Regulation of IRF-3-dependent Innate Immunity by the Papain-like Protease Domain of the Severe Acute Respiratory Syndrome Coronavirus
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV) is a novel coronavirus that causes a highly contagious respiratory disease, SARS, with significant mortality. Although factors contributing to the highly pathogenic nature of SARS-CoV remain poorly understood, it has been reported that SARS-CoV infection does not induce type I interferons (IFNs) in cell culture. However, it is uncertain whether SARS-CoV evades host detection or has evolved mechanisms to counteract innate host defenses. We show here that infection of SARS-CoV triggers a weak IFN response in cultured human lung/bronchial epithelial cells without inducing the phosphorylation of IFN-regulatory factor 3 (IRF-3), a latent cellular transcription factor that is pivotal for type I IFN synthesis. Furthermore, SARS-CoV infection blocked the induction of IFN antiviral activity and the up-regulation of protein expression of a subset of IFN-stimulated genes triggered by double-stranded RNA or an unrelated paramyxovirus. In searching for a SARS-CoV protein capable of counteracting innate immunity, we identified the papain-like protease (PLpro) domain as a potent IFN antagonist. The inhibition of the IFN response does not require the protease activity of PLpro. Rather, PLpro interacts with IRF-3 and inhibits the phosphorylation and nuclear translocation of IRF-3, thereby disrupting the activation of type I IFN responses through either Toll-like receptor 3 or retinoic acid-inducible gene I/melanoma differentiation-associated gene 5 pathways. Our data suggest that regulation of IRF-3-dependent innate antiviral defenses by PLpro may contribute to the establishment of SARS-CoV infection.
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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 | 0.000 |
| 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".