Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.

Tuberculosis (TB) remains a major international health problem. Rapid differentiation of Mycobacterium tuberculosis complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound mode...

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Autores principales:	Ngoc A Dang, Sjoukje Kuijper, Elisabetta Walters, Mareli Claassens, Dick van Soolingen, Gabriel Vivo-Truyols, Hans-Gerd Janssen, Arend H J Kolk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c373b53dde745b3915759e638327ef9
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spelling	oai:doaj.org-article:0c373b53dde745b3915759e638327ef92021-11-18T08:50:38ZValidation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.1932-620310.1371/journal.pone.0076263https://doaj.org/article/0c373b53dde745b3915759e638327ef92013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24146846/?tool=EBIhttps://doaj.org/toc/1932-6203Tuberculosis (TB) remains a major international health problem. Rapid differentiation of Mycobacterium tuberculosis complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound model to distinguish between MTB and NTM. It is based on thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry and partial least square discriminant analysis. Here we report the validation of this model with two independent sample sets, one consisting of 39 MTB and 17 NTM isolates from the Netherlands, the other comprising 103 isolates (91 MTB and 12 NTM) from Stellenbosch, Cape Town, South Africa. All the MTB strains in the 56 Dutch samples were correctly identified and the model had a sensitivity of 100% and a specificity of 94%. For the South African samples the model had a sensitivity of 88% and specificity of 100%. Based on our model, we have developed a new decision-tree that allows the differentiation of MTB from NTM with 100% accuracy. Encouraged by these findings we will proceed with the development of a simple, rapid, affordable, high-throughput test to identify MTB directly in sputum.Ngoc A DangSjoukje KuijperElisabetta WaltersMareli ClaassensDick van SoolingenGabriel Vivo-TruyolsHans-Gerd JanssenArend H J KolkPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e76263 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ngoc A Dang Sjoukje Kuijper Elisabetta Walters Mareli Claassens Dick van Soolingen Gabriel Vivo-Truyols Hans-Gerd Janssen Arend H J Kolk Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
description	Tuberculosis (TB) remains a major international health problem. Rapid differentiation of Mycobacterium tuberculosis complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound model to distinguish between MTB and NTM. It is based on thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry and partial least square discriminant analysis. Here we report the validation of this model with two independent sample sets, one consisting of 39 MTB and 17 NTM isolates from the Netherlands, the other comprising 103 isolates (91 MTB and 12 NTM) from Stellenbosch, Cape Town, South Africa. All the MTB strains in the 56 Dutch samples were correctly identified and the model had a sensitivity of 100% and a specificity of 94%. For the South African samples the model had a sensitivity of 88% and specificity of 100%. Based on our model, we have developed a new decision-tree that allows the differentiation of MTB from NTM with 100% accuracy. Encouraged by these findings we will proceed with the development of a simple, rapid, affordable, high-throughput test to identify MTB directly in sputum.
format	article
author	Ngoc A Dang Sjoukje Kuijper Elisabetta Walters Mareli Claassens Dick van Soolingen Gabriel Vivo-Truyols Hans-Gerd Janssen Arend H J Kolk
author_facet	Ngoc A Dang Sjoukje Kuijper Elisabetta Walters Mareli Claassens Dick van Soolingen Gabriel Vivo-Truyols Hans-Gerd Janssen Arend H J Kolk
author_sort	Ngoc A Dang
title	Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
title_short	Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
title_full	Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
title_fullStr	Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
title_full_unstemmed	Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
title_sort	validation of biomarkers for distinguishing mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0c373b53dde745b3915759e638327ef9
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Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics.

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