Modeling metabolism of the human gut microbiome

Current Opinion in Biotechnology

Volumn 51, Issue , 2018, Pages 90-96

  Magnúsdóttir, Stefanía ^a   Thiele, Ines ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

Author keywords

[No Author keywords available]

Indexed keywords

MICROORGANISMS; PHYSIOLOGY;

ANALYSIS METHOD; CONSTRAINT-BASED; GENOME-SCALE METABOLIC NETWORK; METABOLIC FUNCTION; METABOLIC MODELING; METABOLITE EXCHANGES; MICROBIAL INTERACTIONS; SYSTEMS BIOLOGY;

METABOLISM;

BACTEROIDES; BACTEROIDES THETAIOTAMICRON; COMMUNITY STRUCTURE; EUBACTERIUM; EUBACTERIUM RECTALE; HUMAN; INTESTINE FLORA; KINETICS; METABOLITE; METABOLOMICS; MICROBIAL COMMUNITY; MOLECULAR INTERACTION; ORGANISMAL INTERACTION; PRIORITY JOURNAL; REVIEW; SEQUENCE ALIGNMENT; BIOLOGICAL MODEL; COMPUTER SIMULATION; HOST PATHOGEN INTERACTION; METABOLISM; MICROFLORA; PROCEDURES; SYSTEMS BIOLOGY;

COMPUTER SIMULATION; GASTROINTESTINAL MICROBIOME; HOST-PATHOGEN INTERACTIONS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MICROBIAL INTERACTIONS; MICROBIOTA; MODELS, BIOLOGICAL; SYSTEMS BIOLOGY;

EID: 85038215952     PISSN: 09581669     EISSN: 18790429     Source Type: Journal    
DOI: 10.1016/j.copbio.2017.12.005     Document Type: Review

References (80)

