The abundance of the mRNA translation initiation inhibitor PDCD4 in L6 myotubes is regulated by mTORC1 and the proteasome
Bibliographic record
Abstract
The mammalian target of rapamycin complex 1/ S6 kinase 1 (mTORC1/S6K1) pathway is a critical regulator of muscle protein synthesis and mass. Using L6 myotubes, we characterized the regulation of the mRNA translation inhibitor programmed cell death 4 (PDCD4), a recently described substrate of the mTORCI/S6K1 pathway. Myotubes were cultured in amino acid‐free, serum‐free medium for 12 h, and then refed for 1 or 3 h. Starvation decreased the phosphorylation of S6K1, the PDCD4 kinase (P<0.01). Starvation increased the abundance, but not the phosphorylation, of PDCD4 >4 fold (P<0.001); refeeding reversed the effects of starvation on S6K1 and PDCD4. Rapamycin or MG132 (an inhibitor of the proteasome) prevented the refeeding‐associated decrease in PDCD4 abundance. Because PDCD4 inhibits translation by binding to the eukaryotic initiation factor 4A (eIF4A), we assessed this interaction. Starvation increased the % of PDCD4 bound to eIF4A (P<0.05); this was reversed during refeeding. Rapamycin prevented the effect of refeeding. Thus, we showed that in skeletal muscle cells, the abundance of PDCD4 and its interaction with the initiation factor eIF4A were reversibly modified by nutrients, and that the activities of mTORC1 and of the ubiquitin dependent proteolytic system were required for this regulation. Manipulation of PDCD4 abundance may offer an approach to stimulate mass. 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 | 0.000 |
| 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 | 0.001 |
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".