Literature DB >> 33864753

Urinary metabolomic analysis to identify potential markers for the diagnosis of tuberculosis and latent tuberculosis.

Jiaheng Deng1, Liguo Liu1, Qianting Yang2, Candong Wei1, Haoran Zhang1, Henan Xin1, Shouguo Pan3, Zisen Liu3, Dakuan Wang3, Bo Liu1, Lei Gao1, Rongmei Liu4, Yu Pang4, Xinchun Chen5, Jianhua Zheng6, Qi Jin7.   

Abstract

Tuberculosis (TB) is a serious infectious disease with high infection and mortality rates. 5%-10% of the latent tuberculosis infections (LTBI) are likely to develop into active TB, and there are currently no clinical biomarkers that can distinguish between LTBI, active TB and other non-tuberculosis populations. Therefore, it is necessary to develop rapid diagnostic methods for active TB and LTBI. In this study, urinary metabolome of 30 active TB samples and the same number of LTBI and non-TB control samples were identified and analyzed by UPLC-Q Exactive MS. In total, 3744 metabolite components were obtained in ESI- mode and 4086 in ESI + mode. Orthogonal partial least square discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) showed that there were significant differences among LTBI, active TB and non-TB. Six differential metabolites were screened in positive and negative mode, 3-hexenoic acid, glutathione (GSH), glycochenodeoxycholate-3-sulfate, N-[4'-hydroxy-(E)-cinnamoyl]-l-aspartic acid, deoxyribose 5-phosphate and histamine. The overlapping pathways differential metabolites involved were mainly related to immune regulation and urea cycle. The results showed that the urine metabolism of TB patients was disordered and many metabolic pathways changed. Multivariate statistical analysis revealed that GSH and histamine were selected as potential molecular markers, with area under curve of receiver operating characteristic curve over 0.75. Among the multiple differential metabolites, GSH and histamine changed to varying degrees in active TB, LTBI and the non-TB control group. The levels of GSH and histamine in 48 urinary samples were measured by ELISA in validation phase, and the result in our study provided the potential for non-invasive biomarkers of TB.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Biomarkers; Diagnostics; Glutathione; Histamine; Metabolomics; Tuberculosis; Urine

Year:  2021        PMID: 33864753     DOI: 10.1016/j.abb.2021.108876

Source DB:  PubMed          Journal:  Arch Biochem Biophys        ISSN: 0003-9861            Impact factor:   4.013


  2 in total

Review 1.  Tuberculous Granuloma: Emerging Insights From Proteomics and Metabolomics.

Authors:  Abisola Regina Sholeye; Aurelia A Williams; Du Toit Loots; A Marceline Tutu van Furth; Martijn van der Kuip; Shayne Mason
Journal:  Front Neurol       Date:  2022-03-21       Impact factor: 4.003

2.  Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

Authors:  Xin Hu; Jie Wang; Yingjiao Ju; Xiuli Zhang; Wushou'er Qimanguli; Cuidan Li; Liya Yue; Bahetibieke Tuohetaerbaike; Ying Li; Hao Wen; Wenbao Zhang; Changbin Chen; Yefeng Yang; Jing Wang; Fei Chen
Journal:  BMC Infect Dis       Date:  2022-08-25       Impact factor: 3.667

  2 in total

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