The Early Growth Response Factor-1 Contributes to Interleukin-13 Production by Mast Cells in Response to Stem Cell Factor Stimulation
Bibliographic record
Abstract
Stem cell factor (SCF) is not only critical for mast cell development, but also an important mast cell functional regulator. However, roles of transcription factors involved in SCF-induced effects remain incompletely defined. Early growth response factor-1 (Egr-1) is a member of zinc-finger transcription factor family. Mouse bone marrow-derived mast cells (BMMC) were used to examine a role of Egr-1 in SCF-induced mast cell activation and growth. SCF induced a strong and rapid expression of Egr-1 mRNA as tested by real-time PCR analysis. SCF-induced Egr-1 nuclear translocation and DNA binding were demonstrated by electrophoretic mobility shift assay (EMSA) and immunofluorescence assay. To examine if Egr-1 is required for SCF-induced IL-13 expression, Egr-1-deficient BMMC were used. Levels of SCF-induced IL-13 mRNA and protein were reduced in Egr-1 deficient BMMC when compared with wild-type BMMC. Although Egr-1 is required for macrophage and lymphocyte development, SCF-induced mast cells growth was not affected by Egr-1 deficiency. Interestingly, SCF-induced Egr activation was blocked by a tyrosine kinase inhibitor PP2, suggesting a role of tyrosine phosphorylation in SCF-induced Egr-1 activation. Taken together, our results suggest that Egr-1 is required for SCF-induced IL-13 expression, but not mast cell growth.
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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) | 0.002 | 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".