DOI: 10.57760/sciencedb.05646
title: Quercetin Modulates Liver Metabolic Disorder Induced by Chronic Unpredictable Mild Stress Based on Metabolomics Technology
title: Quercetin Modulates Liver Metabolic Disorder Induced by Chronic Unpredictable Mild Stress Based on Metabolomics Technology

description: Statistical analysis software (SPSS, version 22.0) was used for statistical analysis and receiver operating characteristic (ROC) curve analysis. All data were evaluated by one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) post hoc test. Data are presented as mean ± standard deviation (SD). If the P value is &lt; 0.05, the difference in means is considered statistically significant. GraphPad Prism 5 was used to generate all charts. A heatmap of metabolite was performed by using the software R version 4.0.2.The metabolomics experiment was performed using the Waters ACQUITY UPLC System. The UPLC BEH C18 column (100×2.1mm, 1.7μm) was used for chromatographic separation. Date feature alignment, nontargeted signal detection, and signal integration were performed by Progenesis QI software. Subsequently, EZinfo software (version 2.0) was used for the statistical analysis of metabolite features. Principal component analysis (PCA) was used to assess the repeatability of technical and methodological. Then, partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to examine the best separation between the groups. Furthermore, the model was validated by a cross‐validation tool (SIMCA‐P software; version 14.0) to avoid the risk of overfitting. The S-plot and the value of variable importance in the projection (VIP) scores &gt;1.0 were used to select differential metabolites. Finally, differential metabolites with fold change ≥ 2, and P＜0.05 were filtered as significantly changed metabolites.Preliminary identification of metabolites was performed using the Human Metabolome Database (HMDB). Finally, metabolites were identified based on m/z and MS/MS spectra compared with public databases. Metabolites pathway analysis was performed with Metaboanalyst (http://www.mataboanalyst.ca) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.genome.jp/kegg/kegg2.html) and the Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca).
description: Statistical analysis software (SPSS, version 22.0) was used for statistical analysis and receiver operating characteristic (ROC) curve analysis. All data were evaluated by one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) post hoc test. Data are presented as mean ± standard deviation (SD). If the P value is &lt; 0.05, the difference in means is considered statistically significant. GraphPad Prism 5 was used to generate all charts. A heatmap of metabolite was performed by using the software R version 4.0.2.The metabolomics experiment was performed using the Waters ACQUITY UPLC System. The UPLC BEH C18 column (100×2.1mm, 1.7μm) was used for chromatographic separation. Date feature alignment, nontargeted signal detection, and signal integration were performed by Progenesis QI software. Subsequently, EZinfo software (version 2.0) was used for the statistical analysis of metabolite features. Principal component analysis (PCA) was used to assess the repeatability of technical and methodological. Then, partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to examine the best separation between the groups. Furthermore, the model was validated by a cross‐validation tool (SIMCA‐P software; version 14.0) to avoid the risk of overfitting. The S-plot and the value of variable importance in the projection (VIP) scores &gt;1.0 were used to select differential metabolites. Finally, differential metabolites with fold change ≥ 2, and P＜0.05 were filtered as significantly changed metabolites.Preliminary identification of metabolites was performed using the Human Metabolome Database (HMDB). Finally, metabolites were identified based on m/z and MS/MS spectra compared with public databases. Metabolites pathway analysis was performed with Metaboanalyst (http://www.mataboanalyst.ca) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.genome.jp/kegg/kegg2.html) and the Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca).