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Classification of Isatis indigotica Fortune and Isatis tinctoria Linnaeus via comparative analysis of chloroplast genomes

BMC Genomics volume 24, Article number: 465 (2023) Cite this article

Abstract

Background

Isatis tinctoria Linnaeus and Isatis indigotica Fortune are very inconsistent in their morphological characteristics, but the Flora of China treats them as the same species. In this work, a new technology that differs from conventional barcodes is developed to prove that they are different species and to clarify their classification.

Results and methods

I. indigotica was indistinguishable from I. tinctoria when using ITS2. CPGAVAS2 was used to construct the chloroplast genomes. MAFFT and DnaSP were used to calculate nucleotide polymorphism, the chloroplast genomes of the two have high diversity in the rpl32 ~ trnL-UAG short region. When using this region as a mini barcode, it was found that there are obvious differences in the base numbers of I. tinctoria and different ploidy I. indigotica were found, but diploid and tetraploid I. indigotica had the same number of bases. Moreover, the reconstruction of the maximum likelihood (ML) tree, utilizing the mini-barcode, demonstrated that I. tinctoria and both diploid and tetraploid I. indigotica are located on distinct branches. The genome size of tetraploid I. indigotica was approximately 643.773 MB, the heterozygosity rate was approximately 0.98%, and the repeat sequence content was approximately 90.43%. This species has a highly heterozygous, extremely repetitive genome.

Conclusion

A new method was established to differentiate between I. indigotica and I. tinctoria. Furthermore, this approach provides a reference and basis for the directional breeding of Isatis.

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Introduction

Isatis indigotica Fortune is a biennial herb belonging to Brassicaceae [1]. Its dry leaves and roots are Isatidis Folium and Isatidis Radix, respectively [2]. As a traditional Chinese medicine from Shen Nong’s Materia Medica used for over 2000 years, it functions in cooling the blood and pharynx, clearing away heat and detoxifying. In addition, the herb treats fever and sore throat, spots due to dampness poisoning, coma, and polydipsia [3, 4]. However, in 2001, the Flora of China (FOC) merged. I. indigotica and the Isatis tinctoria Linnaeus into the latter [5] due to their morphological diversity and interspecific transitions; therefore, they should not be considered as two separate species. However, Wang from China investigated the scientific name of I. indigotica in 1982 and found that I. indigotica and I. tinctoria differed greatly in the morphology of seedlings, petals, fruits, rhizomes, etc., theoretically, these may be two different species [6]. Given that accurate species identification of medicinal herbs is an important prerequisite for ensuring the safety and effectiveness of medicinal materials, it is important to understand whether the original plant of Isatidis Radix and Isatidis Folium is I. indigotica or I. tinctoria or if they are the same species. Further identification is thus needed.

The methods of species identification include morphological identification, genetic material identification and molecular markers. Despite the advantages of simple and rapid morphological identification, the plant phenotype is susceptible to variation resulting from its genetic material and external environment, due to its high level of plasticity [7]; therefore, it is difficult to accurately identify species by morphological methods alone. Since genetic material controls the occurrence of biological forms, detection of the number of nuclei, genome size, DNA content, and chromosome number of organs and tissues by methods such as chromosome counting and flow cytometry has gradually become a reliable approach for plant classification [8]. The disadvantage is that two similar species of the same ploidy cannot be differentiated. Due to their advantages of being unaffected by the environment,highly stable, highly polymorphic, and highly accurate, DNA molecular markers have become the most popular method of identifying plant taxa [1]. However, DNA molecular markers can be analysed only from the perspective of nucleic acids or proteins [8], and cannot directly locate genes or genotypes; therefore, they cannot distinguish two species with extremely similar genomes. However, there have been few reports on diploid and tetraploid genomes. Polyploidy research mainly focuses on transcription and metabolism, and most studies are performed using RNAseq-based transcriptome analysis [9, 10]. In view of the above, there is an urgent need to develop a rapid species identification method to identify two species with extremely similar genotypes.

In this work, we collected 26 samples of diploid I. indigotica, 1 sample of tetraploid I. indigotica, and 1 sample of I. tinctoria in China. The complete chloroplast genome was reconstructed using whole-genome Illumina sequencing data available from the NCBI. Comparing the performance of ITS2 and mini-barcoding for I. indigotica and I. tinctoria, genome sequencing analysis and evaluation of tetraploid I. indigotica can further reveal whether it has a highly heterozygous and highly repetitive genome. Therefore, this study will provide a reference for clarifying the germplasm source of I. indigotica and the improving active components, as well as provide a new method for the distinguishing of highly similar species.

Materials and methods

Plant material

Diploid seeds of twenty–six I. indigotica germplasms (1–26), were collected from Xinjiang, Henan, Anhui, Hebei, Shanxi, and Gansu Provinces in China in 2015 and identified by Dr. Qiaosheng Guo of Nanjing Agricultural University. Tetraploid seeds of I. indigotica (27) were provided by Resources Sanjiu Medical & Pharmaceutical Co., Ltd, of China. The I. tinctoria seeds (28) were identified by BaharGuli Huangerhan, a researcher with the Food and Drug Inspection Institute of Altay Prefecture in 2015 (Table S1).

