TAK1 signaling activity links the mast cell cytokine response and degranulation in allergic inflammation
Bibliographic record
Abstract
Mast cells drive the inappropriate immune response characteristic of allergic inflammatory disorders via release of pro-inflammatory mediators in response to environmental cues detected by the IgE-FcεRI complex. The role of TGF-β-activated kinase 1 (TAK1), a participant in related signaling in other contexts, remains unknown in allergy. We detect novel activation of TAK1 at Ser412 in response to IgE-mediated activation under SCF-c-kit potentiation in a mast cell-driven response characteristic of allergic inflammation, which is potently blocked by TAK1 inhibitor 5Z-7-oxozeaenol (OZ). We, therefore, interrogated the role of TAK1 in a series of mast cell-mediated responses using IgE-sensitized murine bone marrow-derived mast cells, stimulated with allergen under several TAK1 inhibition strategies. TAK1 inhibition by OZ resulted in significant impairment in the phosphorylation of MAPKs p38, ERK, and JNK; and mediation of the NF-κB pathway via IκBα. Impaired gene expression and near abrogation in release of pro-inflammatory cytokines TNF, IL-6, IL-13, and chemokines CCL1, and CCL2 was detected. Finally, a significant inhibition of mast cell degranulation, accompanied by an impairment in calcium mobilization, was observed in TAK1-inhibited cells. These results suggest that TAK1 acts as a signaling node, not only linking the MAPK and NF-κB pathways in driving the late-phase response, but also initiation of the degranulation mechanism of the mast cell early-phase response following allergen recognition and may warrant consideration in future therapeutic development.
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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 | 0.001 |
| 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".