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Genetic diversity analysis and DNA fingerprint construction of Zanthoxylum species based on SSR and iPBS markers

BMC Plant Biology volume 24, Article number: 843 (2024) Cite this article

Abstract

Zanthoxylum is a versatile economic tree species utilized for its spice, seasoning, oil, medicinal, and industrial raw material applications, and it has a lengthy history of cultivation and domestication in China. This has led to the development of numerous cultivars. However, the phenomenon of mixed cultivars and confusing names has significantly obstructed the effective utilization of Zanthoxylum resources and industrial development. Consequently, conducting genetic diversity studies and cultivar identification on Zanthoxylum are crucial. This research analyzed the genetic traits of 80 Zanthoxylum cultivars using simple sequence repeat (SSR) and inter-Primer Binding Site (iPBS) molecular markers, leading to the creation of a DNA fingerprint. This study identified 206 and 127 alleles with 32 SSR markers and 10 iPBS markers, respectively, yielding an average of 6.4 and 12.7 alleles (Na) per marker. The average polymorphism information content (PIC) for the SSR and iPBS markers was 0.710 and 0.281, respectively. The genetic similarity coefficients for the 80 Zanthoxylum accessions ranged from 0.0947 to 0.9868 and from 0.2206 to 1.0000, with mean values of 0.3864 and 0.5215, respectively, indicating substantial genetic diversity. Cluster analysis, corroborated by principal coordinate analysis (PCoA), categorized these accessions into three primary groups. Analysis of the genetic differentiation among the three Zanthoxylum (Z. bungeanum, Z. armatum, and Z. piperitum) populations using SSR markers revealed a mean genetic differentiation coefficient (Fst) of 0.335 and a gene flow (Nm) of 0.629, suggesting significant genetic divergence among the populations. Molecular variance analysis (AMOVA) indicated that 65% of the genetic variation occurred within individuals, while 35% occurred among populations. Bayesian model-based analysis of population genetic structure divided all materials into two groups. The combined PI and PIsibs value of the 32 SSR markers were 4.265 × 10− 27 and 1.282 × 10− 11, respectively, showing strong fingerprinting power. DNA fingerprints of the 80 cultivars were established using eight pairs of SSR primers, each assigned a unique numerical code. In summary, while both markers were effective at assessing the genetic diversity and relationships of Zanthoxylum species, SSR markers demonstrated superior polymorphism and cultivar discrimination compared to iPBS markers. These findings offer a scientific foundation for the conservation and sustainable use of Zanthoxylum species.

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Introduction

Zanthoxylum L., a member of the Rutaceae family, is a small evergreen or deciduous tree, shrub, or woody vine. There are approximately 250 species worldwide, primarily found in the tropical and subtropical regions of East Asia and North America [1]. Specifically, China is home to 45 species, 13 varieties, and 2 formas distributed in both the northern and southern regions. The predominant cultivated species in China are Zanthoxylum bungeanum Maxim. and Zanthoxylum armatum DC., commonly referred to as “Huajiao” or “Chinese pepper,” which are used as edible spices [2,3,4]. Moreover, Zanthoxylum species have a wide range of applications, including in food, medicine, ornamental purposes, and soil and water conservation, demonstrating significant economic and ecological benefits.

China serves as the leading producer of Zanthoxylum, boasting the highest yield and cultivation area globally. Furthermore, China has been at the forefront of utilizing and domesticating Zanthoxylum species, with records indicating its use dating back to the 11th to 10th centuries BC [5]. Over the course of extensive cultivation and domestication, a diverse range of Zanthoxylum cultivars and types have emerged. As the cultivation area expands and the exchange of resources between different Zanthoxylum production regions becomes more frequent, the genetic background of Zanthoxylum has become increasingly complex. Additionally, varying classification criteria in different regions have contributed to issues such as cultivar confusion and name ambiguity. Consequently, instances of synonymy, homonymy, and substandard materials often arise in the cultivation and commercial circulation of Zanthoxylum. Morphological identification methods based solely on phenotypic traits prove inadequate for distinguishing these similar materials. This not only compromises the rights and interests of consumers, growers, and breeders but also hinders the development and utilization of Zanthoxylum germplasm resources and the process of cultivar selection [3, 6]. Therefore, conducting extensive research on genetic diversity analysis, genetic map construction, and cultivar identification techniques for Zanthoxylum is highly important. This research will play a crucial role in safeguarding the development of Zanthoxylum germplasm resources and ensuring the healthy growth of the industry.

Molecular markers are extensively utilized in genetic diversity analysis, germplasm resources identification, and genetic map construction of species. Among the various molecular marker technologies available, SSR has gained wide popularity due to their high polymorphism level, reliable repeatability, codominance, and multiple allele variations. It has been chosen as the preferred method for constructing plant DNA fingerprints by the International Union for the Protection of New Plant Varieties (UPOV) [7, 8]. In recent years, several molecular markers have been applied in the study of Zanthoxylum. Li et al. [9] conducted the first genome-wide survey of Zanthoxylum and used 36 Genomic-SSR (G-SSR) markers, which demonstrated polymorphism, to classify 15 Zanthoxylum cultivars into two categories. Using three candidate DNA barcode regions (ITS2, ETS, and trnH psbA), Zhao et al. [10] identified 69 materials representing 13 Chinese pepper species. Feng et al. on the other hand, employed SRAP [3], chloroplast DNA (cpDNA) [4], EST-SSR [11], ISSR [12], and SNP [13] markers to analyze the genetic diversity, phylogenetic relationships, and genetic structure of Zanthoxylum species. Although numerous SSR markers have been identified in Zanthoxylum species, their potential for use in identifying Zanthoxylum germplasm resources has not been validated.

iPBS (inter-Primer Binding Site), proposed in 2010 by Kalendar et al. [14], is a novel molecular marker technology for polymorphism amplification based on reverse transcription transposon sequences. Compared to other molecular marker techniques, iPBS does not require sequence information or primer design in advance. The detection of produced markers can be achieved through agarose gel electrophoresis, a simple, fast, and cost-effective method. The primers used in iPBS are versatile and can be utilized in a wide range of plants and animals. Moreover, iPBS exhibits high polymorphism and reproducibility [14, 15]. As a result of these advantages, iPBS has been increasingly employed in plants for evaluating genetic diversity, as observed in grape [16], safflower (Carthamus tinctorius) [17], and bamboo [18] studies. However, so far, there are no reports on the application of iPBS as a molecular marker in Zanthoxylum. Notably, a study by Hu et al. [19] revealed that approximately 71.2% of the Z. armatum genome and 70.6% of the Z. bungeanum genome consisted of LTR-type reverse transcriptional transposons. Consequently, the reverse transcriptional transposon-based marker approach seems appealing as a tool for fingerprinting Zanthoxylum species.

In this study, we assessed the genetic diversity of 80 Zanthoxylum accessions using both SSR and iPBS molecular markers. Through this analysis, we constructed DNA fingerprints to provide a reference for the assessment of resources and cultivar identification of Zanthoxylum. Furthermore, this research endeavors to establish a scientific foundation for the utilization of Zanthoxylum resources and the protection of intellectual property rights.

Materials and methods

Plant materials and DNA extraction

Eighty plant samples including three Zanthoxylum species (Z. bungeanum, Z. armatum, Z. piperitum) were collected from the Zanthoxylum Germplasm Resource Bank in Hanyuan County, Sichuan Province (Table 1). The sampling process involved selecting well-growing Zanthoxylum species plants, randomly selecting 3 individual samples from each cultivar, and collecting fresh and pest-free Zanthoxylum leaves. These leaves were stored in a -80 °C freezer for future use.

