Serum Metabolomics of Tick-Borne Encephalitis Based on Orbitrap-Mass Spectrometry

Meng Zhang,1,* DeSheng Lu,2,* Hui Sun,2 HaiJun Zheng,2 Ming Cang,1 YanDan Du2 1State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010070, People’s Republic of China; 2Department of Clinical...

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Autores principales: Zhang M, Lu D, Sun H, Zheng H, Cang M, Du Y
Formato: article
Lenguaje:EN
Publicado: Dove Medical Press 2021
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Acceso en línea:https://doaj.org/article/52441d0243a0438cb158891db2112278
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Sumario:Meng Zhang,1,* DeSheng Lu,2,* Hui Sun,2 HaiJun Zheng,2 Ming Cang,1 YanDan Du2 1State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010070, People’s Republic of China; 2Department of Clinical Laboratory, Inner Mongolia Forestry General Hospital (The Second Clinical Medical School of Inner Mongolia, University for the Nationalities), Hulunbuir, Inner Mongolia, People’s Republic of China*These authors contributed equally to this workCorrespondence: YanDan Du; Ming Cang Email Mizh@faith-m.com; mdmzhh_2008@126.comBackground: Tick-borne encephalitis virus (TBEV), the most prevalent arbovirus, causes potentially fatal encephalitis in humans. Prevalent in northeast China, tick-borne encephalitis (TBE) poses a major threat to public health, local economies and tourism. There are no biomarkers for TBE, which is classified serologically and clinically. Due to sample heterogeneity of samples and different detection platforms, obtaining stable markers is a great challenge for metabolomics. Accurate annotation is vital for data mining and interpretation.Objective: To identify reliable biomarkers of TBEV infection.Methods: An untargeted metabolomics analysis of serum from 30 TBE patients and 30 healthy controls was carried out. Liquid chromatography–mass spectrometry (LC-MS)-based metabolomics methods were used to characterize the subjects’ serum metabolic profiles and to screen and validate TBE biomarkers.Results: A total of 3370 molecular features were extracted from each sample, and the peak intensity of each feature was obtained. Pattern analysis, principal component analysis, partial least squares discriminant analysis were used to screen for potential metabolites. Bilirubin, LysoPC (18:1[9Z]), palmitic acid, and CL (8:0/8:0/8:0/8:0) were significantly different. Pathway enrichment analysis showed that these metabolites were in the fatty acid biosynthesis and glycerophospholipid metabolism pathways. The phospholipid family had a significant difference in both the difference ratio and the abundance.Conclusion: Phospholipids may be used to distinguish TBEV patients from healthy controls. TBEV infection affects the normal metabolic activity of host cells, providing insight into the pathogenesis of TBE.Keywords: untargeted metabolomics, TBEV, diagnostic biomarkers