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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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2
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Dixit S, Kumar A, Srinivasan K, Vincent PMDR, Ramu Krishnan N. Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions. Front Bioeng Biotechnol 2024; 11:1335901. [PMID: 38260726 PMCID: PMC10800897 DOI: 10.3389/fbioe.2023.1335901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - P. M. Durai Raj Vincent
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - Nadesh Ramu Krishnan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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3
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Lin HC, Makhlouf A, Vazquez Echegaray C, Zawada D, Simões F. Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs. Development 2023; 150:dev202300. [PMID: 38078653 PMCID: PMC10753584 DOI: 10.1242/dev.202300] [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] [Indexed: 12/18/2023]
Abstract
In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.
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Affiliation(s)
- Hsiu-Chuan Lin
- Department of Biosystems Science and Engineering, ETH Zürich, 4057 Basel, Switzerland
| | - Aly Makhlouf
- MRC Laboratory of Molecular Biology, University of Cambridge, Cambridge CB2 0QH, UK
| | - Camila Vazquez Echegaray
- Molecular Medicine and Gene Therapy, Lund Stem Cell Centre, Wallenberg Centre for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Dorota Zawada
- First Department of Medicine, Cardiology, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
- German Center for Cardiovascular Research (DZHK), Munich Heart Alliance, 80636 Munich, Germany
- Regenerative Medicine in Cardiovascular Diseases, First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
| | - Filipa Simões
- Department of Physiology, Anatomy and Genetics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford OX3 7TY, UK
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4
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Kharbikar L, Konwarh R, Chakraborty M, Nandanwar S, Marathe A, Yele Y, Ghosh PK, Sanan-Mishra N, Singh AP. 3Bs of CRISPR-Cas mediated genome editing in plants: exploring the basics, bioinformatics and biosafety landscape. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2023; 29:1825-1850. [PMID: 38222286 PMCID: PMC10784264 DOI: 10.1007/s12298-023-01397-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 01/16/2024]
Abstract
The recent thrust in research has projected the type II clustered regularly interspaced short palindromic repeats and associated protein 9 (CRISPR-Cas9) system as an avant-garde plant genome editing tool. It facilitates the induction of site-specific double-stranded DNA cleavage by the RNA-guided DNA endonuclease (RGEN), Cas9. Elimination, addition, or alteration of sections in DNA sequence besides the creation of a knockout genotype (CRISPRko) is aided by the CRISPR-Cas9 system in its wild form (wtCas9). The inactivation of the nuclease domain generates a dead Cas9 (dCas9), which is capable of targeting genomic DNA without scissoring it. The dCas9 system can be engineered by fusing it with different effectors to facilitate transcriptional activation (CRISPRa) and transcriptional interference (CRISPRi). CRISPR-Cas thus holds tremendous prospects as a genome-manipulating stratagem for a wide gamut of crops. In this article, we present a brief on the fundamentals and the general workflow of the CRISPR-Cas system followed by an overview of the prospects of bioinformatics in propelling CRISPR-Cas research with a special thrust on the available databases and algorithms/web-accessible applications that have aided in increasing the usage and efficiency of editing. The article also provides an update on the current regulatory landscape in different countries on the CRISPR-Cas edited plants to emphasize the far-reaching impact of the genomic editing technology. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-023-01397-3.
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Affiliation(s)
- Lalit Kharbikar
- ICAR - National Institute of Biotic Stress Management (NIBSM), Raipur, India
- International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Rocktotpal Konwarh
- Department of Biotechnology, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
- Baba Kinaram Research Foundation (BKRF), Bramsthan, Mau, Uttar Pradesh India
| | - Monoswi Chakraborty
- Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Bengaluru, Karnataka India
- International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Shweta Nandanwar
- ICAR - National Institute of Biotic Stress Management (NIBSM), Raipur, India
| | - Ashish Marathe
- ICAR - National Institute of Biotic Stress Management (NIBSM), Raipur, India
| | - Yogesh Yele
- ICAR - National Institute of Biotic Stress Management (NIBSM), Raipur, India
| | - Probir Kumar Ghosh
- ICAR - National Institute of Biotic Stress Management (NIBSM), Raipur, India
| | - Neeti Sanan-Mishra
- International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Anand Pratap Singh
- Baba Kinaram Research Foundation (BKRF), Bramsthan, Mau, Uttar Pradesh India
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5
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Rajabalee N, Siushansian H, Weerapura M, Berton S, Berbatovci F, Hooks B, Geoffrion M, Yang D, Harper ME, Rayner K, Blais A, Sun J. ATF2 orchestrates macrophage differentiation and activation to promote antibacterial responses. J Leukoc Biol 2023; 114:280-298. [PMID: 37403209 DOI: 10.1093/jleuko/qiad076] [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: 10/15/2022] [Revised: 04/22/2023] [Accepted: 06/15/2023] [Indexed: 07/06/2023] Open
Abstract
The differentiation and activation of macrophages are critical regulatory programs that are central to host inflammation and pathogen defense. However, the transcriptional regulatory pathways involved in these programs are not well understood. Herein, we demonstrate that the activity and expression of the transcription factor ATF2 is precisely regulated during primary human monocyte-to-macrophage differentiation and that its activation is linked to M1 polarization and antibacterial responses. Genetic perturbation experiments demonstrated that deletion of ATF2 (THP-ΔATF2) resulted in irregular and abnormal macrophage morphology, whereas macrophages overexpressing ATF2 (THP-ATF2) developed round and pancake-like morphology, resembling classically activated (M1) macrophages. Mechanistically, we show that ATF2 binds to the core promoter of PPM1A, a phosphatase that regulates monocyte-to-macrophage differentiation, to regulate its expression. Functionally, overexpression of ATF2 sensitized macrophages to M1 polarization, resulting in increased production of major histocompatibility complex class II, IL-1β, and IP-10; improved phagocytic capacity; and enhanced control of the intracellular pathogen Mycobacterium tuberculosis. Gene expression profiling revealed that overexpression of ATF2 reprogramed macrophages to promote antibacterial pathways enriched in chemokine signaling, metabolism, and antigen presentation. Consistent with pathways analysis, metabolic profiling revealed that genetic overexpression or stimuli-induced activation of ATF2 alters the metabolic capacity of macrophages and primes these cells for glycolytic metabolism during M1 polarization or bacterial infection. Our findings reveal that ATF2 plays a central role during macrophage differentiation and M1 polarization to enhance the functional capacities of macrophages.
