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Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice

Nature Microbiology volume 6, pages 563–573 (2021)Cite this article

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Abstract

The gut microbiota, which includes Akkermansia muciniphila, is known to modulate energy metabolism, glucose tolerance, immune system maturation and function in humans1,2,3,4. Although A. muciniphila is correlated with metabolic diseases and its beneficial causal effects were reported on host metabolism5,6,7,8, the molecular mechanisms involved have not been identified. Here, we report that A. muciniphila increases thermogenesis and glucagon-like peptide-1 (GLP-1) secretion in high-fat-diet (HFD)-induced C57BL/6J mice by induction of uncoupling protein 1 in brown adipose tissue and systemic GLP-1 secretion. We apply fast protein liquid chromatography and liquid chromatography coupled to mass spectrophotometry analysis to identify an 84 kDa protein, named P9, that is secreted by A. muciniphila. Using L cells and mice fed on an HFD, we show that purified P9 alone is sufficient to induce GLP-1 secretion and brown adipose tissue thermogenesis. Using ligand–receptor capture analysis, we find that P9 interacts with intercellular adhesion molecule 2 (ICAM-2). Interleukin-6 deficiency abrogates the effects of P9 in glucose homeostasis and downregulates ICAM-2 expression. Our results show that the interactions between P9 and ICAM-2 could be targeted by therapeutics for metabolic diseases.

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Fig. 1: A. muciniphila activates brown adipocytes and induces GLP-1 expression.
Fig. 2: A purified GLP-1-inducing protein, P9, from A. muciniphila ameliorates obesity and influences glucose homeostasis by promoting thermogenesis.
Fig. 3: P9 induces GLP-1 secretion through a Ca2+-dependent pathway and ICAM-2.
Fig. 4: P9 regulates glucose homeostasis and promotes thermogenesis through the GLP-1R signalling pathway and IL-6.

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Data availability

RNA-seq data and 16S rRNA gene sequencing data that support the findings of this study have been deposited at the European Nucleotide Archive (ENA) and are publicly available under the accession numbers PRJEB36198 (RNA-seq) and PRJEB36225 (16S rRNA gene sequencing). Whole-genome sequencing data of SNUG-61027 is deposited at the ENA under accession number PRJEB42664 and is publicly available. Similarly, metabolomic data were deposited at MetaboLights under accession number MTBLS1824 and are publicly available. Source data are provided with this paper.

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Acknowledgements

We thank P. Helbling and M. Pavlou (Dualsystems Biotech) for their discussion regarding the LRC assay and proteomics analysis; K. Ge (National Institutes of Health, USA), J. B. Kim (Seoul National University, Korea) and O. J. Kwon (Catholic University, Korea) for providing the immortalized BACs; B. L. Roth (The University of North Carolina at Chapel Hill, USA) for providing HTLA cells; S. J. Lee (Korea University, Korea) for providing the calcium inhibitors; W. K. Huh (Seoul National University, Korea) for providing the GPCR inhibitors; J. S. Han (Seoul National University, Korea) for FPLC usage; Y. K. Oh (Seoul National University, Korea) for infrared camera usage; Y. Chung (Seoul National University, Korea) for temperature-controlled chamber usage; S. H. Yoon, L. Song, J. W. Kim and S. E. Choi (Seoul National University, Korea) for their technical assistance with the tissue sampling and preparation for FACS analysis; T. J. Ahn (KoBioLabs, Inc.) for cytokine analysis of bacterial isolates in cell lines; I. S. Cho (KoBioLabs, Inc.) for producing EcPrc; and the staff at the Samsung Medical Center for providing infant faecal samples. This work was supported by the National Research Foundation of Korea (NRF) (no. NRF-2018R1A2A1A05078258). C.H.C. was supported by the Global Ph.D. Fellowship program and a NRF grant funded by the Korean government (no. NRF-2018H1A2A1061914).

