Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSkwark, Marcin J.en_US
dc.contributor.authorCroucher, Nicholas J.en_US
dc.contributor.authorPuranen, Santerien_US
dc.contributor.authorChewapreecha, Claireen_US
dc.contributor.authorPesonen, Maijuen_US
dc.contributor.authorXu, Yingyingen_US
dc.contributor.authorTurner, Paulen_US
dc.contributor.authorHarris, Simon R.en_US
dc.contributor.authorBeres, Stephen B.en_US
dc.contributor.authorMusser, James M.en_US
dc.contributor.authorParkhill, Julianen_US
dc.contributor.authorBentley, Stephen D.en_US
dc.contributor.authorAurell, Eriken_US
dc.contributor.authorCorander, Jukkaen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.groupauthorProfessorship Kaski Samuelen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.groupauthorCentre of Excellence in Computational Inference, COINen
dc.contributor.groupauthorComplex Systems and Materialsen
dc.contributor.organizationImperial College Londonen_US
dc.contributor.organizationUniversity of Cambridgeen_US
dc.contributor.organizationUniversity of Oxforden_US
dc.contributor.organizationWellcome Trust Sanger Instituteen_US
dc.contributor.organizationHouston Methodist Hospitalen_US
dc.contributor.organizationCornell Universityen_US
dc.contributor.organizationVanderbilt Universityen_US
dc.date.accessioned2017-05-11T09:17:00Z
dc.date.available2017-05-11T09:17:00Z
dc.date.issued2017-02-01en_US
dc.description.abstractRecent 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.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationSkwark, M J, Croucher, N J, Puranen, S, Chewapreecha, C, Pesonen, M, Xu, Y, Turner, P, Harris, S R, Beres, S B, Musser, J M, Parkhill, J, Bentley, S D, Aurell, E & Corander, J 2017, ' Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis ', PLoS Genetics, vol. 13, no. 2, e1006508 . https://doi.org/10.1371/journal.pgen.1006508en
dc.identifier.doi10.1371/journal.pgen.1006508en_US
dc.identifier.issn1553-7390
dc.identifier.issn1553-7404
dc.identifier.otherPURE UUID: ffb5ea08-466b-42c4-b985-b81c90a985d4en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ffb5ea08-466b-42c4-b985-b81c90a985d4en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85014119857&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/11520139/journal.pgen.1006508.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/26005
dc.identifier.urnURN:NBN:fi:aalto-201705114380
dc.language.isoenen
dc.relation.ispartofseriesPLOS GENETICSen
dc.relation.ispartofseriesVolume 13, issue 2en
dc.rightsopenAccessen
dc.titleInteracting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysisen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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