Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2014 / Article
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Sustainable Utilization of TCM Resources

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Research Article | Open Access

Volume 2014 |Article ID 843923 | 11 pages | https://doi.org/10.1155/2014/843923

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

Academic Editor: Robert Henry
Received15 Jun 2014
Accepted22 Jul 2014
Published27 Aug 2014

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 [13]. It possesses various beneficial compounds, including astragalosides, isoflavonoids, isoflavones, isoflavan, and pterocarpan glycosides [46].

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.


Experiment number speciesSampling spot

S1-S5Astragalus membranaceus ShaanxiChina
SD1-SD9Astragalus membranaceus ShaanxiChina
GS1-GS6Astragalus mongholicus Gansu China
NM1-NM10Astragalus mongholicus NeimengChina
SX1-SX10Astragalus mongholicus Shanxi China
HHQ1-HHQ7Astragalus chinensis BeijingChina
CY1-CY6Astragalus scaberrimus BeijingChina
JK1-JK3Malva pusilla ShaanxiChina
MXMedicago sativa ShaanxiChina
HH1-HH7Melilotus officinalis ShaanxiChina
HQ1-HQ12Hedysarum polybotrys GansuChina
XJAstragalus adsurgens BeijingChina

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 [2426]. 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).


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

ITS2
 2FATGCGATACTTGGTGTGAAT94°C 5 min;
 3RGACGCTTCTCCAGACTACAAT94°C 30 s, 56°C 30 s,
72°C 45 s, 40 cycles;
72°C 10 min;
ITS
 4RTCCTCCGCTTATTGATATGC94°C 5 min;
 5FGGAAGTAAAAGTCGTAACAAGG94°C 1 min, 50°C 1 min,
72°C 1.5 min + 3 s/cycle, 30 cycles;
72°C 7 min;
psbA
 fwdPAGTTATGCATGAACGTAATGCTC94°C 4 min;
trnH
 rev THCGCGCATGGTGGATTCACAATCC94°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].


RegionFamilySpeciesAccession number

ITS2FabaceaeMelilotus officinalis U50765, Z97687
FabaceaeAstragalus adsurgens L10757, GU217639, GU217640, GU217641
FabaceaeAstragalus chinensis GQ434365, GQ434366
FabaceaeHedysarum polybotrys GQ434367
FabaceaeAstragalus mongholicus GQ434368, GU217643
FabaceaeAstragalus mongholicus var. dahuricus GU217635
FabaceaeAstragalus membranaceus GU217642, JF421475
FabaceaeCaragana sinica GU217654
FabaceaeMedicago sativa GU217662, Z99236, AF028417, JN617208
FabaceaeMedicago sativa subsp. caerulea AF028418
FabaceaeMedicago sativa subsp. glomerata AF028419
FabaceaeMedicago falcata AF028420
MalvaceaeAlcea rosea AF303023

ITSFabaceaeAstragalus 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
FabaceaeAstragalus mongholicus AF359750, EF685969, HM142282, HM142283, HM142284
HM142285, HM142286, HM142287, HM142288, HM142289
HM142290, JF736665, JF736666, JF736667, JF736668
JF736669, AB787166
FabaceaeAstragalus propinquus AF359751
FabaceaeAstragalus lepsensis AF359752
FabaceaeAstragalus aksuensis AF359753, AB231091
FabaceaeAstragalus hoantchy AF359754, AF521952
FabaceaeAstragalus hoantchy subsp. dshimensis AF359755
FabaceaeAstragalus lehmannianus AF359756
FabaceaeAstragalus sieversianus AF359757
FabaceaeAstragalus austrosibiricus AF359758
FabaceaeAstragalus uliginosus EF685970
FabaceaeAstragalus scaberrimus AB051988
FabaceaeAstragalus chinensis FJ980292, HM142297, AF121681
FabaceaeAstragalus borealimongolicus HM142291, HM142292, HM142293, HM142294, HM142295
HM142296
FabaceaeAstragalus adsurgens HM142298, HM142299, HQ199326
FabaceaeAstragalus mongholicus var. dahuricus HM142300, KC262199
FabaceaeAstragalus zacharensis HM142301
FabaceaeAstragalus melilotoides HM142302
FabaceaeAstragalus scaberrimus HM142303
FabaceaeAstragalus sieversianus AB741299
FabaceaeOxytropis anertii EF685971
FabaceaeCaragana sinica DQ914785, FJ537284, GQ338283
FabaceaeGlycyrrhiza pallidiflora EU591998, GQ246130
FabaceaeMelilotus officinalis AB546796, JF461307, JF461308, JF461309, DQ311985
FabaceaeMedicago sativa GQ488541, AF053142, AY256392, JX017335, JX017336
JX017337, KF938697
FabaceaeOxytropis caerulea GU217599, HQ199316
FabaceaeHedysarum vicioides HM142304, HM142305
FabaceaeHedysarum polybotrys JX017333, JX017334, KF032294
MalvaceaeMalva neglecta EF419478, EF419479
MalvaceaeAlcea rosea AH010172, EF419544, EF679714, JX017319

