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.

  • Article
  • Published:

Liver-derived extracellular vesicles improve whole-body glycaemic control via inter-organ communication

Nature Metabolism volume 6, pages 254–272 (2024)Cite this article

Subjects

Abstract

Small extracellular vesicles (EVs) are signalling messengers that regulate inter-tissue communication through delivery of their molecular cargo. Here, we show that liver-derived EVs are acute regulators of whole-body glycaemic control in mice. Liver EV secretion into the circulation is increased in response to hyperglycaemia, resulting in increased glucose effectiveness and insulin secretion through direct inter-organ EV signalling to skeletal muscle and the pancreas, respectively. This acute blood glucose lowering effect occurs in healthy and obese mice with non-alcoholic fatty liver disease, despite marked remodelling of the liver-derived EV proteome in obese mice. The EV-mediated blood glucose lowering effects were recapitulated by administration of liver EVs derived from humans with or without progressive non-alcoholic fatty liver disease, suggesting broad functional conservation of liver EV signalling and potential therapeutic utility. Taken together, this work reveals a mechanism whereby liver EVs act on peripheral tissues via endocrine signalling to restore euglycaemia in the postprandial state.

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: Confirmation of NAFL and NASH and validation of small EV isolation secreted from the livers of mice.
Fig. 2: The liver EV proteome in mice with NAFL and NASH.
Fig. 3: Liver-secreted EVs improve glycaemic control.
Fig. 4: Liver-secreted EVs do not regulate insulin sensitivity.
Fig. 5: Liver EVs improve glycaemic control through enhanced GE.
Fig. 6: Liver EVs activate cell signalling associated with calcium metabolism.
Fig. 7: Liver-secreted EVs directly stimulate GSIS in primary murine islets.
Fig. 8: Human liver-secreted EVs improve glycaemic control.

Similar content being viewed by others

Data availability

All data are available within the manuscript, extended data, source data files and supplementary files. The analysis in this study used standard techniques and protocols without generating new unique reagents or custom code. The proteomic and phosphoproteomic data generated in this study have been deposited with the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/cgi/GetDataset) via the PRIDE database84, under the identifiers PXD037924, PXD036400 and PXD043137. These data are also provided as supplementary information. Source data are provided with this paper.

References

  1. Chalasani, N. et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology 67, 328–357 (2018).

    Article  PubMed  Google Scholar 

  2. Angulo, P. et al. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 149, 389–397 (2015).

    Article  PubMed  Google Scholar 

  3. Younossi, Z. M. et al. Global epidemiology of nonalcoholic fatty liver disease—meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64, 73–84 (2016).

    Article  PubMed  Google Scholar 

  4. Ooi, G. J. et al. Effect of body mass index, metabolic health and adipose tissue inflammation on the severity of non-alcoholic fatty liver disease in bariatric surgical patients: a prospective study. Obes. Surg. 29, 99–108 (2019).

    Article  PubMed  Google Scholar 

  5. Younossi, Z. M. et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J. Hepatol. 71, 793–801 (2019).

    Article  PubMed  Google Scholar 

  6. Korenblat, K. M., Fabbrini, E., Mohammed, B. S. & Klein, S. Liver, muscle, and adipose tissue insulin action is directly related to intrahepatic triglyceride content in obese subjects. Gastroenterology 134, 1369–1375 (2008).

    Article  PubMed  CAS  Google Scholar 

  7. Hwang, J.H. et al. Increased intrahepatic triglyceride is associated with peripheral insulin resistance: in vivo MR imaging and spectroscopy studies. Am. J. Physiol. Endocrinol. Metab. 293, E1663–E1669 (2007).

    Article  PubMed  CAS  Google Scholar 

  8. Meex, R. C. et al. Fetuin B is a secreted hepatocyte factor linking steatosis to impaired glucose metabolism. Cell Metab. 22, 1078–1089 (2015).

    Article  PubMed  CAS  Google Scholar 

  9. Montgomery, M. K. et al. Deep proteomic profiling unveils arylsulfatase A as a non-alcoholic steatohepatitis inducible hepatokine and regulator of glycemic control. Nat. Commun. 13, 1259 (2022).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  10. Watt, M. J., Miotto, P. M., De Nardo, W. & Montgomery, M. K. The liver as an endocrine organ—linking NAFLD and insulin resistance. Endocr. Rev. 40, 1367–1393 (2019).

    Article  PubMed  Google Scholar 

  11. Jensen-Cody, S. O. & Potthoff, M. J. Hepatokines and metabolism: deciphering communication from the liver. Mol. Metab. 44, 101138 (2021).

