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Identification of species and materia medica within Saussurea subg. Amphilaena based on DNA barcodes

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Identification of species and materia medica within Saussurea subg. Amphilaena based on DNA barcodes https://t.co/buyaE45W91 https://t.co/zPXflu1JA8
PEER-REVIEWED Plant Biology section

Introduction

Saussurea is one of the most species-rich genera in Asteraceae and the taxonomic identification of these species is notoriously difficult (Lipschitz, 1979). Recent radiation, widespread hybridization, and convergent evolution have combined to make the delimitation of these species extremely complicated (Wang et al., 2009). Among the 289 recognized species in the “Flora of China” (FOC), many are very challenging to differentiate, with one or several morphologically similar species (Shi & Raab-Straube, 2011). For example, about nine current widely accepted species are suspected to be conspecific with S. taraxacifolia (Chen, 2015). Since the publication of FOC, the newly described species have totaled more than 60 species (Chen, 2015; Wang et al., 2014; Xu, Hao & Xia, 2014; Chen & Wang, 2018), with an average of 10 species every year, which is a far higher number than that of other genera. These new species have mostly been separated from the known species and at least 10 of them bear the prefix “pseudo” to indicate their similarity in terms of morphology (Chen, 2014; Chen & Yuan, 2015; Wang et al., 2014).

This taxonomic problem particularly affects S. subg. Amphilaena, which is one of the four subgenera of Saussurea, where these species are defined mainly based on the self-transparent and colorful bract that subtends the synflorescence (Fig. 1) (Lipschitz, 1979; Raab-Straube, 2017). This character is a well-known adaptation to high altitudes and it occurs in a number of angiosperm genera from different families (Omori, Takayama & Fls, 2000). Within S. subg. Amphilaena, it has also been documented that this character was derived multiple times and some of the species showing very high similarity, such as S. involucrata and S. obvolata, are actually distantly related according to molecular phylogeny (Wang et al., 2009). In addition, this subgenus is considered to be a result of a recent radiation in the Qinghai–Tibet Plateau where 35 of the total number of 38 species have been recorded (Raab-Straube, 2017). This type of process usually produces many closely related species where one species might resemble several other species, thereby yielding a number of complexes (Simões et al., 2016).

Photographs of six species sampled in the study.

Figure 1: Photographs of six species sampled in the study.

(A) S. bogedaensis, WYJ201607018. (B) S. involucrata, WYJ201607025. (C) S. pubifolia, WYJ201607272. (D) S. luae, WYJ201607286. (E) S. globosa, WYJ201607422. (F) S. erubescens, sn110814017.

Complex taxonomy undoubtedly causes problems with identification, and among the 38 species recognized in the latest monograph, at least 13 species are widely misidentified. For example, S. orgaadayi was long misidentified as S. involucrata (Smirnov, 2004), although both species were described many years ago and the latter is one of the most famous plants in China because of its beauty and usage in traditional Chinese medicine (Chik et al., 2015). In addition, eight species within the S. obvallata complex have been recognized as single species since the establishment of S. obvallata (Raab-Straube, 2017).

Evidently, misidentification can lead to a misunderstanding of biodiversity. In some cases, these errors can even be deadly harmful for humans given that many Saussurea species are used in medicine (Chik et al., 2015; Li, Zhu & Cai, 2000; Yang et al., 2005). In addition to S. involucrata, 14 other species have been formally recorded as medically useful in S. subg. Amphilaena (Table 1) (Cao et al., 2016; Chen, Pei & Zhao, 2010; Jiang, Luo & Xu, 2010; Li, 1999). However, the authentication of species is time-consuming and it requires a specialist taxonomist in most cases. Moreover, some species are found only in areas that are difficult to access, possibly because of their excessive consumption. For example, S. involucrata is currently listed as second-class protected plants due to over-exploitation (Fu & Jin, 1992), while S. wettsteiniana and S. velutina are both endemic to a few mountains in Sichuan, China, and they are difficult to obtain due to their restricted distributions (Shi & Raab-Straube, 2011). Thus, possible substitutes for these species are urgently needed to be ascertained.

Table 1:
List of medicinal plants within Saussurea subg. Amphilaena.
Species Reference
S. involuvcrata Chen, Pei & Zhao (2010) and Chik et al. (2015)
S. globosa Cao et al. (2016) and Li (1999)
S. wettsteiniana Jiang, Luo & Xu (2010)
S. polycolea Jiang, Luo & Xu (2010) and Li (1999)
S. uniflora Jiang, Luo & Xu (2010) and Li (1999)
S. velutina Jiang, Luo & Xu (2010)
S. phaeantha Cao et al. (2016) and Li (1999)
S. orgaadayi Shi & Raab-Straube (2011)
S. tangutica Cao et al. (2016) and Li, Zhu & Cai (2000)
S. bracteata Li (1999)
S. erubescens Cao et al. (2016) and Li (1999)
S. nigrescens Cao et al. (2016) and Li (1999)
S. iodostegia Cao et al. (2016) and Li (1999)
S. glandulosissima Cao et al. (2016), Li (1999) and Yang et al. (2005)
S. sikkimensis Cao et al. (2016), Li (1999) and Yang et al. (2005)
DOI: 10.7717/peerj.6357/table-1

DNA barcoding is a rapid and reliable technique for identifying species based on variations in the sequence of short standard DNA regions. Phylogenetic studies based on these fragments can also help to identify substitute plants. However, the selection of the fragments used for DNA barcoding is a controversial problem. The Plant Working Group of the Consortium for the Barcode of Life (CBOL) proposed using a combination of rbcL and matK as a “core barcode” for identifying land plants (Hollingsworth et al., 2009). Subsequently, trnH-psbA and the nuclear ribosomal internal transcribed spacer (ITS) were proposed as supplementary barcodes for land plants (Kress et al., 2005; Li et al., 2011). In addition, trnK was found to outperform matK in some studies (Cao et al., 2010; Müller & Borsch, 2005).

Previously, the sequences used in DNA barcodes for Saussurea species have been rather limited and only five species have been reported with DNA sequences. Among these species, none have been reported more than two populations, which is obviously insufficient for DNA barcode studies (Wang et al., 2009). Thus, in this study, we performed extensive investigations in the field, and we sequenced five DNA barcode candidates in chloroplasts (matK, trnH-psbA, trnK, and rbcL) and the nuclear ITS. Our main aims were: (i) to evaluate the application of these DNA barcodes in S. subg. Amphilaena; (ii) to develop an objective method for identifying medically important Saussurea species; and (iii) to explore the possible taxonomic problems and potential substitutes for some rare herbs.

