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

• Published in: International journal of general medicine Vol. 14; pp. 7995 - 8005
• Main Authors: Zhang, Meng, Lu, DeSheng, Sun, Hui, Zheng, HaiJun, Cang, Ming, Du, YanDan
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2021; Taylor & Francis Ltd; Dove; Dove Medical Press
• Subjects: Biomarkers; Chromatography; Data analysis; Data mining; Datasets; diagnostic biomarkers; Encephalitis; Ethics; Fatty acids; Forestry; Health aspects; Health care; Homeostasis; Infections; Liquid chromatography; Mass spectrometry; Metabolism; Metabolites; Open source software; Original Research; Patient compliance; Patients; Physiological aspects; Reproducibility; Retention; Scientific imaging; Signal transduction; Systems stability; tbev; Tick-borne encephalitis; untargeted metabolomics; China; Inner Mongolia China; Australia; Germany; TBEV; untargeted metabolomics; diagnostic biomarkers
• ISSN: 1178-7074; 1178-7074
• DOI: 10.2147/IJGM.S331374

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. To identify reliable biomarkers of TBEV infection. 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. 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. 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.