Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

Nature Communications

Volumn 8, Issue , 2017, Pages

  Sung, Jaeyun ^a,b,c   Kim, Seunghyeon ^a,d,e   Cabatbat, Josephine Jill T ^a   Jang, Sungho ^f   Jin, Yong Su ^d,f   Jung, Gyoo Yeol ^f,g   Chia, Nicholas ^g,h,i   Kim, Pan Jun ^a,d  

^a Asia Pacific Center for Theoretical Physics (APCTP)   (South Korea)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

^c BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^d Pohang University of Science and Technology (POSTECH)   (South Korea)

^e ABDUS SALAM INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS   (Italy)

^f UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^g MAYO CLINIC   (United States)

^h MAYO CLINIC ROCHESTER   (United States)

ⁱ Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CELLS AND CELL COMPONENTS; DEGRADATION; DIABETES; DIGESTIVE SYSTEM; GLOBAL PERSPECTIVE; HOMINID; METABOLISM; MICROBIAL ACTIVITY; MICROBIAL COMMUNITY;

ADULT; AGED; ARTICLE; COHORT ANALYSIS; COMMUNITY STRUCTURE; CONTROLLED STUDY; ENZYME DEGRADATION; FEMALE; GENE SEQUENCE; GLUCOSE ABSORPTION; HUMAN; INTESTINE FLORA; MACROMOLECULE; MAJOR CLINICAL STUDY; MALE; METAGENOMICS; MICROBIAL ACTIVITY; MICROBIAL COMMUNITY; MICROBIAL METABOLISM; NON INSULIN DEPENDENT DIABETES MELLITUS; NONHUMAN; PHYLOGENY; BACTERIUM; DATA BASE; METABOLISM; MICROBIOLOGY; TRANSPORT AT THE CELLULAR LEVEL;

BACTERIA; BIOLOGICAL TRANSPORT; DATABASES AS TOPIC; DIABETES MELLITUS, TYPE 2; GASTROINTESTINAL MICROBIOME; HUMANS; MALE; METABOLIC NETWORKS AND PATHWAYS;

EID: 85020381670     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms15393     Document Type: Article

References (70)

