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Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism


The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

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Fig. 1: Gut Veillonella abundance is significantly associated with marathon running.
Fig. 2: V. atypica gavage improves treadmill run time in mice.
Fig. 3: The athlete gut microbiome is functionally enriched for the metabolism of lactate to propionate postexercise.
Fig. 4: Serum lactate crosses the epithelial barrier into the gut lumen, and colorectal propionate instillation is sufficient to enhance treadmill run time.

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Data availability

All raw sequencing data have been uploaded to NCBI and SRA in the form of the BioProjects PRJNA472785 (16S) and PRJNA472768 (MGX). These are linked to associated BioSamples, which in turn are linked to the paired-end read files in the SRA, and correspond to the metadata in the Supplementary Information files.

Code availability

Unless otherwise noted, all plots were generated in R version 3.4.1 with the ggplot2, dplyr, scales, grid and reshape2 packages28,37,38,39,40. Large-scale data analysis was done on AWS, utilizing machines running Ubuntu 16.04. Data curation methods were coded in python version 2.7.12. The Aether package utilized for analysis is available at


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This work was funded by the Synthetic Biology platform at the Wyss Institute for Biologically Inspired Engineering at Harvard University; National Institutes of Health (NIH)/National Human Genome Research Institute grant T32 HG002295 (to J.M.L.; principal investigator: P. J. Park); NIH/National Institute of Diabetes and Digestive and Kidney Diseases grant T32 DK007260 (to T.A.C.; principal investigator: T. K. Blackwell); a National Science Foundation Graduate Research Fellowship Program fellowship (to J.M.L.); National Library of Medicine BIRT grant T15LM007092 (to B.T.T.); an AWS Research Credits for Education Grant (to J.M.L. and A.D.K.); a Smith Family Foundation Award for Excellence in Biomedical Research (to A.D.K.); an American Diabetes Association Pathway to Stop Diabetes Initiator Award (to A.D.K.) and NIH/National Institute of Diabetes and Digestive and Kidney Diseases Diabetes Research Center grant P30DK036836-30 (to J.M.L., T.A.C., T.M., B.T.T., L.-D.P., Z.Y., M.C.W., S.L. and A.D.K.; principal investigator: G. L. King). We acknowledge C. J. Patel and S. R. Stein for statistical advice, and S. Softic for assistance with tail vein injections.

Author information

Authors and Affiliations



J.S., G.M.C. and A.D.K. conceived the project. J.S. collected the athlete samples and metadata. J.M.L. performed the computational analysis and modeling, with assistance from T.A.C., M.C.W., R.C.W., B.T.T., Z.Y. and M.W.H. T.A.C. designed and performed the model organism experiments, with assistance from J.M.L., T.M., A.T., L.-D.P., M.C.W., S.P. and S.L. J.A.-P. and C.B.C. processed the metabolomic samples. J.M.L., T.A.C. and A.D.K. wrote the manuscript. G.M.C. and A.D.K. supervised the project.

Corresponding authors

Correspondence to George M. Church or Aleksandar D. Kostic.

Ethics declarations

Competing interests

J.S. and G.M.C. are co-founders of FitBiomics. Along with A.D.K., they hold equity in FitBiomics.

Additional information

Peer review information: Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Statistical validations of association between Veillonella abundance and marathon running.

a, Histogram of P values (Wald Z-tests) for time coefficient from LOOCV models predicting 16S Veillonella abundance. The red line represents the P value for the model trained without any hold outs. b, Histogram of P values for time coefficients from 1,000 label permutations in GLMM models predicting Veillonella relative abundance. The red line represents the P value for the model trained without any label permutation.

Extended Data Fig. 2 Microbiome composition in control subjects.

a, 16S composition in control subjects. b, Veillonella relative abundance in control subjects.

