De novo synthesis of early growth response factor-1 is required for the full responsiveness of mast cells to produce TNF and IL-13 by IgE and antigen stimulation
Bibliographic record
Abstract
Early growth-response factor 1 (Egr-1) is a zinc-finger transcription factor that plays a regulatory role in the expression of many genes important for inflammation. Whether Egr-1 is involved in IgE-dependent mast-cell activation was investigated. We demonstrated that IgE and antigen (TNP) stimulation induced a rapid expression of Egr-1 mRNA in mouse bone marrow-derived mast cells (BMMCs). As early as 15 to 20 minutes after IgE + TNP stimulation, Egr-1 protein was detectable in the nucleus of BMMCs by immunofluorescence or electrophoretic mobility shift assay. To examine a role for Egr-1 in IgE-dependent cytokine production by mast cells, Egr-1-deficient (Egr-1-/-) BMMCs were developed from the bone marrow cells of Egr-1 knockout mice. Egr-1-/- BMMCs express similar levels of surface c-kit and IgE receptor as compared with those on Egr-1+/+ BMMCs. Importantly, IgE + TNP-induced TNF and IL-13 expression was significantly reduced at both mRNA and protein levels in Egr-1-/- BMMCs as compared with those in Egr-1+/+ BMMCs. Thus, our results suggest that de novo synthesis of Egr-1 represents a novel mechanism in FcepsilonRI signaling and is required for the full responsiveness of IgE-dependent TNF and IL-13 production by 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) | 0.001 | 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 | 0.001 |
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".