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Title: Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis
Authors: Marcin J. Skwark
Nicholas J. Croucher
Santeri Puranen
Claire Chewapreecha
Maiju Pesonen
Ying Ying Xu
Paul Turner
Simon R. Harris
Stephen B. Beres
James M. Musser
Julian Parkhill
Stephen D. Bentley
Erik Aurell
Jukka Corander
Vanderbilt University
Imperial College London
Aalto University
University of Cambridge
Mahidol University
Nuffield Department of Clinical Medicine
Wellcome Trust Sanger Institute
Methodist Hospital Houston
Weill Cornell Medical College
The Royal Institute of Technology (KTH)
Institute of Theoretical Physics Chinese Academy of Sciences
Helsingin Yliopisto
Universitetet i Oslo
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology
Issue Date: 1-Feb-2017
Citation: PLoS Genetics. Vol.13, No.2 (2017)
Abstract: © 2017 Skwark et al. 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.
ISSN: 15537404
Appears in Collections:Scopus 2016-2017

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