In silico analyses for the discovery of tuberculosis drug targets

Journal of Antimicrobial Chemotherapy

Volumn 68, Issue 12, 2013, Pages 2701-2709

  Chung, Bevan Kai Sheng ^a   Dick, Thomas ^b   Lee, Dong Yup ^c  

^a AGENCY FOR SCIENCE   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Antimycobacterials; Bioinformatics; In silico methods; Systems biology; Target identification

Indexed keywords

AMINOTRANSFERASE; ANTIMYCOBACTERIAL AGENT; ARABINOGALACTAN; BINDING PROTEIN; CYCLOSERINE; ETHAMBUTOL; ETHIONAMIDE; GLUTAMATE AMMONIA LIGASE; HISTIDINOL PHOSPHATASE; HISTIDINOLPHOSPHATE AMINOTRANSFERASE; ISONIAZID; MYCOLIC ACID; PYRAZINAMIDE; TRANSCRIPTION FACTOR; UNCLASSIFIED DRUG;

ANALYTIC METHOD; ANTIBACTERIAL ACTIVITY; ARTICLE; BACTERIAL STRAIN; BACTERIUM; CELL METABOLISM; COMPUTER MODEL; CORYNEBACTERIUM GLUTAMICUM; COST EFFECTIVENESS ANALYSIS; DRUG DESIGN; DRUG TARGETING; GENE EXPRESSION; GENE REGULATORY NETWORK; HIGH THROUGHPUT SCREENING; HUMAN; LARGE SCALE PRODUCTION; METABOLOME; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PHARMACOGENETICS; PROSPECTIVE STUDY; PROTEIN PHOSPHORYLATION; PROTEIN PROTEIN INTERACTION; PROTEIN SYNTHESIS; SIGNAL TRANSDUCTION; STRUCTURAL BIOINFORMATICS; SYSTEMS BIOLOGY; THERMODYNAMICS; TUBERCULOSIS;

ANTIMYCOBACTERIALS; BIOINFORMATICS; IN SILICO METHODS; SYSTEMS BIOLOGY; TARGET IDENTIFICATION;

ANTITUBERCULAR AGENTS; BACTERIAL PROTEINS; COMPUTATIONAL BIOLOGY; DRUG DISCOVERY; HUMANS; MYCOBACTERIUM TUBERCULOSIS; PROTEIN INTERACTION MAPS; TUBERCULOSIS;

EID: 84887524955     PISSN: 03057453     EISSN: 14602091     Source Type: Journal    
DOI: 10.1093/jac/dkt273     Document Type: Article

References (71)

