Long dsRNA mediated RNA interference (dsRNAi) is antiviral in interferon competent mammalian cells
Bibliographic record
Abstract
Abstract In invertebrate cells, RNA interference (RNAi) acts as a powerful defense against virus infection by cleaving virally produced long dsRNA into siRNA by Dicer and loaded into RISC which can then destroy/disrupt complementary viral mRNA sequences. Comparatively in mammalian cells, the type I interferon (IFN) pathway is the cornerstone of the innate antiviral response. Although the cellular machinery for RNAi functions in mammalian cells, its role in the antiviral response remains controversial. Here we show that IFN competent mammalian cells engage in dsRNA-mediated RNAi. We found that pre-soaking mammalian cells with concentrations of sequence-specific dsRNA too low to induce IFN production could significantly inhibit viral replication, including SARS-CoV-2. This phenomenon was dependent on dsRNA length, was comparable in effect to transfected siRNAs, and could knockdown multiple sequences at once. Additionally, Dicer-knockout cell lines were incapable of this inhibition, confirming use of RNAi. This represents the first evidence that soaking with gene-specific dsRNA can generate viral knockdown in mammalian cells. Furthermore, demonstrating RNAi below the threshold of IFN induction has uses as a novel therapeutic platform.
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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) | 0.003 | 0.002 |
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".