Author information

Author notes

  1. These authors contributed equally: Hyo Shin Yoon, Chung Hwan Cho.

Authors and Affiliations

  1. Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea

    Hyo Shin Yoon, Chung Hwan Cho, Myeong Sik Yun, Sung Jae Jang, Hyun Ju You, Sung Hyun Moon & GwangPyo Ko

  2. Institute of Health and Environment, Seoul National University, Seoul, Korea

    Hyun Ju You

  3. Bio-MAX/N-Bio, Seoul National University, Seoul, Korea

    Hyun Ju You & GwangPyo Ko

  4. Center for Human and Environmental Microbiome, Seoul National University, Seoul, Korea

    Hyun Ju You & GwangPyo Ko

  5. KoBioLabs Inc., Seoul, Korea

    Jun-hyeong Kim, Sung Hyun Moon, Kiuk Lee, Tae-Wook Nam & GwangPyo Ko

  6. Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea

    Dohyun Han

  7. Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, Korea

    Kwang Hyun Cha

  8. Department of Biotechnology, College of Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seoul, Korea

    Yeon-Ji Kim & Sung-Joon Lee

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Contributions

H.S.Y., C.H.C. and G.K. contributed to the experiment design and interpreted all of the results. H.S.Y. narrowed down to the target protein using the in vitro L cell system, performed BAT-related experiments and isolated bacterial strains from infant faecal samples. C.H.C. contributed to daily administration of treatment in mouse experiments. C.H.C. performed cloning and, with assistance from M.S.Y., carried out protein expression. M.S.Y. and S.J.J. provided technical assistance with tissue sampling. S.J.J. performed QIIME II with correlation analysis and the GLP-1R β-arrestin assay. H.J.Y. participated in discussions and provided all of the assistance. J.-h.K. performed a single injection of P9 in mice and analysed systemic GLP-1. D.H. performed the proteomic analysis. Metabolomic analysis and isolation of the A. muciniphila strain, with assistance from K.H.C., and the culture conditions of A. muciniphila were optimized by S.H.M.; T.-W.N. and K.L. performed the FPLC analysis. S.-J.L. and Y.-J.K. performed the metabolic chamber analysis. H.S.Y. wrote the manuscript with C.H.C. and edited the manuscript. All of the authors discussed the results and approved the final text of the manuscript.

Corresponding author

Correspondence to GwangPyo Ko.

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Competing interests

G.K. is a founder of KoBioLabs Inc., a company that aims to characterize the role of host–microbiome interactions in chronic diseases. The other authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks Marc Donath, Lesley Hoyles and Liping Zhao for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Effects of administration of viable A. muciniphila for 14 weeks on insulin tolerance, adipocyte size, liver steatosis markers and intestinal cytokines levels as well as immune profiling of A. muciniphila effects on macrophage and colon cells.

a, Body mass after administration of a low-fat (LF), high-fat (HF), or a high-fat diet supplemented with A. muciniphila (HF + Akk). b, Intraperitoneal glucose tolerance testing (IPGTT) data are presented in dot plots and as the areas under the curves (AUCs). c, Fat mass (g g body weight (BW)−1), d, plasma insulin concentration, e, liver mass (g g BW−1), and f, H&E staining of liver sections (Scale bar = 100 μm, n = 3 mice per group). g, mRNA expression of fatty acid and ß-oxidation genes in liver. h, Relative abundance of cecal metabolites, including short-chain fatty acids (SCFA), branched-chain amino acids (BCAA), tricarboxylic acid intermediates (TCAi), and metabolites involved in creatine synthesis, quantified by nuclear magnetic resonance (NMR). mRNA expression of cytokines in (i) colon and (j) ileum in HF and HF + Akk group. IL-6 secretion was measured after the stimulation of k, CT26 and l, Raw264.7 cell lines with Lactobacillus spp., Bifidobacterium spp., or Akk (ATCC BAA-835) (cell to bacteria: 1:10). TNF-α expression in response to Akk, Lactobacillus spp., and Bifidobacterium spp in m, CT26 cell lines and n, Raw 264.7 cell lines. The experiment was repeated twice. E. coli lipopolysaccharide (LPS) was used as a positive control. Data are presented as the means ± SEMs. Number of mice per group for a–b: LF: 10, HF: 10, HF + Akk: 10. Number of mice per group for c: LF: 6, HF: 10, HF + Akk: 10. Number of mice per group for d–e and g–j: HF: 10, HF + Akk: 10. Data were analyzed using one-way ANOVA, followed by Tukey’s test for a and b (right panel) and two-way ANOVA, followed by Tukey’s test for b (left panel). For c–e and g–j, the two-tailed unpaired t-test was used to analyze the data. k–n represent the results of three independent experiments performed in duplicate and were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test. **, *** and φφφφ indicate significant differences (P < 0.01, < 0.001 and < 0.0001, respectively).

