1
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Li G, Wu J, Wang X. Predicting functional UTR variants by integrating region-specific features. Brief Bioinform 2024; 25:bbae248. [PMID: 38783704 PMCID: PMC11116830 DOI: 10.1093/bib/bbae248] [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: 01/15/2024] [Revised: 03/30/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.
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Affiliation(s)
- Guangyu Li
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Jiayu Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Xiaoyue Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
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2
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Ryu J, Barkal S, Yu T, Jankowiak M, Zhou Y, Francoeur M, Phan QV, Li Z, Tognon M, Brown L, Love MI, Bhat V, Lettre G, Ascher DB, Cassa CA, Sherwood RI, Pinello L. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nat Genet 2024; 56:925-937. [PMID: 38658794 DOI: 10.1038/s41588-024-01726-6] [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: 09/07/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.
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Affiliation(s)
- Jayoung Ryu
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sam Barkal
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tian Yu
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Martin Jankowiak
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matthew Francoeur
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Quang Vinh Phan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhijian Li
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Manuel Tognon
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computer Science Department, University of Verona, Verona, Italy
| | - Lara Brown
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael I Love
- Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vineel Bhat
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, Quebec, Canada
- Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Christopher A Cassa
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Richard I Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
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3
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Johnson GA, Gould SI, Sánchez-Rivera FJ. Deconstructing cancer with precision genome editing. Biochem Soc Trans 2024; 52:803-819. [PMID: 38629716 PMCID: PMC11088927 DOI: 10.1042/bst20230984] [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: 02/01/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations in their endogenous genetic contexts with high precision and efficiency. Of these, base editing and prime editing are quickly becoming gold-standards in the field due to their versatility and scalability. Here, we review the merits and limitations of these precision genome editing technologies, their application to modern cancer research, and speculate how these could be integrated to address future directions in the field.
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Affiliation(s)
- Grace A. Johnson
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Samuel I. Gould
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Francisco J. Sánchez-Rivera
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
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4
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Kim HS, Kweon J, Kim Y. Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants. Exp Mol Med 2024; 56:861-869. [PMID: 38556550 PMCID: PMC11058232 DOI: 10.1038/s12276-024-01212-3] [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: 07/31/2023] [Revised: 01/15/2024] [Accepted: 01/30/2024] [Indexed: 04/02/2024] Open
Abstract
Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. The CRISPR‒Cas system has emerged as a pivotal tool for genome engineering, enabling the precise incorporation of specific genetic variations, including VUSs, into DNA to facilitate their functional characterization. Additionally, the integration of CRISPR‒Cas technology with sequencing tools allows the high-throughput evaluation of mutations, transforming uncertain genetic data into actionable insights. This allows researchers to comprehensively study the functional consequences of point mutations, paving the way for enhanced understanding and increasing application to precision medicine. This review summarizes the current genome editing tools utilizing CRISPR‒Cas systems and their combination with sequencing tools for functional genomics, with a focus on point mutations.
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Affiliation(s)
- Heon Seok Kim
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, Republic of Korea
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Seongdong-gu, Seoul, Republic of Korea
| | - Jiyeon Kweon
- Department of Cell and Genetic Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yongsub Kim
- Department of Cell and Genetic Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Stem Cell Immunomodulation Research Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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5
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Yang C, Lei Y, Ren T, Yao M. The Current Situation and Development Prospect of Whole-Genome Screening. Int J Mol Sci 2024; 25:658. [PMID: 38203828 PMCID: PMC10779205 DOI: 10.3390/ijms25010658] [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: 11/21/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.
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Affiliation(s)
| | | | | | - Mingze Yao
- Shanxi Provincial Key Laboratory for Medical Molecular Cell Biology, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education and Institute of Biomedical Sciences, Shanxi University, Taiyuan 030006, China; (C.Y.); (Y.L.); (T.R.)
