Exploring the beneficial effect of gut microbiota metabolites on diabetic nephropathy via network pharmacology study

Abstract

Diabetic nephropathy (DN) is one of the severe complications of diabetes, current treatment against DN is still limited. It is suggested that gut microbiota metabolites will be a promising alternative therapy against DN. In this study, we explore the beneficial effect of gut microbiota metabolites on DN via employing network pharmacology study. The targets of metabolites were screen from Similarity Ensemble Approach (SEA) and Swiss Target Prediction (STP). The DN targets were acquired from disease database. The intersecting targets of metabolites and DN were considered crucial targets. The Protein-Protein Interaction (PPI) networks, GO function and KEGG analysis were conducted to identify core target and key signaling pathway. A "Microbiota-Substrate-Metabolites-Targets" network was built to screen the core metabolites. Molecular docking was employed to assess the binding affinity between metabolites and targets. GO functional results indicated that the metabolites were mainly enriched in oxidative stress and inflammation. PPARG, AKT1, IL6 and JUN were the top 4 targets of gut microbiota metabolites regulating DN. Butyrate, Acetate, Indole and 3-Indolepropionic acid were the core gut microbiota metabolites that had beneficial effects on attenuating DN. Molecular docking results indicated that 3-Indolepropionic acid displayed a good binding affinity toward targets of PPARG, AKT1, IL6 and JUN. Our study revealed that the gut microbiota metabolites might exert beneficial effect on attenuating DN by regulating multi-signaling pathway and multi-targets. This work offers us a novel insight into the mechanism of DN from the perspective of beneficial benefits of gut microbiota metabolites.

Keywords: Diabetic nephropathy; Gut microbiota; Gut microbiota metabolites; Network pharmacology.

Fig. 1

The flowchart of the study design.

Fig. 2

The identifications of targets of gut microbiota metabolites against DN. (A) The overlapping targets corresponding to DN between DisGenet and GeneCards. (B) The overlapping targets corresponding to gut microbiota metabolites between SEA and STP. (C) The overlapping targets corresponding between gut microbiota metabolites and DN. (D) The network of Gut-Target-DN. Note: SEA stands for Similarity Ensemble Approach; STP stands for Swiss Target Prediction; DN stands for Diabetic Nephropathy.

Fig. 3

The PPI network cluster analysis. (A) The PPI network from STRING platform. (B) The visualization of PPI network. (C) The Cluster1 of PPI network. (D) The GO-BP function analysis of Cluster1. (E) The Cluster2 of PPI network. (F) The GO-BP function analysis of Cluster2.

Fig. 4

Biological analysis of targets of GM against DN. (A–C) The GO function analysis of hub targets, including Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). (D) The KEGG pathway analysis of hub targets. (E) The KEGG functional annotation analysis of hub targets.

Fig. 5

The identification of core targets against DN. (A) The hub targets of 153 targets and human gut targets. (B) PPI network from STRING platform. (C) The visualization of PPI network. (D) The Degree Centrality of hub targets. (E) The GO function analysis of hub targets. (F) The KEGG pathway analysis of hub targets. (G) The KEGG functional annotation analysis of hub targets. Note: We have got permission to use the KEGG software from the Kanehisa laboratory on 2025/1/28.

Fig. 6

The identification of core metabolites. Note: The red color represented the targets, the gray color represented the metabolites, the blue color represented Substrate, the green color represented gut microbiota.

Fig. 7

Molecular docking of core metabolites with core targets. (A) The heatmap reflected the binding energy between metabolites and targets. (B) 3-Indolepropionic acid-PPARG. (C) 3-Indolepropionic acid-AKT1. (D) 3-Indolepropionic acid-IL6. (E) 3-Indolepropionic acid-JUN.