Computational tools for modeling xenometabolism of the human gut microbiota

Trends in Biotechnology

Volumn 32, Issue 3, 2014, Pages 157-165

  Klünemann, Martina ^a   Schmid, Melanie ^a   Patil, Kiran Raosaheb ^a  

^a EUROPEAN MOLECULAR BIOLOGY LABORATORY   (Germany)

Author keywords

Biotransformation; Functional metagenomics; Metabolic modeling; Microbial communities

Indexed keywords

BIOTRANSFORMATION; FUNCTIONAL METAGENOMICS; METABOLIC MODELING; MICROBIAL COMMUNITIES;

BIOTRANSFORMATION; DATABASES, FACTUAL; HUMANS; MICROBIOTA; MODELS, BIOLOGICAL; SOFTWARE; XENOBIOTICS;

EID: 84894254929     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2014.01.005     Document Type: Review

References (90)

1. Goldman P., et al. Metabolism of drugs by microorganisms in the intestine. Am. J. Clin. Nutr. 1974, 27:1348-1355.
2. Azad Khan A.K., et al. Tissue and bacterial splitting of sulphasalazine. Clin. Sci. 1983, 64:349-354.
3. Sousa T., et al. The gastrointestinal microbiota as a site for the biotransformation of drugs. Int. J. Pharm. 2008, 363:1-25.
4. Clayton T.A., et al. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:14728-14733.
5. Zheng X., et al. Melamine-induced renal toxicity is mediated by the gut microbiota. Sci. Transl. Med. 2013, 5:172ra22.
6. Wallace B., et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 2010, 330:831-835.
7. Wang Z., et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011, 472:57-63.
8. Cimperman L., et al. A randomized, double-blind, placebo-controlled pilot study of Lactobacillus reuteri ATCC 55730 for the prevention of antibiotic-associated diarrhea in hospitalized adults. J. Clin. Gastroenterol. 2011, 45:785-789.
9. Hickson M. Probiotics in the prevention of antibiotic-associated diarrhoea and Clostridium difficile infection. Ther. Adv. Gastroenterol. 2011, 4:185-197.
10. Haiser H.J., et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 2013, 341:295-298.
11. Haiser H.J., Turnbaugh P.J. Developing a metagenomic view of xenobiotic metabolism. Pharmacol. Res. 2013, 69:21-31.
12. Qin J., et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010, 464:59-65.
13. Structure, function and diversity of the healthy human microbiome. Nature 2012, 486:207-214. Human Microbiome Project Consortium.
14. Blaser M., et al. The microbiome explored: recent insights and future challenges. Nat. Rev. Microbiol. 2013, 11:213-217.
15. Levy R., Borenstein E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl. Acad. Sci. U.S.A. 2013, 110:12804-12809.
16. Thiele I., et al. A systems biology approach to studying the role of microbes in human health. Curr. Opin. Biotechnol. 2013, 24:4-12.
17. Kostic A.D., et al. Exploring host-microbiota interactions in animal models and humans. Genes Dev. 2013, 27:701-718.
18. Segata N., et al. Computational meta'omics for microbial community studies. Mol. Syst. Biol. 2013, 9:666.
19. Wikoff W., Anfora A. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:3698-3703.
20. Van Duynhoven J., et al. Metabolic fate of polyphenols in the human superorganism. Proc. Natl. Acad. Sci. U.S.A. 2011, 108(Suppl):4531-4538.
21. Gill S.R., et al. Metagenomic analysis of the human distal gut microbiome. Science 2006, 312:1355-1359.
22. Nicholson J.K., et al. Host-gut microbiota metabolic interactions. Science 2012, 336:1262-1267.
23. Johnson C.H., et al. Xenobiotic metabolomics: major impact on the metabolome. Annu. Rev. Pharmacol. Toxicol. 2012, 52:37-56.
24. Khersonsky O., Tawfik D.S. Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 2010, 79:471-505.
25. Ekins S. Predicting undesirable drug interactions with promiscuous proteins in silico. Drug Discov. Today 2004, 9:276-285.
26. Oguri K. Regiochemistry of cytochrome P450 isozymes. Annu. Rev. Pharmacol. Toxicol. 1994, 34:251-279.
27. Hatzimanikatis V., et al. Exploring the diversity of complex metabolic networks. Bioinformatics 2005, 21:1603-1609.
28. De Groot M.J.L., et al. Metabolite and reaction inference based on enzyme specificities. Bioinformatics 2009, 25:2975-2982.
29. Gao J., et al. The University of Minnesota Pathway Prediction System: multi-level prediction and visualization. Nucleic Acids Res. 2011, 39:W406-W411.
30. Boyer S., et al. Reaction site mapping of xenobiotic biotransformations. J. Chem. Inf. Model. 2007, 47:583-590.
31. Zomorrodi A.R., Maranas C.D. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 2012, 8:e1002363.
32. Klitgord N., Segrè D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 2010, 6:e1001002.
33. Stolyar S., et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 2007, 3:92.
34. Kanehisa M., et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40:D109-D114.
35. Jakoby W.B., Ziegler D.M. The enzymes of detoxication. J. Biol. Chem. 1990, 265:20715-20718.
36. Holzhütter H-G., et al. The virtual liver: a multidisciplinary, multilevel challenge for systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 2012, 4:221-235.
37. Valerio L.G., Long A. The in silico prediction of human-specific metabolites from hepatotoxic drugs. Curr. Drug Discov. Technol. 2010, 7:170-187.
38. Gao J., et al. The University of Minnesota Biocatalysis/Biodegradation Database: improving public access. Nucleic Acids Res. 2010, 38:D488-D491.
39. Nobeli I., et al. Protein promiscuity and its implications for biotechnology. Nat. Biotechnol. 2009, 27:157-167.
40. Foye's Principles of Medicinal Chemistry 2007, Lippincott Williams & Wilkins. D.A. Williams (Ed.).
41. Nikolova N., Jaworska J. Approaches to measure chemical similarity - a review. QSAR Comb. Sci. 2003, 22:1006-1026.
