Next Article in Journal
Bioactive Compounds and Probiotics Mitigate Mastitis by Targeting NF-κB Signaling Pathway
Previous Article in Journal
Molecular Developmental Biology of Fibrodysplasia Ossificans Progressiva: Measuring the Giant by Its Toe
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomolecules
Volume 14
Issue 8
10.3390/biom14081010
biomolecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata

by
Lin Hu
1,2,3,4,
Jiancheng Wang
2,3,4,
Xiyong Wang
2,3,4,
Daoyuan Zhang
2,3,4,
Yanxia Sun
5,
Ting Lu
1,* and
Wei Shi
2,3,4,*
1
College of Forestry and Landscape Architecture, Xinjiang Agricultural University, Urumqi 830011, China
2
State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
Xinjiang Key Lab of Conservation and Utilization of Plant Gene Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4
Turpan Eremophytes Botanical Garden, Chinese Academy of Sciences, Turpan 838008, China
5
Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Biomolecules 2024, 14(8), 1010; https://doi.org/10.3390/biom14081010
Submission received: 11 July 2024 / Revised: 12 August 2024 / Accepted: 13 August 2024 / Published: 15 August 2024
(This article belongs to the Section Molecular Genetics)
Browse Figures
Review Reports Versions Notes

Abstract

:
The conservation biology field underscores the importance of understanding genetic diversity and gene flow within plant populations and the factors that influence them. This study employs Simple Sequence Repeat (SSR) molecular markers to investigate the genetic diversity of the endangered plant species Saussurea involucrata, offering a theoretical foundation for its conservation efforts. Utilizing sequencing results to screen SSR loci, we designed and scrutinized 18 polymorphic microsatellite primers across 112 samples from 11 populations in the Bayinbuluke region. Our findings reveal high genetic diversity (I = 0.837, He = 0.470) and substantial gene flow (Nm = 1.390) among S. involucrata populations (China, Xinjiang), potentially attributed to efficient pollen and seed dispersal mechanisms. Principal Coordinate Analysis (PCoA) indicates a lack of distinct genetic structuring within the Bayinbuluke populations. The cluster analysis using STRUCTURE reflected the genetic structure of S. involucrata to a certain extent compared with PCoA. The results showed that all samples were divided into four groups. To safeguard this species, we advocate for the in situ conservation of all S. involucrata populations in the area. The SSR markers developed in this study provide a valuable resource for future genetic research on S. involucrata.

