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Xiang T, Feng H, Xing XH, Zhang C. GLiDe: a web-based genome-scale CRISPRi sgRNA design tool for prokaryotes. BMC Bioinformatics 2025; 26:1. [PMID: 39754035 PMCID: PMC11699761 DOI: 10.1186/s12859-024-06012-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 12/09/2024] [Indexed: 01/06/2025] Open
Abstract
BACKGROUND CRISPRi screening has become a powerful approach for functional genomic research. However, the off-target effects resulting from the mismatch tolerance between sgRNAs and their intended targets is a primary concern in CRISPRi applications. RESULTS We introduce Guide Library Designer (GLiDe), a web-based tool specifically created for the genome-scale design of sgRNA libraries tailored for CRISPRi screening in prokaryotic organisms. GLiDe incorporates a robust quality control framework, rooted in prior experimental knowledge, ensuring the accurate identification of off-target hits. It boasts an extensive built-in database, encompassing 1,397 common prokaryotic species as a comprehensive design resource. It also provides the capability to design sgRNAs for newly discovered organisms by accepting uploaded design resource. We further demonstrated that GLiDe exhibits enhanced precision in identifying off-target binding sites for the CRISPRi system. CONCLUSIONS We present a web server that allows the construction of genome-scale CRISPRi sgRNA libraries for prokaryotes. It mitigates off-target effects through a robust quality control framework, leveraging prior experimental knowledge within an end-to-end, user-friendly pipeline.
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Affiliation(s)
- Tongjun Xiang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Huibao Feng
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, 91125, USA.
| | - Xin-Hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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2
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Otto RM, Turska-Nowak A, Brown PM, Reynolds KA. A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment. Cell Syst 2024; 15:134-148.e7. [PMID: 38340730 PMCID: PMC10885703 DOI: 10.1016/j.cels.2024.01.003] [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: 05/04/2023] [Revised: 10/13/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan M Otto
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Agata Turska-Nowak
- Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Philip M Brown
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Kimberly A Reynolds
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA; Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA.
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3
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Toch K, Buczek M, Labocha MK. Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans. Genes (Basel) 2023; 14:2080. [PMID: 38003023 PMCID: PMC10671385 DOI: 10.3390/genes14112080] [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: 10/24/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.
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Affiliation(s)
| | | | - Marta K. Labocha
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Ul. Gronostajowa 7, 30-387 Krakow, Poland; (K.T.); (M.B.)
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4
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Kovuri P, Yadav A, Sinha H. Role of genetic architecture in phenotypic plasticity. Trends Genet 2023; 39:703-714. [PMID: 37173192 DOI: 10.1016/j.tig.2023.04.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.
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Affiliation(s)
- Purnima Kovuri
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.
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5
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Mukherjee V, Lenitz I, Lind U, Blomberg A, Nygård Y. CRISPRi screen highlights chromatin regulation to be involved in formic acid tolerance in Saccharomyces cerevisiae. ENGINEERING MICROBIOLOGY 2023; 3:100076. [PMID: 39629247 PMCID: PMC11611036 DOI: 10.1016/j.engmic.2023.100076] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/05/2023] [Accepted: 01/29/2023] [Indexed: 12/07/2024]
Abstract
Formic acid is one of the main weak acids in lignocellulosic hydrolysates that is known to be inhibitory to yeast growth even at low concentrations. In this study, we employed a CRISPR interference (CRISPRi) strain library comprising >9000 strains encompassing >98% of all essential and respiratory growth-essential genes, to study formic acid tolerance in Saccharomyces cerevisiae. To provide quantitative growth estimates on formic acid tolerance, the strains were screened individually on solid medium supplemented with 140 mM formic acid using the Scan-o-Matic platform. Selected resistant and sensitive strains were characterized in liquid medium supplemented with formic acid and in synthetic hydrolysate medium containing a combination of inhibitors. Strains with gRNAs targeting genes associated with chromatin remodeling were significantly enriched for strains showing formic acid tolerance. In line with earlier findings on acetic acid tolerance, we found genes encoding proteins involved in intracellular vesicle transport enriched among formic acid sensitive strains. The growth of the strains in synthetic hydrolysate medium followed the same trend as when screened in medium supplemented with formic acid. Strains sensitive to formic acid had decreased growth in the synthetic hydrolysate and all strains that had improved growth in the presence of formic acid also grew better in the hydrolysate medium. Systematic analysis of CRISPRi strains allowed identification of genes involved in tolerance mechanisms and provided novel engineering targets for bioengineering strains with increased resistance to inhibitors in lignocellulosic hydrolysates.
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Affiliation(s)
- Vaskar Mukherjee
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Department of Biorefinery and Energy, RISE Research Institutes of Sweden, Örnsköldsvik, Sweden
| | - Ibai Lenitz
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Ulrika Lind
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Anders Blomberg
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Yvonne Nygård
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
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6
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Chen SAA, Kern AF, Ang RML, Xie Y, Fraser HB. Gene-by-environment interactions are pervasive among natural genetic variants. CELL GENOMICS 2023; 3:100273. [PMID: 37082145 PMCID: PMC10112290 DOI: 10.1016/j.xgen.2023.100273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/09/2022] [Accepted: 01/31/2023] [Indexed: 04/22/2023]
Abstract
Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.
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Affiliation(s)
- Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Roy Moh Lik Ang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
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7
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Mormino M, Lenitz I, Siewers V, Nygård Y. Identification of acetic acid sensitive strains through biosensor-based screening of a Saccharomyces cerevisiae CRISPRi library. Microb Cell Fact 2022; 21:214. [PMID: 36243715 PMCID: PMC9571444 DOI: 10.1186/s12934-022-01938-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Acetic acid tolerance is crucial for the development of robust cell factories for conversion of lignocellulosic hydrolysates that typically contain high levels of acetic acid. Screening mutants for growth in medium with acetic acid is an attractive way to identify sensitive variants and can provide novel insights into the complex mechanisms regulating the acetic acid stress response. Results An acetic acid biosensor based on the Saccharomyces cerevisiae transcription factor Haa1, was used to screen a CRISPRi yeast strain library where dCas9-Mxi was set to individually repress each essential or respiratory growth essential gene. Fluorescence-activated cell sorting led to the enrichment of a population of cells with higher acetic acid retention. These cells with higher biosensor signal were demonstrated to be more sensitive to acetic acid. Biosensor-based screening of the CRISPRi library strains enabled identification of strains with increased acetic acid sensitivity: strains with gRNAs targeting TIF34, MSN5, PAP1, COX10 or TRA1. Conclusions This study demonstrated that biosensors are valuable tools for screening and monitoring acetic acid tolerance in yeast. Fine-tuning the expression of essential genes can lead to altered acetic acid tolerance. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01938-7.
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Affiliation(s)
- Maurizio Mormino
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ibai Lenitz
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Yvonne Nygård
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
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8
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Bosch-Guiteras N, van Leeuwen J. Exploring conditional gene essentiality through systems genetics approaches in yeast. Curr Opin Genet Dev 2022; 76:101963. [PMID: 35939967 DOI: 10.1016/j.gde.2022.101963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/25/2022] [Accepted: 07/04/2022] [Indexed: 11/25/2022]
Abstract
An essential gene encodes for a cellular function that is required for viability. Although viability is a straightforward phenotype to analyze in yeast, defining a gene as essential is not always trivial. Gene essentiality has generally been studied in specific laboratory strains and under standard growth conditions, however, essentiality can vary across species, strains, and environments. Recent systematic studies of gene essentiality revealed that two sets of essential genes exist: core essential genes that are always required for viability and conditional essential genes that vary in essentiality in different genetic and environmental contexts. Here, we review recent advances made in the systematic analysis of gene essentiality in yeast and discuss the properties that distinguish core from context-dependent essential genes.
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Affiliation(s)
| | - Jolanda van Leeuwen
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
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9
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Hogan AM, Cardona ST. Gradients in gene essentiality reshape antibacterial research. FEMS Microbiol Rev 2022; 46:fuac005. [PMID: 35104846 PMCID: PMC9075587 DOI: 10.1093/femsre/fuac005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 02/03/2023] Open
Abstract
Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.