All seeds were planted at Nanjing Agricultural University (N = 10). The plant height, leaf length, leaf width, petiole length, number of blades, and single leaf area were measured at the growth and flourishing stages, and the taproot length, taproot width, branch number of the taproot, phloem, xylem and hundred-grain weight were measured at the harvest stage. Silique data including hundred-grain weight, silique length, silique width and the length to width ratio of siliques were collected. The leaf area, xylem and phloem were measured using ImageJ software (V1.8.0.345), and other data were measured by a digital micrometer (Dongguan Sanliang Instrument Co., Ltd., China). All data were analysed by principal component analysis (PCA) performed on the Metware Cloud (https://cloud.metware.cn/#/home). The cor program in RStudio-2022.07.1–554 was used for correlation analysis and graph construction.

Chloroplast genome

DNA extraction

I. indigotica and I. tinctoria were selected at the same growth stage to extract genomic DNA (Fig. S1). Total DNA was extracted (kit: Dp360, Beijing Tiangen Biotechnology Co., Ltd, China) from 28 samples. The purified DNA was quantified spectrophotometrically with a microplate reader (Epoch2, BioTek, USA). The ratio of OD260/OD280 is a measure of the degree of protein contamination. For high–quality DNA, the OD260/OD280 reading is between 1.8 and 2.1, and a ratio of 2.0 is a sign of high-quality DNA.

Complete chloroplast genome annotation

The second-generation sequencing data for I. indigotica were downloaded from the NCBI (SRX10395440). GetOrganelle software (V1.7.3.4) was used to assemble and examine the chloroplast genome (I. indigotica). The I. tinctoria chloroplast genome was obtained from the NCBI (accession number NC_028415). The construction of the chloroplast genome was completed using software, and each base was manually determined to ensure its accuracy. The websites Cpgavas2 [11] and Geseq [12] were used to annotate genes of the chloroplast genome. All codons were manually adjusted in order, and the adjusted sequences were visualized by Organellar-GenomeDRAW [13], with default parameters set to construct a chloroplast genome map.

Comparative analyses of the chloroplast genomes of the two species

Geneious 11.1.2 was used to calculate the total length of the genome and each region (inverted repeat regions, small single-copy regions and large single-copy regions) in the two species. It also provided the base composition, GC (AT) content and gene composition of both genomes.

Codon analyses

Although codon usage bias occurs between species or during translation within the same species, some codon usage is high and needs to be fully analysed. As a measure reflecting the genetics and evolution of species, codon preference is frequently examined in similar studies [14]. In this work, we used CodonW to analyse codon preference.

Phylogenetic analysis

MAFFT (V7) was used for alignment, with default parameter settings [15]. IQ-TREE (V1.6.1) was used to construct a maximum likelihood (ML) tree [16]. This method is accurate, fast, flexible and extensively used to determine the genetic relationship between species. The best-fit model was found by ModelFinder, and the phylogenetic tree was reconstructed [17]. The best models for ITS2 (2 F/3R) and ITS2 (P3/E4) were TIM + F + R4 and TIM2 + F + R3, respectively. The best model for the mini-barcode was TIM2 + F + I. The ITS2 (2F3R) outgroup was Nicotiana tabacum (NCBI number: AJ012363). The sequence of I. tinctoria (NCBI number: FJ593182) was downloaded from the NCBI, and alignment analysis was performed. The outgroup of ITS2 (P3E4) was Nicotiana glauca (NCBI number: AJ012363). The I. tinctoria sequence (NCBI number: MT923072) was downloaded from the NCBI and compared for analysis. Phylogenetic tree reconstruction was based on the ML method with 1000 bootstrap replicates. iTOL software (https://itol.embl.de/) was used to display and annotate the phylogenetic tree.

Analysis of sequence divergences

Inverted Repeats Finder was used to detect IR regions, with default parameters. The length had to be at least 20 bp, and 90% sequence similarity was required [18]. To analyse differences in the chloroplast genome, we performed sliding window analysis using DnaSP (V6.11.0179) to evaluate nucleotide variability (PI) in the two species. Among the window parameter settings, the step size and length size were set to 200 and 600 bp, respectively [19]. According to the PI values, the highest point rpl32 ~ trnL–UAG was used for mini-barcode primer design to distinguish I. indigotica from I. tinctoria.