1. Introduction

Biodiversity encapsulates the variety of life forms on Earth, encompassing a spectrum of plants, animals, microorganisms, the genetic codes they harbor, and the ecosystems they forge. It is a multifaceted concept that includes ecosystem diversity, species diversity, and genetic diversity [1]. Among these, genetic diversity stands as a pivotal attribute, underpinning the capacity of species to adapt to environmental shifts and serving as the cornerstone of evolutionary processes [2,3]. Preserving genetic diversity is integral to formulating effective conservation and management strategies [4]. The extent of genetic variation and the population genetic structure of plant species are shaped by a tapestry of factors, including the evolutionary history, distribution range, morphology, reproductive strategies, and seed dispersal mechanisms. These factors are instrumental in crafting strategies for safeguarding species’ genetic variability [5].
Saussurea involucrata (Kar. & Kir.) Sch. Bip (Asteraceae), a perennial flowering and fruiting plant, thrives in alpine environments, often found in slopes, valleys, meadows, and rock crevices at elevations ranging from 2400 to 4100 m [6]. Predominantly native to Xinjiang, China, this species completes its life cycle from germination to flowering and fruiting in approximately six years. Peak blooming occurs from July to August, with a flowering duration of about four months. The unique spatial arrangement of male pre-maturation and hermaphroditic flowers facilitates crosspollination. The plant’s umbrella-shaped inflorescence and the manner in which its flowers open render it well suited to the harsh alpine climate. The extended period of pollination and nectar secretion increases the chances of successful pollination, leading to the production of a substantial number of seeds. The wind-dispersed achenes, equipped with a pappus, enhance seed germination and seedling survival under favorable conditions [7,8,9]. Recognized in traditional Chinese medicine, S. involucrata has been utilized ethno-medically for its diverse pharmacological properties, including anti-inflammatory, antifatigue, radiation-preventive, antitumor, free radical-scavenging, and anti-aging effects [10,11,12]. However, the natural reproduction rate of S. involucrata is inherently low, and its growth is slow [13,14]. The exploitation of its medicinal properties has led to excessive harvesting during its flowering period, impeding achene formation and causing a drastic decline in its natural reserves [15]. The Chinese Species Red List has classified S. involucrata as VU grade (http://protection.especies.cn/chineseredlist/list (accessed on 2 April 2024)), and it has been included in the national protected plant list as a secondarily protected species (http://www.iplant.cn/bhzw/info/1102 (accessed on 2 April 2024)).
The conservation genetics of S. involucrata in the Tianshan Mountains of Xinjiang have revealed high genetic diversity, with the western Tianshan Mountains and particularly the Bayinbuluke area emerging as genetic differentiation centers for the species [16,17]. The Bayinbuluke area, recognized as one of Central Asia’s biodiversity hotspots [18], offers an exemplary setting for studying the origins and conservation of biodiversity.
SSRs, or simple sequence repeats, were initially discovered by Tautz and Renz [19] and later termed microsatellites by Litt and Luty [20]. These short tandem repetitive sequences of 1–6 nucleotides are pervasively present across the genomes of eukaryotes [21]. Characterized by high polymorphism, repeatability, codominant expression, and neutrality, SSRs serve as invaluable tools for elucidating population genetic structures, gene flow, genetic relationships, and population viability. They are instrumental in assessing the impacts of habitat fragmentation and in guiding conservation strategies [22,23].
This study employs SSR markers to assess the genetic diversity of S. involucrata in the genetic differentiation center of the Bayinbuluke area, aiming to provide a scientific basis for the species’ conservation and sustainable management.

2. Materials and Methods

2.1. Plant Sampling and Preservation

In this study, we sampled 11 wild populations of S. involucrata in the Bayinbuluke area; each population was at least 7 km apart. In each population, healthy individuals of S. involucrata were randomly sampled (each individual was at least 50 m apart), and their leaves were collected and dried in silica gel. A total of 112 individuals were sampled from 11 populations. In the process of collection, the latitude and longitude of each sampling population were recorded. The location information of the S. involucrata sampling population and the sampling individuals of each population are shown in Figure 1.

2.2. Identification and Development of Genomic SSRs

In this study, the whole genome sequencing results of S. involucrata obtained from NCBI (project registration number: PRJNA991078) were used as the basis for the development of high polymorphic SSR primers. To achieve this goal, our study considered microsatellite markers with a standard size of 2–6 bp, excluding single nucleotides. In order to determine the microsatellite loci, MISA v2.1 software was used to focus on nucleotide microsatellites with a minimum number of repeats of five [24]. We used Primer 3 software to design primers for specific S. involucrata genomic sequences based on the reading parameters of the microsatellite region. The expected amplified fragment length ranged from 100 to 300 bp [25].

2.3. PCR Amplification and Electrophoresis Detection

Genomic DNA was extracted from S. involucrata plant materials using the DNAsecure plant kit (Tiangen Biotech (Beijing) Co., Ltd., Beijing, China) according to the manufacturer’s guidance. In total, 18 pairs of primers were randomly selected (Table S1). The forward primers 5′ends were labeled with FAM blue, a fluorescent dye (Shanghai General Biotechnology, Shanghai, China), for easy scoring in genotyping. The selected primers were used for the PCR amplification of all sample DNA in a 25 μL reaction system, including 1 μL template DNA, 1 μL upstream and 1 μL downstream primers, 2 × EasyTaq PCR SuperMix 12.5 μL, and ddH2O 9.5 μL. The amplification reaction procedure consisted of three stages: The PCR reaction was carried out in a thermal cycler, with a single step denaturation at 93 °C for 3 min, followed by a denaturation at 94 °C for 30 s, followed by annealing at a temperature of 30 s according to the specific Tm value of each primer, then extending at 65 °C for 90 s, and finally extending at 65 °C for 5 min. The successful amplification was confirmed by 2% agarose gel electrophoresis. PCR products were detected by capillary electrophoresis and fluorescence labeling.

