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, next-generation sequencing of DNA methylation and RNA sequencing were conducted for the blood samples from 51 healthy adults between 20 and 74 years of age and I identified aging-related epigenetic and transcriptomic biomarkers. I 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, I 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, I 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.
노화는 신체 외형적/내재적인 다양한 변화를 수반한다. 이와 연관된 분자 바이오마커들에 대한 연구는 많이 진행되었으나, 마커들간의 조절관계에 대해서는 잘 알려져있지 않다. 본 학위논문에서는 20 ⁓ 70대 비질병 한국인 51명 혈액 샘플의 메틸레이션, 발현량 측정 데이터를 기반으로 노화 관련 유전자 조절 네트워크를 구축하고, 신호흐름 분석을 적용하여 두 오믹스 레이어 간 조절 관계를 분석하였다. 이러한 조절 관계를 기반으로 노화 관련 발현량 변화를 역으로 조절하기 위한 분자 타겟과 약물을 탐색하였으며, 분석을 통해 예측된 JUN 저해 작용이 존에 노화 조절 약물로 알려진 커큐민과의 결합을 통해 이루어지는 것을 확인하였다.
