Feature-based molecular networking and MS2LDA analysis for the dereplication of adjacent bis-tetrahydrofuran Annonaceous acetogenins.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-09-05 DOI:10.1002/pca.3444
Aleksandra Gurgul, Chun-Tao Che
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Abstract

Introduction: Annonaceous acetogenins are a group of natural polyketide compounds possessing notable cytotoxic and antitumor properties. Mass spectrometry (MS) techniques can be used for the structural determination of these compounds, including the location of functional groups along the long alkyl chain.

Objective: This study aims to develop a convenient liquid chromatography (LC)-MS-based method for the dereplication of acetogenins in plant extracts using a molecular networking approach.

Methodology: The LC-electrospray ionization (ESI)-MS/MS spectra of pure adjacent bis-tetrahydrofuran (THF) acetogenins isolated from Uvaria rufa (Annonaceae) were acquired, along with those of the crude ethyl acetate and hexanes fractions of the plant extract, followed by dereplication and molecular networking analysis using the Global Natural Products Social Molecular Networking (GNPS) platform.

Results: A high level of fragmentation of the protonated molecules [M + H]+ was observed at collision energies of 37.5 and 25.0 eV. The application of feature-based molecular networking (FBMN) allowed for distinguishing diastereoisomers based on different retention times in the reversed-phase high-performance liquid chromatography method. The acetogenin possessing one or more additional OH groups on the methyl-terminal chain side of the OH-flanked bis-THF ring unit were grouped separately from those lacking such substructure. Furthermore, the MS2LDA analysis revealed shared Mass2Motifs among acetogenins, confirming the structural relations within the molecular network.

Conclusions: The ESI-MS/MS-based molecular networking method provided an effective strategy for the dereplication of acetogenins in plant extracts. It is anticipated that this molecular networking approach could be extended to other types of acetogenins to facilitate rapid identification of this class of compounds.

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基于特征的分子网络和 MS2LDA 分析,用于相邻双四氢呋喃芒柄蜡素的去复制。
简介茴香属植物苷元是一类天然多酮化合物,具有显著的细胞毒性和抗肿瘤特性。质谱(MS)技术可用于确定这些化合物的结构,包括沿长烷基链的官能团的位置:本研究旨在开发一种基于液相色谱(LC)-质谱(MS)的简便方法,利用分子网络方法去除植物提取物中的炔苷元:方法:利用全球天然产品社会分子网络(GNPS)平台,对从 Uvaria rufa(芒柄菊科)中分离得到的纯邻近双四氢呋喃(THF)乙酰苷元以及植物提取物的乙酸乙酯粗馏分和己烷馏分进行去复制和分子网络分析,获得其液相色谱-电喷雾电离(ESI)-MS/MS 图谱:结果:质子化分子[M + H]+ 在 37.5 和 25.0 eV 的碰撞能量下发生了高水平的碎片化。应用基于特征的分子网络(FBMN),可以根据反相高效液相色谱法中不同的保留时间区分非对映异构体。在羟基侧翼双 THF 环单元的甲基末端链侧具有一个或多个额外羟基的乙酰皂苷元与缺乏此类亚结构的乙酰皂苷元被分开分组。此外,MS2LDA 分析还发现了苷元之间共享的 Mass2Motifs,证实了分子网络中的结构关系:基于 ESI-MS/MS 的分子网络方法为植物提取物中萃取苷元的去复制提供了一种有效的策略。预计这种分子网络方法可扩展到其他类型的苷元,以促进该类化合物的快速鉴定。
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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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