Hui Du1, Yifei Rao1, Ronghua Liu1, Kesui Deng2, Yongmei Guan1, Dewei Luo1, Qiping Mao1, Jianwei Yu2, Tao Bo3, Ziquan Fan3, Hui Ouyang1, Yulin Feng4, Weifeng Zhu5. 1. Jiangxi University of Traditional Chinese Medicine, Nanchang, PR China. 2. The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, PR China. 3. Thermo Fisher Scientific-CN, Shanghai, PR China. 4. State Key Laboratory of Innovative Drug and Efficient Energy-Saving Pharmaceutical Equipment, Nanchang, PR China. 5. Key Laboratory of Ministry of Education of Jiangxi University of Traditional Chinese Medicine, Nanchang, PR China.
Abstract
BACKGROUND: Dyslipidemia is a common, chronic metabolic disease associated with cardiovascular complications. Due to the multiplicity of etiological factors, the pathogenesis of dyslipidemia is still unclear. METHODS: In this study, we combined proteomics and metabolomics methods to analyze the plasma of patients with dyslipidemia and healthy subjects. ITRAQ markers, combined with LC-MS/MS proteomics technology and the UHPLC/Orbitfast-X Tribrid system, were used to establish the metabolite profile in clinical dyslipidemia. RESULTS: A total of 137 differentially expressed proteins were identified, mainly related to biological processes such as protein activation cascades, adaptive immune responses, complement activation, acute inflammatory responses and regulation of acute inflammatory responses. These proteins are involved in the regulation of important metabolic pathways, such as immunity and inflammation, coagulation and hemostasis, lipid metabolism, and oxidation and antioxidant defenses. Analysis of clinical metabolites showed there were 69 different metabolites in plasma, mainly related to glycerolipid, sphingolipid, porphyrin, alpha-linolenic acid, linoleic acid and arachidonic acid metabolism, suggesting that regulation of inflammation and lipid metabolism may be disturbed in patients with dyslipidemia. Among these, significant changes were observed in indole-3-propionic acid (IPA), which is considered a potential biomarker of dyslipidemia. CONCLUSIONS: Combined analysis of proteins and metabolites showed that arachidonic acid, linoleic acid and lipid metabolic pathways were closely related to dyslipidemia. IPA may be a potential biomarker. The information provided in this study may provide new insights into the pathogenesis of animal models of dyslipidemia and related disease models, as well as potential intervention targets. This article is protected by copyright. All rights reserved.
BACKGROUND:Dyslipidemia is a common, chronic metabolic disease associated with cardiovascular complications. Due to the multiplicity of etiological factors, the pathogenesis of dyslipidemia is still unclear. METHODS: In this study, we combined proteomics and metabolomics methods to analyze the plasma of patients with dyslipidemia and healthy subjects. ITRAQ markers, combined with LC-MS/MS proteomics technology and the UHPLC/Orbitfast-X Tribrid system, were used to establish the metabolite profile in clinical dyslipidemia. RESULTS: A total of 137 differentially expressed proteins were identified, mainly related to biological processes such as protein activation cascades, adaptive immune responses, complement activation, acute inflammatory responses and regulation of acute inflammatory responses. These proteins are involved in the regulation of important metabolic pathways, such as immunity and inflammation, coagulation and hemostasis, lipid metabolism, and oxidation and antioxidant defenses. Analysis of clinical metabolites showed there were 69 different metabolites in plasma, mainly related to glycerolipid, sphingolipid, porphyrin, alpha-linolenic acid, linoleic acid and arachidonic acid metabolism, suggesting that regulation of inflammation and lipid metabolism may be disturbed in patients with dyslipidemia. Among these, significant changes were observed in indole-3-propionic acid (IPA), which is considered a potential biomarker of dyslipidemia. CONCLUSIONS: Combined analysis of proteins and metabolites showed that arachidonic acid, linoleic acid and lipid metabolic pathways were closely related to dyslipidemia. IPA may be a potential biomarker. The information provided in this study may provide new insights into the pathogenesis of animal models of dyslipidemia and related disease models, as well as potential intervention targets. This article is protected by copyright. All rights reserved.