UC Irvine AD Translational Center for Disease Model Resources-Microbiome Supplement

项目总结/摘要 阿尔茨海默病（AD）的特征在于淀粉样蛋白斑的进行性出现， 神经纤维缠结，导致神经炎症、神经元损失和痴呆。微生物 暴露调节炎症，并且现在已知其影响AD的发作和进展。 包括动物模型和人类。然而，UCI MODEL-AD动物的微生物组不是 目前正在研究。表征MODEL-AD动物的纵向微生物组将具有 对理解疾病进展以及确保严谨性和可重复性具有重要影响 在不同动物设施中可能具有不同微生物组的动物队列中进行的AD研究，或 甚至是不同的笼子微生物及其代谢产物可能是AD预防和治疗的一部分， 未来因此，我们建议从以下来源试点生成微生物组和代谢组数据： 粪便和盲肠样品，以表征（a）纵向微生物 群落组成，（B）纵向代谢物组成，和（c）疾病相关微生物 和代谢物。我们将包括早发性5xFAD、迟发性hAb-KI、三联抗-HBV-K1、三联抗-HBV-K1和三联抗-HBV-K1的队列。 转基因（3xTg-AD）小鼠，发展斑块和缠结病理，和几个合作 与WT对照沿着交叉。这将使我们能够确定是否有微生物 每个模型，其时间点和样品类型与AD相关生理学最相关， 以及微生物组和代谢组标记物是否在未来的研究中有用。微生物组数据 来自UCI的微生物组数据将与其他MODEL-AD研究中心生成的微生物组数据进行比较。

Project Summary/Abstract 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.