A network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect

Abstract

BackgroundWhile the majority of studies have focused on the association between sex hormones and dementia, emerging evidence supports the role of other hormone signals in increasing dementia risk. However, due to the lack of an integrated view on mechanistic interactions of hormone signaling pathways associated with dementia, molecular mechanisms through which hormones contribute to the increased risk of dementia has remained unclear and capacity of translating hormone signals to potential therapeutic and diagnostic applications in relation to dementia has been undervalued.MethodsUsing an integrative knowledge- and data-driven approach, a global hormone interaction network in the context of dementia was constructed, which was further filtered down to a model of convergent hormone signaling pathways. This model was evaluated for its biological and clinical relevance through pathway recovery test, evidence-based analysis, and biomarker-guided analysis. Translational validation of the model was performed using the proposed novel mechanism discovery approach based on ‘serendipitous off-target effects’.ResultsOur results reveal the existence of a well-connected hormone interaction network underlying dementia. Seven hormone signaling pathways converge at the core of the hormone interaction network, which are shown to be mechanistically linked to the risk of dementia. Amongst these pathways, estrogen signaling pathway takes the major part in the model and insulin signaling pathway is analyzed for its association to learning and memory functions. Validation of the model through serendipitous off-target effects suggests that hormone signaling pathways substantially contribute to the pathogenesis of dementia.ConclusionsThe integrated network model of hormone interactions underlying dementia may serve as an initial translational platform for identifying potential therapeutic targets and candidate biomarkers for dementia-spectrum disorders such as Alzheimer’s disease.

From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

“…In spite of such growing evidence, the molecular mechanism underlying this protective effect is still unclear and thus, aggregation and analysis of disparate knowledge on this topic in the frame of a unified computational model may facilitate the mission of finding a preventive treatment for Alzheimer's disease. In this direction, we have recently proposed a mechanistic model for genomic hormone interactions underlying dementia, which not only reveals the possible molecular connections between the insulin signaling pathway and learning/memory functions but also explains the preventive mode of action by serendipitous off-target effects of several approved drugs [ 33 ].…”