Transcriptome signature of resistance exercise adaptations: mixed muscle and fiber type specific profiles in young and old adults

Abstract

This investigation examined the effects of acute resistance exercise (RE), progressive resistance training (PRT), and age on the human skeletal muscle Transcriptome. Two cohorts of young and old adults [study A: 24 yr, 84 yr (n = 28); study B: 25 yr, 78 yr (n = 36)] were studied. Vastus lateralis biopsies were obtained pre- and 4 h post-RE in conjunction with the 1st and 36th (last) training session as part of a 12-wk PRT program in study A, whereas biopsies were obtained in the basal untrained state in study B. Additionally, the muscle fiber type specific (MHC I and MHC IIa) Transcriptome response to RE was examined in a subset of young and old women from study A. Transcriptome profiling was performed using HG U133 Plus 2.0 Arrays. The main findings were 1) there were 661 genes affected by RE during the 1st and 36th training bout that correlated with gains in muscle size and strength with PRT (termed the Transcriptome signature of resistance exercise adaptations); 2) the RE gene response was most pronounced in fast-twitch (MHC IIa) muscle fibers and provided additional insight into the skeletal muscle biology affected by RE; 3) skeletal muscle of young adults is more responsive to RE at the gene level compared with old adults and age also affected basal level skeletal muscle gene expression. These skeletal muscle Transcriptome findings provide further insight into the molecular basis of sarcopenia and the impact of resistance exercise at the mixed muscle and fiber type specific level.

Heterogeneity in resistance training-induced muscle strength and mass responses in men and women of different ages

“…In previous studies with healthy human subjects, e.g., several genetic variations (Riechman et al 2004;Devaney et al 2009;Walsh et al 2012;Van Deveire et al 2012), differences in skeletal muscle gene Raue et al 2012;Phillips et al 2013) and microRNA expression (Davidsen et al 2011), phosphorylation status of signaling proteins (Mayhew et al 2011;Mitchell et al 2013), androgen receptor concentrations (Ahtiainen et al 2011;Mitchell et al 2013), and satellite cell count (Petrella et al 2008) have been suggested to segregate high and low responders to RTinduced muscle hypertrophy. The physiological aspects of individual variation in phenotype responses to RT are apparently very complex phenomena and more studies specifically focused on high and low responders are required to reveal unambiguously the mechanisms of individual differences in RT-induced adaptations.…”

“…The robust study of individual responses to unilateral upper arm RT by Hubal et al (2005) showed that of 585 subjects, approximately 6 % showed practically no gains in muscle size. Also, other RT studies have reported that, in some subjects, muscle size gains are either minimal or non-existent following a training period Davidsen et al 2011;Raue et al 2012;Mitchell et al 2013;Phillips et al 2013). Similarly to muscle size responses, gains in muscle strength during RT are also highly individual (Hubal et al 2005;Erskine et al 2010).…”

Discovering Differences in Gender-related Skeletal Muscle Aging through the Majority Voting-Based Identification of Differently Expressed Genes

“…The proposed system is also compared with the system presented in [2] and [19], in which genes are identified for three categories (male young versus male old, female young versus female old and male old versus female old) from total of 54623 genes. In order to have a fair comparison, the same number of genes are selected from MFS and compared with the genes identified in [2] and [19].The evaluation metrics used in this study are: Classification accuracy, Sensitivity and Specificity.…”

“…Datasetsare publicly available in the Gene Expression Omnibus (GEO) database [2], [19]. A total of 58 individuals were involved in this investigation:…”

“…Thefirst dataset consists of around 55,000 genes with only 22 samples.While the second dataset [19] involves 55,000 genes with 36 samples. This is considered a significant challenge to machine learning methods,this means that there are a large number of features than samples.…”