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Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis

BACKGROUND: Mycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive exp...

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Autores principales: Garay, Christopher D., Dreyfuss, Jonathan M., Galagan, James E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574064/
https://www.ncbi.nlm.nih.gov/pubmed/26377923
http://dx.doi.org/10.1186/s12918-015-0206-7
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author Garay, Christopher D.
Dreyfuss, Jonathan M.
Galagan, James E.
author_facet Garay, Christopher D.
Dreyfuss, Jonathan M.
Galagan, James E.
author_sort Garay, Christopher D.
collection PubMed
description BACKGROUND: Mycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive experimental work could significantly accelerate research. RESULTS: We have developed E-Flux-MFC, an enhancement of our original E-Flux method that enables the prediction of changes in the production of external and internal metabolites corresponding to gene expression measurements. We have used this method to simulate the changes in the metabolic state of Mycobacterium tuberculosis (MTB). We have validated the accuracy of E-Flux-MFC for predicting changes in lipids and metabolites during a hypoxia time course using previously published metabolomics and transcriptomics data. We have further validated the accuracy of the method for predicting changes in MTB lipids following the deletion and induction of two well-studied transcription factors (TFs). We have applied the method to predict the metabolic impact of the induction of each of the approximately 180 MTB TFs using a previously generated and publically available expression data set. CONCLUSIONS: E-flux-MFC can be used to study global changes in MTB metabolites from gene expression data associated with environmental and genetic perturbations. The application of this method to a data set of MTB TF perturbations provides a resource for studying the large number of TFs whose functions remain unknown. Most TFs impact metabolites indirectly through the propagation of gene expression changes through the regulatory network rather than through their direct regulons. E-Flux-MFC is also applicable to any organism for which accurate metabolic models are available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0206-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-45740642015-09-19 Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis Garay, Christopher D. Dreyfuss, Jonathan M. Galagan, James E. BMC Syst Biol Research Article BACKGROUND: Mycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive experimental work could significantly accelerate research. RESULTS: We have developed E-Flux-MFC, an enhancement of our original E-Flux method that enables the prediction of changes in the production of external and internal metabolites corresponding to gene expression measurements. We have used this method to simulate the changes in the metabolic state of Mycobacterium tuberculosis (MTB). We have validated the accuracy of E-Flux-MFC for predicting changes in lipids and metabolites during a hypoxia time course using previously published metabolomics and transcriptomics data. We have further validated the accuracy of the method for predicting changes in MTB lipids following the deletion and induction of two well-studied transcription factors (TFs). We have applied the method to predict the metabolic impact of the induction of each of the approximately 180 MTB TFs using a previously generated and publically available expression data set. CONCLUSIONS: E-flux-MFC can be used to study global changes in MTB metabolites from gene expression data associated with environmental and genetic perturbations. The application of this method to a data set of MTB TF perturbations provides a resource for studying the large number of TFs whose functions remain unknown. Most TFs impact metabolites indirectly through the propagation of gene expression changes through the regulatory network rather than through their direct regulons. E-Flux-MFC is also applicable to any organism for which accurate metabolic models are available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0206-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-16 /pmc/articles/PMC4574064/ /pubmed/26377923 http://dx.doi.org/10.1186/s12918-015-0206-7 Text en © Garay et al. 2015 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
Garay, Christopher D.
Dreyfuss, Jonathan M.
Galagan, James E.
Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title_full Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title_fullStr Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title_full_unstemmed Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title_short Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis
title_sort metabolic modeling predicts metabolite changes in mycobacterium tuberculosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574064/
https://www.ncbi.nlm.nih.gov/pubmed/26377923
http://dx.doi.org/10.1186/s12918-015-0206-7
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