Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.
Department of Pharmacology and Personalised Medicine, MaCSBio, Maastricht University, Universiteitssingel 60, 6229 ER, Maastricht, The Netherlands.
Nucleic Acids Res. 2019 Jan 10;47(1):85-92. doi: 10.1093/nar/gky1176.
Gene regulatory networks (GRNs) and gene expression data form a core element of systems biology-based phenotyping. Changes in the expression of transcription factors are commonly believed to have a causal effect on the expression of their targets. Here we evaluated in the best researched model organism, Escherichia coli, the consistency between a GRN and a large gene expression compendium. Surprisingly, a modest correlation was observed between the expression of transcription factors and their targets and, most noteworthy, both activating and repressing interactions were associated with positive correlation. When evaluated using a sign consistency model we found the regulatory network was not more consistent with measured expression than random network models. We conclude that, at least in E. coli, one cannot expect a causal relationship between the expression of transcription and factors their targets, and that the current static GRN does not adequately explain transcriptional regulation. The implications of this are profound as they question what we consider established knowledge of the systemic biology of cells and point to methodological limitations with respect to single omics analysis, static networks and temporality.
基因调控网络 (GRNs) 和基因表达数据构成了基于系统生物学的表型分析的核心要素。转录因子表达的变化通常被认为对其靶基因的表达有因果影响。在这里，我们在研究最充分的模式生物大肠杆菌中，评估了 GRN 与大型基因表达综合数据集之间的一致性。令人惊讶的是，观察到转录因子及其靶基因之间的表达呈适度相关性，最值得注意的是，激活和抑制相互作用都与正相关相关。当使用符号一致性模型进行评估时，我们发现与测量的表达相比，调控网络并没有比随机网络模型更一致。我们得出的结论是，至少在大肠杆菌中，不能期望转录因子与其靶基因的表达之间存在因果关系，并且当前的静态 GRN 不能充分解释转录调控。这具有深远的意义，因为它们质疑了我们对细胞系统生物学的既定认识，并指出了在单组学分析、静态网络和时间性方面存在方法学限制。