2.4. Data Analysis

The Hardy–Weinberg (HWE) equilibrium test was performed using GenAlEx 6.5 software. The linkage disequilibrium test was performed using GENEPOP on the Web (https://genepop.curtin.edu.au/genepop_op2.html (accessed on 2 April 2024)) [26]. The number of alleles (Na), effective number of alleles (Ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and other genetic diversity indexes were calculated by Gen Al Ex 6.5 software [27]. The polymorphic information content (PIC) was calculated by Power marker v3.25 software [28]. Nei’s genetic diversity index was calculated using Popgene 32 [29], and principal coordinate analysis (PCoA) was conducted using GenAlEx 6.5 software. The genetic distance (GD) matrix of 11 S. involucrata populations was calculated using the distance-based module in GenAlex 6.5 software. Then, molecular analysis of variance (AMOVA) was used to evaluate the contribution of genetic variation among and within populations. Bayesian assignment testing in STRUCTURE 2.3.4. software was used [30]. The parameters were set to K = 2–13, and each K value was run 20 times. The relevant parameters were set as follows: the length of the Burnin Period was 5 × 104, the number of MCMC Reps after the Burnin was 5 × 105, and the mixed model was used. For the numerical results corresponding to the K value of each run (stored in the Results folder), Structure Harvester was used to find the best ΔK value.

3. Results

3.1. Analysis of the Distribution of SSRs in the Genome of S. involucrata

Utilizing the MISA v2.1 software, we conducted a comprehensive screen of the 168.12 Mb genome of S. involucrata, uncovering a total of 673,244 SSR markers. These markers spanned a total length of 12,818,069 base pairs. The frequency and density of SSRs across the entire genome were calculated to be 4004.61 SSR/Mb and 76,244.85 bp/Mb, respectively. This represents a significant proportion of 7.62% of the genome sequence (Table 1).
The length of the SSR found in the whole genome of S. involucrata was 10~14,184 bp, with an average length of 19.04 bp. The most common repeat sequence length is 12 bp, with 108,391 occurrences, followed by 10 bp and 14 bp, with frequencies of 85,161 and 71,046 respectively (Figure 2).
The repeat motifs of each SSR locus were analyzed, and it was found that the number of repeats ranged from 5 to 3546. Most of the loci had 10 tandem repeats (17.07%), followed by loci with 6 tandem repeats (16.18%) (Figure 3).
In the whole genome of S. involucrata, dinucleotide repeats are the most common, followed by mononucleotide and trinucleotide repeats. The total length of the SSRs in the genome was 12,818,069 bp, and the total length of the SSRs containing one, two, three, four, five, and six nucleotide repeats was 2,264,331 bp, 8,661,590 bp, 1,338,753 bp, 360,580 bp, 61,925 bp, and 130,890 bp, respectively. The average length of each basic sequence was 12.29 bp, 21.34 bp, 18.70 bp, 59.48 bp, 27.62 bp, and 41.28 bp, respectively (Table 2).

3.2. Linkage Disequilibrium Tests

Linkage disequilibrium analysis was performed on the two sites, and the p value of the combination of 9 sites was significant (p < 0.05), which deviated from the balance (Table 3). Therefore, due to linkage disequilibrium, all further analysis removed S4, S31, S35, and other sites.

