Network pharmacology-based strategy for predicting therapy targets of Sanqi and Huangjing in diabetes mellitus

Abstract

BACKGROUND A comprehensive literature search shows that Sanqi and Huangjing (SQHJ) can improve diabetes treatment in vivo and in vitro , respectively. However, the combined effects of SQHJ on diabetes mellitus (DM) are still unclear. AIM To explore the potential mechanism of Panax notoginseng (Sanqi in Chinese) and Polygonati Rhizoma (Huangjing in Chinese) for the treatment of DM using network pharmacology. METHODS The active components of SQHJ and targets were predicted and screened by network pharmacology through oral bioavailability and drug-likeness filtration using the Traditional Chinese Medicine Systems Pharmacology Analysis Platform database. The potential targets for the treatment of DM were identified according to the DisGeNET database. A comparative analysis was performed to investigate the overlapping genes between active component targets and DM treatment-related targets. We constructed networks of the active component-target and target pathways of SQHJ using Cytoscape software and then analyzed the gene functions. Using the STRING database to perform an interaction analysis among overlapping genes and a topological analysis, the interactions between potential targets were identified. Gene Ontology (GO) function analyses and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted in DAVID. RESULTS We screened 18 active components from 157 SQHJ components, 187 potential targets for active components and 115 overlapping genes for active components and DM. The network pharmacology analysis revealed that quercetin, beta-sitosterol, baicalein, etc. were the major active components. The mechanism underlying the SQHJ intervention effects in DM may involve nine core targets (TP53, AKT1, CASP3, TNF, interleukin-6, PTGS2, MMP9, JUN, and MAPK1). The screening and enrichment analysis revealed that the treatment of DM using SQHJ primarily involved 16 GO enriched terms and 13 related pathways. CONCLUSION SQHJ treatment for DM targets TP53, AKT1, CASP3, and TNF and participates in pathways in leishmaniasis and cancer.

Determination of the synergistic anti-influenza effect of Huangqin Su tablet and Oseltamivir and investigation of mechanism of the tablet based on gut microbiota and network pharmacology

“…Thus, it is crucial to investigate the targets and mechanisms of action of baicalein against influenza. Network pharmacology provides a simple platform for Chinese medicine research to evaluate the multitarget effects of drugs and to increase the understanding of the theory of biological network function [ 44 – 47 ]. Therefore, in the present study, a network pharmacology approach was used to comprehensively analyze and predict the targets and pathways of HQS at the protein level.…”

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

“…In this work, 80 potential targets of compounds were obtained by using databases such as TCMSP ( ), 25 STITCH ( ), 26 PubChem ( ), 27 UniProt ( ), 28 Use GeneCards ( ), OMIM ( ), and TTD ( ) 29 databases to collect obesity-related targets.…”

“…In this work, 80 potential targets of compounds were obtained by using databases such as TCMSP (http://lsp.nwu.edu.cn/ tcmsp.php), 25 STITCH (https://stitch.embl.de/), 26 PubChem (https://pubchem.ncbi.nlm.nih.gov), 27 UniProt (https:// www.uniprot.org/), 28 Use GeneCards (https://www. genecards.org/), OMIM (https://www.omim.org/), and TTD (https://db.idrblab.net/ttd/) 29 databases to collect obesity-related targets.…”