Dataset Information

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

ABSTRACT: CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.

SUBMITTER: Ryu J

PROVIDER: S-EPMC10508837 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

Ryu Jayoung J Barkal Sam S Yu Tian T Jankowiak Martin M Zhou Yunzhuo Y Francoeur Matthew M Phan Quang Vinh QV Li Zhijian Z Tognon Manuel M Brown Lara L Love Michael I MI Lettre Guillaume G Ascher David B DB Cassa Christopher A CA Sherwood Richard I RI Pinello Luca L

medRxiv : the preprint server for health sciences 20230910

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of eac ...[more]

PMID: 37732177

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Project description:Over 2.5 million prosthetic joint implantation surgeries occur annually in the United States. Periprosthetic joint infections (PJIs), though occurring in only 1-2% of patients receiving replacement joints, are challenging to diagnose and treat and are associated with significant morbidity. The Gram-positive bacterium Enterococcus faecalis, which can be highly antibiotic-resistant and is a robust biofilm producer on indwelling medical devices, accounts for 2-11% of PJIs. E. faecalis PJIs are understudied compared to those caused by other pathogens, such as Staphylococcus aureus. This motivates the need to generate a comprehensive understanding of E. faecalis PJIs to guide future treatments for these infections. To address this, we describe a panel of E. faecalis strains isolated from the surface of prosthetic joints in a cohort of individuals treated at the Mayo Clinic in Rochester, MN. Here, we present the first complete genome assemblage of E. faecalis PJI isolates. Comparative genomics shows differences in genome size, virulence factors, antimicrobial resistance genes, plasmids, and prophages, underscoring the genetic diversity of these strains. These isolates have strain-specific differences in in vitro biofilm biomass, biofilm burden, and biofilm morphology. We measured robust changes in biofilm architecture and aggregation for all isolates when grown in simulated synovial fluid (SSF). Finally, we evaluated the antibiotic efficacy of these isolates and found strain-specific changes across all strains when grown in SSF. Results of this study highlight the existence of genetic and phenotypic heterogeneity among E. faecalis PJI isolates which will provide valuable insight and resources for future E. faecalis PJI research.ImportancePeriprosthetic joint infections (PJIs) affect ~1-2% of those who undergo joint replacement surgery. Enterococcus faecalis is a Gram-positive opportunistic pathogen that causes ~10% of PJIs in the United States each year, but our understanding of how and why E. faecalis causes PJIs is limited. E. faecalis infections are typically biofilm-associated and can be difficult to clear with antibiotic therapy. Here, we provide complete genomes for four E. faecalis PJI isolates from the Mayo Clinic. These isolates have strain-specific differences in biofilm formation, aggregation, and antibiotic susceptibility in simulated synovial fluid. These results provide important insight into the genomic and phenotypic features of E. faecalis isolates from PJI.

Dataset Information

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

Publications

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

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