Interaction between RAX and PKR Modulates the Effect of Ethanol on Protein Synthesis and Survival of Neurons
Bibliographic record
Abstract
Ethanol exposure inhibits protein synthesis and causes cell death in the developing central nervous system. The double-stranded RNA (dsRNA)-activated protein kinase (PKR), a serine/threonine protein kinase, plays an important role in translational regulation and cell survival. PKR has been well known for its anti-viral response. Upon activation by viral infection or dsRNA, PKR phosphorylates its substrate, the alpha-subunit of eukaryotic translation initiation factor-2 (eIF2alpha) leading to inhibition of translation initiation. It has recently been shown that, in the absence of a virus or dsRNA, PKR can be activated by direct interactions with its protein activators, PACT, or its mouse homologue, RAX. We have demonstrated that exposure to ethanol increased the phosphorylation of PKR and eIF2alpha in the developing cerebellum. The effect of ethanol on PKR/eIF2alpha phosphorylation positively correlated to the expression of PACT/RAX in cultured neuronal cells. Using PKR inhibitors and PKR null mouse fibroblasts, we verified that ethanol-induced eIF2alpha phosphorylation was mediated by PKR. Overexpression of a wild-type RAX dramatically enhanced sensitivity to ethanol-induced PKR/eIF2alpha phosphorylation, as well as translational inhibition and cell death. In contrast, overexpression of a mutant (S18A) RAX inhibited ethanol-mediated PKR/eIF2alpha activation. Ethanol promoted PKR and RAX association in cells expressing wild-type RAX but not in cells expressing S18A RAX. S18A RAX functioned as a dominant negative protein and blocked ethanol-induced inhibition of protein synthesis and cell death. Our results suggest that the interactions between PKR and PACT/RAX modulate the effect of ethanol on protein synthesis and cell survival in the central nervous system.
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) | 0.000 | 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".