Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Species–function relationships shape ecological properties of the human gut microbiome

Nature Microbiology volume 1, Article number: 16088 (2016) Cite this article

Subjects

Abstract

Despite recent progress, the organization and ecological properties of the intestinal microbial ecosystem remain under-investigated. Here, using a manually curated metabolic module framework for (meta-)genomic data analysis, we studied species–function relationships in gut microbial genomes and microbiomes. Half of gut-associated species were found to be generalists regarding overall substrate preference, but we observed significant genus-level metabolic diversification linked to bacterial life strategies. Within each genus, metabolic consistency varied significantly, being low in Firmicutes genera and higher in Bacteroides. Differentiation of fermentable substrate degradation potential contributed to metagenomic functional repertoire variation between individuals, with different enterotypes showing distinct saccharolytic/proteolytic/lipolytic profiles. Finally, we found that module-derived functional redundancy was reduced in the low-richness Bacteroides enterotype, potentially indicating a decreased resilience to perturbation, in line with its frequent association to dysbiosis. These results provide insights into the complex structure of gut microbiome-encoded metabolic properties and emphasize the importance of functional and ecological assessment of gut microbiome variation in clinical studies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Gut reference species' saccharolytic, proteolytic and lipolytic fermentation potential.
Figure 2: Gut metabolic diversification of input, central and ouput functions.
Figure 3: Metabolic consistency between species belonging to the same genus.
Figure 4: Saccharolytic, lipolytic and proteolytic potential diversification across the enteroscape (277 MetaHIT samples).
Figure 5: Enterotype-associated differences in ecosystem resilience and stability indicators (277 MetaHIT samples).

Similar content being viewed by others

References

  1. Ley, R. E., Peterson, D. A. & Gordon, J. I. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124, 837–848 (2006).

    Article  CAS  Google Scholar 

  2. Rajilić-Stojanović, M. & de Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047 (2014).

    Article  Google Scholar 

  3. Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).

    Article  CAS  Google Scholar 

  4. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  Google Scholar 

  5. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nature Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  Google Scholar 

  6. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    Article  CAS  Google Scholar 

  7. 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).

    Article  CAS  Google Scholar 

  8. Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnol. 31, 814–821 (2013).

    Article  CAS  Google Scholar 

  9. Cummings, J. H. & Macfarlane, G. T. The control and consequences of bacterial fermentation in the human colon. J. Appl. Bacteriol. 70, 443–459 (1991).

    Article  CAS  Google Scholar 

  10. 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).

    CAS  PubMed  Google Scholar 

  11. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 42, D459–D471 (2014).

    Article  CAS  Google Scholar 

  12. Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).

    Article  CAS  Google Scholar 

  13. Darzi, Y., Falony, G., Vieira-Silva, S. & Raes, J. Towards biome-specific analysis of meta-omics data. ISME J. 10, 1025–1028 (2016).

    Article  CAS  Google Scholar 

  14. Koch, A. L. Oligotrophs versus copiotrophs. Bioessays 23, 657–661 (2001).

    Article  CAS  Google Scholar 

  15. Pokusaeva, K., Fitzgerald, G. F. & van Sinderen, D. Carbohydrate metabolism in Bifidobacteria. Genes Nutr. 6, 285–306 (2011).

    Article  CAS  Google Scholar 

  16. Thomas, F., Hehemann, J.-H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: the food connection. Front. Microbiol. 2, 93 (2011).

    Article  Google Scholar 

  17. Tyrrell, K. L., Warren, Y. A., Citron, D. M. & Goldstein, E. J. C. Re-assessment of phenotypic identifications of Bacteroides putredinis to Alistipes species using molecular methods. Anaerobe 17, 130–134 (2011).

    Article  Google Scholar 

  18. Carbonero, F., Benefiel, A. C. & Gaskins, H. R. Contributions of the microbial hydrogen economy to colonic homeostasis. Nature Rev. Gastroenterol. Hepatol. 9, 504–518 (2012).

    Article  CAS  Google Scholar 

  19. Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2014).

    Article  Google Scholar 

  20. Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).

    Article  Google Scholar 

  21. Macfarlane, G. T., Gibson, G. R. & Cummings, J. H. Comparison of fermentation reactions in different regions of the human colon. J. Appl. Bacteriol. 72, 57–64 (1992).

    CAS  PubMed  Google Scholar 

  22. Macfarlane, S. & Macfarlane, G. T. Regulation of short-chain fatty acid production. Proc. Nutr. Soc. 62, 67–72 (2003).

    Article  CAS  Google Scholar 

  23. Macfarlane, S., Quigley, M., Hopkins, M., Newton, D. F. & Macfarlane, G. Polysaccharide degradation by human intestinal bacteria during growth under multi-substrate limiting conditions in a three-stage continuous culture system. FEMS Microbiol. Ecol. 26, 231–243 (1998).

    Article  CAS  Google Scholar 

  24. De Preter, V. et al. The prebiotic, oligofructose-enriched inulin modulates the faecal metabolite profile: an in vitro analysis. Mol. Nutr. Food Res. 54, 1791–1801 (2010).

    Article  CAS  Google Scholar 

  25. Zaneveld, J. R., Lozupone, C., Gordon, J. I. & Knight, R. Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives. Nucleic Acids Res. 38, 3869–3879 (2010).

    Article  CAS  Google Scholar 

  26. 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).

    Article  CAS  Google Scholar 

  27. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  Google Scholar 

  28. Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7, e30126 (2012).

