RESUMO
In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.
RESUMO
Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.
Assuntos
Sistemas CRISPR-Cas/genética , Edição de Genes/métodos , Edição de Genes/normas , Síndrome de Hermanski-Pudlak/genética , Aprendizado de Máquina , Síndrome dos Cabelos Torcidos/genética , Moldes Genéticos , Alelos , Sequência de Bases , Proteína 9 Associada à CRISPR/metabolismo , Reparo do DNA/genética , Fibroblastos/metabolismo , Fibroblastos/patologia , Células HCT116 , Células HEK293 , Síndrome de Hermanski-Pudlak/patologia , Humanos , Células K562 , Síndrome dos Cabelos Torcidos/patologia , Reprodutibilidade dos Testes , Especificidade por SubstratoRESUMO
Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.