Identification of mitochondrial related signature associated with immune microenvironment in Alzheimer's disease

被引:20
|
作者
Zhang, Yaodan [1 ,2 ]
Miao, Yuyang [1 ,2 ]
Tan, Jin [1 ,2 ]
Chen, Fanglian [4 ]
Lei, Ping [1 ,2 ,3 ]
Zhang, Qiang [1 ,2 ]
机构
[1] Tianjin Med Univ Gen Hosp, Dept Geriatr, Anshan Rd 154, Tianjin 300052, Peoples R China
[2] Tianjin Geriatr Inst, Anshan Rd 154, Tianjin 300052, Peoples R China
[3] Tianjin Med Univ Gen Hosp, Haihe Lab Cell Ecosyst, Anshan Rd 154, Tianjin 300052, Peoples R China
[4] Tianjin Med Univ Gen Hosp, Tianjin Neurol Inst, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
Alzheimer's disease; Mitochondria; Immune infiltration; Bioinformatics analysis; AMYLOID-BETA; PROTECTS; OVEREXPRESSION; HSP70/HSP75;
D O I
10.1186/s12967-023-04254-9
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disease. Mitochondrial dysfunction and immune responses are important factors in the pathogenesis of AD, but their crosstalk in AD has not been studied. In this study, the independent role and interaction of mitochondria-related genes and immune cell infiltration in AD were investigated using bioinformatics methods.MethodsThe datasets of AD were obtained from NCBI Gene Expression Omnibus (GEO), and the data of mitochondrial genes was from MitoCarta3.0 database. Subsequently, differential expression genes (DEGs) screening and GSEA functional enrichment analysis were performed. The intersection of DEGs and mitochondrial related genes was used to obtain MitoDEGs. The MitoDEGs most relevant to AD were determined by Least absolute shrinkage and selection operator and multiple support vector machine recursive feature elimination, as well as protein-protein interactions (PPI) network and random forest. The infiltration of 28 kinds of immune cells in AD was analyzed by ssGSEA, and the relationship between hub MitoDEGs and the proportion of immune infiltration was studied. The expression levels of hub MitoDEGs were verified in cell models and AD mice, and the role of OPA1 in mitochondrial damage and neuronal apoptosis was investigated.ResultsThe functions and pathways of DEGs were significantly enriched in AD, including immune response activation, IL1R pathway, mitochondrial metabolism, oxidative damage response and electron transport chain-oxphos system in mitochondria. Hub MitoDEGs closely related to AD were obtained based on PPI network, random forest and two machine learning algorithms. Five hub MitoDEGs associated with neurological disorders were identified by biological function examination. The hub MitoDEGs were found to be correlated with memory B cell, effector memory CD8 T cell, activated dendritic cell, natural killer T cell, type 17 T helper cell, Neutrophil, MDSC, plasmacytoid dendritic cell. These genes can also be used to predict the risk of AD and have good diagnostic efficacy. In addition, the mRNA expression levels of BDH1, TRAP1, OPA1, DLD in cell models and AD mice were consistent with the results of bioinformatics analysis, and expression levels of SPG7 showed a downward trend. Meanwhile, OPA1 overexpression alleviated mitochondrial damage and neuronal apoptosis induced by A & beta;1-42.ConclusionsFive potential hub MitoDEGs most associated with AD were identified. Their interaction with immune microenvironment may play a crucial role in the occurrence and prognosis of AD, which provides a new insight for studying the potential pathogenesis of AD and exploring new targets.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer’s disease
    Chenming Liu
    Sutong Xu
    Qiulu Liu
    Huazhen Chai
    Yuping Luo
    Siguang Li
    BMC Medical Genomics, 16
  • [42] Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer's disease
    Liu, Chenming
    Xu, Sutong
    Liu, Qiulu
    Chai, Huazhen
    Luo, Yuping
    Li, Siguang
    BMC MEDICAL GENOMICS, 2023, 16 (01)
  • [43] Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease
    Zhu, Min
    Hou, Tingting
    Jia, Longfei
    Tan, Qihua
    Qiu, Chengxuan
    Du, Yifeng
    NEUROBIOLOGY OF AGING, 2023, 125 : 62 - 73
  • [44] An immune-cell signature marks the brain in Alzheimer’s disease
    Michael T. Heneka
    Nature, 2020, 577 (7790) : 322 - 323
  • [45] Mitochondrial Haplotypes Associated with Biomarkers for Alzheimer's Disease
    Ridge, Perry G.
    Koop, Andre
    Maxwell, Taylor J.
    Bailey, Matthew H.
    Swerdlow, Russell H.
    Kauwe, John S. K.
    Honea, Robyn A.
    PLOS ONE, 2013, 8 (09):
  • [46] Identification of the Prognostic Signature Associated With Tumor Immune Microenvironment of Uterine Corpus Endometrial Carcinoma Based on Ferroptosis-Related Genes
    Liu, Jinhui
    Wang, Yichun
    Meng, Huangyang
    Yin, Yin
    Zhu, Hongjun
    Ni, Tingting
    FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2021, 9
  • [47] Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer's disease
    Ma, Wenhao
    Su, Yuelin
    Zhang, Peng
    Wan, Guoqing
    Cheng, Xiaoqin
    Lu, Changlian
    Gu, Xuefeng
    FRONTIERS IN MOLECULAR NEUROSCIENCE, 2023, 16
  • [48] Identification of a pyroptosis-related prognosis gene signature and its relationship with an immune microenvironment in gliomas
    Xiao, Shengying
    Yan, Zhiguang
    Zeng, Furen
    Lu, Yichen
    Qiu, Jun
    Zhu, Xiaodong
    MEDICINE, 2022, 101 (28) : E29391
  • [49] Identification of Prognostic Glycolysis-Related lncRNA Signature in Tumor Immune Microenvironment of Hepatocellular Carcinoma
    Bai, Yang
    Lin, Haiping
    Chen, Jiaqi
    Wu, Yulian
    Yu, Shi'an
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2021, 8
  • [50] Identification of immune-related gene signature predicting survival in the tumor microenvironment of lung adenocarcinoma
    Mengnan Zhao
    Ming Li
    Zhencong Chen
    Yunyi Bian
    Yuansheng Zheng
    Zhengyang Hu
    Jiaqi Liang
    Yiwei Huang
    Jiacheng Yin
    Cheng Zhan
    Mingxiang Feng
    Qun Wang
    Immunogenetics, 2020, 72 : 455 - 465