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.

You are viewing this page in draft mode.

Microbiome-wide association studies link dynamic microbial consortia to disease

Abstract

Rapid advances in DNA sequencing, metabolomics, proteomics and computational tools are dramatically increasing access to the microbiome and identification of its links with disease. In particular, time-series studies and multiple molecular perspectives are facilitating microbiome-wide association studies, which are analogous to genome-wide association studies. Early findings point to actionable outcomes of microbiome-wide association studies, although their clinical application has yet to be approved. An appreciation of the complexity of interactions among the microbiome and the host's diet, chemistry and health, as well as determining the frequency of observations that are needed to capture and integrate this dynamic interface, is paramount for developing precision diagnostics and therapies that are based on the microbiome.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Sources of metabolites from the human microbiome.
Figure 2: Developing a microbial Global Positioning System to stratify individuals and to guide their treatment.

References

  1. Manichanh, C. et al. Anal gas evacuation and colonic microbiota in patients with flatulence: effect of diet. Gut 63, 401–408 (2014).

    PubMed  Article  Google Scholar 

  2. Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007). This study linked the microbiota to IBD and also demonstrated that various forms of the condition have distinct signatures of microbiota.

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Lewis, J. D. et al. Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn's disease. Cell Host Microbe 18, 489–500 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    ADS  CAS  PubMed  Article  Google Scholar 

  5. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    ADS  PubMed  Article  Google Scholar 

  6. Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Med. 19, 576–585 (2013).

    ADS  CAS  PubMed  Article  Google Scholar 

  7. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012). This study identified high levels of Fusobacterium nucleatum in tissue from human tumours; the bacterium was later confirmed to cause tumours in experiments in animals.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013). This paper provided the first evidence to directly link the gut microbiota to rheumatoid arthritis in people.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. Naseribafrouei, A. et al. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 26, 1155–1162 (2014).

    CAS  PubMed  Article  Google Scholar 

  10. Scheperjans, F. et al. Gut microbiota are related to Parkinson's disease and clinical phenotype. Mov. Disord. 30, 350–358 (2015).

    PubMed  Article  Google Scholar 

  11. Kang, D. W. et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS ONE 8, e68322 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Kostic, A. D., Howitt, M. R. & Garrett, W. S. Exploring host–microbiota interactions in animal models and humans. Genes Dev. 27, 701–718 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Barr, J. J. et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl Acad. Sci. USA 110, 10771–10776 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013). This study provided a mechanism to underpin the high variation between individuals in efficacy of the cardiac drug digoxin, which was suspected (but not yet proven) to be linked to its metabolism by Eggerthella lenta.

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Arthur, J. C. et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338, 120–123 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nature Methods 8, 761–763 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe 15, 382–392 (2014). This study of treatment-naive children who had been freshly diagnosed with Crohn's disease enabled the effects of treatment to be separated from those of the condition.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Kuczynski, J. et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nature Methods 7, 813–819 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S. & Bahler, J. Proportionality: a valid alternative to correlation for relative data. PLoS Comput. Biol. 11, e1004075 (2015).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. Knights, D., Parfrey, L. W., Zaneveld, J., Lozupone, C. & Knight, R. Human-associated microbial signatures: examining their predictive value. Cell Host Microbe 10, 292–296 (2011).

    CAS  PubMed  Article  Google Scholar 

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

    ADS  CAS  PubMed  Article  Google Scholar 

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

  25. Fierer, N. et al. Forensic identification using skin bacterial communities. Proc. Natl Acad. Sci. USA 107, 6477–6481 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. USA 112, E2930–E2938 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013). This paper demonstrated how careful analysis of exact 16S rRNA sequences that avoids clustering into OTUs can reveal fine-grained information that can be useful for forensic matching.

    PubMed Central  Article  Google Scholar 

  28. Noecker, C. et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. mSystems 13, http://dx.doi.org/10.1128/mSystems.00013-15 (2015).

