Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam, Shah Alam, Malaysia.
Phytochem Anal. 2022 Dec;33(8):1235-1245. doi: 10.1002/pca.3175. Epub 2022 Oct 3.
Ficus deltoidea Jack (Moraceae) is a plant used in Malaysia to treat various ailments, including diabetes. The presence of several varieties raises essential questions regarding which is the potential bioactive variety and what are the bioactive metabolites.
Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity.
The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis.
The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action.
NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.
榕属植物三角叶榕(Moraceae)在马来西亚被用于治疗多种疾病,包括糖尿病。由于存在多个品种,因此出现了一些基本问题,例如哪一个品种具有潜在的生物活性,以及具有哪些生物活性代谢产物。
本研究使用核磁共振(NMR)和液相色谱-质谱(LC-MS)分析探索了来自马来西亚半岛的七个品种的植物化学多样性,并将其与α-葡萄糖苷酶抑制活性相关联。
对 NOESY 一维(1D)-NMR 和 LC-MS 数据进行了处理、注释,并使用多元数据分析将其与体外α-葡萄糖苷酶抑制活性相关联。
α-葡萄糖苷酶结果表明,不同品种具有不同的抑制作用,抑制率最高的是三角叶榕变种特伦甘努和变种 kunstleri。此外,不同的生境和植物年龄也可能影响抑制率。NMR 和 LC-MS 图谱的热图根据不同的α-葡萄糖苷酶抑制率水平显示出独特的模式。由 NMR 和 LC-MS 构建的偏最小二乘(PLS)模型进一步证实了三角叶榕品种的α-葡萄糖苷酶抑制率与其代谢产物图谱之间的相关性。使用变量投影重要性(VIP)和相关系数(p(corr))值确定了解释预期抑制作用的高度相关代谢产物。
NMR 和 LC-MS 注释可鉴定黄烷-3-醇和原花青素是关键的生物活性因子。本研究结果表明,多元数据分析可从生物学和化学方面预测草药材料的质量。