Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high...

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Main Authors:	Dutta, Noton K., Bandyopadhyay, Nirmalya, Veeramani, Balaji, Lamichhane, Gyanu, Karakousis, Petros C., Bader, Joel S.
Format:	Online
Language:	English
Published:	American Society of Microbiology 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944818/

id	pubmed-3944818
recordtype	oai_dc
spelling	pubmed-39448182014-03-12 Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs Dutta, Noton K. Bandyopadhyay, Nirmalya Veeramani, Balaji Lamichhane, Gyanu Karakousis, Petros C. Bader, Joel S. Research Article Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes. American Society of Microbiology 2014-02-18 /pmc/articles/PMC3944818/ /pubmed/24549847 http://dx.doi.org/10.1128/mBio.01066-13 Text en Copyright © 2014 Dutta et al. http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license (http://creativecommons.org/licenses/by-nc-sa/3.0/) , which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Dutta, Noton K. Bandyopadhyay, Nirmalya Veeramani, Balaji Lamichhane, Gyanu Karakousis, Petros C. Bader, Joel S.
spellingShingle	Dutta, Noton K. Bandyopadhyay, Nirmalya Veeramani, Balaji Lamichhane, Gyanu Karakousis, Petros C. Bader, Joel S. Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
author_facet	Dutta, Noton K. Bandyopadhyay, Nirmalya Veeramani, Balaji Lamichhane, Gyanu Karakousis, Petros C. Bader, Joel S.
author_sort	Dutta, Noton K.
title	Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
title_short	Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
title_full	Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
title_fullStr	Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
title_full_unstemmed	Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs
title_sort	systems biology-based identification of mycobacterium tuberculosis persistence genes in mouse lungs
description	Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.
publisher	American Society of Microbiology
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944818/
_version_	1612065166894039040

Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

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