Target of neohesperidin in treatment of osteoporosis and its effect on osteogenic differentiation of bone marrow mesenchymal stem cells

doi:10.12307/2025.030

Abstract

Abstract: BACKGROUND: Previous studies have found that neohesperidin can delay bone loss in ovariectomized mice and has the potential to treat osteoporosis, but its specific mechanism of action remains to be explored.
OBJECTIVE: To explore the key targets and possible mechanisms of neohesperidin in the treatment of osteoporosis based on bioinformatics and cell experiments in vitro.
METHODS: The gene expression dataset related to osteoporosis was obtained from GEO database, and the differentially expressed genes were screened and analyzed in R language. The osteoporosis-related targets were screened from GeneCards and DisGeNET databases, and the neohesperidin-related targets were screened from ChEMBL and PubChem databases, and the common targets were obtained by intersection of the three. The String database was used to construct the PPI network of intersection genes, and the key targets were screened. The DAVID database was used for GO and KEGG enrichment analysis. The AutoDock software was used to verify the molecular docking between the neohesperidin and the target protein. The effect of neohesperidin on osteogenic differentiation of C57 mouse bone marrow mesenchymal stem cells was detected. Complete medium was used as blank control group; osteogenic induction medium was used as the control group; and osteogenic induction medium containing different concentrations of neohesperidin (25, 50 μmol/L) was used as experimental group. The expression of alkaline phosphatase, the degree of mineralization, the expression of osteogenic-related genes and target genes during osteogenic differentiation of cells were measured at corresponding time points.
RESULTS AND CONCLUSION: (1) 9 253 differentially expressed genes, 2 161 osteoporosis-related targets, and 326 neohesperidin-related targets were screened. There were 53 common targets among the three. All 53 genes were up-regulated in osteoporosis samples. The PPI network screened the target gene PRKACA of research significance. GO function and KEGG pathway enrichment analysis showed that neohesperidin’s treatment of osteoporosis through PRKACA target mainly depended on biological processes such as protein phosphorylation and protein autophosphorylation, acting on endocrine resistance, proteoglycan in cancer, and estrogen signaling pathway to play a therapeutic role. Molecular docking results showed that neohesperidin had a certain binding ability to the protein corresponding to the target PRKACA. (2) The results of alkaline phosphatase staining showed that neohesperidin could promote the expression of alkaline phosphatase in the early stage of osteogenic differentiation of mesenchymal stem cells. Alizarin red staining showed that neohesperidin could promote the mineralization of osteogenic differentiation of mesenchymal stem cells. RT-qPCR results showed that neohesperidin could increase the mRNA expression of alkaline phosphatase, PRKACA, and osteocalcin. (3) These results indicate that neohesperidin may promote osteogenic differentiation through PRKACA target on the estrogen signaling pathway to prevent and treat osteoporosis.

Key words: bioinformatics, osteoporosis, neohesperidin, bone marrow mesenchymal stem cell, PRKACA gene

CLC Number:

Zhang Zhenyu, Liang Qiujian, Yang Jun, Wei Xiangyu, Jiang Jie, Huang Linke, Tan Zhen. Target of neohesperidin in treatment of osteoporosis and its effect on osteogenic differentiation of bone marrow mesenchymal stem cells[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(7): 1437-1447.

Figures/Tables 5

2.2 骨质疏松症相关靶点在GeneCards数据库得到骨质疏松症相关的基因靶点信息5 630条，以Relevance score > 1为标准对得到的数据进行筛选，剩余1 659个基因靶点。在DisGeNET数据库得到基因靶点信息1 098条。将2个数据库的基因靶点信息进行整合，删除重复项，最终得到2 161个骨质疏松症相关靶点基因。 2.3 新橙皮苷相关靶点在ChEMBL数据库中得到新橙皮苷相关的蛋白靶点信息255条，将筛选后的数据导入UniProt数据库匹配，得到相应的基因靶点255个。在SwissTarget Prediction数据库得到新橙皮苷相关的基因靶点信息100条。将以上两部分基因靶点信息进行整合，删除重复项，最终得到326个新橙皮苷相关靶点基因。 2.4 数据集、疾病靶点、药物靶点的交集基因通过Venny 2.1.0在线作图工具对GEO芯片数据集GSE230665的差异表达基因、骨质疏松症相关靶点基因、新橙皮苷相关靶点基因取交集，并制作韦恩图，见图2A，得到交集基因53个。对交集基因在GSE230665中的表达值进行log10转换，发现53个交集基因的表达量在骨质疏松症样本中都上调，绘制热图，见图2B。 2.5 构建蛋白互作网络将交集基因导入STRING数据库进行分析，绘制交集基因PPI网络图，见图3A。获取TSV数据文件并导入Cytoscape 3.9.1软件进行分析，使用CytoHubba插件根据Betweenness值、Closeness值、Degree值进行拓扑分析，筛选出前10位核心靶点，见图3B-D。进一步研究发现，根据Betweenness值排序的前3位基因分别是MAPK3、PRKACA、EGFR，根据Closeness值和Degree值排序的前3位基因都是EGFR、BCL2、MAPK3，汇总得到4个核心靶点EGFR、MAPK3、BCL2、PRKACA。在中国知网以这4个核心靶点基因分别和“骨质疏松症”为主题进行高级检索，其中只有PRKACA基因未见与骨质疏松症相关的研究报道，因此将PRKACA作为进一步研究的目标靶点基因。"

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