Cholesteryl Ester Transfer Protein Directly Mediates Selective Uptake of High Density Lipoprotein Cholesteryl Esters by the Liver
Bibliographic record
Abstract
OBJECTIVE: To determine whether cholesteryl ester transfer protein (CETP) directly mediates selective uptake of high-density lipoprotein (HDL)-cholesteryl ester (CE) by hepatocytes and to quantify the effects of the CETP inhibitor, torcetrapib, on this process. METHODS AND RESULTS: Using adenovirus-mediated CETP (ad-CETP) expression in primary mouse hepatocytes from either wild-type, low-density lipoprotein (LDL) receptor-/- or SR-BI-/- mice, we demonstrate that CETP enhances the selective accumulation of HDL-derived 3H-CE independently of known lipoprotein receptors. Addition of torcetrapib to the media did not impair the ability of cell-associated CETP to enhance CE uptake but reduced the ability of exogenously added CETP to increase selective uptake by up to 80%. When mice were infected with ad-CETP or ad-Luciferase and treated with daily intravenous injections of torcetrapib or vehicle, hepatic CETP expression resulted in a 50% decrease in HDL cholesterol in vehicle-treated animals versus a 33% decrease in HDL cholesterol in mice treated with torcetrapib. CONCLUSIONS: CETP mediates selective uptake of HDL-CE by hepatocytes by both torcetrapib-sensitive (exogenous CETP) and torcetrapib-insensitive (cell-associated CETP) mechanisms. Hepatic expression of CETP in vivo results in a marked decrease in cholesterol in particles in the HDL density range, consistent with a physiological role for hepatocyte CETP in selective uptake.
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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) | 0.001 | 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 | 0.001 |
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".