Ehya Farveh, Abdul Tehrani Hossein, Garshasbi Masoud, Nabavi Seyed Masood
Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Mol Biol Res Commun. 2017 Sep;6(3):127-140. doi: 10.22099/mbrc.2017.24861.1256.
Many studies have investigated misregulation of miRNAs relevant to multiple sclerosis (MS) pathogenesis. Abnormal miRNAs can be used both as candidate biomarker for MS diagnosis and understanding the disease miRNA-mRNA regulatory network. In this comprehensive study, misregulated miRNAs related to MS were collected from existing literature, databases and via in silico prediction. A multi-staged data integration strategy (including the construction of miRNA-mRNA regulatory network and systematic data analysis) was conducted in order to investigate MS related miRNAs and their regulatory networks. The final outcome was a bi-layer MS related regulatory network constructed with 27 miRNAs (seven of them were novel) and 59 mRNA targets. To verify the accuracy of the bioinformatics strategy three novel and five previously reported miRNAs from the network model were selected for experimental validation using the real-time PCR assay. The obtained results proved the accuracy of the network. The expression of themiR-24 and miR-137(as novel MS candidate biomarker) and miR-16, and miR-181 (as previously reported MS candidate biomarker) showed significant deregulation in 33 MS patients compared to the control. The optimized data integration strategy conducted in this study found two miRNAs (miR-24and miR-16)that can be considered as candidate biomarkers for MS and also has the potential to generate a regulatory network to aid in further understanding the mechanisms underlying this disease.
许多研究已经调查了与多发性硬化症（MS）发病机制相关的微小RNA（miRNA）的调控异常。异常的miRNA既可以用作MS诊断的候选生物标志物，也有助于理解疾病的miRNA-信使核糖核酸（mRNA）调控网络。在这项综合性研究中，通过现有文献、数据库以及计算机模拟预测收集了与MS相关的失调miRNA。为了研究与MS相关的miRNA及其调控网络，实施了一种多阶段数据整合策略（包括构建miRNA-mRNA调控网络和系统数据分析）。最终构建了一个双层的与MS相关的调控网络，该网络由27个miRNA（其中7个是新发现的）和59个mRNA靶标组成。为了验证生物信息学策略的准确性，从网络模型中选择了3个新发现的和5个先前报道的miRNA，采用实时聚合酶链反应（PCR）分析进行实验验证。所得结果证实了该网络的准确性。与对照组相比，miR-24和miR-137（作为新的MS候选生物标志物）以及miR-16和miR-181（作为先前报道的MS候选生物标志物）在33例MS患者中的表达显示出明显失调。本研究中实施的优化数据整合策略发现了两个miRNA（miR-24和miR-16），它们可被视为MS的候选生物标志物，并且有潜力生成一个调控网络，以帮助进一步理解该疾病的潜在机制。
