Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort

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Abstract

Background

The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.

Results

We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state.

Conclusions

Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1194-2) contains supplementary material, which is available to authorized users.

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Most cited references 69

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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QIIME allows analysis of high-throughput community sequencing data.

J. Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh … (2010)

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Diet rapidly and reproducibly alters the human gut microbiome

Lawrence A David, Corinne F. Maurice, Rachel Carmody … (2013)

Long-term diet influences the structure and activity of the trillions of microorganisms residing in the human gut 1–5 , but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here, we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila, and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals 2 , reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi, and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids, and the outgrowth of microorganisms capable of triggering inflammatory bowel disease 6 . In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles.

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Author and article information

Contributors

Elin Org: (372) 737 4039 , elin.org@ut.ee

Yuna Blum: yuna.blum@gmail.com

Silva Kasela: Silva.Kasela@ut.ee

Margarete Mehrabian: mehrabi.m@gmail.com

Johanna Kuusisto: johanna.kuusisto@kuh.fi

Antti J. Kangas: antti.kangas@computationalmedicine.fi

Pasi Soininen: pasi.soininen@uef.fi

Zeneng Wang: WANGZ2@ccf.org

Mika Ala-Korpela: mika.ala-korpela@computationalmedicine.fi

Stanley L. Hazen: HAZENS@ccf.org

Markku Laakso: markku.laakso@uef.fi

Aldons J. Lusis: (310) 825-1359 , jlusis@mednet.ucla.edu

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 13 April 2017

Publication date PMC-release: 13 April 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 70

Affiliations

[1 ]GRID grid.19006.3e, Department of Medicine, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA

[2 ]GRID grid.10939.32, Estonian Genome Centre, , University of Tartu, ; Tartu, 51010 Estonia

[3 ]GRID grid.10939.32, , Institute of Molecular and Cell Biology, University of Tartu, ; Tartu, 51010 Estonia

[4 ]GRID grid.9668.1, , Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, ; Kuopio, Finland

[5 ]GRID grid.410705.7, , Kuopio University Hospital, ; Kuopio, Finland

[6 ]GRID grid.10858.34, Computational Medicine, Faculty of Medicine, , University of Oulu and Biocenter Oulu, ; Oulu, Finland

[7 ]GRID grid.9668.1, NMR metabolomics Laboratory, , School of Pharmacy, University of Eastern Finland, ; Kuopio, Finland

[8 ]GRID grid.239578.2, Department of Cellular and Molecular Medicine, , Cleveland Clinic, ; Cleveland, OH 44195 USA

[9 ]GRID grid.5337.2, Computational Medicine, School of Social and Community Medicine, , University of Bristol and Medical Research Council Integrative Epidemiology Unit at the University of Bristol, ; Bristol, UK

[10 ]GRID grid.19006.3e, Department of Human Genetics, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA

[11 ]GRID grid.19006.3e, Department of Microbiology, Immunology and Molecular Genetics, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA

Article

Publisher ID: 1194

DOI: 10.1186/s13059-017-1194-2

PMC ID: 5390365

PubMed ID: 28407784

SO-VID: 15ed227b-dfa1-4b14-a541-997a848a846a

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 27 November 2016

Date accepted : 16 March 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: HL028481

Award ID: HL30568

Award ID: DK094311

Award Recipient : Aldons J. Lusis

Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;

Award ID: FP7-MC-IOF grant no 330381

Award Recipient : Elin Org

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: host-microbiota interactions,tmao,metabolic traits,serum metabolites,type 2 diabetes

Data availability:

ScienceOpen disciplines: Genetics

Keywords: host-microbiota interactions, tmao, metabolic traits, serum metabolites, type 2 diabetes