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Masinas MPD, Litsios A, Razdaibiedina A, Usaj M, Boone C, Andrews BJ. Expanding TheCellVision.org: A Central Repository for Visualizing and Mining High-Content Cell Imaging Projects. Genetics 2024:iyae044. [PMID: 38518223 DOI: 10.1093/genetics/iyae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/16/2024] [Accepted: 03/13/2024] [Indexed: 03/24/2024] Open
Abstract
We previously constructed TheCellVision.org, a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.org housed two high-content screening (HCS) projects providing genome-scale protein abundance and localization information for the budding yeast Saccharomyces cerevisiae, as well as a comprehensive analysis of the morphology of its endocytic compartments upon systematic genetic perturbation of each yeast gene. Here, we report on the expansion of TheCellVision.org by the addition of two new HCS projects and the incorporation of new global functionalities. Specifically, TheCellVision.org now hosts images from the Cell Cycle Omics project, which describes genome-scale cell cycle-resolved dynamics in protein localization, protein concentration, gene expression, and translational efficiency in budding yeast. Moreover, it hosts PIFiA, a computational tool for image-based predictions of protein functional annotations. Across all its projects, TheCellVision.org now houses > 800,000 microscopy images along with computational tools for exploring both the images and their associated datasets. Together with the newly added global functionalities, which include the ability to query genes in any of the hosted projects using either yeast or human gene names, TheCellVision.org provides an expanding resource for single-cell eukaryotic biology.
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Affiliation(s)
| | - Athanasios Litsios
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
| | - Anastasia Razdaibiedina
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, M5G 1N1, Canada
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
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2
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Longhurst AD, Wang K, Suresh HG, Ketavarapu M, Ward HN, Jones IR, Narayan V, Hundley FV, Hassan AZ, Boone C, Myers CL, Shen Y, Ramani V, Andrews BJ, Toczyski DP. The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2. bioRxiv 2024:2024.03.18.585604. [PMID: 38562687 PMCID: PMC10983909 DOI: 10.1101/2024.03.18.585604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenomics approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued growth inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in promoting G1 progression.
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Affiliation(s)
- Adam D Longhurst
- University of California, San Francisco, San Francisco, CA 94158, USA
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kyle Wang
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Harsha Garadi Suresh
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Mythili Ketavarapu
- Gladstone Institute for Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities Minneapolis MN USA
| | - Ian R Jones
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California
| | - Vivek Narayan
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Frances V Hundley
- University of California, San Francisco, San Francisco, CA 94158, USA
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA 02115, USA
| | - Arshia Zernab Hassan
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities Minneapolis MN USA
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities Minneapolis MN USA
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA 02115, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Vijay Ramani
- Gladstone Institute for Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Brenda J Andrews
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - David P Toczyski
- University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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3
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Litsios A, Grys BT, Kraus OZ, Friesen H, Ross C, Masinas MPD, Forster DT, Couvillion MT, Timmermann S, Billmann M, Myers C, Johnsson N, Churchman LS, Boone C, Andrews BJ. Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle. Cell 2024; 187:1490-1507.e21. [PMID: 38452761 PMCID: PMC10947830 DOI: 10.1016/j.cell.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/01/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.
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Affiliation(s)
- Athanasios Litsios
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Benjamin T Grys
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Oren Z Kraus
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Helena Friesen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Catherine Ross
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Myra Paz David Masinas
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Duncan T Forster
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Mary T Couvillion
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Stefanie Timmermann
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Chad Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nils Johnsson
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | | | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; RIKEN Center for Sustainable Resource Science, Wako 351-0198 Saitama, Japan.
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
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4
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Varland S, Silva RD, Kjosås I, Faustino A, Bogaert A, Billmann M, Boukhatmi H, Kellen B, Costanzo M, Drazic A, Osberg C, Chan K, Zhang X, Tong AHY, Andreazza S, Lee JJ, Nedyalkova L, Ušaj M, Whitworth AJ, Andrews BJ, Moffat J, Myers CL, Gevaert K, Boone C, Martinho RG, Arnesen T. N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity. Nat Commun 2023; 14:6774. [PMID: 37891180 PMCID: PMC10611716 DOI: 10.1038/s41467-023-42342-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
Most eukaryotic proteins are N-terminally acetylated, but the functional impact on a global scale has remained obscure. Using genome-wide CRISPR knockout screens in human cells, we reveal a strong genetic dependency between a major N-terminal acetyltransferase and specific ubiquitin ligases. Biochemical analyses uncover that both the ubiquitin ligase complex UBR4-KCMF1 and the acetyltransferase NatC recognize proteins bearing an unacetylated N-terminal methionine followed by a hydrophobic residue. NatC KO-induced protein degradation and phenotypes are reversed by UBR knockdown, demonstrating the central cellular role of this interplay. We reveal that loss of Drosophila NatC is associated with male sterility, reduced longevity, and age-dependent loss of motility due to developmental muscle defects. Remarkably, muscle-specific overexpression of UbcE2M, one of the proteins targeted for NatC KO-mediated degradation, suppresses defects of NatC deletion. In conclusion, NatC-mediated N-terminal acetylation acts as a protective mechanism against protein degradation, which is relevant for increased longevity and motility.
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Affiliation(s)
- Sylvia Varland
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway.
- Department of Biological Sciences, University of Bergen, N-5006, Bergen, Norway.
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.
| | - Rui Duarte Silva
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal.
- Faculdade de Medicina e Ciências Biomédicas, Universidade do Algarve, 8005-139, Faro, Portugal.
| | - Ine Kjosås
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Alexandra Faustino
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal
| | - Annelies Bogaert
- VIB-UGent Center for Medical Biotechnology, B-9052, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, B-9052, Ghent, Belgium
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, D-53127, Bonn, Germany
| | - Hadi Boukhatmi
- Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes 1, CNRS, UMR6290, 35065, Rennes, France
| | - Barbara Kellen
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal
| | - Michael Costanzo
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Adrian Drazic
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Camilla Osberg
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Katherine Chan
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Xiang Zhang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
| | - Amy Hin Yan Tong
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Simonetta Andreazza
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Juliette J Lee
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Lyudmila Nedyalkova
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Matej Ušaj
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | | | - Brenda J Andrews
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Jason Moffat
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto, ON, M5G 1×8, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, B-9052, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, B-9052, Ghent, Belgium
| | - Charles Boone
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- RIKEN Centre for Sustainable Resource Science, Wako, Saitama, 351-0106, Japan
| | - Rui Gonçalo Martinho
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal.
- Departmento de Ciências Médicas, Universidade de Aveiro, 3810-193, Aveiro, Portugal.
- iBiMED - Institute of Biomedicine, Universidade de Aveiro, 3810-193, Aveiro, Portugal.
| | - Thomas Arnesen
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway.
- Department of Biological Sciences, University of Bergen, N-5006, Bergen, Norway.
- Department of Surgery, Haukeland University Hospital, N-5021, Bergen, Norway.
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5
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Billmann M, Ward HN, Aregger M, Costanzo M, Andrews BJ, Boone C, Moffat J, Myers CL. Reproducibility metrics for context-specific CRISPR screens. Cell Syst 2023; 14:418-422.e2. [PMID: 37201508 DOI: 10.1016/j.cels.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 08/17/2022] [Accepted: 04/07/2023] [Indexed: 05/20/2023]
Abstract
CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background, or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn 53127, Germany.
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
| | - Michael Aregger
- National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA; Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada; Program in Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Research and Learning Centre, 686 Bay Street, Toronto, ON M5G0A4, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA.