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6
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Maes S, Deploey N, Peelman F, Eyckerman S. Deep mutational scanning of proteins in mammalian cells. CELL REPORTS METHODS 2023; 3:100641. [PMID: 37963462 PMCID: PMC10694495 DOI: 10.1016/j.crmeth.2023.100641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.
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Affiliation(s)
- Stefanie Maes
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Nick Deploey
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Frank Peelman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Eyckerman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium.
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7
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Zeng H, Yuan Q, Peng F, Ma D, Lingineni A, Chee K, Gilberd P, Osikpa EC, Sun Z, Gao X. A split and inducible adenine base editor for precise in vivo base editing. Nat Commun 2023; 14:5573. [PMID: 37696818 PMCID: PMC10495389 DOI: 10.1038/s41467-023-41331-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
DNA base editors use deaminases fused to a programmable DNA-binding protein for targeted nucleotide conversion. However, the most widely used TadA deaminases lack post-translational control in living cells. Here, we present a split adenine base editor (sABE) that utilizes chemically induced dimerization (CID) to control the catalytic activity of the deoxyadenosine deaminase TadA-8e. sABE shows high on-target editing activity comparable to the original ABE with TadA-8e (ABE8e) upon rapamycin induction while maintaining low background activity without induction. Importantly, sABE exhibits a narrower activity window on DNA and higher precision than ABE8e, with an improved single-to-double ratio of adenine editing and reduced genomic and transcriptomic off-target effects. sABE can achieve gene knockout through multiplex splice donor disruption in human cells. Furthermore, when delivered via dual adeno-associated virus vectors, sABE can efficiently convert a single A•T base pair to a G•C base pair on the PCSK9 gene in mouse liver, demonstrating in vivo CID-controlled DNA base editing. Thus, sABE enables precise control of base editing, which will have broad implications for basic research and in vivo therapeutic applications.
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Affiliation(s)
- Hongzhi Zeng
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Qichen Yuan
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Fei Peng
- Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dacheng Ma
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Ananya Lingineni
- Department of Bioengineering, Rice University, Houston, TX, 77005, USA
| | - Kelly Chee
- Department of Biosciences, Rice University, Houston, TX, 77005, USA
| | - Peretz Gilberd
- Department of Biosciences, Rice University, Houston, TX, 77005, USA
| | - Emmanuel C Osikpa
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Zheng Sun
- Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Xue Gao
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA.
- Department of Bioengineering, Rice University, Houston, TX, 77005, USA.
- Department of Chemistry, Rice University, Houston, TX, 77005, USA.
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8
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Ryu J, Barkal S, Yu T, Jankowiak M, Zhou Y, Francoeur M, Phan QV, Li Z, Tognon M, Brown L, Love MI, Lettre G, Ascher DB, Cassa CA, Sherwood RI, Pinello L. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.08.23295253. [PMID: 37732177 PMCID: PMC10508837 DOI: 10.1101/2023.09.08.23295253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.
