Karki Reagon, Kodamullil Alpha Tom, Hofmann-Apitius Martin
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.
Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany.
J Alzheimers Dis. 2017;60(2):721-731. doi: 10.3233/JAD-170440.
Various studies suggest a comorbid association between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms.
This study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM.
We have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources.
Using comparative analysis, we have identified several putative "shared pathways". We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD.
The two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.
多项研究表明,阿尔茨海默病(AD)与2型糖尿病(T2DM)之间存在共病关联,这表明可能存在共同的潜在病理生理机制。
本研究旨在在分子水平上系统地构建相关知识模型,以找到一种机制性原理来解释AD与T2DM之间现有的共病关联。
我们采用基于知识的建模方法,使用生物表达语言(BEL)构建了AD和T2DM的两个网络模型,该语言能够从各种知识资源中捕捉和表示分子和临床水平上的因果关系和相关关系。
通过比较分析,我们确定了几个假定的“共享途径”。我们在机制水平上展示了胰岛素信号通路如何与其他重要的AD途径相关,如神经营养因子信号通路、PI3K/AKT信号通路、MTOR信号通路和MAPK信号通路,以及这些途径在AD和T2DM中如何相互串扰。此外,我们提出了一个机制性假设,解释了抗糖尿病药物二甲双胍在AD中的有利和不利影响。
这里介绍的两个可计算模型提供了一个强大的框架,以识别AD和T2DM之间共享的合理机制联系,从而确定新治疗方法的靶向途径。我们的方法还可用于为为什么某些T2DM治疗似乎会增加AD风险的问题提供机制性答案。