The architecture of binding cooperativity between densely bound transcription factors.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-09-20 Epub Date: 2023-07-31 DOI:10.1016/j.cels.2023.06.010
Offir Lupo, Divya Krishna Kumar, Rotem Livne, Michal Chappleboim, Idan Levy, Naama Barkai
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引用次数: 2

Abstract

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

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紧密结合的转录因子之间结合协同性的结构。
转录因子(TF)沿基因组的结合仅限于包含其优选基序的位点的子集。TF结合特异性通常归因于相互作用的TF的共结合;然而,除了具体的例子之外,这个模型还没有经过测试。在这里,我们通过分析共消耗多个转录因子的全基因组后果,定义了定位于重叠启动子的出芽酵母转录因子之间的依赖性。我们描述了单向相互作用,揭示了Msn2是允许TF在其靶启动子处结合的中心因子。相比之下,没有观察到相互合作的情况。特别地,在共耗尽14个类似结合的TF时,Msn2在其优选的启动子处保持结合。总的来说,TF共耗竭的后果是适度的,仅限于启动子的一个子集,并且未能解释DNA结合结构域之外的区域在指导TF结合偏好中的作用。我们的研究结果要求重新评估合作相互作用在引导TF结合偏好中的作用。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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