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Affiliation(s)
- Andrew M Hogan
- Department of Microbiology, University of Manitoba, 45 Chancellor's Circle, Winnipeg, Manitoba R3T 2N2, Canada
| | - Silvia T Cardona
- Department of Microbiology, University of Manitoba, 45 Chancellor's Circle, Winnipeg, Manitoba R3T 2N2, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Room 543 - 745 Bannatyne Avenue, Winnipeg, Manitoba, R3E 0J9, Canada
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10
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Phenomics approaches to understand genetic networks and gene function in yeast. Biochem Soc Trans 2022; 50:713-721. [PMID: 35285506 PMCID: PMC9162466 DOI: 10.1042/bst20210285] [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: 12/10/2021] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 01/03/2023]
Abstract
Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.
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11
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Bock C, Datlinger P, Chardon F, Coelho MA, Dong MB, Lawson KA, Lu T, Maroc L, Norman TM, Song B, Stanley G, Chen S, Garnett M, Li W, Moffat J, Qi LS, Shapiro RS, Shendure J, Weissman JS, Zhuang X. High-content CRISPR screening. NATURE REVIEWS. METHODS PRIMERS 2022; 2:9. [PMID: 37214176 PMCID: PMC10200264 DOI: 10.1038/s43586-022-00098-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.
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Affiliation(s)
- Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Florence Chardon
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Matthew B. Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Tian Lu
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Laetitia Maroc
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Thomas M. Norman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bicna Song
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Geoff Stanley
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Mathew Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Wei Li
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Lei S. Qi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- ChEM-H, Stanford University, Stanford, CA, USA
| | - Rebecca S. Shapiro
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Jonathan S. Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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12
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Decourty L, Malabat C, Frachon E, Jacquier A, Saveanu C. Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast. Nucleic Acids Res 2021; 49:8535-8555. [PMID: 34358317 PMCID: PMC8421204 DOI: 10.1093/nar/gkab680] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 07/19/2021] [Accepted: 08/02/2021] [Indexed: 11/15/2022] Open
Abstract
Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with query mutants related with RNA metabolism and estimated the growth rates of about 700 000 double mutant Saccharomyces cerevisiae strains. The tested targets included the gene deletion collection and 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results, we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1471 genes and 117 biological processes. In addition to these large-scale results, we validated both enhancement and suppression of slow growth measured for specific RNA-related pathways. Thus, negative genetic interactions identified a role for the OCA inositol polyphosphate hydrolase complex in mRNA translation initiation. By analysis of suppressors, we found that Puf4, a Pumilio family RNA binding protein, inhibits ribosomal protein Rpl9 function, by acting on a conserved UGUAcauUA motif located downstream the stop codon of the RPL9B mRNA. Altogether, the results and their analysis should represent a useful resource for discovery of gene function in yeast.
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Affiliation(s)
- Laurence Decourty
- Unité de Génétique des Interactions Macromoléculaires, Département Génomes et Génétique, Institut Pasteur, 75015 Paris, France.,UMR3525, Centre national de la recherche scientifique (CNRS), 75015 Paris, France
| | - Christophe Malabat
- Hub Bioinformatique et Biostatistique, Département de Biologie Computationnelle, Institut Pasteur, 75015 Paris, France
| | - Emmanuel Frachon
- Plate-forme Technologique Biomatériaux et Microfluidique, Centre des ressources et recherches technologiques, Institut Pasteur, 75015 Paris, France
| | - Alain Jacquier
- Unité de Génétique des Interactions Macromoléculaires, Département Génomes et Génétique, Institut Pasteur, 75015 Paris, France.,UMR3525, Centre national de la recherche scientifique (CNRS), 75015 Paris, France
| | - Cosmin Saveanu
- Unité de Génétique des Interactions Macromoléculaires, Département Génomes et Génétique, Institut Pasteur, 75015 Paris, France.,UMR3525, Centre national de la recherche scientifique (CNRS), 75015 Paris, France
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13
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A CRISPR Interference Screen of Essential Genes Reveals that Proteasome Regulation Dictates Acetic Acid Tolerance in Saccharomyces cerevisiae. mSystems 2021; 6:e0041821. [PMID: 34313457 PMCID: PMC8407339 DOI: 10.1128/msystems.00418-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