Table 1 List of 80 Zanthoxylum accessions used in the present study
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Following the method outlined by Porebski et al. [20], DNA was extracted using a modified CTAB method. The concentration and purity of the extracted DNA were subsequently assessed using a NanoDrop One Ultra-Micro UV Spectrophotometer (Thermo Fisher Scientific Inc., USA). The integrity of the DNA was verified through 1% agarose gel electrophoresis. The DNA was uniformly diluted to a concentration of 100 ng/µl and stored in a -40 °C refrigerator as a backup.

SSR primer screening and PCR amplification

Six hundred pairs of primers were selected from the G-SSR primers developed in the previous stage of our group, containing dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, hexanucleotide and complex types of SSR sites, and all of them were tested for specificity and synthesized by Sangon Biotech (Shanghai) Co., Ltd. These primers were used to amplify DNA from seven Zanthoxylum accessions (FGHJ, HYWCXZ ♂, SJSH, NLDHP, YJ, WCTJ, JYQHJ) (Table 1) that exhibited significant morphological differences. Primers with clear target bands, simple band types, and high polymorphism were selected.

PCR reaction system (25 µL): 3G Taq Master Mix for PAGE (Red Dye) (Nanjing Vazyme Biotech Co., Ltd.)12.5 µL; forward and reverse primers: 1.0 µL (10 pmol/L); DNA 100 ng; fill with ddH2O to 25.0 µL. PCR amplification was performed using Touchdown PCR method, with a reaction procedure of pre-denaturation at 95℃ for 6 min; denaturation at 95 °C for 15 s, annealing at 64 °C for 15 s (thereafter, cycling at 64 °C ∼ 54 °C for every 2 °C decrease until 54 °C), and extension at 72 °C for 30 s; denaturation at 95 °C for 15 s, annealing at 54 °C for 15 s, extension at 72 °C for 30 s, and cycling 25 times; extend at 72 °C for another 5 min and stored at 4 °C.

PCR products were detected by 10% nondenaturing polyacrylamide gel electrophoresis at 185 V for 130 min. After silver staining and color development, they were photographed with a camera.

iPBS primer screening and PCR amplification

Eighty-three iPBS primers published by Kalendar et al. [14] were synthesized, and these primers were amplified by PCR using DNA from Zanthoxylum accessions (FGHJ, HYWCXZ ♂, SJSH, NLDHP, YJ, WCTJ, JYQHJ) (Table 1) that exhibited significant morphological differences, and those with clear amplified bands, high polymorphism, and high stability were selected. 83 iPBS primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd.

PCR reaction system (25 µL): 2 × Rapid Taq Master Mix (Nanjing Vazyme Biotech Co., Ltd.) 12.5 µL, iPBS primer 1.0 µL (10 pmol/L), ddH2O 10.5 µL, DNA 1.0 µL. Reaction procedure: Pre-denaturation at 95 °C for 6 min; denaturation at 95 °C for 15 s, annealing at 39.0 ∼ 65.0 °C for 30 s, extension at 72 °C for 1 min, 32 cycles; complete extension at 72 °C for 5 min, stored at 4 °C.

PCR products were detected by 1.2% agarose gel electrophoresis at 100 V for 28 min, and photographed by a gel imaging system at the end of electrophoresis.

Data statistics and analysis

The bands in the SSR and iPBS electrophoresis profiles were counted using Excel 2019 and assigned corresponding “1” or “0” values based on the presence or absence of bands, respectively. These data were used to create a two-dimensional matrix of “0, 1”.

For SSR markers, the data formats were converted using DataFormater software [21]. Genetic parameters such as number of observed alleles (Na), number of effective alleles (Ne), Shannon’s information index (I), expected heterozygosity (He), observed heterozygosity (Ho), fixed coefficient of population genetic differentiation (Fst), gene flow (Nm), probability of identity (PI), and probability of identity among siblings (PIsibs) were computed by GenAlex 6.503 software [22]; and the test materials were principal coordinate analysis (PCoA) and analysis of molecular variance (AMOVA) were performed. The polymorphism information content (PIC) of SSR primers was calculated using PIC-Calc 0.6 software. Genetic similarity coefficients (GS) among the test materials were calculated using NTSYS-pc 2.1 software [23], and the unweighted pair-group method with arithmetic means (UPGMA) in the SAHN module was used for cluster analysis and construction of dendrograms. Population structure analysis was performed by Structure 2.3.2 software [24] with the following parameters: Length of Burin Period = 50,000, Number of MCMC Reps after Burnin = 100,000, K = 1 ∼ 10, and 5 replications for each K value; the results were uploaded to the Structure Harvester website (https://taylor0.biology.ucla.edu/structureHarvester/) to determine the optimal K value; the results corresponding to the optimal K value were subsequently analyzed by repeated sampling through the CLUMPP program; and finally visualized using the distrut program.

For iPBS markers, observed alleles (Na), number of effective alleles (Ne), Shannon’ s information index (I), and Nei’ s gene diversity (H) were calculated for amplified loci and populations by PopGene 1.32 software [25]; PCoA and cluster analysis based on UPGMA method were performed using NTSYS-pc 2.1 software. Since the iPBS markers are dominant markers, the PIC was calculated with reference to the method of Hinze et al. [26]: PICi = 1 - (p2 + q2), where p is the frequency of “1” appearing in the i-th band of the primer and q is the frequency of “0” appearing in the i-th band of the primer; when p = q = 0.5, the PIC value of the dominant marker is the largest (0.5), and the polymorphism of the primer is the highest.

Construction of DNA fingerprint

The SSR primers for constructing fingerprints were screened according to the following conditions: (1) The amplified bands are clear, and the results are stable and reproducible; (2) Primers with high PIC and low PI values; (3) The principle of identifying the most materials with the fewest number of primers was followed; (4) Ensure the uniqueness of the fingerprint of each accession.

The band information amplified by each primer was recorded in Excel 2019 using “0”, “1”, and “9” to signify “no band,” “with band,” and “no amplification,“, respectively, to form a digital fingerprint map. Subsequently, the information (name, Latin name, cultivar types, provenance) of each Zanthoxylum accession was integrated with its fingerprint code and imported into the “Caoliao QR Code” online software (https://cli.im/) to generate QR codes for the fingerprints of 80 Zanthoxylum cultivars.

Results

SSR primer screening and genetic diversity of the markers

A total of 32 pairs of polymorphic SSR primers (Supplementary Table S1) were screened from 600 pairs of primers using seven Zanthoxylum accessions with significant morphological differences. These primers were subsequently used to amplify all peppercorn samples.

A total of 206 (Na) of the 32 pairs of SSR primers were detected in 80 Zanthoxylum accessions. The average number of alleles detected per pair of primers ranged from 3.000 (D27, T6) to 11.000 (P.17), with an average value of 6.438 (Table 2). This finding suggested that the tested Zanthoxylum accessions exhibit relatively abundant allelic variation. The number of effective alleles (Ne) varied from 1.648 (P4.2) to 6.181 (D86), with a mean value of 3.254. Observed heterozygosity (Ho) and expected heterozygosity (He) values indicate the magnitude of genetic variance for different SSR primers, with higher Ho values indicating higher heterozygosity. Among the 32 markers, the Ho values ranged from 0.225 (D112) to 0.950 (D39), and the He values ranged from 0.393 (P4.2) to 0.838 (D86). The mean values for Ho and He were 0.638 and 0.661, respectively. Shannon’s information index (I) varied from 0.677 (P4.2) to 1.937 (D86), with a mean value of 1.336. These results indicate that the tested Zanthoxylum materials exhibit a high degree of genetic variation and rich genetic diversity.

