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Comparative analyses of chloroplast genomes from Six Rhodiola species: variable DNA markers identification and phylogenetic relationships within the genus

Abstract

Background

As a valuable medicinal plant, Rhodiola has a very long history of folk medicine used as an important adaptogen, tonic, and hemostatic. However, our knowledge of the chloroplast genome level of Rhodiola is limited. This drawback has limited studies on the identification, evolution, genetic diversity and other relevant studies on Rhodiola.

Results

Six Rhodiola complete chloroplast genomes were determined and compared to another Rhodiola cp genome at the genome scale. The results revealed a cp genome with a typical quadripartite and circular structure that ranged in size from 150,771 to 151,891 base pairs. High similarity of genome organization, gene number, gene order, and GC content were found among the chloroplast genomes of Rhodiola. 186 (R. wallichiana) to 200 (R. gelida) SSRs and 144 pairs of repeats were detected in the 6 Rhodiola cp genomes. Thirteen mutational hotspots for genome divergence were determined and could be used as candidate markers for phylogenetic analyses and Rhodiola species identification. The phylogenetic relationships inferred by members of Rhodiola cluster into two clades: dioecious and hermaphrodite. Our findings are helpful for understanding Rhodiola's taxonomic, phylogenetic, and evolutionary relationships.

Conclusions

Comparative analysis of chloroplast genomes of Rhodiola facilitates medicinal resource conservation, phylogenetic reconstruction and biogeographical research of Rhodiola.

Peer Review reports

Introduction

As traditional natural plant pharmaceuticals and health food, Rhodiola belongs to the family Crassulaceae and mainly distributed in alpine regions of Asia and Europe [1,2,3]. 73 species of Rhodiola plants are distributed in China, and the Qinghai-Tibet Plateau has the most species [4]. The extract of Rhodiola plants, especially R. crenulata and R. rosea, has various pharmacological effects such as anti-hypoxia, fatigue, tumors, radiation, aging, and improvement of mental and physical functions [5]. Due to the fragile ecological environment of the Qinghai-Tibet Plateau and the lack of artificial cultivation techniques, relying solely on the digging of wild resources can easily lead to the reduction of Rhodiola plant resources and the loss of genetic diversity resources [6].

Due to the variety of Rhodiola plants, the source of commercial medicine of Rhodiola is very complicated, but the pharmacodynamics of different species of Rhodiola have a significant difference in clinical efficacy [7]. The traits of the medicinal plants of Rhodiola and the characteristics of the microstructure are similar [7]. At present, R. crenulata is the only primordial plant of the Rhodiola medicinal herbs contained in the Chinese Pharmacopoeia (2020 Edition). The development and research of its alternative varieties is imminent. In recent years, the mixed use of plant roots and rhizomes of distinct species of Rhodiola has occurred frequently. Researchers have studied the Rhodiola Herbal Slices on the market using DNA barcoding technology, only 40% of the samples are R. crenulata collected in the Chinese Pharmacopoeia [8]. The mixuse of Rhodiola medicinal materials directly affects the safety and efficacy of clinical medications, and coupled with unrestricted collection, the number of wild resources has decreased dramatically. Therefore, in order to realize the protection and sustainable development of Rhodiola plants, it is necessary to conduct in-depth research on the identification and genetic diversity of their species.

To solve the problem of Rhodiola plant identification, Wang et al. developed random amplification polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR) primers to identify R. angusta, R. crenulata, R. bupleuroides, and R. sachalinensis [9]. Li et al. established a method for classifying and identifying R. quadrifida and R. crenulata based on nuclear magnetic resonance 1H-NMR fingerprints-chemical pattern recognition technique [10]. Zhu et al.found that the internal transcribed spacer 2 (ITS2) sequence can effectively distinguish R. crenulata and R. rosea [11]. Booker et al. used nuclear magnetic resonance spectroscopy coupled with high performance thin layer chromatography techniques to comprehensively analyze R. crenulata and R. rosea collected in markets around the world [12]. These advanced identification methods currently used can solve the identification problems of some Rhodiola plants, but they also have the disadvantages of having a narrow application range and high identification cost.

DNA barcoding is a new species identification technology developed in recent years [13]. It eliminates the obstacles of traditional morphological recognition methods that rely on long-term experience. The plant chloroplast (cp) genome, as a research hotspot for screening DNA barcoding sequences, can also be used as a super-barcoding for phylogenetic and species identification studies [14]. The use of the cp genome to solve the problem of difficult classification of related species is of great significance for species identification in herbal medicine and even the entire plant community. Chen et al. proposed that using the whole genome as a super-barcode can effectively identify Ligularia plants [15]. Zhong et al. found that 41 Dendrobium species can be effectively identified based on the whole cp genome, and Dendrobium officinale from 3 different places of production can also be distinguished [16].