Source data

Extended Data Fig. 2 The relative abundance of A. muciniphila correlates with iBAT temperature and GLP-1 secretion and A. muciniphila administration to HF-diet-fed mice increases the M2 macrophage count in adipose tissue.

a, Differences in the relative abundance of bacterial species between the HF and HF + Akk groups are represented by ALDEx2. The differences in abundance between and within each group for individual species were analyzed. Organisms (at the OTU and nearest neighbor species levels) with significant p values are shown as pink circles (Welch’s t-statistic, corrected using the Benjamini-Hochberg method). b, Data showing the 16S rRNA gene count in the HF and HF + Akk groups. c–e, Scatter plots illustrating the statistical relationship (Spearman’s correlation) between the relative abundance of A. muciniphila and metabolic phenotypes: OGTT AUC (oral glucose tolerance test area under the curve), iBAT temp (interscapular brown adipose tissue temperature), and GLP-1 (plasma glucagon-like peptide-1 concentration). f–h, mRNA expression of M1 and M2 macrophage markers in iBAT, igWAT, and epiWAT. i, Gating strategy for detection of M2-like macrophages (CD11b+ CD206+) in iBAT of HF-fed mice. Data are presented as the means ± SEMs. Number of mice per group for a–h: HF: 10, HF + Akk: 10. Data in f–h were analyzed using the two-tailed unpaired t-test.

Source data

Extended Data Fig. 3 Short-chain fatty acids produced by A. muciniphila are not the only factors responsible for the induction of glucagon-like peptide-1 (GLP-1).

a, Glucagon-like peptide 1 (GLP-1) secretion after treatment of NCI-H716 cells with viable A. muciniphila (cell to bacteria ratio, 1:20) or cell-free supernatant (CFS) (10% v/v) (ATCC BAA-835 or SNUG-61027). b, GLP-1 secretion by NCI-H716 cells after treatment with various amounts of A. muciniphila (10–100% v/v). c, GLP-1 secretion after the treatment of NCI-H716 cells with CFS (10% v/v) from A. muciniphila (ATCC BAA-835, SNUG-61027), Korean fecal strains, Lactobacillus spp., or Bifidobacterium spp. d, Short-chain fatty acids (SCFA) derived from the CFS of A. muciniphila, measured by GC-MS. e, GLP-1 secretion by NCI-H716 cells induced by the CFS of A. muciniphila (100% v/v), acetate (1 mM, 10 mM), or propionate (1 mM or 10 mM). Data are presented as the means ± SEMs. The data in a and b represent the results of two independent experiments performed in duplicate. The data in c–e are the results of triplicate analyses. The data in a–e were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test. *, **, and *** indicate significant differences (P < 0.05, < 0.01, and < 0.001, respectively).

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Extended Data Fig. 4 Profiling of A. muciniphila supernatant fractions that induced glucagon-like peptide-1 (GLP-1) secretion.

