Impact of Voluntary Wheel Running on Gene Expression of Histone 3 Lysine 27-Modifying Enzymes in Mouse Skeletal Muscle
Bibliographic record
Abstract
Abstract Skeletal muscle is highly plastic and capable of remodelling its contractile and metabolic properties depending on physical demands. Such remodelling requires modification of chromatin structure to support transcriptional activation and suppression of gene programs. Chromatin dynamics depend, in part, on the acetylation and methylation of histone 3 lysine 27 (H3K27), which is controlled by several H3K27-specific acetyltransferases, deacetylases, methyltransferases and demethylases. Several histone post-translational modifications in muscle have been shown to be modulated by exercise. Here, we sought to examine whether major H3K27 regulators are altered by endurance training. Male and female C57BL/6J mice were provided with voluntary running wheels for 6 weeks and compared to sex-matched sedentary controls with locked running wheels. Gene expression of various slow/oxidative and fast/glycolytic skeletal muscles was then measured. We found voluntary running induced modest changes in H3K27 acetyltransferases, along with several components of the polycomb-repressive complex 2 in a muscle- and sex-specific manner. Our findings indicate that the capacity for both acetylation and methylation of H3K27 is modulated by chronic endurance exercise, suggesting that chromatin dynamics are a mechanistic component of exercise-induced muscle remodelling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".