ) described by an all-atom potential energy function [20] as explained in Methods . This allows generating , in of simulation , structures characterized by a significant secondary content and a small radius of gyration . A movie with a short part of the trajectory ( ) is available as Video S1 . It shows the exploration proceeds mostly by local reorganization of secondary structure elements . From time to time the system unfolds completely , exploring a totally independent topology . A selection of the 30 , 000 structures is represented in Fig . 1-a and a repository , with their all-atom configuration , is available at http://dx . doi . org/10 . 5061/dryad . 1922 . By steepest descent optimization ( see Methods ) we verified that even if these structures have been obtained with an enhanced sampling technique , they closely correspond to local minima of the potential energy surface of VAL60 . Consistently with Ref . [11] , they also correspond closely to local minima of the potential energy surface of polyalanine ( ALA60 ) ( see Methods ) . Even though these structures correspond to local minima , one still wonders if their structural quality is good and if they resemble real proteins . In order to address this issue , we monitored several structural quantities on our dataset . In Fig . 2-a we show the Ramachandran plot of the VAL60 structures . One can see that the dihedrals populate the allowed regions . The relative height of the various peaks is determined by the probability to observe the different secondary structural elements and the random coil in the full dataset . The “stereochemical quality” of the VAL60 set was also assessed using PROCHECK [21] . This program provides an overall quality measure , called G-factor , which takes into account dihedrals , bond lengths and angles , as compared with stereochemical parameters derived from well-refined , high-resolution structures . If the G-factor is higher than −1 . 0 the structure is considered to be “normal” . In Fig . 2-b the G-factor distribution is shown for the VAL60 . For a comparison , we computed the same distribution also for the structures of length smaller than 75 amino acids belonging to the CATH database [19] . We also used PROCHECK to estimate the average hydrogen bond energy . The distributions of this quantity for VAL60 and CATH is shown in Fig . 2-c and compared ( dash line ) with its ideal mean and standard deviation [21] . For the VAL60 set the G-factor and the H-bond energy , though not as good as for CATH , are in accordance with what is expected for realistic proteins . Lastly , in order to check if medium size structures generated by our sampling procedure are representative of the PDB , the VAL60 structures were fragmented in small 5 amino acids long structures and were compared by backbone RMSD [22] to all the fragments of the same length found in CATH . The minimum RMSD value was obtained for each small fragment . The distribution of this quantity is shown in Fig . 2-d . It is found that the VAL60 fragments have on average at least one CATH structure within 0 . 6 Å of RMSD . For all the structural descriptors we considered the VAL60 distributions are similar but not identical to the ones of real proteins , due to the fact that in our simulation we considered an homopolymer formed by only one amino acid , valine . Taken together the data shown in Fig . 2 demonstrate our first major result: finding by molecular dynamics at an all-atom level a library of 30000 protein-like structures . The VAL60 structures obtained in this manner , at a first sight , cannot be distinguished from folds adopted by proteins . In order to understand how many independent structures are actually explored , and if the set contains all the known folds , a measure of the degree of similarity between two protein structures is needed . We used the TM-align approach [23] , which gives , as three quantitative outputs , the coverage , the root mean square distance ( RMSD ) between the aligned residues , and the TM-score ( see Methods ) . Following Ref . [15] , we first checked if the set of structures generated by molecular dynamics reproduces all the known folds . As a target set we here considered the CATH database [19] , that is successfully used in structural studies to classify protein folds . Other choices were also considered ( see Text S1 ) . For each structure in the CATH database , we searched , in the set of the 30 , 000 structures of VAL60 generated by molecular dynamics , for its most similar structure as quantified by the TM-score . In Fig . 1-b , three CATH structures with their respective VAL60 equivalent are shown . As shown in Fig . 3-a , for almost every CATH structure it is possible to find a VAL60 structure that is very similar . For CATH structures of length between 55 and 65 amino acids the average coverage is 75% , and the average RMSD is of only 2 . 8 Å . The VAL60 set reproduces , with even greater success , CATH structures of shorter length . Instead , structures of 65 or more amino acids are reproduced less accurately , as the maximum coverage that can be attained is , by definition , smaller than their length . However , even in these cases , the RMSD restricted to the aligned residues is small , of 3 Å or less . Comparison of the VAL60 set with even longer chains is not considered here: the long chains can contain extra secondary structure elements that do not significantly affect the quality of the alignment but change the topological details of the fold . The excellent capability of the VAL60 set of reproducing the known folds is confirmed by monitoring the progress of exploration as a function of the number of structures found during the simulation . At this purpose , we assumed that a CATH structure is “found” when molecular dynamics explores a VAL60 structure whose TM-score ( with respect to the CATH structure ) is higher than 0 . 45 . Visual inspection reveals that two structures of similar length and of relative TM-score larger than 0 . 45 are structurally and topologically similar . In Fig . 3-b we plot , for different length classes , the fraction of CATH structures that are found as a function of the number of VAL60 structures ( which is approximately proportional to simulation time ) . At the end of the simulation , for length L = 55–65 the fraction of found structures is 86% ( 85% for L = 40–55 and 78% for L = 65–75 ) . 100% of the structures of length L = 40–65 are reproduced within a TM-score of 0 . 4 . This shows that the computational setup used in this work allows us to explore the majority of the folds in nature , at least within the limited range of lengths considered . This is the second main message of our study and confirms the results of Ref . [15] obtained with a simpler potential energy function . The exploration of VAL60 structures by molecular dynamics proceeds in an almost random manner , with no obvious preference for a specific class of folds or secondary structure element . Indeed we checked that it is , on average , equally likely to find a specific CATH structure as finding a VAL60 structure for the second time ( see Methods ) . In other words , in our sampling strategy there is no particular bias for generating a structure observed in nature . However , one realizes that the two sets of structures , CATH and VAL60 , cannot be fully equivalent . Indeed , according to a clustering procedure ( see Methods ) , in the simulation explores independent structures , much more than the structures in CATH ( in a length range between 40 and 75 ) . One could argue that finding or not a one-to-one correspondence might just depend on the chosen similarity threshold [24] . In order to quantitatively investigate this issue , we addressed the following question: Do structural descriptors exist whose distributions are different between the two sets CATH and VAL60 ? If the answer is yes , a biased search mechanism reflecting an evolutionary pressure may be envisaged . Otherwise a random search mechanism in a continuous structure space may be enough to account for the choice of the observed folds out of all possible structures . While at first sight structures belonging to the VAL60 and CATH sets look indistinguishable , a more detailed analysis reveals that several VAL60 structures include a large fraction of parallel -sheets . This secondary structure element is much less common in the CATH set restricted to . We quantify this observation by looking at the distributions of normalized contact order ( CO ) and the contact locality ( CL ) ( see Methods ) . The distribution of CATH is significantly restricted towards lower CO and higher CL values with respect to VAL60 ( see Fig . 4-a ) , consistent with the observation that parallel -sheets are found less frequently in CATH . We have checked that this discrepancy is not due to the specific simulation setup ( see Methods ) . We also checked that the CO distribution computed for the subset of VAL60 that are recognized to be similar to CATH is largely overlapping with the CO distribution for the CATH set ( see Fig . 4-b ) . This demonstrates the consistency of the similarity measure provided by the TM-score . We also analyzed the distribution of the CO restricted to the different structural classes . The bias towards low CO is not effective for all- structures ( see Fig . S6 ) , whereas is active for all- and - structures . All these results suggest that , among all possible conformations physically attainable by polypeptide chains , real protein structures were selected under a bias towards low CO . This is the third main message of our study: As observed with the coarse grained model of ref . [16] , there is no one-to-one correspondence between the PDB library and the ensemble of compact structures with significant secondary content .
By using atomistic simulations and a powerful enhanced sampling technique we have generated a database of structures corresponding to energy minima of a 60 amino acids polypeptide . Clearly , the length of 60 amino acids used in the simulation does not provide a complete representation of the full protein universe , which includes a very large amount of much longer proteins . However , our results indicate that , within the limited length range we considered , the VAL60 set is indeed representative of the space inhabited by real proteins . In fact , this set includes all the folds existing in nature for proteins of similar size , confirming that the observed protein folds are selected based on geometry and symmetry and not on the chemistry of the aminoacid sequence [5]–[15] . However , we find that the known folds form only a small fraction of the full database . Natural folds are indistinguishable in terms of secondary content and compactness from non-natural folds , but are characterized by a relatively small contact order and a relatively high contact locality . Why has nature made this choice ? One can argue that , due to a higher -structure content , large CO structure could have a higher tendency to aggregate . Another possible explanation relies on kinetic accessibility , as the contact order is known to correlate with the folding time of two-state globular proteins [25] . Evolution might have selected the folds under the guidance of a simple principle: reducing the entanglement in the bundle formed by the protein in its folded state . Bundles with shorter loops might be preferable , as they are explored more easily starting from a random coil . How has nature been able to select low contact order structures ? In order to address this issue , we investigated the role of specific amino acids in selecting a fold among the possible structures . At this scope , we compared the correlation between potential energy and CO of the structures obtained by energy minimization of VAL60 and ALA60 ( see Methods ) . Fig . 5 vividly demonstrates that different low energy structures may be discriminated when different sequences are mounted on all the possible “presculpted” structures [12] . Whereas energetically VAL60 prefers structures with high CO and a large content of strands , ALA60 promotes conformations with low CO and which are rich in helices . Evolution , possibly also guided by the kinetic bias hypothesized above , can then proceed by using a repertoire of 20 types of amino acids , to select and design the sequences which minimize the free energy of a desired structure against other competing structures . As a final remark , we believe that the VAL60 structures and the computational procedure to generate them , also with different types of amino acids and with different lengths , may play a key role in future developments . The availability of a rich library of possible folds and realistic decoys could allow for major advances in the two main applicative challenges in protein physics: the prediction of the native state of any given sequence and the design of the sequence folding into a desired fold . They might be also used to check predictions in synthetic biology [26] , [27] . Furthermore the library could be exploited to obtain models of misfolded protein structures related to neurodegenerative diseases [28] . We have shown that generating a huge set of realistic structures is feasible with a computational analysis based only on ab-initio physico-chemical information , with no need of using knowledge-based potentials as in state-of-the-art approaches to protein structure prediction and design [29] .
Molecular dynamics ( MD ) simulations are performed using the AMBER03 [20] force field and the molecular dynamics package GROMACS [30] . Simulations are mainly performed in vacuum , but tests have been performed also in water solution ( see below ) . The temperature is controlled by the Nose-Hoover thermostat , and the integration time step is . In order to explore the conformational space we use bias-exchange metadynamics ( BE-META ) [18] , [31] with 6 replicas . BE-META is a combination of replica exchange [32] and metadynamics [33] , in which multiple metadynamics simulations are performed at the same temperature . Each replica of the system is biased with a one-dimensional metadynamics potential acting on a single collective variable ( CV ) . The CVs are described in detail in [34] and are designed in order to evaluate by a differentiable function of the coordinates the fraction of a secondary structure element ( -helix , parallel -sheet and antiparallel -sheet ) . For instance , for the antiparallel -sheet the variable counts how many pairs of 3-residue fragments in a given protein structure adopt the correct -conformation , measured by the RMSD from an ideal block of antiparallel formed by a pair of three residues . We use six CVs: 3 -CVs each biasing one third of the protein , 1 anti- CV , and 2 para- CV . The Gaussians entering in the metadynamics potential are added every . Their height and width are and 0 . 3 . Exchanges between the biasing potentials are allowed every . The exchanges greatly enhance the capability of the dynamics of exploring new structures [18] , [35] . These parameters have been optimized according to the criteria of Ref . [36] . The main scope of this work is exploring exhaustively the conformational space of an average length polypeptide described by a realistic potential energy function . The final choice of simulating VAL60 in vacuum with at , and then optimizing the configurations with was taken after considering several alternatives . We first considered performing the simulation on a 60-alanine in vacuum ( ALA60 ) , as alanine is used in Ref . [11] . This system was evolved using the BE-META setup described above for generating structures with a high secondary content . However , the structures generated in this manner are too compact to be comparable with experimental structures . Indeed , the histogram of the radius of gyration for ALA60 is peaked approximately 1 Å too low with respect to what observed for real proteins of similar length ( see Fig . S1 ) . This is due to the relatively low steric hindrance of the side chain of ALA . The same histogram computed for VAL60 is instead fully consistent with the distribution observed in real proteins . We also performed test simulations of VAL60 solvated in TIP3P water at . This system was evolved for with the same BE-META setup . In this case structures with a high secondary content are found , but most of these structures are not independent , as the correlation time in water is much larger than in vacuum . More importantly , the structures generated in water have on average a large radius of gyration ( see Fig . S1 ) . This is an indication that at the system explores mainly non-compact structures . Of course , one could perform the simulation at lower temperature , but this would lead to an even larger correlation time , making an exhaustive exploration of the configuration space too time consuming with existing computational resources . Performing the exploration with is not strictly necessary , as test simulations performed with are also able to explore structures with a high secondary content . However , VAL60 with has a relatively high preference for structures ( see Fig . 5 ) . With and structures become approximately isoenergetic for VAL60 , removing a possible bias in the exploration ( see also Fig . S5 ) . The VAL60 set was generated by molecular dynamics in vacuum at 400 K , biasing the system by metadynamics potentials aimed at producing secondary structure elements . One wonders if the structures that are explored in this manner have protein-like topologies only because of the bias , and would fall apart in normal conditions . In order to address this issue , for all the structures generated by molecular dynamics we performed a steepest decent ( SD ) simulation with , aimed at localizing the closest potential energy surface minimum . For the last configuration the RMSD was calculated with respect to the initial structure . The distribution of this quantity is shown in Fig . S2 . Most of the structures do not drift significantly apart from the initial configuration , and the RMSD remains relatively small , within 2 Å in most cases . Thus , we conclude that the VAL60 structures generated by molecular dynamics are close to local energy minima . The set of structures generated in this manner form the database on which we perform the analysis . We also checked if the structures that are generated in this manner are stable if the homopolymer chain is formed by another amino acid . At this purpose , VAL60 structures were chosen randomly . For each of these structure the valines were replaced by alanines ( ALA60 ) . Following the same procedure described above , a SD simulation was run until the closest local minimum is reached . The RMSD from the initial ALA60 configuration was calculated . The distribution of this quantity is shown in Fig . S2 . Quite remarkably , even if one changes the amino acid sequence from VAL60 to ALA60 the structures do not change significantly , remaining within Å of RMSD from the initial structure . This confirms the prediction of Ref . [11] . The similarity between two different structures is assessed using the TM-align algorithm [23] . This method , regardless of the primary sequence of the two proteins , attempts to align their secondary structure elements allowing insertions and deletions of residues . The fraction of aligned residues is called coverage , and is the first measure of similarity . Afterward , the algorithm finds the rotation and translation that minimizes the relative distance between pairs of aligned residues ( RMSD ) . The optimal coverage and RMSD are then combined into a single similarity measure , the TM-score . The original version of the TM-align algorithm has been modified in order to assign the secondary structure elements with more accuracy . Instead of considering only the coordinates as in Ref . [23] , our modified version reads for each protein the secondary structure assignment given by DSSP [37] . When the proteins have different lengths , the length of the target protein is used in the TM-score definition [23] . The TM-score is equal to one for two identical structures . Two structures are considered to represent the same fold if their TM-score is greater than 0 . 45 , while for two randomly chosen structures the TM-score is approximately equal to 0 . 25 . In order to find the independent structures we proceeded as follows: first we selected the structure with the largest number of neighbors , namely with the largest number of structures at a TM-score larger than 0 . 45 . We assign it as the first independent structure and remove it , together with all its neighbors , from the list of structures . We iterate this procedure until the list is empty . In Fig . S3 we plot the number of independent structures found as a function of the number of structures explored by MD . This data can be accurately reproduced with a double exponential fit ( ) , which allows estimating as the number of independent structures that would be explored in an infinitely long MD run . We consider a small fraction of the MD trajectory used for generating the VAL60 dataset . In this fraction of the trajectory independent structures are generated . Using the rest of the trajectory , we compute the number of times that each of these structures is observed ( namely , the number of times a structure with relative TM-score larger than 0 . 45 is visited ) . The histogram of is calculated for 20 different sets , each including 100 VAL60 structures . Its average and standard deviation ( error bars ) are plotted in Fig . S4 . This is compared to the same histogram computed for the CATH set with ( structures ) . Strikingly , the two histograms are very similar , indicating that the probability of finding a CATH structure in this length range is similar to the probability of finding a VAL60 structure a second time . Two residues are considered to be in contact when at least one pair of their heavy atoms is found at a distance smaller than 3 . 5 Å . The contact order ( CO ) [25] is defined as the average sequence separation between contacting residues divided by the chain length . The contact locality ( CL ) , is a structural descriptor that counts the fraction of contacting residue pairs which are formed within the same half of the chain [38] . The total number of pairwise contacts is , where and are the contacts between residues both belonging to the half of the chain towards the N-terminus and the C-terminus , respectively , and are the contacts between residues belonging to different halves of the chain . CL is then defined as . One of the main results described in the work is that , on average , the VAL60 structures have higher CO than CATH structures . In order to find out if the biasing procedure favors high CO structures we separate the VAL60 structures in two classes: low CO ( ) and large CO ( ) , and we calculate the probability to find a structure times in the simulation ( same procedure as above ) . The two distributions with the respective error bars are shown in Fig . S5 . From the graph , it can be concluded that the two distributions are similar but it is marginally easier for VAL60 to re-generate more times low CO structures rather than high CO ones . Thus , the VAL60 system is able to sample low CO structures with a marginally higher efficiency . This is possibly due to the fact that low CO structures are kinetically encountered more often in a random search guided only by a bias towards high secondary structure content . This allows concluding that the large number of high CO structures that is obtained by molecular dynamics is not due to a bias in the sampling procedure . The results found in Fig . 4 show that there is a bias towards low CO structures for the CATH set . In order to find out how this bias acts for different structural classes , the CO distributions was calculated for all- structures and all- structures of CATH and VAL60 . The results are shown in Fig . S6 . While the bias towards low CO is present for all- structures , for all- structures it is not effective . It is also remarkable that the CO distribution for structures in the VAL60 set that are similar to a CATH structure is very similar to the probability distribution for the all- CATH structures . | Protein structure and biological function are determined by their sequence , but proteins of different sequence or function can share the same structure . To rationalize this puzzling observation we explored by computer simulations the universe of all possible folds for proteins of relatively small length . We find that nature exploits a relatively small corner of this universe . Evolution selected this region under the guidance of a simple principle: reducing the entanglement in the bundle formed by the protein in its folded state . This makes bundles with shorter loops preferable . The set of structures that we make available will open a range of practical applications in biomedical sciences . | [
"Abstract",
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"Results",
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"Methods"
] | [
"computational",
"biology/molecular",
"dynamics"
] | 2010 | Exploring the Universe of Protein Structures beyond the Protein Data Bank |
The notion that previous infection by Leishmania spp . in endemic areas leads to robust anti-Leishmania immunity , supports vaccination as a potentially effective approach to prevent disease development . Nevertheless , to date there is no vaccine available for human leishmaniasis . We optimized and assessed in vivo the safety and immunogenicity of an innovative vaccine candidate against human visceral leishmaniasis ( VL ) , consisting of Virus-Like Particles ( VLP ) loaded with three different recombinant proteins ( LJL143 from Lutzomyia longipalpis saliva as the vector-derived ( VD ) component , and KMP11 and LeishF3+ , as parasite-derived ( PD ) antigens ) and adjuvanted with GLA-SE , a TLR4 agonist . No apparent adverse reactions were observed during the experimental time-frame , which together with the normal hematological parameters detected seems to point to the safety of the formulation . Furthermore , measurements of antigen-specific cellular and humoral responses , generally higher in immunized versus control groups , confirmed the immunogenicity of the vaccine formulation . Interestingly , the immune responses against the VD protein were reproducibly more robust than those elicited against leishmanial antigens , and were apparently not caused by immunodominance of the VD antigen . Remarkably , priming with the VD protein alone and boosting with the complete vaccine candidate contributed towards an increase of the immune responses to the PD antigens , assessed in the form of increased ex vivo CD4+ and CD8+ T cell proliferation against both the PD antigens and total Leishmania antigen ( TLA ) . Overall , our immunogenicity data indicate that this innovative vaccine formulation represents a promising anti-Leishmania vaccine whose efficacy deserves to be tested in the context of the “natural infection” .
Leishmaniasis is a spectrum of pathological outcomes caused by different Leishmania spp . , intracellular parasites with a complex life cycle requiring a susceptible host and a permissive vector [1] . Visceral leishmaniasis , the most severe form of the disease , fatal if untreated , is caused by L . donovani and L . infantum , parasite species that migrate to the liver , spleen and bone marrow [2–4] . It has a worldwide distribution , being endemic in 74 countries , representing more than 37% of the total Earth terrestrial area [5] . Every year an estimated 0 . 2 to 0 . 4 million new VL cases occur and more than 20 000 people die , mostly in developing nations where access to healthcare is limited [6] . Furthermore , scarce and sometimes ineffective treatment options challenge leishmaniasis control [7] . Vaccination is considered one of the most cost/effective ways to control Leishmania infection . However , no human leishmaniasis vaccine is currently available . Several candidates have been proposed during the past few decades [8] . Some were shown to be immunogenic and have conferred protection against Leishmania in rodent models . Nevertheless , most of them were discarded after proving to be ineffective in large animals [8 , 9] . Furthermore , most of these studies shared a limitation which may have been responsible for the overestimation of the vaccine candidates effectiveness: they had a binomial focus ( host-parasite ) and disregarded the contribution of the vector , essential in vaccine efficacy determination as highlighted by Peters et al who showed the loss of protection of a potentially-good vaccine candidate when tested in the context of vector-transmitted leishmaniasis [10] . Leishmania parasites are transmitted by sand flies from the genera Lutzomyia and Phlebotomus in a specific vector-Leishmania spp . pairing [11] . During the sand fly blood meal , parasites together with vector derived factors , including saliva , are introduced into host skin [11–13] . Previous exposure to sand fly salivary components has been shown to confer protection against vector-transmitted Leishmania [14 , 15] . Furthermore , in recent studies , protection against natural transmission of Leishmania has been attained by vaccination with defined salivary molecules in animal models for both cutaneous leishmaniasis and VL [16 , 17] . Interestingly , these proteins were shown to improve the protection induced by live anti-Leishmania vaccines [18 , 19] . Fundamentally , the Th1 immune response elicited against a salivary molecule can adversely impact parasite establishment in the host . This study proposes a novel vaccine candidate based on defined antigens of both parasite ( KMP11 and LeishF3+ , the latter a fusion protein consisting of Nucleoside hydrolase , Sterol 24-c-methyltransferase and Cysteine protease B ) , and sand fly vector ( salivary protein LJL143 ) origins , formulated into Influenza virosomes and adjuvanted with GLA-SE , a TLR-4 agonist . The sand fly antigen LJL143 was shown to produce a long lasting Th1 immune response in dogs , which impacted parasite growth in vitro [20] . One of the parasite-derived antigens , KMP-11 , was already demonstrated to be individually effective against VL in the pre-clinical context [21] , as were each of the individual components of the second parasite-derived antigen , the fusion protein LeishF3+ [22–24] . Additionally , LeishF3+ predecessor antigen ( a fusion protein consisting of Nucleoside hydrolase and Sterol 24-c-methyltransferase , but not Cysteine protease B ) , was considered safe and immunogenic in the clinical context ( Phase I trial ) [25] , as were also both the adjuvant and the virosomes [26 , 27] . Influenza virosomes represent a unique vaccine delivery system , flexible but robust , that allows loading of a wide variety of antigens [28 , 29] . The VLP-based antigen formulation has the potential to generate both CD4+ and CD8+ specific memory T cells , the latter due to the potentiation of cross-presentation events [30] . The immune response elicited by the immunization should induce a Th1 phenotype due to the adjuvant chosen and the presence of the sand fly salivary antigen [26 , 31] . In theory , an immunized individual bitten by an infected sand fly and exposed to parasites and vector saliva , will quickly mount both a strong Th1 anti-Leishmania , and a strong Th1-DTH anti-sand fly saliva immune responses , resulting in prevention of infection establishment . Here , we explore the safety and antigenicity of the vaccine candidate , using ex-vivo and in-vivo approaches .
Animal experiments were performed in accordance with the IBMC . INEB Animal Ethics Committee and the Portuguese National Authorities for Animal Health guidelines ( directive 2010/63/EU ) . BPC and ACdS are accredited for animal research ( Portuguese Veterinary Direction—DGAV , Ministerial Directive 113/2013 ) . DGAV approved the animal experimentation presented in this manuscript under the license number 0421/000/000/2013 . The study with human Peripheral Blood Mononuclear Cells ( PBMCs ) was approved by the Hospital de Fuenlabrada ( Madrid , Spain ) Ethics and Research Committee ( protocols APR12-65 and APR14-64 ) , and all participants gave written informed consent to be involved . Four different virosomal preparations have been specifically designed for this study , three of them containing each of the individual antigens , and one containing all the three antigens together . Briefly , a solution containing 1 mg of inactivated Influenza virus A/H1N1/California was pelleted at 286 000g for 1 hour , dissolved in presence of 0 . 5 ml of PBS containing 0 . 1 M of Octaethyleneglycol mono ( n-dodecyl ) ether ( OEG; Sigma Aldrich , MO , USA ) , and then mixed with 32 mg of phosphatidylcholine ( Lipoid Ag , Steinhausen , Switzerland ) dissolved in 1 . 5 ml of PBS-OEG 0 . 1 M . The mixture was centrifuged at 100 000g for 30 min and the supernatant containing Haemagglutinin and Neuraminidase was recovered . For the individual virosomal formulations , the obtained supernatant was then mixed with 2 mg of Leish-F3 ( or Leish-F3+ ) , or KMP11 or LJL143 in presence of detergent . Virosomes were then formed by detergent removal and sterile-filtered . The virosome particles containing the mixture of the three antigens were produced similarly , from 1 mg of starting influenza protein mixed with 1 mg of each antigen ( Leish F3+ , KMP11 and LJL143 ) . Size determination and distribution of the particle population was performed using a Zetasizer Nano instrument ( Malvern Instruments , Malvern , UK ) . Parasite derived and/or VD proteins content in virosome particle was determined by SDS-PAGE Coomassie Stained . Subjects included in this study were residents of a L . infantum post-outbreak area . Up to 14 healthy endemic individuals ( theoretically never exposed to Leishmania ) , 11 asymptomatic subjects ( positivity to the in vitro PBMC proliferation assay to soluble Leishmania antigen ) and 21 cured VL patients ( clinically diagnosed with VL; presence of Leishmania confirmed in blood by PCR; three months after successful treatment with liposomal amphotericin B ) were included in the antigenicity assays . Blood samples were collected at the hospital blood bank and the internal medicine department ( Hospital of Fuenlabrada , Madrid ) . PBMCs were prepared by density gradient centrifugation of heparinized blood samples ( Lymphocyte Isolation Solution , RAFER , Spain ) . PBMCs were adjusted up to 2×106 cells/ml in complete medium ( RPMI 1640 supplemented with 100 U/ml penicillin , 100 μg/ml streptomycin , 2mM L-glutamine , 25mM HEPES and 10% heat inactivated fetal calf serum ) , and cultured in 96-well plates at a density of 2×105 cells per well for 5 days with either KMP11 ( 10 μg/ml ) , LeishF3 ( 10 μg/ml ) , LeishF3+ ( 10 μg/ml ) , LJL143 ( 10 μg/ml ) , soluble leishmanial antigen—SLA ( 10 μg/ml ) or PHA-M ( 5 μg/ml ) in a final volume of 200 μl per well . The supernatants of the in vitro cell cultures were collected and stored at -20°C for cytokine quantification . Interferon-γ , granzyme B , TNF-α , and IL-10 , were quantified in culture supernatants , using the BD Cytometric Bead Array Human Flex Set ( BD Biosciences , NJ , USA ) following the manufacturer’s instructions . Data were acquired using a FACSCalibur flow cytometer and analyzed using the Flow Cytometric Analysis Program Array ( BD Biosciences , NJ , USA ) . Six to eight weeks old male BALB/c mice ( Charles River Laboratories , France ) were maintained under specific-pathogen free conditions at the IBMC facilities , with water and food ad libitum . Animals were immunized intramuscularly with a maximum of 50 μl of the respective formulation in the thigh . The volumes administered were based on the concentration of the antigen/adjuvant preparations , and adjusted to equivalent final volumes with PBS . Unless otherwise stated , BALB/c mice were immunized three times at four weeks intervals , in the two thighs alternately . Four weeks after the last immunization , animals were euthanized by cervical dislocation , under volatile anesthesia ( Isoflurane , Piramal healthcare , Northumberland , UK ) . Two major in vivo experimental set ups originated this work , the first one for optimization purposes , and the second one as the actual pre-clinical trial . Blood from mice was collected through intracardiac puncture under isoflurane anesthesia . One hundred μl of blood were immediately dispensed to a pre-heparinized tube to be used for general hematological determinations . The remaining volume was left to clot , and serum was then collected and stored at -80°C for posterior immunoglobulin titration . Mice were disinfected using 70% ethanol . Thereafter , abdominal skin was cut with sterile scissors and removed to expose the abdomen . Peritonea were then opened using a new pair of sterile scissors and tweezers , and the spleens harvested to pre-weighed 15 mL falcon tubes containing 5 ml of complete RPMI ( Lonza , Switzerland ) [10% heat-inactivated FBS ( Lonza , Switzerland ) , 2 mM L-glutamine , 100 U/ml penicillin and 100 μg/ml streptomycin ( BioWhittaker , Walkersville , MD , USA ) ] supplemented with 50 μM 2-mercaptoethanol ( Sigma-Aldrich , MO , USA ) . Falcon tubes were re-weighed to obtain spleen masses . Splenic single cell suspensions were then obtained using FalconTM Cell Strainers ( Fisher Scientific , MA , USA ) , and their concentrations determined using an EVETM automatic cell counter ( NanoEntek , Seoul , Korea ) . Ten million cells per animal were pelleted , washed twice with PBS and stained for 10 minutes at 37°C with CFSE ( 1 μM; in 1 ml of PBS; Thermo Fisher Scientific , MA , USA ) . Complete RPMI was then added to cell suspensions to stop the reaction , that were then pelleted , re-suspended in complete RPMI and incubated at 4°C for 5 minutes . Afterwards , cells were once more centrifuged ( 5 min , 350 g ) , resuspended in complete RPMI supplemented with 50 μM 2-mercaptoethanol ( Sigma-Aldrich , MO , USA ) , and plated into u-bottom 96-well plates at the final amount of 2 . 5 x 105 cells per well . After plating , different stimuli were added , depending on the experiment: individual non-formulated antigens ( KMP11 , LJL143 or LeishF3 ( + ) ; 10 μg/ml ) , a pool of the three recombinant antigens ( KMP11+LJL143+LeishF3+; 10 μg/ml each ) , and total Leishmania antigen ( TLA; equivalent to 10 parasites per cell ) . Concanavalin A ( 3 μg/ml ) and complete RPMI medium were added as positive and negative controls , respectively . From each animal , a single CFSE staining was performed , being only then the cells divided and stimulated . This warrants that any proliferating cells , regardless the condition , comes from the same initial suspension . Cells were incubated ( 37°C and 5% CO2 ) for three ( positive controls ) or four days ( remaining stimuli ) , and then the originated cell culture supernatants were collected and stored at -80°C for cytokine quantification . Each determination was performed in duplicate . Proliferating CD4+ and CD8+ cell populations were determined by Flow Cytometry , based on the premise that CFSE intensity gradually decreases after each cell division . The anti-mouse monoclonal antibodies used to perform this study were all purchased from BioLegend ( CA , USA ) unless otherwise stated: FITC labeled anti-MHC-II ( I-Ad ) ( AMS-32 . 1 , BD Biosciences , NJ , USA ) , anti-IFN-γ ( XMG1 . 2 ) and anti-IL-17A ( TC11-18H10 . 1 ) ; PE labeled anti-CD8 ( 53–6 . 7 , BD ) , anti-Siglec-F ( E50-2440 , BD ) , anti-IL-4 ( 11B11 ) and anti-IL-6 ( MP5-20F3 ) ; PerCP-Cy5 . 5 labeled anti-Ly6C ( HK1 . 4 ) and anti-TNFα ( MP6-XT22 ) ; PE-Cy7 labeled anti-CD3 ( HA2 ) and anti-CD11b ( M1/70 ) ; APC-Cy7 labeled anti-CD11c ( N418 ) ; APC labeled anti-CD19 ( 6D5 ) , anti-IL-5 ( TRFK5 ) and anti-IL-10 ( JES5-16E3 ) ; BV510 labeled anti-CD4 ( RM4-5 ) and Pacific BlueTM labeled anti-Ly6G ( 1A8 ) . To analyze lymphoid and myeloid cell populations , two panels of antibodies were designed . The lymphoid panel was composed of anti-CD8 , -CD3 , -CD4 , and -CD19 . The Myeloid panel comprised anti-CD11b , -CD11c , -Siglec-F , -Ly6C , -Ly6G and -MHC-II . Surface staining of splenic cells was performed in PBS + 0 . 5% BSA ( 20 min , 4°C ) followed by 15 min fixation with 2% PFA . For intracellular staining ( non-specific cytokine production ) , splenocytes were cultured for 2h with PMA/Ionomycin ( 50/500 ng/ml ) and then Brefeldin A ( 10μg/mL ) was added for 2 additional hours . Cells were surface stained , fixed and permeabilized with 1% saponin ( Sigma-Aldrich , MO , USA ) and then intracellularly stained [33] . Samples were acquired in a FACSCanto ( BD ) and analyzed with FlowJo software v10 ( TreeStar , OR , USA ) . An initial gate plotting FSC-A versus SSC-A was performed . Afterwards , singlets were selected by plotting FSC-A versus FSC-H and the remaining cell populations were resolved . T lymphoid cell populations were defined as CD3+/CD4+ and CD3+/CD8+ while B cells were defined as CD19+ . Non-specific cytokine production by T cells was assessed within CD3+/CD4+ and CD3+/CD8+ cells . Myeloid cell populations were gated as eosinophils ( Siglec-F+/SSC-Hint/high ) , neutrophils ( CD11bhigh/Ly6Ghigh/Siglec-F- ) , DCs ( CD11c+/MHC-IIint/high ) and monocytes/macrophages ( CD11b+/CD11c-/Ly6G-/Siglec-F- ) . Proliferating T cells ( CD4+ or CD8+ ) were defined as CFSEint/low/neg ( FITC channel ) , always comparing each condition with the respective negative control . Cytokines were quantified , according to the manufacturer , using the commercial kits: Mouse IL-10 DuoSet ELISA , ( R&D Systems , MN , USA ) , IL-12p70 , IL-4 and IFN-γ ELISA MAX Deluxe ( BioLegend , CA , USA ) . Uncoagulated murine blood samples were used to obtain a complete blood evaluation , including haemoglobin and hematocrit levels , and total red blood cell , white blood cell , and platelet counts , using an automated blood cell counter ( Sysmex K1000 , Hamburg , Germany ) . For specific immunoglobulin titration assays , high protein binding 96-well plates were coated overnight at 4°C , individually with each one of the three antigens comprising the vaccine formulation ( 1 μg/ml ) , with a pool of the three antigens ( 1 μg/ml each ) , or with soluble Leishmania antigen ( SLA; 1 μg/ml ) ; all solutions were prepared in NaHCO3 0 . 1 M . Additionally , total IgG levels were also determined , using as a coating agent α-mouse IgG ( 1 μg/ml; Southern Biotech , AL , USA ) . Plates were then washed with PBS Tween 0 . 1% , blocked with 1% gelatin in PBS ( blocking buffer ) for 1 hour at 37°C and re-washed . Each serum was then serially diluted ( twofold , 7 dilutions ) in blocking buffer . Wells filled with just blocking buffer were used as blanks . Plates were incubated for 1 hour at 37°C and re-washed . Afterwards IgG and isotypes , IgM and IgE were detected using horseradish peroxidase ( HRP ) coupled α-mouse antibodies [diluted 1:5000 ( IgM , IgE , IgG1 , IgG3 , IgG2b and IgG2a; Southern Biotech , AL , USA ) or 1:8000 ( IgG; Southern Biotech , AL , USA ) in blocking buffer; incubated for 30 minutes , at 37°C] . The plates were washed for a last time , and the substrate ( orthophenyldiamine ( OPD ) in citrate buffer ) was added for 10 minutes , time after which the reaction was stopped with HCl 3 N . Absorbance values were determined at 492 nm in a SynergyTM 2 Multi-Mode Reader ( BioTek instruments , VT , USA ) . The value of the last dilution factor for which the corrected optical density was equal or higher than 0 . 1 was the defined titer of the antibody ( endpoint titer ) , as has been previously described [34] . Results are generally expressed per individual animals/samples , with a representation of the group mean value ± standard deviation . Statistical differences were analyzed using GraphPad Prism v6 . 01 ( CA , USA ) . Mice experimental groups were compared using either the one-way ANOVA or the unpaired t-test . Comparisons between human samples were performed using Mann–Whitney test .
Different experimental groups were designed for the pre-clinical tests of the optimized vaccine candidate in mice to assess , besides the safety of the vaccine components , the influence of several variables in the final outcome of the immunization , such as the contribution of the virosome to the induction of immunogenicity or the possibility of immunodominance of the sand fly salivary protein ( S2 Fig ) . In parallel , the effect of a prime with the sand fly-salivary protein in the final vaccine-elicited immune response was evaluated ( S2 Fig ) . Defined above as essential to increase the antigens immunogenicity , the adjuvant ( GLA-SE ) was administered to all groups . Human immunogenicity studies dictated the replacement of LeishF3 by LeishF3+ as one of the three antigens of the optimized-vaccine .
Because in Leishmania spp . endemic areas the majority of infected persons do not develop clinical symptoms and previous infection leads to robust immunity against the parasite , vaccination is considered as one of the most viable ways to control Leishmania infection . However , to date there is no anti-Leishmania vaccine available for humans [3 , 8] . This work proposes an innovative vaccine concept , consisting on Virus-Like Particles ( VLP ) loaded with 3 different antigens , two from the parasite and one from the sand fly vector , adjuvanted with a TLR4 agonist , as a strong candidate to fill in the existing gap in terms of human anti-Leishmania vaccines . Although already demonstrated as a useful adjuvant in the context of anti-Leishmania vaccination , we considered it essential to determine the effect of GLA-SE on vaccine-elicited immune responses , mainly because the vaccine candidate we propose is much more complex than the one previously tested ( single recombinant fusion protein ) [25] , with a multi-antigen nature and a virosomal component , which may itself have an adjuvant effect [37] . As expected , the adjuvant generally improved the antigen-elicited immune response , both in terms of specific cellular and humoral responses elicited by non-formulated antigens ( S1i and S1ii Fig; PA versus P ) . Furthermore , similar results were obtained for virosome-formulated proteins ( Fig 1i and 1ii; VPA versus PA ) indicating , on one hand , the essentiality of the adjuvant in this vaccination context , and on the other that the Influenza VLP are working mainly as vehicles , and not as adjuvants in this context . For almost two decades in vaccinology , the effect of the antigen dosage in the final outcome of the immunization has been studied and discussed , always in parallel with the concept of antigen affinity [38 , 39] . Here , in order to define the optimal vaccine composition , based on the specific responses elicited , we tested two doses of antigens and adjuvant . A lower antigen/adjuvant dose , although is worse regarding the humoral immune response elicited ( Fig 1ii , S1ii Fig ) , promotes a stronger specific cellular immune response against both LJL143 and LeishF3 ( Fig 1i , S1i Fig ) . These results point therefore to the idea that “less is more” , once it is generally accepted that cellular immunity is essential for Leishmania elimination [8] , and justify the choice of the lower antigen/adjuvant dosages used in the pre-clinical trials per se . In agreement with previous observations [40–42] , we demonstrated the immunogenicity of KMP11 in humans . In fact , KMP11 was the antigen that generated a better response in the VL patients PBMCs stimulation experiments , with a significant increase in IFN-γ production by cells collected from cured VL patients compared with cells from matching endemic controls ( Fig 2i ) . Such an observation was paramount to the final decision to include this particular antigen as a component of the innovative vaccine candidate . A possible explanation for the weak KMP11 immunogenicity detected in mice , which contrasts with previous studies in animals , is the use of the recombinant protein in opposition with the use of different DNA-based or heterologous recombinant live-vaccine approaches [21 , 43–45] . On the other hand , the non-expressive response obtained in human ex vivo immunogenicity studies against LeishF3 ( results generally similar between VL patients and controls; Fig 2i ) was unexpected . A previous study showed the individual immunogenicity of NH and SMT , the two components of the fusion protein LeishF3 , and successfully defined it as immunogenic and safe in a Phase I human clinical trial [25] . However , while the ex vivo immunogenicity assessment done by Coler and colleagues [25] was performed in a cohort from a L . donovani endemic area in Bangladesh , ours was performed using a cohort from a L . infantum endemic area in Spain , which may explain the lower-than-expected reactivity detected . These results led to the characterization of the immunogenicity of a LeishF3 “upgraded version” named LeishF3+ using the same human cohort , and the final substitution of LeishF3 by LeishF3+ in the vaccine formulation due to the observed improvement of the detected responses ( Fig 2ii ) . Interestingly , PBMCs from some individuals of the three different studied groups , including the controls ( Old World human samples ) responded to LJL143 ( Fig 2i ) , a salivary protein from Lutzomyia longipalpis , the vector of VL in the New World . The sand fly salivary Lufaxin-like proteins are found in both the New and Old Worlds sand flies [46] . Within this family , LJL143 from L . longipalpis and PpeSP06 , the homologous salivary protein from P . perniciosus , the main vector of VL in the Mediterranean Basin , share an amino acid sequence conservation of 45% . In line with this evidence , the reactivity , equally detected in samples from infected and non-infected individuals , is a potential indicator of immune cross-recognition of LJL143 , with which in theory , the studied population has not been in contact before . These results further support the inclusion of this antigen in the vaccine formulation , stressing the sand fly salivary protein LJL143 as a potential “broad-spectrum antigen” . All the above mentioned justifies the final composition of the optimized innovative vaccine used in the definitive pre-clinical trials in mice for extrapolation of its safety profile and characterization its in-depth immunogenic profile . Furthermore , several variables in the final outcome of the immunization , such as the contribution of the virosome to the induction of immunogenicity , or the possibility of immunodominance of the sand fly salivary protein , were considered . The values of hematological studies , splenic cell populations , CD4+ T cell non-specific reactivity and IgE specific titers ( Table 1 , S3i–S3iv Fig ) , determined in the pre-clinical trials , potentially indicate the safety of each vaccine component ( proteins , adjuvant and virosome ) . The absence of specific IgE titers deserves to be highlighted , due to the correlation of antigen-specific IgE and vaccine-associated anaphylatic reactions development , shown particularly , but not exclusively for anti-Influenza vaccines [47] . To further explore the safety of the vaccine candidate , a repeated dose toxicity study in rabbits , complying with the WHO Expert Committee on Biological Standardization [48] is ongoing . The different optimized formulations tested are indeed immunogenic , eliciting overall significant specific humoral and cellular immune responses ( Figs 3i–3iv and 4i and 4ii ) . Regarding the humoral responses detected , they were generally mixed in nature ( IgG1/IgG2a ) , indicating a mixed Th1/Th2 phenotype . Furthermore , the improvement of the specific humoral response against LeishF3+ induced by the VLP-based antigen formulations ( Fig 3iii ) deserves to be highlighted , as a possible advantage of the use of formulated antigens without forgetting , however , the debatable relevance of the humoral immune responses in the context of VL [49] . In respect to cellular immune responses detected , they shown distinct magnitudes , depending on each of the individual antigens . Reproducibly , the sand fly-derived antigen induced a more robust response than the parasite-derived ones ( LJL143 ≥ LeishF3+ > KMP11; Fig 4ii ) . Of note , the responses obtained against LeishF3+ were higher than those previously obtained against LeishF3 ( Fig 4ii versus S1i Fig; PA ( 1+1+1 ) versus PA ) . This difference in the magnitude of the responses detected against the three different antigens seems not to be an immunodominance problem , since both cellular and humoral immune responses detected against LeishF3+ ( the parasite derived antigen showing significant responses ) were similar for the groups that received VPA ( 1+1+1 ) and VPA ( 1+5+5 ) ( doses of LJL143 , KMP11 and LeishF3+ , respectively; Figs 3iii and 4ii ) . Although it is a dogma that the protection against Leishmania spp . requires antigen-specific CD4+ and CD8+ T cell responses , the correlates of immunity to human VL are yet to be completely understood [50] . Therefore , the vaccine correlates of protection are still a debatable issue that takes bigger proportions when we add the translatability of animal pre-clinical trials to the equation . This said , the balance between specific IFN-γ and IL-10 production , has been used as predictive of vaccine efficacy in mice [51 , 52] . In our study , through cell proliferation assays we detected in non-primed groups , a higher production of IL-10 than IFN-γ in response to the pool of antigens or to LeishF3+ alone , an either comparable or prevalent IFN-γ over IL-10 response against LJL143 ( depending on the experimental group; Fig 5i and 5ii ) and a limited but prevalent IFN-γ over IL-10 response against total parasite antigens ( TLA; Fig 6ii ) . The Th1 directed response induced by stimulation with TLA , the experimentally closest experimental set up to the infectious process ( deposition of whole parasites in the skin ) is a promising indication of vaccine effectiveness . Nevertheless , we cannot ignore the apparent main Th2 response induced by the pool of antigens and LeishF3+ , and either mixed or Th1 responses induced by LJL143 . One curious observation is that , while the IL-10 levels quantified in the cell proliferation against the pool of antigens are 1 . 5 fold higher than the sum of levels determined in the cell proliferation against the individual antigens ( excluding KMP11 ) , the same comparison gives similar IFN-γ ( Fig 5i and 5ii ) . This particular observation , together with evidence showing that stimulation with high antigen doses leads to enhanced IL-10 production by Th1 CD4+ cells [53] , makes us speculate on the occurrence of a possible regulatory mechanism in vitro as a way to control a vigorous immune response and prevent inflammation-mediated damage . In parallel , in the pre-clinical trial , we evaluated the effect of priming with the sand fly salivary protein in the final vaccine-elicited immune responses . Interestingly , the previous administration of the sand fly saliva derived antigen may be beneficial for the generation of a better response against the parasite-derived antigens , particularly in terms of cellular immunity ( higher CD4+ T cell proliferation against LeishF3+ , KMP11 and TLA; Figs 4ii and 6i ) . Nevertheless , the IL-10 response detected , particularly against the pool of antigens and LJL143 , was higher in the primed animals ( Fig 5i and 5ii ) . On the other hand , the specific IFN-γ response generated by TLA increased tendentiously comparing primed with non-primed animals , and was 5 fold higher than the TLA induced IL-10 response ( Fig 6ii ) , making this vaccination approach interesting to be tested in terms of anti-Leishmania effectiveness , in the context of natural infection ( parasites delivered by the sand fly in the presence of salivary proteins ) . Overall our results indicate that the innovative vaccine candidate tested here represents a promising anti-Leishmania vaccine . Some questions remain that need to be further explored , such as the potential benefits or implications of the predicted constant vector exposure in endemic countries , as well as a probable exposure to Influenza virus , to the final vaccine-induced responses . | Although vaccination is accepted as a potentially effective approach to prevent leishmaniasis , to date there is no vaccine available for human disease . The research on the topic is therefore extremely important , and the design and testing of new vaccine approaches , as well as non-traditional immunization schemes continues to be as relevant as before . This study proposes an innovative vaccine approach for human visceral leishmaniasis , not only due to its multi-antigen nature which contemplates both parasite and vector derived proteins , but also because it explores the possibility of the use of Influenza virosomes as antigen-delivery vehicles . A strong TLR-4 agonist completes the vaccine formulation . Here we show the rationale-behind this vaccine approach , the safety of all the vaccine components in our in vivo context , and immunogenicity studies of the optimized vaccine candidate in mice that explored the contribution of the virosome to the antigen-elicited immune responses . Additionally , we tested an unusual immunization scheme that potentiated the final vaccine-elicited immune responses . This prime-boost immunization approach gives relevance to the use of both parasite and vector derived antigens together as an anti-Leishmania vaccine , and proposes a new strategy for vaccination in endemic areas , where people are constantly exposed to sand fly bites . | [
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... | 2017 | Pre-clinical antigenicity studies of an innovative multivalent vaccine for human visceral leishmaniasis |
The role of CD8 T cells in anti-tuberculosis immunity in humans remains unknown , and studies of CD8 T cell–mediated protection against tuberculosis in mice have yielded controversial results . Unlike mice , humans and nonhuman primates share a number of important features of the immune system that relate directly to the specificity and functions of CD8 T cells , such as the expression of group 1 CD1 proteins that are capable of presenting Mycobacterium tuberculosis lipids antigens and the cytotoxic/bactericidal protein granulysin . Employing a more relevant nonhuman primate model of human tuberculosis , we examined the contribution of BCG- or M . tuberculosis-elicited CD8 T cells to vaccine-induced immunity against tuberculosis . CD8 depletion compromised BCG vaccine-induced immune control of M . tuberculosis replication in the vaccinated rhesus macaques . Depletion of CD8 T cells in BCG-vaccinated rhesus macaques led to a significant decrease in the vaccine-induced immunity against tuberculosis . Consistently , depletion of CD8 T cells in rhesus macaques that had been previously infected with M . tuberculosis and cured by antibiotic therapy also resulted in a loss of anti-tuberculosis immunity upon M . tuberculosis re-infection . The current study demonstrates a major role for CD8 T cells in anti-tuberculosis immunity , and supports the view that CD8 T cells should be included in strategies for development of new tuberculosis vaccines and immunotherapeutics .
Tuberculosis remains one of the major causes of global mortality , and has become increasingly prevalent and deadly as a result of HIV/AIDS pandemic and the emergence of extensively drug resistant ( XDR ) strains of M . tuberculosis [1] . Elucidating the relevant components of anti-tuberculosis immunity is therefore of critical importance and urgency to facilitate the development of safe and effective vaccines for the global battle against tuberculosis . The attenuated Mycobacterium bovis strain Bacille Calmette-Guerin ( BCG ) is currently the sole vaccine for tuberculosis that is approved for use in humans . BCG vaccination has been shown to induce protection against severe tuberculosis in children and nonhuman primates , but it is inconsistent or ineffective at conferring protection against tuberculosis in adults who have been vaccinated early in life [2] , [3] , [4] , [5] , [6] . Although it is widely accepted that CD4 T cells play a critical role in the ability of humans and experimental animals to resist active M . tuberculosis infection [7] , the contribution of CD8 T cells to natural or BCG-induced immunity against tuberculosis remains unclear . Some in vivo studies in mice and bovines or in vitro work in humans provide evidence supporting the contribution of CD8 T cells to immunity against tuberculosis [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , whereas a number of studies in mouse models of tuberculosis have argued against a significant role for CD8 T cells in the control of primary M . tuberculosis infection [19] , [20] , [21] , [22] , [23] , [24] , [25] . However , it is likely that the mouse model of tuberculosis does not accurately reflect the complete picture of how protective immune responses against tuberculosis develop in humans , and better models of this disease are needed to enable a full understanding of this process . We propose that nonhuman primates should provide more relevant models for evaluating a role of human CD8+ T cells in BCG vaccine-induced immunity against tuberculosis [6] , [26] , [27] , [28] , [29] , [30] . Unlike mice and other rodents studied to date , rhesus macaques share with humans a number of important features of the immune system that relate directly to the specificity and functions of CD8 T cells , such as the expression of group 1 CD1 proteins that are capable of presenting M . tuberculosis lipids antigens and the cytotoxic/bactericidal protein granulysin [31] , [32] . Importantly , we and others have shown that disease course and pathology in M . tuberculosis-infected macaques resemble human tuberculosis , and that vaccine-induced immunity to tuberculosis can be experimentally evaluated in rhesus and cynomolgus macaques [6] , [28] , [29] , [33] , [34] . Although the importance of CD4 T cells in nonhuman primate models of tuberculosis has been reported , data are currently lacking on the role of CD8 T cells [35] , [36] .
To directly examine the importance of CD8 T cells in BCG vaccine-induced anti-tuberculosis immunity , six BCG-vaccinated macaques were treated with depleting anti-CD8 antibody , cM-T807 [37] at the same time that they received pulmonary inoculation with M . tuberculosis . Pulmonary M . tuberculosis infection was introduced by bronchoscope-guided inoculation of 3000 CFU of M . tuberculosis into the right caudal lobe [29] . As controls , six BCG-vaccinated moneys were treated similarly with isotype-matched human IgG at the time of infection with M . tuberculosis , and six unvaccinated macaques ( naïve controls ) were treated with saline at the time pulmonary M . tuberculosis was introduced . Treatment with depleting anti-CD8 Ab resulted in profound depletion of CD8 cells during M . tuberculosis infection , with CD3+CD8+ T cells and CD3−CD8+ cells falling to barely detectable levels in blood and BAL fluid at 1–4 weeks after the simultaneous anti-CD8 Ab treatment and M . tuberculosis infection ( Fig . 1A ) . Even at 7 weeks after the anti-CD8 Ab treatment , CD8 T cells remained partially depleted ( Fig . 1A ) . Consistently , peptide antigen-specific IFNγ-producing CD8 T effector cells also became undetectable early after the simultaneous CD8 depletion and M . tuberculosis infection ( Fig . 1B ) . In contrast , the macaques treated with the nonspecific isotype matched control Ab exhibited increased percentages and numbers of CD8 T cells , including peptide-specific CD8 T effector cells , after the M . tuberculosis infection ( Fig . 1A , 1B ) . Thus , we concluded that this method of CD8 T cell depletion was suitable for evaluating the impact of previously immunized CD8 T cells on the progression and outcome of pulmonary tuberculosis infection in the macaque model . To determine the impact of depletion of CD8 lymphocytes including BCG-elicited CD8 T cells on immunity to tuberculosis in the rhesus macaque model , we evaluated four aspects of vaccine efficacy: ( i ) severe clinical manifestations such as marked changes in temperature , progressive coughing , dispnea/distress , anorexia , altered consciousness or weight loss during a 2-month follow-up; ( ii ) bacterial colony counts in BAL fluid at various times after M . tuberculosis infection and in lung tissues at necropsy; ( iii ) gross pathology of the lungs and other thoracic and extrathoracic organs at necropsy; ( iv ) histologic evaluation of tissues for granulomas and other microscopic lesions . None of the macaques in any of the control or treatment groups developed fatal clinical tuberculosis or significant loss of body weight during 2-months of follow-up . This was consistent with earlier reports that Chinese rhesus macaques were more resistant to infections than Indian rhesus macaques , and that adult animals were less susceptible to fatal tuberculosis than juveniles [6] , [27] , [38] . Nevertheless , the CD8 Ab-treated group of BCG-vaccinated macaques showed significantly higher bacterial counts in BAL fluids than the IgG isotype control group at 42 days after M . tuberculosis infection ( Fig . 2A ) . In addition , the CD8 Ab-treated group also had significantly higher bacterial CFU counts in the lung tissues in the right caudal lobe ( M . tuberculosis infection site ) and distant right middle lobe than the isotype IgG-treated group ( Fig . 2B ) . These results suggested that CD8 T cells contributed to BCG vaccine-induced immune control of M . tuberculosis replication in the vaccinated macaques . The next critical question regarding the contribution of CD8 T cells to BCG vaccine-induced immunity against tuberculosis was whether depletion of CD8 lymphocytes in BCG-vaccinated macaques led to more severe tuberculosis lesions . To address this , we performed complete necropsy studies 2 months after M . tuberculosis infection of three groups of macaques . The gross pathology was evaluated in detail in a quantitative fashion using a previously described scoring systems [6] , [28] , [29] , [33] , and then compared among the three groups of M . tuberculosis-infected macaques . The isotype control IgG-treated BCG-vaccinated macaques exhibited apparent protection against tuberculosis compared to the naïve saline-treated group ( Fig . 3 ) . For these macaques with intact BCG-elicited CD8 T cells , M . tuberculosis infection appeared to be well contained by individual granulomas predominantly at the primary infection site in the right caudal lobe ( Fig . 3A ) . In contrast , the CD8 Ab-treated group of BCG-vaccinated macaques exhibited a reduced containment of tuberculosis lesions in the infection site . CD8 Ab-treated macaques showed greater numbers of lung lobes showing extensive coalescing granulomas than isotype IgG-treated control animals ( p<0 . 05 , Fig . 3A ) . These CD8-depleted macaques also exhibited more lobes with severe/extensive caseating and miliary lesions or caseation pneumonia than the isotype IgG-treated controls ( p<0 . 05 , Fig . 3A ) . Tuberculosis lesions in the CD8 Ab-treated macaques were more likely distributed or disseminated in other lobes or opposite lungs and hilar lymph nodes/pleural ( Fig . 3A ) . Furthermore , depletion of CD8 lymphocytes led to systemic dissemination of tuberculosis , as grossly apparent granulomas were identified in extra-thoracic organs in five of 6 macaques treated with anti-CD8 Ab . When total scores of gross tuberculosis lesions were calculated for individual macaques , and compared among the three groups of animals , we found that the CD8 Ab-treated group developed significantly worse gross tuberculosis lesions than the isotype IgG-treated group ( p<0 . 05 , Fig . 3B ) . The severity of tuberculosis following depletion of CD8 lymphocytes was further evaluated at the microscopic level , which revealed that depletion of CD8 lymphocytes during M . tuberculosis infection led to changes in the organization and cellular composition of granulomas . In the isotype control IgG-treated macaques with intact CD8 T cells , M . tuberculosis infection was usually contained by well-organized granulomas infiltrated by numerous lymphocytes and some neutrophils ( Fig . 3C ) . However , in the CD8 Ab-treated macaques , lymphocytic infiltration was reduced and necrosis was more pronounced , especially in the lungs ( Fig . 3C ) . Furthermore , microscopic scanning of a series of tissue sections derived from extrathoracic organs revealed that five of six CD8 Ab-treated BCG-vaccinated macaques had a number of granulomatous lesions in the spleen , whereas only one of six isotype control IgG-treated macaques displayed microscopic granulomas in the spleen . All naïve macaques developed multiple granulomatous lesions in spleens and livers ( Fig . 3C ) . Finally , we sought to examine whether CD8 memory T cells resulting from prior M . tuberculosis infection were also important for anti-tuberculosis immunity . This question is directly relevant to the development of novel vaccines that are based on attenuated M . tuberculosis mutants with virulence gene deletion or other modifications [39] , [40] , and also is important for understanding the potential immunological defects that allow re-infection with M . tuberculosis and reactivation of latent tuberculosis . To generate M . tuberculosis-immunized animals with anti-tuberculosis immune memory , we made use of BCG-vaccinated juvenile rhesus macaques who exhibited rapid recall T cell responses and survived fatal tuberculosis for 2 . 5 months after aerosol challenge with 400 CFU M . tuberculosis . Under these conditions , unimmunized naïve control macaques consistently became moribund due to development of early fatal tuberculosis [6] . These M . tuberculosis-exposed macaques that had transient low levels of bacillus but no evidence of active tuberculosis after the challenge were treated daily for 3 months with a regimen of anti-tuberculosis drugs ( 5 mg/kg Isoniazid plus and 15 mg/kg Pyrazinamide ) , and then rested for 2 months during which they showed no evidence of detectable bacilli in repeated lung washings or of clinical tuberculosis . Two animals were then treated with depleting anti-CD8 Ab , and two with isotype control IgG at the time of re-infection with 3000 CFU M . tuberculosis by aerosol . The CD8 Ab-treated but not isotype IgG-treated macaques showed profound depletion of CD8 lymphocytes in the blood and BAL fluid ( Fig . 4A ) . Importantly , the CD8 Ab-treated macaques with CD8 depletion showed much higher levels of M . tuberculosis burdens in BAL fluid and lung tissues than the IgG-treated control macaques ( Fig . 4B ) . The gross pathology studies showed the isotype IgG-treated macaques displayed no or limited numbers of small non-caseating granulomas ( Fig . 4C ) . In contrast , the CD8-depleted macaques showed dissemination of >0 . 5 cm coalescing or caseating granulomas or tubercle nodules in both lungs ( Fig . 4C ) . When such tuberculosis lesions were scored using the scoring system as described [41] in these individual macaques , the lesions scores of CD8-depleted macaques were significantly worse than the isotype IgG-treated controls ( p<0 . 01 , by nonparametric student t test ) . Consistently , histologic analyses revealed that granulomas in the CD8 Ab-treated macaques with CD8 depletion were large and necrotic in the center , whereas those in the isotype IgG-treated macaques with CD8 intact were small and highly lymphocytic without apparent necrosis ( Fig . 4D ) . These results from M . tuberculosis-immunized , drug-treated macaques provided novel evidence suggesting that M . tuberculosis-elicited CD8 T cells are important for memory protection against tuberculosis after M . tuberculosis re-infection .
Our findings in BCG-vaccinated and M . tuberculosis-immunized macaques strongly support the hypothesis that CD8 T cells play a critical role in host defense against tuberculosis despite that the role of CD3−CD8+ cells cannot be completely ruled out . Although the depletion of other cells expressing CD8 in addition to CD8 memory T cells could potentially have contributed to the observed effects of anti-CD8 treatment , this seems unlikely for several reasons . First of all , the anti-CD8 effect was manifested as a loss of immunological memory induced by previous BCG immunization or M . tuberculosis infection , and such memory is a characteristic of CD8 T cells but not of other cells known to express CD8 such as NK cells . In addition , most macaque dendritic cells , like their human counterparts , do not express CD8 [42] , and thus immune responses of these cells in tuberculosis might not be affected directly by the antibody-mediated CD8 depletion . Furthermore , depletion of a subset of NK cells and potentially other CD8+ cells that do not express TCR/CD3 was at most partial by the anti-CD8 treatment in contrast to the depletion of CD8 T cells which was essentially complete for 3–4 weeks after infection . The memory CD8 T cells conferring anti-tuberculosis immunity in BCG- and M . tuberculosis-immunized macaques may include at least three different CD8 T cell subpopulations: ( i ) peptide-specific classical MHC class I-restricted CD8 T cells; ( ii ) non-classical MHC class I-restricted CD8 T cells [43]; ( iii ) lipid-specific CD1-restricted CD8 T cells [44] . They may also include phosphoantigen-specific Vγ2Vδ2 T cells , the γδ T-cell subset existing only in primates , since macaque Vγ2Vδ2 T cells can express CD8 and exhibit anti-microbial responses during clonal activation/expansion [45] . The relative importance and protective surrogate markers of these different CD8 T cell subpopulations to anti-tuberculosis immunity is currently not known . However , it is noteworthy that only the first of these three populations is present in mice , whereas all three are present in humans . For this reason , studies such as those reported here in nonhuman primates may reveal important activities of CD8 T cells that are not apparent in mouse models of tuberculosis . Our studies add to a growing body of evidence that supports the view that CD8 T cells are of critical importance for effective immunity against M . tuberculosis . Mycobacterium-specific CD8 T cells have been shown to possess effector functions that are likely to assist in the containment and clearance of M . tuberculosis , including cytolytic effector function , cytokine production and direct antimicrobial activity through the production of anti-microbial peptides such as granulysin [31] , [46] , [47] . Even in mice and other rodents which may lack some of the important recognition and effector activities of CD8 T cells in primates , it is noteworthy that studies have shown that approaches to vaccination that augment CD8 T cell responses can provide better protection against challenge with M . tuberculosis than what is achieved with standard BCG vaccination [48] . Such findings , together with the results of the current study , strongly suggest that CD8 T cells should be considered as relevant targets in the design and development of new tuberculosis vaccines and immunotherapeutics .
A total of 22 monkeys were included in these studies . 18 Chinese rhesus ( Macaca mulatta ) , 4–7 years old , were used to assess CD8 T cells for BCG vaccine-induced immunity; 4 juvenile Indian rhesus monkeys , 2 . 9 years old , were used for evaluating M . tuberculosis-immunized CD8 T cells . Studies using all the animals were documented in animal protocols and approved by IACUC . 12 Chinese rhesus monkeys were vaccinated intradermally with 50×106 CFU of BCG Danish ( FDA stock ) , as previously described [33] . 4 months after the vaccination , the monkeys were randomly assigned to CD8 Ab- and isotype IgG-treated groups . Four Indian rhesus monkeys were first vaccinated with BCG , and then challenged with 400 CFU of M . tuberculosis by aerosol as previously described [6] . These vaccinated monkeys survived fatal tuberculosis during a 2 . 5-month follow-up , whereas four control naïve animals became moribund and had to be euthanized due to the development of fatal tuberculosis within 1 . 5 moths after the infection [6] . These M . tuberculosis-exposed monkeys with transient low levels of bacillus but no evidence of active tuberculosis were treated daily for 3 months with anti-TB drugs; Isoniazid ( 5mg/kg ) and Pyrazinamide ( 15mg/kg ) mixing with yoga as previously described [49] , and then rested subsequently for 2 months . M . tuberculosis infection appeared to be “cured” by the antibiotics , since repeated lung washings showed no evidence of detectable bacillus organisms; clinical follow-up demonstrated no evidence of clinical tuberculosis . Two of these monkeys were randomly assigned for anti-CD8 Ab treatment , and the other two assigned for treatment with isotype IgG at the time they were re-infected with 3000 CFU M . tuberculosis by aerosol . For the CD8 Ab-treated group , each monkey was injected intravenously with depleting anti-CD8 Ab cM-T807 [37] at days 0 , 3 , 7 and 10 at doses of 10 mg/kg , 5 mg/kg , 5 mg/kg and 5 mg/kg , respectively . For the isotype control group , each monkey was similarly injected with human IgG at same doses . For the naïve control group , each monkey was injected similarly with saline . At day 0 , each monkey in BCG groups and the naïve group was infected with 3000 CFU of M . tuberculosis Erdman ( the standard challenge stock from FDA ) by the bronchoscope-guided injection of the inoculum into the right caudal lobe as previously described [29] , [33] . For M . tuberculosis-immunized monkeys treated with anti-tuberculosis drugs , M . tuberculosis re-infection was introduced by aerosol challenge with about 3000 CFU M . tuberculosis organisms as previously described [6] . The Inhalation Exposure System used to conduct the aerosol exposure tests was enclosed within a Class III biological safety cabinet . A modified Microbiological Research Establishment type three-jet Collison nebulizer ( BGI , Waltham , MA ) with a precious fluid jar was used to generate a controlled delivery of M . tuberculosis aerosol ( 1–1 . 5 um diameter of droplets ) from a PBS suspension . 106 M . tuberculosis CFU/ml were placed in the nebulizer and monkeys were exposed for 10 min . Samples of the aerosol were collected using all-glass impingers for analyzing M . tuberculosis concentration ( CFU/ml ) . The inhaled doses were determined based on the AGI concentration , sampler volume , sampling rate and respiratory minute volume of individual macaques . The inhaled doses for the individual monkeys ranged from 2800 to 3100 CFU of M . tuberculosis . Prior to BAL , animals were subjected to overnight or 24 h fasting , and were tranquilized i . m . with 1–2 mg/kg xylazine ( Ben Venue Laboratories , Bedford , OH ) and 10 mg/kg ketamine HCl . For BAL , animals also received 0 . 05 mg/kg atropine ( Phoenix Scientific , Inc . , St . Joseph , MO ) i . m . as an anticholinergic and were restrained in an upright position . A pediatric feeding tube was inserted down the larynx , into the trachea through direct visualization with a laryngoscope to the level of the carina . 10 ml of saline were instilled into the trachea and immediately withdrawn and repeated a maximum of 3 times until a total of 12–15 ml BAL fluid was retrieved . PBL were isolated from EDTA blood of the monkeys using Ficoll/diatrizoate gradient centrifugation . Lymph nodes and spleen were carefully teased to generate single-cell suspensions . Tissue pieces from lungs , livers , and kidneys were minced in RPMI medium , as previously described [29] , to collect single cell suspensions ( mainly lymphocytes and tissue macrophages ) . The single cells suspensions from these non-lymphoid organs were divided into three parts: one directly used for mycobacterial CFU counts; one directly saved as pellets for real time quantitation of M . tuberculosis Ag85B RNA; one subjected to isolation of lymphocytes by Ficoll/diatrizoate gradient centrifugation for flow cytometry-based analyses of T cells . Cell surface phenotyping , antibodies used for staining and flow cytometry analyses were described as we previously published [6] , [33] , [45] . PBMC , BAL , and tissue cells were stained with up to 5 Abs ( conjugated to FITC , PE , allophycocyanin , pacific blue , and PE-Cy5 or allophycocyanin-Cy7 ) for at least 15 min . After staining , cells were fixed with 2% formaldehyde-PBS ( Protocol Formalin , Kalamazoo , MI ) prior to analysis on a CyAn ADP flow cytometer ( DakoCytomation , Carpinteria , CA ) . Lymphocytes were gated based on forward- and side-scatters , and pulse-width and at least 40 , 000 gated events were analyzed using Summit Data Acquisition and Analysis Software ( DakoCytomation ) . Absolute cell numbers were calculated based on flow cytometry data and complete blood counts performed on a hematology system ( Advia 120 , Siemens , Tarrytown , NY ) . This was done as previously described [33] , [36] , [50] . 105–106 BAL cells or 106 PBL plus costimulatory mAbs CD28 ( 1 µg/ml ) and CD49d ( 1 µg/ml ) were incubated with PPD ( 25ug/ml ) or media alone in 200 µl final volume for 1 h at 37°C , 5% CO2 followed by an additional 5 h incubation in the presence of brefeldin A ( GolgiPlug , BD ) . After staining cell-surface CD3 , CD4 , and CD8 for 30 min , cells were permeabilized for 45 min ( Cytofix/cytoperm , BD ) and stained another 45 min for IFNγ and perforin before resuspending in 2% formaldehyde-PBS . Quantitation of M . tuberculosis infection was done by measuring bacterial colony counts , as previously described [6] , [29] , [50] . To objectively measure CFU in lungs , a half of cut-sections of the right caudal lobe ( the infection site ) , the right middle lobe or the left caudal lobe from each animal were taken for CFU determination after the extensive gross pathologic evaluation was accomplished . If there were tuberculosis lesions in the respective lobe , a half of the lung tissue containing approximately 50% lesions was taken . If no visible lesions were seen in the respective lobe , a random half of tissue was taken for evaluation . Tissue homogenates were made using a homogenizer ( PRO 200 , PRO Scientific INC , CT ) and diluting the homogenate in sterile PBS + 0 . 05% Tween-80 . 5-fold serial dilutions of samples were plated on Middlebrook 7H11 supplemented with OADC ( 10% ) , glycerol ( 0 . 5% ) , Casamino acids ( 0 . 2%; Becton Dickinson 223120 ) , Lysine ( 80 mg/L; Sigma L8662 ) , Pantothenate ( 24 mg/L; Sigma P5710 ) , Polymyxin B ( 50 , 000 units/L; Sigma P1004 ) , Amphotericin B ( 5 mg/L; Sigma A2942 ) , Nalidixic acid ( 20 mg/L; Sigma N4382 ) , Trimethoprim ( 5 mg/L; Sigma T7883 ) and Azlocillin ( 5 mg/L; Sigma A7926 ) . The plates were then incubated in a 37°C incubator for 3 weeks , and CFU were counted . The minimal detection level of vial mycobacteria is about 2 CFU on a plate from 100 ul of 10 ml lung homogenate . The bronchoaleveolar lavage fluid was decontaminated with the MycoPrep solutions ( Becton Dickinson cat 240862 ) and then plated on the supplemented Middlebrook 7H11 . The method and validation including detection limit , CV and relationship to bacterial colony counts were previously described [expression levels of β-actin in cells were verified and expressed as copies of M . tb RNA in 10 mg-equivalent tissue cells [6] , [29] , [36]] . Complete necropsy was done by three pathologists and 2 veterinary doctors for M . tuberculosis-infected monkeys as previously described [6] , [29] , [33] . Animals were sacrificed by intravenous barbiturate overdose , and immediately necropsied in a biological safety cabinet within a BSL-3 facility . Standard gross pathologic evaluation procedures were followed , with each step recorded and photographed . Multiple specimens from all tissues with gross lesions and remaining major organs were harvested . Specifically , each lobe of lung , bronchial , mesenteric , axillary and inguinal lymph nodes , tonsils , and other major organs were collected and labeled . Gross observations including but not limited to the presence , location , size , number and distribution of lesions were recorded . Two scoring systems were used; one system [28] , [29] was used for scoring TB lesions in lungs infected by bronchoscope-guided inoculation . For each lobe of lung , granuloma prevalence was scored 0 -4 for ( i ) no visible granulomas , ( ii ) 1–3 visible granulomas , ( iii ) 4–10 visible granulomas , ( iv ) >10 visible granulomas , and ( v ) miliary pattern of granulomas , respectively . Granuloma size was scored 0 -3 for ( i ) none present , ( ii ) <1 – 2 mm , ( iii ) 3–4 mm , and ( iv ) >4 mm , respectively . Pulmonary consolidation or atelectasis as viewed from organ exterior and cut surfaces were scored 0 -2 for ( i ) absent , ( ii ) present focally in one lobe , and ( iii ) focally extensive within a lobe or involving multiple lobes . One score was also given for the presence of tuberculosis-related focal parietal pleural adhesions , pleural thickening and opacification , and pulmonary parenchymal cavitation . For hilar lymph nodes , enlargements were scored 0 -3 for ( i ) visible but not enlarged , ( ii ) visibly enlarged unilaterally ( ≤2cm ) , ( iii ) visibly enlarged bilaterally ( ≤2 cm ) , ( iv ) visibly enlarged unilaterally or bilaterally >2 cm , respectively . Tuberculosis lesions in hilar lymph nodes were scored 0 -4 for ( i ) no granulomas visible on capsular or cut surface , ( ii ) focal or multifocal , circumscribed , non-coalescing granulomas , <2mm ( ≥nodes ) , ( iii ) coalescing solid or caseating granulomas occupying<50% of nodal architecture ( ≥nodes ) , ( iv ) coalescing solid or caseating granulomas occupying >50% of nodal architecture , with residual nodal components still recognizable , and ( v ) complete granulomatous nodal effacement and caseation , respectively . One score was also given for tuberculosis-associated changes in other thoracic nodes . The tuberculosis lesions in each extrathoracic organ were scored similarly as each lung lobe . The pathology scoring of infected tissues was conducted in a blinded fashion by the senior pathologist D . H . The other scoring system , as described [41] , was adopted for scoring >0 . 5 cm tubercles' involvement in each lobe of lungs infected by aerosol . Score 0 if no lung lobe involved; score 1 if <¼ of lobe involved; score 2 if ¼–½ of lobe involved; score 9 if >½ but < entire lobe involved; score 16 if entire lobe involved . Adjacent blocks of tissues were collected and fixed in buffered 10% formalin with ionized zinc ( Z-Fix™; Anatech , LTD , Battle Creek , MI ) , frozen with and without Optimal Cutting Temperature ( OCT ) compound ( Sakura Finetek USA , Inc , Torrance , CA ) . Histologic specimens were embedded in paraffin and sectioned at 5um for routine staining with hematoxylin and eosin ( H&E ) and selected staining with Ziehl-Neelsen acid fast stain . The extent of lung involvement for each lung lobe was determined using digital scans of each lobe of lung to record total pixel counts on H&E stained material and specimen area measured in square cm using Image-Pro Plus software ( MediaCybernetics , Silver Spring , MD ) . Granulomas were objectively compared for the size , type of granuloma ( caseous , solid , suppurative , or mixed ) , distribution pattern ( focal , multifocal , coalescing , and invasive ) , and cellular composition ( absence or presence with degree of lymphocytic cuff , mineralization , fibrosis , multinucleated giant cells , and epithelioid macrophages ) between and within monkey groups . The multivariate analysis of variance ( ANOVA ) and nonparametric student t test were used , as previously described [29] , [33] , [36] , to statistically analyze the data for differences in T cell numbers , M . tuberculosis burdens , gross pathology scores or specific lesion sizes between CD8 Ab-treated and isotype IgG-treated or naïve control groups . | Tuberculosis , HIV/AIDS and malaria remain top killers worldwide . Cell-mediated immune responses play a crucial role in immunity against tuberculosis . While CD4 T cells are well described for their protection against tuberculosis , little is known about the role of human CD8 T cells in anti-tuberculosis immunity . Studies done to date in mice have yielded conflicting results regarding the role of mouse CD8 T cells in tuberculosis . Since there are considerable differences in CD8 T cell biology between mice and primates including humans and macaques , studies in humans or macaques are crucial for clarifying human CD8 T cell–mediated immunity against tuberculosis . Thus , we used a macaque tuberculosis model to examine the contribution of CD8 T cells to vaccine-induced immunity against tuberculosis . We found that CD8 T cells play a role in BCG vaccine-induced immune control of Mycobacterium tuberculosis replication and in the vaccine-induced immunity against tuberculosis . Consistently , memory CD8 T cells also play a crucial role in anti-tuberculosis immunity upon M . tuberculosis re-infection . The findings in the current study provide evidence that human CD8 T cells are of importance for anti-tuberculosis immunity , and support the view that CD8 T cells should be targeted for development of new tuberculosis vaccines and immunotherapeutics . | [
"Abstract",
"Introduction",
"Results",
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] | [
"infectious",
"diseases",
"immunology/immunity",
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] | 2009 | A Critical Role for CD8 T Cells in a Nonhuman Primate Model of Tuberculosis |
Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language . The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes . In this study , we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species . To describe phenotypes using ontologies , we used an Entity-Quality ( EQ ) methodology , wherein the affected entity ( E ) and how it is affected ( Q ) are recorded using terms from a variety of ontologies . Using this EQ method , we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man ( OMIM ) . These human annotations were loaded into our Ontology-Based Database ( OBD ) along with other ontology-based phenotype descriptions of mutants from various model organism databases . Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation . We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species . Using these tools , we demonstrate that we can identify , through the similarity of the recorded phenotypes , other alleles of the same gene , other members of a signaling pathway , and orthologous genes and pathway members across species . We conclude that EQ-based annotation of phenotypes , in conjunction with a cross-species ontology , and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone . This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease , which may shorten the lengthy path to identification and understanding of the genetic basis of human disease .
Our understanding of gene function is often informed by comparing the phenotypic consequences of mutation with the canonical “wild-type” in a single organism , as well as between mutants of orthologous genes in different organisms . In particular , model organisms have provided great insight into gene function in humans . The importance and need for automating these cross-species comparisons has become imperative as large-scale mutagenesis screens are conducted in model organisms . A fundamental roadblock for analysis is , however , the lack of a computationally tractable method for describing phenotypes that is applicable across multiple domains of biological knowledge and species ( for example , see [1] ) . Not only does each model organism have its own vocabulary for describing the phenotypic consequences of mutation , but these vocabularies are usually tied to the particular anatomies or physiologies of the organism . Often these descriptions are recorded as free text , and although wonderfully expressive , free text remains difficult to reliably compare with computational methods . For example , a computer program would not be able to recognize the fact that there is a significant similarity between the PAX6 mutations that result in “small eyed” mice , “opaque cornea” in humans , a “malformed retina” in zebrafish , and “eyeless” Drosophila ( Figure 1 ) . Current methodologies traditionally identify animal models on the basis of sequence orthology between the mutant animal model and a human gene . For example , Schuhmacher et al . recently developed a mouse model of human Costello syndrome ( OMIM: #218040 ) , which is a neuro-cardio-facio-cutaneous developmental syndrome resulting from mutations in the H-RAS gene [2] . The mouse H-Ras gene was mutated in the orthologous position as in Costello patients , and the resulting phenotype recapitulates the disease . Occasionally , spontaneous models can be identified by the observation of symptoms reminiscent of human disease , for example the fat aussie mouse develops obesity , type 2 diabetes , and male infertility . This phenotype is similar to human Alström syndrome , which is caused by mutation in the ALMS1 gene [3] . Sequencing and further characterization of fat aussie revealed a mutation in Alms1 , and fat aussie is emerging as a good animal model for understanding Alström syndrome and the function of cilia-localized Alms1 [4] . These examples for identifying animal models of disease relied on knowledge of the genetic basis of the human disease , but there are many human diseases for which it is not yet known . If a researcher could compare human model organism , and even ancestral phenotypes directly , they would have a mechanism to more rapidly identify candidate genes and models of disease . Model organism communities benefit from centralized collections of curated research , where a scientist can search for extensively cross-referenced gene expression , phenotype , and genomic data , referred to as “model organism databases” ( MODs ) . Research in the field of human biology suffers because there is no equivalent resource for the human research community , and linking these diverse datasets requires searching many detached resources . There are , however , several valuable data resources for human phenotypic data , including the Online Mendelian Inheritance in Man ( OMIM ) [5] published by the National Center for Biotechnology Information ( NCBI ) . OMIM contains more than 19 , 000 records , divided between genes and phenotypes/diseases . Approximately 53% of the gene records have detailed allelic variant descriptions and/or general clinical synopses , while 43% of phenotype/disease records have a known molecular basis . OMIM is a text-based resource , and retrieval of information suffers from this fact , as the Entrez searches in Table 1 show . For an individual researcher wanting to know which human mutations may result in an increase in bone size , or a computer script mining OMIM data , free text annotations do not provide the rigor necessary for querying . While successful mining of the literature to relate genes to phenotypes has been shown [6] , it does not provide a mechanism to compare phenotypes directly . One of the most revolutionary tools for the biologist has been the ability to compare sequences using algorithms such as BLAST [7] , which allows one to quantitatively assess similarity between one or more sequences . However , the genetic basis of a disease is often unknown , and in this case a sequence-comparison tool is of no use to identify sequence mutations . If descriptions of phenotypes were based on a common controlled vocabulary—an ontology—they would be structured such that algorithms could be written to compare phenotypes computationally . One of the benefits of using ontologies is the ability to use general-purpose logical inference tools called reasoners ( for example , see [8] ) . Reasoners can assist in query answering and analysis . As an example , consider two different queries , one to find genes expressed in the ZFA:gut , and the other to find genes expressed in the ZFA:epithelium ( we write ontology terms prefixed with the name of the ontology; see Materials and Methods for further explanation ) . We would expect both of these searches to return annotations to the ZFA:intestinal epithelium , because the intestines are a part_of the gut , and the intestinal epithelium is_a type of epithelium ( Figure 2 ) . Analogous to the nucleic and amino acid alphabets and distance matrices used in the BLAST algorithm , ontology terms and their relationships to one another can be used to group and compare phenotypic and gene expression data and can be utilized for cross-species phenotype analysis . A phenotype can be defined as the outcome of a given genotype in a particular environment ( for review see [9] ) and can be described using ontologies to facilitate comparisons . A description of an individual phenotypic character can be recorded using a bipartite “EQ” ( Entity + Quality ) method , where a bearer entity ( such as an anatomical part , cellular process , etc . ) is described by a quality ( such as small , increased temperature , round , reduced length , etc . ) . The EQ method is sufficient for the description of many phenotypes , provided the source ontologies are rich enough . The entity terms may be structures from any anatomy ontology , or biological processes , cellular components , or molecular functions from the Gene Ontology ( GO ) [10] . The quality terms come from the Phenotype and Trait Ontology ( PATO ) , which is designed to be used in combination with species-specific anatomical ontologies or other cross-species entity ontologies ( see , for example , [11]–[13] ) . For instance , a Drosophila “redness of eye” phenotype could be described using the terms “red” from PATO and “eye” from the Fly Anatomy ontology ( FBbt ) into the EQ statement EQ = FBbt:eye + PATO:red . The EQ method has been extended to include related qualities and additional entities , and with a post-composition approach to describe more granular entities . Many MODs already utilize community-specific anatomy ontologies , in addition to GO , for annotation of gene expression and/or phenotype data [14] , [15] , and these methods are described in detail elsewhere [16] , [17] . Ontological reasoning can also be applied to EQ descriptions , just as for a single ontology , because they too represent nodes in a graph structure . For example , queries for cranial cartilage position should return genotypes that have the phenotype ZFA: ceratohyal + PATO:mislocalised_ventrally . Similarly , queries for superstructures of the ceratohyal cartilage , such as cranial cartilage , should also return these genotypes ( Figure 3 ) . Any EQ description can be combined with other EQ descriptions and data , such as genotype , environment , and stage identifiers from other databases or ontologies , to fully express the phenotypic state of an individual or group . For example , one could record the zebrafish phenotype EQ = ZFA:median fin fold + PATO:attenuate at the embryonic stage ZFS:26-somite with genotype fbn2bgw1/gw1 ( AB ) ( defined in the Zebrafish Information Network , ZFIN ) . With this method , phenotypes can be recorded using multiple ontologies in a highly expressive and finely detailed manner while maintaining correct logic and computability . Existing computational tools are inadequate to store and analyze this ontology-based phenotype annotation data in a generic , species-neutral way . In particular , there is a lack of tools for the cross-species comparisons needed to identify gene candidates and animal models of disease . Many existing algorithms have been developed and tested using the GO to measure the semantic similarity of annotations and provide a good starting point for analysis ( for example , see [18]–[21] ) . It was unclear how well these algorithms would work for analyzing datasets using a combination of ontologies . Additionally , cross-species comparisons would not be possible because there were no links between the various anatomical ontologies . Schlicker and Albrecht [22] suggest an information content ( IC ) –based approach to analyzing phenotypic profiles made with multiple ontologies , although they only tested their results with annotations made with the species-neutral GO . Their FunSimMat tool requires a specific list of proteins to compare and therefore does not provide a means to comprehensively search for phenotypically similar genes . PhenomicDB [23] is a cross-species resource that has pulled together annotations from diverse resources and mined free-text phenotypes to provide “phenoclusters” of phenotype-related genes . However , their analysis did not make use of the relationships in the source ontologies . Although known interacting proteins were clustered together , they note that their resulting “phenoclusters” tended to be species-specific due in large part to the community-specific terminologies that were used in the annotations , and not necessarily due to the underlying biology . These existing methods were insufficient for our needs because they were either free-text based or used a limited set of ontologies for annotation , and because they lacked a framework to integrate and compare anatomical entities between organisms . They also lacked metrics for determining significance in similarity calculations . Lastly , apart from the querying aspect , none included a species-neutral method for recording phenotypes de novo . By annotating phenotypes using this EQ method , together with appropriate computational analysis tools , we have a unique opportunity to standardize and query phenotypic data in a rigorous and illuminating manner . In this study , we tested the hypothesis that EQ annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species . We EQ-annotated 11 human disease genes from free-text OMIM descriptions with Phenote software [24] to provide a dataset for cross-species comparison . We compared these annotations to annotations of the mouse and zebrafish orthologs , which required the development of a cross-species unifying ontology ( UBERON ) to provide a bridge between different anatomy ontologies . We also developed new , and extended existing , metrics for measuring the phenotypic similarity between genes . We assessed their relative performance through analysis of known signaling pathways and genetic interactions and show that these data can be queried and compared by phenotype alone to identify biologically meaningful similarities . Furthermore , these annotations provide a resource for a better understanding of existing disease phenotypes . We conclude that this method can facilitate the discovery of new genotype-phenotype associations within and between species .
Although many MODs curate phenotype data using the EQ method , no such annotations existed for human disease genes . Because we required annotations of human diseases in the EQ style for comparison , we proceeded to annotate a small set of gene records from OMIM: ATP2A1 , EPB41 , EXT2 , EYA1 , FECH , PAX2 , SHH , SOX9 , SOX10 , TTN , and TNNT2 . These 11 genes were selected because they were known to be causal for a variety of human diseases and had known mutant orthologs in flies , mice , and/or fish with corresponding EQ descriptions available for comparative analysis . Specifically , our curation process involved translating OMIM textual descriptions into associations between genotypes and phenotypes , where the phenotypes were delineated using EQ descriptions . Specific ontologies were chosen based on their community-wide acceptance and use , as well as their species-specificity and granularity . For annotation of human disease genes from OMIM , and their resulting phenotypes , we utilized the Foundational Model of Anatomy for adult human gross anatomy ( FMA [25] ) and the human developmental anatomy ontology ( EHDAA ) for developing anatomical structures . Additionally we utilized the cell ontology for cell types ( CL [26] ) , CHEBI for chemicals [27] , the GO for sub-cellular components and biological processes , and PATO as the source of qualities presented by these varied entities . Free-text phenotype or disease description was translated into one or more individual EQ phenotypic descriptions , so that a single genotype ( i . e . , one or more variant alleles plus the genetic background , to whatever extent it is known ) could be associated with multiple EQ descriptions . In the following sections , we refer to a “phenotypic profile” as the sum-total of the EQ descriptions for an individual genotype . For example , Figure 1 shows phenotypic profiles for eye phenotypes of PAX6 ortholog mutations in mouse , human , zebrafish , and fruitfly ( also see Table 2 ) . An important thing to note is that any given individual organism presenting a phenotype may manifest only a subset of the EQ descriptions of a complete phenotypic profile for a particular genotype . The PAX6 and ortholog EQ descriptions are based on gross observations of individual eyes , at a particular developmental stage . These genotypes have additional phenotypes not shown in Figure 1 ( different anatomical structures , at other developmental stages , and so forth ) that would contribute to their complete phenotypic profile . Alternatively , other PAX6 genotypes may have different ( or similar ) phenotypic profiles . Therefore , the phenotypic profile for each genotype grows with time as more observations are made , and this information is easily associated with the allele or gene level for comparison . For the 11 selected human disease genes , curators annotated the general description of the phenotypes contained within the body of each OMIM gene record to a general OMIM gene identifier ( i . e . , OMIM:601653 ) . Additionally , any mention of specific alleles was curated to the allelic variant ID ( i . e . , OMIM:601653 . 0001 ) . Therefore , the general OMIM ID is representative of all non-indicated alleles , rather than a general phenotype description of all alleles . Five of the 11 genes were recorded independently by three curators to test for annotation consistency ( to be published elsewhere ) . In total , 1 , 848 annotations comprising 709 distinct descriptions were collected for all 11 genes with 114 alleles ( Table 3 ) . Some descriptions were frequently identical , such as the description EQ = FMA:palate + PATO:cleft being used to annotate 25 genotypes of 3 genes . Of these 709 descriptions , 487 used FMA , 110 used GO , and 4 used CL ontologies to describe the entities . We loaded all annotations and source ontologies ( Table 4 ) into a single OBD instance [28] . Briefly , this is an information system that allows for the construction of complex descriptions using multiple ontologies , and logical reasoning over these descriptions and the annotations that utilize them . OBD also has analysis capabilities that support comparison of like entities ( such as genes , alleles , and genotypes ) based on their shared attributes ( such as their phenotype profiles ) . The reasoning step is required for the comparison step . OBD assigns an IC score to every term or EQ description used to annotate a gene , allele , or genotype . The IC score is a measure of how informative a term or a description is , based on the frequency of annotations with the term and depth in the ontology . The IC score will thus vary depending on the background set of annotations . OBD uses a reasoner to compute IC scores , such that annotations “propagate up the graph , ” and consequently more general terms receive lower IC scores . For example , Figure 2 shows nodes from the zebrafish anatomy ( ZFA ) ontology , each with an IC score . Terms deeper in the ontology are more distinguishing and informative ( i . e . , a term such as ZFA:intestinal epithelium has a higher score , IC = 12 . 4 ) than those at the root ( i . e . , ZFA:anatomical structure , IC = 2 . 72 ) , because all intestinal epithelium phenotypes are also anatomical structure phenotypes . OBD treats phenotypic EQ descriptions in the same way as other terms , and these nodes are assigned IC scores in the same fashion . Just as for the terms , the reasoner can calculate annotation frequencies such that more general EQ descriptions such as ZFA:cranial cartilage + PATO:position have lower IC scores than more specific , less frequently used , and thus more informative descriptions such as ZFA:ceratohyal cartilage + PATO:misplaced ventrally ( Figure 3 ) . OBD can utilize the IC scores of each node to compute various measures of similarity between any two pairs of annotations or phenotypic profiles . We utilized three IC-based metrics as calculated in OBD to perform our analysis in this paper: similarity based on Information Content ( simIC ) , Information Content of the Common Subsumer ( ICCS ) , and maximal Information Content of a pair ( maxIC ) . A non-IC-based metric , the Jaccard similarity coefficient ( simJ ) , was also included in our analysis . These metrics are detailed in [28] and [18] and in the Materials and Methods section below . Figure 4 shows an example of how these different metrics result from a set of genotypes being compared and how phenotypic profiles are promoted to the alleles and genes for comparison at those levels . The simIC metric quantifies the similarity between two phenotypic profiles using the reasoner to determine which EQ phenotype descriptions are shared based on the subsumption hierarchy . If two phenotypic profiles are very similar , we expect their profiles to converge more quickly and share quite specific phenotype descriptions ( i . e . , with high IC scores ) ; conversely , dissimilar profiles will share only a few very general phenotype descriptions in common ( i . e . , with low IC scores ) . Each subsuming EQ also has an IC , and the average of the resulting set of the EQs in common provides the ICCS score . Of this set of EQs that subsume two phenotypic profiles , one will have the highest IC , the maxIC of all pairs . The simJ metric does not use IC but is rather a ratio of the count of all nodes in common to nodes not in common based on the hierarchy . We can directly compare any two items of the same type , such as two genotypes , two alleles , or two genes by promoting annotations from the genotype carrying a particular allele up to the allele itself , or to the affected gene . Figure 4 illustrates the comparison of two phenotypic profiles at the genotype and gene levels , and the calculation of similarity metrics at those different levels . The two profiles share a total of four common subsumers; some of the annotations have a single common subsumer of the different genotypes; others map to two different common subsumers . In this example , genotypes A1/A1 and A3/A3 share an identical annotation to ZFA:ceratohyal cartilage + PATO:mislocalized ventrally with an IC = 12 . 5 , which is therefore one of the common subsuming annotations and , in this case , also the highest scoring common subsumer , or maxIC . As detailed in Figure 3 , ZFA:ceratohyal cartilage + PATO:mislocalized ventrally and ZFA:pharyngeal arch cartilage + PATO:mislocalized phenotypes share the common subsuming parent ZFA:cranial cartilage + PATO:mislocalized . Therefore , the common phenotypes that subsume genotypes A1/A1 and A3/A3 include both of these parent EQ descriptions . The phenotypes of A1/A1 and A3/A3 are promoted up to the alleles A1 and A3 , respectively , and in turn to gene A . In this example , when the comparison is made at the gene level , the highest scoring common subsumer ( the phenotype with the maxIC ) is GO:neural crest cell migration + PATO:duration . The common subsumers of annotations to the anatomy terms are at more generic nodes , due to their convergence point in the ontologies ( Figures 2 and 3 ) . The first test to assess how well the EQ annotation and phenotype comparison methods work was to correctly identify alleles of the same gene based on their phenotype descriptions . We compared the phenotypic profiles of all pair-wise combinations of alleles annotated for each of the 11 OMIM genes using four scoring metrics in OBD ( simIC , ICCS , simJ , and maxIC ) . Our hypothesis was that similarity scores between alleles of the same gene ( i . e . , intra-gene ) would be significantly higher than similarity scores between either one of these alleles and alleles of other genes ( i . e . , inter-gene ) . Only monogenic phenotypic profiles were included in this part of our analysis; digenic genotypes were not included ( for example , OMIM:600725 . 0011/OMIM:603073 has a double mutation in SHH and ZIC2 ) . Figure 5 summarizes the results , showing that without exception , intra-gene allelic variants were more phenotypically similar ( p<0 . 0001 in two-tailed t-test ) to each other than to those of other genes using any of the four metrics . Another way to examine the similarity between genetic variants is to use each allele to query all other alleles to determine which other allele is most similar . Out of all 118 alleles in the analysis , all had their most phenotypically similar genotype in the same gene . Together , these results support our hypothesis that EQ-based phenotype descriptions capture the similarities between alleles of the same gene , and these ontology-based similarity metrics are effective in retrieving related alleles and quantifying their phenotypic similarity . Members of a signaling pathway frequently exhibit similar mutant phenotypes , and therefore we predicted that a query based on the phenotype due to a mutation in one member of a pathway would retrieve other known members of that pathway . We tested this hypothesis on the well-characterized hedgehog-signaling pathway , which regulates patterning and midline development in animals [29] . ZFIN has >2 , 900 genes with mutant phenotypes annotated with the EQ method [13] , including 20 of the 64 known hedgehog pathway members identified in ZFIN [30] . The entity terms were typically drawn from the zebrafish-specific anatomical ontology , as well as from GO , and the quality terms were from PATO . The annotations from ZFIN ( 17 , 494 total , 5 , 157 unique descriptions ) were loaded together with the source ontologies ( Table 4 ) . We queried OBD for genes with mutant phenotypes similar to the mutant phenotype of the zebrafish shha gene ( ZDB-GENE-980526-166 ) . Figure 6 illustrates these results based on the zebrafish hedgehog signaling pathway diagram from KEGG [31] , to which some additional genes have been added based on current knowledge [30] . Table 5 lists the hedgehog pathway members , and other phenotypes significantly similar to shha , in order of their rank by simIC , together with their ranks and scores by the four metrics . Six of the 11 genes scoring as most similar by simIC are known to be members of the hedgehog signaling pathway , seven by simJ , five by ICCS , and three of the top eight by maxIC ( many genes were tied for ninth place , see Table S1 ) . This set of the most similar genes to shha comprised 23 genes total , of which 11 were known pathway members . Assuming a hypergeometric distribution , the chances of retrieving 11 of the 20 mutant pathway members in the top 23 out of 2 , 908 genes at random is very low ( p<E-19 ) . Three known pathway members , bmp2b , hhip , and sufu , were not identified in the top 10 most similar . sufu was the lowest ranking of these at 628 of the 2 , 908 genes compared by simIC ( see Table S1 for additional metrics ) . To further test the similarity algorithm , we performed the reverse query to determine if any hedgehog pathway members were similar to sufu . The most similar pathway member to sufu was hhip ( rank 3 by simIC ) . Intriguing are the additional zebrafish mutants found to have highly similar phenotypes ( for example , lama1 , dharma , ntl , and doc ) , but which are as yet unlinked to the hedgehog pathway , either because they are not yet mapped or are untested in this role . These results show that known , and potentially new , pathway members within the same species can be identified using EQ methodology and the similarity algorithms available within OBD . One of the primary goals of this study was to compare phenotypes across species directly , particularly human to model systems . This goal presented two challenges; first , we needed to include more annotations from additional sources , specifically mouse annotations from MGI [32] , [33] , and disease associations from the human Gene Association Database ( GAD ) [34] . However , these annotations were described using neither PATO nor an anatomical ontology . The MGI annotations use the Mammalian Phenotype ( MP ) ontology , and the GAD uses textual descriptors . To integrate these valuable data , we first created an equivalence mapping of MP terms to EQ descriptions [17] . We also mapped the GAD descriptors to Disease Ontology ( DO ) terms and created a mapping of DO terms to the FMA . These annotations , together with their source ontologies , were loaded into OBD ( Table 4 ) . The second challenge in making cross-species comparisons is that each species of interest has its own unique anatomical ontology . This means that there is no automated method to determine that a zebrafish ZFA:cranial nerve VII phenotype is in fact related to a human FMA:facial nerve phenotype . In initial tests , orthologs scored very poorly in terms of phenotypic profile matches , as might be expected ( unpublished data ) . The majority ( 85% ) of annotations in OBD were made using these species-specific anatomical ontologies , and without a means for linking them across species , only species-neutral ontologies such as GO , CL , and PATO could be used for comparisons . We recognized that the comparisons would be greatly enhanced by providing links between the anatomical structures in the different organismal anatomy ontologies that would allow the search algorithm to identify commonalities in the phenotypic profiles of different organisms . Therefore , we added UBERON to OBD , a multi-species ontology which generalizes over the types of structures represented in the species-centric anatomical ontologies and provides links between these terms and UBERON terms ( see Methods ) [16] . For example , Figure 7 shows how phenotype annotations to the mouse MA:cochlea , the zebrafish ZFA:macula , and the human FMA:pinna may be related via the common superclass ear in UBERON . Our final hypothesis was that sequence orthologs would exhibit similar mutant phenotypes and therefore phenotype descriptions alone would be sufficient to identify orthologs and pathway members . To test this , we queried the complete set of zebrafish and mouse phenotypes , using the phenotypic profiles of the 11 human disease genes annotated from OMIM and our four scoring metrics . Table 6 shows the score and rank of the mouse and zebrafish orthologs when compared to the human disease gene for all four metrics . The full set of returned genes for zebrafish and mouse using all four metrics are available in Table S2–S23 . In the case of the human-zebrafish comparison , seven out of the 11 orthologous genes were returned in the most similar 100 by any metric , with five being in the top 10 by two or more metrics . Three zebrafish genes , pax2a , sox10 , and ttna , were found to be the most similar to their human ortholog ( rank 1 by ICCS and maxIC metrics , as well as by simIC for sox10 ) . The human-mouse comparison revealed fewer orthologous findings , with only 5 of the 10 orthologs ( no annotations for mouse Tnnt2 were available at the time of analysis ) being identified in the most similar 100 genes by any of the metrics . Of these five , four were in the top 10 by two or more metrics . Two mouse genes , Ebp4 . 1 and Eya1 , were the most similar to the human ortholog by two metrics . In some cases , the rankings of the orthologous gene were very similar by the different metrics . For example , comparison of human and mouse EPB41 ranked the mouse ortholog first in the case of ICCS and maxIC , sixth for the simJ metric , and third for the simIC metric . In other cases , the rankings were more variable for the different metrics . For example , mouse Pax2 was ranked as only 45th by the simJ metric , but in the top 10 most similar genes by the simIC and ICCS metrics . Because the most phenotypically similar gene by the four metrics was often not the sequence ortholog , we took a closer look at which genes were the most similar . Table 7 lists the mouse and zebrafish genes most phenotypically similar ( rank 1 ) to the 11 human disease genes , for each of the four metrics . In general , there did not appear to be a significant bias towards one metric in the first-place ranking of orthologs . One ortholog was returned as most similar by each metric in the mouse , and one by simIC , and three each by maxIC and ICCS in the zebrafish . Some of the most similar genes are in the same family as the ortholog ( for example , mouse Epb4 . 1 , Epb4 . 2 , and Epb4 . 9; and zebrafish sox9a and sox10 ) . Other similar genes may participate in the same pathway , for example , mouse Shh and Cdon . Some of the returned genes are known to function in similar locations , such as atp2a1 and ryr1b , which are both sarcoplasmic reticulum calcium channels . These results show that the EQ method of describing phenotypes with species-specific ontologies ( FMA , ZFA , and MP ) , when combined with species-neutral ontologies ( PATO , GO , CL , and ChEBI ) and a species-neutral linking ontology ( UBERON ) , can be used to successfully query for similar phenotypes across species using the similarity algorithms available in OBD .
This is the first effort to systematically record , and computationally compare , phenotype descriptions with the goal of providing a new tool for discovering genotype-phenotype relationships within and across species . We tested our methods incrementally , showing: first , that allelic variants were most phenotypically similar to other allelic variants of the same gene; second , that we could retrieve known pathway members based on the similarity of the mutant phenotypes; and third , that we could identify orthologous genes across species . Together , these tests indicate that automated similarity analysis of structured phenotype descriptions can successfully identify sets of genes with important and informative biological relevance . Specifically , EQ phenotype description used in combination with IC-based similarity metrics and anatomical mapping between organisms provides the resources necessary for both precisely recording the phenotypes observed and subsequent computational comparisons , which are unconstrained by terminological differences between research communities . We used three IC-based metrics to compare phenotypic profiles: simIC , ICCS , and maxIC of a pair . One non-IC-based metric , the Jaccard index ( simJ ) , was also included in our analysis [18] . Of these metrics , ICCS has not been assessed in previous studies . To our knowledge , this is the first attempt to use any of these metrics to score similarity using composite EQ descriptions . All metrics work in conjunction with a reasoner , thus descriptions do not have to be exact matches in order to be considered similar . The simJ metric rewards more specific matches by counting the total common subsuming descriptions over the union of all subsumed descriptions . This means that simJ is potentially open to bias in the ontology structure . We can see this if we compare the GO with the FMA—terms of comparable specificity are often located deeper in the FMA is_a hierarchy due to the use of high-level abstract terms in the FMA . IC-based metrics attempt to overcome biases in ontology structure by associating significance with term usage . High-level terms such as “organ” are used frequently ( recall that we use the reasoner to compute indirect annotations ) , whereas more specific terms such as “lens” are used less frequently . Such matches for lens phenotypes are considered more significant than matches for organ phenotypes . A danger with this method is that the set of annotations may be biased , and therefore score lower than expected . We expected IC-based metrics to fare better with the inter-species comparisons , because we have a reasonably well-sampled distribution of annotations over UBERON . There are still some biases—the zebrafish is well-suited to certain kinds of studies and mouse to others , and the literature and annotations will reflect these differences . For instance , many of the zebrafish annotations are to early developmental processes and structures because this model is well suited to developmental studies . This is a ubiquitous problem when comparing gene expression or function across species . However , it is much harder to evaluate IC-based metrics versus simJ in the context of the inter-species comparisons . If we make the assumption that orthology leads to similar phenotypes , we can use the results in Table 6 to evaluate the metrics . While the results of this study suggest that the derived IC-based metrics maxIC and ICCS may overcome some of these biases ( more orthologs returned as the most similar genes ) , our dataset of 11 human genes does not constitute a large enough sample to statistically compare the different metrics . In the future , we aim to create a “gold standard” set of genotype-phenotype annotations that would minimize literature or experimental bias and is independently annotated by different curators to eliminate errors of commission and omission . This would allow statistical testing of sensitivity and specificity with regard to these similarity metrics . Nevertheless , our results demonstrate conclusively that one can compare phenotypes across organisms using ontology-based metrics to find biologically meaningful results . Furthermore , it is important to use multiple metrics to analyze and rank the overall similarity between genes . The primary limitation of this method is the cost of curation from the literature , both in terms of needing domain experts as well as the time involved . There are several Natural Language Processing efforts to facilitate partial-information extraction to assist curators in identifying relevant material in the literature . For example , Textpresso [47] is able to mark up full-text literature articles for important biologically relevant terms . Adding PATO or other quality ontologies into the workflow could greatly increase the speed at which a curator could annotate the literature . However , automated tools will have errors due to terminological inaccuracy or inadequacy in published reports , and require human curatorial staff to review . This is particularly true for the human dysmorphology field , but recent efforts by a group of clinicians to standardize the terminologies used to describe human phenotypes [48] will be enormously helpful for further automated analysis . Furthermore , coordinating these standardized terminologies with the development of the Human Phenotype Ontology ( HPO ) [49] and in creating OMIM clinical synopses will be a necessity . The HPO was not yet available at the time of our annotation , and will be especially valuable in future cross-species phenotype studies if its development is coordinated with OMIM and the clinical dysmorphology group , and follows the OBO Foundry principles for maximal interoperability [50] . As evidenced by our evaluation of curatorial reproducibility ( to be published elsewhere ) , ontology development is also a factor that must be considered . A fair degree of effort is required to build and maintain ontologies and the relationships between them and this effort must be informed and guided by collaborative interactions with the curators . Some domains , such as behavior , which is minimally represented in the GO , remain poorly represented by ontologies . These insufficiencies are being addressed [51] , [52] and the combinatorial nature of ontologies makes new ontologies easy to add to the analysis as they become available . Another case in point are the current efforts aimed at using ontologies for image annotation ( see , for example , [53] and [54] ) , wherein not only can the images from which the ontology terms are in part defined be easily located , but the term markup of the images themselves can be updated as the ontologies change over time . Some key players in the zebrafish shha search were not included in our analysis because they were based on morpholino knockdowns rather than traditional mutants . Similarly , morpholino phenotype data from five of the 11 orthologs of the human disease genes examined were also not included in the ortholog analysis ( shha , sox9a , sox10 , tnnt2 , and ttna ) . Future enhancements to our database structure will accommodate various mechanisms for diminishing gene function such as gene-specific morpholinos , siRNAs , or chemicals , and this will greatly expand the available dataset for comparison . Databases such as PharmGKB and the Comparative Toxicogenomics Database ( CTD ) , both of which correlate the effects of drugs and/or toxicants to specific gene dysfunction and/or disease states [55] , [56] , and correlate these to specific allelic variants ( PharmGKB only ) , might also be integrated into the system to provide additional reference data . In order to prioritize candidate genes to be studied in the laboratory for a mutation with a defined phenotype , some combination of information is considered . The first we present here , namely the discovery of organisms with similar phenotypes in which the candidate gene may be more easily identified . However , additional information such as chromosomal position and gene expression are also often used in prioritizing candidate genes for sequencing . Since an aim of this method is to increase the efficiency in identifying candidate genes , inclusion of mapping and expression data into the workflow could further refine the search results . MODs are already using anatomy ontologies and the GO cellular component ontology for annotating both gene expression and phenotypes , and this information could be especially informative in cases where no phenotypes have been annotated to the anatomical structures in which they are expressed . In addition , recent literature suggests that much of morphological evolution is tied to mutation in cis-regulatory regions ( for reviews , see [57] , [58] ) . If it is the case that phenotypes fall into distinct classes , for example , morphological , behavioral , or physiological , then it would be interesting to see if groups of phenotypically similar genes are correlated with specific genomic or biologically relevant phenomenon . This type of contextual information can be mined from external databases ( genomic , protein binding results , co-expression , etc . ) and would not only facilitate candidate gene prioritization but may also provide insight as to the molecular basis of gene evolution . Another biologically interesting question we considered was whether zebrafish paralogs would have combined phenotypic profiles that are complementary in toto to their mammalian ortholog . An interesting feature of zebrafish is that they had a genome-wide duplication , which occurred as part of the teleost radiation approximately 350 million years ago , and some of the duplicated genes persist in the modern zebrafish genome [59] . The occurrence of two orthologs in zebrafish of a single mammalian gene provides a unique opportunity to examine the degree to which the phenotypes of mutations in these paralogs are similar or complementary . It is well known that a number of paralogs have diverged so as to become complementary or expanded in their expression patterns and/or functions , whereas others are redundant or nonfunctional [60] , [61] . In many cases only one of a pair of paralogs has been studied by mutational analysis , but the other has been studied using morpholino knockdown reagents . Therefore , the analysis of phenotypic similarity between paralogs will also be facilitated by the future inclusion of the knockdown phenotypes into our dataset . A project that relates and extends this work is the Phenoscape [62] project , which uses ontologies and the EQ method to record evolutionarily variable morphological characters for a large clade of fishes . This group has been very successful in having the comparative morphology community annotate evolutionary phenotypes . The goal is to use these explicitly recorded character states to query MODs for similar phenotypes , thus gaining candidate genes for evolutionary change . It will be interesting to utilize the phenotypic similarity of related species as an added component to the methodology presented here . Both approaches could well inform one another , providing a better understanding of the evolution of signaling pathways and anatomical form . In this study , we show that by using ontologies for phenotype annotation , one can precisely record and quantify similar phenotypes . Annotation of phenotypes using the EQ method will not only facilitate the use of a common language necessary for comparing phenotypes , it will also facilitate the identification of genotypes with similar phenotypes within and across species , providing candidate genes for human disease , evolutionary change , and pathway characterization .
Statistics for free-text query of OMIM records were obtained on 2/6/2009 ( Table 1 ) . Statistics for the number of OMIM gene records with associated phenotypes were obtained by doing a query in OMIM for any gene record ( * or + ) with a filter selecting records with allelic variant descriptions and/or clinical synopses . Statistics for the percentage of OMIM phenotype/disease records with known molecular genetic basis were derived from the table of OMIM statistics at http://www . ncbi . nlm . nih . gov/Omim/mimstats . html , by dividing the count for records with a “Phenotype description , molecular basis known” by the total number of phenotype records ( statistics are as of 8/10/2009 ) . Human genes from OMIM were selected first by ranking by those with known and described mutant homologs in Danio rerio and Drosophila melanogaster , then by having the greatest number of detailed descriptions of alleles in OMIM . We selected the following 11 genes to be annotated from their OMIM record: ATP2A1 ( 108730 ) , EPB41 ( 130500 ) , EXT2 ( 608210 ) , EYA1 ( 601653 ) , FECH ( 177000 ) , PAX2 ( 167409 ) , SHH ( 600725 ) , SOX9 ( 608160 ) , SOX10 ( 602229 ) , TNNT2 ( 191045 ) , and TTN ( 188840 ) . EYA1 , PAX2 , SOX9 , SOX10 , and TTN were selected for recording by three independent curators to test for annotation consistency ( to be published elsewhere ) . Where an OMIM gene record referred to a disease record , the annotators would capture as much general phenotype information about that disease as possible . We write ontology terms prefixed with the name of the ontology; abbreviations are provided at the beginning of this paper . We use ZFA:gut in place of ZFA:0000112 for legibility purposes . The actual computationally parseable form would use the numeric IDs . All OMIM annotations were created with Phenote [24] software , using the “human” configuration . This included the following ontologies: CL , CHEBI , FMA , GO , and EDHAA for entity selection , and PATO for quality selection . All annotations were recorded with provenance assigned to the PubMed identifier ( PMID ) for the original publication as listed in the OMIM record . Ontologies were updated daily during annotation , and any annotations to obsolete terms were reconciled prior to analysis . Annotations , together with reference ontologies , that were analyzed for this paper can be found at the stable URL: http://obo . svn . sourceforge . net/viewvc/obo/phenotype-commons/annotations/OMIM/archive/2009/ . Additional phenotype annotations were retrieved for cross-species comparison from MGI [33] , ZFIN [13] , GAD [63] , NCBI gene [64] , and homologene [65] in September 2008 . Ontologies used in the analysis were downloaded from the OBO Foundry repository [66] in August 2008: BP-XP-UBERON ( December 2008 ) , ChEBI , CL , DO , DO-XP-FMA , EDHAA , FMA , GO-BP , GO-CC , GO-MF , MA , MP-XP , PATO , SO , UBERON , ZFA , and ZFS . To link cross-species annotations made to species-specific anatomy ontologies ( ssAOs ) , we created an “Uber-ontology , ” UBERON , to fill the gap between the general Common Anatomy Reference Ontology ( CARO ) [67] and the ssAOs . The first version of UBERON was generated automatically by aligning existing ssAOs and anatomical reference ontologies , and then partially manually curated . Ontologies referenced include: FMA , MA , EHDAA , ZFA , TAO , NIF , GAID , CL , XAO , MAT , FBbt , AAO , BILA , WBbt , and CARO . Additional details can be found in [17] and [16] . All ontologies were loaded into OBD , together with the annotations from the sources listed in Table 4 . Reasoning was performed over the combined set of annotations , ontologies , and ontology mappings . We used the OBD RuleBasedReasoner to compute the closure of transitive relations and to compute inferred subsumption relationships between EQ descriptions [28] . The phenotype analysis was performed using the OBD System [28] that implements a number of similarity metrics , described as follows . All similarity metrics are based on the reasoned graph , and annotations are propagated up the subsumption hierarchy . Most of these metrics use the IC ( Equation 1 ) of a term or EQ phenotype ( collectively called a description ) , which is the negative log of the probability of that description being used to annotate a gene , allele , or genotype ( collectively called a feature ) . where the probability of a description is the number of features annotated with that description over the total number of features in the database ( Equation 2 ) : Here annotdescription denotes the number of features to which the description applies , after reasoning has been performed . This means that very general descriptions , such as “morphology of anatomical structure , ” which subsume many more specific descriptions , are applicable to a greater number of features and thus have a low IC . The maxIC is obtained by taking all descriptions shared by a pair of features and finding the description ( s ) with the highest IC . This may be an exact match , or it may be a subsuming description inferred by the reasoner . One characteristic of the maxIC score is that it can hide the contributions of annotations not in the maxIC set . This score is equivalent to the “maximum” variant of the Resnick similarity , as described in [18] . This metric attempts to match every description directly annotated in one feature with a directly annotated description in the other feature . Each directly annotated description di is compared against all the descriptions d’1 , d’2 , …in the other feature being compared . The most specific ( highest scoring ) common subsuming description is found , and the unique set of these is called the common subsumers . The ICCS is the average IC of all the common subsumers in this unique set . This measure is shown in Figure 4 where the center triptych shows the common subsumers . The ICCS metric is described in [28] and has not been described previously to our knowledge . It can be considered a composition of the average and maximum Resnick measures as described in [18] . Given two phenotypic profiles , for example the phenotypic profiles of two genes , or two genotypes , or the two profiles generated by two curators annotating the same genotype , we can calculate the sum of the IC scores for ( a ) those phenotype EQ descriptions that are held in common ( the intersection ) and ( b ) the combined total set of phenotype EQ descriptions ( the union ) . Looking at the ratio of these two sums ( those that are shared versus the totality ) , we can obtain a measure of how similar the two phenotypic profiles are , with perfectly identical phenotypes having a score of 1 . The simIC measure is illustrated in ( Equation 3 ) . Here ap denotes the total set of descriptions that can be applied to p , including subsuming descriptions . As an example , given two genotypes , p and q , the simIC is obtained by dividing the sum of ICs for all descriptions in common by the sum of all descriptions in the union . Here , descriptions include the actual descriptions used in the profile , and all subsuming descriptions as determined by the reasoner . This metric penalizes nodes that have differing annotations . We used one additional similarity metric , the simJ , which does not utilize the IC measures . The simJ between two profiles is the ratio between the number of descriptions in common versus the number of descriptions in both profiles . This is also called the “Jaccard index” or the “Jaccard similarity coefficient . ” The number of descriptions in common is called simTO in [18] . The simJ ( Equation 4 ) is a variant of the normalized simTO: Note that for comparisons between two genes , all annotations made to heterozygous and homozygous genotypes were first propagated to the single ( or both , if known ) alleles , and then propagated to their gene parent . The genotype annotations used in each query were excluded from the background set in calculating the overall score ( Figure 5 ) . For the allele-to-allele comparisons , we calculated each metric for all pairwise combinations of alleles . Similarity scores between a pair of alleles were sorted into intra-gene ( same gene ) and inter-gene ( different genes ) sets , and the mean scores for each gene compared . The significance of the difference between the mean scores for each gene was calculated using a two-tailed Student's t-test . For the zebrafish shha query , we also compared this gene against all other zebrafish genes ( 2 , 908 genes in the total set ) . For the inter-species queries , we exhaustively compared each gene against all other genes using simJ and then computed all metrics on the top 250 . | Model organisms such as fruit flies , mice , and zebrafish are useful for investigating gene function because they are easy to grow , dissect , and genetically manipulate in the laboratory . By examining mutations in these organisms , one can identify candidate genes that cause disease in humans , and develop models to better understand human disease and gene function . A fundamental roadblock for analysis is , however , the lack of a computational method for describing and comparing phenotypes of mutant animals and of human diseases when the genetic basis is unknown . We describe here a novel method using ontologies to record and quantify the similarity between phenotypes . We tested our method by using the annotated mutant phenotype of one member of the Hedgehog signaling pathway in zebrafish to identify other pathway members with similar recorded phenotypes . We also compared human disease phenotypes to those produced by mutation in model organisms , and show that orthologous and biologically relevant genes can be identified by this method . Given that the genetic basis of human disease is often unknown , this method provides a means for identifying candidate genes , pathway members , and disease models by computationally identifying similar phenotypes within and across species . | [
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"and... | 2009 | Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation |
The onset of prezygotic and postzygotic barriers to gene flow between populations is a hallmark of speciation . One of the earliest postzygotic isolating barriers to arise between incipient species is the sterility of the heterogametic sex in interspecies' hybrids . Four genes that underlie hybrid sterility have been identified in animals: Odysseus , JYalpha , and Overdrive in Drosophila and Prdm9 ( Meisetz ) in mice . Mouse Prdm9 encodes a protein with a KRAB motif , a histone methyltransferase domain and several zinc fingers . The difference of a single zinc finger distinguishes Prdm9 alleles that cause hybrid sterility from those that do not . We find that concerted evolution and positive selection have rapidly altered the number and sequence of Prdm9 zinc fingers across 13 rodent genomes . The patterns of positive selection in Prdm9 zinc fingers imply that rapid evolution has acted on the interface between the Prdm9 protein and the DNA sequences to which it binds . Similar patterns are apparent for Prdm9 zinc fingers for diverse metazoans , including primates . Indeed , allelic variation at the DNA–binding positions of human PRDM9 zinc fingers show significant association with decreased risk of infertility . Prdm9 thus plays a role in determining male sterility both between species ( mouse ) and within species ( human ) . The recurrent episodes of positive selection acting on Prdm9 suggest that the DNA sequences to which it binds must also be evolving rapidly . Our findings do not identify the nature of the underlying DNA sequences , but argue against the proposed role of Prdm9 as an essential transcription factor in mouse meiosis . We propose a hypothetical model in which incompatibilities between Prdm9-binding specificity and satellite DNAs provide the molecular basis for Prdm9-mediated hybrid sterility . We suggest that Prdm9 should be investigated as a candidate gene in other instances of hybrid sterility in metazoans .
The question of how two species originate from one has fascinated biologists since before Darwin's iconic treatise on the subject [1] . Postzygotic reproductive barriers between species are thought to result from the acquisition of genetic incompatibilities as an incidental by-product of divergence between two populations . In its simplest form , this Dobzhansky-Muller model involves genetic interactions between two loci ( e . g . a and b ) [2] . In isolated populations , new alleles can arise and go to fixation in two isolated populations ( A in one and B in the other ) since they remain compatible with ancestral alleles . However , a negative epistatic interaction between the two new alleles ( A with B ) in hybrids might result in sterility or inviability , a hallmark of postzygotic isolation in hybrids between two species [3] . Theory predicts that additional incompatibilities will accumulate rapidly following an initial genetic incompatibility [4] . One of the earliest postzygotic isolating barriers in interspecies hybrids is the sterility of the heterogametic sex ( XY males or ZW females ) , a pattern referred to as Haldane's rule that holds almost universally across animal taxa [3] , [5] . Examination of early events in speciation that lead to hybrid sterility ( for example [6] , [7] ) is thus vital to gain insight into this mysterious process . The first hybrid sterility gene to be discovered was the Drosophila Odysseus-site homeobox ( OdsH ) gene . The D . mauritiana allele of OdsH causes hybrid male sterility when introgressed into D . simulans together with adjacent loci [8] , [9] . OdsH encodes a presumptive DNA-binding protein which is exclusively expressed in male reproductive tissues [9] . OdsH function within Drosophila species remained unclear until recently ( ablation of the gene in D . melanogaster has a very modest effect on male fertility [10] ) as did the molecular basis for why it causes hybrid sterility . However , the manifestation of hybrid sterility appears to be correlated with rapid evolution of OdsH specifically in its DNA-binding homeobox domain , in the species clade that includes D . mauritiana and D . simulans [11] . A second hybrid sterility gene was discovered not as a Dobzhansky-Muller incompatibility but as a result of gene transposition . Hybrids between D . melanogaster and D . simulans , which carry two 4th chromosomes from D . simulans in an otherwise D . melanogaster genetic background , are sterile . This sterility is caused by the transposition of the JYAlpha gene away from the 4th chromosome in D . simulans [12] . Since JYAlpha is required for male fertility , D . melanogaster male flies that only possess D . simulans 4th chromosomes lack JYAlpha and are therefore sterile . In contrast to OdsH , the biological cause of hybrid sterility is well understood but involves no sequence divergence of the underlying sterility gene and only affects a fraction of F2 hybrids . A third hybrid sterility gene was recently discovered in crosses between the Bogota and USA subpopulations of D . pseudoobscura . F1 males resulting from crosses between Bogota females and USA males are almost completely sterile when young . When aged , however , these F1 males recover partial fertility but produce all female progeny . Intriguingly , a single gene Overdrive ( Ovd ) was found to be causal for both the segregation distortion and hybrid male sterility [13] . Like OdsH , Ovd encodes a putative DNA-binding protein whose biological function is unclear . Like OdsH , rapid evolution of Ovd in the Bogota lineage appears to be associated with hybrid sterility . Genetic results with Ovd strongly suggest that hybrid sterility is a by-product of intraspecies genomic conflict , manifest as segregation distortion [13] . Prdm9 ( Meisetz ) is the fourth hybrid sterility gene , the first to be described in vertebrates . It was discovered in crosses between the mouse subspecies Mus musculus musculus and Mus musculus domesticus . Allelic differences at Prdm9 provide the genetic basis for the Hybrid sterility 1 ( Hst1 ) locus , which together with other genetic loci [6] , [7] , [14] , is responsible for spermatogenic failure in sterile hybrids between Mus m . musculus and Mus m . domesticus [15] . Polymorphism linked to Hst1 is associated with sterility traits not only for Mus m . domesticus strains but also , separately , for Mus m . musculus strains [16] . In natural Mus m . musculus populations these polymorphisms appear to have arisen very recently [16] . Prdm9 is a meiosis-specific gene that is only expressed in germ cells entering meiotic prophase in both female and male mice [17] . Loss of Prdm9 causes sterility in both sexes due to impaired meiotic progression at the pachytene stage . Furthermore , nonsynonymous SNPs in human PRDM9 are associated with infertility and azoospermia via meiotic arrest [18] , [19] . Prdm9 encodes 3 protein isoforms , of which the largest isoform contains an N-terminal KRAB motif , a central histone H3 Lysine-4-methyltransferase ( SET ) domain , and several zinc fingers in its carboxy-terminal region ( Figure 1 ) . Similar zinc fingers in other proteins have been shown to mediate sequence-specific binding to DNA . The number of zinc fingers encoded in mouse Prdm9 appears to directly affect hybrid sterility . Whereas an allele of Prdm9 encoding 13 zinc fingers causes postzygotic hybrid sterility , an allele containing 14 zinc fingers does not ( Figure 1 ) [15] . The finding that changes in a single DNA-binding determinant appears to be causal for hybrid sterility motivated our analysis to study the evolutionary constraints that shape the sequence and copy number of zinc finger motifs in Prdm9 across a broad taxonomic panel of metazoans , starting with rodents .
We sequenced the terminal zinc fingers from the final exons of Prdm9 from 11 rodent species to which we added the genomic sequences of mouse ( C57BL/6J ) and rat Prdm9 ( Figure 2A ) , thereby sampling a ∼25 million year period of rodent phylogeny [20] . The C57BL/6J strain of mice is a mosaic of M . m . musculus , M . m . domesticus and M . m . castaneus [21] . The C57BL/6J mouse genome assembly harbours the M . m . domesticus Prdm9 allele [22] . We found that rodents vary greatly in their numbers of zinc fingers present in the C-terminal array: from 7 in Peromyscus polionotus to 12 in Mus musculus ( Figure 2A ) . Even closely-related species pairs , such as field and water voles ( Microtus agrestis and Arvicola terrestris ) , and M . macedonicus and M . spicilegus , differ in their numbers of zinc fingers ( Figure 2A ) . Rodent Prdm9 zinc finger sequences have been subject to concerted evolution . Many changes in numbers of zinc fingers have resulted from very recent lineage-specific duplications ( Figure 2A ) . Twelve of the 13 rodent species we examined possess at least one pair of Prdm9 zinc fingers that were so recently duplicated that they have identical nucleotide sequences . In one case ( Peromyscus leucopus , Figure 2A ) , Prdm9 encodes a cluster of five zinc fingers that are identical at the nucleotide level , together with another pair of identical zinc fingers . Consistent with concerted evolution , Prdm9 zinc fingers from the same species often form monophyletic clades , even in comparisons of closely related rodents ( Figure S1 ) . Such concerted sequence evolution may result from multiple rounds of zinc finger duplication and deletion ( ‘birth-and-death’ model [23] ) to change zinc finger numbers . However , we favor non-allelic gene conversion as a dominant mechanism [24] since it more easily accounts for the many interdigitated and non-adjacent zinc finger duplications , as well as the complexity of the inferred zinc finger phylogeny . Although more occasional gain and loss of zinc finger sequences have been observed previously for other genes [25] , the extreme degree of sequence similarity between different zinc finger pairs is far greater for Prdm9 than for any other zinc finger gene present in the C57BL/6J mouse genome sequence ( Figure 2B ) . In addition to concerted evolution , our analyses reveal evidence for positive selection at particular codons responsible for DNA binding specificities within Prdm9 zinc fingers in rodents . Due to the high degree of concerted evolution , it is not formally correct to carry out a pairwise analysis of the non-synonymous to synonymous rate ratio ( dN/dS ) when comparing Prdm9 sequences from two different species . Instead , by comparing all Prdm9 zinc fingers within a species , we find that all but one of these 13 rodent species have acquired more amino acid substitutions than would be expected under neutral evolution within their Prdm9 zinc fingers ( Figure 3 ) . For instance , in the Prdm9 encoded zinc fingers from Mus musculus strain C57BL/6J ( Figure 3A ) , two codons are predicted to have evolved under positive selection ( positions labelled −1 and 3 in Figure 3A ) . Intriguingly , positive selection is restricted to only a small number of positions within these zinc finger sequences . Sites labelled −1 , 3 , and 6 were identified as having evolved by positive selection in the majority of the 13 rodent species we examined when comparing all zinc fingers from a particular species ( tabulated in Figure 3B ) . Codons at these sites are turned over rapidly . For instance , two recently diverged vole species , Microtus agrestis and Arvicola terrestris exhibit species-specific codons at positions −1 , 3 and 6 ( Figure S2 ) despite their independent evolution only over the last 0 . 5 million years [26] . In each case , we use the Sitewise Likelihood-ratio method ( SLR ) [27] with p-value thresholds of 0 . 05 after multiple testing correction . Since these methods can be strongly affected by tree topology , we tested both the most likely and other competing topologies to conservatively estimate non-synonymous substitutions; this will reduce the chance of false-positives in our analysis ( see Materials and Methods ) . These unusually elevated values may reflect the sustained action of positive selection , consistent with the elevated rates observed for many rodent species ( Figure 3 ) . Rapid evolution and addition/deletion of zinc fingers ( that provide the basis for hybrid sterility among M . musculus strains [15] ) are thus recurrent across rodent evolution . We also inferred evolutionary rates for each codon from an alignment of every Prdm9 zinc finger from all of these 13 rodent species . Rates for three sites ( sites −1 , 3 and 6 ) , together with a fourth ( site −2 ) , greatly surpass the neutral rate with values of dN/dS up to 8 ( Figure 3C ) . These ratios greatly exceed those found for corresponding positions in other mammalian zinc finger genes [28]–[30] . These three positions ( namely −1 , 3 and 6 ) correspond exactly to the positions known to be involved in sequence-specific DNA-binding [31] , [32]; structural studies have shown that amino acids within the zinc finger α-helix at positions −1 , 3 and 6 make contacts with bases 3 , 2 and 1 in the primary DNA strand respectively , whilst the amino acid at position 2 interacts with the complement of base 4 [33] . Thus the finding that positive selection on residues −1 , 3 and 6 indicates that it has specifically acted to alter DNA-binding preferences encoded by Prdm9 . Based on our findings in rodents , we next undertook a survey of PRDM9 divergence in the primate lineage to ask whether the extraordinary evolution of Prdm9 was limited to rodents alone . In humans , there appear to be two genes that are orthologous to a single mouse Prdm9 , suggesting a recent gene duplication [34] , [35] . These two genes , PRDM7 and PRDM9 , are found at chromosomal locations 16q24 . 3 and 5p14 , respectively . It is clear that since the gene duplication PRDM7 has acquired distinct tissue-specific patterns of expression and has undergone major structural rearrangements , dramatically altering the number of encoded zinc fingers ( 2 in macaques , 5 in orangutans ) while diverging from ancestral patterns of transcript splicing [34] . Furthermore , there is evidence for a frame-disruption affecting PRDM7 in some humans . Consequently , we do not investigate PRDM7 further in this report . Primate PRDM9 appears to show a large variation in numbers of zinc fingers in its C-terminal array similar to what we found in rodents ( Figure 2A ) . Chimpanzee , orangutan , rhesus macaque and marmoset PRDM9 genes encode 15 , 10 , 9 , and 9 C-terminal zinc fingers as opposed to 13 in human PRDM9 ( Figure 4A ) . As in rodents , primate zinc fingers also show evidence for concerted evolution . For example , there are three identical pairs out of the C-terminal array of 13 zinc fingers encoded by human PRDM9 . When we compared the PRDM9 gene sequence between humans and chimpanzees , we found the nucleotide divergence to be 7 . 1% , over 5-fold higher than the divergence observed genome-wide ( 1 . 23% [36] ) although the high degree of concerted evolution complicates this human-chimpanzee ortholog comparison . However , it does appear that much of the divergence has resulted from a combination of positive selection and concerted evolution . Estimated dN/dS values for positions −1 , 3 and 6 of human PRDM9 zinc fingers are 12 . 6 , 9 . 9 and 13 . 9 respectively , substantially greater than 1 . Indeed , either by a species-specific zinc finger analysis ( Figure 4B ) or by a pooled analysis of all primate PRDM9 encoded zinc fingers ( Figure 4C ) , we find strong evidence for positive selection at these positions . Our findings suggest that positive selection and concerted evolution have directly and dramatically altered DNA-binding specificity of the encoded PRDM9 protein in primates as was observed in rodents . For instance , for 12 of the 15 C-terminal array of chimpanzee PRDM9 zinc fingers , codons at position −1 are not found in any human PRDM9 zinc finger at the same position; similarly , 6 human zinc fingers have codons at this position that are not present in the chimpanzee ortholog ( Figure 5A and 5B ) . Like in rodents ( Figure 2 and Figure 3 ) , the PRDM9 genes of closely related primate species are differentiated not only by the numbers of zinc fingers they encode , but also by species-specific codons , particularly at key positions that dictate DNA-binding specificity ( Figure 4 and Figure 5 ) . We next investigated whether positive selection on PRDM9 had left population genetic signatures of selection that still remained evident among modern humans . Each of the two methods we employed exploits SNP data and accounts for issues concerning population structure and growth ( see Materials and Methods ) . Particularly recent selective sweeps are characterized by long extents of linkage disequilibrium ( LD ) that ensue when the haplotype carrying the advantageous allele rises in frequency more rapidly than a neutral allele . Conversely , tests based on this characteristic are particularly sensitive for detecting recent episodes of positive selection [37] . Looking at patterns of LD , we did not find evidence for very recent selective sweeps at PRDM9 . In our test we computed the maximum correlation coefficient ( r2 ) between SNP pairs spanning the PRDM9 locus , and compared these to the empirical distribution of this statistic across the genome . These maximum r2-statistics were not significantly different from the background ( p values of 0 . 24 , 0 . 23 and 0 . 24 for the African , European and Japanese/Chinese population panels ) . Since tests based on long extents of LD or haplotypes are sensitive for very recent sweeps [37] only , while tests based on Tajima's D maintain power until some time after fixation of the advantageous allele [38] , we also used a Tajima's D estimate to investigate whether polymorphisms linked to PRDM9 exhibit an unusual population frequency spectrum . When an advantageous allele has risen to fixation , the extended haplotype associated with it will , for a considerable time thereafter , carry young and low-frequency polymorphisms , which may be observed as a reduction of Tajima's D , defined as the scaled difference of two estimators of heterozygosity which are identical under the standard neutral model [39] . There are significant caveats to the calculation of Tajima's D from genotyping data which bias against the recovery of low frequency SNPs . The Perlegen genotyping data have been shown to provide useful Tajima's D statistics after empirically accounting for this ascertainment bias [40] , [41] . Using these methods , we calculated Tajima's D at the PRDM9 locus [41] in African Americans ( D = −0 . 130; p = 0 . 038 ) , European Americans ( D = −0 . 259 , p = 0 . 068 ) , and Asian Americans ( D = 1 . 7 ) . With the caveat that there might be uneven distribution of ascertainment biases across the genome , there appears to be weak evidence for a recent selective sweep in African Americans . In contrast to PRDM9 , Tajima's D provides no evidence for recent sweeps in any of the three populations at the PRDM7 locus . We were interested in using intraspecies human polymorphisms to gain further insight into the evolutionary forces that drive the concerted evolution of PRDM9 . To this end , we sequenced the terminal PRDM9 zinc finger sequences from 50 Han Chinese individuals , seeking sequence polymorphisms that might have arisen by gene conversion . Under gene conversion , we would expect to observe a nucleotide polymorphism in one zinc finger that is identical to its fixed paralogous base in another . We observed 7 codons containing single nucleotide polymorphisms ( SNPs; blue rectangles in Figure 5A and 5C ) . Of these , 4 ( numbered 1 , 2 , 5 and 7 in Figure 5A and 5C ) represent changes to codons that are not represented among any of the remaining zinc finger sequences and thus are unlikely to have arisen by gene conversion . The remaining 3 changes are to codons that are also present in at least one paralogous position within the other zinc fingers . A separate study identified 17 non-synonymous SNPs within human PRDM9 zinc fingers , of which 13 showed evidence for having arisen by gene conversion from paralogous sequences [18] . We infer , therefore , that non-allelic gene conversion has contributed to the rapid evolution of primate PRDM9 , and this provides a likely mutational mechanism for many other PRDM9 orthologues . What are the functional consequences of these non-synonymous SNPs in PRDM9 ? Two recent genetic association studies have investigated PRDM9 SNPs and their association with azoospermia . The first study [19] did not find correlated SNPs in the C-terminal zinc fingers . However , a second study found that individual nonsynonymous SNPs in the zinc finger domain are associated with a significantly decreased risk of infertility [18] . For instance , human non-synonymous SNPs ( labelled 3 , 6 , 8 and 9 in Figure 5A and 5C ) are associated with decreased risk of sterility in a cohort of Japanese men [18] , of which two ( numbers 3 and 6 ) were found among the 50 Han Chinese individuals we sequenced . In addition , 3 out of 4 non-synonymous SNPs associated with fertility are found at zinc finger position 6 , a site predicted to determine DNA-binding specificity and which we show has evolved under positive selection in human PRDM9 ( Figure 4 and Figure 5A ) . Surprisingly , in each instance , the ‘minor’ allele at each position is associated with protection against sterility in Japanese men [18] . Intriguingly , in both studies , the effect on ameliorating azoospermia or oligospermia was manifest even in the heterozygous condition [18] , [19] , suggesting that PRDM9's effect is semi-dominant ( consistent with results of hybrid sterility seen in mouse Prdm9 ) . In a situation where a minor allele provides a protective benefit against sterility , we might expect that high frequency retention of these alleles would be favored by balancing selection in this population . Consistent with this expectation , we point out that Asian American individuals had a striking Tajima's D of +1 . 7 in contrast to the negative Tajima's D in the other two populations in the Perlegen dataset , although this statistic by itself is not strong evidence of balancing selection given the ascertainment bias . The two evolutionary themes ( concerted evolution and positive selection ) that typify PRDM9 evolution in primates and in rodents also have occurred recurrently across metazoan evolution ( summarized in Figure 6 ) . For instance , we found evidence of concerted evolution among Prdm9-encoded zinc fingers in the sea anemone Nematostella vectensis , the gastropod snail Lottia gigantea , and the polychaete worm Capitella sp . I ( Figure S3 , S4 , S5 ) , organisms that last shared a common ancestor with mammals approximately 700 million years ago [42] . In addition , we find strong evidence of positive selection in zinc fingers of N . vectensis Prdm9 for the same 3 positions ( namely , −1 , 3 and 6 ) also identified from analyses of rodent and primate lineages ( summarized in Figure 6 ) . Estimated dN/dS values for these positions were exceptionally high , ranging between 25 and 32 . A single codon of the Capitella worm Prdm9 zinc fingers also shows evidence of positive selection ( Figure 6 ) . Thus , even early branching metazoans show strong evidence of both concerted evolution and positive selection within Prdm9-encoded zinc fingers . Concerted evolution is also apparent in Prdm9 zinc fingers for many mammals including elephants ( Loxodanta africana ) , cats ( Felis catus ) , common shrews ( Sorex araneus ) , cattle ( Bos taurus ) , muntjak deer ( Muntiacus reevesi and Muntiacus muntjak vaginalis ) , bats ( Myotis lucifugus ) and rabbits ( Oryctolagus cuniculus ) ( data not shown ) . It is also evident among the zinc fingers of Prdm9 from the Atlantic salmon ( Salmo salar ) and the rainbow trout ( Oncorhynchus mykiss ) . Of the four complete zinc fingers in rainbow trout Prdm9 , two are identical in nucleotide sequence , and the remaining pair are more closely-related to each other than they are to those of Prdm9 for the Atlantic salmon ( Figure S6 ) , with which it last shared a common ancestor approximately 20 million years ago [43] . Evidence for positive selection is , however , less compelling outside of these fish , the sea anemone , rodents and primates . This is perhaps owing to the stringent multiple testing correction we employed , especially in cases where there are insufficient zinc fingers to obtain significant power for this kind of analysis ( see Materials and Methods ) . Despite strong evidence of concerted evolution and/or positive selection in many metazoan Prdm9 sequences , this pattern is not universal across all metazoans . In comparisons of Prdm9 in other ray-finned fishes ( including Danio rerio ) and in tunicates ( including Ciona intestinalis ) , we found no evidence for either concerted evolution or positive selection within their zinc fingers . Among mammals , we found two homologs of Prdm9 in the platypus Ornithorhynchus anatinus , but evidence for neither concerted evolution nor positive selection . When we investigated the Prdm9 ortholog in the marsupial Monodelphis domestica and the nematode Caenorhabditis elegans , we were surprised to find a complete loss of all zinc fingers . Despite Prdm9 being essential for fertility in mice , Prdm9 appears lacking in chicken ( Gallus gallus ) , frog ( Xenopus tropicalis ) and fly ( Drosophila melanogaster ) genomes , while the dog ( Canis familiaris ) genome has acquired multiple disruptive mutations ( “pseudogenization” ) within its Prdm9 ortholog [44] . This either implies that Prdm9 function in meiosis is carried out by another gene in these lineages , or that Prdm9's essential function in meiosis is itself lineage- or species-specific .
Our finding of recurrent and dramatic episodes of rapid evolution of Prdm9 in different lineages indicates that the protein-DNA interface at which Prdm9 acts , has frequently altered between , and within , species . These evolutionary observations allow us to revisit some key models of Prdm9 function and how its divergence might give rise to hybrid sterility . The currently prevailing model is that Prdm9 encodes a transcription factor for euchromatic genes during meiosis . Mouse Prdm9 ( Meisetz ) was first discovered for its essential role in meiotic prophase of both male and female meiosis [17] . Its SET domain was later found to catalyse the specific transition from di- to tri-methylation of the Lysine-4 residue on histone H3 ( H3-K4 ) , an activity that is characteristically associated with transcriptional activation [45] . Indeed , by tethering experiments , Prdm9 was shown to be able to activate transcription . Furthermore , in Meisetz−/− testes , the transcriptional regulation of close to 125 genes was disturbed . Thus , Prdm9 ( Meisetz ) was proposed be a master transcriptional regulator of entry into meiosis in mammals , and all data including the intriguing association with human azoospermia [18] , [19] are consistent with this view [17] . However , the accelerated evolution of the Prdm9-DNA interface challenges whether Prdm9's only , or even primary , role is a transcription factor for euchromatic genes . Such a function would leave unexplained why cis-acting ( promoter ) sequences to which Prdm9 binds , would be subject to repeated positive selection over the long time course of metazoan evolution . Rapid evolution at the protein-DNA interface would be especially disfavoured if it was required for fertility . We cannot formally rule out the unprecedented possibility that a transcription factor may evolve rapidly in concert with all of its ( at least 125 [17] ) cis-acting binding sites if indeed Prdm9 directly mediates the transcription activation of meiotic promoters . However , in general , the larger the number of cis-acting sequences that Prdm9 has to bind , the more its DNA-binding would be expected to be evolutionarily constrained which , we suggest , argues against its primary role as a transcription factor . We considered the possibility that the rapid evolution of Prdm9 was actually required for , rather than an impediment to , its function . One of the strongest observations in favor of the transcription model was the fact that the SET domain catalyzed transition from di- to tri-methyl H3-K4 , a chromatin mark most often associated with transcriptional activation . And yet , this chromatin mark is not unique to transcriptional activation . Indeed , the same transition from di- to tri-methyl H3-K4 , distinguishes canonical H3-nucleosomes at centromeric versus pericentric heterochromatic regions at mitotic centromeres of organisms as diverse as flies and humans [46] . Inactivation of a centromere on a human artificial chromosome directly results in loss of H3-K4 dimethylation and accumulation of H3-trimethylation [47] . We hypothesize that Prdm9's essential role in meiosis is directly related to its ability to bind rapidly-evolving DNA elements . While we do not know the identity of these DNA elements , we speculate that Prdm9 may function by binding directly to repetitive DNA sequences that are found at pericentric and centromeric regions ( Figure 7A ) . Such repetitive DNA sequences ( or ‘satellite repeats’ ) evolve exceedingly rapidly across multiple lineages [48]–[52] . It has been previously proposed that this rapid evolution results from centromere-drive [53] , [54] , a process in which meiotic products compete during female meiosis for retention in the egg versus exclusion as polar bodies . The genetic opportunity to ‘cheat’ during female meiosis is the evolutionary thread common among many repetitive DNA elements [55]–[58] . Further , DNA-binding proteins are thought to rapidly evolve their DNA-binding specificity to suppress this ‘meiotic drive’ [59]–[63] . Under this model , rapid changes in satellite-DNA sequences potentially ensuing from centromere-drive are followed by positive selection of non-synonymous substitutions within Prdm9 DNA-binding determinants to counter the deleterious effects of the meiotic ( centromere ) drive process . This would explain not only the rapid evolution and retention of Prdm9 in most metazoans but also the loss of Prdm9 genes in some lineages , when a second satellite-DNA binding protein may have taken over this suppressor function . A recent study on the Drosophila OdsH hybrid sterility gene provides interesting parallels to the Prdm9 study [64] . Due to its evolutionary descent from the unc-4 transcription factor [11] , OdsH was also believed to be a transcription factor . Since the DNA-binding homeobox domain had undergone rapid evolution , hybrid sterility was proposed to result from altered gene expression in Drosophila testis [65] , much the same as it has been suggested for Prdm9 . However , functional analyses of OdsH revealed it to function as a heterochromatin-binding protein , with altered DNA binding resulting in altered heterochromatic localization and chromosome decondensation [64] . A transcription factor function of Prdm9 ( like in OdsH ) may be directly tied to a chromosome decondensation function . Indeed , work from a number of model systems especially the fission yeast Schizosaccharomyces pombe has revealed that transcription of heterochromatic repeats is a prequel and often a pre-requisite for the deposition of heterochromatin-specific histone modifications and proteins required for transcriptional silencing and condensation [66]–[69] . Prdm9 binding to satellite-DNA may facilitate its heterochromatinization by virtue of its transcriptional activity ( Figure 7A ) , and alterations of Prdm9's binding specificity could allow it to act on a wider array of satellite-DNAs , consistent with its semi-dominant effect in hybrid sterility and human azoospermia . The chromosome decondensation and synapsis defects in male meiosis observed in sterile hybrids between M . m . musculus and M . m . domesticus species [15] would be explained by an inability to correctly bind and package satellite DNA ( Figure 7B ) . Indirect consequences of such decondensation could be the transcriptional misregulation of some genes , as observed in Prdm9−/− mice [17] . Alternatively , ‘mismatched’ binding of Prdm9 to centromeric satellite-DNA repeats would result in their inappropriate heterochromatinization , again leading to chromosome condensation defects and male sterility . Under this model , mismatched Prdm9-satellite DNA configurations would be predicted to result in sterility only in hybrid males , but not in hybrid females [53] . We would like to emphasize the current absence of functional data to support such a hypothesis . However , the precedence provided by the OdsH study [64] and the consistent rapid evolution seen at Prdm9's DNA-binding interface provides a simple , testable explanation for the onset of highly context-specific hybrid sterility . Variation in Hst1 ( Prdm9 ) occasions a genetic incompatibility between the Prdm9 DNA-binding protein encoded by this locus and the satellite DNAs to which Prdm9 binds ( or fails to bind ) . The finding that human azoospermia is rescued by heterozygous PRDM9 alleles [18] , [19] , including some that alter DNA-binding preferences , further suggests that a reduced repertoire of satellite-DNA binding ability may be responsible for the meiotic arrest at pachytene seen not only in the hybrid mice species [15] , but also in the Prdm9−/− mice [17] , a possibility that directly lends itself to genetic and cytological scrutiny . The proposal that episodes of meiotic drive and suppression drive hybrid incompatibilities is not new [70] , [71] . Indeed , cryptic meiotic-drive suppressor systems have been uncovered by introgression analyses between different Drosophila species [72] . Moreover , recent studies of hybrid inviability amongst Drosophila species have revealed the very likely role that pericentric heterochromatin plays in the manifestation of genetic incompatibility [73] , [74] . While the molecular function of Prdm9 remains to be fully elucidated , our findings directly implicate the Dobzhansky-Muller incompatibility underlying Prdm9-mediated sterility as residing at a rapidly evolving protein-DNA interface . The onset of interspecies hybrid incompatibilities is widely believed to ensue as the by-product of acquired genetic differences in geographically isolated populations . This process can be imagined to take place in the absence of any selective pressure , purely by genetic drift [75] . However , the accumulation of genetic incompatibilities is more likely with accelerated evolutionary change , especially if recurrent genetic conflicts were driving the divergence . Consistent with this , many hybrid incompatibility genes for both sterility and inviability are associated with dramatic episodes of positive selection [11] , [73] , [76] . Here , we have shown that the Prdm9 gene , which was identified as a hybrid sterility gene in mice [15] , has evolved rapidly due to the dual forces of concerted evolution and positive selection . This rapid evolution is seen not just across the rodent lineage , but also in primates and especially humans , whereby some alleles at positively selected sites are associated with male sterility via azoospermia due to meiotic arrest . Strikingly , rapid evolution of Prdm9 is observed in some fish , in the sea anemone and a polychaete worm and thus , parsimoniously , is an ancestral feature of metazoan evolution , an evolutionary period spanning 700 million years . This recurrent evolution of Prdm9 is in stark contrast to both the Ovd and OdsH hybrid sterility gene in Drosophila , which appear to have evolved rapidly only in isolated lineages in which its role in hybrid sterility is manifest [11] , [13] whereas the gene transposition of JYalpha is also highly lineage-specific [12] . From sequenced transcripts , Prdm9 is known to be expressed in male and female germ-line tissues across diverse metazoans such as trout , cattle , pig , sea urchin , and gastropod snail ( accessions: CR372724 , EF432552 , EW634943 , AM222434 and CAXX2975 ) in line with its previously described expression profile for mouse [17] . Hybrid sterility has been shown to arise from the simple deletion or insertion of a zinc finger domain in Prdm9 in mice [15] . The loss or gain of a single zinc finger is among the least perturbing of all changes in zinc finger number and sequence we have observed . For example , even closely related species , such as humans and chimpanzees , or bank and field voles , or rainbow trout and Atlantic salmon , differ much more dramatically at DNA-binding positions of their Prdm9 zinc fingers ( Figure 3 , Figure 4 , Figure 5 , Figure S2 , Figure S6 ) . Moreover , findings from human genetic association studies demonstrate that even individual amino acid changes in PRDM9 can affect male fertility even within species [18] . Finally , recent studies clearly demonstrate that Hst1 ( Prdm9 ) associated genetic incompatibilities have evolved independently and are polymorphic in both M . m . musculus and M . m . domesticus mouse subspecies [16] . Our study has found even more radical alterations within Prdm9 zinc fingers than are observed in the M . m . musculus x M . m . domesticus cross . These changes , by themselves , may not be sufficient to result in reproductive isolation , as incompatibilities with a ( as yet unknown ) rapidly evolving DNA component would be required for hybrid sterility . In addition , hybrid sterility is clearly affected by multiple other loci [6] , [7] whose discovery will lend further insight into the biological forces behind hybrid sterility . Nevertheless , our findings of recurrent rapid evolution of Prdm9 suggest its candidacy as a postzygotic hybrid sterility gene in other metazoan taxa .
