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Nature DOI:10.1038/nature12337

The Mycobacterium tuberculosis regulatory network and hypoxia.

Publication TypeJournal Article
Year of Publication2013
AuthorsGalagan, JE, Minch, K, Peterson, M, Lyubetskaya, A, Azizi, E, Sweet, L, Gomes, A, Rustad, T, Dolganov, G, Glotova, I, Abeel, T, Mahwinney, C, Kennedy, AD, Allard, R, Brabant, W, Krueger, A, Jaini, S, Honda, B, Yu, W-H, Hickey, MJ, Zucker, J, Garay, C, Weiner, B, Sisk, P, Stolte, C, Winkler, JK, Van de Peer, Y, Iazzetti, P, Camacho, D, Dreyfuss, J, Liu, Y, Dorhoi, A, Mollenkopf, H-J, Drogaris, P, Lamontagne, J, Zhou, Y, Piquenot, J, Park, STae, Raman, S, Kaufmann, SHE, Mohney, RP, Chelsky, D, D Moody, B, Sherman, DR, Schoolnik, GK
JournalNature
Volume499
Issue7457
Pages178-83
Date Published2013 Jul 11
ISSN1476-4687
KeywordsAdaptation, Physiological, Bacterial Proteins, Binding Sites, Chromatin Immunoprecipitation, Gene Expression Profiling, Gene Regulatory Networks, Genomics, Hypoxia, Lipid Metabolism, Metabolic Networks and Pathways, Models, Biological, Mycobacterium tuberculosis, Oxygen, Proteolysis, Reproducibility of Results, RNA, Messenger, Sequence Analysis, DNA, Transcription Factors, Tuberculosis
Abstract

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.

URLhttp://dx.doi.org/10.1038/nature12337
DOI10.1038/nature12337
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/23823726?dopt=Abstract

Alternate JournalNature
PubMed ID23823726
PubMed Central IDPMC4087036
Grant ListT32 AI007509 / AI / NIAID NIH HHS / United States
R01 AI071155 / AI / NIAID NIH HHS / United States
U19 AI 076217 / AI / NIAID NIH HHS / United States
HHSN272200800059C / / PHS HHS / United States
U19 AI076217 / AI / NIAID NIH HHS / United States
R01 AI 071155 / AI / NIAID NIH HHS / United States
HHSN272200800059C / AI / NIAID NIH HHS / United States