Biological composition analysis of a natural medicine, Faeces Vespertilionis, with complex sources using DNA metabarcoding

Abstract

Faeces Vespertilionis is a commonly used fecal traditional Chinese medicine. Traditionally, it is identified relying only on morphological characters. This poses a serious challenge to the composition analysis accuracy of this complex biological mixture. Thus, for quality control purposes, an accurate and effective method should be provided for taxonomic identification of Faeces Vespertilionis. In this study, 26 samples of Faeces Vespertilionis from ten provinces in China were tested using DNA metabarcoding. Seven operational taxonomic units (OTUs) were detected as belonging to bats. Among them, Hipposideros armiger (Hodgson, 1835) and Rhinolophus ferrumequinum (Schober and Grimmberger, 1997) were the main host sources of Faeces Vespertilionis samples, with average relative abundances of 59.3% and 24.1%, respectively. Biodiversity analysis showed that Diptera and Lepidoptera were the most frequently consumed insects. At the species level, 19 taxa were clearly identified. Overall, our study used DNA metabarcoding to analyze the biological composition of Faeces Vespertilionis, which provides a new idea for the quality control of this special traditional Chinese medicine.

Species diversity analysis of commercial Mantidis Ootheca samples contaminated by store pests based on DNA metabarcoding

“…MEGAN parameters were set as: minimum score = 50, maximum expected = 0.01, top percent = 10, minimum support percent = 0.01, minimum support = 1 and weighted LCA algorithm. Species-level taxonomy was assigned when the identity values between the query and reference sequences were above 98% [ 25 ]. The minimum identity to query would be set as 92% to obtain taxonomic information at a higher level for queries which could not be identified as exact species.…”

Species diversity analysis of commercial Mantidis Ootheca samples contaminated by store pests based on DNA metabarcoding

“…MEGAN parameters were set as: minimum score = 50, maximum expected = 0.01, top percent = 10, minimum support percent = 0.01, minimum support = 1 and weighted LCA algorithm. Species-level taxonomy was assigned when the identity values between the query and reference sequences were above 98% [24]. The minimum identity to query would be set as 92% to obtain taxonomic information at a higher level for queries which could not be identi ed as exact species.…”