Meta-analysis of Skeletal Muscle Response to Exercise
The app to meta-analyse skeletal muscle transcriptomic response to inactivity and exercise. Use MetaMEx to get a complete overview the behavior of a specific gene accross all published exercise and inactivity transcriptomic studies.
Use the meta-analysis to obtain a forest plot of the expression of your gene of interest in all available studies.
Customize your population of interest
Use the correlation to find genes with an exercise response correlated to your gene of interest. The analysis includes all exercise and inactivity studies.
Find the legend used for the annotation of studies.
Find the references for all studies of acute exercise included in MetaMEx.
Find the references for all studies of exercise training included in MetaMEx.
Find the references for all studies of inactivity included in MetaMEx.
Welcome to MetaMEx
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 in all available transcriptomic datasets. This section summarizes and explains the main analyses available on MetaMEx.
Start typing the gene name and suggestions will appear in the scroll menu. MetaMEx works with official gene symbols, for instance the official gene name of PGC1α is PPARGC1A.
In order to give a transparent overview of the currently available data, all studies are presented, even if genes are not detected. Older studies, or custom arrays often have a limited number of probes and therefore fewer detected genes. On the other hand, the more recent RNA sequencing datasets often have more depth and detect non-coding RNAs which are not present in gene arrays.
A forest plot is a graphical representation of results from several scientific studies and is typically used to plot meta-analyses. 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.
MetaMEx compiles more than 90 studies which include volunteers of different age, sex, weight, fitness, weight and health. Studies can be included or excluded from the analysis by scrolling at the bottom of the page and checking the boxes. For instance, select males or females by checking the corresponding tick boxes.
- Sex. Choose whether you want males (M) or females (F). Some studies have pooled males and females or did not provide sex information and are labelled as undefined (U).
- Age. Studies in MetaMEx are split into three age groups: young (<35), middle age (35-60) and elderly (>60).
- Fitness. Activity levels were determined based on the description of the cohorts available in the publications. Sedentary is defined as no formal exercise training. Individuals performing exercise for more than 150 min per week and/or having an average VO2max are considered active. Athletes are individuals engaged in formal and regular exercise training and exhibit good to excellent VO2max.
- Weight. Body composition is based on body mass index provided in the publications and the actual definition of lean (BMI<25), overweight (25≤BMI<30), obese (30≤BMI<40) and morbidly obese (BMI≥40).
- Muscle. Most studies do cycling exercise and therefore collect vastus lateralis (quadriceps) biopsies. However, a handful of studies used soleus or biceps biopsies. Sometimes the muscle biopsy is unknow and is therefore annotated as N.A.
Health. MetaMEx includes studies from healthy individuals with no history of disease as well as people diagnosed with metabolic diseases or other chronic conditions such as chronic kidney disease or frailty.
After selecting either acute exercise, exercise training or inactivity, a specific menu will appear on the right of the page. This menu includes parameters such as exercise duration or time of biopsy collection after exercise cessation. Another list will appear under the forest plot to select or unselect specific datasets.
- Acute exercise studies. For acute exercise protocols, it is possible to customize the type of exercise (concentric, eccentric or mixed) and the time of the biopsy collection after exercise cessation.
- Exercise training studies. For exercise training protocols, it is possibly to customize the duration of the training (from 1 week to lifelong) and the time of the biopsy collection after exercise cessation. It is also possible to include/exclude specific studies based on their GEO accession number.
Inactivity studies. MetaMEx includes two inactivity protocols: bed rest or limb immobilization. It is also possible to customize the duration of the inactivity and include/exclude specific studies based on their GEO accession number.
All studies were annotated with as much information as possible about age, weight, health, biopsy, muscle, etc. The title of the studies reflects the clinical data and protocol used for a specific study.
A detailed description of the labels is availabe in Datasets/Annotation.
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 performed individually for each array.
- Robust multiarray averaging was used for affymetrix arrays (oligo package)
- Quantile normalization was used for other microarrays (limma package)
- Variance stabilizing transformation (VST) was used for RNA sequencing datasets (DESeq2 package).
- Moderated t-statistics were calculated for each study with empirical Bayes statistics for differential expression (limma package).
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.
The timeline was calculated by collecting all data available in the database and annotate them by either time of the biopsy or inactivity duration. Moderated t-statistics were calculated with empirical Bayes statistics after blocking for other confounding parameters (sex, age, exercise type…).
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 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 th