Genomic dissection of enhancers uncovers principles of combinatorial regulation and cell type-specific wiring of enhancer–promoter contacts

Abstract

Genomic binding of transcription factors, like the glucocorticoid receptor (GR), is linked to the regulation of genes. However, as we show here, GR binding is a poor predictor of GR-dependent gene regulation even when taking the 3D organization of the genome into account. To connect GR binding sites to the regulation of genes in the endogenous genomic context, we turned to genome editing. By deleting GR binding sites, individually or in combination, we uncovered how cooperative interactions between binding sites contribute to the regulation of genes. Specifically, for the GR target gene GILZ, we show that the simultaneous presence of a cluster of GR binding sites is required for the activity of an individual enhancer and that the GR-dependent regulation of GILZ depends on multiple GR-bound enhancers. Further, by deleting GR binding sites that are shared between different cell types, we show how cell type-specific genome organization and enhancer-blocking can result in cell type-specific wiring of promoter–enhancer contacts. This rewiring allows an individual GR binding site shared between different cell types to direct the expression of distinct transcripts and thereby contributes to the cell type-specific consequences of glucocorticoid signaling.

Expanding the repertoire of glucocorticoid receptor target genes by engineering genomic response elements

“…For instance, GR binds as a dimer to typically imperfect half sites separated by a 3 bp spacer and the exact sequence of the half site, the spacer and of the nucleotides flanking the GR binding sequence (GBS) can modulate GR's activity towards target genes [10,11]. GR can bind to tens of thousands of genomic binding sites, yet seems to regulate a smaller number of genes [4,7,12,13]. Part of the discrepancy between GR binding and gene regulation might be due to GR's inability to activate gene expression for a subset of occupied sites [14].…”

“…Another part could reflect the inability of distal GR binding sites to contribute to gene regulation because they lack the physical proximity to the promoter of a gene. Accordingly, the link between GR binding and gene regulation is especially weak for peaks located at large distances from the promoter of genes except when the three dimensional organization of the genome brings these distal GR peak proximal to the promoter of a gene [13]. Nonetheless, even when taking three dimensional genome organization into account, GR binding is a poor predictor of GR-dependent gene regulation with only a subset of binding events (< 25%) resulting the in the regulation of associated genes [13].…”

“…Accordingly, the link between GR binding and gene regulation is especially weak for peaks located at large distances from the promoter of genes except when the three dimensional organization of the genome brings these distal GR peak proximal to the promoter of a gene [13]. Nonetheless, even when taking three dimensional genome organization into account, GR binding is a poor predictor of GR-dependent gene regulation with only a subset of binding events (< 25%) resulting the in the regulation of associated genes [13]. Recent advances in genome editing now offer opportunities to assay the contribution of endogenous TF-bound regions to gene regulation.…”

Applications of ENCODE data to systematic analyses via data integration

“…Cao et al developed a computational method called joint effect of multiple enhancers (JEME) and applied it to predict EP interactions in 935 human primary cell types, cell lines and tissues by the integration of histone modification, DNase-seq, RNA-seq and other data types [*32]. These analyses have revealed critical insights into EP interactions, including that i) they are not simply determined by genomic proximity [33–35]; ii) multiple enhancers might control the same promoter [36]; and iii) EPs are cell-type specific [37,*38]. …”

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

“…Regulatory SNPs are then grouped based on their predicted capacity to modulate a FU and are jointly subjected to multivariate association with a trait or disease using feature selection with LASSO (L1) penalization 21 . The rationale of this grouping by FUs stems from the idea that as the majority of our chromatin features are TFs (85%, see Supplementary Table 1) and that one TF influences a specific cellular function through accumulation of its downstream effects via multiple SNPs on a series of loci 22 . This optimized variant selection improves the power to identify sets of functional SNPs that may play a role in the etiology of the disease, and map them directly to a specific context -i.e., cell type, transcription factor, stimulation condition.…”