Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Kavvas, Erol; Catoiu, Edward; Mih, Nathan; Yurkovich, James; Seif, Yara; Dillon, Nicholas; Heckmann, David; Anand, Amitesh; Yang, Laurence; Nizet, Victor; Monk, Jonathan; Palsson, Bernhard
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 9
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06634-y
• Languages: English
• ISSNs: 2041-1723

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 thatM. 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. Mycobacterium tuberculosis exhibits complex evolution of antimicrobial resistance (AMR). Here, the authors perform machine learning and structural analysis to identify signatures of AMR evolution to 13 antibiotics.