Structural modification of plasma HDL by phospholipids promotes efficient ABCA1-mediated cholesterol release
Bibliographic record
Abstract
It has been suggested that ABCA1 interacts preferentially with lipid-poor apolipoprotein A-I (apoA-I). Here, we show that treatment of plasma with dimyristoyl phosphatidylcholine (DMPC) multilamellar vesicles generates prebeta(1)-apoA-I-containing lipoproteins (LpA-I)-like particles similar to those of native plasma. Isolated prebeta(1)-LpA-I-like particles inhibited the binding of (125)I-apoA-I to ABCA1 more efficiently than HDL(3) (IC(50) = 2.20 +/- 0.35 vs. 37.60 +/- 4.78 microg/ml). We next investigated the ability of DMPC-treated plasma to promote phospholipid and unesterified (free) cholesterol efflux from J774 macrophages stimulated or not with cAMP. At 2 mg DMPC/ml plasma, both phospholipid and free cholesterol efflux were increased ( approximately 50% and 40%, respectively) in cAMP-stimulated cells compared with unstimulated cells. Similarly, both phospholipid and free cholesterol efflux to either isolated native prebeta(1)-LpA-I and prebeta(1)-LpA-I-like particles were increased significantly in stimulated cells. Furthermore, glyburide significantly inhibited phospholipid and free cholesterol efflux to DMPC-treated plasma. Removal of apoA-I-containing lipoproteins from normolipidemic plasma drastically reduced free cholesterol efflux mediated by DMPC-treated plasma. Finally, treatment of Tangier disease plasma with DMPC affected the amount of neither prebeta(1)-LpA-I nor free cholesterol efflux. These results indicate that DMPC enrichment of normal plasma resulted in the redistribution of apoA-I from alpha-HDL to prebeta-HDL, allowing for more efficient ABCA1-mediated cellular lipid release. Increasing the plasma prebeta(1)-LpA-I level by either pharmacological agents or direct infusions might prevent foam cell formation and reduce atherosclerotic vascular disease.
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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) | 0.001 | 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".