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J. Biol. Chem., Vol. 280, Issue 15, 15084-15096, April 15, 2005

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Global Gene Expression Profiling in Escherichia coli K12

EFFECTS OF OXYGEN AVAILABILITY AND ArcA^*

From the ^aDepartment of Microbiology, Immunology, and Molecular Genetics and the ^mMolecular Biology Institute, University of California, Los Angeles, California 90095-1489 and the Departments of ^dMicrobiology and Molecular Genetics, ^gInformation and Computer Science, ^jBiological Chemistry, and ^kChemical Engineering and Material Science and the ^eInstitute for Genomics and Bioinformatics, University of California, Irvine, California 92697

The ArcAB two-component system of Escherichia coli regulates the aerobic/anaerobic expression of genes that encode respiratory proteins whose synthesis is coordinated during aerobic/anaerobic cell growth. A genomic study of E. coli was undertaken to identify other potential targets of oxygen and ArcA regulation. A group of 175 genes generated from this study and our previous study on oxygen regulation (Salmon, K., Hung, S. P., Mekjian, K., Baldi, P., Hatfield, G. W., and Gunsalus, R. P. (2003) J. Biol. Chem. 278, 29837–29855), called our gold standard gene set, have p values <0.00013 and a posterior probability of differential expression value of 0.99. These 175 genes clustered into eight expression patterns and represent genes involved in a large number of cell processes, including small molecule biosynthesis, macromolecular synthesis, and aerobic/anaerobic respiration and fermentation. In addition, 119 of these 175 genes were also identified in our previous study of the fnr allele. A MEME/weight matrix method was used to identify a new putative ArcA-binding site for all genes of the E. coli genome. 16 new sites were identified upstream of genes in our gold standard set. The strict statistical analyses that we have performed on our data allow us to predict that 1139 genes in the E. coli genome are regulated either directly or indirectly by the ArcA protein with a 99% confidence level.

Received for publication, December 14, 2004 , and in revised form, January 18, 2005.

^* This work was supported in part by National Institutes of Health Grants GM49694 and AI21678 (to R. P. G.) and Grant GM68903 (to G. W. H.) and by the University of California Institute for Genomics and Bioinformatics (Irvine, CA). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

The on-line version of this article (available at http://www.jbc.org) contains a supplemental table.

^b Both authors contributed equally to this work.

^c Present address: Dept. of Microbiology and Molecular Genetics, University of California, Irvine, CA 92697.

^f Recipient of a postdoctoral fellowship from the University of California Biotechnology Research and Education Program.

^h Recipient of Biomedical Informatics Training Program Postdoctoral Fellowship T15 LM-07443 from the National Institutes of Health-National Library of Medicine.

ⁱ Supported by a traineeship from the UCLA-Integrative Graduate Education and Research Traineeship Bioinformatics Program funded by National Science Foundation Grant DGE-9987641.

^l To whom correspondence may be addressed: Dept. of Microbiology and Molecular Genetics, University of California, Medical Science I, Campus Dr., Irvine, CA 92697. Tel.: 949-824-5344; Fax: 949-824-8595; E-mail: gwhatfie{at}uci.edu. ⁿ To whom correspondence may be addressed: Dept. of Microbiology, Immunology, and Molecular Genetics, UCLA, 609 Charles Young Dr. East, 1602A MSB, Los Angeles, CA 90095. Tel.: 310-206-8201; Fax: 310-206-5231; E-mail: robg{at}microbio.ucla.edu.

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