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Small extracellular vesicle-mediated targeting of hypothalamic AMPKα1 corrects obesity through BAT activation

Nature Metabolism volume 3, pages 1415–1431 (2021)Cite this article

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Abstract

Current pharmacological therapies for treating obesity are of limited efficacy. Genetic ablation or loss of function of AMP-activated protein kinase alpha 1 (AMPKα1) in steroidogenic factor 1 (SF1) neurons of the ventromedial nucleus of the hypothalamus (VMH) induces feeding-independent resistance to obesity due to sympathetic activation of brown adipose tissue (BAT) thermogenesis. Here, we show that body weight of obese mice can be reduced by intravenous injection of small extracellular vesicles (sEVs) delivering a plasmid encoding an AMPKα1 dominant negative mutant (AMPKα1-DN) targeted to VMH-SF1 neurons. The beneficial effect of SF1-AMPKα1-DN-loaded sEVs is feeding-independent and involves sympathetic nerve activation and increased UCP1-dependent thermogenesis in BAT. Our results underscore the potential of sEVs to specifically target AMPK in hypothalamic neurons and introduce a broader strategy to manipulate body weight and reduce obesity.

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Fig. 1: Generation and characterization of neuronal-targeted dendritic-cell-derived sEVs.
Fig. 2: Effect of stereotaxic VMH injection of SF1-AMPKα1-DN-loaded sEVs on energy balance in DIO mice.
Fig. 3: Effect of systemic treatment with SF1-AMPKα1-DN-loaded sEVs on hypothalamic AMPK activity in DIO mice.
Fig. 4: Effect of systemic treatment with SF1-AMPKα1-DN-loaded sEVs on energy balance in DIO mice.
Fig. 5: Effect of systemic treatment with SF1-AMPKα1-DN-loaded sEVs on BAT thermogenesis in DIO mice.
Fig. 6: Effect of systemic treatment with SF1-AMPKα1-DN-loaded sEVs on thermogenic pathways in DIO mice.
Fig. 7: Effect of adrenergic blockade on the systemic treatment with SF1-AMPKα1-DN-loaded sEVs in DIO mice.
Fig. 8: Effect of systemic treatment with SF1-AMPKα1-DN-loaded sEVs on energy balance in thermoneutral conditions and UCP1 knockout mice under HFD.

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Data availability

All additional data that support the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.

References

  1. Clemmensen, C., Muller, T. D., Finan, B., Tschop, M. H. & DiMarchi, R. Current and emerging treatment options in diabetes care. Handb. Exp. Pharmacol. 233, 437–459 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Tschop, M. H. et al. Unimolecular polypharmacy for treatment of diabetes and obesity. Cell Metab. 24, 51–62 (2016).

    Article  PubMed  CAS  Google Scholar 

  3. López, M., Nogueiras, R., Tena-Sempere, M. & Dieguez, C. Hypothalamic AMPK: a canonical regulator of whole-body energy balance. Nat. Rev. Endocrinol. 12, 421–432 (2016).

