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The Mycobacterium tuberculosis regulatory network and hypoxia

Nature volume 499, pages 178–183 (2013)Cite this article

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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.

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Figure 1: ChIP-Seq binding shows high sensitivity, reproducibility and sequence specificity.
Figure 2: TF regulatory interaction subnetwork linking hypoxia, lipid metabolism and protein degradation.
Figure 3: Predicting gene expression during hypoxia and re-aeration.
Figure 4: Lipid changes during hypoxia and re-aeration.

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Accessions

Gene Expression Omnibus

Data deposits

Expression data were deposited at GEO (accession number GSE43466). The proteomics data have been deposited in the ProteomeXchange with the identifier PXD000045.

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Acknowledgements

This project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases National Institute of Health, Department of Health and Human Services, under contract no. HHSN272200800059C and U19 AI 076217, R01 AI 071155, the Paul G. Allen Family Foundation (to DRS), the National Science Foundation Pre-doctoral Fellowship Program (to K.M.), and the Burroughs Wellcome Fund Award for Translational Research. We acknowledge D. C. Young for lipidomics mass spectrometry services and advice. We would also like to thank L. Carvalho for his advice on the statistical analysis of the gene expression modelling. We are grateful for the administrative assistance of S. Shiviah and S. Tucker and for the support and advice of V. Di Francesco, K. Lacourciere, P. Dudley and M. Polanski.

Author information

Author notes

  1. Kyle Minch, Matthew Peterson, Anna Lyubetskaya, Elham Azizi, Linsday Sweet and Antonio Gomes: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Engineering, Boston University, Boston, 02215, Massachusetts, USA

    James E. Galagan, Matthew Peterson, Chris Mahwinney, Andrew Krueger, Suma Jaini, Brent Honda, Wen-Han Yu, Christopher Garay, Paul Iazzetti, Diogo Camacho & Jonathan Dreyfuss

  2. Department of Microbiology, Boston University, Boston, 02215, Massachusetts, USA

    James E. Galagan, Sang Tae Park & Sahadevan Raman

  3. Bioinformatics Program, Boston University, Boston, 02215, Massachusetts, USA

    James E. Galagan, Anna Lyubetskaya, Elham Azizi, Antonio Gomes & Irina Glotova

  4. The Eli and Edythe L. Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA

    James E. Galagan, Thomas Abeel, Jeremy Zucker, Brian Weiner, Peter Sisk & Christian Stolte

  5. Seattle Biomedical Research Institute, Seattle, 98109, Washington, USA

    Kyle Minch, Tige Rustad, William Brabant, Mark J. Hickey, Jessica K. Winkler & David R. Sherman

  6. Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, Massachusetts, USA

    Linsday Sweet & D. Branch Moody

  7. Departments of Medicine and of Microbiology and Immunology, Stanford Medical School, Stanford, 94305, California, USA

    Gregory Dolganov, Yang Liu & Gary K. Schoolnik

  8. Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Gent, Belgium,

    Thomas Abeel & Yves Van de Peer

  9. Metabolon Inc., Durham, 27713, North Carolina, USA

    Adam D. Kennedy & Robert P. Mohney

  10. Caprion Proteomics, Inc., Montreal, Quebec H4S 2C8, Canada ,

    René Allard, Paul Drogaris, Julie Lamontagne, Yiyong Zhou, Julie Piquenot & Daniel Chelsky

  11. Department of Immunology, Max Planck Institute for Infection Biology, 10117 Berlin, Germany,

    Anca Dorhoi & Stefan H. E. Kaufmann

  12. Microarray Core Facility, Max Planck Institute for Infection Biology, 10117 Berlin, Germany ,

    Hans-Joachim Mollenkopf

  13. Department of Global Health, Interdisciplinary Program of Pathobiology, University of Washington, Seattle, 98195, Washington, USA

    David R. Sherman

  14. Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford Medical School, Stanford, 94305, California, USA

    Gary K. Schoolnik

Authors
  1. James E. Galagan
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  2. Kyle Minch
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Contributions

