Exploration of the molecular mechanism of modified Danggui Liuhuang Decoction in treating central precocious puberty and its effects on hypothalamic-pituitary-gonadal axis hormones

Abstract

Aim: To evaluate the molecular mechanism of modified Danggui Liuhuang Decoction (MDGLHD) in treating central precocious puberty (CPP).

Methods: CPP-related genes were obtained from GEO dataset, MalaCard, DisGeNET and GeneCards databases. MDGLHT ingredients and targets were obtained in TCMSP, HERB, and SwissTargetPrediction databases. Protein-protein interaction (PPI) network was constructed and analyzed using STRING database and Cytoscape 3.9.1. Genetic ontological (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with DAVID and Metascape databases. Molecular docking was performed with PyMoL and AutoDock-Vina software. The GnRH secretion model was established by E2 induction of GT1-7 cells. CCK-8, ELISA and qRT-PCR were used to detect the effects of MDGLHD on gonadotropin-releasing hormone (GnRH) secretion and endocrine signaling receptor gene expression.

Results: 318 potential targets of MDGLHD in CPP treatment were screened out. Quercetin, kaempferol, and (S)-Canadine were considered to be the most important active ingredients in MDGLHD. Bioinformatics analysis showed that these targets were associated with response to hormone, JAK-STAT signaling pathway and HIF-1 signaling pathway. Quercetin, kaempferol, and (s)-Canadine had good binding affinity with tumor protein p53 (TP53), estrogen receptor 1(ESR1), Jun proto-oncogene (JUN), MYC proto-oncogene (MYC) and AKT serine/threonine kinase 1 (AKT1). In vitro experiments showed that MDGLHD extract can inhibit GnRH secretion and the expression of neuroendocrine signaling receptor protein gene.

Conclusion: MDGLHD treatment of CPP is achieved through multi-components, multi-targets and multi-pathways, and inhibition of GnRH secretion and neuroendocrine signaling.

Keywords: Central precocious puberty; Gonadotropin-releasing hormone; MDGLHD; Molecular Docking; Network Pharmacology.

Fig. 1

Screening of CPP-related genes. A. The volcano map shows DEGs in GSE208722 dataset. Green represents down-regulated genes and red represents up-regulated genes. B. The Venn diagram shows the CPP-related genes are identified by MalaCard, DisGeNET, GeneCards databases and analysis of GSE208722

Fig. 2

Components and targets of MDGLHD. A. The Venn diagram is applied to obtain the target genes of MDGLHD in CPP treatment. B. Cytoscape 3.8.0 software was used for the construction of drug-compound-target network. The yellow and orange oval nodes represent the targets. The blue oval nodes represent pharmaceutical ingredients. The orange diamond-shaped nodes represent medical materials. HQ, Huangqi (astragalus); HB, Huangbai (cortex phellodendri); HL, Huanglian (coptis); HQIN, Huangqin (radix scutellariae); DG, Danggui (angelica); ZM, Zhimu (rhizoma anemarrhenae); DP, Danpi (cortex moutan); ZGB, Zhiguiban (grilled turtle plate); SHUDH, Shudihuang (cooked rehmannia); SHEDH, Shengdihuang (raw rehmannia). C-D. Histogram and interactive network show the results of GO analysis and KEGG enrichment analysis. The same color of the nodes in the network represents a term, and the node size represents the number of genes in the term

Fig. 3

PPI network construction and key module screening. A. Cytoscope software was used to visualize the PPI network of targets. Different colors represent different clustering modules, orange represents cluster 1 (score: 8.353, including 35 nodes and 142 edges); Light purple represents cluster 2 (score: 7.857, including 15 nodes and 55 edges); Cyan represents cluster 3 (score: 4.500, including 5 nodes and 9 edges); Pink represents cluster 4 (score: 4.400, including 6 nodes and 11 edges); Red, green, and purple represent clusters 5, 6, and 7 respectively (score: 3.000, including 3 nodes and 3 edges). B. Cluster 1 and cluster 2 subnetworks. C. STRING database was applied to construct the PPI network of the 50 genes obtained from cluster 1 and cluster 2. D & E. GO analysis and KEGG enrichment analysis were applied to predict the biological functions of the 50 genes

Fig. 4

Screening of key targets of MDGLHD in CPP treatment. A. Screening of key targets using cytoNCA plug-ins of cytoscope software. B. The bar charts show degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC) of 6 key targets

Fig. 5

Molecular docking was applied for investigating the interactions between the bioactive components and the targets. A. Interactions between quercetin with AKT1, STAT3, and ESR1. B. Interactions between kaempferol with AKT1, STAT3 and ESR1. C. Interactions between (s)-canadine with AKT1, STAT3 and ESR1

Fig. 6

MDGLHD extract inhibits the expression of genes related to estrogen secretion and neuroendocrine signals. A. Effect of MDGLHD extract on the viability of GT1-7 cells was evaluated by CCK-8 assay. B. The level of GnRH in the supernatant of the cells of each group was detected by ELISA. C-G. The mRNA expression levels of GnRH (C), GnRHR (D), ERβ (E), Kiss1 (F) and GPR54 (G) in GT1-7 cells of each group were detected by qRT-PCR. *P < 0.05, **P < 0.01, ***P < 0.001