Park, J. H.; Yu, J.; Lucey, B. P.; Brubaker, D. K.

Alzheimer's disease (AD) is a brain disease characterized by deposition of insoluble amyloid-beta; plaque, intraneuronal neurofibrillary tangles, and cognitive dysfunction. AD can be characterized as early-onset or late-onset based on age and genetic factors. For early-onset, these genetic factors can include amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). For late-onset, these can include apolipoprotein E e4 (APOE4), and the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2). Mouse models incorporating these risk factors provide critical knowledge for studying AD pathology and preclinical studies for drug development. However, these transgenic mice depend on early-onset genetic mutations and are deficient in certain AD features that are present in late-onset. Here, we developed innovative non-linear and feature selection procedures for our cross-species translation framework, Translatable Components Regression (TransComp-R), to identify transcriptomic features in mouse models predictive of human late-onset AD pathobiology. We used the cross-species computational translatability links of TransComp-R to perform computational high-throughput drug screening and identified multiple repurposable drugs for AD treatment that targeted the sleep-wake cycle. We tested these predictions in an orthogonal, prospective cohort of human subjects treated with an orexin receptor antagonist, suvorexant. We correlated conserved protein-level biomarkers from our cross-species transcriptomics model with significant reductions in phosphorylated tau in cerebrospinal fluid collected from humans treated with suvorexant. This study demonstrates the power of computational methods like TransComp-R to enhance the utility of murine disease models for discovering new therapeutic approaches for AD.