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A network model of genomic hormone interactions underlying dementia and its translational validation through
Erfan Younesi1, Martin Hofmann-Apitius
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing-SCAI, Schloss Birlinghoven, Sankt Augustin 53754, Germany. erfan.younesi@scai.fraunhofer.de
Background:
While 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.
Methods:
Using 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'.
Results:
Our 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.
Conclusions:
The 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.
Insights
This study reveals a network of seven hormone signaling pathways linked to dementia risk. The findings offer a new platform for developing dementia therapies and diagnostics.
Area of Science:
- Endocrinology
- Neuroscience
- Systems Biology
Background:
- Dementia risk is linked to hormones beyond sex hormones, but mechanisms are unclear.
- Existing research lacks an integrated view of hormone signaling in dementia.
- The potential for hormone-based dementia therapeutics and diagnostics is undervalued.
Purpose of the Study:
- To construct an integrated hormone interaction network for dementia.
- To identify key hormone signaling pathways involved in dementia risk.
- To explore translational applications for dementia diagnosis and treatment.
Main Methods:
- An integrative, data-driven approach was used to build a global hormone interaction network.
- The network was refined to a model of convergent hormone signaling pathways.
- Biological, clinical, and translational relevance was assessed using multiple validation methods.
Main Results:
- A well-connected hormone interaction network underlying dementia was identified.
- Seven key hormone signaling pathways, including estrogen and insulin, converge in this network.
- Validation confirmed that these pathways significantly contribute to dementia pathogenesis.
Conclusions:
- The developed integrated network model provides a translational platform for dementia research.
- This model can aid in identifying novel therapeutic targets for dementia.
- Candidate biomarkers for dementia-spectrum disorders can be discovered using this framework.
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