3.3. Genetic Diversity of the S. involucrata

A comprehensive analysis was conducted on 112 samples collected from 11 populations of S. involucrata, utilizing 15 SSR loci (Table 4). The results illuminated a total of 48 alleles across the loci, with an average of 3.182 alleles per locus, ranging from 2 to 7. Notably, the locus S10 exhibited the highest number of alleles. The average effective number of alleles (Ne) was 2.372, with the highest value of 5.700 observed at the S10 locus and the lowest of 1.452 observed at the S16 locus. The Shannon information index (I) varied from 0.376 to 1.818, while the observed heterozygosity (Ho) ranged from 0 to 1. The expected heterozygosity (He) values spanned from 0.234 to 0.811, and unbiased expected heterozygosity (uHe) ranged between 0.246 and 0.863. The fixation index (Fst) values were between 0.066 and 0.486, with an average of 0.199, indicating considerable genetic differentiation among the samples. The polymorphism information content (PIC) values ranged from 0.883 to 0.990, with an average of 0.971, suggesting that the selected primer pairs demonstrated substantial polymorphism. The Nei’s genetic diversity (Nei’s) values were between 0.234 and 0.811, with an average of 0.470.
The selected markers were 11 amplified populations of S. involucrata, the Na values of 2.467–3.800, and the Ne values of 1.940–2.841. The I value is between 0.599 and 0.950, the Ho value is between 0.341 and 0.431, the He value is between 0.345 and 0.506, the uHe value is between 0.364 and 0.538, and the F value is between −0.111 and 0.403. Hardy–Weinberg equilibrium analysis showed that there were loci deviating from Hardy–Weinberg equilibrium in all populations. Many loci deviated from HWE were found in population 8 (12 loci) (Table 5).

3.4. Genetic Relationship and Population Structure Analysis

Principal coordinate analysis (PCoA) is used to provide a spatial representation of the relative genetic distance between individuals and to determine the consistency of population differentiation defined by cluster analysis. The PCoA of 112 S. involucrata materials is shown in Figure 4. The first principal coordinate and the second principal coordinate account for 4.18% and 2.29% of the total variation of the total coordinate respectively. The principal coordinate analysis (PCoA) showed that the individuals of various populations were mixed with each other, and the 11 populations of S. involucrata could not be clearly grouped. Most of the individuals in each population overlapped, but Populations 2 and 3 were far away from the other populations. Bayesian clustering results show that the provisional statistic ∆K shows that the maximum likelihood value of K = 4 (Figure S1). The results of STRUCTURE showed that there were different degrees of penetration in the population of S. involucrata, and there was an obvious genetic structure (Figure 5).

4. Discussion

4.1. Genetic Diversity of S. involucrata

The implementation of molecular markers has significantly propelled the fields of molecular ecology and population genetics. In our study, SSR markers were identified through the gene sequencing data of S. involucrata, culminating in the development of 18 SSR molecular markers. These markers were evaluated based on their Polymorphism Information Content (PIC), where a value greater than 0.5 indicates high polymorphism, a value between 0.25 and 0.5 suggests medium polymorphism, and a value less than 0.5 denotes low polymorphism [31]. The average PIC value of the 15 SSR markers in this study surpassed 0.5, underscoring their utility for analyzing the genetic diversity and population structure of S. involucrata.
Genetic variation is pivotal for local adaptation and the evolutionary process of species. Unraveling the genetic diversity of rare plants is essential for formulating robust long-term management and conservation strategies [32,33,34]. Our study revealed that S. involucrata possesses considerable genetic diversity (I = 0.837, He = 0.470), surpassing that of other endangered alpine flora such as Eryngium alpinum Lapeyr. (I = 0.283, Nei = 0.198) [35], Isoetes hypsophila Hand.-Mazz. (He = 0.039, Hs = 0.084, I = 0.061) [36], Cerastium alpinum L. (He = 0.085) [37], and Sinadoxa corydalifolia C. Y. Wu, Z. L. Wu & R. F. Huang (He = 0.368) [38]. The genetic diversity of plant species is influenced by a multitude of factors, including the distribution range, population size, life cycle, mating system, and gene flow [39]. Outcrossing plants, such as S. involucrata, typically exhibit higher genetic diversity and lower population differentiation compared to self-pollinating and clonal plants [40,41]. As a perennial and crosspollinated species, S. involucrata benefits from effective pollination mechanisms and long-distance seed dispersal, which contribute to its genetic polymorphism [8]. Due to the outcrossing system, it has high genetic diversity, which has also been reported in other Asteraceae plants [42,43,44]. SSR loci deviated from HWE in 11 populations of S. involucrata. In general, the population deviation from HWE may be due to the high heterozygosity or high invalid allele frequency at this locus [45]. The wild populations of S. involucrata were sporadically distributed, and the deviation of all populations from HWE may be caused by an insufficient population size and individual number.

