Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

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 repercus...

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Bibliographic Details
Published in:Scientific reports 2021-06, Vol.11 (1), p.12317-12317, Article 12317
Main Authors: Lee, Hwang-Yeol, Jeon, Yeonsu, Kim, Yeon Kyung, Jang, Jae Young, Cho, Yun Sung, Bhak, Jong, Cho, Kwang-Hyun
Format: Article
Language:English
Subjects:
631/114/2114
631/443/7
631/553/1833
631/553/2711
Aging
Biomarkers
Curcumin
Degeneration
DNA methylation
DNA sequencing
Epigenetics
Gene expression
Humanities and Social Sciences
multidisciplinary
Next-generation sequencing
Science
Science (multidisciplinary)
Skeletal muscle
Transcriptomics
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-91811-1