Dataset Information

Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode.

ABSTRACT: In this work, an untargeted metabolomic method based on ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) in MSE (E represents collision energy) mode was exploited to determine the dynamic metabolic alterations in the plasma of male C57BL/6 mice during the onset and development of lung carcinoma. Plasma samples were collected from control and model mice (male C57BL/6 mice experimentally inoculated with the Lewis lung carcinoma cells) at 7 and 14 days post-inoculation (DPI). As a result, 15 dysregulated metabolites, including cholesterol sulphate, tiglylcarnitine, 1-palmitoylglycerophosphoinositol, 2-stearoylglycerophosphoinositol, stearoylcarnitine, PC(20:2(11Z,14Z)/16:0), PC(22:4(7Z,10Z,13Z,16Z)/14:0), PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0), PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0), 12,20-Dioxo-leukotriene B4, sphingosine 1-phosphate(d19:1-P), sphingomyelin(d18:0/16:1(9Z)), lysoPC(16:0), lysoPC(18:0) and lysoPC(20:4(5Z,8Z,11Z,14Z)), were identified in the plasma of model mice with xenografts at both 7 and 14 DPI. All the altered metabolites associated with the onset and development of lung carcinoma were involved in the metabolism of glycerophospholipid, fatty acid, sphingolipid and arachidonic acid. The feasible utility of these endogenous biomarkers as potential diagnostic indicators was validated through receiver operating characteristic curve analysis. Collectively, these findings provide a systematic view of metabolic changes linked to the onset and development of lung carcinoma.

SUBMITTER: Wu H

PROVIDER: S-EPMC6170569 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode.

Wu Huan H Chen Yang Y Li Zegeng Z Liu Xianhua X

Royal Society open science 20180919 9

In this work, an untargeted metabolomic method based on ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) in MSE (E represents collision energy) mode was exploited to determine the dynamic metabolic alterations in the plasma of male C57BL/6 mice during the onset and development of lung carcinoma. Plasma samples were collected from control and model mice (male C57BL/6 mice experimentally inoculated with the Lewis lung carcino ...[more]

PMID: 30839735

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Project description:Hyperuricemia (HUA) as a metabolic disease is closely associated with metabolic disorders. The etiology and pathogenesis of HUA are not fully understood, so there is no radical cure so far. Metabolomics, a specialized study of endogenous small molecule substances, has become a powerful tool for metabolic pathway analysis of selected differential metabolites, which is helpful for initially revealing possible development mechanisms of various human diseases. Twenty HUA patients and 20 healthy individuals participated in the experiment, and ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF/MS) was employed to investigate serum samples to find differential metabolites. The statistical techniques used were principal component analysis and orthogonal partial least-squares discriminant analysis. The differences in metabolomics results of samples after pretreatment with different solvents were compared, 38, 20, 26, 28, 33, 50, and 40 potential differential metabolites were found, respectively, in HUA patient samples, and each group involved different metabolic pathways. Repetitive metabolites were removed, 138 differential metabolites in HUA serum were integrated for analysis, and the human body was affected by 7 metabolic pathways of glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and α-linolenic acid metabolism. In this work, the metabolomics approach based on UPLC-Q-TOF/MS was employed to investigate serum metabolic changes in HUA patients, 138 potential differential metabolites related to HUA were identified, which provided associations of lipids, amino acids, fatty acids, organic acids, and nucleosides profiles of HUA individuals. Metabolic pathways involved in glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and a-linolenic acid metabolism shed light on the understanding of the etiology and pathogenesis process of HUA.

Project description:(1) Background: The ability to determine the age of ginseng is very important because the price of ginseng depends on the cultivation period. Since morphological observation is subjective, a new scientific and systematic method for determining the age of ginseng is required. (2) Methods: Three techniques were used for a metabolomics approach. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy was used to analyze powdered ginseng samples without extraction. Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography quadrupole time-of-fight mass spectrometry (GC-TOF/MS) were used to analyze the extracts of 4-, 5-, and 6-year-old ginseng. (3) Results: A metabolomics approach has the potential to discriminate the age of ginseng. Among the primary metabolites detected from NMR spectroscopy, the levels of fumarate and choline showed moderate prediction with an area under the curve (AUC) value of more than 0.7. As a result of UPLC-QTOF/MS-based profiling, 61 metabolites referring to the VIP (variable importance in the projection) score contributed to discriminating the age of ginseng. The results of GC×GC-TOF/MS showed clear discrimination of 4-, 5-, and 6-year-old ginseng using orthogonal partial least-squares discriminant analysis (OPLS-DA) to 100% of the discrimination rate. The results of receiver operating characteristic (ROC) analysis, 16 metabolites between 4- and 5-year-old ginseng, and 18 metabolites between 5- and 6-year-old ginseng contributed to age discrimination in all regions. (4) Conclusions: These results showed that metabolic profiling and multivariate statistical analyses can distinguish the age of ginseng. Especially, it is meaningful that ginseng samples from different areas had the same metabolites for age discrimination. In future studies, it will be necessary to identify the unknown variables and to collaboratively study with other fields the biochemistry of aging in ginseng.

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Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode.

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Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MS<sup>E</sup> mode.

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