Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Journal: Nature communications
Published Date: Oct 17, 2018

Abstract

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.

Authors

  • Erol S Kavvas
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Edward Catoiu
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Nathan Mih
    Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.
  • James T Yurkovich
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Yara Seif
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Nicholas Dillon
    Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
  • David Heckmann
    Heinrich-Heine-Universität, Institute for Computer Science, 40225 Düsseldorf, Germany. Electronic address: david.heckmann@uni-duesseldorf.de.
  • Amitesh Anand
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Laurence Yang
    Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
  • Victor Nizet
    Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
  • Jonathan M Monk
    Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.
  • Bernhard O Palsson
    Department of Bioengineering, University of California, San Diego, CA, USA.

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