Exercise Increases Mitochondrial PGC-1α Content and Promotes Nuclear-Mitochondrial Cross-talk to Coordinate Mitochondrial Biogenesis
Post-publication record
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Bibliographic record
Abstract
Endurance exercise is known to induce metabolic adaptations in skeletal muscle via activation of the transcriptional co-activator peroxisome proliferator-activated receptor γ co-activator 1α (PGC-1α). PGC-1α regulates mitochondrial biogenesis via regulating transcription of nuclear-encoded mitochondrial genes. Recently, PGC-1α has been shown to reside in mitochondria; however, the physiological consequences of mitochondrial PGC-1α remain unknown. We sought to delineate if an acute bout of endurance exercise can mediate an increase in mitochondrial PGC-1α content where it may co-activate mitochondrial transcription factor A to promote mtDNA transcription. C57Bl/6J mice (n = 12/group; ♀ = ♂) were randomly assigned to sedentary (SED), forced-endurance (END) exercise (15 m/min for 90 min), or forced endurance +3 h of recovery (END+3h) group. The END group was sacrificed immediately after exercise, whereas the SED and END+3h groups were euthanized 3 h after acute exercise. Acute exercise coordinately increased the mRNA expression of nuclear and mitochondrial DNA-encoded mitochondrial transcripts. Nuclear and mitochondrial abundance of PGC-1α in END and END+3h groups was significantly higher versus SED mice. In mitochondria, PGC-1α is in a complex with mitochondrial transcription factor A at mtDNA D-loop, and this interaction was positively modulated by exercise, similar to the increased binding of PGC-1α at the NRF-1 promoter. We conclude that in response to acute altered energy demands, PGC-1α re-localizes into nuclear and mitochondrial compartments where it functions as a transcriptional co-activator for both nuclear and mitochondrial DNA transcription factors. These results suggest that PGC-1α may dynamically facilitate nuclear-mitochondrial DNA cross-talk to promote net mitochondrial biogenesis.
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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 | 0.001 |
| 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".