Prdm9 genes , and their 3′ ( carboxy-terminal ) arrays of zinc fingers ( Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 ) , were predicted from genome sequences available from UCSC , Ensembl and JGI genome browsers . Additional Prdm9 sequences were identified from the interrogation of nucleotide sequence databases using TBLASTn . Prediction of 3′ Prdm9 zinc finger sequences is greatly facilitated by their presence in the single 3′ terminal coding exon in all species . Orthology of Prdm9 sequences was confirmed using phylogenetic analysis [44] , by consideration of the KRAB-SET-zinc finger domain architecture that is conserved among many but not all ( including some fish , C . elegans and Monodelphis ) Prdm9 proteins ( see text ) , and by reciprocal best BLAST hits . Details of Prdm9 gene predictions from all species investigated are provided in Dataset S1 . In addition to genomic data obtained for Mus musculus and Rattus norvegicus , sequencing of the final exon of Prdm9 was performed from genomic DNA purified from reproductive tract tissue from a total of 11 additional ( sub- ) species: Mus musculus castaneus , Mus macedonicus , Mus spicilegus , Coelomys pahari , Apodemus sylvaticus , Meriones unguiculateus , Peromyscus leucopus , Peromyscus maniculatus , Peromyscus polionotus , Microtus agrestis and Arvicola terrestris . PCR products were amplified using primers designed from the most highly conserved regions from mouse and rat genomic sequence flanking the last exon; either: Mus-Prdm9-F1 5′ CAAAGAACAAATGAGATCTGAG or Mus-Prdm9-F2 5′ AGAACAGGCCAGACAACAAAT with Mus-Prdm9-R1 5′ GTCTT ( C/T ) CTGTAATTGTTGAGATG or Mus-Prdm9-R2 5′ GCT ( G/A ) TTGGCTTTCTCATTC . Products were amplified using the proof-reading Pfx DNA polymerase ( Invitrogen ) , purified from agarose gels using the Qiaquick gel purification kit ( Qiagen ) and sequenced in both directions from 2 or more independent amplification reactions . Sequence traces were initially curated and assembled using Chromas 2 . 0 ( http://www . technelysium . com . au/chromas . html ) and Bioedit ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) . Genbank accessions are provided in Dataset S2 . Sequencing of the zinc finger repeat domain of PRDM9 was performed from the genomic DNA of 50 Chinese normal control samples . PCR amplification , purification and sequencing was carried out as above using the primers Hs-PRDM9-F 5′-GGCCAGAAAGTGAATCCAGG-3′ and Hs-PRDM9-R 5′-TGAAGCCACCTCACACAGCTG-3′ . Products were gel purified and A-tailed prior to sub-cloning into the pCR4-TOPO vector ( Invitrogen ) . T7 and T3 vector primers were used to sequence mini-prep DNA from positive clones . Genbank accessions are provided in Dataset S2 . Chimpanzee ( Pan troglodytes ) PRDM9 C-terminal zinc fingers were sequenced by PCR using the primers Pt-PRDM9-F 5′-GCCTGACCAAAACATCTACCCTGACC-3′ and Pt-PRDM9-R 5′-GTCATGAAAGTGGCGGATTTG-3′ . PCR products were both directly sequenced as well as cloned into the pCR4-TOPO vector ( Invitrogen ) and six independent clones sequenced using vector-specific primers . The genomic DNA sample was obtained from Coriell ( ID#NG03448 ) . The Genbank accession can be found in Dataset S2 . For the prediction of positively selected sites , we included all zinc finger sequences from the 3′ terminal array only if they were complete ( 28-codon ) and retained , at conserved positions , two cysteine and two histidine residues expected to coordinate a single Zn2+ ion . This excludes , for example , the first two zinc finger motifs in primates and rodents . Phylogenetic trees for each multiple alignment were constructed by applying the Fitch-Margoliash criterion to distance matrices of synonymous substitutions per synonymous site ( dS ) as calculated by the codeml programme [77] , [78] . Tree topologies were accepted if they were corroborated by phyml [79] and treebest ( http://treesoft . sourceforge . net/treebest . shtml ) programs . Amino acid sites under positive selection were inferred using “site likelihood method” ( SLR ) [27] with p-value thresholds of 0 . 05 after multiple testing correction . We observed that inferences of positive selection among sequence similar zinc fingers from the same species were sensitive to tree topology . Nevertheless , the use of alternative less-well supported topologies tended only to increase evidence for positive selection . As a result , we have , conservatively , used inferences from the most strongly supported tree . SLR , and other maximum-likelihood approaches that take account of codon evolution , have proved reliable provided that assumptions in evolutionary models are not greatly violated . One such assumption is vertical inheritance without gene conversion , which is demonstrably violated for Prdm9 . However , gene conversion is more likely to affect analyses of sequence-similar zinc fingers from the same species and is less of a factor in analyzing zinc fingers from all the rodent or primate clades due to the greater sequence divergences involved ( for instance , all identical zinc fingers are essentially treated as one representative sequence in analyses ) . Our inferences of positive selection among all zinc fingers in rodent or primate clades ( Figure 3C and Figure 4C ) are accordingly the most robust to phylogeny variations and show high dN/dS values , and low and significant p-values . Rapid fixation of an advantageous allele changes the pattern of polymorphisms around the locus under selection , and various methods have been developed to formally test whether such patterns are compatible with evolution under a neutral model . Other effects , such as geographical structure , population admixture , non-random mating , and varying population sizes , can also give rise to a departure from the neutral model , thereby confounding this analysis . To address this problem , here we use data from recent large-scale surveys of population variation that allow us to compare our observations to empirical , genomic distributions rather than to model-based predictions . This approach accounts for non-local genomic effects such as population structure and growth , at the expense of some loss of power . Tajima's D values were acquired from the UCSC genome browser for American individuals of African , European and Asian ancestry populations [40] . These were computed at 10 kb intervals , each using 100 kb of data . Since both PRDM7 and PRDM9 span about 20 kb , we took the average of two neighbouring values . For the background distribution , averages were similarly computed for all neighbours . To assess the existence of long haplotype blocks , we used HapMap data ( public release 26 ) . We computed derived allele frequencies ( DAF ) by polarizing using the chimpanzee genome . To avoid miscalls , we removed all potential CpG SNPs . Finally , we used r-squared values computed for SNPs at a minimum distance of 50 kb , as including more proximal SNPs which are often in strong LD would further reduce power . For any locus , we identified all pairs of SNPs spanning the locus that satisfied these filters; the maximum r-squared value among these pairs was taken as the observable for that locus . We computed this value for all genomic loci to create the empirical distribution . The entire procedure was done separately for each of the HapMap populations . Clusters of zinc finger repeats ( Figure 2B ) were identified in each of six possible reading frames of the mouse genome using the hmmsearch programme [80] and a hidden Markov model derived from the SMART domain resource [81] . We discarded all zinc finger clusters which show frameshift or stop codon disruptions , giving 473 putative open reading frames ( ORFs ) . Within each ORF , zinc fingers which do not possess the canonical zinc finger Cys2His2 structure were excluded from subsequent comparisons . A multiple alignment of conceptual cDNA zinc finger sequences was constructed from peptide alignments using the MUSCLE programme [82] . Pairwise cDNA sequence alignments were calculated and the proportions of pairs which were higher than a given threshold calculated . Mouse Prdm9 was an extreme outlier for zinc finger pairwise sequence identities greater than 90% , and also for other thresholds ( data not shown ) . | Speciation , the process by which one species splits into two , involves reproductive barriers between previously interbreeding populations . The question of how speciation occurs has rightly occupied the attention of biologists since before Darwin's “On the Origin of Species . ” Studies of recently diverged species have revealed the presence of hybrid sterility genes ( colloquially referred to as “speciation genes” ) , alleles of which are associated with sterility of interspecies hybrids . Mouse Prdm9 is the only known such gene in vertebrate animals . Here we report that the Prdm9 protein has evolved extremely rapidly in its DNA-binding domain , comprising an array of “zinc fingers . ” This suggests that hybrid sterility may arise from a mismatch between the DNA-binding specificity of Prdm9 and rapidly evolving DNA . We propose that Prdm9 binds to satellite-DNA repeats evolving rapidly within and between different species . Prdm9 evolution is unusual because other hybrid sterility genes appear only to evolve rapidly in isolated bursts , whereas Prdm9 has evolved rapidly over 700 million years , in many rodent species , diverse primates and other metazoans . This leads to the tantalizing possibility that Prdm9 may have served as a “speciation gene” on other occasions in metazoan evolution , a possibility that will now need to be investigated . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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] | [
"evolutionary",
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"biology",... | 2009 | Accelerated Evolution of the Prdm9 Speciation Gene across Diverse Metazoan Taxa |
From October 2014 to March 2015 , French Polynesia experienced for the first time a chikungunya outbreak . Two Aedes mosquitoes may have contributed to chikungunya virus ( CHIKV ) transmission in French Polynesia: the worldwide distributed Ae . aegypti and the Polynesian islands-endemic Ae . polynesiensis mosquito . To investigate the vector competence of French Polynesian populations of Ae . aegypti and Ae . polynesiensis for CHIKV , mosquitoes were exposed per os at viral titers of 7 logs tissue culture infectious dose 50% . At 2 , 6 , 9 , 14 and 21 days post-infection ( dpi ) , saliva was collected from each mosquito and inoculated onto C6/36 mosquito cells to check for the presence of CHIKV infectious particles . Legs and body ( thorax and abdomen ) of each mosquito were also collected at the different dpi and submitted separately to viral RNA extraction and CHIKV real-time RT-PCR . CHIKV infection rate , dissemination and transmission efficiencies ranged from 7–90% , 18–78% and 5–53% respectively for Ae . aegypti and from 39–41% , 3–17% and 0–14% respectively for Ae . polynesiensis , depending on the dpi . Infectious saliva was found as early as 2 dpi for Ae . aegypti and from 6 dpi for Ae . polynesiensis . Our laboratory results confirm that the French Polynesian population of Ae . aegypti is highly competent for CHIKV and they provide clear evidence for Ae . polynesiensis to act as an efficient CHIKV vector . As supported by our findings , the presence of two CHIKV competent vectors in French Polynesia certainly contributed to enabling this virus to quickly disseminate from the urban/peri-urban areas colonized by Ae . aegypti to the most remote atolls where Ae . polynesiensis is predominating . Ae . polynesiensis was probably involved in the recent chikungunya outbreaks in Samoa and the Cook Islands . Moreover , this vector may contribute to the risk for CHIKV to emerge in other Polynesian islands like Fiji , and more particularly Wallis where there is no Ae . aegypti .
Chikungunya virus ( CHIKV; Togaviridae: Alphavirus ) infection usually produces fever , joint pain , maculopapular rash and chronic polyarthralgia [1] . Since its emergence in the Indian Ocean islands in 2005 , CHIKV has caused a series of outbreaks in the Indian subcontinent , South-East Asia , China and Central Africa , and , following an increasing trend , CHIKV also expanded to countries in Europe , the Pacific , the Caribbean and the Americas [2–4] . CHIKV is a single-stranded positive sense RNA virus that genetically has diverged in four lineages: the three original West African , East Central South African ( ECSA ) and Asian lineages; and the new ECSA-derived Indian Ocean lineage ( IOL ) [2] . French Polynesia is a French overseas Territory of about 270 000 inhabitants , located in the East part of the South Pacific Ocean . Until October 2013 and the first appearance of Zika virus ( Flaviviridae: Flavivirus ) , dengue virus ( Flaviviridae: Flavivirus ) used to be the only arbovirus formally proven as circulating in French Polynesia [5] . From October 2014 to March 2015 , French Polynesia experienced its first CHIKV outbreak . Within a few weeks CHIKV transmission expanded to all the districts on the main island Tahiti and then rapidly to several islands in all five archipelagos of French Polynesia ( Society , Marquesas , Tuamotu , Gambier and Austral Islands ) . As of March 2015 , 69 , 000 suspected CHIKV cases had been recorded by the Direction of Health [4 , 6] . Phylogenetic analysis confirmed that the virus was introduced from the Caribbean and that it belonged to the Asian lineage [7] . CHIKV is transmitted by daytime-biting Aedes mosquitoes , mostly the wide distributed Ae . aegypti , but also Ae . albopictus that is able to survive at temperate climates [8–14] . Several other Aedes mosquito species have also been reported as potential vectors for sylvatic transmission of CHIKV in Africa and Asia [15] . In the Pacific region , Ae . aegypti started colonizing the islands in the late 19th and early 20th centuries . In the late 1930s infestations were reported in the North part of the Pacific ( Guam , Palau , Federated States of Micronesia , Marshall Islands… ) and also in Vanuatu ( New Hebrides ) and in the Solomon Islands [16 , 17] . Ae . aegypti is now present in almost all Pacific islands and because its ability to transmit CHIKV had been demonstrated , the Pacific island countries were considered at high risk for CHIKV to emerge [14] . In 2011 , CHIKV was reported for the first time in New Caledonia and local populations of Ae . aegypti were demonstrated as able to transmit CHIKV [11] . In Pacific islands or remote areas where Ae . aegypti is not or poorly present , CHIKV may have been transmitted by endemic Aedes species , like Ae . hensilli in Yap State in 2013 [18] . In French Polynesia , possible contribution of the endemic Ae . polynesiensis species in CHIKV transmission was suspected . Ae . polynesiensis may have settled in the Polynesian islands together with human population migrations from the far west to the east part of the Pacific approximately 1 , 500–3 , 000 years ago [19] . Because the Ae . polynesiensis gravid adult female preferentially looks for natural breeding sites such as coconut shells , tree-holes or crab-holes and because its larvae can develop in brackish water , this mosquito is widely distributed in the Polynesian islands [19–22] . In the 1980s after the occurrence of several outbreaks caused by Ross River virus ( RRV; Togaviridae: Alphavirus ) in Pacific islands , the ability for Ae . polynesiensis to transmit RRV was investigated and demonstrated [3 , 23] . These observations suggested Ae . polynesiensis may also be able to transmit CHIKV . In 1967 , Gilotra and Shah mentioned for the first time the ability of a Samoan population of Ae . polynesiensis to experimentally transmit CHIKV [9] . In the present study , we investigated the vector competence of French Polynesian populations of Ae . aegypti and Ae . polynesiensis for CHIKV .
Ae . aegypti and Ae . polynesiensis mosquito colonies were established in 2014 , using mosquito collected on Tahiti Island in the districts of Toahotu and Atimaono , respectively . For the purpose of the study , F14-generation eggs of each mosquito colony were hatched under negative pressure in tap water for 1 hour . Larvae were reared in plastic trays containing tap water supplemented with bovine liver powder ( MP Biomedicals , USA ) inside a climate chamber ( Sanyo MLR-351H , Japan ) set at 27°C , 80% relative humidity and 12:12h light-dark cycle . Pupae were selected with a ratio of 1 male: 4 females . Adults were then maintained in climatic conditions as indicated above and were given continuous access to 10% sucrose solution . CHIKV strain PF14/300914-109 was isolated from the serum of patient infected in September 2014 in Tahiti , French Polynesia . Amplification of CHIKV was performed by inoculation of Ae . albopictus C6/36 cells [24] routinely maintained at 30°C in RPMI-1640 medium supplemented with non essential amino acids , gentamicin , fungizone ( Amphotericin B ) and 10% heat-inactivated foetal bovine serum ( FBS , Life technologies , USA ) . Serum was inoculated at 1:40 in cell-culture medium adjusted at 1% of FBS for 30 minutes at 30°C . Inoculum was then removed and replaced by fresh 1% FBS cell-culture medium . Infected cells were incubated at 30°C for 4 days . Infected cell-culture supernatant was then harvested and underwent two successive additional passages on C6/36 cells . Each successive passage was performed as follows: infected cell-culture supernatant from the previous passage was inoculated at 3:1 in 1% FBS-cell-culture medium for 1 hour at 37°C with gentle agitation . The inoculum was then replaced by fresh 1% FBS-cell-culture medium and infected cells were incubated at 30°C for 4 days . After the third passage , the infected-cell supernatant was harvested and concentrated by using Centricon Plus-70 centrifugal filter devices ( Millipore , Germany ) as previously described [25] . FBS was added to the CHIKV concentrate at 1:5 before storage at -80°C . Virus titration was performed by inoculating C6/36 cells with serial 10-fold dilutions of virus concentrate on a 96-wells plate . Six days later , C6/36 cells were fixed directly on the plate with 70% ice-cold acetone for 10 minutes . Each well was then incubated 30 minutes at 37°C with Group-A mouse ascitic fluid ( National Institute of Allergy and Infectious Diseases , USA ) diluted 1:100 in PBS followed by 30 minutes incubation at 37°C with fluorescein isothiocyanate-conjugated goat anti-mouse IgG ( Bio-Rad Laboratories , France ) diluted 1:100 . Wells containing infected cells were counted and viral titers in 50% tissue culture infectious dose ( TCID50/mL ) were calculated using the method of Reed and Muench [26] . The day of infection , 24 hours-starved and water-deprived 5-days-old mosquitoes were transferred into four to eight nylon mesh-covered containers of about 70 mosquitoes for each population . Two hundred Ae . aegypti mosquitoes were offered the CHIKV infectious blood meal . For Ae . polynesiensis , as the survival rate in laboratory conditions seemed to be lower than for Ae . aegypti , >400 Ae . polynesiensis females were offered the meal to ensure getting enough mosquitoes surviving at least 9 days later . The infectious meal was prepared with fresh washed bovine red cells , viral concentrate ( 1:22 ) and adenosine triphosphate at 5 mM as phagostimulant . As used in previous studies CHIKV titer in the blood meal was adjusted to 7 log10 TCID50/mL to be close to the viremia levels observed in patients [10 , 11 , 27] . Blood meal maintained at 37°C was offered through a Parafilm-M membrane to Ae . aegypti mosquitoes and through a porcine membrane to Ae . polynesiensis mosquitoes . After 1 hour of free access to the blood meal each fully-engorged female was transferred into a 67 x 26 mm individual plastic container to avoid horizontal transmission during sugar-feeding [28 , 29] . Mosquitoes were given access to 10% sucrose solution and maintained for up to 21 days in the climate chamber set at 27°C , 80% relative humidity and 12:12h light-dark cycle . At days 2 , 6 , 9 , 14 and 21 after the infectious blood meal , a subset of 18 hours sucrose-starved and water-deprived mosquitoes were cold-anesthetized . Legs and wings from each mosquito were carefully removed and the proboscis was inserted into an individual filter tips ART ( Molecular BioProducts , USA ) containing 20 μL of FBS . Mosquitoes were allowed to expectorate saliva for 30 min . Then the FBS was expelled into a microtube containing 80 μL of 1% FBS cell-culture medium and stored at -80°C until tested . Each saliva sample was inoculated to C6/36 cells in a single well of a 96-well plate and 6 days later , infectious wells were determined by indirect immunofluorescent assay as described above . At days 2 , 6 , 9 , 14 and 21 after the infectious blood meal , legs and body ( thorax and abdomen ) of each mosquito were collected in separate microtubes and stored at -80°C until tested . Individual mosquito legs and bodies were separately homogenized with metal beads at 20 Hz for 4 min ( Mixer Mill Retsch MM301 , Germany ) either in cell-culture medium supplemented at 20% FBS for bodies or directly in NucliSENS lysis buffer ( bioMérieux , France ) for legs . Homogenate supernatants were recovered after centrifugation at 20 , 000 x g during 5 minutes . Viral RNA was extracted using NucliSENS miniMAG system ( bioMérieux , France ) according to manufacturer’s instructions . Real time RT-PCR was processed on a CFX96 Touch Real-Time PCR Detection System instrument using iScript One-Step RT-PCR Kit for Probes ( Bio-Rad Laboratories , France ) . The primers and the probe used were previously described [30] . Vector competence is defined as the ability of a mosquito to be infected , to disseminate and finally to be able to transmit a given virus [31] . Information on the ability of the two populations of mosquitoes to get infected was provided by the detection of CHIKV by RT-PCR performed on bodies . The mosquito infection rate was defined as the number of mosquitoes with positive body divided by the number of females tested at each time point . The aptitude of the two mosquito species to disseminate the virus was based on the detection of CHIKV by RT-PCR performed on legs . The viral dissemination efficiency was defined as the number of mosquitoes with positive legs divided by the number of females tested at each time point . Evidence for the potential for each of the species to be able to transmit the virus was given by the detection of replicative CHIKV particles in mosquito saliva . The viral transmission rate was defined as the number of mosquitoes with infectious saliva divided by the number of females tested at each time point . Chi-square test with or without Yates’ correction or Fisher’s exact test were used to assess the differences between the two Aedes species at each time point and between two time points for each species ( Graph Pad Prism software , USA ) .
In our infection experiments , ~90% of Ae . aegypti and 70% of Ae . polynesiensis females were fully-engorged with the blood meal ( Table 1 ) . The mortality rate of initially fully-engorged females was much higher for Ae . polynesiensis compared to Ae . aegypti ( Table 1 ) . At 9 dpi , only 29 Ae . polynesiensis females had survived and all were sacrificed for this collecting day . The infection rate was calculated at different time post-infection except at 2 dpi to prevent any false positive RT-PCR due to remaining infectious blood meal in the mosquito midgut . The mosquito infection rate was ~80% as soon as 6 dpi for Ae . aegypti and ~40% for Ae . polynesiensis ( Table 2 ) . The infection rate was significantly higher for Ae . aegypti compared to Ae . polynesiensis ( p<0 . 001 at 6 dpi and p<0 . 0001 at 9 dpi ) . At 2 dpi , CHIKV was detected in legs from seven Ae . aegypti and one Ae . polynesiensis mosquito , on 38 and 37 females tested respectively ( Table 2 ) . CHIKV dissemination efficiencies increased over days and especially between 6 and 9 dpi in Ae . aegypti ( p<0 . 01; Fig 1 ) . At 6 and 9 dpi , the dissemination rates in legs were significantly higher for Ae . aegypti compared to Ae . polynesiensis ( p<0 . 05 and p<0 . 0001 respectively; Table 2 ) . We observed at 6 dpi that RT-PCR cycle threshold values ( data in S1 Table ) in bodies of Ae . aegypti mosquitoes with negative dissemination were significantly higher than those in mosquitoes with positive dissemination ( p<0 . 0001 , Mann Whitney test ) . Infectious saliva was detected as early as 2 dpi in two Ae . aegypti females and at 6 dpi in one Ae . polynesiensis female ( Table 2 ) . At 9 dpi , transmission rate was 34% for Ae . aegypti and 14% for Ae . polynesiensis . CHIKV transmission efficiency in Ae . aegypti increased regularly up to 14 dpi and then plateaued to reach 53% at 21 dpi ( Fig 1 ) .
In the present study we provided evidence that Ae . aegypti and Ae . polynesiensis from French Polynesia were competent laboratory vectors of CHIKV . We observed that the French Polynesian population of Ae . aegypti displayed CHIKV infection rates similar to those previously reported for Ae . aegypti populations collected in other countries [15] . As previously reported for an Ae . aegypti population from Mayotte , Comoros archipelago , infectious saliva was detected in the French Polynesian Ae . aegypti as early as 2 dpi [10] . Such a short extrinsic incubation period allows the vector to quickly infect susceptible people in the household of an infected patient . Together with the observation that dissemination and transmission efficiencies increased up to 21 dpi , our results support that Ae . aegypti may have been an efficient vector of CHIKV during the outbreak in French Polynesia . For Ae . polynesiensis , although the latter time points ( >9 dpi ) were not available , we found that CHIKV infection and dissemination efficiencies were lower compared to Ae . aegypti . Nevertheless , as vectorial capacity relies on multiple factors ( mosquito densities , feeding behavior… ) Ae . polynesiensis might have been able to sustain CHIKV transmission in areas where Ae . aegypti is poorly represented . Indeed , CHIKV spread very quickly in remote French Polynesian atolls , but also recently caused outbreaks in Pacific islands where Ae . polynesiensis is dominating , like in the Cook Islands and Samoa [20 , 22] . | Chikungunya virus has caused a series of outbreaks in the Pacific from 2011 , including French Polynesia . Aedes ( Ae . ) aegypti mosquito , which has colonized almost all Pacific Island Countries , is reasonably expected to have been involved in the chikungunya outbreaks . In addition , endemic Aedes mosquito species may have sustained chikungunya virus transmission in the less urbanized and most remote islands . In the present study , we demonstrated the ability of French Polynesian populations of Ae . aegypti and Ae . polynesiensis to replicate , disseminate and transmit chikungunya virus under experimental conditions . Our results provide for the first time clear evidence for Ae . polynesiensis to act as an efficient chikungunya vector . These findings corroborate previous observation that endemic Aedes species , like Ae . hensilli in Yap Island , may play a critical role in sustaining chikungunya virus transmission , in place or together with the widely distributed Ae . aegypti and Ae . albopictus . In a context where innovative vector control strategies are mostly focused on targeting the mosquito species considered as the main arbovirus vectors , the potential for endemic Aedes species to take the lead in transmitting such arboviruses should not be neglected . | [
"Abstract",
"Introduction",
"Materials",
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"m... | 2016 | Vector Competence of Aedes aegypti and Aedes polynesiensis Populations from French Polynesia for Chikungunya Virus |
An important question in the literature focusing on motor control is to determine which laws drive biological limb movements . This question has prompted numerous investigations analyzing arm movements in both humans and monkeys . Many theories assume that among all possible movements the one actually performed satisfies an optimality criterion . In the framework of optimal control theory , a first approach is to choose a cost function and test whether the proposed model fits with experimental data . A second approach ( generally considered as the more difficult ) is to infer the cost function from behavioral data . The cost proposed here includes a term called the absolute work of forces , reflecting the mechanical energy expenditure . Contrary to most investigations studying optimality principles of arm movements , this model has the particularity of using a cost function that is not smooth . First , a mathematical theory related to both direct and inverse optimal control approaches is presented . The first theoretical result is the Inactivation Principle , according to which minimizing a term similar to the absolute work implies simultaneous inactivation of agonistic and antagonistic muscles acting on a single joint , near the time of peak velocity . The second theoretical result is that , conversely , the presence of non-smoothness in the cost function is a necessary condition for the existence of such inactivation . Second , during an experimental study , participants were asked to perform fast vertical arm movements with one , two , and three degrees of freedom . Observed trajectories , velocity profiles , and final postures were accurately simulated by the model . In accordance , electromyographic signals showed brief simultaneous inactivation of opposing muscles during movements . Thus , assuming that human movements are optimal with respect to a certain integral cost , the minimization of an absolute-work-like cost is supported by experimental observations . Such types of optimality criteria may be applied to a large range of biological movements .
In order to perform accurate goal-directed movements , the Central Nervous System ( CNS ) has to compute neural commands according to the initial state of the body , the location of the target , and the external forces acting on the limbs . Arm movement planning requires solving redundancy problems related to angular displacements , joint torques , muscular patterns , and neural inputs [1] . Experimental studies reported stereotypical kinematic features during pointing and reaching arm movements ( e . g . , quasi-straight finger paths , bell-shaped finger velocity profiles [2]–[4] ) . These features were found to be robust despite changes in mass , initial/final positions , amplitudes , and speeds of displacements [5]–[9] . Therefore , many studies have attempted to identify the principles of motion planning and control , hypothesizing that movements were optimal with respect to some criteria . The present article addresses the question whether motor planning is optimal according to an identifiable criterion . A promising approach to answer this question , called inverse optimal control , is to record experimental data and try to infer a cost function with regard to which the observed behavior is optimal [10] . In the theory of linear-quadratic control , the question of which quadratic cost is minimized in order to control a linear system along certain trajectories was already raised by R . Kalman [11] . Some methods allowed deducing cost functions from optimal behavior in system and control theory ( linear matrix inequalities , [12] ) and in Markov decision processes ( inverse reinforcement learning , [13] ) . In the field of sensorimotor control and learning , some authors suggested that motor learning results from the optimization of some “loss function” related to the task ( e . g . , pointing accuracy ) providing , therefore , a technique allowing to measure such function from experimental data [14] . Nevertheless , in most optimal control studies focusing on arm movements , a cost function is chosen and used in a mathematical model to check its validity a posteriori by comparing the theoretical predictions to the experimental observations . Kinematic models include minimum hand acceleration [15] and minimum hand jerk criteria [16] . These models produce horizontal arm movements that globally fit well with experimental data , providing smooth symmetric velocity profiles and straight trajectories in space . Dynamic models include minimum torque-change [17] and minimum commanded torque-change [18] criteria . They also accurately reproduce certain types of movements ( point-to-point and via-point movements performed in the horizontal plane ) but in several cases provide non-realistic double-peaked speed profiles ( see for instance Figure 11 in [19] ) . In the Riemannian geometry framework , a model used geodesics to separately determine the geometrical and temporal movement features , allowing therefore a unification of previous computational models [19] . Specifically , the geodesic model accurately predicts the spatiotemporal features of three dimensional arm movements . However it results in hand paths that are excessively curved for planar movements . Additional criteria have also been considered , such as energy-like criteria [20]–[25] and effort related criteria [26] , which minimize the peak value of the work , the metabolic energy expenditure , or the amount of neural control signals necessary to drive the arm . These models quantitatively reproduce some specific features of reaching and grasping , such as trajectories , velocity profiles , or final postures . Stochastic models , which are grounded on the hypothesis that noise in the nervous system corrupts command signals , have also been proposed . The minimum variance model was aimed at minimizing endpoint errors and provides not only accurate simulated trajectories of both eye saccades and arm pointing movements in the horizontal plane , but also the speed-accuracy trade-off described by Fitt's law [27] . In the optimal feedback control theory , noise is assumed to induce movement inaccuracy . If errors interfere with task goals , then the controller corrects deviations from the average trajectory . Otherwise the errors are ignored and , thus , variability in task-irrelevant dimensions is allowed [28]–[30] . Despite extensive literature concerning direct optimal control of arm movements , the hypotheses seem too restrictive in some models . For instance , in several models [19] , [26] , the static ( gravity-related ) and dynamic ( speed-related ) torques are calculated separately; therefore their predictions are independent from the gravity field . This assumption partly relies on the physiological observations that muscle activity patterns show two components: a tonic one ( gravity-related ) and a phasic one ( speed-related ) [31] , [32] . Nevertheless , some authors reported difficulties in solving optimal control problems while taking into account gravitational forces in the optimization process [33] , [34] . Thus , this assumption was also aimed at simplifying computations . Furthermore , the models previously cited are generally not consistent with the observation that the kinematics of arm movements performed in the sagittal plane depends on the direction with respect to gravity ( i . e . , upward versus downward movements ) [35]–[38] whereas such a directional difference is significantly attenuated in microgravity [39] . A possible explanation of these findings would be that the CNS uses the gravity to move the limbs efficiently , rather than simply offset it at each instant . This idea guided the development of the theoretical model presented here . During a movement , the energetic consumption is related to the work of muscular forces . However , work is a signed physical quantity that may cancel itself out , even though both active and resistive forces consume energy in muscles . Therefore , work has to be always counted positive in order to express the energy expenditure of a movement: this is the absolute work of forces . The problem of minimizing this absolute work was never solved previously , despite its apparent simplicity and its potential interest for neurophysiologists . A reason might be the mathematical difficulty due to the non-differentiability of the cost function ( induced by the absolute value function ) . Thus , while most existing models deal with smooth cost functions ( i . e . , functions that have continuous derivatives up to some desired order ) , this study relies on this non-smoothness property . The cost chosen here includes two terms: the first represents the absolute work and the second is proportional to the integral of the squared acceleration . In this article , two theoretical results are reported . Firstly , an “Inactivation Principle” states that minimizing a cost similar to the absolute work implies the presence of simultaneous inactivation of both agonistic and antagonistic muscles acting on a joint during fast movements . Secondly , a reciprocal result is that the presence of such inactivation along optimal trajectories implies the non-smoothness of the cost function . Therefore , by using transversality arguments from Thom's Differential Topology [40] , Pontryagin's Maximum Principle [41] , and Non-smooth Analysis [42] , an equivalence between the non-smoothness of the cost function and the presence of simultaneous inactivation of both agonistic and antagonistic muscles is established . The proposed model permits to simulate accurately the kinematics of fast vertical arm movements with one , two , and three degrees of freedom . Moreover , experimental observations actually show simultaneous silent periods on the electromyographic ( EMG ) signals of opposing muscles during fast arm movements .
The current subsection summarizes the mathematical theory which is more fully presented in the Materials and Methods Section . The reader who may not be interested in the full mathematical development of the model may read this subsection only , as a general survey . Although human vertical arm movements are studied here , the above theoretical results may apply to locomotion , whole-body reaching , and more generally to any mechanical system described in the Mathematical Theory Subsection . Firstly , we show that minimizing the compromise Aw/Ae is consistent with temporal and spatial features of biological arm movements . Secondly , we report simultaneous inactivation of agonistic and antagonistic muscles during arm movements . This suggests that the proposed criterion is also relevant at the muscular level and gives insights concerning the cost minimized during fast arm movements .
A model that minimizes a cost based upon the absolute work ( i . e . , an energetic optimality criterion ) has been shown to allow simulating plausible arm movements in the sagittal plane . This was checked by means of three relevant kinematic features: fingertip path curvature , asymmetry of fingertip velocity profiles , and final arm posture . Since this cost function is non-smooth , the Inactivation Principle can be stated: for a large class of non-smooth cost functions , the net torque acting on a joint is zero during a short period occurring around the mid-path movements that are sufficiently rapid . This principle is also valid if a pair of agonistic-antagonistic actuators is considered , exerting opposite torques . Each of the torques is zero during an inactivation period which still appears if the biomechanics of the muscles is considered , when response times are brief ( a few tens of milliseconds ) . For longer response times , complete inactivation is progressively replaced by low-levels of muscular activities . Such quiet periods in the EMGs of opposing muscles were observed during fast arm movements ( see Figures 4 , 5 , and 6 ) , which suggests that this optimality criterion is suitable . The suitability of a similar non-smooth cost function was also found for animals in a recent study [46] . The author concludes that the locomotor pattern of legged animals is optimized with respect to an energetic cost based upon the “positive work” of forces . However , the direct optimal control approach does not prove that the motor planning process actually minimizes energy expenditure . It just shows that such a criterion is plausible because it provides realistic behavior . Indeed , several other cost functions or theories may lead to similar results . For instance , muscle inactivation was also interpreted as a consequence of the Equilibrium Point hypothesis [47] . According to this interpretation , the threshold position control and the principle of minimal interaction would , together , determine the “Global EMG minima” which appear simultaneously in all muscles during rhythmic movements , near the point of direction reversals . Nevertheless , in the theory proposed here , inactivation is somewhat different: it appears near the time of peak velocity , and the precise interval of inactivity may be different at different joints . Moreover , inactivation is still predicted even if biomechanics of muscles , inertia and external forces are taken into account , which is not the case in Equilibrium Point theory [47] . Alternatively , it could be also considered that the CNS simply activates and deactivates the muscles , explicitly determining inactivation phases . However , this would be an argument against our main assumption that the brain tries to minimize some costs . Here , under this assumption , inactivation provides information on the cost function . The theoretical results also allow us to characterize the non-smoothness of the cost function once the simultaneous inactivation of opposing muscles is measured in practice , during movements presumed as optimal . Using mathematical transversality arguments from differential topology we proved that the minimization of an absolute-work-like cost during arm movements is a necessary condition to obtain inactivation phases along optimal trajectories . In other words , assuming that human movements are optimal with respect to a certain integral cost , the simultaneous inactivation of muscles that we observed provides evidence for an absolute-work-like cost . Notably , this simultaneous inactivation of opposing muscles , which is a singular phenomenon , cannot be predicted by models using smooth cost functions , such as the minimum endpoint variance [27] , the minimum jerk [16] , or the minimum torque-change [17] . Those models would predict deviations from “zero torque” , whereas singularity analysis proves the existence of an exact inactivation period . Simultaneous inactivation periods also appeared on intra-muscular EMG traces recorded from monkeys when performing horizontal arm movements ( see Figure 5 in [48] ) . These findings suggest that the minimization of the energy expenditure may be a basic motor principle for both humans and animals . It should be emphasized that such an equivalence between specific movement features and well-identified properties of the cost function is not common in studies using optimal control approach for movement planning . The simulated movements replicated the experimental records accurately , except , obviously , for the bang-bang command signals which provide non-zero accelerations at the beginning and end of the movement ( see Figure 5 ) . The patterns of motor command are actually smoothed by the biomechanical characteristics ( low-pass filters ) of the muscles . As pointed out by several authors some models have been rejected hastily due to the lack of biological validity of their optimal solutions ( bang-bang behaviors ) [15] , [49] . This problem was also discussed in a study where the authors used a similar non-smooth cost function based upon the “positive work” of forces [23] . They noticed that the abrupt velocity profiles predicted by their model were non-realistic but might actually be smoothed by modeling muscles dynamics . In fact , depending on the precision of modeling , different conclusions may be drawn . This is illustrated in Figure 1 where gradient constraints on the torques lead to smoother motor patterns whereas Figure 10 shows solutions in a simpler case of torque control . In the first case the acceleration is continuous while in the second case the acceleration jumps at the initial and final times ( to make the transition between posture and movement ) . Nevertheless , in both cases , inactivation is present and fingertip velocity profiles reproduce the experimental directional asymmetries . Thus , these relevant features of movements are not affected by such changes in modeling . The reason for not systematically considering more precise levels of modeling is twofold . Firstly , it causes important additional computational difficulties , and secondly , many more parameters , which are not always well-known , appear in the model . Here , the model depends on a few parameters . Firstly , the maximum torque that can be developed by each muscle is finite . In particular , this determines the shortest possible movement duration in order to complete the pointing task . Nevertheless these maximum torques did not seem to be reached in practice ( at least during the movements tested here ) so that their precise values were not important for the present study . Secondly , the weighting parameters that appear in the cost could depend on the individual and the task goal . However , they are not critical with respect to the qualitative behavior of the optimal solutions and , although their values could be discussed , the simulations obtained using this model were accurate for a large range of these parameters . Importantly , the whole theory holds without precise constraints on these parameters . A first example is given by the strongly consistent kinematic difference in the 1-dof case for movements performed in the upward versus the downward direction . For instance , for an upward movement ( 1-dof , 45° and 400 ms ) , the relative time to peak velocity ( TPV ) ranged between 0 . 43 and 0 . 5 for weighting parameters ranging between 0 and 10 . For the corresponding downward movement , TPV ranged between 0 . 57 and 0 . 5 . The classical models [16]–[18] were not able to reproduce this directional difference in the speed profiles observed in vertical arm movement executed with 1-dof [37] . Moreover , it has been found that this difference disappeared for movements performed in the horizontal plane , either in upright or reclined postures [37] , [38] . This behavior is experimentally well established and can be easily verified with simulations . Interestingly , it is predicted by our optimality criterion , whatever the choice of the tuning parameters . A second example concerns the final posture selected by the model . The exact terminal limb configuration depends on these weighting parameters . However , we tested several instances of the model , for weighting parameters ranging between 0 . 05 and 1 . In all instances , the simulated terminal postures were in the range of those measured in practice . In order to check the validity of the present model , its predictions were also compared with well-known experimental findings , without trying to fit the data . The tuning parameters used are defined in the Materials and Methods Section . Movement curvature is known to depend on movement duration [36] , [50] . Here , the 2-dof model predicts a change in the fingertip path curvature ( FPC ) when movement duration varies . For the movements tested in Figure 2 , the FPC ranged between 0 . 18 and 0 . 23 for movement durations of between 0 . 2 s and 1 s . Moreover , the final postures have been found to be invariant with respect to the speed of the movement [8] and to the addition of a mass of 600 g on the forearm [9] . Here , in the 3-dof case , the final posture does not significantly vary with movement duration . For instance , the final postures changed by less than 3° ( maximum change at each joint ) while the movement duration ranged between 0 . 2 s and 1 s ( tested for U and D movements that appeared in the left column of Figure 5 ) . Also , adding a mass of 600 g to the forearm did not change the simulated final limb configuration: the model predicted less than 0 . 5° of variation at each joint . In the proposed model , the final posture is selected as the final limb configuration that minimizes the amount of the compromise Aw/Ae necessary to bring the finger to the target . Movements directed toward a single target were tested for various starting configurations of the arm . It resulted in changes in the final posture ( about 1° , 10° , and 15° of variability at the shoulder , the elbow , and the wrist levels , respectively ) . Thus , the final posture depends on the initial configuration of the arm , in agreement with experimental results [21] . It must be noted that the minimum torque-change and the minimum force-change models failed to predict the curvature of movements when antigravity torques were implied in the optimization process , according to Figure 3 in [33] . In contrast , the finger trajectory for a 2-dof arm predicted by our model ( for the same movements of duration equal to 400 ms ) was quite realistic ( Figure 7A ) . This was also in agreement with the experimental finger paths observed in Figure 4 in [6] for other movements performed in the sagittal plane ( see Figure 7B ) . Although the proposed model was only tested in a sagittal workspace , it appears to be well-suited for a large set of movements and may , thus , motivate future extensions of the model to 3-dimensional movements . Several investigators have proposed that the CNS optimizes inertial forces and compensates gravitational forces at each instant [19] , [26] . Static and dynamic forces were assumed to be controlled separately . Although plausible , this idea is hardly compatible with several experimental results . For instance , when considering an upward movement in the sagittal plane performed with the arm fully-extended ( 1-dof case ) , according to such a viewpoint , agonistic ( anti-gravitational ) muscles should be active throughout the movement ( corresponding to a tonic component of EMGs ) [31] . In this case , a muscular activity counteracting the gravity would be necessary to continuously maintain the arm , as if it were at equilibrium at each instant , and would be noticeable in EMGs . However , EMG recordings showed that the activities of the agonistic muscles were quasi-null near the time of peak velocity suggesting , thus , that no muscle was acting against gravity at this instant . Moreover , it may explain why , after subtracting the tonic activity from rectified EMG data , some authors obtained negative phasic activities of some muscles ( e . g . , see [51] , [52] ) . Rather than resulting from errors in the evaluation of the tonic component of muscles activity , the gravitational and inertial forces could just be integrated into the same motor plan , within the minimization of energy expenditure . In that case , an explicit separation between tonic and phasic activities of muscles could be impossible , at least for fast movements . It must be noted that separating static and dynamic forces is not the same as separating posture and movement . Indeed , static and dynamic forces are present during posture maintaining . Neuro-anatomical and experimental evidences for distinct controls of posture and movement were reported in [53] . Thus , the present results concerning inactivation do not contradict the hypothesis that , while maintaining posture , anti-gravity control seems to be tightly related to the muscular system's viscoelastic properties ( see [54] for a study of equilibrium control during quiet standing ) . This problem was not addressed here since we focused on the control of the transient phase of fast movements . In conclusion , from a methodological point of view , the novelty of the present work is to introduce a hypothetical-deductive approach in studies focusing on motor planning of arm movements . The possible existence of the inactivation phenomenon was deduced from a mathematical analysis which aimed to reproduce directional asymmetries in arm movements performed in the sagittal plane . Then , the presence of these inactivation periods produced by the model was confirmed by the EMG signals obtained from experimental data . The mathematical analysis showed that this inactivation was a necessary and sufficient condition for the minimization of an absolute-work-like cost . As far as we know , this is the first time that such a condition has been proved in studies investigating optimality principles in human movement . These results suggest that , considering that inactivation is a short and quite singular phenomenon , more attention should be paid to this specific movement feature in future studies . Two major conclusions can be drawn:
This section is devoted to technical details and proofs of the results presented in the Theoretical Analysis Subsection . It is organized as follows . Firstly , we present the general setting of the optimal control problem under consideration . Secondly , we present the examples that will be used to illustrate the theory . After presenting some prerequisites that may be helpful to understand the main mathematical results , we state two theorems concerning the Inactivation Principle and the necessity of non-smoothness . Then , some details on the computation of the optimal solutions using Pontryagin's Maximum Principle [41] are reported ( for the 1-dof and 2-dof cases ) . Finally , three extensions of the model are given in the case of i ) gradient constraints on the control; ii ) distinct control of agonistic and antagonistic torques; and iii ) modeling the dynamics of agonistic and antagonistic muscles . | When performing reaching and grasping movements , the brain has to choose one trajectory among an infinite set of possibilities . Nevertheless , because human and animal movements provide highly stereotyped features , motor strategies used by the brain were assumed to be optimal according to certain optimality criteria . In this study , we propose a theoretical model for motor planning of arm movements that minimizes a compromise between the absolute work exerted by the muscles and the integral of the squared acceleration . We demonstrate that under these assumptions agonistic and antagonistic muscles are inactivated during overlapping periods of time for quick enough movements . Moreover , it is shown that only this type of criterion can predict these inactivation periods . Finally , experimental evidence is in agreement with the predictions of the model . Indeed , we report the existence of simultaneous inactivation of opposing muscles during fast vertical arm movements . Therefore , this study suggests that biological movements partly optimize the energy expenditure , integrating both inertial and gravitational forces during the motor planning process . | [
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] | 2008 | The Inactivation Principle: Mathematical Solutions Minimizing the
Absolute Work and Biological Implications for the Planning of Arm Movements |
Until 2008 , human rabies had never been reported in French Guiana . On 28 May 2008 , the French National Reference Center for Rabies ( Institut Pasteur , Paris ) confirmed the rabies diagnosis , based on hemi-nested polymerase chain reaction on skin biopsy and saliva specimens from a Guianan , who had never travelled overseas and died in Cayenne after presenting clinically typical meningoencephalitis . Molecular typing of the virus identified a Lyssavirus ( Rabies virus species ) , closely related to those circulating in hematophagous bats ( mainly Desmodus rotundus ) in Latin America . A multidisciplinary Crisis Unit was activated . Its objectives were to implement an epidemiological investigation and a veterinary survey , to provide control measures and establish a communications program . The origin of the contamination was not formally established , but was probably linked to a bat bite based on the virus type isolated . After confirming exposure of 90 persons , they were vaccinated against rabies: 42 from the case's entourage and 48 healthcare workers . To handle that emergence and the local population's increased demand to be vaccinated , a specific communications program was established using several media: television , newspaper , radio . This episode , occurring in the context of a Department far from continental France , strongly affected the local population , healthcare workers and authorities , and the management team faced intense pressure . This observation confirms that the risk of contracting rabies in French Guiana is real , with consequences for population educational program , control measures , medical diagnosis and post-exposure prophylaxis .
Worldwide , rabies causes approximately 55 , 000 deaths per year [1] . Rabies viruses are transmitted to humans via saliva from bites of carnivores and bats . Bats may be frugivorous , hematophagous or insectivorous . Vampire bats ( 3 main species Desmodus rotundus , Diphylla ecaudata and Diaemus youngi ) feed on blood from warm-blooded animals , e . g . horses and cattle [2] . Rabies in 2005 , transmitted to humans by vampire bats reached new heights in Latin America , where with several outbreaks reportedly concerned 55 human cases , 41 of them in the Amazon region of Brazil . Peru and Brazil had the highest numbers of reported cases from 1975 to 2006 [3] . Bats represent the main vector of human rabies in Brazil [4]–[8] . Near its border with French Guiana , other outbreaks were described in remote rural areas of Portel and Viseu Municipalities , Pará State , northern Brazil . Twenty-one human deaths were attributed to paralytic rabies in those 2 municipalities . Isolates were antigenically characterized as D . rotundus variant 3 [9] . During a recent outbreak , media reports noted that nocturnal biting coincided with the failure of a regional generator that left people without electricity for 6 weeks . Outbreaks of bat-transmitted rabies have been linked to the continued deforestation of the Amazon region , which has displaced vampire bats across northern Brazil and increased their contact with humans . The reasons for the outbreak in Brazil are not yet fully understood . In French Guiana , a French Overseas Department located in South America , 10 cows , 2 dogs and 1 cat died of bat rabies-virus infection between 1984 and 2003 [10] , [11] , but no human case had previously been reported there [10]–[12] . However , on 28 May 2008 , the National Reference Center for Rabies ( Institut Pasteur , Paris ) , confirmed the diagnosis of rabies for a 42-year-old French Guianan man , who had never left this Department and who died in Cayenne , after developing clinically typical meningoencephalitis . Since 14 May , he had complained of nonspecific symptoms , mainly fever , severe asthenia and pain , and had consulted at the Cayenne Hospital Emergency Unit 3 times before being admitted on 21 May in a state of mental confusion; his condition deteriorated rapidly thereafter . He became comatose on the same day and died on 27 May . On 28 May , rabies was diagnosed based on a new reverse-transcription hemi-nested polymerase chain reaction ( RT-hnPCR ) protocol applied to a skin biopsy and saliva specimens . This case illustrates the risk of under-reporting of human rabies based only on clinical criteria and highlights the need for laboratory confirmation to obtain accurate data on disease burden [13]–[15] . Phylogenetic analysis of the isolated virus identified a Lyssavirus ( Rabies virus species ) , closely related to those circulating in hematophagous bats . This identification of the first human case of bat rabies in France resulted in the creation of a national multidisciplinary Crisis Unit under the authority of the French Ministry of Health in Paris . In French Guiana , it was coordinated by the local health authorities and the Center for Treatment Anti-Rabies ( CTAR ) of Institut Pasteur de la Guyane ( IPG ) . Its objectives were to manage the crisis , implement an epidemiological investigation and a veterinary survey , provide control measures and establish a communications program . Herein , we review the methodology used by the Crisis Unit and the consequences of this case on the local perception of rabies .
Immediately following laboratory confirmation of the rabies case , a multidisciplinary Crisis Unit was created at the national level in Paris , France , and locally in Cayenne , Guiana . Nationally , the stakeholders were affiliated with the French Ministry of Health , French Ministry of Agriculture , Institut Pasteur , Paris , and Institut de Veille Sanitaire , St-Maurice . Locally , the involved services were IPG and its CTAR , Departmental Health and Development Direction , Departmental Veterinary Services , Cayenne Hospital and the Regional Epidemiological Cell . An epidemiological investigation was conducted to identify people potentially exposed to rabies virus , who would require post-exposure prophylaxis ( PEP ) . To do so , the following criteria of exposure were used . A person was defined as potentially exposed when: 1 ) he/she was a part of the case's entourage ( family , friends , sexual partners , sport team members , colleagues , visitors ) during the 15 days preceding the onset of the index case's symptoms; 2 ) he/she was a healthcare worker who had cared for the case; or 3 ) he/she had been in contact with animals suspected of being contaminated ( based on their behavior , illness , death ) that were known to have been in contact with the case [16] . An active search was carried out within the case's familial and professional entourage , and in the Cayenne hospital , where the patient had been admitted . A step-by-step search procedure was implemented , questioning all exposed people able to identify other people suspected of being in contact with the index case . All people suspected of exposure had a medical visit at the IPG CTAR , using Questionnaire 2 ( Table S2 ) for the case's familial and professional entourage . For the healthcare workers , the exposure level was assessed for those working in 3 of Cayenne Hospital's departments: emergency unit , intensive care unit and biology laboratories . To assess their possible exposure , specific healthcare worker questionnaire 1 ( Table S1 ) was filled out during a medical consultation with the physician responsible for the hospital's hygiene unit . A healthcare worker was considered to be exposed when: 1 ) he/she had close contact with the index case ( <1 m ) , took part in his resuscitation or performed an act susceptible of generating aerosolization of body fluids; 2 ) he/she had close contact with the case's biological fluids ( laboratory workers ) ; 3 ) he/she had been bitten by the case; or 4 ) he/she failed to comply with general hygiene recommendations . When exposure corresponded to those criteria , the 4-dose Zagreb immunization protocol ( 1 of the schedules recommended by the World Health Organization guidelines [1] ) against rabies was systematically administered . Chi-square or Fisher's exact test , with a risk α of 5% was used for simple comparisons of rates . The statistical analyses were run with SAS version 9 . 1 ( SAS Institute Inc . , Cary , NC , USA ) . The rabies index case had numerous contacts with animals . The objectives of the veterinary survey were to identify the animal at the origin of the rabies infection and to identify other animals possibly contaminated and currently in the incubation period . Suspected animals were listed by questioning the case's family and close contacts . Standardized veterinary questionnaire 3 ( Table S3 ) was completed at IPG , when people in close contact with the case came for their vaccinations , in collaboration with the Regional Epidemiological Cell and the Departmental Veterinary Services . Two types of suspected animals were identified: those still alive and those that had died . Although it was impossible to obtain brain samples from most of the deceased animals , some of their graves could be found and sometimes brain specimens could be recovered . The living suspected animals were placed under veterinary surveillance and some of them were euthanized when suspicious clinical symptoms became manifest . All the animal brain samples were sent to the National Reference Center for Rabies for laboratory diagnosis of rabies . The communications program had several levels ( national and local ) and targeted different types of people ( general population , healthcare workers and veterinary services ) . Several communication means were used: national and local press releases , press conferences , and specific meetings with healthcare workers ( in Cayenne Hospital and within the private-practice network ) and veterinarians in private practice . The local media ( television , radio , print ) were kept informed and recruited to encourage individuals who might have been in contact with the index case to come and consult at the IPG CTAR .
The Crisis Unit met twice daily in French Guiana from 28 May 2008 to 4 July 2008 ( Fig . 1 ) . The stakeholders involved in continental France participated by phone once daily until mid-June then every 2 days thereafter . After each meeting , a brief report was sent to all the stakeholders to share up-to-date information with the entire network of people involved in the epidemiological investigation , control measures and the veterinary survey . It also provided regular updates of the situation to the media and , thus , to the local population . This investigation identified 160 persons suspected of being exposed: 60 within the index case's entourage and 100 in Cayenne Hospital ( Fig . 2 , Table 1 ) . Each one's risk of exposure was systematically assessed by the CTAR , after an individual interview with a doctor . Exposure was confirmed for 90 persons: 42/60 ( 70% ) from the case's entourage and 48/100 ( 48% ) from the hospital . The first medical interview conducted within the hospital identified 48 healthcare workers; the CTAR confirmed all of them . For healthcare workers , the confirmed exposure rate differed among departments: 60% for biology laboratories , 58 . 5% for the intensive care unit and 34 . 1% for the emergency unit ( p = 0 . 04 ) , and significantly so between the former 2 ( p = 0 . 01 ) and the latter 2 ( p = 0 . 02 ) . All 90 exposed individuals were vaccinated against rabies . The immunization program for these people began between 28 May and 13 June , and finished on 4 July . Because reported exposures were considered to be only grade 2 , no rabies immunoglobulins were administered . One of the consequences of human rabies emergence was a higher number of consultations at the CTAR ( Fig . 3 ) , necessitating the reorganization of its functioning and establishment of dedicated chain of consultation for rabies [17] . Exposure of the rabies index case to bats was difficult to assess . He had very frequently slept outside , in a hammock but without using mosquito netting to protect him , in places where bats are numerous . Nobody from his entourage recalled his having been bitten by a bat but , because bats usually bite while its victim is sleeping , it is impossible to formally conclude . For other animals , a field investigation was conducted from 29 May to 9 June to identify retrospectively any animal that might have been the source of contamination of the human case . Five suspected pets were identified: 4 cats and 1 dog . Unfortunately , only 1 exploitable carcass was recovered and it yielded negative results ( Table 2 ) . Four other cats living in the index case's residence , in contact with those pets suspected of having been exposed to the rabies virus , were placed under veterinary observation and , finally , euthanized on 30 May 2008 . All the biological analyses were negative . This survey was unable to identify the source of rabies transmission . A local press conference was organized each week until mid-June . Three reports , on the epidemiological investigation , control measures and veterinary survey , were specifically written for the print media . A free telephone number , dedicated to questions about rabies , was opened within the local Health Authorities Services . Four television and radio reports were prepared within the IPG CTAR . Three meetings were organized at Cayenne Hospital to inform healthcare workers . The first was held on 23 May , before rabies had been biologically confirmed , to anticipate the crisis and assure the medical teams' preparedness to react ( Fig . 1 ) . It was useful because it enabled a quicker healthcare-worker response when the rabies diagnosis was confirmed . Another meeting was held on 2 June to inform private-practice veterinarians in French Guiana of the epidemiological developments and their role in the ongoing epidemiological survey .
The last autochthonous human rabies case identified any French territory was reported in 1924 in continental France [11] . However , the risk remains of humans being exposed to the virus in enzootic countries and not seeking PEP due to ignorance of the rabies risk . Since 1970 , 21 human deaths from rabies have been recorded in France [18]: 20 cases were imported and 1 was transmitted by a corneal transplant . The first human rabies case diagnosed in French Guiana , in May 2008 , and described herein , confirms that the risk of contracting the rabies virus there indeed exists . It was the first case subjected to molecular biology confirmation by the French National Reference Center for Rabies . The patient's initial clinical picture was not typical and he consulted the Cayenne Hospital emergency unit 3 times before being admitted , further emphasizing the need to include rabies in the differential diagnosis of unexplained encephalitis in humans [14] . Rabies was diagnosed intravitam based on RT-hnPCR–detection of viral RNA in saliva and a skin biopsy [13] . The rabies virus responsible was similar to those circulating in hematophagous bats in this part of the world and closely related to those previously isolated from animals in French Guiana with <4% nucleotide divergence in the nucleoprotein gene ( unpublished data ) . The origin of the contamination was not formally established , although an unrecognized vampire-bat bite seems by far the most likely route of transmission . However , as some cases reported in other countries [19] , the source of contamination could also have been feline , because a cat reportedly died in March 2008 , 2 months after having been severely bitten and wounded by a bat . After the public was informed of this case , the number of patients consulting the CTAR increased dramatically [17] , a phenomenon that had previously been observed in continental France [20] . Since 2008 , no other human rabies case has been reported in French Guiana . Recent emerging zoonoses , e . g . , Ebola or Marburg virus hemorrhagic fevers , Nipah virus encephalitis , severe acute respiratory syndrome ( SARS ) , highlight the potential of bats as vectors for transmission of infectious diseases to humans . This potential was already known for rabies encephalitis , since 10 of the 11 Lyssavirus species are transmitted by bats . Rabies control in bats remains very difficult , even though some encouraging experimental results obtained with D . rotundus bats in captivity demonstrated the immunogenicity of the vaccinia-rabies glycoprotein [21] . However , several effective methods are available to limit the access of the bat population to cattle . Furthermore , some preventive and control measures to limit the number of human deaths attributable to rabies transmitted by vampire bats have been successfully implemented [3] . Rabies diagnosis is a key issue . It is routinely based on clinical and epidemiological information , especially when the exposure is reported in a rabies-endemic country . Although techniques for postmortem diagnosis of rabies have been well-established for decades , tests for intravitam diagnosis of human rabies were rarely optimal , and depended entirely on the nature and quality of the sample supplied . Over the past 3 decades , molecular biology tools have contributed to the development of these tests , resulting in more rapid detection of the rabies virus . Several molecular methods are now available that can be used to complement conventional tests for human rabies diagnosis [22] . The 21st century challenges for diagnostic test developers are 2-fold: first , to achieve internationally accepted validation of a test that will then lead to its acceptance by international organizations; second , these tests are mainly needed in developing regions the world , where financial and logistical barriers prevent their implementation [14] , [22] . The question is even more important in that rapid diagnosis of rabies in suspected human cases influences PEP for potential case contacts and ensures appropriate patient management [23] . This first human rabies case in French Guiana means that national and local public health authorities must improve preventive and control measures for the local population and travellers . Rabies prophylaxis requires a multifaceted approach , including health education , PEP , systematic vaccination of dogs and cats , and , sometimes , selective immunization campaigns to control transmission among wild animals , e . g . foxes and hematophagous bats [24] . Since human rabies is almost always fatal if prophylactic measures are not initiated , it is essential to increase awareness of who should receive PEP and when it should be administered . Pre-exposure prophylaxis entails the administration of the rabies vaccine to individuals at high risk for exposure to rabies viruses , e . g . , laboratory workers who handle infected specimens , diagnosticians , veterinarians , animal-control workers , rabies researchers , cave explorers… [25] . PEP consists of a multimodal approach to decrease an individual's likelihood of developing clinical rabies after suspected exposure to the virus . Regimens depend on the victim's vaccination status and involve a combination of wound cleansing , rabies-vaccine inoculation , and administration of human rabies immunoglobulins [25] . When used in a timely and accurate fashion , PEP is nearly 100% effective . However , once clinical rabies manifestations have developed , rabies PEP remains supportive . To date , only 5 well-documented cases of prolonged survival or recovery from rabies have been described and were specifically associated with PEP administration before the onset of symptoms [26] . The recently developed Milwaukee protocol added induction of therapeutic coma to supportive care measures and antivirals , claiming it ensured the recovery of an unvaccinated patient . However , its use has yielded inconsistent outcomes [27] . The impact of this rabies-virus emergence in French Guiana was dramatic , especially in the context of a Department far from continental France . Despite the enormous pressure placed on the crisis-managing team by the local population , healthcare workers and politicians , the number of PEP remained relatively limited compared with previous cases in continental France [16] and other countries [28]–[32] . Notably , no subsequent case developed . This case illustrates the need for further preparedness of public health infrastructures in rabies-enzootic areas that have not yet recorded human rabies cases . Pertinently , lessons learned from other countries , informing public health professionals and a multidisciplinary approach were essential to crisis management of our case [3] , [33] . His case history enhanced the perception of the risk and , consequently , a vast campaign to educate and inform the general population about zoonotic diseases acquired from domestic , as well as wild animals , like bats , was undertaken in French Guiana as had been done in neighboring countries [34] . In addition to these measures , rabies is now more systematically included in the differential diagnosis of human encephalitis cases consulting at French Guiana hospitals . Indeed , 2 suspected human cases , subsequently found negative , were subjected to rabies testing during 2008–2010 period . In parallel , active surveillance of bat rabies has been established to learn more about rabies-virus circulation in the local bat populations . | Until 2008 , rabies had never been described within the French Guianan human population . Emergence of the first case in May 2008 in this French Overseas Department represented a public health event that markedly affected the local population , healthcare workers and public health authorities . The antirabies clinic of French Guiana , located at Institut Pasteur de la Guyane , had to reorganize its functioning to handle the dramatically increased demand for vaccination . A rigorous epidemiological investigation and a veterinary study were conducted to identify the contamination source , probably linked to a bat bite , and the exposed population . Communication was a key factor to controlling this episode and changing the local perception of this formerly neglected disease . Because similar clinical cases had previously been described , without having been diagnosed , medical practices must be adapted and the rabies virus should be sought more systematically in similarly presenting cases . Sharing this experience could be useful for other countries that might someday have to manage such an emergence . | [
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] | [
"medicine"
] | 2012 | First Human Rabies Case in French Guiana, 2008: Epidemiological Investigation and Control |
The human immunodeficiency virus type 1 ( HIV-1 ) exterior envelope glycoprotein , gp120 , possesses conserved binding sites for interaction with the primary virus receptor , CD4 , and also for the co-receptor , generally CCR5 . Although gp120 is a major target for virus-specific neutralizing antibodies , the gp120 variable elements and its malleable nature contribute to evasion of effective host-neutralizing antibodies . To understand the conformational character and immunogenicity of the gp120 receptor binding sites as potential vaccine targets , we introduced structure-based modifications to stabilize gp120 core proteins ( deleted of the gp120 major variable regions ) into the conformation recognized by both receptors . Thermodynamic analysis of the re-engineered core with selected ligands revealed significant stabilization of the receptor-binding regions . Stabilization of the co-receptor-binding region was associated with a marked increase in on-rate of ligand binding to this site as determined by surface plasmon resonance . Rabbit immunization studies showed that the conformational stabilization of core proteins , along with increased ligand affinity , was associated with strikingly enhanced humoral immune responses against the co-receptor-binding site . These results demonstrate that structure-based approaches can be exploited to stabilize a conformational site in a large functional protein to enhance immunogenic responses specific for that region .
Effective vaccines are an extremely important means to control , and even eradicate ( e . g . , smallpox ) global human pandemics caused by viral and bacterial pathogens ( reviewed in [1] and [2] ) . A major correlate of effective anti-viral vaccines is the elicitation of virus-neutralizing antibodies in vaccinated individuals . With approximately 60 million humans infected with HIV-1 overall , the well-documented global pandemic has resulted in a huge burden of human mortality and morbidity , highlighting the need for an effective vaccine . Structure-based development of HIV-1-specific drugs has been enormously successful , and the application of structure-guided vaccine design is an appealing avenue to advance such efforts ( reviewed in [3] ) . Here , we describe a novel effort to apply structural and thermodynamic analysis to inform the design of vaccine immunogens that induce HIV-1-neutralizing antibodies . The HIV-1 infection process begins with interaction of the exterior component of the trimeric envelope glycoprotein ( Env ) complex , gp120 , with the primary receptor protein , CD4 , present on the host cell surface . Interaction of the Env complex ( or functional spike ) with CD4 , induces exposure of or formation of the co-receptor-binding site on gp120 and enables this glycoprotein to bind chemokine receptor molecules ( usually CCR5 or , alternatively , CXCR4 ) expressed on the surface of a subset of CD4+ lymphocytes ( reviewed in [4] ) . These receptor-induced activation events are followed by fusion of the viral and host cell membranes , mediated by the transmembrane glycoprotein , gp41 . It is this series of HIV-1 Env-receptor interactions that are the major focus of research aimed at developing broadly neutralizing antibodies to interrupt the entry process . It is anticipated that if such antibodies can be elicited , they will contribute a major component to protection by an HIV-1 vaccine . CD4 induces extensive conformational alterations in monomeric gp120 as characterized by unusually large entropic changes following gp120-CD4 interaction and by changes in antigenicity [5]–[9] . The flexible gp120 glycoprotein likely presents multiple conformations to the immune system that are not present on the functional spike [5] . In addition , gp120 possesses conserved antigenic determinants that , in principle , might elicit antibodies capable of neutralizing a broad array of HIV-1 isolates . However , gp120 variable regions and non-neutralizing determinants tend to dominate the elicited immune response [10] , [11] . Moreover , extensive Env glycosylation ( “glycan shielding” ) and conformational masking in the context of the functional spike ( i . e . , epitope inaccessibility; see [12] and reviewed in [13] ) make this glycoprotein a difficult target for broadly neutralizing antibodies [12] , [14] , [15] . The receptor-binding structures of gp120 are conserved among diverse viral isolates and represent functionally constrained regions that might serve as targets of broadly neutralizing antibodies . However , structural evidence suggests that , within functional spike , the CD4-binding site ( CD4bs ) is a recessed pocket and the co-receptor-binding site ( or CD4-induced region ) is either not formed or not exposed until gp120 engages CD4 on target cells [16] . In animal models , passive administration of neutralizing antibodies inhibits HIV-1 infection [17]–[20] , demonstrating the proof-of-principle that , if elicited by a vaccine , such antibodies could effectively inhibit viral entry . Typically , effective anti-viral vaccines consist of either live-attenuated or chemically inactivated forms of a given virus . These vaccines usually elicit neutralizing antibodies as a major component of a protective response [2] . However , neither of these approaches has been successful to prevent HIV-1 infection in a safe or effective manner . Much effort has therefore focused on utilizing the HIV-1 envelope glycoproteins as recombinant , subunit vaccines to elicit potent neutralizing antibodies . Due to the aforementioned Env variability , here we focused on eliciting antibodies against the functionally and structurally conserved receptor-binding regions of gp120 . Several studies attempted to elucidate the biophysical factors of the antigen that effect the maturation of host antibody responses [21] , [22] . However , no study to date has tested the impact of conformational stabilization and increased ligand affinity on enhancing the immune responses against discrete conformational regions in the context of a large functional protein . Here , we test the concept that conformational fixation of the conserved receptor-binding sites on the surface of gp120 would enhance elicitation of antibody responses against those target sites . The approach is based upon the high-resolution crystal structures of core gp120 protein in a ternary complex with CD4 and the co-receptor mimetic , CD4-induced ( CD4i ) antibody , 17b , and the unliganded SIV core structure [16] , [23] . The structural information , consistent with the thermodynamic analysis , indicates that major structural rearrangements occur within gp120 following interaction with CD4 [23] . Previously , we exploited this information to design core gp120 molecules with up to 50% stabilization of the CD4bs , and one such protein , Ds12F123 , was co-crystallized with the broadly neutralizing anti-HIV-1 antibody , b12 [24] . However , limitations in protein expression prevented us from introducing additional stabilizing mutations into this molecule to achieve greater conformational stabilization . In the present study , we re-designed the core gp120 , based upon new available structures [25] , to enhance protein folding and expression , and layered upon this , additional mutations to stabilize the CD4-binding site as well as the co-receptor-binding region . Detailed conformational characterization of the receptor-binding sites of these modified proteins are presented . We tested the effects of the stabilizing mutations in regards to the elicitation of antibodies in small animals . We demonstrate that the novel mutagenic stabilization of a discontinuous epitope , typified by an increased on-rate of ligand binding to this region , dramatically increased the immunogenicity and neutralizing capacity of elicited antibodies specific for that epitope region .
To focus the immune response onto the conserved receptor-binding sites , it is important to remove immunodominant regions , such as the V1/V2 and V3 hypervariable loops . Previously , loop truncations demonstrated that such removal was possible; however , structural analysis suggested more optimal designs were feasible [16] , [25] , [26] . For example , the structure of core gp120 with intact V3 loop showed that the previously published Gly-Ala-Gly substitution of V3 residues 298–329 ( to accomplish deletion of V3 ) removed four hydrogen-bonds from β-strand 12 and five hydrogen-bonds from β-strand 13 [25] . We modeled a new substitution ( V3S ) that retained these hydrogen bonds , and added a longer linker ( Figure 1A ) . Further structural analysis indicated that additional trimming of the flexible V1V2 loop to eliminate a naturally occurring cysteine pair might facilitate accommodation of additional pairs of stabilizing cysteines elsewhere in the molecule . Accordingly , a more minimal loop ( V1/V2b ) was modeled with a type II turn connecting strands β2 and β3 , replacing nine residues ( CVGAGSCNT ) with an Ala-Gly-Ala tri-peptide ( see Figure 1 ) . To reduce conformational flexibility and lock core gp120 into its receptor-bound state , we used two tactics: filling hydrophobic pockets and adding inter-domain disulfides . We previously described cavity-filling or “F mutations” to fill the Phe-43 pocket ( where critical contacts are made for CD4 binding ) and other gp120 cavities [24] , [27] , [28] , and also described the introduction of inter-domain cysteine pairs ( disulfides or Ds mutations ) [24] . Here , we used a combination of loop alterations , F mutations and Ds mutations , to create four new immunogens . The “coreV3S” contained the V1/V2b and V3S alterations . Meanwhile “2CC” , “3CC” and “4CC” involved 2 , 3 and 4 additional inter-domain disulfides , in concert with the gp120 cavity-filling mutations ( F1 , F2 and/or F3; Figure 1B and 1C ) . We also expressed two previously described immunogens , core and Ds12F123 [24] , as controls . The new designs resulted in the expression of well-folded gp120 proteins as assessed by their interactions with conformational ligands 17b and b12 ( see below ) . Protein purity and molecular mass were determined by SDS-PAGE analysis followed by Coomassie blue staining ( Figure S1 ) . The purified proteins were tested for recognition by ligands directed against the receptor binding sites , first by ELISA ( Figure 2A ) . In agreement with our previous data , the stabilizing mutations in Ds12F123 enhanced affinity for CD4 binding over that of the parent core protein . Interestingly , the coreV3S protein displayed slightly increased CD4 affinity even without any additional stabilizing modifications , suggesting that the V1V2b and V3S modifications influence formation , stability or accessibility of the CD4 binding region on the gp120 core . Addition of stabilizing mutations to coreV3S protein , however , did not increase the already high affinity binding of CD4 to the modified proteins . We also assessed recognition of the proteins by b12 , a CD4bs-directed antibody that recognizes a surface similar to but not identical with that of CD4 [24] . The core and the coreV3S proteins both displayed similar recognition by b12 , suggesting that the modified truncations of the V1V2- and the V3 loop did not impact upon b12 binding . However , addition of the stabilizing mutations affected b12 binding to different degrees irrespective of the core context ( see Figure 2 ) . These findings are consistent with our earlier observation that the same set of stabilizing mutations reduce b12 affinity to some degree [24] . However , the greater impact of the stabilizing mutations on b12 affinity observed in this study may be a result of the coreV3S protein context or , for the ELISA , by the detection reagent used ( i . e . , rabbit sera raised against the unmodified core gp120 protein ) . We then tested effects on the gp120 co-receptor-binding site assessed by recognition of the cores by the co-receptor mimetic antibody , 17b [6] . As expected , the original core gp120 protein was not recognized by 17b , in contrast to the partially stabilized Ds12F123 protein ( which possesses 2 pairs of cysteines; see also [24] ) . However , the V3S modifications facilitated increased recognition by 17b ( closed circles ) even in the absence of CD4 or stabilizing mutations . Incorporation of 2 , 3 or 4 cysteine pairs did not alter the avid recognition of the coreV3S protein by 17b as determined by ELISA ( Figure 2A ) . These results indicated that somewhat unexpectedly , the V3S structural elements play a critical role in the formation of the 17b-associated co-receptor-binding site . We also determined effects of the structural alterations on the recognition of gp120 by ligands that bound outside the CD4 and co-receptor regions . We selected the monoclonal antibody 2G12 , which binds to a conformational glycan epitope on the outer domain of gp120 that is distal from the CD4bs . The 2CC protein displayed slightly reduced affinity whereas the 3CC and 4CC proteins showed somewhat increased affinity for 2G12 . These small but unanticipated differences in affinities were not contributed by differences in the amounts of proteins used since equal protein quantities were confirmed by both optical density and SDS gel analysis ( data not shown ) . Since the putative stabilizing mutations influenced recognition by the receptor-site-directed ligands , we performed SPR studies to identify the contributions of the individual rate constants in regards to the changes in affinity for these ligands . As shown in Figure 2B , the on-rates ( ka ) of CD4 binding to the stabilized cores remained nearly unchanged relative to the unmodified core . However , there was a subtle and gradual decrease in off-rates upon addition of the cysteine pairs , leading up to twofold increase in CD4 affinity ( 10 . 5 nM for coreV3S versus 5 . 5 nM for 4CC ) . For b12 binding , the on-rates were significantly reduced for the stabilized proteins as reflected in the overall affinities . For the kinetic analysis of 17b binding ( Figure 2C ) , we included the core protein ( without the V3S modifications ) to determine the influence of V3S modification on the antigenicity of the co-receptor-binding site . The core protein was recognized by 17b with extremely low affinity , which was not detectable in the ELISA format ( see also Figure 2A ) . In stark contrast , the newly designed coreV3S protein is recognized by 17b with remarkably high affinity ( 3 nM ) , even in the absence of CD4 . Addition of the stabilizing mutations did not alter the off-rates of 17b interaction to any of the coreV3S variants . However , the on-rates increased significantly , ranging from increases of 11- ( for 2CC ) to 27- ( for 3CC ) to 18-fold ( for 4CC ) over the on-rate observed for 17b binding to coreV3S . Therefore , the enhancement of 17b affinity for the series of V3S-stabilized cores was directly correlated with an increase in the on-rate of antibody binding . To investigate the influences of CD4 interaction with gp120 on 17b binding in the coreV3S protein context , the V3S protein variants were pre-incubated with 10-fold molar excess of sCD4 and the protein mixtures were then analyzed by SPR . As expected , the previously crystallized core protein showed high affinity ( 28 nM ) binding to 17b in the presence of CD4 , with nearly undetectable affinity in the absence of CD4 . Note , however , that the coreV3S displayed nanomolar affinity even in the absence of CD4 , implicating greatly the V3S modifications on stabilization of the 17b epitope , and perhaps the bridging sheet itself ( see Figure 1 and [16] ) . The association rate constant for 17b binding to coreV3S was increased 10-fold in the presence of CD4 , resulting in nearly a 10-fold increase in the observed affinity . The 2CC protein exhibited only a 1 . 5-fold gain in the on-rate of 17b binding and a 2 . 5-fold increase in overall 17b affinity in the presence of CD4 . For cores containing the 3CC or 4CC mutations , pre-incubation with CD4 did not alter 17b affinity , suggesting that the 3CC and 4CC mutations mimic closely the conformational effects induced by CD4 relative to formation or stabilization of the co-receptor-binding site . To evaluate the extent of stabilization of the receptor-binding sites on gp120 , we performed isothermal titration calorimetry ( ITC ) and determined the apparent change in enthalpy ( ΔH ) and entropy ( −TΔS ) upon binding of coreV3S variants to the primary receptor , CD4 , or to the co-receptor mimetic antibody , 17b . The complete thermodynamic cycle of CD4 and 17b binding were measured in the two possible orders: A and B ( Figure 3A ) . In order A , CD4 was combined with gp120 ( A1 ) , followed by 17b binding to the gp120-CD4 complex ( A2 ) ; in order B , 17b was combined with gp120 ( B1 ) , followed by CD4 binding to the gp120-17b complex ( B2 ) . The thermodynamic values of these interactions are summarized in Figure 3B and 3C . In A1 reactions , the enthalpy of CD4 interaction was approximately 56 kcal/mol for the coreV3S protein and approximately 27 kcal/mol for each of the mutants , indicating that approximately 50% less bond formations/reformations and/or solvent displacement occurred when the stabilizing mutations were present . This observation was supported by approximately 65% reduction in change in entropy for the mutants ( approximately 16 kcal/mol ) compared to the coreV3S protein ( 45 kcal/mol ) , indicating that the mutations substantially stabilized the coreV3S protein into the CD4-bound conformation . Interestingly , the calculated entropy values were similar for all the mutants , suggesting that Ds12F123 mutations , present in 2CC , account for most of the effect in stabilizing the CD4-binding region . In A2 reaction , addition of 17b to the coreV3S-CD4 complexes introduced further conformational rigidity in gp120 , as accounted by an entropy change of 17 . 5 kcal/mol . However , entropy of this interaction was significantly decreased by the stabilizing mutations in 2CC and 3CC , indicating that besides stabilizing the CD4-binding site , the structure-guided mutations further stabilized the co-receptor-binding site in a manner beyond that achievable by interaction with CD4 itself . To measure the effects of the mutations on stabilizing the co-receptor-binding site , the 17b antibody was titrated with each envelope variant ( B1 reactions ) . 17b binding to the coreV3S yielded approximately 52 kcal/mol of favorable enthalpy and approximately 40 kcal/mol of change in compensating unfavorable entropy , suggesting that 17b binding alone can induce similarly large conformational ( and/or solvation ) effects to the gp120 core . The stabilizing mutations reduced the enthalpy change during the B1 reaction by almost 50% . Consistent with these data , we observed as well a significant decrease in the entropy change in the presence of 2 , 3 or 4 pairs of cysteines , leading up to 75% reduced entropy for 17b interaction as compared to the ∼40 kcal/mol value obtained for 17b-coreV3S . These results indicated that the selected mutations significantly stabilized the conformation of the co-receptor-binding site . We then measured the thermodynamics of CD4 binding to these gp120-17b complexes ( B2 reactions ) . CD4 binding to coreV3S-17b complexes resulted in a 17 . 3 kcal/mol of entropy change . However , the entropy value of CD4 binding to 17b complexes with 2CC , 3CC or 4CC proteins , each containing the respective stabilizing mutations , did not change relative to the parental protein . The entropy of CD4 binding to gp120-17b complexes ( approximately 16 kcal/mol ) therefore perhaps resulted from reduced flexibility of gp120 elements distal from the CD4-binding pocket , and/or from solvent effects . The B2 reactions also showed less negative ΔG values in the presence of the stabilizing mutations , suggesting that 17b alone can induce a most thermodynamically favorable conformation of the CD4-binding site , and presence of the current set of stabilizing mutations actually conferred a slightly negative impact on the 17b-induced CD4-binding region . The kinetic and thermodynamic characterizations of the coreV3S envelope variants revealed considerable stabilization of both the CD4bs and the co-receptor binding site , associated particularly with enhanced on-rate and affinity by 17b . Therefore , we tested impact of these modifications on elicitation of antibody responses in vivo . To allow better statistical analysis , we immunized 14 rabbits with each protein immunogen . The overall immune response after each inoculation was analyzed by ELISA to measure IgG binding to either core or to coreV3S proteins . In all cases ( except for BSA ) , high titers of anti-gp120 antibodies were detected after two inoculations , with end-point titers reaching 1 . 5×105 ( Figure S2 ) . The binding titers did not substantially increase with additional inoculations . After four inoculations , we sought to analyze the overall breadth of HIV neutralization elicited by these immunogens . Due to the large number of sera , we screened the neutralization activity at a 1∶5 dilution of each serum against viruses pseudotyped with clade B ( 9 isolates ) or clade C ( 1 isolate ) or clade A ( 1 isolate ) HIV-1 envelope glycoproteins . The results , shown as percent neutralization of viral entry , are summarized in Figure 4 . Autologous ( HXBc2 ) neutralization was achieved by all sera . Interestingly , although the breadth was somewhat limited , the 3CC and 4CC stabilized V3S immunogens elicited a trend of higher neutralization responses against several primary HIV-1 clade B isolates , namely SF162 , SS1196 and ADA ( a typically neutralization-resistant isolate ) , and a clade C isolate , MW965 when compared to responses elicited by the coreV3S ( Figure 4 ) . In addition , we analyzed core and Ds12F123 proteins ( lacking the V3S modifications ) for immunogenicity and obtained similar results to the V3S equivalents ( Figure S3 ) . Because percent neutralization is a rough approximation of the actual inhibitory titer , we confirmed these data by deriving inhibitory dilution 50% values ( ID50 ) of all sera for selected isolates ( HXBc2 , SS1196 and MW965; see Figure S4 ) . Since the cysteine-based mutagenesis resulted in significant stabilization and enhanced ligand affinity of the co-receptor-binding site , we employed an assay which detects the presence of functionally active antibodies specific for the co-receptor binding site that is conserved between HIV-1 and HIV-2 ( see Figure 5A and Methods; [29] ) . Most sera elicited by coreV3S demonstrated little cross neutralization of the HIV-2 isolate ( see Figure 5A ) . However , in stark contrast , very potent HIV-2 neutralization responses were elicited by the stabilized core immunogens , 3CC and 4CC . Moderate neutralization was elicited by the 2CC immunogen . In the 3CC and 4CC elicited sera , we observed low-titer and inconsistent neutralization of SF162 , SS1196 and ADA that parallels the more consistent neutralization of a particular clade C HIV-1 isolate , MW965 , and the HIV-2 isolate , 7312AV434M , by these sera . Interestingly , both of the latter two isolates are known to be sensitive to co-receptor binding site-directed antibodies under the conditions tested . Following the initial neutralization analysis at a single dilution of the sera , we determined ID50 values of all immune sera against the indicator HIV-2 isolate ( Figure 5A ) . Compared to the non-stabilized coreV3S , the stabilized proteins elicited significantly more potent HIV-2 neutralizing responses in the order of 4CC>3CC>2CC>coreV3S . The linear regression analysis of the HIV-2 neutralization titers and corresponding immunogen properties ( ligand affinity , on-rate of ligand binding and stabilization of the 17b epitope ) showed distinct linear correlations in all cases ( Figure 5B ) . In a parallel immunogenicity study , performed in guinea pigs , the V3S or 3CC modifications were introduced into a DNA prime , recombinant adenovirus ( rAd ) regimen in a gp120 core context or in the previously described gp145ΔCFI and gp140ΔCFI contexts ( Figure S5 , panel A; [30] ) . High serum titers of HIV-2 cross-neutralizing antibodies were detected in guinea pigs that were inoculated with the 3CC-containing DNA/rAd immunogens only ( Figure S5 , panel B ) . Next , we examined the effects of conformational stabilization on the elicitation of antibodies to the CD4 binding region . Since there is no HIV neutralization assay available yet to specifically map serum immune responses against the CD4bs , we performed competition ELISA experiments with CD4 as previously described ( Figure S6; [28] ) . We also established a similar competition assay with the CD4bs antibody , b12 , based upon our observation that the presence of excess 17b antibody does not affect the binding of b12 to the coreV3S protein ( Figure S7 ) . Results from both competition assays indicated that all three of the stabilized immunogens elicited CD4bs-directed antibodies , although , in particular instances , to lesser extents compared to the coreV3S immunogen . Therefore , the modest neutralization capacity elicited by the coreV3S variants ( Figure 4 ) indicated that if we have elicited neutralizing antibodies against the CD4 binding region , they are not of the breadth or potency of b12 or CD4 itself . These data were consistent with binding analysis following the differential adsorptions on selected sera described below . The HIV-1 HXBc2 gp120 variants coreV3S and 4CC proteins , elicited strikingly different levels of CD4i antibodies as determined by the HIV-2 cross-neutralization assay ( Figure 5 ) . However , sera derived from immunized animals from both the coreV3S and the 4CC group potently neutralized the autologous virus , HXBc2 . We therefore sought to characterize the target specificity of the elicited neutralizing antibodies . We performed differential adsorption of antibody subpopulations from each serum in a previously described process [31] , [32] , followed by binding analysis and neutralization assays as described below . Due to the necessity to adsorb out all binding antibodies in this process by gp120 protein excess , this is not a high throughput assay in hyper-immune animals . Therefore , immune sera from one coreV3S-immunized rabbit and one 4CC-immunized rabbit were selected for the analysis . The sera were incubated with Dynabeads covalently conjugated with one of the following indicator proteins: gp120WT , to adsorb out all gp120-directed antibodies; gp120D368R , to adsorb out all but CD4bs-directed antibodies , and gp120I420R , to adsorb out all but CD4i antibodies . Following selective adsorptions , performed in gp120 excess , the flow-throughs from these reactions were first analyzed by ELISA to verify completion of adsorption and to determine relative prevalence of each antibody type ( Figure 6A ) . Complete adsorption in each reaction was confirmed by the lack of binding of the adsorption flow-throughs ( FT; containing non-adsorbed antibodies ) to the same protein target that was attached to the corresponding beads . The titers of either CD4bs-directed antibodies or co-receptor-binding site-directed antibodies were determined from binding of corresponding depleted serum to gp120WT protein . The coreV3S protein elicited much higher titer of CD4bs-directed antibodies than CD4i antibodies ( Figure 6A , left panel ) , and the conformationally stabilized 4CC protein dramatically shifted this response towards eliciting much higher CD4i antibodies than CD4bs-directed antibodies ( right panel ) . This type of differential analysis ( previously described in reference [32] ) is subject to less off-target effects than are cross-competition assays and is a more definitive means to map binding specificities delineated by selected gp120 point mutations . Next , the potency of the selectively adsorbed immune sera fractions was tested in selected neutralization assays . The coreV3S protein elicited low levels of CD4i binding site antibodies ( Figure 6A , left panel ) , but the serum did not neutralize HIV-2 ( Figure 6B , left panel ) . In contrast , only the CD4i antibody population ( gp120 I420R FT ) of the 4CC-immunized sera potently neutralized the HIV-2 isolate ( Figure 6B , right panel ) and , as well , the highly 17b-sensitive clade C isolate , MW965 ( Figure 6C , bottom panel ) . Similarly , only the CD4i antibody population in the 4CC serum neutralized yet another HIV-1 clade B isolate , SS1196 , but the potency of neutralization observed under the experimental conditions used here was relatively low ( data not shown ) . The same adsorbed immune serum fractions were then analyzed in an HXBc2 neutralization assay . This sensitive isolate is neutralized by both non-potent CD4bs-directed antibodies and by the non-potent co-receptor-binding-site-directed antibodies . As shown in Figure 6C , left panel , coreV3S immune serum mediated neutralization of HXBc2 mostly by CD4bs-directed antibodies ( gp120D368R FT ) , the first time elicitation of antibodies of such specificity by an Env-based immunogen has been demonstrated . Analysis of serum ID50 values ( Figure S3 ) indicated that the 3CC protein trended toward the elicitation of slightly higher HXBc2 neutralization titers than those elicited by the 4CC protein while the potency of HIV-2 neutralization was reversed between these groups . The data are another indication that the stabilizing mutations affect the neutralization specificity elicited by the core proteins .
Due to the pressing need for an effective HIV-1 vaccine , and due to the limits of current Env-based immunogens to elicit neutralization breadth , we pursued HIV-1 Env structure-guided immunogen design to determine if this line of investigation will better elicit virus neutralizing antibodies . Here , we demonstrate that the structure-based redesigning of the HIV-1 envelope core glycoprotein increased folding and expression of a series of related , mutagenically stabilized molecules . Structure-guided protein design led to stabilization of both the CD4-binding site and the co-receptor-binding region of gp120 . The data clearly demonstrated that thermodynamic stabilization of the co-receptor-binding site was associated with a marked increase in the on-rate of binding of the co-receptor mimetic , 17b antibody . Furthermore , when immunized into small animals , stabilization of the gp120 core resulted in a dramatic enhancement of the functional antibody response against the CD4-enhanced co-receptor binding region shared by HIV-1 and HIV-2 . These results suggest that , in general , specific regions of an immunogen might be rendered more immunodominant by direct conformational stabilization ( in this case , cysteine-pair mutagenesis ) of that region resulting in reduced entropy . These results are appealing from a thermodynamic perspective , as the ligand affinity ( ΔG ) can be more favorable with a reduction in entropy ( −TΔS ) . This thermodynamic relationship would predict that if a given epitope ( or circumscribed region ) is pre-fixed into a desired conformation , a ligand ( i . e . , 17b or perhaps a “naïve” B cell receptor ) specific for that site will not be required to initiate “induced-fit” [33] and will therefore bind to the site with an increased on-rate [34] . Potentially , the overall affinity may increase ( assuming no negative impact on the off-rate ) , and in regards to the B cell receptor , a faster on-rate may enhance epitope recognition , resulting in more efficient activation of that B cell . The finding described in this study , demonstrate that conformational stabilization of a discrete protein region can alter the quantity and quality of the antibody response to that protein region . They suggest that ligand stabilization or improved ligand affinity , especially ligand on-rate , might be used as parameters to focus functional antibody responses on specific regions on the surface of a complex , conformationally sensitive and multi-epitope protein . Recently we reported that the elicitation of co-receptor-directed antibodies is dependent upon interaction of gp140 glycoprotein immunogens with endogenous primate CD4 molecules [35] . A mechanism for this important observation is provided in this current study as we clearly demonstrate that mutagenic stabilization of g120 , in a similar manner to that achieved by CD4 binding , locks the co-receptor-binding-site into a single conformation that is well recognized by the naïve B cell repertoire in rabbits . This principle might be applicable for viruses that undergo receptor-induced conformational changes to accomplish entry , and for which a vaccine is lacking ( e . g . , Ebola ) . Deletion of immunodominant variable regions of Env-based anti-viral subunit vaccines may also have broader applicability . The protein redesign described here to improve expression revealed some interesting observations relative to recognition by the 17b antibody and implications on the bridging sheet in the coreV3S context . Our earlier data indicated that the previously crystallized core protein could not bind 17b unless induced by CD4 [16] . In this study , the previously described core protein was modified at the base of the V3 loop and at the base of the V1V2 loop . Somewhat unexpectedly , we observed that the newly designed coreV3S protein was recognized by 17b with very high affinity even in the absence of CD4 , and that CD4 binding affinity of this protein was markedly improved . A plausible explanation of this modified antigenicity is that restoration of the β12 and β13 strands on the outer domain indirectly aids in formation of bridging sheet elements that are critical for 17b recognition . Restoration of these strands may also impart stability at the base of the Phe 43 cavity , located above ( CD4 binding site , see Figure 1 ) . These implications are somewhat in conflict with the unliganded SIV core structure , which shows the bridging sheet β-strands in a non-CD4-bound orientation , but perhaps represent differences in the structural elements present between core and coreV3S proteins and/or differences between HIV and SIV [23] . However , these data are consistent with the initial analysis of 17b recognition , which revealed that 17b binds well to full-length gp120 possessing the V3 loop , but not at all to a V3-loop deleted protein [6] , confirmed by our recent studies [28] , [35] . Complete V3 loop deletion was performed in the original HIV-1 and SIV crystallized cores proteins . However , because interaction of CD4 with V3 loop-deleted gp120 completely restores 17b binding , this suggests that CD4 can compensate for the ( artificial ) instability of the bridging sheet region imparted by full truncation of the V3 stem . Yet unresolved , then , is the structure of the receptor-binding sites in the context of the static functional spike ( i . e . , pre-receptor bound state ) . The data described above are consistent with the model that the co-receptor-binding site can exist in the context of the static viral spike , but accessibility to antibody is limited unless steric constraints are reduced by receptor engagement . In previous studies , we have shown that the Phe 43 cavity-filling mutations partially lock gp120 into the receptor/co-receptor-bound conformation [24] , [28] . To achieve greater stabilization , we added two pairs of cysteines and additional cavity-filling mutations to core gp120 [24] and analyzed the thermodynamic effects of these mutations on core gp120 . Interestingly , we showed clearly that 17b itself can stabilize gp120 into the conformation recognized by CD4 ( B1 reactions ) and that the Ds mutations used in this study have no further effect on this conformation ( B2 reactions ) . However , it is noteworthy that , a relatively constant amount of entropy change ( 15 . 1 to 17 . 3 kcal/mol ) was always detected upon addition of CD4 to gp120-17b complexes irrespective of the presence of the stabilizing mutations . We assume that this entropy is either accounted for stabilization of elements distal from the cysteine pairs themselves or , alternatively , results from some unanticipated solvent displacement effects . Increases in 17b affinity or increased stabilization of the 17b epitope alone were not always associated with all differences in the immunogenicity described here . For example , between 2CC and 3CC , increased 17b affinity correlated very significantly with increased elicitation of CD4i antibodies , although the degree of stabilization achieved was similar in these immunogens . In contrast , the increase in 17b affinity was minimal from 3CC to 4CC , although there was a substantial difference in epitope stabilization . This difference correlated with the enhanced elicitation of CD4i antibodies by 4CC . Therefore , ligand affinity and epitope stabilization both contributed to the overall altered immune responses elicited by the current set of immunogens . The same set of mutations that stabilized the co-receptor-binding site also stabilized the CD4bs , generally considered a more desired target of the study design because of the neutralization capacity of both CD4 itself and of the CD4bs antibody , b12 . In fact , we clearly demonstrate the elicitation of CD4bs antibodies both by ligand cross-competition and by selective adsorption . However , the unmodified cores appear to elicit this type of activity more efficiently than the stabilized cores ( see Figure 6A , Figure S6 and Figure S7 ) . It might be that the stabilization process itself subtly altered the CD4-binding surface on gp120 and actually reduced cross-reactivity with natural sequences found on the virus . Alternatively , the stabilized CD4i site perhaps became immuno-dominant and out-competed CD4bs-directed responses . Furthermore , although stabilization of the CD4bs , relative to its starting entropy , approached that of the co-receptor-binding site , the absolute values of CD4-related entropy and affinity did not . Analysis of the sera from one representative animal immunized with coreV3S group compared to one representative animal immunized with the 4CC protein demonstrated a shift of antibody response towards the 17b epitope , correlating with the increased 17b affinity . Consistent with these results , immunization studies using synthetic peptide immunogens indicate that the kinetics of antigen recognition influence epitope-driven repertoire selection and antibody maturation [22] . Achieving slower ligand off-rate may have the potential to improve immune response , although that property may not always be approachable by structure-based design and might be dependent upon the context . This might be in part due to the uncertainty of the factors that define which elements on the surface of a complex protein are most immunogenic . It was suggested that all accessible domains on the surface of a multi-determinant antigen can potentially induce primary B cell responses [36] . However , only those that interact with naive B cells with high affinity will generate an avid antibody response [37] . Here , we demonstrate in this complex model system that it is conformational fixation , associated with increased 17b on-rate and overall affinity , which drives the elicitation of functionally cross-neutralizing antibodies directed toward the gp120 co-receptor binding region . This class of antibodies was extensively studied for their unique properties of posttranslational tyrosine sulfation and preferential VH gene usage [38] , [39] . Although to date there are no identified co-receptor binding-site-directed monoclonal antibodies that potently neutralize diverse primary isolates , our recent study implicates antibodies with specificities to this site contribute to neutralization in broadly reactive HIV-1 patient sera [32] . CD4-induced antibodies were also associated with partial control of SHIV challenge in macaques [40] . In the current study , the stabilized immunogens elicited moderate neutralization responses against a few Tier 1 HIV-1 isolates that are typically sensitive to antibodies directed to the gp120 variable loop 3 , a component absent in the immunogens tested here . Additional analysis may be warranted to determine if the neutralization activity observed in selected sera is indeed mediated by CD4-induced antibodies as was determined here for the Tier 1 isolates , MW965 and SS1196 . Thus , the co-receptor-binding region , usually occluded on most primary isolates , remains an intriguing target due to its conservation , especially if there exists an as yet-to-be-defined subset of antibodies that can access elements of this region on circulating isolates . In addition to CD4-induced responses , the principle established in this study may have important implications for proper stabilization of the CD4bs to generate more broadly cross-reactive and neutralizing antibodies to this heavily shielded , receptor-binding region towards the development of a broadly protective HIV-1 vaccine . Beyond HIV-specific vaccine development , the viral envelope glycoprotein and its ligands under study here provide a model system to establish “proof-of-principle” regarding targeted immunogenicity . Such principles may extend to the design of vaccines against other pathogens capable of humoral immune evasion .
To design a more optimal V3 truncation , hydrogen bonding at the V3 base was examined in the gp120 core with V3 structure ( PDB ID 2B4C ) . To preserve observed hydrogen bonds , additional residues between the last residue in the β12-strand and the first in the β13-strand were retained . To design a shortened V1/V2 truncation , type II turns were modeled onto strands β2 and β3 to determine the shortest that preserved full β2-hydrogen bonding to strand β21 . Lastly , to determine where stabilizing disulfide bonds might be introduced into the gp120 core structure , a distance matrix between all C·β atoms was calculated [41] ) . All Cβ inter-domain pairs with distances between 3–6 Å were analyzed with explicit modeling disulfide pairs , using the interactive software provided by the program “O” [42] . Plasmids for the expression of HXBc2 gp120 core and Ds12F123 proteins have been described before [43] . Plasmids expressing other immunogens ( listed in Figure 1B ) were derivated either by Quick change PCR mutagenesis ( for 3CC and 4CC ) or by de novo gene synthesis ( coreV3S and 2CC ) . All immunogen proteins were expressed in serum-free medium by transient transfection of HEK293T cells and purified over antibody columns as described earlier [28] . The core protein was purified over b12 affinity column and all proteins with V3S modifications were purified over 17b affinity columns as was the Ds12F123 protein . Expression and purification of proteins used in serum adsorption analyses have been described elsewhere [31] , [32] . The antigenicity of WT and mutant envelope proteins ( Figure 2 ) and the anti-gp120 antibody titers in immunized sera were determined by ELISA analyses as described in Dey et el . , 2007 [28] . All ITC reactions were performed at 37°C as described in Dey et al . , 2007 [28] . The concentration of gp120 in the sample cell was approximately 4 µM and that of sCD4 or 17b in the syringe was approximately 40 µM . The molar concentrations of the proteins were calculated using the following molar extinction coefficients: core , 1 . 35; Ds12F123 , 1 . 4; coreV3S , 1 . 33; 2CC , 1 . 5; 3CC , 1 . 5; 4CC , 1 . 5; sCD4 , 0 . 93 and IgG17b , 1 . 47 . The specific activity of sCD4 and 17b was determined as described previously [28] , [44] . The values for enthalpy ( ΔH ) , entropy ( ΔS ) , and the association rate constant ( Ka ) were obtained by fitting the data to a nonlinear least-squares analysis with Origin software . For the second step of a two-step reaction , gp120 concentration was recalculated as per the final volume at the end of the first reaction , which is equal to the sample cell volume plus the total volume of the first injectant . Before the second titration , the sample volume equivalent to the volume of the first injectant was removed from the sample cell . All kinetic reactions were performed at RT on a Biacore3000 surface plasmon resonance spectrometer . To prepare binding surfaces with approximately 500 RU per cell , ligands ( 7 µg/ml in 10 mM NaOAc , pH 5 . 5 buffer ) were immobilized on CM5 chip by the amine coupling method following manufacturer's protocol . The reference cell received only NaOAc buffer . Analytes were serially diluted in the HEPES-EP reaction buffer at concentrations ranging from 6 . 2 nM to 400 nM for sCD4 and b12 or 6 . 2 nM to 50 nM for 17b . Association was allowed for 3 min at 30 µl/min . To determine 17b binding in the presence of CD4 , gp120 dilutions were pre-incubated for 30 min at RT with 10-fold molar excess of 2-domain sCD4 . Dissociation was determined by washing off bound analyte over the next 5 min . The chip surface was regenerated with two injections ( 60 sec each ) of 10 mM Glycine , pH 3 . 0 . The kinetic rate constants were obtained by fitting the curves to 1∶1 Langmuir binding model using BIAevaluation software . Approximately 12 weeks old female New Zealand White rabbits were inoculated with 50 µg of affinity-purified protein formulated in GlaxoSmithKline Adjuvant System AS01B , injected intramuscularly by splitting the protein-adjuvant mix in the two hind legs at 4 weeks intervals . Serum was collected 8–10 days after each inoculation . Serum preparation and heat inactivation of complement systems were performed as described earlier [28] . All rabbits were housed and maintained in the AAALAC-accredited BIOCON , Inc ( Rockville , MD ) under specific pathogen-free conditions . All experiments were approved by the Animal Care and Use Committee of the Vaccine Research Center and BIOCON , Inc . Production and neutralization of pseudotyped HIV-1isolates were described earlier [10] , [28] , [31] . To control for non-specific effects in the assay , preimmune sera , BSA-AS01B-immunized antisera and a pseudovirus expressing murine leukemia virus envelope were analyzed [28] , [45] . Neutralization of HIV-2 strain 7312A/V434M was performed in the presence of non-neutralizing levels of sCD4 and analyzed as previously described [28] , [29] . Antibody populations directed towards the CD4bs or the CD4i sites were separated by absorbing rabbit immune sera on protein-coated dynabeads as previously described [31] . In brief , sera were diluted between 1∶4 and 1∶20 in DMEM/10% FBS and 1000 µl of diluted sera was incubated with 500 µl of beads at room temperature for 30 minutes , followed by a second adsorption with 250 ul of beads . After serum adsorption , beads were removed with a magnet followed by centrifugation and were stored in PBS/0 . 2%BSA/0 . 02% sodium azide buffer at 4°C . | Vaccination is an effective means to control worldwide human diseases caused by viruses and other pathogens . Most viral vaccines work by inducing the immune system to generate neutralizing antibodies . The human immunodeficiency virus ( HIV ) continues to cause huge tolls in terms of human death and disease . The generation of neutralizing antibodies against HIV remains a key but elusive goal for the development of an effective vaccine . Here , we describe a novel approach that uses atomic-level structures of the HIV surface protein , gp120 , together with extensive biophysical analysis of this protein , to design modified vaccine candidates . Immunization with these modified gp120 proteins revealed a new relationship between structure-guided protein stability and the efficient elicitation of antibodies against the highly conserved co-receptor binding site of HIV . These data demonstrate the potential for using the design principles established here to develop improved antibody-generating HIV vaccines and for vaccines against other pathogens . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/immunodeficiency",
"viruses",
"immunology/immune",
"response",
"virology",
"biophysics/biomacromolecule-ligand",
"interactions",
"virology/immune",
"evasion"
] | 2009 | Structure-Based Stabilization of HIV-1 gp120 Enhances Humoral Immune Responses to the Induced Co-Receptor Binding Site |
Disruption of synapses underlies a plethora of neurodevelopmental and neurodegenerative disease . Presynaptic specialization called the active zone plays a critical role in the communication with postsynaptic neuron . While the role of many proteins at the active zones in synaptic communication is relatively well studied , very little is known about how these proteins are transported to the synapses . For example , are there distinct mechanisms for the transport of active zone components or are they all transported in the same transport vesicle ? Is active zone protein transport regulated ? In this report we show that overexpression of Par-1/MARK kinase , a protein whose misregulation has been implicated in Autism spectrum disorders ( ASDs ) and neurodegenerative disorders , lead to a specific block in the transport of an active zone protein component- Bruchpilot at Drosophila neuromuscular junctions . Consistent with a block in axonal transport , we find a decrease in number of active zones and reduced neurotransmission in flies overexpressing Par-1 kinase . Interestingly , we find that Par-1 acts independently of Tau-one of the most well studied substrates of Par-1 , revealing a presynaptic function for Par-1 that is independent of Tau . Thus , our study strongly suggests that there are distinct mechanisms that transport components of active zones and that they are tightly regulated .
Effective communication between neurons is maintained by synapses via their pre- and postsynaptic specializations called active zones and postsynaptic densities respectively . Active zones are composed of many proteins that are important for the efficient release of synaptic vesicles- a pre-requisite for efficacious neuronal communication[1 , 2] . Proteins present at the active zones form an important presynaptic network for the regulation of vesicle release at all chemical synapses . Indeed , many proteins that regulate synapses are disrupted in both neurodevelopmental as well as neurodegenerative diseases[3–5] . One such protein , microtubule associated regulatory protein ( MARK ) / partitioning-defective 1 ( Par-1 ) is implicated in both neurodevelopmental [6–8]and neurodegenerative diseases[9–12] but the mechanisms by which it disrupts synapses is unclear . MARK1 levels are elevated in Autism spectrum disorders ( ASDs ) , a neurodevelopmental disorder[6] . Interestingly , MARK1 is overexpressed specifically in the prefrontal cortex- a region highly implicated in ASDs [6] . On the other hand , MARK4 is overexpressed in neurodegenerative diseases and is thought to hyperphosphorylate Tau [13 , 14] . Indeed , the site that is phosphorylated by the MARK/Par-1 is hyperphosphorylated in post-mortem brains of patients with frontotemporal dementia ( FTD ) [11] . Thus , elevated levels or activity of MARK/Par-1 is implicated in both neurodevelopmental and neurodegenerative diseases . While there is good evidence for the role of MARK/Par-1 in regulating postsynaptic density during development[15] , it is unclear whether it has any presynaptic role . In this study , we show for the first time that presynaptic overexpression of Par-1 regulates the axonal transport of an active zone protein- Bruchpilot ( BRP ) . Decreased axonal transport of BRP due to presynaptic overexpression of Par-1 lead to a significant decrease in the number of BRP marked active zones at the synaptic terminals . Furthermore , consistent with a decrease in BRP protein at the synapse[16] , ultrastructural analysis demonstrated a decrease in the number of dense bars and deficits in synaptic transmission . Finally , our data show that MARK/Par-1 affects the axonal transport of BRP independent of endogenous Drosophila Tau ( dTau ) , implicating that a novel substrate of MARK/Par-1 mediates the axonal transport of BRP . Together , these data suggest that different components of active zones are transported separately by distinct mechanisms , and that these processes are likely to be tightly regulated by kinases .
Increase in levels or activity of Par-1/MARK is associated with both neurodevelopmental disorders like ASD[6–8] and neurodegenerative disorders like FTD[11] . Since synapse is the common underlying unit disrupted in both these disorders[3–5] we wanted to test the effects of elevated levels of presynaptic Par-1 on synapses . To test this , we overexpressed Par-1 presynaptically using the UAS-GAL4 binary system[17] . BG380-GAL4[18] driver was used to overexpress of Par-1 ( Par-1OE ) specifically in presynaptic neurons . Neuromuscular Junction ( NMJ ) preparations were then stained with antibodies against the active zone marker ( Bruchpilot ( BRP ) , [16] ) , synaptic vesicle marker ( DVGLUT[19] ) and neuronal membrane marker ( Horse Radish Peroxidase ( HRP ) [20] ) to visualize synapses . Overexpression of Par-1 in presynaptic neurons resulted in significant accumulation of BRP in axons ( Fig 1A and 1B ) . This was observed using multiple presynaptic drivers ( S1 Fig ) . While all the tested presynaptic drivers showed qualitatively similar increases in accumulation of BRP in axons , driving the same transgene postsynaptically using postsynaptic driver G7-Gal4[21] did not result in the accumulation of BRP within axons ( S1 Fig ) , suggesting that this was a cell-autonomous effect . Surprisingly , DVGLUT or HRP did not accumulate within the axons ( Fig 1A and 1C ) , indicating that overexpression of Par-1 may result in specific accumulation of only a subset of synaptic cargo in the axons . Importantly , overexpression of inactive Par-1 ( Par-1T408A , [22] ) did not result in accumulation of BRP within axons ( Fig 1A and 1B ) , indicating that BRP accumulations were unlikely to be an unintended consequence of overexpression of Par-1 kinase . To further test our hypothesis that overexpression of Par-1 may lead to a specific axonal transport defect of BRP; we labeled the axons using markers of various cargoes that are transported within the axons . We used the following markers: Liprin-α ( another marker of active zones , [23] ) , and disabled ( DAB , a marker for endocytic zones , [24] ) . The levels of Liprin-α and DAB in the axons of flies overexpressing Par-1 were similar to the levels of these proteins in WT flies ( Fig 2A–2C ) , providing further evidence that overexpression of Par-1 results in specific accumulation of BRP in axons . Next , we tested the transport of mitochondria , which is mediated by Milton and Miro[25] . To test this , we generated flies that express mito-GFP in the presynaptic neurons along with overexpression of Par-1 . To account for the possible “dilution” of GAL4 due to two UAS promoters ( UAS-mito-GFP and UAS-Par-1OE ) , we generated flies that carry UAS-GFP and UAS-Par-1 as a control . Expression of mito-GFP in wild type flies showed many mitochondria within the axons . Consistent with our hypothesis , flies overexpressing mito-GFP in Par-1 overexpression background showed no significant changes in the levels ( Fig 2A , 2B and 2C ) or size ( S3 Fig ) of mitochondria within the axons while still showing accumulations of BRP , indicating that overexpression of Par-1 does not affect mitochondrial transport . Finally , to test the possibility that increased transcription of BRP may lead to accumulation of BRP in axons[26] , we compared the BRP protein levels in the ventral nerve cords ( VNC ) between WT , Par-1OE and Par-1T408A flies . No significant differences were noted between the levels of BRP protein in the VNCs of these genotypes ( Fig 1E ) . To confirm these data we also performed western blots using anti-BRP antibody on WT and Par-1OE flies and did not observe any significant difference in the levels of BRP protein ( Fig 1D ) . These data indicate that increased accumulations of BRP in axons of flies overexpressing Par-1 are unlikely to be due to increased levels of BRP protein . Taken together , our results strongly suggest that overexpression of Par-1 specifically affects the transport of BRP in the axons . To test whether block in axonal transport of BRP would lead to decreased levels of BRP at the synapses , we labeled the NMJ synapses with BRP[16] and HRP . Although we expected to see only a reduction in BRP at the synaptic terminals , we were surprised to find that there were some interesting differences between WT synapses and those overexpressing Par-1 . First , as expected , there were significant reductions in the number of active zones marked by BRP ( Fig 3A and 3B ) . Second , while the synaptic span was not significantly different in the flies overexpressing Par-1 , the size of synaptic boutons was significantly reduced ( S2 Fig ) . However , these changes did not affect the apposition of synapses quantified using number of BRP puncta apposed to DGluRIII patches , a marker of postsynaptic density ( S2 Fig ) [27] . These data show that although overexpression of Par-1 may cause specific defects in BRP transport , these defects may possibly lead to other changes at the synapse . It is not clear whether all of these changes are caused due to a block in axonal transport of BRP but levels of other synaptic proteins like Liprin-α and DAB are unaffected in flies overexpressing Par-1 ( S3 Fig ) . We also compared the ratio of Liprin-α , DAB and BRP between the axons and the synapses . We found that the ratio was unchanged in Liprin-α and DAB . However , as expected , we found a significant increase in the ratio of BRP at axons versus synapses ( S3 Fig ) . Interestingly , overexpression of Par-1T408A does not show an increase in BRP within the axons or a reduction in BRP at the synapse arguing that these effects are not merely a consequence of overexpression of Par-1 ( S2 Fig ) . To confirm our light-level findings , we performed ultrastructural studies at WT and Par-1OE synapses . Consistent with previous reports showing that a reduction in BRP causes a decrease in number of T-bars[16 , 28] , we found a significant decrease in the total number of T-bars per active zones at the synapses of flies overexpressing Par-1 as compared to WT ( Fig 4A and 4B ) . Taken together , our data so far demonstrate that overexpression of Par-1 in neurons leads to a specific block in axonal transport of BRP , which is the likely caused due to the reduction in T-bars at synapses . To test whether these synaptic changes result in defects in neurotransmission , we performed intracellular electrophysiological recordings from WT , Par-1OE and Par-1T408A flies . We did not observe any change in the amplitude ( Fig 5A and 5C ) of mini excitatory junction potentials ( mEJPs ) , suggesting that the postsynaptic apparatus was unperturbed in Par-1OE flies . However , the frequency of mEJPs was significantly reduced consistent with the decrease in number of release sites marked by BRP in Par-1OE ( Fig 5A and 5D ) . Furthermore , there was a dramatic reduction in the excitatory junction potential ( EJP ) amplitude in Par-1OE flies ( Fig 5B and 5E ) pointing to a presynaptic defect . Calculation of the quantal content ( EJP amplitude/mEJP amplitude ) [29] showed a decrease in quantal content ( Fig 5F ) in flies overexpressing Par-1 . These data are consistent with presynaptic deficits and are likely a consequence of fewer T-Bars[16 , 28] and reduced size of synaptic boutons . Taken together , our data suggest that presynaptic elevation in the levels of Par-1 has both structural and functional consequences for the synapse . Microtubules play an important role in axonal transport of synaptic cargo[30] . MARK/Par-1 kinase phosphorylates Tau[31]-a microtubule associated protein that binds and helps stabilize microtubules . Phosphorylation of Tau has been postulated to lead to its detachment from the microtubules leading to their destabilization[32 , 33] . Thus , overexpression of Par-1 is expected to hyperphosphorylate Tau and lead to its detachment from microtubules making them unstable . To begin testing these possibilities , we first tested the levels of dTau using Western blot analysis on protein extracts from the ventral nerve cords ( VNCs ) of WT , Par-1OE and Par-1T408A flies using two previously characterized antibodies[34 , 35] . The levels of dTau were not significantly different between these genotypes ( Fig 6B and 6C ) , suggesting that Par-1 overexpression does not alter the levels of Tau in neurons . This raises the possibility that overexpressed Par-1 does not localize to the microtubules and is therefore unable to phosphorylate it . To test this possibility , we stained the axons of Par-1OE flies with anti-Par-1 antibodies and compared its localization in WT axons . In Par-1OE flies , Par-1 localized prominently within axons along with microtubules ( S4 Fig ) indicating that Par-1 localization to the microtubules was not hampered . We also tested the possibility that Par-1T408A may not localize to axons and therefore would not phosphorylate its substrate ( Tau ) . However , Par-1T408A localized similar to that of overexpressed wild type Par-1 ( S4 Fig ) . These experiments suggest that activity of Par-1 kinase is important to affect the transport of BRP within the axons . Having established that overexpressed Par-1 can localize to the microtubules , we next wanted to test whether excess phosphorylation of Tau might cause its detachment from microtubules rendering them unstable[32 , 33] . To test this , we first confirmed that overexpression of Par-1 could phosphorylate endogenous dTau . For this , we used an antibody that specifically recognizes the phospho-Ser262 on Tau ( pS262 ) that is phosphorylated by Par-1[36] . We found that overexpression of Par-1 leads to an increase in dTau phosphorylation at Ser262 site ( Fig 6B and 6D ) . We then stained the axons of WT , Par-1OE , and Par-1T408A flies using the marker for stable microtubules-acetylated tubulin[37] . Distribution and levels of acetylated tubulin were unchanged in Par-1 overexpressing flies as compared to WT ( Fig 7A and 7B ) , indicating that microtubule stability was uncompromised in flies overexpressing Par-1 . Finally , we wanted to test whether dTau was mislocalized because of overexpression of Par-1 . To test this we used the previously generated anti-dTau antibody[34] but because this antibody has not been used for staining axons or synapses , we first tested its specificity . For this , we performed co-localization experiments with overexpressed tauGFP ( S5 Fig ) . We found that overexpressed TauGFP co-localizes with anti-Tau antibody in axons . Furthermore , dtauko[38] larval axons did not show any specific Tau staining within axons , ( S5 Fig ) demonstrating the specificity of anti-dTau antibody . Next , to test whether dTau localizes to microtubules we performed co-localization experiments of dTau with Tubulin-a marker for microtubules ( Fig 6A ) . As expected , dTau and Tubulin co-localized in the axons indicating that dTau localizes to the microtubules in the axons . We then tested whether dTau was mislocalized because of overexpression of Par-1 but did not find any evidence of mislocalization of dTau in flies overexpressing Par-1 ( Fig 6A ) within the axons . We also double labeled the axons for Tubulin and dTau to ascertain that dTau was still localized to the microtubules in flies overexpressing Par-1 ( Fig 6A ) . These experiments suggest that instability of MT due to hyperphosphorylation of Tau is an unlikely reason for accumulations of BRP in axons of Par-1OE flies . Having shown that the levels of endogenous Tau were not altered in flies overexpressing Par-1 , and that stability of microtubules was uncompromised , we wondered whether overexpression of Tau , which has been shown to cause neurodegeneration[39 , 40] , might have an effect on axonal transport of BRP . To test this possibility , we overexpressed tauGFP in the presynaptic neurons . First , to confirm that dTau was overexpressed , we stained the axons with the anti-GFP antibody[41] ( S5 Fig ) and found that dTau was overexpressed . We then stained the axons of tauGFP flies with antibodies against BRP to test whether dTau overexpression affected the axonal transport of BRP . We did not observe any significant difference in the levels or size of BRP puncta within the axons ( S6 Fig ) in flies overexpressing dTau as compared to WT . These data show that dTau overexpression does not cause accumulation of BRP within the axons . Finally , to test whether dTau may not be the endogenous substrate of Par-1 that mediates axonal transport of BRP , we generated a fly that overexpressed Par-1 in a dtau transheterozygote ( Df ( 3R ) tauMR22/+ , [35] ) ( Par-1OE , tauMR22/+ ) because tauMR22 mutants are embryonic lethal[34 , 35] . To confirm that tauMR22 heterozygotes had at least a 50% decrease because of deletion of one copy of dTau , we stained the tauMR22 heterozygous larvae with anti-dTau antibody . Levels of dTau in axons were reduced by ~70% ( S7 Fig ) in tauMR22 heterozygotes . If dTau were to mediate the effects of Par-1 overexpression on the axonal transport of BRP , we expect to see at least a partial suppression of BRP accumulations within the axons of flies that have reduced dTau levels . To test this , we stained WT , Par-1OE and Par-1OE , tauMR22/+ fly NMJs with antibodies against BRP . As expected , Par-1OE showed elevated levels of BRP in axons as compared to WT ( Fig 8A and 8B ) . However , the levels of BRP protein in the axons of Par-1OE and tauMR22/+ flies were quantitatively similar , demonstrating that dTau is unlikely to be the substrate of Par-1 that mediates the axonal transport deficits elicited by elevated levels of Par-1 . Finally , we confirmed that BRP transport was unaffected in tauMR22 transheterozygotes as well as dtauKO flies ( S8 Fig ) . Together , these data strongly support the idea that BRP accumulations observed within the axons for Par-1 overexpressing flies are independent of Tau .
MARK/Par-1 is an essential gene that is required for cell polarity[35 , 42 , 43] and therefore , is essential for proper embryogenesis . MARK/Par-1 is also enriched in neurons and has been shown to be important in neuronal development[44 , 45] . Elegant studies in C . elegans have shown that SYD2 and Liprin-α- two active zone proteins- are important in synapse assembly and maturation[23 , 46 , 47] . Interestingly , while SYD2/ Liprin-α can interact with ( Rab3 interacting molecule ) RIM/Unc10[48] respectively these interactions are dispensable for active zone maturation[23] . The maturation of active zones instead depends on the interaction with BRP homolog , ELKS both in C . elegans and mice[23 , 49] . Since our data shows that axonal transport of BRP is deficient in Par-1 overexpressing flies , this may affect the development or maturation of active zone , which may in turn have functional consequences as suggested by our data that shows reduced synaptic transmission in flies overexpressing Par-1 . Decrease in number of T-bars in Par-1 overexpression flies is not accompanied by a change in apposition of active zones and PSDs suggesting a developmental defect and such a defect could arise due the synaptic instability[50 , 51] . Finally , MARK/Par-1 levels are increased in postmortem brains of children diagnosed with ASD[6] . Importantly , the increase in MARK is specific to pre-frontal cortex in ASD , a region of brain most affected in ASDs[6] . Our data would suggest that increase in MARK/Par-1 might lead to defects in active zone formation or maturation in these areas . These questions need to be addressed by future studies . Hyperphosphorylation of Tau has been hypothesized to be the underlying cause of neurodegeneration[13] . MARK/Par-1 can phosphorylate Tau at serine 262[31] , which has been shown to be hyperphosphorylated in postmortem Alzheimer’s disease ( AD ) patient brains[52] and it has been demonstrated that MARK/Par-1 can function as a “initiator kinase”[9] for the cascade that hyperphosphorylates Tau . In vitro , hyperphosphorylation of Tau can cause its detachment from microtubules leading to their destabilization[32] . Microtubules serve as “highways” on which the transport of synaptic cargo is dependent[30] . Thus , overexpression of Par-1 could lead to hyperphosphorylation of Tau and could cause axonal transport deficits . Our data suggest that axonal transport defects caused due to Par-1 overexpression are independent of endogenous Drosophila Tau ( dTau ) . Although Drosophila Par-1 can phosphorylate Tau in vitro[9] , previous studies have shown that it can act independently of Tau in vivo[35] , suggesting additional substrates of Par-1 could possibly regulate specific transport of active zone protein , BRP . Furthermore , many studies suggest that axonal transport is likely to precede overt neurodegeneration[53–56] . It is tempting to speculate that one possibility is that transport of active zone proteins could be an initial event that leads to synaptic dysfunction , another symptom that precedes neuronal degeneration[57] . Thus , although the ultimate neuronal degeneration could be driven by hyperphosphorylation of Tau , other proteins may play a role in setting the stage for Tau pathology . Indeed , in vivo observation of axonal transport in a mouse model of neurodegeneration suggests that axonal transport can occur early in the neuronal pathology and is likely not driven by Tau[58] . PTVs ( Piccolo-Bassoon transport vesicles ) have been shown to carry largely active zone components[59] in mammalian cell culture studies . However , some of the main components of PTVs for example , Basoon have no homologs in invertebrates[16] . Recent studies in flies and C . elegans have shed some light on the mechanisms of active zone transport . For example , mutants in imac ( kinesin 3 homolog in flies ) have severe reductions in BRP protein at the synapses[60] . However , these flies also have reduction in synaptic vesicles[60] , suggesting that imac may transport both synaptic vesicles and active zone components . Supporting this argument , studies in C . elegans show that synaptic vesicles and active zone components are transported together[61] . However , a recent study in flies suggests that active zone components could be transported in distinct vesicles[62] . This study found that BRP and RIM-binding protein ( RBP ) can be co-transported[62] . Intriguingly , RBP and BRP transport could be uncoupled[62] . Indeed , our data supports such an idea and suggests the possibility that BRP could be transported via a distinct mechanism . Overexpression of Par-1 leads to specific accumulation of BRP while mitochondria; markers for synaptic vesicles and other active zone proteins do not accumulate . Our data also demonstrate that this process is not mediated Tau . One possible target of Par-1 as suggested earlier[35] is another microtubule binding protein Futsch[63] . Intriguingly , presynaptic reduction of Futsch leads to a reduction in active zone numbers and leads to defects in neurotransmission[64] . This possibly remains to be determined . Par-1 phosphorylates a conserved KXGS motif and our initial analysis suggests that Futsch and its vertebrate homolog , MAP1B[63] contains one KXGS motif . Interestingly , Discs Large ( Dlg ) , a homolog of PSD-95[18] is also phosphorylated by Par-1 kinase[15] and Dlg also contains only one KXGS motif that can be phosphorylated , suggesting that presence of single KXGS motif might be enough for Par-1 to phosphorylate a protein . Further analysis is required to test whether Futsch may be involved in the regulation of transport of BRP . Interestingly , at the synapse , Futsch is present closer to BRP than microtubules[64] thus making it a plausible target for mediating the transport of BRP . Since the transport defects we observe are so specific one alternative is that Par-1 directly phosphorylates BRP . Similar to Futsch , BRP also has a KXGS motif that is present at its conserved N-terminus . Thus , Par-1 could phosphorylate BRP and directly affect its transport . Since the N-terminus of BRP is more conserved with the vertebrate ERC2 and C . elegans ELKS protein[16] , it is likely that such a mechanism might also be conserved . These intriguing possibilities should be explored in future studies . Par-1 is activated by LKB1 by phosphorylating it on the threonine 408 . Our data suggest that Threonine 408 is necessary for the manifestation of BRP transport phenotype . Overexpression of inactive Par-1 ( Par-1T408A ) does not lead to BRP accumulation within axons , suggesting that inactive Par-1 cannot induce the accumulation of BRP within the axons . However , overexpression of LKB1 in neurons is unable to induce accumulation of BRP within axons ( S9 Fig ) suggesting that while activation of Par-1 by LKB1 might be indeed important in increasing the toxicity of Tau[12] , it may not be necessary to induce BRP accumulation in the axons . Furthermore , this raises the possibility of a novel upstream regulator of Par-1 kinase that might be important in regulating the transport of BRP within axons . Thus , our current study demonstrates that distinct mechanisms exist to transport components of active zones like BRP and that availability of these components is likely regulated tightly by kinases such as Par-1 kinase .
Flies were reared in medium containing Nutri-FlyTM Bloomington formulation ( Genesee Scientific , San Diego , CA ) , Jazz mix ( Fisher Scientific , Waltham , MA , USA ) , sugar and powdered yeast ( Genesee Scientific ) in an 8:5:1:1 ratio and made according to standard procedures . The following fly stocks were used in this study: UAS-Par-1[12] , UAS-Par-1T408A [22] , BG380-Gal4[18] , Df ( 3R ) tauMR22[34 , 35] , UAS-GFP[65] , and UAS-mito-GFP[66] . BG380-Gal4 was obtained from Aaron DiAntonio , Washington University Medical School ( St . Louis , MO , USA ) . Df ( 3R ) tauMR22 was a generous gift from Daniel St . Johnston , University of Cambridge ( UK ) . UAS-Par-1 and UAS-PAR-1T408A were obtained from Bingwei Lu , Stanford School of Medicine ( Stanford , CA , USA ) . Larvae were dissected and stained as described previously[27 , 67] . Following primary antibodies were used: anti-BRP ( 1:250 ) [16] , anti-Tubulin ( E7 ) ( 1:100 ) ( obtained from the Developmental Studies Hybridoma Bank ) , anti-GFP ( 1:500 ) [41] ( obtained from abcam ) , anti-DVGLUT ( 1:10 , 000 ) [19] ( gift from Aaron Diantonio , Washington University Medical School ) , anti-Liprin-α ( 1:500 ) [68] ( gift from Stephan Sigrist , Free University Berlin ) , anti-DAB [24] ( gift from Richard Ordway , Pennsylvania State University ) , and anti-dTau ( 1:1000 ) [34 , 35] ( gift from Doris Kretzschmar , Oregon Health and Science University and Daniel St . Johnston , University of Cambridge ) . Dylight conjugated goat anti-HRP antibody ( 1:1 , 000 ) , Goat Cy3- , and Alexa 488 conjugated secondary antibodies against mouse , rabbit , and chicken IgG ( 1:1000 ) were obtained from Jackson ImmunoResearch , West Grove , PA . All axonal imaging was done between segments A2–A4 . All the NMJ imaging was done at muscle 4 , segment A2 –A4 . Imaging and analysis of intensity of proteins within axons were done as described previously[26] . Staining intensities of various proteins within the axons and the NMJs were quantified by using MetaMorph software ( Molecular Devices , Sunnyvale , CA , USA ) . For axons and NMJs , HRP was used to set the color threshold . Only the axonal compartment and NMJ region was used to measure the intensity of the red , green and blue ( HRP ) channels . The intensity of HRP did not vary significantly within the same experimental group . Active zones were counted manually by counting the puncta stained by an anti-BRP antibody . Statistical analysis and graphs were generated using GraphPad Prism ( GraphPad Software , Inc . ) . Student T-tests and One-way ANOVA followed by Dunnett’s or Tukey’s multiple comparison tests were performed to compare each group with other samples . Intracellular electrophysiological recordings were performed on muscle 6 , segment A2-A4 as previously described[69] . Quantal content was determined by dividing the mean EJP amplitude by the mean mEJP amplitude ( EJP/ mEJP ) . The cells across all genotypes had similar mean input resistances and resting membrane potentials . Statistical analysis and graphs were generated using GraphPad Prism ( GraphPad Software , Inc . ) . One-way ANOVA followed by Dunnett’s or Tukey’s multiple comparison tests were performed to compare each group with other samples . Western blots were performed as described in[28] and run on 8% SDS-PAGE gels . Briefly , heads of flies were separated manually and 20 heads were used to extract lysates using 1x SDS buffer . 6 head equivalent lysate was loaded into each well and probed for dTau using anti-dTau antibody ( 1:10 , 000 ) [34 , 35] ( gift from Doris Kretzschmar , Oregon Health and Science University and Daniel St . Johnston , University of Cambridge ) . 30 head equivalent lysate against anti- BRP ( 1:100 ) [16] ( Developmental Studies Hybridoma Bank ) and anti-Tau ( phospho S262 ) ( 1:1000 ) ( abcam ) [9] were performed according to Gorska-Andrzejak et al [70] . In all experiments Syx1A Antibody ( 8C3 ) ( 1:100 ) [71] ( Developmental Studies Hybridoma Bank ) was used as a loading control . Image J was used to analyze the intensity of bands on the western blots and the “Gel analysis” function in the program was used to quantify the intensity of the bands . Ratios of the intensities of WT , Par-1OE , or Par-1T408A bands to that of Syntaxin bands were measured and used for calculating the statistical differences between the genotypes . Statistical analysis was generated using GraphPad Prism ( GraphPad Software , Inc . ) . Student T-tests and one-way ANOVA followed by Dunnett’s or Tukey’s multiple comparison tests were performed to compare each group with other samples . Samples for ultrastructural analysis were performed as previously described[28] . The larval head and tail were pinned and a dorsal slit was made lengthwise , thus filleting the larvae–in Tannic acid . The larvae were then post-fixed in 1% osmium tetroxide for 1 hr at 4°C . The larvae were dehydrated through 60 , ( 1x , 7 min ) 70 , 80 , 95 and 100% EtOH ( 2x , 10 min each step ) , transferred into propylene oxide ( 2x , 10 min ) , then into a 1:1 mixture of propylene oxide and Eponate , and left o/n , capped and at room temp . The larvae were then placed into fresh Eponate and into a mould , oriented and allowed to polymerize at 70°C . Thin sections were made and placed on superfrost/plus micro slide and stained with Toluidine Blue “O” . Type 1b boutons from NMJ6/7 in segment A2-A4 from WT and Par-1OE larval neuromuscular junctions were identified from the thin sections . Sections were cut at 50 nm with a diamond knife , picked up on formvar coated , copper slot grids , and stained with 2% aqueous uranyl acetate for 15 min followed by lead citrate stain for 1 min . Samples were observed and photographed in a JEM-1400 ( JOEL , Japan ) transmission electron microscope . Active zones and T-bars were quantified manually . Statistical analysis and graphs were generated using GraphPad Prism ( GraphPad Software , Inc . ) . One-way ANOVA followed by Dunnett’s or Tukey’s multiple comparison tests were performed to compare each group with other samples . | Synapses consist of pre- and postsynaptic partners . Proper function of active zones , a presynaptic component of synapse , is essential for efficacious neuronal communication . Disruption of neuronal communication is an early sign of both neurodevelopmental as well as neurodegenerative diseases . Since proteins that reside in active zones are used so frequently during the neuronal communication , they must be constantly replenished to maintain active zones . Axonal transport of these proteins plays an important role in replenishing these vital components necessary for the health of active zones . However , the mechanisms that transport components of active zones are not well understood . Our data suggest that there are distinct mechanisms that transport various active zone cargoes and this process is likely regulated by kinases . Further , our data show that disruption in the transport of one such active zone components causes reduced neuronal communication emphasizing the importance of the process of axonal transport of active zone protein ( s ) for neuronal communication . Understanding the processes that govern the axonal transport of active zone components will help dissect the initial stages of pathogenesis in both neurodevelopmental and neurodegenerative diseases . | [
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"cellular",... | 2017 | Active zone proteins are transported via distinct mechanisms regulated by Par-1 kinase |
The apicoplast organelle of the malaria parasite Plasmodium falciparum contains metabolic pathways critical for liver-stage and blood-stage development . During the blood stages , parasites lacking an apicoplast can grow in the presence of isopentenyl pyrophosphate ( IPP ) , demonstrating that isoprenoids are the only metabolites produced in the apicoplast which are needed outside of the organelle . Two of the isoprenoid biosynthesis enzymes are predicted to rely on iron-sulfur ( FeS ) cluster cofactors , however , little is known about FeS cluster synthesis in the parasite or the roles that FeS cluster proteins play in parasite biology . We investigated two putative FeS cluster synthesis pathways ( Isc and Suf ) focusing on the initial step of sulfur acquisition . In other eukaryotes , these proteins can be located in multiple subcellular compartments , raising the possibility of cross-talk between the pathways or redundant functions . In P . falciparum , SufS and its partner SufE were found exclusively the apicoplast and SufS was shown to have cysteine desulfurase activity in a complementation assay . IscS and its effector Isd11 were solely mitochondrial , suggesting that the Isc pathway cannot contribute to apicoplast FeS cluster synthesis . The Suf pathway was disrupted with a dominant negative mutant resulting in parasites that were only viable when supplemented with IPP . These parasites lacked the apicoplast organelle and its organellar genome – a phenotype not observed when isoprenoid biosynthesis was specifically inhibited with fosmidomycin . Taken together , these results demonstrate that the Suf pathway is essential for parasite survival and has a fundamental role in maintaining the apicoplast organelle in addition to any role in isoprenoid biosynthesis .
Iron-sulfur ( FeS ) clusters are ancient protein cofactors found in most organisms . These cofactors have a variety of roles including the transfer of single electrons , donation of sulfur atoms , initiation of free radical chemistry , oxygen sensing , and purely structural roles [1] , [2] . FeS clusters are found in a variety of forms , but the most common are cubane 4Fe-4S , cuboidal 3Fe-4S , and binuclear 2Fe-2S clusters [3] . Proteins typically bind these clusters through cysteine residues , although other amino acids have been shown to be involved in coordinating the cofactor [1] . Proteins containing FeS clusters are typically sensitive to oxygen and the clusters rapidly degrade in extracellular environments . Thus , clusters are synthesized de novo by one of three known FeS biosynthetic pathways . The Nif pathway , the first synthesis pathway described , is primarily found in nitrogen-fixing bacteria [4] . The Isc and Suf pathways are the dominant FeS cluster synthesis pathways found in eukaryotes , and are also present in bacteria and archaea [5] , [6] . In eukaryotes , the Isc pathway is mitochondrial [5] while the Suf pathway has thus far been found in species harboring a plastid organelle and has been localized to the chloroplast in Arabidopsis thaliana [7] , [8] . The protozoan parasite Blastocystis , which lacks a plastid , contains components of the Suf pathway in the cytosol [9] . While the protein components of the Isc and Suf machinery are quite different , both pathways follow the same basic steps of sulfur mobilization , cluster assembly , and cluster transfer ( Figure 1 ) . The Isc and Suf systems both depend on a cysteine desulfurase to mobilize sulfur from L-cysteine . The cysteine desulfurases of the eukaryotic Isc pathway ( IscS ) and of the Suf pathway ( SufS ) are only active when in complex with a partner protein ( Figure 1 ) . Isd11 , a component of eukaryotic Isc pathways , is essential for mitochondrial FeS cluster synthesis in Saccharomyces cerevisiae and Trypanosoma brucei [10] , [11] , [12] but is not present in prokaryotes [13] . In the absence of Isd11 , yeast IscS is prone to aggregation [10] , [11] . Isd11 has a conserved LYK/R motif that is essential for its ability to activate IscS cysteine desulfurase activity [14] . SufS is activated by SufE , an accessory protein which is found in both prokaryotic and eukaryotic Suf pathways . Unlike Isd11 , SufE forms a persulfide bond with the mobilized sulfur atom and acts to transfer the persulfide sulfur to the SufBCD assembly machinery [15] . Bacterial SufE has been shown to accelerate the cysteine desulfurase activity of SufS [16] , [17] . In the presence of SufE , the Vmax of Escherichia coli SufS is increased eight fold and an additional rate enhancement of 32 fold is observed when the assembly machinery ( SufBCD complex ) is present to accept the sulfur from SufE [16] . In E . coli , SufE does not interact with the Isc cysteine desulfurase [16] while in A . thaliana SufE has been shown to localize to both mitochondria as well as chloroplasts and serves to activate both cysteine desulfurases [18] . Malaria parasites harbor a plastid organelle called the apicoplast that is thought to have arisen from two sequential endosymbiotic events [19] . The apicoplast harbors biochemical pathways of prokaryotic origin such as type II fatty acid synthesis ( FASII ) , lipoate synthesis , tRNA modification , and 2-C-methyl-D-erythritol 4-phosphate ( MEP ) isoprenoid biosynthesis [20] . Enzymes in these pathways are predicted to require FeS cluster cofactors . In prokaryotes , lipoate synthase ( LipA ) , the tRNA modification enzyme MiaB , as well as the MEP enzymes IspG and IspH contain 4Fe-4S clusters [21] , [22] , [23] , [24] , [25] . The activity of these FeS proteins is in turn thought to be dependent on the 2Fe-2S electron transfer protein ferredoxin ( Fd ) [26] , [27] . In malaria parasites , only Fd and IspH have thus far been shown to contain FeS clusters [27] . The MEP isoprenoid biosynthesis pathway , the target of the antimalarial fosmidomycin , was recently shown to be essential for the survival of erythrocytic stage malaria parasites [28] , [29] . Parasites cultured in the presence of the MEP pathway product IPP ( isopentenyl pyrophosphate ) were no longer sensitive to fosmidomycin . Additionally , supplementation with IPP allowed parasites to survive without the apicoplast organelle , demonstrating that isoprenoids are the only metabolites produced in the apicoplast that are needed outside the organelle [28] . FeS cluster proteins are likely required for the production of essential isoprenoids . However , the synthesis of FeS clusters themselves has not been well characterized in malaria parasites . Only the P . falciparum SufC protein , part of the SufBCD assembly complex , has been studied to date , and was demonstrated to be an active ATPase localized to the apicoplast [30] . In this report , we investigated two putative FeS cluster synthesis pathways ( Isc and Suf ) , focusing on the initial step of sulfur acquisition . In P . falciparum , SufS and its partner SufE were found exclusively in the apicoplast and SufS was shown to have cysteine desulfurase activity in a complementation assay . IscS and its effector Isd11 were solely mitochondrial , suggesting that the Isc pathway does not contribute to apicoplast FeS cluster synthesis . We disrupted the Suf pathway using a dominant negative mutant of SufC and showed that these parasites only survive when cultured in the presence of IPP . Furthermore , these parasites lack the apicoplast organelle and its organellar genome – a phenotype not observed when isoprenoid biosynthesis was specifically inhibited with fosmidomycin . Taken together , these results demonstrate that the Suf pathway has a fundamental role in maintaining the apicoplast organelle in addition to any role in isoprenoid biosynthesis .
Bioinformatic studies suggest that the genomes of Plasmodium spp . encode both Isc and Suf proteins , including candidate cysteine desulfurases [20] , [31] , [32] , [33] , [34] . In most eukaryotes the cysteine desulfurases of the Isc and Suf pathways act in complex with the effector proteins Isd11 and SufE , respectively . SufE is essential for Suf FeS cluster synthesis in E . coli [15] , [16] , [35] , but was originally thought to be absent from malaria parasites [32] , [34] . More recent bioinformatic studies identified a potential sufE gene [30] , [36] and a candidate isd11 gene [33] , [37] . We used the PATS [38] , PlasmoAP [39] , and PlasMit [40] algorithms to predict the subcellular localization of P . falciparum Suf and Isc pathway proteins ( Table 1 ) . Most of the Suf pathway proteins were predicted to be apicoplast localized while the Isc proteins were predicted to be mitochondrial . In other systems , however , there is precedence for dual localization and crosstalk between components of Isc and Suf pathways . In Arabidopsis , SufE is dually localized to chloroplasts and mitochondria and activates both the Isc and Suf cysteine desulfurases [18] . In E . coli , SufE serves only the Suf pathway; however , the cluster transfer proteins are interchangeable between the pathways . SufA can rescue an IscA knockout , demonstrating that SufA can interact with the rest of the Isc pathway proteins; likewise , IscA can interact with the Suf machinery [35] . In S . cerevisiae , IscS has been localized to the mitochondria as well as the nucleus where it has a poorly defined but essential role [41] . In order to understand how the P . falciparum Suf and Isc pathways are partitioned in the parasite , we localized the IscS and SufS cysteine desulfurases and their effector proteins Isd11 and SufE in blood stage parasites . We localized the SufS and SufE proteins in P . falciparum by expressing protein constructs fused to a C-terminal green fluorescent protein ( GFP ) tag . For SufS , the leader peptide ( SufSlp ) consisting of the first 59 amino acids was appended to GFP , since this region was predicted by the PATS algorithm [38] to contain the organellar targeting peptide . The mycobacteriophage Bxb1 integrase method was used to generate parasite strains with a single copy of SufSlp-GFP integrated into a specific recombination site in the P . falciparum genome [42] , [43] . Live fluorescence microscopy demonstrated the presence of GFP fluorescence in an elongated organelle distinct from the parasite mitochondrion , which is typical of apicoplast morphology ( Figure 2A ) . To verify localization to the apicoplast , we performed immunofluorescence analysis using antibodies against the apicoplast marker acyl carrier protein ( ACP ) ( Figures 2B and S1 ) . We also visualized the processing of this fusion protein upon import into the apicoplast by western blot using an antibody against GFP ( Figure 2C ) . There was a small amount of unprocessed SufSlp-GFP while the majority of the fusion protein ran as a smaller processed species consistent with a cleavage event that occurs upon import into the apicoplast [44] . Full length SufE ( SufEfl-GFP ) could not be expressed in P . falciparum when driven by the strong calmodulin ( CaM ) promoter . Therefore , we used the lower strength ribosomal L2 protein ( RL2 ) promoter [45] . SufEfl-GFP parasites displayed the same ramified pattern as SufS expressing parasites by live microscopy ( Figure 3A ) . Detection by immunofluorescence demonstrated co-localization of SufEfl-GFP with the ACP apicoplast marker ( Figures 3B and S2 ) . As observed for SufS , SufEfl-GFP also appears to be processed , consistent with import into the apicoplast ( Figure 3C ) . These results demonstrate that SufS and SufE are localized to the apicoplast of erythrocytic stage P . falciparum and that SufE does not appear to be dually localized as observed in A . thaliana [18] . In E . coli , the Isc and Suf pathways are partially redundant; deletions of essential elements of either pathway result in conditional lethality while deletion of both pathways is lethal [35] . E . coli deficient in the Suf pathway are more sensitive to iron starvation and oxidative stress than wild type or Isc deficient strains [35] . We used the iron starvation phenotype to test the cysteine desulfurase activity of SufS in E . coli . ΔsufS E . coli transformed with the mature ( processed ) form of SufS ( pGEXT-SufS60 ) were able to grow in the presence of an iron chelator ( 2 , 2′-dipyridyl ) while ΔsufS E . coli transformed with empty vector ( pGEXT ) were unable to grow ( Figure 4 ) . Thus , SufS can complement the loss of EcSufS , demonstrating that the parasite protein has cysteine desulfurase activity . This result also demonstrates that SufS is able to participate in an active E . coli Suf complex , even though mature SufS is only 30% identical to EcSufS . We next wanted to know whether SufS is the only cysteine desulfurase that functions in the apicoplast . We localized IscS , the only other candidate cysteine desulfurase in malaria parasites , and its effector protein Isd11 , using the same strategy described above for the Suf proteins . A full-length IscS construct ( IscSfl ) fused to GFP co-localized with mitotracker in live fluorescence microscopy ( Figure 5A ) . Additionally , the 35 amino acid leader peptide of IscS ( IscSlp , as predicted by PlasMit [40] ) is sufficient to target GFP to the mitochondrion ( Figure S3 ) . We used the same integration strategy to localize a full-length construct of Isd11 ( Isd11fl ) , which in yeast is necessary to activate IscS . Live fluorescence showed complete co-localization with mitotracker indicating the exclusive presence of Isd11 in the mitochondrion ( Figure 5B ) . Thus , both IscS and Isd11 are mitochondrial and there is no evidence of additional nuclear localization of IscS as reported for S . cerevisiae IscS [41] . Taken together , these results suggest that SufS and SufE are solely responsible for sulfur acquisition for FeS synthesis in the apicoplast and we next attempted to determine whether this activity is essential in blood stage malaria parasites . A conserved cysteine ( at residue 51 ) in E . coli SufE is required for rapid transfer of sulfur from SufS to the SufBCD complex ( Figure 1 ) , and mutant SufE ( C51S ) binds to SufS and the SufBCD complex in a nonproductive manner [15] , [16] . We attempted to interfere with iron-sulfur cluster synthesis and downstream metabolic pathways by generating an overexpression construct of P . falciparum SufE with the equivalent cysteine substituted with serine , SufE ( C154S ) -HA . We were able to select parasites expressing SufE ( C154S ) -HA ( Figure S4A ) in the presence of 200 µM IPP , however this parasite line was not dependent on IPP for growth ( Figure S4B ) . Western blot analysis identified two protein bands for SufE ( C154S ) -HA , consistent with processing of the apicoplast leader peptide , and immunofluorescence showed that the protein co-localized with the apicoplast marker ACP ( Figure S4C ) . Thus , SufE ( C154S ) -HA was expressed and properly trafficked to the apicoplast organelle , but ultimately failed to interfere with apicoplast metabolism enough to make these parasites dependent on IPP supplementation . Although SufE ( C154S ) -HA failed to act as a dominant negative mutant , this construct helps to confirm the apicoplast localization observed with SufE-GFP in Figure 3 . We designed another dominant negative mutant based on the recent finding that an active site lysine in E . coli SufC is required for ATPase activity and for accumulation of iron on the SufBCD assembly complex [46] . We generated a construct of P . falciparum SufC driven by the calmodulin promoter ( CaM ) with the active site lysine substituted with alanine , SufC ( K140A ) -HACaM . We were unable to select parasites expressing the SufC ( K140A ) -HACaM construct , suggesting that expression of this construct is toxic . To bypass this toxicity , we then transfected parasites with the SufC ( K140A ) -HACaM construct in the presence of 200 µM IPP and were able to select transgenic parasites . Western blot analysis showed a single band consistent with the expected molecular weight of our dominant negative construct ( Figure 6A ) . However , unlike the apicoplast proteins shown in Figures 2C , 3C and S4A , there was no indication of apicoplast leader peptide processing with the SufC ( K140A ) -HACaM construct . Presumably , this construct was initially targeted to the apicoplast , but over time its expression led to apicoplast dysfunction and a loss of apicoplast leader peptide processing . Consistent with the effects of a dominant negative likely disrupting isoprenoid biosynthesis , these parasites were only able to grow when supplied with IPP ( Figure 6B ) . We next examined the condition of the apicoplast in dominant negative parasites . We generated a control parasite line expressing the apicoplast targeting peptide of the acyl carrier protein ( ACP ) fused to the red fluorescent protein mCherry . Parasites expressing ACP-mCherry ( ACP-mCh ) were cultured for six days with IPP in the presence or absence of 100 nM azithromycin ( 1× IC50 ) . Azithromycin treatment is known to result in loss of the apicoplast organelle [28] , [47] . Parasites treated with azithromycin ( 1× Az ) were compared to untreated parasites and dominant negative parasites using a PCR assay . We amplified genes from the apicoplast genome ( sufB ) , the mitochondrial genome ( cox1 ) and the nuclear genome ( sufS ) . All parasite lines maintained their mitochondrial and nuclear genomes . However , in contrast to the wild type parasites , the azithromycin-treated parasites and the SufC ( K140A ) -HACaM dominant negative parasites no longer contained sufB , indicating that both strains had lost the apicoplast genome ( Figure 6C ) . The localization of apicoplast marker ACP in dominant negative parasites , and the SufC ( K140A ) -HA protein itself , were examined by immunofluorescence and found to be present in multiple foci spread throughout the cell rather than in a single apicoplast organelle ( Figures 6D and S5 ) . The same phenotype was observed when the apicoplast was chemically disrupted [28] , confirming that the apicoplast had been similarly disrupted in the dominant negative parasites . It is possible that high level expression of SufC ( K140A ) -HACaM could interfere with secretory pathway function , leading to general toxicity and loss of the apicoplast . To test this , we generated a parasite line expressing SufC ( K140A ) -HA from the weaker strength RL2 ( ribosomal protein L2 ) promoter [45] , SufC ( K140A ) -HARL2 . In contrast to the CaM driven construct presented in Figure 6A , we were unable to detect the protein by western blot , even when the blot was loaded with ten-fold more SufC ( K140A ) -HARL2 parasite material . Despite the lower expression level , this mutant line was also dependent on continuous supplementation with IPP for growth ( Figure 7A ) and PCR analysis indicated that these parasites no longer contained the apicoplast sufB gene ( Figure 7B ) . Furthermore , immunofluorescence analysis of this line showed localization of ACP to multiple puncta spread throughout the cell similar to that seen with the strong promoter ( Figure 7C ) . Thus , even when expressed at a lower level , the SufC ( K140A ) -HARL2 dominant negative construct still causes the loss of the apicoplast organelle . We next generated a parasite line expressing wild type SufC-HA driven by the CaM promoter , SufC-HACaM , to test whether overexpression of this construct would lead to loss of the apicoplast . Unlike the SufC ( K140A ) -HACaM line shown in Figure 6 , the SufC-HACaM line is not dependent on IPP for growth , has not lost the sufB gene , and appears to contain a single intact apicoplast organelle ( Figure 7 ) . The SufC-HACaM construct is expressed in this parasite line and co-localizes with ACP in the apicoplast organelle ( Figure S6 ) . Notably , the SufC-HACaM construct is processed in a manner consistent with apicoplast import ( Figure S6A ) where as the SufC ( K140A ) -HACaM construct is not ( Figure 6A ) . Similarly , endogenous ACP protein is processed in the SufC-HACaM line , but not in the dominant negative line ( Figure S7 ) . Taken together , these data demonstrate that the toxicity of the dominant negative construct is not due to the expression level or the presence of the HA tag , but rather depends solely on the K140A mutation . Isoprenoids produced by the MEP pathway are the only metabolites produced in the apicoplast that are required outside of the organelle during the erythrocytic stages [28] . It is not known , however , whether the MEP pathway is required for maintenance of the apicoplast itself . To test this , we treated parasites with either azithromycin , to target all apicoplast functions , or fosmidomycin , which specifically targets the MEP pathway . We inhibited the MEP pathway in the presence of IPP by treating ACP-mCh parasites with 50 µM fosmidomycin ( 100× IC50 ) , 100 µM fosmidomycin ( 200× IC50 ) , 100 nM azithromycin ( 1× IC50 ) , or no drug for six days . In subsequent growth experiments , only the azithromycin-treated parasites were dependent on IPP for growth ( Figure 8A ) . Consistent with this growth phenotype , these parasites also lacked the apicoplast gene sufB ( Figure 8B ) . These results indicate that treatment with azithromycin leads to loss of the apicoplast organelle while treatment with fosmidomycin does not . The ACP-mCherry produced by fosmidomycin-treated parasites and untreated control parasites is trafficked to a single branched organelle consistent with normal apicoplast morphology ( Figure 8C ) . By contrast , parasites treated in parallel with azithromycin contain ACP-mCherry in multiple foci throughout the cell ( Figure 8C ) . Thus , inhibition of the MEP pathway with fosmidomycin does not lead to loss of the apicoplast organelle . The dominant negative disruption of the Suf pathway results in similar molecular and cellular phenotypes as the general disruption of the apicoplast by azithromycin and not the specific inhibition of the MEP pathway by fosmidomycin ( Figures 6 and 7 ) . These results suggest that in addition to providing FeS clusters for isoprenoid biosynthesis enzymes , the Suf pathway also plays a role in the maintenance of the apicoplast organelle .
