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Insights into the processes that drive the evolution of drug resistance in Mycobacterium tuberculosis.
Nguyen, Quang Huy; Contamin, Lucie; Nguyen, Thi Van Anh; Bañuls, Anne-Laure.
Afiliação
  • Nguyen QH; Department of Pharmacological, Medical and Agronomical Biotechnology University of Science and Technology of Hanoi Vietnam Academy of Science and Technology (VAST) Hanoi Vietnam.
  • Contamin L; Institute of Research for Development UMR MIVEGEC (CNRS-IRD-University of Montpellier) Montpellier France.
  • Nguyen TVA; LMI Drug Resistance in South East Asia (LMI DRISA) University of Science and Technology of Hanoi Vietnam Academy of Science and Technology (VAST) Hanoi Vietnam.
  • Bañuls AL; Institute of Research for Development UMR MIVEGEC (CNRS-IRD-University of Montpellier) Montpellier France.
Evol Appl ; 11(9): 1498-1511, 2018 Oct.
Article em En | MEDLINE | ID: mdl-30344622
ABSTRACT
At present, the successful transmission of drug-resistant Mycobacterium tuberculosis, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, in human populations, threatens tuberculosis control worldwide. Differently from many other bacteria, M. tuberculosis drug resistance is acquired mainly through mutations in specific drug resistance-associated genes. The panel of mutations is highly diverse, but depends on the affected gene and M. tuberculosis genetic background. The variety of genetic profiles observed in drug-resistant clinical isolates underlines different evolutionary trajectories towards multiple drug resistance, although some mutation patterns are prominent. This review discusses the intrinsic processes that may influence drug resistance evolution in M. tuberculosis, such as mutation rate, drug resistance-associated mutations, fitness cost, compensatory mutations and epistasis. This knowledge should help to better predict the risk of emergence of highly resistant M. tuberculosis strains and to develop new tools and strategies to limit the development and spread of MDR and XDR strains.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article