Capturing functional epigenomes for insight into metabolic diseases

Abstract

Background: Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied.

Scope of the review: We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases.

Major conclusions: Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression.

Keywords: Adipose tissue; DNA methylation; Epigenomics; Metabolic diseases; Next-generation sequencing; Regulatory elements.

Figure 1

Summary of complex diseases and trait drivers. Graphical representation of contributing factors underlying complex disease risk including the interplay between environmental and genetic factors (black arrows) that impact epigenetics traits and provide insight into molecular mechanisms underlying disease etiology (gray arrows). This potential “causal pathway” (solid arrows) involving epigenetic changes impacting phenotype and disease is distinguished from a “reactive pathway”, in which the phenotype or disease state causes an epigenetic change (dotted arrow). Epigenetic traits are referred to here by CpG (DNA) methylation and histone modifications. Methylated CpGs are shown in orange and unmethylated CpGs are depicted in purple. Similarly, histone tails enriched in modifications correlating to inactive chromatin are shown in yellow (i.e., H3K9me2/3 and H3K27me3) and those enriched at active regulatory elements are presented in pink (i.e., H3K4me1, H3K4me3, and H3K27ac). Figure was created with Biorender.com.

Figure 2

Metabolic disease-linked tissue epigenome maps from the International Human Epigenome Consortium (IHEC). Publicly accessible epigenome profiles from the IHEC portal (http://epigenomes.ca/ihec;2018-10; hg19 reference). The 824 available datasets are summarized (A) in table format, (B) per consortium, (C) per tissue type, and (D) per epigenetic mark category. Figure panels were adapted from the IHEC portal graphical content.

Figure 3

Comparison of DNA methylation profiling at adipose regulatory regions by available methods. CpGs captured by the 450K array, EPIC array, and MCC-Seq using the adipose tissue panel published in [34] are contrasted at different genomic regions. Overlaps with typical profiles of WGBS in visceral adipose tissue and adipocyte nuclei histone marks for H3K4me1 and H3K4me3 (NIH Roadmap; donor 92) are also depicted. (A) Lack of coverage by array-based methods can be noted at multiple adipose enhancer elements mapping to an intergenic region on chr13. (B) The fine-mapping potential by MCC-Seq at the previously identified HDL-linked 450K array probe [69] locating to an intragenic region in the MYO5C locus (highlighted in light gray) is exemplified.