PICOLINIC ACID, A CATABOLITE OF TRYPTOPHAN, HAS AN ANABOLIC EFFECT ON MYOBLASTS AND INCREASES MOBILITY IN C. ELEGANS
Bibliographic record
Abstract
Abstract Osteoporosis, sarcopenia, and osteosarcopenia (simultaneous occurrence of these diseases) are highly prevalent in older persons and associated with falls and fractures. Compounds with dual anabolic effects on muscle and bone could become the treatment of choice for these conditions. We have previously reported the anabolic effect of picolinic acid (PIC) on bone in vitro and in vivo. However, its effect on muscles remains to be elucidated. Therefore, the aim of the study was to examine the effect of PIC on muscle. C2C12 myoblasts were treated with PIC (1, 50 and 100 µM) or vehicle for 7 days. Transcription factors involved in myogenesis were evaluated by western blot, and the fusion index and myotubules’ length were calculated in confocal microscopy images at timed intervals (Day 1, 3, 5 and 7). In addition, C. elegans were treated with 1mM PIC, and the frequency of thrashing (mobility) was evaluated at timed intervals (Day 1, 8 and 16). PIC-treated myoblasts showed a higher and earlier expression of myosin II and MyoD transcription factors (p < 0.001). On day 5, PIC-treated myotubes were significantly longer when treated with 50 µM (+80.7 µm, p=0.001) and 100 µM (+73.0 µm, p=0.002) than vehicle-treated controls. The frequency of thrashing was also significantly increased in the PIC-treated C. elegans at all timed intervals (p < 0.001). In conclusion, this study demonstrates that, in addition to its anabolic effect on bone, PIC also has an anabolic effect on muscle, which opens up exciting possibilities for further exploration in animal models and future geroscience research.
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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".