The fecal metabolome as a functional readout of the gut microbiome

Jonas Zierer, Matthew A Jackson, Gabi Kastenmüller, Massimo Mangino, Tao Long, Amalio Telenti, Robert P Mohney, Kerrin S Small, Jordana T Bell, Claire J Steves, Ana M Valdes, Tim D Spector, Cristina Menni, Jonas Zierer, Matthew A Jackson, Gabi Kastenmüller, Massimo Mangino, Tao Long, Amalio Telenti, Robert P Mohney, Kerrin S Small, Jordana T Bell, Claire J Steves, Ana M Valdes, Tim D Spector, Cristina Menni

Abstract

The human gut microbiome plays a key role in human health 1 , but 16S characterization lacks quantitative functional annotation 2 . The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host-microbiome interactions 3 . 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.

Conflict of interest statement

Competing financial interests

RPM is employee of Metabolon, Inc. TL and AT were employees of HLI, Inc. at the time this work was conducted. TDS is co-founder of MapMyGut Ltd. All other authors declare no competing financial interests.

Figures

Figure 1. Number of measured fecal metabolites.
Figure 1. Number of measured fecal metabolites.
1116 metabolites were detected in 786 fecal samples. (a) 570 of those were detected in at least 80% of all samples and 345 were detected in less than 80% but more than 20% of all samples. The first were analyzed continuously, while we dichotomized the latter in present/absent. 210 metabolites that were present in less than 20% of the samples were excluded from further analysis. (b) 469 metabolites where observed in both, fecal and blood samples of the sample individuals, while 647 metabolites are unique to feces. 499 of these 647 metabolites were observed in at least 20% of the fecal samples.
Figure 2. Association of the fecal metabolome…
Figure 2. Association of the fecal metabolome with age.
We compared the fecal metabolome between the oldest (>75 yrs., n=79) and youngest decile (-6) in a 10-fold cross-validation.
Figure 3. Associations of fecal metabolites with…
Figure 3. Associations of fecal metabolites with gut microbiome corespond to microbial effect on visceral fat.
Visceral fat mass was significantly associated with 43 fecal amino acids (all positively) (n=647) and 32 OTUs (n=540) (6 positively in orange, 26 negatively in green). Red tiles indicate positive associations between these metabolites and OTUs (β > 0) and blue tiles negative associations (β 5%). Microbial associations with fecal metabolites correspond to their respective associations with visceral fat, indicating that the microbial metabolic profile is more closely related to the host phenotype than taxonomy.
Figure 4. Intraclass correlation of fecal metabolites…
Figure 4. Intraclass correlation of fecal metabolites in MZ and DZ twins.
The intraclass correlation coefficients (ICCs) were calculated from variance components of a one-way analysis of variance separately for monozygotic (MZ, n=148 pairs) and dizygotic (DZ, n=155 pairs) twins for each metabolite. Positive values of their respective differences indicate more similar metabolic profiles between MZ than DZ twins.
Figure 5. Host genetic influence on the…
Figure 5. Host genetic influence on the fecal metabolome
Genome-wide association studies were conducted for 428 heritable fecal metabolites and 31,226 fecal metabolite ratios (n=739). P-values were calculated using the score test implemented in GEMMA. (a) The Manhattan plot illustrates the genetic associations of fecal metabolites in the discovery sample. The horizontal line indicates the Bonferroni cutoff of 1.2×10-10. Three loci (red) pass the Bonferroni threshold. (b) The second Manhattan plot shows genetic associations with metabolite ratios in the discovery sample. The horizontal line indicates the Bonferroni cutoff of (p<1.6×10-12). Two loci pass the threshold, however only the association with 1,3-dimethylurate / 5-acetylamino-6-amino-3-methyluracil (p = 6.2×10-21, red) passed filtering by p-gain (p-gain > 8.9×105) and thus is considerably stronger than the association of each individual metabolite. Boxplots, QQ-plots, and regional association plots for each of the four loci are shown in Supplementary Figures 1-3.

