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Enhancers looping to target genes

Nature Genetics volume 49, pages 15641565 (2017) | Download Citation

High-resolution maps of enhancer–promoter interactions in rare primary human T cell subsets and coronary artery smooth muscle cells link variants associated with autoimmune and cardiovascular diseases to target genes. This represents an important step forward for mapping genes involved in complex diseases.

The first successful genome-wide association study (GWAS) raised the question of what genes are the targets of the identified disease risk variants. GWAS have mapped thousands of variants associated with a range of phenotypes, from biometric traits to complex immune diseases1. Despite this success, it has been a major challenge to translate the associated variants into molecular mechanisms2. As the vast majority of disease-associated variants fall outside of protein-coding sequence, something conceptually as simple as assigning disease variants to their target genes has been a major challenge for geneticists. To overcome this problem, different approaches have been taken: nomination of a candidate gene on the basis of functional relevance to disease biology, reporting of the nearest gene to the variant or claiming a gene for which the same variant affects gene expression (that is, is an expression quantitative trait locus (eQTL)). All of these approaches, however, lack a direct link between the associated variant and target gene. Howard Chang, William Greenleaf and colleagues3 generated a high-resolution map of enhancer–promoter interactions in rare disease-relevant cell types, thus mapping physical interactions between regulatory elements containing variants associated with autoimmune and cardiovascular diseases and target genes.

T cell gene regulation in 3D

Gene expression is regulated through complex interactions between regulatory elements and gene promoters, which, when disrupted, may lead to disease development. Mapping of chromatin contacts has tremendously advanced in the last decade owing to the development of chromosomal conformation capture techniques. However, these methods often require tens of millions of cells to identify chromatin interactions, a prohibitively high number for the rare cell types that are often critical for disease biology4.

In the study by Mumbach et al.3, the authors used the histone modification that correlates with active enhancers and promoters (H3K27ac) as a bait for their recently developed HiChIP method, a combination of chromatin immunoprecipitation (ChIP) and Hi-C assays for mapping protein-centric chromatin interactions in as few as 50,000 cells5. They generated high-resolution maps of enhancer–promoter contacts in primary naive CD4+ T cells, T regulatory (Treg) cells and T helper type 17 (TH17) cells. The authors identified over 10,000 chromatin loops that were shared by all three cell types. Importantly, 91% of the loop anchors were associated with either an enhancer or a promoter, with a median distance of 130 kb between the anchors.

Could the same information be recovered from linear 1D data? The chromatin contact maps allowed the authors to characterize spatial features, such as enhancer skipping, higher-order enhancer structures, promoter–promoter interactions and enhancers interacting with different gene promoters in a cell-type-specific manner, that otherwise would be unrecognized in standard 1D ChIP–seq data. On average, 36% of the cell-type-specific loops would not be captured by the differential signal in 1D H3K27ac ChIP–seq data alone. Additionally, cell-type-specific interactions often mapped to accessible chromatin regions shared by all three cell types. Together, the findings emphasize that, even in highly related cell types, a proportion of enhancer-interacting signals will only be captured in 3D.

Disease-associated variants in a spatial context

Armed with the cell-type-specific enhancer interactions for the three T cell subsets, Mumbach et al.3 went on to test whether the generated data could provide a link between noncoding disease-associated variants and target genes (Fig. 1). Importantly, enrichment of variants associated with autoimmune diseases in H3K27ac-marked enhancers has previously been reported for different T cell subsets6,7, including those studied by the authors. When overlapping autoimmune disease–associated variants in intergenic regions with the interaction loops, the authors found that the disease-associated variants interacted with from zero to ten target genes3. For the 684 autoimmune disease–associated variants analyzed, there were 2,597 target genes mapped through the chromatin interactions. Critically, only 14% of target genes were the nearest gene to the disease-associated variant, 86% of variants skipped at least one gene before reaching the target gene and 64% of variants connected to more than one gene. This emphasizes the further need to map 3D interactions across different cell types to understand disease mechanisms.

Figure 1: Integrating GWAS SNPs with HiChIP enhancer loop data can identify disease-relevant target genes for disease-associated variants.
Figure 1

This study provides a valuable framework for mapping disease-associated variants to target genes. The approach is not limited to autoimmune diseases and can be broadly applied to other complex traits. Indeed, the authors extended it to variants associated with coronary artery disease (CAD) and showed strong enrichment of these variants in HiChIP enhancer–promoter interactions generated from primary human coronary artery smooth muscle cells.

What are the immediate next applications? HiChIP interactions can be captured in as few as 50,000 cells, allowing for functional dissection of disease-associated variants in chromatin loops generated from rare pathogenic cell types or scarce material obtained from patients. This will be a critical step: if GWAS-identified variants are to be translated into molecular consequences, it is essential to work with the most relevant cell types for each trait. In fact, the authors observed that the majority of the TH17- and Treg-specific loops with the strongest interaction signals showed discovery rates below 15% in promoter capture Hi-C experiments on bulk CD4+ cells.

This study is a much needed step forward in the functional interrogation of GWAS-identified variants, but a big challenge remains: how can coupled variant–gene pairs be translated into other molecular and cellular functions? For instance, the authors report that the risk allele of the rs1537373 variant showed increased interaction with the CDKN2A promoter and the enhancer in the long noncoding RNA (lncRNA) ANRIL. This is a terrific example of what the field is up against, as not only will disease-associated variants synergistically act on multiple genes, but there may also be more complex gene-regulatory mechanisms involved, like ones affecting the function of noncoding RNAs.


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  1. Gosia Trynka is at the Wellcome Trust Sanger Institute, Cambridge, UK.

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The author declares no competing financial interests.

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Correspondence to Gosia Trynka.

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