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Xiao MS, Damodaran AP, Kumari B, Dickson E, Xing K, On TA, Parab N, King HE, Perez AR, Guiblet WM, Duncan G, Che A, Chari R, Andresson T, Vidigal JA, Weatheritt RJ, Aregger M, Gonatopoulos-Pournatzis T. Genome-scale exon perturbation screens uncover exons critical for cell fitness. Mol Cell 2024; 84:2553-2572.e19. [PMID: 38917794 PMCID: PMC11246229 DOI: 10.1016/j.molcel.2024.05.024] [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: 10/01/2023] [Revised: 04/04/2024] [Accepted: 05/24/2024] [Indexed: 06/27/2024]
Abstract
CRISPR-Cas technology has transformed functional genomics, yet understanding of how individual exons differentially shape cellular phenotypes remains limited. Here, we optimized and conducted massively parallel exon deletion and splice-site mutation screens in human cell lines to identify exons that regulate cellular fitness. Fitness-promoting exons are prevalent in essential and highly expressed genes and commonly overlap with protein domains and interaction interfaces. Conversely, fitness-suppressing exons are enriched in nonessential genes, exhibiting lower inclusion levels, and overlap with intrinsically disordered regions and disease-associated mutations. In-depth mechanistic investigation of the screen-hit TAF5 alternative exon-8 revealed that its inclusion is required for assembly of the TFIID general transcription initiation complex, thereby regulating global gene expression output. Collectively, our orthogonal exon perturbation screens established a comprehensive repository of phenotypically important exons and uncovered regulatory mechanisms governing cellular fitness and gene expression.
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Affiliation(s)
- Mei-Sheng Xiao
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Arun Prasath Damodaran
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA.
| | - Bandana Kumari
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Ethan Dickson
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Kun Xing
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Tyler A On
- Molecular Targets Program, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Nikhil Parab
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Helen E King
- EMBL Australia and Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
| | - Alexendar R Perez
- Laboratory of Biochemistry and Molecular Biology, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA; Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Wilfried M Guiblet
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA
| | - Gerard Duncan
- Protein Characterization Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, MD 21701, USA
| | - Anney Che
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, MD 21701, USA
| | - Raj Chari
- Genome Modification Core, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, MD 21702, USA
| | - Thorkell Andresson
- Protein Characterization Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, MD 21701, USA
| | - Joana A Vidigal
- Laboratory of Biochemistry and Molecular Biology, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Robert J Weatheritt
- EMBL Australia and Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2010, Australia
| | - Michael Aregger
- Molecular Targets Program, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA.
| | - Thomas Gonatopoulos-Pournatzis
- RNA Biology Laboratory, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, MD 21702, USA.
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2
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Lin K, Chang YC, Billmann M, Ward HN, Le K, Hassan AZ, Bhojoo U, Chan K, Costanzo M, Moffat J, Boone C, Bielinsky AK, Myers CL. A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds. Sci Rep 2024; 14:2508. [PMID: 38291084 PMCID: PMC10828508 DOI: 10.1038/s41598-024-51735-y] [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: 09/20/2023] [Accepted: 01/09/2024] [Indexed: 02/01/2024] Open
Abstract
Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screening platform by generating a DNA damage response (DDR)-focused custom sgRNA library targeting 1011 genes with 3033 sgRNAs. We performed five proof-of-principle compound screens and found that the compounds' known modes-of-action (MoA) were enriched among the compounds' CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale with 20-fold fewer resources than commonly used genome-wide libraries and produce biologically informative CGI profiles.
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Affiliation(s)
- Kevin Lin
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Ya-Chu Chang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Khoi Le
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Arshia Z Hassan
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Urvi Bhojoo
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Katherine Chan
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jason Moffat
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
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3
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Chang YC, Lin K, Baxley RM, Durrett W, Wang L, Stojkova O, Billmann M, Ward H, Myers CL, Bielinsky AK. RNF4 and USP7 cooperate in ubiquitin-regulated steps of DNA replication. Open Biol 2023; 13:230068. [PMID: 37607592 PMCID: PMC10444366 DOI: 10.1098/rsob.230068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/27/2023] [Indexed: 08/24/2023] Open
Abstract
DNA replication requires precise regulation achieved through post-translational modifications, including ubiquitination and SUMOylation. These modifications are linked by the SUMO-targeted E3 ubiquitin ligases (STUbLs). Ring finger protein 4 (RNF4), one of only two mammalian STUbLs, participates in double-strand break repair and resolving DNA-protein cross-links. However, its role in DNA replication has been poorly understood. Using CRISPR/Cas9 genetic screens, we discovered an unexpected dependency of RNF4 mutants on ubiquitin specific peptidase 7 (USP7) for survival in TP53-null retinal pigment epithelial cells. TP53-/-/RNF4-/-/USP7-/- triple knockout (TKO) cells displayed defects in DNA replication that cause genomic instability. These defects were exacerbated by the proteasome inhibitor bortezomib, which limited the nuclear ubiquitin pool. A shortage of free ubiquitin suppressed the ataxia telangiectasia and Rad3-related (ATR)-mediated checkpoint response, leading to increased cell death. In conclusion, RNF4 and USP7 work cooperatively to sustain a functional level of nuclear ubiquitin to maintain the integrity of the genome.
