Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers

Abstract

Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation.

An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level

“…Super enhancers (SE) are involved in the cancer-specific regulation of essential genes in human cancers. The same SEs might function differently across cancers in terms of which and how gene targets are regulated [ 20 , 21 , 43 ]. To understand the SE divergence across human cancers, we revisited SE profiles for 28 cancer types based on the public H3K27Ac ChIP-seq data of the NCI-60 cancer cell lines [ 19 ].…”

“…Recently, we and others demonstrated the functionally critical structures inside SEs [ 21 , 22 , 24 ], motivating the study of SE divergence across cancers at a higher resolution, i.e., at the constituent enhancer (CE) level. We applied our tool DASE [ 21 ] to compare SE constituent differences between pair-wise cell lines (1770 comparisons across 60 cell lines, with an average of 9530 CEs per pair and 7 CEs per SE). For a given SE, DASE statistically evaluated the CE patterns between cell lines by considering both the divergence between cell lines and the consistency of two replicates within each cell line.…”

“…Differential SEs were identified with at least one types of differential changes ( FDR<0 . 05) by DASE [ 21 ]. Error bars indicate standard deviation.…”

Super-Enhancers and Their Parts: From Prediction Efforts to Pathognomonic Status

“…ROSE has proven to be a useful tool in identifying super-enhancers across different cell types and species [ 79 , 80 , 81 ]. Additionally, ROSE has been extensively used in cancer research to identify the key regulatory elements that drive the expression of oncogenes and tumour-suppressor genes [ 82 , 83 ].…”

Experimental Validation and Prediction of Super-Enhancers: Advances and Challenges

“…The authors categorized differential SEs into five major groups based on their overall activity and structural alterations: overall-change, shortened, hollowed, shifted, and others, which allowed them to link SEs with different numbers of genes and assess their gene expressions’ divergent impacts. Besides that, DASE demonstrated the increased power of identifying cell line-specific SE regulation when applied to similar cell lines [ 77 ].…”