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Estrogen receptor alpha deficiency in cardiomyocytes reprograms the heart-derived extracellular vesicle proteome and induces obesity in female mice

Nature Cardiovascular Research volume 2, pages 268–289 (2023)Cite this article

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Abstract

Dysregulation of estrogen receptor alpha (ERα) has been linked with increased metabolic and cardiovascular disease risk. Here, we generate and characterize cardiomyocyte-specific ERα knockout (ERαHKO) mice to assess the role of ERα in the heart. The most striking phenotype was obesity in female ERαHKO but not male ERαHKO mice. Female ERαHKO mice showed cardiac dysfunction, mild glucose and insulin intolerance and reduced ERα gene expression in skeletal muscle and white adipose tissue. Transcriptomic, proteomic, lipidomic and metabolomic analyses revealed evidence of contractile and/or metabolic dysregulation in heart, skeletal muscle and white adipose tissue. We show that heart-derived extracellular vesicles from female ERαHKO mice contain a distinct proteome associated with lipid and metabolic regulation, and have the capacity to metabolically reprogram the target skeletal myocyte proteome with functional impacts on glycolytic capacity and reserve. This multi-omics study uncovers a cardiac-initiated and sex-specific cardiometabolic phenotype regulated by ERα and provides insights into extracellular vesicle-mediated interorgan communication.

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Fig. 1: Phenotypic differences between floxed control and ERαHKO old mice (>12 months).
Fig. 2: Cardiac, skeletal muscle and adipose molecular phenotype of female ERαHKO old mice (>12 months).
Fig. 3: Transcriptomics analyses in heart and white adipose tissue from female ERαHKO old mice.
Fig. 4: Lipidomic analyses in female and male ERαHKO old mice.
Fig. 5: Metabolomic analyses in female ERαHKO old mice.
Fig. 6: Proteomic analyses of mouse cardiac-derived extracellular vesicles.
Fig. 7: Female ERαHKO heart-derived EV-mediated cell proteome and functional reprogramming of C2C12 myotubes.
Fig. 8: Summary of findings and potential mechanisms responsible for the phenotype in female ERαHKO mice based on functional and multi-omics analyses.

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Data availability

Data generated or analyzed during this study are included in this published article (and its Supplementary Information) or are available from data repositories. Source data are provided with this paper.

Metabolomics and lipidomics data are available from the NIH Common Fund’s National Metabolomics Data Repository (NMDR) website. The metabolomics dataset is available at Metabolomics Workbench96, assigned Study ID ST002228 or accessed directly at https://doi.org/10.21228/M89D8V. The lipidomics dataset is available under study ID ST002229 or accessed directly at https://doi.org/10.21228/M89D8V. Transcriptomics data (RNA-seq) is available in BioStudies under the accession number E-MTAB-12564, or accessed directly via https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12564.

Proteomics data are available from the ProteomeXchange Consortium via the PRIDE partner repository (http://www.proteomexchange.org/). Experimental parameters are submitted to EV-TRACK knowledgebase (EV-TRACK ID: EV220410). A list of samples and RAW data are available in ProteomeXchange Consortium via the PRIDE partner repository, under accession numbers PXD023570 (heart tissue spectral library), PXD027811 (cardiac EV proteomics) and PXD033078/PXD038482 (proteome reprogramming of C2C12 myoblasts with cardiac EVs). For proteomics, tissue enrichment and cell marker analyses were performed using Human Protein Atlas (https://www.proteinatlas.org/humanproteome/tissue/), and CellMarker Database (http://biocc.hrbmu.edu.cn/CellMarker/help.jsp). Global proteomes of each group were analyzed using CellTalk Database (http://tcm.zju.edu.cn/celltalkdb/) to identify potential EV–protein interactors based on known ligand–receptor partners. Functional enrichment annotations were retrieved using g:Profiler (https://biit.cs.ut.ee/gprofiler/). MitoCarta3.0 (https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways/) was used to interrogate annotated mouse mitochondrial genes.

References

  1. Menazza, S. & Murphy, E. The expanding complexity of estrogen receptor signaling in the cardiovascular system. Circ. Res. 118, 994–1007 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Heine, P. A., Taylor, J. A., Iwamoto, G. A., Lubahn, D. B. & Cooke, P. S. Increased adipose tissue in male and female estrogen receptor-alpha knockout mice. Proc. Natl. Acad. Sci. USA 97, 12729–12734 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Davis, K. E. et al. The sexually dimorphic role of adipose and adipocyte estrogen receptors in modulating adipose tissue expansion, inflammation, and fibrosis. Mol Metab. 2, 227–242 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ribas, V. et al. Skeletal muscle action of estrogen receptor alpha is critical for the maintenance of mitochondrial function and metabolic homeostasis in females. Sci. Transl. Med. 8, 334ra354 (2016).

    Article  Google Scholar 

  5. Iñigo, M. R. et al. Estrogen receptor-α in female skeletal muscle is not required for regulation of muscle insulin sensitivity and mitochondrial regulation. Mol Metab. 34, 1–15 (2020).

    Article  PubMed  Google Scholar 

  6. Wend, K., Wend, P. & Krum, S. Tissue-specific effects of loss of estrogen during menopause and aging. Front. Endocrinol. https://doi.org/10.3389/fendo.2012.00019 (2012).

