Exploring the health benefits of gut microbiota metabolites on combating ulcerative colitis via network pharmacology, bioinformatics and molecular docking

Abstract

Ulcerative colitis (UC) is a leading health challenge worldwide. The evidence of the benefits of gut microbiota metabolites for the treatment of ulcerative colitis is accumulating, but the underlying mechanism remains to be further elucidated. Hence, the aim of this study was to decipher the role of gut microbiota metabolites in the management of UC through employing network pharmacology approach. The targets of gut microbiota metabolites were acquired from gutMgene database, similarity ensemble approach (SEA) and SwissTargetPrediction (STP) respectively. The ulcerative colitis related targets were acquired from GeneCards and DisGeNet database. DAVID platform was used to identify key pathways. Molecular docking was used to assess the binding affinity of metabolites with targets. The final core targets were PPARG, IL6 and AKT1. IL-17 signaling pathway and Toll-like receptor signaling pathway were regarded as critical pathway involved in the development of ulcerative colitis. Equol, Butyrate, Acetate and Propionate were identified as the key metabolites against ulcerative colitis. Molecular docking results demonstrated that Equol displayed strong binding affinity to the core targets. the key gut microbiota metabolites exerted beneficial effects on ulcerative colitis through interacting with multi-targets and multi-pathways, these findings highlighted the potential clinical application of gut microbiota metabolites to the treatment of ulcerative colitis.

Keywords: Gut microbiota; Metabolites; Molecular Docking; Network Pharmacology; Ulcerative colitis.

Fig. 1

The flowchart of the study design.

Fig. 2

The identifications of targets of gut microbiota metabolites against UC. (A) The overlapping targets corresponding to gut microbiota metabolites between SEA and STP. (B) The overlapping targets between UC targets and metabolites. (C) The overlapping targets between gut microbiota and 46 targets. (D) The network of Gut-Metabolites-UC. Note: SEA stands for Similarity Ensemble Approach; STP stands for Swiss Target Prediction; UC stands for ulcerative colitis.

Fig. 3

The PPI network analysis. (A) The PPI network from STRING platform. (B) The visualization of PPI network. Note: PPI network could decode the interactions among different targets that could help identify core targets by using different measures.

Fig. 4

Biological analysis of targets of metabolites against UC. (A) The GO function analysis of hub targets, including Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). (B) The KEGG pathway analysis of hub targets. (C) The visualization of GO functions by employing chord plot. (D) The network of BP-CC-MF-Pathway-Target. Note: GO referring to Gene Ontology is powerful platform to elucidate the biological process, cellular component and molecular function of identified targets. KEGG referring to Kyoto Encyclopedia of Genes and Genomes is widely used platform to identified the signaling pathway.

Fig. 5

The KEGG pathway enrichment. (A) IL-17 pathway with core targets being involved. (B) Toll like receptor pathway with core targets being involved.

Fig. 6

The gut microbiota (G)-metabolite(M)-target(T)-pathway(P) network. The green node represented gut microbiota, The brown node represented gut microbiota metabolites, The red node represented target and The blue node represented pathway. Note: The visualization of GMPT network could help better understand the relationships among all the elements.

Fig. 7

Molecular docking of core metabolites with core targets. (A) The heatmap reflected the binding energy between metabolites and targets. (B) Equol-PPARG. (C) Equol -IL6. (D) Equol -AKT1. Note: The binding energy < 0 kcal/mol suggested a stable binding ability between component and target. The smaller the binding energy is, the more stable the binding ability is.