Diabetes: A Candidate Disease for Efficient DNA Methylation Profiling

Methylation has been implied in a number of biological processes and has been shown to vary under environmental influences as well as in age. Most results on the correlation of methylation patterns with phenotypic characteristics of cells have been obtained by analysis of very few or even single genomic fragments for methylation. However, variation of methylation may more often than not be a phenomenon that affects multiple genomic loci. The role of methylation has been most conclusively demonstrated in complex disease, with cancer being the most prominent example. The influence of aging and environmental influences such as diet seems to be on global methylation patterns, in turn exerting local effects on groups of genes. Hence, methylation seems literally to be orchestrating complex genetic systems. It could, therefore, be considered an archetypal “genomics” parameter. In consequence, technologies used to analyze methylation patterns should be as industrialized as possible to capture the local events across the entire genome. Epigenomics’ research team is the first to have achieved the industrialized production of genome sequence–specific wide methylation data. Our microarray and mass-spectrometry–based detection platform currently allow the analysis of up to 50,000 methylation positions per day, for the first time making methylation data amenable to sophisticated information mining. The information content of methylation position has never been analyzed using the high-dimensional statistical methods that are recognized to be required for the analysis of, for example, mRNA expression profiles or proteomic data. As methylation patterns are nothing but a quasi-digital form of expression data, their information content must be evaluated using similar but adapted algorithms. This article presents a broad set of studies that demonstrate that methylation yields information that is comparable or even superior to the current state of the art, namely, mRNA profiling. We argue that the resulting robust, digital and—because of the highly stable nature of DNA as the analyte—more reproducible information could become the “gold standard” for clinical diagnostics and disease gene identification in age-related, environmentally influenced and epigenetic disease in general, substituting for mRNA expression.