  7. Murphy, E. Estrogen signaling and cardiovascular disease. Circ. Res. 109, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Blenck, C. L., Harvey, P. A., Reckelhoff, J. F. & Leinwand, L. A. The importance of biological sex and estrogen in rodent models of cardiovascular health and disease. Circ. Res. 118, 1294–1312 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Skavdahl, M. et al. Estrogen receptor-beta mediates male–female differences in the development of pressure overload hypertrophy. Am. J. Physiol. Heart Circ. Physiol. 288, H469–H476 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Gronemeyer, H., Gustafsson, J.-Å. & Laudet, V. Principles for modulation of the nuclear receptor superfamily. Nat. Rev. Drug Discov. 3, 950–964 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Hevener, A. L., Ribas, V., Moore, T. M. & Zhou, Z. The impact of skeletal muscle ERα on mitochondrial function and metabolic health. Endocrinology https://doi.org/10.1210/endocr/bqz017 (2020).

  12. Mendelsohn, M. E. & Karas, R. H. Molecular and cellular basis of cardiovascular gender differences. Science 308, 1583–1587 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. Pugach, E. K., Blenck, C. L., Dragavon, J. M., Langer, S. J. & Leinwand, L. A. Estrogen receptor profiling and activity in cardiac myocytes. Mol. Cell. Endocrinol. 431, 62–70 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ropero, A. B. et al. Heart estrogen receptor alpha: distinct membrane and nuclear distribution patterns and regulation by estrogen. J. Mol. Cell. Cardiol. 41, 496–510 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Lizotte, E., Grandy, S. A., Tremblay, A., Allen, B. G. & Fiset, C. Expression, distribution and regulation of sex steroid hormone receptors in mouse heart. Cell. Physiol. Biochem. 23, 75–86 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Schonbrunn, A. Editorial: antibody can get it right: confronting problems of antibody specificity and irreproducibility. Mol. Endocrinol. 28, 1403–1407 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hinton, A. O. Jr. et al. Estrogen receptor-α in the medial amygdala prevents stress-induced elevations in blood pressure in females. Hypertension 67, 1321–1330 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Xu, P. et al. Estrogen receptor-α in medial amygdala neurons regulates body weight. J. Clin. Invest. 125, 2861–2876 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Feng, Y., Manka, D., Wagner, K. U. & Khan, S. A. Estrogen receptor-alpha expression in the mammary epithelium is required for ductal and alveolar morphogenesis in mice. Proc. Natl. Acad. Sci. USA 104, 14718–14723 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Saito, K. & Cui, H. Emerging roles of estrogen-related receptors in the brain: potential interactions with estrogen signaling. Int. J. Mol. Sci. https://doi.org/10.3390/ijms19041091 (2018).

  21. Huss, J. M., Garbacz, W. G. & Xie, W. Constitutive activities of estrogen-related receptors: transcriptional regulation of metabolism by the ERR pathways in health and disease. Biochim. Biophys. Acta 1852, 1912–1927 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Bostrom, P. et al. A PGC1-alpha-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature 481, 463–468 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Feldmann, H. M., Golozoubova, V., Cannon, B. & Nedergaard, J. UCP1 ablation induces obesity and abolishes diet-induced thermogenesis in mice exempt from thermal stress by living at thermoneutrality. Cell Metab. 9, 203–209 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Abel, E. D. et al. Cardiac hypertrophy with preserved contractile function after selective deletion of GLUT4 from the heart. J. Clin. Invest. 104, 1703–1714 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dudek, J. Role of cardiolipin in mitochondrial signaling pathways. Front. Cell Dev. Biol. 5, 90 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wiklander, O. P. et al. Extracellular vesicle in vivo biodistribution is determined by cell source, route of administration and targeting. J. Extracell. Vesicles 4, 26316 (2015).

    Article  PubMed  Google Scholar 

  28. Greening, D. W. & Simpson, R. J. Understanding extracellular vesicle diversity—current status. Expert Rev. Proteomics 15, 887–910 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  30. Zhang, C. C., Krieg, S. & Shapiro, D. J. HMG-1 stimulates estrogen response element binding by estrogen receptor from stably transfected HeLa cells. Mol. Endocrinol. 13, 632–643 (1999).

    Article  CAS  PubMed  Google Scholar 

  31. Chau, K. Y., Lam, H. Y. & Lee, K. L. Estrogen treatment induces elevated expression of HMG1 in MCF-7 cells. Exp. Cell. Res. 241, 269–272 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Zhang, F. et al. Four-and-a-half-LIM protein 1 downregulates estrogen receptor-α activity through repression of AKT phosphorylation in human breast cancer cell. Int. J. Biochem. Cell Biol. 44, 320–326 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Manavathi, B., Acconcia, F., Rayala, S. K. & Kumar, R. An inherent role of microtubule network in the action of nuclear receptor. Proc. Natl. Acad. Sci. USA 103, 15981–15986 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Le Gall, L. et al. Optimized method for extraction of exosomes from human primary muscle cells. Skelet. muscle 10, 20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hartwig, S. et al. Exosomal proteins constitute an essential part of the human adipose tissue secretome. Biochim. Biophys. Acta Proteins Proteom. 1867, 140172 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Patel, S. K. et al. Comprehensive profiling of plasma exosomes using data-independent acquisitions—new tools for aging cohort studies. Preprint at bioRxiv https://doi.org/10.1101/2021.02.27.433188 (2021).