    Article  PubMed  CAS  Google Scholar 

  4. Cui, H., López, M. & Rahmouni, K. The cellular and molecular bases of leptin and ghrelin resistance in obesity. Nat. Rev. Endocrinol. 13, 338–351 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Muller, T. D., Clemmensen, C., Finan, B., Dimarchi, R. D. & Tschop, M. H. Anti-obesity therapy: from rainbow pills to polyagonists. Pharmacol. Rev. 70, 712–746 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Dragano, N. R. V., Ferno, J., Dieguez, C., Lopez, M. & Milbank, E. Recent updates on obesity treatments: available drugs and future directions. Neuroscience 437, 215–239 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Kahn, B. B., Alquier, T., Carling, D. & Hardie, D. G. AMP-activated protein kinase: ancient energy gauge provides clues to modern understanding of metabolism. Cell Metab. 1, 15–25 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Schneeberger, M. & Claret, M. Recent insights into the role of hypothalamic AMPK signaling cascade upon metabolic control. Front. Neurosci. 6, 185 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Hardie, D. G., Schaffer, B. E. & Brunet, A. AMPK: an energy-sensing pathway with multiple inputs and outputs. Trends Cell Biol. 26, 190–201 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Carling, D. AMPK signalling in health and disease. Curr. Opin. Cell Biol. 45, 31–37 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Lopez, M. AMPK wars: the VMH strikes back, return of the PVH. Trends Endocrinol. Metab. 29, 135–137 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. López, M. et al. Hypothalamic AMPK and fatty acid metabolism mediate thyroid regulation of energy balance. Nat. Med. 16, 1001–1008 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Martínez de Morentin, P. B. et al. Nicotine induces negative energy balance through hypothalamic AMP-activated protein kinase. Diabetes 61, 807–817 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Whittle, A. J. et al. Bmp8b increases brown adipose tissue thermogenesis through both central and peripheral actions. Cell 149, 871–885 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Martínez de Morentin, P. B. et al. Estradiol regulates brown adipose tissue thermogenesis via hypothalamic AMPK. Cell Metab. 20, 41–53 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Martins, L. et al. A functional link between AMPK and orexin mediates the effect of BMP8B on energy balance. Cell Rep. 16, 2231–2242 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Martínez-Sánchez, N. et al. Hypothalamic AMPK–ER stress–JNK1 axis mediates the central actions of thyroid hormones on energy balance. Cell Metab. 26, 212–229 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Seoane-Collazo, P. et al. SF1-specific AMPKalpha1 deletion protects against diet-induced obesity. Diabetes 67, 2213–2226 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Buzas, E. I., Gyorgy, B., Nagy, G., Falus, A. & Gay, S. Emerging role of extracellular vesicles in inflammatory diseases. Nat. Rev. Rheumatol. 10, 356–364 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Milbank, E., Martinez, M. C. & Andriantsitohaina, R. Extracellular vesicles: pharmacological modulators of the peripheral and central signals governing obesity. Pharmacol. Ther. 157, 65–83 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Martinez, M. C. & Andriantsitohaina, R. Extracellular vesicles in metabolic syndrome. Circ. Res. 120, 1674–1686 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Malloci, M. et al. Extracellular vesicles: mechanisms in human health and disease. Antioxid. Redox Signal. 30, 813–856 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. López, M. et al. Hypothalamic fatty acid metabolism mediates the orexigenic action of ghrelin. Cell Metab. 7, 389–399 (2008).

    Article  PubMed  CAS  Google Scholar 

  24. Tang, K. et al. Delivery of chemotherapeutic drugs in tumour cell-derived microparticles. Nat. Commun. 3, 1282 (2012).

    Article  PubMed  CAS  Google Scholar 

  25. Dalli, J. et al. Microparticle alpha-2-macroglobulin enhances pro-resolving responses and promotes survival in sepsis. EMBO Mol. Med. 6, 27–42 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Kamerkar, S. et al. Exosomes facilitate therapeutic targeting of oncogenic KRAS in pancreatic cancer. Nature 546, 498–503 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Quah, B. J. & O’Neill, H. C. The immunogenicity of dendritic cell-derived exosomes. Blood Cells Mol. Dis. 35, 94–110 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Alvarez-Erviti, L. et al. Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nat. Biotechnol. 29, 341–345 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Colombo, M., Raposo, G. & Thery, C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu. Rev. Cell Dev. Biol. 30, 255–289 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Kumar, P. et al. Transvascular delivery of small interfering RNA to the central nervous system. Nature 448, 39–43 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Domingos, R. F. et al. Characterizing manufactured nanoparticles in the environment: multimethod determination of particle sizes. Environ. Sci. Technol. 43, 7277–7284 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. Parker, K. L. & Schimmer, B. P. Steroidogenic factor 1: a key determinant of endocrine development and function. Endocr. Rev. 18, 361–377 (1997).

    Article  CAS  PubMed  Google Scholar 

  33. Choi, Y. H., Fujikawa, T., Lee, J., Reuter, A. & Kim, K. W. Revisiting the ventral medial nucleus of the hypothalamus: the roles of SF-1 neurons in energy homeostasis. Front. Neurosci. 7, 71 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Corley, D. R., Li, X., Lei, Z. M. & Rao, C. V. Potential regulation of GnRH gene by a steroidogenic factor-1-like protein. Mol. Hum. Reprod. 6, 671–676 (2000).

    Article  CAS  PubMed  Google Scholar 

  35. Mellon, P. L. et al. Immortalization of hypothalamic GnRH neurons by genetically targeted tumorigenesis. Neuron 5, 1–10 (1990).

