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Feng Q, Li Q, Zhou H, Wang Z, Lin C, Jiang Z, Liu T, Wang D. CRISPR technology in human diseases. MedComm (Beijing) 2024; 5:e672. [PMID: 39081515 PMCID: PMC11286548 DOI: 10.1002/mco2.672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/01/2024] [Accepted: 07/01/2024] [Indexed: 08/02/2024] Open
Abstract
Gene editing is a growing gene engineering technique that allows accurate editing of a broad spectrum of gene-regulated diseases to achieve curative treatment and also has the potential to be used as an adjunct to the conventional treatment of diseases. Gene editing technology, mainly based on clustered regularly interspaced palindromic repeats (CRISPR)-CRISPR-associated protein systems, which is capable of generating genetic modifications in somatic cells, provides a promising new strategy for gene therapy for a wide range of human diseases. Currently, gene editing technology shows great application prospects in a variety of human diseases, not only in therapeutic potential but also in the construction of animal models of human diseases. This paper describes the application of gene editing technology in hematological diseases, solid tumors, immune disorders, ophthalmological diseases, and metabolic diseases; focuses on the therapeutic strategies of gene editing technology in sickle cell disease; provides an overview of the role of gene editing technology in the construction of animal models of human diseases; and discusses the limitations of gene editing technology in the treatment of diseases, which is intended to provide an important reference for the applications of gene editing technology in the human disease.
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Affiliation(s)
- Qiang Feng
- Laboratory Animal CenterCollege of Animal ScienceJilin UniversityChangchunChina
- Research and Development CentreBaicheng Medical CollegeBaichengChina
| | - Qirong Li
- Laboratory Animal CenterCollege of Animal ScienceJilin UniversityChangchunChina
| | - Hengzong Zhou
- Laboratory Animal CenterCollege of Animal ScienceJilin UniversityChangchunChina
| | - Zhan Wang
- Laboratory Animal CenterCollege of Animal ScienceJilin UniversityChangchunChina
| | - Chao Lin
- School of Grain Science and TechnologyJilin Business and Technology CollegeChangchunChina
| | - Ziping Jiang
- Department of Hand and Foot SurgeryThe First Hospital of Jilin UniversityChangchunChina
| | - Tianjia Liu
- Research and Development CentreBaicheng Medical CollegeBaichengChina
| | - Dongxu Wang
- Laboratory Animal CenterCollege of Animal ScienceJilin UniversityChangchunChina
- Department of Hand and Foot SurgeryThe First Hospital of Jilin UniversityChangchunChina
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2
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Guan Z, Jiang Z. A systematic method for solving data imbalance in CRISPR off-target prediction tasks. Comput Biol Med 2024; 178:108781. [PMID: 38936075 DOI: 10.1016/j.compbiomed.2024.108781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 06/05/2024] [Accepted: 06/15/2024] [Indexed: 06/29/2024]
Abstract
Accurately identifying potential off-target sites in the CRISPR/Cas9 system is crucial for improving the efficiency and safety of editing. However, the imbalance of available off-target datasets has posed a major obstacle in enhancing prediction performance. Despite several prediction models have been developed to address this issue, there remains a lack of systematic research on handling data imbalance in off-target prediction. This article systematically investigates the data imbalance issue in off-target datasets and explores numerous methods to process data imbalance from a novel perspective. First, we highlight the impact of the imbalance problem on off-target prediction tasks by determining the imbalance ratios present in these datasets. Then, we provide a comprehensive review of various sampling techniques and cost-sensitive methods to mitigate class imbalance in off-target datasets. Finally, systematic experiments are conducted on several state-of-the-art prediction models to illustrate the impact of applying data imbalance solutions. The results show that class imbalance processing methods significantly improve the off-target prediction capabilities of the models across multiple testing datasets. The code and datasets used in this study are available at https://github.com/gzrgzx/CRISPR_Data_Imbalance.
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Affiliation(s)
- Zengrui Guan
- School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
| | - Zhenran Jiang
- School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China.
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3
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Yaish O, Orenstein Y. Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges. Nucleic Acids Res 2024; 52:6777-6790. [PMID: 38813823 PMCID: PMC11229338 DOI: 10.1093/nar/gkae428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 04/12/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The CRISPR/Cas9 system is a highly accurate gene-editing technique, but it can also lead to unintended off-target sites (OTS). Consequently, many high-throughput assays have been developed to measure OTS in a genome-wide manner, and their data was used to train machine-learning models to predict OTS. However, these models are inaccurate when considering OTS with bulges due to limited data compared to OTS without bulges. Recently, CHANGE-seq, a new in vitro technique to detect OTS, was used to produce a dataset of unprecedented scale and quality. In addition, the same study produced in cellula GUIDE-seq experiments, but none of these GUIDE-seq experiments included bulges. Here, we generated the most comprehensive GUIDE-seq dataset with bulges, and trained and evaluated state-of-the-art machine-learning models that consider OTS with bulges. We first reprocessed the publicly available experimental raw data of the CHANGE-seq study to generate 20 new GUIDE-seq experiments, and hundreds of OTS with bulges among the original and new GUIDE-seq experiments. We then trained multiple machine-learning models, and demonstrated their state-of-the-art performance both in vitro and in cellula over all OTS and when focusing on OTS with bulges. Last, we visualized the key features learned by our models on OTS with bulges in a unique representation.
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Affiliation(s)
- Ofir Yaish
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Yaron Orenstein
- Department of Computer Science, Bar-Ilan University, Ramat Gan 5290002, Israel
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel
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4
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Ben-Tov D, Mafessoni F, Cucuy A, Honig A, Melamed-Bessudo C, Levy AA. Uncovering the dynamics of precise repair at CRISPR/Cas9-induced double-strand breaks. Nat Commun 2024; 15:5096. [PMID: 38877047 PMCID: PMC11178868 DOI: 10.1038/s41467-024-49410-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 06/05/2024] [Indexed: 06/16/2024] Open
Abstract
CRISPR/Cas9 is widely used for precise mutagenesis through targeted DNA double-strand breaks (DSBs) induction followed by error-prone repair. A better understanding of this process requires measuring the rates of cutting, error-prone, and precise repair, which have remained elusive so far. Here, we present a molecular and computational toolkit for multiplexed quantification of DSB intermediates and repair products by single-molecule sequencing. Using this approach, we characterize the dynamics of DSB induction, processing and repair at endogenous loci along a 72 h time-course in tomato protoplasts. Combining this data with kinetic modeling reveals that indel accumulation is determined by the combined effect of the rates of DSB induction processing of broken ends, and precise versus error repair. In this study, 64-88% of the molecules were cleaved in the three targets analyzed, while indels ranged between 15-41%. Precise repair accounts for most of the gap between cleavage and error repair, representing up to 70% of all repair events. Altogether, this system exposes flux in the DSB repair process, decoupling induction and repair dynamics, and suggesting an essential role of high-fidelity repair in limiting the efficiency of CRISPR-mediated mutagenesis.
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Affiliation(s)
- Daniela Ben-Tov
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Fabrizio Mafessoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Amit Cucuy
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Arik Honig
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Cathy Melamed-Bessudo
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel.
| | - Avraham A Levy
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel.
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5
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Bergman S, Tuller T. Strong association between genomic 3D structure and CRISPR cleavage efficiency. PLoS Comput Biol 2024; 20:e1012214. [PMID: 38848440 PMCID: PMC11189236 DOI: 10.1371/journal.pcbi.1012214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/20/2024] [Accepted: 05/30/2024] [Indexed: 06/09/2024] Open
Abstract
CRISPR is a gene editing technology which enables precise in-vivo genome editing; but its potential is hampered by its relatively low specificity and sensitivity. Improving CRISPR's on-target and off-target effects requires a better understanding of its mechanism and determinants. Here we demonstrate, for the first time, the chromosomal 3D spatial structure's association with CRISPR's cleavage efficiency, and its predictive capabilities. We used high-resolution Hi-C data to estimate the 3D distance between different regions in the human genome and utilized these spatial properties to generate 3D-based features, characterizing each region's density. We evaluated these features based on empirical, in-vivo CRISPR efficiency data and compared them to 425 features used in state-of-the-art models. The 3D features ranked in the top 13% of the features, and significantly improved the predictive power of LASSO and xgboost models trained with these features. The features indicated that sites with lower spatial density demonstrated higher efficiency. Understanding how CRISPR is affected by the 3D DNA structure provides insight into CRISPR's mechanism in general and improves our ability to correctly predict CRISPR's cleavage as well as design sgRNAs for therapeutic and scientific use.
