Dissection of the Endogenous Cellular Pathways of PCSK9-induced Low Density Lipoprotein Receptor Degradation
Bibliographic record
Abstract
Elevated levels of plasma low density lipoprotein (LDL)-cholesterol, leading to familial hypercholesterolemia, are enhanced by mutations in at least three major genes, the LDL receptor (LDLR), its ligand apolipoprotein B, and the proprotein convertase PCSK9. Single point mutations in PCSK9 are associated with either hyper- or hypocholesterolemia. Accordingly, PCSK9 is an attractive target for treatment of dyslipidemia. PCSK9 binds the epidermal growth factor domain A (EGF-A) of the LDLR and directs it to endosomes/lysosomes for destruction. Although the mechanism by which PCSK9 regulates LDLR degradation is not fully resolved, it seems to involve both intracellular and extracellular pathways. Here, we show that clathrin light chain small interfering RNAs that block intracellular trafficking from the trans-Golgi network to lysosomes rapidly increased LDLR levels within HepG2 cells in a PCSK9-dependent fashion without affecting the ability of exogenous PCSK9 to enhance LDLR degradation. In contrast, blocking the extracellular LDLR endocytosis/degradation pathway by a 4-, 6-, or 24-h incubation of cells with Dynasore or an EGF-AB peptide or by knockdown of endogenous autosomal recessive hypercholesterolemia did not significantly affect LDLR levels. The present data from HepG2 cells and mouse primary hepatocytes favor a model whereby depending on the dose and/or incubation period, endogenous PCSK9 enhances the degradation of the LDLR both extra- and intracellularly. Therefore, targeting either pathway, or both, would be an effective method to reduce PCSK9 activity in the treatment of hypercholesterolemia and coronary heart disease.
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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".