In the apicoplast of Plasmodium falciparum there are several pathways that are predicted to rely on FeS cluster cofactors ( Figure 9 ) , and one of these pathways is known to be essential for erythrocytic stage growth . An early step in MEP isoprenoid synthesis is the target for the antimalarial fosmidomycin [29] which is currently being evaluated in human trials as a partner drug with piperaquine . Recently , it was shown that supplementing parasites with isopentenyl pyrophosphate ( IPP , one of the two final products of the MEP pathway ) rescues sensitivity to antibiotics targeting apicoplast maintenance ( e . g . chloramphenicol , clindamycin , doxycycline ) , demonstrating that isoprenoid synthesis is essential for blood stage parasite growth [28] . Antibiotic-treated parasites no longer contain an intact apicoplast or the organellar genome , however , these abnormalities should not affect the expression of the MEP pathway proteins . All of the enzymes in the MEP pathway are nuclear encoded and should still be produced under conditions in which the apicoplast is disrupted by antibiotic treatment . This is certainly true for the nuclear encoded apicoplast protein ACP , which is still produced regardless of whether the apicoplast is disrupted ( Figures 6 , 7 and S7 ) . Unlike ACP , the enzymes that catalyze the penultimate and final steps of isoprenoid synthesis ( IspG and IspH , respectively ) should both contain FeS clusters [22] , [27] . As described below , these clusters should not be available in parasites that lack an apicoplast . SufB is one of the few non-housekeeping genes encoded in the apicoplast genome . In other systems , SufB plays an essential role in FeS cluster assembly and is the scaffold on which the clusters are built [35] , [46] . When apicoplast maintenance is disrupted , SufB , and thereby FeS cluster synthesis , should be lost; this would then lead to disruption of the MEP pathway . Consistent with these expectations , we found that disruption of the Suf pathway with the SufC ( K140A ) -HACaM dominant negative mutant was toxic to blood stage malaria parasites . Parasites were only viable if supplemented with IPP , indicating that disruption of the Suf pathway ultimately leads to loss of the MEP isoprenoid biosynthesis pathway ( Figure 6 ) . Thus , the Suf pathway supports the MEP pathway and is essential for the survival of blood stage malaria parasites . In addition to the MEP pathway , the apicoplast of malaria parasites harbors a type II fatty acid synthesis ( FASII ) pathway which is essential for liver stage development [48] , [49] . The FASII pathway consumes acetyl-CoA [50] which is produced by the apicoplast-localized pyruvate dehydrogenase ( PDH ) enzyme complex [51] . Like the FASII pathway , a complete PDH complex ( composed of four proteins ) is essential during liver stage parasite development [52] . PDH is modified with the protein cofactor lipoate [53] which should be required for enzymatic activity . The synthesis of lipoate in the apicoplast is catalyzed by lipoate synthase ( LipA ) , which we have shown contains 4Fe-4S clusters ( Figure S8 ) . These FeS clusters not only need to be synthesized , but they probably also need to be continuously repaired . One of the FeS clusters in E . coli LipA is destroyed every time lipoate is formed , making turnover of LipA dependent on replacing this FeS cluster [54] . Thus , FeS cluster synthesis in the apicoplast should ultimately be required for lipoate synthesis , PDH activity , and the function of the FASII pathway known to be critical for liver stage development in rodent and human malaria parasite species . In organisms expressing both an Isc and a Suf pathway , such as E . coli , the Isc pathway acts as the default FeS synthesis pathway while the Suf pathway is expressed under conditions of prolonged oxidative stress and iron starvation [35] . It has been suggested that the Suf pathway is more efficient than the Isc pathway under conditions of oxidative stress [55]; this would be an attractive characteristic of the FeS cluster synthesis pathway expressed in oxygen producing compartments such as the plant chloroplast or the ancestral photosynthetic apicoplast [6] , [56] . However , the modern apicoplast appears to maintain a reducing environment and is highly resistant to oxidative stress [57] . This protective environment may enhance the activity of enzymes sensitive to oxygen , such as LipA ( Figure S8 ) , but it is not clear whether the parasite Suf pathway retains the tolerance to oxidative stress conditions displayed by its orthologs in plants and bacteria . FeS clusters are synthesized by ancient , highly conserved pathways , at least one of which is found in all organisms [58] . We have confirmed the presence of the Suf pathway in the P . falciparum apicoplast ( Figures 2 , 3 , S1 and S2 ) and demonstrated the activity of the cysteine desulfurase SufS , the first enzyme in the pathway ( Figure 4 ) . In 2003 , another group localized IscS as a test of a transfection method [59] . They fused GFP to what was at the time predicted to be the first 135 amino acids of IscS , however , the amino-terminus of the current gene model differs from the sequence used in that study . We repeated the localization using the current gene model which aligns more closely with eukaryotic IscS sequences . P . falciparum IscS and Isd11 both localized exclusively to the mitochondrion ( Figures 5 and S3 ) . The subcellular partitioning of the Isc and Suf pathways demonstrates that they function independently of each other , and are likely both essential for erythrocytic stage parasite growth . The same general pattern of organellar partitioning of the Isc and Suf pathways is observed in the only other plastid-containing organism in which both pathway components have been localized , A . thaliana [6] . P . falciparum appears to differ from A . thaliana , however , in that we observe SufE solely in the apicoplast while one of the Arabidopsis SufE homologs appears to be dually localized between the chloroplasts and the mitochondria and has been shown to activate mitochondrial AtIscS [18] . AtIsd11 is only 18% identical to Isd11 from P . falciparum and AtIscS lacks the extended amino terminus of IscS present in P . falciparum and S . cerevisiae . There may be functional differences between these IscS homologs that affect their ability to be stimulated by effector proteins . FeS cluster modified proteins in the P . falciparum mitochondrion are involved in redox regulation , metabolism , and participate in the electron transport chain . Complex III ( cytochrome bc1 ) is the target of the antimalarial atovaquone , which prevents binding of reduced ubiquinone and also blocks electron transfer from the Rieske type 2Fe-2S cluster , implying that the Isc pathway is essential for blood stage parasite growth [60] . This makes the Isc pathway an attractive drug target , however it is closely related to the host Isc pathway . Closer study of the Isc pathway found in parasites may identify exploitable differences between mitochondrial FeS cluster synthesis in the parasite and in the human host . The Suf pathway is not found in humans , and the work presented here shows that it is required for the maintenance of the apicoplast organelle . If the Suf pathway was only needed to activate certain MEP enzymes , we would expect disruption of the Suf pathway to have similar effects as inhibition of the MEP pathway . This , however , was not the case . As shown in Figure 8 , inhibition of the MEP pathway by the specific inhibitor fosmidomycin does not lead to dependence on IPP for growth , loss of the apicoplast gene sufB , or observable changes in organelle morphology . In contrast to fosmidomycin treatment , disruption of the Suf pathway with the dominant negative mutant SufC ( K140A ) -HACaM results in loss of the apicoplast organelle . Dominant negative parasites depend on IPP for growth , have lost the sufB gene , and no longer contain an intact apicoplast organelle ( Figures 6 ) . One possible explanation for this broader phenotype is that high level expression of SufC ( K140A ) -HACaM interfered with secretory pathway function , leading to general toxicity . This seems unlikely , however , since these parasites still traffic ACP into punctate foci in the cell ( Figures 6C and S5 ) , consistent with the membrane-bound secretory vesicles observed by Yeh and coworkers [28] . To address this issue , we generated two additional parasite lines . The first expressed the same dominant negative mutant driven by the lower strength RL2 promoter . This parasite line , SufC ( K140A ) -HARL2 , displayed the same loss of apicoplast phenotype , demonstrating the potency of the dominant negative SufC ( K140A ) mutation ( Figure 7 ) . We also generated a parasite line expressing a wild type construct of SufC driven by the strong calmodulin promoter . This SufC-HACaM construct differs from the toxic dominant negative SufC ( K140A ) -HACaM construct by a single amino acid , yet had none of the molecular and cellular phenotypes associated with loss of the apicoplast organelle ( Figures 7 and S6 ) . Thus , the K140A point mutation is solely responsible for disrupting apicoplast metabolism leading to loss of the organelle . How does the dominant negative mutant interfere with apicoplast metabolism ? SufC is known to bind to SufB [30] and presumably forms the SufBCD iron-sulfur cluster assembly complex observed in other organisms ( Figure 1 ) . The SufC ( K140A ) mutant was designed to form a nonproductive complex with endogenous SufB and SufD , thereby limiting the availability of these proteins for cluster assembly . The dominant negative mutant should decrease cluster synthesis , but it could also affect iron homeostasis in the apicoplast , a phenomenon that is difficult to study since organellar iron import and storage mechanisms are not known . We attempted to interfere with the Suf pathway at an earlier step ( sulfur acquisition ) with the SufE ( C154S ) mutant , but this construct did not have a dominant negative phenotype , even when overexpressed with the strong calmodulin promoter ( Figure S4 ) . It may be that sulfur acquisition is not the rate limiting step in the parasite Suf pathway or that the SufE mutant does not interact with other Suf proteins as observed in the E . coli system . The most likely effect of the SufC ( K140A ) dominant negative mutant is inactivation of apicoplast FeS proteins . Known and predicted FeS proteins are shown in Figure 9 , including four FeS enzymes ( LipA , IspH , IspG and MiaB ) and ferredoxin ( Fd ) . Are any of these proteins likely to be required for apicoplast maintenance during blood stage parasite growth ? As described above , LipA is responsible for lipoylating the PDH and ultimately supporting fatty acid biosynthesis in the apicoplast . Since components of the FASII pathway and subunits of the PDH complex ( albeit not the lipoylated E2 subunit ) have been successfully deleted in blood stage malaria parasites [48] , [49] , [52] , LipA is presumed to be similarly dispensable and not required for apicoplast maintenance . Although IspH and IspG should be essential for isoprenoid biosynthesis in blood stage parasites , loss of these enzymes should have the same effect as inhibition with fosmidomycin . As shown in Figure 8 , inhibition of isoprenoid biosynthesis does not result in loss of the apicoplast organelle . This result also suggests that the final FeS enzyme , MiaB , is not required for apicoplast maintenance . MiaB presumably functions in conjunction with an upstream enzyme , MiaA , in the maturation of tRNAs . MiaA has not been studied in malaria parasites , but in most eukaryotes and bacteria this enzyme transfers isopentenyl groups to a specific adenosine base in the anticodon loop of certain tRNAs [61] , [62] . MiaB is a methylthiolase that further modifies the isopentenyladenosine tRNA base with a CH3S group [63] , [64] . If P . falciparum MiaB functions in an analogous way , then its activity depends on isoprenoid biosynthesis , a pathway that we have shown is not required for apicoplast maintenance . Importantly , MiaA enzymes use the MEP pathway product DMAPP ( dimethylallyl pyrophosphate ) as the source of isopentenyl groups and would not be able to use the IPP that we supply in our parasite culture conditions unless there is an IPP/DMAPP isomerase present . Thus , based on their predicted activities ( these enzymes could have additional noncanonical activities ) , these four FeS enzymes do not appear to be good candidates to explain why disrupting FeS cluster synthesis leads to loss of the apicoplast organelle . Among the predicted apicoplast FeS proteins in Figure 9 , ferredoxin stands out as the most integral to apicoplast function . P . falciparum Fd contains a 2Fe-2S cluster and has been shown to act as an electron donor to IspH [27] . Other apicoplast pathways may also depend on Fd , since it is predicted to be the preferred electron transfer partner for the other apicoplast FeS enzymes ( LipA , IspG and MiaB ) and may be required to provide reducing equivalents during certain steps of FeS cluster biosynthesis [36] , [65] . Because of its role in FeS synthesis , reduced Fd metalation could have an exaggerated effect by further limiting the production of its own FeS clusters . Even if Fd is required for FeS synthesis , it still does not provide an explanation for how the apicoplast is lost since the downstream FeS enzymes do not have obvious roles in apicoplast maintenance . Loss of the organelle may instead be linked to how redox balance is maintained in the apicoplast . Fd in conjunction with its associated reductase , ferredoxin-NADP+-reductase ( FNR ) , is the only known redox system in the apicoplast [66] . Perturbation of the Fd/FNR system could lead to increased sensitivity to oxidative stress , as observed in other systems [67] . Since the apicoplast is known to be a highly reducing environment [57] , failure of this protective system could lead to oxidative damage , particularly of the organellar DNA , and subsequent loss of the organelle . Regardless of the mechanism , it is clear that Suf pathway dysfunction results in a disruption of apicoplast maintenance . Since the enzymes which comprise the Suf pathway are distinct from anything found in the human host , they are attractive targets for inhibition . The Suf pathway appears to lie at the root of apicoplast metabolic function and inhibition of the pathway should block the growth of blood stage and liver stage malaria parasites .
The genes in this study were amplified from gDNA or cDNA prepared from blood stage P . falciparum Dd2 strain parasites and inserted into the pLN-GFP transfection plasmid described by Nkrumah and coworkers [42] . In some cases , the calmodulin ( CaM ) promoter of pLN-GFP was substituted with the weaker strength ribosomal L2 protein ( RL2 ) promoter [45] , and in other cases the GFP tag was removed or replaced with mCherry ( mCh ) or a hemagglutinin tag ( HA ) . The iscS gene ( PF3D7_0727200 ) was amplified from gDNA with primers IscS . AvrII . F and IscS . fl . BsiWI . R and inserted into pRL2-GFP , generating plasmid pRL2-IscSfl-GFP ( see Table S1 for primer sequences ) . Nucleotides encoding the 35 amino acid IscS leader peptide ( IscSlp ) were amplified from pRL2-IscSfl-GFP vector using primers IscS . AvrII . F and IscS . 35 . BsiWI . R and ligated into pLN-GFP to generate pLN-IscSlp-GFP . The in-frame intron in isd11 ( PF3D7_1311000 ) was confirmed by amplifying this gene from cDNA with primers Isd11 . AvrII . F and Isd11 . BsiWI . R and inserting into pRL2-GFP , generating plasmid pRL2-Isd11fl-GFP . The sufS gene ( PF3D7_0716600 ) was amplified from cDNA using the primers SufS . TOPO . F and SufS . TOPO . R and ligated into cloning vector pET100/D-TOPO ( Invitrogen ) . Nucleotides encoding the leader peptide of SufS were amplified using the primers SufS . AvrII . F and SufS . 59 . BsiWI . R and ligated into pLN-GFP , generating plasmid pLN-SufSlp-GFP . Amplification of sufE ( PF3D7_0206100 ) from cDNA confirmed the four exon gene model , but consistently resulted in a frame-shifted amplicon . Gene synthesis ( GeneArt ) was used to generate the sufE gene flanked by AvrII and BsiWI endonuclease sites which were used to subclone into the pRL2-GFP transfection plasmid generating pRL2-SufEfl-GFP . A transfection vector was created to express mCherry red fluorescent protein in the apicoplast organelle . The gene encoding mCherry was amplified with primers mCh . BsiWI . F and mCh . AflII . R and inserted into the pLN-TP-ACP-GFP vector described by Gallagher et al . [68] . The resulting transfection vector , pLN-TP-ACP-mCh , encodes mCherry instead of GFP . Constucts SufE ( C154S ) , SufC ( PF3D7_1413500 ) and SufC ( K140A ) were synthezised ( GeneArt ) with flanking AvrII and BsiWI sites and inserted into a pLN plasmid modified to have a carboxy-terminal single HA tag , generating pLN-SufE ( C154S ) -HA , pLN-SufC-HA and pLN-SufC ( K140A ) -HA . The SufC ( K140A ) -HA coding region was digested from this plasmid with AvrII and BsiWI and inserted into pRL2 to generate pRL2-SufC ( K140A ) -HA . P . falciparum transfections were performed using the Bxb1 mycobacteriophage integrase system in Dd2 strain parasites containing the attB recombination site [42] in combination with a red blood cell ( RBC ) preloading technique [43] . Infected red blood cells ( iRBC ) were first observed between 11 and 27 days after beginning selection with 2 . 5 µg/mL blasticidin . Insertion of the transgene at the attB site was confirmed by PCR using the primers P1 , P2 , P3 , and P4 ( Table S1 ) as described by Spalding et al . [43] . Genomic DNA from each integrated parasite line was purified and used to verify the transgene sequence with primers GFP . R or pLN . 790 . R and either RL2 . F or CaM . F , as appropriate ( Table S1 ) . Parasites were maintained in human red blood cell culture at 2% hematocrit using the general method described by Trager and Jensen [69] . Briefly , blood stage parasites were cultured in RPMI 1640 supplemented with 10% human serum , 28 mM NaCO3H , 25 mM HEPES , and 0 . 09 mM hypoxanthine . Cultures were gassed with 92% N2 , 3% O2 , 5% CO2 and incubated in sealed 75 cm2 flasks at 37°C . For the chemical bypass experiments , 0 . 5 ml or 1 ml parasite cultures were maintained in 24 or 48 well plates and supplemented during daily feedings with 200 µM isopentenyl pyrophosphate ( Sigma ) . Parasite cultures with a parasitemia between 2% and 15% were incubated for 30 minutes at 37°C with 12 . 5 nM mitotracker CMX-Ros ( Invitrogen ) and 1 µg/mL 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Cells were washed three times for 5 minutes at 37°C with RPMI or PBS and then sealed on a slide for observation on a Nikon Eclipse 90i equipped with an automated z-stage . A series of images spanning 4 µm were acquired with 0 . 2 µm spacing and images were deconvolved with VOLOCITY software ( PerkinElmer ) to report a single combined z -stack image . Parasites were fixed and permeabilized for immunofluorescence studies . Live parasites were mixed with 4% paraformaldehyde and 0 . 0075% glutaraldehyde in PBS and placed on poly-lysine coated glass slides for 30 minutes at room temperature . The slides were then incubated with 1% Triton X-100 in PBS for 10 minutes and then washed three times for 5 minutes with PBS . Sodium borohydride ( 0 . 1 g/L in PBS ) was used to reduce any remaining unreacted aldehydes followed by three more 5 minutes washes in PBS . The slides were then blocked with 3% bovine serum albumin for an hour and then probed with the appropriate primary antibodies [1∶500 rabbit or rat αACP [57] , 1∶50 Living Colors mouse αGFP JL-8 ( Clontech ) , or 1∶50 rat αHA mAb 3F10 ( Roche ) ] . Slides were washed three times for 5 minutes with PBS , and then incubated with the appropriate secondary antibodies [1∶3 , 000 goat αRabbit IgG Alexa Fluor 594 , 1∶1 , 000 goat αMouse IgG Alexa Fluor 488 ( Invitrogen ) , or 1∶1 , 000 goat αRat IgG Alexa Fluor 488 ( Invitrogen ) ] for one hour at room temperature . The slides were washed three times for five minutes with PBS , then mounted with Prolong Gold antifade reagent with DAPI ( Invitrogen ) . Expression of SufS and SufE in transgenic parasites was verified by western blot . Host RBCs from 5 mL cultures at 5–15% parasitemia were permeabilized with 0 . 2% saponin in PBS for 5 min on ice and then washed repeatedly in PBS until the supernatant was clear . Purified parasites were then lysed in gel loading buffer and parasite proteins were resolved on a NuPage 4–12% Bis-Tris reducing gel ( Invitrogen ) and transferred onto nitrocellulose . The nitrocellulose membrane was blocked for at least one hour with 5% milk in PBS and probed overnight at 4°C with 1∶5 , 000 Living Colors mouse αGFP JL-8 ( Clonetech ) in 1% milk . The membrane was washed with PBS three times and probed with 1∶20 , 000 sheep αMouse IgG horseradish peroxidase ( HRP ) secondary antibody ( GE Healthcare ) for at least one hour at room temperature . After three additional washes , the blot was visualized with SuperSignal West Pico detection solution ( Thermo Scientific ) and exposed to film . All constructs expressed in E . coli were cloned into the pGEXT vector which expresses the parasite proteins fused to a cleavable amino terminal glutathione-s-transferase ( GST ) tag [70] . Mature lipA ( encoding residues 89 to 415 of PF3D7_1344600 ) was amplified from cDNA using Pfu polymerase and primers LipA . EcoRI . F and LipA . PstI . R ( Table S1 ) . This amplicon was digested with PstI and EcoRI and ligated into vector pMALcHT [71] . Primers LipA . BamHI . F and LipA . EcoRI . R ( Table S1 ) were used to subclone LipA , generating plasmid pGEXT-LipA89 . A construct of mature sufS ( encoding residues 60 to 546 of PF3D7_0716600 ) was amplified from vector pET100/D-TOPO ( described above ) using primers SufS . BamHI . F and SufS . EcoRI . R ( Table S1 ) , generating plasmid pGEXT-SufS60 . E . coli containing a deletion of sufS ( ΔsufS , Keio collection JW1670 ) were transformed with either empty vector , pGEXT , or pGEXT-SufS60 . Each strain was grown overnight at 37°C in MinE medium as modified by Allary et al . [53] . The overnight culture was used to plate 1 µL of 1 . 0 OD600 on MinE agar plates containing 100 µM 2 , 2′-dipyridyl . The plates were incubated for 48 hrs at 30°C and inspected for bacterial growth . BL21 Star ( DE3 ) E . coli containing the pLysE plasmid were transformed with pGEXT-LipA89 construct produced above . In order to culture the protein in conditions of minimal oxygen , E . coli were grown in flat bottom flasks filled three quarters full with LB medium . When cells reached an OD600 of 0 . 6 they were induced with 0 . 4 mM IPTG for 10 hours at 20°C . Cells were harvested by centrifugation , flash frozen in liquid nitrogen , and stored under the liquid layer . The cell pellet was transferred to a Bactron IV ( Shell Labs ) anaerobic chamber flooded with 5% hydrogen , 5% carbon dioxide , and 90% nitrogen . A palladium catalyst was used to maintain <30 ppm oxygen . Cells were resuspended in anaerobic lysis buffer ( 20 mM Na/K phosphate [pH 7 . 5] , 200 mM NaCl , 2 g/L lysozyme , and 1 mM phenylmethylsulfonyl fluoride [PMSF] ) and incubated at room temperature until cell lysis was apparent . After lysis , 2 . 5 µg/mL DNase I was added and incubated for 30 minutes at room temperature . The lysate was transferred to an air tight container and centrifuged to separate the soluble and insoluble fractions . GST-LipA89 was purified using a 5 mL GST-Trap HP column ( GE Healthcare ) connected to a peristaltic pump in the anaerobic chamber . Plasmids pLN-SufE ( C154S ) -HA , pLN-SufC ( K140A ) -HA , pRL2-SufC ( K140A ) -HA and pLN-SufC-HA were used to generate transgenic parasite lines . As described above , these parasite lines were maintained in the presence of 200 µM IPP . Protein expression was confirmed by western blot using the methods described above with 1∶1 , 000 rat αHA mAb 3F10 ( Roche ) and 1∶20 , 000 goat αRat IgG horseradish peroxidase ( HRP ) secondary antibody ( GE Healthcare ) secondary antibody . Growth assays were conducted in triplicate using 24 well culture plates and initiated at a parasitemia of 0 . 5% . Over a six day period , parasitemia was assessed by flow cytometry using a FACSCalibur cell sorting machine ( Becton Dickinson ) . Samples of 10 µl from each well were incubated with 10 µl of 5 µM dihydroethidium for 15 minutes at 37°C in the dark . Results were analyzed by FlowJo software ( Tree Star Inc . , Ashland , OR ) . Whole cell PCR was used to amplify representative genes from the nuclear ( sufS ) , apicoplast ( sufB ) , and mitochondrial ( cox1 ) genomes ( Table S1 ) . Phusion High-Fidelity DNA Polymerase ( New England BioLabs ) was used in accordance with the manufacturer's directions in 25 µL reactions containing 1 µL of parasite culture . The processing of endogenous ACP was visualized by western blot using the 4% formaldehyde 0 . 1% glutaraldehyde fixation conditions previously described [57] to prevent ACP from diffusing out of the blot membrane . The blot was probed with 1∶5 , 000 rabbit αACP [57] primary and 1∶3 , 500 donkey αRabbit IgG horseradish peroxidase ( HRP ) secondary antibody ( GE Healthcare ) . Parasites transfected with the pLN-TA-ACP-mCherry vector were supplemented with 200 µM IPP and treated with 100 nM azithromycin , 50 µM fosmidomycin , 100 µM fosmidomycin , or no drug for 6 days . All four ACP-mCherry ( ACP-mCh ) lines were then tested for IPP dependence and analyzed by live epifluorescence microscopy and whole cell PCR as descibed above . | Iron is essential for the survival of blood stage P . falciparum and is used primarily in the synthesis of iron-sulfur ( FeS ) cluster cofactors . We investigated the role that ( FeS ) clusters play in malaria parasites . We demonstrated that the synthesis of FeS clusters is partitioned between two organelles: the Isc pathway is mitochondrial while the Suf pathway is found exclusively in the apicoplast organelle . Attempts to interfere with the Suf pathway through a dominant negative approach were only successful when parasite cultures were supplemented with an isoprenoid product . This result demonstrates that isoprenoid biosynthesis depends on a functional Suf pathway . Unexpectedly , we also observed the complete loss of the apicoplast organelle when we disrupted the Suf pathway . This phenotype does not result from inhibition of isoprenoid biosynthesis; we treated parasites with high levels of the isoprenoid inhibitor fosmidomycin without any loss of the apicoplast organelle . These results demonstrate that the Suf pathway has a fundamental role in maintaining the apicoplast organelle in addition to any role in isoprenoid biosynthesis . Inhibition of the Suf pathway , which is not found in humans , will block the growth of malaria parasites . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Suf Iron-Sulfur Cluster Synthesis Pathway Is Required for Apicoplast Maintenance in Malaria Parasites |
Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex , yet the fundamental principles underlying the origin of grid-cell firing are still debated . Grid-like patterns could emerge via Hebbian learning and neuronal adaptation , but current computational models remained too abstract to allow direct confrontation with experimental data . Here , we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity . Through rigorous mathematical analysis applicable in the linear limit , we quantitatively predict the requirements for grid-pattern formation , and we establish a direct link to classical pattern-forming systems of the Turing type . Our study lays the groundwork for biophysically-realistic models of grid-cell activity .
Grid cells are neurons of the medial entorhinal cortex ( mEC ) tuned to the position of the animal in the environment [1 , 2] . Unlike place cells , which typically fire in a single spatial location [3 , 4] , grid cells have multiple receptive fields that form a strikingly-regular triangular pattern in space . Since their discovery , grid cells have been the object of a great number of experimental and theoretical studies , and they are thought to support high-level cognitive functions such as self-location [e . g . 5 , 6] , spatial navigation [e . g . 7–9] , and spatial memory [10 , 11] . Nevertheless , to date , the mechanisms underlying the formation of grid spatial patterns are yet to be understood [12 , 13] . The attractor-network theory proposes that grid fields could arise from a path-integrating process , where bumps of neural activity are displaced across a low-dimensional continuous attractor by self-motion cues [14–21] . The idea that self-motion inputs could drive spatial firing is motivated by the fact that mammals can use path integration for navigation [22] , that speed and head-direction signals have been recorded within the mEC [23 , 24] , and that , in the rat [1 , 25] but not in the mouse [26 , 27] , grid firing fields tend to persist in darkness . However , grid-cell activity may rely also on non-visual sensory inputs—such as olfactory or tactile cues—even in complete darkness [28] . Additionally , the attractor theory alone cannot explain how grid fields are anchored to the physical space , and how the properties of the grid patterns relate to the geometry of the enclosure [29–31] . A different explanation for the formation of grid-cell activity is given by the so-called oscillatory-interference models [32–36] . In those models , periodic spatial patterns are generated by the interference between multiple oscillators whose frequencies are controlled by the velocity of the animal . Speed-modulated rhythmic activity is indeed prominent throughout the hippocampal formation in rodents and primates [37–40] , particularly within the theta frequency band ( 4-12 Hz ) . Additionally , reduced theta rhythmicity disrupts grid-cell firing [41 , 42] , and grid-cell phase precession [43] is intrinsically generated by interference models; but see [44] . Despite their theoretical appeal , however , these models cannot explain grid-cell activity in the absence of continuous theta oscillations in the bat [45] , and they are inconsistent with the grid-cell membrane-potential dynamics as measured intracellularly [46 , 47]; see [48] for a hybrid oscillatory-attractor model . Here we focus on the idea that grid-cell activity does not originate from self-motion cues , but rather from a learning process driven by external sensory inputs . In particular , it was proposed that grid patterns could arise from a competition between persistent excitation by spatially-selective inputs and the reluctance of a neuron to fire for long stretches of time [49–53] . In this case , Hebbian plasticity at the input synapses could imprint a periodic pattern in the output activity of a single neuron . Spatially-selective inputs , i . e . , inputs with significant spatial information , are indeed abundant within the mEC [54–56] and its afferent structures [57–61] And spike-rate adaptation , which is ubiquitous in the brain [62] , could hinder neuronal firing in response to persistent excitation . Kropff and Treves [49] explored this hypothesis by means of a computational model; see also [63–67] and Sec Related models for similar works . The emergence of grid-like patterns was demonstrated with theoretical arguments and with numerical simulations of a rate-based network . However , because of a relatively abstract level of description , the outcomes of the model could not be easily confronted with experimental data . Specifically , the simulations included a network-level normalization mechanism that constrained the mean and the sparseness of the output activity , and it remained unsettled whether grid patterns could emerge in a single-cell scenario . Additionally , the synaptic weights did not obey Dale’s law . And the robustness of the model was not tested against shot noise due to stochastic spiking . Finally , the link between the numerical simulations and the underlying mathematical theory remained rather loose . To overcome these issues , we propose here a single-cell spiking model based on similar principles as the model by Kropff and Treves [49] , but that is , on the one hand , more biologically realistic , and on the other hand , better suited for mathematical treatment . Importantly , we show that grid patterns can emerge from a single-cell feed-forward mechanism needless of any network-level interaction ( although recurrent dynamics may be still required to explain the coherent alignment of grid patterns [1] ) . To increase biological plausibility , we consider a stochastic spiking neuron model , and we constrain the synaptic weights to non-negative values ( Dale’s law ) . Finally , by studying the model analytically , we quantitatively predict the requirements for grid-pattern formation , and we establish a direct link to classical pattern-forming systems via the Turing instability [68] .
We consider a single cell that receives synaptic input from N spatially-tuned excitatory neurons . Input spike trains S i in ( t ) ≔ ∑ k δ ( t - t i , k in ) for i = 1 , 2 , … , N are generated by independent inhomogeneous Poisson processes with instantaneous rates r i in ( t ) where δ ( t ) is the Dirac delta function , and t i , k in is the timing of the kth input spike at synapse i . Similarly , the output spike train S out ( t ) ≔ ∑ k δ ( t - t k out ) is generated by an inhomogeneous Poisson process with instantaneous rate rout ( t ) where t k out denotes the timing of the kth output spike . We assume that inputs are integrated linearly at the output , and that the output neuron is equipped with an intrinsic spike-rate adaptation mechanism , that is , r out ( t ) ≔ r 0 + ∫ 0 ∞ d τ K ( τ ) ∑ i = 1 N w i S i in ( t - τ ) ( 1 ) where r0 is a baseline rate , wi is the synaptic weight of input neuron i , and the function K is a temporal filter modeling the spike-rate adaptation dynamics . Note that the instantaneous output rate rout depends only on the temporal history of the input spikes and that there is no reset mechanism after the emission of an output spike . The impulse response of the adaptation kernel K is the sum of two exponential functions: K ( t ) ≔ { 1 τ S exp ( - t τ S ) - μ τ L exp ( - t τ L ) for t ≥ 0 0 for t < 0 ( 2 ) where τS and τL are the short and long filter time constants ( 0 < τS < τL ) , and the parameter μ > 0 sets the filter integral ∫ 0 ∞ d t K ( t ) = 1 - μ ( Fig 1A ) . Intuitively , at the arrival of an input spike , the firing probability of the output neuron is first increased for a short time that is controlled by the time constant τS , and then decreased for a longer time that is controlled by the time constant τL . This second hyper-polarization dynamics effectively hinders the neuron to fire at high rates for long stretches of time , mimicking a spike-rate adaptation mechanism [69–71] . From a signal-processing perspective , the adaptation kernel K performs a temporal band-pass filtering of the input activity ( Fig 1B ) , and the two time constants τS and τL control the resonance frequency kres at which the filter response is maximal . Note that in Sec Pattern formation with after-spike potentials we study a variant of the present model where neuronal adaptation is obtained though after-spike hyperpolarizing potentials associated to the output activity of the neuron . We assume spike-timing dependent plasticity ( STDP ) at the input synapses [e . g . 72–76] . Input and output spikes trigger weight changes Δwi according to the following rule: where η ≪ 1 is a small learning rate , and the STDP learning window W ( Δt ) sets the weight change as a function of the time difference Δt ≔ tpre − tpost between pre- and post-synaptic spikes . We consider a symmetric STDP learning window [77] W ( Δ t ) ≔ W tot 2 τ W exp ( - | Δ t | / τ W ) ( 5 ) where the time constant τW > 0 controls the maximal time lag at which plasticity occurs , and W tot = ∫ - ∞ ∞ d t W ( t ) is the integral of the learning window . The first part of the learning rule ( Eq 3 ) is the classical Hebbian term whereas the second part ( Eq 4 ) is a local normalization term that stabilizes the average synaptic strength w av = N - 1 ∑ i = 1 N w i and prevents the individual weights to grow unbounded . This normalization term mimics local homoeostatic processes observed experimentally [78–80]; see also [81] for a review . The parameters α > 0 and β set , respectively , the rate of weight decay and the target average weight wav ( Sec Weight normalization ) . Importantly , the synaptic weights are constrained to non-negative values by imposing the hard bounds w i ≥ 0 ∀ i . ( 6 ) We consider excitatory inputs with firing rates r i in that are tuned to the spatial position of a virtual rat exploring a square arena of side-length L , i . e . , r i in ( t ) ≔ Ψ i in ( x t ) ( 7 ) where xt is the position of the virtual rat at time t , and Ψ i in is a spatial tuning curve . We characterize the spatial tuning curves Ψ i in in two alternative scenarios: The first scenario , which is reminiscent of hippocampal place-cell activity [3 , 82 , 83] , is easier to study analytically and cheaper to simulate numerically . The second scenario , which is reminiscent of parasubicular activity [57–61] , is motivated by the anatomy of the entorhinal circuit ( Sec Input spatial tuning and the origin of grid-cell patterns ) . In both cases , we consider circularly-symmetric receptive fields that cover the arena evenly . Indeed , place fields in open environments do not show systematic shape biases , and , in the absence of specific reward or goal locations , their centres are roughly homogeneously distributed [3 , 57–61 , 82 , 83] . Note , however , that border-like inputs [84 , 85]—which are not radially-symmetric—are present in the real system , but not explicitly modeled here . Finally , for simplicity , we assume periodic boundaries at the edges of the arena . The movement of the virtual rat follows a smooth random walk that satisfies the following three assumptions: ( i ) the movement speed v is constant in time; ( ii ) the random walk is isotropic and ergodic with respect to the auto-covariance; ( iii ) the virtual-rat trajectories are smooth within time stretches shorter than the time length τmax = 5τL of the adaptation kernel K ( Fig 1A ) . Note that assumption ( i ) is obviously not valid in general . However , because synaptic plasticity acts on a time scale that is much slower than behaviour , the relevant variable for pattern formation is the rat running speed averaged over long stretches of time ( e . g . minutes ) , which can be considered approximately constant . We assume an average running speed of 25 cm/s , which is experimentally plausible [86] . Assumptions ( ii ) and ( iii ) hold by ignoring directional anisotropies deriving from the geometry of the environment , and by observing that experimental rat trajectories are approximately straight over short running distances ( e . g , over distances shorter than 25 cm ) [86] . Mathematically , the two-dimensional virtual-rat trajectories xt are sampled from the stochastic process d X t d t ≔ v [ cos ( θ t ) , sin ( θ t ) ] with θ t = σ θ W t , ( 11 ) where the angle θt sets the direction of motion and W t is a standard Wiener process ( Fig 2 ) . The parameters v and σθ control the speed of motion and the tortuosity of the trajectory . Note that we also perform simulations with variable running speeds . In this case , the speed is sampled from an Ornstein-Uhlenbeck process with long-term mean v ¯ = v . The grid-cell model presented above is studied both analytically and numerically . In this section , we obtain an equation for the average dynamics of the synaptic weights , and we derive the requirements for spatial pattern formation . In Sec Numerical results on grid-pattern formation we demonstrate the emergence of grid-like activity by simulating both the detailed spiking model and the averaged system . The analytical results presented here may be skipped by the less mathematically-inclined reader . We study structure formation in the activity of an output cell by averaging the weight dynamics resulting from the stochastic activation of input and output neurons ( Sec Model of neural activity ) and the STDP learning rule ( Sec Model of synaptic plasticity ) , while a virtual rat explores a two-dimensional enclosure and the inputs are spatially tuned ( Secs Model of input spatial tuning and Model of spatial exploration ) . We take both ensemble averages across spike-train realizations and temporal averages within a time window of length T . The averaging time length T separates the time scale of neural activation ( of the order of the width τW of the learning window W ) from the time scale τstr of structure formation , i . e . , τW ≪ T ≪ τstr . Because τstr is inversely proportional to the learning rate η ( Eq 29 ) , such averaging is always possible provided that the learning rate η is small enough . In other words , we assume that within a time T , the virtual rat has roughly explored the entire environment , but the synaptic weights did not change considerably . In this case , the dynamics of the synaptic weights wi is approximated by a drift-diffusion process , where the deterministic drift term reads [74] η - 1 d w ¯ i d t = ( β - α w ¯ i ) 〈 S i in ( t ) 〉 ¯ + ∫ - ∞ ∞ d s W ( s ) 〈 S i in ( t + s ) S out ( t ) ¯ 〉 ( 12 ) with w ¯ i ≥ 0 . The functions S i in and Sout denote input and output spike trains ( Sec Model of neural activity ) , the angular brackets denote ensemble averages over input and output spike trains , and the overbars denote temporal averages , i . e . , f ¯ ( t ) ≔ T - 1 ∫ t - T t d s f ( s ) . Following Kempter et al . [74] we derive 〈 S i in ( t + s ) S out ( t ) ¯ 〉 = 〈 S i in ( t + s ) 〉 〈 S out ( t ) 〉 ¯ + w ¯ i 〈 S i in ( t ) 〉 ¯ K ( - s ) , ( 13 ) where the ensemble averages read 〈 S i in ( t ) 〉 = r i in ( t ) ( 14 ) 〈 S out ( t ) 〉 = 〈 r out ( t ) 〉 = ( 1 ) r 0 + ∫ 0 ∞ d τ K ( τ ) ∑ j = 1 N w j r j in ( t - τ ) . ( 15 ) Finally , from Eqs 12–15 we obtain η - 1 d d t w ¯ i = ∑ j = 1 N C i j w ¯ j - a w ¯ i + b with w ¯ i ≥ 0 ( 16 ) where we defined C i j ≔ ∫ 0 ∞ d τ K ( τ ) ∫ - ∞ ∞ d s W ( s ) r i in ( t + s ) r j in ( t - τ ) ¯ ( 17 ) a ≔ r av [ α - ∫ - ∞ ∞ d s W ( s ) K ( - s ) ] ( 18 ) b ≔ r av ( W tot r 0 + β ) . ( 19 ) Note that in deriving Eq 16 we approximated the temporal average of the input rates r i in ¯ with the spatial average rav of the input tuning curves Ψ i in . This approximation holds with the assumption that in a time T the virtual rat roughly covers the entire space evenly . By ignoring the non-linear weight constraints w ¯ i ≥ 0 , the average weight dynamics is described by a linear system with coupling terms Cij ( Eq 16 ) . The coefficients Cij are given by the temporal correlations of the input rates r i in and r j in , filtered by the adaptation kernel K and the STDP learning window W ( Eq 17 ) . To further simplify the calculations , we assume that the low-pass filtering introduced by the STDP learning window can be neglected for the purpose of studying pattern formation . In particular , we assume that the learning window W decays much faster than the changes in the input correlations r i in ( t + s ) r j in ( t - τ ) ¯ ( Eq 17 ) , which holds for τW ≪ σ/v . In this case , we obtain C i j ≈ W tot ∫ 0 ∞ d τ K ( τ ) r i in ( t ) r j in ( t - τ ) ¯ ( 20 ) where Wtot is the integral of the learning window ( Eq 5 ) . Finally , by assuming smooth virtual-rat trajectories at constant speed v , the correlation matrix Cij can be estimated solely from the input tuning curves Ψ i in and the adaptation kernel K ( Sec Input correlation for general inputs , Eq 47 ) : where L2 is the area explored by the virtual rat . In Eq 21 , the matrix element Cij is obtained by integrating the spatial cross-correlation of the input tuning curves Ψ i in ⋆ Ψ j in over circles of radius τv , and by weighting each integral with the amplitude of the adaptation kernel K at time τ . Note that Eq 21 holds for generic spatial tuning curves Ψ i in . In Sec Mathematical results on grid-pattern formation we derived an equation for the average dynamics of the synaptic weights wi , under the STDP learning rule and the stochastic activation of input and output neurons ( Eq 16 ) . In the case of spatially-regular inputs , we then computed the systems eigenvalue spectrum λ ( k ) in terms of the Gaussian input tuning curve G and the temporal adaptation kernel K ( Eq 32 ) . We showed that periodic spatial patterns could emerge if the eigenvalue spectrum λ ( k ) had a global maximum λmax > 0 at a frequency kmax > 0 ( Fig 4 ) . Fig 5A shows the eigenvalue spectrum λ ( k ) for a choice of the parameter values such that this condition is satisfied . With adaptation time constants τS = 0 . 1 s and τL = 0 . 16 s ( Eq 2 , Fig 1 , star in Fig 4 ) , and Gaussian input receptive fields of size σ = 6 . 25 cm ( Eq 9 ) , the eigenvalue spectrum peaks at the critical frequency kmax = 3 m−1 . In the following , we simulate the emergence of grid-like patterns in this scenario .
We assumed that the feed-forward input activity is spatially tuned . Such spatial tuning could be provided by hippocampal place cells , or by other cortical or sub-cortical structures with less regular spatial firing . From a theoretical point of view , we find that grid patterns emerge faster with place-cell-like inputs , i . e . , with inputs having a single receptive field in space . From an anatomical point of view , both scenarios seem plausible . On the one hand , grid-cell activity requires excitatory drive from the hippocampus [102] , which projects to the deep layers of the mEC [103 , 104] where grid cells are found [23 , 60] . On the other hand , parasubicular inputs target layer II of the mEC [61 , 105–108] where grid cells are most abundant [23 , 60] . Although a small fraction of parasubicular cells already shows grid-like tuning [60 , 61] , the activity in parasubiculum is often characterized by multiple spatially-irregular fields [57–61] similar to those assumed in our model ( Fig 9A ) . That grid-cell activity could originate from parasubicular inputs is further supported by the detailed layout of the entorhinal circuit . Layer II principal neurons segregate into stellate and pyramidal cells , which are distinguished by their morphology , intrinsic properties [69] , and immunoreactivity [109–111] . Interestingly , pyramidal-cell somata cluster into anatomical patches [110 , 111] , which are preferentially targeted by parasubicular axons [61]; and the spiking activity in parasubiculum precedes the activity of layer II pyramidal cells by a few degrees in the theta cycle [61] . Such a network configuration suggests that grid patterns may originate in the layer II pyramidal cells via parasubicular inputs , and be inherited by the stellate cells via feed-forward projections . Consistent with this view is that both stellate and pyramidal cells show grid spatial tuning [55] , and that direct intra-laminar connections are found from pyramidal onto stellate cells and not vice-versa [112 , 113]; but see [114] . In summary , our model is consistent with entorhinal grid-cell activity originating either in the superficial layers via parasubicular input or in the deep layers via hippocampal input . It is also possible that multiple sites of origin exist , and that grid-like tuning is inherited—and even sharpened—via feed-forward projections from the deep to the superfical layers [115–119] or from the superficial to the deep layers [104] . Our grid-cell model relies on the presence of a spike-rate adaptation mechanism . Spike-rate adaptation has been observed throughout the cortex [62] , and is prominent in layer II of the mEC , in both stellate and pyramidal neurons [69 , 70] . Yoshida et al . [71] also reported a dorso-ventral gradient in the adaptation strength of layer II entorhinal cells . However , because adaptation was found to be stronger ventrally than dorsally , Yoshida et al . [71] interpreted their results as evidence against grid-cell models based on adaptation . Yet the critical variable controlling the grid scale is not the strength of adaptation , but rather its temporal dynamics ( Fig 7 ) , which was not systematically analyzed [71]; see also [101] for a similar discussion on this point . We modeled spike-rate adaptation by applying a temporal kernel K to the input spike trains ( Eq 1 ) . The kernel K , was composed of a brief depolarization peak and a slower hyper-polarizing potential ( on a time scale of hundreds of milliseconds ) . Such a slow hyper-polarizing potential reduced the output firing rate in response to persistent excitation , and it filtered the input activity in a low-frequency band ( i . e . with a resonance frequency of about 1 Hz , see Fig 1 ) . The shape of the kernel was motivated by long-lasting hyper-polarizing potentials following excitatory post-synaptic potentials found in hippocampal CA1 pyramidal neurons [120] , although similar responses have not been observed in the mEC yet . However , the formation of grid-cell patterns could rely on any other cellular or synaptic mechanism that effectively acts as a band-pass filter on the input activity . A candidate mechanism is the after-spike hyperpolarizing potential ( AHP ) . AHPs are indeed observed in the superficial layers of the mEC where single action potentials are followed by both a fast ( 2-5 ms ) and a medium AHP ( 20-100 ms ) [69 , 97 , 121] . To assess whether such hyperpolarizing potentials could underlie grid-pattern formation , we extended our model to account for AHPs ( Sec Pattern formation with after-spike potentials ) . However , we found that grids at typical spatial scales cannot be obtained by AHPs alone . Yet after-spike potentials could amplify the effects of a band-pass filtering mechanism that is already present at the input . Spike-rate adaptation could also rely on hyperpolarization-activated cation currents ( Ih ) , which depend on HCN channels [122 , 123] . Fast Ih currents ( mediated by HCN1 channels ) have been shown to control the theta-frequency resonance of entorhinal stellate cells in vitro [95 , 97 , 124–126] . Instead , slower Ih currents ( mediated by HCN2-4 channels ) could generate in entorhinal cells the low-frequency resonance assumed by our model ( Fig 1B ) . We propose that grid-cell patterns emerge from a synaptic reorganization of the mEC network , which is assumed to be plastic . This is in line with both LTP and LTD being reported in the entorhinal cortex [121 , 127–130] , but see also [131] . Additionally , asymmetric STDP was observed in the mEC [76] . Although we used a symmetric learning window in our model , the exact window shape has little effect on grid-pattern formation , provided that its temporal width ( on the order of tens of milliseconds ) is much shorter than the correlation length of the input activities ( on the order of hundreds of milliseconds ) . Structure formation via Hebbian learning is typically a slow process . In our model , grid-like patterns emerge on a time scale that is inversely proportional to the learning rate η and to the maximal eigenvalue λmax ( Eq 29 ) . The latter depends on the spatial density ρ = N/L2 of input receptive fields , on the integral Wtot of the learning window , on the shapes of the input-tuning curves G , and on the dynamics of the adaptation kernel K ( Eq 32 ) . Because most of these quantities are under-constrained by empirical data , a direct comparison with experimental time scales remains difficult . Yet learning shall be slow enough such that the input correlations that drive structure formation dominate over random fluctuations of the synaptic weights , which are due to the random walk of the virtual rat and the shot noise of the stochastic spiking . In our simulations , we find that this requires tens of hours of spatial exploration ( Fig 6A ) . Such slow process may seem in contrast with grid-cell activity appearing immediately in a novel environment [1 , 132] . However , grid-like tuning may not need to be learned in each environment anew , but rather recalled—and possibly refined—from the experience of similar environments explored in the past . Although hippocampal place cells [133 , 134] and entorhinal non-grid spatial cells [56] seem to remap completely in novel spaces , pattern formation could still leverage on residual correlations across environments that are hardly observable from the simultaneous recordings of only a few tens of neurons . Additionally , grid-cell learning could generalize across spatial contexts through border and boundary-vector inputs [84 , 85] , which are invariant across environments . We suggest that a structure in the synaptic weights may be formed during the animal’s ontogenetic development , i . e . , within a two-week period after the animal leaves the nest [135–137] . Consistent with this hypothesis is that stable spatial firing is observed before grid-cell maturation , e . g . , hippocampal place cells develop prior to grid cells [135 , 136] and irregular spatial cells are present before grid cells [137] . We studied the emergence of grid patterns in a purely single-cell model , ignoring any network-level interaction between the neurons . However , because excitatory and inhibitory recurrent circuits have been described in the mEC [19 , 20 , 112 , 113 , 138] , grid cells are likely to be mutually coupled [139 , 140] . Such recurrent connections could explain the modular organization of grid-cell properties [93 , 101] and their coherent responses to environmental changes [139] . Feedback interactions within a module may also amplify an initially broad grid-tuning given by the feed-forward inputs , similarly to the sharpening of receptive fields in visual cortex [141 , 142] . Finally , recurrent dynamics may sustain grid-like activity when the feed-forward inputs are temporally untuned , like in attractor models [14] . Still , spatially-tuned feed-forward inputs could be required for the initial formation of grid-like patterns [see e . g . 21] . Our work—and the one by Kropff and Treves [49]—belong to a broad category of grid-cell models based on spatially-tuned feed-forward inputs and Hebbian synaptic plasticity [63–67] . In all these models , periodic spatial patterns arise via a common underlying principle: the input correlations that drive the dynamics of the synaptic weights have the form of a Mexican-hat kernel ( Fig 3 ) . What distinguishes the models among each other—and generates distinct predictions—is the specific mechanism by which such Mexican-hat interactions are obtained . In our model , a Mexican-hat kernel results from the intrinsic adaptation dynamics of the output neuron , which controls the grid scale directly ( Fig 7 ) . By contrast , in the models by Castro and Aguiar [63] and Stepanyuk [64] , Mexican-hat correlations arise from the learning rule itself , i . e . , by assuming that synaptic plasticity switches between LTP and LTD based on pre- and post-synaptic activities [143] . In this case , the grid spatial scale shall be affected by interfering with the learning rule . In a different model , Dordek et al . [65] obtain Mexican-hat correlations by constraining the input activity to be effectively zero-mean . The authors discuss that such a zero-mean constraint could originate either from lateral inhibition or from a zero-mean temporal filter controlling the output activity of the neuron . In the latter case , the model by Dordek et al . [65] is analogous to the present one . We note , however , that effectively zero-mean inputs are neither necessary nor sufficient for grid patterns to emerge . Instead , pattern formation depends on the dynamics of the temporal filter and on the shape of the input tuning curves , but not on their means . This can be easily understood by considering the system’s eigenvalue spectrum in Fourier space ( Eq 30 ) , where the zero-frequency mode ( k = 0 ) is not relevant for the emergence of spatially-periodic patterns . Also note that the smallest grid scales in our model are obtained with negative-mean temporal filters ( Fig 4 ) . Yet our results agree with the ones of Dordek et al . [65] in that the non-linearity introduced by imposing non-negative synaptic weights is sufficient for a triangular symmetry to emerge . Alternatively , Mexican-hat correlations could emerge from phase-precessing feed-forward inputs [66] . In this case , grid-cell activity shall be impaired when phase precession is disrupted . Finally , Weber and Sprekeler [67] proposed a model where the interplay between spatially-narrow feed-forward excitation and spatially-broad feed-forward inhibition generates a Mexican-hat kernel . This model predicts that the grid scale shall be affected by manipulating inhibitory inputs to the mEC . We presented a single-cell model for the origin of grid-cell activity based on Hebbian synaptic plasticity and spike-rate adaptation . Our work builds upon the model by Kropff and Treves [49] and improves its original formulation in several aspects: 1 ) grid-like patterns emerge form a purely single-cell mechanism independently of any network-level interaction; 2 ) neuronal activities are spike-based and stochastic; 3 ) the input synaptic weights are purely excitatory; 4 ) the dynamics of the synaptic weights is studied analytically and linked to classical Turing-like patterns . The present model makes the following experimental predictions . First , grid-cell patterns shall be affected by disrupting synaptic plasticity during ontogenetic development , which is consistent with preliminary data from Dagslott et al . [144] . Second , adult grid-cell activity shall be influenced by systematic behavioral or environmental biases in the first weeks of spatial exploration , e . g . , by rising animals in environments without boundaries or with non-zero surface curvature [52 , 145] . Third , the grid scale shall be affected by three factors: 1 ) the spatial tuning-width of the feed-forward inputs; 2 ) the average speed of the rat during ontogenetic development; 3 ) the time constant of the recovery from spike-rate adaptation . Fourth , grids at larger scales shall develop faster as compared to grids at smaller scales ( Fig 4 ) . We believe that manipulations of the intrinsic adaptation properties of single cells are key to distinguish our model from other feed-forward models based on Hebbian learning ( Sec Related models ) . To this end , further experimental work shall be devoted to pinpoint the biophysical mechanisms underlying adaptation in the mEC . Extensions of the present model could also explain how the geometry of the enclosure affects grid-cell symmetry [99] , and how grid-like tuning emerges in non-spatial contexts [146 , 147] . To conclude , our study contributes to a better understanding of the fundamental principles governing grid-cell activity , and lays the groundwork for more biophysically-realistic grid-cell models .
Here we derive the dynamics of the mean synaptic weight w av = N - 1 ∑ i = 1 N w ¯ i for a neuron with N synapses and temporally-averaged weights w ¯ i . We recall the weight dynamics in Eq 16 η - 1 d d t w ¯ i = ∑ j = 1 N C i j w ¯ j - a w ¯ i + b with w ¯ i ≥ 0 . ( 34 ) By taking the average over the index i at both sides of Eq 34 we obtain η - 1 d d t w av = ( N C av - a ) w av + b ( 35 ) where we defined the mean correlation Cav ≔ N−2∑ij Cij . Note that we used the property ∑j Cij = NCav for all i , which holds true for translation-invariant inputs . Therefore , for NCav < a , the mean weight wav decays exponentially with time constant τ av ≔ 1 η ( a - N C av ) ( 36 ) to the normalization level w av ∞ ≔ b a - N C av . ( 37 ) In this section we estimate the input correlation matrix C i j ≈ ( 20 ) W tot ∫ 0 ∞ d τ K ( τ ) r i in ( t ) r j in ( t - τ ) ¯ with i , j = 1 , … , N ( 38 ) for general spatial tuning curves Ψ i in and smooth movement trajectories of the virtual rat ( Sec Model of spatial exploration ) . We start by computing the temporal average r i in ( t ) r j in ( t - τ ) ¯ of the product between the input activities r i in ( t ) and the delayed input activities r j in ( t - τ ) . We assume that the stochastic process Xt controlling the virtual-rat trajectory ( Eq 11 ) is ergodic with respect to the auto-covariance , i . e . , 1 T ∫ 0 T d t x t x t - τ = 〈 X t , X t - τ 〉 for T → ∞ ( 39 ) where the angular brackets denote statistical expectation . By using this ergodicity property ( Eq 39 ) and the spatial tuning of the inputs ( Eq 7 ) , we derive r i in ( t ) r j in ( t - τ ) ¯ = Ψ i in ( x t ) Ψ j in ( x t - τ ) ¯ ≈ 〈 Ψ i in ( X t ) Ψ j in ( X t - τ ) 〉 . ( 40 ) Note that Eq 40 is only valid in an approximate sense because Eq 39 assumes T → ∞ , but the averaging time window has finite length T ≪ τstr where τstr is structure-formation time constant ( Eq 29 ) . From Eq 40 follows r i in ( t ) r j in ( t - τ ) ¯ ≈ ⟨ Ψ i in ( X t ) Ψ j in ( X t - τ ) ⟩ ( 41 ) ≔ ∫ ∫ d x d x ′ Ψ i in ( x ) Ψ j in ( x ′ ) p ( x , t , x ′ , t - τ ) ( 42 ) = ∫ ∫ d x d x ′ Ψ i in ( x ) Ψ j in ( x ′ ) p ( x ′ , t - τ | x , t ) p ( x , t ) ( 43 ) = 1 L 2 ∫ ∫ d x d x ′ Ψ i in ( x ) Ψ j in ( x ′ ) p ( x ′ , t - τ | x , t ) ( 44 ) where the integrals in Eqs 42–44 run over all positions in the environment ( a square arena of side-length L ) , and p ( x , t , x′ , t − τ ) is the joint probability density of the virtual rat being at position x at time t and at position x′ at time t − τ . From Eqs 43 to 44 , we used the fact that , for large times t , the virtual rat has equal probability of being in any position x , i . e . , p ( x , t ) = 1/L2 . Eq 44 shows that the temporal average r i in ( t ) r j in ( t - τ ) ¯ can be estimated from the input tuning curves Ψ i in and Ψ j in , and the conditional probability density p ( x′ , t − τ|x , t ) . This conditional probability density has not yet been solved for correlated random walks in two dimensions [148] . Nevertheless , an additional approximation is possible . Because the temporal average r i in ( t ) r j in ( t - τ ) ¯ is weighted by the adaptation adaptation kernel K ( τ ) ( Eq 38 ) , and K ( τ ) is negligible for τ > τmax ≈ 5τL ( Eq 2 ) , we are interested in the conditional probability p ( x′ , t − τ|x , t ) only at lags τ < τmax . In this case , for movement trajectories that are sufficiently smooth , we can assume that in a time τ the virtual rat has moved to a position x at distance |x − x′| = τv from the initial position x′ , that is p ( x ′ , t - τ | x , t ) ≈ δ ( | x - x ′ | - τ v ) 2 π τ v ( 45 ) where v is the speed of the virtual rat ( Eq 11 ) , and the denominator ensures that ∫ dx′p ( x′ , t − τ|x , t ) = 1; see also Fig 2 for exemplary virtual-rat trajectories in this scenario . We now use Eq 45 in Eq 44 , and let z ≔ x′ − x: ( 46 ) From Eq 46 , the temporal average r i in ( t ) r j in ( t - τ ) ¯ is approximated by the integral of the spatial cross-correlation Ψ i in ⋆ Ψ j in over a circle of radius τv . Finally , by using Eq 46 in Eq 20 , we obtain ( 47 ) In this section we compute the input correlation function C and its Fourier spectrum C ^ in the case of spatially-regular inputs ( see Sec Weight dynamics for spatially-regular inputs ) . First , we rewrite the input correlation matrix Cij in Eq 21 as a continuous function C ( r , r′ ) by labeling neurons according to their receptive-field centers r and r′: where Ψ r in ( x ) ≔ G ( | x - r | ) is a Gaussian input tuning curve centered at position r ( Eq 9 ) . Because the inputs are translation invariant , the correlation function C depends only on the translation vector u ≔ r − r′: where Ψ 0 in ( x ) ≔ G ( | x | ) is the tuning curve centered at the origin 0 = ( 0 , 0 ) . Next , we substitute in Eq 50 the definition of the integral operator in Eq 46: C ( u ) ≈ W tot L 2 ∫ 0 ∞ d τ K ( τ ) ∫ d z δ ( | z | - τ v ) 2 π τ v Ψ 0 in ⋆ Ψ 0 in | u + z . ( 51 ) It is easy to see that the auto-correlation of a Gaussian is still a Gaussian: Ψ 0 in ⋆ Ψ 0 in | u = L 4 r av 2 4 π σ 2 exp ( - | u | 2 4 σ 2 ) ( 52 ) from which we derive Ψ 0 in ⋆ Ψ 0 in | u + z = L 4 r av 2 4 π σ 2 [ exp ( - | u | 2 + | z | 2 4 σ 2 ) exp ( - | u | | z | cos ( φ ) 2 σ 2 ) ] ( 53 ) where φ is the angle between the vectors u and z . Finally , by expressing in polar coordinates the vector z ≔ |z|[cos ( φ ) , sin ( φ ) ] , from Eqs 51 and 53 we obtain C ( u ) ≈ W tot L 2 r av 2 4 π σ 2 ∫ 0 ∞ d τ K ( τ ) exp ( - | u | 2 + ( τ v ) 2 4 σ 2 ) I 0 ( - | u | τ v 2 σ 2 ) ( 54 ) where I 0 ( x ) ≔ 1 / ( 2 π ) ∫ 0 2 π d φ exp ( x cos ( φ ) ) is the zeroth-order modified Bessel function of the first kind . In this section we estimate the expected eigenvalue spectrum 〈λirr ( k ) 〉 for spatially-irregular inputs ( Secs Spatially-irregular inputs and Pattern formation with spatially-irregular inputs ) . We recall that , for spatially-regular inputs , in Sec Mathematical results on grid-pattern formation we obtained ( Eq 32 ) : λ ( k ) ≈ ρ W tot L 2 4 π 2 G ˜ 2 ( k ) ︸ = ( 58 ) | Ψ ^ 0 in ( k ) | 2 K ˜ sp ( k ) - a with k ≔ | k | ≠ 0 ( 65 ) where G ˜ and K ˜ sp are the zeroth-order Hankel transforms of the input tuning curve G ( Eq 9 ) and of the equivalent adaptation kernel in space Ksp ( Eqs 31 and 61 ) . Note that the parameters ρ , L , Wtot , and a do not depend on k . From Eq 65 , the eigenvalue spectrum λ ( k ) is linearly-related to the input power spectrum | Ψ ^ 0 in ( k ) | 2 where Ψ 0 in ( x ) ≔ G ( | x | ) is an input tuning curve centered at the origin 0 ≔ ( 0 , 0 ) ( Sec Input correlation for spatially-regular inputs ) . Here , in analogy to Eq 65 , we assume that the expected eigenvalue spectrum 〈λirr ( k ) 〉 for spatially-irregular inputs is linearly-related to the expected input power 〈 | Ψ ^ p in ( k ) | 2 〉 , that is , 〈 λ irr ( k ) 〉 ≈ ρ W tot L 2 ⟨ | Ψ ^ p in ( k ) | 2 ⟩ K ˜ sp ( k ) - a with k ≠ 0 ( 66 ) where Ψ ^ p in ( k ) is the two-dimensional Fourier transform of the spatially-irregular tuning curve Ψ p in ( x ) , and the angular brackets denote statistical expectation across input realizations ( see Eq 56 for a definition of the two-dimensional Fourier transform ) . The validity of this assumption is confirmed numerically at the end of this section . Let us compute the expected input power spectrum 〈 | Ψ ^ p in ( k ) | 2 〉 . We recall that the input maps Ψ p in ( x ) are obtained by the superimposing M Gaussian receptive fields ( Eq 10 ) Ψ p in ( x ) ≔ 1 β p ∑ m = 1 M A p m G ( | x - r p m | ) for p = 1 , 2 , … , N ( 67 ) with G ( r ) = ( 9 ) L 2 r av 2 π σ 2 exp ( - r 2 2 σ 2 ) and β p ≔ ∑ m = 1 M A p m . ( 68 ) The field amplitudes Apm ≥ 0 are uniformly distributed in the range ( 0 , 1 ) , and the receptive field centers rpm are uniformly distributed in the environment ( see Fig 9A for examples ) . From Eq 67 we derive | Ψ ^ p in ( k ) | = 2 π β p G ˜ ( k ) | ∑ m = 1 M A p m exp ( − 2 π j r p m · k ) | ︸ ≕ α p ( 69 ) where G ˜ ( k ) is the zeroth-order Hankel transform of the Gaussian function G ( r ) . In deriving Eq 69 , we used the shift property of the Fourier transform and the equivalence between the Fourier and the zeroth-order Hankel transforms for circularly-symmetric functions ( Eq 58 ) . Finally , from Eq 69 we obtain ⟨ | Ψ ^ p in ( k ) | 2 ⟩ = 4 π 2 G ˜ 2 ( k ) Φ with Φ ≔ ⟨ α p 2 β p 2 ⟩ . ( 70 ) Therefore , for spatially-irregular inputs , the expected power spectrum 〈 | Ψ ^ p in ( k ) | 2 〉 is proportional to the power spectrum 4 π 2 G ˜ 2 ( k ) of a single Gaussian G with scale factor Φ ≥ 0 . Note that for |k| = 0 we obtain Φ = 1 ( Eqs 69 and 70 ) , which means that the average rate rav is independent of the number M of input receptive fields and their specific spatial arrangement . Using Eq 70 in Eq 66 yields 〈 λ irr ( k ) 〉 ≈ ρ W tot L 2 4 π 2 G ˜ 2 ( k ) K ˜ sp ( k ) Φ - a with k ≠ 0 . ( 71 ) Finally , from Eqs 65 and 71 we find ( Eq 33 ) 〈 λ irr ( k ) 〉 ≈ Φ λ ( k ) + a ( 1 - Φ ) . ( 72 ) In the next section we estimate the scale factor Φ for |k| > 0 . Here we study whether grid-like patterns could emerge by means of after-spike hyperpolarizing potentials ( see discussion in Sec Spike-rate adaptation ) . To this end , we consider a model of the output neural activity that is alternative to the one presented in the main text ( Sec Model of neural activity , Eq 1 ) . We model input post-synaptic potentials ( PSPs ) with a kernel Kin applied to the input spike trains S j in , and we model output after-spike hyperpolarizing potentials ( AHPs ) with a kernel Kout applied to the output spike train Sout: r out ( t ) ≔ r 0 + ∫ 0 ∞ d s K out ( s ) S out ( t - s ) + ∫ 0 ∞ d τ K in ( τ ) ∑ j = 1 N w j S j in ( t - τ ) ( 84 ) where r0 ≥ 0 is a baseline firing rate . First , we show that the average dynamics of Eq 84 can be rewritten in terms of an equivalent kernel Keq applied to the input spikes only . We average Eq 84 across input and output spike train realizations: 〈 r out ( t ) 〉 = r 0 + ∫ 0 ∞ d s K out ( s ) 〈 r out ( t - s ) 〉 + ∫ 0 ∞ d τ K in ( τ ) ∑ j = 1 N w j r j in ( t - τ ) . ( 85 ) And by taking the Fourier transform f ^ ( ω ) ≔ ∫ d t f ( t ) exp ( - j ω t ) ; f ( t ) = 1 2 π ∫ d ω f ^ ( ω ) exp ( j ω t ) ( 86 ) at both sides of Eq 85 we obtain 〈 r ^ out ( ω ) 〉 = r 0 δ ( ω ) + K ^ in ( ω ) 1 - K ^ out ( ω ) ∑ j = 1 N w j r ^ j in ( ω ) . ( 87 ) From Eqs 85 to 87 we assumed that the input and the output kernels are causal , i . e . , Kin , out ( t ) = 0 for t < 0 , and that the output kernel has integral different from 1 , i . e . , K ^ out ( 0 ) = ∫ 0 ∞ d t K out ( t ) ≠ 1 . Finally , by defining the equivalent filter K ^ eq ( ω ) ≔ K ^ in ( ω ) 1 - K ^ out ( ω ) , ( 88 ) the inverse Fourier transform of Eq 87 reads 〈 r out ( t ) 〉 = r 0 + ∫ 0 ∞ d τ K eq ( τ ) ∑ j = 1 N w j r j in ( t - τ ) , ( 89 ) which is equivalent to Eq 15 with Keq = K . Next , we compute the equivalent filter Keq for a simple choice of the input and output kernels K in ( t ) ≔ { 1 τ in exp ( - t τ in ) for t ≥ 0 0 for t < 0 ( 90 ) and K out ( t ) ≔ { - μ out τ out exp ( - t τ out ) for t ≥ 0 0 for t < 0 ( 91 ) where τin , τout > 0 are decay time constants , and the parameter μout > 0 scales the integral of the output kernel ∫ 0 ∞ d t K out ( t ) = - μ out . We assume that the input kernel Kin ( modeling an incoming PSP ) decays faster than the output kernel Kout ( modeling an output AHP ) , i . e . , τin < τout . From the definition of the filter Keq in Eq 88 we obtain K ^ eq ( ω ) = 1 / τ in 1 / τ in - ( 1 + μ out ) / τ out ︸ ≕ H [ 1 / τ in - 1 / τ out 1 / τ in + j ω - μ out / τ out ( 1 + μ out ) / τ out + j ω ] ( 92 ) where we used K ^ in ( ω ) = 1 / τ in 1 / τ in + j ω and K ^ out ( ω ) = - μ out / τ out 1 / τ out + j ω . ( 93 ) Finally , the inverse Fourier transform of Eq 92 reads K eq ( t ) = H · [ ( 1 τ in - 1 τ out ) exp ( - t τ in ) - μ out τ out exp ( - t τ out / ( 1 + μ out ) ) ] ( 94 ) for t ≥ 0 and Keq ( t ) = 0 for t < 0 . Eq 94 shows that the equivalent filter Keq is a difference of two exponentials , similarly to the kernel K in Eq 2 . Note however that the two exponentials are scaled differently as compared to the original filter K . Additionally , if the integral of the output kernel is negative , the integral of the equivalent filter is always positive ( Eq 88 with ω = 0 ) . To test whether spatially-periodic patterns could still emerge in this scenario , we compute the eigenvalue spectrum λ ( k ) and the critical spatial frequency kmax by using Eqs 31 and 32 with K = Keq . Surprisingly , we find that typical grid scales ( e . g . , kmax > 2 m−1 ) are obtained for output-kernel time constants of the order of seconds , which seem biologically unrealistic ( Fig 11 ) . Therefore , we conclude that AHPs alone are not sufficient to generate grid-like patterns . Nevertheless , AHPs could still support structure formation by amplifying the effects of a band-pass filter that is already present at the input . Model parameters and derived quantities are summarized in Tables 1 and 2 . | When an animal explores an environment , grid cells activate at multiple spatial locations that form a strikingly-regular triangular pattern . Grid cells are believed to support high-level cognitive functions such as navigation and spatial memory , yet the origin of their activity remains unclear . Here we focus on the hypothesis that grid patterns emerge from a competition between persistent excitation by spatially-selective inputs and the reluctance of a neuron to fire for long stretches of time . Using a computational model , we generate grid-like activity by only spatially-irregular inputs , Hebbian synaptic plasticity , and neuronal adaptation . We study how the geometry of the output patterns depends on the spatial tuning of the inputs and the adaptation properties of single cells . The present work sheds light on the origin of grid-cell firing and makes specific predictions that could be tested experimentally . | [
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"pat... | 2017 | A single-cell spiking model for the origin of grid-cell patterns |
Human coronaviruses ( HCoV ) are respiratory pathogens that may be associated with the development of neurological diseases , in view of their neuroinvasive and neurotropic properties . The viral spike ( S ) glycoprotein is a major virulence factor for several coronavirus species , including the OC43 strain of HCoV ( HCoV-OC43 ) . In an attempt to study the role of this protein in virus spread within the central nervous system ( CNS ) and neurovirulence , as well as to identify amino acid residues important for such functions , we compared the sequence of the S gene found in the laboratory reference strain HCoV-OC43 ATCC VR-759 to S sequences of viruses detected in clinical isolates from the human respiratory tract . We identified one predominant mutation at amino acid 758 ( from RRSR↓ G758 to RRSR↓R758 ) , which introduces a putative furin-like cleavage ( ↓ ) site . Using a molecular cDNA infectious clone to generate a corresponding recombinant virus , we show for the first time that such point mutation in the HCoV-OC43 S glycoprotein creates a functional cleavage site between the S1 and S2 portions of the S protein . While the corresponding recombinant virus retained its neuroinvasive properties , this mutation led to decreased neurovirulence while potentially modifying the mode of virus spread , likely leading to a limited dissemination within the CNS . Taken together , these results are consistent with the adaptation of HCoV-OC43 to the CNS environment , resulting from the selection of quasi-species harboring mutations that lead to amino acid changes in viral genes , like the S gene in HCoV-OC43 , which may contribute to a more efficient establishment of a less pathogenic but persistent CNS infection . This adaptative mechanism could potentially be associated with human encephalitis or other neurological degenerative pathologies .
Human coronaviruses ( HCoV ) are enveloped positive-stranded RNA viruses belonging to the family Coronaviridae in the order Nidovirales and are mostly responsible for upper respiratory tract infections [1] . Being opportunistic pathogens , they have also been associated with other more serious human pathologies , such as pneumonia and bronchiolitis , and even meningitis [2–4] in more vulnerable populations . Moreover , at least HCoV-229E and HCoV-OC43 are naturally neuroinvasive and neurotropic in humans [5] . Indeed , we have previously reported that HCoV can infect and persist in human neural cells [6–8] , and in human brains [9] . Moreover , the OC43 strain ( HCoV-OC43 ) induces encephalitis in susceptible mice , with neurons being the main target of infection [10 , 11] . Enveloped viruses use different types of proteins to induce fusion of the host-cell membrane to their own in order to initiate infection . For coronaviruses , the spike ( S ) protein is responsible for cell entry [12] , and was shown to be a major factor of virulence in the central nervous system ( CNS ) for several coronavirus species , including HCoV-OC43 . We previously reported that persistent HCoV-OC43 infections of human neural cell lines led to the appearance of predominant point mutations in the putative receptor-binding domain of the S glycoprotein gene [13] and that these mutations were sufficient to significantly increase neurovirulence and modify neuropathology in BALB/c mice [14] . In order to identify amino acid residues in the S glycoprotein that are involved in viral spread within the CNS , we compared the sequence of the gene encoding the viral S protein in the laboratory reference strain HCoV-OC43 ( ATCC VR-759 ) with sequences of the S gene in viruses detected in clinical isolates from sputum of upper and lower respiratory tract of seven children , aged 3 to 36 months , admitted to the University Hospital of Caen , France , in 2003 [15] , as well as with all S protein sequences found in the NCBI data bank . This characterization led to the identification of predominant mutations , including one at the amino acid Gly758 , which introduces a putative furin-like protease cleavage site RRSR↓R758 in the viral S protein [16] . Several class 1 viral fusion proteins , such as the coronavirus S protein , are proteolytically processed during infection of the host cell , a mechanism that is often essential for the initiation of infection of receptor-bearing cells , tissue tropism and in eventual pathogenesis [17–20] . Moreover , its cleavage by different types of host proteases , including furin-like proteases designated proprotein convertases ( PCs ) that cleave at paired basic residues [20] are involved in various steps of coronavirus infection [21–23] . In the present study , we show for the first time , that while the S glycoprotein of the laboratory reference strain HCoV-OC43 ATCC VR-759 is not cleaved by host cell proteases , the sequences of more than 60 clinical isolates reveal a common G758R resulting from a single nucleotide polymorphism ( SNP ) in the S gene . This creates a functional PC-cleavage site between the S1 and S2 portions of the viral S glycoprotein , thereby modulating viral spread and neurovirulence in susceptible mice , without affecting the neuroinvasive capacities of the virus or its infectivity ( capacity to infect ) of a neuronal cell line . These results , which suggest that PC-cleavage can be dispensable for efficient infection by HCoV-OC43 , appear surprising compared to other coronaviruses , for which S protein cleavage is required for efficient virus infection [21 , 23 , 24] . Importantly , our results may help to better characterize the possible adaptation of HCoV-OC43 to the CNS environment , which , in the end , results in a decreased neurovirulence potentially associated to a modified spreading and a more efficient mechanism for the establishment of a persistent infection in human CNS , a phenomenon that could influence the severity of human viral encephalitis or exacerbate neurological degenerative pathologies of unknown etiologies .
We first sought to investigate the potential biological function of the viral G758R mutation located in the HCoV-OC43 S gene between the S1 and S2 domains , detected in the viral S protein of several clinical isolates from human sputum of upper and lower respiratory tract . Accordingly , we introduced this mutation in the infectious cDNA clone of HCoV-OC43 ( pBAC-OC43FL ) [25] to produce a recombinant mutated rOC/SG758R virus , and we first studied its neuroinvasive and neurovirulent properties compared to reference rOC/ATCC virus ( Fig 1 ) . For this , 10 day-old BALB/c mice were inoculated by the intranasal ( IN ) route [10 , 14] and survival curves were obtained ( Fig 1A ) . After infection with the reference virus , over half of BALB/c mice died within the first 15 days post-infection , with symptoms of social isolation and hunched backs . In comparison , the viral mutant was less neurovirulent , with about 30% of mortality . Despite this difference in survival , there were no changes in the symptoms induced by the mutant virus compared to reference strain . Mice were also investigated for variation of weight during infection ( Fig 1B ) as previously described [14]: mice infected with mutant virus showed a delay in body weight gain of about 50% at 9 days post-infection compared to control mice . Comparison of survival curves of mice infected by both variants , coupled with weight variations , suggested that the rOC/SG758R variant was less neurovirulent than reference virus rOC/ATCC after inoculation by the IN route . To determine whether the slight difference in neurovirulence between the two viruses could be related to differences in viral replication kinetics within the CNS , brains ( Fig 1C ) and spinal cords ( Fig 1D ) were harvested and infectious virus production was evaluated every 2 days over a period of 21 days post-infection ( dpi ) . Even though the difference in neurovirulence between the two viruses did not correlate with a different amount in production of infectious viral particles in the CNS ( brain and spinal cord ) , there was a delay in viral replication kinetics of the mutant virus compared to reference virus . Viral spread in mouse brain ( Fig 2 ) was also studied with a focus on the olfactory bulb and the hippocampus regions , because we have previously determined that these regions are primarily infected by the reference virus strain [14] . At 5 dpi , viral antigens were already present everywhere in the olfactory bulb infected by the reference wild-type virus ( Fig 2A ) , compared to mutant virus for which antigens were only scarcely distributed . At 7 dpi , the kinetics was restored as the mutant infected this region as efficiently as the reference virus . In the hippocampus , we observed the same trend: no viral antigens were detected in this region for the mutant virus at 7 dpi , whereas the spread of both viruses was similar at 9 dpi ( Fig 2B ) . When viruses had spread to all regions of the brain , activation of astrocytes and microglial cells was evident in all infected regions ( S1 Fig ) . Even though no precise quantitation was performed , a slight increase in the number of astrocytes was observed in the olfactory bulb ( S1A Fig ) and in the hippocampus ( S1B Fig ) of mice infected by the reference virus compared to mutant virus . Activation of microglial cells was evident in the hippocampus region for both variants at 9 dpi ( S1C Fig ) . As the mutant virus S protein harbors a SNP present in respiratory clinical isolates , we also evaluated viral dissemination towards the respiratory tract . Neither infectious virus particles nor viral RNA were detectable in the lungs of all the mice tested . Having demonstrated that both virus variants retained their neuroinvasive and neurovirulent capacities in our mouse model after intranasal ( IN ) inoculation , we sought to study the spreading and neurovirulent capacities of the two recombinant viruses after intracerebral ( IC ) inoculation , as this route results in a more reproducible infection associated with a better control of viral doses introduced into the brain . In order to do so , 21 day-old female BALB/c mice were used [11] and experiments were performed by characterizing mouse survival and weight curves , clinical symptoms of encephalitis and viral replication in brain and spinal cord ( Fig 3 ) . There was a significant difference in survival after inoculation with either virus ( Fig 3A ) : the mutant virus , like the sham control , induced no mortality compared to reference virus , which led to a 20% mortality rate over a period of 21 days . We then measured the weight of mice during the infection ( Fig 3B ) , and observed that there was a significant delay in body weight gain for the reference virus and the mutant virus compared to the sham control between 7 and 11 dpi , which correlates with the survival curves . Using a clinical score scale based on neurological symptoms of mice described in the Materials and Methods section [26] , we next studied the clinical symptoms of mice after injection of both variants ( Fig 3C and 3D ) . The only clinical sign caused by mutant virus was the abnormal flexion of the four limbs ( level 1 ) whereas mice infected by the reference virus developed encephalitis associated with the 4 different levels of clinical scores . No clinical signs were noted for sham mice . Taken together , survival and weight curves coupled with the clinical scores indicate that the mutant virus was less neurovirulent than reference virus after IC inoculation in 21-day old mice . Given our observation that reference virus was more neurovirulent compared to the mutant virus after inoculation by the IC route , we wished to evaluate whether this correlated with a difference in viral replication in the CNS . Brains and spinal cords were harvested and infectious virus titers were assayed every 2 days for a period of 21 dpi ( Fig 3E and 3F ) . The difference in neurovirulence did not correlate with a significant difference in the amount of infectious viral particles in the brain ( Fig 3E ) . However , there was a drastic difference in the production of infectious virus between both variants in the spinal cord ( Fig 3F ) : virus titers of the reference strain ( rOC/ATCC ) were almost identical to what was detected in the brain , whereas the less virulent mutant ( rOC/SG758R ) reached the spinal cord only in one out of thirty infected mice . In this mouse , an important delay and a production of viral infectious particles close to the limit of detection suggested that mutant virus had difficulty reaching this portion of the CNS . Histological examination of infected mice revealed that the infected regions were similar following infection by both viruses in the brain , but that the kinetics were different ( Fig 4 ) . Indeed , as was the case after the IN route of infection , the IC route of infection also led to a delay in viral replication in the olfactory bulb and in the hippocampus , as no viral antigens were detected before 7 dpi for the mutant virus ( compared to 5 dpi for the reference virus ) . As in 10 day-old BALB/c mice infected IN , when virus had spread to all regions of the brain , activation of astrocytes and microglial cells was evident in all infected regions ( S2 Fig ) . As seen in 10 day-old mice after IN inoculation , even though no precise quantitation was performed , a slight increase in the number of astrocytes in the olfactory bulb ( S2A Fig ) and in the hippocampus ( S2B Fig ) could be observed in brains of mice infected by reference virus compared to the mutant virus . The same was observed for microglial cells at 7 dpi in the olfactory bulb ( S2C Fig ) and in the hippocampus ( S2D Fig ) . In order to further study the role of the G758R mutation on the biology of both HCoV-OC43 variants , we first evaluated the kinetics of viral replication and spread within mixed primary CNS cultures from BALB/c mice over a period of 72 h post-infection ( hpi ) . Using immunofluorescence , we observed no change in cell tropism , with neurons remaining the main target of infection by both virus variants ( Fig 5 ) , even though astrocytes could also be infected later in the infection ( S3 Fig ) as we previously reported [10] . Interestingly , we did observe a delay in viral spread in neurons for the mutant virus at 8 and 24 hpi compared to the reference strain ( Fig 5 ) . Interestingly , even though the infection was shown to be productive for both variants in primary CNS cultures from BALB/c mice , there was a significant increase in the total amount of infectious virus in the cell culture supernatant ( free virus ) between 48 and 72 hpi for the mutant virus compared to the reference virus rOC/ATCC ( Fig 6B ) . As the G758R mutation creates a putative furin-like cleavage site [16] in the S glycoprotein previously reported to influence viral infectivity [20–22 , 24] , we wished to evaluate whether cleavage was indeed associated with the delayed spreading in neuronal cells , the increased release of infectious virus and eventually with neurovirulence . As seen in Fig 6 , our data correlated with a much stronger cleavage of the S protein of the rOC/SG758R mutant into S1/S2 fragments , compared to reference virus rOC/ATCC at 24 and 48 hpi ( Fig 6C; whole cell lysate ) , which was even more obvious at 48 hpi in the cell supernatant ( Fig 6D ) . In order to evaluate whether this cleavage of the viral S protein also took place in human cells , we made use of the differentiated LA-N-5 neuronal cell line described in the Materials and Methods section [27] and showed , first , that the kinetics of viral replication was similar to that observed between both viruses in murine primary cells ( Fig 7A and 7B ) , as there was a significant increase of virus release for the rOC/SG758R mutant and , second , that the cleavage of the S protein into S1/S2 fragments was again predominantly detected in the cell culture supernatant ( Fig 7D ) compared to the protein associated with cells ( Fig 7C ) . Again , this cleavage was more evident for mutant than for reference virus . Similar results were obtained with HRT-18 cells . Even though the S protein of HCoV-OC43 reference virus was present mostly in the uncleaved form , our results also show that there are intermediate size bands between the uncleaved and furin-like cleaved forms of the protein . These secondary bands may be unspecific degradation products , but we suggest that among these intermediate size fragments seen on SDS-PAGE , there could be a fragment corresponding to the S protein cleaved at a potential alternative site ( S2’ in Figs 6 , 7 , 8 and S4 , the latter showing corresponding overexpositions ) . In an attempt to determine whether the mutation identified at amino acid 758 ( G758R ) in the viral S protein could indeed create a furin-like cleavage site , we used a cell-permeable general inhibitor of furin-like PCs to investigate the potential involvement of these proteases in the process . Differentiated LA-N-5 cells were infected in the presence of different concentrations of the decanoylated furin-like inhibitor ( dec-RVKR-cmk ) , and at 48 hpi , proteins in the supernatant were harvested and analyzed ( Fig 8 ) . As expected , the S glycoprotein of the reference virus was not cleaved at all ( Fig 8A ) whereas the cleavage of the S protein of mutant virus was inhibited in a dose-dependent manner by dec-RVKR-cmk ( Fig 8B ) . To evaluate whether the kinetics of viral replication was also affected , supernatants were harvested over a period of 48 hpi , and evaluation of infectious viral particles revealed no significant differences ( S5 Fig ) . As the furin-like inhibitor reduced the cleavage of the S protein for the rOC/SG758R variant , we sought to identify which proprotein convertase ( s ) could play a role in cleavage of the S glycoprotein during infection . As shown in Fig 8C and 8D , a synthetic peptide containing the sequence of reference virus ( RRSRG ) , was only cleaved by recombinant furin after 15 hours , likely at RRSR↓G . On the other hand , the synthetic peptide containing the sequence of mutant virus ( RRSRR ) was cleaved in only 30–60 minutes , likely at RRSR↓R , by furin and less so by three additional proprotein convertases: PACE4 , PC5/6 and much less efficiently by PC7 . Having shown that proprotein convertases are able to cleave the viral S glycoprotein in vitro , we sought to determine whether this cleavage could be associated with a change in viral particle morphology for the rOC/SG758R variant compared to reference rOC/ATCC . Our observations by transmission electron microscopy ( TEM ) , suggest that the typical coronavirus double crown-shape of the HCoV-OC43 virion was present in two different forms in cell supernatants that were harvested at 48 hpi during infection of mixed primary CNS cultures from BALB/c mice . Indeed , Fig 9A ( left panel ) represents the first type of morphology , which we named “long” for long S peplomers as measurements of the spike ( S ) and the hemagglutinin-esterase ( HE ) peplomer is shown for the same particle in the right panel . The same relative length of S and HE proteins were previously determined for other coronaviruses [28] . The second type of crown morphology , which we named “short” for short S peplomers is presented in Fig 9B . This short S morphology shows normal HE peplomers of similar length . For a more accurate characterization of the spike length on viral particles from both variants , we measured the spike length of an equal number of virions ( 10 for each virus ) , for which the crown presented long ( long S ) or short ( short S ) peplomers ( Fig 9C ) . The average length of the spike associated with a long S type of crown morphology was 24 nm , whereas the average length of the spikes on short S virions was about 15 nm . This apparent average difference of 9 nm represents a reduction of about 37 . 5% in the total length of the spike , which could presumably play a role in viral infectivity . Therefore , we next counted the number of viral particles that have a long-S or short-S crown for both viruses ( Fig 9D ) and found a significant difference , which tended to demonstrate that viral particles of the mutant virus were mostly in the short S state ( about 72% ) compared to the reference virus , which showed mainly long S crown ( about 68% ) . Given that the rOC/SG758R variant was less neurovirulent and presented a delay in dissemination within the mouse CNS compared to rOC/ATCC reference virus , two observations that can relate to a difference in viral infectivity in neuronal cells , we sought to evaluate whether there was a correlation between the morphological differences of the crown of viral particles ( Fig 9A–9D ) and their relative infectivity ( capacity to infect the target cell ) . No significant differences were found in the ratio of infectious viral particles over total viral particles ( evaluated by the number of viral genome present in viral stocks used for all experiments ) , which establishes itself at 1/200 for both variants ( Fig 9E ) . Furthermore the amount of viral RNA associated with infected LA-N-5 cells remained the same over a period of 16 hours ( Fig 9F ) , suggesting that attachment and cell entry was similar for both viruses .
Being opportunistic pathogens , HCoV are naturally neuroinvasive and neurotropic in humans [5] . Herein , making use of our cDNA infectious clone , we show that a single nucleotide polymorphism ( SNP ) naturally found in the S gene of all known HCoV-OC43 contemporary clinical isolates leads to a G758R mutation in the S protein , without significantly affecting the virus neuroinvasive properties and infectivity in cell culture . However , this mutation was sufficient to modify viral spread and neurovirulence in susceptible mice by modulating the cleavage of the S protein , which appears related to furin-like activity in susceptible neuronal cells . Even though the rOC/SG758R mutant harbors a SNP present in respiratory clinical isolates , we were not able to detect any viral presence in the respiratory tract of infected mice . Further studies are underway to try to identify other naturally occurring S mutations that could be important for viral spread to the respiratory tract in mice . Nevertheless , our results indicate that , despite the difference in neurovirulence , the recombinant virus rOC/SG758R retains its full neuroinvasive properties even though there was a delay in viral spread and in the production of infectious virus ( Figs 1–4 ) . This phenomenon may in part explain the mutant reduced neurovirulence accompanied by less severe neurological symptoms and a less frequent spread to the spinal cord , as previously reported for other S protein mutants of HCoV-OC43 [14] and for the murine coronavirus , MHV [29] . When viruses had spread to all regions of the brain , the innate immune response was well established , as observed by astrogliosis and microgliosis after both routes of infection where we detected viral antigens [10 , 14 , 30] and S1 and S2 Figs . The stronger astrogliosis and microgliosis observed after infection by the reference virus may also be related to a faster spread throughout the CNS compared to the mutant virus [14] . The same difference in viral spread was confirmed in primary cultures of mouse brain cells , where both variants were still infecting neurons as primary targets ( Fig 5 ) , even though astrocytes could also be infected ( S3 Fig ) . This is in agreement with our previous reports in these cultures [14] and underlines the fact that the change in neurovirulence was not associated with a change in cell tropism as was previously shown for MHV [31] , but could rather be related to a modification in the spread between infected neurons . The differential neurovirulence and spread could certainly be the consequence of the G758R mutation in the S protein , which introduces a typical furin-like recognition site [16 , 32] probably recognized by cellular proprotein convertases ( PCs ) as this mutation was the only difference found in the whole genome of both recombinant viruses used in the present study . Proteolytic cleavage of coronaviruses S proteins was characterized several years ago for the murine coronavirus [33] . Since then , several reports have indicated that PCs appear to be important for cell-cell fusion and/or virus entry into host cells [21–23] , or during transport of the newly assembled virions through the secretory pathway of the producer cell [21 , 34–36] for different coronaviruses including MHV , SARS-CoV and FIPV[21 , 23 , 37 , 38] . The data presented in Figs 6 and 7 clearly show that the S protein harboring the G758R mutation is more easily cleaved during infection . This S1/S2 cleaved version of the S protein is easily detected in the free virus present in cell culture supernatant but it is barely detectable in cell-associated proteins . Taken together , these results strongly suggest that this cleavage of S takes place during the late steps of infection , probably during particle assembly and egress , as it was previously shown for MERS-CoV [35] and MHV [39] . Furthermore , cleavage of the HCoV-OC43 S glycoprotein also has an impact on pathology , as it decreases neurovirulence and spread within the CNS . It is highly interesting to note that this association between decreased virulence and cleavage of coronavirus S glycoprotein was only suggested for FCoV [32] . In fact , for other coronaviruses , including the murine ( MHV ) and the bovine coronavirus ( BCoV ) , no clear association was established between S cleavage and virulence [40 , 41] . The data presented in Fig 8A and 8B strongly suggest that PCs can indeed be involved in the cleavage of HCoV-OC43 S protein during infection of neuronal cells . Inhibition of furin-like protease ( PCs ) was already demonstrated for other coronaviruses like MERS-CoV and MHV with the same type of inhibitor [21 , 35] . These results are supported by observation of S protein in reference virus rOC/ATCC ( Fig 8A ) for which the ratio of uncleaved S protein over S1/S2 cleaved form remained equal at all inhibitor concentrations . In contrast , this ratio increased for mutant virus rOC/SG758R ( Fig 8B ) in a dose-dependent manner . Results presented in Fig 8C and 8D bring even more interesting new information about which of these proteases could be involved in the actual cleavage . Indeed , the synthetic peptide harboring the G758R mutation was cleaved with much more efficiency by PCs than the model peptide mimicking the reference virus S protein . Furthermore , even though furin represented the most efficient convertase , PC5/6 and PACE4 , and to a much lesser extent PC7 , were also able to cleave the synthetic peptide and could therefore cleave the HCoV-OC43 S protein during infection of susceptible cells as it was previously shown for SARS-CoV [42] . Inhibition of furin-like activity during infection of human neuronal cells , lead us to suggest that PCs ( most notably furin ) are the cognate proteases involved in the S protein cleavage at amino acid 758 ( Fig 8 ) . The number and morphology of glycoproteins on virions can modulate infectivity for different RNA viruses harboring a class I fusion protein , including other coronaviruses [43–46] . In the case of HCoV-OC43 , the apparent modification of crown-shaped virions ( Fig 9A–9D ) , associated with the observed differential S protein cleavage , does not seem to increase or decrease viral infectivity ( Fig 9E and 9F ) . Moreover , inhibition of furin-like-activity did not influence the capacity of the viruses to enter these cells ( S5 Fig ) . Therefore , even though the S cleavage associated with furin-like activity was shown to influence viral entry for IBV [24] , the HCoV-OC43 S protein cleavage by PCs did not appear to modulate infectivity as it was shown for MERS-CoV [47] . On the other hand , the modified virion morphology associated with preferential cleavage of the rOC/SG758R HCoV-OC43 variant S protein at the S1/S2 domain interface correlates with a decrease CNS viral spread and neurovirulence in susceptible mice . Indeed , the delays in spreading in both primary cultures ( Fig 5 ) and within the CNS ( Fig 2 ) were observed despite a more efficient release of infectious rOC/SG758R particles in the cell culture medium as compared to the reference rOC/ATCC virus , a relationship that may seem counterintuitive but is in fact reminiscent of the cell-to-cell mode of propagation prevailing for a growing list of viruses [48] . For example , HTLV-1 is famously inefficient at spreading through free-virus particles diffusion , the particles remaining instead associated to the plasma membrane from where productive transfer towards target cell occurs [49 , 50] . By analogy , it is tempting to speculate that S1/S2 HCoV-OC43 spike cleavage limits the amount of particles at the plasma membrane available for a cell-to-cell transfer to naive neurons . This can explain the difference in kinetics of dissemination between both viruses and the difficulty for mutant rOC/SG758R to reach the spinal cord even though it does disseminate throughout the brain . This hypothesis may appear in contradiction with the previously documented positive impact of S1/S2 spike cleavage on MHV and SARS-CoV cell-to-cell transfer occurring upon fusion-dependent syncytium formation [21 , 22 , 39] . Furthermore , even though syncytium formation upon MHV infection has often been linked to S cleavage , in some instances , this type of cell-cell fusion was shown to occur without cleavage of the S protein [51–53] and the MHV-2 strain S protein can be cleaved without being able to induce syncytia [54] . In fact , regardless of the cleavage status of its S protein , HCoV-OC43 was never able to induce syncytia in any type of cells we studied and we therefore tend to think that distinct , but not mutually exclusive , cell-to-cell propagation mechanisms may prevail among coronaviruses like it does for other enveloped viruses , especially within the CNS , where cell-cell movement of viruses may take place at synapses [48] . Altogether , these observations suggest that the influence of spike cleavage on coronavirus propagation is not an absolute prerequisite and therefore , cannot per se predict accurately the efficiency of cell-to-cell spread . The reasons underlying this variable outcome are still unclear but may well reside in the different virus receptors , structural features and attachment factors exploited by coronaviruses . Given the expected influence of virus spread on neurovirulence , host survival and potentially establishment of CNS viral persistence , further studies are indeed warranted to characterize the underlying mechanisms associated with HCoV-OC43 spread within the CNS . The SDS-PAGE ( Figs 6 , 7 , 8 and S4 ) shows intermediate size fragments migrating between the uncleaved and furin-like cleaved forms of the S protein that may represent unspecific degradation products . However , analysis of the S protein gene sequences of HCoV-OC43 , revealed a second putative cleavage site ( S2’ ) between amino acid 899 and 903 ( KASSR ) . If functional , this second putative cleavage site could be used by other types of cell proteases , including trypsin , TMPRSS and cathepsins [20 , 55–57] to produce a fragment of such molecular weight . Further studies to characterize the possible involvement of this second putative cleavage site and the identification of host proteases involved in the potential processing of the HCoV-OC43 S protein are ongoing . Taken together , the results of the current study indicate for the first time that HCoV-OC43 is clearly able to infect neuronal cells and to spread with or without the need for a furin-like S protein cleavage . The difference in viral spread within CNS and in brain primary cultures , associated with the increase of infectious viral particles in the culture medium for the virus harboring the G758R mutation present in all known clinical isolates , as well as the absence of modification in infectivity between the two viruses , strongly suggests that the PC-activity-associated cleavage of HCoV-OC43 S protein plays a more important role during the egress and viral budding from infected cells , which could influence the mode of viral transmission between CNS cells . This is of importance to better understand the mechanisms underlying viral spread within the CNS , potentially associated with an adaptation of HCoV-OC43 to this particular environment . Even though HCoV-OC43 reference strain is highly neurovirulent , we have already shown that its RNA persists in the mouse CNS for up to one year in a significant proportion of infected mice [10] . Nevertheless , the delayed dissemination and reduced neurovirulence of mutant rOC/SG758R increase host survival and therefore could favor the establishment of CNS viral persistence associated with a potential viral adaptation to the CNS environment , which could result in the selection of better adapted quasi-species , as it was shown for MHV [58] . In the end , such a persistent infection in the human CNS could , in certain circumstances , be associated with recurrent human encephalitis or neurological degenerative pathologies . Therefore , the observation that HCoVs are naturally neuroinvasive in both mice and humans [9 , 59 , 60] underlines the need to further characterize viral and cellular determinants of these neuroinvasive properties . Understanding mechanisms and consequences of virus interactions with the nervous system is essential to better understand potentially pathologically relevant consequences and in the design of diagnostic and therapeutic strategies , including modulation of host proteases such as proprotein convertases .
All animal experiments were approved by the Institutional Animal Care and Use Ethics Committee ( IACUC ) of the Institut National de la Recherche Scientifique ( INRS ) and conform to the Canadian Council on Animal Care ( CCAC ) . Animal care and used protocols numbers 1304–02 and 1205–03 were issued by the IACUC of INRS for the animal experiments described herein . The wild-type reference virus HCoV-OC43 ( VR-759 ) was obtained in the 1980s from the American Type Culture Collection ( ATCC ) . The recombinant HCoV-OC43 virus ( rOC/ATCC ) was generated using the full-length cDNA clone pBAC-OC43FL and displayed the same phenotypic properties as the wild-type virus , as previously described [25] . This recombinant virus was used as the reference control virus for all experiments . Using site-directed mutagenesis ( Stratagene QuikChange Multisite-directed mutagenesis kit ) as recommended by the supplier , we introduced a point mutation in the gene coding for the spike glycoprotein of HCoV-OC43 at nucleotide 2272 , corresponding to an amino acid change at position 758 ( corresponding recombinant virus designated rOC/SG758R ) . Each cDNA clone was transfected in BHK-21 cells , amplified by two passages in the HRT-18 cell line , and sequenced to make sure that only the introduced G758R mutation was present and that no other mutations appeared . The HRT-18 cell line ( a gift from the late David Brian , University of Tennessee ) was cultured in minimal essential medium alpha ( MEM-alpha; Life Technologies ) supplemented with 10% ( vol/vol ) fetal bovine serum ( FBS; PAA GE Healthcare ) and was used to produce viral stocks . The LA-N-5 cell line ( a kind gift of Stephan Ladisch , George Washington University School of Medicine ) was cultured in RPMI medium supplemented with 15% ( vol/vol ) fetal bovine serum ( FBS ) , 10 mM HEPES , 1 mM sodium pyruvate , and 100 μM non-essential amino acids ( Gibco- Invitrogen ) . LA-N-5 cells were differentiated into human neurons as previously described [61] . Briefly , cells were seeded in Cell+ petri dishes ( 5x105 cells in RPMI medium supplemented with 10% ( vol/vol ) FBS , 10 mM HEPES , 1 mM sodium pyruvate , and 100 μM non-essential amino acids . The next day and every 2 days for 6 days , the medium was replaced with the same medium supplemented with 10% ( vol/vol ) FBS and 10 μM all-trans retinoic acid ( Sigma-Aldrich ) . Before infection , differentiated LA-N-5 cells in Petri dishes , and 24-well plates were pretreated with furin inhibitor Decanoyl-Arg-Val-Lys-Arg-chloromethylketone ( Dec-RVLR-cmk; Bachem N-1505 ) at different concentrations ( 5-10-20-40 μM ) for 2 h at 37°C . The medium was removed and cells were infected at a defined multiplicity of infection ( MOI ) of 0 . 1 , with reference and mutant virus and incubated for 2 h at 37°C without furin inhibitor , washed with PBS and incubated at 37°C with fresh RPMI containing Dec-RVLR-cmk at the concentrations used before infection . At 6 , 24 and 48 hpi , supernatants and cells were harvested separately for protein extraction and evaluation of infectious virus production . Embryos at 14 to 16 days of gestation were removed from pregnant anesthetized CD1 mice . The cortex and hippocampus of the embryonic pup brains were harvested and placed in Hanks balanced salt solution ( HBSS ) medium , without Ca2+ and Mg2+ , supplemented with 1 . 0 mM sodium pyruvate and 10 mM HEPES buffer . The tissues were incubated in 5 ml of HBSS+trypsin-EDTA 0 . 5% ( ratio 10:1 respectively ) for 15 min at 37°C with gentle tilting to mix . After digestion , the tissues were washed 5 minutes three times with HBSS , and the medium was removed and replaced by fresh HBSS medium ( without Ca2+ and Mg2+ , supplemented with 1 . 0 mM sodium pyruvate and 10 mM HEPES buffer ) . Tissues were gently pipetted up and down with a Pasteur pipette to dissociate the cells . After a decantation step of 5 min at room temperature , supernatants were transferred in a 50 ml tube with 36 mL of neurobasal medium ( Invitrogen ) supplemented with 0 . 5 mM GlutaMAX-I ( Life Technologies ) , 10 mM HEPES buffer , B27 supplement ( Life Technologies ) , gentamycin and 10% ( vol/vol ) of Horse serum ( Life Technologies ) . This step was performed twice to increase the final amount of cells . Cells were then seeded at 2x105 cells/cm2 and grown on collagen+poly-D-lysine ( 3:1 for a final concentration at 50 μg/mL for both ) -treated glass coverslips in the same medium , which was replaced by fresh neurobasal medium without horse medium the next day . The medium was changed every 2 days after and the cultures were ready for infection after 7 days in culture . The HRT-18 and LA-N-5 cells as well a primary mouse CNS cell cultures were infected at a defined MOI of 0 . 1 , or mock-infected and then incubated at 33°C ( HRT-18 ) or 37°C ( LA-N-5 cell line and primary cultures ) , for 2 h ( for virus adsorption ) , and incubated at 33°C with fresh MEM-alpha supplemented with 1% ( vol/vol ) FBS ( for HRT-18 cells ) or at 37°C with fresh neurobasal medium with B27-GlutaMAX-I ( for primary murine CNS cell cultures ) or at 37°C with fresh RPMI medium supplemented with 2 . 5% ( vol/vol ) FBS ( for LA-N-5 cells ) for different periods of time . Female BALB/c mice ( Jackson Laboratories ) aged 22 days post-natal ( dpn ) or 10 dpn were inoculated respectively by the IC route with 102 . 5 or the intranasal route with 103 . 25 of 50% tissue culture infective doses ( TCID50 ) recombinant virus , as previously described [14] . Groups of 10 mice infected by each recombinant virus were observed on a daily basis over a period of 21 dpi , and survival and weight variations were evaluated . Clinical scores were evaluated using a scale with 4 distinctive levels ( 0 to 3 ) ; where 0 was equivalent to the asymptomatic mouse; 1 for mice symptoms of abnormal flexion of the four limbs [10] . Mice presenting social isolation , ruffled fur , hunched backs and weight loss were classified as number 2 and number 3 was attributed to mice that were in moribund state or dead . This neurological scale was adapted from Burrer et al . already published for several viruses [26] . Mouse brain and spinal cord tissues or cell culture supernatants were processed for the presence and quantification of infectious virus by an indirect immunoperoxidase assay ( IPA ) on HRT-18 cells , as previously described [62] . Briefly , HRT-18 cells were incubated with the mouse primary antibody 4 . 3E4 ( dilution 1/50 ) that detects the S protein of HCoV-OC43 . After three PBS washes , cells were incubated with a secondary horseradish peroxidase-conjugated goat anti-mouse immunoglobulin antibody diluted 1/500 ( Kirkegaard & Perry Laboratories ) . Finally , immune complexes were detected by incubation with 0 . 025% ( wt/vol ) 3 , 3-diaminobenzidine tetrahydrochloride ( Sigma-Aldrich ) and 0 . 01% ( vol/vol ) hydrogen peroxide in PBS 1X , and infectious virus titers were calculated by the Karber method , as previously described [62] . For immunohistochemistry , perfusion with 4% ( wt/vol ) paraformaldehyde ( PFA ) was performed on five infected BALB/c mice for each recombinant virus , every 2 days , between 1 and 15 dpi . Sagittal brain sections were prepared at a thickness of 60 μm with a Lancer Vibratome . Serial sections were collected and incubated overnight with primary antibodies , as previously described [11] . For detection of viral antigens , 1/1000 dilutions of ascites fluid from the 4 . E . 11 . 3 hybridoma secreting a murine monoclonal antibody against the viral N protein were used [8] . Astrocytes were identified with a rabbit anti-glial fibrillary acidic protein antibody ( GFAP; Dako ) diluted 1/500 , and activated macrophages/microglia by a rabbit anti-Iba 1 ( Wako ) diluted 1/500 for 10 day-old BALB/c mice , or a rat anti-Mac 2 monoclonal antibody diluted 1/50 for 21 day-old female BALB/c mice . For immunofluorescence staining , primary murine CNS cell cultures were washed with sterile PBS and then fixed with 4% ( wt/vol ) paraformaldehyde for 30 min at room temperature . After washing , cells were permeabilized with 100% methanol at -20°C for 5 min . The samples were then incubated with primary antibodies: a polyclonal rabbit anti-glial fibrillary acidic protein ( GFAP ) antibody ( 1/1000; Dako ) , and a monoclonal mouse anti-S protein antibody ( 1/2 of 4 . 3 . E4 hybridoma supernatant ) ( S3 Fig ) or polyclonal rabbit anti-S protein of the bovine coronavirus ( BCoV ) at 1/1000 dilution and a monoclonal mouse anti-microtubule-associated protein 2 ( MAP2 ) antibody ( at a dilution of 1/1000 ) , 1 h at room temperature . After three washes with PBS , cells were incubated in the dark for 1 h at room temperature with the secondary fluorescent antibodies Alexa Fluor 568 goat anti-rabbit ( 1/1000; Life Technologies ) or Alexa Fluor 488 anti-mouse ( 1/1000; Life Technologies ) . After three PBS washes , tissue sections were incubated for 5 min at room temperature with 4’ , 6-diamidino-2 phenylindole ( DAPI; 1 μg/ml; Life Technologies ) washed once with PBS and water and then mounted with Immuno-Mount mounting medium ( Fisher Scientific ) . Immuno-histochemical and fluorescent staining were observed under a Nikon Eclipse E800 microscope with a QImaging Retiga-EXi Fast 1394 digital camera using Procapture system software . Proteins in the cell culture medium and cell-associated proteins were extracted using RIPA buffer ( 150 mM NaCl , 50 mM Tris , pH 7 . 4 , 1% ( v/v ) NP-40 , 0 . 25% ( w/v ) sodium deoxycholate , 1 mM EDTA ) supplemented with the Protease cocktail inhibitor ( Sigma ) and the Halt-Phosphatase inhibitor ( Pierce ) . Harvested cells were pipetted up and down into RIPA buffer , incubated on ice for 20 min , centrifuged for 10 min at 4°C at 17 , 000×g and supernatants were stored at −80°C until further analyzed . Protein concentrations were determined using the BCA Protein assay kit ( Novagen ) , according to the manufacturer's instructions . Equal amounts of proteins were subjected to SDS-PAGE using a Criterion 4–12% gradient gel , or a Tris-Glycine 4–15% gradient gel , transferred to PVDF membrane with a semi-dry trans-blot apparatus ( BIO-RAD ) . Membranes were blocked overnight at 4°C with TBS buffer containing 1% ( v/v ) Tween ( TBS-T ) and 5% ( w/v ) non-fat milk , then incubated with the monoclonal mouse anti-S protein antibody 4 . 3E4 ( hybridoma supernatant 1/2 ) for 1 h at room temperature . After three washes of 10 min with TBS-T , the membranes were incubated with a secondary anti-mouse antibody coupled to horseradish peroxidase ( GE Life Sciences ) and detection was performed using the enhanced chemiluminescence ( ECL ) kit ( BIO-RAD ) using Kodak-X-Omat L-S film ( Kodak ) . For the observation of viral particles by Transmission Electron Microscopy ( TEM ) , 200 μL of the supernatant of infected mixed primary cultures of murine CNS were ultracentrifuged on a nickel grid at 50 , 000 rpm for 5 min . The grids were then dried with bibulous paper before negative staining of 1 min with a drop of 3% phosphotungstic acid ( PTA ) . Real time RT-PCR for the absolute quantitation of viral RNA ( genome ) in viral stocks and during infection of LA-N-5 cells , was modified from Vijgen and collaborators [63] using the Taqman technology and the use of cRNA standards for the generation of a standard curve and to evaluate the absolute number of viral genome in samples with the MEGAshortscript kit ( Ambion/Life Technologies ) [63 , 64] . Briefly , total RNA was extracted with the Qiazol reagent ( Qiagen ) for HRT-18 cell culture supernatant to evaluate the amount of viral genome in virus stock and with Qiagen RNeasy mini extraction kit according to the manufacturer's instructions for total RNA extraction following infection of LA-N-5 cells at 0 . 5 , 2 , 4 , 8 , and 16 hpi . cRNA standards were constructed exactly as described elsewhere made as previously described [63] . RNA concentrations were evaluated in all samples and quantified using a ND1000 spectrophotometer ( Nanodrop ) . Real-time quantitative RT-PCR was performed with the TaqMan-RNA-to-CT 1-Step kit ( Applied Biosystems/Life Technologies ) in a 20 μL reaction mixture with 10 μL of 2x TaqMan RT-PCR Mix ( containing ROX as a passive reference dye ) , 900 nM of forward and reverse primers , and 200 nM of FAM BHQ1-TP probe . Four μL of extracted RNA for supernatant samples and cRNA standards ( serial dilutions ) , or 0 . 5 μg of total RNA for cell-associated ( LA-N-5 cells infection ) were used for the reaction . Amplification and detection were performed in a StepOnePlus Realtime PCR system apparatus and analysis were performed with the StepOne software version 2 . 3 ( Applied Biosystems ) . The synthetic peptides N-322: VDYSKNRRSRGAITTGY; sequence of rOC/ATCC reference virus S protein ( amino acid 748–764 ) and N-321: VDYSKNRRSRRAITTGY; sequence of rOC/SG758R virus S protein ( amino acid 748–764 ) were made in-house at the laboratory of Dr . Robert Day . The underlined amino acid represents the G758R polymorphism between reference and clinical isolates . Briefly , recombinant PC enzymes were first titrated using the Dec-RVKR-chloromethylketone inhibitor . Cleavage assays of coronavirus derived peptides ( 42 μg/tube ) were carried out with 5 nM of each PC with BSA in a final volume of 80 μl . The reaction was stopped with TFA ( 1% final ) . HPLC analysis ( 0–30% acetonitrile gradient , 0 . 5%/min ) was done and quantification was obtained with peak area relative to T = 0 min . Peaks obatined were also collected and identified using MALDI-TOF . For cell experiments , statistical analysis were conducted by one-way analysis of variance ( ANOVA ) , followed by Tukey’s post hoc test , or a t-test . For mice experiments , results were compared using two non-parametric statistical tests: Kruskal-Wallis and Mann-Whitney . Survival rates were plotted as Kaplan–Meier survival curves and were compared using the log rank ( Mantel–Cox ) test . Statistical significance was defined as p < 0 . 05 . | Human coronaviruses ( HCoV ) are respiratory pathogens involved in a sizable proportion of common colds . They have over the years been associated with the development of neurological diseases , given their demonstrated neuroinvasive and neurotropic properties . The viral spike ( S ) glycoprotein appears to be associated with these neurologic features and is a major factor of virulence for several coronavirus species , including HCoV-OC43 . To further characterize the role of this protein in neurovirulence and virus spread within the CNS , we sought to identify amino acid residues that may be important for this function . Our data revealed that one of them , G758R , introduces a functional furin-like cleavage site in the S protein ( RRSR↓R758 ) . This change in S protein mostly impacts neurovirulence , which seems associated with a modified viral dissemination , without significantly affecting its neuroinvasive capacity . This mutation , found in all characterized contemporary human clinical respiratory isolates , underlines previous findings that naturally existing field isolates of HCoV-OC43 variants still possess the capacity to invade the CNS where they could eventually adapt and establish a persistent human CNS infection , a mechanism potentially associated with human encephalitis or neurodegenerative pathologies of unknown etiologies . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | Cleavage of a Neuroinvasive Human Respiratory Virus Spike Glycoprotein by Proprotein Convertases Modulates Neurovirulence and Virus Spread within the Central Nervous System |
The spectrum of the clinical presentation and severity of malaria infections is broad , ranging from uncomplicated febrile illness to severe forms of disease such as cerebral malaria ( CM ) , acute lung injury ( ALI ) , acute respiratory distress syndrome ( ARDS ) , pregnancy-associated malaria ( PAM ) or severe anemia ( SA ) . Rodent models that mimic human CM , PAM and SA syndromes have been established . Here , we show that DBA/2 mice infected with P . berghei ANKA constitute a new model for malaria-associated ALI . Up to 60% of the mice showed dyspnea , airway obstruction and hypoxemia and died between days 7 and 12 post-infection . The most common pathological findings were pleural effusion , pulmonary hemorrhage and edema , consistent with increased lung vessel permeability , while the blood-brain barrier was intact . Malaria-associated ALI correlated with high levels of circulating VEGF , produced de novo in the spleen , and its blockage led to protection of mice from this syndrome . In addition , either splenectomization or administration of the anti-inflammatory molecule carbon monoxide led to a significant reduction in the levels of sera VEGF and to protection from ALI . The similarities between the physiopathological lesions described here and the ones occurring in humans , as well as the demonstration that VEGF is a critical host factor in the onset of malaria-associated ALI in mice , not only offers important mechanistic insights into the processes underlying the pathology related with malaria but may also pave the way for interventional studies .
Malaria is one of the most devastating diseases in the world today . The total burden of disease has recently been estimated to be higher than 500 million episodes annually being responsible for 18% of all childhood deaths in sub-Saharan Africa , equivalent to 800 , 000 deaths each year . It is caused by Apicomplexan parasites of the genus Plasmodium , which are transmitted through the bite of a female Anopheles mosquito . Infection begins when an infected mosquito bites a mammalian host and deposits Plasmodium sporozoites under the skin . These then enter the circulatory system to reach the liver where they infect hepatocytes leading to the release of thousands of merozoites into the bloodstream , initiating the symptomatic stage of the infection ( reviewed in [1] , [2] ) . In endemic areas , many infections in semi-immune and immune children and adults present themselves as uncomplicated febrile illness . In more severe disease , non-immune individuals may exhibit a number of syndromes including severe anemia ( SA ) , cerebral malaria ( CM ) or respiratory distress ( ALI/ARDS ) [1] . While CM is the most studied form of severe P . falciparum malaria , ALI/ARDS are not only important complications in severe P . falciparum malaria but have been also described in P . vivax and P . ovale malaria . Malaria-associated ALI/ARDS causes high mortality and is more common in adults than in children and pregnant women , with non-immune individuals being more prone to develop this condition [3] . Malaria-associated pathogenesis is considered multi-factorial , with both host and Plasmodium factors playing critical roles [1] , [4] . Nevertheless , the mechanisms responsible for severe malaria's high morbidity and mortality remain poorly understood [5] . This explains why no therapeutic strategies attempting to control the onset of severe malaria have been successfully developed . Laboratory mice infected with natural species of rodent malaria are indispensable tools in the search for pathways involved in the different syndromes developed during infection [6] . Here , we report on a rodent model for malaria-associated ALI . Thirty to 60% of the DBA/2 mice infected with P . berghei ANKA showed not only dyspnea before death but also airway obstruction , hypoxemia , pleural effusion , pulmonary hemorrhage and edema , and increased lung vessel permeability . In this model , ALI is associated with high levels of circulating VEGF and its blockade during infection led to protection of mice from this syndrome , opening new avenues to the treatment of this form of severe malaria .
With the aim of identifying host factors involved in the onset of distinct severe malaria syndromes , we investigated the cause of death of different mouse strains infected with the same rodent Plasmodium strain . Infection of 3 different mouse strains , C57BL/6 , BALB/c and DBA/2 mice , with P . berghei ANKA-infected red blood cells ( iRBCs ) showed 3 significantly distinct patterns of survival curves ( P<0 . 05 for C57BL/6 versus DBA/2 , P<0 . 01 for DBA/2 versus BALB/c and P<0 . 001 for C57BL/6 versus BALB/c ) . As previously described , all C57BL/6 mice infected with P . berghei ANKA succumbed within 6–9 days ( n = 7 , Figure 1A ) due to the development of a complex neurological syndrome consisting of hemi- or paraplegia , head deviation , tendency to roll-over on stimulation , ataxia and convulsions . Given its similarities to human CM , this neurological syndrome is referred to as experimental cerebral malaria ( ECM ) ( reviewed in [7] ) . On the other hand , BALB/c mice are much less susceptible to developing ECM when infected with P . berghei ANKA . Thus , none of these mice died with ECM ( n = 9 ) dying later ( after 15 days of infection ) with hyperparasitemia ( HP ) ( >50% of infected red blood cells ) ( Figure 1A ) without exhibiting any neurological symptoms . DBA/2 mice infected with P . berghei ANKA showed a pattern of survival distinct from the previous two strains . These mice died between days 7 and 20 after infection ( Figure 1A ) . Thorough examination allowed us to discriminate two different phenotypes in P . berghei ANKA-infected DBA/2 mice: one that occurred in mice that died up to day 12 after infection and the other that occurred in mice that succumbed from day 12 onwards . The mice that died after 12 days of infection showed signs of severe anemia , consistent with their high levels of parasitemia ( >50% , Figure 1B ) . This is similar to the HP phenotype , also observed for BALB/c mice . Importantly , none of the DBA/2 mice that died between days 7–12 after infection showed any symptoms of ECM ( as observed for C57BL/6 mice ) . Instead , these mice showed dyspnea before death and airway obstruction , as determined by enhanced pause ( Penh ) . These mice show significantly higher Penh values as well as lower respiratory frequency , than non-infected and P . berghei ANKA-infected DBA/2 mice that died later with HP ( Figure 1C , D , E ) . Importantly , these mice are hypoxemic after the onset of the symptoms , with PaO2/fraction of inspired oxygen ( FIO2 ) values below 300 mmHg and significantly lower than non-infected and P . berghei ANKA-infected DBA/2 mice without symptoms ( P<0 . 001; Figure 1F ) . Post-mortem studies revealed that the main pulmonary necroscopic finding observed in 100% of these mice was pleural effusion . Analysis of the pleural fluid from these mice ( n = 10 ) revealed to be an exsudate ( high total protein content , 59 . 4±11 . 7 mg/ml , showing specific-gravity >1 . 020 , 1 . 030±0 . 004 ) that contained inflammatory cells such as neutrophils ( 57 . 6±11 . 7% ) , lymphocytes ( 28 . 5±15 . 1% ) , monocytes and macrophages ( 13 . 8±6 . 8% ) , as well as both infected and non-infected red blood cells . ALI and ARDS are both disorders of the lung with similar features to those described above for P . berghei ANKA infected DBA mice , such as dyspnea and respiratory insufficiency ( as first symptoms ) as well as inflammatory infiltrates and hypoxemia . Importantly , ALI and ARDS differ only in the degree of hypoxemia , defined as PaO2/FiO2 ≤300 mmHg ( for ALI ) or ≤200 mmHg ( for ARDS ) . Thus , P . berghei ANKA infected DBA/2 mice , which show all these features including hypoxemia with PaO2/fraction of inspired oxygen ( FIO2 ) values between 200 and 300 mmHg , represent a model of malaria-associated ALI . Importantly , we also noted that none of these features were observed in DBA/2 mice infected with other Plasmodium strains , including P . berghei NK65 , P . chabaudi chabaudi AS and P . yoelii yoelii 17X ( data not shown ) suggesting that the onset of malaria-associated ALI in mice depends on the specific P . berghei ANKA-DBA/2 combination . Given that these mice die within a similar time scale as C57BL/6 mice infected with P . berghei ANKA , we sought to determine the main differences between the ECM and malaria-associated ALI syndromes . The main CNS ( central nervous system ) necroscopic and histological findings in P . berghei ANKA-infected C57BL/6 mice were hemorrhages in the cranium , brain and cerebellum ( Figure 2A ) . Histopathological examination also showed multifocal hemorrhages in white and grey matters ( pyramidal , molecular and granular layers , perivascular , hippocampus , and bulb ) and congestive blood vessels in 100% of the C57BL/6 mice showing ECM symptoms ( Figure 2A , B ) . However , only 20% of the P . berghei ANKA-infected DBA/2 mice with ALI symptoms showed some hemorrhagic foci ( data not shown ) , which were much smaller and less frequent than the ones observed in P . berghei ANKA-infected C57BL/6 mice . In addition , none of the mice show ECM symptoms ( Figure 2B ) . A hallmark of ECM is the disruption of the blood-brain barrier ( BBB ) . Indeed , 100% of P . berghei ANKA-infected C57BL/6 presented , during the onset of ECM , disruption of the BBB , as revealed by a ∼15-fold increase in Evans blue accumulation in brain parenchyma , as compared with non-infected C57BL/6 controls ( Figure 3A , P<0 . 0001 ) . In contrast , BBB disruption was not observed in P . berghei ANKA-infected DBA/2 or BALB/c mice ( Figure 3A ) . Instead , lung vessel permeability was significantly higher in infected DBA/2 mice showing ALI symptoms ( P<0 . 001 ) but not in C57BL/6 or BALB/c mice ( Figure 3B ) . Pulmonary edema has been correlated with impaired gas exchange within the lungs , ultimately leading to severe respiratory failure and death [8] . This condition can originate from a number of insults involving damage to the alveoli capillary membrane , including direct pulmonary injury ( e . g . , pulmonary infection ) and indirect injury ( e . g . , sepsis ) [8] . Indeed , while severe pulmonary edema and hemorrhages were observed in 100% of the P . berghei ANKA-infected DBA/2 mice showing ALI symptoms , this was a rare event in mice dying with ECM and was never extensive or severe enough to constitute the cause of death . Altogether , these data show that the two experimental syndromes are distinct . While in ECM the brain is the major affected organ , the lung is the key organ in the onset of ALI in P . berghei ANKA-infected DBA/2 mice . Major histopathological changes in the lungs of DBA/2 mice after the onset of ALI are characterized by inflammatory cellular infiltration ( neutrophil-dominant and foamy macrophages in the alveolar and interstitial sites ) as well as marked alveolar edema and hemorrhage . It is interesting to note that both DBA/2 and BALB/c mice showing HP also show interstitial pneumonia but the histopathological features were very distinct from those of DBA/2 mice with ALI . Mice with HP showed a thickened alveolar septum with some mononuclear inflammatory cells . The lung pattern observed in C57BL/6 after the onset of ECM was characterized by a discrete presence of mononuclear inflammatory cells and/or polymorphonuclear leucocytes but , in most cases without thickening of the alveolar septum ( Figure 4A , B ) . Our next objective was to determine the cause of the development of malaria-associated ALI , using P . berghei ANKA-infected DBA/2 mice as a model . Pulmonary edema , i . e . , fluid build-up in the lung alveoli , originates in the loss of the integrity of the alveolar-capillary barrier ( Figure 3B ) . Indeed , increased alveolar permeability is considered to be the key functional abnormality underlying malaria-associated ALI/ARDS in humans , as seen in ALI/ARDS due to other causes . However , the mechanisms underlying the onset of this syndrome in humans are still not known . It has been shown that vascular endothelial growth factor , VEGF , plays a critical role in angiogenesis but also in vascular permeability [9] . Indeed , systemic overexpression of VEGF has been shown to cause widespread capillary leakage in multiple organs , especially in the lungs [10] . Moreover , high levels of VEGF in plasma were found in ARDS patients [11] . To address the role of VEGF in malaria-associated ALI , VEGF levels in the sera of P . berghei ANKA-infected DBA/2 mice were measured throughout infection . VEGF levels remained constant throughout infection , except for the mice that developed ALI , which showed a significant increase in VEGF levels by day 7 after infection ( Figure 5A ) . The systemic increase in VEGF in the sera seems to originate from de novo production in the spleen ( Figure 5B ) , as shown by the correlation between mRNA levels in the spleen and serum protein levels ( Figure 5C ) . No major alterations of VEGF levels were observed in DBA/2 mice infected with P . berghei NK65 , P . c . chabaudi AS or P . yoelii yoelii 17X , similarly to C57BL/6 and BALB/c mice infected with P . berghei ANKA , none of which developed ALI symptoms ( Figure 5D ) . Altogether , these data show that high VEGF levels in plasma correlate with the onset of ALI in malaria-infected mice . VEGF is known to cause an increase in lung vascular permeability , which strongly supports the idea that higher levels of VEGF in serum might be the cause of malaria-associated ALI . These results strongly suggest that increased levels of VEGF in circulation originate from de novo production in the spleen and may be the cause of death in P . berghei ANKA-infected DBA/2 mice that develop ALI . Thus , we next asked whether P . berghei ANKA-infected splenectomized DBA/2 mice would be protected from developing ALI . The results clearly show that the spleen is required for the onset of malaria-associated ALI , which correlates with VEGF levels in circulation ( Figura 6A–C ) . Infected DBA/2 mice that did not develop ALI not only showed unaltered levels of VEGF in the sera ( Figure 5A ) , but also showed a significant increase in the levels of the soluble form of the VEGF receptor ( sFLT1 ) ( Figure 7A ) , known to neutralize excess VEGF in circulation [12] , [13] , [14] , [15] . Therefore , it is reasonable to think that interfering in vivo with VEGF levels might protect mice from the onset of malaria-associated ALI . To this end , sFLT1-expressing adenoviruses were administered intravenously ( i . v . ) into DBA/2 mice on days 3 and 5 after infection with P . berghei ANKA . LacZ-expressing adenoviruses were administered to control mice . Administration of sFLT1-expressing adenoviruses led to a significant increase of sFLT1 expression ( Figure 7B , 1 . 8 fold , P<0 . 05 ) . While in the control group approximately 70% ( n = 8 mice out of 11 ) of the mice died with malaria-associated ALI symptoms , only 18% ( n = 2 mice out of 11 ) of the mice treated with sFLT1-expressing adenoviruses succumbed to this syndrome ( Figure 7C ) . The protection from malaria-associated ALI fully correlated with a significant decrease in VEGF levels in circulation ( 66% decrease between mice developing malaria-associated ALI and non-ALI in the group receiving LacZ-adenoviruses and 63% decrease between malaria-associated ALI-developing mice receiving LacZ-adenoviruses and non-ALI mice receiving sFLT1-adenoviruses , P<0 . 05 or P<0 . 005 , respectively ) ( Figure 7D ) . Altogether , these data demonstrate that VEGF is a critical host factor for the onset of malaria-associated ALI in mice . Despite the distinct outcomes observed , the host inflammatory response has been postulated to play a major role in the onset of distinct severe forms of malaria infection [16] . In the case of P . berghei ANKA-infected DBA/2 mice , it is also tempting to speculate that an uncontrolled inflammatory response of the host to the parasite might be the primary cause of the observed VEGF increase . This hypothesis is strongly supported not only by the presence of inflammatory cells in the pleural exsudate but also by the fact that the spleen is the major contributor to VEGF increase . We have previously shown that administration of a potent anti-inflammatory molecule , carbon monoxide ( CO ) , suppresses the pathogenesis of ECM [17] , [18] . Interestingly , a similar administration of exogenous CO has been shown to be beneficial on a number of lung injury models ( reviewed in [19] ) . When CO ( 250 parts per million; p . p . m . ) was administered for 72 h , starting at day 2 after infection , it prevented death of P . berghei ANKA-infected DBA/2 mice by ALI ( Figure 8A ) without significant alterations in the parasitemia ( Figure 8B ) but with a significant impairment in the increase on the levels of VEGF in circulation ( P<0 . 01; Figure 8B ) . Moreover , our histopathological observations showed that lungs from mice under CO administration did not present hemorrhages and pulmonary edema ( Figure 8D–F ) . These data not only reveal a means of preventing the onset of malaria-associated ALI but also strongly suggest that , as for ECM , the host inflammatory response may play an important role in the onset of this severe malaria syndrome .
Once thought to be near eradication , malaria is now one of the most prevalent infectious diseases worldwide , with a toll of nearly 1 million deaths per year in regions where infection is endemic . These deaths are at the most severe end of a scale of pathologies affecting approximately 500 million people per year and can be due to the onset of distinct syndromes . These include cerebral malaria ( CM ) , acute lung injury ( ALI ) , acute respiratory distress syndrome ( ARDS ) , and severe anemia , among other pathologies . The outcome of infection is influenced by the genetics of both host and parasite [4] , [20] . This is particularly visible in rodent models of infection , as different strains of mice infected with different Plasmodium strains develop a variety of pathologies , ranging from lethal to self-resolving [18] , [21] . Rodent models that mimic certain aspects of the human CM , anemia syndromes and pregnancy-associated malaria have been established [6] , [22] , [23] . We now report that DBA/2 mice infected with P . berghei ANKA constitute a model for malaria-associated ALI , where the cause of death is respiratory failure . It is important to note that P . berghei ANKA-infected DBA/2 mice have been previously described as CM-resistant [24] . However , another report has described these mice as a resolving CM model . Interestingly , the authors also noted changes in vascular permeability in DBA/2 mice during what they called “mild cerebral malaria” phase . They further state that the even distribution of these changes suggests a response to a circulating factor , although they do not speculate on which factor that might be [25] . Our present detailed pathological study , of the brain and the lungs of P . berghei ANKA- infected DBA/2 mice , indicates that the cause of death of these mice is respiratory failure . In humans , while patients with uncomplicated malaria usually present fever and non-specific symptoms , severe and complicated malaria is characterized by multiorgan involvement including ALI/ARDS . Recent years have witnessed a shift in the profile of patients with complicated malaria ( reviewed in [3] ) . Multi-organ system failure and respiratory complications are being increasingly reported not only for P . falciparum infections but also for malaria caused by P . vivax [26] , [27] , [28] , [29] , P . ovale [30] and P . malariae [31] , usually considered benign Plasmodium species . In fact , it has been suggested that as many as 5% of patients with uncomplicated malaria and 20–30% of patients with severe and complicated malaria requiring intensive care unit admission may develop ALI/ARDS , often after treatment has been initiated [3] . Pregnant women with severe P . falciparum infection are particularly prone to developing ALI/ARDS , which is associated with high mortality [32] , [33] . It is therefore of the utmost importance that a rodent model of such syndrome becomes available . Moreover , post-mortem studies on human patients dying with severe P . falciparum malaria have revealed histopathological findings , such as heavy edematous lungs and hemorrhages [34] , [35] , very similar to the ones we describe here for P . berghei ANKA-infected DBA/2 mice developing malaria-associated ALI . Mild lung pathology has been previously reported in C57BL/6 mice infected with P . berghei ANKA [36] , [37] . Our present study confirms that C57BL/6 mice died with a significant loss of the integrity of the BBB , causing all the ECM symptoms observed prior to death , but also showed some level of lung pathology . However , none of those mice presented pleural effusion or exsudate in their pleural cavities . Moreover , while pulmonary edema and hemorrhages were observed in 100% of the P . berghei ANKA-infected DBA/2 mice showing ALI symptoms , this was a rare event in P . berghei ANKA-infected C57BL/6 mice and was never severe enough to constitute the cause of death . Plasmodium blood stage infection is known to cause multi-organ pathology but the level of pathology varies from organ to organ depending on the host-Plasmodium combination . Here , we clearly show that infection of C57BL/6 or DBA/2 mice with P . berghei ANKA results into two distinct models of severe malaria; the former developing a neurological syndrome while the latter causing death due to respiratory failure in approximately half of the infected mice . Importantly , our data also show that a host factor plays a critical role in the establishment of malaria-associated ALI . Indeed , the present data demonstrates that P . berghei ANKA only causes malaria-associated ALI in DBA/2 mice . Interestingly , DBA/2 mice have been shown to respond quite strongly to angiogenic stimuli [38] and this might be the reason why a proportion of these mice are not able to control the levels of VEGF , leading to the onset of ALI during a P . berghei ANKA infection . It should also be noted that a model named “malaria lung syndrome” , where C3H/z mice infected with P . berghei K173 also die very early in infection and show notably edematous lungs and pleural effusion , has been described more than 25 years ago [39] . Although it would be very interesting to test the levels of VEGF in these mice , the unavailability of this strain of mice from the major animal houses makes this experiment very difficult to perform . But why is VEGF responsible for the onset of malaria-associated ALI ? VEGF has long been known for its activity as a regulator of vessel permeability [13] . In fact VEGF was primarily termed vascular permeability factor , for its ability to induce vascular leakage , rather than for its growth factor activity [40] . VEGF increases vascular permeability 50 , 000 times more efficiently than does histamine [41] . Interestingly , VEGF also plays a central role in the formation and maintenance of lung vasculature [42] . However , when VEGF levels are altered , lung disease frequently follows . Plasma VEGF levels in subjects with non-malaria ALI/ARDS are strongly elevated compared to controls and values higher than two-fold have been associated with mortality [11] . The association between VEGF levels and mortality due to respiratory failure does not mean that VEGF effects are restricted to the lung , but simply highlights the importance of vascular integrity for lung function . Another example in which VEGF and lung injury are involved in response to a pathogenic microorganism has recently been reported [43] . Pseudomonas aeruginosa is a pathogenic bacterium that colonizes the lungs and may lead to lung disease in immunocompromized patients . Interestingly , while aerosol delivery of this bacterium causes fatal disease in DBA/2 mice , other mouse strains are able to resolve infection . DBA/2 mice display progressive deterioration of lung pathology with extensive alveolar exsudate and edema formation together with significantly increase levels of VEGF that seem to result from an uncontrolled host inflammatory response [43] . Indeed , a cross-talk between angiogenesis and inflammation has long been proposed [44] . Similarly , P . berghei ANKA-infected DBA/2 mice treated with a potent anti-inflammatory molecule prior to the onset of ALI show significantly reduced levels of VEGF in sera and are fully protected from this syndrome of severe malaria . Numerous studies have measured VEGF levels in malaria patients [45] , [46] , [47] but none of these studies included a group of individuals for which the cause of death was ALI/ARDS . On the other hand , it was recently shown that P . falciparum-infected red blood cells induce VEGF secretion from human mast cells , a cell population highly represented in the spleen [48] . Importantly , while ALI affects pregnant women infected with P . falciparum [32] , the VEGF pathway seems to play an important role during chronic placental malaria and hypertension in first-time mothers [49] . It remains to be established whether these observations are in any way connected . The similarities between the physiopathological lesions described in the rodent model reported here and those occurring in humans pave the way for a better understanding of the malaria-associated pathology and may contribute to the design of novel rational intervention strategies .
C57BL/6 , BALB/c and DBA-2 mice were bred and housed in the specific pathogen-free facilities of the Instituto de Gulbenkian de Ciência . The mice were then transferred to the Instituto de Medicina Molecular at least 72 h prior to experimentation . All protocols were approved by the Animal Care Committee of the Instituto de Medicina Molecular , following Institutional , National , and European Union guidelines . P . berghei ANKA , P . berghei NK65 , P . yoelii 17X or P . chabaudi AS were used after one in vivo passage in C57BL/6 , BALB/c or DBA-2 mice . Mice were infected via intraperitoneal ( ip ) inoculation with 106–107 infected red blood cells . Infected mice were monitored twice daily for clinical symptoms of ECM including hemi- or paraplegia , head deviation , tendency to roll-over on stimulation , ataxia and convulsions or ALI , including dyspnea . Parasitemia was determined by Giemsa staining followed by microscopic counting and expressed as percentage of infected red blood cells . Brains or lungs were harvested from mice under different experimental conditions when clinical signs of ECM , ALI or HP were noticed . Tissues were fixed in buffered 10% ( v/v ) formaldehyde for paraffin embedding and Hematoxylin-Eosin staining . Pulmonary function was assessed in unrestrained conscious mice placed in a barometric plethysmographic chamber ( Buxco Electronics , Sharon , CT ) , where respiratory parameters were measured every day for 10 minutes . Since these measurements can be performed every day in the same group of mice , the group classification was only performed by the end of each experiment after determining the cause of death . The enhanced pause ( Penh ) , a dimensionless value indicative of airway obstruction , as well as respiratory frequency , were used to determine respiratory resistance and were calculated as previously described [50] . Mice were gently heated in their cages with a heat lamp to increase peripheral blood flow . The mice were then restrained in a restraining device , and the ventral artery of the tail was nicked by carefully plunging a small scalpel blade diagonally into the artery . Heparin was swabbed onto the skin before it was cut to minimize clotting . About 100 mL of blood was collected in a lithium-heparin ( 50 IU/ml ) containing capillary tube Blood in the capillary tube was mixed by placing a small metal fragment into the tube and then passing a magnet along the length of the tube several times . The samples were analyzed immediately with i-STAT cartridge CG8+ ( pH , PCO2 , PO2 , Na , K , iCA , Glu , Hct ) using the i-STAT® System Analyzer ( Abbott Laboratories ) . Mice were injected intravenously ( iv ) with 0 . 2 ml of 1–2% Evans Blue ( Sigma ) when clinical symptoms of ECM , ALI or HP were noticed . Mice were sacrificed one hour later and brains or lungs were weighted and placed in formamide ( 2 ml ) ( Merck ) ( 37°C , 48 h ) to extract Evans Blue dye from the tissue . Absorbance was measured at λ = 620 nm ( Bio Rad SmartSpec 3000 ) . Evans Blue concentration was calculated from a standard curve and is expressed as µg of Evans Blue per g of brain or lung tissue . Mice were placed in a gastight 60 L capacity chamber and exposed to CO for the times indicated , as described elsewhere [18] . Briefly , 1% CO ( Aga Linde ) was mixed with air in a stainless steel cylinder to obtain a final concentration of 250 ppm . CO was provided continuously at a flow rate of ∼12 L/min . CO concentration was monitored using a CO analyzer ( Interscan Corporation , Chatsworth ) . Controls were maintained in a similar chamber without CO . Mouse VEGF and sFLT1 levels in plasma or serum samples were determined using a commercial ELISA kit ( R&D Systems ) following the manufacturer's instructions . Once again , and since only small volumes of blood from the mouse tail vein can be used for this determination , the group classification was only performed by the end of each experiment after determining the cause of death . Extraction of total RNA from lungs , spleen , liver and kidney , from mice with ALI and HP symptoms , was performed using RNeasy Mini Kit ( Qiagen ) , according to the manufacturer's instructions . Non-infected mice were used as controls and as baseline levels . After extraction , RNA concentration and quality were determined using a NanoDrop ND-100 spectrophotometer ( NanoDrop Technologies ) . One microgram of total RNA was reverse-transcribed to single-strand cDNA using the AMV Reverse Transcriptase protocol ( Roche Applied Science ) . VEGF transcripts in the cDNA pool obtained from the reverse transcriptase reaction were quantified by real-time quantitative fluorogenic PCR . SYBR Green PCR Master Mix ( Applied Biosystems ) was used to quantify gene expression according to the manufacturer's instructions . RNA expression levels were calculated using the ABIPrism 7000 SDS Software , and normalized against the expression levels of the housekeeping gene hypoxanthine guanine phosphoribosyltransferase ( HPRT ) . An adenoviral vector carrying the sFLT1 gene was produced using the same LR Clonase II enzyme recombination reaction as described above , but using the pAd/CMV/V5-DEST Gateway vector ( Ad; Invitrogen ) as destination vector . Once the sFLT1-containing Ad vector was established , an adenoviral stock was produced . A vector containing the LacZ gene was used as a control . After purification from the enzymatic reaction , the Pac I-digested vectors were transfected into 293A cells , with Lipofectamine 2000 ( Invitrogen ) as the transfection reagent in Opti-MEM I Medium ( Gibco/Invitrogen ) without serum . Cells were incubated overnight in a 5% CO2 incubator at 37°C . Media were replaced the following day with complete medium ( DMEM with 10% Foetal Calf Serum , 2 mM glutamine , 0 . 1 mM non essential aminoacids and 100 U/mL penicillin , 0 . 1 mg/mL streptomycin ) . Forty-eight hours post-transfection , cells were trypsinized and transfered to sterile 10 cm tissue culture plates containing 10 mL complete medium . Media were replaced every other day until day 8 , when visible regions of cytopathic effect ( CPE ) were observed . Infection was allowed to proceed for an additional 2 days until ∼80% CPE was observed . Adenovirus-containing cells were harvested by squirting cells off the plate with a pipette . A crude viral lysate was prepared by 3 consecutive freeze-thaw cycles ( 30 minutes at −80°C , followed by 15 minutes at 37°C ) . This crude lysate was further amplified by infection of 293A cells . After 3 days , amplified viral stocks were obtained using the freeze-thaw procedure described before . Amplified adenoviral stocks were titered using 293A cells and stored at − 80°C until use . For samples in which n>5 , statistical analysis were performed using unpaired Student t or ANOVA parametric tests . Normal distributions were confirmed using the Kolmogorov-Smirnov test . For samples in which n<5 , statistical analysis were performed using Kruskall-Wallis or Wilcoxon non-parametric tests . All survival curves were compared using Student t , Mann-Whitney e Kolmogorov-Smirnov tests . P<0 . 05 was considered significant . | Malaria remains a major source of morbidity and mortality throughout the tropical regions of the world causing up to 1 million deaths every year , mainly in children . Although infection with malaria parasites is common , only 1 to 2% of infections lead to severe life-threatening disease characterized by a range of clinical features including coma , severe anemia , respiratory distress , metabolic acidosis , or multiorgan failure . Animal models of infection are indispensable tools to better understand the dynamic host-parasite interactions that lead to the onset of different infection outcomes . We now show that DBA/2 mice infected with P . berghei ANKA constitute a rodent model for malaria-associated acute lung injury ( ALI ) . Up to 60% of these infected mice develop respiratory problems including dyspnea , airway obstruction and hypoxemia and die soon after . The most common pathological findings were pleural effusion , pulmonary hemorrhage and edema , features common to human malaria patients that show life-threatening respiratory distress . Malaria-associated ALI in this model correlates with high levels of circulating vascular permeability factor , VEGF , and its blockage by different means leads to protection from ALI . The existence of such a model of disease will certainly contribute to a better understanding of malaria-associated pathology and possibly to the design of novel intervention strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/protozoal",
"infections",
"pathology/histopathology",
"microbiology/parasitology",
"infectious",
"diseases/respiratory",
"infections"
] | 2010 | VEGF Promotes Malaria-Associated Acute Lung Injury in Mice |
ADP-glucose pyrophosphorylase ( AGPase ) , a key allosteric enzyme involved in higher plant starch biosynthesis , is composed of pairs of large ( LS ) and small subunits ( SS ) . Current evidence indicates that the two subunit types play distinct roles in enzyme function . Recently the heterotetrameric structure of potato AGPase has been modeled . In the current study , we have applied the molecular mechanics generalized born surface area ( MM-GBSA ) method and identified critical amino acids of the potato AGPase LS and SS subunits that interact with each other during the native heterotetrameric structure formation . We have further shown the role of the LS amino acids in subunit-subunit interaction by yeast two-hybrid , bacterial complementation assay and native gel . Comparison of the computational results with the experiments has indicated that the backbone energy contribution ( rather than the side chain energies ) of the interface residues is more important in identifying critical residues . We have found that lateral interaction of the LS-SS is much stronger than the longitudinal one , and it is mainly mediated by hydrophobic interactions . This study will not only enhance our understanding of the interaction between the SS and the LS of AGPase , but will also enable us to engineer proteins to obtain better assembled variants of AGPase which can be used for the improvement of plant yield .
ADP-glucose pyrophosphorylase ( AGPase ) is a key regulatory allosteric enzyme involved in starch biosynthesis in higher plants . It catalyzes the rate limiting reversible reaction and controls the carbon-flux in the α-glucan pathway by converting Glucose-1-phosphate and ATP to ADP-glucose and pyrophosphate using Mg2+ as the cofactor [1]–[3] . Regulation of almost all AGPases depends on the 3-phosphoglyceric acid to inorganic phosphate ratio ( 3PGA/Pi ) . While 3-PGA functions as the main stimulator , Pi inhibits the activity of enzyme [3]–[5] . Plant AGPases consist of pairs of small ( SS , or α ) and large ( LS , or β ) subunits thereby constituting a heterotetrameric structure ( α2β2 ) . These two subunits are encoded by two distinct genes [6] . In potato tuber AGPase the sequence identity between the different subunits is 53% suggesting a common ancestral gene [7] , [8] . The molecular weights of tetrameric AGPases range from 200 to 240 kDa depending on the tissue and plant species . Specifically , molecular weights of LS and SS in potato tuber AGPase are 51 kDa and 50 kDa , respectively [6] . It was found that SS and LS have different roles in the enzyme functionality . SS was shown to have both catalytic and regulatory functions whereas LS is mainly responsible for regulating the allosteric properties of SS [9]–[12] . These results were also supported by the studies that showed LS was incapable of assembling into a catalytically active oligomeric structure , whereas SS was able to form a homotetramer with catalytic properties [9] , [13] . However , this SS homotetramer showed defective properties in terms of catalysis and regulation . It required higher concentrations of 3-PGA for activation and was more sensitive to Pi inhibition . These results suggested that LS was essential for the enzyme to function efficiently [11] , [14] , [15] . Alternatively , recent studies have indicated that the LS may bind to substrates glucose-1 phosphate and ATP . The binding of the LS to substrates may allow the LS to interact cooperatively with the catalytic SS in binding substrates and effectors and , in turn , influence net catalysis [12] , [16]–[18] . In addition , specific regions from both the LS and the SS were found to be important for subunit association and enzyme stability [15] . Also , using chimeric maize/potato small subunits , Cross et al . [19] found a polymorphic motif in the SS which is critical for subunit interaction . They have concluded that a 55-amino acid region between the residues 322–376 directly interacts with LS and significantly contributes to the overall enzyme stability . Recently crystal structure of SS was found in a homotetrameric form by Jin et al . [20] . Neither the LS nor the heterotetrameric AGPase ( α2β2 ) structure have been solved yet . This is due to the difficulty of obtaining AGPase in stable form . However , it is critical to elucidate the native heterotetrameric AGPase structure and identify the key residues taking place in subunit-subunit interactions to obtain a more detailed picture of the enzyme . Understanding the structure and the hot spot residues in the subunit interface will enable us to manipulate the native enzyme to get a stable form which can be utilized for improving the yield of crops . The feasibility of such an approach has been shown previously [21] , [22] . We modeled the LS structure of potato tuber AGPase and proposed a model for the heterotetrameric AGPase [23] . In this study , we extended our previous work by examining our AGPase model to identify important residues mediating the interactions between the LS and the SS both by computational and experimental techniques . Based on Molecular mechanics generalized born surface area ( MM-GBSA ) method , two distinct LS domains are involved in LS-SS subunit interaction . The residues of the potato AGPase LS Asn97 , Pro327 , Ile330 , Ile335 , Ile339 , Ile340 , and His342 are involved in lateral interaction with the potato AGPase SS whereas residues Arg45 , Arg88 , Arg92 , and Trp135 are involved in longitudinal interaction with the potato AGPase SS . The effect of these mutations on the interactions of the LS and the SS of potato AGPase were further characterized in vivo using the bacterial complementation and the yeast two-hybrid methods . Also , experimental results indicated that the backbone ΔGbinding energy of the interface amino acids is a decisive parameter for the subunit-subunit interaction rather than side chain ΔGbinding or total ΔGbinding energies . This study will highlight the important structural aspects of AGPase structure and provide insights for further attempts to engineer a more functional form of the enzyme .
To determine the critical amino acid residues of the potato AGPase LS that interact with potato AGPase SS , we performed MM-GBSA method which calculates the binding free energy and decomposes the energy at the amino acid level . The binding free energy differences for the longitudinal ( D2 ) and lateral ( D1 ) dimers of the modeled heterotetramer [23] ( see Figure 1 ) obtained from MM-GBSA method are shown in Table 1 . It is observed that in all of the dimeric interactions , favorable ΔEelec terms are compensated by unfavorable ΔGpolar terms . Hence , total electrostatic interactions ΔGelec , favor binding of subunits . Contributions from van der Waals and non-polar solvation energies also favor interactions thus being the major forces that drive the association of subunits . These results are in agreement with our previous work [23] . In this study , the definition for hot spots is as follows: If a residue shows 3 . 0 kcal/mol energy drop in dimer formation compared to its subunit form ( |ΔGbinding|>3 . 0 kcal/mol ) , then it is considered as a hot spot . Hot-spot residues for D1 and D2 and their binding free energy components together with the standard deviations are shown in Table 2 and Table 3 , respectively . For a residue to be considered in interface its absolute SASA must decrease at least 1Å2 upon subunit complexation and it must satisfy this condition for at least 160 of the snapshots . Based on these requirements , a total of 79 ( 38 in LS and 41 in SS , data not shown ) residues in D1 were classified to be part of interfaces . A total of 19 out of 79 interface residues ( 8 in LS and 11 in SS ) in D1 , are hot-spots . The hot-spot residues in LS are mostly non-polar in general with the exception of Asn97 , Thr328 , and His342 . Seven of the hot-spots in SS for D1 are also non-polar , too . Residues SSLys288 , SSTyr308 , SSLys313 and SSThr320 make up the polar region in this interface . Overall interaction in the lateral dimer is mediated by amino acids that have hydrophobic side chain ( Figure 2A ) . When we looked at the D2 , we identified total of 53 amino acids ( 27 in LS and 26 in SS , data not shown ) as interface residues . Number of hot-spots ( five ) in D2 is relatively less than the residues in D1 . In contrast to D1 hot-spots , which are generally non-polar , there are three basic hot spot amino acids ( Arg45 , Arg88 , Arg92 in LS ) in this interface . The remaining two residues are Trp135 in LS and Trp120 in SS ( Figure 2B ) . A recent MD study indicated that stable complexes prefer to use hydrophobic interactions rather than polar interactions [24] ) in concordance with previous studies [25]–[28] . Further , the critical residues are found to be less mobile in the interfaces [29] contributing more to the stability . Here , we observe that D1 , which is the lateral dimer , is more stable compared to D2 ( longitudinal dimer ) ( see Table 1 , the last row ) . As can be seen from Table 2 , Tyr308 in SS ( in D1 ) shows the highest free energy difference with a |ΔGbinding| value of 6 . 75 kcal/mol upon complexation . We see that favorable contributions to ΔGbinding for this residue are dominated by Eele ( –5 . 42 kcal/mol ) and Evdw ( −6 . 63 kcal/mol ) . Indeed , several H-bonds are formed by Tyr308 and several polar residues ( with the Thr93 , Asn97 and Thr320 in LS ) . Tyr308 is also in close contact with non-polar residues , such as Pro322 in LS which account for the favorable van der Waals interactions . The unfavorable contribution of polar solvation energy ( 6 . 12 kcal/mol ) comes from these interactions and it is observed to be compensated by the favorable electrostatic term . The backbone and side chain contributions to the total free energy are −1 . 99 and −4 . 75 kcal/mol , respectively for this residue . Pro327 in LS has the second highest |ΔGbinding| energy difference with a value of 5 . 03 kcal/mol . It should be noted that this residue is highly conserved and makes van der Waals contacts with Gly40 , Ala41 , Ile285 , Ile324 and the aromatic ring of Tyr43 in SS . These interactions explain the hydrophobic contribution of Pro327 to the total |ΔGbinding| . The backbone and side chain contributions to the total free energy are −1 . 80 and −3 . 24 kcal/mol , respectively for Pro327 . Shown in Figure 3A are the three important isoleucine residues in LS , Ile330 , Ile335 and Ile340 that constitute a hydrophobic core at the inner layer of β-helix domain . The bulky side-chain groups of these residues make strong hydrophobic interactions with each other as well as their counterparts in SS . In fact , favorable ΔGbinding for these amino acids are mainly driven by the van der Waals forces ( see Table 2 ) . Eele terms on the other hand are canceled by the desolvation penalties during dimer formation . Also , noteworthy about Ile330 , Ile335 and Ile340 is that they form total of six highly conserved H-bonds with Ser322 , Ala317 and Ser312 in SS respectively ( Figure 3A ) . Even though the effects of these H-bonds are counter balanced with the polar solvation terms , they contribute strongly to help the LS and SS β-helix domains to maintain their correct orientation relative to each other by providing structural constrains in subunit association . Modeled structure of LS reveals that side-chain of Ile339 is excluded from the hydrophobic core of β-helix domain . Instead this bulky group faces Ile355 , another isoleucine whose side-chain is also excluded from the hydrophobic core , Thr303 and Lys313 in SS . Again Evdw term plays a dominant role for the favorable energy state of this residue . Lys313 in SS has a remarkable feature in terms of electrostatic and polar solvation energies . Upon complex formation this residue is surrounded by many non-polar amino acids such as Leu302 , Ala338 , Ile339 in LS and Leu315 , Val329 in SS which are responsible for the high ΔGpolar term . However , it also contacts with LS Thr303 Gln304 , Glu305 and polar groups and takes part in several H-bonds with these residues . These electrostatic interactions strongly favor the ΔGbinding for Lys313 . It should be noted that , backbone free energy contributions of almost all residues are high ( except Asn97 , His342 in LS ) . This is especially important to decide whether the side chains or backbone interactions are important to define critical residues , hot spots , in AGPase complex . As shown in Table 3 , 80% of the hot-spots in D2 belong to LS in the longitudinal association . It is also worth mentioning that contributions of side-chain atoms in dimer stabilization are much higher than the backbone atoms in this group . This might suggest that longitudinal interactions are not as optimized as the lateral interactions and may further mean that even single alanine mutations on these residues can have deleterious effects in subunit-subunit interactions . We see that Evdw term has no contribution for Arg45 stabilization in LS during dimerization ( Figure 3B ) . Consequently , nearly all the contributions come from electrostatic interactions . This residue makes H-bonds with Ser83 and Glu448 in the LS and Glu124 in the SS . Although Arg45 is not surrounded with hydrophobic amino acids upon complexation it suffers from desolvation effects . One possible explanation for the high desolvation free energy might be that residues found in the close proximity of Arg45cannot sufficiently mimic the solvent environment in complex form . Trp135 in LS is enclosed by both polar and non-polar groups . Residues in the first group are Asn142 in LS , Asn68 , Gln100 and Ser101 in SS which are the constituents of Eele . Second group of amino acids include Val136 in LS , Ala70 and Pro102 in SS ( see Figure 3B ) . Contributions of Val136 and Ala70 to the Evdw term might be smaller than Pro102 since the aromatic group of Trp135 can get involved in strong hydrophobic interactions with the side-chain of this residue . Several residues were reported to be critical in the crystal structure of homotetrameric SS by Jin et al [20] . To compare these amino acids with the corresponding residues in our AGPase model , free energy decomposition scheme was also applied to D3 and D4 ( Figure 1C ) . Table 4 and Table 5 show interface amino acids in SS and their ΔGbinding values in our AGPase model and in the crystal structure of homotetrameric SS , respectively . All of the residues listed in Table 4 were found to be part of interfaces according to our analysis . Four of the residues in the D3 ( Tyr308 , Pro310 , Val319 and Ile324 ) and Trp120 in D4 were also classified as hot-spots in our AGPase model . All the other residues , except for the Glu94 , also have negative ΔGbinding values which mean that they are stabilized upon complex formation . However , they were not considered as hot-spots since their change in ΔGbinding values according to free energy decomposition are higher than our cutoff value ( −3 . 0 kcal/mol ) . We see that while the important amino acids reported by Jin et al . [20] have a total of −36 . 81 kcal/mol ΔGbinding energy in our model , they are less stabilized in the homotetrameric SS with a ΔGbinding value of −29 . 03 kcal/mol ( Table 5 ) . In other words , those residues are more stabilized if they interact with residues from LS instead of residues from SS . This is especially true for Tyr308 , Lys313 , Ile324 and Glu124 . In addition , Glu94 has smaller positive ΔGbinding energy in our model . These results support the fact that a SS chain prefers to interact with a LS instead of another SS in terms of thermodynamic stability . It has been previously shown that the expression of the cDNA sequences of the potato tuber LS and SS subunits yielded a functional heterotetrameric enzyme capable of complementing a mutation in the single AGPase ( glgC ) structural gene of Escherichia coli [13] . This heterologous complementation provides a powerful genetic approach to obtain biochemical information on the specific roles of the LS and the SS in enzyme function [13] , [30] . We performed site-directed mutagenesis experiments based on the results of the MM-GBSA method . Computationally identified hot spots of the LS were mutated and then expressed in an E . coli glgC− ( containing pML10 ) . The ability of LS mutants to form a functional heterotetrameric AGPase was assessed by exposing mutant colonies to I2 vapor to monitor the glycogen accumulation . The residues of the LS listed in Tables 2 and 3 were mutated . Cells carrying the LS Pro327Ala , Thr328Ala , Ile330Lys , Ile335Arg , or Ile339Ala/Ile340Ala mutants within the αβ domain along with the wildtype ( WT ) SS displayed an impaired glycogen accumulation compared to cells co-expressing WT potato AGPase genes ( Table 6 and Figure 4A , C ) . On the other hand , cells expressing the LSHis342Ala , LSAsn97Ala and WT SS demonstrated a comparable glycogen accumulation compared with cells co-expressing potato AGPase genes ( Figure 4A ) . As a control , Lys334 ( ΔGbinding = −2 . 19 kcal/mol ) and Lys336 ( ΔGbinding = −2 . 83 kcal/mol ) adjacent to the Ile335 were replaced with Ala . These LS mutants were transformed into E . coli glgC− cell lines containing the WT SS . Cells were exposed to iodine staining to see the effect of mutation on the heterotetrameric assemblies . As seen in Figure 4C cells harboring those mutants were stained with iodine . These results suggested that altering amino acid residues of hot spots of the LS disturbed the heterotetrameric AGPase assemblies in E . coli . Our results are in agreement with previously reported data where they showed that lateral interaction is mainly mediated by the hydrophobic amino acids in homotetrameric enzymes of the potato SS and Agrobacterium AGPases within the αβ domain of AGPase [20] , [31] . Also , changing the size and polarity of any amino acids in the interacting region of LS disturbed heterotetrameric structure of potato AGPase in E . coli . For example when Ile339 , and Ile340 were changed to Ala ( smaller R-side chain ) , there were no heterotetrameric assemblies between the potato LS and SS AGPase subunits in E . coli ( Figure 4A ) . Similarly mutating the positions at Ile330 to Lys and Ile335 to Arg , which have different charge , again disturbed heterotetrameric assembly in E . coli . Next , we investigated the role of amino acids ( Arg45 , Arg88 , Arg92 and Trp135 ) of the potato AGPase LS in longitudinal interaction with the potato AGPase SS by bacterial complementation and yeast two hybrid assays . Residues , Arg45 , Arg88 , and Arg92 were mutated to the Ala whereas Trp135 was mutated to Arg by site-directed mutagenesis in pML7 vector . Mutants were transformed into E . coli glgC− ( with the pML10 ) . Only the LSArg88Ala mutants have glycogen deficient phenotype and they were unable to complement glgC− gene compared to cells containing wildtype AGPase genes ( Table 6 , Figure 4b ) . To see if Arg88 is solely responsible for the interaction with SS , as a control we replaced adjacent amino acids Asn87 ( ΔGbinding = − . 123 kcal/mol ) and His89 ( ΔGbinding = −0 . 11 kcal/mol ) to Ala . Bacterial complementation result indicated that cells harboring mutant LS constructs can complement glgC− in E . coli ( Figure 4C ) . The LSArg88Ala mutant colonies were unable to grow on the selective medium compared with cells carrying WT genes in yeast two hybrid experiments ( Figure 5 ) . Bacterial cells carrying LSArg45Ala or LSArg92Ala mutants displayed an identical phenotype with the cells containing WT LS and WT SS in E . coli ( Figure 4B ) . Moreover , cells co-expressing the LSTrp135Arg mutant and WT SS demonstrated moderate staining compared to cells expressing WT genes ( Figure 4B ) . To see the direct effects of mutations on heterotetrameric assemblies , WT and mutant constructs ( of LS ) were expressed with the WT SS in E . coli glgC− using IPTG and nalidixic acid ( see Materials and Methods ) . Then , cells were disrupted in the presence of protease inhibitors and 10 µg of total protein of each sample were subjected to 10% SDS-PAGE followed by Western Blot analysis using anti-LS and anti-SS antibodies . As shown in Figure 6 all the LS mutants and SS proteins were detected around 50 kDa as expected in the cell-free extract . Then , crude extract of these samples were analyzed by 3–13% gradient native PAGE to determine if these mutated LS subunits were able to assemble with their counterpart WT subunits to form the oligomeric structure . The expressed LS and SS by themselves can not be seen as homotetrameric structures in native gel compared with when both subunits are together ( Figure 6 , look for SS only , LS only and WT AGPase ) . It is worth to note that , although SS homotetramer can form in vitro , they are not stable enough to be seen by native gel . The WT AGPase was detected around 200 kDa compared with trimeric BSA control . The LS mutants of Arg88Ala , Pro327Ala , Ile330Lys , Ile335Arg , Ile339Ala/Ile340Ala were unable to form heterotetrameric structures . If these amino acids of the LS directly contribute to the interaction with the SS , one would expect to see nearby amino acids not interfering with heterotetrameric interaction . Therefore , we have randomly changed adjacent amino acids of Arg88 and Ile335 and analyzed by the native gel . Replacement of the both Asn87 and His89 to Ala did not effect heterotetrameric formation ( Figure 6 ) . Likewise , changing amino acids of Lys334 and Lys336 into Ala resulted in heterotetrameric structure ( Figure 6 ) . These results are in agreement with staining data that indicated that we successfully identified amino acid residues of the LS that mediates interaction with the SS . When we analyzed the backbone energy contribution of Arg45 , Arg88 , Arg92 , and Trp135 of the LS with interaction of the SS , Arg88 had the highest backbone energy ( Table 3 and 4 ) . Then , we hypothesized that these residues themselves may not be enough to interrupt the heterotetrameric assembly and we subsequently generated double mutants . The LS double mutants , Trp135Arg/Arg45Ala and Trp135Arg/Arg92Ala , were transformed into the E . coli glgC− containing the pML10 ( WT SS ) . As seen in Figure 4B , both LS mutants were not able to complement glgC− gene in E . coli and in turn glycogen production . These results point out that the backbone energy of these residues showed an additive effect when they combined and caused disruption of the heterotetrameric assemblies .
