Matthew Francoeur

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

Binding of transcription factors (TFs) at gene regulatory elements controls cellular epigenetic state and gene expression. Current genome-wide chromatin profiling approaches have inherently limited resolution, complicating assessment of TF occupancy and co-occupancy, especially at individual alleles. In this work, we introduce Accessible Chromatin by Cytosine Editing Site Sequencing with ATAC-seq (ACCESS-ATAC), which harnesses a double-stranded DNA cytosine deaminase (Ddd) enzyme to stencil TF binding locations within accessible chromatin regions. We optimize bulk and single-cell ACCESS-ATAC protocols and develop computational methods to show that the increased resolution compared with ATAC-seq improves the accuracy of TF binding site prediction. We use ACCESS-ATAC to perform genome-wide allelic occupancy and co-occupancy imputation for 64 TFs each in HepG2 and K562, revealing that the propensity of a majority of TFs to co-occupy nearby motifs oscillates with a period approximating the helical turn of DNA. Altogether, ACCESS-ATAC expands the resolution and capabilities of bulk and single-cell epigenomic profiling.

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.

Data repository for the paper` Joint genotypic and phenotypic outcome modeling improves base editing variant effect measurement`. Includes data required to reproduce analysis in the manuscript through the workflow in https://github.com/pinellolab/bean_manuscript.