Predicting medicinal resources in Ranunculaceae family by a combined approach using DNA barcodes and chemical metabolites

Plant taxonomy based on molecular phylogenetic study and/or chemosystematics study has become increasingly important in exploring and utilizing medicinal resources due to the advent of big data era. In this study, we proposed a classifying approach combining DNA and chemical metabolites for the prediction of new medicinal resources. Specifically, we obtained 104 ITS2 barcodes and 847 chemical metabolites from 104 species in Ranunculaceae. Then, phylogenetic tree based on the ITS2 barcode and clustering tree based on structural similarity of metabolites were separately constructed. In addition, we tested the classifying accuracy of the two methods by Baker`s correlation coefficient and the result showed that phylogenetic tree based on the ITS2 barcode was more accurate, giving a higher score of 0.627, whereas clustering tree based on chemical metabolites obtained a lower score of 0.301. Therefore, the natural products of plants might be described using these clades found by ITS2-based methods, and thus new metabolites of plants might be predicted due to the close relationships in a given clade. Using this combined method, 53 plants with structurally similar metabolites were included in 9 plant groups and currently unknown species-metabolite relations were predicted. Finally, 26.92% species in Ranunculaceae were found to contain the predicted metabolites after verification using two alternative KNApSAcKCore and ChEBI databases. As a whole, the combined approach can successfully classify plants and predict specialized natural products based on plant taxa.