The Mycobacterium tuberculosis regulatory network and hypoxia (original) (raw)

Accession codes

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.

References

1. Manabe, Y. C. & Bishai, W. R. Latent _Mycobacterium tuberculosis_-persistence, patience, and winning by waiting. Nature Med. 6, 1327–1329 (2000)
   Article CAS Google Scholar
2. Flynn, J. L. & Chan, J. Tuberculosis: latency and reactivation. Infect. Immun. 69, 4195–4201 (2001)
   Article CAS PubMed Google Scholar
3. Schnappinger, D. et al. Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: insights into the phagosomal environment. J. Exp. Med. 198, 693–704 (2003)
   Article CAS PubMed Google Scholar
4. Yang, X., Nesbitt, N. M., Dubnau, E., Smith, I. & Sampson, N. S. Cholesterol metabolism increases the metabolic pool of propionate in Mycobacterium tuberculosis . Biochemistry 48, 3819–3821 (2009)
   Article CAS PubMed Google Scholar
5. Miner, M. D., Chang, J. C., Pandey, A. K., Sassetti, C. M. & Sherman, D. R. Role of cholesterol in Mycobacterium tuberculosis infection. Indian J. Exp. Biol. 47, 407–411 (2009)
   CAS Google Scholar
6. Chang, J. C. et al. igr genes and Mycobacterium tuberculosis cholesterol metabolism. J. Bacteriol. 191, 5232–5239 (2009)
   Article CAS PubMed Google Scholar
7. Daniel, J., Maamar, H., Deb, C., Sirakova, T. D. & Kolattukudy, P. E. Mycobacterium tuberculosis uses host triacylglycerol to accumulate lipid droplets and acquires a dormancy-like phenotype in lipid-loaded macrophages. PLoS Pathog. 7, e1002093 (2011)
   Article CAS PubMed Google Scholar
8. Low, K. L. et al. Triacylglycerol utilization is required for regrowth of in vitro hypoxic nonreplicating Mycobacterium bovis bacillus Calmette-Guerin. J. Bacteriol. 191, 5037–5043 (2009)
   Article CAS PubMed Google Scholar
9. Russell, D. G., Mwandumba, H. C. & Rhoades, E. E. Mycobacterium and the coat of many lipids. J. Cell Biol. 158, 421–426 (2002)
   Article CAS PubMed Google Scholar
10. Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods 4, 651–657 (2007)
    Article CAS PubMed Google Scholar
11. Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007)
    Article ADS CAS PubMed Google Scholar
12. Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007)
    Article ADS CAS PubMed Google Scholar
13. Ehrt, S. et al. Controlling gene expression in mycobacteria with anhydrotetracycline and Tet repressor. Nucleic Acids Res. 33, e21 (2005)
    Article PubMed Google Scholar
14. Ehrt, S. & Schnappinger, D. Controlling gene expression in mycobacteria. Future Microbiol. 1, 177–184 (2006)
    Article CAS PubMed Google Scholar
15. Klotzsche, M., Ehrt, S. & Schnappinger, D. Improved tetracycline repressors for gene silencing in mycobacteria. Nucleic Acids Res. 37, 1778–1788 (2009)
    Article CAS PubMed Google Scholar
16. Farnham, P. J. Insights from genomic profiling of transcription factors. Nature Rev. Genet. 10, 605–616 (2009)
    Article CAS PubMed Google Scholar
17. MacQuarrie, K. L., Fong, A. P., Morse, R. H. & Tapscott, S. J. Genome-wide transcription factor binding: beyond direct target regulation. Trends Genet. 27, 141–148 (2011)
    Article CAS PubMed Google Scholar
18. Galagan, J., Lyubetskaya, A. & Gomes, A. ChIP-Seq and the complexity of bacterial transcriptional regulation. Curr. Top. Microbiol. Immunol. 363, 43–68 (2013)
    CAS PubMed Google Scholar
19. Chauhan, S., Sharma, D., Singh, A., Surolia, A. & Tyagi, J. S. Comprehensive insights into Mycobacterium tuberculosis DevR (DosR) regulon activation switch. Nucleic Acids Res. 39, 7400–7414 (2011)
    Article CAS PubMed Google Scholar
20. Gautam, U. S., Chauhan, S. & Tyagi, J. S. Determinants outside the DevR C-terminal domain are essential for cooperativity and robust activation of dormancy genes in Mycobacterium tuberculosis . PLoS ONE 6, e16500 (2011)
    Article ADS CAS PubMed Google Scholar
21. Vasudeva-Rao, H. M. & McDonough, K. A. Expression of the Mycobacterium tuberculosis _acr_-coregulated genes from the DevR (DosR) regulon is controlled by multiple levels of regulation. Infect. Immun. 76, 2478–2489 (2008)
    Article CAS PubMed Google Scholar
22. Cho, B. K., Federowicz, S., Park, Y. S., Zengler, K. & Palsson, B. O. Deciphering the transcriptional regulatory logic of amino acid metabolism. Nature Chem. Biol. 8, 65–71 (2012)
    Article CAS Google Scholar
23. Colangeli, R. et al. The multifunctional histone-like protein Lsr2 protects mycobacteria against reactive oxygen intermediates. Proc. Natl Acad. Sci. USA 106, 4414–4418 (2009)
    Article ADS CAS Google Scholar
24. Gordon, B. R. et al. Lsr2 is a nucleoid-associated protein that targets AT-rich sequences and virulence genes in Mycobacterium tuberculosis . Proc. Natl Acad. Sci. USA 107, 5154–5159 (2010)
