Evolutionary dynamics of prokaryotic transcriptional regulatory networks
Evolutionary dynamics of prokaryotic transcriptional regulatory networks
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DOI：
10.1016/j.jmb.2006.02.019
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发表时间：
2006-04-28
影响因子：
5.6
通讯作者：
Aravind, L
中科院分区：
文献类型：
--
作者：
Babu, MM;Teichmann, SA;Aravind, L
复杂的转录调控网络的结构已经在某些模式生物中得到了广泛的研究。然而，这些网络在生物体中的进化动力学，这将揭示适应性调节变化的重要原则，知之甚少。我们使用已知的转录调控网络的大肠杆菌科尔，以分析该网络在175个原核基因组的保守模式，并预测这些生物体的调控网络的组件。我们观察到，转录因子通常比它们的靶基因保守性更低，并且独立于它们进化，不同的生物体进化出不同的转录因子库，以响应特定的信号。我们发现，原核转录调控网络的发展主要是通过广泛的修补转录相互作用在当地的水平嵌入orthopathic基因在不同类型的监管图案。不同的转录因子在不同的生物体中独立地成为主导的调控中心，这表明它们已经收敛地获得了类似的无标度拓扑结构。我们注意到，在广泛的系统发育范围内具有相似生活方式的生物体往往会保留等效的相互作用和网络图案。因此，生物体特异性的最佳网络设计似乎已经演变，由于选择特定的转录因子和转录相互作用，允许响应普遍的环境刺激。本文介绍的生物网络分析方法可普遍应用于其他网络的研究，这些预测可用于指导具体的实验。出版社：Elsevier Ltd
The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia Coll to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments. Published by Elsevier Ltd.