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Affiliation(s)
- Shaked Bergman
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- The Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, Israel
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6
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Vora DS, Bhandari SM, Sundar D. DNA shape features improve prediction of CRISPR/Cas9 activity. Methods 2024; 226:120-126. [PMID: 38641083 DOI: 10.1016/j.ymeth.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/27/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors. This study evaluates the improvement in the performance of similar predictors upon inclusion of epigenetic and DNA shape feature groups in the conventionally used sequence-based Cas9 target and off-target datasets. The approach involved the utilization of neural networks trained on a diverse range of parameters, allowing us to systematically assess the performance increase for the meticulously designed datasets- (i) sequence only, (ii) sequence and epigenetic features, and (iii) sequence, epigenetic and DNA shape feature datasets. The addition of DNA shape information significantly improved predictive performance, evaluated by Akaike and Bayesian information criteria. The evaluation of individual feature importance by permutation and LIME-based methods also indicates that not only sequence features like mismatches and nucleotide composition, but also base pairing parameters like opening and stretch, that are indicative of distortion in the DNA-RNA hybrid in the presence of mismatches, influence model outcomes.
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Affiliation(s)
- Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
| | - Sakshi Manoj Bhandari
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India; School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
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7
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Dimitrievska M, Bansal D, Vitale M, Strouboulis J, Miccio A, Nicolaides KH, El Hoss S, Shangaris P, Jacków-Malinowska J. Revolutionising healing: Gene Editing's breakthrough against sickle cell disease. Blood Rev 2024; 65:101185. [PMID: 38493007 DOI: 10.1016/j.blre.2024.101185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024]
Abstract
Recent advancements in gene editing illuminate new potential therapeutic approaches for Sickle Cell Disease (SCD), a debilitating monogenic disorder caused by a point mutation in the β-globin gene. Despite the availability of several FDA-approved medications for symptomatic relief, allogeneic hematopoietic stem cell transplantation (HSCT) remains the sole curative option, underscoring a persistent need for novel treatments. This review delves into the growing field of gene editing, particularly the extensive research focused on curing haemoglobinopathies like SCD. We examine the use of techniques such as CRISPR-Cas9 and homology-directed repair, base editing, and prime editing to either correct the pathogenic variant into a non-pathogenic or wild-type one or augment fetal haemoglobin (HbF) production. The article elucidates ways to optimize these tools for efficacious gene editing with minimal off-target effects and offers insights into their effective delivery into cells. Furthermore, we explore clinical trials involving alternative SCD treatment strategies, such as LentiGlobin therapy and autologous HSCT, distilling the current findings. This review consolidates vital information for the clinical translation of gene editing for SCD, providing strategic insights for investigators eager to further the development of gene editing for SCD.
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Affiliation(s)
- Marija Dimitrievska
- St John's Institute of Dermatology, King's College London, London SE1 9RT, UK
| | - Dravie Bansal
- St John's Institute of Dermatology, King's College London, London SE1 9RT, UK
| | - Marta Vitale
- St John's Institute of Dermatology, King's College London, London SE1 9RT, UK
| | - John Strouboulis
- Red Cell Hematology Lab, Comprehensive Cancer Center, School of Cancer & Pharmaceutical Sciences, King's College London, United Kingdom
| | - Annarita Miccio
- Laboratory of Chromatin and Gene Regulation During Development, Imagine Institute, INSERM UMR1163, Paris 75015, France
| | - Kypros H Nicolaides
- Women and Children's Health, School of Life Course & Population Sciences, Kings College London, London, United Kingdom; Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom
| | - Sara El Hoss
- Red Cell Hematology Lab, Comprehensive Cancer Center, School of Cancer & Pharmaceutical Sciences, King's College London, United Kingdom.
| | - Panicos Shangaris
- Women and Children's Health, School of Life Course & Population Sciences, Kings College London, London, United Kingdom; Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom; Peter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
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8
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Danaeifar M, Najafi A. Artificial Intelligence and Computational Biology in Gene Therapy: A Review. Biochem Genet 2024:10.1007/s10528-024-10799-1. [PMID: 38635012 DOI: 10.1007/s10528-024-10799-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.
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Affiliation(s)
- Mohsen Danaeifar
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran.
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9
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Luo Y, Chen Y, Xie H, Zhu W, Zhang G. Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT. Comput Biol Med 2024; 169:107932. [PMID: 38199209 DOI: 10.1016/j.compbiomed.2024.107932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024]
Abstract
Off-target effects of CRISPR/Cas9 can lead to suboptimal genome editing outcomes. Numerous deep learning-based approaches have achieved excellent performance for off-target prediction; however, few can predict the off-target activities with both mismatches and indels between single guide RNA (sgRNA) and target DNA sequence pair. In addition, data imbalance is a common pitfall for off-target prediction. Moreover, due to the complexity of genomic contexts, generating an interpretable model also remains challenged. To address these issues, firstly we developed a BERT-based model called CRISPR-BERT for enhancing the prediction of off-target activities with both mismatches and indels. Secondly, we proposed an adaptive batch-wise class balancing strategy to combat the noise exists in imbalanced off-target data. Finally, we applied a visualization approach for investigating the generalizable nucleotide position-dependent patterns of sgRNA-DNA pair for off-target activity. In our comprehensive comparison to existing methods on five mismatches-only datasets and two mismatches-and-indels datasets, CRISPR-BERT achieved the best performance in terms of AUROC and PRAUC. Besides, the visualization analysis demonstrated how implicit knowledge learned by CRISPR-BERT facilitates off-target prediction, which shows potential in model interpretability. Collectively, CRISPR-BERT provides an accurate and interpretable framework for off-target prediction, further contributes to sgRNA optimization in practical use for improved target specificity in CRISPR/Cas9 genome editing. The source code is available at https://github.com/BrokenStringx/CRISPR-BERT.
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Affiliation(s)
- Ye Luo
- College of Engineering, Shantou University, Shantou, 515063, China
| | - Yaowen Chen
- College of Engineering, Shantou University, Shantou, 515063, China
| | - HuanZeng Xie
- College of Engineering, Shantou University, Shantou, 515063, China
| | - Wentao Zhu
- College of Engineering, Shantou University, Shantou, 515063, China
| | - Guishan Zhang
- College of Engineering, Shantou University, Shantou, 515063, China.
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10
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Guo Y, Xue Z, Gong M, Jin S, Wu X, Liu W. CRISPR-TE: a web-based tool to generate single guide RNAs targeting transposable elements. Mob DNA 2024; 15:3. [PMID: 38303094 PMCID: PMC10832116 DOI: 10.1186/s13100-024-00313-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/13/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND The CRISPR/Cas systems have emerged as powerful tools in genome engineering. Recent studies highlighting the crucial role of transposable elements (TEs) have stimulated research interest in manipulating these elements to understand their functions. However, designing single guide RNAs (sgRNAs) that are specific and efficient for TE manipulation is a significant challenge, given their sequence repetitiveness and high copy numbers. While various sgRNA design tools have been developed for gene editing, an optimized sgRNA designer for TE manipulation has yet to be established. RESULTS We present CRISPR-TE, a web-based application featuring an accessible graphical user interface, available at https://www.crisprte.cn/ , and currently tailored to the human and mouse genomes. CRISPR-TE identifies all potential sgRNAs for TEs and provides a comprehensive solution for efficient TE targeting at both the single copy and subfamily levels. Our analysis shows that sgRNAs targeting TEs can more effectively target evolutionarily young TEs with conserved sequences at the subfamily level. CONCLUSIONS CRISPR-TE offers a versatile framework for designing sgRNAs for TE targeting. CRISPR-TE is publicly accessible at https://www.crisprte.cn/ as an online web service and the source code of CRISPR-TE is available at https://github.com/WanluLiuLab/CRISPRTE/ .
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Affiliation(s)
- Yixin Guo
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China
| | - Ziwei Xue
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China
| | - Meiting Gong
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China
| | - Siqian Jin
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China
| | - Xindi Wu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China
| | - Wanlu Liu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Zhejiang, Hangzhou, 310003, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China.
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11
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Toufikuzzaman M, Hassan Samee MA, Sohel Rahman M. CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction. Brief Bioinform 2024; 25:bbad530. [PMID: 38388680 PMCID: PMC10883906 DOI: 10.1093/bib/bbad530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/24/2024] Open
Abstract
CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.
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Affiliation(s)
- Md Toufikuzzaman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - M Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
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12
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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] [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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13
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Motoche-Monar C, Ordoñez JE, Chang O, Gonzales-Zubiate FA. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules 2023; 13:1698. [PMID: 38136570 PMCID: PMC10741458 DOI: 10.3390/biom13121698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 12/24/2023] Open
Abstract
Over the past decade, genetic engineering has witnessed a revolution with the emergence of a relatively new genetic editing tool based on RNA-guided nucleases: the CRISPR/Cas9 system. Since the first report in 1987 and characterization in 2007 as a bacterial defense mechanism, this system has garnered immense interest and research attention. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system capable of modifying the genomes of a wide range of organisms. The refinement of these modifications encompasses diverse approaches, including the development of more accurate nucleases, understanding of the cellular context and epigenetic conditions, and the re-designing guide RNAs (gRNAs). Considering the critical importance of the correct performance of CRISPR/Cas9 systems, our scope will emphasize the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting the evolution of their computational architecture and gRNA characteristics over the years. Our study explains computational approaches that use machine learning techniques, neural networks, and gRNA/target interactions data to enable predictions and classifications. This review could open the door to a dynamic community that uses up-to-date algorithms to optimize and create promising gRNAs, suitable for modern CRISPR/Cas9 engineering.