  29. Koenigsknecht, M. J. et al. Dynamics and establishment of Clostridium difficile infection in the murine gastrointestinal tract. Infect. Immun. 83, 934–941 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. Hsiao, E. Y. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451–1463 (2013). This study showed that the phenotype of a mouse model of autism spectrum disorder could be traced, in part, to a single molecule (4-ethylphenylsulfate) and a shift in the microbiota that can be partially restored using a probiotic.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Belda-Ferre, P. et al. The human oral metaproteome reveals potential biomarkers for caries disease. Proteomics 15, 3497–3507 (2015).

    CAS  PubMed  Article  Google Scholar 

  32. Willing, B. P. et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854 (2010).

    PubMed  Article  Google Scholar 

  33. Erickson, A. R. et al. Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn's disease. PLoS ONE 7, e49138 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Jansson, J. et al. Metabolomics reveals metabolic biomarkers of Crohn's disease. PLoS ONE 4, e6386 (2009).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Michail, S. et al. Altered gut microbial energy and metabolism in children with non-alcoholic fatty liver disease. FEMS Microbiol. Ecol. 91, 1–9 (2015).

    PubMed  Article  CAS  Google Scholar 

  36. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015). This paper compared two discordant studies of microbiomes in type 2 diabetes and showed that the alleged effect of diabetes could be attributed mostly to differences in use of metformin, which has an unexpectedly large effect on the microbiome, between the two populations.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013). This study demonstrated that phenotypes such as increased adiposity could be transferred from people to mice using personalized culture collections.

    Article  CAS  PubMed  Google Scholar 

  38. Li, H. & Jia, W. Cometabolism of microbes and host: implications for drug metabolism and drug-induced toxicity. Clin. Pharmacol. Ther. 94, 574–581 (2013).

    CAS  PubMed  Article  Google Scholar 

  39. Nicholson, J. K., Lindon, J. C. & Holmes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999).

    CAS  PubMed  Article  Google Scholar 

  40. Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nature Rev. Drug Discov. http://dx.doi.org/10.1038/nrd.2016.32 (2016).

  41. da Silva, R. R., Dorrestein, P. C. & Quinn, R. A. Illuminating the dark matter in metabolomics. Proc. Natl Acad. Sci. USA 112, 12549–12550 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Gilbert, J. A. & Henry, C. Predicting ecosystem emergent properties at multiple scales. Environ. Microbiol. Rep. 7, 20–22 (2015).

    PubMed  Article  Google Scholar 

  43. Allen, L. et al. Pyocyanin production by Pseudomonas aeruginosa induces neutrophil apoptosis and impairs neutrophil-mediated host defenses in vivo. J. Immunol. 174, 3643–3649 (2005).

    CAS  PubMed  Article  Google Scholar 

  44. Puertollano, E., Kolida, S. & Yaqoob, P. Biological significance of short-chain fatty acid metabolism by the intestinal microbiome. Curr. Opin. Clin. Nutr. Metab. Care 17, 139–144 (2014).

    CAS  PubMed  Article  Google Scholar 

  45. Cimermancic, P. et al. Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell 158, 412–421 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Donia, M. S. et al. A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 158, 1402–1414 (2014). This paper showed that the human microbiome harbours many biosynthetic gene clusters, including those required for the production of antibiotics.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Cummings, J. H. Fermentation in the human large intestine: evidence and implications for health. Lancet 1, 1206–1209 (1983).

    CAS  PubMed  Article  Google Scholar 

  48. Huda-Faujan, N. et al. The impact of the level of the intestinal short chain fatty acids in inflammatory bowel disease patients versus healthy subjects. Open Biochem. J. 4, 53–58 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Ríos-Covián, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7, 185 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  50. Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Nougayrède, J. P. et al. Escherichia coli induces DNA double-strand breaks in eukaryotic cells. Science 313, 848–851 (2006).

    ADS  PubMed  Article  CAS  Google Scholar 

  53. Putze, J. et al. Genetic structure and distribution of the colibactin genomic island among members of the family Enterobacteriaceae. Infect. Immun. 77, 4696–4703 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

    ADS  CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  Google Scholar 

  56. Allegretti, J. R. et al. Recurrent Clostridium difficile infection associates with distinct bile acid and microbiome profiles. Aliment. Pharmacol. Ther. 43, 1142–1153 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Maurice, C. F., Haiser, H. J. & Turnbaugh, P. J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152, 39–50 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Mani, S., Boelsterli, U. A. & Redinbo, M. R. Understanding and modulating mammalian-microbial communication for improved human health. Annu. Rev. Pharmacol. Toxicol. 54, 559–580 (2014).

