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Authentication of the Herbal Medicine Angelicae Dahuricae Radix Using an ITS Sequence-Based Multiplex SCAR Assay.

Pureum NohWook Jin KimSungyu YangInkyu ParkByeong-Cheol Moon
Published in: Molecules (Basel, Switzerland) (2018)
The accurate identification of plant species is of great concern for the quality control of herbal medicines. The Korean Pharmacopoeia and the Pharmacopoeia of the People's Republic of China define Angelicae Dahuricae Radix (Baek-Ji in Korean and Bai-zhi in Chinese) as the dried roots of Angelica dahurica or A. dahurica var. formosana belonging to the family Apiaceae. Discrimination among Angelica species on the basis of morphological characteristics is difficult due to their extremely polymorphic traits and controversial taxonomic history. Furthermore, dried roots processed for medicinal applications are indistinguishable using conventional methods. DNA barcoding is a useful and reliable method for the identification of species. In this study, we sequenced the internal transcribed spacer (ITS) region of nuclear ribosomal RNA genes in A. dahurica, A. dahurica var. formosana, and the related species A. anomala and A. japonica. Using these sequences, we designed species-specific primers, and developed and optimized a multiplex sequence-characterized amplified region (SCAR) assay that can simply and rapidly identify respective species, and verify the contamination of adulterant depending on the polymerase chain reaction (PCR) amplification without sequencing analysis in a single PCR reaction. This assay successfully identified commercial samples of Angelicae Dahuricae Radix collected from Korean and Chinese herbal markets, and distinguished them from adulterants. This multiplex SCAR assay shows a great potential in reducing the time and cost involved in the identification of genuine Angelicae Dahuricae Radix and adulterant contamination.
Keyphrases
  • high throughput
  • real time pcr
  • quality control
  • bioinformatics analysis
  • genetic diversity
  • risk assessment
  • single cell
  • human health
  • nucleic acid
  • dna methylation
  • health risk
  • data analysis