Iyappan Anandhi, Gündel Michaela, Shahid Mohammad, Wang Jiali, Li Hui, Mevissen Heinz-Theodor, Müller Bernd, Fluck Juliane, Jirsa Viktor, Domide Lia, Younesi Erfan, 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 Information Technology, Bonn, Germany.
J Alzheimers Dis. 2016 Apr 12;52(4):1343-60. doi: 10.3233/JAD-151178.
Molecular signaling pathways have been long used to demonstrate interactions among upstream causal molecules and downstream biological effects. They show the signal flow between cell compartments, the majority of which are represented as cartoons. These are often drawn manually by scanning through the literature, which is time-consuming, static, and non-interoperable. Moreover, these pathways are often devoid of context (condition and tissue) and biased toward certain disease conditions. Mining the scientific literature creates new possibilities to retrieve pathway information at higher contextual resolution and specificity. To address this challenge, we have created a pathway terminology system by combining signaling pathways and biological events to ensure a broad coverage of the entire pathway knowledge domain. This terminology was applied to mining biomedical papers and patents about neurodegenerative diseases with focus on Alzheimer's disease. We demonstrate the power of our approach by mapping literature-derived signaling pathways onto their corresponding anatomical regions in the human brain under healthy and Alzheimer's disease states. We demonstrate how this knowledge resource can be used to identify a putative mechanism explaining the mode-of-action of the approved drug Rasagiline, and show how this resource can be used for fingerprinting patents to support the discovery of pathway knowledge for Alzheimer's disease. Finally, we propose that based on next-generation cause-and-effect pathway models, a dedicated inventory of computer-processable pathway models specific to neurodegenerative diseases can be established, which hopefully accelerates context-specific enrichment analysis of experimental data with higher resolution and richer annotations.
分子信号通路长期以来一直被用于证明上游因果分子与下游生物学效应之间的相互作用。它们展示了细胞区室之间的信号流，其中大部分以示意图表示。这些示意图通常是通过查阅文献手动绘制的，既耗时又静态，且不可互操作。此外，这些通路往往缺乏背景信息（条件和组织），并且偏向于某些疾病状态。挖掘科学文献为以更高的背景分辨率和特异性检索通路信息创造了新的可能性。为应对这一挑战，我们通过结合信号通路和生物学事件创建了一个通路术语系统，以确保广泛覆盖整个通路知识领域。该术语被应用于挖掘有关神经退行性疾病（重点是阿尔茨海默病）的生物医学论文和专利。我们通过将源自文献的信号通路映射到健康和阿尔茨海默病状态下人类大脑的相应解剖区域，展示了我们方法的强大之处。我们展示了如何利用这一知识资源来确定一种解释已批准药物雷沙吉兰作用机制的推定机制，并展示了如何利用该资源对专利进行指纹识别，以支持发现阿尔茨海默病的通路知识。最后，我们提出，基于下一代因果通路模型，可以建立一个专门的、针对神经退行性疾病的计算机可处理通路模型清单，有望以更高的分辨率和更丰富的注释加速对实验数据的背景特异性富集分析。
