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Record W4390403330 · doi:10.26685/urncst.530

Investigating SHP and PCSK9 Interactions in Cholesterol-Mediated Cardiovascular Diseases: A Research Protocol

2023· article· en· W4390403330 on OpenAlexaff
Moon Young Bae, Rachel Kim, Luyu Wang

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsPCSK9Transcription factorProprotein convertaseDownregulation and upregulationSmall heterodimer partnerKexinBiologySmall interfering RNACell biologyLiver X receptorKnockout mouseFarnesoid X receptorGene silencingElectrophoretic mobility shift assayNuclear receptorLDL receptorCholesterolReceptorLipoproteinEndocrinologyBiochemistryGeneTransfection

Abstract

fetched live from OpenAlex

Introduction: Improper cholesterol metabolism results in accumulation of low-density lipoproteins (LDL). High levels of LDL cholesterol deposits in blood vessels, forming plaques and contributing to various cardiovascular diseases (CVD). The nuclear farnesoid X receptor (FXR) regulates the transcription of genes involved in cholesterol metabolism and is a therapeutic target for cholesterol dysregulation. Studies conducted on immortalized human hepatocytes demonstrate FXR signaling-induced downregulation of proprotein convertase subtilisin/kexin type 9 (PCSK9) expression. PCSK9 is an LDL receptor-degrading enzyme whose upregulation is implicated in cholesterol-mediated diseases. Specifically, the FXR target gene SHP (small heterodimer partner) is a transcriptional regulator that has been implicated in an inverse relationship with PCSK9 expression. The biomolecular mechanism mediating this relationship has not been explored, meriting investigation into a potential novel axis of cholesterol metabolism. We hypothesize that SHP is a direct repressor of PCSK9 transcription. Methods: To investigate, we will knock out SHP expression in the liver hepatocyte cell line AML12 using small interfering RNAs (siRNAs). To confirm SHP knockout on transcriptomic and proteomic levels, reverse transcription quantitative PCR (RT-qPCR) and Western blotting will be performed. To assess SHP binding to the promoter region of PCSK9, an electrophoretic mobility supershift (EMSA) assay will be performed on unstimulated or chenodeoxycolic acid (CDCA)-stimulated AML12 cells that have undergone SHP or control knockouts. Western blotting will quantitate PCSK9 protein expression following SHP knockout in CDCA-stimulated and unstimulated conditions. Results: Results from EMSA are expected to demonstrate SHP binding to the promoter region of PCSK9 in a transcription factor complex to repress transcription. SHP knockout models are expected to show upregulated PCSK9 expression at transcriptomic and proteomic levels. Discussion: If successful, our study presents a novel perspective on cholesterol metabolism by characterizing the inhibitory effect of SHP on PCSK9 expression. This underlines the critical role of FXR signaling in PCSK9 regulation, and knockout models and assay techniques provide valuable evidence of this regulatory role. Conclusion: This study will establish an enhanced understanding of the SHP/PCSK9 pathway within broader pathways of cholesterol metabolism. Further research may explore therapies targeting the SHP/PCSK9 pathway to manage CVD downstream of cholesterol dysregulation.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0500.016

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 head0.156
GPT teacher head0.506
Teacher spread0.350 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2023
Admission routes1
Has abstractyes

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