psbA-trnHFabaceaeAstragalus membranaceus f. pallidipurpureus GQ139474
FabaceaeAstragalus adsurgens GU396749, GU396750, GU396751, KF011553
FabaceaeAstragalus mongholicus GU396754, AB787167
FabaceaeAstragalus membranaceus GQ139475, GQ139476, GQ139477, GQ139478, GQ139479
GQ139480, GQ139481, GQ139482, GQ139483, GU396752
GU396753
FabaceaeCaragana sinica GU396767, KJ025053
FabaceaeOxytropis caerulea GU396771
FabaceaeMedicago sativa GU396781, HQ596768, HE966707
FabaceaeGlycyrrhiza pallidiflora GU396807
FabaceaeMelilotus officinalis HE966710
MalvaceaeMalva neglecta EF419597, EF419598, HQ596765, HQ596765
MalvaceaeAlcea rosea EF419662, EF679744

matKFabaceaeAstragalus membranaceus EF685992, HM142232, HM142233, HM142234, HM142235
HM142236, HM142237, HM142238, HM142239, HM142240
HM142254
FabaceaeAstragalus mongholicus EF685993, HM142241, HM142242, HM142243, HM142244
HM142245, HM142246, HM142247, HM142255, HM142256
FabaceaeAstragalus uliginosus EF685994, HM142262
FabaceaeAstragalus mongholicus var. dahuricus HM049531, HM142260
FabaceaeAstragalus chinensis HM049533, HM142263
FabaceaeAstragalus adsurgens HM049537, HM142258, HM142259, AY920437
FabaceaeAstragalus borealimongolicus HM142248, HM142249, HM142250, HM142251, HM142252
HM142253
FabaceaeAstragalus zacharensis HM142261
FabaceaeAstragalus melilotoides HM142264
FabaceaeAstragalus scaberrimus HM142265
FabaceaeAstragalus sieversianus AB741343
FabaceaeMedicago sativa AF522108, HQ593363, HM851138, AY386881, HE967439
AF169289
FabaceaeOxytropis anertii EF685995, HM142266
FabaceaeOxytropis caerulea HM049544
FabaceaeGlycyrrhiza pallidiflora EF685997, HM142269, JQ619944
FabaceaeHedysarum vicioides EF685996, HM142257, HM142267
FabaceaeCaragana sinica HM049541
FabaceaeMelilotus officinalis HE970723
MalvaceaeMalva neglecta EU346788, HQ593360, JN894566, JN894571, JN895781
JQ412262,
MalvaceaeAlcea rosea EU346805

rbcLFabaceaeMedicago sativa Z70173
FabaceaeAstragalus membranaceus EF685978, HM142199, HM142200, HM142201, HM142202
HM142203, HM142204, HM142205, HM142206, HM142207
HM142221
FabaceaeAstragalus mongholicus EF685979, HM142208, HM142209, HM142210, HM142211
HM142212, HM142213, HM142214, HM142222, HM142223
FabaceaeAstragalus uliginosus EF685980, HM142225
FabaceaeHedysarum vicioides EF685982, U74246, HM142224, HM142227,
FabaceaeAstragalus adsurgens EF685984
FabaceaeAstragalus borealimongolicus HM142215, HM142216, HM142217, HM142218, HM142219
HM142220,
FabaceaeOxytropis anertii EF685981, HM142226
FabaceaeGlycyrrhiza pallidiflora EF685983, AB012129, HM142228
FabaceaeCaragana sinica FJ537233
FabaceaeMelilotus 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.


Markers COIITS2ITSmatKrbcLpsbA-trnH

Number of sequences3972185654374
Average GC content/%43.2950.8052.9731.1442.8821.77
Genetic distance
 Min0.00000.00000.00000.00000.00000.0000
 Max0.00867.94945.31300.28010.03492.2701
 Average0.00191.07920.35080.07110.01160.5080
Identification efficiency/%
 BLAST 1/%10.2612.5030.8129.2323.2629.73
 Nearest distance/%33.3327.7852.4366.1537.2141.89

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.


Marker (Mean ± SD)COIITS2ITSmatKrbcLpsbA-trnH

Theta2.2260 ± 6.29610.0030 ± 0.00460.0271 ± 0.04040.0021 ± 0.00350.0011 ± 0.00200.2415 ± 0.4777
Coalescent depth0.0001 ± 0.00040.0040 ± 0.00460.1423 ± 0.39580.0032 ± 0.00500.0016 ± 0.00300.4109 ± 0.5683
All intraspecific distance9.3280 ± 0.00030.0021 ± 0.00240.1153 ± 0.30510.0014 ± 0.00220.0002 ± 0.00110.3093 ± 0.4300
Theta prime0.0012 ± 0.00080.0617 ± 0.03020.0603 ± 0.03710.0091 ± 0.00610.0024 ± 0.00350.3083 ± 0.2887
Minimum interspecific distance0.0008 ± 0.00100.0440 ± 0.03860.0168 ± 0.01960.0066 ± 0.00660.0023 ± 0.00350.0423 ± 0.0380
All interspecific distance0.0007 ± 0.00100.0343 ± 0.03890.1066 ± 0.28330.0071 ± 0.00640.0015 ± 0.00290.3166 ± 0.4070
Gap rate/%87.5090.91/51.5230.43/

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.

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.

4. Discussion and Conclusions

Radix Astragali is reported to possess 47 bioactive compounds and has many bioactive properties [3237]. 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.


DNA barcodesParameters
Average genetic distanceIdentification efficiencyGap rateInter- to intraspecific variationBarcoding gapTotal score
BLAST1Nearest distances

ITS2812888448
ITS6282200662
psbA-trnH6261802254
rbcL4121446848
matK4142444252
COI261060630

*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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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.

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