a, Schematic workflow used to identify GLP-1-inducing fractions derived from A. muciniphila. b, The cell-free supernatant (CFS) of A. muciniphila was fractionated using molecular size cut-off filters, as indicated. A volume of each fraction containing 5 mg of protein was used to treat NCI-H716 cells. c, 100–300 kDa filtrate and the 30–100 kDa filtrate of A. muciniphila were treated with proteinase K (100 µg ml−1) as indicated (+PK). A control sample was used that comprised BHI broth containing 5% FBS and was treated with proteinase K. The effects of each on GLP-1 expression was then analyzed. d, 100 K filtrates of control media and A. muciniphila were separated by ion exchange fast protein liquid chromatography (FPLC) and 26 fractions (m1–m26) were obtained. Each fraction was used to treat NCI-H716 cells and GLP-1 secretion was analyzed by ELISA. e, Concentrates of the m2–m4 fractions of Control and SNUG-61027 were separated by size columns (to generate fractions G1–G34) using the FPLC system, then the procedures listed above were followed. f, Venn diagram of the identified proteins. Sample 1, Sample 2, and Sample 3 are the 100 K concentrates obtained from size cut-off filtration, concentrates from the ion exchange chromatography, and concentrates from the size-exclusion chromatography, respectively. g, SDS-PAGE gel of the expressed proteins were processed in parallel in the same gel. h, Nine proteins identified by LC-MS/MS from Sample 3. Nine candidate proteins were cloned using an E.coli expression system according to the protein sequences for the SNUG-61027 strain. The sequence similarity for each protein between SNUG-61027 and ATCC-BAA-835 was analyzed. Data are presented as the means ± SEMs. The data in b–e represent the results of triplicate experiments and were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test.

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Extended Data Fig. 5 Intraperitoneal administration of P9 improves glucose homeostasis and body mass in normal chow (NC) diet-fed mice.

a, Weight gain was measured in NC-fed mice administered candidate proteins intraperitoneally (i.p.) (P1, P5, P9, or Amuc_1100) at 100 μg per mouse for 2 weeks. b, Oral glucose tolerance testing (OGTT) was performed and the AUCs were calculated. The data in c represent the effects of P9 derived from A. muciniphila and EcPrc (S41 family derived from E. coli) on GLP-1 secretion by NCI-H716 cells. d, Plasma GLP-1 levels were measured after the single i.p. injection (300 μg per mouse) of each respected group. Data are presented as the means ± SEMs. Number of mice per group for a and b: NC: 8, P1: 8, P5: 8, P9: 8, Amuc_1100: 8 and for d, NC: 8, ErPrc: 8, Acetate: 8, Propionate: 8. Data were analyzed using one-way ANOVA, followed by Tukey’s post-hoc test for a, d. For b, data were analyzed using two-way ANOVA, followed by Tukey’s post-hoc test. The data in c are the results of duplicate experiments and were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test. * or φ indicate significant differences, P < 0.05.

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Extended Data Fig. 6 Oral administration of P9 reduces adipose tissue mass and induces thermogenesis in high-fat diet-fed mice.

a, Gross appearance of the adipose tissue depots. b, Representative infrared thermographic images of the temperatures of the mice are presented at room temperature (RT) (top) and after a cold shock at 5 °C for 4 h (bottom) (n = 3 per group).

Extended Data Fig. 7 Indirect calorimetry and body composition analysis following acute treatment with P9.

Mice were fed a high-fat diet and were orally administered P9 protein 100 µg or vehicle for 10 days (n = 8 mice per group). Oxygen consumption (VO2), carbon dioxide production (VCO2), respiratory quotient (RQ), and energy expenditure were calculated every 3 min using METABOLISM software (V2.2.01, Panlab-Harvard Apparatus). Fatty acid oxidation (FAO) was calculated using the following formula: (1.6946×VO2)-(1.7012×VCO2). a, Body composition of the mice (% fat mass, lean mass), measured using a Minispec LF90 analyzer. b, Energy expenditure, c, FAO, and d, RQ. Data are presented as the means ± SEMs. Number of mice per group: HF: 8, HF + P9: 8. Data were analyzed using the two-tailed unpaired t-test. *, ** and *** indicate significant differences (P < 0.05, < 0.01 and < 0.001, respectively).

