Mammalian genomes possess multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, yet it is unclear how these enhancers coordinate to achieve this task. Here, we combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network possesses a nested multi-layer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify non-coding variant pairs associated with pathogenic genes in diseases beyond Genome-Wide Association Studies (GWAS) analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.

中文翻译：

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用于稳健基因组调控的嵌套上位增强子网络

哺乳动物基因组拥有多个跨越超长距离（> 兆碱基）的增强子来调节重要基因，但尚不清楚这些增强子如何协调以完成这项任务。在这里，我们将多重 CRISPRi 筛选与机器学习相结合，以定义定量增强子-增强子相互作用。我们发现超长距离增强子网络具有嵌套的多层结构，赋予基因表达功能稳健性。实验表征表明，增强子上位性由三维染色体相互作用和 BRD4 浓缩维持。协同增强子的机器学习预测提供了一种有效的策略来识别与全基因组关联研究 (GWAS) 分析之外的疾病中致病基因相关的非编码变体对。