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Synthetic ecology of the human gut microbiota


Despite recent advances in sequencing and culturing, a deep knowledge of the wiring and functioning of the human gut ecosystem and its microbiota as a community is still missing. A holistic mechanistic understanding will require study of the gut microbiota as an interactive and spatially organized biological system, which is difficult to do in complex natural communities. Synthetic gut microbial ecosystems can function as model systems to further current understanding of the composition, stability and functional activities of the microbiota. In this Review, we provide an overview of the current synthetic ecology strategies that can be used towards a more comprehensive understanding of the human gut ecosystem. Such approaches that integrate in vitro experiments using cultured isolates with mathematical modelling will enable the ultimate goal: translating mechanistic and ecological knowledge into novel and effective therapies.

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Fig. 1: Timeline of select highlights in the sequencing and culturing of the human gut microbiota.
Fig. 2: Gaps between sequenced, cultured and experimentally verified interacting bacterial species in the human gut microbiota.
Fig. 3: In vitro culture systems for batch and continuous culture of gut microbiota.
Fig. 4: Connections between different types of human gut microbiota models.


  1. 1.

    Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    CAS  Google Scholar 

  2. 2.

    Lloyd-Price, J., Abu-Ali, G. & Huttenhower, C. The healthy human microbiome. Genome Med. 8, 51–62 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Gensollen, T., Iyer, S. S., Kasper, D. L. & Blumberg, R. S. How colonization by microbiota in early life shapes the immune system. Science 352, 539–544 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Honda, K. & Littman, D. R. The microbiota in adaptive immune homeostasis and disease. Nature 535, 75–84 (2016).

    CAS  Google Scholar 

  5. 5.

    Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).

    PubMed  Google Scholar 

  6. 6.

    Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).

    CAS  PubMed  Google Scholar 

  7. 7.

    Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

    CAS  PubMed  Google Scholar 

  9. 9.

    Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    CAS  PubMed  Google Scholar 

  10. 10.

    Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLOS ONE 5, e9085 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Tilg, H. & Moschen, A. R. Microbiota and diabetes: an evolving relationship. Gut 63, 1513–1521 (2014).

    CAS  PubMed  Google Scholar 

  13. 13.

    Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).

    CAS  PubMed  Google Scholar 

  15. 15.

    Tramontano, M. et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat. Microbiol. 3, 514–522 (2018). This study applied high-throughput techniques to study the growth characteristics in 96 gut species.

    CAS  Google Scholar 

  16. 16.

    Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).

    CAS  PubMed  Google Scholar 

  17. 17.

    De Roy, K., Marzorati, M., Van den Abbeele, P., Van de Wiele, T. & Boon, N. Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities. Environ. Microbiol. 16, 1472–1481 (2014).

    Google Scholar 

  18. 18.

    Großkopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).

    Google Scholar 

  19. 19.

    Johns, N. I., Blazejewski, T., Gomes, A. L. C. & Wang, H. H. Principles for designing synthetic microbial communities. Curr. Opin. Microbiol. 31, 146–153 (2016).

    Google Scholar 

  20. 20.

    Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005). This is the first paper to quantitatively predict the number of gut microbial species missing from culture collections.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat. Biotechnol. 37, 186–192 (2019). This report provides an introduction to the science, practice and applications of phenotypically multiplexed isolation of human gut bacteria.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lagier, J. C. et al. Microbial culturomics: paradigm shift in the human gut microbiome study. Clin. Microbiol. Infect. 18, 1185–1193 (2012). This paper provides an introduction to the science, practice and applications of high-throughput isolation of human gut bacteria.

    CAS  Google Scholar 

  23. 23.

    Rettedal, E. A., Gumpert, H. & Sommer, M. O. A. Cultivation-based multiplex phenotyping of human gut microbiota allows targeted recovery of previously uncultured bacteria. Nat. Commun. 5, 4714 (2014).

    CAS  PubMed  Google Scholar 

  24. 24.

    Zou, Y. et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol. 37, 179–185 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    NIH Human Microbiome Project. View dataset. HMPDACC (2019).

  26. 26.

    Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662.e20 (2019). Pasolli et al. contribute an impressive addition to our genome-level knowledge of human gut inhabitants based on innovative analysis of existing data.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gibson, G. R. & Wang, X. Regulatory effects of bifidobacteria on the growth of other colonic bacteria. J. Appl. Bacteriol. 77, 412–420 (1994).

    CAS  PubMed  Google Scholar 

  30. 30.

    Lievin, V. et al. Bifidobacterium strains from resident infant human gastrointestinal microflora exert antimicrobial activity. Gut 47, 646–652 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Touré, R., Kheadr, E., Lacroix, C., Moroni, O. & Fliss, I. Production of antibacterial substances by bifidobacterial isolates from infant stool active against Listeria monocytogenes. J. Appl. Microbiol. 95, 1058–1069 (2003).

    Google Scholar 

  32. 32.

    Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).

    CAS  PubMed  Google Scholar 

  33. 33.

    Berry, D. & Loy, A. Stable-isotope probing of human and animal microbiome function. Trends Microbiol. 26, 999–1007 (2018).

    CAS  Google Scholar 

  34. 34.

    Lahti, L., Salojärvi, J., Salonen, A., Scheffer, M. & De Vos, W. M. Tipping elements in the human intestinal ecosystem. Nat. Commun. 5, 4344 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Saeidnia, S., Manayi, A. & Abdollahi, M. From in vitro E experiments to in vivo and clinical studies; pros and cons. Curr. Drug Discov. Technol. 12, 218–224 (2016).

    Google Scholar 

  36. 36.

    Wilmes, P., Marta, C. & Tom, Vd. W. Resolving host–microbe interactions in the gut: the promise of in vitro models to complement in vivo research. Curr. Opin. Microbiol. 24, 28–33 (2018).

    Google Scholar 

  37. 37.

    Crost, E. H. et al. Mechanistic insights into the cross-feeding of Ruminococcus gnavus and Ruminococcus bromii on host and dietary carbohydrates. Front. Microbiol. 9, 2558 (2018).

    Google Scholar 

  38. 38.

    D'Hoe, K. et al. Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community. eLife 7, e37090 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Falony, G. et al. In vitro kinetic analysis of fermentation of prebiotic inulin-type fructans by Bifidobacterium species reveals four different phenotypes. Appl. Environ. Microbiol. 75, 454–461 (2009).

    CAS  Google Scholar 

  40. 40.

    Rivière, A., Selak, M., Geirnaert, A., Van den Abbeele, P. & De Vuyst, L. Complementary mechanisms for degradation of inulin-type fructans and arabinoxylan oligosaccharides among bifidobacterial strains suggest bacterial cooperation. Appl. Environ. Microbiol. 84, e02893-17 (2018).

    Google Scholar 

  41. 41.

    Berner, A. Z. et al. Novel polyfermentor intestinal model (PolyFermS) for controlled ecological studies: validation and effect of pH. PLOS ONE 8, e77772 (2013).

    CAS  Google Scholar 

  42. 42.

    Van den Abbeele, P. et al. Incorporating a mucosal environment in a dynamic gut model results in a more representative colonization by lactobacilli. Microb. Biotechnol. 5, 106–115 (2012).

    PubMed  Google Scholar 

  43. 43.

    Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157–e8157 (2018). The authors successfully predict the in vitro dynamics of a 12-species gut community from mono-cultures and co-cultures, thereby illustrating that higher-order interactions do not dominate the dynamics.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Kim, H. J., Li, H., Collins, J. J. & Ingber, D. E. Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip. Proc. Natl Acad. Sci. USA 113, E7–E15 (2016).

    CAS  PubMed  Google Scholar 

  45. 45.

    Kim, H. J., Huh, D., Hamilton, G. & Ingber, D. E. Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab Chip 12, 2165–2174 (2012).

    CAS  Google Scholar 

  46. 46.

    Shah, P. et al. A microfluidics-based in vitro model of the gastrointestinal human–microbe interface. Nat. Commun. 7, 11535 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Marzorati, M. & Van De Wiele, T. An advanced in vitro technology platform to study the mechanism of action of prebiotics and probiotics in the gastrointestinal tract. J. Clin. Gastroenterol. 2015, S124–S125 (2016).

    Google Scholar 

  48. 48.

    Zoetendal, E. G. et al. Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces. Appl. Environ. Microbiol. 68, 3401–3407 (2002).

    CAS  Google Scholar 

  49. 49.

    Tytgat, H. L. P., Nobrega, F. L., van der Oost, J. & de Vos, W. M. Bowel biofilms: tipping points between a healthy and compromised gut? Trends Microbiol. 27, 17–25 (2019). This study gives an excellent introduction to gut biofilms and their role in pathology.

    CAS  Google Scholar 

  50. 50.

    Elzinga, J., van der Oost, J., de Vos, W. M. & Smidt, H. The use of defined microbial communities to model host–microbe interactions in the human gut. Microbiol. Mol. Biol. Rev. 83, e00054-18 (2019).

    Google Scholar 

  51. 51.

    Schaedler, R. W., Dubos, R. & Costello, R. The development of the bacterial flora in the gastrointestinal tract of mice. J. Exp. Med. 122, 59–66 (1965). This classic study describes one of the first model artificial communities.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Byndloss, M. X., Pernitzsch, S. R. & Bäumler, A. J. Healthy hosts rule within: ecological forces shaping the gut microbiota. Mucosal Immunol. 11, 1299–1305 (2018). This report provides a clear overview of the role of the human host in controlling the human gut microbiota; especially of interest is the role of anaerobiosis and its failure in a variety of gut afflictions.

    CAS  PubMed  Google Scholar 

  53. 53.

    Salzman, N. H. et al. Enteric defensins are essential regulators of intestinal microbial ecology. Nat. Immunol. 11, 76–83 (2010).

    CAS  Google Scholar 

  54. 54.

    Nguyen, T. L. A., Vieira-Silva, S., Liston, A. & Raes, J. How informative is the mouse for human gut microbiota research? Dis. Model. Mech. 8, 1–16 (2015).

    CAS  Google Scholar 

  55. 55.

    von Martels, J. Z. H. et al. The role of gut microbiota in health and disease: in vitro modeling of host–microbe interactions at the aerobe–anaerobe interphase of the human gut. Anaerobe 44, 3–12 (2017).

    Google Scholar 

  56. 56.

    Fair, K. L., Colquhoun, J. & Hannan, N. R. F. Intestinal organoids for modelling intestinal development and disease. Phil. Trans. R. Soc. B 373, 20170217 (2018).

    Google Scholar 

  57. 57.

    Lagkouvardos, I. et al. The Mouse Intestinal Bacterial Collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat. Microbiol. 1, 16131 (2016).

    CAS  PubMed  Google Scholar 

  58. 58.

    Sommer, M. O. A. Advancing gut microbiome research using cultivation. Curr. Opin. Microbiol. 27, 127–132 (2015).

    CAS  Google Scholar 

  59. 59.

    Bilen, M. et al. The contribution of culturomics to the repertoire of isolated human bacterial and archaeal species. Microbiome 6, 94 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Oren, A., Garrity, G. M. & Parte, A. C. Why are so many effectively published names of prokaryotic taxa never validated? Int. J. Syst. Evol. Microbiol. 68, 2125–2129 (2018).

    Google Scholar 

  61. 61.

    Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).

    CAS  Google Scholar 

  62. 62.

    Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Huys, G. R. B. & Raes, J. Go with the flow or solitary confinement: a look inside the single-cell toolbox for isolation of rare and uncultured microbes. Curr. Opin. Microbiol. 44, 1–8 (2018).

    CAS  Google Scholar 

  64. 64.

    Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011). This study paves the way for in vivo synthetic ecology in model organisms.

    CAS  PubMed  Google Scholar 

  65. 65.

    Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).

    CAS  PubMed  Google Scholar 

  66. 66.

    Wu, F. & Dekker, C. Nanofabricated structures and microfluidic devices for bacteria: from techniques to biology. Chem. Soc. Rev. 45, 268–280 (2016).

    CAS  PubMed  Google Scholar 

  67. 67.

    Hsu, R. H. et al. Microbial interaction network inference in microfluidic droplets. Cell Syst. (2019).

    PubMed  Google Scholar 

  68. 68.

    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).

    Google Scholar 

  69. 69.

    Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015). In this work, the authors parameterize the gLV model to identify gut bacteria inhibiting C. difficile, which were subsequently shown to confer infection resistance in mice.

    CAS  Google Scholar 

  70. 70.

    Stein, R. R. et al. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7, e30916 (2018).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

    CAS  PubMed  Google Scholar 

  73. 73.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2018).

    CAS  Google Scholar 

  75. 75.

    Gonze, D., Lahti, L., Raes, J. & Faust, K. Multi-stability and the origin of microbial community types. ISME J. 11, 2159–2166 (2017).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Goyal, A., Dubinkina, V. & Maslov, S. Multiple stable states in microbial communities explained by the stable marriage problem. ISME J. 12, 2823–2834 (2018).

    PubMed  Google Scholar 

  77. 77.

    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).

    Google Scholar 

  78. 78.

    Cremer, J., Arnoldini, M. & Hwa, T. Effect of water flow and chemical environment on microbiota growth and composition in the human colon. Proc. Natl Acad. Sci. USA 114, 6438–6443 (2017).

    CAS  PubMed  Google Scholar 

  79. 79.

    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, 56–56 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

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

    CAS  PubMed  Google Scholar 

  81. 81.

    Song, H.-S., Cannon, W., Beliaev, A. & Konopka, A. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2, 711–752 (2014).

