Role of epidermal growth factor receptor transactivation in endothelin-1-induced enhanced expression of Gi protein and proliferation in A10 vascular smooth muscle cells
Bibliographic record
Abstract
We have recently shown that vasoactive peptides such as angiotensin II (Ang II) and endothelin-1 (ET-1) increase the expression of Gi proteins and the proliferation of A10 vascular smooth muscle cells (VSMC) through mitogen-activated protein (MAP) kinase-phosphoinositide (PI) 3-kinase pathways. This study was intended to examine the implication of epidermal growth factor receptor (EGFR) activation in ET-1-induced enhanced expression of Gi proteins and proliferation of A10 VSMC, and to further investigate the underlying mechanisms responsible for these increases. Cell proliferation was determined by [(3)H]thymidine incorporation and the expression of Gi proteins; extracellular signal-regulated kinases 1 and 2 (ERK1/2) and EGFR phosphorylation was determined by Western blotting. Treatment of A10 VSMC with ET-1 enhanced the expression of Gi proteins, which was attenuated by BQ123 and BQ788, antagonists of ET(A) and ET(B) receptor respectively. In addition, ET-1 enhanced the phosphorylation of EGFR in A10 VSMC, which was restored to the control levels by EGFR inhibitor and ETA and ETB receptor antagonists. Furthermore, ET-1 also augmented the proliferation and ERK1/2 phosphorylation of A10 VSMC, which were restored to the control levels by inhibition of EGFR. These data suggest that ET-1 transactivates EGFR, which, through MAP kinase signaling, may contribute to the enhanced expression of Gi proteins and thus increased proliferation of A10 VSMC.
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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".