Predicting the virulence of MRSA from its genome sequence

  1. Ruth C. Massey1,12
  1. 1Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, United Kingdom;
  2. 2College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom;
  3. 3Department of Clinical Microbiology, School of Medicine, Dokuz Eylul University, 35210 Konak, Turkey;
  4. 4Centre for Biomolecular Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom;
  5. 5Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198-5900, USA;
  6. 6Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, United Kingdom;
  7. 7The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, United Kingdom;
  8. 8Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom;
  9. 9Department of Human Genetics, Emory University, Atlanta, Georgia 30322, USA;
  10. 10Department of Rheumatology and Inflammation Research, University of Gothenburg, 405 30 Gothenburg, Sweden
    1. 11 These authors contributed equally to this work.

    Abstract

    Microbial virulence is a complex and often multifactorial phenotype, intricately linked to a pathogen’s evolutionary trajectory. Toxicity, the ability to destroy host cell membranes, and adhesion, the ability to adhere to human tissues, are the major virulence factors of many bacterial pathogens, including Staphylococcus aureus. Here, we assayed the toxicity and adhesiveness of 90 MRSA (methicillin resistant S. aureus) isolates and found that while there was remarkably little variation in adhesion, toxicity varied by over an order of magnitude between isolates, suggesting different evolutionary selection pressures acting on these two traits. We performed a genome-wide association study (GWAS) and identified a large number of loci, as well as a putative network of epistatically interacting loci, that significantly associated with toxicity. Despite this apparent complexity in toxicity regulation, a predictive model based on a set of significant single nucleotide polymorphisms (SNPs) and insertion and deletions events (indels) showed a high degree of accuracy in predicting an isolate’s toxicity solely from the genetic signature at these sites. Our results thus highlight the potential of using sequence data to determine clinically relevant parameters and have further implications for understanding the microbial virulence of this opportunistic pathogen.

    Footnotes

    • 12 Corresponding author

      E-mail r.c.massey{at}bath.ac.uk.

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.165415.113.

      Freely available online through the Genome Research Open Access option.

    • Received August 19, 2013.
    • Accepted February 25, 2014.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0.

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