使用生物信息学分析研究 1 型糖尿病与轻度认知障碍之间的分子机制,重点关注免疫反应。

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response.

机构信息

Department of Pathology and Pathophysiology, Faculty of Basic Medical Sciences, Kunming Medical University, Kunming, PR China.

Department of Biochemistry and Molecular Biology, Faculty of Basic Medical Sciences, Kunming Medical University, Kunming, PR China.

出版信息

Int Immunopharmacol. 2024 Dec 5;142(Pt B):113256. doi: 10.1016/j.intimp.2024.113256. Epub 2024 Sep 27.

Abstract

The immune system is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive impairment (MCI) remains unclear. This study investigated the underlying immune response mechanisms that contribute to the development and progression of T1DM and MCI. Microarray datasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. To explore the functional implications of these DEGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using ClusterProfiler. Protein-protein interaction networks for the DEGs were constructed using the STRING database and visualized using Cytoscape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell method. Gene set enrichment analysis was used to gain in-depth insights into the functional characteristics of T1DM and MCI. Immune-related genes were obtained from the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosis, and the diagnostic value was assessed using the receiver operating characteristic curve. The identified genes were validated using quantitative polymerase chain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, of which 232 and 378 were upregulated and downregulated, respectively. Immune response analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expression increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnected disorders. These findings will facilitate the development of therapeutic interventions and identification of potential therapeutic targets.

摘要

免疫系统参与了多种疾病的发展和进程。1 型糖尿病(T1DM)是一种自身免疫性疾病,会影响多种其他疾病的进展;然而,T1DM 与轻度认知障碍(MCI)之间的联系尚不清楚。本研究旨在探讨导致 T1DM 和 MCI 发生和发展的潜在免疫反应机制。从基因表达综合数据库中检索到 MCI(GSE63060)和 T1DM(GSE30208)的微阵列数据集。使用 limma 包鉴定差异表达基因(DEGs)。为了探讨这些 DEGs 的功能意义,使用 ClusterProfiler 进行基因本体论和京都基因与基因组百科全书通路富集分析。使用 STRING 数据库构建 DEGs 的蛋白质-蛋白质相互作用网络,并使用 Cytoscape 可视化。使用分子复合物检测算法分析 DEGs。使用 xCell 方法分析 T1DM 和 MCI 患者的免疫细胞浸润情况。使用基因集富集分析深入了解 T1DM 和 MCI 的功能特征。从 GeneCards 和 ImmPort 数据库中获取免疫相关基因。使用机器学习算法识别与 T1DM 和 MCI 诊断相关的潜在免疫相关基因,并使用接收者操作特征曲线评估诊断价值。使用定量聚合酶链反应验证鉴定的基因。在 T1DM 和 MCI 数据集,610 个 DEGs 表现出一致的趋势,其中 232 个上调,378 个下调。免疫反应分析显示与 MCI 和 T1DM 相关的免疫细胞发生显著变化。使用免疫相关基因、DEGs 和机器学习技术,我们在 MCI 和 T1DM 组中鉴定出 CD3D。随着疾病的进展,我们观察到 T1DM 小鼠的认知功能逐渐下降。CD3D 表达随着疾病严重程度的增加而增加;CD3D 主要影响 CD4+T 细胞。本研究揭示了 T1DM 和 MCI 之间的复杂相互作用,为免疫失调与认知障碍之间的复杂关系提供了新的见解,并扩展了我们对这两种相互关联疾病的认识。这些发现将有助于开发治疗干预措施,并确定潜在的治疗靶点。

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