Activation of ET<sub>B</sub>receptors regulates the abundance of ET‐1 mRNA in vascular endothelial cells
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The factors that influence the cellular levels of endothelin-1 (ET-1) include transcription, mRNA localization, stability and translation, post-translational maturation of preproET-1 and degradation of ET-1. We investigated the regulation of ET-1 mRNA abundance by extracellular ET-1 in porcine aortic endothelial cells (PAECs). EXPERIMENTAL APPROACH: Passsage one cultures of PAECs were incubated in starving medium in the presence or absence of ET-1 and antagonists or pharmacological inhibitors. PreproET-1 mRNA, endothelin-1 promoter activity, Erk and p38 MAPK activation were determined. KEY RESULTS: Exogenous ET-1 reduced cellular ET-1 mRNA content: a reduction of 10 000-fold was observed after 4 h. ET-1 simultaneously reduced the stability of ET-1 mRNA and increased the loading of RNA Polymerase II at the endothelin-1 promoter. In the absence of exogenous ET-1, the ETB-selective antagonist, BQ788, increased ET-1 mRNA. An ETA-selective antagonist had no effect. ET-1 mRNA returned to control levels within 24 h. Whereas activation of p38 MAPK induced by ET-1 peaked at 30 min and returned to control levels within 90 min, Erk1/2 remained active after 4 h of stimulation. Inhibition of p38 MAPK prevented the ET-1-induced decrease in ET-1 mRNA. In contrast, Erk1/2 inhibition increased ET-1 mRNA. Similarly, inhibition of receptor internalization increased ET-1 mRNA in the presence or absence of exogenous ET-1. CONCLUSIONS AND IMPLICATIONS: These results suggest that extracellular ET-1 regulates the abundance of ET-1 mRNA in PAECs, in an ETB receptor-dependent manner, by modulating both mRNA stability and transcription via mechanisms involving receptor endocytosis and both ERK and p38 MAPK pathways.
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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) | 0.002 | 0.001 |
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".