1
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Katti A, Vega-Pérez A, Foronda M, Zimmerman J, Zafra MP, Granowsky E, Goswami S, Gardner EE, Diaz BJ, Simon JM, Wuest A, Luan W, Fernandez MTC, Kadina AP, Walker JA, Holden K, Lowe SW, Sánchez Rivera FJ, Dow LE. Generation of precision preclinical cancer models using regulated in vivo base editing. Nat Biotechnol 2024; 42:437-447. [PMID: 37563300 DOI: 10.1038/s41587-023-01900-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/10/2023] [Indexed: 08/12/2023]
Abstract
Although single-nucleotide variants (SNVs) make up the majority of cancer-associated genetic changes and have been comprehensively catalogued, little is known about their impact on tumor initiation and progression. To enable the functional interrogation of cancer-associated SNVs, we developed a mouse system for temporal and regulatable in vivo base editing. The inducible base editing (iBE) mouse carries a single expression-optimized cytosine base editor transgene under the control of a tetracycline response element and enables robust, doxycycline-dependent expression across a broad range of tissues in vivo. Combined with plasmid-based or synthetic guide RNAs, iBE drives efficient engineering of individual or multiple SNVs in intestinal, lung and pancreatic organoids. Temporal regulation of base editor activity allows controlled sequential genome editing ex vivo and in vivo, and delivery of sgRNAs directly to target tissues facilitates generation of in situ preclinical cancer models.
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Affiliation(s)
- Alyna Katti
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Adrián Vega-Pérez
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Miguel Foronda
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jill Zimmerman
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Maria Paz Zafra
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Biosanitary Research Institute (IBS)-Granada, Granada, Spain
| | - Elizabeth Granowsky
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Sukanya Goswami
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Eric E Gardner
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Bianca J Diaz
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Janelle M Simon
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandra Wuest
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wei Luan
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | | | - Scott W Lowe
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Francisco J Sánchez Rivera
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lukas E Dow
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
- Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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2
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Yuan T, Wu L, Li S, Zheng J, Li N, Xiao X, Zhang H, Fei T, Xie L, Zuo Z, Li D, Huang P, Feng H, Cao Y, Yan N, Wei X, Shi L, Sun Y, Wei W, Sun Y, Zuo E. Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes. Cell Discov 2024; 10:20. [PMID: 38378648 PMCID: PMC10879117 DOI: 10.1038/s41421-023-00624-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/09/2023] [Indexed: 02/22/2024] Open
Abstract
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.
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Affiliation(s)
- Tanglong Yuan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Leilei Wu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Shiyan Li
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jitan Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, China
| | - Nana Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Xiao Xiao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Haihang Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Tianyi Fei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Long Xie
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Zhenrui Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Di Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, China
| | | | - Hu Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Yaqi Cao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Nana Yan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Xinming Wei
- Epigenic Therapeutics, Inc., Shanghai, China
| | - Lei Shi
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Yongsen Sun
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Wu Wei
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
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3
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Dixit S, Kumar A, Srinivasan K, Vincent PMDR, Ramu Krishnan N. Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions. Front Bioeng Biotechnol 2024; 11:1335901. [PMID: 38260726 PMCID: PMC10800897 DOI: 10.3389/fbioe.2023.1335901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - P. M. Durai Raj Vincent
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - Nadesh Ramu Krishnan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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4
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Yuan B, Zhang S, Song L, Chen J, Cao J, Qiu J, Qiu Z, Chen J, Zhao XM, Cheng TL. Engineering of cytosine base editors with DNA damage minimization and editing scope diversification. Nucleic Acids Res 2023; 51:e105. [PMID: 37843111 PMCID: PMC10639057 DOI: 10.1093/nar/gkad855] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/25/2023] [Accepted: 09/22/2023] [Indexed: 10/17/2023] Open
Abstract
Cytosine base editors (CBEs), which enable precise C-to-T substitutions, have been restricted by potential safety risks, including DNA off-target edits, RNA off-target edits and additional genotoxicity such as DNA damages induced by double-strand breaks (DSBs). Though DNA and RNA off-target edits have been ameliorated via various strategies, evaluation and minimization of DSB-associated DNA damage risks for most CBEs remain to be resolved. Here we demonstrate that YE1, an engineered CBE variant with minimized DNA and RNA off-target edits, could induce prominent DSB-associated DNA damage risks, manifested as γH2AX accumulation in human cells. We then perform deaminase engineering for two deaminases lamprey LjCDA1 and human APOBEC3A, and generate divergent CBE variants with eliminated DSB-associated DNA damage risks, in addition to minimized DNA/RNA off-target edits. Furthermore, the editing scopes and sequence preferences of APOBEC3A-derived CBEs could be further diversified by internal fusion strategy. Taken together, this study provides updated evaluation platform for DSB-associated DNA damage risks of CBEs and further generates a series of safer toolkits with diversified editing signatures to expand their applications.