For a long time, due to the difficulties in collecting samples and specimens, the phylogenetic relationship within Rhodiola is still poorly understood [17]. Mayuzumi et al. proposed that Rhodiola as a distinct genus from Sedum and a close relationship between Rhodiola and Pseudosedum [3]. A recent molecular phylogenetic study using plastome genomes sampled about 12 representative species of Rhodiola supported Rhodiola was divided into dioecious clade and hermaphrodite clade [4]. Although these studies provided new and important insights into the phylogeny of Rhodiola, a broader sampling scheme is needed to better understand the phylogenetic relationships of Rhodiola.

In this study, we sequenced the cp genomes of R. tangutica, R. wallichiana, R. quadrifida, R. bupleuroides, R. gelida, and R. henryi using Illumina technology followed by reference-guided assembly of de novo contigs. Our aims were: 1) to detect the variations of long repeats and SSRs in 6 Rhodiola cp genomes; 2) to identify divergence hotspots as potential genetic markers for Rhodiola DNA barcoding; and 3) to reconstruct a phylogeny for Rhodiola species using protein coding sequences of the cp genome and infer their phylogenetic location within Crassulaceae.

Results

General features of the Six Rhodiola cp genomes

The cp genomes of R. tangutica (2.4 Gb), R. wallichiana (2.1 Gb), R. quadrifida (2.4 Gb), R. bupleuroides (2.2 Gb), R. gelida (2.1 Gb), and R. henryi (2.3 Gb) were sequenced with approximately 2.0 Gb of paired-end reads, respectively. Clean reads were achieved by removing adaptors and low-quality read pairs. The recovered clean reads for R. quadrifida, R. tangutica, R. wallichiana, R. bupleuroides, R. gelida, and R. henryi were 1,737,149, 1,013,832, 839,613, 973,418, 866,547, and 1,092,141, respectively (Table S1). Six Rhodiola complete cp genome maps (Fig. 1) were obtained through de novo genome sequencing and assembly with the reference R. rosea (MH410216) genome. The average organelle coverage for R. quadrifida, R. tangutica, R. wallichiana, R. bupleuroides, R. gelida, and R. henryi with the reference genome reached 1,378, 262, 254, 193, 203, and 313, respectively (Table S1).

Fig. 1
figure 1

Gene maps of the complete cp genome of six species of Rhodiola. Genes drawn outside of the map circle are transcribed clockwise, while those drawn inside are transcribed counter-clockwise. The darker gray in the inner circle corresponds to GC content while the lighter gray corresponds to AT content

The cp genome size ranged from 150,771 bp in R. quadrifida to 151,891 bp in R. henryi, which included 82,211 bp (R. tangutica) to 83,095 bp (R. gelida) large single-copy (LSC) regions, and 16,991 bp (R. quadrifida) to 17,104 bp (R. tangutica) small single-copy (SSC) regions, separated by a pair of 25,773 bp (R. quadrifida) to 25,887 bp (R. henryi) inverted repeat (IR) regions (Fig. 1; Table S1). There were 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes that were identified in each Rhodiola cp genome (Table S2). Among these unique genes, 15 genes harbored one intron and three genes (ycf3, clpP, and rps12) harbored two introns (Table S2).

Long repeats and SSRs

Repeat sequences have been applied extensively for phylogeny, population genetics, genetic mapping, and forensic studies [18]. A total of 144 pairs of repeats were detected in the 6 Rhodiola cp genomes, with the repeat length range from 30 to 62 bp (Fig. 2A). The cp genomes of 6 Rhodiola have 5, 15, 8, 11, 12 and 9 forward repeats and 10, 14, 13, 16, 13 and 14 palindromic repeats (Fig. 2B). Reverse repeats and complementary repeats only exist in the cp genome of R. gelida (Fig. 2B). The long repeat lengths of 30, 31, 32, 40, and 41 bp existed in all 6 Rhodiola cp genomes (Fig. 2A). Long repeat lengths of 33, 34, and 36 bp were found the least often and only existed in the R. gelida and R. bupleuroides cp genomes, respectively (Fig. 2A).