    Article ADS CAS Google Scholar
25. Rustad, T. R., Harrell, M. I., Liao, R. & Sherman, D. R. The enduring hypoxic response of Mycobacterium tuberculosis . PLoS ONE 3, e1502 (2008)
    Article ADS PubMed Google Scholar
26. Kendall, S. L. et al. A highly conserved transcriptional repressor controls a large regulon involved in lipid degradation in Mycobacterium smegmatis and Mycobacterium tuberculosis . Mol. Microbiol. 65, 684–699 (2007)
    Article CAS PubMed Google Scholar
27. Nesbitt, N. M. et al. A thiolase of Mycobacterium tuberculosis is required for virulence and production of androstenedione and androstadienedione from cholesterol. Infect. Immun. 78, 275–282 (2010)
    Article CAS Google Scholar
28. Gao, C. H., Yang, M. & He, Z. G. Characterization of a novel ArsR-like regulator encoded by Rv2034 in Mycobacterium tuberculosis . PLoS ONE 7, e36255 (2012)
    Article ADS CAS PubMed Google Scholar
29. Gonzalo-Asensio, J. et al. PhoP: a missing piece in the intricate puzzle of Mycobacterium tuberculosis virulence. PLoS ONE 3, e3496 (2008)
    Article ADS PubMed Google Scholar
30. Gonzalo Asensio, J. et al. The virulence-associated two-component PhoP-PhoR system controls the biosynthesis of polyketide-derived lipids in Mycobacterium tuberculosis . J. Biol. Chem. 281, 1313–1316 (2006)
    Article PubMed Google Scholar
31. Ryndak, M., Wang, S. & Smith, I. PhoP, a key player in Mycobacterium tuberculosis virulence. Trends Microbiol. 16, 528–534 (2008)
    Article CAS PubMed Google Scholar
32. Abramovitch, R. B., Rohde, K. H., Hsu, F. F. & Russell, D. G. aprABC: a Mycobacterium tuberculosis complex-specific locus that modulates pH-driven adaptation to the macrophage phagosome. Mol. Microbiol. 80, 678–694 (2011)
    Article CAS PubMed Google Scholar
33. Singh, A. et al. Mycobacterium tuberculosis WhiB3 maintains redox homeostasis by regulating virulence lipid anabolism to modulate macrophage response. PLoS Pathog. 5, e1000545 (2009)
    Article PubMed Google Scholar
34. Ernst, J., Vainas, O., Harbison, C. T., Simon, I. & Bar-Joseph, Z. Reconstructing dynamic regulatory maps. Mol. Syst. Biol. 3, 74 (2007)
    Article PubMed Google Scholar
35. Garton, N. J. et al. Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum. PLoS Med. 5, e75 (2008)
    Article PubMed Google Scholar
36. Park, H. D. et al. Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis . Mol. Microbiol. 48, 833–843 (2003)
    Article CAS PubMed Google Scholar
37. Baek, S. H., Li, A. H. & Sassetti, C. M. Metabolic regulation of mycobacterial growth and antibiotic sensitivity. PLoS Biol. 9, e1001065 (2011)
    Article CAS PubMed Google Scholar
38. Cox, J. S., Chen, B., McNeil, M. & Jacobs, W. R., Jr Complex lipid determines tissue-specific replication of Mycobacterium tuberculosis in mice. Nature 402, 79–83 (1999)
    Article ADS CAS PubMed Google Scholar
39. Camacho, L. R., Ensergueix, D., Perez, E., Gicquel, B. & Guilhot, C. Identification of a virulence gene cluster of Mycobacterium tuberculosis by signature-tagged transposon mutagenesis. Mol. Microbiol. 34, 257–267 (1999)
    Article CAS PubMed Google Scholar
40. Converse, S. E. et al. MmpL8 is required for sulfolipid-1 biosynthesis and Mycobacterium tuberculosis virulence. Proc. Natl Acad. Sci. USA 100, 6121–6126 (2003)
    Article ADS CAS Google Scholar
41. Domenech, P. et al. The role of MmpL8 in sulfatide biogenesis and virulence of Mycobacterium tuberculosis . J. Biol. Chem. 279, 21257–21265 (2004)
    Article CAS Google Scholar
42. Rousseau, C. et al. Production of phthiocerol dimycocerosates protects Mycobacterium tuberculosis from the cidal activity of reactive nitrogen intermediates produced by macrophages and modulates the early immune response to infection. Cell. Microbiol. 6, 277–287 (2004)
    Article CAS Google Scholar
43. Nazarova, E. V. et al. Role of lipid components in formation and reactivation of Mycobacterium smegmatis “nonculturable” cells. Biochemistry 76, 636–644 (2011)
    CAS Google Scholar
44. Ojha, A. K. et al. Growth of Mycobacterium tuberculosis biofilms containing free mycolic acids and harbouring drug-tolerant bacteria. Mol. Microbiol. 69, 164–174 (2008)
    Article CAS PubMed Google Scholar
45. Ojha, A. K., Trivelli, X., Guerardel, Y., Kremer, L. & Hatfull, G. F. Enzymatic hydrolysis of trehalose dimycolate releases free mycolic acids during mycobacterial growth in biofilms. J. Biol. Chem. 285, 17380–17389 (2010)
    Article CAS PubMed Google Scholar
46. Arnvig, K. & Young, D. Non-coding RNA and its potential role in Mycobacterium tuberculosis pathogenesis. RNA Biol. 9, 427–436 (2012)
    Article CAS PubMed Google Scholar

