Systems Biology of Tuberculosis: Insights for Drug Discovery

Chapter

Abstract

Tuberculosis has been a global health concern for decades and the emergence of resistant strains and co-infection with HIV warrant newer approaches to identify anti-tubercular drugs and targets. The availability of many “omics”-scale data sets, together with the advances in computation and modelling have enabled the application of several systems-level modelling techniques in drug discovery. In this chapter, we focus on how systems-level modelling of Mtb can provide us insights on various aspects of the pathogen, from metabolic pathways to protein-protein interaction networks, and how such models lend themselves to the identification of new and potentially improved drug targets.We present a brief overview of the modelling of mycobacterial metabolism, transcriptome and host-pathogen interactions, as well as how various models can be exploited for a rational identification of potential drug targets. Systems-level modelling and simulation of pathogenic organisms has an immense potential to impact most drug discovery programmes.

References

  1. .
    R. Albert and H. G. Othmer. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J Theor Biol, 223(1):1–18, 2003PubMedCrossRefGoogle Scholar
  2. .
    P. W. Anderson. More is different. Science, 177(4047):393–396, 1972PubMedCrossRefGoogle Scholar
  3. .
    S. Anishetty, M. Pulimi, and G. Pennathur. Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Comput Biol Chem, 29(5):368–378, 2005PubMedCrossRefGoogle Scholar
  4. .
    G. Apic, T. Ignjatovic, S. Boyer, and R. B. Russell. Illuminating drug discovery with biological pathways. FEBS Lett, 579(8):1872–1877, 2005PubMedCrossRefGoogle Scholar
  5. .
    G. Balázsi, A. P. Heath, L. Shi, and M. L. Gennaro. The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest. Mol Syst Biol, 4:225, 2008PubMedCrossRefGoogle Scholar
  6. .
    A-L. Barabási and Z. N. Oltvai. Network biology: Understanding the cell’s functional organization. Nat Rev Genet, 5(2):101–113, 2004Google Scholar
  7. .
    C. E. Barry III, R. E. Lee, K. Mdluli, A. E. Simpson, B. G. Schroeder, R. A. Slayden, and Y. Yuan. Mycolic acids: Structure, biosynthesis and physiological functions. Prog Lipid Res, 37:143–179, 1998PubMedCrossRefGoogle Scholar
  8. .
    S. A. Becker and B. Ø. Palsson. Three factors underlying incorrect in silico predictions of essential metabolic genes. BMC Syst Biol, 2:14, 2008PubMedCrossRefGoogle Scholar
  9. .
    E. Beretta, M. Carletti, D. E. Kirschner, and S. Marino. Stability analysis of a mathematical model of the immune response with delays, In: Mathematics for life science and medicine, pages 177–206. Springer, Berlin, 2007Google Scholar
  10. .
    D. J. V. Beste, T. Hooper, G. Stewart, B. Bonde, C. Avignone-Rossa, M. E. Bushell, P. Wheeler, S. Klamt, A. M. Kierzek, and J. McFadden. GSMN-TB: A web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol, 8:R89, 2007PubMedCrossRefGoogle Scholar
  11. .
    H. P. J. Bonarius, G. Schmid, and J. Tramper. Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends Biotechnol, 15(8): 308–314, 1997CrossRefGoogle Scholar
  12. .
    S. Bonora and G. Di Perri. Interactions between antiretroviral agents and those used to treat tuberculosis: Clinical pharmacology of antiretroviral drugs. Curr Opin HIV & AIDS, 3:306–312, 2008CrossRefGoogle Scholar
  13. .
    H. I. Boshoff, T. G. Myers, B. R. Copp, M. R. McNeil, M. Wilson, and C. E. Barry III. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism: Novel insights into drug mechanisms of action. J Biol Chem, 279(38): 40174–40184, 2004PubMedCrossRefGoogle Scholar
  14. .
    A. Brückner, C. Polge, N. Lentze, D. Auerbach, and U. Schlattner. Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci, 10(6):2763–2788, 2009PubMedCrossRefGoogle Scholar
  15. .
    L. Cabusora, E. Sutton, A. Fulmer, and C. V. Forst. Differential network expression during drug and stress response. Bioinformatics, 21(12):2898–2905, 2005PubMedCrossRefGoogle Scholar
  16. .
