Integrative analysis of Multiple Sclerosis using a systems biology approach
Integrative analysis of Multiple Sclerosis using a systems biology approach
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DOI：
10.1038/s41598-018-24032-8
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发表时间：
2018-04-04
影响因子：
4.6
通讯作者：
Husi, Holger
中科院分区：
文献类型：
--
作者：
Cervantes-Gracia, Karla;Husi, Holger
多发性硬化症(MS)是一种以中枢神经系统炎性脱髓鞘事件为特征的慢性自身免疫性疾病。尽管对多发性硬化症进行了40多年的研究，但其病因仍不清楚。这项研究的目的是识别多发性硬化症中报道最频繁和调控最一致的分子，以生成分子相互作用网络，从而识别解除调控的过程和途径，从而深入了解多发性硬化症的潜在分子机制。在综合系统生物学方法的推动下，基因表达谱数据集被合并并分层为“未治疗”和“治疗”组，并另外与其他疾病模式进行比较。来自数据集比较的分子识别符与我们的多发性硬化症数据库(musce；www.padb.org/musce)匹配。从5079个具有统计学意义的分子中，组内的相关性分析确定了一组由16个高置信度基因组成的小组，这些基因是单纯多发性硬化症表型所特有的，而“治疗”组反映了与自身免疫性疾病相关的常见模式。途径和基因本体聚类确定干扰素伽马信号通路是所有重要分子中最相关的，病毒感染是所有观察到的下游事件最可能的原因。这种无假设的方法揭示了不同MS表型中最重要的分子事件，可以用于进一步的详细研究。
Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammatory-demyelinating events in the central nervous system. Despite more than 40 years of MS research its aetiology remains unknown. This study aims to identify the most frequently reported and consistently regulated molecules in MS in order to generate molecular interaction networks and thereby leading to the identification of deregulated processes and pathways which could give an insight of the underlying molecular mechanisms of MS. Driven by an integrative systems biology approach, gene-expression profiling datasets were combined and stratified into "Non-treated" and "Treated" groups and additionally compared to other disease patterns. Molecular identifiers from dataset comparisons were matched to our Multiple Sclerosis database (MuScle;www.padb.org/muscle). From 5079 statistically significant molecules, correlation analysis within groups identified a panel of 16 high-confidence genes unique to the naive MS phenotype, whereas the "Treated" group reflected a common pattern associated with autoimmune disease. Pathway and gene-ontology clustering identified the Interferon gamma signalling pathway as the most relevant amongst all significant molecules, and viral infections as the most likely cause of all down-stream events observed. This hypothesis-free approach revealed the most significant molecular events amongst different MS phenotypes which can be used for further detailed studies.