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Nuttle X, Burt ND, Currall B, Moysés-Oliveira M, Mohajeri K, Bhavsar R, Lucente D, Yadav R, Tai DJC, Gusella JF, Talkowski ME. Parallelized engineering of mutational models using piggyBac transposon delivery of CRISPR libraries. CELL REPORTS METHODS 2024; 4:100672. [PMID: 38091988 PMCID: PMC10831954 DOI: 10.1016/j.crmeth.2023.100672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024]
Abstract
New technologies and large-cohort studies have enabled novel variant discovery and association at unprecedented scale, yet functional characterization of these variants remains paramount to deciphering disease mechanisms. Approaches that facilitate parallelized genome editing of cells of interest or induced pluripotent stem cells (iPSCs) have become critical tools toward this goal. Here, we developed an approach that incorporates libraries of CRISPR-Cas9 guide RNAs (gRNAs) together with inducible Cas9 into a piggyBac (PB) transposon system to engineer dozens to hundreds of genomic variants in parallel against isogenic cellular backgrounds. This method empowers loss-of-function (LoF) studies through the introduction of insertions or deletions (indels) and copy-number variants (CNVs), though generating specific nucleotide changes is possible with prime editing. The ability to rapidly establish high-quality mutational models at scale will facilitate the development of isogenic cellular collections and catalyze comparative functional genomic studies investigating the roles of hundreds of genes and mutations in development and disease.
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Affiliation(s)
- Xander Nuttle
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
| | - Nicholas D Burt
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin Currall
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mariana Moysés-Oliveira
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Kiana Mohajeri
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; PhD program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Riya Bhavsar
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Diane Lucente
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rachita Yadav
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Derek J C Tai
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - James F Gusella
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Michael E Talkowski
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
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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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Ghamsari R, Rosenbluh J, Menon AV, Lovell NH, Alinejad-Rokny H. Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers. Cancers (Basel) 2023; 15:3566. [PMID: 37509229 PMCID: PMC10377346 DOI: 10.3390/cancers15143566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.
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Affiliation(s)
- Reza Ghamsari
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Joseph Rosenbluh
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - A Vipin Menon
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW 2052, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, UNSW Sydney, Sydney, NSW 2052, Australia
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Li X, Wang C, Peng T, Chai Z, Ni D, Liu Y, Zhang J, Chen T, Lu S. Atomic-scale insights into allosteric inhibition and evolutional rescue mechanism of Streptococcus thermophilus Cas9 by the anti-CRISPR protein AcrIIA6. Comput Struct Biotechnol J 2021; 19:6108-6124. [PMID: 34900128 PMCID: PMC8632846 DOI: 10.1016/j.csbj.2021.11.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 12/26/2022] Open
Abstract
CRISPR-Cas systems are prokaryotic adaptive immunity against invading phages and plasmids. Phages have evolved diverse protein inhibitors of CRISPR-Cas systems, called anti-CRISPR (Acr) proteins, to neutralize this CRISPR machinery. In response, bacteria have co-evolved Cas variants to escape phage's anti-CRISPR strategies, called anti-anti-CRISPR systems. Here we explore the anti-CRISPR allosteric inhibition and anti-anti-CRISPR rescue mechanisms between Streptococcus thermophilus Cas9 (St1Cas9) and the anti-CRISPR protein AcrIIA6 at the atomic level, by generating mutants of key residues in St1Cas9. Extensive unbiased molecular dynamics simulations show that the functional motions of St1Cas9 in the presence of AcrIIA6 differ substantially from those of St1Cas9 alone. AcrIIA6 binding triggers a shift of St1Cas9 conformational ensemble towards a less catalytically competent state; this state significantly compromises protospacer adjacent motif (PAM) recognition and nuclease activity by altering interdependently conformational dynamics and allosteric signals among nuclease domains, PAM-interacting (PI) regions, and AcrIIA6 binding motifs. Via in vitro DNA cleavage assays, we further elucidate the rescue mechanism of efficiently escaping AcrIIA6 inhibition harboring St1Cas9 triple mutations (G993K/K1008M/K1010E) in the PI domain and identify the evolutionary landscape of such mutational escape within species. Our results provide mechanistic insights into Acr proteins as natural brakes for the CRISPR-Cas systems and a promising potential for the design of allosteric Acr peptidomimetics.
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Affiliation(s)
- Xinyi Li
- Department of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Chengxiang Wang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ting Peng
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Zongtao Chai
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Duan Ni
- The Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
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Li Y, Zhou LQ. dCas9 techniques for transcriptional repression in mammalian cells: Progress, applications and challenges. Bioessays 2021; 43:e2100086. [PMID: 34327721 DOI: 10.1002/bies.202100086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 01/10/2023]
Abstract
Innovative loss-of-function techniques developed in recent years have made it much easier to target specific genomic loci at transcriptional levels. CRISPR interference (CRISPRi) has been proven to be the most effective and specific tool to knock down any gene of interest in mammalian cells. The catalytically deactivated Cas9 (dCas9) can be fused with transcription repressors to downregulate gene expression specified by sgRNA complementary to target genomic sequence. Although CRISPRi has huge potential for gene knockdown, there is still a lack of systematic guidelines for efficient and widespread use. Here we describe the working mechanism and development of CRISPRi, designing principles of sgRNA, delivery methods and applications in mammalian cells in detail. Finally, we propose possible solutions and future directions with regard to current challenges.
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Affiliation(s)
- Yuanyuan Li
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li-Quan Zhou
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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