Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities
Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities
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DOI：
10.1073/pnas.1702581114
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发表时间：
2017-09-19
影响因子：
11.1
通讯作者：
Palsson, Bernhard O.
中科院分区：
文献类型：
--
作者：
Fang, Xin;Sastry, Anand;Palsson, Bernhard O.
转录调控网络(TRN)已经被深入研究了25年。然而，即使对于大肠杆菌TRN--可能是最具特征的TRN--仍有几个问题存在。在这里，我们解决三个问题：(I)我们对大肠杆菌TRN的知识有多完整；(Ii)我们能用这个TRN预测基因表达有多好；以及(Iii)我们对TRN的理解有多可靠？首先，我们构建了一个高可信的TRN(HiTRN)，它由147个转录因子(TF)组成，调控1,538个转录单位(TU)，编码1,764个基因。3797个高度可信的调控相互作用收集自已发表的、经过验证的染色质免疫沉淀(CHIP)数据和RegulonDB。对于21种不同的TF敲除，高达63%的差异表达基因在hiTRN中通过调控级联追溯到被敲除的TF。其次，我们训练有监督的机器学习算法，使用441个样本预测给定TF活动的1,364个TU的表达。该算法准确地预测了86%(1,364个TU中的1,174个)TU的特定条件表达，而193个TU(14%)的预测效果好于随机TRN。第三，我们确定了10个调控模块，它们的定义对TRN或表达纲要的变化是健壮的。使用替代变量分析，我们还确定了三个系统地影响基因表达的未建模因素。我们的计算工作流程从不同的数据类型全面描述了生物体的TRN的预测能力和系统级功能。
Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN-probably the best characterized TRN-several questions remain. Here, we address three questions: (i) How complete is our knowledge of the E. coli TRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism's TRN from disparate data types.