Muscle-Derived microRNAs Correlated with Thigh Lean Mass Gains during Progressive Resistance Training in Older Adults.

Aging Exercise Physiology Muscle Mass Resistance Training

Journal

Journal of applied physiology (Bethesda, Md. : 1985)
ISSN: 1522-1601
Titre abrégé: J Appl Physiol (1985)
Pays: United States
ID NLM: 8502536

Informations de publication

Date de publication:
27 Jun 2024
Historique:
medline: 27 6 2024
pubmed: 27 6 2024
entrez: 27 6 2024
Statut: aheadofprint

Résumé

Resistance training (RT) remains the most effective treatment for age-related declines in muscle mass. However, many older adults experience attenuated muscle hypertrophy in response to RT when compared to younger adults. This may be attributed to underlying molecular processes that are dysregulated by aging and exacerbated by improperly prescribed RT weekly volume, intensity, and/or frequency doses. MicroRNA (miRNA) are key epigenetic regulators that impact signaling pathways and protein expression within cells, are dynamic and responsive to exercise stimuli, and are often dysregulated in diseases. In this study, we used untargeted miRNA-seq to examine miRNA in skeletal muscle and serum-derived exosomes of older adults (n = 18, 11M/7F, 66±1y) who underwent 3x/wk RT for 30 weeks [e.g., high intensity 3x/wk (HHH, n = 9) or alternating high-low-high intensity (HLH, n = 9)], after a standardized four-week wash-in. Within each tissue, miRNAs were clustered into modules based on pairwise correlation using Weighted Gene Correlation Network Analysis (WGCNA). Modules were tested for association with the magnitude of RT-induced thigh lean mass (TLM) change (as measured by DXA). While no modules were unique to training dose, we identified miRNA modules in skeletal muscle associated with TLM gains irrespective of exercise dose. Using miRNA-target interactions, we analyzed key miRNAs in significant modules for their potential regulatory involvement in biological pathways. Findings point toward potential miRNAs that may be informative biomarkers and could also be evaluated as potential therapeutic targets as an adjuvant to RT in order to maximize skeletal muscle mass accrual in older adults.

Identifiants

pubmed: 38932684
doi: 10.1152/japplphysiol.00680.2023
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : HHS | National Institutes of Health (NIH)
ID : T32HD071866
Organisme : HHS | National Institutes of Health (NIH)
ID : P2CHD086851
Organisme : HHS | National Institutes of Health (NIH)
ID : 5R01AG017896-07

Auteurs

Samia M O'Bryan (SM)

Department of Cell, Developmental, and Integrative Biology;, University of Alabama at Birmingham, Birmingham, Alabama, United States.

Kaleen M Lavin (KM)

Human Health Performance and Resilience, Florida Institute for Human and Machine Cognition, Pensacola, FL, United States.

Zachary A Graham (ZA)

Healthspan, Resilience, and Performance Research, Florida Institute for Human and Machine Cognition, Pensacola, FL, United States.

Devin J Drummer (DJ)

Military Nutrition Division, University of Alabama at Birmingham, Natick, MA, United States.

S Craig Tuggle (SC)

Healthspan, Resilience, and Performance Research, Florida Institute for Human and Machine Cognition, Pensacola, FL, United States.

Kendall Van Keuren-Jensen (K)

Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, AZ, United States.

Rebecca Reiman (R)

Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, AZ, United States.

Eric Alsop (E)

Center for Noninvasive Diagnostics, Translational Genomics Research Institute, Phoenix, AZ, United States.

Madhavi P Kadakia (MP)

Biochem and Mol. Biol., Wright State University, Dayton, OH, United States.

Michael P Craig (MP)

Boonshoft School of Medicine, Wright State University, Dayton, OH, United States.

Jin Zhang (J)

Neuroscience, Cell Biology & Physiology, Wright State University, Dayton, OH, United States.

Marcas M Bamman (MM)

Healthspan, Resilience, and Performance Research, Florida Institute for Human and Machine Cognition, Pensacola, FL, United States.

Classifications MeSH