Enhanced expression of Egr‐1 in vascular smooth muscle cells from spontaneously hypertensive rats: role of Giα proteins
Bibliographic record
Abstract
Early growth response, Egr‐1, is known to activate the expression of several genes implicated in the development of vascular dysfunction. Earlier studies have shown that angiotensin II (AngII) enhances the expression of Egr‐1 and Giα proteins in vascular smooth muscle cells (VSMC). In addition, the role of enhanced levels of endogenous Ang II in enhanced expression of Giα proteins in spontaneously hypertensive rats (SHR) has also been shown. The present study was therefore undertaken to examine if VSMC from SHR also exhibit enhanced expression of Egr‐1 proteins and explore the underlying molecular mechanisms mediating this effect. The levels of Egr‐1 protein in VSMC from SHR were not different in 2 weeks old SHR as compared to WKY (Wistar‐Kyoto rats), however, it started increasing at 4 weeks and at 12 weeks, it was significantly increased by 80%. Knockdown of Giα‐2 proteins, using antisense and siRNA treatment, attenuated the enhanced expression of Egr‐1 proteins to control levels. In addition, the knockdown of Egr‐1 by siRNA also attenuated the expression of Giα‐2 and Giα‐3 proteins in VSMC from 12 week old SHR. The enhanced levels of Egr‐1 protein were attenuated by losartan (an AT1 receptor antagonist), BQ123 (an ETA receptor antagonist), BQ788 (an ETB receptor antagonist) but not by PD123319 (an AT2 receptor antagonist) in VSMC from 12 week SHR. These results suggest that VSMC from SHR exhibit an overexpression of Egr‐1 which is due to the enhanced levels of endogenous Ang II, ET‐1 and Giα proteins. In addition, Egr‐1 also regulates the expression of Giα proteins in VSMC from SHR and suggests a cross‐talk between Giα and Egr‐1. Support or Funding Information Supported by CIHR This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".