LDLR variant classification through activity-normalized prime editing screening

Phillip J. Zhou, Minja Velimirovic, Tian Yu, Vojislav Gligorovski, Nicolas Mathis, Jing Zhao, Quang Vinh Phan, Felicitas Vogd, Jayoung Ryu, Qisheng Pan, Atharva Tyagi, David B. Ascher, Gerald Schwank, Luca Pinello, Christopher A. Cassa, Richard I. Sherwood
2025 · doi:10.64898/2025.12.16.694467
preprint OA: closed CC-BY-NC-ND-4.0
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This study developed an activity-normalized prime editing screen to classify low-density lipoprotein receptor (LDLR) variants based on their functional impact.

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The paper studied how inherited LDLR coding variants affect LDL-cholesterol uptake, aiming to classify the large fraction of variants currently labeled as uncertain in familial hypercholesterolemia. The authors developed an activity-normalized prime editing screening pipeline testing 5,184 LDLR variants by pairing a genotypic reporter with each pegRNA to correct for variable editing efficiency, and by denoising scores using statistical estimation across missense variants at the same position. They found a continuous spectrum of variant functional effects, robust separation of pathogenic versus benign ClinVar variants, concordance between functional scores and LDL-C levels from UK Biobank participants, and—after calibrating evidence to ACMG/AMP guidelines plus integrating other evidence sources—a majority of unclassified rare variants reaching thresholds for reclassification, while also identifying LDLR variants that enhance LDL-C uptake via increased interaction with apolipoprotein B and capturing candidate splice-altering coding variants not modeled by cDNA screens. The paper does not explicitly state limitations in the provided text excerpt, though the assay’s calibration to clinical interpretation frameworks and comparisons to other editing/screening modalities represent key caveats. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Background Inherited variants in the LDL receptor (LDLR) gene are the most common cause of familial hypercholesterolemia (FH), significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt intervention with lipid-lowering therapies; however, the majority of LDLR variants observed in the population have uncertain or absent clinical classifications, limiting the potential to improve clinical management.

Methods

We developed an innovative, activity-normalized prime editing screening pipeline to measure the impact of 5,184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Through pairing a genotypic outcome reporter with every prime editing guide RNA (pegRNA), we adjust phenotypic measurements to account for variable editing efficiency, extending activity normalization to prime editing for the first time at this scale. Further, we use a statistical estimation approach that leverages measurements for all missense variants at a given position to denoise the resulting scores.

Results

We show that prime editing-mediated reporter editing correlates with endogenous variant installation frequency, allowing activity normalization to improve imputation of LDLR variant effect. Our optimized prime editing assay identifies a broad, continuous spectrum of variant functional effects. We achieve robust separation of pathogenic vs. benign ClinVar variants and concordance between experimentally derived functional scores and LDL-C levels measured in UK Biobank participants. Further, when calibrating the strength of evidence provided by this functional screening data to align with the ACMG/AMP variant interpretation guidelines, and integrating additional sources of evidence, a majority of currently unclassified rare LDLR variants meet evidence thresholds for reclassification. We use the broad coverage of this screen to gain insight into how apolipoproteins bind to LDLR. In particular, we identify and characterize rare LDLR variants that enhance LDL-C uptake through increased interaction with apolipoprotein B. Finally, we compare prime editing-based functional scores with those derived from recent base editing and cDNA-based LDLR variant screens, showing that these approaches all show robust correlation with clinically observed LDL-C levels and computational scores, while prime editing identifies candidate splice-altering coding variants that are not modeled by cDNA screening.

Conclusions

Altogether, our approach demonstrates the power of prime editing to significantly improve understanding of how variants in LDLR impact function and contribute to FH. Competing Interest Statement Luca Pinello has financial interests in Edilytics, Inc., and SeQure Dx, Inc. Gerald Schwank is a scientific advisor of Prime Medicine and co-founder of Nerai Bio. Authors' interests were reviewed and are managed by MassGeneral Brigham HealthCare in accordance with their conflict of interest policies.

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License: CC-BY-NC-ND-4.0