Control of Paip1-Eukayrotic Translation Initiation Factor 3 Interaction by Amino Acids through S6 Kinase
Bibliographic record
Abstract
The simultaneous interaction of poly(A)-binding protein (PABP) with eukaryotic translation initiation factor 4G (eIF4G) and the mRNA 3' poly(A) tail promotes translation initiation. We previously showed that the interaction of PABP-interacting protein 1 (Paip1) with PABP and eukaryotic translation initiation factor 3 (eIF3; via the eIF3g subunit) further stimulates translation. Here, we demonstrate that the interaction of eIF3 with Paip1 is regulated by amino acids through the mTORC1 signaling pathway. The Paip1-eIF3 interaction is impaired by the mTORC1 inhibitors, rapamycin and PP242. We show that ribosomal protein S6 kinases 1 and 2 (S6K1/2) promote the interaction of eIF3 with Paip1. The enhancement of Paip1-eIF3 interaction by amino acids is abrogated by an S6K inhibitor or shRNA against S6K1/2. S6K1 interacts with eIF3f and, in vitro, phosphorylates eIF3. Finally, we show that S6K inhibition leads to a reduction in translation by Paip1. We propose that S6K1/2 phosphorylate eIF3 to stimulate Paip1-eIF3 interaction and consequent translation initiation. Taken together, these data demonstrate that eIF3 is a new translation target of the mTOR/S6K pathway.
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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".