Exploring Key Mitochondrial Autophagy Genes in Schizophrenia through Integrated Bioinformatics Approaches
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摘要:
目的 利用3D脑类器官的单细胞及外周血转录组数据，结合机器学习，深入分析线粒体自噬基因在精神分裂症（schizophrenia，SCZ）中的作用。 方法 结合两种机器学习算法，通过外周血RNA测序数据，识别精神分裂症和健康对照组之间表达存在差异的线粒体自噬相关基因，探讨线粒体自噬基因与免疫细胞和炎症因子间的相互关系；利用单细胞综合分析，探讨基于线粒体自噬基因的信号通路和特异1性转录因子。 结果 通过机器学习，鉴定了7个在精神分裂症患者中表达的关键线粒体自噬基因。基于Mitoscore分析，在单细胞层面，现高线粒体自噬活性的神经元（Mitohigh_Neuron）通过SPP1信号通路与内皮细胞形成新的相互作用。 结论 鉴定了精神分裂症患者中两种具有线粒体自噬特征的亚型及7个关键线粒体自噬基因，为理解该病的发病机制提供新的视角。 Abstract:Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids, combined with machine learning, to analyze the role of mitochondrial autophagy genes in schizophrenia （SCZ）. Methods By integrating two machine learning algorithms, we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data. The relationship between mitophagy gene, immune cells and inflammatory factors was further explored. Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes. Results Using machine learning, seven key mitophagy genes expressed in schizophrenia patients were identified. Based on Mitoscore analysis, at the single-cell level, neurons with high mitochondrial autophagy activity （Mitohigh_Neuron） formed new interactions with endothelial cells via the SPP1 signaling pathway. Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia, providing new insights into the pathogenesis of the disease. -
Key words:
- Schizophrenia /
- Mitochondrial autophagy /
- Neurons /
- Single-cell sequencing /
- Machine learning
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图 1 在SCZ中表达的24个线粒体自噬基因分析
A：SCZ组与CT组24个线粒体自噬基因热图；B：SCZ组与CT组差异表达基因集与线粒体自噬基因集交集火山图；C：SCZ组与CT组24个线粒体自噬基因的条形图。*P < 0.05；**P < 0.01；***P < 0.001。
Figure 1. Analysis of 24 mitochondrial autophagy genes expressed in SCZ
图 2 线粒体自噬基因相互作用关系图
A：24个显著差异线粒体自噬基因的热图；B：6个显著差异表达线粒体自噬基因的热图；C：CSNK2B与TOOM40的相关性；D：CSNK2B与TOOM20的相关性；E：CSNK2B与MAP1LC3A的相关性；F：CSNK2B与MAP1LC3B的相关性；G：CSNK2B与MFN1的相关性图。
Figure 2. Diagram of mitophagy gene interactions
图 3 基于机器学习获得的7个关键线粒体自噬基因建立的诊断模型和OR值
A：由7个关键线粒体自噬基因建立的诊断模型的ROC曲线；B：由7个关键线粒体自噬基因建立的诊断模型OR值的森林图。
Figure 3. Diagnostic model established based on seven key mitochondrial autophagy genes obtained through machine learning and their odds ratios （OR values）
图 4 分析线粒体自噬基因与免疫细胞浸润的关系。
A：24个线粒体自噬基因与19个免疫细胞的CIBERSORT相关性分析热图；B：7个关键线粒体自噬基因与19个免疫细胞的CIBERSORT相关性热图；C～I：7个关键线粒体自噬基因：MFN1，TOMM40，MAP1LC3B，CSNK2A2，PGAM5，CSNK2B，ATG12和Neutrophils_MCPcounter丰度的相关性分析。*P < 0.05；**P < 0.01；***P < 0.001。
Figure 4. Analysis of the relationship between mitophagy genes and immune cell infiltration.
图 5 7个关键线粒体自噬基因与28个炎症因子的相关性热图分析
*P < 0.05；**P < 0.01；***P < 0.001。
Figure 5. Heat map analysis of correlation between 7 top mitophagy genes and 28 inflammatory factors
图 6 基于线粒体自噬基因表达和免疫谱的不同亚型SCZ患者分析
A：两种亚型中24个线粒体自噬基因的相关热图；B：两种亚型的年龄相关性分析；C：两种亚型的性别相关分析；D：两种亚型核心基因构建的临床诊断预测模型的相关性分析；E：两种亚型中24个线粒体自噬基因差异表达分析柱状图；F：两种亚型免疫细胞浸润评分的差异分析；G：炎症因子在两种亚型中的差异表达分析。*P < 0.05；**P < 0.01；***P < 0.001。
Figure 6. Analysis of different subgroups of SCZ patients based on Characteristics of mitophagy gene expression and immunologic profile
图 7 WGCNA和fGSEA分析结果
A：经过一致性评分，将SCZ样本分为两种稳定类型：Cluster1和Cluster2；B：WGCNA分析的尺度独立性（左图）和平均连通性（右图）；C：基因聚类图；D：12个模块与两类SCZ样本之间的特征关联图；E：red模块中关键基因的GO分析条形图；F：两类SCZ样品生物过程富集分析聚类图。
Figure 7. Results of WGCNA and fGSEA analysis
图 8 SCZ与CT供体来源的3D脑类器官的单细胞特征比较
A：CT和SCZ的单细胞簇结果鉴定出19个细胞簇的UMAP图；B：每个亚簇中显著标记基因表达谱的气泡图；C：单细胞主成分分析主要集中在6个成分：星形胶质细胞、神经元、增殖细胞、内皮细胞、少突胶质细胞和髓系细胞；D：神经元线粒体自噬评分的两个UMAP图；E：SCZ组和CT组神经元亚群线粒体自噬评分差异有统计学意义（P < 0.0001）；F：神经元单细胞降维注释得到的12个神经元细胞亚群；G：12个神经细胞亚群线粒体自噬谱小提琴图；H：对线粒体自噬评分的神经元亚群进行逆向时序分析，线粒体自噬评分高的第9组主要位于发育轨迹的末端。
Figure 8. Single-cell profiles of SCZ versus CT donor-derived 3D brain organoids
图 9 特异性转录因子亚细胞簇的差异分布
A：高线粒体自噬评分的第9簇和低线粒体自噬评分的第11簇；B：7个特定转录因子在亚细胞簇9和11中的分布差异。
Figure 9. Differential distributionof subcellular clusters of specific transcription factors.
图 10 细胞通讯结果
A：细胞群之间相互作用数量的统计分析。向外的箭头表示表达配体的细胞，指向配体的箭头表示表达受体的细胞；B：交互作用的概率/强度值（强度是概率值的总和）；C：在气泡图中显示多个配体-受体介导的细胞关系之间的相互作用；D：细胞信号传导模式，横轴为细胞类型，纵轴为通路；E：配体-受体信号通路CALCR、VISFATIN、SPP1介导的细胞间相互作用热图；F：细胞亚组中显著差异转录因子的曲线下面积（AUC）值热图；G：两个细胞亚群中显著差异转录因子的平均调控活性；H：两个细胞亚群特异性转录因子评分指标散点图。
Figure 10. Cell communication results
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