    Google Scholar 

  82. 82.

    Widder, S. et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 10, 2557–2568 (2016).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    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).

    CAS  Google Scholar 

  84. 84.

    Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).

    CAS  PubMed  Google Scholar 

  86. 86.

    Cao, H.-T., Gibson, T. E., Bashan, A. & Liu, Y.-Y. Pitfalls in inferring human microbial dynamics from temporal metagenomics data. Bioessays 39, 1600188 (2017).

    Google Scholar 

  87. 87.

    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589–589 (2011).

    PubMed  Google Scholar 

  88. 88.

    Heinken, A. & Thiele, I. Anoxic conditions promote species-specific mutualism between gut microbes in silico. Appl. Environ. Microbiol. 81, 4049–4061 (2015).

    CAS  Google Scholar 

  89. 89.

    Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Devoid, S. et al. Automated genome annotation and metabolic model reconstruction in the SEED and Model SEED. Methods Mol. Biol. 985, 17–45 (2013).

    CAS  PubMed  Google Scholar 

  91. 91.

    Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).

    PubMed  Google Scholar 

  93. 93.

    Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    CAS  PubMed  Google Scholar 

  94. 94.

    Tissier, H. Recherches sur la flore intestinale des nourissons: état normal et pathologique (G. Carré et C. Naud, 1900).

  95. 95.

    Finegold, S. M., Attebery, H. R. & Sutter, V. L. Effect of diet on human fecal flora: comparison of Japanese and American diets. Am. J. Clin. Nutr. 27, 1456–1469 (1974).

    CAS  PubMed  Google Scholar 

  96. 96.

    Moore, W. E. & Holdeman, L. V. Human fecal flora: the normal flora of 20 Japanese-Hawaiians. Appl. Microbiol. 27, 961–979 (1974).

    CAS  Google Scholar 

  97. 97.

    Millar, M. R. et al. Application of 16S rRNA gene PCR to study bowel flora of preterm infants with and without necrotizing enterocolitis. J. Clin. Microbiol. 34, 2506–2510 (1996).

    CAS  Google Scholar 

  98. 98.

    Wilson, K. H. & Blitchington, R. B. Human colonic biota studied by ribosomal DNA sequence analysis. Appl. Environ. Microbiol. 62, 2273–2278 (1996). This pioneering gut 16S rRNA gene study lays the foundations for the microbiome field.

    CAS  Google Scholar 

  99. 99.

    Chassard, C., Delmas, E., Lawson, P. A. & Bernalier-Donadille, A. Bacteroides xylanisolvens sp. nov., a xylan-degrading bacterium isolated from human faeces. Int. J. Syst. Evol. Microbiol. 58, 1008–1013 (2008).

    CAS  Google Scholar 

  100. 100.

    Derrien, M., Vaughan, E. E., Plugge, C. M. & de Vos, W. M. Akkermansia municiphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 54, 1469–1476 (2004).

    CAS  Google Scholar 

  101. 101.

    Duncan, S. H., Louis, P. & Flint, H. J. Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl. Environ. Microbiol. 70, 5810–5817 (2004).

    CAS  Google Scholar 

  102. 102.

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

    CAS  PubMed  Google Scholar 

  103. 103.

    Dethlefsen, L., Huse, S., Sogin, M. L. & Relman, D. A. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16s rRNA sequencing. PLOS Biol. 6, e280 (2008).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Schnorr, S. L. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    CAS  Google Scholar 

  108. 108.

    McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1538 (2018).

    CAS  PubMed  Google Scholar 

  110. 110.

    Belenguer, A. et al. Two routes of metabolic cross-feeding between Bifidobacterium adolescentis and butyrate-producing anaerobes from the human gut. Appl. Environ. Microbiol. 72, 3593–3599 (2006).

    CAS  Google Scholar 

  111. 111.

    Bunesova, V., Lacroix, C. & Schwab, C. Mucin cross-feeding of infant bifidobacteria and Eubacterium hallii. Microb. Ecol. 75, 228–238 (2018).

    CAS  PubMed  Google Scholar 

  112. 112.

    Das, P., Ji, B., Kovatcheva-Datchary, P., Bäckhed, F. & Nielsen, J. In vitro co-cultures of human gut bacterial species as predicted from co-occurrence network analysis. PLOS ONE 13, e0195161 (2018).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Egan, M. et al. Cross-feeding by Bifidobacterium breve UCC2003 during co-cultivation with Bifidobacterium bifidum PRL2010 in a mucin-based medium. BMC Microbiol. 14, 282 (2014).

