Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning

Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in ma...

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Vydané v:Cell Ročník 182; číslo 2; s. 463
Hlavní autori: Arbab, Mandana, Shen, Max W, Mok, Beverly, Wilson, Christopher, Matuszek, Żaneta, Cassa, Christopher A, Liu, David R
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 23.07.2020
Predmet:
Animals
Gene Editing - methods
Gene Library
Humans
Machine Learning
Mice
Mouse Embryonic Stem Cells - cytology
Mouse Embryonic Stem Cells - metabolism
Point Mutation
RNA, Guide, CRISPR-Cas Systems
transversion base editing
machine learning
base editing
disease correction
precision genome editing
ISSN:1097-4172, 1097-4172
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Shrnutí:Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in mammalian cells and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes (R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1097-4172
1097-4172
DOI:10.1016/j.cell.2020.05.037