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Genome–microbiome interplay provides insight into the determinants of the human blood metabolome

Nature Metabolism volume 4, pages 1560–1572 (2022)Cite this article

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Abstract

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome–microbiome control. Additionally, interaction effects, where a metabolite–microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

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Fig. 1: Study design and cross-sectional variances in metabolites explained by host genetics, gut bacterial genera or both.
Fig. 2: Metabolites associated with both genetics and the microbiome.
Fig. 3: Gene–microbiome interactions.
Fig. 4: Gene–microbiome interactions explain variation in blood metabolite levels.
Fig. 5: Host genetic and gut microbial associations with secondary bile acids.
Fig. 6: Host genetic and gut microbial associations with sphingosine and ceramides.

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Data availability

Qualified researchers can access the full Arivale deidentified dataset supporting the findings in this study for research purposes through signing of a data use agreement. Enquiries to access the data can be made at data-access@isbscience.org and will be responded to within seven business days. The raw 16S amplicon sequencing data can be found in the Sequencing Read Archive under Bioproject nos. PRJNA826530 and PRJNA826648. External databases used were the hg19 human reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13), SILVA database v.128 (https://www.arb-silva.de/) and NCBI dbSNP database build 155 (https://www.ncbi.nlm.nih.gov/snp/).

Code availability

Pipelines for 16S amplicon data processing and analysis can be found at https://github.com/Gibbons-Lab/mbtools. GWAS workflows, Jupyter notebooks, intermediate data files and Python code for reproducing the regression models and figures have been deposited at https://github.com/Gibbons-Lab/2021_gene_environment_interactions.

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Acknowledgements

S.M.G. and C.D. were supported by the Washington Research Foundation Distinguished Investigator Award and start-up funds from the Institute for Systems Biology. Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (NIH) under award no. R01DK133468 (to S.M.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in designing, carrying out or interpreting the work presented in the manuscript. T.W. was supported by a generous gift from C. Ellison. Further support came from the NIH (grant no. U19AG023122) awarded by the National Institute on Aging (to N.R.).

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  1. These authors contributed equally: Christian Diener, Chengzhen L. Dai.

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Christian Diener, Chengzhen L. Dai, Tomasz Wilmanski, Priyanka Baloni, Brett Smith, Noa Rappaport, Leroy Hood, Andrew T. Magis & Sean M. Gibbons

  2. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Leroy Hood & Sean M. Gibbons

  3. eScience Institute, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

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Contributions

Conceptualization was carried out by C.L.D., C.D., S.M.G. and A.T.M. Methodology was the responsibility of C.D. and C.L.D. C.D., C.L.D., T.W., N.R. and B.S. performed formal analysis. C.D., C.L.D., B.S., S.M.G. and A.T.M. carried out investigations. Data curation was undertaken by C.L.D., B.S. and A.T.M. C.D., C.L.D. and S.M.G. wrote the original draft. Writing, review and editing were carried out by C.D., C.L.D., T.W., P.B., S.M.G. and A.T.M. Visualization was undertaken by C.D. and C.L.D. Supervision and project administration were performed by L.H., A.T.M. and S.M.G.

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Correspondence to Andrew T. Magis or Sean M. Gibbons.

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Competing interests

C.L.D., B.S. and A.T.M. are former employees of, and held stock options in, Arivale, Inc. L.H. is a former shareholder of Arivale. Arivale is no longer a commercially operating company as of April 2019. The remaining authors report no competing interests.

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Nature Metabolism thanks Aleksandar D. Kostic and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Genome-wide association study results.

Genome-wide association p-values for variants associated with at least one metabolite (only p < 1e-5 shown here). Red dashed line denotes genome-wide significance (p < 5.37e-11). Gray bars denote separation between chromosomes. N = 1,000 (training cohort). The full list of p-values from the GWAS analysis can be found in Supplementary Table S4.

Extended Data Fig. 2 Gene-microbiome interactions explain variation in blood metabolite levels (unadjusted data).

Shown are six selected examples where the genomic background modulates the associations between bacterial genus-level abundances and metabolite levels. In (A-F) metabolite abundances were log-transformed, but no adjustment for common confounders, was performed here. In (A-F) “ρ” denotes the Spearman’s Rank-Order correlation coefficient of the regression and “p” denotes the p-value under a two-sided Spearman’s correlation test. The solid line indicates an ordinary least squares regression line relating the transformed metabolite abundance with the transformed genus abundance within each group. The shaded area is the 95% confidence interval of the regression. Associations were corrected for sex, age, age², sex:age, and sex:age² interactions, BMI, microbiome vendor, metabolomics batch, and the first 5 principal components of genetic ancestry (N = 1,569). Metabolite names ending in “*” or “**” indicate compounds for which a standard is not available, but Metabolon is confident in its identity.

Supplementary information

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Supplementary Table 1.

Supplementary Tables 1–5.

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Diener, C., Dai, C.L., Wilmanski, T. et al. Genome–microbiome interplay provides insight into the determinants of the human blood metabolome. Nat Metab 4, 1560–1572 (2022). https://doi.org/10.1038/s42255-022-00670-1

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