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探索重度抑郁症和双相情感障碍中的基因调控相互作用网络并预测治疗分子:一种生物信息学方法。

Exploring gene regulatory interaction networks and predicting therapeutic molecules among major depressive disorder and bipolar disorder: A bioinformatics approach.

作者信息

Chauhan Abhimanyu, Jain Chakresh Kumar

机构信息

Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, Uttar Pradesh 201307, India.

Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, Uttar Pradesh 201307, India.

出版信息

J Affect Disord. 2025 Apr 15;375:64-74. doi: 10.1016/j.jad.2025.01.078. Epub 2025 Jan 17.

DOI:10.1016/j.jad.2025.01.078
PMID:39828023
Abstract

BACKGROUND

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are two common psychiatric disorders that have a substantial influence on people's mental health and quality of life. The identification of regulatory networks and potential drugs for both disorders enhances our understanding of these conditions and facilitates the development of targeted and effective therapies.

METHODS

This study employed network-based methods to identify gene regulatory networks and potential therapeutics for Major Depressive Disorder (MDD) and Bipolar Disorder (BD). We identified intersecting genes, predicted miRNAs and transcription factors, and constructed the TF-miRNA-hub gene network. Modules, enrichment analysis, and motifs were identified, and potential drugs targeting disease-associated genes were discovered using the DSigDB from the Enricher database.

RESULTS

We identified five common hub genes (AKT1, IL1B, IL6, MAPK3, TNF) in MDD and BD protein-protein interaction networks. Our analysis revealed three microRNAs (hsa-let-7d-5p, hsa-let-7a-5p, hsa-mir-34a-5p) and two transcription factors (NFKB1, RELA) targeting these hub genes, which are also involved in various disorders and pathways, including cancer, hepatitis B, and the TNF signalling pathway. Notably, we identified 10 potential drug candidates targeting these hubs, providing valuable insights into MDD and BD's molecular mechanisms and potential therapeutic targets.

LIMITATION

Further experimental validation required to confirm the computational predictions.

CONCLUSION

The findings emphasize the importance of regulatory network motifs discovery in understanding the disorders-dynamics and therapeutics. These results provide the ground work for developing the common targeted interventions for MDD and BD.

摘要

背景

重度抑郁症(MDD)和双相情感障碍(BD)是两种常见的精神疾病,对人们的心理健康和生活质量有重大影响。识别这两种疾病的调控网络和潜在药物,有助于我们加深对这些疾病的理解,并推动有针对性的有效治疗方法的开发。

方法

本研究采用基于网络的方法,来识别重度抑郁症(MDD)和双相情感障碍(BD)的基因调控网络和潜在治疗方法。我们确定了交叉基因,预测了微小RNA(miRNA)和转录因子,并构建了转录因子-微小RNA-枢纽基因网络。识别了模块、富集分析和基序,并使用来自Enricher数据库的DSigDB发现了针对疾病相关基因的潜在药物。

结果

我们在MDD和BD的蛋白质-蛋白质相互作用网络中确定了五个共同的枢纽基因(AKT1、IL1B、IL6、MAPK3、TNF)。我们的分析揭示了靶向这些枢纽基因的三种微小RNA(hsa-let-7d-5p、hsa-let-7a-5p、hsa-mir-34a-5p)和两种转录因子(NFKB1、RELA),它们也参与各种疾病和通路,包括癌症、乙型肝炎和TNF信号通路。值得注意的是,我们确定了10种靶向这些枢纽的潜在药物候选物,为MDD和BD的分子机制及潜在治疗靶点提供了有价值的见解。

局限性

需要进一步的实验验证来证实计算预测结果。

结论

这些发现强调了在理解疾病动态和治疗方面发现调控网络基序的重要性。这些结果为开发针对MDD和BD的共同靶向干预措施奠定了基础。

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