The Mycobacterium tuberculosis regulatory network and hypoxia.
|Title||The Mycobacterium tuberculosis regulatory network and hypoxia.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Galagan 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|
|Date Published||2013 Jul 11|
|Keywords||Adaptation, Physiological, Anoxia, Bacterial Proteins, Binding Sites, Chromatin Immunoprecipitation, Gene Expression Profiling, Gene Regulatory Networks, Genomics, Lipid Metabolism, Metabolic Networks and Pathways, Models, Biological, Mycobacterium tuberculosis, Oxygen, Proteolysis, Reproducibility of Results, RNA, Messenger, Sequence Analysis, DNA, Transcription Factors, Tuberculosis|
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.
|Grant List||HHSN272200800059C / AI / NIAID NIH HHS / United States |
HHSN272200800059C / / PHS HHS / United States
R01 AI 071155 / AI / NIAID NIH HHS / United States
T32 AI007509 / AI / NIAID NIH HHS / United States
U19 AI 076217 / AI / NIAID NIH HHS / United States