Table 2 The genetic diversity statistics of 32 SSR markers in 80 Zanthoxylum accessions
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The PIC values of the 32 pairs of primers ranged from 0.400 (P4.2) to 0.827 (D11), with an average of 0.710. There were 30 pairs of primers with PIC values > 0.5, indicating that the screened primers had high polymorphism. These primers can effectively reveal the genetic diversity of the tested Zanthoxylum accessions and are suitable for DNA fingerprinting.

Genetic relationship and cluster analysis of Zanthoxylum based on SSR markers

Genetic similarity coefficients (GS) are commonly used to evaluate the extent of genetic similarity among individuals. In this study, the genetic similarity coefficient matrix of 80 Zanthoxylum accessions was obtained using NTSYS-pc 2.1 software (Supplementary Figure S1). The GS values ranged from 0.0947 to 0.9868, with an average of 0.3864, indicating noticeable variation in the genetic backgrounds of the test materials. Notably, the GS value between ‘JJYH’ and ‘ZHJ’ was the smallest (0.0947), indicating that these two plants had the highest genetic variation and the furthest genetic relationship. Conversely, the GS value between ‘LZHHJ’ and ‘BSJ’ was the largest (0.9868), indicating that these two plants had very close genetic relationships. Additionally, the frequency distribution of the 3160 GSs obtained from the two-by-two comparison of the test samples revealed that the majority of the GSs fell within the range of 0.1 to 0.5, accounting for 77.5% of the total (Supplementary Figure S2). Among them, the largest number of Zanthoxylum accessions had GS values ranging from 0.1 to 0.2, accounting for 26.17% of the total. Overall, these results indicate that the 80 Zanthoxylum accessions possess a diverse range of genetic characteristics and a broad genetic background.

The cluster analysis results demonstrated that using 32 SSR markers, it was possible to completely distinguish the 80 Zanthoxylum accessions (Fig. 1). With a GS threshold of 0.2217, the test accessions could be classified into three classes (I, II, and III). Class I consisted of 57 Z. bungeanum accessions, class II consisted of 17 Z. armatum accessions, and class III consisted of 6 Z. piperitum accessions. It is worth noting that “MYWCQHJ” (37) and “YJ” (67) in class II aggregate into a subclass at a GS value of 0.348. After calculation, it was found that the average GS values of “MYWCQHJ” and “YJ” with the other 15 Z. armatum accessions were 0.356 and 0.365, respectively, indicating that they have a distant genetic relationship with other Z. armatum accessions. Similarly, in class I, “HYWC ♂” (30) and " HYWC ♀” (31) clustered into a subclass at a GS of 0.312, showing a distant relationship with other Z. bungeanum accessions. Furthermore, we noticed that the GS values of “BSJ” (26) from Hebei and “LZHHJ” (52) from Henan amounted to 0.987, suggesting minimal genetic differences and a possible case of synonymy. Additionally, certain Zanthoxylum accessions from different source areas are clustered together, such as “LNDHP” (25) from Gansu and “MXLYJ” (40) from Sichuan, as well as “DJ” (27) from Gansu and “RCHJ” (54) from Shanxi. This clustering may be attributed to the frequent trade and introductions of Zanthoxylum between various regions. Moreover, the high correlation coefficient (0.977) calculated using the matrix comparison plot module of NTSYS-pc 2.1 software indicates the accuracy of the clustering results.

Fig. 1
figure 1

UPGMA clustering tree of 80 Zanthoxylum accessions based on SSR markers

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Genetic diversity and differentiation of the Zanthoxylum population based on SSR markers

In this study, a total of 80 Zanthoxylum accessions were categorized into three populations based on the species: Z. bungeanum (Pop1), Z. armatum (Pop2), and Z. piperitum (Pop3). The genetic diversity analysis revealed that, among all three populations, Pop1 exhibited the highest Na, Ne, Ha, He, and I value (Table 3), suggesting that Pop1 possessed the highest genetic diversity. Pop2 had the second highest level, while Pop3 had the lowest. The coefficient of genetic differentiation (Fst) between the populations was calculated, yielding Fst values of 0.242 for Pop1 and Pop2, 0.335 for Pop1 and Pop3, and 0.429 for Pop2 and Pop3. The mean Fst was 0.335 (Fst > 0.25), indicating significant genetic differentiation between the three populations. AMOVA further demonstrated that genetic variation in Zanthoxylum species existed mainly within individuals (65%), with relatively little variation between populations (35%) (Table 4). Additionally, the average Nm was 0.629 (Table 2), suggesting limited gene exchange among individuals within each population, potentially attributed to the phenomenon of apomixis in Zanthoxylum species.

Table 3 The genetic diversity statistics among 3 populations of Zanthoxylum species
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Table 4 The AMOVA of 3 populations of Zanthoxylum species
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Furthermore, Nei’s genetic distance and genetic concordance study revealed that the genetic distance among the populations ranged from 0.854 to 1.190, with a mean value of 0.972. The genetic concordance ranged from 0.304 to 0.426, with a mean value of 0.383 (Table 5), indicating low genetic similarity and a high degree of genetic differentiation among the three populations. Pop2 and Pop3 exhibited the greatest genetic distance, representing the most distant relationship, whereas Pop1 and Pop2 displayed the smallest genetic distance, indicating a more recent relationship.

Table 5 Unbiased estimation of Nei’s genetic distance and genetic identity in 3 populations of Zanthoxylum species
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Principal coordinate analysis indicated that the first two principal coordinates accounted for 46.12% of the genetic variation among the 80 Zanthoxylum accessions. Principal coordinate 1 explained 31.71% of the variation, while principal coordinate 2 accounted for 14.41% (Fig. 2). The analysis classified the 80 Zanthoxylum accessions into three groups: the first group included 57 accessions of Z. bungeanum, the second group comprised 17 accessions of Z. armatum, and the third group consisted of 6 accessions of Z. piperitum. These findings were consistent with the results obtained from cluster analysis.

Fig. 2
figure 2

Principal coordinate analysis of 3 populations of Zanthoxylum species based on SSR markers

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Population structure analysis of Zanthoxylum based on SSR markers

In order to understand the genetic background and gene penetration of 80 Zanthoxylum accessions, the population structure of the test materials was analyzed by Structure software based on Bayesian modeling and the Q-values (Supplementary Table S2) (Pritchard et al., 2000) (probability that the i-th material has its genomic variation originating from the k-th subgroup) was counted. The results showed that Delta K has an optimal value when K = 2 (Fig. 3), therefore, the 80 Zanthoxylum accessions can be classified into 2 groups: Pop1 (blue) and Pop2 (orange) (Fig. 4); where Pop1 includes 63 accessions, mainly Z. bungeanum and Z. piperitum, and Pop2 includes 17 accessions, mainly Z. armatum.

