Year: 2023
Type: article
Abstract: Background Alzheimer’s disease (AD) therapeutics have largely been unsuccessful in alleviating disease burden in those afflicted by the disease. The TREAT‐AD Consortium is an international group of academic researchers dedicated to identifying novel molecular targets for AD from underexplored areas of disease linked pathology. Method We generate 19 disease biology specific networks, Biological Domains (biodomain), from pathway reconstructions based on interactions from pathway databases. The networks are composed of gene lists which represent Gene Ontology terms (GO Terms) that best describe AD endophenotypes. We interrogate these networks to identify plausibly causal genes using a weighted Key Driver Analysis (KDA). The networks are also used as priors for training Graph Neural Networks (GNN) on transcriptomic data. Result Because a biodomain network object has genes and GO Terms that are annotated to other biodomains, we inferred inter‐biodomain relationships. Analysis of the directed biodomain network provided predictions of the ordering of processes that contribute to AD progression. Molecular targets inferred to be causal across multiple biodomains provide druggable targets that are upstream of AD‐correlated processes. For example, we found gene expression changes in the lipid metabolism biodomain that are driven by genes annotated to autophagy and immune response. Conclusion Alzheimer’s Disease biology can be subdivided into networks which represent processes that are notable to AD patients. Genes from these networks will be prioritized to identify candidate therapeutic targets that are well situated to affect the network biology. TREAT‐AD investigators will validate these targets in cell models and generate experimental resources to support further target development.
Source: Alzheimer s & Dementia
DOI: 10.1002/alz.080089
Authors: Stephen Keegan, Gregory A. Cary, Jesse C Wiley, Jake Gockley, Karina Leal, Robert R. Butler, Anna K Greenwood, Gregory W Carter
Institutions: Jackson Laboratory, Sage Bionetworks, Stanford University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4390196147