    Article  CAS  PubMed  Google Scholar 

  36. Coyral-Castel, S. et al. The effect of AMP-activated kinase activation on gonadotrophin-releasing hormone secretion in GT1-7 cells and its potential role in hypothalamic regulation of the oestrous cyclicity in rats. J. Neuroendocrinol. 20, 335–346 (2008).

    Article  CAS  PubMed  Google Scholar 

  37. Beall, C. et al. Mouse hypothalamic GT1-7 cells demonstrate AMPK-dependent intrinsic glucose-sensing behaviour. Diabetologia 55, 2432–2444 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Schimmer, B. P. & White, P. C. Minireview: steroidogenic factor 1: its roles in differentiation, development, and disease. Mol. Endocrinol. 24, 1322–1337 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Warner, A. et al. Inappropriate heat dissipation ignites brown fat thermogenesis in mice with a mutant thyroid hormone receptor alpha1. Proc. Natl Acad. Sci. USA 110, 16241–16246 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang, Q. et al. AMPK/alpha-ketoglutarate axis dynamically mediates DNA demethylation in the Prdm16 promoter and brown adipogenesis. Cell Metab. 24, 542–554 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cannon, B. & Nedergaard, J. Brown adipose tissue: function and physiological significance. Physiol. Rev. 84, 277–359 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Morrison, S. F., Madden, C. J. & Tupone, D. Central neural regulation of brown adipose tissue thermogenesis and energy expenditure. Cell Metab. 19, 741–756 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Contreras, C. et al. The brain and brown fat. Ann. Med 47, 150–168 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Gonzalez-Garcia, I. et al. Estradiol regulates energy balance by ameliorating hypothalamic ceramide-induce ER stress. Cell Rep. 25, 413–423 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Alvarez-Crespo, M. et al. Essential role of UCP1 modulating the central effects of thyroid hormones on energy balance. Mol. Metab. 5, 271–282 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Claret, M. et al. AMPK is essential for energy homeostasis regulation and glucose sensing by POMC and AgRP neurons. J. Clin. Invest. 117, 2325–2336 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Foretz, M., Guigas, B., Bertrand, L., Pollak, M. & Viollet, B. Metformin: from mechanisms of action to therapies. Cell Metab. 20, 953–966 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Foretz, M. & Viollet, B. Therapy: metformin takes a new route to clinical efficacy. Nat. Rev. Endocrinol. 11, 390–392 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Campderros, L. et al. Brown adipocytes secrete GDF15 in response to thermogenic activation. Obesity (Silver Spring) 27, 1606–1616 (2019).