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Affiliation(s)
- Cristofer Motoche-Monar
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
| | - Julián E. Ordoñez
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
| | - Oscar Chang
- Departamento de Electrónica, Universidad Simon Bolivar, Caracas 1080, Venezuela
- MIND Research Group, Model Intelligent Networks Development, Urcuquí 100119, Ecuador
| | - Fernando A. Gonzales-Zubiate
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
- MIND Research Group, Model Intelligent Networks Development, Urcuquí 100119, Ecuador
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14
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Chen Q, Chuai G, Zhang H, Tang J, Duan L, Guan H, Li W, Li W, Wen J, Zuo E, Zhang Q, Liu Q. Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints. Nat Commun 2023; 14:7521. [PMID: 37980345 PMCID: PMC10657421 DOI: 10.1038/s41467-023-42695-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/19/2023] [Indexed: 11/20/2023] Open
Abstract
The powerful CRISPR genome editing system is hindered by its off-target effects, and existing computational tools achieved limited performance in genome-wide off-target prediction due to the lack of deep understanding of the CRISPR molecular mechanism. In this study, we propose to incorporate molecular dynamics (MD) simulations in the computational analysis of CRISPR system, and present CRISOT, an integrated tool suite containing four related modules, i.e., CRISOT-FP, CRISOT-Score, CRISOT-Spec, CRISORT-Opti for RNA-DNA molecular interaction fingerprint generation, genome-wide CRISPR off-target prediction, sgRNA specificity evaluation and sgRNA optimization of Cas9 system respectively. Our comprehensive computational and experimental tests reveal that CRISOT outperforms existing tools with extensive in silico validations and proof-of-concept experimental validations. In addition, CRISOT shows potential in accurately predicting off-target effects of the base editors and prime editors, indicating that the derived RNA-DNA molecular interaction fingerprint captures the underlying mechanisms of RNA-DNA interaction among distinct CRISPR systems. Collectively, CRISOT provides an efficient and generalizable framework for genome-wide CRISPR off-target prediction, evaluation and sgRNA optimization for improved targeting specificity in CRISPR genome editing.
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Affiliation(s)
- Qinchang Chen
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Guohui Chuai
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Haihang Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jin Tang
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Liwen Duan
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Huan Guan
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Wenhui Li
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Wannian Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jiaying Wen
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Erwei Zuo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Qing Zhang
- Roche R&D Center (China) Ltd., China Innovation Center of Roche, Shanghai, 201203, China.
- Ailomics Therapeutics, Shanghai, 201203, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
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15
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Joshi A, Yang SY, Song HG, Min J, Lee JH. Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress. BIOLOGY 2023; 12:1400. [PMID: 37997999 PMCID: PMC10669554 DOI: 10.3390/biology12111400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/01/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023]
Abstract
Abiotic stresses extensively reduce agricultural crop production globally. Traditional breeding technology has been the fundamental approach used to cope with abiotic stresses. The development of gene editing technology for modifying genes responsible for the stresses and the related genetic networks has established the foundation for sustainable agriculture against environmental stress. Integrated approaches based on functional genomics and transcriptomics are now expanding the opportunities to elucidate the molecular mechanisms underlying abiotic stress responses. This review summarizes some of the features and weblinks of plant genome databases related to abiotic stress genes utilized for improving crops. The gene-editing tool based on clustered, regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has revolutionized stress tolerance research due to its simplicity, versatility, adaptability, flexibility, and broader applications. However, off-target and low cleavage efficiency hinder the successful application of CRISPR/Cas systems. Computational tools have been developed for designing highly competent gRNA with better cleavage efficiency. This powerful genome editing tool offers tremendous crop improvement opportunities, overcoming conventional breeding techniques' shortcomings. Furthermore, we also discuss the mechanistic insights of the CRISPR/Cas9-based genome editing technology. This review focused on the current advances in understanding plant species' abiotic stress response mechanism and applying the CRISPR/Cas system genome editing technology to develop crop resilience against drought, salinity, temperature, heavy metals, and herbicides.
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Affiliation(s)
- Alpana Joshi
- Department of Bioenvironmental Chemistry, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea;
- Department of Agriculture Technology & Agri-Informatics, Shobhit Institute of Engineering & Technology, Meerut 250110, India
| | - Seo-Yeon Yang
- Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea; (S.-Y.Y.); (H.-G.S.)
| | - Hyung-Geun Song
- Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea; (S.-Y.Y.); (H.-G.S.)
| | - Jiho Min
- School of Chemical Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
| | - Ji-Hoon Lee
- Department of Bioenvironmental Chemistry, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea;
- Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea; (S.-Y.Y.); (H.-G.S.)
- Institute of Agricultural Science & Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea
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16
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Noshay J, Walker T, Alexander W, Klingeman D, Romero J, Walker A, Prates E, Eckert C, Irle S, Kainer D, Jacobson D. Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering. Nucleic Acids Res 2023; 51:10147-10161. [PMID: 37738140 PMCID: PMC10602897 DOI: 10.1093/nar/gkad736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/07/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023] Open
Abstract
CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been developed for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and new public resource, produced with quantum chemical tensors, for interpreting and predicting sgRNA efficiency. Feature engineering for sgRNA efficiency is performed using an explainable-artificial intelligence model: iterative Random Forest (iRF). By encoding quantitative attributes of position-specific sequences for Escherichia coli sgRNAs, we identify important traits for sgRNA design in bacterial species. Additionally, we show that expanding positional encoding to quantum descriptors of base-pair, dimer, trimer, and tetramer sequences captures intricate interactions in local and neighboring nucleotides of the target DNA. These features highlight variation in CRISPR-Cas9 sgRNA dynamics between E. coli and H. sapiens genomes. These novel encodings of sgRNAs enhance our understanding of the elaborate quantum biological processes involved in CRISPR-Cas9 machinery.
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Affiliation(s)
- Jaclyn M Noshay
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Tyler Walker
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - William G Alexander
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dawn M Klingeman
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jonathon Romero
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - Angelica M Walker
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - Erica Prates
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Carrie Eckert
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Stephan Irle
- Computational Sciences and Engineering, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - David Kainer
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Daniel A Jacobson
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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17
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Newton MD, Losito M, Smith QM, Parnandi N, Taylor BJ, Akcakaya P, Maresca M, van Eijk P, Reed SH, Boulton SJ, King GA, Cuomo ME, Rueda DS. Negative DNA supercoiling induces genome-wide Cas9 off-target activity. Mol Cell 2023; 83:3533-3545.e5. [PMID: 37802026 DOI: 10.1016/j.molcel.2023.09.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 05/30/2023] [Accepted: 09/07/2023] [Indexed: 10/08/2023]
Abstract
CRISPR-Cas9 is a powerful gene-editing technology; however, off-target activity remains an important consideration for therapeutic applications. We have previously shown that force-stretching DNA induces off-target activity and hypothesized that distortions of the DNA topology in vivo, such as negative DNA supercoiling, could reduce Cas9 specificity. Using single-molecule optical-tweezers, we demonstrate that negative supercoiling λ-DNA induces sequence-specific Cas9 off-target binding at multiple sites, even at low forces. Using an adapted CIRCLE-seq approach, we detect over 10,000 negative-supercoiling-induced Cas9 off-target double-strand breaks genome-wide caused by increased mismatch tolerance. We further demonstrate in vivo that directed local DNA distortion increases off-target activity in cells and that induced off-target events can be detected during Cas9 genome editing. These data demonstrate that Cas9 off-target activity is regulated by DNA topology in vitro and in vivo, suggesting that cellular processes, such as transcription and replication, could induce off-target activity at previously overlooked sites.
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Affiliation(s)
- Matthew D Newton
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0HS, UK; Single Molecule Imaging, MRC-London Institute of Medical Sciences, Du Cane Road, London W12 0HS, UK; DSB Repair Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Marialucrezia Losito
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0HS, UK; Single Molecule Imaging, MRC-London Institute of Medical Sciences, Du Cane Road, London W12 0HS, UK; Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Quentin M Smith
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0HS, UK; Single Molecule Imaging, MRC-London Institute of Medical Sciences, Du Cane Road, London W12 0HS, UK
| | - Nishita Parnandi
- DSB Repair Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Benjamin J Taylor
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Pinar Akcakaya
- Genome Engineering, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden
| | - Marcello Maresca
- Genome Engineering, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden
| | - Patrick van Eijk
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff CF14 4AW, UK
| | - Simon H Reed
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff CF14 4AW, UK
| | - Simon J Boulton
- DSB Repair Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Graeme A King
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK.
| | | | - David S Rueda
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0HS, UK; Single Molecule Imaging, MRC-London Institute of Medical Sciences, Du Cane Road, London W12 0HS, UK.