4.2. Genetic Differentiation of S. involucrata

Our analysis of molecular variation (AMOVA) among the 11 populations of S. involucrata indicated a higher degree of genetic differentiation within populations (97.440%), with a relatively minor contribution from genetic variation between populations (Table 6). This finding aligns with previous studies conducted in the Western Tianshan [16]. The breeding system is a determinant factor in the genetic variation observed within plant populations [46,47]. In plants that undergo crosspollination, the majority of genetic variation is distributed among individuals within a population, with a smaller proportion attributed to variation between populations [48]. S. involucrata’s floral biology, featuring male prematurity and hermaphroditism, along with its inflorescence and flower-opening mechanisms, promotes crosspollination [8].
Understanding gene flow in endangered plants is fundamental for their conservation and management. Moderate to high gene flow among populations is essential to preventing inbreeding depression and preserving genetic variation [49]. According to Wright, the gene flow (Nm) can be categorized into high (≥1.0), medium (0.250–0.99), and low (0.0–0.249) levels, with Nm values greater than 1 indicating significant gene flow between populations [50]. Our study demonstrated that S. involucrata exhibits substantial levels of gene flow mediated by pollen or seeds (mean Nm = 1.390 > 1). The sexual reproduction strategy of S. involucrata relies heavily on pollinators, particularly those belonging to the Bumblebeeidae family. Bumblebees, with their large body size and robust environmental adaptability, are capable of withstanding low temperatures and facilitating crosspollination over extended periods and distances. Additionally, the achene of S. involucrata, equipped with a long pappus, can be dispersed by wind, while the fruit can be spread through surface runoff. These mechanisms, along with the pappus’s ability to retain water and adhere to other organisms, contribute to the species’ diverse dispersal methods, primarily windborne with elements of water and animal-borne dissemination [8]. In order to clarify the genetic relationship of 112 accessions of S. involucrata from 11 populations, we used two different clustering methods. Using a two-dimensional PCoA distance matrix to visualize the relationship between samples makes it difficult to group and analyze them. In contrast, the cluster analysis using STRUCTURE showed a clear genetic structure between populations. S. involucrata relies on wind to spread seeds [8]. This mode of transmission may promote long-distance gene exchange. Wind can carry seeds to places far away from the mother plant, increasing the mating opportunities with other S. involucrata individuals. Therefore, different populations have frequent gene exchanges. The strong gene exchange of wind-borne plants has also been reported in other studies [51,52]. Geographical distance is not the only explanatory factor for the genetic differentiation of the S. involucrata population. The overall topography of the sampling site is high in the northwest and low in the southeast [53], the climate between the mountains is more complex, and the wind direction is more changeable. There are no mountains in the middle area of the sampling site that affect gene exchange. The altitude of the three sampling sites of the S. involucrata population is lower than that of other eastern sampling sites, and the western S. involucrata population has a stronger influence on it. Different terrains may form different niches, prompting species to adapt to specific environmental conditions, thus affecting the genetic structure [54]. Terrain differences can lead to changes in the microclimate, such as temperature, humidity, and wind speed, which may affect the survival and reproduction of organisms [55]. S. involucrata Population 1 is located upstream of the wind direction in this area, and the mountains block the gene exchange with S. involucrata Population 2. A more in-depth investigation of S. involucrata outside the study area can further explore the reasons for the special genetic structure.

