Project description:Enhancers play key roles in gene regulation. However, comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Through gene regulatory network modeling, we identified that dynamic cell state transitions, a critical missing component in prevalent enhancer discovery strategies, can be utilized to improve the cells’ sensitivity to enhancer perturbation. Guided by the modeling results, we performed a mid-transition CRISPRi-based enhancer screen utilizing human embryonic stem cell definitive endoderm differentiation as a dynamic transition system. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core lineage-specifying transcription factors (TFs), including many enhancers with weak to moderate effects. Integrating the screening results with enhancer activity measurements (ATAC-seq, H3K27ac ChIP-seq) and three-dimensional enhancer-promoter interaction information (CTCF looping, Hi-C), we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Together, our dynamic network-guided enhancer screen and the CIA enhancer prediction model provide generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.
