Abstract 452: Detailed Characterization of the Role of the Epidermal Growth Factor Precursor Homology Domain A of the Low-density Lipoprotein Receptor in Proprotein Convertase Subtilisin/kexin-type 9 Binding
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin-type 9 (PCSK9) promotes the LDL receptor (LDLR) degradation, thereby preventing clearance of LDL-C levels and playing a central regulatory role in cholesterol homeostasis. PCSK9 interacts with the LDLR at the cell surface and binds the receptor with a much higher affinity at the acidic environment of the endosome. Consequently, the receptor traffics from the endosome to the lysosome for degradation, rather than being recycled. Previously, 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 (pH7.4) and that leucine at the position 318 in the EGF-A is critical for PCSK9 binding. Here, we further characterized the role of EGF-A in the binding of PCSK9 to the LDLR in more details. We found that mutation of Asp299 to Val or Arg329 to Pro in the EGF-A significantly reduced PCSK9 binding at pH 7.4. In addition, we observed that deletion of EGF-A significantly reduced PCSK9 binding at the acidic endosomal environment (pH 6.0) and that PCSK9 bound to purified recombinant EGF-A in a pH-dependent manner with a stronger binding at pH6.0. However, mutation of Leu318 to Arg or His306 to Tyr that dramatically enhanced PCSK9 binding at pH7.4 had no significant effect on the binding of PCSK9 to the LDLR at pH6.0. Thus, our findings demonstrate that EGF-A of the LDLR plays an important role in PCSK9 binding at the cell surface (pH 7.4) and the endosomal environment (pH6.0). Our data also indicate that different amino acid residues in EGF-A are involved in PCSK9 binding to the LDLR at the cell surface and in the endosome.
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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) | 0.003 | 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".