Systematic identification of metabolites controlling gene expression in E. coli
Systematic identification of metabolites controlling gene expression in E. coli
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DOI：
10.1038/s41467-019-12474-1
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发表时间：
2019-10-02
影响因子：
16.6
通讯作者：
Link, Hannes
中科院分区：
文献类型：
--
作者：
Lempp, Martin;Farke, Niklas;Link, Hannes
代谢通过代谢产物和转录因子之间的变构相互作用来控制基因的表达。这些相互作用通常是通过体外测试来测量的，但还没有方法在体内从基因组规模上识别它们。在这里，我们展示了动态转录组和代谢组数据识别控制大肠杆菌转录因子的代谢物。通过在饥饿和生长之间切换大肠杆菌培养物，我们会引起强烈的代谢物浓度变化和基因表达变化。利用网络成分分析，我们计算了209个转录调控因子的活性，并将它们与代谢产物关联起来。例如，这种方法捕捉到了环腺苷酸对C反应蛋白调节的体内动力学。通过检验所有转录因子和代谢产物之间的相关性，我们预测了71个转录因子的可能效应因子，并在体外验证了五个相互作用。这些结果表明，结合转录组学和代谢组学产生了关于代谢-转录相互作用的假设，这些相互作用推动了生理状态之间的转换。
Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.