Investigating the negative regulatory role of neurogranin in IgE-mediated mast cell activation
Bibliographic record
Abstract
Mast cells are granulocytic immune sentinels that drive the chronic inflammatory state characteristic of allergic pathologies, through the release of a plethora of pro-inflammatory mediators following IgE-mediated activation. Calcium mobilization following mast cell activation is essential for degranulation and pro-inflammatory cytokine/chemokine production, processes that require the Ca2+/calmodulin (CaM)-dependent phosphatase, calcineurin. Neurogranin is a protein that regulates Ca2+-mediated signaling pathways by binding and sequestering of CaM, subsequently acting as a critical suppressor of calcineurin phosphatase activity. Neurogranin was previously thought to exist exclusively in the brain; however, this is the first study to demonstrate the presence of neurogranin in mast cells. Since Ca2+/CaM signaling is so critical to the inflammatory response, the objective of this study was to characterize the role of neurogranin in allergen activated mast cells using bone marrow-derived mast cells (BMMCs) from Nrgn+/- mice. In this study, BMMCs from Nrgn+/- and Nrgn+/+ mice were sensitized with IgE overnight and stimulated the next day with TNP-BSA under SCF potentiation. Early phase mast cell degranulation, measured through a -hexosaminidase assay, was demonstrated to be uninterrupted in neurogranin deficient BMMCs. In the late phase, neurogranin was found to function as a negative regulator of the transcription of IL-6 and IL-13 genes, as well as the secretion of IL-6 and TNF. These observed functional effects of neurogranin deficiency were found to be independent of modulation of the MAPK signaling pathway. Inhibitory signals that terminate inappropriate mast cell responses and limit undesirable inflammation remain incompletely defined, and these findings are the first to suggest that neurogranin has an integral role in IgE-mediated mast cell inhibitory 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) | 0.000 | 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) | 0.000 | 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".