  37. Domenighetti, A. A. et al. Loss of FHL1 induces an age-dependent skeletal muscle myopathy associated with myofibrillar and intermyofibrillar disorganization in mice. Hum. Mol. Genet. 23, 209–225 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Hwang, H. et al. Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes. Diabetes 59, 33–42 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Machuki, J. O., Zhang, H. Y., Harding, S. E. & Sun, H. Molecular pathways of oestrogen receptors and β-adrenergic receptors in cardiac cells: recognition of their similarities, interactions and therapeutic value. Acta Physiol. https://doi.org/10.1111/apha.12978 (2018).

  40. Bupha-Intr, T. & Wattanapermpool, J. Regulatory role of ovarian sex hormones in calcium uptake activity of cardiac sarcoplasmic reticulum. Am. J. Physiol. Heart Circ. Physiol. 291, H1101–H1108 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Liu, C. G. et al. 17β-oestradiol regulates the expression of Na+/K+-ATPase β1-subunit, sarcoplasmic reticulum Ca2+-ATPase and carbonic anhydrase iv in H9C2 cells. Clin. Exp. Pharmacol. Physiol. 34, 998–1004 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Dufour, C. R. et al. Genome-wide orchestration of cardiac functions by the orphan nuclear receptors ERRα and γ. Cell Metab. 5, 345–356 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Giguère, V., Yang, N., Segui, P. & Evans, R. M. Identification of a new class of steroid hormone receptors. Nature 331, 91–94 (1988).

    Article  PubMed  Google Scholar 

  44. Liu, D., Zhang, Z., Gladwell, W. & Teng, C. T. Estrogen stimulates estrogen-related receptor alpha gene expression through conserved hormone response elements. Endocrinology 144, 4894–4904 (2003).

    Article  CAS  PubMed  Google Scholar 

  45. Devanathan, S. et al. An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: functional, metabolic, and differential network analysis. PLoS ONE 9, e101900 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hewitt, S. C. et al. Biological and biochemical consequences of global deletion of exon 3 from the ER alpha gene. FASEB J. 24, 4660–4667 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Dupont, S. et al. Effect of single and compound knockouts of estrogen receptors alpha (ERα) and beta (ERβ) on mouse reproductive phenotypes. Development 127, 4277–4291 (2000).

    Article  CAS  PubMed  Google Scholar 

  48. Xu, Y. et al. Distinct hypothalamic neurons mediate estrogenic effects on energy homeostasis and reproduction. Cell Metab. 14, 453–465 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Trask, R. V. & Billadello, J. J. Tissue-specific distribution and developmental regulation of M and B creatine kinase mRNAs. Biochim. Biophys. Acta 1049, 182–188 (1990).

    Article  CAS  PubMed  Google Scholar 

  50. Luo, J. et al. Class IA phosphoinositide 3-kinase regulates heart size and physiological cardiac hypertrophy. Mol. Cell. Biol. 25, 9491–9502 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Brüning, J. C. et al. A muscle-specific insulin receptor knockout exhibits features of the metabolic syndrome of NIDDM without altering glucose tolerance. Mol. Cell 2, 559–569 (1998).

    Article  PubMed  Google Scholar 

  52. Buzaglo-Azriel, L. et al. Loss of muscle MTCH2 increases whole-body energy utilization and protects from diet-induced obesity. Cell Rep. 14, 1602–1610 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Chen, W., Zhang, X., Birsoy, K. & Roeder, R. G. A muscle-specific knockout implicates nuclear receptor co-activator MED1 in the regulation of glucose and energy metabolism. Proc. Natl. Acad. Sci. USA 107, 10196–10201 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lung, D. K., Reese, R. M. & Alarid, E. T. Intrinsic and extrinsic factors governing the transcriptional regulation of ESR1. Horm. Cancer 11, 129–147 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Fatima, L. A. et al. Estrogen receptor 1 (ESR1) regulates VEGFA in adipose tissue. Sci. Rep. 7, 16716 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Couse, J. F., Lindzey, J., Grandien, K., Gustafsson, J. A. & Korach, K. S. Tissue distribution and quantitative analysis of estrogen receptor-alpha (ERα) and estrogen receptor-beta (ERβ) messenger ribonucleic acid in the wild-type and ERα-knockout mouse. Endocrinology 138, 4613–4621 (1997).

    Article  CAS  PubMed  Google Scholar 

  57. Santos, R. S. et al. Activation of estrogen receptor alpha induces beiging of adipocytes. Mol. Metab. 18, 51–59 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Even, P. C. & Nadkarni, N. A. Indirect calorimetry in laboratory mice and rats: principles, practical considerations, interpretation and perspectives. Am. J. Physiol. Regul. Integr. Comp. Physiol. 303, R459–R476 (2012).

    Article  CAS  PubMed  Google Scholar 

  59. Ribas, V. et al. Impaired oxidative metabolism and inflammation are associated with insulin resistance in ERα-deficient mice. Am. J. Physiol. Endocrinol. Metab. 298, E304–E319 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Grueter, C. E. et al. A cardiac microRNA governs systemic energy homeostasis by regulation of MED13. Cell 149, 671–683 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Baskin, K. K. et al. MED13-dependent signaling from the heart confers leanness by enhancing metabolism in adipose tissue and liver. EMBO Mol. Med. 6, 1610–1621 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Woodall, B. P. et al. Alteration of myocardial GRK2 produces a global metabolic phenotype. JCI Insight 5, e123848 (2019).

