Human gut microbes impact host serum metabolome and insulin sensitivity

Journal name:
Nature
Volume:
535,
Pages:
376–381
Date published:
DOI:
doi:10.1038/nature18646
Received
Accepted
Published online

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.

At a glance

Figures

  1. Overview of the workflow integrating human phenotypes, fasting serum metabolome, gut microbiome data and mouse feeding experiments.
    Figure 1: Overview of the workflow integrating human phenotypes, fasting serum metabolome, gut microbiome data and mouse feeding experiments.

    a, Metabolites were summarized as co-abundance clusters, and functional module and species abundance profiles extracted from gut microbiome data. b, Features were filtered for significant positive or negative associations with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome. c, Metabolite clusters were divided into IR- and IS-metabotypes and associated with microbiome functional modules. d, Microbial driver species for the functional module associations with HOMA-IR were identified using a leave-one-out analysis. e, Experimental design for P. copri feeding mouse experiment. HFD, high-fat diet; OGTT, oral glucose tolerance test; ITT, insulin tolerance test; MetS, metabolic syndrome.

  2. Association map of the three-tiered analyses integrating the phenome, the gut microbiome and the fasting serum metabolome in 277 non-diabetic individuals with available metagenomic data.
    Figure 2: Association map of the three-tiered analyses integrating the phenome, the gut microbiome and the fasting serum metabolome in 277 non-diabetic individuals with available metagenomic data.

    The left panel shows significant associations (Mann–Whitney U-test FDR < 0.1) between microbial functional modules and the indicated phenotypes; colouring indicates direction of association. The right panel shows associations between the same modules and serum metabolite clusters. Colouring represents the median Spearman correlation coefficient between metabolite clusters and the indicated functional modules, corrected for background distribution (SCCbg.adj., see Methods), where MWU FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001.

  3. The ratio between the gut microbiome potential for BCAA biosynthesis and inward transport is linked to fasting serum BCAA levels and IR in humans and can be attributed to a few driver species, including P. copri that induces an aggravation of glucose intolerance in mice concomitantly with elevated serum BCAA levels.
    Figure 3: The ratio between the gut microbiome potential for BCAA biosynthesis and inward transport is linked to fasting serum BCAA levels and IR in humans and can be attributed to a few driver species, including P. copri that induces an aggravation of glucose intolerance in mice concomitantly with elevated serum BCAA levels.

    a, The total abundance of all microbial species containing genes coding for BCAA biosynthesis (Spearman correlation coefficient (SCC) = 0.30, P = 5.3 × 10−7) and inward transport (SCC = −0.17, P = 4.5 × 10−3) potential is shown in green and blue, respectively, for 277 non-diabetic individuals ordered by their HOMA-IR levels. The slopes are significantly different (P = 4.0 × 10−8, see Methods). b, Fasting serum BCAA levels in 291 non-diabetic individuals ordered by HOMA-IR levels (SCC: leucine = 0.40, isoleucine = 0.44, valine = 0.49, P < 4 × 10−12). c, d, The effect of specific microbial species on associations between BCAA biosynthesis and transport, respectively, and HOMA-IR in 277 non-diabetic individuals; illustrated by the change in background-adjusted median SCC (SCCbg.adj) between HOMA-IR and the BCAA biosynthesis/transport functional modules when a given species has been excluded from the analysis (see Methods). e, Suggested model of the microbiome contribution to serum BCAA levels and IR. Fasting serum BCAA levels are influenced by microbial BCAA biosynthesis and uptake; these levels in turn may influence host insulin sensitivity. f, Changes in mouse serum BCAA levels (mean ± s.e.m.) after P. copri or sham gavaging for two weeks before challenge. Asterisks indicate significance between P. copri-gavaged (n = 12) and sham-gavaged control mice (n = 12) and significance relative to before challenge (likelihood ratio test, *P < 0.05; **P < 0.01). g, Insulin tolerance test. The P. copri-gavaged mice (n = 12) had significantly higher serum glucose levels compared to sham-gavaged controls (n = 12, P = 0.032, repeated measures two-way ANOVA) after three weeks challenge. Mean ± s.e.m. is depicted. Asterisks indicate significant differences at individual time points (repeated measures two-way ANOVA): *P < 0.05; **P < 0.01. h, Faecal P. copri abundance (arbitrary units, quantitative PCR normalized 16S rDNA) as a function of HOMA-IR two weeks post bacterial challenge (SCC = 0.46, P = 0.040).

  4. Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls.
    Extended Data Fig. 1: Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls.

    An overview of the same traits is shown in Supplementary Table 1. The 75 non-diabetic controls are a subset of the 291 non-diabetic individuals matched to the type 2 diabetes patients by age, sex and BMI and used for comparative analyses.

