Glycogen synthase kinase 3 (GSK3) is a critical regulator of allergen-mediated mast cell activation
Bibliographic record
Abstract
Abstract Mast cells are granulated immune sentinels responsible for perpetuating allergic inflammatory events. Allergen-induced FcɛRI signaling leads to the activation of MAPK, PLC and PI3K/Akt signaling cascades, which are responsible for mediating a biphasic mast cell response, characterized by degranulation in the early phase and sustained release of pro-inflammatory mediators in the late phase. Glycogen synthase kinase 3 (GSK3) is a constitutively active serine/threonine kinase and a major convergence point for several highly conserved signaling cascades, including the PI3K/Akt pathway. Due to its central role in regulating downstream pro-inflammatory signals, GSK3 has become a therapeutic target in inflammatory pathologies, however, the role that GSK3 plays in allergen-induced FcɛRI signaling has yet to be characterized. Thus, the objective of this study was to determine the functional role of GSK3 in allergen-activated mast cells. Sensitized murine bone marrow-derived mast cells were incubated with the GSK3 inhibitor CHIR99021 and stimulated with allergen. CHIR99021 treatment significantly inhibited degranulation dose-dependently to 63% (10 μM, p<0.001) and 43% (20 μM, p<0.001) of the control. Release of cytokines TNF (p<0.01), IL-6 (p<0.001), IL-13 (p<0.01) and chemokines CCL1 (p<0.001), CCL2 (p<0.05) and CCL3 (p<0.001) were inhibited following treatment at 20 μM. Finally, inhibition of GSK3 was found to significantly reduce downstream phosphorylation of JNK (10 & 20 μM, p<0.05), while ERK and p38 remained unaffected. These results are the first to characterize GSK3 as a central regulator of allergen-induced FcɛRI signaling, making it an intriguing target to attenuate mast cell functionality in pathological contexts.
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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) | 0.002 | 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".