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Affiliation(s)
- Bo Yuan
- Institute of Pediatrics, National Children's Medical Center, Children's Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuqian Zhang
- Institute of Pediatrics, National Children's Medical Center, Children's Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
- Department of Pediatrics, Qilu Hospital of Shandong University, Ji’nan 250012, China
| | - Liting Song
- Institute of Science and Technology for Brain-inspired Intelligence, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jinlong Chen
- Institute of Pediatrics, National Children's Medical Center, Children's Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
| | - Jixin Cao
- Institute of Science and Technology for Brain-inspired Intelligence, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jiayi Qiu
- Institute of Pediatrics, National Children's Medical Center, Children's Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
| | - Zilong Qiu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- Songjiang Hospital, Songjiang Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingqi Chen
- Institute of Science and Technology for Brain-inspired Intelligence, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-inspired Intelligence, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Tian-Lin Cheng
- Institute of Pediatrics, National Children's Medical Center, Children's Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
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5
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Koeppel J, Weller J, Peets EM, Pallaseni A, Kuzmin I, Raudvere U, Peterson H, Liberante FG, Parts L. Prediction of prime editing insertion efficiencies using sequence features and DNA repair determinants. Nat Biotechnol 2023; 41:1446-1456. [PMID: 36797492 PMCID: PMC10567557 DOI: 10.1038/s41587-023-01678-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/18/2023] [Indexed: 02/18/2023]
Abstract
Most short sequences can be precisely written into a selected genomic target using prime editing; however, it remains unclear what factors govern insertion. We design a library of 3,604 sequences of various lengths and measure the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems in varying DNA repair contexts. We find that length, nucleotide composition and secondary structure of the insertion sequence all affect insertion rates. We also discover that the 3' flap nucleases TREX1 and TREX2 suppress the insertion of longer sequences. Combining the sequence and repair features into a machine learning model, we can predict relative frequency of insertions into a site with R = 0.70. Finally, we demonstrate how our accurate prediction and user-friendly software help choose codon variants of common fusion tags that insert at high efficiency, and provide a catalog of empirically determined insertion rates for over a hundred useful sequences.
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Affiliation(s)
| | | | | | | | - Ivan Kuzmin
- Department of Computer Science, University of Tartu, Tartu, Estonia
| | - Uku Raudvere
- Department of Computer Science, University of Tartu, Tartu, Estonia
| | - Hedi Peterson
- Department of Computer Science, University of Tartu, Tartu, Estonia
| | | | - Leopold Parts
- Wellcome Sanger Institute, Hinxton, UK.
- Department of Computer Science, University of Tartu, Tartu, Estonia.
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6
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Weller J, Pallaseni A, Koeppel J, Parts L. Predicting Mutations Generated by Cas9, Base Editing, and Prime Editing in Mammalian Cells. CRISPR J 2023; 6:325-338. [PMID: 37339457 DOI: 10.1089/crispr.2023.0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023] Open
Abstract
The first fruits of the CRISPR-Cas revolution are starting to enter the clinic, with gene editing therapies offering solutions to previously incurable genetic diseases. The success of such applications hinges on control over the mutations that are generated, which are known to vary depending on the targeted locus. In this review, we present the current state of understanding and predicting CRISPR-Cas cutting, base editing, and prime editing outcomes in mammalian cells. We first provide an introduction to the basics of DNA repair and machine learning that the models rely on. We then overview the datasets and methods created for characterizing edits at scale, as well as the insights that have been derived from them. The predictions generated from these models serve as a foundation for designing efficient experiments across the broad contexts where these tools are applied.
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7
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Lue NZ, Liau BB. Base editor screens for in situ mutational scanning at scale. Mol Cell 2023:S1097-2765(23)00431-8. [PMID: 37390819 DOI: 10.1016/j.molcel.2023.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 07/02/2023]
Abstract
A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.
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Affiliation(s)
- Nicholas Z Lue
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brian B Liau
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
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8
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Cheng X, Li Z, Shan R, Li Z, Wang S, Zhao W, Zhang H, Chao L, Peng J, Fei T, Li W. Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches. Nat Commun 2023; 14:752. [PMID: 36765063 PMCID: PMC9912244 DOI: 10.1038/s41467-023-36316-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/26/2023] [Indexed: 02/12/2023] Open
Abstract
A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org .
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Affiliation(s)
- Xiaolong Cheng
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, 20010, USA
| | - Zexu Li
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China
| | - Ruocheng Shan
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA
- Department of Computer Science, George Washington University, Washington, DC, 20052, USA
| | - Zihan Li
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China
| | - Shengnan Wang
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China
| | - Wenchang Zhao
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China
| | - Han Zhang
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China
| | - Lumen Chao
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, 20010, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Teng Fei
- National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China.
| | - Wei Li
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, 20010, USA.
