Endothelial lipase enhances low density lipoprotein binding and cell association in THP-1 macrophages
Bibliographic record
Abstract
OBJECTIVE: Endothelial lipase (EL) is expressed in macrophages in human atherosclerotic lesions. However, its specific metabolic role in human macrophages has not been fully explored. METHODS: The present study used lentivirus containing either shRNA or cDNA for EL to decrease or increase EL expression, respectively in THP-1 macrophages to investigate the consequence on LDL binding and cell association. RESULTS: EL suppression significantly decreased the binding and cell association of native LDL (52% and 33%) and mildly oxLDL (43% and 36%) as well as extensively oxLDL binding (36%) in THP-1 macrophages. EL overexpression markedly increased the binding and cell association of native LDL (3.1- and 2.2-fold), mildly oxLDL (1.9- and 1.4-fold), and extensively oxLDL (1.5- and 1.5-fold). An inactive mutant EL compromised EL-mediated cell association of native and mildly oxLDL but not extensively oxLDL. Heparinase treatment almost completely eliminated EL-mediated native and oxLDL binding and cell association in THP-1 macrophages. LDL receptor blocking by antibodies decreased EL-mediated native LDL binding and cell association by 24% and 54%, respectively. Neither receptor associated protein or CD36 antibody treatment led to changes in EL-mediated lipoprotein binding and cell association. Furthermore, wild-type and the catalytically inactive mutant EL increased lipid accumulation in THP-1 macrophages. CONCLUSIONS: EL expression promotes the binding and uptake of native and oxidized LDL in THP-1 macrophages in a heparan sulfate proteoglycan-dependent manner, and the LDL receptor was partly responsible for the EL-enhanced uptake of native LDL.
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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 | 0.001 |
| 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".