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

Tanglong Yuan, Nana Yan, Tianyi Fei, Jitan Zheng, Juan Meng, Nana Li, Jing Liu, Haihang Zhang, Long Xie, Wenqin Ying, Di Li, Lei Shi, Yongsen Sun, Yongyao Li, Yixue Li, Yidi Sun () and Erwei Zuo ()
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Tanglong Yuan: Chinese Academy of Agricultural Sciences
Nana Yan: Chinese Academy of Agricultural Sciences
Tianyi Fei: Chinese Academy of Sciences
Jitan Zheng: Chinese Academy of Agricultural Sciences
Juan Meng: Chinese Academy of Sciences
Nana Li: Chinese Academy of Agricultural Sciences
Jing Liu: Chinese Academy of Agricultural Sciences
Haihang Zhang: Chinese Academy of Agricultural Sciences
Long Xie: Chinese Academy of Agricultural Sciences
Wenqin Ying: Chinese Academy of Sciences
Di Li: Chinese Academy of Agricultural Sciences
Lei Shi: Chinese Academy of Agricultural Sciences
Yongsen Sun: Chinese Academy of Agricultural Sciences
Yongyao Li: Chinese Academy of Agricultural Sciences
Yixue Li: Chinese Academy of Sciences Shanghai
Yidi Sun: Chinese Academy of Sciences
Erwei Zuo: Chinese Academy of Agricultural Sciences

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-25217-y

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