Materials and Methods

Taxon sampling

In total, 20 species were sampled in the present study, including 18 from the 38 species recognized in the latest monograph on S. subg. Amphilaena (Raab-Straube, 2017), one recently published species, S. bogedaensis (Chen & Wang, 2018), and a Jurinea species, which was selected as an outgroup according to a previous study (Wang et al., 2009). Photos of some species are presented in Fig. 1. Our sample focus on medical resources and 15 species formally recorded in the medical literature were included in the analyses (Table 1). For most of the species in the ingroup, we collected from two or more populations, with more than three individuals from each population. In total, we collected 132 individuals and their details are listed in Table 2.

Table 2:
The name, locality, voucher and GenBank accession number for the samples used in this study.
Species Locality (All from China) Voucher/Individual Latitude (°) Longitude (°) Altitude (m) GenBank accession number (ITS, matK, rbcL, trnK, trnH-psbA)
S. bogedaensis Qitai, Xinjiang WYJ201607018b, 140 43.45321 89.55213 3,471 MH003705 MH070617 MH070870 MH070996 MH070743
S. bogedaensis Qitai, Xinjiang WYJ201607018a, 167 43.45321 89.55213 3,471 MH003706 MH070618 MH070871 MH070997 MH070744
S. bogedaensis Qitai, Xinjiang WYJ201607018, 378 43.45321 89.55213 3,471 MH003707 MH070619 MH070872 MH070998 MH070745
S. bogedaensis Qitai, Xinjiang WYJ201308006, 38 43.44370 89.58167 3,386 MH003708 MH070620 MH070873 MH070999 MH070746
S. bogedaensis Qitai, Xinjiang WYJ201308006, 39 43.44370 89.58167 3,386 MH003709 MH070621 MH070874 MH071000 MH070747
S. bogedaensis Qitai, Xinjiang WYJ201308006, 40 43.44370 89.58167 3,386 MH003710 MH070622 MH070875 MH071001 MH070748
S. bracteata Qumalai, Qinghai WYJ201207537, 114 34.84716 94.94569 4,621 MH003711 MH070623 MH070876 MH071002 MH070749
S. bracteata Cuomei, Xizang WYJ201607213, 151 28.51474 91.45611 4,934 MH003712 MH070624 MH070877 MH071003 MH070750
S. bracteata Cuomei, Xizang WYJ201607213, 153 28.51474 91.45611 4,934 MH003713 MH070625 MH070878 MH071004 MH070751
S. bracteata Yushu, Qinghai WYJ201607043, 160 35.05681 93.01225 4,644 MH003714 MH070626 MH070879 MH071005 MH070752
S. bracteata Yushu, Qinghai WYJ201607043, 161 35.05681 93.01225 4,644 MH003715 MH070627 MH070880 MH071006 MH070753
S. bracteata Yushu, Qinghai WYJ201607043, 162 35.05681 93.01225 4,644 MH003716 MH070628 MH070881 MH071007 MH070754
S. bracteata Jilong, Xizang WYJ201607099, 173 28.93494 85.39376 5,108 MH003717 MH070629 MH070882 MH071008 MH070755
S. bracteata Jilong, Xizang WYJ201607099, 174 28.93494 85.39376 5,108 MH003718 MH070630 MH070883 MH071009 MH070756
S. bracteata Jilong, Xizang WYJ201607099, 175 28.93494 85.39376 5,108 MH003719 MH070631 MH070884 MH071010 MH070757
S. bracteata Geermu, Qinghai WYJ201607053f, 204 32.98834 91.98589 5,120 MH003720 MH070632 MH070885 MH071011 MH070758
S. bracteata Geermu, Qinghai WYJ201607041, 248 35.51127 93.72552 4,525 MH003721 MH070633 MH070886 MH071012 MH070759
S. bracteata Geermu, Qinghai WYJ201607041, 249 35.51127 93.72552 4,525 MH003722 MH070634 MH070887 MH071013 MH070760
S. erubescens Luqu, Gansu sn110814017, 123 34.59103 102.48699 3,345 MH003723 MH070635 MH070888 MH071014 MH070761
S. erubescens Luqu, Gansu sn110814018, 124 34.59121 102.48657 3,367 MH003724 MH070636 MH070889 MH071015 MH070762
S. erubescens Luqu, Gansu sn110814017, 353 34.59103 102.48699 3,345 MH003725 MH070637 MH070890 MH071016 MH070763
S. erubescens Luqu, Gansu sn110815020, 355 33.59203 101.48659 3,451 MH003726 MH070638 MH070891 MH071017 MH070764
S. erubescens Xiahe, Gansu Ikeda200713210, 371 35.20252 102.52181 3,342 MH003727 MH070639 MH070892 MH071018 MH070765
S. globosa Aba, Sicuan WYJ-2011-175, 109 33.63526 102.35556 3,470 MH003728 MH070640 MH070893 MH071019 MH070766
S. globosa Baoxing, Sicuan WYJ201607422, 168 30.49153 102.48188 3,992 MH003729 MH070641 MH070894 MH071020 MH070767
S. globosa Kangding, Sicuan WYJ201209151, 318 30.05441 101.96308 3,841 MH003730 MH070642 MH070895 MH071021 MH070768
S. globosa Kangding, Sicuan WYJ201209158, 329 30.05564 101.97304 3,864 MH003731 MH070643 MH070896 MH071022 MH070769
S. globosa Kangding, Sicuan WYJ201209157, 331 30.13242 101.56306 3,974 MH003732 MH070644 MH070897 MH071023 MH070770
S. globosa EF420926
S. globosa Xiangcheng, Sicuan WYJ201209234, 337 28.93118 99.79842 3,764 MH003733
S. globosa Xiangcheng, Sicuan WYJ-2011-069, 80 28.53118 99.45658 3,835 MH003734 MH070645 MH070898 MH071024 MH070771
S. globosa Xiangcheng, Sicuan WYJ-2011-069, 81 28.53118 99.45658 3,835 MH003735 MH070646 MH070899 MH071025 MH070772
S. involucrata Urumqi, Xinjiang WYJ201607025a, 163 43.10847 86.84220 3,564 MH003736 MH070647 MH070900 MH071026 MH070773
S. involucrata Urumqi, Xinjiang WYJ201607025c, 165 43.10847 86.84220 3,564 MH003737 MH070648 MH070901 MH071027 MH070774
S. involucrata Tekesi, Xinjiang WYJ201308184, 24 43.09915 82.68382 3,678 MH003738 MH070649 MH070902 MH071028 MH070775
S. involucrata Tekesi, Xinjiang WYJ201308184, 26 43.09915 82.68382 3,678 MH003739 MH070650 MH070903 MH071029 MH070776
S. involucrata Urumqi, Xinjiang WYJ201308203, 372 43.11985 86.82125 3,768 MH003740 MH070651 MH070904 MH071030 MH070777
S. involucrata Urumqi, Xinjiang WYJ201308203, 374 43.11985 86.82125 3,768 MH003741 MH070652 MH070905 MH071031 MH070778
S. involucrata Xinyuan, Xinjiang WYJ201308188, 390 43.33469 84.01032 3,543 MH003742 MH070653 MH070906 MH071032 MH070779
