Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

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

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Geach, T. Gut microbiota: mucin-munching bacteria modulate glucose metabolism. Nat. Rev. Endocrinol. 13, 66 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Chevalier, C. et al. Gut microbiota orchestrates energy homeostasis during cold. Cell 163, 1360–1374 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Parekh, P. J., Balart, L. A. & Johnson, D. A. The influence of the gut microbiome on obesity, metabolic syndrome and gastrointestinal disease. Clin. Transl. Gastroenterol. 6, e91 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sonnenburg, J. L. & Backhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066–9071 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cani, P. D. et al. Endocannabinoids—at the crossroads between the gut microbiota and host metabolism. Nat. Rev. Endocrinol. 12, 133–143 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Plovier, H. et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 23, 107–113 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Depommier, C. et al. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat. Med. 25, 1096–1103 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377, 13–27 (2017).

  10. Lim, M. Y. et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut 66, 1031–1038 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Dao, M. C. et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 65, 426–436 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Karlsson, C. L. et al. The microbiota of the gut in preschool children with normal and excessive body weight. Obesity 20, 2257–2261 (2012).

    Article  PubMed  Google Scholar 

  13. Santacruz, A. et al. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br. J. Nutr. 104, 83–92 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Dao, M. C. et al. Akkermansia muciniphila abundance is lower in severe obesity, but its increased level after bariatric surgery is not associated with metabolic health improvement. Am. J. Physiol. Endocrinol. Metab. 317, E446–E459 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Zhang, L. et al. Akkermansia muciniphila can reduce the damage of gluco/lipotoxicity, oxidative stress and inflammation, and normalize intestine microbiota in streptozotocin-induced diabetic rats. Pathog. Dis. https://doi.org/10.1093/femspd/fty028 (2018).

  16. Liu, J. et al. Gypenosides reduced the risk of overweight and insulin resistance in C57BL/6J mice through modulating adipose thermogenesis and gut microbiota. J. Agric. Food Chem. 65, 9237–9246 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Gao, X. et al. Polyphenol- and caffeine-rich postfermented pu-erh tea improves diet-induced metabolic syndrome by remodeling intestinal homeostasis in mice. Infect. Immun. https://doi.org/10.1128/IAI.00601-17 (2018).

  18. Depommier, C. et al. Pasteurized Akkermansia muciniphila increases whole-body energy expenditure and fecal energy excretion in diet-induced obese mice. Gut Microbes https://doi.org/10.1080/19490976.2020.1737307 (2020).

  19. Gribble, F. M. & Reimann, F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. Nat. Rev. Endocrinol. 15, 226–237 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Greiner, T. U. & Backhed, F. Microbial regulation of GLP-1 and L-cell biology. Mol. Metab. 5, 753–758 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang, L. et al. A purified membrane protein from Akkermansia muciniphila or the pasteurised bacterium blunts colitis associated tumourigenesis by modulation of CD8+ T cells in mice. Gut https://doi.org/10.1136/gutjnl-2019-320105 (2020).

  22. Tolhurst, G. et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes 61, 364–371 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Psichas, A. et al. The short chain fatty acid propionate stimulates GLP-1 and PYY secretion via free fatty acid receptor 2 in rodents. Int. J. Obes. 39, 424–429 (2015).

