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

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dc.contributor.author Skwark, Marcin J.
dc.contributor.author Croucher, Nicholas J.
dc.contributor.author Puranen, Santeri
dc.contributor.author Chewapreecha, Claire
dc.contributor.author Pesonen, Maiju
dc.contributor.author Xu, Ying Ying
dc.contributor.author Turner, Paul
dc.contributor.author Harris, Simon R.
dc.contributor.author Beres, Stephen B.
dc.contributor.author Musser, James M.
dc.contributor.author Parkhill, Julian
dc.contributor.author Bentley, Stephen D.
dc.contributor.author Aurell, Erik
dc.contributor.author Corander, Jukka
dc.date.accessioned 2017-03-24T08:18:02Z
dc.date.available 2017-03-24T08:18:02Z
dc.date.issued 2017-02
dc.identifier.citation Skwark , M J , Croucher , N J , Puranen , S , Chewapreecha , C , Pesonen , M , Xu , Y 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 , 1006508 . https://doi.org/10.1371/journal.pgen.1006508
dc.identifier.other PURE: 81958822
dc.identifier.other PURE UUID: a46d8d0d-e582-4c94-8a08-bdee413b7994
dc.identifier.other WOS: 000395719300003
dc.identifier.other Scopus: 85014119857
dc.identifier.uri http://hdl.handle.net/10138/178231
dc.description.abstract 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. en
dc.format.extent 24
dc.language.iso eng
dc.relation.ispartof PLoS Genetics
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject DIRECT-COUPLING ANALYSIS
dc.subject STREPTOCOCCUS-PNEUMONIAE
dc.subject PENICILLIN RESISTANCE
dc.subject STATISTICAL-METHODS
dc.subject CONTACT PREDICTION
dc.subject RESIDUE CONTACTS
dc.subject EVOLUTION
dc.subject PROTEINS
dc.subject ASSOCIATION
dc.subject COEVOLUTION
dc.subject 1183 Plant biology, microbiology, virology
dc.subject 1184 Genetics, developmental biology, physiology
dc.subject 112 Statistics and probability
dc.title Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis en
dc.type Article
dc.contributor.organization Department of Mathematics and Statistics
dc.contributor.organization Jukka Corander / Principal Investigator
dc.contributor.organization Biostatistics Helsinki
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1371/journal.pgen.1006508
dc.relation.issn 1553-7404
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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