Zebrafish CERKL Enhances Host TBK1 Stability and Simultaneously Degrades Viral Protein via Ubiquitination Modulation
Bibliographic record
Abstract
Abstract In the viral infection process, host gene function is usually reported as either defending the host or assaulting the virus. In this study, we demonstrated that zebrafish ceramide kinase-like (CERKL) mediates protection against viral infection via two distinct mechanisms: stabilization of TANK-binding kinase 1 (TBK1) through impairing K48-linked ubiquitination and degradation of spring viremia of carp virus (SVCV) P protein by dampening K63-linked ubiquitination, resulting in an improvement of the host immune response and a decline in viral activity in epithelioma papulosum cyprini (EPC) cells. On SVCV infection, ifnφ1 expression was increased or blunted by CERKL overexpression or knockdown, respectively. Subsequently, we found that CERKL localized in the cytoplasm, where it interacted with TBK1 and enhanced its stability by impeding the K48-linked polyubiquitination; meanwhile, the antiviral capacity of TBK1 was significantly potentiated by CERKL. In contrast, CERKL also interacted with and degraded SVCV P protein to disrupt its function in viral proliferation. Further mechanism analysis revealed K63-linked deubiquitination is the primary means of CERKL-mediated SVCV P protein degradation. Taken together, our study reveals a novel mechanism of fish defense against viral infection: the single gene cerkl is both a shield for the host and a spear against the virus, which strengthens resistance.
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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".