    Article  CAS  Google Scholar 

  50. Johann, K. et al. Thyroid-hormone-induced browning of white adipose tissue does not contribute to thermogenesis and glucose consumption. Cell Rep. 27, 3385–3400.e3 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Seoane-Collazo, P. et al. Central nicotine induces browning through hypothalamic kappa opioid receptor. Nat. Commun. 10, 4037 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Johnson, D. H., Flask, C. A., Ernsberger, P. R., Wong, W. C. & Wilson, D. L. Reproducible MRI measurement of adipose tissue volumes in genetic and dietary rodent obesity models. J. Magn. Reson. Imaging 28, 915–927 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hu, H. H., Chen, J. & Shen, W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. MAGMA 29, 259–276 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. Hong, C. W., Fazeli Dehkordy, S., Hooker, J. C., Hamilton, G. & Sirlin, C. B. Fat quantification in the abdomen. Top. Magn. Reson. Imaging 26, 221–227 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Contreras, C. & Lopez, M. Ceramide sensing in the hippocampus: the lipostatic theory and Ockham’s razor. Mol. Metab. 3, 90–91 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Heras, V. et al. Central ceramide signaling mediates obesity-induced precocious puberty. Cell Metab. 32, 951–966 e958 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Vazquez, M. J. et al. SIRT1 mediates obesity- and nutrient-dependent perturbation of pubertal timing by epigenetically controlling Kiss1 expression. Nat. Commun. 9, 4194 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sanchez-Garrido, M. A. et al. Intergenerational influence of paternal obesity on metabolic and reproductive health parameters of the offspring: male-preferential impact and involvement of Kiss1-mediated pathways. Endocrinology 159, 1005–1018 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Zadra, G. et al. A novel direct activator of AMPK inhibits prostate cancer growth by blocking lipogenesis. EMBO Mol. Med. 6, 519–538 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Tang, Y. C., Williams, B. R., Siegel, J. J. & Amon, A. Identification of aneuploidy-selective antiproliferation compounds. Cell 144, 499–512 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The research leading to these results has received funding from the Xunta de Galicia (R.N. grant no. 2016-PG057, M.L. grant no. 2016-PG068); EuroNanoMed III (R.A. and M.L. grant no. EURONANOMED2019-050/ENAMEP); Junta de Andalucía (M.T.-S. grant no. P12-FQM-01943); Ministerio de Ciencia y Universidades cofunded by the FEDER Program of EU (C.D. grant no. BFU2017-87721; M.T.-S. grant no. BFU2014-57581-P, R.N. grant no. BFU2015-70664R; M.L. grant no. RTI2018–101840-B-I00 and grant no. BFU2015-70454-REDT/Adipoplast); USA National Institutes of Health (K.R. grant no. HL084207); the German Research Foundation (DFG) (grant nos. MI1242/3-2 to J.M. and OE723/2-1 to R.O.); the USA Department of Veterans Affairs (K.R. grant no. I01BX004249); The University of Iowa Fraternal Order of Eagles Diabetes Research Center (K.R.); European Research Council ERC (C.G.-C. STG grant AstroNeuroCrosstalk no. 757393); Atresmedia Corporación (R.N. and M.L. grant no. 2017-PO004) and ‘la Caixa’ Foundation (grant no. ID 100010434), under grant agreement no. LCF/PR/HR19/52160022 (M.L.). E.M. was the recipient of a Predoctoral Fellowship of the Nanofar Erasmus Mundus Program. I.G.-G. is the recipient of a fellowship from Alexander von Humboldt Foundation (ref. 3.3, ESP-1206916-HFST-P) and European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie actions (grant no. 842080, H2020-MSCA-IF-2018). T.S. is the recipient of a research contract from the Miguel Servet Program (CPII17/00027, Instituto de Salud Carlos III). The CiMUS is supported by the Xunta de Galicia (grant no. 2016-2019, ED431G/05). CIBER de Fisiopatología de la Obesidad y Nutrición is an initiative of ISCIII. INSERM U1063 is supported by the Institut National de Santé et Recherche Médicale and Université d’Angers. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. We thank S. Recoquillon and M. Wertheimer for technical assistance. The TEM analysis was performed in the Service Commun d’Imageries et d’Analyses Microscopiques of University of Angers. We thank SCAHU staff (Université d’Angers) for taking care of animals. The PET–CT analysis was performed in the Molecular Imaging Unit of the Department of Nuclear Medicine of USC.

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Authors and Affiliations

  1. Department of Physiology, CiMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela, Spain

    Edward Milbank, Nathalia R. V. Dragano, Ismael González-García, Marcos Rios Garcia, Verónica Rivas-Limeres, Cristina Contreras, Juan Cuñarro, Begoña Porteiro, Anxo Vidal, Carlos Diéguez, Rubén Nogueiras & Miguel López

  2. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Madrid, Spain

    Edward Milbank, Nathalia R. V. Dragano, Ismael González-García, Marcos Rios Garcia, Verónica Rivas-Limeres, Francisco Ruiz-Pino, Cristina Contreras, Juan Cuñarro, Begoña Porteiro, Aleix Gavaldà-Navarro, Juan Roa, Francesc Villarroya, Carlos Diéguez, Rubén Nogueiras, Manuel Tena-Sempere & Miguel López

  3. SOPAM, U1063, INSERM, University of Angers, SFR ICAT, Bat IRIS-IBS, Angers, France

    Edward Milbank, Liliana Perdomo, Grégory Hilairet, Patricia Mallegol, Luisa Vergori, M. Carmen Martínez & Ramaroson Andriantsitohaina

  4. Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), German Center for Diabetes Research (DZD), Neuherberg, Germany

    Ismael González-García & Cristina García-Cáceres

  5. Department of Cell Biology, Physiology and Immunology, University of Córdoba, Instituto Maimónides de Investigación Biomédica (IMIBIC)/Hospital Universitario Reina Sofía, Córdoba, Spain

