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Systems Biology of Tuberculosis pp 37–54Cite as

Probing Gene Regulatory Networks to Decipher Host–Pathogen Interactions

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Abstract

Given the pivotal role that macrophages play in determining the outcome of infection, it is now becoming apparent that a better understanding of the molecular interplay between the pathogen and this host cell type will be crucial for developing more effective strategies for tuberculosis therapy. In this context the need to capture the dynamic features of this crosstalk, so as to dissect the evolving stages of host–pathogen equilibration, is also beginning to be appreciated. A promising way to probe the ongoing dialog between the macrophage and the pathogen is through gene expression profiling. An analysis of the gene expression pattern of the infected host cell on the one hand, and that of the infecting pathogen on the other, provides a coarse grained insight into the nature and dynamics of interactions between these two entities. While much more work needs to be done in this direction, initial studies are beginning to shed light on the mechanisms by which the pathogen equilibrates within the host intracellular environment. However, an important goal here would be to extract the gene regulatory networks that emerge within the pathogen and the host cell, and to then precisely map the interface between these two networks. In addition to yielding important information on crosstalk mechanisms, such mapping should also help to identify novel targets for drug development.

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References

  1. Rengarajan J, Bloom BR, Rubin EJ (2005) Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages. Proc Natl Acad Sci USA 102(23):8327–8332

    Article  PubMed  CAS  Google Scholar 

  2. Sassetti CM, Boyd DH, Rubin EJ (2001) Comprehensive identification of conditionally essential genes in mycobacteria. Proc Natl Acad Sci USA 98(22):12712–12717

    Article  PubMed  CAS  Google Scholar 

  3. Kuijl C, Savage ND, Marsman M, Tuin AW, Janssen L, Egan DA, Ketema M, van den Nieuwendijk R, van den Eeden SJ, Geluk A, Poot A, van der Marel G, Beijersbergen RL, Overkleeft H, Ottenhoff TH, Neefjes J (2007) Intracellular bacterial growth is controlled by a kinase network around PKB/AKT1. Nature 450(7170):725–730

    Article  PubMed  CAS  Google Scholar 

  4. Jayaswal S, Kamal MA, Dua R, Gupta S, Majumdar T, Das G, Kumar D, Rao KV (2010) Identification of host-dependent survival factors for intracellular Mycobacterium tuberculosis through an siRNA screen. PLoS Pathog 6(4):e1000839

    Article  PubMed  Google Scholar 

  5. Kumar D, Nath L, Kamal MA, Varshney A, Jain A, Singh S, Rao KV (2010) Genome-wide analysis of the host intracellular network that regulates survival of Mycobacterium tuberculosis. Cell 140(5):731–743

    Article  PubMed  CAS  Google Scholar 

  6. Karim AF, Chandra P, Chopra A, Siddiqui Z, Bhaskar A, Singh A, Kumar D (2011) Express path analysis identifies a tyrosine kinase Src-centric network regulating divergent host responses to Mycobacterium tuberculosis infection. J Biol Chem 286(46):40307–40319

    Article  PubMed  CAS  Google Scholar 

  7. Nau GJ, Richmond JF, Schlesinger A, Jennings EG, Lander ES, Young RA (2002) Human macrophage activation programs induced by bacterial pathogens. Proc Natl Acad Sci USA 99(3):1503–1508

    Article  PubMed  CAS  Google Scholar 

  8. Ehrt S, Schnappinger D, Bekiranov S, Drenkow J, Shi S, Gingeras TR, Gaasterland T, Schoolnik G, Nathan C (2001) Reprogramming of the macrophage transcriptome in response to interferon-gamma and Mycobacterium tuberculosis: signaling roles of nitric oxide synthase-2 and phagocyte oxidase. J Exp Med 194(8):1123–1140

    Article  PubMed  CAS  Google Scholar 

  9. Ragno S, Romano M, Howell S, Pappin DJ, Jenner PJ, Colston MJ (2001) Changes in gene expression in macrophages infected with Mycobacterium tuberculosis: a combined transcriptomic and proteomic approach. Immunology 104(1):99–108

