Er-Chen Decoction Alleviates High-Fat Diet-Induced Nonalcoholic Fatty Liver Disease in Rats through Remodeling Gut Microbiota and Regulating the Serum Metabolism

Abstract

Many studies have found that the dysfunction in gut microbiota and the metabolic dysfunction can promote nonalcoholic fatty liver disease (NAFLD) development. Er-Chen decoction (EC) can be used in the treatment of NAFLD. However, the mechanism of this hepatoprotection is still unknown. In this study, we constructed a rat model with NAFLD fed with high-fat chow and administered EC treatment. The therapeutic effects of EC on NAFLD were evaluated by measuring transaminases, blood lipid levels, and pathological changes in the liver. In addition, we measured the effects of EC on liver inflammatory response and oxidative stress. The changes in gut microbiota after EC treatment were studied using 16S rRNA sequencing. Serum untargeted metabolomics analysis was also used to study the metabolic regulatory mechanisms of EC on NAFLD. The results showed that EC decreased the serum transaminases and lipid levels and improved the pathological changes in NAFLD rats. Furthermore, EC enhanced the activities of SOD and GSH-Px and decreased MDA level in the liver. EC treatment also decreased the gene and protein levels of IL-6, IL-1β, and TNF-α in the liver and serum. The 16S rRNA sequencing and untargeted metabolomics indicated that EC treatment affected the gut microbiota and regulated serum metabolism. Correlation analysis showed that the effects of EC on taurine and hypotaurine metabolism, cysteine and methionine metabolism, and vitamin B6 metabolism pathways were associated with affecting in the abundance of Lactobacillus, Dubosiella, Lachnospiraceae, Desulfovibri, Romboutsia, Akkermansia, Intestinimonas, and Candidatus_saccharimonas in the gut. In conclusion, our study confirmed the protective effect of EC on NAFLD. EC could treat NAFLD by inhibiting oxidative stress, reducing inflammatory responses, and improving the dysbiosis of gut microbiota and the modulation of the taurine and hypotaurine metabolism, cysteine and methionine metabolism, and vitamin B6 metabolism pathways in serum.

Figure 1

EC treatment decreases the body weight in NAFLD model rats. Control, model, simvastatin, EC-low dose, and EC-high dose (n = 10 per group) groups are shown. Data are reported as the mean ± SD. ^##P < 0.01 compared with the control group; ^∗∗P < 0.01 compared with the model group.

Figure 2

EC treatment improved the hepatosteatosis in NAFLD model rats. (a, b) H&E staining indicated that EC treatment ameliorated the pathological changes of the liver in NAFLD rats. Black arrows indicate the steatosis of hepatocytes, red arrows indicate lobular inflammation, and yellow arrows hepatic cord disorder (200x). (c, d) Oil Red O staining shows that EC treatment reduced the lipid accumulation in the liver (200x). Control, model, simvastatin, EC low-dose, and EC high-dose (n = 10 per group) groups are shown. Data are reported as the mean ± SD. ^##P < 0.01 compared with the control group; ^∗P < 0.05 compared with the model group; ^∗∗P < 0.01 compared with the model group.

Figure 3

EC treatment reduced the inflammatory response in NAFLD model rats: (a) The levels of IL-6, IL-1β, and TNF-α in serum were decreased after EC treatment. (b) EC treatment downregulated the mRNA expression of IL-6, IL-1β, and TNF-α in the liver. Control, model, simvastatin, EC low-dose, and EC high-dose (n = 10 per group) groups are shown. Data are reported as the mean ± SD. ^##P < 0.01 compared with the control group; ^∗P < 0.05 compared with the model group; ^∗∗P < 0.01 compared with the model group.

Figure 4

EC treatment affected the gut microbiota community in NAFLD model rats. (a) There were no significant differences in Shannon and Simpson indexes among each group. (b, c) PCoA and system clustering tree indicated that beta diversity of gut microbiota between EC high-dose and control groups was more similar than that between the model and control groups (“C” indicates control group; “M” indicates model group; “E” indicates EC high-dose group). (d) The different numbers of OTUs are shown using Venn diagram. (e, f) EC treatment reduced the F to B ratio in gut microbiota at the phylum level. (g) At the genus level, EC treatment affected the relative abundances of Lactobacillus, Dubosiella, Desulfovibrio, Romboutsis, Intestinimonas and C._saccharimonas in NAFLD rats. (h, i) The differential metabolic pathways (written in red) of EC on NAFLD were predicted using PICRUSt analysis based on the 16S rRNA sequencing data. Control, model, and EC high-dose (n = 6 per group) groups are shown. Data are reported as the mean ± SD. ^##P < 0.01 compared with the control group; ^∗P < 0.05 compared with the model group; ^∗∗P < 0.01 compared with the model group.

Figure 5

EC modulated the metabolites in serum in NAFLD rats. (a) PCA among groups. (b, c) OPLS-DA of untargeted metabolomics data between the control and model groups and the relative coefficient of loading plots. (d, e) OPLS-DA untargeted metabolomics data between the model and EC high-dose groups and the relative coefficient of loading plots. (f, g) Pathway analysis of differential metabolites between control and model groups (f) and between model and EC high-dose groups (g). The common pathways were written in red. a, arachidonic acid metabolism; b, taurine and hypotaurine metabolism; c, alpha-linolenic acid metabolism; d, cysteine and methionine metabolism; e, vitamin B6 metabolism; f, synthesis and degradation of ketone bodies; g, butanoate metabolism; h, sulfur metabolism; i, linoleic acid metabolism.

Figure 6

Spearman correlation analysis of gut microbiota with differential abundance and differential metabolites in the serum (heatmap). Red grids indicate positive correlations between gut microbiota and metabolites (correlation analysis value > 0.1), and blue grids indicate negative correlations between gut microbiota and metabolites (correlation analysis value < −0.1). ^∗P < 0.05; ^∗∗P < 0.01.

Figure 7

Spearman correlation analysis between general statement, biochemical markers, and proinflammatory cytokines and gut microbiota (heatmap). Red grids indicate positive correlations between general statement, biochemical markers, and proinflammatory cytokines and gut microbiota (correlation analysis value > 0.1), while blue grids indicate negative correlations between general statement, biochemical markers, and proinflammatory cytokines and gut microbiota (correlation analysis value < −0.1). ^∗P < 0.05; ^∗∗P < 0.01.