Abstract

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

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Accessions

Primary accessions

European Nucleotide Archive

Data deposits

Raw nucleotide data can be found for all samples used in the study in the European Nucelotide Archive (accession numbers: ERP003612, ERP004605, MetaHIT samples; ERP014713, 16S rDNA from mouse experiment). The metabolomics data has been deposited in the MetaboLights database (http://www.ebi.ac.uk/metabolights/) under accession number: MTBLS351.

References

  1. 1.

    et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013)

  2. 2.

    et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012)

  3. 3.

    et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013)

  4. 4.

    et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015)

  5. 5.

    et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143, 913–916 (2012)

  6. 6.

    et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013)

  7. 7.

    , , & Gut microbiota composition and activity in relation to host metabolic phenotype and disease risk. Cell Metab. 16, 559–564 (2012)

  8. 8.

    et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011)

  9. 9.

    et al. Branched-chain amino acids and insulin metabolism: The Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Care 39, 582–588 (2016)

  10. 10.

    et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J. Clin. Invest. 121, 1402–1411 (2011)

  11. 11.

    et al. Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations. Diabetologia 52, 684–690 (2009)

  12. 12.

    et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62, 639–648 (2013)

  13. 13.

    , & Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2, 65–75 (2014)

  14. 14.

    et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419 (1985)

  15. 15.

    , & Metabolic syndrome – a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet. Med. 23, 469–480 (2006)

  16. 16.

    Microbial metabolism of dietary phenolic compounds in the colon. Phytochem. Rev. 7, 407–429 (2008)

  17. 17.

    et al. The gut microbiota modulates host energy and lipid metabolism in mice. J. Lipid Res. 51, 1101–1112 (2010)

  18. 18.

    et al. Structure-based design and mechanisms of allosteric inhibitors for mitochondrial branched-chain α-ketoacid dehydrogenase kinase. Proc. Natl Acad. Sci. USA 110, 9728–9733 (2013)

  19. 19.

    et al. Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study. Lancet Diabetes Endocrinol. 2, 810–818 (2014)

  20. 20.

    , , , & Metabolic phenotyping of a model of adipocyte differentiation. Physiol. Genomics 39, 109–119 (2009)

  21. 21.

    et al. De novo lipogenesis in the differentiating human adipocyte can provide all fatty acids necessary for maturation. J. Lipid Res. 52, 1683–1692 (2011)

  22. 22.

    Ceramides in insulin resistance and lipotoxicity. Prog. Lipid Res. 45, 42–72 (2006)

  23. 23.

    et al. CerS2 haploinsufficiency inhibits β-oxidation and confers susceptibility to diet-induced steatohepatitis and insulin resistance. Cell Metab. 20, 687–695 (2014)

  24. 24.

    et al. Obesity-induced CerS6-dependent C16:0 ceramide production promotes weight gain and glucose intolerance. Cell Metab. 20, 678–686 (2014)

  25. 25.

    et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2008)

  26. 26.

    et al. Interspecies systems biology uncovers metabolites affecting C. elegans gene expression and life history traits. Cell 156, 759–770 (2014)

  27. 27.

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

  28. 28.

    et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011)

  29. 29.

    et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013)

  30. 30.

    et al. Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut 62, 1112–1121 (2013)

  31. 31.

    et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009)

  32. 32.

    , & Amino acid biosynthesis in the spirochete Leptospira: evidence for a novel pathway of isoleucine biosynthesis. J. Bacteriol. 117, 203–211 (1974)

  33. 33.

    Amino acid biosynthesis and its regulation. Annu. Rev. Biochem. 47, 532–606 (1978)

  34. 34.

