Lipoprotein electrostatic properties regulate hepatic lipase association and activity
Bibliographic record
Abstract
The effect of lipoprotein electrostatic properties on the catalytic regulation of hepatic lipase (HL) was investigated. Enrichment of serum or very low density lipoprotein (VLDL) with oleic acid increased lipoprotein negative charge and stimulated lipid hydrolysis by HL. Similarly, enrichment of serum or isolated lipoproteins with the anionic phospholipids phosphatidylinositol (PI), phosphatidic acid, or phosphatidylserine also increased lipoprotein negative charge and stimulated hydrolysis by HL. Anionic lipids had a small effect on phospholipid hydrolysis, but significantly stimulated triacylglyceride (TG) hydrolysis. High density lipoprotein (HDL) charge appears to have a specific effect on lipolysis. Enrichment of HDL with PI significantly stimulated VLDL-TG hydrolysis by HL. To determine whether HDL charge affects the association of HL with HDL and VLDL, HL-lipoprotein interactions were probed immunochemically. Under normal circumstances, HL associates with HDL particles, and only small amounts bind to VLDL. PI enrichment of HDL blocked the binding of HL with HDL. These data indicate that increasing the negative charge of HDL stimulates VLDL-TG hydrolysis by reducing the association of HL with HDL. Therefore, HDL controls the hydrolysis of VLDL by affecting the interlipoprotein association of HL. Lipoprotein electrostatic properties regulate lipase association and are an important regulator of the binding and activity of lipolytic enzymes.
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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 | 0.001 | 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".