Abstract

The human gut microbiome plays a key role in human health1, but 16S characterization lacks quantitative functional annotation2. The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host–microbiome interactions3. In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability (H2) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits.

  • Subscribe to Nature Genetics for full access:

    $59

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    O’Hara, A. M. & Shanahan, F. The gut flora as a forgotten organ. EMBO Rep. 7, 688–693 (2006).

  2. 2.

    Frias-Lopez, J. et al. Microbial community gene expression in ocean surface waters. Proc. Natl. Acad. Sci. USA 105, 3805–3810 (2008).

  3. 3.

    Marcobal, A. et al. A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. ISME J. 7, 1933–1943 (2013).

  4. 4.

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

  5. 5.

    Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

  6. 6.

    Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

  7. 7.

    Clarke, G. et al. Minireview: Gut microbiota: the neglected endocrine organ. Mol. Endocrinol. 28, 1221–1238 (2014).

  8. 8.

    Cangelosi, G. A. & Meschke, J. S. Dead or alive: molecular assessment of microbial viability. Appl. Environ. Microbiol. 80, 5884–5891 (2014).

  9. 9.

    O’Toole, P. W. & Claesson, M. J. Gut microbiota: changes throughout the lifespan from infancy to elderly. Int. Dairy J. 20, 281–291 (2010).

  10. 10.

    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

  11. 11.

    Romero-Corral, A. et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int. J. Obes. (Lond) 32, 959–966 (2008).

  12. 12.

    Arora, T. & Bäckhed, F. The gut microbiota and metabolic disease: current understanding and future perspectives. J. Intern. Med. 280, 339–349 (2016).

  13. 13.

    Parséus, A. et al. Microbiota-induced obesity requires farnesoid X receptor. Gut 66, 429–437 (2017).

  14. 14.

    Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

  15. 15.

    Beaumont, M. et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 17, 189 (2016).

  16. 16.

    Pallister, T. et al. Untangling the relationship between diet and visceral fat mass through blood metabolomics and gut microbiome profiling. Int. J. Obes. (Lond) 41, 1106–1113 (2017).

  17. 17.

    Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

  18. 18.

    Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK Twins. Cell Host Microbe 19, 731–743 (2016).

  19. 19.

    Petersen, A.-K. et al. On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinformatics 13, 120 (2012).

  20. 20.

    Weimann, A., Sabroe, M. & Poulsen, H. E. Measurement of caffeine and five of the major metabolites in urine by high-performance liquid chromatography/tandem mass spectrometry. J. Mass Spectrom. 40, 307–316 (2005).

  21. 21.

    Nyéki, A., Buclin, T., Biollaz, J. & Decosterd, L. A. NAT2 and CYP1A2 phenotyping with caffeine: head-to-head comparison of AFMU vs. AAMU in the urine metabolite ratios. Br. J. Clin. Pharmacol. 55, 62–67 (2003).

  22. 22.

    Shin, S.-Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

  23. 23.

    Raffler, J. et al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 11, e1005487 (2015).

  24. 24.

    GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  25. 25.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

  26. 26.

    Bastian, F. et al. Data Integration in the Life Sciences (Springer, Berlin and Heidelberg, 124–131, 2008).

  27. 27.

    McDonagh, E. M. et al. PharmGKB summary: very important pharmacogene information for N-acetyltransferase 2. Pharmacogenet. Genomics 24, 409–425 (2014).

  28. 28.

    Meinl, W., Sczesny, S., Brigelius-Flohé, R., Blaut, M. & Glatt, H. Impact of gut microbiota on intestinal and hepatic levels of phase 2 xenobiotic-metabolizing enzymes in the rat. Drug Metab. Dispos. 37, 1179–1186 (2009).

  29. 29.

    Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

  30. 30.

    Keegan, K. P., Glass, E. M. & Meyer, F. MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol. Biol. 1399, 207–233 (2016).