Fig. 2
figure 2

The number of long repeats in the whole cp genome sequence of the 6 Rhodiola species. A Frequency of repeats more than 30 bp long. B Frequency of repeat type

Simple sequence repeats (SSRs) are usually 1–6 bp tandem repeat DNA sequences and are widely used as molecular markers with their polymorphic to identify closely related species [19]. In our study, SSRs in 6 Rhodiola cp genomes were identified using MISA software. The number of SSRs in the 6 Rhodiola species ranged from 186 (R. wallichiana) to 200 (R. gelida) (Fig. 3A). The numbers and distribution of all SSR types were similar and conserved in the 6 Rhodiola cp genomes, except for pentanucleotide, which only existed in R. bupleuroides (Fig. 3B). Mononucleotide repeat motifs were occupied the largest proportion in these SSRs, ranging from 63% (R. bupleuroides) to 65% (R. henryi). No hexanucleotide repeat motifs were found in the 6 Rhodiola cp genomes (Fig. 3B).

Fig. 3
figure 3

Analysis of SSRs in the 6 Rhodiola cp genomes. A Frequency of common motifs in the 6 Rhodiola cp genomes. B Number of different SSR types detected in the 6 Rhodiola cp genomes

Divergence hotspots

We selected eight Rhodiola cp genome sequences to be compared and plotted using the mVISTA software with the annotated cp genome of R. rosea as a reference to elucidate the level of sequence divergence (Fig. 4). Based on the overall sequence identity, the results indicated that the coding regions exhibit lower divergence levels than the non-coding regions and two IR regions exhibit higher conservation than the remaining sequences across the whole chloroplast genome, as can be seen in other plants [20, 21]. Furthermore, the results showed that the ycf1 and trnH-GUG-psbA sequences of Rhodiola were highly divergent regions.

Fig. 4
figure 4

Sequence alignment of chloroplast genomes of eight Rhodiola species. Sequence identity plot comparing the chloroplast genomes with R. rosea as a reference using mVISTA. The grey arrows and thick black lines above the alignment indicate genes with their orientation. The Y-axis represents the identity from 50 to 100%

In addition, the nucleotide diversity (Pi) values were calculated to evaluate the sequence divergence among 22 Rhodiola cp genomes (Table S3). The genetic distance of all 75 protein-coding genes varied from 0 to 0.01143 (ycf1) with an average value of 0.00444 (Fig. 5A). Based on a considerably higher Pi value of > 0.009, we found seven highly variable regions (ycf1, rps15, ndhF, rpoC1, rps8, rpl20, rps18, and matK) (Fig. 5A). These values of the non-coding regions ranged from 0 to 0.04122 (trnH-GUG-psbA) with an average value of 0.01057 (Fig. 5B). A total of five mutational hotspots that showed high values of PI (≥ 0.02) were identified, including trnH-GUG-psbA, rps15-ycf1, trnG-GCC-trnR-UCU, trnC-GCA-petN, and ndhF-rpl32. The analysis revealed that the protein-coding regions exhibit lower divergence levels than non-coding regions. These hotspot regions could be utilized as potential molecular markers for phylogenetic studies and the identification of Rhodiola species.

Fig. 5
figure 5

The nucleotide variability (Pi) values were compared among 22 Rhodiola species. A The P-distance value of protein-coding genes. B The P-distance value of intergenic regions

Phylogenetic analysis within Rhodiola

Complete cp genomes comprise abundant phylogenetic information, which could be applied to evolution and phylogenetic studies of angiosperms because of several advantages, such as high accuracy and resolution [22]. In order to clarify the phylogenetic position of six newly assembled Rhodiola within the Saxifragales, phylogenetic tree was constructed. The phylogenetic tree showed that all Rhodiola species formed a monophyletic clade and then were classified into two separate branches (Fig. 6), which is inconsistent with the record in Flora of China [1]. The dioecious clade composed of nine dioecious Rhodiola species, and the hermaphrodite clade included all the hermaphrodite species.

Fig. 6
figure 6

Phylogenetic tree based on 38 protein-coding genes shared by the cp genomes of 55 Saxifragales species. The tree was generated using a ML method with 1000 bootstrap replicates. Numbers on the nodes indicate bootstrap values. Different colors represent species belonging to different families

Materials and methods

All methods were performed in accordance with the relevant guidelines and regulations.

Plant materials and DNA extraction

Wild plant materials of Rhodiola were collected in Nyingchi (Tibet, China). The specimens of Rhodiola have been kept at the Tibet Agriculture & Animal Husbandry University and Kunming Institute of Botany. Total genomic DNA was extracted from silica-gel-dried leaves using the modified CTAB (cetyltrimethylammonium bromide) method [23]. The quantity and quality of extracted genomic DNA were determined by gel electrophoresis and NanoDrop 2000 Spectrophotometer (Thermo Scientific, Carlsbad, CA, USA).