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Affiliation(s)
- Nusrah Rajabalee
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Hannah Siushansian
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Milani Weerapura
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Stefania Berton
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Fjolla Berbatovci
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Breana Hooks
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Michele Geoffrion
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa Heart Institute, 40 Ruskin Road, Ottawa, Ontario K1Y 4W7, Canada
| | - Dabo Yang
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Katey Rayner
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa Heart Institute, 40 Ruskin Road, Ottawa, Ontario K1Y 4W7, Canada
| | - Alexandre Blais
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Éric Poulin Centre for Neuromuscular Disease, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
| | - Jim Sun
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
- Centre for Infection, Immunity and Inflammation, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada
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6
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Zhou Y, Wang L, Lu Z, Yu Z, Ma L. Optimized minimal genome-wide human sgRNA library. Sci Rep 2023; 13:11569. [PMID: 37464007 PMCID: PMC10354020 DOI: 10.1038/s41598-023-38810-6] [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: 03/02/2023] [Accepted: 07/14/2023] [Indexed: 07/20/2023] Open
Abstract
Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-based knockout screening is revolting the genetic analysis of a cellular or molecular phenotype in question but is challenged by the large size of single-guide RNA (sgRNA) library. Here we designed a minimal genome-wide human sgRNA library, H-mLib, which is composed of 21,159 sgRNA pairs assembled based on a dedicated selection strategy from all potential SpCas9/sgRNAs in the human genome. These sgRNA pairs were cloned into a dual-gRNA vector each targeting one gene, resulting in a compact library size nearly identical to the number of human protein-coding genes. The performance of the H-mLib was benchmarked to other CRISPR libraries in a proliferation screening conducted in K562 cells. We also identified groups of core essential genes and cell-type specific essential genes by comparing the screening results from the K562 and Jurkat cells. Together, the H-mLib exemplified high specificity and sensitivity in identifying essential genes while containing minimal library complexity, emphasizing its advantages and applications in CRISPR screening with limited cell numbers.
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Affiliation(s)
- Yangfan Zhou
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
- School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, 310030, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, 310024, Zhejiang, China
| | - Lixia Wang
- School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, 310030, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, 310024, Zhejiang, China
- School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Zhike Lu
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
- School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, 310030, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, 310024, Zhejiang, China
| | - Zhenxing Yu
- School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, 310030, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, 310024, Zhejiang, China
- School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Lijia Ma
- School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, 310030, Zhejiang, China.
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, 310024, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China.
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7
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Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, Kulkarni DA, Moggs J, Newham P, Nelson MR, Sistare FD, Ward LD, Yuan J. Using human genetics to improve safety assessment of therapeutics. Nat Rev Drug Discov 2023; 22:145-162. [PMID: 36261593 DOI: 10.1038/s41573-022-00561-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 02/07/2023]
Abstract
Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.
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Affiliation(s)
| | - Aimee M Deaton
- Amgen, Cambridge, MA, USA.,Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Alberto Del Rio-Espinola
- Novartis Institutes for BioMedical Research, Basel, Switzerland.,GentiBio Inc., Cambridge, MA, USA
| | | | - Mark Fielden
- Amgen, Thousand Oaks, MA, USA.,Kate Therapeutics, San Diego, CA, USA
| | | | - Jonathan Moggs
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Frank D Sistare
- Merck & Co., West Point, PA, USA.,315 Meadowmont Ln, Chapel Hill, NC, USA
| | - Lucas D Ward
- Amgen, Cambridge, MA, USA. .,Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Jing Yuan
- Amgen, Cambridge, MA, USA.,Pfizer, Cambridge, MA, USA
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8
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Forward Genetic Screens as Tools to Investigate Role and Mechanisms of EMT in Cancer. Cancers (Basel) 2022; 14:cancers14235928. [PMID: 36497409 PMCID: PMC9735433 DOI: 10.3390/cancers14235928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 12/02/2022] Open
Abstract
Epithelial-mesenchymal transition (EMT) is a process of cellular plasticity regulated by complex signaling networks. Under physiological conditions, it plays an important role in wound healing and organ repair. Its importance for human disease is given by its central role in chronic fibroproliferative diseases and cancer, which represent leading causes of death worldwide. In tumors, EMT is involved in primary tumor growth, metastasis and therapy resistance. It is therefore a major requisite to investigate and understand the role of EMT and the mechanisms leading to EMT in order to tackle these diseases therapeutically. Forward genetic screens link genome modifications to phenotypes, and have been successfully employed to identify oncogenes, tumor suppressor genes and genes involved in metastasis or therapy resistance. In particular, transposon-based insertional mutagenesis screens and CRISPR-based screens are versatile and easy-to-use tools applied in recent years to discover and identify novel cancer-related mechanisms. Here, we review the contribution of forward genetic screens to our understanding of how EMT is regulated and how it is involved in various aspects of cancer. Based on the current literature, we propose these methods as additional tools to investigate EMT.
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9
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Yedier-Bayram O, Gokbayrak B, Kayabolen A, Aksu AC, Cavga AD, Cingöz A, Kala EY, Karabiyik G, Günsay R, Esin B, Morova T, Uyulur F, Syed H, Philpott M, Cribbs AP, Kung SHY, Lack NA, Onder TT, Bagci-Onder T. EPIKOL, a chromatin-focused CRISPR/Cas9-based screening platform, to identify cancer-specific epigenetic vulnerabilities. Cell Death Dis 2022; 13:710. [PMID: 35973998 PMCID: PMC9381743 DOI: 10.1038/s41419-022-05146-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/24/2022] [Accepted: 07/28/2022] [Indexed: 01/21/2023]
Abstract
Dysregulation of the epigenome due to alterations in chromatin modifier proteins commonly contribute to malignant transformation. To interrogate the roles of epigenetic modifiers in cancer cells, we generated an epigenome-wide CRISPR-Cas9 knockout library (EPIKOL) that targets a wide-range of epigenetic modifiers and their cofactors. We conducted eight screens in two different cancer types and showed that EPIKOL performs with high efficiency in terms of sgRNA distribution and depletion of essential genes. We discovered novel epigenetic modifiers that regulate triple-negative breast cancer (TNBC) and prostate cancer cell fitness. We confirmed the growth-regulatory functions of individual candidates, including SS18L2 and members of the NSL complex (KANSL2, KANSL3, KAT8) in TNBC cells. Overall, we show that EPIKOL, a focused sgRNA library targeting ~800 genes, can reveal epigenetic modifiers that are essential for cancer cell fitness under in vitro and in vivo conditions and enable the identification of novel anti-cancer targets. Due to its comprehensive epigenome-wide targets and relatively high number of sgRNAs per gene, EPIKOL will facilitate studies examining functional roles of epigenetic modifiers in a wide range of contexts, such as screens in primary cells, patient-derived xenografts as well as in vivo models.
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Affiliation(s)
- Ozlem Yedier-Bayram
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Bengul Gokbayrak
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Alisan Kayabolen
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Ali Cenk Aksu
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Ayse Derya Cavga
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
- Biostatistics, Bioinformatics and Data Management Core, KUTTAM, Istanbul, Türkiye
| | - Ahmet Cingöz
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Ezgi Yagmur Kala
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Goktug Karabiyik
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Rauf Günsay
- Koç University School of Medicine, Istanbul, Türkiye
| | - Beril Esin
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
| | - Tunc Morova
- Koç University School of Medicine, Istanbul, Türkiye
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Fırat Uyulur
- Koç University Department of Computational Biology, Istanbul, Türkiye
| | - Hamzah Syed
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
- Biostatistics, Bioinformatics and Data Management Core, KUTTAM, Istanbul, Türkiye
- Koç University School of Medicine, Istanbul, Türkiye
| | - Martin Philpott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sonia H Y Kung
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Nathan A Lack
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
- Koç University School of Medicine, Istanbul, Türkiye
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Tamer T Onder
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye.
- Koç University School of Medicine, Istanbul, Türkiye.
| | - Tugba Bagci-Onder
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye.
- Koç University School of Medicine, Istanbul, Türkiye.