    Article  PubMed  CAS  Google Scholar 

  12. Misu, H. et al. A liver-derived secretory protein, selenoprotein P, causes insulin resistance. Cell Metab. 12, 483–495 (2010).

    Article  PubMed  CAS  Google Scholar 

  13. Xu, J. et al. Fibroblast growth factor 21 reverses hepatic steatosis, increases energy expenditure, and improves insulin sensitivity in diet-induced obese mice. Diabetes 58, 250–259 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Montgomery, M. K. et al. SMOC1 is a glucose-responsive hepatokine and therapeutic target for glycemic control.Sci. Transl. 12, eaaz8048 (2020).

    Article  CAS  Google Scholar 

  15. Michael, D. J., Cai, H., Xiong, W., Ouyang, J. & Chow, R. H. Mechanisms of peptide hormone secretion. Trends Endocrinol. Metab. 17, 408–415 (2006).

    Article  PubMed  CAS  Google Scholar 

  16. Isaac, R., Reis, F. C. G., Ying, W. & Olefsky, J. M. Exosomes as mediators of intercellular crosstalk in metabolism. Cell Metab. 33, 1744–1762 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Ji, Y. et al. Hepatocyte-derived exosomes from early onset obese mice promote insulin sensitivity through miR-3075. Nat. Metab. 3, 1163–1174 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Albanese, M. et al. MicroRNAs are minor constituents of extracellular vesicles that are rarely delivered to target cells. PLoS Genet. 17, e1009951 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Crewe, C. et al. An endothelial-to-adipocyte extracellular vesicle axis governed by metabolic state. Cell 175, 695–708 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Ying, W. et al. Adipose tissue macrophage-derived exosomal miRNAs can modulate in vivo and in vitro insulin sensitivity. Cell 171, 372–384 (2017).

    Article  PubMed  CAS  Google Scholar 

  21. Brunt, E. M. et al. Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology 53, 810–820 (2011).

    Article  PubMed  CAS  Google Scholar 

  22. Marchesini, G. et al. EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. J. Hepatol. 64, 1388–1402 (2016).

    Article  Google Scholar 

  23. De Nardo, W. et al. Proteomic analysis reveals exercise training induced remodelling of hepatokine secretion and uncovers syndecan-4 as a regulator of hepatic lipid metabolism. Mol. Metab. 60, 101491 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Giebel, B. & Helmbrecht, C. Methods to analyze EVs. Methods Mol. Biol. 1545, 1–20 (2017).

    Article  PubMed  CAS  Google Scholar 

  25. Rontogianni, S. et al. Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping. Commun. Biol. 2, 325 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gidlöf, O. et al. Proteomic profiling of extracellular vesicles reveals additional diagnostic biomarkers for myocardial infarction compared to plasma alone. Sci. Rep. 9, 8991 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  27. Wang, S., Kojima, K., Mobley, J. A. & West, A. B. Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease. EBioMedicine 45, 351–361 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Thompson, A. G. et al. CSF extracellular vesicle proteomics demonstrates altered protein homeostasis in amyotrophic lateral sclerosis. Clin. Proteomics 17, 31 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Xie, Y. et al. Membrane-bound HSP70-engineered myeloma cell-derived exosomes stimulate more efficient CD8+ CTL− and NK-mediated antitumour immunity than exosomes released from heat-shocked tumour cells expressing cytoplasmic HSP70. J. Cell Mol. Med. 14, 2655–2666 (2010).

    Article  PubMed  CAS  Google Scholar 

  30. Deng, Z.-B. et al. Adipose tissue exosome-like vesicles mediate activation of macrophage-induced insulin resistance. Diabetes 58, 2498–2505 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Kim, D.-K. et al. Chromatographically isolated CD63+CD81+ extracellular vesicles from mesenchymal stromal cells rescue cognitive impairments after TBI. Proc. Natl Acad. Sci. USA 113, 170–175 (2016).

    Article  PubMed  ADS  CAS  Google Scholar 

  32. Ding, M. et al. Exosomes isolated from human umbilical cord mesenchymal stem cells alleviate neuroinflammation and reduce amyloid-beta deposition by modulating microglial activation in Alzheimer’s disease. Neurochem. Res. 43, 2165–2177 (2018).

    Article  PubMed  CAS  Google Scholar 

  33. Peters, D. T. et al. Asialoglycoprotein receptor 1 is a specific cell-surface marker for isolating hepatocytes derived from human pluripotent stem cells. Development 143, 1475–1481 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes.Science 367, eaau6977 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Charoenviriyakul, C., Takahashi, Y., Morishita, M., Nishikawa, M. & Takakura, Y. Role of extracellular vesicle surface proteins in the pharmacokinetics of extracellular vesicles. Mol. Pharm. 15, 1073–1080 (2018).