    CAS  PubMed  Article  Google Scholar 

  59. Wallace, B. D. et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010). This paper demonstrated that the cancer therapeutic drug irinotecan causes severe diarrhoea because of its reactivation and metabolism by bacterial β-glucuronidases; inhibiting these enzymes with a drug that targets the bacteria, rather than the host, reduces toxicity.

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. ElRakaiby, M. et al. Pharmacomicrobiomics: the impact of human microbiome variations on systems pharmacology and personalized therapeutics. OMICS 18, 402–414 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Clayton, T. A., Baker, D., Lindon, J. C., Everett, J. R. & Nicholson, J. K. Pharmacometabonomic identification of a significant host–microbiome metabolic interaction affecting human drug metabolism. Proc. Natl Acad. Sci. USA 106, 14728–14733 (2009). This study provided the first link between the toxicity of a drug (in this case, acetaminophen, a widely used analgesic) and microbial metabolism.

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Wilson, I. D. Drugs, bugs, and personalized medicine: pharmacometabonomics enters the ring. Proc. Natl Acad. Sci. USA 106, 14187–14188 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Quinn, R. A. et al. From sample to multi-omics conclusions in under 48 hours. mSystems http://dx.doi.org/10.1128/mSystems.00038-16 (2016).

  64. Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Gibbons, S. M. et al. Ecological succession and viability of human-associated microbiota on restroom surfaces. Appl. Environ. Microbiol. 81, 765–773 (2015).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Quick, J. et al. Rapid draft sequencing and real-time nanopore sequencing in a hospital outbreak of Salmonella. Genome Biol. 16, 114 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nature Med. 22, 250–253 (2016).

    CAS  PubMed  Article  Google Scholar 

  69. Goyal, M. S., Venkatesh, S., Milbrandt, J., Gordon, J. I. & Raichle, M. E. Feeding the brain and nurturing the mind: linking nutrition and the gut microbiota to brain development. Proc. Natl Acad. Sci. USA 112, 14105–14112 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Biteen, J. S. et al. Tools for the microbiome: nano and beyond. ACS Nano 10, 6–37 (2016).

    CAS  PubMed  Article  Google Scholar 

  71. Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 1262073 (2015).

    PubMed  Article  CAS  Google Scholar 

  72. Sam Ma, Z. et al. Network analysis suggests a potentially 'evil' alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Sci. Rep. 5, 8275 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015); erratum 17, 852 (2015).

    PubMed  Article  CAS  Google Scholar 

  74. The Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project: dynamic analysis of microbiome–host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).

  75. Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  76. Weingarden, A. et al. Dynamic changes in short- and long-term bacterial composition following fecal microbiota transplantation for recurrent Clostridium difficile infection. Microbiome 3, 10 (2015). This paper introduced animation techniques that revealed the transformation of the whole microbiota during faecal microbiota transplantation for C. difficile infection.

    PubMed  PubMed Central  Article  Google Scholar 

  77. Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108 (suppl. 1), 4578–4585 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  78. Lozupone, C. A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. Shade, A. et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 5, e01371-14 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  81. DiGiulio, D. B. et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112, 11060–11065 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. Koren, O. et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470–480 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Wang, Z. et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. 35, 904–910 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015). This study showed that individual glycaemic responses could be predicted using the microbiome; it also revealed that although population averages match conventional glycaemic-index values, the responses of individuals are highly idiosyncratic and dependent on the microbiome.

    CAS  PubMed  Article  Google Scholar 

  85. Teng, F. et al. Prediction of early childhood caries via spatial-temporal variations of oral microbiota. Cell Host Microbe 18, 296–306 (2015).

