A novel assay uncovers an unexpected role for SR-BI in LDL transcytosis
Bibliographic record
Abstract
AIMS: Retention of low-density lipoprotein (LDL) cholesterol beneath the arterial endothelium initiates an inflammatory response culminating in atherosclerosis. Since the overlying endothelium is healthy and intact early on, it is likely that LDL passes through endothelial cells by transcytosis. However, technical challenges have made confirming this notion and elucidating the mechanisms of transcytosis difficult. We developed a novel assay for measuring LDL transcytosis in real time across coronary endothelial cell monolayers; we used this approach to identify the receptor involved. METHODS AND RESULTS: Murine aortas were perfused ex vivo with LDL and dextran of a smaller molecular radius. LDL (but not dextran) accumulated under the endothelium, indicating that LDL transcytosis occurs in intact vessels. We then confirmed that LDL transcytosis occurs in vitro using human coronary artery endothelial cells. An assay was developed to quantify transcytosis of DiI-LDL in real time using total internal reflection fluorescence microscopy. DiI-LDL transcytosis was inhibited by excess unlabelled LDL, while degradation of the LDL receptor by PCSK9 had no effect. Instead, LDL colocalized partially with the scavenger receptor SR-BI and overexpression of SR-BI increased LDL transcytosis; knockdown by siRNA significantly reduced it. Excess HDL, the canonical SR-BI ligand, significantly decreased LDL transcytosis. Aortas from SR-BI-deficient mice were perfused ex vivo with LDL and accumulated significantly less sub-endothelial LDL compared with wild-type littermates. CONCLUSION: We developed an assay to quantify LDL transcytosis across endothelial cells and discovered an unexpected role for SR-BI. Elucidating the mechanisms of LDL transcytosis may identify novel targets for the prevention or therapy of atherosclerosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".