Allergen‐derived proteinases: Isolation, characterization and signaling via proteinase‐activated receptors (PARs)
Bibliographic record
Abstract
Objective To identify proteolytic enzymes in insect and mould asthma allergens and assess their ability to signal via the PARs. Methods Allergen‐derived trypsin‐like enzymes known to contribute to lung inflammation were visualized (western blot) with serine proteinase activity‐based probes (ABP). Enzymes were purified using ion‐exchange chromatography and analyzed for their ABP reactivity & substrate‐inhibitor kinetic profiles. Each enzyme was tested for its activation of PAR–dependent calcium signaling in PAR 1 & PAR 2 ‐expressing KNRK cells. ABP‐labeled enzymes were isolated by avidin affinity chromatography and their peptide sequences determined (mass spectrometry). Results Both allergens contained ABP‐labeled proteinases with MWs of 20–26 kDa. Each enzyme had distinct kinetic substrate (Km) and inhibitor (Ki) profiles. The enzymes all caused PAR 2 calcium signaling, and differentially activated PAR 1 . Sequencing revealed that the allergen proteinases were homologous with previously described trypsin‐like enzymes. Conclusions Diverse allergens contain multiple biochemically distinct trypsin‐like proteinases that are able to target PARs 1 and 2. Thus, PARs and their activating allergen proteinases may be attractive therapeutic targets for the treatment of allergen‐induced asthma. Support: Canadian Institutes of Health Research, NIH USA and Lung Association of Alberta & NWT
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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".