    Article  PubMed  CAS  Google Scholar 

  10. Volpe E, Cappelli G, Grassi M, Martino A, Serafino A, Colizzi V, Sanarico N, Mariani F (2006) Gene expression profiling of human macrophages at late time of infection with Mycobacterium tuberculosis. Immunology 118(4):449–460

    PubMed  CAS  Google Scholar 

  11. Shi S, Nathan C, Schnappinger D, Drenkow J, Fuortes M, Block E, Ding A, Gingeras TR, Schoolnik G, Akira S, Takeda K, Ehrt S (2003) MyD88 primes macrophages for full-scale activation by interferon-gamma yet mediates few responses to Mycobacterium tuberculosis. J Exp Med 198(7):987–997

    Article  PubMed  CAS  Google Scholar 

  12. Wang JP, Rought SE, Corbeil J, Guiney DG (2003) Gene expression profiling detects patterns of human macrophage responses following Mycobacterium tuberculosis infection. FEMS Immunol Med Microbiol 39(2):163–172

    Article  PubMed  Google Scholar 

  13. Hu CH, Xie JP, Li Y, Yue J, Xu YZ, Wang HH (2004) Differential expression of apoptosis-related gene induced by clinical and laboratory Mycobacterium tuberculosis strain in macrophages U937 revealed by oligonucleotide microarray. Yi Chuan Xue Bao 31(3):231–235

    PubMed  CAS  Google Scholar 

  14. Keller C, Lauber J, Blumenthal A, Buer J, Ehlers S (2004) Resistance and susceptibility to tuberculosis analysed at the transcriptome level: lessons from mouse macrophages. Tuberculosis (Edinb) 84(3–4):144–158

    Article  Google Scholar 

  15. Blumenthal A, Lauber J, Hoffmann R, Ernst M, Keller C, Buer J, Ehlers S, Reiling N (2005) Common and unique gene expression signatures of human macrophages in response to four strains of Mycobacterium avium that differ in their growth and persistence characteristics. Infect Immun 73(6):3330–3341

    Article  PubMed  CAS  Google Scholar 

  16. Calamita H, Ko C, Tyagi S, Yoshimatsu T, Morrison NE, Bishai WR (2005) The Mycobacterium tuberculosis SigD sigma factor controls the expression of ribosome-associated gene products in stationary phase and is required for full virulence. Cell Microbiol 7(2):233–244

    Article  PubMed  CAS  Google Scholar 

  17. Khajoee V, Saito M, Takada H, Nomura A, Kusuhara K, Yoshida SI, Yoshikai Y, Hara T (2006) Novel roles of osteopontin and CXC chemokine ligand 7 in the defence against mycobacterial infection. Clin Exp Immunol 143(2):260–268

    Article  PubMed  CAS  Google Scholar 

  18. Pai RK, Pennini ME, Tobian AA, Canaday DH, Boom WH, Harding CV (2004) Prolonged toll-like receptor signaling by Mycobacterium tuberculosis and its 19-kilodalton lipoprotein inhibits gamma interferon-induced regulation of selected genes in macrophages. Infect Immun 72(11):6603–6614

    Article  PubMed  CAS  Google Scholar 

  19. Raju B, Hoshino Y, Belitskaya-Levy I, Dawson R, Ress S, Gold JA, Condos R, Pine R, Brown S, Nolan A, Rom WN, Weiden MD (2008) Gene expression profiles of bronchoalveolar cells in pulmonary TB. Tuberculosis (Edinb) 88(1):39–51

    Article  CAS  Google Scholar 

  20. Schreiber T, Ehlers S, Heitmann L, Rausch A, Mages J, Murray PJ, Lang R, Holscher C (2009) Autocrine IL-10 induces hallmarks of alternative activation in macrophages and suppresses antituberculosis effector mechanisms without compromising T cell immunity. J Immunol 183(2):1301–1312