    J-C. Camus, M. J. Pryor, C. Medigue, and S. T. Cole. Re-annotation of the genome sequence of Mycobacterium tuberculosis H37Rv. Microbiology, 148(10):2967–2973, 2002Google Scholar
  17. .
    B. L. Claus and D. J. Underwood. Discovery informatics: Its evolving role in drug discovery. Drug Discov Today, 7:957–966, 2002PubMedCrossRefGoogle Scholar
  18. .
    S. T. Cole, R. Brosch, J. Parkhill, T. Garnier, C. Churcher, D. Harris, S. V. Gordon, K. Eiglmeier, S. Gas, C. E. Barry III, F. Tekaia, K. Badcock, D. Basham, D. Brown, T. Chillingworth, R. Connor, R. Davies, K. Devlin, T. Feltwell, S. Gentles, N. Hamlin, S. Holroyd, T. Hornsby, K. Jagels, A. Krogh, J. McLean, S. Moule, L. Murphy, K. Oliver, J. Osborne, M. A. Quail, M.-A. Rajandream, J. Rogerand, S. Rutter, K. Seeger, J. Skelton, R. Squares, S. Squares, J. E. Sulston, K. Taylor, S. Whitehead, and B. G. Barrell. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature, 393:537–544, 1998PubMedCrossRefGoogle Scholar
  19. .
    A. Cornish-Bowden and M. L. Cárdenas. Metabolic analysis in drug design. C R Biol, 326(5):509–515, 2003PubMedCrossRefGoogle Scholar
  20. .
    D. C. Crick, S. Mahapatra, and P. J. Brennan. Biosynthesis of the arabinogalactan-peptidoglycan complex of Mycobacterium tuberculosis. Glycobiology, 11:107R–118R, 2001PubMedCrossRefGoogle Scholar
  21. .
    P. Csermely, V. Ágoston, and S. Pongor. The efficiency of multi-target drugs: The network approach might help drug design. Trends Pharmacol Sci, 26:178–182, 2005PubMedCrossRefGoogle Scholar
  22. .
    T. Dandekar, B. Snel, M. A. Huynen, and P. Bork. Conservation of gene order: A fingerprint of proteins that physically interact. Trends Biochem Sci, 23(9):324–328, 1998PubMedCrossRefGoogle Scholar
  23. .
    E. J. Davidov, J. M. Holland, E. W. Marple, and S. Naylor. Advancing drug discovery through systems biology. Drug Discov Today, 8(4):175–183, 2003PubMedCrossRefGoogle Scholar
  24. .
    J. Doyle. Computational biology. Beyond the spherical cow. Nature, 411(6834):151–152, 2001Google Scholar
  25. .
    P. Draper and M. Daffé. The cell envelope of Mycobacterium tuberculosis with special reference to the capsule and outer permeability barrier. In: Stewart T. Cole, Kathleen D. Eisenach, David N. McMurray, and William R. Jacobs Jr., editors, Tuberculosis and the tubercle bacillus, pages 261–273. American Society of Microbiology Press, 2005Google Scholar
  26. .
    E. Dubnau, J. Chan, C. Raynaud, V. P. Mohan, M. A. Lanéelle, K. Yu, A. Quémard, I. Smith, and M. Daffé. Oxygenated mycolic acids are necessary for virulence of Mycobacterium tuberculosis in mice. Mol Microbiol, 36(3):630–637, 2000PubMedCrossRefGoogle Scholar
  27. .
    J. S. Edwards and B. Ø. Palsson. The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. Proc Natl Acad Sci USA, 97 (10):5528–5533, 2000PubMedCrossRefGoogle Scholar
  28. .
    J. S. Edwards, M. W. Covert, and B. Ø. Palsson. Metabolic modelling of microbes: The flux-balance approach. Environ Microbiol, 4(3):133–133, 2002PubMedCrossRefGoogle Scholar
  29. .
    S. Fields and O. Song. A novel genetic system to detect protein-protein interactions. Nature, 340(6230):245–246, 1989PubMedCrossRefGoogle Scholar
  30. .
    C. V. Forst. Host-pathogen systems biology. Drug Discov Today, 11(5–6):220–227, 2006PubMedCrossRefGoogle Scholar
  31. .
    J. Förster, I. Famili, P. Fu, B. Ø. Palsson, and J. Nielsen. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res, 13(2):244–253, 2003PubMedCrossRefGoogle Scholar
  32. .
