The Nucleotide-Binding Domain and Leucine-Rich Repeat Protein–3 Inflammasome Is Not Activated in Airway Smooth Muscle Upon Toll-Like Receptor–2 Ligation
Bibliographic record
Abstract
Inflammasomes have emerged as playing key roles in inflammation and innate immunity. A growing body of evidence has suggested that the nucleotide-binding domain and leucine-rich repeat protein-3 (NLRP3) inflammasome is important in chronic airway diseases such as asthma and chronic obstructive pulmonary disease. Inflammasome activation results, in part, in pro-IL-1β processing and the secretion of the proinflammatory cytokine IL-1β. Because asthma exacerbations are associated with elevated concentrations of secreted IL-1β, we addressed whether the NLRP3 inflammasome is activated under in vitro conditions that mimic infectious exacerbations in asthma. Primary cultures of airway smooth muscle (ASM) cells were treated with infectious stimuli (mimicked using the Toll-like receptor-2 agonist Pam3CSK4, a synthetic bacterial lipopeptide). Whereas Pam3CSK4 robustly up-regulated ASM cytokine expression in response to TNF-α and significantly enhanced IL-1β mRNA expression, we were unable to detect IL-1β in the cell supernatants. Thus, IL-1β was not secreted and therefore was unable to act in an autocrine manner to promote the amplification of ASM inflammatory responses. Moreover, Toll-like receptor-2 ligation did not enhance NLRP3 or caspase-1 expression in ASM cells, and NLRP3 and caspase-1 protein were not present in the ASM layer of tracheal sections from human donors. In conclusion, these data demonstrate that the enhanced synthetic function of ASM cells, induced by infectious exacerbations of airway inflammation, is NLRP3 inflammasome-independent and IL-1β-independent. Activation of the NLRP3 inflammasome by invading pathogens may prove cell type-specific in exacerbations of airway inflammation in asthma.
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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 | 0.001 | 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".