DOI: 10.57760/sciencedb.06968
title: Quercetin Modulates Liver Metabolic Profile in the Chronic Unpredictable Mild Stress Rat Model Based on Metabolomics Technology
title: Quercetin Modulates Liver Metabolic Profile in the Chronic Unpredictable Mild Stress Rat Model Based on Metabolomics Technology

description: Statistical analysis software (SAS, version 9.4) was used for statistical analysis. Two-way ANOVA was used to evaluate the main and interaction effects of the CUMS and quercetin. The post hoc least significant difference (LSD) test was used to analyze the multiple comparisons. Data are presented as mean ± standard deviation (SD). If the P value is &lt; 0.05, the difference in means is considered statistically significant. SPSS (27.0) was used for receiver operating characteristic (ROC) curve analysis. 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 experiment was performed using the Waters ACQUITY UPLC System. The UPLC BEH C18 column (100×2.1mm, 1.7μm) was used for chromatographic separation. The mobile phase consists of 0.1% formic acid (solvent A) and acetonitrile (solvent B). Liquid phase gradient setting was as follows: 0 to 0.5min, 98% A; 0.5 to 1.5min, 80% A; 1.5 to 6min, 30% A; 6 to 12min, 2% A; 12 to 14min, 30% A; 14 to 16min, 98% A. The flow rate was set to 0.45mL/min and the injection volume was 2μL. The temperature of the autosampler and the column were set to 4℃ and 35℃. The QC sample was injected after every 10 measurements to monitor the stability of the instrument and evaluate the reproducibility of the UPLC-MS systems.Mass spectrometry was performed with an electrospray ionization source in positive and negative ion modes. The mass range ranged from m/z 50 to 1000. The cone voltage was 35V. The capillary voltage in the positive and negative modes was 3000V and 2800V, respectively. The cone gas with a flow rate of 50L/h and desolvation gas with a flow rate of 650L/h. The desolvation temperature and source temperature was 300°C and 100°C, respectively. The scan time of 0.4s was used throughout the experiment, with an interval scan time of 0.1s. Leucine enkephalin was applied as a lock mass in positive and negative ionization modes ([M+H]+ = 556.2771, [M−H]− = 554.2615). The maximum allowed mass deviation was 10ppm for MS and MS/MS scan tolerance was set at 0.5Da.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) was 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 (SAS, version 9.4) was used for statistical analysis. Two-way ANOVA was used to evaluate the main and interaction effects of the CUMS and quercetin. The post hoc least significant difference (LSD) test was used to analyze the multiple comparisons. Data are presented as mean ± standard deviation (SD). If the P value is &lt; 0.05, the difference in means is considered statistically significant. SPSS (27.0) was used for receiver operating characteristic (ROC) curve analysis. 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 experiment was performed using the Waters ACQUITY UPLC System. The UPLC BEH C18 column (100×2.1mm, 1.7μm) was used for chromatographic separation. The mobile phase consists of 0.1% formic acid (solvent A) and acetonitrile (solvent B). Liquid phase gradient setting was as follows: 0 to 0.5min, 98% A; 0.5 to 1.5min, 80% A; 1.5 to 6min, 30% A; 6 to 12min, 2% A; 12 to 14min, 30% A; 14 to 16min, 98% A. The flow rate was set to 0.45mL/min and the injection volume was 2μL. The temperature of the autosampler and the column were set to 4℃ and 35℃. The QC sample was injected after every 10 measurements to monitor the stability of the instrument and evaluate the reproducibility of the UPLC-MS systems.Mass spectrometry was performed with an electrospray ionization source in positive and negative ion modes. The mass range ranged from m/z 50 to 1000. The cone voltage was 35V. The capillary voltage in the positive and negative modes was 3000V and 2800V, respectively. The cone gas with a flow rate of 50L/h and desolvation gas with a flow rate of 650L/h. The desolvation temperature and source temperature was 300°C and 100°C, respectively. The scan time of 0.4s was used throughout the experiment, with an interval scan time of 0.1s. Leucine enkephalin was applied as a lock mass in positive and negative ionization modes ([M+H]+ = 556.2771, [M−H]− = 554.2615). The maximum allowed mass deviation was 10ppm for MS and MS/MS scan tolerance was set at 0.5Da.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) was 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).