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Towards a pathway inventory of the human brain for modeling disease mechanisms underlying neurodegeneration

: Iyappan, A.; Gündel, M.; Shahid, M.; Wang, J.; Li, H.; Mevissen, H.-T.; Müller, B.; Fluck, J.; Jirsa, V.; Domide, L.; Younesi, E.; Hofmann-Apitius, M.


Journal of Alzheimer's disease : JAD 52 (2016), Nr.4, S.1343-1360
ISSN: 1875-8908
ISSN: 1387-2877
Fraunhofer SCAI ()

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.