Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

Tanglong Yuan,Wenqin Ying,Jitan Zheng,Yongyao Li,Nana Li,Nana Yan,Haihang Zhang,Yixue Li,Yidi Sun,Di Li,Juan Meng,Lei Shi,Long Xie,Tianyi Fei,Jing Liu,Erwei Zuo,Yongsen Sun

doi:10.1038/s41467-021-25217-y

Abstract

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites.

Highlights

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable
We first compared the efficiency of C-to-G base editing using uracil-DNA glycosylase (UNG) from human, E. coli, mouse, or C. elegans to substitute uracil-DNA glycosylase inhibitor (UGI) of BE3 (Supplementary Fig. 1)
For 34 endogenous sites in HEK293Tcells, we found that C-to-G BE (CGBE) variants with the E. coli or C. elegans UNG achieved much higher C-to-G transversion efficiency than that with human UNG (Fig. 1a)

Summary

Introduction

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. These engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites. We determine the motif preferences of these OPTI-CGBEs using a sgRNA library comprising 41,388 sequences, and develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These CGBE variants expand the scope of base editing and provide selection criteria for future gene editing that requires C-to-G transversion

Objectives

Methods

Results

Conclusion