A Novel Approach Based on Gut Microbiota Analysis and Network Pharmacology to Explain the Mechanisms of Action of Cichorium intybus L. Formula in the Improvement of Hyperuricemic Nephropathy in Rats

Abstract

Background: Cichorium intybus L. formula (CILF) is a traditional Chinese medicine (TCM) widely used in the treatment of gout and hyperuricemic nephropathy (HN). The aim of this research was to investigate the potential protective effect of CILF against HN and elucidated the underlying mechanism.

Methods: CILF water extract was administered to an HN rat model established by adenine combined with ethambutol. The levels of uric acid (UA), serum urea nitrogen (UREA), and creatinine (CREA) were detected. Changes in the pathology and histology of the kidney were observed by hematoxylin-eosin staining. The 16S rRNA of the gut microbiota was sequenced. The binding ability of the main ingredients of CILF to key targets was analyzed by network pharmacology and molecular docking. The expression levels of the related mRNAs and proteins in the kidney were evaluated by RT-qPCR and immunohistochemistry analysis.

Results: CILF administration significantly alleviated increases in UA, UREA, and CREA, structural damage, and kidney dysfunction. Gut microbiota analysis was applied to explore the pharmacological mechanism of the effects of CILF on bacterial diversity and microbiota structure in HN. CILF decreased the abundance of Bacteroides. In addition, it increased the abundance of Lactobacillaceae, Erysipelotrichaceae, Lachnospiraceae, Ruminococcaceae, and Bifidobacterium. Based on network pharmacology and molecular docking analysis, CILF profoundly influenced the IL17, TNF and AGE-RAGE signaling pathway. Additionally, CILF inhibited the expression of STAT3, VEGFA and SIRT1 to improve the symptoms of nephropathy. Our research suggested that CILF protects against kidney dysfunction in rats with HN induced by adenine combined with ethambutol.

Conclusion: Our findings on the anti-HN effects of CILF and its mechanism of action, from the viewpoint of systems biology, and elaborated that CILF can alter the diversity and community structure of the gut microbiota in HN, providing new approaches for the prevention and treatment of HN.

Keywords: Cichorium intybus L. formula; gut microbiota; hyperuricemic nephropathy; molecular docking; network pharmacology.

Figure 1

Main chemical constituents of CILF. (A) UPLC-Q/Orbitrap HRMS total ion chromatogram of CILF. (B) UPLC chromatogram of CILF at a wavelength of 254 nm. 1, Shanzhiside; 2, Inopyranosid; 3, Chlorogenic acid; 4, Cryptochlorogenic acid; 5, Isochlorogenic Acid B; 6, Isochlorogenic acid A; 7, Isochlorogenic acid C; 8, Genipin 1-gentiobioside; 9, Puerarin; 10, Capparoside A; 11, Puerarin 6”-O-xyloside; 12, Daidzin.

Figure 2

Effects of CILF on serum biochemistry and H&E staining in rats. Data are shown as mean ± SD (n=6). (A) Schematic experimental design for CILF treatment. (B) UA index. (C) CREA index. (D) UREA index. (E) Representative photographs of H&E staining. (i) CG (magnification × 10); (ii) CG (magnification × 40); (iii) HNG (magnification × 10); (iv) HNG (magnification × 40); (v) AG (magnification × 10); (vi) AG (magnification × 40); (vii) CHG (magnification × 10); (viii) CHG (magnification × 40). (ix) CLG (magnification × 10); (x) CLG (magnification × 40). Data are presented as mean ± SD (n = 6). **p < 0.01, ^##p < 0.01 vs the HNG; *p < 0.05, ***p < 0.001 vs the CG.

Figure 3

The relative abundance of the species with the highest abundance in the sample at each taxonomic level. (A) The bar plot of relative abundance at the Phylum level. (B) The bar plot of relative abundance at the Family level. (C) The bar plot of relative abundance at the Genus level. (D) The bar plot of high abundance Genus. (E) The bar plot of high abundance Species. CG; HNG; AG; CHG; and CLG (n=6).

Figure 4

CILF regulates the diversity of intestinal flora in HN rats and guts microbiota α-diversity based on Chao1 index, PD index, Shannon index and Simpson index. (A) Chao1 curves. (B) PD whole tree curves. (C) Simpson curves. (D) Shannon curves. (E) Rarefaction Curve. (F) OTU Rank abundance curves. *p < 0.05.

Figure 5

The β-diversity of gut microbiota based on NMDS analysis. (A) PCoA based on Bray-Curtis distance. (B) PCoA is based on Jaccard distance. (C) PCoA based on Unweighted UniFrac distance. (D) PCoA based on Weighted UniFrac distance. (E) NMDS based on Bray-Curtis distance. (F) NMDS based on Jaccard distance. (G) NMDS based on Unweighted UniFrac. (H) NMDS based on Weighted UniFrac.

Figure 6

Differential species analysis and functional enrichment of the ko metabolic pathway at 3 different levels. (A) LDA score of Lefse. (B) Cladogram of Lefse. (C) Random forest analysis. (D) Ko analysis of level 1. (E) Ko analysis of level 2. (F) Ko analysis of level 3.

Figure 7

Network construction and pathway and functional enrichment analysis of the effect of CILF on HN. (A) Potential active ingredient‑target‑disease network. The different colors of the symbols represent the following: square yellow represents the seven herbal medicines, purple represents protein targets, dark green represents chicory, red represents dandelion, light green represents angelica dahurica, dark blue represents mulberry leaf, dark pink represents lilium, light blue represents common ingredients of herbs, dark yellow represents radix puerariae, circular yellow represents gardenia. (B) Venn. (C) Frequency analysis of protein targets. (D) Protein-protein interaction (PPI) network. Node information as mentioned here: query proteins and first shell of interactors (colored nodes), the second shell of interactors (white nodes), proteins of unknown 3D structure (empty nodes), and some 3D structure is known or predicted (filled nodes). (E) core PPI network. (F) GO function analysis. The gradual change in color represents the change in probability (G) KEGG pathway enrichment analysis. The gradual change in color represented the change in probability.

Figure 8

Molecular docking and visualization. (A-C) Docking mode between the TNF target protein and 3 key components with the lowest Vina scores. (D–F) Docking mode between the VEGFA target protein and 3 key components with the lowest Vina score. (G–I) Docking mode between the SIRT1 target protein and 3 key components with the lowest Vina score.

Figure 9

CILF inhibited kidney injury in adenine combined with ethambutol-induced HN rats. (A) Immunohistochemical analysis of SIRT1, STAT3 and VEGFA (×400). (B) SIRT1 expression positive area. (C) STAT3 expression positive area. (D) VEGFA expression positive area. (E–G) Expression of Sirt1, Stat3 and Vegfa was determined by RT-qPCR. CG; HNG; AG; CHG; and CLG. The data are expressed as the mean ± SD (n=6). *p < 0.05, **p < 0.01, ***p < 0.001 vs CG, ^##p < 0.01, ^###p < 0.001 vs HNG.