Evidence-Based Complementary and Alternative Medicine

Volume 2014 (2014), Article ID 843923, 11 pages

http://dx.doi.org/10.1155/2014/843923

Research Article

Integrated Analysis for Identifying Radix Astragali and Its Adulterants Based on DNA Barcoding

Sihao Zheng,¹ Dewang Liu,² Weiguang Ren,¹ Juan Fu,¹ Linfang Huang,¹ and Shilin Chen³

¹Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China

²School of Pharmacy, Inner Mongolia Medical University, Inner Mongolia 010080, China

³Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China

Received 15 June 2014; Accepted 22 July 2014; Published 27 August 2014

Academic Editor: Robert Henry

Copyright © 2014 Sihao Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Radix Astragali is a popular herb used in traditional Chinese medicine for its proimmune and antidiabetic properties. However, methods are needed to help distinguish Radix Astragali from its varied adulterants. DNA barcoding is a widely applicable molecular method used to identify medicinal plants. Yet, its use has been hampered by genetic distance, base variation, and limitations of the bio-NJ tree. Herein, we report the validation of an integrated analysis method for plant species identification using DNA barcoding that focuses on genetic distance, identification efficiency, inter- and intraspecific variation, and barcoding gap. We collected 478 sequences from six candidate DNA barcodes (ITS2, ITS, psbA-trnH, rbcL, matK, and COI) from 29 species of Radix Astragali and adulterants. The internal transcribed spacer (ITS) sequence was demonstrated as the optimal barcode for identifying Radix Astragali and its adulterants. This new analysis method is helpful in identifying Radix Astragali and expedites the utilization and data mining of DNA barcoding.

1. Introduction

Radix Astragali (Huang Qi), a commonly used Chinese medicinal material, is mainly sourced from the plants of Astragalus membranaceus and Astragalus mongholicus according to Chinese Pharmacopoeia (2010 edition). Radix Astragali is widely used for its antiperspirant, antidiuretic, and antidiabetic properties and as a tonic drug [1–3]. It possesses various beneficial compounds, including astragalosides, isoflavonoids, isoflavones, isoflavan, and pterocarpan glycosides [4–6].

Due to the high market demand for Radix Astragali, a diverse group of adulterants with similar-morphological characteristics from genuses, such as Astragalus, Hedysarum, and Malva are often used in its stead [7]. The traditional methods used to identify Radix Astragali for use as a medicinal material, such as morphological and microscopic identification [8], thin-layer chromatography and Ultraviolet spectroscopy [9], Fourier Transform infrared spectroscopy (FTIR) [10], and high performance liquid chromatography (HPLC) [11], all, require specialized equipment and training. Several PCR-based molecular methods have been developed, providing an alternative means of identification. Multiplex PCR methods of DNA fragment analysis, such as randomly amplified polymorphic DNA (RAPD) [12] or amplified fragment length polymorphism (AFLP) [13], are unstable for the results to identify. DNA barcoding is a widely used molecular marker technology, first proposed by Hebert et al. [14, 15]. It uses a standardized and conserved, but diverse, DNA sequence to identify species and uncover biological diversity [16, 17]. In previous studies, various coding sequences for identifying Radix Astragali and its adulterants have been used, such as the 5S-rRNA spacer domain [18], 3′ untranslated region (3′ UTR) [19], ITS (internal transcribed spacer region) and 18S rRNA [3, 20, 21], ITS2 [22], ITS1 [6], matK (maturase K) and rbcL (ribulose 1, 5-bisphosphate carboxylase) of chloroplast genome, and coxI (cytochrome c oxidase 1) of the mitochondrial genome [23]. However, sequence analysis was mainly focused on genetic distance, variable sites, amplified polymorphisms, and the use of a modified neighbor-joining (NJ) algorithm, Bio-NJ tree, which were basic analyses limited to particular species. A more effective method of molecular identification is necessary. The current study evaluates the identification reliability and efficiency of DNA barcoding for the identification of Radix Astragali using six indicators of genetic distance, identification efficiency, intra- and interspecific variation, gap rate, and barcoding gap. Six barcodes were selected for identification because they are commonly used in plant, especially in medicinal plant. We collected Radix Astragaliand several of its adulterants reported in previous research and downloaded the genetic sequences from the GenBank database. A total of 29 species (including 19 species of Astragalus) and 478 sequences from six barcodes were used to validate the new method for identifying Radix Astragali and adulterants and to accelerate the data utilization of DNA barcoding.

