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QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data
BACKGROUND: Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately esti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986090/ https://www.ncbi.nlm.nih.gov/pubmed/31992201 http://dx.doi.org/10.1186/s12864-020-6486-3 |
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author | Anyansi, Christine Keo, Arlin Walker, Bruce J. Straub, Timothy J. Manson, Abigail L. Earl, Ashlee M. Abeel, Thomas |
author_facet | Anyansi, Christine Keo, Arlin Walker, Bruce J. Straub, Timothy J. Manson, Abigail L. Earl, Ashlee M. Abeel, Thomas |
author_sort | Anyansi, Christine |
collection | PubMed |
description | BACKGROUND: Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis. Here we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, and a list of drugs for which resistance-conferring mutations (or heteroresistance) have been predicted within the sample. RESULTS: We show that QuantTB has a high degree of resolution and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1× coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of 50 paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach. CONCLUSION: QuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients – even in low-coverage (1×) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples. |
format | Online Article Text |
id | pubmed-6986090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69860902020-01-30 QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data Anyansi, Christine Keo, Arlin Walker, Bruce J. Straub, Timothy J. Manson, Abigail L. Earl, Ashlee M. Abeel, Thomas BMC Genomics Research Article BACKGROUND: Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis. Here we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, and a list of drugs for which resistance-conferring mutations (or heteroresistance) have been predicted within the sample. RESULTS: We show that QuantTB has a high degree of resolution and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1× coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of 50 paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach. CONCLUSION: QuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients – even in low-coverage (1×) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples. BioMed Central 2020-01-28 /pmc/articles/PMC6986090/ /pubmed/31992201 http://dx.doi.org/10.1186/s12864-020-6486-3 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Anyansi, Christine Keo, Arlin Walker, Bruce J. Straub, Timothy J. Manson, Abigail L. Earl, Ashlee M. Abeel, Thomas QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title | QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title_full | QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title_fullStr | QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title_full_unstemmed | QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title_short | QuantTB – a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data |
title_sort | quanttb – a method to classify mixed mycobacterium tuberculosis infections within whole genome sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986090/ https://www.ncbi.nlm.nih.gov/pubmed/31992201 http://dx.doi.org/10.1186/s12864-020-6486-3 |
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