Freddolino Peter L, Amemiya Haley M, Goss Thomas J, Tavazoie Saeed
Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
PLoS Biol. 2021 Jun 25;19(6):e3001306. doi: 10.1371/journal.pbio.3001306. eCollection 2021 Jun.
Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display-high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein-DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation.
自由生活的细菌通过数百种DNA结合蛋白的精确相互作用对基因表达进行重新编程，从而适应环境变化。对细菌生理学的预测性理解要求我们在一系列环境和基因扰动下全面监测所有此类蛋白质-DNA相互作用。在这里，我们表明使用体内蛋白质占据显示技术的优化版本（体内蛋白质占据显示-高分辨率，IPOD-HR）可以进行这样的全局观察，并展示了对大肠杆菌的初步应用。我们观察到大肠杆菌的蛋白质-DNA相互作用组组织成2个不同的原型特征：（1）高度动态的条件依赖性转录因子（TF）占据；（2）核仁因子在千碱基尺度上的稳定占据，形成类似于真核异染色质的沉默结构域。我们表明，在一系列条件下的占据动态可以迅速揭示细菌的全局转录调控组织。除了发现以前隐藏的调控逻辑外，我们还表明这些观察结果可用于通过计算确定大多数活性TF的序列特异性模型。我们的研究表明，对蛋白质占据的全局观察与统计推断相结合，可以快速、系统地揭示细菌基因组的转录调控和结构特征。这种能力对于不适合常规基因操作的非模式细菌尤为关键。
