Alzheimer?s disease is the most common cause of dementia in the elderly, but there are a number of other related dementias that exhibit substantial overlap in the behavioral, cognitive, and neuropathological manifestations of the disease. In fact, the majority of dementia cases likely arise from the co-occurrence of one or more of these AD and AD-related pathologies, with very few individuals exhibiting ?pure? Alzheimer?s pathology (e.g., only amyloid plaques). This complexity makes diagnosis and therapeutic development challenging, a problem exacerbated by a paucity of accurate animal models for ADRD that faithfully recapitulate the full spectrum of the molecular, cellular, cognitive, and behavioral pathologies of these dementias. In response to PAR-19-167, we will create a panel of genetically diverse knock-in mice harboring known mutations associated with AD and several related dementias using precise genomic editing to ensure biologically-relevant gene expression patterns and levels.
In Aim 1, we will use CRISPR/Cas9 to create mice carrying combinations of disease-causing mutations in App, Psen1, Mapt, Tardbp, and Snca to produce a set of ?core? strains we expect to better capture the complexity of ADRD. To capture the role of genetic background in disease risk, we will then cross these ?core? mice to four genetic backgrounds known to promote susceptibility or resilience of ADRD (DBA/2J, FVB/NJ, WSB/EiJ, and C57Bl/6J). We will then leverage our expertise in high-throughput mouse neurobehavioral phenotyping to screen 16 new ADRD strains to identify the lines that best model ADRD.
In Aim 2, we will use our deep phenotyping pipeline to fully characterize our top strains across the entire spectrum of ADRD-related symptoms, including both cognitive and non-cognitive domains. We will also use high-field MRI, histopathological measurements, and molecular phenotypes to assess effects on brain structure, extent of neuropathologies, and impact on gene networks and pathways associated with disease. Finally, in Aim 3, we will validate our new models for use in basic science and preclinical studies by determining concordance between mouse and human data and use network modeling approaches to identify early drivers of disease that predict late-stage outcomes in humans. This project will produce much-needed new models for AD and related dementias that will greatly enhance our understanding of the pathological mechanisms underlying these diseases. Finally, all of the models produced here will be distributed to the community via the JAX Repository. We will also make all of the phenotyping data publicly available using resources such as Mouse Phenome Database, GeneWeaver, and Synapse.
Alzheimer?s disease dementia is typically associated with memory loss and accumulation of amyloid plaques, but a majority of individuals actually show mixed pathological changes in the brain, which are often associated with worse outcomes and cognitive decline than in individuals with only amyloid pathology. Unfortunately, the influence of these additional changes on disease severity and progression is poorly understood, largely because we still lack mouse models that recapitulate these additional pathologies, particularly in the presence of comorbidities relevant to human health and disease. Our objective here is to, for the first time, create models of Alzheimer?s disease and its related dementias that more accurately capture the complexity of this devastating disease in humans.