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使用多指标统计分析来表征和区分四种在β淀粉样蛋白沉积方面存在差异的阿尔茨海默病转基因小鼠品系的性能。

Use of multimetric statistical analysis to characterize and discriminate between the performance of four Alzheimer's transgenic mouse lines differing in Abeta deposition.

作者信息

Leighty Ralph E, Nilsson Lars N G, Potter Huntington, Costa David A, Low Mark A, Bales Kelly R, Paul Steven M, Arendash Gary W

机构信息

Memory and Aging Research Laboratory, SCA 110, University of South Florida, Tampa, FL 33620, USA.

出版信息

Behav Brain Res. 2004 Aug 12;153(1):107-21. doi: 10.1016/j.bbr.2003.11.004.

Abstract

Behavioral assessment of genetically-manipulated mouse lines for Alzheimer's disease has become an important index for determining the efficacy of therapeutic interventions and examining disease pathogenesis. However, the potential for higher level statistical analyses to assist in these goals remains largely unexplored. The present study thus involved multimetric statistical analyses of behavioral and beta-amyloid (Abeta) deposition measures from four PDAPP-derived transgenic mouse lines that differ in extent of Abeta deposition. For all four lines, multiple behavioral measures obtained from a comprehensive task battery administered at 15-16 months of age were collectively examined by correlation, factor, and discriminant function analyses. In addition, both compact and total beta-amyloid (Abeta) histologic measures were determined from the same animals. Widespread intra- and inter-task correlations were evident, with impairment in all three water tasks (Morris maze, platform recognition, and radial arm water maze) correlating extensively with Abeta deposition in hippocampus and cerebral cortex. By elucidating the underlying relationships among measures, factor analysis revealed a single primary factor (Factor 1) that loaded most cognitive measures, particularly those for working memory and recognition. Abeta deposition measures loaded exclusively on this primary factor. In individual animals, only factor scores derived from this primary factor were correlated with Abeta deposition. Both of these findings again underscore the association between cognitive impairment and Abeta deposition. Finally, discriminant function analysis (step-wise forward method) was able to distinguish between all four AD transgenic lines based on behavioral performance alone, as well as when Abeta deposition measures were included. Our results demonstrate the utility of higher level, multimetric analysis of behavioral measures from AD transgenic mice. Analyses such as these will be very beneficial for the functional evaluation of therapeutic interventions against AD and for finding behavioral measures that can serve as predictors of pathology.

摘要

对用于阿尔茨海默病的基因操纵小鼠品系进行行为评估,已成为确定治疗干预效果和研究疾病发病机制的重要指标。然而,高级统计分析在助力实现这些目标方面的潜力在很大程度上仍未得到探索。因此,本研究对来自四个不同β-淀粉样蛋白(Aβ)沉积程度的PDAPP衍生转基因小鼠品系的行为和β-淀粉样蛋白(Aβ)沉积测量值进行了多指标统计分析。对于所有四个品系,对在15 - 16月龄时通过综合任务组获得的多个行为测量值,共同进行了相关性分析、因子分析和判别函数分析。此外,还从同一批动物中确定了致密型和总β-淀粉样蛋白(Aβ)的组织学测量值。任务内和任务间广泛存在相关性,所有三项水任务(莫里斯迷宫、平台识别和放射状臂水迷宫)的损伤都与海马体和大脑皮层中的Aβ沉积广泛相关。通过阐明测量值之间的潜在关系,因子分析揭示了一个单一的主要因子(因子1),该因子加载了大多数认知测量值,特别是那些用于工作记忆和识别方面的测量值。Aβ沉积测量值仅加载在这个主要因子上。在个体动物中,只有从这个主要因子得出的因子得分与Aβ沉积相关。这两个发现再次强调了认知障碍与Aβ沉积之间的关联。最后,判别函数分析(逐步向前法)能够仅基于行为表现,以及在纳入Aβ沉积测量值时,区分所有四个AD转基因品系。我们的结果证明了对AD转基因小鼠行为测量值进行高级别、多指标分析的实用性。此类分析对于针对AD的治疗干预的功能评估以及寻找可作为病理预测指标的行为测量值将非常有益。

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