Vascular proinflammatory responses by aldosterone are mediated via c-Src trafficking to cholesterol-rich microdomains: role of PDGFR
Bibliographic record
Abstract
AIMS: We demonstrated c-Src activation as a novel non-genomic signalling pathway for aldosterone in vascular smooth muscle cells (VSMCs). Here, we investigated molecular mechanisms and biological responses of this phenomenon, focusing on the role of lipid rafts/caveolae and platelet-derived growth factor receptor (PDGFR) in c-Src-regulated proinflammatory responses by aldosterone. METHODS AND RESULTS: Studies were performed in cultured VSMCs from Wistar-Kyoto (WKY) rats and caveolin-1 knockout (Cav 1(-/-)) and wild-type mice. Aldosterone stimulation increased c-Src phosphorylation and trafficking to lipid rafts/caveolae. Cholesterol depletion with methyl-β-cyclodextrin abrogated aldosterone-induced phosphorylation of c-Src and its target, Pyk2. Aldosterone effects were recovered by cholesterol reload. Aldosterone-induced c-Src and cortactin phosphorylation was reduced in caveolin-1-silenced and Cav 1(-/-) VSMCs. PDGFR is phosphorylated by aldosterone within cholesterol-rich fractions of VSMCs. AG1296, a PDGFR inhibitor, prevented c-Src phosphorylation and translocation to cholesterol-rich fractions. Aldosterone induced an increase in adhesion molecule protein content and promoted monocyte adhesion to VSMCs, responses that were inhibited an by cholesterol depletion, caveolin-1 deficiency, AG1296 and PP2, a c-Src inhibitor. Mineralocorticoid receptor (MR) content in flotillin-2-rich fractions and co-immunoprecipitation with c-Src and PDGFR increased upon aldosterone stimulation, indicating MR-lipid raft/signalling association. CONCLUSION: We demonstrate that aldosterone-mediated c-Src trafficking/activation and proinflammatory signalling involve lipid rafts/caveolae via PDGFR.
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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".