Kristen Mary Shannon O'Connell - US grants

PROJECT SUMMARY/ABSTRACT Memory impairment and cognitive deficits are the most prominent feature of Alzheimer?s disease (AD); hence, current AD research has focused predominantly on CNS regions related to learning and memory, such as the hippocampus. However, one of the most consistently reported phenotypes in AD patients is weight loss, which may precede memory loss and cognitive decline by as much as 17 years, raising the question of whether hypothalamic dysfunction is an early cause of AD or merely coincident with disease onset? We propose that hypothalamic dysfunction during the preclinical stage of AD is an early causative step in a cascade of events culminating in dementia, which arises from complex interactions between genetic and environmental (GxE) risk factors that include diet and obesity. Our overall objective is to discover genetic variants and networks that modulate body weight across the lifespan, a clinically relevant biomarker of hypothalamic dysfunction that is predicative of cognitive status later in life. To this end, in Aim 1 we will use a novel mouse panel that incorporates high-risk human FAD mutations on a segregated background of genetic diversity (BXD panel) to identify modifiers that contribute to variation in body weight that is associated with cognitive decline. In Aim 2, we will identify genetic variants that modulate susceptibility to HFD, and derive directed networks and molecular pathways mediating the impact of diet and obesity on AD symptoms via causal inference analysis. In Aim 3, we will evaluate a priori candidates Igf1r, Esr2, and Apbb2 and up to 10 candidates from Aims 1 and 2, establishing the feasibility and independence of Aims. Successful completion of these aims will yield critical new insight into the pathogenesis of AD, including how modifiable environmental factors influence susceptibility and risk. Our deliverables include potential new biomarkers for early detection and new therapeutic strategies targeting the very earliest preclinical stages of the disease to delay or even prevent AD.

PROJECT SUMMARY 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.