Granzyme B inhibits translation of viral proteins and viral replication through proteolytic cleavage of the eukaryotic initiation factor 4 gamma 3 (154.35)
Bibliographic record
Abstract
Abstract Cytotoxic T lymphocytes (CTL) are the major killer of virus-infected cells. Granzyme B (GrB) from CTL induces apoptosis in target cells by cleavage and activation of substrates such as caspase-3. However, while undergoing apoptosis, cells can still produce infectious viruses unless a mechanism exists to specifically inhibit viral production. Using proteomic approaches, we found a novel GrB target that plays a major role in protein synthesis: eukaryotic initiation factor 4 Gamma 3 (eIF4G3). We hypothesized a novel role for GrB to arrest translation of viral proteins by targeting eIF4G3, which contains the GrB recognition sequence IESD1408S. We showed that GrB specifically cleaves eIF4G3 since the mutant eIF4G-Δ, with Ala substituted for D1408, was resistant to GrB degradation. Translational inhibition by GrB was reversed by the inclusion of eIF4G-Δ, showing that eIF4G3 is the critical GrB substrate involved in translation. In Jurkat cells, GrB or CTL treatment resulted in reduced rate of translation. We infected Jurkat cells with vaccinia virus (VV) and showed that viral protein synthesis and viral replication was inhibited in response to GrB or CTL. Transfection of eIF4G-Δ reversed the halt in protein synthesis induced by GrB. This demonstrated for the first time that GrB could prevent the production of VV by targeting the translational machinery of the host. These results represent a major new insight into what constitutes an effective CTL response to a virus-infected cell.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".