1. Nowak, M. A. Evolutionary Dynamics: exploring the Equations of Life (Harvard Univ. Press, 2006).
2. Sanchez, A. & Gore, J. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol. 11, e1001547 (2013).
3. Mackie, R. I., White, B. A. & Isaacson, R. E. Gastrointestinal Microbiology (Chapman & Hall, 1997).
4. Blaut, M. Ecology and physiology of the intestinal tract. Curr. Top. Microbiol. Immunol. 358, 247-272 (2013).
5. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59-65 (2010).
6. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804-810 (2007).
7. Donohoe, D. R. et al. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13, 517-526 (2011).
8. Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446-450 (2013).
9. Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661-672 (2014).
10. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohns disease. Cell Host Microbe 15, 382-392 (2014).
11. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292-298 (2012).
12. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55-60 (2012).
13. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480-484 (2009).
14. Barabasi, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
15. Hood, L. Tackling the microbiome. Science 336, 1209 (2012).
16. Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).
17. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
18. 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. USA 109, 594-599 (2012).
19. Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).
20. Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl Acad. Sci. USA 110, 12804-12809 (2013).
21. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449-6454 (2015).
22. Heinken, A. & Thiele, I. Systematic prediction of health-relevant humanmicrobial co-metabolism through a computational framework. Gut Microbes 6, 120-130 (2015).
23. Kettle, H., Louis, P., Holtrop, G., Duncan, S. H. & Flint, H. J. Modelling the emergent dynamics and major metabolites of the human colonic microbiota. Environ. Microbiol. 17, 1615-1630 (2015).
24. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320-331 (2015).
25. Magnusdottir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81-89 (2017).
26. Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99-103 (2013).
27. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419-425 (2013).
28. Markle, J. G. et al. Sex differences in the gut microbiome drive hormonedependent regulation of autoimmunity. Science 339, 1084-1088 (2013).
29. Mueller, S. et al. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: A cross-sectional study. Appl. Environ. Microbiol. 72, 1027-1033 (2006).
30. Wu, G. D. et al. Linking long-Term dietary patterns with gut microbial enterotypes. Science 334, 105-108 (2011).
31. Noble, D., Mathur, R., Dent, T., Meads, C. & Greenhalgh, T. Risk models and scores for type 2 diabetes: systematic review. BMJ 343, d7163 (2011).
32. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262-266 (2015).
33. Everard, A. et al. Cross-Talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066-9071 (2013).
34. Everard, A. et al. Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice. Diabetes 60, 2775-2786 (2011).
35. De Vadder, F. et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell 156, 84-96 (2014).
36. Gao, Z. et al. Butyrate improves insulin sensitivity and increases energy expenditure in mice. Diabetes 58, 1509-1517 (2009).
37. Lin, H. V. et al. Butyrate and propionate protect against diet-induced obesity and regulate gut hormones via free fatty acid receptor 3-independent mechanisms. PLoS ONE 7, e35240 (2012).
38. den Besten, G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lipid. Res. 54, 2325-2340 (2013).
39. Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Backhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332-1345 (2016).
40. Rios-Covian, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7, 185 (2016).
41. Wong, J. M., de Souza, R., Kendall, C. W., Emam, A. & Jenkins, D. J. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235-243 (2006).
42. Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451-455 (2013).
43. Honda, K., Moto, M., Uchida, N., He, F. & Hashizume, N. Anti-diabetic effects of lactic acid bacteria in normal and type 2 diabetic mice. J. Clin. Biochem. Nutr. 51, 96-101 (2012).
44. Attene-Ramos, M. S., Wagner, E. D., Plewa, M. J. & Gaskins, H. R. Evidence that hydrogen sulfide is a genotoxic agent. Mol. Cancer Res. 4, 9-14 (2006).
45. Bartram, H. P. et al. Proliferation of human colonic mucosa as an intermediate biomarker of carcinogenesis: effects of butyrate, deoxycholate, calcium, ammonia, and pH. Cancer Res. 53, 3283-3288 (1993).
46. Hughes, R., Magee, E. A. & Bingham, S. Protein degradation in the large intestine: relevance to colorectal cancer. Curr. Issues Intest. Microbiol. 1, 51-58 (2000).
47. Leschelle, X. et al. Isolation of pig colonic crypts for cytotoxic assay of luminal compounds: effects of hydrogen sulfide, ammonia, and deoxycholic acid. Cell Biol. Toxicol. 18, 193-203 (2002).
48. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559-563 (2014).
49. Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902-903 (2015).
50. Albenberg, L. et al. Correlation between intraluminal oxygen gradient and radial partitioning of intestinal microbiota. Gastroenterology 147, 1055-1063 e1058 (2014).
51. Baughn, A. D. & Malamy, M. H. The strict anaerobe Bacteroides fragilis grows in and benefits from nanomolar concentrations of oxygen. Nature 427, 441-444 (2004).
52. Khan, M. T. et al. The gut anaerobe Faecalibacterium prausnitzii uses an extracellular electron shuttle to grow at oxic-Anoxic interphases. ISME J. 6, 1578-1585 (2012).
53. Ma, L. et al. Gene-Targeted microfluidic cultivation validated by isolation of a gut bacterium listed in Human Microbiome Projects Most Wanted taxa. Proc. Natl Acad. Sci. USA 111, 9768-9773 (2014).
54. Rettedal, E. A., Gumpert, H. & Sommer, M. O. Cultivation-based multiplex phenotyping of human gut microbiota allows targeted recovery of previously uncultured bacteria. Nat. Commun. 5, 4714 (2014).
55. Bakken, J. S. et al. Treating Clostridium difficile infection with fecal microbiota transplantation. Clin. Gastroenterol. Hepatol. 9, 1044-1049 (2011).
56. Rohlke, F. & Stollman, N. Fecal microbiota transplantation in relapsing Clostridium difficile infection. Therap. Adv. Gastroenterol. 5, 403-420 (2012).
57. Chiu, H. C., Levy, R. & Borenstein, E. Emergent biosynthetic capacity in simple microbial communities. PLoS Comput. Biol. 10, e1003695 (2014).
58. Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589 (2011).
59. Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104-1115 (2014).
60. Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205-208 (2015).
61. Gerber, G. K., Onderdonk, A. B. & Bry, L. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS Comput. Biol. 8, e1002624 (2012).
62. Gibson, T. E., Bashan, A., Cao, H. T., Weiss, S. T. & Liu, Y. Y. On the origins and control of community types in the human microbiome. PLoS Comput. Biol. 12, e1004688 (2016).
63. Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).
64. Meehan, C. J. & Beiko, R. G. Lateral gene transfer of an ABC transporter complex between major constituents of the human gut microbiome. BMC Microbiol. 12, 248 (2012).
65. Stecher, B. et al. Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae. Proc. Natl Acad. Sci. USA 109, 1269-1274 (2012).
66. Sung, J., Hale, V., Merkel, A. C., Kim, P.-J. & Chia, N. Metabolic modeling with big data and the gut microbiome. Appl. Transl. Genom. 10, 10-15 (2016).
67. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811-814 (2012).
68. Sung, J. et al. Data from: Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mc1j9 (2017).
69. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017).
70. Oberhardt, M. A. et al. Harnessing the landscape of microbial culture media to predict new organism-media pairings. Nat. Commun. 6, 8493 (2015).