Download references

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
   You can also search for this author inPubMed Google Scholar
2. Kyle Minch
   You can also search for this author inPubMed Google Scholar
3. Matthew Peterson
   You can also search for this author inPubMed Google Scholar
4. Anna Lyubetskaya
   You can also search for this author inPubMed Google Scholar
5. Elham Azizi
   You can also search for this author inPubMed Google Scholar
6. Linsday Sweet
   You can also search for this author inPubMed Google Scholar
7. Antonio Gomes
   You can also search for this author inPubMed Google Scholar
8. Tige Rustad
   You can also search for this author inPubMed Google Scholar
9. Gregory Dolganov
   You can also search for this author inPubMed Google Scholar
10. Irina Glotova
    You can also search for this author inPubMed Google Scholar
11. Thomas Abeel
    You can also search for this author inPubMed Google Scholar
12. Chris Mahwinney
    You can also search for this author inPubMed Google Scholar
13. Adam D. Kennedy
    You can also search for this author inPubMed Google Scholar
14. René Allard
    You can also search for this author inPubMed Google Scholar
15. William Brabant
    You can also search for this author inPubMed Google Scholar
16. Andrew Krueger
    You can also search for this author inPubMed Google Scholar
17. Suma Jaini
    You can also search for this author inPubMed Google Scholar
18. Brent Honda
    You can also search for this author inPubMed Google Scholar
19. Wen-Han Yu
    You can also search for this author inPubMed Google Scholar
20. Mark J. Hickey
    You can also search for this author inPubMed Google Scholar
21. Jeremy Zucker
    You can also search for this author inPubMed Google Scholar
22. Christopher Garay
    You can also search for this author inPubMed Google Scholar
23. Brian Weiner
    You can also search for this author inPubMed Google Scholar
24. Peter Sisk
    You can also search for this author inPubMed Google Scholar
25. Christian Stolte
    You can also search for this author inPubMed Google Scholar
26. Jessica K. Winkler
    You can also search for this author inPubMed Google Scholar
27. Yves Van de Peer
    You can also search for this author inPubMed Google Scholar
28. Paul Iazzetti
    You can also search for this author inPubMed Google Scholar
29. Diogo Camacho
    You can also search for this author inPubMed Google Scholar
30. Jonathan Dreyfuss
    You can also search for this author inPubMed Google Scholar
31. Yang Liu
    You can also search for this author inPubMed Google Scholar
32. Anca Dorhoi
    You can also search for this author inPubMed Google Scholar
33. Hans-Joachim Mollenkopf
    You can also search for this author inPubMed Google Scholar
34. Paul Drogaris
    You can also search for this author inPubMed Google Scholar
35. Julie Lamontagne
    You can also search for this author inPubMed Google Scholar
36. Yiyong Zhou
    You can also search for this author inPubMed Google Scholar
37. Julie Piquenot
    You can also search for this author inPubMed Google Scholar
38. Sang Tae Park
    You can also search for this author inPubMed Google Scholar
39. Sahadevan Raman
    You can also search for this author inPubMed Google Scholar
40. Stefan H. E. Kaufmann
    You can also search for this author inPubMed Google Scholar
41. Robert P. Mohney
    You can also search for this author inPubMed Google Scholar
42. Daniel Chelsky
    You can also search for this author inPubMed Google Scholar
43. D. Branch Moody
    You can also search for this author inPubMed Google Scholar
44. David R. Sherman
    You can also search for this author inPubMed Google Scholar
45. Gary K. Schoolnik
    You can also search for this author inPubMed Google Scholar

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 toJames E. Galagan.

Ethics declarations

Competing interests

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)

PowerPoint slides

Rights and permissions

About this article

Cite this article

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

Download citation

• Received: 30 December 2011
• Accepted: 23 May 2013
• Published: 03 July 2013
• Issue Date: 11 July 2013
• DOI: https://doi.org/10.1038/nature12337