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6
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Suresh HG, Bonneil E, Albert B, Dominique C, Costanzo M, Pons C, David Masinas MP, Shuteriqi E, Shore D, Henras AK, Thibault P, Boone C, Andrews BJ. K29-linked unanchored polyubiquitin chains disrupt ribosome biogenesis and direct ribosomal proteins to the Intranuclear Quality control compartment (INQ). bioRxiv 2023:2023.05.03.539259. [PMID: 37205480 PMCID: PMC10187189 DOI: 10.1101/2023.05.03.539259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Ribosome assembly requires precise coordination between the production and assembly of ribosomal components. Mutations in ribosomal proteins that inhibit the assembly process or ribosome function are often associated with Ribosomopathies, some of which are linked to defects in proteostasis. In this study, we examine the interplay between several yeast proteostasis enzymes, including deubiquitylases (DUBs), Ubp2 and Ubp14, and E3 ligases, Ufd4 and Hul5, and we explore their roles in the regulation of the cellular levels of K29-linked unanchored polyubiquitin (polyUb) chains. Accumulating K29-linked unanchored polyUb chains associate with maturing ribosomes to disrupt their assembly, activate the Ribosome assembly stress response (RASTR), and lead to the sequestration of ribosomal proteins at the Intranuclear Quality control compartment (INQ). These findings reveal the physiological relevance of INQ and provide insights into mechanisms of cellular toxicity associated with Ribosomopathies.
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7
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Andrews BJ. A decade of G3: Genes|Genomes|Genetics: a unified home for genetics and genomics research. G3 (Bethesda) 2021; 11:6365208. [PMID: 34544148 PMCID: PMC8496312 DOI: 10.1093/g3journal/jkab247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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8
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van Leeuwen J, Pons C, Tan G, Wang JZ, Hou J, Weile J, Gebbia M, Liang W, Shuteriqi E, Li Z, Lopes M, Ušaj M, Dos Santos Lopes A, van Lieshout N, Myers CL, Roth FP, Aloy P, Andrews BJ, Boone C. Systematic analysis of bypass suppression of essential genes. Mol Syst Biol 2021; 16:e9828. [PMID: 32939983 PMCID: PMC7507402 DOI: 10.15252/msb.20209828] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/15/2022] Open
Abstract
Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole-genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane-associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line-specific essentiality in the Cancer Dependency Map (DepMap) project.
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Affiliation(s)
- Jolanda van Leeuwen
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Guihong Tan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Jason Zi Wang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jing Hou
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Jochen Weile
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Marinella Gebbia
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Wendy Liang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Ermira Shuteriqi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Zhijian Li
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Maykel Lopes
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland
| | - Matej Ušaj
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Andreia Dos Santos Lopes
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland
| | - Natascha van Lieshout
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Frederick P Roth
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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9
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Kong KYE, Fischer B, Meurer M, Kats I, Li Z, Rühle F, Barry JD, Kirrmaier D, Chevyreva V, San Luis BJ, Costanzo M, Huber W, Andrews BJ, Boone C, Knop M, Khmelinskii A. Timer-based proteomic profiling of the ubiquitin-proteasome system reveals a substrate receptor of the GID ubiquitin ligase. Mol Cell 2021; 81:2460-2476.e11. [PMID: 33974913 PMCID: PMC8189435 DOI: 10.1016/j.molcel.2021.04.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 03/15/2021] [Accepted: 04/19/2021] [Indexed: 01/01/2023]
Abstract
Selective protein degradation by the ubiquitin-proteasome system (UPS) is involved in all cellular processes. However, the substrates and specificity of most UPS components are not well understood. Here we systematically characterized the UPS in Saccharomyces cerevisiae. Using fluorescent timers, we determined how loss of individual UPS components affects yeast proteome turnover, detecting phenotypes for 76% of E2, E3, and deubiquitinating enzymes. We exploit this dataset to gain insights into N-degron pathways, which target proteins carrying N-terminal degradation signals. We implicate Ubr1, an E3 of the Arg/N-degron pathway, in targeting mitochondrial proteins processed by the mitochondrial inner membrane protease. Moreover, we identify Ylr149c/Gid11 as a substrate receptor of the glucose-induced degradation-deficient (GID) complex, an E3 of the Pro/N-degron pathway. Our results suggest that Gid11 recognizes proteins with N-terminal threonines, expanding the specificity of the GID complex. This resource of potential substrates and relationships between UPS components enables exploring functions of selective protein degradation.
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Affiliation(s)
| | - Bernd Fischer
- Computational Genome Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Meurer
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Ilia Kats
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Zhaoyan Li
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Frank Rühle
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Joseph D Barry
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Daniel Kirrmaier
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany; Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Veronika Chevyreva
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Bryan-Joseph San Luis
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Michael Costanzo
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Brenda J Andrews
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Michael Knop
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany; Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany.
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10
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Arita Y, Kim G, Li Z, Friesen H, Turco G, Wang RY, Climie D, Usaj M, Hotz M, Stoops EH, Baryshnikova A, Boone C, Botstein D, Andrews BJ, McIsaac RS. A genome-scale yeast library with inducible expression of individual genes. Mol Syst Biol 2021; 17:e10207. [PMID: 34096681 PMCID: PMC8182650 DOI: 10.15252/msb.202110207] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/27/2021] [Accepted: 04/30/2021] [Indexed: 11/09/2022] Open
Abstract
The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.
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Affiliation(s)
- Yuko Arita
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
| | - Griffin Kim
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | - Zhijian Li
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Helena Friesen
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Gina Turco
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | | | - Dale Climie
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Matej Usaj
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Manuel Hotz
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | | | | | - Charles Boone
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | | | - Brenda J Andrews
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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11
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Mattiazzi Usaj M, Yeung CHL, Friesen H, Boone C, Andrews BJ. Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations. Cell Syst 2021; 12:608-621. [PMID: 34139168 DOI: 10.1016/j.cels.2021.05.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 05/12/2021] [Indexed: 12/26/2022]
Abstract
Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.
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Affiliation(s)
- Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Clarence Hue Lok Yeung
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Helena Friesen
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada; RIKEN Centre for Sustainable Resource Science, Wako, Saitama 351-0198, Japan
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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12
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Rahman M, Billmann M, Costanzo M, Aregger M, Tong AHY, Chan K, Ward HN, Brown KR, Andrews BJ, Boone C, Moffat J, Myers CL. A method for benchmarking genetic screens reveals a predominant mitochondrial bias. Mol Syst Biol 2021; 17:e10013. [PMID: 34018332 PMCID: PMC8138267 DOI: 10.15252/msb.202010013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 12/29/2022] Open
Abstract
We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene-pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria-associated signal within co-essentiality networks derived from these data and explore the basis of this signal. Our analysis and time-resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.
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Affiliation(s)
- Mahfuzur Rahman
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Maximilian Billmann
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | | | | | - Amy H Y Tong
- Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | - Henry N Ward
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | | | - Brenda J Andrews
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Charles Boone
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Jason Moffat
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Chad L Myers
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
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13
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Abstract
Complex genetic interactions occur when mutant alleles of multiple genes combine to elicit an unexpected phenotype, which could not be predicted given the expectation based on the combination of phenotypes associated with individual mutant alleles. Trigenic Synthetic Genetic Array (τ-SGA) methodology was developed for the systematic analysis of complex interactions involving combinations of three gene perturbations. With a series of replica pinning steps of the τ-SGA procedure, haploid triple mutants are constructed through automated mating and meiotic recombination. For example, a double-mutant query strain carrying two mutant alleles of interest, such as a deletion allele of a nonessential gene and a conditional temperature-sensitive allele of an essential gene, is crossed to an input array of yeast mutants, such as the diagnostic array set of ~1200 mutants, to generate an output array of triple mutants. The colony-size measurements of the resulting triple mutants are used to estimate cellular fitness and quantify trigenic interactions by incorporating corresponding single- and double-mutant fitness estimates. Trigenic interaction networks can be further analyzed for functional modules using various clustering and enrichment analysis tools. Complex genetic interactions are rich in functional information and provide insight into the genotype-to-phenotype relationship, genome size, and speciation.