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

  35. 35.

    et al. NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745–757 (2011)

  36. 36.

    et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012)

  37. 37.

    et al. Gram-negative bacteria aggravate murine small intestinal Th1-type immunopathology following oral infection with Toxoplasma gondii. J. Immunol. 177, 8785–8795 (2006)

  38. 38.

    et al. Gut dendritic cell activation links an altered colonic microbiome to mucosal and systemic T-cell activation in untreated HIV-1 infection. Mucosal Immunol. 9, 24–37 (2016)

  39. 39.

    et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 22, 971–982 (2015)

  40. 40.

    et al. Differential metabolic impact of gastric bypass surgery versus dietary intervention in obese diabetic subjects despite identical weight loss. Sci. Transl. Med. 3, 80re2 (2011)

  41. 41.

    & Branched-chain amino acids in metabolic signalling and insulin resistance. Nat. Rev. Endocrinol. 10, 723–736 (2014)

  42. 42.

    et al. A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat. Med. 22, 421–426 (2016)

  43. 43.

    , , , & Adipose tissue branched chain amino acid (BCAA) metabolism modulates circulating BCAA levels. J. Biol. Chem. 285, 11348–11356 (2010)

  44. 44.

    et al. Global transcript profiles of fat in monozygotic twins discordant for BMI: pathways behind acquired obesity. PLoS Med. 5, e51 (2008)

  45. 45.

    et al. Brain insulin lowers circulating BCAA levels by inducing hepatic BCAA catabolism. Cell Metab. 20, 898–909 (2014)

  46. 46.

    et al. A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Eur. J. Cardiovasc. Prev. Rehabil. 10, 377–386 (2003)

  47. 47.

    , , , & Data analysis tool for comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry. Anal. Chem. 83, 3058–3067 (2011)

  48. 48.

    et al. GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21, 1635–1638 (2005)

  49. 49.

    , , , & Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics for studies of body fluids and tissues. Methods Mol. Biol. 708, 247–257 (2011)

  50. 50.

    , , & MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010)

  51. 51.

    & WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)

  52. 52.

    & Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46, i11 (2012)

  53. 53.

    & Data Analysis and Regression. A Second Course in Statistics, 203–209 (Addison–Wesley, 1977)

  54. 54.

    , & Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008)

  55. 55.

    et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014)

  56. 56.

    et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS One 7, e47656 (2012)

  57. 57.

    & Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006)

  58. 58.

    , , , & SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics 26, 2977–2978 (2010)

  59. 59.

    et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014)

  60. 60.

    et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010)

  61. 61.

    , , , & Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013)

  62. 62.

    , , , & UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

  63. 63.

    Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010)

  64. 64.

    et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)

  65. 65.

    , , & Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007)

  66. 66.

    , , & Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013)

  67. 67.

    & phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013)

  68. 68.

    et al. vegan: Community Ecology Package. R package version 2.3-3. (2016)

  69. 69.

    ggplot2: Elegant Graphics for Data Analysis. (Springer, 2009)

  70. 70.

    R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2012)

  71. 71.

    Graphical models in applied multivariate statistics. (John Wiley & Sons, 1990)

  72. 72.

    ppcor: Partial and semipartial (Part) correlation. R package version 1.0. (2012)

  73. 73.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  74. 74.

    , , & Using the correct statistical test for the equality of regression coefficients. Criminology 36, 859–866 (1998)

  75. 75.

    , & FAM21 directs SNX27–retromer cargoes to the plasma membrane by preventing transport to the Golgi apparatus. Nat. Commun. 7, (2016)

