Mitochondrial Biogenesis Drives a Vicious Cycle of Metabolic Insufficiency and Mitochondrial DNA Deletion Mutation Accumulation in Aged Rat Skeletal Muscle Fibers
Bibliographic record
Abstract
Aged muscles possess dysfunctional fibers that contain intracellular expansions of somatically derived mitochondrial DNA deletion mutations. At high abundance, these mutations disrupt the expression of mitochondrially-encoded protein subunits of the electron transport chain resulting in aerobic respiration deficient muscle fiber segments. These fiber segments atrophy and break contributing to the loss of muscle mass and function that occurs with age. By combining micro-dissection of individual muscle fibers with microarray analysis, we observed the response induced within these abnormal muscle fibers and detected an increase in many genes affecting metabolism and metabolic regulation. The transcriptional profile and subsequent protein validation suggested that a non-compensatory program of mitochondrial biogenesis was initiated. We hypothesized that this non-adaptive program of mitochondrial biogenesis was driving mtDNA deletion mutation accumulation. We tested this hypothesis by treating aged rats with β-Guanidinopropionic acid, a compound that stimulates mitochondrial biogenesis. β-Guanidinopropionic acid treatment increased muscle mitochondrial genome copy number and resulted in a 3.7 fold increase in the abundance of electron transport chain negative muscle fiber segments. We conclude that in electron transport system abnormal muscle fiber segments, a vicious cycle of metabolic insufficiency and non-compensatory mitochondrial biogenesis drive mtDNA deletion mutation accumulation.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".