Article series: Microbiome

Metagenome-wide association studies: fine-mining the microbiome

Journal name:
Nature Reviews Microbiology
Year published:
Published online


Metagenome-wide association studies (MWAS) have enabled the high-resolution investigation of associations between the human microbiome and several complex diseases, including type 2 diabetes, obesity, liver cirrhosis, colorectal cancer and rheumatoid arthritis. The associations that can be identified by MWAS are not limited to the identification of taxa that are more or less abundant, as is the case with taxonomic approaches, but additionally include the identification of microbial functions that are enriched or depleted. In this Review, we summarize recent findings from MWAS and discuss how these findings might inform the prevention, diagnosis and treatment of human disease in the future. Furthermore, we highlight the need to better characterize the biology of many of the bacteria that are found in the human microbiota as an essential step in understanding how bacterial strains that have been identified by MWAS are associated with disease.

At a glance


  1. Identifying associations using MWAS.
    Figure 1: Identifying associations using MWAS.

    Although metagenome-wide association studies (MWAS) can, in principle, be used to study associations between the microbiome and any trait, studies to date have focused on identifying associations between the microbiome and disease. a | A typical cohort to be studied by MWAS would include a group of healthy individuals (top left panel, yellow) and a group of individuals with a disease (top left panel, red). However, MWAS can also be used to compare the microbiomes of individuals in a longitudinal study: before and after a certain intervention, such as a drug treatment (top right panel) or dietary intervention (not shown); or in a natural process, such as the development of an infant (bottom left panel) or the progression of a disease (not shown). Finally, an MWAS may be designed to compare the microbiomes at different body sites for a cohort of individuals with a disease (bottom right panel). b | The microbiomes of samples that are taken from different body sites, such as oral plaque, saliva, stool (representing the gut microbiome) or skin, can be studied by MWAS. c | DNA extraction, library preparation and metagenomic shotgun sequencing of the samples generates a dataset of sequencing reads. Bioinformatics tools (not shown) are used to assemble the metagenomic reads into contigs. d | Genes that are predicted from contigs are compiled into a gene catalogue, or an existing reference gene catalogue that is representative of the data could be readily used. The relative abundance of a gene can be quantified by determining the number of sequencing reads that align to that gene in the reference catalogue. Furthermore, phylogenetic or functional annotation and grouping of the predicted genes allows the quantification of microbial taxa or functional pathways in the samples and comparisons between samples. e | Genes (or contigs, which can contain several genes and intergenic regions) that have abundances that co-vary in samples can be clustered into strain-level taxonomic units (known, according to the clustering algorithm used, as metagenomic linkage groups (MLGs), metagenomic clusters (MGCs) or metagenomic species (MGSs). f | Associations with a disease can be identified for individual microbial genes, taxa or functions. In addition, classifiers can be constructed using supervised machine learning to assign each sample to a certain category, such as healthy or diseased. g | Associations that are identified by MWAS can be validated using additional metagenomic datasets, such as samples from additional cohorts or timepoints, or using other forms of omics data. For studies that seek to identify causal relationships between a disease and the microbiome, associations that are identified by MWAS can be used to suggest hypotheses for further investigation by animal models. These experiments may involve the microbial transplant of specific species or sets of species, and/or the study of the response of the microbiome to dietary changes or drug treatment.

  2. Changes to the gut microbiome that are associated with type 2 diabetes.
    Figure 2: Changes to the gut microbiome that are associated with type 2 diabetes.

    a | In healthy individuals, the gut microbiome is enriched for taxa that are associated with an increased capacity for the production of metabolites, such as short-chain fatty acids (SCFAs)13, 14, that promote intestinal integrity and energy homeostasis through absorption by the gut epithelium and signalling through host receptors to induce regulatory T cells (Treg), which restricts inflammation and may even promote tissue repair37. SCFAs also stimulate the secretion of glucagon-like peptide 1 (GLP1) and peptide YY by intestinal L cells (not shown) to control glucose homeostasis and regulate food intake10, 11. These taxa and functions tend to be depleted in the gut microbiomes of individuals with type 2 diabetes or obesity13, 14, 18. b | In individuals with type 2 diabetes, metagenome-wide association studies (MWAS) suggest that changes to the gut microbiome are associated with metabolic dysfunction and inflammation. For example, an increased potential for the production of hydrogen sulfide and lipopolysaccharide (LPS) could stimulate inflammation. However, the gut microbiomes of individuals with type 2 diabetes who were treated with the anti-diabetic drug metformin showed a decrease in the abundance of Intestinibacter spp. and an increase in the abundances of species in the Enterobacteriaceae family, such as Escherichia coli, compared with individuals with type 2 diabetes who did not receive metformin treatment. The increase in the abundance of E. coli seemed to correlate with an increase in the secretion of GLP1. BCAA, branched-chain amino acid; C. hathewayi, Clostridium hathewayi; C. ramosum, Clostridium ramosum; C. symbiosum, Clostridium symbiosum; E. lenta, Eggerthella lenta; F. prausnitzii, Faecalibacterium prausnitzii; R. intestinalis, Roseburia intestinalis; R. inulinivorans, Roseburia inulinivorans.