    Article  PubMed  CAS  Google Scholar 

  21. Talaat AM, Lyons R, Howard ST, Johnston SA (2004) The temporal expression profile of Mycobacterium tuberculosis infection in mice. Proc Natl Acad Sci USA 101(13):4602–4607

    Article  PubMed  CAS  Google Scholar 

  22. Weiss DJ, Evanson OA, Deng M, Abrahamsen MS (2004) Sequential patterns of gene expression by bovine monocyte-derived macrophages associated with ingestion of mycobacterial organisms. Microb Pathog 37(4):215–224

    Article  PubMed  CAS  Google Scholar 

  23. Widdison S, Watson M, Piercy J, Howard C, Coffey TJ (2008) Granulocyte chemotactic properties of M. tuberculosis versus M. bovis-infected bovine alveolar macrophages. Mol Immunol 45(3):740–749

    Article  PubMed  CAS  Google Scholar 

  24. Thuong NT, Dunstan SJ, Chau TT, Thorsson V, Simmons CP, Quyen NT, Thwaites GE, Thi Ngoc Lan N, Hibberd M, Teo YY, Seielstad M, Aderem A, Farrar JJ, Hawn TR (2008) Identification of tuberculosis susceptibility genes with human macrophage gene expression profiles. PLoS Pathog 4(12):e1000229

    Article  PubMed  Google Scholar 

  25. Tailleux L, Waddell SJ, Pelizzola M, Mortellaro A, Withers M, Tanne A, Castagnoli PR, Gicquel B, Stoker NG, Butcher PD, Foti M, Neyrolles O (2008) Probing host pathogen cross-talk by transcriptional profiling of both Mycobacterium tuberculosis and infected human dendritic cells and macrophages. PLoS One 3(1):e1403

    Article  PubMed  Google Scholar 

  26. Tailleux L, Neyrolles O, Honore-Bouakline S, Perret E, Sanchez F, Abastado JP, Lagrange PH, Gluckman JC, Rosenzwajg M, Herrmann JL (2003) Constrained intracellular survival of Mycobacterium tuberculosis in human dendritic cells. J Immunol 170(4):1939–1948

    PubMed  CAS  Google Scholar 

  27. Li Z, Li P, Krishnan A, Liu J (2011) Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics 27(19):2686–2691

    Article  PubMed  CAS  Google Scholar 

  28. Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, Thorsson V (2006) The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7(5):R36

    Article  PubMed  Google Scholar 

  29. Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1):172–181

    Article  PubMed  CAS  Google Scholar 

  30. di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23(3):377–383

    Article  PubMed  Google Scholar 

  31. Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629):102–105

    Article  PubMed  CAS  Google Scholar 

  32. Yeung MK, Tegner J, Collins JJ (2002) Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA 99(9):6163–6168

    Article  PubMed  CAS  Google Scholar 

  33. Li X, Rao S, Jiang W, Li C, Xiao Y, Guo Z, Zhang Q, Wang L, Du L, Li J, Li L, Zhang T, Wang QK (2006) Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling. BMC Bioinformatics 7:26

    Article  PubMed  Google Scholar 

  34. Mukhopadhyay ND, Chatterjee S (2007) Causality and pathway search in microarray time series experiment. Bioinformatics 23(4):442–449

    Article  PubMed  CAS  Google Scholar 

  35. Schafer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764

    Article  PubMed  Google Scholar 

  36. Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18):3594–3603

    Article  PubMed  CAS  Google Scholar 

  37. Zou M, Conzen SD (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21(1):71–79

    Article  PubMed  CAS  Google Scholar 

  38. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620

    Article  PubMed  CAS  Google Scholar 

  39. Hartemink AJ, Gifford DK, Jaakkola TS, Young RA (2001) Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac Symp Biocomput 6:422–433

    Google Scholar 

  40. Vohradsky J (2001) Neural model of the genetic network. J Biol Chem 276(39):36168–36173

    Article  PubMed  CAS  Google Scholar 

  41. Mestl T, Plahte E, Omholt SW (1995) A mathematical framework for describing and analysing gene regulatory networks. J Theor Biol 176(2):291–300