Validation of primers

PCR amplification was performed in a 20-µl total reaction, including 10 µl of CWBIO 2×Es MasterMix Dye, a 10-µM solution of mini-barcode primer (FR, 1 µl), approximately 7 µl of ddH2O, and 1 µl of DNA from 28 samples. The PCR program was as follows: 94 °C for 2 min, 94 °C for 30 s, 30 cycles including 58 °C for 30 s and 72 °C for 30 s, 72 °C for 2 min, and 4 °C to stop the reaction. The PCR amplicons were sequenced in both directions using the above primer (FR) on an AB 3730Xl DNA Sequencer (Applied Biosystems, U.S.A.) by Tsingke Biological Co., Ltd. The sequencing results were spliced using DNAMAN software (V10), and consistent sequences were obtained by bidirectional splicing for analysis.

Genome survey of tetraploid I. indigotica

Extraction and sequencing of genomes

The CTAB method was used to extract genomic DNA from tetraploid I. indigotica (27). The MGI-2000 platform was used for sequencing at Shenzhen Huada Gene Technology Service Co., Ltd, and a 350 bp library was built. The amount of sequencing data was 50× for subsequent analysis.

Data filtering

SOAPnuke (V1.5.3) was used to filter the original sequencing data to remove low-quality reads, connectors and PCR duplicates [20]. The remaining clean reads were then used for subsequent analysis.

K-mer statistics and genome assessment

The reads obtained from sequencing were divided into K-mers, and statistical analysis of the genome and miscellaneous and duplicate records was performed. Jellyfish (V2.2.10) was used to quickly calculate the frequency of K-mers [21]. Genomescope software was fitted with the spectrum of the K-mer [22]. Smudgeplot software was used to determine the characteristics of the tetraploid I. indigotica genome.

Results

Morphological characteristics of the two species

Morphological data of I. indigotica and I. tinctoria were recorded in different periods, and the results were analysed by PCA, as shown in Fig. 1A. All the samples of I. indigotica (1–26) were placed into one class, and tetraploid I. indigotica (27) and I. tinctoria (28) were placed into one class each. Specific phenotypic data are shown in Table S2. Most of the morphological data were significantly positively correlated, as shown in Fig. 1B, where the xylem and length to width ratio of siliques were significant (P < 0.001). The results shown in Fig. 1 indicate that the two species can be distinguished. However, because the morphological data were highly inconsistent, they did not provide strong evidence, and further verification is needed at the molecular level.

Fig. 1
figure 1

Analysis of differences in morphological data in 28 samples (A: PCA of morphological data of I. indigotica and I. tinctoria. B: Correlation analysis between morphological data of 28 samples. *P < 0.05, **P < 0.01, ***P < 0.001)

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Complete chloroplast genome features of the two species

The two species displayed a similar tetrad structure as shown in Fig. 2. The chloroplast genome sizes of I. indigotica and I. tinctoria were 153,827 bp, and 156,670 bp, respectively. There were 132 genes, including 8 rRNA genes, 37 tRNAs, and 87 protein coding genes. I. indigotica included a pair of IR regions of 26,272 bp each, separated by an LSC region of 83,577 bp, and an SSC region of 17,706 bp. I. tinctoria had a pair of IR regions of 26,995 bp each, separated by a LSC region of 84,907 bp, and a SSC region of 17,773 bp. The total GC content was 36.5%, while the corresponding values of the LSC, SSC and IR regions were 34.2%, 29.7% and 42.3%, respectively (Table 1). The nucleotide composition of the chloroplast genomes of the two species was biased towards T and A. The two species had the same LSC region, and each region contained ATCG (Table 2).

Fig. 2
figure 2

Gene map of the complete chloroplast genomes of the two Isatis species. Genes on the inside of the circle are transcribed clockwise, whereas those outside are transcribed counterclockwise. The light grey and dark grey shading within the inner circle correspond to the percentages of A + T and G + C contents, respectively

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Table 1 Comparison of the chloroplast genome organization of the two Isatis species
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Table 2 Base composition in a single strand of the cp. genome of the two species
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Table 3 shows related genes, including self–replicating genes, genes involved in photosynthesis, genes with unknown functions and other genes in the two species [19]. Fifteen genes (rpl2, rpl16, rps16, rpoC1, trnA–UGC, trnE–UCC, trnK–UUU, trnL–UAA, trnV–UAC, trnT–UGU, ndhA, ndhB, petB, petD, and atpF) each contained one intron, and two genes, i.e., clpP and ycf3, harboured two introns each.

Table 3 Gene contents in the chloroplast genomes of the two Isatis species
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Codon usage analyses

The usage frequency of 20 amino acids was between 1.63% and 9.38%, as shown in Fig. 3. Arg, Leu and Ser were the most abundant amino acids, and the most commonly used codons were AGA, TTA, and TCT. Met and Trp were represented by only one codon, ATG and TGG, respectively, and other amino acids were encoded by 2–5 amino acids. Four synonymous codons were used for Ala, Gly, Pro, Thr, and Val, and the most commonly used codons were GCT, GCA, CCT, ACT and GTA. Three synonymous codons was observed only for IIe, and the most commonly used codons was ATT. Asn, Asp, Cys, Glu, Gln, His, Lys, Phe, and Tyr, which were used at different frequencies, were encoded by AAT, GAT, TGT, GAA, CAA, CAT, AAA, TTT and TAT, respectively. In the two species, 35 codons had a RSCU > 1, of which 30 codons ended in A/T, accounting for 85.71%. Twenty-six codons had RSCU < 1, all of which ended in CG.

