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Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa
Mycobacterium tuberculosis (MTB) is a pathogenic bacterium accountable for 10.6 million new infections with tuberculosis (TB) in 2021. The fact that the genetic sequences of M. tuberculosis vary widely provides a basis for understanding how this bacterium causes disease, how the immune system respon...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102689/ https://www.ncbi.nlm.nih.gov/pubmed/37059737 http://dx.doi.org/10.1038/s41597-023-02112-3 |
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author | Laamarti, Meriem El Fathi Lalaoui, Yasmine Elfermi, Rachid Daoud, Rachid El Allali, Achraf |
author_facet | Laamarti, Meriem El Fathi Lalaoui, Yasmine Elfermi, Rachid Daoud, Rachid El Allali, Achraf |
author_sort | Laamarti, Meriem |
collection | PubMed |
description | Mycobacterium tuberculosis (MTB) is a pathogenic bacterium accountable for 10.6 million new infections with tuberculosis (TB) in 2021. The fact that the genetic sequences of M. tuberculosis vary widely provides a basis for understanding how this bacterium causes disease, how the immune system responds to it, how it has evolved over time, and how it is distributed geographically. However, despite extensive research efforts, the evolution and transmission of MTB in Africa remain poorly understood. In this study, we used 17,641 strains from 26 countries to create the first curated African Mycobacterium tuberculosis (MTB) classification and resistance dataset, containing 13,753 strains. We identified 157 mutations in 12 genes associated with resistance and additional new mutations potentially associated with resistance. The resistance profile was used to classify strains. We also performed a phylogenetic classification of each isolate and prepared the data in a format that can be used for phylogenetic and comparative analysis of tuberculosis worldwide. These genomic data will extend current information for comparative genomic studies to understand the mechanisms and evolution of MTB drug resistance. |
format | Online Article Text |
id | pubmed-10102689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101026892023-04-16 Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa Laamarti, Meriem El Fathi Lalaoui, Yasmine Elfermi, Rachid Daoud, Rachid El Allali, Achraf Sci Data Data Descriptor Mycobacterium tuberculosis (MTB) is a pathogenic bacterium accountable for 10.6 million new infections with tuberculosis (TB) in 2021. The fact that the genetic sequences of M. tuberculosis vary widely provides a basis for understanding how this bacterium causes disease, how the immune system responds to it, how it has evolved over time, and how it is distributed geographically. However, despite extensive research efforts, the evolution and transmission of MTB in Africa remain poorly understood. In this study, we used 17,641 strains from 26 countries to create the first curated African Mycobacterium tuberculosis (MTB) classification and resistance dataset, containing 13,753 strains. We identified 157 mutations in 12 genes associated with resistance and additional new mutations potentially associated with resistance. The resistance profile was used to classify strains. We also performed a phylogenetic classification of each isolate and prepared the data in a format that can be used for phylogenetic and comparative analysis of tuberculosis worldwide. These genomic data will extend current information for comparative genomic studies to understand the mechanisms and evolution of MTB drug resistance. Nature Publishing Group UK 2023-04-14 /pmc/articles/PMC10102689/ /pubmed/37059737 http://dx.doi.org/10.1038/s41597-023-02112-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Laamarti, Meriem El Fathi Lalaoui, Yasmine Elfermi, Rachid Daoud, Rachid El Allali, Achraf Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title | Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title_full | Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title_fullStr | Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title_full_unstemmed | Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title_short | Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa |
title_sort | afro-tb dataset as a large scale genomic data of mycobacterium tuberuclosis in africa |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102689/ https://www.ncbi.nlm.nih.gov/pubmed/37059737 http://dx.doi.org/10.1038/s41597-023-02112-3 |
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