    Article  PubMed  Google Scholar 

  63. Rolfe, D. F. & Brown, G. C. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol. Rev. 77, 731–758 (1997).

    Article  CAS  PubMed  Google Scholar 

  64. Kersten, S. Integrated physiology and systems biology of PPARα. Mol Metab 3, 354–371 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, Z. et al. iTRAQ-based proteomic analysis reveals key proteins affecting muscle growth and lipid deposition in pigs. Sci. Rep. 7, 46717 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. van Weeghel, M. et al. Increased cardiac fatty acid oxidation in a mouse model with decreased malonyl-CoA sensitivity of CPT1B. Cardiovasc. Res. 114, 1324–1334 (2018).

    Article  PubMed  Google Scholar 

  67. Neels, J. G. & Grimaldi, P. A. Physiological functions of peroxisome proliferator-activated receptor β. Physiol. Rev. 94, 795–858 (2014).

    Article  CAS  PubMed  Google Scholar 

  68. Khumukcham, S. S. & Manavathi, B. Two decades of a protooncogene HPIP/PBXIP1: uncovering the tale from germ cell to cancer. Biochim. Biophys. Acta, Rev. Cancer 1876, 188576 (2021).

    Article  CAS  PubMed  Google Scholar 

  69. Cowling, B. S. et al. Four and a half LIM protein 1 gene mutations cause four distinct human myopathies: a comprehensive review of the clinical, histological and pathological features. Neuromuscul. Disord. 21, 237–251 (2011).

    Article  PubMed  Google Scholar 

  70. Friedrich, F. W. et al. Evidence for FHL1 as a novel disease gene for isolated hypertrophic cardiomyopathy. Hum. Mol. Genet. 21, 3237–3254 (2012).

    Article  CAS  PubMed  Google Scholar 

  71. Gan, L. et al. Ischemic heart-derived small extracellular vesicles impair adipocyte function. Circ. Res. 130, 48–66 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Harvey, J. M., Clark, G. M., Osborne, C. K. & Allred, D. C. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J. Clin. Oncol. 17, 1474–1481 (1999).

    Article  CAS  PubMed  Google Scholar 

  73. Clark, G. M., Osborne, C. K. & McGuire, W. L. Correlations between estrogen receptor, progesterone receptor, and patient characteristics in human breast cancer. J. Clin. Oncol. 2, 1102–1109 (1984).

    Article  CAS  PubMed  Google Scholar 

  74. Broberg, A. M. et al. Prevention, detection and management of heart failure in patients treated for breast cancer. Curr. Heart Fail. Rep. 17, 397–408 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Scalzo, R. L. et al. Breast cancer endocrine therapy promotes weight gain with distinct adipose tissue effects in lean and obese female mice. Endocrinology https://doi.org/10.1210/endocr/bqab174 (2021).

  76. Nagy, A. Cre recombinase: the universal reagent for genome tailoring. Genesis 26, 99–109 (2000).

    Article  CAS  PubMed  Google Scholar 

  77. Weeks, K. L. et al. FoxO1 is required for physiological cardiac hypertrophy induced by exercise but not by constitutively active PI3K. Am. J. Physiol. Heart Circ. Physiol. 320, H1470–H1485 (2021).

    Article  CAS  PubMed  Google Scholar 

  78. Donner, D. G., Kiriazis, H., Du, X. J., Marwick, T. H. & McMullen, J. R. Improving the quality of preclinical research echocardiography: observations, training, and guidelines for measurement. Am. J. Physiol. Heart Circ. Physiol. 315, H58–h70 (2018).

    Article  PubMed  Google Scholar 

  79. Lancaster, G. I. & Henstridge, D. C. Body composition and metabolic caging analysis in high-fat fed mice. J. Vis. Exp. https://doi.org/10.3791/57280 (2018).

  80. Bond, S. T. et al. Deletion of Trim28 in committed adipocytes promotes obesity but preserves glucose tolerance. Nat. Commun. 12, 74 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Bond, S. T. et al. Tissue-specific expression of Cas9 has no impact on whole-body metabolism in four transgenic mouse lines. Mol. Metab. 53, 101292 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. McMullen, J. R. et al. Deletion of ribosomal S6 kinases does not attenuate pathological, physiological, or insulin-like growth factor 1 receptor-phosphoinositide 3-kinase-induced cardiac hypertrophy. Mol. Cell. Biol. 24, 6231–6240 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Shioi, T. et al. Akt/protein kinase B promotes organ growth in transgenic mice. Mol. Cell. Biol. 22, 2799–2809 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).

    Article  PubMed  Google Scholar 

  85. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  86. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  87. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–d551 (2021).

    Article  CAS  PubMed  Google Scholar 

  90. Ge, S. X., Son, E. W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 19, 534 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Luo, W. & Brouwer, C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830–1831 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 29, e45 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Claridge, B. et al. Proteome characterisation of extracellular vesicles isolated from heart. Proteomics 21, e2100026 (2021).

    Article  PubMed  Google Scholar 

  94. Lozano, J. et al. Scalable generation of nanovesicles from human-induced pluripotent stem cells for cardiac repair. Int. J. Mol. Sci. https://doi.org/10.3390/ijms232214334 (2022).

  95. McMahon, D. K. et al. C2C12 cells: biophysical, biochemical, and immunocytochemical properties. Am. J. Physiol. 266, C1795–C1802 (1994).