Fig. 3
figure 3

Delta K values for different numbers of populations assumed (K) in the STRUCURE analysis

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Fig. 4
figure 4

Population genetic structure of 80 Zanthoxylum accessions. Each rectangular column in the figure represents one accession, and the color and color scale of the columns represent the subpopulation to which it belongs and the proportion of the subpopulation it occupies (Blue represents Pop1, and Orange represents Pop2). The number on the X-axis is the accession number

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Of the 80 Zanthoxylum accessions, 69 had Q-values ≥ 0.8, with a mean value of 0.99, indicating that these materials were from a single source, with a simple genetic background and a lack of genetic exchange between subgroups; 11 accessions had Q-values < 0.8 with a mean value of 0.66, suggesting that these materials possessed a mixed origin with a relatively complex genetic composition.

Fingerprinting power of SSR markers and DNA fingerprint construction

PI is an important parameter for assessing the fingerprinting power of molecular markers, with lower values indicating higher fingerprinting efficiency of the markers [27]. According to the results in Table 2, the PI values of the 32 SSR markers ranged from 0.046 (D86) to 0.428 (P4.2), with an average value of 0.173. Assuming that all loci segregate independently, the probability of finding two random individuals with identical genotypes at the 32 marker loci is estimated to be 4.265 × 10− 27, i.e., it is almost impossible to find two different individuals with identical genotypes, suggesting that the markers developed in this study have strong fingerprinting power. PIsibs is considered to be the upper limit of PI [28], and the range of PIsibs values for the 32 SSR markers was 0.342 (D86) to 0.660 (P4.2), and the PIsibs value for all marker combinations was 1.282 × 10− 11.

Based on these results, combined with the results of primer amplification, eight SSR markers (D11, D23, D49, D81, D86, N63, P4.11, P4.17) with low PI values (the average value was 0.096) were screened to compose a core set of markers used to construct the fingerprinting of Zanthoxylum. Through the combination of these eight markers, 80 fingerprinting profiles with unique correspondences were obtained. The digital codes of 80 Zanthoxylum cultivars and their corresponding cultivar types, seed source locations and other information were merged to generate a QR code for fingerprinting (Fig. 5).

Fig. 5
figure 5

Fingerprint information of 80 Zanthoxylum cultivars based on SSR markers

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iPBS primer screening and analysis of primer polymorphisms

Ten iPBS primers with high polymorphism and clear banding patterns were selected from a pool of 83 primers for analysis of genetic diversity in the 80 Zanthoxylum accessions (Supplementary Table S3).

A total of 127 bands were amplified from the ten selected primers, 120 of which were found to be polymorphic (Table 6). The number of bands per primer ranged from 4 to 21, with an average of 12.7 bands. The polymorphism ratio per primer ranged from 75 to 100%, with an average of 93.1%. The PIC values of the primers ranged from 0.201 to 0.324, with an average of 0.281. Notably, primer 2242 exhibited the highest level of polymorphism, with a PIC value of 0.324, while primer 2083 had the lowest level, with a PIC value of 0.201.

Table 6 The amplification results and genetic diversity index of 80 Zanthoxylum accessions by 10 iPBS primers
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Genetic diversity analysis of Zanthoxylum based on iPBS markers

The genetic diversity indices of the 80 Zanthoxylum accessions were calculated with PopGene 1.32 software (Table 6), and the results showed that the mean values of Na, Ne, H and I were 1.9313, 1.3760, 0.2346 and 0.3703, respectively, indicating that the genetic variation among the 80 Zanthoxylum accessions was relatively high.

Genetic similarity coefficient matrices of 80 Zanthoxylum accessions were obtained via NTSYS-pc 2.1 software (Supplementary Figure S3). GS varied from 0.2206 to 1.0000, with an average of 0.5215; among them, the GS values of ‘MSQHJ’ and ‘HYWC ♂’, and ‘WCTJ’ and ‘HYWC ♂’ were all 0.2206, which indicated that they were the most distantly related. There were five groups of Zanthoxylum accessions with GS values of 1; these results, in combination with the SSR marker results, indicated that these materials were very close to each other and had highly similar genetic backgrounds; on the other hand, these results also indicated that the 10 iPBS markers in this study had limited discriminatory ability. Statistics on the frequency distribution of GS values of the test materials were found (Supplementary Figure S4), and the GS values were mainly distributed between 0.3 and 0.7, accounting for 74.56%, with the largest number of Zanthoxylum samples with GS values between 0.3 and 0.4 accounting for 27.09%.

Cluster analysis of Zanthoxylum based on iPBS markers

Based on the matrix of genetic similarity coefficients, a dendrogram depicting iPBS marker clustering of 80 Zanthoxylum accessions was constructed using the UPGMA method (Fig. 6). The analysis revealed that these 80 Zanthoxylum accessions could be categorized into three distinct groups, Group I, Group II, and Group III, representing Z. bungeanum, Z. armatum, and Z. piperitum, respectively, with a GS threshold of 0.3683. Notably, ‘MYWCQHJ’ did not cluster within any group associated with Z. armatum. This phenomenon may be attributed to two factors. First, this could be due to the limited number of iPBS markers utilized in this study. Second, this difference might be attributed to the unique characteristics of the ‘MYWCQHJ’ cultivar itself, as evidenced by its separate clustering within Group I. The correlation coefficient, computed using the Matrix comparison plot module in the NTSYS-pc 2.1 software, was found to be 0.966, underscoring the high accuracy of the clustering results.

Fig. 6
figure 6

UPGMA clustering tree of 80 Zanthoxylum accessions based on iPBS markers

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Furthermore, the principal coordinate analysis results concurred with the cluster analysis results. The 80 Zanthoxylum accessions were divided into three distinct categories (Fig. 7): the first category consisted of one accession of Z. armatum (‘MYWCQHJ’), 57 accessions of Z. bungeanum, the second category comprised 16 accessions of Z. armatum, and the third category included 6 accessions of Z. piperitum. This alignment between the two analyses strengthens the validity of the obtained classifications.

Fig. 7
figure 7

Principal coordinate analysis of 80 Zanthoxylum accessions based on iPBS markers

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Genetic and cluster analysis of Zanthoxylum based on SSR + iPBS markers

The genetic similarity coefficient matrix (Supplementary Figure S5) and clustering tree diagram (Fig. 8) were constructed through the integration of SSR and iPBS molecular marker data. The finding revealed that among the 80 Zanthoxylum accessions, the GS ranged from 0.1747 to 0.9921, with an average value of 0.4422. This indicates a significant disparity in the genetic backgrounds of the accessions. It should be noted that ‘HYWC ♂’ and ‘MSQHJ’ exhibited the lowest GS values (0.1747), while ‘BSJ’ and ‘LZHHJ’ demonstrated the highest GS values (0.9921). Among the Z. bungeanum species, ‘HYWC ♂’ and ‘XZHJ’ had the smallest GS values (0.3072), while ‘BSJ’ and ‘LZHHJ’ had the highest GS values (0.9921). In the case of Z. armatum, ‘MYWCQHJ’ and ‘LQYH’ had the smallest GS values (0.3611), while ‘MSQHJ’ and ‘WCTJ’ had the largest GS values (0.9833). Finally, within the Z. piperitum category, ‘JWCYH’ and ‘HSJ’ had the lowest GS values (0.6837), while ‘ZCSJ’ and ‘ZCSJ ♂’ had the highest GS values (0.9878).

