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The Mycobacterium tuberculosis regulatory network and hypoxia

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.

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. 1

    Manabe, Y. C. & Bishai, W. R. Latent Mycobacterium tuberculosis-persistence, patience, and winning by waiting. Nature Med. 6, 1327–1329 (2000)

    CAS  Article  Google Scholar 

  2. 2

    Flynn, J. L. & Chan, J. Tuberculosis: latency and reactivation. Infect. Immun. 69, 4195–4201 (2001)

    CAS  Article  PubMed  Google Scholar 

  3. 3

    Schnappinger, D. et al. Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: insights into the phagosomal environment. J. Exp. Med. 198, 693–704 (2003)

    CAS  Article  PubMed  Google Scholar 

  4. 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)

    CAS  Article  PubMed  Google Scholar 

  5. 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. 6

    Chang, J. C. et al. igr genes and Mycobacterium tuberculosis cholesterol metabolism. J. Bacteriol. 191, 5232–5239 (2009)

    CAS  Article  PubMed  Google Scholar 

  7. 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)

    CAS  Article  PubMed  Google Scholar 

  8. 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)

    CAS  Article  PubMed  Google Scholar 

  9. 9

    Russell, D. G., Mwandumba, H. C. & Rhoades, E. E. Mycobacterium and the coat of many lipids. J. Cell Biol. 158, 421–426 (2002)

    CAS  Article  PubMed  Google Scholar 

  10. 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)

    CAS  Article  PubMed  Google Scholar 

  11. 11

    Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007)

    ADS  CAS  Article  PubMed  Google Scholar 

  12. 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)

    ADS  CAS  Article  PubMed  Google Scholar 

  13. 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. 14

    Ehrt, S. & Schnappinger, D. Controlling gene expression in mycobacteria. Future Microbiol. 1, 177–184 (2006)

    CAS  Article  PubMed  Google Scholar 

  15. 15

    Klotzsche, M., Ehrt, S. & Schnappinger, D. Improved tetracycline repressors for gene silencing in mycobacteria. Nucleic Acids Res. 37, 1778–1788 (2009)

    CAS  Article  PubMed  Google Scholar 

  16. 16

    Farnham, P. J. Insights from genomic profiling of transcription factors. Nature Rev. Genet. 10, 605–616 (2009)

    CAS  Article  PubMed  Google Scholar 

  17. 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)

    CAS  Article  PubMed  Google Scholar 

  18. 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. 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)

    CAS  Article  PubMed  Google Scholar 

  20. 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)

    ADS  CAS  Article  PubMed  Google Scholar 

  21. 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)

    CAS  Article  PubMed  Google Scholar 

  22. 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)

    CAS  Article  Google Scholar 

  23. 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)

    ADS  CAS  Article  Google Scholar 

  24. 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)

    ADS  CAS  Article  Google Scholar 

  25. 25

    Rustad, T. R., Harrell, M. I., Liao, R. & Sherman, D. R. The enduring hypoxic response of Mycobacterium tuberculosis . PLoS ONE 3, e1502 (2008)

    ADS  Article  PubMed  Google Scholar 

  26. 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)

    CAS  Article  PubMed  Google Scholar 

  27. 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)

    CAS  Article  Google Scholar 

  28. 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)

    ADS  CAS  Article  PubMed  Google Scholar 

  29. 29

    Gonzalo-Asensio, J. et al. PhoP: a missing piece in the intricate puzzle of Mycobacterium tuberculosis virulence. PLoS ONE 3, e3496 (2008)

    ADS  Article  PubMed  Google Scholar 

  30. 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. 31

    Ryndak, M., Wang, S. & Smith, I. PhoP, a key player in Mycobacterium tuberculosis virulence. Trends Microbiol. 16, 528–534 (2008)

    CAS  Article  PubMed  Google Scholar 

  32. 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)

    CAS  Article  PubMed  Google Scholar 

  33. 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. 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. 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. 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)

    CAS  Article  PubMed  Google Scholar 

  37. 37

    Baek, S. H., Li, A. H. & Sassetti, C. M. Metabolic regulation of mycobacterial growth and antibiotic sensitivity. PLoS Biol. 9, e1001065 (2011)

    CAS  Article  PubMed  Google Scholar 

  38. 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)

    ADS  CAS  Article  PubMed  Google Scholar 

  39. 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)

    CAS  Article  PubMed  Google Scholar 

  40. 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)

    ADS  CAS  Article  Google Scholar 

  41. 41

    Domenech, P. et al. The role of MmpL8 in sulfatide biogenesis and virulence of Mycobacterium tuberculosis . J. Biol. Chem. 279, 21257–21265 (2004)

    CAS  Article  Google Scholar 

  42. 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)

    CAS  Article  Google Scholar 

  43. 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. 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)

    CAS  Article  PubMed  Google Scholar 

  45. 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)

    CAS  Article  PubMed  Google Scholar 

  46. 46

    Arnvig, K. & Young, D. Non-coding RNA and its potential role in Mycobacterium tuberculosis pathogenesis. RNA Biol. 9, 427–436 (2012)

    CAS  Article  PubMed  Google Scholar 

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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

Affiliations

Authors

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.

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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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