Picolinic acid, a tryptophan metabolite, exhibits anabolic effects in muscle cells and improves lifespan and movement in <i>C. elegans</i>
Bibliographic record
Abstract
Compounds promoting anabolic effects on muscle and bone may offer an ideal treatment for osteosarcopenia while potentially impacting healthspan and lifespan. We previously demonstrated the anabolic effects of picolinic acid (PIC), a tryptophan metabolite, on bone both in vitro and in vivo. However, its effects on muscle and potential additional effects on lifespan and healthspan are not yet fully understood. This study aimed to investigate PIC's effects on muscle cells in vitro and its impact on mobility and lifespan in an animal model. Murine C2C12 and human myoblasts were treated with PIC (1, 50, and 100 µM) or vehicle for 5 days. Myogenic regulatory factors (MRFs) were evaluated, and the fusion index and myotubules' length were calculated at timed intervals (day 1, 3, and 5). In vivo, Caenorhabditis elegans were treated with increasing doses of PIC, and their lifespan and rate of movement (thrashing) were evaluated at timed intervals. PIC-treated myoblasts showed a higher and earlier expression of MRFs. On day 3, PIC-treated myotubes were significantly more fused and longer when treated with PIC than vehicle-treated controls. C. elegans treated with 1 mM of PIC showed a significantly longer lifespan. In addition, the mobility of PIC-treated C. elegans was significantly increased at all timed points. In conclusion, this study demonstrates that, besides its anabolic effect on bone, PIC has an anabolic effect on muscle, which is also associated with a longer lifespan in PIC-treated C. elegans. This evidence opens up promising avenues for further exploration of PIC as a novel therapy for osteosarcopenia with additional effects on healthspan and lifespan.
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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".