Weng Weipin, Fu Jianhan, Cheng Fan, Wang Yixuan, Zhang Jie
Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China.
Department of Neurology, Center for Cognitive Neurology, Fujian Medical University Union Hospital, Fuzhou, China.
Mol Neurobiol. 2024 Aug;61(8):6013-6030. doi: 10.1007/s12035-023-03907-6. Epub 2024 Jan 24.
Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.
尽管越来越多的证据表明阿尔茨海默病(AD)与昼夜节律紊乱(CRD)之间存在密切关联,但很少有研究关注昼夜节律对AD患者免疫细胞水平的影响。我们旨在阐明昼夜节律相关基因对AD患者T细胞免疫功能影响的潜在机制。通过加权基因共表达网络分析和机器学习算法(LASSO、支持向量机递归特征消除法和随机森林),共使用112个脑样本构建了与CRD相关的模型。采用单样本基因集富集分析(ssGSEA)方法计算CRD评分,以量化CRD状态。利用脑脊液细胞的单细胞转录组数据,我们研究了高风险和低风险CRD组中CD4+T细胞的代谢及细胞间通讯。应用连通性图谱(CMap)探索靶向CRD的小分子药物,并在AD中进一步验证标志性基因GPR4的表达。基于23个昼夜节律相关基因的CRD评分算法能够有效区分AD数据集中的CRD状态。单细胞分析显示,CRD评分高的CD4+T细胞具有代谢减退的特征。细胞通讯分析表明,CRD状态下CD4+T细胞可能参与促进CD8+T细胞黏附,这可能有助于T细胞浸润至脑实质。总体而言,本研究揭示了AD中昼夜节律的潜在内涵,为理解CRD状态下T细胞代谢重编程提供了思路。