    PubMed  PubMed Central  Google Scholar 

  114. 114.

    Falony, G., Calmeyn, T., Leroy, F. & De Vuyst, L. Coculture fermentations of bifidobacterium species and Bacteroides thetaiotaomicron reveal a mechanistic insight into the prebiotic effect of inulin-type fructans. Appl. Environ. Microbiol. 75, 2312–2319 (2009).

    CAS  Google Scholar 

  115. 115.

    Newton, D. F., MacFarlane, S. & MacFarlane, G. T. Effects of antibiotics on bacterial species composition and metabolic activities in chemostats containing defined populations of human gut microorganisms. Antimicrob. Agents Chemoth. 57, 2016–2025 (2013).

    CAS  PubMed  Google Scholar 

  116. 116.

    Pinto, F., Medina, D. A., Pérez-Correa, J. R. & Garrido, D. Modeling metabolic interactions in a consortium of the infant gut microbiome. Front. Microbiol. 8, 2507 (2017).

    Google Scholar 

  117. 117.

    Ze, X., Duncan, S. H., Louis, P. & Flint, H. J. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J. 8, 2507 (2012).

    Google Scholar 

  118. 118.

    Gottstein, W., Olivier, B. G., Bruggeman, F. J. & Teusink, B. Constraint-based stoichiometric modelling from single organisms to microbial communities. J. R. Soc. Interface 13, 20160627 (2016).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Gonze, D., Coyte, K. Z., Lahti, L. & Faust, K. Microbial communities as dynamical systems. Curr. Opin. Microbiol. 44, 41–49 (2018).

    Google Scholar 

  120. 120.

    Rosindell, J., Hubbell, S. P. & Etienne, R. S. The unified neutral theory of biodiversity and biogeography at age ten. Trends Ecol. Evol. 26, 340–348 (2011).

    Google Scholar 

  121. 121.

    Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. (2018).

    Google Scholar 

  122. 122.

    Röttjers, L. & Faust, K. From hairballs to hypotheses — biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).

    Google Scholar 

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The authors thank all members of the Raes laboratory, as well as D. Gonze, for the many discussions that contributed to this work. G.H. is supported by the European Union’s Horizon 2020 Research and Innovation Programme (EU H2020) AD-GUT project, grant agreement no. 686271. The Raes laboratory is supported by the VIB Grand Challenges Programme, the Rega Institute for Medical Research, KU Leuven, the FWO EOS Programme (30770923), FP7 METACARDIS (305312) and the H2020 initiatives SYSCID (733100), PIBD-SET (668023) and IMMUNAID (779295); K.F. has received funding from the European Research Council (ERC) under EU H2020 grant agreement no. 801747.

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G.V. provided scoping and coordination. G.V. and A.C.G. wrote the introduction and the sections on in vitro systems and future strategies; A.C.G. made draft figures; G.R.B.H. contributed the section on isolation; and K.F., the section on mathematical modelling as well as the table. J.R. provided overall guidance. All authors polished and approved the text.

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Correspondence to Jeroen Raes.

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Nature Reviews Microbiology thanks Samuel Forster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Related links

Human Microbiome Project:

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Qualitative and quantitative changes in the intestinal microbiota that alter their metabolic activities and local distributions.


The process by which one organism uses the metabolic products of another organism.


A type of cooperative bidirectional cross-feeding whereby two organisms gain through the metabolic reactions of each other.


A type of cross-feeding whereby the metabolic reactions of one organism are required for the growth of another organism.


The ability of a system to withstand disturbance.


The ability of a system to recover from disturbance.

Metagenome-assembled genomes

(MAGs). Genomes assembled using sequencing data from environmental samples.

Tipping points

Thresholds at which, if conditions are changed beyond that level, the system suddenly switches to a different state.

Operational taxonomic units

(OTUs). Operational definitions used to classify and group closely related organisms.


Groups of phylogenetically related organisms.

Xenobiotic transformations

Biological transformation of the chemical structure of a molecule normally absent from the microbial ecosystem, such as pharmaceuticals, pesticides and so forth.


Nutrients that, besides macronutrient components, also have pharmaceutical properties.


Without microbial commensals.


Tissue-culture-generated structures from certain human cell types, in this paper to be understood as differentiated gut tissue.

Core microbiome

The collection of gut organisms common to the majority of subjects in a given population.


Serial dilution of a microbial sample to extremely low densities that allow the isolation of single cells.

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Vrancken, G., Gregory, A.C., Huys, G.R.B. et al. Synthetic ecology of the human gut microbiota. Nat Rev Microbiol 17, 754–763 (2019).

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