Tuning of mitochondrial pathways by muscle work: from triggers to sensors and expression signaturesThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Performance of striated muscle relies on the nerve-driven activation of the sarcomeric motor and coupled energy supply lines. This biological engine is unique; its mechanical and metabolic characteristics are not fixed, but are tailored by functional demand with exercise. This remodelling is specific for the imposed muscle stimulus. This is illustrated by the increase in local oxidative capacity with highly repetitive endurance training vs. the preferential initiation of sarcomerogenesis with strength training regimes, where high-loading increments are imposed. The application of molecular biology has provided unprecedented insight into the pathways that govern muscle plasticity. Time-course analysis indicates that the adjustments to muscle work involve a broad regulation of transcript expression during the recovery phase from a single bout of exercise. Highly resolving microarray analysis demonstrates that the specificity of an endurance-exercise stimulus is reflected by the signature of the transcriptome response after muscle work. A quantitative match in mitochondrial transcript adjustments and mitochondrial volume density after endurance training suggests that the gradual accumulation of expressional microadaptations underlies the promotion of fatigue resistance with training. This regulation is distinguished from control of muscle growth via the load-dependent activation of sarcomerogenesis. Discrete biochemical signalling systems have evolved that sense metabolic perturbations during exercise and trigger a specific expression program, which instructs the remodelling of muscle makeup. A drop in muscle oxygen tension and metabolite perturbations with exercise are recognized as important signals in the genome-mediated remodelling of the metabolic muscle phenotype in humans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".