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Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis
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2017 (English)In: PLoS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 13, no 2, e1006508Article in journal (Refereed) Published
Abstract [en]

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.

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PUBLIC LIBRARY SCIENCE , 2017. Vol. 13, no 2, e1006508
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Biological Sciences
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URN: urn:nbn:se:kth:diva-204109DOI: 10.1371/journal.pgen.1006508ISI: 000395719300003PubMedID: 28207813ScopusID: 2-s2.0-85014119857OAI: oai:DiVA.org:kth-204109DiVA: diva2:1085194
Note

QC 20170328

Available from: 2017-03-28 Created: 2017-03-28 Last updated: 2017-04-07Bibliographically approved

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CiteExportLink to record
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