1. LeBlanc, J.G., Milani, C., de Giori, G.S., Sesma, F., van Sinderen, D., Ventura, M., Bacteria as vitamin suppliers to their host: a gut microbiota perspective. Curr Opin Biotechnol 24 (2013), 160–168.
2. Metges, C.C., Contribution of microbial amino acids to amino acid homeostasis of the host. J Nutr 130 (2000), 1857s–1864s.
3. Flint, H.J., Scott, K.P., Duncan, S.H., Louis, P., Forano, E., Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3 (2012), 289–306.
4. Duncan, S.H., Scott, K.P., Ramsay, A.G., Harmsen, H.J., Welling, G.W., Stewart, C.S., Flint, H.J., Effects of alternative dietary substrates on competition between human colonic bacteria in an anaerobic fermentor system. Appl Environ Microbiol 69 (2003), 1136–1142.
5. Shafquat, A., Joice, R., Simmons, S.L., Huttenhower, C., Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol 22 (2014), 261–266.
6. Clemente, J.C., Ursell, L.K., Parfrey, L.W., Knight, R., The impact of the gut microbiota on human health: an integrative view. Cell 148 (2012), 1258–1270.
7. Dicksved, J., Halfvarson, J., Rosenquist, M., Jarnerot, G., Tysk, C., Apajalahti, J., Engstrand, L., Jansson, J.K., Molecular analysis of the gut microbiota of identical twins with Crohn's disease. ISME J 2 (2008), 716–727.
8. Greenblum, S., Turnbaugh, P.J., Borenstein, E., Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci U S A 109 (2012), 594–599.
9. Ley, R.E., Bäckhed, F., Turnbaugh, P., Lozupone, C.A., Knight, R.D., Gordon, J.I., Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A 102 (2005), 11070–11075.
10. Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490 (2012), 55–60.
11. Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., Magrini, V., Mardis, E.R., Gordon, J.I., An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444 (2006), 1027–1031.
12. Claesson, M.J., Jeffery, I.B., Conde, S., Power, S.E., O/’Connor, E.M., Cusack, S., Harris, H.M.B., Coakley, M., Lakshminarayanan, B., O/'sullivan, O., et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488 (2012), 178–184.
13. Yatsunenko, T., Rey, F.E., Manary, M.J., Trehan, I., Dominguez-Bello, M.G., Contreras, M., Magris, M., Hidalgo, G., Baldassano, R.N., Anokhin, A.P., et al. Human gut microbiome viewed across age and geography. Nature 486 (2012), 222–227.
14. David, L.A., Maurice, C.F., Carmody, R.N., Gootenberg, D.B., Button, J.E., Wolfe, B.E., Ling, A.V., Devlin, A.S., Varma, Y., Fischbach, M.A., et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505 (2014), 559–563.
15. Carmody, R.N., Gerber, G.K., Luevano, J.M. Jr, Gatti, D.M., Somes, L., Svenson, K.L., Turnbaugh, P.J., Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17 (2015), 72–84.
16. Suez, J., Korem, T., Zeevi, D., Zilberman-Schapira, G., Thaiss, C.A., Maza, O., Israeli, D., Zmora, N., Gilad, S., Weinberger, A., et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514 (2014), 181–186.
17. David, L.A., Materna, A.C., Friedman, J., Campos-Baptista, M.I., Blackburn, M.C., Perrotta, A., Erdman, S.E., Alm, E.J., Host lifestyle affects human microbiota on daily timescales. Genome Biol, 15, 2014, R89.
18. Human Microbiome Project Consortium, Structure, function and diversity of the healthy human microbiome. Nature 486 (2012), 207–214.
19. Palsson, B., Systems Biology: Constraint-based Reconstruction and Analysis. 2015, Cambridge University Press.
20. Oberhardt, M.A., Palsson, B.O., Papin, J.A., Applications of genome-scale metabolic reconstructions. Mol Syst Biol, 5, 2009, 320.
21. Heinken, A., Thiele, I., Systems biology of host–microbe metabolomics. Wiley Interdiscip Rev Syst Biol Med 7 (2015), 195–219.
22. Thiele, I., Palsson, B.O., A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5 (2010), 93–121.
23. Henry, C.S., DeJongh, M., Best, A.A., Frybarger, P.M., Linsay, B., Stevens, R.L., High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotech 28 (2010), 977–982.
24. Arkin, A.P., Stevens, R.L., Cottingham, R.W., Maslov, S., Henry, C.S., Dehal, P., Ware, D., Perez, F., Harris, N.L., Canon, S., et al. The DOE Systems Biology Knowledgebase (KBase). 2016, bioRxiv.
25. Karp, P.D., Latendresse, M., Paley, S.M., Krummenacker, M., Ong, Q.D., Billington, R., Kothari, A., Weaver, D., Lee, T., Subhraveti, P., et al. Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform 17 (2016), 877–890.
26. Reed, J.L., Patel, T.R., Chen, K.H., Joyce, A.R., Applebee, M.K., Herring, C.D., Bui, O.T., Knight, E.M., Fong, S.S., Palsson, B.O., Systems approach to refining genome annotation. Proc Natl Acad Sci U S A 103 (2006), 17480–17484.
27. Fleming, R.M.T., Vlassis, N., Thiele, I., Saunders, M.A., Conditions for duality between fluxes and concentrations in biochemical networks. J Theor Biol 409 (2016), 1–10.
28. Fleming, R.M.T., Thiele, I., Nasheuer, H.P., Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli. Biophys Chem 145 (2009), 47–56.
29. Haraldsdottir, H.S., Thiele, I., Fleming, R.M., Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. Biophys J 102 (2012), 1703–1711.
