Gene expression variability in human skeletal muscle transcriptome responses to acute resistance exercise
Bibliographic record
Abstract
NEW FINDINGS: What is the central question of this study? Does exercise, independent of random error and within-subject variability, contribute to the variability in gene expression responses to an acute bout of resistance exercise? What is the main finding and its importance? A reanalysis of publicly available microarray data revealed that variability in observed gene expression responses for a subset of genes could be partially attributable to an effect of acute resistance exercise. These finding support the notion that individual responsiveness explains a portion of the variability in observed gene expression responses to acute resistance exercise. ABSTRACT: The purpose of this study was to use publicly available transcriptomic data to determine whether variability in gene expression responses to an acute bout of acute resistance exercise (ARE) can be attributable to an effect of ARE per se. We examined microarray data from a previous study that collected skeletal muscle biopsies before and 24 h after ARE or a no-exercise time-matched control period (CTL). By subtracting the standard deviation in the observed responses to CTL from ARE, we determined that ARE contributed to the variability in the observed gene expression responses for many (∼31,000), but not all, transcripts included on the Affymetrix Human Genome chips. ARE had a large effect on variability in the observed gene expression responses in 1290 genes that was not attributed to any technical/biological variability associated with repeated measurements. Pathway analysis using WebGestalt revealed that several of these 1290 genes are involved in pathways known to regulate skeletal muscle adaptations to chronic resistance training. These results suggest that variability in the observed gene expression responses for a subset of genes could be partially attributable to an effect of ARE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".