Chloroplast genome sequencing, assembly and annotation

Genomic DNA was randomly fragmented by sonication (Covaris, M220). Paired-end sequencing libraries were constructed according to the Illumina standard protocol (Illumina, San Diego, CA, USA). Sample sequencing was carried out on an Illumina Hiseq X-Ten platform. Total genomic DNAs were also sent to BGI (Shenzhen, China) for library (400 bp) preparation for genome skimming sequencing. Paired-end (150 bp) sequencing was conducted on the Illumina HiSeq X-10 platform, generating 2 Gb data per sample. Raw reads were filtered by quality control software NGS QC Toolkit v2.3.333 to obtain high quality Illumina data [24].

Next, filtered reads were de novo assembled using NOVOPlasty [25] with parameters of K-mer (33). These assembled chloroplast genomes were annotated in GeSeq [26], coupled with manually edited start and stop codons in Geneious 11.1.4 [27] (Biomatters Ltd., Auckland, New Zealand) with a reference R. rosea chloroplast genome (Genbank accession number MH410216). In addition, all tRNA genes were further verified using tRNAscan-SE v1.21 [28]. The border region between the inverted repeat (IR) and the large single copy (LSC), also between inverted repeats and small single copy (SSC) junction were determined through local BLAST software. Finally, the circular gene maps of Rhodiola plastomes were drawn utilizing the Organellar Genome DRAW tool (OGDRAW) [29].

Comparative genomic analysis and molecular marker identification

To detect variations within Rhodiola cp genomes, we compared the cp genomes of R. crenulata, R. rosea, and the six newly assembled Rhodiola cp genomes by mVISTA [30]. The nucleotide diversity of the Rhodiola cp genomes was detected by DNA Sequence Polymorphism (DnaSP) software [31].

Characterization of repeat sequence and SSRs

The long repetitive sequences were detected using REPuter with a 30 bp minimum repeat size and a Hamming distance of 3 [32]. Simple sequence repeats (SSRs) in the cp genomes were identified via the MISA perl script [33] with the minimum number of repeats set to 8, 5, 3, 3, 3 and 3 for mono-, di-, tri-, tetra-, penta-, and hexa-nucleotides, respectively.

Phylogenetic analysis

The phylogenetic analysis was performed on six newly assembled Rhodiola cp genomes, another 55 Saxifragales species, and one outgroup Rosa rugosa, all of which were down loaded from the NCBI (Table S4) except those of six newly assembled Rhodiola cp genomes. Molecular phylogenetic trees, using aligned sequences of 38 protein-coding genes (Table S5) with MAFFT 7.0 [34] and adjusted manually where necessary, were constructed using IQ-TREE (Nguyen et al.,2015) and MrBayes 3.2.6 software [35] under the GTRGAMMA model.

Discussion

Six Rhodiola cp genomes were sequenced and assembled in our study, and this information was used to identify candidate DNA markers and infer Rhodiola phylogeny. The size of the cp genome, the length of the SSC, LSC, and IR regions, the content of the GC, and gene content demonstrated a high degree of similarity among the genomes, implying that Rhodiola species shared low diversity [4]. The IR region, however, has a larger GC content than the LSC and SSC regions. In most angiosperm chloroplasts, there are 74 protein-coding genes, with an additional five in a few species [36]. Six newly assembled Rhodiola cp genomes contain 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes, which is consistent with previous studies [4].

Repetitive sequences play crucial roles in chloroplast genome arrangement and sequence divergence [37]. Reverse repeats and complementary repeats only exist in the cp genome of R. gelida, reflecting the fact that Rhodiola chloroplast genomes exhibited a significant difference in type, length and number of repeats. Chloroplast SSR markers are efficient genetic resources to investigate population genetics and biogeography of closely related taxa due to their relatively richness, high reproducibility and polymorphism [38, 39]. Wang et al. used 11 ISSR primers to reveal the interspecific or intraspecific genetic differences and diversity of four Rhodiola species [9].In our study, A and T nucleotides were the most common, while tandem G or C repeats were quite rare (Fig. 3A), which was in concordance with the other research results [40, 41].These SSR markers could be used to examine the genetic structure, differentiation, diversity, and maternity in the 6 Rhodiola species and their relative species in future studies.

