Modulation of eIF2alpha Phosphorylation and PKR Activation by Nck
Bibliographic record
Abstract
Phosphorylation of the α‐subunit of the eukaryotic initiation factor 2 (eIF2) on Ser 51 is an early event associated with downregulation of protein synthesis at the level of translation and constitutes a potent mechanism to overcome various stress conditions. In mammals, four eIF2α‐kinases PERK, PKR, HRI and GCN2, activated following specific stresses, have been involved in this process. In a first study, we demonstrated that the adaptor protein Nck, classically implicated in receptor tyrosine kinases signal transduction, modulates eIF2α‐kinases‐mediated eIF2αSer 51 phosphorylation in a specific manner. In fact, we showed that Nck reduces eIF2α phosphorylation in conditions activating PKR or HRI as we reported for PERK, but fails to do so in conditions activating GCN2. Herein, we report that Nck reduces PKR activation in response to dsRNA. In addition, we found that Nck reduces dsRNA‐induced activation of p38MAPK, a PKR‐downstream substrate, and cell death. Finally, we show that Nck interacts with inactive PKR. All together, these results suggest that Nck could regulate threshold levels of PKR activation. Our study reveals the existence of a novel mechanism regulating phosphorylation of eIF2α on Ser 51 under various stress conditions and identifies Nck as a regulator of the tumor suppressor and antiviral protein kinase PKR. Supported by the Natural Sciences and Engineering Research Council of Canada (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 | 0.000 |
| 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".