Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

Seminars in Immunology

Volumn 26, Issue 6, 2014, Pages 610-622

  Rienksma, Rienk A ^a   Suarez Diez, Maria ^a   Spina, Lucie ^b   Schaap, Peter J ^a   Martins Dos Santos, Vitor A P ^a,c  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b UNIVERSITÉ PAUL SABATIER   (France)

^c LIFEGLIMMER GMBH   (Germany)

Author keywords

Constraint based metabolic model; Gene essentiality; Metabolic model; Metabolic state; Mycobacterium tuberculosis; Systems biology

Indexed keywords

CARBON 13; BACTERIAL PROTEIN; CARBON; TUBERCULOSTATIC AGENT;

BACTERIAL GENE; BACTERIAL GENOME; BACTERIAL GROWTH; BACTERIAL METABOLISM; HOST PATHOGEN INTERACTION; MATHEMATICAL MODEL; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; QUANTITATIVE ANALYSIS; REVIEW; DRUG EFFECTS; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; MICROBIOLOGY; MOLECULARLY TARGETED THERAPY; MULTIDRUG RESISTANCE; PATHOLOGY; STATISTICAL MODEL; SYSTEMS BIOLOGY; TUBERCULOSIS, MULTIDRUG-RESISTANT; TUBERCULOSIS, PULMONARY;

ANTITUBERCULAR AGENTS; BACTERIAL PROTEINS; CARBON ISOTOPES; DRUG RESISTANCE, MULTIPLE, BACTERIAL; GENE EXPRESSION REGULATION, BACTERIAL; GENE REGULATORY NETWORKS; GENOME, BACTERIAL; HOST-PATHOGEN INTERACTIONS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, STATISTICAL; MOLECULAR TARGETED THERAPY; MYCOBACTERIUM TUBERCULOSIS; SYSTEMS BIOLOGY; TUBERCULOSIS, MULTIDRUG-RESISTANT; TUBERCULOSIS, PULMONARY;

EID: 84920022371     PISSN: 10445323     EISSN: 10963618     Source Type: Journal    
DOI: 10.1016/j.smim.2014.09.013     Document Type: Review

References (85)