The whole cp genome contains abundant mutation sites, which can be used directly as a super barcode for species identification. As with hypervariable regions of the genome, they can also be screened out as potential molecular markers [42, 43]. At present, many species have been successfully identified based on the chloroplast genome, especially the species with frequent hybridization and apomixis [43,44,45]. Ycf1, rps15, ndhF, rpoC1, rps8, rpl20, rps18 and matK genes in CDS showed significant variation and high sequence variations were found in intergenic regions as follows: trnH-GUG-psbA, rps15-ycf1, trnG-GCC-trnR-UCU, trnC-GCA-petN, and ndhF-rpl32 (Fig. 6). These regions can also be used as candidate markers for elucidating the phylogenetic relationship among Rhodiola species. In many species, TrnH-GUG-psbA and matK are the most mutated hotspots for species identification, such as Kengyilia [46], Apocynaceae [47] and Orchidinae [48]. Ycf1 marker has good species identification resolution in Pinus at the within-genus level relationships [49]. Ycf1 has a better effect on the identification of Rhodiola due to its longer sequence (~ 5800 bp). We recommend that the ycf1 gene be used to reconstruct phylogenetic relationships of Rhodiola where there is a lack of genomic information.

Our phylogenetic analysis strongly supported the monophyly of Rhodiola species, which is consistent with previous studies [3, 4]. All Rhodiola species are classified into two separate branches (dioecious and hermaphrodite), which supports the view of Zhao et al. [4]. What interests us is that R. wallichiana was gathered in the hermaphrodite clade, and we think that we may have selected a rare unisexualis among R. wallichiana. So, we speculate that unisexual R. wallichiana and bisexual R. wallichiana may have huge genetic differences. In addition, the dioecious clade also contains R. integrifolia, which has been temporarily classified as a hybrid between R. rosea and R. rhodantha [50]. Our phylogenetic analyses also revealed that there are close relationships between Crassulaceae and Saxifragaceae, supporting the view that there is a common origin between them. In some traditional angiosperm classification systems, Saxifragaceae is the largest group of garbage bins in angiosperms, and many branches that are unrelated in evolution are forcibly pieced together into a highly multi-line group. Our phylogeny suggested that the Penthoraceae and Haloragidaceae were clustered into one clade, indicating their close relationship. The APGII research considers that Penthoraceae can be selectively combined with Haloragidaceae [51], our research seems to support this view. However, since there is only one chloroplast genome data in two families and lack of data on the cp genome of more species, we believe that when more species in the two families have been sequenced to accurately determine their evolutionary relationship.

Conclusions

In this study, we determined and characterized six cp genome sequences of Rhodiola, which are commonly used as Tibetan medicinal materials. The size of the genome, the structure and organization of genes were shown to be conservative, which is similar to those reported cp genomes of Rhodiola species. To develop molecular markers for future phylogeographic and population genetic studies, thirteen mutational hotspots were identified. The results of phylogenetic analysis showed that Rhodiola species were clustered into two clades: dioecious and hermaphrodite, with strong support values. The complete cp genome sequences that were newly assembled facilitate medicinal resource conservation, phylogenetic reconstruction, and biogeographical research of Rhodiola.

Availability of data and materials

The datasets supporting the results of this publication are included within the article and Additional files 1, 2, 3, 4, 5. The chloroplast genome data of 6 cp genome sequences of Rhodiola used for analysis could be obtained from NCBI, and their accession numbers are as follow: R. wallichiana, OL742458; R. henryi, OL742459; R. gelida, OL742460; R. bupleuroides, OL742461; R. tangutica, OL742462; R. quadrifida, OL742463. Data for our newly assembled six Rhodiola chloroplast genomes are also available in Supplementary S6. Voucher specimens of 6 Rhodiola were deposited in the Tibet Agriculture & Animal Husbandry University and Kunming Institute of Botany and identified by Professor Xiaozhong Lan, and their accession numbers are as follow: R. wallichiana, WH-2013–029; R. henryi, GanQL691; R. gelida, ChenSL1377; R. bupleuroides, LiuJQ-08XZ-062; R. tangutica, ChenSL0277; R. quadrifida, ChenSL0149.

References

  1. Fu SX, Fu KJ. The Flora of China. Sci Press. 1984;34(1):159–60.

    Google Scholar 

  2. Panossian A, Wikman G, Sarris J. Rosenroot (Rhodiola rosea): Traditional use, chemical composition, pharmacology and clinical efficacy. Phytomedicine. 2010;17(7):481–93.

    CAS  PubMed  Article  Google Scholar 

  3. Mayuzumi S, Ohba H. The Phylogenetic Position of Eastern Asian Sedoideae (Crassulaceae) Inferred from Chloroplast and Nuclear DNA Sequences. Syst Bot. 2004;29(3):587–98.

    Article  Google Scholar 

  4. Zhao D-N, Ren Y, Zhang J-Q. Conservation and innovation: Plastome evolution during rapid radiation of Rhodiola on the Qinghai-Tibetan Plateau. Mol Phylogenet Evol. 2020;144:106713.

    PubMed  Article  Google Scholar 

  5. Hong DX, Su JS, Wen J, Zhang J, Zhang Y. Resource investigation about Tibetan medicine Rhodiola kirilowii. Zhongguo Zhong Yao Za Zhi. 2017;42(6):1202–6.