S. involucrata Urumqi, Xinjiang WYJ201308203, 41 43.11985 86.82125 3,768 MH003743 MH070654 MH070907 MH071033 MH070780
S. involucrata Xinyuan, Xinjiang WYJ201308188, 47 43.33469 84.01032 3,543 MH003744 MH070655 MH070908 MH071034 MH070781
S. involucrata Xinyuan, Xinjiang WYJ201308188, 48 43.33469 84.01032 3,543 MH003745 MH070656 MH070909 MH071035 MH070782
S. involucrata Dushanzi, Xinjiang WYJ201308131, 61 43.77545 84.45615 2,684 MH003746 MH070657 MH070910 MH071036 MH070783
S. involucrata Dushanzi, Xinjiang WYJ201308131, 63 43.77545 84.45615 2,684 MH003747 MH070658 MH070911 MH071037 MH070784
S. iodostegia Datong, Shanxi WYJ201507117, 107 39.05578 113.65927 2,514 MH003748 MH070659 MH070912 MH071038 MH070785
S. iodostegia Datong, Shanxi WYJ201507117, 108 39.05578 113.65927 2,514 MH003749 MH070660 MH070913 MH071039 MH070786
S. iodostegia Weixian, Hebei WYJ201309004, 20 39.91413 114.96546 2,237 MH003750 MH070661 MH070914 MH071040 MH070787
S. iodostegia Weixian, Hebei WYJ201309004, 21 39.91413 114.96546 2,237 MH003751 MH070662 MH070915 MH071041 MH070788
S. iodostegia Weixian, Hebei WYJ201309004, 22 39.91413 114.96546 2,237 MH003752 MH070663 MH070916 MH071042 MH070789
S. iodostegia Mentougou, Beijing WYJ201507105, 27 40.03633 115.47206 2,048 MH003753 MH070664 MH070917 MH071043 MH070790
S. iodostegia Mentougou, Beijing WYJ201507105, 28 40.03633 115.47206 2,048 MH003754 MH070665 MH070918 MH071044 MH070791
S. iodostegia Mentougou, Beijing WYJ201507105, 29 40.03633 115.47206 2,048 MH003755 MH070666 MH070919 MH071045 MH070792
S. luae Linzhi, Xizang WYJ201607286a, 271 29.59022 94.59631 4,121 MH003756
S. luae Linzhi, Xizang WYJ201607286a, 272 29.59022 94.59631 4,121 MH003757
S. luae Linzhi, Xizang WYJ201607286b, 273 29.59022 94.59631 4,121 MH003758 MH070667 MH070920 MH071046 MH070793
S. luae Linzhi, Xizang WYJ201607286c, 283 29.59022 94.59631 4,121 MH003759
S. luae Linzhi, Xizang LJQ2620, 316 28.48051 93.36541 4,225 MH003760 MH070668 MH070921 MH071047 MH070794
S. nigrescens Tianzhu, Gansu LJQ1480, 314 36.41075 102.45620 1,900 MH003761 MH070669 MH070922 MH071048 MH070795
S. nigrescens Sunan, Gansu LJQ1517, 315 37.23345 102.32444 2,651 MH003762 MH070670 MH070923 MH071049 MH070796
S. nigrescens Huangyuan, Qinghai Liu1603, 320 36.20387 98.14870 3,700 MH003763 MH070671 MH070924 MH071050 MH070797
S. nigrescens Huangzhong, Qinghai WYJ200611, 347 36.50087 101.57164 3,641 MH003764 MH070672 MH070925 MH071051 MH070798
S. nigrescens Menyuan, Qinghai LJQ-QLS-2008-0065, 82 37.37502 101.62422 2,654 MH003765 MH070673 MH070926 MH071052 MH070799
S. nigrescens Menyuan, Qinghai LJQ-QLS-2008-0065, 83 37.37502 101.62422 2,654 MH003766 MH070674 MH070927 MH071053 MH070800
S. nigrescens Menyuan, Qinghai LJQ-QLS-2008-0065, 84 37.37502 101.62422 2,654 MH003767 MH070675 MH070928 MH071054 MH070801
S. glandulosissima Chayu, Xizang WYJ201607321, 257 29.32542 97.134728 3,949 MH003768 MH070676 MH070929 MH071055 MH070802
S. glandulosissima Linzhi, Xizang WYJ201607298, 264 29.627012 94.635744 4,433 MH003769 MH070677 MH070930 MH071056 MH070803
S. glandulosissima Linzhi, Xizang WYJ201607298, 379 29.627012 94.635744 4,433 MH003770 MH070678 MH070931 MH071057 MH070804
S. glandulosissima Chayu, Xizang WYJ201607321, 382 29.32542 97.134728 3,949 MH003771 MH070679 MH070932 MH071058 MH070805
S. glandulosissima Chayu, Xizang WYJ201607321, 383 29.32542 97.134728 3,949 MH003772 MH070680 MH070933 MH071059 MH070806
S. orgaadayi Altay, Xinjiang WYJ201308041, 11 47.21846 89.87999 3,541 MH003773 MH070681 MH070934 MH071060 MH070807
S. orgaadayi Altay, Xinjiang WYJ201308041, 12 47.21846 89.87999 3,541 MH003774 MH070682 MH070935 MH071061 MH070808
S. orgaadayi Altay, Xinjiang WYJ201308041, 360 47.21846 89.87999 3,541 MH003775 MH070683 MH070936 MH071062 MH070809
S. phaeantha Xiaojing, Sicuan WYJ201209126, 1 30.99918 102.3644 3,642 MH003776 MH070684 MH070937 MH071063 MH070810
S. phaeantha Xiaojing, Sicuan WYJ201209126, 2 30.99918 102.3644 3,642 MH003779 MH070687 MH070940 MH071066 MH070813
S. phaeantha Qilian, Gansu WYJ201607014, 195 38.60685 99.48221 4,096 MH003777 MH070685 MH070938 MH071064 MH070811
S. phaeantha Qilian, Gansu WYJ201607014, 196 38.60685 99.48221 4,096 MH003778 MH070686 MH070939 MH071065 MH070812
S. phaeantha Maqin, Qinghai LJQ1718, 317 34.47733 100.23956 3,210 MH003780 MH070688 MH070941 MH071067 MH070814
S. phaeantha Xinghai, Qinghai sn110718001, 349 35.58868 99.98818 2,654 MH003781 MH070689 MH070942 MH071068 MH070815
S. phaeantha Xinghai, Qinghai sn120811001, 351 34.32412 99.35641 2,641 MH003782 MH070690 MH070943 MH071069 MH070816
S. phaeantha Xinghai, Qinghai sn120801130, 354 35.38821 99.78935 2,684 MH003783 MH070817
S. polycolea Linzhi, Xizang WYJ201607292, 229 29.62701 94.63574 4,433 MH003784 MH070691 MH070944 MH071070 MH070818
S. polycolea Linzhi, Xizang WYJ201607292, 230 29.62701 94.63574 4,433 MH003785 MH070692 MH070945 MH071071 MH070819
S. polycolea Linzhi, Xizang WYJ201607292, 231 29.62701 94.63574 4,433 MH003786 MH070693 MH070946 MH071072 MH070820
S. polycolea Langxian, Xizang WYJ201607279, 269 28.883036 93.356181 4,472 MH003787 MH070694 MH070947 MH071073 MH070821
S. polycolea Langxian, Xizang WYJ201607279, 270 28.883036 93.356181 4,472 MH003788 MH070695 MH070948 MH071074 MH070822
S. polycolea Linzhi, Xizang Liu07257, 334 29.62201 94.63554 4,231 MH003789 MH070696 MH070949 MH071075 MH070823
S. pubifolia Jiacha, Xizang WYJ201607272a, 206 29.03175 92.35724 4,796 MH003790 MH070697 MH070950 MH071076 MH070824
