Mast cell tryptase and proteinase‐activated receptor 2 induce hyperexcitability of guinea‐pig submucosal neurons
Bibliographic record
Abstract
Mast cells that are in close proximity to autonomic and enteric nerves release several mediators that cause neuronal hyperexcitability. This study examined whether mast cell tryptase evokes acute and long-term hyperexcitability in submucosal neurons from the guinea-pig ileum by activating proteinase-activated receptor 2 (PAR2) on these neurons. We detected the expression of PAR2 in the submucosal plexus using RT-PCR. Most submucosal neurons displayed PAR2 immunoreactivity, including those colocalizing VIP. Brief (minutes) application of selective PAR2 agonists, including trypsin, the activating peptide SL-NH2 and mast cell tryptase, evoked depolarizations of the submucosal neurons, as measured with intracellular recording techniques. The membrane potential returned to resting values following washout of agonists, but most neurons were hyperexcitable for the duration of recordings (> 30 min-hours) and exhibited an increased input resistance and amplitude of fast EPSPs. Trypsin, in the presence of soybean trypsin inhibitor, and the reverse sequence of the activating peptide (LR-NH2) had no effect on neuronal membrane potential or long-term excitability. Degranulation of mast cells in the presence of antagonists of established excitatory mast cell mediators (histamine, 5-HT, prostaglandins) also caused depolarization, and following washout of antigen, long-term excitation was observed. Mast cell degranulation resulted in the release of proteases, which desensitized neurons to other agonists of PAR2. Our results suggest that proteases from degranulated mast cells cleave PAR2 on submucosal neurons to cause acute and long-term hyperexcitability. This signalling pathway between immune cells and neurons is a previously unrecognized mechanism that could contribute to chronic alterations in visceral function.
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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".