The data presented in this paper allow us to reach the following conclusions . First , critical amino acid residues of the potato LS AGPase subunit that interact with SS subunit were identified using MM-GBSA and experimental methods . Lateral interaction between the LS and SS subunits was mainly mediated by the hydrophobic amino acids as shown previously for homotetrameric AGPase . For the first time we have shown the amino acids of the LS subunit that are important for such interactions The amino acids Asn97 , Pro327 , Ile330 , Ile335 , Ile339 , Ile340 , and His342 are critical for the interaction with the SS of AGPase . Longitudinal interaction by the LS AGPase with the SS subunit is mediated by the Arg42 , Arg88 , Arg92 , and Trp135 . Second , we found that dimer 1 is much more stable compared with dimer 2 due to the hydrophobic interaction in dimer 1 . Finally , backbone energy is an important deterministic parameter for the protein-protein interaction .
Potato tuber AGPase large and small subunits share 53% sequence identity according to the CLUSTALW [37] results . Such a high sequence identity between the subunits allows us to model the potato LS AGPase structure using the homology modeling . In our previous study [23] , we have predicted the three dimensional structure of the LS using the SWISS-Model homology modeling server . Then , we proposed three possible models for the native heterotetrameric AGPase based on the crystal structure of homotetrameric SS using MM-PBSA [23] . A schematic presentation of a proposed heterotetrameric structure can be seen in Figure 1A . The lateral and longitudinal dimers of LS and SS are labeled as D1 and D2 , respectively ( Figure 1B ) . Explicit solvent molecular dynamics simulations for the representative structures of the native AGPase , D1 and D2 , were performed using NAMD software [38] with parm96 force field [39] . Starting structures were solvated in rectangular boxes of TIP3P [40] water molecules . Distances between the edge of the boxes and the closest atom of the solutes were adjusted to at least 10 Å . Counter ions were added , 10 Na+ atoms , in order to neutralize the systems . All the histidine residues were charged as +1 at their Nε atoms in order to establish unity . Particle Mesh Ewald ( PME ) method [41] was used to treat the long range electrostatic interactions and a direct space non-bonded cut off value was taken as 9 Å . Water molecules and the hydrogen atoms were constrained by applying the SHAKE algorithm [42] . Langevin piston Nose-Hoover method [43] , as implemented in NAMD , was used to keep the pressure of the systems constant together with the periodic boundary conditions ( PBC ) . Time step of the simulations were 2 fs . Systems were first minimized for 104 steps using conjugate gradient method and keeping the backbone atoms of the solute atoms fixed . Minimization was completed by an additional 104 steps with all the atoms relaxed to remove the bad contacts . The systems were then gradually heated from 0 K to 300 K in 150 ps using canonical ensembles ( NVT ) during which the Cα atoms of the solutes were restrained by applying 2 kcal mol−1 Å−2 force constants . Subsequent shift into isothermal-isobaric ( NPT ) ensembles was done and harmonic restraints on the Cα atoms were gradually removed in 80 ps after which the systems were equilibrated with an additional 100 ps . NPT simulations were performed for 8 ns at 300 K from which the last 4 ns was used to extract the snapshots with 20 ps time intervals . The 200 snapshots were then used for interface residue identification and binding free energy calculations together with free energy decomposition scheme ( see ref [23] ) . Snapshots are taken from the last 4 ns of the simulations ( 200 snapshots with 20 ps intervals ) . Interface residues were determined using NACCESS [44] based on the implementation of Lee and Richards method . Calculations were performed for each of the complex and subunits separately excluding the hydrogen atoms . As probe radius values for the calculation of solvent accessible surface area ( SASA ) , we have used 1 . 4 Å together with a z-slice value of 0 . 05 Å . Residues that showed 1 Å2 decrease in their SASA upon complex formation were considered as part of the interface . These amino acids were then further screened by an additional criterion to eliminate the pseudo interface residues . Residues that satisfy the above condition for at least 80% of the last 6 ns simulation time ( 160 snapshots ) were treated as the true interface amino acids . In this study , MM-GBSA [45] , [46] method was mainly employed to calculate the binding free energy of molecules in an equilibrium state . In this approach , binding free energy of a complex is calculated by taking snapshots from a molecular dynamics trajectory and computing the average energy of these snapshots according to the formula in Eq ( 1 ) ; ( 1 ) where Gcomplex , Greceptor , Gligand are the energies of the complex , receptor and ligand respectively . Each term on the right hand side of Eq 1 can be represented as shown in the following equation: ( 2 ) where EMM is the total mechanical energy of the molecule in gas phase , Gsol is the solvation free energy and TS is the entropic term . Each term in Eq ( 2 ) can be written as follows: ( 3 ) where EMM represents the bonded and non-bonded interactions as a sum of electrostatic ( columbic ) , van der Waals ( Lennard-Jones ) and internal strain ( bonds , angles and dihedrals ) energies . This term is calculated by classical molecular-mechanics methods using standard force fields such as parm96 force field [39] . Solvation free energy of a molecule is calculated as the sum of a polar and a non-polar term: ( 4 ) where electrostatic contribution to the solvation energy ( Gpolar ) is computed in a continuum solvent environment by using the GBSA method . Non-polar solvation energy ( Gnon-polar ) , which is considered to be the sum of a solute-solvent van der Waals interaction and solvent-solvent cavity formation energy , is approximated by using an empirical formula such as Gnon-polar = α×SASA . According to this formula , non-polar solvation energy of a molecule is proportional to the solvent accessible surface area ( SASA ) of that molecule in a solvent , where α was taken as 0 . 005 kcal•Å−2 [47] , [48] . The entropic term in Eq ( 2 ) is considered as the summation of vibrational , rotational and translational contributions where vibrational term can be calculated by normal-mode analysis or quasi-harmonic analysis: ( 5 ) The entropic term is found to be much smaller than the other two terms ( in Eq . 2 ) in many applications of estimating relative binding free energies [46] . Since the calculation of entropic contribution is computationally expensive , this term can be omitted if qualitative results , rather than quantitative , are considered to be more important . This is also true for different ligands that show similar binding affinities and modes for a given receptor [49] , [50] . However , neglect of entropic terms may lead to miscalculation of binding free energy , hence individual contributions of amino acids to total binding energy , if they show significant conformational change upon complex formation . In our study , this issue is more important for hot-spots in D2 ( those found in β-helix domain ) compared with the hot-spots in D1 since they are relatively more flexible in separate receptor ( LS ) and ligand ( SS ) forms . The last 4 ns of the simulations for both lateral and longitudinal dimeric interactions between the LS and SS pairs were analyzed by MM-GBSA method as implemented in AMBER8 package [51] ( with igb = 2 ) with the modified Bondi radii ( mbondi2 ) [47] which is appropriate for macromolecules such as proteins . The trajectories were post processed in order to strip off the water molecules and counter ions before the calculations . 200 snapshots with 20 ps intervals were extracted for each complex , receptor and ligand structures from single trajectories . We analyzed the autocorrelation functions of effective free energies and found that the correlations drop to 0 . 1 in 20 ps ( see ref [23] ) . In all the calculations the LS was treated as the receptor and the SS as the ligand . Gas phase energies ( EMM ) of the proteins were calculated by the SANDER module applying no cutoff value for non-bonded interactions . Dielectric constants for the solute and solvent were taken as 1 and 80 , respectively; and the solvent probe radius was adjusted to 1 . 4 Å . Residues in interfaces of the subunits that showed at least 3 kcal/mol energy decrease , upon complexation , according to the per-residue free energy decomposition were considered as hot-spots . The cDNAs of potato AGPase LS and SS were PCR amplified using pML7 and pML10 plasmids as template , respectively . The restriction sites , NcoI and BamHI , were introduced using primers at Table 7 . Then , the PCR products were subjected to restriction enzymes and ligated into pGADT7 and pGBKT7 vectors to construct pGAD-SS and pGBKT7-LS plasmids for the yeast two-hybrid assay . E . coli DH5α strain was used during the manipulation of plasmids . For bacterial complementation assays , plasmids pML7 and pML10 were used . Site-directed mutations of the specified hot spot residues were introduced to potato AGPase LS by PCR . Plasmids pML7 , pGBT7K-LS , or pGAD-SS were used as template . PCR reaction was performed in a total volume of 50 µl containing approximately 50 ng of plasmid samples , 20 pmol of each primer , 0 . 2 mM dNTPs , and 2 . 5 unit Dream Taq DNA polymerase ( MBI Fermentas ) with appropriate primers indicated at Table I . Conditions for the 18 cycles of amplification reaction were 95°C for 30 s , 50°C for 30 s and 68°C for 14 min . Before the first cycle reaction mixtures were kept at 95°C for 4 min and at the end of the 18th cycle an additional 68°C extension period was applied for 10 min . Samples were then treated with DpnI restriction enzyme to remove the template DNA and transformed into E . coli . Transformed cells were seeded and selected on appropriate selective medium . The presence of the specific mutations was verified by DNA sequencing throughout Burc Laboratory ( Istanbul , Turkey ) . Yeast-two hybrid assays were performed as described previously [23] . Briefly , the constructs containing wildtype ( WT ) or mutant LS were sequentially transformed into the cells as in the following procedure . First , pGAD-SS was transformed into AH109 cells . Transformed cells were plated on SD/-Leu medium . A single colony was inoculated in liquid SD/-Leu medium for competent cell preparation . Then , constructs that contain the WT or mutant LS were transferred into AH109/pGAD-SS cells . Transformed cells were seeded onto SD/-Trp -Leu medium and the interaction between the SS and the WT or mutant LS was scored on the SD/-Leu -Trp-His medium . The WT or mutant LS cDNA containing pML7 plasmids were sequentially transformed into E . coli AC70R1–504 ( glgC− ) , carrying the SS cDNA expression plasmid pML10 . The particular contribution of each mutant to the LS-SS interaction was evaluated by their ability to complement the glgC− mutation and synthesize glycogen on Kornberg medium enriched with 2% glucose . Glycogen accumulation phenotypes was detected by iodine staining [52] . AC70R1–504 ( glgC− ) cells were grown in 25 ml of LB medium and then induced with 10 mg/L of nalidixic acid and 200 µM isopropyl- b-D-thiogalactopyranoside ( IPTG ) at room temperature for 20 h when the culture OD600 reached 1–1 . 2 . The cells were harvested by centrifugation and disrupted by sonication in 1 ml lysis buffer [1× Tris-buffered saline ( TBS ) , 200 µg/ml lysozyme , 5 mg/ml protease inhibitor ( Sigma ) , and 1 mM phenylmethylsulfonyl fluoride ( PMSF ) ( Roche ) ] . The crude homogenate was centrifuged at 14 , 000 g for 10 min . The resulting supernatant was used . Protein levels were determined by Bradford assay ( Bradford 1976 ) according to the manufacturer's ( Bio-Rad ) instructions ( Bio-Rad Laboratories , CA , USA ) . SDS–PAGE was performed using a Bio-Rad Mini-PROTEAN III electrophoresis cell . Cell lysates containing 10 µg total protein were electrophoresed on a 10% separating gel . Gels were run at 150 V for 1 . 5 h . After SDS–PAGE , gels were transferred to polyvinylidene difluoride membrane ( Biotrace PVDF , Pall Corporation , FL , USA ) with a Mini-Trans-Blot electrophoretic transfer cell ( Bio-Rad ) at 90 V for 1 hr . After pre-blocking with 5% BSA dissolved in Tris-buffered saline ( TBS ) , the membrane was incubated with anti-LS or anti-SS primary antibodies ( 1∶2000 diluted in 0 . 15% Tween20/TBS ) for 1 hr at room temperature . After a series of washes the membrane was subsequently incubated with HRP-conjugated secondary anti-rabbit IgG antibody ( 1∶5000 diluted in 0 . 15% Tween20/TBS ) ( S41176 , Sigma ) for 1 hr . Proteins were visualized by Amersham ECL plus western blotting detection system ( GE Healthcare , Amersham , UK ) . The blot was exposed to autoradiography film . Native-PAGE was performed using a Bio-Rad Mini-PROTEAN III electrophoresis cell . Cell lysates containing 10 µg total protein was mixed with Laemmli's sample loading buffer except β-mercaptoethanol and reducing agent . Samples were electrophoresed on 3–13% polyacrylamide gradient gel ( pH 7 . 0 ) with 1X running buffer ( 192 mM Glycine , 25 mM Tris , pH 7 . 0 ) at constant 100 V at 4°C for 2 hrs . Western blotting and protein visualization were performed as described above . The observed position of protein complexes was compared with BSA oligomer running pattern . | ADP-glucose pyrophosphorylase ( AGPase ) is a key heterotetrameric allosteric enzyme involved in plant starch biosynthesis . In this study , we have applied computational and experimental methods to identify critical amino acids of the AGPase large and small subunits that interact with each other during the heterotetrameric structure formation . During the comparison of the computational with the experimental results we also noted that the backbone energy contribution of the interface residues is more important in identifying critical residues . This study will enable us to use a rational approach to obtain better assembled mutant AGPase variants and use them for the improvement of the plant yield . | [
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"computationa... | 2009 | Investigation of the Interaction between the Large and Small Subunits of Potato ADP-Glucose Pyrophosphorylase |
Analogous to genetically distinct alleles , epialleles represent heritable states of different gene expression from sequence-identical genes . Alleles and epialleles both contribute to phenotypic heterogeneity . While alleles originate from mutation and recombination , the source of epialleles is less well understood . We analyze active and inactive epialleles that were found at a transgenic insert with a selectable marker gene in Arabidopsis . Both converse expression states are stably transmitted to progeny . The silent epiallele was previously shown to change its state upon loss-of-function of trans-acting regulators and drug treatments . We analyzed the composition of the epialleles , their chromatin features , their nuclear localization , transcripts , and homologous small RNA . After mutagenesis by T-DNA transformation of plants carrying the silent epiallele , we found new active alleles . These switches were associated with different , larger or smaller , and non-overlapping deletions or rearrangements in the 3′ regions of the epiallele . These cis-mutations caused different degrees of gene expression stability depending on the nature of the sequence alteration , the consequences for transcription and transcripts , and the resulting chromatin organization upstream . This illustrates a tight dependence of epigenetic regulation on local structures and indicates that sequence alterations can cause epigenetic changes at some distance in regions not directly affected by the mutation . Similar effects may also be involved in gene expression and chromatin changes in the vicinity of transposon insertions or excisions , recombination events , or DNA repair processes and could contribute to the origin of new epialleles .
Epialleles are heritable states of different gene expression from sequence-identical genes and have been described in several organisms [1]–[3] . Like genetically different alleles , epialleles contribute to phenotypic heterogeneity [4]–[5] . While the mutagenic processes creating DNA sequence allele variations are relatively well understood , little is known about how and when epialleles originate , and it is difficult to investigate this in statu nascendi . In plants , epialleles were described as natural variants [6]–[9] , mutation-induced [10]–[12] , or associated with tissue-culture [13]–[15] . Once established , epialleles can acquire stability over many generations; however , they have much higher reversion rates than genetic alleles . Therefore , analyzing the switch from one epigenetic state to the other at well-characterized epialleles can provide insight into their natural origin . Pairs of epialleles are characterized by antithetic histone modifications at the associated nucleosomes , transcriptional activity at the expressed form , and transcriptional gene silencing ( TGS ) at the other . In some fungi , mammals , and higher plants , the latter is connected with cytosine methylation at the epiallele [e . g . 6] , [16]–[17] . Several pairs of epialleles in plants define easily scored phenotypes like morphology [6] , [10] , development [9] , pigmentation [7] , [18] , or reporter gene expression [19]–[20] . Some epialleles , as well as many other epigenetically controlled genes , have been used for mutant screens and have helped identify many different proteins and RNAs whose presence or absence can cause transient or stable changes of epiallele expression , or influence epigenetic regulation in general . There is also a wealth of data on the influence of drug treatments , sequence determinants , and the role of genomic neighborhood , on epigenetic regulation . Arabidopsis thaliana has been the plant model of choice for genetic analysis of switching between epiallelic states , based on the rich genetic and genomic resources available . The experimental system in our study is based on a pair of epialleles in Arabidopsis thaliana containing either an expressed or silent hygromycin phosphotransferase gene ( HPT ) . Active transcription confers resistance to the antibiotic while the inactive epiallele renders the plant sensitive . Gene expression can be selected for on antibiotic-containing medium but does not affect the plants during non-selective growth . The epialleles were found in tetraploid plants obtained by regeneration from protoplasts [20] . While some lines had resistant progeny and expressed the HPT gene , other lines had silenced the HPT and produced only sensitive progeny . The R and S epialleles ( determining resistance and sensitivity on hygromycin , respectively ) were maintained in their particular expression state after diploidization and for all generations of self-pollination analyzed so far ( Figure S1 ) . Beside their differences in transcription , they also differ in DNA methylation [21] . We screened for a switch between the epialleles , by scoring for restored hygromycin resistance after T-DNA mutagenesis of the diploid S line . We identified two trans-acting factors whose nature indicated an epigenetic ‘double lock’ at the silent epiallele [22] . In contrast to many other silent genes , silencing could only be released by simultaneous interference with methylation of DNA and histones . Six mutations from the same screen were mapped to the resistance gene itself . These cis-mutations provided the opportunity to study the nature and effect of DNA sequence changes on gene expression , chromatin organization , and genetic stability . We describe these new alleles in detail and compare them with the R and S epialleles . We show that different , and non-overlapping , sequence changes downstream of the HPT gene can restore the expression of the upstream promoter , to a similar extent as the mutations interfering with the chromatin factors in trans . Such small sequence alterations that cause epigenetic changes at some distance may also be involved in gene expression and chromatin changes in the vicinity of transposon insertions/excisions , recombination events , or DNA repair processes and may thereby contribute to the origin of new epialleles .
The HPT gene is inserted in an AT-rich intergenic region on Arabidopsis thaliana chromosome 3 [20] . Previous investigations , and published data from genome-wide screens for chromatin features [20] , [23]–[24] , indicated that the genomic localization itself is unlikely to influence the epigenetic state of the HPT gene , as no prominent epigenetic modifications are present in the neighborhood of the insertion . Resistant and sensitive Arabidopsis lines with the different epialleles had been generated from the same progenitor line homozygous for the HPT gene , thereby being supposedly isogenic . Nevertheless , the lack of transcription initiation in the hygromycin-sensitive lines could have been due to a DNA sequence mutation in a regulatory region , for example , a transcription factor binding site . Also , the structure of the insert had not been analyzed in detail . Therefore , active and inactive versions were amplified from genomic DNA of the respective lines . Both epialleles are potentially fully functional and have identical sequences . The 35S promoter ( P1 ) is flanked upstream by a 661 bp fragment derived from the plasmid vector ( V1 ) . A rearrangement between two vector molecules prior to , or during , the integration of the transgene into the plant genome caused a duplication of the adjacent vector sequence ( V2 ) and the 35S promoter ( P2 ) , resulting in two tandem repeats ( Figure 1A ) . The polyadenylation signal from the CaMV 35S terminator following the HPT ORF lacks 151 bp compared to the transformation construct and has therefore lost its termination function ( ΔT ) , causing read through of the P1 transcript into the flanking plant genome sequence ( Figure 1A ) . P2 is followed by a 505 bp non-protein coding fragment ( NC ) harboring sequences of bovine carrier DNA used to assist PEG-mediated direct gene transfer to mesophyll protoplasts [25] , interspersed with 54 nucleotides without homology to known sequences . This heterologous DNA is transcribed by P2 , giving rise to a smaller non-coding transcript ( P2 transcript ) ( Figure 1A ) . Resistant plants produce the longer P1 and the shorter P2 transcripts , while both promoters are inactive in sensitive plants ( Figure 1B and Figure S6 ) . Therefore , the isogenic inserts differ only by gene expression , and R and S represent true epialleles . The different expression states were suspected to originate from distinct chromatin configuration , and previous studies had provided evidence for opposing DNA methylation at the epialleles , especially pronounced at the transcription factor binding sites ( [20]–[21] , Figure 1C ) . As DNA methylation and silencing are usually correlated with specific changes of the DNA-associated proteins , we investigated histone modifications and nucleosome occupancy at the epialleles by chromatin immunoprecipitation . This revealed significant differences between the epialleles along the whole transgenic insert . While expressing lines ( R ) were primarily marked by trimethylation of histone H3 at lysine residue 4 ( H3K4me3 ) , typically enriched in euchromatic regions , epialleles in silenced lines ( S ) have nucleosomes with a modification characteristic of heterochromatin , namely dimethylated lysines at position 9 ( H3K9me2 ) ( Figure 1D ) . These marks , also including low levels of H3 dimethylated at position 27 ( H3K27me2 ) , only extend a short distance from the transgene into the flanking plant DNA ( Figure S2 ) , indicating limited spreading in transcriptional direction . Beside the specific modifications , we also observed an overall reduced association with H3 in line R compared to S ( Figure 1E ) , probably rendering the promoters more accessible for the transcription machinery . While the epialleles clearly differed in their local chromatin configuration , this did not have any effect on their nuclear localization ( Figure S3 ) . Both epialleles were stably inherited over a minimum of eight generations of self-pollination , without any evidence for spontaneous switches in the germ line . To also study the stability of epialleles in undifferentiated cells , we initiated callus cultures , starting with cotyledons of resistant , sensitive , and non-transgenic plants , and propagated the calli for up to six months under non-selective conditions . We screened callus tissue at several time points for its ability to grow under hygromycin selection for up to 5 weeks . Calli derived from R lines were resistant whereas calli obtained from S or non-transgenic lines died on selection plates . We also determined chromatin modifications and DNA methylation in callus tissue grown on non-selective medium , with results comparable to those of leaf tissue ( Figure S4 ) . This demonstrates similar states and stable maintenance of epialleles even upon dedifferentiation . We screened for the involvement of antisense and/or small RNAs in silencing maintenance . Significant promoter activity of the NC region was excluded ( Figure S5A ) , and specific antisense RNA in line S could also not be detected , neither by northern blotting ( Figure S5B ) nor by RT-PCR ( data not shown ) . Nevertheless , we generated libraries from size-fractionated 19 nt to 26 nt RNAs prepared from flower buds of plants containing either the sensitive or resistant epiallele . Both libraries were sequenced ( Table S1 ) and the reads screened for alignment with the transgenic insert . The library from the R plants had only 59 reads ( 3 per 1 million reads ) with only one sequence with a match in the epiallele ( Figure 2A , Table S3 ) . In line S , we found 2661 ( 129 per 1 million reads ) matching the epiallele , with a predominant length of 24 nucleotides ( Figure 2A , Table S2 and Table S3 ) , the size class known to be primarily involved in RNA-directed DNA methylation ( RdDM ) . This is significantly more than in R , but still relatively little , compared to an individual miRNA ( 820 reads per 1 million for miRNA165 ) or to siRNA from a repetitive sequence ( >1000 reads per 1 million for TSI [26] ) . The reads in S were distributed along the epiallele but mostly outside the HPT coding region . Importantly , among all reads specific for the silent epiallele we found an sRNA peak ( 671 reads , 476 antisense and 195 sense ) covering 61 bp in the middle of the 505 bp non-coding sequence of the P2 transcript ( Figure 2B ) . The most abundant sRNAs overlap with the 54 nucleotides of unknown origin . However , this sequence encompasses 28 nucleotides that are homologous to the most 5′ end of the 35S promoter ( Figure 2B ) . In short , these results indicate very stable and completely isogenic epialleles that differ only in their transcriptional activity . DNA methylation , suppressing chromatin marks , and sRNAs , are specifically enriched at the transcriptionally inactive epiallele; while the counterpart produces high transcript levels , lacks DNA methylation and sRNAs , and carries modifications characteristic of open chromatin ( Figure 2C ) . In addition to the trans-acting mutants identified in a screen for restored HPT expression after mutagenesis of line S [22] , we identified six hygromycin-resistant plants in which the mutant phenotype was genetically linked to the resistance gene itself ( ‘cis-mutations’ , RΔ1-6 ) . All these mutants produced progeny that could grow on hygromycin selection plates ( Figure 3A ) , connected with restoration of variable amounts of P1 and P2 transcripts ( Figure 3B ) . Northern blot analysis of cis-mutant RNA revealed P1 transcripts of smaller size in all cis-mutants compared to those from the active R line ( Figure 3C ) . The length is reduced to different extents , indicating several independent mutational changes of the sequence . An extended northern blot analysis , with either total RNA or poly ( A ) -enriched RNA , showed that the P1 transcript in all lines besides RΔ6 is polyadenylated ( Figure S6 ) , likely due to a flanking sequence with some similarity to a polyA signal . While no P2 transcript from the second promoter is detectable in RΔ1 , RΔ2 , RΔ4 , and RΔ6 , there is a signal in RΔ3 and RΔ5 , including in the poly ( A ) fraction ( Figure S6C , S6D ) . To characterize the P1 transcripts , and to identify the transcriptional termination sites in the cis-mutants , we performed 3′-RACE . We also analyzed the genomic DNA of all cis-mutants after amplification of the transgenic insert from genomic DNA and aligned DNA and RNA sequences ( Figure 3D ) . This verified six different sequence rearrangements within the 3′ region: mainly deletions , but also one case of an inserted plant DNA fragment ( RΔ3 ) . The mutants RΔ1 and RΔ2 have both lost the duplicated promoter P2 and the NC sequence . The vector duplication was partially ( RΔ1 ) or completely ( RΔ2 ) deleted , as was part of the flanking plant sequence . The deletions in RΔ4 , RΔ5 , and RΔ6 did not or only partially affect the P2 promoter , and two of them maintain also the NC sequence . The rearrangement in RΔ3 is most complex: here , a 1243 bp plant DNA sequence derived from a position 1 . 2 kb upstream of the transgene location was inserted between the P1 transcript and the downstream vector fragment . In the mutants RΔ1 , RΔ2 , RΔ3 , and RΔ4 , the P1 transcripts are terminated at the ( first ) site of rearrangement , while the transcripts go beyond the breakpoints in RΔ5 and RΔ6 . Only RΔ3 and RΔ5 are able to produce the P2 transcript , as in these cases , the P2 promoter is complete and the heterologous sequence downstream was only slightly affected by mutagenesis ( Figure 3D ) . Nevertheless , the P2 transcript levels are much lower than in the R line ( Figure 3B ) . Interestingly , there is no overlap between the deletions in all individual cis-mutants , but the rearrangements had either affected the second promoter copy ( RΔ1 , RΔ2 , RΔ6 ) , or the DNA template for the P2 transcript ( RΔ1 , RΔ2 and RΔ4 ) , or the connection between both sequences ( RΔ3 , RΔ5 ) . All cis-mutants were tested for effects outside of the epiallele by analyzing the degree of genome-wide methylation at endogenous repeats and by introgressing a transcriptionally silent marker gene coding for β-glucuronidase from line L5 , shown to be affected by other epigenetic mutations [27]–[28] . None of the cis-mutants changed the modification or expression of these markers ( Figure S7 ) . Therefore , it is unlikely that they have an effect outside of the epiallele . Due to the hygromycin selection in the screen , all cis-mutants were expected to have a functional resistance marker gene . Indeed , the upstream promoter P1 and the HPT coding region were intact and identical in RΔ1-6 and hence potential new epialleles of the resistance gene . Therefore , we compared the chromatin state in this region . We found reduced DNA methylation levels in cis-mutants compared to S ( Figure 4A ) , and a detailed bisulfite methylation analysis confirmed an overall reduction of DNA methylation in the promoter region of cis-mutants ( Figure 4B , 4C ) . However , the degree of hypomethylation , and the distribution of the remaining methylated cytosine residues , do not support a direct and linear correlation with expression levels . Although RΔ2 , RΔ3 , and RΔ4 show the strongest reduction of CG methylation , especially at the transcription factor binding sites ( Figure 4B , asterisk ) , and have expression levels comparable to R ( Figure 3B ) , methylation in RΔ5 is similar to RΔ3 and RΔ4 , although P1 transcript expression is much lower . Also , RΔ3 and RΔ4 have even gained CHH methylation in the 5′ region . Concomitant with the loss of DNA methylation , the modification specific for the silent state ( H3K9me2 ) was changed in favor of the active mark ( H3K4me3 ) in P1 and P1-transcribed regions , as demonstrated by ChIP ( Figure 4D ) . One mutant ( RΔ1 ) maintained a high level of H3K9me2 similar to that of the silent epiallele . Nonetheless , it also acquired a remarkable amount of H3K4me3 , although less than other cis-mutants . Independent of the modifications , and similar to the resistant line , cis-mutants showed a decreased level of H3 association , indicating that the sequence rearrangements had also affected the nucleosome density ( Figure 4E ) . On the whole , the cis-mutants demonstrate that structural rearrangements can cause significant changes in transcriptional activation and chromatin configuration at the previously silent epiallele . These changes are surprisingly divergent and reflect specific effects of similar but not overlapping deletions . The extreme stability of R and S epialleles through many generations and in callus cultures raised the question of expression stability in the cis-mutants . Most structurally rearranged derivatives displayed similar stability and provided comparable hygromycin resistance over several generations of homozygous cis-mutants ( S4 to S6 tested ) . RΔ2 , RΔ3 , and RΔ4 produced resistant progeny in consecutive generations . Resistance in RΔ5 and RΔ6 was lower in S4 ( 56% and 61% , respectively ) , but maintained this level up to S6 . In contrast , RΔ1 plants that were clearly hygromycin-resistant in S4 ( 84% ) generated partially sensitive S5 and fully sensitive S6 progeny ( Figure 5A ) . This correlates well with the loss of unmethylated sites at the transgenic insert ( Figure 5B ) , similar to gradual loss of resistance over 5 generations described for another marker gene [29] . The instability in RΔ1 does not correspond with additional sequence changes , as the same rearranged structure ( Figure 3D ) is maintained in subsequent generations . Rather , it correlates with the epigenetic state , since RΔ1 was characterized by the bivalent histone modifications ( Figure 4D ) . The re-silencing in generation S6 of RΔ1 allowed us to compare silencing maintenance at promoter 1 between this line and the S epiallele . We tested plants of both lines after growth in the presence of zebularine [reducing DNA methylation , 30] or DZNep [reducing histone methylation and also DNA methylation via SAHH]-[inhibition , 22 , 31] . Zebularine alone did not reactivate promoter P1 in line S , but in RΔ1S6 , and DZNep-induced activation was twice as high in RΔ1S6 compared to S ( Figure 5C ) . This indicates that S and RΔ1S6 differ in the stringency of silencing , either due to presence or absence of the P2 promoter and transcript , or to the lineage history of RΔ1S6 from a recently active state . The presence of the P2 promoter in RΔ3 - 6 and the expression of the P2 transcript in RΔ3 and 5 , which do not cause re-silencing in later generations , make the latter explanation more likely .
The thorough analysis of the HPT transgene in its two opposite expression states has revealed sequence identity over the full length of the insertion , significant differences in chromatin modifications and few , but silencing-specific , small RNA molecules . Chromatin differences are restricted to the affected sequence , with no hint of genome-wide changes or modified localization of the genomic region within the nucleus . Together with heritability of the expression states over many generations , and their maintenance even upon de-differentiation , the data prove the transcriptionally active and the silenced version to be authentic epialleles . Their occurrence in Arabidopsis , the best studied model for epigenetic research in plants , and the easy assay for the selectable hygromycin resistance conferred by the active state , made this pair of epialleles convenient tools for studying maintenance and switching of epigenetic states . After mutagenesis , we identified several hygromycin-resistant plants in which mutations in the epiallele sequence downstream of the HPT coding region had reactivated the previously silenced epiallele . Combining DNA and RNA sequence analysis and characterization of chromatin modifications , we found that these structural changes of the DNA sequence caused substantial upstream changes in chromatin and transcriptional activity . Beyond the complex and mutually dependent interplay of chemical modifications of the DNA and the associated histones , and longer and small , coding and non-coding RNAs described in numerous cases , the results presented here have shown that even small and non-overlapping modifications of the genomic template , outside of the promoter and open reading frame , can modify transcription and chromatin states in the vicinity . These changes are not minor: the bacterial gene HPT coding for hygromycin phosphotransferase is a selectable marker gene applied in numerous plant transformation experiments [32] , but plants need a significant amount of HPT transcript to produce enough protein to detoxify the antibiotic . Minor reactivation in the background of some epigenetic mutants tested in a reverse genetic approach ( data not shown ) was not sufficient . Therefore , the stringent assay for restored hygromycin resistance required a substantial change , as in the case of the trans-acting mutants from the same screen that revealed a double lock of two simultaneous chromatin modifications [22] . HPT expression levels are indeed similar between cis-and trans-acting mutants . Although the transgenic marker allowed this convenient selection for drastic epigenetic switches , without affecting plants under non-selective conditions , it could have been considered not representative for other , plant-endogenous or general cases . However , a recent publication [33] describes an interesting mutation that affects expression of the gene for nodulation factor SUNN in Medicago truncatula . The mutation is closely linked to the SUNN gene , acts only in cis but does not change the DNA sequence of the SUNN gene itself . Although the nature of this mutation is not yet identified , it could exert its effect in a similar way to the cis-mutants described here , especially since the ‘like sunn supernodulator’ mutant phenotype is occasionally unstable , like the hygromycin resistance in RΔ1 , 5 , and 6 . Other examples may be found upon further inspection of natural transcript level variation between regions with very similar gene sequences in plants [e . g . 8] or in the connection between chromatin structure and trinucleotide repeat expansion in mammals [for review 34] . Transcriptional gene silencing is often associated with the presence of homologous sequences in the genome [e . g . 35]–[37] , and intentional rearrangements from complex inserts to single copies by site-specific recombinase eliminate silencing [e . g . 38] . Therefore , when we started the analysis of the sequence changes in the cis-mutants , we were expecting a clear dependence of reactivation on loss of the duplicated region . This is not the case , since all cis-mutants , with the exception of RΔ2 , still retain some duplicated regions . Also against expectation , a loss of the non-coding sequence homologous to the most abundant small RNAs is not a prerequisite for reactivation ( RΔ3 , RΔ5 , and RΔ6 ) . Furthermore , a loss of the small transcript starting from the P2 promoter is not necessary ( RΔ3 and RΔ5 ) , although its level in these mutants is not as high as in R plants . It should be kept in mind that neither the tandem sequence duplications , nor either of the two transcripts , are sufficient to initiate silencing , since R plants ( with the complete insert and substantial transcription from P1 and P2 ) are fully resistant and stable . This is distinct from the FWA gene where tandem repeats are necessary and sufficient for silencing and DNA methylation [39] . Considering the lack of DNA methylation and small RNAs at the HPT insert in R plants , it is possible that the initial steps of silencing do not occur , are not efficient enough to start the reinforcing mechanism [39] , or are inhibited by efficient transcription [40] . However , such conditions must have been overruled on the rare occasions that produced the silent epiallele in the first place . The deletions in the different cis-mutants do not overlap in a specific region , and the smallest change is the loss of just 65 bp ( RΔ5 ) . Apparently , rather than affecting a specific sequence , the rearrangements change the overall organization at this locus . These changes can have variable consequences for the upstream promoter , causing either decisive , stable epigenetic switches ( RΔ2 , RΔ3 , RΔ4 ) or leading to ambivalent states that can later fall back into silencing ( RΔ1 ) . How such small genetic heterogeneity , that does not affect coding or regulatory regions , can cause extreme epigenetic diversity at a promoter elsewhere remains an open question . The sequence changes could exert their effect by modifying the distance to flanking regulatory regions , the nucleosome arrangement or density , the association with DNA-binding molecules , or any higher order structure within the DNA . It is clearly different from the ‘spreading’ effect of silencing often associated with RdDM [41]–[42]: it causes activation ( not silencing ) , goes against ( not along with ) the direction of transcription , and the most abundant of the relatively few small RNAs does not match the affected sequence of the upstream promoter . The results emphasize the mutual dependence between genetic and epigenetic factors , while indicating that these do not necessarily act at overlapping genomic sites . Similar effects might explain some of the associated changes in gene expression in the vicinity of small or large sequence modifications by transposon or recombination events . One example at a similar distance might be the transposon-dependent loss and gain of DNA methylation and inverse gene expression regulating sex determination in melon , at a site just 1 . 5 kb away from the insertion/excision site [43] . The relatively high number of cis-mutants in the screen was plausible in retrospective: mutations outside of the epiallele released silencing only if they reduce two epigenetic marks simultaneously . This is achieved by a few special mutations [22] or theoretically by rare double mutations and explains the low number of trans-acting mutants . In the study here , the genetic changes were found after mutagenesis by Agrobacterium-mediated T-DNA transformation [22] , although none of the cis-mutations was connected with an integrated fragment of the incoming T-DNA . T-DNA transformation is also known to create mutations unlinked , or independent , from the site of integration [44] and can cause complex chromosome rearrangements [45]–[46] . Successful , and possibly also attempted , integrations occur at sites of microhomologies between T-DNA and plant DNA [47]–[48] . The incoming T-DNA [49] has some homology with the terminator sequences in the epiallele ( ΔT ) , and in fact , the deletion sites in two cis-mutants ( RΔ2 , RΔ3 ) are near , or in , this sequence . The other deletions are close to promoter copy P2 that has no homology with the T-DNA , but potentially reflect a recombination hotspot in the 35S promoter sequence [50] . Alternatively , the double strand breaks connected with completed or aborted integration might stimulate repair via homologous recombination between the duplicated sequences of the epiallele ( RΔ3 ) . This would indeed have selected for 3′ rearrangements since those affecting the upstream copy are likely to lose the functional HPT cassette . All together , the R and S epialleles described here provide an example of identical DNA sequences with converse expression states and specific epigenetic configuration that are faithfully transmitted to progeny . However , sequence changes in the vicinity of the silent epiallele can induce an epigenetic switch to the opposite state . These can have different degrees of stability , depending on the complex interplay between the nature of the sequence alteration , the consequences for transcription and transcripts , and the chromatin organization ( Figure 6 ) . This also illustrates a tight dependence of epigenetic regulation on local structures and makes it likely that DNA rearrangements can potentially change or induce new epialleles outside the affected region .
Arabidopsis thaliana lines with R and S epialleles in accession Zürich and mutagenesis of line S were described previously [20] , [22] . Stratified seeds were surface-sterilized with 5% sodium hypochlorite and 0 . 05% Tween-80 for 6 min , washed and air-dried overnight . Sterilized seeds were germinated and grown in Petri dishes containing agar-solidified germination medium ( GM ) in growth chambers under 16 h light/8 h dark cycles at 21°C . For drug treatments , seeds were sown and plants grown on GM plates with hygromycin ( 10 µg/ml , Calbiochem ) , zebularine ( 40 µM , Sigma ) or 3-deazaneplanocin ( DZNep , 2 µM , donated by Dr . Victor Marquez ) under the conditions described above . Genomic DNA was isolated from 3 week-old seedlings using either DNeasy Plant Mini Kit ( Qiagen ) or Phytopure ( Amersham ) , following the manufacturers' protocols , except that genomic DNA was eluted in sterile water . Total RNA extraction from 3 week-old seedlings was performed with RNeasy Plant Mini Kit ( Qiagen ) including an on-column DNase I digest ( Qiagen ) . For Southern blot analysis , 10 µg of genomic DNA were digested overnight with 20 U restriction enzymes . For methylation-specific Southern blot analysis , the methylation-sensitive restriction enzymes ( HpaII , blocked by mCG and mCHG , and MspI , blocked only by mCHG ) were used . Digested samples were electrophoretically separated on 1 . 2% TAE agarose gels , depurinated for 10 min in 250 mM HCl , denaturated for 30 min in denaturation solution containing 0 . 5 M NaOH and 1 . 5 M NaCl and neutralized twice in 0 . 5 M Tris , 1 . 5 M NaCl and 1 mM EDTA at pH7 . 2 for 15 min . For northern blot analysis of total and poly ( A ) RNA , 5 µg of RNA were denatured with 15% glyoxal and 50% DMSO for 1 h at 50°C and separated using 1 . 5% agarose gels in 10 mM sodium phosphate buffer pH7 in a Sea2000 circular flow electrophoresis chamber ( Elchrom Scientific ) . DNA and RNA gels were blotted onto Hybond N+ ( Amersham ) membranes overnight with 20× SSC , washed and UV-crosslinked using a Stratalinker ( Stratagene ) . Hybridization was performed as described [51] . Radioactively labeled sequence-specific probes were synthesized from 25 ng of DNA using the Rediprime labeling kit ( Amersham ) and 50 µCi dCTP-α-32P ( Amersham or Hartmann Analytic ) and purified on G50 Probequant ( Amersham ) columns . Signals were detected with phosphoimager screens ( Bio-Rad ) and scanned with a Molecular Imager FX ( Bio-Rad ) . 3′-RACE was performed with the SMART RACE cDNA Amplification Kit ( Clontech ) according to the instructions . Total RNA ( 700 ng ) was treated with DNaseI ( Fermentas ) , then reverse-transcribed with RevertAidRT ( Fermentas ) with 3-RACE A primer ( 5–AAGCAGTGGTATCAACGCAGAGTAC ( T ) 30V N–3 ) in a 20 µl reaction . Two µl of cDNA reaction were used as template in 3′-RACE PCR . For this , Advantage 2 PCR Kit ( Clontech ) was used according to instructions . A control primer ( Actin , Act2F primer: 5-GCCATCCAAGCTGTTCTCTC-3 ) and gene-specific primers were used in combination with UniA_45 ( 5–CTAATACGACTCACTATAGGGCAAGCAGTGGTATCAACGCAGAGT–3 ) . RNA samples were treated with DNase I ( MBI Fermentas ) for 30 min at 37°C to remove residual DNA contamination . The reaction was inactivated by addition of EDTA and incubation at 65°C for 10 min . Reverse transcription was performed on 1 µg of RNA with 0 . 2 µg of random hexamer primers ( MBI Fermentas ) using 1 U RevertAid H Minus M-MuLV-RTase ( MBI Fermentas ) in the presence of 20 U RiboLock Ribonuclease inhibitor at 42°C for 1 . 5 h . Real time PCR analysis was performed with the 2× SensiMix Plus SYBR & Fluorescein Kit ( Quantace ) protocol using an iQ5 Real-Time-PCR System ( BioRad Laboratories ) . The obtained Ct values were analyzed with the iQ5 Optical System Software Version 2 . 0 ( Bio-Rad ) , applying the mathematical model for relative quantification in Excel ( Microsoft ) as described [52] . All primer sequences are listed in Table S4 . After treatment with RNase A and proteinase K , 1–2 µg of genomic DNA were digested overnight with BamHI ( MBI Fermentas ) . Subsequent bisulphite conversion was carried out using the Epitect Conversion Kit ( Qiagen ) and controlled for completion as described [21] , [53] . Converted DNA was used for PCR amplification . PCR-amplified DNA was cloned using pGEM-Teasy ( Promega ) and ligation mixes transformed into DH5α cells ( Invitrogen ) and sequenced by terminal-labeling using BigDye Terminator v3 . 1 ( Applied Biosystems ) . The sequence information obtained was analyzed with CyMATE , www . gmi . oeaw . ac . at/cymate [54] , and Excel ( Microsoft ) . ChIP was performed as described ( http://mescaline . igh . cnrs . fr/EpiGeneSys/www/images/protopdf/p13 . pdf ) using 3 week-old seedlings . The chromatin was immuno-precipitated with antibodies to histone H3 ( Abcam , ab1791 ) , H3K4me3 ( Upstate , 07-473 ) , H3K9me2 ( T . Jenuwein 4677 and Abcam ab1220 ) , and H3K27me2 ( Upstate , 07-473 ) . Immunoprecipitated DNA was purified using a Qiagen PCR Purification Kit and eluted in 50 µl of EB buffer . Quantitative real-time PCR was carried out in a total reaction volume of 25 µl and qPCR conditions were according to the 2× SensiMix Plus SYBR & Fluorescein Kit ( Quantace ) protocol using an iQ5 Real-Time-PCR System ( BioRad Laboratories ) . qPCR data were evaluated as a ratio to input DNA [55] . Small RNA was isolated from either pooled inflorescences or seedlings ( 21 days old ) using the mirVana miRNA Isolation Kit ( Ambion ) . Small RNA libraries were generated as previously described [56] and sequenced using the Illumina G2 platform . After clipping the adapter sequence by vectorstrip software from EMBOSS package [57] , small RNA reads were screened for homology with the epiallele sequence using bowtie [58] , allowing only perfect matches ( Table S3 ) . Reads homologous to tRNA , rRNA , snRNA , snoRNA , mitochondrial RNAs , and chloroplast RNAs were removed by custom Perl scripts . The total number of reads that mapped to a certain region was computed as sum of 1/N_i ( N_i is the number of times the read i was mapped ) . It was then normalized to indicate the number of each read per million bp ( adapted from the RPKM concept in RNA-Seq , [59] . A threshold of 10 reads was chosen for any sequence to be taken into account . For the epiallele region , the normalized number of mapped reads was computed at single bp scale . For a more detailed view on a selected region , the analysis was performed with SiLoMa [60] . Additional methods are described in Text S1 . | In contrast to alleles , epialleles have identical DNA sequence and differ only in gene expression and chromatin features . Epialleles are heritable and can also contribute to phenotypes . How this variation originates is unclear . In this study , we analyzed two epialleles found in Arabidopsis for the difference between their chromatin features and their potential to change state . We mutagenized plants with the inactive epiallele and recovered mutants with restored gene expression . In several cases , this was connected with different rearrangements downstream of the epiallele that caused a switch of the epigenetic configuration further upstream . Therefore , sequence alterations , for example by transposon activity or recombination events , may trigger similar heritable changes of chromatin and gene expression in their proximity and could create new epialleles . | [
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"molecul... | 2011 | Genetic Rearrangements Can Modify Chromatin Features at Epialleles |
What are the features of movement encoded by changing motor commands ? Do motor commands encode movement independently or can they be represented in a reduced set of signals ( i . e . synergies ) ? Motor encoding poses a computational and practical challenge because many muscles typically drive movement , and simultaneous electrophysiology recordings of all motor commands are typically not available . Moreover , during a single locomotor period ( a stride or wingstroke ) the variation in movement may have high dimensionality , even if only a few discrete signals activate the muscles . Here , we apply the method of partial least squares ( PLS ) to extract the encoded features of movement based on the cross-covariance of motor signals and movement . PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest . We use this approach to explore how the main downstroke flight muscles of an insect , the hawkmoth Manduca sexta , encode torque during yaw turns . We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli . We ask whether this pair of muscles acts as a muscle synergy ( a single linear combination of activity ) consistent with their hypothesized function of producing a left-right power differential . Alternatively , each muscle might individually encode variation in movement . We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke . At first , the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque . However , when we consider the PLS features , the muscles reveal independent encoding of torque . Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation . PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional sensory or motor transformations .
Control of animal movement is accomplished through the coordinated action of many parallel motor signals activating many muscles . To understand how motor spikes are transformed into action requires knowledge of how movement is encoded in these patterns of neuromuscular activation . Unfortunately , we cannot reliably predict the movement resulting from a particular motor signal simply from a muscle’s anatomy and static function ( e . g . an “extensor” ) [1–4] . The same pattern of activation to the same muscle , but in different dynamic contexts can even produce turning torques in opposite directions [2] . Moreover , both motor signals ( the inputs to the motor transform ) and movements ( the outputs ) are typically high dimensional and we may not be able to record all relevant motor signals electrophysiologically . Understanding how muscles work together to encode movement is therefore a computational challenge of both 1 ) dimensionality and 2 ) incomplete representation . High dimensionality is ubiquitous in both sensory and motor transformations . Dimensionality reduction techniques used in sensory neuroscience are typically one-sided , meaning that a high dimensional stimulus is reduced to describe variation in a single spiking neuron [5] . Similar use of one-sided dimensionality reduction in the motor transformation identifies patterns in high dimensional representations of movement encoded in the activity of individual muscles e . g . [6] . Conversely , the dimensionality of the neural signals can be high , as is the case for many central brain recordings , but in many experimental designs the representation of motor output is restricted to few dimensions , or even discrete states , e . g . [7] . Developing techniques that represent high dimensional movement encoded in high dimensional neural signals remains challenging [8] , yet is ever more pressing as such datasets become the norm . The second challenge for motor encoding is one of incompleteness . Muscle synergies , patterns of variation in activation across multiple muscles , are hypothesized to reduce the dimensionality of the motor commands and provide high level encoding of movement features [9 , 10] . However , not all variation in muscle activation might affect movement dynamics ( and vice versa ) . If we consider all variation in a high dimensional description of movement as potentially relevant , we are likely to include variation that is not encoded by the muscles we are able to record from . This challenge exists for sensory encoding as well . Only certain features of a complex stimulus may be encoded by the neurons under consideration . Spike triggering is one way to extract only relevant variation . This method conditions the stimulus on the spiking of an individual neuron ( or muscle ) thereby limiting the reduced stimulus description to what is encoded in that spike . However , we can only align to a single neuron or discrete patterns of spiking across many neurons [5 , 6] . Relating activation of multiple muscles to a rich description of movement demands a reliable way to 1 ) reduce dimensionality when both input and output have multiple dimensions and 2 ) extract only the changes in movement that covary with the subset of muscles recorded . Here , we develop a feature encoding analysis based on the partial least squares ( PLS ) method of two-sided dimensionality reduction [11 , 12] . By two-sided , we mean that PLS uses variation in both input and output to reduce dimensionality , addressing the two challenges above . Using this approach , we analyze the movement encoded in the flight muscles of the hawkmoth , Manduca sexta , to test a muscle synergy hypothesis for flight muscle coordination [13 , 14] . The term “muscle synergy” has many meanings [9 , 10 , 15–20] , but it is usually represented as a set of muscles that act in a fixed proportion , or in proportions undergoing a fixed time-varying pattern [10 , 20] . In their most general sense , muscle synergies are linear combinations of variables describing muscle activation that capture variation with fewer dimensions than the complete set of variables [9] . To avoid confusion , this use of muscle synergy differs from “information synergy” where two signals jointly provide more information than the sum of their individual contributions [21] . It is also different from the terminology of synergistic ( vs . antagonistic ) muscles , which refers to muscles acting on the same joint to produce movement in the same ( vs . opposite ) direction . A variety of reasons have been posed for the existence of synergies , most notably for simplifying control by reducing the number of independent motor signals an animal’s brain must control [10] . In invertebrates some synergies exist simply due to anatomy because individual motor neurons can innervate multiple muscles ( e . g . [22] ) . Even when innervation is separate , motor units can fire in very tight synchrony , acting like a simple synergy . In Manduca , each of the main downstroke muscles ( dorsolongitudinal muscles or DLMs ) has five discrete subunits ( Fig 1A ) , each innervated by a separate motor neuron ( one per subunit ) [13 , 23] . However , the whole muscle fires as one combined motor unit because the timing of the motor neurons is very precisely synchronized [23] . In fact , each DLM is driven by only a single muscle potential during each wingstroke ( Fig 1B; [13 , 14] ) . Activation of the DLMs therefore varies only in the timing of the spike rather than magnitude ( e . g . number of spikes ) . During straight flight the timing of the left and right DLM are also very precisely synchronized ( spikes occur within < 1 ms of each other ) , but during turning the timing is modulated over an ~8 ms window [14] . The fact that the DLMs have any potential for control has only recently been appreciated [14 , 25] because this subtle timing modulation occurs over such a narrow range . Nonetheless , such small shifts produce large changes in mechanical power output [14 , 26] . Prior to this discovery , control of the wings was thought to be the exclusive domain of the small steering muscles that trim and tension the wingstroke [13] . The question we pose here is whether the two DLMs encode independent aspects of turning torque production or if they act as a synergy . Three reasons why these muscles could act as a synergy are: 1 ) They are activated at the midpoint of a steep but monotonic region of the power-phase curve , meaning that small changes in timing produce large , but nearly linear , changes in power output [14 , 26] . Therefore the timing difference between the left and right muscle’s spike translates into a power differential [14] . 2 ) Variation in the timing of activation is also highly correlated between the DLMs , meaning that little independent variation exists that could encode movement separately in each muscle [14] . 3 ) The two muscles both attach to the same large exoskeletal plate that deforms to drive wing motions , resulting in mechanical coupling of their action [24] . As we will see , the synergy hypothesis is supported when we consider wingstroke-averaged turning torque , but fails to describe the variation in motor features captured by the PLS analysis . The tractability of analyzing invertebrate motor commands combined with the ability to record turning torque affords an opportunity to test hypotheses about muscle synergies with spike-level resolution . The coordination of the moth’s downstroke muscles is a very simple synergy hypothesis: that one variable describing the combination of these two muscles’ activities has as much predictive power as considering the two muscles independently . Manduca flight muscle is also a good system for assessing the PLS approach because the timing of activation of multiple muscles translates into a continuously varying , high dimensional pattern of torque throughout the wingstroke . Our goal is to identify the few relevant dimensions of torque that are encoded by changes in the DLMs and then to use these to assess the synergy hypothesis . Even though we focus on a specific hypothesis about an insect’s flight motor program , we use this system to show how PLS feature analysis may be generally applied to produce a data-driven decomposition of two simultaneously measured datasets .
Manduca sexta is a large crepuscular hawkmoth capable of agile , maneuverable flight . It demonstrates a strong visual tracking ( optomotor ) response to oscillating wide-field optical patterns [27] . This behavior is ecologically relevant because moths must hover and feed while visually tracking flowers’ movement [28 , 29] . Flight in hawkmoths is powered by a left-right pair of DLMs and a pair of upstroke muscles ( the dorsoventral muscles or DVMs; Fig 1A ) [24] . However , a suite of several smaller steering muscles further trim or tension the movement of the wings , and contribute to the control of turning [13 , 30] . It is therefore still challenging to isolate the turning dynamics encoded in the DLMs’ activation alone . We flew seven moths ( mixed sex ) under open-loop , visually driven ( i . e . optomotor ) flight conditions that produced left and right turning behaviors . The data used here are from the same moths used previously and under conditions elaborated on in [14] . In brief , bipolar tungsten electrodes inserted through the scutum on the dorsal surface of each moth recorded from the DLMs ( Fig 1B ) . We tethered moths to a custom optical torque-meter that produced an output voltage dependent on the yaw ( left-right ) turning torque of the animal ( Fig 1C ) [14] . Following at least a minute of warm-up shivering , moths produced full-amplitude wingstrokes spontaneously or when we elicited flight with a light touch to the neck region . The visual stimulus consisted of a sinusoidal grating of light and dark bars with a spatial frequency of 0 . 05 cycles degree-1 , oscillating sinusoidally at 1 Hz . Because wingstroke frequency is much faster ( ~25 Hz ) , this slow variation in optic flow magnitude produced individual wingstrokes spanning a wide range of average yaw torque . We recorded electromyograms ( EMGs ) from the left and right DLMs and detected spikes using simple threshold crossing . Invertebrate EMGs usually afford resolution of individual muscle potentials , or spikes , whereas vertebrate recordings typically record from many motor units simultaneously , obscuring individual spikes [9 , 10] . Trials were analyzed further only if the right DLM’s average spike rate exceeded 18 spikes sec-1 , corresponding to the lowest flight flapping frequency . To extract the torque within each wingstroke , we had to decouple the internal dynamics of the torquemeter from the forces applied by the animal . We modeled the torquemeter as a forced , damped rotational oscillator . Iϕ¨+Cϕ˙+κϕ=τ ( t ) . ( 1 ) where ϕ is the angle of rotation , I is the moment of inertia , C is the torsional damping coefficient , and κ is the torsional stiffness . We fit the spring , damping , and inertial parameters using a series of calibration trials following [31] . Our analytical goal was to relate a measured set of motor signals to the resulting movement , extract the relevant dimensions of variation in movement , and use this set of motor features to test the synergy hypothesis . During periodic movement , the motor signals form a matrix U of k timing ( or phase ) variables {u1 , … , uk} , characterizing neural or muscular action potentials ( hereafter “spikes” ) for each of N periods of movement ( e . g . wingstrokes ) . During rhythmic movement with a characteristic period T , the motor output matrix is an N x b matrix , M , where b is the number of samples of a set of movement variables over T . Since the DLMs each only receive a single spike of activation per wingstroke , we defined the N x 2 , U signal matrix using the time of the left ( tL ) and the right ( tR ) muscle’s spikes relative to the zero phase onset of each wingstroke ( Fig 1B ) . These variables were each centered and scaled by their variances . The N x 500 , M movement matrix was composed of 50 ms ( 500 samples ) long waveforms , {τ1 . … τ500} , based on a typical tethered wingbeat frequency of 20 Hz and a 10 kHz sampling of torque . The ensemble of wingstrokes were centered and scaled by their overall variance , s . Our approach to extracting motor features follows four steps ( Fig 1D–1H ) : 1 ) Alignment of the torque waveforms , 2 ) dimensionality reduction of the movement matrix , M , to extract a reduced basis , termed motor features , 3 ) synergy testing and 4 ) reconstruction of the torque waveforms from the motor signals , U . By comparing how well torque was encoded by different combinations of the motor signals , U , we asked if these signals act as a synergy or independently encode information about movement . In the independence model , the activation of each muscle {tL , tR} contributes significantly to predicting torque , and these two variables ( the two columns of U ) cannot be reduced to a single variable . We pose two synergy models , constructed as different linear combinations of the motor signals . The first is based on an a priori physiological hypothesis that the timing difference Δt between the muscles’ spikes could translate directly into a power differential between the muscles [14 , 25] . We refer to this as the differential synergy model . The second synergy model is entirely data-driven: we extract the first principal component ( tPCA—the PCA synergy model ) of the two motor variables {tL , tR} . This closely mirrors the existing methods of muscle synergy calculation in the literature [9] . However , we use PCA instead of the sign-dependent nonnegative matrix factorization ( NMF ) technique [32] because timing can shift positively or negatively . We call this the empirical synergy model . Finally , we test a redundancy model , in which the variation in torque is equally well explained by only one muscle’s activation . In summary , we use the full set of motor signals , U , and torque samples , M , to identify all the variation in movement that cross-correlates with changes in any U variable ( Fig 1F ) . Using this same feature basis , we then test whether either of the synergy models can fully explain the variation in the feature basis or if the independence model ( full U ) is needed ( Fig 1G ) . This avoids circularity because we identify the torque features contingent on U , but all the synergy and independence models are subsets or combinations of U . We test the different synergy , independence , and redundancy models by how well they explain variation in the projection of torque onto the feature basis ( the PLS “scores” ) and also how well the competing models perform in reconstruction of the entire torque waveform ( Fig 1H ) . We aligned the torque signal from each wingstroke in two different ways . We first produced an alignment that did not depend on the spikes directly . We computed the Hilbert transform of the torque signal , and filtered around the dominant 20 Hz wingstroke frequency ( 3–35 Hz bandpass , 8th order Type II Chebychev ) . The Hilbert transform returns a periodic function whose value estimates the phase of the original time series data and has been used for both gait [33] and rat whisking analyses [34] . After transforming the raw voltage signal ( Fig 1C ) into actual torque ( Fig 1E ) , each torque segment was aligned to the zero phase crossing of each wingstroke ( “phase-triggered” ) . As an alternative , we also aligned the torque to the timing of the right DLM’s action potential ( “spike-triggered”; Fig 1E ) . In both alignments , the resulting wingstrokes were assembled into the movement matrix , M ( Fig 2A and 2B ) . We applied two dimensionality reduction methods to the two different waveform alignments to determine which best captured the variations in torque that correspond to the motor timing variables in U . We first applied standard principal component analysis ( PCA ) by computing the eigendecomposition of the covariance matrix of M alone for both the phase- and spike-triggered ensembles . Note that this is different from the PCA used on the motor signals , U , to create the empirical synergy model . We compared the one-sided PCA analysis of M with a two-sided , cross-covariance decomposition based on the partial least squares ( PLS ) method ( Fig 1F ) . We explored how effectively these reduced descriptions of the torque output ( termed motor features ) captured variation in the motor signals {tL , tR} via standard regression of the features onto the signals . Partial least squares regression , hereafter PLS , extracts features from one dataset of predictor variables , here the animal’s movement M , to maximize each successive feature’s cross-covariance with an arbitrarily large set of predicted variables , here the motor signals U ( Table 1 ) [11 , 12 , 35] . PLS regression is one of several methods based on Wold’s original Projection onto Latent Structures [11] , which are used widely in chemometrics [35] . PLS regression uses an iterative approach that greedily extracts features in one dataset ( here M ) that maximally predict the remaining variance in another dataset ( here U ) . The set of features identified from PLS therefore are not guaranteed to maximize a global statistical property of the data [36] , although each subsequent feature is optimal for that iteration . This approach has been shown to have good predictability in empirical datasets ranging from neural imaging analyses [37] , ecology [38] , geometric morphometrics [39 , 40] , and paleontology [41] . We implement the faster Statistically-Inspired Modification of PLS ( SIMPLS ) developed by de Jong [35] rather than the original Non-Linear Iterative PLS ( NIPALS ) [11] that requires iterative optimization steps . Because we extract features of the motor output that maximize their ability to reconstruct the motor signals , the direction of the PLS regression is from M to U . This also allows the number of relevant motor output features to exceed the rank of U . The weights are the left and right singular values ( SVs ) from an SVD of the cross covariance matrix of M and U . Projecting these into M and U gives the loadings , which are the features and the coefficients of these projections are the scores . The SIMPLS algorithm , applied to U and M , has the following steps: In using cross-covariance to isolate relevant variation , PLS-based methods are similar to canonical correlation analysis ( CCA ) [42] . However , CCA is symmetric , uses only a single cross-covariance decomposition , and only extracts a number of features up to the smaller of the rank of the inputs or the rank of the outputs ( here only two ) . In contrast , PLS is a greedy , iterative algorithm that captures the unique contributions of successive features in describing the motor signals while preserving the asymmetry through a regression step [12] . It can therefore produce a number of features up to the number of variables describing the movement , given by the dimensionality of M . We compared the synergy and independence models first by how well the motor signals could predict the magnitude of each feature ( its score ) and the mean torque produced over a wingstroke ( Fig 1G ) . We quantify model performance using the predicted residual sum of squares ( PRESS ) . This form of leave-one-out cross-validation sequentially withholds each individual wingstroke from the analysis and then predicts the withheld wingstroke . If the two-variable independence model has greater predictive power than the one-variable synergy or redundancy models , then each muscle contributes significantly to the decoding of torque . If the independence model does not significantly improve PRESS , then at least one of the reduced models are favored . Note that the independence model cannot explain less variance than the synergy or redundancy models because these two alternatives are subsets of the independence model with one fewer free parameters . We used ANOVAs and paired tests to compare across models using each animal as a separate observation for model testing . In addition to comparing how well motor signals encoded the features , we also tested how well we could reconstruct the wingstroke torques from the features ( Fig 1H ) . The torque waveforms , M′ , can be approximated from PLS features as a sum of the average motor output ( the spike-triggered average—MSTA ) and the features weighted by their scores: M′=MSTA+sKPT . ( 16 ) The scaling factor , s , corrects for the original centering and scaling of the M matrix . The final score and loading vectors for the motor signal U matrix , D = {d1 , … , dn} and Q = {q1 , … , qn} define the dimensions of U maximally predicted in a least squares sense by each feature , pi . This approach is similar to reverse reconstruction of the stimulus given a set of spikes [5] . We first used the STA of all torque waveforms as a comparison for reconstruction ( only the first term of Eq . 16 ) . To demonstrate the efficacy of wingstroke-averaged methods , we next considered the STA with just the average torque over the whole wingstroke , < τ > , reconstructed from the synergy and independence models and added as a constant offset ( the mean torque models ) . This is the same as assuming that only wingstroke-to-wingstroke changes are encoded in the motor signals . We then reconstructed the torque using Equation 15 , which includes the STA and the first two motor features: M′=MSTA+sk1p1T+sk2p2T . ( 17 ) These reconstructions are the reduced-dimension representations of each measured wingstroke derived from the feature analysis . Finally , we reconstructed these waveforms from the motor signals themselves by predicting the features’ scores ( k1 and k2 in Eq . 17 ) from the motor signals . These reconstructions were based on using the independence , redundancy , differential synergy , and empirical synergy representations of U to generate the k’s ( via the regression equations ) . As our main metric of reconstruction performance we used the RMS power of the residual ( error ) torque waveform normalized to RMS of the actual waveform . We also considered the residual , or unexplained , variance in the model , calculated as 1—r2 , where r is the correlation coefficient . RMSE is sensitive to small , but systematic deviations ( e . g . offsets ) whereas unexplained variance is sensitive to small phase shifts . Throughout the analyses we used repeated measures ANOVAs with each animal contributing an observation for each decile of turning and for each model ( i . e . model and decile were each factors ) . Since we were primarily interested in comparing to the best model ( the actual two feature waveform or the independence model ) , we used Hsu’s “multiple comparisons to the best” ( MCB ) test [43]rather than a Tukey comparison of all pairs . In paired comparisons we use paired t-tests . We confirmed that non-parametric tests ( Kruskal-Wallis and pairwise Wilcoxon tests ) did not affect our conclusions . Statistical tests were performed in Matlab ( Mathworks , Natick , MA , USA ) with Hsu’s MCB tests performed in JMP ( SAS Institute , Cary , NC , USA ) . To test the predictive power of the reconstructions , we cross-validated the feature analysis using 70% of each decile of the data as a training set to predict the remaining 30% . Cross-validation was repeated one thousand times . We also reconstructed each individual wingstroke’s torque , rather than the decile averages . In this case , the maximum reconstruction performance is likely to be limited because the motor commands from the main flight muscles should only predict a portion of the overall variation in the wingstroke . However , the ability of these motor commands to predict the scores ( ki ) of each PLS feature ( pi ) should remain high because these include only variation in torque corresponding to flight muscle variation .
We first determined whether the torque waveforms varied in more than just their mean . After alignment via phase- or spike-triggering , we separated the resulting ensembles of wingstrokes into deciles ordered from left to right turns by mean torque . We found substantial variation , confirming our ability to visually induce a range of motor outputs ( Fig 2C and 2D ) . While mean torque varied smoothly across deciles , there were changes in shape of the wingstroke , including phase shifts . These were stronger in some animals than in others ( Fig 2D ) . In animal J ( Fig 2C ) , the amplitude of the torque around ventral wingstroke reversal ( ~25 ms ) varied and a secondary peak arose in the middle of the upstroke ( ~37 ms ) . In animal L ( Fig 2D ) , a similar double peak formed between 50 and 80% of the wingstroke cycle and there was a prominent phase shift . These patterns were consistent for both phase and spike-triggered ensembles . Our next goal was to determine the dimensions along which the torque signal covaried with the motor signals and which of the four combinations of alignment ( phase- vs . spike-triggering ) and dimensionality method ( PCA vs . PLS ) best captured this variation . In PCA , the spike-triggered waveforms required fewer features to reach the same explanatory power as the phase-triggered waveforms ( Fig 3A and 3B ) . This is presumably because the spike-triggered ensemble contains more implicit information about the timing variables . PCA features are ranked in order of their ability to describe variation in the motor output . This ordering does not correspond to each feature’s ability to predict motor timing variables: some higher-ranked PCA features explained more motor signal variation than lower-ranked features . Some lower-rank PCA features also describe variability in the waveform ensemble that is not correlated with spike timing . As a result , the cumulative sum of the variance explained by the PCA features did not have a constant plateau ( Fig 3C ) . Ranking features based on how well movement , M , explains muscle activity , U , is exactly the strength of PLS and , as expected , the PLS features are ordered such that they explain successively more of the variance in the motor timing signals ( Fig 3D–3F ) . The spike- and phase-triggered alignments performed comparably under PLS decomposition ( Fig 3D and 3E ) presumably because the spike timing information is now incorporated in the dimensionality reduction . However , with spike triggering , there was smoother accumulation of variance concentrated in the first features ( Fig 3F ) . Including Δt along with tL and tR in the original U matrix did not affect the number of motor features extracted from torque ( Fig 3G ) . The cumulative variance explained in timing variables does not reach 100% because features iteratively maximize the cross-covariance; the procedure aims to capture variance explained by the timing variables , not all variance in the output . The amount of variance explained also differs across animals . To combine all individuals , we scaled variation explained to the variance captured with 10 features ( Fig 3H and 3I ) . We did the same for the random features extracted from resampling the torque for each animal . The first two features explain significantly more variation than chance in the timing of both the left and the right muscle . While the variance generally drops off rapidly after the first feature , the second feature was important in some animals and so we retain it . Our conclusions about synergies do not depend on the inclusion of the second feature . In each animal analyzed , the two PLS motor features form a low dimensional basis describing the torque or movement matrix M and vary with left-right turning . Features generally had four periods of oscillation per wingstroke , consistent with the mean torque waveform , or spike-triggered average ( “STA”; Fig 4A ) . The score of the first feature correlated with the degree of turning from left- to rightmost . The second feature’s score had a maximum at intermediate torque values , corresponding to straight flight , and decreased during extreme left and right turns ( Fig 4B ) . The shapes of the extracted features make sense given our understanding of the control of flight . Varying the amplitude of the first feature has the greatest effect on torque at the beginning and midpoint of the wingstroke ( Fig 4A ) . These points correspond to ventral and dorsal wingstroke reversals , critical moments for flight control [44] . Furthermore , the features oscillate at approximately four times the wingstroke frequency , which is consistent with the patterns observed in the aerodynamics of a robotic Drosophila wing model [45] . Having identified PLS features that capture the variation in torque relating to variation in the motor signals , we now use this feature basis to test for synergies ( Fig 1G ) . We compared the variation in the torque captured by reduced combinations of the motor signals ( the synergy or redundancy models ) with that predicted by the two signals together ( the independence model ) . We predicted mean torque and the identified torque features through regression and cross-validation ( PRESS ) . When we predicted only the mean torque , but not the torque features , the one-variable synergy models performed as well as the two-variable independence models ( p > 0 . 1; Fig 4C ) , supporting the interpretation that the two muscle commands act as a synergy . However , when we considered either of the two PLS features , we found that the independence model significantly outperformed the redundancy or synergy models ( p < 0 . 05 in all cases; Fig 4D and 4E ) . This was true even for the first feature alone , which indicates that our conclusions do not rely on the iterated extraction of features—even the first feature is encoded in both tL and tR . Rather than just predicting the feature scores , we next addressed how well these descriptions of the motor commands and PLS features could reconstruct the entire torque waveform . The first two PLS features produced reconstructions of torque that closely matched the decile means ( Figs 5A , 5B and 6A ) , describing 95% of the variance and 70% of the residual variance after correlating the STA to the waveforms ( Fig 6B ) . If we predicted the amount of each feature ( its score ) from the motor signals , the reconstruction was necessarily worse than if we used the measured feature scores ( P < 0 . 001; Figs 5 , 7A ) ; compare cyan and green ) , but the resulting reconstructions were still significantly better than those based on mean torque alone ( P < 0 . 003; Fig 7A , 7B and 7D ) or using the STA without any added features ( P < 0 . 0004; Fig 6 ) . The PLS feature-based analysis improved reconstruction primarily by matching the shape and phase of the torque waveform ( Fig 5A and 5B ) . The independence model accurately reconstructed 91% of the average torque waveform across all turning deciles ( Fig 7B ) and 53% of the residual variance after accounting for the STA . Wingstrokes during more extreme turns were less completely reconstructed ( Fig 7A and 7B ) , but this is not surprising given that there are other muscles involved in turning whose activity we did not record . If we restrict the reconstruction to the torque projected into the two-feature subspace , the motor signals are even more accurate and consistent across deciles ( Fig 5 ) . Using the RMSE waveforms for all the deciles , we first confirmed that adding more features beyond the first two did not improve reconstruction ( Fig 7C ) . Reconstruction based on the independence model ( using both tR and tL to predict the feature scores in Equation 16 ) outperformed both synergy models and the redundancy model in minimizing the RMS error in the torque—even when controlling for the differences across deciles ( effect of model in two-factor ANOVA with decile; P < 0 . 05 in all cases; Fig 7A and 7D ) . As before , we only rejected the synergy models when considering PLS features because when considering the mean torque alone , the independence and synergy models were equivalent ( two-factor ANOVA; P > 0 . 1; Fig 7A and 7E ) . To summarize reconstruction performance across all deciles into a single performance measure , we took the mean ratio between the decile RMSE of each model and the RMSE of the two-feature reconstruction , which is the best two-dimensional reconstruction possible: This allows for paired tests across all animals and wingstrokes ( Fig 8A; paired t-tests comparing feature-based synergy models to independence P < 0 . 001 in all cases; average-torque based: P = 0 . 94 ) . The conclusions held even if the most extreme left and right turning deciles ( 20% of the data ) were excluded from the analysis ( feature based: P < 0 . 003; average torque based: P = 0 . 7 ) . To test if the rejection of the synergy hypothesis was due to statistical bias introduced by not including a linear combination of tR and tL in the original U matrix of motor signals , we repeated the entire analysis including Δt in U . The independence model still outperformed synergy models in the feature based cases ( Fig 8B; feature based: P < 0 . 0004; average torque based: P = 0 . 98 ) . One concern about using the spike-triggered ensemble is that variation in torque may itself be phase locked . In that case , spike triggering could introduce a spike-timing dependent signal that might bias our data . It is unlikely that variation due to changes in the muscle’s timing would be phase locked independent of muscle timing , but to check for this bias we repeated the feature extraction and reconstruction using the phase-triggered torque waveforms rather than spike-triggered ensembles . The results were the same as before ( Fig 8C; feature based: P < 0 . 001; average torque based: P = 0 . 94 ) . Finally we challenged the models to be more predictive . First , we tested that the results were robust to cross-validation ( Fig 8D ) . Second , we reconstructed the torque of individual wingstrokes rather than decile averages . RMS errors were naturally higher ( Fig 9A ) , but the reconstructed waveforms were highly correlated with the measured torque ( Fig 9B ) and the synergy models were still rejected in feature-based analyses ( paired t-test compared to independence model; all P < 10−7 ) .
Only two out of 500 possible motor features were required to describe the variation in torque encoded in changes in DLM activation during visually-induced turning ( Fig 3D ) . Reconstruction of decile averages ( Fig 7C ) , cross-validated predictions ( Fig 8D ) and individual wingstrokes ( Fig 9 ) are all improved by incorporating PLS features . We found that retaining the timing of both DLM activations captured more of the variance in torque than compressing these timing variables into a single linear combination , or synergy ( Fig 4D and 4E ) . The independence model also more accurately decodes within-wingstroke torque by reconstruction ( Figs 7D , 7E and 8A ) . The need to retain independent variation in the DLMs' activation was only revealed using the PLS feature basis . The synergy models are sufficient to account for the torque averaged over the wingstroke ( Figs 4C , 7D and 8 ) . When considering turning torque only from wingstroke to wingstroke , the difference in timing ( the differential synergy ) may be sufficient to describe dynamics . However , independent variation in the two muscles is necessary to encode within-wingstroke dynamics . This does not imply that each muscle’s activation is orthogonal . These within-wingstroke dynamics are likely critical to flight control . A recent Floquet analysis treating the periodic dynamics of flapping flight rather than just the wingstroke-averaged forces demonstrated that ignoring within-wingstroke dynamics alters the stable and unstable modes of moth flight [46] . The advantages of the PLS-based approach come in dealing with spike-resolved motor signals and in testing synergies in terms of how well motor variation is encoded rather than how much variation in muscle activation is described . Most synergy analyses consider a large number of muscles , each described by a single , time-varying activation variable [9 , 10 , 19] . This study connects individual spike-level encoding of movement with muscle synergy questions . Additionally , while it is common to identify the force vectors or movements that correspond to muscle synergies [19 , 47] , the extraction of synergies is usually an independent dimensionality reduction . Considering only the variation in muscle activation does not address whether or not this variation is relevant for movement generation . Here , we decompose a high-dimensional representation of torque to identify a relevant basis dependent on the variation in the muscles’ activity . We assess synergy performance against this basis . Whether muscle synergies represent a general strategy to simplify control remains an open question [10 , 20] . We found that a muscle representation that retains the independent timing of both muscle activations captures more behavioral variation in torque output than three specific synergy alternatives [14] . If recordings from more muscles were included , more complex synergies might exist . For example , some of the information encoded in the timing of the left DLM might also be shared with a steering muscle’s activity . This is always a concern with encoding studies , which alone do not establish causality . Fortunately , in this case we do know that the DLMs are causally involved in producing turning torque [14] . More importantly , while we cannot say if a larger set of recordings might not express synergies , we do know that the variation in the downstroke muscles must contribute to at least two separate synergies—they cannot be expressed in a single linear dimension even with more muscles included . For the present analysis , we constructed synergies from linear combinations of the motor signals , U , as a separate step from the PLS feature identification to be consistent with prior synergy analyses [9 , 32] . However , PLS provides an alternative way of constructing synergies . We obtain not only the motor features , P , but also a matrix of equal dimension Q , whose vectors , qi , represent a non-orthogonal set of synergies for the motor signals , U ( Table 1 , Equation 5 ) . This approach scales well as dimensionality increases and could also be advantageous for analyzing population encoding of complex sensory stimuli . In our case , reanalysis using the first vector q1 instead of the first PC of the DLMs’ activations does not change our conclusions . It is important to note that these methods are linear , and that our conclusions about the nature of motor encoding are based on the quality of linear correlation . It is possible that nonlinear analyses that take into account higher-order correlations may reach different conclusions . For example , information-based methods provide an alternative to covariance approaches that satisfy some of these same goals . The technique of maximally informative dimensions [48] seeks a feature basis which captures the most mutual information between input and output , where the output typically is the occurrence of a single spike [49] . This approach can likely be generalized to discover complex output symbols that preserve mutual information [50] . Such techniques can handle naturalistic stimuli and minimize a priori assumptions about the statistical structure of the data , but they typically require estimating the marginal ( or conditional ) probability distribution of the outputs with respect to the inputs . This is very challenging when the dimensionality of both the input and output becomes large ( although see [51] ) . In contrast , PLS readily scales to a large number of inputs and outputs because it relies only on estimating the cross-covariance structure in the data . That DLMs encode discernible torque variation is itself surprising because these muscles were long thought not to have any significant role in control [13] . DLMs can cause significant turning variation because small changes in their timing produce large changes in power [14] . However , other muscles almost certainly play a role in turning as well . In particular , small steering muscles modulate wingstroke angle ( e . g . the 3rd axillary muscle ) and demonstrate correlated phases of activation [13 , 30 , 52] . The role of steering muscles is one reason why the PLS approach is critical . PLS extracts features from the movement matrix , M , only if they covary with motor signals , U , and should ignore variation that is due to steering muscles but not encoded in the DLMs . Therefore , while a mechanistic understanding of turning control is not yet complete , our results do indicate that each DLM independently encodes information about the optomotor response . One reason why synergies may be present in nervous systems is to allow task control with a smaller set of command variables [9 , 10] . Optomotor sensory input is bilateral , at least in flies , because each eye possesses distinct populations of left and right directionally selective cells [53] . However , this information could be centralized into a single descending command setting the timing difference between the flight muscles . The fact that we reject a reduced representation for the pair of DLMs indicates that visual information can separately modify the descending commands to each of the two DLMs , in addition to its known feedback to steering muscles [13] . A PLS dimensionality reduction of torque incorporates multiple , continuous variables to describe the motor signals , while PCA can only incorporate information about the timing of one spiking event ( or pattern of spikes ) , via spike triggering [5] . By ranking features based on the cross-covariance , the PLS approach only captures motor variation relevant to the changing motor signals , while to some extent excluding components that are unrelated [12 , 35] . These factors account for the improved performance of PLS compared to PCA ( Fig 3 ) and should only improve further as the dimensionality of both the motor signals and of movement increases . Our analysis considering only two muscles is the minimal case in which PLS could outperform a spike-triggered PCA . Even in this case , the abilities of PLS to deal with the challenges of multiple high dimensional datasets and incomplete representation are necessary to discover independent encoding . Overall , PLS-based feature analysis is a data-efficient method to extract a reduced representation of a multiple input-multiple output ( MIMO ) data set . Here , the approach improves predictability of wingstroke variability and our understanding of the motor program . In contrast to encoding in the central nervous system [54–56] and sensory systems , there have been few applications of dimensionality reduction approaches to encoding in the peripheral motor system . Just as sensory encoding reveals the patterns of stimuli to which neurons respond , the encoding of movement in the activation of multiple muscles reveals the structure of the motor program . New approaches like PLS-based feature analysis set the stage for understanding how the peripheral nervous system represents locomotor control . | Understanding movement control is challenging because the brains of nearly all animals send motor command signals to many muscles , and these signals produce complex movements . In studying animal movement , one cannot always record all the motor commands an animal uses or know all the ways in which movement varies in response . A combined approach is necessary to find the relevant patterns: the changes in movement that correspond to changes in the recorded motor commands . Techniques exist to identify simple patterns in either the motor commands or the movements , but in this paper we develop an approach that identifies patterns in both simultaneously . We use this technique to understand how agile flying insects control aerial turns . The two main downstroke muscles of moths are thought to produce turns by creating a power difference between the left and right wings . The moth’s brain may only need to specify the difference in activation between the two muscles . We discover that moth’s brain actually has independent control over each muscle , and this separate control increases the moth’s ability to adjust turning within a single wingstroke . Our computational approach reveals sophisticated patterns of movement processing even in the small nervous systems of insects . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [] | 2015 | Dual Dimensionality Reduction Reveals Independent Encoding of Motor Features in a Muscle Synergy for Insect Flight Control |
Atrio-ventricular conduction disease is a common feature in Mendelian rhythm disorders associated with sudden cardiac death and is characterized by prolongation of the PR interval on the surface electrocardiogram ( ECG ) . Prolongation of the PR interval is also a strong predictor of atrial fibrillation , the most prevalent sustained cardiac arrhythmia . Despite the significant genetic component in PR duration variability , the genes regulating PR interval duration remain largely elusive . We here aimed to dissect the quantitative trait locus ( QTL ) for PR interval duration that we previously mapped in murine F2 progeny of a sensitized 129P2 and FVBN/J cross . To determine the underlying gene responsible for this QTL , genome-wide transcriptional profiling was carried out on myocardial tissue from 109 F2 mice . Expression QTLs ( eQTLs ) were mapped and the PR interval QTL was inspected for the co-incidence of eQTLs . We further determined the correlation of each of these transcripts to the PR interval . Tnni3k was the only eQTL , mapping to the PR-QTL , with an established abundant cardiac-specific expression pattern and a significant correlation to PR interval duration . Genotype inspection in various inbred mouse strains revealed the presence of at least three independent haplotypes at the Tnni3k locus . Measurement of PR interval duration and Tnni3k mRNA expression levels in six inbred lines identified a positive correlation between the level of Tnni3k mRNA and PR interval duration . Furthermore , in DBA/2J mice overexpressing hTNNI3K , and in DBA . AKR . hrtfm2 congenic mice , which harbor the AKR/J “high-Tnni3k expression” haplotype in the DBA/2J genetic background , PR interval duration was prolonged as compared to DBA/2J wild-type mice ( “low-Tnni3k expression” haplotype ) . Our data provide the first evidence for a role of Tnni3k in controlling the electrocardiographic PR interval indicating a function of Tnni3k in atrio-ventricular conduction .
Atrio-ventricular ( AV ) conduction delay describes the impairment of the electrical continuity between the atria and the ventricles and is characterized by prolongation of the PR interval on the surface electrocardiogram ( ECG ) . AV delay of varying severity is a common feature in Mendelian rhythm disorders and is associated with sudden cardiac death [1] . PR interval prolongation is also a strong predictor of atrial fibrillation ( AF ) [2] and is therefore considered an intermediate phenotype for this condition [3] . AF is the most commonly observed sustained cardiac arrhythmia , with an age dependent prevalence of up to 9% [4] . Identification of genetic determinants of AV conduction delay is essential for understanding the underlying molecular mechanisms and for the possibility of development of targeted treatments and prevention strategies . There is a strong heritable component in the variability of the PR interval [5]–[7] and although genome-wide approaches have highlighted several causal loci [3] , a major proportion of the heritability and the underlying genes remains elusive . The identification of these genetic factors in the human population has been difficult owing to wide genetic heterogeneity and an uncontainable environment . We here exploit the homogeneous genetic background and controlled environment of inbred laboratory mouse strains to identify a novel genetic modifier of the PR interval . We have previously detected a quantitative trait locus ( QTL ) for the PR interval ( PR-QTL ) on chromosome 3 in a conduction disease sensitized mouse F2 progeny of mice harboring the cardiac voltage-gated sodium channel gene mutation Scn5a1798insD/+ , generated from a 129P2-Scn5a1798insD/+×FVBN/J-Scn5a1798insD/+ cross [8] . 129P2-Scn5a1798insD/+ and FVBN/J-Scn5a1798insD/+ mice recapitulate many of the electrocardiographic ( ECG ) manifestations seen in patients carrying the homologous mutation SCN5A-1795insD , including cardiac conduction defects . Importantly , 129P2-Scn5a1798insD/+ and FVBN/J-Scn5a1798insD/+ mice display different severity of conduction disease [9] , [10] . These differences in conduction disease severity are likely due to a complex interplay of multiple modifier loci . We previously exploited these strain effects on cardiac conduction to map a QTL on mouse chromosome 3 that influences the variance in PR interval [8] . The aim of the present study was to dissect this QTL in detail to identify the underlying gene responsible for the variation in PR interval . As genetic variation underlying a QTL may act through effects on gene expression [11] , we tested the presence of such variability in our F2 population . We integrated genome-wide transcriptional profiles of myocardial tissue with single nucleotide polymorphism ( SNP ) mapping in our hybrid mouse population to delineate genetic loci in association with variance in gene expression . These expression QTLs ( eQTLs ) where assessed for overlap with the PR interval QTL . Of the thus found 16 eQTLs only Tnni3k expression levels both correlated to the PR interval and had a high cardiac specific expression . We integrated genome-wide transcriptional profiles of myocardial tissue with genotypic data in F2 progeny from the 129P2-Scn5a1798insD/+×FVBN/J-Scn5a1798insD/+ cross to uncover eQTLs that overlapped with the chromosome 3 PR interval QTL . Of these eQTLs , only the transcript for Tnni3k both correlated to the PR interval and was highly and specifically expressed in heart; Tnni3k was thus identified as a very strong candidate for the effect . The role of Tnni3k was subsequently validated in silico using phenotypic data from the mouse phenome database [12] . Further validation was performed by testing the correlation of Tnni3k expression level with PR interval in 6 inbred mouse strains harboring 3 independent haplotypes at the Tnni3k genomic locus . Finally , the role of Tnni3k in modulation of the PR interval was validated in vivo in ( i ) congenic mice harboring the high-Tnni3k expression haplotype of the AKR/J strain in the DBA/2J ( low expression of Tnni3k ) genetic background and ( ii ) in DBA/2J mice overexpressing human TNNI3K .
The identification of a main-effect PR interval QTL in the distal portion of chromosome 3 has been reported previously [8] . In brief , we combined ECG and genome-wide genotypic data in 502 F2 progeny generated from an FVBN/J-Scn5a1798insD+/−×129P2-Scn5a1798insD+/− intercross to map QTLs for ECG parameters . The most significant SNP association with PR interval was rs13477506; explaining 3 . 9% ( p = 7 . 71×10−5 ) of the observed variance in PR interval [8] . As genetic variation underlying a QTL may act on the trait through effects on gene expression [11] we tested the presence of such variability in the F2 mice . We measured genome-wide gene expression profiles of cardiac tissue in 109 previously genotyped F2 mice using microarrays . Normalized log-transformed intensities of individual transcripts were used as quantitative inputs for eQTL mapping . Considering a multiple-comparison corrected significance threshold of p<1 . 88×10−6 ( LOD>6 . 83 ) , we uncovered 16 eQTLs within the 1 . 5 LOD drop for the PR interval chromosome 3 QTL ( Table 1 ) . Of these 16 eQTLs , seven were deemed cis-regulated ( cognate gene and eQTL SNP physically map to the same genomic region ) indicating a possible direct effect of the underlying genetic variation on transcript levels; whereas nine eQTLs were labeled as trans-regulated ( cognate gene physically maps to a different genomic region than the eQTL SNP ) suggesting indirect effects such as transcription factor levels influencing the transcript level . To determine whether any of the identified eQTLs could be a candidate for the effect of the Chr3 PR-QTL we tested correlation of the transcript levels with the PR interval duration . Only four transcripts showed significant correlation to the PR interval , namely , Eif4e ( rho −0 . 246 , p<0 . 01 ) , Gipc2 ( rho −0 . 300 , p<0 . 001 ) , Socs2 ( rho 0 . 279 , p<0 . 001 ) and Tnni3k ( rho 0 . 263 , p<0 . 01 ) . Data from the GEO database indicates that of these only Tnni3k displays a high cardiac specific expression pattern [13] , [14] . We therefore investigated Tnni3k further as the prime candidate for the effect at the chromosome 3 PR-QTL . Inspection of the genome-wide LOD plot for Tnni3k ( Figure 1 ) shows a very sharp LOD peak with a maximum LOD score of 56 . 5 at rs13477506 . The allele effect-size plot for the Tnni3k cis-eQTL at rs13477506 is shown in Figure 2A . Carriership of the 129P2-derived A-allele at the chromosome 3 locus is associated with heightened Tnni3k expression ( Figure 2A , ( Illumina probe ILMN_3023962 , representative for all Tnni3k probe IDs ) and prolonged PR interval ( Figure 2B ) [8] . Univariate analysis showed that the genotype at rs13477506 explains 88% ( p<2×10−16 ) of the observed variance in Tnni3k transcript abundance . Differential Tnni3k mRNA expression was confirmed by quantitative RT-PCR in cardiac tissue ( Figure 2A , dark colors ) . We next examined the haplotypes at the locus of interest in a panel of inbred mouse strains using the mouse phylogeny viewer [15] ( Figure 3C ) . We identified three haplotypes in the Tnni3K eQTL region ( indicated as red , green and blue in Figure 3C ) . The red haplotype ( including DBA/2J ) contains rs49812611 , which is associated with nonsense mediated decay , leading to low levels of Tnni3k transcript [16] . This variant is absent in the strains with the blue and green haplotypes . To determine whether these different genomic backgrounds in the different mouse strains were related to the PR interval we first looked at the published PR interval in publicly accessible data in the mouse phenome database [12] , [17] , [18] . Interestingly , this uncovered a significantly longer PR interval in inbred strains with the green ( similar to 129P2 ) haplotype compared to strains with the red ( DBA/2J ) or blue ( FVBN/J ) haplotype ( P<0 . 001 ) ( www . phenome . jax . org ) . Since Tnni3k expression levels have been shown to be low in DBA/2J ( red haplotype ) and FVBN/J ( blue haplotype ) mice ( which both have short PR intervals ) , and high in 129/P2 and AKR/J strains ( both green haplotype and both having long PR interval durations ) ( Figure 2A and Wheeler et al . [16] ) , PR interval duration in these various mouse lines appears to correspond to the ( predicted ) Tnni3k expression levels . We next validated the correlation between Tnni3k expression levels and PR interval duration in a diverse panel of inbred mice with the three different haplotypes ( red , green and blue ) and a further two wild-derived inbred lines for which no haplotype information is available ( WSB/EiJ & PWD/PhJ ) [19] . In these lines we determined cardiac expression levels of Tnni3k by qRT-PCR and measured surface ECGs for assessment of PR interval duration . As shown in Figure 4B , Tnni3k expression levels significantly correlate to PR interval indices ( rho = 0 . 475 , p = 0 . 012 ) , thus validating the correlation observed in silico . To exclude effects from loci that differ between the parental inbred strains but which map elsewhere in the genome , we measured ECGs in congenic DBA . AKR-Hrtfm2 mice . These mice harbor approximately 20 Mb of the AKR/J ( green , ‘high-Tnni3k expression’ ) haplotype at the Tnni3k locus in the genetic background of the DBA/2J inbred mice; pure DBA/2J mice have short PR intervals and low Tnni3k levels ( red haplotype ) ( Figure 3 and Figure 4B ) . In DBA . AKR-Hrtfm2 mice the level of Tnni3k is indistinguishable from that in AKR/J mice while the rest of the genetic background is the same as that of DBA/2J . Strikingly , DBA . AKR-Hrtfm2 mice completely recapitulate the long PR interval of the AKR/J mice , suggesting that the difference in the level of Tnni3k expression is a major cause of the difference in PR interval duration between these lines ( Figure 5 ) . An overview of all the ECG paramemeters measured is given in Table 2 . In order to exclude the contribution of any of the 117 other genes present in the congenic region to the observed effects in the DBA . AKR-Hrtfm2 mice we measured ECGs in DBA/2J mice overexpressing human TNNI3K . As expected , the PR interval in these overexpression mice ( with ∼20× overexpression of hTNNI3K [16] ) was extremely prolonged ( Figure 5 ) . As prolonged PR interval indicates a possible role for Tnni3k in atrial and/or atrio-ventricular conduction we next investigated whether Tnni3k protein is also present in the atria besides its known presence ventricle [16] . We therefore performed Western Blotting on atrial ( A ) and ventricular ( V ) protein lysates from AKR/J ( high Tnni3k expression ) and DBA/2J ( low Tnni3k expression ) . Tnni3k protein was detected in both atrial and ventricular lysates in AKR/J and as expected was undetectable in DBA/2J hearts ( Figure 6 ) . In AKR/J hearts expression appeared higher in atria compared to ventricle .
We here dissect the PR interval QTL on mouse chromosome 3 identifying the causal gene . We used eQTL mapping to identify genes whose expression is genetically regulated by variation within the QTL region . We tested the correlation of these genes to PR interval and , based on its in vivo expression pattern , selected Tnni3k as candidate gene for the effect . We subsequently validated the role of Tnni3k by in silico haplotype-phenotype correlation and by assessing the relation between Tnni3k expression levels and PR interval in 6 inbred mouse lines . Finally , the causality of Tnni3k was proven by in vivo studies in ( i ) congenic mice harboring a “high-Tnni3k–long-PR-interval” haplotype in a “low-Tnni3k-short PR” genetic background , and ( ii ) mice overexpressing Tnni3k . These in vivo studies provided unequivocal evidence for the involvement of Tnni3k in regulation of PR interval duration . Genetic variation is known to affect a phenotype by altering the transcriptional activity of genes [20] . Thus , eQTL mapping , integrating genome-wide transcript profiling and genetic data in a hybrid population as carried out here , provides a powerful tool for the identification of causal genes at QTLs impacting on complex traits . Moreover , coupling eQTL detection with transcript-trait correlation analysis as performed here further aids prioritization of physiologically relevant genes within the QTL region [21] . Online databases providing genotypic and phenotypic information on diverse inbred mouse lines have been instrumental in the identification and validation of the causal gene underlying the PR interval QTL . Expression pattern data deposited in the Gene Expression Omnibus ( GEO ) database [13] , [22] aided our selection of Tnni3k as the prime candidate among the 4 identified eQTLs whose transcripts correlated to the PR interval . In silico validation of the possible influence of Tnni3k expression levels on the PR interval was made possible by the mouse phylogeny viewer ( http://msub . csbio . unc . edu/ ) [19] and the mouse phenome database ( http://phenome . jax . org/ ) [12] . Furthermore , we used the information in the phylogeny viewer to select the panel of 6 inbred mouse lines in such a way that they represented all possible independent haplotypes at the Tnni3k locus . To functionally validate the role of Tnni3k in the in vivo regulation of PR interval we studied the DBA . AKR-Hrtfm2 congenic mouse line harboring the high-Tnni3k expression AKR/J QTL region in the low-Tnni3k expression DBA/2J genetic background as well as transgenic mice overexpressing human TNNI3K . Strikingly , the congenic mice recapitulate perfectly the PR interval of the high-Tnni3k expression AKR/J donor line; furthermore , the hTNNI3K overexpression mice , as expected , display an extremely prolonged PR interval . Taken together this implies a dose-dependent effect of Tnni3k on atrio-ventricular conduction . Of note , hTNNI3K overexpression mice displayed longer QRS duration in comparison to DBA/2J wild-type mice . This concurs with our previous observation of a QTL for QRS duration post-flecainide overlapping the PR-QTL on mouse chromosome 3 [8] . Tnni3k was recently identified as a genetic modifier of disease progression in the Csqtg mouse model of cardiomyopathy [16] . In this model , with cardiac-specific overexpression of Calsequestrin ( Csq ) , Tnni3k was shown to be the causative gene underlying the heart failure modifier 2 ( Hrtfm2 ) locus , with high levels of Tnni3k accelerating the progression of the cardiomyopathic phenotype . Little is known thus far about the physiological role of Tnni3k . Tnni3k encodes for cardiac Troponin I-interacting kinase 3 , initially identified as a cardiac-specific protein kinase interacting with cardiac Troponin I in a yeast-two hybrid assay [23] . Its phosphorylation target ( s ) remain unknown [16] . The protein structure is predicted to contain an Ankyrin repeat domain besides the the Serine/Threonine-Tyrosine universal kinase domain , most likely involved in extensive protein-protein interactions . The molecular mechanism whereby Tnni3k impacts on atrio-ventricular conduction requires further in-depth studies . Cardiac conduction slowing may stem from multiple mechanisms affecting cardiomyocyte depolarization , cell-cell electrical communication via gap-junctional coupling , or fibrosis , processes which may not necessarily be mutually exclusive . It is tempting to speculate that cardiac ion channels and/or gap junction proteins ( connexins ) may be direct targets of Tnni3k phosporylation . In conclusion , we identified Tnni3k as the causal gene for a mouse PR interval QTL . Our findings provide the first evidence for Tnni3k in modulation of the electrocardiographic PR interval , indicating a previously unknown role for Tnni3k in atrio-ventricular conduction .
The transgenic 129P2-Scn5a1798insD/+ ( 129P2-MUT ) and FVBN/J-Scn5a1798insD/+ ( FVBN/J-MUT ) mice were generated as previously described [24] . ( 129P2xFVBN/J ) -Scn5a1798insD/+ F1 mice ( F1-MUT ) were reared from these mice , and subsequently intercrossed to produce 120 Scn5a1798insD/+ F2 progeny . Generation of the congenic DBA . AKR . hrtfm2 and hTNNI3K-tg mouse lines was published previously [16] The inbred strains ( WSB/Eij , Molf/Eij , DBA/2j , AKR/J , PWD/PhJ ) were obtained from Jackson Laboratory . All mice were supplied with the same SDS diet ( SDS CRM ( E ) PL; Special Diets Services , UK ) and water ad libitum and maintained on a 12-hour light/dark cycle in a temperature and humidity controlled environment . All experiments were performed in accordance with governmental and institutional guidelines for animal use in research . All F2 mice were genotyped for the Scn5a1798insD/+ mutation as previously described [7] , only mice heterozygous for the mutation were used . For the genome-wide scan , the F2-MUT and three mice from each parental strain were genotyped across the 19 autosomes and X chromosome by means of an Illumina Golden Gate mouse medium density ( 768 SNP ) panel . This genotyping was carried out at Harvard Partners Center for Genetics and Genomics ( HPCGG , Cambridge MA , USA ) . Mice with call rates <95% and single nucleotide polymorphisms ( SNPs ) with a call rate <95% and a minor allele frequency ( MAF ) <0 . 45 were removed from the analyses . Genotyping errors were identified using error LOD scores [13] . All analysed strains were assessed for the presence of rs49812611 ECG analysis of F2-MUT and inbred mice at 12 to 14 weeks of age ( n = 109 ) was performed as previously described [10] . Briefly , mice were weighed , lightly anaesthetized by isoflurane inhalation ( 4 . 0% v/v induction; 0 . 8–1 . 2% v/v maintenance ) with 800 ml/min oxygen and allowed to acclimatize for 5 minutes . The ambient temperature within the ECG recording hood was kept warm by means of a heat lamp . A 3-lead surface ECG was acquired digitally from subcutaneous 23-gauge needle electrodes at each limb of mice in the prone position using the Powerlab acquisition system ( ADInstruments ) . Each channel was amplified and sampled at a rate of 1 kHz and a high-pass filter setting of 15 Hz . Baseline surface ECG traces were recorded for the duration of 5 minutes . A 3 minute ECG trace was analyzed for HR , and the signal average ECG ( SAECG ) calculated from each of leads I and II , aligned at QRS maximum , was analyzed for PR duration using the LabChart7Pro software ( ADInstruments ) and utilized for subsequent QTL mapping . We excluded mice that exhibited ECG parameter standard deviations greater than 1 . 5 ms between leads . Mice were sacrificed at 12–14 weeks by CO2-O2 asphyxiation followed by cervical dislocation . Hearts were excised , washed in 1×PBS , and dissected left ventricular ( LV ) free-wall flash-frozen in liquid N2 . Total RNA was extracted from LV samples ( n = 109 ) using the QIAGEN RNeasy mini kit 50 protocol for isolating total RNA from animal tissue using spin technology ( QIAGEN Inc . ) according to manufacturer's protocol . Total RNA yield ( µg ) and purity ( 260 nm∶280 nm ) were determined spectrophotometrically using the NanoDrop spectrophotometer ( USA ) . The integrity ( RIN>9 . 0 ) of the re-suspended total RNA was determined using the RNA Nano Chip Kit on the Bioanalyzer 2100 and the 2100 Expert software ( Agilent technologies ) . Synthesis , amplification and purification of anti-sense RNA was performed by using the Illumina TotalPrep RNA Amplification Kit ( Ambion art . No . AM-IL1791 ) following the Illumina Sentrix Array Matrix expression protocol at ServiceXS ( Leiden , The Netherlands ) . A total of 750 ng biotinylated cRNA was hybridized onto the MouseREf-8v2 Expression BeadChip ( Illumina ) . The raw scan data were read using the beadarray package ( version 1 . 12 . 1 ) [14] , available through Bioconductor [15] . Quality control using the function calculateBeadLevelScores showed no evidence of low-quality arrays . Illumina's default pre-processing steps were performed using beadarray . In short , estimated background was subtracted from the foreground for each bead . For replicate beads , outliers greater than 3 median absolute deviations ( MADs ) from the median were removed and the average signal was calculated for the remaining intensities . A variance-stabilization transformation [16] was applied to the summarized data in order to remove the mean-variance relationship in the intensities . Resulting data was then quantile normalized [17] . eQTL mapping was performed using the R/eqtl package based on the R-statistical program , as previously described [18] . Briefly , for each transcript probe ( n = 26 , 529 ) a genome-wide scan was performed with genotype and the covariates sex , weight and age as main-effects . The logarithm-of-odds ( LOD ) scores were calculated by interval mapping using the expectation-maximization ( EM ) algorithm . A multiple-transcript genome-wide significance threshold ( p<1 . 88×10−6 , Bonferroni correction for 26 , 529 transcript traits ) was applied . Corresponding empirical LOD threshold was determined using 10 , 000 permutations ( swapping phenotypes ( ECG parameters , sex , age and weight ) and genotypes , thus destroying the phenotype-genotype relationship , but maintaining the LD patterns between markers ) , this corresponded to a LOD score threshold of 6 . 83 . A cis-eQTL was called when the genomic distance between the mapping SNP and transcript was less than 10 Mb [19] . Transcripts for which we identified cis-eQTLs co-localizing with ECG trait QTL were tested for correlations with the respective ECG indices for conduction . Spearman's correlation coefficients ( rho ) were generated with a commercial program ( SPSS software , ver . 16 . 0; SPSS , Chicago , IL ) . mRNA expression levels of Tnni3k were validated in LV tissue samples ( n = 10 in each group ) by quantification using the LightCycler system for real-time RT-PCR ( Roche Applied Science ) . Quantitative PCR data was analyzed with the LinRegPCR program [2] . All samples were processed in triplicate and expression levels were normalized to GAPDH . Protein was isolated from left ventricular and atrial tissue from a snap frozen mouse heart using standard procedures . In short: the tissue was homogenized in ice-cold RIPA buffer ( 50 mM Tris HCl pH 7 . 6 , 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS ) supplemented with protease inhibitors ( Complete Mini; Roche ) and Sodium Orthovanadate ( final concentration 0 . 5 mM ) using magnalyser ceramic beads ( Roche scientific , 03358941001 ) for 60″ . The unsoluable parts were spun down ( 30 sec at 4°C , 13000 rpm ) , the supernatant was transferred to a fresh tube and protein concentrations were determined using a BCA protein assay kit ( Thermo Fisher Scientific ) . Protein lysates were run on a 8% acrylamide gel and blotted on a pre-equilibrated PVDF Immobilon-P membrane ( Millipore ) by means of a semidry system . Blots were cut at appropriate heights and probed with primary antibodies ( 1∶2000 α-mTnni3k rbt [16] 1∶10 000 α-ILK1 ( 4G9 ) ( 3856 , Cell Signalling ) as loading control . HRP conjugated secondary antibodies were detected with ECL-Plus ( Amersham ) . Chemiluminescent signals were visualized using a digital image analyzer ( LAS-4000 Lite; Fujifilm ) . We analyzed the haplotype structure in the 1 . 5 LOD drop region of the Tnni3k eQTL using the mouse phylogeny viewer [19] . PR interval data for 9 strains with known haplotype structure was downloaded from the mouse phenome database [12] , [18] and analysed by Student's T-test ( after testing for normal distribution ) for differences between the strains harboring the red versus the green haplotype . FVBN/J was excluded from this test , as no data for other strains with the same haplotype was available . Expression data was deposited in the public gene expression omnibus ( GEO ) database of NCBI GEO database: GSE19741 . | Atrio-ventricular ( AV ) conduction disease ( delay ) , characterized by prolongation of the PR interval on the surface electrocardiogram ( ECG ) , is a common feature in Mendelian rhythm disorders and is associated with sudden cardiac death . Prolongation of the PR interval is also a strong predictor of atrial fibrillation ( AF ) , the most common sustained cardiac arrhythmia . Although there is a substantial heritable component to the variability of the PR interval , the causative genes remain largely elusive . The identification of these genetic factors in the human population has been difficult owing to wide genetic heterogeneity and an uncontainable environment . We here exploited the homogeneous genetic background and controlled environment of inbred laboratory mouse strains to detect a genetic modifier of the PR interval . We identify Tnni3k as prime candidate for the modulation of the PR interval duration and suggest a new role for this gene , in the modulation of atrio-ventricular conduction . | [
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"l... | 2012 | Dissection of a Quantitative Trait Locus for PR Interval Duration Identifies Tnni3k as a Novel Modulator of Cardiac Conduction |
Francisella tularensis ( Ft ) causes a frequently fatal , acute necrotic pneumonia in humans and animals . Following lethal Ft infection in mice , infiltration of the lungs by predominantly immature myeloid cells and subsequent myeloid cell death drive pathogenesis and host mortality . However , following sub-lethal Ft challenge , more mature myeloid cells are elicited and are protective . In addition , inflammasome-dependent IL-1β and IL-18 are important for protection . As Nlrp3 appears dispensable for resistance to infection with Francisella novicida , we considered its role during infection with the virulent Type A strain SchuS4 and the attenuated Type B live vaccine strain LVS . Here we show that both in vitro macrophage and in vivo IL-1β and IL-18 responses to Ft LVS and SchuS4 involve both the Aim2 and Nlrp3 inflammasomes . However , following lethal infection with Francisella , IL-1r- , Caspase-1/11- , Asc- and Aim2-deficient mice exhibited increased susceptibility as expected , while Nlrp3-deficient mice were more resistant . Despite reduced levels of IL-1β and IL-18 , in the absence of Nlrp3 , Ft infected mice have dramatically reduced lung pathology , diminished recruitment and death of immature myeloid cells , and reduced bacterial burden in comparison to wildtype and inflammasome-deficient mice . Further , increased numbers of mature neutrophil appear in the lung early during lethal Ft infection in Nlrp3-deficient mice . Finally , Ft infection induces myeloid and lung stromal cell death that in part requires Nlrp3 , is necrotic/necroptotic in nature , and drives host mortality . Thus , Nlrp3 mediates an inflammasome-independent process that restricts the appearance of protective mature neutrophils and promotes lethal necrotic lung pathology .
Pulmonary tularemia is an acute , necrotizing , and highly lethal pneumonia caused by the highly pathogenic zoonotic bacterium Francisella tularensis ( Ft ) [1] . The Type A ( F . tularensis tularensis ) and Type B ( F . tularensis holarctica ) strains cause disease in both animals and humans [2] . Type A strains ( e . g . SchuS4 ) are highly pathogenic to humans and animals and inhalation of as few as 10 cfu of SchuS4 causes lethal disease in humans and mice [1] . Thus , Type A strains are classified as category ‘A’ biothreat agents by the CDC [3] ) . Although used to model pulmonary tularemia in mice , the attenuated Type B live vaccine strain ( Ft LVS ) is not pathogenic to humans . Another strain , Francisella novicida ( Fn ) is closely related to Ft and highly pathogenic in rodents , but nonpathogenic in humans [3] . During lethal pulmonary tularemia , Ft infects lung phagocytes and replicate intracellularly [1–2] . Instead of eliciting effective innate immune responses capable of controlling bacteria , immature myeloid cells/myeloid-suppressor cells are recruited [4] . These immature cells are ineffective phagocytes , but prone to necrosis resulting in necrotic lung damage and subsequent death of mice . In contrast , during sublethal infection , infiltrating mature neutrophils and inflammatory monocytes/macrophages outnumber immature myeloid cells and are essential for protection of surviving mice . Thus , the necrotizing inflammation and extensive tissue damage associated with lethal disease during pulmonary tularemia can be attributed to this dysregulated myeloid cell response [4] . How these immature myeloid cells are recruited , how they die , and how dying cells result in lung pathology during pulmonary tularemia is not known . Previous studies have suggested apoptosis as a mode of myeloid cell death through active Caspase-3 in myeloid cells in the spleen , liver , and lungs of Type A strain KU49 infected mice [5 , 6] . In contrast , another study reported that activated Caspase-3 or AnnexinV expression was rarely observed at 3 days post-infection in lungs of mice infected with SchuS4 [7] . However , while we observed that Ft induces necrotic changes in myeloid cells including immature cells in the lungs [4] , how these cells die and how that death contributes to lethal lung damage is unknown . Although production of the pro-inflammatory cytokines IL-1β , IL-6 and TNFα is delayed during tularemia [8–10] , mice deficient for these cytokines or the relevant receptors are more susceptible to Ft infection [11–13] . Previous studies have also clearly shown a protective role for IL-1β in mice during Fn infection [14–16] . A recent study examined intranasal Ft LVS infection of IL-1-/- and IL-18-/- mice , revealing increased susceptibility of IL-18 deficient mice and a critical role for IL-1β in the early production of protective anti-Ft LPS IgM by B1a B cells [13] . These studies suggest that early inflammatory cytokine responses , such as that of IL-1β and IL-18 are important for survival . Several studies have investigated the protective role of the Aim2/Asc/Caspase-1 inflammasome axis in resistance to subcutaneous infection with Fn [14–19] . Aim2 binds dsDNA which assembles an Aim2 inflammasome via oligomerization of ASC and recruitment/activation of proCaspase-1 to enzymatically process proIL-1β and proIL-18 [17 , 20 , 21] . The Aim2 inflammasome also promotes Caspase-1-dependent cell death ( pyroptosis ) [17 , 20] . Indeed , recognition of Fn dsDNA by Aim2 appears solely responsible for Fn elicited inflammasome activation as mouse Nlrp1 , Nlrp3 , and Nlrc4 do not respond to Fn [14 , 15] . In contrast , in human cells both Aim2 and NLRP3 inflammasomes respond to Fn and Ft LVS [22] . NLRP3 also seeds inflammasome formation , but is activated by a wide array of stimuli and likewise can promote pyroptotic and Asc-dependent , but Caspase-1-independent ( pyronecrotic ) death of myeloid cells during infection [21 , 23–26] . Although two Ft LVS studies using the LVS mutants LVSΔripA and FTL-0325 report that IL-1β responses enhanced by these mutants are independent of Nlrp3 [27 , 28] , whether Nlrp3 is involved in the inflammasome response to Ft LVS or SchuS4 in mice is unclear . However , the in vivo response of Nlrp3-/- mice to infection with the Ft LVS or SchuS4 strain , most relevant to human disease , is essentially unexplored . Here , we report that Nlrp3-/- mice exhibit resistance to Ft infection through mature myeloid cell response in lungs and decreased myeloid and lung cell death during pulmonary tularemia . Consistent with previous reports , IL-1r-/- , Asc-/- , Casp-1/11-/- , and Aim2-/- mice were more susceptible to Ft infection , but a significant proportion of Nlrp3-/- mice survive . Despite limited IL-1β and IL-18 production , Ft-infected Nlrp3-/- mice had reduced lung pathology , lower bacterial burden , and fewer dead lung myeloid and stromal cells when compared to wildtype and inflammasome-deficient mice . A mature population of neutrophils appearing in the lung on day 1 post-infection is necessary for protection . We also demonstrate that Ft-elicited cell death is likely due to a necrotic/necroptotic mechanism involving Nlrp3 , but Asc/Caspase-1 inflammasome-independent . Our results suggest that while Asc and Caspase-1-mediated IL-1β and IL-18 play protective roles , Nlrp3 is a host susceptibility factor detrimental during Ft infection .
A variety of gram negative bacteria activate the NLRP3 inflammasome [26] . However , it is well-established that protective innate immunity to F . novicida requires activation of the Aim2 inflammasome and that the Nlrp3 inflammasome is not required [14 , 17–19 , 29] . Further , elaboration of IL-1β by Fn infected macrophages requires neither Nlrp3 nor Nlrc4 , while Asc is indispensable [29] . We previously reported that in human macrophages , both AIM2 and NLRP3 mediate the inflammasome response to F . novicida and Ft LVS [22] . Nevertheless , the role of Nlrp3 in Ft infection has not been well studied . Further , LVS and SchuS4 are the Francisella strains most relevant to epidemics of human tularemia [3] . To establish whether Nlrp3 is involved in the inflammasome response to LVS and SchuS4 , BMDM from C57BL6J wildtype and Nlrp3-/- mice were infected with Ft LVS , SchuS4 or Fn and their IL-1β and IL-18 responses measured . The corresponding responses of Casp1/11-/- , Asc-/- , and Aim2-/- BMDM were evaluated as controls . As expected , IL-1β and IL-18 elaboration by these cells is dramatically reduced in the absence of Casp1/11 and Asc ( Fig 1 ) . Interestingly , IL-1β production was significantly reduced following Ft LVS or SchuS4 infection of cells from Nlrp3-/- mice ( Fig 1A ) . Curiously , macrophages from Aim2-/- mice produced more IL-1β than wildtype cells in response to LVS infection but IL-1β production following SchuS4 infection was limited ( Fig 1A ) . In contrast , IL-1β production following Fn infection was not reduced by deficiency in Nlrp3 , but was abrogated without Aim2 as previously reported [17–19] . Surprisingly , the IL-18 response pattern differed . In the absence of Nlrp3 or Aim2 , the macrophage IL-18 response to Ft LVS or SchuS4 infection was reduced by about 50% ( Fig 1B ) . Infected Asc-/- and Casp-1/11-/- macrophages produced little IL-18 ( Fig 1B ) . However , IL-18 processing by Nlrp3-deficient macrophages following Fn infection was significantly reduced , but still robust , while that of cells-deficient for Aim2 , Asc or Casp-1/11 was similar to negative controls ( Fig 1B ) . These observed changes in IL-1β and IL-18 are likely due to inflammasome-specific differences in the response to the bacterial strains , as other inflammasome-independent pro-inflammatory cytokines , including IL-6 ( Fig 1C ) and others ( S1 Fig ) were unaffected indicating that TLR responses are unaffected . Further , none of these genetic deficiencies altered macrophage infection by the Francisella strains used in this study , nor were proIL-1β protein levels substantially altered ( S1 Fig ) . These results suggest that the specific Francisella strains differ in their utilization of the Nlrp3 and Aim2 inflammasomes for macrophage IL-1β and IL-18 responses . Further , Nlrp3 and Aim2 dependent LDH release was also observed for Ft LVS ( S1 Fig ) . Collectively , our data reveal that Nlrp3 is responsive to Francisella strains other than Fn and thus Nlrp3 may have an important role in immunity and pathogensis of pulmonary tularemia . The Aim2 inflammasome response is critical for resistance to intradermal infection with F . novicida [14–19] . Further , IL-1β and IL-18 are also important for resistance to pulmonary challenge with LVS [13] . Since Nlrp3 is required for LVS and SchuS4 elicited IL-1β/IL-18 response , we considered whether Nlrp3 is important for resistance to pulmonary Ft infection . Wildtype and Nlrp3-/- mice were infected intranasally with a lethal dose of Ft LVS ( 1000 cfu ) and monitored for survival . All wildtype mice succumbed to lethal LVS infection between 8–10 days ( Fig 2A ) . Surprisingly , a large percentage ( ~50% ) of Nlrp3-/- mice survived lethal LVS infection . Compared to lethally infected wildtype mice , Nlrp3-/- mice had reduced bacterial burden in lungs ( Fig 2A ) , spleen and liver ( S2A Fig ) that was evident at 3 dpi and became significant at 5 dpi . Approximately 15% of Nlrp3-/- mice survived infection with a four-fold higher challenge dose , but all succumbed with a 20-fold higher challenge dose ( S2B Fig ) . Thus , resistance to Ft LVS observed in Nlrp3-/- mice is challenge dose-dependent . Periodic clinical observations including weight loss and decreased activity indicated that all the mice were infected with Ft ( S2C Fig ) . Ft LVS-infected wildtype mice exhibit overt pathological changes characterized by necrotizing inflammation in lungs , spleen and liver that correlates with a loss of pulmonary function and death [4] . In the lungs , this inflammation is characterized by progressive mixed cellular infiltration , serous to fibrinous exudates with cellular debris , and necrosis culminating in loss of airway space and function . Consistent with increased survival , the lungs of Nlrp3-/- mice exhibited less necrosis and more preserved airway space in comparison to wildtype mice ( Fig 2B ) . Remarkably , while all wildtype mice succumbed to infection with the highly virulent and clinically relevant Type A strain SchuS4 ( 10 cfu ) by 6–7 days , a small , but significant proportion ( 20% ) of Nlrp3-/- mice survived ( Fig 2C ) . Similar to LVS infection , lung bacterial burdens were significantly reduced in Nlrp3-/- mice at 5 days post-SchuS4 infection ( Fig 2C ) . However , upon challenge with a larger inoculum ( 150 cfu ) , all Nlrp3-/- mice succumbed to infection ( S2D Fig ) . Curiously , although previous studies have shown that Asc-/- , Casp-1/11-/- and Aim2-/- mice are susceptible to intradermal Fn infection [13 , 17 , 18 , 29] , a significant proportion of Nlrp3-/- mice ( 25% ) survived intranasal infection with a lethal dose of Fn ( 20 cfu ) compared with wildtype mice which all died by 6–7 days ( Fig 2D ) . As with LVS and SchuS4 infection , the lungs of Nlrp3-/- mice displayed reduced bacterial burdens at 3 and 5 days post-Fn infection ( Fig 2D ) . Together , these data demonstrate a Francisella strain-independent , detrimental role for Nlrp3 in the pathogenesis of pulmonary tularemia . IL-1 family cytokines mediate inflammatory processes essential for innate and adaptive immunity [30] . IL-1β and IL-18 are critical for protective immunity against subcutaneous Fn infection [14–16] as well as pulmonary Ft LVS infection [13] . Although Fn elicited IL-1β responses are Nlrp3-independent , we observed significant protection against pulmonary Fn infection in Nlrp3-deficienct mice . However , the diminished IL-1β/IL-18 response of Nlrp3-deficient macrophages after Ft infection suggests that this response could be significantly impaired in Nlrp3-/- mice , yet sufficient to provide protection against Francisella . Alternatively , Nlrp3 might play an inflammasome-independent role in the pathogenesis of pulmonary tularemia in wildtype mice . Interestingly , levels of IL-1β and IL-18 in the lung are markedly reduced in Nlrp3-/- mice with slower kinetics over the initial 6 days of infection with LVS ( Fig 3A ) or SchuS4 ( S3A Fig ) infection , but are not completely ablated . Lung IL-1β and IL-18 levels in Ft infected mice deficient in Casp-1/11 , Asc , and Aim2 were largely comparable to those of Nlrp3-/- mice , with the exception of Aim2-/- mice which had more IL-18 in their lungs at 6 dpi ( Fig 3B ) . Lung IL-6 and TNFα responses were similar in all the mouse strains compared to to those of wildtype mice following Ft LVS infection ( Fig 3C ) or SchuS4 infection ( S3B Fig ) . Protection of Nlrp3-/- mice despite greatly reduced IL-1β/IL-18 responses , similar to those of Casp-1/11-/- mice , was unexpected . Further , these results are seemingly contradictory to reports demonstrating the importance of IL-1β and IL-18 for protection against Francisella . We therefore considered whether other inflammasome-deficient mice were similarly protected . Unlike Nlrp3-deficient mice , Aim2-/- , Caspase-1/11-/- , Asc-/- , and IL-1R-/- mice died within 8–11 dpi after lethal Ft LVS infection ( Fig 3D ) or between 6 and 7 dpi after SchuS4 ( S3C Fig ) . In addition , lung bacterial burdens in these mice were approximately twice those of wildtype mice at 5 dpi ( Fig 3E ) , but bacterial loads in the spleen and liver were similar to wildtype ( S3D Fig ) . Further , following intransal instillation of a 50% lethal dose of Ft LVS nearly all Nlrp3-/- mice survived while all of the IL-1R-/- or other inflammasome component-deficient mice succumbed to infection ( Fig 3F ) . After Ft LVS infection IL-1β and IL-18 levels are similar between Aim2- and Nlrp3-deficient mice , yet mice deficient in Aim2 , ASC , or caspase-1/11 do not reproduce the survival phenotype of Nlrp3 mice . These levels are insufficient to protect Aim2-deficient mice , and do not account for the increased survival of Nlrp3-deficient mice . Thus , the detrimental impact of Nlrp3 is unlikely to be inflammasome-dependent . The Nlrp3 inflammasome inhibitor MCC950 specifically blocks Nlrp3:Asc interaction and downstream caspase-1 activation without impacting the Aim2 inflammasome [31] . Wildtype mice infected with a lethal dose of Ft LVS and treated with MCC950 were not protected ( Fig 3G ) . However , MCC950 treatment of wildtype mice receiving a 50% lethal dose of Ft LVS resulted in a complete loss of protection ( Fig 3H ) . Although these mice are Nlrp3-sufficient , inhibition of the Nlrp3 inflammasome results in a phenotype similar to mice lacking ASC and caspase-1 . Collectively , these results demonstrate that the detrimental role of Nlrp3 during pulmonary Ft infection is independent of the inflammasome . Our results also suggest that the diminished levels of IL-1β and IL-18 in the lungs of Nlrp3-/- mice may be sufficient to support their critical protective function during pulmonary tularemia . A recent study reported that Ft-specific IgM produced by B1a B cells was significantly reduced in Il-1b/- , Il-1b-/-/Il-1a-/- , or Il-1r1-/- mice compared to C57BL/6J wildtype or Il-1a-/- mice and implicated as an explanation for susceptibility of IL-1β-deficient mice to pulmonary Ft LVS infection [13] . This study also demonstrated the importance of IL-18 for resistance to Ft infection [13] . Our results with Nlrp3-deficient mice appear to contradict these findings . However , while the serum level of IL-1β is reduced systemically in Nlrp3-/- , Asc-/- , Casp1/11-/- and Aim2-/- mice , IL-18 is only moderately reduced in Nlrp3-/- , Asc-/- and Casp1/11-/- mice , and is elevated in Aim2-/- mice ( Fig 4A and 4B ) . Although the serum IL-1β/IL-18 response follows a similar trend to that in the lungs , the magnitude of the systemic response is quite low by comparison ( compare with Fig 3A and 3B ) . Further , no difference could be detected in the innate Ft LPS-specific IgM level ( Fig 4C ) . Thus , reduced serum levels of IL-1β/IL-18 in mice deficient for inflammasome components does not appear to impact Ft LPS-specific IgM and does not correlate with their pattern of survival/mortality , suggesting that sufficient IL-1β/IL-18 is available to facilitate production of these antibodies . There was also no difference in IgG or IgA antibody levels in serum ( Fig 4D ) or BAL fluid ( S4 Fig ) of these mice at 6 dpi . As levels of anti-Ft LPS antibodies in serum and BAL fluid were comparable between Nlrp3-deficient and wildtype mice , it is unlikely that differences in the Ft-LPS-specific IgM antibody response play a critical role in the survival of Nlrp3-deficient mice . During Ft infection , both myeloid ( PMN and macrophages ) and lymphoid ( T and B cells ) cells are thought important for protection [32–36] . However , we recently reported that necrotic lung damage and host death during lethal pulmonary tularemia is accompanied by predominating infiltration of the lung by death-prone immature myeloid cells with myeloid-derived suppressor cell ( MDSC ) phenotypes and function , specifically immature “band” neutrophils/PMN-MDSC ( pMDSC ) and monocytic-MDSC ( mMDSC ) [4] . Accordingly , Ly6Ghi neutrophils and F4/80+ macrophages capable of controlling bacteria are not prevalent in the lungs of lethally infected mice . We also showed that while eliciting mature neutrophils and macrophage is protective , neutrophils are essential [4] . Thus , the resistance of Nlrp3-/- mice to Ft LVS might result from a change in the type or extent of the myeloid cell response . The total number of lung cells recovered and the frequencies of T and NK cells were similar between wildtype , Casp1/11-/- , and Nlrp3-/- mice over the course of infection ( S5 Fig ) . Interestingly , the number of lung CD11b+ myeloid cells in Nlrp3-/- mice was also comparable to that in Casp1/11-/- and wildtype mice until 6 dpi ( Fig 5A ) , demonstrating that myeloid cell influx is largely unaffected . However , Ly6Ghi ( i . e . mature ) neutrophils were notably more abundant in the lungs of Nlrp3-/- mice than that of wildtype mice at 1 dpi , similar to wildtype mice at 3 dpi , and only somewhat less abundant at 6 dpi ( Fig 5B ) . The Ly6Ghi mature neutrophils were lower in Asc-/- , Casp1/11-/- or Aim2-/- mice than that of Nlrp3-/- mice at 1 dpi ( S5 Fig ) . Further , numbers of detrimental pMDSC were also decreased in lungs of Nlrp3-/- mice at 1 dpi ( Fig 5C ) , whereas the number of these cells in Casp1/11-/- and wildtype mice were similar . Numbers of F4/80+ macrophages were unchanged between Nlrp3-/- and Casp1/11-/- mice through day 3 post-infection , but declined by day 6 ( Fig 5D ) . Immature and ineffective mMDSC were somewhat less abundant in the lungs of Nlrp3-/- mice compared to Casp1/11-/- and wildtype mice at 6 dpi ( Fig 5E ) . Thus , the resistance of Nlrp3-/- mice to lethal Ft infection is unlikely to result from improved numbers or function among mature macrophages or a reduction in immature , ineffective mMDSC , but instead correlates with increased numbers of mature neutrophils in the lung at 1 dpi . The appearance of neutrophils in the lung on day 1 precedes reduced bacterial burden over the course of infection in Nlrp3-/- mice , suggesting that Nlrp3 promotes host lethality in wildtype mice by restricting the appearance of these cells . Ft infected Nlrp3-/- mice were therefore depleted of neutrophils with anti-Gr-1 antibody . Ft infected Nlrp3-/- mice depleted of Gr-1+ cells succumb to infection ( Fig 5F ) , indicating that the neutrophils observed at 1 dpi are critical for protection . We previously observed that lung recruited myeloid cells in the neutrophil lineage are mostly immature in Ft-infected mice [4] . As such , the immature neutrophils ( pMDSC ) and mMDSC population appear lower in Nlrp3-/- mice , suggesting that eliciting mature neutrophils may be sufficient for protection . To further evaluate this idea , mice were administered a low dose of E . coli LPS ( 10 or 100 μg/mouse ) by the intranasal route ( Fig 5G ) . LPS-treatment elicited a mature neutrophil response with predominant neutrophils and some macrophages at 48 h post-treatment , but prior to infection ( S5E Fig ) . Neutrophil numbers remained high in these mice at day 1 post-infection , but declined at later time points ( Fig 5G ) , while the magnitude of the macrophage response , although higher at day 3 , is similar to those seen in wildtype and Nlrp3-/- mice infected with Ft LVS . As an alternative approach , we adoptively transferred neutrophils isolated from the bone marrow cells of the naïve wildtype mice . LPS stimulation prior to infection was completely protective and transfer of BM neutrophils protected approximately 30% of infected mice ( Fig 5H ) . Further , Ft LVS was effectively controlled in the lungs of low-dose LPS treated mice ( Fig 5I ) and those receiving transferred neutrophils ( Fig 5J ) . Thus , we conclude that Nlrp3 prevents lung recruitment , maturation , or survival of mature neutrophils , that are otherwise capable of promoting clearance of Ft which preserves lung architecture and increases the survival of mice during pulmonary infection . The protective neutrophil response in Nlrp3-/- mice may result from improved neutrophil recruitment . The cytokines/chemokines IL-17 , mouse KC , and MCP-1 are important for myeloid cell recruitment and maturation [37] . We evaluated their expression in the lungs of wildtype and Nlrp3-/- mice with Casp1/11-/- mice as a control representing inflammasome-deficient mice . IL-17 was increased in Nlrp3-/- mice at 1 dpi , was equivalent at 3 dpi , but reduced at 6 dpi compared to wildtype mice ( Fig 5K ) , but levels of KC and MCP-1 did not differ between Ft infected Nlrp3-/- and wildtype mice . Except for reduced IL-17 and slightly elevated KC levels at 6 dpi , the levels of these soluble mediators in the lungs of Ft infected Casp1/11-/- mice were essentially identical to wildtype controls ( Fig 5K ) . Since IL-17 levels increase concomitantly with the appearance of neutrophils in the lungs of Nlrp3-/- mice at 1 dpi , IL-17 may be negatively regulated by Nlrp3 early during infection . In this case , neutralization of IL-17 is expected to reverse the protective phenotype leading to increased mortality . However , administration of anti-IL-17 did not alter the survival of Nlrp3-deficient mice ( Fig 5L ) . Thus , although neutrophils are essential for protection against Ft in Nlrp3-/- mice , IL-17 is dispensable during this protection . IL-1 also recruits neutrophils and IL-1r-deficient mice are susceptible to sublethal infection with Ft LVS . Accordingly , neutrophil recruitment to the lungs was significantly reduced in Ft LVS infected mice lacking the IL-1r ( Fig 5M ) . As Nlrp3-deficient mice have reduced IL-1β production , the increased neutrophil numbers in the lung occur despite diminished IL-1β levels . These observations suggest that improved neutrophil recruitment may not account for their increased numbers and that other mechanisms should be considered . Our data suggest that Nlrp3 prevents an early neutrophil response that effectively controls Ft replication and potentially restricts overt cellular inflammation that contributes to tissue pathology and acute death . Consistent with the preservation of airway space ( see Fig 2 ) , overall lung pathology scores are lower in Nlrp3-/- mice when compared to wildtype or Casp1/11-/- mice ( Fig 6A ) . However , a further analysis of individual criterion used for pathology scoring revealed that the site of inflammation ( mostly at peri-bronchiolar , peri-vascular and alveolar regions ) with involvement of neutrophil ( PMN ) /macrophages infiltration was essentially identical between wildtype and Nlrp3-/- mice ( Fig 6B ) . In contrast , Nlrp3-/- mice had fewer inflammatory foci ( mostly small and patchy ) versus many large inflammatory foci seen in wildtype or Casp1/11-/- mice ( Fig 6C ) . Importantly , the extent of necrosis was also less severe in Nlrp3-/- mice with fewer and smaller necrotic foci in the lung epithelial parenchyma ( Fig 6D ) . Consistently , in the absence of Nlrp3 , lung damage is significantly reduced as reflected by significantly lower LDH release in BAL fluid ( Fig 6E ) and in situ assessment of LDH release in lung tissue by immunohistochemistry ( Fig 6F ) . Indeed , necrotizing inflammation is a hallmark of pulmonary tularemia and is associated with the death of myeloid cells which constitute the lethal pulmonary inflammatory response to Ft [4] . Next , we quantified the number of dead cells ( 7-AAD+ ) among the recoverable fraction of the single cell suspension obtained from lungs by flow cytometry and in situ histological evaluation of inflammatory/necrotic foci by microscopy . At day 3 post-infection , although limited , PMN death was evident and comparable between wildtype , Asc-/- , and Casp1/11-/- mice , but reduced in Nlrp3-/- mice as assessed by 7-AAD staining ( Fig 6G ) . While macrophage death was also reduced in Nlrp3-/- and Asc-/- mice at day 3 , death of macrophages from Casp1/11-/- mice was comparable to wildtype . At day 6 , approximately 30% of lung infiltrating PMN cells and macrophage from wildtype mice were dead . In contrast , death of these cells was reduced in Nlrp3-/- , Asc-/- , and Casp1/11-/- mice . At day 6 when necrotic changes are most evident on histological observation , all inflammasome component-deficient strains had reduced numbers of dead PMN cells and macrophages ( Fig 6H and S5 Fig ) . Consistent with lung necrosis scores ( Fig 6D ) , only Nlrp3-/- mice had significantly fewer dead lung epithelial cells . These results suggest that Nlrp3 drives the death of myeloid and epithelial cells and contributes to necrotic injury in the lung , yet various mechanisms including ASC and caspase-1-dependent processes also occur . However , unlike Nlrp3-deficient mice , ASC- and caspase-1/11-deficient mice show no protection against Ft LVS infection , no protective early neutrophil response , and no reduction in lung pathology . These observations are consistent with an inflammasome-independent role for Nlrp3 in promoting host mortality perhaps via Nlrp3-mediated myeloid cell death . Nlrp3 is involved in two distinct forms of cell death , caspase-1 dependent pyroptosis [23 , 38–42] and Asc-dependent , but caspase-1 independent pyronecrosis [23 , 38 , 43] . However , while apoptosis ( caspase-3-mediated ) and pyroptosis ( caspase-1-mediated ) have been implicated in Ft or Fn-induced macrophage death [5 , 6 , 14 , 18 , 29 , 44] , it is unclear whether Nlrp3 is involved . On in vitro infection , Ft induced necrosis in BMDM in a dose- and time-dependent manner at an MOI of 100 or greater at 24 hours post-infection ( hpi ) as measured by LDH release . At an MOI of 1 or 10 , no cell death was observed up to 24 hpi ( Fig 6I ) . As macrophages can also undergo necroptosis , a form of regulated/programmed necrotic cell death [45] , we sought to distinguish between an apoptotic , pyroptotic , pyronecrotic , or necroptotic mechanism of Nlrp3-mediated cell death . While Ft-induced death of BMDM from Casp-1/11- and Asc-deficient mice was indistinguishable from wildtype controls , significantly less cell death was observed in Nlrp3-/- BMDM ( Fig 6J ) . Further , caspase-3 inhibitor ( z-DEVD-fmk ) treatment did not reduce Ft-induced cell death in BMDM from any of these mouse strains . This result suggests a novel Nlrp3-mediated form of cell death independent of caspase-3 , caspase-1 and Asc . Interestingly , pre-treatment with necrostatin-1 ( Nec-1 ) , an inhibitor of RIP1/3-mediated necroptosis , reduced cell death in wildtype , Casp1/11-/- , and Asc-/- BMDM , but did not further reduce the death of Nlrp3-/- BMDM . Further , necrosis was reduced in cultured alveolar macrophages ( MØ ) and PMN from Nlrp3-/- mice , but was comparable for cells isolated from wildtype or Casp1/11-/- mice ( Fig 6K ) . Necrotic damage was also observed in lung epithelium in Ft-infected mice and reduced in the absence of Nlrp3 . To examine whether Ft kills epithelial cells , we infected the LA-4 lung epithelial cell line with Ft LVS and examined LDH release at 24 hours . As with myeloid cells , death of these cells was inhibited by Nec-1 , but was insensitive to caspase-3 inhibition ( Fig 6L ) , suggesting that Ft elicits lung epithelial cell necrosis . Collectively , these data suggest that Nlrp3-dependent , but Asc , caspase-1/11 , and caspase-3-independent cell death , which is likely necrosis/necroptotic , contributes to death of lung myeloid and epithelial cells during pulmonary tularemia . As the deficiency of Nlrp3 limits neutrophil cell death early during infection and lung epithelial cell death later in infection , Nlrp3-dependent necroptosis might help explain the pathology and host mortality accompanying pulmonary tularemia . Ft-induced necrosis is blunted in myeloid cells from Nlrp3-/- mice and Nec-1 protects cultured cells for Ft-elicited cell death , but the behavior of mature cells in vitro may not reflect the mechanism contributing to tissue damage and death . Following lethal LVS infection , 25% of mice treated with Nec-1 survived ( Fig 7A ) and necrotic lung damage was also reduced ( Fig 7B ) , while wildtype control mice and those treated with a caspase-3 inhibitor were not protected and had comparable pathology . As Casp1/11-/- mice also showed no protection ( Fig 3 ) , these data suggest that Nec-1 sensitive necroptotic cell death contributes to host mortality and that caspase-1- or caspase-3-dependent forms of cell death do not . During the course of these studies , we learned that in addition to RIPK1 , Nec-1 also inhibits inhibits IDO [46] , which could complicate our results . However , the Nec-1 derivative Nec-1s retains RIP1K specificity but does not inhibit IDO [46] . Therefore we repeated our survival experiments using Nec-1s . As with Nec-1 treatment , approximately 25% of mice treated with Nec-1s survived lethal Ft LVS infection ( Fig 7C ) . Importantly , Nec-1s treatment of Nlrp3-deficient mice did not significantly improve the resistance of these mice to lethal Ft infection ( Fig 7C ) , strongly suggesting that Nlrp3 and RIPK1 are acting on the same pathway . Further , Nec-1s treatment of wildtype mice resulted in improved bacterial control ( Fig 7D ) , similar to that seen with Ft LVS infected Nlrp3-deficient mice . These data support the hypothesis that Nlrp3 mediates a necroptotic program that contributes to irreversible damage of the lung during lethal pulmonary tularemia . Overall , our findings support the conclusion that during pulmonary tularemia Nlrp3 drives host mortality in an inflammasome-independent fashion that prevents an early neutrophil response important for host protection by promoting myeloid cell death . Moreover , the pathologic effects of Nlrp3 can be inhibited by the RIP1 inhibitor , Nec-1s , strongly implicating Nlrp3-dependent cell death as a key determinant of host susceptibility to Francisella .
Pulmonary tularemia is a frequently fatal , acute necrotic pneumonia in humans and animals caused by various sub-species of the environmental bacterium Francisella tularensis ( Ft ) . Most human cases of pulmonary tularemia result from infection with F . tularensis holarctica ( the parent strain of the live vaccine strain; LVS ) or F . tularensis tularensis ( e . g . SchuS4 ) , while and Francisella novicida ( Fn ) cause disease in rodents . As a vaccine strain , Ft LVS is non-pathogenic to humans and Fn rarely causes human disease [1–3] . Despite intensive research efforts , the mechanism by which Ft elicits fatal disease is poorly understood . Many studies have reported that the Asc/Caspase-1 axis and , in particular , the Aim2 , inflammasome which generates IL-1β and IL-18 is critical for resistance to Fn [14–19] . Owing to the emphasis on IL-1β and the still unexplained inability of Fn to elicit an Nlrp3 inflammasome response in mouse macrophages , the role of Nlrp3 during Ft infection has not been explored further . In addition , with the exception of a few studies [13 , 28] , the roles played by Nlrp3 , Aim2 or other inflammasomes during infection with Ft LVS and SchuS4 have not been investigated . Given the extensive genetic similarity ( 97% nucleotide identity ) between Ft and Fn [47] , differences in virulence and pathogenesis are thought to arise from differential regulation of homologous genes and distinct roles for their products [3] . We have noted that both Fn and Ft LVS are capable of utilizing the NLRP3 inflammasome in human cells [22] . Thus , the present study sought to examine the role of Nlrp3 in the mouse model of pulmonary tularemia caused by Ft LVS or SchuS4 . Interestingly , despite Ft strain-specific differences in the IL-1β/IL-18 responses of bone marrow-derived macrophages and differential reliance upon Nlrp3 or Aim2 inflammasomes , Nlrp3-deficient mice exhibited various degrees of resistance ( decreased susceptibility ) to lethal pulmonary infection with Fn , Ft LVS , and SchuS4 . This finding demonstrates , for the first time , that Nlrp3 is activated by Francisella species in general , acting as a host susceptibility factor driving the pathogenesis of pulmonary tularemia . Consistent with improved resistance , lung bacterial burden and lung pathology were significantly reduced in Ft-infected Nlrp3-/- mice . Mice deficient for Caspase-1/11 , Aim2 , or Asc , however , displayed increased susceptibility despite levels of inflammatory cytokines similar to Nlrp3-deficient mice , clearly indicating an inflammasome-independent role for Nlrp3 in the pathogenesis of pulmonary tularemia . Our results confirm that Asc and Caspase-1 contribute to protection , while Nlrp3 , independent of inflammasome , is detrimental during Ft infection [14–19 , 38–43] . We find that Nlrp3-deficient mice exhibit an early , protective , mature neutrophil response accompanied by reduced numbers of immature myeloid cells , a response that is absent in wildtype mice and mice lacking Caspase-1 . Our results suggest that Nlrp3 prevents this mature response , perhaps via Nlrp3-dependent cell death , and that Nlrp3 contributes to a dysregulated myeloid cell response that drives necrotic pathology and host susceptibility during pulmonary tularemia . Prior to this study , the role of inflammsome activating proteins important for IL-1β/IL-18 responses in mice following infection with Ft strains other than Fn was essentially unknown . Unlike Fn which relies almost exclusively upon Aim2 inflammasome for macrophage production of both IL-1β and IL-18 , Ft LVS and SchuS4 utilize both Aim2 and Nlrp3 . However , we noted an interesting difference between Ft LVS and SchuS4 in their activation of Aim2 and Nlrp3 in macrophages . While the IL-1β response to SchuS4 strictly required Nlrp3 , Aim2 was only partially required . However , Ft LVS required Nlrp3 for IL-1β responses , which were approximately 2-fold higher in the absence of Aim2 . IL-18 responses to either Ft LVS or SchuS4 , however , were dependent upon both proteins . Curiously , Aim2-independent production of IL-1β following Ft LVS infection was not observed in vivo . This disparity suggests that aspects of inflammasome activation in macrophages may differ based on in vivo versus in vitro context or may reflect the phenotypic response of mature macrophages versus the immature cells found in the lungs of Ft infected mice [4] . Despite these differences , how the Nlrp3-inflammasome is engaged in Ft infection and how Fn relies entirely upon the Aim2 inflammasome in macrophages remain unknown . Consistently both Aim2 and Nlrp3 require the Asc adapter protein [20 , 48] , suggesting the possibility of negative regulation via competition for Asc . Fn replicates faster intracellularly and causes macrophage cell death more rapidly than Ft LVS . It is possible that release of bacterial and cellular dsDNA following cell demise could engage the Aim2-inflammasome . Interestingly , with Ft LVS macrophages , Aim2 seems to negatively regulate the Nlrp3 response , as Aim2-/- cells generate higher amounts of IL-1β . Although we do not further pursue the mechanism responsible for increased IL-1β release in Aim2-/- mice following Ft LVS infection in this report , this effect was consistently observed in all our experiments . The increased susceptibility of Casp1/11-/- , Asc-/- , and IL-1r1-/- mice to Ft LVS infection in this study and IL-1β-/- mice in a previous study [13] clearly underscore the critical requirement of IL-1β/IL-18 for protective immunity . Consistently , Casp1/11-/- and Asc-/- mice had reduced level of processed IL-1β/IL-18 . In contrast , while Nlrp3-/- and Aim2-/- mice had levels of IL-1β and 1L-18 similar to those of Caspase-1/11 and Asc-deficient mice; only mice lacking Nlrp3 were protected from lethal Ft infection . Further , the Nlrp3 inflammasome inhibitor MCC950 did not result in any protection . This clearly indicates an inflammasome and IL-1/IL-18-independent mechanism for Nlrp3 that promotes susceptibility . As IL-1β/IL-18 levels are reduced in Ft LVS infected Nlrp3-/- mice , the amounts of these cytokines required for protection during pulmonary tularemia may be much lower than previously thought . Alternatively , higher levels of IL-1β or IL-18 in the absence of Nlrp3 might afford greater protection . Consistent with this alternative , IL-1r1 is thought to be important for the generation of protective Ft-LPS specific IgM [13] , however we observed no significant alterations in anti-Ft-LPS IgM in inflammasome component-deficient mice . Moreover , MCC950 treatment increased the susceptibility of wildtype mice receiving a 50% lethal dose of Ft LVS , presumably due to reduced IL-1β/IL-18 production . Thus , the precise role that IL-1β and IL-18 play in the absence of Nlrp3 as well as the levels required for protection remain to be addressed . Infiltration of the Ft-infected lung by immature myeloid cells/myeloid-derived suppressor cells ( MDSC ) is associated with necrotic lung damage and host death , as we recently reported [4] . Appearance of these immature cells was comparable between wildtype controls and Capase-1/11-deficient mice lacking intact Nlrp3 and Aim2 inflammasome function . Indeed , Caspase-1/11- and Aim2-deficiency is associated with ineffective clearance of Ft , as evidenced by bacterial burdens comparable or higher than control mice later in infection . Thus , the immature myeloid response appears to be inflammasome-independent . However , improved clearance of Ft in Nlrp3-deficient mice correlates with a necessary early ( day 1 ) mature neutrophil ( PMN ) response that appears critical for inhibiting further replication of Ft in the lungs and a corresponding decrease in PMN-MDSC at 6 dpi . This is consistent with our recent demonstration that a mature neutrophil response in sub-lethal Ft LVS infection supports bacterial clearance and survival [4] and with the protection against Ft LVS infection provided by eliciting mature neutrophils with low dose intranasal instillation of LPS or by direct transfer of neutrophils . Immature monocytic cells ( mMDSC ) declined late during Ft LVS infection of Nlrp3-/- mice , but at the same time point , mature F4/80+ macrophages were also decreased . Yet , although similar in magnitude , macrophage numbers were higher with low-dose LPS instillation . In contrast to the clear role of neutrophils , whether mature macrophages or reduced numbers of mMDSC also contribute to bacterial control and resistance in these mice is unknown . Recently , it has been reported that Nlrp3 regulates chemokine-mediated functions and recruitment of neutrophils contributing to hepatic ischemia-perfusion injury independent of inflammasome [43] . In this previous study , Nlrp3 regulates the function of KC and thereby reduced neutrophil recruitment in Nlrp3-/- mice when compared to Asc-/- or Casp1-/- mice . However , excluding a significant , but seemingly unnecessary increase in IL-17 levels at day 1 post-infection in Nlrp3-/- mice , differences in the level of KC or MCP-1 that might impact myeloid cell recruitment were not discernable between Nlrp3-/- , Asc-/- , Casp1/11-/- and Aim2-/- mice . This lack of difference suggests that the early , protective , neutrophil response is independent of the actions of these chemokines as well as IL-17 . This is somewhat surprising as IL-17 has been implicated in promoting Th17 responses that appear to be protective in mice infected with an Ft LVS mutant which fails to elicit PGE2 [49] . IL-1 is important for neutrophil recruitment during Ft LVS infection , but Nlrp3-deficient mice have increased neutrophil numbers despite diminished IL-1β . These early neutrophils may represent the lung-associated , marginated pool of neutrophils which may be mobilized to the lung upon infection , but how this might occur during infection has not been evaluated [50–51] . Whether Nlrp3 directly or indirectly negatively regulates neutrophil maturation or recruitment is unclear . Alternatively , death of neutrophils owing to Nlrp3 may be responsible for restricting the number of mature neutrophils . Acute necrotic lung injury correlates with dying myeloid cells , loss of pulmonary function , and death during lethal Ft infection [4] . Consistent with sustained recruitment of immature myeloid cells/MDSC , Casp1/11-/- mice exhibited severe necrotizing inflammatory changes in the lung . However , the size and number of inflammatory foci in the lung and accompanying necrotic damage was markedly lower in Nlrp3-/- mice resulting in notable preservation of lung architecture . These changes point to an Nlrp3-mediated cell death mechanism . Curiously , preservation of lung architecture has been observed previously in Nlrp3-deficient mice infected with Klebsiella , but these mice nevertheless succumb due to insufficient IL-1β production [39] . Nlrp3 is reported to induce two forms of cell death . The first is inflammasome-dependent pyroptosis and requires caspase-1 activation [23 , 38–42] . The second is Nlrp3- and Asc-dependent , but Casp-1-independent and termed pyronecrosis [39 , 43] . Importantly , both share some features of classical necrosis and are appreciated as distinct forms of programmed cell death [23] . It is unclear which forms of cell death mediate the necrotic damage evident during lethal pulmonary tularemia . Intriguingly , severe necrosis during pulmonary tularemia requires Nlrp3 , but appears largely independent of caspase-1 and Asc in both myeloid and lung epithelial cells . Our in vitro infection data suggest that Ft-induced BMDM cell death is non-apoptotic ( Caspase-3 independent ) , non-pyroptotic ( Caspase-1-independent ) , and non-pyronecrotic ( Asc-independent ) . Our data also suggest that an Nlrp3-dependent necrosis/necroptotic ( Nec-1-sensitive ) pathway likely predominates in myeloid cells dying during Ft infection . Although Nlrp3 function in myeloid-lineage cells is well documented , Nlrp3 expression in epithelial cells also plays a critical role during inflammation [52–56] . We also observe reduced lung stromal cell death in Ft infected Nlrp3-deficient mice and Ft-infected epithelial cells die by a mechanism that is also sensitive to inhibition with Nec-1 . While Francisella has been reported to induce caspase-3 activation and apoptotic cell death in the lung [5 , 6] , our data suggests that caspase-3 may be less important by implicating Nlrp3 in a form of cell death distinct from apoptosis , pyroptosis , and pyronecrosis . Indeed , Ft LVS infected mice treated with a caspase-3 inhibitor are not protected , while treatment with the RIPK1 inhibitor Nec-1s is protective and results in reduced bacterial burden . Whether RIPK1 is responsible for restricting the early mature neutrophil response by promoting the death of these cells is unclear . Nec-1s treatment does not result in an increase in lung neutrophils following Ft LVS infection , while macrophage numbers are higher at days 3 and 6 post-infection ( S7 Fig ) , suggesting that the early neutrophil response is restricted by Nlrp3 , but not by RIPK1 . However , DMSO is known to diminish lung neutrophil numbers [57] and reduces both neutrophil and macrophage bacteriocidal function without enhancing host susceptibility to infection [58] , important caveats suggesting that differences in neutrophil survival may be obscured . Such inhibition may also account for the larger bacterial numbers and less pronounced bacterial control seen in the Nec-1s experiment . Of note , Nec-1s treatment does not significantly improve the survival of Ft LVS infected Nlrp3-deficient mice , suggesting that Nlrp3 activation of RIPK1 , whether direct or indirect , is likely . This supports the hypothesis that Nlrp3 promotes lethality through inducing necroptosis , although the cell populations critically impacted in vivo and how necroptosis contributes to the lack of an early , protective neutrophil response are still unknown . In addition , the precise mechanisms of cell death involved during infection remain unclear as at later times post infection , myeloid cell death also appears to involve Asc and Caspase-1/11 . Deciphering whether Nlrp3 is required for the apoptotic cell death observed during infection with the Type A strain SchuS4 [5 , 6] , a caspase-3 independent mechanism , or both , will be of considerable interest and require further study . More importantly , dissecting how Nlrp3 is involved in epithelial cell death and whether dying myeloid cells or direct infection are responsible for necrotic damage to the lung stroma will likely be of interest to those interested in the pathogenesis of acute lung injury during acute necrotic pneumonias and others exploring the functions of Nlrp3 . Lastly , early infiltration of neutrophils is important for protection and likely controls Ft numbers , which may ultimately reduce myeloid and epithelial cell death . These cells may represent the pulmonary-associated marginated pool of neutrophils that are thought to be poised to respond to infection . If demonstrated , susceptibly of these to Nlrp3-mediated cell death would isolate a role for Nlrp3 in restricting a key step in the appearance of neutrophils early during pulmonary infection . Are there other inflammasome-independent mechanism involving Nlrp3 that may contribute to our observations ? Recently , Nlrp3 was demonstrated to cooperate with IRF4 that drives IL-4 transcription and positively regulate differentiation of Th2 cells [59] . In this system , Nlrp3 deficiency increased Th1-dependent responses which exerted significant control of disease in mouse models of asthma and metastatic melanoma [59] . In contrast , during Leishmaniasis , Nlrp3 also promotes Th2-biased adaptive immunity in an inflammasome-dependent manner through IL-18 [60] . Although these two studies report divergent mechanisms by which Nlrp3 favors Th2 immune responses , it is understood that Nlrp3 plays a critical role in restricting Th1 responses . However , in the Ft infection model we observed no decrease in Th2 cytokines such as IL-4 or IL-10 in Nlrp3-deficient mice at any time following infection , but IFNγ production was slightly elevated at 6 days post-infection ( S3F Fig ) . Importantly , lung necrosis is already moderated at 3 days post-infection in Nlrp3-/- mice , at which point bacterial numbers in the lung also appear to be declining . Given the requirement for the mature neutrophils early in infection and the late appearance of elevated IFNγ , it is unlikely that resistance to Ft in the absence of Nlrp3 results from transcriptional alterations in lung Th1/Th2 cytokines during pulmonary tularemia . Collectively our data demonstrates that Nlrp3 acts as a host susceptibility factor during Francisella infection . Independent of its role in the inflammasome , Nlrp3 prevents the appearance of neutrophils in the lung early during Francisella infection and ultimately contributes to lung damage and host mortality likely via necrotic/necroptotic death of myeloid and stromal cells .
