The role of PGC-1α on mitochondrial function and apoptotic susceptibility in muscle
Bibliographic record
Abstract
Mitochondria are critical for cellular bioenergetics, and they mediate apoptosis within cells. We used whole body peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC-1alpha) knockout (KO) animals to investigate its role on organelle function, apoptotic signaling, and cytochrome-c oxidase activity, an indicator of mitochondrial content, in muscle and other tissues (brain, liver, and pancreas). Lack of PGC-1alpha reduced mitochondrial content in all muscles (17-44%; P < 0.05) but had no effect in brain, liver, and pancreas. However, the tissue expression of proteins involved in mitochondrial DNA maintenance [transcription factor A (Tfam)], import (Tim23), and remodeling [mitofusin 2 (Mfn2) and dynamin-related protein 1 (Drp1)] did not parallel the decrease in mitochondrial content in PGC-1alpha KO animals. These proteins remained unchanged or were upregulated (P < 0.05) in the highly oxidative heart, indicating a change in mitochondrial composition. A change in muscle organelle composition was also evident from the alterations in subsarcolemmal and intermyofibrillar mitochondrial respiration, which was impaired in the absence of PGC-1alpha. However, endurance-trained KO animals did not exhibit reduced mitochondrial respiration. Mitochondrial reactive oxygen species (ROS) production was not affected by the lack of PGC-1alpha, but subsarcolemmal mitochondria from PGC-1alpha KO animals released a greater amount of cytochrome c than in WT animals following exogenous ROS treatment. Our results indicate that the lack of PGC-1alpha results in 1) a muscle type-specific suppression of mitochondrial content that depends on basal oxidative capacity, 2) an alteration in mitochondrial composition, 3) impaired mitochondrial respiratory function that can be improved by training, and 4) a greater basal protein release from subsarcolemmal mitochondria, indicating an enhanced mitochondrial apoptotic susceptibility.
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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 | 0.000 |
| 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".