  31. 31.

    Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).

  32. 32.

    Tigchelaar, E. F. et al. Gut microbiota composition associated with stool consistency. Gut 65, 540–542 (2016).

  33. 33.

    Moayyeri, A., Hammond, C. J., Valdes, A. M. & Spector, T. D. Cohort profile: TwinsUK and healthy ageing twin study. Int. J. Epidemiol. 42, 76–85 (2013).

  34. 34.

    Evans, A. et al. High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. J. Postgenomics Drug Biomark. Dev. 4, S24–S36 (2014).

  35. 35.

    Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009).

  36. 36.

    Dehaven, C. D., Evans, A. M., Dai, H. & Lawton, K. A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J. Cheminform. 2, 9 (2010).

  37. 37.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108, 4516–4522 (2011).

  38. 38.

    Jackson, M. A., Bell, J. T., Spector, T. D. & Steves, C. J. A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units. PeerJ 4, e2341 (2016).

  39. 39.

    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

  40. 40.

    Westcott, S. L. & Schloss, P. D. De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units. PeerJ 3, e1487 (2015).

  41. 41.

    Menni, C. et al. Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health. Obesity (Silver Spring) 24, 1380–1388 (2016).

  42. 42.

    Kaul, S. et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring) 20, 1313–1318 (2012).

  43. 43.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 51 (2015).

  44. 44.

    van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).

  45. 45.

    Dejean, S. et al. mixOmics: Omics Data Integration Project, http://mixomics.org/ (2013).

  46. 46.

    Neale, M. & Cardon, L. Methodology for Genetic Studies of Twins and Families (Springer Netherlands, Houten, the Netherlands, 1994).

  47. 47.

    Wolak, M. E., Fairbairn, D. J. & Paulsen, Y. R. Guidelines for estimating repeatability. Methods Ecol. Evol. 3, 129–137 (2012).

  48. 48.

    Long, T. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568–578 (2017).

  49. 49.

    Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).

  50. 50.

    Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

  51. 51.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  52. 52.

    Speed, D., Hemani, G., Johnson, M. R. & Balding, D. J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021 (2012).

  53. 53.

    Zhao, J. H. gap: genetic analysis package. J. Stat. Softw. 23, 1–18 (2007).

  54. 54.

    Schaefer, J., Opgen-Rhein, R. & Strimmer., K. GeneNet: Modeling and Inferring Gene Networks, https://CRAN.R-project.org/package=GeneNet (2014).

  55. 55.

    Fruchterman, T. M. J. & Reingold, E. M. Graph drawing by force-directed placement. Software-Practice Exp. 21, 1129–1164 (1991).

  56. 56.

    Csardi, G. & Nepusz, T. The igraph Software Package for Complex Network Research (InterJournal Complex Systems 1695, 2006).

  57. 57.

    Väremo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

Download references

Acknowledgements

The study was funded by the Wellcome Trust, European Community's Seventh Framework Programme (FP7/2007-2013). The study also received support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and the Biomedical Research Centre based at Guy's and St Thomas’ NHS Foundation Trust in partnership with King's College London, by the Chronic Disease Research Foundation and by the Denise Coates Foundation. HLI, Inc., collaborated with King's College London to produce the metabolomics data from Metabolon, Inc. C.M. was funded by the MRC AIM HY (MR/M016560/1) project grant. We thank J. Goodrich and R. Ley for support in sequencing the fecal samples.

