Metabolite-Content-Guided Prediction of Medicinal/Edible Properties in Plants for Bioprospecting

Metabolite-content (MC) refers to all small molecules which are the products or intermediates of metabolism within an organism. The metabolite-contents of plants which involve numerous secondary metabolites are highly related to their nutritional and medicinal features. Previous researches have confirmed that phylogeny-guided approaches have been seen as one of the timeefficient and informative approaches to plant-based drug discovery. However, the phylogenetic reconstruction of plants is not determined conclusively from genomic sequence data. Here, we investigate the systematic value of metabolite-contents of plants, especially the predictive power of metabolite-content data in exploration of edible and medicinal properties for bioprospecting. In this study, we reconstructed the phylogenetic tree for a set of plants which are distributed in different genera and families by their metabolite-content data obtained from KNApSAcK Core DB. We used a network based approach to abstract structurally similar metabolites as features, and measure the phylogenetic distance by a binary method. We also reconstructed phylogenetic trees based on plastid markers rbcL, matK and ITS2 for the same set of plants, to investigate the predictive power of these two approaches, sequenceand MC-based approaches, in guiding the prediction of medicinal/edible properties. Our results reveal that besides the genomic sequence data, metabolite-content data is also closely associated with medicinal and edible bioactivity of plants and can be used to explore the medicinal/edible properties in a different perspective from sequence-based approach. Our study therefore provides a new approach for plant bioprospecting, and the predictive power of metabolite-content data for medicinal/edible plants will also be improved with the improvement and completeness of the metabolite-content database.