Characterization of the role of EGF-A of low density lipoprotein receptor in PCSK9 binding
Bibliographic record
Abstract
Proprotein convertase subtilisin kexin-like 9 (PCSK9) promotes the degradation of low density lipoprotein receptor (LDLR) and plays an important role in regulating plasma LDL-cholesterol levels. We have shown that the epidermal growth factor precursor homology domain A (EGF-A) of the LDLR is critical for PCSK9 binding at the cell surface (pH 7.4). Here, we further characterized the role of EGF-A in binding of PCSK9 to the LDLR. We found that PCSK9 efficiently bound to the LDLR but not to other LDLR family members. Replacement of EGF-A in the very low density lipoprotein receptor (VLDLR) with EGF-A of the LDLR promoted the degradation of the mutant VLDLR induced by PCSK9. Furthermore, we found that PCSK9 bound to recombinant EGF-A in a pH-dependent manner with stronger binding at pH 6.0. We also identified amino acid residues in EGF-A of the LDLR important for PCSK9 binding. Mutations G293H, D299V, L318D, and L318H reduced PCSK9 binding to the LDLR at neutral pH without effect at pH 6.0, while mutations R329P and E332G reduced PCSK9 binding at both pH values. Thus, our findings reveal that EGF-A of the LDLR is critical for PCSK9 binding at the cell surface (neutral pH) and at the acidic endosomal environment (pH 6.0), but different determinants contribute to efficient PCSK9 binding in different pH environments.
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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".