A Dataset for Constructing the Network Pharmacology of Overactive Bladder and Its Application to Reveal the Potential Therapeutic Targets of Rhynchophylline

Abstract

Objectives: Network pharmacology is essential for understanding the multi-target and multi-pathway therapeutic mechanisms of traditional Chinese medicine. This study aims to evaluate the influence of database quality on target identification and to explore the therapeutic potential of rhynchophylline (Rhy) in treating overactive bladder (OAB). Methods: An OAB dataset was constructed through extensive literature screening. Using this dataset, we applied network pharmacology to predict potential targets for Rhy, which is known for its therapeutic effects but lacks a well-defined target profile. Predicted targets were validated through in vitro experiments, including DARTS and CETSA. Results: Our analysis identified Rhy as a potential modulator of the M3 receptor and TRPM8 channel in the treatment of OAB. Validation experiments confirmed the interaction between Rhy and these targets. Additionally, the GeneCards database predicted other targets that are not directly linked to OAB, corroborated by the literature. Conclusions: We established a more accurate and comprehensive dataset of OAB targets, enhancing the reliability of target identification for drug treatments. This study underscores the importance of database quality in network pharmacology and contributes to the potential therapeutic strategies for OAB.

Keywords: network pharmacology; overactive bladder; rhynchophylline; target identification.

Figure 1

Workflow of the current study.

Figure 2

KEGG and GO enrichment results. The KEGG and GO enrichment analysis based on self-built dataset (A–C) and the GeneCards database (D–F). (A,B), the KEGG enrichment based on self-built database; (C), the GO enrichment based on self-built dataset; (D,E), the KEGG enrichment based on GeneCards database; (F), the GO enrichment based on GeneCards database.

Figure 3

Drug–disease intersecting targets. (A) Intersecting targets collected from the self-built dataset. (B) Intersecting targets collected from public databases.

Figure 4

Molecular docking analysis reveals the best binding positions of the Rhy (sticks in yellow) with the lowest binding free energy in the ligand-binding cavity of CHRM3 (PDB ID 5ZHP) and TRMP8 (PDB ID 7WRC). (A) The three-dimensional diagram illustrates the interactions of Rhy (yellow sticks) with CHRM3 at the same binding position of crystallized 9EC (purple sticks). (B) The two-dimensional diagram indicates the interactions of Rhy with the amino acid residues in the binding pocket of CHRM3. (C) The three-dimensional diagram illustrates the interactions of Rhy (yellow sticks) with TRMP8 at the same binding position of crystallized icilin (purple sticks). (D) The two-dimensional diagram indicates the interactions of Rhy with the amino acid residues in the binding pocket of TRMP8. Green lines show H-bonding with different distances between ligands and specific amino acid residues. The spoked arcs indicate amino acid residues providing non-bonded interactions with the ligand.

Figure 5

Identification of TRPM8 and M3 as direct targets of Rhy. (A) The DARTS–Western blotting experiment was used to verify that TRPM8 significantly increased in tissue lysate with Rhy. (B) The DARTS–Western blotting experiment was used to verify that M3 significantly increased in tissue lysate with Rhy. (C) CETSA temperature gradient experiment. (D) The CETSA experiment was used to verify that the degradation degree of TRPM8 with Rhy was lower than that of the HT group. (E) The CETSA experiment was used to verify that the degradation degree of M3 with Rhy was lower than that of the HT group.