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10
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Ancos-Pintado R, Bragado-García I, Morales ML, García-Vicente R, Arroyo-Barea A, Rodríguez-García A, Martínez-López J, Linares M, Hernández-Sánchez M. High-Throughput CRISPR Screening in Hematological Neoplasms. Cancers (Basel) 2022; 14:3612. [PMID: 35892871 PMCID: PMC9329962 DOI: 10.3390/cancers14153612] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 02/01/2023] Open
Abstract
CRISPR is becoming an indispensable tool in biological research, revolutionizing diverse fields of medical research and biotechnology. In the last few years, several CRISPR-based genome-targeting tools have been translated for the study of hematological neoplasms. However, there is a lack of reviews focused on the wide uses of this technology in hematology. Therefore, in this review, we summarize the main CRISPR-based approaches of high throughput screenings applied to this field. Here we explain several libraries and algorithms for analysis of CRISPR screens used in hematology, accompanied by the most relevant databases. Moreover, we focus on (1) the identification of novel modulator genes of drug resistance and efficacy, which could anticipate relapses in patients and (2) new therapeutic targets and synthetic lethal interactions. We also discuss the approaches to uncover novel biomarkers of malignant transformations and immune evasion mechanisms. We explain the current literature in the most common lymphoid and myeloid neoplasms using this tool. Then, we conclude with future directions, highlighting the importance of further gene candidate validation and the integration and harmonization of the data from CRISPR screening approaches.
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Affiliation(s)
- Raquel Ancos-Pintado
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - Irene Bragado-García
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - María Luz Morales
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Roberto García-Vicente
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Andrés Arroyo-Barea
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - Alba Rodríguez-García
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Joaquín Martínez-López
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Medicine, Medicine School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain
| | - María Linares
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - María Hernández-Sánchez
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
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11
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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12
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Ibrahim AU, Al-Turjman F, Sa’id Z, Ozsoz M. Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:35143-35171. [PMID: 32837247 PMCID: PMC7276962 DOI: 10.1007/s11042-020-09010-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/16/2020] [Accepted: 05/01/2020] [Indexed: 05/02/2023]
Abstract
Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10-18 M), Femtomolar (10-15 M) and Picomolar (10-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10-9 M and micromolar 10-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.
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Affiliation(s)
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Near East University, Nicosia, 10 Mersin, Turkey
| | - Zubaida Sa’id
- Department of Biomedical Engineering, Near East University, Nicosia, 10 Mersin, Turkey
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, 10 Mersin, Turkey
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13
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Pacini C, Dempster JM, Boyle I, Gonçalves E, Najgebauer H, Karakoc E, van der Meer D, Barthorpe A, Lightfoot H, Jaaks P, McFarland JM, Garnett MJ, Tsherniak A, Iorio F. Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun 2021; 12:1661. [PMID: 33712601 PMCID: PMC7955067 DOI: 10.1038/s41467-021-21898-7] [Citation(s) in RCA: 118] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/18/2021] [Indexed: 01/14/2023] Open
Abstract
CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.
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Affiliation(s)
- Clare Pacini
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | | | - Emanuel Gonçalves
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Hanna Najgebauer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Emre Karakoc
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Andrew Barthorpe
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Howard Lightfoot
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Patricia Jaaks
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Mathew J Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Francesco Iorio
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Human Technopole, Milano, Italy.
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14
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Choi A, Jang I, Han H, Kim MS, Choi J, Lee J, Cho SY, Jun Y, Lee C, Kim J, Lee B, Lee S. iCSDB: an integrated database of CRISPR screens. Nucleic Acids Res 2021; 49:D956-D961. [PMID: 33137185 PMCID: PMC7779034 DOI: 10.1093/nar/gkaa989] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/29/2020] [Accepted: 10/13/2020] [Indexed: 12/01/2022] Open
Abstract
High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.
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Affiliation(s)
- Ahyoung Choi
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Insu Jang
- Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Heewon Han
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Min-Seo Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jinhyuk Choi
- Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jieun Lee
- Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Sung-Yup Cho
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Yukyung Jun
- Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul 03760, Republic of Korea.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, U.S.A
| | - Charles Lee
- Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul 03760, Republic of Korea.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, U.S.A.,Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - Jaesang Kim
- Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul 03760, Republic of Korea.,Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Byungwook Lee
- Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Sanghyuk Lee
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.,Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul 03760, Republic of Korea.,Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea
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15
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Cui Y, Cheng X, Chen Q, Song B, Chiu A, Gao Y, Dawson T, Chao L, Zhang W, Li D, Zeng Z, Yu J, Li Z, Fei T, Peng S, Li W. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Res 2021; 49:D848-D854. [PMID: 33010154 PMCID: PMC7778972 DOI: 10.1093/nar/gkaa809] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/12/2020] [Accepted: 09/30/2020] [Indexed: 12/21/2022] Open
Abstract
High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.
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Affiliation(s)
- Yingbo Cui
- Sanyi Road, Changsha, Hunan Province, People's Republic of China
| | - Xiaolong Cheng
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Qing Chen
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Bicna Song
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Anthony Chiu
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA.,School of Medicine and Health Sciences, George Washington University, 2300 I Street NW, Washington, DC 20037, USA
| | - Yuan Gao
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA.,Department of Biochemistry and Molecular Biology, George Washington University, 2300 I Street NW, Washington, DC 20037, USA
| | - Tyson Dawson
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA.,Institute for Biomedical Sciences, George Washington University, 2300 I Street NW, Washington, DC 20037, USA.,Computational Biology Institute, Milken Institute School of Public Health, George Washington University, 45085 University Drive, Ashburn, VA 20148, USA
| | - Lumen Chao
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Wubing Zhang
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. 450 Brookline Ave., Boston MA 02215, USA
| | - Dian Li
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. 450 Brookline Ave., Boston MA 02215, USA
| | - Zexiang Zeng
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. 450 Brookline Ave., Boston MA 02215, USA
| | - Jijun Yu
- Beijing Key Laboratory of Therapeutic Gene Engineering Antibody. Beijing, People's Republic of China
| | - Zexu Li
- College of Life and Health Sciences, Northeastern University. 110819 Shenyang, People's Republic of China
| | - Teng Fei
- College of Life and Health Sciences, Northeastern University. 110819 Shenyang, People's Republic of China
| | - Shaoliang Peng
- Lushan South Road, Changsha, Hunan Province, People's Republic of China
| | - Wei Li
- Center for Genetic Medicine Research, Children's National Hospital. 111 Michigan Ave NW, Washington, DC 20010, USA.,Department of Genomics and Precision Medicine, George Washington University. 111 Michigan Ave NW, Washington, DC 20010, USA
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16
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O’Brien AR, Burgio G, Bauer DC. Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing. Brief Bioinform 2021; 22:308-314. [PMID: 32008042 PMCID: PMC7820861 DOI: 10.1093/bib/bbz145] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 10/21/2019] [Accepted: 10/22/2019] [Indexed: 12/26/2022] Open
Abstract
The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly.