    Article  CAS  PubMed  Google Scholar 

  96. Rai, A. et al. Exosomes derived from human primary and metastatic colorectal cancer cells contribute to functional heterogeneity of activated fibroblasts by reprogramming their proteome. Proteomics 19, e1800148 (2019).

    Article  PubMed  Google Scholar 

  97. Poh, Q. H., Rai, A., Carmichael, I. I., Salamonsen, L. A. & Greening, D. W. Proteome reprogramming of endometrial epithelial cells by human trophectodermal small extracellular vesicles reveals key insights into embryo implantation. Proteomics 21, e2000210 (2021).

    Article  PubMed  Google Scholar 

  98. Greening, D. W. et al. Chronic methamphetamine interacts with BDNF Val66Met to remodel psychosis pathways in the mesocorticolimbic proteome. Mol. Psychiatry 26, 4431–4447 (2021).

    Article  PubMed  Google Scholar 

  99. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  100. Kompa, A. R. et al. Sustained subcutaneous delivery of secretome of human cardiac stem cells promotes cardiac repair following myocardial infarction. Cardiovasc. Res. 117, 918–929 (2021).

    Article  CAS  PubMed  Google Scholar 

  101. Duivenvoorden, H. M. et al. Myoepithelial cell-specific expression of stefin A as a suppressor of early breast cancer invasion. J. Pathol. 243, 496–509 (2017).

    Article  CAS  PubMed  Google Scholar 

  102. Tyanova, S. & Cox, J. Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Methods Mol. Biol. 1711, 133–148 (2018).

    Article  CAS  PubMed  Google Scholar 

  103. Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2018).

    Article  PubMed Central  Google Scholar 

  104. Shao, X. et al. CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice. Brief. Bioinform. 22, bbaa269(2020).

    Article  Google Scholar 

  105. Rai, A., Fang, H., Claridge, B., Simpson, R. J. & Greening, D. W. Proteomic dissection of large extracellular vesicle surfaceome unravels interactive surface platform. J. Extracell. Vesicles 10, e12164 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Théry, C. et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles 7, 1535750 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    Article  CAS  PubMed  Google Scholar 

  108. Doncheva, N. T., Morris, J. H., Gorodkin, J. & Jensen, L. J. Cytoscape StringApp: network analysis and visualization of proteomics data. J. Proteome Res. 18, 623–632 (2019).

    Article  CAS  PubMed  Google Scholar 

  109. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge R. Chooi, N. Cemerlang, Y. Alexander, E. Masterman and Y. Liu (Baker Heart and Diabetes Institute) for technical support. This work was supported by the National Heart Foundation of Australia (Grant-In-Aid: ID G 08M 3760 and G 11M 6111 to J.R.M.; Vanguard 105072 to D.G.), National Health and Medical Research Council (NHMRC) project grant (1139489 and 1057741 to D.G.), Future Fund (MRF1201805 to D.G.), Pankind (to D.G.) and the Victorian Government’s Operational Infrastructure Support Program. J.R.M. was supported by a NHMRC Senior Research Fellowship (grant no. 1078985) and is supported by a Baker Fellowship. B.C.B. is supported by an Alice Baker and Eleanor Shaw Fellowship (The Baker Foundation, Australia). K.L.W. is supported by a National Heart Foundation Future Leader Fellowship (award ID 102539). B.C. and H.F. are supported by an Australian Government Training Program (RTP) scholarship and Baker Institute Bright Sparks Scholarship Top Up. E.D.A. is supported by the American Heart Association (20SFRN35120123). The metabolomics component of this project used NCRIS-enabled Metabolomics Australia infrastructure at the University of Melbourne and was funded through BioPlatforms Australia. We acknowledge N. Carvajal from the Department of Genomic Medicine at the Alfred Research Alliance for assistance with sequencing.

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Author notes

  1. These authors contributed equally: Yow Keat Tham, Bianca C Bernardo.

Authors and Affiliations

  1. Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Yow Keat Tham, Bianca C. Bernardo, Bethany Claridge, Gunes S. Yildiz, Liesel Min-Linn Woon, Simon Bond, Haoyun Fang, Jenny Y. Y. Ooi, Aya Matsumoto, Jieting Luo, Celeste M. K. Tai, Claudia A. Harmawan, Helen Kiriazis, Daniel G. Donner, Natalie A. Mellett, Kevin Liu, Ruidong Xiang, Manika Singh, Michael Inouye, Peter J. Meikle, Kate L. Weeks, Brian G. Drew, David W. Greening & Julie R. McMullen

  2. Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia

    Yow Keat Tham, Simon Bond, Jenny Y. Y. Ooi, Helen Kiriazis, Daniel G. Donner, Peter J. Meikle, Kate L. Weeks, Brian G. Drew, David W. Greening & Julie R. McMullen

  3. Department of Diabetes, Central Clinical School, Monash University, Clayton, Victoria, Australia

    Yow Keat Tham, Bianca C. Bernardo, Simon Bond, Jenny Y. Y. Ooi, Peter J. Meikle, Kate L. Weeks & Julie R. McMullen

  4. Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia

    Bianca C. Bernardo

  5. Department of Biochemistry and Chemistry, La Trobe University, Melbourne, Victoria, Australia

    Bethany Claridge & David W. Greening

  6. Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia

    Bethany Claridge, Peter J. Meikle, Brian G. Drew, David W. Greening & Julie R. McMullen

