SCIENCE CHINA Life Sciences • RESEARCH PAPERS •

June 2010 Vol.53 No.6: 701–708 doi: 10.1007/s11427-010-4009-1

Assessment of candidate plant DNA barcodes using the Rutaceae family LUO Kun1,2, CHEN ShiLin1*, CHEN KeLi2, SONG JingYuan1, YAO Hui1, MA XinYe1, ZHU YingJie3, PANG XiaoHui1, YU Hua1, LI XiWen1,4 & LIU Zhen2 1

Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; 2 College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430061, China; 3 School of Bioscience and Engineering, Southwest Jiaotong University, Chengdu 610031, China; 4 Department of Chemistry, Tsinghua University, Beijing 100084, China Received October 20, 2009; accepted February 5, 2010

DNA barcoding is a rapidly developing frontier technology that is gaining worldwide attention. Here, seven regions (psbA-trnH, matK, ycf5, rpoC1, rbcL, ITS2, and ITS) with potential for use as DNA barcodes were tested for their ability to identify 300 samples of 192 species from 72 genera of the family Rutaceae. To evaluate each barcode’s utility for species authentication, PCR amplification efficiency, genetic divergence, and barcoding gaps were assessed. We found that the ITS2 region exhibited the highest inter-specific divergence, and that this was significantly higher than the intra-specific variation in the “DNA barcoding gap” assessment and Wilcoxon two-sample tests. The ITS2 locus had the highest identification efficiency among all tested regions. In a previous study, we found that ITS2 was able to discriminate a wide range of plant taxa, and here we confirmed that ITS2 was also able to discriminate a number of closely related species. Therefore, we propose that ITS2 is a promising candidate barcode for plant species identification. DNA barcoding, ITS2, Rutaceae, identification

Citation:

Luo K, Chen S L, Chen K L, et al. Assessment of candidate plant DNA barcodes using the Rutaceae family. Sci China Life Sci, 2010, 53: 701–708, doi: 10.1007/s11427-010-4009-1

DNA barcoding, which was first proposed as a species identification system by Hebert et al. [1], is a new technique that uses DNA sequences from a small fragment of the genome to identify species. Recently, DNA barcoding has become a hotspot of biodiversity research [2,3]. In 2003, Hebert et al. [4] examined sequence divergence in more than 13000 congeneric pairs, including representatives from 11 phyla (with the exception of Cnidaria) and found that species-level diagnoses could routinely be obtained through CO1 analysis. In subsequent research [5-7], the CO1 gene came to be recognized as the standard DNA barcode for animals. Because plant species are more prone to hybridiza*Corresponding author (email: [email protected]) © Science China Press and Springer-Verlag Berlin Heidelberg 2010

tion and reticulate evolutionary histories, the study of plant barcodes is much more complicated than that of animals [8,9]. In recent years, a number of single loci and combined loci have been proposed as barcode sequences for plant identification [10-12], but no consensus has been reached. In 2009, based on the performance of seven plastid DNA regions (atpF-atpH, matK, rbcL, rpoB, rpoC1, psbK-psbI, and psbA-trnH) among 907 samples from 550 species, the Plant Working Group of the Consortium for the Barcode of Life recommended the combination of rbcL and matK as the plant barcode [13]. In 2010, Chen et al. [14] compared seven candidate DNA barcodes (psbA-trnH, matK, rbcL, rpoC1, ycf5, ITS2, and ITS) from medicinal plant species and tested the discrimination ability of ITS2 in more than life.scichina.com

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6600 plant samples. They determined the ITS2 region as a novel DNA barcode for identifying medicinal plant species. An ideal barcode sequence is universal in a number of families and efficient when there are a lot of related species. Although some scholars have carried out DNA barcoding research in related species and genera [15-18], no one has extensively evaluated the Rutaceae family. Throughout the world, there are 180 genera and about 1300-1600 species in the Rutaceae family. About 28 genera with 150 species (including both introduced and cultivated species) are found in China. Plants of this family have great economic value. Genera like Zanthoxylum, Citrus, Phellodendron, and Evodia are sources of medicinal herbs; Citrus is a famous aromatic; Citrus, Poncirus, and Fortunella are good fruit resources; and Murraya, Atalantia, and Citrus are important garden greening plants. Correct species identification is the basis for the study of plants, and it is also fundamental to resource conservation. Traditional plant identification methods are based on morphological characteristics. With the development of molecular biology, DNA molecular marker technology has been widely used in various fields. The new molecular species identification technique of DNA barcoding has the following characteristics: (i) no restrictions on individual morphological characteristics; (ii) no impact on the individual developmental stage; and (iii) simple methodology (http://phe.rockefeller.edu/barcode/). Therefore, we selected the Rutaceae family to assess the identification efficiency of candidate DNA barcode sequences among many related species.

