摘要
目地:目地:本研究目的证实微小NRA(miNRA)在癫痫动物模型中的异常表达,探索导致癫痫发作潜在的基因片段。方法:从NCBI GEO数据库中下载癫痫大鼠齿状回扁桃体电击下颞叶7天,14天,30天和90天miRNA表达基因片段GSE49850的20个样本及相应的20个对照样本,比较找到电刺激样本miRNA的表达显著不同基因序列。整合两个miRNA数据库miRDB和microRNA.org中的预测靶基因,从中挑选目标miRNA的靶基因。用DAVID软件对筛选得到的靶基因进行功能富集分析,并对miRNA和目标基因的互补序列分析。结果:与对照组比较,rno-miRNA-187证实电刺激后下调表达且与所对应的预测靶基因功能与神经系统密切相关。序列CUGUGC是rno-miR-187与部分交集靶基因结合的共有序列。结论:rno-miR-187电击刺激后四个不同阶段表达持续下调,与神经系统功能密切关联,其表达特异性可能成为癫痫大鼠后颞叶组织中miRNA变化的分子靶标,所调控的靶基因可能为颞叶组织与癫痫疾病关联的基因。
Objective: This study aimed to identify the role of aberrant mi RNAs expression in epilepsy and to explore the potential genes associated with epilepsy.Methods: The miRNA expression profile GSE49850, including20 samples of rat epileptic dentate gyrusat 7, 14, 30 and 90 days after electrical stimulation and other 20 samples of sham time-matched controls, was downloaded from the Gene Expression Omnibus database. The significantly differentially expressed miRNAs were identified in stimulated samples at each time point compared to time-matched controls. The target genes of significantly differentially expressed miRNAs were screened from miDRB and microRNA.org database used in network construction and GO(Gene Ontology)enrichment analysis. Furthermore, the complementary site sequences of miRNAs and target genes were analyzed. Results: rno-miRNA-187 was identified to be significantly down-regulated in stimulated groups compared with the time-matched controls. GO terms related to the nervous system were mostly enriched. Moreover, the sequence CUGUGC was derived as the consensus binding element for rno-miR-187 and its target genes. Conclusion: Our study suggested that miR-187 may play an important role in epilepsy development and progression via regulating numerous target genes. The elucidation of the underlying mechanism of the role of miR-187 in epilepsy may make it a potential therapeutic option.
出处
《中国妇幼健康研究》
2017年第S2期675-676,共2页
Chinese Journal of Woman and Child Health Research
关键词
癫痫
差异表达miRNA
相互作用网络
功能富集分析
Epilepsy
rno-miR-187
Differentially expressed miRNAs
Interaction network
Complementary sequence analysis