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E. coli response to MMC

ABSTRACT: Changes in gene expression after treatment of E. coli cultures with mitomycin C were assessed using gene array technology. Unexpectedly, a large number of genes (nearly 30% of all genes) displayed significant changes in their expression level. Analysis and classification of expression profiles of the corresponding genes allowed us to assign this large number of genes into a dozen to two dozen small clusters of genes with similar expression profiles. This assignment allowed us to describe systematically the changes in the level of gene expression in response to DNA damage. Among the damage-induced genes more than a hundred are novel. From those genes involved in DNA metabolism that have not been previously shown to be induced by DNA damage, for example, the mutS gene involved in mismatch repair is especially noteworthy. In addition to the SOS response, we observed the induction of other stress response pathways such as those of oxidative stress and osmotic protection. Among the genes that are down-regulated in response to DNA damage are numerous protein biosynthesis genes. Analysis of the gene expression data highlighted the essential involvement of sigmaS regulated genes and the general stress response network in the response to DNA damage. Keywords = DNA damage, recA, SOS Keywords: other

ORGANISM(S): Escherichia coli

PROVIDER: GSE33 | GEO | 2002/02/26

SECONDARY ACCESSION(S): PRJNA84805

REPOSITORIES: GEO

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Project description:Shimoni2009 - Escherichia Coli SOS Simple model, involving only the basic components of the circuit, sufficient to explain the peaks in the promoter activities of recA and lexA. This model is described in the article: Stochastic analysis of the SOS response in Escherichia coli. Shimoni Y, Altuvia S, Margalit H, Biham O PloS one. 2009; 4(5):e5363 Abstract: BACKGROUND: DNA damage in Escherichia coli evokes a response mechanism called the SOS response. The genetic circuit of this mechanism includes the genes recA and lexA, which regulate each other via a mixed feedback loop involving transcriptional regulation and protein-protein interaction. Under normal conditions, recA is transcriptionally repressed by LexA, which also functions as an auto-repressor. In presence of DNA damage, RecA proteins recognize stalled replication forks and participate in the DNA repair process. Under these conditions, RecA marks LexA for fast degradation. Generally, such mixed feedback loops are known to exhibit either bi-stability or a single steady state. However, when the dynamics of the SOS system following DNA damage was recently studied in single cells, ordered peaks were observed in the promoter activity of both genes (Friedman et al., 2005, PLoS Biol. 3(7):e238). This surprising phenomenon was masked in previous studies of cell populations. Previous attempts to explain these results harnessed additional genes to the system and deployed complex deterministic mathematical models that were only partially successful in explaining the results. PRINCIPAL FINDINGS: Here we apply stochastic methods, which are better suited for dynamic simulations of single cells. We show that a simple model, involving only the basic components of the circuit, is sufficient to explain the peaks in the promoter activities of recA and lexA. Notably, deterministic simulations of the same model do not produce peaks in the promoter activities. SIGNIFICANCE: We conclude that the double negative mixed feedback loop with auto-repression accounts for the experimentally observed peaks in the promoter activities. In addition to explaining the experimental results, this result shows that including additional regulations in a mixed feedback loop may dramatically change the dynamic functionality of this regulatory module. Furthermore, our results suggests that stochastic fluctuations strongly affect the qualitative behavior of important regulatory modules even under biologically relevant conditions, thus emphasizing the importance of stochastic analysis of regulatory circuits. This model is hosted on BioModels Database and identified by: MODEL2937159804. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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E. coli response to MMC

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