Nested epistasis enhancer networks for robust genome regulation.

Area of Science:

• Genomics
• Molecular Biology
• Systems Biology

Background:

• Mammalian genomes contain numerous enhancers that regulate gene expression over vast distances (megabases).
• The coordination mechanisms among these long-range enhancers remain largely unknown.
• Understanding enhancer interactions is crucial for deciphering gene regulation and its role in disease.

Purpose of the Study:

• To elucidate the coordination mechanisms of ultralong distance enhancers in mammalian genomes.
• To define quantitative enhancer-enhancer interactions using advanced screening and computational methods.
• To explore the functional implications of enhancer networks in gene expression and disease.

Main Methods:

• Utilized multiplexed CRISPR interference (CRISPRi) screening to perturb enhancer activity.
• Employed machine learning algorithms to analyze screening data and predict enhancer interactions.
• Performed experimental characterization to validate findings, focusing on 3D genome architecture and protein condensations.

Main Results:

• Identified a nested, multilayered architecture within ultralong distance enhancer networks.
• Demonstrated that this architecture confers functional robustness to gene expression.
• Found that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation.
• Developed a machine learning approach to predict synergistic enhancers.

Conclusions:

• Unveiled the concept of nested epistasis enhancer networks.
• Provided a framework for understanding how enhancers cooperate over long genomic distances.
• Established a strategy for identifying disease-associated noncoding variants by predicting synergistic enhancers.
• Advanced the understanding of gene regulation in cellular function and human diseases.