    Article  PubMed  CAS  Google Scholar 

  36. Roh, E., Song, D. K. & Kim, M.-S. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp. Mol. Med. 48, e216 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Hu, X. et al. Extracellular vesicles from adipose-derived stem cells promote microglia M2 polarization and neurological recovery in a mouse model of transient middle cerebral artery occlusion. Stem Cell Res. Ther. 13, 21 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Alford, F. P., Henriksen, J. E., Rantzau, C. & Beck-Nielsen, H. Glucose effectiveness is a critical pathogenic factor leading to glucose intolerance and type 2 diabetes: an ignored hypothesis. Diabetes Metab. Res. Rev. 34, e2989 (2018).

    Article  PubMed  CAS  Google Scholar 

  39. Small, L. et al. Comparative analysis of oral and intraperitoneal glucose tolerance tests in mice. Mol. Metab. 57, 101440 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Khatua, A. K., Taylor, H. E., Hildreth, J. E. & Popik, W. Exosomes packaging APOBEC3G confer human immunodeficiency virus resistance to recipient cells. J. Virol. 83, 512–521 (2009).

    Article  PubMed  CAS  Google Scholar 

  41. Xie, Z. et al. Adipose-derived exosomes exert proatherogenic effects by regulating macrophage foam cell formation and polarization.J. Am. Heart Assoc. 7, e007442 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Gao, K. et al. Exosomes derived from septic mouse serum modulate immune responses via exosome-associated cytokines. Front. Immunol. 10, 1560 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Wilson-Grady, J. T., Haas, W. & Gygi, S. P. Quantitative comparison of the fasted and re-fed mouse liver phosphoproteomes using lower pH reductive dimethylation. Methods 61, 277–286 (2013).

    Article  PubMed  CAS  Google Scholar 

  44. Linn, T. C., Pettit, F. H. & Reed, L. J. Alpha-keto acid dehydrogenase complexes. X. Regulation of the activity of the pyruvate dehydrogenase complex from beef kidney mitochondria by phosphorylation and dephosphorylation. Proc. Natl Acad. Sci. USA 62, 234–241 (1969).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  45. Campbell, J. E. & Newgard, C. B. Mechanisms controlling pancreatic islet cell function in insulin secretion. Nat. Rev. Mol. Cell Biol. 22, 142–158 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Pearson, T., Greiner, D. L. & Shultz, L. D. Humanized SCID mouse models for biomedical research. Curr. Top. Microbiol. Immunol. 324, 25–51 (2008).

    PubMed  CAS  Google Scholar 

  47. Lo, J. C. Y. et al. Obesity does not promote tumorigenesis of localized patient-derived prostate cancer xenografts. Oncotarget 7, 47650–47662 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Thomou, T. et al. Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature 542, 450–455 (2017).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  49. Koenen, M. T. et al. Extracellular vesicles from steatotic hepatocytes provoke pro-fibrotic responses in cultured stellate cells.Biomolecules 12, 698 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Németh, K. et al. Extracellular vesicle release and uptake by the liver under normo- and hyperlipidemia. Cell Mol. Life Sci. 78, 7589–7604 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Yan, C. et al. A high-fat diet attenuates AMPK α1 in adipocytes to induce exosome shedding and nonalcoholic fatty liver development in vivo. Diabetes 70, 577–588 (2021).

    Article  PubMed  CAS  Google Scholar 

  52. Zhang, R. et al. A potential target for diabetic vascular damage: high glucose-induced monocyte extracellular vesicles impair endothelial cells by delivering miR-142-5p. Front. Bioeng. Biotechnol. 10, 913791 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Burger, D. et al. High glucose increases the formation and pro-oxidative activity of endothelial microparticles. Diabetologia 60, 1791–1800 (2017).

    Article  PubMed  CAS  Google Scholar 

  54. Povero, D. et al. Lipid-induced toxicity stimulates hepatocytes to release angiogenic microparticles that require Vanin-1 for uptake by endothelial cells. Sci. Signal. 6, ra88 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Sabatier, F. et al. Type 1 and type 2 diabetic patients display different patterns of cellular microparticles. Diabetes 51, 2840–2845 (2002).

    Article  PubMed  CAS  Google Scholar 

  56. Tramontano, A. F. et al. Circulating endothelial microparticles in diabetes mellitus. Mediators Inflamm. 2010, 250476 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Cheng, V., Kashyap, S. R., Schauer, P. R., Kirwan, J. P. & McCrae, K. R. Restoration of glycemic control in patients with type 2 diabetes mellitus after bariatric surgery is associated with reduction in microparticles. Surg. Obes. Relat. Dis. 9, 207–212 (2013).

