Lindholm, M. E. | bioRxiv Science

bioRxiv2019

Differential response trajectories to acute exercise in blood and muscle

A physically active lifestyle is essential for maintaining health, and is a powerful way to prevent chronic disease. However, the molecular mechanisms that drive exercise adaptation and transduce its beneficial effects, are incompletely understood. Here, we combined data from 49 studies that measured the whole transcriptome in humans before and after exercise to provide the power to draw novel observations not seen in any individual study alone. The resulting curated and standardized resource includes samples from skeletal muscle (n=1,260) and blood (n=726) in response to endurance or resistance exercise and training. Using a linear mixed effects meta-regression model selection strategy, we detected specific time patterns and novel regulatory modulators of the acute exercise response. Acute and long term responses to exercise were transcriptionally distinct. Exercise induced a more pronounced inflammatory response in skeletal muscle of older individuals. We identified multiple sex-specific response genes, where MTMR3 is a novel exercise-regulated gene. These results deepen our understanding of the transcriptional responses to exercise and provide a powerful resource for future research efforts in exercise physiology and medicine.

physiology↗

bioRxiv2019

An epigenetic clock for skeletal muscle

BackgroundAgeing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples, and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue. MethodsTo address this, we developed a more accurate, muscle-specific epigenetic clock based on the genome-wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets (18-89 years old, 22% women, 99% Caucasian), all generated with Illumina HumanMethylation arrays (HM27, HM450 or HMEPIC). We also took advantage of the large number of samples to conduct an epigenome-wide association study (EWAS) of age-associated DNA methylation patterns in skeletal muscle. ResultsThe newly developed clock uses 200 CpGs to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 CpGs of the pan-tissue clock. The muscle clock outperformed the pan-tissue clock, with a median error of only 4.6 years across datasets (vs 13.1 years for the pan-tissue clock, p < 0.0001) and an average correlation of {rho} = 0.62 between actual and predicted age across datasets (vs {rho} = 0.51 for the pan-tissue clock). Lastly, we identified 180 differentially methylated regions (DMRs) with age in skeletal muscle at a False Discovery Rate < 0.005. However, Gene Set Enrichment Analysis did not reveal any enrichment for Gene Ontologies. ConclusionsWe have developed a muscle-specific epigenetic clock that predicts age with better accuracy than the pan-tissue clock. We implemented the muscle clock in an R package called MEAT available on Bioconductor to estimate epigenetic age in skeletal muscle samples. This clock may prove valuable in assessing the impact of environmental factors, such as exercise and diet, on muscle-specific biological ageing processes.

bioinformatics↗