Calcineurin–Rcan1 Interaction Contributes to Stem Cell Factor–Mediated Mast Cell Activation
Bibliographic record
Abstract
The receptor for stem cell factor (SCF) is expressed on mast cells and hematopoietic progenitors. SCF-induced signaling pathways remain incompletely defined. In this study, we identified calcineurin and regulator of calcineurin 1 (Rcan1) as novel components in SCF signaling. Calcineurin activity was induced in SCF-stimulated primary mouse and human mast cells. NFAT was activated by SCF in bone marrow-derived mast cells (BMMCs) and mouse bone marrow cells, which contain hematopoietic progenitors. SCF-mediated activation also induced expression of Rcan1 in BMMCs. Rcan1-deficient BMMCs showed increased calcineurin activity and enhanced transcriptional activity of NF-κB and NFAT, resulting in increased IL-6 and TNF production following SCF stimulation. These results suggest that Rcan1 suppresses SCF-induced activation of calcineurin and NF-κB. We further demonstrated that SCF-induced Rcan1 expression is dependent on the transcription factor early growth response 1 (Egr1). Interestingly, SCF-induced Egr1 was also suppressed by Rcan1, suggesting a negative regulatory loop between Egr1 and Rcan1. Together, our findings revealed that calcineurin contributes to SCF-induced signaling, leading to NFAT activation, which, together with NF-κB and Egr1, is suppressed by Rcan1. Considering the wide range of biological functions of SCF, these novel regulatory mechanisms in SCF signaling may have broad implications.
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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".