Distinct Skeletal Muscle Gene Regulation from Active Contraction, Passive Vibration, and Whole Body Heat Stress in Humans
Bibliographic record
Abstract
UNLABELLED: Skeletal muscle exercise regulates several important metabolic genes in humans. We know little about the effects of environmental stress (heat) and mechanical stress (vibration) on skeletal muscle. Passive mechanical stress or systemic heat stress are often used in combination with many active exercise programs. We designed a method to deliver a vibration stress and systemic heat stress to compare the effects with active skeletal muscle contraction. PURPOSE: The purpose of this study is to examine whether active mechanical stress (muscle contraction), passive mechanical stress (vibration), or systemic whole body heat stress regulates key gene signatures associated with muscle metabolism, hypertrophy/atrophy, and inflammation/repair. METHODS: Eleven subjects, six able-bodied and five with chronic spinal cord injury (SCI) participated in the study. The six able-bodied subjects sat in a heat stress chamber for 30 minutes. Five subjects with SCI received a single dose of limb-segment vibration or a dose of repetitive electrically induced muscle contractions. Three hours after the completion of each stress, we performed a muscle biopsy (vastus lateralis or soleus) to analyze mRNA gene expression. RESULTS: We discovered repetitive active muscle contractions up regulated metabolic transcription factors NR4A3 (12.45 fold), PGC-1α (5.46 fold), and ABRA (5.98 fold); and repressed MSTN (0.56 fold). Heat stress repressed PGC-1α (0.74 fold change; p < 0.05); while vibration induced FOXK2 (2.36 fold change; p < 0.05). Vibration similarly caused a down regulation of MSTN (0.74 fold change; p < 0.05), but to a lesser extent than active muscle contraction. Vibration induced FOXK2 (p < 0.05) while heat stress repressed PGC-1α (0.74 fold) and ANKRD1 genes (0.51 fold; p < 0.05). CONCLUSION: These findings support a distinct gene regulation in response to heat stress, vibration, and muscle contractions. Understanding these responses may assist in developing regenerative rehabilitation interventions to improve muscle cell development, growth, and repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".