IRF3 and IRF7 Phosphorylation in Virus-infected Cells Does Not Require Double-stranded RNA-dependent Protein Kinase R or IκB Kinase but Is Blocked by Vaccinia Virus E3L Protein
Bibliographic record
Abstract
Induction of interferon-alpha (IFNalpha) gene expression in virus-infected cells requires phosphorylation-induced activation of the transcription factors IRF3 and IRF7. However, the kinase(s) that targets these proteins has not been identified. Using a combined pharmacological and genetic approach, we found that none of the kinases tested was responsible for IRF phosphorylation in cells infected with Newcastle disease virus (NDV). Although the broad-spectrum kinase inhibitor staurosporine potently blocked IRF3 and -7 phosphorylation, inhibitors for protein kinase C, protein kinase A, MEK, SAPK, IKK, and protein kinase R (PKR) were without effect. Both IkappaB kinase and PKR have been implicated in IFN induction, but cells genetically deficient in IkappaB kinase, PKR, or the PKR-related genes PERK, IRE1, or GCN2 retained the ability to phosphorylate IRF7 and induce IFNalpha. Interestingly, PKR mutant cells were defective for response to double-stranded (ds) RNA but not to virus infection, suggesting that dsRNA is not the only activating viral component. Consistent with this notion, protein synthesis was required for IRF7 phosphorylation in virus-infected cells, and the kinetics of phosphorylation and viral protein production were similar. Despite evidence for a lack of involvement of dsRNA and PKR, vaccinia virus E3L protein, a dsRNA-binding protein capable of inhibiting PKR, was an effective IRF3 and -7 phosphorylation inhibitor. These results suggest that a novel cellular protein that is activated by viral products in addition to dsRNA and is sensitive to E3L inhibition is responsible for IRF activation and reveal a novel mechanism for the anti-IFN effect of E3L distinct from its inhibition of PKR.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".