Global gene expression profiling in Escherichia coli K12 -: The effects of oxygen availability and FNR
Global gene expression profiling in Escherichia coli K12 -: The effects of oxygen availability and FNR
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DOI：
10.1074/jbc.m213060200
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发表时间：
2003-08-08
影响因子：
4.8
通讯作者：
Gunsalus, RP
中科院分区：
文献类型：
--
作者：
Salmon, K;Hung, SP;Gunsalus, RP
这里介绍的工作是系统生物学长期目标的第一步，即完整阐明生物体的基因调控网络。为此，我们采用了DNA微阵列技术，以确定参与调控网络，促进大肠杆菌细胞从有氧到厌氧生长状态的过渡基因。我们还报告了这些基因的一个子集，由全球无氧代谢，FNR调节蛋白的鉴定。对这些数据的分析表明，当E.大肠杆菌细胞转变为厌氧生长状态，这些基因中有712个（49%）的表达直接或间接受FNR调节。这里提出的结果也表明FNR和亮氨酸响应调节蛋白（Lrp）的调控网络之间的相互作用。由于计算方法来分析和解释高维DNA微阵列数据仍处于早期阶段，因为数据分析的基本问题仍在整理，这项工作的重点是针对发展的方法，以确定差异表达的基因与高水平的信心。特别是，我们描述了一种方法，用于识别基因表达模式（集群）从多个扰动实验的基础上表现出高概率差异表达值的基因的子集。
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.