Molecular Medicine Research Centre, Universitätsklinikum Jena, Jena, Germany; Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Proteomics. 2023 May 30;280:104890. doi: 10.1016/j.jprot.2023.104890. Epub 2023 Mar 24.
This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SIGNIFICANCE: As the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.
本研究采用系统生物学和高通量技术分析多发性硬化症病理生理学的复杂分子成分，结合来自多个组学源的数据，以确定潜在的生物标志物，并提出多发性硬化症治疗的治疗靶点和重新利用药物。本研究使用 geWorkbench、CTD 和 COREMINE 分析 GEO 微阵列数据集和多发性硬化症蛋白质组学数据，以识别与多发性硬化症疾病相关的差异表达基因。使用 Cytoscape 及其插件构建蛋白质-蛋白质相互作用网络，并进行功能富集分析以识别关键分子。还使用 DGIdb 创建药物-基因相互作用网络以提出药物。本研究使用 GEO、蛋白质组学和文本挖掘数据集确定了 592 个与多发性硬化症疾病相关的差异表达基因 (DEG)。拓扑网络研究发现 37 个 DEG 很重要，其中 6 个被确定为多发性硬化症病理生理学的最重要因素。此外，我们还提出了针对这些关键基因的六种药物。本研究中鉴定的关键分子在多发性硬化症中失调，可能在疾病机制中发挥关键作用，值得进一步研究。此外，我们还提出重新利用某些 FDA 批准的药物治疗多发性硬化症。我们的计算机模拟结果得到了一些靶基因和药物的先前实验研究的支持。意义：随着长期的研究继续发现神经退行性疾病中的新病理区域，我们在这里采用系统生物学方法来确定多发性硬化症的分子和病理生理学起源，并确定多发性硬化症的关键基因，这些基因有助于候选新的生物标志物并提出新的药物。