2. Materials and Methods

2.1. Materials Information

A total of 77 specimens were collected from two origins of Radix Astragali, along with seven adulterants. Radix Astragali specimens were collected from Inner Mongolia, Shaan xi, and Gan su provinces in the People’s Republic of China, which are the main producing areas. The collection information is shown in Table 1. All corresponding voucher specimens were deposited in the Herbarium of the Institute of Medicinal Plant Development at the Chinese Academy of Medical Sciences in Beijing, China. The GenBank accession number of the ITS2 in this experiment was orderly KJ999296–KJ999344, the accession number of ITS sequences was orderly KJ999345–KJ999416, and the accession number of psbA-trnH was orderly KJ999256–KJ999295. The sequences added in the subsequent analysis, including ITS, ITS2, psbA-trnH, matK, and rbcL, were downloaded from the GenBank database.

Table 1: Taxon sampling information of astragalus and its adulterants.

Experiment number
species
Sampling spot

S1-S5
Astragalus membranaceus
Shaanxi
China

SD1-SD9
Astragalus membranaceus
Shaanxi
China

GS1-GS6
Astragalus mongholicus
Gansu
China

NM1-NM10
Astragalus mongholicus
Neimeng
China

SX1-SX10
Astragalus mongholicus
Shanxi
China

HHQ1-HHQ7
Astragalus chinensis
Beijing
China

CY1-CY6
Astragalus scaberrimus
Beijing
China

JK1-JK3
Malva pusilla
Shaanxi
China

MX
Medicago sativa
Shaanxi
China

HH1-HH7
Melilotus officinalis
Shaanxi
China

HQ1-HQ12
Hedysarum polybotrys
Gansu
China

XJ
Astragalus adsurgens
Beijing
China

2.2. DNA Extraction, PCR Amplification, and Sequencing

The material specimens were naturally dried and 30 mg of dried plant material was used for the DNA extraction. Samples were rubbed for two minutes at a frequency of 30 r/s in a FastPrep bead mill (Retsch MM400, Germany), and total genomic DNA was isolated from the crushed material according to the manufacturer’s instructions (Plant Genomic DNA Kit, Tiangen Biotech Co., China). We made the following modifications to the protocol: chloroform was diluted with isoamyl alcohol (24 : 1 in the same volume) and buffer solution GP2 with isopropanol (same volume). The powder, 700 μL of 65°C GP1, and 1 μL β-mercaptoethanol were mixed for 10–20 s before being incubated for 60 minutes at 65°C. Then, 700 μL of the chloroform:isoamyl alcohol mixture was added and the solution was centrifuged for 5 minutes at 12000 rpm (~13400 ×g). Supernatant was removed and placed into a new tube before adding 700 μL isopropanol and blending for 15–20 minutes. The mixture was centrifuged in CB3 spin columns for 40 s at 12000 rpm. The filtrate was discarded and 500 μL GD (adding quantitative anhydrous ethanol before use) was added before centrifuging at 12000 rpm for 40 s. The filtrate was discarded and 700 μL PW (adding quantitative anhydrous ethanol before use) was used to wash the membrane before centrifuging for 40 s at 12000 rpm. This step was repeated with 500 μL PW, followed by a final centrifuge for 2 minutes at 12000 rpm to remove residual wash buffer. The spin column was dried at room temperature for 3–5 minutes and then centrifuged for 2 minutes at 12000 rpm to obtain the total DNA.