    Article  PubMed  Google Scholar 

  58. Gesmundo, I. et al. Adipocyte-derived extracellular vesicles regulate survival and function of pancreatic β cells.JCI Insight 6, e141962 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Mleczko, J. et al. Extracellular vesicles from hypoxic adipocytes and obese subjects reduce insulin-stimulated glucose uptake.Mol. Nutr. Food Res. 62, 1700917 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Pan, Y. et al. Adipocyte-secreted exosomal microRNA-34a inhibits M2 macrophage polarization to promote obesity-induced adipose inflammation. J. Clin. Invest. 129, 834–849 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Merrins, M. J., Corkey, B. E., Kibbey, R. G. & Prentki, M. Metabolic cycles and signals for insulin secretion. Cell Metab. 34, 947–968 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. López-Bermudo, L. et al. Contribution of liver and pancreatic islet crosstalk to β-cell function/dysfunction in the presence of fatty liver. Front. Endocrinol. 13, 892672 (2022).

    Article  Google Scholar 

  63. Zhao, Y. et al. Liver governs adipose remodelling via extracellular vesicles in response to lipid overload. Nat. Commun. 11, 719 (2020).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  64. Furman, B. L. Streptozotocin-induced diabetic models in mice and rats. Curr. Protoc. Pharmacol. 1, e78 (2021).

    Article  CAS  Google Scholar 

  65. Kleiner, D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41, 1313–1321 (2005).

    Article  PubMed  Google Scholar 

  66. Vella, L. J. et al. A rigorous method to enrich for exosomes from brain tissue. J. Extracell. Vesicles 6, 1348885 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Ma, C. et al. Extracellular vesicles secreted by glioma stem cells are involved in radiation resistance and glioma progression.Int. J. Mol. Sci. 23, 2770 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Espejo, C. et al. Cathelicidin-3 associated with serum extracellular vesicles enables early diagnosis of a transmissible cancer. Front. Immunol. 13, 858423 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. van der Vlist, E. J., Nolte-‘t Hoen, E. N., Stoorvogel, W., Arkesteijn, G. J. & Wauben, M. H. Fluorescent labeling of nano-sized vesicles released by cells and subsequent quantitative and qualitative analysis by high-resolution flow cytometry. Nat. Protoc. 7, 1311–1326 (2012).

    Article  PubMed  Google Scholar 

  70. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  71. Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    Article  PubMed  CAS  Google Scholar 

  72. Humphrey, S. J., Karayel, O., James, D. E. & Mann, M. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat. Protoc. 13, 1897–1916 (2018).

    Article  PubMed  CAS  Google Scholar 

  73. Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell Proteomics 14, 1400–1410 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  PubMed  CAS  Google Scholar 

  75. Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 787 (2020).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  76. Péronnet, F. & Massicotte, D. Table of nonprotein respiratory quotient: an update. Can. J. Sport Sci. 16, 23–29 (1991).

    PubMed  Google Scholar 

  77. Miotto, P. M. & Holloway, G. P. In the absence of phosphate shuttling, exercise reveals the in vivo importance of creatine-independent mitochondrial ADP transport. Biochem. J. 473, 2831–2843 (2016).

    Article  PubMed  CAS  Google Scholar 

  78. Yang, C.-H. et al. Neuropeptide Y1 receptor antagonism protects β-cells and improves glycemic control in type 2 diabetes. Mol. Metab. 55, 101413 (2022).

    Article  PubMed  CAS  Google Scholar 

  79. Mason, R. R. et al. PLIN5 deletion remodels intracellular lipid composition and causes insulin resistance in muscle. Mol. Metab. 3, 652–663 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Dodd, G. T. et al. Intranasal targeting of hypothalamic PTP1B and TCPTP reinstates leptin and insulin sensitivity and promotes weight loss in obesity. Cell Rep. 28, 2905–2922 (2019).

    Article  PubMed  CAS  Google Scholar 

  81. Bandyopadhyay, G. K., Yu, J. G., Ofrecio, J. & Olefsky, J. M. Increased malonyl-CoA levels in muscle from obese and type 2 diabetic subjects lead to decreased fatty acid oxidation and increased lipogenesis; thiazolidinedione treatment reverses these defects. Diabetes 55, 2277–2285 (2006).

