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      Functional Analysis With a Barcoder Yeast Gene Overexpression System

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          Abstract

          Systematic analysis of gene overexpression phenotypes provides an insight into gene function, enzyme targets, and biological pathways. Here, we describe a novel functional genomics platform that enables a highly parallel and systematic assessment of overexpression phenotypes in pooled cultures. First, we constructed a genome-level collection of ~5100 yeast barcoder strains, each of which carries a unique barcode, enabling pooled fitness assays with a barcode microarray or sequencing readout. Second, we constructed a yeast open reading frame (ORF) galactose-induced overexpression array by generating a genome-wide set of yeast transformants, each of which carries an individual plasmid-born and sequence-verified ORF derived from the Saccharomyces cerevisiae full-length EXpression-ready (FLEX) collection. We combined these collections genetically using synthetic genetic array methodology, generating ~5100 strains, each of which is barcoded and overexpresses a specific ORF, a set we termed “barFLEX.” Additional synthetic genetic array allows the barFLEX collection to be moved into different genetic backgrounds. As a proof-of-principle, we describe the properties of the barFLEX overexpression collection and its application in synthetic dosage lethality studies under different environmental conditions.

          Most cited references34

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          The genetic landscape of a cell.

          A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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            Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

            The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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              A global protein kinase and phosphatase interaction network in yeast.

              The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                ggg
                ggg
                ggg
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                1 October 2012
                October 2012
                : 2
                : 10
                : 1279-1289
                Affiliations
                [* ]Department of Molecular Genetics, University of Toronto, Ontario M5S 1A8, Canada
                []Banting and Best Department of Medical Research, University of Toronto, Ontario M5G 1L6, Canada
                []Donnelly Centre, University of Toronto, Ontario M5S 3E1, Canada, and
                [§ ]Department of Pharmaceutical Sciences, University of Toronto, Ontario M5S 3M2, Canada
                Author notes

                Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.112.003400/-/DC1.

                [1]

                Present address: Biomolecular Sciences Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario, P3E 2C6, Canada.

                [2]

                Present address: Department of Biochemistry, Microbiology and Immunology, The Apoptosis Research Centre, Children’s Hospital of Eastern Ontario, University of Ottawa, 401 Smyth Road, Ottawa, Ontario, K1H 8K1, Canada.

                [3 ]Corresponding authors: Donnelly Centre, 160 College Street, Room 230, University of Toronto, Ontario M5S 3E1, Canada. E-mails: brenda.andrews@ 123456utoronto.ca ; corey.nislow@ 123456utoronto.ca ; charlie.boone@ 123456utoronto.ca
                Article
                GGG_003400
                10.1534/g3.112.003400
                3464120
                23050238
                ddb693aa-db1d-4592-8280-3fc146cf9c5a
                Copyright © 2012 A. C. Douglas et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 June 2012
                : 22 August 2012
                Categories
                Investigations
                Custom metadata
                v1

                Genetics
                synthetic dosage lethality,gene overexpression,barcoders,barflex array
                Genetics
                synthetic dosage lethality, gene overexpression, barcoders, barflex array

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