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Affiliation(s)
- Elena Kuzmin
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada
| | - Brenda J Andrews
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
| | - Charles Boone
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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14
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Kintaka R, Makanae K, Namba S, Kato H, Kito K, Ohnuki S, Ohya Y, Andrews BJ, Boone C, Moriya H. Genetic profiling of protein burden and nuclear export overload. eLife 2020; 9:54080. [PMID: 33146608 PMCID: PMC7673788 DOI: 10.7554/elife.54080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 11/01/2020] [Indexed: 12/11/2022] Open
Abstract
Overproduction (op) of proteins triggers cellular defects. One of the consequences of overproduction is the protein burden/cost, which is produced by an overloading of the protein synthesis process. However, the physiology of cells under a protein burden is not well characterized. We performed genetic profiling of protein burden by systematic analysis of genetic interactions between GFP-op, surveying both deletion and temperature-sensitive mutants in budding yeast. We also performed genetic profiling in cells with overproduction of triple-GFP (tGFP), and the nuclear export signal-containing tGFP (NES-tGFP). The mutants specifically interacted with GFP-op were suggestive of unexpected connections between actin-related processes like polarization and the protein burden, which was supported by morphological analysis. The tGFP-op interactions suggested that this protein probe overloads the proteasome, whereas those that interacted with NES-tGFP involved genes encoding components of the nuclear export process, providing a resource for further analysis of the protein burden and nuclear export overload.
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Affiliation(s)
- Reiko Kintaka
- Donnelly Center for Cellular and Biomolecular Research, Department of Medical Genetics, University of Toronto, Toronto, Canada
| | - Koji Makanae
- Research Core for Interdisciplinary Sciences, Okayama University, Okayama, Japan
| | - Shotaro Namba
- Matching Program Course, Okayama University, Okayama, Japan
| | - Hisaaki Kato
- Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
| | - Keiji Kito
- Department of Life Sciences, School of Agriculture, Meiji University, Tokyo, Japan
| | - Shinsuke Ohnuki
- Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yoshikazu Ohya
- Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Brenda J Andrews
- Donnelly Center for Cellular and Biomolecular Research, Department of Medical Genetics, University of Toronto, Toronto, Canada
| | - Charles Boone
- Donnelly Center for Cellular and Biomolecular Research, Department of Medical Genetics, University of Toronto, Toronto, Canada.,RIKEN Center for Sustainable Resource Science, Wako, Japan
| | - Hisao Moriya
- Research Core for Interdisciplinary Sciences, Okayama University, Okayama, Japan.,Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
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15
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Kuzmin E, VanderSluis B, Nguyen Ba AN, Wang W, Koch EN, Usaj M, Khmelinskii A, Usaj MM, van Leeuwen J, Kraus O, Tresenrider A, Pryszlak M, Hu MC, Varriano B, Costanzo M, Knop M, Moses A, Myers CL, Andrews BJ, Boone C. Exploring whole-genome duplicate gene retention with complex genetic interaction analysis. Science 2020; 368:eaaz5667. [PMID: 32586993 PMCID: PMC7539174 DOI: 10.1126/science.aaz5667] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 05/06/2020] [Indexed: 12/25/2022]
Abstract
Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.
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Affiliation(s)
- Elena Kuzmin
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Anton Khmelinskii
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
| | | | | | - Oren Kraus
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Amy Tresenrider
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Michael Pryszlak
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ming-Che Hu
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda Varriano
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Knop
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
- Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alan Moses
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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16
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Aregger M, Lawson KA, Billmann M, Costanzo M, Tong AHY, Chan K, Rahman M, Brown KR, Ross C, Usaj M, Nedyalkova L, Sizova O, Habsid A, Pawling J, Lin ZY, Abdouni H, Wong CJ, Weiss A, Mero P, Dennis JW, Gingras AC, Myers CL, Andrews BJ, Boone C, Moffat J. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat Metab 2020; 2:499-513. [PMID: 32694731 PMCID: PMC7566881 DOI: 10.1038/s42255-020-0211-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase (FASN), whose product catalyses the formation of long-chain fatty acids. FASN-mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2 and ACACA. Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.
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Affiliation(s)
- Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Urology, Department of Surgery, University of Toronto
- Corresponding authors: , , ,
| | - Maximillian Billmann
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Amy H. Y. Tong
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Katherine Chan
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
| | - Kevin R. Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Catherine Ross
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lucy Nedyalkova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Olga Sizova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Habsid
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Judy Pawling
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Zhen-Yuan Lin
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Hala Abdouni
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Cassandra J. Wong
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Patricia Mero
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - James W. Dennis
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Brenda J. Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - 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
- Corresponding authors: , , ,
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17
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Mattiazzi Usaj M, Sahin N, Friesen H, Pons C, Usaj M, Masinas MPD, Shuteriqi E, Shkurin A, Aloy P, Morris Q, Boone C, Andrews BJ. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol Syst Biol 2020; 16:e9243. [PMID: 32064787 PMCID: PMC7025093 DOI: 10.15252/msb.20199243] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/16/2019] [Accepted: 01/15/2020] [Indexed: 12/13/2022] Open
Abstract
Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.
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Affiliation(s)
| | - Nil Sahin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | | | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
| | - Matej Usaj
- The Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | | | - Aleksei Shkurin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)Barcelona, CataloniaSpain
| | - Quaid Morris
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- Computational and Systems Biology ProgramMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Charles Boone
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
| | - Brenda J Andrews
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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18
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Abstract
The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.
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Affiliation(s)
- Barbara Mair
- Donnelly Centre, University of Toronto, ON, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada.
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19
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VanderSluis B, Costanzo M, Billmann M, Ward HN, Myers CL, Andrews BJ, Boone C. Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell. Curr Opin Microbiol 2018; 45:170-179. [PMID: 30059827 DOI: 10.1016/j.mib.2018.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 06/26/2018] [Accepted: 06/27/2018] [Indexed: 01/01/2023]
Abstract
Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.
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Affiliation(s)
- Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Henry N Ward
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada; RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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20
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Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R, Chen Y, Usaj M, Balint A, Mattiazzi Usaj M, van Leeuwen J, Koch EN, Pons C, Dagilis AJ, Pryszlak M, Wang JZY, Hanchard J, Riggi M, Xu K, Heydari H, San Luis BJ, Shuteriqi E, Zhu H, Van Dyk N, Sharifpoor S, Costanzo M, Loewith R, Caudy A, Bolnick D, Brown GW, Andrews BJ, Boone C, Myers CL. Systematic analysis of complex genetic interactions. Science 2018; 360:eaao1729. [PMID: 29674565 PMCID: PMC6215713 DOI: 10.1126/science.aao1729] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 02/23/2018] [Indexed: 12/11/2022]
Abstract
To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.
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Affiliation(s)
- Elena Kuzmin
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - 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
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Yiqun Chen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Attila Balint
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Mojca Mattiazzi Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jolanda van Leeuwen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Andrius J Dagilis
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Michael Pryszlak
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jason Zi Yang Wang
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Julia Hanchard
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Margot Riggi
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- Department of Biochemistry, University of Geneva, 1211 Geneva, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Kaicong Xu
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Hamed Heydari
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Ermira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Hongwei Zhu
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Nydia Van Dyk
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Sara Sharifpoor
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Robbie Loewith
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Amy Caudy
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Daniel Bolnick
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Grant W Brown
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
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21
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Lu AX, Chong YT, Hsu IS, Strome B, Handfield LF, Kraus O, Andrews BJ, Moses AM. Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins. eLife 2018; 7:e31872. [PMID: 29620521 PMCID: PMC5935485 DOI: 10.7554/elife.31872] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 03/30/2018] [Indexed: 01/29/2023] Open
Abstract
The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.