4.3. Conservation of S. involucrata

Preserving genetic variation is a cornerstone of conservation efforts for endangered and threatened species [56]. Insights into genetic variation among and within populations are vital for crafting effective conservation management strategies [57]. Given the unique pharmacological properties of S. involucrata and its burgeoning applications in medicine, skincare, and health care [58], the overexploitation of this species has led to a precipitous decline in its natural reserves, ecological devastation, and challenges in cultivation [59]. Despite a ban on wild harvesting in China, the loss of wild S. involucrata in Xinjiang is estimated at approximately 40 tons annually [60]. Climate change, specifically the rising snowline in the Tianshan Mountains, has drastically reduced the habitable range for S. involucrata, making it increasingly rare below the 3400 m elevation [60]. The constriction of populations increases the risk of losing genetic polymorphism due to genetic drift and inbreeding. Urgent conservation measures are warranted to ensure the long-term survival of this rare species. Considering the scarcity of wild populations and the minimal genetic differentiation among them, it is imperative to protect all populations in the Bayinbuluke area. Enhancing in situ conservation efforts can help maintain current population sizes, establish protected areas for S. involucrata, and bolster its adaptability. To safeguard this precious medicinal resource, the Xinjiang Uygur Autonomous Region government has prohibited mountain mining and mandated a three-year restoration period postharvest [61]. Considering S. involucrata’s sole mode of reproduction being seed-based, its naturally low germination and survival rates—approximately 3% under natural conditions—and its 5–6-year maturation period from germination to flowering and fruiting [62], tissue culture techniques can be employed to expedite plant propagation and address the shortfall in natural resources.

5. Conclusions

The genetic analysis of S. involucrata underscores the importance of conserving genetic diversity and implementing targeted protection strategies. Our findings highlight the species’ high genetic diversity and the significant gene flow among populations, which are crucial for its evolutionary potential and adaptability. The conservation measures proposed are essential for maintaining the species’ resilience and ensuring its survival in the face of environmental challenges and anthropogenic pressures. The development and application of SSR markers offer a valuable tool for future research and conservation efforts, providing a foundation for evidence-based management strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biom14081010/s1, Figure S1: the best grouping number was 4 based on the DK estimation. Table S1: Characteristics of 18 pairs of polymorphic SSR primers in Saussurea involucrate.

Author Contributions

Writing—review and editing, T.L.; Resources, Y.S.; Project administration and investigation, J.W.; Investigation, X.W.; Project administration, D.Z.; Writing—original, L.H.; Writing—review and editing, W.S. All authors edited and provided a critical review of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

Key Protected Wild Plants Investigation Project in Northern Xinjiang (DZXJZB2022039) and The Second Comprehensive Scientific Study of the Tibetan Plateau: Survey Evaluation of Saussurea involucrata and Ammopiptanthus nanus (2019QZKK05020207).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Acknowledgments