1. World Health Organization Global Tuberculosis Report 2013 2013, World Health Organization.
2. Gengenbacher M., Kaufmann S.H.E. Mycobacterium tuberculosis: success through dormancy. FEMS Microbiol Rev 2012, 36:514-532.
3. Ma Z., Lienhardt C., McIlleron H., Nunn A.J., Wang X. Global tuberculosis drug development pipeline: the need and the reality. Lancet 2010, 375:2100-2109.
4. Sassetti C.M., Boyd D.H., Rubin E.J. Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 2003, 48:77-84.
5. Griffin J.E., Gawronski J.D., Dejesus M.A., Ioerger T.R., Akerley B.J., Sassetti C.M. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog 2011, 7:e1002251.
6. Sacchettini J.C., Rubin E.J., Freundlich J.S. Drugs versus bugs: in pursuit of the persistent predator Mycobacterium tuberculosis. Nat Rev Microbiol 2008, 6:41-52.
7. Bordbar A., Lewis N.E., Schellenberger J., Palsson B.O., Jamshidi N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 2010, 6:422.
8. Wipperman M.F., Sampson N.S., Thomas S.T. Pathogen roid rage: cholesterol utilization by Mycobacterium tuberculosis. Crit Rev Biochem Mol Biol 2014, 1-25.
9. 13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for intracellular Mycobacterium tuberculosis. Chem Biol 2013, 20:1012-1021.
10. Gouzy A., Larrouy-Maumus G., Wu T-D., Peixoto A., Levillain F., Lugo-Villarino G., et al. Mycobacterium tuberculosis nitrogen assimilation and host colonization require aspartate. Nat Chem Biol 2013, 9:674-676.
11. Gouzy A., Larrouy-Maumus G., Bottai D., Levillain F., Dumas A., Wallach J.B., et al. Mycobacterium tuberculosis exploits asparagine to assimilate nitrogen and resist acid stress during infection. PLoS Pathog 2014, 10:e1003928.
12. Bhat A., Vashisht R., Chandra N. Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid. Syst Synth Biol 2010, 4:299-309.
13. Haggart C.R., Bartell J.A., Saucerman J.J., Papin J.A. Whole-genome metabolic network reconstruction and constraint-based modeling. Methods in enzymology 2011, 411-433. Academic Press, [chapter 21]. M.V. Daniel Jameson, V.W. Hans (Eds.).
14. Raman K., Rajagopalan P., Chandra N. Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLoS Comput Biol 2005, 1:e46.
15. Kim H.U., Kim S.Y., Jeong H., Kim T.Y., Kim J.J., Choy H.E., et al. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol Syst Biol 2011, 7.
16. Schuetz R., Kuepfer L., Sauer U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 2007, 3:119.
17. Bird B.R., Stewart W.E., Lightfoot E.N. Transport phenomena 2007, John Wiley & Sons, United States of America. revised 2nd ed.
18. Lewis N.E., Nagarajan H., Palsson B.O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 2012, 10:291-305.
19. Chandrasekaran S., Price N.D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2010, 107:17845-17850.
20. Bordbar A., Monk J.M., King Z.A., Palsson B.O. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 2014, 15:107-120.
21. Schellenberger J., Palsson B.O. Use of randomized sampling for analysis of metabolic networks. J Biol Chem 2009, 284:5457-5461.
22. Bordel S., Agren R., Nielsen J. Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 2010, 6:e1000859.
23. Jamshidi N., Palsson B.O. 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.
24. Lofthouse E.K., Wheeler P.R., Beste D.J.V., Khatri B.L., Wu H., Mendum Tom A., et al. Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex. PLOS ONE 2013, 8:e75913.
25. Phong W.Y., Lin W., Rao S.P.S., Dick T., Alonso S., Pethe K. Characterization of phosphofructokinase activity in Mycobacterium tuberculosis reveals that a functional glycolytic carbon flow is necessary to limit the accumulation of toxic metabolic intermediates under hypoxia. PLOS ONE 2013, 8:e56037.
26. Oberhardt M.A., Puchałka J., Martins Dos Santos V.A.P., Papin J.A. Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol 2011, 7:e1001116.
27. Becker S.A., Feist A.M., Mo M.L., Hannum G., Palsson B.O., Herrgard M.J. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2:727-738.
28. Fell D.A., Small J.R. Fat synthesis in adipose tissue - an examination of stoichiometric constraints. Biochem J 1986, 238:781-786.
29. Marrakchi H., Lanéelle M.-A., Daffé M. Mycolic acids: structures, biosynthesis, and beyond. Chem Biol 2014, 21:67-85.
30. Beste D.J., Hooper T., Stewart G., Bonde B., Avignone-Rossa C., Bushell M.E., et al. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol 2007, 8:R89.
