REGγ regulates antiviral response by activating TBK1-IFNβ signaling through degradation of PPP2CB
Bibliographic record
Abstract
Although significant progressions in antiviral studies of IFNβ have been demonstrated, the role of the proteasome in modulating cross-talk between TBK1-IFNβ signaling and viral replication during viral infection is not fully elucidated. Here, we discover that deficiency of REGγ, a proteasome activator, significantly reduces IFNβ production and increases viral replications in mice, leading to increased mortality in virus infection models. Our mechanistic study indicates that REGγ interacts with and degrades the protein phosphatase subunit Protein Phosphatase 2 Catalytic Subunit Beta (PPP2CB). This degradation disrupts the dephosphorylation of TBK1 and its interaction with IRF3, resulting in the activation of IFNβ-mediated antiviral signaling. In response to viral infection, up-regulation of REGγ in macrophages accelerates the degradation of PPP2CB, which increases the activation of TBK1-IRF3-IFNβ axis and thereby restricts viral replications and pathology. Interestingly, IFNβ enhances REGγ expression in viral infection, forming a positive feedback regulatory loop. In conclusion, our work demonstrates that REGγ is a positive modulator of IFNβ signaling during antiviral response, highlighting that this procedure is regulated via REGγ degradation of PPP2CB and provides a new insight into the coordination between antiviral response and proteasome activity. Thus, REGγ-proteasome activity and phosphatase PPP2CB may be potential targets in host defense against viruses.
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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) | 0.001 | 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".