Drastic Up-Regulation of FcεRI on Mast Cells Is Induced by IgE Binding Through Stabilization and Accumulation of FcεRI on the Cell Surface
Bibliographic record
Abstract
It has been shown that IgE binding to FcepsilonRI on mast cells results in increased FcepsilonRI expression, which in turn enhances IgE-dependent chemical mediator release from mast cells. Therefore, prevention of the IgE-mediated FcepsilonRI up-regulation would be a promising strategy for management of allergic disorders. However, the mechanism of IgE-mediated FcepsilonRI up-regulation has not been fully elucidated. In this study, we analyzed kinetics of FcepsilonRI on peritoneal mast cells and bone marrow-derived mast cells. In the presence of brefeldin A, which prevented transport of new FcepsilonRI molecules to the cell surface, levels of IgE-free FcepsilonRI on mast cells decreased drastically during culture, whereas those of IgE-bound FcepsilonRI were stable. In contrast, levels of FcgammaRIII on the same cells were stable even in the absence of its ligand, indicating that FcepsilonRI alpha-chain, but not beta- and gamma-chains, was responsible for the instability of IgE-free FcepsilonRI. As far as we analyzed, there was no evidence to support the idea that IgE binding to FcepsilonRI facilitated synthesis and/or transport of FcepsilonRI to the cell surface. Therefore, the stabilization and accumulation of FcepsilonRI on the cell surface through IgE binding appears to be the major mechanism of IgE-mediated FcepsilonRI up-regulation.
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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".