References

    1. O’Hara AM, Shanahan F. The gut flora as a forgotten organ. EMBO Rep. 2006;7:688–693.
    1. Frias-Lopez J, et al. Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci U S A. 2008;105:3805–10.
    1. Marcobal A, et al. A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. ISME J. 2013;7:1933–43.
    1. Ley R, Turnbaugh P, Klein S, Gordon J. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444:1022–3.
    1. Turnbaugh PJ, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–484.
    1. Pedersen HK, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016;535:376–81.
    1. Clarke G, et al. Gut Microbiota: The Neglected Endocrine Organ. Mol Endocrinol. 2014;28:1221–1238.
    1. Cangelosi GA, Meschke JS. Dead or alive: molecular assessment of microbial viability. Appl Environ Microbiol. 2014;80:5884–91.
    1. O’Toole PW, Claesson MJ. Gut microbiota: Changes throughout the lifespan from infancy to elderly. Int Dairy J. 2010;20:281–291.
    1. Yatsunenko T, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222–227.
    1. Romero-Corral A, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond) 2008;32:959–66.
    1. Arora T, Bäckhed F. The gut microbiota and metabolic disease: current understanding and future perspectives. J Intern Med. 2016;280:339–49.
    1. Parséus A, et al. Microbiota-induced obesity requires farnesoid X receptor. Gut. 2017;66:429–437.
    1. Shoaie S, et al. Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome. Cell Metab. 2015;22:320–31.
    1. Beaumont M, et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 2016;17:189.
    1. Pallister T, et al. Untangling the relationship between diet and visceral fat mass through blood metabolomics and gut microbiome profiling. Int J Obes (Lond) 2017;41:1106–1113.
    1. Goodrich JK, et al. Human Genetics Shape the Gut Microbiome. Cell. 2014;159:789–799.
    1. Goodrich JK, et al. Genetic Determinants of the Gut Microbiome in UK Twins. Cell Host Microbe. 2016;19:731–743.
    1. Petersen A-K, et al. On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinformatics. 2012;13:120.
    1. Weimann A, Sabroe M, Poulsen HE. Measurement of caffeine and five of the major metabolites in urine by high-performance liquid chromatography/tandem mass spectrometry. J Mass Spectrom. 2005;40:307–16.
    1. Nyéki A, Buclin T, Biollaz J, Decosterd LA. NAT2 and CYP1A2 phenotyping with caffeine: head-to-head comparison of AFMU vs. AAMU in the urine metabolite ratios. Br J Clin Pharmacol. 2003;55:62–7.
    1. Shin S-Y, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50.
    1. 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. 2015;11:e1005487.
    1. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–213.
    1. Zerbino DR, et al. Ensembl 2018. Nucleic Acids Res. 2017;46:754–761.
    1. Bastian F, et al. Data Integration in the Life Sciences. Springer; Berlin Heidelberg: 2008. Bgee: Integrating and Comparing Heterogeneous Transcriptome Data Among Species; pp. 124–131.
    1. McDonagh EM, et al. PharmGKB summary: very important pharmacogene information for N-acetyltransferase 2. Pharmacogenet Genomics. 2014;24:409–25.
    1. 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. 2009;37:1179–86.
    1. Lozupone C, Knight R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Appl Environ Microbiol. 2005;71:8228–8235.
    1. Keegan KP, Glass EM, Meyer F. MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. Methods Mol Biol. 2016;1399:207–33.
    1. Vandeputte D, et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65:57–62.
    1. Tigchelaar EF, et al. Gut microbiota composition associated with stool consistency. Gut. 2016;65:540–2.
    1. Moayyeri A, Hammond CJ, Valdes AM, Spector TD. Cohort Profile: TwinsUK and Healthy Ageing Twin Study. Int J Epidemiol. 2013;42:76–85.
    1. 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. 2014;4:S24–S36.
    1. Evans AM, DeHaven CD, 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. 2009;81:6656–6667.
    1. Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform. 2010;2:9.
    1. Caporaso JG, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108(Suppl):4516–22.
    1. Jackson MA, Bell JT, Spector TD, Steves CJ. A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units. PeerJ. 2016;4:e2341.
    1. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–2200.
    1. Westcott SL, Schloss PD. De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units. PeerJ. 2015;3:e1487.
    1. Menni C, et al. Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health. Obesity. 2016;24:1380–1388.
    1. Kaul S, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring) 2012;20:1313–8.
    1. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67:51.
    1. van Buuren S, Groothuis-Oudshoorn K. mice : Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45:1–67.
    1. Dejean S, et al. mixOmics: Omics Data Integration Project. 2013.
    1. Neale M, Cardon L. Methodology for genetic studies of twins and families. Springer; Netherlands: 1994.
    1. Wolak ME, Fairbairn DJ, Paulsen YR. Guidelines for estimating repeatability. Methods Ecol Evol. 2012;3:129–137.
    1. Telenti A, et al. Deep sequencing of 10,000 human genomes. Proc Natl Acad Sci U S A. 2016;113:11901–11906.
    1. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–4.
    1. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
    1. Speed D, Hemani G, Johnson MR, Balding DJ. Improved Heritability Estimation from Genome-wide SNPs. Am J Hum Genet. 2012;91:1011–1021.
    1. Zhao JH. gap: Genetic Analysis Package. J Stat Softw. 2007;23:1–18.
    1. Schaefer J, Opgen-Rhein R, Strimmer K. GeneNet: Modeling and Inferring Gene Networks. 2014
    1. Fruchterman TMJ, Reingold EM. Graph Drawing by Force-directed Placement. Software-Practice Exp. 1991;21:1129–1164.
    1. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal Complex Sy. 2006:1695.
    1. 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. 2013;41:4378–4391.

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