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Affiliation(s)
- Alex X Lu
- Department of Computer ScienceUniversity of TorontoTorontoCanada
| | - Yolanda T Chong
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoCanada
| | - Ian Shen Hsu
- Department of Cell and Systems BiologyUniversity of TorontoTorontoCanada
| | - Bob Strome
- Department of Cell and Systems BiologyUniversity of TorontoTorontoCanada
| | | | - Oren Kraus
- Department of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada
| | - Brenda J Andrews
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoCanada
| | - Alan M Moses
- Department of Computer ScienceUniversity of TorontoTorontoCanada
- Department of Cell and Systems BiologyUniversity of TorontoTorontoCanada
- Center for Analysis of Genome Evolution and FunctionUniversity of TorontoTorontoCanada
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22
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Xie JL, Qin L, Miao Z, Grys BT, Diaz JDLC, Ting K, Krieger JR, Tong J, Tan K, Leach MD, Ketela T, Moran MF, Krysan DJ, Boone C, Andrews BJ, Selmecki A, Ho Wong K, Robbins N, Cowen LE. The Candida albicans transcription factor Cas5 couples stress responses, drug resistance and cell cycle regulation. Nat Commun 2017; 8:499. [PMID: 28894103 PMCID: PMC5593949 DOI: 10.1038/s41467-017-00547-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 07/06/2017] [Indexed: 12/16/2022] Open
Abstract
The capacity to coordinate environmental sensing with initiation of cellular responses underpins microbial survival and is crucial for virulence and stress responses in microbial pathogens. Here we define circuitry that enables the fungal pathogen Candida albicans to couple cell cycle dynamics with responses to cell wall stress induced by echinocandins, a front-line class of antifungal drugs. We discover that the C. albicans transcription factor Cas5 is crucial for proper cell cycle dynamics and responses to echinocandins, which inhibit β-1,3-glucan synthesis. Cas5 has distinct transcriptional targets under basal and stress conditions, is activated by the phosphatase Glc7, and can regulate the expression of target genes in concert with the transcriptional regulators Swi4 and Swi6. Thus, we illuminate a mechanism of transcriptional control that couples cell wall integrity with cell cycle regulation, and uncover circuitry governing antifungal drug resistance.Cas5 is a transcriptional regulator of responses to cell wall stress in the fungal pathogen Candida albicans. Here, Xie et al. show that Cas5 also modulates cell cycle dynamics and responses to antifungal drugs.
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Affiliation(s)
- Jinglin L Xie
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
| | - Longguang Qin
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
| | - Zhengqiang Miao
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
| | - Ben T Grys
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Jacinto De La Cruz Diaz
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA
| | - Kenneth Ting
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
| | - Jonathan R Krieger
- The Hospital for Sick Children, SPARC Biocentre, Toronto, ON, Canada, M5G 0A4
| | - Jiefei Tong
- The Hospital for Sick Children, Program in Cell Biology, Peter Gilgan Centre for Research and Learning, Toronto, ON, Canada, M5G 0A4
| | - Kaeling Tan
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
| | - Michelle D Leach
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
- Aberdeen Fungal Group, School of Medical Sciences, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Abderdeen, AB252ZD, UK
| | - Troy Ketela
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
| | - Michael F Moran
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
- The Hospital for Sick Children, SPARC Biocentre, Toronto, ON, Canada, M5G 0A4
- The Hospital for Sick Children, Program in Cell Biology, Peter Gilgan Centre for Research and Learning, Toronto, ON, Canada, M5G 0A4
| | - Damian J Krysan
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA
- Department of Pediatrics and Microbiology/Immunology, University of Rochester, Rochester, NY, 14642, USA
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Brenda J Andrews
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Anna Selmecki
- Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE, 68178, USA
| | - Koon Ho Wong
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
| | - Nicole Robbins
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1
| | - Leah E Cowen
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1.
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23
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Abstract
Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.
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Affiliation(s)
- Jolanda van Leeuwen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1.,Department of Molecular Genetics, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1.,Department of Molecular Genetics, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1
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24
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Hart T, Tong AHY, Chan K, Van Leeuwen J, Seetharaman A, Aregger M, Chandrashekhar M, Hustedt N, Seth S, Noonan A, Habsid A, Sizova O, Nedyalkova L, Climie R, Tworzyanski L, Lawson K, Sartori MA, Alibeh S, Tieu D, Masud S, Mero P, Weiss A, Brown KR, Usaj M, Billmann M, Rahman M, Constanzo M, Myers CL, Andrews BJ, Boone C, Durocher D, Moffat J. Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens. G3 (Bethesda) 2017; 7:2719-2727. [PMID: 28655737 PMCID: PMC5555476 DOI: 10.1534/g3.117.041277] [Citation(s) in RCA: 280] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 06/12/2017] [Indexed: 12/26/2022]
Abstract
The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.
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Affiliation(s)
- Traver Hart
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | | | - Katie Chan
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | | | | | - Michael Aregger
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | | | - Nicole Hustedt
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G1X5, Canada
| | - Sahil Seth
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Avery Noonan
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Andrea Habsid
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Olga Sizova
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | | | - Ryan Climie
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | | | - Keith Lawson
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | | | - Sabriyeh Alibeh
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - David Tieu
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
| | - Sanna Masud
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
| | - Patricia Mero
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | | | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
- Canadian Institute for Advanced Research, Toronto, Ontario M5G1Z8, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
- Canadian Institute for Advanced Research, Toronto, Ontario M5G1Z8, Canada
| | - Daniel Durocher
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G1X5, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Ontario M5S3E1, Canada
- Canadian Institute for Advanced Research, Toronto, Ontario M5G1Z8, Canada
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25
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Abstract
Recent analysis of genome sequences has identified individuals that are healthy despite carrying severe disease-associated mutations. A possible explanation is that these individuals carry a second genomic perturbation that can compensate for the detrimental effects of the disease allele, a phenomenon referred to as suppression. In model organisms, suppression interactions are generally divided into two classes: genomic suppressors which are secondary mutations in the genome that bypass a mutant phenotype, and dosage suppression interactions in which overexpression of a suppressor gene rescues a mutant phenotype. Here, we describe the general properties of genomic and dosage suppression, with an emphasis on the budding yeast. We propose that suppression interactions between genetic variants are likely relevant for determining the penetrance of human traits. Consequently, an understanding of suppression mechanisms may guide the discovery of protective variants in healthy individuals that carry disease alleles, which could direct the rational design of new therapeutics.
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Affiliation(s)
- Jolanda van Leeuwen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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26
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Yang F, Sun S, Tan G, Costanzo M, Hill DE, Vidal M, Andrews BJ, Boone C, Roth FP. Identifying pathogenicity of human variants via paralog-based yeast complementation. PLoS Genet 2017; 13:e1006779. [PMID: 28542158 PMCID: PMC5466341 DOI: 10.1371/journal.pgen.1006779] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 06/09/2017] [Accepted: 04/25/2017] [Indexed: 11/21/2022] Open
Abstract
To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation. Functional complementation assays of human disease-associated gene variants can reveal many more human disease variants at high confidence than current computational approaches, even using highly-diverged model organisms. However, this has generally only been possible for a minority of human disease genes for which orthologous complementation is known in the relevant model organism, so that alternative assays are urgently needed. Here we show that complementation relationships can be found for many additional human disease genes by exploiting paralogous human-yeast gene relationships, and that disease variant identification using paralogy-based assays performs on par with orthology-based assays.
