Abstract
Mouse models are vital for preclinical research on Alzheimer’s disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.
Highlights
Alzheimer’s disease (AD) is the most common form of dementia, characterized by loss of cognitive functions such as memory and the presence of hallmark amyloid and tau histopathologies in the brain
Differential Gene Expression Analysis Following normalization, we evaluated human study GSE48350 for differential gene expression to generate a permissive list of differentially expressed genes (DEGs) for subsequent principal component analysis (PCA)
The TASTPM mouse model samples evaluated in this study are driven by mutations identified in dominantly inherited forms of AD, as a goal of this study was to leverage the existing wider availability of Early Onset Alzheimer’s Disease (EOAD) mouse models as compared to late onset AD (LOAD) mouse models
Summary
Alzheimer’s disease (AD) is the most common form of dementia, characterized by loss of cognitive functions such as memory and the presence of hallmark amyloid and tau histopathologies in the brain. Understanding of disease progression is currently limited, and the majority of existing, FDA-approved therapies for AD only provide symptomatic relief (von Schaper, 2018). The majority of existing AD mouse models are based on mutations identified in Early Onset Alzheimer’s Disease (EOAD). Most EOAD cases can be classified as familial or dominant inheritance forms of the disease that commonly involve an aggressive mutation(s) along the hallmark amyloid cascade to account for disease onset. There are mouse models such as the 3xTg (APPswe, MAPT P301L, PSEN1 M146V) model which combine amyloid and tau mutations (Oddo et al, 2003). Each of these individual mouse models incompletely recapitulates known AD outcomes. Tau models show aggregated mutant tau, which has a different conformation than the wild type tau that makes up Alzheimer tangles, and neuronal loss but an absence of amyloid plaque deposition
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