C57BL/6J wild-type , Aim2-/- ( Aim2Gt ( CSG445 ) Byg ) , Casp1/11-/- ( Casp1tm1Flv ) , IL-1r-/- ( Il1r1tm1Imx ) , and CD45 . 1 ( B6 . SJL-Ptprca Pepcb/BoyJ ) congenic mice were purchased from Jackson laboratories . Nlrp3-/- and Asc-/- mice were described previously [61] . All the mice were housed and bred in the Animal Resources Facility at Albany Medical College . Experiments were conducted using male and female mice ( 8–10 weeks ) . All animals were maintained in the animal resource facility at Albany Medical College and handled in strict accordance with good animal practice as defined by the United States Public Health Service and Department of Agriculture . All animal work was approved ( ACUP #12–04001 , 12–04002 and 12–04003 ) by the Albany Medical College Institutional Animal Care and Use Committee ( IACUC ) and adhered to the regulations of the Public Health Service ( PHS ) policy on Humane Care and Use of Laboratory Animals . Ft SchuS4 and LVS were cultured in modified Muller Hinton ( MH ) or Brain Heart Infusion ( BHI ) broth as described [62] . All experiments utilizing SchuS4 were conducted within the Albany Medical College , CDC-certified BSL-3 facility . Bacterial inocula were prepared in sterile PBS by serial dilution to defined cfu numbers . Mice were anesthetized by i . p injection of 80–100μl/mouse of Ketamine ( 20mg/ml ) and Xylazine ( 1mg/ml ) mixture . Anesthetized mice were infected i . n with 40 μl of inoculum instilled in a single nare . An equal volume of inoculum was plated on MH chocolate agar to confirm actual cfu numbers . Sham-inoculated controls received an equal volume of PBS or appropriate vehicle medium . Blood was collected by submandibular venipuncture [63] and mice were euthanized with a mixture of Ketamine and Xylazine followed by cervical dislocation . Necropsy was performed , gross lesions were noted , and organs ( lungs , liver and spleen ) were collected aseptically to prepare tissue homogenate ( for bacterial counting and/or cytokine measurements ) , single cell suspensions ( for immunophenotyping ) , or histology ( for pathological assessment ) as described previously [4] . For lung homogenate preparation , either whole lungs or a half of the lungs containing pieces ( consistent size for each mouse ) from middle lobe , post-caval lobe , the right superior lobe and the left lung lobes were collected in sterile PBS . For histology , either the entire lung lobes or representative pieces each from the right superior and inferior lobes and a half of the left lung lobes were collected in 10% buffered formalin . Either all or half of the spleen was collected in formalin for histology . As well , pieces of liver from left lateral lobe and medial lobe were collected in formalin . Formalin fixed tissues were processed by standard histological procedures and 4μm-thick sections were cut and stained with hematoxylin and eosin ( HE ) . Sections of lungs , spleen or liver were examined for the location of inflammatory foci , type of infiltrating cells and the extent of necrotic changes in parallel with sections from uninfected or Ft-infected lungs and scored using the criteria described previously [4] . Immunohistochemical ( IHC ) analysis for identification of myeloid cell types ( Ly6G+ , Ly6C+ , or CD11b+ ) or localization of LDH was performed in formalin-fixed paraffin-embedded tissue sections , as described previously [4] . BAL fluid was collected from control and Ft-infected mice as described previously [4] . The cell-free clear supernatants were used immediately for LDH assays or stored frozen at -80°C for protein estimation . LDH assay was performed following manufacturer’s instruction using the Cytotox96 non-radioactive kit ( Promega ) . The cell pellets were used for flow cytometry or cytospin smear preparation for differential cell counting ( Giemsa ) . Tissue homogenates prepared from whole or pieces of lungs/spleen/or liver were plated onto MH chocolate agar as described previously [4] . After 2 days , colony counts were performed and bacterial numbers ( cfu ) were calculated . Results are expressed as log10 cfu/ml/organ . By using Mouse Group I and II Luminex assay kits ( BioRad ) , cytokines/chemokines were estimated in clear tissue homogenates [4] . Myeloid and lymphoid cell types were evaluated by multi-color flow cytometry as described previously [4] . Briefly , single cell suspensions from collagenase-digested lung or spleen were surface stained with either lymphoid markers lymphoid markers ( CD3 , CD4 , CD8 , NK1 . 1 , B220 , CD19 , Terr119 ) , myeloid markers ( CD11b , CD11c , F4/80 , Gr-1 , Ly6C , Ly6G ) and/or cell activation markers ( CD80 , CD86 , MHCII , PD-L1 or CD115 ) for 30 min . Cells were fixed in 1% paraformaldehyde ( PFA ) and cytometry was performed on an LSRII ( Becton Dickinson ) . For cell death analysis in lung cells , surface marker stained cells were stained with 7-AAD , washed twice and fixed in 1% PFA , prior to run in LSRII . Flow cytometry data were analyzed using FlowJo software ( v10 . 0 . 1 ) . Specific cell populations are represented as a mean percentage or total numbers for Ft-infected mice at various dpi in comparison to uninfected control mice ( 0 dpi ) . Bone marrow cells were isolated from the femurs and tibias of six to eight week-old mice to enrich bone marrow-derived macrophages ( BMDM ) as described previously [58] . F4/80+ lung macrophages were isolated from BAL fluid and Gr-1+ PMN cells were isolated from bone marrow cells by using magnetic beads ( Miltenyi ) as described previously [4] . Cells were cultured in DMEM containing L-cell supernatant and adherent cells were used for the infection studies . Cells were infected with Ft LVS at different MOI ( 1 , 10 , 100 or 200 ) and cell death analyses were done at different time points ( 3 , 6 or 24 h ) . In some experiments , cells were pre-treated ( 30 min ) with z-DEVD-fmk ( 50 μM , final concentration ) , Nec-1 ( 50 μM ) or vehicle alone and infected with LVS at MOI = 100 . After 24 h , cell culture supernatants were tested for LDH activity as described [4] . For lung cells , single cell suspensions were stained with surface markers followed by 7-AAD staining and analyzed in LSRII as described ( P ) . Within myeloid cell subsets , the frequencies of 7-AAD+ cells were identified . For in vitro cultured cells , medium was removed; cells were harvested gently and stained with 7-AAD or TUNEL kit , or both , and then analyzed in an LSRII to count dead cells . BAL fluid and cell culture supernatants were tested for LDH activity as described [4] . Mature Gr-1+ PMN cells were isolated from the bone marrow cells of CD45 . 1 donor mice and the cells ( 1 x 106 ) were transferred to a group of naïve recipient CD45 . 2 mice by intra-tracheal intubation as described previously [4] . As well , naïve CD3+ T cells isolated from spleen were transferred to other group of recipient mice . The next day , these mice were infected with Ft LVS ( 1000 cfu ) and survival was monitored . In other experiments , to elicit mature myeloid cell responses , naïve C57BL/6 mice were instilled i . n with LPS ( E . coli , O55:B5 ) at 100μg or 10μg/mouse . After 48 h , these mice were infected with Ft LVS ( 1000 cfu ) and survival was monitored . Two mice in each group were euthanized at 48 hr to analyze the BAL fluid cell counts and were found to have more number of cells than naïve control mice . For depletion of PMN cells , mice were i . p injected with anti-Gr-1 ( RB8-8C5 , BioXcell ) or isotype control rat IgG2b mAb antibody ( 200μg/mouse ) at 1 day prior and after Ft LVS infection . Cell depletion antibodies were purchased from BioXcell ( Lebanon , NH ) . Antibody depleted mice were infected with Ft LVS ( 1000 cfu ) and survival was monitored . Mice were administered i . p with anti-IL-17 antibody ( Rat IgG1 , clone TC11-18H10 . 1; Biolegend ) or its isotype control antibody ( 200μg/mouse ) at 1 and 3 dpi . Following infection , these mice were monitored for survival . At indicated experiments , Ft-infected mice were treated i . p . with Casp3-inhibitor z-DEVD-fmk ( 200 μg/mouse ) , Nec-1 ( 200 μg/mouse ) or Nec-1s ( 200 μg/mouse ) daily between 2–6 dpi . Ft-infected mice were treated with MCC950 ( 250μg/mouse ) or glyburide ( 500μg/mouse ) daily between 2–7 dpi and monitored for survival . Statistical analysis and data compilation were done using GraphPad Prizm ( ver 6 ) . Student’s t-test or a parametric ANOVA test with Tukey’s post-test was used for statistical comparisons between groups . For survival analysis , Log-rank ( Mantel-Cox ) test was used . The p<0 . 05 was considered significant . | The Nlrp3 inflammasome is critical for various innate and adaptive immune responses through elaboration of IL-1β and IL-18 . In contrast to the anticipated minimal , or perhaps absent , role of Nlrp3 in the pathogenesis of pulmonary tularemia , we find that Nlrp3 is a host susceptibility factor . Likely through promoting necrotic/necroptotic cell death , Nlrp3 contributes to the immature myeloid response and necrotic pathology that characterize lethal infection with Francisella tularensis . | [
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"... | 2016 | Inflammasome-Independent NLRP3 Restriction of a Protective Early Neutrophil Response to Pulmonary Tularemia |
Diethylcarbamazine is a drug that is used for the treatment of filariasis in humans and animals; it also has effects on intestinal nematodes , but its mechanism of action remains unclear . Emodepside is a resistance-busting anthelmintic approved for treating intestinal parasitic nematodes in animals . The novel mode of action and resistance-breaking properties of emodepside has led to its use against intestinal nematodes of animals , and as a candidate drug for treating filarial parasites . We have previously demonstrated effects of emodepside on SLO-1 K+-like currents in Ascaris suum . Here , we demonstrate that diethylcarbamazine , which has been proposed to work through host mediated effects , has direct effects on a nematode parasite , Ascaris suum . It increases activation of SLO-1 K+ currents and potentiates effects of emodepside . Our results suggest consideration of the combination of emodepside and diethylcarbamazine for therapy , which is predicted to be synergistic . The mode of action of diethylcarbamazine may involve effects on parasite signaling pathways ( including nitric oxide ) as well as effects mediated by host inflammatory mediators .
Infections with parasitic nematodes are a global concern for human and animal health . These infections come in the form of gastrointestinal nematodes , like ascariasis infections , hookworm infections and trichuriasis infections , as well as infections transmitted by biting insects , like filariasis . Over 1 billion people are infected with parasitic nematodes [1] , especially in the tropical regions where the combination of poor sanitation , warm and moist conditions creates the conducive environment for survival and spread of these parasites . Infections of parasitic nematodes of farm animals cause massive loss of food production and also lead to animal welfare issues . Parasitic nematode infections cause cognitive impairment of humans and , in humans and animals , stunted growth , anemia , sometimes swollen limbs and sometimes death . In the absence of effective vaccines or sanitation , anthelmintic drugs are required for both treatment and prophylaxis . There are a limited number of drug classes available and their frequent use has produced resistance in animals [2] and concerns about development of resistance in humans [3] , [4] . One way to slow the speed of development of resistance is to use the drugs that are available in a more targeted manner [5] and to use synergistic combinations of drugs [6] . In this paper we explore effects of diethylcarbamazine and describe the interactive effects of the combination of emodepside and diethylcarbamazine . Emodepside , a semisynthetic derivative of PF1022A , has a novel mechanism of action , different from other anthelmintics; it is effective against a broad spectrum of parasitic nematodes , including soil-transmitted nematodes [7] , [8] . Emodepside has a complex mode of action involving activation of a voltage-activated calcium-dependent K+ channel ( SLO-1 ) at the neuromuscular junction [9] , [10] and potentiation of its effects by drugs that increase levels of nitric oxide [11] . Emodepside has potential as a drug for the treatment of filarial parasites: it is larvicidal and adulticidal in vitro and in vivo but the efficacy of emodepside against filariae depends on species , being quite low against Brugia pahangi and Brugia malayi in comparison to other filariae [12] , [13] , [14] . Diethylcarbamazine citrate is an established antifilarial drug which has been used since 1947 for the treatment of lymphatic filariasis and loiasis . It is still an important and effective antifilarial drug but its mode of action is not fully described . Diethylcarbamazine has been suggested to have an indirect , host mediated mode of action: it appears to alter host arachidonic acid and nitric oxide metabolic pathways , which in an unknown way leads to immobilization and sequestration of the microfilariae [15] . Diethylcarbamazine activity against B . malayi microfilariae is abolished in inducible nitric oxide synthase knockout mice ( iNOS−/− ) , suggesting that diethylcarbamazine activity is dependent on host inducible nitric oxide synthase ( iNOS ) and nitric oxide , [16] . We were interested to determine: how diethylcarbamazine would affect calcium-dependent SLO-1 K+ currents in isolated Ascaris suum muscle flap preparations and; how diethylcarbamazine interacts with emodepside . The interest was prompted by observations in vertebrates [17] which show that nitric oxide activates SLO-1 K+ channels and observations on Ascaris indicating the presence nitric oxide synthase [18] and of SLO-1 K+ channels which show positive modulation by a nitric oxide pathway [11] , [19] . We hypothesized that diethylcarbamazine , with effects on arachidonic acid and nitric oxide pathways , may increase activation of SLO-1 K+ currents in Ascaris suum muscle and potentiate effects of emodepside on membrane potential . We conducted experiments in the presence of sufficient calcium to allow activation of the SLO-1 K+ currents . Here we show that diethylcarbamazine , by itself , can increase activation of SLO-1 K+ currents and potentiate effects of emodepside .
Adult Ascaris suum were collected weekly from JBS Swift and Co . pork processing plant , Marshalltown , IA and maintained for up to 4 days in Locke's solution ( NaCl 155 mM , KCl 5 mM , CaCl2 2 mM , NaHCO3 1 . 5 mM , glucose 5 mM ) at 32°C . About 1 cm of the anterior part of the worm , 4 cm from the head , was cut-out and the cylindrical worm piece cut open along a lateral line to form a muscle flap . After removing the gut to expose muscle cells , the muscle flap was pinned to a 35×10 mm Sylgard-lined Petri-dish containing low-potassium , high-calcium Ascaris perienteric fluid ( APF ) ( mM: NaCl 23 , Na acetate 110 , KCl 3 , CaCl2 6 , MgCl2 5 , glucose 11 , HEPES 5 , pH adjusted to 7 . 6 with NaOH ) . A 20-gauge perfusion needle , placed directly over the muscle bag being recorded from , delivered the drugs in APF at a rate of 4 mL min−1 . We employed the two-micropipette current-clamp and voltage-clamp techniques to investigate the effects of diethylcarbamazine and emodepside on A . suum muscle bag . Micropipettes were pulled on a Flaming Brown Micropipette Puller ( Sutter Instrument Co . , Novato , CA , USA ) and filled with 3 M potassium acetate . Resistance of the voltage-sensing micropipettes was between 20–30 MΩ but the tip of the current-injecting micropipette was broken to have a resistance of 3–6 MΩ . A 1320A Digidata , an Axoclamp 2B amplifier and pClamp 8 . 2 software ( Molecular Devices , Sunnyvale , CA , USA ) were used for the recordings . The resting membrane potential of cells selected for recording were stable and between −25 mV and −35 mV and had input conductances less than 4 . 0 µS . The current-clamp protocol consisted of injection of 40 nA hyperpolarizing pulses for 500 ms and recording the change in membrane potential with the voltage-sensing micropipette . In the voltage-clamp protocol , the muscle bag was held at −35 mV and then stepped to 0 , 5 , 10 , 15 , 20 , 25 and 30 mV to activate the K+ currents . We used a leak subtraction protocol [11] that averaged four 5 mV hyperpolarizing pre-pulses to obtain the leak subtraction current before each depolarizing step and which was scaled by the amplitude of the depolarizing step for leak subtraction . The leak subtraction was under the control of pClamp software . The leak subtraction procedure was not modified otherwise by voltage , emodepside or emodepside and diethylcarbamazine . Recordings were rejected if the conductance of the muscle cells increased abruptly , indicating cell membrane damage , or if the conductance increased above 4 µS . Acquired data were displayed on a Pentium IV desktop computer and the currents were leak-subtracted . We analyzed the leak subtracted K+ current at the 0 mV step potential because the emodepside effect was biggest at this potential [11] . All chemicals and drugs were purchased from Sigma Aldrich ( St Louis , MO , USA ) except emodepside , which was generously supplied by Achim Harder ( Bayer HealthCare AG , Leverkusen , Germany ) . Emodepside stocks of 2 mM in 100% DMSO were prepared every two weeks . The working emodepside concentration was prepared so that the final DMSO concentration did not exceed 0 . 1% . To avoid problems with emodepside coming out of solution , we did not keep it longer than the two weeks and in some cases , we prepared fresh emodepside for every experiment . Effects of drug applications were measured after 10 min and post-drug measurements made after a 20 min wash in drug-free solutions . Graph Pad Prism Software ( version 5 . 0 , San Diego , CA , USA ) and Clampfit 9 . 2 ( Molecular Devices ) were used for data analysis . The activation curve was fitted by the Boltzmann equation G = Gmax/[1 + exp { ( V50 - V ) /Kslope}] , where G = conductance , Gmax = maximal conductance change , V50 = half-maximal voltage and Kslope = slope factor .
We have shown that emodepside increases SLO-1 K+ currents by shifting V50 of the voltage-activation curve in the hyperpolarizing direction [11] . Consequently , we investigated the effects of diethylcarbamazine on the voltage-activation of SLO-1 K+ currents by itself and in combination with emodepside . Fig . 1 A shows a representative recording of the control SLO-1 K+ current produced by holding the cell at -50 mV and stepping to 0 mV; the arrow , Fig . 1 A indicates the time-point in the depolarizing pulse at which the current measurements were taken for plotting results . The application of diethylcarbamazine by itself significantly increased the peak K+ currents by 21±3% ( p<0 . 01 , n = 4 , paired t-test , Fig . 1 A & B ) . The application of 100 µM diethylcarbamazine and 1 µM emodepside together increased the SLO-1 K+ currents by 47±9% , ( p<0 . 01 , n = 4 , paired t-test , Fig . 1 A & B ) . The K+ currents were further increased over 20 min despite wash out by 72±15% ( p<0 . 01 , n = 4 , paired t-test ) , Fig . 1 A & B . We have previously described how effects of emodepside continue to increase slowly over time and do not wash off [11] . Similar effects were observed in this set of experiments during the post-emodepside and post-diethylcarbamazine period . We therefore measure drug effects after a fixed period of 10 min following drug applications to standardize observations [11] . The slow increase and lack of wash-off may be explained by the lipophilic nature of emodepside and activation of signaling cascades that include nitric oxide . Next , we determined the effect of emodepside plus diethylcarbamazine on the voltage-sensitivity of the SLO-1 K+ current , V50 . In the representative conductance-voltage plot displayed in Fig . 2 A , diethylcarbamazine by itself shifted the curve in the hyperpolarizing direction , making the channels more sensitive to depolarization . In the presence of both emodepside and diethylcarbamazine , there was a further hyperpolarizing shift of V50 . Fig . 2 B shows that 100 µM diethylcarbamazine decreased the average V50 from 7 . 6±0 . 6 mV to 6 . 2±0 . 6 mV ( p<0 . 001 , n = 5 , paired t-test , Table 1 ) . In the presence of the combination of 100 µM diethylcarbamazine and 1 µM emodepside , V50 decreased to 4 . 4±0 . 9 mV ( p<0 . 01 , n = 5 , paired t-test , Table 1 ) and the decrease continued to 3 . 1±1 . 1 mV during the post-emodepside period ( p<0 . 01 , n = 4 , paired t-test , Table 1 ) . In separate experiments we tested the effects of 1 µM emodepside alone and observed that by itself only produced a 30% increase the SLO-1 K+ current ( Table 1 ) . These experiments demonstrated that diethylcarbamazine mimicked the effect and , increased the effect of emodepside on V50 of the SLO-1 K+ . Although 10 µM diethylcarbamazine was without effect on the resting membrane potential , 100 µM diethylcarbamazine produced a small , slow ∼1 mV hyperpolarization of the membrane potential in 3 of 5 separate preparations . However the effect of 100 µM diethylcarbamazine after emodepside pre-treatment was much bigger . Fig . 3A shows a representative recording where 1 µM emodepside caused a hyperpolarization of −5 . 3 V and in the presence of the 1 µM emodepside , 100 µM diethylcarbamazine caused a hyperpolarization of −11 . 1 mV . In a series of experiments on ten different preparations , 1 µM emodepside by itself , caused a slow hyperpolarization of −5 . 1±0 . 8 mV of the Ascaris suum muscle membrane potential ( p<0 . 01 , n = 10 , paired t-test , Fig . 3B ) . Addition of 100 µM diethylcarbamazine significantly increased the hyperpolarization produced by 1 µM emodepside to −10 . 0±2 . 0 mV ( p<0 . 01 , n = 15 unpaired t-test , Fig . 3B ) . The hyperpolarization caused by 1 µM emodepside plus 100 µM diethylcarbamazine was sustained even after washing as are the effects of emodepside alone , an effect that may be explained by the lipophilic nature of the compounds and/or accumulation of second messengers in a signaling cascade [11] .
Diethylcarbamazine is used mostly for treatment of filariasis in humans but has been used for treatment of intestinal nematode parasites [20] . Details of its mode of action remain to be defined however; it has been suggested that the effects of diethylcarbamazine are mediated via the host innate immune system [15] rather than by a direct effect on the parasite . Our observations show that 100 µM ( but not 10 µM ) diethylcarbamazine has a direct effect on the parasite ( independent of the host ) raising the possibility that its therapeutic mode of action also involves a direct effect . The antifilarial action of diethylcarbamazine appears to involve host arachidonic acid metabolism via cyclooxygenase & 5-lipoxygenase and , in addition nitric oxide metabolic pathways via inducible nitric oxide synthase [16] . A role for nitric oxide and the inducible nitric oxide pathway is suggested by the experiments involving microfliarial infected iNOS−/− mice which showed no clearance response following treatment with diethylcarbamazine [16] . In addition to the treatment of filariasis , diethylcarbamazine , as a single dose treatment , has modest effects on intestinal nematode parasite infections including ascariasis and trichuriasis but is more effective when combined with ivermectin or albendazole [21] . Although diethylcarbamazine is a piperazine derivative , diethylcarbamazine does not mimic the effects of piperazine by acting as a GABA agonist on parasite muscles [22] . Here we observed that diethylcarbamazine , in the presence of sufficient calcium , had a direct effect on the worm preparation and increased activation of the SLO-1 K+ channel currents by shifting the V50 in the hyperpolarizing direction . SLO-1 channels are calcium-dependent K+ channels that are pharmacologically different from delayed rectifier K+ channels . In low-calcium , even high concentrations of diethylcarbamazine ( mM ) do not activate SLO-1 K+ currents showing that this action requires calcium [22] . High , mM concentrations of diethylcarbamazine , in low-calcium conditions , inhibits nicotinic acetylcholine currents and a delayed rectifier K+ current [22] but these high-concentration effects are non-selective . The effect of diethylcarbamazine here in our parasite preparations is to produce opening of SLO-1 K+ channels that does not involve the host . The outcome will be a reduction in the excitability of nerve and muscles tissues where the channels are present . A reduction in muscle excitability is predicted to inhibit motility and thus lead to exclusion from the intestinal tract . The Ascaris SLO-1 K+ channels , which are activated by calcium , are also activated by nitric oxide and emodepside [10] , [11] , [19] . Vertebrate SLO-1 K+ ( BK ) channels are activated by calcium , nitric oxide , arachidonic acid and fatty acid metabolites [23] , [24] but are less sensitive to emodepside [25] . The synergistic action of diethylcarbamazine and emodepside on membrane potential and the voltage-activated SLO-1 K+ current seen in our Ascaris experiments could be explained by a combination of effects on SLO-1 K+ channels by: a ) effects of diethylcarbamazine on Ascaris arachidonic metabolic pathways; b ) effects of diethylcarbamazine on Ascaris nitric oxide pathways and; c ) direct effects of emodepside on nematode SLO-1 K+ channels [10] , [25] . In conclusion , our observations show that diethylcarbamazine has a direct effect on a nematode parasite and its effects synergize with emodepside on SLO-1 K+ channels . These observations suggest the potential for the combination of diethylcarbamazine and emodepside as an anthelmintic treatment . | Filarial parasites and soil-transmitted nematodes ( STNs ) are Neglected Tropical Diseases ( NTDs ) that affect millions of people in the developing world . There is an urgent need for novel drugs and improved use of existing drugs , because of concerns about the development of resistance . The mode of action of one of these drugs , diethylcarbamazine , remains unclear , despite the fact that it has been used for a long time for treatment and prevention of filariae and STNs . The resistance-busting anthelmintic emodepside also has effects against filariae and STNs , with a mode of action that involves activation of nematode SLO-1 K+ channels . The effects of both diethylcarbamazine and emodepside may be increased by inflammatory mediators , which suggests that the effects of diethylcarbamazine and emodepside will be additive . We used our Ascaris suum preparation to test the activation of SLO-1 K+ channels by diethylcarbamazine and its potentiating effect on emodepside . Our results suggest potential for diethylcarbamazine and emodepside in combination therapy for parasitic nematodes . | [
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"helm... | 2014 | Diethylcarbamazine Increases Activation of Voltage-Activated Potassium (SLO-1) Currents in Ascaris suum and Potentiates Effects of Emodepside |
Phylogenetic networks are rooted , directed , acyclic graphs that model reticulate evolutionary histories . Recently , statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci . Bi-allelic markers , most notably single nucleotide polymorphisms ( SNPs ) and amplified fragment length polymorphisms ( AFLPs ) , provide a powerful source of genome-wide data . In a recent paper , a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers . The generative process assumed by the method combined both a model of evolution for the bi-allelic markers , as well as the multispecies coalescent . A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker . Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers . Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees . Unlike the case of species trees , the algorithm is no longer polynomial-time on all instances of phylogenetic networks . Furthermore , the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data . Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data , as well as a data set of multiple New Zealand species of the plant genus Ourisia ( Plantaginaceae ) . We implemented the method in the publicly available , open-source PhyloNet software package .
The availability of genome-wide data from many species and , in some cases , many individuals per species , has transformed the study of evolutionary histories , and given rise to phylogenomics—the inference of gene and species evolutionary histories from genome-wide data . Consider a data set S = {S1 , … , Sm} consisting of the molecular sequences of m loci under the assumptions of free recombination between loci and no recombination within a locus . The likelihood of a species phylogeny Ψ ( topology and parameters ) is given by L ( Ψ | S ) = ∏ i = 1 m L ( Ψ | S i ) = ∏ i = 1 m ∫ G p ( S i | g ) p ( g | Ψ ) d g ( 1 ) where the integration is taken over all possible gene trees . The term p ( Si|g ) is the likelihood of gene tree g given the sequence data of locus i [1] . The term p ( g|Ψ ) is the density function ( pdf ) of gene trees given the species phylogeny and its parameters . For example , Rannala and Yang [2] derived this pdf under the multispecies coalescent ( MSC ) . This formulation underlies the Bayesian inference methods of [2–4] . Debate has recently ensued regarding the size of genomic regions that would be recombination-free ( or almost recombination-free ) and could truly have a single underlying evolutionary tree [5 , 6] . One way to overcome this issue is to use unlinked single nucleotide polymorphisms ( SNPs ) or amplified fragment length polymorphisms ( AFLPs ) . Such data provide a powerful signal for inferring species phylogenies and the issue of recombination within a locus becomes irrelevant . Furthermore , as long as those markers are sampled far enough from each other the assumption of free recombination among loci holds . Indeed , this is the basis of the SNAPP method that was recently introduced in [7] . Since a bi-allelic SNP or AFLP marker has no signal by itself to resolve much of the branching patterns of a gene genealogy , a major contribution of Bryant et al . was an algorithm for analytically computing the integration in Eq ( 1 ) for bi-allelic markers . While trees constitute an appropriate model of the evolutionary histories of many groups of species , it is well known that other groups of species have evolutionary histories that are reticulate [8] . Horizontal gene transfer is ubiquitous in prokaryotes [9 , 10] , and several bodies of work are pointing to much larger extent and role of hybridization in eukaryotic evolution than once thought [8 , 11–15] . Not only does hybridization play an important role in the genomic diversification of several eukaryotic groups , but increasing evidence is pointing to the adaptive role it has played , for example , in wild sunflowers [16] , humans [17] , macaques [18] , mice [19] , butterflies [20] , and mosquitoes [21 , 22] . Reticulate evolutionary histories are best modeled by phylogenetic networks . Two statistical methods were recently introduced for inference under the formulation given by Eq ( 1 ) , when Ψ is a phylogenetic network [23 , 24] , and other methods were also introduced for statistical inference of phylogenetic networks using gene tree estimates as the input data [25–29] . The methods of [23 , 24] assume that the data for each locus consists of a sequence alignment that has no recombination . In this paper , we devise an algorithm that builds on the algorithm of [7] for analytically computing the integral in Eq ( 1 ) when Ψ is a phylogenetic network . In other words , our algorithm allows for computing the likelihood of a phylogenetic network from unlinked bi-allelic markers while analytically integrating out the gene trees for the individual markers . We couple this likelihood function with priors on the phylogenetic network and its parameters to obtain a Bayesian formulation , and then employ the reversible-jump MCMC ( RJMCMC ) kernel from [23] to sample the posterior of the phylogenetic networks and their associated parameters given the bi-allelic data . We implemented our algorithm and the RJMCMC sampler in PhyloNet [30] , which is a publicly available open-source software package for inferring and analyzing reticulate evolutionary histories . We studied the performance of our method on simulated and biological data . For simulations , we extended the framework of [7] so that the evolution of bi-allelic markers could be simulated within the branches of a phylogenetic network . For the biological data , we analyzed two data sets of multiple New Zealand species of the plant genus Ourisia ( Plantaginaceae ) . The results on the simulated data show very good accuracy and robustness as reflected by the method’s ability to recover the true phylogenetic networks and their associated parameters even when the underlying assumptions of the method are violated . For the biological data , the method recovers two established hybrids and their putative parents correctly . The proposed method and Bayesian sampler provide a new tool for biologists to infer reticulate evolutionary histories , while also account for the complexity arising from incomplete lineage sorting , from bi-allelic markers , thus complementing existing tools that use gene tree estimates or sequence alignments of the individual loci as the input data . The use of such bi-allelic markers , particularly when they are sampled far enough across the genome , completely sidesteps potential problems that could arise due to the presence of recombination within loci .
A phylogenetic X -network , or X -network for short , Ψ is a rooted , directed , acyclic graph ( DAG ) whose leaves are bijectively labeled by set X of taxa . We denote by V ( Ψ ) and E ( Ψ ) the sets of nodes and edges , respectively , of the phylogenetic network Ψ . Every node of the network has in-degree 1 , which we call a tree node , or in-degree 2 , which we call a reticulation node . The only exception is special node s whose in-degree is 0 and out-degree is 1; the edge ( s , r ) defines the branch above the root . The edges whose head is a reticulation node are the reticulation edges of the network; all other edges constitute the tree edges of the network . Every edge is directed forward in time . We assume all phylogenies considered here ( trees and networks ) are binary—no node has out-degree higher than 2 . Here , we use the bottom of a branch to refer to the end of the branch that is farther from the root of the network , and use the top of a branch to refer to the end of the branch that is closer to the root . Given that the coalescent views the evolution of alleles backward in time , we say that a lineage enters a branch to mean a lineage that exists at the bottom of that branch . Similarly , we say a lineage exits a branch to mean a lineage that exists at the top of that branch . Each node in the network has a species divergence time and each edge b has an associated population mutation rate θb = 4Nbμ . This parameter is typically referred to in the literature as the ( rescaled ) population size . Given the length τ of a branch in units of expected number of mutations per site , the length of that branch in coalescent units is 2τ/θ , assuming diploid individuals . The branch above the root , ( s , r ) , is infinite in length so that all lineages that enter it coalesce on it eventually . For every pair of reticulation edges e1 and e2 that share the same reticulation node , we associate an inheritance probability , γ , such that γ e 1 , γ e 2 ∈ [ 0 , 1 ] with γ e 1 + γ e 2 = 1 . We denote by Γ the vector of inheritance probabilities corresponding to all the reticulation nodes in the phylogenetic network . We use Ψ to refer to the topology , species divergence times , population mutation rates , and inheritance probabilities of the phylogenetic network . That is , here we include Γ as part of Ψ . An X -phylogenetic tree , or X -tree , is an X -network with no reticulation nodes . A gene tree is an X -tree . Each node in the gene tree has an associated coalescence time . In the algorithm below , we make use of a coloring function c: ( E ( g ) , t ) → {0 , 1} , similar to that used in [7] , where c ( e , t ) indicates the color , or allele , at time t along the branch e of gene tree g . We will follow [7] in calling the two colors red and green . Looking forward in time ( from the root toward the leaves ) , let u and v be the mutation rate from red allele to green allele and the mutation rate from green allele to red allele , respectively . The stationary distribution of the red and green alleles at the root is given by v/ ( u + v ) and u/ ( u + v ) , respectively . Observed alleles are indicated by values of the coloring function c at gene tree leaves . Given a gene history embedded within the branches of the network , the numbers and types of lineages at both ends of each branch of the network are needed to compute the likelihood . Let x be a branch in the phylogenetic network . We denote by n x T and n x B the total numbers of lineages at the top and bottom of x , respectively , and by r x T and r x B the numbers of red lineages at the top and bottom of x , respectively . See Fig 1 for an illustration . Let x be an arbitrary branch in the phylogenetic network and let R x be the event that for every external branch z that is a descendant of x , the actual number of red alleles in z equals to r z B . We define two partial likelihoods: F x B is the product of the likelihood of a subtree rooted at the bottom of x and the probability P r [ n x B = n ] , and F x T is the product of the likelihood at the top of branch x and the probability P r [ n x T = n ] . In the case of a species tree ( i . e . , no reticulation nodes in the species phylogeny ) , the partial likelihood vectors F x B and F x T are given by [7] F x B ( n , r ) = P r [ R x | n x B = n , r x B = r ] P r [ n x B = n ] ( 2 ) and F x T ( n , r ) = P r [ R x | n x T = n , r x T = r ] P r [ n x T = n ] . ( 3 ) Here F x B and F x T are indexed by nonnegative integers n and r , where r ≤ n . Let M be the maximum possible value of n x B and n x T over all branches . Then , each of F x B and F x T has at most l = ( 1 + ( M + 1 ) ) ( M + 1 ) /2 entries . In the case of a species tree , the path from a leaf to the root is unique . However , this might not be the case for phylogenetic networks: If there is a reticulation node on a path from a leaf to the root , then multiple paths exist between that leaf and the root . This is the issue that necessitates modifying the algorithm of [7] significantly , and that leads to much larger computational requirements in the case of phylogenetic networks . The key idea behind the modification is as follows . As the algorithm proceeds to compute the likelihood in a bottom-up fashion from the leaves to the root , whenever a reticulation node is encountered , the current set of lineages is bipartitioned in every possible way so that one side of the bipartition tracks one parent of the reticulation node and the other side tracks the other parent . As the network has a unique root , the two sides of each bipartition eventually come back together at an ancestral node . At that point , these two sides are merged properly . To achieve this proper merger , we introduce “labeled partial likelihoods , ” or LPL . Like the case of [7] , LPLs are not “real” partial likelihoods . The reason for this is that when partial likelihood vectors are split ( described below ) , those become symbolic terms that do not evaluate to partial likelihoods until they are merged later . This is analogous to the difference between ancestral configurations on species trees [31] and their labeled counterparts on phylogenetic networks [32] , where the latter are in many cases just symbolic terms that do not evaluate to true ( partial ) likelihood values . Given a phylogenetic network Ψ with k reticulation nodes numbered 0 , 1 , ⋯ , k − 1 , an LPL P is an element of [ 0 , 1 ] l × Z k , where the first element of the pair is a partial likelihood as in [7] . The second element is the label to keep track of partial likelihoods that originated from a split of the same partial likelihood at a reticulation node so that these two could be merged . More formally , we say two LPLs P1 = ( F1 , s1 ) and P2 = ( F2 , s2 ) , where |s1| = |s2| , are compatible if and only if for every 0 ≤ i < |s1| , either s1 ( i ) = s2 ( i ) or s1 ( i ) ⋅ s2 ( i ) = 0 . We denote by P x T and P x B the sets of LPLs that are associated with the top and bottom of branch x , respectively . These two quantities are computed in a bottom-up fashion , proceeding from the leaves of the network towards its root . Once the LPLs at the root are computed , the overall likelihood of a given site is computed . As the algorithm proceeds from the leaves towards the root , it needs to compute LPLs at the leaves , the top of a branch , the bottom of reticulation edges , and the bottom of tree edges . We now describe each of those computations; the overall algorithm is simply a bottom-up traversal of the network while applying the appropriate computation as a node is encountered . Our algorithm computes the likelihood of a phylogenetic network given a set of biallelic markers . This algorithm computes matrix exponential along every branch , and processes the network’s nodes in a post-order traversal . Computation at a leaf takes O ( 1 ) time . At a reticulation node , the time consumption increases after each reticulation node is processed , due to the accumulation of ( split ) LPLs . In the last processed reticulation node , the number of LPLs in its descendant is at most O ( n4 ( k−1 ) ) . There are at most O ( n4 ) new LPLs generated due to decompose-and-split operation for each original LPL . Therefore the time complexity of processing a reticulation node is at most O ( n4k ) . We adopted the same approximation of matrix exponential as in [7] , so the time complexity of computing matrix exponentiation is O ( n2 ) , and computation along every branch is at most O ( n4k+2 ) . At a tree node , computation is mostly spent on evaluating Eq ( 13 ) . Let n be the number of individuals present under an internal tree node . Then , this evaluation takes O ( n4 ) time for a pair of compatible LPLs . The total time consumption of processing tree nodes also depends on the number of LPLs . Assuming k reticulation nodes in the phylogenetic network , there are at most O ( n4k ) pairs of compatible LPLs . Therefore the time complexity of processing a tree node is O ( n4k+4 ) . In total , the time complexity of the algorithm is O ( mn4k+4 ) , where m is the number of species , n is the total number of lineages sampled from the species , and k is the number of reticulation nodes . Notice that when k = 0 , which means the species phylogeny is a tree , the time complexity is O ( mn4 ) , which is the running time of the SNAPP algorithm without fast Fourier transforms . To speed up computation , and since markers are independent , computations for the individual markers are parallelized by multi-threading . Furthermore , the data is preprocessed so that the unique marker patterns are identified and their probabilities are computed only once and reused for for all markers with the same patterns ( states for the taxa ) . The prior on the phylogenetic network is the same as that employed in [23] , which we review briefly here . The prior is given by p ( Ψ | ν , δ , η , ζ , α , β ) = p n u m r e t ( Ψ | ν ) × p d i a m ( Ψ | η ) × p d i v ( Ψ | δ ) × p p o p ( Ψ | ζ ) × p i n h ( Ψ | α , β ) . ( 15 ) Here , p ( Ψ|ν ) is a Poisson prior on the number of reticulation nodes , normalized by the number of networks with the same number of reticulation nodes as Ψ . pdiam ( Ψ|η ) is an exponential prior on the diameters of reticulation nodes . The diameter of a reticulation node is the sum of the branch lengths on the cycle that contains the reticulation node in the underlying undirected graph of the network . pdiv ( Ψ|δ ) is an exponential prior on the divergence times . Rannala and Yang used independent Gamma distributions for time intervals ( branch lengths ) instead of divergence times . However , in the absence of any information on the number of edges of the species network as well as the time intervals , it is computationally intensive to infer the hyperparameters of independent Gamma distributions . Currently , we use a uniform distribution ( as in BEST [33] ) . ppop ( Ψ|ζ ) is a Gamma prior on the population mutation rate . For ppop , we use the Gamma distribution Γ ( 2 , ζ ) with mean value 2ζ and shape parameter 2 . pinh ( Ψ|α , β ) is a Beta prior , with parameters α and β , on the inheritance probabilities . Unless there is some specific knowledge on the inheritance probabilities , a uniform prior on [0 , 1] is adopted by setting α = β = 1 . It is important to note here that if the topology of Ψ does not follow the phylogenetic network definition ( e . g . , has a cycle ) , then p ( Ψ|ν , δ , η , ψ ) = 0 . This is crucial since , in the MCMC kernels we employ for sampling the posterior distribution , we allow the moves to produce directed graphs that slightly deviate from the definition; in this case , having the prior be 0 guarantees that the proposal is rejected . Using the strategy , rather than defining only “legal” moves simplifies the calculation of the Hastings ratios . However , the sampler always guarantees that the divergence times are consistent; that is , no node has a divergence time smaller than or equal to the divergence time of any of its descendants . We employed the reversible-jump MCMC , or RJMCMC [34] algorithm implemented in PhyloNet [30] to sample from the posterior distribution given by p ( Ψ | S ) ∝ L ( Ψ | S ) p ( Ψ ) , ( 16 ) where Ψ here denotes the topology of the network and all its parameters , and p ( Ψ ) is the prior on the network and its parameters as described above . We make use of only the 12 proposals designed for sampling phylogenetic networks and their parameters described in [23] , but not the proposals aimed at sampling gene trees , as gene trees are integrated out . We implemented in PhyloNet [30] a program to simulate bi-allelic markers on a given phylogenetic network . Bryant et al . [7] simulated bi-allelic markers by first generating gene trees inside a species tree ( under the multispecies coalescent model ) , and then simulating the markers down the gene trees . In our case , we replaced the first step by generating gene trees inside a phylogenetic network under the multispecies network coalescent [26]; the second step of simulating bi-allelic markers down gene trees remains the same as that employed in [7] . When requiring the data set to contain only polymorphic sites , if the generated site is not polymorphic , we discard both gene tree and markers , and repeat until a polymorphic site is generated .
Two small subsets of a larger AFLP data set of multiple New Zealand species of the plant genus Ourisia ( Plantaginaceae ) [39] were analyzed , including previously unpublished AFLP profiles from two different hybrid individuals O . × cockayneana and O . × prorepens ( herbarium codes follow [40] [continuously updated] ) . There are both morphological [41] and molecular ( Meudt unpubl . ) data supporting the hybrid nature of these two individuals . Although other Ourisia hybrid combinations have been reported in New Zealand [41] , O . × cockayneana and O . × prorepens are perhaps the most common , both involve O . caespitosa as a putative parent , and both have been formally named . Each data subset comprised five diploid individuals in total , which means ten haploid individuals were effectively analyzed due to the correction for dominant markers . A Poisson distribution with λ = 1 . 5 as the prior on the number of reticulations , an exponential prior with λ = 2 . 0 as the prior on the species divergence times , and a Gamma distribution with α = 2 . 0 and β = 0 . 05 as the prior on the population mutation rates were adopted . An MCMC chain was run on each data subset for 1 . 5 × 106 iterations with 2 × 105 burn-in iterations , and a sample was collected every 500 iterations . We used following commands:
Phylogenetic networks allow for representing evolutionary relationships that involve both vertical and horizontal transmission of genetic material . Extensions of the multispecies coalescent process to include hybridization events have facilitated the development of statistical methods for inferring and analyzing phylogenetic networks from gene tree estimates and sequence data . A major challenge with using gene tree estimates as the input to species phylogeny inference methods is the error in these estimates . While using the sequence data directly overcomes this issue , the problem of recombinations within loci can confound inferences . Using bi-allelic markers from individual , independent loci could provide a way to avoid both the gene tree uncertainty and recombination problems ( the two are not necessarily independent ) . Furthermore , it is important to note that many biological studies use data sets that consists of bi-allelic markers and no available sequence alignment data for individual loci . Bryant et al . recently devised an algorithm for inferring species trees from bi-allelic genetic markers while analytically integrating out the gene trees for the individual loci [7] . In this paper , we extended their algorithm significantly so as the likelihood of a phylogenetic network given bi-allelic markers could be computed while integrating out the gene trees . This method complements existing ones that use gene tree estimates or sequence alignments as input for statistical inference of phylogenetic networks . We implemented a Bayesian method for sampling the posterior of phylogenetic networks and their associated parameters from bi-allelic data , and studied its performance on both simulated and empirical data . The results indicate a very good performance of the method . This work adds a powerful method to the biologist’s toolbox that allows for estimating reticulate evolutionary histories . A major bottleneck of the method is its computational requirements . While the SNAPP method is very time consuming on species trees , our method is much more time consuming given that reticulations in the phylogenetic network give rise to an explosion of the number of partial likelihoods that need to be computed and stored . More generally , the number of taxa in a data set has more of an effect on the running time of the method than the number of loci does . In particular , two aspects of the phylogenetic network under consideration affect the computational requirements of the method: The number of leaves under the reticulation nodes and the diameter of each of the reticulation nodes . As discussed above , the set of lineages entering a reticulation node must be bipartitioned in every possible way . This number of lineages is dependent on the number of leaves under that reticulation node . For example , if a single individual is sampled from a single species that exist under the reticulation node , then the number of bipartitions is very small ( only two bipartitions exist ) . However , if n individuals are sampled from a single species that exist under the reticulation node or one individual is sampled per n species that exist under the reticulation node , then a number of bipartitions on the order of 2n arises . This computation becomes much more demanding if there are more reticulation nodes on the path to a lowest articulation node . As for the diameter—which is the number of branches on the paths between the two parents of the reticulation node and a lowest articulation node above them , the larger its value , the more demanding the computation becomes . An important direction for future research is improving the computational requirements of the method to scale up to data sets with many taxa . | The availability of genomic data has revolutionized the study of evolutionary histories and phylogeny inference . Inferring evolutionary histories from genomic data requires , in most cases , accounting for the fact that different genomic regions could have evolutionary histories that differ from each other as well as from that of the species from which the genomes were sampled . In this paper , we introduce a method for inferring evolutionary histories while accounting for two processes that could give rise to such differences across the genomes , namely incomplete lineage sorting and hybridization . We introduce a novel algorithm for computing the likelihood of phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference method . Analyses of synthetic and empirical data sets show a very good performance of the method in terms of the estimates it obtains . | [
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"meth... | 2018 | Bayesian inference of phylogenetic networks from bi-allelic genetic markers |
Increasing evidence suggests that chromatin modifications have important roles in modulating constitutive or alternative splicing . Here we demonstrate that the PWWP domain of the chromatin-associated protein Psip1/Ledgf can specifically recognize tri-methylated H3K36 and that , like this histone modification , the Psip1 short ( p52 ) isoform is enriched at active genes . We show that the p52 , but not the long ( p75 ) , isoform of Psip1 co-localizes and interacts with Srsf1 and other proteins involved in mRNA processing . The level of H3K36me3 associated Srsf1 is reduced in Psip1 mutant cells and alternative splicing of specific genes is affected . Moreover , we show altered Srsf1 distribution around the alternatively spliced exons of these genes in Psip1 null cells . We propose that Psip1/p52 , through its binding to both chromatin and splicing factors , might act to modulate splicing .
Pre-mRNA splicing occurs co-transcriptionally [1] , whilst the nascent transcript is still associated with the chromatin template . However , until recently there has been little consideration of how chromatin structure might influence the control of splicing . Initial studies indicated a link between promoters and alternative splicing [2]–[4] and this has been extended to histone modifications enriched at promoters . For example , Gcn5 mediated histone acetylation at promoters in yeast has been shown to facilitate recruitment of splicing factors [5] and mammalian GCN5-containing complexes interact with pre-mRNA splicing factors [6] . The chromatin remodeller CHD1 , which recognises a histone mark ( H3K4me3 ) enriched at active promoters , also interacts with spliceosome components and affects the rate of mRNA splicing [7] . A link between the rate of transcriptional elongation and splicing [8]–[10] has led to a consideration of how chromatin structure within the body of genes might also influence splicing . Increased levels of histone acetylation in gene bodies lead to exon skipping , likely through enhanced RNA polymerase II processivity [11] . Conversely , HP1γ , which binds to H3K9me3 , favors inclusion of alternative exons , possibly by decreasing RNA polymerase II elongation rate [12] . Trimethylation of H3 at lysine 36 ( H3K36me3 ) is enriched at exons , particularly those of highly expressed genes [13]– and its level at alternatively spliced exons is reported to correlate with their inclusion into the spliced transcript [13] . An explanation for this may come from observations that pre-mRNA splicing itself affects the deposition of this histone modification [18] , [19] . A direct link between H3K36me3 and an effect on mRNA splicing comes from the observation that MRG15 , a protein whose chromodomain can recognise H3K36me3 , recruits polypyrimidine tract binding protein ( PTB ) to alternatively spliced exons [20] . It was not clear whether this is a unique interaction or whether there are other systems that connect H3K36me3 to alternative splicing . PC4 and SF2 interacting protein 1 ( Psip1 ) has been implicated in transcriptional regulation and mRNA splicing in vitro [21] , but its function in vivo is poorly understood . It has been implicated in developmental gene regulation [22] and in guiding the integration of human immunodeficiency virus ( HIV ) into the host genome [23]–[26] . Psip1 encodes two protein isoforms - p52 and p75 - generated by alternative splicing within intron 9 , and whose relative levels vary between tissues [21] , [27] . The p75 isoform , also known as lens epithelium derived growth factor ( Ledgf ) , has a C-terminal integrase binding domain ( IBD ) ( Figure 1A ) that binds the integrases of HIV-1 and other lentiviruses , preventing their degradation by the proteosome [28] and tethering them to host chromosomes [28]–[33] . In Psip1 mutant cells , HIV/lentivirus infection is impaired and sites of viral integration into the host genome are altered [24]–[26] . Though the normal cellular function of Psip1/p75 has not been established , the IBD binds to RAM2/JPO2 - a myc-associated transcriptional regulator [34] , [35] and p75 is tethered , via Menin and in an IBD-dependant manner , to MLL H3K4 histone methyltransferase [36] . The p52 isoform of Psip1 lacks the IBD ( Figure 1A ) and does not interact with Menin . Instead , Psip1/p52 has been purified with PC4 transcriptional co-activator [37] , and had been shown to immunoprecipitate ( IP ) with , and to modulate the activity of , the splicing factor SRSF1 ( previously known as SF2/ASF ) [38] , indicating that it might have a role in mRNA processing . Here we show that the N-terminal PWWP domain , common to both Psip1 isoforms , can specifically recognize H3K36me3 and , that like H3K36me3 , Psip1/p52 is enriched at expressed genes and often at the downstream exons of those genes . We demonstrate that Psip1/p52 , but not p75 , also interacts with proteins known to be involved in splicing and RNA processing and co-localizes with splicing factor-enriched speckles in the nucleus . Furthermore , we show that there is altered alternative splicing in Psip1 mutant cells and that this is attributable to loss of function of the p52 isoform . We demonstrate altered association of Srsf1 with the genome in the absence of functional Psip1 , including around some exons whose inclusion or exclusion into mRNA is altered in Psip1 mutant cells . We propose that Psip1/p52 provides a new example of communication between chromatin and the regulation of mRNA splicing .
GFP-tagged full-length , and β-gal tagged gene-trap , versions of Psip1/p75 have been reported on mitotic chromosomes [22] , [30] , [39] , [40] . The N-terminal PWWP ( Pro-Trp-Trp-Pro ) domain ( Figure 1A ) is required for chromatin association [41] . PWWP belongs to the Tudor ( Royal ) family of protein domains , which are known to bind methylated lysines , including in histones [42] and the PWWP domains of Brpf1 , Dnmt3a , MSH-6 , NSD1 , NSD2 and N-PAC have been shown to specifically bind H3K36me3 [43]–[45] . To determine if the Psip1 PWWP domain directly interacts with modified histone tails , we used histone tail peptide arrays containing in total 59 different modifications of H3 , H4 , H2A , and H2B tails in 384 different combinations . In two independent experiments , we observed that GST-tagged Psip1 PWWP domain bound H3K36me3 with high specificity - signal from H3K36me2 , H3K36me and corresponding unmodified peptide spots were not above background ( Figure 1B and 1C , Table S1 ) . Direct binding of p52 with H3K36me3 was confirmed by peptide pulldown ( Figure 1D ) . Immunoblotting with antibodies recognizing different H3 methylation states confirmed a specific enrichment of H3K36me3 in Psip1 IPs from nuclear extracts ( Figure 1E ) . We assessed the genomic distribution of Psip1 in mouse embryonic fibroblasts ( MEFs ) by chromatin immunoprecipitation ( ChIP ) using αPsip1 antibody A300-847 ( see below ) and hybridization to a custom tiling array . The hybridization pattern was compared to that from H3K36me3 and H3K4me3 ChIPs . The large-scale distributions of H3K36me3 and Psip1/p52 were similar to each other and both appeared to be enriched at active genes ( Figure 2A ) . Across the entire array , levels of both Psip1/p52 and H3K36me3 were significantly higher at active genes than inactive genes or intergenic regions , and furthermore were especially enriched at the exons compared to the introns of expressed genes ( p<0 . 05 ) ( Figure 2B ) . Visual inspection of specific genes revealed a similar distribution of Psip1/p52 and H3K36me3 at some downstream exons ( Figure 2C and 2D ) , distinct from the peak of H3K4me3 at promoters . However , there is also evidence for some enrichment of Psip1/p52 near the transcription start sites ( TSSs ) suggesting multiple modes of Psip1 association to chromatin . Correlation between the distribution of Psip1/p52 and H3K36me3 ( Spearman's rank correlation coefficient ρ = 0 . 38 , p<0 . 05 ) was stronger than that between Psip1/p52 and H3K4me3 ( ρ = −0 . 05 ) or between H3K36me3 and H3K4me3 ( ρ = 0 . 013 ) . To determine whether there are other interacting partners for Psip1 isoforms , apart from H3K36me3 , we performed immunoprecipitation with two different antibodies . Antibody A300-847 was raised against an epitope present in both p52 and p75 ( a . a . 225–275 ) ( Figure 1A ) and indeed detects both isoforms by immunoblot ( Figure 3A ) . However , A300-847 efficiently IPs the Psip1 p52 isoform , but not p75 ( Figure 3B ) . This is likely due to masking of the A300-847 epitope in the p75 tertiary structure . In agreement with this , Ge et al [38] also reported that antibodies generated against recombinant p52 could recognize both p52 and p75 by immunoblot , but could not IP Psip1/p75 under native conditions . In addition to Psip1/p52 itself , a large number of other proteins were co-immunoprecipitated from NIH3T3 cells using A300-847 ( Figure S1A ) . Mass spectrometry revealed that ≈95% of them are known to function in pre-mRNA processing . Grouping the mascot hits according to their known function ( s ) and/or key domains revealed; SR proteins , DEAD/H box helicases , proteins of the U5 snRNP , hnRNP proteins , and other proteins known to function in pre-mRNA processing ( Table 1 ) . Apart from these , a few other transcription related proteins were identified . In agreement with the report of its co-purification with p52 , Srsf1 was one of the major hits [38] . The specificity of A300-847 antibody for wild-type ( wt ) Psip1/p52 is evidenced by the absence of immunoprecipitation of Srsf1 and other SR proteins in extracts prepared from MEFs homozygous for a gene-trap integration into Psip1 ( Psip1gt/gt ) ( Figure S1 ) in which the A300-847 epitope is 3′ to the site of gene trap integration , and so is absent from the resulting fusion protein ( Figure 1A ) [22] . Antibody A300-848 specifically recognizes the extreme C-terminus – amino acids ( a . a . ) 480 to 530 - of Psip1/p75 ( Figure 1A ) and so detects endogenous p75 , but not p52 , in immunoblots and IPs ( Figure 3A and 3B ) . Only a few transcription related proteins , in addition to p75 itself , were IP'ed from nuclear extracts by A300-848 ( data not shown ) . These data indicate a cellular link between Psip1/p52 and the splicing machinery . Immunoblotting of the IP from RNase treated nuclear extracts indicated that Psip1/p52 interacts mainly with the hypophosphorylated form of SRSF1 ( Figure 3C ) . Phosphorylation levels of SR proteins are known to modulate alternative splicing and alter SR protein distribution in relative to splicing-factor enriched nuclear speckles [46]–[48] . GST-p52 pull down of T7-SRSF1 ( over expressed HEK-293T cells ) , confirmed direct interaction of Psip1/p52 with SRSF1 and that the Psip1 PWWP domain is not sufficient for this ( Figure 3D ) . Furthermore , GST-p52 pulldown of SRSF1 mutants which mimic hypo ( RG ) and hyper ( RD ) phosphorylation ( serine residues within RS/SR dipeptide repeats of RS domain substituted with Glycine: RG or Aspartic acid: RD ) [49] shows higher affinity of Psip1/p52 for hypophosphorylated SRSF1 compared to the hyperphosphorylated form ( Figure 3E ) . GST pulldown also confirms interaction with SRSF3 , but shows that Psip1/p52 does not simply interact non-specifically with all SR proteins , since there is no direct interaction with SRSF2 ( SC35 ) ( Figure 3E ) . Identification of Srsf2 by mass spectrometry in the A300-847 immunoprecipitate presumably is the result of indirect association with other splicing proteins ( Table 1 ) . Unphosphorylated SRSF1 has been reported to associate with chromatin , especially the H3 tail and to be sensitive to H3 tail post-translational modifications [50] . To investigate whether absence of Psip1 causes any loss of Srsf1 chromatin association in vivo , chromatin purified by ChIP for H3K36me3 was analyzed by immunoblotting . Levels of Srsf1 associated with H3K36me3 modified chromatin were greatly reduced in Psip1−/− MEFs cells that do not have detectable Psip1/Ledgf [25] , compared to wild type ( Figure 3F ) . As controls , the levels of H3K36me3 associated Ptb and Srsf2 were not changed in the Psip1−/− IPed chromatin compared to wild type , These results confirm that Psip1/p52 specifically recruits Srsf1 to H3K36me3 chromatin in vivo , but not Ptb , which has been shown to be recruited to H3K36me3 chromatin through MRG-15 [20] . To investigate whether SRSF1 alone can bind to H3K36me3 in vitro , or whether this occurs via interaction with Psip1 , we pulled-down HeLa core histones with T7-SRSF1 , with or without addition of Psip1/p52 . Immunoblotting with antibodies recognizing different methylated states of H3 revealed a specific enrichment of H3K36me3 in the presence of Psip1/p52 compared to SRSF1 alone ( Figure 3G ) . These results suggest that Psip1/p52 can aid the recruitment of specific splicing factors , including SRSF1 , to H3K36me3 modified chromatin . Given the preponderance of splicing/RNA-binding proteins co-immunoprecipitating with Psip1/p52 but not p75 ( Figure 3 and Table 1 ) , we investigated the nuclear localizations of Psip1 isoforms . Antibody A300-848 revealed that , as for Psip1gt/gt [22] , endogenous p75 is associated with chromosomes in mitotic cells ( Figure 4A ) and is generally distributed in the nucleoplasm at interphase . Immunostaining with A300-847 also showed association with mitotic chromosomes , but at interphase revealed numerous nuclear foci reminiscent of splicing-factor enriched nuclear speckles [51] ( Figure 4A ) . Co-immunostaining for Psip1/p52/p75 and SRSF2 , a marker for the splicing-factor enriched nuclear speckles , confirmed this ( Figure 4B ) . Splicing-factor enriched nuclear speckles become larger and less numerous upon the inhibition of transcription with actinomycin D [40] . Concomitantly , Psip1/p52 also became redistributed to these foci . In contrast , there was no correspondence between the sub-nuclear distribution of Psip1/p75 and splicing-factor enriched nuclear speckles ( Figure 4C ) . To identify whether there are specific exons whose splicing in vivo might be affected by Psip1/p52 , we analyzed patterns of alternative splicing in RNA prepared from primary MEFs from three different Psip1gt/gt and corresponding wild type littermate embryos . Psip1gt/gt mutant mice were generated from ES cells with a gene trap integrated between exons 8 and 9 of Psip1 . This results in the production of a protein in which only the N-terminal 208 a . a . of Psip1 are present ( arrowed in Figure 1A ) and are fused to the β-geo reporter [40] . We used a custom Affymetrix microarray containing 40 , 443 exon junction probe sets derived from 7 , 175 genes with one or more predicted alternative transcripts and analyzed the data with ASPIRE 3 software [52] . Splicing changes were detected in 95 alternative exons with a score that , in our past experience , can be validated by RT-PCR with high ( >90% success; ΔI rank ≥1 , or ≤−1 ) [53] , [54] . Out of these , 58 exons , from 55 genes , appeared to have decreased inclusion in the mutant MEFs and 37 exons , from 35 genes , had increased inclusion ( Table S2 ) . The gene-trap in Psip1gt/gt is between exons 8 and 9 ( Figure 1A ) [22] so the resulting mRNA lacks exons 9-15 . This was evident from the microarray results , which detected Psip1 exons 11 and 12 as those with the most decreased inclusion in the whole analysis ( Table S2 ) . At the other extreme , the most increased inclusion of alternative exons in Psip1gt/gt was at Ptprc . In mutant cells , increased alternative exon inclusion for Ptprc , Ppfibp , Rapgef6 , Rasgrp3 and Ogfrl1 , all of which have a ΔI>1 , and altered 3′ splice site utilization at alternative exon 4 of Sorb2 ( ΔI of <−1 ) , was confirmed by semi-quantitative RT-PCR of RNA from primary MEFs derived from three wild type and three Psip1gt/gt litter mates ( Figure 5A ) . Primer pairs spanned across regions subject to alternative splicing to generate PCR products of different sizes dependent on exon skipping or inclusion ( Table S3 ) . A 2–3 fold increase in the ratio of included∶skipped exon bands was seen in mutant cells compared to wild-type . The absence of alternative splicing at the alternative exons of Csnk1d , Alg9 and Tpp2 exon 24 , which were not detectably altered in the microarray , was also confirmed by RT-PCR ( Figure 5B , 5D ) . To examine the splicing of specific alternative exons , RT-PCR was also carried out across specific constitutive exon - constitutive exon junctions and across constitutive exon - alternative exon junctions of Vcan , Tpp2 and Diap2 where microarray analysis had indicated increased exon skipping in Psip1gt/gt cells ( ΔI≤−1 ) ( Table S2 ) . This confirmed the decreased inclusion of alternatively spliced exons in Psipgt/gt cells ( Figure 5C ) . To rule out the possibility of amplification bias , RT-PCR using primers spanning constitutive exons at either the 5′ or 3′ end of Tpp2 , Vcan and Diap2 were tested ( Figure 5D ) . Although the gene-trapped Psip1 protein produced in Psipgt/gt cells is truncated and co-localizes with concentrations of chromatin instead of splicing factors [22] , [40] , we wished to confirm a role for Psip1 in the regulation of alternative splicing using an independently derived mutant allele . Therefore , splicing patterns of specific genes were also examined in Psip1−/− MEFs in which deletion of Psip1 exon 3 leads to the absence of detectable Psip1/Ledgf protein [25] . As for Psip1gt/gt ( Figure 5A and 5B ) altered patterns of splicing at Vcan , Tpp2 , Diap2 and Sorb2 were detected in RNA prepared from Psip1−/− MEFs compared to wild-type controls ( Figure 5E ) . Since the mutations in both Psip1gt/gt and Psip1−/− affect both p52 and p75 isoforms , we determined whether dysregulated alternative splicing could be directly attributed to p52 rather than p75 by complementing Psip1−/− MEFs with expression of either p52 and p75 ( Figure 5G ) . Only expression of p52 rescued the changes in alternative splicing pattern in Psip1−/− cells . Expression of Psip1/p75 did not restore splicing patterns of the tested genes ( Figure 5E ) . Consistent with the microarray , RT-PCR of alternative exons of Csnk1d , Alg9 and alternative exon 24 of Tpp2 were not significantly altered by loss of Psip1 ( Psip1−/− ) or by functional rescue of those cells with either p52 or p75 ( Figure 5F ) . Our data suggest that the absence of Psip1/p52 alters the splicing pattern of alternative exons and that this might be mediated by perturbed association of splicing factors at specific genomic loci . SR proteins such as SRSF1 can affect alternative splicing patterns through their recruitment to both alternatively and constitutively spliced exons [55] . Therefore , we examined the enrichment of H3K36me3 , Psip1/p52 , and Srsf1 across some gene loci subject to alternative splicing , by ChIP and hybridization to a custom microarray encompassing 8 . 2 megabases of the mouse genome including loci whose splicing pattern we have shown ( Figure 5 ) is altered in Psip1gt/gt cells . In addition Srsf1 binding was analyzed by ChIP from cells lacking Psip1 ( Psip1−/− MEFs ) ( Figure 6 ) . The correlation between sites of Srsf1 localization and the Psip1 bound sites in wild-type cells ( ρ = 0 . 35 p<0 . 05 ) , was reduced ( ρ = 0 . 25 ) in Psip1−/− cells . In Psip1−/− cells Srsf1 binding was lost from the 5′ side of Vcan exon 7 ( Figure 6A ) , whose inclusion into processed mRNA is reduced in Psip1gt/gt cells ( Figure 5 ) . Similarly , at Diap2 Srsf1 binding in Psip1−/− cells was lost to the 3′ side of exon 5 ( Figure 6B ) whose inclusion is reduced in Psip1gt/gt cells ( Figure 5B ) . However , the affects of Psip1 loss on Srsf1 chromatin binding are complex . At Ppfibp1 , where there is increased alternative exon inclusion in Psip1gt/gt cells ( Figure 5 ) , sites of Srsf1 binding seems displaced toward the alternatively spliced exon 10 , and away from the downstream constitutively spliced exon 11 in mutant cells ( Figure 6C ) . This likely reflects a shift in the balance between different modes of Srsf1 recruitment across this locus in the absence of Psip1 .
Tri-methylation of H3K36 is elevated in the expressed exons compared to introns , which suggested it is linked to splicing . A recent report showed specific recruitment of the splicing factor PTB to H3K36me3 modified chromatin at the FGFR2 gene via MRG15 [20] . It was not clear whether other similar proteins exist to recruit different splicing factors to H3K36me3 modified nucleosomes . Our results suggest that there is a more extensive family of chromatin proteins which can bind to H3K36me3 and also recruit splicing factors to facilitate alternative splicing . However , recent investigations [18] , [19] also propose a plausible but not mutually exclusive model , in which splicing modulates the level of H3K36me3 . This suggests that there is extensive interplay between H3K36me3 chromatin modification and alternative splicing . We demonstrate that the short ( p52 ) isoform of Psip1 modulates the inclusion or exclusion of alternative exons in specific mRNAs , probably by interacting both with chromatin and proteins involved in pre-mRNA splicing . Despite containing almost all the a . a . residues of p52 , the longer ( p75 ) Psip1 isoform neither co-IPs , nor co-localizes , with splicing related proteins ( Figure 4 ) . This , together with the inability of the A300-847 antibody to IP p75 , even though its epitope is present in the protein sequence and recognized in denatured p75 by immunoblot ( Figure 3 ) , suggests that protein folding of Psip1/p75 occludes both the A300-847 epitope and the region capable of interaction with splicing factors . Differential localization and interaction with the transcriptional regulation machinery or with splicing proteins has previously been reported for different isoforms of another protein – WT1 [56]–[58] . We add Psip1 to the recently identified group of PWWP-containing proteins - Brpf1 , Dnmt3a , MSH-6 , NSD1 , NSD2 and N-PAC - that have been shown to be able to bind H3K36me3 [43]–[45] ( Figure 1 and Figure 2 ) . This establishes the PWWP members of the ‘royal’ family of protein domains as reader of this histone modification , that has been associated with the exons of active genes [13]–[16] , [59] and whose deposition onto chromatin has recently been linked to the process of splicing itself [18] , [19] . MRG15 uses a chromo-domain for methylated histone binding [20] . The chromo-domain , like PWWP , is also a royal family protein domain [42] , but the chromo-domain of MRG15 is structurally more similar to the PWWP domain of DNMT3b than to that of more typical chromo-domain proteins that recognize H3K9me3 or H3K27me3 [60] . Psip1/p75 was demonstrated to be important for guiding HIV/lentiviral integration to the body of genes [24]–[26] . Our demonstration that the N-terminal PWWP domain , shared by both p52 and p75 Psip1 isoforms , recognizes and binds to H3K36me3 provides a mechanistic explanation for this pattern of HIV integration . There is a growing awareness of the interactions between splicing factors and RNA polymerase II elongation [61] and emerging evidence now highlights the role of histone modifications in this process . At gene promoters , the chromo-domains of CHD1 recognize H3K4me3 [62] , [63] and CHD1 interacts with the SF3a subcomplex of the U2 snRNP to then facilitate mRNA splicing post-initiation [7] . Similarly , in yeast , the histone acetyltransferase GCN5 , found at the promoter regions of active genes , also interacts with components of the U2 snRNP [5] . MRG15 and Psip1/p52 now provide two examples of H3K36me3 binding proteins that can influence the recruitment of splicing components to chromatin . MRG15 interacts with the RNA–binding protein PTB to regulate alternative splicing [20] . In contrast , we found interactions between Psip1/p52 and; several SR-containing proteins – including Srsf1 ( Figure 3 and Table 1 ) , components of the U5 snRNP and other proteins involved in RNA processing . Furthermore , we show that the absence of functional p52 affects alternative splicing of defined endogenous genes in vivo ( Figure 5 ) and alters the pattern of Srsf1 binding across alternatively spliced gene loci ( Figure 6 ) . Differential expression of SR proteins is important for tissue-specific alternative splicing and is abundant in brain and testis [64] , [65] where , compared to other tissues , mRNA for the p52 isoform of Psip1 is also at high levels compared to that of p75 [21] . Amongst other Psip1 co-immunoprecipitating proteins are many DExD/H box family putative RNA helicases . One of these is DDX10 which , like Psip1/Ledgf , is found as a fusion partner with Nup98 in myeloid leukaemias and myelodysplastic syndromes [66]–[69] , perhaps indicative of their function in a common pathway that is mis-regulated in these malignancies . The presence of the H3K36 methyltransferase NSD1 as another Nup98 fusion partner [70] [71] suggests that the splicing-H3K36me3 connection might be implicated in the aetiology of these myeloid disorders .
A modified histone peptide array ( Active motif , #13005 ) was blocked in TBST buffer ( 10 mM Tris/HCl pH 8 . 3 , 0 . 05% Tween-20 , 150 mM NaCl ) containing 5% non-fat dried milk at 4°C overnight . The membrane was washed with TBST for 5 min , and incubated with 10 ηM purified GST-tagged Psip1 PWWP domain , or GST protein alone , at room temperature ( rt ) for 1 h in interaction buffer ( 100 mM KCl , 20 mM HEPES pH 7 . 5 , 1 mM EDTA , 0 . 1 mM DTT , 10% glycerol ) . After washing in TBST , the membrane was incubated with goat α-GST ( GE Healthcare #27- 4577-01 , 1∶5000 dilution in TBST ) for 1 h at rt . The membrane was then washed 3× with TBST for 10 min each at rt and incubated with horseradish peroxidase conjugated α Goat antibody ( Invitrogen #81-1620 1∶12000 in TBST ) for 1 h at rt . The membrane was submerged in ECL developing solution ( Pierce , #32209 ) , imaged ( Image-quant , GE Healthcare ) and the data quantified using array analyzer software ( Active motif ) . Biotinylated histone H3 ( Ana spec 64440-025 ) and H3K36me3 ( Ana spec 64441-025 ) peptides coupled to Streptavidin magnetic beads ( Invitrogen 656-01 ) , and were used to pull-down GST-p52 as described ( http://www . epigenome-noe . net/WWW/researchtools/protocol . php ? protid=46 ) . Mouse GST-p52 and GST-PWWP ( a . a . 1–97 ) , were cloned into pDEST-PGEX6P . Proteins were expressed in BL21 Codonplus E . coli and purified on glutathione sepharose using standard protocols . Human SRSF1 and Human Psip1/p52 open reading frames were cloned into pCG-T7 and pEGFP vector with CMV promoters . pIRES2-eGFP-p52-HA and pIRES2-eGFP-p75-HA were kindly gifted by Prof . Alan Engelman ( Dana-Farber Cancer Institute ) . Immunoblotting was performed with the following antibody dilutions A300 847 ( 1∶2000 ) , A300-848 ( 1;3000 ) , αH3K36me3 ( Abcam AB9050 , 1∶500 ) . αH3K9me2 ( Abcam ab7312 , 1∶500 ) αH3K4me3 ( Millipore 07-473 , 1∶500 ) , αPan H3 ( Abcam Ab 1791 ) αSRSF1 ( 1∶300 ) , αSRSF1 ( Invitrogen 32-4500 1∶2000 ) αPCNA ( Santa Cruz , Sc56 ) αT7 ( Novagen , 65922 ) , Detection was by ECL . Mouse embryonic fibroblast ( MEF ) lines were derived from 13 . 5 day old Psip1gt/gt embryos and their corresponding wild-type littermates [22] . They were maintained for three passages in DMEM supplemented with 15% Fetal calf serum ( FCS ) , non-essential amino acids , sodium pyruvate , L-glutamine , and Penicillin/Streptomycin and cultured at 37°C . Psip1−/− and corresponding wild-type MEFs ( gift of Alan Engelman ) [25] were maintained in DMEM supplemented with 10% FCS and Pencillin/Streptomycin . They were transfected with Lipofectamine and GFP+ve FACS-sorted cells were harvested after 72 hrs . MEFs were harvested by trypsinizing and fixed immediately with 1% formaldehyde ( 25°C , 10 min ) in PBS , and stopped with 0 . 125M Glycine . Chromatin immunoprecipitation ( ChiP ) was performed as described previously [72] . Nuclei were sonicated using a Diagenode Bioruptor ( Liege , full power 30 s on , 30 s off , in an icebath for 50 min ) to produce fragments of <300 bp . An arbitrary concentration of 200 µg chromatin was incubated with 4 µg rabbit IgG ( Santa Cruz , sc-2025 ) , Psip1 antibodies ( A300-847 ) , H3K36me3 antibodies ( Abcam , Ab 9050-100 ) , αH3K4me3 ( Millipore 07-473 ) or αSRSF1 ( Invitrogen 32-4500 ) and washed , eluted and cross-links reversed . To analyze proteins associated with H3K36me3 , αH3K36me3 ChIP'ed chromatin was heated at 95 C in the presence of 1× Laemmli buffer for 10 min , separated on 4–20% SDS-PAGE , transferred onto a PVDF membrane , and probed with αSRSF1 , α SRSF2 , αSRSF3 , αPTB ( Invitrogen 32-4800 ) αPsip1 ( A300-847A ) , and αH3K36me3 antibodies . Instead of species-specific secondary antibodies , HRP coupled Clean-Blot IP Detection Reagent ( Thermo Scientific Prod . No . 21230 ) was used to avoid cross reactivity of HRP coupled antibody to denatured IgGs in the gel . For analysis in Figure 2 , WGA2 amplified ChIP DNA and input DNA were labeled and hybridized according to the manufacturer's protocol to a 3×720 , 000 probe custom microarray containing specific tiled regions encompassing 8 . 2 megabases of the mouse genome ( Nimblegen ) . Array platform number is GPL13276 and the GEO accession numbers for ChIP data are; Psip1: GSM697402 , GSM697403 , GSM697404 , GSM697405 , H3K36me3: GSM697406 , GSM697407 , GSM697408 , GSM697409 , H3K4me3: GSM697410- GSM697411 . Biological replicates were performed for all the ChIP array experiments and the data were analyzed in R/Bioconductor ( http://genomebiology . com/2004/5/10/R80 ) using the Epigenome ( PROT43 ) protocol ( http://www . epigenome-noe . net/WWW/researchtools/protocol . php ? protid=43 ) with the following parameters; The mean signal intensity of the 4 replicate probes present on each array was calculated . Loess normalization was used within arrays to correct for dye bias , and scale normalization was used within replicate groups to control inter-array variability . Log enrichment for each group was calculated by subtracting the mean log2 input intensities from the mean of log2 ChIP-enriched intensities . Probes were tested for significant enrichment using the significance analysis of microarrays ( SAM ) technique [73] , and the local false discovery rate based on the SAM statistic was calculated using the Locfdr R package [74] . A false discovery rate of 0 . 05 was used as the significance cutoff . The spearman rank correlation coefficient was used to assess the correlation between replicate experiments . The spearman rank correlation coefficient was used on all log enrichment scores between data from Psip1 ChIP and remaining groups to determine , significance and strength of their relationship . To determine if overlaps between Psip1 , H3K36me3 and H3K4me3 enriched probes were significant , 1000 randomized datasets were produced and the 95th percentile of the resultant overlaps was used as a significance cutoff . To determine the enrichment of probes over genomic features , probes were selected based on the following criteria . Genes were classified as expressed in MEF if they had been detected on an Illumina microarray ( unpublished data ) with a p value of detection <0 . 01 . Genes classified as non-expressed in MEF cells were defined if they had a p value of detection >0 . 5 and a signal intensity less than 0 . Only those genes that contained significantly enriched Psip1/p52 and H3K36 me3 signal were used for analysis . Exonic probes were defined as those that fall within an exon - probes falling within the 5′UTR and <200 bp from TSS were excluded . Intronic regions were defined as those that fall within an intron and >200 bp from the intron start or end site . Intergenic regions probes were selected from probes that are more than 1 Kbp from either the transcriptional start sites or transcriptional end sites of a gene . The significance of differences between genomic regions was calculated using a Wilcoxon rank sum test , with a p value cutoff <0 . 05 . For data in Figure 6 , WGA2 amplified ChIP DNA and input DNA were labeled and hybridized to a 3×720 , 000 probe custom microarray containing specific tiled regions encompassing 8 . 2 megabases of the mouse genome ( Nimblegen ) . Array platform number is GPL14175 and the GEO accession numbers for ChIP data are; Psip1: GSM782590 , H3K36me3: GSM782591 , Srsf1 ( Wt MEFs ) : GSM782592 , GSM782593 , Srsf1 ( Psip1−/− MEFs ) : GSM782594 , GSM782595 . The median signal of replicate probes was taken prior to normalization . Data was normalized as above . Because levels of Srsf1 binding were generally quite low we used quantized correlation coefficients ( QCC ) , which are less effected by the amount of binding signal present in the data , to determine the correlation between replicate experiments [75] . Across the entire array the QCC between Srsf1 replicates was 0 . 37 in wild-type cells and 0 . 18 in Psip1−/− cells likely reflecting a loss of overall Srsf1 binding captured in the mutant cells . However , considering only the regions on the array around exons , where most Srsf1 binding is likely to be located , the QCC in wild-type cells rises to 0 . 5 and to 0 . 23 in mutant cells . Enriched probes were identified as those above a threshold defined using the upperBoundNull method from Ringo Bioconductor Package [76] . Probes above the threshold must also be located within 300 bp of 2 or more probes to be called enriched . A hypergeometric test was applied to determine significant overlap between enriched probe groups . Nuclear extract was prepared from NIH 3T3 cells according to [77] with the following modifications: after precipitation with 1/10th vol of 4 M ( NH4 ) 2SO4 and mixing for 20 min , the lysate was cleared by centrifugation at 116000g in a TL-100 ultracentrifuge ( Beckman , Mountain View , CA ) . The supernatant was dialyzed against 3 changes of buffer C ( 25 mM Hepes pH 7 . 6 , 150 mM KCl , 12 . 5 mM MgCl2 , 0 . 1 mM EDTA , 10% ( v/v ) glycerol , 1 mM DTT , 0 . 2 mM PMSF and complete protease inhibitors ( Roche ) ) and flash frozen in liquid nitrogen . The extracts were quantified by Bradford assay ( Bio-Rad ) . A total of 200 µg nuclear extract were immunoprecipitated by incubation for 45 minutes at 4°C with 5 µg rabbit IgG ( Santa Cruz , sc-2027 ) or αPSIP1 p52/p75 ( A300-847 ) or a-p75 ( A300-348 ) together with 10 µl Protein A Dynal beads . After washing three times with buffer C , but containing 200 mM KCl , for 10 min each , the bound proteins were boiled in SDS sample buffer , separated on a 4–20% tris glycine polyacrylamide gel and either stained with colloidal coomassie ( Invitrogen ) to identify the proteins , or transferred to nitrocellulose membrane for western blotting . Individual protein bands or 1 cm2 gel pieces were cut and subjected to mass spectrometry analysis . Excised gel pieces were treated with trypsin at 37°C and the peptides extracted with 10% formic acid . Peptides were separated using an UltiMate nanoLC ( LC Packings , Amsterdam ) equipped with a PepMap C18 trap & column . The eluent was sprayed into a Q-Star XL tandem mass spectrometer ( Applied Biosystems , Foster City , CA ) and analyzed in Information Dependent Acquisition ( IDA ) mode , performing 1 s of MS followed by 3 s MSMS analyses of the 2 most intense peaks seen by MS . The MS/MS data file generated was analyzed using the Mascot 2 . 1 search engine ( Matrix Science , London , UK ) against UniProt April 2009 ( 7966092 sequences ) or NCBInr March 2010 ( 10530540 sequences ) databases with no species restriction . The data was searched with tolerances of 0 . 2 Da for the precursor and fragment ions . The Mascot search results were accepted if a protein hit included at least 2 peptides with a score above the homology threshold . For p52 pulldown , T7 tagged SRSF1 , SRSF3 and RG and RD mutants of SRSF1 and GFP- SRSF2 , were overexpressed in 293T cells 49 , 78 , and the cell lysates incubated with Glutathione beads coupled with p52 in GST lysis buffer . Unbound proteins were washed 5 times with the same buffer . Bound proteins were separated on 12% SDS PAGE . After transferring to nitrocellulose membrane , the proteins were probed with αT7 monoclonal antibody ( Novagen ) and imaged . For histone pulldowns , 1 µg of T7 tagged SRSF1 , purified from 293T cells , was incubated with T7 beads in GST lysis buffer for 1 hr at 4°C . After washing unbound proteins in same buffer , 1 µg of GST-p52 and 1 µg HeLa core histones ( Active motif , cat . 53501 ) were added and incubated for 3 hrs . Unbound proteins were washed off 5 times with the same buffer and bound proteins were separated on 17% SDS-PAGE . After transferring to nitrocellulose membrane , the proteins were probed with αH3K36me3 antibodies and imaged . The membrane was then stripped and reprobed with αH3K9me2 and αH3K4me3 antibodies . Cells grown on slides were fixed in 3% paraformaldheyde ( pFa ) as previously described [79] and incubated with primary antibodies; rabbit A300-847 ( 1∶200 dilution , Bethyl laboratories , ) which recognizes an epitope ( a . a . 225–275 ) present in both p52 and p75 , A300-848 ( 1∶200 , Bethyl laboratories ) which recognizes only p75 ( a . a . 480–530 ) , mouse monoclonal αSc35 ( 1∶50 , Sigma S4045 ) . Secondary antibodies , and image capture by wide-field epifluorescence microscopy were as previously described [79] . Confocal analysis was performed using a Zeiss LSM510 confocal microscope . Microarray analysis of alternative splicing was performed as described [53] . Five hundred ηg total RNA , isolated from primary MEFs derived from three littermates of E13 . 5 wild-type or Psip1gt/gt embryos [22] , were used to generate sense-strand cDNA ( Ambion WT expression kit #411974 ) . Purified cDNA was fragmented and labelled with biotin-conjugated nucleotides using terminal transferase ( Affymetrix , #900670 ) . Arrays were hybridized with labelled cDNA for 16 h at 50°C in 7% dimethylsulfoxide . Washing and detection were performed in an Affymetrix Fluidics Station using standard protocols for eukaryotic targets [53] . Scanned microarrays were analyzed using ASPIRE3 ( Analysis of SPlicing Isoform Reciprocity , version 3 ) [52] , which predicts splicing changes from reciprocal sets of microarray probes that recognize either inclusion or skipping of an alternative exon . Data were quantified as the change in the fraction of exon inclusion ( ΔI ) , where a value of 1 . 0 indicates a 100% increase , and −1 . 0 a 100% decrease in exon inclusion . Primers corresponding to exons flanking the alternate spliced exons were designed ( Table S2 ) . 5 µg of RNA was reverse transcribed with superscript reverse transcriptase II ( Invitrogen ) using random primers , and each of the forward primers were labeled with 32P γ-ATP . PCR was performed for 24–30 cycles , and the products were separated on a 5% denaturing polyacrylamide gel and analyzed by autoradiography for 3–16 h . or separated on 1 . 5% agarose gel , | The regulated processing of mRNAs by splicing of exons and introns has the potential to increase the information content of the genome . Various splicing factors have been identified whose binding to cis-acting sequences can influence whether an alternative exon is included or excluded ( skipped ) in the mature mRNA . However , increasing evidence suggests that the chromatin template also has an important role in modulating splicing . Here we identify a chromatin-associated protein Psip1/Ledgf that can bind to a histone modification enriched at active genes and that can also interact with other proteins involved in mRNA splicing . Loss of Psip1 reduces the chromatin association of specific splicing proteins and alters the pattern of alternative splicing . We propose that Psip1 , through its binding to both chromatin and splicing factors , might act to modulate splicing . | [
"Abstract",
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"Materials",
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"Methods"
] | [
"gene",
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] | 2012 | Psip1/Ledgf p52 Binds Methylated Histone H3K36 and Splicing Factors and Contributes to the Regulation of Alternative Splicing |
The precise mechanism by which the binding of a class I cytokine to the extracellular domain of its corresponding receptor transmits a signal through the cell membrane remains unclear . Receptor activation involves a cytokine-receptor complex with a 1∶2 stoichiometry . Previously we used our transient-complex theory to calculate the rate constant of the initial cytokine-receptor binding to form a 1∶1 complex . Here we computed the binding pathway leading to the 1∶2 activation complex . Three cytokine systems ( growth hormone , erythropoietin , and prolactin ) were studied , and the focus was on the binding of the extracellular domain of the second receptor molecule after forming the 1∶1 complex . According to the transient-complex theory , translational and rotation diffusion of the binding entities bring them together to form a transient complex , which has near-native relative separation and orientation but not the short-range specific native interactions . Subsequently conformational rearrangement leads to the formation of the native complex . We found that the changes in relative orientations between the two receptor molecules from the transient complex to the 1∶2 native complex are similar for the three cytokine-receptor systems . We thus propose a common model for receptor activation by class I cytokines , involving combined scissor-like rotation and self-rotation of the two receptor molecules . Both types of rotations seem essential: the scissor-like rotation separates the intracellular domains of the two receptor molecules to make room for the associated Janus kinase molecules , while the self-rotation allows them to orient properly for transphosphorylation . This activation model explains a host of experimental observations . The transient-complex based approach presented here may provide a strategy for designing antagonists and prove useful for elucidating activation mechanisms of other receptors .
Cytokines are a large family of small proteins that bind to specific cell surface receptors to initiate signals critical for cell proliferation , differentiation , and apoptosis . Among the best characterized cytokines are class I helical cytokines , including growth hormone ( GH ) , erythropoietin ( EPO ) , and prolactin ( PRL ) . Each of these cytokines has two receptor binding sites , referred to as site 1 and site 2 , with high and low affinities , respectively . Each cytokine receptor consists of an extracellular domain ( ECD ) and an intracellular domain ( ICD ) , connected by a single transmembrane helix ( TMH ) . The ECD in turn is composed of two β-sandwich subdomains linked by a short hinge [1] . It is well known that the binding of two receptor molecules , to site 1 and site 2 on the cytokine , results in receptor activation , leading to transphosphorylation of two Janus kinase 2 ( JAK2 ) molecules , each associated with a receptor ICD at a proline-rich region ( box 1 ) . Once phosphorylated , the JAK2 molecules initiate downstream signaling [2]–[5] . The structures of the 1∶2 complexes of GH , EPO , and PRL with the ECDs of the corresponding receptors have been determined [1] , [6] , [7] ( Figure 1 ) . The structures are overall similar , but differ in many details . Each cytokine contacts both ECD subdomains of each receptor molecule around the hinge . The two C-terminal subdomains are nearly parallel to each other ( and presumably to the normal of the cell membrane ) , while the two N-terminal domains lie on a plane parallel to the membrane , at 130°–160° angles . These structures have been very valuable , but they do not reveal the rearrangement of the two ECDs induced by the cytokine binding . Since the structures lack the TMHs and the ICDs , there is also no information on the ICDs' rearrangement , which initiates downstream signaling . The aim of the present study is to compute the cytokine-induced rearrangement of the ECDs and develop a detailed model for receptor activation . In the early model proposed by Fuh et al . [8] for GH receptor activation , GH first binds to one receptor molecule via site 1 , and then recruits the second receptor molecules via site 2 . This sequential receptor-dimerization model was based on three important observations . First , site 1 has much higher affinity than site 2 . Second , a G120R mutation disrupting site 2 did not affect receptor binding to site 1 but abolished GH-induced cell proliferation . Third , the dose response curve of cell proliferation was bell-shaped , suggesting that engagement of each receptor molecule by a separate GH molecule ( via site 1 ) interferes with receptor dimerization and signaling . It is now clear that receptors likely exist as preformed dimers in the absence of the cytokines [9]–[11] . For both GH receptor ( GHR ) and EPO receptor ( EPOR ) , the TMHs are implicated in dimer formation [10] , [12] , [13] . However , dimerization alone is insufficient for activation . For example , two EPO mimetic peptides ( EMP1 and EMP33 ) bind to EPOR to form 1∶2 complexes , but in each of these complexes the ECDs ( and their subdomains ) have an orientational arrangement that is different from that in the EPO: ( EPOR ) 2 complex [6] , [14] , [15] . ( EMP1 and EMP33 each are present as dimers in the complexes with two EPORs . We treat these dimers as a single ligand and refer to the stoichiometry of the complexes as 1∶2 . ) In signaling EMP1 acted as a partial agonist but EMP33 as an antagonist . Seubert et al . [16] engineered EPOR dimers by replacing the ECDs with a dimeric coiled coil . Through deletions of up to 6 residues , they explored the full range of relative orientation of the two TMHs in the EPOR dimers , and found one of them to be constitutively active in cell proliferation . For GHR , Rowlinson et al . [17] found monoclonal antibodies that competed against GH for GHR binding but failed to act as agonists , again indicating that dimerization is insufficient for activation . Brown et al . [10] demonstrated constitutive dimer formation of GHR by FRET experiments , and after inserting alanine residues in the TMH or in the sequence immediately before box 1 , observed constitutive activity . Interestingly , constitutive activity required different numbers of inserted alanine residues in the TMH and before box 1 . The deletion and insertion results of Seubert et al . [16] and Brown et al . [10] suggest that rotation of the TMH is involved in receptor activation . However , the orientational rearrangement of the ECDs that is induced by cytokine binding and triggers the TMH rotation remains unclear . Even in binding to a preformed dimer , it is still believed that engagement of site 1 precedes engagement of site 2 [5] , [10] , [18] . The initial step , i . e . , the binding of a cytokine to the first receptor molecule ( R1 ) via site 1 , leads to a 1∶1 complex . The 1∶1 complex is very likely an on-pathway intermediate since the structures of the 1∶1 complexes formed by GH and GHR ECD [19] , [20] and by PRL and PRL receptor ( PRLR ) ECD [21] are very similar to those in the corresponding 1∶2 complexes [1] , [7] , [22] . The 1∶1 complexes were obtained by introducing the site-2 disrupting mutation G120R to GH and a corresponding mutation , G129R , to PRL . Recently we calculated the rate constants for forming the 1∶1 complexes of PRL , GH , and EPO [23] , using our transient-complex theory [24] . These rate constants differ by 5000-fold , mostly arising from differing levels of charge complementarity across the site-1 interface . Moreover , the rate constants of the initial binding apparently anti-correlate with the circulation concentrations of the cytokines , such that the pseudo-first order receptor binding rate constants are close to the limits set by the half-lives of the receptors , ensuring their participation in cytokine binding before internalization and degradation . The transient complex in a binding process refers to an intermediate that has near-native relative separation and orientation but not the short-range specific interactions of the native complex , and is formed by translational and rotational diffusion of the subunits . The transient complex is located at the rim of the energy well of the native complex , and is therefore a late on-pathway intermediate . Structural differences between the transient complex and the native complex reveal the orientational rearrangement of the subunits at the late stage of the binding process . This stage starts after some of the native contacts are already in proximity , but before the precise fit of all the native contacts . As such it is at a critical juncture of the binding process . Yet its characterization enjoys certain technical advantages . First , because we focus on the late stage , we completely avoid any issues concerning how the subunits reach the transient complex , such as 2-dimensional diffusion of the membrane-bound receptors . Second , because the transient complex is formed before the formation of the stereospecific native contacts , we also avoid the necessity of accurately treating the native contacts . Instead , the transient-complex ensemble is largely dictated by the shape of the binding interface . Here we applied the transient-complex theory to study the binding of a second receptor molecule ( R2 ) to a 1∶1 complex , to form the 1∶2 activation complex . By calculating the transient complex for this step , we identified the orientational rearrangement between the ECDs of R1 and R2 leading to receptor activation . Similar rotational motions were found for three cytokine-receptor systems ( GH , EPO , and PRL with their receptors ) . At the start of the late-stage orientational rearrangement , R2 is loosely bound to the 1∶1 complex around site 2 of the cytokine , with the C-terminal subdomains of R1 and R2 far apart . R1 and R2 then rotate like a scissor , around an axis along the N-terminal subdomain of R2 , to close up the membrane-proximal ends of the two C-terminal subdomains . In addition , R1 and R2 both self-rotate but to different extents , such that the angle between the two N-terminal subdomains is reduced . We propose that the scissor-like rotation separates the intracellular domains of the two receptor molecules to make room for the associated Janus kinase molecules , while the self-rotation allows them to orient properly for transphosphorylation . This common model for receptor activation explains a host of experimental observations on the three cytokine-receptor systems .
Each transient complex was an ensemble of configurations located at the rim of the native-complex energy well . It was generated from the structure of the 1∶2 complex and would be a late on-pathway intermediate , even if R2 came from a preformed receptor dimer . As noted above , the transient complex was identified by mapping the energy landscape over the native-complex energy well and the surrounding region . The internal conformations of R2 and the 1∶1 complex ( referred to as two subunits ) were fixed at those in the 1∶2 native complex . This is justified since the available structures of the isolated 1∶1 complexes of the GH and PRL systems [19]–[21] are very similar to those in the corresponding 1∶2 complexes [1] , [7] , [22] ( with Cα RMSDs of ∼1 . 2 Å ) ; similarly the structures of apo GHR [10] and of apo EPOR [9] as well as EPORs in EMP1: ( EPOR ) 2 and EMP33: ( EPOR ) 2 [14] , [15] are similar to the R2 structures in the respective 1∶2 complexes for GH and EPO ( with Cα RMSDs of ∼1 . 3 Å ) . In particular , there is no evidence for significant change in the relative orientation between the N-terminal and C-terminal subdomains of either ECD upon forming any 1∶2 complex . ( Calculations using some of these alternative structures as well as those taken from molecular dynamics simulations of the 1∶2 complexes produced similar results . ) There were then only six remaining degrees of freedom in mapping the inter-subunit energy landscape: three for relative separation and three for relative rotation . To facilitate describing the orientational rearrangement on going from the transient complex to the native complex , we refer to the N-terminal and C-terminal subdomains of the R1 ECD as N1 and C1 , and analogously N2 and C2 for the subdomains of R2 . We present orientational changes as rotations of R2 relative to R1 . To that end , we define a coordinate system in which the z axis is the long axis of C1 ( directed upward ) , the y axis is perpendicular to the long axes of C1 and N2 , and consequently the x axis is in the plane defined by the two long axes and roughly parallel to the N2 long axis ( Figure 2A ) . We refer to the view into the z axis as top view , and the view into the x axis as side view . Figure 2B–D presents the configurations of the receptor molecules in the 1∶2 native complexes of the three systems in these two viewing directions . In Figure 3 we display 5 representative transient-complex configurations each for the GH-GHR , EPO-EPOR , and PRL-PRLR systems . The top view shows that , for each of the three systems , R2 undergoes clockwise rotation around the z axis on going from the transient complex to the 1∶2 native complex . This “self-rotation” is most prominent for N2 and less so for C2 , since the latter is roughly parallel to the rotation axis ( i . e . , z axis ) . Meanwhile the side view shows that , again for each of the three systems , R2 undergoes counterclockwise rotation around the x axis on going from the transient complex to the 1∶2 native complex . This “scissor-like rotation” brings together the membrane-proximal ends of C1 and C2 . To quantitatively characterize the orientational rearrangement , we define two angles: γ for the angle between the projections of the N1 and N2 long axes on the x-y plane; and φ for the angle between the projections of the C1 and C2 long axes on the y-z plane . The values of these angles in the native complexes of are: γ = 163° and φ = −7° in GH: ( GHR ) 2; γ = 132° and φ = 0° in EPO: ( EPOR ) 2; and γ = 157° and φ = 20° in PRL: ( PRLR ) 2 ( Figure 2B–D ) . From the transient complex to the native complex , clockwise self-rotation can be recognized as a decrease in γ , and scissor-like rotation can be recognized as a decrease in φ . The distributions of γ and φ in the transient complexes of the three systems are shown in Figures S1 , S2 , and S3 . The distributions are asymmetric with respect to the γ and φ values in the native complexes , with higher values more favored in the transient complexes , supporting the self-rotation and scissor-like rotation illustrated in Figure 3 on going from the transient complex to the native complex . Mark and co-workers [27] , [28] carried out molecular dynamics simulations of ( GHR ) 2 after removing GH from its 2∶1 complex and of ( PRLR ) 2 after removing PRL from its 2∶1 complex . In the former simulations they found prominent self-rotation corresponding to that depicted in the top view of Figure 3A . In the latter simulations they found prominent scissor-like rotation corresponding to that depicted in the side view of Figure 3C . The simulation results thus accord well with our transient-complex calculations . Examination of the structures of the three 1∶2 native complexes revealed that the asymmetry in φ can be attributed to the wrapping of a C1 loop ( between strands A and B ) around C2 ( Figure S4 ) . A C2 configuration with φ lower than the native value tends to encounter steric clash with the C1 loop . In contrast , C1 presents a relatively flat surface on the side of the native C2 where φ is higher than the native value , allowing the sampling of the high φ values . In the cases of GH: ( GHR ) 2 and PRL: ( PRLR ) 2 , the extended N-terminal tail of the cytokine enforces the asymmetry in γ by providing an additional interaction surface for N2 configurations with γ higher than the native value . Recent experimental results of Jomain et al . [21] have implicated a role of the PRL N-terminal tail in receptor activation . The dictation of the transient-complex ensemble by the interface shape is reminiscent of observations on the binding of a ribotoxin to an RNA loop on the ribosome [29]; there ribosomal proteins around the binding interface were found to shift the positioning of the transient-complex ensemble . Our transient-complex calculations revealed the ECD orientational rearrangements of the three receptor dimers induced by the binding of the corresponding cytokines . These orientational rearrangements are similar , involving both self-rotation and scissor-like rotation , and are largely dictated by the shape of binding interface . The orientational rearrangement of the ECDs has to be transmitted via the TMHs to the ICDs , to properly position and orient the associated JAK2 molecules for transphosphorylation . Based on our previous study [23] and the present results on the three cytokine-receptor systems , we propose a common model for receptor activation illustrated in Figure 4 ( see also Supplementary Video S1 ) . First a cytokine binds to an unoccupied receptor R1 via site 1 to forms a 1∶1 complex . Then R2 in the preformed dimer approaches site 2 . Initially the ECD N-terminal subdomains of R1 and R2 are separated at ∼180° and the membrane-proximal ends of the two ECD C-terminal subdomains are apart . Subsequently the two ECDs undergo scissor-like rotation to bring together the membrane-proximal ends of the two C-terminal subdomains , and simultaneously self-rotation to reduce the angle between the N-terminal subdomains . As a result of the scissor-like rotation , the ECD-TMH linkers and the N-terminals of the TMHs move closer , while the C-terminals of the TMHs and the box-1 regions of the ICDs are separated , making room for the associated JAK2 molecules . Meanwhile the self-rotation allows the JAK2 molecules to orient properly for transphosphorylation . Our calculations were based on the structures of the 1∶2 complexes of the three cytokines with the corresponding receptor ECDs . These structures are likely preserved in the 1∶2 complexes involving the full-length receptors bound to cell membranes , for the following reasons . First , structures of the receptor ECDs in apo form and in 1∶1 and 1∶2 complexes with their cytokines have been determined by different groups . As noted above , the multiple structures for each system are all very similar , attesting to their stability . Second , the ECD of each receptor is separated from the TMH by a linker of ∼10 residues , suggesting minimal perturbation of the ECD by the TMH in the full-length receptor . While separating the ECDs from the TMHs , the linkers play the important role of relaying the rotational motions of the ECDs to the TMHs . ( A similar role was identified for an inter-domain linker in the activated of a ligand-gated ion channel [30] . ) The ECD orientational rearrangements of the receptor dimers determined here occur after the two receptor molecules are loosely bound , and thus the fact that the molecules reach this state via diffusion in the 2-dimensional membrane has no bearing . The resulting motions of the TMHs and box-1 regions are speculated , but seem to be supported by a host of experimental observations , as we detail below . Our transient-complex calculations identified a common rotational pathway that receptor dimers are likely to follow upon ligand binding . If the rotations induced are incomplete , then the ligand will likely act as a partial agonist or antagonist . This conclusion is supported by the EPOR partial agonist EMP1 and antagonist EMP33 . In EMP1: ( EPOR ) 2 , γ = 168° and φ = 39° ( Figure 2C ) . Both values are higher than the counterparts in EPO: ( EPOR ) 2 , just like those in the transient complex of EPO: ( EPOR ) 2 ( Figure S2 ) . That is , in terms of receptor orientational arrangement , EMP1: ( EPOR ) 2 and the transient complex of EPO: ( EPOR ) 2 deviate from EPO: ( EPOR ) 2 from the same direction . The receptor configuration induced by EMP1 can thus be viewed as an intermediate along the way to the fully activated configuration as found in EPO: ( EPOR ) 2 , explaining why EMP1 is only a partial agonist . In EMP33: ( EPOR ) 2 , γ = 182° and φ = 38° ( Figure 2C ) , the former angle deviating even more than that in EMP1: ( EPOR ) 2 from the counterpart in EPO: ( EPOR ) 2 . The receptor configuration induced by EMP33 is thus an earlier intermediate compared to that induced by EMP1 , and hence EMP33 is an antagonist . The fact that EMP1 is a partial agonist but EMP33 is an antagonist despite the similar φ angles of EMP1: ( EPOR ) 2 and EMP33: ( EPOR ) 2 directly supports our contention that both scissor-like rotation and self-rotation are required for receptor activation ( see below for further discussion ) . We also calculated the transient complexes formed by EMP1 and EMP33 with EPOR , and found that they too followed the common rotational pathway of the GH-GHR , EPO-EPOR , and PRL-PRLR systems . The distributions of γ and φ for the EMP1 and EMP33 transient-complex ensembles are shown in Figure S2 . Figure S5 displays 5 representative configurations each for the EMP1 and EMP33 transient complexes . Clockwise self-rotation ( top view ) and scissor-like rotation ( side view ) similar to those shown in Figure 3 are also seen in approaching the native complexes here . From the distributions of γ and φ in Figure S2 , it can seen that the EMP33 transient complex is comprised of configurations closely clustered around the EMP33 native complex , and they all fall inside the configurational space of the EMP1 transient complex . It appears that EMP33 locks the receptor dimer in the configurations found in the EMP1 transient complex and prevents it from further orientational rearrangement toward more active configurations . EMP33 differs from EMP1 by two additional bromine atoms on Tyr4 residues ( located in site 1 and site 2 ) of the dimeric ligand . The additional contacts seem key to the locking action of EMP33 . Our analysis on the complexes of EMP1 and EMP33 with EPOR suggests a strategy for designing antagonists based on transient-complex calculations . One first uses the configurations constituting the transient complex of a full agonist as targets; ligands ( like EMP1 ) that stabilize these transient-complex configurations may be candidates for partial agonists . In the next iteration , configurations constituting the transient complex of a thus designed partial agonist become targets; ligands ( like EMP33 ) that stabilize the new generation of transient-complex configurations may be candidates for antagonists . This process may be further iterated . Constitutively active receptors obtained by Seubert et al . [16] and Brown et al . [10] through deletion or insertion mutations on TMHs demonstrate the involvement of self-rotation in receptor activation . Insertions and deletions move residues on the C-terminal side of the point of mutation along the helical wheel . This has the same effect as self-rotation on the associations JAK2s . Each deleted ( inserted ) residue in the TMH corresponds to a 103° counterclockwise ( clockwise ) rotation ( top view ) . Starting with the state in which R2 is loosely bound to the 1∶1 complex ( Figure 4C ) , we find that , after either deleting three residues or inserting four residues on the TMHs , the associated JAK2s are oriented in proximity ( Figure S6 ) , similar to that brought about by the receptor self-rotation in our activation model ( Figure 4D ) . These are precisely the numbers of deleted and inserted residues that Seubert et al . [16] and Brown et al . [10] found to result in constitutive activity . We emphasize , however , both self-rotation and scissor-like rotation are required in our model of receptor activation . We note that the dimeric coiled coil replacing the ECDs in the constitutively active EPOR mutant engineered by Seubert et al . [16] would likely bring the N-terminals of the TMHs together , thus achieving the same effect as cytokine-induced scissor-like rotation . Other experimental observations also support the proposed role of scissor-like rotation in receptor activation . Zhang et al . [31] found that a disulfide linkage between Cys241 residues , located in the middle of the ECD-TMH linkers ( Figure 4 ) , occurred only after forming the GH: ( GHR ) 2 complex . This observation suggests that the ECD-TMH linkers are apart before GH binding and come into contact in the 1∶2 complex . This movement of the linkers is just what is brought about by the scissor-like rotation of R1 and R2 ( Figure 4 ) . Brooks et al . [32] using FRET observed that GHR ICDs moved part by ∼9 Å in an active receptor dimer relative to an inactive dimer . They concluded that reorientation ( akin to our self-rotation ) is critical but insufficient for full activation . Their observation and conclusion are in line with our model of receptor activation . Recently Liu and Brooks [33] replicated the alanine-insertion of Brown et al . [10] on PRLR . In contrast to the results of Brown et al . for GHR , Liu and Brooks did not find any constitutively active dimer after inserting up to four alanines . Since it takes seven residues to cover all positions on a helix wheel , insertions of five and six alanines would be required to complete the full range of relative orientation of the two TMHs . It is possible that the five- or six-alanine insertion mutant would be constitutively active . It is also possible that none of these alanine-insertion PRLR mutants has sufficient scissor-like rotation for activation . Other experiments can be designed to further test our model of cytokine receptor activation . For example , inter-receptor distances at different positions along the z axis could be obtained by double cross-linking with bifunctional reagents , which bridge between two receptor molecules and can be used as molecular rulers [34] . The distances , before and after cytokine binding , between residues in the ECD-TMH linkers and between residues in the box-1 regions will be particularly useful for validating and refining our model . It will then even be worthwhile to start building structural models for receptor constructs that are truncated only after the box-1 region , as either preformed dimer or in an activated complex . Orientational rearrangements such as self-rotation have been implicated in the activation of thrombopoietin receptor and many tyrosine kinase receptors [35]–[37] . The detailed activation model presented here for three cytokine receptors and our approach based on transient-complex calculations will be useful for elucidating the activation mechanisms of a wide range of receptors . In conclusion , our calculations suggest that R2 undergoes a combined scissor-like rotation and self-rotation to reach the activated state upon binding to the cytokine-R1 complex . The similar observations in all the three cytokine-receptor systems allow us to propose a common model for class I cytokine receptor activation . Both the scissor-like and self-rotation are required for the activation
The implementation of our transient-complex theory used the structures of native complexes as input . Here native complex referred to a 1∶2 complex comprised of one cytokine molecule and two receptor molecules . The structures of the 1∶2 complexes of the GH-GHR , EPO-EPOR , PRL-PRLR , EMP1-EPOR , and EMP33-EPOR systems were from Protein Data Bank entries 3HHR [1] , 1EER [6] , 3NPZ [7] , 1EBP [14] , and 1EBA [15] , respectively . In the complex containing either EMP1 or EMP33 , the EPO mimetic peptide was present as a dimer . All hydrogen atoms were added and energy minimized by the AMBER program . The N-terminal tail of GH ( residues 1 to 5 ) changes orientation on going from the 1∶1 complex to the 1∶2 complex , from extending sideways to wrapping around R2 . We used the orientation of the N-terminal tail of GH in the 1∶1 complex , but counted those N-terminal residues in touch with R2 in the 1∶2 complex when calculating contacts for determining the transient complex ( see below ) . The N-terminal tail of PRL ( residues 1 to 10 ) is disordered in both the 1∶1 and 1∶2 complexes , and shows an ensemble of conformations in the NMR structure of the unbound state ( Protein Data Bank entry 1RW5 ) [38] . Jomain et al . [21] implicated a role of the N-terminal tail in receptor activation . We thus chose to build the N-terminal tail by Modeller ( version 9v8 ) [39] , in an orientation wrapping R2 and similar to that in one of the NMR models for the unbound PRL . To further mimic the situation with the GH-GHR system , we pulled the N-terminal tail so that it extended sideways . The subsequent treatment of this N-terminal tail when determining the transient complex was the same as described for the GH-GHR system . The implementation of our transient-complex theory for protein-protein association has been described previously [23]–[25] . Briefly , while fixing the 1∶1 complex in space , R2 was translated and rotated around the native-complex configuration . The three translational degrees of freedom were represented by the displacement vector r between the centers of the binding surfaces on the two subunits . The binding surfaces were defined by heavy atoms making <5 Å cross-interface contacts in the native complex . Of the three rotational degrees of freedom , two were a unit vector e attached to the mobile R2 and the remaining one was the rotational angle χ around the unit vector . The unit vector was perpendicular to the least-squares plane of the interface heavy atoms . To sample the native-complex energy well and the transition region to the unbound state , the six translational and rotational coordinates ( r , e , χ ) were randomly generated , with the magnitude , r , of r restricted: r≤rcut . The value of rcut was automatically determined to ensure that the clash-free fraction of the randomly generated configurations was ≥10−4 [25] . The resulting rcut values were 6 , 6 , 12 , 6 , and 7 Å for the GH-GHR , EPO-EPOR , PRL-PRLR , EMP1-EPOR , and EMP33-EPOR systems , respectively . Clash between the 1∶1 complex and R2 was detected exhaustively over all inter-subunit atom pairs . For each clash-free configuration , the total number , Nc , of contacts , either native or nonnative , made by a list of “interaction-locus” atoms across the binding interface was calculated as a surrogate of short-range interaction energy . The interaction-locus atoms were selected from the interface atoms as follows . Native pairs of the interface heavy atoms were sorted in ascending order of interatomic distances; each pair was then evaluated against preceding pairs for possible elimination . Specifically , a pair was eliminated if it was within 3 . 5 Å of a preceding pair on either side of the interface . The final remaining list constituted the interaction-locus atoms . The purpose of the selection process was twofold: to increase the chance that retained native pairs were distinct from each other; and to decrease the chance of nonnative contacts so that there was a proper balance between native and nonnative contacts . The value of Nc in a randomly generated configuration was calculated by counting the number of native contacts and nonnative contact . The upper limit in distance for forming a native contact was the native distance plus 3 . 5 Å . To count nonnative contacts , the native distance of each native pair was split in half to define the contact radii of the two atoms . A nonnative contact was considered formed when the interatomic distance was less than the sum of their contact radii plus 2 . 5 Å . The Nc values of the GH-GHR , EPO-EPOR , PRL-PRLR , EMP1-EPOR , and EMP33-EPOR native complexes were 56 , 32 , 81 , 31 and 44 , respectively . As the two subunits moved apart , Nc decreased gradually and the range of allowed rotation angles , as indicated by the standard deviation in χ of the clash-free configurations , increased sharply . The midpoint of this sharp transition ( where Nc≡Nc* ) defined the transient complex ( Figure S7 ) [25] . From 8×106 clash-free configurations , the values of Nc* were determined to be 12 , 15 , 16 , 19 , and 13 , respectively , for the five systems , and the 9 , 114 , 48 , 078 , 2 , 276 , 19 , 407 , and 13 , 361 configurations with these respective Nc values constituted the transient-complex ensembles . By calculating the basal rate constant to reach the transient complex and the electrostatic interaction energy within the transient complex , the transient-complex theory further predicts the protein association rate constant in solution . Details of these two components and the calculated rate constants are given in Supporting Text S1 . | Class I cytokines activate their receptors via a 1∶2 complex , but the conformational rearrangements leading to receptor activation remain unclear . To elucidate the activation mechanism , here we calculated the transient complex , an on-pathway intermediate close to the 1∶2 complex . Similar rotational motions were found for three cytokine ( growth hormone , erythropoietin , and prolactin ) receptors on going from the transient complex to the 1∶2 complex . They involve both scissor-like rotation between the extracellular domains of two receptor molecules and self-rotation of the molecules . Based on these results , we propose a common model for receptor activation by class I cytokines . The model explains a number of experimental observations , including differences in receptor orientations between erythropoietin and its antagonistic and partially agonistic mimetics . Transient complexes present a novel type of targets for designing antagonists . The detailed activation model developed here and our transient-complex based approach will be useful for studying the activation mechanisms of other receptors . | [
"Abstract",
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] | [
"biology",
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] | 2012 | A Common Model for Cytokine Receptor Activation: Combined Scissor-Like Rotation and Self-Rotation of Receptor Dimer Induced by Class I Cytokine |
Herpes simplex virus 1 ( HSV-1 ) establishes latency in trigeminal ganglia ( TG ) sensory neurons of infected individuals . The commitment of infected neurons toward the viral lytic or latent transcriptional program is likely to depend on both viral and cellular factors , and to differ among individual neurons . In this study , we used a mouse model of HSV-1 infection to investigate the relationship between viral genomes and the nuclear environment in terms of the establishment of latency . During acute infection , viral genomes show two major patterns: replication compartments or multiple spots distributed in the nucleoplasm ( namely “multiple-acute” ) . Viral genomes in the “multiple-acute” pattern are systematically associated with the promyelocytic leukemia ( PML ) protein in structures designated viral DNA-containing PML nuclear bodies ( vDCP-NBs ) . To investigate the viral and cellular features that favor the acquisition of the latency-associated viral genome patterns , we infected mouse primary TG neurons from wild type ( wt ) mice or knock-out mice for type 1 interferon ( IFN ) receptor with wt or a mutant HSV-1 , which is unable to replicate due to the synthesis of a non-functional ICP4 , the major virus transactivator . We found that the inability of the virus to initiate the lytic program combined to its inability to synthesize a functional ICP0 , are the two viral features leading to the formation of vDCP-NBs . The formation of the “multiple-latency” pattern is favored by the type 1 IFN signaling pathway in the context of neurons infected by a virus able to replicate through the expression of a functional ICP4 but unable to express functional VP16 and ICP0 . Analyses of TGs harvested from HSV-1 latently infected humans showed that viral genomes and PML occupy similar nuclear areas in infected neurons , eventually forming vDCP-NB-like structures . Overall our study designates PML protein and PML-NBs to be major cellular components involved in the control of HSV-1 latency , probably during the entire life of an individual .
Herpes simplex virus 1 ( HSV-1 ) is a neurotropic virus that establishes a life-long latent infection in the trigeminal ganglia ( TG ) ( or Gasserian ganglia ) of the infected human host . From time to time the virus asymptomatically or symptomatically reactivates from the latency stage producing epithelial lesions , most of the time on the face but also in the eye , inducing severe pathologies such as keratitis [1] . HSV-1 infection is also associated with pathologies of the central nervous system ( CNS ) , such as encephalitis , especially after primary infection of newborn children with deficiencies in their innate immunity due to genetic alteration of two genes coding proteins involved in the intrinsic antiviral response [2] . In mouse models reproducing latent infection , HSV-1 has also been shown to lead to brain pathologies following reactivation through retrograde transport of the viral particles towards the CNS [3 , 4] . During latency the virus is in a transcriptionally restricted state . Of the about 80 genes transcribed during lytic infection , only a family of long non-coding RNAs is produced abundantly during latency . These latency associated transcripts ( LATs ) arise from the transcription of an 8 . 3 kb primary RNA that is processed in two major LATs of 1 . 5 kb and 2 kb and several microRNAs with cellular and viral targets [5–12] . The precise role of LATs is a matter of debate; however , a point of convergence among the many studies of LATs is that their initial production would favor the survival of the infected neurons and the coordination of the infectious process towards the latency transcriptional program and reactivation [13–17] . The lytic cycle is the alternative transcriptional program and is characterized by a temporarily regulated transcriptional program , which starts with the expression of immediate early ( IE ) , then early ( E ) , and finally late ( L ) genes ( reviewed in [18 , 19] ) . Three proteins favor the onset of the lytic cycle , namely ICP4 , ICP0 , and VP16 . ICP4 is an IE protein and the major viral transactivator that induces the transcription of viral genes of all kinetics [20] . ICP4 is essential for the virus to enter the replication stage and for productive infection [21] . ICP0 is also an IE protein . ICP0 is a RING-finger protein that possesses SUMO-targeted E3 ubiquitin ligase ( STUbL ) activity [22 , 23] . ICP0 induces the proteasomal degradation of many cellular proteins , including components of the promyelocytic leukemia nuclear bodies ( PML-NBs or ND10 ) , and centromeres [24–29] . As a consequence , ICP0 induces the destabilization of PML-NBs and centromere chromatin , which contributes to creation of a nuclear environment suitable for lytic infection [29–34] . VP16 ( α-TIF ) is a virion-associated multifunctional protein that transactivates the expression of the five viral IE genes through its interaction with two cellular proteins , HCF-1 , a cell cycle regulator and Oct-1 , a transcription factor [35–40] . In the viral particle , HSV-1 genome is a 150-kb double stranded naked linear DNA . Upon entry into the nucleus , the viral genome does not integrate in the host cell genome , instead remaining as an extrachromosomal entity . As such , it sustains a process of circularization and associates with chromatin remodeling factors to be chromatinized [41–43] . Chromatinization of the viral genome during latency plays a major regulatory role , and post-translational modifications of histones associated to key viral promoters determines the fate of the latency/reactivation process [39 , 42 , 43] . However , latent viral genomes are present in multiple copies within the nucleus of infected neurons in mouse models and human [44–46] , and little is known about the molecular determinants that enable one neuron rather than another to sustain reactivation . In contrast , within an individually reactivating neuron , whether some viral genomes are more prone to lead to a complete lytic transcriptional program is unknown . The question is legitimate since in a recent study we reported that the viral genomes were non-randomly distributed in the nucleus of latently infected mouse TG neurons [47] . Latent viral genomes showed two major patterns namely “single” ( a single viral genome spot detected in the nucleus ) and “multiple-latency” ( up to 20–30 spots detected ) differentially distributed in the nucleus . The “single” pattern was exclusively associated with the promyelocytic leukemia nuclear bodies ( PML-NBs ) , forming structures known as viral DNA-containing PML-NBs or vDCP-NBs . In the “multiple-latency” pattern some viral genomes co-localized with PML-NBs , while others co-localized with centromeres , or were distributed in the nucleoplasm distal from PML-NBs and centromeres . Importantly , the expression of LATs from individual genomes was observed only for viral genomes neither associated with PML-NBs nor with centromeres . These data highlighted the previously anticipated heterogeneity of HSV-1 latency at the molecular level , and confirmed the major role played by the nuclear environment in the maintenance of latency and probably the reactivation process . In the present study , using a fluorescent in situ hybridization ( FISH ) approach combined to immunofluorescence ( IF ) , we investigated the interaction between viral genomes and nuclear proteins within TG neurons of latently infected mice and during the whole process of latency establishment ( from 4 to 28 days post infection , dpi ) . We detected viral genomes in neurons and satellite cells at 4 and 6 dpi , but only in neurons at > 6 dpi . In satellite cells , viral genomes showed only replication compartment ( RC ) patterns , whereas in neurons both RC and “multiple-acute” patterns were detected . From 4 to 14 dpi both patterns progressively disappeared , and transformed from14dpi onwards to the latency-associated “single” and “multiple-latency” patterns . Expression of two lytic program-associated proteins , ICP4 and ICP27 , was detected only in cells with the RC pattern . LAT expression was detected in “multiple-latency” but not “multiple-acute” pattern-containing neurons . Interestingly , at 4 to 8 dpi , a subset of RC-containing neurons showed LAT expression . The “multiple-acute” viral genomes co-localized with PML , Daxx , ATRX , SUMO-1 and SUMO-2/3 proteins in structures similar to vDCP-NBs but with a difference in number per infected neurons ( up to 10 vDCP-NBs/neuron at 6 dpi ) . To gain a better insight into the cellular and viral factors that could lead to the formation of vDCP-NBs or “multiple-latency” patterns , cultures of mouse primary TG neurons from wt mice or knock-out mice for the type I interferon ( IFN ) receptor were infected with wt or temperature-sensitive ( ts ) mutant viruses . The results indicates that defects in the onset of the lytic program due to the absence of functional ICP4 , combined with the absence of functional ICP0 were the two viral features that led to the formation of vDCP-NBs . In contrast , the type I IFN signaling pathway was required for the formation of a “multiple-latency”-like pattern , demonstrating the essential role of innate immunity in the acquisition of latency-associated viral genome patterns . Finally , immuno-FISH analyses of human TG showed a close spatial distribution between latent HSV-1 genomes and PML protein in neurons , which suggests that , similar to the situation in the mouse model , HSV-1 latency in human is probably tightly linked to the activity of PML-NBs .
In a previous study , we described the distribution of viral genomes in the nucleus of latently infected mouse TG neurons ( 28 days post-infection , dpi ) . We found that two major patterns were detectable; i . e . , “single” ( hereafter S ) and “multiple-latency” ( hereafter ML ) . Neurons harboring those patterns differed in LATs expression , with S- and ML-containing neurons being negative and positive , respectively . These viral genome patterns are likely to be among the key features that determine which neurons sustain reactivation . It was thus essential to characterize the nuclear distribution of the viral genomes during the whole process of establishing latency . Mice were infected and TGs were harvested at fixed times ( 0 , 4 , 6 , 8 , 11 , 14 , 18 , 22 , and 28 dpi ) after inoculation . At 6 dpi , two major viral genome patterns were observed , which we named “replication compartment” ( RC ) and “multiple-acute” ( MA ) ( Fig 1Ai and 1Aii ) . Some RC-containing neurons clearly showed annexation of the interchromosomal space ( Fig 1Ai ) , as described previously in cultured cells [48] . The MA was distinguishable from the ML pattern on the basis of the following structural and temporal observations: ( i ) viral genome spots in the MA pattern were often larger than those in the ML pattern; ( ii ) neurons with the MA pattern showed up to 10 spots per nucleus , whereas neurons with the ML pattern could contain up to 50 detectable viral genome spots; ( iii ) viral genomes in the MA pattern co-localized with PML ( see Fig 2Avi in this study , and Fig . 5C in [47] for a more precise analysis ) , forming the previously described “viral DNA-containing PML-NBs ( vDCP-NBs , up to 10 per infected neuron ) [47] , whereas in the ML pattern only one or two spots of viral genome co-localized with PML [47]; ( iv ) MA pattern is detectable during acute infection and mainly at 6 dpi , whereas ML pattern build up begins from 8 dpi and then persists until latency per se ( 28 dpi ) ( Fig 1B ) . We analyzed the proportion of neurons with the various viral genome patterns during the whole establishment of latency period from 4 to 28 dpi . Data were collected from two to three mice and are presented as estimations within three percentage ranges ( 0 to 10% , between 10 and 25% , and > 25% ) . We could distinguish five major patterns: RC , MA , ML , 4-3-2 spots ( 4-3-2 ) , and S-single+ ( S+ ) ( Fig 1B ) . RC were visible only during the early stages of acute infection ( from 4 to 6–8 dpi ) , MA appeared from 6 dpi and persisted not beyond 11 dpi , and 4-3-2 was detectable only during a short period between 11 and 14 dpi; ML and S were the two major patterns observed during latency ( 28 dpi , see [47] ) and started to build up from 6–8 dpi for the former and 8–11 dpi for the latter . Similar to S and MA , the 4-3-2 patterns corresponded to vDCP-NBs . An intriguing observation was a change in the number of vDCP-NBs per infected neuron from 6 dpi ( up to 10 per neuron ) to 14–28 dpi ( only 1 per neuron ) , with an intermediate situation consisting in the 4-3-2 pattern ( 11 to 14 dpi ) . These data suggested the possibility of fusion of the vDCP-NBs as the process of establishment of latency progressed . To investigate this possibility we used an in vitro model involving infection of human primary fibroblasts with a replication-defective HSV-1 mutant , in1374 . This virus does not replicate at 38 . 5°C and forms vDCP-NBs in human primary fibroblasts and in neurons ( see Figs S1A and 4B ) . Cells were harvested at 6 h to 7 dpi and processed for immuno-FISH to visualize viral genomes and PML . At 6 hpi the number of vDCP-NBs was 1 to 20/nucleus with an average of 5 and an average area of viral spots of 30 . 5 nm2 ( minimum 1 . 8 nm2 , maximum 120 . 6 nm2 ) . The average number of vDCP-NBs/nucleus decreased over time to 1 to 6 vDCP-NBs/nucleus with an average of 2 at 7 dpi , and an average area of the viral spots of 116 . 9 nm2 ( minimum 19 . 8 nm2 , maximum 279 nm2 ) ( S1B–S1D Fig ) . Given the experimental conditions used to perform cell infections ( see Materials and Methods ) , it is unlikely that the decrease in viral spot number is due to loss of viral genomes over time due to multiple cell divisions . We counted the number of cells/well at the start and end of each experiment; the results indicated little cell division ( S1E Fig ) . We then determined the total number of viral genomes by quantitative PCR at the start and end of each experiment to rule out an effect of viral genome degradation . Although , we observed a slight ( but not significant ) loss of viral genomes between 6 and 24 hpi ( possibly due to limited cell division at the start of the experiment due to inertia of cells seeded 24 h before the infection ) , no significant loss of viral genomes was detected ( S1F Fig , Student’s t-test ) . Data obtained in vitro on the amount and size of vDCP-NBs , combined with the in vivo observations , suggested that vDCP-NBs are dynamic structures probably capable of fusion as the establishment of latency progressed . Overall , the in vivo data anticipate changes in the viral genome patterns during the whole process of establishment of latency until the system achieves the physiological , cellular and molecular conditions that enable stable latency to be maintained . We then determined the presence of HSV-1 genomes in cells other than neurons . TG from mice infected for 4 to 6 dpi were analyzed by immuno-FISH , and neurons were specifically labeled with an antibody recognizing neurofilaments . We detected viral genomes under the RC pattern in neurons and satellite cells ( Fig 1Ci and 1Ciii ) , whereas MA was exclusively detected in neurons ( Fig 1Cii ) . RC- but not MA ( or ML , S2 Fig ) -containing neurons were positive for ICP4 and ICP27 , two of the major proteins of the lytic cycle ( Fig 1Di–1Div ) . This confirmed that during acute infection , neurons positive and negative for productive infection-associated proteins harbored different HSV-1 genome patterns . Overall , these data emphasized the discrepancies in the viral genome nuclear distribution between infected neurons during the whole process of establishment of latency , and the link between these patterns and the capacity of the infected neuron to support a lytic cycle or latency . RC-containing neurons are likely to become productively infected , whereas MA-containing neurons are likely among those that will support latency . One of the molecular characteristics of latency is a switch in the virus transcriptional program towards the quasi-exclusive expression of an abundant lncRNA known as LAT . We thus analyzed LAT expression in neurons containing the various viral genome patterns . We performed combined RNA/DNA FISH on TG harvested from acutely infected mice . RC-containing neurons were in their vast majority negative for LAT , with the exception of a few ( < 1% ) ( Fig 1Ei and 1Eii ) . This was not unexpected , as previous studies reported that some latently infected neurons could experience an aborted lytic program [49 , 50] . MA and S-containing neurons were negative for LAT ( Fig 1Eiii and 1Ev; lower neuron ) , with the exception of rare MA-containing neurons that contained discrete LAT signals juxtaposed to viral genomes ( Fig 1Eiv ) . The only neurons frequently positive for LAT detection were those with the ML and S+ patterns ( Fig 1Ev upper neuron and vi ) as described previously [47] . Of note is that , with the exception of the few neurons positive for LAT at 4–8 dpi , LAT was readily detectable only from 10–14dpi . We previously showed that the MA viral genome pattern co-localizes with , PML , Daxx and ATRX , three of the major constituents of the PML-NBs , thereby forming vDCP-NBs [47] . RC-containing neurons lacked the typical PML-NB staining for PML , Daxx and ATRX ( Fig 2Ai , 2Aiii and 2Av ) , unlike those containing the MA pattern , which showed vDCP-NBs ( Fig 2Aii , 2Aiv and 2Avi ) . We noticed an increase in both Daxx and ATRX signals in infected compared to uninfected TGs at 6 dpi ( S3 Fig ) . We then performed WBs on whole TG extracts from uninfected and infected mice to analyze the signal of both proteins ( Fig 2B ) . The model of virus inoculation used ( upper left lip , see Materials and Methods ) allows the mouse to be heavily infected in the left TG but not in the right TG . WBs were performed on the two TGs ( left: infected , right: not infected ) of two mice . Similar to what was previously observed for PML [47] , we detected an increase in the amount of both proteins in the infected compared to the uninfected TGs . These data showed that during acute infection , and similarly to PML , the overall amount of Daxx and ATRX increased , probably as a result of the antiviral response mediated in the entire TG by type 1 interferons . Small Ubiquitin MOdifier ( SUMO ) proteins are also major components of the PML-NBs and are involved in the intrinsic antiviral response against HSV-1 infection in cell cultures [23 , 51] . We analyzed the involvement of SUMOs in the control of virus infection in TG neurons , by co-detecting SUMOs and viral genomes during the whole process of establishment of latency . SUMO-1 and SUMO-2/3 were found in PML-NBs in uninfected neurons ( Fig 2Ci and 2Di ) . In RC-containing neurons , similar to PML , Daxx , and ATRX , SUMO-1 and SUMO-2/3 did not show the punctate pattern characteristic of their presence in PML-NBs ( Fig 2Cii and 2Dii ) . In MA-containing neurons , SUMO-1 was infrequently ( < 20% of infected neurons ) found co-localized with not more than one vDCP-NB ( Fig 2Ciii ) , whereas SUMO-2/3 was frequently ( > 50% of infected neurons ) co-localized with all the vDCP-NBs ( Fig 2Diii ) . Co-localization of SUMO-2/3 with vDCP-NBs persisted until 28 dpi , and SUMO-1 was found to be more systematically co-localized with vDCP-NBs in the S pattern from 14 dpi onwards ( Fig 2Civ and 2Div ) . These data suggest the involvement of SUMO proteins in control of the incoming viral genomes , in accordance with their previously described intrinsic antiviral activity . However , in neurons , the activity and nuclear dynamics of SUMO-1 and SUMO-2/3 could differ with regard to their association with vDCP-NBs . ICP0 is involved in the proteasomal degradation of several components of the PML-NBs , inducing the destabilization of these nuclear bodies . RC-containing neurons were consistently negative for the presence of PML-NBs . In these neurons , could ICP0 have induced destabilization of the PML-NBs , favoring the lytic cycle ? Although we possess several ICP0 antibodies that have been used by us and others in immunocytochemistry , we have not detected an ICP0 signal within infected TG neurons by either IF or immuno-DNA FISH . Indeed , ICP0 in infected cell cultures shows a nuclear punctate pattern that is difficult to distinguish from the nonspecific signal in neurons from TG samples . An indirect way to analyze ICP0 activity in infected nuclei is to detect the disappearance of its cellular substrates . In addition to PML , the centromeric protein A ( CENP-A ) is another ICP0 substrate , and ICP0 efficiently induces proteasomal degradation of centromeric proteins in mouse cells [28] . TG samples from mice infected for 6 days were processed by immuno-DNA FISH to determine the fate of PML or CENP-A signals in infected neurons . All RC-containing neurons were negative for PML-NBs but positive for the CENP-A signal ( Fig 2Fi and 2Fii ) . Several hypotheses arose from these data: ( i ) ICP0 is less efficient in inducing the degradation of centromeric proteins than PML in mouse neurons , ( ii ) ICP0 is synthesized in , but does not reach the nucleus of , infected neurons , ( iii ) ICP0 is not efficiently synthesized in neurons , ( iv ) some neurons lack PML-NBs and are more susceptible to lytic infection even in the absence of functional ICP0 . Concerning the latter , IF analyses of TG samples from uninfected mice showed that not all neurons contained PML-NBs ( Fig 2E ) . Moreover , a previous study suggested that ICP0 remained in the cytoplasm of HSV-1-infected TG neurons in culture [52] . These data do not exactly fit with our results ( see S4Ci Fig ) , possibly due to the heterogeneity in the type of neurons found in a TG , combined with the use of different methods of purification of neurons in the two studies . Finally , a recent study demonstrated that a neuron-specific microRNA , miR-138 , targets ICP0 mRNA , preventing ICP0 synthesis at least in cultured cells [53] . Therefore , our data , together with those of other laboratories suggested that during acute infection the interplay between cellular ( including PML-NB-associated proteins ) and viral factors is likely to determine the extent of virally-induced modification of the nuclear environment . Depending on the degree of modification , the lytic or latent transcriptional program will then be favored , leading to acquisition of the corresponding viral genome pattern . To gain further insight into the cellular and molecular features that favor acquisition of the various viral genome patterns , especially vDCP-NBs and ML , which are associated with the latency process , we established mouse primary TG neuron cultures ( S4A Fig ) . We first characterized the PML-NBs in the neurons and found that Daxx , ATRX , SUMOs and PML proteins were detectable in the nuclear bodies in most neurons ( S4Bi–S4Biv Fig ) . We then infected the neuron cultures with HSV-1wt 24H to detect the synthesis of viral IE proteins involved in the onset of lytic infection , such as ICP0 , ICP4 and ICP27 ( S4Ci–S4Civ Fig ) . All proteins could be found in the nucleus of the infected neurons . We then performed immuno-FISH on similarly infected neurons to detect simultaneously viral genomes and neuronal or viral markers . As expected , all infected neurons showed RC , and expressed lytic viral proteins as exemplified by specific detection of ICP4 , ICP27 , or viral proteins ( Fig 3Ai–3Aiv ) . ICP0 could not be detected in these experiments because none of the antibody that we usually use was suitable for our immuno-FISH protocol . We then analyzed the fate of PML-NBs and associated proteins . The majority ( about 88% ) of infected neurons did not show PML-NBs although some ( about 12% ) contained PML dots characteristic of PML-NBs ( Fig 3D ) . About half of these infected neurons ( Fig 3C ) showed co-localization of PML-NB-associated proteins with RC ( Fig 3B ) . This was not unexpected , as previous studies have described the presence of PML in RC of HSV-1-infected cells [54 , 55] . Because ICP0 is directly involved in the destabilization of PML-NBs in infected non-neuronal cells , we performed infections with a deletion virus unable to express ICP0 . Neurons were infected for 24 h with the dl1403 mutant virus and PML-NBs were analyzed . In infected neurons , PML was found both in dots and co-localized with the RC ( S5Ai Fig ) . Dots of PML were frequently located at the edge of and all around the RC ( S5Aii Fig ) . SUMO proteins remained co-localized with PML in the PML dots ( S5Avi Fig ) ; however Daxx and ATRX were absent from the remaining PML dots ( S5Aiii to S5Av Fig ) . In infected mice , a virus with deletion of the thymidine kinase ( TK ) gene is able to replicate more or less efficiently at the site of inoculation but its replication in TG neurons is severely impaired [56 , 57] . To determine if the absence of TK alone could explain the formation of vDCP-NBs in infected neurons , we infected neurons with a TK mutant HSV-1 virus 17/tBTK- and analyzed the viral genome patterns at 48 hpi . Neurons with RC and without PML-NBs were exclusively observed , likely as a result of ICP0 expression ( S5Bi to S5Biv Fig ) . Taken together , these data showed that , under our experimental conditions , which were compatible with the detection of viral genomes by FISH , if neurons are infected through the cell body and not through the axon as in natural infections , the balance between pro- and antiviral features favors the onset of lytic infection and formation of RC . Studies performed in infected mice using HSV-1 or pseudorabies virus ( PRV ) , another neurotropic herpesvirus infecting pigs , showed that VP16 , which is present in the virion tegument , is inefficiently transported through the axon to the cell body of infected neurons [58 , 59] . Another study described the distinct regulation of the VP16 promoter ( normally a late promoter ) in TG neurons , which could be activated early after infection by neuron-specific factors [60] . The stochastic activation of the VP16 promoter in neurons would thus enable the early synthesis of VP16 during acute infection as well as reactivation from latency . This would favor the entry of the virus to the lytic cycle through the activation of IE genes , including ICP4 and ICP0 . In that context , the efficiency of the PML-NB–associated intrinsic antiviral response is likely to be inversely proportional to the synthesis of VP16 , ICP4 and ICP0 , and influence the acquisition of the latency viral genome pattern . To test this hypothesis , we infected neurons with the temperature-sensitive virus , in1374 , which inefficiently expressed functional ICP4 at the restrictive temperature of 38 . 5°C , and lacks functional VP16 and ICP0 due to an insertion of 12 nt in the transactivation domain of the former and to deletion of part of the RING finger of the latter [61–63] . Neurons were first infected at the permissive temperature of 32°C . As expected , the virus showed the same features as HSV-1wt in terms of the formation of RC co-localized with ICP4 ( Fig 4Ai and 4Aii ) , and ICP8 , a subunit of the viral DNA replication complex ( Fig 4Aiii ) . PML , Daxx , ATRX , and SUMO signals were similar to those obtained in neurons infected with the ICP0 mutant dl1403 , with PML aggregates surrounding the RC and co-localizing with SUMOs , and Daxx and ATRX disappearing from these structures ( Figs 4Aiv–4Avii and S3A ) . We then infected neurons at the restrictive temperature to inactivate ICP4 . Under these conditions , viral genomes showed a different pattern and were detected as spots in the nucleus of infected neurons ( Fig 4B ) . The co-detection of PML , Daxx , ATRX and SUMOs showed perfect co-localization of all proteins with the viral genomes , resulting in formation of structures reminiscent of vDCP-NBs ( Fig 4Bi–4Biv ) . Infection with in1330 virus , which contains and expresses a functional VP16 ( and hence expresses functional IE proteins ICP27 , 22 and 47 ) , also led to the formation of vDCP-NBs at 38 . 5°C ( Fig 4Ci–4Civ ) . Infections with the tsK virus , which is the parental virus that expresses tsICP4 at 38 . 5°C and contains functional VP16 and ICP0 , exhibited mainly RC ( S6 Fig ) . This is possibly explained by the expression of a fraction of functional ICP4 at 38 . 5°C that under our experimental conditions of infection of neurons is sufficient to activate the lytic transcriptional program . If the formation of vDCP-NBs is the default viral genome pattern for a virus unable to replicate in neurons , than infection of mice with a virus deficient in replication in neurons should lead to the exclusive formation of vDCP-NBs . Mice were infected with the TKDM21 mutant virus , which can replicate at the inoculation site , but is unable to replicate in neurons due to a deletion in the TK gene [56 , 57 , 64] ( S7A and S7B Fig ) . Mice at 6 dpi were sacrificed and immuno-FISH was performed on TG samples to detect HSV-1 genomes and PML . Complete TGs of four mice were analyzed , and few neurons ( 48 in total ) were detected with a positive signal for the viral genome . The small number of positive neurons may be due to weak replication of the virus in the lip ( S7A Fig ) . This small number of positive neurons hampers precise analysis . However , none of the thousands of neurons analyzed in the four mice showed an RC pattern . All positive neurons showed vDCP-NBs comprising 35 ( 73% ) with a “single” ( one spot ) pattern , 8 ( 17% ) with two spots , 3 with three spots ( 6% ) , and 2 with four spots ( 4% ) ( S7C Fig ) . These data , although obtained from few detectable infected neurons , tend to confirm those obtained in the cultured neurons and suggest that the inability of a virus to start replication in neurons will automatically lead to the formation of vDCP-NBs probably due to the additional absence of ICP0 synthesis due to cellular miR control [53] . Overall , these data demonstrated that the formation of vDCP-NBs resulted from both the failure of the virus to start the lytic program due to the inefficient synthesis of ICP4 ( and thus to undergo replication ) , and the absence of functional ICP0 . Although the presence of functional VP16 per se did not directly impact the formation of vDCP-NBs , its stochastic synthesis in neurons during acute infection likely increases the probability of ICP4 synthesis , and thus the start of the lytic program and the formation of RC . Our previous data showed that latently infected neurons containing vDCP-NBs were deficient in the expression of the 2 kb LAT , and that viral genomes trapped in the vDCP-NBs were unable to synthesize the primary 8 . 3 kb LAT transcript [47] . These data raise the question of whether the genomes in the vDCP-NBs are permanently silenced , or if they retain the capacity to resume transcription following exposure to stress that could affect the vDCP-NBs . In1374 contains a HCMV-lacZ cassette whose transcription is shut down upon infection of human fibroblasts at 38 . 5°C and resumes upon treatment with the histone deacetylase inhibitor , trichostatin A ( TSA ) [65 , 66] . Similarly , mouse primary TG neurons quiescently infected with a non-replicative HSV-1 virus containing a pCMV-GFP transgene were shown to resume GFP expression upon the addition of TSA [67] . Neurons infected with in1374 for 3 days were treated or not with 2 μM TSA for 24 h at 32°C and RT-qPCR was first performed to quantify LacZ re-expression under these experimental conditions ( Fig 4D ) . Dual RNA-DNA FISH was then performed to detect LacZ transcripts and HSV-1 genomes . Without TSA , viral genomes in the vDCP-NBs showed no sign of LacZ transcription ( Fig 4Ei ) . Addition of TSA led to the observation of three further patterns: ( i ) LacZ-negative RC-like structures in close proximity to PML-NBs ( Fig 4Eii , pattern 1 ) ; ( ii ) RC-like structures juxtaposed with LacZ and PML signals ( Fig 4Eiii , pattern 2 ) ; and ( iii ) vDCP-NBs containing LacZ signal ( Fig 4Eiv , pattern 3 ) . To determine whether PML-NBs juxtaposed to stress-induced RC are missing Daxx and/or ATRX , similar to the HSV-1 dl1403 ( ICP0- ) -infected cultured neurons showing RC ( see S5A Fig ) , we analyzed Daxx and ATRX behavior in RC-containing neurons . First , we confirmed that TSA treatment alone did not affect the localization of Daxx and ATRX at the PML-NBs in uninfected neurons ( S8A Fig ) . Unlike PML , the majority of the stress-induced RCs did not juxtapose with Daxx or ATRX in spots , which suggested that the two proteins leave the RC-associated PML-NBs ( S8B and S8C Fig ) . However , some neurons exhibited stress-induced RC in the vicinity of Daxx or ATRX spots , albeit with diffuse Daxx and ATRX signals throughout the nucleoplasm . Moreover , some neurons showed a Daxx signal co-localized with RCs . The two latter most likely reflect transitory situations before the complete disappearance of Daxx and ATRX from RC-associated PML-NBs . These data suggest that upon transcriptional reactivation leading to HSV-1 replication , PML remains in spots juxtaposed to the RC whereas Daxx and ATRX are more labile and tend to leave the RC-associated PML-NB . Daxx and ATRX behavior is in accordance with their previously described mutual contribution to intrinsic antiviral resistance to HSV-1 infection [34] . PML-associated pattern 3 ( Fig 4Eiv ) was reminiscent of the MA genome-associated discrete LAT signal reported previously ( see Fig 1Eiv ) . TG neurons from 6 to 8 days HSV-1wt–infected mice were analyzed for the expression of LAT together with the detection of viral genomes and PML protein . Rare neurons were indeed positive for a discrete LAT signal associated with vDCP-NBs ( Fig 4F ) . Together , these data showed that: ( i ) a virus contained in a vDCP-NB is unlikely to be definitively silenced provided that a stimulus sufficient to modify the transcriptional equilibrium and/or the PML-NBs dynamic is applied to the neuron; ( ii ) at least during the first stages of establishment of latency in mice ( 6–8 dpi ) , viruses associated with vDCP-NBs could show some transcription of LAT before being completely silenced during latency per se ( 28–30 dpi ) . A previous study performed in cultured cells showed that viral genomes juxtaposed to the PML-NBs were more prone to initiate replication [68] . Other studies suggested that HSV-1 transcription is more likely to occur in the vicinity of PML-NBs [69 , 70] . Our data , together with those of other groups , show that PML-NBs could have a dual role in viral infection; on the one hand , the capacity to silence incoming viral DNA , and on the other hand , and following appropriate stimuli , to serve as a nuclear platform for virus reactivation , although during this event the protein content of PML-NBs is likely modified in terms of Daxx and ATRX leaving the nuclear body . Type 1 IFNs are produced very early upon alphaherpesvirus infection , which limits virus replication and spread both in vitro and in vivo [71–74] . IFNα was previously shown to induce a quiescent state of HSV-1 that resembles latency in cultured primary porcine TG neurons [75] . Given the changes in the viral genome patterns observed during acute infection ( see Fig 1 ) , we anticipated that type 1 IFN could take part in those changes by preventing the onset of lytic infection even in the presence of functional ICP4 , provided that functional VP16 and ICP0 are missing . We infected neurons with in1374 at 32°C in the presence or absence of IFNα . Without IFNα , only RC-containing neurons were observed ( Fig 5Ai ) . Treatment of neurons with IFNα decreased the number of neurons showing the RC pattern , and induced the formation of multiple spots in the nucleus , some of which co-localized with PML-NBs or centromeres , a pattern reminiscent of the ML pattern in vivo ( Fig 5Aii and 5B ) [47] . Quantification of the effect of IFNα on pattern acquisition ( Fig 5C ) showed that without IFNα treatment infection with in1374 led to the formation of RC in ~91 ( ±3 . 5 ) % of neurons . IFNα treatment favored the ML-like pattern , with 82 ( ±1 . 7 ) % of neurons showing this pattern . Infection with in1330 or tsK induced the formation of RC in almost all infected neurons ( > 98% ) , irrespective of IFNα addition , consistent with the essential contribution of VP16 and ICP0 to the onset of the lytic cycle . Type I IFNs share the same receptor , type I interferon receptor ( IFNAR ) [76] . To gain insight into the impact of the IFN signaling pathway on viral genome pattern acquisition , we infected TG neurons harvested from IFNAR KO mice with in1374 at 32°C . Irrespective of treatment with IFNα , nearly all infected neurons ( > 98% , two experiments ) showed the formation of RC ( Fig 5D ) , whereas infection of wt C57BL/6 neurons yielded results similar to those of OF1-infected neurons . These data confirmed that the type I IFN signaling pathway plays a major role in viral genome pattern acquisition , and favors formation of the ML-like pattern provided that functional VP16 and ICP0 are not produced in infected neurons . The formation of vDCP-NBs is an important hallmark of HSV-1 latency in mice and likely highlights a close inter-connection at the molecular level between the intrinsic antiviral activity of PML-NBs and the latent viral genomes . To investigate this association in the context of HSV-1 latency in human , we performed analyses of human TGs . The left and right TGs of five patients ( Fig 6A ) were collected and then processed to analyze the presence of HSV-1 genomes by PCR and possible reactivation by RT-PCR of viral transcripts , and also by immuno-FISH . PCR data showed that all TGs were positive for viral genomes ( Fig 6B ) , and one patient was reactivating HSV-1 at the time of collection ( Fig 6C ) . To avoid any misinterpretation due to reactivation , we further analyzed in priority the TGs that did not show any signs of reactivation at the molecular level . We performed IF using several neuronal markers to correlate the structural analysis of the cells with biochemical markers specific to neurons . Neurons appeared as large cells , usually containing a bright cluster of cytoplasmic autofluorescence due to lipofuscin , and with markedly fainter nuclear DAPI staining than that of satellite cells ( Fig 6Di–6Diii ) . PML-NBs appeared in neurons as large nucleoplasmic aggregates of PML protein ( Fig 6E ) . Detection of the 2 kb LAT showed bright staining throughout the nucleoplasm , irrespective of the probe and stain used ( Fig 6F ) . Samples from TGs were subjected to simultaneous detection of LAT , HSV-1 genomes and PML . Several observations were made in neurons positive for HSV-1 DNA ( Fig 6Gi–6Giii ) : ( i ) infected neurons unequivocally showed systematic disappearance of the large aggregates of PML-NBs observed in uninfected neurons; ( ii ) observations at high magnification showed that PML staining was reorganized under a single clustered nuclear signal , most of the time forming a large “doughnut-shaped” structure; ( iii ) viral genomes were detected as multiple spots clustered in a discrete region of the nucleoplasm; ( iv ) the clusters of viral genomes constantly co-localized with the PML clusters , and when PML was under the “doughnut-shaped” structure , HSV-1 genomes were localized inside those structures; and ( v ) all neurons positive for HSV-1 genomes were also positive for LAT . Overall , these data confirmed the close proximity of viral genomes and PML during latency in human TG neurons , and the occasional presence of vDCP-NB-like structures .
