The PCSK9 discovery, an inactive protease with varied functions in hypercholesterolemia, viral infections, and cancer
Bibliographic record
Abstract
In 2003, the sequences of mammalian proprotein convertase subtilisin/kexin type 9 (PCSK9) were reported. Radiolabeling pulse-chase analyses demonstrated that PCSK9 was synthesized as a precursor (proPCSK9) that undergoes autocatalytic cleavage in the endoplasmic reticulum into PCSK9, which is then secreted as an inactive enzyme in complex with its inhibitory prodomain. Its high mRNA expression in liver hepatocytes and its gene localization on chromosome 1p32, a third locus associated with familial hypercholesterolemia, other than LDLR or APOB, led us to identify three patient families expressing the PCSK9 variants S127R or F216L. Although Pcsk9 and Ldlr were downregulated in mice that were fed a cholesterol-rich diet, PCSK9 overexpression led to the degradation of the LDLR. This led to the demonstration that gain-of-function and loss-of-function variations in PCSK9 modulate its bioactivity, whereby PCSK9 binds the LDLR in a nonenzymatic fashion to induce its degradation in endosomes/lysosomes. PCSK9 was also shown to play major roles in targeting other receptors for degradation, thereby regulating various processes, including hypercholesterolemia and associated atherosclerosis, vascular inflammation, viral infections, and immune checkpoint regulation in cancer. Injectable PCSK9 monoclonal antibody or siRNA is currently used in clinics worldwide to treat hypercholesterolemia and could be combined with current therapies in cancer/metastasis. In this review, we present the critical information that led to the discovery of PCSK9 and its implication in LDL-C metabolism. We further analyze the underlying functional mechanism(s) in the regulation of LDL-C, as well as the evolving novel roles of PCSK9 in both health and disease states.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".