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Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome

Nature Microbiology volume 2, Article number: 16221 (2017) Cite this article

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Abstract

Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

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Figure 1: Early life environment determines gut microbiome structure.
Figure 2: A GWAS identifies host genetic loci that impact gut microbiome composition and abundances.
Figure 3: Association of microbial abundance with host phenotypes and their implications for human disease.
Figure 4: Dietary and microbial influences on metabolite profiles.

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  • 14 July 2017

    In the PDF version of this article previously published, the year of publication provided in the footer of each page and in the 'How to cite' section was erroneously given as 2017, it should have been 2016. This error has now been corrected. The HTML version of the article was not affected.

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Acknowledgements

The authors thank S.E. Cates, N.N. Robinson and G.D. Shaw in the Systems Genetics Core at UNC for technical assistance and M.H. Stoiber for helpful discussions, especially regarding statistical analysis. This work was primarily supported by funding from the Office of Naval Research under ONR contract N0001415IP00021 (J.J., J.H.M. and A.M.S.). Additional support was provided by the Low Dose Scientific Focus Area, Office of Biological and Environmental Research, US Department of Energy (G.K., J.H.M. and A.M.S.) and the Lawrence Berkeley National Laboratory Directed Research and Development (LDRD) program funding under the Microbes to Biomes (M2B) initiative (S.C., B.B., G.K., J.H.M. and A.M.S.). C.N. was supported by an NSF IGERT DGE-1258485 fellowship and in part by New Innovator Award DP2 AT007802-01 to E.B. Partial support was also provided under the Microbiomes in Transition (MinT) Initiative as part of the Laboratory Directed Research and Development Program at PNNL. Metabolomic measurements were performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the US DOE OBER and located at PNNL in Richland, Washington. PNNL and LBNL are multi-program national laboratories operated by Battelle for the DOE under contract DE-AC05-76RLO 1830 and the University of California for the DOE under contract DE AC02-05CH11231, respectively.

Author information

Author notes

  1. Antoine M. Snijders, Sasha A. Langley and Young-Mo Kim: These authors contributed equally to this work.

Authors and Affiliations

  1. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Antoine M. Snijders, Sasha A. Langley, Yurong Huang, Gary H. Karpen & Jian-Hua Mao

  2. Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, 99352, Washington, USA

    Young-Mo Kim, Colin J. Brislawn, Erika M. Zink, Sarah J. Fansler, Cameron P. Casey, Janet K. Jansson & Thomas O. Metz

  3. Department of Genome Sciences, University of Washington, Seattle, 98105, Washington, USA

    Cecilia Noecker & Elhanan Borenstein

  4. Department of Genetics, Systems Genetics Core Facility, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA

    Darla R. Miller

  5. Department of Molecular and Cell Biology, University of California, Berkeley, 94720, California, USA

    Gary H. Karpen

  6. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Susan E. Celniker & James B. Brown

  7. Department of Computer Science and Engineering, University of Washington, Seattle, 98195, Washington, USA

    Elhanan Borenstein

  8. Santa Fe Institute, Santa Fe, 87501, New Mexico, USA

    Elhanan Borenstein

Authors
  1. Antoine M. Snijders
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Contributions

A.M.S., J.-H.M. and J.K.J. conceived and designed the study. A.M.S. and J.-H.M. performed the mouse experiments, acquired the data, performed data analysis, interpreted results and co-wrote the manuscript. S.A.L. performed data analysis, interpreted results and co-wrote the manuscript. T.O.M. and Y.-M.K. performed metabolome data analysis, interpreted results and co-wrote the manuscript. C.J.B. performed microbiome data analysis and interpreted results. C.N. performed metabolic modelling-based taxonomic and metabolomics integration. E.M.Z. prepared microbiome samples and performed GC–MS-based metabolomics analysis. S.J.F. carried out microbiome sequencing. C.P.C. performed metabolome data analysis and interpreted results. D.R.M. acquired data. Y.H. performed in vivo experiments and collected data. G.H.K. and S.E.C. interpreted results and co-wrote the manuscript. J.B.B. supervised the integrative data analysis, interpreted results and co-wrote the manuscript. E.B. performed data analysis, interpreted results and co-wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Janet K. Jansson, Thomas O. Metz or Jian-Hua Mao.

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Competing interests

The authors declare no competing financial interests

Supplementary information

Supplementary Information

Supplementary Figures 1-10, Supplementary Tables 1-12 (PDF 36706 kb)

Supplementary Table 2

Normalized amplicon abundance. (XLSX 4809 kb)

Supplementary Table 3

Differentially abundant fecal operational taxonomic units (OTUs) between animal facility built environments (BE1 vs BE2). (XLSX 320 kb)

Supplementary Table 4

P-values for each genetic locus obtained using Mann-Whitney U test for all OTUs. (XLSX 237480 kb)

Supplementary Table 5

Joint QTL intervals and candidate genes. (XLSX 88 kb)

Supplementary Table 6

Linkage analysis of microbial families. (XLSX 8304 kb)

Supplementary Table 7

Candidate genes in genetic loci associated with specific microbial families. (XLSX 167 kb)

Supplementary Table 10

Metabolomics data including original intensity of the detected metabolites from murine feces and their zscored transformed values in separate tabs. (XLSX 677 kb)

Supplementary Table 11

Metabolite profiles in fecal samples of four CC strains maintained on different diets. (XLSX 84 kb)

Supplementary Table 12

A list of all metabolites assayed and analyzed in terms of community metabolic potential for each subset of the data, detailing correlations between metabolomics data and community metabolic potential scores and potential taxonomic contributors. (XLSX 256 kb)

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Snijders, A., Langley, S., Kim, YM. et al. Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome. Nat Microbiol 2, 16221 (2017). https://doi.org/10.1038/nmicrobiol.2016.221

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