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Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance
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 computa...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193043/ https://www.ncbi.nlm.nih.gov/pubmed/30333483 http://dx.doi.org/10.1038/s41467-018-06634-y |
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author | Kavvas, Erol S. Catoiu, Edward Mih, Nathan Yurkovich, James T. Seif, Yara Dillon, Nicholas Heckmann, David Anand, Amitesh Yang, Laurence Nizet, Victor Monk, Jonathan M. Palsson, Bernhard O. |
author_facet | Kavvas, Erol S. Catoiu, Edward Mih, Nathan Yurkovich, James T. Seif, Yara Dillon, Nicholas Heckmann, David Anand, Amitesh Yang, Laurence Nizet, Victor Monk, Jonathan M. Palsson, Bernhard O. |
author_sort | Kavvas, Erol S. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6193043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61930432018-10-19 Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance Kavvas, Erol S. Catoiu, Edward Mih, Nathan Yurkovich, James T. Seif, Yara Dillon, Nicholas Heckmann, David Anand, Amitesh Yang, Laurence Nizet, Victor Monk, Jonathan M. Palsson, Bernhard O. Nat Commun Article 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. Nature Publishing Group UK 2018-10-17 /pmc/articles/PMC6193043/ /pubmed/30333483 http://dx.doi.org/10.1038/s41467-018-06634-y Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kavvas, Erol S. Catoiu, Edward Mih, Nathan Yurkovich, James T. Seif, Yara Dillon, Nicholas Heckmann, David Anand, Amitesh Yang, Laurence Nizet, Victor Monk, Jonathan M. Palsson, Bernhard O. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title | Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title_full | Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title_fullStr | Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title_full_unstemmed | Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title_short | Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
title_sort | machine learning and structural analysis of mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193043/ https://www.ncbi.nlm.nih.gov/pubmed/30333483 http://dx.doi.org/10.1038/s41467-018-06634-y |
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