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Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study
BACKGROUND: In many countries, regular monitoring of the emergence of resistance to anti-tuberculosis drugs is hampered by the limitations of phenotypic testing for drug susceptibility. We therefore evaluated the use of genetic sequencing for surveillance of drug resistance in tuberculosis. METHODS:...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science ;, The Lancet Pub. Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968368/ https://www.ncbi.nlm.nih.gov/pubmed/29574065 http://dx.doi.org/10.1016/S1473-3099(18)30073-2 |
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author | Zignol, Matteo Cabibbe, Andrea Maurizio Dean, Anna S Glaziou, Philippe Alikhanova, Natavan Ama, Cecilia Andres, Sönke Barbova, Anna Borbe-Reyes, Angeli Chin, Daniel P Cirillo, Daniela Maria Colvin, Charlotte Dadu, Andrei Dreyer, Andries Driesen, Michèle Gilpin, Christopher Hasan, Rumina Hasan, Zahra Hoffner, Sven Hussain, Alamdar Ismail, Nazir Kamal, S M Mostofa Khanzada, Faisal Masood Kimerling, Michael Kohl, Thomas Andreas Mansjö, Mikael Miotto, Paolo Mukadi, Ya Diul Mvusi, Lindiwe Niemann, Stefan Omar, Shaheed V Rigouts, Leen Schito, Marco Sela, Ivita Seyfaddinova, Mehriban Skenders, Girts Skrahina, Alena Tahseen, Sabira Wells, William A Zhurilo, Alexander Weyer, Karin Floyd, Katherine Raviglione, Mario C |
author_facet | Zignol, Matteo Cabibbe, Andrea Maurizio Dean, Anna S Glaziou, Philippe Alikhanova, Natavan Ama, Cecilia Andres, Sönke Barbova, Anna Borbe-Reyes, Angeli Chin, Daniel P Cirillo, Daniela Maria Colvin, Charlotte Dadu, Andrei Dreyer, Andries Driesen, Michèle Gilpin, Christopher Hasan, Rumina Hasan, Zahra Hoffner, Sven Hussain, Alamdar Ismail, Nazir Kamal, S M Mostofa Khanzada, Faisal Masood Kimerling, Michael Kohl, Thomas Andreas Mansjö, Mikael Miotto, Paolo Mukadi, Ya Diul Mvusi, Lindiwe Niemann, Stefan Omar, Shaheed V Rigouts, Leen Schito, Marco Sela, Ivita Seyfaddinova, Mehriban Skenders, Girts Skrahina, Alena Tahseen, Sabira Wells, William A Zhurilo, Alexander Weyer, Karin Floyd, Katherine Raviglione, Mario C |
author_sort | Zignol, Matteo |
collection | PubMed |
description | BACKGROUND: In many countries, regular monitoring of the emergence of resistance to anti-tuberculosis drugs is hampered by the limitations of phenotypic testing for drug susceptibility. We therefore evaluated the use of genetic sequencing for surveillance of drug resistance in tuberculosis. METHODS: Population-level surveys were done in hospitals and clinics in seven countries (Azerbaijan, Bangladesh, Belarus, Pakistan, Philippines, South Africa, and Ukraine) to evaluate the use of genetic sequencing to estimate the resistance of Mycobacterium tuberculosis isolates to rifampicin, isoniazid, ofloxacin, moxifloxacin, pyrazinamide, kanamycin, amikacin, and capreomycin. For each drug, we assessed the accuracy of genetic sequencing by a comparison of the adjusted prevalence of resistance, measured by genetic sequencing, with the true prevalence of resistance, determined by phenotypic testing. FINDINGS: Isolates were taken from 7094 patients with tuberculosis who were enrolled in the study between November, 2009, and May, 2014. In all tuberculosis cases, the overall pooled sensitivity values for predicting resistance by genetic sequencing were 91% (95% CI 87–94) for rpoB (rifampicin resistance), 86% (74–93) for katG, inhA, and fabG promoter combined (isoniazid resistance), 54% (39–68) for pncA (pyrazinamide resistance), 85% (77–91) for gyrA and gyrB combined (ofloxacin resistance), and 88% (81–92) for gyrA and gyrB combined (moxifloxacin resistance). For nearly all drugs and in most settings, there was a large overlap in the estimated prevalence of drug resistance by genetic sequencing and the estimated prevalence by phenotypic testing. INTERPRETATION: Genetic sequencing can be a valuable tool for surveillance of drug resistance, providing new opportunities to monitor drug resistance in tuberculosis in resource-poor countries. Before its widespread adoption for surveillance purposes, there is a need to standardise DNA extraction methods, recording and reporting nomenclature, and data interpretation. FUNDING: Bill & Melinda Gates Foundation, United States Agency for International Development, Global Alliance for Tuberculosis Drug Development. |
format | Online Article Text |
