Stabilisation of p53 enhances reovirus-induced apoptosis and virus spread through p53-dependent NF-κB activation
Bibliographic record
Abstract
BACKGROUND: Naturally oncolytic reovirus preferentially kills cancer cells, making it a promising cancer therapeutic. Mutations in tumour suppressor p53 are prevalent in cancers, yet the role of p53 in reovirus oncolysis is relatively unexplored. METHODS: Human cancer cell lines were exposed to Nutlin-3a, reovirus or a combination of the two and cells were processed for reovirus titration, western blot, real-time PCR and apoptosis assay using Annexin V and 7-AAD staining. Confocal microscopy was used to determine translocation of the NF-κB p65 subunit. RESULTS: We show that despite similar reovirus replication in p53(+/+) and p53(-/-) cells, stabilisation of p53 by Nutlin-3a significantly enhanced reovirus-induced apoptosis and hence virus release and dissemination while having no direct effect on virus replication. Enhanced apoptosis by Nutlin-3a was not observed in p53(-/-) or p53 knockdown cells; however, increased expression of Bax and p21 are required. Moreover, elevated NF-κB activation in reovirus-infected cells following Nutlin-3a treatment was necessary for enhanced reovirus-induced apoptosis, as synergistic cytotoxicity was overcome by specific NF-κB inhibitors. CONCLUSION: Nutlin-3a treatment enhances reovirus-induced apoptosis and virus spread through p53-dependent NF-κB activation, and combination of reovirus and Nutlin-3a might represent an improved therapy against cancers harbouring wild-type p53.
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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".