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Affiliation(s)
- Fan Yang
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Song Sun
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Guihong Tan
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Michael Costanzo
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - David E. Hill
- Center for Cancer Systems Biology (CCSB), Dana- Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana- Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brenda J. Andrews
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Charles Boone
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
| | - Frederick P. Roth
- Donnelly Centre, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Center for Cancer Systems Biology (CCSB), Dana- Farber Cancer Institute, Boston, Massachusetts, United States of America
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
- * E-mail:
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27
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van Leeuwen J, Pons C, Mellor JC, Yamaguchi TN, Friesen H, Koschwanez J, Ušaj MM, Pechlaner M, Takar M, Ušaj M, VanderSluis B, Andrusiak K, Bansal P, Baryshnikova A, Boone CE, Cao J, Cote A, Gebbia M, Horecka G, Horecka I, Kuzmin E, Legro N, Liang W, van Lieshout N, McNee M, San Luis BJ, Shaeri F, Shuteriqi E, Sun S, Yang L, Youn JY, Yuen M, Costanzo M, Gingras AC, Aloy P, Oostenbrink C, Murray A, Graham TR, Myers CL, Andrews BJ, Roth FP, Boone C. Exploring genetic suppression interactions on a global scale. Science 2017; 354:354/6312/aag0839. [PMID: 27811238 DOI: 10.1126/science.aag0839] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 10/04/2016] [Indexed: 12/21/2022]
Abstract
Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression.
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Affiliation(s)
- Jolanda van Leeuwen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Carles Pons
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Joseph C Mellor
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Takafumi N Yamaguchi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Helena Friesen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - John Koschwanez
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Mojca Mattiazzi Ušaj
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Maria Pechlaner
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, A-1190 Vienna, Austria
| | - Mehmet Takar
- Department of Biological Sciences, Vanderbilt University, 1161 21st Avenue South, Nashville, TN 37232, USA
| | - Matej Ušaj
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Kerry Andrusiak
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Pritpal Bansal
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Anastasia Baryshnikova
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Claire E Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jessica Cao
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Atina Cote
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Marinella Gebbia
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Gene Horecka
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Ira Horecka
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Elena Kuzmin
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Nicole Legro
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Wendy Liang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Natascha van Lieshout
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Margaret McNee
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Fatemeh Shaeri
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Ermira Shuteriqi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Song Sun
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Lu Yang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Ji-Young Youn
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Michael Yuen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Catalonia, Spain
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, A-1190 Vienna, Austria
| | - Andrew Murray
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, 1161 21st Avenue South, Nashville, TN 37232, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. .,Canadian Institute for Advanced Research, 180 Dundas Street West, Toronto, Ontario M5G 1Z8, Canada
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Frederick P Roth
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. .,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada.,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Canadian Institute for Advanced Research, 180 Dundas Street West, Toronto, Ontario M5G 1Z8, Canada.,Department of Computer Science, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Canadian Institute for Advanced Research, 180 Dundas Street West, Toronto, Ontario M5G 1Z8, Canada
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28
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Abstract
Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.
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Affiliation(s)
- Oren Z Kraus
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Ben T Grys
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jimmy Ba
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Yolanda Chong
- Cellular Pharmacology, Discovery Sciences, Janssen Pharmaceutical Companies, Johnson & Johnson, Beerse, Belgium
| | - Brendan J Frey
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Program on Genetic Networks, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Program on Learning in Machines & Brains, Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Program on Genetic Networks, Toronto, ON, Canada
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Program on Genetic Networks, Toronto, ON, Canada
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29
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Grys BT, Lo DS, Sahin N, Kraus OZ, Morris Q, Boone C, Andrews BJ. Machine learning and computer vision approaches for phenotypic profiling. J Cell Biol 2016; 216:65-71. [PMID: 27940887 PMCID: PMC5223612 DOI: 10.1083/jcb.201610026] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 11/18/2016] [Accepted: 11/21/2016] [Indexed: 11/27/2022] Open
Abstract
Grys et al. review computer vision and machine-learning methods that have been applied to phenotypic profiling of image-based data. Descriptions are provided for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data. With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.
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Affiliation(s)
- Ben T Grys
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Dara S Lo
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Nil Sahin
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Oren Z Kraus
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| | - Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada .,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada .,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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30
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Styles EB, Founk KJ, Zamparo LA, Sing TL, Altintas D, Ribeyre C, Ribaud V, Rougemont J, Mayhew D, Costanzo M, Usaj M, Verster AJ, Koch EN, Novarina D, Graf M, Luke B, Muzi-Falconi M, Myers CL, Mitra RD, Shore D, Brown GW, Zhang Z, Boone C, Andrews BJ. Exploring Quantitative Yeast Phenomics with Single-Cell Analysis of DNA Damage Foci. Cell Syst 2016; 3:264-277.e10. [PMID: 27617677 PMCID: PMC5689480 DOI: 10.1016/j.cels.2016.08.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 05/27/2016] [Accepted: 08/11/2016] [Indexed: 01/12/2023]
Abstract
A significant challenge of functional genomics is to develop methods for genome-scale acquisition and analysis of cell biological data. Here, we present an integrated method that combines genome-wide genetic perturbation of Saccharomyces cerevisiae with high-content screening to facilitate the genetic description of sub-cellular structures and compartment morphology. As proof of principle, we used a Rad52-GFP marker to examine DNA damage foci in ∼20 million single cells from ∼5,000 different mutant backgrounds in the context of selected genetic or chemical perturbations. Phenotypes were classified using a machine learning-based automated image analysis pipeline. 345 mutants were identified that had elevated numbers of DNA damage foci, almost half of which were identified only in sensitized backgrounds. Subsequent analysis of Vid22, a protein implicated in the DNA damage response, revealed that it acts together with the Sgs1 helicase at sites of DNA damage and preferentially binds G-quadruplex regions of the genome. This approach is extensible to numerous other cell biological markers and experimental systems.
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Affiliation(s)
- Erin B Styles
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Karen J Founk
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Lee A Zamparo
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Computer Sciences, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Tina L Sing
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Dogus Altintas
- Department of Molecular Biology, NCCR Program "Frontiers in Genetics", Institute of Genetics, Genomics, Geneva (iGE3), University of Geneva, 30, quai Ernest-Ansermet, 1211 Geneva 4, Switzerland
| | - Cyril Ribeyre
- Department of Molecular Biology, NCCR Program "Frontiers in Genetics", Institute of Genetics, Genomics, Geneva (iGE3), University of Geneva, 30, quai Ernest-Ansermet, 1211 Geneva 4, Switzerland
| | - Virginie Ribaud
- Department of Molecular Biology, NCCR Program "Frontiers in Genetics", Institute of Genetics, Genomics, Geneva (iGE3), University of Geneva, 30, quai Ernest-Ansermet, 1211 Geneva 4, Switzerland
| | - Jacques Rougemont
- Laboratory of Computational Systems Biology, Ecole Polytéchnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - David Mayhew
- Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Adrian J Verster
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Daniele Novarina
- Dipartimento di Bioscienze, Universita' degli Studi di Milano, 20122 Milano, Italy
| | - Marco Graf
- Institute of Molecular Biology (IMB), Ackermannweg 4, Mainz 55128, Germany
| | - Brian Luke
- Institute of Molecular Biology (IMB), Ackermannweg 4, Mainz 55128, Germany
| | - Marco Muzi-Falconi
- Dipartimento di Bioscienze, Universita' degli Studi di Milano, 20122 Milano, Italy
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robi David Mitra
- Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - David Shore
- Department of Molecular Biology, NCCR Program "Frontiers in Genetics", Institute of Genetics, Genomics, Geneva (iGE3), University of Geneva, 30, quai Ernest-Ansermet, 1211 Geneva 4, Switzerland
| | - Grant W Brown
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Zhaolei Zhang
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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31
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Andrews BJ, Walhout AJM, Iyengar R. Quantitative human cell encyclopedia. Sci Signal 2016. [DOI: 10.1126/scisignal.aah4406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Quantitative cataloging of molecular entities in human cells will enable a better understanding of human physiology.
