Mast Cell Tryptase Activates Peripheral Blood Eosinophils to Release Granule-Associated Enzymes
Bibliographic record
Abstract
BACKGROUND: Mast cells and eosinophils are important effector cells in asthma. Understanding their interactions is essential for studying asthma pathophysiology. Inflammatory mediators released from mast cells, such as arachidonic acid metabolites, TNF and IL-5, are important in eosinophil biology. However, little is known about the effects of mast cell-specific mediators, such as tryptase, on eosinophils. Our objective was to investigate the effects of mast cell tryptase on human peripheral blood eosinophils. METHODS: Peripheral blood eosinophils isolated from asthmatic individuals were activated using various concentrations of tryptase- and protease-activated receptor-2 (PAR-2)-activating peptides (PAR-2 AP). Eosinophil activation was evaluated by the release of granule mediators, superoxide release, estimation of eosinophil survival, changes in intracellular Ca2+ concentration and mitogen-activated protein kinase activation. RESULTS: Tryptase induced the release of eosinophil peroxidase and beta-hexosaminidase from peripheral blood eosinophils but had no effect on RANTES release. Eosinophils isolated from two thirds of our donors responded to tryptase, while the remainder appeared not to respond. Release of granule mediators was dependent on tryptase enzymatic activity. To identify the mechanism of eosinophil activation by tryptase, we studied the expression of PAR-2 by eosinophils and its function. Using RT-PCR, we amplified PAR-2 from eosinophils. However, flow cytometry failed to detect significant PAR-2 expression on the surface of eosinophils. The PAR-2 AP SLIGRL-NH2 did not induce eosinophil activation by any of the methods we employed. CONCLUSION: Our data indicate that mast cell tryptase may affect eosinophil activation status independently of PAR-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".