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      Global transcriptional responses of fission and budding yeast to changes in copper and iron levels: a comparative study

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          Abstract

          Analysis of genome-wide responses to changing copper and iron levels in budding and fission yeast reveals conservation of only a small core set of genes and remarkable differences in the responses of the two yeasts to excess copper.

          Abstract

          Background

          Recent studies in comparative genomics demonstrate that interspecies comparison represents a powerful tool for identifying both conserved and specialized biologic processes across large evolutionary distances. All cells must adjust to environmental fluctuations in metal levels, because levels that are too low or too high can be detrimental. Here we explore the conservation of metal homoeostasis in two distantly related yeasts.

          Results

          We examined genome-wide gene expression responses to changing copper and iron levels in budding and fission yeast using DNA microarrays. The comparison reveals conservation of only a small core set of genes, defining the copper and iron regulons, with a larger number of additional genes being specific for each species. Novel regulatory targets were identified in Schizosaccharomyces pombe for Cuf1p ( pex7 and SPAC3G6.05) and Fep1p ( srx1, sib1, sib2, rds1, isu1, SPBC27B12.03c, SPAC1F8.02c, and SPBC947.05c). We also present evidence refuting a direct role of Cuf1p in the repression of genes involved in iron uptake. Remarkable differences were detected in responses of the two yeasts to excess copper, probably reflecting evolutionary adaptation to different environments.

          Conclusion

          The considerable evolutionary distance between budding and fission yeast resulted in substantial diversion in the regulation of copper and iron homeostasis. Despite these differences, the conserved regulation of a core set of genes involved in the uptake of these metals provides valuable clues to key features of metal metabolism.

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          Most cited references78

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          Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

          Y. H. Yang (2002)
          There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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            Genomic expression programs in the response of yeast cells to environmental changes.

            We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
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              Remodeling of yeast genome expression in response to environmental changes.

              We used genome-wide expression analysis to explore how gene expression in Saccharomyces cerevisiae is remodeled in response to various changes in extracellular environment, including changes in temperature, oxidation, nutrients, pH, and osmolarity. The results demonstrate that more than half of the genome is involved in various responses to environmental change and identify the global set of genes induced and repressed by each condition. These data implicate a substantial number of previously uncharacterized genes in these responses and reveal a signature common to environmental responses that involves approximately 10% of yeast genes. The results of expression analysis with MSN2/MSN4 mutants support the model that the Msn2/Msn4 activators induce the common response to environmental change. These results provide a global description of the transcriptional response to environmental change and extend our understanding of the role of activators in effecting this response.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2007
                3 May 2007
                : 8
                : 5
                : R73
                Affiliations
                [1 ]EMBL Outstation-Hinxton, European Bioinformatics Institute, Cambridge CB10 1SD, UK
                [2 ]Cancer Research UK Fission Yeast Functional Genomics Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, UK
                [3 ]Complex Genetics Group, UMC Utrecht, Department of Biomedical Genetics, 3584 CG Utrecht, The Netherlands
                [4 ]Genomics Laboratory, UMC Utrecht, Department for Physiological Chemistry, 3584 CG Utrecht, The Netherlands
                [5 ]Genetics Department, University Medical Center Groningen, Groningen, The Netherlands
                Article
                gb-2007-8-5-r73
                10.1186/gb-2007-8-5-r73
                1929147
                17477863
                3fce51fb-acd6-48e8-8865-0cec3d998b86
                Copyright © 2007 Rustici 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.

                History
                : 28 July 2006
                : 31 January 2007
                : 3 May 2007
                Categories
                Research

                Genetics
                Genetics

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