Translational Repression Mediates Activation of Nuclear Factor Kappa B by Phosphorylated Translation Initiation Factor 2
Bibliographic record
Abstract
Numerous stressful conditions activate kinases that phosphorylate the alpha subunit of translation initiation factor 2 (eIF2alpha), thus attenuating mRNA translation and activating a gene expression program known as the integrated stress response. It has been noted that conditions associated with eIF2alpha phosphorylation, notably accumulation of unfolded proteins in the endoplasmic reticulum (ER), or ER stress, are also associated with activation of nuclear factor kappa B (NF-kappaB) and that eIF2alpha phosphorylation is required for NF-kappaB activation by ER stress. We have used a pharmacologically activable version of pancreatic ER kinase (PERK, an ER stress-responsive eIF2alpha kinase) to uncouple eIF2alpha phosphorylation from stress and found that phosphorylation of eIF2alpha is both necessary and sufficient to activate both NF-kappaB DNA binding and an NF-kappaB reporter gene. eIF2alpha phosphorylation-dependent NF-kappaB activation correlated with decreased levels of the inhibitor IkappaBalpha protein. Unlike canonical signaling pathways that promote IkappaBalpha phosphorylation and degradation, eIF2alpha phosphorylation did not increase phosphorylated IkappaBalpha levels or affect the stability of the protein. Pulse-chase labeling experiments indicate instead that repression of IkappaBalpha translation plays an important role in NF-kappaB activation in cells experiencing high levels of eIF2alpha phosphorylation. These studies suggest a direct role for eIF2alpha phosphorylation-dependent translational control in activating NF-kappaB during ER stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".