Neuropeptides activate human mast cell degranulation and chemokine production
Bibliographic record
Abstract
During neuronal-induced inflammation, mast cells may respond to stimuli such as neuropeptides in an FcepsilonRI-independent manner. In this study, we characterized human mast cell responses to substance P (SP), nerve growth factor (NGF), calcitonin gene-related peptide (CGRP) and vasoactive intestinal polypeptide (VIP) and compared these responses to human mast cell responses to immunoglobulin E (IgE)/anti-IgE and compound 48/80. Primary cultured mast cells, generated from CD34(+) progenitors in the presence of stem cell factor and interleukin-6 (IL-6), and human cultured mast cells (LAD2) were stimulated with these and other stimuli (gastrin, concanavalin A, radiocontrast media, and mannitol) and their degranulation and chemokine production was assessed. VIP and SP stimulated primary human mast cells and LAD cells to degranulate; gastrin, concanavalin A, radiocontrast media, mannitol, CGRP and NGF did not activate degranulation. While anti-IgE stimulation did not induce significant production of chemokines, stimulation with VIP, SP or compound 48/80 potently induced production of monocyte chemoattractant protein-1, inducible protein-10, monokine induced by interferon-gamma (MIG), RANTES (regulated on activation, normal, T-cell expressed, and secreted) and IL-8. VIP, SP and compound 48/80 also activated release of tumour necrosis factor, IL-3 and granulocyte-macrophage colony-stimulating factor, but not IL-4, interferon-gamma or eotaxin. Human mast cells expressed surface neurokinin 1 receptor (NK1R), NK2R, NK3R and VIP receptor type 2 (VPAC2) but not VPAC1 and activation of human mast cells by IgE/anti-IgE up-regulated expression of VPAC2, NK2R, and NK3R. These studies demonstrate the pattern of receptor expression and activation of mast cell by a host of G-protein coupled receptor ligands and suggest that SP and VIP activate a unique signalling pathway in human mast cells. These results are likely to have direct relevance to neuronally induced inflammatory diseases.
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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 | 0.001 |
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".