Download references

Acknowledgements

The authors wish to thank S. Castillo, M. Sysi-Aho, A. Ruskeepää, U. Lahtinen, A. Forman, T. Lorentzen, B. Andreasen, G. J. Klavsen, M. J. Nielsen, B. Pedersen, M. T. F. Damgaard and L. B. Rosholm for technical assistance, D. R. Mende and J. R. Kultima for their help in data processing and tool provision, C. Ekstøm and S. Ditlevsen for statistical and mathematical assistance, respectively, and T. F. Toldsted and G. Lademann for management assistance. C. B. Newgard and A. Vaag are thanked for critical comments on our manuscript. The present study is initiated and funded by the European Community’s Seventh Framework Program (FP7/2007-2013): MetaHIT, grant agreement HEALTH-F4-2007-201052. Additional funding came from The Lundbeck Foundation Centre for Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care (LuCamp, http://www.lucamp.org), Metagenopolis grant ANR-11-DPBS-0001 and FP7 METACARDIS HEALTH-F4-2012-305312. J.R., S.V.-S. and G.F. are funded by the Rega institute for Medical Research, KU Leuven, the Agency for Innovation by Science and Technology (IWT), Marie Curie Actions FP7 People COFUND - Proposal 267139 and the Fund for Scientific Research Flanders (FWO). M.O. is also supported by Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research, Decision No. 250114) and EU FP7 Project TORNADO (project 222720). F.H. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 600375. The Center for Biological Sequence Analysis and the Novo Nordisk Foundation Center for Basic Metabolic Research have in addition received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The Novo Nordisk Foundation Center for Protein Research received funding from the Novo Nordisk Foundation (grant agreement NNF14CC0001). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (http://www.metabol.ku.dk).

Author information

Author notes

    • Helle Krogh Pedersen
    • , Valborg Gudmundsdottir
    • , Henrik Bjørn Nielsen
    • , Tuulia Hyotylainen
    •  & Trine Nielsen

    These authors contributed equally to this work.

    • MetaHIT Consortium

    Lists of participants and their affiliations appear in the Supplementary Information.

Affiliations

  1. Center for Biological Sequence Analysis, Dept. of Systems Biology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark

    • Helle Krogh Pedersen
    • , Valborg Gudmundsdottir
    • , Henrik Bjørn Nielsen
    • , Damian R. Plichta
    • , Lars I. Hellgren
    • , Susanne Brix
    •  & Søren Brunak
  2. University of Örebro, SE-702 81 Örebro, Sweden

    • Tuulia Hyotylainen
  3. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland

    • Tuulia Hyotylainen
    •  & Matej Oresic
  4. VTT Technical Research Centre of Finland, FI-02044 Espoo, Finland

    • Tuulia Hyotylainen
    • , Ismo Mattila
    • , Päivi Pöhö
    •  & Matej Oresic
  5. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Trine Nielsen
    • , Manimozhiyan Arumugam
    • , Torben Hansen
    •  & Oluf Pedersen
  6. Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark

    • Benjamin A. H. Jensen
    • , Jacob Bak Holm
    • , Karsten Kristiansen
    •  & Jun Wang
  7. European Molecular Biology Laboratory, 69117 Heidelberg, Germany

    • Kristoffer Forslund
    • , Falk Hildebrand
    • , Shinichi Sunagawa
    •  & Peer Bork
  8. Department of Bioscience Engineering, Vrije Universiteit Brussel, 1050 Brussels, Belgium

    • Falk Hildebrand
    •  & Jeroen Raes
  9. Center for the Biology of Disease, VIB, 3000 Leuven, Belgium

    • Falk Hildebrand
    • , Gwen Falony
    • , Sara Vieira-Silva
    •  & Jeroen Raes
  10. MGP MetaGénoPolis, INRA, Université Paris-Saclay, 78350 Jouy en Josas, France

    • Edi Prifti
    • , Emmanuelle Le Chatelier
    • , Florence Levenez
    • , Joel Doré
    •  & S. Dusko Ehrlich
  11. Institute of Cardiometabolism and Nutrition (ICAN), 75013 Paris, France

    • Edi Prifti
  12. Department of Microbiology and Immunology, Rega Institute, KU Leuven, 3000 Leuven, Belgium

    • Gwen Falony
    • , Sara Vieira-Silva
    •  & Jeroen Raes
  13. Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France

