Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Konrad-Adenauer-Straße, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Endenicher Allee 19C, Bonn 53113, Germany.
Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay050.
Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities [e.g. Alzheimer's disease (AD) and Parkinson's disease] are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy. A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language. After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and anti-epileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD. We have made the Epilepsy Knowledge Assembly available at https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel and queryable through NeuroMMSig at http://neurommsig.scai.fraunhofer.de. The source code used for analysis and tutorials for reproduction are available on GitHub at https://github.com/cthoyt/epicom.
横断面流行病学研究表明，几种神经系统疾病在癫痫患者中的发病率高于普通人群。一些合并症[例如阿尔茨海默病（AD）和帕金森病]也是癫痫发作的危险因素；这表明它们可能与癫痫具有共同的病理生理机制。采用基于文献的方法来确定癫痫与其合并症之间的基因重叠，以作为疾病共同遗传基础（或遗传多效性）的替代指标，这是首次尝试确定共同机制。虽然研究结果确定了神经障碍是疾病中基因重叠最高的一组疾病，但这种分析不足以确定癫痫及其合并症之间可能存在的共同机制。这促使我们使用专门的文献挖掘和知识组装方法，该方法使用生物表达语言捕获癫痫的因果模型。在使用癫痫及其危险因素、合并症和抗癫痫药物的信息丰富知识组装后，使用一种新颖的比较机制富集方法来提出几种下游效应器（包括 GABA 受体、GABA 能途径等），这些效应器可以解释卡马西平在癫痫和 AD 两种情况下的治疗效果。我们已经在 https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel 上提供了癫痫知识库，并可以通过 http://neurommsig.scai.fraunhofer.de 上的 NeuroMMSig 进行查询。可在 https://github.com/cthoyt/epicom 上获取用于分析的源代码和重现教程。