J.E.G. led the project with G.K.S., oversaw ChIP-Seq, wrote the paper and produced figures, discussed results and implications, oversaw data integration, and performed analyses. K.M. co-designed and performed ChIP and transcriptomic experiments, discussed results and implications, and commented on the manuscript. M.P. developed the analysis pipeline for ChIP-Seq data, performed all ChIP-Seq data analysis, and contributed multiple figures and text. A.L. performed all analysis of the integration of TF induction transcriptomics with ChIP-Seq data, contributed to analysis of ChIP-Seq binding data, and contributed multiple figures and text. E.A. developed the predictive models of gene expression, and contributed all corresponding figures and text. L.S. performed lipidomics experiments and data analysis, discussed the results and implications, and contributed figure and text to the paper. A.G. developed the improved blind deconvolution algorithm for ChIP-Seq, contributed to analysis of all ChIP-Seq data, and contributed corresponding figures. T.R. designed and performed hypoxic time course and transcriptomic experiments, discussed results and implications and commented on the manuscript. G.D. performed all RT–PCR transcriptomics experiments and contributed analyses to the paper. I.G. performed the DREM analysis and provided corresponding the figure. T.A. analysed ChIP-Seq data, developed the interfaces for data sharing and public release, and provided text. C.M. performed all library preparation and sequencing for ChIP-Seq. A.D.K. performed the metabolomics measurements, data analysis and their interpretation, discussed the results and implications and commented on the manuscript. R.A. was responsible for overview of bioinformatics and statistical data analysis. W.B. performed hypoxic time course, ChIP and transcriptomic experiments, and discussed results and implications. A.K. performed the experimental analysis of KstR de-repression and provided the corresponding figure. S.J. performed the experimental analysis of KstR de-repression, and provided the corresponding figure. M.J.H. produced individual MTB strains for ChIP-Seq experiments, and discussed results and implications. J.Z. developed and curated the MTB metabolic model. C.G. contributed to analysis of profiling data. J.K.W. performed ChIP and transcriptomic experiments, and discussed results and implications. Y.V.P. provided support and advice. P.I. contributed to the analysis of KstR expression and the validation of KstR binding sites. B.W. contributed to the ChIP-Seq analysis pipeline. P.S. and C.S. developed the interfaces for data sharing and public release. D.C. contributed to initial network analysis. J.D. contributed to analysis of profiling data. Y.L. contributed expression data for TB under different lipids. P.D. was responsible for experimental design and mass spectrometry analysis. J.L. was responsible for coordinating sample analysis, data generation, annotation and results reporting Y.Z. was responsible for proteomics statistical data analysis. J.P. was responsible for analysis of LC-MS and LC-MS/MS data analysis, protein identification and maintenance of annotation databases. A.D. and H.-J.M. discussed the results and implications and commented on the manuscript. B.H. and W.-H.Y. developed the ChIP protocol; S.T.P. developed the ChIP protocol, performed the KstR RT–PCR experiments, and performed the MTB KstR native promoter ChIP-Seq experiments. S.R. developed the ChIP protocol, oversaw experimental work on KstR and commented on the manuscript. S.H.E.K. discussed the results and implications and commented on the manuscript. R.P.M. performed the metabolomics measurements, data analysis, and their interpretation; discussed the results and implications and commented on the manuscript. D.C. was responsible for overall scientific direction of the proteomic core. D.B.M. oversaw lipidomics experiments, contributed to integration of methods across mass spectral platforms, discussed the results and implications and commented on the manuscript. D.R.S. oversaw the hypoxic culture, ChIP and transcriptomic experiments, discussed results and implications, provided text and commented extensively on the manuscript. G.K.S. led the project with J.E.G., oversaw RT–PCR experiments, discussed results and implications, provided text and commented extensively on the manuscript. G.K.S. and D.R.S. are co-last authors.

Corresponding author

Correspondence to James E. Galagan.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Methods, Supplementary Figures 1-29, Supplementary Tables 1-5 and Supplementary References. (PDF 9391 kb)

Supplementary Data

This file contains a summary table of MTB TFs mapped using Chip-Seq. (PDF 1338 kb)

Supplementary Data

This zipped file contains a Cytoscape file containing MTB metabolic network reconstruction. (ZIP 586 kb)

Supplementary Data

This zipped file contains a Cytoscape file containing MTB regulatory network model. (ZIP 908 kb)

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Galagan, J., Minch, K., Peterson, M. et al. The Mycobacterium tuberculosis regulatory network and hypoxia. Nature 499, 178–183 (2013). https://doi.org/10.1038/nature12337

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