Trouillon Julian, Huber Alexandra E, Trabesinger Yannik, Sauer Uwe
Institute of Molecular Systems Biology, ETH Zürich, Zürich, 8093, Switzerland.
Mol Syst Biol. 2025 Jul 16. doi: 10.1038/s44320-025-00132-2.
The activity of bacterial transcription factors (TFs) is typically modulated through direct interactions with small molecules. However, these input signals remain unknown for most TFs, even in well-studied model bacteria. Identifying these signals typically requires tedious experiments for each TF. Here, we develop a systematic workflow for the identification of TF input signals in bacteria based on metabolomics and transcriptomics data. We inferred the activity of 173 TFs from published transcriptomics data and determined the abundance of 279 metabolites across 40 matched experimental conditions in Escherichia coli. By correlating TF activities with metabolite abundances, we successfully identified previously known TF-metabolite interactions and predicted novel TF effector metabolites for 41 TFs. To validate our predictions, we conducted in vitro assays and confirmed a predicted effector metabolite for LeuO. As a result, we established a network of 80 regulatory interactions between 71 metabolites and 41 E. coli TFs. This network includes 76 novel interactions that encompass a diverse range of chemical classes and regulatory patterns, bringing us closer to a comprehensive TF regulatory network in E. coli.
细菌转录因子（TFs）的活性通常通过与小分子的直接相互作用来调节。然而，即使在研究充分的模式细菌中，大多数TFs的这些输入信号仍然未知。识别这些信号通常需要对每个TF进行繁琐的实验。在这里，我们基于代谢组学和转录组学数据开发了一种用于识别细菌中TF输入信号的系统工作流程。我们从已发表的转录组学数据中推断出173个TFs的活性，并在大肠杆菌的40个匹配实验条件下测定了279种代谢物的丰度。通过将TF活性与代谢物丰度相关联，我们成功识别了先前已知的TF-代谢物相互作用，并预测了41个TFs的新型TF效应代谢物。为了验证我们的预测，我们进行了体外试验，并证实了LeuO的一种预测效应代谢物。结果，我们建立了一个由71种代谢物和41个大肠杆菌TFs之间的80个调控相互作用组成的网络。该网络包括76个新型相互作用，涵盖了多种化学类别和调控模式，使我们更接近大肠杆菌中全面的TF调控网络。
