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Nat Commun ; 12(1): 4902, 2021 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-34385461

RESUMO

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.


Assuntos
Sistemas CRISPR-Cas , Citidina Desaminase/metabolismo , Edição de Genes/métodos , Aprendizado de Máquina , Uracila-DNA Glicosidase/metabolismo , Animais , Sequência de Bases , Sítios de Ligação/genética , Caenorhabditis elegans/genética , Códon/genética , Citidina Desaminase/genética , Escherichia coli/genética , Feminino , Biblioteca Gênica , Células HEK293 , Humanos , Camundongos , Reprodutibilidade dos Testes , Uracila-DNA Glicosidase/genética
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