Myogenin Regulates Exercise Capacity and Skeletal Muscle Metabolism in the Adult Mouse
Bibliographic record
Abstract
Although skeletal muscle metabolism is a well-studied physiological process, little is known about how it is regulated at the transcriptional level. The myogenic transcription factor myogenin is required for skeletal muscle development during embryonic and fetal life, but myogenin's role in adult skeletal muscle is unclear. We sought to determine myogenin's function in adult muscle metabolism. A Myog conditional allele and Cre-ER transgene were used to delete Myog in adult mice. Mice were analyzed for exercise capacity by involuntary treadmill running. To assess oxidative and glycolytic metabolism, we performed indirect calorimetry, monitored blood glucose and lactate levels, and performed histochemical analyses on muscle fibers. Surprisingly, we found that Myog-deleted mice performed significantly better than controls in high- and low-intensity treadmill running. This enhanced exercise capacity was due to more efficient oxidative metabolism during low- and high-intensity exercise and more efficient glycolytic metabolism during high-intensity exercise. Furthermore, Myog-deleted mice had an enhanced response to long-term voluntary exercise training on running wheels. We identified several candidate genes whose expression was altered in exercise-stressed muscle of mice lacking myogenin. The results suggest that myogenin plays a critical role as a high-level transcriptional regulator to control the energy balance between aerobic and anaerobic metabolism in adult skeletal muscle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".