S. pubifolia Jiacha, Xizang WYJ201607272b, 207 29.03175 92.35724 4,796 MH003791 MH070698 MH070951 MH071077 MH070825
S. pubifolia Jiacha, Xizang WYJ201607272c, 208 29.03175 92.35724 4,796 MH003792 MH070699 MH070952 MH071078 MH070826
S. pubifolia Jiacha, Xizang WYJ-2011-057, 94 29.02165 92.35714 4,786 MH003793 MH070700 MH070953 MH071079 MH070827
S. sikkimensis Cuona, Xizang WYJ201607242, 156 27.92057 91.84863 3,970 MH003794 MH070701 MH070954 MH071080 MH070828
S. sikkimensis Yadong, Xizang WYJ201607150e, 186 27.48592 88.90708 4,102 MH003795 MH070702 MH070955 MH071081 MH070829
S. sikkimensis Yadong, Xizang WYJ201607150c, 187 27.48592 88.90708 4,102 MH003796 MH070703 MH070956 MH071082 MH070830
S. sikkimensis Yadong, Xizang WYJ201607150f, 385 27.48592 88.90708 4,102 MH003797 MH070704 MH070957 MH071083 MH070831
S. sikkimensis Yadong, Xizang WYJ201607150 h, 386 27.48592 88.90708 4,102 MH003798 MH070705 MH070958 MH071084 MH070832
S. sikkimensis Cuona, Xizang WYJ201607242, 388 27.92057 91.84863 3,970 MH003799 MH070706 MH070959 MH071085 MH070833
S. sikkimensis Cuona, Xizang WYJ201607242, 389 27.92057 91.84863 3,970 MH003800 MH070707 MH070960 MH071086 MH070834
S. tangutica Qilian, Gansu WYJ201607013, 226 38.60685 99.48221 4,096 MH003801 MH070708 MH070961 MH071087 MH070835
S. tangutica Qilian, Gansu WYJ201607013, 228 38.60685 99.48221 4,096 MH003802 MH070709 MH070962 MH071088 MH070836
S. tangutica Zhiduo, Qinghai WYJ201207279, 328 33.85203 95.61335 3,948 MH003803 MH070710 MH070963 MH071089 MH070837
S. tangutica Kangding, Sicuan sn120801019, 332 30.05093 101.96437 3,987 MH003804 MH070711 MH070964 MH071090 MH070838
S. tangutica Kangding, Sicuan sn120801019, 335 30.05093 101.96437 3,987 MH003805 MH070712 MH070965 MH071091 MH070839
S. tangutica Zhiduo, Qinghai WYJ201207279, 340 33.85203 95.61335 3,948 MH003806 MH070713 MH070966 MH071092 MH070840
S. uniflora Cuona, Xizang WYJ201607254, 142 27.765831 91.90194 4,138 MH003807 MH070714 MH070967 MH071093 MH070841
S. uniflora Cuona, Xizang WYJ201607254, 143 27.765831 91.90194 4,138 MH003808 MH070715 MH070968 MH071094 MH070842
S. uniflora Cuona, Xizang WYJ201607254, 144 27.765831 91.90194 4,138 MH003809 MH070716 MH070969 MH071095 MH070843
S. uniflora Yadong, Xizang WYJ201607151c, 145 27.48592 88.90708 4,102 MH003810 MH070717 MH070970 MH071096 MH070844
S. uniflora Yadong, Xizang WYJ201607151a, 146 27.48592 88.90708 4,102 MH003811 MH070718 MH070971 MH071097 MH070845
S. uniflora Yadong, Xizang WYJ201607151b, 147 27.48592 88.90708 4,102 MH003812
S. uniflora Cuona, Xizang WYJ201607243, 197 27.92057 91.84863 3,970 MH003813 MH070719 MH070972 MH071098 MH070846
S. veitchiana Xinglong, Hebei WYJ201507098, 302 40.59808 117.47655 2,032 MH003814 MH070720 MH070973 MH071099 MH070847
S. veitchiana Xinglong, Hebei WYJ201507098, 303 40.59808 117.47655 2,032 MH003815 MH070721 MH070974 MH071100 MH070848
S. veitchiana Nuanchuan, Henan WYJ201507135, 52 33.67057 111.79417 1,651 MH003816 MH070722 MH070975 MH071101 MH070849
S. veitchiana Nuanchuan, Henan WYJ201507135, 53 33.67057 111.79417 1,651 MH003817 MH070723 MH070976 MH071102 MH070850
S. veitchiana Nuanchuan, Henan WYJ201507135, 54 33.67057 111.79417 1,651 MH003818 MH070724 MH070977 MH071103 MH070851
S. veitchiana Nuanchuan, Henan WYJ201507135, 55 33.67057 111.79417 1,651 MH003819 MH070725 MH070978 MH071104 MH070852
S. veitchiana Shenlongjia, Hubei WYJ201507160, 57 31.43997 110.307149 3,098 MH003820 MH070726 MH070979 MH071105 MH070853
S. veitchiana Shenlongjia, Hubei WYJ201507160, 58 31.43997 110.307149 3,098 MH003821 MH070727 MH070980 MH071106 MH070854
S. veitchiana Shenlongjia, Hubei WYJ201507160, 59 31.43997 110.307149 3,098 MH003822 MH070728 MH070981 MH071107 MH070855
S. veitchiana Wuxi, Chongqing WYJ201507184, 64 31.43791 109.15498 1,795 MH003823 MH070729 MH070982 MH071108 MH070856
S. veitchiana Wuxi, Chongqing WYJ201507184, 65 31.43791 109.15498 1,795 MH003824 MH070730 MH070983 MH071109 MH070857
S. veitchiana Wuxi, Chongqing WYJ201507184, 66 31.43791 109.15498 1,795 MH003825 MH070731 MH070984 MH071110 MH070858
S. veitchiana Wuxi, Chongqing WYJ201507184, 67 31.43791 109.15498 1,795 MH003826 MH070732 MH070985 MH071111 MH070859
S. velutina Xiaojin, Sichuan WYJ201209124, 339 30.99441 102.82915 4,000 MH003827 MH070733 MH070986 MH071112 MH070860
S. velutina Xiaojin, Sichuan WYJ201209124, 342 30.99441 102.82915 4,000 MH003828 MH070734 MH070987 MH071113 MH070861
S. velutina Xiaojin, Sichuan WYJ201209124, 76 30.99441 102.82915 4,000 MH003829 MH070735 MH070988 MH071114 MH070862
S. velutina Xiaojin, Sichuan WYJ201209124, 77 30.99441 102.82915 4,000 MH003830 MH070736 MH070989 MH071115 MH070863
S. velutina Xiaojin, Sichuan WYJ201209124, 78 30.99441 102.82915 4,000 MH003831 MH070737 MH070990 MH071116 MH070864
S. wettsteiniana Mianning, Sichuan WYJ201607408a, 176 29.00106 102.14985 3,381 MH003832 MH070738 MH070991 MH071117 MH070865
S. wettsteiniana Mianning, Sichuan WYJ201607408b, 177 29.00106 102.14985 3,381 MH003833 MH070739 MH070992 MH071118 MH070866
S. wettsteiniana Mianning, Sichuan WYJ201607402, 178 29.00106 102.14985 3,381 MH003834 MH070740 MH070993 MH071119 MH070867
S. wettsteiniana Mianning, Sichuan WYJ201607402, 284 29.00106 102.14985 3,381 MH003835 MH070741 MH070994 MH071120 MH070868
Jurinea multiflora Tuoli, Xinjiang WYJ201308102, 377 45.73564 83.14712 1,753 MH003704 MH070616 MH070869 MH070995 MH070742
DOI: 10.7717/peerj.6357/table-2

