Endothelial Lipase Promotes Apolipoprotein AI-Mediated Cholesterol Efflux in THP-1 Macrophages
Bibliographic record
Abstract
OBJECTIVE: Endothelial lipase (EL) is expressed by macrophages within atherosclerotic lesions. We investigated the influence of EL expression on cholesterol efflux in macrophages. METHODS AND RESULTS: The present study used lentivirus to introduce either EL shRNA for loss-of-function studies or EL cDNA for gain-of-function studies to investigate the role of EL in apoAI-mediated cholesterol efflux. ApoAI-mediated cholesterol efflux was decreased after EL suppression, but increased with EL overexpression in free cholesterol labeled and acLDL loaded THP-1 macrophages. Similar findings were observed in THP-1 macrophages after exogenous EL addition and in transfected 293 cells. EL-related apoAI-mediated cholesterol efflux decreased after treatment with heparin or catalytic inactivation (S149A mutation or tetrahydrolipstatin) alone, and completely inhibited in combination. Furthermore, EL expression did not change ABCA1 expression, but was positively correlated with apoAI binding to macrophages and 293 cells. This effect was mitigated after heparin treatment but not influenced by catalytic inactivation via tetrahydrolipstatin or the S149A mutation. Moreover, EL expression was positively associated with lysophosphatidylcholine production and inversely with phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin levels. Lysophosphatidylcholine treatment dose-dependently stimulated apoAI-mediated cholesterol efflux in THP-1 macrophages. CONCLUSIONS: EL appears to promote apoAI-mediated cholesterol efflux through catalytic and noncatalytic-dependent mechanisms.
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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 | 0.001 |
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".