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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
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
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Research Article Metabolism Muscle biology

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

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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.

Authors

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

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Figure 2

Machine learning identifies CD63 EVs as a major contributor to exercise-induced changes in adipose miR-1.

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Machine learning identifies CD63 EVs as a major contributor to exercise-...
(A) Schematic diagram outlining the workflow of CLASSify. (B) Nonlinear predictors of the exercise-induced changes in miR-1 levels in adipose tissue identified by 1 or more of the 3 nonparametric models: K-Nearest Neighbors, Neural Network, and Logistic Regression. Participants were divided into low (< 0.5-fold change; n = 7), medium (0.5–2.0-fold change; n = 15), and high (> 2.0-fold change; n = 10) miR-1 based on changes in adipose miR-1 in response to acute exercise. The relationship among exercise-induced changes in adipose miR-1 and (C) muscle miR-1 primary transcript 1b (pri-miR-1b), (D) BMI, (E) CD63 count, and (F) age. Data are expressed with min-to-max box plots. BMI, body mass index; BL, baseline; T-0, 0-minute time point; T-30, 30-minute time point; T-90, 90-minute time point.

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