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Affiliation(s)
- Aidan R O’Brien
- Health and Biosecurity, CSIRO, Sydney, NSW, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Gaetan Burgio
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Denis C Bauer
- Health and Biosecurity, CSIRO, Sydney, NSW, Australia
- Department of Biomedical Sciences in the Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
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17
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Störtz F, Minary P. crisprSQL: a novel database platform for CRISPR/Cas off-target cleavage assays. Nucleic Acids Res 2021; 49:D855-D861. [PMID: 33084893 PMCID: PMC7778913 DOI: 10.1093/nar/gkaa885] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/23/2020] [Accepted: 10/17/2020] [Indexed: 12/20/2022] Open
Abstract
With ongoing development of the CRISPR/Cas programmable nuclease system, applications in the area of in vivo therapeutic gene editing are increasingly within reach. However, non-negligible off-target effects remain a major concern for clinical applications. Even though a multitude of off-target cleavage datasets have been published, a comprehensive, transparent overview tool has not yet been established. Here, we present crisprSQL (http://www.crisprsql.com), an interactive and bioinformatically enhanced collection of CRISPR/Cas9 off-target cleavage studies aimed at enriching the fields of cleavage profiling, gene editing safety analysis and transcriptomics. The current version of crisprSQL contains cleavage data from 144 guide RNAs on 25,632 guide-target pairs from human and rodent cell lines, with interaction-specific references to epigenetic markers and gene names. The first curated database of this standard, it promises to enhance safety quantification research, inform experiment design and fuel development of computational off-target prediction algorithms.
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Affiliation(s)
- Florian Störtz
- Department of Computer Science, University of Oxford, Parks Road, Oxford OX1 3QD, UK
| | - Peter Minary
- Department of Computer Science, University of Oxford, Parks Road, Oxford OX1 3QD, UK
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18
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Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, van der Meer D, Shepherd R, Pignatelli M, Iorio F, Garnett MJ. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets. Nucleic Acids Res 2021. [PMID: 33068406 DOI: 10.6084/m9.figshare.16924132.v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.
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Affiliation(s)
- Lisa Dwane
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Fiona M Behan
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | | | - Wanjuan Yang
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | | | | | | | - Francesco Iorio
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Human Technopole, 20157 Milano, Italy
| | - Mathew J Garnett
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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19
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Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, van der Meer D, Shepherd R, Pignatelli M, Iorio F, Garnett MJ. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets. Nucleic Acids Res 2021; 49:D1365-D1372. [PMID: 33068406 PMCID: PMC7778984 DOI: 10.1093/nar/gkaa882] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/22/2020] [Accepted: 10/14/2020] [Indexed: 01/10/2023] Open
Abstract
CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.
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Affiliation(s)
- Lisa Dwane
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Fiona M Behan
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | | | - Wanjuan Yang
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | | | | | | | - Francesco Iorio
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Human Technopole, 20157 Milano, Italy
| | - Mathew J Garnett
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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20
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Kuang D, Weile J, Li R, Ouellette TW, Barber JA, Roth FP. MaveQuest: a web resource for planning experimental tests of human variant effects. Bioinformatics 2020; 36:3938-3940. [PMID: 32251504 PMCID: PMC7320626 DOI: 10.1093/bioinformatics/btaa228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 01/27/2020] [Accepted: 04/01/2020] [Indexed: 11/22/2022] Open
Abstract
Summary Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes. Availability and implementation MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Da Kuang
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Roujia Li
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Tom W Ouellette
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jarry A Barber
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
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21
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Henkel L, Rauscher B, Schmitt B, Winter J, Boutros M. Genome-scale CRISPR screening at high sensitivity with an empirically designed sgRNA library. BMC Biol 2020; 18:174. [PMID: 33228647 PMCID: PMC7686728 DOI: 10.1186/s12915-020-00905-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/20/2020] [Indexed: 12/22/2022] Open
Abstract
Background In recent years, large-scale genetic screens using the CRISPR/Cas9 system have emerged as scalable approaches able to interrogate gene function with unprecedented efficiency and specificity in various biological contexts. By this means, functional dependencies on both the protein-coding and noncoding genome of numerous cell types in different organisms have been interrogated. However, screening designs vary greatly and criteria for optimal experimental implementation and library composition are still emerging. Given their broad utility in functionally annotating genomes, the application and interpretation of genome-scale CRISPR screens would greatly benefit from consistent and optimal design criteria. Results We report advantages of conducting viability screens in selected Cas9 single-cell clones in contrast to Cas9 bulk populations. We further systematically analyzed published CRISPR screens in human cells to identify single-guide (sg) RNAs with consistent high on-target and low off-target activity. Selected guides were collected in a novel genome-scale sgRNA library, which efficiently identifies core and context-dependent essential genes. Conclusion We show how empirically designed libraries in combination with an optimized experimental design increase the dynamic range in gene essentiality screens at reduced library coverage. Supplementary information The online version contains supplementary material available at 10.1186/s12915-020-00905-1.
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Affiliation(s)
- Luisa Henkel
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, BioQuant and Medical Faculty Mannheim, D-69120, Heidelberg, Germany
| | - Benedikt Rauscher
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, BioQuant and Medical Faculty Mannheim, D-69120, Heidelberg, Germany
| | - Barbara Schmitt
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, BioQuant and Medical Faculty Mannheim, D-69120, Heidelberg, Germany
| | - Jan Winter
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, BioQuant and Medical Faculty Mannheim, D-69120, Heidelberg, Germany
| | - Michael Boutros
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, BioQuant and Medical Faculty Mannheim, D-69120, Heidelberg, Germany.
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22
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Zhang Y, Zhao G, Ahmed FYH, Yi T, Hu S, Cai T, Liao Q. In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster. Front Oncol 2020; 10:584404. [PMID: 33123486 PMCID: PMC7567020 DOI: 10.3389/fonc.2020.584404] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 08/24/2020] [Indexed: 12/11/2022] Open
Abstract
The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the in silico methods play a formidable role to support and boost experimental work. However, various tools based on distinctive design principles and frameworks harbor unique characteristics that are likely to confuse users about how to choose the most appropriate one for their purpose. In this review, we will present a comprehensive overview and comparisons on the in silico methods from the aspects of CRISPR/Cas system identification, guide RNA design, and post-experimental assistance. Furthermore, we establish the hypotheses in light of the new trends around the technical optimization and hope to provide significant clues for future tools development.
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Affiliation(s)
- Yuwei Zhang
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Guofang Zhao
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Fatma Yislam Hadi Ahmed
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Tianfei Yi
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Shiyun Hu
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Ting Cai
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Qi Liao
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
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23
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Michlits G, Jude J, Hinterndorfer M, de Almeida M, Vainorius G, Hubmann M, Neumann T, Schleiffer A, Burkard TR, Fellner M, Gijsbertsen M, Traunbauer A, Zuber J, Elling U. Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles. Nat Methods 2020; 17:708-716. [PMID: 32514112 DOI: 10.1038/s41592-020-0850-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 04/27/2020] [Indexed: 12/13/2022]
Abstract
CRISPR-Cas9 screens have emerged as a transformative approach to systematically probe gene functions. The quality and success of these screens depends on the frequencies of loss-of-function alleles, particularly in negative-selection screens widely applied for probing essential genes. Using optimized screening workflows, we performed essentialome screens in cancer cell lines and embryonic stem cells and achieved dropout efficiencies that could not be explained by common frameshift frequencies. We find that these superior effect sizes are mainly determined by the impact of in-frame mutations on protein function, which can be predicted based on amino acid composition and conservation. We integrate protein features into a 'Bioscore' and fuse it with improved predictors of single-guide RNA activity and indel formation to establish a score that captures all relevant processes in CRISPR-Cas9 mutagenesis. This Vienna Bioactivity CRISPR score (www.vbc-score.org) outperforms previous prediction tools and enables the selection of sgRNAs that effectively produce loss-of-function alleles.