    Article  CAS  Google Scholar 

  24. Li, Z. et al. Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit. Gut 67, 1269–1279 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Yadav, H., Lee, J. H., Lloyd, J., Walter, P. & Rane, S. G. Beneficial metabolic effects of a probiotic via butyrate-induced GLP-1 hormone secretion. J. Biol. Chem. 288, 25088–25097 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chambers, E. S. et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut 64, 1744–1754 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Lund, M. L. et al. L-cell differentiation is induced by bile acids through GPBAR1 and Paracrine GLP-1 and serotonin signaling. Diabetes 69, 614–623 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Albaugh, V. L. et al. Role of bile acids and GLP-1 in mediating the metabolic improvements of bariatric surgery. Gastroenterology 156, 1041–1051 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Brighton, C. A. et al. Bile acids trigger GLP-1 release predominantly by accessing basolaterally located G protein-coupled bile acid receptors. Endocrinology 156, 3961–3970 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chimerel, C. et al. Bacterial metabolite indole modulates incretin secretion from intestinal enteroendocrine L cells. Cell Rep. 9, 1202–1208 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lebrun, L. J. et al. Enteroendocrine L cells sense LPS after gut barrier injury to enhance GLP-1 secretion. Cell Rep. 21, 1160–1168 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Gribble, F. M. & Reimann, F. Enteroendocrine cells: chemosensors in the intestinal epithelium. Annu. Rev. Physiol. 78, 277–299 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Tennoune, N. et al. Bacterial ClpB heat-shock protein, an antigen-mimetic of the anorexigenic peptide α-MSH, at the origin of eating disorders. Transl. Psychiatry 4, e458 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Everard, A. et al. Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice. Diabetes 60, 2775–2786 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Donaldson, G. P. et al. Gut microbiota utilize immunoglobulin A for mucosal colonization. Science 360, 795–800 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ansaldo, E. et al. Akkermansia muciniphila induces intestinal adaptive immune responses during homeostasis. Science 364, 1179–1184 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shin, N. R. et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 63, 727–735 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Krieger, J. P. et al. Glucagon-like peptide-1 regulates brown adipose tissue thermogenesis via the gut-brain axis in rats. Am. J. Physiol. Regul. Integr. Comp. Physiol. 315, R708–R720 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Beiroa, D. et al. GLP-1 agonism stimulates brown adipose tissue thermogenesis and browning through hypothalamic AMPK. Diabetes 63, 3346–3358 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Lynch, L. et al. iNKT cells induce FGF21 for thermogenesis and are required for maximal weight loss in GLP1 therapy. Cell Metab. 24, 510–519 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ottman, N. et al. Pili-like proteins of Akkermansia muciniphila modulate host immune responses and gut barrier function. PLoS ONE 12, e0173004 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ellingsgaard, H. et al. Interleukin-6 enhances insulin secretion by increasing glucagon-like peptide-1 secretion from L cells and alpha cells. Nat. Med. 17, 1481–1489 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kahles, F. et al. GLP-1 secretion is increased by inflammatory stimuli in an IL-6-dependent manner, leading to hyperinsulinemia and blood glucose lowering. Diabetes 63, 3221–3229 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Ottman, N. et al. Characterization of outer membrane proteome of Akkermansia muciniphila reveals sets of novel proteins exposed to the human intestine. Front. Microbiol. 7, 1157 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hauge, M. et al. Gq and Gs signaling acting in synergy to control GLP-1 secretion. Mol. Cell. Endocrinol. 449, 64–73 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Ulven, T. Short-chain free fatty acid receptors FFA2/GPR43 and FFA3/GPR41 as new potential therapeutic targets. Front. Endocrinol. 3, 111 (2012).

    Article  Google Scholar 

  48. Husted, A. S., Trauelsen, M., Rudenko, O., Hjorth, S. A. & Schwartz, T. W. GPCR-mediated signaling of metabolites. Cell Metab. 25, 777–796 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Gorina, R., Lyck, R., Vestweber, D. & Engelhardt, B. β2 integrin-mediated crawling on endothelial ICAM-1 and ICAM-2 is a prerequisite for transcellular neutrophil diapedesis across the inflamed blood-brain barrier. J. Immunol. 192, 324–337 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Kuhre, R. E., Frost, C. R., Svendsen, B. & Holst, J. J. Molecular mechanisms of glucose-stimulated GLP-1 secretion from perfused rat small intestine. Diabetes 64, 370–382 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Flamez, D. et al. Altered cAMP and Ca2+ signaling in mouse pancreatic islets with glucagon-like peptide-1 receptor null phenotype. Diabetes 48, 1979–1986 (1999).

