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

Author

Listed:
  • Erol S. Kavvas

    (University of California)

  • Edward Catoiu

    (University of California)

  • Nathan Mih

    (University of California
    University of California)

  • James T. Yurkovich

    (University of California
    University of California)

  • Yara Seif

    (University of California)

  • Nicholas Dillon

    (University of California
    University of California)

  • David Heckmann

    (University of California)

  • Amitesh Anand

    (University of California)

  • Laurence Yang

    (University of California)

  • Victor Nizet

    (University of California
    University of California)

  • Jonathan M. Monk

    (University of California)

  • Bernhard O. Palsson

    (University of California
    University of California
    University of California)

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.

Suggested Citation

  • Erol S. Kavvas & Edward Catoiu & Nathan Mih & James T. Yurkovich & Yara Seif & Nicholas Dillon & David Heckmann & Amitesh Anand & Laurence Yang & Victor Nizet & Jonathan M. Monk & Bernhard O. Palsson, 2018. "Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06634-y
    DOI: 10.1038/s41467-018-06634-y
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