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PgmNr 206: Interrogating regulatory consequences of genetic variation in DNA associated proteins.

C. Wu 1, 2; S. Shleizer-Burko 2; A. Goren 2; M. Gymrek 2, 3

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1) Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA.; 2) Department of Medicine, University of California San Diego, La Jolla, CA; 3) Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA

Understanding the mechanistic impact of a specific mutation is a key challenge in human genomics. Mutations in proteins including transcription factors, chromatin regulators or splicing factors may cause widespread transcriptomic changes. Intriguingly, different mutations in the same gene can cause distinct phenotypic changes, ranging from no impact to severe health consequences. For example, mutations in FOXC1 may lead either to Anterior Segment Dysgenesis with the Rieger subtype or the Axenfield subtype depending on where they fall within the protein. A potential explanation of these diverse phenotypes is that different mutations may act by a variety of mechanisms, including loss/gain of function or reduced/enhanced activity. For example, mutations in the binding domain of a protein may have profound impact on binding affinity, whereas mutations elsewhere in the protein may have little impact. Genome editing allows the study of specific mutations. However, generating cell lines with precise edits remains an inefficient process making it infeasible using standard methods to study more than a handful of mutations. Here we develop a multiplexed genome-editing assay to measure the regulatory effects of dozens of genetic variants in a particular DNA-associated protein simultaneously using single-cell RNAseq (scRNAseq). The pipeline consists of (1) base editing to efficiently introduce multiple edits to the protein of interest in a pool of cells and (2) scRNAseq on the pool of edited cells. scRNAseq data is used to determine which cells received which edit and to identify differentially expressed genes induced by each target mutation. We also develop a simulation framework to determine experimental parameters required to obtain robust results across a range of mutation types and scRNAseq platforms. We tested the feasibility of our approach by introducing a library of 6-8 sgRNAs for three genes harboring known pathogenic mutations (GATA4, EP300, and FOXC1) and perform scRNAseq on the edited pool to identify genome-wide transcriptomic changes. We achieved editing efficiencies of up to 53%, compared to less than 5% using homology-directed repair. We are additionally generating clonal cell lines with known pathogenic mutations as validation data for the full pipeline. Once established, our approach will provide a valuable platform for simultaneously characterizing the transcriptomic impact of dozens to hundreds of pathogenic variants in protein-coding genes.