Modulated MAPK signaling during mast cell differentiation promotes an impaired inflammatory phenotype
Bibliographic record
Abstract
Abstract Mast cells are innate immune cells derived from hematopoietic stem cells. Mechanisms controlling IL-3-directed mast cell differentiation are incompletely defined. MAPK contributes to IL-3R-mediated signaling, yet specific contributions to maturation of functional mast cells are undetermined. We aimed to evaluate the role of the MAPK signaling nodes—JNK, ERK, and p38—in mast cell differentiation, and explore contributions to epigenetic modifier expression. Bone marrow-derived mast cell (BMMC) cultures were established from C57BL/6 mice and differentiated for 7 weeks in the presence or absence of MAPK inhibitors (JNK, JNK-IN-8; ERK, SCH772984; p38, GW856553X). Following inhibitor withdrawal (5 days), phenotypic and functional characteristics of IgE-sensitized BMMCs were assessed using flow cytometry, ELISA, and β-hexosaminidase release assays. Cells differentiated under JNK inhibition showed a significant impairment in the secretion of IL-6 (p<0.05) and IL-13 (p<0.05) and a non-significant impairment in TNF and CCL1 24 hrs post allergen challenge. Degranulation was also significantly decreased (by 25%, p<0.05) in JNK-inhibitor-cultured BMMCs. Expression of c-kit and level of sensitization by IgE was unaffected by impaired JNK signaling. Treatment with p38 and ERK inhibitors did not affect receptor expression, cytokine secretion, or degranulation. Interrogation of MAPK-dependent modifiers of histone acetylation by qPCR and western blot showed that JNK-mediated contributions to mast cell functional responses were independent of HDACs and HATs examined. These results position JNK as a critical contributor to normal mast cell differentiation and potential target in modulating mast cell phenotype.
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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) | 0.002 | 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".