    Article  PubMed  CAS  Google Scholar 

  82. Jain, S. S. et al. Additive effects of insulin and muscle contraction on fatty acid transport and fatty acid transporters, FAT/CD36, FABPpm, FATP1, 4 and 6. FEBS Lett. 583, 2294–2300 (2009).

    Article  PubMed  CAS  Google Scholar 

  83. Stahl, A., Evans, J. G., Pattel, S., Hirsch, D. & Lodish, H. F. Insulin causes fatty acid transport protein translocation and enhanced fatty acid uptake in adipocytes. Dev. Cell 2, 477–488 (2002).

    Article  PubMed  CAS  Google Scholar 

  84. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank the team at the Ian Holmes Imaging Centre and Melbourne Mass Spectrometry and Proteomics facility of the Bio21 Institute at the University of Melbourne for instrument support, the infrastructure within the Melbourne Murine Metabolic Phenotyping Platform for calorimetry assessment, the Melbourne Histology Platform at the University of Melbourne, M. Shambrook (La Trobe University) for instrument support for the NTA, D. Johnson for technical assistance with running the imaging flow cytometry, and D. Keating (Flinders University) and M. Kebede (University of Sydney) for helpful discussions. P.M.M. was supported by post-doctoral fellowships through the Natural Sciences and Engineering Research Council of Canada (PDF-516731-2018) and Canadian Institutes of Health Research (application no. 430145). This work was supported by a National Health and Medical Research Council (NHMRC) Ideas grant (M.J.W., P.M.M., P.R.B., grant no. APP1184309), NHMRC Investigator grants (P.M.M., grant no. APP2018187; B.J.P., grant no. APP2009642), a Diabetes Australia Research Trust grant (P.M.M.), a Medicine, Dentistry and Health Sciences Early Career Researcher grant (P.M.M., grant no. ECR1782020) and Department of Anatomy and Physiology seed grant (P.M.M.). Graphical illustrations were created with BioRender.com.

Author information

Authors and Affiliations

  1. Department of Anatomy and Physiology, School of Biomedical Sciences, Faculty of Medicine Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia

    Paula M. Miotto, Stacey N. Keenan, William De Nardo, Cait A. Beddows, Gio Fidelito, Garron T. Dodd, Benjamin L. Parker & Matthew J. Watt

  2. St. Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia

    Chieh-Hsin Yang & Kim Loh

  3. Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, Victoria, Australia

    Andrew F. Hill

  4. Institute for Health and Sport, Victoria University, Footscray, Victoria, Australia

    Andrew F. Hill

  5. Centre for Obesity Research and Education, Department of Surgery, Monash University, Melbourne, Victoria, Australia

    Paul R. Burton

  6. Department of Medicine, University of Melbourne, Fitzroy, Victoria, Australia

    Kim Loh

Authors
  1. Paula M. Miotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chieh-Hsin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stacey N. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William De Nardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cait A. Beddows
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gio Fidelito
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Garron T. Dodd
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Benjamin L. Parker
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrew F. Hill
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paul R. Burton
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kim Loh
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Matthew J. Watt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.M.M. and M.J.W. conceptualized the study design and funded the research. P.M.M., C.-H.Y., S.N.K., W.D.N., C.A.B., G.F., G.T.D. and B.L.P. performed the experiments. P.M.M., C.-H.Y., S.N.K., G.F. and B.L.P. analysed the data. G.T.D., A.F.H., B.L.P. and K.L. provided the resources and guided the experimental design. P.R.B. provided the human samples. P.M.M. and M.J.W. wrote the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Matthew J. Watt.

Ethics declarations

Competing interests

M.J.W. received the research funding from Gilead Sciences and CSL, as well as honoraria from Gilead Sciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

Additional information

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

Extended data

Extended Data Fig. 1 Characteristics of diet-induced glucose intolerance and extracellular vesicle purity.

