Apolipoprotein A1 and high-density lipoprotein limit low-density lipoprotein transcytosis by binding SR-B1
Bibliographic record
Abstract
Atherosclerosis results from the deposition and oxidation of LDL and immune cell infiltration in the sub-arterial space leading to arterial occlusion. Studies have shown that transcytosis transports circulating LDL across endothelial cells lining blood vessels. LDL transcytosis is initiated by binding to either scavenger receptor B1 (SR-B1) or activin A receptor-like kinase 1 on the apical side of endothelial cells leading to its transit and release on the basolateral side. HDL is thought to partly protect individuals from atherosclerosis due to its ability to remove excess cholesterol and act as an antioxidant. Apolipoprotein A1 (APOA1), an HDL constituent, can bind to SR-B1, raising the possibility that APOA1/HDL can compete with LDL for SR-B1 binding, thereby limiting LDL deposition in the sub-arterial space. To examine this possibility, we used in vitro approaches to quantify the internalization and transcytosis of fluorescent LDL in coronary endothelial cells. Using microscale thermophoresis and affinity capture, we find that SR-B1 and APOA1 interact and that binding is enhanced when using the cardioprotective variant of APOA1 termed Milano (APOA1-Milano). In male mice, transiently increasing the levels of HDL reduced the acute deposition of fluorescently labeled LDL in the atheroprone inner curvature of the aorta. Reduced LDL deposition was also observed when increasing circulating wild-type APOA1 or the APOA1-Milano variant, with a more robust inhibition from the APOA1-Milano. The results suggest that HDL may limit SR-B1-mediated LDL transcytosis and deposition, adding to the mechanisms by which it can act as an atheroprotective particle.
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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".