The ACE2/Ang (1–7)/MasR axis as an emerging target for antihypertensive peptides
Bibliographic record
Abstract
Food protein-derived bioactive peptides, particularly antihypertensive peptides, are important constituents of functional foods or nutraceuticals. Most antihypertensive are identified as the inhibitors of angiotensin converting enzyme (ACE), a key enzyme responsible for the generation of angiotensin II (Ang II), which is a vasoconstricting peptide. Hence, ACE has long been used as a universal target to identify antihypertensive peptides. Angiotensin converting enzyme 2 (ACE2), is a homolog of ACE but uses Ang II as its key substrate to produce angiotensin (1-7), exerting vasodilatory activity via the mas receptor (MasR). Therefore, ACE2 functions in the opposite way as ACE and is an emerging novel target for cardiovascular therapy. The potential of food protein-derived bioactive peptides in targeting ACE2 has been rarely explored. While, recently we found that IRW, an egg white ovotransferrin-derived antihypertensive peptide, reduced blood pressure in spontaneously hypertensive rats via the ACE2/Ang (1-7)/MasR axis, indicating a new mechanism of food protein-derived bioactive peptides in reducing blood pressure. The objectives of this review are to summarize the functions of the ACE2/Ang (1-7)/MasR axis and to examine its potential roles in the actions of food protein-derived antihypertensive peptides. The interaction between antihypertensive peptides and the ACE2/Ang (1-7)/MasR axis will also be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".