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



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.
Date made available22 Feb 2017
PublisherDryad Digital Repository

Contact person

Associated persons

  • Marcin Skwark Vanderbilt University (Creator)
  • Nicholas J Croucher (Contributor)
  • Santeri Puranen (Contributor)
  • Claire Chewapreecha (Contributor)
  • Maiju Pesonen (Contributor)
  • Yingying Xu (Contributor)
  • Paul Turner (Contributor)
  • Simon R Harris (Contributor)
  • Stephen B. Beres (Contributor)
  • James M. Musser (Contributor)
  • Julian Parkhill (Contributor)
  • Stephen D Bentley (Contributor)
  • Erik Aurell (Contributor)
  • Jukka Corander University of Helsinki, University of Oslo, University of Cambridge, Wellcome Trust Sanger Institute (Contributor)

Associated research units

ID: 21293366