Network pharmacology and molecular docking to explore Siraitia grosvenorii’s potential mechanism in preventing and treating proliferative diabetic retinopathy

Yehong Zhou, Fuxing Shu, S. Sarsaiya, Hu Jiang, Chengyan Jiang, Ting Qu, Ruixia Wang
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Abstract

Although Siraitia grosvenorii (abbreviated as S.g.) is frequently used to prevent and cure diabetes problems, the precise mechanism underlying its ability to do so remains unknown. Through network pharmacology and molecular docking techniques, we studied the early molecular mechanisms of S.g in the treating of proliferative diabetic retinopathy (PDR) in this study. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen the active compounds and related targets of S.g. Oral bioavailability (OB) 30% and drug likeness (DL) 0.18 were used as screening criteria. The active compounds without knowledge of a probable target were excluded. The Uniprot database included converted symbols for the associated targets. GEO2R was used to explore several genes related to PDR. Using jvenn web service to intersect targets of S.g and PDR. The Xiantao Academic Online website was used to examine the expression patterns of intersect targets in PDR samples. The STRING database was used to create a protein-protein interaction (PPI) network of intersecting targets. Cytoscape software was used to show the PPI network, MCODE software was used to evaluate the network’s core proteins, and CytoHubba software was used to extract the important networks of the top three targets. Omicshare platform carried a functional analysis using the Gene Ontology (GO) and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Pymol, AutoDock Vina software, Schrödinger Software were used to conduct molecular docking experiments or pockets search on the top three targets. The results showed that 85 targets were matched to six active compounds of S.g. 18 intersect targets were found. Seven DEGs were up-regulated and eleven genes were down-regulated when these targets were divided into two groups. TNF, PTGS2, and CASP3 were the main targets, according to the PPI network. The intersect targets were mostly related to angiogenesis, cell proliferation, oxidative stress, inflammatory response, and metabolism. It was discovered that the core targets TNF, PTGS2, and CASP3 had various levels of affinity for their respective compounds. Interestingly, multiple good drug-forming pockets for CASP3 and PTGS2 targets were identified through Schrödinger software. In particular, six compounds bind to the top three core targets to inhibit IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Pathways in cancer and 14 other signaling pathways to inhibit inflammation, apoptosis, oxidative stress, arachidonic acid metabolism, and angiogenesis to prevent and treat PDR. The study’s findings, which served as a guide for the widespread use of S.g in PDR clinical practise, included multi-substances and targets of S.g to prevent and cure PDR.
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网络药理学和分子对接探索罗汉果防治增殖性糖尿病视网膜病变的潜在机制
尽管罗汉果(缩写为S.g.)经常被用于预防和治疗糖尿病问题,但其能力的确切机制仍然未知。本研究通过网络药理学和分子对接技术,研究了S.g治疗增殖性糖尿病视网膜病变(PDR)的早期分子机制。利用中药系统药理学(TCMSP)数据库筛选S.g.口服生物利用度(OB)30%和药物相似性(DL)0.18的活性化合物和相关靶点。排除了不知道可能靶点的活性化合物。Uniprot数据库包括相关目标的转换符号。GEO2R用于探索与PDR相关的几个基因。使用jvenn web服务来交叉S.g和PDR的目标。利用仙桃学术在线网站对PDR样本中交叉靶标的表达模式进行了研究。STRING数据库用于创建交叉靶标的蛋白质-蛋白质相互作用(PPI)网络。Cytoscape软件用于显示PPI网络,MCODE软件用于评估网络的核心蛋白,CytoHubba软件用于提取前三个靶标的重要网络。Omicshare平台使用基因本体论(GO)进行了功能分析,并使用京都基因和基因组百科全书(KEGG)进行了途径富集分析。Pymol、AutoDock-Vina软件和Schrödinger软件用于对前三个目标进行分子对接实验或口袋搜索。结果表明,共有85个靶标与S.g.的6个活性化合物相匹配。共发现18个交叉靶标。当这些靶标被分为两组时,7个DEG被上调,11个基因被下调。根据PPI网络,TNF、PTGS2和CASP3是主要靶点。交叉靶点主要与血管生成、细胞增殖、氧化应激、炎症反应和代谢有关。发现核心靶标TNF、PTGS2和CASP3对它们各自的化合物具有不同水平的亲和力。有趣的是,通过Schrödinger软件确定了CASP3和PTGS2靶点的多个良好的药物形成口袋。特别是,六种化合物与前三个核心靶点结合,以抑制IL-17信号通路、糖尿病并发症中的AGE-RAGE信号通路、癌症中的通路和14种其他信号通路,以抑制炎症、细胞凋亡、氧化应激、花生四烯酸代谢和血管生成,从而预防和治疗PDR。该研究结果为s.g在PDR临床实践中的广泛使用提供了指导,包括s.g预防和治疗PDR的多种物质和靶点。
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