Enhanced expression of G<sub>q</sub>α and PLC-β1 proteins contributes to vascular smooth muscle cell hypertrophy in SHR: role of endogenous angiotensin II and endothelin-1
Bibliographic record
Abstract
Vascular Gqα signaling has been shown to contribute to cardiac hypertrophy. In addition, angiotensin II (ANG II) was shown to induce vascular smooth muscle cell (VSMC) hypertrophy through Gqα signaling; however, the studies on the role of Gqα and PLC-β1 proteins in VSMC hypertrophy in animal model are lacking. The present study was therefore undertaken to examine the role of Gqα/PLC-β1 proteins and the signaling pathways in VSMC hypertrophy using spontaneously hypertensive rats (SHR). VSMC from 16-wk-old SHR and not from 12-wk-old SHR exhibited enhanced levels of Gqα/PLC-β1 proteins compared with age-matched Wistar-Kyoto (WKY) rats as determined by Western blotting. However, protein synthesis as determined by [(3)H]leucine incorporation was significantly enhanced in VSMC from both 12- and 16-wk-old SHR compared with VSMC from age-matched WKY rats. Furthermore, the knockdown of Gqα/PLC-β1 in VSMC from 16-wk-old SHR by antisense and small interfering RNA resulted in attenuation of protein synthesis. In addition, the enhanced expression of Gqα/PLC-β1 proteins, enhanced phosphorylation of ERK1/2, and enhanced protein synthesis in VSMC from SHR were attenuated by the ANG II AT1 and endothelin-1 (ET-1) ETA receptor antagonists losartan and BQ123, respectively, but not by the ETB receptor antagonist BQ788. In addition, PD98059 decreased the enhanced expression of Gqα/PLC-β1 and protein synthesis in VSMC from SHR. These results suggest that the enhanced levels of endogenous ANG II and ET-1 through the activation of AT1 and ETA receptors, respectively, and MAP kinase signaling, enhanced the expression of Gqα/PLC-β1 proteins in VSMC from 16-wk-old SHR and result in VSMC hypertrophy.
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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) | 0.001 | 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".