30. Green, M.L., Karp, P.D., Genome annotation errors in pathway databases due to semantic ambiguity in partial EC numbers. Nucleic Acids Res 33 (2005), 4035–4039.
31. Satish Kumar, V., Dasika, M.S., Maranas, C.D., Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics, 8, 2007, 212.
32. Thiele, I., Vlassis, N., Fleming, R.M., fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30 (2014), 2529–2531.
33. Biggs, M.B., Papin, J.A., Metabolic network-guided binning of metagenomic sequence fragments. Bioinformatics, 32, 2016, 867.
34. Biggs, M.B., Papin, J.A., Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PLoS Comput Biol, 13, 2017, e1005413.
35. Rolfsson, O., Palsson, B.O., Thiele, I., The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions. BMC Syst Biol, 5, 2011, 155.
36. Thiele, I., Heinken, A., Fleming, R.M., A systems biology approach to studying the role of microbes in human health. Curr Opin Biotechnol 24 (2013), 4–12.
37. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., de Wouters, T., Juste, C., Rizkalla, S., Chilloux, J., et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab 22 (2015), 320–331 Introduced the Community And Systems-level INteractive Optimization (CASINO) toolbox and used it to model metabolic interactions within microbiomes of four different microbes. They validated their in silico simulations using metabolomics data.
38. Shoaie, S., Karlsson, F., Mardinoglu, A., Nookaew, I., Bordel, S., Nielsen, J., Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep, 3, 2013, 2532.
39. Heinken, A., Thiele, I., Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. Gut Microbes 6 (2015), 120–130 Introduced the largest human gut microbial community model to date and simulated the metabolic interactions between microbial communities and a human small intestinal cell. Commensal communities enabled more key host metabolic pathways than pathogenic communities.
40. Magnusdottir, S., Heinken, A., Kutt, L., Ravcheev, D.A., Bauer, E., Noronha, A., Greenhalgh, K., Jager, C., Baginska, J., Wilmes, P., et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 35 (2017), 81–89 Describes the generation and refinement of hundreds of human gut microbial genome-scale metabolic reconstructions that can be used in microbiome and host–microbiome metabolic modeling.
41. Stolyar, S., Van Dien, S., Hillesland, K.L., Pinel, N., Lie, T.J., Leigh, J.A., Stahl, D.A., Metabolic modeling of a mutualistic microbial community. Mol Syst Biol, 2007, 3.
42. Biggs, M.B., Medlock, G.L., Kolling, G.L., Papin, J.A., Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 7 (2015), 317–334.
43. Zomorrodi, A.R., Maranas, C.D., OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol, 8, 2012, e1002363.
44. Khandelwal, R.A., Olivier, B.G., Roling, W.F., Teusink, B., Bruggeman, F.J., Community flux balance analysis for microbial consortia at balanced growth. PLoS ONE, 8, 2013, e64567.
45. Klitgord, N., Segre, D., Environments that induce synthetic microbial ecosystems. PLoS Comput Biol, 6, 2010, e1001002.
46. Chubiz, L.M., Granger, B.R., Segre, D., Harcombe, W.R., Species interactions differ in their genetic robustness. Front Microbiol, 6, 2015, 271.
47. Freilich, S., Zarecki, R., Eilam, O., Segal, E.S., Henry, C.S., Kupiec, M., Gophna, U., Sharan, R., Ruppin, E., Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun, 2, 2011, 589.
48. Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D.R., Bork, P., Patil, K.R., Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci U S A 112 (2015), 6449–6454 Used metabolic modeling of microbial communities to assess minimum growth requirements of microbiomes. Cooperating microbes require fewer metabolites in their common medium than other communities.
49. Harcombe, W.R., Riehl, W.J., Dukovski, I., Granger, B.R., Betts, A., Lang, A.H., Bonilla, G., Kar, A., Leiby, N., Mehta, P., et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7 (2014), 1104–1115.
50. Zomorrodi, A.R., Islam, M.M., Maranas, C.D., d-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synth Biol 3 (2014), 247–257.
51. Louca, S., Doebeli, M., Calibration and analysis of genome-based models for microbial ecology. Elife, 4, 2015, e08208.
52. Henson, M., Phalak, P., Byproduct cross feeding and community stability in an in silico biofilm model of the gut microbiome. Processes, 5, 2017, 13.
53. Bauer, E., Zimmermann, J., Baldini, F., Thiele, I., Kaleta, C., BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLOS Comput Biol 13 (2017), 1–22 Describes an agent-based platform that allows the spatiotemporal metabolic modeling of individual microbes in communities.
54. Bauer, E., Thiele, I., From Metagenomic Data to Personalized Computational Microbiotas: Predicting Dietary Supplements for Crohn's Disease. 2017 https://arxiv.org/abs/1709.06007.
55. van Hoek, M.J.A., Merks, R.M.H., Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst Biol, 11, 2017, 56.
