Enhanced genomic stability of new miRNA-regulated oncolytic coxsackievirus B3
Bibliographic record
Abstract
Genetic modification of coxsackievirus B3 (CVB3) by inserting target sequences (TS) of tumor-suppressive and/or organ-selective microRNAs (miRs) into viral genome can efficiently eliminate viral pathogenesis without significant impacts on its oncolytic activity. Nonetheless, reversion mutants (loss of miR-TS inserts) were identified as early as day 35 post-injection in ∼40% immunodeficient mice. To improve the stability, here we re-engineered CVB3 by (1) replacing the same length of viral genome at the non-coding region with TS of cardiac-selective miR-1/miR-133 and pancreas-enriched miR-216/miR-375 or (2) inserting the above miR-TS into the coding region (i.e., P1 region) of viral genome. Serial passaging of these newly established miR-CVB3s in cultured cells for 20 rounds demonstrated significantly improved stability compared with the first-generation miR-CVB3 with 5'UTR insertion of miR-TS. The safety and stability of these new miR-CVB3s was verified in immunocompetent mice. Moreover, we showed that these new viruses retained the ability to suppress lung tumor growth in a xenograft mouse model. Finally, we observed that miR-CVB3 with insertion in P1 region was more stable than miR-CVB3 with preserved length of the 5'UTR, whereas the latter displayed significantly higher oncolytic activity. Overall, we presented here valid strategies to enhance the genomic stability of miR-CVB3 for virotherapy.
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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".