    Article  CAS  PubMed  Google Scholar 

  52. Lee, J. H., Wen, X., Cho, H. & Koo, S. H. CREB/CRTC2 controls GLP-1-dependent regulation of glucose homeostasis. FASEB J. 32, 1566–1578 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Shin, S. et al. CREB mediates the insulinotropic and anti-apoptotic effects of GLP-1 signaling in adult mouse β-cells. Mol. Metab. 3, 803–812 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Bala, V. et al. Release of GLP-1 and PYY in response to the activation of G protein-coupled bile acid receptor TGR5 is mediated by Epac/PLC-epsilon pathway and modulated by endogenous H2S. Front. Physiol. 5, 420 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wunderlich, F. T. et al. Interleukin-6 signaling in liver-parenchymal cells suppresses hepatic inflammation and improves systemic insulin action. Cell Metab. 12, 237–249 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Stanford, K. I. et al. Brown adipose tissue regulates glucose homeostasis and insulin sensitivity. J. Clin. Invest. 123, 215–223 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Lang Lehrskov, L. et al. Interleukin-6 delays gastric emptying in humans with direct effects on glycemic control. Cell Metab. https://doi.org/10.1016/j.cmet.2018.04.008 (2018).

  58. Kang, C. S. et al. Extracellular vesicles derived from gut microbiota, especially Akkermansia muciniphila, protect the progression of dextran sulfate sodium-induced colitis. PLoS ONE 8, e76520 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gupta, V. Glucagon-like peptide-1 analogues: an overview. Indian J. Endocrinol. Metab. 17, 413–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. John B. Buse, J. B. et al. 2019 update to: management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 43, 487–493 (2020).

    Article  PubMed  Google Scholar 

  61. Filippatos, T. D., Panagiotopoulou, T. V. & Elisaf, M. S. Adverse effects of GLP-1 receptor agonists. Rev. Diabet. Stud. 11, 202–230 (2014).

    Article  PubMed  Google Scholar 

  62. Park, Y. K. et al. Distinct roles of transcription factors KLF4, Krox20, and peroxisome proliferator-activated receptor γ in adipogenesis. Mol. Cell. Biol. https://doi.org/10.1128/MCB.00554-16 (2017).

  63. Lee, H. et al. Quantitative proteomic analysis identifies AHNAK (neuroblast differentiation-associated protein AHNAK) as a novel candidate biomarker for bladder urothelial carcinoma diagnosis by liquid-based cytology. Mol. Cell. Proteomics 17, 1788–1802 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Frei, A. P. et al. Direct identification of ligand-receptor interactions on living cells and tissues. Nat. Biotechnol. 30, 997–1001 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. Frei, A. P., Moest, H., Novy, K. & Wollscheid, B. Ligand-based receptor identification on living cells and tissues using TRICEPS. Nat. Protoc. 8, 1321–1336 (2013).

    Article  PubMed  Google Scholar 

  68. Le Gall, G. et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).

    Article  PubMed  Google Scholar 

  69. Kroeze, W. K. et al. PRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome. Nat. Struct. Mol. Biol. 22, 362–369 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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

Authors
  1. Hyo Shin Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung Hwan Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Myeong Sik Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sung Jae Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hyun Ju You
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun-hyeong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dohyun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kwang Hyun Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sung Hyun Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kiuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yeon-Ji Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sung-Joon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tae-Wook Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. GwangPyo Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

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).

Source data

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.

Source data

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.

Source data

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).

Source data

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.

Source data

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.

Source data

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).

Source data

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.

Peer Review File

Source data

Source Data Fig. 1

Numerical source data.

Source Data Fig. 2

Numerical source data.

Source Data Fig. 3

Numerical source data.

Source Data Fig. 4

Numerical source data.

Source Data Extended Data Fig. 1

Numerical source data.

Source Data Extended Data Fig. 2

Numerical source data.

Source Data Extended Data Fig. 3

Numerical source data.

Source Data Extended Data Fig. 4

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

Source Data Extended Data Fig. 4

Numerical source data.

Source Data Extended Data Fig. 5

Numerical source data.

Source Data Extended Data Fig. 7

Numerical source data.

Source Data Extended Data Fig. 8

Numerical source data.

Source Data Extended Data Fig. 9

Numerical source data.

Source Data Extended Data Fig. 10

Numerical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-00880-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology