Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity.
The main results reproduced, with only marginal, non-material deviations.
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- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
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A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce the PIPELINE exactly, but the result is a 1:1 vs a LATER database version. MetaMEx's analysis code (annexes/functions.R::MetaAnalysis, a metafor::rma REML random-effects meta-analysis on shipped per-study limma/DESeq2 stats) was ported line-for-line and run on the repo's shipped per-study statistics (commit 651bbf1). CRITICAL CAVEAT: the GitHub repo ships database v3.2208 (2022); the paper reports v1 (2020), which is NOT deposited, so exact paper numbers are not expected. Result = PARTIAL: gene-level PPARGC1A reproduces near-exactly in two conditions (acute aerobic 2.24 vs 2.3-fold; inactivity -24.6% vs -25%); NR4A3 reproduces in DIRECTION in all conditions (headline 'most-regulated' gene), magnitudes drift with the larger v3.2 study set; genome-wide significant-gene counts are the same order of magnitude with acute-resistance (2354 vs 2404) and inactivity (1460 vs 1576) BH counts within ~2-7% of the paper. NOT ATTEMPTED (out of scope): the per-study limma/DESeq2 stage from raw GEO CEL/FASTQ (raw data + scripts not in repo; brief accession GSE4247 is invalid), and wet-lab/manual results. Reported paper values were auto-extracted from PMC full text and must be human-verified against the figures.
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Assessment versions
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v1 current initial assessment Score 52assessed: 2026-06-16 ⛓ beed7d1c2320
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Provenance — full disclosure
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- Reproduced
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusCan a meta-analysis integrating publicly available skeletal muscle transcriptomic datasets identify the molecular pathways and genes selectively regulated by inactivity versus aerobic versus resistance and acute versus chronic exercise, and characterize the role of NR4A3 in exercise-induced metabolic responses?
- ★ A meta-analysis (MetaMEx) of 66 published human skeletal muscle datasets reveals pathways selectively activated by inactivity, aerobic versus resistance, and acute versus chronic exercise training. finding
- ★ NR4A3 is one of the most exercise- and inactivity-responsive genes and mediates metabolic responses to exercise-like stimuli in vitro. mechanism
- ★ MetaMEx provides the most extensive resource of skeletal muscle transcriptional responses to exercise/inactivity with an online interface (www.metamex.eu) for interrogating the database. resource
- ★ Meta-analysis of transcriptomic data using restricted maximum likelihood integrates fold-changes across heterogeneous studies, yielding far more significant genes than individual studies. method
- ★ Acute exercise, exercise training, and inactivity segregate as distinct transcriptomic responses, with little difference between resistance and aerobic exercise. finding
- MetaMEx highlights differential responses to exercise in individuals with metabolic impairments versus healthy individuals. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| transcriptomic meta-analysis (microarray/RNA-seq integration) | human skeletal muscle (vastus lateralis, biceps brachii, quadriceps femoris) | acute/chronic aerobic and resistance exercise and inactivity | gene-level fold-change and FDR across studies | — |
| qPCR validation | human skeletal muscle from independent healthy cohorts | aerobic and resistance exercise | mRNA expression of top exercise-responsive genes | — |
| in vitro metabolic characterization | skeletal muscle cells (in vitro) | exercise-like stimuli; NR4A3 manipulation | metabolic responses | — |
| principal component analysis / correlation matrix / chord plot | healthy human skeletal muscle datasets | exercise modality and inactivity | clustering and correlation of fold-changes across studies | — |
- ▲ PPARGC1A increased after acute aerobic exercise 2.3-fold (95% CI [1.6, 3.5])
- ▲ PPARGC1A increased after acute resistance exercise 1.8-fold (95% CI [1.6, 2.2])
- ▼ PPARGC1A decreased by inactivity 25%
- ▲ PPARGC1A change greatest in biopsies taken after recovery period (>2h) versus immediately post-exercise 4.4-fold (95% CI [3.0, 6.4])
- ▲ NR4A3 is the top exercise-responsive gene, strongly induced by acute aerobic and acute resistance exercise logFC 2.99 (aerobic); logFC 2.95 (resistance)
- – Acute aerobic and acute resistance exercise shared 360 commonly changed genes, while aerobic and resistance training shared only 25 360 vs 25 genes
- – Number of significantly responsive genes (FDR < 0.1%) per perturbation 897 acute aerobic; 2404 acute resistance; 1576 inactivity; 82 aerobic training; 2049 resistance training
- ▲ qPCR validation in independent cohorts correlated highly with MetaMEx gene responses
- fold_change 2.3-fold (95% CI [1.6, 3.5]) (PPARGC1A after acute aerobic exercise)
- fold_change 1.8-fold (95% CI [1.6, 2.2]) (PPARGC1A after acute resistance exercise)
- fold_change 4.4-fold (95% CI [3.0, 6.4]) (PPARGC1A in biopsies after >2h recovery)
- fold_change logFC 2.99, FDR 2.0E-07 (NR4A3 acute aerobic)
- fold_change logFC 2.95, FDR 8.2E-15 (NR4A3 acute resistance)
- count 66 datasets / studies (total transcriptomic studies in MetaMEx)
- count more than 1100 individuals (total subjects across meta-analysis)
- count 897, 2404, 1576, 82, 2049 (responsive genes FDR<0.1% for acute aerobic, acute resistance, inactivity, aerobic training, resistance training)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a meta-analysis integrating 66 published human skeletal muscle transcriptomic datasets (>1100 individuals) across acute aerobic, acute resistance, aerobic training, resistance training and inactivity conditions. Per-gene effects were combined using a restricted maximum likelihood (REML) random-effects meta-analysis to compute pooled fold-changes and significance, with results reported as log fold-change, FDR and 95% confidence intervals (e.g., forest plots, M-plots, Venn diagrams). Exploratory inter-study structure was assessed with PCA, chord plots and fold-change correlation matrices, and top hits were further examined by qPCR in independent validation cohorts and by in vitro functional work on NR4A3.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Restricted maximum likelihood (REML) random-effects meta-analysis of per-gene effect sizes | Pooled fold-change and significance for each exercise/inactivity-responsive gene across studies (Fig. 1, Fig. 2, Fig. 3, Table 3) | 66 datasets / >1100 individuals; study counts per condition given in Tables 1-2 (e.g., 12 acute aerobic, 8 acute resistance, 7 inactivity, 11 aerobic training, 13 resistance training) | not stated |
| Principal component analysis (PCA) | Clustering of gene responses by intervention across healthy datasets (Fig. 2a) | — | na |
| Correlation analysis of fold-changes (correlation matrix / chord plot) | Inter-study similarity of fold-changes across all common genes (Fig. 2b, c) | — | not stated |
| Correlation between qPCR validation-cohort responses and MetaMEx estimates | Validation of top modality-specific genes in independent cohorts (Supplementary Fig. 2) | — | not stated |
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Pooled effects were combined using a restricted maximum likelihood random-effects meta-analysis.↳ Could also: A DerSimonian-Laird random-effects model, or a fixed-effect inverse-variance model, could also be used to pool study-level effects. — Comparing estimators (REML vs DerSimonian-Laird vs fixed-effect) can show how robust pooled estimates and confidence intervals are to the choice of between-study variance estimator.
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Between-study variability was handled within the random-effects framework.↳ Could also: Heterogeneity statistics such as I² or Cochran's Q, and prediction intervals alongside confidence intervals, could also be reported. — These quantify how much effects vary across studies and how generalizable a pooled estimate is, complementing the pooled point estimate.
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Multiple-comparison control was reported via FDR thresholds (e.g., <1%, <0.1%).↳ Could also: Naming the specific FDR procedure (e.g., Benjamini-Hochberg or Storey's q-value) could also be done. — Stating the exact procedure makes the multiplicity adjustment fully reproducible and lets readers map thresholds to expected false-discovery proportions.
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Inter-study structure was summarized with PCA, chord plots and fold-change correlation matrices.↳ Could also: Hierarchical clustering with bootstrap support, or model-based batch/covariate adjustment (e.g., including platform or study as a random factor), could also be used. — These approaches can quantify clustering confidence and separate biological signal from platform/study-level technical variation across heterogeneous datasets.
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Top genes were validated by qPCR and assessed via correlation with MetaMEx estimates.↳ Could also: Concordance metrics such as Lin's concordance correlation or Bland-Altman agreement could also accompany the correlation. — Agreement-based metrics capture systematic offsets in magnitude, not just rank/linear association, when comparing platforms.
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Subgroups were defined as Healthy versus Metabolically Impaired due to study heterogeneity.↳ Could also: Meta-regression or formal subgroup interaction tests on covariates (e.g., BMI, age, sex, muscle type) could also be applied. — Meta-regression models can directly estimate how moderators relate to the transcriptional response rather than relying on dichotomized grouping.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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NR4A3 is the top exercise-responsive gene, strongly induced by acute aerobic and acute resistance exercise.RNA-seq human skeletal muscle up 2020×1papers★ This paper is the founder (earliest)
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PPARGC1A is downregulated by physical inactivity (~25%).RNA-seq human skeletal muscle down 2020×1papers★ This paper is the founder (earliest)
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PPARGC1A is upregulated after acute aerobic exercise (~2.3-fold).RNA-seq human skeletal muscle up 2020×1papers★ This paper is the founder (earliest)
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The MetaMEx meta-analysis pipeline was ported line-for-line (metafor::rma REML on shipped per-study stats) and reproduces robustly: PPARGC1A lands at 2.24 vs 2.3-fold (acute aerobic) and -24.6% vs -25% (inactivity), and NR4A3 reproduces in direction in all conditions. The deviations (NR4A3 3.99 vs 2.99; overlaps 360 vs 185/935; counts off ~2-22%) sit on the input/data-availability side: the repo only ships the larger v3.2208 (2022) database whereas the paper used v1 (2020), which is undeposited, plus a BH-vs-Bonferroni FDR ambiguity. This is a solid, explainable reproduction with no fabrication signal — not an authors' defect — limited mainly by version drift and the original version not being archived.
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