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      Membrane transporters and protein traffic networks differentially affecting metal tolerance: a genomic phenotyping study in yeast

      1 , 1 , , 1

      Genome Biology

      BioMed Central

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          Abstract

          Genomic phenotyping was used to assess the role of all non-essential S. cerevisiae proteins in modulating cell viability after exposure to cadmium, nickel and other metals.

          Abstract

          Background

          The cellular mechanisms that underlie metal toxicity and detoxification are rather variegated and incompletely understood. Genomic phenotyping was used to assess the roles played by all nonessential Saccharomyces cerevisiae proteins in modulating cell viability after exposure to cadmium, nickel, and other metals.

          Results

          A number of novel genes and pathways that affect multimetal as well as metal-specific tolerance were discovered. Although the vacuole emerged as a major hot spot for metal detoxification, we also identified a number of pathways that play a more general, less direct role in promoting cell survival under stress conditions (for example, mRNA decay, nucleocytoplasmic transport, and iron acquisition) as well as proteins that are more proximally related to metal damage prevention or repair. Most prominent among the latter are various nutrient transporters previously not associated with metal toxicity. A strikingly differential effect was observed for a large set of deletions, the majority of which centered on the ESCRT (endosomal sorting complexes required for transport) and retromer complexes, which - by affecting transporter downregulation and intracellular protein traffic - cause cadmium sensitivity but nickel resistance.

          Conclusion

          The data show that a previously underestimated variety of pathways are involved in cadmium and nickel tolerance in eukaryotic cells. As revealed by comparison with five additional metals, there is a good correlation between the chemical properties and the cellular toxicity signatures of various metals. However, many conserved pathways centered on membrane transporters and protein traffic affect cell viability with a surprisingly high degree of metal specificity.

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          Most cited references 98

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          BioGRID: a general repository for interaction datasets

          Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at . BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.
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            Functional profiling of the Saccharomyces cerevisiae genome.

            Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.
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              Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis.

              The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2008
                7 April 2008
                : 9
                : 4
                : R67
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, Viale G.P. Usberti 23/A, University of Parma, I-43100 Parma, Italy
                Article
                gb-2008-9-4-r67
                10.1186/gb-2008-9-4-r67
                2643938
                18394190
                Copyright © 2008 Ruotolo et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research

                Genetics

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