Predicting medicinal resources in Ranunculaceae family by a combined approach using DNA barcodes and chemical metabolites |
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| Institution: | 1. Key Laboratory of Functional Yeast, China National Light Industry & Hubei Key Laboratory of Natural Products Research and Development, College of Biological and Pharmaceutical Sciences, China Three Gorges University, Yichang 443002, PR China;2. School of Biotechnology and Food Engineering, Changshu Institute of Technology, Suzhou 215500, PR China;3. Three Gorges Public Inspection and Testing Center, Yichang 443005, PR China;4. Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, Three Gorges University Medical College, China Three Gorges University, Yichang 443002, PR China;5. Agricultural College, Hubei Three Gorges Polytechnic, Yichang 443000, PR China;1. College of Pharmacy, Fujian University of Traditional Chinese Medicine, No.1 Qiuyang Road, Fujian 350108, PR China;2. Department of Phytochemistry, School of Pharmacy, Naval Medical University, No. 325, Guohe Road, Shanghai 200433, PR China;3. School of Pharmaceutical Sciences and Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming 650500, Yunnan, PR China;1. Université des frères Mentouri-Constantine 1, Département de chimie, Laboratoire d′Obtention de Substances Thérapeutiques (LOST), Campus Chaabet-Ersas, 25000 Constantine, Algeria;2. Université de Reims Champagne Ardenne, CNRS, ICMR UMR 7312, 51097 Reims, France;1. Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi 10072, Viet Nam;2. Graduate University of Science and Technology, VAST, 18 Hoang Quoc Viet, Cau Giay, Hanoi 10072, Viet Nam;3. Institute of Marine Biochemistry, VAST, 18 Hoang Quoc Viet, Cau Giay, Hanoi 10072, Viet Nam;4. Thai Nguyen University of Agriculture and Forestry, Quyet Thang, Thai Nguyen 24119, Viet Nam;1. Facultad de Ciencias Químicas, Universidad Autonoma de Nuevo Leon, Av. Universidad S/N, Ciudad Universitaria, CP 66451 San Nicolás de los Garza, Nuevo León, Mexico;2. Departamento de Bioquímica y Medicina Molecular, Facultad de Medicina/Hospital Universitario, Dr. José Eleuterio González, Universidad Autonoma de Nuevo Leon, Madero y Aguirre Pequeño, Mitras Centro, Monterrey, Nuevo León C.P. 64460, Mexico;3. Departamento de Química Analítica, Facultad de Medicina, Universidad Autonoma de Nuevo Leon. Madero y Aguirre Pequeño, Mitras Centro, Monterrey, Nuevo León C.P. 64460, Mexico;4. Nantes Université, Cibles et médicaments des infections et de l′immunité, IICiMed, UR 1155, F-44000 Nantes, France |
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| Abstract: | 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. |
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| Keywords: | ITS2 barcode Machine learning Metabolites Plant taxonomy |
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