G-Protein Coupled Receptor 30 (GPR30): A Novel Regulator of Endothelial Inflammation
Bibliographic record
Abstract
Estrogen, the female sex hormone, is known to exert anti-inflammatory and anti-atherogenic effects. Traditionally, estrogen effects were believed to be largely mediated through the classical estrogen receptors (ERs). However, there is increasing evidence that G-protein coupled receptor 30 (GPR30), a novel estrogen receptor, can mediate many estrogenic effects on the vasculature. Despite this, the localization and functional significance of GPR30 in the human vascular endothelium remains poorly understood. Given this background, we examined the subcellular location and potential anti-inflammatory roles of GPR30 using human umbilical vein endothelial cells as a model system. Inflammatory changes were induced by treatment with tumor necrosis factor (TNF), a pro-inflammatory cytokine involved in atherogenesis and many other inflammatory conditions. We found that GPR30 was located predominantly in the endothelial cell nuclei. Treatment with the selective GPR30 agonist G-1 partially attenuated the TNF induced upregulation of pro-inflammatory proteins such as intercellular cell adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1). This effect was completely abolished by the selective GPR30 antagonist G-15, suggesting that it was indeed mediated in a GPR30 dependent manner. Interestingly, estrogen alone had no effects on TNF-treated endothelium. Concomitant activation of the classical ERs blocked the anti-inflammatory effects of G-1, indicating opposing effects of GPR30 and the classical ERs. Our findings demonstrate that endothelial GPR30 is a novel regulator of the inflammatory response which could be a potential therapeutic target against atherosclerosis and other inflammatory diseases.
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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".