DNA extraction, PCR amplification, and sequencing

Genomic DNA was extracted from dried leaves in silica gel using the CTAB method (Doyle, 1987). Five regions (rbcL, matK, trnH-psbA, trnK, and ITS) (Berends, Jones & Mullet, 1990; Ford et al., 2009; Olmstead et al., 1992; Sang, Crawford & Stuessy, 1997; White et al., 1990), were amplified and sequenced using the primers listed in Table 3. A PCR reaction mixture comprising 25 µL was prepared and amplified according to the procedure described by Wang et al. (2009). The PCR products were sent to the Beijing Genomics Institute for commercial sequencing. Sequences were aligned using CLUSTALX v.2.1 (Thompson et al., 1997) with the default settings and adjusted manually with Bioedit v.7.0.5 (Hall, 1999). All of the sequences were registered in GenBank (Table 2).

Table 3:
List of the primers used in this study.
Primer Fragment Sequence(5′–3′) Reference
ITS4 ITS TCCTCCGCTTATTGATATGC White et al. (1990)
ITS1 ITS AGAAGTCGTAACAAGGTTTCCGTAGG White et al. (1990)
trnK(UUU) trnK TTAAAAGCCGAGTACTCTACC Berends, Jones & Mullet (1990)
rps16 trnK AAAGTGGGTTTTTATGATCC Berends, Jones & Mullet (1990)
psbA psbA GTTATGCATGAACGTAATGCTC Sang, Crawford & Stuessy (1997)
trnH psbA CGCGCATGGTGGATTCACAATCC Sang, Crawford & Stuessy (1997)
matK-xf matK TAATTTACGATCAATTCATTC Ford et al. (2009)
matK-5r matK GTTCTAGCACAAGAAAGTCG Ford et al. (2009)
rbcL1 rbcL ATGTCACCACAAACAGAGACTAAAGC Olmstead et al. (1992)
rbcL911 rbcL TTTCTTCGCATGTACCCGC Olmstead et al. (1992)
DOI: 10.7717/peerj.6357/table-3

Data analysis

We constructed 31 datasets for ITS, psbA-trn H, matK, and trnK, either individually or in different combinations. For the combination of ITS and each chloroplast loci, incongruence length difference (ILD) was preferred to test the incongruence (Farris et al., 1995) using PAUP version 4b10 (Swofford, 2003). For each dataset, the inter- and intraspecific genetic divergences were calculated as described by Meyer & Paulay (2005) and used to determine whether a barcoding gap was present. For each dataset, best close match (BCM) and two tree-based methods comprising neighbor-joining (NJ) and Bayesian inference (BI) were employed to analyze the five single markers and their different combinations. BCM analysis was conducted using the SPIDER package in R (Brown et al., 2012). NJ trees were constructed using PAUP with the Kimura two-parameter model (Swofford, 2003). Support for nodes was assessed based on 100,000 bootstrap replicates. BI analysis was implemented using MrBayes on XSEDE (v3.2.6) (Ronquist et al., 2012) and the optimal models for each marker were determined according to Akaike’s information criterion with jModelTest2 in XSEDE (v2.1.6) (Darriba et al., 2012). Species were considered to be identified successfully if individual samples of a species clustered in species-specific monophyletic clades.