General PCR reaction conditions and universal DNA barcode primers were used for the ITS, ITS2, and psbA-trnH barcodes, as presented in Table 2 [24–26]. PCR amplification was performed on 25-μL reaction mixtures containing 2 μL DNA template (20–100 ng), 8.5 μL ddH2O, 12.5 μL 2× Taq PCR Master Mix (Beijing TransGen Biotech Co., China), and 1/1-μL forward/reverse (F/R) primers (2.5 μM). The reaction mixtures were amplified in a 9700 GeneAmp PCR system (Applied Biosystems, USA). Amplicons were visualized by electrophoresis on 1% agarose gels. Purified PCR products were sequenced in both directions using the ABI 3730XL sequencer (Applied Biosystems, USA).

Table 2: Primers and PCR reaction conditions.

Primer name
Primer sequences (5′-3′)
PCR reaction condition

ITS2

    2F
ATGCGATACTTGGTGTGAAT
94°C 5 min;

    3R
GACGCTTCTCCAGACTACAAT
94°C 30 s, 56°C 30 s,

72°C 45 s, 40 cycles;

72°C 10 min;

ITS

    4R
TCCTCCGCTTATTGATATGC
94°C 5 min;

    5F
GGAAGTAAAAGTCGTAACAAGG
94°C 1 min, 50°C 1 min,

72°C 1.5 min + 3 s/cycle, 30 cycles;

72°C 7 min;

psbA

    fwdPA
GTTATGCATGAACGTAATGCTC
94°C 4 min;

trnH

    rev TH
CGCGCATGGTGGATTCACAATCC
94°C 30 s, 55°C 1 min,

72°C 1 min, 35 cycles;

72°C 10 min;

2.3. Sequence Assembly, Alignment, and Analysis

Sequencing peak diagrams were obtained and proofread, and then contigs were assembled using a CodonCode Aligner 5.0.1 (CodonCode Co., USA). Complete ITS2 sequences were obtained using the HMMer annotation method, based on the Hidden Markov model (HMM) [27]. All of the sequences were aligned using ClustalW, in combination with 317 sequences from six commonly used barcodes (ITS2, ITS, psbA-trnH, matK, rbcL, and COI), which were downloaded from the GenBank database (Table 3). Sequence genetic distance and GC content were calculated using the maximum composite likelihood model. Maximum likelihood (ML) trees were constructed based on the Tamura-Nei model, and bootstrap tests were conducted using 1000 repeats to assess the confidence of the phylogenetic relationships by MEGA 6.0 software [28]. The barcoding gap, defined as the spacer region between intra- and interspecific genetic variations, and identification efficiency, based on BLAST1 and K2P nearest distance, were performed by the Perl language algorithm (Putty) [25, 29, 30].

Table 3: Sequences from GenBank for identifying Astragalus and its adulterants.

Region
Family
Species
Accession number

ITS2
Fabaceae
Melilotus officinalis
U50765, Z97687

Fabaceae
Astragalus adsurgens
L10757, GU217639, GU217640, GU217641

Fabaceae
Astragalus chinensis
GQ434365, GQ434366

Fabaceae
Hedysarum polybotrys
GQ434367

Fabaceae
Astragalus mongholicus
GQ434368, GU217643

Fabaceae
Astragalus mongholicus var. dahuricus
GU217635

Fabaceae
Astragalus membranaceus
GU217642, JF421475

Fabaceae
Caragana sinica
GU217654

Fabaceae
Medicago sativa
GU217662, Z99236, AF028417, JN617208

Fabaceae
Medicago sativa subsp. caerulea
AF028418

Fabaceae
Medicago sativa subsp. glomerata
AF028419

Fabaceae
Medicago falcata
AF028420

Malvaceae
Alcea rosea
AF303023

ITS
Fabaceae
Astragalus membranaceus
AF359749, EF685968, EU852042, FJ572044, GU289659