1 1.1

Materials and methods Taxon sampling

To select the most suitable DNA barcoding fragments, a total of 300 closely related samples belonging to 192 species from 72 diverse genera of the family Rutaceae were chosen. The first set of plant samples were collected in 9 provinces and 1 municipality of China (Beijing, Guangxi, Yunnan, Anhui, Hainan, Sichuan, Fujian, Chongqing, Jilin, and Hubei) and comprised 32 samples belonging to 19 species from nine diverse genera (Supplementary Table 1, online supporting data). All corresponding voucher samples were deposited in the Herbarium of the Institute of Medicinal Plant Development, Chinese Academy of Medicinal Sciences. A second set of plant sequences was downloaded from GenBank (Supplementary Table 2, online supporting data), which comprised 268 samples belonging to 182 species from 70 diverse genera, in which there were 29 genera containing no less than two species and 10 genera containing no less than five species. Forty-nine species were represented by less than two samples, and eight species were represented by less than five samples. The research materials covered more than 70% of the genera of the Rutaceae family in the territory of China, including most of the im-

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portant genera (Poncirus, Phellodendron, Citrus, and Evodia). 1.2

Methods

1.2.1 PCR amplification and sequencing of candidate DNA barcodes Leaf tissues were first dried in silica gel. A total of 10 mg of each of the dried tissues was rubbed for one minute at a frequency of 30 times/second in a FastPrep bead mill (Retsch MM400, Germany). DNA extractions were performed using the Plant Genomic DNA Kit (Tiangen Biotech Co., China), according to the manufacturer’s instructions. The ITS2 sequence forward primer was 5ƍ-GCGATACTTG GTGTGAAT-3ƍ, and the reverse primer was 5ƍ-GACGCTT CTCCAGACTACAAT-3ƍ. PCR amplification was performed in 25 ȝL reaction mixtures containing approximately 30 ng of genomic DNA template, 1 × PCR buffer without MgCl2, 2.0 mmol L–1 MgCl2, 0.2 mmol L–1 of each dNTP, 0.1 ȝmol L–1 of each primer (synthesized by Sangon Co., China), and 1.0 U Taq DNA Polymerase (Biocolor BioScience & Technology Co., China) in a Peltier Thermal Cycler PTC0200 (BioRad Lab, Inc., USA). Other primers for the DNA barcodes to be tested and general PCR reaction conditions were obtained from previous studies [14-16]. Purified PCR products were sequenced in both directions with the primers used for PCR amplification on a 3730XL sequencer (Applied Biosystems, USA). 1.2.2 Data analyses To estimate the quality of the generated sequence traces, the original forward and reverse sequences were assembled using CodonCode Aligner 3.0 (CodonCode Co., USA). Sequence alignment and checking were carried out using ClustalW. All sequences containing ITS2 or psbA-trnH were retrieved according to Keller et al. [19] and GenBank annotations. Sequences with more than ten ambiguous nucleotides and those shorter than 100 bp were discarded. The inter/intra-specific variation of the samples was calculated according to Kress et al. [8] and Song et al. [15]. After the number of samples was expanded, we followed the method of Meyer et al. [20]. Wilcoxon signed rank tests were used as described previously [14]. The distribution of inter/intra-specific variability was compared using DNA barcoding gaps with the software TAXON DNA [21] and Wilcoxon two-sample tests. Two methods of species identification [14], including BLAST1 and the nearest distance method, were performed as described previously [22].