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9
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Fan J, Shi L, Liu Q, Zhu Z, Wang F, Song R, Su J, Zhou D, Chen X, Li K, Xue L, Sun L, Mao F. Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase. Nucleic Acids Res 2022; 51:D1249-D1256. [PMID: 36350608 PMCID: PMC9825451 DOI: 10.1093/nar/gkac967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/10/2022] Open
Abstract
CRISPR-Cas base editing (BE) system is a powerful tool to expand the scope and efficiency of genome editing with single-nucleotide resolution. The editing efficiency, product purity, and off-target effect differ among various BE systems. Herein, we developed CRISPRbase (http://crisprbase.maolab.org), by integrating 1 252 935 records of base editing outcomes in more than 50 cell types from 17 species. CRISPRbase helps to evaluate the putative editing precision of different BE systems by integrating multiple annotations, functional predictions and a blasting system for single-guide RNA sequences. We systematically assessed the editing window, editing efficiency and product purity of various BE systems. Intensive efforts were focused on increasing the editing efficiency and product purity of base editors since the byproduct could be detrimental in certain applications. Remarkably, more than half of cancer-related off-target mutations were non-synonymous and extremely damaging to protein functions in most common tumor types. Luckily, most of these cancer-related mutations were passenger mutations (4840/5703, 84.87%) rather than cancer driver mutations (863/5703, 15.13%), indicating a weak effect of off-target mutations on carcinogenesis. In summary, CRISPRbase is a powerful and convenient tool to study the outcomes of different base editors and help researchers choose appropriate BE designs for functional studies.
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Affiliation(s)
| | | | | | - Zhipeng Zhu
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China,Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Fan Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province 225009, China
| | - Runxian Song
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China,State Key Laboratory of Tree Genetics and Breeding, Forestry College, Northeast Forestry University, Harbin 150040, China
| | - Jimeng Su
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province 225009, China
| | - Degui Zhou
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China,Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China,Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
| | - Xiao Chen
- Laboratory of Marine Protozoan Biodiversity & Evolution, Marine College, Shandong University, Weihai 264209, China
| | - Kailong Li
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Lixiang Xue
- Correspondence may also be addressed to Lixiang Xue.
| | - Lichao Sun
- Correspondence may also be addressed to Lichao Sun.
| | - Fengbiao Mao
- To whom correspondence should be addressed. Tel: +86 10 87132318;
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Xie X, Li F, Tan X, Zeng D, Liu W, Zeng W, Zhu Q, Liu YG. BEtarget: a versatile web-based tool to design guide RNAs for base editing in plants. Comput Struct Biotechnol J 2022; 20:4009-4014. [PMID: 35983232 PMCID: PMC9355906 DOI: 10.1016/j.csbj.2022.07.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 11/23/2022] Open
Abstract
BEtarget supports the gRNA design of base editing with different types of PAM. BEtarget provides an interactive and customized visualization interface. BEtarget can automatically detect the coordinates of coding regions (exons) in the genomic sequence of the target gene.
CRISPR-dependent base editors enable direct nucleotide conversion without the introduction of double-strand DNA break or donor DNA template, thus expanding the CRISPR toolbox for genetic manipulation. However, designing guide RNAs (gRNAs) for base editors to enable gene correction or inactivation is more complicated than using the CRISPR system for gene disruption. Here, we present a user-friendly web tool named BEtarget dedicated to the design of gRNA for base editing. It is currently supported by 46 plant reference genomes and 5 genomes of non-plant model organisms. BEtarget supports the design of gRNAs with different types of protospacer adjacent motifs (PAM) and integrates various functions, including automatic identification of open reading frame, prediction of potential off-target sites, annotation of codon change, and assessment of gRNA quality. Moreover, the program provides an interactive interface for users to selectively display information about the desired target sites. In brief, we have developed a flexible and versatile web-based tool to simplify complications associated with the design of base editing technology. BEtarget is freely accessible at https://skl.scau.edu.cn/betarget/.
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Li M, Huo YX, Guo S. CRISPR-Mediated Base Editing: From Precise Point Mutation to Genome-Wide Engineering in Nonmodel Microbes. Biology (Basel) 2022; 11:571. [PMID: 35453770 PMCID: PMC9024924 DOI: 10.3390/biology11040571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/27/2022] [Accepted: 04/02/2022] [Indexed: 12/23/2022]
Abstract
Nonmodel microbes with unique and diverse metabolisms have become rising stars in synthetic biology; however, the lack of efficient gene engineering techniques still hinders their development. Recently, the use of base editors has emerged as a versatile method for gene engineering in a wide range of organisms including nonmodel microbes. This method is a fusion of impaired CRISPR/Cas9 nuclease and base deaminase, enabling the precise point mutation at the target without inducing homologous recombination. This review updates the latest advancement of base editors in microbes, including the conclusion of all microbes that have been researched by base editors, the introduction of newly developed base editors, and their applications. We provide a list that comprehensively concludes specific applications of BEs in nonmodel microbes, which play important roles in industrial, agricultural, and clinical fields. We also present some microbes in which BEs have not been fully established, in the hope that they are explored further and so that other microbial species can achieve arbitrary base conversions. The current obstacles facing BEs and solutions are put forward. Lastly, the highly efficient BEs and other developed versions for genome-wide reprogramming of cells are discussed, showing great potential for future engineering of nonmodel microbes.
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Affiliation(s)
| | - Yi-Xin Huo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Beijing 100081, China;
| | - Shuyuan Guo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Beijing 100081, China;
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