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18
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Liu Y, Fan R, Yi J, Cui Q, Cui C. A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency. Comput Biol Med 2023; 165:107476. [PMID: 37696181 DOI: 10.1016/j.compbiomed.2023.107476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
CRISPR/Cas9 system is a powerful tool for genome editing. Numerous studies have shown that sgRNAs can strongly affect the efficiency of editing. However, it is still not clear what rules should be followed for designing sgRNA with high cleavage efficiency. At present, several machine learning or deep learning methods have been developed to predict the cleavage efficiency of sgRNAs, however, the prediction accuracy of these tools is still not satisfactory. Here we propose a fusion framework of deep learning and machine learning, which first deals with the primary sequence and secondary structure features of the sgRNAs using both convolutional neural network (CNN) and recurrent neural network (RNN), and then uses the features extracted by the deep neural network to train a conventional machine learning model with LGBM. As a result, the new approach overwhelmed previous methods. The Spearman's correlation coefficient between predicted and measured sgRNA cleavage efficiency of our model (0.917) is improved by over 5% compared with the most advanced method (0.865), and the mean square error reduces from 7.89 × 10-3 to 4.75 × 10-3. Finally, we developed an online tool, CRISep (http://www.cuilab.cn/CRISep), to evaluate the availability of sgRNAs based on our models.
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Affiliation(s)
- Yu Liu
- Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Rui Fan
- Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Jingkun Yi
- Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Qinghua Cui
- Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.
| | - Chunmei Cui
- Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.
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19
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Zhang G, Luo Y, Dai X, Dai Z. Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities. Brief Bioinform 2023; 24:bbad333. [PMID: 37775147 DOI: 10.1093/bib/bbad333] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 10/01/2023] Open
Abstract
In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient on-target activity and reduced off-target mutations. In the last 5 years, an increasing number of deep learning-based methods have achieved breakthrough performance in predicting sgRNA on- and off-target activities. Nevertheless, it is worthwhile to systematically evaluate these methods for their predictive abilities. In this review, we conducted a systematic survey on the progress in prediction of on- and off-target editing. We investigated the performances of 10 mainstream deep learning-based on-target predictors using nine public datasets with different sample sizes. We found that in most scenarios, these methods showed superior predictive power on large- and medium-scale datasets than on small-scale datasets. In addition, we performed unbiased experiments to provide in-depth comparison of eight representative approaches for off-target prediction on 12 publicly available datasets with various imbalanced ratios of positive/negative samples. Most methods showed excellent performance on balanced datasets but have much room for improvement on moderate- and severe-imbalanced datasets. This study provides comprehensive perspectives on CRISPR/Cas9 sgRNA on- and off-target activity prediction and improvement for method development.
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Affiliation(s)
- Guishan Zhang
- College of Engineering, Shantou University, Shantou 515063, China
| | - Ye Luo
- College of Engineering, Shantou University, Shantou 515063, China
| | - Xianhua Dai
- School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University, Guangzhou 510006, China
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20
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Pagis A, Alfi O, Kinreich S, Yilmaz A, Hamdan M, Gadban A, Panet A, Wolf DG, Benvenisty N. Genome-wide loss-of-function screen using human pluripotent stem cells to study virus-host interactions for SARS-CoV-2. Stem Cell Reports 2023; 18:1766-1774. [PMID: 37703821 PMCID: PMC10545482 DOI: 10.1016/j.stemcr.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 09/15/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019, has become a global health concern. Therefore, there is an immense need to understand the network of virus-host interactions by using human disease-relevant cells. We have thus conducted a loss-of-function genome-wide screen using haploid human embryonic stem cells (hESCs) to identify genes involved in SARS-CoV-2 infection. Although the undifferentiated hESCs are resistant to SARS-CoV-2, their differentiated definitive endoderm (DE) progenies, which express high levels of ACE2, are highly sensitive to the virus. Our genetic screening was able to identify the well-established entry receptor ACE2 as a host factor, along with additional potential novel modulators of SARS-CoV-2. Two such novel screen hits, the transcription factor MAFG and the transmembrane protein TMEM86A, were further validated as conferring resistance against SARS-CoV-2 by using CRISPR-mediated mutagenesis in hESCs, followed by differentiation of mutant lines into DE cells and infection by SARS-CoV-2. Our genome-wide genetic screening investigated SARS-CoV-2 host factors in non-cancerous human cells with endogenous ACE2 expression, providing a unique platform to identify novel modulators of SARS-CoV-2 cytopathology in human cells.
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Affiliation(s)
- Ariel Pagis
- The Azrieli Center for Stem Cells and Genetic Research, Department of Genetics, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Or Alfi
- Clinical Virology Unit, Hadassah Hebrew University Medical Center, Jerusalem 91120, Israel; Lautenberg Center for General and Tumor Immunology, The Hebrew University, Jerusalem 91121, Israel
| | - Shay Kinreich
- The Azrieli Center for Stem Cells and Genetic Research, Department of Genetics, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Atilgan Yilmaz
- The Azrieli Center for Stem Cells and Genetic Research, Department of Genetics, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Leuven Stem Cell Institute, Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
| | - Marah Hamdan
- Clinical Virology Unit, Hadassah Hebrew University Medical Center, Jerusalem 91120, Israel
| | - Aseel Gadban
- The Azrieli Center for Stem Cells and Genetic Research, Department of Genetics, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Amos Panet
- Department of Biochemistry, Faculty of Medicine, The Hebrew University, Jerusalem 91121, Israel
| | - Dana G Wolf
- Clinical Virology Unit, Hadassah Hebrew University Medical Center, Jerusalem 91120, Israel; Lautenberg Center for General and Tumor Immunology, The Hebrew University, Jerusalem 91121, Israel.
| | - Nissim Benvenisty
- The Azrieli Center for Stem Cells and Genetic Research, Department of Genetics, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
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21
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Tsai HH, Kao HJ, Kuo MW, Lin CH, Chang CM, Chen YY, Chen HH, Kwok PY, Yu AL, Yu J. Whole genomic analysis reveals atypical non-homologous off-target large structural variants induced by CRISPR-Cas9-mediated genome editing. Nat Commun 2023; 14:5183. [PMID: 37626063 PMCID: PMC10457329 DOI: 10.1038/s41467-023-40901-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
CRISPR-Cas9 genome editing has promising therapeutic potential for genetic diseases and cancers, but safety could be a concern. Here we use whole genomic analysis by 10x linked-read sequencing and optical genome mapping to interrogate the genome integrity after editing and in comparison to four parental cell lines. In addition to the previously reported large structural variants at on-target sites, we identify heretofore unexpected large chromosomal deletions (91.2 and 136 Kb) at atypical non-homologous off-target sites without sequence similarity to the sgRNA in two edited lines. The observed large structural variants induced by CRISPR-Cas9 editing in dividing cells may result in pathogenic consequences and thus limit the usefulness of the CRISPR-Cas9 editing system for disease modeling and gene therapy. In this work, our whole genomic analysis may provide a valuable strategy to ensure genome integrity after genomic editing to minimize the risk of unintended effects in research and clinical applications.
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Affiliation(s)
- Hsiu-Hui Tsai
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Hsiao-Jung Kao
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ming-Wei Kuo
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital and School of Medicine, Taipei, Taiwan
| | - Chun-Min Chang
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Yin Chen
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Hsiao-Huei Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Pui-Yan Kwok
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Cardiovascular Research Institute, Institute for Human Genetics, and Department of Dermatology, University of California, San Francisco, USA
| | - Alice L Yu
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Pediatrics, University of California, San Diego, USA
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - John Yu
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.
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22
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Hussen BM, Rasul MF, Abdullah SR, Hidayat HJ, Faraj GSH, Ali FA, Salihi A, Baniahmad A, Ghafouri-Fard S, Rahman M, Glassy MC, Branicki W, Taheri M. Targeting miRNA by CRISPR/Cas in cancer: advantages and challenges. Mil Med Res 2023; 10:32. [PMID: 37460924 PMCID: PMC10351202 DOI: 10.1186/s40779-023-00468-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
Clustered regulatory interspaced short palindromic repeats (CRISPR) has changed biomedical research and provided entirely new models to analyze every aspect of biomedical sciences during the last decade. In the study of cancer, the CRISPR/CRISPR-associated protein (Cas) system opens new avenues into issues that were once unknown in our knowledge of the noncoding genome, tumor heterogeneity, and precision medicines. CRISPR/Cas-based gene-editing technology now allows for the precise and permanent targeting of mutations and provides an opportunity to target small non-coding RNAs such as microRNAs (miRNAs). However, the development of effective and safe cancer gene editing therapy is highly dependent on proper design to be innocuous to normal cells and prevent introducing other abnormalities. This study aims to highlight the cutting-edge approaches in cancer-gene editing therapy based on the CRISPR/Cas technology to target miRNAs in cancer therapy. Furthermore, we highlight the potential challenges in CRISPR/Cas-mediated miRNA gene editing and offer advanced strategies to overcome them.