  5. The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome.
    Extended Data Fig. 2: The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome.

    ac, Venn diagrams resuming the number of serum metabolite clusters (a), species (b) and microbiome functional modules (c) that are associated with the three HOMA-IR, HOMA-IRBMIadj and metabolic syndrome phenotypes at FDR < 0.1. d, The number of microbiome functional modules associated with HOMA-IR, gene richness and HOMA-IRGeneRichness.adj. The metabolite cluster associations are based on all 291 non-diabetic individuals whereas the species and KEGG module associations were estimated on the 277 non-diabetic individuals with microbiome data. MetS, metabolic syndrome.

  6. Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals.
    Extended Data Fig. 3: Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals.

    Spearman correlations between all fasting serum metabolite clusters (top panel, molecular lipids; bottom panel, polar metabolites) and clinical phenotypes. The metabolites in each panel are clustered by their correlation profile (see dendrogram). The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The names of the 19 metabolite clusters making up the IR- and IS-metabotypes are highlighted with blue (IR-metabotype) and red (IS-metabotype), respectively.

  7. Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals.
    Extended Data Fig. 4: Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals.

    Spearman correlations between continuous physiological traits and the 81 species significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes (Extended Data Fig. 1). The species are clustered by their correlation profile. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001.

  8. Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals.
    Extended Data Fig. 5: Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals.

    Spearman correlations between species that were significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes and the 19 metabolite clusters making up the IR- and IS-metabotypes. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The left sidebar represents positive (blue) or negative (red) correlations between the species and the indicated phenotypes (FDR < 0.1). MetS, metabolic syndrome.

  9. Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals.
    Extended Data Fig. 6: Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals.

    a, b, The abundances of T2DCAG00385: P. copri (orange) and T2DCAG00050: B. vulgatus (blue), shown for all non-diabetic individuals arranged by decreasing P. copri abundance and increasing B. vulgatus abundance (a), and arranged by total abundance of both species with HOMA-IR levels shown above (b).

  10. Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals.
    Extended Data Fig. 7: Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals.

    a, b, Spearman correlations between species and both the BCAA-containing metabolite cluster (a, M10) and insulin resistance (b, HOMA-IR) in individuals with detectable abundances of the respective species. FDRs of 0.1 and 0.05 are denoted with dotted and dashed lines, respectively. Colour intensity represents mean species abundance in individuals where the species was observed.

  11. Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals.
    Extended Data Fig. 8: Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals.

    The five most important microbial species driving the association between the indicated microbiome functional modules and insulin resistance (HOMA-IR) are shown (see Supplementary Table 9 for effect sizes). Each species is highlighted with a different colour. The left sidebar represents positive (blue) or negative (red) associations between the functional modules and the indicated phenotypes (FDR <0.1). MetS, metabolic syndrome.

  12. 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.
    Extended Data Fig. 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.

    KEGG orthologous gene groups (squares) and metabolites (circles) are coloured by their Spearman correlation with HOMA-IR in the non-diabetic individuals (n = 277 for KEGG orthologous gene groups, n = 291 for metabolites), or coloured grey if no information was available. The network is adapted from KEGG pathway maps (pathways hsa00290 and hsa02010).

  13. 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.
    Extended Data Fig. 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.

    a, Oral glucose tolerance test. The P. copri-gavaged mice (n = 12) had significantly higher serum glucose levels compared to sham-gavaged controls (n = 12, P = 0.02, Mann–Whitney U-test for AUC) after two weeks of the gavage challenge. Mean ± s.e.m. is depicted. Stars indicate significant differences at individual time points (repeated measurements two-way ANOVA): *P < 0.05; **P < 0.01. b, Plasma insulin was measured before and 15 min post glucose bolus, P = 0.80, Mann–Whitney U-test, bars represents mean ± s.e.m., n = 12. c, Microbiota taxa summary plots on family level after the two given time points, that is, pre high-fat diet (HFD) and post HFD plus gavage. HFD feeding significantly changed the microbial community (adonis P = 0.001) while bacterial gavaging had negligible effect. Data represent mean values. n = 12 per group (one sample from the sham group at time point −3 weeks did not go successfully through the 16S rDNA amplicon sequencing and is therefore represented by 11 samples). ‘Unclassified’ refers to reads that could not be classified to any taxonomy. ‘Other’ refers to reads that could not be classified at family level. d, P. copri changes in mouse faecal samples after P. copri gavaging as determined by quantitative PCR. Bars show the change in P. copri levels relative to before P. copri or sham challenge (bars represents mean ± s.e.m., n = 12, P = 0.0058, Mann–Whitney U-test).

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Author information

  1. These authors contributed equally to this work.

    • Helle Krogh Pedersen,
    • Valborg Gudmundsdottir,
    • Henrik Bjørn Nielsen,
    • Tuulia Hyotylainen &
    • Trine Nielsen
  2. Lists of participants and their affiliations appear in the Supplementary Information.

    • MetaHIT Consortium

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

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 financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

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.

