Exploration of variations in proteome and metabolome for predictive diagnostics and personalized treatment
Xianquan Zhan1, Ying Long2, Miaolong Lu2
1Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China; Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China; State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China; The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan 410008, PR China.
Abstract:
Genome mutually interacts with internal and external environmental factors to result in different phenome that contains two important elements of proteins and metabolites, which link genome to predictive, preventive and personalized medicine (PPPM) or precision medicine (PM). Proteomic variations are the final presentation of the genomic and transcriptomic variations, and are involved in a wide range of variations including copy number of protein, splicing, post-translational modifications, translocation/re-distribution, spatial conformation, and pathway-network systems. Metabolomic variations are the comprehensive results originated from all types of in vivo substances, and are involved in a wide range of alterations of metabolites generated from sugars, lipids, proteins, and nucleic acids, and metabolic network systems. Currently the studies on variations in proteome and in metabolome are much insufficient in the width and depth in the fields of proteomics and metabolomics. The development of high-throughput, high-sensitivity, and especially high-reproducibility approaches is necessary to maximize the coverage of variations in proteome and in metabolome. The studies of proteomic and metabolomic variations directly result in the discovery of effective biomarkers to clarify molecular mechanisms of a disease, determine reliable therapeutic targets, and benefit precise prediction, diagnosis, and prognosis assessment. It has more important scientific values in PPPM or PM. BIOLOGICAL SIGNIFICANCE.
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