(A) Body weight over 20 weeks consumption of control (n = 30), NAFL (n = 15), and NASH diet (n = 15) across 1 cohort. *, significant from Control; #, significant from NASH. P < 0.001. (B) Liver and epididymal (EWAT) tissue mass in response to Control (n = 29), NAFL (n = 14), or NASH (n = 15 liver, n = 13 EWAT) across 1 cohort. *, significant from Control; #, significant from NASH. P < 0.001. (C-D) Blood glucose levels during an oral glucose tolerance test (2 g/kg) (C) and the corresponding glucose area under the curve (AUC; D) of mice fed different dietary interventions (n = 15 control, n = 9 NAFL, n = 13 NASH) across 1 cohort. *, significant from Control; #, significant from NASH. P < 0.001. (E) Serum insulin during the oral glucose tolerance test (n = 5 control, n = 9 NAFL, n = 9 NASH) across 1 experiment. *, significant from Control; #, significant from NASH. P = 0.01. (F) Steatosis score in Control (n = 8), NAFL (n = 10), and NASH (n = 10) livers derived from histopathology across 1 cohort. P = 0.1. (G) Lobular inflammation score in Control (n = 8), NAFL (n = 10), and NASH (n = 10) livers derived from histopathology across 1 cohort. P = 0.017. (H) Hepatocyte ballooning score in Control, NAFL, and NASH livers derived from histopathology (n = 8 control, n = 10 NAFL, n = 10 NASH) across 1 cohort. *, significant from Control; #, significant from NAFL. P < 0.001. (I) ATP / ADP ratio in liver slices and whole liver from Control, NAFL, and NASH mice (n = 6/group for liver slices, n = 9 for whole liver pooled from Control, NAFL, and NASH mice) across 1 experiment. P = 0.07. (J) Endotoxin levels in the circulation and in extracellular vesicle (EV) preparations (each bar represents an individual biological sample) across 1 experiment. Data are expressed as mean ± SEM. Each datapoint represents an independent biological sample. Data was analysed using a repeated measures two-way ANOVA (A, C, E) or one-way ANOVA (B (EWAT), D, F, H, I) followed by a Student Newman Keuls post-hoc, or Kruskal Wallis one-way ANOVA on ranks (B (liver), G) followed by a Dunn’s test.

Source data

Extended Data Fig. 2 Pathways analysis of the liver extracellular vesicle proteome in response to NAFL or NASH.

(A-B) Top 20 biological processes identified using pathways enrichment analysis (Metascape.com) in NAFL relative to control conditions (A), and in NASH relative to control conditions (B). n = 10/group. P values indicated within figure. (C-D) Pathway enrichment analysis of disease functions and toxicology using Ingenuity Pathways Analysis in NAFL (C) and NASH (D) compared to healthy control mice. n = 10/group. P < 0.001. Data represented as z-score or -log10 P-value, derived from proteomics analysis using unpaired two-tailed t-test.

Source data

Extended Data Fig. 3 Glucose tolerance testing in mice in response to extracellular vesicles derived from various sources and glucose-stimulated extracellular vesicle secretion.

(A-C) The GTT glucose area under the curve (AUC) in control (A, n = 8 saline, n = 9 NAFL EV, n = 7 NASH EV), NAFL (B, n = 8/group), or NASH (C, n = 8 saline, n = 9 Control EV) mice in response to saline or liver EVs across 1 cohort. P < 0.001. *, significant from Saline. (D) Blood glucose and AUC during GTT in female mice in response to saline or liver EV (n = 3/group) across 1 cohort. *, significant from NAFL EV injection. P < 0.001, P = 0.02 (inset). (E-F) Blood glucose (E, P = 0.001) and serum insulin (F, P < 0.001) during GTT in control mice that received i.v injection of saline or liver EV (n = 6 saline, n = 4 NAFL EV) across 1 cohort. *, significant from NAFL EV injection. (G-I) Blood glucose levels and the AUC (inset) of Control mice injected intraperitoneally with saline or empty liposomes (G, n = 5/group, P < 0.001 (main effect: Time), P = 0.91 (inset)), adipose-derived EVs (H, n = 7 saline, n = 6 adipose EV, P < 0.001 (main effect: Time), P = 0.76 (inset)), or serum derived EVs (I, n = 4 saline, n = 6 serum EV, P < 0.001 (main effect: Time), P = 0.603 (inset)) across 2 cohorts. (J) Serum insulin during an oral GTT in response to administration of saline (n = 6), adipose-derived EVs (n = 5), or serum EVs (n = 5) across 1 experiment. P < 0.001 (main effect: Time). (K-L) Nanoparticle-tracking analysis of liver EVs from liver slices in response to high glucose (20 mM), in the absence or presence of (K, P = 0.994) saline (n = 6), glucagon (n = 5), or norepinephrine (n = 5), and (L, P = 0.22) saline (n = 6) or insulin (n = 5) across 1 experiment for each comparison. (M) Volcano plot comparison of the liver EV proteome secreted by healthy liver slices in response to high glucose (20 mM, n = 6) relative to low glucose (2 mM, n = 5) across 1 experiment. (N-Q) Gating strategy for imaging flow cytometry in Control mice (n = 4/group) that were fasted and subjected to an oral gavage of water (vehicle) or glucose. The unfocused bright field signals were excluded using Gradient RMS feature (N). Particles with relative area smaller than the speed beads were gated by the discriminating aspect ratio and area of particles (previously optimised) (O). CD63-PE-Cy7 and CD9-APC positive particles were gated (P). ASGPR positive particles were plotted and gated based on the isotype control (Q). 1 independent experiment. Data are expressed as mean ± SEM. Each datapoint represents an independent biological sample. Data was analysed using a one-way ANOVA (A, K (calculated from AUC), repeated measures two-way ANOVA (D, E, F, G, H, I, J) with Student Newman Keuls post-hoc, or unpaired two-tailed t-test (B, C, D (inset), G (inset), H (inset), I (inset), L (calculated from AUC), M).

