Dexamethasone and FK506 inhibit expression of distinct subsets of chemokines in human mast cells
Bibliographic record
Abstract
Mast cells produce a large amount of several chemokines after cross-linking of FcεRI and participate in the pathogenesis of allergic diseases. The objective of this study was to comprehensively investigate FcεRI-mediated chemokine induction in human mast cells and the effect of a corticosteroid (dexamethasone) and a calcineurin inhibitor (FK506). Human peripheral bloodderived mast cells were stimulated with anti-IgE Ab in the resence of dexamethasone or FK506. Gene expression profiles were evaluated using GeneChip and confirmed by real-time PCR, and chemokine concentrations were measured by cytometric bead arrays and ELISA. Expression of eight chemokines was significantly induced in mast cells by anti-IgE stimulation. Induction of CCL2, CCL7, CXCL3, and CXCL8 by anti-IgE was significantly inhibited by dexamethasone but was enhanced by FK506. In contrast, induction of CCL1, CCL3, CCL4, and CCL18 was significantly inhibited by FK506 but, with the exception of CCL1, was enhanced by dexamethasone. Combination of dexamethasone and FK506 suppressed production of all chemokines by anti-IgE stimulation. Studies using protease inhibitors indicate that mast cell proteases may degrade several of the chemokines. These results suggest that corticosteroids and calcineurin inhibitors inhibit expression of distinct subsets of chemokines, and a combination of these drugs almost completely suppresses the induction of all chemokine genes in human mast cells in response to FcεRI-dependent stimulation. This implies that a combination of a corticosteroid and a calcineurin inhibitor may be more effective than each single agent for the treatment of allergic diseases in which mast cell-derived chemokines play a major role.
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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) | 0.001 | 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".