  7. David Geffen School of Medicine, University of California, Los Angeles, California, CA, USA

    E. Dale Abel

  8. Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Sohaib A. Khan

  9. Metabolomics Australia, Bio21 Institute, The University of Melbourne, Melbourne, Victoria, Australia

    David P. De Souza & Sheik Nadeem Elahee Doomun

  10. Department of Anatomy and Physiology, University of Melbourne, Melbourne, Victoria, Australia

    Kate L. Weeks

  11. Central Clinical School, Monash University, Melbourne, Victoria, Australia

    Brian G. Drew & Julie R. McMullen

  12. Department of Physiology, Anatomy and Microbiology, La Trobe University, Bundoora, Victoria, Australia

    David W. Greening & Julie R. McMullen

  13. Department of Physiology, Monash University, Melbourne, Victoria, Australia

    Julie R. McMullen

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  1. Yow Keat Tham
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Contributions

Y.K.T. contributed to project coordination, experimental design, animal experiments, molecular work, lipidomics, data analysis, preparation of figures and provided intellectual input. B.C.B. contributed to project coordination, experimental design, animal experiments, molecular work, analyzed data, provided intellectual input and preparation of figures. L.M.L.W. and S.G.Y. contributed to animal experiments, molecular analyses and analyzed data. B.C. contributed to EV and proteomic experiments, analyses, interpretation of data, related figures and text. H.F. contributed to proteomic informatics. B.C., G.S.Y. and L.M.L.W. contributed equally. J.Y.Y.O. contributed to animal experiments and provided intellectual input. A.M. and J.L. contributed to Langendorff-isolated heart experiments. C.M.K.T. contributed to data analysis, and preparation of figures. C.A.H. contributed to echocardiography analysis and analyzed data. H.K. contributed to cardiac function assessment (echocardiography). D.G.D. contributed to echocardiography analysis. S.B. and B.G.D. contributed to metabolic mouse and C2C12 studies, analyses and interpretation. B.G.D. contributed to RNA-seq preparation, analysis and interpretation. K.L. contributed to RNA-seq sample preparation and sequencing. R.X., M.S. and M.I. contributed to RNA-seq analyses and interpretation. E.D.A. and S.A.K. provided critical mouse models, expertise regarding these models and intellectual input. N.A.M. and P.J.M. contributed to lipidomic methods, analyses and interpretation. D.P.D.S. and S.N.E.D. contributed to metabolomics experiments, analyses and interpretation. K.L.W. contributed to experimental design of Cre related studies, analyzed data and provided intellectual input. D.W.G. contributed to EV and proteomic experimental design and analyses, interpretation of data, related figures and text. J.R.M. contributed to project development and coordination, experimental design, animal experiments, data analysis, writing of the manuscript and preparation of figures. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to David W. Greening or Julie R. McMullen.

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Extended Data

Extended Data Fig. 1 Basal morphological differences between control and ERαHKO mice (12-14 weeks).

a, Body weight and tibia length of male and female floxed control (FC) and knockout (KO) mice, b,c, Graphical representation of heart weight (HW), kidney weight (KW), liver weight (LivW), brown adipose tissue weight (BAT), white adipose tissue weight (WAT), normalized to tibia length (TL) or body weight (BW) of females (b) and males (c). For all panels, Male FC n=6, Male KO n=7, Female FC n=9, Female KO n=4. n represent independent animals. For all panels, lines show mean±SEM. *P<0.05, two-sided, unpaired t-test. For data that failed the Shapiro-Wilk normality test (male: BW, WAT/TL, WAT/BW; female: WAT/TL), a two-sided Mann–Whitney test was performed. *P<0.05.

Source data

Extended Data Fig. 2 Cardiac Akt signaling in female control and ERαHKO mice in response to IGF1.

Western blot (left panel) and quantitation (right panel) showing phosphorylation of Akt (pAkt) relative to total Akt (tAkt) in female hearts from floxed control (FC) and knockout (KO) mice (12-14 weeks) in response to a bolus dose of insulin-like growth factor 1 (IGF1) or no stimulation (injection of PBS/no injection). Lines show mean±SEM, P<0.05, n=3/group; n represents data from independent animals. 2 way ANOVA (genotype and IGF1 stimulation/no stimulation) with Fisher’s post hoc pairwise tests (two-sided).

Source data

Extended Data Fig. 3 Analysis of genes associated with cardiac contractility, stress and fibrosis in the hearts of male and female control and ERαHKO mice (>12 months).

a, Gene expression of SERCA2a, αMHC, and βMHC relative to Hprt1 by qPCR in male control (FC) and knockout (KO) hearts. Lines show mean±SEM. FC n=4, KO n=7. Gene expression of atrial natriuretic peptide (ANP, Npaa), B-Type natriuretic peptide (BNP, Nppb), Collagen 1 and 3 relative to Hprt1 in b, Male and c, Female FC and KO hearts. Lines show mean±SEM. Male FC n=4, Male KO n=7, Female FC n=8, Female KO n=7; n represent data from independent animals. For all panels, two-sided, unpaired t-test; no significant differences between groups.

