Systems Analysis of a RIG-I Agonist Inducing Broad Spectrum Inhibition of Virus Infectivity
Bibliographic record
Abstract
The RIG-I like receptor pathway is stimulated during RNA virus infection by interaction between cytosolic RIG-I and viral RNA structures that contain short hairpin dsRNA and 5' triphosphate (5'ppp) terminal structure. In the present study, an RNA agonist of RIG-I was synthesized in vitro and shown to stimulate RIG-I-dependent antiviral responses at concentrations in the picomolar range. In human lung epithelial A549 cells, 5'pppRNA specifically stimulated multiple parameters of the innate antiviral response, including IRF3, IRF7 and STAT1 activation, and induction of inflammatory and interferon stimulated genes - hallmarks of a fully functional antiviral response. Evaluation of the magnitude and duration of gene expression by transcriptional profiling identified a robust, sustained and diversified antiviral and inflammatory response characterized by enhanced pathogen recognition and interferon (IFN) signaling. Bioinformatics analysis further identified a transcriptional signature uniquely induced by 5'pppRNA, and not by IFNα-2b, that included a constellation of IRF7 and NF-kB target genes capable of mobilizing multiple arms of the innate and adaptive immune response. Treatment of primary PBMCs or lung epithelial A549 cells with 5'pppRNA provided significant protection against a spectrum of RNA and DNA viruses. In C57Bl/6 mice, intravenous administration of 5'pppRNA protected animals from a lethal challenge with H1N1 Influenza, reduced virus titers in mouse lungs and protected animals from virus-induced pneumonia. Strikingly, the RIG-I-specific transcriptional response afforded partial protection from influenza challenge, even in the absence of type I interferon signaling. This systems approach provides transcriptional, biochemical, and in vivo analysis of the antiviral efficacy of 5'pppRNA and highlights the therapeutic potential associated with the use of RIG-I agonists as broad spectrum antiviral agents.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".