The interaction of chromosomal loci with their nuclear environment affects the transcriptional activity of particular genes [77] . Nuclear architecture is thus likely to greatly influence the fate of infection with nuclear-replicating viruses , such as herpesviruses . In this study , we demonstrated that the interaction of latent HSV-1 genomes with the nuclear environment is impacted by the activity of cellular components , such as PML-NBs and type I IFNs , but also by viral features such as the ability of the virus to enter the lytic cycle and to express ICP0 . During establishment of HSV-1 latency in the mouse model , viral genomes adopt several nuclear patterns before reaching the two main patterns that are found during stable latency . The nuclear distribution of viral genomes changes greatly until 14 dpi , and stabilizes thereafter . This is in agreement with previous reports of extinction of lytic gene expression , acquisition of chromatin markers , and expression of LAT , which are major molecular features of HSV-1 latency and usually evident by 14 dpi [78–80] . This indicates that the battle between the virus and the infected neurons involves multiple changes in the interaction between the viral genomes and the nuclear environment until reaching a stable situation suitable for both the virus and the host cell . The RC pattern was found in neurons and non-neuronal cells , whereas vDCP-NBs were found only in neurons . The expression of lytic proteins in infected neurons during acute infection was detectable only in RC-containing neurons . These data , combined with the observation that vDCP-NBs are nuclear structures found during the whole process of establishment of latency until latency per se , suggest that vDCP-NBs play a major role in pushing the virus towards latency . To that extent , the presence in the vDCP-NBs of SUMO proteins , which were shown in cultured cells to participate in intrinsic antiviral resistance to HSV-1 infection [23] , strengthens the idea that PML-NBs in general and vDCP-NBs in particular are nuclear relays of the cellular intrinsic antiviral response to HSV-1 . Studies performed in vivo and in vitro showed that VP16 expression likely plays a major role in the onset of the lytic program in neurons [60] , and a neuron-specific microRNA , miR-138 , targets ICP0 mRNA to prevent its synthesis [53] . Therefore , during the initial infection of neurons , the concomitant absence ( or reduced amount below a threshold ) of VP16 and ICP0 probably prevents the virus from entering the lytic cycle . Cultured neurons infected with a virus that is unable to express a functional ICP4 , and which concomitantly does not express functional ICP0 , mimicked the infectious process that in vivo leads to the formation of vDCP-NBs . This demonstrates that as long as the virus is unable to activate the lytic program due to the absence of functional ICP4 ( probably linked to the absence of VP16 in vivo ) , then the only other viral feature required for the formation of vDCP-NBs is the absence of functional ICP0 . Besides the formation of vDCP-NBs , another hallmark of the nuclear distribution of HSV-1 latent genomes is the ML pattern . The ML pattern-containing neurons accumulated in in vivo-infected TGs with a slight delay compared to the vDCP-NBs . In cultured neurons , type I IFNα induced the formation of an ML-like pattern in a non-functional VP16 and ICP0 context . The type I IFN pathway was essential for acquisition of the ML-like pattern because infection of cultured TG neurons prepared from IFNAR KO mice did not result in formation of the ML-like pattern in the presence of IFNα . Two lines of evidences suggest that the ML-like pattern mimics that in vivo; i . e . , ( i ) a subset of viral genomes in the ML-like pattern co-localized with PML , and ( ii ) some viral genomes co-localized with centromeres . Previous studies showed that the tegument protein VP16 was inefficiently transported from the axon termini to the cell body , which precluded the initiation of lytic infection in neurons [58 , 59] . However , a subset of neurons supports lytic infection during the early stages of acute infection of mouse TG . This could be due to a change in the kinetics of VP16 expression in neurons whose promoter could acquire IE-like activity in some neurons due to its activation by an as-yet-unknown neuron-specific feature [60] . The absence of VP16 axonal transport , the stochastic regulation of its promoter , and the absence of ICP0 synthesis due to miR-138 activity are likely to lead to a nuclear environment that would favor the establishment of latency through the formation of vDCP-NBs and/or ML patterns , depending on the type I IFN signaling context of the infected neuron . Type I IFNs ( IFNα and ß ) are major actors in the interplay between the virus , the cell , and the immune response [81] . The IFN response was shown to build up within the infected TG during establishment of latency in mice by both autocrine and paracrine signaling pathways [72] . Previous reports suggested that TGs of infected mice could sustain several waves of virus infection from the site of inoculation at the periphery by means of a positive inter-site “feedback loop” [82] . This suggests that the immune environment of the TG in general , and the neurons in particular , is likely to be different between the first wave of infection and those following . It is thus likely that a virus entering a neuron from a second wave of infection will face a different antiviral environment more prepared to face the virus infection , especially through enhancement of IFN-associated innate immunity . This will inevitably lead to some molecular changes in the nucleus , and the viral genome ML pattern acquisition might reflect these nuclear changes . It is worth mentioning that two of the major components of the PML-NBs , PML and Sp100 , are encoded by interferon-stimulated genes ( ISGs ) , which leads to an overall increase in the protein levels following IFN stimulation . Our previous and present data showed that PML , Daxx and ATRX levels increased in the TG during acute infection [47] . Given that PML in the PML-NBs represents only 10% of the total nuclear PML [83] , the increase in PML by type I IFN stimulation is likely to increase the “free” pool of PML in the nucleoplasm , promoting its repressor-associated activity . Moreover , the overall increase in Daxx and ATRX , two chaperones of histone H3 . 3 [84] , might result in changes in the chromatinization of the viral genomes , which could be linked to changes in their nuclear distribution . IFNα has also been shown to increase HDACs activity , which favors the acquisition of repressive chromatin marks [85] . It is interesting to note that facultative heterochromatin repressive marks associated with latent viral genomes begin to accumulate on the viral genomes by 7 dpi during acute infection in a mouse ocular model of infection [80] . This roughly corresponds to the time at which ML pattern-containing neurons start to appear in our lip model ( Fig 1B ) . Therefore , ML pattern formation might be tightly linked to the acquisition of specific chromatin-associated marks . We are currently investigating this aspect of viral genome dynamics . HSV-1 latency , rather than being an inert situation , is a dynamic equilibrium likely generated by multiple attempts by the virus to complete full reactivation , which is repressed most of the time by tight control by the innate and adaptive immune responses [86–89] . Humans latently infected by HSV-1 undergo multiple spontaneous symptomatic , asymptomatic , and aborted reactivations that could be restricted at different stages of the reactivation process . Despite the differences in the physiology and history of infection between a several-year latently infected human and few-week infected mice , we observed a close spatial overlap between viral genome and PML protein signals in HSV-1–infected human TG neurons , similar to what is observed in mouse TG neurons . Viral genomes were detected as multiple spots grouped in a restricted area of the nucleus . PML-NBs systematically disappeared in infected neurons and PML protein accumulated in the same areas as the viral genomes , eventually forming structures reminiscent of vDCP-NBs . These data show that HSV-1 latency/reactivation in human TG neurons is likely to be closely associated with PML protein and PML-NBs activity . Overall , this study describes the nuclear architecture and nuclear distribution of viral genomes as major determinants of HSV-1 latency . It confirms the close interrelation between PML-NBs and HSV-1 genomes in the establishment of latency through the formation of vDCP-NBs . Finally , it confirms that the spatial organization of HSV-1 genomes and PML is conserved in latently infected neurons in human TG , which indicates PML-NBs to be major HSV-1 genome interactants during latency and probably reactivation .
All procedures involving experimental animals conformed to the ethical standards of the Association for Research in Vision and Ophthalmology ( ARVO ) Statement for the use of animals in research , and were approved by the local Ethics Committee of the Institute for Integrative Biology of the Cell ( I2BC ) and the Ethics Committee for Animal Experimentation ( CEEA ) 59 ( Paris I ) under the number 2012–0047 and in accordance with European Community Council Directive 86/609/EEC . All animals received unlimited access to food and water . Human biological samples and associated data were obtained from Cardiobiotec Biobank ( CRB-HCL Hospices Civils de Lyon BB-0033-00046 ) . All tissue samples were obtained according to French ethics regulations ( specifically , informed consent was obtained from patients for all samples ) . Cardiobiotec is authorized by the French Ministry of Social Affairs and Health ( DC2011-1437 ) , with transfer authorization AC 2013–1867 . The HSV-1 SC16 wild-type ( wt ) and thymidine kinase ( TK ) mutant ( TKDM21 ) strains were used for mouse infections and have been characterized previously [64 , 90] . HSV-1 17 syn + ( 17+ ) wt and mutant strains were used for infections of primary mouse TG neuron cultures . HSV-1 mutants 17/tBTK- and dl1403 are deleted in TK and ICP0 genes , respectively [82 , 91] . The HSV-1 mutant tsK expresses a temperature-sensitive variant of the major viral transcriptional activator ICP4 [92 , 93] . In1374 expresses a temperature-sensitive variant of the major viral transcriptional activator ICP4 [61] , and is derived from in1312 , a virus derived from the VP16 insertion mutant in1814 [62] , which also carries a deletion/frameshift mutation in the ICP0 open reading frame [63] and contains an HCMV-lacZ reporter cassette inserted into the UL43 gene of in1312 [66] . Virus in1330 is a VP16 rescuant of in1312 [65] . All of these viruses have been used and described previously [65] . All HSV-1 strains were grown in BHK-21 cells ( ATCC , CCL-10 ) and titrated in U2OS cells ( ATCC , HTB-96 ) . TsK was grown and titrated at 31°C . Viruses derived from in1312 were grown and titrated at 31°C in the presence of 3 mM hexamethylene bisacetamide [94] . Mice were inoculated and TG processed as described previously [47 , 90 , 95 , 96] . Briefly , 6-week-old inbred female BALB/c mice ( Janvier Labs ) were inoculated with 106 PFU of virus into the upper-left lip . Mice were sacrificed at the indicated times from 0 to 28 dpi . Frozen sections of mouse TG were prepared as described previously [47 , 96] . Primary mouse TG neuron cultures were established from BALB/c , OF1 , or C57BL/6 wt ( Janvier Labs ) or IFNAR KO ( The Jackson Laboratory ) mice , following a procedure described previously [97] . Briefly , 6–8-week-old mice were sacrificed before TG removal . TG were incubated at 37°C for 20 min in papain ( 25 mg ) ( Worthington ) reconstituted with 5 mL Neurobasal A medium ( Invitrogen ) and for 20 min in Hank’s balanced salt solution ( HBSS ) containing dispase ( 4 . 67 mg/mL ) and collagenase ( 4 mg/mL ) ( Sigma ) on a rotator , and mechanically dissociated . The cell suspension was layered twice on a five-step OptiPrep ( Sigma ) gradient , followed by centrifugation for 20 min at 800 g . The lower ends of the centrifuged gradient were transferred to a new tube and washed twice with Neurobasal A medium supplemented with 2% B27 supplement ( Invitrogen ) and 1% penicillin–streptomycin ( PS ) . Cells were counted and plated on poly-D-lysine ( Sigma ) - and laminin ( Sigma ) -coated , eight-well chamber slides ( Millipore ) at a density of 8 , 000 cells per well . Neuronal cultures were maintained in complete neuronal medium , consisting of Neurobasal A medium supplemented with 2% B27 supplement , 1% PS , L-glutamine ( 500 μM ) , nerve growth factor ( NGF; 50 ng/mL , Invitrogen ) , glial-cell-derived neurotrophic factor ( GDNF; 50 ng/mL , PeproTech ) , and the mitotic inhibitors fluorodeoxyuridine ( 40 μM , Sigma ) and aphidicolin ( 16 . 6 μg/mL , Sigma ) for the first 3 days . The medium was then replaced with fresh medium without fluorodeoxyuridine and aphidicolin . Primary human fibroblasts BJ cells ( ATCC , CRL-2522 ) were grown in Dulbecco’s Modified Eagle’s Medium ( DMEM ) supplemented with 10% fetal bovine serum , L-glutamine ( 1% v/v ) , 10 IU/mL penicillin , and 100 mg/mL streptomycin . BJ cells stop their division by contact inhibition , therefore to limit their division , cells were seeded until confluence before being infected at a multiplicity of infection ( m . o . i . ) of 3 , and maintained in 2% serum throughout the experiment . Lips were biopsied at the region of virus inoculation ( commissural region ) immediately after the animals were euthanized , and TGs were harvested as described in the experimental procedures . Tissues were ground in microtubes containing 250μL of ice-cold PBS . Three rounds of freezing/thawing were applied using liquid nitrogen , and samples were centrifuged and supernatants stored at -80°C until use . Serial dilutions were used to titrate the virus on VERO cells ( ATCC , CCL-81 ) . HSV-1 DNA FISH probes were Cy3 labeled by nick-translation . Cosmids 14 , 28 and 56 [98] comprising a total of ~90 kb of the HSV-1 genome were labeled by nick-translation ( Invitrogen ) with dCTP-Cy3 ( GE Healthcare ) , and stored in 100% formamide ( Sigma-Aldrich ) . The DNA-FISH and immuno-DNA FISH procedures for TG sections have been described previously [47 , 96] . Briefly , frozen sections were thawed , rehydrated in 1x PBS and permeabilized in 0 . 5% Triton X-100 . Heat based unmasking was performed in 100 mM citrate buffer , sections were post-fixed using a standard methanol/acetic acid procedure , and dried for 10 min at RT . DNA denaturation of section and probe was performed for 5 min at 80°C , and hybridization was carried out overnight at 37°C . Sections were washed 3 x 10 min in 2 x SSC and for 3 x 10 min in 0 . 2 x SSC at 37°C , and nuclei were stained with Hoechst 33258 ( Invitrogen ) . All sections were mounted under coverslips using Vectashield mounting medium ( Vector Laboratories ) and stored at 4°C until observation . For immuno-DNA FISH , frozen sections were treated as described for DNA-FISH up to the antigen-unmasking step . Tissues were then incubated for 24 h with the primary antibody . After three washes , secondary antibody was applied for 1 h . Following immunostaining , the tissues were post-fixed in 1% PFA , and DNA FISH was carried out from the methanol/acetic acid step onward . The same procedures were used for infected neuronal cultures except that the cells were fixed in PFA 2% before permeabilization . RNA FISH probe labeling and RNA FISH procedures were performed as described previously [96] . Biotinylated single-strand LAT RNA probe was prepared by in vitro transcription ( Ambion ) using plasmid pSLAT-2 as a template ( gift from S . Efstathiou , University of Cambridge , UK ) . Biotinylated LacZ probe was prepared from the pCMV-LacZ plasmid ( Clontech ) using the nick-translation procedure ( Invitrogen ) . Frozen sections were treated as described for DNA FISH up to the antigen-unmasking step using solutions containing 2 mM of the RNAse inhibitor ribonucleoside vanadyl complex . The sections were pre-hybridized in 50% formamide/2 × SSC and hybridized overnight with 60 ng of RNA probe in a 50% formamide buffer at 65°C for LAT and 37°C for LacZ . Sections were washed in 50% formamide/2 × SSC at 65°C , and in 2 × SSC at room temperature . Detection was performed using streptavidin-HRP conjugate , followed by Tyramide Signal Amplification ( TSA , Invitrogen ) with an Alexa Fluor 350- or 488-conjugated substrate , according to the manufacturer’s guidelines . The DNA-FISH procedure was performed starting from the methanol/acetic acid post-fixation step . TGs were collected at 6 or 28 dpi and snap-frozen . Frozen tissues were ground , thawed in lysis buffer ( 10 mM Tris-EDTA , pH 8 . 0 ) containing a protease inhibitor cocktail , and briefly sonicated . Protein extracts were homogenized using QiaShredders ( Qiagen ) . Protein concentration was estimated by the Bradford method . Extracted proteins were analyzed by Western blotting using appropriate antibodies . The following primary antibodies were used: Mouse Mab anti-mouse PML ( mAb3739; Millipore ) , anti-human PML ( clone 5E10; Roel van Driel or clone PG-M3; Santa Cruz ) , anti-SUMO-1 ( clone 5B12; MBL ) , anti-SUMO-2/3 ( clone 1E7; MBL ) , anti-NF160 ( Invitrogen ) , anti-ßIII tubulin ( MAB1637; Millipore ) , anti-ICP0 ( Mab11060 ) , anti-ICP4 ( clone 10F1; Virusys ) , anti-ICP27 ( Virusys ) ; rabbit Mab anti-SUMO-1 ( clone Y299; Abcam ) , anti-SUMO-2/3 ( Mab4971; Cell Signaling ) : rabbit polyclonal anti-ATRX ( H-300; Santa Cruz Biotechnology ) , anti-Daxx ( M-112; Santa Cruz Biotechnology ) , anti-NF200 ( Pierce ) , anti-SUMO-1 ( 4930; Cell Signaling ) , anti-SUMO-2/3 ( ab3742; Abcam ) , anti-ICP0 ( Rab190 ) , anti-VP16 ( ab4808; Abcam ) , and anti-pan-HSV-1 ( LSBio ) were used . All secondary antibodies were Alexa Fluor-conjugated and were raised in goats ( Invitrogen ) . Observations and most image collections were performed using an inverted Cell Observer microscope ( Zeiss ) with a Plan-Apochromat ×100 N . A . 1 . 4 objective and a CoolSnap HQ2 camera from Molecular Dynamics ( Ropper Scientific ) , or a Zeiss LSM 510 confocal microscope . Raw images were processed using the ImageJ software ( NIH ) . Detection of LacZ transcripts in in1374-infected cultured neurons was performed using the FastLane Cell cDNA kit ( Qiagen ) using the following primers: LacZ fwd: 5’ GCAGCAACGAGACGTCA 3’ , LacZ rev: 5’ GAAAGCTGGCTACAGGAAG 3’ . Detection of HSV-1 genomes in cell and TG extracts was performed using primers targeting: TK fwd: 5’ GGAGGACAGACACATCGACC 3’ , rev: 5’ CGAAAGCTGTCCCCAATCCT 3’ and LAT fwd: 5’ CCCACGTACTCCAAGAAGGC 3’ , rev: 5’ AGACCCAAGCATAGAGAGCCAG 3’ . RT-PCR for the detection of viral lytic mRNAs or LAT in human or mouse TG and cell extracts was performed using primers targeting: ICP0 fwd: 5’ GGT-GTA-CCT-GAT-AGT-GGG-CG 3’ , rev: 5’ GCT-GAT-TGC-CCG-TCC-AGA-TA 3’; ICP4 fwd: 5’CGT-GGT-GGT-GCT-GTA-CTC-G 3’ , rev: 5’ GCT-CGG-CGG-ACC-ACT-C 3’; ICP27 fwd: 5’ ATG-TGC-ATC-CAC-CAC-AAC-CT 3’ , rev: 5’ TCC-TTA-ATG-TCC-GCC-AGA-CG 3’; UL30 fwd: 5’ TGT-TTC-GCG-TGT-GGG-ACA-TA 3’ , rev: 5’ TTG-TCC-TTC-AGG-ACG-GCT-TC 3’; VP16 fwd: 5’ TGC-GGG-AGC-TAA-ACC-ACA-TT 3’ , rev: 5’ TCC-AAC-TTC-GCC-CGA-ATC-AA 3’; and LAT ( see above ) . | Establishment of latency of herpes simplex virus 1 ( HSV-1 ) at the cellular level results from the combination of a series of complex molecular events involving cellular and viral-associated features . HSV-1 establishes latency in trigeminal ganglia ( TG ) sensory neurons . HSV-1 genomes remain as extrachromosomal DNA; their initial interaction with the nuclear architecture is likely to determine commitment toward the lytic or the latent transcriptional program . Among the major nuclear components that influence the infection process the promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) play a major role as nuclear relays of the intrinsic antiviral response . In this study , using infected mice and cultured mouse primary TG neuron models , as well as human TGs , we investigated the interaction between HSV-1 genomes and the nuclear environment in individual neurons . We found that the inability of HSV-1 to initiate a lytic program at the initial stages of infection led to the formation of latency-associated viral DNA-containing PML-NBs ( vDCP-NBs ) , or another pattern if the type 1 interferon pathway was activated prior to infection . vDCP-NB–like structures were also present in neurons of latently infected human TGs , designating PML-NBs as major nuclear components involved in the control of HSV-1 latency for the entire life of an individual . | [
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"simplex... | 2016 | Latency Entry of Herpes Simplex Virus 1 Is Determined by the Interaction of Its Genome with the Nuclear Environment |
Cell fate can be determined by asymmetric segregation of gene expression regulators . In the budding yeast Saccharomyces cerevisiae , the transcription factor Ace2 accumulates specifically in the daughter cell nucleus , where it drives transcription of genes that are not expressed in the mother cell . The NDR/LATS family protein kinase Cbk1 is required for Ace2 segregation and function . Using peptide scanning arrays , we determined Cbk1′s phosphorylation consensus motif , the first such unbiased approach for an enzyme of this family , showing that it is a basophilic kinase with an unusual preference for histidine −5 to the phosphorylation site . We found that Cbk1 phosphorylates such sites in Ace2 , and that these modifications are critical for Ace2′s partitioning and function . Using proteins marked with GFP variants , we found that Ace2 moves from isotropic distribution to the daughter cell nuclear localization , well before cytokinesis , and that the nucleus must enter the daughter cell for Ace2 accumulation to occur . We found that Cbk1 , unlike Ace2 , is restricted to the daughter cell . Using both in vivo and in vitro assays , we found that two critical Cbk1 phosphorylations block Ace2′s interaction with nuclear export machinery , while a third distal modification most likely acts to increase the transcription factor's activity . Our findings show that Cbk1 directly controls Ace2 , regulating the transcription factor's activity and interaction with nuclear export machinery through three phosphorylation sites . Furthermore , Cbk1 exhibits a novel specificity that is likely conserved among related kinases from yeast to metazoans . Cbk1 is functionally restricted to the daughter cell , and cannot diffuse from the daughter to the mother . In addition to providing a mechanism for Ace2 segregation , these findings show that an isotropically distributed cell fate determinant can be asymmetrically partitioned in cytoplasmically contiguous cells through spatial segregation of a regulating protein kinase .
Cells can adopt divergent fates upon division by unequally distributing molecules or structures that direct distinct gene expression programs . The genesis of this asymmetry rests on the cell's underlying architecture , and can involve segregation of mRNAs , transcription factors , and cell surface receptors [1 , 2] . Unquestionably critical for metazoan development , asymmetric gene expression is also important in unicellular eukaryotes . In the budding yeast Saccharomyces cerevisiae , for example , unequal partitioning of specific transcription factors causes mother and daughter cells to express different genes late in division [3–7] . Asymmetry of intracellular cell fate determinants requires their physical segregation as well as a mechanism to ensure that they do not act before the differentiating cells are functionally separated . In a number of well-characterized cases , transcriptional regulators are directly partitioned by cytoskeleton-associated machinery . In Drosophila melanogaster , differentiation of neuroblasts and ganglion mother cells ( GMCs ) is achieved through asymmetric segregation of the transcription factor Prospero's protein and mRNA , in association with the adaptor proteins Miranda and Staufen [8–10] . Miranda's segregation to the cortex of the presumptive GMC involves the actin cytoskeleton and the opposing activities of myosin VI and myosin II; this is mitotically regulated by the anaphase-promoting complex/cyclosome [11] . In the next cell cycle , Prospero translocates to the GMC nucleus , where it regulates transcription of GMC-specific genes . In budding yeast , daughter cells are prevented from switching mating types by the asymmetrically segregated transcriptional repressor Ash1 . This partitioning also depends on the actin cytoskeleton: ASH1 mRNA is transported by a class V myosin to the bud tip during mitosis and tethered to the daughter cell cortex . It remains there until the beginning of the next cell cycle , whereupon it is translated to produce the Ash1 repressor protein [4–6] . Asymmetric gene expression is also important in the last step of budding yeast cell division , but is generated by a different mechanism . Final separation of mother and daughter yeast cells requires removal of a chitin-rich septum constructed between the cells during cytokinesis [12] . Destruction of this septum occurs from the daughter side . This asymmetry is due to a daughter-specific transcriptional program driven by the transcriptional activator Ace2 , which accumulates specifically in the daughter cell nucleus and induces expression of enzymes involved in septum degradation [3 , 13–15] . Partitioning of this transcription factor is independent of mechanisms required for ASH1 segregation [7] , and remains incompletely understood . Ace2′s activation and daughter nucleus accumulation are coordinated with mitotic exit [7 , 16] . The transcription factor first localizes faintly to both mother and daughter nuclei , then accumulates to high levels exclusively in the daughter nucleus at the end of mitosis . The timing of this accumulation relative to cytokinesis is uncertain . Ace2′s nuclear import is likely blocked by mitotic cyclin-dependent kinase ( CDK ) phosphorylation of sites near its nuclear localization sequence ( NLS ) [15]: this inhibition is presumably reversed when CDK phosphorylations are removed by the phosphatase Cdc14 during mitotic exit . Ace2 nuclear export depends on the exportin Crm1/Xpo1 [7 , 17] . Loss of nuclear export results in symmetric Ace2 accumulation in both mother and daughter nuclei , indicating that the transcription factor is isotropically distributed in mother and daughter cells and that its asymmetry is probably not due to selective import in the daughter cell nucleus or degradation in the mother cell nucleus . Ace2 is controlled by a conserved signaling pathway termed the Regulation of Ace2 and Morphogenesis ( RAM ) network [3 , 7 , 13 , 18] . Cells lacking RAM network function fail to separate , growing as large clusters of cells connected by the primary septum between mother and daughter cells . This separation defect is the result of failure to segregate and activate Ace2 in daughter cells and thus a lack of expression of the Ace2 target genes required for septum destruction . It is unclear how the RAM network promotes the daughter-specific segregation and activation of Ace2 . Cbk1 , a protein kinase of the broadly conserved NDR/LATS family [19] , is a critical component of the RAM network . Cbk1 localizes to the bud neck and daughter cell nuclei during mitosis; the kinase's nuclear localization requires Ace2 [3 , 7 , 18] . Cbk1 kinase activity is critical for Ace2 localization and activation: in cells lacking Cbk1 , Ace2 localizes faintly to both mother and daughter nuclei and cannot activate transcription of its target genes [7 , 20] . The kinase phosphorylates an N-terminal fragment of Ace2 in vitro , suggesting a direct regulatory connection [20] . However , the identity and functional significance of Cbk1 phosphorylation sites within Ace2 are unknown . In this study , we sought to understand how Cbk1 controls Ace2 . We used an unbiased approach to elucidate the kinase's phosphorylation consensus motif and find a distinctive specificity that is likely conserved in related kinases across large evolutionary distances . This motif identified three Cbk1 phosphorylation sites within Ace2 that are crucial for the transcription factor's asymmetric distribution and function . Our in vivo and in vitro analyses of the functional significance of these sites indicate that Cbk1 phosphorylation controls Ace2 in two distinct ways: by directly blocking its interaction with nuclear export machinery and by enhancing its activity as a transcription factor . We also found that Cbk1 promotes Ace2 segregation well before cytokinesis and that the kinase is functionally partitioned to the daughter cell , allowing it to phosphorylate Ace2 and generate asymmetry from an initially isotropically distributed pool of the transcription factor .
Ace2 is initially cytoplasmically distributed in both mother and daughter cells [3 , 7 , 13] , and it is therefore possible that its partitioning reflects specific regulation of the transcription factor in the daughter cell following cytokinesis . We determined whether Ace2 accumulates in the daughter cell nucleus before or after cytoplasmic separation of the dividing cells by time-lapse microscopy , comparing localization of Ace2 and the actomyosin ring component Myo1 tagged with spectrally distinct fluorescent proteins . Myo1 remains at the bud neck until cytoplasmic division is complete [21] . Before loss of Myo1 from the bud neck , small cytoplasmic proteins have been shown to freely exchange between mother and daughter cells [22] . Remarkably , we found that Ace2 localizes to daughter cell nuclei significantly prior to cytokinesis , as determined by the disappearance of Myo1 from the bud neck ( Figure 1A ) . Thus , the transcription factor becomes asymmetrically distributed to the daughter nucleus while the mother and daughter cells are still cytoplasmically contiguous . Ace2′s precytokinetic partitioning suggests there is an intrinsic difference between the cytoplasmic environments of the bud and the mother cell . We therefore assessed Ace2 accumulation in nuclei that divide entirely within mother cells by examining Ace2-GFP localization in an arp1Δ bub2Δ mutant strain . Cells lacking the dynactin component ARP1 fail to orient the mitotic spindle properly , and nuclear division frequently occurs entirely within the mother [23] . This normally results in a mitotic checkpoint arrest; this is eliminated by deletion of the checkpoint component BUB2 [24] . As in wild-type cells , arp1Δ bub2Δ cells in which nuclei migrated into daughters exhibited strong daughter-specific Ace2 nuclear accumulation ( Figure 1B , cell a , and 1C , right ) . In contrast , Ace2 did not accumulate in nuclei of 95% of cells in which nuclear division occurred in the mother cell ( Figure 1B , cells b and c , and 1C , left ) . Therefore , the dividing nucleus must enter the daughter cell for nuclear retention of Ace2 to occur , and the form of Ace2 that localizes to the nucleus cannot diffuse from daughter to mother cell . Ace2 accumulates strongly and equally in both mother and daughter nuclei when nuclear export is globally blocked [7] . Thus , it is unlikely that Ace2 is sequestered in the daughter cell cytoplasm or specifically degraded in the mother cell , and asymmetry may involve selective inhibition of nuclear export from the daughter nucleus [7] . One plausible mechanism for asymmetric distribution may be “anchoring” of Ace2 in the daughter nucleus by daughter-specific activation of its DNA binding capability . We therefore constructed an Ace2-GFP allele in which amino acids predicted to be essential for zinc finger-mediated DNA binding are mutated ( Figure 2A ) . We analyzed the ability of this Ace2 allele , ace2-8Z-GFP , to bind its native target DSE1 using a quantitative chromatin immunoprecipitation ( ChIP ) assay . This allele fails to bind its target promoter ( Figure 2B ) , and cells carrying this ace2-8Z–GFP allele fail to separate and do not express Ace2 target genes ( Figure 2C and 2D ) . To assay ace2-8Z-GFP localization in mother–daughter pairs that fail to separate , we briefly labeled cells with rhodamine-conjugated lectin concanavalin A , which binds stably to the cell wall , and then allowed cells to grow in the absence of the fluorescent lectin . Using this analysis , mother cells were labeled with a red fluorophore , whereas daughter cells were unlabeled , allowing for unambiguous identification of mother–daughter pairs in clumps of cells . Despite lack of functional association with its target genes , ace2-8Z-GFP still localized exclusively to the daughter cell nucleus ( Figure 2D ) . Thus , Ace2 asymmetry does not arise through daughter-specific activation of the transcription factor's DNA binding . In vivo and in vitro evidence suggests that phosphorylation by Cbk1 directly controls Ace2 partitioning and function [3 , 7 , 13 , 25] . Cbk1 localizes to the cortex of the growing daughter cell , as well as to the bud neck , and accumulates in the daughter nucleus [3 , 13 , 25] . Concentrating activated Cbk1 specifically in daughter cells may create a modified pool of Ace2 that could generate asymmetric localization and activation of the transcription factor . To determine whether Cbk1 that is competent to enter the nucleus is partitioned to the daughter cell , we examined the kinase's nuclear localization after nuclear export block using a sensitized Crm1 ( also referred to as Xpo1 ) allele and leptomycin B ( LMB ) [26] . Unlike Ace2 , which localized to both mother and daughter nuclei in LMB-treated cells ( Figure 3A ) , Cbk1 partitioned exclusively to daughter cell nuclei in 96% of cells in which nuclear localization was visible ( Figure 3B ) , consistent with recent findings [27] . Thus , the form of Cbk1 that is able to enter nuclei is only present in daughter cells . Since Cbk1 kinase activity is required for asymmetric localization and activation of Ace2 , we sought to identify the sites at which Cbk1 phosphorylates Ace2 . Cbk1 is related to protein kinases that prefer basic amino acids in their phosphorylation consensus motif , and prior analysis of the Cbk1-related kinase Dbf2 suggests that these kinases phosphorylate the simple basic motif Arg-X-X- ( Ser/Thr ) , where X is any amino acid [28] . To better understand the specificity of Cbk1 and related enzymes , we determined Cbk1′s phosphorylation consensus motif using positional scanning peptide arrays [28 , 29] . In marked contrast to other basophilic kinases , which generally show limited specificity , Cbk1 exhibits a strong preference for the sequence His-X-Arg-Arg-X-Ser/Thr ( Figure 4A ) . Additional selectivity is also seen for Ser in the −2 position , aliphatic amino acids in the +1 position , and for His in the +2 position . Intriguingly , the Cbk1-related Drosophila kinase Warts/Lats and Human LATS1 have recently been shown to phosphorylate substrates at sequences that match the Cbk1 consensus motif [30–32] . Thus , this novel and highly specific consensus motif is evidently conserved among NDR/LATS family kinases , present in eukaryotes from diverse phyla . Previously , we demonstrated that Cbk1 phosphorylates itself both in vivo and in vitro at a conserved site in the kinase activation loop via an intramolecular reaction [20] . Cbk1′s autophosphorylation site does not match the consensus motif we have defined . This is unsurprising: activation segment autophosphorylation sites can correspond poorly to authentic consensus motifs [33] , presumably because they are a special case combining high local concentration and the protein's tertiary structure . We scanned Ace2 for potential Cbk1 sites using a motif with His at −5 and the basic amino acids Lys or Arg at the −3 and/or −2 positions and identified four candidate sites: S113 , S122 , S137 , and S436 ( Figure 4B ) . We performed in vitro kinase reactions using an Escherichia coli expressed GST-tagged fragment of Ace2 ( amino acids 42–242 ) that contains three of the four putative phosphorylation sites and Cbk1-HA immunoprecipitated from yeast . Larger fragments containing all four sites were prohibitively difficult to express and purify . Cbk1-HA efficiently phosphorylated this Ace2 fragment , as well as mutant Ace2 fragments in which each of the single putative phosphoacceptor serines were replaced with alanines ( Figure S1 ) . However , replacement of all three serines abolished phosphorylation , demonstrating that these constitute bona fide Cbk1 in vitro phosphorylation sites ( Figure 4C ) . Consistent with this conclusion , recent mass spectrometric studies have shown that at least one of these sites ( S122 ) is likely phosphorylated in vivo [34 , 35] . Based on these in vitro phosphorylation results , we propose that Cbk1 phosphorylates motifs with His at the −5 position and the basic amino acids Lys or Arg at either the −3 or −2 positions , concisely noted as HX ( K/R ) ( K/R ) X ( S/T ) . To evaluate the importance of Cbk1′s specificity for His at the −5 position , we compared in vitro phosphorylation of GST-ace2-S113A/S137A fragment , which contains a single phosphoacceptor site ( S122 ) , with phosphorylation of GST-ace2-S113A/S137A/H117A , in which His in the −5 position of the S122 site is mutated . This latter fragment was not phosphorylated , indicating that His in the −5 position is critical for Cbk1′s efficient phosphorylation of its cognate sites ( Figure 4C ) . As discussed further below , these data are in agreement with recent findings for Drosophila Warts/Lats [30 , 31] , further emphasizing the conservation of this substrate specificity among diverse eukaryotes . To determine the in vivo significance of Cbk1 phosphorylation of Ace2 , we constructed a series of Ace2 mutant alleles representing all combinations of Ala substitutions at all four phosphorylation sites . For each of these alleles , we determined the abundance , localization , competence to promote cell separation , and ability to induce transcription of two Ace2 target genes ( DSE1 and CTS1 ) using quantitative real-time polymerase chain reaction ( RT-PCR ) . For a subset of alleles , we used fluorescence microscopy to quantify the nuclear accumulation of each GFP-tagged protein . We integrated all mutant alleles at the endogenous chromosomal locus , C-terminally tagged with ether GFP or HA; all alleles were expressed at similar levels ( Figure S2 ) . To assay cell separation , we counted the number of cells in each group of cells using differential interference contrast ( DIC ) microscopy . Wild-type cells do not have a separation defect , and accumulated in groups of one to two cells , although larger groups sometimes formed ( Figure 5A ) . In contrast , ace2Δ cells accumulated as large groups often containing 30 or more cells per group ( Figure 5A ) , identical to the separation defect seen in cbk1Δ cells ( Figure S3 ) and consistent with previous results [20] . Single Ala substitutions at the Cbk1 phosphorylation sites did not affect Ace2-GFP function , as measured by cell separation and ability to activate target transcription ( Figures S3 and S4 ) . Furthermore , they did not affect partitioning of the transcription factor to the daughter cell ( unpublished data ) . Two phosphorylation sites ( S122 and S137 ) lie within Ace2′s putative nuclear export sequence ( NES ) [17] . We constructed a double-mutant ace2-S122A/S137A allele ( referred to as ace2-2A ) and found that elimination of these sites significantly affected Ace2′s partitioning to the daughter cell , yielding a substantial increase in the fraction of cells in which the transcription factor is present at low levels in both mother and daughter nuclei ( Figure 5B and 5C ) . However , the ace2-2A allele conferred only an intermediate cell separation defect and an incomplete reduction of target gene expression , indicating that it supplied significant in vivo function ( Figures 5A and 5D ) . In contrast , cells carrying a triple-mutant ace2-S122A/S137A/S436A ( ace2-3A ) allele failed to separate , forming large clusters of cells , and failed to activate CTS1 and DSE1 transcription ( Figures 5A and 5D ) . This triple-mutant ace2-3A-GFP no longer accumulated specifically in daughter cell nuclei and instead mislocalized to both mother and daughter nuclei ( Figure 5B ) . The amount of ace2-3A-GFP present in nuclei was significantly reduced relative to wild type and was comparable to the amount of Ace2-GFP present in nuclei of cells in a cbk1Δ strain ( Figure 5C ) . We assayed the ability of ace2-3A-HA to bind to its target promoters using quantitative ChIP and found that binding is severely impaired compared to the wild-type allele ( Figure 5E ) . Intriguingly , the ace2-2A-HA allele , which retains the Cbk1 consensus site at Ser 436 , still associates with these promoters ( Figure 5E ) . Thus , phosphorylation of the Ser 436 site may promote or stabilize DNA interaction . Taken together , these data indicate that three Cbk1 phosphorylation sites on Ace2 are required for proper transcription factor function and segregation . Our findings suggest that modification of sites S122 and S137 blocks Ace2′s interaction with Crm1 . Alternatively , phosphorylation of the sites may promote an intramolecular rearrangement or recruitment of another protein that antagonizes this interaction . To evaluate this directly , we first verified that amino acids 122–150 of Ace2 were sufficient to interact with purified Crm1 in an in vitro pulldown assay ( Figure S5 ) ; intriguingly , this does not require Ran-GTP . To determine the effect of phosphorylation , we obtained biotinylated synthetic peptides consisting of amino acids 122–150 and incorporated phosphoserine at positions 122 or 137 . Immobilizing these peptides on streptavidin-sepharose allowed us to qualitatively assess their affinity for Crm1 . Ace2 ( 122–150 , pS122 ) bound Crm1 more weakly than unphosphorylated peptide , and the interaction was virtually abolished with Ace2 ( 122–150 , pS137 ) ( Figure 6A ) . However , dephosphorylating the phosphopeptides with λ-phosphatase restored binding to both , confirming that differences in Crm1 affinity were due to the phosphoryl groups and not to differences in the efficiency of peptide immobilization . Therefore , phosphorylation of S137 , and to a lesser extent S122 , directly antagonizes the interaction of Ace2 with nuclear export machinery . We reasoned that replacement of Cbk1 phosphorylation sites with the acidic amino acids Asp or Glu might mimic Cbk1 phosphorylation and suppress the Ace2 loss of function seen in cbk1Δ cells . Substitution of S122 by Asp or Glu partially rescued cell separation in vivo ( unpublished data ) , and substitution of both S122 and S137 to Asp significantly increased cell separation ( Figure 6B ) . We also assayed in vitro interaction with Crm1 using an N-terminally GST-tagged fragment containing amino acids 122–150 of Ace2 . We found that replacement of either S122 or S137 with either acidic amino acid ( Asp or Glu ) significantly reduced interaction with Crm1 ( Figure S5 and unpublished data ) . Consistent with our in vivo results , in vitro interaction was only fully blocked by a double substitution of both S122 and S137 sites . Mutations within the nuclear export region ( F127V or G128E ) allow Ace2 to accumulate in both mother and daughter nuclei and to drive transcription of target genes in the absence of Cbk1 function [25] . Biotinylated Ace2 ( 122–150 , F127V ) showed very weak binding to Crm1 in vitro ( and treatment with λ-phosphatase had no effect ) ( Figure 6A ) ; thus , we predicted that the F127V mutation would restore activity to the ace2-3A allele , which exhibits defects similar to a cbk1Δ strain ( Figure S3 ) . Consistent with this , addition of the F127V substitution to ace2-3A allele restored mother–daughter separation to the level seen in an ace2-F127V cbk1Δ strain ( Figure 6B ) : phenotypic suppression was incomplete in both cases . We measured the fluorescence intensity of ace2-F127V-3A-GFP allele in individual mother and daughter nuclei and found that it was substantially increased , to approximately half of the daughter nuclei accumulation seen in a wild-type cell ( Figure 6C ) . Since the F127V mutation results in Ace2 that localizes to both mother and daughter nuclei evenly , we would indeed predict that the maximal accumulation of this allele in cells would only reach half that seen in a wild-type allele , in which Ace2 solely accumulates in the daughter cell nucleus . The ace2-F127V-3A allele also increased transcription of both the CTS1 and DSE1 target genes ( Figure 6D ) . Interestingly , this rescue is considerably larger for the CTS1 gene than the DSE1 gene , suggesting these genes are differentially sensitive to Ace2 activity .
Ace2′s accumulation in daughter cell nuclei and association with target promoters is precisely linked with the end of mitosis [16 , 36] . This coordination likely reflects a transition from mitotic CDK phosphorylation , which blocks Ace2′s nuclear import [15 , 16] , to postmitotic positive regulation by the NDR/LATS kinase Cbk1 . The transcription factor's asymmetric partitioning , as well as its dependency on Cbk1 function , can be eliminated by treatments that block its nuclear export [7 , 27] . These findings show that Ace2 is initially isotropically distributed in the mother and daughter cells and that elevating its intranuclear concentration increases expression of Ace2 target genes; they suggest that the transcription factor might be regulated through control of its nucleocytoplasmic shuttling . Our results illuminate the mechanism by which Cbk1 controls Ace2′s activity and asymmetric localization . Early in the cell cycle , Cbk1 accumulates at the cortex of the daughter cell , where it participates in bud morphogenesis . Upon mitotic exit , the kinase is then enabled to interact with and phosphorylate Ace2 in the daughter cell . We found that Cbk1 phosphorylates Ace2 at three functionally important sites , producing two distinct regulatory effects on the transcription factor . Phosphorylation of amino acids within the Ace2 NES ( S122 and S137 ) block its interaction with the exportin Crm1 and promote its retention in the daughter nucleus; our in vitro studies exclude the possibility that phosphorylation promotes recruitment of an accessory factor or distal inhibitory domain of Ace2 . This direct control of NES function is likely a general mechanism for regulation of nucleocytoplasmic shuttling: although not determined with fully purified components , phosphorylation of sites in the cyclin B1 NES likely blocks exportin binding [37] . Modification of an additional site proximal to the DNA-binding domain ( S436 ) may play a role in enhancing transcriptional activity . These distinct regulatory inputs appear to act in parallel to produce a more sharply defined gene expression response . Phosphorylation of Ace2′s NES allows it to accumulate to high levels in the daughter cell nucleus . These NES modifications are required for Ace2 partitioning , but they are not fully necessary for induction of Ace2-responsive genes . The ace2-2A allele lacking these phosphorylation sites retains the ability to induce target genes due to the presence of the S436 phosphorylation site ( Figure 5D ) . Similarly , the S436 site is not necessary for Ace2 function when the NES phosphorylation sites are present , as seen in the ace2-S436A allele , indicating that accumulation of large amounts of Ace2 in the nucleus can still drive expression of target genes , albeit at a reduced level ( Figure S4 ) . Conversely , nuclear localization is not sufficient for full function: cell separation and activation of DSE1 transcription remain partially compromised in a mutant that accumulates in both mother and daughter nuclei , but cannot be phosphorylated at S436 ( ace2-F127V-3A ) . Thus , both regulatory inputs by Cbk1 phosphorylation are important for Ace2 distribution and function . Remarkably , Cbk1-mediated accumulation of Ace2 in the daughter cell nucleus occurs significantly before cytokinesis . This regulation exemplifies a distinct system for partitioning an otherwise isotropically distributed transcription factor in cells that remain cytoplasmically contiguous . Nuclear localization of Cbk1 requires Ace2 [3 , 7]; therefore , we propose that Cbk1 phosphorylates Ace2 in the daughter cell cytoplasm , and the proteins enter the nucleus as a complex . The mechanism that restricts activation of Ace2 by Cbk1 to the daughter cell remains unclear . We propose two plausible models . One possibility is that a barrier at the bud neck restricts diffusion of cytoplasmic proteins between mother and daughter cells: although GFP exchanges rapidly between mother and daughter cells [22] , the mobility of larger complexes has not been investigated . Therefore , Ace2 might diffuse freely between mother and daughter cells , whereas the Ace2-Cbk1 complex that forms in the daughter cell might not pass through the bud neck . Alternatively , regulatory proteins that antagonize the Ace2-Cbk1 interaction could be localized at or near the bud neck , establishing a steep activation gradient between mother and daughter cells . We previously demonstrated that phosphorylation of Cbk1′s CT motif following mitosis is not necessary for its kinase activity , but is critical for its ability to regulate Ace2 [20] , and reversal of this modification by phosphatases concentrated near the bud neck could allow for spatial control of Cbk1 function . A similar effect may be achieved by dephosphorylation of Ace2 at the bud neck . Our findings also reveal a remarkable evolutionary conservation of substrate specificity in NDR/LATS family kinases: basic phosphorylation motifs with a marked preference for His at the −5 position . Using this motif , we were able to identify critical Cbk1 phosphorylation sites in Ace2 and demonstrated that Cbk1 can phosphorylate sites in which either Lys or Arg are present at the −3 or −2 positions . The Cbk1-related Drosophila kinase Warts/Lats has recently been shown to phosphorylate the transcriptional coactivator Yorkie ( Yki ) at sequences that match the Cbk1 consensus motif to regulate nuclear localization [30 , 31] . Similarly , human LATS1 phosphorylates the mammalian Yki ortholog YAP at these consensus sequences [32] . In Ace2 , modification directly blocks interaction with the exportin Crm1 , while phosphorylation of Yki recruits a 14-3-3 protein that antagonizes nuclear import [30 , 31] . Thus , the yeast and metazoan kinases have inverse functional output: Cbk1 promotes nuclear accumulation of a transcription factor , whereas Warts/LATS acts to suppress it . Despite this difference , regulation of Ace2 by Cbk1 should provide important insight into regulation by this highly conserved family of protein kinases .
All strains generated and used in this study are listed in Table 1 . Ace2 point mutants were created by site-directed QuikChange mutagenesis of pELW487 using Pfu Turbo ( Stratagene ) and integrated into ELY128 along with a C-terminal Longtine GFP::KanMX or HA::TRP1 tag [38] using two-fragment PCR . LMB-sensitive strains were created in ELY570 by integration of a C-terminal Longtine GFP::KanMX tag at the Ace2 or Cbk1 loci . ELY798 was generated by integration of a C-terminal Longtine GFP::KanMX tag at the Ace2 locus . Arp1 was deleted using the Euroscarf::LEU2 deletion plasmid [39] . All plasmids generated and used in this study are listed in Table 2 . Oligos used for PCR are listed in Table 3 . BL21 ( DE3 ) expression cells containing N-terminally glutathione S-transferase ( GST ) -tagged Ace2 mutant fragments were grown to an optical density at 600 nm ( OD600 ) = 0 . 7 in LB/AMP medium at 37 °C and induced with 0 . 5 mM IPTG at 24 °C for 4 h . Cells were spun down and pellets were frozen at −20 °C , then lysed in GST lysis buffer ( 40 mM Tris [pH 8] , 150 mM NaCl , 0 . 5% Triton X-100 , 1 μg/ml pepstatin , 0 . 5 mM leupeptin , and 1 mM PMSF ) with 0 . 1 mg/ml lysozyme and DNase treated . Lysates were cleared by 20 min centrifugation at 10 , 000 rpm . Glutathione-sepharose beads ( Amersham ) prewashed with GST lysis buffer were added to lysates and incubated at 4 °C for 3 h rotating . Beads were washed 2× 10 ml with GST lysis buffer followed by 2× 10 ml with GST wash buffer ( 50 mM Tris [pH 9] , 200 mM NaCl ) in a column . Beads were incubated in GST elution buffer ( GST wash buffer + 25 mM glutathione ) for 30 min and eluted by gravity drip . Protein concentrations were measured by Bradford assay ( Bio-Rad ) , using BSA to generate a standard curve . Glycerol was added to a final concentration of 10% , and proteins were stored at −80 °C . N-terminally GST-tagged Cbk1-T743D kinase domain was expressed and purified as described above with an additional 1 h incubation at 4 °C postlysis with 2 mM ATP and 10 mM MgSO4 to remove the 70 kDa copurifying contaminating band . Proteins were concentrated in an Amicon Ultra Centricon 30 kDa MWCO , exchanging into storage buffer ( 20 mM Tris [pH 8] , 150 mM NaCl , 10% glycerol ) . Crm1 was cloned into pET100 ( Invitrogen ) , expressed as a hexahistidine fusion in BL21 ( DE3 ) RIL , and purified by chromatography on Ni-NTA resin ( Qiagen ) . Purified Crm1 was dialyzed into PBS/KMD buffer ( 25 mM sodium phosphate , 150 mM NaCl , 3 mM KCl , 1 mM MgCl2 , 2 mM DTT ) , flash frozen in liquid nitrogen , and stored at −80 °C . Wild-type and mutant Ace2 ( 122–150 ) were cloned into pGEX-4T1 ( Amersham ) and expressed as GST fusions in BL21 ( DE3 ) RIL . Positional scanning oriented-peptide library screening was performed as previously described [29] . Briefly , solution-phase kinase reactions were performed in parallel on 198 separate biotinylated , partially degenerate , oriented peptide libraries ( Anaspec ) arrayed in a 384-well microtiter plate in a 22 row × nine column format . Each peptide library contains an N-terminal biotin tag , a 50:50 mix of serine and threonine at the orienting phosphoacceptor residue , a single second fixed amino acid located between the −5 and +4 position , and a mixture of amino acids at all other positions . Individual libraries contain any of the 20 natural amino acids as well as phosphothreonine and phosphotyrosine in the second fixed position , corresponding to the 22 rows . Scanning across the columns in the array moves the position of the fixed amino acid from −5 to +4 relative to the fixed phosphoacceptor residue . Kinase reactions were performed at 30 °C for 6 h in a total volume of 16 μl containing 7 . 92 μg of recombinant purified Cbk1 kinase domain , 31 . 25 μM peptide library , 100 μM ATP , and 200 μCi of γ-32P-ATP , in 150 mM NaCl , 10 mM MnCl2 , 1 mM Tris ( 2-carboxyethyl ) phosphine ( TCEP ) , and 50 mM Tris ( pH 7 . 5 ) . Following incubation , 2 μl of each reaction were simultaneously transferred to a streptavidin-coated membrane ( Promega SAM2 biotin capture membrane ) using a 384-slot pin replicator ( VP Scientific ) . The membrane was washed three times with 140 mM NaCl , 0 . 1% SDS , 10 mM Tris ( pH 7 . 4 ) , three times with 2 M NaCl , twice with 2 M NaCl containing 1% H3PO4 , and once with water . The extent of peptide library phosphorylation was determined by imaging the membrane with a phosphorimager ( Molecular Dynamics ) . For GST pulldown experiments , GST fusions were immobilized on glutathione-sepharose by incubating bead slurry with E . coli lysate containing GST protein at 4 °C for 15 min on a rotator . Beads were washed five times in PBS/KMD and incubated with His6-Xpo1 ( 5 μM final in 100 μl ) at 4 °C for 15 min on a rotator . Beads were washed five times with 50 mM Tris , 150 mM NaCl , 0 . 1% Tween-20 ( pH 7 . 5 ) ( TBST ) , and bound protein was eluted by boiling in SDS-PAGE sample buffer for 10 min . Samples were separated by SDS-PAGE , stained with GelCode Blue ( Pierce ) , and visualized on an Odyssey ( Li-Cor ) fluorescence scanner . Cbk1-HA cells ( 225 OD600 ) grown to mid-logarithmic phase were lysed and immunoprecipitated as described previously [20] . Immunoprecipitates used in kinase assays were stored in yeast wash buffer ( 50 mM Tris [pH 7 . 5] , 150 mM NaCl , 1 mM dithiothreitol , 0 . 5 mM leupeptin , and 1 μg/ml pepstatin ) at 4 °C overnight . Kinase assays were performed as described previously [20] . Briefly , immunoprecipitated Cbk1-HA bound to protein G sepharose ( Invitrogen ) was divided and resuspended in kinase buffer ( 20 mM Tris [pH 6 . 8] , 150 mM NaCl ) . A total of 10 μg of purified GST-Ace2 substrate was added to reaction buffer containing 5 mM MnCl2 , 20 μM cold ATP , and 10 μCi/μl γ-32P-ATP . Reactions were incubated at room temperature for 1 h and quenched by addition of 5× SDS-PAGE sample buffer and 10 min incubation at 85 °C . Proteins were resolved on a 10% SDS-PAGE gel and transferred to a PVDF membrane ( Pall ) . Fragment phosphorylation was evaluated using a Storm 860 Imager . Blots were incubated with anti-GST 1:10 , 000 ( Sigma ) and anti-HA 1:1 , 000 ( 12CA5 , a gift of R . Lamb , Northwestern University , Evanston , Illinois ) monoclonal antibodies overnight , then washed 3× with TBST and incubated with IRDye-800 goat anti-mouse secondary antibody ( Rockland ) ( 1:5 , 000 ) for 2 h at room temperature . Blots were imaged using an Odyssey scanner , and protein concentrations quantified using Odyssey software . Frozen Ace2-HA mutant pellets ( 5 OD600 ) were resuspended in 200 μl of 8M Urea , 50 mM HEPES ( pH 7 . 4 ) , and 200 μl of fine glass beads were added . Cells were lysed in a multivortexor at maximum speed for 5 min . A total of 20 μl of 25% SDS was added , and lysates were incubated at 65 °C for 5 min followed by centrifugation at 13 . 2g for 5 min . Cleared lysates were collected , and protein concentration was measured by Bradford assay . A total of 25 μg of lysate per lane was loaded onto an 8% SDS-PAGE gel and transferred to PVDF membranes . Blots were blotted with anti-HA 1:1 , 000 for 2 h at room temperature then stained with IRDye-800 conjugated goat anti-mouse antibodies ( 1:5 , 000 ) for 2 h . Images were collected and quantified using Odyssey software . Cells were grown in synthetic medium to early log phase and pulse-labeled with rhodamine-conjugated concanavalin A ( Vector ) for 10 min , followed by 70 min of growth . Cells were imaged in synthetic dextrose medium at 25 °C with a 100×/1 . 45 NA oil-immersion objective using fluorescence/differential interference contrast microscopy with an Axiovert 200M ( Carl Zeiss MicroImaging ) and photographed with a Cascade II-512 camera ( Photometrics ) . Contrast enhancement of images was performed using Openlab software . GFP Z-stacks were taken , and the brightest individual nuclei were measured for fluorescence intensity using Openlab software . For wild-type cells , only daughter nuclei were quantified; individual nuclei in mother–daughter pairs were scored for each Ace2 mutant . For cell separation quantification , cells were sonicated 2× 15 s prior to microscopy . Cells used for LMB experiments were grown in synthetic medium for 4 h , spun down , and resuspended in fresh medium containing 10 ng/μl LMB and grown for 30 min , then used for microscopy . Biotinylated peptides were synthesized by the MIT Biopolymers Laboratory: Biotin-Ahx-Y-SGTAIFGFL-GHNKTLSISSLQQSILNMSK ( wild-type ) , Biotin-Ahx-Y-pSGTAIFGFLGHNKTLSISSLQQSILNMSK ( pS122 ) , Biotin-Ahx-Y-SGTAIFGFLGHNKTLpSISSLQQSILNMSK ( pS137 ) , Biotin-Ahx-Y-SGTAIVGFLGHNKTLSISSLQQSILNMSK ( F127V ) , where Ahx denotes aminohexanoic acid . Synthesis of the dually phosphorylated ( pS122/pS137 ) peptide was unsuccessful . For each biotin-peptide binding experiment , 10 nmol of peptide were dephosphorylated by treatment with 800 units of λ-phosphatase ( New England Biolabs ) at 30 °C for 60 min in a reaction volume of 100 μl ( 50 mM Tris , 100 mM NaCl , 0 . 1 mM EGTA , 2 mM DTT , 0 . 01% Brij 35 , 2 mM MnCl2 [pH 7 . 5] ) ; 900 μl of PBS/KMD , and 20 μl of streptavidin-sepharose ( 50% slurry in PBS/KMD; Amersham ) were then added , and the suspension incubated at 4 °C for 15 min on a rotator . Non-phosphatase treated peptides were bound to steptavidin-sepharsoe similarly . Streptavidin beads with immobilized peptide were washed three times in PBS/KMD and incubated with His6-Crm1 ( 5 μM final in 100 μl ) at 4 °C for 15 min on a rotator . Beads were washed five times with TBST , and bound protein was eluted by boiling in SDS-PAGE sample buffer for 10 min . Samples were separated by SDS-PAGE , stained with GelCode Blue , and visualized on an Odyssey fluorescence scanner . RNA extracted from 5 OD600 of cells grown to mid-logarithmic phase was prepared as described [40] . A total of 4 μg of RNA was reverse transcribed using 7 μM T120V primer and reverse transcriptase at 42 °C , for 1 h; 50 ng of total product was used in subsequent quantitative PCR reactions using CTS1 , DSE1 , and ACT1 primers . Standard curves for each primer were generated using serial dilutions of yeast genomic DNA and linear regression analysis of cycle threshold ( Ct ) values . Quantification of cDNA template concentrations were calculated using the standard curve for each primer . Cells ( 70 OD600 ) grown to mid-logarithmic phase were incubated with 1% formaldehyde at room temperature for 30 min then filtered and washed with 100 mM Tris ( pH 7 . 0 ) and flash frozen with liquid nitrogen . Pellets were lysed in ChIP lysis buffer ( 50 mM HEPES [pH 7 . 5] , 140 mM NaCl , 1 mM EDTA , 1% Triton-X-100 , 0 . 1% NaDOC , 0 . 5 mM pepstatin , and 1 μg/ml leupeptin ) by bead beating in a multivortexor . Lysates were cleared by centrifugation at 7 , 000 rpm for 10 min , and pellets were resuspended in ChIP lysis buffer ( without detergent ) and sonicated , setting 7 , 10× 10 s with 10 s rests . One percent Triton-X-100 and 0 . 1% NaDOC were added and centrifuged 10 min at 14 , 000 rpm . Supernatant was collected and incubated with 2 μg of anti-HA on ice for 30 min . Protein-G Dynabeads ( Dynal Biotech ASA ) were incubated with lysate 1 h , 4 °C , rotating . Input samples were collected and incubated with elution buffer ( 50 mM Tris [pH 8] , 10 mM EDTA , 1% SDS ) , 65 °C overnight . Beads were washed 10× with ChIP lysis buffer and eluted with elution buffer at 65 °C , 15 min , then washed with 50 mM Tris ( pH 8 ) , 10 mM EDTA , 0 . 67% SDS . Eluates and washes were pooled , and incubated 65 °C overnight . DNA was purified using QIAquick PCR Purification Kit ( Qiagen ) and used in Q-PCR reactions with primers for DSE1 , SCW11 , and ACT1 loci . Standard curves and quantification were performed as described above . | Cells can differentiate by segregating molecules that direct expression of specific sets of genes to one of the two cells produced by division . This generally occurs by direct mechanical movement or asymmetric anchoring of these molecules , which act after division to influence gene expression . In this study , we define a different mechanism by which the budding yeast transcription regulator Ace2 is asymmetrically partitioned . We show that Ace2 moves from uniform distribution to strong accumulation in the daughter nucleus while mother and daughter cells are still connected , and that the enzyme Cbk1 directly controls this segregation by attaching phosphate to specific sites on Ace2 . We also demonstrate that Cbk1 is restricted to the daughter cell . Using both biochemical and live-cell experiments , we show that the Cbk1-mediated modifications activate Ace2 and block its interaction with nuclear export machinery , trapping it in the daughter cell nucleus . In addition to demonstrating Cbk1′s remarkable biochemical similarity to related enzymes in multicellular organisms , our analysis shows that a uniformly distributed regulator of gene expression can be made asymmetrically active in connected cells through the direct action of a localized modifying enzyme . | [
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] | 2008 | The NDR/LATS Family Kinase Cbk1 Directly Controls Transcriptional Asymmetry |
Prokaryotes benefit from having accessory genes , but it is unclear how accessory genes can be linked with the core regulatory network when developing adaptations to new niches . Here we determined hierarchical core/accessory subsets in the multipartite pangenome ( composed of genes from the chromosome , chromid and plasmids ) of the soybean microsymbiont Sinorhizobium fredii by comparing twelve Sinorhizobium genomes . Transcriptomes of two S . fredii strains at mid-log and stationary growth phases and in symbiotic conditions were obtained . The average level of gene expression , variation of expression between different conditions , and gene connectivity within the co-expression network were positively correlated with the gene conservation level from strain-specific accessory genes to genus core . Condition-dependent transcriptomes exhibited adaptive transcriptional changes in pangenome subsets shared by the two strains , while strain-dependent transcriptomes were enriched with accessory genes on the chromid . Proportionally more chromid genes than plasmid genes were co-expressed with chromosomal genes , while plasmid genes had a higher within-replicon connectivity in expression than chromid ones . However , key nitrogen fixation genes on the symbiosis plasmid were characterized by high connectivity in both within- and between-replicon analyses . Among those genes with host-specific upregulation patterns , chromosomal znu and mdt operons , encoding a conserved high-affinity zinc transporter and an accessory multi-drug efflux system , respectively , were experimentally demonstrated to be involved in host-specific symbiotic adaptation . These findings highlight the importance of integrative regulation of hierarchical core/accessory components in the multipartite genome of bacteria during niche adaptation and in shaping the prokaryotic pangenome in the long run .
Prokaryotes play important roles in recycling nutrients and forming pathogenic or mutualistic associations with eukaryotes . It has been established that many ecologically important processes are differentially mediated by prokaryotes at the strain level [1] . This is partially explained by the fact that even closely related strains of bacteria and archaea can have great differences in their genomes due to a high rate of turnover in gene content , so that there are core genes shared by all members of a taxonomic group and accessory genes present in only a subset of the members [2 , 3] . However , it is still puzzling why and how prokaryotes maintain such a high degree of genome content variability [4] . It is widely accepted that certain accessory genes can benefit their host by conferring the ability to occupy new niches , despite the existence of putative junk genes in the pangenome [4 , 5] . However , it is largely unexplored to what extent these accessory functions are linked with the core regulatory network during the development of adaptations to new ecological niches . Soil bacteria able to form nitrogen-fixing nodules on legumes , collectively called rhizobia , have global impacts on sustainable agriculture and the nitrogen cycle . These facultative microsymbionts need a cluster of key symbiosis genes called nod/nif/fix , which are located on a horizontally transferable plasmid or a genomic island , to establish a mutualistic interaction with legume plants [6–10] . The ability to form nitrogen-fixing nodules on legumes has been reported for hundreds of species in alpha- and beta-proteobacteria [11] . Among the 122 complete genome sequences from twelve genera of rhizobia available in the GenBank database ( on March , 30th 2018 ) , 107 genomes from eleven genera have two or more DNA molecules , a genome architecture described as a multipartite genome . This multipartite organization is found in approximately 11% of 1 , 708 bacterial genomes analyzed in a recent study [12] . Each DNA molecule with a separate origin of replication in bacterial genomes is referred to as a replicon . The largest replicon , with most of the core genes , is known as a chromosome , while megaplasmids ( above 350 kb in size ) and plasmids refer to replicons lacking core genes and are characterized with significantly biased signatures such as GC content and dinucleotide composition compared to the chromosome [12 , 13] . The term “chromid” was recently introduced to refer to a replicon with plasmid-type maintenance and replication systems , but carrying some core genes and having sequence signatures more similar to chromosomes than plasmids and megaplasmids [12 , 13] . Accumulating evidence has suggested distinct roles of different replicons in rhizobial adaptations to either saprophytic or symbiotic conditions [14–17] , though the coordinated regulation of core and accessory functions in these multipartite genomes is largely unexplored . A multipartite genome , composed of at least a chromosome , a chromid , and a megaplasmid ( the symbiosis plasmid ) , is present in most sequenced genomes within the Sinorhizobium genus , which includes microsymbionts associated with the important legume crops alfalfa and soybean [18–20] . The chromid genes in Sinorhizobium associated with the same legume host show a higher differentiation level compared to the other two replicons [21 , 22] . In contrast to the symbiosis plasmid , which shows evidence of horizontal gene transfer , the chromid core genes have a phylogeny generally congruent with that of chromosomal core genes [21] . An engineered chromosome containing essential core genes transferred from the chromid is sufficient for growth of a model microorganism Sinorhizobium meliloti in a sterile bulk soil environment [16] . Metabolic modeling suggests that the chromosome of S . meliloti also contributes to fitness in rhizosphere , and the chromid shows a greater fitness contribution in the rhizosphere than in bulk soil [15 , 22] . By contrast , transcriptomics studies of free-living and symbiotic Sinorhizobium strains have demonstrated a specific up-regulation of many genes on the symbiosis plasmid within legume nodules , where core functions are generally down-regulated consistent with the growth arrest status of nitrogen-fixing rhizobia [23–25] . However , scattered genetic evidence suggests that genes located on the chromosome and the chromid can also contribute to the integration and optimization of symbiotic functions in diverse rhizobia including Sinorhizobium [25–30] . It has been proposed that the rhizobium-legume symbiosis requires optimization through a long-term evolutionary process involving integration of lineage-specific accessory genes ( those genes only present in a limited subset of related strains , species or genera ) with the regulatory network of core genomes [26 , 31] , but there is little direct evidence as yet [30 , 32] . There is a need for omics-based comparative analyses of the variation in the contents , regulation and integration of core and accessory genes under different conditions . In this study , we investigate how core and accessory genes are organized and integrated in the multipartite genome of the soybean microsymbiont , Sinorhizobium fredii . To this end , complete genome sequences were obtained for S . fredii CCBAU45436 and CCBAU25509 , which have an overlapping host range . The genes of these two genomes were divided into four hierarchical core/accessory subsets based on comparative genomics analyses with ten published genomes of Sinorhizobium spp . Then the global transcriptomic profiles of the two test strains were determined at exponential and stationary phases in free-living cultures , and at the symbiotic stage within the nodules of cultivated and wild soybeans . By analyzing this transcriptomic and genomic information , we obtained a global integration pattern of core and accessory genes under different conditions , and identified novel genes involved in symbiotic adaptations . These findings will be discussed in the more general context of the organization and evolution of the prokaryotic pangenome in relation to ecological adaptations .
S . fredii CCBAU45436 and CCBAU25509 ( Fig 1A ) , which are effective microsymbionts of local soybean cultivars grown in northern China [33] , induced normal nitrogen-fixing nodules and non-fixing nodule-like structures , respectively , on the roots of soybean accession C08 ( Fig 1B ) , which is a close relative of the sequenced soybean cultivar Williams 82 [34 , 35] . They both established nitrogen-fixing nodules on the wild soybean accession W05 ( Fig 1B ) , which has recently been sequenced [36] . Complete genome sequences for CCBAU45436 and CCBAU25509 were first obtained by assembling Illumina data generated previously [26] . In this study , full assembly of these genomes were achieved by new PacBio and Ion Torrent sequencing data ( S1 Table ) , and Sanger sequencing of PCR products was used to fill assembly gaps when necessary . The general features of CCBAU45436 and CCBAU25509 genomes are summarized in S2 Table . CCBAU25509 has a typical tripartite genome , consisting of a chromosome ( cSF25509; 4 . 20 Mb ) , a chromid ( pSF25509b; 2 . 21 Mb ) and a symbiosis plasmid ( pSF25509a; 0 . 40 Mb ) . In the CCBAU45436 genome , two additional smaller plasmids , pSF45436d ( 0 . 20 Mb ) and pSF45436e ( 0 . 17 Mb ) were also found besides the chromosome ( cSF45436; 4 . 16 Mb ) , the chromid ( pSF45436b; 1 . 96 Mb ) and the symbiosis plasmid ( pSF45436a; 0 . 42 Mb ) . By including ten published genomes of Sinorhizobium ( Fig 1A and S1 Fig ) , the gene homologs shared by CCBAU45436 and CCBAU25509 were each divided into three hierarchical core subsets ( Fig 2A ) : subset I , gene homologs present in all Sinorhizobium strains; subset II , those present in all S . fredii strains excluding subset I; subset III , those shared by CCBUA45436 and CCBAU25509 but not present in all S . fredii strains , i . e . excluding subsets I and II . The remaining accessory genes of CCBAU45436 or CCBAU25509 were defined as subset IV . As expected , genes within each of these hierarchical core/accessory subsets were unevenly distributed on different replicons in the two strains ( Fig 2B and S3 Table; Pearson’s chi-square test , P < 0 . 001 ) . Around 80% of the subset I genes were concentrated on chromosomes . Genes within subsets II and IV were overrepresented on chromids . The symbiosis plasmids were characterized by their enrichment with the subset III genes ( 58%-59% genes on the symbiosis plasmid ) and to a lesser extent with the subset II genes ( 23%-25% ) . Two replicons ( pSF45436d and pSF45436e ) specific to CCBAU45436 were extremely enriched with the subset IV genes ( 69 . 3% and 84 . 6% ) . To investigate how core and accessory genes with biased replicon distributions were integrated during adaptations , we used RNA-seq to obtain transcriptomes of the two test strains under three conditions: ( 1 ) free-living culture in the mid-log phase ( non-stress ) , ( 2 ) free-living culture in the nutrient-starved stationary phase ( abiotic stress ) , and ( 3 ) symbiotic bacteroids within the nodules of cultivated and/or wild soybeans ( biotic stress ) ( S4 Table ) . For convenience , genes were classified into four expression levels ( Level_1-Level_4 ) using arbitrary cut-offs at the first , second and third quartiles of the expression profiles based on the RPKM ( reads per kilobase per million mapped reads ) value of each gene under test condition . The distribution of these genes across different transcriptional levels under test conditions was analyzed for each replicon ( Fig 3 and S2 Fig ) . On the chromosomes and chromids , the proportion of genes expressed at levels higher than the first quartile ( above Level_1 ) decreased along with reduced gene conservation levels ( from subset I to subset IV ) under all test conditions ( Fig 3 and S2 Fig ) . This phenomenon can also be found in the transcriptional profiles of symbiosis plasmid genes under symbiotic conditions but not in free-living cultures , particularly for highly expressed genes ( Level_4 ) . There was generally an increased number of highly expressed genes ( Level_4 ) in subsets I-IV of the symbiosis plasmid in legume nodules compared to free-living cultures . By contrast , the proportion of high-expressed ( Level_4 ) subset I genes on the chromosome was notably reduced under symbiotic conditions and in the stationary phase compared to that of mid-log phase . The chromid genes did not exhibit drastic changes in the proportions of different transcriptional levels under test conditions , except a notable increase of highly expressed genes ( Level_4 ) at the stationary phase compared to the mid-log phase . Although transcriptional levels showed a strong dependence on both the replicon location and the conservation levels , log-linear analysis indicated that replicon and core/accessory status were independently related to gene expression levels ( all P < 0 . 001 ) . To further investigate how genes within different hierarchical core/accessory subsets would respond to different growth conditions , dendrograms based on gene expression distance ( GE distance , defined in Materials and Methods ) were constructed . When we examined the expression profiles of shared genes within each of subset I , subset II and subset III , the profiles of the two strains were closely matched with respect to growth phases and symbiotic conditions ( Fig 4A–4C ) , while the expression profiles of the strain-specific genes ( subset IV ) were , inevitably , clustered by strain ( Fig 4D ) . The overall picture is that , for all gene subsets , expression in nodules is more similar to expression in exponential phase than in stationary phase and , for all subsets that they share , the difference between the two strains is less than the effect of growth conditions . Although similar condition-dependent clustering patterns were observed for subsets I-III ( Fig 4A–4C ) , the average gene expression level under each condition decreased with reduced gene conservation level ( from subset I to subset IV ) ( Fig 4E ) . Moreover , the higher expression plasticity ( gene expression variance among conditions ) was observed for the more conserved subsets ( Fig 4F ) , and subset IV showed the least variance in expression plasticity . As expected , further analyses of the differentially expressed genes ( DEGs , Log2R > 1 . 732 , FDR < 0 . 001 ) based on pairwise comparisons showed that DEGs were significantly enriched in subset I and/or subset II , while depleted in subset III and/or subset IV ( S5 Table , all P < 0 . 05 ) . It is noteworthy that up-regulated and down-regulated genes had distinct enrichment patterns across the core/accessory subsets ( S3 Fig and S5 Table ) . Genes down-regulated at the stationary phase or in the symbiotic nodules compared to the mid-log phase were enriched in subset I ( the genus core genes ) , while the up-regulated ones were enriched in subsets II and III ( the genus accessory genes shared by the two test strains ) ( Pearson’s chi-square test , all P < 0 . 05 ) . These results provided another line of strong evidence for differential roles of core genes with different conservation levels during environmental adaptation . To further dissect this phenomenon , we then examined the condition-dependent co-expressed genes . Genes could be divided into four groups based on k-means clustering of their transcriptional profiles ( Gr . 1-4; Fig 5A ) . Gr . 4 consisted of genes constitutively expressed or non-expressed under all conditions , while Gr . 1 , Gr . 2 and Gr . 3 consisted of those up-regulated at mid-log phase , stationary phase and symbiotic stage in nodules respectively ( Fig 5A ) . Genes within different condition-dependent groups were unevenly distributed in the hierarchical core/accessory subsets ( Fig 5B ) . Gr . 4 was overrepresented within subsets III and IV ( Pearson’s chi-square test , all P < 0 . 001 ) . Gr . 1 genes were enriched in subset I , Gr . 3 genes in subsets II-IV , while Gr . 2 genes in none of them ( Fig 5B ) . Among different replicons , the chromosomes and symbiosis plasmids were enriched with Gr . 1 genes and Gr . 3 genes , respectively , while both Gr . 2 and Gr . 3 genes were overrepresented on the chromids ( Fig 5C ) , indicating a replicon-dependent gene regulation under test conditions . Functional annotations of genes within Gr . 1-4 were further analyzed regarding COG categories . Gr . 1 , Gr . 2 and Gr . 3 were respectively enriched in the COG category J ( translation , ribosomal structure and biogenesis ) , S/W ( S: function unknown; W: extracellular structures ) and P/X ( P: inorganic ion transport and metabolism; X: mobilome: prophages , transposons ) ( Fig 5D ) . Among the 4 , 931 single-copy orthologous genes shared by CCBAU45436 and CCBAU25509 , the DEGs between these two strains ( 151 at the mid-log phase , 292 at the stationary phase , and 197 within the nodules of G . soja W05; Log2R > 1 . 732 , FDR < 0 . 001 ) were significantly enriched in the hierarchical core/accessory subset III ( Fig 6A and S6 Table ) . This provides further evidence that the differential regulation of intraspecies accessory genes may contribute to bacterial diversification . Consistent with results described above that genes within different hierarchical core/accessory subsets exhibited a biased replicon distribution pattern ( Fig 2 ) , the strain-dependent DEGs were significantly enriched on the chromids , and the non-symbiosis plasmid pSF45436d ( Fig 6B & S6 Table ) . The biased distribution of condition-dependent co-expressed genes and strain-dependent DEGs with respect to core/accessory genomes and replicons raised the question of whether accessory genes have been integrated in a replicon-dependent way among S . fredii strains . Therefore , we investigated the gene connectivity ( co-expression of gene pairs ) within or between replicons in gene co-expression networks constructed from the transcriptional profiles of S . fredii CCBAU45436 and CCBAU25509 ( described in Materials and Methods ) . When the genes from all replicons were pooled together , a significant decrease in gene connectivity was revealed in parallel with the decreasing conservation level of the genes ( from subset I to subset III ) ( Fig 7A and S4 Fig ) . This correlation was observed on chromosomes and symbiosis plasmids , but not on chromids and other plasmids ( pSF45436d/e ) ( Fig 7A and S4 Fig ) . A larger fraction ( 68% ) of chromid genes were linked to the chromosome than were the symbiosis plasmid genes ( 36% ) ( Fig 7B and S4 Fig ) , indicating that chromids are more closely associated with chromosomes than symbiosis plasmids in terms of transcriptional regulation . On the other hand , the symbiosis plasmid possessed a larger fraction ( 46% ) of within-replicon gene connectivity than the chromid ( 23% ) ( Fig 7B and S4 Fig ) , and most of the within-replicon gene connectivity on the symbiosis plasmid was linked to genes required to support symbiotic nitrogen fixation , such as nif and fix genes ( S5 Fig ) . Nevertheless , more than half ( 54% ) of the gene connectivity associated with the symbiosis plasmid was between-replicon ( Fig 7B and S4 Fig ) . Both the typical symbiosis genes with high within-replicon gene connectivity and certain genes with low within-replicon gene connectivity can show a high level of between-replicon gene connectivity ( S5 Fig ) . These genes with between-replicon connectivity could be interesting candidates for further functional analyses of the optimization of symbiosis . CCBAU45436 can form effective nodules on both the wild soybean , G . soja W05 , and the cultivated soybean , G . max C08 . This allowed us to investigate the potentially adaptive transcriptional profiles of rhizobia in the nodules of a cultivated soybean compared to those in wild soybean nodules . There were 42 and 77 genes down-regulated and up-regulated , respectively , in CCBAU45436 bacteroids within C08 nodules compared to those in W05 nodules ( Log2R > 1 , FDR < 0 . 001; S1 Dataset ) . These DEGs were slightly enriched in the subset II ( harboring 24 . 4% of DEGs and 14 . 9% of the total number of genes; Pearson’s chi-square test , P < 0 . 05 ) but were not enriched in any one of the replicons . To uncover potential candidate genes essential for host adaptation , we constructed mutants for ten representative genes ( S6 Fig and S7 Table ) that were up-regulated in C08 nodules compared to W05 nodules . These representative genes were among those with the highest log2R values and covered the four conservation levels ( subsets I-IV; S1 Dataset ) . Eight of the mutants exhibited indistinguishable symbiotic phenotypes on both W05 and C08 compared to the wild type ( S8 Table ) , but ΔznuA and mdtA::pVO had significant effects ( Table 1 ) . The Sinorhizobium core genes znuA/B/C ( in subset I ) encode the conserved zinc transporter components , and the in-frame deletion mutant of znuA ( ΔznuA ) formed a reduced number of nodules ( 34 . 9% - 48 . 4% , respectively , compared to wild type , P < 0 . 01 ) on both W05 and C08 , but with higher fresh weight per nodule ( 167% - 247% of wild type , respectively , P < 0 . 05 ) ( Table 1 and S7 Fig ) . C08 plants nodulated by ΔznuA had lower leaf chlorophyll content , 80 . 7% of that from C8 soybean plants inoculated with the wild-type strain ( P < 0 . 0001 ) , which was not significantly different from the uninoculated control ( Table 1 and S7 Fig ) . However , the same ΔznuA mutant was still fully effective in supporting the growth of W05 ( Table 1 and S7 Fig ) . The mutant for mdtA , which is found together with mdtB/C in an operon that encodes a putative multi-drug efflux system , was ineffective on both W05 and C08 as indicated by the significantly reduced chlorophyll content of these host leaves compared to those from plants inoculated with the wild-type strain ( Table 1 and S7 Fig ) . Notably , the mdtA mutant induced many root bumps on C08 but not on W05 ( S7 Fig ) and the mdt operon is present in CCBAU45436 but not in CCBAU25509 ( i . e . it is in subset IV ) . Both znu and mdt operons are located on the chromosome .
The transferable symbiosis island or symbiosis plasmid is the major reason for an ever increasing collection of rhizobial germplasm associated with diverse legumes [8–11 , 37] . The increased contribution of genes on symbiosis plasmids and dramatically reduced contribution of chromosomal genes to the transcriptomes of nitrogen-fixing bacteroids within nodules were observed for both of the S . fredii strains in this study ( Fig 3 and S2 Fig ) and in previous transcriptomic studies of S . meliloti 1021 and S . fredii NGR234 [24 , 38 , 39] . Notably , genes on the symbiosis plasmids of CCBAU45436 and CCBAU25509 that were highly expressed ( Level_4 ) in nodules included genes belonging to pangenome subsets I-IV ( Fig 3 and S2 Fig ) . These findings support a model that the symbiosis plasmid harbors genes of different conservation levels that contribute to symbiotic adaptation . However , a higher level of between-replicon connectivity than within-replicon connectivity was observed for symbiosis plasmids in the co-expression networks ( Fig 7B and S4 Fig ) . Key genes involved in nitrogen fixation ( nif/fix ) have a considerable degree of both within- and between-replicon gene connectivity ( S5 Fig ) . Genes involved in inorganic ion transport and metabolism ( COG category P ) , and those belonging to the COG category X ( mobilome: prophages , transposons ) were found to be up-regulated within nodules ( Fig 5D ) . Indeed , some transporters provide elements ( such as iron , molybdenum , and sulfur ) essential for nitrogenase activity [25 , 40 , 41] . The high-affinity transporters for phosphate and zinc were required by S . fredii to effectively fix nitrogen in soybean nodules [25] . Genes encoding these transporters , and many of those directly involved in nitrogen-fixation , such as nifH/D/K , belong to COG category P . The activation of mobile elements under symbiotic conditions has been widely observed in many transcriptome analyses [24 , 28 , 42] , and was recently found to have an important role in the adaptive evolution of rhizobial symbiotic compatibility [17] . The conserved znu and accessory mdt of CCBAU45436 contributed to symbiotic adaptation to G . max C08 , but to a lesser extent to the symbiosis with G . soja W05 ( Table 1 and S7 Fig ) . The zinc transporter encoded by znu can import zinc under low-zinc conditions [43 , 44] . This indicates possibly different nodule environments of W05 and C08 with respect to the zinc ion concentration . Although the mdtA mutant did not induce pseudonodules ( root bumps ) on W05 ( S7 Fig ) , mdt contributed to the symbiotic efficiency of CCBAU45436 on W05 ( Table 1 ) . A reasonable explanation might be that genes other than mdt have been recruited by CCBAU25509 to optimize its symbiosis with W05 . This view is supported by our recent finding that strain-specific accessory genes can be recruited by different Sinorhizobium strains in optimization of symbiosis with the same legume host [27] . Since both znu and mdt are located on the chromosome , this suggests that chromosomal core and accessory genes can be recruited by S . fredii to optimize the symbiotic functions in a host-dependent manner . These results increase our understanding of the integration of key symbiosis genes with the diverse genomic backgrounds of rhizobia as characterized by their large phylogenetic diversity [31 , 32] . Co-expression analysis of the two S . fredii strains under different conditions unveiled a higher level of gene connectivity between chromids and chromosomes than that between symbiosis plasmids and chromosomes ( Fig 7B and S4 Fig ) . This is in line with the computational prediction of the regulatory network in S . meliloti , i . e . the preference for cross-regulation between the chromosome and chromid , as opposed to the symbiosis plasmid [45] . A recent study of the S . meliloti metabolome revealed that removal of the chromid has a larger effect on the metabolome than loss of the symbiosis plasmid [46] . These findings support the hypothesis of the ancient integration of chromid functions with those on the chromosome [13] . Indeed , some essential genes can be found on the chromid , but not on the symbiosis plasmid , of Sinorhizobium strains [16 , 47 , 48] . Moreover , in contrast to genes on symbiosis plasmids , chromid core genes are more likely to have a congruent phylogeny with that of the species tree of Sinorhizobium [21] . It was reported that chromids contribute to the intraspecies differentiation of S . meliloti strains [22] . This is in line with the enrichment of strain-specific genes ( subset IV ) on chromids of the two S . fredii strains . Here we reveal that the chromid gene pool also makes a significant contribution to inter-species differentiation in Sinorhizobium , as approximately 38 . 7% of the subset II are located on the chromids of S . fredii . When the transcriptional profiles of single-copy genes were compared between CCBAU45436 and CCBAU25509 , DEGs were significantly enriched on chromids under all test conditions ( Fig 6B ) . It has been demonstrated in Escherichia coli that strain-dependent DEGs were more polymorphic or divergent than other genes , indicating the role of differential gene regulation in bacterial diversification [49 , 50] . These findings indicate that the expression pattern of genes on chromids may evolve relatively rapidly , which echoes a report that genes evolve faster on chromids than on chromosomes [51] . Those genes up-regulated at stationary phase were enriched on chromids of the two S . fredii strains , and were over represented with genes of unknown function and those involved in modifying extracellular structures , indicating a role of chromids in stress adaptation . Notably , the average level of gene connectivity for chromid genes was generally lower than that for those from chromosome and symbiosis plasmids under test conditions ( Fig 7A and S4 Fig ) . This may be due to a critical role of chromids in intra- and inter-species diversification and in adaptation to more diverse niches [15 , 16] that were not effectively covered in this study . In line with this view , the chromid of S . meliloti was enriched with genes that were up-regulated under osmotic stress conditions [52] . Moreover , genetic and metabolic modelling studies show that the chromosome alone is sufficient for the growth of S . meliloti in sterile soil , while the chromid may confer more specialized functions in the rhizosphere [15 , 16] . Likewise , among six extrachromosomal replicons including the symbiosis plasmid pRL10 of R . leguminosarum Rlv3841 , many genes of pRL8 are specifically up-regulated in the rhizosphere of pea , but not in that of alfalfa and sugar beet [14] , indicating a contribution by pRL8 to host-specific fitness . Therefore , in addition to the well-known symbiosis plasmid essential for symbiotic adaptation , extra-chromosomal replicons including chromids may offer rhizobia novel adaptations that are needed in soils and rhizospheres characterized by highly fluctuating levels of nutrients and stress factors . The transcriptional profiles of pangenome subsets I-III exhibited a strong condition-dependent clustering pattern ( Fig 4A–4C ) rather than a strain-dependent one as observed for the subset IV ( Fig 4D ) . These results are consistent with the recent comparative transcriptomic analyses of E . coli strains under free-living conditions , which revealed that the gene expression distances of core genes between strains were mainly dependent on the culture conditions rather than phylogenetic relatedness [50] , though a later independent study also identified a large number of strain-dependent transcripts in addition to condition-dependent ones [49] . Distinct characteristics of test conditions among different studies may exert variable strength of influence on clustering patterns . Earlier transcriptomic studies of E . coli strains under free-living conditions revealed a positive correlation between ortholog frequency ( % E . coli genomes exhibiting gene ) and expression level [50] . In our study , the average expression level of a gene under each test condition ( free-living or symbiotic ) is positively related to its conservation level in four hierarchical subsets of the S . fredii pangenome ( Fig 4E ) from strain-specific to genus core . The most recently acquired genes , such as those of subset IV , showed the lowest variation in expression levels between different conditions , whereas the more conserved subsets III , II and I exhibited increasing expression plasticity ( Fig 4F ) . Moreover , the more conserved a gene is , the higher its level of gene connectivity in the co-expression network ( Fig 7A and S4 Fig ) . These findings highlight that transcriptional regulation contributes to the development of the more conserved pangenome subsets , and the newer pangenome members are less intensively integrated with the core regulation network involved in environmental adaptations . It has been hypothesized that the prokaryotic pangenome mainly results from adaptive , not neutral , evolution [4] , and this appears to be true at least for the subsets I-III of the S . fredii pangenome . For those newly acquired genes with few interaction partners in the pangenome , earlier bioinformatics analysis suggests that they may take many million years to be integrated into regulatory interaction networks [53] . Prokaryotic core and accessory genome components are analogous to the operating system and applications ( apps ) of smartphone [54] . This work provides further evidence of the organization , regulation and integration of apps with the operating system in the prokaryotic multipartite genome of S . fredii . We demonstrated that the average level of gene expression , the variation of gene expression between environments , and the gene connectivity degree within co-expression networks are positively related to the conservation level of a gene . There are replicon biases in genes of different conservation levels , in genes up-regulated under specific conditions , and in the connectivity of genes within co-expression networks . Moreover , chromosomal loci znu and mdt operons were identified as novel players in host-specific adaptations , which are generally thought to be the domain of the symbiosis plasmid . These findings shed new light on our understanding of the coordinated regulation of core and accessory genes of rhizobia , facultative microsymbionts of legumes . Similar strategy can be used to study other prokaryotes , which are subject to diverse stimuli in the ever-changing circumstances .
S . fredii strains were cultured at 28°C in tryptone-yeast extract ( TY ) medium [55] , and E . coli strains at 37°C in Luria-Bertani ( LB ) medium . When required , the media were supplemented with the appropriate antibiotics at final concentrations of 30 μg/ml for nalidixic acid , 10 μg/ml for trimethoprim , 10 μg/ml for tetracycline , 50 μg/ml for kanamycin , and 30 μg/ml for gentamicin . Plant growth and inoculation was performed according to the method previously described [25] . Seeds of G . max C08 were surface-sterilized by successive treatments with 95% ethanol for 30 sec and 3% ( w/v ) NaClO for 5 min , and were then washed 6 times by autoclaved deionized water . For seeds of G . soja W05 , a pre-treating step in concentrated sulfuric acid for 2 min was needed before the surface-sterilization . The surface-sterilized seeds were germinated on 0 . 6% agar plates in the dark at 28°C for 36–48 hours . Then , germinated seeds were planted in vermiculite wetted with low-N nutrient solution in Leonard jars [56] and were inoculated with 1 ml of physiological saline suspension ( OD600 = 0 . 2 ) of rhizobia per plant . Plants were grown at 24°C with 12-h day and night cycles for 30 days . Nodules for bacteroid isolation or RNA extraction were harvested , immediately frozen in liquid nitrogen , and then stored at -80°C until use . Illumina paired-end sequences have been previously obtained for the genomes of S . fredii strains CCBAU45436 and CCBAU25509 [26] . In this study , PacBio and Ion Torrent sequencing technologies were used to get sequences of larger genomic libraries of these two strains ( S1 Table ) . Error correction and a hybrid model were used to perform genome assembly by Celera Assembler V8 . 3 [57] . Sanger sequencing of PCR products was then used to close sequence gaps . Gene prediction and functional annotation were performed by RAST [58] and Blast2GO [59] . In this study , twelve Sinorhizobium genomes , spanning five S . fredii strains ( CCBAU45436 , CCBAU25509 , HH103 , NGR234 and USDA257 ) , six S . meliloti strains ( Rm1021 , AK83 , BL225C , GR4 , Rm41 and SM11 ) and one S . medicae strain ( WSM419 ) , were used for comparative genomics analyses . Protein sequences encoded by these genomes were collected and clustered by CD-HIT [60] to generate a ( 0 , 1 ) -matrix describing the distribution of all gene orthologs ( >70% identity over at least 80% of the length of the smallest protein ) in the pangenome of twelve Sinorhizobium strains . Based on this matrix , the core and accessory genomes of S . fredii CCBAU45436 and CCBAU25509 were defined at three different levels: between CCBAU45436 and CCBAU25509 , among S . fredii strains , and among Sinorhizobium strains ( S1 Fig ) . Using this information , the genomes of S . fredii CCBAU45436 and CCBAU25509 were divided into four hierarchical core/accessory subsets ( Fig 2A ) . Free-living bacterial cultures in TY medium at mid-log phase ( OD600 = 0 . 6 ) and stationary phase ( OD600 = 4 . 5 ) were harvested by centrifugation at 4°C and 12 , 000 rpm for 10 min . Bacterial RNA extraction was performed using RNApure Bacteria Kit ( CWBIO ) according to the manufacturer’s recommendation . Bacteroids were isolated from nodules using a method described earlier [24] and ground in liquid nitrogen before RNA extraction . Total RNA from nodules ( a mixture of plant and bacterial RNA ) induced by CCBAU45436 was also extracted using the TAKARA RNAiso plus reagent . Strand-specific RNA sequencing was carried out by BGI-Shenzhen with Next Generation Sequencing ( NGS ) . In brief , the integrity and quality of all RNA samples were checked with Agilent Bioanalyzer 2100 ( Agilent Technologies ) . Genomic DNA contamination was removed by DNase I digestion ( 30 min at 37°C ) . Total RNA was then treated with the Ribo-Zero rRNA removal kit to remove the ribosomal RNA . The ribosomal RNA-depleted samples were then used to construct whole transcriptome libraries following the manufacturer's instructions ( Illumina ) and the resultant products were sequenced on an Illumina Hiseq 2000 platform ( Illumina ) . Two independent cultures and two sets of nodules were used to prepare RNA samples . Clean reads in fastq files were mapped to the reference genomes of S . fredii CCBAU45436 or CCBAU25509 using Bowtie2 ( default parameters ) [61] . Summary statistics for the clean reads data and mapping results are shown in S2 Table . The number of mapped reads for each protein-coding gene was extracted from sorted bam files by HTseq-count ( -a 0 ) [62] . DESeq2 was used to identify DEGs ( Log2R > 1 . 732 or 1 , FDR < 0 . 001 ) using raw counts data as input [63] . Strain-specific genes and multi-copy genes shared by S . fredii CCBAU45436 and CCBAU25509 were omitted when calling DEGs between these two strains . The expression plasticity of a gene was defined as the variance of the Log2-transformed RPKM values of this gene across all transcriptomes of each tested strain . Dendrograms of samples were built from normalized RPKM data using the dendextend package in R [64] , where multi-copy genes were excluded while the values of strain-unique genes were set to zero for the strain that lacked them . This RPKM dataset were first Log2-transformed before calculating Euclidean distance between each sample pair and the final hierarchical clustering ( hclust , method = average ) . This RPKM dataset , after the removal of strain-unique genes and the addition of nifHDK-1 genes , was also used for a weighted and signed gene co-expression network analysis by using R statistical package WGCNA [65] . The numbers of co-expressed gene pairs with a Pearson’s correlation coefficient ( r ) above 0 . 8 were counted to calculate gene degree values . Condition-dependent co-expression groups were divided by k-means clustering of the RPKM dataset of each test strain by using Gene Cluster 3 . 0 [66] . The two independent RPKM datasets were Log2-transformed , filtered ( at least two observations > 4 and Max-Min > 1 ) and centered by gene , respectively , before the final clustering ( k = 3 ) . Strains , plasmids and primers used in this study are listed in S7 Table . The schematic diagrams illustrating the construction of the mutants of representative differentially expressed genes are shown in S6 Fig . In brief , the internal DNA fragments of target genes , which could serve as homology arms for exchanging , were amplified by PCR amplification and each cloned into pVO155 , a plasmid used for gene inactivation via site-specific insertion [67] . The resulting pVO155 derivatives were then conjugated into S . fredii CCBAU45436 and insertion mutants were screened on the TY-agar plates supplied with 30 μg/ml nalidixic acid and 50 μg/ml kanamycin and verified by colony PCR and Sanger sequencing . All enrichment analyses used in this study were performed by using the Pearson’s chi-square test , and the Benjamini-Hochberg FDR controlling procedure was used for P-value correction in multiple comparisons . Correlations were determined with the cor . test R command using the nonparametric Kendall’s s statistic . Two-tailed Student’s t-test was used to compare the symbiotic phenotypes between the wild type and mutant strains . | Prokaryotic pangenomes are characterized by a high rate of turnover in gene content , with core genes shared by all members of a taxonomic group and accessory genes present in only a subset of the members . Accessory functions could serve as an arsenal enabling prokaryotes to develop adaptations to new niches . Therefore , prokaryotic core and accessory components are analogous to the operating system and applications ( apps ) of smartphones . However , it is puzzling how these accessory functions are linked with the core regulatory network in prokaryotes during niche adaptations . Here we address this question by investigating the adaptive regulation of hierarchical core/accessory subsets in the multipartite pangenome ( chromosome , chromid and plasmid ) of Sinorhizobium fredii , which is a facultative microsymbiont of soybeans . The level and variation of gene expression , and gene connectivity revealed in transcriptomes under free-living and symbiotic conditions are positively correlated with the gene conservation level , i . e . from strain-specific accessory genes to genus core . Replicon-dependent organization and adaptive regulation of hierarchical core/accessory subsets suggest distinct roles of different replicons not only in environmental adaptation but also intra- and inter-species differentiation . Among core and accessory genes with host-specific upregulation patterns , we experimentally identified novel symbiotic players involved in host-specific adaptation . | [
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... | 2018 | Coordinated regulation of core and accessory genes in the multipartite genome of Sinorhizobium fredii |
We performed a genome-scale chromatin immunoprecipitation ( ChIP ) -chip comparison of two modifications ( trimethylation of lysine 9 [H3me3K9] and trimethylation of lysine 27 [H3me3K27] ) of histone H3 in Ntera2 testicular carcinoma cells and in three different anatomical sources of primary human fibroblasts . We found that in each of the cell types the two modifications were differentially enriched at the promoters of the two largest classes of transcription factors . Specifically , zinc finger ( ZNF ) genes were bound by H3me3K9 and homeobox genes were bound by H3me3K27 . We have previously shown that the Polycomb repressive complex 2 is responsible for mediating trimethylation of lysine 27 of histone H3 in human cancer cells . In contrast , there is little overlap between H3me3K9 targets and components of the Polycomb repressive complex 2 , suggesting that a different histone methyltransferase is responsible for the H3me3K9 modification . Previous studies have shown that SETDB1 can trimethylate H3 on lysine 9 , using in vitro or artificial tethering assays . SETDB1 is thought to be recruited to chromatin by complexes containing the KAP1 corepressor . To determine if a KAP1-containing complex mediates trimethylation of the identified H3me3K9 targets , we performed ChIP-chip assays and identified KAP1 target genes using human 5-kb promoter arrays . We found that a large number of genes of ZNF transcription factors were bound by both KAP1 and H3me3K9 in normal and cancer cells . To expand our studies of KAP1 , we next performed a complete genomic analysis of KAP1 binding using a 38-array tiling set , identifying ~7 , 000 KAP1 binding sites . The identified KAP1 targets were highly enriched for C2H2 ZNFs , especially those containing Krüppel-associated box ( KRAB ) domains . Interestingly , although most KAP1 binding sites were within core promoter regions , the binding sites near ZNF genes were greatly enriched within transcribed regions of the target genes . Because KAP1 is recruited to the DNA via interaction with KRAB-ZNF proteins , we suggest that expression of KRAB-ZNF genes may be controlled via an auto-regulatory mechanism involving KAP1 .
Certain modifications of the core histones have been associated with either active or inactive gene expression . For example , acetylation of histone H3 on lysines 9 and 14 is associated with regions of the chromatin that are undergoing transcription in that particular cell type [1–4] . Although histone H3 methylation can be associated with active chromatin ( e . g . , methylation of lysines 4 , 36 , and 79 ) , methylation of lysines 9 or 27 ( H3me3K9 or H3me3K27 , respectively ) is often found in regions of silenced chromatin [5–11] . Histone acetylation is a dynamic mark , being controlled by the counteracting effects of histone acetyltransferases and deacetylases , providing a means of rapidly altering transcription of a particular gene in response to changes in environmental signals or position in the cell cycle [12] . In contrast , histone methylation is generally believed to be a more stable mark , suggesting that this modification may be more useful for conferring long-term gene repression , such as that needed for the permanent repression of tissue-specific genes in differentiated tissues . However , recent studies have indicated that members of the Jumonji protein family can demethylate lysine 9 [13–16] . These studies have , in general , examined the effect of Jumonji proteins on global levels of H3me3K9 ( e . g . , using western blots and/or by fluorescent microscopy ) or on H3me3K9 at repetitive elements [15] , rather than the H3me3K9 bound to individual genes . Therefore , the role of the Jumonji proteins in gene regulation is still unclear . To date , no histone demethylases that target H3me3K27 have been reported . The technique of chromatin immunoprecipitation ( ChIP ) has been used to demonstrate the presence of H3me3K9 or H3me3K27 at specific human loci [17–20] . Promoter regions bound by H3me3K27 have been identified in both normal and cancer cells [8 , 21 , 22] . In general , genes whose promoters are bound by H3me3K27 are expressed at very low levels . In contrast , few studies have shown localization of H3me3K9 to promoter regions . Instead , H3me3K9 has been found at repetitive elements [6] , leading to the hypothesis that it is involved in repression . However , one study did show that an unspecified form of histone H3 methylated K9 is associated with RB1-mediated repression of a mammalian promoter [23] and other studies have shown association of binding of H3me3K9 with transcriptional repression of a promoter region after artificially tethering of a factor that can recruit a histone methytransferase [24 , 25] . Perhaps the best examples for association of transcriptional repression of an endogenous gene with the presence of H3me3K9 comes from an analysis of the POU5F1 promoter during differentiation [20] and of ASCL2 after knockdown of a Jumonji family member [13] . In contrast , others have found that H3me3K9 associates with promoters that are bound by the RNA polymerase II complex [8] or with actively transcribed genes [17 , 19 , 26] . For example , one study found high levels of H3me3K9 over the highly transcribed gamma globin locus [26] and a second study found that H3me3K9 was localized to the coding region of several active genes [17] . Clearly , a comprehensive comparison of H3me3K9 and H3me3K27 binding sites in human cells is needed to provide insight into the relative roles of these two modifications in gene expression . We have now compared the binding patterns of H3me3K9 and H3me3K27 at ~26 , 000 human promoters in four different cell populations , identifying thousands of promoters bound by each type of modified histone . Our studies indicate that the two marks segregate differentially with the two most common types of transcriptional regulators . We have also shown that many of the promoters bound by H3me3K9 are also bound by the corepressor KAP1 ( also known as TIF1B or TRIM28 ) . Finally , we present a genome-wide screen and characterization of KAP1 target genes in Ntera2 cells . Our results suggest that Krüppel-associated box ( KRAB ) -zinc finger ( ZNF ) transcription factors participate in an autoregulatory loop involving the KAP1 protein and trimethylation of histone H3 on lysine 9 .
We used ChIP-chip assays with high-density oligonucleotide arrays to analyze the binding patterns of H3me3K9 and H3me3K27 through 5 kb of 26 , 000 human promoters ( see Table S1 for a list of all arrays used in this study ) . We began by using an antibody that specifically recognizes histone H3 only when it is trimethylated on lysine 9 and an antibody that recognizes histone H3 only when it is trimethylated on lysine 27 . We performed ChIP-chip assays using Ntera2 cells and obtained ranked lists using the Maxfour program , which ranks each promoter region based on the average of the intensities of the four consecutive probes ( of the 50 that represent each promoter ) that have the highest enrichment values ( Bieda et al . , manuscript in preparation ) . We found that the sets of target genes that are bound by H3me3K9 and H3me3K27 are essentially mutually exclusive ( Figure 1A ) . To determine which specific types of target genes were selectively silenced by the different histone modifications , we compared the promoters that were in the list of top 2 , 000 H3me3K9 targets and the promoters that were in the list of top 2 , 000 H3me3K27 targets using the program DAVID ( http://david . abcc . ncifcrf . gov ) . This program allows a functional classification of a set of genes based on Gene Ontology descriptions . In addition to classifying genes into different sets , the program provides a p-value that indicates the probability that the set of genes was identified by chance based on the number of genes in the genome that fall into that particular category . We found that although the sets of promoters bound by H3me3K9 and H3me3K27 were different , both histone modifications were specifically enriched at the promoters of genes involved in transcription ( Table 1; see also Table S2 for a more detailed list of the significantly enriched gene categories ) . Interestingly , the two histone modifications targeted two distinctly different classes of transcription factor genes . Similar to previous studies [8 , 21 , 27] , the set of developmentally related homeobox transcription factors were the most highly enriched class of transcription factor genes bound by H3me3K27 ( Figure 1B ) . In contrast , homeobox genes were not bound by H3me3K9 . Instead , H3me3K9 bound specifically to ZNF transcription factors . Examples of enrichment of H3me3K27 at a homeobox gene cluster and of H3me3K9 at a ZNF gene cluster can be seen in Figure 2 . To determine if the selective binding of H3me3K27 and H3me3K9 to the promoters of different families of transcription is commonly observed , we performed ChIP-chip experiments using antibodies to H3me3K9 and H3me3K27 with primary cultures of normal fetal lung fibroblasts , adult foot fibroblasts , and newborn foreskin fibroblasts . As shown in Figure 1 , analysis of 26 , 000 promoters revealed that ZNF genes were enriched in the H3me3K9 targets while homeobox genes were enriched in the H3me3K27 targets in all four cell lines ( see also Table S2 for a list of the significantly enriched gene categories for the fibroblast studies ) . We have previously reported that components of the Polycomb repressive complex 2 colocalize with H3me3K27 in F9 cells and mouse embryonic stem cells [8] . However , due to the fact that H3me3K9 and H3me3K27 are bound to different sets of genes , it is likely that a different repression complex is responsible for the H3me3K9 modifications . Several different proteins can methylate histone H3 on lysine 9 [28 , 29] . A hallmark of such proteins is the presence of a 130-aa Su ( var ) , Enhancer of zeste , Trithorax ( SET ) domain . There are numerous proteins encoded in the human genome that contain SET domains , several of which ( e . g . , G9a , SUV39H1/2 , EHMT1 , and SETDB1 ) have been recently characterized as functional histone methyltransferases . The different biological roles played by the different histone methylases is not yet clear . However , SUV39H1/2 is thought to be responsible for methylation in pericentric heterochromatin , whereas G9a may be involved in methylation in euchromatin regions [28 , 30] . None of the histone methyltransferases contain DNA binding motifs and thus must be brought to the chromatin via interaction with other proteins . It has been proposed that complexes that mediate methylation of H3me3K9 may be recruited to promoters via E2F family members . For example , SUV39H1 may be recruited to promoters by RB1 [31] and EHMT1 may be recruited to promoters by E2F6 [32] . A recent study has suggested that EHMT2 can be recruited to the DNA via interaction with an orphan nuclear receptor called NR0B2 [33] . SETDB1 , another protein that has a SET homology domain [34] , may be responsible for the H3K9 methylation that is maintained in the SUV39H1/2 double knockout mouse [35] . It is thought that SETDB1 is brought to the DNA in a complex with a corepressor called KAP1 [34 , 36] . For example , artificially tethering KAP1 to chromatin can result in gene silencing , methylation of lysine 9 , and recruitment of SETDB1 [24] . This suggests that KAP1 may play a role in performing the methylation of lysine 9 of H3 . However , most studies of KAP1 have used artificial genomic tethers ( such as Gal4KAp1 fusion proteins ) due to the fact that KAP1 target genes have not been identified . Therefore , to address the possibility that KAP1 might colocalize with H3me3K9 marks , we first needed to identify KAP1 target genes . We began by choosing two different antibodies to KAP1 , one rabbit polyclonal and one mouse monoclonal antibody . We reasoned that if the same sites were identified using two different antibodies , then we would have confidence that the KAP1 ChIP experiments were identifying true binding sites . Because we did not know if KAP1 prefers to bind to promoter regions or to regions distant from core promoters , we first applied the amplicons made from the KAP1 ChIP samples to ENCODE arrays ( which represent approximately 1% of the human genome , including ~400 genes; see http://www . genome . gov/10005107 ) . We identified two sites at the highest stringency level of the Tamalpais Peaks program [37] that were bound by KAP1 using both the monoclonal and polyclonal KAP1 antibodies ( Figure 3 ) . Peak identification required that at least six oligomer probes in a row ( spanning 238 bp ) have an enrichment value in the top 2% of all probes on the array ( providing a p-value of p < 0 . 0001 ) . The two identified KAP1 binding sites are located within the transcribed region of IFNAR2 in ENCODE region ENm005 and within the transcribed region of ATP11A in ENCODE region ENr132 . To confirm the ChIP-chip results , we prepared a new set of KAP1 amplicons , as well as H3me3K9 amplicons , and performed PCR analysis using primers specific to the two KAP1 binding sites . As seen in Figure 3B , not only did we confirm binding of KAP1 to these sites , but we also demonstrated that the same sites were bound by H3me3K9 . With confidence that our KAP1 ChIP-chip assays could identify true KAP1 binding sites , we next performed duplicate ChIP experiments using two independent sets of Ntera2 cells and antibodies to KAP1 , SUZ12 , H3me3K9 , and H3me3K27 , prepared amplicons , and performed ChIP-chip experiments using a two-array set of ~26 , 000 human promoters . As described above , target promoters were identified with the Maxfour peak-calling program . A list of high confidence target genes was generated by selecting the top ranked 2 , 000 promoters that were bound in the two independent experiments for each antibody . We then determined how many of the SUZ12 or KAP1 targets were also co-occupied by H3me3K9 and H3me3K27 . This led to a conservative , but high confidence set of co-occupied targets , since each target had to be identified in four out of four arrays . In support of the hypothesis that the modification of H3me3K9 must be accomplished by a complex other than Polycomb repressive complex 2 , we saw no significant overlap between SUZ12 targets and H3me3K9 targets ( Figure 4A ) . However , as expected , we found that many promoters were bound by both SUZ12 and H3me3K27 . We next compared the top 2 , 000 KAP1 targets to the top 2 , 000 H3me3K9 or top 2 , 000 H3me3K27 targets . We found that a fourth of KAP1 targets also carried the histone modification H3me3K9 but only 11% of KAP1 targets were bound by H3me3K27 ( Figure 4B ) . To determine if the promoters that were bound by both KAP1 and H3me3K9 represented a distinct class of genes , we again used the DAVID analysis program . Interestingly , the promoters bound by both KAP1 and H3me3K9 in Ntera2 cells were highly enriched for ZNF transcription factor genes ( p-value 5E−27 ) . Similarly , ChIP-chip experiments using antibodies to KAP1 and H3me3K9 in primary cultures of foot fibroblasts also revealed that the target genes occupied by both KAP1 and H3me3K9 were highly enriched for ZNF transcription factors ( p-value 2E−32 ) . The significant overlap between KAP1 and H3me3K9 suggests that KAP1 may indeed be functioning as a corepressor in a complex that mediates methylation of lysine 9 of H3 . To test this hypothesis , we examined the expression level of different classes of KAP1 and H3me3K9 target genes . To do so , we used NimbleGen expression arrays to analyze RNA levels of the KAP1 target genes in Ntera2 cells , using an average of data obtained from expression arrays probed with RNA isolated from two different cultures of Ntera2 cells . For comparison , we have also analyzed the expression levels of the top 20% of all RNAs on the NimbleGen array . Of the top 2 , 000 KAP1 and top 2 , 000 H3me3K9 target genes , 1 , 952 and 1 , 842 , respectively , were represented on the NimbleGen expression array . As can been seen in Figure 5 , the genes whose promoters are bound by KAP1 and/or H3me3K9 are , in general , expressed at low levels . The repression of targets bound by KAP1 and H3me3K9 is particularly evident in the subcategory of ZNF target genes or KRAB-ZNF target genes . Previous studies of human transcription factors have indicated that although some transcription complexes are often bound near core promoter regions [37] , many transcription complexes bind throughout the genome , with perhaps 20%–30% of the detected sites near core promoters [38 , 39] . In particular , we noted that the two KAP1 binding sites identified using ENCODE arrays were located within transcribed regions and not at core promoters . Thus , it is likely that promoter arrays would not identify a complete list of genes regulated by KAP1 . Therefore , we performed a genome-wide ChIP-chip experiment using KAP1 ChIP samples from Ntera2 cells and a set of 38 arrays , which were composed of 50mers spaced about 100 nt apart that represented the entire nonrepetitive portion of the human genome ( see Table S3 for the genomic coordinates on each array ) . We used the Tamalpais peak-calling program ( [37]; see also http://chipanalysis . genomecenter . ucdavis . edu/cgi-bin/tamalpais . cgi ) and identified sites that represented regions spanning at least four probes in a row that were in the top 2% of all probes on the array . Using these criteria , we identified ~7 , 000 KAP1 binding sites in the human genome ( see Table S4 ) . A comparison of the number of binding sites identified on each chromosome using promoter arrays and the genome tiling arrays is shown in Table 2 . Inspection of the chromosomal location of the KAP1 binding sites indicated that , in general , the larger chromosomes contained more KAP1 targets than did the smaller chromosomes . However , there were several cases in which large clusters of KAP1 targets resulted in a higher-than-expected number of targets on a particular chromosome . For example , Chromosomes 7 and 19 were highly enriched for KAP1 targets using the whole-genome arrays ( Table 2 ) . Interestingly , Chromosome 19 contains clusters of ZNF transcription factor genes , with 266 of the approximately 800 total human ZNF genes located mainly within 11 large familial clusters [40] . The clustered binding pattern of KAP1 targets on Chromosome 19 can be seen in Figure 6A . To determine if the KAP1 targets that were identified using the whole-genome array were similar to those identified using the promoter arrays , we analyzed the set of ~7 , 000 KAP1 binding sites using the DAVID program . As expected from the large number of KAP1 binding sites on Chromosomes 19 and 7 , the Gene Ontology analysis indicated that a large number of the genome-wide KAP1 targets are ZNF transcription factors ( Figure 7 ) . Thus , both the promoter arrays and the whole-genome arrays identified ZNF genes as KAP1 targets . The high number of targets on Chromosomes 19 and 7 is not due to large-scale spreading throughout a region , as we have previously shown for SUZ12 and H3me3K27 [8] . Although there are many binding sites on each of these chromosomes , the average KAP1 binding site spans only 820 bp ( Table S4 ) . PCR analysis using a new biological replicate of KAP1 amplicons confirmed binding of KAP1 and H3me3K9 to ZNF genes ( Figure 6B ) . Four of the binding sites analyzed are located within the transcribed region of ZNF genes on Chromosome 19 ( ZNF554 , ZNF426 , ZNF333 , and ZNF433 ) and two binding sites are located in distal regions on Chromosome 7 ( >100kb from the transcription start sites ( TSSs ) of ZNF genes ZNF479 and ZNF679 ) . Having identified a large set of KAP1 targets in a completely unbiased manner , we could now determine the preferred binding location for KAP1 . The distance of KAP1 binding sites to the nearest transcription start site was determined using knownGenes from the University of California Santa Cruz genome browser ( http://genome . ucsc . edu ) . Although we did note that 24% of the KAP1 binding sites were located more than 50 kb upstream or downstream of transcription start sites of known genes , these were not considered in our location analysis; it is possible that many of these KAP1 target sites that appear to be extremely far from transcription start sites are , in fact , near to start sites of as-yet undiscovered genes or transcripts . As shown in Figure 8A , we performed the location analysis within 50 kb upstream or downstream of a known gene for two categories of KAP1 target genes , ZNF target genes and non-ZNF target genes . We found that for the majority of non-ZNF target genes the KAP1 binding site is located in the core promoter region ( defined as 5 kb upstream or downstream of the start site of transcription ) . The remaining binding sites are distributed fairly evenly in the regions between 5 kb and 50 kb upstream or downstream of the start sites . In contrast , the location analysis was distinctly different for the subset of ZNF genes that are KAP1 target genes . It was very striking that many of the KAP1 binding sites associated with ZNF genes are downstream of the start site ( Figure 8A ) . A more detailed analysis of the KAP1 sites that are located downstream of the start sites of ZNF genes revealed that 74% were located within the transcribed regions . Interestingly , the majority of these KAP1 binding sites were found towards the 3′ end of the ZNF gene ( Figure 8B ) .
Unexpectedly , we have shown that the major targets of KAP1-mediated repression are ZNF genes themselves . For example , in Ntera2 cells KAP1 binds to 60% ( 212 of 355 ) of all KRAB-ZNF genes in the genome . This suggests that KAP1 represses transcription of many KRAB-ZNF genes due to its recruitment to their transcribed regions by interaction with the few KRAB-ZNF proteins that are expressed in a cell ( Figure 9 ) . An analysis of the expression level of the different KRAB-ZNF proteins in Ntera2 cells confirmed that the majority of KRAB-ZNF genes—such as the KAP1 target genes ZNF426 , ZNF333 , and ZNF554—are not expressed . However , a few KRAB-ZNF genes are not bound by KAP1 in Ntera2 cells and are highly transcribed , suggesting that they may play a role in the recruitment of KAP1 to the other KRAB-ZNF target genes . Thus , our studies support an autoregulatory model in which KAP1 represses the expression of hundreds of ZNF genes due to its recruitment to chromatin by a small set of ZNF proteins that are expressed in a particular cell . As indicated above , we have shown that the most highly enriched class of target genes for KAP1 is ZNF genes , many of which encode KRAB-ZNF proteins . Interestingly , we find a difference in the location of KAP1 binding sites in the ZNF genes versus other targets . In accordance with our identification of a large set of KAP1 targets using promoter arrays , we find that thousands of the KAP1 targets identified on the whole-genome tiling arrays show localization at the promoter region ( defined as 5 kb upstream or downstream of the start site ) . In contrast , the KAP1 binding sites associated with ZNF genes are predominantly localized within transcribed gene regions , near the 3′ end of the gene . This suggests that the recruitment of KAP1 to the ZNF genes and/or the function of KAP1 in regulating expression of ZNF genes might be different than for other KAP1 targets . The other ~200 DNA binding transcription factors that are KAP1 targets but are not ZNFs show a promoter-localized KAP1 binding pattern ( unpublished data ) . Thus , the 3′ end-localized KAP1 binding pattern is unique to ZNF transcription factors . A recent study using DamID , rather than ChIP , found that 37% of CBX1 ( HP1-BETA ) targets and 48% of the SUV39H1 targets correspond to KRAB-ZNF genes on Chromosome 19 [44] . CBX1 is thought to be able to recognize methylated lysine 9 . In addition , it has been shown that KAP1 interacts with CBX1 [45] and that this interaction is required for the KAP1 corepressor activity [24] . Thus , our finding that KAP1 plus H3me3K9 binds to KRAB-ZNF genes fits well with the previous study showing that CBX1 plus SUV39H1 bind to this same class of genes . However , Vogel et al . [44] found that CBX1 coats large domains of Chromosome 19 , ranging up to 4 MB , whereas we saw very limited spreading of KAP1 . It is possible that KAP1 binding within the transcribed region of KRAB-ZNF genes remains localized but recruitment of the methyltransferase results in spreading of the H3me3K9 mark . It is also possible that differences in chromatin binding patterns could be due to differences in the cell types used in the two experiments . Our genomic tiling arrays were performed using Ntera2 testicular carincoma cells whereas the CBX1 study used MCF7 breast cancer cells . We have proposed that the general repression of KRAB-ZNF genes is accomplished by recruitment of the KAP1 corepressor to their transcribed regions via interaction with one or more of the KRAB-ZNF proteins that is highly expressed in that particular cell type . It is likely that either simultaneously with or subsequent to KAP1 recruitment a histone methyltransferase such as SUV39H1 or SETDB1 associates with KAP1 , resulting in trimethylation of histone H3 at lysine 9 . In support of this hypothesis , we have preliminary evidence that reduction of the levels of KAP1 in human 293 cells by stable expression of small interfering RNAs results in a reduction of the levels of H3me3K9 at KAP1 binding sites ( Figure S1 ) . Due to the fact that we can identify KAP1 bound promoters that are not bound by H3meK9 , we suggest that the recruitment of the histone methyltransferase is a step subsequent to KAP1 binding to the chromatin . This hypothesis is also supported by the finding that forced expression of KAP1 leads to the subsequent trimethylation of lysine 9 of histone H3 of a chromatinized reporter gene [24] . It is also possible that a histone demethylase is associated with the promoters that are bound by KAP1 but not H3me3K9 [13 , 14]; future studies will be focused on this aspect of the model . In addition , studies are in progress to identify the specific KRAB-ZNFs responsible for 3′ end localized autoregulatory repression of the KRAB-ZNF family in different types of human cells .
Ntera2 and HEK293 cells were grown in Dulbecco's Modified Eagle Medium supplemented with 10% FBS , 2mM glutamine , and 1% penicillin/streptomycin . The stable KAP1 knockdown cell line K928-cl10 was grown as above , with the addition of 10ug/ml puromycin [24] . Human fibroblast cultures were propagated in Dulbecco's Modified Eagle Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin until 80% confluent . Fibroblasts were then synchronized in G0 by the addition of media containing only 0 . 1% fetal bovine serum for 48 h [46] . All cells were incubated at 37 °C in a humidified 5% CO2 incubator . ChIP assays ( 1 × 107 cells/assay ) were performed following the protocol provided at http://genomics . ucdavis . edu/farnham and http://genomecenter . ucdavis . edu/expression_analysis . The primary antibodies used in this study were as follows: rabbit polyclonal KAP1 IgG ( ab10483; Abcam , http://www . abcam . com ) , mouse monoclonal KAP1 IgG ( ab22553; Abcam ) , two different rabbit polyclonal H3me3K9 IgGs ( ab1186 and ab1186; Abcam ) , rabbit polyclonal H3me3K27 IgG ( 07–449; Upstate/Millipore , http://www . upstate . com ) , and rabbit polyclonal SUZ12 IgG ( ab12201; Abcam ) . The secondary rabbit anti-mouse IgG was purchased from MP Biomedicals ( 55436; http://www . mpbio . com ) . The nonspecific rabbit IgG used as a negative control in the ChIP assays was purchased from Alpha Diagnostic International ( 20009–5; http://www . 4adi . com ) . For PCR analysis of the ChIP samples prior to amplicon generation , QIAquick-purified ( Qiagen , http://www1 . qiagen . com ) immunoprecipitates were dissolved in 50 μl of water . Standard PCR reactions using 2 μl of the immunoprecipitated DNA were performed . PCR products were separated by electrophoresis through 1 . 5% agarose gels and visualized using ethidium bromide . Amplicons were prepared by adapting the standard protocol for whole-genome amplification using the Sigma GenomePlex WGA kit ( http://www . sigmaaldrich . com ) as described in O'Geen et al . [47] . Briefly , the initial random fragmentation step was eliminated and DNA from an entire ChIP sample or from 10 ng of total chromatin was amplified . This usually provides enough sample for one array hybridization . However , amplicons for the whole-genome tiling array set ( 38 arrays ) were prepared from ten pooled ChIP samples . A detailed protocol for the WGA method is provided at http://genomics . ucdavis . edu/farnham and http://genomecenter . ucdavis . edu/expression_analysis . Amplicons were applied either to ENCODE arrays , 5-kb promoter arrays or to the human genome tiling array set consisting of 38 arrays ( see http://www . nimblegen . com for details ) . The labeling and hybridization of DNA samples for ChIP-chip analysis was performed by NimbleGen Systems , except that ENCODE arrays were hybridized at University of California Davis . Briefly , each DNA sample ( 1 μg ) was denatured in the presence of 5′-Cy3- or 5′-Cy5-labeled random nonamers ( TriLink Biotechnologies , http://www . trilinkbiotech . com ) and incubated with 100 units ( exo- ) Klenow fragment ( NEB , http://www . neb . com ) and dNTP mix [6 mM each in TE buffer ( 10 mM Tris/1 mM EDTA , pH 7 . 4; Invitrogen , http://www . invitrogen . com ) ] for 2 h at 37 °C . Reactions were terminated by addition of 0 . 5 M EDTA ( pH 8 . 0 ) , precipitated with isopropanol , and resuspended in water . Then , 13 μg of the Cy5-labeled ChIP sample and 13 μg of the Cy3-labeled total sample were mixed , dried down , and resuspended in 40 μl of NimbleGen Hybridization Buffer ( NimbleGen Systems ) plus 1 . 5 μg of human COT1 DNA . After denaturation , hybridization was carried out in a MAUI Hybridization System ( BioMicro Systems , http://www . biomicro . com ) for 18 h at 42 °C . The arrays were washed using NimbleGen Wash Buffer System ( NimbleGen Systems ) , dried by centrifugation , and scanned at 5-μm resolution using the GenePix 4000B scanner ( Axon Instruments , http://www . axon . com ) . Fluorescence intensity raw data were obtained from scanned images of the oligonucleotide tiling arrays using NIMBLESCAN 2 . 0 extraction software ( NimbleGen Systems ) . For each spot on the array , log2-ratios of the Cy5-labeled test sample versus the Cy3-labeled reference sample were calculated . Then , the biweight mean of this log2 ratio was subtracted from each point; this procedure is approximately equivalent to mean normalization of each channel . Sites bound by KAP1 on the ENCODE arrays were identified using the highest stringency level ( six consecutive probes above the 98th percentile threshold , p < 0 . 0001 ) of the Tamalpais peak-calling algorithm previously described [37]; see also http://genomics . ucdavis . edu/farnham . However , the peak-calling algorithm was adapted slightly to identify KAP1 sites on the whole-genome tiling arrays ( we used four consecutive probes above the 98th percentile threshold , p < 0 . 05 ) . This adjustment was made due to the difference in probe spacing between ENCODE arrays ( 38 bp ) and the whole-genome arrays ( 100 bp ) . A complete list of KAP1 binding sites identified on the whole-genome tiling array is provided as Table S4 . The 5-kb promoter array set consists of two individual arrays ( promoter 1 and promoter 2 ) . Two different designs , HG17 and HG18 , were used in this study; the exact design used for each experiment is indicated in Table S1 . The HG17 promoter array set covers 4 . 2 kb upstream and 800 bp downstream of the TSS , whereas the HG18 promoter array set covers −3 . 5kb upstream and 750 bp downstream of the TSS . Regions on the 5-kb promoter arrays bound by the individual factors were determined using the Maxfour peak-calling method ( Bieda et al . , in preparation ) . Briefly , a value was assigned based on the highest mean of four consecutive probes in each promoter . Promoters were then ranked by their Maxfour values for promoter 1 and promoter 2 separately . The list of the top 2 , 000 targets for a 5-kb promoter array set was then created by combining the 1 , 000 highest ranked promoters from promoter array 1 and the 1 , 000 highest ranked promoters from promoter array 2 . The location analysis of KAP1 binding sites was performed using the knownGene database available at http://genome . ucsc . edu ( HG17; assembly May 2004 ) . Functional annotations were performed using the program Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) 2 . 1 ( http://david . abcc . ncifcrf . gov ) , as previously described [8] . Total RNA was prepared from 5 × 106 Ntera cells using RNAeasy Kit ( Qiagen ) following the manufacturer's instructions . RNA quality was ensured using the Agilent Systems Bioanalyzer ( http://www . agilent . com ) . The RNA was hybridized to human whole-genome expression microarrays from NimbleGen , which contain probes for every human gene based on genome build HG18 from the University of California Santa Cruz database ( more details at http://www . nimblegen . com ) . Total RNA ( 10 μg ) was used to synthesize cDNA using the SuperScript Double-Stranded cDNA synthesis kit ( Invitrogen ) . The labeling of RNA samples , array hybridization and preliminary RNA expression analysis ( data normalization ) was performed by NimbleGen Systems .
The National Center for Biotechnology Information ( NCBI ) Entrez ( http://www . ncbi . nlm . nih . gov/gquery/gquery . fcgi ) accession numbers for the genes discurssed in this paper are KAP1 ( also known as TRIM28 , TIF1B , TF1B , and RNF96 ) , NM_005762 and SUZ12 ( also known as JJAZ1 , KIAA0160 , and CHET9 ) , NM_015355 . | Methylation of lysines 9 or 27 of histone H3 ( H3me3K9 or H3me3K27 , respectively ) has been associated with silenced chromatin . However , a comprehensive comparison of the regions of the genome bound by these two types of modified histone H3 has not been performed . Therefore , we compared the binding patterns of H3me3K9 and H3me3K27 at ~26 , 000 human promoters in four different cell populations . Our studies indicated that the two marks segregate differentially with the two most common types of transcriptional regulators; H3me3K27 is highly enriched at homeobox genes and H3me3K9 is highly enriched at zinc-finger genes ( ZNFs ) . We showed that many of the promoters bound by H3me3K9 are also bound by the corepressor KAP1 . A genome-wide screen for KAP1 target genes revealed a difference in the location of KAP1 binding sites in the ZNF genes versus other targets . In general , KAP1 binding sites were localized to core promoter regions . However , KAP1 binding sites associated with ZNF genes are near the 3′ end of the coding region . Our results suggest that the KRAB-ZNF family members participate in an autoregulatory loop involving binding of the KAP1 protein to the 3′ end of the ZNF target genes , resulting in trimethylation of H3K9 and transcriptional repression . | [
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] | 2007 | Genome-Wide Analysis of KAP1 Binding Suggests Autoregulation of KRAB-ZNFs |
Cdc6p is an essential component of the pre-replicative complex ( pre-RC ) , which binds to DNA replication origins to promote initiation of DNA replication . Only once per cell cycle does DNA replication take place . After initiation , the pre-RC components are disassembled in order to prevent re-replication . It has been shown that the N-terminal region of Cdc6p is targeted for degradation after phosphorylation by Cyclin Dependent Kinase ( CDK ) . Here we show that Mck1p , a yeast homologue of GSK-3 kinase , is also required for Cdc6 degradation through a distinct mechanism . Cdc6 is an unstable protein and is accumulated in the nucleus only during G1 and early S-phase in wild-type cells . In mck1 deletion cells , CDC6p is stabilized and accumulates in the nucleus even in late S phase and mitosis . Overexpression of Mck1p induces rapid Cdc6p degradation in a manner dependent on Threonine-368 , a GSK-3 phosphorylation consensus site , and SCFCDC4 . We show evidence that Mck1p-dependent degradation of Cdc6 is required for prevention of DNA re-replication . Loss of Mck1 activity results in synthetic lethality with other pre-RC mutants previously implicated in re-replication control , and these double mutant strains over-replicate DNA within a single cell cycle . These results suggest that a GSK3 family protein plays an unexpected role in preventing DNA over-replication through Cdc6 degradation in Saccharomyces cerevisiae . We propose that both CDK and Mck1 kinases are required for Cdc6 degradation to ensure a tight control of DNA replication .
To constitute the pre-RC and initiate DNA replication , all six-components of the Origin Recognition Complex ( Orc1-6p ) bind to replication origins followed by Cdc6p , Cdt1p and the Mcm2-7p complex [1] . Then the pre-RC has to be activated by the Dbf kinase-Cdc7p complex , resulting in the formation of a bidirectional replication fork in which the Mcm complex acts as a replicative helicase [1] . Finally , DNA polymerase synthesizes new strands of DNA . The cell cycle progression is driven by the Cyclin/CDK complex . Of the nine cyclins in S . cerevisiae six are B-type cyclins ( Clb1-6 ) [2] and there is a single CDK ( Cdc28 ) . Cdc28-Clb activity is required to initiate DNA replication [3]–[5] . Eukaryotes ensure that DNA is replicated once and only once per cell cycle . There are multiple overlapping mechanisms to prevent re-initiation of DNA replication . Pre-RC components such as Cdc6 , Mcm2–7 , and the ORC complex are phosphorylated by Cyclin/CDK to prevent a second round of DNA replication from occurring before mitosis . Cdc6 is phosphorylated by Cyclin/CDK complex at the N-terminal region and is targeted for ubiquitin-mediated proteolysis in S . cerevisiae [6]–[8] . The MCM complex is translocated to the cytoplasm after phosphorylation by Cdk activity [9] , [10] . Orc2 and Orc6 are also phosphorylated in a CDK-dependent manner [11] , [12] . In addition to these mechanisms , a direct recruitment of the cyclin-CDK complex Clb5p-Cdc28p to the origin of replication is an important component of re-replication control [13] . The Clb5p recruitment to the origin is accomplished by binding of the Clb5p hydrophobic patch substrate-targeting domain [14]–[16] to an Arg-X-Leu ( RXL ) target sequence in the Orc6p subunit of the ORC origin recognition complex [13] . This Clb5 binding to Orc6 after origin licensing serves as a local switch to inhibit DNA re-replication by preventing Cdt1/Mcm2–7 loading onto the origin [17] . The ORC6-rxl mutation strongly synergized with other mutations previously implicated in re-replication control including: N-terminal deletions in Cdc6 which stabilize the protein ( CDC6ΔNT ) [13] , mutations which force nuclear localization of the Mcm complex ( MCM7-NLS ) [11] , and mutations blocking Orc2 ( ORC2-ps ) and Orc6 phosphorylation ( ORC6-ps ) [18] . Such multiple mutant strains strongly over-replicate DNA within a single cell cycle [13] . ORC6-rxl GAL-CDC6ΔNT cells are viable , but show moderate DNA re-replication when incubated in galactose [19] . The cell cycle in the ORC6-rxl GAL-CDC6ΔNT cells arrest at G2/M phase due to DNA damage checkpoint activation [19] . Moderate cell viability in the ORC6-rxl GAL-CDC6ΔNT cells was heavily dependent on DNA damage checkpoint components such as MRE11 gene . Cell viability was reduced and DNA re-replication was enhanced in mre11 ORC6-rxl GAL-CDC6ΔNT cells [19] . It is known that Rad53 is phosphorylated upon DNA damage checkpoint activation . Rad53 was hyperphosphorylated in ORC6-rxl GAL-CDC6ΔNT cells [19] , suggesting that DNA damage was induced . We concluded that DNA re-replication most likely causes double strand breaks which in turn activates the DNA damage checkpoint response [19] . To identify a new component that inhibits DNA re-replication in S . cerevisiae , synthetic genetic array ( SGA analysis ) [20] was performed using an ORC6-rxl strain to eliminate Clb5-Orc6 binding . We found that mck1 deletion cells combined with the ORC6-rxl mutation showed synthetic lethality . The MCK1 gene in S . cerevisiae encodes a serine/threonine protein kinase homologous to mammalian glycogen synthase kinase-3 ( GSK-3 ) [21] . Mammalian GSK-3 was initially identified as an enzyme involved in the control of glycogen metabolism [22] . GSK-3 kinase is highly conserved through evolution and plays an important role in the Wnt signaling pathway in the mammalian system ( for a review , see [23] ) . One of the interesting features of GSK-3 kinase is its role in protein degradation . GSK-3 phosphorylates cyclin D1 to promote its nuclear export and subsequent degradation in the mammalian system [24] . Yeast Mck1p has diverse biological functions . Mck1p stimulates calcineurin signaling [25]–[27] and binds stress-response elements to activate transcription [27] therefore cells lacking Mck1p are hot and cold sensitive [28] . Mck1 is also implicated in mitosis and meiosis . Yeast MCK1 has been isolated as a dosage suppressor of centromere ( CEN ) DNA mutation in CDEIII , suggesting that Mck1 has a role in centromere/kinetochore function [28] . The mck1 mutant exhibits poor sporulation [29] , and sensitivity to benomyl , a microtubule destabilizing drug [28] . Cdc6 levels are regulated by three distinct mechanisms: transcription [30] , ubiquitin-mediated proteolysis [7] , [8] , [31] , [32] and nuclear localization [33] . Here we show that Mck1p has a novel function in inhibition of DNA re-replication by Cdc6p degradation through the GSK-3 consensus site at T368 .
Synthetic genetic array ( SGA analysis ) [20] was performed using ORC6-rxl , to eliminate Clb5-Orc6 binding , in order to identify a new component in the regulation of DNA re-replication in S . cerevisiae . We found that mck1 deletion cells showed synthetic lethality in cells containing the ORC6-rxl mutation . It is interesting that mck1 was the only deletion strain that caused synthetic lethality in the ORC6-rxl cells among 4700 deletion strains tested , and that we did not obtain other GSK-3 orthologs in this screening . Tetrad analysis confirmed the genetic interaction between ORC6-rxl and mck1 deletion strains ( Figure 1A ) . Haploid progenies , which contain both ORC6-rxl and Δmck1 mutations , were not able to grow on YEPD plates whereas single mutants grew fine . We also tested if the mck1 deletion genetically interacts with the other orc mutants such as the Orc6 phosphorylation site mutant ( ORC6-ps ) and the Orc2 phosphorylation site mutant ( ORC2-ps ) . Deletion of MCK1 reduced cell growth in the ORC6-ps cells ( Figure 1A ) . Furthermore , the mck1 deletion caused severe growth defects in the ORC2-ps cells ( Figure 1A ) . Thus , mck1 deletion caused synthetic lethality or semi-lethality with DNA re-replication-prone orc mutants in general . This strongly suggests that Mck1p has a function in DNA replication control . The mck1 deletion strain did not have genetic interactions with other pre-RC mutants such as MCM7-NLS or CDC6ΔNT ( data not shown ) . To investigate the molecular basis of the synthetic lethality between Δmck1 and ORC6-rxl , we generated partial loss of function mutants of mck1 by PCR mutagenesis . Among them , mck1-16 allele exhibited semi-synthetic lethality at high temperature ( 36 degrees ) when combined with ORC6-rxl mutation ( Figure 1B ) . Consistent with this effect being due to the disruption of Clb5-Orc6 protein interaction by the ORC6-rxl mutation , the clb5 mck1-16 cells were also semi-lethal when incubated at 36 degrees ( Figure 1B ) . To analyze the terminal phenotype of the mck1-16 ORC6-rxl strain , cells were incubated either at permissive or non-permissive temperatures and cell cycle profiles were analyzed by flow cytometry analysis . The mck1-16 ORC6-rxl cells showed G2/M arrest after 4 hours incubation at 36 degrees ( Figure 1C , top right ) , with some cells showing a DNA content over 2C ( Figure 1C , arrow ) , suggesting re-replicated DNA . Cell morphologies of the mck1-16 ORC6-rxl mutants were further analyzed . The mck1-16 ORC6-rxl cells incubated at 36 degrees for 4 hours showed large budded cells with a single nuclei visualized by propidium iodide staining of DNA ( Figure 1D ) . This phenotype is reminiscent of cells with DNA re-replication found in our previous report [19] . Nuclear division did not occur in the mck1-16 ORC6-rxl cells . Their cell cycle is arrested during G2 or early mitosis , most likely due to DNA damage checkpoint activated by DNA re-replication . This is similar to our previous observation that mitotic arrest in the ORC6-rxl CDC6ΔNT cells was due to DNA damage [19] . Previously we have shown that the ORC6-rxl mutant causes semi-synthetic lethality with a CDC6ΔNT mutant . The ORC6-rxl CDC6ΔNT cells are arrested during mitosis with moderate DNA re-replication followed by DNA damage . Viability of the ORC6-rxl CDC6ΔNT cells was heavily dependent on an intact DNA damage checkpoint gene such as MRE11 , a component of the MRX complex [19] . Rad53 , a transducer kinase required for DNA damage checkpoint activation , was hyperphosphorylated in the ORC6-rxl CDC6ΔNT cells . To directly test if DNA damage checkpoint is activated in the mck-16 ORC6-rxl cells , Rad53 phosphorylation status was analyzed by Western blotting . Rad53 was only hyperphosphorylated in the mck-16 ORC6-rxl cells when incubated at 37 degrees ( Figure 2A ) . We tested if the viability of the mck1-16 ORC6-rxl mutant also relies on DNA damage checkpoint . We found that cell viability of the mck-16 ORC6-rxl cells even at the permissive temperature ( 30 degrees ) required MRE11 ( Figure 2B ) . Next , the cell cycle profile of the mre11 mck-16 ORC6-rxl cells was examined . DNA re-replication was greatly enhanced in the mre11 mck-16 ORC6-rxl cells at the non-permissive temperature , indicating that DNA damage checkpoint activation limits DNA re-replication in the mck-16 ORC6-rxl cells ( Figure 2C ) . Above all , we conclude that an induction of DNA re-replication in the mck-16 ORC6-rxl cells triggered DNA damage leading to cell cycle arrest by DNA damage checkpoint activation . Several parallel and partially overlapped molecular mechanisms ensure that cells do not re-initiate DNA replication at origins that have already fired . We have previously shown that ORC6-rxl CDC6ΔNT cells are mitotic arrested without extensive DNA re-replication [19] . However , multiple mutant strains such as ORC6-rxl , ps CDC6ΔNT MCM7-NLS ORC2-ps strongly over-replicate DNA within a single cell cycle [13] . We tested if mck1 deletion also synergizes with other pre-RC mutations . An addition of either MCM7-NLS or ORC2-ps mutation to the ORC6-rxl mck1-16 did not enhance lethality ( Figure 3A ) . However , cells containing ORC6-rxl , ps mck1-16 MCM7-NLS and ORC2-ps mutations showed stronger lethality ( Figure 3A ) . Flow cytometry analysis showed that DNA re-replication was enhanced in the ORC6-rxl , ps mck1-16 MCM7-NLS ORC2-ps mutant after 4 hours incubation at the non-permissive temperature ( Figure 3B , bottom right ) . ORC6-rxl , ps MCM7-NLS ORC2-ps cells with wild type MCK1 grew normally and did not induce significant re-replication ( Figure 3A and 3B bottom left ) . These results show that Mck1p contributes to the inhibition of DNA re-replication and suggest that the mechanism involved is likely to be distinct from the known mechanisms acting at the level of ORC and MCM proteins . The semi-lethal phenotype of ORC6-rxl Δmck1 cells ( Figure 1D ) was reminiscent of ORC6-rxl CDC6ΔNT cells [13] . Moreover , the deletion of MCK1 interacted genetically with ORC6-rxl ( Figure 1A ) but not CDC6ΔNT ( data not shown ) . These observations led us to hypothesize that Mck1p could function in DNA replication control by regulating Cdc6 . To further test this model , we examined if mck1 deletion behaved similarly to CDC6ΔNT in its interactions with mutations in the cyclin genes . CDC6ΔNT genetically interacts with the clb5 deletion mutant , but not with other B-type cyclins [34] . We also tested if mck1 deletion cells genetically interact with other cyclin mutants in a similar way that CDC6ΔNT does . Table 1 summarizes the genetic interaction between mck1 and cyclin mutants . The mck1 deletion cells were semi-lethal in the ORC6-rxl mutant cells and also showed synthetic lethality with clb5 deletion cells because ORC6-rxl is a binding mutant for Clb5p . However , the mck1 deletion cells did not cause synthetic lethality with other B-type cyclin mutants such as clb1 , 2 , 3 , 4 or 6 ( Table 1 ) . Therefore , mck1 deletion genetically interacts specifically with clb5 deletion . It has been shown that Clb5p binds to Orc6p through the Clb5p hydrophobic patch substrate-targeting domain [14] . We tested if clb5-hpm ( Clb5 hydrophobic patch mutant ) causes synthetic lethality with Δmck1 cells and found that there was a genetic interaction between clb5-hpm and Δmck1 ( Table 1 ) . Moreover neither mck1 nor CDC6ΔNT caused lethality in clb5pCLB2 , a mutant in which Clb2 is controlled under Clb5 promoter . Thus , we conclude that the Δmck1 cells require Clb5p-Orc6p protein binding for their survival . We also found that deletion of CLB6 rescues Δmck1 Δclb5 semi-lethality . We have previously shown that lethality in clb5 CDC6ΔNT cells can be rescued by the deletion of CLB6 [34] and proposed the idea that the S-phase cyclin Clb6 initiates DNA replication , but fails to inhibit DNA re-replication . Therefore , the DNA re-replication phenotype is suppressed if CLB6 is deleted by the reduction of initiation of DNA replication . Mitotic cyclins regulate DNA replication in the clb5 clb6 ORC6-rxl cells . We speculate that deletion of CLB6 rescues Δmck1 Δclb5 cells in the same manner . From these results we conclude that the mck1 deletions genetically interacted with cyclin mutants in a way similar to that of stabilized CDC6ΔNT , reinforcing a model in which Mck1p acts in the same pathway as Cdc6p . Because lack of Mck1p and stabilization of Cdc6p ( Cdc6ΔNT ) exhibited similar genetic interaction with DNA re-replication mutants , we speculated that Mck1p could control the stability of Cdc6p . To test this possibility , the Cdc6 protein ( Cdc6-HA ) expressed under inducible GAL1 promoter in mitotically arrested cells was examined in wild type or Δmck1 backgrounds . We found that the Cdc6 protein level was sustained at a higher level during mitosis in the mck1 deletion cells than in wild type cells even after Cdc6 expression was shut off by glucose ( Figure 4A ) . It is important to mention that CDC6 was expressed under the GAL1 promoter , excluding possible involvement of CDC6 transcription by Mck1 in this experiment . To test if Mck1 regulates Cdc6p post-translational levels , endogenous Cdc6 synthesis was blocked by cycloheximide . In the mitotically arrested wild type cells , Cdc6 protein was rapidly depleted by addition of cycloheximide ( Figure S1 ) . In the mitotic mck1 deletion cells , the cdc6 protein level was high and remained stable after cycloheximide , excluding the possibility that Mck1p regulates Cdc6p by translation . These results strongly suggest that Mck1p controls Cdc6 protein levels by affecting degradation rates . To further explore the possible involvement of Mck1p in Cdc6p degradation , Protein A-tagged Cdc6 protein integrated at the genome locus was examined in the wild type or mck1 deletion cells by Western blotting throughout a single cell cycle progression . We noticed a dramatic accumulation of Cdc6 protein in the mck1 deletion cells ( Figure 4B ) . In wild type cells , Cdc6p was expressed transiently during G1 phase , 10 minutes after alpha-factor release , and suppressed throughout S-phase . Then Cdc6p was expressed again for a short time during mitosis , 70 minutes after alpha-factor release ( Figure 4B , upper panel ) . This is consistent with a previous report by Drury et al [32] . While in the mck1 deletion cells , Cdc6p was not expressed during alpha-factor arrest but was expressed 10 min after alpha-factor release and continued to accumulate during S-phase and mitosis ( Figure 4B , lower panel ) . The increase in Cdc6 protein level is unlikely to be due to an alteration in the cell cycle progression of Δmck1 cells because the kinetics of the cell cycle progression was similar in these two strains as judged by budding index ( Figure 4B ) . To confirm that Cdc6p is stabilized during mitosis in the mck1 deletion strain , CDC6-ProteinA or mck1 CDC6-ProteinA strains were arrested in mitosis by nocodazole and were synchronously released into the cell cycle by washing . A small amount of Cdc6p was detectable at time zero in nocodazole arrested wild type cells ( Figure 4D , left ) . This amount was transiently increased 10–20 minutes after release . This is consistent with a previous report that Cdc6 protein is expressed in late mitosis and degraded after the G1/S transition [7] . In contrast , Cdc6p was stabilized throughout mitotic progression in the mck1 deletion cells ( Figure 4D , right ) . To further confirm if Cdc6 is stabilized in the mck1 deletion cells , we visualized Cdc6p localization in vivo . We introduced a GFP-tag into the C-terminus of the chromosomal copy of the CDC6 gene to allow endogenous expression . The CDC6-GFP fusion appears to be fully functional as a CDC6-GFP strain and did not show any growth defect in any of the conditions tested ( data not shown ) . Consistent with previously published localization patterns of overexpression , Cdc6-GFP [33] , [35] protein localized and accumulated in the nucleus in late mitotic cells ( large budded cells with divided nuclei ) or in unbudded G1 cells ( Figure 4C ) . The Cdc6-GFP signal was undetectable in the cells with small to large buds , confirming tight regulation of Cdc6 abundance by rapid degradation after S-phase onset . In sharp contrast , Cdc6-GFP was constitutively found in the nucleus throughout the cell cycle in mck1 deletion cells ( Figure 4C ) . This localization analysis was consistent with Western blot results that Cdc6p is stabilized in mck1 deletion cells during S-phase and mitosis , as shown in Figure 4B and 4D . We also tested if overexpression of Mck1 promotes rapid Cdc6p degradation . Exogenously expressed Mck1p under the GALL promoter significantly reduced Cdc6p protein levels 10 minutes after the addition of galactose ( Figure 5A , top right ) . This result supports the idea that Mck1p promotes Cdc6p degradation . We next examined if Mck1-mediated Cdc6 degradation is due to SCFCDC4 ubiquitin ligase . When cdc4-1 CDC6-prA mck1 GALL-MCK1 strain was incubated at 26 degrees , Cdc6p was rapidly degraded followed by galactose addition ( Figure 5B ) . This is consistent with results in Figure 5A . When Cdc4 was inactivated at 36 degrees , Cdc6 became stable and was not degraded even after Mck1 overexperssion ( Figure 5B ) . This result suggests that Mck1p phosphorylates Cdc6p to be subsequently recognized by SCFCDC4 complex for degradation . GSK-3 kinases phosphorylate the first serine or threonine residues in the consensus site followed by a phospho-serine or phospho-threonine at the position +4 [S/T-XXX-pS/T] [36] . There are two potential GSK-3 consensus phosphorylation sites in Cdc6p , TPESS ( 39–43 ) and TPTTS ( 368–372 ) ( Figure 6A ) . To test if Mck1p binds Cdc6p at the GSK-3 consensus sites , we performed a yeast two-hybrid assay . We examined whether Mck1p , fused with Gal4 activation domain ( GAD ) , interacts with various truncated CDC6 mutants fused to the LexA DNA binding domain . Mck1p interacted with the C-terminal region of Cdc6p ( aa341–390 ) and not with the N-terminus ( aa 1–47 ) ( Figure 6B ) . The mutation at T368M or S372A abolished two-hybrid interaction between Mck1p-Cdc6p indicating that Mck1p targets Cdc6p through the GSK consensus site at 368–372 ( Figure 6B ) . The physical interaction between Mck1p and Cdc6p was also confirmed by co-immunoprecipitation ( Co-IP ) assay using the MCK1-MYC GAL-CDC6ΔNT-HA strain . Mck1p interacted with Cdc6ΔNTp , indicating that Mck1p interacts with Cdc6p , and the protein interaction was mediated through the C-terminal region in Cdc6p ( Figure 6C ) . The protein binding between Mck1p and Cdc6p was observed only in mitotic arrested cells blocked by nocodazole and not in asynchronous culture or G1-arrested cells ( data not shown ) . Therefore the physical interaction between Mck1p and Cdc6p is likely primed by mitotic CDK phosphorylation of the S372 site ( see next section ) . We also noticed that Cdc6ΔNT migrates slower in the co-IP samples than the input , consistent with the idea that only the phosphorylated form of Cdc6 , probably targeted by CDK , binds to Mck1 ( Figure 6C ) . A GSK-3 kinase usually requires priming [36] . In Cdc6 , the predicted priming site is located at S372 based on the amino acid sequence . After priming , the GSK-3 kinase phosphorylates the target site at the first serine or threonine that corresponds to T368 ( see discussion ) . Next , we tested to see if mutations at the GSK-3 consensus phosphorylation site in CDC6 cause lethality in orc mutants like the mck1 deletion does . To prove that the C-terminus GSK-3 consensus site 368–372 in CDC6 was involved in the inhibition of DNA re-replication , the potential phosphorylation site ( T368 ) and the priming phosphorylation site ( S372 ) were altered to alanine . The CDC6-T368A S372A in a 2 micron plasmid was transformed into wild type , ORC6-rxl , ORC6-ps or ORC6-rxl , ps mutants . Colonies formed when either CDC6 wild type or CDC6 T368A S372A plasmids were transformed into the ORC6-wild type strain ( Figure 7B , top left ) . In contrast , the CDC6 T368A S372A plasmid ( but not CDC6-wt ) was toxic in the ORC6-rxl cells , as transformants gave very few visible colonies ( Figure 7B , top right ) . This effect was even more pronounced in ORC6-rxl , ps cells and , in this case , even the CDC6-wt plasmid appeared somewhat toxic ( Figure 7B , bottom right ) . The CDC6-T368A S372A plasmid did not induce toxicity in the ORC6-ps cells ( Figure 7B , bottom left ) which confirmed the result that mck1 did not genetically interact with ORC6-ps mutation ( Figure 1 ) . The plasmid harboring CDC6-T368A or CDC6-S372A single mutation was also toxic in the ORC6-rxl strain ( Figure S2 ) . These results suggest that the interaction of Cdc6p with Mck1p and/or its phosphorylation by Mck1p contributes to the down-regulation of Cdc6p levels . To confirm that Cdc6p is phosphorylated by Mck1 in vivo , we analyzed the Mck1-dependent mobility shift of Cdc6p in the cdc4-1 mutant background by western blot . We used cdc4-1 mutant to prevent degradation of phosphorylated Cdc6 and examined the effect of Mck1 on the phosphorylation status of Cdc6p . Cdc6p in the wild type cells migrated slower that that in the Δmck1 deletion cells indicating that Cdc6p is hyper-phosphorylated in wild type cells . ( Figure 7C ) . In the mck1 deletion cells , the signal of the higher molecular weight band was abrogated and the lower band was abundant suggesting that Cdc6p is less phosphorylated and more stable ( Figure 7C right ) . To confirm that the slow migrating band of Cdc6p in the wild type cells is due to phosphorylation , protein extracts from wild type cells were treated with CIP ( calf intestine phosphatase ) . After the CIP treatment , the slower migrating band of Cdc6p disappeared and the faster-migrating band was observed at the same level as that in Δmck1 cells . It suggests that the band shift between wild type and Δmck1 is due to phosphorylation ( Figure 7C and 7D ) . Finally we tested if Mck1p dependent destabilization of the Cdc6p is mediated by the T368 residue . The mck1 GALL-MCK1 CDC6-proteinA CDC6T368A strain contains both wild type Cdc6 ( tagged with protein A ) and Cdc6T368A ( no tag ) . First the cells were arrested in mitosis with nocodazole and then released into galactose to overexpress Mck1p . Wild type Cdc6p was degraded rapidly after Mck1p overexpression , which is consistent with previous results in Figure 5A ( Figure 7E , upper panel ) . In contrast , Cdc6T368A protein was resistant to degradation and was stable even after Mck1p overexpression ( Figure 7E , lower panel ) . We also observed faster migration of Cdc6T368A protein than the wild type Cdc6p by western blot ( Figure S3 ) . We conclude that Cdc6p is phosphorylated at T368 by Mck1p to induce its degradation .
In this study , we show that a GSK-3-like kinase , Mck1p , is involved in the inhibition of DNA re-replication through its role in Cdc6p turnover in S . cerevisiae . There are 8 CDK consensus sites in CDC6 . The first 47 amino acids at the N-terminus of Cdc6 are targeted by Cyclin/CDK and are critical for SCFcdc4 dependent proteolysis [7] . Stabilization of Cdc6p in mck1 deletion cells suggests that CDK-dependent phosphorylation at the N-terminus of Cdc6 is not sufficient enough for CDC6p degradation in vivo , that Mck1-dependent phosphorylation through T368 site is also required . The Cdc6 T368A mutant was resistant to Mck1p-dependent degradation ( Figure 7E ) . Nocodazole was added to the media throughout this experiment , therefore Cdc6 stabilization by the T368A mutation , even after Mck1p overexpression , is not due to a change in cell cycle progression . This is of particular interest because activation of CDK promotes both DNA replication and Cdc6p degradation at the same time . The requirement of Mck1 for Cdc6p degradation most likely ensures that degradation of Cdc6p occurs only after origin firing has been initiated . Three distinct Cdc6p degradation modes have been proposed by Diffley's group [32] . Mode1 degradation during G1 phase is independent of Cdc6 CDK consensus sites and is mediated neither by SCF nor APC . The Cdc6p degradation by Mode 2 and Mode 3 are triggered later during the cell cycle . Mode3 is required for Cdc6 degradation during mitosis . The Cdc6p degradation by Mck1p accounts for the mode3 mechanism based on the Cdc6p stabilization pattern during mitosis in mck1 deletion ( Figure 4A ) . Diffley's group has reported that the Cdc6 T368M mutation leads to Cdc6p stabilization during mitosis and the mutation is resistant to mode 3 proteolysis by SCFcdc4 complex [6] . In this study , we showed that Mck1-dependent Cdc6 phosphorylation is targeted by SCFCDC4 complex for degradation ( Figure 5B ) . Therefore , Mck1 , most likely , phosphorylates Cdc6 and the phosphorylation at T368 is recognized by Cdc4 . It is not clear if mode 3 requires CDK activity . Therefore Mck1p may promote complete Cdc6 degradation during mitosis in addition to its degradation mechanism through CDK phosphorylation . Further studies are required to test if Mck1 could also promote Cdc6 degradation via Mode 1 or Mode 2 . There are two potential GSK-3 sites S/TXXXpS/T in Cdc6 , at 39-43 and 368-372 amino acid residues . It has been reported that these sites share sequence similarities and are targeted for SCFCDC4 dependent proteolysis [6] . Our yeast-two hybrid assay showed a specific interaction between Mck1p and Cdc6p through the GSK-3 consensus site located at residues 368–372 ( TPTTS ) . This GSK-3 site in Cdc6p , amino acid 368–372 , is also shared by two potential CDK phosphorylation sites 368–371 ( TPTT ) and 372–275 ( SPVK ) . The former partially matches with a minimal consensus CDK phosphorylation site ( S/T-P ) whereas the latter perfectly matches an optimal CDK site , with a basic residue at the +3 position . It is important to note that Cdc6 is a very good substrate of the B-type Cyclin/CDK complex [37] . The GSK-3 kinase and CDK could share substrate specificity [38] . GSK-3 kinases require “priming” phosphorylation by another kinase on their substrates [36] . The priming site is usually located C-terminally of the GSK-3 phosphorylation site , at the +4 position , which corresponds to S372 in Cdc6 . After priming , GSK-3 recognizes its target and can phosphorylate the first serine or threonine residue , which corresponds to T368 in Cdc6 . Thus , C-terminal Cdc6p ( aa 341–390 ) , including the GSK-3 consensus phosphorylation sequence , is sufficient for Mck1 binding and their interaction likely depends on phosphorylation of S372 by CDK ( Figure 6B ) . We propose a model in which S372 is phosphorylated by cyclin/CDK first in order to induce phosphorylation at T368 by Mck1p kinase . This priming model allows Cdc6 to create Cdc4 diphospho-degrons which is an efficient Cdc4 recognition site . David Morgan's group shows that Eco1 is primed by CDK and DDK in order to be targeted by Mck1 , which creates Cdc4 recognition site ( personal communication ) . Mck1 is involved in the degradation of SCFCDC4 substrates such as Rcn1and Hsl1 [25] , [26] , [39] . Therefore , the priming model to create Cdc4 diphospho-degrons seems to be a universal mechanism to regulate protein degradation . Mck1p protein levels are not cell cycle-regulated ( data not shown ) therefore Mck1 activation is not regulated by its own expression level . This result supports the idea of the priming hypothesis in which Mck1 can target its substrate , Cdc6p , only after Cdc6 is phosphorylated by cyclin/CDK in a cell cycle-dependent manner . Given the requirement of T368 for Mck1 dependent degradation of Cdc6 , Mck1 most likely phosphorylates this residue directly in vivo . However , it is formally possible that Mck1 affects Cdc4 function other than Cdc6 . We favor the model that Mck1 directly phosphorylates Cdc6 to promote Cdc4-dependent degradation based on our results in Figure 5B , Figure 7C and 7D . Whether or not SCFCDC4 or other targets such as Sic1 are also phosphorylated by Mck1 is an interesting future study . The glycogen synthase kinase-3 ( GSK-3 ) was originally identified as a kinase that inactivates glycogen synthase [40] . In higher eukaryotes , there are two isoforms , GSK-3α and GSK-3β , that regulate various cellular processes including Wnt signaling [41] and insulin signaling [42] , [43] . The yeast homologue of GSK3 , Mck1p , also has diverse biological functions ( see introduction ) . This is the first evidence to show that Mck1p or any GSK-3 kinase controls DNA replication . Whether GSK-3 kinases contribute to the regulation of DNA replication at other targets should be investigated further .
