Functional Expression of Neurokinin 1 Receptors on Mast Cells Induced by IL-4 and Stem Cell Factor
Bibliographic record
Abstract
It is widely accepted that neurokinin 1 (NK(1)) receptors are not generally expressed on mast cells but little is known about their expression in inflammation. The present study shows expression of NK(1) receptors on bone marrow-derived mast cells (BMMC) under the influence of IL-4 or stem cell factor (SCF). Highest expression was found when both cytokines are present. Six days of coculture with the cytokines IL-4 and SCF showed significant expression of NK(1) receptors (NK(1) receptor(+)/c-kit(+) BMMC; control: 7%, IL-4/SCF: 16%), while 12 days of cytokine coculture increased this expression to 37% positive cells. A longer coculture with IL-4 and SCF did not give an additional effect. Increased expression in IL-4/SCF-treated BMMC was further confirmed using Western blot analysis. Next, we demonstrated the functional relevance of NK(1) receptor expression for mast cell activation, resulting in an enhanced degranulation upon stimulation by substance P. BMMC activation was significantly diminished by the NK(1) receptor antagonist RP67580 (10 micro M) when stimulated with low concentrations of substance P. The inactive enantiomer RP65681 had no effect. In addition, BMMC cultured from bone marrow of NK(1) receptor knockout mice showed significantly decreased exocytosis to low concentrations of substance P. The present study clearly shows that NK(1) receptor-induced activation contributes significantly at low physiological substance P concentrations (<100 micro M). In conclusion, BMMC were shown to express NK(1) receptors upon IL-4/SCF coculture. This expression of NK(1) receptors has been demonstrated to be of functional relevance and leads to an increase in the sensitivity of BMMC to substance P.
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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) | 0.001 | 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".