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Affiliation(s)
- Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sam Barkal
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Tian Yu
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matthew Francoeur
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Quang Vinh Phan
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhijian Li
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Manuel Tognon
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computer Science Department, University of Verona, Verona, Italy
| | - Lara Brown
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael I. Love
- Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, QC H1T 1C8, Canada
- Faculté de Médecine, Université de Montréal, Montréal, QC H3T 1J4, Canada
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Christopher A. Cassa
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard I. Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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9
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Sahu S, Sullivan TL, Mitrophanov AY, Galloux M, Nousome D, Southon E, Caylor D, Mishra AP, Evans CN, Clapp ME, Burkett S, Malys T, Chari R, Biswas K, Sharan SK. Saturation genome editing of 11 codons and exon 13 of BRCA2 coupled with chemotherapeutic drug response accurately determines pathogenicity of variants. PLoS Genet 2023; 19:e1010940. [PMID: 37713444 PMCID: PMC10529611 DOI: 10.1371/journal.pgen.1010940] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/27/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023] Open
Abstract
The unknown pathogenicity of a significant number of variants found in cancer-related genes is attributed to limited epidemiological data, resulting in their classification as variant of uncertain significance (VUS). To date, Breast Cancer gene-2 (BRCA2) has the highest number of VUSs, which has necessitated the development of several robust functional assays to determine their functional significance. Here we report the use of a humanized-mouse embryonic stem cell (mESC) line expressing a single copy of the human BRCA2 for a CRISPR-Cas9-based high-throughput functional assay. As a proof-of-principle, we have saturated 11 codons encoded by BRCA2 exons 3, 18, 19 and all possible single-nucleotide variants in exon 13 and multiplexed these variants for their functional categorization. Specifically, we used a pool of 180-mer single-stranded donor DNA to generate all possible combination of variants. Using a high throughput sequencing-based approach, we show a significant drop in the frequency of non-functional variants, whereas functional variants are enriched in the pool of the cells. We further demonstrate the response of these variants to the DNA-damaging agents, cisplatin and olaparib, allowing us to use cellular survival and drug response as parameters for variant classification. Using this approach, we have categorized 599 BRCA2 variants including 93-single nucleotide variants (SNVs) across the 11 codons, of which 28 are reported in ClinVar. We also functionally categorized 252 SNVs from exon 13 into 188 functional and 60 non-functional variants, demonstrating that saturation genome editing (SGE) coupled with drug sensitivity assays can enhance functional annotation of BRCA2 VUS.
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Affiliation(s)
- Sounak Sahu
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Teresa L. Sullivan
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Alexander Y. Mitrophanov
- Statistical Consulting and Scientific Programming, Frederick National Laboratory for Cancer Research, National Institutes of Health, Frederick, Maryland, United States of America
| | | | - Darryl Nousome
- CCR Bioinformatics Resource, Leidos Biomedical Sciences, Inc. Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
| | - Eileen Southon
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Dylan Caylor
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Arun Prakash Mishra
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Christine N. Evans
- Genome Modification Core, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
| | - Michelle E. Clapp
- Genome Modification Core, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
| | - Sandra Burkett
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Tyler Malys
- Statistical Consulting and Scientific Programming, Frederick National Laboratory for Cancer Research, National Institutes of Health, Frederick, Maryland, United States of America
| | - Raj Chari
- Genome Modification Core, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
| | - Kajal Biswas
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
| | - Shyam K. Sharan
- Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America
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10
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Lue NZ, Liau BB. Base editor screens for in situ mutational scanning at scale. Mol Cell 2023; 83:2167-2187. [PMID: 37390819 PMCID: PMC10330937 DOI: 10.1016/j.molcel.2023.06.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 07/02/2023]
Abstract
A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.
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Affiliation(s)
- Nicholas Z Lue
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brian B Liau
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
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11
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Awwad SW, Serrano-Benitez A, Thomas JC, Gupta V, Jackson SP. Revolutionizing DNA repair research and cancer therapy with CRISPR-Cas screens. Nat Rev Mol Cell Biol 2023; 24:477-494. [PMID: 36781955 DOI: 10.1038/s41580-022-00571-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 02/15/2023]
Abstract
All organisms possess molecular mechanisms that govern DNA repair and associated DNA damage response (DDR) processes. Owing to their relevance to human disease, most notably cancer, these mechanisms have been studied extensively, yet new DNA repair and/or DDR factors and functional interactions between them are still being uncovered. The emergence of CRISPR technologies and CRISPR-based genetic screens has enabled genome-scale analyses of gene-gene and gene-drug interactions, thereby providing new insights into cellular processes in distinct DDR-deficiency genetic backgrounds and conditions. In this Review, we discuss the mechanistic basis of CRISPR-Cas genetic screening approaches and describe how they have contributed to our understanding of DNA repair and DDR pathways. We discuss how DNA repair pathways are regulated, and identify and characterize crosstalk between them. We also highlight the impacts of CRISPR-based studies in identifying novel strategies for cancer therapy, and in understanding, overcoming and even exploiting cancer-drug resistance, for example in the contexts of PARP inhibition, homologous recombination deficiencies and/or replication stress. Lastly, we present the DDR CRISPR screen (DDRcs) portal , in which we have collected and reanalysed data from CRISPR screen studies and provide a tool for systematically exploring them.
