SR‐BI, CD36, and caveolin‐1 contribute positively to cholesterol efflux in hepatic cells
Bibliographic record
Abstract
In non-hepatic cells, scavenger receptor class B type I (SR-BI), cluster of differentiation 36 (CD36), and caveolin-1 were described as mediators of cholesterol efflux, the first step of reverse cholesterol transport (RCT). Stable transformants of HepG2 cells overexpressing SR-BI, CD36, or caveolin-1 were generated, as well as cells overexpressing both caveolin-1 and SR-BI or caveolin-1 and CD36 in order to address the effect of caveolin-1 on both receptor activities. These cells were analyzed for their ability to efflux cholesterol to HDL(3). Our results show that overexpressing SR-BI, CD36, or caveolin-1 increases cholesterol efflux by 106, 92, and 48%, respectively. Moreover, the dual overexpressions of caveolin-1 and SR-BI or caveolin-1 and CD36 lead to a more prominent increase in cholesterol efflux. Studies were also conducted with primary cultures of SR-BI knockout (KO), CD36 KO, and SR-BI/CD36 double-KO (dKO) mice. SR-BI KO and SR-BI/CD36 dKO hepatic cells show 41 and 56% less cholesterol efflux, respectively, than normal hepatic cells. No significant difference was observed between the efflux of normal and CD36 KO cells. The difference between the role of human and murine CD36 correlated with the absence of CD36 dimers in mouse caveolae/rafts. Overall, our results show that SR-BI is clearly involved in cholesterol efflux in mouse and human hepatic cells, while CD36 plays a significant role in human cells.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".