Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Mol Cell. 2020 Oct 15;80(2):359-373.e8. doi: 10.1016/j.molcel.2020.09.005. Epub 2020 Sep 28.
Eukaryotic gene expression regulation involves thousands of distal regulatory elements. Understanding the quantitative contribution of individual enhancers to gene expression is critical for assessing the role of disease-associated genetic risk variants. Yet, we lack the ability to accurately link genes with their distal regulatory elements. To address this, we used 3D enhancer-promoter (E-P) associations identified using split-pool recognition of interactions by tag extension (SPRITE) to build a predictive model of gene expression. Our model dramatically outperforms models using genomic proximity and can be used to determine the quantitative impact of enhancer loss on gene expression in different genetic backgrounds. We show that genes that form stable E-P hubs have less cell-to-cell variability in gene expression. Finally, we identified transcription factors that regulate stimulation-dependent E-P interactions. Together, our results provide a framework for understanding quantitative contributions of E-P interactions and associated genetic variants to gene expression.
真核生物基因表达调控涉及数千个远端调控元件。了解单个增强子对基因表达的定量贡献对于评估与疾病相关的遗传风险变异体的作用至关重要。然而，我们缺乏将基因与其远端调控元件准确关联的能力。为了解决这个问题，我们使用了通过标签扩展进行的分裂池识别相互作用（SPRITE）鉴定的 3D 增强子-启动子（E-P）关联来构建基因表达的预测模型。我们的模型显著优于使用基因组邻近性的模型，并且可用于确定不同遗传背景下增强子缺失对基因表达的定量影响。我们表明，形成稳定 E-P 枢纽的基因在基因表达方面具有更小的细胞间变异性。最后，我们确定了调节刺激依赖性 E-P 相互作用的转录因子。总之，我们的研究结果为理解 E-P 相互作用及其相关遗传变异对基因表达的定量贡献提供了一个框架。
