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

Author

Listed:
  • 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
    Fujian Medical University)

  • 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
    Guangxi University)

  • 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)

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.

Suggested Citation

  • 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 & Erwei, 2021. "Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25217-y
    DOI: 10.1038/s41467-021-25217-y
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    Cited by:

    1. Huawei Tong & Haoqiang Wang & Xuchen Wang & Nana Liu & Guoling Li & Danni Wu & Yun Li & Ming Jin & Hengbin Li & Yinghui Wei & Tong Li & Yuan Yuan & Linyu Shi & Xuan Yao & Yingsi Zhou & Hui Yang, 2024. "Development of deaminase-free T-to-S base editor and C-to-G base editor by engineered human uracil DNA glycosylase," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Jianli Tao & Daniel E. Bauer & Roberto Chiarle, 2023. "Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Nana Yan & Hu Feng & Yongsen Sun & Ying Xin & Haihang Zhang & Hongjiang Lu & Jitan Zheng & Chenfei He & Zhenrui Zuo & Tanglong Yuan & Nana Li & Long Xie & Wu Wei & Yidi Sun & Erwei Zuo, 2023. "Cytosine base editors induce off-target mutations and adverse phenotypic effects in transgenic mice," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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