Alzheimer?s disease (AD) is characterized by the progressive appearance of amyloid plaques and neurofibrillary tangles, leading to neuroinflammation, neuronal loss and dementia. Microbial exposures modulate inflammation, and are now known to impact the onset and progression AD in both animal models and humans. However, the microbiomes of the UCI MODEL-AD animals are not currently being studied. Characterizing longitudinal microbiomes of MODEL-AD animals will have important impact in understanding disease progression as well ensuring the rigor and reproducibility of AD studies in animal cohorts that may have different microbiomes in different animal facilities or even different cages. Microbes and their metabolites may be part of AD prevention and treatment in the future. We therefore propose to pilot the generation of microbiome and metabolome data from fecal and cecal samples in young as well as older mice to characterize (a) longitudinal microbial community composition, (b) longitudinal metabolite composition, and (c) disease-associated microbes and metabolites. We will include cohorts of the early onset 5xFAD, late onset hAb-KI, the triple transgenic (3xTg-AD) mice that develop both plaque and tangle pathology, and several collaborative crosses along with WT controls. This will allow us to determine if there are microbes associated with each model, which timepoints and sample types are most associated with AD relevant physiology, and whether microbiome and metabolome markers will be useful in future studies. Microbiome data from UCI will be compared with microbiome data being generated at the other MODEL-AD sites.
Alzheimer?s disease (AD) is currently an irreversible neurodegenerative disorder and the most common cause of dementia among older adults. The potential for inflammation and microbial exposure to impact Alzheimer?s disease onset and progression is an active area of investigation. This supplement will enable microbiome and metabolome data collection from existing AD mouse models in order to study microbial associations with AD, understand gender, age and other factors associated with the microbiome, and design rigorous, reproducible and translatable animal model studies of AD.
