Mutations in the Glycoprotein of Vesicular Stomatitis Virus Affect Cytopathogenicity: Potential for Oncolytic Virotherapy
Bibliographic record
Abstract
Vesicular stomatitis virus (VSV) has been widely used to characterize cellular processes, viral resistance, and cytopathogenicity. Recently, VSV has also been used for oncolytic virotherapy due to its capacity to selectively lyse tumor cells. Mutants of the matrix (M) protein of VSV have generally been preferred to the wild-type virus for oncolysis because of their ability to induce type I interferon (IFN) despite causing weaker cytopathic effects. However, due to the large variability of tumor types, it is quite clear that various approaches and combinations of multiple oncolytic viruses will be needed to effectively treat most cancers. With this in mind, our work focused on characterizing the cytopathogenic profiles of four replicative envelope glycoprotein (G) VSV mutants. In contrast to the prototypic M mutant, VSV G mutants are as efficient as wild-type virus at inhibiting cellular transcription and host protein translation. Despite being highly cytopathic, the mutant G(6R) triggers type I interferon secretion as efficiently as the M mutant. Importantly, most VSV G mutants are more effective at killing B16 and MC57 tumor cells in vitro than the M mutant or wild-type virus through apoptosis induction. Taken together, our results demonstrate that VSV G mutants retain the high cytopathogenicity of wild-type VSV, with G(6R) inducing type I IFN secretion at levels similar to that of the M mutant. VSV G protein mutants could therefore prove to be highly valuable for the development of novel oncolytic virotherapy strategies that are both safe and efficient for the treatment of various types of cancer.
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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) | 0.000 | 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".