SGA analysis was performed as previously described [19] , [20] . A query strain , MATalpha ORC6-rxl::LEU2 mfa::MFA1pr-HIS3 trp1 ade2 can1 leu2 his3 lys2 ura3 , was placed on YEPD in rectangle plates . Then deletion mutant arrays ( MATa geneX::KanMX TRP1 ADE2 met15 leu2 ura3 his3 ) were put on top of the query strains . The resulting diploid cells were sporulated on the plates containing 2% agar , 1% potassium acetate , 0 . 1% yeast extracts , 0 . 05% glucose , supplemented with uracil and histidine . After incubation at 22 degrees for 5 days , the spores were pinned onto haploid selection plates ( SD-His/Leu/Arg plus canavanine ) to select for MATa mfa::MFA1pr-HIS3 ORC6-rxl::LEU2 progeny , followed by pinning onto YEPD plates containing G418 to select out the deletion array mutants . Finally , double mutants were placed on SD-His/Leu/Arg plus canavanine plus G418 for 2 days . The proliferation of those that contained haploid cells was scored visually . The deletion sets used in this study were obtained from EuroScarf and are derivatives of BY4741 [44] . First , GAL-CDC6-HA or mck1 GAL-CDC6-HA strains were grown in raffinose-containing media and then galactose was added to express Cdc6-HA for 2 hours . The cell cycle was blocked during mitosis by nocodazole at the concentration of 15 µg/ml for 2 hours . Next , glucose was added to the media to shut off the GAL expression ( Figure 4A ) . CDC6-PRA or mck1 CDC6-PRA strains were grown in liquid YEPD to log-phase at 30 degrees and then treated with alpha-factor at the concentration of 100 nM for 2 hours . The cells were washed with YEPD three times to release the cell cycle from G1 . Samples were collected every 10 minutes for 80 minutes for Figure 4B . To block the cell cycle during mitosis , CDC6-PRA or mck1 CDC6-PRA strains were treated with nocodazole at the concentration of 15 µg/ml for 2 . 5 hours at 30 degrees . The mitotic block was released by washing cells with YEPD twice . Samples were collected every 10 minutes for 60 minutes for Figure 4D . For Figure 5 and 7D , cells were treated with nocodazole for 2 hours and then switched to YEPD or YEPG containing nocodazole at 15 µg/ml . All strains used , except for SGA analysis , are derivatives of W303 ( strain list in Table S1 ) . Standard methods were used for mating and tetrad analysis . DNA transformation was performed by the lithium acetate method [45] . To generate mck1 or mre11 deletion in the W303 background , genes disrupted by a KanMX cassette in BY4741 haploid deletion libraries ( EuroScarf ) were amplified by PCR . The PCR product containg the KanMX cassette with MCK1 flanking region was transformed into the wild type W303 strain . The resulting mck1 deletion cells in W303 were confirmed by PCR . The MCK1-MYC strain was generated by PCR genomic integration of a PCR product containing a MYC tag and a TRP gene [46] . GAL-CDC6ΔNT-HA strain and plasmid were kindly provided by Dr . Stephen Bell . The ORC6-rxl , ORC6-ps , ORC2-ps and MCM7-NLS mutations were described previously [11] , [13] , [34] . CDC6-proteinA strain was generated as previously described [47] . Rad53-FLAG strain was obtained from Dr . Petrini [48] . To generate GALL-MCK1 , MCK1 gene was cloned into GALL-pRS405 plasmid at BamHI and SpeI sites using MCK1 plasmid provided by Dr . P . Hieter [28] . The resulting GALL-MCK1/pRS405 plasmid was cut with BstEII , and the linearized plasmid was transformed into bar1 mck1::KanMX CDC6-prA::HIS3 strain to integrate GALL-MCK1 at LEU locus . Cdc6-GFP strain was made by direct transformation of a GFP cassette [49] in BY4741 and subsequently back crossed to W303-1B three times for Figure 4C . CDC6 plasmid was generated by PCR method using W303 wild type genomic DNA . The resulting PCR product was cloned into pYES2 . 1 Topo TA plasmid ( Invitrogen ) for Figure 7B . The CDC6/pYES2 . 1 plasmid was subjected to site-directed mutagenesis using QuickChange Site-directed mutagenesis kit ( Agilent Technologies , CA ) to introduce T368A S372A mutation for Figure 7B . CDC6/pRS406 plasmid was generated by PCR cloning . CDC6 gene including the endogeneous promoter ( 300 bp upstream from the start codon ) was amplified by PCR using primers that contain BamHI and XhoI , and cloned into pRS406 at BamH1and XhoI sites . The CDC6/pRS406 plasmid was used as a temperate to generate CDC6-T368A/pRS406 . Site-directed mutagenesis was performed as described above . The resulting plasmids were cut with NcoI to integrate the mutated CDC6 at URA3 locus in mck1 GALL-MCK1 CDC6-proteinA strain for Figure 7E . A temperature sensitive mutant of MCK1 was generated using a previously described method [34] . MCK1 gene was cloned into pRS414 at BamHI and SpeI sites from MCK1 plasmid provided by Dr . P . Hieter [28] . The MCK1/pRS414 plasmid was mutagenized by PCR mutagenesis to introduce random mutations in the MCK1 gene as previously described [50] . The mutagenized mck1/pRS414 plasmid was transformed into mck1 orc6-rxl strain containing MCK1/pRS416 plasmid . The mck1 orc6-rxl cells containing mutagenized mck1 plasmid were tested for its viability at 37 degrees . The mutagenized mck1/pRS414 plasmid ( mck1-16 mutation ) was isolated from the strain and was inserted into the pRS406 plasmid at BamHI SpeI sites . The resulting mck1-16/pRS406 plasmid was cut with BstEII restriction enzyme and was integrated at the URA3 genome locus . Sequence analysis identified two mutations in the temperature sensitive mck1-16 allele , resulting in P275L and E357G . A 50-ml culture of each strain was grown to log-phase an OD595 of 0 . 5 was reached . The cell pellets were washed in cold TE buffer , and resuspended with 400 µl of protein extraction buffer [20 mM HEPES , pH 7 . 4 , 110 mM potassium acetate , 2 mM MgCl2 , 0 . 1% Tween 20 , 1 mM DTT , 2 µg/ml DNaseI , protease inhibitor cocktail ( Sigma-Aldrich , MO ) and phosphatase inhibitor ( Sigma-Aldrich , MO ) ] . Acid-washed glass beads ( 0 . 15 g ) were added , and cells were disrupted by FastPrep ( MP Biomedicals , OH ) for 20 seconds , twice , at speed 6 . Samples were centrifuged and 10 µl of supernatants were kept for Western blotting as “INPUT” . The remaining protein extracts were subjected to co-immunoprecipitation ( Co-IP ) . Agarose beads conjugated with anti-MYC antibody ( A7470 ) ( Sigma-Aldrich , MO ) were pre-incubated with 5% BSA in protein extraction buffer for 1 hour at 4 degrees to reduce non-specific binding first . Then the beads were mixed with the protein extract supernatants and rotated for 2 hours at 4 degrees . Beads were washed with protein extraction buffer five times . After the final wash , 30 µl of 2× sample buffer was added to the beads , and the protein was denatured at 95 degrees for 5 minutes . Proteins were separated by SDS-PAGE with Novex 4–20% Tris-Glycine polyacrylamide gel ( Invitrogen , CA ) except Figure 7C with 7% acrylamide large gel . The proteins on the gels were transferred to PVDF membrane ( Millipore , MA ) . Western blot analysis was performed using anti-MYC antibody 9E10 ( M4439 ) ( Sigma-Aldrich , MO ) at 1∶4000 dilution , anti-HA antibody 3F10 ( Roche , IN ) at 1∶4000 dilution and anti-FLAG antibody ( A8592 ) ( Sigma-Aldrich , MO ) at 1∶4000 dilution . Cdc6-proteinA was visualized using anti-peroxidase soluble complex antibody produced in rabbit ( P1291 ) ( Sigma-Aldrich , MO ) at 1∶4000 dilution . Cdc6 was detected using anti-Cdc6 antibody ( 9H8/5 ) ( Abcam , MA ) at 1∶500 dilution . Log phase cultures of Cdc6-GFP expressing cells in SC medium supplemented with 20 mg/L adenine were imaged live with an Eclipse E600 fluorescence microscope ( Nikon ) equipped with a DC350F CCD camera ( Andor ) and 100× , NA 1 . 45 , or 60× , NA 1 . 4 , oil objectives . The images were captured with NIS-Elements software ( Nikon ) and prepared using Photoshop software . DNA content analysis by FACScanto ( BD Biosciences , NJ ) was performed as described previously [51] . The pBTM116 constructs containing various Cdc6 mutants were obtained from Dr . J . Diffley's lab [6] . Full length MCK1 was cloned into pACT at BamHI and XhoI sites by PCR method . The MCK1/pACT and each of the various CDC6/pBMT116 plasmids were co-transformed into L40 strain and plated on SD-Leu/Trp plates [52] . The colonies were transferred to nitrocellulose membrane and kept at −80 degrees overnight . The membrane was placed on whatman paper soaked with 3 ml of Z buffer , [60 mM Na2HPO4 , 40 mM NaH2PO4 , 10 mM KCl , 1 mM MgSO4] with 300 µg/ml X-gal and 0 . 044 M 2-mercaptoethanol . The membrane was incubated at 30 degree overnight to visualize the blue colonies . | DNA replication is a fundamental cellular process that takes place in all living organisms . This cellular event has to be tightly regulated to ensure an accurate genome integrity such that DNA replication takes place only once per cell cycle . Here we show a mechanism by which DNA re-replication is controlled by Cyclin Dependent Kinase ( CDK ) and a yeast GSK-3 kinase ( Mck1p ) in S . cerevisiae . We found that Mck1p promoted Cdc6 protein degradation . Mck1p targets Cdc6p through a GSK-3 consensus site ( T368 ) , and Cdc6p protein degradation was also mediated through the same T368 site . The GSK-3 kinase has diverse cellular functions in higher eukaryotes including roles in tumorigenesis . This finding is particularly important , since this is the first evidence to show that a GSK-3 family kinase regulates DNA replication . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2012 | A Yeast GSK-3 Kinase Mck1 Promotes Cdc6 Degradation to Inhibit DNA Re-Replication |
Melioidosis is an important cause of morbidity and mortality in East Asia . Recurrent melioidosis occurs in around 10% of patients following treatment either because of relapse with the same strain or re-infection with a new strain of Burkholderia pseudomallei . Distinguishing between the two is important but requires bacterial genotyping . The aim of this study was to develop a simple scoring system to distinguish re-infection from relapse . In a prospective study of 2 , 804 consecutive adult patients with melioidosis presenting to Sappasithiprasong Hospital , NE Thailand , between1986 and 2005 , there were 141 patients with recurrent melioidosis with paired strains available for genotyping . Of these , 92 patients had relapse and 49 patients had re-infection . Variables associated with relapse or re-infection were identified by multivariable logistic regression and used to develop a predictive model . Performance of the scoring system was quantified with respect to discrimination ( area under receiver operating characteristic curves , AUC ) and categorization ( graphically ) . Bootstrap resampling was used to internally validate the predictors and adjust for over-optimism . Duration of oral antimicrobial treatment , interval between the primary episode and recurrence , season , and renal function at recurrence were independent predictors of relapse or re-infection . A score of <5 correctly identified relapse in 76 of 89 patients ( 85% ) , whereas a score ≥5 correctly identified re-infection in 36 of 52 patients ( 69% ) . The scoring index had good discriminative power , with a bootstrap bias-corrected AUC of 0 . 80 ( 95%CI: 0 . 73–0 . 87 ) . A simple scoring index to predict the cause of recurrent melioidosis has been developed to provide important bedside information where rapid bacterial genotyping is unavailable .
Melioidosis , a serious Gram-negative infection caused by Burkholderia pseudomallei , is endemic across much of rural East and South Asia and in northern Australia [1] . The causative organism is present in the environment in these areas and infection is acquired by bacterial inoculation or inhalation . B . pseudomallei causes 20% of community-acquired septicemias in northeast Thailand [2] , and is the most common cause of fatal community-acquired bacteremic pneumonia in Darwin , Australia [3] . Acute melioidosis is treated with parenteral treatment for at least 10 days , followed by oral treatment for 20 weeks [1] . The overall mortality of acute melioidosis is 50% in NE Thailand ( 35% in children ) , and 19% in Australia [1] , [4] . Recurrent infection occurs despite 20 weeks of antimicrobial treatment and is the most important complication in survivors , affecting 13% of Thai patients who survive the primary episode [5] . A study that compared the bacterial genotype of strain pairs isolated during primary and recurrent melioidosis in over one hundred patients demonstrated that three quarters of cases were due to relapse ( paired isolates had the same genotype ) , and one quarter were due to re-infection with a new strain [6] . Clinically this is an important distinction , with implications for epidemiology , investigation and management , but the overwhelming majority of medical centers treating patients with melioidosis in Asia do not have the facilities to perform bacterial genotyping and recurrence is usually considered to be synonymous with relapse . In addition , isolates from the primary episode are usually unavailable because bacterial strains are not routinely frozen . The purpose of this study was to define the association of readily accessible factors with relapse or re-infection , and to use these to develop a simple scoring system to help distinguish the most probable cause of recurrent melioidosis .
Study patients were adults ( ≥15 years ) with culture-confirmed recurrent melioidosis who presented to Sappasithiprasong Hospital , Ubon Ratchathani , northeast Thailand between June 1986 and September 2005 and who were included in prospective studies of antimicrobial chemotherapy during this period . The standard of care throughout the study period was inpatient intravenous antimicrobial therapy , followed by a prolonged course of oral drugs . The prospective studies were either trials comparing parenteral antimicrobial regimens or trials comparing oral eradicative treatment regimens , as previously described ( see [7] for list of published trials ) . Patients were followed up for recurrent melioidosis as a secondary outcome for trials comparing parenteral drugs and as a primary outcome for trials comparing oral treatment regimens . Patients with suspected melioidosis were identified by twice-daily active case finding in the medical and intensive care wards . As part of eligibility screening for the clinical trials a history and examination was performed and samples taken for culture from suspected cases ( blood culture , throat swab , respiratory secretions , pus or surface swab from wounds and skin lesions ) . Microbiology specimens were cultured for the presence of B . pseudomallei , as described previously [8] . Additional passive surveillance was undertaken via the diagnostic microbiology laboratory for patients on the surgical and pediatric wards with cultures positive for B . pseudomallei . All isolates were stored in trypticase soy broth with 15% glycerol at −80°C . A history and full clinical examination was performed on all cases of culture proven melioidosis . Details of history , examination , laboratory results , antimicrobial treatment and clinical course were maintained on a password protected computer database . Patients who survived the primary episode received oral eradicative treatment and were followed up monthly for one year , then yearly thereafter . Oral antimicrobial regimens were as described elsewhere [7] . Patients with recurrence were identified from the history , patient notes and by cross-reference with our database . Follow up data in this study was to February 2007 . Ethical permission for all clinical trials was obtained from the Ethical and Scientific Review Subcommittee of Thai Ministry of Public Health . Patients gave written informed consent to participate in the trials . Single isolates obtained from the first and recurrent episode were compared using a combination of PFGE and MLST , as described previously [6] , [9] . Recurrent melioidosis was defined as the development of new symptoms and signs of infection in association with a culture positive for B . pseudomallei following initial response to oral antibiotic therapy . Relapse and re-infection were defined on the basis of typing of isolates from the first and subsequent episode ( s ) . Isolates from the same patient with an identical banding pattern on PFGE were considered to represent a single strain and these patients were classified as having relapse . Isolates from the same patient that differed by one or more bands were examined using a screening approach based on MLST , as described previously [6] . Isolates from the same patient with a different sequence type ( ST ) were classified as representing re-infection , while those with an identical ST were classified as representing relapse . All B . pseudomallei isolates were tested for susceptibility to the antimicrobial drugs used to treat melioidosis ( meropenem , ceftazidime , amoxicillin-clavulanic acid , chloramphenicol , doxycycline and trimethoprim/sulfamethoxazole ( TMP-SMX ) ) . This was performed using the disk diffusion method with the exception of TMP-SMX , which was assessed using the Etest ( AB Biodisk , Solna , Sweden ) [10] . All isolates defined as intermediate or resistant to a given drug by disk diffusion were tested further using the E-test . Interpretative standards were based on CLSI guidelines , which lists resistance for ceftazidime , amoxicillin-clavulanic acid , doxycycline and TMP-SMX as ≥32 mg/L , ≥32 mg/L , ≥16 mg/l and ≥4/76 mg/L , respectively , and intermediate resistance as 16 mg/L , 16 mg/L , 8 mg/l and N/A , respectively [11] . Diabetes mellitus was defined as either pre-existing , or a new diagnosis as defined by the American Diabetes Association criteria [12] . Impaired renal function was defined as an estimated glomerular filtration rate ( GFR ) below 60 mL/min/1 . 73 m2 at admission . GFR was estimated using an abbreviated form of the Modification of Diet in Renal Disease study equation [13] . Hypotension was defined as a systolic blood pressure less than 90 mmHg , acute renal failure as a 50% decrease in the baseline-calculated GFR [14] , and respiratory failure as the need for mechanical ventilation . The time between the primary episode and recurrent episode was measured from the start of oral antimicrobial therapy to the clinical onset of culture-confirmed recurrent infection . The primary outcome of interest was cause of recurrent infection . Comparison between relapse and re-infection for each variable was performed using Fisher's exact test or the Wilcoxon-Mann-Whitney test , as appropriate . We selected potential predictor variables to study based on our collective clinical experience and information from other studies [5]–[7] . The variables considered included sex , age , diabetes , estimated GFR during recurrent infection , body sites involved in the primary and recurrent episode , complications of recurrent infection , antimicrobial treatment given for the primary episode , patterns of antimicrobial resistance for the primary and recurrent isolates , calendar month of presentation of recurrent episode , and duration between primary and recurrent episode . The creatinine level on recurrent episode was missing for 17 patients ( 12% ) and the most recent creatinine levels during follow-up before the recurrent episode were used instead . Variables associated with relapse/re-infection at p<0 . 20 were included as independent variables in a multivariable logistic regression model with relapse/re-infection as the dependent variable . Variables were removed one at a time from the model if the p-value as determined by the likelihood ratio test was >0 . 05 , least significant variable first . To double check that no significantly predictive variables were removed during this process , each de-selected variable was tested in turn with the final model and reintroduced into the model if p<0 . 05 [15] . Variables in the final model were used to construct a scoring system . For simplicity , estimated GFR was categorized into four levels ( <30 , 30 to <60 , 60 to <90 or ≥90 ) based on clinical practice guidelines [13] . Time to recurrent melioidosis was dichotomized ( <1 year or ≥1 year ) and duration of oral treatment received on primary episode was categorized into four levels ( <8 weeks , 8 to <16 weeks , 16 to 20 weeks or >20 weeks ) based on previous knowledge [6] , [7] . These dummy variables were used in a multivariable logistic regression analysis . The coefficient for each variable was multiplied by 10 and rounded off to the nearest integer . A total score was calculated by summing the points from each variable for each patient , and the results plotted on a receiver-operator characteristic curve . The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the regression model . Discrimination referred to the ability to distinguish re-infection from relapse , and was quantified by the area under receiver operating characteristic curves ( AUC ) . Bootstrap resampling procedures were used to assess the internal validity of the model and to adjust for over-fitting or over-optimism . The apparent performance of the scoring system ( AUC ) on the original data set may be better than the performance in another data set . One thousand random bootstrap samples were drawn with replacement from the original data set . The logistic regression model and scoring system generated from the bootstrap sample was evaluated in the bootstrap sample and in the original sample . The bootstrap sample set represented training data and the original sample set represented test data . The difference between the performances in both sets was an estimate of the optimism in the apparent performance . This difference was averaged to obtain a stable estimate of the optimism . The optimism was subtracted from the apparent performance to estimate the internally validated performance . All analyses were performed using the statistical software STATA/SE version 9 . 0 ( StataCorp LP , College Station , Tx . ) .
A total of 2 , 804 adult patients with culture-confirmed melioidosis were seen during the 19-year study period . Of these , 1 , 401 ( 50% ) adult patients died during admission . Of the adults who survived , 1 , 001 ( 71% ) patients presented to follow up clinic at least once . Median duration of follow-up for patients without recurrence was 65 weeks ( 25th percentile-75th percentile , 22–179 weeks; range , 1–954 weeks ) . A total of 194 episodes of culture-confirmed recurrent melioidosis occurred in 170 ( 17% ) patients . Of these , 148 ( 76% ) strain pairs from the primary and recurrent episode were available for genotyping from 141 patients . Bacterial genotyping had been performed previously for 122 episodes in 115 patients [6] , and genotyping of the remainder was performed during this study . Of the 148 episodes of recurrent melioidosis , 98 episodes in 92 ( 65% ) patients were defined by genotyping as relapse . Four of these patients relapsed twice and 1 patient relapsed three times . The other 50 episodes in 49 ( 35% ) patients were due to re-infection . One patient had re-infection after completing treatment for an episode of relapse . For the purposes of this study , only the 141 first episodes of recurrent melioidosis ( 92 relapse and 49 re-infection ) were analyzed . All B . pseudomallei isolates associated with the primary episode of recurrent infection were susceptible to ceftazidime , amoxicillin-clavulanic acid and doxycycline , while 21/141 ( 15% ) were resistant to TMP-SMX . All isolates associated with recurrence were susceptible to ceftazidime . Strains associated with re-infection were resistant to amoxicillin-clavulanic acid , doxycycline and TMP-SMX in 2% ( 1/49 ) , 2% ( 1/49 ) and 16% ( 8/49 ) of cases , respectively , while , strains associated with relapse were resistant in 1% ( 1/92 ) , 1% ( 1/92 ) and 12% ( 11/92 ) , respectively ( p>0 . 05 , all ) . Two patients with relapse associated with the development of bacterial resistance to amoxicillin-clavulanic acid ( MIC from 2 to 16 mg/L ) or doxycycline ( MIC from 1 to 96 mg/L ) received antimicrobial treatment with the respective agent for at least 8 weeks prior to relapse . The majority of patients with re-infection presented in the rainy season , the period of greatest melioidosis incidence , while patients with relapse presented throughout the calendar year without evident seasonality ( p = 0 . 002 , Figure 1A ) . Demographic characteristics and clinical features are shown in Table 1 . Sex and age were comparable between the two groups . Diabetes mellitus was the most common underlying condition in both relapse and re-infection . Impaired renal function was present in 55 ( 60% ) of 92 patients with relapse and 39 of 49 ( 80% ) patients with re-infection ( p = 0 . 02 ) . Distribution of infection and organ involvement during primary infection and at time of recurrence was not different between patients with relapse and re-infection . There was no difference in severity of infection between relapse and re-infection as defined by hypotension , acute renal failure or respiratory failure ( p>0 . 05 in all cases ) . Death occurred in 17 ( 18% ) patients with relapse and 13 ( 27% ) patients with re-infection ( p = 0 . 29 ) . On univariable analysis , the duration of oral antibiotic treatment for the primary episode was significantly shorter for patients with relapse than re-infection ( p<0 . 001 ) . The median time to relapse was also significantly shorter than time to re-infection ( 6 months versus 24 months , p<0 . 001 ) ( Figure 1B ) . On multivariable analysis , significant independent predictors of re-infection were the presence of a low GFR on admission for the recurrent episode , an interval between the primary infection , and recurrence of more than one year and calendar period of presentation ( rainy season ) . Short duration of oral antimicrobial treatment for first episode of infection was predictive for relapse ( Table 2 ) . The AUC for this model was 0 . 81 ( 95% CI: 0 . 74–0 . 89 ) , and the Hosmer-Lemeshow goodness-of-fit test was not significant for lack of fit ( Hosmer-Lemeshow statistics = 9 . 24 , df = 8 , p = 0 . 32 , ) . A scoring system was generated based on a combination of predictors of re-infection or relapse in the final logistic regression model ( Figure 2 ) . Factors associated with re-infection ( time to recurrence more than one year , presentation during the rainy season or with reduced renal function ) were given a positive score . Factors associated with relapse were given a negative score . A non-linear association was found between the duration of oral treatment received and predictive value of relapse . A score was reached based on the accumulation of points from the four variables . The AUC for the re-infection score was 0 . 80 ( 95%CI: 0 . 73–0 . 87 ) after applying the bootstrap correction . The predictive ability of the risk index model for relapse and re-infection is depicted in Figure 3 . A score of less than 5 correctly identified relapse in 76 of 89 patients ( 85% ) in this group , whereas a score of more than or equal to 5 correctly identified re-infection in 36 of 52 patients ( 69% ) .
Determining the cause of recurrence in infectious diseases is important as relapse and re-infection have different implications for disease control and clinical management . Relapse reflects treatment failure , in which antimicrobial regimen , elimination of a persistent focus and drug adherence are the main concerns . This contrasts with re-infection , which involves exogenous infection with a new strain and therefore has implications for disease prevention and health education strategies . In clinical practice , cause and management of recurrent infection is highly complex and standard second-line drug regimen may be recommended where individualized retreatment schemes are not practical [16] . In recurrent melioidosis , if all recurrent episodes are assumed to be relapse due to failure of primary eradicative treatment ( TMP-SMX based regimen ) , then inferior secondary treatment ( amoxycillin-clavulanic acid ) may be used despite the presence of an organism that is still sensitive to TMP-SMX [17] , [18] . Use of inferior second-line drugs would unnecessary expose patients with re-infection to a higher risk of relapse from this new episode than would otherwise be the case [7] . In addition , non-medical treatment , the prevention of re-infection , remains ignored . For many infectious diseases , the clinical differentiation of relapse from re-infection is difficult or impossible , and genotyping has generally been used for this purpose . Examples include tuberculosis [19] , [20] , malaria [21] , [22] , Staphylococcus aureus bacteremia [23] , pneumococcal bacteremia [24] , infective endocarditis [25] and nosocomial infections [26] , [27] . Two typing methods were used in this study since MLST can resolve any uncertainty that arises during the interpretation of DNA macrorestriction patterns generated by PFGE [6] . The MLST scheme has been shown to confirm cluster assignments based on PFGE results in common organisms [28]–[30] . However , genotyping techniques are not widely available for tropical infections in endemic areas . In addition , isolates are rarely stored outside of the research setting , making it impossible to compare isolates associated with the primary and recurrent infection . Clinical differences between re-infection and relapse have been proposed for Lyme disease , although a scoring system was not developed [31] . Scoring systems have been described to predict outcome from melioidosis [32] , and to predict a number of other events including atrial fibrillation after cardiac surgery [33] . To our knowledge , our scoring system is the first clinically-based scoring system to differentiate between relapse and re-infection in any infectious disease . It is rapid and simple to use , necessitating data on only four easy to assess factors . This scoring index can be used where bacterial genotyping is unavailable , which covers nearly all melioidosis-endemic regions . The factors associated with recurrent melioidosis are similar to those reported for recurrence of Lyme disease ( relapse after previous inadequate treatment and within a short period , and re-infection during the ‘high’ season when ticks increase in numbers ) [31] , and may represent features that could be used for assessing other infectious diseases . Using genotyping to compare primary and recurrent isolates to distinguish between relapse or re-infection could be confounded by two major factors . First , ‘re-infection’ could actually represent relapse in the event that primary infection was caused by simultaneous infection with more than one bacterial strain , and different strains were picked by chance for genotyping [34] . This is unlikely in melioidosis since infection with more than one strain of B . pseudomallei occurs in less than 2% of cases [35] . ‘Re-infection’ could also actually represent relapse if genetic events occurred in vivo that led to alteration of one of the seven housekeeping genes that are sequenced in order to generate a sequence type . This would be predicted to be extremely unlikely as MLST is based on the sequence of housekeeping genes which are under neutral selection pressure [36] . Second , ‘relapse’ could actually represent re-infection in the event that re-infection was caused by a different strain that was nonetheless indistinguishable on genotyping from the first infecting strain . This would happen when infection sources were clonal or had limited genetic diversity , but this is highly unlikely in melioidosis as the B . pseudomallei population in the environment is extremely diverse [37] . Our finding of a non-linear association between duration of oral treatment received for the primary episode and predictive value of relapse is consistent with a previous analysis; patients treated for more than 20 weeks may have included those with a slow response to treatment or who had more complicated or severe disease associated with a higher risk of treatment failure and relapse [7] . Bacteremia and multifocal infection during the primary episode have been identified as risk factors for relapse compared to patients who did not have relapse [7]; however , these two variables were not significantly different between the relapse and re-infection groups . B . pseudomallei isolates obtain from patients with primary infection and re-infection were not resistant to amoxicillin-clavulanic acid and doxycycline , a finding that is consistent with previous studies [38] , [39] . Acquired antimicrobial resistance in relapse organisms was also uncommon . A number of factors may relate to this: acquired resistant to ceftazidime is infrequent and related to fatal outcome during the acute episode of infection [40]; acquired resistance to carbapenems has never been observed in our patients; and patients who had incomplete treatment with oral eradicative drugs mainly abandoned their treatment due to drug side effects , which may not increase the risk of selection of resistance [1] . This scoring system will not affect prescribing practice relating to the initial treatment of recurrent melioidosis; standard first-line parenteral antimicrobials are recommended for the treatment of both relapse and re-infection as acquired resistance to either ceftazidime or carbapenems is uncommon . In general , first line oral eradicative treatment ( TMP-SMX ) should be used if the organism isolated is susceptible to this drug . However , the scoring system could help to identify the cause of recurrent melioidosis and may lead to individualized oral eradicative treatment and management . Patients with recurrent infection require a detailed history of initial treatment including duration of each drug used and compliance , and any lifestyle modification made by the patient that reduces exposure to environmental B . pseudomallei . For patients with predicted re-infection , first-line eradicative treatment should be used and education provided on prevention of further re-infection . For patients with predicted relapse , efforts should be focused on patient compliance and completion of a course of therapy of adequate duration . The second-line , less effective amoxycillin-clavulanic acid should be used in patients with relapse only where in vivo failure of TMP-SMX is considered possible . We propose that this scoring system can provide timely and important bedside information where bacterial genotyping is unavailable , though it would be important to validate it in different settings , particularly those outside northeast Thailand . | Melioidosis is a serious infectious disease caused by the Gram-negative bacterium , Burkholderia pseudomallei . This organism is present in the environment in areas where melioidosis is endemic ( most notably East Asia and Northern Australia ) , and infection is acquired following bacterial inoculation or inhalation . Despite prolonged oral eradicative treatment , recurrent melioidosis occurs in approximately 10% of survivors of acute melioidosis . Recurrent melioidosis can be caused by relapse ( failure of initial eradicative treatment ) or re-infection with a new infection . The aim of this study was to develop a simple scoring system to distinguish between re-infection and relapse , since this has implications for antimicrobial treatment of the recurrent episode , but telling the two apart normally requires bacterial genotyping . A prospective study of melioidosis patients in NE Thailand conducted between 1986 and 2005 identified 141 patients with recurrent melioidosis . Of these , 92 patients had relapse and 49 patients had re-infection as confirmed by genotyping techniques . We found that relapse was associated with previous inadequate treatment and shorter time to clinical features of recurrence , while re-infection was associated with renal insufficiency and presentation during the rainy season . A simple scoring index to help distinguish between relapse and re-infection was developed to provide important bedside information where rapid bacterial genotyping is unavailable . Guidelines are provided on how this scoring system could be implemented . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] | 2008 | A Simple Scoring System to Differentiate between Relapse and Re-Infection in Patients with Recurrent Melioidosis |
Translocation of the Helicobacter pylori ( Hp ) cytotoxin-associated gene A ( CagA ) effector protein via the cag-Type IV Secretion System ( T4SS ) into host cells is a major risk factor for severe gastric diseases , including gastric cancer . However , the mechanism of translocation and the requirements from the host cell for that event are not well understood . The T4SS consists of inner- and outer membrane-spanning Cag protein complexes and a surface-located pilus . Previously an arginine-glycine-aspartate ( RGD ) -dependent typical integrin/ligand type interaction of CagL with α5β1 integrin was reported to be essential for CagA translocation . Here we report a specific binding of the T4SS-pilus-associated components CagY and the effector protein CagA to the host cell β1 Integrin receptor . Surface plasmon resonance measurements revealed that CagA binding to α5β1 integrin is rather strong ( dissociation constant , KD of 0 . 15 nM ) , in comparison to the reported RGD-dependent integrin/fibronectin interaction ( KD of 15 nM ) . For CagA translocation the extracellular part of the β1 integrin subunit is necessary , but not its cytoplasmic domain , nor downstream signalling via integrin-linked kinase . A set of β1 integrin-specific monoclonal antibodies directed against various defined β1 integrin epitopes , such as the PSI , the I-like , the EGF or the β-tail domain , were unable to interfere with CagA translocation . However , a specific antibody ( 9EG7 ) , which stabilises the open active conformation of β1 integrin heterodimers , efficiently blocked CagA translocation . Our data support a novel model in which the cag-T4SS exploits the β1 integrin receptor by an RGD-independent interaction that involves a conformational switch from the open ( extended ) to the closed ( bent ) conformation , to initiate effector protein translocation .
Infection with the gastric pathogen Helicobacter pylori ( Hp ) is associated with a spectrum of pathologies , ranging from mild gastritis to peptic ulcers and gastric cancer [1] . However , the molecular mechanisms underlying the development of Hp-associated gastroduodenal diseases are still poorly defined . Two major virulence factors of Hp that have been associated with disease induction are the vacuolating cytotoxin ( VacA ) and the cytotoxin-associated antigen A ( CagA ) , both of which are delivered into eukaryotic target cells . VacA , a secreted multifunctional protein toxin , induces intracellular vacuoles in epithelial cells , inhibits T lymphocyte proliferation and modulates T cell function [reviewed in 2] . Using the β2 integrin subunit CD18 as a cellular receptor for uptake [3] , VacA efficiently down-regulates transcription of several cytokines or chemokines in T cells [4] . CagA , an immunodominant protein of 120–170 kDa , is encoded on the cag pathogenicity island ( cag-PAI ) . The cag-PAI comprises a total of 27 genes , encoding the cag-Type IV Secretion System ( T4SS ) in Hp [5] . Upon direct contact with gastric epithelial cells , CagA is translocated into host cells via the cag-T4SS [6] and immediately tyrosine-phosphorylated at a variable number of so-called EPIYA motifs by kinases of the Src and c-Abl family [7] , [8] . CagA interacts with a large set of host proteins in phosphorylation-dependent and -independent ways and is considered as a bacterial oncoprotein that exerts multiple effects on host signal transduction pathways , the cytoskeleton and cellular junctions [reviewed in 9] . A further hallmark of cag-PAI positive Hp strains is their ability to induce the secretion of chemokines upon contact with epithelial cells , such as interleukin-8 ( IL-8 ) [10] . The function of each cag-PAI-encoded component for CagA delivery and IL-8 secretion has been studied by a systematic mutagenesis approach [11] . Translocation-competent Hp strains harbour membrane protrusions consisting of a central filament , carrying on its surface the cag-PAI encoded proteins CagY ( HP0527 ) , a VirB10 homologous protein [12] , CagX ( HP0528 , VirB9-homologue ) and CagT ( HP0532 , VirB7-homologue ) [13] . Although the ultrastructure and the biochemical composition of these protrusions has not been clarified yet , we would refer to these structures as type IV secretion system pili . Furthermore , the α5β1 integrin heterodimer has recently been identified as a receptor for the Hp pilus-associated adhesin CagL [14] . Integrins represent a family of about 24 different αβ heterodimeric receptors that mediate cell-cell , cell-extracellular matrix and cell-pathogen interactions and govern migration and anchorage of almost all kinds of cells . Each of the non-covalently associated subunits contains a large N-terminal extracellular domain , a transmembrane segment and a short C-terminal cytoplasmic tail . Affinity for biological ligands is regulated by inside-out and outside-in signalling . The bent conformation of the integrin heterodimer represents the physiological low-affinity state , whereas inside–out signalling and ligand binding induces a large-scale conformational rearrangement , in which the integrin extends from the bent into an extended , open conformation [15] . Hp T4SS-pilus-associated CagL was suggested to bind via its arginine-glycine-aspartate ( RGD ) motif to α5β1 integrin , a process described as essential for CagA translocation and activation of focal adhesion kinase ( FAK ) and Src kinase [14] . In the present study we show that further components of the cag-T4SS , such as CagY ( HP0527 ) and the effector protein CagA interact with distinct extracellular domains of β1 integrin . These components are located along or at the tip of the T4SS-pilus . We propose a model that suggests conformational changes of the integrin heterodimer as a basis for CagA translocation .
Hp translocates its effector protein CagA via the cag-T4SS into a number of different cell types in vitro [16] . Recently it was shown that CagA translocation is dependent on the interaction of the Hp T4SS-pilus-associated protein CagL , binding in an RGD-dependent way to α5β1 integrin on the host cell [14] . However , nothing is known about the mechanism of CagA translocation and the involvement of other T4SS components in this process . Using a different approach , we also identified β1 integrin as a cellular receptor for the cag-T4SS . Three independent Hp strains ( P12 , P145 , P217 ) were applied to study host cell requirements for CagA translocation using several human and animal cell lines ( data not shown ) . Of special interest were human promyelocytic leukaemia ( HL60 ) cells , which were fully competent for CagA translocation ( Figure 1A , lanes 1–3 and 7 ) , whereas , HL60 cells differentiated to a granulocyte-like phenotype ( dHL60 cells ) revealed only a very weak CagA-P signal ( Figure 1A , lanes 4–6 and 8 ) . Thus , the capacity of Hp to translocate CagA varies considerably , even for the same type of cell , dependent on its cellular differentiation stage . Flow cytometry revealed elevated β2 , but significantly reduced β1 integrin levels on the surface of dHL60 , as compared to HL60 cells ( Figure 1C ) . We therefore concentrated on β1 integrin as a potential receptor for the T4SS . CagA translocation was completely absent for epithelial ( GE11 ) or fibroblast-like ( GD25 ) β1 integrin knockout cells , but was functional in genetically complemented GE11β or GD25β cells ( Figure 1B ) [17] . In agreement with Kwok et al . [14] , these data independently confirmed the important finding that β1 integrin is essential for CagA translocation . CagL is the only protein encoded on the cag-PAI which carries an RGD motif and therefore might be recognized by the α5β1 integrin receptor in a typical integrin/ligand–like fashion . Whereas Kwok et al . [14] specifically concentrated on the CagL/α5β1 integrin interaction , we chose a systematic approach to identify possible T4SS-integrin interactions and applied a yeast two-hybrid ( YTH ) screen using the GAL4 Matchmaker system ( Clontech ) ( Figure 2A ) . Since various cell lines expressing different α/β1 integrin combinations proved successful for CagA translocation ( data not shown ) , the β1 subunit was considered as important and the extracellular portion of the human β1 integrin gene was used as bait . As prey for the YTH screen each of the 27 cag-PAI-encoded proteins were assayed [18] . Positive interactions were obtained for the extracellular part of β1 integrin with the N-terminal region of CagA ( HP0547a ) , the C-terminal ( VirB10-homologous ) portion of CagY ( HP0527c ) and with CagI ( HP0540 ) ( Figure 2A , Figure S1 ) . Similar results were obtained when bait and prey were exchanged ( data not shown ) . To confirm the YTH data , pulldown experiments using Hp T4SS-associated proteins were performed ( see Figure S2A for preparation of extracts ) using functional α1β1 and α5β1 integrin heterodimers ( Chemicon ) coupled to magnetic beads . CagA was specifically pulled down from wt cell lysates by α1β1 or α5β1 integrin beads , but not by controls ( Tris-blocked beads ) ( Figure 2B ) . The ectopic expression of cagA from the shuttle plasmid pJP66 [19] in a P12ΔPAI strain demonstrated that CagA alone is able to interact with β1 integrin without any other component of the cag-PAI . Preferentially the upper band of CagY and only small amount of the lower band of CagY was pulled down by the same procedure ( Figure 2B , lower panel ) . Again , precipitation of CagY from a cagA-negative Hp background confirmed an interaction of CagY with integrin , independent of CagA . The putative interaction of β1 integrin with CagI could not yet be verified by pulldown assays , due to the lack of a specific functional antibody against CagI . We therefore used as a further method a cell-based assay to determine binding of the corresponding GST-Cag fusion proteins to β1 integrin-proficient ( GE11β ) , versus β1 integrin-deficient cells ( GE11 ) by flow cytometry ( Figure 2C and Figure S2B , C ) . GST did not bind β1–integrin-dependent , but purified GST-CagA , GST-CagI and GST-CagYc bound significantly more efficiently to GE11β as compared to GE11 cells ( Figure 2C ) . Interestingly , GST-CagY , but not the other GST fusion proteins , bound more efficiently when the integrins were activated by Mn2+ . The Yersinia invasin ( GST-Inv397 ) [20] , known to specifically bind β1 integrin , showed a similar behaviour in these binding assays as the GST-Cag proteins ( Figure 2C ) . To exclude that these GST fusion proteins would bind unspecifically to the GE11β cells , we also generated unrelated cag-PAI GST fusion proteins , such as GST-Cagβ ( HP0524 ) , GST-CagG ( HP0542 ) and GST-CagZ ( HP0526 ) , which did not show β1 integrin-specific binding to the cells ( Figure S2B ) . These data confirmed the β1 integrin-specific binding of CagI from the YTH assay and verified CagI as an additional cag-T4SS component interacting with β1 integrin . Unexpectedly , neither α1β1 , nor α5β1 integrin beads specifically pulled down CagL from membrane or soluble fractions of Hp wt cells using our precipitation conditions ( data not shown ) . We therefore also generated GST-CagL and GST-CagL-RAD mutant protein . Although both purified proteins revealed a rather weak interaction to β1 integrin , the binding was completely independent from the RGD motif of CagL ( Figure S2C ) . Taken together , these data verified CagA ( the translocated effector protein ) , CagY and CagI as direct interaction partners of different β1 integrin heterodimers . The fact that β1 integrin in combination with different integrin α chains ( α1 , α5 ) precipitated the Cag proteins confirmed that these proteins bind to the β1 subunit , rather than the α subunit of the heterodimer and strongly support the YTH results . To allow binding of CagA , CagY and CagI to the integrin receptor , these T4SS components should be accessible at the surface of the T4SS pilus . CagY is known as an essential component of the membrane-spanning T4SS complex , but in addition its surface- or T4SS pilus-association has also been demonstrated [12] , [13] . In addition to CagY , we also verified CagA on the pilus by field emission scanning electron microscopy ( FESEM ) ( Protocol S1 ) ( Figure S3 ) [12] . Anti-CagA-coupled gold particles preferentially labelled the pilus tip , with one or rarely two gold grains only , but no staining of the pilus base and only rare background staining of the bacterial or eukaryotic cell surfaces was visible ( Figure S3A-I ) . To investigate a binding of the cag-T4SS pilus to β1 Integrin during the infection process , confocal laser scanning microscopy ( CLSM ) and life cell imaging were applied . The gfp-expressing Hp P12 wild type ( wt ) strain , but not the equally well binding P12ΔPAI mutant strain , showed a rapid co-localization with β1 Integrin upon infection of AGS cells ( Figure 1E ) . Quantification data revealed that roughly 10% of Hp P12 wt or P12ΔcagA , but only 2 . 5% of P12ΔPAI bacteria co-localized with β1 Integrin ( Figure 1D ) . A P12ΔcagA deletion mutant still showed co-localization to β1 integrin , which can be explained by its binding via CagY or CagI . We wondered why CagL was detected neither in our YTH screen , nor the pulldown assays . To reassess the described RGD-dependent interaction of CagL and α5β1 integrin [14] , we first generated a defined cagL deletion mutant in Hp P12 . The strain was genetically complemented in the Hp recA locus by mutated cagL genes encoding RAD , RGA or ΔRGD versions of CagL ( Figure 3A ) . AGS cells infected with the P12ΔcagL strain failed to translocate CagA and to induce IL-8 , as described earlier [11] , [14] , but surprisingly all three distinct Hp cagL mutant strains ( cagL-RAD , cagL-RGA and cagLΔRGD ) behaved identical to the P12 wt strain concerning CagA translocation , as well as IL-8 induction ( Figure 3B ) . This clearly indicated that under infection conditions an RGD-mediated interaction of CagL with α5β1 integrin either is not existent , or not necessary for CagA translocation , or for IL-8 induction . To judge the specificity and the strength of CagA binding to β1 integrin , surface plasmon resonance measurements were performed . The expression of recombinant CagA is problematic because the protein is rapidly degraded [21] , [22] . We considered this property as we purified a stable N-terminal fragment ( 100 kDa ) of CagA , lacking the C-terminal 33kDa domain [22] . The protein was used for binding studies with purified α5β1 integrin and αVβ3 integrin ( Clontech ) as a negative control . CagA binds with high affinity to α5β1 integrin ( KD = 0 . 153+/−0 . 096 nM ) ( Figure 3C ) and with a 2-log higher KD value to αVβ3 integrin ( KD = 33 . 4+/−18 nM ) ( Figure 3D ) , demonstrating the avidity and the specificity of CagA for β1 integrin binding . Thus , the KD value of α5β1 and CagA is approximately hundredfold lower as compared to the same integrin with its cognate ligand , fibronectin , which is dependent on an RGD motif ( KD = 15 nM ) [23] . This strong and specific binding suggests an important function for this interaction . Specific binding of CagA or CagY to the β1 integrin subunit of the heterodimer on the host cell surface might stimulate integrin clustering and internalization . Cholesterol depletion of AGS cells by methyl-β-cyclodextrin strongly reduced CagA translocation in a dose and time-dependent manner ( Table 1 ) . Calpeptin inhibits the Ca2+-dependent protease calpain , which is required for the release of integrins from the cytoskeleton and for clustering in lipid rafts . Calpeptin treatment completely abrogated CagA translocation ( Figure S4 , Table 1 ) and together with the methyl-β-cyclodextrin data strongly suggested that the organization of β1 integrins into lipid rafts and integrin clustering is essential for CagA translocation , as recently confirmed [24] . To clarify whether β1 integrin-mediated signalling might be necessary for CagA translocation , we used CHO cells stably transfected with either a full-length human β1A gene , a deletion comprising the complete cytoplasmic tail ( β1TR ) or constructs containing only the transmembrane and the common region of the β1A tail ( β1COM ) [25] . Surface expression of β1A integrin was verified by flow cytometry ( data not shown ) . With exception of the CHO vector control , all cell lines were competent for CagA translocation by Hp P217 and with lower efficiency in the P12 strain ( Figure 4A ) . The generally low efficiency of CagA translocation into β1 reconstituted CHO cells might be due to a combination of human β1 integrin with the endogenous hamster α integrin chains . Nevertheless , these data suggest that the cytoplasmic tail of β1 integrin and therefore outside-in signalling via the integrin β1 chain is not essential for CagA translocation , although these data do not exclude that such a signalling occurs under in vivo conditions . To further substantiate these findings and to prove whether CagA translocation via the T4SS may occur independently of β1 integrin signalling , we applied an integrin linked kinase ( ILK ) gene knockdown . ILK binds to the β1 integrin cytoplasmic domain , thereby directly coupling outside-in integrin signalling to a variety of downstream signal transduction pathways [26] . Production of the ILK protein was reduced by ∼80% at 60h after transfection of the ILK siRNA ( Figure 4B ) . Interestingly , knockdown of ILK had no effect on the capacity of CagA translocation by any of the three Hp strains used ( Figure 4B ) , providing strong evidence that CagA translocation solely depends on the extracellular part of β1 integrin and its clustering in lipid rafts , but does not necessarily need the β1 integrin/ILK downstream signalling pathway . β1 integrin heterodimers binding to ligands , such as fibronectin , collagen or laminin , involves the α chain and the β chain [27] . Integrin head domains are able to adopt two alternative conformations , termed open ( high affinity ) and closed ( low affinity ) , which are modulated via binding of metal ions , such as Ca2+ ( stabilising closed conformation , deactivating ) , or Mg2+ or Mn2+ ( stabilising open conformation , activating ) . [27] . Deactivation of integrins by treatment with Ca2+ or EDTA did not interfere , but the intracellular Ca2+-chelator BAPTA significantly reduced either translocation or tyrosine-phosphorylation of CagA in AGS cells . In contrast , extracellular activation of integrins ( Mn2+ ) significantly enhanced CagA translocation and its tyrosine-phosphorylation ( Table 1 ) . Treatment of AGS cells with proteases , such as trypsin or thrombin , which leads to detachment of the cells , but not cleavage of integrin heterodimers , resulted in slightly enhanced , rather than abrogated CagA translocation efficiency ( Table 1 ) . This observation might possibly be explained by an indirect activation of β1 integrin through Proteinase-Activated Receptor-2 ( PAR-2 ) [28] . Natural ligands of α5β1 integrin , such as fibronectin , Yersinia enterocolitica invasin [29] or RGD peptides , even in high quantities , did not alter CagA translocation efficiency ( Table 1 ) . We next used a set of specific anti-β1 monoclonal antibodies ( mAbs ) , including stimulatory ( N29 , 8E3 , 12G10 , 9EG7 , B3B11 ) , inhibitory ( JB1A , AIIB2 ) , and neutral ones ( K20 , LM534 ) , to check for interference with the binding of Cag proteins to β1 integrin and thus eventually block CagA translocation ( Table 1 ) . Antibodies targeting different domains of β1 integrin were shown to bind to AGS cells ( Figure 5A , D ) . These well-defined mAbs cover essentially all domains and conformations of the β1 integrin chain ( Figure 5A ) , but with the exception of 9EG7 , none of them was able to interfere with CagA translocation ( Figure 5B , C and Table 1 ) . According to its function , mAb AIIB2 detached AGS cells from the tissue culture plate . This is due to its interaction with the ligand binding domain and its β1 integrin deactivation , but this binding did not block CagA translocation ( Figure 5B ) . Thus , our data show a novel type of interaction of the cag-T4SS with β1 integrins , which is independent of the integrin α chain and a typical integrin/ligand interactions , as well as RGD-motifs in any of the Cag proteins . Several studies have indicated that a close apposition of the α and β subunits in the membrane-proximal region and the so-called bent structure of the heterodimer are characteristic for the low affinity state of integrins [30] , [31] . In contrast , the extended conformation , characterized by separated legs ( comprising the β1 I-EGF1-4/β-tail and the α chain Calf-1/Calf-2 domains ) , represents the high affinity state [15] . Certain allosteric β1 integrin antibodies are able to modulate integrin activity rather specifically by stabilizing a distinct affinity state of the integrin [32] . 9EG7 is a β1-specific mAb with a binding epitope in the I-EGF2-4 region , which is strongly exposed upon manganese treatment or ligand binding ( Figure 5E ) [33] . The mAb 9EG7 stabilizes and probably fixes conformational changes in the integrin heterodimer , which means that 9EG7 binding of activated integrin will no longer allow its inactivation/bending . Interestingly , mAb 9EG7 completely blocked CagA translocation in AGS cells , whereas Mn2+ alone enhanced , rather than reduced CagA translocation ( Figure 5B ) . A papain-generated Fab fragment of mAb 9EG7 binds in a Mn2+-dependent way to the integrin receptor ( Figure 5F ) , but is unable to block CagA translocation ( Figure 5C ) . A direct competition for binding of 9EG7 and GST-Cag proteins to the same integrin epitope was excluded , as measured by FACS analysis ( Figure 5G ) . The important function of the integrin activation state was supported by the human cervix cell line HeLa . This cell line produced normal levels of β1 integrin on the cell surface , as determined by flow cytometry ( data not shown ) , but was only very inefficiently , or not at all able to act as host cell for CagA translocation by certain Hp strains ( Figure 6A ) . An in vitro phosphorylation assay ruled out a possible defect in CagA tyrosine phosphorylation ( data not shown ) . The binding capacity of mAb 9EG7 revealed a 20% difference between non-activated and activated ( Mn2+ ) state of the cells , whereas for AGS cells the difference was approx . 65% . These data suggest that , due to an unknown mechanism , HeLa cells apparently produce constantly activated β1 integrin , which might be locked in the active state unable to switch back to the closed , inactive conformation , similar to the situation obtained by 9EG7 binding . This could explain the limited ability of Hp to translocate CagA .
The cag-Type IV Secretion System ( cag-T4SS ) of Hp constitutes one of the most important virulence factors of this gastric bacterial pathogen . The mechanism by which Hp translocates CagA into host epithelial cells is still not well understood . An important finding was that the cag-T4SS apparently does not inject its effector protein CagA randomly into target cells , but uses the α5β1 integrin as a cellular receptor for the pilus-associated adhesin CagL [14] . CagL is the only cag-PAI encoded protein carrying an RGD sequence , which is present in certain extracellular matrix proteins and known as a typical integrin/ligand interaction motif [34] . In the present study , we describe the mammalian β1 integrin in different combinations with integrin α chains as a receptor for the Hp T4SS . Convincing data for a functional role of β1 integrin were obtained by the promyelocytic HL60 cell line . Non-differentiated promyelocytic HL60 cells , producing high levels of β1 integrin on their cell surface , but not differentiated dHL60 cells , with low levels of surface-associated β1 , translocated CagA very efficiently ( Figure 1A , C ) . These data were substantiated by using integrin-deficient murine fibroblast ( GD25 ) or epithelial ( GE11 ) cells , which were completely resistant to CagA translocation by Hp , but could be functionally restored upon re-expression of the β1 integrin ( GD25β , GE11β ) ( Figure 1B ) . Here , we performed a systematic YTH screen to identify all proteins of the cag-PAI interacting with the β1 integrin receptor . We identified the translocated effector protein CagA , the C-terminal domain of the secretion apparatus component CagY and CagI as binding partners of the integrin receptor . Biochemical evidence for a receptor function of α1β1 ( a collagen and laminin receptor ) or α5β1 integrin ( a fibronectin receptor ) was obtained by ( i ) pulldown experiments using Hp lysates ( Figure 2B ) , ( ii ) direct binding of the corresponding GST-Cag fusion proteins to β1 integrin as determined by FACS analysis ( Figure 2C ) , or ( iii ) by surface plasmon resonance measurements ( Figure 3C , D ) . CagA binds β1 integrin with a significantly lower KD value as α5β1 integrin binds in a RGD dependent way to fibronectin , its natural ligand . CagA affinity for αVβ3 is significantly lower ( approx . 100-fold ) as compared to α5β1 , demonstrating the high specificity of CagA for the β1 heterodimer . CagA also binds with significantly higher affinity as postulated for CagL to α5β1 integrin . CagA carries a C-terminal translocation signal , but also the N-terminus is essential [19] . So far , the role of the N-terminal portion of CagA had remained elusive , but this specific binding to β1 could explain its important role in the translocation process . The exceptionally high affinity of CagA for the integrin receptor might compensate for the relative low abundance of CagA at the tip of the cag-T4SS pilus and suggests an important function for the surface-associated CagA . The integrin binding might have a structural role in triggering integrin rearrangements , whereas only the cytoplasmic ( non-pilus-associated ) CagA might act as the translocated effector molecule when a translocation-competent configuration has been established ( see model Figure 7 ) . Whereas our data support the essential role of β1 integrin for the process of CagA translocation , the RGD-dependent binding of CagL to the integrin receptor could not be verified . Using Hp extracts , we were able to show here a direct interaction of native ( non-recombinant ) CagA or CagY with integrin heterodimers , however we could not confirm an interaction of native CagL with the α5β1 integrin . It is possible that in the native pilus-associated CagL the RGD motif is buried within the protein and not accessible to interaction . In recombinant overexpression systems , this motif could be exposed , due to partially incorrect folding . Our genetic complementation data support this theory . We are able to successfully rescue CagA translocation with the complementation of CagL mutants , independently of the RGD status of the protein . Possible failures in complementation are known for Hp due to frequent secondary mutations , often in the cag-PAI [35] . In support of these genetic data we also showed that binding of GST-CagL protein to β1 integrin is very low , as compared to CagA , CagI or CagYc . More important , the binding of purified GST-CagL to β1 integrin was completely independent from its RGD motif . Thus , we show on the functional as well as on the binding level that the RGD motif of CagL is not essential for the protein function . To further study the type of interaction between the β1 integrin heterodimer and the Cag proteins , we used typical β1 integrin ligands , such as RGD peptide , fibronectin or Yersinia invasin protein , to possibly interfere with CagA translocation ( Table 1 ) . None of these known ligands was able to block CagA translocation , indicating that the Cag proteins use different sites on the integrin for interaction . Kwok et al showed that Escherichia coli strain HB101 that expresses Yersinia invasin inhibited CagA phosphorylation in AGS cells . It might be possible that E . coli expressing invasin on the surface could sterically inhibit H . pylori to bind and translocate CagA , just by blocking the access to the integrins . Interaction of integrins with its ligands results in integrin clustering . Inhibition of integrin clustering into lipid rafts using methyl-β-cyclodextrin or calpeptin strongly reduced CagA translocation , indicating that Hp-mediated clustering of β1 integrin heterodimers on the cell surface might be essential for this process . To determine whether integrin signalling might play a role for CagA translocation , signalling-deficient , truncated versions of β1 integrin receptor were used . Unexpectedly , neither the β1 integrin cytoplasmic tail , nor signalling via the integrin linked kinase was necessary for CagA translocation , indicating that only the extracellular domains of the β1 integrin is important . CHO cells derived from hamster generally showed a lower CagA translocation efficiency as compared to human AGS cells ( Figure 4A ) . We assume that human/hamster integrin heterodimers , generated upon transfection are the reason , an effect also seen for murine GE11 cells ( human/mouse integrin heterodimers ) ( Figure 1B ) . H . pylori P217 shows a very strong CagA phosphorylation in AGS cells due to its high number of EPIYA motifs ( 8 motifs as compared to 4 in P12 ) , resulting in more efficient tyrosine phosphorylation as P12 in CHO cells . To obtain insight which domains of the extracellular part of the integrin receptor are important , a set of defined monoclonal antibodies against various β1 integrin domains were applied ( Figure 5A , Table 1 ) . None of these antibodies , even those blocking the integrin ligand interaction ( AIIB2 , 12G10 ) , were able to block CagA translocation , except mAb 9EG7 . This is in contrast to many viruses , which use integrins as receptors or co-receptors for entry into different host cells [36]–[38] . Most viruses known to use integrins as entry receptors have been shown to do so by extracellular matrix ( ECM ) protein mimicry , which means that viral proteins contain an RGD or any other conserved integrin recognition motif . Thus , specific antibodies , which block integrin ligand interaction , usually abrogate virus infection in vitro [36]–[38] . Taken together our data suggest that the cag-T4SS uses the extracellular portion of integrin to mediate entry of the effector protein into the cell by a different mechanism , probably independent from ECM protein mimicry and the usual integrin ligand interaction . What is the difference in the effect of mAb 9EG7 in comparison to all the other β1 specific mAbs used in this study ? 9EG7 binds an epitope in the β1 integrin which is close to the fulcrum at the genu and is buried in the inactive ( bent ) state of the integrin receptor . Mn2+ treatment or ligand binding opens the integrin into the extended conformation and the epitope is free for antibody binding . When 9EG7 is bound , the integrin cannot move back into the bent conformation , probably due to sterical problems with the bulky Fc part of the antibody or its ability to crosslink integrin chains . In addition , we cannot exclude the possibility that 9EG7 might prevent the interaction of the β1 integrin subunit with a co-receptor necessary for the translocation process , although there is no evidence for a co-receptor being involved . The 9EG7 Fab fragment still needs activation of the integrin for binding ( Figure 5E , F ) . This indicates that its binding is unchanged , but the lack of the Fc chain will not cause the effects presumed for the complete mAb , due to the smaller size of the Fab fragment and its inability to crosslink . Our data lead us to propose a model whereby the rearrangement of the integrin from the extended , open conformation , binding the T4SS components , to a bent conformation ( bent closed ) is an essential step in the process of CagA translocation ( see Figure 7 for a model ) . We propose that this mechanics of the integrin , which is associated with a closer approximation of the integrin head to the cellular membrane [27] , brings the pilus closer to the cellular membrane . When this conformational change is inhibited , CagA translocation cannot occur .
Antibodies against phosphotyrosine were obtained from Santa Cruz ( PY99 ) or Upstate ( 4G10 ) , polyclonal horseradish peroxidase ( HRP ) and alkaline phosphatase-conjugated anti-mouse IgG , anti-rat IgG and anti-rabbit IgG antisera , HRP-conjugated streptavidin , Heptakis ( 2 , 6-di-O-methyl ) -β-cyclodextrin ( Heptakis ) , fibronectin from human plasma , RGD ( Gly-Arg-Gly-Asp-Ser-Pro-Lys ) and RAD ( Gly-Arg-Ala-Asp-Ser-Pro-Lys ) peptides , protease inhibitors PMSF , Leupeptin and Pepstatin were obtained from Sigma . Purified human α1β1 ( from smooth muscle ) , α5β1 ( from placenta ) and αVβ3 ( from placenta ) were purchased from Clontech ( Millipore ) . Integrin α5β1 and monoclonal anti-β1 integrin Clone LM534 were purchased from Chemicon International . CD29 FITC and CD18 PE were purchased from BD Biosciences , and anti-β1 integrin antibody ( Clone 4B7 ) was from Calbiochem . Rat anti-human β1 integrin inactivating antibody AIIB2 was extracted from hybridoma cells supernatant . For antibodies and their sources see Table 1 . To detect CagL , a rabbit antibody against a purified CagL fusion protein was used [18] . The Invitrogen system consisting of the entry vector pDONR207 and the destination vectors pDEST-GADT7 ( prey vector ) and pDEST-GBKT7 ( bait vector ) were used . Yeast two-hybrid bait and prey libraries were generated comprising the external β1 integrin gene sequence and the cag-PAI genes . For the cag-PAI genes , 22 full-length open reading frames ( excluding N-terminal signal sequences ) , and 10 partial open reading frames were amplified from chromosomal DNA of strain 26695 by nested PCR , and cloned in the bait and prey vectors exactly as described [18] . Bait and prey plasmids were transformed into the haploid Saccharomyces cerevisiae strains Y187 and AH109 . Diploid yeast cells were selected after mating and selection on SD medium lacking tryptophan ( Trp− ) and leucine ( Leu− ) , thus generating all possible combinations of bait and prey plasmids . After growth on SD-Trp−Leu− medium , yeast colonies were transferred to SD-Trp−Leu−His− medium in order to select for interactions . Growth after 3 to 6 days indicated bait-prey interactions . Additionally , the stringency of this screen was enhanced by selection on SD-Trp−Leu−His− medium containing the competitive inhibitor 3-aminotriazole ( 5 mM ) . Cells were infected with Hp at 70–90% confluency with a multiplicity of infection ( MOI ) of 60 . For synchronization experiments , cells were detached with PBS/2 mM EDTA , seeded and after 24 hours synchronized overnight in serum free media . Before infection , RPMI ( GIBCO ) complete media ( CM ) containing 10% Fetal Calf Serum ( GIBCO ) was added to cells , counting this point as time 0 . To test different inhibitors , 1 , 2 and 4h infections were performed after 60 min from addition of CM . After infection , supernatants from 2 & 4h experiments were collected , cells were harvested in PBS with protease inhibitors ( pepstatin 1µM , leupeptin 1µM , PMSF 1mM ) and phosphatase inhibitor sodium vanadate ( 1mM ) . Harvested cells were centrifuged at 500×g for 10 min at 4°C and pellets lysed in RIPA buffer with protease and phosphatase inhibitors and DNase I for later SDS-PAGE and immunoblot analysis under non-reducing conditions . SiMAG magnetic beads ( Chemicell ) were coated with 50 µg α5β1 integrin/10 mg beads following the manufacturer's instructions . Beads were saturated with 1 M Tris-HCl ( pH 7 . 5 ) . 1 ml Hp ( OD550 of 2 ) in PBS with protease inhibitors was treated with lysozyme ( 10 mg/ml , 4 mM EDTA ) for 30 min at RT , DNase I was added ( 1 µg/ml ) and bacteria were lysed by ultrasonication on ice . Soluble proteins ( Soluble I ) and membranes were separated by ultracentrifugation . Membranes were resuspended in 1 ml HSL ( High Salt Lysis , 25mM Tris-HCl , pH 7 . 4 , 0 . 05% Triton-X100 , 4 mM MgCl2 , 3 mM MnCl2 , 150 mM NaCl ) buffer with protease inhibitors , sonicated and centrifuged at 4°C , 13 . 000 rpm for 1min to collect the soluble fraction ( Soluble II ) . Soluble II was used for protein pulldown . After pulldown , 3µl beads were incubated at 4°C for 1 h , washed 3 times with HSL 400 ( HSL with 400 mM NaCl ) buffer , boiled and used for SDS-PAGE ( non-reducing conditions ) . Proteins were transferred to PVDF membranes , and blotted with the antisera indicated . Blots were routinely stripped and reprobed with the indicated antisera ( α-actin or α-β1 integrin antibodies ) as loading controls . Blots shown are representative of three independent experiments . For co-localization experiments the integrin β1-specific monoclonal antibody 4B7 was labelled with AlexaFluor568 according to the manufacturer's instructions ( 10 mol Alexa Fluor568/mol antibody ) . AGS cells were grown in 35 mm glass bottom dishes ( MatTek ) to 60–70% confluency . Cells were washed once with PBS ( without Ca2+ and Mg2+ ) and infected with GFP-expressing P12 wt or P12ΔPAI grown on serum-free media at an MOI of 60 . 2 µg/ml Alexa Fluor 568-labelled antibody against β1 integrin was added . Infection was performed for 7 min . at 37°C and PBS was exchanged before microscopy studies . For quantification of co-localization , assays were recorded over a time range of 50s and picture sequences were analyzed for co-localization events of single bacteria and integrin β1 clusters . Percent co-localization was calculated from ratio of bacteria co-localizing with integrin to total adherent bacteria . Imaging was done using an UltraView LCI spinning disc confocal system ( PerkinElmer ) fitted on a Nikon Eclipse TE300 microscope equipped with a temperature- and CO2-controllable environment chamber . Images were taken with the black/white ORCA ER Camera ( Hamamatsu ) . Pictures were taken and edited using LCI UltraView software . For immunofluorescence assays , an Olympus BX 64 microscope and Cell ∧P software were used . CagA gene was cloned into a vector expressing an TEV cleavable His-tag fusion CagA ( pHAR3011–CagA ) as described in [22] . Briefly , the protein was expressed in BL21 cells induced o/n at 20°C with 1mM isopropyl-β-D-galactopyranoside ( IPTG ) . Harvested cells were lysed by sonication in buffer A ( 10 mM Na Phosphate pH7 . 5 , 5mM immidazole , 500 mM NaCl , 10% glycerol ) containing DNaseI , lysozyme , one mini complete EDTA-free protease inhibitor cocktail tablet ( Roche ) . After centrifugation , the supernatant was loaded onto a His-trap column ( GE Healthcare ) and eluted with a linear gradient of immidazole ( 5 to 500 mM ) in buffer A . Two major N-terminal fragments ( 29 kDa and 100kDa , assessed by the His-tag presence ) were eluted together with minor degradation products . Fractions were concentrated and the buffer was exchanged to 10mM Tris pH 7 . 5 , 150mM NaCl . The two fragments were separated by gel filtration using Superdex S75 10/300 GL column ( GE Healthcare ) . Fractions containing ∼95% pure 100 kDa fragment ( residue 1 to approximately 885 ) were pooled , concentrated and reloaded on the same column to ascertain stability . The protein was finally concentrated to 1 . 8 mg/ml . Using a BiacoreX unit , the purified 100 kDa N-terminal fragment of CagA was attached to a CM5 chip ( BiaCore ) using the standard amine coupling procedure . The flow-cell 1 was treated similarly without coupling of a protein and was used as a reference . Integrin binding to the reference was negligible . For the evaluation of the interaction of proteins , a Tris buffer was used containing 24 mM Tris-HCl pH 7 . 5 , 137 mM NaCl and 2 . 4 mM KCl . Injection of the integrin proteins was for 1 min using a flow of 60 µl/min . Subsequently , dissociation was evaluated for 200 sec . Regeneration of the chip took place between each measurement using a solution of Tris 20 mM pH 7 . 4 , 150 mM NaCl , 1 mM MgCl2 , 1 mM CaCl2 and 0 . 0125% Triton , resulting in a stable baseline and retaining activity . BiaEvaluation software ( version 4 . 1 ) was used for the evaluation of the dissociation constant using a 1∶1 langmuir model of binding . 1 mg of 9EG7 mAb ( rat ) ( BD Biosciences ) was digested using beads coupled to Papain ( Pierce ) following the manufacturers' instructions . Fab fragments were detected by western blotting using a rabbit anti rat Fab ( Rockland Immunochemicals ) . Binding capacity of the Fab fragments to AGS cells was evaluated by flow cytometry . Secondary antibodies anti-rat Alexa488 and anti-rabbit Alexa488 were from Molecular Probes . Data are presented as mean+/−SEM . Differences between groups were assessed by the paired , two-tailed Student's t-test , or by the Mann-Whitney U test for unpaired groups depending on the data set of concern ( see figure legends ) . | Integrins are single transmembrane proteins present on almost all types of cells . They are composed of an α and a β subunit , which together form the ligand binding pocket , able to interact with extracellular matrix proteins . The best known binding domain on integrin ligands is the RGD domain . Many bacterial , but also viral pathogens exploit this ligand-binding domain to interact with integrins on the host cell . Helicobacter pylori , a common bacterial pathogen associated with gastric diseases , was recently added to this list . One of H . pylori's most important factors associated with gastric pathologies is the CagA protein . This protein is directly injected into host cells through the Cag Type IV Secretion System ( cag-T4SS ) . Previous studies demonstrated that the cag-T4SS requires integrins for the injection ( translocation ) of CagA into cells . We provide evidence that three proteins , CagA , CagI and CagY , interact with integrins in an RGD-independent way . Additionally , our data point out that the Cag apparatus needs the physical capacity of a β1 integrin heterodimer to change from an active/extended conformation to a closed/bent conformation . This novel kind of integrin interaction opens a new way in which pathogens can use receptors on cells . | [
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"he... | 2009 | Helicobacter pylori Type IV Secretion Apparatus Exploits β1 Integrin in a Novel RGD-Independent Manner |
Newborns are more susceptible to severe disease from infection than adults , with maturation of immune responses implicated as a major factor . The type I interferon response delays mortality and limits viral replication in adult mice in a model of herpes simplex virus ( HSV ) encephalitis . We found that intact type I interferon signaling did not control HSV disease in the neonatal brain . However , the multifunctional HSV protein γ34 . 5 involved in countering type I interferon responses was important for virulence in the brain in both age groups . To investigate this observation further , we studied a specific function of γ34 . 5 which contributes to HSV pathogenesis in the adult brain , inhibition of the cellular process of autophagy . Surprisingly , we found that the beclin binding domain of γ34 . 5 responsible for inhibiting autophagy was dispensable for HSV disease in the neonatal brain , as infection of newborns with the deletion mutant decreased time to mortality compared to the rescue virus . Additionally , a functional beclin binding domain in HSV γ34 . 5 did not effectively inhibit autophagy in the neonate , unlike in the adult . Type I IFN responses promote autophagy in adult , a finding we confirmed in the adult brain after HSV infection; however , in the newborn brain we observed that autophagy was activated through a type I IFN-independent mechanism . Furthermore , autophagy in the wild-type neonatal mouse was associated with increased apoptosis in infected regions of the brain . Observations in the mouse model were consistent with those in a human case of neonatal HSV encephalitis . Our findings reveal age-dependent differences in autophagy for protection from HSV encephalitis , indicating developmental differences in induction and regulation of this innate defense mechanism after HSV infection in the neonatal brain .
Disease due to viral infection is a complex consequence of interactions between both viral and host factors . Herpes simplex virus ( HSV ) infections cause a wide spectrum of outcomes in humans , ranging from asymptomatic acquisition to lethal dissemination and encephalitis [1] . Newborns are particularly susceptible to poor neurologic outcomes of central nervous system ( CNS ) disease from HSV [2] . Over half of neonatal HSV infections result in disseminated disease or encephalitis , with long-term neurologic morbidity in 2/3 of those who survive encephalitis . In contrast , HSV infection in the adult population is often subclinical [3] . Either serotype of HSV may cause disease in newborns ( HSV-1 or HSV-2 ) , but emerging data suggests a rising incidence of HSV-1 genital infection [4] , and a parallel predominance of HSV-1 as a cause of newborn disease [5] , [6] . The disparate outcomes between HSV-infected neonates and adults suggest an age-dependent difference in susceptibility to disease based on host factors . Multiple layers of immunity are involved in the host response to HSV infection , and differences in immune responses of newborns compared with adults likely contribute to their increased susceptibility [7] . Additionally , multiple host signals important in immunity are targeted by the virus for modulation [8] , and it is not clear how HSV may manipulate these responses differently in the newborn . The HSV γ34 . 5 protein is important for counteracting host antiviral responses to allow viral replication in the nervous system [9] , [10] . It is required for complete virulence in the adult mouse brain [9] , [10] , and alters host responses through the type I interferon ( IFN ) , PKR , and RNAse L signaling pathways during early infection [8] . Within the γ34 . 5 protein are domains that specifically target host translational arrest [11] , [12] and type I IFN response induction through TANK-binding kinase 1 ( TBK1 ) [13] , [14] . Recently , γ34 . 5 has also been shown to specifically inhibit initiation of autophagy in infected cells [15] , [16] . Autophagy is critical for control of neurotropic viruses , including HSV , in the murine CNS [16]–[19] . This mechanism contributes to innate antiviral responses , and is thought to be particularly important in post-mitotic cells such as neurons to avoid cell death . Sensing of viral nucleic acid in an infected cell initiates type I IFN responses , activating the double-stranded RNA ( dsRNA ) -dependent protein kinase PKR which in turn induces autophagy [15] . The HSV γ34 . 5 protein binds and inhibits the autophagy initiating protein beclin 1 , counteracting the host autophagic response [16] , [19] . Production of γ34 . 5 in cells potently inhibits autophagy , preventing formation of the characteristic LC3-GFP puncta in serum-starved cells [17] . Autophagy is important in normal neonatal physiology , and rapid upregulation of the autophagic machinery shortly after birth is required for survival in response to the sudden interruption in nutrient supply [20] . Proper regulation of autophagy is required for normal brain development in the neonate , with functional deficiencies in beclin 1 regulatory proteins leading to poor control of proliferation of CNS cells and excessive apoptotic cell death [21] . Although the basal levels of autophagy are elevated in the critical neonatal period of neurodevelopment , little is known about the neonatal autophagic response in the context of infection . We demonstrate here that in contrast to the adult , the type I IFN response does not alter the outcome of HSV infection in the neonatal mouse brain . However , the HSV γ34 . 5 protein involved in countering type I IFN responses is required for full virulence in the neonatal mouse brain . Further investigation of a specific function of γ34 . 5 revealed that the autophagy inhibiting function of this protein , while important for neuropathogenesis in the adult , is dispensable for disease in the neonatal murine brain . Unlike in the adult , autophagy is activated in the neonatal brain during HSV infection and this activation is independent of type I interferon signaling . Additionally , we provide evidence that autophagy may be activated in human neonatal HSV encephalitis . Our findings suggest development-specific differences in the induction and regulation of autophagy during HSV infection of the CNS .
Prior data from our group and others suggests that host immune responses may contribute to pathology after HSV infection in the newborn CNS [22]–[24] . We used viruses lacking specific genes , which interact with the type I inflammatory response , and mice lacking specific inflammatory signals , to test whether newborn disease would be altered after HSV infection . To determine the contribution of the type I IFN response to the pathogenesis of HSV in the neonatal brain , we inoculated 7-day-old wild-type ( WT ) or type I IFN receptor knockout ( IFNAR KO ) mice intracranially ( IC ) with HSV-1 . Both WT and IFNAR KO newborns had 100% mortality by 3 days after inoculation with 1000 PFU virus ( Fig . 1A ) . Viral titer in the neonatal brain at mortality was equivalent independent of intact type I IFN signaling ( Fig . 1B ) . To confirm this was not an inoculum effect , we inoculated WT neonatal mice at a ten-fold lower dose of 100 plaque-forming units ( PFU ) . There were no differences in time to mortality or overall mortality in the CNS after inoculation with 100 PFU compared to 103 PFU ( Fig . 1A , 1B ) , further illustrating the exceptional susceptibility of the neonatal brain to infection . Mean log-transformed viral titer was between 107 and 109 PFU/g in the groups receiving different inocula , and was actually statistically higher in the group , which received less virus . Our finding that the type I IFN response did not protect newborns from HSV disease suggested that a viral factor important to counteracting this response might be dispensable for CNS pathogenesis in this population . We inoculated 7-day-old newborn mice with an HSV-1 F strain mutant deleted in both copies of the γ34 . 5 gene ( R3616 ) or its marker rescue ( HSV-1 ( F ) R ) [9] . Unexpectedly , the R3616 virus was significantly attenuated for mortality in the WT neonate ( Fig . 2A ) . Replication was also defective in the CNS of WT newborns for the R3616 virus , which was detected from the brain of only one pup ( Fig . 2B ) . Interestingly , several WT pups infected with R3616 had weight loss at day 9–10 after inoculation as the only detectable clinical symptom , with all affected pups regaining lost weight by day 14 . Removing type I IFN signaling in the newborn mouse restored some virulence to the R3616 virus , but it remained attenuated for both mortality and replication relative to HSV-1 ( F ) R ( Fig . 2A , 2B ) . For both WT and IFNAR KO newborns , mortality after IC inoculation and replication of the marker rescue virus HSV-1 ( F ) R in the CNS were comparable to WT HSV-1 F . Results in adult mice inoculated IC with R3616 or HSV-1 ( F ) R were consistent with prior studies [9] and comparable to our observations in newborn mice , with no mortality or CNS replication in WT adults after inoculation with R3616 ( Fig . 2C , 2D ) . Mortality and replication of R3616 in adult IFNAR KO mice demonstrated a similar phenotype as observed in newborns , with some restoration of virulence and viral replication , though not to the same extent as for inoculations with HSV-1 ( F ) R . In contrast to our observations in newborns , adult mice demonstrated a significant dependence on type I IFN signaling after IC inoculation with WT virus , as has been previously shown [25] . WT adult mice had a 50% survival rate after inoculation with 104 PFU HSV-1 ( F ) R , with a median time to death of 10 days ( Fig . 2C ) , while adult IFNAR KO mice had 100% mortality and a median survival time of 3 . 5 days . Among mice that died , HSV-1 titer in the brain was more than 100 fold higher in IFNAR KO adult mice inoculated with HSV-1 ( F ) R compared to WT adult mice ( Fig . 2D ) . These data suggest that the type I IFN response makes a larger contribution to control of WT HSV infection in the adult mouse brain than in the newborn brain , but that HSV-1 F γ34 . 5 is important for neurovirulence in both age groups . Genetic ablation of type I IFN signaling did not restore full virulence to the R3616 mutant virus in either age group . HSV-1 F is a virulent strain of HSV initially isolated from a facial lesion [26] . As the phenotypic characteristics of a mutant virus may depend on the genetic background in which the mutation is made [10] , we conducted similar experiments using viruses on the HSV-1 strain 17 genetic background . WT newborns inoculated with an HSV-1 strain 17 mutant 17termA , which lacks both copies of γ34 . 5 , had delayed mortality relative to littermates inoculated with the marker rescue virus 17termAR ( Fig . 3A ) . However , unlike our observations with viruses on the F background , these mice eventually succumbed to infection , and both viruses were present at similar levels in brain homogenates at the time of death ( Fig . 3B ) . Interestingly , unlike our observation using R3616 , genetic deletion of the type I IFN receptor completely restored virulence of 17termA at the inoculum tested , with comparable replication of both viruses in the CNS of IFNAR KO pups ( Fig . 3A , 3B ) . Results in WT adult mice inoculated IC with 17termA or 17termAR were consistent with prior studies [10] and comparable to our observations in newborn mice , with delayed mortality and evidence of CNS replication in WT adults after inoculation with 17termA ( Fig . 3C , 3D ) . Together , these data indicate that the HSV γ34 . 5 protein plays a critical role in the pathogenesis of WT HSV in both the neonatal and adult brain in vivo . However , the restoration of virulence of the 17termA mutant in IFNAR KO newborns suggests that the type I IFN signaling pathway is induced during infection in the newborn brain , though not to an extent which controls disease from WT virus . Our data suggest limited contribution of type I IFN signaling to protection of newborns against CNS disease from WT HSV-1 , but involvement of HSV-1 γ34 . 5 in promoting CNS disease in both newborns and adults . Type I IFN signaling activates PKR , which promotes induction of autophagy [15] . HSV γ34 . 5 directly inhibits autophagy via an interaction with beclin 1 , contributing to CNS disease in adult mice [16] . A possible explanation for our observations in the newborn is that an already blunted type I IFN response is completely suppressed by WT HSV-1 in the CNS , and autophagy is not sufficiently activated to provide protection against CNS disease . Deletion of γ34 . 5 could allow enough type I IFN signaling to promote activation of autophagy , which is not suppressed by the virus , attenuating disease . To specifically determine the importance of HSV modulation of the autophagic pathway through beclin 1 to pathogenesis in the neonatal CNS , we inoculated mice using a mutant virus ( dBBD ) containing a deletion in γ34 . 5 of 20 amino acids required for beclin 1 binding ( Fig . 4A ) , compared with its rescue virus d68HR . Interestingly , neonatal mice inoculated with the dBBD mutant virus had a slightly shorter time to mortality and an increased overall mortality compared to d68HR ( Fig . 4B ) . Furthermore , viral replication of the dBBD virus in the neonatal mouse brain was similar to the d68HR rescue virus ( Fig . 4C ) . These observations were not due to absence of production of beclin 1 in newborn brains , as protein was detected at similar levels to adult mice ( Fig . 4D ) . In contrast to newborns , adult mice inoculated with the dBBD virus had a significantly increased overall survival compared with adult mice inoculated with d68HR ( Fig . 4E ) , and replication of dBBD in the adult CNS was reduced relative to d68HR ( Fig . 4F ) , consistent with prior reports [16] . These results demonstrate that a domain of HSV γ34 . 5 important for binding beclin 1 to inhibit autophagy is dispensable for mortality and viral replication in the neonatal mouse brain , but important in the adult . The observation that a beclin binding domain of HSV-1 γ34 . 5 is dispensable for HSV pathogenesis in newborn brains ( Fig . 4 ) is consistent with the hypothesis that there is defective activation of autophagy by the newborn host after infection . To investigate the induction of autophagy in the newborn CNS after infection , we inoculated WT neonatal mice IC with WT HSV-1 and sacrificed them for immunohistochemical ( IHC ) analysis three days later , at the height of neurologic symptoms but prior to mortality . Several regions in the neonatal CNS stained positive for HSV antigen , which was most commonly detected in the hippocampus , caudate , putamen , and cerebellum ( Fig . 5A ) . Sections adjacent to those found positive for HSV were evaluated further for the autophagy markers LC3 and p62 . Surprisingly , these markers were floridly positive in the neonatal mouse brain after infection with WT virus , which retains beclin 1 binding activity . Abundant LC3 and p62 were similarly detected in infected regions of neonatal mice infected with the dBBD virus ( Fig . 5A , middle panel ) , as would be expected with a virus unable to inhibit autophagy . Similar staining of brain regions from control uninfected neonatal mice lacked immunoreactivity for LC3 and p62 . IHC analyses of similarly infected adult brains demonstrated HSV antigen in several different brain regions , including the hippocampus , caudate , putamen , cerebellum , periaqueductal gray , and cortex . However , in adjacent sections these regions were absent for p62 and LC3 when WT virus was used ( Fig . 5C , top row ) , but detectable in adult brains after infection with mutant HSV-1 deleted for beclin 1 binding ( dBBD ) ( Fig . 5C , middle row ) , consistent with the expected inhibition of autophagy by WT but not dBBD virus . Although LC3 is specifically incorporated into the developing autophagosome [27] , [28] , detection by IHC analysis indicates activation of the autophagic process , but not completion . To demonstrate formation of mature autophagosomes in the neonatal brain during HSV infection [29] , we imaged infected tissue by transmission electron microscopy . We detected abundant cytoplasmic double membrane vesicles characteristic of autophagosomes , containing electron-dense bodies consistent with HSV virions ( Fig . 5B ) . Taken together , these results demonstrate autophagy is activated in the murine neonatal brain after infection with either WT or dBBD HSV-1 , but in adult brains only when HSV-1 is unable to interact with beclin 1 . We have presented evidence that autophagy is activated in the neonatal brain during HSV infection ( Fig . 5 ) , despite data supporting a limited contribution of type I IFN signaling to protection of newborns against CNS disease from WT HSV-1 ( Fig . 1–3 ) . This led us to investigate the contribution of type I IFN signaling to activation of autophagy in the newborn brain . We inoculated IFNAR KO neonatal mice IC with either the dBBD mutant virus or its marker rescue , d68HR . Similar to our findings in WT neonatal mice ( Fig . 4 ) , there was no difference in time to mortality or overall mortality between dBBD and d68HR in IFNAR KO neonatal mice ( Fig . 6A ) . Furthermore , viral titers in the IFNAR KO brain were similar at mortality in mice inoculated with the beclin binding mutant HSV or its marker rescue ( Fig . 6B ) . Since autophagy is activated in the WT newborn CNS despite an apparently blunted type I IFN response , we investigated the activation of autophagy in IFNAR KO newborns during HSV infection . Both LC3 and p62 were abundantly positive by IHC on serial sections in IFNAR KO neonatal brains infected with either dBBD ( Fig . 6C , bottom panel ) or d68HR ( Fig . 6C , top panel ) . These data suggest that intact type I IFN signaling is not required for activation of autophagy in the neonatal murine brain during HSV-1 infection . In contrast , in the adult the induction of PKR activity by type I IFN signaling provides a link between type I IFN and activation of autophagy [29] . To confirm this link in the adult brain , we inoculated adult IFNAR KO mice IC with either dBBD or d68HR . HSV-1 suppression of autophagy is not required for virulence in IFNAR KO adults , as mortality from the dBBD mutant was equivalent to the rescue virus levels ( Fig . 6D ) . Additionally , viral titers at mortality were similar for the two viruses in these experiments ( Fig . 6E ) . Consistent with the expectation that type I IFN signaling is required for induction of autophagy , infected IFNAR KO adult mouse brains were absent by immunohistochemical staining for positive markers of autophagy after inoculation with either dBBD or d68HR ( Fig . 6F ) . Taken together , these results suggest that type I IFN signaling is dispensable for effective activation of autophagy in the neonatal mouse brain after HSV-1 infection , but required in the adult mouse brain . Studies of neonatal CNS diseases such as hypoxic-ischemic injury suggest an association between activation of autophagy and apoptotic cell death in the brain [30] . To investigate whether there was similar evidence of apoptosis in regions of the infected newborn brain which demonstrated activated autophagy , we performed TUNEL staining in serial sections from infected mouse brain samples . TUNEL staining was abundant in areas of the neonatal mouse brain infected with wild-type HSV-1 or dBBD ( Fig . 7A ) , but only scantly positive in a region heavily infected with HSV-1 or dBBD in the adult murine brain ( Fig . 7B ) . To further confirm that the DNA fragmentation observed in the infected neonatal brain is due to apoptosis , we stained additional sections for cleaved caspase-3 , a specific marker of apoptotic cell death [31] . Cleaved caspase-3 was detected in regions of the brain in which TUNEL-positive staining was observed ( Fig . 7A ) . Moreover , cells immunoreactive for cleaved caspase-3 were more abundant in the infected neonatal brain than in the adult brain ( Fig . 7B ) . Morphologically , the caspase-3 positive cells were consistent with neurons and adjacent glial cells . Interestingly , foci of apoptotic cells in the brains of HSV-infected neonatal mice coincided with areas of intense staining for autophagic markers , suggesting a link between activation of autophagy and cellular apoptosis during HSV infection of the developing brain . Although animal models of disease often enhance our general understanding of disease pathogenesis in mammalian hosts , we sought to confirm our observations of activated autophagy and apoptosis in newborn HSV encephalitis by studying sections of brain from a human case of neonatal HSV encephalitis . Staining of infected human brain tissue with hematoxylin and eosin ( H&E ) revealed several cells exhibiting cytopathic effects consistent with HSV infection , including eosinophilic nuclear inclusions , nuclear swelling , and multinucleate cells ( Fig . 8 , top row ) . Cells displaying these morphologic changes consistent with HSV infection stained positive for HSV antigen . These HSV antigen-positive cells were morphologically consistent with neuronal and microglial cells . Furthermore , infected cells in adjacent sections were positive for the markers of activated autophagy LC3 and p62 ( Fig . 8 , top row ) , which displayed the granular cytosolic appearance characteristic of autophagic punctae . Regions of activated autophagy also demonstrated TUNEL-positive staining . In comparison , analysis of a similar region of brain tissue at autopsy from a human neonate which died of non-neurologic disease was negative for cytopathic effect by H&E , and staining was negative for markers of autophagy or for HSV antigen ( Fig . 8 , bottom row ) . TUNEL staining in the control sample was only scantly positive . Collectively , these results demonstrate that autophagy and cellular apoptosis are activated during human neonatal HSV encephalitis .
Here we report an age-dependent difference in autophagy as a mechanism for protection from HSV encephalitis . Our key findings are: ( a ) in the newborn brain , HSV remains virulent independent of its ability to bind and inhibit beclin 1 ( Fig . 4 ) ; ( b ) CNS disease from HSV occurs in newborn brain despite the activation of autophagy , which is not inhibited by virus possessing the ability to bind beclin 1 ( Fig . 5 ) ; and ( c ) type I IFN signaling is not required to initiate autophagy in the newborn brain after infection ( Fig . 6 ) . These observations stand in sharp contrast to the situation in the adult brain , in which autophagy is stimulated by type I IFN responses , and HSV-1 requires suppression of autophagy to promote disease . Inflammatory signaling via type I IFN in newborns is generally blunted compared with adults [32] , and consistent with this observation , we found that signals mediated by the type I IFN receptor in the neonate did not alter the outcome of WT HSV-1 infection in the CNS ( Fig . 1–3 ) . However , deletion of the HSV-1 protein γ34 . 5 , which interacts at different points with PKR-dependent signals and participates in countering host type I IFN responses [12] , [33]–[36] , attenuated CNS disease in both neonatal and adult WT mice ( Fig . 2–3 ) . Additionally , IFNAR KO newborns were equally susceptible to disease from virus deleted of γ34 . 5 on the strain 17 background as to the WT virus ( Fig . 3 ) , suggesting that type I IFN responses are not completely absent in the newborn CNS , but are likely to be more easily overcome by suppressive mechanisms present in HSV-1 than are similar signals in the adult CNS . The γ34 . 5 protein is multifunctional and includes domains that interact with different host signaling pathways , including signals mediated by TANK-binding kinase-1 [13] , [32] , the protein phosphatase PP1α to counter host translational arrest mediated by PKR [11] , [12] , and beclin 1 for inhibition of autophagy [15] , [16] . Although we have demonstrated that the autophagy inhibiting function of γ34 . 5 is dispensable for pathogenesis of HSV in the neonatal CNS , the importance of γ34 . 5 in mediating disease suggests that the other functions of this protein contribute to disease in the developing brain , an active area of investigation . Activation of autophagy during inflammatory responses typically involves PKR-dependent mechanisms that are augmented by type I IFN signaling [37] , [38] . We observed this in experiments in adult mice , where absence of the type I IFN receptor resulted in absence of LC3 staining in the CNS ( Fig . 6F ) as compared with WT adult mice ( Fig . 5C ) . In distinct contrast , HSV infection of the neonatal brain activated autophagy independent of type I interferon signaling ( Fig . 6C ) . Moreover , deletion of a domain of γ34 . 5 responsible for binding beclin 1 did not suppress autophagy in the newborn brain ( Fig . 5 ) , consistent with the hypothesis that a beclin 1-independent mechanism promotes autophagy initiation during inflammatory responses in the neonatal brain . Although beclin 1 is considered to be the central initiating protein for autophagy , in some circumstances it may be dispensable for activation of autophagy [39] , [40] . Recent studies suggest that the cellular prior protein ( PrP ) is a positive regulator of autophagy in the CNS during HSV infection [41] . Notably , this study demonstrated that genetic deletion of PrP in adult mice restored virulence of a beclin 1 binding mutant to wild-type HSV-1 levels . Furthermore , in vitro replication of the BBD mutant in PrP-knockout cells was only observed in glial cells , and not mature neurons , consistent with our previous observation that mature neurons are not the primary target of HSV infection in the neonatal CNS [23] . Finally , PrP is produced early in life in the rodent brain [42] , and inflammatory signals outside of type I IFNs may induce PrP production in the brain [43] . Together , these observations suggest that the newborn brain may respond to infection with production of cytokines that promote PrP , which in turn may stimulate induction of autophagy . This provides a possible mechanism for type I IFN-independent , beclin 1-independent promotion of autophagy as we observed in the newborn brain . We observed an association between activation of autophagy and increased apoptosis in infected regions of the newborn but not adult brain ( Fig . 7 ) . Cellular regulation of these processes is complex and overlapping , with upstream events in both processes often triggered by the same signals [44] , [45] . Although autophagy can inhibit the induction of cell death pathways , including apoptosis and necrosis [46] , an association between excessive autophagy and cell death has been proposed , but whether cell death occurs because of autophagy or despite autophagy has been debated [47] . Consistent with our observations in the HSV-infected neonatal brain , models of neonatal hypoxic-ischemic injury identified an association between the activation of autophagy and the presence of apoptotic markers in the hippocampus [30]; knock-out of the essential autophagy gene ATG7 in this model decreased apoptotic cell death . The developing brain differs from the mature adult brain in having both increased neurogenesis [48] and increased neuronal apoptosis [49] , as circuits are sculpted to ultimately create the networks which process information in the mature animal . Control of these processes is complex and not well understood [50] , with even less known about the influence of infection and inflammation on these processes . Our data suggest that infection may perturb developmental regulation of cell death in the nervous system , possibly triggering apoptosis in additional cells during infection . The increased susceptibility to infection in newborns has been the subject of a great deal of study , with numerous differences identified in immune defense of newborns compared with adults [32] , [51] . Our study has identified a previously unappreciated difference in the newborn response to CNS infection relative to the adult , which is associated with increased cell death in the brain . Moreover , we provide evidence that our observations in a mouse model of infection are relevant to human disease , as activation of autophagy was demonstrated in a human case of neonatal encephalitis ( Fig . 8 ) . Combined with prior observations in a different newborn CNS disease [30] , our results suggest that excessive autophagy in the developing brain may more generally contribute to newborn pathology . This distinction from the cytoprotective role of autophagy in the adult has important therapeutic implications . Development of autophagy inducing drugs could provide clinical benefit in adult CNS infections [52] , but differential outcomes associated with development could lead to detrimental responses in a younger population . Future therapeutics in the newborn will need to be catered to the unique physiology of the developing brain .
The HSV-1 F-strain virus ( kindly provided by Bernard Roizman , University of Chicago , Chicago , Illinois , USA ) is a low-passage clinical strain of HSV-1 originally obtained from a facial lesion and isolated in Hep-2 cells [26] . The mutant HSV-1 deleted of both copies of γ34 . 5 , R3616 , and the rescue virus with both copies of γ34 . 5 restored , HSV-1 ( F ) R , were also provided by Bernard Roizman and are previously described [9] . The mutant virus deleted of both copies of γ34 . 5 on the HSV-1 strain 17 background ( 17TermA ) and its marker rescue with both copies of γ34 . 5 restored ( 17TermAR ) were provided by Richard Thompson [10] . The HSV-1 virus deleted in the beclin 1-binding domain of γ34 . 5 encoding amino acids 68–87 ( termed here dBBD ) and its marker rescue control d68HR ( kindly provided by David Leib , Dartmouth University , Lebanon , NH , USA ) were constructed by homologous recombination [16] , [53] . The recombinant HSV-1 expressing GFP from the UL3/4 intragenic region [54] was constructed by homologous recombination ( kindly provided by Yasushi Kawaguchi , Nagoya University Graduate School of Medicine , Nagoya , Japan ) . Vero cells were cultured in Dulbecco's modification of Eagle's ( DME ) medium plus 10% fetal bovine serum ( FBS ) and 1% penicillin-streptomycin , and were used for the propagation and titering of virus . Plaque titrations were performed by standard methods . The mouse strains used have been previously described , including the 129S2 ( WT ) and interferon-α/β receptor knock-out ( IFNAR KO ) mice [55] on the 129S2 genetic background . Mice were maintained in specific-pathogen-free conditions until transfer to a containment facility just prior to infection . Breeding pairs were regularly monitored , with males separated from gravid females prior to delivery . Pups were inoculated at seven days of age , which from an immunologic perspective corresponds most closely to humans at birth [56] . Virus was diluted in PBS containing 1% inactivated calf serum and 0 . 1% glucose ( PBS-GCS ) to deliver a target intracranial ( IC ) inoculum of 1×103 PFU/pup . Infections of 8-10 week old adult mice were included for comparison with newborn infections , with target inocula of 1×104 PFU/mouse . For IC inoculation of either adult or newborn mice , a positive displacement syringe with a 26-gauge needle and a needle guard was used to inoculate 5 µL total volume into the brain . The needle was placed in the approximate region of the hippocampus , equidistant between the lambda and bregma through the left parietal bone lateral to the sagittal suture . Experiments also included control mice injected IC in an identical manner using the same volume PBS-GCS . Infected mice were monitored daily for signs of neurologic disease , including lethargy , seizure , automatisms , ataxia , and hunched posture . Mice displaying severe signs of illness were immediately sacrificed . Brains were harvested from infected and control mice . Mice used for immunohistochemical analysis were perfused as described below . Tissues for titering were weighed , homogenized in DMEM with 5% inactivated calf serum and 1% ciprofloxacin , and sonicated . Tissue homogenates were stored at −70°C until analysis . All statistical analyses were performed using Prism 5 . 01 ( GraphPad Software ) . Kaplan-Meier survival statistical analysis was performed using the log-rank ( Mantel-Cox ) test . Comparisons of viral titers between different groups of mice was done by Student's t-test , using log-transformed values . Anesthetized mice were subjected to intracardiac perfusion with 4% paraformaldehyde in PBS . Whole brains were removed and post-fixed in 4% paraformaldehyde and subsequently embedded in paraffin . Four-µm-thick sections were mounted on glass slides . Antigen retrieval was performed manually using either a high pH Tris or citric-acid based solution ( Vector Labs ) at 95°C for 10 minutes . IHC staining was performed with anti-HSV antigen ( Dako ) diluted 1∶5000 , anti-LC3 ( Nanotools ) diluted 1∶400 , anti-cleaved caspase-3 ( Cell Signaling ) diluted 1∶500 , or anti-p62 ( Abnova ) diluted 1∶2000 with the Vectastain Elite ABC kit ( Vector Labs ) . HRP labeled secondary antibodies were visualized after treatment with the chromagen diaminobenzidine ( DAB , Vector Labs ) . Finally , the slides were washed in tap water , counterstained in Gill's Hematoxylin , and imaged with the EVOS XL core cell imaging system . Western blots were performed on whole brain homogenates using a 1∶1000 dilution of anti-beclin 1 antibody ( BD Biosciences ) and anti- GAPDH ( Abcam ) as a loading control . Blots were visualized and densitometry analysis was performed using the LI-COR Odyssey system . Paraffin-embedded sections were assayed for DNA fragmentation using the TUNEL technique ( EMD Millipore ) and counterstained with Methyl Green ( Vector Labs ) . H&E staining was performed using Gill's Hematoxylin and Eosin Y solution . Neonatal mice were inoculated as previously described with a recombinant HSV-1 expressing GFP [54] to allow for identification of infected regions . Mice were sacrificed at post-inoculation day 3 , the brains were removed , and infected areas were dissected under a GFP scope ( EVOS ) . Fresh tissue was post-fixed in 2% paraformaldehyde , 2 . 5% glutaraldehyde , and 0 . 1 M cacodylate . Samples were embedded in acrylic resin and thin sectioned by standard protocols and imaged with the FEI Tecnai Spirit G2 120 kV TEM . Permission for the use of human postmortem tissue for this study was obtained from the Ann & Robert H . Lurie Children's Hospital of Chicago Privacy Board , in accordance with US Federal Regulations 45 CFR 46 . 160 and 164 . Samples of brain tissue were obtained at autopsy from a seven week old male who presented at one month of age with symptoms of encephalitis , found to be HSV-2 positive in cerebrospinal fluid and subsequently brain tissue , who received treatment with acyclovir prior to death secondary to neurologic devastation . Control tissue was obtained at autopsy from a two day old male delivered at 30 weeks gestational age , who suffered acute gastrointestinal perforation one day prior to death from respiratory failure . There were no identified neurologic or infectious complications contributing to the death of this patient . Animal care and use in this study were in accordance with institutional and NIH guidelines , as set forth in the "Guide for the Care and Use of Laboratory Animals" ( National Academies Press , 2011 ) . Northwestern also accepts as mandatory the PHS "Policy on Humane Care and Use of Laboratory Animals by Awardee Institutions" and NIH "Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training . " All studies were approved by the Northwestern University Animal Care and Use Committee under the Animal Welfare Assurance Number A3283-01 , Protocol 2013–2054 . | Disease after infection with a pathogen results from an intersection between the infectious agent and the host . Newborns are particularly susceptible to infectious illness compared to adults , and HSV infection commonly results in devastating encephalitis . We studied the interaction of HSV with the type I interferon pathway and found that a specific activity of the viral protein γ34 . 5 , which counters host autophagy to promote encephalitis in adults , was not required to cause disease in newborns . Furthermore , autophagy was not inhibited by HSV in the neonate and was not activated by type I interferon signaling , unlike in the adult . Activated autophagy was associated with increased apoptosis , which may contribute to the increased pathology in newborns . Our findings reveal development-specific differences in the pathogenesis of HSV encephalitis , including a distinct role for autophagy in the neonatal brain . | [
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] | 2015 | Differential Reliance on Autophagy for Protection from HSV Encephalitis between Newborns and Adults |
Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development . Each gene's regulatory program is determined by module-level regulation ( e . g . co-regulation via the same signaling system ) , as well as gene-specific determinants that can fine-tune expression . We present a novel approach , Modular regulatory network learning with per gene information ( MERLIN ) , that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks . Using edge- , regulator- and module-based comparisons of simulated networks of known ground truth , we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction , while additionally identifying modular organization of the regulatory networks . We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation . Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses . The inferred networks are enriched for regulators with genetic or physical interactions , supporting the inference , and identify modules of functionally related genes bound by the same transcriptional regulators . Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development .
Regulatory networks that connect regulators ( signaling proteins and transcription factors ) to target genes are core information processing components in cells and control what genes must be expressed when [1]–[3] . Eukaryotic regulatory networks have several organizational properties: ( 1 ) regulatory networks are modular , enabling multiple genes to be simultaneously regulated through the same regulatory mechanisms [4] , [5] , ( 2 ) individual genes are often regulated by multiple transcription factors that combinatorially bind to promoters of genes [6]–[9] . Activation of upstream signaling proteins and their downstream transcription factors alters global gene expression in dynamic ways and often , upstream regulators are themselves regulated , via feedback and feed-forward loops [2] , [10] . These dynamic patterns can be readily quantified through advances in regulatory genomics , enabling us to describe cellular states by signature patterns of expression and chromatin modifications . Computational reconstruction of regulatory networks provide a powerful approach to dissect these states relying on the premise that the expression patterns of genes encoding upstream regulators are predictive of the expression of other target genes of that signaling system [11]–[16] . A major challenge that remains is to combine these regulatory network properties of individual genes and sets of genes in a module to build predictive models of system state . Computational methods for network reconstruction can be broadly classified into two groups: ( 1 ) per-gene methods ( Figure 1B ) , which infer a regulatory network one gene at a time [17]–[21] , and ( 2 ) per-module methods [14] , [22] , [23] ( Figure 1C ) , which infer a regulatory network by grouping similarly expressed genes into modules and inferring a single regulatory program for the module . While the per-gene methods can infer precise regulatory logic of every gene , considering each gene separately ignores the modular organization of networks . On the other hand , per-module approaches learn concise and modular structures , but they simplify the regulatory network by requiring all genes in the module to have the same regulatory program . This simplification comes at the cost of important regulatory information at individual genes , such as variations in transcription factor interactions due to gene-specific promoter architecture . Thus , while per-module approaches succeed in identifying regulators that affect larger module-level behavior , they cannot identify the regulators that are important from an individual genes perspective because they do not incorporate gene-specific parameters . We propose a novel regulatory network reconstruction approach , Modular regulatory network learning with per gene information , MERLIN , that combines the strengths of per-gene and per-module network inference methods ( Figure 1D ) . Specifically , our approach learns separate regulatory programs for each gene , but constrains the network using a probabilistic graphical model such that genes in the same module have similar , but not identical , regulatory programs . Furthermore the algorithm learns both the network structure and network parameters that can be used to predict expression in a test condition . Comparison of our approach to state of the art per-gene and per-module methods clearly identified the strengths of our approach in accurately recovering both edge and module-based regulatory information . We applied our method to published transcriptome measurements of yeast stress responses [24] and a new human embryonic stem cell differentiation dataset . In both processes , MERLIN inferred transcription factors and signaling proteins that work in concert to regulate the same module , allowing us to predict the upstream signaling networks that function together in the cell . We identify regulatory networks recapitulating the combinatorial transcriptional control of amino acid metabolism genes [7] , [24] , and additionally implicate the HOG1 MAP kinase to be the upstream regulator of numerous modules associated with osmotic and cell wall stress . In humans , we predict regulators from major signaling pathways including Notch and Hedgehog pathways for modules associated with the maintenance of pluripotency and with the onset of cellular differentiation .
A mathematical model of a regulatory network has two components: the structure specifies the regulators of a target gene , and the logic , encoded in mathematical functions , describes the sign and magnitude of individual and combinations of regulators that specify the expression of that gene . Several different mathematical functions could relate the expression of the upstream regulators to the mRNA level of a target , e . g . boolean functions , differential equations , probabilistic functions . The MERLIN approach is based on a probabilistic graphical model representation of a regulatory network ( Figure 1A ) [17] , [21] , [25] , [26] . Within our probabilistic graphical model , both genes and their regulators ( which can be targets as well ) are represented as random variables whose associated probability distributions represent the range of values a gene can take in different microarray or RNA-seq experiments . In probabilistic graphical models , the mathematical functions relating the level of a regulator to the level of a gene is a conditional probability distribution , specifying the probability of a target gene to take a specific expression value given the expression values of its regulators . We use a conditional Gaussian model for the conditional distribution , with mean of the Gaussian derived as a linear function of the expression levels of the regulators ( See Materials and Methods ) . We assume that we have measured expression levels of both gene targets and encoded regulators under multiple conditions , and regulators and target genes co-vary under different conditions . To reconstruct the regulatory network from given gene expression data we need to infer both the structure as well as parameters of the mathematical functions . Our network inference approach , MERLIN , combines the two popular strategies of expression-based network inference approaches described above: per-gene [18] , [19] , and per-module [14] approaches . MERLIN learns the gene-specific regulatory programs while imposing a module constraint as a probabilistic prior . Instead of selecting regulators independently for each gene , the prior enables us to take into account regulators that are predicted to regulate other genes co-expressed with in the same module . In this way we impose a “soft” module constraint so that two genes in a module are favored to have similar but not necessarily identical regulators as in a per-module approach . The MERLIN learning algorithm begins with a set of modules , which are typically defined by an expression-based clustering step , and a set of candidate regulators of gene expression ( e . g . all transcription factors , kinases , phosphatases annotated in an organism ) . It then iterates over two steps ( Figure 1E ) : ( a ) a regulator identification step , and ( b ) a module inference step . In the regulator identification , the modules are kept fixed and the regulator sets of each gene are identified by adding new regulators that reduce the prediction error of a gene 's expression value from the expression values of the regulators , while using a probabilistic “module” prior on the graph that favors regulators regulating other genes in 's module , ( Materials and Methods ) . The prior enables us to favor graph structures that are more modular . In the module inference step , genes are grouped into modules using co-expression and co-regulation based on the inferred set of regulators for a pair of genes . Co-expression is measured by the Pearson's correlation between two gene expression profiles . Co-regulation is measured by the similarity of inferred regulators for each gene ( Materials and Methods ) . The algorithm repeats these two phases of the algorithm until convergence . In addition to the module prior , we also use a model complexity prior that penalizes excessive parameters in the model ( for example , due to a large number of regulators ) . Such a complexity prior avoids over-fitting the model to the data . Both the structure complexity prior and the module prior are controlled by user-defined parameters , and can be flexibly adjusted to control how strongly we want to impose each prior . We compared the quality of networks inferred by MERLIN to those inferred from three other algorithms using several criteria defined below . The algorithms include a linear regression per-gene network inference method ( LINEARREGR ) , GENIE3 a state-of-the-art per-gene network inference algorithm [19] , and Module networks ( MODNET ) from Segal et al . [14] . The LINEARREGR approach that we used is a special case of MERLIN where we set the module prior to zero; this served as the baseline to study the gain in performance by adding the “module” constraint in our MERLIN approach . We also considered a Bayesian network as a baseline ( Figure S1 ) , but the performance was much worse than any of the methods above . We used both simulated gene expression data where the ground truth of the networks generating the data were known , as well as real gene expression data where the ground truth networks are not known . Simulated data was generated for networks of different number of genes , n = 100 , 200 , 300 , 400 , 500 , 1000 genes using GeneNetWeaver ( GNW ) [27] . This simulator takes networks as inputs and uses stochastic differential equations to generate simulated expression data . The simulated data had 100 , 200 , 300 , 400 , 500 and 1 , 000 measurements generated by perturbing one node and propagating the system to steady state . The simulated networks were generated so that they were modular , that is , genes in the same module tended to share more regulators than genes in different modules ( Materials and Methods ) . We defined three criteria to compare the inferred networks: ( a ) Edge-based comparison used fold enrichment and the area under the precision-recall curve ( AUPR ) [28] , to assess edge overlap between the simulated ground truth network and inferred networks ( Figure 2A , Figure S1A , Materials and Methods ) , ( b ) Regulator-based comparison measured the number of regulators whose targets significantly matched between the true and inferred networks ( FDR , Figure 2B ) , ( c ) Module-based comparison measured how many regulators associated with modules in the ground truth networks matched regulators associated with these modules in the inferred networks ( Figure 2C ) . The AUPR edge-based comparison requires a ranking of edges and does not require us to specify a particular cutoff . The edge-based fold enrichment , regulator- and module-based measures require use to define a network . Because GENIE3 does not provide a discrete network but rather a ranking of all edges , and since the edge ranking does not translate into edge confidence , we considered networks with the top 20% or the top 40% edges from the GENIE3 output . On simulated data MERLIN outperformed LINEARREGR and MODNET using edge- , regulator- and module-based metrics ( Figure 2A–C ) , suggesting that adding a module prior is beneficial for inferring better networks . MERLIN was significantly better than GENIE3 using fold enrichment ( t-test -value <0 . 003 ) , and both approaches were at par using AUPR ( Figure 2A , t-test -value = 0 . 5 ) , suggesting an overall improved performance than GENIE3 . On both regulator- and module-based metrics GENIE3's performance depended greatly on the threshold used to define a network; in no case was it better than MERLIN , but significantly worse on the module-regulator relations than MERLIN ( t-test -value <0 . 04 ) . We were surprised to see that MODNET did not perform well on the simulated network datasets . This is likely due to the extremely sparse networks MODNET infers . We observe a more comparable performance , although still low , when considering the yeast regulatory networks . We next compared the network inference algorithms using a well-studied yeast dataset from Gasch et al [24] comprising 2 , 355 genes and 466 candidate regulators catalogued in Segal et al . [14] , where regulators included both transcription factors as well as signaling proteins such as kinases or phosphatases ( Figure 3A ) . Because the ground truth network is not available , we assessed the quality of the inferred networks based on their overlap with other reconstructions of yeast transcriptional regulatory networks using ChIP-chip [6] , [9] , ChIP-exo [29] , evolutionary conservation [30] , and curated TF motifs from protein binding microarrays [31] . These networks include edges with some experimental evidence to suggest the presence of a regulatory edge: ( a ) Gordan , network derived from motif instances from position weight matrices from protein binding arrays followed by manual curation [31] , ( b ) Harbison et al . 's ChIP-chip data considering the exponential ( Harbison exp ) and other conditions ( Harbison other ) separately [6] , ( c ) A recent ChIP-chip data under normal ( Venter 25C ) and heat shock ( Venter 37C ) conditions from Venter et al . [9] , ( d ) Yeastract , a public database comprising regulatory edges based on ChIP-chip , and factor knockout [32] , ( e ) Rhee , high-resolution ChIP-exo neworks from Rhee et al for four transcription factors [29] , ( f ) MacIsaac , a network which combined ChIP-chip data [6] , and evolutionary conserved transcription factor motif instances to derive a regulatory edge between a transcription factor and a target gene [30] . While these networks are not perfect in reflecting the ground truth of the yeast regulatory network because ChIP-chip or -seq networks are condition-specific and the conditions do not overlap completely with the conditions from which we have mRNA data , an enrichment in these edges provides support of our inferred networks . Furthermore , the MacIsaac et al . network was used as the gold standard yeast regulatory network by the DREAM consortium [20] . A comparison based simply on the number of edges in the networks showed that MODNET inferred the most sparse network ( Figure 3A ) , including 3 , 185 connecting 1 , 821 genes and 60 regulators , whereas the GENIE3 networks had the most edges ( 20 , 000 at top 20% ) . The size of the networks inferred by MODNET ( 3 , 185 ) , MERLIN ( 6 , 319 ) and LINEARREGR ( 6 , 860 ) most closely matched the size of the MacIsaac network ( 4 , 153 edges ) . Using edge-based measures we found that the network inferred by MERLIN had the highest fold enrichment with the different networks compared to the other methods ( Figure 3B ) ( t-test -value <0 . 003 ) . Interestingly , none of the inferred networks were enriched in Venter et al's network measured in exponentially growing cells , but exhibited enrichment in the network measured in heat shock stress , suggesting all methods can capture condition-specific edges to some extent , and are also internally consistent with each other . Comparisons using regulator-based measures showed that MERLIN's inferred network was as good or better than other inferred networks exhibiting overlap of ChIP-chip targets of as many regulators as any other method ( Figure 3C ) . For module-based comparisons ( Figure 3D ) , since true modules were not known , we used curated gene sets as modules . This included Gene Ontology Slim terms [33] and Yeastcyc bio-chemical pathways downloaded from the Saccharomyces Genome Database [34] . We treated the MacIsaac network as the true network and compared the number of regulator-module relationships from the MacIsaac network with those from the inferred regulatory networks . We found that in all measures MERLIN was better or as good as other methods with only a slight decrease in enrichment for the Ecocyc pathways . In all these measures the relative performance of MODNET was closer to the other networks compared to simulated networks . As a final comparison , we asked whether the modules inferred by MODNET and MERLIN represented targets of specific TFs by examining each module for enrichment of a TFs' ChIP-based targets in the module . This analysis was possible only for MODNET and MERLIN which infer modules but not for any of the per-gene methods . We found both MODNET and MERLIN modules were enriched for the ChIP targets of a large number of transcriptions ( Figure 3A , column 5 ) . This suggests that the module information captured in both MERLIN and MODNET represent co-regulated sets of genes , and allows us to gain new insight into the module-level properties of networks that are not evident in the per-gene methods . Overall , we find that MERLIN performs as well or better than other methods on different types of metrics . Per-gene methods did not reveal any module structure and thus assessing whether TF's targets were associated in modules was not possible . The per-module method , MODNET , performed poorly on edge-based metrics , but had better performance on the yeast regulatory network which had more genes ( although we cannot rule out that simulated networks are not perfect ) . Thus MERLIN combines the strengths of both the per-gene and per-module network inference methods , inferring high quality reconstructions of individual regulatory edges , as well as high confidence target sets localized to specific modules . We next used MERLIN results from the Gasch et al data [24] to study the regulatory network from a module point of view and to gain additional insight into the regulation of yeast stress responses . We focused our attention on 106 modules with five or more genes , which together encapsulated 80% of the genes in the original dataset ( Figure 4A , B ) . To test our approach on another dataset , we applied MERLIN to infer transcriptional regulatory networks in a very different biological context: during differentiation of human embryonic stem cells to neural progenitor cells . Four time courses were available that represent the first seven or eleven days of differentiation from the pluripotent state ( either ES or iPS cells ) to states that represent early neural precursor cell types . Each cell line was treated by two different conditions to induce neural differentiation ( Materials and Methods ) . The final states are not likely identical in all four time courses , but they share many characteristics and all are representative of early neural differentiation , so we concatenated them into a single dataset . To study the interplay between transcription factors and signaling proteins during differentiation , we included as regulators , transcription factors from a recent comparative study of human and mouse from [41] and proteins annotated as phosphatases and kinases from Uniprot [42] . After initial data pre-processing to remove unchanging genes ( Materials and Methods ) , we gave as input to MERLIN 5670 genes and 823 regulators ( 535 transcription factors and 288 phosphatases and kinases ) . We focused on the high confidence MERLIN-inferred network of 4647 genes , 90% of which were organized into 94 modules , with at least 5 member genes , associated with 326 regulators . We examined these modules for biological function based on enrichment of genes annotated with Gene Ontology processes [33] , genes annotated in pathways in the Molecular Signature Database ( MSigDB , [43] ) , ChIP-seq targets of transcription factors from ENCODE [44] , and motif instances of transcription factors in DNAse1 hypersensitive sites [45] . We discuss these results below ( detailed module profiles and enrichment analysis are available from the web-supplement http://pages . discovery . wisc . edu/~sroy/merlin ) .
We found that while GENIE3 , a state-of-the-art per-gene method performed well using edge-based metrics , when applied to yeast , it did not perform as well on module-based measures . In contrast , MODNET , a state-of-the-art module-based method , performed poorly using edge-based measures , but performed better on module-based measures . This improvement in performance was due to the module information allowing us to restrict ourselves to targets that are co-expressed in a module and thereby exhibit coherent function . Indeed identifying co-expressed sets of genes is a pre-requisite to identifying meaningful cis-regulatory elements enriched with a set of genes . A per-gene method does not provide such information making it difficult to identify the regulatory modules comprising genes sets that are co-regulated by multiple regulators . MERLIN's strengths are in its ability to combine the complementary advantages of both classes of methods . An additional advantage of MERLIN is that it is based on a probabilistic graphical model , which infers network parameters in addition to structure . Our preliminary work on assessing expression prediction on a holdout set shows that MERLIN outperforms LINEARREGR on more genes than it is outperformed , suggesting that incorporating the module constraints can also benefit the predictive power of the model ( Figure S2 ) . Our application of MERLIN to the yeast stress response data and the human embryonic stem cell differentiation data reveals its ability to dissect the transcriptional regulatory programs especially in large datasets that measure diverse conditional responses . In particular , in the human embryonic stem cell differentiation data , the module with genes that are up-regulated in the later time points but not in the earlier ( ES ) time points had little or no enrichment in the ChIP-seq targets . Incidentally , ChIP-seq datasets were generated in the H1-ES ( human embryonic stem cells ) cell line , and the modules in which there was enrichment comprised genes that were most expressed in the earlier time points that reflect a more ES-like state . MERLIN therefore captures context-specific regulatory interactions . Such interactions are most enlightening for modules exhibiting induced expression in conditions or tissues that have not been studied in great detail , perhaps due to under-sampling of these biological responses . Even in the yeast stress response data , which is a very well studied dataset , we derived new insight into the role of HOG1 and the downstream modules that might be regulated through intermediate transcription factors as we discuss below . Our ability to capture these regulatory networks likely centers on the inherent feedback and feed-forward loops in eukaryotic transcriptional responses: genes encoding signaling proteins are often themselves targets of the pathways they encode . In other cases , genes encoding regulatory proteins ( especially negative regulators ) are augmented in anticipation of their future need; nonetheless , their expression patterns remain predictive of physiologically related genes . These features are likely to be common to many different responses across diverse organisms . An important difference between the yeast and the human dataset was the number of biological conditions the genes were measured in . In particular , in yeast we had more than a dozen environmental perturbations whereas in human we had four relatively similar kinds of perturbations . While the relatively uniform nature of expression dynamics in this dataset enables us to identify the major patterns of expression as two large modules , adding more diverse perturbations can help us identify smaller fine-grained modules as in the yeast dataset that are easier to interpret biologically and for follow-up studies with smaller functional assays . There are several directions of future work associated with MERLIN . An immediate step is to more accurately model expression levels from next-generation sequencing data by considering conditional negative binomial or Poisson distributions [62] . MERLIN can be easily applied to other regulatory genomics datasets including global chromatin states that are becoming increasingly available using approaches similar to Marbach et al [63] , taking a weighted union of different MERLIN inferred networks . Another , perhaps more principled , way of integrating such datasets would be through extending MERLIN's prior to incorporate more detailed features of the promoter architecture of a gene such as sequence-specific motifs and nucleosome occupancies . On a related note , it is possible to combine different types of proteomic datasets within the MERLIN framework , e . g . using measured protein levels of transcription factors and signaling proteins and existing physical interactions to predict the mRNA levels of genes . Such extensions can likely better capture the transcription factors based on ChIP-chip than what we are able to do based on expression alone that might miss changes such as post-translational modifications on the regulators . Specifically focusing on temporal dynamics , one direction of research is to predict the expression state based on observations made at a previous state , which can model delays in transcriptional responses . MERLIN can also be extended to capture non-linear relationships between a target and a regulator expression profile . This can be done using a random forest regression approach which has the additional advantage that the trees can be gleaned to identify combinatorial rules of regulatory logic or through an S-system model that models both non-linear and temporal dynamics [16] . The increasing abundance of environment-specific , tissue-specific and disease-specific transcriptional profiles , especially for poorly characterized organisms , makes our ability to infer regulatory networks especially important . Approaches such as MERLIN that identify the gene-specific regulatory information for individual genes , while revealing the global modular organization of regulatory networks can significantly advance our understanding of wiring and combinatorial regulation of transcriptional responses governing cellular states .
The MERLIN approach is based on a probabilistic graphical model of network inference where the goal is to infer regulatory networks by maximizing the likelihood of observed expression data given a network structure [14] , [25] . We use a similar notation as described by Segal et al [14] . Let denote the set of random variables , each taking a value from the domain . Each variable , in turn represents the gene or a regulator and there are total genes . Thus is a possible expression level of a gene measured in a microarray or from an RNA-seq experiment ( See data pre-processing for more details of what we mean by level ) . A subset of variables where denote the candidate regulators . We assume that we have a set of gene expression measurements for the genes denoted by , where denotes the joint assignment of expression values for all genes in the sample . The model that MERLIN learns has three components: . denotes the unknown regulatory network of interest describing the regulatory relationships between genes and regulators . Note that a regulator itself is also a gene and we can infer its regulators as well . denotes the set of module memberships of each gene , where and denotes the total number of modules . denotes the set of parameters , with each denoting the parameters of the conditional distribution , , of a target gene and its regulators , . Several forms are possible for . For example if we assume is a linear combination of the levels of the regulators , is the set of regression coefficients for each regulator selected for a gene . If we want to capture non-linear relationships , we can use a regression tree , where would represent a collection of means and variances of a target gene at each leaf node . As we discuss in the score below , we assume that and its regulators are distributed according to a multivariate Gaussian . The MERLIN code and additional results on the Gasch stress data are available as a web-supplement at http://pages . discovery . wisc . edu/~sroy/merlin . There are three parameters in MERLIN that need to be specified: ( 1 ) controls the number of edges in the network , that is the overall sparsity of the network , ( 2 ) , controls the extent of modularity in the network , ( 3 ) is the cutoff for deciding at what point we must stop the hierarchical clustering of the modules . To determine how the parameters influence the final structure , we carried out an extensive simulation experiment on networks of different sizes genes , and different modular structures . We used GeneNetWeaver ( GNW ) [27] to generate simulated expression data . We generated the structure of these networks outside of GNW in order to impose different extents of modularity by controlling a parameter . We partitioned the data into random non-overlapping modules , which included both a set of target genes and a set of regulator genes . For each edge we used to probabilistically determine whether a regulatory edge would be added between a target and regulator gene with the same module membership , or different modules . Thus a higher value of would favor greater regulatory modularity . To assess the quality of the inferred network structure , we used F-score , which is defined as the harmonic mean of precision and recall . Precision in turn is the ratio of the true positives to the number of edges inferred , and recall is the ratio of the true positives to the total number of edges in the true network . The F-score gives us a single number that assesses the quality of the inferred network . The closer F-score is to 1 , the better the performance . We also computed module statistics to assess the size and coverage of genes in each module: ( a ) number of good-sized modules , where a good-sized module must have at least 5 genes , ( b ) percentage of total genes that are in the good-sized modules . Ideally we would be able to include as many genes as possible in these good sized modules without losing biological coherence . We found that the most important parameter that affected F-score was , which controls the total number of edges in the network ( Figure S3 , S4 ) . This was true for both the high modularity ( ) and low modularity ( ) networks . For all networks of different sizes the optimal value as determined by the best F-score was . We also tried to pick based on the cross-validation error , and picked to have the lowest cross-validation error , but had considerably lower performance as measured by F-score . For a fixed , the other parameters had less affect on the F-score . The value of controlled the number of modules , with small values of producing too many small clusters ( <5 genes ) and large values of producing very large clusters ( Figure S5 ) . In general , the optimal value of ranged from , regardless of low or high modularity networks . Finally for a given , higher values of parameter tended to increase the number of good sized modules ( at least five genes ) and also the coverage of genes in these modules . The value of also affected the structure recovery . In particular for for the network with 500 nodes , a value of to had a greater preference of low for the low modularity networks . This suggests that this parameter can effectively capture modular networks , however not very high values of are required to do so . Finally , we studied the effect of the parameter on the modularity of the network ( Figure S6 ) . Because the networks we infer are directed regulatory networks , standard measures of modularity which are defined for undirected graphs were not sufficient . We defined a measure of regulatory modularity ( defined below ) that measures the extent of shared regulators of genes in a module compared to shared regulators with genes outside a module . We find that as we increase , the estimated modularity of the network increases , but asymptotes after = 8 . We applied MERLIN to two transcriptomic datasets , one in yeast [24] , and one in human ( Manuscript in preparation , some of the data was released in [50] ) . The yeast expression data was obtained from Gasch et al [24] and comprised 173 microarray measurements . The data was pre-processed by Segal et al , to remove genes that did not change significantly producing a total of genes of which genes were signaling proteins and were transcription factors . We replaced missing values of a gene with its mean from other samples where its expression was available . For the human data , we had RNA-seq read counts from four time courses , two each for two cell lines: human ES line H1 and human iPS cell line DF19 . 7 . We assembled these reads into a per gene count using RSEM [65] and transformed the counts , for each gene in the sample as . Next we computed the mean of each time course and subtracted the mean . Thus each expression level in the RNA-seq data , , where denotes the mean from a time course . The data were thus zeromean transformed for each time course separately . While this transformation does not account for the over-dispersed nature of RNA-seq data , we found that clustering the RNA-seq data using a Gaussian mixture gave us good performance as measured by Gene Ontology enrichment . Extending MERLIN to handle the “count” nature of RNA-seq data to reconstruct networks is an area of future work . After this we filtered out genes that changed less than ±1 in all time points . This produced a total of 5670 genes , of which 535 were transcription factors and 288 were either a kinase or phosphatase as annotated in Uniprot . To apply MERLIN to each of these data sets we started with five random initializations . For the yeast data we split the data into five equal folds and learned models on four fifths of the data , and repeated this five times . A high confidence network was that which had an edge in three of the five random initializations . For the human data since we did not have as much data as in yeast , we used the entire data , but generated different MERLIN networks by starting with five different random initializations of clusterings . We created a high confidence network by considering edges that were present in all five random initializations . MERLIN was applied on the different datasets using , and . We selected these settings based on our experiments on simulated data where we found that the optimal values of modularity ranged between to . We defined regulatory modularity for a set of modules , and a regulatory network in the following manner . First , for any pair of genes , , in the modules , we compute a regulatory similarity , as , where is the number of shared regulators between and , and and are the number of regulators for and respectively . Regulatory modularity for a module is defined as , where where , and , where . thus measures the within module regulatory similarity , and measures the regulatory similarity between genes in module and all other genes . The denominators serve to normalize the and measures such that they are always between 0 and 1 . The regulatory modularity for a module ranges between −1 and 1 . For each module with regulators ( including ChIP-enriched regulators ) , we extracted the number of genetic interactions from the BioGRID database [66] . Next from our candidate set of regulators , we extracted a random set of regulators of size and also obtained the number of genetic interactions among proteins in the random set . We repeated the random set selection 100 times , and estimated a mean and standard deviation on the number of edges expected by chance . We next computed a -score using this background distribution . We considered a regulator set to have significant number of interactions if the -score was greater than 1 . We repeated this process for protein protein interactions as well . We compared the performance of MERLIN on simulated networks to the performance of three other algorithms using edge-based , regulator-based and module-based metrics . These comparisons were done on simulated ground truth networks where the true networks were known . We used GeneNetWeaver ( GNW ) to generate simulated expression data [27] for networks of 100 , 200 , 300 , 400 , 500 and 1 , 000 nodes . GNW takes networks as inputs and uses a stochastic differential equation model to generate expression data . To generate data from GNW we used a similar strategy as was used in the DREAM project on network inference comparisons [20] . A set of measurements for all nodes was the steady-state values reached when the system was perturbed by simulating a gene knockout . All genes were knocked out , one at a time , producing datasets of size 100 , 200 , 300 , 400 , 500 or 1 , 000 measurements for the networks of different sizes . To assess the biological meaning of modules inferred by MERLIN we used gene set enrichment analysis of various modules using the hypergeometric test followed by an FDR correction method of Benjamini and Hotchberg . We considered only modules of size at least 5 genes . These gene sets included genes annotated with a Gene Ontology process [33] ( yeast and human ) , ChIP-chip or ChIP-seq targets of transcription factors ( yeast [30] and human [44] ) , gene sets from Molecular Signature Database ( MSigDB , humans [43] ) , or genes with motif instances in DNase I hypersensitive sites ( DHSs ) [45] . To assess the association of Modules 2 , 19 and 37 in the Gasch stress data we obtained a curated Hog1 signaling network from Tiger et al . [67] and combined it with a curated gene set from Gasch lab ( unpublished ) , and asked whether any module members or the regulators of the module were enriched in this list . Based on a hypergeometric test of overlap we found regulators of Module 2 to be significantly enriched ( -value <0 . 02 ) , and a lower stringency of ( -value <0 . 3 ) for the other modules . Module 19's regulators were also enriched for genes in this list ( hypergeometric -value <0 . 13 ) . To map ChIP-seq and DNAse1 sites in the human dataset we focussed on ±2000 bps of the transcription start site ( TSS ) of a gene , where the TSS coordinates were obtained from Gencode10 as used in the ENCODE project [68] . Motifs were obtained from Jaspar [69] , and DHSs were obtained from Thurman et al [45] , downloaded from http://ftp . ebi . ac . uk/pub/databases/ensembl/encode/integration_data_jan2011/byDataType/openchrom/jan2011/combined_peaks/ . To find genes with motif instances of a transcription factor we used the Finding Individual Motif Occurrences ( FIMO ) from MEME suite [70] to scan the DHSs with a q-value <1E-5 . To map a gene to a ChIP-seq peak of a transcription factor we obtained peak calls from the ENCODE project from http://ftp . ebi . ac . uk/pub/databases/ensembl/encode/integration_data_jan2011/byDataType/peaks/jan2011/spp/optimal/hub/ , and associated a gene to the transcription factor if its peak was within ±2000 bps of the TSS of the gene . | The state of a cell is largely determined by the genes the cell expresses . Transcriptional control of gene expression is exerted by transcription factor proteins that bind to regulatory regions of genes and affect their expression . Transcriptional programs have a modular organization enabling multiple genes to be coordinately regulated , and at the same time are fine-tuned for each gene through interactions of transcription factors with a gene's regulatory region . Transcription factors are themselves controlled by upstream signaling proteins , that in turn can be transcriptionally controlled . This complex process of gene expression control is described by a regulatory network that captures who regulates whom . A key challenge in systems biology is to reconstruct regulatory networks that capture precise gene-specific regulatory information , as well as the modular organization of transcriptional programs . We developed a novel regulatory network inference approach , MERLIN , Modular regulatory network learning with per gene information . When applied to examine transcriptional responses in two distinct processes , stress response and cellular differentiation , MERLIN accurately reconstructed regulatory programs of individual genes while revealing regulatory module organization and predicted upstream signaling proteins for regulatory modules . MERLIN is applicable to different environmental , developmental and disease contexts to dissect regulatory programs and ultimately build network-based predictive models of cellular states . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks |
Detection of Trypanosoma cruzi antigens in clinical samples is considered an important diagnostic tool for Chagas disease . The production and use of polyclonal antibodies may contribute to an increase in the sensitivity of immunodiagnosis of Chagas disease . Polyclonal antibodies were raised in alpacas , rabbits , and hens immunized with trypomastigote excreted-secreted antigen , membrane proteins , trypomastigote lysate antigen and recombinant 1F8 to produce polyclonal antibodies . Western blot analysis was performed to determine specificity of the developed antibodies . An antigen capture ELISA of circulating antigens in serum , plasma and urine samples was developed using IgY polyclonal antibodies against T . cruzi membrane antigens ( capture antibody ) and IgG from alpaca raised against TESA . A total of 33 serum , 23 plasma and 9 urine samples were analyzed using the developed test . Among serum samples , compared to serology , the antigen capture ELISA tested positive in 55% of samples . All plasma samples from serology positive subjects were positive in the antigen capture ELISA . All urine positive samples had corresponding plasma samples that were also positive when tested by the antigen capture ELISA . Polyclonal antibodies are useful for detection of circulating antigens in both the plasma and urine of infected individuals . Detection of antigens is direct evidence of the presence of the parasite , and could be a better surrogate of current infection status .
Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , is endemic to many parts of the Americas [1–3] . This parasite infects a wide variety of wild and domestic mammals including humans [2] . The disease is transmitted by insect vectors ( members of the Triatominae family ) with metacyclic trypomastigotes present in their feces . Parasite trypomastigotes gain access to the tissue and circulatory systems at the bite site , through wounds caused by scratching the bite site , or through the mucous membranes [4] . Other transmission routes include congenital transmission , blood transfusions , organ transplantation , oral transmission through the consumption of food contaminated with feces from infected insects , and accidental laboratory exposure [1 , 3 , 4] . It has been estimated that Chagas disease affects approximately 8 million people and may cause about 12 , 000 deaths each year ( 45 , 000 in the 1980s and 23 , 000 in the 1990s ) [2 , 5] . Bolivia is the country with highest endemicity with a prevalence of up to 80–90% in rural areas [6 , 7] . Two main phases can be distinguished in Chagas disease , the acute and chronic phase , each with different characteristics . The acute phase occurs at the beginning of the infection and is characterized by patent parasitemia with most of the patients asymptomatic . Approximately 75% of acute cases are in children under 10 years old [3 , 8] . In most cases , patients are asymptomatic ( 95% ) , however when the inoculation site is the conjunctiva mucous membrane , the characteristic Romaña’s sign , an eyelid edema , may appear [9 , 10] . The chronic phase can appear years to decades following the acute phase . This phase is characterized by a very low parasitemia and the most suitable methods for diagnosis are immunological assays [11] . Unlike the acute phase , about one third of infected patients will develop chronic phase symptoms . These symptoms mainly include heart diseases such as cardiomyopathy which is associated with heart insufficiency , sometimes leading to mortality including sudden death in some patients [10 , 12] . The disease may also affect the gastrointestinal system causing mega colon or mega esophagus [9 , 13] . Diagnosis of Chagas disease is based on clinical and laboratory assessment . Most laboratory assays are dependent on the detection of antibodies . Two positive results , preferably based on serological methods of distinct mechanisms ( e . g . , whole-parasite lysate and recombinant antigens ) , are required for an individual to be considered Chagas disease positive [3 , 14] . As with most of the of serological assays , these tests are not indicative of current infection when used alone and may cross-react with other parasitic diseases such as leishmaniasis and malaria , depending on the antigen used in the assay [15] . Immunological diagnosis is based on the use of parasite derived antigens . Trypomastigote excreted-secreted antigen ( TESA ) is used in Western blot ( TESA-Blot ) for both acute and chronic phase diagnosis , generating a characteristic pattern of bands depending on the strain of T . cruzi used . TESA-blot is highly specific and sensitive; sera of infected individuals identify protein bands of 130–200 kDa in the acute phase , while sera from individuals in the chronic phase identify protein bands of 150–160 kDa [16] . However , Western blot is not very economical to produce , requiring special training and a sophisticated laboratory . While antibody detection is indirect evidence of infection , detection of any antigenic fraction of the parasite is considered the equivalent of finding the whole parasite . Antigen detection may even occur prior to development antibodies at detectable levels [17] . Several reports have shown the presence of different proteins of T . cruzi in the urine of infected animals and humans [18–20] . These proteins have been used to develop monoclonal and polyclonal specific antibodies to be used for antigen detection both in urine and serum samples [17 , 19 , 21] . Due to the variety of circulating and excreted antigens , targeting a specific protein decreases sensitivity because the presence of antigens is variable and depends on different factors such as the phase of the disease [21 , 22] , renal injury [20] among others . The antigen 1F8 is a recombinant protein with a molecular weight of 24–25 kDa derived from a protein found in the flagellum of T . cruzi [23] . This calcium binding protein is used as antigen in an ELISA for the diagnosis of both acute and chronic Chagas disease [24] with high sensitivity and specificity; but detection of the presence of this protein in sera or urine samples has not been performed . The use of antigens of the parasite for the production of polyclonal antibodies designated for diagnosis or for therapy has been a very important tool for research . IgY is a type of immunoglobulin , and the major one in birds , with a molecular weight of 180 kDa . This is much larger than the IgG of most mammals , often about 159 kDa [25] . One of the most important characteristics of IgY antibodies is that they are able to recognize different epitopes than the antibodies raised in the mammals usually do [26] . In addition , IgY does not activate the complement system , providing a great advantage when used as capture antibody in immunoassays [27] . Antibodies from camelids ( IgG2 and IgG3 ) because of their lower size probably recognize inaccessible epitopes that may not be recognized by mammalian antibodies [28–30] . To produce polyclonal antibodies , we used three different animals: alpacas , rabbits , and hens and immunized them with different T . cruzi antigens . The resulting antibodies were then used to develop an antigen capture ELISA and tested for their ability to discriminate and identify individuals infected with T . cruzi the agent of Chagas disease .
Human sera , plasma , and urine samples were archived samples obtained from previous studies . The Human Ethics Committee of the Universidad Peruana Cayetano Heredia approved the use of these archived samples . The Animal Ethics Committee of the Universidad Peruana Cayetano Heredia approved the protocols for the use of animals for antibody production , approval Code 61549—Cons-CIEA-029-2014 . The Animal Ethics Committee of the Universidad Peruana Cayetano Heredia is registered in the Office of Laboratory Animal Welfare , Department of Health and Human Services , National Institutes of Health ( NIH—USA ) and follows its rules and laws . The use of animals in this study was performed following the Deontological Code of the Medical Veterinary College of Peru; The Care and Use of Experimental Animals . Canadian Council on Animal Care 1980 and the Australian code of practice for the care and use of animals for scientific purposes 1997 . In order to produce polyclonal antibodies alpacas , rabbits , and hens were immunized using four different antigens: trypomastigote lysate antigen ( TLA ) , trypomastigote membrane proteins ( TMP ) , trypomastigote excretory-secretory antigen ( TESA ) , and a commercial recombinant 1F8 T . cruzi antigen ( Genway Biotech Inc , CA-USA ) . TLA and TMP were obtained from T . cruzi Y strain trypomastigotes . Parasites were washed three times using cold PBS at 2 , 000 x g for 10 min before antigen preparation . For TLA the parasite pellet was resuspended in 2 ml PBS , frozen and thawed three times using a dry ice-ethanol bath , and sonicated ( Misonix , Sonicator 3000 ) for 4 cycles at 30 s ON , 60 s OFF ( Output Power: 3 ) . The suspension was centrifuge at 13 , 500 x g for 20 min . The supernatant was recovered and used as antigen . TMP were extracted using a modify protocol [31] . The parasite pellet was resuspended in 200 μl of 10 mM Tris-HCI , pH 7 . 4 , 140 mM NaC1 , and 2 . 0% Triton X-114 ( Triton X-114 buffer ) , the tube was incubated 90 min at 4°C , and centrifuged at 10 , 000 x g for 15 min at 4°C . The detergent phase , found at bottom of the tube , was mixed with an equal volume of Triton X-114 buffer , incubated 1 min at 37°C , and centrifuged for15 min at 10 , 000 x g at room temperature . The detergent phase was used as antigen . The TESA antigen was harvested from T . cruzi Y strain growth in LLC-MK2 cells as previously described [15] . After the sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , and transfer to nitrocellulose , the protein band ( 150–160 kDa ) was excised from the nitrocellulose paper using the reaction of chronic positive sera as a reference . The paper was digested by incubating the excised paper with 250 μl of dimethyl sulfoxide ( DMSO ) per 20 mm2 of nitrocellulose for 1h at room temperature on a rocker mixer . After the incubation , 0 . 05 M carbonate/bicarbonate buffer ( pH = 9 . 6 ) was added drop by drop until a volume equivalent to the DMSO used for extracting the antigen from the nitrocellulose paper was added . The mixture was then centrifuged at 4°C for 10 min at 10 , 000 x g and the pellet washed by centrifugation at 4°C for 10 min at 10 , 000 x g with 0 . 1 volumes of phosphate buffered saline ( PBS , pH = 7 . 4 ) . The pellet was re-suspended in an equal volume of PBS . After production , all antigens were stored at -80°C ( no more than one month ) until used . Alpacas were initially immunized using complete Freund’s adjuvant and boosted with incomplete Freund’s adjuvant ( Sigma—Aldrich , MO-USA ) . Rabbits and chickens were immunized using one volume of antigen and one volume of Sigma Adjuvant System ( SAS , S6322—Sigma—Aldrich ) . Three , two years old female alpacas ( Huacaya breed ) , eight hens ( New Hampshire breed ) and eight , two months old rabbits ( New Zealand breed ) were immunized according to the scheme showed in Table 1 . Alpaca immunization was carried out according to previous references [32–34] . The blood from alpacas and rabbits were collected and centrifuged at 1 , 100 x g for 10 min to obtain the sera . The sera were stored at -20°C until used . The collected eggs were maintained at 4°C prior to antibody purification . The HiTrap Protein A columns ( GE Healthcare Life Sciences , PA , USA ) was initially used to separate IgG3 and IgG1 from alpaca . The non-bound fraction was further purified using HiTrap Protein G ( GE Healthcare Life Sciences ) to separate the IgG2 . Rabbit IgG was purified by affinity chromatography using HiTrap Protein A column ( GE Healthcare Life Sciences ) following manufacturer's instructions . After two months of immunization , all the eggs were pooled according to antigen and time of collection . Egg yolk was separated from the white and proteins were extracted as described before with some modifications [35 , 36] . Briefly , for one yolk ( about 15 ml volume ) , 30 ml of PBS were added and mixed carefully for 5 min . Then , 15 ml of chloroform were added and mixed again . The mixture was refrigerated at 4°C for 1–2 h then centrifuged at 1 , 100 x g for 10 min at room temperature . The upper phase was collected and dialyzed overnight with PBS then concentrated using an Amicon YM 100 filter unit ( Amicon , Millipore , Darmstadt , Germany ) . To remove lipid residues and proteins completely , the concentrated samples were further purified with the Pierce Chicken IgY purification kit ( Thermo Fisher Scientific ) following manufacturer’s instructions . Finally , IgY antibodies were purified using the HiTrap IgY Purification columns ( General Electric , Uppsala , Sweden ) following manufacturer's instructions . To verify the purity of antibodies , SDS-PAGE under non-reducing conditions was performed . Alpaca IgG1 , IgG2 , and IgG3 , and rabbit IgG samples were diluted 1:10 using sample buffer 1 [100mM Tris-HCl ( pH = 6 . 8 ) , 4% ( w/v ) dodecyl sulfate ( SDS ) , 0 . 2% ( w/v ) bromophenol blue , and 20% ( v/v ) glycerol ) ] . IgY samples were diluted 1:4 using a sample buffer 2 [62 . 6mM Tris-HCl ( pH = 6 . 8 ) , 2% ( v/v ) SDS , 25% ( v/v ) glycerol ) ] . After electrophoresis , the gels were stained using 0 . 25% Coomassie blue . To corroborate that the antibodies were reacting with the target parts of the parasite an indirect immunofluorescence assay ( IFA ) was performed using T . cruzi Y strain epimastigotes . Briefly , epimastigotes were harvested from LIT cultures ( liver infusion tryptose medium ) and washed by centrifugation at 1 , 100 x g for 10 min using 1% formalin in PBS and resuspended in PBS to a final concentration of 103 epimastigotes/ml . A total of 20 μl/well of epimastigote suspension was fixed on poly-L-lysine pretreated slides . Fixed epimastigotes were then incubated with each of the purified antibodies at 37°C for 45 min , washed three times with PBS , and incubated with Goat Anti-Llama IgG H&L ( FITC ) , Goat Anti-Rabbit IgG H&L ( FITC ) or Goat Anti-Chicken IgY H&L ( FITC ) diluted in PBS , 0 . 002% Evans blue , and incubated at 37°C for 30 min . Slides were observed at 400X under an immunofluorescence microscope . Three milliliters of urine sample were lyophilized and reconstituted in 300 μl of PBS ( pH 7 . 2 ) . Serum or plasma samples were pretreated as previously described [37] . Briefly , 50 μl of sample was diluted with 60 μl of PBS , 0 . 05% Tween 20 , 1 . 0% milk– 0 . 2% Bovine Serum Albumin ( BSA ) and heated at 56°C for 30 min . Initially different combinations of the developed polyclonal antibodies were used to standardize an antigen capture ELISA using TLA spiked urine or sera samples . The final protocol consisted in sensitizing a Nunc Maxisorp 96 well plate ( Nunc Nalgene , Rochester , NY ) , overnight at 4°C , with anti-membrane IgY ( 4 μg/ml ) in carbonate-bicarbonate buffer ( pH = 9 . 6 , capture antibody ) . The plate was washed three times with PBS , 0 . 05% Tween 20 , blocked with PBS , 0 . 05% Tween 20 , 6% semi-skimmed milk , 1% BSA for 2 h at room temperature . Following washing , 100 μl/well of pretreated samples of either urine , serum or plasma samples pretreated were added and the plate was incubated at 37°C for 1 h . The plate was washed again and 100 μl of detection antibody ( alpaca anti-TESA IgG ) at 4 μg/ml in PBS , 0 . 05%Tween 20 , 1% milk , 0 . 2% BSA was added and incubated at 37°C for 1 h . After the final wash , goat-anti-llama peroxidase conjugate ( Bethyl , Laboratories Inc . ) was added at 1: 7500 in PBS , 0 . 05%Tween 20 , 1% milk , 0 . 2% BSA and incubated at 37°C for 30 min . After washing , the plate was developed using OPD ( Sigma FAST Sigma-Aldrich ) as substrate for 15 min . The reaction was stopped using 2 M H2SO4 and the plate was read at 490 nm using the VERSA Max ELISA plate reader ( Molecular Devices , LLC , Sunnyvale , CA ) . A sample was considered positive if the absorbance ( optical density , OD ) obtained in the ELISA was higher than the cut-off value . The cut-off value was determined using the mean plus two standard deviations of the absorbance obtained from all samples negative by serology and qPCR including samples from the volunteers . Diagnosis of Chagas disease in human samples was based on serological assays . ELISA was performed using Chagatek Wiener Recombinante v3 . 0 ELISA ( Wiener laboratories , Rosario , Argentina ) . Western blot analysis was performed using same TESA used for antibody production . Indirect hemagglutination assay ( IHA ) was performed using the Chagas Polychaco kit ( Lemos Laboratories , Buenos Aires , Argentina ) . Real time PCR ( qPCR ) was performed using primers and TaqMan probes targeting the nuclear satellite DNA of T . cruzi as described previously [38–40] . Serum ( n = 30 ) , plasma ( n = 23 ) and urine samples ( n = 6 ) were archived samples from HIV positive adults . None of the subjects received or was receiving treatment at the moment of enrollment . A sample was considered positive or negative for Chagas disease by serology if they tested positive or negative to all of the following assays: ELISA , TESA blot and IHA , respectively . All serum samples were collected in Santa Cruz , Bolivia; 18 were positive and 12 negative to Chagas disease by serology . Plasma samples and urine samples were collected in Cochabamba , Bolivia . Among the plasma samples , 20 were positive and 3 were negative for Chagas disease by serology . Four Chagas-positive and 2 Chagas-negative individuals provided urine samples . Three serum samples and respective urine samples , obtained from healthy adult volunteers from Lima , Peru ( non-endemic for Chagas disease ) , were included in the analysis . All samples from these volunteers tested negative in all serology assays and with qPCR . The qPCR was performed using clot samples and phenol chloroform extraction [41] . A sample with a quantification cycle ( Cq ) equal or greater than 40 was considered qPCR negative . Cross-reactivity of the Ag-ELISA was determined using serum and plasma samples from adults positive for malaria ( n = 5 ) , toxoplasmosis ( n = 5 ) and leishmaniasis ( n = 4 ) . Malaria serum samples were positive for Plasmodium vivax by both thick blood smear and PCR . Toxoplasmosis plasma samples were from HIV-positive individuals who tested positive for Toxoplasma gondii by ELISA ( ELISA-IBL international , Hamburg , Germany ) . Leishmaniasis samples ( 2 plasma and 2 serum ) were from adult subjects infected with mucocutaneous leishmaniasis diagnosed by ELISA and PCR .
The antibodies purified from alpaca , when analyzed by SDS- PAGE and Coomassie blue stained , showed that the isotypes IgG3 and IgG2 purified have a molecular weight ranging between 100–120 kDa while the IgG1 fraction showed a molecular weight of 170 kDa . The IgG2 fraction was not completely purified , it showed traces of IgG1 ( Fig 1A ) . The IgG purified from rabbits showed a major band at 180 kDa ( Fig 1B ) while the IgY of chicken showed a major band at 190 kDa ( Fig 1C ) . When TLA was used as antigen for western blot and tested with IgG3 from alpaca immunized with TLA , two proteins bands of 40 and 50 kDa were recognized . This pattern of bands was similar to pattern of protein bands recognize by sera from chronic patients . When IgG3 from alpaca was evaluated against membrane antigen , by western blot , it recognized a major band of 50 kDa; the sera of Chagas chronic subjects also recognized this protein band ( Fig 2A ) . The IgG purified from rabbits and IgY purified from eggs immunized with 1F8 antigen detected a band of 25 . 8 kDa; which did the pre-immune antibodies not recognize ( S1A and S1B Fig ) . When western blot was performed using TESA , the IgG3 from alpaca , the IgG from rabbit , and the IgY from eggs immunized with TESA recognized a band of 150-160kDa concordant with the SAPA ( Shed Acute Phase Antigen ) pattern detected by the serum from Chagas acute patients . This protein band also coincides with one of the intense protein bands recognized by the serum of Chagas chronic patients . In addition , the IgG from rabbit and IgY from eggs detected a cross-reacting protein band of 200 kDa also recognized by their pre-immune antibodies ( Fig 2B and 2C ) . By western blot analysis the evaluation of IgG purified from rabbit and IgY from hens immunized with the membrane antigen , showed similar band patterns of both IgG and IgY pre-immune and post-immune ( Fig 2B and 2C ) . In the IFA , the pre-immune and post-immune IgG from rabbit immunized with the membrane antigen shown similar fluorescent patterns ( S2A Fig ) . In contrast , only post-immune IgY showed fluorescence in the IFA ( S2B Fig ) . Because of these results IgG from rabbits immunized with membrane were not used for further assays . Among the serum samples the antigen capture ELISA ( Ag-ELISA ) identified as positive 10 out of the 18 serology positive samples , 9 of these samples were also qPCR positive samples , while all 15 samples negative by both serology and qPCR yield a negative result on the Ag-ELISA ( Table 2 ) . Among serum samples , and considering the serology as gold standard , the sensitivity and specificity of the Ag-ELISA were 56% ( 10/18 ) and 100% ( 15/15 ) respectively . None of the samples tested for cross-reactivity tested positive in the Ag-ELISA . Of the 23 plasma samples only three were negative both by serology and qPCR . The Ag-ELISA was positive for all the serology positive samples ( n = 20 ) , four of the serology positive samples were qPCR negative but Ag-ELISA positive ( Table 2 ) . Overall , among the plasma samples the sensitivity and specificity of the Ag-ELISA , compared to serology were 100% . All the urine samples that tested positive to the Ag-ELISA also tested positive on the Ag-ELISA performed in their respective plasma samples ( Table 3 ) . Three samples were urine samples collected from volunteers and sera instead of plasma was analyzed; both sera and urine tested negative to the Ag-ELISA . Antigen detection limit was determined by two-fold dilution of the TLA antigen from 1 μg/ml to 0 . 975 ng/ml; the detection limit was 3 . 9 ng/ml .
