Skeletal Muscle Ribosome and Mitochondrial Biogenesis in Response to Different Exercise Training Modalities
Bibliographic record
Abstract
Skeletal muscle adaptations to resistance and endurance training include increased ribosome and mitochondrial biogenesis, respectively. Such adaptations are believed to contribute to the notable increases in hypertrophy and aerobic capacity observed with each exercise mode. Data from multiple studies suggest the existence of a competition between ribosome and mitochondrial biogenesis, in which the first adaptation is prioritized with resistance training while the latter is prioritized with endurance training. In addition, reports have shown an interference effect when both exercise modes are performed concurrently. This prioritization/interference may be due to the interplay between the 5’ AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin complex 1 (mTORC1) signaling cascades and/or the high skeletal muscle energy requirements for the synthesis and maintenance of cellular organelles. Negative associations between ribosomal DNA and mitochondrial DNA copy number in human blood cells also provide evidence of potential competition in skeletal muscle. However, several lines of evidence suggest that ribosome and mitochondrial biogenesis can occur simultaneously in response to different types of exercise and that the AMPK-mTORC1 interaction is more complex than initially thought. The purpose of this review is to provide in-depth discussions of these topics. We discuss whether a curious competition between mitochondrial and ribosome biogenesis exists and show the available evidence both in favor and against it. Finally, we provide future research avenues in this area of exercise physiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".