Global view of enhancer–promoter interactome in human cells

Abstract

Enhancer mapping has been greatly facilitated by various genomic marks associated with it. However, little is available in our toolbox to link enhancers with their target promoters, hampering mechanistic understanding of enhancer-promoter (EP) interaction. We develop and characterize multiple genomic features for distinguishing true EP pairs from noninteracting pairs. We integrate these features into a probabilistic predictor for EP interactions. Multiple validation experiments demonstrate a significant improvement over state-of-the-art approaches. Systematic analyses of EP interactions across 12 cell types reveal several global features of EP interactions: (i) a larger fraction of EP interactions are cell type specific than enhancers; (ii) promoters controlled by multiple enhancers have higher tissue specificity, but the regulating enhancers are less conserved; (iii) cohesin plays a role in mediating tissue-specific EP interactions via chromatin looping in a CTCF-independent manner. Our approach presents a systematic and effective strategy to decipher the mechanisms underlying EP communication.T ranscriptional enhancers represent the primary basis for differential gene expression. These elements regulate cell type specificity, development, and metazoan evolution, with many human diseases resulting from altered enhancer action (1, 2).A key gap in our knowledge is an understanding of how enhancers select specific promoters for activation. Linkage of enhancers and target promoters is challenged by enhancer properties. First, increasing evidence suggests that enhancers are not located adjacent to their target promoters. Instead, they are positioned tens of kilobases away and contact their targets via long-range interactions (3-6). Second, enhancers are position independent, i.e., they may be located either upstream or downstream of the regulated promoter.Experimental approaches to identifying enhancer targets have largely relied on chromosome conformation capture (3C) (7) and its variants such as circularized chromosome conformation capture (4C) and genome-wide chromosome conformation capture (Hi-C) (8), all of which determine the relative frequency of direct physical contact between linearly separated DNA sequences. Unlike 3C and 4C, Hi-C is a truly genome-wide technology, but its current resolution (1 Mbp) in general is not high enough to distinguish individual enhancer-promoter (EP) interactions (9). Newer methods such as ChIP-loop (10) and chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) (11) combine the principles of 3C and ChIP to identify chromatin interactions mediated by protein factors. However, the assays are technically challenging and currently have a high false-negative rate (5, 12). Therefore, computational work, if successful, can complement experimental protocols and allow prioritization of future experiments much more effectively.The most common computational approach is assigning the nearest promoter of an enhancer as its target. Improvements to this basic approach have be...

EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types

“…Since it is a supervised model, the training cell types may have a significant effect on the performance of the model in different cell types. We used newly reported enhancer-promoter interactions as the gold standard to assess the quality of our IM-PET prediction (He et al, 2014). The performance of predictions in these new cell types (AUC 0.9) are similar the predictions we evaluated in the original IM-PET prediction ( Figure S3).…”

“…We used a recent developed algorithm Integrated Method for Predicting Enhancer Targets (IM-PET) to predict enhancer targets (He et al, 2014). IM-PET predicts enhancer-promoter by integrating four features using a Random Forest classifier.…”

Enhancer methylation dynamics contribute to cancer plasticity and patient mortality

“…S2I). In order to assess the validity of our eDMR-gene pair predictions, we compared our results to other methods that identify enhancer-promotor associations based on physical interactions IM-PET (He et al 2014), ChIA-PET, Hi-C (Teng et al 2015), and transcriptional activities of interacting enhancer-promoters (cap analysis gene expression; CAGE) (Andersson et al 2014). Our model predicted eDMR-gene pairs separated by 400 kb or less at a precision rate of 75% or better ( Fig.…”

“…Recent studies have used these features to predict enhancer-gene interactions Hellman 2013, 2014;Andersson et al 2014;He et al 2014); however, these methods provide limited insight into the roles of enhancers in cancer, in particular, the transcriptional consequences of abnormal enhancer methylation.…”

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes

“…It is difficult to predict enhancer-promoter associations using a single parameter, so that machine learning methods to combine several parameters have been proposed [47][48][49]. These methods showed high accuracy in predicting enhancer-promoter associations (I tried to use some of the tools, but they did not work properly.…”