The online database of skeletal muscle transcriptomic response to exercise and inactivity. On this website, you will be able to explore how specific genes respond to acute exercise, exercise training and inactivity. Let’s start!
MetaMEx works with official gene symbols, for instance here, the official gene name of PGC1α is PPARGC1A. MetaMEx compiles more than 90 studies performed under slighlty different conditions. So first, have a look at the following lists:
All studies were annotated with as much information as possible about age, weight, health, biopsy, muscle, etc. The title of the studies reflect the clinical data and protocol used for a specific study. See Datasets>Annotation for a detailled description of the labels.
The left-hand columns list the names of the studies, followed by the fold-change (log2), false discovery rate (FDR) and sample size (n) for each individual study. The right-hand column is a plot of the fold-change (log2) represented by a square and the 95% confidence intervals represented by horizontal lines. The area of each square is proportional to the study's weight (sample size) in the meta-analysis. The overall meta-analysed score is represented by a diamond on the bottom line, the lateral points of which indicate confidence intervals.
Old arrays, or custom arrays often have a limited number of detected genes. For instance, the GSE7286 dataset only includes data for about 1000 genes. On the other hand, the more recent RNA sequencing datasets often have more than 20.000 genes. In order to give a transparent overview of the currently available data, all studies are presented, even if genes are not detected.
Whenever possible, we downloaded the raw data and re-processed studies using the same pipeline. That means that the normalization methods that we used might differ from the ones used by the original authors. In addition, samples were often insufficiently annotated to allow us to run proper paired-statistics comparing pre/post interventions. We therefore had to used unpaired statistics and lost power in the process. Finally, many studies pooled individuals of different age and BMI to have higher sample size. To allow proper comparison in the meta-analysis, we split these studies into sub groups and analyzed them separately, therefore reducing the sample size and statistical power.
The meta-analysis was created by collecting publicly available studies on mRNA expression levels in human skeletal muscle after exercise or inactivity. Statistics were first perfomed individually for each array.
The meta-analysis summary was calculated using restricted maximum likelihood (metafor package). The analysis was weighted using sample size (n) to adjust for studies with small number of volunteers.
Studies that have used the MetaMEx database:
MetaMEx content and code are published under the Creative Commons Attribution-NonCommercial 4.0 International CC BY-NC 4.0.
If you use data from MetaMEx for publication, teaching or scientific presentations, please cite: Nicolas J. Pillon, Brendan M. Gabriel, Lucile Dollet, Jonathon A. Smith, Laura Sardon Puig, Javier Botella, David J. Bishop,Anna Krook and Juleen R. Zierath. Transcriptomic Profiling of Skeletal Muscle Adaptations to Exercise and Inactivity. Nat Commun. 2020; 11:470.
This work was supported by the Marie Sklodowska-Curie Actions (European Commission), the Novo Nordisk Foundation, the Swedish Diabetes Foundation, the Swedish Research Council, the Strategic Research Program in Diabetes at Karolinska Institutet, the Stockholm County Council, the Swedish Research Council for Sport Science and the EFSD European Research Programme on New Targets for Type 2 Diabetes supported by an educational research grant from MSD.