NARC-1/PCSK9 and Its Natural Mutants
Bibliographic record
Abstract
The discovery of autosomal dominant hypercholesterolemic patients with mutations in the PCSK9 gene, encoding the proprotein convertase NARC-1, resulting in the missense mutations suggested a role in low density lipoprotein (LDL) metabolism. We show that the endoplasmic reticulum-localized proNARC-1 to NARC-1 zymogen conversion is Ca2+-independent and that within the zymogen autocatalytic processing site SSVFAQ [downward arrow]SIP Val at P4 and Pro at P3' are critical. The S127R and D374Y mutations result in approximately 50-60% and > or =98% decrease in zymogen processing, respectively. In contrast, the double [D374Y + N157K], F216L, and R218S natural mutants resulted in normal zymogen processing. The cell surface LDL receptor (LDLR) levels are reduced by 35% in lymphoblasts of S127R patients. The LDLR levels are also reduced in stable HepG2 cells overexpressing NARC-1 or its natural mutant S127R, and this reduction is abrogated in the presence of 5 mm ammonium chloride, suggesting that overexpression of NARC-1 increases the turnover rate of the LDLR. Adenoviral expression of wild type human NARC-1 in mice resulted in a maximal approximately 9-fold increase in circulating LDL cholesterol, while in LDLR-/- mice a delayed approximately 2-fold increase in LDL cholesterol was observed. In conclusion, NARC-1 seems to affect both the level of LDLR and that of circulating apoB-containing lipoproteins in an LDLR-dependent and -independent fashion.
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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".