PAR‐2‐induced epithelial Cl‐ secretion involves EGFr transactivation
Bibliographic record
Abstract
Proteinase-activated receptor (PAR)-2 is activated by trypsin-like serine proteinases. PAR-2 activation stimulates intestinal epithelial Cl- secretion, but the underlying stimulation-secretion coupling pathways are unclear. We have studied these pathways in the SCBN intestinal epithelial cell culture model. Methods 1. SCBN monolayers were grown on Snapwell supports and mounted in modified Ussing chambers. Short circuit current (Isc) was monitored as a measure of net electrogenic ion transport. The roles of PKC, MAP kinase (ERK1/2), Src, matrix metallo-proteinases (MMPs) and the EGF receptor (EGFr) in the response to the PAR-2 activating peptide SLIGRL-NH2 were determined using specific inhibitors (GFX, PD98059, PP1, GM6001, PD153035). 2. Western blot analysis was conducted for ERK1/2, Src and EGFr phosphorylation in response to PAR-2 activation. Results 1. Pretreatment with GFX resulted in a reduction (p<0.05) in the response to PAR-2 activation by SLIGRL-NH2. PD98059 reduced responses to PAR-2 activation as did pretreatment with PD153035 (p<0.05). Inhibition of Src, but not MMPs, resulted in a significant decrease in response to PAR-2 activation by SLIGRL-NH2. 2. As shown by Western blot, PAR-2 activation resulted in EGFr, Src and ERK1/2 phosphorylation. Inhibition of Src, but not MMPs, resulted in significantly reduced EGFr phsophorylation following activation of PAR-2 with SLIGRL-NH2. Furthermore, inhibition of Src, EGFr or PKC prevented ERK1/2 phosphorylation. Conclusions PAR-2 activation induces epithelial Cl− secretion that is mediated by Src-dependent EGF receptor transactivation, subsequent ERK1/2 activation and PKC-mediated signaling. Funding: Canadian Institutes of Health Research.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".