Discriminative analysis of Ficus deltoidea Jack varieties using multiplatform metabolomics and development of bioactivity prediction model / Noraini Kasim

Kasim, Noraini (2020) Discriminative analysis of Ficus deltoidea Jack varieties using multiplatform metabolomics and development of bioactivity prediction model / Noraini Kasim. PhD thesis, Universiti Teknologi MARA.

Abstract

Ficus deltoidea Jack (Moraceae) or locally known as ‘mas cotek’ is a popular herb possessing various medicinal attributes. Ethnobotanically, Ficus deltoidea has been claimed to possess antidiabetic properties, the leaf decoction is commonly used as an alternative for type 2 diabetes management. Morphology distinction of the seven main varieties (var. angustifolia, var. bilobata, var. deltoidea, var. intermedia, var. kunstleri, var. motleyana and var. trengganuensis) is challenging due to the extreme leaf heterophylly and unclear varietal boundaries. This study aims to establish an analytical method for distinguishing the seven varieties found in Peninsular Malaysia based on comprehensive metabolite analysis. The seven varieties were fingerprinted and profiled using HPLC, MS and NMR. Mass-based dereplication identified 26 compounds comprising of flavanols, proanthocyanidins, hydrocinnamic acids, furanocoumarins and flavone glycosides. In addition, high resolution MS in this work was able to detect new hydrocinnamic acids; 2-O-acetyl-3-O-trans-p-coumaroyltartaric acid and 2-O-acetyl-3-O-cis-p-coumaroyltartaric acid in F. deltoidea with high confidence. Interestingly, var. intermedia was found to contain a unique compound, absent in all other varieties, oxypeucedanin hydrate. Apart for that, generally the varieties contain the same set of metabolites but differ in the quantity. The chemical markers of F. deltoidea, vitexin and isovitexin, were quantified in all varieties using UHPLC analysis. Their content were significantly different across the varieties. Isovitexin content was highest in var. angustifolia with 12.97 μg/g of dry plant material while vitexin content was highest in var. deltoidea with 27.21 μg/g of dry plant material. Multivariate data analysis PLSDA and HCA revealed the existence of three groups based on the differentiation in the metabolite content. Group 1 consists of var. bilobata, group 2 consists of var. intermedia and angustifolia, and group 3 consists of var. motleyana, var. deltoidea, var. kunstleri and var. trengganuensis. Intra-variety analysis of var. trengganuensis from several locations revealed that they are not significantly different. The chemical profiles were quite consistent regardless of nature the plants, cultivated or wild. Even plants growing among bushes on sandy soils and the ones growing on palm trees were similar. At 100 ppm, α-glucosidase inhibitory activity of all collected 112 samples ranges from no inhibition to 71.50%, indicating the varied biological properties. Consequently, PLS predictive model for α-glucosidase inhibitory activity based on the metabolite profiles was constructed. The findings suggest that varieties intermedia (CH), trengganuensis and kunstleri are more superior in terms of α-glucosidase inhibitory activity. Metabolites correlated to α-glucosidase inhibitory activity were identified as 4-aminobutyrate, malic acid, epicatechin, catechin, afzelechin and isoleucine. The validated predictive model was found to be very accurate with the root mean squared error of prediction (RMSEP); 5.4. The correlation model can be useful tool in quality control of F. deltoidea herbal products especially for identification of correct varieties possessing optimal biological properties, eliminating the need for routine bioactivity testing for all samples.

Metadata

Item Type: Thesis (PhD)
Creators:
Creators
Email / ID Num.
Kasim, Noraini
2012497394
Contributors:
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Email / ID Num.
Thesis advisor
Ismail, Nor Hadiani (Prof. Dr.)
UNSPECIFIED
Subjects: T Technology > TP Chemical technology > Biotechnology > Plant biotechnology
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Applied Sciences
Programme: Doctor of Philosophy
Keywords: Herbal medicine; metabolomics; herbal analysis; multivariate data analysis; Ficus deltoidea Jack
Date: April 2020
URI: https://ir.uitm.edu.my/id/eprint/61110
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