    Article  PubMed  CAS  Google Scholar 

  42. McAdams HH, Arkin A (1997) Stochastic mechanisms in gene expression. Proc Natl Acad Sci USA 94(3):814–819

    Article  PubMed  CAS  Google Scholar 

  43. Hasty J, McMillen D, Isaacs F, Collins JJ (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2(4):268–279

    Article  PubMed  CAS  Google Scholar 

  44. Smolen P, Baxter DA, Byrne JH (2000) Mathematical modeling of gene networks. Neuron 26(3):567–580

    Article  PubMed  CAS  Google Scholar 

  45. Glass L, Kauffman SA (1973) The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol 39(1):103–129

    Article  PubMed  CAS  Google Scholar 

  46. Kauffman S (1969) Homeostasis and differentiation in random genetic control networks. Nature 224(5215):177–178

    Article  PubMed  CAS  Google Scholar 

  47. Huang S (1999) Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. J Mol Med (Berl) 77(6):469–480

    Article  CAS  Google Scholar 

  48. Shmulevich I, Lahdesmaki H, Dougherty ER, Astola J, Zhang W (2003) The role of certain Post classes in Boolean network models of genetic networks. Proc Natl Acad Sci USA 100(19):10734–10739

    Article  PubMed  CAS  Google Scholar 

  49. Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9(10):770–780

    Article  PubMed  CAS  Google Scholar 

  50. Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2):261–274

    Article  PubMed  CAS  Google Scholar 

  51. Kumar D, Srikanth R, Ahlfors H, Lahesmaa R, Rao KV (2007) Capturing cell-fate decisions from the molecular signatures of a receptor-dependent signaling response. Mol Syst Biol 3:150

    Article  PubMed  Google Scholar 

  52. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA Jr, Marks JR, Dressman HK, West M, Nevins JR (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439(7074):353–357

    Article  PubMed  CAS  Google Scholar 

  53. Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, D’Amico M, Pestell RG, West M, Nevins JR (2003) Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 34(2):226–230

    Article  PubMed  CAS  Google Scholar 

  54. Sweet-Cordero A, Mukherjee S, Subramanian A, You H, Roix JJ, Ladd-Acosta C, Mesirov J, Golub TR, Jacks T (2005) An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 37(1):48–55

    PubMed  CAS  Google Scholar 

  55. Chang JT, Carvalho C, Mori S, Bild AH, Gatza ML, Wang Q, Lucas JE, Potti A, Febbo PG, West M, Nevins JR (2009) A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell 34(1):104–114

    Article  PubMed  CAS  Google Scholar 

  56. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935

    Article  PubMed  CAS  Google Scholar 

  57. Brunet JP, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA 101(12):4164–4169

    Article  PubMed  CAS  Google Scholar 

  58. Carvalho CM, Chang J, Lucas JE, Nevins JR, Wang Q, West M (2008) High-dimensional sparse factor modeling: applications in gene expression genomics. J Am Stat Assoc 103(484):1438–1456

    Article  PubMed  CAS  Google Scholar 

  59. Kim HD, Shay T, O’Shea EK, Regev A (2009) Transcriptional regulatory circuits: predicting numbers from alphabets. Science 325(5939):429–432

    PubMed  CAS  Google Scholar 

  60. Beer MA, Tavazoie S (2004) Predicting gene expression from sequence. Cell 117(2):185–198

    Article  PubMed  CAS  Google Scholar 

  61. Das D, Nahle Z, Zhang MQ (2006) Adaptively inferring human transcriptional subnetworks. Mol Syst Biol 2(2006):0029

    PubMed  Google Scholar 

  62. Nguyen DH, D’Haeseleer P (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Mol Syst Biol 2(2006):0012

    PubMed  Google Scholar 

  63. Raveh-Sadka T, Levo M, Segal E (2009) Incorporating nucleosomes into thermodynamic models of transcription regulation. Genome Res 19(8):1480–1496

