Identifying the molecular systems that influence cognitive resilience to Alzheimer's disease in genetically diverse mice

Sarah E. Heuer, Sarah M. Neuner, Niran Hadad, Kristen M.S. O'Connell, Robert W. Williams, Vivek M. Philip, Chris Gaiteri, Catherine C. Kaczorowski

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Individual differences in cognitive decline during normal aging and Alzheimer's disease (AD) are common, but the molecular mechanisms underlying these distinct outcomes are not fully understood. We utilized a combination of genetic, molecular, and behavioral data from a mouse population designed to model human variation in cognitive outcomes to search for the molecular mechanisms behind this population-wide variation. Specifically, we used a systems genetics approach to relate gene expression to cognitive outcomes during AD and normal aging. Statistical causal-inference Bayesian modeling was used to model systematic genetic perturbations matched with cognitive data that identified astrocyte and microglia molecular networks as drivers of cognitive resilience to AD. Using genetic mapping, we identified Fgf2 as a potential regulator of the astrocyte network associated with individual differences in short-term memory. We also identified several immune genes as regulators of a microglia network associated with individual differences in long-term memory, which was partly mediated by amyloid burden. Finally, significant overlap between mouse and two different human coexpression networks provided strong evidence of translational relevance for the genetically diverse AD-BXD panel as a model of late-onset AD. Together, this work identified two candidate molecular pathways enriched for microglia and astrocyte genes that serve as causal AD cognitive biomarkers, and provided a greater understanding of processes that modulate individual and population-wide differences in cognitive outcomes during AD.

Original languageEnglish
Pages (from-to)355-371
Number of pages17
JournalLearning and Memory
Volume27
Issue number9
DOIs
StatePublished - Sep 2020
Externally publishedYes

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