Network Pharmacology-Based Approaches of Rheum undulatum Linne and Glycyrriza uralensis Fischer Imply their Regulation of Liver Failure with Hepatic Encephalopathy in Mice

Abstract

Rheum undulatum and Glycyrrhiza uralensis have been used as supplementary ingredients in various herbal medicines. They have been reported to have anti-inflammatory and antioxidant effects and, therefore, have potential in the treatment and prevention of various liver diseases. Considering that hepatic encephalopathy (HE) is often associated with chronic liver failure, we investigated whether an R. undulatum and G. uralensis extract mixture (RG) could reduce HE. We applied systems-based pharmacological tools to identify the active ingredients in RG and the pharmacological targets of RG by examining mechanism-of-action profiles. A CCl₄-induced HE mouse model was used to investigate the therapeutic mechanisms of RG on HE. We successfully identified seven bioactive ingredients in RG with 40 potential targets. Based on an integrated target-disease network, RG was predicted to be effective in treating neurological diseases. In animal models, RG consistently relieved HE symptoms by protecting blood-brain barrier permeability via downregulation of matrix metalloproteinase-9 (MMP-9) and upregulation of claudin-5. In addition, RG inhibited mRNA expression levels of both interleukin (IL)-1β and transforming growth factor (TGF)-β1. Based on our results, RG is expected to function various biochemical processes involving neuroinflammation, suggesting that RG may be considered a therapeutic agent for treating not only chronic liver disease but also HE.

Keywords: Glycyrriza uralensis; MMP-9; Rheum undulatum; hepatic encephalopathy; neuroinflammation.

Figure 1

Characterization of RUE (Rheum undulatum Linne) (A) and GUE (Glycyrriza uralensis) (B) by UPLC chromatogram. (A) UPLC chromatogram of five commercial standards (left) and marker compounds in GUE (right). Each peak represents sennoside A (340 nm), emodin (254 nm), chrysophanol (254 nm), aloe-emodin (254 nm), and rhein (254 nm), respectively. (B) UPLC chromatogram of theww commercial standards (left) and marker compounds in RUE (right). Each peak represents glycyrrhizin acid (254 nm), liquiritigenin (280 nm), and isoliquiritigenin (280 nm), respectively.

Figure 1

Characterization of RUE (Rheum undulatum Linne) (A) and GUE (Glycyrriza uralensis) (B) by UPLC chromatogram. (A) UPLC chromatogram of five commercial standards (left) and marker compounds in GUE (right). Each peak represents sennoside A (340 nm), emodin (254 nm), chrysophanol (254 nm), aloe-emodin (254 nm), and rhein (254 nm), respectively. (B) UPLC chromatogram of theww commercial standards (left) and marker compounds in RUE (right). Each peak represents glycyrrhizin acid (254 nm), liquiritigenin (280 nm), and isoliquiritigenin (280 nm), respectively.

Figure 2

Systems pharmacology-based approach. (A) Compound–target network (C–T network). The C–T network was constructed by connecting the candidate compounds (green rectangles) of R. undulatum (red circle) and G. uralensis (blue circle) with their potential targets (yellow rectangles). The C–T network was composed of 141 compound–target links generated from the connection of the 7 candidate compounds to 40 targets. (B) ClueGO analysis of the predicted targets. The pie chart represents the molecular function, immune system processes and reactome pathways of the targets identified in the network analysis. (C) Target–disease network (T–D network). In the T–D network, candidate targets were connected to the related diseases. Target proteins (40, yellow ellipses) were connected to 135 diseases (green circles), which could be assigned into 18 separated groups (orange squares).

Figure 2

Systems pharmacology-based approach. (A) Compound–target network (C–T network). The C–T network was constructed by connecting the candidate compounds (green rectangles) of R. undulatum (red circle) and G. uralensis (blue circle) with their potential targets (yellow rectangles). The C–T network was composed of 141 compound–target links generated from the connection of the 7 candidate compounds to 40 targets. (B) ClueGO analysis of the predicted targets. The pie chart represents the molecular function, immune system processes and reactome pathways of the targets identified in the network analysis. (C) Target–disease network (T–D network). In the T–D network, candidate targets were connected to the related diseases. Target proteins (40, yellow ellipses) were connected to 135 diseases (green circles), which could be assigned into 18 separated groups (orange squares).

Figure 3

RG ameliorates neurobehavioral changes in CCl₄-induced hepatic damage model. Neurobehavioral changes observed in the open field tests (OFTs) in CCl4-induced hepatic damage model. (A) Representative traces of mouse movement in the experimental field. (B) % action categories (resting, slow, and fast) in both center and periphery zones by mice. (C) Distance travelled in both center and periphery zones by mice. Data were analyzed for statistical significance using ANOVA with Tukey’s test for ad hoc multiple comparison implemented in GraphPad Prism. Data were presented as mean ± SD for three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001 when compared with vehicle group; ^#p < 0.05, ^##p < 0.01, ^###p < 0.001 when compared with group CCl₄.

Figure 3

RG ameliorates neurobehavioral changes in CCl₄-induced hepatic damage model. Neurobehavioral changes observed in the open field tests (OFTs) in CCl4-induced hepatic damage model. (A) Representative traces of mouse movement in the experimental field. (B) % action categories (resting, slow, and fast) in both center and periphery zones by mice. (C) Distance travelled in both center and periphery zones by mice. Data were analyzed for statistical significance using ANOVA with Tukey’s test for ad hoc multiple comparison implemented in GraphPad Prism. Data were presented as mean ± SD for three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001 when compared with vehicle group; ^#p < 0.05, ^##p < 0.01, ^###p < 0.001 when compared with group CCl₄.

Figure 4

RG ameliorates CCl₄-induced histological changes in cerebral cortex. Mice were given with repeated injections of CCl₄ (5 mg/kg, 2 times/week, 4 weeks), whereas vehicle mice instead received saline (n = 4). The CCl₄ + RG group was treated with RG (10 mg/kg of R plus 100 mg/kg of G) with CCl₄. After four weeks of CCl₄ injection, mice were sacrificed. (A) Histopathological change (when stained with hematoxylin and eosin; H&E) and (B) astrogliosis (glial fibrillary acidic protein, GFAP) in cerebral cortex. Magnification: 400×.

Figure 5

RG suppresses BBB disruption and inflammation in mice with CCl4-induced HE. Mice were treated with CCl₄, and RG as described in Figure 3. RT-qPCR analysis of the mRNA expression levels of (A) MMP-9, (C) TGF-β1, and (D) IL-1β. (B) The protein levels of claudin-5 were assessed by western blot, in which β-actin served as a loading control. Protein levels were presented as relative band intensities to control (vehicle treated) group. Data were represented by mean ± SD.

Figure 6

RG protects CCl₄-induced liver toxicity in mice. Mice were given with repeated injections of CCl₄ (5 mg/kg, 2 times/week, 4 weeks), whereas vehicle mice instead received saline (n = 4). The CCl₄ + RG group was treated with RG (10 mg/kg of R plus 100 mg/kg of G) with CCl₄. After four weeks of CCl₄ injection, mice were sacrificed. (A) The activities ALT and (B) ammonia were assayed by using semi-automated blood chemistry analyzer after 4 weeks of treatment. Data were represented by the mean ± SD. (C) Representative photomicrographs of liver sections processed for H&E (upper row) and Masson’s trichrome (lower row) staining with vehicle, CCl₄, and CCl₄ + RG. Scale bars = 80 μm.