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      Promoters maintain their relative activity levels under different growth conditions

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          Abstract

          • Between any two conditions, the activity of most promoters changes by a constant global scaling factor that depends only on the conditions and not on the promoter's identity.

          • The value of the global scaling factor between any two conditions corresponds to the change in growth rate and magnitude of the condition-specific response.

          • When specific groups of genes are activated, they also tend to change according to scaling factors, changing the degree to which the entire group is activated, while preserving the ratios between genes within the group.

          • Altogether, a handful of scaling factors are sufficient for quantitatively describing genome-wide expression profiles across conditions.

          Abstract

          Most genes change expression levels across conditions, but it is unclear which of these changes represents specific regulation and what determines their quantitative degree. Here, we accurately measured activities of ∼900 S. cerevisiae and ∼1800 E. coli promoters using fluorescent reporters. We show that in both organisms 60–90% of promoters change their expression between conditions by a constant global scaling factor that depends only on the conditions and not on the promoter's identity. Quantifying such global effects allows precise characterization of specific regulation—promoters deviating from the global scale line. These are organized into few functionally related groups that also adhere to scale lines and preserve their relative activities across conditions. Thus, only several scaling factors suffice to accurately describe genome-wide expression profiles across conditions. We present a parameter-free passive resource allocation model that quantitatively accounts for the global scaling factors. It suggests that many changes in expression across conditions result from global effects and not specific regulation, and provides means for quantitative interpretation of expression profiles.

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          Most cited references55

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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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            Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

            We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
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              Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

              A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.
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                Author and article information

                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2013
                29 October 2013
                29 October 2013
                : 9
                : 701
                Affiliations
                [1 ]Department of Computer Science and Applied Mathematics, Weizmann Institute of Science , Rehovot, Israel
                [2 ]Department of Molecular Cell Biology, Weizmann Institute of Science , Rehovot, Israel
                [3 ]Department of Plant Sciences, Weizmann Institute of Science , Rehovot, Israel
                Author notes
                [a ]Department of Computer Science and Applied Mathematics, Weizmann Institute of Science , 234 Herzl Street, Rehovot 76100, Israel. Tel.:+972 89346488; Fax:+972 89346488; eran.segal@ 123456weizmann.ac.il
                Article
                msb201359
                10.1038/msb.2013.59
                3817408
                24169404
                9e5b1d21-95a2-4f80-811a-85abb8183e03
                Copyright © 2013, EMBO and Macmillan Publishers Limited

                This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/.

                History
                : 05 August 2013
                : 27 September 2013
                Categories
                Article

                Quantitative & Systems biology
                gene expression,growth rate,modeling,promoter activity,transcription regulation

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