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32
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Mattiazzi Usaj M, Styles EB, Verster AJ, Friesen H, Boone C, Andrews BJ. High-Content Screening for Quantitative Cell Biology. Trends Cell Biol 2016; 26:598-611. [PMID: 27118708 DOI: 10.1016/j.tcb.2016.03.008] [Citation(s) in RCA: 153] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 03/21/2016] [Accepted: 03/22/2016] [Indexed: 12/25/2022]
Abstract
High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future.
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Affiliation(s)
| | - Erin B Styles
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Adrian J Verster
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Helena Friesen
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada.
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33
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Styles EB, Friesen H, Boone C, Andrews BJ. High-Throughput Microscopy-Based Screening in Saccharomyces cerevisiae. Cold Spring Harb Protoc 2016; 2016:pdb.top087593. [PMID: 27037080 DOI: 10.1101/pdb.top087593] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The budding yeastSaccharomyces cerevisiaehas served as the pioneer model organism for virtually all genome-scale methods, including genome sequencing, DNA microarrays, gene deletion collections, and a variety of proteomic platforms. Yeast has also provided a test-bed for the development of systematic fluorescence-based imaging screens to enable the analysis of protein localization and abundance in vivo. Especially important has been the integration of high-throughput microscopy with automated image-processing methods, which has allowed researchers to overcome issues associated with manual image analysis and acquire unbiased, quantitative data. Here we provide an introduction to automated imaging in budding yeast.
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Affiliation(s)
- Erin B Styles
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Helena Friesen
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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34
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Sun S, Yang F, Tan G, Costanzo M, Oughtred R, Hirschman J, Theesfeld CL, Bansal P, Sahni N, Yi S, Yu A, Tyagi T, Tie C, Hill DE, Vidal M, Andrews BJ, Boone C, Dolinski K, Roth FP. An extended set of yeast-based functional assays accurately identifies human disease mutations. Genome Res 2016; 26:670-80. [PMID: 26975778 PMCID: PMC4864455 DOI: 10.1101/gr.192526.115] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 03/08/2016] [Indexed: 12/19/2022]
Abstract
We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.
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Affiliation(s)
- Song Sun
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada; Department of Medical Biochemistry and Microbiology, Uppsala University, SE-75123 Uppsala, Sweden
| | - Fan Yang
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Guihong Tan
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Rose Oughtred
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Jodi Hirschman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Chandra L Theesfeld
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Pritpal Bansal
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Nidhi Sahni
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Song Yi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Analyn Yu
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Tanya Tyagi
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Cathy Tie
- Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Kara Dolinski
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada; Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Canadian Institute for Advanced Research, Toronto, Ontario, M5G 1Z8, Canada
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35
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Affiliation(s)
- Charles Boone
- Donnelly Centre and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
| | - Brenda J Andrews
- Donnelly Centre and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
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36
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Parts L, Liu YC, Tekkedil MM, Steinmetz LM, Caudy AA, Fraser AG, Boone C, Andrews BJ, Rosebrock AP. Heritability and genetic basis of protein level variation in an outbred population. Genome Res 2014; 24:1363-70. [PMID: 24823668 PMCID: PMC4120089 DOI: 10.1101/gr.170506.113] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population’s average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.
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Affiliation(s)
- Leopold Parts
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, M5S3E1, Canada
| | - Yi-Chun Liu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada
| | - Manu M Tekkedil
- 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, California 94305, USA
| | - Amy A Caudy
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, M5S3E1, Canada
| | - Andrew G Fraser
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, M5S3E1, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, M5S3E1, Canada
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, M5S3E1, Canada
| | - Adam P Rosebrock
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S3E1, Canada;
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37
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Rossi D, Galvão FC, Bellato HM, Boldrin PEG, Andrews BJ, Valentini SR, Zanelli CF. eIF5A has a function in the cotranslational translocation of proteins into the ER. Amino Acids 2014; 46:645-53. [PMID: 24306454 DOI: 10.1007/s00726-013-1618-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2013] [Accepted: 11/01/2013] [Indexed: 10/25/2022]
Abstract
The putative eukaryotic translation initiation factor 5A (eIF5A) is a highly conserved and essential protein present in all organisms except bacteria. To be activated, eIF5A requires the conversion of a specific residue of lysine into hypusine. This hypusine modification occurs posttranslationally in two enzymatic steps, and the polyamine spermidine is the substrate. Despite having an essential function in translation elongation, the critical role played by eIF5A remains unclear. In addition to demonstrating genetic interactions with translation factors, eIF5A mutants genetically interact with mutations in YPT1, which encodes an essential protein involved in endoplasmic reticulum (ER)-to-Golgi vesicle transport. In this study, we investigated the correlation between the function of eIF5A in translation and secretion in yeast. The results of in vivo translocation assays and genetic interaction analyses suggest a specific role for eIF5A in the cotranslational translocation of proteins into the ER, but not in the posttranslational pathway. Additionally, we observed that a block in eIF5A activation up-regulates stress-induced chaperones, which also occurs when SRP function is lost. Finally, loss of eIF5A function affects binding of the ribosome-nascent chain complex to SRP. These results link eIF5A function in translation with a role of SRP in the cell and may help explain the dual effects of eIF5A in differential and general translation.
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Affiliation(s)
- Danuza Rossi
- Department of Biological Sciences, School of Pharmaceutical Sciences, Univ Estadual Paulista, UNESP, Araraquara, SP, Brazil
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38
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Handfield LF, Chong YT, Simmons J, Andrews BJ, Moses AM. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput Biol 2013; 9:e1003085. [PMID: 23785265 PMCID: PMC3681667 DOI: 10.1371/journal.pcbi.1003085] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Accepted: 04/19/2013] [Indexed: 12/11/2022] Open
Abstract
Protein subcellular localization has been systematically characterized in budding yeast using fluorescently tagged proteins. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches that have been trained to recognize predefined image classes based on statistical features. Here, we present an unsupervised analysis of protein expression patterns in a set of high-resolution, high-throughput microscope images. Our analysis is based on 7 biologically interpretable features which are evaluated on automatically identified cells, and whose cell-stage dependency is captured by a continuous model for cell growth. We show that it is possible to identify most previously identified localization patterns in a cluster analysis based on these features and that similarities between the inferred expression patterns contain more information about protein function than can be explained by a previous manual categorization of subcellular localization. Furthermore, the inferred cell-stage associated to each fluorescence measurement allows us to visualize large groups of proteins entering the bud at specific stages of bud growth. These correspond to proteins localized to organelles, revealing that the organelles must be entering the bud in a stereotypical order. We also identify and organize a smaller group of proteins that show subtle differences in the way they move around the bud during growth. Our results suggest that biologically interpretable features based on explicit models of cell morphology will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images. The location of a particular protein in the cell is one of the most important pieces of information that cell biologists use to understand its function. Fluorescent tags are a powerful way to determine the location of a protein in living cells. Nearly a decade ago, a collection of yeast strains was introduced, where in each strain a single protein was tagged with green fluorescent protein (GFP). Here, we show that by training a computer to accurately identify the buds of growing yeast cells, and then making simple fluorescence measurements in context of cell shape and cell stage, the computer could automatically discover most of the localization patterns (nucleus, cytoplasm, mitochondria, etc.) without any prior knowledge of what the patterns might be. Because we made the same, simple measurements for each yeast cell, we could compare and visualize the patterns of fluorescence for the entire collection of strains. This allowed us to identify large groups of proteins moving around the cell in a coordinated fashion, and to identify new, complex patterns that had previously been difficult to describe.
