Identification of epigenetic markers to analyze phenotypic characteristics using the DNA methylome profiles

Abstract

Epigenome-wide studies of DNA methylation across the epigenetic landscape provide insights into the pluripotency of embryonic stem cells (ESCs). Differentiating into somatic and germ cells, ESCs exhibit varying degrees of pluripotency, and epigenetic changes occurring in this process have emerged as important factors explaining stem cell pluripotency. Further, tissue differences or age differences make a change of their DNA methylation status clear. In this study, we first constructed a machine learning model that predicts degrees of pluripotency for mouse ESCs using paired scBS-seq and scRNA-seq data of mice,. Since the biological activities of non-CpG markers have yet to be clarified, we tested the predictive power of CpG and non-CpG markers, as well as a combination thereof, in the model. Through rigorous performance evaluation with both internal and external validation, we discovered that a model using both CpG and non-CpG markers predicted the pluripotency of ESCs with the highest prediction performance (0.956 AUC, external test). The prediction model consisted of 16 CpG and 33 non-CpG markers. We investigated CpG and non-CpG methylation in relation to mouse stem cell pluripotency and developed a model thereon that successfully predicts the pluripotency of mouse ESCs. Second, by using WGBS profiles for three regional brain tissue, we identified brain regional aging markers and investigated the regional DNA methylation differences. DNA methylation is an important regulatory mechanism involved in brain development, learning, memory and aging, and is a stable epigenetic modification of DNA. In brain regional methylome profiles, we investigated age-independent and -dependent DNA methylation status across sub-regions of the brain by profiling the mouse methylome. Next, we investigated the correlations between these methylation patterns and gene expressions of brain tissues through transcriptome profiling. It was confirmed that tissue-specific methylations are related to a clear functional aspect compared to aging-related methylation while age-specific patterns have more individual variances. We provide a multi-omics profile here and present tissue-aging-related methylation markers.