Global gene expression profiling in Escherichia coli K12 -: The effects of leucine-responsive regulatory protein

被引:109
|
作者
Hung, SP
Baldi, P
Hatfield, GW [1 ]
机构
[1] Univ Calif Irvine, Coll Med, Dept Microbiol & Mol Genet, Irvine, CA 92697 USA
[2] Univ Calif Irvine, Coll Med, Dept Biol Chem, Irvine, CA 92697 USA
[3] Univ Calif Irvine, Inst Genomics & Bioinformat, Irvine, CA 92697 USA
[4] Univ Calif Irvine, Dept Informat & Comp Sci, Irvine, CA 92697 USA
[5] Univ Calif Irvine, Sch Engn, Dept Chem Engn & Mat Sci, Irvine, CA 92697 USA
[6] Univ Calif Irvine, Coll Med, Dept Biol Chem, Irvine, CA 92697 USA
[7] Univ Calif Irvine, Coll Med, Dept Microbiol & Mol Genet, Irvine, CA 92697 USA
关键词
D O I
10.1074/jbc.M204044200
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 Irp(+) and Irp(-)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.
引用
收藏
页码:40309 / 40323
页数:15
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