Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer's disease and glioblastoma.

IF 4.5 2区 医学 Q2 GERIATRICS & GERONTOLOGY Frontiers in Aging Neuroscience Pub Date : 2025-02-13 eCollection Date: 2025-01-01 DOI:10.3389/fnagi.2025.1536142
Xuan Xu, Jiaqi Wang, Tong Chen, Shuaibin Wang, Fei Wang, Junwen He, Xiang-Yu Meng, Yin Shen
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Abstract

Introduction: Alzheimer's disease (AD) and glioblastoma (GBM) are severe neurological disorders that pose significant global healthcare challenges. Despite extensive research, the molecular mechanisms, particularly those involving mitochondrial dysfunction, remain poorly understood. A major limitation in current studies is the lack of cell-specific markers that effectively represent mitochondrial dynamics in AD and GBM.

Methods: In this study, we analyzed single-cell transcriptomic data using 10 machine learning algorithms to identify mitochondria-associated cell-specific markers. We validated these markers through the integration of gene expression and methylation data across diverse cell types. Our dataset comprised single-nucleus RNA sequencing (snRNA-seq) from AD patients, single-cell RNA sequencing (scRNA-seq) from GBM patients, and additional DNA methylation and transcriptomic data from the ROSMAP, ADNI, TCGA, and CGGA cohorts.

Results: Our analysis identified four significant cross-disease mitochondrial markers: EFHD1, SASH1, FAM110B, and SLC25A18. These markers showed both shared and unique expression profiles in AD and GBM, suggesting a common mitochondrial mechanism contributing to both diseases. Additionally, oligodendrocytes and their interactions with astrocytes were implicated in disease progression, particularly through the APP signaling pathway. Key hub genes, such as HS6ST3 and TUBB2B, were identified across different cellular subpopulations, highlighting a cell-specific co-expression network linked to mitochondrial function.

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破译新的线粒体特征:多组学分析揭示了阿尔茨海默病和胶质母细胞瘤的跨疾病标记和少突胶质细胞通路。
阿尔茨海默病(AD)和胶质母细胞瘤(GBM)是严重的神经系统疾病,对全球医疗保健构成重大挑战。尽管进行了广泛的研究,但分子机制,特别是涉及线粒体功能障碍的分子机制,仍然知之甚少。目前研究的一个主要限制是缺乏有效代表AD和GBM线粒体动力学的细胞特异性标记物。方法:在本研究中,我们使用10种机器学习算法分析单细胞转录组数据,以识别线粒体相关的细胞特异性标记。我们通过整合不同细胞类型的基因表达和甲基化数据来验证这些标记。我们的数据集包括来自AD患者的单核RNA测序(snRNA-seq),来自GBM患者的单细胞RNA测序(scRNA-seq),以及来自ROSMAP、ADNI、TCGA和CGGA队列的额外DNA甲基化和转录组数据。结果:我们的分析确定了四种重要的跨疾病线粒体标记:EFHD1、SASH1、FAM110B和SLC25A18。这些标记在AD和GBM中显示出共同和独特的表达谱,表明这两种疾病有共同的线粒体机制。此外,少突胶质细胞及其与星形胶质细胞的相互作用与疾病进展有关,特别是通过APP信号通路。关键枢纽基因,如HS6ST3和TUBB2B,在不同的细胞亚群中被鉴定出来,突出了与线粒体功能相关的细胞特异性共表达网络。
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来源期刊
Frontiers in Aging Neuroscience
Frontiers in Aging Neuroscience GERIATRICS & GERONTOLOGY-NEUROSCIENCES
CiteScore
6.30
自引率
8.30%
发文量
1426
期刊介绍: Frontiers in Aging Neuroscience is a leading journal in its field, publishing rigorously peer-reviewed research that advances our understanding of the mechanisms of Central Nervous System aging and age-related neural diseases. Specialty Chief Editor Thomas Wisniewski at the New York University School of Medicine is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
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