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Affiliation(s)
- Bashdar Mahmud Hussen
- Department of Biomedical Sciences, Cihan University-Erbil, Erbil, Kurdistan Region 44001 Iraq
- Department of Clinical Analysis, College of Pharmacy, Hawler Medical University, Erbil, Kurdistan Region 44001 Iraq
| | - Mohammed Fatih Rasul
- Department of Pharmaceutical Basic Science, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region 44001 Iraq
| | - Snur Rasool Abdullah
- Medical Laboratory Science, Lebanese French University, Erbil, Kurdistan Region 44001 Iraq
| | - Hazha Jamal Hidayat
- Department of Biology, College of Education, Salahaddin University-Erbil, Erbil, Kurdistan Region 44001 Iraq
| | - Goran Sedeeq Hama Faraj
- Department of Medical Laboratory Science, Komar University of Science and Technology, Sulaymaniyah, 46001 Iraq
| | - Fattma Abodi Ali
- Department of Medical Microbiology, College of Health Sciences, Hawler Medical University, Erbil, Kurdistan Region 44001 Iraq
| | - Abbas Salihi
- Department of Biology, College of Science, Salahaddin University-Erbil, Erbil, Kurdistan Region 44001 Iraq
- Center of Research and Strategic Studies, Lebanese French University, Erbil, 44001 Iraq
| | - Aria Baniahmad
- Institute of Human Genetics, Jena University Hospital, 07747 Jena, Germany
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 374-37515 Iran
| | - Milladur Rahman
- Department of Clinical Sciences, Malmö, Section for Surgery, Lund University, 22100 Malmö, Sweden
| | - Mark C. Glassy
- Translational Neuro-Oncology Laboratory, San Diego (UCSD) Moores Cancer Center, University of California, San Diego, CA 94720 USA
| | - Wojciech Branicki
- Faculty of Biology, Institute of Zoology and Biomedical Research, Jagiellonian University, 31-007 Kraków, Poland
| | - Mohammad Taheri
- Institute of Human Genetics, Jena University Hospital, 07747 Jena, Germany
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, 374-37515 Iran
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23
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Zhang H, Yan J, Lu Z, Zhou Y, Zhang Q, Cui T, Li Y, Chen H, Ma L. Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities. Cell Discov 2023; 9:48. [PMID: 37193681 DOI: 10.1038/s41421-023-00549-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/21/2023] [Indexed: 05/18/2023] Open
Abstract
Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the "sweet spot" in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.
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Affiliation(s)
- Heng Zhang
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
- AIdit Therapeutics, Hangzhou, Zhejiang, China
| | - Jianfeng Yan
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
- AIdit Therapeutics, Hangzhou, Zhejiang, China
| | - Zhike Lu
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Yangfan Zhou
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | | | | | - Yini Li
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Hui Chen
- AIdit Therapeutics, Hangzhou, Zhejiang, China
| | - Lijia Ma
- Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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24
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Sherkatghanad Z, Abdar M, Charlier J, Makarenkov V. Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review. Brief Bioinform 2023; 24:7130974. [PMID: 37080758 DOI: 10.1093/bib/bbad131] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 04/22/2023] Open
Abstract
CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA-DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.
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Affiliation(s)
- Zeinab Sherkatghanad
- Departement d'Informatique, Universite du Quebec a Montreal, H2X 3Y7, Montreal, QC, Canada
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, 3216, Geelong, VIC, Australia
| | - Jeremy Charlier
- Departement d'Informatique, Universite du Quebec a Montreal, H2X 3Y7, Montreal, QC, Canada
| | - Vladimir Makarenkov
- Departement d'Informatique, Universite du Quebec a Montreal, H2X 3Y7, Montreal, QC, Canada
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25
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Naeem M, Alkhnbashi OS. Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects. Int J Mol Sci 2023; 24:ijms24076261. [PMID: 37047235 PMCID: PMC10094584 DOI: 10.3390/ijms24076261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
The CRISPR-Cas system has evolved into a cutting-edge technology that has transformed the field of biological sciences through precise genetic manipulation. CRISPR/Cas9 nuclease is evolving into a revolutionizing method to edit any gene of any species with desirable outcomes. The swift advancement of CRISPR-Cas technology is reflected in an ever-expanding ecosystem of bioinformatics tools designed to make CRISPR/Cas9 experiments easier. To assist researchers with efficient guide RNA designs with fewer off-target effects, nuclease target site selection, and experimental validation, bioinformaticians have built and developed a comprehensive set of tools. In this article, we will review the various computational tools available for the assessment of off-target effects, as well as the quantification of nuclease activity and specificity, including web-based search tools and experimental methods, and we will describe how these tools can be optimized for gene knock-out (KO) and gene knock-in (KI) for model organisms. We also discuss future directions in precision genome editing and its applications, as well as challenges in target selection, particularly in predicting off-target effects.
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26
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Bhoopalan SV, Yen JS, Levine RM, Sharma A. Editing human hematopoietic stem cells: advances and challenges. Cytotherapy 2023; 25:261-269. [PMID: 36123234 DOI: 10.1016/j.jcyt.2022.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 07/29/2022] [Accepted: 08/08/2022] [Indexed: 02/07/2023]
Abstract
Genome editing of hematopoietic stem and progenitor cells is being developed for the treatment of several inherited disorders of the hematopoietic system. The adaptation of CRISPR-Cas9-based technologies to make precise changes to the genome, and developments in altering the specificity and efficiency, and improving the delivery of nucleases to target cells have led to several breakthroughs. Many clinical trials are ongoing, and several pre-clinical models have been reported that would allow these genetic therapies to one day offer a potential cure to patients with diseases where limited options currently exist. However, there remain several challenges with respect to establishing safety, expanding accessibility and improving the manufacturing processes of these therapeutic products. This review focuses on some of the recent advances in the field of genome editing of hematopoietic stem and progenitor cells and illustrates the ongoing challenges.
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Affiliation(s)
- Senthil Velan Bhoopalan
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jonathan S Yen
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Rachel M Levine
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Akshay Sharma
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
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27
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Brooks IR, Sheriff A, Moran D, Wang J, Jacków J. Challenges of Gene Editing Therapies for Genodermatoses. Int J Mol Sci 2023; 24:2298. [PMID: 36768619 PMCID: PMC9916788 DOI: 10.3390/ijms24032298] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Genodermatoses encompass a wide range of inherited skin diseases, many of which are monogenic. Genodermatoses range in severity and result in early-onset cancers or life-threatening damage to the skin, and there are few curative options. As such, there is a clinical need for single-intervention treatments with curative potential. Here, we discuss the nascent field of gene editing for the treatment of genodermatoses, exploring CRISPR-Cas9 and homology-directed repair, base editing, and prime editing tools for correcting pathogenic mutations. We specifically focus on the optimisation of editing efficiency, the minimisation off-targets edits, and the tools for delivery for potential future therapies. Honing each of these factors is essential for translating gene editing therapies into the clinical setting. Therefore, the aim of this review article is to raise important considerations for investigators aiming to develop gene editing approaches for genodermatoses.
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Affiliation(s)
| | | | | | | | - Joanna Jacków
- St John’s Institute of Dermatology, King’s College London, London SE1 9RT, UK
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28
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Alipanahi R, Safari L, Khanteymoori A. CRISPR genome editing using computational approaches: A survey. FRONTIERS IN BIOINFORMATICS 2023; 2:1001131. [PMID: 36710911 PMCID: PMC9875887 DOI: 10.3389/fbinf.2022.1001131] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/19/2022] [Indexed: 01/13/2023] Open
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one's-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.
