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The fecal metabolome as a functional readout of the gut microbiome

Nature Genetics volume 50, pages 790–795 (2018)Cite this article

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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.

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Fig. 1: Number of measured fecal metabolites.
Fig. 2: Association of the fecal metabolome with age.
Fig. 3: Associations between fecal metabolites and the gut microbiome correspond to microbial effects on visceral fat.
Fig. 4: Intraclass correlation of fecal metabolites in monozygotic and dizygotic twins.
Fig. 5: Host genetic influence on the fecal metabolome.

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References

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  Google Scholar 

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

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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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

  1. Jonas Zierer

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

  2. Tao Long

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

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

Authors and 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

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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.

Corresponding authors

Correspondence to Tim D. Spector or Cristina Menni.

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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.

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Integrated Supplementary Information

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.

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.

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.

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.

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

Supplementary Figures and Supplementary Table

Supplementary Figures 1–5 and Supplementary Table 1

Reporting Summary

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%

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

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

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

Supplementary Table 6: Annotation of fecal metabolites

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

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

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

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Zierer, J., Jackson, M.A., Kastenmüller, G. et al. The fecal metabolome as a functional readout of the gut microbiome. Nat Genet 50, 790–795 (2018). https://doi.org/10.1038/s41588-018-0135-7

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