GU289660, GU289661, GU289662, GU289663, GU289664

HM142272, HM142273, HM142274, HM142275, HM142276

HM142277, HM142278, HM142279, HM142280, HM142281

HQ891827, JX017320, JX017321, JX017322, JX017323

JX017324, JX017325, JX017326, JX017327, JX017328

JX017329, JX017330, JX017331, JX017332, AF121675

Fabaceae
Astragalus mongholicus
AF359750, EF685969, HM142282, HM142283, HM142284

HM142285, HM142286, HM142287, HM142288, HM142289

HM142290, JF736665, JF736666, JF736667, JF736668

JF736669, AB787166

Fabaceae
Astragalus propinquus
AF359751

Fabaceae
Astragalus lepsensis
AF359752

Fabaceae
Astragalus aksuensis
AF359753, AB231091

Fabaceae
Astragalus hoantchy
AF359754, AF521952

Fabaceae
Astragalus hoantchy subsp. dshimensis
AF359755

Fabaceae
Astragalus lehmannianus
AF359756

Fabaceae
Astragalus sieversianus
AF359757

Fabaceae
Astragalus austrosibiricus
AF359758

Fabaceae
Astragalus uliginosus
EF685970

Fabaceae
Astragalus scaberrimus
AB051988

Fabaceae
Astragalus chinensis
FJ980292, HM142297, AF121681

Fabaceae
Astragalus borealimongolicus
HM142291, HM142292, HM142293, HM142294, HM142295

HM142296

Fabaceae
Astragalus adsurgens
HM142298, HM142299, HQ199326

Fabaceae
Astragalus mongholicus var. dahuricus
HM142300, KC262199

Fabaceae
Astragalus zacharensis
HM142301

Fabaceae
Astragalus melilotoides
HM142302

Fabaceae
Astragalus scaberrimus
HM142303

Fabaceae
Astragalus sieversianus
AB741299

Fabaceae
Oxytropis anertii
EF685971

Fabaceae
Caragana sinica
DQ914785, FJ537284, GQ338283

Fabaceae
Glycyrrhiza pallidiflora
EU591998, GQ246130

Fabaceae
Melilotus officinalis
AB546796, JF461307, JF461308, JF461309, DQ311985

Fabaceae
Medicago sativa
GQ488541, AF053142, AY256392, JX017335, JX017336

JX017337, KF938697

Fabaceae
Oxytropis caerulea
GU217599, HQ199316

Fabaceae
Hedysarum vicioides
HM142304, HM142305

Fabaceae
Hedysarum polybotrys
JX017333, JX017334, KF032294

Malvaceae
Malva neglecta
EF419478, EF419479

Malvaceae
Alcea rosea
AH010172, EF419544, EF679714, JX017319

psbA-trnH
Fabaceae
Astragalus membranaceus f. pallidipurpureus
GQ139474

Fabaceae
Astragalus adsurgens
GU396749, GU396750, GU396751, KF011553

Fabaceae
Astragalus mongholicus
GU396754, AB787167

Fabaceae
Astragalus membranaceus
GQ139475, GQ139476, GQ139477, GQ139478, GQ139479

GQ139480, GQ139481, GQ139482, GQ139483, GU396752

GU396753

Fabaceae
Caragana sinica
GU396767, KJ025053

Fabaceae
Oxytropis caerulea
GU396771

Fabaceae
Medicago sativa
GU396781, HQ596768, HE966707

Fabaceae
Glycyrrhiza pallidiflora
GU396807

Fabaceae
Melilotus officinalis
HE966710

Malvaceae
Malva neglecta
EF419597, EF419598, HQ596765, HQ596765

Malvaceae
Alcea rosea
EF419662, EF679744

matK
Fabaceae
Astragalus membranaceus
EF685992, HM142232, HM142233, HM142234, HM142235

