Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer's Disease
Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer's Disease
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DOI：
10.3389/fnins.2019.00392
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发表时间：
2019-04-24
影响因子：
4.3
通讯作者：
Mias, George, I
中科院分区：
文献类型：
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作者：
Brooks, Lavida R. K.;Mias, George, I
阿尔茨海默病(AD)已被美国疾病控制和预防中心(CDC)列为美国第六大死因。AD是一个重大的保健负担，因为它的发病率增加(特别是在老年人口中)，而且缺乏有效的治疗和预防方法。随着预期寿命的增加，疾控中心预计到2060年AD病例将增加到1500万。衰老以前一直与AD的易感性有关，目前正在努力有效地区分正常和AD年龄相关的脑退化和记忆丧失。AD以神经元功能为靶点，可由于淀粉样β蛋白斑块和细胞内神经纤维缠结的积聚而导致神经元丢失。我们的研究旨在确定健康对照组和AD受试者基因表达谱中的时间变化。我们使用AD和健康队列中可公开获得的微阵列表达数据进行了荟萃分析。在我们的荟萃分析中，我们选择了报告捐赠者年龄和性别的数据集，并使用Affymetrix和Illumina微阵列平台(8个数据集，2088个样本)。原始微阵列表达数据被重新分析，并跨阵列归一化。然后，我们进行了方差分析，使用了一个线性模型，该模型包含了年龄、组织类型、性别和疾病状态作为影响，以及考虑批次效应的研究，并包括因素之间的二元交互作用。我们的结果确定了3,735个有统计学意义的差异(Bonferroni调整p<；0.05)，在AD和健康对照组之间，我们过滤了生物学效应(组间平均差异的10%双尾分位数)，获得了352个基因。被鉴定为丰富的有趣的途径包括神经退行性疾病途径(包括AD)，以及线粒体翻译和功能障碍，突触小泡周期和GABA能突触，以及神经系统中的基因本体论术语丰富，跨化学突触传递和线粒体翻译。总体而言，我们的方法使我们能够有效地结合多个可用的微阵列数据集，并识别AD和健康个体之间的基因表达差异，包括完全年龄和组织类型的考虑。我们的发现提供了潜在的基因和途径关联，可以被靶向地改善AD的诊断和潜在的治疗或预防。
Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6th leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population), and the lack of effective treatments and preventive methods. With an increase in life expectancy, the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal and AD age-related brain degeneration and memory loss. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles. Our study aims to identify temporal changes within gene expression profiles of healthy controls and AD subjects. We conducted a meta-analysis using publicly available microarray expression data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We then performed an analysis of variance, using a linear model that incorporated age, tissue type, sex, and disease state as effects, as well as study to account for batch effects, and included binary interactions between factors. Our results identified 3,735 statistically significant (Bonferroni adjusted p < 0.05) gene expression differences between AD and healthy controls, which we filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, transmission across chemical synapses and mitochondrial translation. Overall our approach allowed us to effectively combine multiple available microarray datasets and identify gene expression differences between AD and healthy individuals including full age and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve AD diagnostics and potentially treatment or prevention.