Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

White to brown/beige adipocytes conversion is a possible therapeutic strategy to tackle the current obesity epidemics. While mitochondria are key for energy dissipation in brown fat, it is unknown if they can drive adipocyte browning. Here, we show that the mitochondrial cristae biogenesis protein optic atrophy 1 (Opa1) facilitates cell-autonomous adipocyte browning. In two cohorts of patients with obesity, including weight discordant monozygotic twin pairs, adipose tissue OPA1 levels are reduced. In the mouse, Opa1 overexpression favours white adipose tissue expandability as well as browning, ultimately improving glucose tolerance and insulin sensitivity. Transcriptomics and metabolomics analyses identify the Jumanji family chromatin remodelling protein Kdm3a and urea cycle metabolites, including fumarate, as effectors of Opa1-dependent browning. Mechanistically, the higher cyclic adenosine monophosphate (cAMP) levels in Opa1 pre-adipocytes activate cAMP-responsive element binding protein (CREB), which transcribes urea cycle enzymes. Flux analyses in pre-adipocytes indicate that Opa1-dependent fumarate accumulation depends on the urea cycle. Conversely, adipocyte-specific Opa1 deletion curtails urea cycle and beige differentiation of pre-adipocytes, and is rescued by fumarate supplementation. Thus, the urea cycle links the mitochondrial dynamics protein Opa1 to white adipocyte browning.

Background DNA methylation may play a role in the progression from normative to problematic drinking and underlie adverse health outcomes associated with alcohol misuse. We examined the association between alcohol consumption and DNA methylation patterns using 3 approaches: a conventional epigenome‐wide association study (EWAS); a co‐twin comparison design, which controls for genetic and environmental influences that twins share; and a regression of age acceleration, defined as a discrepancy between chronological age and DNA methylation age, on alcohol consumption. Methods Participants came from the Finnish Twin Cohorts (FinnTwin12/FinnTwin16; N = 1,004; 55% female; average age = 23 years). Individuals reported the number of alcoholic beverages consumed in the past week, and epigenome‐wide DNA methylation was assessed in whole blood using the Infinium HumanMethylation450 BeadChip. Results In the EWAS, alcohol consumption was significantly related to methylation at 24 CpG sites. When evaluating whether differences between twin siblings (185 monozygotic pairs) in alcohol consumption predicted differences in DNA methylation, co‐twin comparisons replicated 4 CpG sites from the EWAS and identified 23 additional sites. However, when we examined qualitative differences in drinking patterns between twins (heavy drinker vs. light drinker/abstainer or moderate drinker vs. abstainer; 44 pairs), methylation patterns did not significantly differ within twin pairs. Finally, individuals who reported higher alcohol consumption also exhibited greater age acceleration, though results were no longer significant after controlling for genetic and environmental influences shared by co‐twins. Conclusions Our analyses offer insight into the associations between epigenetic variation and levels of alcohol consumption in young adulthood.