VEGFA Induction in Skeletal Muscle Under Hypoxia and Exercise: A Review of the Epigenetic Role of H3K27me3
Bibliographic record
Abstract
Skeletal muscle adapts through various structural and molecular changes that support vascular remodelling. Among these, angiogenesis enhances capillary density and improves oxygen and nutrient delivery to active muscle fibres. Vascular endothelial growth factor A (VEGFA) is a key regulator of this process and is robustly induced in skeletal muscle during exercise, largely via activation of Hypoxia-inducible Factor 1-alpha (HIF-1α). However, HIF-1α activity diminishes with prolonged training, suggesting the need for additional mechanisms to sustain VEGFA expression. This review synthesizes findings from vascular biology, epigenetics, and exercise physiology to explore the potential role of histone modification H3K27me3 in regulating VEGFA expression during hypoxic and exercise-induced stress in skeletal muscle. Evidence from endothelial cells indicates that H3K27me3, a repressive histone mark, can be removed by the demethylase JMJD3 to enable VEGFA transcription in response to hypoxia. Although this mechanism is well characterized in vascular tissue, recent studies suggest similar epigenetic changes occur in skeletal muscle, particularly at promoters of exercise-responsive genes like PGC-1α. These findings support the hypothesis that epigenetic regulation through H3K27me3 demethylation may contribute to sustained VEGFA expression as HIF-1α activity declines with training. However, direct evidence in human skeletal muscle remains limited. Histone demethylation may represent a key mechanism supporting angiogenesis in skeletal muscle under exercise and hypoxic conditions. These epigenetic mechanisms may also be relevant for skeletal muscle adaptation to exercise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".