The application of multi-locus DNA metabarcoding in traditional medicines

• Published in: Journal of Food Composition and Analysis
• Main Authors: Arulandhu, Alfred J., Staats, Martijn, Hagelaar, Rico, Peelen, Tamara, Kok, E.J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: Customs authority; DNA extraction; Declared and undeclared ingredients; Endangered species; Multi-locus DNA metabarcoding; Traditional medicines; Ursus arctos
• Online Access: https://research.wur.nl/en/publications/the-application-of-multi-locus-dna-metabarcoding-in-traditional-m; https://doi.org/10.1016/j.jfca.2019.03.007

Traditional medicines (TMs) are globally traded and the consumer market is estimated to be $83 billion per annum. The diversity of TM matrices and poor quality of DNA extracted from highly processed TMs makes it challenging to apply standardized DNA-based procedures for ingredient analysis. In the present study, multiple DNA extraction methods were compared for the ability to obtain amplifiable DNA from TMs belonging to different matrices. The best performing DNA extraction was used to successfully obtain DNA from 18 TMs that were subsequently analyzed with a multi-locus DNA metabarcoding method to assess the species composition. In the analysis mini-barcodes accounted for the identification of most of the taxa in the TMs. The plant (ITS2) and animal (mini-16S) mini-barcode markers showed to allow species level identification of targets. In a few cases, full-length barcode markers, requiring higher quality DNA, proved to be critically informative at this level. The applied strategy resulted in the identification of a wide range of declared and undeclared ingredients, including endangered species (Ursus arctos and Aloe sp.). In 14 TMs less than 65% of the identified taxa matched the product label, and in two TMs none of the identified species matched the ingredients list. The current study shows that a multi-locus DNA metabarcoding approach is an informative analytical tool for species identification in TMs, including the potential identification of endangered species.