Author information

Author notes

    • Jonas Zierer

    Present address: Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA

    • Tao Long

    Present address: Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

  1. These authors jointly directed this work: Tim D. Spector, Cristina Menni.

Affiliations

  1. Department for Twin Research & Genetic Epidemiology, King’s College London, London, UK

    • Jonas Zierer
    • , Matthew A. Jackson
    • , Massimo Mangino
    • , Kerrin S. Small
    • , Jordana T. Bell
    • , Claire J. Steves
    • , Ana M. Valdes
    • , Tim D. Spector
    •  & Cristina Menni
  2. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany

    • Jonas Zierer
    •  & Gabi Kastenmüller
  3. NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK

    • Massimo Mangino
  4. Human Longevity, Inc, San Diego, CA, USA

    • Tao Long
    •  & Amalio Telenti
  5. Metabolon, Inc, Durham, NC, USA

    • Robert P. Mohney
  6. NIHR Nottingham Biomedical Research Centre, Nottingham, UK

    • Ana M. Valdes
  7. School of Medicine, University of Nottingham, Nottingham, UK

    • Ana M. Valdes

Authors

  1. Search for Jonas Zierer in:

  2. Search for Matthew A. Jackson in:

  3. Search for Gabi Kastenmüller in:

  4. Search for Massimo Mangino in:

  5. Search for Tao Long in:

  6. Search for Amalio Telenti in:

  7. Search for Robert P. Mohney in:

  8. Search for Kerrin S. Small in:

  9. Search for Jordana T. Bell in:

  10. Search for Claire J. Steves in:

  11. Search for Ana M. Valdes in:

  12. Search for Tim D. Spector in:

  13. Search for Cristina Menni in:

Contributions

Conceived and designed the experiments: A.T., T.D.S., and C.M. Performed the experiments: R.P.M. Analyzed the data: J.Z., M.A.J., T.L., and C.M. Contributed reagents/materials/analysis tools: M.M., G.K., T.L., A.T., K.S.S., C.J.S., J.T.B., and A.M.V. Wrote the manuscript: J.Z., M.A.J., R.P.M., A.M.V., T.D.S., and C.M.. All authors revised the manuscript.

Competing interests

R.P.M. is an employee of Metabolon, Inc. T.L. and A.T. were employees of HLI, Inc. at the time this work was conducted. TDS is a co-founder of MapMyGut Ltd. All other authors declare no competing financial interests.

Corresponding authors

Correspondence to Tim D. Spector or Cristina Menni.

Integrated Supplementary Information

  1. Supplementary Figure 1 Genetic associations of fecal metabolites.

    We found three fecal metabolites and one metabolite ratio significantly associated with genetic loci. Each panel shows one of these associations with the respective lead SNP. 3-hydroxyhexanoate was found in less than 80% of all samples and was, thus, analyzed as dichotomous trait. The other metabolites are observed in at least 80% of the samples and were analyzed as continuous traits.

  2. Supplementary Figure 2 Q–Q plots for host genetic associations with fecal metabolites.

    Each panel shows the qq-plot for one of the (a-c) three metabolites and (d) metabolite ratio, which have a genome-wide significant association with a genetic locus in the discovery cohort (n = 739). P-values were calculated using the score test implemented in GEMMA.

  3. Supplementary Figure 3 Associations of fecal metabolites with caffeine metabolism.

    (a) Caffeine metabolism pathway showing the generation of the two metabolites that form the fecal metabolite ratio associated with NAT2 genetic variants. (b) Box plot showing the relationship between circulating caffeine (bottom versus top tertile) and the metabolite ratio 1,3-dimethylurate/5-acetylamino-6-amino-3-methyluracil (n = 444). (c) Box plot showing the relationship between coffee intake (from food frequency questionnaires) and 1,3-dimethylurate/5-acetylamino-6-amino-3-methyluracil (n = 676). P-values were calculated from linear mixed models.

  4. Supplementary Figure 4 Multivariate dependencies of fecal metabolites and the gut microbiome.

    We used a Gaussian graphical model to illustrate multivariate dependencies of fecal metabolite levels and gut microbes (n = 644). Microbes on the y-axis are ordered by taxonomy, metabolites on the x-axis by pathway and hierarchical clustering of the partial correlation matrix. Connections indicate significant (FDR < 5%) shrinkage partial correlations of fecal metabolites and microbial OTUs, given all other metabolites and microbes in the model. Edges are colored by the metabolic pathway.

