MKK3 regulates mast cell IL-4 production through Egr1. (151.9)
Bibliographic record
Abstract
Abstract Mast cells play a central role in allergic inflammation and are activated through crosslinking of IgE receptors, initiating a signaling cascade resulting in production of biologically potent mediators. Signaling pathways in the regulation of specific mediators remain incompletely defined. Here, we examined the role of mitogen-activated protein kinase (MAPK) kinase 3 (MKK3) in IgE-dependent mast cell activation. In an in vivo model of passive cutaneous anaphylaxis, MKK3-deficient mice showed a deficit in late phase IgE-dependent inflammation. To characterize the mechanism of this deficiency, we cultured bone marrow-derived mast cells (BMMCs) from wild-type and MKK3 knockout (KO) mice. We found that IgE-mediated mast cell activation induced rapid MKK3 phosphorylation by 5 minutes, diminishing slowly after 6 hours. In MKK3-deficient BMMCs, phosphorylation of p38 was reduced at early and later time points. Among 40 cytokines tested using a protein array, IL-4 was the only cytokine specifically down-regulated in MKK3-deficient BMMCs. Furthermore, early growth response-1 (Egr1) bound to the promoter of IL-4 in FcϵRI-activated mast cells and Egr1 transcription factor activity was diminished from 1-6 hours in MKK3-deficient BMMCs. Thus, MKK3 is a specific regulator of IgE-induced IL-4 production likely through regulating Egr1 activity in mast cells.
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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 | 0.001 |
| 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".