How is Mitochondrial Biogenesis Affected in Mitochondrial Disease?
Bibliographic record
Abstract
Mitochondrial biogenesis occurs when the tissue energy demand is chronically increased to stress the ATP producing capacity of the preexisting mitochondria. In muscle, endurance training is a metabolic stress that is capable of inducing mitochondrial biogenesis, the consequence of which is improved performance during exercise. Expansion of the mitochondrial volume requires the coordinated response of the nuclear and mitochondrial genomes. During acute exercise, the initial signaling events are the perturbations in ATP turnover and calcium (Ca) concentrations caused by the contractile process. These alterations activate signal transduction pathways which target transcription factors involved in gene expression. Nuclear gene products are then posttranslationally imported into mitochondria. One of these, Tfam, is important for the regulation of mitochondrial DNA (mtDNA) gene expression. In muscle, a broad range of mitochondrial-specific diseases due to mutations in nuclear DNA or mtDNA exist, termed mitochondrial myopathies. These mutations result in dysfunctional mitochondrial assembly which ultimately leads to reduced ATP production. Mitochondrial myopathy patients exhibit a variety of compensatory responses which attempt to reconcile this energy deficiency, but the extent and the type of compensatory adaptations are disease-specific. Understanding the role of exercise in mediating these compensatory responses leading to mitochondrial biogenesis could help us in prescribing exercise designed to improve mitochondrial function in patients with mitochondrial myopathies. In addition, numerous other diseases (e.g., neurological disorders, cancer, diabetes, and cardiomyopathies), as well as the aging process, have etiologies or consequences attributed, in part, to mitochondrial dysfunction. Thus, insight gained by investigating the steps involved in exercise-induced mitochondrial biogenesis may help us to understand the underlying basis of these other disease states.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".