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The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome

Drug resistance is a known risk factor for poor tuberculosis (TB) treatment outcomes, but the contribution of other bacterial factors to poor outcomes in drug-susceptible TB is less well understood. Here, we generate a population-based dataset of drug-susceptible Mycobacterium tuberculosis (MTB) iso...

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Autores principales: Chen, Yiwang, Jiang, Qi, Peierdun, Mijiti, Takiff, Howard E, Gao, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188106/
https://www.ncbi.nlm.nih.gov/pubmed/37133242
http://dx.doi.org/10.7554/eLife.84815
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author Chen, Yiwang
Jiang, Qi
Peierdun, Mijiti
Takiff, Howard E
Gao, Qian
author_facet Chen, Yiwang
Jiang, Qi
Peierdun, Mijiti
Takiff, Howard E
Gao, Qian
author_sort Chen, Yiwang
collection PubMed
description Drug resistance is a known risk factor for poor tuberculosis (TB) treatment outcomes, but the contribution of other bacterial factors to poor outcomes in drug-susceptible TB is less well understood. Here, we generate a population-based dataset of drug-susceptible Mycobacterium tuberculosis (MTB) isolates from China to identify factors associated with poor treatment outcomes. We analyzed whole-genome sequencing (WGS) data of MTB strains from 3196 patients, including 3105 patients with good and 91 patients with poor treatment outcomes, and linked genomes to patient epidemiological data. A genome-wide association study (GWAS) was performed to identify bacterial genomic variants associated with poor outcomes. Risk factors identified by logistic regression analysis were used in clinical models to predict treatment outcomes. GWAS identified fourteen MTB fixed mutations associated with poor treatment outcomes, but only 24.2% (22/91) of strains from patients with poor outcomes carried at least one of these mutations. Isolates from patients with poor outcomes showed a higher ratio of reactive oxygen species (ROS)-associated mutations compared to isolates from patients with good outcomes (26.3% vs 22.9%, t-test, p=0.027). Patient age, sex, and duration of diagnostic delay were also independently associated with poor outcomes. Bacterial factors alone had poor power to predict poor outcomes with an AUC of 0.58. The AUC with host factors alone was 0.70, but increased significantly to 0.74 (DeLong’s test, p=0.01) when bacterial factors were also included. In conclusion, although we identified MTB genomic mutations that are significantly associated with poor treatment outcomes in drug-susceptible TB cases, their effects appear to be limited.
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spelling pubmed-101881062023-05-17 The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome Chen, Yiwang Jiang, Qi Peierdun, Mijiti Takiff, Howard E Gao, Qian eLife Genetics and Genomics Drug resistance is a known risk factor for poor tuberculosis (TB) treatment outcomes, but the contribution of other bacterial factors to poor outcomes in drug-susceptible TB is less well understood. Here, we generate a population-based dataset of drug-susceptible Mycobacterium tuberculosis (MTB) isolates from China to identify factors associated with poor treatment outcomes. We analyzed whole-genome sequencing (WGS) data of MTB strains from 3196 patients, including 3105 patients with good and 91 patients with poor treatment outcomes, and linked genomes to patient epidemiological data. A genome-wide association study (GWAS) was performed to identify bacterial genomic variants associated with poor outcomes. Risk factors identified by logistic regression analysis were used in clinical models to predict treatment outcomes. GWAS identified fourteen MTB fixed mutations associated with poor treatment outcomes, but only 24.2% (22/91) of strains from patients with poor outcomes carried at least one of these mutations. Isolates from patients with poor outcomes showed a higher ratio of reactive oxygen species (ROS)-associated mutations compared to isolates from patients with good outcomes (26.3% vs 22.9%, t-test, p=0.027). Patient age, sex, and duration of diagnostic delay were also independently associated with poor outcomes. Bacterial factors alone had poor power to predict poor outcomes with an AUC of 0.58. The AUC with host factors alone was 0.70, but increased significantly to 0.74 (DeLong’s test, p=0.01) when bacterial factors were also included. In conclusion, although we identified MTB genomic mutations that are significantly associated with poor treatment outcomes in drug-susceptible TB cases, their effects appear to be limited. eLife Sciences Publications, Ltd 2023-05-03 /pmc/articles/PMC10188106/ /pubmed/37133242 http://dx.doi.org/10.7554/eLife.84815 Text en © 2023, Chen et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Genetics and Genomics
Chen, Yiwang
Jiang, Qi
Peierdun, Mijiti
Takiff, Howard E
Gao, Qian
The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title_full The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title_fullStr The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title_full_unstemmed The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title_short The mutational signatures of poor treatment outcomes on the drug-susceptible Mycobacterium tuberculosis genome
title_sort mutational signatures of poor treatment outcomes on the drug-susceptible mycobacterium tuberculosis genome
topic Genetics and Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188106/
https://www.ncbi.nlm.nih.gov/pubmed/37133242
http://dx.doi.org/10.7554/eLife.84815
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