Prevention of Angiotensin II–Mediated Renal Oxidative Stress, Inflammation, and Fibrosis by Angiotensin-Converting Enzyme 2
Bibliographic record
Abstract
Angiotensin-converting enzyme 2 (ACE2) is a monocarboxypeptidase capable of metabolizing angiotensin (Ang) II into Ang 1 to 7. We hypothesized that ACE2 is a negative regulator of Ang II signaling and its adverse effects on the kidneys. Ang II infusion (1.5 mg/kg⁻¹/d⁻¹) for 4 days resulted in higher renal Ang II levels and increased nicotinamide adenine dinucleotide phosphate oxidase activity in ACE2 knockout (Ace2(-/y)) mice compared to wild-type mice. Expression of proinflammatory cytokines, interleukin-1β and chemokine (C-C motif) ligand 5, were increased in association with greater activation of extracellular-regulated kinase 1/2 and increase of protein kinase C-α levels. These changes were associated with increased expression of fibrosis-associated genes (α-smooth muscle actin, transforming growth factor-β, procollagen type Iα1) and increased protein levels of collagen I with histological evidence of increased tubulointerstitial fibrosis. Ang II-infused wild-type mice were then treated with recombinant human ACE2 (2 mg/kg⁻¹/d⁻¹, intraperitoneal). Daily treatment with recombinant human ACE2 reduced Ang II-induced pressor response and normalized renal Ang II levels and oxidative stress. These changes were associated with a suppression of Ang II-mediated activation of extracellular-regulated kinase 1/2 and protein kinase C pathway and Ang II-mediated renal fibrosis and T-lymphocyte-mediated inflammation. We conclude that loss of ACE2 enhances renal Ang II levels and Ang II-induced renal oxidative stress, resulting in greater renal injury, whereas recombinant human ACE2 prevents Ang II-induced hypertension, renal oxidative stress, and tubulointerstitial fibrosis. ACE2 is an important negative regulator of Ang II-induced renal disease and enhancing ACE2 action may have therapeutic potential for patients with kidney 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) | 0.001 | 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) | 0.001 | 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".