Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-04-19 DOI:10.1016/j.cels.2023.02.003
Rosa Martinez-Corral, Minhee Park, Kelly M Biette, Dhana Friedrich, Clarissa Scholes, Ahmad S Khalil, Jeremy Gunawardena, Angela H DePace
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Abstract

Transcription factors (TFs) control gene expression, often acting synergistically. Classical thermodynamic models offer a biophysical explanation for synergy based on binding cooperativity and regulated recruitment of RNA polymerase. Because transcription requires polymerase to transition through multiple states, recent work suggests that "kinetic synergy" can arise through TFs acting on distinct steps of the transcription cycle. These types of synergy are not mutually exclusive and are difficult to disentangle conceptually and experimentally. Here, we model and build a synthetic circuit in which TFs bind to a single shared site on DNA, such that TFs cannot synergize by simultaneous binding. We model mRNA production as a function of both TF binding and regulation of the transcription cycle, revealing a complex landscape dependent on TF concentration, DNA binding affinity, and regulatory activity. We use synthetic TFs to confirm that the transcription cycle must be integrated with recruitment for a quantitative understanding of gene regulation.

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转录动力学协同作用:一个复杂的景观揭示了整合建模和合成生物学。
转录因子(TFs)控制基因表达,通常协同作用。经典热力学模型为基于结合协同性和RNA聚合酶的调节募集的协同作用提供了生物物理解释。由于转录需要聚合酶在多个状态中转换,最近的研究表明,“动能协同”可以通过tf作用于转录周期的不同步骤而产生。这些类型的协同作用并不是相互排斥的,很难从概念上和实验上加以区分。在这里,我们模拟并构建了一个合成电路,其中tf与DNA上的单个共享位点结合,这样tf就不能通过同时结合来协同作用。我们将mRNA的产生建模为TF结合和转录周期调控的功能,揭示了依赖于TF浓度、DNA结合亲和力和调控活性的复杂景观。我们使用合成tf来证实转录周期必须与基因募集相结合,以定量了解基因调控。
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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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