MAPK Kinase 3 Specifically Regulates FcεRI-Mediated IL-4 Production by Mast Cells
Bibliographic record
Abstract
Mast cells play a central role in allergic inflammation and are activated through cross-linking of FcεRI receptor-bound IgE, initiating a signaling cascade resulting in production of biologically potent mediators. Signaling pathways in the regulation of specific mediators remain incompletely defined. In this study, we examined the role of 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-deficient mice. We found that FcεRI-mediated mast cell activation induced rapid MKK3 phosphorylation by 5 min, diminishing slowly after 6 h. 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 downregulated in MKK3-deficient BMMCs. Reduced IL-4 expression was seen in the local skin of MKK3-deficient mice following passive cutaneous allergic reaction. 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 in MKK3-deficient BMMCs. Finally, mast cell-deficient mice reconstituted with MKK3-deficient BMMCs displayed a significantly impaired late-phase allergic inflammatory response. Thus, mast cell MKK3 signaling contributes to IgE-dependent allergic inflammation and is a specific regulator of FcεRI-induced IL-4 production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".