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Affiliation(s)
- Samah W Awwad
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Almudena Serrano-Benitez
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
| | - John C Thomas
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
| | - Vipul Gupta
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Stephen P Jackson
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
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12
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Liang Y, Chen F, Wang K, Lai L. Base editors: development and applications in biomedicine. Front Med 2023; 17:359-387. [PMID: 37434066 DOI: 10.1007/s11684-023-1013-y] [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: 02/23/2023] [Accepted: 05/19/2023] [Indexed: 07/13/2023]
Abstract
Base editor (BE) is a gene-editing tool developed by combining the CRISPR/Cas system with an individual deaminase, enabling precise single-base substitution in DNA or RNA without generating a DNA double-strand break (DSB) or requiring donor DNA templates in living cells. Base editors offer more precise and secure genome-editing effects than other conventional artificial nuclease systems, such as CRISPR/Cas9, as the DSB induced by Cas9 will cause severe damage to the genome. Thus, base editors have important applications in the field of biomedicine, including gene function investigation, directed protein evolution, genetic lineage tracing, disease modeling, and gene therapy. Since the development of the two main base editors, cytosine base editors (CBEs) and adenine base editors (ABEs), scientists have developed more than 100 optimized base editors with improved editing efficiency, precision, specificity, targeting scope, and capacity to be delivered in vivo, greatly enhancing their application potential in biomedicine. Here, we review the recent development of base editors, summarize their applications in the biomedical field, and discuss future perspectives and challenges for therapeutic applications.
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Affiliation(s)
- Yanhui Liang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, 572000, China
| | - Fangbing Chen
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, 572000, China
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, 529020, China
| | - Kepin Wang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, 572000, China
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, 529020, China
| | - Liangxue Lai
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China.
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, 572000, China.
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, 529020, China.
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13
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Yu T, Fife JD, Adzhubey I, Sherwood R, Cassa CA. Joint estimation and imputation of variant functional effects using high throughput assay data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.06.23284280. [PMID: 36711907 PMCID: PMC9882428 DOI: 10.1101/2023.01.06.23284280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (Functional Substitution Estimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1, p=2.24×10-51), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53, FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10-6). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.
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Affiliation(s)
- Tian Yu
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - James D. Fife
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ivan Adzhubey
- Department of Biomedical Informatics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts
| | - Richard Sherwood
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christopher A. Cassa
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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14
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Lattanzi A, Maddalo D. The CRISPR Revolution in the Drug Discovery Workflow: An Industry Perspective. CRISPR J 2022; 5:634-641. [PMID: 35917561 DOI: 10.1089/crispr.2022.0002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
In a relatively short time, the pharmaceutical industry has witnessed a rapid integration of the CRISPR technology in multiple areas of research, development, therapy, and diagnostics. A unique feature to this system compared with other technologies is the exceptional versatility in adapting to the broad range of needs across the drug discovery pipeline, such as target identification, cell engineering, and in vivo modeling. As a consequence, the CRISPR toolbox has been evolving to address key questions around preclinical and clinical drug development. In this review, we provide a high-level perspective of how CRISPR has impacted several aspects of the drug discovery workflow and the future ahead for this exciting technology.