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Affiliation(s)
- Georg Michlits
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
| | - Julian Jude
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Matthias Hinterndorfer
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Melanie de Almeida
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Gintautas Vainorius
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
| | - Maria Hubmann
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
| | - Tobias Neumann
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Alexander Schleiffer
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Thomas Rainer Burkard
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Michaela Fellner
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Max Gijsbertsen
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria
| | - Anna Traunbauer
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Johannes Zuber
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
- Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria.
| | - Ulrich Elling
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria.
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24
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Ohnmacht J, May P, Sinkkonen L, Krüger R. Missing heritability in Parkinson's disease: the emerging role of non-coding genetic variation. J Neural Transm (Vienna) 2020; 127:729-748. [PMID: 32248367 PMCID: PMC7242266 DOI: 10.1007/s00702-020-02184-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/24/2020] [Indexed: 02/01/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder caused by a complex interplay of genetic and environmental factors. For the stratification of PD patients and the development of advanced clinical trials, including causative treatments, a better understanding of the underlying genetic architecture of PD is required. Despite substantial efforts, genome-wide association studies have not been able to explain most of the observed heritability. The majority of PD-associated genetic variants are located in non-coding regions of the genome. A systematic assessment of their functional role is hampered by our incomplete understanding of genotype-phenotype correlations, for example through differential regulation of gene expression. Here, the recent progress and remaining challenges for the elucidation of the role of non-coding genetic variants is reviewed with a focus on PD as a complex disease with multifactorial origins. The function of gene regulatory elements and the impact of non-coding variants on them, and the means to map these elements on a genome-wide level, will be delineated. Moreover, examples of how the integration of functional genomic annotations can serve to identify disease-associated pathways and to prioritize disease- and cell type-specific regulatory variants will be given. Finally, strategies for functional validation and considerations for suitable model systems are outlined. Together this emphasizes the contribution of rare and common genetic variants to the complex pathogenesis of PD and points to remaining challenges for the dissection of genetic complexity that may allow for better stratification, improved diagnostics and more targeted treatments for PD in the future.
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Affiliation(s)
- Jochen Ohnmacht
- LCSB, University of Luxembourg, Belvaux, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, Belvaux, Luxembourg
| | - Patrick May
- LCSB, University of Luxembourg, Belvaux, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, Belvaux, Luxembourg
| | - Rejko Krüger
- LCSB, University of Luxembourg, Belvaux, Luxembourg.
- Luxembourg Institute of Health (LIH), Transversal Translational Medicine, Strassen, Luxembourg.
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg.
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25
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Baryshnikova A. Data libraries - the missing element for modeling biological systems. FEBS J 2020; 287:4594-4601. [PMID: 32100391 PMCID: PMC7687078 DOI: 10.1111/febs.15261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/19/2020] [Accepted: 02/24/2020] [Indexed: 11/29/2022]
Abstract
The primary bottleneck in understanding and modeling biological systems is shifting from data collection to data analysis and integration. This process critically depends on data being available in an organized form, so that they can be accessed, understood, and reused by a broad community of scientists. A proven solution for organizing data is literature curation, which extracts, aggregates, and distributes findings from publications. Here, I describe the benefits of extending curation practices to datasets, especially those that are not deposited in centralized databases. I argue that dataset curation (or ‘data librarianship’ as I suggest we call it) will overcome many barriers in data visibility and reusability and make a unique contribution to integration and modeling.
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26
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Chen CC, Li B, Millman SE, Chen C, Li X, Morris JP, Mayle A, Ho YJ, Loizou E, Liu H, Qin W, Shah H, Violante S, Cross JR, Lowe SW, Zhang L. Vitamin B6 Addiction in Acute Myeloid Leukemia. Cancer Cell 2020; 37:71-84.e7. [PMID: 31935373 PMCID: PMC7197326 DOI: 10.1016/j.ccell.2019.12.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 09/02/2019] [Accepted: 12/06/2019] [Indexed: 12/15/2022]
Abstract
Cancer cells rely on altered metabolism to support abnormal proliferation. We performed a CRISPR/Cas9 functional genomic screen targeting metabolic enzymes and identified PDXK-an enzyme that produces pyridoxal phosphate (PLP) from vitamin B6-as an acute myeloid leukemia (AML)-selective dependency. PDXK kinase activity is required for PLP production and AML cell proliferation, and pharmacological blockade of the vitamin B6 pathway at both PDXK and PLP levels recapitulated PDXK disruption effects. PDXK disruption reduced intracellular concentrations of key metabolites needed for cell division. Furthermore, disruption of PLP-dependent enzymes ODC1 or GOT2 selectively inhibited AML cell proliferation and their downstream products partially rescued PDXK disruption induced proliferation blockage. Our work identifies the vitamin B6 pathway as a pharmacologically actionable dependency in AML.
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Affiliation(s)
- Chi-Chao Chen
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 10065, USA
| | - Bo Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Scott E Millman
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Cynthia Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Xiang Li
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 10065, USA
| | - John P Morris
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Allison Mayle
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yu-Jui Ho
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Evangelia Loizou
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 10065, USA
| | - Hui Liu
- Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Weige Qin
- Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Hardik Shah
- Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sara Violante
- Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Justin R Cross
- Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Scott W Lowe
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Howard Hughes Medical Institute, New York, NY 10065, USA.
| | - Lingbo Zhang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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27
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Barman A, Deb B, Chakraborty S. A glance at genome editing with CRISPR–Cas9 technology. Curr Genet 2019; 66:447-462. [DOI: 10.1007/s00294-019-01040-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 12/16/2022]
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28
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Sayed S, Paszkowski-Rogacz M, Schmitt LT, Buchholz F. CRISPR/Cas9 as a tool to dissect cancer mutations. Methods 2019; 164-165:36-48. [PMID: 31078796 DOI: 10.1016/j.ymeth.2019.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 12/26/2022] Open
Abstract
The CRISPR/Cas9 system is transforming many biomedical disciplines, including cancer research. Through its flexible programmability and efficiency to induce DNA double strand breaks it has become straightforward to introduce cancer mutations into cells in vitro and/or in vivo. However, not all mutations contribute equally to tumorigenesis and distinguishing essential mutations for tumor growth and survival from biologically inert mutations is cumbersome. Here we present a method to screen for the functional relevance of mutations in high throughput in established cancer cell lines. We employ the CRISPR/Cas9 system to probe cancer vulnerabilities in a colorectal carcinoma cell line in an attempt to identify novel cancer driver mutations. We designed 100 high quality sgRNAs that are able to specifically cleave mutations present in the colorectal carcinoma cell line RKO. An all-in-one lentiviral library harboring these sgRNAs was then generated and used in a pooled screen to probe possible growth dependencies on these mutations. Genomic DNA at different time points were collected, the sgRNA cassettes were PCR amplified, purified and sgRNA counts were quantified by means of deep sequencing. The analysis revealed two sgRNAs targeting the same mutation (UTP14A: S99delS) to be depleted over time in RKO cells. Validation and characterization confirmed that the inactivation of this mutation impairs cell growth, nominating UTP14A: S99delS as a putative driver mutation in RKO cells. Overall, our approach demonstrates that the CRISPR/Cas9 system is a powerful tool to functionally dissect cancer mutations at large-scale.