    M. S. Glickman, J. S. Cox, and W. R. Jacobs Jr. A novel mycolic acid cyclopropane synthetase is required for cording, persistence, and virulence of Mycobacterium tuberculosis. Mol Cell, 5(4):717–727, 2000PubMedCrossRefGoogle Scholar
  33. .
    S. Gupta, S. S. Bisht, R. Kukreti, S. Jain, and S. K. Brahmachari. Boolean network analysis of a neurotransmitter signaling pathway. J Theor Biol, 244(3):463–469, Feb 2007PubMedCrossRefGoogle Scholar
  34. .
    S. Hasan, S. Daugelat, P. S. Rao, and M. Schreiber. Prioritizing genomic drug targets in pathogens: Application to Mycobacterium tuberculosis. PLoS Comput Biol, 2(6):e61, 2006Google Scholar
  35. .
    P. J. Hunter and T. K. Borg. Integration from proteins to organs: The Physiome project. Nat Rev Mol Cell Biol, 4(3):237–243, 2003PubMedCrossRefGoogle Scholar
  36. .
    T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18 Suppl 1: S233–S240, 2002Google Scholar
  37. .
    P-E. Jacques, A. L. Gervais, M. Cantin, J-F. Lucier, G. Dallaire, G. Drouin, L. Gaudreau, J. Goulet, and J. Brzezinski. MtbRegList, a database dedicated to the analysis of transcriptional regulation in Mycobacterium tuberculosis. Bioinformatics, 21 (10):2563–2565, 2005Google Scholar
  38. .
    N. Jamshidi and B. Ø. Palsson. Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol, 1:26, 2007PubMedCrossRefGoogle Scholar
  39. .
    H. Jeong, S. P. Mason, A-L. Barabási, and Z. N. Oltvai. Lethality and centrality in protein networks. Nature, 411(6833):41–42, 2001Google Scholar
  40. .
    Y. Kalidas and N. Chandra. Pocketdepth: A new depth based algorithm for identification of ligand binding sites in proteins. J Struct Biol, 161(1):31–42, 2008PubMedCrossRefGoogle Scholar
  41. .
    K. J. Kauffman, P. Prakash, and J. S. Edwards. Advances in flux balance analysis. Curr Opin Biotechnol, 14(5):491–496, 2003PubMedCrossRefGoogle Scholar
  42. .
    S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol, 22(3):437–467, 1969PubMedCrossRefGoogle Scholar
  43. .
    D. Kirschner and S. Marino. Mycobacterium tuberculosis as viewed through a computer. Trends Microbiol, 13(5):206–211, 2005PubMedCrossRefGoogle Scholar
  44. .
    E. M. Marcotte, M. Pellegrini, H-L. Ng, D. W. Rice, T. O. Yeates, and D. Eisenberg. Detecting protein function and protein-protein interactions from genome sequences. Science, 285(5428):751–753, 1999Google Scholar
  45. .
    S. Marino and D. E. Kirschner. The human immune response to Mycobacterium tuberculosis in lung and lymph node. J Theor Biol, 227(4):463–486, 2004PubMedCrossRefGoogle Scholar
  46. .
    S. Marino, S. Pawar, C. L. Fuller, T. A. Reinhart, J. L. Flynn, and D. E. Kirschner. Dendritic cell trafficking and antigen presentation in the human immune response to Mycobacterium tuberculosis. J Immunol, 173(1):494–506, 2004PubMedGoogle Scholar
  47. .
    S. Marino, E. Beretta, and D. E. Kirschner. The role of delays in innate and adaptive immunity to intracellular bacterial infection. Math Biosci Eng, 4(2):261–288, 2007aPubMedCrossRefGoogle Scholar
  48. .
    S. Marino, D. Sud, H. Plessner, L. P. Lin, J. Chan, J. L. Flynn, and D. E. Kirschner. Differences in reactivation of tuberculosis induced from anti-TNF treatments are based on bioavailability in granulomatous tissue. PLoS Comput Biol, 3(10):1909–1924, 2007bPubMedCrossRefGoogle Scholar
  49. .
    K. Mdluli and M. Spigelman. Novel targets for tuberculosis drug discovery. Curr Opin Pharmacol, 6(5):459–467, 2006PubMedCrossRefGoogle Scholar
  50. .