    PubMed  Google Scholar 

  6. Qiang W, Xiao R, Lan F, Yan Q. Study on Stage, Questions and Strategies of Resource Plant Rhodiola L. J Xinjiang Agric Univ. 2002;25(4):57–62.

    Google Scholar 

  7. Li T, Zhang H. Application of microscopy in authentication of traditional Tibetan medicinal plants of five Rhodiola (Crassulaceae) alpine species by comparative anatomy and micromorphology. Microsc Res Tech. 2010;71(6):448–58.

    Article  Google Scholar 

  8. Xin T, Li X, Yao H, Lin Y, Ma X, Cheng R, Song J, Ni L, Fan C, Chen S. Survey of commercial Rhodiola products revealed species diversity and potential safety issues. Sci Rep. 2015;5:8337.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Wang ML, Ren XL, Cui JL, Wang JH. Genetic diversity of wild plants in Rhodiola L. with two molecular marker methods of RAPD and ISSR. Chin Tradit Herb Drugs. 2016;47:469–73.

  10. Li T, Su C, Li LX, Li C, Si MX. Identification of Rhodiola quadrifida and Rhodiola crenulata based on NMR fingerprint and chemical pattern recognition method. Chin Tradit Herb Drugs. 2018;49:3918–25.

    Google Scholar 

  11. Zhu R-W, Li Y-C, Zhong D-L, Zhang J-Q. Establishment of the most comprehensive ITS2 barcode database to date of the traditional medicinal plant Rhodiola (Crassulaceae). Sci Rep. 2017;7(1):10051.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Booker A, Zhai L, Gkouva C, Li S, Heinrich M. From Traditional Resource to Global Commodities:-A Comparison of Rhodiola Species Using NMR Spectroscopy-Metabolomics and HPTLC. Front Pharmacol. 2016;7:254.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. Hebert PDN, Gregory TR. The Promise of DNA Barcoding for Taxonomy. Syst Biol. 2005;54(5):852–9.

    PubMed  Article  Google Scholar 

  14. Jansen RK, Zhengqiu C, Raubeson LA, Henry D, Depamphilis CW, James LM, Müller KF, Mary GB, Haberle RC, Hansen AK. Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns. Proc Natl Acad U S A. 2007;104(49):19369–74.

    CAS  Article  Google Scholar 

  15. Chen X, Zhou J, Cui Y, Wang Y, Duan B, Yao H. Identification of Ligularia Herbs Using the Complete Chloroplast Genome as a Super-Barcode. Front Pharmacol. 2018;9:695.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. Zhong ZM. Study on identification and systematic classification of Dendrobium with DNA barcode. Guangzhou: Guangzhou University of Chinese Medicine; 2018.

  17. Zhang JQ, Meng SY, Wen J, Rao GY. Phylogenetic Relationships and Character Evolution of Rhodiola (Crassulaceae) based on Nuclear Ribosomal ITS and Plastid trnL-F and psbA-trnH Sequences. Syst Bot. 2014;39(2):441–51.

    Article  Google Scholar 

  18. Bull NL. Compound Microsatellite Repeats: Practical and Theoretical Features. Genome Res. 1999;9(9):830–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Zhao K, Li L, Lu Y, Yang J, Zhang Z, Zhao F, Quan H, Ma X, Liao Z, Lan X. Characterization and Comparative Analysis of Two Rheum Complete Chloroplast Genomes. Biomed Res Int. 2020;2020(4):1–11.

    Google Scholar 

  20. Kim SH, Yang J, Park J, Yamada T, Maki M, Kim SC. Comparison of Whole Plastome Sequences between Thermogenic Skunk Cabbage Symplocarpus renifolius and Nonthermogenic S. nipponicus (Orontioideae; Araceae) in East Asia. Int J Mol Sci. 2019;20(19):4678.

    CAS  PubMed Central  Article  Google Scholar 

  21. Asaf S, Khan AL, Khan AR, Waqas M, Kang SM, Khan MA, Lee SM, Lee IJ. Complete Chloroplast Genome of Nicotiana otophora and its Comparison with Related Species. Front Plant Sci. 2016;7:843.

    PubMed  PubMed Central  Article  Google Scholar 

  22. Bremer B, Bremer K, Chase MWF, Michael F, Reveal JL, Soltis DE, Soltis PS, Stevens PF, Anderberg AA, Moore MJ, Olmstead RG. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot J Linn Soc. 2009;161(2):105–21.