Fig. 8
figure 8

UPGMA clustering tree of 80 Zanthoxylum accessions based on SSR + iPBS markers

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Upon reaching a GS of 0.2657, the 80 Zanthoxylum accessions were divided into three classes: Class I represented Z. bungeanum, Class II represented Z. armatum, and Class III represented Z. piperitum. At a GS of 0.4856, Class I could be further divided into five subclasses. The first subclass comprised 43 Z. bungeanum cultivars, including all the accessions from Shaanxi (12/12), nearly all the accessions from Gansu (13/14), and almost half of the accessions from Sichuan (8/17). These three provinces are geographically close to each other and are major areas for Zanthoxylum production. The mixing of Zanthoxylum cultivars from these regions could be attributed to frequent introductions and resource exchange. Additionally, the first subclass included three cultivars from southwestern Yunnan and a few Zanthoxylum cultivars from northern regions, such as Hebei, Henan, Shandong, and Shanxi. The second subclass comprised eight Zanthoxylum cultivars, five from Sichuan, two from Hebei, and one from Gansu. The third subclass included ‘LWDHP’ and ‘LWXHP’ from Shandong and ‘PSDHP’ from Shanxi. The fourth subclass consisted of two special cultivars, ‘HYWC ♂’ and ‘HYWC ♀’, while the remaining ‘ZHJ’ accessions formed a separate fifth subclass. In Class II, ‘MYWCQHJ’ and ‘YJ’ were found to be distantly related to the other Z. armatum accessions and clustered into separate subclasses with GS values of 0.3909 and 0.4917, respectively. Overall, the clustering results revealed that the Z. bungeanum and Z. armatum cultivars from various source locations exhibited some mixing and were not exclusively clustered based on geographic differences. Clustering analysis utilizing only SSR or iPBS markers also confirmed this phenomenon. Conversely, combining the results of both markers provided a more accurate classification and effectively represented the genetic relationships among the tested Zanthoxylum accessions.

Discussion

Genetic diversity of Zanthoxylum

Genetic diversity serves as the foundation for the long-term survival and evolutionary advancement of species. The extent of genetic diversity within a species determines its evolutionary potential and ability to withstand adverse environmental factors [29]. In the case of plants, research on genetic diversity is crucial for comprehending the level of genetic variation and genetic structure within species. This serves as a significant indicator for evaluating the genetic potential of germplasm resources. Additionally, these findings could lead to resource utilization, germplasm innovation, and varietal improvement while also providing recommendations for resource conservation and management [30, 31].

Molecular markers represent an effective method for studying species genetic diversity. There are various types of molecular markers with different characteristics. By combining different molecular markers, researchers can examine different segments of the genome, thereby enhancing the coverage and uniformity of polymorphic loci. This approach compensates for any limitations and drawbacks associated with using a single type of molecular marker, enabling researchers to gain a comprehensive understanding of the species’ genetic information and enhancing the credibility of their findings [32].

The aim of this study was to assess the genetic diversity and relatedness among 80 Zanthoxylum accessions using SSR and iPBS molecular markers. SSR molecular markers are known for their superior variability and broad distribution within the genome. They are widely utilized across numerous genetic-related fields due to their codominance, high polymorphism, reproducibility, and consistent results [7]. In this study, we identified a total of 206 allelic variations among the 80 Zanthoxylum accessions using 32 selected SSR markers. Each marker displayed an average of 6.438 alleles (Na), an effective number of alleles (Ne) of 3.254, a Shannon’s information index (I) of 1.336, and PIC values ranging from 0.400 to 0.827, with an average of 0.710. Notably, 30 markers exhibited high polymorphism levels (PIC > 0.5). Among the genetic diversity indices, Na and the PIC are particularly important for assessing molecular marker polymorphisms [33]. In this study, the values for these two indices were greater than those reported by Li et al. [9] (Na = 3.5; PIC = 0.48) and Feng et al. [13] (Na = 4.636) in Zanthoxylum. Taken together, these findings indicate that the SSR markers employed in this study exhibited overall high polymorphism, revealing the genetic diversity of the tested Zanthoxylum accessions.

Compared to SSR molecular labeling technology, iPBS molecular labeling technology offers a simpler, faster, and more cost-effective approach. Throughout this study, 10 iPBS primers were employed to amplify a total of 127 bands across the 80 Zanthoxylum accessions. The average polymorphism rate of the primers was 93.1%. The PIC values ranged from 0.201 to 0.324, with an average of 0.281, indicating a moderate level of polymorphism, consistent with research findings in Phoenix dactylifera [34] (PIC = 0.287) and Psidium guajava [35] (PIC = 0.287). By combining the results of both sets of molecular markers, it was observed that the genetic diversity index obtained through iPBS markers was significantly lower than that obtained through SSR markers. This finding suggested that SSR markers possess greater polymorphism and are more suitable for analyzing the genetic diversity of Zanthoxylum germplasm resources. Such disparity is likely influenced by the number of markers used in this study; utilizing 32 SSR markers increases the likelihood of detecting greater genetic variation than does the use of only 10 iPBS markers. Moreover, SSR markers are codominant markers that distinguish between pure and heterozygous genotypes, thus conferring a greater advantage in revealing species genetic diversity than dominant markers. In summary, the utilization of both molecular markers revealed a considerable level of genetic diversity within the 80 Zanthoxylum accessions.

Genetic relationship ofZanthoxylum.

The genetic similarity coefficient is a useful tool for evaluating genetic similarity. A higher genetic similarity coefficient indicates a closer genetic relationship and greater similarity between two individuals or groups, while a lower coefficient suggests greater genetic differentiation and greater genetic diversity [36]. Among the 80 Zanthoxylum accessions, the ranges of GS values obtained through the SSR, iPBS, and SSR + iPBS methods were 0.0947 ∼ 0.9868, 0.2206 ∼ 1.0000, and 0.1747 ∼ 0.9921, respectively, with statistically significant differences. The average GS values were 0.3864, 0.5215, and 0.4422, respectively, indicating relatively rich genetic diversity and a high level of genetic variation among the tested Zanthoxylum accessions. SSR markers exhibited a wider range of GS variation and smaller average GS values than did the other markers, suggesting that SSR markers are more effective at detecting genetic variation. The genetic relationships revealed by the two marker types were consistent. For instance, in the iPBS results, GS values of 1 were obtained between ‘FXDHP’ and ‘GJDHP’, ‘BSJ’ and ‘LZHHJ’, and ‘MSQHJ’ and ‘WCTJ’. These same groups also had relatively large GS values (0.9744, 0.9868, and 0.9730) according to the SSR results, indicating very close genetic relationships. This may be attributed to inconsistent naming of the same cultivar in different regions, known as the phenomenon of synonymy. In summary, both SSR and iPBS markers can be employed to assess the phylogenetic relationships of the Zanthoxylum species. However, SSR markers showed greater diversity and a more comprehensive reflection of the phylogenetic relationships, suggesting it has greater polymorphism. Additionally, SSR + iPBS markers compensated for the limitations of iPBS markers and provided a more accurate representation of the genetic relationships among the tested Zanthoxylum accessions. The cluster analysis findings also supported these conclusions. Based on the SSR, iPBS, and SSR + iPBS markers, the 80 Zanthoxylum accessions were divided into three categories (Z. bungeanum, Z. armatum, and Z. piperitum), and closely related Zanthoxylum species were grouped together. However, when iPBS markers were used, ‘MYWCQHJ’, which belongs to Z. armatum, was clustered with Z. bungeanum cultivars, indicating that SSR markers provided more accurate results. Furthermore, it is possible that the unique characteristics of ‘MYWCQHJ’ contributed to this clustering result, as evidenced by the presence of multiple unique loci or band patterns (Supplementary Figure S6). The calculated mean GS value of ‘MYWCQHJ’ compared to those of the other 16 accessions of Z. armatum was only 0.391 (based on SSR + iPBS markers), indicating a distant relationship. These findings highlight the unique genetic variation of ‘MYWCQHJ’, which may prove valuable in future efforts related to germplasm innovation and the development of new cultivars. Additionally, on the clustering tree diagrams of both markers, it was observed that some Zanthoxylum accessions from the same region were not clustered together (Fig. 8). These findings suggest that long-term cultivation, domestication of Zanthoxylum species, and trading and introduction between different regions may have contributed to this phenomenon. Notably, the single Zanthoxylum accession from Germany was not grouped separately but instead clustered together with Chinese Zanthoxylum, indicating a shared origin, consistent with previous research conducted by Feng [37].

