Article series: Microbiome

Metagenome-wide association studies: fine-mining the microbiome

Journal name:
Nature Reviews Microbiology
Volume:
14,
Pages:
508–522
Year published:
DOI:
doi:10.1038/nrmicro.2016.83
Published online

Abstract

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

Figures

  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.

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Affiliations

  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

Competing interests statement

J.W. is the CEO of iCarbonX.

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Author details

  • 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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