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Diagnosis of Clostridium difficile infection using an UPLC–MS based metabolomics method

Metabolomics volume 14, Article number: 102 (2018) Cite this article

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.

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Abbreviations

CD:

Clostridium difficile

LDA:

Linear discriminant analysis

LOOCV:

Leave-one-out cross-validation

PCA:

Principal components analysis

PLS-DA:

Partial least squares discriminate analysis

UPLC–MS:

Ultra performance liquid chromatography–mass spectroscopy

VIP:

Variable importance in the projection

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Funding

This work was supported by the National Natural Science Foundation of China (No. 81601803 and No. 31500678), Natural Science Foundation of Hunan Province (2017JJ3481 and 2017JJ3490) and Xiangya Sinobio way Health Research Fund (No.xywm2015I11).

Author information

Affiliations

  1. Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People’s Republic of China

    Pengcheng Zhou, Sidi Liu, Xiujuan Meng, Juping Duan, Xinrui Xiong, Xun Huang, Yuhua Chen, Chunhui Li & Anhua Wu

  2. Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People’s Republic of China

    Ning Zhou

  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

    Li Shao

  4. Department of Infectious Diseases, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shanxi, People’s Republic of China

    Jianzhou Li

  5. Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People’s Republic of China

    Xuegong Fan & Yixiang Zheng

  6. Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan, People’s Republic of China

    Shujuan Ma

Authors
  1. Pengcheng Zhou
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  2. Ning Zhou
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  3. Li Shao
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  4. Jianzhou Li
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  7. Juping Duan
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  8. Xinrui Xiong
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  9. Xun Huang
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  10. Yuhua Chen
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  11. Xuegong Fan
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  12. Yixiang Zheng
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  13. Shujuan Ma
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  14. Chunhui Li
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  15. Anhua Wu
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Corresponding authors

Correspondence to Chunhui Li or Anhua Wu.

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Zhou, P., Zhou, N., Shao, L. et al. Diagnosis of Clostridium difficile infection using an UPLC–MS based metabolomics method. Metabolomics 14, 102 (2018). https://doi.org/10.1007/s11306-018-1397-x

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  • DOI: https://doi.org/10.1007/s11306-018-1397-x

Keywords

  • Clostridium difficile infection
  • Diagnostic test
  • Metabolomics
  • Ultra performance liquid chromatography–mass spectrometry