HM142236, HM142237, HM142238, HM142239, HM142240

HM142254

Fabaceae
Astragalus mongholicus
EF685993, HM142241, HM142242, HM142243, HM142244

HM142245, HM142246, HM142247, HM142255, HM142256

Fabaceae
Astragalus uliginosus
EF685994, HM142262

Fabaceae
Astragalus mongholicus var. dahuricus
HM049531, HM142260

Fabaceae
Astragalus chinensis
HM049533, HM142263

Fabaceae
Astragalus adsurgens
HM049537, HM142258, HM142259, AY920437

Fabaceae
Astragalus borealimongolicus
HM142248, HM142249, HM142250, HM142251, HM142252

HM142253

Fabaceae
Astragalus zacharensis
HM142261

Fabaceae
Astragalus melilotoides
HM142264

Fabaceae
Astragalus scaberrimus
HM142265

Fabaceae
Astragalus sieversianus
AB741343

Fabaceae
Medicago sativa
AF522108, HQ593363, HM851138, AY386881, HE967439

AF169289

Fabaceae
Oxytropis anertii
EF685995, HM142266

Fabaceae
Oxytropis caerulea
HM049544

Fabaceae
Glycyrrhiza pallidiflora
EF685997, HM142269, JQ619944

Fabaceae
Hedysarum vicioides
EF685996, HM142257, HM142267

Fabaceae
Caragana sinica
HM049541

Fabaceae
Melilotus officinalis
HE970723

Malvaceae
Malva neglecta
EU346788, HQ593360, JN894566, JN894571, JN895781

JQ412262,

Malvaceae
Alcea rosea
EU346805

rbcL
Fabaceae
Medicago sativa
Z70173

Fabaceae
Astragalus membranaceus
EF685978, HM142199, HM142200, HM142201, HM142202

HM142203, HM142204, HM142205, HM142206, HM142207

HM142221

Fabaceae
Astragalus mongholicus
EF685979, HM142208, HM142209, HM142210, HM142211

HM142212, HM142213, HM142214, HM142222, HM142223

Fabaceae
Astragalus uliginosus
EF685980, HM142225

Fabaceae
Hedysarum vicioides
EF685982, U74246, HM142224, HM142227,

Fabaceae
Astragalus adsurgens
EF685984

Fabaceae
Astragalus borealimongolicus
HM142215, HM142216, HM142217, HM142218, HM142219

HM142220,

Fabaceae
Oxytropis anertii
EF685981, HM142226

Fabaceae
Glycyrrhiza pallidiflora
EF685983, AB012129, HM142228

Fabaceae
Caragana sinica
FJ537233

Fabaceae
Melilotus officinalis
JQ933405, JX848463

3. Results

3.1. Sequence Information and Identification Efficiency

A total of 478 sequences for six barcodes were analyzed, from which 161 sequences were obtained from Astragalus Radix and its adulterants. Sequence information and identification success rates are listed in Table 4. The average GC content of six barcodes was discrepant, and ITS and ITS2 regions from nuclear ribosomal DNA performed higher than other barcodes (52.97% versus 50.80%). Among the six barcodes, ITS2 provided the largest average genetic distance (1.0792), and rbcL was the smallest (0.0349). All of the six barcodes obtained a zero value for the minimum genetic distance. In terms of identification efficiency, the nearest distance method was superior to the BLAST1 method for all of the six barcodes. Moreover, ITS and the psbA-trnH and matK regions provided a higher rate of success than the other three barcodes using the BLAST1 method. However, matK, ITS, and psbA-trnH performed better than the other three barcodes, based on the nearest distance method. ITS and psbA-trnH obtained higher genetic distances, so the matK, ITS, and psbA-trnH barcodes were the preferable methods for identifying Radix Astragali and its adulterants based on superior sequencing efficiency and identification efficiency.

Table 4: The information of identification efficiency for six barcodes.

