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J. Biol. Chem., Vol. 277, Issue 43, 40309-40323, October 25, 2002
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From the Leucine-responsive regulatory protein
(Lrp) is a global regulatory protein that affects the expression of
multiple genes and operons in bacteria. Although the physiological
purpose of Lrp-mediated gene regulation remains unclear, it has been
suggested that it functions to coordinate cellular metabolism with the
nutritional state of the environment. The results of gene expression
profiles between otherwise isogenic lrp+ and
lrp
Global Gene Expression Profiling in Escherichia coli
K12
THE EFFECTS OF LEUCINE-RESPONSIVE REGULATORY PROTEIN*,
§,
**, and**§§
Departments of Microbiology and Molecular
Genetics and of ¶ Biological Chemistry, College of Medicine, the
Department of Chemical Engineering and
Material Sciences, School of Engineering, the Department of
Information and Computer Science, and the ** Institute for
Genomics and Bioinformatics, University of California,
Irvine, California 92697
strains of Escherichia coli
support this suggestion. The newly discovered Lrp-regulated genes
reported here are involved either in small molecule or macromolecule
synthesis or degradation, or in small molecule transport and
environmental stress responses. Although many of these regulatory
effects are direct, others are indirect consequences of Lrp-mediated
changes in the expression levels of other global regulatory proteins.
Because computational methods to analyze and interpret high dimensional
DNA microarray data are still an early stage, 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 a Bayesian statistical framework for a
posterior estimate of the standard deviation of gene measurements based
on a limited number of replications. We also describe an algorithm to
compute a posterior estimate of differential expression for each gene based on the experiment-wide global false positive and false negative level for a DNA microarray data set. This allows the
experimenter to compute posterior probabilities of differential
expression for each individual differential gene expression measurement.
*
This work was supported in part by the Institute of Genomics
and Bioinformatics (University of California, Irvine) and National Institutes of Health Grant GM-55073 (to G. W. H.), by a
Laurel Wilkening faculty innovation award, and by a Sun Microsystems award (to P. B.).The costs of publication of this
article were defrayed in part by the
payment of page charges. The 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 all of the raw and processed data for the
experimental results reported here.
§
Supported by a training grant from the University of California
Biotechnology Research and Education Program.
§§
To whom correspondence should be addressed: Dept. of Microbiology
and Molecular Genetics, College of Medicine, University of California,
Irvine, CA 92697. Tel.: 949-824-5344; Fax: 949-824-8598; E-mail:
gwhatfie@uci.edu.
Copyright © 2002 by The American Society for Biochemistry and Molecular Biology, Inc.
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