  5. Supplementary Figure 5 Effect of storage time on fecal metabolite levels.

    To assess the effect of storage (a) in the participants’ fridge before being stored in the TwinsUK Biobank and (b) in the freezer at -80 °C before being analyzed, we calculated linear regression models of fecal metabolites against both measures (n = 786). Here we present qq-plots where the dashed lines indicate Bonferroni-cutoff.

Supplementary information

  1. Supplementary Figures and Supplementary Table

    Supplementary Figures 1–5 and Supplementary Table 1

  2. Reporting Summary

  3. Supplementary Table 2: Variance components and phenotype associations of fecal metabolites

    Complete list of all analyzed metabolites with the proportion of samples in which each metabolite was observed (n), and the relative standard deviation (RSD) if the metabolite was present in at least 90% of quality control samples. Values in the columns 6-8 indicate the amount of variance attributed to the compartment of additive genetic factors (A or heritability), common/shared environmental factors (C) and unique environmental factors (E) estimated with structural equation modelling on 148 MZ and 155 DZ twin pairs (n=606). Similarly, values in column 9 indicate the proportion of variance explained by gut microbial composition (M) estimated from UniFrac beta diversities using linear mixed models (n=644). Finally, subsequent columns indicate each metabolite association with age, gender, BMI (n=786), microbial alpha diversity (n=644), and visceral fat mass (n=647) obtained using linear regression models for metabolites present in more than 80% of the samples and logistic regression models for metabolites present in less than 80% (but more than 20%) of samples. Green cells indicate significant results passing FDR=5%

  4. Supplementary Table 3: Genome-wide associations of fecal metabolites and their ratios

    Genome-wide association studies were conducted for 428 heritable metabolites and 31,226 heritable metabolite ratios using GEMMA (n=739). This table lists all associations of genetic variants with fecal metabolites passing a p-value of 10-5 and associations of metabolite ratios passing 10-8. Metabolite annotation is given in Supplementary Table 7

  5. Supplementary Table 4: Associations of fecal metabolites with the gut microbiome

    Associations between fecal metabolite levels and gut microbial operational taxonomic units (OTUs) and higher taxonomical levels were calculated using mixed linear regression models correcting for Shannon alpha diversity, age, sex, BMI, storage time, and family relationships (n=644). Nominally significant associations are flagged by *, FDR significant associations by ** and Bonferroni significant associations with ***. The annotation of metabolites and OTUs can be found in Supplementary tables 6 and 7, respectively

  6. Supplementary Table 5: Gaussian graphical model integrating fecal metabolites and gut microbes

    Gaussian graphical models combining fecal metabolites and microbial OTUs were calculated using GeneNet (n=644). The table contains the shrinkage partial correlation and the corresponding false discovery rate for each edge. Annotation of fecal metabolites and bacterial OTUs can be found in Supplementary tables 6 and 7, respectively

  7. Supplementary Table 6: Annotation of fecal metabolites

    The table lists the IDs, biochemical names, and pathway annotations for all analyzed fecal metabolites

  8. Supplementary Table 7: Annotation of OTUs

    Microbial sequencing data was clustered in organizational taxonomical units (OTUs) using the de novo approach. The table contains all analyzed OTUs along with their representative sequences as well as the corresponding taxonomical annotation from the GreenGenes Database

  9. Supplementary Dataset 1: Regional association plots

    Regional association plots were created for all significant associations of genetic loci with fecal metabolites using the web tool SNIPA (http://snipa.helmholtz-muenchen.de/). Colors indicate the strength of linkage disequilibrium (LD) with the sentinel SNP. The chromosomal positions are based on GRC37 and Ensembl v82 was used for gene annotations

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41588-018-0135-7

Rights and permissions

To obtain permission to re-use content from this article visit RightsLink.