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Affiliation(s)
- Shady Sayed
- Carl Gustav Carus Faculty of Medicine, UCC, Section Medical Systems Biology, TU Dresden, Germany; National Center for Tumor Diseases (NCT), University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Maciej Paszkowski-Rogacz
- Carl Gustav Carus Faculty of Medicine, UCC, Section Medical Systems Biology, TU Dresden, Germany
| | - Lukas Theo Schmitt
- Carl Gustav Carus Faculty of Medicine, UCC, Section Medical Systems Biology, TU Dresden, Germany
| | - Frank Buchholz
- Carl Gustav Carus Faculty of Medicine, UCC, Section Medical Systems Biology, TU Dresden, Germany; National Center for Tumor Diseases (NCT), University Hospital Carl Gustav Carus, TU Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
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29
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Feddersen CR, Wadsworth LS, Zhu EY, Vaughn HR, Voigt AP, Riordan JD, Dupuy AJ. A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells. BMC Genomics 2019; 20:497. [PMID: 31208320 PMCID: PMC6580595 DOI: 10.1186/s12864-019-5888-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 06/06/2019] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The introduction of genome-wide shRNA and CRISPR libraries has facilitated cell-based screens to identify loss-of-function mutations associated with a phenotype of interest. Approaches to perform analogous gain-of-function screens are less common, although some reports have utilized arrayed viral expression libraries or the CRISPR activation system. However, a variety of technical and logistical challenges make these approaches difficult for many labs to execute. In addition, genome-wide shRNA or CRISPR libraries typically contain of hundreds of thousands of individual engineered elements, and the associated complexity creates issues with replication and reproducibility for these methods. RESULTS Here we describe a simple, reproducible approach using the SB transposon system to perform phenotypic cell-based genetic screens. This approach employs only three plasmids to perform unbiased, whole-genome transposon mutagenesis. We also describe a ligation-mediated PCR method that can be used in conjunction with the included software tools to map raw sequence data, identify candidate genes associated with phenotypes of interest, and predict the impact of recurrent transposon insertions on candidate gene function. Finally, we demonstrate the high reproducibility of our approach by having three individuals perform independent replicates of a mutagenesis screen to identify drivers of vemurafenib resistance in cultured melanoma cells. CONCLUSIONS Collectively, our work establishes a facile, adaptable method that can be performed by labs of any size to perform robust, genome-wide screens to identify genes that influence phenotypes of interest.
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Affiliation(s)
- Charlotte R. Feddersen
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Lexy S. Wadsworth
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Eliot Y. Zhu
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Hayley R. Vaughn
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Andrew P. Voigt
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Jesse D. Riordan
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
| | - Adam J. Dupuy
- Department of Anatomy & Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52246 USA
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52246 USA
- Department of Anatomy & Cell Biology, Cancer Biology Graduate Program, University of Iowa, MERF, 375 Newton Road, Iowa City, IA 3202 USA
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30
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Oughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L, Chang C, Kolas N, O’Donnell L, Leung G, McAdam R, Zhang F, Dolma S, Willems A, Coulombe-Huntington J, Chatr-aryamontri A, Dolinski K, Tyers M. The BioGRID interaction database: 2019 update. Nucleic Acids Res 2019; 47:D529-D541. [PMID: 30476227 PMCID: PMC6324058 DOI: 10.1093/nar/gky1079] [Citation(s) in RCA: 858] [Impact Index Per Article: 171.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 10/15/2018] [Accepted: 11/22/2018] [Indexed: 12/17/2022] Open
Abstract
The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.
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Affiliation(s)
- Rose Oughtred
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Chris Stark
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Bobby-Joe Breitkreutz
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Jennifer Rust
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Lorrie Boucher
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Christie Chang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Nadine Kolas
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Lara O’Donnell
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Genie Leung
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Rochelle McAdam
- Arthur and Sonia Labatt Brain Tumor Research Center and Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
| | - Frederick Zhang
- Arthur and Sonia Labatt Brain Tumor Research Center and Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
| | - Sonam Dolma
- Arthur and Sonia Labatt Brain Tumor Research Center and Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
| | - Andrew Willems
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Jasmin Coulombe-Huntington
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec H3C 3J7, Canada
| | - Andrew Chatr-aryamontri
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec H3C 3J7, Canada
| | - Kara Dolinski
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Mike Tyers
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec H3C 3J7, Canada
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31
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Doggett K, Williams BB, Markmiller S, Geng FS, Coates J, Mieruszynski S, Ernst M, Thomas T, Heath JK. Early developmental arrest and impaired gastrointestinal homeostasis in U12-dependent splicing-defective Rnpc3-deficient mice. RNA (NEW YORK, N.Y.) 2018; 24:1856-1870. [PMID: 30254136 PMCID: PMC6239176 DOI: 10.1261/rna.068221.118] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 09/20/2018] [Indexed: 05/10/2023]
Abstract
Splicing is an essential step in eukaryotic gene expression. While the majority of introns is excised by the U2-dependent, or major class, spliceosome, the appropriate expression of a very small subset of genes depends on U12-dependent, or minor class, splicing. The U11/U12 65K protein (hereafter 65K), encoded by RNPC3, is one of seven proteins that are unique to the U12-dependent spliceosome, and previous studies including our own have established that it plays a role in plant and vertebrate development. To pinpoint the impact of 65K loss during mammalian development and in adulthood, we generated germline and conditional Rnpc3-deficient mice. Homozygous Rnpc3-/- embryos died prior to blastocyst implantation, whereas Rnpc3+/- mice were born at the expected frequency, achieved sexual maturity, and exhibited a completely normal lifespan. Systemic recombination of conditional Rnpc3 alleles in adult (Rnpc3lox/lox ) mice caused rapid weight loss, leukopenia, and degeneration of the epithelial lining of the entire gastrointestinal tract, the latter due to increased cell death and a reduction in cell proliferation. Accompanying this, we observed a loss of both 65K and the pro-proliferative phospho-ERK1/2 proteins from the stem/progenitor cells at the base of intestinal crypts. RT-PCR analysis of RNA extracted from purified preparations of intestinal epithelial cells with recombined Rnpc3lox alleles revealed increased frequency of U12-type intron retention in all transcripts tested. Our study, using a novel conditional mouse model of Rnpc3 deficiency, establishes that U12-dependent splicing is not only important during development but is indispensable throughout life.