  3. Model for a gut microbial basis for the development of colorectal cancer.
    Figure 3: Model for a gut microbial basis for the development of colorectal cancer.

    a,b | Associations that were identified by metagenome-wide association studies (MWAS)31, 32 suggest that bacterial species that are usually of low abundance in the gut, and the toxins that they produce, could become more abundant in response to lifestyle or dietary changes, such as an increase in the consumption of red meat and a decrease in the consumption of fruits, vegetables and fibre. Some bacterial species that are most commonly described as anaerobic oral bacteria, such as Fusobacterium spp. and Parvimonas micra, have been identified by MWAS as being associated with colorectal cancer31, 32, 33, 35. Functional changes in the gut microbiome might involve an increase in the production of carcinogens through processes such as amino acid fermentation and the metabolism of bile acids37. By contrast, bacterial species that produce the metabolites butyrate and lactate, which facilitate the maintenance of the colonic epithelium, can be depleted in the gut microbiomes of individuals with colorectal cancer. c | Dysbiosis of the gut microbiota can result in an impairment of gut barrier function, which increases the exposure of the gut epithelium to microorganisms and their metabolites37, 40; some of these metabolites are mutagens that might promote carcinogenesis.

  4. The oral and gut microbiomes of individuals with rheumatoid arthritis.
    Figure 4: The oral and gut microbiomes of individuals with rheumatoid arthritis.

    The microbiome might interact with both genetic and environmental factors that influence the risk of developing rheumatoid arthritis45, 46. Using metagenome-wide association studies (MWAS) to examine both the oral and gut microbiomes of individuals with rheumatoid arthritis has shown an overlap between the microbiomes from the two body sites, with an enrichment of several bacterial species, including Lactobacillus salivarius, at both sites. Sets of bacterial species were also shown to have correlated changes in abundance between the oral and gut microbiomes of individuals with rheumatoid arthritis: for example, the abundance of Klebsiella pneumoniae in the gut microbiome was positively correlated with the abundance of Lactococcus spp. in the oral microbiome, whereas the abundance of Clostridium asparagiforme in the gut microbiome was negatively correlated with the abundance of Prevotella intermedia in the oral microbiome45. As such, sampling at one body site may reveal information about the microbiome at another site45. HLA-DRB1, major histocompatibility complex, class II, DRβ1; PADI4, peptidyl arginine deiminase 4; PTPN22, protein tyrosine phosphatase non-receptor type 22; TNFAIP3, tumour necrosis factor-α-induced protein 3.


