E3 ligase HECTD3 promotes RNA virus replication and virus-induced inflammation via K33-linked polyubiquitination of PKR
Bibliographic record
Abstract
Uncontrolled viral replication and excessive inflammation are the main causes of death in the host infected with virus. Hence inhibition of intracellular viral replication and production of innate cytokines, which are the key strategies of hosts to fight virus infections, need to be finely tuned to eliminate viruses while avoid harmful inflammation. The E3 ligases in regulating virus replication and subsequent innate cytokines production remain to be fully characterized. Here we report that the deficiency of the E3 ubiquitin-protein ligase HECTD3 results in accelerated RNA virus clearance and reduced inflammatory response both in vitro and in vivo. Mechanistically, HECTD3 interacts with dsRNA-dependent protein kinase R (PKR) and mediates Lys33-linkage of PKR, which is the first non-proteolytic ubiquitin modification for PKR. This process disrupts the dimerization and phosphorylation of PKR and subsequent EIF2α activation, which results in the acceleration of virus replication, but promotes the formation of PKR-IKK complex and subsequent inflammatory response. The finding suggests HECTD3 is the potential therapeutic target for simultaneously restraining RNA virus replication and virus-induced inflammation once pharmacologically inhibited.
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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".