Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach

Abstract

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Figure 1

Graphical abstract.

Figure 2

(A) Venn diagram between metabolites and disease-related targets. (B) Tissue and cell-specific distribution of genes. Pa-GenBase dataset via the Metascape web server was used to generate the figure. (C) Chromosomal location of genes. The ShinyGO web tool was used to determine the chromosomal position of genes.

Figure 3

(A) The biological processes, cellular components, and molecular function identified in the Gene Ontology enrichment analysis of OP targets. (B) The biological processes, cellular components, and molecular function identified in the Gene Ontology enrichment analysis of Obesity targets. (C) Kyoto Encyclopedia of Genes and Genomes pathways identified in the enrichment analysis of OP targets. (D) Kyoto Encyclopedia of Genes and Genomes pathways identified in the enrichment analysis of Obesity-target. GO and KEGG enrichment analysis was performed using EnrichR.

Figure 4

(A) Identification of Hub proteins. (B, C) PPI network of Hub proteins (the nodes represent proteins, and the edges represent protein–protein interactions).

Figure 5

RNA tissue specificity in (A) bone marrow and (B) adipocyte using the Human Protein Atlas database.

Figure 6

3D Interactions of Osteoporosis related targets-their related metabolites and control (Estrogen). (A) AKT1-Indole (798), (B) AKT1-Oestrogen (9907745), (C) TP53-Compound K (9852086), (D) TP53-Urolithin A, (5488186) (E) TP53-Oestrogen (9907745), and (F) MAPK8-genipin (442424), (G) MAPK8-Oestrogen (9907745). All the figures were generated using BIOVIA Discovery Studio Visualizer v19.1 (BIOVIA).

Figure 7

3D Interactions of Obesity-related targets-their related metabolites and control (Rosiglitazone). (A) HMOX1-10-Oxo-11-octadecenoic acid (10308378), (B) HMOX1-Rosiglitazone (77999), (C) IL18-Equol (91469), (D) IL18-Indole (798), (E) IL18-Rosiglitazone (77999), (F) TP53-Compound K (9852086), (G) TP53-Urolithin A (5488186), (H) TP53-Rosiglitazone (77999), (I) MAPK8-genipin (442424), and (J) MAPK8-Rosiglitazone (77999), (K) G6PD-Urolithin A (5488186), (L) G6PD-Rosiglitazone (77999). All the figures were generated using BIOVIA Discovery Studio Visualizer v19.1 (BIOVIA).

Figure 8

Molecular dynamic simulation of compound K and TP53 protein complex. (A) RMSD, (B) RMSF, (C) ROG, (D) SASA, (E) H-Bond, and (F) MM-GBSA. RMSD – root-mean-square deviation; RMSF – root-mean-square fluctuation; ROG – radius of gyration; SASA – solvent-accessible surface area; MM-GBSA – molecular biomechanics generalized Born surface area. Desmond v6.3 Program in Schrödinger 2023–3 under the Linux platform was used to perform molecular dynamic simulation.

Figure 9

The protein–ligand contact was retrieved from the molecular dynamic simulation trajectory.