ENHANCING INTERACTIONS BETWEEN MASR AND ETBR AS A NEW VASOPROTECTIVE STRATEGY
Bibliographic record
Abstract
Objective: We demonstrated that the MAS and ETB receptors physically interact and may be involved in the protective actions of Ang(1–7) in endothelial cells (ECs). We assessed the biological role of MAS/ETBR interaction in the vasculature, focusing on the NO pathway. Design and method: Human ECs were stimulated with Ang(1–7) (10-7 M) in the presence/absence of A779 (MAS antagonist, 10-5 M), BQ788 (ETBR antagonist, 10-5 M) and MAS/ETBR peptide disruptor (10-5 M). ETBR expression and AKT/eNOS activation were evaluated by immunoblotting and NO production by DAF-FM fluorescence. Results: Ang(1–7) increased ETBR expression (50%), an effect blocked by A779 (p < 0.05 vs control). Ang(1–7) also induced AKT (40%) and eNOS (35%) activation; followed by an increase in NO production (2 fold) (p < 0.05 vs control). These effects were inhibited by A779, BQ788 and the MAS/ETBR disruptor. In a high throughput screening of a 20K protein:protein interaction modulator library, we discovered 23 potential enhancers of the MAS/ETBR interaction, where we tested 4 in ECs (Enh1–4: 10–5 M). All four MAS/ETBR enhancers increased eNOS activation with variable responses (Enh1: 44.2%; Enh2: 65.4%; Enh3: 80.7%; Enh4: 94.9% vs control, p < 0.05), but only treatment with Enh1 and Enh2 increased NO production (Enh1: 109.4%; Enh2: 46.5% vs control, p < 0.05) in ECs. Mesenteric resistance arteries were pre-incubated with Enh1–4 for 30 minutes prior to acetylcholine (Ach) curves, to assess endothelium-dependent relaxation. Enh1 decreased ACh-induced relaxation (Emax: 52.8 ± 2.9 vs Emax control: 70.4 ± 3.3), while Enh4 increased ACh-induced relaxation (Emax: 96.7 ± 4.6 vs Emax control: 70.4 ± 3.3), p < 0.05. Conclusions: Promoting the MAS/ETBR interaction with specific enhancers leads to eNOS activation and vasorelaxation, where Enh1–4 have variable effects. Current studies are focusing on identifying the most sensitive enhancer that will provide vasoprotection, and potentially new strategies in the treatment of vascular disease.
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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".