JNK signaling during IL-3–mediated differentiation contributes to the c-kit–potentiated allergic inflammatory capacity of mast cells
Bibliographic record
Abstract
Mast cells are leukocytes that mediate various aspects of immunity and drive allergic hypersensitivity pathologies. Mast cells differentiate from hematopoietic progenitor cells in a manner that is largely IL-3 dependent. However, molecular mechanisms, including the signaling pathways that control this process, have yet to be thoroughly investigated. Here, we examine the role of the ubiquitous and critical mitogen-activated protein kinase signaling pathway due to its position downstream of the IL-3 receptor. Hematopoietic progenitor cells were harvested from the bone marrow of C57BL/6 mice and differentiated to bone marrow-derived mast cells in the presence of IL-3 and mitogen-activated protein kinase inhibitors. Inhibition of the JNK node of the mitogen-activated protein kinase pathway induced the most comprehensive changes to the mature mast cell phenotype. Bone marrow-derived mast cells differentiated during impaired JNK signaling expressed impaired c-kit levels on the mast cell surface, first detected at week 3 of differentiation. Following 1 wk of inhibitor withdrawal and subsequent stimulation of IgE-sensitized FcεRI receptors with allergen (TNP-BSA) and c-kit receptors with stem cell factor, JNK-inhibited bone marrow-derived mast cells exhibited impediments in early-phase mediator release through degranulation (80% of control), as well as late-phase secretion of CCL1, CCL2, CCL3, TNF, and IL-6. Experiments with dual stimulation conditions (TNP-BSA + stem cell factor or TNP-BSA alone) showed that impediments in mediator secretion were found to be mechanistically linked to reduced c-kit surface levels. This study is the first to implicate JNK activity in IL-3-mediated mast cell differentiation and also identifies development as a critical and functionally determinative period.
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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) | 0.001 | 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".