RNAi Screening in Tumor Cells Identifies Artificial microRNAs That Improve Oncolytic Virus Replication
Bibliographic record
Abstract
Background/Objectives: Oncolytic viruses infect and kill tumor cells while leaving normal cells unharmed. They are often attenuated through the reduction in their ability to antagonize antiviral defenses, leading to robust replication in tumor cells, which often possess defects in antiviral pathways, while minimizing replication in normal cells. However, not all tumors have defects in their antiviral defenses, and virus replication in these tumors is minimal, thus limiting therapeutic benefits. Therefore, identifying and modulating host factors that regulate virus replication in oncolytic virus-resistant cancer cells, but not normal cells, could lead to increased replication in these tumors and potentially improved therapeutic outcomes. Methods: To identify host factors that modulate oncolytic virus replication in tumor cells, we conducted an RNA interference screen by using a replication-competent library of Sindbis virus recombinants individually enabled with the capacity to elicit RNA interference in host genes via the expression of artificial microRNAs. Since the expression of artificial microRNAs is coupled to virus replication, this results in the selective enrichment of viral clones which express an artificial microRNA that promotes virus replication. Results: By using this approach, the serial passage of the Sindbis virus–artificial microRNA library in a tumor cell line followed by the deep sequencing of the selected viral populations led to the identification of several artificial microRNA sequences that were enriched. Furthermore, the identified artificial miRNA sequences increased the replication of several oncolytic viruses both in vitro and in vivo, ultimately leading to an enhanced therapeutic effect. Conclusions: Altogether, our study highlights the utility of this screening platform in identifying artificial microRNAs that enhance oncolytic virus efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".