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Affiliation(s)
- Annalisa Lattanzi
- Department of Molecular Biology and Genentech, Inc., South San Francisco, California, USA
| | - Danilo Maddalo
- Department of Translational Oncology, Genentech, Inc., South San Francisco, California, USA
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15
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CRISPR screening in cancer stem cells. Essays Biochem 2022; 66:305-318. [PMID: 35713228 DOI: 10.1042/ebc20220009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/04/2022] [Accepted: 06/07/2022] [Indexed: 12/14/2022]
Abstract
Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal ability. Increasing evidence points to the critical roles of CSCs in tumorigenesis, metastasis, therapy resistance, and cancer relapse. As such, the elimination of CSCs improves cancer treatment outcomes. However, challenges remain due to limited understanding of the molecular mechanisms governing self-renewal and survival of CSCs. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screening has been increasingly used to identify genetic determinants in cancers. In this primer, we discuss the progress made and emerging opportunities of coupling advanced CRISPR screening systems with CSC models to reveal the understudied vulnerabilities of CSCs.
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16
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Saturation variant interpretation using CRISPR prime editing. Nat Biotechnol 2022; 40:885-895. [DOI: 10.1038/s41587-021-01201-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 12/20/2021] [Indexed: 12/13/2022]
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17
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Nambiar TS, Baudrier L, Billon P, Ciccia A. CRISPR-based genome editing through the lens of DNA repair. Mol Cell 2022; 82:348-388. [PMID: 35063100 PMCID: PMC8887926 DOI: 10.1016/j.molcel.2021.12.026] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 01/22/2023]
Abstract
Genome editing technologies operate by inducing site-specific DNA perturbations that are resolved by cellular DNA repair pathways. Products of genome editors include DNA breaks generated by CRISPR-associated nucleases, base modifications induced by base editors, DNA flaps created by prime editors, and integration intermediates formed by site-specific recombinases and transposases associated with CRISPR systems. Here, we discuss the cellular processes that repair CRISPR-generated DNA lesions and describe strategies to obtain desirable genomic changes through modulation of DNA repair pathways. Advances in our understanding of the DNA repair circuitry, in conjunction with the rapid development of innovative genome editing technologies, promise to greatly enhance our ability to improve food production, combat environmental pollution, develop cell-based therapies, and cure genetic and infectious diseases.
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Affiliation(s)
- Tarun S Nambiar
- Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lou Baudrier
- Department of Biochemistry and Molecular Biology, Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive N.W., Calgary, Alberta T2N 4N1, Canada
| | - Pierre Billon
- Department of Biochemistry and Molecular Biology, Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive N.W., Calgary, Alberta T2N 4N1, Canada.
| | - Alberto Ciccia
- Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
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18
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Pallaseni A, Peets E, Koeppel J, Weller J, Vanderstichele T, Ho U, Crepaldi L, van Leeuwen J, Allen F, Parts L. OUP accepted manuscript. Nucleic Acids Res 2022; 50:3551-3564. [PMID: 35286377 PMCID: PMC8989541 DOI: 10.1093/nar/gkac161] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 02/19/2022] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts.
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Affiliation(s)
| | | | | | | | | | - Uyen Linh Ho
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | | | - Jolanda van Leeuwen
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | | | - Leopold Parts
- To whom correspondence should be addressed. Tel: +44 1223 834 244;
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19
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Tian P, Zhong M, Wei GH. Mechanistic insights into genetic susceptibility to prostate cancer. Cancer Lett 2021; 522:155-163. [PMID: 34560228 DOI: 10.1016/j.canlet.2021.09.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022]
Abstract
Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.
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Affiliation(s)
- Pan Tian
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Mengjie Zhong
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Gong-Hong Wei
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China.
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20
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Hayward SB, Ciccia A. Towards a CRISPeR understanding of homologous recombination with high-throughput functional genomics. Curr Opin Genet Dev 2021; 71:171-181. [PMID: 34583241 PMCID: PMC8671205 DOI: 10.1016/j.gde.2021.08.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 12/26/2022]
Abstract
CRISPR-dependent genome editing enables the study of genes and mutations on a large scale. Here we review CRISPR-based functional genomics technologies that generate gene knockouts and single nucleotide variants (SNVs) and discuss how their use has provided new important insights into the function of homologous recombination (HR) genes. In particular, we highlight discoveries from CRISPR screens that have contributed to define the response to PARP inhibition in cells deficient for the HR genes BRCA1 and BRCA2, uncover genes whose loss causes synthetic lethality in combination with BRCA1/2 deficiency, and characterize the function of BRCA1/2 SNVs of uncertain clinical significance. Further use of these approaches, combined with next-generation CRISPR-based technologies, will aid to dissect the genetic network of the HR pathway, define the impact of HR mutations on cancer etiology and treatment, and develop novel targeted therapies for HR-deficient tumors.
