Abstract
Base editors derived from CRISPR-Cas9 systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. Here, we test the ability of the Target-AID base editor to systematically modify genes genome-wide by targeting yeast essential genes. We mutate around 17,000 individual sites in parallel across more than 1500 genes. We identify over 700 sites at which mutations have a significant impact on fitness. Using previously determined and preferred Target-AID mutational outcomes, we find that gRNAs with significant effects on fitness are enriched in variants predicted to be deleterious based on residue conservation and predicted protein destabilization. We identify key features influencing effective gRNAs in the context of base editing. Our results show that base editing is a powerful tool to identify key amino acid residues at the scale of proteomes.
Highlights
Base editors derived from CRISPR-Cas[9] systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput
To associate each gRNA in the library to specific base editing outcomes, we developed a simple model based on the yeast data included in the original Target-AID study as well as our own work[15,23]
Using a base editor that channels mutational outcomes such as cytidine deaminase-uracil glycosylase inhibitor (UGI) fusion can address this problem[15] but decreases the number of mutations explored during the experiment
Summary
Base editors derived from CRISPR-Cas[9] systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. By using a base editor with a narrow and welldefined activity window[15], we select gRNAs generating a limited number of predictable edits in yeast essential genes This allows us to use gRNAs as a readout for the effect of the mutations, similar to commonly used barcode-sequencing approaches to measure fitness effects. We orthogonally validate fitness effects of mutagenesis outcomes using classical genetics approaches and show that they overlap with those predicted to be deleterious We use this data to investigate which factors influence base editing efficiency and find multiple gRNAs and target properties that affect mutagenesis and that can be optimized for future experiments in specific genomic spaces
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