    • Joel Doré
  14. Steno Diabetes Center, DK-2820 Gentofte, Denmark

    • Ismo Mattila
    • , Kajetan Trošt
    •  & Matej Oresic
  15. Faculty of Pharmacy, University of Helsinki, FI-00014 Helsinki, Finland

    • Päivi Pöhö
  16. Institute of Microbiology, ETH Zurich, CH-8092 Zurich, Switzerland

    • Shinichi Sunagawa
  17. Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Torben Jørgensen
    •  & Oluf Pedersen
  18. Research Centre for Prevention and Health, Centre for Health, Capital region, Glostrup Hospital, DK-2600 Glostrup, Denmark

    • Torben Jørgensen
  19. BGI-Shenzhen, 518083 Shenzhen, China

    • Karsten Kristiansen
    •  & Jun Wang
  20. Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia

    • Jun Wang
  21. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau

    • Jun Wang
  22. Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong

    • Jun Wang
  23. Faculty of Health Sciences, University of Southern Denmark, DK-5000 Odense, Denmark

    • Torben Hansen
  24. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany

    • Peer Bork
  25. Max Delbrück Centre for Molecular Medicine, D-13125 Berlin, Germany

    • Peer Bork
  26. Department of Bioinformatics, University of Wuerzburg, D-97074 Würzburg, Germany

    • Peer Bork
  27. Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Søren Brunak
  28. King’s College London, Centre for Host–Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, SE1 9RT London, UK

    • S. Dusko Ehrlich

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Contributions

O.P., S.D.E. and P.B. devised the study. O.P., S.D.E., S.Bru. and H.B.N. designed the study protocol and supervised all parts of the project. H.B.N. and S.Bru. led the data integration, the bioinformatics analyses and did the primary interpretation of analytical outcomes in close collaboration with H.K.P. and V.G. H.K.P., V.G., B.A.H.J., T.Hy., E.P., D.P., S.S., F.H., K.F., J.B.H. and S.V.-S. performed data analyses. T.N., T.Ha. and O.P. composed the clinical protocol, carried out phenotyping of study participants including collection of biological samples and physiological data generation and interpretation. F.L. performed DNA extraction and J.D. supervised DNA extraction. J.W. supervised DNA sequencing and gene profiling. M.O., T.Hy., I.M., K.T. and P.P. performed profiling of serum metabolomics and serum lipidomics. B.A.H.J., K.K., J.B.H. and S.Bri. performed mouse experiments. H.B.N., H.K.P. and V.G. drafted the first versions of the paper with critical and substantial contributions from O.P., S.Bru., T.N., J.R., K.F., F.H., M.O., L.I.H., D.P., G.F., P.B. and S.D.E. All authors approved the final version. MetaHIT consortium members provided support and constructive criticism throughout MetaHIT research operations.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Søren Brunak or Matej Oresic or S. Dusko Ehrlich or Oluf Pedersen.

Reviewer Information Nature thanks J. Garrett, L. Groop, C. Lozupone, G. Siuzdak and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data

Extended data figures

  1. 1.

    Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls.

  2. 2.

    The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome.

  3. 3.

    Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals.

  4. 4.

    Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals.

  5. 5.

    Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals.

  6. 6.

    Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals.

  7. 7.

    Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals.

  8. 8.

    Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals.

  9. 9.

    An in-depth view of the microbial BCAA biosynthesis pathway and BCAA inward transport system, illustrating the correlations between microbial KEGG orthologous gene groups and serum metabolites with human insulin resistance.

  10. 10.

    Oral glucose tolerance test after two weeks of P. copri or sham gavaging and 16S rDNA amplicon sequencing of faecal samples from mice after three weeks of treatment with P.copri or sham.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains full legends for Supplementary Tables 1-17, Supplementary Results and Discussion and a list of additional MetaHIT consortium members.

Excel files

  1. 1.

    Supplementary Data

    This file contains Supplementary Tables 1-17 – see Supplementary Information document for full legends.

About this article

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DOI

https://doi.org/10.1038/nature18646

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