ACE2 Activation by Tripeptide IRW (Ile-Arg-Trp) Depends on the G Protein-Coupled Receptor 30 Signaling Cascade
Bibliographic record
Abstract
This study aimed to understand how specific cell-bound receptors influence ACE2 activation by IRW. Our results showed that G protein-coupled receptor 30 (GPR30), a 7-transmembrane domain protein, was involved in IRW-mediated ACE2 increase. IRW treatment (50 μM) significantly increased the GPR30 pool levels (3.2 ± 0.5 folds) ( p < 0.001). IRW treatment also boosted the consecutive GEF (guanine nucleotide exchange factor) activity (2.2 ± 0.2 folds) ( p < 0.001), and GNB1 levels (2.0 ± 0.5 folds) ( p < 0.05), associated with the functional subunits of G proteins, in cells. These results were translated in hypertensive animal studies as well ( p < 0.05), indicated by an increase in the aortal levels of GPR30 ( p < 0.01); further experiments showed an increase in downstream PIP3/PI3K/Akt pathway activation following IRW treatment. The blockade of GPR30 by an antagonist and siRNA in cells abolished the ACE2-activating ability of IRW, as shown by the depleted levels of ACE2 mRNA ( p < 0.001), protein levels in whole cells and membrane, angiotensin (1–7) ( p < 0.01), and ACE2 promoter HNF1α ( p < 0.05). Finally, the GPR30 blockade in ACE2-overexpressing cells using the antagonist ( p < 0.01) and siRNA ( p < 0.05) significantly depleted the innate cellular pool of ACE2, thus confirming the relationship between the membrane-bound GPR30 and ACE2. Overall, these results showed that the vasodilatory peptide IRW could activate ACE2 via the membrane-bound receptor GPR30.
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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) | 0.001 | 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".