Source data

Extended Data Fig. 4 ERα (Esr1) and Cre expression in tissues from male control and ERαHKO mice (>12 months).

a, qPCR quantitation of ERα relative to Hprt1 in skeletal muscle (Sk Mus), white adipose tissue (WAT), kidney and liver from male control (FC) and knockout (KO) mice. b, qPCR quantitation of Cre in heart, skeletal muscle (Sk mus), kidney, liver and adipose (BAT) from male FC and KO mice. Lines show mean±SEM. For panel a and b: FC n=4, KO n=7; n represent data from independent animals. *P<0.05, two-sided, unpaired t-test (panel a and b). For data that failed the Shapiro-Wilk normality test (Panel a: skeletal muscle, kidney), a two-sided Mann Whitney test was performed. *P<0.05. For b, ^ very small but significant (P=0.046) increase in Cre expression was identified in the kidney and adipose from male KO mice. This is considered within the noise of detection for a gene not endogenously expressed. c, Western blot showing Cre and α-tubulin in hearts from FC and KO mice, and multiple tissues from KO mice. He=heart, Kid=kidney, Liv=liver, WAT=white adipose tissue, BAT=brown adipose tissue, Sk=skeletal muscle, Lu=lung. A signal for Cre was only observed in hearts from KO male mice (>12 months). Cre protein expression was assessed across multiple non-cardiac tissues once in multiple male tissues and once in multiple female tissues (Fig. 2h). For heart it was done in duplicate in both males and females.

Source data

Extended Data Fig. 5 Analysis of cardiac function, insulin and glucose tolerance of male and female control and ERαHKO mice (>12 months).

a, Left, echocardiography images of female control (FC) and knockout (KO) heart. In a parasternal long axis view, the left ventricle length from the aortic annulus to the endocardial border at the apex at diastole and systole. LA=left atrium. Right, quantitation of longitudinal fractional shortening (FS), cardiac index (CI), and heart rate in females and males. Lines show mean±SEM. Male FC n=5, Male KO n=5, Female FC n=8, Female KO n=7. *P<0.05, two-sided, unpaired t-test. b, Insulin tolerance tests (ITT) and glucose tolerance tests (GTT) were performed at ~52 and 55 weeks, respectively. Data shown as mean±SEM. For ITT, Female FC n=7, Female KO n=7, Male FC n=5, Male KO n=4. For GTT, Female FC n=8, Female KO n=7, Male FC n=5, Male KO=5. N.B. For ITT, the insulin injection was unsuccessful in 1 female FC and 1 male KO. *P<0.05, NS=not significant. Curves were compared using two-way ANOVA. All n represent data from independent animals.

Source data

Extended Data Fig. 6 Body weight, food intake, physical activity and respiration in male and female control and ERαHKO mice.

a, Body weight in female and male control (FC) and knockout (KO) mice tracked over time. Data shown as mean±SEM. Male FC n=5, Male KO n=5, Female FC n=8, Female KO n=7. Curves were compared using two-way ANOVA. NS=not significant. b-g, Mice were placed in a metabolic cage on a chow diet for 72h at ~53 weeks of age. Measurements were made in the final 24h. b, Food intake. c, Physical activity assessed via beam breaks over a 12h day/night cycle. d, Averaged oxygen consumption per minute over a 12h day/night cycle. e, Averaged carbon dioxide production per minute over a 12h day/night cycle. f, Respiratory exchange ratio (RER). g, Energy expenditure (EE) relative to grams of lean mass (gLM). For b-g, lines indicate mean±SEM. Male FC n=5, Male KO n=5, Female FC n=8, Female KO n=7; all n represent data from independent animals. In panels b-g, data analyzed with two-sided, unpaired t-test within the same sex. No significant differences between groups.

Source data

Extended Data Fig. 7 Transcriptomics of the PGC-1 cascade (PPARα and ERRα) and mitochondrial DNA in the heart.

a, Heatmap highlighting the regulation of PPARα and ERRα interactors and target genes from heart of from female ERαHKO (FKO) and female floxed control mice (FFC; all mice >12 months; n=4/group; n represents data from independent animals); majority of genes presented had differential expression p-values<0.05 (Benjamini-Hochberg corrected and two-sided), see Supplementary Table 6. STRING: functional protein association networks; experimental evidence only, high confidence cut off >0.7. Selected PPARα and ERRα target genes with roles in metabolism or contractility. PGC1α: Pdk1, Pcx, Acsl1; ERRα: Acsl1, Mfn2, Atp2a2, Mef2a* (*may be an upstream regulator; PMID: 32212902), Prkab1. b, Heatmap of significantly differentially expressed mitochondrial genes from the mouse mito database (Mouse MitoCarta3.0: 1140 mitochondrial genes) in the heart from female ERαHKO (FKO) and female floxed control mice (FFC; all mice >12 months; N=4/group). Genes presented had differential expression p-values<0.05 (Benjamini-Hochberg corrected and two-sided), see Supplementary Table 6. Hierarchical clustering was used to group genes and samples. c, Heart mitochondrial DNA (mtDNA) content from mice (12-14 weeks), expressed as a ratio of mtDNA/nuclear (Nuc) DNA. Data represent mean. Male FC n=3, Male KO n=3, Female FC n=5, Female KO n=5; n represent data from independent animals. Data analyzed with two-sided, unpaired t-test within the same sex.

Source data

Extended Data Fig. 8 C2C12 myotubes treated with heart-derived EVs from female and male ERαHKO mice.

