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额叶转录组数据的汇总分析确定了阿尔茨海默病大脑中关键的线粒体自噬基因变化。

Pooled analysis of frontal lobe transcriptomic data identifies key mitophagy gene changes in Alzheimer's disease brain.

作者信息

Mei Taoyu, Li Yuan, Orduña Dolado Anna, Li Zhiquan, Andersson Robin, Berliocchi Laura, Rasmussen Lene Juel

机构信息

Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.

Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

出版信息

Front Aging Neurosci. 2023 Jun 9;15:1101216. doi: 10.3389/fnagi.2023.1101216. eCollection 2023.

Abstract

BACKGROUND

The growing prevalence of Alzheimer's disease (AD) is becoming a global health challenge without effective treatments. Defective mitochondrial function and mitophagy have recently been suggested as etiological factors in AD, in association with abnormalities in components of the autophagic machinery like lysosomes and phagosomes. Several large transcriptomic studies have been performed on different brain regions from AD and healthy patients, and their data represent a vast source of important information that can be utilized to understand this condition. However, large integration analyses of these publicly available data, such as AD RNA-Seq data, are still missing. In addition, large-scale focused analysis on mitophagy, which seems to be relevant for the aetiology of the disease, has not yet been performed.

METHODS

In this study, publicly available raw RNA-Seq data generated from healthy control and sporadic AD post-mortem human samples of the brain frontal lobe were collected and integrated. Sex-specific differential expression analysis was performed on the combined data set after batch effect correction. From the resulting set of differentially expressed genes, candidate mitophagy-related genes were identified based on their known functional roles in mitophagy, the lysosome, or the phagosome, followed by Protein-Protein Interaction (PPI) and microRNA-mRNA network analysis. The expression changes of candidate genes were further validated in human skin fibroblast and induced pluripotent stem cells (iPSCs)-derived cortical neurons from AD patients and matching healthy controls.

RESULTS

From a large dataset (AD: 589; control: 246) based on three different datasets (i.e., ROSMAP, MSBB, & GSE110731), we identified 299 candidate mitophagy-related differentially expressed genes (DEG) in sporadic AD patients (male: 195, female: 188). Among these, the AAA ATPase VCP, the GTPase ARF1, the autophagic vesicle forming protein GABARAPL1 and the cytoskeleton protein actin beta ACTB were selected based on network degrees and existing literature. Changes in their expression were further validated in AD-relevant human models, which confirmed their down-regulation in AD conditions.

CONCLUSION

Through the joint analysis of multiple publicly available data sets, we identify four differentially expressed key mitophagy-related genes potentially relevant for the pathogenesis of sporadic AD. Changes in expression of these four genes were validated using two AD-relevant human models, primary human fibroblasts and iPSC-derived neurons. Our results provide foundation for further investigation of these genes as potential biomarkers or disease-modifying pharmacological targets.

摘要

背景

阿尔茨海默病(AD)患病率不断上升,正成为一项缺乏有效治疗方法的全球健康挑战。线粒体功能缺陷和线粒体自噬最近被认为是AD的病因,与自噬机制(如溶酶体和吞噬体)成分异常有关。已经对AD患者和健康对照者的不同脑区进行了几项大型转录组学研究,其数据是可用于了解这种疾病的重要信息的巨大来源。然而,对这些公开可用数据(如AD RNA测序数据)的大规模整合分析仍然缺失。此外,尚未对似乎与该疾病病因相关的线粒体自噬进行大规模聚焦分析。

方法

在本研究中,收集并整合了从健康对照和散发性AD患者脑额叶的死后人类样本中生成的公开可用原始RNA测序数据。在批次效应校正后,对合并数据集进行性别特异性差异表达分析。从所得的差异表达基因集中,根据其在线粒体自噬、溶酶体或吞噬体中的已知功能作用,鉴定候选线粒体自噬相关基因,随后进行蛋白质-蛋白质相互作用(PPI)和微小RNA-信使核糖核酸网络分析。在来自AD患者和匹配的健康对照的人皮肤成纤维细胞和诱导多能干细胞(iPSC)衍生的皮质神经元中进一步验证候选基因的表达变化。

结果

基于三个不同数据集(即ROSMAP、MSBB和GSE110731)的大型数据集(AD:589;对照:246),我们在散发性AD患者中鉴定出299个候选线粒体自噬相关差异表达基因(DEG)(男性:195个,女性:188个)。其中,基于网络度数和现有文献,选择了AAA三磷酸腺苷酶VCP、鸟苷三磷酸酶ARF1、自噬泡形成蛋白GABARAPL1和细胞骨架蛋白肌动蛋白β ACTB。在与AD相关的人类模型中进一步验证了它们表达的变化,这证实了它们在AD条件下的下调。

结论

通过对多个公开可用数据集的联合分析,我们鉴定出四个差异表达的关键线粒体自噬相关基因,它们可能与散发性AD的发病机制相关。使用两种与AD相关的人类模型(原代人成纤维细胞和iPSC衍生的神经元)验证了这四个基因表达的变化。我们的结果为进一步研究这些基因作为潜在生物标志物或疾病修饰药理学靶点提供了基础。

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