31. Griffin J.E., Pandey A.K., Gilmore S.A., Mizrahi V., McKinney J.D., Bertozzi C.R., et al. Cholesterol catabolism by Mycobacterium tuberculosis requires transcriptional and metabolic adaptations. Chem Biol 2012, 19:218-227.
32. Colijn C., Brandes A., Zucker J., Lun D.S., Weiner B., Farhat M.R., et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5:e1000489.
33. Fang X., Wallqvist A., Reifman J. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Syst Biol 2010, 4:160.
34. Duarte N.C., Becker S.A., Jamshidi N., Thiele I., Mo M.L., Vo T.D., et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A 2007, 104:1777-1782.
35. Chindelevitch L., Stanley S., Hung D., Regev A., Berger B. MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis. Genome Biol 2012, 13:r6.
36. Beste D.J., Peters J., Hooper T., Avignone-Rossa C., Bushell M.E., McFadden J. Compiling a molecular inventory for Mycobacterium bovis BCG at two growth rates: evidence for growth rate-mediated regulation of ribosome biosynthesis and lipid metabolism. J Bacteriol 2005, 187:1677-1684.
37. Youmans A.S., Youmans G.P. Ribonucleic acid, deoxyribonucleic acid, and protein content of cells of different ages of Mycobacterium tuberculosis and the relationship to immunogenicity. J Bacteriol 1968, 95:272-279.
38. 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 2011, 7:e1002093.
39. McMahon M.D., Rush J.S., Thomas M.G. Analyses of MbtB, MbtE, and MbtF suggest revisions to the mycobactin biosynthesis pathway in Mycobacterium tuberculosis. J Bacteriol 2012, 194:2809-2818.
40. Voskuil M.I., Bartek I., Visconti K., Schoolnik G.K. The response of Mycobacterium tuberculosis to reactive oxygen and nitrogen species. Front Microbiol 2011, 2.
41. Brooksbank C., Cameron G., Thornton J. The European Bioinformatics Institute's data resources: towards systems biology. Nucleic Acids Res 2005, 33:D46-D53.
42. Acland A.A.R., Barrett T., Beck J., Benson D.A., Bollin C., Bolton E., et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2013, 41:D8-D20.
43. Kanehisa M., Goto S., Hattori M., Aoki-Kinoshita K.F., Itoh M., Kawashima S., et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, 34:D354-D357.
44. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 1988, 28:31-36.
45. Daffé M., Crick D.C., Jackson M. Genetics of capsular polysaccaharides and cell envelope (glyco)lipids. Microbiol Spectrum 2014, 2:1-46.
46. 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli. Metab Eng 2011, 13:38-48.
47. 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.
48. Garnier T., Eiglmeier K., Camus J.C., Medina N., Mansoor H., Pryor M., et al. The complete genome sequence of Mycobacterium bovis. Proc Natl Acad Sci U S A 2003, 100:7877-7882.
49. Cole S.T., Brosch R., Parkhill J., Garnier T., Churcher C., Harris D., et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998, 393:537-544.
50. Brosch R., Gordon S.V., Garnier T., Eiglmeier K., Frigui W., Valenti P., et al. Genome plasticity of BCG and impact on vaccine efficacy. Proc Natl Acad Sci U S A 2007, 104:5596-5601.
51. Parker S.K., Curtin K.M., Vasil M.L. Purification and characterization of mycobacterial phospholipase A: an activity associated with mycobacterial cutinase. J Bacteriol 2007, 189:4153-4160.
52. Yang X., Nesbitt N.M., Dubnau E., Smith I., Sampson N.S. Cholesterol metabolism increases the metabolic pool of propionate in Mycobacterium tuberculosis. Biochemistry 2009, 48:3819-3821.
53. Kendall S.L., Burgess P., Balhana R., Withers M., Ten Bokum A., Lott J.S., et al. Cholesterol utilization in mycobacteria is controlled by two TetR-type transcriptional regulators: kstR and kstR2. Microbiology 2010, 156:1362-1371.
54. Kendall S.L., Withers M., Soffair C.N., Moreland N.J., Gurcha S., Sidders B., et al. A highly conserved transcriptional repressor controls a large regulon involved in lipid degradation in Mycobacterium smegmatis and Mycobacterium tuberculosis. Mol Microbiol 2007, 65:684-699.
55. Nesbitt N.M., Yang X., Fontán P., Kolesnikova I., Smith I., Sampson N.S., et al. A thiolase of Mycobacterium tuberculosis is required for virulence and production of androstenedione and androstadienedione from cholesterol. Infect Immun 2010, 78:275-282.
56. Uhía I., Galán B., Medrano F.J., García J.L. Characterization of the KstR-dependent promoter of the gene for the first step of the cholesterol degradative pathway in Mycobacterium smegmatis. Microbiology 2011, 157:2670-2680.
