预测大肠杆菌中转录因子的输入信号
Julian Trouillon1, Alexandra E Huber1, Yannik Trabesinger1
1Institute of Molecular Systems Biology, ETH Zürich, Zürich, 8093, Switzerland.
Molecular systems biology
|July 16, 2025
概括
研究人员开发了一种新的工作流程,以识别调节细菌转录因子 (TF) 的小分子信号. 这种方法使用代谢学和转录学数据来发现大肠杆菌中的新型TF代谢物相互作用.
科学领域:
- 微生物学 微生物学
- 系统生物学 系统生物学
- 生物化学 生物化学
背景情况:
- 细菌转录因子 (TF) 活性主要由小分子调节,但对于大多数TF来说,这些信号是未知的.
- 识别TF输入信号通常需要耗时,TF特定的实验方法.
研究的目的:
- 开发一个系统的工作流程来识别细菌TF输入信号,使用集成的OMIC数据.
- 预测大肠杆菌中的新型TF代谢物相互作用.
主要方法:
- 从现有的转录组学数据推断了173个TF的活动.
- 在大肠杆菌的40个实验条件中,279种代谢物的量化丰富性.
- 与代谢物丰度相关的TF活动,以确定调控相互作用.
主要成果:
- 成功识别了已知的TF-代谢物相互作用,并预测了41种新的TF效应物代谢物.
- 通过体外测试验证了LeuO TF的预测效应物代谢物.
- 建立了80种调节性相互作用的网络,涉及71种代谢物和41种大肠杆菌TF,包括76种新型相互作用.
结论:
- 开发的工作流提供了一种系统的方法来发现细菌中的TF输入信号.
- 已识别的网络显著扩大了已知的大肠杆菌的调节相互作用,为各种化学类和调节模式提供了洞察力.
- 这项工作推进了构建在大肠杆菌中全面的TF监管网络的目标.
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