TAK1 contributes to IgE-FcɛRI-mediated calcium mobilization and mast cell degranulation
Bibliographic record
Abstract
Abstract Allergic inflammation is an inappropriate immune response, typically triggered by activated mast cells that have been IgE-sensitized to innocuous environmental agents. Allergic inflammatory events can be split into two phases, early and late, initiated by allergen-mediated mast cell activation. The early phase of the allergic response occurs within minutes of an allergen binding and crosslinking IgE-FcɛRI complexes on the surface of a sensitized mast cell. This recognition of a perceived ‘threat’ rapidly induces mast cell activation and subsequent release of pro-inflammatory mediators from preformed granules, a process known as degranulation. We have identified TAK1 as a novel contributor to induced mast cell signaling events and aim to characterize the mechanistic contribution of TAK1 in mast cell degranulation. To this end, we have employed a primary murine model of bone marrow-derived mast cells (BMMC) to examine contributions of TAK1 to IgE-mediated calcium mobilization and degranulation. β-hexosaminidase release assays and Indo-1-based spectrofluorometry demonstrated a significant inhibition (−67% ±1.44 p<0.0001) in degranulation and a reduction in calcium transients (both peak amplitude p<0.01 and area under curve p<0.05) in allergically-activated BMMCs treated with the TAK1 inhibitor, 5Z-7-oxozeaneol (OZ). These results were supported by restoration of normal degranulation with the use of an inactive OZ analog (5Z), whereas an alternative TAK1 inhibitor (AZ) also resulted in degranulation inhibition (−32% ± 2.64 p<0.0001). These results provide novel evidence to support the mechanistic control of mast cell degranulation by TAK1, highlighting its potential as a therapeutic target for allergic pathologies.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".