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      Global Analysis of Extracytoplasmic Stress Signaling in Escherichia coli

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          Abstract

          The Bae, Cpx, Psp, Rcs, and σ E pathways constitute the Escherichia coli signaling systems that detect and respond to alterations of the bacterial envelope. Contributions of these systems to stress response have previously been examined individually; however, the possible interconnections between these pathways are unknown. Here we investigate the dynamics between the five stress response pathways by determining the specificities of each system with respect to signal-inducing conditions, and monitoring global transcriptional changes in response to transient overexpression of each of the effectors. Our studies show that different extracytoplasmic stress conditions elicit a combined response of these pathways. Involvement of the five pathways in the various tested stress conditions is explained by our unexpected finding that transcriptional responses induced by the individual systems show little overlap. The extracytoplasmic stress signaling pathways in E. coli thus regulate mainly complementary functions whose discrete contributions are integrated to mount the full adaptive response.

          Author Summary

          Bacteria possess various signaling systems that sense and respond to environmental conditions. The bacterial envelope is at the front line for most external stress conditions; its components sense perturbations and transmit signals to induce transcriptional reprogramming, leading to an adaptive response. In Escherichia coli, at least five response pathways, called Bae, Cpx, Psp, Rcs, and σ E, are induced in response to envelope stress. To date, these pathways have been studied mainly individually, and the interconnections and/or overlaps between them have not been extensively characterized. The present study establishes two important characteristics of stress response in E. coli: first, that a given stress solicits the combined responses of several pathways; second, that each individual pathway controls a discrete set of genes involved in the response, and shows little overlap with other pathways. Based on previous knowledge and the present data, we propose that an environmental stress probably impacts on the cell envelope by inducing numerous alterations, each of which may be perceived by different pathways of the stress response and contributes to adapting the cell to different aspects of the stress damage. The extracytoplasmic stress signaling pathways in E. coli thus regulate mainly complementary functions whose discrete contributions are integrated to mount the full adaptive response.

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Normalization of cDNA microarray data.

            Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.
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              Singular value decomposition for genome-wide expression data processing and modeling.

              We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                September 2009
                September 2009
                18 September 2009
                : 5
                : 9
                : e1000651
                Affiliations
                [1 ]Laboratoire de Signalisation et Réseaux de Régulations Bactériens, Université Paris-Sud 11, CNRS, UMR8621, Institut de Génétique et Microbiologie Bâtiment 400, Orsay Cedex, France
                [2 ]Centre de Génétique Moléculaire, UPR2167 CNRS, Gif/Orsay DNA Microarray Platform (GODMAP), Université Paris-Sud 11, Gif sur Yvette, France
                [3 ]Institut de Chimie des Substances Naturelles, UPR2301 CNRS, IMAGIF qPCR-Platform, Gif sur Yvette, France
                Université Paris Descartes, INSERM U571, France
                Author notes
                [¤]

                Current address: LBPA-CNRS-UMR 8113, ENS Cachan, Cachan, France

                Conceived and designed the experiments: SBM RB EJ SI AJ PB. Performed the experiments: SBM YN NR RB AJ PB. Analyzed the data: SBM YN NR RB EJ SI AJ PB. Contributed reagents/materials/analysis tools: EJ SI PB. Wrote the paper: SBM YN AJ PB. Supervised the project: PB, AJ.

                Article
                08-PLGE-RA-1723R3
                10.1371/journal.pgen.1000651
                2731931
                19763168
                40abe7ed-36d6-4b3a-a99c-1dd9f901c240
                Bury-Moné et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 15 December 2008
                : 17 August 2009
                Page count
                Pages: 17
                Categories
                Research Article
                Genetics and Genomics/Functional Genomics
                Genetics and Genomics/Gene Expression
                Microbiology/Microbial Growth and Development
                Microbiology/Microbial Physiology and Metabolism

                Genetics
                Genetics

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