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Abstract 4364195: A transformative LDL cholesterol–lowering <i>in vivo</i> CRISPR gene editing medicine that functionally upregulates LDLR in mice and non-human primates

2025· article· en· W4415794162 on OpenAlexaff
Judith A. Newmark, Paul J. Wrighton, Salvatore Iovino, Parth H. Amin, Morgan N. Thompson, Salu Rizal, Ruhong Dong, Zhen Wei, Jimit Girish Raghav, Michael Jaskolka, Benjamin A. Diner, Vikram Soman, Tushare Jinadasa, Ameya Apte, Meng Wu, Steve Bottega, Luis M. Agosto, Deep Majithia, Shreya Jambard, Linnea Jansson-Fritzberg, Mark Jones, Briana Steward, James Bochicchio, Stephen Pietrasiewicz, E. Rubio, Nisha Chander, Kieu Lam, Steve Reid, Michael Dinsmore, Tanya M. Teslovich, Jenny Xie, Anshul Gupta, Linda C. Burkly

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLDL receptorGenome editingDownregulation and upregulationCRISPRFamilial hypercholesterolemiaUntranslated regionPCSK9Cas9Gene

Abstract

fetched live from OpenAlex

Background: Elevated low-density lipoprotein cholesterol (LDL-C) is a key risk factor for atherosclerotic cardiovascular disease (ASCVD), and reducing LDL-C lowers major adverse cardiovascular event risk. Heterozygous familial hypercholesterolemia (HeFH) is a genetic disease characterized by high LDL-C and ASCVD risk, with ~90% of genetically confirmed cases due to autosomal dominant, loss-of-function mutations in the LDLR gene. Despite multiple treatment options, many patients fail to reach target LDL-C levels. Furthermore, evidence suggests a favorable benefit and safety profile with extremely low LDL-C (10–40 mg/dL) and supports aggressively lowering LDL-C in ASCVD. Hypothesis: We have developed a novel CRISPR-based one-time treatment strategy to edit the LDLR gene to achieve LDLR upregulation and significant LDL-C lowering in patients with HeFH and ASCVD. Our strategy was informed by a naturally occurring gain-of-function deletion in the 3′ untranslated region (UTR) of LDLR that led to a mean 74% lower LDL-C compared with non-carriers, and no adverse effects. We employ dual gRNAs to delete negative regulatory regions of the LDLR 3′ UTR to upregulate mRNA and protein expression. Methods and Results: A potent human dual gRNA pair was identified through primary human hepatocyte screening. To demonstrate in vivo proof of concept, Ldlr +/− mice were injected with lipid nanoparticles (LNPs) containing a CRISPR nuclease mRNA, and a surrogate gRNA pair targeting the orthologous genomic region. Mice treated with LNPs demonstrated >15-fold increase in LDLR protein in the liver and >80% biomarker reduction. Administration of two different LNP formulations (Genevant Sciences) to non-human primates was well tolerated as indicated by maintenance of body weight, temperature, and clinical presentation, and an acceptable clinical chemistry, hematology, and coagulation profile. Terminal liver samples demonstrated productive editing, LDLR mRNA upregulation, and increased LDLR protein. Notably, treatment induced a remarkable >90% mean decrease in serum LDL-C. Conclusions: Our findings demonstrate preclinical validation of an alternate mechanism for robust LDL-C lowering by deleting 3′ UTR regulatory elements in the LDLR gene resulting in potent LDLR upregulation. This in vivo strategy represents a potentially transformative one-time treatment for lowering LDL-C in patients with HeFH and ASCVD to significantly reduce cardiovascular risk.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.285
Teacher spread0.278 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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