Stimulation of the RIG-I/MAVS Pathway by Polyinosinic:Polycytidylic Acid Upregulates IFN-β in Airway Epithelial Cells with Minimal Costimulation of IL-8
Bibliographic record
Abstract
Pharmacological stimulation of the antiviral cytokine IFN-β in the airways may help to counter deleterious virus-induced exacerbations in chronic inflammatory lung diseases (asthma, chronic obstructive pulmonary disease, or cystic fibrosis). Polyinosinic-polycytidylic acid [poly(I:C)] is a known inducer of IFN-β but also costimulates an inflammatory response. The latter response is undesirable given the pre-existing airway inflammation in these diseases. The objective of our study was to identify conditions for poly(I:C) to selectively upregulate IFN-β in airway epithelial cells without a concomitant inflammatory response. The inflammatory response was gauged by production of the chemokine IL-8. Using cell lines and primary airway epithelial cells (both submerged and well-differentiated), we observed that pure poly(I:C) stimulated IFN-β mainly through the TLR3/TRIF pathway and IL-8 through an unidentified pathway. The magnitude of the IL-8 response stimulated by pure poly(I:C) matched or even exceeded that of IFN-β. Furthermore, this IL-8 response could not be pharmacologically downregulated without affecting IFN-β. In contrast, we show that stimulation of the RIG-I/MAVS pathway, such as when poly(I:C) is delivered intracellularly in a complex with liposomes or via nucleofection, selectively stimulates IFN-β with low IL-8 costimulation. The magnitude of IFN-β stimulation by liposome-encapsulated poly(I:C) is markedly diminished in well-differentiated cells. In conclusion, it is feasible to augment IFN-β production in airway epithelial cells without excessive costimulation of IL-8 if the RIG-I/MAVS pathway is stimulated, such as via liposomal delivery of poly(I:C). Better cytoplasmic delivery vehicles are needed to efficiently stimulate this pathway in well-differentiated cells.
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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) | 0.001 | 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".