Trafficking Dynamics of PCSK9-Induced LDLR Degradation: Focus on Human PCSK9 Mutations and C-Terminal Domain
Bibliographic record
Abstract
PCSK9 is a secreted ligand and negative post-translational regulator of low-density lipoprotein receptor (LDLR) in hepatocytes. Gain-of-function (GOF) or loss-of-function (LOF) mutations in PCSK9 are directly correlated with high or low plasma LDL-cholesterol levels, respectively. Therefore, PCSK9 is a prevailing lipid-lowering target to prevent coronary heart diseases and stroke. Herein, we fused monomeric fluorescent proteins to PCSK9 and LDLR to visualize their intra- and extracellular trafficking dynamics by live confocal microscopy. Fluorescence recovery after photobleaching (FRAP) showed that PCSK9 LOF R46L mutant and GOF mutations S127R and D129G, but not the LDLR high-affinity mutant D374Y, significantly accelerate PCSK9 exit from the endoplasmic reticulum (ER). Quantitative analysis of inverse FRAP revealed that only R46L presented a much slower trafficking from the trans-Golgi network (TGN) to the plasma membrane and a lower mobile fraction likely suggesting accumulation or delayed exit at the TGN as an underlying mechanism. While not primarily involved in LDLR binding, PCSK9 C-terminal domain (CTD) was found to be essential to induce LDLR degradation both upon its overexpression in cells or via the extracellular pathway. Our data revealed that PCSK9 CTD is required for the localization of PCSK9 at the TGN and increases its LDLR-mediated endocytosis. Interestingly, intracellular lysosomal targeting of PCSK9-ΔCTD was able to rescue its capacity to induce LDLR degradation emphasizing a role of the CTD in the sorting of PCSK9-LDLR complex towards late endocytic compartments. Finally, we validated our dual fluorescence system as a cell based-assay by preventing PCSK9 internalization using a PCSK9-LDLR blocking antibody, which may be expended to identify protein, peptide or small molecule inhibitors of PCSK9.
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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 | 0.000 |
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".