[GW28-e0939]

Discovery of dynamical network biomarkers (DNB) during the progression of atherosclerosis using multiple omic techniques and systems biology

Jing Ge^{1 2} Xiaoya Fan^{1 2} Xinli Xue^{2 3} Gaopeng Li^{1 2} Shanshan Zhong^{2 3} Xia Shen^{3 5} Huiyong Yin^{2 3 4 5} Luonan Chen^{1 2 5}
1.Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2.University of the Chinese Academy of Sciences, CAS, Beijing 100049, China
3.Key Laboratory of Food Safety Research, Institute for Nutritional Sciences (INS), Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200031, China
4.Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing 100000, China
5.School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China

Objectives: Aim to develop a prediction model to identify the early signals of the critical transition in atherosclerosis, and definethe major metabolic networks that regulate the homoestasis of blood vessel, which may help for the early detection and prevention of atherosclerosis.
Methods: Combining omics data and theoretical analysis to establish a theoretical model for early prediction of the dysregulation of blood vessel homeostasis and atherosclerotic lesion formation from the viewpoint of systems biology.
Results: First we used the LDLR^−/− mouse model feeding western diet to mimic the onset and progression of atherosclerosis. The pathological characteristics of aortic roots by oil red and H&E staining showed that the atherosclerotic lesion gradually increased over time. Second, we integrated high throughput data including different-stage data of RNA-seq and metabolomics/lipidomics to discover regulatory networks of atherosclerosis and identify dynamical network biomarkers (DNB) to characterize the critical transition from normal vessel to atherosclerotic lesion. By analyzing the RNA-seq data of aortas, we identified the critical tipping point in the progression of atherosclerosis at the genetic level and discovered a group of DNB which appear to play driving roles in the progression of this disease. At last, we attempted to validate the theoretical results with biological experiments.
Conclusions: We develop a prediction model to identify the early signals of the critical transition in atherosclerosis, and discovered a group of DNB which appear to play driving roles in the progression of this disease. Thesemay help for the early detection and prevention of atherosclerosis.