RNA interference of influenza A virus replication by microRNA-adapted lentiviral loop short hairpin RNA
Bibliographic record
Abstract
Limitations of the current vaccines and antivirals against influenza A virus (IAV) pandemic underscore the urgent need for developing novel anti-influenza strategies. RNA interference (RNAi) induced by small interfering RNA (siRNA) has become a powerful new means to inhibit viral infection in a gene-specific manner. However, the efficacy of the siRNA delivery platform and the relatively high cost of administration have hindered widespread application of siRNA. In this study, we developed a microRNA (miRNA)-30-based lentivirus delivery system by embedding a synthetic short hairpin RNA (shRNA) stem into the context of endogenous precursor of miRNA-30 (shRNAmir) to express a silencer of the influenza gene. We showed that the miRNA-based lentivirus vector was able to express and process a single nucleoprotein (NP)-targeting shRNAmir, which could potently inhibit IAV replication. We further showed that miRNA-based lentivirus vector carrying tandemly linked NP and polymerase PB1 shRNAmirs could express and process double shRNAmirs. Despite the relatively low levels of NP and PB1 miRNAs produced in the stably transduced cells, the combination of two miRNAs exerted a great degree of inhibition on influenza infection. Given the advantage of combinatorial RNAi in preventing emergence of mutant virus, miRNA-based lentiviral vectors are valuable tools for anitiviral activities. To the best of our knowledge, this is the first study demonstrating that a miRNA-based RNAi strategy can be applied for better control of influenza virus infection.
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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) | 0.000 | 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".