Human gut microbes impact host serum metabolome and insulin sensitivity (original) (raw)

Accession codes

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.

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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

  1. Helle Krogh Pedersen, Valborg Gudmundsdottir, Henrik Bjørn Nielsen, Tuulia Hyotylainen and Trine Nielsen: These authors contributed equally to this work.
  2. MetaHIT Consortium: Lists of participants and their affiliations appear in the Supplementary Information.

Authors and Affiliations

  1. Center for Biological Sequence Analysis, Dept. of Systems Biology, Technical University of Denmark, Kongens Lyngby, DK-2800, Denmark
    Helle Krogh Pedersen, Valborg Gudmundsdottir, Henrik Bjørn Nielsen, Damian R. Plichta, Lars I. Hellgren, Susanne Brix & Søren Brunak
  2. University of Örebro, Örebro, SE-702 81, Sweden
    Tuulia Hyotylainen
  3. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland
    Tuulia Hyotylainen & Matej Oresic
  4. VTT Technical Research Centre of Finland, Espoo, FI-02044, 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, Copenhagen, DK-2200, Denmark
    Trine Nielsen, Manimozhiyan Arumugam, Torben Hansen & Oluf Pedersen
  6. Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, DK-2100, Denmark
    Benjamin A. H. Jensen, Jacob Bak Holm, Karsten Kristiansen & Jun Wang
  7. European Molecular Biology Laboratory, Heidelberg, 69117, Germany
    Kristoffer Forslund, Falk Hildebrand, Shinichi Sunagawa & Peer Bork
  8. Department of Bioscience Engineering, Vrije Universiteit Brussel, Brussels, 1050, Belgium
    Falk Hildebrand & Jeroen Raes
  9. Center for the Biology of Disease, VIB, Leuven, 3000, Belgium
    Falk Hildebrand, Gwen Falony, Sara Vieira-Silva & Jeroen Raes
  10. MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, 78350, France
    Edi Prifti, Emmanuelle Le Chatelier, Florence Levenez, Joel Doré & S. Dusko Ehrlich
  11. Institute of Cardiometabolism and Nutrition (ICAN), Paris, 75013, 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, Jouy-en-Josas, 78350, France
    Joel Doré
  14. Steno Diabetes Center, Gentofte, DK-2820, Denmark
    Ismo Mattila, Kajetan Trošt & Matej Oresic
  15. Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, 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, Copenhagen, DK-2200, Denmark
    Torben Jørgensen & Oluf Pedersen
  18. Research Centre for Prevention and Health, Centre for Health, Capital region, Glostrup Hospital, Glostrup, DK-2600, Denmark
    Torben Jørgensen
  19. BGI-Shenzhen, Shenzhen, 518083, 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, Odense, DK-5000, Denmark
    Torben Hansen
  24. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, 69120, Germany
    Peer Bork
  25. Max Delbrück Centre for Molecular Medicine, Berlin, D-13125, Germany
    Peer Bork
  26. Department of Bioinformatics, University of Wuerzburg, Würzburg, D-97074, Germany
    Peer Bork
  27. Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2200, Denmark
    Søren Brunak
  28. King’s College London, Centre for Host–Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, London, SE1 9RT, UK
    S. Dusko Ehrlich

Authors

  1. Helle Krogh Pedersen
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  2. Valborg Gudmundsdottir
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  3. Henrik Bjørn Nielsen
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  4. Tuulia Hyotylainen
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  5. Trine Nielsen
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  6. Benjamin A. H. Jensen
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  7. Kristoffer Forslund
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  8. Falk Hildebrand
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  9. Edi Prifti
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  10. Gwen Falony
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  11. Emmanuelle Le Chatelier
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  12. Florence Levenez
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  13. Joel Doré
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  14. Ismo Mattila
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  15. Damian R. Plichta
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  16. Päivi Pöhö
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  17. Lars I. Hellgren
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  18. Manimozhiyan Arumugam
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  19. Shinichi Sunagawa
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  20. Sara Vieira-Silva
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  21. Torben Jørgensen
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  22. Jacob Bak Holm
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  23. Kajetan Trošt
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  24. MetaHIT Consortium
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  25. Karsten Kristiansen
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  26. Susanne Brix
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  27. Jeroen Raes
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  28. Jun Wang
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  29. Torben Hansen
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  30. Peer Bork
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  31. Søren Brunak
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  32. Matej Oresic
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  33. S. Dusko Ehrlich
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  34. Oluf Pedersen
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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.

Corresponding authors

Correspondence toSøren Brunak, Matej Oresic, S. Dusko Ehrlich or Oluf Pedersen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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 figures and tables

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.

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.

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

a–c, 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.

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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.

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.

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).

Extended Data Figure 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.

Extended Data Figure 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.

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.

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).

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.

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

Supplementary Information

This file contains full legends for Supplementary Tables 1-17, Supplementary Results and Discussion and a list of additional MetaHIT consortium members. (PDF 531 kb)

Supplementary Data

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Pedersen, H., Gudmundsdottir, V., Nielsen, H. et al. Human gut microbes impact host serum metabolome and insulin sensitivity.Nature 535, 376–381 (2016). https://doi.org/10.1038/nature18646

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