1. Gandy M, Zumla A. The resurgence of disease: social and historical perspectives on the 'new' tuberculosis. Soc Sci Med 2002; 55: 385-96; discussion 97-401.
2. Sacchettini JC, Rubin EJ, Freundlich JS.Drugs versus bugs: in pursuit of the persistent predator Mycobacteriumtuberculosis. Nat Rev Microbiol 2008; 6: 41-52.
3. Somoskovi A, Parsons LM, Salfinger M. Themolecular basis of resistance to isoniazid, rifampin, and pyrazinamide in Mycobacterium tuberculosis. Respir Res 2001; 2: 164-8.
4. Zhang Y, Yew WW. Mechanisms of drug resistance in Mycobacterium tuberculosis. Int J Tuberc Lung Dis 2009; 13: 1320-30.
5. Espinal MA, Kim SJ, Suarez PG et al. Standard short-course chemotherapy for drug-resistant tuberculosis: treatment outcomes in 6 countries. JAMA 2000; 283: 2537-45.
6. Koul A, Arnoult E, Lounis N et al. The challenge of newdrug discovery for tuberculosis. Nature 2011; 469: 483-90.
7. Barry CE 3rd, Boshoff HI, Dartois V et al. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. Nat Rev Microbiol 2009; 7: 845-55.
8. Brotz-oesterhelt H, Sass P. Postgenomic strategies in antibacterial drug discovery. Future Microbiol 2010; 5: 1553-79.
9. Dick T, Young D.Howantibacterials reallywork: impact on drug discovery. Future Microbiol 2011; 6: 603-4.
10. Gandotra S, Schnappinger D, Monteleone M et al. In vivo gene silencing identifies the Mycobacterium tuberculosis proteasome as essential for the bacteria to persist in mice. Nat Med 2007; 13: 1515-20.
11. Wei JR, Krishnamoorthy V,Murphy K et al. Depletion of antibiotic targets has widely varying effects on growth. Proc Natl Acad Sci USA 2011; 108: 4176-81.
12. Kohanski MA, Dwyer DJ, Hayete B et al. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 2007; 130: 797-810.
13. Fischer HP. Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery. Biotechnol Annu Rev 2005; 11: 1-68.
14. Pujol A,Mosca R, Farres J et al.Unveiling the role of networkand systems biology in drug discovery. Trends Pharmacol Sci 2010; 31: 115-23.
15. Strange K. The end of 'naive reductionism': rise of systems biology or renaissance of physiology? Am J Physiol Cell Physiol 2005; 288: C968-74.
16. Aderem A. Systems biology: its practice and challenges. Cell 2005; 121: 511-3.
17. Fisher J, Henzinger TA. Executable cell biology. Nat Biotechnol 2007; 25:1239-49.
18. Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev 2007; 21: 1010-24.
19. Kanehisa M, Goto S, Hattori M et al. From genomics to chemical genomics: newdevelopments in KEGG.NucleicAcids Res 2006; 34:D354-7.
20. Caspi R, Altman T, Dale JM et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2010; 38: D473-9.
21. Galagan JE, Sisk P, Stolte C et al. TB database 2010: overview and update. Tuberculosis (Edinb) 2010; 90: 225-35.
22. Bruggeman FJ, Westerhoff HV. The nature of systems biology. Trends Microbiol 2007; 15: 45-50.
23. Bansal M, Belcastro V, Ambesi-impiombato A et al. How to infer gene networks from expression profiles. Mol Syst Biol 2007; 3: 78.
24. Imoto S, Tamada Y, Savoie CJ et al. Analysis of gene networks for drug target discovery and validation. Methods Mol Biol 2007; 360: 33-56.
25. Rachman H, Strong M, Ulrichs T et al. Unique transcriptome signature of Mycobacterium tuberculosis in pulmonary tuberculosis. Infect Immun 2006; 74: 1233-42.
26. Marcotte EM, Pellegrini M, Ng HL et al. Detecting protein function and protein-protein interactions from genome sequences. Science 1999; 285: 751-3.
27. Pellegrini M, Marcotte EM, Thompson MJ et al. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA 1999; 96: 4285-8.
28. Dandekar T, Snel B, Huynen M et al. Conservation of gene order:a fingerprint of proteins that physically interact. Trends Biochem Sci 1998; 23: 324-8.
29. Overbeek R, Fonstein M, D'souza M et al. The use of gene clusters to infer functional coupling. Proc Natl Acad Sci USA 1999; 96: 2896-901.
30. Strong M, Mallick P, Pellegrini M et al. Inference of protein function and protein linkages in Mycobacterium tuberculosis based on prokaryotic genome organization: a combined computational approach. Genome Biol 2003; 4: R59.
31. Wingender E, Chen X, Hehl R et al. TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res 2000; 28: 316-9.
32. Von Mering C, Huynen M, Jaeggi D et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 2003; 31: 258-61.
33. Krawczyk J, Kohl TA, Goesmann A et al.From Corynebacterium glutamicum to Mycobacterium tuberculosis-towards transfers of generegulatory networks and integrated data analyses with MycoRegNet. Nucleic Acids Res 2009; 37: e97.
34. Cui T, Zhang L, Wang X et al. Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis. BMC Genomics 2009; 10: 118.
