Computational design of genomic transcriptional networks with adaptation to varying environments
Computational design of genomic transcriptional networks with adaptation to varying environments
复制标题
DOI：
10.1073/pnas.1200030109
复制
发表时间：
2012-09-18
影响因子：
11.1
通讯作者：
Jaramillo, Alfonso
中科院分区：
文献类型：
--
作者：
Carrera, Javier;Elena, Santiago F.;Jaramillo, Alfonso
转录谱已被广泛用作揭示基因对遗传和环境扰动的共调控的工具。在少数情况下，这些共调节已被用于推断全球转录调控模型。在这里，使用大量的转录组信息可用于细菌大肠杆菌，我们试图了解的设计原则，确定其转录组的调节。结合转录组和信号数据，我们开发了一个进化的计算程序，允许获得替代的基因组转录调控网络（GTRN），仍然保持其适应性的动态环境。我们将我们的方法应用于E。coli GTRN，并表明它可以重新连接到更简单的转录调控结构。这些重新连接的GTRN仍然保持着对波动环境的整体生理反应。重新连接的GTRN含有减少73%的调节操纵子。具有相似功能和跨环境的协调表达模式的基因被聚集成更长的受调控操纵子。这些合成GTRN更敏感，对挑战性环境表现出更强大的响应。这一结果说明，E. coliGTRN不一定是由于对环境扰动的鲁棒性选择，但进化的偶然性可能也很重要。我们还讨论了需求理论的背景下，我们的方法的局限性。我们的程序将是有用的，作为一种新的方式来分析全球转录调控网络和合成生物学的从头设计的基因组。
Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.