Characterizing Cellular Responses During Oncolytic Maraba Virus Infection
Bibliographic record
Abstract
The rising demand for powerful oncolytic virotherapy agents has led to the identification of Maraba virus, one of the most potent oncolytic viruses from Rhabdoviridae family which displays high selectivity for killing malignant cells and low cytotoxicity in normal cells. Although the virus is readied to be used for clinical trials, the interactions between the virus and the host cells is still unclear. Using a newly developed interferon-sensitive mutant Maraba virus (MG1), we have identified two key regulators of global translation (4E-BP1 and eIF2α) as being involved in the regulation of protein synthesis in the infected cells. Despite the translational arrest upon viral stress, we showed an up-regulation of anti-apoptotic Bcl-xL protein that provides a survival benefit for the host cell, yet facilitates effective viral propagation. Given the fact that eIF5B canonically regulates 60S ribosome subunit end joining and is able to replace the role of eIF2 in delivering initiator tRNA to the 40S ribosome subunit upon the phosphorylation of eIF2α we have tested whether eIF5B mediates the translation of target mRNAs during MG1 infection. Our results show that the inhibition of eIF5B significantly down-regulates the level of Bcl-xL steady-state mRNA, thus indirectly attenuates viral propagation.
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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 | 0.000 |
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".