Source data

Extended Data Fig. 4 Glucose tolerance testing in mice in response to extracellular vesicles derived from various treatments, tissue insulin sensitivity in isolated tissue, and blood glucose and insulin levels during a hyperinsulinemic-euglycemic clamp.

(A-D) Gating strategy for imaging flow cytometry in NAFL mice (n = 3 fasted, n = 4 glucose gavage) that were fasted and subjected to an oral gavage of water (vehicle) or glucose. The unfocused bright field signals were excluded using Gradient RMS feature (A). Particles with relative area smaller than the speed beads were gated by the discriminating aspect ratio and area of particles (previously optimised) (B). CD63-PE-Cy7 and CD9-APC positive particles were gated (C). ASGPR positive particles were plotted and gated based on the isotype control (D). 1 independent experiment. (E) Glucose AUC during an oral GTT in Control (n = 6/group) and NAFL mice (n = 5/group) in response to sonicated liver EVs across 1 cohort. P < 0.001 (main effect). (F-G) Glucose AUC during an oral GTT in Control (F, n = 9 saline, n = 10 NAFL EV, n = 9 ‘shaved EV) or NAFL (G, n = 5 saline, n = 3 Control EV, n = 6 ‘shaved’ EV) mice that received saline or ‘shaved’ liver EVs across 2 cohorts (control) and 1 cohort (NAFL). P < 0.001. *, significant from Saline; #, significant from Control EV. (H) Representative cryo-electron microscopy of ‘shaved’ liver EV preparation. 1 experiment. (I) Nanoparticle tracking analysis of ‘shaved’ liver EV. 1 experiment, n = 4 biological replicates graphed as an average. (J-K) Blood glucose levels and AUC (inset) during an oral GTT (J), and serum insulin (K) of mice that received ICV of saline/liver EVs during 1 experiment (n = 9/group). P < 0.001 (main effect: time). (L-N) Glucose uptake in isolated soleus muscle (L, n = 3 saline, n = 4 Control EV, P = 0.61, basal and insulin-stimulated samples are paired from the same animal within each treatment), white adipose tissue (M, n = 7 saline/basal, n = 6 saline/insulin, n = 7 Control EV/basal, n = 6 Control EV/insulin, P = 0.73), and brown adipose tissue (N, n = 7/group, P = 0.932) collected from NAFL mice and treated ex vivo with saline or liver EVs (10 μg/ml) obtained from healthy Control mice 1 hr prior to and during the experiment. 1 cohort across tissues. Basal and insulin-stimulated samples from adipose are paired from the same animal across both treatments, with exception of 1 mouse for the EWAT comparison (that is, basal saline vs. basal EV). (O) Blood glucose levels at baseline (that is, fasted state) and during a hyperinsulinemic-euglycemic clamp in Control mice (n = 7/group) following intraperitoneal injection of NAFL EV 1 hr prior to the experiment. P < 0.001 (main effect). 1 independent cohort. (P) Serum insulin at baseline (that is, fasted state) and during a hyperinsulinemic-euglycemic clamp in Control mice (n = 6 saline, 9 NAFL EV) following intraperitoneal injection of NAFL EV 1 hr prior to the experiment. P < 0.001 (main effect). 1 independent cohort. Data are expressed as mean ± SEM. Each datapoint represents an independent biological sample. Data was analysed using a one-way ANOVA (F, G), two-way ANOVA (E, L, M, N) or repeated measures two-way ANOVA (J, K, O, P) with Student Newman Keuls post-hoc analyses, or unpaired two-tailed t-test (J (inset)). *, significant main effect compared to basal.

Source data

Extended Data Fig. 5 Effect of liver extracellular vesicles in regulating glucose effectiveness.

