Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR that is shed by Furin
Bibliographic record
Abstract
ABSTRACT The hepatic carbohydrate-recognizing asialoglycoprotein receptor (ASGR1) mediates the endocytosis/lysosomal degradation of desialylated glycoproteins following binding to terminal galactose/N-acetylgalactosamine. Human heterozygote-carriers of ASGR1- deletions exhibited ∼34% lower risk of coronary artery disease and ∼10-14% non-HDL-cholesterol reduction. Since PCSK9 is a major degrader of LDLR, the regulation of LDLR and/or PCSK9 by ASGR1 was studied. Thus, we investigated the role of endogenous/overexpressed ASGR1 on LDLR degradation and functionality by Western-blot and immunofluorescence in HepG2 naïve and HepG2-PCSK9-knockout cells. ASGR1, like PCSK9, targets LDLR and both interact with/enhance the degradation of the receptor independently. Such lack of cooperativity between PCSK9 and ASGR1 on LDLR expression was confirmed in livers of wild-type (WT) versus Pcsk9 -/- mice. ASGR1-knockdown in HepG2 naïve cells significantly increased total (∼1.2-fold) and cell-surface (∼4-fold) LDLR protein. In HepG2-PCSK9-knockout cells ASGR1-silencing led to ∼2-fold higher levels of LDLR protein and DiI-LDL uptake, associated with ∼9-fold increased cell-surface LDLR. Overexpression of WT-ASGR1/2 reduced primarily the immature non-O-glycosylated LDLR (∼110 kDa), whereas the triple Gln 240 /Trp 244 /Glu 253 Ala-mutant (loss of carbohydrate-binding) reduced the mature form of the LDLR (∼150 kDa), suggesting that ASGR1 binds the LDLR in sugar-dependent and -independent fashion. Furin sheds ASGR1 at R KM K 103 ↓ into a secreted form, likely resulting in a loss-of-function on LDLR. LDLR is the first example of a liver-receptor ligand of ASGR1. Additionally, we demonstrate that lack of ASGR1 enhances LDLR levels and DiI-LDL incorporation, independently of PCSK9. Overall, silencing of ASGR1 and PCSK9 may lead to higher LDL-uptake by hepatocytes, thereby providing a novel approach to further reduce LDL-cholesterol.
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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".