Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Can Mediate Degradation of the Low Density Lipoprotein Receptor-Related Protein 1 (LRP-1)
Bibliographic record
Abstract
Elevated LDL-cholesterol (LDLc) levels are a major risk factor for cardiovascular disease and atherosclerosis. LDLc is cleared from circulation by the LDL receptor (LDLR). Proprotein convertase subtilisin/kexin 9 (PCSK9) enhances the degradation of the LDLR in endosomes/lysosomes, resulting in increased circulating LDLc. PCSK9 can also mediate the degradation of LDLR lacking its cytosolic tail, suggesting the presence of as yet undefined lysosomal-targeting factor(s). Herein, we confirm this, and also eliminate a role for the transmembrane-domain of the LDLR in mediating its PCSK9-induced internalization and degradation. Recent findings from our laboratory also suggest a role for PCSK9 in enhancing tumor metastasis. We show herein that while the LDLR is insensitive to PCSK9 in murine B16F1 melanoma cells, PCSK9 is able to induce degradation of the low density lipoprotein receptor-related protein 1 (LRP-1), suggesting distinct targeting mechanisms for these receptors. Furthermore, PCSK9 is still capable of acting upon the LDLR in CHO 13-5-1 cells lacking LRP-1. Conversely, PCSK9 also acts on LRP-1 in the absence of the LDLR in CHO-A7 cells, where re-introduction of the LDLR leads to reduced PCSK9-mediated degradation of LRP-1. Thus, while PCSK9 is capable of inducing degradation of LRP-1, the latter is not an essential factor for LDLR regulation, but the LDLR effectively competes with LRP-1 for PCSK9 activity. Identification of PCSK9 targets should allow a better understanding of the consequences of PCSK9 inhibition for lowering LDLc and tumor metastasis.
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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 | 0.001 |
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".