UCLA

Alzheimer’s disease (AD) is a major global health challenge, with limited therapeutic options currently available. As a multifactorial disorder, AD is characterized by pathology involving multiple molecular and cellular pathways. Advancing our understanding of AD pathogenesis and identifying effective disease-modifying therapies requires continuous innovation to improve research models. A critical step toward this goal is the development of novel experimental models that address existing gaps in the field. In this dissertation, I characterize two novel mouse genetic tools designed to advance AD research. The first is a novel Bacterial Artificial Chromosome (BAC) transgenic model targeting the understudied AD risk gene MS4A4E, localized within the GWAS-identified MS4A gene cluster. Using immunohistochemistry and bulk RNA sequencing, I discovered sex-specific effects on AD-like pathology in the 5xFAD mouse background. Notably, 7-month-old male BAC-MS4A/5xFAD mice had exacerbation of both amyloid pathology and upregulated DAM-like genes, especially enriched in microglial pathways. This BAC transgenic model provides a valuable platform for investigating the role of MS4A4E in AD pathogenesis and its influence on microglial function.The second model presented is the application of the Mononucleotide Repeat Frameshift (MORF) 3 model, which was previously developed in our lab to visualize the detailed morphology of genetically-defined cell populations. Applying this cutting edge mouse genetic tool to AD research, I demonstrated that axonal pathology, specifically axonal spheroids, can be visualized and quantified in layer 5 pyramidal neurons in an amyloid AD mouse model. Preliminary findings suggest that axonal spheroid density increases with age and differs between brain regions. This genetic tool provides a method to characterize axonal pathology as a potential indicator of disease burden.Together, these genetic tools expand the repertoire of experimental models available for studying Alzheimer’s disease. Here, I demonstrate how these models can provide new insights into the mechanisms underlying disease progression and facilitate more precise characterization of pathological features.