2

Results

2.1 The efficiency of PCR amplification and the success rate of sequencing The efficiency of PCR amplification, the success rate of

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sequencing, and the effective sequence ratio of the seven candidates were compared. We found that psbA-trnH provided the highest efficiency of PCR amplification, followed by ITS2, with ITS being the lowest. ITS had the highest success rate of sequencing, followed by rbcL, with matK being the lowest. ITS and matK performed poorly in this test, and therefore, they were not included in the subsequent experiments (Figure 1). 2.2 Inter/intra-specific variation and the length of each sequence Among the five remaining candidate regions (ITS2, rbcL,

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psbA-trnH, ycf5, and rpoC1), ITS2 had the highest inter-specific divergence, followed by psbA-trnH. Meanwhile, ITS2 and psbA-trnH had higher intra-specific divergence. rbcL generated the longest amplification fragment and ITS2 was the shortest. However, the lengths of ITS2 and psbA-trnH were different in different species. The length differences of the psbA-trnH sequences were greater than those of the ITS2 sequences. The other three sequences demonstrated no differences in length between samples (Tables 1, 2). To verify the results, we validated the conclusions with a number of examples from both the experimental data and from the GenBank database of the Rutaceae family. In

Figure 1 The efficiency of PCR amplification, the success rate of sequencing, and the effective sequence ratio of the seven candidates. Note: PCR amplification was considered as successful when there was a clear and bright band; a high quality sequence was judged to be successful sequencing. Table 1 Sequence length and inter-specific variation of five candidate sequencesa) Species (number of individuals sampled) Citrus (7 spp.)

ITS2, length (bp)/% variation (number of species amplified)

rbcL, length (bp)/% variation (number of species amplified)

rpoC1, length (bp)/% variation (number of species amplified)

ycf5, length (bp)/% variation (number of species amplified)

424-434(6)/0.23,0.23, 4.90,4.90,4.90,0.00,4. 63,4.63,4.63,4.63,4.6 3,4.63,0.00,0.00,0.00 (2.86)

703(5)/0.00,0.57,0.57, 0.00,0.57,0.57,0.00,0.0 0,0.57,0.57(0.34)

487(5)/0.00,0.21,0.21 ,0.00,0.21,0.21,0.00,0 .00,0.21 0.21, (0.13)

376(3)/0.53,0.53,0.00 (0.35)

448(1)/

703(2)/1.73(1.73)

487(1)/

376(1)/

419-422(2)/ 1.93(1.93) 446(2)/0.45(0.45)

703(2)/0.00(0.00)

487(2)/0.00(0.00)

376(2)/0.00(0.00)

703(2)/1.0(1.0)

487(2)/0.20(0.20)

376(2)/2.43(2.43)

Evodia (2 spp.)

231(7)/0.00, 3.09, 5.00, 2.65, 5.00, 3.09, 5.00, 2.65, 3.09, 5.00, 2.65, 1.75, 1.31, 0.87, 1.75, 1.31(2.60) 223(1)/

Phellodendron(2 spp.)

226(1)/

Zanthoxylum (3 spp.) Clausena (1 spp.)

224-225(2)/ 13.44(13.44) (0)/

447(1)//

703(1)/

(0)/

376(1)/

Dictamnus (1 spp.)

222(1)/

373(1)/

(0)/

(0)/

(0)/

Glycosmis (1 spp.) Murraya (1 spp.) Poncirus (1 spp.)

221(1)/ 222(1)/ 236(1)/

(0)/ 473(1)/ 447(1)/

703(1)/ 703(1)/ 703(1)/

(0)/ 487(1)/ (0)/

376(1)/ 376(1)/ 376(1)/

Mean sequence divergence (%) Range of sequence length (bp)

3.54

2.51

0.55

0.12

0.84

221-236

373-447

703

487

376

a) “” Indicates that the data are missing.

0.00, 0.00, 5.00, 5.00, 0.39,

psbA-trnH, length (bp)/% variation (number of species amplified)

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comparisons of inter-specific genetic distances among congeneric species using five candidate barcodes, ITS2 and psbA-trnH exhibited significantly larger variation among species than the other three candidates. Additionally, their minimum inter-specific variation was much larger than their maximum intra-specific variation (Coalescent depth). For the other candidate barcodes, there was no significant differences (Table 3). 2.3 Validation of the different sequences’ inter/intraspecific variations Using Wilcoxon signed rank tests, we confirmed that ITS2

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provided the highest inter-specific divergence between congeneric species. rpoC1 showed the lowest variation between conspecific individuals and ITS2 showed the highest (Tables 4, 5). 2.4

