An Unbiased Mass Spectrometry Approach Identifies Glypican-3 as an Interactor of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) and Low Density Lipoprotein Receptor (LDLR) in Hepatocellular Carcinoma Cells
Bibliographic record
Abstract
The mechanism of LDL receptor (LDLR) degradation mediated by the proprotein convertase subtilisin/kexin type 9 (PCSK9) has been extensively studied; however, many steps within this process remain unclear and still require characterization. Recent studies have shown that PCSK9 lacking its Cys/His-rich domain can still promote LDLR internalization, but the complex does not reach the lysosome suggesting the presence of an additional interaction partner(s). In this study we carried out an unbiased screening approach to identify PCSK9-interacting proteins in the HepG2 cells' secretome using co-immunoprecipitation combined with mass spectrometry analyses. Several interacting proteins were identified, including glypican-3 (GPC3), phospholipid transfer protein, matrilin-3, tissue factor pathway inhibitor, fibrinogen-like 1, and plasminogen activator inhibitor-1. We then validated these interactions by co-immunoprecipitation and Western blotting. Furthermore, functional validation was examined by silencing each candidate protein in HepG2 cells using short hairpin RNAs to determine their effect on LDL uptake and LDLR levels. Only GPC3 and phospholipid transfer protein silencing in HepG2 cells significantly increased LDL uptake in these cells and displayed higher total LDLR protein levels compared with control cells. Moreover, our study provides the first evidence that GPC3 can modulate the PCSK9 extracellular activity as a competitive binding partner to the LDLR in HepG2 cells.
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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) | 0.000 | 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".