MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH, UK.
Nucleic Acids Res. 2010 Nov;38(20):6841-56. doi: 10.1093/nar/gkq612. Epub 2010 Jul 14.
In prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called transcription factors (TFs). In this study, we map the complete repertoire of ∼300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of nonredundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug-induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, defined as those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions such as drug induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and, in general, transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic data sets.
在原核生物中，基因表达的调控主要在转录水平上进行。转录又由一组称为转录因子 (TFs) 的 DNA 结合因子介导。在这项研究中，我们将细菌模型大肠杆菌中的约 300 个 TF 的完整库映射到许多非冗余实验条件下的基因表达数据上，并表明 TF 通常比其他基因类别的表达水平低。我们还证明了不同的条件具有不同数量的活性 TF，平均约占总库的 15%，某些应激和药物诱导的条件表现出高达三分之一的 TF 集合。我们的结果还表明，激活剂比抑制剂更频繁地表达，这表明在细菌中激活启动子可能比抑制更为常见。最后，为了了解 TF 与不同条件的关联，并阐明它们与其他 TF 的动态相互作用，我们开发了一种基于网络的框架来识别作为标记的 TF，这些标记被定义为负责特定条件转录重布线的 TF。这种方法使我们能够确定几个标记 TF 在各种特殊条件（如药物诱导或生长条件变化）中处于中心地位，我们将根据先前报道的实验结果对此进行讨论。进一步的分析表明，大多数鉴定出的标记有效地控制它们的调控子的表达，并且通常，大多数条件的转录程序可以通过非常少的 TF 有效地重新布线。还发现接近度是一种关键的中心性度量，可以帮助在调控网络中成功识别标记 TF。我们的结果表明，本研究中开发的基于网络的方法在理解其他相互作用数据集方面具有应用价值。
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