Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-03-15 Epub Date: 2023-01-24 DOI:10.1016/j.cels.2022.12.008
Simon Alamos, Armando Reimer, Clay Westrum, Meghan A Turner, Paul Talledo, Jiaxi Zhao, Emma Luu, Hernan G Garcia
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Abstract

How enhancers interpret morphogen gradients to generate gene expression patterns is a central question in developmental biology. Recent studies have proposed that enhancers can dictate whether, when, and at what rate promoters engage in transcription, but the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal promoter-proximal synthetic enhancer in embryos of Drosophila melanogaster. Here, a gradient of the Dorsal activator is read by a single Dorsal DNA binding site. Using live imaging to quantify transcriptional activity, we found that a single binding site can regulate whether promoters engage in transcription in a concentration-dependent manner. By modulating the binding-site affinity, we determined that a gene's decision to transcribe and its transcriptional onset time can be explained by a simple model where the promoter traverses multiple kinetic barriers before transcription can ensue.

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最小合成增强子揭示了通过形态发生梯度控制转录参与的概率及其时间。
增强子如何解释形态发生梯度以产生基因表达模式是发育生物学中的一个核心问题。最近的研究表明,增强子可以决定启动子是否、何时以及以何种速率参与转录,但内源性增强子的复杂性需要具有太多自由参数的理论模型来定量剖析这些调控策略。为了克服这一限制,我们在果蝇胚胎中建立了一种最小启动子近端合成增强子。这里,通过单个Dorsal DNA结合位点读取Dorsal激活剂的梯度。使用活体成像来量化转录活性,我们发现单个结合位点可以以浓度依赖的方式调节启动子是否参与转录。通过调节结合位点的亲和力,我们确定基因转录的决定及其转录开始时间可以用一个简单的模型来解释,在这个模型中,启动子在转录之前穿过多个动力学屏障。
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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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