背景:阿尔茨海默病(AD)与大脑中tau和淀粉样β蛋白的异常积累有关,导致神经原纤维缠结,神经纤维线和细胞外淀粉样β斑块。治疗仅限于症状管理,疾病改善疗法是不可用的。为了推进治疗方法的搜索,继续需要通过确认现有假设和生成新假设来确定疾病干预的目标.
方法:我们进行了一项mRNA-seq研究,以鉴定来自颞上回的死后脑样本中与AD相关的基因(STG,n=76),和额下回(IFG,n=65)大脑区域。差异表达基因(DEGs)被鉴定为校正性别和替代变量以捕获未被预先计划的协变量解释的隐藏变异。这项研究的结果与加速医学伙伴关系-阿尔茨海默病(AMP-AD)倡议的转录组研究进行了比较。过度表达和基因集富集分析(GSEA)用于鉴定疾病相关途径。进行了蛋白质-蛋白质相互作用(PPI)和加权基因共表达网络分析(WGCNA)分析,并鉴定了共表达的基因模块及其中心基因,并将其与其他感兴趣的表型性状相关联。
结果:在STG中,AD病例和认知正常对照之间有数百个mRNAs差异表达,而在IFG中没有和很少的转录物满足相同的标准(调整后的p小于0.05和倍数变化大于1.2)。这些发现在基因集水平上与来自AMP-AD的三个队列中的两个一致。PPI分析表明,DEGs的蛋白质-蛋白质相互作用比随机偶然的预期富集。过度表达和GSEA分析表明基因在神经炎症中起作用,淀粉样蛋白-β,自噬和贩运在AD疾病过程中很重要。在基因层面,在本研究和MSBB研究中(AMP-AD计划中的三个队列之一),来自STG的10个基因在小胶质细胞基因中富集(TREM2,C3AR1,ITGAX,OLR1、CD74和HLA-DRA),但也包括具有更广泛细胞类型表达模式的基因,如CDK2AP1。在具有独立研究支持证据的DEG中,CDK2AP1(在星形胶质细胞中表达最丰富)是与死前认知测量(最后一次迷你精神状态检查评分)和神经原纤维缠结负荷相关最强的转录物,但也与淀粉样蛋白斑块负荷相关。而OLR1是与淀粉样蛋白斑块负荷相关性最强的转录本。GSEA和过度表达分析揭示了与免疫过程相关的基因集,包括中性粒细胞脱颗粒,白细胞介素10信号,和干扰素γ信号,补体和凝血级联,磷脂酰肌醇信号系统,本研究丰富了吞噬体和神经递质受体以及突触后信号传递,并在独立研究中进行了复制。
结论:这项研究确定了差异基因组,与三个AMP-AD队列中的两个(ROSMAP和MSBB)常见,并强调小胶质细胞和星形胶质细胞是与AD临床诊断相关的DGE的关键细胞类型,和/或死前认知测量以及神经病理学指标。未来的荟萃分析和因果推理分析将有助于查明最相关的途径和基因进行干预。
BACKGROUND: Alzheimer\'s disease (AD) is associated with abnormal tau and amyloid-β accumulation in the brain, leading to neurofibrillary tangles, neuropil threads and extracellular amyloid-β plaques. Treatment is limited to symptom management, a disease-modifying therapy is not available. To advance search of therapy approaches, there is a continued need to identify targets for disease intervention both by confirming existing hypotheses and generating new hypotheses.
METHODS: We conducted a mRNA-seq study to identify genes associated with AD in post-mortem brain samples from the superior temporal gyrus (
STG, n = 76), and inferior frontal gyrus (IFG, n = 65) brain regions. Differentially expressed genes (DEGs) were identified correcting for gender and surrogate variables to capture hidden variation not accounted for by pre-planned covariates. The results from this study were compared with the transcriptome studies from the Accelerated Medicine Partnership - Alzheimer\'s Disease (AMP-AD) initiative. Over-representation and gene set enrichment analysis (GSEA) was used to identify disease-associated pathways. Protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) analyses were carried out and co-expressed gene modules and their hub genes were identified and associated with additional phenotypic traits of interest.
RESULTS: Several hundred mRNAs were differentially expressed between AD cases and cognitively normal controls in the
STG, while no and few transcripts met the same criteria (adjusted p less than 0.05 and fold change greater than 1.2) in the IFG. The findings were consistent at the gene set level with two out of three cohorts from AMP-AD. PPI analysis suggested that the DEGs were enriched in protein-protein interactions than expected by random chance. Over-representation and GSEA analysis suggested genes playing roles in neuroinflammation, amyloid-β, autophagy and trafficking being important for the AD disease process. At the gene level, 10 genes from the
STG that were consistently differentially expressed in this study and in the MSBB study (one of the three cohorts within the AMP-AD initiative) were enriched in microglial genes (TREM2, C3AR1, ITGAX, OLR1, CD74, and HLA-DRA), but also included genes with a broader cell type expression pattern such as CDK2AP1. Among the DEGs with supporting evidence from an independent study, CDK2AP1 (most abundantly expressed in astrocyte) was the transcript with strongest association with antemortem cognitive measure (last Mini-Mental State Examination score) and neurofibril tangle burden but also associated with amyloid plaque burden, while OLR1 was the transcript with strongest association with amyloid plaque burden. GSEA and over-representation analyses revealed gene sets related to immune processes including neutrophil degranulation, interleukin 10 signaling, and interferon gamma signaling, complement and coagulation cascades, phosphatidylinositol signaling system, phagosome and neurotransmitter receptors and postsynaptic signal transmission were enriched from this study and replicated in an independent study.
CONCLUSIONS: This study identified differential gene sets, common with two out of three AMP-AD cohorts (ROSMAP and MSBB) and highlights microglia and astrocyte as the key cell-types with DGEs associated with AD clinical diagnosis, and/or antemortem cognitive measure as well as neuropathological indices. Future meta-analysis and causal inferential analysis will be helpful in pinpointing the most relevant pathways and genes to intervene.