    Article  Google Scholar 

  23. Allen GC, Floresvergara MA, Krasynanski S, Kumar S, Thompson WF. A modified protocol for rapid DNA isolation from plant tissues using cetyltrimethylammonium bromide. Nat Protoc. 2006;1(5):2320.

    CAS  PubMed  Article  Google Scholar 

  24. Patel RK, Jain M. NGS QC Toolkit: A Platform for Quality Control of Next-Generation Sequencing Data. PLoS One. 2012;7:e30619.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Nicolas D, Patrick M, Guillaume S. NOVOPlasty: de novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 2017;45(4):e18.

    Google Scholar 

  26. Tillich M, Lehwark P, Pellizzer T, Ulbricht-Jones ES, Fischer A, Bock R, Greiner S. GeSeq - versatile and accurate annotation of organelle genomes. Nucleic Acids Res. 2017;45(1):6–11.

    Article  CAS  Google Scholar 

  27. Matthew K, Richard M, Amy W, Steven SH, Matthew C, Shane S, Simon B, Alex C, Sidney M, Chris D. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647–9.

    Article  Google Scholar 

  28. Brooks A, Lowe T. The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs. Nucleic Acids Res. 2005;33:686–9.

    Article  CAS  Google Scholar 

  29. Marc L, Oliver D, Sabine K, Ralph B. OrganellarGenomeDRAW–a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets. Nucleic Acids Res. 2013;41(Web Server issue):575.

    Google Scholar 

  30. Frazer KA, Lior P, Alexander P, Rubin EM, Inna D. VISTA: computational tools for comparative genomics. Nucleic Acids Res. 2004;32(Web Server issue):273–9.

    Article  CAS  Google Scholar 

  31. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, Sánchez-Gracia A. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34(12):3299–302.

    CAS  PubMed  Article  Google Scholar 

  32. Kurtz S, Choudhuri JV, Ohlebusch E, Schleiermacher C, Stoye J, Giegerich R. REPuter: the manifold applications of repeat analysis on a genomic scale. Nucleic Acids Res. 2001;29(22):4633–42.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Thiel T, Michalek W, Varshney RK, Graner A. Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet. 2003;106(3):411–22.

    CAS  PubMed  Article  Google Scholar 

  34. Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol Biol Evol. 2013;30(4):772–80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Ronquist F, Teslenko M, Der Mark PV, Ayres DL, Darling AE, Hohna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP. MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space. Syst Biol. 2012;61(3):539–42.

    PubMed  PubMed Central  Article  Google Scholar 

  36. Millen RS, Olmstead RG, Adams KL, Palmer JD, Lao NT, Heggie L, Kavanagh TA, Hibberd JM, Gray JC, Morden CW. Many Parallel Losses of infA from Chloroplast DNA during Angiosperm Evolution with Multiple Independent Transfers to the Nucleus. Plant Cell. 2001;13(3):645–58.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Guisinger MM, Kuehl JV, Boore JL, Jansen RK. Extreme reconfiguration of plastid genomes in the angiosperm family Geraniaceae: rearrangements, repeats, and codon usage. Mol Biol Evol. 2011;28(1):583–600.

    CAS  PubMed  Article  Google Scholar 

  38. Tian S, Lu P, Zhang Z. Chloroplast genome sequence of Chongming lima bean (Phaseolus lunatus L.) and comparative analyses with other legume chloroplast genomes. BMC Genomics. 2021;22:194.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Huang J, Chen R, Li X. Comparative Analysis of the Complete Chloroplast Genome of Four Known Ziziphus Species. Genes. 2017;8(12):340.

    PubMed Central  Article  CAS  Google Scholar 

  40. Kuang DY, Wu H, Wang YL, Gao LM, Zhang SZ, Lu L. Complete chloroplast genome sequence of Magnolia kwangsiensis (Magnoliaceae): implication for DNA barcoding and population genetics. Genome. 2011;54(8):663–73.

    PubMed  Article  Google Scholar 

  41. Wang W, Yu H, Wang J, Lei W, Gao J, Qiu X, Wang J. The Complete Chloroplast Genome Sequences of the Medicinal Plant Forsythia suspensa (Oleaceae). Int J Mol Sci. 2017;18(11):2288.

    PubMed Central  Article  CAS  Google Scholar 

  42. Nock CJ, Waters DL, Edwards MA, Bowen SG, Rice N, Cordeiro GM, Henry RJ. Chloroplast genome sequences from total DNA for plant identification. Plant Biotechnol J. 2011;9(3):328–33.

    CAS  PubMed  Article  Google Scholar 

  43. Zhao K, Li L, Quan H, Yang J, Lan X. Comparative Analyses of Chloroplast Genomes From 14 Zanthoxylum Species: Identification of Variable DNA Markers and Phylogenetic Relationships Within the Genus. Front Plant Sci. 2021;11:605793.