Chagas disease remains an important health problem worldwide . Diagnosis is based on antibody detection by several methods , however antibody detection is not necessarily indicative of current infection . Thus , antigen detection might be a better determinate of current Chagas infection . To our knowledge , this study represents the first study that demonstrated the usefulness of polyclonal antibodies for the diagnosis of Chagas disease in an ELISA format . We have developed polyclonal antibodies against a variety of T . cruzi antigens in alpacas , rabbits , and hens ( eggs ) , with adequate sensitivity and specificity as to be employed for antigen detection in clinical samples obtained from infected individuals . A combination of chicken IgY developed against T . cruzi membrane antigens ( capture antibody ) with alpaca polyclonal antibodies developed against excretory/secretory antigens ( detection antibody ) in a sandwich ELISA format was useful for the detection of circulating antigens in sera or plasma samples as well as excreted antigens present in the urine samples of infected individuals . Development and uses of polyclonal antibodies in rabbits and hens have been widely described before with inherent variations depending on the nature of the antigen and the dose and route of administration [42 , 43] . Although only two animals ( a pair of hens or rabbits ) were used to produce antibodies against each antigen , the immune response of each animal was similar as demonstrated by Western blot analysis . Rabbits did not produce a good antibody response when immunized with T . cruzi membrane antigens , since the Western blot and IFA analysis showed that the pre-immune response was similar to post-immune response . Probably the lack of immune response to these antigens was inherent to the rabbits and not due to the antigen preparation , since both hens and alpacas produced good antibody response . Moreover , the antibodies produced in hens ( eggs ) against membrane antigens were used in this study to detect T . cruzi antigens in clinical samples with high sensitivity and specificity . The use of heavy chain antibodies ( modified into nano-antibodies ) has been recently explored against Trypanosomes [44 , 45] suggesting that alpacas may be capable of generating an adequate immune response against complex mixtures of T . cruzi antigens . Here we have shown the usefulness of alpaca antibodies for the detection of circulating antigens in different clinical samples . Several studies describe the use of antigen detection for the diagnosis of Chagas disease [18 , 20 , 46–49] , but all are oriented to the detection of T . cruzi antigens in urine samples; and all demonstrate the presence of these antigens by Western blot and complicated pre-analytical handling of urine samples . Although Western blot is a standard technique that is widely used is most developed settings , it is still difficult to access in Chagas endemic settings . ELISA based diagnosis is more accessible and useful in these settings . The methodology presented here is simple , accessible and does not require the use of sophisticated laboratory equipment . Although urine samples were lyophilized in this study for practical reasons , ethanol precipitated of antigens [18] perform similarly . They do not differ in the final result from lyophilized antigens , as tested with TLA spiked urine samples and with the positive control samples . Thus , ethanol precipitation might be a good alternative to lyophilization . Only nine urine samples were analyzed in this study . Although there was a perfect correlation between Ag-ELISA in plasma and urine samples , those results need to be corroborated with a larger number of samples . Lower levels of antigen were detected via the Ag-ELISA in serum samples as compared to the corresponding plasma samples , even in those samples that were qPCR positive . The reason for this low performance is unclear , although it is probable that the pre-treatment technique ( heating of samples ) might not liberate the T . cruzi antigens from the immune complexes associated to this disease [29 , 50] . Immune complexes may be trapped within the fibrin clot , lowering the availability of antigen . Alternatively , since the test was performed with archived samples , the antigens may have degraded during the storage process . Further analysis is required to clarify this issue . A high background was observed on the ELISA technique described here . The cut-off value ( mean plus two standard deviations ) was the highest for urine samples , and the lowest for serum samples . The high cut-off values may be a consequence of the nature of the antibodies and probably could be improved with the use of better blocking agents and techniques . We have developed polyclonal antibodies that are useful for the detection of circulating antigens in serum , plasma , and urine samples of human subjects infected with Chagas disease using a simple ELISA technique . The antigen detection strategy described here is a promising methodology for the diagnosis of Chagas disease and , because it detects antigens , may be a good surrogate of current infection . Pretreatment of samples is straightforward , and the ELISA is a simple and more accessible technique than the currently described strategies oriented to the detection of T . cruzi antigens in urine samples . | Current diagnosis of Chagas disease is still cumbersome . Diagnosis is based on antibody detection with at least two assays of distinct mechanisms . If a discrepancy exists , a third assay must be performed . However , detection of antibodies is not indicative of current infection . Molecular-based techniques such as qPCR have been used for diagnosis and as a gold standard in the demonstration of therapeutic failure , but availability of genomic material depends on the presence of parasites in the bloodstream . Detection of parasite-derived antigens represents a better alternative for diagnosis , as several proteins are secreted/excreted by the parasites and may be detected in blood and in the urine of infected individuals . This study describes the development of polyclonal antibodies raised against different Trypanosoma cruzi antigens and their applicability for the diagnosis of Chagas disease using the widely-used ELISA format . | [
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"pro... | 2017 | Polyclonal antibodies for the detection of Trypanosoma cruzi circulating antigens |
Schistosomal myeloradiculopathy ( SMR ) , the most severe and disabling ectopic form of Schistosoma mansoni infection , is caused by embolized ova eliciting local inflammation in the spinal cord and nerve roots . The treatment involves the use of praziquantel and long-term corticotherapy . The assessment of therapeutic response relies on neurological examination . Supplementary electrophysiological exams may improve prediction and monitoring of functional outcome . Vestibular evoked myogenic potential ( VEMP ) triggered by galvanic vestibular stimulation ( GVS ) is a simple , safe , low-cost and noninvasive electrophysiological technique that has been used to test the vestibulospinal tract in motor myelopathies . This paper reports the results of VEMP with GVS in patients with SMR . A cross-sectional comparative study enrolled 22 patients with definite SMR and 22 healthy controls that were submitted to clinical , neurological examination and GVS . Galvanic stimulus was applied in the mastoid bones in a transcranial configuration for testing VEMP , which was recorded by electromyography ( EMG ) in the gastrocnemii muscles . The VEMP variables of interest were blindly measured by two independent examiners . They were the short-latency ( SL ) and the medium-latency ( ML ) components of the biphasic EMG wave . VEMP showed the components SL ( p = 0 . 001 ) and ML ( p<0 . 001 ) delayed in SMR compared to controls . The delay of SL ( p = 0 . 010 ) and of ML ( p = 0 . 020 ) was associated with gait dysfunction . VEMP triggered by GVS identified alterations in patients with SMR and provided additional functional information that justifies its use as a supplementary test in motor myelopathies .
Schistosomal myeloradiculopathy ( SMR ) is the most severe and disabling ectopic form of Schistosoma mansoni infection [1–3] . Although its prevalence is unknown [1–4] , it has been found to be 6% of non-traumatic transverse myelopathies in endemic areas [5 , 6] . In SMR , acute transverse myelitis and radiculitis occur as a result of local inflammatory response against the embolized ova in the vessels of the spinal cord , mainly at the lower thoracic and lumbar levels . The anomalous migration of parasites to the central nervous system is explained by a retrograde venous flow into the Batson vertebral epidural venous plexus , which is connected to the portal venous system , were worms are typically located [7–10] . In the acute phase of SMR , the patients present with lumbar and/or lower limbs pain , generally associated with sensitive and motor alterations of lower limbs as well as bladder , intestinal and sexual dysfunctions . If not promptly and adequately diagnosed and treated , the patients remain with serious spinal cord injury sequelae , commonly handicapped , and eventually die because of infectious complications [1 , 2 , 4 , 11] . The diagnosis is based on clinical presentation , evidence of schistosomal infection , magnetic resonance imaging ( MRI ) and the exclusion of other causes [1–4 , 6 , 11] . Treatment involves the use of antischistosomal drug ( e . g . praziquantel ) and corticosteroids [2 , 4 , 11] . There is no consensus about the recommended doses and duration of steroid therapy [4 , 11] . A recommendation of 3–4 weeks using high daily dose of oral steroids ( e . g . prednisone , 1 . 5–2 mg/kg/day ) followed by tapering and then complete discontinuation within 3–4 months reduces the risk of severe adverse effects [4] . However , the steroid withdrawal before six months may result in relapse with worse motor sequelae , thus other authors advocate the use of prednisone 1mg/kg/day for 6 months and then start tapering the dose [2 , 3 , 11] . Hence , the final decision to interrupt corticosteroids may be based on the therapeutic response and not on guideline protocols . The monitoring of the spinal recovery relies mainly on neurological examination , since MRI normalization after treatment may not mirror clinical outcome [2] . Therefore , a supplementary exam is necessary to better guide therapeutic decisions . Galvanic vestibular stimulation ( GVS ) is a simple , safe , low-cost and easily reproducible technique used to trigger the vestibular evoked myogenic potential ( VEMP ) [12–14] , which has been used to investigate medullar function in spinal cord injury due to trauma , tumor , ischemia and infection [15–20] . A transcranial GVS is applied on the mastoid bones as a binaural configuration affecting the firing rates of the irregular primary vestibular afferents , exciting on the negative ( cathode ) and inhibiting on the positive ( anode ) side of the electrodes [21–23] . The unexpected vestibular stimulus exerts a strong influence on body posture with a protective muscular reflex to maintain postural control:the trunk and limbs sway toward the anode , followed by a counteracting movement [14 , 21 , 23 , 24] . VEMP is the response recorded by electromyography ( EMG ) in the muscles involved in balance control such as sternocleidomastoid , paraspinal , triceps brachii , tibialis anterior , soleus and gastrocnemius [12–14 , 25] . After the GVS appliance , VEMP is captured from muscles of lower limbs through EMG , showing the reflex that crossed the entire neuro-axis , until the lumbar spinal segments . The evoked potential descends through the reticulospinal and the vestibulospinal tracts with similar velocity to the corticospinal tract and integrates motor and sensitivity information [12–14 , 19] . VEMP recorded in the soleus or gastrocnemius muscle produces a biphasic EMG wave , with the short-latency ( SL ) component initiating at approximately 60ms after stimulus onset , followed by the medium-latency ( ML ) component , which initiates at around 100ms after stimulus onset and is in the opposite polarity of the SL [12–14 , 22 , 23] . The components of the lower limbs EMG response , although recorded consecutively , do not seem to be related to the same spinal pathway . SL is considered a stable and direct measure of the vestibulospinal reflex , via reticulospinal tract whereas ML is a component of integration , polysynaptic and driven from the semicircular canals to the vestibulospinal tract [12 , 16 , 23 , 25] . The aim of this study was to investigate the spinal cord function of patients with SMR using VEMP with GVS . The results were compared to those of a healthy control group and were crossed with data from neurological examination .
The Ethics Committee of Federal University of Minas Gerais , Brazil , approved this study ( protocol n° 11895813 . 1 . 00005149 ) and it was conducted according to the principles expressed in the Declaration of Helsinki . All subjects gave their informed and written consent . This was a comparative cross-sectional study that enrolled 22 patients with SMR and 22 healthy controls . It was performed between September 2013 and August 2014 at the infectious diseases outpatient clinic of Federal University of Minas Gerais , in Belo Horizonte , Brazil . All participants ( n = 44 ) were submitted to anamnesis , clinical and neurological examination . Patients with urinary dysfunction were submitted to urodynamics to confirm neurogenic bladder . Neurogenic bowel was clinically diagnosed if patients presented fecal incontinence and/or fecal constipation with regular need for laxatives , enema or manual maneuvers . Erectile dysfunction was defined according to the Sexual Health Inventory for Men ( SHIM ) [26] . All participants underwent GVS with EMG recording of VEMP in the gastrocnemius muscle . The VEMP variables studied were the onset ( in milliseconds ) of short-latency ( SL ) and of medium latency ( ML ) waves . Sample size was calculated a priori with the software G*Power 3 . 1 . 9 . 2 to achieve the power of 80% and the significance of 5% based on SL means and standard deviations of patients with HTLV-1 associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) and healthy control subjects already published [17] . SL mean was chosen over ML mean because of its smaller difference between healthy controls and patients . A minimum of 11 patients and 11 control individuals was necessary . Since no research had been published using GVS in the evaluation of SMR , the sample size was doubled to 22 in each group . EpiData ( EpiData Data Entry , Data Management and basic Statistical Analysis System . Odense Denmark , EpiData Association , 2000–2008 ) was used to build the data bank and SPSS version 15 . 0 ( SPSS , Inc . , Chicago , IL , USA ) was used for all statistical analyses . Double entered data had asymmetric distribution for all continuous variables , but height ( Shapiro-Wilk test ) . Therefore , non-parametric tests were used for all analyses ( Mann-Whitney test for independent samples , Wilcoxson test for dependent samples , Spearman for correlation ) , with the exception of Student T test for comparison of height between groups . ROC curve was done to analyze the diagnostic performance of SL and ML . The confounders were controlled by linear regression . The level of significance was 5% . The potential confounders age [24] and height [18] did not show any influence on interest variables in the linear regression model .
Among the 22 patients with SMR , 17 were male ( 77% ) and the ages varied between 20 and 70 years ( median: 42 , interquartile range: 28–52 ) . The control group consisted of 22 healthy individuals , 12 male ( 55% ) , with ages between 19 to 70 years ( median: 30 , interquartile range: 26–44 ) . Clinical characteristics of both groups are described and compared in Table 1 . The groups were statistically similar regarding age , sex , height and body mass index . Time of SMR diagnosis ranged from one month to 16 years ( median: 61 months , interquartile range: 27–144 ) . Manifestations/sequelae of each patient with SMR are described in S1 Table and their frequency is shown in Table 2 . The urodynamics confirmed neurogenic bladder in 13 patients . One presented urodynamics consistent with infra-bladder obstruction . Two refused to do the exam and received the diagnosis of neurogenic bladder based on signs and symptoms of urinary retention with onset at the acute phase of SMR ( S1 Table ) . Three patients were receiving SMR treatment with prednisone 1mg/kg/day ( patients 2 , 4 and 7; patient 7 was undergoing treatment of SMR recurrence ) ( S1 Table ) . The frequency of the affected spinal cord segment is indicated in Table 3 . SL and ML responses were delayed in patients with SMR compared to controls ( Table 4 ) . Concerning the morphology of the waves in the SMR group , according to the examiner A , one patient had altered SL and ML and another had altered ML; according to examiner B , three patients had altered SL and four had altered ML . Examiner B did not analyze the EMG of two healthy control individuals . Correlation between two independent examiners measurements was moderate for SL ( r = 0 . 542 , p<0 . 001 ) and strong for ML ( r = 0 . 834 , p<0 . 001 ) . The area under the ROC curve was 0 . 814 for SL ( p = 0 . 001 ) and 0 . 861 for ML ( p<0 . 001 ) . The SL ( p = 0 . 010 ) and ML ( p = 0 . 024 ) were more delayed in patients with gait disturbance in the SMR group . No other alteration in the neurological examination was associated with delay in VEMP response .
Neurophysiology techniques are usually used for assessing spinal cord injury , including motor evoked potential with transcranial magnetic stimulation , somatosensory evoked potentials , electroneuromyography , nerve-conduction studies , and motor-evoked potentials as the VEMP [29 , 30] . In patients with SMR , VEMP with acoustic stimulation and electroneuromyography have already been investigated [19 , 31] . The electroneuromyography was more sensitive to detect schistosomal radiculopathy than the MRI [31] . The limitation of this type of exam in SMR is the lack of direct spinal cord evaluation . VEMP using acoustic stimulation was shown to be altered in 10 out of 29 ( 34% ) patients with definite SMR [19] . VEMP can be trigger either by acoustic or by galvanic stimulation . The difference is that VEMP with acoustic stimuli generates EMG responses captured in the sternocleidomastoid muscle , innerved by cervical spinal segments , via the medial vestibulospinal tract while the galvanic stimulus generates the evoked response measurable in the lower limbs , innerved by lumbar spine , via the lateral vestibulospinal tract [27] . Taking into consideration that SMR affects more commonly the spine in the lower thoracic , lumbar segments and conus , the galvanic stimulus may be a good supplementary diagnostic tool for follow-up . This is the first study that uses GVS in patients with SMR . The alteration of VEMP in the lower limbs that was triggered by GVS indicates dysfunction in the reticulospinal and the vestibulospinal tracts , which are located in the anterior and lateral spinal cord [15–17 , 28] . CUNHA et al ( 2013 ) used GVS to study VEMP in 13 patients with HAM/TSP and found absent waves in around 70% of the patients . When present , the waves were delayed: SL 67±8 and ML 130±3ms in HAM/TSP patients versus SL 55±4 and ML 112±10ms in normal controls ( p = 0 . 001 ) [17] . In another study testing GVS in 21 patients with spinal cord injury , ILES et al ( 2004 ) found 50% of absent VEMP responses . Latency was delayed when the responses were present and the more severe the spinal cord impairment , the longer were the latencies [15] . LIECHTI et al ( 2008 ) reported ML with a mean of 130ms in 8 patients with spinal cord injury whereas normal subjects had ML with a mean of 110ms ( p<0 . 050 ) [16] . These authors did not report SL results because it was often indistinguishable from the baseline [15 , 16] . Some level of difficulty in defining SL was also observed in the present study , resulting in only moderate correlation between two independent examiners for SL measurements , whereas correlation was strong for ML . In addition , area under the ROC curve was greater for ML than for SL . Therefore , ML was shown to be the most reliable component of VEMP in lower limbs to define alteration . Others studies confirm the importance of ML , which is considered the wave that represents the motor-sensory integration [12 , 15–17 , 28] . The patients with SMR that could not stand were not included , because EMG responses were recorded in the muscles engaged in the maintenance of standing posture . It is possible to record EMG responses from erectors spinae muscles following GVS in sitting patients . In fact , this study was already done to define the level of spinal cord injury [15] . However , in the case of SMR , GVS would not be useful for wheelchair patients . The most important information is about the subclinical functional alterations seen by VEMP while imaging and neurological examination are normal or slightly altered . The diagnosis of acute SMR is based on clinical signs and symptoms , parasitological confirmation and MRI [2–4] . MRI may be used for follow-up , but images frequently normalize after the beginning of the treatment even in patients with incomplete recovery and may not get worse in case of recurrence [2] . In fact , the follow-up of SMR is a challenge since it ultimately relies on neurological examination to guide therapeutic decisions such as the withdrawal of steroids treatment or the reintroduction in case of recurrence . In addition , unresponsive or uncertain cases or even uncooperative patients raise the need for supplementary functional studies . Finally , variables of functional prognostic value could indicate patients who may benefit from longer corticosteroid treatment [2] . In the context of developing countries where SMR and other infectious myelopathies , i . e . HAM/TSP [32] are endemic , a simple , inexpensive and noninvasive neurophysiology technique such as VEMP with GVS can contribute to a better diagnosis and follow-up assistance . Longitudinal studies are going to be done to clarify these hypotheses . In conclusion , the SL and ML components of VEMP triggered by GVS and recorded in the lower limbs were delayed or absent in patients with SMR , especially in those with gait disturbance . The component ML was more accurate than the SL component . These results showed that VEMP triggered by GVS identified vestibulospinal deficit in patients with SMR . The use of this exam may improve prediction and monitoring of functional outcome during the treatment of SMR by providing additional information on the spinal cord of these patients . | Schistosomal myeloradiculopathy is a rare and severe form of schistosomiasis caused by Schistosoma mansoni , a blood-dwelling worm that lives in the intestinal veins of infected people . The parasite produces eggs that travel in the blood flow and can be trapped in different organs , including the spinal cord . Local inflammation leads to myeloradiculopathy , whose classical symptoms are back pain , numbness and weakness of the legs , erectile dysfunction and urinary retention . Precocious and long-duration therapy with high dose of corticosteroids is necessary to avoid severe sequelae , such as disability to walk . The recommended duration of the treatment varies from three weeks to six months . The physician usually decides when to stop steroids based on physical examination . A neurophysiology exam could improve the evaluation of therapeutic response and the diagnosis of recurrences . Vestibular Evoked Myogenic Potential ( VEMP ) with Galvanic Vestibular Stimulation ( GVS ) , which is a simple , non-invasive and inexpensive exam , could be used in this context . We assessed the characteristics of VEMP with GVS in patients and found that it was altered in comparison to controls , demonstrating that this exam is a promising tool to add electrophysiological information on the spine to the physical examination . | [
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"su... | 2016 | Vestibular Evoked Myogenic Potential (VEMP) Triggered by Galvanic Vestibular Stimulation (GVS): A Promising Tool to Assess Spinal Cord Function in Schistosomal Myeloradiculopathy |
Infection with the Streptococcus suis ( S . suis ) epidemic strain can cause Streptococcal toxic shock-like syndrome ( STSLS ) , which is characterized by a cytokine storm , dysfunction of multiple organs and a high incidence of mortality despite adequate treatment . Despite some progress concerning the contribution of the inflammatory response to STSLS , the precise mechanism underlying STSLS development remains elusive . Here , we use a murine model to demonstrate that caspase-1 activity is critical for STSLS development . Furthermore , we show that inflammasome activation by S . suis is mainly dependent on NLRP3 but not on NLRP1 , AIM2 or NLRC4 . The important role of NLRP3 activation in STSLS is further confirmed in vivo with the NLRP3 inhibitor MCC950 and nlrp3-knockout mice . By comparison of WT strain with isogenic strains with mutation of various virulence genes for inflammasome activation , Suilysin is essential for inflammasome activation , which is dependent on the membrane perforation activity to cause cytosolic K+ efflux . Moreover , the mutant strain msly ( P353L ) expressing mutagenic SLY without hemolytic activity was unable to activate the inflammasome and does not cause STSLS . In summary , we demonstrate that the high membrane perforation activity of the epidemic strain induces a high level of NLRP3 inflammasome activation , which is essential for the development of the cytokine storm and multi-organ dysfunction in STSLS and suggests NLRP3 inflammasome as an attractive target for the treatment of STSLS .
Streptococcus suis ( S . suis ) is a major swine pathogen that is responsible for severe economic losses in the porcine industry and represents a significant threat to human health [1–4] . To date , more than 1600 human S . suis infections have been reported worldwide [4 , 5] , and the infection has been identified as the leading and second-leading cause of adult meningitis in Vietnam and Thailand [2] . S . suis infection mainly induces meningitis , sepsis , arthritis , endocarditis , and endophthalmitis , and the pooled case-fatality rate is 12 . 8% [5] . However , two large-scale human S . suis epidemics in China ( the first was 25 cases with 14 deaths in Jiangsu in 1998 , and the second was 204 cases with 38 deaths in Sichuan in 2005 ) raised serious concerns for global public health and challenged the conventional perception that S . suis infections are sporadic in humans [2 , 6 , 7] . This infection causes unusual development of Streptococcal toxic-shock-like syndrome ( STSLS ) , including the hallmarks of acute high fever , blood spots , hypotension , shock , and dysfunction of multiple organs , as well as acute death ( mortality is more than 80% despite adequate treatment ) [7 , 8] . At present , how the epidemic strain causes STSLS and leads to high mortality remains unclear . A retrospective clinical investigation showed high tumor necrosis factor-alpha ( TNF-α ) , interleukin ( IL ) -1β , IL-6 , IL-8 , IL-12 , and interferon-γ ( IFN-γ ) levels in the blood of patients with STSLS [6] . Subsequent studies further confirmed that the induction of an inflammatory cytokine storm was essential for STSLS [9 , 10] , which was further supported by the finding that inhibition of the excessive inflammatory response with anti-inflammatory drugs improved survival against STSLS [11] . Together , these data highlight the great potential that comprehensive understanding of the molecular mechanisms by which S . suis induces a high level of inflammatory responses may contribute to identify new therapeutic targets for S . suis-caused conditions , including STSLS [11 , 12] . IL-1β secretion is tightly controlled by the assembly of a multiprotein complex called the inflammasome [13 , 14] . To date , a few types of inflammasomes ( NLRP1 , NLRP3 , NLRC4 , AIM2 , etc . ) have been described , and the NLRP3 inflammasome has been under intense investigation given its link with a vast number of diseases [13 , 15 , 16] . Upon activation , NLRP3 is recruited to the dispersed trans-Golgi network to form multiple puncta that induces ASC polymerization and makes pro-caspase-1 ( pro-casp1 ) into an active protease [17] . In turn , caspase-1 ( casp1 ) mediates the processing of several targets , including pro-IL-1β and pro-IL-18 , into their biologically active forms and induces their secretion by triggering pyroptosis through cleaved gasdermin D ( GSDMD ) [18–21] . IL-1β and IL-18 secretion may further induce IL-6 , IL-8 , IL-17 , and IFN-γ expression , thereby resulting in inflammatory conditions such as fever and septic shock [22] . Owing to the high levels of blood IL-1β and its inflammatory mediators in patients with STSLS [6] , we hypothesized that the inflammasome could contribute to STSLS . Here , we demonstrated for the first time that a high level of inflammasome activation was essential for induction of the cytokine storm and the dysfunction of multiple organs—the hallmarks of STSLS .
STSLS is characterized by high bacterial burden , an inflammatory cytokine storm , multi-organ dysfunction , and ultimately acute host death [6–8] . In a murine model , S . suis epidemic strain SC-19 infection induced an acute and extremely high inflammatory cytokine response , including increased IL-1β , IL-18 , TNF-α , IL-17A , and IFN-γ levels ( Fig 1A ) , high bacterial burden ( Fig 1B ) , and high CK ( creatine kinase ) , ALT ( alanine aminotransferase ) , AST ( aspartate aminotransaminase ) , and LDH ( lactate dehydrogenase ) levels in the blood ( Fig 1C ) , resulting in evident injury in multiple organs , such as severe congestion and dense infiltration of inflammatory cells in the lung , severe congestion in the spleen , and severe vacuolated degeneration and necrosis in the liver ( Fig 1D ) . In addition , all infected mice presented with severe clinical signs and died within two days ( n = 10 ) ( Fig 1E and 1F ) . Moreover , the level of inflammatory response and organ damage caused by SC-19 is much higher than classical virulent P1/7 strain , which could also cause high mortality [10] . Thus , murine infection with SC-19 mimicked the STSLS observed in humans . To evaluate the effect of the inflammasome on STSLS , an inhibitor ( inh ) of casp1 , Ac-YVAD-CHO , was intraperitoneally injected into the infected mice 1 h after infection . Ac-YVAD-CHO treatment significantly reduced the IL-1β and IL-18 levels ( Fig 1A ) , indicating that the secretion of IL-1β and IL-18 depended mainly on casp1 activity . In contrast , TNF-α production was not significantly inhibited by the treatment , which suggested that inhibition of the inflammasome with Ac-YVAD-CHO could not significantly inhibit the casp1-unrelated pro-inflammatory cytokine response ( Fig 1A ) . IL-17A and IFN-γ induction was also inhibited by Ac-YVAD-CHO ( Fig 1A ) since these cytokines are reported as downstream effectors of the inflammasome [23–25] . Because the bacterial burden in the blood did not significantly decrease at the given time point ( Fig 1B ) , the decreased inflammatory response was not due to a decreased bacterial load , the trigger for activation of this inflammatory signaling pathway . Furthermore , inhibition of casp1 activity also reduced the levels of CK and AST in the blood ( Fig 1C ) , alleviated inflammation and injury in multiple organs ( Fig 1D ) , reduced clinical signs and promoted survival ( Fig 1E and 1F ) . Ac-YVAD-CHO was not an exclusive inhibitor for casp1 , and it also exhibited some activity against caspase-4/5 [26] , which directly recognized intracellular LPS for non-canonical inflammasome activation [27–29] . Therefore , these data indicate a potential critical role of casp1-based inflammasome activation in STSLS . To understand the mechanism underlying STSLS development and to identify the type of inflammasome that is activated in response to S . suis infection , we constructed four types of inflammasome complexes in the 293T cell line ( S1 Fig ) . S . suis could clearly induce cleavage of pro-casp1 and pro-IL-1β and secretion of IL-1β in 293T cells expressing the NLRP3 inflammasome complex but not in cells expressing the other three ( NLRP1 , NLRC4 , or AIM2 ) inflammasome complexes ( Fig 2A ) . In contrast , poly ( dA:dT ) mainly activated the AIM2 inflammasome , as described previously [30] ( Fig 2A ) . These results indicated that NLRP3 was required for inflammasome activation in response to S . suis epidemic strain SC-19 infection . To further confirm whether NLRP3 was indispensable for inflammasome activation induced by S . suis , an nlrp3-deficient human acute monocytic leukemia THP-1 cell line ( THP-1-nlrp3-/- ) and a control cell line ( THP-1-nlrp3+/+ ) were constructed using clustered regularly interspaced short palindromic repeats ( CRISPR ) technology . Similar to cardiac glycosides ouabain , which activates the NLRP3 inflammasome [31] , SC-19 infection induced cleavage of pro-casp1 and pro-IL-1β and secretion of IL-1β in THP-1-nlrp3+/+ cells , but the activation was significantly inhibited in nlrp3-/- cells ( Fig 2B ) . This study was also performed using the murine macrophage cell line J774a . 1 with nlrp3 gene knockout ( J774a . 1-nlrp3-/- ) and the control cell line J774a . 1-nlrp3+/+ ( S2 Fig ) . Thus , NLRP3 was mainly responsible for inflammasome activation induced by S . suis epidemic strain SC-19 infection . NLRP3 inflammasome activation can be attributed to several cellular events , including the presence of a P2X7 receptor agonist ( extracellular ATP ) , ROS production , mitochondrial damage , lysosomal damage , formation of large nonspecific pores in the cell membrane , and cytosolic K+ efflux [32–34] . Activation of the inflammasome by SC-19 was not inhibited by the single treatment of the P2X7 antagonist KN-62 , the ROS scavenger N-acetyl-L-cysteine ( NAC ) , the phagocytosis inhibitor cytochalasin B , or the lysosomal inhibitor bafilomycin A ( Fig 2C ) , indicating that inflammasome activation by S . suis was not dependent on the each single event or was dependent on these complicate events . However , the activation was significantly inhibited in the K+-rich media ( Fig 2D ) . Although K+ efflux-independent NLRP3 inflammasome activation by small molecules targeting mitochondria had been observed [35] , these results indicated that inflammasome activation in response to SC-19 infection was primarily dependent on K+ efflux , an essential process for recruitment of NLRP3 to the dispersed trans-Golgi network to cause K+-efflux-dependent NLRP3 activation [17] . Because SC-19 specifically activated the NLRP3 inflammasome in vitro , we further investigated the role of NLRP3 in STSLS with a small-molecule inhibitor of the NLRP3 inflammasome , MCC950 , which blocks NLRP3-induced ASC oligomerization [36] . MCC950 effectively blocked inflammasome activation by SC-19 in vitro ( S3 Fig ) . MCC950 treatment significantly reduced IL-1β level in response to SC-19 infection in mice ( Fig 3A ) . As downstream effects of inflammasome activation , S . suis infection-induced IL-6 and IFN-γ levels were also significantly decreased by MCC950 treatment ( Fig 3A ) . Therefore , NLRP3 inflammasome activation induced by S . suis significantly contributed to the inflammatory cytokine storm . MCC950 treatment also reduced the CK and AST levels in the blood ( Fig 3B ) , alleviated injury in multiple organs ( Fig 3C ) , decreased clinical signs ( Fig 3D ) , and promoted host survival ( Fig 3E ) , although the bacterial burden in the blood was not significantly changed at the given time point ( Fig 3F ) . These indicated that blocking NLRP3 inflammasome could significantly inhibit STSLS caused by SC-19 infection . To direct investigate the role of NLRP3 in STSLS , the comparison of infection was also performed on nlrp3-/- mice and nlrp3+/+ mice . Similar effects were observed for infection of SC-19 on nlrp3-/- mice , it induced significantly decreased levels of IL-1β and IFN-γ comparing to the infection on nlrp3+/+ mice ( Fig 4A ) , while the bacterial burden in the blood did not significantly decrease at the given time point ( Fig 4B ) . The infection on nlrp3-/- also caused significantly decreased levels of CK , AST , and LDH in the blood ( Fig 4C ) , decreased injury in multiple organs ( Fig 4D ) , decreased clinical signs ( Fig 4E ) , and promoted host survival ( Fig 4F ) . These results suggested that the NLRP3 inflammasome activation was essential for STSLS development following epidemic S . suis strain SC-19 infection . Although we identified the NLRP3 inflammasome as being essential for STSLS development , it was also important to identify the component of S . suis involved in inflammasome activation . To identify the component of S . suis involved in inflammasome activation , we found that live , but not heat-inactivated , S . suis strain SC-19 induced very obvious cleavage of pro-casp1 , pro-IL-1β , and GSDMD ( Fig 5A and 5B ) , which resulted in pyroptosis and benefited the secretion of mature IL-1β and IL-18 [19–21] . Furthermore , the secretion of IL-1β was specific because treatment with either live or heat-inactivated S . suis did not induce significantly more TNF-α at the indicated time point ( Fig 5C ) . Consistent with the results obtained in THP-1 cells , live , but not heat-killed , S . suis was required for IL-1β secretion , and IL-1β activation was inhibited by the casp1 inh in isolated murine peritoneal macrophages ( S4A Fig ) and bone marrow neutrophils ( S4B Fig ) . Thus , live , but not heat-killed , SC-19 infection activated the inflammasome . To further identify the component of S . suis that contributes to inflammasome activation , D-alanylation of lipoteichoic acid ( DLTA ) [37 , 38] , the capsular polysaccharides ( CPS ) structure [39] , and SLY [38 , 40–42] , which are directly involved in the virulence of S . suis , were selected for evaluation of their roles in inflammasome activation . The isogenic mutants for dlta ( Δdlta ) ( S5 Fig ) or cpsEF ( ΔcpsEF ) induced pro-casp1 , pro-IL-1β and GSDMD cleavage ( Fig 5B ) and IL-1β secretion ( Fig 5C ) , similar to the wild-type ( WT ) strain . However , the isogenic sly mutant ( Δsly ) completely lost the ability to induce cleavage of pro-casp1 , pro-IL-1β and GSDMD ( Fig 5B ) and secretion of IL-1β ( Fig 5C ) , but it did not block TNF-α secretion ( Fig 5C ) . In contrast , the complemental SLY strain could restore the ability for induction of inflammasome ( S6 Fig ) . Furthermore , the purified recombinant SLY ( rSLY ) induced pro-casp1 , pro-IL-1β and GSDMD cleavage and IL-1β secretion in a dose-dependent manner ( Fig 5D and 5E and S2 Fig ) . These data indicated that SLY of S . suis activated the inflammasome . Because SLY is a member of the pore-forming cholesterol-dependent cytolysin family of toxins [43 , 44] , we further evaluated the role of SLY in inflammasome activation by adding exogenous cholesterol , which can inhibit binding of SLY to host cells [45 , 46] . Although cholesterol crystals induced the NLRP3 inflammasome [47] , the addition of solubilized cholesterol at the given concentrations inhibited the pro-casp1 , pro-IL-1β and GSDMD cleavage ( Fig 5F ) and IL-1β secretion ( Fig 5G ) induced by SC-19 in a dose-dependent manner . In contrast , the addition of solubilized cholesterol at the given concentration did not significantly inhibit the IL-1β secretion induced by the NLRP3 agonist ouabain ( Fig 5F and 5G ) . These studies indicated that inflammasome activation in response to S . suis epidemic strain SC-19 infection required the binding of SLY to host cells . Structural analysis of S . suis SLY indicated that P353L would result in a loss of hemolytic activity while retaining the biological activity of erythrocyte aggregation [43] , which was further confirmed in a biological experiment using recombinant SLY [45] . To elucidate the mechanism underlying SLY-induced inflammasome activation , we constructed a mutant strain containing the P353L point substitution in SLY [msly ( P353L ) ] to analyze the contribution of the membrane perforation activity of SLY to inflammasome activation ( S7A Fig ) . Compared with WT strain inoculation , msly ( P353L ) strain inoculation failed to activate the inflammasome ( Figs 2B , 5B and 5C and S2 Fig ) . The inability of msly ( P353L ) to activate the inflammasome was not due to failed SLY expression , because the amount of SLY in the supernatants of cells treated with msly ( P353L ) was not less than that in the supernatants of cells treated with the WT strain ( S7C and S7D Fig ) . Therefore , our data strongly suggested that the membrane perforation activity of SLY was very important for inflammasome activation during S . suis infection . Previous studies have indicated that SLY may confer bacterial resistance to complement-mediated killing [38 , 48] and contribute to enhanced host inflammation [42] , which ultimately contributes to S . suis virulence . The non-hemolytic mutant msly ( P353L ) retained its resistance to complement-mediated killing , while the Δsly mutant did not ( S7E Fig ) . Therefore , the non-hemolytic mutant msly ( P353L ) could be used to further confirm the effect of NLRP3 inflammasome activation on STSLS . As expected , msly ( P353L ) did not induce high levels of the inflammasome-regulated pro-inflammatory cytokines IL-1β and IL-18 or the downstream effectors , including IL-17A and IFN-γ , in contrast with the WT strain , but the mutant could still induce comparatively high levels of the inflammasome-unrelated cytokine TNF-α ( Fig 6A ) . Notably , the trend in the induction of these inflammasome-related cytokines by the mutant was similar to the effect on nlrp3-deficient mice with SC-19 strain infection ( Fig 4 ) . These data suggested that membrane perforation activity was required for inflammasome activation in vivo and that inflammasome activation was essential for the development of the inflammatory cytokine storm following SC-19 infection . Interestingly , msly ( P353L ) infection did not result in high levels of ALT , AST , LDH and CK in the blood ( Fig 6B ) , indicating that the mutant did not cause severe multi-organ injury , an essential aspect of STSLS . Furthermore , the bacterial burden was comparable in mice infected with the SC-19 or its mutant strain at the given time points ( Fig 6C ) , which suggested that the decreased inflammasome activation was not attributable to differential bacterial load . The SC-19 strain caused severe damage to multiple organs and acute death with severe clinical signs; in contrast , 90% of the mice infected with msly ( P353L ) survived , and only moderate clinical signs and alleviated organ damage were observed during the study ( Fig 6D–6F ) . These data further confirmed that membrane perforation activity was required for inflammasome activation and full virulence of the epidemic strain SC-19 , which can cause STSLS . In summary , these experiments further supported our hypothesis that the membrane perforation activity of SLY leaded to NLRP3 inflammasome activation that was essential for the induction of STSLS following epidemic S . suis infection .
Highly virulent S . suis infection in humans , pigs , and mice induces STSLS , which is characterized by high bacterial burden , a cytokine storm , multi-organ dysfunction , and ultimately acute host death [8 , 10 , 49] . However , no superantigen responsible for toxic shock syndrome was detected in S . suis [7] , indicating that the mechanism underlying STSLS is different from that of toxic shock syndrome . Although a few studies have indicated that an excessive inflammatory response is responsible for STSLS development [6] and that targeting the pathway may be a potential therapeutic strategy [11 , 12] , the precise mechanism underlying STSLS remains elusive . In addition to being a characteristic of acute and fulminating infectious diseases , the “cytokine storm” plays an essential role in the associated high mortality [50 , 51] . Therefore , suppression of inflammatory genes is an appealing strategy for preventing death due to severe infections [50] . The “cytokine storm” contributes to STSLS and high mortality [6]; however , the underlying mechanism was unknown . Among these cytokines , IFN-γ plays a broad and important role in severe inflammatory responses and organ injury during shock syndrome [10 , 52 , 53] . In the present study , NLRP3 inflammasome activation was responsible for high IFN-γ level , multi-organ dysfunction , and mortality in response to epidemic S . suis infection ( Figs 3–4 ) . These findings further demonstrated that NLRP3 inflammasome activation was important for S . suis-causing cytokine storm . The pore-forming toxins have been reported to activate the inflammasome through various means [29 , 54–56] . For extracellular Gram- bacteria , the toxins could help the bacterial outer membrane vesicles to escape from early endosomes [29] , which was important for non-canonical inflammasome activation through caspase-11/4/5 to recognize the intracellular LPS [57 , 58] . For extracellular Gram+ bacteria , the precise underlying mechanism remains unclear . Inflammasome activation by SC-19 was blocked in K+-rich media , which could also inhibit inflammasome activation by Streptococcus pneumonia [55] . The present study further indicated that the activation by this toxin could not be inhibited by any one of the inhibitors that block inflammasome activation by extracellular ATP and other stimulators ( Fig 2 ) , which indicated that the toxin activated inflammasome through various means . However , the activation by the toxin could be inhibited in the K+-rich media ( Fig 2 ) , providing a direct explanation for SLY activation of the inflammasome: SLY-induced formation of large pores might cause cytosolic K+ efflux-dependent NLRP3 inflammasome activation , which could further result in pro-casp1 , pro-IL-1β , and GSDMD cleavage , leading to pyroptosis and facilitating the secretion of mature IL-1β and IL-18 [19–21] , which ultimately leads to severe inflammation and STSLS . In fact , the association of SLY with the virulence of S . suis has been known for decades [40–42 , 59] . Although SLY does not seem to be a critical virulent factor for some strains [40] , it is essential for the full virulence of the epidemic strain , which can cause STSLS [60] . SLY was first confirmed to be involved in resistance to complement-mediated killing [38 , 48] and to contribute to the virulence of S . suis [42] . Recently , SLY was demonstrated to be the main stimulus for TNF-α production independently of its membrane perforation ability [61] , and it was also involved in the invasive infection caused by S . suis [46 , 62–64] . Here , we demonstrated that SLY was essentially responsible for the high level of inflammasome activation by S . suis ( Fig 5 ) because the isogenic sly mutant showed no obvious ability to activate the inflammasome and inflammasome activation was significantly inhibited by soluble cholesterol , the target molecule in the cell membrane for SLY binding [44] . Furthermore , the membrane perforation activity of SLY was indispensable for inflammasome activation ( Fig 5 ) . Undoubtedly , all these pathogenic functions of SLY may contribute to the virulence of S . suis [46 , 62 , 63 , 65 , 66] . To further determine the significance of inflammasome activation by SLY for virulence , we constructed the mutant msly ( P353L ) , which expresses SLY with a point mutation that resulted in a defect in hemolytic activity . The strain retained complement-mediated killing ability but lost its membrane perforation activity and the ability to activate the inflammasome ( S7 Fig ) . Interestingly , the mutant maintained its ability to resist bacterial clearance and induced high levels of TNF-α , similar to the WT strain ( the epidemic strain ) , but could not significantly induce high levels of inflammasome-related cytokines , which was similar to the effect of inflammasome inhibitors on S . suis infection . As a result , the mutant could not cause the cytokine storm and multi-organ failure ( Fig 6 ) . Therefore , the present study strongly indicates that the membrane perforation activity of SLY is important for causing high levels of NLRP3 inflammasome activation , which is essential for STSLS development . However , it is still difficult to explain why the epidemic strain causes STSLS while other sly+ strains ( such as the P1/7 strain ) do not . Interestingly , the epidemic strain expressed higher levels of SLY [67] , which further activated the inflammasome ( S8 Fig ) . Surprisingly , a novel hemolysis-related gene was identified in the 89K pathogenicity island ( 89K PI ) , which could increase SLY expression [68] . Because the 89K PI was specifically present in the genome of the epidemic S . suis strain [69] and could be transferred in a T4SS-mediated horizontal manner [70] , increased SLY expression due to the acquisition of the 89K PI might explain why the epidemic strain suddenly had the ability to cause high level of inflammasome activation and STSLS development . Therefore , it would be worthy to further elicit the mechanism underlying the regulation of SLY by the 89K PI . In conclusion , we identified an important mechanism by which the epidemic S . suis strain causes STSLS ( Fig 7 ) . First , S . suis infection may activate the transcription of genes involved in the inflammasome through pattern-recognition receptors , such as Toll-like receptor ( TLR ) [9 , 61 , 71 , 72] . Then , acquisition of the 89K PI enables the strain to increase SLY expression , the high membrane perforation activity of which causes several events , including cytosolic K+ efflux , an essential event for NLRP3 inflammasome activation . Thus , strong activation of the inflammasome is an important mechanism by which this strain causes the cytokine storm , multi-organ dysfunction , and a high mortality rate , which are hallmarks of STSLS . Therefore , our study provides an explanation for STSLS development and indicates that the NLRP3 inflammasome is an attractive target for the treatment of STSLS .
The S . suis epidemic strain SC-19 , which shows high pathogenicity in humans , mice and pigs [11 , 73] , was used in the present study . The isogenic mutants for cpsEF ( ΔcpsEF ) [74] , sly ( Δsly ) [75] , dlta ( Δdlta ) and a mutant [msly ( P353L ) ] containing a point substitution P353L were originally from strain SC-19 ( S5 and S7 Figs ) . The S . suis strain P1/7 , which induces only sporadic cases of meningitis and sepsis in pigs [76] , was used as a non-STSLS-causing control . The sly gene and its predicted upstream promoter was constructed into a S . suis-E . coli shuttle vector pSET2 [77] , and then introduced into Δsly strain to obtain the complemented SLY on Δsly strain ( Δsly-Csly ) . The experimental infectious studies were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals Monitoring Committee of Hubei Province , China , and the protocol was approved by the Scientific Ethics Committee of Huazhong Agricultural University ( Permit Number: HZAUMO-2015-014 ) . All efforts were made to minimize the suffering of the animals . Five- to six-week-old Balb/c mice with similar body weights were randomly divided into groups of 10 mice and challenged with 0 . 5 mL of S . suis strains ( 8 × 108 CFU/mL ) by an intraperitoneal ( i . p . ) injection to evaluate the pathogenicity of the different S . suis strains . To evaluate the effect of casp1 and NLRP3 signaling on S . suis infection , 100 μg of the casp1 inh Ac-YVAD-CHO ( Merck Millipore , 400015-1MG , Germany ) or PBS as a control; or 37 . 5 μg of MCC950 ( Selleck , S7809 , USA ) , a selective inh of NLRP3 , or a PBS control were injected intraperitoneally 1 h post-infection with S . suis . The experimental infections were also performed on nlrp3-/- mice ( C57BL/6 background , purchased from the Jackson Laboratory ) and nlrp3+/+ mice ( C57BL/6 ) to direct evaluate the effect of nlrp3 on STSLS development . All the mice were monitored three times a day for seven days for clinical signs and assigned clinical scores as follows [78]: 0 = normal response to stimuli; 1 = ruffled coat and slow response to stimuli; 2 = respond only to repeated stimuli; 3 = non-responsive or walking in circles; and 4 = dead . Mice exhibiting extreme lethargy or neurological signs ( score = 3 ) were considered moribund and were humanely euthanized . In addition to the evaluation of mortality , experimental infections were also performed with mice to evaluate the effect of various treatments on the cytokine response , blood biochemistry , and bacterial burden during S . suis infection . At the indicated time points post-infection with S . suis , mice in each group were euthanized by carbon dioxide inhalation , and blood was collected via cardiac puncture . Fifty microliters of blood was withdrawn for bacterial load analysis . The remaining blood was used to prepare plasma for analysis of the CK , ALT , AST , and LDH levels with a VITALAB SE Chemistry Analyzer and for analysis of the IL-1β ( eBioscience , E09327-1647 , USA ) , TNF-α ( eBioscience , E09483-1670 , USA ) , IL-6 ( eBioscience , 88-7064-88 , USA ) , IL-17A ( eBioscience , 88-7371-88 , USA ) , IL-18 ( Sino Biological , SEK50073 , China ) , and IFN-γ ( eBioscience , 88-7134-88 , USA ) levels using commercial ELISA kits . Peritoneal lavage fluid was also collected from each mouse with 2 mL of PBS to analyze the bacterial load and cytokine levels . The lung , kidney , liver and spleen tissues were collected and fixed in 10% neutral buffered formalin and routinely processed in paraffin . Sections with a thickness of 2 to 3 mm were cut for hematoxylin and eosin staining for histopathologic evaluation as previously described [11] . The collected blood samples were serially diluted and then plated on Tryptic Soy Agar plates to evaluate the bacterial load . The THP-1 nlrp3 knockout cell line ( THP-1-nlrp3-/- ) was constructed using CRISPR technology [79] . sgRNA ( GGATCTTCGCTGCGATCAAC ) for human nlrp3 was designed with an online CRISPR Design Tool ( http://tools . genome-engineering . org ) and then constructed into a lentiCRISPR v2 vector ( Addgene , 52961 ) to produce the plasmid lentiCRISPR v2-hunlrp3 . Then , HEK 293FT cells ( ATCC source ) were transfected with lentiCRISPR v2-hunlrp3 , psPAX2 ( Addgene , 12260 ) , and pMD2 . G ( Addgene , 12259 ) to produce lentivirus for disruption of the nlrp3 gene . The lentivirus was then used to transduce THP-1 cells at an MOI = 0 . 5 . After transduction , the THP-1 cells were cultured in the presence of 1μg/mL puromycin ( Selleck , S7417 , USA ) for 5 days . The surviving THP-1 cells were diluted into 96-well plates at a concentration of 1 cell/200 μL and cultured in the presence of 1μg/mL puromycin . The THP-1-nlrp3-/- cell line was identified by a western blot assay with NLRP3 antibody ( CST , 15101S , USA ) and then by DNA sequencing of the nlrp3 gene . The control cell line ( THP-1-nlrp3+/+ ) was also constructed according to the same procedure using the original lentiCRISPR v2 plasmid . The nlrp3 knockout cell line derived from the murine macrophage cell line J774a . 1 ( J774a . 1-nlrp3-/- ) and its control cell line ( J774a . 1-nlrp3+/+ ) were constructed according to the same procedure used for THP-1 cells . The designed sgRNA targeted the murine nlrp3 gene and contained the sequence GAAGATTACCCGCCCGAGAA , and the concentration of puromycin for selection of J774a . 1-nlrp3-/- or J774a . 1-nlrp3+/+ cells was 2 . 5 μg/mL . Cell supernatants were collected , and LDH release was quantified using a CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega , USA ) according to the manufacturer’s instructions . The percentage of cytotoxicity was calculated based on LDH release in the total cell lysates . THP-1 cells ( ATCC source ) were differentiated into macrophage-like cells by treatment with 50 nM phorbol myristate acetate ( PMA ) ( Sigma , P8139-1MG ) overnight . The differentiated cells ( 2 × 106 /mL ) were primed with LPS ( Sigma , L4391 ) at 0 . 5 μg/mL for 4 h and then infected with S . suis strains ( 2 × 107 /mL ) or stimulated with ATP ( Sigma , A2383 ) for 30 min or ouabain ( Sigma , O3125 ) for 2 h in the presence of the following inhibitors: cholesterol ( Sigma , C8667-1G ) , 5 μM cytochalasin B ( Sigma , C274 ) , 100 nM KN-62 ( Santa Cruz , SC-3560 , USA ) , 2 . 5 mM NAC ( Sigma , 1009005 ) , 50 nM bafilomycin A ( InvivoGen , tlrl-baf1 , USA ) , 100 μM casp1 inh , Ac-YVAD-CHO , or the controls containing the corresponding solvents . Then , 100-μL aliquots of the cell culture supernatants were collected to determine human TNF-α ( Dakewe Group , DKW12-1720-096 , China ) and IL-1β ( eBioscience , 88-7261-88 , USA ) secretion levels using commercial available ELISA kits . The cellular proteins were extracted in Laemmli sample buffer . The proteins in the supernatants were precipitated with 20% trichloroacetic acid on ice for 30 minutes and then washed 3 times with ice-cold acetone . After the last wash , the acetone was removed by vacuum , and the pellets were allowed to air dry for 5 minutes and then dissolved in Laemmli sample buffer . The proteins were subjected to immunoblot analysis with antibodies for the detection of casp1 ( Cell Signaling , 3866S , USA ) , GSDMD ( Proteintech , 66387-1-Ig , USA ) , or IL-1β ( Proteintech , 16806-1-AP , USA ) . Actin was also detected as an internal control using a specific antibody ( Proteintech , 66009-1-AP , USA ) . The THP-1-nlrp3-/- cell line and its control cell line ( THP-1-nlrp3+/+ ) were also subjected to detection of inflammasome activation according to the procedure described for THP-1 cells . Inflammasome activation was also performed using the murine macrophage cell line J774a . 1 with the nlrp3 gene knockout ( J774a . 1-nlrp3-/- ) and its control cell line J774a . 1-nlrp3+/+ via western blotting with antibodies against casp1 ( R&D MAB6215 , USA ) and IL-1β ( BIO vision , 5129-30T ) and with ELISA kits for TNF-α ( eBioscience , E09483-1670 , USA ) and IL-1β ( eBioscience , E09327-1647 , USA ) . Murine peritoneal macrophages and bone marrow neutrophils were isolated according to a procedure described previously [74] . Detection of inflammasome activation in isolated murine peritoneal macrophages and bone marrow neutrophils was also performed as described for THP-1 cells with ELISA kits for TNF-α ( eBioscience , E09483-1670 ) and IL-1β ( eBioscience , E09327-1647 ) . THP-1 cells ( ATCC source ) were differentiated into macrophage-like cells by treatment with 50 nM PMA ( Sigma , P8139-1MG , USA ) overnight . The differentiated cells ( 2 × 106 /mL ) were primed with LPS ( Sigma , L4391 ) at 0 . 5 μg/mL for 4 h and then treated with K+-rich media containing 45 mM KCL ( Sigma , 746436 ) or Na+-rich media containing 45 mM NaCl ( Sigma , S5886 ) for 1 h , followed by treatment with S . suis strain SC-19 for 2 h . The supernatants of the cells were collected for IL-1β and LDH detection . 293T cells ( ATCC source ) ( 1 × 106 /mL ) were co-transfected with 0 . 3 μg , 0 . 1 μg , and 0 . 2 μg of expression plasmids encoding human Flag-tagged pro-IL-1β , Flag-tagged pro-casp1 , and Myc-tagged ASC , respectively , and with 0 . 3 μg of plasmid for co-expression of GFP with NLRP3 , NLRP1 , NLRC4 , or AIM2 . The expression of these inflammasome components was confirmed by western blotting with a Myc-tag antibody ( CST , 2272S , USA ) and a FLAG-tag antibody ( MBL , M185-3L , USA ) and by examination of GFP expression with a fluorescence microscope ( Nikon 80I; Tokyo , Japan ) . At 24 h post-transfection , cells were infected with S . suis strain SC-19 for 2 h or transfected with poly ( dA:dT ) ( Invivogen , tlrl-patn , USA ) for 12 h . Then , cell supernatants were collected for the western blot assay with antibodies against casp1 ( Cell Signaling , 3866S , USA ) and IL-1β ( Proteintech , 16806-1-AP , USA ) and for determination of IL-1β ( eBioscience , 88-7261-88 , USA ) . Unless otherwise specified , the data were analyzed using two-tailed , unpaired t-tests . All assays were repeated at least three times , and the data were expressed as the mean ± standard deviations . For the animal infection experiments , comparisons of survival rates and clinical scores were analyzed with a log-rank test or two-way RM ANOVA , respectively , using GraphPad Prism 6 . For all tests , a value of p < 0 . 05 was considered the threshold for significance . | The two large-scale human Streptococcus suis epidemics have caused unusual development of Streptococcal Toxic-Shock-like Syndrome ( STSLS ) and high incidence of mortality despite adequate treatments . However , how the epidemic strain causes STSLS remained to be elucidated . Because an excessive high level of inflammasome-regulated cytokine was detected in the blood of STSLS patients , we used a murine model to identify the role of inflammasome activation on the development of STSLS . We found that NLRP3 activation contributed to STSLS with the pharmacological inhibition and NLRP3-/- mice . We identified a novel mechanism of STSLS in that increased suilysin expression in S . suis highly virulent strain could induce high level of cytosolic K+ efflux , an essential event for NLRP3 inflammasome activation , and then further cause a cytokine storm , dysfunction of multiple organs and a high incidence of mortality , the characters of STSLS . Therefore , our study provides insights for STSLS development and highlights NLRP3 inflammasome as an attractive target for the treatment of STSLS . | [
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"biolo... | 2019 | An NLRP3 inflammasome-triggered cytokine storm contributes to Streptococcal toxic shock-like syndrome (STSLS) |
The exosome functions throughout eukaryotic RNA metabolism and has a prominent role in gene silencing in yeast . In Arabidopsis , exosome regulates expression of a “hidden” transcriptome layer from centromeric , pericentromeric , and other heterochromatic loci that are also controlled by small ( sm ) RNA-based de novo DNA methylation ( RdDM ) . However , the relationship between exosome and smRNAs in gene silencing in Arabidopsis remains unexplored . To investigate whether exosome interacts with RdDM , we profiled Arabidopsis smRNAs by deep sequencing in exosome and RdDM mutants and also analyzed RdDM-controlled loci . We found that exosome loss had a very minor effect on global smRNA populations , suggesting that , in contrast to fission yeast , in Arabidopsis the exosome does not control the spurious entry of RNAs into smRNA pathways . Exosome defects resulted in decreased histone H3K9 dimethylation at RdDM-controlled loci , without affecting smRNAs or DNA methylation . Exosome also exhibits a strong genetic interaction with RNA Pol V , but not Pol IV , and physically associates with transcripts produced from the scaffold RNAs generating region . We also show that two Arabidopsis rrp6 homologues act in gene silencing . Our data suggest that Arabidopsis exosome may act in parallel with RdDM in gene silencing , by epigenetic effects on chromatin structure , not through siRNAs or DNA methylation .
High-throughput analyses have revealed that eukaryotic genomes are pervasively transcribed [1]–[4] , and the majority of the transcriptional activity takes place outside of protein-coding genes , producing non-coding RNAs ( ncRNAs ) derived from genome regions once thought to be transcriptionally silent , including intergenic and heterochromatic regions [1]–[3] , [5] . Pervasive transcription constitutes a risk for the cell , as it can be associated with expansion of TEs , loss of genomic stability and defects in gene expression . However , recent studies have also shown that ncRNAs themselves can have important regulatory functions , including the establishment and maintenance of the epigenetic architecture of eukaryotic genomes . In some cases , long ncRNAs serve directly as molecular scaffolds for recruiting chromatin modifiers [6] , [7] , whereas in other cases ncRNAs are processed by the RNAi machinery into short interfering siRNAs that guide DNA methylation and chromatin modifications to homologous regions of the genome [8] , [9] . Thus , RNA-mediated heterochromatin formation requires an affected region to be transcribed for transcriptional silencing to occur . Many of the ncRNA transcripts earned the term “hidden” because they remain invisible unless RNA degradation is prevented , for example , by inactivation of the degradation machinery [1] , [3] , [4] , [10]–[14] , raising the important question of how these ncRNAs are regulated . The exosome complex plays a central role in RNA metabolism in eukaryotes . Evolutionarily conserved from archaea to humans , the exosome is a stable complex of RNase-like and RNA binding proteins that catalyzes 3′ to 5′ processing and decay of various RNA substrates [15] . The current view of eukaryotic exosome structure is based mostly on studies done in yeast and human . The eukaryotic exosome has nuclear and cytoplasmic forms that share ten components . The key structural feature is a nine-subunit donut-shaped structure called the exosome ring . Six of the subunits , RNase PH domain-containing proteins Rrp41 , Rrp42 , Rrp43 , Rrp45 , Rrp46 and Mtr3 , are organized into a hexameric ring , capped on one side by a trimer of subunits that contain S1 and KH RNA binding domains ( Rrp40 , Rrp4 and Csl4 ) [16] , [17] . The 9-subunit ring structure has no catalytic activity in yeast and human , due to amino acid replacements that disable binding of RNA , phosphate ion , or catalysis [16] , [17] . The exosome active sites are contributed by the tenth protein , Rrp44 ( Dis3 ) , which has endonucleolytic and exonucleolytic activities and considered to be the tenth subunit of the exosome core [18] , [19] . In addition to Rrp44 , the nuclear form of the eukaryotic exosome is associated with a second active 3′ to 5′ exonuclease , Rrp6 [20] , [21] . Most functions of the exosome are dependent on cofactors . One of the notable complexes associated with the nuclear exosome is the Trf-Air-Mtr4 polyadenylation ( TRAMP ) complex endowed with a poly ( A ) polymerase activity that stimulates degradation [22]–[24] . The plant exosome might differ from yeast and human exosomes , as its ring subunit Atrrp41p appears to retain an active site and was also shown to have catalytic activity in vitro [1] , [25] . Our previous genome-wide study using tiling microarrays to examine exosome targets in Arabidopsis revealed that a large number of exosome substrates correspond to ncRNAs originated from promoters , 5′UTRs , intergenic regions , repetitive elements and TEs [1] . Many of these ncRNAs derive from centromeric and pericentromeric regions and other heterochromatic loci known to give rise to smRNAs that participate in silencing of these loci [26] . In Arabidopsis , the main and most-studied pathway for transcriptional gene silencing of repetitive elements and transposons is the siRNA-based silencing mechanism known as RNA-dependent DNA methylation ( RdDM ) [9] , [27]–[29] . The effects of exosome depletion on these ncRNAs and , potentially , on smRNAs are unlikely to be attributable to indirect effects of exosome depletion on the expression of RdDM pathway components , since no genes acting in siRNA biogenesis , siRNA-mediated transcriptional gene silencing ( TGS ) , DNA methylation or demethylation , or histone H3K9 modifications were found to be affected in these lines [1] . RdDM induces de novo methylation of cytosines in all sequence contexts at the region of siRNA–DNA or siRNA-RNA sequence homology . This silencing pathway requires two plant-specific RNA polymerases , Pol IV and Pol V , specializing in transcriptional gene silencing ( TGS ) [28] , although transcriptional activity of Arabidopsis Pol II was also reported to be involved in siRNA-directed gene silencing [30] . The mechanistic details of RNA-dependent silencing are not fully understood and also appear to vary from one genomic location to another , but the RdDM pathway likely consists of three main steps: ( i ) siRNA production from transcripts that are likely transcribed by RNA Pol IV [9] , ( ii ) synthesis of non-coding RNAs that could serve as scaffolds by RNA Pol V and/or Pol II at some of the loci [30] , [31] , and ( iii ) assembly of AGO-siRNA effector complexes to recruit methylation machinery to complementary sequences [9] . In siRNA biogenesis , RNA Pol IV transcripts are made double-stranded by RNA-DEPENDENT RNA POLYMERASE 2 ( RDR2 ) , processed into 24 nt siRNA by DICER-LIKE 3 ( DCL3 ) , and then incorporated into ARGONAUTE ( AGO4 and possibly AGO6 ) to form an AGO-siRNA complex [32]–[35] . The AGO-siRNA complex and other RdDM effectors [31] , [35]–[37] , assemble on scaffold RNA to form a guiding complex that recruits DNA methyltransferases and histone methyltransferases to direct the silencing of specific genomic loci through a mechanism that is not fully understood . Pol IV is thought to initiate RdDM pathway , whereas Pol V and AGO4-associated siRNAs function downstream from Pol IV to promote DNA methylation by recruiting the silencing complex to targeted loci . RNA Pol IV , Pol V and Pol II activities in RdDM are functionally diversified and coordinated; however , it is not yet clear how they are functionally integrated in heterochromatin silencing . The model system in which siRNA-mediated silencing is the best understood mechanistically is fission yeast . In S . pombe RNA Pol II carries out the functions attributed to Pol IV and Pol V in plants , therefore , it generates both siRNA precursors and scaffold transcripts to which siRNAs bind at loci that are subject to siRNA-mediated silencing . Exosome defects in S . pombe were reported to result in the loss of transcriptional silencing from centromeric , silent mating type , and telomeric loci [38]–[40] . In S . pombe , in the absence of exosome-mediated degradation , abundant aberrant RNA species enter the RNAi pathway and interfere with heterochromatic silencing through competition for RNAi biogenesis machinery , resulting in the dramatic decrease in centromeric siRNAs [38]–[40] . Recently , it was also shown that exosome plays an important role in remodeling of facultative heterochromatin [41] . Earlier work in plants also suggested that aberrant RNAs could enter RNAi pathways unless they are degraded by the 5′ to 3′ pathway [42] . However , the role of the exosome complex in smRNA metabolism in Arabidopsis has not been examined . It is also not known whether the Arabidopsis exosome complex interacts with the RdDM silencing pathway . To answer these questions we employed next-generation sequencing to profile populations of smRNAs in exosome-depleted plants , and in mutants of RdDM pathway genes . Unexpectedly , we found that loss of the exosome subunits had little effect on the global populations of smRNAs and had no affect on the level of DNA methylation in examined RdDM loci; rather , it resulted in a reduction of histone H3K9 dimethylation . We propose that the Arabidopsis exosome may coordinate the transcriptional interplay of RNA polymerases Pol II , Pol V and Pol IV , to achieve the appropriate level of transcriptional repression of heterochromatic loci .
Previously , we found that the majority of transcripts upregulated in RRP4 and RRP41 exosome depletion mutants originate from the promoters , repeats , intergenic , and siRNA generating regions [1] . Most of these regions harbor repeats and TEs that are known to be silenced by RdDM through siRNAs . Since microarray experiments allow estimation of only the length of affected regions , but not the exact length of affected transcripts , we set out to examine whether the exosome is involved in down regulation of these regions through regulating either quantity or quality of smRNAs . To characterize any changes in smRNA populations that occur in response to exosome depletion , we employed next-generation sequencing to deep sequence the smRNA populations in depletion mutants of exosome subunits RRP4 and RRP41 . Null T-DNA insertion mutations in RRP4 and RRP41 are lethal; therefore , we used inducible RNA-interference ( iRNAi ) constructs to reduce RRP4 and RRP41 . The seedlings of RRP4 ( rrp4-i ) or RRP41 ( rrp41-i ) transgenic plants grown on estradiol-containing medium to induce the RNAi constructs subsequently exhibit a growth arrest ( [1] , Figure 1A ) . We selected the earliest time-point of estradiol treatment corresponding to the accumulation of underprocessed 5 . 8S rRNA species ( the hallmark of the exosome defect ) , but before growth retardation , to minimize changes in gene expression that did not result directly from exosome depletion [1] . Small RNA libraries for Illumina sequencing were generated from the seedlings of rrp4-i and rrp41-i iRNAi lines grown with and without estradiol ( Table S1 ) and smRNAs between 15- and 32 nt in length were selected and mapped to the Arabidopsis genome ( TAIR version 9 ) . We first examined the smRNAs from the iRNAi transgenes used for inactivation of RRP4 or RRP41 [1] . As expected , these silencing cassettes generate silencer sequences corresponding to RRP4 or RRP41 ( mapping to AT1G03360 and AT3G61620 loci ) . Profiling silencer sequences by size and by first nucleotide revealed that the majority of the silencer sequences are 21 , 22 and 24 nt and start with 5′U or 5′A ( Figure S1 ) , suggesting that they are preferentially loaded into Ago1 , Ago2 and Ago4 complexes [43] to silence their target . Silencer sequences produced from iRNAi transgenes were filtered out and libraries without silencer reads were termed FLR , for filtered reads ( Table S1 ) . Each library was normalized either to the total number of mapped non-redundant reads or to the total number of non-redundant filtered reads ( FLR ) , multiplied by 106 ( RPM , reads per million ) . Both methods of normalization produced similar results; therefore , only data normalized using filtered reads ( FLR ) are presented graphically in this study . We then classified smRNAs based on their size , the nature of their first nucleotide , and their genomic features . The majority of functional smRNAs in A . thaliana range from 21 to 24 nt . Our libraries were constructed using 15–32 nt smRNAs; therefore , we were able to detect any effect exosome depletion might have on smRNA metabolism . We found that exosome defect does not lead to changes in smRNAs in the 15–19 nt and 26–32 nt categories ( data not shown ) . Importantly , the group of 20–25 nt smRNAs , which contains the majority of functional smRNAs , was present in similar proportions , although with minor variations , relative to the number of total reads in the libraries of both of exosome depletion mutants and in their corresponding non-induced lines , and constituted about half of total smRNAs mapped to the genome ( Table S1 , Figure 1B ) . Therefore , for simplicity we graphed only data corresponding either to 20–25 nt smRNAs , or to smRNAs of one specific length . In addition , the depletion of either RRP4 or RRP41 , which are both essential for exosome function , with slight variations , had no effect on the smRNA size distribution ( Figure 1B ) or the frequencies of their first nucleotide ( Figure 1C ) . All together , these results suggest that defects in exosome function do not lead to accumulation of un-degraded smRNA fragments or to any changes in the cleavage bias of Dicer proteins . Also , exosome depletion did not change proportions of smRNAs mapped to different classes of RNAs , such as mRNAs , tRNAs , rRNAs , and snoRNAs ( Figure 1D ) . Therefore , unlike the situation in S . pombe , where exosome acts as a negative regulator of siRNA biogenesis , Arabidopsis exosome does not act to prevent spurious RNAs from entering RNAi pathway . In Arabidopsis , repeats and TEs are silenced by siRNAs through RdDM; therefore , we examined the effect of exosome loss on 20–25 nt smRNAs corresponding specifically to TEs and repeats . Surprisingly , we saw no changes in the groups of smRNAs mapped to tandem repeats ( TR ) , inverted repeats ( IR ) , dispersed repeats ( DR ) or the group of TEs in both exosome mutants ( Figure 2A and 2B ) . The diverse heterochromatic siRNAs participating in TE silencing are mostly 24-mers and are Pol IV- and/or Pol V-dependent [9] . Most siRNA production relies on Pol IV , but there are also Pol V-dependent and Pol IV-independent siRNA-generating loci [44] , [45] . Therefore , to examine whether the exosome complex functionally overlaps with the components of the RdDM pathway , we constructed lines containing rrp4-i or rrp41-i iRNAi and mutations affecting Pol IV , Pol V , RDR2 and DCL3 , which are nrpd1 , nrpe1 , dcl3 and rdr2 respectively ( allele numbers provided in Methods ) . This approach also allowed us to confirm that smRNAs observed in exosome depletion lines are siRNAs produced by components of the RdDM pathway and not short RNA degradation products accumulated in the absence of functional exoribonucleolytic complex . Pol IV , Pol V , RDR2 and DCL3 are not essential for viability [27] , [29] , [46] . Combining mutations in nrpd1 , nrpe1 , dcl3 and rdr2 with rrp41-i iRNAi knock-down line did not exacerbate the phenotypes of single exosome depletion mutants ( Figure 1A ) . We next analyzed the smRNAs corresponding to repeats and TEs produced in the rrp41/nrpd1 and rrp41/nrpe1 double mutants ( Figure 2C ) and the rrp41/rdr2 and rrp41/dcl3 double mutants ( Figure 2D ) . Similar to previous reports , we observed a significant reduction in the amount of smRNAs corresponding to TEs , TRs and IRs in nrpd1 , nrpe1 , rdr2 , and dcl3 mutants [27] , [44] , [47] , [48] . Depletion of the exosome in nrpd1 , nrpe1 and rdr2 mutants had no effect on the amount of TE and repeat-associated smRNAs produced in these mutants ( Table S2 , Figure 2C and 2D ) . Depletion of rrp41 in dcl3 led to a minor restoration of this defect in all groups of repeats and TEs . In the absence of dcl3 , other Arabidopsis Dicer proteins are known to process dcl3 substrates [49]; therefore this minor restoration most likely resulted from compensatory effects of other DICER proteins ( Table S2 , Figure 2D ) . Profiling repeat- and transposable element-generated smRNAs by their size confirmed that the exosome defect did not affect the group of 20–25 nt smRNAs even in Pol IV , Pol V , RDR2 and DCL3 deficient genetic backgrounds . Typically , siRNAs participating in RdDM are 24 nt long; therefore we profiled smRNAs mapping to transposable elements by length , but observed no change in abundance of 24 nt smRNAs ( Figure 2E ) . Further analysis of the 24 nt smRNAs mapped specifically to the different transposable element superfamilies led to the same conclusion ( Figure 2F and 2G ) . We therefore concluded that there are no significant changes in the populations of siRNAs corresponding to repeats and TE superfamilies in exosome depletion mutants . We also did not observe any significant differences in amounts of mature 21-mer miRNAs . The results of our sequencing analysis were confirmed by Northern blot analysis ( Table S3 , Figure 3 , Figure S2 ) . Together , these data suggest that the Arabidopsis exosome complex is not involved in siRNA metabolism on a global scale . Nevertheless , we can not exclude the possibility that exosome might control a small number of smRNA precursor transcripts at a few specific loci that would have been missed in our experiments and with the data processing approach we took while dissecting differences on genomic level . To further investigate whether the exosome participates in gene silencing and interacts with the RdDM pathway , we examined the transcription patterns of several specific loci regulated through RdDM . solo LTR and AtSN1 are the heterochromatic loci for which the role of RdDM players in their silencing and interactions between them are best-understood [30] , [31] , [50]–[52] . Transcriptional silencing of solo LTR and AtSN1 heterochromatic loci are dependent on Pol IV and Pol V [30] , [31] , [50]–[52] . Based on previous studies , both solo LTR and AtSN1 loci can be subdivided into region A and an adjacent region B [30] , [31] . Region A represents the siRNA-generating region likely transcribed by Pol IV , and region B gives rise to the ncRNAs that are proposed to serve as a scaffold for recruiting siRNA-mediated complexes that form heterochromatin ( Figure 4A ) . Pol V was proposed to produce transcripts which serve as the scaffolds [31] , although in case of solo LTR , Pol II was also shown to be involved [30] . We then used real-time RT–PCR to examine the levels of transcript produced from region A , as a measure of the silencing status of each locus . We found that exosome defects resulted in accumulation of polyadenylated ncRNA produced from both regions A and B of solo LTR ( Figure 4B ) . We then compared the amplitudes of the region A derepression in the rrp41 , with rrp41 iRNAi/nrpd1 and rrp41 iRNAi/nrpe1 double mutants relative to the respective single mutants . As previously reported by others [30] , [31] , we observed solo LTR to be significantly derepressed in Pol IV and Pol V single mutants ( Figure 4C and 4F ) . Interestingly , only the combination of exosome defect with mutation of Pol V , but not with mutation of Pol IV , resulted in the synergistic increase of region A transcript ( Figure 4C ) . Reverse transcription with oligo dT primers does not discriminate between transcripts originating from either DNA strand; thus an elevated level of polyadenylated transcript could result from transcription from either one of the DNA strands . Therefore , to find out which of the transcripts increased in abundance , we carried out strand-specific RT-PCR for the A and B regions . Following standard nomenclature , the top transcript ( also called top strand RNA ) corresponds to the transcript identical to the sequence of the DNA top strand ( and therefore produced from the bottom DNA strand ) , and the bottom transcript is identical to the sequence of DNA bottom strand . The scaffold RNAs were reported to correspond to region B top strand [30] , [31] . Similar to previous results [30] , [31] , we observed region A top and bottom transcripts to be significantly derepressed in Pol IV and Pol V single mutants ( Figure 4D and 4E ) , and depletion of RRP41 lead to increased accumulation of the region A top and bottom transcripts ( inserts in Figure 4D and 4E ) . Interestingly , we found that the bottom transcript was synergistically derepressed in rrp41 iRNAi/nrpe1 double mutants relative to nrpe1 and rrp41 iRNAi single mutants , while no change was observed in rrp41 iRNAi/nrpd1 double mutants ( Figure 4D ) . Despite the fact that the exosome defect equally affected the levels of both top and bottom region A transcripts , combining the exosome defect with either Pol IV or Pol V mutants had no additive or synergistic effect on the level of region A top transcript . Surprisingly , the level of expression of region A top transcript was even somewhat decreased in rrp41 iRNAi/nrpd1 and rrp41 iRNAi/nrpe1 , compared to nrpd1 and nrpe1 single mutants , opposite to the pattern we observed for the bottom strand ( Figure 4E ) . Production of scaffold transcripts is central in silencing of the locus and it was reported that even in the presence of functional Pol IV and siRNAs , silencing of solo LTR fails when scaffold RNAs are not produced [30] , [31] . We therefore examined the scaffold-producing region B and found that the exosome also affects the amount of region B top transcript , but there is no synergistic increase of this transcript in rrp41 iRNAi/nrpe1 double mutants ( Figure 4F and 4G ) . When we examined AtSN1 , we observed a very similar synergistic increase in the level of the siRNA-producing region A of bottom strand transcript of AtSN1 in rrp41 iRNAi/nrpe1 mutants ( Figure 4H and 4I ) . Together , these results suggest that the exosome participates in controlling the amount of top transcripts emanating from the scaffold-producing region B of solo LTR , and thus may contribute to the repression of region A through regulating the level of region B transcripts . The solo LTR , AtSN1 and IGN5 loci are silenced primarily by RdDM , through siRNA mediated de novo methylation of DNA [9] , [30] , [31] . We reasoned that if the exosome is involved in controlling the amount of RNA expressed from these loci in a siRNA-dependent manner , then the exosome defect might affect the amount of siRNAs generated from these regions . To address this question , we first compared solo LTR and AtSN1-specific smRNAs . We found that production of smRNAs from the siRNA-generating A regions was not altered in rrp4-i or rrp41-i mutants relative to WT ( Figure 5A and 5B ) , similar to the results of the global smRNA analysis we described above . The increased amount of smRNAs observed in dcl3 mutants is because in the absence of DCL3 , the other Dicer proteins process DCL3 substrates [49] . In order to make sure that the smRNAs produced from one strand of region A are not masking the smRNAs produced from the opposite strand in exosome depletion mutants , we also analyzed these smRNA populations in a strand-specific manner . However , the patterns of strand-specific siRNAs were very similar to the patterns we observed previously and siRNAs were not affected by exosome depletion ( Figure 5C and 5D ) . We examined an additional region controlled by RdDM , the IGN5 locus [31] , and found that IGN5-specific smRNAs are also not affected in exosome mutants , similar to solo LTR and AtSN1 loci ( Figure S3C ) . This implies that the increase in accumulation of transcripts in exosome-depleted plants was not a result of siRNA defect . To verify this directly , we examined the patterns of DNA methylation in these regions by using methylation sensitive restriction enzymes ( Figure 5E ) . The DNA of the solo LTR region was examined by two different assays ( Figure 5E and 5F ) . We found that , consistent with the results of the region-specific siRNA analysis , de novo DNA methylation was not changed in rrp41-i plants ( Figure 5A–5D ) . Taken together , these results indicate that an increase in transcript accumulation is not caused by the loss of de novo methylation and the region is still silenced by RdDM . It also suggests that in the examined loci , the exosome complex functions independently of RdDM . Interestingly , the increased amount of transcripts accumulated in these regions does not contribute to increased smRNA amounts in the exosome-depleted plants . This was observed regardless of whether these transcripts originated from siRNA-generating regions , or adjacent regions . Indeed , even several thousand-fold upregulation of region A transcript in iRNAi/nrpe1 mutants ( Figure 4B , 4C , 4G and 4H ) does not produce any increase in the amount of siRNAs ( Figure 5A–5D ) . DNA methylation and histone modification are two major epigenetic marks regulating gene expression and chromatin state in plants . Monomethylated histone H3 lysine 27 ( H3K27me1 ) and dimethylated histone H3 lysine 9 ( H3K9me2 ) are hallmarks of heterochromatin , and silencing of solo LTR , AtSN1 and IGN5 loci also involves histone modifications [30] , [31] . Although de novo methylation does not directly affect the level of H3K9me2 , it does affect the level of H3K27me1 [31] , suggesting that in addition to histone modification pathways , which are dependent on RdDM , other , RdDM-independent , pathways also contribute to transcriptional silencing of these regions . We therefore used chromatin immunoprecipitation ( ChIP ) to examine whether the exosome is involved in regulation of histone modifications in these loci . Similar to the results reported by others [30] , [31] , we observed a significant decrease in the level of H3K9me2 in the solo LTR locus in nrpd1 and nrpe1 mutants , which affect Pol IV and Pol V , respectively . We found that RRP41 depletion also led to a decrease in H3K9me2 but less than observed in nrpd1 and nrpe1 mutants ( Figure 6A ) . The decrease in level of this repressive histone modification also correlated with a mild increase in RNA Pol II occupancy in the solo LTR region , as would be expected with a release of transcriptional block ( Figure 6B ) . The rrp41 iRNAi/nrpe1 double mutant did not exhibit any additive or synergistic effect on the loss of H3K9me2 relative to respective single mutants . When we examined AtSN1 , we found that the level of H3K9me2 was mildly decreased in all mutants tested ( Figure 6A ) . For AtSN1 , it was previously suggested that RNA Pol III is the main RNA polymerase transcribing the region when the region is in a derepressed state [31] , although RNA Pol II was also reported to be associated with this region [30] . We found that RNA Pol II occupancy in AtSN1 was very low but it increased significantly in rrp41 iRNAi/nrpe1 double mutants ( Figure 6B ) , in accordance with the synergistic increase of the transcript level we observed ( Figure 4H and 4I ) . Depletion of another exosome subunit , RRP4 , caused a similar loss of H3K9me2 at solo LTR and AtSN1 loci ( Figure 6C ) . We then chose several additional regions , termed REG3 and REG4 ( Figure S3A ) , that are mildly upregulated in exosome mutants according to our previous microarray analysis [1] , and examined them using ChIP . REG3 harbors a MuDR transposon , and REG4 is situated in a tandem repeat area . Neither of these loci produces smRNAs or is controlled by DNA methylation ( Figure 6E and data not shown ) . We found that the H3K9me2 in these loci was similarly affected by exosome depletion ( Figure 6C ) , while the level of H3K27 methylation in these regions didn't show any difference ( Figure 6D ) . These results suggest that the exosome may participate in maintaining chromatin structure in these regions as well , and does so by specifically affecting the level of H3K9me2 in addition to controlling the level of transcripts . We then examined exosome association with ncRNA loci . Detection of some protein–nascent mRNA interactions by ChIP were reported previously for proteins working on RNA , but the results of our attempts to localize tagged exosome subunits at solo LTR locus have proven inconclusive . Transcripts from region A are normally below the level of detection in wild-type plants , but transcription from the region B adjacent to solo LTR has been previously documented in wild-type plants [1] , [30] , [31] . In order to confirm that the exosome directly associates with these transcripts , we conducted RNA immunoprecipitation ( RIP ) using plants carrying a transgene expressing RRP41-TAP , and examined the ncRNAs associated with the exosome by RT-PCR . No region A transcripts were detected in immunoprecipitates , but we found that region B transcripts were co-precipitated with exosome ( Figure 7A ) . These data suggest that in wild-type plants , exosome physically associates with polyadenylated transcripts produced from region B of solo LTR . In contrast to solo LTR , we did not detect a physical association of exosome with AtSN1 region B transcript ( Figure 7A ) . This implies that exosome depletion may not directly affect the silencing of AtSN1 . However , we observed that exosome depletion resulted in accumulation of transcript in the AtSN1 locus and we detected a synergistic derepression of the locus in rrp41/nrpe1 mutants , similar to solo LTR locus ( Figure 4H and 4I ) . Most likely the regulation of AtSN1 is more complex because an additional RNA polymerase , RNA Pol III , is involved . AtSN1 is transcribed mostly by RNA Pol III [31] , [53] , suggesting that the double deficiency in exosome and Pol V may increase both Pol II and Pol III access to the locus . We also observed the increased Pol II association with AtSN1 in rrp41/nrpe1 mutants by ChIP assay using anti-Pol II ( Figure 6B ) , which is consistent with the results of qRT-PCR . Therefore , it is also possible that the loss of exosome function may lead to the alteration of chromatin structure in regions adjacent to AtSN1 and thus affect the stability of silencing in AtSN1 indirectly . Nevertheless , these results are similar to the interplay between exosome and Pol V observed for solo LTR . The 9-subunit exosome complex is catalytically inactive in yeast and human . Instead , active sites are contributed by Rrp44 ( Dis3 ) and by the subunit Rrp6 , which is substoichiometric , nuclear-specific , and not essential for viability . Degradation of S . cerevisiae nuclear ncRNAs depends on polyadenylation by the TRAMP complex and involves Rrp6 , the subunit that is also responsible for elimination of heterochromatic RNAs in S . pombe [20] , [22]–[24] , [39]–[41] . In Arabidopsis there are three RRP6-like proteins – nuclear localized RRP6L1 and RRP6L2 , and cytoplasmic RRP6L3; these were suggested to be functional homologues of RRP6 [54] . None of the RRP6-like proteins co-purified with the exosome complex in our proteomic studies [1] , but may have been underrepresented in our preparations . In addition , RRP6L2 was later shown to have at least some commonalities with core exosome substrates [54] . We therefore examined whether the Arabidopsis RRP6-like proteins control the amount of ncRNA at the solo LTR locus . To determine this , we used T-DNA insertion alleles in RRP6L1 , RRP6L2 and RRP6L3 . We isolated the rrp6l1-2 allele from the University of Wisconsin BASTA population ( Ws ecotype ) , and the alleles of the rrp6l2-2 and rrp6l3-1 are SALK alleles ( Col-0 ecotype ) . To control for effects of ecotype , we compared the amount of region A transcript in rrp6l3-1 , rrp6l2-2 , rrp6l1-2/rrp6l2-2 mutants to Col-0 wild type plants , and rrp6l1-2 , rrp6l1-2/rrp6l2-2 mutants to Ws ecotype plants ( Figure 7B and 7C ) . We found that , similar to depletion of the core subunits RRP4 and RRP41 , rrp6l1-2 and rrp6l2-2 mutants exhibited increased accumulation of transcripts produced from region A . As would be expected based on cytoplasmic localization of RRP6L3 protein , no effect was observed in rrp6l3-1 mutants . To our surprise , we observed a dramatic derepression of region A in rrp6l1-2/rrp6l2-2 double mutants , suggesting that both RRP6L1 and RRP6L2 proteins are involved in the silencing of this region and might have a redundant function in this process . We also examined the status of solo LTR DNA methylation in rrp6l1-2 , rrp6l2-2 , and rrp6l1-2/rrp6l2-2 double mutants . We found that methylation was not affected in these mutants regardless of the extent of derepression of the region ( Figure 7D ) , consistent with the results obtained using rrp4-i and rrp41-i depletion mutants . Taken together , these results indicate that the observed increase in transcript accumulation is not caused by the loss of de novo methylation and the region is still methylated by RdDM . This further confirms that the exosome complex functions independently of the RdDM pathway .
The exosome functions in virtually all aspects of RNA metabolism and it appears to also have a prominent role in transcriptional gene silencing in different species [1] , [10] , [38]–[41] , [55]–[59] . This study examined the role of the exosome complex in metabolism of smRNAs and explored the possible relationship between the exosome and the RdDM pathway in gene silencing in Arabidopsis . Our results showed that exosome-mediated silencing did not produce global changes in smRNA profiles , nor in DNA methylation at specific loci . However , we did find effects on histone methylation , indicating that the exosome may regulate chromatin structure , thereby playing an important role in maintenance of gene silencing on a much broader scale than the RdDM pathway . It is clear from our results using suppression of key exosome components that plants have an exosome-dependent pathway that relies on ncRNAs to target heterochromatin . Our finding that the increase in ncRNA transcribed from heterochromatic loci in exosome-depleted plants did not lead to an increase in levels of smRNA indicates that exosome function in Arabidopsis differs from that in fission yeast . In fission yeast , exosome defects have a dramatic effect on siRNAs leading to redistribution of the spectrum of Ago1-associated siRNAs , from mostly repeat-associated to those derived predominantly from exosome substrates such as rRNA and tRNA [39] , indicative of exosome acting as a negative regulator of siRNA biogenesis . Our data indicate that the Arabidopsis exosome most likely lost this function during evolution , meaning that exosome substrates do not compete with siRNA precursors for siRNA biogenesis machinery and spurious transcripts do not enter RNAi pathways in plants . Additionally , it suggests that perhaps only very few of the ncRNA transcripts controlled by the exosome could be bona fide siRNA precursors . One of the reasons for this could be the fact that plants evolved two plant-specific RNA polymerases , Pol IV and Pol V , which specialize in siRNA-mediated TGS . Pol IV is required for biogenesis of the majority of 24-nt siRNAs and is supported by Pol V , which is responsible for production of a subset of siRNAs [31] , [44] , [45] , [60] . It is also plausible that there might be other unknown plant-specific ribonucleases that specialize in controlling stability of siRNAs or the amount of siRNA precursors generated by Pol IV and/or Pol V in plants . We also cannot rule out the possibility that some of the transcripts controlled by the exosome in a small subset of loci are legitimate siRNA precursors; this definitely warrants further in-depth investigation . siRNA-dependent RdDM is thought to be the main pathway for transcriptional gene silencing of repetitive elements and transposons in plants [27] , [28] , [31] , [61] , [62] , although existence of other DNA methylation-independent gene silencing pathways have also been reported [63]–[71] . One of the DNA methylation-independent gene silencing pathways is mediated by MOM1 ( Morpheus' molecule 1 ) protein [63] , [65] , which predominantly silences transposons and loci harboring sequences related to gypsy-like transposons . Activation of transcription in mom1 mutants occurs with no change in DNA methylation , histone modifications or chromatin condensation , and the investigation of the relationship between RdDM and MOM1 revealed a very complex interplay between these two pathways [63] , [69] , [72]–[74] . However , a reduction in H3K9 dimethylation was reported in some loci in mom1 mutants and it was suggested that MOM1 may transduce RdDM signals to repressive histone modifications by an unknown mechanism [75] . Also , a recent study of MORC family ATPases revealed that mutation of AtMORC1 or AtMORC6 caused derepression of DNA methylated genes and TEs without any loss of DNA methylation , change in histone methylation or alteration of siRNA levels [71] . These proteins are involved in alteration of chromosome superstructure and are likely to act downstream of DNA methylation . These results indicate that there are multiple parallel pathways for DNA methylation-independent gene silencing in Arabidopsis . The exosome-mediated silencing we observed here bears some similarities to the silencing observed for MOM1 and MORC; for example , they show effects on repetitive sequences and an absence of effects on siRNAs , although there are notable differences as well . Here we show that , similar to MOM1 and MORC mechanisms , exosome-dependent gene silencing also affects repetitive sequences and acts independent of RdDM , although our results are limited in scope . Characterization of the relationship between these pathways remains an interesting topic for future study . The different silencing pathways likely have different functions , depending on the genomic region , the nature of the regulated sequences , and the precision and dynamics of silencing required . For example , methylated sequences can affect the expression of nearby genes . The expression of nearby genes is negatively correlated with the density of methylated , but not unmethylated TEs . Methylated TEs are preferentially removed from gene-dense regions over time and TE families that have a higher proportion of methylated insertions are distributed farther from genes [76] , arguing that positional effects and the surrounding landscape most likely contributes to the choice of silencing mechanisms and the interplay between them . There are multiple mechanisms by which the exosome can be envisioned to participate in gene silencing in Arabidopsis . Heterochromatin assembly is used by all eukaryotes in gene silencing . In addition to repressive histone modifications employed by all organisms , humans and plants widely use DNA methylation as well , and ncRNAs play a central role in the control of chromatin structure in all organisms . While ncRNA-mediated silencing proceeds through multiple mechanisms some of which are organism-specific , the end result appears to be the same repressive histone modifications . For example , budding yeast , which lacks RNAi machinery , employs strategies that include , but not limited to , the use of antisense , cryptic or read-through transcripts , as well as transcripts originating from divergent promoters to guide histone modifications . Fission yeast is more similar to higher eukaryotes and uses all of the above strategies in addition to utilizing RNAi as well . However , DNA methylation is not used by budding and fission yeast . Plants , on the other hand , evolved very sophisticated epigenetic mechanisms that include the use of both RNAi-dependent and RNAi-independent pathways to guide DNA methylation and histone modifications for gene silencing [9] , [31]–[33] , [44] , [45] , [47] , [61] , [68] , [70] , [75] , [77] , [78] . Exosome complex proved to be amazingly versatile in impacting gene silencing in budding and fission yeasts . In fission yeast , the organism which takes full advantage of RNAi machinery to regulate its gene expression , the exosome is involved in silencing of both facultative and constitutive heterochromatin by acting in several different pathways through smRNAs , produced in either an RNAi-dependent or RNAi-independent manner [38] , [39] , [79] , [80] . It was also found to act through surveillance of RNA quantity and quality as well as by collaborating with termination machinery [40] , [41] , [57] , [80] , [81] , similarly to the manner exosome participates in gene silencing in bakers yeast , which lacks RNAi machinery [55] , [58] , [59] . In Arabidopsis , silencing of repetitive elements involves siRNA-dependent DNA methylation guided by homologous siRNAs [9] . Repressive histone modifications always appear to accompany DNA methylation , however , the mechanistic link between them is not yet fully understood . In budding and fission yeasts , degradation of nuclear ncRNAs depends on polyadenylation by the TRAMP complex and involves Rrp6 . We also found that mutations in two RRP6-like proteins AtRRP6 L1and AtRRP6 L2 led to significant dereperession of solo LTR ( Figure 7B , 7C and 7D ) and occurred in a DNA methylation-independent manner as in rrp4 and rrp41 ( Figure 7F ) . These results suggest that Atrrp6s may be true nuclear catalytic subunits of Arabidopsis exosome , or may also work independently of core exosome . It will be interesting to examine whether another putative exosome catalytic subunit AtRrp44a [J . Lee and J . Chekanova unpublished data] is involved in this process , and whether components of the TRAMP complex also participate . We also observed that the exosome physically associates with the polyadenylated ncRNA transcripts from scaffold producing regions . We could not reliably crosslink the exosome to the DNA of the target locus by ChIP ( data not shown ) , although this could simply reflect the difficulty of reliably crosslinking proteins to DNA through RNA , or it could mean that the exosome binds to the transcripts after they are released from the locus and that exosome-mediated regulation of the transcripts may be important for maintenance of chromatin structure around the locus . H3K9 dimethylation was reported to be disturbed and lost when isolated Arabidopsis nuclei were treated with RNase A [82] , meaning that histone modification may be affected by RNA level and/or RNA in close proximity to the target loci . In fission yeast , the mutation of Cid14 , one of the subunits of the TRAMP complex , results in accumulation of aberrant heterochromatic RNA close to the target loci and leads to a mild decrease in H3K9 methylation . It was recently shown that decrease of H3K9 methylation in yeast is the result of HP1 protein ( Heterochromatin Protein1 ) , which binds to H3K9me2 heterochromatin and propagates H3K9me2 spreading , being titrated by an excess of heterochromatic RNA [83] . In our study , we also observed a combination of the transcripts accumulation in exosome mutants relative to WT with a weak decrease in H3K9me2 levels in solo LTR ( Figure 6A ) . Taken together , these data could suggest that a similar mechanism to regulate the stability of chromatin structure might operate in plants . However , LHP1 ( Like-HP1 ) , the closest Arabidopsis homolog of yeast HP1 , has specificity for H3K27me3 [84] , not H3K9me2 , and the rrp41 iRNAi/nrpe1 double mutant did not exhibit any additive or synergistic effect on the loss of H3K9me2 relative to respective single mutants as well , suggesting that the loss of H3K9me2 observed in the exosome mutants is unlikely to result from an unknown functional homolog of Arabidopsis HP1 simply titrating an excess of ncRNA off chromatin , as reported in fission yeast . Our results showed that the exosome depletion produced no effect on siRNAs and DNA methylation of solo LTR , AtSN1 and IGN5 loci , arguing that the exosome complex functions independently of RdDM . However , our findings also indicated that the exosome is involved in the silencing of these loci and does interact with the RdDM pathway , possibly through its functional interaction with RNA Pol V . The converging transcripts we observed in the rrp41-i and rrp4-i mutants in solo LTR and AtSN1 suggest that the exosome is involved in regulation of either processing or level of RNA from these loci ( Figure 4A–4I , and model Figure 8 ) . We found that production of smRNAs from the siRNA-generating A regions was totally abolished in rrp41/nrpd1 double mutant ( Figure 5A–5D ) , ruling out a possibility for these transcripts to serve as a double stranded precursors for RNA Pol IV-independent siRNAs . We also found that the exosome physically associates with the polyadenylated transcripts produced from the scaffold region ( region B ) and exhibits synergistic derepression of the locus ( region A ) when combined with a Pol V mutant , while there was no change in the derepression in rrp41/nrpd1 double mutants ( Figure 4B , 4C , 4H and 4I ) . Based on these results , we speculate that RNA polymerase V may function in gene silencing of these loci in two ways , the first acting in the DNA- methylation-dependent RdDM pathway , and the second acting independently of a DNA-methylation . Indeed , RdDM- independent roles of Pol V in silencing of 5S rDNA [31] , [85] and several other loci [82] were previously reported . A recent genome-wide study of Pol V-associated loci also hints at the possibility of Pol V having unknown functions in addition to the function it plays in the RdDM pathway [45] . The DNA-methylation-independent function of Pol V may then be in addition to its function in RdDM , and may operate in parallel to the exosome pathway . If this is the case , the depletion of both rrp41 and nrpd1 may not lead to synergistic derepression because it would be compensated by the RdDM-independent function of Pol V . However , deficiencies in exosome and Pol V would result in synergistic desilencing due to the loss of three different pathways . Both Pol II and Pol V were reported to be responsible for the transcription of scaffold RNA and be required for silencing [30] , [31] , although it is not known how their activities are functionally integrated . It is also not known how Pol V initiation sites are chosen , but they appear to be promoter independent [31] . Perhaps transcription by Pol II helps maintain open chromatin architecture at this site , and together with the resulting noncoding RNAs facilitates Pol V transcription initiation . Alternative possibility is that Pol II produces two distinct pools of transcripts , one of which is controlled by the exosome , and the exosome functions by regulating the Pol II transcripts that are distinct from the transcripts that are used in RdDM pathway . This possibility would be very interesting to examine , particularly in light of the yeast exosome involvement in gene silencing through regulation of cryptic transcripts , transcripts originating from divergent promoters and read-through transcripts [4] , [55] , [58] , [59] . How the Arabidopsis exosome complex and the exosome controlled ncRNAs facilitate recruitment of chromatin modifiers in order to enforce silencing through repressive histone modifications remains an interesting topic of future studies . We suggest that the exosome may coordinate the transcriptional interplay of RNA polymerases Pol II and Pol V to achieve the right level of transcriptional repression of heterochromatic loci ( Figure 8 ) . In summary , our data suggest that the exosome likely acts in a parallel pathway to RdDM pathways in gene silencing , possibly affecting the transcriptional interplay of different RNA polymerases to modulate repression of heterochromatic sequences . The mechanisms that link this RNA metabolic complex , the epigenetic modification of histone methylation , and heterochromatic silencing in plants remain to be elucidated . Our results indicate that there is no one-size-fits-all pathway or mechanism that exclusively governs silencing of all loci; rather , different loci and different players in RdDM interact with different pathways and are silenced by different , likely overlapping mechanisms . The positional effects and the surrounding landscape most likely also play important roles in the choice of silencing mechanisms and the interplay between them . This may reflect the crucial importance of silencing in developmental gene regulation and in maintenance of genomic stability by suppression of invasive sequences .
iRNAi lines of exosome subunits RRP4 and RRP41 , RNA Pol IV ( SALK_128428 . 20 . 10 , nrpd1a-3 , nrpd1-3 ) , RNA Pol V ( SALK_029919 , nrpd1b-11 , nrpe1-11 ) , RDR2 ( SAIL_1277808 , rdr2-1 ) , and DCL3 ( SALK_005512 . 38 . 70 . x0 , dcl3-1 ) mutants were described previously [1] , [27] , [33] , [86] . rrp41 iRNAi/nrpd1-3 , rrp41 iRNAi/nrpe1-11 , rrp4 iRNAi/nrpd1-3 , and rrp4 iRNAi/nrpe1-11 double mutants were obtained by crossing of rrp41 iRNAi and rrp4 iRNAi with nrpd1/nrpe1-11 line . rrp41 iRNAi/dcl3-1 , rrp41 iRNAi/rdr2-1 double mutants were obtained by crossing . The alleles of the rrp6l2-2 and rrp6l3-1 correspond to SALK_011429 and SALK_122492 lines , respectively . The rrp6l1-2 allele was isolated from the University of Wisconsin BASTA population . The ecotype background is Col-0 for all Salk alleles and Ws for University of Wisconsin alleles . To induce iRNAi , seedlings were germinated and grown for 7 days on ½× MS plates with 8 mM 17β-estradiol , as described before [1] . Total RNA was isolated from 7-day-old seedlings using the mirVana miRNA isolation kit ( Ambion ) according to the manufacturer's protocol . The total RNA sample was used for sequencing library construction using the Small RNA sample Prep v1 . 5 kit and TruSeq Small RNA Sample Prep kit ( Illumina , San Diego , CA ) according to the manufacturer's instructions . The smRNA libraries were sequenced using the Illumina Genetic Analyzer II ( by DNA Core Facility , University of Missouri ) and Illumina HiSeq 2000 ( by Biotechnology Center , University of Wisconsin ) according to the manufacturer's instructions . HiSeq 2000 sequencing reads were demultiplexed using Casava v 1 . 8 ( by Bioinformatic Resource Center , University of Wisconsin ) before further bioinformatic analysis Data processing was done using available tools and custom in-house UNIX shell programming [43] , [75] , [87]–[90] . The raw sequences in Illumina GAIIx and demultiplexed HiSeq 2000 sequencing reads were trimmed removing adapter using “fastx_clipper” in the FASTX-Toolkit ( version 0 . 0 . 13 ) [91] and smRNAs with lengths between 15- and 32-nt were selected and mapped to the Arabidopsis genomic sequences ( TAIR9 version ) using BOWTIE ( version 0 . 12 . 7 ) [92] . Reads that failed to perfectly map to the nuclear genome with no mismatches , and reads present in fewer than two counts were discarded . All Arabidopsis lines used in this study carried iRNAi cassette transgenes used for inactivation of either RRP4 or RRP41 exosome subunit genes [1] . These silencing cassettes generate a number of 21- , 22- and 24-nt silencer sequences corresponding to RRP4 or RRP41 genes ( mapping to AT1G03360 and AT3G61620 loci ) , respectively . Therefore , silencer sequences produced from iRNAi transgenes were filtered out from each library and libraries were analyzed separately to ensure accurate interpretations . The remaining smRNA reads , termed FLR for filtered reads , were used for further analysis . Each library was normalized either to the total number of mapped non-redundant reads or to the total number of non-redundant filtered reads ( FLR ) , multiplied by 106 ( rpm , reads per million ) . Both methods of normalizations were compared and found to produce results which lead to identical interpretations , therefore , only data analyzed using filtered reads are presented in this study . Classification of small RNAs was performed by BEDTools ( v2 . 10 . 0 ) [93] and in-house UNIX shell programming using the following databases: TAIR9 annotations for protein coding and non-coding features ( tRNA , rRNA , ncNRA , miRNA , snRNA , snoRNA , and transposable elements [76] ) , miRBase ( release 18 ) [94] or mature miRNA annotations . Some smRNAs match more than one annotation category; therefore the sum of the numbers is bigger than the total input number . The small RNA reads with 20 to 25 nt length were calculated and plotted versus the sum of their normalized reads per million ( rpm ) . The relative frequencies of each 5′ terminal nucleotide of the small RNAs were calculated ( Tables S1 , S2 ) and represented graphically . Repetitive genomic features were classified using TAIR9 Tandem Repeat Finder ( version 4 . 04 ) [95] and Inverted Repeat Finder ( version 3 . 05 ) [96] . Annotation of dispersed repeats was done with Repeat Masker ( version 3-3-0 ) [97] . For analysis of locus-specific expression of smRNAs ( solo LTR , AtSN1 , IGN5 , REG3 , and REG4 ) , the expressed normalized reads per million ( rpm ) were calculated for respective genomic locus and locus-specific datasets were plotted for comparisons . Total RNA was isolated from 7-day-old seedlings using the Trizol method . For RT-qPCR , 1–4 µg of total RNA digested with DNase I ( Fermentas ) was reverse transcribed 1 hour either at 50°C ( for oligo-dT primer ) or 55°C ( for specific primers ) using 60–100 units SuperScript III Reverse Transcriptase ( Invitrogen ) . Transcripts were quantified by RT-qPCR using the comparative threshold cycle method ( ΔΔCt , primers listed in Table S4 ) , using Actin2 ( At3g18780 ) as endogenous reference . Polyacrylamide Northern Blot analyses were performed as described [25] . Genomic DNA was isolated from 7-day-old seedlings using a DNeasy kit ( QIAGEN ) . The methylation analysis using DNA sensitive methylation enzymes was followed as described [27] , [31] , [77] . ChIP was performed as described [98] . One gram of 7-day-old seedlings was used for each experiment . All ChIP experiments were reproduced at least twice on each of the two or more biological replicates . The normalization was done relative to input using [99] . Anti-RNA Pol II ( ab817 ) and anti-H3K9me2 ( ab1220 ) were obtained from Abcam , and anti-H3K27me1 antibody from Upstate . An equal amount of chromatin not treated with antibody was used as the mock antibody control . The ChIPed DNA was purified using PCR purification kit ( Fermentas ) before being used for qPCR . The primer sets used for the PCR are listed in Table S4 . RIP assays were performed by adapting an existing protocol [100] . Transgenic plants expressing TAP-tagged RRP41 at physiological levels [1] were used in the experiment . Two grams of 2-week-old seedlings were collected and fixed with 1% formaldehyde . For RRP41-RNA complex purification , the chromatin solution was incubated overnight with prewashed IgG Sepharose 6 Fast Flow ( GE Healthcare ) at 4°C . Immunoprecipitated RNA was purified with phenol: chloroform and cDNA synthesis was performed using SuperScript III reverse transcriptase ( Invitrogen ) and random hexamers ( Promega ) . The primer sets used for the PCR are listed in Table S4 . | To maintain genomic stability and prevent expansion of invasive genomic sequences such as transposable elements ( TEs ) , eukaryotes have evolved defensive mechanisms to control them . Here , we examine the role of the Arabidopsis exosome complex in such mechanisms . Evolutionarily conserved from archaea to humans , the exosome is a stable complex of RNase-like and RNA binding proteins that plays a central role in RNA metabolism in eukaryotes . Depletion of the exosome allows some repetitive sequences to escape from silencing . Most of these transcripts emanate from centromeric and pericentromeric chromosomal regions and other heterochromatic loci , and many derive from repetitive and transposable elements . In plants , TEs are targeted for de novo DNA methylation by smRNA–mediated pathways . However , we found that exosome depletion has only minor effects on smRNA populations that are acting in the main silencing mechanism in Arabidopsis , siRNAs–dependent DNA methylation RdDM . Instead , exosome depletion affects histone H3K9 dimethylation , an epigenetic mark that affects chromatin structure and thus alters transcription . Our data suggest that the exosome collaborates in gene silencing , likely acting in a parallel pathway to other mechanisms . We also propose that the Arabidopsis exosome may coordinate the transcriptional interplay of different RNA polymerases to modulate repression of some repetitive sequences . | [
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"o... | 2013 | The Role of the Arabidopsis Exosome in siRNA–Independent Silencing of Heterochromatic Loci |
By directly affecting structure , dynamics and interaction networks of their targets , post-translational modifications ( PTMs ) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control . Despite the great importance of understanding how PTMs affect proteins at the atomistic level , a systematic framework for treating post-translationally modified amino acids by molecular dynamics ( MD ) simulations , a premier high-resolution computational biology tool , has never been developed . Here , we report and validate force field parameters ( GROMOS 45a3 and 54a7 ) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs . The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies ( RMSE = 4 . 2 kJ/mol over the validation set ) . Using this tool , we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids , with the extent of change in many cases being comparable to the complete range spanned by native amino acids .
Proteins in the cell continually get covalently modified in different post-translational , enzyme-controlled reactions [1]–[3] . Additionally , protein modifications frequently arise in a non-controlled fashion as well , mainly as a consequence of oxidative stress [4] . While enzymatic post-translational modifications ( PTMs ) play important regulatory roles in a large number of different cellular processes , non-enzymatic PTMs are predominantly linked with protein damage and are involved in age-related diseases such as neurodegenerative disorders , diabetes and cancer [2] , [4]–[7] . Despite the general importance of PTMs in different biological contexts , their effect on protein structure , dynamics and interaction networks at the atomistic level remains poorly understood . In particular , molecular dynamics ( MD ) simulations , a widely used high-resolution computational method for studying biomolecular properties and behavior [8]–[10] , have been limited to unmodified , native proteins due to a surprising deficiency of suitable tools and systematically developed parameters for treating PTMs , with only sporadic exceptions [11]–[16] . MD simulations capture atomic and molecular motions based on Newton's equation of motion and an empirical potential energy function that defines interactions between simulated particles . The latter is defined by a force field , i . e . a self-consistent set of physically realistic equations and semi-empirical parameters describing all interactions in a given system . Force-field parameters are typically obtained by fitting atomic or molecular properties of small molecules against calculated quantum-mechanical or experimentally measured data . As the applied parameterization strategies often differ from each other , considerably different parameter values have been derived in many cases [17]–[20] . Here , we develop force field parameters for over 250 different types of enzymatic and non-enzymatic modifications of amino-acid side chains as well as protein termini within the context of GROMOS 45a3 [19] and 54a7 [21] , [22] force fields ( Table S1 ) . We choose GROMOS force fields because of their widespread usage , high accuracy in reproducing experimental results and general transferability of parameters when it comes to identical chemical groups in different compounds [21] ( e . g . from the hydroxyl group of tyrosine to the hydroxyl group of 7-hydroxytryptophan ) . The functional form of a typical force field is exemplified in equation 1 for GROMOS class force fields , ( 1 ) with parameters highlighted using boldface letters and RF representing a reaction field contribution to the electrostatic interactions . The non-bonded interaction terms in the GROMOS force field are primarily parameterized against thermodynamic data of small molecules , either in the pure liquid state , or in aqueous or nonpolar solution . Therefore , we validate the obtained parameters by reproducing experimental hydration free energies ( HFEs ) , a measure of hydrophobicity and arguably one of the most important amino-acid properties with implications in protein folding , ligand binding or protein-lipid interactions . Finally , we analyze physico-chemical properties related to hydrophobicity of all parameterized PTMs according to their type and compare them against the 20 canonical amino acids .
One of the principal objectives in our parameterization has been the coverage of experimentally known PTMs , which is as complete as possible . Following an exhaustive literature search and analysis of an online PTM database PTMdb [23] , we have compiled a diverse list of enzymatic and non-enzymatic PTMs , including phosphorylation , methylation , acetylation , hydroxylation , carboxylation , carbonylation , nitration , deamidation and many others ( Figure 1a , Table S1 ) , covering a total of 259 distinct PTM reactions or 110 non-redundant post-translationally modified amino acids and protein termini . The lower number in the latter case reflects the fact that different PTM reactions can lead to the same modified product ( e . g . glutamic semialdehyde is a product of both arginine and proline carbonylation ) . We have generated GROMOS 45a3 ( Dataset S1 ) and 54a7 ( Dataset S2 ) force field parameters for the non-redundant set of compounds by either direct transfer or analogy to already parameterized compounds including amino acids , nitrogenous bases and other small molecules or completely novel parameterization ( see Methods for more details ) . How well do the obtained parameters cover the space of biologically relevant PTMs ? To address this question , we have analyzed PTMs that have been experimentally verified ( 72 , 984 ) and annotated as such in the UniProt database [24] ( 21 , 411 protein entries , Dataset S3 . Phosphorylation is by-far the most abundant modification type in the UniProt database ( 78 . 5% of all UniProt PTMs ) , followed by acetylation , hydroxylation and methylation ( Figure 1b ) . Note that terminal PTMs account for a sizable fraction of all annotated modification at 8 . 3% . Strikingly , the parameterized compounds reported herein match every annotated phosphorylation modification , 99 . 9% of acetylation , 99 . 2% of hydroxylation and 99 . 7% of methylation modifications , for a grand-total coverage of 98 . 5% of all PTMs reported in UniProt ( Figure 1c ) . Concerning PTMs that are not covered by our parameters , they are all extremely rare , each accounting for less than 0 . 5% of all UniProt PTMs . Finally , we provide parameters for 33 PTMs ( Table S1 ) , mostly non-enzymatic ones , that have to date not been reported in UniProt . HFE , a free energy difference between a compound solvated in water and the same compound in the gas phase , is an experimentally measurable property related to hydrophobicity , and it has been originally used to re-parameterize the GROMOS force field in 2004 [21] . A proper description of functional groups in the hydrated phase is of crucial importance for virtually all relevant biomolecular processes , so we have used the same thermodynamic quantity to validate the parameters obtained in the present study . To the best of our knowledge , experimental HFEs are available for the exact side chain analogs of 13 parameterized PTMs only and we have therefore in the validation set also included compounds , which are chemically related to PTM side chains for which no experimental HFEs were available , for a total of 26 different molecules ( only a single representative compound was included for each group of PTMs involving the same chemical moiety , Table 1 ) . Note that the additional compounds related to PTM side chains have been parameterized in the same way as the relevant PTMs . We have used MD simulations and the thermodynamic integration ( TI ) approach [25] ( see Methods for more details ) to calculate the HFEs for neutral forms small-molecule analogs of the canonical amino-acid side chains and for the compounds in the validation set using both the 45a3 ( Table S2 ) and 54a7 ( Table 1 ) parameter sets of the GROMOS force field . As a consequence of the parameterization strategy behind them , the canonical amino acids exhibit an excellent agreement with experimental HFEs when it comes to the 54a7 parameter set , with a root-mean-square error ( RMSE ) of 3 . 3 kJ/mol ( RT = 2 . 5 kJ/mol at room temperature ) and an almost perfect correlation with experimental HFEs ( correlation coefficient R2 = 0 . 98 ) ( Figure 2 ) . Remarkably , the newly generated GROMOS 54a7 force field parameters of PTM-related compounds exhibit a nearly equal level of matching of experimental HFEs with an RMSE of 4 . 2 kJ/mol ( Table 1 ) and a correlation coefficient R2 of 0 . 94 ( Figure 2 ) over 25 different compounds , excluding a single outlier , 2-nitrophenol ( Figure 2 , red X symbol ) . This compound , containing nitro and hydroxyl groups attached to a benzene ring , deviates from the experimental value by 14 . 6 kJ/mol . Considering the outlier 2-nitrophenol in more detail , additional calculations have shown that p-cresol ( a tyrosine side-chain analog ) , o-cresol , m-cresol and nitrobenzene , compounds containing either a hydroxyl group or a nitro group attached to a benzene ring , agree well with experimental HFEs with an overall RMSE of 2 . 7 kJ/mol only . This suggests that , although parameters of individual groups do reproduce experimental HFEs , the agreement with experiment may significantly worsen if they appear in combination . In order to test this , we have calculated HFEs of 3- and 4-nitrophenol and compared them against experimental values . Interestingly , the calculated HFEs of both compounds match experimental values ( Table 1 ) suggesting either that these groups exert a specific influence on each other only in 2-nitrophenol or that the experimentally measured HFE may simply not be reliable for this compound . To account for the former possibility , we have derived a set of parameters de novo for 2-nitrophenol that closely match its experimental HFE with an absolute value of the deviation of 1 . 8 kJ/mol ( Table 1 ) . Note that we report both versions of nitrotyrosine ( Table S1 ) , a cognate PTM to 2-nitrophenol . Finally , we have also excluded 4-methylimidazole ( a histidine side-chain analog ) and 1-methylimidazole from the HFE analysis of the canonical amino acids and PTMs , respectively , even though experimental HFEs are available for both compounds . Since histidine exists in two tautomeric states , described by different parameters , the calculated HFE depends on the choice of the state used for calculations , with one matching the experimental HFE and the other varying by approximately 20 kJ/mol ( Table 1 ) . Consequently , the same problem exists for 1′- and 3′-methylhistidine , whose parameters are based on those of histidine , where one tautomer matches while the other deviates from the experimental HFE ( Table 1 ) . In contrast to GROMOS 54a7 , the 45a3 parameter set does not reproduce experimental HFEs well ( Table S2 and Figure S1 ) . Namely , the slope of 0 . 79 and the offset of 3 . 8 kJ/mol of the regression line suggest that the calculated HFEs are largely overestimated ( RMSE = 10 . 8 kJ/mol ) for the amino-acid side chain analogs , as observed previously [21] . The same effect persists for the PTM compounds , with a RMSE from experimental HFEs of 15 kJ/mol ( Figure S1 ) . As the GROMOS 45a3 parameter set was not parameterized to match experimental HFEs for polar compounds , such level of deviation was to be expected . Due to a lack of pertinent experimental data , seven parameterized PTMs ( carboxylysine , homocitrulline , citrulline , S-carbamoyl-cysteine , S-nitrosocysteine , 2-oxo-histidine and pyruvic acid ) have remained unrepresented in the validation set , and therefore unverified in terms of reproducing experimental HFEs . To further assess the quality of the parameters for these compounds , we have compared them to those obtained by the Automated Topology Builder [26] , a widely used online service for automated parameterization of small molecules compatible with the GROMOS 54a7 force field . While manually curated approaches are arguably superior to automated ones , it is reassuring to see that the two sets of parameters match closely . For example , we have observed close agreement between the sets of partial charges obtained using the two methods for these seven compounds , with a Pearson correlation coefficient R of 0 . 93 and an overall RMSD of 0 . 2 e− . As an application of the newly developed PTM parameters , we focus on the changes in several key physico-chemical properties of amino acids introduced by PTMs . Interestingly , the majority of post-translationally modified amino acids are larger in size than their native counterparts , with more than 85% of PTMs increasing the molecular weight and more than 80% of PTMs increasing the solvent accessible surface area ( SASA ) of the affected residues ( Table S3 ) as calculated on energy-minimized ( using the GROMOS 54a7 parameter set ) configurations of PTMs and canonical amino acids . What is more , PTMs introduce significant changes in the electrostatic properties of target residues as illustrated in the case of net charge and dipole moment ( Table S3 ) . For example , 42% of all PTMs studied here undergo a charge change of 1 e− or more in absolute value , with 88% of such changes resulting in a more negatively charged species . Moreover , the average absolute value of the change in dipole moment upon PTM equals 1 . 7 Debye , which is comparable in magnitude to the average dipole moment of 2 . 7 Debye or its standard deviation of 1 . 9 Debye as calculated in both cases over all unmodified residues using GROMOS 54a7 parameters and energy-minimized configurations . Finally , given the general importance of hydrophobicity in various biological processes , it is critical to understand in a quantitative manner how PTMs modulate the hydrophobicity of target amino acids . To address this question , we have used TI and GROMOS 54a7 parameters to calculate HFEs of all parameterized PTMs in neutral protonation states , since the available experimental data is insufficient for such an analysis . Our results show that methylation and carbonylation modifications increase HFEs on average by 18 . 6 kJ/mol and 20 . 5 kJ/mol , respectively , while hydroxylation modifications exhibit an opposite effect and decrease HFEs by on average 25 . 1 kJ/mol ( Figure 3a ) . These changes are extremely relevant if one considers the fact that the two central quartiles of the distribution of HFEs for canonical amino acids span the range from approximately −40 kJ/mol to −20 kJ/mol ( Figure 3a ) . Furthermore , the most extreme cases , i . e . symmetric di-methylation of arginine ( ΔHFE = 46 . 2 kJ/mol ) and di-hydroxylation of phenylalanine ( ΔHFE = −60 . 3 kJ/mol ) are comparable in absolute values to the total span of the canonical amino acid HFEs ( −49 . 4 kJ/mol to −3 . 2 kJ/mol , Figure 3a ) . In other words , the effect of some PTMs on the HFEs of target amino acids is as large as the difference which would arise by mutating the most hydrophobic to the most hydrophilic canonical amino acid or vice versa . While some of these effects agree well with what one would qualitatively expect , for a number of PTMs our results are the first to provide a quantitative framework for such an analysis . As both calculation and experimental measurement of HFEs are limited to neutral compounds only , the above analysis does not take into account charged modifications such as phosphorylation . To address this , we have used the molecular hydrophobicity potential ( MHP ) [27] approach to estimate hydrophobicity of all parameterized PTMs using their protonation states at physiological pH . MHP values are semi-empirical estimates of logP , a given compound's partition coefficient between water and the non-polar solvent octanol and are widely used in computational drug design [28] , [29] . Similarly to the HFEs analysis , MHP calculations show that carbonylation and methylation are hydrophobicity-increasing modifications ( Figure 3b ) , in contrast to phosphorylation and hydroxylation , which are hydrophilicity-increasing modifications . Finally , this analysis shows that PTMs can drastically change hydrophobic/hydrophilic properties of affected residues , e . g . arginine carbonylation shifts a highly hydrophilic to a highly hydrophobic residue , while cysteine oxidation does exactly the opposite ( Figure 3c ) . By changing the chemical nature of affected residues , PTMs frequently completely alter their physico-chemical properties such as hydrophobicity , a feature with potentially far-reaching biological implications [11] , [12] , [30] .
Despite the importance of understanding PTMs at the molecular level , MD simulations of post-translationally modified proteins lag significantly behind the studies of unmodified proteins , and this seems primarily due to a general lack of suitable computational tools and simulation parameters for treating PTMs . This study is to the best of our knowledge the first-ever effort to develop force-field parameters for the large majority of known PTMs in a systematic fashion . We have generated GROMOS force field ( 45a3 and 54a7 ) parameters for over 250 different enzymatic and non-enzymatic PTMs , spanning a wide range of modification types with a close to complete coverage of experimentally verified PTMs ( Figure 1 ) . Since GROMOS 54a7 force field parameters were fitted to reproduce experimental HFEs , we have tested the quality of the PTM parameters , obtained by manually curating the parameters of different groups mostly in analogy to canonical amino acids , by comparing the calculated HFEs against the experimental values . The newly generated parameters compatible with the GROMOS 54a7 parameter set reproduce experimental HFEs almost equally well as the original ones ( Table 1 and Figure 2 ) . Overall , only a few parameterized PTMs have not been directly validated against experimental HFEs due to a lack of experimentally available data . In those cases , however , good matching with the parameters obtained using an orthogonal , fully automated approach [26] lends support to the general validity of the reported parameters . However , one should emphasize that the full range of validity of the presented parameters could and should be delineated only by directly comparing MD simulations of different post-translationally modified proteins in biologically relevant contexts with relevant experimental data . To date , PTMs in MD simulations have been treated in separate studies using different procedures and force fields , typically focusing on a single modification at a time [11] , [13] , [16] . Additionally , there are some available tools for automated generation of parameters ( e . g . the AMBER [31] feature antechamber and online tools SwissParam [32] , PRODRG [33] , ATB [26] and q4md-forcefieldtools [34] ) , however , envisioned for small molecules rather than protein PTMs . The parameters reported herein have comparative advantage over these sources along three principal directions . First , we provide exclusively human curated and validated PTM force-field parameters , which are mutually fully consistent as well as being consistent with canonical amino acids . Second , we provide PTM parameters in both GROMOS [35] and GROMACS [36] format , widely used MD simulation packages ( supporting GROMOS version 11 and GROMACS versions 3 . × and newer ) , suitable for immediate simulation of modified proteins without any additional work required . This should be contrasted with the above tools that provide parameters for isolated compounds only . Finally , in combination with a publicly available online tool for introducing PTMs of choice to a user-supplied protein 3D structure ( Vienna-PTM server , http://vienna-ptm . univie . ac . at ) [37] , we provide a comprehensive , user-friendly toolkit for studying PTMs using MD simulations . During their lifecycle in the cell , almost all proteins undergo one or more different PTMs affecting their structure , dynamics and interaction networks and , subsequently , their function through direct alteration of chemical and physico-chemical properties of target residues ( Figure 3 ) . The force field parameters presented here , together with the Vienna-PTM webserver , provide a systematic framework required to study the effects of PTMs using MD simulations . As a first step in this direction , we have here compared the hydrophobicity-related variables ( HFEs and MFP values ) of native and modified amino acids and quantitatively showed that PTMs can have an extremely strong , biologically significant effect in this context . It has already been documented that some PTMs exert their biological effect through a general modification of the hydrophobicity of their targets . For example , lysine trimethylation is known to directly affect the binding of retinoic acid receptors , which regulate genes involved in growth , differentiation and apoptosis , to their partners via an increase in site-specific hydrophobicity [38] . Moreover , acetylated and methylated lysine residues in histones , i . e . , some of the key components of the histone code , are recognized by the hydrophobic binding pockets of bromo- and chromo-domains based on the difference in hydrophobicity between the modified and unmodified lysines [39] . Furthermore , we have recently shown that carbonylation , which affects lysine , arginine , proline and threonine residues , drastically increases local propensity for aggregation in proteins by affecting the hydrophobicity of the modified sites [11] . While other , more specific effects of PTMs on the structure , dynamics and interaction profile of target proteins are certainly important , a major change in hydrophobicity , net charge , isoelectric point or any other general physico-chemical property caused by a PTM at a given site could certainly have major biological repercussions . We believe that our present study will provide a solid foundation for exploring such timely and important issues in the future . However , this is only one possible application of the PTM force-field parameters reported herein . From direct MD simulations to biomolecular structure refinement to computational free energy estimation and drug design , these parameters expand the range of MD methodLology to a large class of biomolecular systems of paramount importance . It is our hope that this advance will play a catalytic role in bringing together realistic cell biology , dominated by PTMs , and the quantitative , reductionist power of structural biology and chemistry , as embodied in the MD method , and help shed light on a broad spectrum of important biological questions at the microscopic level .
One of the aims of the GROMOS force fields is to allow for the transfer of parameters between chemically similar groups in different compounds . Accordingly , we have derived GROMOS 45a3 and 54a7 force field parameters describing 110 post-translationally modified amino acids and protein termini ( Table S1 ) by either novel parameterization or direct transfer from or analogy to already parameterized compounds including amino acids , nitrogenous bases and other small molecules according to the following principles and rationales . General principles: Modification type-specific principles: We include detailed descriptions of parameter choices as comments in Dataset S1 and Dataset S2 . We have used the thermodynamic integration approach [25] , a widely used computational method based on MD simulations , to calculate hydration free energies ( HFEs ) of neutral forms of small-molecule analogs of 14 amino-acid side chains ( the same set as in Oostenbrink et al . [21] ) , compounds from the validation set and side chain analogs of all parameterized PTMs with a charge neutral protonation state . Non-bonded ( van der Waals and Coulomb ) interactions , coupled to a parameter λ , were scaled down to zero in a stepwise manner in vacuum and water . Free energy changes of these processes were calculated as the integral of the ensemble average of the derivative of the total Hamiltonian of the system with respect to λ , over the interval from λ = 0 to λ = 1 . For vacuum calculations , three independent simulations , each 5 ns long , were run at 21 equally spaced λ-points with the temperature kept at 500 K and additional random kicks introduced by Langevin dynamics integration method [44] , in order to avoid convergence problems due to inefficient sampling of the conformational space . Water simulations were run in five independent copies , each 0 . 5 ns long , at 21 equally spaced λ-points , together with 10 additional λ-points placed in the regions of the least smoothness of the integrated curve , using SPC explicit water [45] , a reaction field electrostatic scheme with a cutoff of rc = 1 . 4 nm and the dielectric constant of εrf = 65 and a Berendsen thermostat and barostat keeping the temperature and pressure at 300 K ( τT = 0 . 05 ps ) and 1 bar ( τp = 1 ps and compressibility = 4 . 5×10−5 bar−1 ) [46] . A soft-core formalism [47] was used to avoid singularities of the potential energy . The aforementioned integrals were evaluated by the generalized Simpson's rule for non-equidistant nodes using the averages over the independent simulations at each λ-point . HFEs were calculated as the difference between the change in free energy upon the removal of non-bonded interactions calculated in vacuum and calculated in water . | Post-translational modifications , i . e . chemical changes of protein amino acids , play a key role in different cellular processes , ranging from enzymatic activation to transcription and translation regulation to disease development and aging . However , our understanding of their effects on protein structure , dynamics and interaction networks at the atomistic level is still largely incomplete . In particular , molecular dynamics simulations , despite their power to provide a high-resolution insight into biomolecular function and underlying mechanisms , have been limited to unmodified , native proteins due to a surprising deficiency of suitable tools and systematically developed parameters for treating modified proteins . To fill this gap , we develop and validate force field parameters , an essential part of the molecular dynamics method , for more than 250 different types of enzymatic and non-enzymatic post-translational modifications . Additionally , using this tool , we quantitatively show that microscopic properties of target amino acids , such as hydrophobicity , are greatly affected by the majority of modifications . The parameters presented in this study greatly expand the range of applicability of computational methods , and in particular molecular dynamics simulations , to a large set of new systems with utmost biological and biomedical importance . | [
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"protein"... | 2013 | A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications |
Lassa fever afflicts tens of thousands of people in West Africa annually . The rapid progression of patients from febrile illness to fulminant syndrome and death provides incentive for development of clinical prognostic markers that can guide case management . The small molecule profile of serum from febrile patients triaged to the Viral Hemorrhagic Fever Ward at Kenema Government Hospital in Sierra Leone was assessed using untargeted Ultra High Performance Liquid Chromatography Mass Spectrometry . Physiological dysregulation resulting from Lassa virus ( LASV ) infection occurs at the small molecule level . Effects of LASV infection on pathways mediating blood coagulation , and lipid , amino acid , nucleic acid metabolism are manifest in changes in the levels of numerous metabolites in the circulation . Several compounds , including platelet activating factor ( PAF ) , PAF-like molecules and products of heme breakdown emerged as candidates that may prove useful in diagnostic assays to inform better care of Lassa fever patients .
Lassa virus ( LASV ) , an Old World arenavirus , is the etiological agent of Lassa fever [1] . Lassa fever is endemic to West Africa , with tens of thousands of cases or more estimated to occur annually [2] . The case fatality rate ( CFR ) in acutely ill Lassa fever patients presenting while viremic was 29–31% in Nigeria [3 , 4] and 69% in Sierra Leone [5] . During a recent surge of Lassa fever cases in Nigeria the CFRs were >50% [6] . Women who are pregnant develop severe disease with increased frequency and have a Lassa fever CFR as high as 90% , with fetal death , miscarriage or spontaneous abortion occurring in nearly all cases [7 , 8] . Recent cases in Togo , Benin and areas of Nigeria that rarely have Lassa fever [6 , 9 , 10] , coupled with serological studies in Mali [11–14] , suggest that efforts to improve Lassa fever surveillance should continue [15] . Lassa fever ranks among the most common of the viral hemorrhagic fevers that are imported from Africa [16–20] . Recently , the first case of Lassa fever contracted outside Africa was reported in Germany [21] . There is no approved Lassa fever vaccine , and the only available treatment , ribavirin , is effective only during early infection [5 , 22 , 23] . Management of Lassa fever principally involves supportive therapy such as fluid replenishment for dehydration [5] . Lassa fever presents in its early stages as a febrile illness indiscernible from multiple diseases of infectious etiology that are common in West Africa , such as malaria , typhoid , leptospirosis , influenza and various arbovirus-induced illnesses [24] . After the brief nondescript prodrome , progression to fulminant disease occurs rapidly [25] . Lassa fever diagnostics include enzyme linked immunosorbant assays ( ELISA ) detecting LASV antigen ( Ag ) , human anti-LASV IgG and IgM , a rapid lateral flow immunoassay [5] , and polymerase chain reaction based assays detecting viral genomic RNA [26] . Diagnostics can provide the serostatus of patients presenting with a febrile illness and offer a quantitative view of antibody responses . However , the presence of anti-LASV IgM lacks utility as a reliable marker of recent LASV infection [27] , and IgM persistence can be confounding as a strategy to monitor disease progression [28] . High viremia is an indicator for poor outcome in Lassa fever [28] . At present , few other markers exist that accurately inform clinical management of Lassa fever patients or patients that survive the acute illness . Increases in serum markers of hepatic damage , particularly aspartate aminotransferase ( AST ) , increase during the acute stage of Lassa fever and levels correlate with fatal outcome in Lassa fever [28–30] . Likewise , the serum levels of cytokines interleukin 6 , 8 , and 10 ( IL-6 , IL-8 , IL-10 ) , macrophage inflammatory protein 1 alpha & beta ( MIP-1α/β ) , and interferons-alpha/gamma ( INF- α/γ ) are altered during the course of the disease [28 , 31–35] . However , subjects that survive also display very high serum levels of liver enzymes or various cytokines , which limits the prognostic value of these markers . Lassa virus exhibits tropism for circulating leukocytes , and there is evidence to suggest LASV infection results in endothelial dyregulation [36–38] that results in loss of intravascular volume [39] . These circulatory pathologies suggest that the serum of a Lassa fever patient may be an informative , clinically relevant medium to monitor perturbations to homeostasis and may have utility in tracking the trajectory of disease . Furthermore , analysis of Lassa fever at the small molecule level may reveal intermediates of cellular pathways disrupted by LASV replication , suggesting pathogenic mechanisms that the virus utilizes and identifying markers for prognostic diagnostics and potential targets for intervention strategies . Herein , we report findings from a Liquid Chromatography Mass Spectrometry ( LCMS ) serum metabolomics investigation of a heterogeneous clinical population presenting with febrile illness and triaged to the Lassa Fever Ward at Kenema Government Hospital ( KGH ) in Sierra Leone .
The Tulane University Institutional Review Board and the Sierra Leone Ethics and Scientific Research Committee approved this project . Patients were referred to the KGH Viral Hemorrhagic Fever Ward from the hospital’s general ward or from regional health centers on the basis of suspicion of Lassa fever . Patients who met the case definition of Lassa fever as defined by Khan et al . [40] were admitted and cared for by the ward’s trained staff . All adult subjects provided written informed consent for publication of their case details . A parent or guardian of child participants provided written informed consent on their behalf . Small blood volumes ( approximately five ml ) for serum separation were collected from patients presenting to KGH with febrile illnesses that met preclinical criteria of suspected Lassa fever for diagnostic purposes . Patient samples received a coded designation and were collected in serum vacutainer tubes . Blood samples were allowed to coagulate for 20 minutes at room temperature . Serum was separated from coagulated blood by centrifugation ( 200 x g , 20 minutes at room temperature ) . For consented subjects for which there was excess serum not needed for clinical evaluations , aliquots of the serum fraction were stored in cryovials at -20°C prior to processing for metabolite analysis . A lateral flow immunoassay requiring only a drop of blood obtained with a safety lancet and capable of detecting LASV antigenemia within 15 minutes was utilized to triage cases for possible LASV infection [28 , 35] . Serum from subjects was subsequently tested using recombinant antigen-based Lassa fever antigen- , IgM- and IgG-capture enzyme-linked immunosorbent assays ( ELISA ) [24] . Limits of detection and quantitation of the ELISA were based on the upper 95th percentile obtained with a panel of sera from U . S . and Sierra Leonean donors lacking detectable LASV antigens or immunoglobulin M or G ( IgM , and IgG ) antibodies to LASV recombinant proteins . All sera collected at the Lassa Fever Ward , KGH is treated as if it contains replication-competent LASV . Serum samples were prepared via a validated metabolomics preparation method utilizing ice-cold methanol for extraction [41 , 42] . Separated serum samples were depleted of protein by addition to one part sera ( 100 μL ) of 4 parts ice-cold methanol ( 400 μL ) , the mixture was vortexed vigorously for 10 seconds , and incubated 1 hour at -20°C followed by centrifugation at 14 , 000 x g , 15 minutes , 4°C . The supernatant was collected and transferred to a new , sterile vial and dried under vacuum . The resultant small-molecule containing pellets were stored in desiccated , sealed containers and shipped to Tulane University where they were gamma-irradiated . Small molecule containing pellets were dissolved in a solution of 95:5 water:acetonitrile transferred to autosampler vials , and held at -20°C or 4°C immediately prior to analysis [43] . All reagents utilized were HPLC grade . LCMS methods was optimized based upon a meta-sample consisting of an equal-volume mix of all 50 samples . Detection of metabolites was performed via HPLC separation with ESI-MS ( electrospray mass spectrometry ) detection . HLPC was performed with an aqueous normal-phase , hydrophilic interaction chromatography ( ANP/HILIC ) HPLC column: a Cogent Diamond Hydride Type-C column with 4 μm particles and dimensions of 150 mm length and 2 . 1 mm diameter was used with an Agilent 1290 HPLC system ( Agilent Technologies , Santa Clara , CA ) . Two identical Diamond Hydride columns were connected in series to obtain better separations . The column were maintained at 60°C with a flow rate of 900 μL/min . Chromatography was as follows: solvent consisted of H20 with 0 . 1% ( v/v ) formic acid for channel “A” and acetonitrile with 0 . 1% formic acid for channel “B” . Following column equilibration at 98% B , the sample was injected via autosampler , and the column was flushed for 2 . 0 min to waste . From 2 . 0 min to 14 . 5 min , the gradient was linearly ramped from 98% to 65% B . From 14 . 5 min to 16 . 0 min , the gradient was ramped from 65% to 25% B . From 14 . 5 to 18 . 0 min the column was held at 25% B , and from 18 . 0 to 18 . 2 minutes the gradient was ramped from 25% to 98% B . From 18 . 2 to 20 . 0 minutes the column was re-equilibrated with 98% B . An Agilent 6538 Q-TOF with dual-ESI source mass spectrometer was used for all analyses . Resolution was approximately 20 , 000 and accuracy was 1 ppm . Source parameters were: drying gas 12 L/min , nebulizer 60 psi , capillary voltage 3500V , capillary exit 100V . Spectra were collected in positive mode from 50 to 1700 m/z at a rate of 1 Hz . Raw spectral data in . d format where uploaded to XCMS Online ( Version 1 . 0 . 42 ) and processed as pairwise comparisons using parameters optimized for data acquired with UPLC on an Agilent 6538 MS [44] . Data has been deposited in the XCMS Public archive ( https://xcmsonline . scripps . edu/landing_page . php ? pgcontent=mainPage ) under the identifier Lassa_Serum_PLOS_NTD . All statistical analyses were carried out using the R statistical software package [45] . Raw mass spectral intensity values and a unique identifier for specific spectral features were extrapolated from XMCS output and compiled into . csv files for machine learning analysis with predefined outcome . Machine learning algorithms built into the R package were utilized with outputs quantifying the sensitivity , specificity , accuracy , and/or receiver operating character computed depending on diagnostic ( binary ) or prognostic ( multi-outcome ) analysis . The Random Forest algorithm was employed for all analyses reported . The Random Forest algorithm was set to select features through permutation of the data set yielding the strongest indicators of the input features . The datasets where run with 10-fold cross validation ensuring that ranked output features where selected on importance after predefined , multiple rounds of random training and testing . FacoMineR was utilized for a Principle Components Analyses [46] .
A panel of 49 serum samples from patients presenting to Kenema Government Hospital ( KGH ) in Sierra Leone and triaged to the Viral Hemorrhagic Fever Ward ( LFW ) with febrile illnesses was analyzed by LCMS . These subjects presented with varying serological status and are representative of the spectrum of illnesses during Lassa fever from acute disease to convalescence ( Fig 1 ) . Serum samples where drawn upon admittance . Diagnostic tests for patients were performed the same day the sample was drawn . Twenty subjects tested positive for the presence of LASV in their blood by either antigen-capture ELISA or RT-PCR and were considered to have acute Lassa fever . Five patients died and were classified as having fatal Lassa fever ( FL ) . 15 of these patients survived and were classified as having non-fatal Lassa fever ( NFL ) . 21 subjects without measurable LASV in there blood tested positive for the presence of anti-LASV Immunoglobulin M ( IgM ) and/or anti-Lassa IgG by ELISA , and were considered to have survived infection with LASV . This group was subdivided into post-Lassa fever patients that were acutely ill ( PLAFI , n = 9 ) or those whose illness was non-acute and presented after an extended period of illness ( PLNAFI , n = 11 ) . Nine patients presenting with a febrile illness , but testing negative for the presence of LASV and either anti-Lassa IgM or IgG and were classified as non-Lassa febrile illness ( NLF ) controls . There were 28 female and 21 male patient sera screened ( 57% female ) with gender information not available for one patient ( S1 Table ) . The mean age was 26 . 0±13 . 9 years with age information not available for one patient . 7 patients died ( 14% total sample group ) with a mean terminal time point ( time in days since the onset of symptoms ) of 11 . 4±5 . 1 days ( with time since symptom onset not available for two patients ) . The mean age for patients who died was 24 . 6 ±9 . 7 years . The antiviral drug ribavirin has been reported have some efficacy in the treatment of Lassa fever , particularly if treatment is begun early during the course of the illness . In this cohort eleven patients with Lassa virus viremia at the time of admission , as well as three IgM positive patients received ribavirin . Spectral features of protonated , sodiated , and potassiated ( m/z = 245 . 0852 , 267 . 0775 , & 283 . 0408; rt = 4 . 30 , 4 . 32 , 4 . 34 , respectively ) ribavirin adducts where detected in these samples . Three of the 11 ( 21% ) ribavirin treated acute Lassa fever patients died . Individual samples produced between 3100 and 6900 different spectral features . LCMS analyses allowed for the putative identification of small molecules in serum from the cohort of febrile patients presenting to KGH . Principle Components Analysis ( PCA ) of these features indicated that Lassa fever patients with different outcomes and patients at various stages during and after LASV infection segregated according to their serum small molecule profiles ( Fig 2 ) . Patients with active or prior LASV infection had profiles that were distinct from febrile patients without serological evidence of current or prior LASV exposure . The small molecule profiles of patients with fatal and non-fatal Lassa fever were also distinct . The overall serum small molecule profile of patients with evidence of prior exposure to LASV and presenting to the Lassa fever Ward after an extended period of illness ( post-LASV non-acute ) most closely resembled the profile of nonfatal Lassa fever patients . Patients with evidence of prior exposure to LASV , but presenting with an acute illness , showed a distinct small molecule PCA profile . We used the LCMS data to perform cluster analysis of serum metabolites in subjects with different outcomes following Lassa virus infection , survivors of Lassa virus infection and febrile controls ( Fig 3 ) . Platelet activity is depressed during Lassa fever , particularly in terminal patients [29 , 47] . 24 platelet-activating factor/platelet-activating factor-like molecules were putatively identified and expressed at variable levels in the serum of febrile patients presenting to KGH ( Fig 3A , S2 Table ) . Protonated and sodiated adducts of phosphatidylcholine , platelet-activating factor ( PAF ) C-16 , its metabolic precursors Lyso-PAF C-16 and Arachidonoyl PAF C-16 , and 9 additional PAF-like lipids were putatively identified via manual m/z screening . Heat maps of the levels of PAF or PAF-like species illustrate the levels from low ( red ) , intermediate ( black ) to high ( green ) in the patient groups . The cluster analysis indicated that nearly all PAFs or PAF-like molecules were present in lower amounts in the serum of patients with fatal Lassa fever than in patients that survived the acute infection ( nonfatal Lassa fever ) . Post-Lassa patients had higher levels of PAF or PAF-like molecules than fatal Lassa fever patients , with the subgroup of patients presenting with an acute illness displaying higher levels than the non-acute group . Non-Lassa febrile illness patients had the highest overall levels of PAF or PAF-like molecules . Extracted ion chromatograms of selected metabolites were analyzed ( Fig 4 ) . This analysis confirms the lower levels of two PAFs , PAF4 ( PC ( O-16:1 ( 11Z ) /2:0 ) H+ , m/z 522 . 3504 and PAF 7 ( PC ( O-18:2 ( 9Z , 12Z ) /2:0 ) Na+ , 570 . 3463 ) in patients with fatal Lassa fever compared to patients with non Lassa fever ( Fig 4A and 4B ) . Products of hemoglobin breakdown and various nucleosides were among other spectral features that were putatively identified in the LCMS data set ( Fig 3B , S3 Table ) . Certain of these metabolites were expressed at variable levels in the serum of febrile patients presenting to KGH . For example extracted ion chromatograms confirm that the hemoglobin breakdown products D-urobilinogen and I-urobilin sharing m/z 591 . 3195 were reduced in patients with fatal Lassa fever compared to Lassa Negative patients ( Fig 4C ) . A spectral feature consistent with the protonated adduct of 7-methylinosine is detected with m/z 283 . 1016 ( theoretical m/z = 283 . 1037 ) significantly elevated in the sera of Lassa fever patients who died compared to patients with a non-Lassa febrile illness and other patient groups ( Fig 4D ) . There were several spectral features that could not be putatively identified by their precise mass , and were designated as unknown metabolites . Lipids constituted the most abundant class of molecules assigned putative identifications in serum samples from the cohort of subjects presenting with febrile illnesses to KGH . 153 substituents of the primary lipid classes were putatively identified included fatty acids and conjugates , fatty esters , glycerophosphocholines , glycerolipids , diacyglycerols , glycerophospholipids , prenol , sterol , sphingolipids , vitamin D3 and derivative species ( Fig 3D , S5 Table ) . Approximately half of the lipids were present in lower amounts in the serum of patients with fatal Lassa fever than in patients with non-fatal Lassa fever . Lipids as a class were generally higher in post-LASV group of patients presenting with an acute illness or in patients with a non-Lassa febrile illness than in the other patient groups . Random Forest machine learning provided a quantitative assessment of the ability for metabolomics data to discriminate between patients in different serological groups . Several metabolites showed significantly different levels in different groups of patients ( Fig 5 ) . For example , PAF4 ( PC ( O-16:1 ( 11Z ) /2:0 ) H+ , m/z 522 . 3504 ) and PAF6 ( PC ( O-18:1 ( 10E ) /2:0 ) H+ , m/z 550 . 3808 ) were found in significantly lower levels in patients that succumbed to Lassa fever compared to those that survived acute infection ( Fig 5A ) . PAF8 ( PC ( O-18:2 ( 9Z , 12Z ) /2:0 ) H+ , m/z 548 . 3552 ) and M5 ( Unknown 2 , m/z 187 . 0693 ) were found in significantly higher levels in non-Lassa febrile illness patient than in patients that succumbed to Lassa fever ( Fig 5B ) . M4 ( Fibrin monomer breakdown product Na+ , m/z 168 . 075 ) and M12 ( D-Urobilinogen/I-Urobilin Na+ , m/z 613 . 3223 ) among other metabolites was higher in non-Lassa febrile illness patients compared to the combined groups of patients that presented with acute Lassa fever ( Fatal plus nonfatal , Fig 5C ) . Other PAFs or PAF-like molecules , including PAF7 ( PC ( O-18:2 ( 9Z , 12Z ) /2:0 ) Na+ , m/z 570 . 3463 ) and PAF12 ( PC ( O-14:0/2:0 ) Na+ , m/z 613 . 3223 ) distinguished acute Lassa fever patients ( fatal plus nonfatal ) from patients with prior LASV infection ( Acute and non-acute presentations , Fig 5D ) . The random forests machine-learning algorithm is also able to rank the power of the input variables in predicting sensitivity and specificity for placement in a given serological group . For example , M9 ( Mesobilirubinogen H+ , 593 . 3334 ) provided a sensitivity of 1 and a specificity of . 89 when comparing fatal Lassa fever and nonfatal Lassa fever ( Table 1 ) . PAF6 ( PC ( O-18:1 ( 10E ) /2:0 ) H+ , 550 . 38080 ) provided a sensitivity of 1 and a specificity of . 78 when comparing the same groups . Receiver operator characteristic ( ROC ) curves for both comparisons had a value of 1 ( S1A Fig ) . Among several metabolites with promising diagnostic potential four , PAF4 ( PC ( O-16:1 ( 11Z ) /2:0 ) H+ , 522 . 3504 ) , PAF8 ( PC ( O-18:2 ( 9Z , 12Z ) /2:0 ) H+ , 548 . 3552 ) , M2 ( Unknown 1 H+ , 102 . 0537 ) and M5 ( Unknown 2 , 187 . 0693 ) , showed sensitivities , specificities and ROC of 1 when comparing patients with a fatal outcome from Lassa fever versus nonLassa febrile illness ( Table 2 , S1B Fig ) . The algorithm was also able to identify a number of metabolites with possible diagnostic potential for discriminating between acute Lassa fever patients ( fatal plus nonfatal ) and non-Lassa febrile illness patients ( S6 Table , S1C Fig ) or patients with prior LASV infection ( acute or non-acute presentation , S7 Table , S1D Fig ) .
LASV induces a dynamic physiological dysregulation within the circulatory system of infected humans , which is manifest in changes in the levels of numerous metabolites . Pathways mediating blood coagulation , hemoglobin breakdown and lipid , amino acid , nucleic acid metabolism are affected during or following LASV infection . Further investigation of these metabolic pathways may inform discovery of novel therapeutic targets for Lassa fever . Metabolites that differentiate Lassa fever patients at various stages of disease , as well those that differentiated these patients from other febrile illness patients presenting to KGH , have been identified . Several compounds , including PAF , PAF-like molecules and products of heme breakdown emerged as candidates that may prove useful in diagnostic assays to inform better care of Lassa fever patients . Several PAFs or PAF-like molecules demonstrated high sensitivity and specificity for discriminating between patients that ultimately succumbed to fatal Lassa fever and those that survived . The primary physiological role of platelets is to aggregate at the site of endothelial injury where they initiate the clotting cascade to block circulatory leak [48] . Human and nonhuman primates infected with LASV develop clotting abnormalities that manifest in abnormal in vitro platelet aggregation [29 , 39 , 49 , 50] . Levels of platelets in the blood and platelet survival times are normal or only slightly depressed in Lassa patients . Abnormal platelet aggregation correlated with the presence of hemorrhage and with the severity of disease . Here , we demonstrate that levels of PAF or PAF-like molecules were decreased in Lassa fever patients that succumbed to their infection . An as yet to be identified inhibitor of platelet aggregation was identified in the blood of patients with Lassa fever as well as the in patients with Argentine hemorrhagic fever , which is caused by Junin virus , an arenavirus related to LASV [47 , 51] . The contribution of PAF and PAF-like molecules to hemorrhagic fever pathogenesis appears to be complex . In dengue virus infected patients , PAF appears to be a contributing factor to vascular leakage [52] and higher expression of PAF-degrading acetylhydrolase ( PAF-AH ) correlates with lower frequency of dengue fever , but not dengue hemorrhagic fever in two ethnically distinct populations [53] . In a murine model of dengue genetic knockout or chemical inhibition of the platelet-activating factor receptor ( PAFR ) resulted in a less severe disease and increased survival in those animals deficient or inhibited for PAFR [54] . Additional studies will be required to determine if decreased PAF mediated platelet activation contributes to the hemorrhagic manifestations of severe Lassa fever , and whether or PAFs or PAF-like molecules can serve as diagnostic or prognostic markers . Hemoglobin breakdown products were identified as potential prognostic biomarkers . Mesobilirubinogen ( M9 ) exhibited a specificity of 1 and sensitivity of >86% in discriminating between fatal Lassa fever and either non-fatal Lassa fever or non-Lassa febrile illness . Two confounding factors concerning the presence of heme breakdown products are worth noting for consideration in future studies . First is the malaria endemic locale where over 75% of febrile patients test positive for Plasmodium spp [25] . Second , 35% of the LF patients received ribavirin treatment , a drug attributed to development of anemia [55] . In the present study , serum lipids were the most frequently identified molecular class and also the most frequently identified as decreased in fatal Lassa fever . Similar results were obtained previously in a study of lymphocytic choriomeningitis virus ( LCMV ) infected mice , a small animal model of arenavirus infection . Stearoyl lysophosphocholine ( 18:0 ) , identified with positive ion m/z 524 . 37057 , had reduced signal intensity in the serum of infected animals [43] . Proteolytic breakdown products are also observed in this murine arenavirus infection model . The dipeptides γ-glutamyl-Valine , γ-glutamyl-Leucine , and prolyl-hydroxyproline were present in lower amounts in the plasma over the course of LCMV infection in mice [43] . We observed both increased and decreased amounts of a several peptide species . However , no consistent pattern emerged to suggest a mechanism that might account for differences in Lassa fever patients , survivors or febrile controls . Despite the observed differences amongst patient groups there were no peptide or lipid species identified that showed diagnostic sensitivity and specificity approaching that of PAFs or heme breakdown products . In this regard , additional features in the present dataset merit continued investigation to obtain a definite chemical identification . Unknown 1 ( H+ m/z 102 . 0537 , NH4+ m/z 119 . 08 ) was significantly elevated in serum samples from patients with fatal Lassa fever compared to those with non-Lassa febrile illness . A second unknown spectral feature detected at m/z 187 . 0693 showed a significant reduction in sera from Lassa fever patients that died compared to non-Lassa febrile patients . Virus load , the levels of liver enzymes and certain cytokines have predictive value in the outcomes of Lassa fever [28 , 35 , 56 , 57] . Assays to measure these parameters are generally not feasible in austere environments . Many rural health posts across the Lassa fever zone in West Africa are challenged by lack of electricity , minimal lab infrastructure and lack of access to training . LCMS technology is also not feasible in field clinics in West Africa where Lassa fever patients are prevalent . Therefore , simple assays such as dipstick style chromatographic assays , including lateral flow immunoassays , have gained acceptance , and can be conducted with minimal resources and training [35 , 58–60] . Several small molecules with high biomarker potential were identified including adducts of the modified nucleoside 1-methylinosine . The protonated , sodiated , and potassiated adducts of 1-methylinosine are elevated in the urine of cancer patients [61 , 62] and in the plasma of patients in renal failure whereupon removal possesses biomarker utility for effective hemodialysis [63] . Reagents used in urinalysis sticks for assaying 1-methylinosine , and similar approaches for quantifying heme breakdown products and other metabolites potentially could be adapted for a Lassa fever prognostic/diagnostic panel . While these studies have putatively identified a number of compounds with altered serum levels during LASV disease , unequivocal identity of a compound by LCMS requires comparison of the spectrum of the metabolite with a reference standard , which will be pursued for molecules with diagnostic potential . Furthermore , the panel of candidate biomarkers , including PAF , PAF-like molecules and heme breakdown products , must be investigated for diagnostic efficacy alone and in combination in prospective clinical studies . Another limitation of the current study is the relatively low numbers of patients samples analyzed . Additional metabolomics profiling using a larger number of patient samples should be conducted , including LCMS analysis of metabolites in noninvasive specimens such as saliva or urine . Larger panels including other bodily fluids may expand the panel of potential metabolites with diagnostic potential . Metabolic markers for early diagnosis and prognosis of patients at high risk for development of fatal Lassa fever could be integrated into existing clinical and laboratory algorithms for LASV diagnosis and prognosis and improve outcomes of this often fatal disease by identifying cases at greatest risk of death . In addition , the application of metabolomics to reveal fundamental LASV pathogenic mechanisms will potentially provide new targets for therapeutic interventions . | Lassa fever afflicts tens of thousands of people in West Africa each year . The disease progresses rapidly , but there are no tests available to determine which patients are at high risk for dying . We measured the levels of small molecules in the blood of febrile patients with and without infection by LASV that presented to Kenema Government Hospital in Sierra Leone using Ultra High Performance Liquid Chromatography Mass Spectrometry ( LCMS ) , which identifies compounds based on their precise mass . Computational analyses were used to identify compounds that differed in patients with an acute LASV infection , patients with evidence of prior exposure to LASV and patients with fever , but who did not have evidence of exposure to LASV . Several serum metabolites , including factors that are involved in blood clotting and breakdown products of heme , were identified that may prove useful in diagnostic assays that will inform better care of Lassa fever patients or development of therapeutic interventions . | [
"Abstract",
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"neglect... | 2017 | Metabolomics analyses identify platelet activating factors and heme breakdown products as Lassa fever biomarkers |
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