in silico data of HELIYON-D-23-33818
In network pharmacology study, herbal targets for Daehwang and disease targets for epilepsy were retrieved from various databases. A protein-protein interaction network was established using the STRING database, and the core targets were identified through topological analysis. Finally, enrichment analysis was performed using the DAVID tool to uncover the underlying mechanism.
Steps to reproduce
1. in silico identification of active compounds in Rheum tanguticum for treating epilepsy The compounds associated with RT were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) version 2.3 and the Herbal-Ingredient-Target Platform (HIT) version 2.0 using “Da huang” or “Dahuang” as the keyword. Subsequently, the synonym names, Chemical Abstracts Service (CAS), PubChem compound IDs (CID), and International Chemical Identifier (InChIKey) numbers of the compounds were introduced into the PubChem database to obtain the compound structures and ensure that the compounds were recognizable for further steps. 2. in silico identification of target genes of R. tanguticum for treating epilepsy The target genes of the RT active compounds were sourced from several databases, including TCMSP version 2.3, HIT version 2.0, Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM), and SWISS Target Prediction databases. For TCMSP and HIT, the names, CIDs, and CAS numbers of the compounds were entered into the HIT 2.0 database to obtain the target genes. For BATMAN-TCM, the compound CIDs were entered, and the predicted potential genes (including known genes) with a Score_cutoff = 20 were selected. To identify disease targets related to epilepsy, DisGeNET version 7.0, Genecards version 5.9, and the Therapeutic Target Database (TTD) were consulted. All disease targets listed in the TTD and targets with a gene-disease association score greater than 0.1 in DisGeNET, as well as those with a relevance score above 10 in Genecards, were selected. 3. in silico construction of protein-protein interaction network and analysis of signaling pathways To examine the protein interactions among the overlapping targets, a protein-protein interaction (PPI) network was constructed. This network was achieved using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, specifying the species specified as "H. sapiens" and a medium confidence level of 0.4 (Szklarczyk et al., 2021). Subsequently, the topology of the protein interaction network was analyzed using Cytoscape 3.9.0 software. In order to investigate and uncover the relevant biological processes and molecular pathways in the treatment of epilepsy by RT, an enrichment analysis of Gene Ontology (GO) and KEGG pathways was conducted by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), with an adjusted p-value threshold of 0.01 (following Benjamini's correction). GO analyses focus on the molecular functions, cellular components, and biological processes associated with a target, while KEGG pathway enrichment analyses explore the involvement of targets in various pathways and activities. To further understand the mechanisms of RT, a network of "herb-compound-target-pathway" was constructed using Cytoscape 3.9.0. (Other details will be automatically disclosed upon publication of the paper.)
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)
National Research Council of Science & Technology (NST) grant by the Korea government (MSIT)