Farnesol prevents aging-related muscle weakness in mice through enhanced farnesylation of Parkin-interacting substrate
Bibliographic record
Abstract
Peroxisome proliferator–activated receptor-γ coactivator-1α (PGC-1α) is a master regulator of mitochondrial biogenesis. Reduced PGC-1α abundance is linked to skeletal muscle weakness in aging or pathological conditions, such as neurodegenerative diseases and diabetes; thus, elevating PGC-1α abundance might be a promising strategy to treat muscle aging. Here, we performed high-throughput screening and identified a natural compound, farnesol, as a potent inducer of PGC-1α. Farnesol administration enhanced oxidative muscle capacity and muscle strength, leading to metabolic rejuvenation in aged mice. Moreover, farnesol treatment accelerated the recovery of muscle injury associated with enhanced muscle stem cell function. The protein expression of Parkin-interacting substrate (PARIS/ Zfp746 ), a transcriptional repressor of PGC-1α, was elevated in aged muscles, likely contributing to PGC-1α reduction. The beneficial effect of farnesol on aged muscle was mediated through enhanced PARIS farnesylation, thereby relieving PARIS-mediated PGC-1α suppression. Furthermore, short-term exercise increased PARIS farnesylation in the muscles of young and aged mice, whereas long-term exercise decreased PARIS expression in the muscles of aged mice, leading to the elevation of PGC-1α. Collectively, the current study demonstrated that the PARIS–PGC-1α pathway is linked to muscle aging and that farnesol treatment can restore muscle functionality in aged mice through increased farnesylation of PARIS.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".