Identification of potential PAR‐regulating proteinases in agricultural dust using an activity‐based serine proteinase probe
Bibliographic record
Abstract
In previous work from our laboratories (PMIDs 26092994, 24152160, 21270400) we found that environmental allergens, including cockroach‐ and barn‐derived particulates, contain proteolytic enzymes that contribute to lung inflammation by cleaving and activating proteinase‐activated receptors (PARs). Preliminary work using in‐gel zymography demonstrated the presence of multiple serine proteinase inhibitor‐sensitive enzymes in hog barn dust extract (HDE: PMID 26092994). Because these HDE enzymes were blocked by alkylation of the active serine in many of the proteinases with PMSF and related agents, we reasoned that an activity‐based serine proteinase probe (ABP: PMID16554154) would also be able to alkylate the enzymes in the HDE. Using this novel approach (PMID 21270400), we ABP‐biotinylated the enzymes in HDE with ABPs selective for tryptic, chymotryptic, and elastase enzymes, respectively. Our data show differential reactivity of these ABP probes with enzymes in the HDE, identifying several enzymes in the mass range from 10 to 36 kDa, overlapping with the mass of ABP‐labeled porcine trypsin (28 kDa). Porcine trypsin itself is a major PAR‐regulating proteinase that is present in HDE, and this enzyme may play a prominent role in triggering lung inflammation in agricultural workers. Thus, HDE trypsin and other proteinases represent novel therapeutic targets both for preventing dust‐induced disease and for monitoring as environmental risk factors in many agricultural settings. Support or Funding Information Supported by the Canadian CIHR (MDH), The Lung Association of Alberta‐NWT (MDH), and NIOSH 2R01‐0H‐008539 (DJR)
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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) | 0.001 | 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".