    P. Nunn, B. Williams, K. Floyd, C. Dye, G. Elzinga, and M. Raviglione. Tuberculosis control in the era of HIV. Nat Rev Immunol, 5(10):819–826, 2005PubMedCrossRefGoogle Scholar
  51. .
    J. A. Papin, T. Hunter, B. Ø. Palsson, and S. Subramaniam. Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol, 6(2):99–111, 2005PubMedCrossRefGoogle Scholar
  52. .
    M. Pellegrini, E. M. Marcotte, M. J. Thompson, D. Eisenberg, and T. O. Yeates. Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proc Natl Acad Sci USA, 96(8):4285–4288, 1999PubMedCrossRefGoogle Scholar
  53. .
    G. Ramachandraiah and N. Chandra. Sequence and structural determinants of mannose recognition. Proteins, 39(4):358–364, 2000PubMedCrossRefGoogle Scholar
  54. .
    K. Raman. Systems-level modelling and simulation of Mycobacterium tuberculosis: Insights for drug discovery. PhD thesis, Indian Institute of Science, Bangalore, 2008Google Scholar
  55. .
    K. Raman and N. Chandra. Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol, 8:234, 2008PubMedCrossRefGoogle Scholar
  56. .
    K. Raman and N. Chandra. Flux balance analysis of biological systems: Applications and challenges. Brief Bioinform, 10(4):435–449, 2009PubMedCrossRefGoogle Scholar
  57. .
    K. Raman, P. Rajagopalan, and N. Chandra. Flux balance analysis of mycolic acid pathway: Targets for anti-tubercular drugs. PLoS Comput Biol, 1(5):e46, 2005Google Scholar
  58. .
    K. Raman, Y. Kalidas, and N. Chandra. targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol, 2(1):109, 2008Google Scholar
  59. .
    K. Raman, R. Vashisht, and N. Chandra. Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis. Mol Biosyst, 5:1740–1751, 2009PubMedCrossRefGoogle Scholar
  60. .
    K. Raman, A. G. Bhat, and N. Chandra. A systems perspective of hostpathogen interactions: Predicting disease outcome in tuberculosis. Mol Biosyst, 6:516–530, 2010PubMedCrossRefGoogle Scholar
  61. .
    K. Raman, Y. Kalidas, and N. Chandra. Model-driven drug discovery: Principles and practices, Biological database modeling, pages 163–188. Artech House, New York, 2007Google Scholar
  62. .
    J. C. J. Ray and D. E. Kirschner. Requirement for multiple activation signals by anti-inflammatory feedback in macrophages. J Theor Biol, 241(2):276–294, 2006PubMedCrossRefGoogle Scholar
  63. .
    J. C. J. Ray, J. Wang, J. Chan, and D. E. Kirschner. The timing of TNF and IFN-γ signaling affects macrophage activation strategies during Mycobacterium tuberculosis infection. J Theor Biol, 252(1):24–38, 2008PubMedCrossRefGoogle Scholar
  64. .
    J. L. Reed, I. Famili, I Thiele, and B. Ø. Palsson. Towards multidimensional genome annotation. Nat Rev Genet, 7(2):130–141, 2006aGoogle Scholar
  65. .
    J. L. Reed, T. R. Patel, K. H. Chen, A. R. Joyce, M. K. Applebee, D. D. Herring, O. T. Bui, E. M. Knight, S. S. Fong, and B. Ø. Palsson. Systems approach to refining genome annotation. Proc Natl Acad Sci USA, 103(46):17480–17484, Nov 2006bPubMedCrossRefGoogle Scholar
  66. .
    C. M. Sassetti, D. M. Boyd, and E. J. Rubin. Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol, 48(1):77–84, 2003PubMedCrossRefGoogle Scholar
  67. .
    D. Schnappinger, S. Ehrt, M. I. Voskuil, Y. Liu, J. A. Mangan, I. M. Monahan, G. Dolganov, B. Efron, P. D. Butcher, C. Nathan, and G. K. Schoolnik. Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: Insights into the phagosomal environment. J Exp Med, 198(5):693–704, 2003PubMedCrossRefGoogle Scholar
  68. .
    J. L. Segovia-Juarez, S. Ganguli, and D. E. Kirschner. Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. J Theor Biol, 231(3):357–376, 2004Google Scholar
  69. .
