Regulation of p53 mRNA by AMP Kinase (AMPK) activation in C2C12 myoblasts
Bibliographic record
Abstract
p53 protein is important for basal and exercise‐induced mitochondrial biogenesis in skeletal muscle. As shown previously, pifithrin‐α (15 μM) a specific inhibitor of p53, reduced the AICAR‐induced increase in mitochondrial content in myoblasts. This demonstrates that p53 partly mediates AMPK‐induced mitochondrial adaptations. Here, we investigated whether p53 mRNA content and stability were modulated by AMPK. Two transcript variants (TV) of murine p53 have been discovered. p53 TV2 mRNA was expressed at 60% lower levels than p53 TV1 in C2C12 myoblasts. AICAR treatment (3 days) reduced p53 TV1 and TV2 mRNA levels by 28% and 13%, respectively, compared to control. To investigate whether this reduced mRNA was due to changes in mRNA stability, we generated reporter constructs containing the full length (R2) 3′untranslated region (UTR), or a shorter construct without the AU‐rich region and the poly‐adenylation signal (R1) of p53 TV2 mRNA. Following transfection, luciferase activity of both R1 and R2 was only ~15% of control, indicating that even the shorter 3′UTR could mediate p53 mRNA decay. AICAR treatment further decreased luciferase activity of both constructs by ~35% compared to untreated cells. This suggests that AMPK regulates p53 by reducing mRNA stability and content. Our findings shed light on the complexity of p53 regulation and have significance for p53‐based therapeutic interventions. (Funded by NSERC)
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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 | 0.001 |
| 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 | 0.001 |
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".