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.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls. (244 KB)

    An overview of the same traits is shown in Supplementary Table 1. The 75 non-diabetic controls are a subset of the 291 non-diabetic individuals matched to the type 2 diabetes patients by age, sex and BMI and used for comparative analyses.

  2. Extended Data Figure 2: The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome. (276 KB)

    ac, Venn diagrams resuming the number of serum metabolite clusters (a), species (b) and microbiome functional modules (c) that are associated with the three HOMA-IR, HOMA-IRBMIadj and metabolic syndrome phenotypes at FDR < 0.1. d, The number of microbiome functional modules associated with HOMA-IR, gene richness and HOMA-IRGeneRichness.adj. The metabolite cluster associations are based on all 291 non-diabetic individuals whereas the species and KEGG module associations were estimated on the 277 non-diabetic individuals with microbiome data. MetS, metabolic syndrome.

  3. Extended Data Figure 3: Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals. (556 KB)

    Spearman correlations between all fasting serum metabolite clusters (top panel, molecular lipids; bottom panel, polar metabolites) and clinical phenotypes. The metabolites in each panel are clustered by their correlation profile (see dendrogram). The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The names of the 19 metabolite clusters making up the IR- and IS-metabotypes are highlighted with blue (IR-metabotype) and red (IS-metabotype), respectively.

  4. Extended Data Figure 4: Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals. (859 KB)

    Spearman correlations between continuous physiological traits and the 81 species significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes (Extended Data Fig. 1). The species are clustered by their correlation profile. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001.

  5. Extended Data Figure 5: Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals. (693 KB)

    Spearman correlations between species that were significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes and the 19 metabolite clusters making up the IR- and IS-metabotypes. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The left sidebar represents positive (blue) or negative (red) correlations between the species and the indicated phenotypes (FDR < 0.1). MetS, metabolic syndrome.

  6. Extended Data Figure 6: Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals. (277 KB)

    a, b, The abundances of T2DCAG00385: P. copri (orange) and T2DCAG00050: B. vulgatus (blue), shown for all non-diabetic individuals arranged by decreasing P. copri abundance and increasing B. vulgatus abundance (a), and arranged by total abundance of both species with HOMA-IR levels shown above (b).

  7. Extended Data Figure 7: Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals. (320 KB)

    a, b, Spearman correlations between species and both the BCAA-containing metabolite cluster (a, M10) and insulin resistance (b, HOMA-IR) in individuals with detectable abundances of the respective species. FDRs of 0.1 and 0.05 are denoted with dotted and dashed lines, respectively. Colour intensity represents mean species abundance in individuals where the species was observed.

  8. Extended Data Figure 8: Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals. (1,695 KB)

    The five most important microbial species driving the association between the indicated microbiome functional modules and insulin resistance (HOMA-IR) are shown (see Supplementary Table 9 for effect sizes). Each species is highlighted with a different colour. The left sidebar represents positive (blue) or negative (red) associations between the functional modules and the indicated phenotypes (FDR <0.1). MetS, metabolic syndrome.

  9. Extended Data Figure 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. (283 KB)

    KEGG orthologous gene groups (squares) and metabolites (circles) are coloured by their Spearman correlation with HOMA-IR in the non-diabetic individuals (n = 277 for KEGG orthologous gene groups, n = 291 for metabolites), or coloured grey if no information was available. The network is adapted from KEGG pathway maps (pathways hsa00290 and hsa02010).

  10. Extended Data Figure 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. (247 KB)

    a, Oral glucose tolerance test. The P. copri-gavaged mice (n = 12) had significantly higher serum glucose levels compared to sham-gavaged controls (n = 12, P = 0.02, Mann–Whitney U-test for AUC) after two weeks of the gavage challenge. Mean ± s.e.m. is depicted. Stars indicate significant differences at individual time points (repeated measurements two-way ANOVA): *P < 0.05; **P < 0.01. b, Plasma insulin was measured before and 15 min post glucose bolus, P = 0.80, Mann–Whitney U-test, bars represents mean ± s.e.m., n = 12. c, Microbiota taxa summary plots on family level after the two given time points, that is, pre high-fat diet (HFD) and post HFD plus gavage. HFD feeding significantly changed the microbial community (adonis P = 0.001) while bacterial gavaging had negligible effect. Data represent mean values. n = 12 per group (one sample from the sham group at time point −3 weeks did not go successfully through the 16S rDNA amplicon sequencing and is therefore represented by 11 samples). ‘Unclassified’ refers to reads that could not be classified to any taxonomy. ‘Other’ refers to reads that could not be classified at family level. d, P. copri changes in mouse faecal samples after P. copri gavaging as determined by quantitative PCR. Bars show the change in P. copri levels relative to before P. copri or sham challenge (bars represents mean ± s.e.m., n = 12, P = 0.0058, Mann–Whitney U-test).

Supplementary information

PDF files

  1. Supplementary Information (531 KB)

    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. Supplementary Data (647 KB)

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

Additional data