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A motif preferred adenine base editor with minimal bystander and off-targets editing

Mengyu Shang,Yinuo Li,Qiuyu Cao,Jingxuan Ren,Yuqiang Zeng,Jinxin Wang,Rachel V. L. Gonzalez,Xiaohui Zhang

Published 2025 in Nature Communications

ABSTRACT

47% of hereditable diseases are caused by single C•G-to-T•A base conversions, which means efficient A-to-G base editing tools (ABEs) have great potential for the treatment of these diseases. However, the existing efficient ABEs, while catalyzing targeted A-to-G conversion, cause high A or C bystander editing and off-target events, which poses safety concerns for their clinical applications. To overcome this shortcoming, we have developed ABE8e-YA (ABE8e with TadA-8e A48E) for efficient and accurate editing of As in YA motifs with YAY > YAR (Y = T or C, R = A or G) hierarchy through structure-oriented rational design. Compared with ABE3.1, which is currently the only ABE version with a YAC motif preference, ABE8e-YA exhibits an average A-to-G editing efficiency improvement of an up to 3.1-fold increase in the indicated YA motif while maintaining reduced bystander C editing and minimized DNA or RNA off-targets. Additionally, we demonstrate that ABE8e-YA efficiently and precisely corrects pathogenic mutations in human cells, suggesting its high suitability for addressing 9.3% of pathogenic point mutations, higher than that of ABE8e and ABE9. Moreover, by using ABE8e-YA, we efficiently and precisely generate hypocholesterolemia and tail-loss mouse models mimicking human-associated disease, as well as performed in vivo mouse proprotein convertase subtilisin/kexin type 9 (Pcsk9) base editing for hypercholesterolemia gene therapy. Together these data indicate its great potential in broad applications for disease modeling and gene therapy. Efficient adenine base editors (ABEs) often induce bystander and off-target effects. Here, Shang et al. developed a YA (Y = T or C) motif preferred ABE(ABE8e-YA) that minimizes these effects, enabling precise and effective pathogenic mutation correction, disease modeling, and in vivo gene therapy.

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