Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations.-猫眼课题宝

DOI：

10.1186/1752-0509-3-39

发表时间：

2009-04-14

期刊：

BMC systems biology

影响因子：

通讯作者：

中科院分区：

生物2区

文献类型：

作者：

Zare H;Sangurdekar D;Srivastava P;Kaveh M;Khodursky A

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2689187/?report=classic

依赖于转录调控因子及其靶标表达协方差的网络重构方法忽略了一个事实，即调控因子及其靶标的转录可以被不同和/或独立控制。这种疏忽会导致许多错误的预测。然而，通过对共调控基因群的转录活性进行建模和估计，可以准确预测基因调控相互作用。本文使用不完整的调控连通性和表达数据来构建大肠杆菌转录调控的共识网络。该网络通过协方差模型更新，协方差模型描述了由共同调节器控制的基因集的活性。所提出的模型选择算法用于根据两组独立的表达数据注释大肠杆菌中最可能的调控相互作用，每组数据都包含多种条件下的许多微阵列实验。通过实验和文献调查验证了关键的调控预测。此外，转录因子的估计活性谱被用来描述它们对环境和遗传扰动以及药物治疗的反应。已记录的共调控基因（核心调控子）的转录活性信息应该足以发现新的靶基因，其转录活性与核心调控子成员的活性显着共变化。通过将基于规则的方法应用于两个非常不同的数据集，我们能够推导出一个高度重要的共识网络，这证明了该策略的有效性。我们相信这种方法可以用来重建其他生物的基因调控网络，其中部分已知的相互作用是可用的。

Network reconstruction methods that rely on covariance of expression of transcription regulators and their targets ignore the fact that transcription of regulators and their targets can be controlled differently and/or independently. Such oversight would result in many erroneous predictions. However, accurate prediction of gene regulatory interactions can be made possible through modeling and estimation of transcriptional activity of groups of co-regulated genes. Incomplete regulatory connectivity and expression data are used here to construct a consensus network of transcriptional regulation in Escherichia coli (E. coli). The network is updated via a covariance model describing the activity of gene sets controlled by common regulators. The proposed model-selection algorithm was used to annotate the likeliest regulatory interactions in E. coli on the basis of two independent sets of expression data, each containing many microarray experiments under a variety of conditions. The key regulatory predictions have been verified by an experiment and literature survey. In addition, the estimated activity profiles of transcription factors were used to describe their responses to environmental and genetic perturbations as well as drug treatments. Information about transcriptional activity of documented co-regulated genes (a core regulon) should be sufficient for discovering new target genes, whose transcriptional activities significantly co-vary with the activity of the core regulon members. Our ability to derive a highly significant consensus network by applying the regulon-based approach to two very different data sets demonstrated the efficiency of this strategy. We believe that this approach can be used to reconstruct gene regulatory networks of other organisms for which partial sets of known interactions are available.