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Screening of key candidate genes and pathways for osteocytes involved in the differential response to different types of mechanical stimulation using a bioinformatics analysis

机译:使用生物信息学分析筛选对不同类型机械刺激的差异响应中涉及不同类型的机械刺激的骨细胞的关键候选基因和途径

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摘要

This study aimed to predict the key genes and pathways that are activated when different types of mechanical loading are applied to osteocytes. mRNA expression datasets (series number of GSE62128 and GSE42874) were obtained from Gene Expression Omnibus database (GEO). High gravity-treated osteocytic MLO-Y4 cell-line samples from GSE62128 (Set1), and fluid flow-treated MLO-Y4 samples from GSE42874 (Set2) were employed. After identifying the differentially expressed genes (DEGs), functional enrichment was performed. The common DEGs between Set1 and Set2 were considered as key DEGs, then a protein-protein interaction (PPI) network was constructed using the minimal nodes from all of the DEGs in Set1 and Set2, which linked most of the key DEGs. Several open source software programs were employed to process and analyze the original data. The bioinformatic results and the biological meaning were validated by in vitro experiments. High gravity and fluid flow induced opposite expression trends in the key DEGs. The hypoxia-related biological process and signaling pathway were the common functional enrichment terms among the DEGs from Set1, Set2 and the PPI network. The expression of almost all the key DEGs (Pdk1, Ccng2, Eno2, Egln1, Higd1a, Slc5a3 and Mxi1) were mechano-sensitive. Eno2 was identified as the hub gene in the PPI network. Eno2 knockdown results in expression changes of some other key DEGs (Pdk1, Mxi1 and Higd1a). Our findings indicated that the hypoxia response might have an important role in the differential responses of osteocytes to the different types of mechanical force.
机译:该研究旨在预测当不同类型的机械载荷应用于骨细胞时被激活的关键基因和途径。从基因表达综合数据库(Geo)获得mRNA表达数据集(系列GSE62128和GSE42874)。使用来自GSE62128(Set1)的高重力处理的骨细胞MLO-Y4细胞系样品,以及来自GSE42874(Set2)的流体流动处理的MLO-Y4样品。在鉴定差异表达基因(DEGS)之后,进行官能富集。 Set1和Set2之间的常见次数被认为是键参数,然后使用SET1和SET2中的所有DEG的最小节点构建蛋白质 - 蛋白质相互作用(PPI)网络,其连接了大部分键。采用几个开源软件程序来处理和分析原始数据。通过体外实验验证了生物信息结果和生物学意义。高重力和流体流动诱导关键的表达趋势。缺氧相关的生物过程和信号通路是Set1,Set2和PPI网络中的常见功能性富集术语。几乎所有关键DEG(PDK1,CCNG2,ENO2,EGLN1,HIGD1A,SLC5A3和MXI1)的表达是机械敏感的。 ENO2被鉴定为PPI网络中的集线基因。 ENO2敲低导致其他一些关键DEG的表达式更改(PDK1,MXI1和HIGD1A)。我们的研究结果表明,缺氧反应可能在骨细胞对不同类型的机械力的差异反应中具有重要作用。

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