Resistance to Vesicular Stomatitis Virus Infection Requires a Functional Cross Talk between the Eukaryotic Translation Initiation Factor 2α Kinases PERK and PKR
Bibliographic record
Abstract
Phosphorylation of the alpha (alpha) subunit of the eukaryotic translation initiation factor 2 (eIF2) leads to the inhibition of protein synthesis in response to diverse stress conditions, including viral infection. The eIF2alpha kinase PKR has been shown to play an essential role against vesicular stomatitis virus (VSV) infection. We demonstrate here that another eIF2alpha kinase, the endoplasmic reticulum-resident protein kinase PERK, contributes to cellular resistance to VSV infection. We demonstrate that mouse embryonic fibroblasts (MEFs) from PERK(-/-) mice are more susceptible to VSV-mediated apoptosis than PERK(+/+) MEFs. The higher replication capacity of VSV in PERK(-/-) MEFs results from their inability to attenuate viral protein synthesis due to an impaired eIF2alpha phosphorylation. We also show that VSV-infected PERK(-/-) MEFs are unable to fully activate PKR, suggesting a cross talk between the two eIF2alpha kinases in virus-infected cells. These findings further implicate PERK in virus infection, and provide evidence that the antiviral and antiapoptotic roles of PERK are mediated, at least in part, via the activation of PKR.
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".