Abstract
Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.
Highlights
Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling
We investigated the regulation of differentially expressed gene (DEG) markers, which results from changes in the methylation states of differentially methylated position (DMP) markers
To use the DMP markers whose methylation states are correlated with expression levels of transcription factors (TFs) genes as inputs in signal flow analysis of the gene regulatory network (GRN), we calculated the correlation between the methylation states of each DMP marker and the expression levels of nearby TF genes
Summary
Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. We identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. The causal relationship between variations in biomarkers and the specific cellular dysfunction or phenotypes related to aging remains largely unknown To investigate these molecular mechanisms and causal relationships, molecular dynamics studies have been conducted to predict and control gene expression or protein phosphorylation changes in d iseases[22,23,24,25,26] such as cancer and diabetes from the Institute (PGI), Genome Research Foundation (GRF), Osong 28160, Republic of Korea. For integrative analysis, investigating the changes in methylation and gene expression in a network model requires understanding the causal relationships between two different omics layers. It is difficult to determine which methylation/expression event is the leading cause and which is the result of the causal relationship represented in the feedback structures, as DNA methylation and gene expression are regulated by each other
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