    CAS  PubMed  Article  Google Scholar 

  86. Huang, S. et al. Predictive modeling of gingivitis severity and susceptibility via oral microbiota. ISME J. 8, 1768–1780 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  87. Zhang, X. et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nature Med. 21, 895–905 (2015).

    CAS  PubMed  Article  Google Scholar 

  88. Ding, T. & Schloss, P. D. Dynamics and associations of microbial community types across the human body. Nature 509, 357–360 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

    ADS  CAS  PubMed  Article  Google Scholar 

  90. Walters, W. A., Xu, Z. & Knight, R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 588, 4223–4233 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. Kang, D., Shi, B., Erfe, M. C., Craft, N. & Li, H. Vitamin B12 modulates the transcriptome of the skin microbiota in acne pathogenesis. Sci. Transl. Med. 7, 293ra103 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  92. Sinha, R., Abnet, C. C., White, O., Knight, R. & Huttenhower, C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 16, 276 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  93. Sinha, R. et al. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol. Biomarkers Prev. 25, 407–416 (2016).

    PubMed  Article  Google Scholar 

  94. Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. de Goffau, M. C. et al. Fecal microbiota composition differs between children with β-cell autoimmunity and those without. Diabetes 62, 1238–1244 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. Giongo, A. et al. Toward defining the autoimmune microbiome for type 1 diabetes. ISME J. 5, 82–91 (2011).

    CAS  PubMed  Article  Google Scholar 

  97. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260–273 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. Scher, J. U. et al. Periodontal disease and the oral microbiota in new-onset rheumatoid arthritis. Arthritis Rheum. 64, 3083–3094 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  99. Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl Acad. Sci. USA 108 (suppl. 1), 4592–4598 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  100. Yin, J. et al. Dysbiosis of gut microbiota with reduced trimethylamine-N-oxide level in patients with large-artery atherosclerotic stroke or transient ischemic attack. J. Am. Heart Assoc. 4, e002699 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  101. Tang, W. H. et al. Gut microbiota-dependent trimethylamine N-oxide (TMAO) pathway contributes to both development of renal insufficiency and mortality risk in chronic kidney disease. Circ. Res. 116, 448–455 (2015).

    CAS  PubMed  Article  Google Scholar 

  102. Xu, R., Wang, Q. & Li, L. A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat. BMC Genomics 16 (suppl. 7), S4 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  103. Tang, W. H. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. Zhu, W. et al. Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell 165, 111–124 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. Hsiao, E. Y., McBride, S. W., Chow, J., Mazmanian, S. K. & Patterson, P. H. Modeling an autism risk factor in mice leads to permanent immune dysregulation. Proc. Natl Acad. Sci. USA 109, 12776–12781 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. Liu, Z., DeSantis, T. Z., Andersen, G. L. & Knight, R. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Res. 36, e120 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. Gilbert, J. A. et al. Meeting report: the terabase metagenomics workshop and the vision of an Earth microbiome project. Stand. Genomic Sci. 3, 243–248 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  108. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. Quinn, R. A. et al. Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome. ISME J. 10, 1483–98 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  110. Ridlon, J. M., Kang, D. J., Hylemon, P. B. & Bajaj, J. S. Bile acids and the gut microbiome. Curr. Opin. Gastroenterol. 30, 332–338 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  111. Gill, S. R. et al. Metagenomic analysis of the human distal gut microbiome. Science 312, 1355–1359 (2006). This study provided the first metagenomic gene catalogue of the human gut.

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352 (2009).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  115. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).

    PubMed  Google Scholar 

  117. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011).

    CAS  PubMed  Article  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. Vijay-Kumar, M. et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328, 228–231 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. Parks, B. W. et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 17, 141–152 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  122. Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

This work and the work in the authors' laboratories that it describes was supported in part by awards from the US National Institutes of Health, the US Department of Energy, the US National Science Foundation, the Alfred P. Sloan Foundation, the Crohn's and Colitis Foundation of America and the US Office of Naval Research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rob Knight.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reprints and permissions information is available at www.nature.com.reprints.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gilbert, J., Quinn, R., Debelius, J. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016). https://doi.org/10.1038/nature18850

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature18850

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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