id | pubmed-5968368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier Science ;, The Lancet Pub. Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-59683682018-06-01 Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study Zignol, Matteo Cabibbe, Andrea Maurizio Dean, Anna S Glaziou, Philippe Alikhanova, Natavan Ama, Cecilia Andres, Sönke Barbova, Anna Borbe-Reyes, Angeli Chin, Daniel P Cirillo, Daniela Maria Colvin, Charlotte Dadu, Andrei Dreyer, Andries Driesen, Michèle Gilpin, Christopher Hasan, Rumina Hasan, Zahra Hoffner, Sven Hussain, Alamdar Ismail, Nazir Kamal, S M Mostofa Khanzada, Faisal Masood Kimerling, Michael Kohl, Thomas Andreas Mansjö, Mikael Miotto, Paolo Mukadi, Ya Diul Mvusi, Lindiwe Niemann, Stefan Omar, Shaheed V Rigouts, Leen Schito, Marco Sela, Ivita Seyfaddinova, Mehriban Skenders, Girts Skrahina, Alena Tahseen, Sabira Wells, William A Zhurilo, Alexander Weyer, Karin Floyd, Katherine Raviglione, Mario C Lancet Infect Dis Article BACKGROUND: In many countries, regular monitoring of the emergence of resistance to anti-tuberculosis drugs is hampered by the limitations of phenotypic testing for drug susceptibility. We therefore evaluated the use of genetic sequencing for surveillance of drug resistance in tuberculosis. METHODS: Population-level surveys were done in hospitals and clinics in seven countries (Azerbaijan, Bangladesh, Belarus, Pakistan, Philippines, South Africa, and Ukraine) to evaluate the use of genetic sequencing to estimate the resistance of Mycobacterium tuberculosis isolates to rifampicin, isoniazid, ofloxacin, moxifloxacin, pyrazinamide, kanamycin, amikacin, and capreomycin. For each drug, we assessed the accuracy of genetic sequencing by a comparison of the adjusted prevalence of resistance, measured by genetic sequencing, with the true prevalence of resistance, determined by phenotypic testing. FINDINGS: Isolates were taken from 7094 patients with tuberculosis who were enrolled in the study between November, 2009, and May, 2014. In all tuberculosis cases, the overall pooled sensitivity values for predicting resistance by genetic sequencing were 91% (95% CI 87–94) for rpoB (rifampicin resistance), 86% (74–93) for katG, inhA, and fabG promoter combined (isoniazid resistance), 54% (39–68) for pncA (pyrazinamide resistance), 85% (77–91) for gyrA and gyrB combined (ofloxacin resistance), and 88% (81–92) for gyrA and gyrB combined (moxifloxacin resistance). For nearly all drugs and in most settings, there was a large overlap in the estimated prevalence of drug resistance by genetic sequencing and the estimated prevalence by phenotypic testing. INTERPRETATION: Genetic sequencing can be a valuable tool for surveillance of drug resistance, providing new opportunities to monitor drug resistance in tuberculosis in resource-poor countries. Before its widespread adoption for surveillance purposes, there is a need to standardise DNA extraction methods, recording and reporting nomenclature, and data interpretation. FUNDING: Bill & Melinda Gates Foundation, United States Agency for International Development, Global Alliance for Tuberculosis Drug Development. Elsevier Science ;, The Lancet Pub. Group 2018-06 /pmc/articles/PMC5968368/ /pubmed/29574065 http://dx.doi.org/10.1016/S1473-3099(18)30073-2 Text en © 2018 World Health Organization http://creativecommons.org/licenses/by/3.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Zignol, Matteo Cabibbe, Andrea Maurizio Dean, Anna S Glaziou, Philippe Alikhanova, Natavan Ama, Cecilia Andres, Sönke Barbova, Anna Borbe-Reyes, Angeli Chin, Daniel P Cirillo, Daniela Maria Colvin, Charlotte Dadu, Andrei Dreyer, Andries Driesen, Michèle Gilpin, Christopher Hasan, Rumina Hasan, Zahra Hoffner, Sven Hussain, Alamdar Ismail, Nazir Kamal, S M Mostofa Khanzada, Faisal Masood Kimerling, Michael Kohl, Thomas Andreas Mansjö, Mikael Miotto, Paolo Mukadi, Ya Diul Mvusi, Lindiwe Niemann, Stefan Omar, Shaheed V Rigouts, Leen Schito, Marco Sela, Ivita Seyfaddinova, Mehriban Skenders, Girts Skrahina, Alena Tahseen, Sabira Wells, William A Zhurilo, Alexander Weyer, Karin Floyd, Katherine Raviglione, Mario C Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title | Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title_full | Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title_fullStr | Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title_full_unstemmed | Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title_short | Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
title_sort | genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968368/ https://www.ncbi.nlm.nih.gov/pubmed/29574065 http://dx.doi.org/10.1016/S1473-3099(18)30073-2 |
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