A Trypsin‐like Protease from <i>Alternaria alternata</i> Allergens Promotes Airway Inflammation through Activation of Protease‐activated Receptor‐2/β‐arrestin Signaling
Bibliographic record
Abstract
Fungal Alternaria alternata exposure is correlated with increased morbidity and a higher risk of fatal asthma attacks in asthmatic patients. Alternaria allergens contain serine proteases capable of activating proteinase‐activated receptor‐2 (PAR 2 ) and promote airway inflammation that is dependent upon protease activity. We have previously shown that PAR 2 ‐dependent airway inflammation is mediated through the β‐arrestin‐2 activated pathway. Thus, we hypothesized that proteases from Alternaria filtrates will promote activation of the β‐arrestin‐2 (βarr2) dependent cellular signaling pathways that mediate airway inflammation in murine models of Alternaria ‐induced asthma. We identify a single alkaline serine protease, with trypsin‐like characteristics, AASP (Alternaria Alkaline Serine Protease) that can activate PAR 2 , as demonstrated by recruitment of β‐arrestin‐2 to PAR 2 and mobilization of intracellular Ca 2+ in cultured cells. Using histological and flow cytometric analyses, we demonstrate that Alternaria‐ induced airway inflammation requires both PAR 2 and βarr2, as demonstrated by the reduced recruitment of leukocytes (particularly eosinophils and CD4+ T‐cells) into the lung, goblet cell hyperplasia, and thickening of the lung epithelium in PAR 2 −/− or βarr2 −/− , compared to wild‐type mice. These inflammation parameters are also reduced by treatment of mice with Soybean Trypsin Inhibitor (SBTI) during the administration of Alternaria allergens, indicating that AASP is likely necessary for the inflammation induced by Alternaria . These are the first experiments demonstrating that the PAR 2 ‐β‐arrestin‐dependent signaling axis is important for asthma induced by household allergens. Thus, targeting this pathway via biased antagonism of PAR 2 could be a new avenue of therapeutic intervention for 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 | 0.000 | 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".