CRISPR interference (CRISPRi) is a powerful tool to study cellular physiology under different growth conditions, and this technology provides a means for screening changed expression of essential genes. In this study, a Saccharomyces cerevisiae CRISPRi library was screened for growth in medium supplemented with acetic acid. Acetic acid is a growth inhibitor challenging the use of yeast for the industrial conversion of lignocellulosic biomasses. Tolerance to acetic acid that is released during biomass hydrolysis is crucial for cell factories to be used in biorefineries. The CRISPRi library screened consists of >9,000 strains, where >98% of all essential and respiratory growth-essential genes were targeted with multiple guide RNAs (gRNAs). The screen was performed using the high-throughput, high-resolution Scan-o-matic platform, where each strain is analyzed separately. Our study identified that CRISPRi targeting of genes involved in vesicle formation or organelle transport processes led to severe growth inhibition during acetic acid stress, emphasizing the importance of these intracellular membrane structures in maintaining cell vitality. In contrast, strains in which genes encoding subunits of the 19S regulatory particle of the 26S proteasome were downregulated had increased tolerance to acetic acid, which we hypothesize is due to ATP salvage through an increased abundance of the 20S core particle that performs ATP-independent protein degradation. This is the first study where high-resolution CRISPRi library screening paves the way to understanding and bioengineering the robustness of yeast against acetic acid stress. IMPORTANCE Acetic acid is inhibitory to the growth of the yeast Saccharomyces cerevisiae, causing ATP starvation and oxidative stress, which leads to the suboptimal production of fuels and chemicals from lignocellulosic biomass. In this study, where each strain of a CRISPRi library was characterized individually, many essential and respiratory growth-essential genes that regulate tolerance to acetic acid were identified, providing a new understanding of the stress response of yeast and new targets for the bioengineering of industrial yeast. Our findings on the fine-tuning of the expression of proteasomal genes leading to increased tolerance to acetic acid suggest that this could be a novel strategy for increasing stress tolerance, leading to improved strains for the production of biobased chemicals.
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14
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Schaffer LV, Ideker T. Mapping the multiscale structure of biological systems. Cell Syst 2021; 12:622-635. [PMID: 34139169 PMCID: PMC8245186 DOI: 10.1016/j.cels.2021.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 01/14/2023]
Abstract
Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.
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Affiliation(s)
- Leah V Schaffer
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA.
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15
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Costanzo M, Hou J, Messier V, Nelson J, Rahman M, VanderSluis B, Wang W, Pons C, Ross C, Ušaj M, San Luis BJ, Shuteriqi E, Koch EN, Aloy P, Myers CL, Boone C, Andrews B. Environmental robustness of the global yeast genetic interaction network. Science 2021; 372:372/6542/eabf8424. [PMID: 33958448 DOI: 10.1126/science.abf8424] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/30/2021] [Indexed: 12/18/2022]
Abstract
Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.
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Affiliation(s)
- Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Jing Hou
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Vincent Messier
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Justin Nelson
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Catherine Ross
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Matej Ušaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Emira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca I Estudis Avaçats (ICREA), Barcelona, Spain
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. .,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.,RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Brenda Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
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16
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Reynolds KA, Rosa-Molinar E, Ward RE, Zhang H, Urbanowicz BR, Settles AM. Accelerating biological insight for understudied genes. Integr Comp Biol 2021; 61:2233-2243. [PMID: 33970251 DOI: 10.1093/icb/icab029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.
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Affiliation(s)
- Kimberly A Reynolds
- The Green Center for Systems Biology and the Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Eduardo Rosa-Molinar
- Department of Pharmacology & Toxicology, The University of Kansas, Lawrence, KS 66047, USA
| | - Robert E Ward
- Department of Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Hongbin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Breeanna R Urbanowicz
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia 30602, USA
| | - A Mark Settles
- Bioengineering Branch, NASA Ames Research Center, Moffett Field, CA USA
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17
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Halder V, McDonnell B, Uthayakumar D, Usher J, Shapiro RS. Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions. FEMS Microbiol Rev 2021; 45:fuaa055. [PMID: 33145589 DOI: 10.1093/femsre/fuaa055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022] Open
Abstract
Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .
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Affiliation(s)
- Viola Halder
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Brianna McDonnell
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Deeva Uthayakumar
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Jane Usher
- Medical Research Council Centre for Medical Mycology, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter EX4 4QD, UK
| | - Rebecca S Shapiro
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
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18
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McGlincy NJ, Meacham ZA, Reynaud KK, Muller R, Baum R, Ingolia NT. A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast. BMC Genomics 2021; 22:205. [PMID: 33757429 PMCID: PMC7986282 DOI: 10.1186/s12864-021-07518-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 03/08/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND CRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis. RESULTS We present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription. CONCLUSIONS Taken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast.
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Affiliation(s)
| | - Zuriah A Meacham
- Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA
| | - Kendra K Reynaud
- Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA
- Biophysics Graduate Group, University of California, Berkeley, CA, 94720, USA
| | - Ryan Muller
- Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA
| | - Rachel Baum
- Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA
| | - Nicholas T Ingolia
- Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA.
- Biophysics Graduate Group, University of California, Berkeley, CA, 94720, USA.
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, 94720, USA.
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19
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Gutmann F, Jann C, Pereira F, Johansson A, Steinmetz LM, Patil KR. CRISPRi screens reveal genes modulating yeast growth in lignocellulose hydrolysate. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:41. [PMID: 33568224 PMCID: PMC7874482 DOI: 10.1186/s13068-021-01880-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/07/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND Baker's yeast is a widely used eukaryotic cell factory, producing a diverse range of compounds including biofuels and fine chemicals. The use of lignocellulose as feedstock offers the opportunity to run these processes in an environmentally sustainable way. However, the required hydrolysis pretreatment of lignocellulosic material releases toxic compounds that hamper yeast growth and consequently productivity. RESULTS Here, we employ CRISPR interference in S. cerevisiae to identify genes modulating fermentative growth in plant hydrolysate and in presence of lignocellulosic toxins. We find that at least one-third of hydrolysate-associated gene functions are explained by effects of known toxic compounds, such as the decreased growth of YAP1 or HAA1, or increased growth of DOT6 knock-down strains in hydrolysate. CONCLUSION Our study confirms previously known genetic elements and uncovers new targets towards designing more robust yeast strains for the utilization of lignocellulose hydrolysate as sustainable feedstock, and, more broadly, paves the way for applying CRISPRi screens to improve industrial fermentation processes.
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Affiliation(s)
- Friederike Gutmann
- European Molecular Biology Laboratory (EMBL), Structural and Cell Biology Unit, 69117, Heidelberg, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Cosimo Jann
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117, Heidelberg, Germany.
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
| | - Filipa Pereira
- European Molecular Biology Laboratory (EMBL), Structural and Cell Biology Unit, 69117, Heidelberg, Germany.
| | - Andreas Johansson
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117, Heidelberg, Germany
| | - Lars M Steinmetz
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117, Heidelberg, Germany
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Genome Technology Center, Palo Alto, CA, 94304, USA
| | - Kiran R Patil
- European Molecular Biology Laboratory (EMBL), Structural and Cell Biology Unit, 69117, Heidelberg, Germany.
- MRC Toxicology Unit, University of Cambridge, Cambridge, UK.
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20
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Mathis AD, Otto RM, Reynolds KA. A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth. Nucleic Acids Res 2021; 49:e6. [PMID: 33221881 PMCID: PMC7797047 DOI: 10.1093/nar/gkaa1073] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/02/2020] [Accepted: 10/22/2020] [Indexed: 11/13/2022] Open
Abstract
A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that 'escape' the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.
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Affiliation(s)
- Andrew D Mathis
- The Green Center for Systems Biology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ryan M Otto
- The Green Center for Systems Biology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Kimberly A Reynolds
- The Green Center for Systems Biology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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21
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Liu Z, Miller D, Li F, Liu X, Levy SF. A large accessory protein interactome is rewired across environments. eLife 2020; 9:e62365. [PMID: 32924934 PMCID: PMC7577743 DOI: 10.7554/elife.62365] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 12/30/2022] Open
Abstract
To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.
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Affiliation(s)
- Zhimin Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Darach Miller
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
| | - Xianan Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
- SLAC National Accelerator LaboratoryMenlo ParkUnited States
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22
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Sun S, Baryshnikova A, Brandt N, Gresham D. Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states. Mol Syst Biol 2020; 16:e9167. [PMID: 32449603 PMCID: PMC7247079 DOI: 10.15252/msb.20199167] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 03/18/2020] [Accepted: 03/31/2020] [Indexed: 01/13/2023] Open
Abstract
Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing (Bar-seq) to test the role of ~4,000 gene deletion mutants and ~12,000 pairwise interactions between all non-essential genes and the protein kinase genes TOR1, RIM15, and PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect up to 10-fold more genetic interactions in quiescent cells than proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15, shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals, suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, and highlights the conditional dependence of epistasis.
