Wild-type PCSK9 inhibits LDL clearance but does not affect apoB-containing lipoprotein production in mouse and cultured cells
Bibliographic record
Abstract
Mutations in Proprotein Convertase Subtilisin Kexin 9 (PCSK9) have been associated with autosomal dominant hypercholesterolemia. In vivo kinetic studies indicate that LDL catabolism was impaired and apolipoprotein B (apoB)-containing lipoprotein synthesis was enhanced in two patients presenting with the S127R mutation on PCSK9. To understand the physiological role of PCSK9, we overexpressed human PCSK9 in mouse and cellular models as well as attenuated the endogenous expression of PCSK9 in HuH7 hepatoma cells using RNA interference. Here, we show that PCSK9 dramatically impairs the expression of the low density lipoprotein receptor (LDLr) and, in turn, LDL cellular binding as well as LDL clearance from the plasma compartment in C57BL6/J mice but not in LDLr-deficient mice, establishing a definitive role for PCSK9 in the modulation of the LDLr metabolic pathway. In contrast to data obtained in S127R-PCSK9 patients presenting with increased apoB production, our study indicates that wild-type PCSK9 does not significantly alter the production and/or secretion of VLDL apoB in either cultured cells or mice. Finally, we show that unlike PCSK9 overexpression in mice, the S127R mutation in patients led to increased VLDL apoB levels, suggesting a potential gain of function for S127R-PCSK9 in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".