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J. Biol. Chem., Vol. 278, Issue 32, 29837-29855, August 8, 2003
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Global Gene Expression Profiling in Escherichia coli K12
THE EFFECTS OF OXYGEN AVAILABILITY AND FNR*
¶||¶¶
From the
Department of Microbiology, Immunology,
and Molecular Genetics, and the Molecular Biology Institute, UCLA, Los
Angeles, California 90095-1489, the ¶Department
of Microbiology and Molecular Genetics, the **School
of Information and Computer Science, the
Department of Biological Chemistry,
and the ||Institute for Genomics and
Bioinformatics, University of California, Irvine, California 92697
The work presented here is a first step toward a long term goal of systems biology, the complete elucidation of the gene regulatory networks of a living organism. To this end, we have employed DNA microarray technology to identify genes involved in the regulatory networks that facilitate the transition of Escherichia coli cells from an aerobic to an anaerobic growth state. We also report the identification of a subset of these genes that are regulated by a global regulatory protein for anaerobic metabolism, FNR. Analysis of these data demonstrated that the expression of over one-third of the genes expressed during growth under aerobic conditions are altered when E. coli cells transition to an anaerobic growth state, and that the expression of 712 (49%) of these genes are either directly or indirectly modulated by FNR. The results presented here also suggest interactions between the FNR and the leucine-responsive regulatory protein (Lrp) regulatory networks. Because computational methods to analyze and interpret high dimensional DNA microarray data are still at an early stage, and because basic issues of data analysis are still being sorted out, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe an approach for identifying gene expression patterns (clusters) obtained from multiple perturbation experiments based on a subset of genes that exhibit high probability for differential expression values.
Received for publication, December 20, 2002 , and in revised form, May 15, 2003.
* This work was supported in part by National Institutes of Health Grants GM49694, AI21678 (to R. G.), GM55073, and GM68903 (to G. W. H.) and by the University of California, Irvine, Institute of Genomics and Bioinformatics. 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 the raw and processed data for the experimental results and may be
downloaded in tabular format as Excel files.
Both authors contributed equally to this work.
To whom correspondence (computation) may be addressed. Tel.: 949-824-5344;
Fax: 949-824-8595; E-mail:
gwhatfie{at}uci.edu.
¶¶ To whom correspondence (biology) may be addressed. Tel.: 310-206-8201; Fax: 310-206-5231; E-mail: robg{at}microbio.ucla.edu.
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