  1. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 5965 (2010).
    This study details the first gene catalogue of the human gut microbiome that is assembled from next-generation sequencing data.
  2. Methé, B. A. et al. A framework for human microbiome research. Nature 486, 215221 (2012).
  3. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834841 (2014).
    This study details a high-quality reference gene catalogue that is compiled from 1,267 samples across three continents, and identifies many differences in the gut microbiome between healthy Chinese and Danish individuals.
  4. Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 12581270 (2012).
  5. Sommer, F. & Bäckhed, F. The gut microbiota — masters of host development and physiology. Nat. Rev. Microbiol. 11, 227238 (2013).
  6. Marchesi, J. R. et al. The gut microbiota and host health: a new clinical frontier. Gut 65, 330339 (2015).
  7. Xu, Y. et al. Prevalence and control of diabetes in Chinese adults. JAMA 310, 948959 (2013).
  8. Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085 (2010).
  9. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 5560 (2012).
    The first MWAS, which establishes the MLG method and identifies associations between the gut microbiome and type 2 diabetes.
  10. Brubaker, P. L. & Anini, Y. Direct and indirect mechanisms regulating secretion of glucagon-like peptide-1 and glucagon-like peptide-2. Can. J. Physiol. Pharmacol. 81, 10051012 (2003).
  11. Zhou, J. et al. Peptide YY and proglucagon mRNA expression patterns and regulation in the gut. Obesity (Silver Spring) 14, 683689 (2006).
  12. Sun, J. et al. Pancreatic β-cells limit autoimmune diabetes via an immunoregulatory antimicrobial peptide expressed under the influence of the gut microbiota. Immunity 43, 304317 (2015).
  13. Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99103 (2013).
  14. Pyra, K. A., Saha, D. C. & Reimer, R. A. Prebiotic fiber increases hepatic acetyl CoA carboxylase phosphorylation and suppresses glucose-dependent insulinotropic polypeptide secretion more effectively when used with metformin in obese rats. J. Nutr. 142, 213220 (2012).
  15. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262266 (2015).
  16. Shin, N.-R. et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 63, 727735 (2014).
  17. Lee, H. & Ko, G. Effect of metformin on metabolic improvement and gut microbiota. Appl. Environ. Microbiol. 80, 59355943 (2014).
  18. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541546 (2013).
  19. Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585588 (2013).
  20. Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 10221023 (2006).
  21. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480484 (2009).
  22. Jemal, A. et al. Global cancer statistics. CA Cancer J. Clin. 61, 6990 (2011).
  23. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359E386 (2015).
  24. Brenner, H., Kloor, M. & Pox, C. P. Colorectal cancer. Lancet 383, 14901502 (2014).
  25. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292298 (2012).
  26. Castellarin, M. et al. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res. 22, 299306 (2012).
  27. Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207215 (2013).
  28. Gur, C. et al. Binding of the Fap2 protein of Fusobacterium nucleatum to human inhibitory receptor TIGIT protects tumors from immune cell attack. Immunity 42, 344355 (2015).
  29. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260270 (2012).
  30. Weir, T. L. et al. Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PLoS ONE 8, e70803 (2013).
  31. Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).
  32. Feng, Q. et al. Gut microbiome development along the colorectal adenoma–carcinoma sequence. Nat. Commun. 6, 6528 (2015).
  33. Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut (2015).
  34. Marchesi, J. R. et al. Towards the human colorectal cancer microbiome. PLoS ONE 6, e20447 (2011).
  35. Nakatsu, G. et al. Gut mucosal microbiome across stages of colorectal carcinogenesis. Nat. Commun. 6, 8727 (2015).
  36. Tjalsma, H., Boleij, A., Marchesi, J. R. & Dutilh, B. E. A bacterial driver-passenger model for colorectal cancer: beyond the usual suspects. Nat. Rev. Microbiol. 10, 575582 (2012).
  37. Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661672 (2014).
  38. Iida, N. et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342, 967970 (2013).
  39. Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971976 (2013).
  40. Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800812 (2013).
  41. Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 10791084 (2015).
  42. Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 10841089 (2015).
  43. McInnes, I. B. & Schett, G. The pathogenesis of rheumatoid arthritis. N. Engl. J. Med. 365, 22052219 (2011).
  44. Demoruelle, M. K., Deane, K. D. & Holers, V. M. When and where does inflammation begin in rheumatoid arthritis? Curr. Opin. Rheumatol. 26, 22642271 (2014).
  45. Zhang, X. et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 21, 895905 (2015).
    This study extends MWAS to the oral microbiome, and identifies potential markers in the oral and gut microbiomes for rheumatoid arthritis and its treatment by drugs.
  46. Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013).
  47. Scher, J. U. et al. Periodontal disease and the oral microbiota in new-onset rheumatoid arthritis. Arthritis Rheum. 64, 30833094 (2012).
  48. Deane, K. D. & El-Gabalawy, H. Pathogenesis and prevention of rheumatic disease: focus on preclinical RA and SLE. Nat. Rev. Rheumatol. 10, 212228 (2014).
  49. Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
  50. Fu, J. et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res. 117, 817824 (2015).
  51. Qin, N. et al. Alterations of the human gut microbiome in liver cirrhosis. Nature 513, 859864 (2014).
  52. Ding, T. & Schloss, P. D. Dynamics and associations of microbial community types across the human body. Nature 509, 357360 (2014).
  53. Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690703 (2015).
    This study details the largest cohort of infants for which gut microbiomes have been longitudinally profiled for one year from birth; draft genomes were assembled from each sample through the binning of contigs instead of genes.
  54. Sonnenburg, J. L. et al. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 307, 19551959 (2005).
  55. Koropatkin, N. M., Cameron, E. A. & Martens, E. C. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10, 323335 (2012).
  56. Kashnap, P. et al. Genetically dictated change in host mucus carbohydrate landscape exerts a diet-dependent effect on gut microbiota. Proc. Natl Acad. Sci. USA 110, 1705917064 (2013).
  57. Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426429 (2013).
    This study identifies glycan-utilizing colonization factors in Bacteroides spp. that are responsible for saturable colonization of individual Bacteroides species in mice.
  58. Motta, J.-P. et al. Hydrogen sulfide protects from colitis and restores intestinal microbiota biofilm and mucus production. Inflamm. Bowel Dis. 21, 10061017 (2015).
  59. Benson, A. K. et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl Acad. Sci. USA 107, 1893318938 (2010).
  60. Benson, A. K. Host genetic architecture and the landscape of microbiome composition: humans weigh in. Genome Biol. 16, 203 (2015).
  61. Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789799 (2014).
  62. van Opstal, E. J. & Bordenstein, S. R. Rethinking heritability of the microbiome. Science 349, 11721173 (2015).
