Pengcheng Zhou1, Ning Zhou2, Li Shao3, Jianzhou Li4, Sidi Liu1, Xiujuan Meng1, Juping Duan1, Xinrui Xiong1, Xun Huang1, Yuhua Chen1, Xuegong Fan5, Yixiang Zheng5, Shujuan Ma6, Chunhui Li7, Anhua Wu8. 1. Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. 2. Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China. 3. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China. 4. Department of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shanxi, People's Republic of China. 5. Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China. 6. Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan, People's Republic of China. 7. Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. lichunhui@csu.edu.cn. 8. Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. xywuanhua@csu.edu.cn.
Abstract
INTRODUCTION: The fecal metabolome of Clostridium difficile (CD) infection is far from being understood, particularly its non-volatile organic compounds. The drawbacks of current tests used to diagnose CD infection hinder their application. OBJECTIVE: The aims of this study were to find new characteristic fecal metabolites of CD infection and develop a metabolomics model for the diagnosis of CD infection. METHODS: Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was used to characterize the fecal metabolome of CD positive and negative diarrhea and healthy control stool samples. RESULTS: Diarrhea and healthy control samples showed distinct clusters in the principal components analysis score plot, and CD positive group and CD negative group demonstrated clearer separation in a partial least squares discriminate analysis model. The relative abundance of sphingosine, chenodeoxycholic acid, phenylalanine, lysophosphatidylcholine (C16:0), and propylene glycol stearate was higher, and the relative abundance of fatty amide, glycochenodeoxycholic acid, tyrosine, linoleyl carnitine, and sphingomyelin was lower in CD positive diarrhea groups, than in the CD negative group. A linear discriminant analysis model based on capsiamide, dihydrosphingosine, and glycochenodeoxycholic acid was further constructed to identify CD infection in diarrhea. The leave-one-out cross-validation accuracy and area under receiver operating characteristic curve for the training set/external validation set were 90.00/78.57%, and 0.900/0.7917 respectively. CONCLUSIONS: Compared with other hospital-onset diarrhea, CD diarrhea has distinct fecal metabolome characteristics. Our UPLC-MS metabolomics model might be useful tool for diagnosing CD diarrhea.
INTRODUCTION: The fecal metabolome of Clostridium difficile (CD) infection is far from being understood, particularly its non-volatile organic compounds. The drawbacks of current tests used to diagnose CD infection hinder their application. OBJECTIVE: The aims of this study were to find new characteristic fecal metabolites of CD infection and develop a metabolomics model for the diagnosis of CD infection. METHODS: Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was used to characterize the fecal metabolome of CD positive and negative diarrhea and healthy control stool samples. RESULTS:Diarrhea and healthy control samples showed distinct clusters in the principal components analysis score plot, and CD positive group and CD negative group demonstrated clearer separation in a partial least squares discriminate analysis model. The relative abundance of sphingosine, chenodeoxycholic acid, phenylalanine, lysophosphatidylcholine (C16:0), and propylene glycol stearate was higher, and the relative abundance of fattyamide, glycochenodeoxycholic acid, tyrosine, linoleyl carnitine, and sphingomyelin was lower in CD positive diarrhea groups, than in the CD negative group. A linear discriminant analysis model based on capsiamide, dihydrosphingosine, and glycochenodeoxycholic acid was further constructed to identify CD infection in diarrhea. The leave-one-out cross-validation accuracy and area under receiver operating characteristic curve for the training set/external validation set were 90.00/78.57%, and 0.900/0.7917 respectively. CONCLUSIONS: Compared with other hospital-onset diarrhea, CD diarrhea has distinct fecal metabolome characteristics. Our UPLC-MS metabolomics model might be useful tool for diagnosing CD diarrhea.
Authors: N Berry; B Sewell; S Jafri; C Puli; S Vagia; A M Lewis; D Davies; E Rees; C L Ch'ng Journal: J Hosp Infect Date: 2014-04-12 Impact factor: 3.926
Authors: Gregory H Norris; Christina Jiang; Julia Ryan; Caitlin M Porter; Christopher N Blesso Journal: J Nutr Biochem Date: 2016-01-04 Impact factor: 6.048
Authors: Marije K Bomers; Michiel A van Agtmael; Hotsche Luik; Merk C van Veen; Christina M J E Vandenbroucke-Grauls; Yvo M Smulders Journal: BMJ Date: 2012-12-13