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Record W1972933596 · doi:10.1016/j.febslet.2015.01.007

MMP‐2 inhibits PCSK9‐induced degradation of the LDL receptor in Hepa1‐c1c7 cells

2015· article· en· W1972933596 on OpenAlexafffund
Xiang Wang, Evan Berry, Samuel Hernandez‐Anzaldo, Difei Sun, Ayinuer Adijiang, Liang Li, Dawei Zhang, Carlos Fernández-Patrón

Bibliographic record

VenueFEBS Letters · 2015
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta InnovatesUniversity of Alberta
KeywordsLDL receptorPCSK9KexinProprotein convertaseMatrix metalloproteinaseReceptorEndocytosisChemistryCell biologyWestern blotLipoproteinMolecular biologyBiologyCholesterolBiochemistry

Abstract

fetched live from OpenAlex

Low-density lipoprotein receptor (LDLR) catalyzes the uptake of LDL-cholesterol by liver and peripheral organs. The function of the LDLR is antagonized by pro-protein convertase subtilisin/kexin type 9 (PCSK9), which binds to LDLR at the plasma membrane inducing LDLR degradation. Here, we report that matrix metalloproteinase-2 (MMP-2) interacts with and cleaves PCSK9, as evidenced by proteomic, chemical cross-linkage, blue native-PAGE and domain-specific antibodies Western blot analyses. Furthermore, MMP-2 overexpression renders Hepa1-c1c7 cells resistant to PCSK9-induced LDLR degradation. The data suggest that pathological MMP-2 overexpression may protect the LDLR from PCSK-9-induced degradation.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.250
Teacher spread0.216 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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Same venueFEBS LettersSame topicCholesterol and Lipid MetabolismFrench-language works237,207