The Ebola Virus VP35 Protein Inhibits Activation of Interferon Regulatory Factor 3
Bibliographic record
Abstract
The Ebola virus VP35 protein was previously found to act as an interferon (IFN) antagonist which could complement growth of influenza delNS1 virus, a mutant influenza virus lacking the influenza virus IFN antagonist protein, NS1. The Ebola virus VP35 could also prevent the virus- or double-stranded RNA-mediated transcriptional activation of both the beta IFN (IFN-beta) promoter and the IFN-stimulated ISG54 promoter (C. Basler et al., Proc. Natl. Acad. Sci. USA 97:12289-12294, 2000). We now show that VP35 inhibits virus infection-induced transcriptional activation of IFN regulatory factor 3 (IRF-3)-responsive mammalian promoters and that VP35 does not block signaling from the IFN-alpha/beta receptor. The ability of VP35 to inhibit this virus-induced transcription correlates with its ability to block activation of IRF-3, a cellular transcription factor of central importance in initiating the host cell IFN response. We demonstrate that VP35 blocks the Sendai virus-induced activation of two promoters which can be directly activated by IRF-3, namely, the ISG54 promoter and the ISG56 promoter. Further, expression of VP35 prevents the IRF-3-dependent activation of the IFN-alpha4 promoter in response to viral infection. The inhibition of IRF-3 appears to occur through an inhibition of IRF-3 phosphorylation. VP35 blocks virus-induced IRF-3 phosphorylation and subsequent IRF-3 dimerization and nuclear translocation. Consistent with these observations, Ebola virus infection of Vero cells activated neither transcription from the ISG54 promoter nor nuclear accumulation of IRF-3. These data suggest that in Ebola virus-infected cells, VP35 inhibits the induction of antiviral genes, including the IFN-beta gene, by blocking IRF-3 activation.
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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 | 0.001 |
| 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".