Quantitative Proteomic Analysis of PCSK9 Gain of Function in Human Hepatic HuH7 Cells
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays an important role in cholesterol homeostasis, mediating degradation of the liver low-density lipoprotein receptor (LDLR). In fact, gain- and loss-of-function PCSK9 variations in human populations associate with hyper- or hypo- cholesterolemia, respectively. Exactly how PCSK9 promotes degradation of the LDLR, the identity of the other biomolecules involved in this process, and the global effect of PCSK9 on other proteins has not been thoroughly studied. Here we employ stable isotope labeling with amino acids in cell culture (SILAC) to present the first quantitative, subcellular proteomic study of proteins affected by the stable overexpression of a gain-of-function PCSK9 membrane-bound chimera (PCSK9-V5-ACE2) in comparison to control, empty vector transfections in a human hepatocyte (HuH7) cell line. The expression level of 327 of 5790 peptides was modified by PCSK9-V5-ACE2 overexpression. Immunoblotting was carried out for the control transferrin receptor, shown to be unaffected in cells overexpressing PCSK9-V5-ACE2, thus validating our SILAC results. We also used immunoblotting to confirm the novel SILAC results of up- and down-regulation of several proteins in cells overexpressing PCSK9-V5-ACE2. Moreover, we documented the novel down-regulation of the EH domain binding protein-1 (EHBP1) in a transgenic PCSK9 mouse model and its up-regulation in a PCSK9 knockout mouse model.
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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".