Metagenome-wide association studies: fine-mining the microbiome

Wang, Jun; Jia, Huijue

doi:10.1038/nrmicro.2016.83

Cited by 358 publications

(319 citation statements)

References 120 publications

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“…CCoDA differs from earlier approaches to quantifying variability in microbiome function in several key ways. First, we focus explicitly on the variability of gene family abundance, not differences in mean abundance between predefined groups, as has been done to reveal pathways whose abundance differs between body sites [69] or disease states [6]. …”

Section: Discussionmentioning

confidence: 99%

“…Shotgun metagenomics is revolutionizing our ability to identify protein-coding genes from these microbes and associate gene levels with disease [6], drug efficacy [7] or side-effects [8], and other host traits. For instance, human gut microbiota associated with a traditional high-fiber agrarian diet encoded gene families involved in cellulose and xylan hydrolysis, which were absent in age-matched controls eating a typical Western diet [9].…”

Section: Introductionmentioning

confidence: 99%

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Proteobacteria explain significant functional variability in the human gut microbiome

2017

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BackgroundWhile human gut microbiomes vary significantly in taxonomic composition, biological pathway abundance is surprisingly invariable across hosts. We hypothesized that healthy microbiomes appear functionally redundant due to factors that obscure differences in gene abundance between individuals.ResultsTo account for these biases, we developed a powerful test of gene variability called CCoDA, which is applicable to shotgun metagenomes from any environment and can integrate data from multiple studies. Our analysis of healthy human fecal metagenomes from three separate cohorts revealed thousands of genes whose abundance differs significantly and consistently between people, including glycolytic enzymes, lipopolysaccharide biosynthetic genes, and secretion systems. Even housekeeping pathways contain a mix of variable and invariable genes, though most highly conserved genes are significantly invariable. Variable genes tend to be associated with Proteobacteria, as opposed to taxa used to define enterotypes or the dominant phyla Bacteroidetes and Firmicutes.ConclusionsThese results establish limits on functional redundancy and predict specific genes and taxa that may explain physiological differences between gut microbiomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0244-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proteobacteria explain significant functional variability in the human gut microbiome

2017

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“…One approach to identify the association is using metagenome-wide association study (MWAS), which takes advantages of huge taxa data discovered using metagenomics and applies the concept of genome-wide association study (GWAS) for the association analysis. Instead of using single nucleotide polymorphisms (SNPs) as the explanatory variables, MWAS employs the abundance of a taxa (a metagenomic species or a metagenomic gene cluster) as the explanatory variables (Wang and Jia, 2016), and MWAS has been successfully used for several human diseases such as type 2 diabetes (Karlsson et al, 2013). Another advantage of the huge taxa data from a metagenomics is to use machine learning methods such as the Random Forest (RF) model or Support Vector Machine model, to integrate the abundance of metagenomic species for phenotypic prediction (Soueidan and Nikolski, 2016; Wang and Jia, 2016).…”

Section: Introductionmentioning

confidence: 99%

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

et al. 2017

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Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.

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“…The human intestinal microbiome encompasses at least 100 trillion microorganisms, which can influence the immune system and health conditions, including cancer [3–5]. A growing body of evidence indicates that diet, lifestyle, and drugs can influence the composition of the gut microbiota and that the gut microbiota can modulate the development and progression of gastrointestinal tract neoplasms [6,7].…”

Section: Introductionmentioning

confidence: 99%

The role of intestinal bacteria in the development and progression of gastrointestinal tract neoplasms

Mima

Ogino²,

Nakagawa

et al. 2017

Surgical Oncology

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More than 100 trillion microorganisms inhabit the human intestinal tract and play important roles in health conditions and diseases, including cancer. Accumulating evidence demonstrates that specific bacteria and bacterial dysbiosis in the gastrointestinal tract can potentiate the development and progression of gastrointestinal tract neoplasms by damaging DNA, activating oncogenic signaling pathways, producing tumor-promoting metabolites such as secondary bile acids, and suppressing antitumor immunity. Other bacterial species have been shown to produce short-chain fatty acids such as butyrate, which can suppress inflammation and carcinogenesis in the gastrointestinal tract. Consistent with these lines of evidence, clinical studies using metagenomic analyses have shown associations of specific bacteria and bacterial dysbiosis with gastrointestinal tract cancers, including esophageal, gastric, and colorectal cancers. Emerging data demonstrate that intestinal bacteria can modulate the efficacy of cancer chemotherapies and novel targeted immunotherapies such as anti-CTLA4 and anti-CD274 therapies, the process of absorption, and the occurrence of complications after gastrointestinal surgery. A better understanding of the mechanisms by which the gut microbiota influence tumor development and progression in the intestine would provide opportunities to develop new prevention and treatment strategies for patients with gastrointestinal tract cancers by targeting the intestinal microflora.

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Metagenome-wide association studies: fine-mining the microbiome

Cited by 358 publications

References 120 publications

Proteobacteria explain significant functional variability in the human gut microbiome

Proteobacteria explain significant functional variability in the human gut microbiome

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

The role of intestinal bacteria in the development and progression of gastrointestinal tract neoplasms

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