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Vitamin B12 has multiple biochemical functions including in the one-carbon cycle generating a methyl group for DNA methylation, and metabolism of fatty acids and amino acids to generate energy via the citric acid cycle. The aim of our study was to use a combined epigenomic and transcriptomic approach to identify novel genes mediating the effect of B12 on adipogenesis.Human pre-adipocytes (CHUB-S7) were treated with a range of B12 (0-500 nM) concentrations from the day of cell seeding until harvesting in discovery and validation experiments prior to genome-wide methylation analysis using the Illumina HumanMethylation 450Beadchip. For transcriptomic analysis, RNA-seq libraries were run on the Illumina HiSeq 2500. To further investigate the expression of any genes on human adipogenesis, a second human preadipocyte strain was studied (SGBS) by real-time quantitative PCR (qRT-PCR).A combined epigenetic and transcriptomic approach in differentiated human pre-adipocyte cell line, CHUB-S7, identified that the Human cartilage chitinase 3-like protein 2 ( CHI3L2) gene was hypo-methylated and had increased expression in low B12 conditions. Furthermore, there was an approximately 1000-fold increase in CHI3L2 expression in the early days of adipocyte differentiation, which paralleled an increase of lipid droplets in differentiated SGBS cells and an increased expression level of markers of mature adipocytes.In summary, we have identified a potential role of the human cartilage chitinase 3-like protein 2 ( CHI3L2) in adipocyte function in the presence of low B12 levels.

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.