56. El-Semman, I.E., Karlsson, F.H., Shoaie, S., Nookaew, I., Soliman, T.H., Nielsen, J., Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC Syst Biol, 8, 2014, 41.
57. Mardinoglu, A., Shoaie, S., Bergentall, M., Ghaffari, P., Zhang, C., Larsson, E., Bäckhed, F., Nielsen, J., The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol Syst Biol, 2015, 11 Combined in silico metabolic modeling with results from in vivo mouse models to assess the effects of the microbiome on the host amino acid metabolism.
58. Sahoo, S., Thiele, I., Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Hum Mol Genet 22 (2013), 2705–2722.
59. Heinken, A., Thiele, I., Anoxic conditions promote species-specific mutualism between gut microbes in silico. Appl Environ Microbiol 81 (2015), 4049–4061.
60. Steinway, S.N., Biggs, M.B., Loughran, T.P. Jr, Papin, J.A., Albert, R., Inference of network dynamics and metabolic interactions in the gut microbiome. PLOS Comput Biol, 11, 2015, e1004338.
61. Granger, B.R., Chang, Y.-C., Wang, Y., DeLisi, C., Segrè, D., Hu, Z., Visualization of metabolic interaction networks in microbial communities using VisANT 5.0. PLOS Comput Biol, 12, 2016, e1004875.
62. Budinich, M., Bourdon, J., Larhlimi, A., Eveillard, D., A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PLOS ONE, 12, 2017, e0171744.
63. Chan, S.H.J., Simons, M.N., Maranas, C.D., SteadyCom: predicting microbial abundances while ensuring community stability. PLOS Comput Biol, 13, 2017, e1005539 Introducing the SteadyCom optimization framework for microbial communities. The authors used the framework to predict in silico a gut microbial community composition and matched their predictions with published experimental data.
64. 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, 6, 2010, 422.
65. Heinken, A., Sahoo, S., Fleming, R.M., Thiele, I., Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4 (2013), 28–40 First study using metabolic modeling to simulate host-gut microbe metabolism. The presence of the microbe model rescued the metabolic phenotype of the mouse model simulated with an inborn error of metabolism.
66. Vo, T.D., Palsson, B.O., Building the power house: recent advances in mitochondrial studies through proteomics and systems biology. Am J Physiol Cell Physiol 292 (2007), C164–C177.
67. Brunk, E., Sahoo, S., Zielinski, D.C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Gonzalez, G.A.P., Aurich, M.K., et al. Recon3D: a resource enabling a three-dimensional view of gene variation in human metabolism. Nat Biotech, 2017 (in press).
68. Heinken, A., Khan, M.T., Paglia, G., Rodionov, D.A., Harmsen, H.J., Thiele, I., Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe. J Bacteriol 196 (2014), 3289–3302.
69. Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S.N., Richelle, A., Heinken, A., Haraldsdottir, H.S., Keating, S.M., Vlasov, V., Wachowiak, J., et al. Creation and Analysis of Biochemical Constraint-Based Models: The COBRA Toolbox v3.0. 2017 https://arxiv.org/abs/1710.04038.
70. Opdam, S., Richelle, A., Kellman, B., Li, S., Zielinski, D.C., Lewis, N.E., A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst, 4, 2017 318-329.e316.
71. Machado, D., Herrgard, M., Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol, 10, 2014, e1003580.
72. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., The Sequence alignment/map format and SAMtools. Bioinformatics 25 (2009), 2078–2079.
73. Heintz-Buschart, A., May, P., Laczny, C.C., Lebrun, L.A., Bellora, C., Krishna, A., Wampach, L., Schneider, J.G., Hofgan, A., De Beaufort, C., et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol, 2, 2016, 16180.
74. Beger, R.D., Dunn, W., Schmidt, M.A., Gross, S.S., Kirwan, J.A., Cascante, M., Brennan, L., Wishart, D.S., Oresic, M., Hankemeier, T., et al. Metabolomics enables precision medicine: ‘A White Paper, Community Perspective’. Metabolomics, 12, 2016, 149.
75. Heinken, A., Ravcheev, D.A., Baldini, F., Heirendt, L., Fleming, R.M.T., Thiele, I., Personalized Modeling of the Human Gut Microbiome Reveals Distinct Bile Acid Deconjugation and Biotransformation Potential in Healthy and IBD Individuals. 2017, 10.1101/229138.
76. Wikoff, W.R., Anfora, A.T., Liu, J., Schultz, P.G., Lesley, S.A., Peters, E.C., Siuzdak, G., Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A 106 (2009), 3698–3703.
77. Martin, F.P., Wang, Y., Sprenger, N., Yap, I.K., Lundstedt, T., Lek, P., Rezzi, S., Ramadan, Z., van Bladeren, P., Fay, L.B., et al. Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model. Mol Syst Biol, 4, 2008, 157.
78. Heirendt, L., Thiele, I., Fleming, R.M.T., DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics, 33, 2017, 1421.
79. Gudmundsson, S., Thiele, I., Computationally efficient flux variability analysis. BMC Bioinformatics, 11, 2010, 489.
80. Thiele, I., Swainston, N., Fleming, R.M., Hoppe, A., Sahoo, S., Aurich, M.K., Haraldsdottir, H., Mo, M.L., Rolfsson, O., Stobbe, M.D., et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 31 (2013), 419–425.