Effects of the Prosegment and pH on the Activity of PCSK9
Bibliographic record
Abstract
PCSK9, a target for the treatment of dyslipidemia, enhances the degradation of the LDL receptor (LDLR) in endosomes/lysosomes, up-regulating LDL-cholesterol levels. Whereas the targeting and degradation of the PCSK9-LDLR complex are under scrutiny, the roles of the N- and C-terminal domains of PCSK9 are unknown. Although autocatalytic zymogen processing of PCSK9 occurs at Gln(152)↓, here we show that human PCSK9 can be further cleaved in its N-terminal prosegment at Arg(46)↓ by an endogenous enzyme of insect High Five cells and by a cellular mammalian protease, yielding an ∼4-fold enhanced activity. Removal of the prosegment acidic stretch resulted in ∼3-fold higher binding to LDLR in vitro, in ≥4-fold increased activity on cellular LDLR, and faster cellular internalization in endosome/lysosome-like compartments. Finally, swapping the acidic stretch of PCSK9 with a similar one found in the glycosylphosphatidylinositol-anchored heparin-binding protein 1 does not impair PCSK9 autoprocessing, secretion, or activity and confirmed that the acidic stretch acts as an inhibitor of PCSK9 function. We also show that upon short exposure to pH values 6.5 to 5.5, an ∼2.5-fold increase in PCSK9 activity on total and cell surface LDLR occurs, and PCSK9 undergoes a second cleavage at Arg(248), generating a two-chain PCSK9-ΔN(248). At pH values below 5.5, PCSK9 dissociates from its prosegment and loses its activity. This pH-dependent activation of PCSK9 represents a novel pathway to further activate PCSK9 in acidic endosomes. These data enhance our understanding of the functional role of the acidic prosegment and on the effect of pH in the regulation of PCSK9 activity.
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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) | 0.001 | 0.000 |
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".