Determining How Alzheimer's Disease Alters Hippocampal Network Signatures of Memory in Mouse Models

项目摘要 随着美国人口的老龄化，影响老年人的疾病正成为一个日益关注的问题。最 这些疾病中突出的是阿尔茨海默病（AD），其是一种以进行性痴呆为特征的痴呆形式。 认知能力下降，影响了11%的65岁以上的美国人口。在美国，AD是第六大原因 每年花费2360亿美元AD的主要遗传危险因素是APOE的ε4变体 基因（apoE 4），其在所有AD患者的65-80%中被发现。尽管这种流行，没有成功的 预防性治疗、治疗或治愈AD。为这项奖学金提出的研究将有助于填补两个关键 通过解剖AD如何改变海马网络活动来研究这些差异。 我们对AD病理生理学的理解中的一个重要空白是如何预测未来的认知能力下降 健康的老年人。这将允许在神经变性之前测试潜在的预防性治疗 无法修复的进步本申请中提出的研究将确定是否存在异常海马 网络活动可用于预测未来的学习和记忆障碍。这个工具可以被用来 小鼠模型以监测AD缺陷随年龄的进展并测量化合物的功效 在行为症状发作之前给药。 我们对AD理解的第二个重大差距是特定类型的神经元如何独特地贡献 认知缺陷将AD研究的重点缩小到小细胞群体将允许治疗 在体外和体内筛选中特异性靶向这些细胞。本申请中提出的研究将 试图通过以下方式诱导或挽救空间学习和记忆以及海马网络活动缺陷： 操纵一个非常小的神经元群体，特别是GABA能中间神经元。 总之，本文提出的研究将使用表达apoE 4的小鼠实现三个目的 作为一个模型：（1）测量海马网络活动在衰老过程中的退化，以及这种退化是如何发生的。 退化预测空间学习和记忆在以后的年龄，（2）确定特定的作用， 空间学习记忆中的GABA能中间神经元亚类和海马网络特征 （3）分析apoE 4在这些中间神经元亚类中的特异性表达如何影响 空间学习和记忆以及海马网络活动的缺陷。拟议的研究将 评估认知能力下降的关键生物标志物，并阐明潜在的疾病机制和细胞 未来治疗的目标。

Project Summary As the U.S. population ages, diseases that affect older adults are becoming a growing concern. The most prominent of these diseases is Alzheimer's disease (AD), a form of dementia characterized by progressive cognitive decline which affects 11% of the US population over age of 65. In the US, AD is the sixth leading cause of mortality and costs $236 billion annually. The leading genetic risk factor for AD is the ε4 variant of the APOE gene (apoE4), which is found in 65-80% of all AD patients. Despite this prevalence, there is no successful preventative therapy, treatment, or cure for AD. The research proposed for this fellowship will help fill two critical gaps by dissecting how AD alters hippocampal network activity. One important gap in our understanding of AD pathophysiology is how to predict future cognitive decline in healthy older adults. This would allow testing of potential preventative therapies before the neurodegeneration advances beyond repair. The research proposed in this application will determine if aberrant hippocampal network activity can be used to predict future learning and memory impairment. This tool could be harnessed in mouse models to monitor progression of AD deficits over aging and to measure the efficacy of compound administration before behavioral symptom onset. A second significant gap in our understanding of AD is how specific types of neurons uniquely contribute to cognitive deficits. Narrowing the focus of AD research towards a small cell population would allow therapies to specifically target these cells in in vitro and in vivo screens. The research proposed in this application will attempt to induce or rescue spatial learning and memory and hippocampal network activity deficits by manipulating a very small population of neurons, specifically GABAergic interneurons. In summary, the research proposed herein will accomplish three aims using mice which express apoE4 as a model: (1) measure the degeneration of hippocampal network activity across aging and how this degeneration predicts spatial learning and memory at later ages, (2) determine the role of specific GABAergic interneuron subclasses in spatial learning and memory and hippocampal network signatures thereof, and (3) dissect how apoE4 expression specifically in these interneuron subclasses contributes to deficits in spatial learning and memory and hippocampal network activity. The proposed research will assess a crucial biomarker for cognitive decline and elucidate potential disease mechanisms and cellular targets for future therapies.