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. 2018 Mar 15;27(6):1106-1121.
doi: 10.1093/hmg/ddy006.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Shaza B Zaghlool  1   2 , Dennis O Mook-Kanamori  3 , Sara Kader  1 , Nisha Stephan  1 , Anna Halama  1 , Rudolf Engelke  4 , Hina Sarwath  4 , Eman K Al-Dous  5 , Yasmin A Mohamoud  5 , Werner Roemisch-Margl  6 , Jerzy Adamski  7   8 , Gabi Kastenmüller  6   8 , Nele Friedrich  9 , Alessia Visconti  10 , Pei-Chien Tsai  10 , Tim Spector  10 , Jordana T Bell  10 , Mario Falchi  10 , Annika Wahl  11   12 , Melanie Waldenberger  11   12 , Annette Peters  11   12   13 , Christian Gieger  11   12 , Marija Pezer  14 , Gordan Lauc  14 , Johannes Graumann  4   15 , Joel A Malek  5 , Karsten Suhre  1
Affiliations

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Shaza B Zaghlool et al. Hum Mol Genet. .

Abstract

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

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Figures

Figure 1.
Figure 1.
Hypothesis tested in this study. Exposure to physiological challenges, such as an increased BMI, smoking or dysregulated glycemic control leads physiological changes that translate into changes in intermediate molecular phenotypes, such as metabolite levels that are detectable in different body fluids, blood circulating lipids, proteins and protein glycosylation. These then further induce changes in DNA methylation at specific regulatory sites of genes that are required to counter this insult. Note that this view does not exclude that changes in the expression of certain genes may not also result in further changes in molecular phenotypes. Hence, despite the fact that we found here three cases of causality from metabolite to CpG, cases with reverse directionality are also likely to exist.
Figure 2.
Figure 2.
Multi-omics dataset and study design. A total of 388 individuals participated in the initial QMDiab study. A total of 359 samples had DNA methylation data and at least one other deep-molecular trait.
Figure 3.
Figure 3.
Evidence supporting the hypothesis that genetically induced changes in metabolite levels are causal to the associated changes in methylation levels. The instrumental variables here were identified using the BIOS server (75) and SNiPA (76). The three-way associations were evaluated using the KORA dataset (N∼1800). The P-values (PIVW) shown are associated with the estimate (Wald test). In all three cases presented here (see Table 6 for details), the associations between SNP and CpG methylation can be fully explained via the metabolite. This suggests that the metabolic trait is causal to the association between metabolite and CpG.
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