Low Density Lipoprotein Binds to Proprotein Convertase Subtilisin/Kexin Type-9 (PCSK9) in Human Plasma and Inhibits PCSK9-mediated Low Density Lipoprotein Receptor Degradation
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type-9 (PCSK9) is a secreted protein that binds to the epidermal growth factor-like-A domain of the low density lipoprotein receptor (LDLR) and mediates LDLR degradation in liver. Gain-of-function mutations in PCSK9 are associated with autosomal dominant hypercholesterolemia in humans. Size-exclusion chromatography of human plasma has shown PCSK9 to be partly associated with undefined high molecular weight complexes within the LDL size range. We used density gradient centrifugation to isolate LDL in plasma pooled from 5 normolipidemic subjects and report that >40% of total PCSK9 was associated with LDL. Binding of fluorophore-labeled recombinant PCSK9 to isolated LDL in vitro was saturable with a K(D) ∼ 325 nM. This interaction was competed >95% by excess unlabeled PCSK9, and competition binding curves were consistent with a one-site binding model. An N-terminal region of the PCSK9 prodomain (amino acids 31-52) was required for binding to LDL in vitro. LDL dose-dependently inhibited binding and degradation of cell surface LDLRs by exogenous PCSK9 in HuH7 cells. LDL also inhibited PCSK9 binding to mutant LDLRs defective at binding LDL. These data suggest that association of PCSK9 with LDL particles in plasma lowers the ability of PCSK9 to bind to cell surface LDLRs, thereby blunting PCSK9-mediated LDLR degradation.
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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) | 0.002 | 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".