Multimodal single‐cell/nucleus RNA‐sequencing data analysis uncovers molecular networks between disease‐associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease. (1st February 2022)

• Record Type: Journal Article
• Authors: Cheng, Feixiong; Xu, Jielin; Zhang, Pengyue; Zhou, Yadi; Pieper, Andrew A.; Cummings, Jeffrey L.; Leverenz, James B.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-01
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.051952
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Systematic identification of molecular networks in disease relevant immune cells of the nervous system is critical for elucidating the underlying pathophysiology of Alzheimer's disease (AD). Two key immune cell types, disease‐associated microglia (DAM) and disease‐associated astrocytes (DAA), are biologically involved in AD pathobiology. Therefore, uncovering molecular determinants of DAM and DAA will enhance our understanding of AD biology, potentially identifying novel therapeutic targets for AD treatment. Method: We systematically investigate molecular networks between DAM and DAA in order to uncover novel therapeutic targets for AD. Specifically, we develop a network‐based methodology that leverages single‐cell/nucleus RNA‐sequencing data from both transgenic mouse models and AD patient brains, as well as drug‐target network, metabolite‐enzyme associations, the human protein‐protein interactome, and large‐scale longitudinal patient data. We prioritize repurposed drugs for potential treatment of AD by identifying those that specifically reverse dysregulated gene expression of microglia and astrocytes. Finally, top drug candidates are selected to be validated further using the state‐of‐the‐art pharmacoepidemiologic observations of a longitudinal patient database with 7.2 million subjects. Result: Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R ) and DAA (i.e., NFKB1, FOS, and JUN ) that are significantly enriched by neuro‐inflammatory pathways and well‐known genetic variants (i.e., BIN1 ). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Lastly, integrative metabolite‐enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network‐based prediction and retrospective case‐control observations with 7.2 million subjects, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio (HR) = 0.86, 95% confidence interval (CI) 0.83‐0.89, p<1.0x10 ‐8 ). Propensity score‐matching cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR=0.74, 95% CI 0.68‐0.81, p<1.0x10 ‐8 ) compared to fluticasone after adjusting age, gender, and disease comorbidities. Conclusion: In summary, we present a network‐based, multimodal methodology for single‐cell/nucleus genomics‐informed drug discovery in AD that has identified fluticasone and mometasone as potential treatments.