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Extended Data Fig. 8 The induction of GLP-1 secretion, by P9 involves activation of the CREB signaling pathway, but was attenuated by GPCR antagonists.

a, Kinase phospho-profiles after P9 or vehicle treatment of NCI-H716 cells for 10 min, performed using a Proteome profile phospho-kinase array kit (Red square: p-CREB, Blue square: p-HSP27) (experiments were performed in duplicate). b, Ca2+ influx into NCI-H716 (left) and GLUTag (right) cells after treatment with 50 μg ml−1 or 100 μg ml−1 of P9 (n = 6 per group). c, NCI-H716 cells were treated with calcium inhibitors (10 μM) 15 min before the P9 treatment (100 μg ml−1) for 2 h, then GLP-1 secretion was quantified (n = 6 per group). d, GLP-1 receptor (GLP-1R) beta-arrestin activity test data (n = 3 per group) (exendin-4: GLP-1R agonist). e, NCI-H716 cells were treated with GPCR antagonists (10 μM) or vehicle (DMSO) for 15 min before P9 treatment (50 μg ml−1), then incubated for an additional 2 h, after which GLP-1 secretion was analyzed (n = 6 per group). Data are presented as the means ± SEMs. The data in c and e represent the results of three independent experiments. The data in a and c–e were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test.

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Extended Data Fig. 9 P9 induces IL-6 secretion by macrophage cell lines and IL-6 directly regulates thermogenic gene expression in brown adipocytes.

a, IL-6 expression in Raw264.7 cells after P1, P5, or P9 treatment (10 μg ml−1) overnight. b, Thermogenic gene expression in immortalized brown preadipocytes (BAC) and c, Ucp1 gene expression in primary preadipocytes derived from interscapular brown adipose depots treated with recombinant mouse IL-6 (200 ng ml−1) for 6 h. d, GLP-1 secretion by GLUTag cells after treatment of IL-6 in glucose condition (0.1 mM). e, GLP-1 secretion by GLUTag cells after treatment of IL-6 or P9 or IL-6 together with P9 in glucose condition (0.1 mM). Data are presented as the means ± SEMs. Data were analyzed using the Kruskal-Wallis test, followed by Dunn’s post-hoc test for a, d, and e; and by the Mann-Whitney test for b and c. The data represent the results of three independent experiments performed in duplicate, except in b, where they represent the results of two independent experiments performed in duplicate.

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Extended Data Fig. 10 P9 promotes thermogenesis via the GLP-1R signaling pathway and IL-6.

a, Weight gain and food intake, and b, OGTT and ITT data after 8 weeks of HF-diet-feeding (WT: n = 8, IL-6KO: n = 6). c, Insulin tolerance testing (ITT) and AUC data for wild type (WT) and IL-6 knockout (IL-6 KO) HF-fed mice that were orally administered P9 (100 μg per mouse) ± i.p. injected with exendin 9–39 (a GLP-1R antagonist, 0.8 μg per mouse) for 8 weeks. n = 8 mice per group for WT (left) and n = 6 mice per group for IL-6 KO (right) mice. d, Weight gain, e, food intake (g per mouse per day), and f, temperatures of the iBAT and eye, measured after a cold shock at 5 °C for 4 h by infrared thermography (n = 8 mice per group). Data are presented as the means ± SEMs. Number of mice per group for c–e: WT HF: 7, HF + P9: 8, HF + P9 + Ex9–39: 7; IL-6 KO mice HF: 6, HF + P9: 6, HF + P9 + Ex9–39: 6. Number of mice per group for f: WT HF: 8, HF + P9: 8, HF + P9 + Ex9–39: 8; IL-6 KO mice: HF: 6, HF + P9: 6, HF + P9 + Ex9–39: 6. Data were analyzed using two-way ANOVA, followed by Tukey’s post-hoc test for c (left panel), d, and e. For the data in c (right panel), one-way ANOVA, followed by Tukey’s post-hoc test was used. The unpaired t-test was used to analyze the data in f. *, ***, and **** indicate significant differences (P < 0.05, < 0.001, and < 0.0001, respectively).

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Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7 include the primers used for protein expression and mRNA analysis, RNA-seq data and human samples. All of the tables are explained individually.

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Unprocessed SDS–PAGE gel of the expressed proteins labelled in Extended Data Fig. 4h.

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Yoon, H.S., Cho, C.H., Yun, M.S. et al. Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice. Nat Microbiol 6, 563–573 (2021). https://doi.org/10.1038/s41564-021-00880-5

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