Regulatory networks coordinating mitochondrial quality control in skeletal muscle
Bibliographic record
Abstract
The adaptive plasticity of mitochondria within a skeletal muscle is regulated by signals converging on a myriad of regulatory networks that operate during conditions of increased (i.e., exercise) and decreased (inactivity, disuse) energy requirements. Notably, some of the initial signals that induce adaptive responses are common to both conditions, differing in their magnitude and temporal pattern, to produce vastly opposing mitochondrial phenotypes. In response to exercise, signaling to peroxisome proliferator-activated receptor (PPAR)-γ coactivator-1α (PGC-1α) and other regulators ultimately produces an abundance of high-quality mitochondria, leading to reduced mitophagy and a higher mitochondrial content. This is accompanied by the presence of an enhanced protein quality control system that consists of the protein import machinery as well chaperones and proteases termed the mitochondrial unfolded protein response (UPRmt). The UPRmt monitors intraorganelle proteostasis, and strives to maintain a mito-nuclear balance between nuclear- and mtDNA-derived gene products via retrograde signaling from the organelle to the nucleus. In addition, antioxidant capacity is improved, affording greater protection against oxidative stress. In contrast, chronic disuse conditions produce similar signaling but result in decrements in mitochondrial quality and content. Thus, the interactive cross talk of the regulatory networks that control organelle turnover during wide variations in muscle use and disuse remain incompletely understood, despite our improving knowledge of the traditional regulators of organelle content and function. This brief review acknowledges existing regulatory networks and summarizes recent discoveries of novel biological pathways involved in determining organelle biogenesis, dynamics, mitophagy, protein quality control, and antioxidant capacity, identifying ample protein targets for therapeutic intervention that determine muscle and mitochondrial health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".