C2C12 myotubes treated with vehicle (Veh, volume matched PBS), EVs from female ERαHKO mice (F_KO), or male ERαHKO mice (M_KO). Due to limited EV material, EVs were pooled from each group (3 independent animals/group), 1 well C2C12/group for proteomics (analyzed in triplicate). Volcano plot of cell proteome following EV treatment ((5.3 µg/mL); a, F_KO versus M_KO group, based on two-sided, student’s unpaired t-test. Significantly up-(red) and down-(blue) regulated proteins with p-value < 0.05 highlighted. b, Scatter plot of the log2 fold changes of proteins differentially expressed (p-value < 0.05; F_KO vs Veh or M_KO vs Veh (x-/y-axis); two-sided, student’s unpaired t-test) in response to EV treatment. Subgroups of proteins reveal opposing effect of sex-specific EV treatments on C2C12 myotubes comparing to Veh, in which highly expressed (orange) or downregulated (green) proteins in F_KO versus M_KO (p-value < 0.05) are indicated. c, Lollipop plot of functional enrichment analysis of significantly dysregulated proteins in F KO vs M KO (using g:Profiler) as highlighted in panel B (GO: BP/Reactome/KEGG pathway analysis; adjusted p-value < 0.01). Supplementary Tables 22-23.

Source data

Extended Data Fig. 9 Cardiac derived EVs from female ERαHKO mice increased glycolytic capacity in C2C12 myotubes.

The extra cellular acidification rate (ECAR) and oxygen consumption rate (OCR) were measured in differentiated C2C12 myotubes. a, Profiles of glycolytic stress test (ECAR) and Mito Stress Test (OCR) data in differentiated C2C12 myotubes treated for 16 hours with EV’s isolated from ex vivo female mouse cardiac tissues, indicating sequential injections into media of Glucose, Oligomycin, 2-D-Glucose (ECAR) and Oligomycin, FCCP and Rotenone/Antimycin A (OCR). Glycolytic stress test and Mito stress test metabolic parameters as calculated from experimental profiles for C2C12 myotubes treated with b, female derived cardiac EVs and c, male derived cardiac EVs. Values are means ±SEM, 5-6 replicates/EV treatment, P<0.05 considered significant via two-sided, unpaired t-test. FCCP - carbonyl cyanite-4 (trifluoromethoxy) phenylhydrazone. For panel a and b, ECAR: Female FC/KO EVs n=6/5, OCR: Female FC/KO EVs n=5/6. For panel c, ECAR and OCR: Male FC/KO EVs n=5/6. Due to limited material, EVs were pooled from each group of animals (6 independent animals in female FC, female ERαHKO, male FC and male ERαHKO) and n represents individual well replicates for each group as indicated.

Source data

Extended Data Fig. 10 Summary schematic of the female ERαHKO heart indicative of metabolic dysregulation and increased glucose utilization.

(1) ERαHKO (KO) hearts displayed a trend for higher Cd36 expression, which would be expected to increase uptake of circulating fatty acids into cardiomyocytes. (2) Overall profile for fatty acid utilization was reduced based on a decrease in gene expression of PGC1α, PPARα and CPT1A, and acylcarnitine lipids. (3) Concurrently, lipidomic profiling demonstrated increased levels of diacylglycerides (DG) and triacylglycerides (TG). (4) Enrichment analyses and KEGG pathway mapping of transcriptome results indicate upregulation of gene sets associated with diabetic cardiomyopathy, reactive oxygen species and proteaosome/ubiquitin activity. (5) Further to this, MtDNA ratio assessment suggested a trend for decrease in overall mitochondria abundance, (6) potentially resulting in the compensatory increase of OXPHOS complexes in fewer and/or dysfunctional mitochondria in a heart with energy defects. (7) Metabolomic profiling showed increase in metabolites involved in glucose-related pathways, further highlighting an increase in glucose utilization as fuel for ATP generation. Data represented as mean ± SEM. For metabolomics y-axis represents abundance/integrated area: FC n=8, KO n=7. For gene expression: Cd36 FC n=7, KO n=4; Ppargc1a and Ppara FC n=8, KO n=7; Cpt1a FC n=7, KO n=5. For lipidomics: FC n=8, KO=7; *p<0.05. Solid arrows indicate significant changes, dotted arrows indicate trends. The images were obtained from Servier Medical Art (https://smart.servier.com/).

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13 (including legends and and uncropped western blots) and Supplementary Tables 30–33

Reporting Summary

Supplementary Information

Supplementary Tables 1–29.

Supplementary Data 1

Source data for Supplementary Tables 1–5.

Supplementary Data 2

Source data for Supplementary Figures 1–9.

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Source Data Fig. 1

Statistical source data for Fig. 1 is within source data for Supplementary Tables 2 and 3 in Supplementary Data File 1 within tabs Supplementary Data 2 and 3.

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Statistical source data for Fig. 3 is within Supplementary Tables 6, 8 and 10–13, and within the repository listed for RNA-seq data.

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Statistical source data for all figures/Extended Data figures with clearly named tabs for each figure/Extended Data figure, within Supplementary Tables 18–21, and within the repository listed for proteomics data.

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Tham, Y.K., Bernardo, B.C., Claridge, B. et al. Estrogen receptor alpha deficiency in cardiomyocytes reprograms the heart-derived extracellular vesicle proteome and induces obesity in female mice. Nat Cardiovasc Res 2, 268–289 (2023). https://doi.org/10.1038/s44161-023-00223-z

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