Barcoding gap assessment

Ideally, the genetic variation of a DNA barcode should have separate, non-overlapping distributions between intra- and inter-specific samples. Here, the results demonstrated that the intra/inter-specific variation of ITS2, psbA-trnH, rpoC1, and ycf5 exhibited distinct gaps, but when intra-specific variation between conspecific individuals and inter-specific

Table 2 Sequence length and intra-specific variation for five candidate sequencesa) rbcL, length (bp)/ % divergence (mean)

rpoC1, length (bp)/ % divergence (mean)

ycf5, length (bp)/ % divergence (mean)

ITS2, Length (bp)/ % divergence (mean)

psbA-trnH, length (bp)/ % divergence (mean)

231(3)/0.43,1.32,0. 87(0.87)

703(0)/

487(3)/0.00(0.00)

376(1)/

231(2)/0.87(0.87)

433(5)/0.24,0.00,0.46,0.24,0 .24,0.00,0.00,0.46,0.70,0.46 (0.28) 433(2)/0.46(0.46)

703(1)/

487(0)/

376(1)/

Tomentosa (2)

231(2)/0.00(0.00)

424(2)/0.00(0.00)

703(2)/0.00(0.00)

487(1)/

376(1)/

Citru medica (3)

223(1)/

448(1)/

703(1)/

487(1)/

376(0)/

Genus (number of species sampled) Citrus grandis (6)

Citrus grandis var.

Evodia rutaecarpa (2) Murray exotica (5)

222(4)/0.00(0.00)

473(3)/0.85,0.43,0.42 (0.57)

703(3)/0.00(0.00)

487(2)/0.00

376(4)/0.00(0.00)

Mean sequence divergence (%) Range of sequence length (bp)

0.40

0.34

0.00

0.00

0.00

221-231

424473

703

487

376

a) “” indicates that the data are missing.

Table 3

Analysis of inter-specific divergence between congeneric species and intra-specific variation for the whole sample ITS2

psbA-trnH

rbcL

rpoC1

ycf5

All inter-specific distance

0.0422±0.0468

0.0402±0.0510

0.0069±0.0051

0.0016±0.0017

0.0099±0.0128

Theta prime

0.0844±0.0768

0.0656±0.0987

0.0090±0.0068

0.0024±0.0027

0.0099±0.0128

Minimum inter-specific distance

0.0553±0.0832

0.0489±0.1030

0.0073±0.0081

0.0021±0.0029

0.0099±0.0128

All intra-specific distance

0.0123±0.0104

0.0029±0.0028

0.0028±0.0051

0.0000±0.0000

0.0000±0.0000

Theta

0.0115±0.0157

0.0023±0.0028

0.0042±0.0061

0.0000±0.0000

0.0000±0.0000

Coalescent depth

0.0172±0.0180

0.0045±0.0051

0.0043±0.0061

0.0000±0.0000

0.0000±0.0000

Table 4 Wilcoxon signed rank test for inter-specific variation W+ ITS2 ITS2 ITS2 psbA-trnH psbA-trnH psbA-trnH psbA-trnH rbcL rbcL ycf5

W rbcL rpoC1 ycf5 ITS2 rbcL rpoC1 ycf5 ycf5 rpoC1 rpoC1

Relative rank, n, P value W+ = 28.0, W = 0, n = 7, Pİ0.0176 W+ = 55.0, W = 0, n = 10, Pİ0.0049 W+ = 1.0, W = 0, n = 1, Pİ0.3173 W+ = 27.0, W = 379.0, n = 28, Pİ6.1193×10-15 W+ = 66.0, W = 0, n = 11, Pİ0.0033 W+ = 28.0, W = 0, n = 7, Pİ0.0180 W+ = 3.0, W = 0, n = 2, Pİ0.1797 W+ = 1.0, W = 0, n = 1, Pİ0.3173 W+ = 3.0, W = 0, n = 2, Pİ0.1572 W+ = 0.0, W = 3.0, n = 2, Pİ0.1797

Result ITS2 > rbcL ITS2 > rpoC1 ITS2 = ycf5 psbA-trnH << ITS2 psbA-trnH > rbcL psbA-trnH > rpoC1 psbA-trnH = ycf5 rbcL = ycf5 rbcL = rpoC1 ycf5 = rpoC1

