Expression of caveolin‐1 in hepatic cells increases oxidized LDL uptake and preserves the expression of lipoprotein receptors
Bibliographic record
Abstract
Oxidized LDL (OxLDL) that are positively associated with the risk of developing cardiovascular diseases are ligands of scavenger receptor-class B type I (SR-BI) and cluster of differentiation-36 (CD36) which can be found in caveolae. The contribution of these receptors in human hepatic cell is however unknown. The HepG2 cell, a human hepatic parenchymal cell model, expresses these receptors and is characterized by a very low level of caveolin-1. Our aim was to define the contribution of human CD36, SR-BI, and caveolin-1 in the metabolism of OxLDL in HepG2 cells and conversely the effects of OxLDL on the levels/localization of these receptors. By comparing mildly (M)- and heavily (H)-OxLDL metabolism between control HepG2 cells and HepG2 cells overexpressing CD36, SR-BI, or caveolin-1, we found that (1) CD36 increases M- and H-OxLDL-protein uptake; (2) SR-BI drives M-OxLDL through a degradation pathway at the expense of the cholesterol ester (CE) selective uptake pathway; (3) caveolin-1 increases M- and H-OxLDL-protein uptake and decreases CE selective uptake from M-OxLDL. Also, incubation with M- or H-OxLDL decreases the levels of SR-BI and LDL-receptor in control HepG2 cells which can be overcome by caveolin-1 expression. In addition, OxLDL move CD36 from low to high buoyant density membrane fractions, as well as caveolin-1 in cells overexpressing this protein. Thus, hepatic caveolin-1 expression has significant effects on OxLDL metabolism and on lipoprotein receptor levels.
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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 | 0.000 |
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".