    Francisco Ruiz-Pino, Juan Roa & Manuel Tena-Sempere

  6. Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, IA, USA

    Donald A. Morgan & Kamal Rahmouni

  7. Clinical Neurosciences Research Laboratory, Instituto de Investigación Sanitaria, Santiago de Compostela, Spain

    Ramón Iglesias-Rey & Tomás Sobrino

  8. Institut de Biomedicina de la Universitat de Barcelona—Institut de Recerca Hospital Sant Joan de Déu, IBUB-IRSJD, Barcelona, Spain

    Aleix Gavaldà-Navarro & Francesc Villarroya

  9. Institute for Endocrinology and Diabetes—Molecular Endocrinology, Center of Brain Behavior and Metabolism CBBM, University of Lübeck, Lübeck, Germany

    Rebecca Oelkrug & Jens Mittag

  10. Medizinische Klinik and Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany

    Cristina García-Cáceres

  11. FiDiPro Program, Institute of Biomedicine, University of Turku, Turku, Finland

    Manuel Tena-Sempere

Authors
  1. Edward Milbank
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  2. Nathalia R. V. Dragano
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  3. Ismael González-García
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  9. Patricia Mallegol
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  11. Ramón Iglesias-Rey
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Contributions

E.M., N.R.V.D., I.G.-G., M.R.-G., F.R.-P., V.R.-L., C.C., J.C. and B.P. performed the in vivo experiments, analytical methods and collected and analysed the data. E.M., L.P., G.H., P.M. and L.V. generated and validated the sEVs. F.R.-P., J.R. and M.T.-S. analysed testis and adrenal function. D.A.M. and K.R. performed and analysed the SNA studies. R.I.-R. and T.S. performed the NMR studies. A.G.-N. and F.V. performed the experiments with brown adipocytes. N.R.V.D., I.G.-G. and C.G.-C. performed the immunohistochemistry studies. R.O. and J.M. provided the ucp1 null mice. E.M., N.R.V.D., I.G.-G., A.V., F.V., C.D., R.N., C.G.-C., M.T.-S., M.C.M., J.M., K.R., R.A. and M.L. analysed, interpreted and discussed the data. E.M., M.C.M., R.A. and M.L. developed the hypothesis and conceived and designed the experiments. E.M. and M.L. made the figures and wrote the paper. All authors revised and edited the paper. R.A. and M.L. jointly supervised this work, secured funding, coordinated the project and served as guarantors. M.L. is the lead contact for this study.

Corresponding authors

Correspondence to Ramaroson Andriantsitohaina or Miguel López.

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Competing interests

E.M., M.C.M., R.A. and M.L. declare that the research described in this paper is patent pending: European Patent Application EP21382763.7 entitled ‘Populations of small extracellular vesicles for use in the treatment of obesity’, European Patent Office (EPO). The other authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Christopher Madden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt.

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

Extended data

Extended Data Fig. 1 Characterization of SF1-AMPKα1-DN loaded neuronal-targeted dendritic cell-derived sEVs.

a, Western blotting using antibodies against Lamp2b in native and Lamp2b-RVG sEVs. b, Quantification of Lamp2b levels in native (n = 4 samples) and Lamp2b-RVG (n = 5 samples) sEVs in % of native control; P = 0.00031. c, Western blotting using antibodies against GRP94 in Jaws II cells (lane 1), unmodified native sEVs (lane 2) and Lamp2b-RVG sEVs (lane 3). d, Circular representation of the SF1-AMPKα1-DN encoding plasmid. e, Example of curve obtained by nanoparticle tracking analysis of a sample of native (left panel) and SF1-AMPKα1-DN loaded Lamp2b-RVG sEVs (right panel). The graph represents concentration of sEVs (particles/mL) according to the size (nm). f, Electron microscopy image of SF1-AMPKα1-DN loaded Lamp2b-RVG sEVs showing specific round shape and average size of ~70 nm vesicles. g, Agarose gel electrophoresis of native (lane 1), Lamp2b-RVG (lane 2), SF1-AMPKα1-DN loaded Lamp2b-RVG sEVs (lane 3) and negative control H2O (lane 4) of AMPK and GAPDH. h, Agarose gel electrophoresis of SF1-AMPKα1-DN loaded Lamp2b-RVG sEVs treated with DNAse (lane 1), DNAse + Triton X-100 0.2% (lane 2) and Triton X-100 0.2% (lane 3) of AMPK and GAPDH. i, Quantification of pACCα/ACCα in primary astrocytes treated for 24 h with native and Lamp2b-RVG (n = 6 samples/group) sEVs. j, Quantification of pACCα/ACCα in Neuro2A cells treated for 24 h with native and Lamp2b-RVG (n = 3 samples per group) sEVs. Data expressed as mean ± SEM. ***P < 0.001 vs. Control. Statistical significance was assessed by two-sided Student’s t-test.

