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脑缺血蛋白质相互作用网络中的富俱乐部组织

A Rich-Club Organization in Brain Ischemia Protein Interaction Network.

作者信息

Alawieh Ali, Sabra Zahraa, Sabra Mohammed, Tomlinson Stephen, Zaraket Fadi A

机构信息

Department of Neurosciences, Medical University of South Carolina, Charleston, SC 29425.

Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.

出版信息

Sci Rep. 2015 Aug 27;5:13513. doi: 10.1038/srep13513.

Abstract

Ischemic stroke involves multiple pathophysiological mechanisms with complex interactions. Efforts to decipher those mechanisms and understand the evolution of cerebral injury is key for developing successful interventions. In an innovative approach, we use literature mining, natural language processing and systems biology tools to construct, annotate and curate a brain ischemia interactome. The curated interactome includes proteins that are deregulated after cerebral ischemia in human and experimental stroke. Network analysis of the interactome revealed a rich-club organization indicating the presence of a densely interconnected hub structure of prominent contributors to disease pathogenesis. Functional annotation of the interactome uncovered prominent pathways and highlighted the critical role of the complement and coagulation cascade in the initiation and amplification of injury starting by activation of the rich-club. We performed an in-silico screen for putative interventions that have pleiotropic effects on rich-club components and we identified estrogen as a prominent candidate. Our findings show that complex network analysis of disease related interactomes may lead to a better understanding of pathogenic mechanisms and provide cost-effective and mechanism-based discovery of candidate therapeutics.

摘要

缺血性中风涉及多种具有复杂相互作用的病理生理机制。解读这些机制并了解脑损伤的演变过程是开发成功干预措施的关键。我们采用一种创新方法,利用文献挖掘、自然语言处理和系统生物学工具来构建、注释和管理脑缺血相互作用组。经过管理的相互作用组包括人类和实验性中风脑缺血后失调的蛋白质。对相互作用组的网络分析揭示了一种富俱乐部组织,表明存在一个由对疾病发病机制有突出贡献的紧密互联的枢纽结构。对相互作用组的功能注释揭示了突出的通路,并突出了补体和凝血级联在通过富俱乐部激活引发和放大损伤中的关键作用。我们对可能对富俱乐部成分具有多效性作用的假定干预措施进行了计算机模拟筛选,并确定雌激素是一个突出的候选者。我们的研究结果表明,对疾病相关相互作用组进行复杂的网络分析可能有助于更好地理解致病机制,并提供具有成本效益且基于机制的候选治疗药物发现。

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