Zhang Xi, Shao Zhihua, Xu Sutong, Liu Qiulu, Liu Chenming, Luo Yuping, Jin Lingjing, Li Siguang
Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Neurology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Front Aging Neurosci. 2021 Feb 9;13:605970. doi: 10.3389/fnagi.2021.605970. eCollection 2021.
Parkinson's disease (PD) is an age-related and second most common neurodegenerative disorder. In recent years, increasing evidence revealed that peripheral immune cells might be able to infiltrate into brain tissues, which could arouse neuroinflammation and aggravate neurodegeneration. This study aimed to illuminate the landscape of peripheral immune cells and signature genes associated with immune infiltration in PD. Several transcriptomic datasets of substantia nigra (SN) from the Gene Expression Omnibus (GEO) database were separately collected as training cohort, testing cohort, and external validation cohort. The immunoscore of each sample calculated by single-sample gene set enrichment analysis was used to reflect the peripheral immune cell infiltration and to identify the differential immune cell types between PD and healthy participants. According to receiver operating characteristic (ROC) curve analysis, the immunoscore achieved an overall accuracy of the area under the curve (AUC) = 0.883 in the testing cohort, respectively. The immunoscore displayed good performance in the external validation cohort with an AUC of 0.745. The correlation analysis and logistic regression analysis were used to analyze the correlation between immune cells and PD, and mast cell was identified most associated with the occurrence of PD. Additionally, increased mast cells were also observed in our PD model. Weighted gene co-expression network analysis (WGCNA) was used to selected module genes related to a mast cell. The least absolute shrinkage and selection operator (LASSO) analysis and random-forest analysis were used to analyze module genes, and two hub genes RBM3 and AGTR1 were identified as associated with mast cells in the training cohort. The expression levels of RBM3 and AGTR1 in these cohorts and PD models revealed that these hub genes were significantly downregulated in PD. Moreover, the expression trend of the aforementioned two genes differed in mast cells and dopaminergic (DA) neurons. In conclusion, this study not only exhibited a landscape of immune infiltrating patterns in PD but also identified mast cells and two hub genes associated with the occurrence of PD, which provided potential therapeutic targets for PD patients (PDs).
帕金森病（PD）是一种与年龄相关的、第二常见的神经退行性疾病。近年来，越来越多的证据表明外周免疫细胞可能能够浸润到脑组织中，这可能引发神经炎症并加重神经退行性变。本研究旨在阐明帕金森病中外周免疫细胞的概况以及与免疫浸润相关的特征基因。从基因表达综合数据库（GEO）中分别收集了几个黑质（SN）的转录组数据集作为训练队列、测试队列和外部验证队列。通过单样本基因集富集分析计算的每个样本的免疫评分用于反映外周免疫细胞浸润，并识别帕金森病患者与健康参与者之间差异的免疫细胞类型。根据受试者工作特征（ROC）曲线分析，免疫评分在测试队列中的曲线下面积（AUC）总体准确率分别为0.883。免疫评分在外部验证队列中表现良好，AUC为0.745。采用相关性分析和逻辑回归分析来分析免疫细胞与帕金森病之间的相关性，发现肥大细胞与帕金森病的发生最为相关。此外，在我们的帕金森病模型中也观察到肥大细胞增加。使用加权基因共表达网络分析（WGCNA）来选择与肥大细胞相关的模块基因。采用最小绝对收缩和选择算子（LASSO）分析和随机森林分析来分析模块基因，并在训练队列中确定了两个与肥大细胞相关的枢纽基因RBM3和AGTR1。这些队列和帕金森病模型中RBM3和AGTR1的表达水平表明，这些枢纽基因在帕金森病中显著下调。此外，上述两个基因在肥大细胞和多巴胺能（DA）神经元中的表达趋势不同。总之，本研究不仅展示了帕金森病中免疫浸润模式的概况，还确定了与帕金森病发生相关的肥大细胞和两个枢纽基因，为帕金森病患者提供了潜在的治疗靶点。