Extended Data Fig. 3 Statistical validations of the association between Veillonella gavage and mouse treadmill run time.

a, Density plot of maximum run times in the AB/BA crossover study. A two-sided Shapiro–Wilk normality test on the maximum run times for each mouse in each treatment group resulted in P = 0.67, with the null hypothesis that the distribution of data is normal (n = 64). b, 95% confidence intervals for the coefficient effect on treadmill run time in AB/BA crossover (Wald Z-tests, n = 64). Center values are the regression estimate for each coefficient. Error bars represent the 95% confidence interval. c, Histogram of P values for the treatment coefficient from LOOCV models predicting treadmill run time. The red line represents the P value for the model trained without any hold outs (Wald Z-tests, n = 64). d, Histogram of P values for the treatment coefficient from 1,000 label permutations in GLMM models predicting treadmill run time. The red line represents the P value for the model trained without any label permutation (Wald Z-tests, n = 64 per permutation).

Extended Data Fig. 4 AB/BA crossover study results segregated by individual mouse.

Each of the 32 facets (each representing an individual mouse) has six longitudinal treadmill run times plotted (three pre- and three post-treatment crossover). The shapes of the points represent the treatment sequence. Each mouse facet has two horizontal lines showing the mean run time when dosed with L. bulgaricus (light blue) or V. atypica (light red). Each facet has a GLMM fit to all data in a treatment sequence (green), a LOOCV GLMM fit trained on all mice except for the mouse the facet represents (red), and a GLMM fit showing the change in intercept related to random effect for each mouse (blue).

Extended Data Fig. 5 Difference in maximum treadmill run time.

Difference in maximum run time between V. atypica and L. Bulgaricus gavage treatment periods, segregated into ‘responders’ and ‘non-responders’ to V. atypica treatment (n = 32).

Extended Data Fig. 6 Mouse serum cytokine response.

a,b, Cytokines after V. atypica and L. bulgaricus gavage. Each mouse sample is represented as an individual point, with the central bar representing the mean and error bars representing s.e.m. (n = 64, 32 and 32 for baseline, L. bulgaricus and V. atypica, respectively). c,d, Cytokines after intrarectal propionate instillation. Each mouse sample is represented as an individual point, with the central bar representing the mean and error bars representing s.e.m. (n = 32, 16 and 16 for baseline, L. bulgaricus and V. atypica, respectively). P values were determined by one-way ANOVA followed by Tukey’s posthoc test.

Extended Data Fig. 7 GLUT4 measurement in mice following gavage.

a, Representative section of western blot showing GLUT4 abundance in pre-exercise states, as well as following L. bulgaricus and V. atypica gavage. A stain-free control was used to normalize the densitometry analysis shown. The experiment was performed once (n = 8). b, Fold-change in GLUT4 abundance. Each point represents an individual mouse sample, the centre bar represents the mean and error bars represent s.e.m. (n = 8).

Extended Data Fig. 8 Metagenomic analysis of athlete gut microbiome samples.

a, Fraction of putative Veillonella relative abundance from metagenomics (calculated utilizing MetaPhlAn2) before and after exercise in rowers and runners. b, Significant alleles (calculated from pairwise ANOVA) that are present in each of the 87 samples. c, The aforementioned 396 significant alleles segregated by exercise state and sample. d, Histogram comparing non-redundant gene family size and annotation fraction.

Extended Data Fig. 9 Enzyme-level abundance analysis of the methylmalonyl-CoA pathway.

Enzyme-resolution, log-transformed relative abundances of differentially abundant non-redundant gene families mapped by EC ID to methylmalonyl-CoA pathway components. a, Pathway in aggregate. bi, Individual reactions in the pathway (n = 8). Data are represented as violin plots, which display the distribution of data as a rotated kernel density distribution.

Extended Data Fig. 10 Lactate clearance following IP injection in mice.

a, Mice were gavaged either V. atypica or L. bulgaricus and, 5 h later, injected with sodium lactate (750 mg kg−1). Blood lactate was measured 5 min postinjection and every subsequent 10 min (n = 8). Points are means ± s.e.m. b, Area under the curve (AUC) was determined for each mouse and compared between treatments. Each mouse is represented as an individual point, with the central bar representing the mean and error bars representing s.e.m. (P = 0.72 by two-sided unpaired t-test, n = 8).

Supplementary information

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Supplementary Tables 1–8

Source Data

Extended Data Fig. 7

Unprocessed western blot

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Scheiman, J., Luber, J.M., Chavkin, T.A. et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat Med 25, 1104–1109 (2019).

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