A step-by-step progressive strategy exploring whole metabolic profiles in vivo for Polygalae Radix with/without licorice.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-09-23 DOI:10.1002/pca.3452
Tiantian Zhao, Ningning Zhao, Junpeng Xing, Zhong Zheng, Zhiqiang Liu, Shu Liu
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Abstract

Introduction: Polygalae Radix (PR) is known to relieve toxicity and increase efficiency in various diseases after processing. However, there were few studies for aromatic carboxylic acids (ACAs) due to the limited detection, especially for the metabolites within m/z 100-2000.

Objectives: This study aims to elucidate the whole metabolism of PR with/without licorice (LP), focusing on metabolites within m/z 100-2000 and pharmacodynamics in vivo.

Material and methods: This study was established by the combination of multidimensional ultra-high performance liquid chromatography coupled with a mass spectrometer (UPLC-MS) technology with protein sedimentation method to analyze metabolites in plasma, brain, colon, and stomach contents. Quantitative monitoring ACAs was enhanced with our novel stable isotope derivatization (SILD) technique. And then the pharmacokinetics (PK) study of relatively large metabolites was carried out. A targeted network pharmacology approach was established to avoid false positive results, mapping interactions relevant to Alzheimer's disease (AD), and other conditions.

Results: The 85 polygala metabolites were qualitatively analyzed in plasma, brain, colon, and stomach contents. The 11 types of relatively large metabolites and 8 types of ACAs were quantitatively monitored. Among them, nine types of relatively large metabolites were assessed through PK studies. In targeted network pharmacology, it highlighted the significance of small molecular metabolites, including ACAs et al, which were frequently overlooked. LP may play a more key role mainly through neural active ligand-receptor interaction, AD, and pertussis pathways. These findings have outlined a step-by-step strategy for in-depth research in vivo, laying a foundation for further verification of biological function.

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采用循序渐进的策略,探索含/不含甘草的远志在体内的整体代谢情况。
简介众所周知,远志(Polygalae Radix,PR)经加工后具有解毒、增效等功效。然而,由于对芳香族羧酸(ACA)的检测有限,尤其是对 m/z 100-2000 范围内的代谢物的研究很少:本研究旨在阐明含/不含甘草(LP)的 PR 的整个代谢过程,重点是 m/z 100-2000 范围内的代谢物和体内药效学:本研究采用多维超高效液相色谱-质谱联用技术(UPLC-MS),结合蛋白沉淀法分析血浆、脑、结肠和胃内容物中的代谢物。我们的新型稳定同位素衍生化(SILD)技术加强了对 ACA 的定量监测。然后对相对较大的代谢物进行了药代动力学(PK)研究。为了避免假阳性结果,我们建立了一种有针对性的网络药理学方法,绘制了与阿尔茨海默病(AD)和其他疾病相关的相互作用图:结果:对血浆、大脑、结肠和胃内容物中的 85 种远志代谢物进行了定性分析。定量监测了 11 种相对较大的代谢物和 8 种 ACA。其中,通过 PK 研究评估了 9 种相对较大的代谢物。在靶向网络药理学中,它强调了小分子代谢物(包括 ACA 等)的重要性,而这些代谢物往往被忽视。LP可能主要通过神经活性配体-受体相互作用、AD和百日咳途径发挥更关键的作用。这些发现为在体内进行深入研究勾画了一个循序渐进的策略,为进一步验证生物学功能奠定了基础。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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