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Affiliation(s)
- Ya-Chu Chang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kevin Lin
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ryan M. Baxley
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wesley Durrett
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Liangjun Wang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Olivera Stojkova
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Henry Ward
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
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Bock C, Datlinger P, Chardon F, Coelho MA, Dong MB, Lawson KA, Lu T, Maroc L, Norman TM, Song B, Stanley G, Chen S, Garnett M, Li W, Moffat J, Qi LS, Shapiro RS, Shendure J, Weissman JS, Zhuang X. High-content CRISPR screening. NATURE REVIEWS. METHODS PRIMERS 2022; 2:9. [PMID: 37214176 PMCID: PMC10200264 DOI: 10.1038/s43586-022-00098-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.
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Affiliation(s)
- Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Florence Chardon
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Matthew B. Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Tian Lu
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Laetitia Maroc
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Thomas M. Norman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bicna Song
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Geoff Stanley
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Mathew Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Wei Li
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Lei S. Qi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- ChEM-H, Stanford University, Stanford, CA, USA
| | - Rebecca S. Shapiro
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Jonathan S. Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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5
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Common computational tools for analyzing CRISPR screens. Emerg Top Life Sci 2021; 5:779-788. [PMID: 34881774 PMCID: PMC8786280 DOI: 10.1042/etls20210222] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 12/13/2022]
Abstract
CRISPR–Cas technology offers a versatile toolbox for genome editing, with applications in various cancer-related fields such as functional genomics, immunotherapy, synthetic lethality and drug resistance, metastasis, genome regulation, chromatic accessibility and RNA-targeting. The variety of screening platforms and questions in which they are used have caused the development of a wide array of analytical methods for CRISPR analysis. In this review, we focus on the algorithms and frameworks used in the computational analysis of pooled CRISPR knockout (KO) screens and highlight some of the most significant target discoveries made using these methods. Lastly, we offer perspectives on the design and analysis of state-of-art multiplex screening for genetic interactions.
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Aregger M, Xing K, Gonatopoulos-Pournatzis T. Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening. Nat Protoc 2021; 16:4722-4765. [PMID: 34508260 DOI: 10.1038/s41596-021-00595-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 06/17/2021] [Indexed: 02/08/2023]
Abstract
CRISPR-based forward genetic screening represents a powerful approach for the systematic characterization of gene function. Recent efforts have been directed toward establishing CRISPR-based tools for the programmable delivery of combinatorial genetic perturbations, most of which are mediated by a single nuclease and the expression of structurally identical guide backbones from two promoters. In contrast, we have developed CHyMErA (Cas hybrid for multiplexed editing and screening applications), which is based on the co-expression of Cas9 and Cas12a nucleases in conjunction with a hybrid guide RNA (hgRNA) engineered by the fusion of Cas9 and Cas12a guides and expressed from a single U6 promoter. CHyMErA is suitable for the high-throughput deletion of genetic segments including the excision of individual exons. Furthermore, CHyMErA enables the concomitant targeting of two or more genes and can thus be used for the systematic mapping of genetic interactions in mammalian cells. CHyMErA can also be applied for the perturbation of paralogous gene pairs, thereby allowing the capturing of phenotypic roles that would otherwise be masked because of genetic redundancy. Here, we provide instructions for the cloning of hgRNA screening libraries and individual hgRNA constructs and offer guidelines for designing and performing combinatorial pooled genetic screens using CHyMErA. Starting with the generation of Cas9- and Cas12a-expressing cell lines, CHyMErA screening can be implemented within 15-20 weeks.
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Affiliation(s)
- Michael Aregger
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.
| | - Kun Xing
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
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