Genetic differentiation and genetic structure of Zanthoxylum

Gene differentiation (Fst) and gene flow (Nm) are crucial parameters for assessing genetic variation among populations, and they exhibit an inverse correlation wherein higher differentiation coefficients indicate lower levels of gene flow [38]. For Fst, the following categories are generally utilized: Fst ranges between 0 and 0.05, which suggests negligible genetic differentiation between populations; 0.05 and 0.15, which signifies a moderate degree of genetic differentiation; 0.15 and 0.25, which indicates a substantial degree of genetic differentiation; and Fst > 0.25, which signifies a high degree of genetic differentiation [39]. For Nm, it is generally accepted that Nm > 1 indicates that there is frequent gene exchange between populations, which prevents genetic differentiation of populations due to genetic drift and contributes to the maintenance of genetic stability of populations, while Nm < 1 indicates that gene flow is not sufficient to counteract the effects of genetic drift, thus contributing to the increase of genetic differentiation between populations [40]. In this study, we used SSR markers to analyze the genetic differentiation characteristics of three Zanthoxylum populations (Pop1, Pop2, and Pop3). The Fst values were 0.242, 0.335, and 0.429 between Pop1 and Pop2, Pop1 and Pop3, and Pop2 and Pop3, respectively, suggesting a high level of genetic differentiation among the three populations. Moreover, the mean Nm was 0.629 (< 1), indicating limited gene exchange among the populations. This can be attributed to the fusionless reproductive characteristics of Zanthoxylum species and the high levels of genetic differentiation among populations, which hinder gene flow [37]. Additionally, the AMOVA results indicated a high level of genetic differentiation among the tested Zanthoxylum accessions, with genetic variation predominantly arising within individuals (65%), while 35% of the genetic variation originated from between populations. Both cluster analysis and PCoA accurately categorized the 80 Zanthoxylum accessions into three groups corresponding to the three different Zanthoxylum species populations (Pop1, Pop2, and Pop3). The genetic analysis revealed substantial genetic distance (0.972) and low genetic concordance (0.383) among these three populations, further highlighting their high level of genetic differentiation. Geographical isolation is an important factor leading to population differentiation, due to environmental heterogeneity, genetic variation, and limited gene flow, resulting in the independent evolution of populations in different geographical regions [13, 41]. The distinct growth environments of these three groups contributed significantly to their differentiation, with Z. armatum found in frost-free regions of southwestern China characterized by warm and humid climates; Z. bungeanum exhibiting resilience and adaptability to wide areas with harsh climates (subtropical and temperate zones); mainly distributed in northern regions of the Qinling Mountains-Huaihe River in China [19]; and Z. piperitum concentrated in certain parts of Japan. Over an extended period, the combination of natural and artificial selection has limited genetic exchange between these Zanthoxylum populations, leading to significant differentiation. Generally, higher genetic diversity indicates greater complexity of plant diversity and greater potential for environmental adaptation [42]. Among the three populations, the Z. bungeanum population (Pop1) exhibited the highest genetic diversity, while the Z. piperitum population (Pop3) displayed the lowest. This discrepancy may be attributed to the number of samples and actual cultivars, as well as the stronger environmental adaptability and wider geographic distribution of Z. bungeanum. Consequently, Z. bungeanum germplasm resources can serve as crucial genetic breeding material for future cultivar selection and breeding endeavors.

Unlike the results of UPGMA cluster analysis and PCoA, Bayesian model-based population structure analysis classified the 80 Zanthoxylum accessions into two subgroups (Fig. 4), of which six Z. piperitum materials were not classified into a separate category. The reasons for this discrepancy have to do with the fact that the different methods take different computational approaches or provide different amounts of information [37]; on the other hand, it may be related to the small number of Z. piperitum material used in this study. Most of the 80 Zanthoxylum accessions (86%) had a single genetic component (Q-value ≥ 0.8), and only a few materials (14%) showed a mixture of both gene pools (Q-value < 0.8), suggesting a lack of genetic exchange between Zanthoxylum subgroups, which is consistent with the results of the analysis of population genetic differentiation.

Construction of DNA fingerprint map and fingerprinting power

DNA fingerprinting is a molecular-level method used to identify different biological individuals by utilizing molecular markers. It is not influenced by environmental factors or by the developmental stage of organisms. In the case of plants, DNA fingerprinting is valuable for accurately and rapidly identifying cultivars, offering convenience for germplasm resource management, evaluation, protection of cultivar rights, and crop breeding [43]. Among several molecular markers, SSR markers are widely regarded as the preferred method for constructing plant DNA fingerprints. They have been recognized as one of the most powerful marker systems for identifying plant cultivar and have been successfully applied across multiple species [8, 43]. For instance, He et al. [44] established the genetic fingerprints of 33 standard flue-cured tobacco varieties using 48 SSR markers and developed identification technology for new tobacco varieties based on SSR markers. Chen et al. [43] created a DNA fingerprinting database of 128 excellent oil camellia cultivar using highly variable SSR markers.

PI and PIsibs are widely used as indicators of the fingerprinting power of molecular markers in studies of fingerprinting construction [28, 45]. In this study, the combined PI value of 32 SSR markers was 4.265 × 10− 27, and the low PI value showed high fingerprinting power. However, Waits et al. [28] argued that the assumption of independent segregation among sites does not hold because the substructure of plant populations is shaped by environmental and anthropogenic selection, leading to a possible overestimation of the theoretical PI, and thus PIsibs are usually used as a conservative upper limit for the PI; specifically, PI values of 1 × 10− 4 ∼ 1 × 10− 2 are considered sufficient for application to the identification of individuals in natural populations. The PI and PIsibs values in this study were much lower than the putative values, indicating that the 32 SSR markers have a very high potential for fingerprinting. Therefore, we combined eight pairs of primers to construct DNA fingerprints for 80 Zanthoxylum cultivars, each of which was assigned a unique numerical code. However, it should be noted that the number of Zanthoxylum cultivars that can be identified by this fingerprint method is limited. As the number of Zanthoxylum accessions used for identification increases and new cultivars are introduced and promoted, the number of new variant sites will increase as well. In such cases, timely and periodic updates to the fingerprint will be required to ensure its ongoing role in future research and application.

