Zebrafish Uba1 Degrades IRF3 through K48-Linked Ubiquitination to Inhibit IFN Production
Bibliographic record
Abstract
Abstract Fish IFN regulatory factor 3 (IRF3) is a crucial transcription factor in the IFN activation signaling pathway, which leads to IFN production and a positive cycle. Unrestricted IFN expression results in hyperimmune responses and therefore, IFN must be tightly regulated. In the current study, we found that zebrafish Ub-activating enzyme (Uba1) negatively regulated IRF3 via the K-48 ubiquitin proteasome degradation of IRF3. First, ifn expression stimulated by spring viraemia of carp virus infection was blunted by the overexpression of Uba1 and enhanced by Uba1 knockdown. Afterward, we found that Uba1 was localized in the cytoplasm, where it interacted with and degraded IRF3. Functional domains analysis revealed that the C-terminal ubiquitin-fold domain was necessary for IRF3 degradation by Uba1 and the N-terminal DNA-binding domain of IRF3 was indispensable for the degradation by Uba1.The degradation of IRF3 was subsequently impaired by treatment with MG132, a ubiquitin proteasome inhibitor. Further mechanism analysis revealed that Uba1 induced the K48-linked Ub-proteasomal degradation of IRF3. Finally, the antiviral capacity of IRF3 was significantly attenuated by Uba1. Taken together, our study reveals that zebrafish Uba1 interacts with and activates the ubiquitinated degradation of IRF3, providing evidence of the IFN immune balance mechanism in fish.
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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) | 0.002 | 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".