Plasma Membrane Tetraspanin CD81 Complexes with Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) and Low Density Lipoprotein Receptor (LDLR), and Its Levels Are Reduced by PCSK9
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an important factor in plasma cholesterol regulation through modulation of low density lipoprotein receptor (LDLR) levels. Naturally occurring mutations can lead to hyper- or hypocholesterolemia in human. Recently, we reported that PCSK9 was also able to modulate CD81 in Huh7 cells. In the present study, several gain-of-function and loss-of-function mutants as well as engineered mutants of PCSK9 were compared for their ability to modulate the cell surface expression of LDLR and CD81. Although PCSK9 gain-of-function D374Y enhanced the degradation both receptors, D374H and D129N seemed to only reduce LDLR levels. In contrast, mutations in the C-terminal hinge-cysteine-histidine-rich domain segment primarily affected the PCSK9-induced CD81 degradation. Furthermore, when C-terminally fused to an ACE2 transmembrane anchor, the secretory N-terminal catalytic or hinge-cysteine-histidine-rich domain domains of PCSK9 were able to reduce CD81 and LDLR levels. These data confirm that PCSK9 reduces CD81 levels via an intracellular pathway as reported for LDLR. Using immunocytochemistry, a proximity ligation assay, and co-immunoprecipitation, we found that the cell surface level of PCSK9 was enhanced upon overexpression of CD81 and that both PCSK9 and LDLR interact with this tetraspanin protein. Interestingly, using CHO-A7 cells lacking LDLR expression, we revealed that LDLR was not required for the degradation of CD81 by PCSK9, but its presence strengthened the PCSK9 effect.
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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 | 0.001 |
| 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".