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Affiliation(s)
- Siyu Sun
- Center for Genomics and Systems BiologyNew York UniversityNew YorkNYUSA
- Department of BiologyNew York UniversityNew YorkNYUSA
| | | | - Nathan Brandt
- Center for Genomics and Systems BiologyNew York UniversityNew YorkNYUSA
- Department of BiologyNew York UniversityNew YorkNYUSA
| | - David Gresham
- Center for Genomics and Systems BiologyNew York UniversityNew YorkNYUSA
- Department of BiologyNew York UniversityNew YorkNYUSA
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23
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Zhang Z, Bendixsen DP, Janzen T, Nolte AW, Greig D, Stelkens R. Recombining Your Way Out of Trouble: The Genetic Architecture of Hybrid Fitness under Environmental Stress. Mol Biol Evol 2020; 37:167-182. [PMID: 31518427 PMCID: PMC6984367 DOI: 10.1093/molbev/msz211] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Hybridization between species can either promote or impede adaptation. But we know very little about the genetic basis of hybrid fitness, especially in nondomesticated organisms, and when populations are facing environmental stress. We made genetically variable F2 hybrid populations from two divergent Saccharomyces yeast species. We exposed populations to ten toxins and sequenced the most resilient hybrids on low coverage using ddRADseq to investigate four aspects of their genomes: 1) hybridity, 2) interspecific heterozygosity, 3) epistasis (positive or negative associations between nonhomologous chromosomes), and 4) ploidy. We used linear mixed-effect models and simulations to measure to which extent hybrid genome composition was contingent on the environment. Genomes grown in different environments varied in every aspect of hybridness measured, revealing strong genotype–environment interactions. We also found selection against heterozygosity or directional selection for one of the parental alleles, with larger fitness of genomes carrying more homozygous allelic combinations in an otherwise hybrid genomic background. In addition, individual chromosomes and chromosomal interactions showed significant species biases and pervasive aneuploidies. Against our expectations, we observed multiple beneficial, opposite-species chromosome associations, confirmed by epistasis- and selection-free computer simulations, which is surprising given the large divergence of parental genomes (∼15%). Together, these results suggest that successful, stress-resilient hybrid genomes can be assembled from the best features of both parents without paying high costs of negative epistasis. This illustrates the importance of measuring genetic trait architecture in an environmental context when determining the evolutionary potential of genetically diverse hybrid populations.
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Affiliation(s)
- Zebin Zhang
- Division of Population Genetics, Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Devin P Bendixsen
- Division of Population Genetics, Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Thijs Janzen
- Max Planck Institute for Evolutionary Biology, Plön, Germany.,Institute of Biology and Environmental Sciences, University of Oldenburg, Oldenburg, Germany
| | - Arne W Nolte
- Max Planck Institute for Evolutionary Biology, Plön, Germany.,Institute of Biology and Environmental Sciences, University of Oldenburg, Oldenburg, Germany
| | - Duncan Greig
- Max Planck Institute for Evolutionary Biology, Plön, Germany.,Centre for Life's Origins and Evolution (CLOE), Department of Genetics, Evolution, and Environment, University College London, London, United Kingdom
| | - Rike Stelkens
- Division of Population Genetics, Department of Zoology, Stockholm University, Stockholm, Sweden.,Max Planck Institute for Evolutionary Biology, Plön, Germany
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24
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Eguchi Y, Bilolikar G, Geiler-Samerotte K. Why and how to study genetic changes with context-dependent effects. Curr Opin Genet Dev 2019; 58-59:95-102. [PMID: 31593884 DOI: 10.1016/j.gde.2019.08.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/21/2019] [Accepted: 08/29/2019] [Indexed: 01/18/2023]
Abstract
The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.
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Affiliation(s)
- Yuichi Eguchi
- Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States
| | - Gaurav Bilolikar
- Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States
| | - Kerry Geiler-Samerotte
- Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States.
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