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Table 5

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Wilcoxon signed rank test for intra-specific variationa) W+

W

ITS2

rbcL

Relative Rank, n, P value

Result

W+ = 4.0, W = 2.0, n = 3, Pİ0.5930

ITS2 = rbcL

ITS2

rpoC1

W+ = 15.0, W = 0, n = 5, Pİ0.0422

ITS2 > rpoC1

ITS2

ycf5

W+ = 1.0, W = 9.0, n = 4, Pİ0.1305

ITS2 = ycf5

psbA-trnH

ITS2

W+ = 25.0, W = 111.0, n = 16, Pİ0.0260

psbA-trnH < ITS2

psbA-trnH

rbcL

W+ = 6.0, W = 0, n = 3, Pİ0.1088

psbA-trnH = rbcL

psbA-trnH

rpoC1

W+ = 10.0, W = 0, n = 4, Pİ0.0679

psbA-trnH = rpoC1

psbA-trnH

ycf5

W+ = 1.0, W = 5.0, n = 3, Pİ0.2850

psbA-trnH = ycf5

rbcL

rpoC1

rbcL

ycf5

ycf5

rpoC1

 W+ = 0, W = 3.0, n = 2, Pİ0.1305 

 rbcL = ycf5 

a) “” indicates the missing data. The Wilcoxon signed-rank test focuses on the same part of the two tested sequences, so the asymmetry of each sequence causes a partial loss of results. For example, rbcL and rpoC1 have no intra-specific Wilcoxon test result because there were not two or more samples of one species having rbcL and rpoC1 at the same time in the study. ycf5 and rpoC1 have no intra-specific result for the same reason.

divergence between all hetero-specifics were calculated using rbcL, there was significant overlap without gaps (Figure 2). Furthermore, Wilcoxon two-sample tests showed that ITS2 and psbA-trnH have extremely significant differences between their mean inter-specific divergence and the corresponding intra-specific variation, and that the difference of ITS2 is more significant. 2.5 Evaluation of the species authentication capability of the barcodes We used the BLAST 1 method and the nearest distance method to simultaneously assess the identification efficiency of the sequences. The results indicated that ITS2 has the highest success rate at the species level with both methods (Table 6).

3 3.1 ily

Discussion Selection of a DNA barcode for the Rutaceae fam-

The key to DNA barcoding is to find a suitable barcode sequence. In animals, a portion of CO1 is well established as the standard universal barcode. However, progress in plants has been hampered by slow substitution rates in mitochondrial DNA. The proposed chloroplast barcoding regions mainly include coding (rpoC1, matK, rbcL, and ycf5) and non-coding regions (psbA-trnH). In addition, nr ITS has also been suggested as a potential plant barcode by Kress et al. [8]. In recent years, researchers have mainly studied the ITS and ITS1 regions because of their higher inter-specific divergences compared to the ITS2 region [8,11]. However, universal primers and PCR conditions for ITS and ITS1 for a broad range of taxa have not been found [10,14,23,24]. At

the Second International Barcode Conference in 2007, ITS was even excluded from the candidate plant barcode sequences [12]. On the other hand, many researchers have already proposed ITS2 as a suitable marker applicable for phylogenetic reconstruction and taxonomic classification [18,25,26]. The advantages of ITS2 include its relative ease of amplification using one pair of universal primers selected by our group. In addition, we are able to accurately detect the ITS2 intergenic region with Hidden Markov Models [19]. This significant advantage makes data processing more accurate and reliable. In a previous study, we found that ITS2 had excellent discrimination ability among more than 6600 plant samples [14]. Here, we tested seven DNA regions (psbA-trnH, rbcL, matK, ycf5, rpoC1, ITS, and ITS2) using 300 plant samples belonging to 192 closely related species from 72 genera from the Rutaceae family. We found that the ITS2 region exhibited the highest inter-specific divergence, and this was significantly higher than the intra-specific variation in the “DNA barcoding gap” assessment and Wilcoxon two-sample tests. Furthermore, the ITS2 locus had the highest identification efficiency among all regions tested. CBOL recommended an rbcL and matK composite sequence as a plant barcode in their latest study [13]. The rbcL sequence is easy to be aligned and amplified. There are a number of rbcL data resources in GenBank. It was thought that rbcL would place an unidentified specimen into a family and a genus [11,16]. However, it lacked discriminating power at the species level. The matK sequence showed much higher levels of sequence variation and provided better species discrimination, but the problem was how to improve PCR primer sets to enhance its “universality” [10,14]. In our study, the efficiency of PCR amplification of matK was too low to complete the data analysis. PsbA-trnH has one of the fastest rates of evolution in the

