Reconstructing protein interactions at enhancer-promoter regions in prostate cancer

Abstract DNA-binding proteins (DBPs) and in particular transcription factors interact with enhancers and their target genes through enhancer-promoter (E-P) interactions. Technological advancements such as chromosome conformation capture allow to identify E-P interactions, but the protein networks involved have not yet been characterized. Most importantly, the role of nuclear protein networks in human diseases has been so far poorly investigated. Prostate cancer (PrCa) heritability is associated with variations in enhancers that affect specific gene expression. Here, we introduce a novel approach, called Promoter-ENhancer-GUided Interaction Networks (PENGUIN), to identify protein-protein interactions (PPI) in E-P interactions and apply it to our PrCa dataset. PENGUIN integrates chromatin interactions between a promoter and its enhancers defined by high-coverage H3K27ac-HiChIP data, with a tissue-specific PPI network inferred from DNA-binding motifs and refined with gene expression. Among a total of 4,314 E-P networks, PENGUIN performed unsupervised clustering. We functionally validated this clustering procedure by searching for enrichments of specific biological features. We confirmed PENGUIN structural classification of E-P networks by showing a clear differential enrichment of the architectural protein CTCF. Next, and directly related to our PrCa case study, we observed that one of our 8 main clusters, containing 273 promoters, is particularly enriched for PrCA associated single nucleotide polymorphisms (SNPs) and oncogenes. Our approach proposes a mechanistic explanation for 208 PrCa SNPs falling either inside the binding sites of DNA-binding proteins (DBPs) or within genes encoding for intermediate proteins bridging E-P contacts. PENGUIN not only confirmed the relevance of key regulators in PrCa, but also identified new candidates for intervention, opening up new directions to identify molecular targets for disease treatment.