Discovery and Use of Long dsRNA Mediated RNA Interference to Stimulate Antiviral Protection in Interferon Competent Mammalian Cells
Bibliographic record
Abstract
In invertebrate cells, RNA interference (RNAi) acts as a powerful immune defense that stimulates viral gene knockdown thereby preventing infection. With this pathway, virally produced long dsRNA (dsRNA) is cleaved into short interfering RNA (siRNA) by Dicer and loaded into the RNA-induced silencing complex (RISC) which can then destroy/disrupt complementary viral mRNA sequences. Comparatively, in mammalian cells it is believed that the type I interferon (IFN) pathway is the cornerstone of the innate antiviral response. In these cells, dsRNA acts as a potent inducer of the IFN system, which is dependent on dsRNA length, but not sequence, to stimulate an antiviral state. Although the cellular machinery for RNAi is intact and functioning in mammalian cells, its role to trigger an antiviral response using long dsRNA (dsRNAi) remains controversial. Here we show that dsRNAi is not only functional but has a significant antiviral effect in IFN competent mammalian cells. We found that pre-soaking mammalian cells with concentrations of sequence specific dsRNA too low to induce IFN production could significantly inhibit vesicular stomatitis virus expressing green fluorescent protein (VSV-GFP), and the human coronaviruses (CoV) HCoV-229E and SARS-CoV-2 replication. This phenomenon was shown to be dependent on dsRNA length, was comparable in effect to transfected siRNAs, and could knockdown multiple sequences at once. Additionally, knockout cell lines revealed that functional Dicer was required for viral inhibition, revealing that the RNAi pathway was indeed responsible. These results provide the first evidence that soaking with gene-specific long dsRNA can generate viral knockdown in mammalian cells. We believe that this novel discovery provides an explanation as to why the mammalian lineage retained its RNAi machinery and why vertebrate viruses have evolved methods to suppress RNAi. Furthermore, demonstrating RNAi below the threshold of IFN induction has uses as a novel therapeutic platform, both antiviral and gene targeting in nature.
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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 | 0.001 |
| 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 | 0.001 |
| 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".