{"title":"包含 DNA 序列以外内源因素的深度学习模型提高了碱基编辑结果的预测准确性。","authors":"Tanglong Yuan, Leilei Wu, Shiyan Li, Jitan Zheng, Nana Li, Xiao Xiao, Haihang Zhang, Tianyi Fei, Long Xie, Zhenrui Zuo, Di Li, Pinzheng Huang, Hu Feng, Yaqi Cao, Nana Yan, Xinming Wei, Lei Shi, Yongsen Sun, Wu Wei, Yidi Sun, Erwei Zuo","doi":"10.1038/s41421-023-00624-1","DOIUrl":null,"url":null,"abstract":"<p><p>Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.</p>","PeriodicalId":9674,"journal":{"name":"Cell Discovery","volume":null,"pages":null},"PeriodicalIF":13.0000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879117/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.\",\"authors\":\"Tanglong Yuan, Leilei Wu, Shiyan Li, Jitan Zheng, Nana Li, Xiao Xiao, Haihang Zhang, Tianyi Fei, Long Xie, Zhenrui Zuo, Di Li, Pinzheng Huang, Hu Feng, Yaqi Cao, Nana Yan, Xinming Wei, Lei Shi, Yongsen Sun, Wu Wei, Yidi Sun, Erwei Zuo\",\"doi\":\"10.1038/s41421-023-00624-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.</p>\",\"PeriodicalId\":9674,\"journal\":{\"name\":\"Cell Discovery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2024-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879117/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Discovery\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1038/s41421-023-00624-1\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Discovery","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41421-023-00624-1","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.
Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.
Cell DiscoveryBiochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
24.20
自引率
0.60%
发文量
120
审稿时长
20 weeks
期刊介绍:
Cell Discovery is a cutting-edge, open access journal published by Springer Nature in collaboration with the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (CAS). Our aim is to provide a dynamic and accessible platform for scientists to showcase their exceptional original research.
Cell Discovery covers a wide range of topics within the fields of molecular and cell biology. We eagerly publish results of great significance and that are of broad interest to the scientific community. With an international authorship and a focus on basic life sciences, our journal is a valued member of Springer Nature's prestigious Molecular Cell Biology journals.
In summary, Cell Discovery offers a fresh approach to scholarly publishing, enabling scientists from around the world to share their exceptional findings in molecular and cell biology.