Results

The PCR amplification ranged from about 73% (trnK) to 93% (ITS), while sequencing success rates from about 95% for the three chloroplast loci to 100% for the ITS, as shown in Table 4. The length after alignment, the variable sites, the interspecific or intraspecific genetic distance for each locus as well as the p values of ILD test between ITS and each chloroplast locus are also listed in Table 4. The mean intraspecific genetic distances for each species based on ITS and the four cp markers combined are listed in Table 5, and those for the mean interspecific genetic distances are shown in Table 6. The distributions of the intraspecific and interspecific distances for each species based on the five separate markers are shown in Fig. 2. In general, the mean interspecific distances were higher than the intraspecific distances for the five markers. However, the ranges of the intra- and interspecific distances overlapped for all the barcodes tested in this study.

Table 4:
List of statistics information of five DNA barcodes and the result of incongruence length difference (ILD) analysis between ITS and each chloroplast locus.
DNA region ITS trnH-psbA matK rbcL trnK
PCR success (%) 92.7 77 89.6 91.6 72.9
Sequencing success (%) 100 96.18 95.42 95.42 95.42
Aligned sequence length (bp) 656 444 711 634 656
No. indel (length in bp) 3 (1) 5 (1–3) 0 0 4 (1)
No. variated sites 111 22 18 8 28
No. sampled species (individual) 19 (131) 19 (131) 19 (131) 19 (131) 19 (131)
Interspecific distance mean (range) (%) 0.011 (0-0.028) 0.004(0–0.028) 0.003(0–0.008) 0.002(0–0.006) 0.004(0–0.012)
Intraspecific distance mean (range) (%) 0.001(0–0.005) 0.002(0–0.021) 0.001(0–0.006) 0.001(0–0.006) 0.001(0–0.009)
p values of ILD test between ITS 0.02 0.001 0.12 0.001
DOI: 10.7717/peerj.6357/table-4
Table 5:
Mean intraspecies distance (%) of ITS and the combined sequences of four chloroplast loci for each species.
Species ITS Chloroplast
S. bogedaensis 0.0 0.02
S. bracteata 0.0 0.00
S. erubescens 0.0 0.00
S. glandulosissima 0.1 0.07
S. globosa 0.2 0.04
S. involucrata 0.2 0.06
S. iodostegia 0.0 0.05
S. luae 0.0 0.29
S. nigrescens 0.0 0.00
S. orgaadayi 0.0 0.00
S. phaeantha 0.4 0.04
S. polycolea 0.0 0.07
S. pubifolia 0.0 0.00
S. sikkimensis 0.2 0.06
S. tangutica 0.1 0.46
S. uniflora 0.1 0.15
S. veitchiana 0.1 0.39
S. velutina 0.0 0.21
S. wettsteiniana 0.0 0.00
DOI: 10.7717/peerj.6357/table-5
Table 6:
The pairwise distances (%) of ITS (lower left) and the combined chloroplast loci (upper right) from 19 species of Saussurea.
(1) S. bogedaensis, (2) S. bracteata, (3) S. erubescens, (4) S. globosa, (5) S. involucrate, (6) S. iodostegia, (7) S. luae, (8) S. nigrescens, (9) S. glandulosissima, (10) S. orgaadayi, (11) S. phaeantha, (12) S. polycolea, (13) S. pubifolia, (14) S. sikkimensis, (15) S. tangutica, (16) S. uniflora, (17) S. veitchiana, (18) S. velutina, (19) S. wettsteiniana.
CP ITS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
1 0.30 0.26 0.28 0.22 0.62 0.32 0.34 0.28 0.22 0.28 0.34 0.30 0.41 0.46 0.34 0.55 0.34 0.26
2 1.92 0.04 0.06 0.17 0.57 0.19 0.29 0.22 0.16 0.06 0.12 0.00 0.35 0.35 0.23 0.50 0.16 0.21
3 1.52 2.77 0.02 0.13 0.53 0.14 0.25 0.18 0.12 0.02 0.08 0.04 0.31 0.31 0.19 0.46 0.12 0.16
4 1.53 2.88 0.61 0.15 0.55 0.17 0.27 0.20 0.15 0.05 0.10 0.06 0.34 0.33 0.22 0.48 0.15 0.19
5 0.93 2.58 2.14 2.14 0.48 0.19 0.21 0.14 0.09 0.15 0.20 0.17 0.27 0.33 0.21 0.42 0.20 0.13
6 1.96 3.33 1.85 1.60 2.47 0.59 0.53 0.54 0.49 0.55 0.60 0.57 0.51 0.71 0.55 0.37 0.57 0.53
7 1.07 0.72 1.90 1.78 1.72 2.31 0.31 0.18 0.19 0.17 0.21 0.19 0.37 0.39 0.25 0.52 0.23 0.23
8 1.83 3.19 1.72 1.47 2.34 0.34 2.12 0.26 0.21 0.27 0.32 0.29 0.31 0.45 0.22 0.32 0.19 0.25
9 1.35 2.69 1.56 1.31 1.92 1.74 1.69 1.60 0.14 0.20 0.24 0.22 0.33 0.34 0.22 0.47 0.26 0.18
10 1.41 3.08 2.30 2.35 2.02 2.28 2.21 2.17 2.16 0.15 0.20 0.16 0.27 0.32 0.21 0.42 0.20 0.12
11 1.53 2.84 1.60 1.45 2.14 1.92 1.84 1.78 1.31 2.34 0.10 0.06 0.34 0.33 0.22 0.48 0.15 0.19
12 1.09 2.42 1.36 1.06 1.69 1.48 1.43 1.35 0.87 1.89 0.89 0.12 0.37 0.37 0.26 0.53 0.20 0.24
13 1.61 1.32 2.22 2.23 2.26 3.00 0.23 2.84 2.37 2.76 2.51 2.10 0.35 0.35 0.23 0.50 0.16 0.21
14 1.11 2.44 1.34 1.08 1.71 1.49 1.38 1.36 0.71 1.91 1.07 0.64 2.12 0.51 0.34 0.48 0.35 0.31
15 1.63 2.98 1.58 1.59 1.47 2.57 2.01 2.42 2.06 2.67 2.20 1.78 2.32 1.81 0.42 0.65 0.40 0.35
16 1.00 2.33 1.27 0.97 1.44 1.38 1.34 1.26 0.78 1.80 0.96 0.53 2.01 0.55 1.70 0.46 0.24 0.25
17 2.10 3.48 2.06 1.74 2.62 1.52 2.36 1.30 1.72 2.93 2.02 1.62 2.81 1.64 2.50 1.53 0.45 0.46
18 2.21 2.91 2.49 2.50 2.50 2.94 2.04 2.80 2.31 3.04 2.50 2.05 2.59 2.07 2.66 1.96 3.09 0.24
19 1.73 3.05 1.88 1.70 2.35 1.80 1.85 1.69 1.19 2.39 1.65 1.25 2.77 1.09 2.45 1.16 2.27 2.71
DOI: 10.7717/peerj.6357/table-6
Relative distributions of intraspecific and interspecific distances calculated with ITS (A), rbcL (B), trnH-psbA (C), matK (D), and trnK (E).