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Affiliation(s)
| | - Leila Safari
- Department of Computer Engineering, University of Zanjan, Zanjan, Iran,*Correspondence: Leila Safari,
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29
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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30
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Srivastava R. Applications of artificial intelligence multiomics in precision oncology. J Cancer Res Clin Oncol 2023; 149:503-510. [PMID: 35796775 DOI: 10.1007/s00432-022-04161-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023]
Abstract
Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning (ML) approaches. NGS data are able to detect the abnormalities and mutations in the oncogenes. These multi-omics analyses are used for risk prediction, early diagnosis, accurate prognosis, and identification of biomarkers in cancer patients. The availability of these cancer data and their analysis may provide insights into the biology of the disease, which can be used for the personalized treatment of cancer patients. Bioinformatics tools are delivering this promise by managing, integrating, and analyzing these complex datasets. The clinical outcomes of cancer patients are improved by the use of various innovative methods implicated particularly for diagnosis and therapeutics. ML-based artificial intelligence (AI) applications are solving these issues to a great extent. AI techniques are used to update the patients on a personalized basis about their treatment procedures, progress, recovery, therapies used, dietary changes in lifestyles patterns along with the survival summary of previously recovered cancer patients. In this way, the patients are becoming more aware of their diseases and the entire clinical treatment procedures. Though the technology has its own advantages and disadvantages, we hope that the day is not so far when AI techniques will provide personalized treatment to cancer patients tailored to their needs in much quicker ways.
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Affiliation(s)
- Ruby Srivastava
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India.
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31
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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32
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Cancellieri S, Zeng J, Lin LY, Tognon M, Nguyen MA, Lin J, Bombieri N, Maitland SA, Ciuculescu MF, Katta V, Tsai SQ, Armant M, Wolfe SA, Giugno R, Bauer DE, Pinello L. Human genetic diversity alters off-target outcomes of therapeutic gene editing. Nat Genet 2023; 55:34-43. [PMID: 36522432 PMCID: PMC10272994 DOI: 10.1038/s41588-022-01257-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/01/2022] [Indexed: 12/23/2022]
Abstract
CRISPR gene editing holds great promise to modify DNA sequences in somatic cells to treat disease. However, standard computational and biochemical methods to predict off-target potential focus on reference genomes. We developed an efficient tool called CRISPRme that considers single-nucleotide polymorphism (SNP) and indel genetic variants to nominate and prioritize off-target sites. We tested the software with a BCL11A enhancer targeting guide RNA (gRNA) showing promise in clinical trials for sickle cell disease and β-thalassemia and found that the top candidate off-target is produced by an allele common in African-ancestry populations (MAF 4.5%) that introduces a protospacer adjacent motif (PAM) sequence. We validated that SpCas9 generates strictly allele-specific indels and pericentric inversions in CD34+ hematopoietic stem and progenitor cells (HSPCs), although high-fidelity Cas9 mitigates this off-target. This report illustrates how genetic variants should be considered as modifiers of gene editing outcomes. We expect that variant-aware off-target assessment will become integral to therapeutic genome editing evaluation and provide a powerful approach for comprehensive off-target nomination.
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Affiliation(s)
| | - Jing Zeng
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Linda Yingqi Lin
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Manuel Tognon
- Department of Computer Science, University of Verona, Verona, Italy
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - My Anh Nguyen
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Nicola Bombieri
- Department of Computer Science, University of Verona, Verona, Italy
| | - Stacy A Maitland
- Department of Molecular, Cell and Cancer Biology, Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Varun Katta
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Shengdar Q Tsai
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Myriam Armant
- TransLab, Boston Children's Hospital, Boston, MA, USA
| | - Scot A Wolfe
- Department of Molecular, Cell and Cancer Biology, Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Verona, Italy.
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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33
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Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions. Diagnostics (Basel) 2022; 13:diagnostics13010100. [PMID: 36611392 PMCID: PMC9818832 DOI: 10.3390/diagnostics13010100] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. MATERIALS AND METHODS We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. RESULTS Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. CONCLUSION In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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Siegner SM, Ugalde L, Clemens A, Garcia-Garcia L, Bueren JA, Rio P, Karasu ME, Corn JE. Adenine base editing efficiently restores the function of Fanconi anemia hematopoietic stem and progenitor cells. Nat Commun 2022; 13:6900. [PMID: 36371486 PMCID: PMC9653444 DOI: 10.1038/s41467-022-34479-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022] Open
Abstract
Fanconi Anemia (FA) is a debilitating genetic disorder with a wide range of severe symptoms including bone marrow failure and predisposition to cancer. CRISPR-Cas genome editing manipulates genotypes by harnessing DNA repair and has been proposed as a potential cure for FA. But FA is caused by deficiencies in DNA repair itself, preventing the use of editing strategies such as homology directed repair. Recently developed base editing (BE) systems do not rely on double stranded DNA breaks and might be used to target mutations in FA genes, but this remains to be tested. Here we develop a proof of concept therapeutic base editing strategy to address two of the most prevalent FANCA mutations in patient hematopoietic stem and progenitor cells. We find that optimizing adenine base editor construct, vector type, guide RNA format, and delivery conditions leads to very effective genetic modification in multiple FA patient backgrounds. Optimized base editing restored FANCA expression, molecular function of the FA pathway, and phenotypic resistance to crosslinking agents. ABE8e mediated editing in primary hematopoietic stem and progenitor cells from FA patients was both genotypically effective and restored FA pathway function, indicating the potential of base editing strategies for future clinical application in FA.
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Affiliation(s)
- Sebastian M. Siegner
- grid.5801.c0000 0001 2156 2780Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Laura Ugalde
- grid.5515.40000000119578126Division of Hematopoietic Innovative Therapies, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIEMAT/CIBERER) and Advanced Therapies Unit, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), Madrid, Spain
| | - Alexandra Clemens
- grid.5801.c0000 0001 2156 2780Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Laura Garcia-Garcia
- grid.5515.40000000119578126Division of Hematopoietic Innovative Therapies, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIEMAT/CIBERER) and Advanced Therapies Unit, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), Madrid, Spain
| | - Juan A. Bueren
- grid.5515.40000000119578126Division of Hematopoietic Innovative Therapies, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIEMAT/CIBERER) and Advanced Therapies Unit, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), Madrid, Spain
| | - Paula Rio
- grid.5515.40000000119578126Division of Hematopoietic Innovative Therapies, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIEMAT/CIBERER) and Advanced Therapies Unit, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), Madrid, Spain
| | - Mehmet E. Karasu
- grid.5801.c0000 0001 2156 2780Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Jacob E. Corn
- grid.5801.c0000 0001 2156 2780Department of Biology, ETH Zurich, Zurich, Switzerland
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Yaish O, Asif M, Orenstein Y. A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction. Brief Bioinform 2022; 23:bbac157. [PMID: 35595297 DOI: 10.1093/bib/bbac157] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/24/2022] [Accepted: 04/07/2022] [Indexed: 11/14/2022] Open
Abstract
CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this editing technique is quite accurate in the target region, there may be many unplanned off-target sites (OTSs). Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of OTSs) produced by experimental techniques to detect OTSs with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect OTSs, was used to produce a dataset of unprecedented scale and quality (>200 000 OTS over 110 guide RNAs). In addition, the same study included in cellula GUIDE-seq experiments for 58 of the guide RNAs. Here, we fill the gap in previous computational methods by utilizing these data to systematically evaluate data processing and formulation of the CRISPR OTSs prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive OTSs to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between guide RNAs and their OTSs as a feature. Finally, we present predictive off-target in cellula models based on both in vitro and in cellula data and compare them to state-of-the-art methods in predicting true OTSs. Our conclusions will be instrumental in any future development of an off-target predictor based on high-throughput datasets.
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Affiliation(s)
- Ofir Yaish
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Maor Asif
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Yaron Orenstein
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
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Vora DS, Verma Y, Sundar D. A Machine Learning Approach to Identify the Importance of Novel Features for CRISPR/Cas9 Activity Prediction. Biomolecules 2022; 12:1123. [PMID: 36009017 PMCID: PMC9405635 DOI: 10.3390/biom12081123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022] Open
Abstract
The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features. The binding energy estimation of the gRNA-DNA hybrid as well as the Cas9-gRNA-DNA hybrid allowed generating better performing machine learning models for the prediction of Cas9 activity. The analysis of feature contribution towards the model output on a limited dataset indicated that energy features played a determining role along with the sequence features. The binding energy features proved essential for the prediction of on-target activity and off-target sites. The plateau, in the performance on unseen datasets, of current machine learning models could be overcome by incorporating novel features, such as binding energy, among others. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA).
