Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK.
J Alzheimers Dis. 2019;68(4):1635-1656. doi: 10.3233/JAD-181085.
Microarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer's disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations.
Meta-analyze publicly available transcriptomic data from multiple brain-related disorders to identify robust transcriptomic changes specific to AD brains.
Twenty-two AD, eight schizophrenia, five bipolar disorder, four Huntington's disease, two major depressive disorder, and one Parkinson's disease dataset totaling 2,667 samples and mapping to four different brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum) were analyzed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction.
Meta-analysis identified 323, 435, 1,023, and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe, and cerebellum brain regions, respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-Seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. In addition, biological pathways involved in the "metabolism of proteins" and viral components were significantly enriched across AD brains.
This study identified transcriptomic changes specific to AD brains, which could make a significant contribution toward the understanding of AD disease mechanisms and may also provide new therapeutic targets.
微阵列技术已经确定了阿尔茨海默病（AD）大脑中特定基因和生物途径表达的失衡。然而，个体 AD 研究之间缺乏可重复性，许多相关的神经退行性和精神健康障碍表现出类似的干扰。
对来自多种与大脑相关的疾病的公开转录组数据进行荟萃分析，以确定 AD 大脑特有的稳健转录组变化。
对 22 个 AD、8 个精神分裂症、5 个双相情感障碍、4 个亨廷顿病、2 个重度抑郁症和 1 个帕金森病数据集进行分析，共 2667 个样本，映射到四个不同的大脑区域（颞叶、额叶、顶叶和小脑）。在每个数据集内分别进行差异表达分析，然后使用称为自适应加权单侧校正的组合 p 值方法进行荟萃分析。
荟萃分析确定了 323、435、1023 和 828 个分别特定于 AD 颞叶、额叶、顶叶和小脑的差异表达基因。其中 7 个基因在所有 AD 大脑区域中都受到一致干扰，SPCS1 基因表达模式在 RNA-Seq 数据中复制。另外 19 个基因在受到斑块和缠结影响的 AD 大脑区域中受到特异性干扰，这表明它们可能参与了 AD 神经病理学。此外，与 AD 大脑相关的生物途径包括“蛋白质代谢”和病毒成分显著富集。
本研究确定了 AD 大脑特有的转录组变化，这可能对理解 AD 疾病机制做出重大贡献，并可能为新的治疗靶点提供依据。
