Identify 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 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 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 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 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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[1] Jauhar S,Johnstone M,McKenna P J. Schizophrenia[J]. Lancet,2022,399(10323):473-486. doi: 10.1016/S0140-6736(21)01730-X [2] Sullivan P F,Yao S,Hjerling-Leffler J. Schizophrenia genomics: Genetic complexity and functional insights[J]. Nat Rev Neurosci,2024,25(9):611-624. doi: 10.1038/s41583-024-00837-7 [3] Momozawa Y,Mizukami K. Unique roles of rare variants in the genetics of complex diseases in humans[J]. J Hum Genet,2021,66(1):11-23. doi: 10.1038/s10038-020-00845-2 [4] Owen M J,Legge S E,Rees E,et al. Genomic findings in schizophrenia and their implications[J]. Mol Psychiatry,2023,28(9):3638-3647. doi: 10.1038/s41380-023-02293-8 [5] Roberts R C. Mitochondrial dysfunction in schizophrenia: With a focus on postmortem studies[J]. Mitochondrion,2021,56(1):91-101. [6] Ni P,Chung S. Mitochondrial dysfunction in schizophrenia[J]. Bioessays,2020,42(6):e1900202. doi: 10.1002/bies.201900202 [7] Chen W,Zhao H,Li Y. Mitochondrial dynamics in health and disease: Mechanisms and potential targets[J]. Sig Transduct Target Ther,2023,8(1):333. doi: 10.1038/s41392-023-01547-9 [8] Di Rienzo M,Romagnoli A,Refolo G,et al. Role of AMBRA1 in mitophagy regulation: Emerging evidence in aging-related diseases[J]. Autophagy,2024,20(12):1-14. [9] Roberts R C. Mitochondrial dysfunction in schizophrenia: With a focus on postmortem studies[J]. Mitochondrion,2021,56:91-101. doi: 10.1016/j.mito.2020.11.009 [10] Andreazza A C,Nierenberg A A. Mitochondrial dysfunction: At the core of psychiatric disorders?[J]. Biol Psychiatry,2018,83(9):718-719. doi: 10.1016/j.biopsych.2018.03.004 [11] Zhu Y,Zhang J,Deng Q,et al. Mitophagy-associated programmed neuronal death and neuroinflammation[J]. Front Immunol,2024,15(13):1460286. [12] Shivakumar V,Rajasekaran A,Subbanna M,et al. Leukocyte mitochondrial DNA copy number in schizophrenia[J]. Asian J Psychiatr,2020,53(89):102193. [13] Sebastian R,Song Y,Pak C. Probing the molecular and cellular pathological mechanisms of schizophrenia using human induced pluripotent stem cell models[J]. Schizophr Research,2024,22(1):1-42 [14] Newman A M,Liu C L,Green M R,et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods,2015,12(5):453-457. doi: 10.1038/nmeth.3337 [15] Zeng D,Ye Z,Shen R,et al. IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures[J]. Frontiers Immunology,2021,12(10):687975. [16] Simonsen A T,Hansen M C,Kjeldsen E,et al. Systematic evaluation of signal-to-noise ratio in variant detection from single cell genome multiple displacement amplification and exome sequencing[J]. BMC Genomics,2018,19(1):681. doi: 10.1186/s12864-018-5063-5 [17] Trigo D,Vit ó ria J J,da Cruz E Silva O A B. Novel therapeutic strategies targeting mitochondria as a gateway in neurodegeneration[J]. Neural Regeneration Research,2023,18(5):991-995. doi: 10.4103/1673-5374.355750 [18] Zhao Y,Shen W,Zhang M,et al. DDAH-1 maintains endoplasmic reticulum-mitochondria contacts and protects dopaminergic neurons in Parkinson's disease[J]. Cell Death Disease,2024,15(6):399. doi: 10.1038/s41419-024-06772-w [19] Ma K,Chen G,Li W,et al. Mitophagy,mitochondrial homeostasis,and cell fate[J]. Front Cell Dev Biol,2020,24(8):467. [20] Papageorgiou M P,Filiou M D. Mitochondrial dynamics and psychiatric disorders: The missing link[J]. Neurosci Biobehav Rev,2024,165(10):105837. [21] Chen S,Sarasua S M,Davis N J,et al. TOMM40 genetic variants associated with healthy aging and longevity: A systematic review[J]. BMC Geriatr,2022,22(1):667. doi: 10.1186/s12877-022-03337-4 [22] Choudhury M,Fu T,Amoah K,et al. Widespread RNA hypoediting in schizophrenia and its relevance to mitochondrial function[J]. Sci Adv,2023,9(14):eade9997. doi: 10.1126/sciadv.ade9997 [23] Bonam S R,Bayry J,Tschan M P,et al. Progress and challenges in the use of MAP1LC3 as a legitimate marker for measuring dynamic autophagy in vivo[J]. Cells,2020,9(5):1321. doi: 10.3390/cells9051321 [24] Liang M Z,Lu T H,Chen L. Timely expression of PGAM5 and its cleavage control mitochondrial homeostasis during neurite re-growth after traumatic brain injury[J]. Cell Biosci,2023,13(1):96. doi: 10.1186/s13578-023-01052-0 [25] Cheng M,Lin N,Dong D,et al. PGAM5: A crucial role in mitochondrial dynamics and programmed cell death[J]. Eur J Cell Biol,2021,100(1):151144. doi: 10.1016/j.ejcb.2020.151144 [26] Lystad A H,Carlsson S R,Simonsen A. Toward the function of mammalian ATG12-ATG5-ATG16L1 complex in autophagy and related processes[J]. Autophagy,2019,5(8):1485-1486. [27] Yang K,Yu B,Cheng C,et al. Mir505-3p regulates axonal development via inhibiting the autophagy pathway by targeting ATG12[J]. Autophagy,2017,13(10):1679-1696. doi: 10.1080/15548627.2017.1353841 期刊类型引用(11)
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9. 白璧辉,谢兴文,李鼎鹏,许伟,李宁,潘鑫戊. 我国近5年来骨质疏松症流行病学研究现状. 中国骨质疏松杂志. 2018(02): 253-258 . 百度学术
10. 章镇南,吴斌,董忠. 福州地区老年骨折患者回顾性分析. 中国骨质疏松杂志. 2018(07): 949-953 . 百度学术
11. 朱星华,郭勇,张智海. 社区中老年居民骨质疏松症认知度调查. 中国现代医生. 2017(22): 117-120+133 . 百度学术
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