Fig. 3
figure 3

Codon content of amino acids encoding proteins in the chloroplast genome of the two species (A:I. indigotica.B:I. tinctoria)

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Analysis of inverted repeats and barcoding

Although chloroplast genomes are highly conserved in terms of structure and size, expansion and contraction of the LSC/IRb, IRb/SSC, SSC/IRa, and IRa/SSC boundaries can lead to changes in the length of the junction region, in turn causing changes in the chloroplast genome [24]. The rps19 gene was located at the junction of LSC/IRb; the ndhF and ycf1 genes were located at the junction of IRb/SSC, and most of the ndhF gene extended to the SSC region. The ycf1 gene was located at the SSC/IRa junction, and most of it extended to the SSC region. The trnH gene was located at the IRa/SSC junction, where the distance between trnH and the JLA of I. indigotica was 3 bp, and the rpl2 gene was slightly contracted towards the IRa region. Fig. 4 shows that the IR/SC junction has almost the same length, with only slight dilation and contraction, indicating that the chloroplast genomes of the two species are highly similar.

Fig. 4
figure 4

Comparison of chloroplast genome genome sequences of I. indigotica and I. tinctoria at the junction of the LSC, LSC, IR (IRa and IRb), and SSC regions

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Traditional barcoding was performed using the nuclear genome sequence ITS2 (2 F: ATGCGATACTTGGTGTGAAT, 3R: GACGCTTCTCCAGACTACAAT; P3: YGACTCTCGGCAACGGATA, E4: RGTTTCTTTTCCTCCGCTTA). Due to the highly variable DNA regions of the chloroplast genome, researchers have attempted to look for differences in these genomes to differentiate species [23]. The chloroplast genomes of two Isatis species, including 132 genes, were analysed to estimate nucleotide diversity (Pi). Fig. 5 shows that the mean value of nucleotide variability for the two species is 0.00161, ranging from 0.00001 to 0.08333. We found that the rpl32 ~ trnL–UAG region showed a higher Pi value (0.08333), and thus it was considered a candidate marker to distinguish these two species. Primer design for the rpl32 ~ trnL–UAG region was performed using primer3 (F:ACCTTGATGCAATAAT.AAACAAAGA, R: AAAATGAAAACTTCTCCAAAATGC).

Fig. 5
figure 5

Nucleotide diversity (Pi) based on sliding window analysis of I. indigotica and I. tinctoria chloroplast genomes

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Phylogenetic analysis

In recent years, as the most advanced method, high-throughput sequencing has provided convincing data for species identification and assisted in the establishment of phylogenies [24]. In Fig. 6, despite the addition of NCBI sequences of I. tinctoria (NCBI numbers: FJ593182 and AJ012363), I. tinctoria (Habahe) was still mixed with I. indigotica. This indicates that ITS2 (2F3R/P3E4) cannot be used to distinguish between the two species. However, the chloroplast genome, as an indicator of the phylogenetic relationships of inferred plant groups, is reliable. According to Fig. 7, there were 28 samples of I. indigotica and I. tinctoria (explant 1 and sterile seedlings cultured on MS media 2 and 3). The mini-barcode ML tree could accurately identify two species with high statistical support (bootstraps of 99%). Using DNAMAN to align the sequences of rpl32 ~ trnL–UAG in 28 samples, the sequences of diploid and tetraploid I. indigotica (1–27) were consistent. The sequences of I. indigotica (1–27) and I. tinctoria (28) were markedly different, and base mismatches and deletions were present. The mini-barcode can effectively distinguish I. indigotica from I. tinctoria.

Fig. 6
figure 6

Maximum likelihood (ML) phylogenetic tree reconstruction based on ITS2 (circle size at the nodes on the tree represents the bootstrap value). [A: ITS2 with 2 F/3R primers. B: ITS2 with P3/E4 primers]

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Fig. 7
figure 7

Maximum likelihood (ML) phylogenetic tree reconstruction based on the mini-barcode (numbers at the nodes on the tree represent bootstrap values) and differences in sequence and ploidy between I. indigotica and I. tinctoria

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Filtering data volume and statistics

As shown in Table 4, filtering of raw sequencing data was performed by SOAPnuke (V1.5.3). The sample insert size was 300–400 bp, the length was 150 bp, the amount of clean data was 61.58 GB, and the GC content was 38.88%. The samples were thus qualified for subsequent experimental analysis.

