Agonist‐dependent modulation of arterial endothelin<sub>A</sub>receptor function
Bibliographic record
Abstract
BACKGROUND AND PURPOSE Endothelin-1 (ET-1) causes long-lasting vasoconstrictions. These can be prevented by ET(A) receptor antagonists but are only poorly reversed by these drugs. We tested the hypothesis that endothelin ET(A) receptors are susceptible to allosteric modulation by endogenous agonists and exogenous ligands. EXPERIMENTAL APPROACH Rat isolated mesenteric resistance arteries were pretreated with capsaicin and studied in wire myographs, in the presence of L-NAME and indomethacin to concentrate on arterial smooth muscle responses. KEY RESULTS Endothelins caused contractions with equal maximum but differing potency (ET-1 = ET-2 > ET-3). ET-1(1-15) neither mimicked nor antagonized these effects in the absence and presence of ET(16-21). 4(Ala) ET-1 (ET(B) agonist) and BQ788 (ET(B) antagonist) were without effects. BQ123 (peptide ET(A) antagonist) reduced the sensitivity and relaxed the contractile responses to endothelins. Both effects depended on the agonist (pK(B): ET-3 = ET-1 > ET-2; % relaxation: ET-3 = ET-2 > ET-1). Also, with PD156707 (non-peptide ET(A) antagonist) agonist-dependence and a discrepancy between preventive and inhibitory effects were observed. The latter was even more marked with bulky analogues of BQ123 and PD156707. CONCLUSIONS AND IMPLICATIONS These findings indicate allosteric modulation of arterial smooth muscle ET(A) receptor function by endogenous agonists and by exogenous endothelin receptor antagonists. This may have consequences for the diagnosis and pharmacotherapy of diseases involving endothelins.
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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) | 0.002 | 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".