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

Nature Medicine

Volumn 25, Issue 7, 2019, Pages 1104-1109

  Scheiman, Jonathan ^a,b,c   Luber, Jacob M ^a,d,e   Chavkin, Theodore A ^a,d   MacDonald, Tara ^a,d   Tung, Angela ^a,b   Pham, Loc Duyen ^d   Wibowo, Marsha C ^a,d   Wurth, Renee C ^c,f   Punthambaker, Sukanya ^a,b   Tierney, Braden T ^a,d   Yang, Zhen ^d,g   Hattab, Mohammad W ^b   Avila Pacheco, Julian ^e   Clish, Clary B ^e   Lessard, Sarah ^a,d   Church, George M ^a,b   Kostic, Aleksandar D ^a,d  

^a HARVARD MEDICAL SCHOOL   (United States)

^b HARVARD UNIVERSITY   (United States)

^c Fitbiomics   (United States)

^d JOSLIN DIABETES CENTER   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^f HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^g UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

LACTIC ACID; PROPIONIC ACID; GAMMA INTERFERON; GLUCOSE TRANSPORTER 4; LACTATE SODIUM; TUMOR NECROSIS FACTOR; PROPIONIC ACID DERIVATIVE;

EXERCISE; LACTATE BLOOD LEVEL; LETTER; MARATHON RUNNER; METABOLISM; METAGENOMICS; NONHUMAN; PRIORITY JOURNAL; VEILLONELLA; VEILLONELLA ATYPICA; ALGORITHM; ARTICLE; BACTERIUM; BACTEROIDETES; BIOINFORMATICS; BODY MASS; COLUMN CHROMATOGRAPHY; DAIRY PRODUCT; DNA EXTRACTION; DNA SEQUENCING; ELITE ATHLETE; EXHAUSTION; FECES ANALYSIS; FIRMICUTES; GASTROINTESTINAL TRACT; GRAIN; HUMAN; LACTATE METABOLISM; LACTOBACILLUS DELBRUECKII SUBSP. BULGARICUS; MARATHON RUNNING; MASS SPECTROMETRY; MENSTRUATION; MICROBIOME; MICROORGANISM; MOLLICUTES; MOUSE; PERFORMANCE; PHYSIOLOGY; POLYMERASE CHAIN REACTION; PROTEOBACTERIA; QUANTITATIVE ANALYSIS; QUESTIONNAIRE; TAXONOMY; VEILLONELLA DISPAR; VEILLONELLA PARVULA; VERRUCOMICROBIA; ANIMAL; ATHLETE; C57BL MOUSE; INTESTINE FLORA; RUNNING;

ANIMALS; ATHLETES; EXERCISE; GASTROINTESTINAL MICROBIOME; HUMANS; LACTIC ACID; METAGENOMICS; MICE; MICE, INBRED C57BL; PROPIONATES; RUNNING; VEILLONELLA;

EID: 85068219526     PISSN: 10788956     EISSN: 1546170X     Source Type: Journal    
DOI: 10.1038/s41591-019-0485-4     Document Type: Article

References (40)

1. Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).
2. Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).
3. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
4. Dusko Ehrlich, S. & The MetaHIT Consortium. in Metagenomics of the Human Body 307–316 (Springer, 2011).
5. Petersen, L. M. et al. Community characteristics of the gut microbiomes of competitive cyclists. Microbiome 5, 98 (2017).
6. Clarke, S. F. et al. Exercise and associated dietary extremes impact on gut microbial diversity. Gut 63, 1913–1920 (2014).
7. Garvie, E. I. Bacterial lactate dehydrogenases. Microbiol. Rev. 44, 106–139 (1980).
8. Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
9. Luber, J. M., Tierney, B. T., Cofer, E. M., Patel, C. J. & Kostic, A. D. Aether: leveraging linear programming for optimal cloud computing in genomics. Bioinformatics 34, 1565–1567 (2017).
10. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
11. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
12. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
13. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
14. Van den Bogert, B., Boekhorst, J., Smid, E. J., Zoetendal, E. G. & Kleerebezem, M. Draft genome sequence of Veillonella parvula HSIVP1, isolated from the human small intestine. Genome Announc. 1, e00977-13 (2013).
15. Ng, S. K. C. & Hamilton, I. R. Carbon dioxide fixation by Veillonella parvula M4 and its relation to propionic acid formation. Can. J. Microbiol. 19, 715–723 (1973).
16. Phypers, B. & Pierce, J. M. T. Lactate physiology in health and disease. Contin. Educ. Anaesth. Crit. Care Pain 6, 128–132 (2006).
17. Kimura, I. et al. Short-chain fatty acids and ketones directly regulate sympathetic nervous system via G protein-coupled receptor 41 (GPR41). Proc. Natl Acad. Sci. USA 108, 8030–8035 (2011).
18. Pluznick, J. A novel SCFA receptor, the microbiota, and blood pressure regulation. Gut Microbes 5, 202–207 (2014).
19. Pluznick, J. L. et al. Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proc. Natl Acad. Sci. USA 110, 4410–4415 (2013).
20. Chambers, E. S. et al. Acute oral sodium propionate supplementation raises resting energy expenditure and lipid oxidation in fasted humans. Diabetes Obes. Metab. 20, 1034–1039 (2018).
21. Araghizadeh, F. & Abdelnaby, A. in Colorectal Surgery (eds Bailey, H. R., Billingham, R. P., Stamos, M. J. & Snyder, M. J.) 3–17 (Elsevier Health Sciences, 2012).
22. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
23. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
24. 2 delivery, and performance during exercise. J. Appl. Physiol. 51, 1493–1499 (1981).
25. Pimentel, G. et al. Blood lactose after dairy product intake in healthy men. Br. J. Nutr. 118, 1070–1077 (2017).
26. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D & R Development Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-117 http://CRAN.R-project.org/package=nlme (2014).
27. Bolker, B. M. coefplot2: Coefficient Plots. R package version 0.1.3.3 http://r-forge.r-project.org/R/?group_id=1059 (2012).
28. Wickham, H. & Chang, W. ggplot2: An implementation of the grammar of graphics. R package version 1 http://CRAN.R-project.org/package=ggplot2 (2015).
29. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
30. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
31. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 38, D473–D479 (2010).
32. Jones, E et al. SciPy: Open source scientific tools for python, http://www.scipy.org/ (2001).
33. Lemon, J. et al. plotrix: Various plotting functions. R package version 3.7 https://cran.r-project.org/package=plotrix (2007).
34. Robinson, O., Dylus, D. & Dessimoz, C. Phylo.io: interactive viewing and comparison of large phylogenetic trees on the web. Mol. Biol. Evol. 33, 2163–2166 (2016).
35. Kolde, R. Pheatmap: pretty heatmaps. R package version 61 (2012).
36. Fujisaka, S. et al. Diet, genetics, and the gut microbiome drive dynamic changes in plasma metabolites. Cell Rep. 22, 3072–3086 (2018).
37. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
38. Wickham, H. & Francois, R. dplyr: A grammar of data manipulation. R package version 0.4.3 (2015).
39. Murrell, P. The grid graphics package. R. News 2, 14–19 (2002).
40. Wickham, H. reshape2: Flexibly reshape data: a reboot of the reshape package. R package version 1 (2012).