Table 4 Filter data statistics
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K-mer analysis

K-mers of 17–31 units were selected for statistical analysis, and the frequency of the 17–31-mers was quickly calculated using Jellyfish software (V2.2.10). The results are shown in Table 5, and the statistics are as follows. A negative binomial distribution model (Genomescope) was used, and 19 K-mer classes were selected for statistical analysis. The results demonstrated that the genome size was 643.77 MB, the heterozygosity rate was approximately 0.98%, the duplication rate was 90.43%, the fault tolerance rate was 0.55%, and the effective sequencing depth was 95.13 times. Fig. 8 A shows the size, depth, and predicted ploidy of the genome, indicating that aaaa was 96.7%, aabb was 1.18%, aabc was 0.001%, and abcd was 0.001%.

Table 5  K-mer analysis data statistics
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Fig. 8
figure 8

Genomic assessment of tetraploid I. indigotica. [A:19 K-mer distribution curve from GenomeScope 2 (the abscissa is the 19 K-mer depth (coverage), and the ordinate is the 19 K-mer frequency at this depth). B. Genomic ploidy evaluation of the smudgeplot

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Genome ploidy was assessed using a smudgeplot, and the results are shown in Fig. 8B. K-mer pairs from 0 to 22,000 were used to examine the genome, and it was found that the genotype frequencies were AAAB 0.37, AABB 0.15, AAAABB 0.15, AB 0.14, and others 0.29. Based on the results in Fig. 8AB, it was found that the species is autotetraploid and has a highly heterozygous and ultra-repetitive genome.

Discussion

Mini barcode effectively distinguishes I. indigotica from I. tinctoria

With technological development, the methods of plant classification and identification are constantly updated, and plants are classified on the basis of morphological characteristics, tissue structure, ploidy analysis and genetic composition. Generally, ploidy analysis is performed on chromosomes and DNA content [4] to compensate for the lack of morphological and tissue structure information for identification. I. indigotica is mostly cultivated in various parts of China, and may be diploid or tetraploid (chromosome number of 2n = 14 and 2n = 28, respectively). Most I. tinctoria plants are wild and tetraploid (the number of chromosomes is 2n = 28) [1, 25]. Therefore, tetraploid I. indigotica and I. tinctoria cannot be distinguished by ploidy analysis. Amplification using standard, variable, easily amplified, relatively short DNA fragments, known as DNA barcoding, is emerging as a new biometric system [26]. In contrast to morphology, it can support classification basis at the gene level, which can compensate for the lack of identification information due to the similarity in chromosomal ploidy. Although the above methods are effective, they may not be able to accurately identify species with highly similar genotypes.

In this work, it was found that the chloroplast genomes of I. indigotica and I. tinctoria were 153,827 bp and 156,670 bp long, respectively, and they encoded a total of 132 genes. The chloroplast genomes of these two species are highly conserved and similar, It may not be possible to accurately distinguish using traditional barcoding techniques. This work confirmed this speculation; ITS2 (2F3R/P3E4) could not be used to distinguish between I. indigotica and I. tinctoria. The species were relocated to a branch on the NJ tree in Fig. 6. Given that the above techniques are the most advanced among plant classification techniques, FOC treating I. indigotica and I. tinctoria as one species seems to be correct. The mini-barcode showed different results. In the phylogenetic analysis, I. tinctoria formed a monophyletic clade with high bootstrap support separate from I. indigotica. On the phylogenetic tree, diploid and tetraploid I. indigotica belonged to a single DNA–barcode category that was different from that of I. tinctoria. I. indigotica and I. tinctoria are clearly not the same species because of their different ploidies and different genes. Due to the different evolutionary rules of the chloroplast genome and nuclear genome sequences, where the former is biparentally inherited [27] and the latter is maternally inherited [28], it is necessary to take into account both the chloroplast genome and nuclear genome for species identification.

Despite the versatility of the molecular markers developed, they have not been adequately tested to identify the homology of polyploid I. indigotica. However, they can distinguish between I. indigotica and I. tinctoria. The findings of this study are consistent with research results reported by Chinese scholars [6] as well as some European scholars who have ensured that they are two independent species based on chemical composition and molecular markers [29]. However, their merging in the FOC is not supported. This method will be used to guide the relevant quality control research on other Chinese herbal medicines, especially adulterants and counterfeits, and will be continuously applied in related research fields.

The source of tetraploid I. indigotica

Polyploidy is a natural phenomenon of plant evolution and an important evolutionary process in adaptation to the natural environment [30]. It is also an important step in the formation of new species. Angiosperms are mostly polyploid, with more than approximately 70% including many important crops, such as Brassica napus [31], Triticum aestivum [32] and Gossypium spp [33]. Compared with diploid plants, polyploid plants are large and have the advantages of enhanced resistance, improved quality and increased yield [34].Autopolyploidy refers to the phenomenon in which the chromosomes of an organism come from different species and are generally produced by the direct doubling of diploid chromosomes [35]. Allopolyploidy refers to chromosomes in an organism from different species, which is the product of hybridization doubling of different species or autopolyploid hybridization of different species [36]. I. indigotica is not highly fertile due to self-incompatibility [37]; therefore, it is important to clarify the source of germplasm [38].

The use of autopolyploid plant breeding can increase the content of secondary metabolites and active ingredients [39]. The active components in the leaves and roots of tetraploid I. indigotica were more abundant than those in diploid I. indigotica [39]. Tetraploid I. indigotica exhibited greater resistance and higher yields, while being more adaptable [40]. The above results do not indicate the source of polyploid I. indigotica as a wild-type or artificially doubled polyploid. In this work, the K-mers in the tetraploid I. indigotica genome sequence were analysed, and Genomescope analysis and a smudgeplot were combined to find that the genome was highly heterozygous and extremely repetitive, and the species was revealed to be an artificially doubled polyploid, not an allopolyploid.

Given that genome sequencing has become the most effective tool for species identification and assessment [41], the technique can be used as a reference for the identification of polyploid genomes, through which it can be applied to other complex genomes, not to mention highly similar homologous genomes, to identify their origin [42]. This approach makes up for the shortcomings of mini-barcodes, taking into account the chloroplast genome and high-throughput sequencing technology to completely identify them, and reveals the source of tetraploid I. indigotica. This study provides molecular evidence for the identification of the original species of Isatidis Radix and Isatidis Folium used as traditional medicinal materials in China.

Conclusions

In this study, we successfully used a mini-barcode to distinguish I. indigotica and I. tinctoria. The mini–barcode primers rpl32 ~ trnL–UAG could accurately identify these two species. Tetraploid I. indigotica is an autotetraploid, and this species has a highly heterozygous and extremely repetitive genome, which can be used to analyse the evolutionary relationships within I. indigotica. Mini-barcoding can help to further elucidate the genetic diversity of I. indigotica and I. tinctoria. It can provide a reference for phylogenetic and evolutionary studies.

Data Availability

The chloroplast genome of I. indigotica has been uploaded to the NCBI database (GenBank accession number OP620952) and the chloroplast genome fasta, gb and sqn files were uploaded to Figshare: https://figshare.com/articles/thesis/genbank_Isatis_ indigotica_Fortune_sqn/21,371,487. The chloroplast genome of I. tinctoria was obtained from the NCBI (accession number NC_028415). The de novo clean reads of tetraploid I. indigotica were uploaded to the NCBI SRA database under BioProject PRJNA888437.

References

  1. Chen S, Zhu Z, Ma H, Yang J, Guo Q. DNA barcodes for discriminating the medicinal plant Isatis indigotica Fort. (Cruciferae) and its adulterants. Biochem Syst Ecol. 2014;57:287–92.

    Article  CAS  Google Scholar 

  2. Sun HJ, Wu YX, Wu ZF, Han F, Wang YQ. Optimization of vacuum assisted heat reflux extraction process of radix isatidis using least squares-support vector machine algorithm. Phytochem Lett. 2021;43:108–13.

    Article  Google Scholar 

  3. Kong WJ, Zhao YL, Shan LM, Xiao XH, Guo WY. Investigation on the spectrum-effect relationships of EtOAc extract from Radix Isatidis based on HPLC fingerprints and microcalorimetry. J Chromatogr B. 2008;87:109–14.

    Article  Google Scholar 

  4. Ding C, Zhu H. Isatidis Folium alleviates acetaminophen-induced liver injury in mice by enhancing the endogenous antioxidant system. Environ Toxicol. 2020;35:1251–9.

    Article  PubMed  CAS  Google Scholar 

  5. Zhou T, Lu L, Yang G. Flora of China. Beijing: Natural Science Press; 2001.

    Google Scholar 

  6. Cheo TY, Chen CM, Zhuang TD, Ying SG. Comments on the chinese generic names of Clerodendrum L. and Isatis L. J Syst Evol. 1978;16:98–100.

    Google Scholar 

  7. Gentil MV, Maury S. Characterization of epigenetic biomarkers using new molecular approaches. Genomics-assisted crop improvement. Dordrecht. 2007; 351–370.

  8. Olszewski W, Darzynkiewicz Z, Rosen PP, Schwartz MK, Melamed MR. Flow cytometry of breast carcinoma: I. Relation of DNA ploidy level to histology and estrogen receptor. Cancer. 2015;48:980–4.

    Article  Google Scholar 

  9. Yang H, Tao Y, Zheng Z, Sweetingham MW, Howieson JG. Application of next-generation sequencing for rapid marker development in molecular plant breeding: a case work on anthracnose disease resistance in Lupinus angustifolius L. BMC Genomics. 2012;13:1–12.

    Article  Google Scholar 

  10. Zhang ZZ, Tan MP, Zhang YY, Jia Y, Zhu SX, Wang J, et al. Integrative analyses of targeted metabolome and transcriptome of Isatidis Radix autotetraploids highlighted key polyploidization-responsive regulators. BMC Genomics. 2021;22:1–13.

    Article  Google Scholar 

  11. Tillich M, Lehwark P, Pellizzer T, Ulbricht-Jones ES, Fischer A, Bock R, et al. GeSeq—versatile and accurate annotation of organelle genomes. Nucleic Acids Res. 2017;45:6–11.

    Article  Google Scholar 

  12. Shi L, Chen H, Jiang M, Wang L, Wu X, Huang L, et al. CPGAVAS2, an integrated plastome sequence annotator and analyzer. Nucleic Acids Res. 2019;47:65–73.

    Article  Google Scholar 

  13. Greiner S, Lehwark P, Bock R. OrganellarGenomeDRAW (OGDRAW) version 1.3.1: expanded toolkit for the graphical visualization of organellar genomes. Nucleic Acids Res. 2019;47:59–63.

    Article  Google Scholar 

  14. Park I, Yang S, Kim WJ, Song JH, Lee HS, Lee HO, et al. Sequencing and comparative analysis of the chloroplast genome of Angelica polymorpha and the development of a novel indel marker for species identification. Molecules. 2019;24:1038.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Nguyen LT, Schmidt HA, Arndt V, Quang MB. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.

    Article  PubMed  CAS  Google Scholar 

  17. Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler AV, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27:573–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Tan W, Gao H, Jiang WL, Zhang HY, Yu XL, Liu EW, et al. The complete chloroplast genome of Gleditsia sinensis and Gleditsia japonica: genome organization, comparative analysis, and development of taxon specific DNA mini-barcodes. Sci Rep-UK. 2020;10:1–13.

    Google Scholar 

  20. Chen YX, Chen YS, Shi CM, Huang ZB, Zhang Y, Li SK, et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience. 2018;7:120.

    Article  Google Scholar 

  21. Audano P, Vannberg F. KAnalyze: a fast versatile pipelined K-mer Alexandra, D., Tuller. T. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 2014; 42: 9171–9181.

  22. Ranallo TR, Jaron KS, Schatz MC. GenomeScope 2.0 and smudgeplot for reference-free profiling of polyploid genomes. Nat Commun. 2020;11:1–10.

    Google Scholar 

  23. Jiao LC, Lu Y, He T, Li JN, Yin YF. A strategy for developing high-resolution DNA barcodes for species discrimination of wood specimens using the complete chloroplast genome of three Pterocarpus species. Planta. 2019;250:95–104.

    Article  PubMed  CAS  Google Scholar 

  24. Yu X, Tan W, Zhang H, Gao H, Tian X. Complete chloroplast genomes of Ampelopsis humulifolia and Ampelopsis japonica: molecular structure, comparative analysis, and phylogenetic analysis. Plants. 2019;8:410–4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Tuo ZY, Hader A, Zhou GL. A report on chromosome numbers and karyotypes of 12 species of Brassicaceae from Xinjiang. J Xinjiang Agricultural Univ. 2012;35:439–45.

    Google Scholar 

  26. Ren BQ, Chen ZD. DNA barcoding plant life. Chin Bull bot. 2010;45:1–12.

    CAS  Google Scholar 

  27. Barnard-Kubow KB, McCoy MA, Galloway LF. Biparental chloroplast inheritance leads to rescue from cytonuclear incompatibility. New Phytol. 2017;213:1466–76.

    Article  PubMed  CAS  Google Scholar 

  28. Camus MF, Alexander-Lawrie B, Sharbrough J, Hurst. GDD. Inheritance through the cytoplasm. Heredity, 2022: 1–13.

  29. Spataro G, Taviani P, Negri V. Genetic variation and population structure in a eurasian collection of Isatis tinctoria L. Gen Res Crop Evol. 2007;54:573–84.

    Article  Google Scholar 

  30. Havananda T, Brummer EC, Doyle JJ. Complex patterns of autopolyploid evolution in alfalfa and allies (Medicago sativa; Leguminosae). Am J Bot. 2011;98:1633–46.

    Article  PubMed  Google Scholar 

  31. Qu C, Jia L, Fu F, Zhao HY, Lu K, Wei LJ, et al. Genome-wide association mapping and identification of candidate genes for fatty acid composition in Brassica napus L. using SNP markers. BMC Genomics. 2017;18:1–17.

    Google Scholar 

  32. Duan S, Liu B, Zhang YY, Li GL, Guo XL. Genome-wide identification and abiotic stress-responsive pattern of heat shock transcription factor family in Triticum aestivum L. BMC Genomics. 2019;20:1–20.

    Article  Google Scholar 

  33. Ali F, Qanmber G, Wei Z, Yu DQ, Li YH, Gan L, et al. Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses. BMC Genomics. 2020;21:1–15.

    Article  Google Scholar 

  34. Mao JJ. The creating of new germplasm of autotetrraploid non-heading chinese cabbage and radish. Dissertation, Nanjing Agricultural University. 2016.

  35. Doyle JJ, Coate JE. Autopolyploidy: an epigenetic macromutation. AM J BOT. 2020;107:1097–110.

    Article  PubMed  Google Scholar 

  36. Harun ar Rashid. Phenotypic and genetic effects in brassica rapa and brassica oleracea autotetraploids and trisomics of B.RAPA. Dissertation, Huazhong Agricultural University. 2021.

  37. Gibson E, Tielborger K, Seifan M. Equivocal evidence for a change in balance between selfing and pollinator-mediated reproduction in annual Brassicaceae growing along a rainfall gradient.J SYST EVOL. 2022; 60:196–207.

  38. Zhang HL, Zhang SN, Zhang W, Zhang ZC. Study on the induction of autotetraploid radish by colchicine. J Nanjing Agricultural Univ. 2008;31:47–50.

    Google Scholar 

  39. Zhou YY. Cytological and transcriptomic studies of the autopolyploid and cabbage isatis allopolyploid progeny. Huazhong Agricultural University; 2014. Dissertation.

  40. Pan X, Xiao Y, Wang Z, Zhang L, Tang K. Tetraploids Isatis indigotica are more responsive and adaptable to stresses than the diploid progenitor based on changes in expression patterns of a cold inducible ii CPK1. Biologia. 2008;63:535–41.

    Article  CAS  Google Scholar 

  41. Manchanda N, Portwood JL, Woodhouse MR, Arun S, Seetharam, Carolyn J, Lawrence-Dill, et al. GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations. BMC Genomics. 2020;21:1–9.

    Article  Google Scholar 

  42. Stott CM, Bobay LM. Impact of homologous recombination on core genome phylogenie. BMC Genomics. 2020;21:1–10.

    Article  Google Scholar 

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Acknowledgements

We thank all authors for their contributions to this work. I. indigotica (27) was supplied by China Resources Sanjiu Medical & Pharmaceutical Co., Ltd. I. tinctoria (28) was supplied by the researcher Bahar Guli Huangerhan of the Food and Drug Inspection Institute of Altay Prefecture.

Funding

This work was supported by the Jiangsu Modern Agricultural Industrial Technology System Construction Project (JATS|[2022]461,JATS[2022]291).

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Author notes

  1. Yong Su and Man Zhang these authors share co-first authorship.

Authors and Affiliations

  1. Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing City, 210095, Jiangsu Province, PR China

    Yong Su, Man Zhang, Qiaosheng Guo, Min Wei, Hongzhuan Shi, Tao Wang, Chang Liu & Jianmin Huang

  2. China Resources Sanjiu Medical & Pharmaceutical Co., Ltd, Shenzhen City, 518000, PR China

    Min Wei, Zhengzhou Han & Huihui Liu

Authors
  1. Yong Su
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Contributions

Yong Su: Software, Formal analysis, Investigation, Writing–original draft. Man Zhang: Software, Formal analysis, Visualization. Qiaosheng Guo: Conceptualization, Writing–review & editing. Min Wei: Conceptualization. Hongzhuan Shi: conceptualization. Tao Wang: conceptualization. Zhengzhou Han: Investigation, Writing–review & editing. Huihui Liu: Writing–review & editing. Chang Liu: Writing–review & editing. Jianmin Huang: Writing–review & editing. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Qiaosheng Guo.

Ethics declarations

Ethics approval and consent to participate

This species was not included in the list of rare and endangered species in China. It was necessary to collect seeds for scientific research to solve the classification. Under the entrustment of China Resources Sanjiu Medical & Pharmaceutical Co., Ltd, diploid seeds of I. indigotica (1 ~ 26) and tetraploid seeds of I. indigotica (27) were collected in various provinces of China. I. tinctoria seeds (28) were collected in the Xinjiang Uygur Autonomous Region of China. The seeds of I. indigotica (1 ~ 27) were identified by Dr. Qiaosheng Guo of Nanjing Agricultural University. The seeds of I. tinctoria (28) were identified by the researcher BaharGuli Huangerhan of the Food and Drug Inspection Institute of Altay Prefecture in the Xinjiang Uygur Autonomous Region. The voucher specimen was kept at the Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing, Jiangsu Province. The deposition number of I. indigotica (1 ~ 27) was IIF-1506001 to IIF-1506027, and that of I. tinctoria (28) was ITL-1506001. All the samples were for plant research use only. The use of seeds in this study complied with the relevant institutional, national, and international guidelines and legislation.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Electronic supplementary material

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Supplementary Material 1:

Figure S1. Morphological Characteristics of I. indigotica and I. tinctoria.

Table S1. The information of the seeds.

Table S2. Morphological data in I. indigotica and I. tinctoria.

Sequence of ITS2-2F(1~28).

Sequence of ITS2-p3(1~28).

Sequence of mini-barcode(1~28)

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Su, Y., Zhang, M., Guo, Q. et al. Classification of Isatis indigotica Fortune and Isatis tinctoria Linnaeus via comparative analysis of chloroplast genomes. BMC Genomics 24, 465 (2023). https://doi.org/10.1186/s12864-023-09534-8

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Keywords

BMC Genomics

ISSN: 1471-2164

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