Exercise is associated with younger methylome and transcriptome profiles in human skeletal muscle

Exercise training prevents age-related decline in muscle function. Targeting epigenetic aging is a promising actionable mechanism and late-life exercise mitigates epigenetic aging in rodent muscle. Whether exercise training can decelerate, or reverse epigenetic aging in humans is unknown. Here, we performed a powerful meta-analysis of the methylome and transcriptome of an unprecedented number of human skeletal muscle samples (n = 3176). We show that: (1) individuals with higher baseline aerobic fitness have younger epigenetic and transcriptomic profiles, (2) exercise training leads to significant shifts of epigenetic and transcriptomic patterns toward a younger profile, and (3) muscle disuse "ages" the transcriptome. Higher fitness levels were associated with attenuated differential methylation and transcription during aging. Furthermore, both epigenetic and transcriptomic profiles shifted toward a younger state after exercise training interventions, while the transcriptome shifted toward an older state after forced muscle disuse. We demonstrate that exercise training targets many of the age-related transcripts and DNA methylation loci to maintain younger methylome and transcriptome profiles, specifically in genes related to muscle structure, metabolism, and mitochondrial function. Our comprehensive analysis will inform future studies aiming to identify the best combination of therapeutics and exercise regimes to optimize longevity

Ancillary Table A1

<p>FASEBJ Supplemental Material for The human skeletal muscle transcriptome: sex differences, alternative splicing and tissue homogeneity assessed with RNA sequencing</p

Supplemental Material S1

<p>Supplemental material for FASEBJ manuscript 14-255000: The human skeletal muscle transcriptome: sex differences, alternative splicing and tissue homogeneity assessed with RNA sequencing</p

The mitochondrial multi-omic response to exercise training across tissues

Mitochondria volume adjusted differential analysis results RData files represent the omics differential analysis results as a single large table per ome. In R use the `load` command to add the data frame to your session. These results files were generated using code from this repo: https://github.com/MoTrPAC/motrpac-rat-training-mitochondria, specifically see the notebooks in the dea folder. Each ome has its own file: TRANSCRPT (for RNA-seq transcriptomics, PROT for protein abundance, and PHOSPHO for phosphosites). Each file provides a data frame with the result of each specific time point per sex. These differential analysis results are based on a single model of all time points per sex, for each measured analyte. For example, in the TRNSCRPT table, consider all male time points of a gene, then their statistics are based on a single DESeq2 model, from which timewise contrasts were extracted. The data field "selection_fdr" can be used to identify the analytes that were selected at 5% FDR when considering all time points. This is referred to as the "training-level" analysis in the main paper, see https://www.biorxiv.org/content/10.1101/2022.09.21.508770v2.abstract. For the full documentation of these data frames and their fields see the following GitHub repository: motrpac.github.io/MotrpacRatTraining6moData After downloading and installing the package, see e.g., ? TRNSCRPT_ADRNL_DA for the full description of the data format of the adrenal gland differential analysis results. This will include the description of each data field including: feature, assay, sex, comparison group, logFC, p-value, etc. Cardiolipin data Cardiolipin quantification per tissue is available as csv files

Endurance training changes muscle DNA methylation

<p>Data from the paper:</p> <p><strong>An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training</strong></p> <p>Maléne E Lindholm*, Francesco Marabita*, David Gomez-Cabrero, Helene Rundqvist, Tomas J Ekström, Jesper Tegnér & Carl Johan Sundberg</p> <p>*equal contribution</p> <p>Accepted for publication in Epigenetics</p

Research.chalmers.se : building the next generation research information infrastructure at Chalmers University of Technology

research.chalmers.se is the new research infrastructure - repository platform and CRIS - that is currently being developed by the library at Chalmers University of Technology, Sweden.With the aim of eventually becoming the place for all relevant research information at the university, and built in accordance with the principles of UX (User eXperience) and agile development (SCRUM) and with a steady focus on value and continuous deliveries, research.chalmers.se is set to replace most of the current research information infrastructure at the university. It will provide new services for collecting, curating and providing quality data, as well as tools for analysis, sharing and promotion of research output by new and up-to-date means.research.chalmers.se is (or will be)- creating and preserving values - research output and data repository with qualitativedata, researcher profiles, open access.- promoting open access publishing, by proving the value of knowledge sharing, visibilityand impact as a researcher.- collecting and curating all kinds of research information, including publications,research data and information about research projects and other research activities.- built with responsive design that is adapted to current user needs. - using current standards for validity and sustainability, such as ORCID, DOI andFundRef IDs.- providing new ways of exploring and analyzing data, such as altmetrics, open APIs andvisualization tools.In the first phase (starting in 2014) a complete system for handling research project and grant information has been developed, together with integration of the local HR system for persistent and structured data about staff and organisation.In the current phase (2016-) repository services are being developed, such as a new publication database and a new digital repository, along with services for sharing and collecting data.The next phase(s) will include handling of research data, research activities, learning objects and tools for bibliometric analysis.This poster will show some of the current and future features and the principles of UX and agile development, as well as the experiences of moving out of the comfort zone and dealing with new, non-publication related data, while sustaining and enhancing existing data and current services. It will discuss the challenges and possible solutions, when handling different kinds of research information for use and re-use, in the long run enabling the comprehension of the big picture of research at Chalmers University of Technology