Literature DB >> 28747591

Assessment of Genetic and Chemical Variability in Curcumae Longae Rhizoma (Curcuma longa) Based on DNA Barcoding Markers and HPLC Fingerprints.

Zhonggang Duan1,2, Wei Song2, Kuan Chen2, Xue Qiao2, Min Ye2.   

Abstract

Curcumae Longae Rhizoma (Curcuma longa L.) is an important traditional Chinese medicine with multiple beneficial effects. To elucidate the genetic and chemical differences among Curcumae Longae Rhizoma samples, three DNA barcoding markers (rbcL, matK, and ITS-LSU D1/D3) and HPLC fingerprinting were employed in this study. The discriminatory power of rbcL and matK was low, as they only detected one sequence type that showed 100% similarity with more than 20 congeneric species in the Barcode of Life Data Systems (BOLD) database. In contrast, ITS-LSU D1/D3 showed sufficient discriminatory power to precisely identify all of the market samples as C. longa L. in a BLAST search as well as differentiate each sample based on 2-10 ITS-LSU D1/D3 haplotypes with intragenomic variability (mean p-distance: 0.7%, range: 0-2.6%; mean number of differences: 9.6 sites, range: 0-38 sites). HPLC fingerprinting of 13 commercial samples showed a similarity that ranged from 0.769 to 0.996, indicating that the sample quality varied. A cluster analysis based on 5 common peak areas from the HPLC chromatogram resulted in two groups. Group I included 9 samples with a relatively high chemical content, and group II contained 4 samples with a low chemical content. A Mantel test revealed a low correlation (r=0.1721, p=0.047) between genetic and chemical differences. Our findings suggest that the integrated approach of ITS-LSU D1/D3 DNA barcoding and HPLC fingerprinting provides a comprehensive, precise, and convenient method to clarify the genetic and chemical differences in Curcumae Longae Rhizoma.

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Keywords:  Curcuma longa; DNA barcoding; fingerprint; intragenomic variability

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Year:  2017        PMID: 28747591     DOI: 10.1248/bpb.b17-00020

Source DB:  PubMed          Journal:  Biol Pharm Bull        ISSN: 0918-6158            Impact factor:   2.233


  1 in total

1.  Ingredients, Anti-Liver Cancer Effects and the Possible Mechanism of DWYG Formula Based on Network Prediction.

Authors:  Yao Li; Han-Min Li; Zhi-Cheng Li; Ming Yang; Rui-Fang Xie; Zhi Hua Ye; Xiang Gao; Xin Zhou
Journal:  Onco Targets Ther       Date:  2020-05-15       Impact factor: 4.147

  1 in total

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