Extracellular vesicle transfer of miR-1 to adipose tissue modifies lipolytic pathways following resistance exercise

Benjamin I. Burke, Ahmed Ismaeel, Douglas E. Long, Lauren A. Depa, Peyton T. Coburn, Jensen Goh, Tolulope P. Saliu, Bonnie J. Walton, Ivan J. Vechetti, Bailey D. Peck, Taylor R. Valentino, C. Brooks Mobley, Hasiyet Memetimin, Dandan Wang, Brian S. Finlin, Philip A. Kern, Charlotte A. Peterson, John J. McCarthy, Yuan Wen

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Extracellular vesicles (EVs) have emerged as important mediators of intertissue signaling and exercise adaptations. In this human study, we provide evidence that muscle-specific microRNA-1 (miR-1) was transferred to adipose tissue via EVs following an acute bout of resistance exercise. Using a multimodel machine learning automation tool, we discovered muscle primary miR-1 transcript and CD63+ EV count in circulation as top explanatory features for changes in adipose miR-1 levels in response to resistance exercise. RNA-Seq and in-silico prediction of miR-1 target genes identified caveolin 2 (CAV2) and tripartite motif containing 6 (TRIM6) as miR-1 target genes downregulated in the adipose tissue of a subset of participants with the highest increases in miR-1 levels following resistance exercise. Overexpression of miR-1 in differentiated human adipocyte-derived stem cells downregulated these miR-1 targets and enhanced catecholamine-induced lipolysis. These data identify a potential EV-mediated mechanism by which skeletal muscle communicates with adipose tissue and modulates lipolysis via miR-1.

Original languageEnglish
Article numbere182589
JournalJCI insight
Volume9
Issue number21
DOIs
StatePublished - Nov 8 2024

Bibliographical note

Publisher Copyright:
Copyright: © 2024, Burke et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Funding

The authors would like to thank the University of Kentucky Center for Applied Artificial Intelligence for the use of CLASSify. The authors would also like to thank all the participants for participating in this study. BIB, AI, DEL, IJV, BSF, PAK, CAP, JJM, and YW conceived and designed the experiments in this manuscript. BIB, AI, DEL, LAD, PTC, JG, TPS, BJW, IJV, BDP, TRV, CBM, HM, and YW performed the experiments. BIB, AI, DW, and YW analyzed all data. BIB, AI, BSF, PAK, CAP, JJM, and YW interpreted data. BIB, AI, and YW prepared the figures and drafted the manuscript. All authors read and approved the final manuscript. Alphabetical order of last name was used to assign authorship order for co–first authors. CP and JM were supported by the National Institutes of Health (R01DK119619). Additional funding for this study was from R01 DK124626 to PK, and the clinical research was supported by CTSA grant UL1TR001998.

FundersFunder number
University of Kentucky Center for Applied Artificial Intelligence
National Institutes of Health (NIH)R01DK119619, R01 DK124626
National Institutes of Health (NIH)
Georgia Clinical and Translational Science AllianceUL1TR001998
Georgia Clinical and Translational Science Alliance

    ASJC Scopus subject areas

    • General Medicine

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