Interferon regulatory factor 3 mediates effective antiviral responses to human coronavirus 229E and OC43 infection
Bibliographic record
Abstract
Interferon regulatory factors (IRFs) are key elements of antiviral innate responses that regulate the transcription of interferons (IFNs) and IFN-stimulated genes (ISGs). While the sensitivity of human coronaviruses to IFNs has been characterized, antiviral roles of IRFs during human coronavirus infection are not fully understood. Type I or II IFN treatment protected MRC5 cells from human coronavirus 229E infection, but not OC43. Cells infected with 229E or OC43 upregulated ISGs, indicating that antiviral transcription is not suppressed. Antiviral IRFs, IRF1, IRF3 and IRF7, were activated in cells infected with 229E, OC43 or severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2). RNAi knockdown and overexpression of IRFs demonstrated that IRF1 and IRF3 have antiviral properties against OC43, while IRF3 and IRF7 are effective in restricting 229E infection. IRF3 activation effectively promotes transcription of antiviral genes during OC43 or 229E infection. Our study suggests that IRFs may be effective antiviral regulators against human coronavirus 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".