<i>Pseudomonas aeruginosa</i> Elastase Disables Proteinase-Activated Receptor 2 in Respiratory Epithelial Cells
Bibliographic record
Abstract
Pseudomonas aeruginosa, a major lung pathogen in cystic fibrosis (CF) patients, secretes an elastolytic metalloproteinase (EPa) contributing to bacterial pathogenicity. Proteinase-activated receptor 2 (PAR2), implicated in the pulmonary innate defense, is activated by the cleavage of its extracellular N-terminal domain, unmasking a new N-terminal sequence starting with SLIGKV, which binds intramolecularly and activates PAR2. We show that EPa cleaves the N-terminal domain of PAR2 from the cell surface without triggering receptor endocytosis as trypsin does. As evaluated by measurements of cytosolic calcium as well as prostaglandin E(2) and interleukin-8 production, this cleavage does not activate PAR2, but rather disarms the receptor for subsequent activation by trypsin, but not by the synthetic receptor-activating peptide, SLIGKV-NH(2). Proteolysis by EPa of synthetic peptides representing the N-terminal cleavage/activation sequences of either human or rat PAR2 indicates that cleavages resulting from EPa activity would not produce receptor-activating tethered ligands, but would disarm PAR2 in regard to any further activating proteolysis by activating proteinases. Our data indicate that a pathogen-derived proteinase like EPa can potentially silence the function of PAR2 in the respiratory tract, thereby altering the host innate defense mechanisms and respiratory functions, and thus contributing to pathogenesis in the setting of a disease like CF.
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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 | 0.000 |
| 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".