    Article  PubMed  CAS  Google Scholar 

  64. Segal E, Raveh-Sadka T, Schroeder M, Unnerstall U, Gaul U (2008) Predicting expression patterns from regulatory sequence in Drosophila segmentation. Nature 451(7178):535–540

    Article  PubMed  CAS  Google Scholar 

  65. Negre N, Brown CD, Ma L, Bristow CA, Miller SW, Wagner U, Kheradpour P, Eaton ML, Loriaux P, Sealfon R, Li Z, Ishii H, Spokony RF, Chen J, Hwang L, Cheng C, Auburn RP, Davis MB, Domanus M, Shah PK, Morrison CA, Zieba J, Suchy S, Senderowicz L, Victorsen A, Bild NA, Grundstad AJ, Hanley D, MacAlpine DM, Mannervik M, Venken K, Bellen H, White R, Gerstein M, Russell S, Grossman RL, Ren B, Posakony JW, Kellis M, White KP (2011) A cis-regulatory map of the Drosophila genome. Nature 471(7339):527–531

    Article  PubMed  CAS  Google Scholar 

  66. Oliveri P, Tu Q, Davidson EH (2008) Global regulatory logic for specification of an embryonic cell lineage. Proc Natl Acad Sci USA 105(16):5955–5962

    Article  PubMed  CAS  Google Scholar 

  67. Hegde SR, Manimaran P, Mande SC (2008) Dynamic changes in protein functional linkage networks revealed by integration with gene expression data. PLoS Comput Biol 4(11):e1000237

    Article  PubMed  Google Scholar 

  68. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113

    Article  PubMed  CAS  Google Scholar 

  69. Bonde BK, Beste DJ, Laing E, Kierzek AM, McFadden J (2011) Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis. PLoS Comput Biol 7(6):e1002060

    Article  PubMed  CAS  Google Scholar 

  70. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8

    Article  PubMed  Google Scholar 

  71. Trevino S 3rd, Sun Y, Cooper TF, Bassler KE (2012) Robust detection of hierarchical communities from Escherichia coli gene expression data. PLoS Comput Biol 8(2):e1002391

    Article  PubMed  CAS  Google Scholar 

  72. Manganelli R, Provvedi R, Rodrigue S, Beaucher J, Gaudreau L, Smith I (2004) Sigma factors and global gene regulation in Mycobacterium tuberculosis. J Bacteriol 186(4):895–902

    Article  PubMed  CAS  Google Scholar 

  73. Hartkoorn RC, Sala C, Uplekar S, Busso P, Rougemont J, Cole ST (2012) Genome-wide definition of the SigF regulon in Mycobacterium tuberculosis. J Bacteriol 194(8):2001–2009

    Article  PubMed  CAS  Google Scholar 

  74. Balazsi G, Heath AP, Shi L, Gennaro ML (2008) The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest. Mol Syst Biol 4:225

    Article  PubMed  Google Scholar 

  75. Kussell E, Leibler S (2005) Phenotypic diversity, population growth, and information in fluctuating environments. Science 309(5743):2075–2078

    Article  PubMed  CAS  Google Scholar 

  76. Chao MC, Rubin EJ (2010) Letting sleeping dos lie: does dormancy play a role in tuberculosis? Annu Rev Microbiol 64:293–311

    Article  PubMed  CAS  Google Scholar 

  77. Chauhan S, Sharma D, Singh A, Surolia A, Tyagi JS (2011) Comprehensive insights into Mycobacterium tuberculosis DevR (DosR) regulon activation switch. Nucleic Acids Res 39(17):7400–7414

    Article  PubMed  CAS  Google Scholar 

  78. Ford CB, Lin PL, Chase MR, Shah RR, Iartchouk O, Galagan J, Mohaideen N, Ioerger TR, Sacchettini JC, Lipsitch M, Flynn JL, Fortune SM (2011) Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat Genet 43(5):482–486

