Angiotensin-(1–7) activates growth-stimulatory pathways in human mesangial cells
Bibliographic record
Abstract
Angiotensin-(1-7) [Ang-(1-7)] is generated in part via ACE2-dependent degradation of angiotensin II (ANG II). In proximal tubular cells, Ang-(1-7) inhibits ANG II-stimulated phosphorylation of the mitogen-activated protein kinases (MAPKs) p38, extracellular signal-related kinase (ERK1/ERK2), and c-jun N-terminal kinase (JNK), suggesting that Ang-(1-7) protects against ANG II-mediated tubulointerstitial injury. We determined the effect of Ang-(1-7) on signaling and growth responses in cultured human mesangial cells. Ang-(1-7) increased phosphorylation of p38, ERK1/ERK2, and JNK MAPKs, which was blocked by the Ang-(1-7) antagonist A-779. Neither the AT(1) receptor antagonist losartan, nor the AT(2) antagonist PD123319 affected specific binding of [(125)I]Ang-(1-7) or Ang-(1-7)-stimulated p38 phosphorylation. Ang-(1-7) increased cell arachidonic acid release, an effect blocked by A-779. The p38 MAPK antagonist SB202190 completely prevented Ang-(1-7)-stimulated release of arachidonic acid, whereas inhibitors of ERK or JNK had no effect. Ang-(1-7) significantly enhanced DNA synthesis and increased production of transforming growth factor-beta1 (TGF-beta1), fibronectin, and collagen IV. Both A-779 and SB202190 blocked the Ang-(1-7)-stimulated increases in TGF-beta1, fibronectin, and collagen IV. These data indicate that Ang-(1-7) activates MAPK phosphorylation via binding to a specific receptor in human mesangial cells. Stimulation of p38 MAPK phosphorylation by Ang-(1-7) leads to release of arachidonic acid and production of TGF-beta1 and extracellular matrix proteins. We conclude that Ang-(1-7) exerts growth-stimulatory effects in human mesangial cells.
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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".