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Record W3153020707 · doi:10.1101/2021.04.11.439364

Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR that is shed by Furin

2021· preprint· en· W3153020707 on OpenAlexaff
Delia Susan‐Resiga, Emmanuelle Girard, Rachid Essalmani, Anna Roubtsova, Jadwiga Marcinkiewicz, Rabeb Mouna Derbali, Alexandra Evagelidis, Jae Hyun Byun, Paul Lebeau, Richard C. Austin, Nabil G. Seidah

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsLDL receptorAsialoglycoprotein receptorPCSK9EndocytosisBiologyReceptorMolecular biologyKexinChemistryBiochemistryCholesterolLipoproteinHepatocyteIn vitro

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.226
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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