Type I IFN Triggers RIG-I/TLR3/NLRP3-dependent Inflammasome Activation in Influenza A Virus Infected Cells
Bibliographic record
Abstract
Influenza A virus (IAV) triggers a contagious and potentially lethal respiratory disease. A protective IL-1β response is mediated by innate receptors in macrophages and lung epithelial cells. NLRP3 is crucial in macrophages; however, which sensors elicit IL-1β secretion in lung epithelial cells remains undetermined. Here, we describe for the first time the relative roles of the host innate receptors RIG-I (DDX58), TLR3, and NLRP3 in the IL-1β response to IAV in primary lung epithelial cells. To activate IL-1β secretion, these cells employ partially redundant recognition mechanisms that differ from those described in macrophages. RIG-I had the strongest effect through a MAVS/TRIM25/Riplet–dependent type I IFN signaling pathway upstream of TLR3 and NLRP3. Notably, RIG-I also activated the inflammasome through interaction with caspase 1 and ASC in primary lung epithelial cells. Thus, NS1, an influenza virulence factor that inhibits the RIG-I/type I IFN pathway, strongly modulated the IL-1β response in lung epithelial cells and in ferrets. The NS1 protein derived from a highly pathogenic strain resulted in increased interaction with RIG-I and inhibited type I IFN and IL-1β responses compared to the least pathogenic virus strains. These findings demonstrate that in IAV-infected lung epithelial cells RIG-I activates the inflammasome both directly and through a type I IFN positive feedback loop.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".