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Affiliation(s)
| | - Yolanda T. Chong
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Jibril Simmons
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
| | - Brenda J. Andrews
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Alan M. Moses
- Department of Computer Science, University of Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
- * E-mail:
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39
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Srikumar T, Lewicki MC, Costanzo M, Tkach JM, van Bakel H, Tsui K, Johnson ES, Brown GW, Andrews BJ, Boone C, Giaever G, Nislow C, Raught B. Global analysis of SUMO chain function reveals multiple roles in chromatin regulation. ACTA ACUST UNITED AC 2013; 201:145-63. [PMID: 23547032 PMCID: PMC3613684 DOI: 10.1083/jcb.201210019] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Multiple large-scale analyses in yeast implicate SUMO chain function in the
maintenance of higher-order chromatin structure and transcriptional repression
of environmental stress response genes. Like ubiquitin, the small ubiquitin-related modifier (SUMO) proteins can form
oligomeric “chains,” but the biological functions of these
superstructures are not well understood. Here, we created mutant yeast strains
unable to synthesize SUMO chains (smt3allR) and
subjected them to high-content microscopic screening, synthetic genetic array
(SGA) analysis, and high-density transcript profiling to perform the first
global analysis of SUMO chain function. This comprehensive assessment identified
144 proteins with altered localization or intensity in
smt3allR cells, 149 synthetic genetic
interactions, and 225 mRNA transcripts (primarily consisting of stress- and
nutrient-response genes) that displayed a >1.5-fold increase in
expression levels. This information-rich resource strongly implicates SUMO
chains in the regulation of chromatin. Indeed, using several different
approaches, we demonstrate that SUMO chains are required for the maintenance of
normal higher-order chromatin structure and transcriptional repression of
environmental stress response genes in budding yeast.
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Affiliation(s)
- Tharan Srikumar
- Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada
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40
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Wagih O, Usaj M, Baryshnikova A, VanderSluis B, Kuzmin E, Costanzo M, Myers CL, Andrews BJ, Boone CM, Parts L. SGAtools: one-stop analysis and visualization of array-based genetic interaction screens. Nucleic Acids Res 2013; 41:W591-6. [PMID: 23677617 PMCID: PMC3692131 DOI: 10.1093/nar/gkt400] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.
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Affiliation(s)
- Omar Wagih
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, Canada
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41
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Bastajian N, Friesen H, Andrews BJ. Bck2 acts through the MADS box protein Mcm1 to activate cell-cycle-regulated genes in budding yeast. PLoS Genet 2013; 9:e1003507. [PMID: 23675312 PMCID: PMC3649975 DOI: 10.1371/journal.pgen.1003507] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Accepted: 03/27/2013] [Indexed: 11/19/2022] Open
Abstract
The Bck2 protein is a potent genetic regulator of cell-cycle-dependent gene expression in budding yeast. To date, most experiments have focused on assessing a potential role for Bck2 in activation of the G1/S-specific transcription factors SBF (Swi4, Swi6) and MBF (Mbp1, Swi6), yet the mechanism of gene activation by Bck2 has remained obscure. We performed a yeast two-hybrid screen using a truncated version of Bck2 and discovered six novel Bck2-binding partners including Mcm1, an essential protein that binds to and activates M/G1 promoters through Early Cell cycle Box (ECB) elements as well as to G2/M promoters. At M/G1 promoters Mcm1 is inhibited by association with two repressors, Yox1 or Yhp1, and gene activation ensues once repression is relieved by an unknown activating signal. Here, we show that Bck2 interacts physically with Mcm1 to activate genes during G1 phase. We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes. The Bck2-Mcm1 interaction requires valine 69 on Mcm1, a residue known to be required for interaction with Yox1. Overexpression of BCK2 decreases Yox1 localization to the early G1-specific CLN3 promoter and rescues the lethality caused by overexpression of YOX1. Our data suggest that Yox1 and Bck2 may compete for access to the Mcm1-ECB scaffold to ensure appropriate activation of the initial suite of genes required for cell cycle commitment.
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Affiliation(s)
- Nazareth Bastajian
- The Donnelly Centre and the Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Helena Friesen
- The Donnelly Centre and the Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Brenda J. Andrews
- The Donnelly Centre and the Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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42
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Koch EN, Costanzo M, Bellay J, Deshpande R, Chatfield-Reed K, Chua G, D'Urso G, Andrews BJ, Boone C, Myers CL. Conserved rules govern genetic interaction degree across species. Genome Biol 2012; 13:R57. [PMID: 22747640 PMCID: PMC3491379 DOI: 10.1186/gb-2012-13-7-r57] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2011] [Accepted: 07/02/2012] [Indexed: 11/10/2022] Open
Abstract
Background Synthetic genetic interactions have recently been mapped on a genome scale in the budding yeast Saccharomyces cerevisiae, providing a functional view of the central processes of eukaryotic life. Currently, comprehensive genetic interaction networks have not been determined for other species, and we therefore sought to model conserved aspects of genetic interaction networks in order to enable the transfer of knowledge between species. Results Using a combination of physiological and evolutionary properties of genes, we built models that successfully predicted the genetic interaction degree of S. cerevisiae genes. Importantly, a model trained on S. cerevisiae gene features and degree also accurately predicted interaction degree in the fission yeast Schizosaccharomyces pombe, suggesting that many of the predictive relationships discovered in S. cerevisiae also hold in this evolutionarily distant yeast. In both species, high single mutant fitness defect, protein disorder, pleiotropy, protein-protein interaction network degree, and low expression variation were significantly predictive of genetic interaction degree. A comparison of the predicted genetic interaction degrees of S. pombe genes to the degrees of S. cerevisiae orthologs revealed functional rewiring of specific biological processes that distinguish these two species. Finally, predicted differences in genetic interaction degree were independently supported by differences in co-expression relationships of the two species. Conclusions Our findings show that there are common relationships between gene properties and genetic interaction network topology in two evolutionarily distant species. This conservation allows use of the extensively mapped S. cerevisiae genetic interaction network as an orthology-independent reference to guide the study of more complex species.
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43
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Witkin KL, Chong Y, Shao S, Webster MT, Lahiri S, Walters AD, Lee B, Koh JLY, Prinz WA, Andrews BJ, Cohen-Fix O. The budding yeast nuclear envelope adjacent to the nucleolus serves as a membrane sink during mitotic delay. Curr Biol 2012; 22:1128-33. [PMID: 22658600 DOI: 10.1016/j.cub.2012.04.022] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2012] [Revised: 03/17/2012] [Accepted: 04/12/2012] [Indexed: 11/17/2022]
Abstract
The mechanisms that dictate nuclear shape are largely unknown. Here we screened the budding yeast deletion collection for mutants with abnormal nuclear shape. A common phenotype was the appearance of a nuclear extension, particularly in mutants in DNA repair and chromosome segregation genes. Our data suggest that these mutations led to the abnormal nuclear morphology indirectly, by causing a checkpoint-induced cell-cycle delay. Indeed, delaying cells in mitosis by other means also led to the appearance of nuclear extensions, whereas inactivating the DNA damage checkpoint pathway in a DNA repair mutant reduced the fraction of cells with nuclear extensions. Formation of a nuclear extension was specific to a mitotic delay, because cells arrested in S or G2 had round nuclei. Moreover, the nuclear extension always coincided with the nucleolus, while the morphology of the DNA mass remained largely unchanged. Finally, we found that phospholipid synthesis continued unperturbed when cells delayed in mitosis, and inhibiting phospholipid synthesis abolished the formation of nuclear extensions. Our data suggest a mechanism that promotes nuclear envelope expansion during mitosis. When mitotic progression is delayed, cells sequester the added membrane to the nuclear envelope associated with the nucleolus, possibly to avoid disruption of intranuclear organization.