42. Nantasenamat C., et al. Advances in computational methods to predict the biological activity of compounds. Expert Opin. Drug Discov. 2010, 5:633-654.
43. Kirchmair J., et al. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model. 2012, 52:617-648.
44. Marchant C.A., et al. In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol. Mech. Methods 2008, 18:177-187.
45. Darvas F. MetabolExpert, an expert system for predicting metabolism of substances. QSAR in Environmental Toxicology-II 1987, 71-81. Springer. K.L.E. Kaiser, D. Riedel (Eds.).
46. Medema M.H., et al. Computational tools for the synthetic design of biochemical pathways. Nat. Rev. Microbiol. 2012, 10:191-202.
47. Moriya Y., et al. PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res. 2010, 38:W138-W143.
48. Chen L., et al. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics 2010, 11:293.
49. Kuhn M., et al. Large-scale prediction of drug-target relationships. FEBS Lett. 2008, 582:1283-1290.
50. Mu F., et al. Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 2011, 27:1537-1545.
51. Oh M., et al. Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J. Chem. Inf. Model. 2007, 47:1702-1712.
52. Schomburg I., et al. BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res. 2013, 41:D764-D772.
53. Muto A., et al. Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J. Chem. Inf. Comput. Sci. 2013, 10.1021/ci3005379.
54. Shu Y.Z., et al. Metabolism of levamisole, an anti-colon cancer drug, by human intestinal bacteria. Xenobiotica 1991, 21:737-750.
55. Li M., Wang B. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:2117-2122.
56. Yamada T., et al. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours. Mol. Syst. Biol. 2012, 8:581.
57. Henry C.S., et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 2010, 28:977-982.
58. Patil K.R., Nielsen J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. U.S.A. 2005, 102:2685-2689.
59. Zomorrodi A.R., et al. Mathematical optimization applications in metabolic networks. Metab. Eng. 2012, 14:672-686.
60. Oberhardt M.A., et al. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 2009, 5:320.
61. Lewis N.E., et al. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 2012, 10:291-305.
62. Finley S.D., et al. In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene. BMC Syst. Biol. 2010, 4:7.
63. Allison K., et al. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 2011, 473:216-220.
64. Maurice C.F., et al. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 2013, 152:39-50.
65. Booijink C.C.G.M., et al. Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed. Appl. Environ. Microbiol. 2010, 76:5533-5540.
66. Jacobs D.M., et al. (1)H NMR metabolite profiling of feces as a tool to assess the impact of nutrition on the human microbiome. NMR Biomed. 2008, 21:615-626.
67. Segrè D., et al. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. U.S.A. 2002, 99:15112-15117.
68. Brochado A.R., et al. Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS Comput. Biol. 2012, 8:e1002758.
69. Modi S.R., et al. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature 2013, 499:219-222.
70. Lepage P., et al. A metagenomic insight into our gut's microbiome. Gut 2013, 62:146-158.
71. Rey F.E., et al. Metabolic niche of a prominent sulfate-reducing human gut bacterium. Proc. Natl. Acad. Sci. U.S.A. 2013, 110:13582-13587.
72. Dunne C. Adaptation of bacteria to the intestinal niche: probiotics and gut disorder. Inflamm. Bowel Dis. 2001, 7:136-145.
73. Yang H., et al. Niche heterogeneity determines bacterial community structure in the termite gut (Reticulitermes santonensis). Environ. Microbiol. 2005, 7:916-932.
74. Hao W-L., Lee Y-K. Microflora of the gastrointestinal tract: a review. Methods Mol. Biol. 2004, 268:491-502.
75. Hooper L., et al. Interactions between the microbiota and the immune system. Science 2012, 336:1268-1273.
76. Costello E., Stagaman K. The application of ecological theory toward an understanding of the human microbiome. Science 2012, 336:1255-1262.
77. Abubucker S., et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 2012, 8:e1002358.
78. Kolmeder C.A., et al. Comparative metaproteomics and diversity analysis of human intestinal microbiota testifies for its temporal stability and expression of core functions. PLoS ONE 2012, 7:e29913.
79. Lee H., et al. Bacterial charity work leads to population-wide resistance. Nature 2010, 467:82-85.
80. Riley M., Wertz J. Bacteriocins: evolution, ecology, and application. Annu. Rev. Microbiol. 2002, 56:117-137.
81. Janga S.C., Babu M.M. Network-based approaches for linking metabolism with environment. Genome Biol. 2008, 9:239.
82. Borenstein E., et al. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:14482-14487.
83. Taffs R., et al. In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Syst. Biol. 2009, 3:114.
84. Freilich S., et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2011, 2:589.
85. Rodrigo G., et al. DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 2008, 24:2554-2556.
86. Rocha I., et al. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 2010, 4:45.
87. Chhabra R.S. Intestinal absorption and metabolism of xenobiotics. Environ. Health Perspect. 1979, 33:61-69.
88. Toxicology of the Gastrointestinal Tract 2007, CRC Press. S.C. Gad (Ed.).
89. Kaminsky L.S., Zhang Q-Y. The small intestine as a xenobiotic-metabolizing organ. Drug Metab. Dispos. 2003, 31:1520-1525.
90. Björkholm B., et al. Intestinal microbiota regulate xenobiotic metabolism in the liver. PLoS ONE 2009, 4:e6958.