35. Kushwaha SK, Shakya M. Protein interaction network analysis- approach for potential drug target identification in Mycobacterium tuberculosis. J Theor Biol 2010; 262: 284-94.
36. Raman K, Rajagopalan P, Chandra N. Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLoS Comput Biol 2005; 1: e46.
37. Beste DJ, Hooper T, Stewart G et al. GSMN-TB: a web-based genome-scale networkmodel of Mycobacterium tuberculosismetabolism. Genome Biol 2007; 8: R89.
38. Jamshidi N, Palsson BO. Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 2007; 1: 26.
39. Cole ST, Brosch R, Parkhill J et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998; 393: 537-44.
40. Kinnings SL, Xie L, Fung KH et al. The Mycobacterium tuberculosis drugome and its polypharmacological implications. PLoS Comput Biol 2010; 6: e1000976.
41. Colijn C, Brandes A, Zucker J et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009; 5: e1000489.
42. Stelling J.Mathematicalmodels inmicrobial systems biology. Curr Opin Microbiol 2004; 7: 513-8.
43. Berger SI, Iyengar R. Network analyses in systems pharmacology.Bioinformatics 2009; 25: 2466-72.
44. Raman K, Chandra N.Mycobacteriumtuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 2008; 8: 234.
45. Balazsi G, Heath AP, Shi L et al. The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest. Mol Syst Biol 2008; 4: 225.
46. Joshi RS, Jamdhade MD, Sonawane MS et al. Resistome analysis of Mycobacterium tuberculosis: identification of aminoglycoside 2'-Nacetyltransferase (AAC) as co-target for drug designing. Bioinformation 2013; 9: 174-81.
47. Verkhedkar KD, Raman K, Chandra NR et al.Metabolome based reaction graphs of M. tuberculosis and M. tuberculosis: a comparative network analysis. PLoS One 2007; 2: e881.
48. Yeh I,Hanekamp T, Tsoka S et al. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res 2004; 14: 917-24.
49. Rahman SA, Schomburg D. Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks. Bioinformatics 2006; 22: 1767-74.
50. Kim TY, Kim HU, Lee SY.Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks. Metab Eng 2010; 12: 105-11.
51. Hasan S, Daugelat S, Rao PS et al. Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis. PLoS Comput Biol 2006; 2: e61.
52. Raman K, Yeturu K, Chandra N. targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2008; 2: 109.
53. Oberhardt MA, Chavali AK, Papin JA. Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol Biol 2009; 500: 61-80.
54. Reed JL, Palsson BO. Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 2003; 185: 2692-9.
55. Knorr AL, Jain R, Srivastava R. Bayesian-based selection of metabolic objective functions. Bioinformatics 2006; 23: 351-7.
56. Schuetz R, Kuepfer L, Sauer U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 2007; 3: 119.
57. Gianchandani EP, Oberhardt MA, Burgard AP et al. Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 2008; 9: 43.
58. Segre D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA 2002; 99: 15112-7.
59. Shlomi T, Berkman O, Ruppin E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci USA 2005; 102: 7695-700.
60. Wiechert W. 13Cmetabolic flux analysis. Metab Eng 2001; 3: 195-206.
61. Beste DJ, Bonde B, Hawkins N et al. 13C metabolic flux analysis identifies an unusual route for pyruvate dissimilation in mycobacteria which requires isocitrate lyase and carbon dioxide fixation. PLoS Pathog 2011; 7: e1002091.
62. Chung BK, Lee DY. Flux-sumanalysis: ametabolite-centric approach for understanding the metabolic network. BMC Syst Biol 2009; 3: 117.
63. Lee DY, Chung BK, Yusufi FN et al. In silico genome-scalemodeling and analysis for identifying anti-tubercular drug targets. Drug Dev Res 2011; 72: 121-9.
64. Singh VK, Ghosh I. Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets. Theor Biol Med Model 2006; 3: 27.
65. Fang X, Wallqvist A, Reifman J. A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis. BMC Syst Biol 2009; 3: 92.
66. Vashisht R, Mondal AK, Jain A et al. Crowd sourcing a new paradigmfor interactome driven drug target identification in Mycobacterium tuberculosis. PLoS One 2012; 7: e39808.
67. Bordbar A, Lewis NE, Schellenberger J et al. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 2010; 6: 422.
68. Kitano H.Biological robustness incomplexhost-pathogensystems. Prog Drug Res 2007; 64: 239-63.
69. Knox C, Law V, Jewison T. et al. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011; 39: D1035-41.
70. Zhang Y. The magic bullets and tuberculosis drug targets. Annu Rev Pharmacol Toxicol 2005; 45: 529-64.
71. Zhang Y, Amzel LM. Tuberculosis drug targets. Curr Drug Targets 2002; 3: 131-54.