Markers
COI
ITS2
ITS
matK
rbcL
psbA-trnH

Number of sequences
39
72
185
65
43
74

Average GC content/%
43.29
50.80
52.97
31.14
42.88
21.77

Genetic distance

    Min
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000

    Max
0.0086
7.9494
5.3130
0.2801
0.0349
2.2701

    Average
0.0019
1.0792
0.3508
0.0711
0.0116
0.5080

Identification efficiency/%

    BLAST 1/%
10.26
12.50
30.81
29.23
23.26
29.73

    Nearest distance/%
33.33
27.78
52.43
66.15
37.21
41.89

3.2. Intra- and Interspecific Variation Analysis Using Six Parameters

Six parameters to analyze intraspecific variation and interspecific divergence were employed to assess the utility of six DNA barcodes (Table 5). We expected the “minimum interspecific distance” would be higher than the “coalescent depth” (maximum intraspecific distance). Therefore, we first utilized the “gap rate” to indicate the distinctness, calculated by the formula: (minimum interspecific distance − maximum intraspecific distance)/minimum interspecific distance. Results show that the ITS2, COI, matK, and rbcL regions outperformed the ITS and psbA-trnH regions for gap rates. However, when we compared all of the average inter- and intraspecific distances, the ITS2, rbcL, matK, and psbA-trnH regions performed better than the ITS and COI regions. Therefore, in terms of intra- and interspecific variation, ITS2, matK, and rbcL are the preferable options for identifying Radix Astragali and its adulterants.

Table 5: Analysis of interspecific divergence and intraspecific variation for six barcodes.

Marker (Mean ± SD)
COI
ITS2
ITS
matK
rbcL
psbA-trnH

Theta
2.2260 ± 6.2961
0.0030 ± 0.0046
0.0271 ± 0.0404
0.0021 ± 0.0035
0.0011 ± 0.0020
0.2415 ± 0.4777

Coalescent depth
0.0001 ± 0.0004
0.0040 ± 0.0046
0.1423 ± 0.3958
0.0032 ± 0.0050
0.0016 ± 0.0030
0.4109 ± 0.5683

All intraspecific distance
9.3280 ± 0.0003
0.0021 ± 0.0024
0.1153 ± 0.3051
0.0014 ± 0.0022
0.0002 ± 0.0011
0.3093 ± 0.4300

Theta prime
0.0012 ± 0.0008
0.0617 ± 0.0302
0.0603 ± 0.0371
0.0091 ± 0.0061
0.0024 ± 0.0035
0.3083 ± 0.2887

Minimum interspecific distance
0.0008 ± 0.0010
0.0440 ± 0.0386
0.0168 ± 0.0196
0.0066 ± 0.0066
0.0023 ± 0.0035
0.0423 ± 0.0380

All interspecific distance
0.0007 ± 0.0010
0.0343 ± 0.0389
0.1066 ± 0.2833
0.0071 ± 0.0064
0.0015 ± 0.0029
0.3166 ± 0.4070

Gap rate/%
87.50
90.91
/
51.52
30.43
/

3.3. Barcoding Gap Analysis

Analysis of the DNA barcoding gap presents the divergence of inter- and intraspecies and indicates separate, nonoverlapping distribution between specimens in an ideal situation [25]. In our study (Figure 1), the rbcL, COI, ITS, and matK regions possessed less relative distribution of inter- and intraspecific variation than psbA-trnH and ITS2, although there were no nonoverlapping regions for the six barcodes. Hence, the rbcL, COI, ITS, and matK regions are more successful at identifying Radix Astragali and its adulterants, from the standpoint of barcoding gap analysis.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 1: Barcoding gap for six barcodes.

3.4. ML Tree Analysis

Maximum likelihood (ML) is a general statistical criterion in widespread use for the inference of molecular phylogenies [31]. An ML tree visually revealed the relationship between species. As the results show (Figure 2), psbA-trnH successfully differentiated Radix Astragali and its adulterants. Furthermore, it produced areas of obvious separation for Radix Astragali. The remaining five barcodes also differentiated Radix Astragali and its adulterants. Each species clustered together, separate from other species. Considering the difficult amplification and sequencing and fast and accurate identification purpose of DNA barcoding, we did not add all the sequence data of ITS2 and psbA-trnH to build ML tree and subsequent analysis.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: ML tree for six barcodes. *The different color and shape for different species in clusters presented the identification of different barcodes.

