白头翁有效成分的UHPLC-QTOF-MS/MS及网络药理学分析对溃疡性结肠炎的影响。

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Jiaojiao Zhang, Xing Chen, Yuman Li, Xue Ma, Nuo Xu, Tuanjie Wang, Yun Shi, Kunming Qin
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引用次数: 0

摘要

背景:白头翁(pulsatilae radix, PR)是一种药用根茎植物,是一种著名的中草药,主要用于清热、解毒、凉血和抗炎。本研究旨在通过超高效液相色谱-四极杆飞行时间质谱(UHPLC-Q-TOF-MS/MS)、网络药理学、分子对接等综合手段,探讨PR对溃疡性结肠炎(UC)的治疗作用机制。方法:采用UHPLC-Q-TOF-MS/MS对其成分进行系统分析。通过SwissTargetPrediction和PharmMapper数据库确定活性成分的潜在靶点,而从GeneCard、OMIM和其他相关数据库检索uc相关疾病靶点。利用Venn分析确定PR和UC之间的重叠目标。Cytoscape软件促进了化合物-疾病-靶点网络的构建。利用STRING数据库生成交叉靶点的蛋白-蛋白相互作用(protein-protein interaction, PPI)网络,利用CytoNCA插件对核心靶点进行鉴定。使用DAVID平台进行基因本体(GO)和京都基因与基因组百科全书(KEGG)途径富集分析。最后,利用PyMOL进行关键组分与靶蛋白的分子对接。结果:共鉴定出27个活性化合物、237个药物靶点和4622个疾病靶点。交叉分析揭示了141个共享目标,而PPI网络确定了10个枢纽目标。GO和KEGG富集分析表明,枢纽靶点主要与磷酸化、细胞质功能、核受体活性以及与晚期糖基化终产物受体(AGE-RAGE)信号传导、T细胞受体(TCR)信号传导、脂质和胆固醇代谢以及各种癌症相关途径相关。分子对接实验表明,(+)-松脂醇、菊苣酸、β-脱皮酮、白头草皂苷D、23-HBA和AB4与PIK3R1、TLR4和ESR1结合稳定,其中AB4与ESR1形成最稳定的配合物。结论:本研究建立了快速有效的UHPLC-Q-TOF-MS/MS表征PR主要化学成分的方法,通过网络药理学和分子对接,研究了PR参与UC治疗的有效成分和潜在机制,为后续的药效学实验研究和机制研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analysis of Effective Components of Pulsatillae radix Using UHPLC-QTOF-MS/MS and Network Pharmacology to Investigate its Effects on Ulcerative Colitis.

Background: Pulsatillae radix (PR), a medicinal root plant and a well-known Chinese herbal remedy, is primarily used for its heat-clearing, detoxifying, blood-cooling, and antiinflammatory properties. This study aimed to investigate the underlying mechanisms by which PR exerts therapeutic effects on ulcerative colitis (UC) through an integrated approach, combining ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS), network pharmacology, and molecular docking.

Methods: The constituents of PR were systematically analyzed using UHPLC-Q-TOF-MS/MS. Potential targets of active components were identified via the SwissTargetPrediction and PharmMapper databases, while UC-related disease targets were retrieved from GeneCard, OMIM, and other relevant databases. Overlapping targets between PR and UC were determined using Venn analysis. Cytoscape software facilitated the construction of the compound-disease-target network. The STRING database was employed to generate a protein-protein interaction (PPI) network for the intersecting targets, and core targets were identified using the CytoNCA plugin. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID platform. Lastly, molecular docking of key components with target proteins was carried out using PyMOL.

Results: A total of 27 active compounds, 237 drug targets, and 4622 disease targets were identified. Intersection analysis revealed 141 shared targets, while the PPI network identified 10 hub targets. GO and KEGG enrichment analyses indicated that the hub targets were primarily associated with phosphorylation, cytoplasmic functions, nuclear receptor activity, as well as pathways related to the advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling, T cell receptor (TCR) signaling, lipid and cholesterol metabolism, and various cancer-related pathways. Molecular docking experiments demonstrated that (+)- pinoresinol, cichoric acid, β-ecdysone, pulsatilla saponin D, 23-HBA, and AB4 exhibited stable binding to PIK3R1, TLR4, and ESR1, with AB4 forming the most stable complex with ESR1.

Conclusion: This study established a rapid and effective UHPLC-Q-TOF-MS/MS method for characterizing the main chemical components of PR. Using network pharmacology and molecular docking, the active components and potential mechanisms of PR involved in the UC treatment were investigated, providing a foundation for future experimental studies on pharmacodynamics and the underlying mechanisms.

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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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