    Article  PubMed  CAS  Google Scholar 

  79. Gill WP, Harik NS, Whiddon MR, Liao RP, Mittler JE, Sherman DR (2009) A replication clock for Mycobacterium tuberculosis. Nat Med 15(2):211–214

    Article  PubMed  CAS  Google Scholar 

  80. Magombedze G, Mulder N (2012) A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages. J Theor Biol 292:44–59

    Article  PubMed  Google Scholar 

  81. Mawuenyega KG, Forst CV, Dobos KM, Belisle JT, Chen J, Bradbury EM, Bradbury AR, Chen X (2005) Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling. Mol Biol Cell 16(1):396–404

    Article  PubMed  CAS  Google Scholar 

  82. Sanz J, Navarro J, Arbues A, Martin C, Marijuan PC, Moreno Y (2011) The transcriptional regulatory network of Mycobacterium tuberculosis. PLoS One 6(7):e22178

    Article  PubMed  CAS  Google Scholar 

  83. Strong M, Graeber TG, Beeby M, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D (2003) 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

    Article  PubMed  CAS  Google Scholar 

  84. Venancio TM, Aravind L (2009) Reconstructing prokaryotic transcriptional regulatory networks: lessons from actinobacteria. J Biol 8(3):29

    Article  PubMed  Google Scholar 

  85. Wang Y, Cui T, Zhang C, Yang M, Huang Y, Li W, Zhang L, Gao C, He Y, Li Y, Huang F, Zeng J, Huang C, Yang Q, Tian Y, Zhao C, Chen H, Zhang H, He ZG (2010) Global protein-protein interaction network in the human pathogen Mycobacterium tuberculosis H37Rv. J Proteome Res 9(12):6665–6677

    Article  PubMed  CAS  Google Scholar 

  86. Hegde SR, Rajasingh H, Das C, Mande SS, Mande SC (2012) Understanding communication signals during Mycobacterial latency through predicted genome-wide protein interactions and Boolean modeling. PLoS One 7(3):e33893

    Article  PubMed  CAS  Google Scholar 

  87. Drumm JE, Mi K, Bilder P, Sun M, Lim J, Bielefeldt-Ohmann H, Basaraba R, So M, Zhu G, Tufariello JM, Izzo AA, Orme IM, Almo SC, Leyh TS, Chan J (2009) Mycobacterium tuberculosis universal stress protein Rv2623 regulates bacillary growth by ATP-Binding: requirement for establishing chronic persistent infection. PLoS Pathog 5(5):e1000460

    Article  PubMed  Google Scholar 

  88. He H, Bretl DJ, Penoske RM, Anderson DM, Zahrt TC (2011) Components of the Rv0081-Rv0088 locus, which encodes a predicted formate hydrogenlyase complex, are coregulated by Rv0081, MprA, and DosR in Mycobacterium tuberculosis. J Bacteriol 193(19):5105–5118

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. International Centre for Genetic Engineering and Biotechnology, New Delhi, India

    Kanury V. S. Rao & Dhiraj Kumar

  2. National Centre for Cell Science, Pune, India

    Shekhar C. Mande

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  1. Kanury V. S. Rao
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  2. Dhiraj Kumar
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  3. Shekhar C. Mande
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Correspondence to Kanury V. S. Rao or Shekhar C. Mande .

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  1. Microbial and Cellular Sciences, University of Surrey, Guildford District, Guildford, GU2 5XH, Surrey, United Kingdom

    Johnjoe McFadden

  2. School of Biomedical and Molecular Scien, University of Surrey, Stag Hill, Guildford, GU2 7XH, Surrey, United Kingdom

    Dany J.V. Beste

  3. Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, GU2 5XH, Surrey, United Kingdom

    Andrzej M. Kierzek

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Rao, K.V.S., Kumar, D., Mande, S.C. (2013). Probing Gene Regulatory Networks to Decipher Host–Pathogen Interactions. In: McFadden, J., Beste, D., Kierzek, A. (eds) Systems Biology of Tuberculosis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4966-9_3

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