4. Discussion and Conclusions

Radix Astragali is reported to possess 47 bioactive compounds and has many bioactive properties [32–37]. Various Radix Astragali preparations are commercially available, not only in China as a TCM component, but also in the United States, as dietary supplements [38]. However, due to increasing demand, substitutes and adulterants have flooded the market. Traditional identification methods, such as morphological and microscopic methods, are limited by the lack of explicit criteria for character selection or coding and, thus, mainly depend on subjective assessments. Although chemical methods are able to distinguish between different species, it is difficult to differentiate sibling species that possess similar chemical compositions. In addition, chemical methods are unable to provide accurate species authentication. Several types of molecular markers for characterizing genotypes are useful in identifying plant species. For example, RAPD has been used to estimate genetic diversity in plant populations based on amplification of random DNA fragments and comparisons of common polymorphisms. DNA barcoding is advocated for species identification, due to its universal applicability, simplicity, and scientific accuracy. However, the analysis methods for DNA barcodes were limited. With the development of molecular biology and bioinformatics, a more improved analytic method for DNA barcoding can be established to identify Radix Astragali and closely related species.

In this study, we validated a new analytical method for identifying Radix Astragali using DNA barcoding. Seventy-seven specimens of Radix Astragali and its adulterants were collected, and the sequences of 29 species reported in the literature were downloaded from the GenBank database. Based on the 478 sequences for six barcodes (ITS2, ITS from nuclear genome; psbA-trnH, rbcL, and matK from chloroplast genome; COI from mitochondrial genome), genetic distance and ML Tree were calculated by MEGA 6.0 software, and identification efficiency, intra- and interspecific variation, and barcoding gap were calculated using the Perl language algorithm. Results of the six indicators assessed are shown in Table 6. ITS and psbA-trnH outperformed other barcodes in terms of identification efficiency. ITS2 performed better in terms of genetic distance, gap rate, and inter- and intraspecific variation. RbcL performed better in terms of barcoding gap and inter- and intraspecific variation. Although ITS2 was part of the ITS sequence, it performed poorly in identification efficiency. Therefore, we suggest that the ITS sequence is the optimal barcode, and that the psbA-trnH region is a complementary barcode for identifying Radix Astragali and its adulterants.

Table 6: Six indicators assessed for DNA barcoding.

DNA barcodes
Parameters

Average genetic distance
Identification efficiency
Gap rate
Inter- to intraspecific variation
Barcoding gap
Total score

BLAST1
Nearest distances

ITS2
8
12
8
8
8
4
48

ITS
6
28
22
0
0
6
62

psbA-trnH
6
26
18
0
2
2
54

rbcL
4
12
14
4
6
8
48

matK
4
14
24
4
4
2
52

COI
2
6
10
6
0
6
30

*The total score of six parameters was set by 10, 30, 30, 10, 10, and 10 in order. Identification efficiency based on two methods was set by 30 score because of its importance for identification.

In conclusion, we describe a new analytical method for the use of DNA barcoding in the identification of Radix Astragali. Six indicators, including average genetic distance, BLAST1 and the nearest distance method for identification efficiency, inter- and intraspecific variation, and gap rate were tested to evaluate six DNA barcodes using bioinformatics software and the Perl language algorithm. The ITS sequence was the optimal barcode for identifying Radix Astragali and its adulterants. This method provides a novel means for accurate identification of Radix Astragali and its adulterants and improves the utilization of DNA barcoding in identifying medicinal plant species.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

Thanks are due to the National Natural Science Foundation of China (nos. 81274013, 8130069, and 81473315) and the National Science and Technology Major Projects for “Major New Drugs Innovation and Development” (no. 2011BAI07B01).

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