This application is being submitted in response to NOT-RM-19-009. Enhancers are context-specific regulatory elements that play a critical role in transcriptional control of biological processes such as differentiation and activation; however, the dynamic nature of enhancers and their ability to affect genes over great distances have made identifying the targets of enhancers an enormous challenge. The 4D Nucleome project has made great strides towards understanding enhancers and their role in gene regulation and has generated a variety of data sets characterizing enhancers, 3D chromatin structure, and gene transcription across biological time courses. This has created a dire need for computational methods designed specifically for, and taking full advantage of, the temporal aspects of existing and forthcoming time-course data sets generated by the 4D nucleome project and others. Here we propose a computational approach to predict enhancer-gene pairs by leveraging the temporal patterns of enhancer strength, chromatin contacts, and gene expression. We will apply this method to multi-omic time courses of cellular activation and differentiation to identify putative enhancers and their target genes. We will then validate select enhancer-gene pairs using genome editing and qPCR. This work will identify novel context-specific enhancer-gene pairs and produce a new computational tool to extract these pairings from forthcoming multi-omic time-course data sets from the 4D Nucleome project.
Enhancers are context-specific regulatory elements that play a critical role in transcriptional control of biological processes such as differentiation and activation; however, the dynamic nature of enhancers and their ability to affect genes over great distances has made identifying the targets of enhancers an enormous challenge. Since most disease-associated genetic variants reside in non-coding regulatory elements, effective strategies to map enhancers to their target genes would drastically improve our ability to understand and develop treatments for human disease. Here we propose a new strategy for accurate prediction of enhancer-gene pairs using temporally resolved multi-omic data generated by the 4D Nucleome Project.
