Transcriptome features of striated muscle aging and predictability of protein level changes

AbstractRNA and protein levels correlate only partially and some transcripts are better correlated with their protein counterparts than others. This suggests that in aging and disease studies, some transcriptomics markers may carry more information in predicting protein-level changes. Here we applied a computational data analysis workflow to predict which transcriptomic changes are more likely relevant to protein-level regulation in striated muscle aging. The protein predictability of each transcript is estimated from existing large proteogenomics data sets, then transferred to new total RNA sequencing data comparing skeletal muscle and cardiac muscle in young adult (~4 months) mice vs. early aging (~20 months) mice. Aging cardiac and skeletal muscles both invoke transcriptomic changes in innate immune system and mitochondria pathways but diverge in extracellular matrix processes. On an individual gene level, we identified 611 age-associated signatures in skeletal and cardiac muscles at 10% FDR, including a number of myokine and cardiokine encoding genes. We estimate that about 48% of the aging-associated transcripts may predict protein levels well (r ≥ 0.5). In parallel, a comparison of the identified aging-regulated genes with public human transcriptomics data showed that only 35–45% of the identified genes show an age-dependent expression in corresponding human tissues. Finally, integrating both RNA-protein correlation and human conservation across data sources, we nominate 134 prioritized aging striated muscle signature genes that are predicted to correlate strongly with protein levels and that show age-dependent expression in humans. These prioritized signatures may hold promise to understanding heart and skeletal muscle physiology in human and mouse aging.