MetAR: A semi-automated meta-analysis of skeletal muscle androgen receptors association with age

Abstract

Ageing is associated with reduced skeletal muscle mass and function. Testosterone, the main androgenic hormone, exerts its anabolic effects in muscle through androgen receptor (AR) mediated pathways, with evidence suggesting that AR availability may represent a rate-limiting factor in androgen signalling. Rodent studies report age-related reductions in AR expression, however findings in human muscle are inconclusive. This study aimed to characterise age-related changes in AR expression and its upstream and downstream regulators in human skeletal muscle. To this end, we developed a novel R-based pipeline, MetAR, for reproducible meta-analyses of publicly available RNA sequencing (RNA-seq) datasets, to investigate associations between target gene expression and variables of interest. Eligible datasets included skeletal muscle samples from healthy adult males aged [≥]18 years, with an age range of [≥] 10 years and sample size [≥] 6. Raw counts data were downloaded, appraised and trimmed mean of M-values (TMM) normalised. Dataset-level associations between age and target gene expression were modelled and random-effects meta-analyses performed. Sixteen RNA-seq datasets (n = 364; age 18-92 years) were included. AR expression was negatively associated with age ({beta} = -0.006 log2 TMM-Counts Per Million (CPM) per year, p < 0.001, 4.4% decline per decade, I-squared=53.8%, 95% prediction interval [-0.0143 to +0.0014]). Age was associated with a reduction in expression of regulators of AR stability, transcriptional activity and nuclear transport, as well as downstream regulators relevant to AR signalling. These findings suggest multi-faceted age-associated remodelling of AR expression, signalling and nuclear transport, potentially explaining a component of age-associated muscle loss.