Title: Nested epistasis enhancer networks for robust genome regulation
Author: Xueqiu Lin, Yanxia Liu, Shuai Liu, Xiang Zhu, Lingling Wu, Yanyu Zhu, Dehua Zhao, Xiaoshu Xu, Augustine Chemparathy, Haifeng Wang, Yaqiang Cao, Muneaki Nakamura, Jasprina N. Noordermeer, Marie La Russa, Wing Hung Wong, Keji Zhao, Lei S. Qi
Abstract: 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.