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Affiliation(s)
- Keren L Witkin
- Laboratory of Cell and Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
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44
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Sharifpoor S, Nguyen Ba AN, Youn JY, van Dyk D, Friesen H, Douglas AC, Kurat CF, Chong YT, Founk K, Moses AM, Andrews BJ. Erratum to: A quantitative literature-curated gold standard for kinase-substrate pairs. Genome Biol 2012. [PMCID: PMC3446286 DOI: 10.1186/gb-2012-13-5-417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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45
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Nguyen Ba AN, Yeh BJ, van Dyk D, Davidson AR, Andrews BJ, Weiss EL, Moses AM. Proteome-wide discovery of evolutionary conserved sequences in disordered regions. Sci Signal 2012; 5:rs1. [PMID: 22416277 DOI: 10.1126/scisignal.2002515] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
At least 30% of human proteins are thought to contain intrinsically disordered regions, which lack stable structural conformation. Despite lacking enzymatic functions and having few protein domains, disordered regions are functionally important for protein regulation and contain short linear motifs (short peptide sequences involved in protein-protein interactions), but in most disordered regions, the functional amino acid residues remain unknown. We searched for evolutionarily conserved sequences within disordered regions according to the hypothesis that conservation would indicate functional residues. Using a phylogenetic hidden Markov model (phylo-HMM), we made accurate, specific predictions of functional elements in disordered regions even when these elements are only two or three amino acids long. Among the conserved sequences that we identified were previously known and newly identified short linear motifs, and we experimentally verified key examples, including a motif that may mediate interaction between protein kinase Cbk1 and its substrates. We also observed that hub proteins, which interact with many partners in a protein interaction network, are highly enriched in these conserved sequences. Our analysis enabled the systematic identification of the functional residues in disordered regions and suggested that at least 5% of amino acids in disordered regions are important for function.
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Affiliation(s)
- Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario M5S 3B2, Canada
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46
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Kurat CF, Lambert JP, van Dyk D, Tsui K, van Bakel H, Kaluarachchi S, Friesen H, Kainth P, Nislow C, Figeys D, Fillingham J, Andrews BJ. Restriction of histone gene transcription to S phase by phosphorylation of a chromatin boundary protein. Genes Dev 2012; 25:2489-501. [PMID: 22156209 DOI: 10.1101/gad.173427.111] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The cell cycle-regulated expression of core histone genes is required for DNA replication and proper cell cycle progression in eukaryotic cells. Although some factors involved in histone gene transcription are known, the molecular mechanisms that ensure proper induction of histone gene expression during S phase remain enigmatic. Here we demonstrate that S-phase transcription of the model histone gene HTA1 in yeast is regulated by a novel attach-release mechanism involving phosphorylation of the conserved chromatin boundary protein Yta7 by both cyclin-dependent kinase 1 (Cdk1) and casein kinase 2 (CK2). Outside S phase, integrity of the AAA-ATPase domain is required for Yta7 boundary function, as defined by correct positioning of the histone chaperone Rtt106 and the chromatin remodeling complex RSC. Conversely, in S phase, Yta7 is hyperphosphorylated, causing its release from HTA1 chromatin and productive transcription. Most importantly, abrogation of Yta7 phosphorylation results in constitutive attachment of Yta7 to HTA1 chromatin, preventing efficient transcription post-recruitment of RNA polymerase II (RNAPII). Our study identified the chromatin boundary protein Yta7 as a key regulator that links S-phase kinases with RNAPII function at cell cycle-regulated histone gene promoters.
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Affiliation(s)
- Christoph F Kurat
- The Donnelly Center, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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47
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Sharifpoor S, van Dyk D, Costanzo M, Baryshnikova A, Friesen H, Douglas AC, Youn JY, VanderSluis B, Myers CL, Papp B, Boone C, Andrews BJ. Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs. Genome Res 2012; 22:791-801. [PMID: 22282571 DOI: 10.1101/gr.129213.111] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase-substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks.
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Affiliation(s)
- Sara Sharifpoor
- Department of Molecular Genetics, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S3E1, Canada
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Echtenkamp FJ, Zelin E, Oxelmark E, Woo JI, Andrews BJ, Garabedian M, Freeman BC. Global functional map of the p23 molecular chaperone reveals an extensive cellular network. Mol Cell 2012; 43:229-41. [PMID: 21777812 DOI: 10.1016/j.molcel.2011.05.029] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2010] [Revised: 03/31/2011] [Accepted: 05/10/2011] [Indexed: 12/22/2022]
Abstract
In parallel with evolutionary developments, the Hsp90 molecular chaperone system shifted from a simple prokaryotic factor into an expansive network that includes a variety of cochaperones. We have taken high-throughput genomic and proteomic approaches to better understand the abundant yeast p23 cochaperone Sba1. Our work revealed an unexpected p23 network that displayed considerable independence from known Hsp90 clients. Additionally, our data uncovered a broad nuclear role for p23, contrasting with the historical dogma of restricted cytosolic activities for molecular chaperones. Validation studies demonstrated that yeast p23 was required for proper Golgi function and ribosome biogenesis, and was necessary for efficient DNA repair from a wide range of mutagens. Notably, mammalian p23 had conserved roles in these pathways as well as being necessary for proper cell mobility. Taken together, our work demonstrates that the p23 chaperone serves a broad physiological network and functions both in conjunction with and sovereign to Hsp90.
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Affiliation(s)
- Frank J Echtenkamp
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, 601 S. Goodwin Avenue, Urbana, IL 61801, USA
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Cokol M, Chua HN, Tasan M, Mutlu B, Weinstein ZB, Suzuki Y, Nergiz ME, Costanzo M, Baryshnikova A, Giaever G, Nislow C, Myers CL, Andrews BJ, Boone C, Roth FP. Systematic exploration of synergistic drug pairs. Mol Syst Biol 2011; 7:544. [PMID: 22068327 PMCID: PMC3261710 DOI: 10.1038/msb.2011.71] [Citation(s) in RCA: 227] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 08/11/2011] [Indexed: 01/20/2023] Open
Abstract
Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways ('specific synergy'). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, 'promiscuous synergy' can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.
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Affiliation(s)
- Murat Cokol
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Hon Nian Chua
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Murat Tasan
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Beste Mutlu
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Zohar B Weinstein
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Yo Suzuki
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Synthetic Biology and Bioenergy, J. Craig Venter Institute, San Diego, CA, USA
| | - Mehmet E Nergiz
- Department of Computer Engineering, Faculty of Engineering, Zirve University, Gaziantep, Turkey
| | - Michael Costanzo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Anastasia Baryshnikova
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Guri Giaever
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Corey Nislow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Frederick P Roth
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Mt Sinai Hospital, Toronto, Ontario, Canada
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA
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50
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Dixon SJ, Andrews BJ, Boone C. Exploring the conservation of synthetic lethal genetic interaction networks. Commun Integr Biol 2011; 2:78-81. [PMID: 19704894 DOI: 10.4161/cib.7501] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2008] [Accepted: 11/25/2008] [Indexed: 11/19/2022] Open
Abstract
High-throughput studies have enabled the large-scale mapping of synthetic lethal genetic interaction networks in the budding yeast Saccharomyces cerevisiae (S. cerevisiae). Recently, complementary high-throughput methods have been developed to map genetic interactions in the fission yeast Schizosaccharomyces pombe (S. pombe), enabling comparative analyses of genetic interaction networks between S. pombe and S. cerevisiae, two species separated by hundreds of millions of years of evolution. The resultant data has providing our first view of a possible core genetic interaction network shared between two distantly related eukaryotes, and identified numerous species-specific interactions that may contribute to the unique biology of these two different organisms. These and other results suggest that comparative interactomic studies will provide novel insights into the structure of genetic interaction networks.
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Affiliation(s)
- Scott J Dixon
- Banting and Best Department of Medical Research; Terrence Donnelly Center for Cellular and Biomolecular Research; University of Toronto; Toronto, ON CA
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