Integrating Molecular Biology and Network Pharmacology to Analyze the Antidepressant Mechanism of Glycyrrhizaglabra.

IF 1.7 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS Combinatorial chemistry & high throughput screening Pub Date : 2025-01-01 DOI:10.2174/0113862073295662240715070530
Liting Liang, Yongmei Jiang, Linghan Kuang, Xingxin Liu, Jingjing Luo, Jiaji Ling, Wenjing Wu
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Abstract

Introduction: Antidepressants have adverse effects and induce drug resistance when used excessively or frequently. Therefore, adjuvants are needed to reduce the use of antidepressants during treatment. Traditional Chinese medicine (TCM) is an important adjunctive approach to depression with safety, environmental protection, and low toxicity. Glycyrrhizaglabra (licorice, GG) is a plant commonly used in various herbal remedies.

Method: To explore the potential antidepressant-related targets of Glycyrrhizaglabra (GG) and its underlying mechanisms, we utilized a combination of animal behavioral experiments, molecular biology, and network pharmacology to analyze the antidepressant effects of GG. Initially, we conducted behavioral assays to verify the capacity of GG to mitigate depressive-like behaviors in mice. Subsequently, we selected 56 active compounds and 695 target compounds of licorice from TCMSP. The PPI network screened 80 core targets for enrichment analysis. Lastly, Western blot and ELISA techniques were utilized to authenticate and corroborate the predicting outcomes of PPI and enrichment analysis.

Result: GG extracts reversed lipopolysaccharide (LPS)-induced depression-like behavior in behavioral tests. The results of enrichment analysis showed that,GG significantly affected neurodegeneration pathways, neuroactive ligand-receptor interaction, cAMP signaling pathway, serotonergic synapse, dopaminergic synapse, and MAPK signaling pathway. Mechanistic studies showed that GG reduced IL-1β, IL-6, and TNF-α levels, 5-HTRA1 expression, and GSK3β phosphorylation in mouse hippocampus. It also increased BDNF and DRD1 expression and CREB and ERK1/2 phosphorylation.

Conclusion: Our experimental results demonstrate that GG targets multiple proteins associated with depression, influencing diverse pathways and consequently regulating depressive-like behaviors in mice.

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结合分子生物学和网络药理学分析甘草的抗抑郁机制
导言过度或频繁使用抗抑郁药会产生不良反应并诱发耐药性。因此,在治疗过程中需要辅助药物来减少抗抑郁药的使用。中药是治疗抑郁症的一种重要辅助方法,具有安全、环保、低毒等特点。甘草(Glycyrrhizaglabra,GG)是一种常用于各种中药疗法的植物:我们研究了甘草的抗抑郁活性、其活性成分以及潜在的抑郁相关靶点。我们将动物行为学和分子生物学实验与网络药理学相结合,分析了 GG 的抗抑郁机制。在行为测试中,GG 提取物逆转了脂多糖(LPS)诱导的抑郁样行为。我们从 TCMSP 中筛选出了 56 个甘草活性化合物和 695 个目标化合物。PPI 网络筛选了 80 个核心靶标进行富集分析。结果表明,甘草可明显影响神经退行性变通路、神经活性配体-受体相互作用、cAMP信号通路、5-羟色胺能突触、多巴胺能突触和MAPK信号通路:机理研究表明,GG能降低小鼠海马中IL-1β、IL-6和TNF-α的水平、5-HTRA1的表达以及GSK3β的磷酸化。它还能增加 BDNF 和 DRD1 的表达以及 CREB 和 ERK1/2 的磷酸化:结论:这表明 GG 对这些蛋白质产生了作用,从而影响了介导抑郁症发病机制的多种途径。
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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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