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

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 () and Bernhard O. Palsson ()
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Erol S. Kavvas: University of California
Edward Catoiu: University of California
Nathan Mih: University of California
James T. Yurkovich: University of California
Yara Seif: University of California
Nicholas Dillon: University of California
David Heckmann: University of California
Amitesh Anand: University of California
Laurence Yang: University of California
Victor Nizet: University of California
Jonathan M. Monk: University of California
Bernhard O. Palsson: University of California

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-06634-y

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