Figure 2: Relative distributions of intraspecific and interspecific distances calculated with ITS (A), rbcL (B), trnH-psbA (C), matK (D), and trnK (E).

The discriminatory powers of all the loci both individually and in different combinations based on the three methods are listed in Table 7 (Figs. S1S59). In general, BCM achieved higher success rates, followed by NJ and BI, but there were a few exceptions. Among the results obtained with a single barcode, ITS (84.2–93.2%) had the highest species discriminatory power, followed by trnK (15.8–36%), matK (10.5–16.8%), and trnH-psbA (5.2–27%). Among the combinations of two barcodes, ITS + rbcL had the highest discriminatory success (89.5–100%), whereas that of matK and rbcL, which was suggested as the core barcode by CBOL (CBOL Plant Working Group, 2009), was only 10.5–25.6%. The three-region combination of ITS + rbcL + trnH-psbA recovered the highest number of monophyletic species (18) in the NJ tree (94.7%). Only five species were successfully discriminated (26.3%) by either the NJ or BI trees using the combination of all four cp markers, i.e., matK + rbcL + trnH-psbA + trnK.

Table 7:
Species resolution using the Best Close Match method and the tree-based method with five barcodes and their combinations.
Sequences Number Best close match (%) BI (%) NJ (%)
Correct Ambiguous Incorrect No match Threshold
ITS 132 93.2 6.8 0.0 0.0 0.45 84.2 84.2
trnK 125 36.0 61.6 2.4 0.0 0.91 15.8 15.8
matK 125 16.8 83.2 0.0 0.0 0.56 10.5 10.5
psbA 126 27.0 71.4 0.8 0.8 1.12 5.2 5.2
rbcL 125 12.0 88.0 0.0 0.0 0.63 0.0 0.0
ITS+trnK 125 98.4 0.0 1.6 0.0 0.53 79.0 84.2
ITS+matk 125 96.0 3.2 0.8 0.0 0.36 79.0 84.2
ITS+psbA 126 96.0 4.0 0.0 0.0 0.54 84.2 89.5
ITS+rbcL 125 100.0 0.0 0.0 0.0 0.38 89.5 89.5
trnK+matK 125 52.0 45.6 2.4 0.0 0.72 26.3 26.3
trnK+psbA 125 52.0 44.8 3.2 0.0 0.99 21.1 21.1
trnK+rbcL 125 37.6 60.8 1.6 0.0 0.77 15.8 15.8
matK+psbA 125 49.6 48.8 1.6 0.0 0.77 21.1 15.8
matK+rbcL 125 25.6 74.4 0.0 0.0 0.59 10.5 10.5
psbA+rbcL 125 30.4 68.8 0.8 0.0 0.83 10.5 5.2
ITS+matK+psbA 125 96.0 3.2 0.8 0.0 0.54 68.4 89.5
ITS+trnK+matK 125 98.4 0.0 1.6 0.0 0.54 73.7 89.5
ITS+trnK+rbcL 125 98.4 0.0 1.6 0.0 0.51 84.2 89.5
ITS+matK+rbcL 125 99.2 0.0 0.8 0.0 0.39 79.0 89.5
ITS+rbcL+psbA 125 100.0 0.0 0.0 0.0 0.57 79.0 94.7
ITS+trnK+psbA 125 98.4 0.0 1.6 0.0 0.68 79.0 89.5
trnK+matK+rbcL 125 52.0 45.6 2.4 0.0 0.69 26.3 26.3
trnK+matK+psbA 125 63.2 35.2 1.6 0.0 0.82 26.3 26.3
matK+psbA+rbcL 125 49.6 49.6 0.8 0.0 0.72 21.1 21.1
rbcL+trnK+psbA 125 55.2 41.6 3.2 0.0 0.86 15.8 21.1
ITS+matK+psbA+rbcL 125 99.2 0.0 0.8 0.0 0.57 68.4 84.2
ITS+matK+psbA+trnK 125 98.4 0.0 1.6 0.0 0.64 73.7 84.2
ITS+matK+rbcL+trnK 125 98.4 0.0 1.6 0.0 0.52 73.7 84.2
ITS+rbcL+trnK+psbA 125 98.4 0.0 1.6 0.0 0.66 79.0 84.2
trnK+matK+psbA+rbcL 125 63.2 35.2 1.6 0.0 0.77 26.3 26.3
ITS+trnK+matK+psbA+rbcL 125 98.4 0.0 1.6 0.0 0.64 79.0 84.2
DOI: 10.7717/peerj.6357/table-7

Discussion

Proposed DNA barcodes for S. subg. Amphilaena

Among the fragments tested in the present study, ITS obtained a much higher success rate compared with the other loci. In addition, all of the combinations without ITS yielded much lower success rates, regardless of the method used (Table 7). Moreover, the rate of successful PCR (92.7%) was more or less higher for ITS than the other fragments (72.9–91.6%). It has also been reported that this fragment is highly efficient in other Asteraceae genera (Gao et al., 2010; Gong et al., 2016). However, an intrinsic problem with this fragment is that an individual may have undergone recent hybridization, thereby resulting in multiple mosaic sites (Li et al., 2011). In S. subg. Amphilaena, two species failed to form monophyletic clades in the BI and NJ trees, which could be attributed to the presence of multiple mosaic sites (Fig. 3). However, ITS performed better than the other fragments in S. subg. Amphilaena, and thus we propose that this fragment should be the first or best choice when selecting only one of the current candidates.

Phylogenetic tree based on Bayesian analysis of ITS.

Figure 3: Phylogenetic tree based on Bayesian analysis of ITS.

