IgE alone stimulates mast cell adhesion to fibronectin via pathways similar to those used by IgE + antigen but distinct from those used by Steel factor
Bibliographic record
Abstract
We recently demonstrated that immunoglobulin E (IgE), in the absence of cross-linking agents, activates signaling pathways in healthy murine bone marrow-derived mast cells (BMMCs) and that this activation enhances BMMC survival, at least in part, via secretion of autocrine-acting cytokines. We report herein that IgE alone also triggers the adhesion of both BMMCs and connective tissue mast cells (CTMCs) to the connective tissue component, fibronectin (FN). This adhesion occurs to the same extent as that triggered by optimal levels of Steel factor (SF) or IgE + antigen (IgE + Ag) and is mediated by an increased avidity of the integrin very late antigen 5 (VLA-5). Moreover, this IgE-induced adhesion, which is prolonged compared with that elicited by SF or IgE + Ag, requires phosphatidylinositol 3-kinase (PI3K), phospholipase C gamma (PLCgamma), and extracellular calcium but not extracellular-regulated kinase (Erk) or p38. Interestingly, we found, using the calcium channel blocker, 2-APB (2-aminoethoxydiphenyl borate) and Lyn-/- BMMCs that both IgE- and IgE + Ag-induced adhesion to FN require extracellular calcium entry, whereas SF does not. Furthermore, our data suggest that FN acts synergistically with IgE to prolong intracellular phosphorylation events and to enhance IgE-induced inflammatory cytokine production and BMMC survival.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".