Establishing Genotype-to-Phenotype Relationships Between Alzheimer’s Related BIN1 Variants

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PROJECT SUMMARY ABSTRACT Genome-wide association studies (GWAS) have identified several risk factors associated with altered probability of late onset Alzheimer’s disease (LOAD). In this regard, variation in bridging integrator 1 (BIN1) have been identified as being strongly associated with increased risk of AD. To that end, several preliminary studies have suggested that BIN1 not only modulates amyloid and tau pathology but also modulates various inflammatory and cell homeostatic pathways. Nonetheless, the mechanistic links between BIN1 and AD remain poorly defined. Moreover, there is a paucity of research examining the effects of BIN 1 variation on the manifestation or augmentation of AD-related phenotypes. In this proposal, we will use our collective expertise in stem cell bioengineering, neurodegenerative disease modeling, and genome engineering to investigate the relationship between specific BIN 1 variants and AD risk. In the first aim, we will using our highly efficient gene editing approach to introduce BIN1 variants into isogenic hiPSCs from healthy non-demented control (NDC) and AD patients. In the second aim, we will employ isogenic hiPSC lines in a 3-D co-culture model to test the hypothesis that these BIN variants exert their risk-modifying effects through (i) modulation of amyloid precursor protein (APP) processing and Aβ secretion and (ii) alteration in tau hyperphosphorylation and internalization. In addition, we will use RNA-seq analysis to identify signaling pathways, gene regulatory networks, and transcriptional targets that are independently influenced by the presence of BIN variation and disease status. In addition, through the use of BIN1 knockout hiPSCs we will be able to determine if these BIN1 variants induce their effects occur through gain- or loss-of-function mechanisms. Overall, a more thorough understanding of the mechanisms by which variation in BIN1 contributes to the likelihood of AD onset will have a significant impact on the design of therapeutic interventions.