DataSheet1_Precise Species Detection in Traditional Herbal Patent Medicine, Qingguo Wan, Using Shotgun Metabarcoding.docx

As one of the high-incidence diseases in the world, pharyngitis seriously affects the lives of those with the condition. Qingguo Wan is a herbal medicine used for treating pharyngitis, and its quality evaluation is currently only accomplished via traditional identification. However, precise identification becomes challenging with fake products on the market or fungal contamination during the production process. This study used the Illumina NovaSeq platform for targeting the ITS2, psbA-trnH, matK, and rbcL sequences to survey the species composition of lab-made and commercial samples. The results showed that a total of 34.56 Gb of raw data that was obtained represented more than 0.23 billion reads. After assembly, annotation, and operational taxonomic unit clustering, 103, 12, 10, and 12 OTUs were obtained, which belonged to the ITS2, psbA-trnH, matK, and rbcL sequences of the mock lab-made and commercial samples. The analytical results indicated that the sequences of all the prescription ingredients were successfully obtained in the two lab-made samples. The positive control medicinal Panax quinquefolius L. sequence was obtained in HSZY175, while Scutellaria baicalensis Georgi, Lonicera japonica Thunb. Menispermum dauricum DC. and Paeonia lactiflora Pall. were detected in the three commercial samples. The detection results of the other four herbs in different fragments were not all the same. In addition, a total of 28 fungi OTUs, representing 19 families and 20 genera, were obtained from both the commercial and mock lab-made samples. Aspergillus, Cladosporium, and Penicillium dominated among the 20 genera. This study demonstrated that the shotgun metabarcoding method is a powerful tool for the molecular identification of the biological ingredients in Qingguo Wan. It can be used to effectively supplement traditional methods while providing a new technique for the quality evaluation of Qingguo Wan.