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解析精神分裂症、双相情感障碍和重度抑郁症中的转录组特征。

Deciphering transcriptomic signatures in schizophrenia, bipolar disorder, and major depressive disorder.

作者信息

Kumar Rajesh, Kumar Vinod, Kumar Ashwani, Rana Sandeep Singh

机构信息

Department of Applied Psychology, Guru Jambheshwar University of Science & Technology, Hisar, India.

CSIR - Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India.

出版信息

Front Psychiatry. 2025 Jul 14;16:1574458. doi: 10.3389/fpsyt.2025.1574458. eCollection 2025.


DOI:10.3389/fpsyt.2025.1574458
PMID:40727846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12301984/
Abstract

Schizophrenia (SCZ), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) are severe psychiatric conditions that share overlapping clinical symptoms, yet they differ in their underlying molecular mechanisms. Despite extensive research, the biological foundations of these disorders remain incompletely understood. In this study, we performed a large-scale transcriptomic analysis by integrating 557 publicly available RNA-seq datasets from post-mortem brain tissues, spanning multiple regions, to better understand the shared and distinct molecular features of these disorders. Using systematic bioinformatic approaches, we identified differentially expressed genes (DEGs) and investigated associated biological pathways, regulatory transcription factors, and drug-gene interactions. Our analysis revealed notable overlap in gene expression profiles, particularly between SCZ and BD, suggesting common molecular pathways underlying these disorders. At the same time, each disorder also demonstrated unique transcriptional patterns, supporting the existence of disorder-specific mechanisms. Brain region-specific analyses further highlighted spatial heterogeneity in gene expression, with significant differences observed in regions such as the hippocampus and dorsolateral prefrontal cortex (DLPFC). The transcription factor enrichment analysis revealed distinct regulatory programs driving each disorder: MDD pathology appears regulated by ASCL3, MYOG, HNF1B, RUNX3, FOXA1 and STAT4; BD exhibited predominant control by immune-regulatory factors including FOSL1, FOSL2, PLSCR1, RELB, BATF3, IRF and NFKB1; while SCZ demonstrated unique regulation through ATF5, CREB3L3, SNAI1, NFIL3, CEBPB, RELB and IRF transcription factors. Moreover, our drug-gene interaction analysis uncovered promising therapeutic targets, with several differentially expressed genes showing potential for drug repurposing, particularly in relation to antipsychotics and immunomodulatory agents. Our comprehensive transcriptomic analysis reveals both shared molecular mechanisms and distinct immune signatures across schizophrenia, bipolar disorder, and major depressive disorder, advancing our understanding of psychiatric pathophysiology while highlighting the heterogeneous nature of these conditions. These findings establish a critical foundation for developing targeted, patient-specific therapeutic interventions that address the underlying biological complexity of major psychiatric disorders.

摘要

精神分裂症(SCZ)、双相情感障碍(BD)和重度抑郁症(MDD)是严重的精神疾病,它们具有重叠的临床症状,但潜在的分子机制有所不同。尽管进行了广泛的研究,但这些疾病的生物学基础仍未完全了解。在本研究中,我们通过整合来自多个脑区尸检脑组织的557个公开可用的RNA测序数据集进行了大规模转录组分析,以更好地了解这些疾病的共同和独特分子特征。使用系统的生物信息学方法,我们鉴定了差异表达基因(DEG),并研究了相关的生物学途径、调控转录因子和药物-基因相互作用。我们的分析揭示了基因表达谱中的显著重叠,特别是在SCZ和BD之间,这表明这些疾病存在共同的分子途径。同时,每种疾病也表现出独特的转录模式,支持了疾病特异性机制的存在。脑区特异性分析进一步突出了基因表达的空间异质性,在海马体和背外侧前额叶皮质(DLPFC)等区域观察到显著差异。转录因子富集分析揭示了驱动每种疾病的不同调控程序:MDD病理似乎受ASCL3、MYOG、HNF1B、RUNX3、FOXA1和STAT4调控;BD主要受包括FOSL1、FOSL2、PLSCR1、RELB、BATF3、IRF和NFKB1在内的免疫调节因子控制;而SCZ通过ATF5、CREB3L3、SNAI1、NFIL3、CEBPB、RELB和IRF转录因子表现出独特的调控。此外,我们的药物-基因相互作用分析发现了有前景的治疗靶点,几个差异表达基因显示出药物重新利用的潜力,特别是与抗精神病药物和免疫调节药物有关。我们的综合转录组分析揭示了精神分裂症、双相情感障碍和重度抑郁症之间的共同分子机制和独特的免疫特征,在推进我们对精神病理生理学理解的同时,突出了这些疾病的异质性。这些发现为开发针对主要精神疾病潜在生物学复杂性的靶向、个性化治疗干预措施奠定了关键基础。

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本文引用的文献

[1]
Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database.

IBRO Neurosci Rep. 2024-7-21

[2]
Single-cell genomics and regulatory networks for 388 human brains.

Science. 2024-5-24

[3]
Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents.

Transl Psychiatry. 2024-3-26

[4]
Large-scale animal model study uncovers altered brain pH and lactate levels as a transdiagnostic endophenotype of neuropsychiatric disorders involving cognitive impairment.

Elife. 2024-3-26

[5]
Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review.

Brief Bioinform. 2024-1-22

[6]
DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

Nucleic Acids Res. 2024-1-5

[7]
GRIN2A (NR2A): a gene contributing to glutamatergic involvement in schizophrenia.

Mol Psychiatry. 2023-9

[8]
Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings.

Transl Psychiatry. 2023-4-19

[9]
Evaluation of Biological Variation of Different Clinical Laboratory Analytes in the Blood of Healthy Subjects.

Cureus. 2023-3-16

[10]
Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment.

Mol Psychiatry. 2023-5

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