  63. Rausch, P. et al. Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc. Natl Acad. Sci. USA 108, 1903019035 (2011).
  64. Pickard, J. M. et al. Rapid fucosylation of intestinal epithelium sustains host–commensal symbiosis in sickness. Nature 514, 638641 (2014).
  65. Goto, Y. et al. Innate lymphoid cells regulate intestinal epithelial cell glycosylation. Science 345, 1254009 (2014).
  66. Franzosa, E. A. et al. Sequencing and beyond: integrating molecular 'omics' for microbial community profiling. Nat. Rev. Microbiol. 13, 360372 (2015).
  67. Sridharan, G. V. et al. Prediction and quantification of bioactive microbiota metabolites in the mouse gut. Nat. Commun. 5, 5492 (2014).
  68. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell. Metab. 22, 320331 (2015).
  69. Xu, J. et al. Structural modulation of gut microbiota during alleviation of type 2 diabetes with a Chinese herbal formula. ISME J. 9, 552562 (2014).
  70. Lukens, J. R. et al. Dietary modulation of the microbiome affects autoinflammatory disease. Nature 516, 246249 (2014).
  71. Hsiao, E. Y. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 14511463 (2013).
  72. Atarashi, K. et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500, 232236 (2013).
  73. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
    This study shows that co-housing mice that have received microbial transplants from an obese twin with mice that have received microbial transplants from a lean twin prevents the development of obesity-associated phenotypes.
  74. Xiao, L. et al. A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 11031108 (2015).
    This paper details the first gene catalogue for the gut microbiome of laboratory mice, which reports differences from the human gut microbiome as well as between mice.
  75. Bäckhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979984 (2007).
  76. Mukherji, A., Kobiita, A., Ye, T. & Chambon, P. Homeostasis in intestinal epithelium is orchestrated by the circadian clock and microbiota cues transduced by TLRs. Cell 153, 812827 (2013).
  77. Olszak, T. et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 336, 489493 (2012).
  78. Braniste, V. et al. The gut microbiota influences blood–brain barrier permeability in mice. Sci. Transl Med. 6, 263ra158 (2014).
  79. Erny, D. et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat. Neurosci. 18, 965977 (2015).
  80. Lacombe, A. et al. Lowbush wild blueberries have the potential to modify gut microbiota and xenobiotic metabolism in the rat colon. PLoS ONE 8, e67497 (2013).
  81. Hildebrand, F. et al. A comparative analysis of the intestinal metagenomes present in guinea pigs (Cavia porcellus) and humans (Homo sapiens). BMC Genomics 13, 514 (2012).
  82. Cabreiro, F. & Gems, D. Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans. EMBO Mol. Med. 5, 13001310 (2013).
  83. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 10271031 (2006).
  84. Lukovac, S. et al. Differential modulation by Akkermansia muciniphila and Faecalibacterium prausnitzii of host peripheral lipid metabolism and histone acetylation in mouse gut organoids. mBio 5, e01438-14 (2014).
  85. Kim, K., Lee, S. & Ryu, C.-M. Interspecific bacterial sensing through airborne signals modulates locomotion and drug resistance. Nat. Commun. 4, 1809 (2013).
  86. Cuskin, F. et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature 517, 165169 (2015).
  87. Stowell, S. R. et al. Microbial glycan microarrays define key features of host-microbial interactions. Nat. Chem. Biol. 10, 470476 (2014).
  88. Wang, X. et al. Cloning and variation of ground state intestinal stem cells. Nature 522, 173178 (2015).
  89. Joice, R., Yasuda, K., Shafquat, A., Morgan, X. C. & Huttenhower, C. Determining microbial products and identifying molecular targets in the human microbiome. Cell. Metab. 20, 731741 (2014).
  90. Dobkin, J. F., Saha, J. R., Butler, V. P., Neu, H. C. & Lindenbaum, J. Inactivation of digoxin by Eubacterium lentum, an anaerobe of the human gut flora. Trans. Assoc. Am. Physicians 95, 2229 (1982).
  91. Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295298 (2013).
  92. Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31, 533538 (2013).
  93. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822828 (2014).
    This study reports the use of MGSs to assemble genomes, 238 of which met the Human Microbiome Project (HMP) high-quality draft genome standard.
  94. Kuleshov, V. et al. Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome. Nat. Biotechnol. 34, 6469 (2015).
  95. McLean, J. S. et al. Candidate phylum TM6 genome recovered from a hospital sink biofilm provides genomic insights into this uncultivated phylum. Proc. Natl Acad. Sci. USA 110, E2390E2399 (2013).
  96. Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431437 (2013).
  97. Reyes, A. et al. Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature 466, 334338 (2010).
  98. Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241244 (2011).
  99. Minot, S. et al. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res. 21, 16161625 (2011).
  100. Modi, S. R., Lee, H. H., Spina, C. S. & Collins, J. J. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature 499, 219222 (2013).
  101. Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447460 (2015).
  102. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 4550 (2012).
    This study represents the first analysis of genomic variations, such as SNPs, in the gut microbiome.
  103. Hu, Y. et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat. Commun. 4, 2151 (2013).
  104. Greenblum, S., Carr, R. & Borenstein, E. Extensive strain-level copy-number variation across human gut microbiome species. Cell 160, 583594 (2015).
  105. Aagaard, K. et al. The placenta harbors a unique microbiome. Sci. Transl Med. 6, 237ra65 (2014).
  106. Oh, J. et al. Biogeography and individuality shape function in the human skin metagenome. Nature 514, 5964 (2014).
  107. Kultima, J. R. et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS ONE 7, e47656 (2012).
  108. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207214 (2012).
  109. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174180 (2011).
  110. Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 11961199 (2013).
  111. Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881884 (2013).
  112. Dupont, C. L. et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 6, 11861199 (2012).
  113. Freitas, T. A., Li, P.-E., Scholz, M. B. & Chain, P. S. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res. 43, e69 (2015).
  114. Spanogiannopoulos, P., Bess, E. N., Carmody, R. N. & Turnbaugh, P. J. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14, 273287 (2016).
  115. Gill, S. & Panda, S. A. Smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell. Metab. 22, 789798 (2015).
  116. Kovatcheva-Datchary, P. et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell. Metab. 22, 971982 (2015).
  117. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 10791094 (2015).
    This study shows that the composition of the gut microbiota, integrated with other parameters, can be used to predict the blood glucose level of an individual after a certain meal and facilitate dietary interventions.
  118. Olle, B. Medicines from microbiota. Nat. Biotechnol. 31, 309315 (2013).
  119. Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455459 (2015).
  120. Wang, Z. et al. Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis. Cell 163, 15851595 (2015).
    This study identifies a chemical analogue of choline that shows success in inhibiting the production of trimethylamine by the gut microbiota.

