Hargis Kendra E, Blalock Eric M
University of Kentucky College of Medicine, Department of Pharmacology and Nutritional Science, Lexington, KY, United States.
University of Kentucky College of Medicine, Department of Pharmacology and Nutritional Science, Lexington, KY, United States.
Behav Brain Res. 2017 Mar 30;322(Pt B):311-328. doi: 10.1016/j.bbr.2016.05.007. Epub 2016 May 4.
Aging is the biggest risk factor for idiopathic Alzheimer's disease (AD). Recently, the National Institutes of Health released AD research recommendations that include: appreciating normal brain aging, expanding data-driven research, using open-access resources, and evaluating experimental reproducibility. Transcriptome data sets for aging and AD in humans and animal models are available in NIH-curated, publically accessible databases. However, little work has been done to test for concordance among those molecular signatures. Here, we test the hypothesis that brain transcriptional profiles from animal models recapitulate those observed in the human condition. Raw transcriptional profile data from twenty-nine studies were analyzed to produce p-values and fold changes for young vs. aged or control vs. AD conditions. Concordance across profiles was assessed at three levels: (1) # of significant genes observed vs. # expected by chance; (2) proportion of significant genes showing directional agreement; (3) correlation among studies for magnitude of effect among significant genes. The highest concordance was found within subjects across brain regions. Normal brain aging was concordant across studies, brain regions, and species, despite profound differences in chronological aging among humans, rats and mice. Human studies of idiopathic AD were concordant across brain structures and studies, but were not concordant with the transcriptional profiles of transgenic AD mouse models. Further, the five transgenic AD mouse models that were assessed were not concordant with one another. These results suggest that normal brain aging is similar in humans and research animals, and that different transgenic AD model mice may reflect selected aspects of AD pathology.
衰老是特发性阿尔茨海默病(AD)最大的风险因素。最近,美国国立卫生研究院发布了AD研究建议,其中包括:认识正常脑老化、扩大数据驱动研究、使用开放获取资源以及评估实验可重复性。人类和动物模型中衰老及AD的转录组数据集可在国立卫生研究院管理的、公众可访问的数据库中获取。然而,在测试这些分子特征之间的一致性方面所做的工作很少。在此,我们检验这样一个假设,即动物模型的脑转录谱概括了人类中观察到的转录谱。对来自29项研究的原始转录谱数据进行分析,以得出年轻与年老或对照与AD状态下的p值和倍数变化。在三个层面评估各转录谱之间的一致性:(1)观察到的显著基因数量与偶然预期的数量;(2)显示方向一致性的显著基因比例;(3)各研究中显著基因效应大小之间的相关性。在不同脑区的个体内发现了最高的一致性。尽管人类、大鼠和小鼠在实际年龄上存在显著差异,但正常脑老化在各项研究、脑区和物种之间是一致的。特发性AD的人类研究在脑结构和各项研究之间是一致的,但与转基因AD小鼠模型的转录谱不一致。此外,所评估的五个转基因AD小鼠模型彼此之间也不一致。这些结果表明,人类和实验动物的正常脑老化相似,并且不同的转基因AD模型小鼠可能反映了AD病理学的某些特定方面。