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DNA methylation is a major regulatory process of gene transcription, and aberrant DNA methylation is associated with various diseases including cancer. Many compounds have been reported to modify DNA methylation states. Despite increasing interest in the clinical application of drugs with epigenetic effects, and the use of diagnostic markers for genome-wide hypomethylation in cancer, large-scale screening systems to measure the effects of drugs on DNA methylation are limited. In this study, we improved the previously established fluorescence polarization-based global DNA methylation assay so that it is more suitable for application to human genomic DNA. Our methyl-sensitive fluorescence polarization (MSFP) assay was highly repeatable (inter-assay coefficient of variation = 1.5%) and accurate (r2 = 0.99). According to signal linearity, only 50–80 ng human genomic DNA per reaction was necessary for the 384-well format. MSFP is a simple, rapid approach as all biochemical reactions and final detection can be performed in one well in a 384-well plate without purification steps in less than 3.5 hours. Furthermore, we demonstrated a significant correlation between MSFP and the LINE-1 pyrosequencing assay, a widely used global DNA methylation assay. MSFP can be applied for the pre-screening of compounds that influence global DNA methylation states and also for the diagnosis of certain types of cancer.
Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.
Background: The quantification of global DNA methylation has been established in epigenetic screening. As more practicable alternatives to the HPLC-based gold standard, the methylation analysis of CpG islands in repeatable elements (LINE-1) and the luminometric methylation assay (LUMA) of overall 5-methylcytosine content in “CCGG” recognition sites are most widely used. Both methods are applied as virtually equivalent, despite the hints that their results only partly agree. This triggered the present agreement assessments.
Results: Three different human cell types (cultured MCF7 and SHSY5Y cell lines treated with different chemical modulators of DNA methylation and whole blood drawn from pain patients and healthy volunteers) were submitted to the global DNA methylation assays employing LINE-1 or LUMA-based pyrosequencing measurements. The agreement between the two bioassays was assessed using generally accepted approaches to the statistics for laboratory method comparison studies. Although global DNA methylation levels measured by the two methods correlated, five different lines of statistical evidence consistently rejected the assumption of complete agreement. Specifically, a bias was observed between the two methods. In addition, both the magnitude and direction of bias were tissue-dependent. Interassay differences could be grouped based on Bayesian statistics, and these groups allowed in turn to re-identify the originating tissue.
Conclusions: Although providing partly correlated measurements of DNA methylation, interchangeability of the quantitative results obtained with LINE-1 and LUMA was jeopardized by a consistent bias between the results. Moreover, the present analyses strongly indicate a tissue specificity of the differences between the two methods.