Estrogen Inhibits LDL (Low-Density Lipoprotein) Transcytosis by Human Coronary Artery Endothelial Cells via GPER (G-Protein–Coupled Estrogen Receptor) and SR-BI (Scavenger Receptor Class B Type 1)
Bibliographic record
Abstract
Objective- The atheroprotective effects of estrogen are independent of circulating lipid levels. Whether estrogen regulates transcytosis of LDL (low-density lipoprotein) across the coronary endothelium is unknown. Approach and Results- Using total internal reflection fluorescence microscopy, we quantified transcytosis of LDL across human coronary artery endothelial cells from multiple donors. LDL transcytosis was significantly higher in cells from men compared with premenopausal women. Estrogen significantly attenuated LDL transcytosis by endothelial cells from male but not female donors; transcytosis of albumin was not affected. Estrogen caused downregulation of endothelial SR-BI (scavenger receptor class B type 1), and overexpression of SR-BI was sufficient to restore LDL transcytosis. Similarly, depletion of SR-BI by siRNA attenuated endothelial LDL transcytosis and prevented any further effect of estrogen. In contrast, treatment with estrogen had no effect on SR-BI expression by liver cells. Inhibition of estrogen receptors α and β had no effect on estrogen-mediated attenuation of LDL transcytosis. However, estrogen's effect on LDL transcytosis was blocked by depletion of the GPER (G-protein-coupled estrogen receptor). GPER was found to be enriched in endothelial cells compared with hepatocytes and is reported to signal via transactivation of the EGFR (epidermal growth factor receptor); inhibition of EGFR prevented the effect of estrogen on LDL transcytosis and SR-BI mRNA. Last, SR-BI expression was significantly higher in human coronary artery endothelial cells from male compared with premenopausal female donors. Conclusions- Estrogen significantly inhibits LDL transcytosis by downregulating endothelial SR-BI; this effect requires GPER.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".