High throughput screen identifies lysosomal acid phosphatase 2 (ACP2) to regulate IFN-1 responses to potentiate oncolytic VSV∆51 activity
Bibliographic record
Abstract
Strategies in genetic and pharmacological modulation of innate immunity to enhance oncolytic virotherapy (OV) efficacy are being explored. We have recently characterized the ability for vanadium-based compounds, a class of pan-phosphatase (PP) inhibitors, to potentiate OVs. We next sought to identify PPs that could be targeted to enhance OVs, akin to vanadium. By conducting a high-throughput screen of a library of silencing RNA (siRNA) targeting human PPs, we uncovered several PPs that robustly enhanced infectivity and oncolysis of the oncolytic vesicular stomatitis virus (VSV∆51). Knockdown of our top validated hit, lysosomal acid phosphatase 2 (ACP2), increased VSV∆51 viral titers by over 20-fold. In silico analysis by RNA sequencing revealed ACP2 to regulate antiviral type I interferon (IFN-1) signaling pathways, similar to vanadium. To further exploit this mechanism for therapeutic gain, we encoded a short-hairpin RNA (shRNA) against ACP2 into oncolytic vesicular stomatitis virus (VSV∆51) under a miR-30 promoter. This bioengineered OV demonstrated expression of the miR-30 promoter, knockdown of ACP2, repression and ultimately, showed markedly enhanced viral VSV∆51 particle production compared to its non-targeting control counterpart. Altogether, this study identifies IFN-1 regulating PP targets, namely ACP2, that may prove instrumental in increasing the therapeutic efficacy of OVs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".