Requirement for the eIF4E Binding Proteins for the Synergistic Down-Regulation of Protein Synthesis by Hypertonic Conditions and mTOR Inhibition
Bibliographic record
Abstract
The protein kinase mammalian target of rapamycin (mTOR) regulates the phosphorylation and activity of several proteins that have the potential to control translation, including p70S6 kinase and the eIF4E binding proteins 4E-BP1 and 4E-BP2. In spite of this, in exponentially growing cells overall protein synthesis is often resistant to mTOR inhibitors. We report here that sensitivity of wild-type mouse embryonic fibroblasts (MEFs) to mTOR inhibitors can be greatly increased when the cells are subjected to the physiological stress imposed by hypertonic conditions. In contrast, protein synthesis in MEFs with a double knockout of 4E-BP1 and 4E-BP2 remains resistant to mTOR inhibitors under these conditions. Phosphorylation of p70S6 kinase and protein kinase B (Akt) is blocked by the mTOR inhibitor Ku0063794 equally well in both wild-type and 4E-BP knockout cells, under both normal and hypertonic conditions. The response of protein synthesis to hypertonic stress itself does not require the 4E-BPs. These data suggest that under certain stress conditions: (i) translation has a greater requirement for mTOR activity and (ii) there is an absolute requirement for the 4E-BPs for regulation by mTOR. Importantly, dephosphorylation of p70S6 kinase and Akt is not sufficient to affect protein synthesis acutely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".