Source data

Extended Data Fig. 2 Control of hypothalamic nuclei dissections.

Quantification of Pomc, Sf1 and Hcrt/orexin mRNA levels in ARC, VMH and LHA dissections [Pomc: ARC n = 20 mice, VMH n = 20 mice, LHA n = 19 mice; Sf1: ARC n = 19 mice, VMH n = 19 mice, LHA n = 19 mice; Hcrt: ARC n = 20 mice, VMH n = 20 mice, LHA n = 19 mice; box plots indicate median (middle line), 25th, 75th percentile (box) and 10th-90th percentiles (whiskers; minima and maxima, respectively)]. Data expressed as mean ± SEM. ***P < 0.001 vs. Pomc ARC, Sf1 VMH and Hcrt LHA. Statistical significance was assessed by two-sided ANOVA.

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Extended Data Fig. 3 Effect of systemic treatment with SF1-AMPKα1-DN loaded sEVs on hypothalamic AMPK activity in DIO mice.

a, Representative confocal images depicting GFAP (green), pACCα (magenta) and merged reactivity in brain sections (control n = 8 fields, 4 mice/group; SF1-AMPKα1-DN n = 8 fields, 4 mice/group) after 24 h of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded sEVs; scale bars represent 20 µm. b, Representative confocal images depicting Iba1 (green), pACCα (magenta) and merged reactivity in brain sections after 24 h of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded sEVs; scale bars represent 20 µm. c, Negative controls for pACCα and SF1 double immunofluorescence. Representative confocal images depicting DAPI (blue), Alexa594, with or without SF1 (red), Alexa 488 with or without pACCα (green) and merged reactivity in brain sections; scale bars represent 20 µm. The experiments were repeated 3 times. d, Quantification of pACCα fluorescence in ARC, DMH and PVH (quantification per field; ARC control n = 12 fields, 3 mice/group; SF1-AMPKα1-DN n = 8 fields, 2 mice/group; DMH control n = 12 fields, 3 mice/group; SF1-AMPKα1-DN n = 12 fields, 3 mice/group; PVH control n = 4 fields, 1 mice/group; SF1-AMPKα1-DN n = 12 fields, 3 mice/group) after 24 h of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded sEVs. Data expressed as mean ± SEM. Statistical significance was assessed by two-sided Student’s t-test.

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Extended Data Fig. 4 Effect of systemic treatment with SF1-AMPKα1-DN loaded sEVs on central and peripheral tissues in DIO mice.

a, Quantification of pACCα/ACCα levels in cortex, thalamus and cerebellum after 28-day of intravenous injection with control (non-loaded; cortex n = 7 mice; thalamus n = 7 mice; cerebellum n = 6 mice) or SF1-AMPKα1-DN loaded (cortex n = 7 mice; thalamus n = 7 mice; cerebellum n = 7 mice) sEVs. b, Quantification of pACCα/ACCα levels in liver, adrenal gland, testis, BAT, heart and skeletal muscle after 28-day of intravenous injection with control (non-loaded, liver n = 7 mice; adrenal n = 7 mice; testis n = 6 mice; BAT n = 7 mice; heart n = 7 mice; skeletal muscle n = 6 mice) or SF1-AMPKα1-DN loaded (liver n = 7 mice; adrenal n = 7 mice; testis n = 7 mice; BAT n = 7 mice; heart n = 7 mice; skeletal muscle n = 7 mice) sEVs. c, Quantification of pACCα/ACCα levels in brown adipocytes after 24 h of incubation with control (non-loaded) or SF1-AMPKα1-DN loaded (n = 5 samples/group) sEVs. d, Quantification of circulating testosterone levels after 28-day of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded (n = 7 mice/group) sEVs. e, Quantification of mRNA levels of steroidogenic enzymes (STAR, p450scc and 17β-HSD3) in testis after 28-day of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded (n = 7 mice/group) sEVs. f, Quantification of circulating CORT levels after 28-day of intravenous injection with control (non-loaded, n = 7 mice) or SF1-AMPKα1-DN loaded (n = 6 mice) sEVs. g, Quantification of mRNA levels of P450scc and STAR in adrenals after 28-day of intravenous injection with control (non-loaded; P450scc n = 5 mice; STAR n = 6 mice) or SF1-AMPKα1-DN loaded (P450scc n = 5 mice/group; STAR n = 5 mice/group) sEVs. h, Quantification of circulating LH levels after 28-day of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded (n = 6 mice/group) sEVs. i, Quantification of mRNA levels of LH β subunit in pituitary after 28-day of intravenous injection with control (non-loaded, n = 6 mice) or SF1-AMPKα1-DN loaded (n = 7 mice) sEVs. a-i, j, Quantification of BAT UCP1 levels at 1, 2, 3 and 7 days after a single injection in the tail vein of control (non-loaded; day 1 n = 6 mice; day 2 n = 5 mice; day 3 n = 5 mice; day 7 n = 5 mice) or SF1-AMPKα1-DN sEVs (day 1 n = 4 mice; day 2 n = 4 mice; day 3 n = 5 mice; day 7 n = 5 mice) at day 0; day 1 P = 0.023; day 2 P = 0.0054. Data expressed as mean ± SEM. *P < 0.05 and **P < 0.01 vs. Control. Statistical significance was assessed by two-sided Student’s t-test.

