Suppression of endothelial or lipoprotein lipase in THP-1 macrophages attenuates proinflammatory cytokine secretion
Bibliographic record
Abstract
LPL and endothelial lipase (EL) are associated with macrophages in human atherosclerotic lesions, and overexpression of LPL in mouse macrophages is associated with a greater extent of atherosclerosis. To investigate potential mechanisms by which macrophage-derived lipase expression may mediate proatherogenic effects, we used lentivirus-mediated RNA interference to suppress the expression of either LPL or EL within THP-1 macrophages. After suppression of either LPL or EL, significant decreases in the concentration of interleukin-1beta, interleukin-6, monocyte chemoattractant protein-1, and tumor necrosis factor-alpha were observed. Incubation of THP-1 macrophages with either mildly or extensively oxidized LDL consistently decreased cytokine expression, which was additive to that contributed by lipase suppression. Decreased lipase expression was also associated with an altered lipid composition, with reduced percentages of cholesterol (unesterified and esterified), triglycerides, and lysophosphatidylcholine. Microarray data indicated a decreased expression of proinflammatory genes, growth factors, and antiapoptotic genes. By contrast, there was an increased expression of lipoprotein receptors (scavenger receptor 1, low density lipoprotein receptor, scavenger receptor class B type I, and CD36). Thus, we conclude that the suppression of either LPL or EL decreases proinflammatory cytokine expression and influences the lipid composition of THP-1 macrophages. These results provide further insight into the specific metabolic and potential pathological roles of LPL and EL in human macrophages.
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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) | 0.001 | 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".