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Affiliation(s)
- Samuel B Hayward
- Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Alberto Ciccia
- Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, United States.
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Tam B, Sinha S, Qin Z, Wang SM. Comprehensive Identification of Deleterious TP53 Missense VUS Variants Based on Their Impact on TP53 Structural Stability. Int J Mol Sci 2021; 22:11345. [PMID: 34768775 PMCID: PMC8583684 DOI: 10.3390/ijms222111345] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/30/2022] Open
Abstract
TP53 plays critical roles in maintaining genome stability. Deleterious genetic variants damage the function of TP53, causing genome instability and increased cancer risk. Of the large quantity of genetic variants identified in TP53, however, many remain functionally unclassified as variants of unknown significance (VUS) due to the lack of evidence. This is reflected by the presence of 749 (42%) VUS of the 1785 germline variants collected in the ClinVar database. In this study, we addressed the deleteriousness of TP53 missense VUS. Utilizing the protein structure-based Ramachandran Plot-Molecular Dynamics Simulation (RPMDS) method that we developed, we measured the effects of missense VUS on TP53 structural stability. Of the 340 missense VUS tested, we observed deleterious evidence for 193 VUS, as reflected by the TP53 structural changes caused by the VUS-substituted residues. We compared the results from RPMDS with those from other in silico methods and observed higher specificity of RPMDS in classification of TP53 missense VUS than these methods. Data from our current study address a long-standing challenge in classifying the missense VUS in TP53, one of the most important tumor suppressor genes.
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Affiliation(s)
| | | | | | - San Ming Wang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (B.T.); (S.S.); (Z.Q.)
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22
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Huang C, Li G, Wu J, Liang J, Wang X. Identification of pathogenic variants in cancer genes using base editing screens with editing efficiency correction. Genome Biol 2021; 22:80. [PMID: 33691754 PMCID: PMC7945310 DOI: 10.1186/s13059-021-02305-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 02/24/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Millions of nucleotide variants are identified through cancer genome sequencing and it is clinically important to identify the pathogenic variants among them. By introducing base substitutions at guide RNA target regions in the genome, CRISPR-Cas9-based base editors provide the possibility for evaluating a large number of variants in their genomic context. However, the variability in editing efficiency and the complexity of outcome mapping are two existing problems for assigning guide RNA effects to variants in base editing screens. RESULTS To improve the identification of pathogenic variants, we develop a framework to combine base editing screens with sgRNA efficiency and outcome mapping. We apply the method to evaluate more than 9000 variants across all the exons of BRCA1 and BRCA2 genes. Our efficiency-corrected scoring model identifies 910 loss-of-function variants for BRCA1/2, including 151 variants in the noncoding part of the genes such as the 5' untranslated regions. Many of them are identified in cancer patients and are reported as "benign/likely benign" or "variants of uncertain significance" by clinicians. Our data suggest a need to re-evaluate their clinical significance, which may be helpful for risk assessment and treatment of breast and ovarian cancer. CONCLUSIONS Our results suggest that base editing screens with efficiency correction is a powerful strategy to identify pathogenic variants in a high-throughput manner. Applying this strategy to assess variants in both coding and noncoding regions of the genome could have a direct impact on the interpretation of cancer variants.
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Affiliation(s)
- Changcai Huang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Guangyu Li
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jiayu Wu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Junbo Liang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
| | - Xiaoyue Wang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
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