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Extended Data Fig. 5 Effect of systemic treatment with SF1-AMPKα1-DN sEVs on circulating and hemodynamic parameters in DIO mice.

a-i, Quantification of circulating leptin (a; control n = 12 mice, SF1-AMPKα1-DN n = 13 mice), GDF15 (b; control n = 7 mice, SF1-AMPKα1-DN n = 8 mice), IL-6 (c; control n = 6 mice, SF1-AMPKα1-DN n = 5 mice), IP-10 (d; control n = 6 mice, SF1-AMPKα1-DN n = 5 mice), triglycerides (e; control n = 7 mice, SF1-AMPKα1-DN n = 6 mice), cholesterol (f; control n = 7 mice, SF1-AMPKα1-DN n = 6 mice), NEFA (g; control n = 7 mice, SF1-AMPKα1-DN n = 6 mice, P = 0,0142), AST (h; control n = 8 mice, SF1-AMPKα1-DN n = 8 mice) and ALT (i; control n = 8 mice, SF1-AMPKα1-DN n = 8 mice) after 28-day of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded sEVs. j-m, Quantification of heart rate (j, control n = 6 mice, SF1-AMPKα1-DN n = 6 mice), systolic arterial pressure (k; control n = 6 mice, SF1-AMPKα1-DN n = 6 mice), diastolic arterial pressure (l; n control n = 6 mice, SF1-AMPKα1-DN n = 6 mice) and mean arterial pressure (m; n = 6 mice/group) after 24 h of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded sEVs. Data expressed as mean ± SEM. Statistical significance was assessed by two-sided Student’s t-test.

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Extended Data Fig. 6 Effect of systemic treatment with SF1-AMPKα 1-DN sEVs on BAT and skeletal muscle thermogenic markers in DIO mice.

a, Quantification of UCP1 protein levels in the BAT after 28-day of intravenous injection with control (non-loaded) or SF1-AMPKα1-DN loaded (n = 7 mice/group) sEVs; P = 0.029. b, Quantification of mRNA levels of thermogenic markers (Ucp3, Gpd2, Pparɣ, Sln, Ryr1, Atp2a2) in skeletal muscle after 28-day of intravenous injection with control (non-loaded; Ucp3 n = 7 mice; Gpd2 n = 7 mice; Pparγ n = 7 mice; Sln n = 6 mice; Ryr n = 7 mice; Atp2a2 n = 6 mice) or SF1-AMPKα1-DN loaded (Ucp3 n = 7 mice; Gpd2 n = 7 mice; Pparγ n = 7 mice; Sln n = 6 mice; Ryr n = 7 mice; Atp2a2 n = 6 mice) sEVs. Data expressed as mean ± SEM. *, **P < 0.05 vs. Control. Statistical significance was assessed by two-sided Student´s t-test.

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Milbank, E., Dragano, N.R.V., González-García, I. et al. Small extracellular vesicle-mediated targeting of hypothalamic AMPKα1 corrects obesity through BAT activation. Nat Metab 3, 1415–1431 (2021). https://doi.org/10.1038/s42255-021-00467-8

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