Hydrolysis Products Generated by Lipoprotein Lipase and Endothelial Lipase Differentially Impact THP‐1 Macrophage Cell Signalling Pathways
Bibliographic record
Abstract
Macrophages express lipoprotein lipase (LPL) and endothelial lipase (EL) within atherosclerotic plaques; however, little is known about how lipoprotein hydrolysis products generated by these lipases might affect macrophage cell signalling pathways. We hypothesized that hydrolysis products affect macrophage cell signalling pathways associated with atherosclerosis. To test our hypothesis, we incubated differentiated THP-1 macrophages with products from total lipoprotein hydrolysis by recombinant LPL or EL. Using antibody arrays, we found that the phosphorylation of six receptor tyrosine kinases and three signalling nodes--most associated with atherosclerotic processes--was increased by LPL derived hydrolysis products. EL derived hydrolysis products only increased the phosphorylation of tropomyosin-related kinase A, which is also implicated in playing a role in atherosclerosis. Using electrospray ionization-mass spectrometry, we identified the species of triacylglycerols and phosphatidylcholines that were hydrolyzed by LPL and EL, and we identified the fatty acids liberated by gas chromatography-mass spectrometry. To determine if the total liberated fatty acids influenced signalling pathways, we incubated differentiated THP-1 macrophages with a mixture of the fatty acids that matched the concentrations of liberated fatty acids from total lipoproteins by LPL, and we subjected cell lysates to antibody array analyses. The analyses showed that only the phosphorylation of Akt was significantly increased in response to fatty acid treatment. Overall, our study shows that macrophages display potentially pro-atherogenic signalling responses following acute treatments with LPL and EL lipoprotein hydrolysis products.
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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".