Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Abstract

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

“…These metrics can also be used as a tool for metabolic databases in order to more easily identify gaps within current metabolic pathway graphs by using a new information provided by mGWAS. In this multi-omics era, we anticipate that large scale studies looking for associations between genomics, proteomics and metabolomics signals will soon become a new standard, as illustrated by recent studies in mice and humans [53,54], resulting in additional protein-metabolite pairs, for which the computation of SRD values may add great value. × 10 −10 for metabolite concentrations and p ≤ 5.08×10 −13 for pairwise metabolite ratios) involving 187 unique metabolites and 145 loci, annotated as 132 causal genes [11].…”

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

“…These metrics can also be used as a tool for metabolic databases in order to more easily identify gaps within current metabolic pathway graphs by using new information provided by mGWAS. In this multi-omics era, we anticipate that large scale studies looking for associations between genomics, proteomics and metabolomics signals will soon become a new standard, as illustrated by recent studies in mice and humans, 57 , 58 , 59 resulting in additional protein-metabolite pairs, for which the computation of SRD values may add great value.…”

Defining Blood Plasma and Serum Metabolome by GC-MS

“…The trend of shifting from genome-wide association studies (GWAS) to metabolome-wide association studies (MWAS) has been gaining momentum since 2008. The results of consolidation of several omics layers appear more and more often [209][210][211][212][213].…”