Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury
Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury
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DOI：
10.1007/s11064-020-02973-9
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发表时间：
2020-01-30
影响因子：
4.4
通讯作者：
Zhang, Lin
中科院分区：
文献类型：
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作者：
Xiao, Xiao;Bai, Peng;Zhang, Lin
高通量和生物信息学技术已被广泛应用于研究创伤性脑损伤（TBI）的关键分子，但还没有研究整合现有的TBI相关数据集进行分析。在这项研究中，四个可用的表达数据集的液压冲击损伤（FPI）和假样本从大鼠海马进行了分析。共鉴定出248个差异表达基因（DEG）和10个差异表达microRNA（DEMIs）。然后，使用基因本体（GO）和京都基因和基因组百科全书（KEGG）途径分析进行功能注释。大多数DEG富含术语炎性免疫应答。应用Cytoscape软件中的MCODE插件构建蛋白质-蛋白质相互作用（PPI）网络，发现18个hub基因在细胞周期途径中富集。此外，使用短时间序列表达挖掘器（STEM）进行时间序列（3 h、6 h、12 h、24 h和48 h）分析。显著表达的基因被分配到24个模式聚类，其中4个显著上升趋势聚类。发现Fcgr 2a、Bcl 2a 1、Cxcl 16和Gbp 2四种DEG在所有时间点均差异表达。利用miRWalk3.0和Cytoscape软件对53个DEG和8个DEMIs进行分析，发现它们构成了一个miRNA-mRNA负调控网络。此外，通过qRT-PCR验证了8个枢纽基因的mRNA水平。这些DEG，DEMI，和时间依赖性的表达模式，促进我们的知识的分子机制的过程中TBI的大鼠海马，并有可能提高TBI的诊断和治疗。
High-throughput and bioinformatics technology have been broadly applied to demonstrate the key molecules involved in traumatic brain injury (TBI), while no study has integrated the available TBI-related datasets for analysis. In this study, four available expression datasets of fluid percussion injury (FPI) and sham samples from the hippocampus of rats were analysed. A total of 248 differentially expressed genes (DEGs) and 10 differentially expressed microRNAs (DEMIs) were identified. Then, functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Most of the DEGs were enriched for the term inflammatory immune response. The MCODE plug-in in the Cytoscape software was applied to build a protein-protein interaction (PPI) network, and 18 hub genes were demonstrated to be enriched in the cell cycle pathway. Besides, time sequence (3 h, 6 h, 12 h, 24 h, and 48 h) profile analysis was performed using short time-series expression miner (STEM). The significantly expressed genes were assigned into 24 pattern clusters with four significant uptrend clusters. Four DEGs, Fcgr2a, Bcl2a1, Cxcl16, and Gbp2, were found to be differentially expressed at all time-points. Fifty-three DEGs and eight DEMIs were identified to form a miRNA-mRNA negative regulatory network using miRWalk3.0 and Cytoscape. Moreover, the mRNA levels of eight hub genes were validated by qRT-PCR. These DEGs, DEMIs, and time-dependent expression patterns facilitate our knowledge of the molecular mechanisms underlying the process of TBI in the hippocampus of rats and have the potential to improve the diagnosis and treatment of TBI.