Integrated hepatic transcriptome and metabolome reveal the mechanisms of Jiangtang Tiaozhi formula on improving glycolipid metabolic disorder

Figure 1 Jiangtang Tiaozhi formula attenuated glycolipid metabolic disorder in vivo. A: Fasting glucose of mice during treatment (n = 8); B: Oral glucose tolerance test (OGTT) (n = 8); C: Body weight (n = 8); D: Triglyceride (n = 8); E: Total cholesterol (n = 8); F: Fasting glucose of mice after 8 weeks of treatment (n = 8); G: Area under the curve of OGTT (n = 8). ^aP < 0.05. ^bP < 0.01. ^cP < 0.001. NS: Not significant; JTTZF: Jiangtang Tiaozhi formula; OGTT: Oral glucose tolerance test; TG: Triglyceride; TC: Total cholesterol; AUC: Area under the curve.

Figure 2 Jiangtang Tiaozhi formula modulates liver transcriptome profiles. A: Principal components analysis [control vs model vs Jiangtang Tiaozhi formula (JTTZF) mice, n = 8]; B: Volcano plots (model vs control mice, n = 8); C: Volcano plots (JTTZF vs model mice, n = 8); D: Heatmap of representative differentially expressed genes (control vs model vs JTTZF mice, n = 8), with red indicating up-regulation and blue indicating down-regulation; E: Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to annotate the biological function of differential genes(model vs control mice, n = 8); F: KEGG pathway analysis to annotate the biological function of differential genes (JTTZF vs model mice, n = 8). JTTZF: Jiangtang Tiaozhi formula; PC: Principal component.

Figure 3 The messenger RNA levels of potential genes by real-time polymerase chain reaction. A: Ces1d; B: Ces2a; C: Cry1; D: Cidea; E: Fabp3; F: Fabp4; G: Gprc5b; H: Gpnmb. ^aP < 0.05. ^bP < 0.01. ^cP < 0.001.

Figure 4 Multivariate statistical analysis of liver samples among the control, model, and Jiangtang Tiaozhi formula groups. A: Principal components analysis (PCA) score plot of liver samples among control, model, and Jiangtang Tiaozhi formula (JTTZF) groups in the positive ion mode; B: PCA score plot of liver samples among control, model, and JTTZF groups in the negative ion mode; C: Scores plots of partial least squares discriminant analysis (PLS-DA) between control and model groups in the positive ion mode; D: Scores plots of PLS-DA between control and model groups in the negative ion mode; E: Scores plots of PLS-DA between and between model and JTTZF groups in the positive ion mode; F: Scores plots of PLS-DA between model and JTTZF groups in the negative ion mode; G: Permutation tests of PLD-DA models between control and model groups in the positive ion mode; H: Permutation tests of PLD-DA models between control and model groups in the negative ion mode; I: Permutation tests of PLD-DA models between model and JTTZF groups in the positive ion mode; J: Permutation tests of PLD-DA models between model and JTTZF groups in the negative ion mode. PC: Principal component; J: Jiangtang Tiaozhi formula group; Mo: Model group; C: Control group; Cor: Correlation.

Figure 5 Jiangtang Tiaozhi formula modulates liver metabolite profiles. A: Volcano plots under the positive ion mode (model vs control mice, n = 8); B: Volcano plots under the ion mode (model vs control mice, n = 8); C: Volcano plots under the positive ion mode [Jiangtang Tiaozhi formula (JTTZF) vs model mice, n = 8]; D: Volcano plots under the negative ion mode (JTTZF vs model mice, n = 8); E: Overview of metabolic pathway analysis of liver metabolism (model vs control mice, n = 8); F: Overview of metabolic pathway analysis of liver metabolism (JTTZF vs model mice, n = 8). J: Jiangtang Tiaozhi formula group; Mo: Model group; C: Control group; VIP: Variable important in projection; TCA: Tricarboxylic acid cycle.

Figure 6 Integrated transcriptomics and metabolomics analyses of Jiangtang Tiaozhi formula on improving glycolipid metabolic disorder.