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Affiliation(s)
- Karen Doggett
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Ben B Williams
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Sebastian Markmiller
- Department of Surgery, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria 3050, Australia
| | - Fan-Suo Geng
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Janine Coates
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
| | - Stephen Mieruszynski
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Matthias Ernst
- Olivia Newton-John Cancer Research Institute and La Trobe University School of Cancer Medicine, Heidelberg, Victoria 3050, Australia
| | - Tim Thomas
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Joan K Heath
- Development and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria 3052, Australia
- Department of Surgery, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria 3050, Australia
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32
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Schuster A, Erasimus H, Fritah S, Nazarov PV, van Dyck E, Niclou SP, Golebiewska A. RNAi/CRISPR Screens: from a Pool to a Valid Hit. Trends Biotechnol 2018; 37:38-55. [PMID: 30177380 DOI: 10.1016/j.tibtech.2018.08.002] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 08/09/2018] [Accepted: 08/09/2018] [Indexed: 02/07/2023]
Abstract
High-throughput genetic screens interfering with gene expression are invaluable tools to identify gene function and phenotype-to-genotype interactions. Implementing such screens in the laboratory is challenging, and the choice between currently available technologies based on RNAi and CRISPR/Cas9 (CRISPR-associated protein 9) is not trivial. Identifying reliable candidate hits requires a streamlined experimental setup adjusted to the specific biological question. Here, we provide a critical assessment of the various RNAi/CRISPR approaches to pooled screens and discuss their advantages and pitfalls. We specify a set of best practices for key parameters enabling a reproducible screen and provide a detailed overview of analysis methods and repositories for identifying the best candidate gene hits.
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Affiliation(s)
- Anne Schuster
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg
| | - Hélène Erasimus
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg
| | - Sabrina Fritah
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg
| | - Petr V Nazarov
- Genomics and Proteomics Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg
| | - Eric van Dyck
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg
| | - Simone P Niclou
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg; KG Jebsen Brain Tumour Research Center, Department of Biomedicine, University of Bergen, Bergen, Norway; Co-senior authors.
| | - Anna Golebiewska
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg; Co-senior authors.
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33
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Jeong HH, Kim SY, Rousseaux MWC, Zoghbi HY, Liu Z. CRISPRcloud: a secure cloud-based pipeline for CRISPR pooled screen deconvolution. Bioinformatics 2018; 33:2963-2965. [PMID: 28541456 DOI: 10.1093/bioinformatics/btx335] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 05/23/2017] [Indexed: 11/14/2022] Open
Abstract
Summary We present a user-friendly, cloud-based, data analysis pipeline for the deconvolution of pooled screening data. This tool, CRISPRcloud, serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. Moreover, CRISPRcloud serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. Taken together, the framework described in this study is expected to accelerate development of web-based bioinformatics tool for handling all studies which include next generation sequencing data. Availability and implementation http://crispr.nrihub.org. Contact zhandong.liu@bcm.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hyun-Hwan Jeong
- Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
| | - Seon Young Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
| | - Maxime W C Rousseaux
- Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
| | - Huda Y Zoghbi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA.,Department of Pediatrics.,Baylor College of Medicine, Howard Hughes Medical Institute, Houston, TX, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA.,Department of Pediatrics
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34
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You Q, Zhong Z, Ren Q, Hassan F, Zhang Y, Zhang T. CRISPRMatch: An Automatic Calculation and Visualization Tool for High-throughput CRISPR Genome-editing Data Analysis. Int J Biol Sci 2018; 14:858-862. [PMID: 29989077 PMCID: PMC6036748 DOI: 10.7150/ijbs.24581] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 02/28/2018] [Indexed: 01/05/2023] Open
Abstract
Custom-designed nucleases, including CRISPR-Cas9 and CRISPR-Cpf1, are widely used to realize the precise genome editing. The high-coverage, low-cost and quantifiability make high-throughput sequencing (NGS) to be an effective method to assess the efficiency of custom-designed nucleases. However, contrast to standardized transcriptome protocol, the NGS data lacks a user-friendly pipeline connecting different tools that can automatically calculate mutation, evaluate editing efficiency and realize in a more comprehensive dataset that can be visualized. Here, we have developed an automatic stand-alone toolkit based on python script, namely CRISPRMatch, to process the high-throughput genome-editing data of CRISPR nuclease transformed protoplasts by integrating analysis steps like mapping reads and normalizing reads count, calculating mutation frequency (deletion and insertion), evaluating efficiency and accuracy of genome-editing, and visualizing the results (tables and figures). Both of CRISPR-Cas9 and CRISPR-Cpf1 nucleases are supported by CRISPRMatch toolkit and the integrated code has been released on GitHub (https://github.com/zhangtaolab/CRISPRMatch).
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Affiliation(s)
- Qi You
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Centre for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou 225009, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
| | - Zhaohui Zhong
- Department of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiurong Ren
- Department of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fakhrul Hassan
- Department of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yong Zhang
- Department of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Tao Zhang
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Centre for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou 225009, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
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35
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Wilson LOW, Reti D, O'Brien AR, Dunne RA, Bauer DC. High Activity Target-Site Identification Using Phenotypic Independent CRISPR-Cas9 Core Functionality. CRISPR J 2018; 1:182-190. [PMID: 31021206 DOI: 10.1089/crispr.2017.0021] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The activity of CRISPR-Cas9 target sites can be measured experimentally through phenotypic assays or mutation rate and used to build computational models to predict activity of novel target sites. However, currently published models have been reported to perform poorly in situations other than their training conditions. In this study, we hence investigate how different sources of data influence predictive power and identify the best data set for the most robust predictive model. We use the activity of 28,606 target sites and a machine learning approach to train a predictive model of CRISPR-Cas9 activity, outperforming other published methods by an average increase in accuracy of 80% for prediction of the degree of activity and 13% for classification into active and inactive categories. We find that using data sets that measure CRISPR-Cas9 activity through sequencing provides more accurate predictions of activity. Our model, dubbed TUSCAN, is highly scalable, predicting the activity of 5000 target sites in under 7 s, making it suitable for genome-wide screens. We conclude that sophisticated machine learning methods can classify binary CRISPR-Cas9 activity; however, predicting fine-scale activity scores will require larger data sets directly measuring Indel insertion rate.
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Affiliation(s)
| | - Daniel Reti
- 1 Health and Biosecurity, CSIRO , Sydney, Australia .,2 Faculty of Engineering, UNSW , Sydney, Australia
| | - Aidan R O'Brien
- 1 Health and Biosecurity, CSIRO , Sydney, Australia .,3 Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University , Canberra, Australia
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36
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Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery. Biochem Pharmacol 2018; 152:84-93. [PMID: 29551586 DOI: 10.1016/j.bcp.2018.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 03/13/2018] [Indexed: 11/21/2022]
Abstract
The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data.
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37
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Rauscher B, Heigwer F, Henkel L, Hielscher T, Voloshanenko O, Boutros M. Toward an integrated map of genetic interactions in cancer cells. Mol Syst Biol 2018; 14:e7656. [PMID: 29467179 PMCID: PMC5820685 DOI: 10.15252/msb.20177656] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Revised: 01/20/2018] [Accepted: 01/23/2018] [Indexed: 12/13/2022] Open
Abstract
Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.
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Affiliation(s)
- Benedikt Rauscher
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Florian Heigwer
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Luisa Henkel
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oksana Voloshanenko
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Michael Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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38
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Modernizing Human Cancer Risk Assessment of Therapeutics. Trends Pharmacol Sci 2017; 39:232-247. [PMID: 29242029 DOI: 10.1016/j.tips.2017.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/17/2017] [Accepted: 11/17/2017] [Indexed: 12/14/2022]
Abstract
Cancer risk assessment of therapeutics is plagued by poor translatability of rodent models of carcinogenesis. In order to overcome this fundamental limitation, new approaches are needed that enable us to evaluate cancer risk directly in humans and human-based cellular models. Our enhanced understanding of the mechanisms of carcinogenesis and the influence of human genome sequence variation on cancer risk motivates us to re-evaluate how we assess the carcinogenic risk of therapeutics. This review will highlight new opportunities for applying this knowledge to the development of a battery of human-based in vitro models and biomarkers for assessing cancer risk of novel therapeutics.
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39
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Abstract
Exciting new technologies are often self-limiting in their rollout, as access to state-of-the-art instrumentation or the need for years of hands-on experience, for better or worse, ensures slow adoption by the community. CRISPR technology, however, presents the opposite dilemma, where the simplicity of the system enabled the parallel development of many applications, improvements and derivatives, and new users are now presented with an almost paralyzing abundance of choices. This Review intends to guide users through the process of applying CRISPR technology to their biological problems of interest, especially in the context of discovering gene function at scale.
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Affiliation(s)
- John G Doench
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
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40
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Michlits G, Hubmann M, Wu SH, Vainorius G, Budusan E, Zhuk S, Burkard TR, Novatchkova M, Aichinger M, Lu Y, Reece-Hoyes J, Nitsch R, Schramek D, Hoepfner D, Elling U. CRISPR-UMI: single-cell lineage tracing of pooled CRISPR-Cas9 screens. Nat Methods 2017; 14:1191-1197. [PMID: 29039415 DOI: 10.1038/nmeth.4466] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 09/11/2017] [Indexed: 12/12/2022]
Abstract
Pooled CRISPR screens are a powerful tool for assessments of gene function. However, conventional analysis is based exclusively on the relative abundance of integrated single guide RNAs (sgRNAs) between populations, which does not discern distinct phenotypes and editing outcomes generated by identical sgRNAs. Here we present CRISPR-UMI, a single-cell lineage-tracing methodology for pooled screening to account for cell heterogeneity. We generated complex sgRNA libraries with unique molecular identifiers (UMIs) that allowed for screening of clonally expanded, individually tagged cells. A proof-of-principle CRISPR-UMI negative-selection screen provided increased sensitivity and robustness compared with conventional analysis by accounting for underlying cellular and editing-outcome heterogeneity and detection of outlier clones. Furthermore, a CRISPR-UMI positive-selection screen uncovered new roadblocks in reprogramming mouse embryonic fibroblasts as pluripotent stem cells, distinguishing reprogramming frequency and speed (i.e., effect size and probability). CRISPR-UMI boosts the predictive power, sensitivity, and information content of pooled CRISPR screens.
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Affiliation(s)
- Georg Michlits
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Maria Hubmann
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Szu-Hsien Wu
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Gintautas Vainorius
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Elena Budusan
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Sergei Zhuk
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Thomas R Burkard
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),Vienna, Austria
| | - Maria Novatchkova
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),Vienna, Austria
| | - Martin Aichinger
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),Vienna, Austria
| | - Yiqing Lu
- Center for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - John Reece-Hoyes
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA
| | - Roberto Nitsch
- Discovery Sciences RAD, AstraZeneca R&D, Gothenburg, Sweden
| | - Daniel Schramek
- Center for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | | | - Ulrich Elling
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria
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41
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Montalbano A, Canver MC, Sanjana NE. High-Throughput Approaches to Pinpoint Function within the Noncoding Genome. Mol Cell 2017; 68:44-59. [PMID: 28985510 PMCID: PMC5701515 DOI: 10.1016/j.molcel.2017.09.017] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 09/13/2017] [Accepted: 09/13/2017] [Indexed: 12/26/2022]
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas nuclease system is a powerful tool for genome editing, and its simple programmability has enabled high-throughput genetic and epigenetic studies. These high-throughput approaches offer investigators a toolkit for functional interrogation of not only protein-coding genes but also noncoding DNA. Historically, noncoding DNA has lacked the detailed characterization that has been applied to protein-coding genes in large part because there has not been a robust set of methodologies for perturbing these regions. Although the majority of high-throughput CRISPR screens have focused on the coding genome to date, an increasing number of CRISPR screens targeting noncoding genomic regions continue to emerge. Here, we review high-throughput CRISPR-based approaches to uncover and understand functional elements within the noncoding genome and discuss practical aspects of noncoding library design and screen analysis.
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Affiliation(s)
- Antonino Montalbano
- New York Genome Center, New York, NY, USA; Department of Biology, New York University, New York, NY, USA
| | | | - Neville E Sanjana
- New York Genome Center, New York, NY, USA; Department of Biology, New York University, New York, NY, USA.
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42
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Shang W, Wang F, Fan G, Wang H. Key elements for designing and performing a CRISPR/Cas9-based genetic screen. J Genet Genomics 2017; 44:439-449. [PMID: 28967615 DOI: 10.1016/j.jgg.2017.09.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 07/09/2017] [Accepted: 09/04/2017] [Indexed: 12/26/2022]
Abstract
Reverse genetic screens are invaluable for uncovering gene functions, but are traditionally hampered by some technical limitations. Over the past few years, since the advent of the revolutionary CRISPR/Cas9 technology, its power in genome editing has been harnessed to overcome the traditional limitations in reverse genetic screens, with successes in various biological contexts. Here, we outline these CRISPR/Cas9-based screens, provide guidance on the design of effective screens and discuss the potential future directions of development of this field.
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Affiliation(s)
- Wanjing Shang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gaofeng Fan
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Haopeng Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
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43
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Affiliation(s)
- Joshua A. Meier
- Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- New York Genome Center, 101 Avenue of the Americas, New York, NY 10013
- Department of Biology, New York University, 100 Washington Square East, New York, NY 10003
| | - Feng Zhang
- Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Neville E. Sanjana
- New York Genome Center, 101 Avenue of the Americas, New York, NY 10013
- Department of Biology, New York University, 100 Washington Square East, New York, NY 10003
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44
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Transcriptome modeling and phenotypic assays for cancer precision medicine. Arch Pharm Res 2017; 40:906-914. [PMID: 28766239 DOI: 10.1007/s12272-017-0940-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 07/27/2017] [Indexed: 01/02/2023]
Abstract
Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery.
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45
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Abstract
Genetic screens are powerful tools to identify components that make up biological systems. Perturbations introduced by methods such as RNA interference (RNAi) or CRISPR/Cas9-mediated genome editing lead to biological phenotypes that can be examined to understand the molecular function of genes in the cell. Over the years, many of such experiments have been conducted providing a wealth of knowledge about genotype-to-phenotype relationships. These data are a rich source of information and it is in a common interest to make them available in a simplified and integrated format. Thus, an important challenge is that genetic screening data can be stored in databases in standardized ways, allowing users to gain new biological insights through data mining and integrated analyses. Here, we provide an overview of available phenotype databases for human cells. We review in detail two databases for high-throughput screens, GenomeRNAi and GenomeCRISPR, and describe how these resources are integrated into the German Network for Bioinformatics Infrastructure de.NBI as part of the European infrastructure for life-science information ELIXIR.
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Affiliation(s)
- Benedikt Rauscher
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany
| | - Erica Valentini
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany
| | - Ulrike Hardeland
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany
| | - Michael Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, 69120 Heidelberg, Germany.
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