Probucol treatment is associated with an ABCA1-independent mechanism of cholesterol efflux to lipid poor apolipoproteins from foam cell macrophages
Bibliographic record
Abstract
Objective: Probucol is a cholesterol-lowering agent whose ability to prevent atherosclerosis is currently under study. Herein, we investigate the putative mechanism of probucol by observation of changes in cellular cholesterol efflux and lipid droplet morphology in macrophages. Results: The inhibitory activity of probucol was assessed in non-foam or foam cell macrophages expressing ABCA1 generated by treatment with fetal calf serum (FCS) alone or in combination with acetylated LDL, respectively. Probucol inhibited cholesterol efflux to apolipoprotein A-I (apoA-I) by 31.5±0.1% in THP-1 non-foam cells and by 18.5±0.2% in foam cells. In probucol-treated non-foam THP-1 cells, nascent high density lipoprotein (nHDL) particles with a diameter < 7 nm were generated, while in probucol-treated THP-1 foam cells nHDL particles of > 7 nm in diameter containing cholesterol were produced. Foam cells also displayed a significant accumulation of free cholesterol at the plasma membrane, as measured by percent cholestenone formed. Intracellularly, there was a significant decrease in lipid droplet number and an increase in size in probucol-treated THP-1 foam cells when compared to non-treated cells. Conclusions: We report for the first time that probucol is unable to completely inhibit cholesterol efflux in foam cells to the same extent as in non-foam cells. Indeed, functional nHDL is released from foam cells in the presence of probucol. This difference in inhibitory effect could potentially be explained by changes in the plasma membrane pool as well as intracellular cholesterol storage independently of ABCA1.
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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) | 0.001 | 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".