Systematic Discovery and Pathway Analyses of Metabolic Disturbance in COVID-19  

作　　者：Bo-Wen Li Xing Fan Wen-Jing Cao He Tian Si-Yu Wang Ji-Yuan Zhang Sin Man Lam Jin-Wen Song Chao Zhang Shao-Hua Zhang Zhe Xu Ruo-Nan Xu Jun-Liang Fu Lei Huang Tian-Jun Jiang Ming Shi Fu-Sheng Wang Guang-Hou Shui 

机构地区：[1]LipidALL Technologies Company Limited,Changzhou,Jiangsu 213022,China [2]Department of Infectious Diseases,Fifth Medical Center of Chinese PLA General Hospital,National Clinical Research Center for Infectious Diseases,Beijing 100039,China [3]State Key Laboratory of Molecular Developmental Biology,Institute of Genetics and Developmental Biology,Chinese Academy of Sciences,Beijing 100101,China [4]Bengbu Medical University,Bengbu,Anhui 233000,China

出　　处：《Infectious Diseases & Immunity》2021年第2期74-85,共12页感染性疾病与免疫（英文）

基　　金：This work was supported by grants from the Youth Talent Lifting Project(2020-JCJQ-QT-034);the National Science and Technology Major Project of the Ministry of Science and Technology of China(2017ZX10202102-004-002).

摘　　要：Background:The ongoing global coronavirus disease 2019(COVID-19)pandemic is posing a serious public health threat to nations worldwide.Understanding the pathogenesis of the disease and host immune responses will facilitate the discovery of therapeutic targets and better management of infected patients.Metabolomics technology can provide an unbiased tool to explore metabolic perturbation.Methods:Twenty-six healthy controls and 50 COVID-19 patients with mild,moderate,and severe symptoms in the Fifth Medical Center of PLA General Hospital from January 22 to February 16,2020 were recruited into the study.Fasting blood samples were collected and subject to metabolomics analysis by liquid chromatography–mass spectrometry.Metabolite abundance was measured by peak area and was log-transformed before statistical analysis.The principal component analysis,different expression analysis,and metabolic pathway analysis were performed using R package.Co-regulated metabolites and their associations with clinical indices were identified by the weighted correlation network analysis and Spearman correlation coefficients.The potential metabolite biomarkers were analyzed using a random forest model.Results:We uncovered over 100 metabolites that were associated with COVID-19 disease and many of them correlated with disease severity.Sets of highly correlated metabolites were identified and their correlations with clinical indices were presented.Further analyses linked the differential metabolites with biochemical reactions,metabolic pathways,and biomedical MeSH terms,offering contextual insights into disease pathogenesis and host responses.Finally,a panel of metabolites was discovered to be able to discriminate COVID-19 patients from healthy controls,and also another list for mild against more severe cases.Our findings showed that in COVID-19 patients,citrate cycle,sphingosine 1-phosphate in sphingolipid metabolism,and steroid hormone biosynthesis were downregulated,while purine metabolism and tryptophan metabolism were disturbed.Conc

关 键 词：COVID-19 Functional metabolites Host immune response Metabolomics Pathway analysis 

分 类 号：R563.1[医药卫生—呼吸系统]