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Affiliation(s)
- Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Yugesh Verma
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
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Xie J, Liu M, Zhou L. CRISPR-OTE: Prediction of CRISPR On-Target Efficiency Based on Multi-Dimensional Feature Fusion. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Pan X, Qu K, Yuan H, Xiang X, Anthon C, Pashkova L, Liang X, Han P, Corsi GI, Xu F, Liu P, Zhong J, Zhou Y, Ma T, Jiang H, Liu J, Wang J, Jessen N, Bolund L, Yang H, Xu X, Church GM, Gorodkin J, Lin L, Luo Y. Massively targeted evaluation of therapeutic CRISPR off-targets in cells. Nat Commun 2022; 13:4049. [PMID: 35831290 PMCID: PMC9279339 DOI: 10.1038/s41467-022-31543-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/20/2022] [Indexed: 11/09/2022] Open
Abstract
Methods for sensitive and high-throughput evaluation of CRISPR RNA-guided nucleases (RGNs) off-targets (OTs) are essential for advancing RGN-based gene therapies. Here we report SURRO-seq for simultaneously evaluating thousands of therapeutic RGN OTs in cells. SURRO-seq captures RGN-induced indels in cells by pooled lentiviral OTs libraries and deep sequencing, an approach comparable and complementary to OTs detection by T7 endonuclease 1, GUIDE-seq, and CIRCLE-seq. Application of SURRO-seq to 8150 OTs from 110 therapeutic RGNs identifies significantly detectable indels in 783 OTs, of which 37 OTs are found in cancer genes and 23 OTs are further validated in five human cell lines by targeted amplicon sequencing. Finally, SURRO-seq reveals that thermodynamically stable wobble base pair (rG•dT) and free binding energy strongly affect RGN specificity. Our study emphasizes the necessity of thoroughly evaluating therapeutic RGN OTs to minimize inevitable off-target effects.
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Affiliation(s)
- Xiaoguang Pan
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biology, Copenhagen University, Copenhagen, Denmark
| | - Kunli Qu
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biology, Copenhagen University, Copenhagen, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Hao Yuan
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xi Xiang
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Christian Anthon
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Liubov Pashkova
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Xue Liang
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biology, Copenhagen University, Copenhagen, Denmark
| | - Peng Han
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biology, Copenhagen University, Copenhagen, Denmark
| | - Giulia I Corsi
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Fengping Xu
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Research, BGI-Shenzhen, Shenzhen, China
| | - Ping Liu
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | - Jiayan Zhong
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | - Yan Zhou
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Tao Ma
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | - Hui Jiang
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | - Junnian Liu
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
| | - Jian Wang
- BGI-Research, BGI-Shenzhen, Shenzhen, China
| | - Niels Jessen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Lars Bolund
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Huanming Yang
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- IBMC-BGI Center, the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Xun Xu
- BGI-Research, BGI-Shenzhen, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, China
| | - George M Church
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
| | - Yonglun Luo
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China.
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
- BGI-Research, BGI-Shenzhen, Shenzhen, China.
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
- IBMC-BGI Center, the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
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Pulido-Quetglas C, Johnson R. Designing libraries for pooled CRISPR functional screens of long noncoding RNAs. Mamm Genome 2022; 33:312-327. [PMID: 34533605 PMCID: PMC9114037 DOI: 10.1007/s00335-021-09918-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/09/2021] [Indexed: 02/01/2023]
Abstract
Human and other genomes encode tens of thousands of long noncoding RNAs (lncRNAs), the vast majority of which remain uncharacterised. High-throughput functional screening methods, notably those based on pooled CRISPR-Cas perturbations, promise to unlock the biological significance and biomedical potential of lncRNAs. Such screens are based on libraries of single guide RNAs (sgRNAs) whose design is critical for success. Few off-the-shelf libraries are presently available, and lncRNAs tend to have cell-type-specific expression profiles, meaning that library design remains in the hands of researchers. Here we introduce the topic of pooled CRISPR screens for lncRNAs and guide readers through the three key steps of library design: accurate annotation of transcript structures, curation of optimal candidate sets, and design of sgRNAs. This review is a starting point and reference for researchers seeking to design custom CRISPR screening libraries for lncRNAs.
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Affiliation(s)
- Carlos Pulido-Quetglas
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Graduate School of Cellular and Biomedical Sciences, University of Bern, 3012, Bern, Switzerland
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland.
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland.
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland.
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Magis W, DeWitt MA, Wyman SK, Vu JT, Heo SJ, Shao SJ, Hennig F, Romero ZG, Campo-Fernandez B, Said S, McNeill MS, Rettig GR, Sun Y, Wang Y, Behlke MA, Kohn DB, Boffelli D, Walters MC, Corn JE, Martin DI. High-level correction of the sickle mutation is amplified in vivo during erythroid differentiation. iScience 2022; 25:104374. [PMID: 35633935 PMCID: PMC9130532 DOI: 10.1016/j.isci.2022.104374] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 12/21/2022] Open
Abstract
Background A point mutation in sickle cell disease (SCD) alters one amino acid in the β-globin subunit of hemoglobin, with resultant anemia and multiorgan damage that typically shortens lifespan by decades. Because SCD is caused by a single mutation, and hematopoietic stem cells (HSCs) can be harvested, manipulated, and returned to an individual, it is an attractive target for gene correction. Results An optimized Cas9 ribonucleoprotein (RNP) with an ssDNA oligonucleotide donor together generated correction of at least one β-globin allele in more than 30% of long-term engrafting human HSCs. After adopting a high-fidelity Cas9 variant, efficient correction with minimal off-target events also was observed. In vivo erythroid differentiation markedly enriches for corrected β-globin alleles, indicating that erythroblasts carrying one or more corrected alleles have a survival advantage. Significance These findings indicate that the sickle mutation can be corrected in autologous HSCs with an optimized protocol suitable for clinical translation. The gene editing protocol corrects the sickle mutation in ∼30% of engrafting cells Random assortment of engrafting stem cell clones without clonal dominance was shown Corrected erythroid cells are preferentially enriched compared with unedited cells
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Computational tools and resources for CRISPR/Cas genome editing. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00027-4. [PMID: 35341983 PMCID: PMC10372911 DOI: 10.1016/j.gpb.2022.02.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 12/21/2022]
Abstract
The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genomic editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduced the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focused on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights on the limitations of available computational tools that surely help researchers of this field for further optimization. Lastly, we suggested a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.
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Cardinali B, Provenzano C, Izzo M, Voellenkle C, Battistini J, Strimpakos G, Golini E, Mandillo S, Scavizzi F, Raspa M, Perfetti A, Baci D, Lazarevic D, Garcia-Manteiga JM, Gourdon G, Martelli F, Falcone G. Time-controlled and muscle-specific CRISPR/Cas9-mediated deletion of CTG-repeat expansion in the DMPK gene. MOLECULAR THERAPY. NUCLEIC ACIDS 2022; 27:184-199. [PMID: 34976437 PMCID: PMC8693309 DOI: 10.1016/j.omtn.2021.11.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/28/2021] [Indexed: 12/14/2022]
Abstract
CRISPR/Cas9-mediated therapeutic gene editing is a promising technology for durable treatment of incurable monogenic diseases such as myotonic dystrophies. Gene-editing approaches have been recently applied to in vitro and in vivo models of myotonic dystrophy type 1 (DM1) to delete the pathogenic CTG-repeat expansion located in the 3′ untranslated region of the DMPK gene. In DM1-patient-derived cells removal of the expanded repeats induced beneficial effects on major hallmarks of the disease with reduction in DMPK transcript-containing ribonuclear foci and reversal of aberrant splicing patterns. Here, we set out to excise the triplet expansion in a time-restricted and cell-specific fashion to minimize the potential occurrence of unintended events in off-target genomic loci and select for the target cell type. To this aim, we employed either a ubiquitous promoter-driven or a muscle-specific promoter-driven Cas9 nuclease and tetracycline repressor-based guide RNAs. A dual-vector approach was used to deliver the CRISPR/Cas9 components into DM1 patient-derived cells and in skeletal muscle of a DM1 mouse model. In this way, we obtained efficient and inducible gene editing both in proliferating cells and differentiated post-mitotic myocytes in vitro as well as in skeletal muscle tissue in vivo.
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Affiliation(s)
- Beatrice Cardinali
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Claudia Provenzano
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Mariapaola Izzo
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Christine Voellenkle
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Jonathan Battistini
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Georgios Strimpakos
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Elisabetta Golini
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Silvia Mandillo
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Ferdinando Scavizzi
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Marcello Raspa
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
| | - Alessandra Perfetti
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Denisa Baci
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Dejan Lazarevic
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Geneviève Gourdon
- Sorbonne Université, Inserm, Institut de Myologie, Centre de Recherche en Myologie, 75013 Paris, France
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Germana Falcone
- Institute of Biochemistry and Cell Biology, National Research Council, Monterotondo, 00015 Rome, Italy
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Krohannon A, Srivastava M, Rauch S, Srivastava R, Dickinson BC, Janga SC. CASowary: CRISPR-Cas13 guide RNA predictor for transcript depletion. BMC Genomics 2022; 23:172. [PMID: 35236300 PMCID: PMC8889671 DOI: 10.1186/s12864-022-08366-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/03/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Recent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which may impact the effectiveness of transcript depletion of target sequences. However, our understanding of the features and corresponding methods which can predict whether a specific sgRNA will effectively knockdown a transcript is very limited. RESULTS Here we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system - CIRTS, confirming the robustness and reproducibility of our model's sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in HeLa cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner. CONCLUSIONS Application of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.
