Acute activation of <scp>SERCA</scp> with <scp>CDN1163</scp> attenuates <scp>IgE</scp> ‐mediated mast cell activation through selective impairment of <scp>ROS</scp> and p38 signaling
Bibliographic record
Abstract
Abstract Mast cells are granulocytic immune sentinels present in vascularized tissues that drive chronic inflammatory mechanisms characteristic of allergic pathologies. IgE‐mediated mast cell activation leads to a rapid mobilization of Ca 2+ from intracellular stores, which is essential for the release of preformed mediators via degranulation and de novo synthesized proinflammatory cytokines and chemokines. Given its potent signaling capacity, the dynamics of Ca 2+ localization are highly regulated by various pumps and channels controlling cytosolic Ca 2+ concentrations. Among these is sarco/endoplasmic reticulum Ca 2+ ‐ATPase (SERCA), which functions to maintain low cytosolic Ca 2+ concentrations by actively transporting cytosolic Ca 2+ ions into the endoplasmic reticulum. In this study, we characterized the role of SERCA in allergen‐activated mast cells using IgE‐sensitized bone marrow‐derived mast cells (BMMCs) treated with the SERCA activating compound, CDN1163, and simultaneously stimulated with allergen through FcεRI under stem cell factor (SCF) potentiation. Acute treatment with CDN1163 was found to attenuate early phase mast cell degranulation along with reactive oxygen species (ROS) production. Additionally, treatment with CDN1163 significantly reduced secretion of IL‐6, IL‐13, and CCL3, suggesting a role for SERCA in the late phase mast cell response. The protective effects of SERCA activation via CDN1163 treatment on the early and late phase mast cell response may be driven by the selective suppression of p38 MAPK signaling. Together, these findings implicate SERCA as an important regulator of the mast cell response to allergen and suggest SERCA activity may offer therapeutic potential targeting allergic pathologies, warranting further investigation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".