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

Abstract

Background Alzheimer'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. Methods The 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. Results The 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 Aβ1-42. Conclusions Five 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.

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning

“…Using the R package “rms,” a nomogram model ( 28 ) based on the differentially expressed hub MitoDEGs was built to calculate the diagnosis probability of AD patients. The receiver operating characteristic (ROC) curve analysis was used to show the prediction performance of hub MitoDEGs and risk score.…”

The Influence of Myeloid-Derived Suppressor Cell Expansion in Neuroinflammation and Neurodegenerative Diseases

“…The study of the peculiar gene expression of these cell populations allowed us to identify five hub mitochondria-related differential expression genes MitoDEGs (BDH1, TRAP1, OPA1, DLD, SPG7 genes) which represent potential pathological biomarkers. Their mRNA expression levels, except for SPG7, decreased in AD and negatively correlated with immune cell infiltration in the brain tissue [27].…”

“…The search method employed in this review produced six results. One consists of bioinformatic research to highlight peculiar gene expression in AD cases [27]. Two papers investigated MDSC expansion in AD patients and looked at their influence on T cells [28,29].…”

A review and analysis of key biomarkers in Alzheimer’s disease

“…Enrichment analysis of these hub genes revealed disruptions in mitochondrial complexes in the context of AD pathogenesis. Zhang et al (2023a) used PPI network, random forest, and two machine learning algorithms to obtain hub mitochondrial-related differentially expressed genes (MitoDEGs) closely related to AD. Zhao et al (2023) identified six mitophagy-related hub genes (MRHGs) that be used as valuable diagnostic biomarkers for AD.…”