Natural Products Laboratory, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands.
Pharmacognosy Research Group, Louvain Drug Research Institute, Université catholique de Louvain, UCLouvain, Avenue E. Mounier, 72, B1.72.03, B- 1200, Brussels, Belgium.
Metabolomics. 2019 Feb 21;15(3):27. doi: 10.1007/s11306-019-1487-4.
The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products.
Create a multivariate model to identify antiplasmodial metabolites from H NMR data of two African medicinal plants, Keetia leucantha and K. venosa.
The extracts of twigs and leaves of Keetia species were measured by H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation.
Unsupervised H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples.
NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.
抗药性的增加和疟疾治疗效果的缺乏促使人们迫切需要发现新的抗疟药物，从而重新开始筛选来自传统医学的次生代谢产物。在植物代谢组学中，基于 NMR 的策略被认为是一种黄金方法，它既能提供化学特征的整体视图，又能在代谢组学和生物活性之间建立相关性，成为从天然产物中开发药物的基石。
从两种非洲药用植物 Keetia leucantha 和 K. venosa 的 H NMR 数据中创建一个多变量模型来识别抗疟代谢产物。
用 H NMR 测量 Keetia 属树枝和叶子的提取物，然后将光谱提交给正交偏最小二乘法（OPLS）以进行抗疟相关性分析。
无监督的 H NMR 分析表明，组织的影响高于物种，三萜类化合物的信号与 Keetia 树枝比叶子更相关。基于 Keetia 属的 OPLS-DA 相关三萜信号与 K. leucantha 相关，表现出比 K. venosa 更高浓度的三萜和苯丙素缀合三萜。通过 OPLS 进行的体外抗疟相关性验证，适用于所有 Keetia 样本，表明苯丙素缀合三萜与生物活性高度相关，而无环角鲨烯则被发现是生物活性低的样本中的主要代谢物。
基于 NMR 的代谢组学结合有监督的多元数据分析是识别植物提取物中生物活性代谢物的有力策略。此外，统计全相关光谱学与 2D NMR 的结合允许对不同的三萜进行详细分析，克服了它们在脂肪族区域结构相似和峰重叠带来的挑战。