In comparison to SSR markers, iPBS markers have been less frequently employed to construct DNA fingerprints. Zeng et al. [46] successfully constructed fingerprints of 85 Cymbidium goeringii germplasm resources using two iPBS primers. Demirel et al. [47] used 17 iPBS markers to fingerprint and genetically analyze 151 potato genotypes. These studies demonstrated the feasibility of constructing plant fingerprints using iPBS markers. For our study, we selected 10 iPBS primers with high polymorphism and clear amplification bands from a pool of 83 primers. However, we found that these 10 iPBS markers were not sufficient to completely differentiate the 80 Zanthoxylum cultivars.

Notably, specific bands were observed in the amplification results for SSR markers, indicating that allelic loci, such as ‘HYWC ♂’, ‘HYWC ♀’, ‘MYWCQHJ’, and ‘YJ’, can serve as important molecular traits for cultivar identification (Supplementary Figure S6). Considering factors such as the ease of banding, number of available markers, polymorphic information content of the primers, and amplification stability, we believe that SSR markers are more suitable for constructing DNA fingerprints of Zanthoxylum species. However, it is important to acknowledge that iPBS markers have valuable potential when genomic information is lacking for a species. Moreover, for materials that are difficult to identify using a single molecular marker, a combination of multiple markers can improve identification efficiency.

Currently, with the decreasing cost of high-throughput sequencing technology, the construction of DNA fingerprints using SSR and/or SNP markers has become the most popular choice [48]. Future research can focus on the development of these two marker types, as well as the collection of more comprehensive Zanthoxylum germplasm resources, to construct a more perfect fingerprint map. This endeavor holds significant importance for the conservation and development of Zanthoxylum germplasm resources.

Conclusions

This study aimed to assess the genetic diversity, genetic relationships, population genetic differentiation, and genetic structure of 80 Zanthoxylum accessions using 32 G-SSR markers and 10 iPBS markers. Additionally, a DNA fingerprint of Zanthoxylum cultivars was constructed. The findings of this research demonstrated that the 80 Zanthoxylum accessions exhibit a significant level of genetic diversity. Both the SSR and iPBS markers were effective at revealing the genetic relationship of Zanthoxylum species, with SSR markers providing a more comprehensive reflection of the genetic variation within the tested accessions. Moreover, limited genetic exchange was observed among the three populations of Zanthoxylum, resulting in noticeable genetic differentiation. In terms of discriminatory ability, SSR markers demonstrated greater strength than iPBS markers. Furthermore, the construction of DNA fingerprints for the 80 Zanthoxylum cultivars was achieved using eight pairs of SSR primers. These findings have significant implications for the conservation and utilization of Zanthoxylum resources, offering a valuable scientific foundation.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Zhang M, Wang J, Zhu L, et al. Zanthoxylum Bungeanum Maxim. (Rutaceae): a systematic review of its traditional uses, Botany, Phytochemistry, Pharmacology, Pharmacokinetics, and Toxicology. Int J Mol Sci. 2017;18(10):2172.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Guo T, Deng YX, Xie H, et al. Antinociceptive and anti-inflammatory activities of ethyl acetate fraction from Zanthoxylum armatum in mice. Fitoterapia. 2011;82(3):347–51.

    Article  PubMed  Google Scholar 

  3. Feng S, Yang T, Liu Z, et al. Genetic diversity and relationships of wild and cultivated Zanthoxylum germplasms based on sequence-related amplified polymorphism (SRAP) markers. Genet Resour Crop Evol. 2015;62:1193–204.

    Article  Google Scholar 

  4. Feng S, Liu Z, Chen L, et al. Phylogenetic relationships among cultivated Zanthoxylum species in China based on cpDNA markers. Tree Genet Genomes. 2016;12:1–9.

    Article  Google Scholar 

  5. Xie DY, Chen QY. Utilization of Chinese prickly ash in ancient Chinese people’s Diet. Chuxiong Normal Univ. 2013;28:10–3.

    Google Scholar 

  6. Chen X, Tian L, Tian J, et al. Extensive sampling provides New insights into phylogenetic relationships between Wild and Domesticated Zanthoxylum species in China. Horticulturae. 2022;8(5):440.

    Article  Google Scholar 

  7. Guichoux E, Lagache L, Wagner S, et al. Current trends in microsatellite genotyping. Mol Ecol Resour. 2011;11(4):591–611.

    Article  PubMed  Google Scholar 

  8. UPOV. Guidelines for DNA-profiling: Molecular marker selection and database construction/BMT Guidelines (proj.9). Geneva. 2007; Switzerland:3–4.

  9. Li J, Li S, Kong L, Wang L, Wei A, Liu Y. Genome survey of Zanthoxylum Bungeanum and development of genomic-SSR markers in congeneric species. Biosci Rep. 2020;40(6):BSR20201101.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhao LL, Feng SJ, Tian JY, et al. Internal transcribed spacer 2 (ITS 2) barcodes: a useful tool for identifying Chinese Zanthoxylum. Appl Plant Sci. 2018;6(6):e01157.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Feng S, Zhao L, Liu Z, et al. De novo transcriptome assembly of Zanthoxylum bungeanum using Illumina sequencing for evolutionary analysis and simple sequence repeat marker development. Sci Rep. 2017;7(1):16754.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Feng S, Yang T, Li X, et al. Genetic relationships of Chinese prickly ash as revealed by ISSR markers. Biologia. 2015;70:45–51.

    Article  Google Scholar 

  13. Feng S, Liu Z, Hu Y, Tian J, Yang T, Wei A. Genomic analysis reveals the genetic diversity, population structure, evolutionary history and relationships of Chinese pepper. Hortic Res. 2020;7:158.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kalendar R, Antonius K, Smýkal P, Schulman AH. iPBS: a universal method for DNA fingerprinting and retrotransposon isolation. Theor Appl Genet. 2010;121(8):1419–30.

    Article  PubMed  Google Scholar 

  15. Amom T, Nongdam P. The use of molecular marker methods in plants: a review. Int J Curr Res Rev. 2017;9(17):1–7.

    Google Scholar 

  16. Milovanov A, Zvyagin A, Daniyarov A, et al. Genetic analysis of the grapevine genotypes of the Russian Vitis ampelographic collection using iPBS markers. Genetica. 2019;147:91–101.

    Article  PubMed  Google Scholar 

  17. Ali F, Yılmaz A, Nadeem MA, et al. Mobile genomic element diversity in world collection of safflower (Carthamus tinctorius L.) panel using iPBS-retrotransposon markers. PLoS ONE. 2019;14(2):e0211985.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Amom T, Tikendra L, Apana N, et al. Efficiency of RAPD, ISSR, iPBS, SCoT and phytochemical markers in the genetic relationship study of five native and economical important bamboos of North-East India. Phytochemistry. 2020;174:112330.

    Article  PubMed  Google Scholar 

  19. Hu L, Xu Z, Fan R, et al. The complex genome and adaptive evolution of polyploid Chinese pepper (Zanthoxylum Armatum and Zanthoxylum Bungeanum). Plant Biotechnol J. 2023;21(1):78–96.

    Article  PubMed  Google Scholar 

  20. Porebski S, Bailey LG, Baum BR. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol Biology Report. 1997;15:8–15.

    Article  Google Scholar 

  21. Fan W, Gai H, Sun X, et al. DataFormater, a software for SSR data formatting to develop population genetics analysis. Mol Plant Breed. 2016;14:265–70.