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Figure 2 Barcoding gaps of the five candidate sequences. Table 6 Comparison of the identification efficiency of the five candidate sequences using different methods of species identificationa) Marker ITS2 psbA-trnH rbcL rpoC1 ycf5

Method of species identification BLAST 1 Distance BLAST 1 Distance BLAST 1 Distance BLAST 1 Distance BLAST 1 Distance

Number of samples 197 197 65 65 67 67 20 20 14 14

Correct identification Species level % Genus level % 89.3 100 77.7 100 83.1 100 75.4 97.0 77.6 97.0 74.6 97.0 40.0 100 40.0 100 78.6 85.7 78.6 85.7

Incorrect identification Species level % Genus level % 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Ambiguous identification Species level % Genus level % 10.7 0 22.3 0 16.9 0 24.6 3.0 22.4 3.0 25.4 3.0 60.0 0 60.0 0 21.4 14.3 21.4 14.3

a) In the BLAST 1 method, correct identification means the query sample is the same species with the best BLAST hit; ambiguous identification means the several species of the best BLAST hit contain the species of the query sample; incorrect identification means the query sample species is not among the best BLAST hit species. In the nearest distance method, correct identification means the query sample is the species with the smallest genetic distances among samples; ambiguous identification means the query sample species is among the several species with the smallest genetic distance; incorrect identification means the query sample is not among the specie with the smallest genetic distance.

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chloroplast intergenic region. In addition, it presents a conserved sequence of about 75 bp at both ends that can be used to design universal primers [27]. As a result, psbAtrnH has become accepted as a potential plant barcode in recent years [8,11,28]. Our study showed that the interspecific divergence of the psbA-trnH locus is higher than that of other chloroplast loci investigated. PsbA-trnH also demonstrated excellent reliability for authentication (100% at the genus level, 83.8% at the species level). Therefore, we suggest that psbA-trnH can be used as a complementary barcode with ITS2 for a broad range of plant taxa. 3.2

Samples not successfully identified

We chose the ITS2 sequence as a DNA barcode for the identification of species in the Rutaceae family in this paper. Among the 197 samples tested, 21 samples could not be identified, of which 18 samples belong to Citrus. The systematic classification Citrus has received more attention than any other genus since it was established by Linnaeus in 1753. There have been many arguments about the classification of this genus, especially the division of species [29–31]. There are two main reasons for this. First, species in Citrus are easy to bud mutation, species of this genus can crossbreed easily and the progeny are fertile. Somatic cell mutation rates are high. Citrus has a long history of artificial cultivation, and therefore, there are many morphologic types in Citrus. In addition, there are different subjective views on the evolution of Citrus, resulting in different classification standards. The unsuccessfully identified species of this paper are mainly cultivars. These species are selected by bud mutation, which is a somatic cell mutation. This kind of mutation creates little difference between the maternal plant and the mutant. Thus, barcodes that use characteristic genetic information can hardly distinguish them, and this is in accordance with the results of Francois et al. [32]. The results also show that these species have a very close relationship with each other, and some of them may be classified improperly. All of these results illustrate the importance of a clear and adequate definition of species for DNA barcode classification. DNA barcodes have been widely used in animals, and they are rapidly being introduced to plants, although some controversies about their utilization remain. DNA barcoding will help non-professionals identify species quickly and accurately. Although they cannot replace traditional taxonomy, DNA barcodes are a useful tool for taxonomists, because the digital information comprising the DNA sequence is accurate, abundant, and uniquely reproducible [13,33,34]. This study assessed DNA barcodes for plants, and the results provide new methods and ideas for molecular plant identification and genetic relationship studies.

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We would like to thank ZHAO Na for her help in the experiment. We also thank LIN YuLin for identification of species in the Rutaceae family. This work was supported by the National International Cooperation Program (Grant No. 2007DFA30990) and Special Scientific Research Project of Health Profession (Grant No. 200802043). 1

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