Stephania is a medicinal plants-rich genus of Menispermaceae. However, the identification of morphologically-similar species in Stephania is difficult using the currently reported methods. The indiscriminate overexploitation of Stephania plants has… Click to show full abstract
Stephania is a medicinal plants-rich genus of Menispermaceae. However, the identification of morphologically-similar species in Stephania is difficult using the currently reported methods. The indiscriminate overexploitation of Stephania plants has resulted in clinical misuse and endangerment of many species, which necessitates the development of an efficient and reliable method for species authentication. Therefore, six candidate DNA barcode sequences (ITS, ITS2, psbA-trnH, matK, rbcL, and trnL-F) were tested for their capacity to identify Stephania species. The barcodes were analyzed either as a single region or in combination by tree-based [neighbor-joining (NJ) and Bayesian inference (BI)], distance-based (PWG-distance), and sequence similarity-based (TaxonDNA) methods. Amplification and sequencing success rates were 100% for all six candidate barcodes. A comparison of six barcode regions showed that ITS exhibited the highest number of variable and informative sites (182/179), followed by psbA-trnH (173/162). DNA barcoding gap assessment showed that interspecific distances of the six barcodes were greater than intraspecific distances. The identification results showed that species discrimination rates of combination barcodes were higher than those of single-region barcodes. Based on best match and best close match methods, the ITS+psbA-trnH combination exhibited the highest discrimination power (93.93%). Further, all Stephania species could be resolved in the phylogenetic trees based on ITS+psbA-trnH (NJ, BI). This study demonstrates that DNA barcoding is an efficient method to identify Stephania species and recommends that the ITS+psbA-trnH combination is the best DNA barcode for the identification of Stephania species.
               
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