    PubMed  PubMed Central  Article  Google Scholar 

  44. Jansen RK, Cai Z, Raubeson LA, Daniell H, Boore JL. Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns. Proc Natl Acad Sci. 2008;104(49):19369–74.

    Article  Google Scholar 

  45. Xue S, Shi T, Luo W, Ni X, Iqbal S, Ni Z, Huang X, Yao D, Shen Z, Gao Z. Comparative analysis of the complete chloroplast genome among Prunus mume, P. armeniaca, and P. salicina. Hortic Res. 2019;6(1):13.

    Article  CAS  Google Scholar 

  46. Luo X, Tinker NA, Xing F, Zhang H, Sha L, Kang H, Ding C, Jing L, Li Z, Yang R. Phylogeny and maternal donor of Kengyilia species (Poaceae: Triticeae) based on three cpDNA (matK, rbcL and trnH-psbA) sequences. Biochem Syst Ecol. 2012;44:61–9.

    CAS  Article  Google Scholar 

  47. Cabelin V, Alejandro G. Efficiency of matK, rbcL, trnH-psbA, and trnL-F (cpDNA) to Molecularly Authenticate Philippine Ethnomedicinal Apocynaceae Through DNA Barcoding. Pharmacogn Mag. 2016;12(46):384–8.

    CAS  Article  Google Scholar 

  48. Jin WT, Schuiteman A, Chase MW, Li JW, Chung SW, Hsu TC, Jin XH. Phylogenetics of subtribe Orchidinae s.l. (Orchidaceae; Orchidoideae) based on seven markers (plastid matK, psaB, rbcL, trnL-F, trnH-psbA, and nuclear nrITS, Xdh): implications for generic delimitation. BMC Plant Biol. 2017;17(1):222.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. Olsson S, Grivet D, Cidvian J. Species-diagnostic markers in the genus Pinus: evaluation of the chloroplast regions matK and ycf1. For Sys. 2018;27(3):e016.

  50. Hermsmeier U, Grann J, Plescher A. Rhodiola integrifolia: hybrid origin and Asian relatives. Botany-Botanique. 2012;90(11):1186–90.

    CAS  Article  Google Scholar 

  51. Bremer B, Bremer K, Chase M, Reveal J, Zmarzty S. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG II. Bot J Linn Soc. 2003;141(4):399–436.

    Article  Google Scholar 

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Acknowledgements

We are grateful to all reviewers for their valuable comments on the manuscript.

Funding

This work was supported by the Tibet Autonomous Region Office and School Joint Foundation (Grant No. XZ202001ZR0038G), The National Natural Science Foundation of China (Grant No. 81660628), The Special Support Plan for the Talent of Fifth WANREN Plan in China(Grant No.2020–366),The Forth National Survey of Traditional Chinese Medicine Resources, Chinese or Tibet Medicinal Resources Investigation in Tibet Autonomous Region (State Administration of Chinese Traditional Medicine 20190411–542222), The Key R & D program of Yunnan Province, China (grant no. 202103AC100003), The Large-scale Scientific Facilities of the Chinese Academy of Sciences (Grant No. 2017-LSF-GBOWS-02).

Author information

Authors and Affiliations

Authors

Contributions

KZ and XL conceived and designed the work. HQ, ZZ, and JY collected the samples. KZ performed the experiments and analyzed the data; KZ and LL wrote the manuscript; ZL and XL revised the manuscript. All authors gave final approval of the paper.

Corresponding author

Correspondence to Xiaozhong Lan.

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Not applicable. No specific permits were required for the collection of specimens for this study. This research was carried out in compliance with the relevant laws of China.

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The authors declare that they have no competing interest.

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Supplementary Information

Additional file 1:

Table S1. Summary of six Rhodiola chloroplast genome characteristics.

Additional file 2: Table S2.

List of genes in six Rhodiola chloroplast genomes.

Additional file 3: Table S3.

List of species used to evaluate the sequence divergence.

Additional file 4: Table S4.

List of species used for phylogenetic tree construction.

Additional file 5: Table S5.

List of 38 protein-coding genes used for phylogenetic tree construction.

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Zhao, K., Li, L., Quan, H. et al. Comparative analyses of chloroplast genomes from Six Rhodiola species: variable DNA markers identification and phylogenetic relationships within the genus. BMC Genomics 23, 577 (2022). https://doi.org/10.1186/s12864-022-08834-9

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Keywords

  • Rhodiola
  • Chloroplast genome
  • Divergent hotspots
  • Phylogenetic analysis