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  1. iCarbonX, Shahe Industrial Zone, No.4018 Qiaoxiang Road, Nanshan District, Shenzhen 518083, China.

    • Jun Wang
  2. Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen 518083, China.

    • Jun Wang &
    • Huijue Jia

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J.W. is the CEO of iCarbonX.

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  • Jun Wang

    Jun Wang is the founder and CEO of iCarbonX, Shenzhen, China, and was formerly the CEO of BGI, Shenzhen, China. He graduated from Peking University, Beijing, China, with a Ph.D. in genomics and a Bachelor's degree in artificial intelligence. At BGI, he has authored more than 400 peer-reviewed original papers, of which more than 100 were published in Cell, Nature, Nature research journals or Science, and he was recognized by in Nature as one of “Ten people who mattered this year” in 2012. His current research at iCarbonX focuses on omics data and the development of an artificial intelligence engine to interpret these data, with a view to enable every individual to better manage their digital life.

  • Huijue Jia

    Huijue Jia studied biological sciences at Fudan University, Shanghai, China, prior to studying for her Ph.D. in the laboratory of Eckhard Jankowsky at Case Western Reserve University, Cleveland, Ohio, USA, where she worked on RNA helicases. She completed postdoctoral work on DNA demethylation in the laboratory of Yi Zhang at the University of North Carolina at Chapel Hill, USA, before joining Nature Communications as an editor in 2012, where she covered a wide range of topics that were related to DNA and RNA. In 2013, she joined BGI, Shenzhen, China, where she is now in charge of the Trans-omics Centre for Complex Diseases. Her current research focuses on understanding the human microbiome and on the use of multiomics to investigate complex diseases.

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