Rcan1 negatively regulates FcɛRI-mediated signaling and mast cell function
Bibliographic record
Abstract
Aggregation of the high affinity IgE receptor (Fc epsilonRI) activates a cascade of signaling events leading to mast cell activation. Subsequently, inhibitory signals are engaged for turning off activating signals. We identified that regulator of calcineurin (Rcan) 1 serves as a negative regulator for turning off Fc epsilonRI-mediated mast cell activation. Fc epsilonRI-induced Rcan1 expression was identified by suppression subtractive hybridization and verified by real-time quantitative polymerase chain reaction and Western blotting. Deficiency of Rcan1 led to increased calcineurin activity, increased nuclear factor of activated T cells and nuclear factor kappaB activation, increased cytokine production, and enhanced immunoglobulin E-mediated late-phase cutaneous reactions. Forced expression of Rcan1 in wild-type or Rcan1-deficient mast cells reduced Fc epsilonRI-mediated cytokine production. Rcan1 deficiency also led to increased Fc epsilonRI-mediated mast cell degranulation and enhanced passive cutaneous anaphylaxis. Analysis of the Rcan1 promoter identified a functional Egr1 binding site. Biochemical and genetic evidence suggested that Egr1 controls Rcan1 expression. Our results identified Rcan1 as a novel inhibitory signal in Fc epsilonRI-induced mast cell activation and established a new link of Egr1 and Rcan1 in Fc epsilonRI signaling.
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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 | 0.000 | 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".