57. Galagan J.E. Genomic insights into tuberculosis. Nat Rev Genet 2014, 15:307-320.
58. Radusky L., Defelipe L.A., Lanzarotti E., Luque J., Barril X., Marti M.A., et al. TuberQ: a Mycobacterium tuberculosis protein druggability database. Database 2014, 2014.
59. Chang J.C., Miner M.D., Pandey A.K., Gill W.P., Harik N.S., Sassetti C.M., et al. igr Genes and Mycobacterium tuberculosis cholesterol metabolism. J Bacteriol 2009, 191:5232-5239.
60. Petrusma M., Dijkhuizen L., van der Geize R. Structural features in the KshA terminal oxygenase protein that determine substrate preference of 3-ketosteroid 9α-hydroxylase enzymes. J Bacteriol 2012, 194:115-121.
61. Chakraborty S., Gruber T., Barry C.E., Boshoff H.I., Rhee K.Y. Para-aminosalicylic acid acts as an alternative substrate of folate metabolism in mycobacterium tuberculosis. Science 2013, 339:88-91.
62. Blazier A.S., Papin J.A. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 2012, 3:299.
63. Hyduke D.R., Lewis N.E., Palsson B.O. Analysis of omics data with genome-scale models of metabolism. Mol Biosyst 2013, 9:167-174.
64. Reed J.L. Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 2012, 8:e1002662.
65. Machado D., Herrgard M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 2014, 10:e1003580.
66. Boshoff H.I., Myers T.G., Copp B.R., McNeil M.R., Wilson M.A., Barry C.E. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. J Biol Chem 2004, 279:40174-40184.
67. Fang X., Wallqvist A., Reifman J. Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia. PLoS Comput Biol 2012, 8:e1002688.
68. Bonde B.K., Beste D.J., Laing E., Kierzek A.M., McFadden J. Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis. PLoS Comput Biol 2011, 7:e1002060.
69. Schnappinger D., Ehrt S., Voskuil M.I., Liu Y., Mangan J.A., Monahan I.M., et al. Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: insights into the phagosomal environment. J Exp Med 2003, 198:693-704.
70. Garton N.J., Waddell S.J., Sherratt A.L., Lee S-M., Smith R.J., Senner C., et al. Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum. PLoS Med 2008, 5:e75.
71. Beste D.J.V., Laing E., Bonde B., Avignone-Rossa C., Bushell M.E., McFadden J.J. Transcriptomic analysis identifies growth rate modulation as a component of the adaptation of mycobacteria to survival inside the macrophage. J Bacteriol 2007, 189:3969-3976.
72. Balázsi G., Heath A.P., Shi L., Gennaro M.L. The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest. Mol Syst Biol 2008, 4.
73. van Dam J.C.J., Schaap P.J., Martins Dos Santos V.A.P., Suarez-Diez M. Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis. BMC Syst Biol 2014, 8:111.
74. Teusink B., Wiersma A., Jacobs L., Notebaart R.A., Smid E.J. Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation. PLoS Comput Biol 2009, 5:e1000410.
75. Shi L., Sohaskey C.D., Pfeiffer C., Datta P., Parks M., McFadden J., et al. Carbon flux rerouting during Mycobacterium tuberculosis growth arrest. Mol Microbiol 2010, 78:1199-1215.
76. Gouzy A., Poquet Y., Neyrolles O. A central role for aspartate in Mycobacterium tuberculosis physiology and virulence. Front Cell Infect Microbiol 2013, 3:68.
77. Niederweis M. Nutrient acquisition by mycobacteria. Microbiology 2008, 154:679-692.
78. Fang X., Wallqvist A., Reifman J. A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis. BMC Syst Biol 2009, 3:92.
79. Thiele I., Swainston N., Fleming R.M.T., Hoppe A., Sahoo S., Aurich M.K., et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013, 31:419-425.
80. Amaral L., Viveiros M. Why thioridazine in combination with antibiotics cures extensively drug-resistant Mycobacterium tuberculosis infections. Int J Antimicrob Agents 2012, 39:376-380.
81. Varma A., Boesch B.W., Palsson B.O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl Environ Microbiol 1993, 59:2465-2473.
82. Varma A., Palsson B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60:3724-3731.
83. Andries K., Verhasselt P., Guillemont J., Göhlmann H.W.H., Neefs J.-M., Winkler H., et al. A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science 2005, 307:223-227.
84. Makarov V., Manina G., Mikusova K., Möllmann U., Ryabova O., Saint-Joanis B., et al. Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis. Science 2009, 324:801-804.
85. 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.