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Nested epistasis enhancer networks for robust genome regulation.

Xueqiu LinYanxia LiuShuai LiuXiang ZhuLingling WuYanyu ZhuDehua ZhaoXiaoshu XuAugustine ChemparathyHaifeng WangYaqiang CaoMuneaki NakamuraJasprina N NoordermeerMarie La RussaWing Hung WongKeji ZhaoLei S Qi
Published in: Science (New York, N.Y.) (2022)
Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network has a nested multilayer 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 noncoding variant pairs associated with pathogenic genes in diseases beyond genome-wide association studies analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.
Keyphrases
  • binding protein
  • transcription factor
  • machine learning
  • gene expression
  • case control
  • genome wide
  • genome wide association
  • induced apoptosis
  • deep learning
  • big data
  • single cell
  • mass spectrometry
  • cancer therapy