    Article  CAS  Google Scholar 

  29. Jeffery, I. B., Claesson, M. J., O'Toole, P. W. & Shanahan, F. Categorization of the gut microbiota: enterotypes or gradients? Nature Rev. Microbiol. 10, 591–592 (2012).

    Article  CAS  Google Scholar 

  30. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    Article  CAS  Google Scholar 

  31. Bäckhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A. & Gordon, J. I. Host–bacterial mutualism in the human intestine. Science 307, 1915–1920 (2005).

    Article  Google Scholar 

  32. Manor, O. & Borenstein, E. Systematic characterization and analysis of taxonomic drivers of functional shifts in the human microbiome (2016); http://elbo.gs.washington.edu/software_fishtaco.html

  33. Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).

    Article  CAS  Google Scholar 

  34. Gorkiewicz, G. et al. Alterations in the colonic microbiota in response to osmotic diarrhea. PLoS One 8, e55817 (2013).

    Article  CAS  Google Scholar 

  35. Flores, G. E. et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 15, 531 (2014).

    Article  Google Scholar 

  36. McCann, K. S. The diversity–stability debate. Nature 405, 228–233 (2000).

    Article  CAS  Google Scholar 

  37. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    Article  CAS  Google Scholar 

  38. Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    Article  CAS  Google Scholar 

  39. Relman, D. A. The human microbiome: ecosystem resilience and health. Nutr. Rev. 70(Suppl 1), S2–S9 (2012).

    Article  Google Scholar 

  40. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  Google Scholar 

  41. Markowitz, V. M. et al. The integrated microbial genomes (IMG) system in 2007: data content and analysis tool extensions. Nucleic Acids Res. 36, D528–D533 (2008).

    Article  CAS  Google Scholar 

  42. Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).

    Article  CAS  Google Scholar 

  43. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    Article  CAS  Google Scholar 

  44. Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nature Methods 10, 1196–1199 (2013).

    Article  CAS  Google Scholar 

  45. Campbell, J. W., Morgan-Kiss, R. M. & Cronan, J. E. A new Escherichia coli metabolic competency: growth on fatty acids by a novel anaerobic beta-oxidation pathway. Mol. Microbiol. 47, 793–805 (2003).

    Article  CAS  Google Scholar 

  46. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  Google Scholar 

  47. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  Google Scholar 

  48. Talavera, G. & Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was funded by FP7 METACARDIS HEALTH-F4-2012- 305312 and IWT-SBO 100016. S.V.S. is funded by Marie Curie Actions FP7 People COFUND Proposal 267139 (acronym OMICS@VIB) and the Fund for Scientific Research-Flanders (FWO-V). S.C., M.V.C. and G.L.M. are funded by FWO-V. D.V. is funded by the Agency for Innovation by Science and Technology (IWT). The authors thank Dr Greenblum and collaborators and Dr Korem and collaborators for providing data.

Author information

Author notes

  1. Sara Vieira-Silva and Gwen Falony: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Microbiology and Immunology, KU Leuven–University of Leuven, Rega Institute, Herestraat 49, B-3000 Leuven, Belgium

    Sara Vieira-Silva, Gwen Falony, Youssef Darzi, Gipsi Lima-Mendez, Doris Vandeputte, Mireia Valles-Colomer, Samuel Chaffron & Jeroen Raes

  2. VIB, Center for the Biology of Disease, Herestraat 49, B-3000 Leuven, Belgium

    Sara Vieira-Silva, Gwen Falony, Youssef Darzi, Gipsi Lima-Mendez, Roberto Garcia Yunta, Doris Vandeputte, Mireia Valles-Colomer, Falk Hildebrand, Samuel Chaffron & Jeroen Raes

  3. Microbiology Unit, Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium

    Youssef Darzi, Gipsi Lima-Mendez, Roberto Garcia Yunta, Doris Vandeputte, Falk Hildebrand, Samuel Chaffron & Jeroen Raes

  4. Niigata University Graduate School of Medical and Dental Sciences 1-757 Asahimachi-dori, Chuo-ku, 951-8510, Niigata, Japan

    Shujiro Okuda

Authors
  1. Sara Vieira-Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gwen Falony
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Youssef Darzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gipsi Lima-Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roberto Garcia Yunta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shujiro Okuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Doris Vandeputte
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mireia Valles-Colomer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Falk Hildebrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Samuel Chaffron
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jeroen Raes
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.V.S. and G.F. contributed equally to this work. S.V.S. and G.F. designed the study, collected and processed data, performed the analyses and wrote the paper. G.F., S.V.S., D.V., M.V.C. and Y.D. curated the gut-specific metabolic modules. J.R. designed the study and co-wrote the paper. Y.D., R.G.Y., F.H. and S.O. participated in data collection and processing. G.L.M., Y.D., R.G.Y., S.O., D.V. and S.C. participated in statistical analyses. Y.D. participated in graphical representation design.

Corresponding author

Correspondence to Jeroen Raes.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–9, Supplementary Data legends 1–3, Supplementary Table legends 1–11. (PDF 3080 kb)

Supplementary Data 1

Gut-specific metabolic modules formatted for easy integration into bioinformatics pipelines (TXT 25 kb)

Supplementary Data 2

Gut reference genomes phylogenetic tree (TXT 14 kb)

Supplementary Information

Supplementary Tables 1–11 (XLSX 666 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vieira-Silva, S., Falony, G., Darzi, Y. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat Microbiol 1, 16088 (2016). https://doi.org/10.1038/nmicrobiol.2016.88

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/nmicrobiol.2016.88

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing