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

Research output: Contribution to journalArticle


  • Marcin J. Skwark
  • Nicholas J. Croucher
  • Santeri Puranen

  • Claire Chewapreecha
  • Maiju Pesonen

  • Yingying Xu
  • Paul Turner
  • Simon R. Harris
  • Stephen B. Beres
  • James M. Musser
  • Julian Parkhill
  • Stephen D. Bentley
  • Erik Aurell

  • Jukka Corander

Research units

  • Vanderbilt University
  • Imperial College London
  • University of Cambridge
  • University of Oxford
  • Wellcome Trust Sanger Institute
  • Houston Methodist Hospital
  • Cornell University
  • KTH Royal Institute of Technology
  • Chinese Academy of Sciences
  • University of Helsinki
  • University of Oslo


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.


Original languageEnglish
Article numbere1006508
JournalPLoS Genetics
Issue number2
Publication statusPublished - 1 Feb 2017
MoE publication typeA1 Journal article-refereed

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