(A) Glucose area under the curve (AUC) during an oral glucose tolerance test in streptozotocin (STZ) treated mice that received an intraperitoneal injection of saline or Control liver EVs (n = 4 control saline, n = 6/group STZ) across 1 cohort. *, significant from Control + saline; #, significant from STZ + saline. P = 0.001. (B-D) Whole body calorimetry and activity in Control mice over 2 hours in response to saline or liver EVs. Fat oxidation (B, P = 0.58), energy expenditure (C, P = 0.04), and physical activity (D, P = 0.54). n = 4/group. 1 experiment. *, significant from Saline. (E) Serum insulin in STZ mice in response to saline or liver EVs, followed by gavage with vehicle (n = 8 saline/H2O) or glucose (n = 8 saline/high glucose (HG), n = 7 liver control EV/HG) 1 hr later across 2 cohorts. P = 0.245. (F) Plasma membrane CAV3 content in STZ mice in response to saline or liver EVs followed by oral gavage with vehicle (n = 8 saline/H2O) or glucose (n = 8 saline/HG, n = 9 control EV + HG) 1 hr later across 2 cohorts. P = 0.918. (G) Pathways enrichment analysis for phosphosites regulated in soleus muscle from Control mice following high glucose (HG + saline, n = 5) treatment relative to low glucose (LG + saline, n = 4) in the presence of saline. q < 0.05. (H) Pathways enrichment analysis for phosphosites regulated in soleus muscle from Control mice following high glucose treatment and liver EVs (HG + EV, n = 5) relative to HG and saline (HG + saline, n = 5). q < 0.05. (I) Glucose uptake between no drug, dantrolene (5 μM), and KN-62 (10 μM) under LG (2 μM, n = 5 no drug, n = 3 dantrolene, n = 3 KN-62) and HG (20 μM, n = 7 no drug, n = 5 dantrolene, n = 8 KN-62) conditions in soleus muscle. P < 0.001 main effect: condition. Same values reassessed from Figs. 6E and F. Data are expressed as mean ± SEM. Each data point represents an independent biological sample. Data was analysed using one-way ANOVA (A, F) and two- way repeated measures ANOVA (E) with Student Newman Keuls post-hoc, or unpaired two-tailed t-test (B, C, D, G, H).

Source data

Extended Data Fig. 6 Effect of liver extracellular vesicles on insulin secretion and cellular insulin content.

(A-E) Glucose-stimulated insulin secretion from isolated healthy murine islets in the presence or absence of NAFL liver EVs (10 μg/ml), when treated with tolbutamide (A, n = 10 saline/2.8 mM or 20 mM, n = 12 NAFL EV/2.8 mM, n = 11 NAFL EV/20 mM, P < 0.001 (main effect)), diazoxide (B, n = 4 saline/group, n = 6 NAFL EV/group, P = 0.364), arginine (C, n = 6/group, P < 0.001 (main effect)), alanine (D, n = 8 saline/group, n = 11 NAFL EV/group, P < 0.008), or exendin 4 (E, n = 5/group, P = 0.019) across 2 experiments involving isolations from 9 mice. *, significant from low glucose (2.8 mM) within same condition (that is, saline or liver EV) or significant main effect of high glucose (20 mM); #, significant from saline within 20 mM glucose condition. (F-J) Islet insulin content in response to tolbutamide (F, n = 5 saline/2.8 mM, n = 4 saline/20 mM, n = 5/group NAFL EV, P = 0.820), diazoxide (G, n = 6/group, P = 0.004 (main effect)), arginine (H, n = 6 saline/2.8 mM, n = 5 saline/20 mM, n = 6/group NAFL EV, P = 0.744), alanine (I, n = 9 saline/2.8 mM, n = 11 saline/20 mM, n = 11/group NAFL EV, P = 0.543), and exendin 4 (J, n = 5/group, P = 0.720). Data are expressed as mean ± SEM. Data was analysed using two-way repeated measures ANOVA (A, B, C, D, E, H, J) or two-way ANOVA (F, G, I) with Student Newman Keuls post-hoc analyses.

Source data

Extended Data Table 1 Patient clinical variables
Full size table
Extended Data Table 2 Primer sequences
Full size table

Supplementary information

Reporting Summary

Supplementary Tables 1–5

Supplementary Tables 1–5: Table 1: Liver EV proteomic analysis. NAFL versus control. Table 2: Liver EV proteomic analysis. NASH versus control. Table 3: Liver EV proteomic analysis. HG versus LG stimulation. Table 4: Skeletal muscle phosphoproteome. HG versus LG. Table 5: Skeletal muscle phosphoproteome. HG EV versus saline.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Unprocessed immunoblots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed immunoblots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Table. 1

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miotto, P.M., Yang, CH., Keenan, S.N. et al. Liver-derived extracellular vesicles improve whole-body glycaemic control via inter-organ communication. Nat Metab 6, 254–272 (2024). https://doi.org/10.1038/s42255-023-00971-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-023-00971-z

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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