    Google Scholar 

  22. Peakall ROD, Smouse PE. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006;6(1):288–95.

    Article  Google Scholar 

  23. Rohlf FJ. NTSYS 2.1: numerical taxonomic and multivariate analysis system. New York, Exeter Software. 2000.

  24. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yeh FC, Yang R, Boyle TJ, et al. POPGENE 32, Microsoft Windows-based freeware for population genetic analysis. Edmonton: Molecular biology and biotechnology centre, University of Alberta; 2000.

    Google Scholar 

  26. Hinze LL, Fang DD, Gore MA, et al. Molecular characterization of the Gossypium diversity reference set of the US national cotton germplasm collection. Theor Appl Genet. 2015;128:313–27.

    Article  PubMed  Google Scholar 

  27. Taberlet P, Luikart G. Non-invasive genetic sampling and individual identification. Biol J Linn Soc. 1999;68(1–2):41–55.

    Article  Google Scholar 

  28. Waits LP, Luikart G, Taberlet P. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol. 2001;10(1):249–56.

    Article  PubMed  Google Scholar 

  29. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89(3):583–90.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mohammadis A, Prasanna BM. Analysis of genetic diversity in crop plants. Crop Sci. 2003;43:1235–48.

    Article  Google Scholar 

  31. Bhandari HR, Bhanu AN, Srivastava K, et al. Assessment of genetic diversity in crop plants-an overview. Adv Plants Agric Res. 2017;7(3):279–86.

    Google Scholar 

  32. Gogoi B, Wann SB, Saikia SP. Comparative assessment of ISSR, RAPD, and SCoT markers for genetic diversity in Clerodendrum species of North East India. Mol Biol Rep. 2020;47:7365–77.

    Article  PubMed  Google Scholar 

  33. Nie XH, Li YR, Tian SL, et al. Construction of DNA fingerprint map and analysis of genetic diversity for Chinese chestnut cultivars (lines). Acta Horticulturae Sinica. 2022;49(11):2313–24.

    Google Scholar 

  34. Al-Najm A, Luo S, Ahmad NM, et al. Molecular variability and genetic relationships of date palm (‘Phoenix dactylifera’L.) Cultivars based on inter-primer binding site (iPBS) markers. Aust J Crop Sci. 2016;10(5):732–40.

    Article  Google Scholar 

  35. Mehmood A, Luo S, Ahmad NM, et al. Molecular variability and phylogenetic relationships of guava (Psidium guajava L.) cultivars using inter-primer binding site (iPBS) and microsatellite (SSR) markers. Genet Resour Crop Evol. 2016;63:1345–61.

    Article  Google Scholar 

  36. Mliki A, Staub JE, Zhangyong S, et al. Genetic diversity in African cucumber (Cucumis sativus L.) provides potential for germplasm enhancement. Genet Resour Crop Evol. 2003;50:461–8.

    Article  Google Scholar 

  37. Feng SJ. Genetic andphylogenetic structure of Zanthoxylum species. Northwest A&F University; 2017.

  38. Yang W, Bai Z, Wang F, et al. Analysis of the genetic diversity and population structure of Monochasma Savatieri Franch. Ex Maxim using novel EST-SSR markers. BMC Genomics. 2022;23(1):597.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wright S. Evolution in mendelian populations. Genetics. 1931;16:97–159.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Slatkin M. Gene flow and the geographic structure of natural populations. Volume 236. New York, N.Y.): Science; 1987. pp. 787–92. 4803.

    Google Scholar 

  41. Chen S, Xu Y, Helfant L, Geographical, Isolation. Buried depth, and Physicochemical Traits Drive the Variation of Species Diversity and Prokaryotic Community in three typical hypersaline environments. Microorganisms. 2020;8(1):120.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Booy G, Hendriks RJJ, Smulders MJM, et al. Genetic diversity and the survival of populations. Plant Biol. 2000;2(04):379–95.

    Article  Google Scholar 

  43. Chen Y, Dai X, Hou J, et al. DNA fingerprinting of oil camellia cultivars with SSR markers. Tree Genet Genomes. 2016;12:1–8.

    Article  Google Scholar 

  44. He B, Geng R, Cheng L, Yang X, Ge H, Ren M. Genetic diversity and fingerprinting of 33 standard flue-cured tobacco varieties for use in distinctness, uniformity, and stability testing. BMC Plant Biol. 2020;20(1):378.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tarroux E, DesRochers A, Tremblay F. Molecular analysis of natural root grafting in Jack pine (Pinus banksiana) trees: how does genetic proximity influence anastomosis occurrence? Tree Genet Genomes. 2014;10(3):667–77.

    Article  Google Scholar 

  46. Zeng YH, He JZ, Long QY, et al. Genetic diversity analysis and fingerprinting construction of wild Cymbidium goeringii from Leye, Guangxi by iPBS. Southwest China J Agricultural Sci. 2023;36(01):11–9.

    Google Scholar 

  47. Demirel U, Tındaş İ, Yavuz C, et al. Assessing genetic diversity of potato genotypes using inter-PBS retrotransposon marker system. Plant Genetic Resour. 2018;16(2):137–45.

    Article  Google Scholar 

  48. Nybom H, Weising K, Rotter B. DNA fingerprinting in botany: past, present, future. Invest Genet. 2014;5(1):1–35.

    Article  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the National Key R&D Program of China (2019YFD1001200) and the Research on the Selection and Breeding of New High-Quality and Labor-Saving Cultivars of Chinese Pepper and Supporting Technology(2021YFYZ0032).

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Authors and Affiliations

  1. College of Horticulture and Gardening, Yangtze University, Jingzhou, Hubei, 434025, China

    Xiaoxi Zhang, Weiwei Zhang, Feng Xu, Jiabao Ye & Yongling Liao

  2. Sichuan Academy of Forestry, Chengdu, Sichuan, 610081, China

    Wei Chen, Zhiwu Yang & Chengrong Luo

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Contributions

XZ, WZ and FX designed the experiments; XZ and WC performed the experiments; ZY and CL participated in the collection of resources; XZ, JY and YL analyzed the data; XZ wrote the manuscript. All the authors have read and agreed to the published version of the manuscript.

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Correspondence to Weiwei Zhang or Feng Xu.

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Electronic supplementary material

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12870_2024_5373_MOESM1_ESM.jpg

Supplementary Figure S1: Genetic similarity coefficient matrix heatmap of 80 Zanthoxylum accessions based on SSR markers

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Supplementary Figure S2: Frequency distribution of genetic similarity coefficients of 80 Zanthoxylum accessions based on SSR markers

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Supplementary Figure S3: Genetic similarity coefficient matrix heatmap of 80 Zanthoxylum accessions based on iPBS markers

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Supplementary Figure S4: Frequency distribution of genetic similarity coefficients of 80 Zanthoxylum accessions based on iPBS markers

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Supplementary Figure S5: Genetic similarity coefficient matrix heatmap of 80 Zanthoxylum accessions based on SSR + iPBS markers

Supplementary Figure S6: The amplification results of 80 Zanthoxylum accessions by SSR primers “D50” and “N76”

Supplementary Table S1: The sequence information of 32 pairs of SSR primers

Supplementary Table S2: Q-values of 80 Zanthoxylum accessions in two groups

Supplementary Table S3: The sequence information of 10 iPBS primers

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Zhang, X., Chen, W., Yang, Z. et al. Genetic diversity analysis and DNA fingerprint construction of Zanthoxylum species based on SSR and iPBS markers. BMC Plant Biol 24, 843 (2024). https://doi.org/10.1186/s12870-024-05373-1

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