    I. Smith. Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clin Microbiol Rev, 16(3):463–496, 2003PubMedCrossRefGoogle Scholar
  70. .
    M. Strong, T. G. Graeber, M. Beeby, M. Pellegrini, M. J. Thompson, T. O. Yeates, and D. Eisenberg. Visualization and interpretation of protein networks in Mycobacterium tuberculosis based on hierarchical clustering of genome-wide functional linkage maps. Nucleic Acids Res, 31(24):7099–7109, 2003PubMedCrossRefGoogle Scholar
  71. .
    D. Sud, C. Bigbee, J. L. Flynn, and D. E. Kirschner. Contribution of CD8 +  T cells to control of Mycobacterium tuberculosis infection. J Immunol, 176(7): 4296–4314, 2006PubMedGoogle Scholar
  72. .
    K. Takayama, C. Wang, and G. S. Besra. Pathway to synthesis and processing of mycolic acids in Mycobacterium tuberculosis. Clin Microbiol Rev, 18:81–101, 2005PubMedCrossRefGoogle Scholar
  73. .
    J. Thakar, M. Pilione, G. Kirimanjeswara, E. T. Harvill, and R. Albert. Modeling systems-level regulation of host immune responses. PLoS Comput Biol, 3(6):e109, 2007Google Scholar
  74. .
    T. Thomas. Boolean formalization of genetic control circuits. J Theor Biol, 42(3):563–585, 1973PubMedCrossRefGoogle Scholar
  75. .
    K. D. Verkhedkar, K. Raman, N. Chandra, and S. Vishveshwara. Metabolome based reaction graphs of M. tuberculosis and M. leprae: A comparative network analysis. PLoS One, 2(9):e881, 2007Google Scholar
  76. .
    P. K. Vinod, B. Konkimalla, and N. Chandra. In-silico pharmacodynamics: Correlation of adverse effects of H2-antihistamines with histamine N-methyl transferase binding potential. Appl Bioinform, 5(3):141–150, 2006CrossRefGoogle Scholar
  77. .
    C. Von Mering, L. J. Jensen, M. Kuhn, S. Chaffron, T. Doerks, B. Krüger, B. Snel, and P. Bork. STRING 7 – recent developments in the integration and prediction of protein interactions. Nucleic Acids Res, 35(Database issue):358–362, 2007Google Scholar
  78. .
    J. S. Waddell and P. D. Butcher. Microarray analysis of whole genome expression of intracellular Mycobacterium tuberculosis. Curr Mol Med, 7(3):287–296, 2007PubMedCrossRefGoogle Scholar
  79. .
    S. J. Waddell, R. A. Stabler, K. Laing, L. Kremer, R. C. Reynolds, and G. S. Besra. The use of microarray analysis to determine the gene expression profiles of Mycobacterium tuberculosis in response to anti-bacterial compounds. Tuberculosis (Edinb), 84(3–4):263–274, 2004Google Scholar
  80. .
    S. J. Waddell, P. D. Butcher, and N. G. Stoker. Rna profiling in host-pathogen interactions. Curr Opin Microbiol, 10(3):297–302, 2007PubMedCrossRefGoogle Scholar
  81. .
    S. J. Waddell, K. Laing, C. Senner, and P. D. Butcher. Microarray analysis of defined Mycobacterium tuberculosis populations using rna amplification strategies. BMC Genomics, 9:94, 2008PubMedCrossRefGoogle Scholar
  82. .
    J. E. Wigginton and D. E. Kirschner. A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. J Immunol, 166(3):1951–1967, 2001PubMedGoogle Scholar
  83. .
    World Health Organisation. Global tuberculosis control: Surveillance, planning, financing: WHO report 2008. World Health Organisation, 2008 ISBN 978-9241563543Google Scholar
  84. .
    K. Yeturu and N. Chandra. PocketMatch: A new algorithm to compare binding sites in protein structures. BMC Bioinform, 9:543, 2008CrossRefGoogle Scholar
  85. .
    D. Young, J. Stark, and D. E. Kirschner. Systems biology of persistent infection: Tuberculosis as a case study. Nat Rev Microbiol, 6(7):520–528, 2008PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Bioinformatics CentreIndian Institute of ScienceBangaloreIndia

Personalised recommendations