Modular organization of enhancer network provides transcriptional robustness in mammalian development

Abstract

Enhancer clusters, pivotal in mammalian development and diseases, can organize as enhancer networks to control cell identity and disease genes; however, the underlying mechanism remains largely unexplored. Here, we introduce eNet 2.0, a comprehensive tool for enhancer networks analysis during development and diseases based on single-cell chromatin accessibility data. eNet 2.0 extends our previous work eNet 1.0 by adding network topology, comparison and dynamics analyses to its network construction function. We reveal modularly organized enhancer networks, where inter-module interactions synergistically affect gene expression. Moreover, network alterations correlate with abnormal and dynamic gene expression in disease and development. eNet 2.0 is robust across diverse datasets. To facilitate application, we introduce eNetDB (https://enetdb.huanglabxmu.com), an enhancer network database leveraging extensive scATAC-seq (single-cell assay for transposase-accessible chromatin sequencing) datasets from human and mouse tissues. Together, our work provides a powerful computational tool and reveals that modularly organized enhancer networks contribute to gene expression robustness in mammalian development and diseases.