We found that it was difficult to identify the best second choice after ITS. TrnK performed much better than rbcL in terms of its efficiency when used individually, but its combination with ITS obtained contradictory results, i.e., ITS + trnK was inferior to ITS + rbcL in terms of efficiency. This contradictory result was unexpected and it is not common in other taxa (Cao et al., 2010; Müller & Borsch, 2005). We attributed this result to higher degree of congruence of the concatenated sequences of rbcL and ITS (P = 0.12 for ILD test), in compare to trnK and ITS (P = 0.001). But it might derive from some other mechanisms, such as the higher rate of mutation for trnK that could have caused differentiation within species, but not high enough to form distinct genetic differentiation among species, and thus a failure to cluster as a monophyletic group in line with species (Naciri, Caetano & Salamin, 2012; Petit & Excoffier, 2009). Therefore, we suggest that using trnK alone is problematic and instead we propose to use rbcL as complementary to ITS because this combination could identify all 19 of the sampled species based BCM, and 17 by NJ or BI (89%) (Table 7) (Fig. 4).

Phylogenetic tree based on Bayesian analysis of ITS + rbcL.

Figure 4: Phylogenetic tree based on Bayesian analysis of ITS + rbcL.

The two loci comprising trnH -psbA and matK were affected by the same problem as trnK, with higher mutation rates and barcode efficiencies compared with rbcL when used individually, but lower efficiency when combined with ITS. Thus, their combination with ITS + rbcL failed to significantly increase the success rate and lower results were even obtained in some cases (Table 7). However, among the combinations without ITS, the combination with higher mutation rates was more efficient than those with lower mutation rates, e.g., trnK + trnH-psbA was better than matK + rbcL, which was proposed previously as the core DNA barcode for plants (Hollingsworth et al., 2009). Therefore, if ITS is subjected to hybridization, we propose that the priority order should be the following: trnK > trnH-psbA >  matK > rbcL. Moreover, the combination with more loci performed better than that with less loci. However, even the combination of all four loci was not sufficient to discriminate each species and new fragments should be considered.

Insights into taxonomic problems based on DNA barcodes

Most of the analyses failed to identify the species within two groups, i.e., S. luae vs. S. publifolia and S. globosa vs. S. erubescens (Figs. 35; Table 7). We found that these failures might have been attributable to taxonomic problems. For the first group, we found that S. luae was rather heterogeneous in terms of the ITS sequences. Some cp sequences were slightly differentiated compared with S. velutina, but the others were closer to those in S. glandulosissima or S. uniflora (Fig. 5). By contrast, the ITS sequences lacked variance and after excluding the mosaic sites, they were closely related in S. pubifolia or S. bracteata (Fig. 3). These nuclear-cytoplasmic inconsistencies suggest that hybridization may have occurred among these species.

Phylogenetic tree based on Bayesian analysis of trnK +matK +psbA +rbcL.

Figure 5: Phylogenetic tree based on Bayesian analysis of trnK +matK +psbA +rbcL.

The second group comprising S. globosa and S. erubescens was often confused in previous studies because the latter resembles a smaller form of S. globosa, which has various forms across its distribution (Raab-Straube, 2017). In agreement with the morphology, the genetic distance between the cp sequences within S. erubescens was zero whereas that within S. globosa was 0.04% (Table 5), which is even larger than that between S. erubescens and S. globosa (Table 6). The ITS sequences had a very similar pattern and the rich mosaic sites in both species also indicated differentiation accompanying substantial gene flow (Naciri, Caetano & Salamin, 2012). Both the BI and NJ methods found that S. globosa formed a clade within which S. erubescens nested as a monophyletic clade (Fig. 3). Based on these results, we propose that S. globosa might be a species with a series of differentiated populations where S. erubescens represents one of the most obvious. The current delimitation might need revision on the basis of extensive morphological as well as genetic diversity across the distribution range of both species.

Identification of the medicinal species and the potential substitutes

All of the known medically important species could be identified using our proposed DNA barcodes, i.e., ITS + rbcL or ITS alone (Table 7; Figs. 34). Moreover, some species such as S. bogedaensis, S. glandulosissima, S. polycolea, S. wettsteiniana, and S. orgaadayi could be identified with the cp DNA barcodes (Fig. 5). This high rate of success was unexpected because some species such as the two species in the S. obvallata complex (S. glandulosissima and S. sikkimensis) have been morphologically confused for many years and they were only separated very recently (Raab-Straube, 2017). Their distinction is indicative of difference in bioactive components. Therefore, our results caution against their indiscriminating usage in medicine.

Barcode sequences can also help to identify substitutes for medically useful species because closely related species might possibly share the same or similar secondary metabolites and bioactivities (Zhou et al., 2014). Thus, we propose that nine of the 15 medically useful species might be substituted by their close relatives according to the molecular phylogenetic context. Six of these species, which formed three groups, are also morphologically similar, i.e., S. involucrata and S. orgaadayi or S. bogedaensis, S. globosa and S. erubescens, and S. wettsteiniana and S. glandulosissima (Fig. 3) (Raab-Straube, 2017). Among the remaining three species, S. bracteata appears to be closely related to S. pubifolia whereas S. iodostegia and S. nigrescens are closely related to each other according to phylogenetic tree (Fig. 3). These affinities were not expected according to their morphology, but they are possibly due to convergent evolution or radiation in Saussurea (Wang et al., 2009). Secondary metabolomes or bioactivities are wanted to confirm their similarity.

Conclusion

Based on the sequence statistics, inter- and intraspecific distances, SPIDER, and phylogenetic analyses, it is concluded that internal transcribed spacer (ITS) + rbcL or ITS + rbcL + psbA-trnH could distinguish all of the species, while the ITS alone could identify all of the 15 medical plants. However, the species identification rates based on plastid barcodes were low, i.e., 0% to 36% when analyzed individually, and 63% when all four loci were combined. Thus, we recommend using ITS + rbcL as the DNA barcode for S. subg. Amphilaena or the ITS alone for medical plants.

Supplemental Information

BI and NJ trees of different combinations of DNA regions areas

DOI: 10.7717/peerj.6357/supp-1

psbA sequences in FASTA format

DOI: 10.7717/peerj.6357/supp-2

matK sequences in FASTA format

DOI: 10.7717/peerj.6357/supp-3

rbcL sequences in FASTA format

DOI: 10.7717/peerj.6357/supp-4

trnK sequences in FASTA format

DOI: 10.7717/peerj.6357/supp-5

ITS sequences in FASTA format

DOI: 10.7717/peerj.6357/supp-6