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Record W2174098513 · doi:10.1016/j.celrep.2015.11.006

GRP94 Regulates Circulating Cholesterol Levels through Blockade of PCSK9-Induced LDLR Degradation

2015· article· en· W2174098513 on OpenAlexafffund
Steve Poirier, Maya Mamarbachi, Wan‐Ting Chen, Amy S. Lee, Gaétan Mayer

Bibliographic record

VenueCell Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Cancer InstituteNational Institutes of HealthFondation Institut de Cardiologie de MontréalHeart and Stroke Foundation of Canada
KeywordsLDL receptorPCSK9ReceptorInternal medicineEndocrinologyHepatocyteCholesterolChemistryLipoproteinCell biologySecretionBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Clearance of circulating low-density lipoprotein cholesterol (LDLc) by hepatic LDL receptors (LDLR) is central for vascular health. Secreted by hepatocytes, PCSK9 induces the degradation of LDLR, resulting in higher plasma LDLc levels. Still, it remains unknown why LDLR and PCSK9 co-exist within the secretory pathway of hepatocytes without leading to complete degradation of LDLR. Herein, we identified the ER-resident GRP94, and more precisely its client-binding C-terminal domain, as a PCSK9-LDLR inhibitory binding protein. Depletion of GRP94 did not affect calcium homeostasis, induce ER stress, nor did it alter PCSK9 processing or its secretion but greatly increased its capacity to induce LDLR degradation. Accordingly, we found that hepatocyte-specific Grp94-deficient mice have higher plasma LDLc levels correlated with ∼ 80% reduction in hepatic LDLR protein levels. Thus, we provide evidence that, in physiological conditions, binding of PCSK9 to GRP94 protects LDLR from degradation likely by preventing early binding of PCSK9 to LDLR within the ER.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

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)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.270
Teacher spread0.234 · 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 teacher head, 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

Citations80
Published2015
Admission routes2
Has abstractyes

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