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Affiliation(s)
- Alexander Krohannon
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), 535 West Michigan St, Indianapolis, IN, 46202, USA
| | - Mansi Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), 535 West Michigan St, Indianapolis, IN, 46202, USA
| | - Simone Rauch
- Department of Chemistry, The University of Chicago, Chicago, IL, USA
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois, 60637, USA
| | - Rajneesh Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), 535 West Michigan St, Indianapolis, IN, 46202, USA
| | - Bryan C Dickinson
- Department of Chemistry, The University of Chicago, Chicago, IL, USA
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), 535 West Michigan St, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translation Sciences (HITS), 410 West 10th Street, Indianapolis, IN, 46202, USA.
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, IN, 46202, USA.
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Roy RK, Debashree I, Srivastava S, Rishi N, Srivastava A. CRISPR/ Cas9 Off-targets: Computational Analysis of Causes, Prediction,
Detection, and Overcoming Strategies. Curr Bioinform 2022. [DOI: 10.2174/1574893616666210708150439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
:
CRISPR/Cas9 technology is a highly flexible RNA-guided endonuclease (RGEN)
based gene-editing tool that has transformed the field of genomics, gene therapy, and genome/
epigenome imaging. Its wide range of applications provides immense scope for understanding
as well as manipulating genetic/epigenetic elements. However, the RGEN is prone to
off-target mutagenesis that leads to deleterious effects. This review details the molecular and cellular
mechanisms underlying the off-target activity, various available detection tools and prediction
methodology ranging from sequencing to machine learning approaches, and the strategies to
overcome/minimise off-targets. A coherent and concise method increasing target precision would
prove indispensable to concrete manipulation and interpretation of genome editing results that
can revolutionise therapeutics, including clarity in genome regulatory mechanisms during development.
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Affiliation(s)
- Roshan Kumar Roy
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Noida 201313, India
| | - Ipsita Debashree
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Noida 201313, India
| | - Sonal Srivastava
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Noida 201313,India
| | - Narayan Rishi
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Noida 201313,India
| | - Ashish Srivastava
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Noida 201313,India
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Fu R, He W, Dou J, Villarreal OD, Bedford E, Wang H, Hou C, Zhang L, Wang Y, Ma D, Chen Y, Gao X, Depken M, Xu H. Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity. Nat Commun 2022; 13:474. [PMID: 35078987 PMCID: PMC8789861 DOI: 10.1038/s41467-022-28028-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/04/2022] [Indexed: 12/20/2022] Open
Abstract
The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. To systematically explore the sequence determinants governing CRISPR/Cas9 specificity, here we describe a dual-target system to measure the relative cleavage rate between off- and on-target sequences (off-on ratios) of 1902 gRNAs on 13,314 synthetic target sequences, and reveal a set of sequence rules involving 2 factors in off-targeting: 1) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context; 2) an "epistasis-like" combinatorial effect of multiple mismatches, which are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. These sequence rules lead to the development of MOFF, a model-based predictor of Cas9-mediated off-target effects. Moreover, the "epistasis-like" combinatorial effect suggests a strategy of allele-specific genome editing using mismatched guides. With the aid of MOFF prediction, this strategy significantly improves the selectivity and expands the application domain of Cas9-based allele-specific editing, as tested in a high-throughput allele-editing screen on 18 cancer hotspot mutations.
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Affiliation(s)
- Rongjie Fu
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Wei He
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Jinzhuang Dou
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Oscar D Villarreal
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Ella Bedford
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Helen Wang
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Connie Hou
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Liang Zhang
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Yalong Wang
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA
| | - Dacheng Ma
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Yiwen Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Xue Gao
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
- Department of Chemistry, Rice University, Houston, TX, 77005, USA
- Department of Bioengineering, Rice University, Houston, TX, 77005, USA
| | - Martin Depken
- Kavli Institute of NanoScience and Department of BionanoScience, Delft University of Technology, Delft, 2629HZ, the Netherlands
| | - Han Xu
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, 78957, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- The Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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47
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A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage. Comput Struct Biotechnol J 2022; 20:5813-5823. [PMID: 36382194 PMCID: PMC9630617 DOI: 10.1016/j.csbj.2022.10.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/21/2022] [Accepted: 10/08/2022] [Indexed: 11/30/2022] Open
Abstract
CRISPR/Cas9 technology has greatly accelerated genome engineering research. The CRISPR/Cas9 complex, a bacterial immune response system, is widely adopted for RNA-driven targeted genome editing. The systematic mapping study presented in this paper examines the literature on machine learning (ML) techniques employed in the prediction of CRISPR/Cas9 sgRNA on/off-target cleavage, focusing on improving support in sgRNA design activities and identifying areas currently being researched. This area of research has greatly expanded recently, and we found it appropriate to work on a Systematic Mapping Study (SMS), an investigation that has proven to be an effective secondary study method. Unlike a classic review, in an SMS, no comparison of methods or results is made, while this task can instead be the subject of a systematic literature review that chooses one theme among those highlighted in this SMS. The study is illustrated in this paper. To the best of the authors' knowledge, no other SMS studies have been published on this topic. Fifty-seven papers published in the period 2017–2022 (April, 30) were analyzed. This study reveals that the most widely used ML model is the convolutional neural network (CNN), followed by the feedforward neural network (FNN), while the use of other models is marginal. Other interesting information has emerged, such as the wide availability of both open code and platforms dedicated to supporting the activity of researchers or the fact that there is a clear prevalence of public funds that finance research on this topic.
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48
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Zhang ZR, Jiang ZR. Effective use of sequence information to predict CRISPR-Cas9 off-target. Comput Struct Biotechnol J 2022; 20:650-661. [PMID: 35140885 PMCID: PMC8804193 DOI: 10.1016/j.csbj.2022.01.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/05/2022] [Accepted: 01/08/2022] [Indexed: 12/05/2022] Open
Abstract
The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP.
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Abstract
Background:
The CRISPR system can quickly achieve the editing of different gene loci by
changing a small sequence on a single guide RNA. But the off-target event limits the further development
of the CRISPR system. How to improve the efficiency and specificity of this technology and minimize
the risk of off-target have always been a challenge. For genome-wide CRISPR Off-Target Cleavage
Sites (OTS) prediction, an important issue is data imbalance, that is, the number of true OTS identified
is much less than that of all possible nucleotide mismatch loci.
Method:
In this work, based on the sequence-generating adversarial network (SeqGAN), positive offtarget
sequences were generated to amplify the off-target gene locus OTS dataset of Cpf1. Then we
trained the data by a deep Convolutional Neural Network (CNN) to obtain a predictor with stronger
generalization ability and better performance.
Results:
In 10-fold cross-validation, the AUC value of the CNN classifier after SeqGAN balance was
0.941, which was higher than that of the original 0.863 and over-sampling 0.929. In independence testing,
the AUC value of the CNN classifier after SeqGAN balance was 0.841, which was higher than that
of the original 0.833 and over-sampling 0.836. The PR value was 0.722 after SeqGAN, which was also
about higher 0.16 than the original data and higher about 0.03 than over-sampling.
Conclusion:
The sequence generation antagonistic network SeqGAN was firstly used to deal with data
imbalance processing on CRISPR data. All the results showed that the SeqGAN can effectively generate
positive data for CRISPR off-target sites.
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Affiliation(s)
- Wen Li
- Institute of Computing Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiao-Bo Wang
- Institute of Applied Physics and Computational Mathematics, Beijing 100083, China
| | - Yan Xu
- Institute of Computing Technology, University of Science and Technology Beijing, Beijing 100083, China
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50
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Abstract
:
Clustered regularly interspaced short palindromic repeats along with CRISPR-associated protein
mechanisms preserve the memory of previous experiences with DNA invaders, in particular spacers
that are embedded in CRISPR arrays between coordinate repeats. There has been a fast progression in
the comprehension of this immune system and its implementations; however, there are numerous points
of view that anticipate explanations to make the field an energetic research zone. The efficiency of
CRISPR-Cas depends upon well-considered single guide RNA; for this purpose, many bioinformatics
methods and tools are created to support the design of greatly active and precise single guide RNA. Insilico
single guide RNA architecture is a crucial point for effective gene editing by means of the
CRISPR technique. Persistent attempts have been made to improve in-silico single guide RNA formulation
having great on-target effectiveness and decreased off-target effects. This review offers a summary
of the CRISPR computational tools to help different researchers pick a specific tool for their work according
to pros and cons, along with new thoughts to make new computational tools to overcome all existing
limitations.
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Affiliation(s)
- Mohsin Ali Nasir
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave,
West Hi-Tech Zone, Chengdu 611731, China
| | - Samia Nawaz
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave,
West Hi-Tech Zone, Chengdu 611731, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave,
West Hi-Tech Zone, Chengdu 611731, China
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