Aldosterone Activates Vascular p38MAP Kinase and NADPH Oxidase Via c-Src
Bibliographic record
Abstract
Increasing evidence indicates that aldosterone elicits vascular effects through nongenomic signaling pathways. We tested the hypothesis that aldosterone induces activation of vascular mitogen-activated protein (MAP) kinases and NADPH oxidase via c-Src-dependent mechanisms in vascular smooth muscle cells (VSMCs). Aldosterone effects on activation of c-Src, p38MAP kinase, and NADPH oxidase, and incorporation of [3H]proline, an index of collagen synthesis, were assessed in cultured rat VSMCs. Studies were performed in the absence and presence of eplerenone, a selective mineralocorticoid receptor blocker, PP2, a selective Src inhibitor, and SB212190, a selective p38MAPK inhibitor. Phosphorylation of c-Src was dose-dependently increased by aldosterone, with maximal responses obtained at 10(-7) mol/L. Aldosterone increased p38MAP kinase phosphorylation, NAD(P)H oxidase activation, and [3H]proline incorporation. These responses were abrogated by eplerenone and almost abolished by PP2. Aldosterone-stimulated incorporation of [3H]proline was significantly reduced by SB212190, indicating that p38MAP kinase plays a role in profibrotic actions of aldosterone. To unambiguously demonstrate the importance of aldosterone in c-Src signaling, VSMCs from c-Src+/+ and c-Src+/- mice were also studied. Aldosterone increased phosphorylation of c-Src, p38MAP kinase, and cortactin, a Src-specific substrate, in c-Src+/+ VSMCs, but not in c-Src-deficient cells. Taken together, our findings demonstrate that nongenomic signaling by aldosterone occurs through c-Src-dependent pathways. These processes may play an important role in profibrotic actions of aldosterone.
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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 | 0.000 |
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".