We thank Wang Shaojie and Liu Zhongjun of the Xinjiang Uygur Autonomous Region Forestry and Grassland Bureau for their help in the collection of Saussurea involucrata samples.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Rawat, U.; Agarwal, N. Biodiversity: Concept, threats and conservation. Environ. Conserv. J. 2015, 16, 19–28. [Google Scholar] [CrossRef]
  2. Pauls, S.U.; Nowak, C.; Bálint, M.; Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 2013, 22, 925–946. [Google Scholar] [CrossRef]
  3. Jump, A.S.; Marchant, R.; Peñuelas, J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 2009, 14, 51–58. [Google Scholar] [CrossRef]
  4. Ellegren, H.; Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 2016, 17, 422–433. [Google Scholar] [CrossRef] [PubMed]
  5. De Kort, H.; Prunier, J.G.; Ducatez, S.; Honnay, O.; Baguette, M.; Stevens, V.M.; Blanchet, S. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 2021, 12, 516. [Google Scholar] [CrossRef] [PubMed]
  6. Chik, W.-I.; Zhu, L.; Fan, L.-L.; Yi, T.; Zhu, G.-Y.; Gou, X.-J.; Tang, Y.-N.; Xu, J.; Yeung, W.-P.; Zhao, Z.-Z.; et al. Saussurea involucrata: A review of the botany, phytochemistry and ethnopharmacology of a rare traditional herbal medicine. J. Ethnopharmacol. 2015, 172, 44–60. [Google Scholar] [CrossRef]
  7. He, L.; Fan, S. From Saussurea bracteata to Saussurea tianschanica. For. Hum. 2018, 4, 66–73. [Google Scholar]
  8. Dai, P. Study on Reproductive Ecology of Saussurea involucrata. Master’s Thesis, Xinjiang Agricultural University, Urumqi, China, 2008. [Google Scholar]
  9. Xie, X.; Zhu, Y. Survival wisdom of Saussurea involucrata. Xinjiang For. 2023, 3, 31–32. [Google Scholar] [CrossRef]
  10. Yi, T.; Chen, H.-B.; Zhao, Z.-Z.; Jiang, Z.-H.; Cai, S.-Q.; Wang, T.-M. Identification and Determination of the Major Constituents in the Traditional Uighur Medicinal Plant Saussurea involucrata by LC-DAD-MS. Chromatographia 2009, 69, 537–542. [Google Scholar] [CrossRef]
  11. Zhai, K.; Wang, C. Research progress of Saussurea involucrata. Hubei Agric. Sci. 2009, 48, 2869–2873. [Google Scholar]
  12. Zhuang, L.; Li, W. Utilization, development and protection of Saussurea involucrata resources in Xinjiang. Resour. Environ. Arid. Areas 2006, 2, 195–202. [Google Scholar] [CrossRef]
  13. Kuo, C.-L.; Agrawal, D.-C.; Chang, H.-C.; Chiu, Y.-T.; Huang, C.-P.; Chen, Y.-L.; Huang, S.-H.; Tsay, H.-S. In vitro culture and production of syringin and rutin in Saussurea involucrata (Kar. et Kir.)—An endangered medicinal plant. Bot. Stud. 2015, 56, 12. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, R.; Liu, J.; Liu, S.; Guan, S.; Jiao, P. Characterization of the complete chloroplast genome of Saussurea involuerata (Compositae), an endangered species endemic to China. Mitochondrial DNA Part B 2020, 5, 511–512. [Google Scholar] [CrossRef]
  15. Yuan, X.F.; Dai, Z.H.; Wang, X.D.; Zhao, B. Assessment of genetic stability in tissue-cultured products and seedlings of Saussurea involucrata by RAPD and ISSR markers. Biotechnol. Lett. 2009, 31, 1279–1287. [Google Scholar] [CrossRef] [PubMed]
  16. Wei, S.; Yang, W.; Wang, X.; Hou, Y. High genetic diversity in an endangered medicinal plant, Saussurea involucrata (Saussurea, Asteraceae), in western Tianshan Mountains, China. Conserv. Genet. 2017, 18, 1435–1447. [Google Scholar] [CrossRef]
  17. Hu, L.; Lu, T.; Wang, X.; Wang, J.; Shi, W. Conservation Priorities and Demographic History of Saussurea involucrata in the Tianshan Mountains and Altai Mountains. Life 2023, 13, 2209. [Google Scholar] [CrossRef]
  18. Zhang, B.; Yao, Y.; Cheng, W.; Zhou, C.; Lu, Z.; Chen, X.; Alshir, K.; ErDowlet, I.; Zhang, L.; Shi, Q. Human-Induced Changes to Biodiversity and Alpine Pastureland in the Bayanbulak Region of the East Tienshan Mountains. Mt. Res. Dev. 2002, 22, 383–389. [Google Scholar] [CrossRef]
  19. Tautz, D.; Renz, M. Simple sequences are ubiquitous repetitive components of eukaryotic genomes. Nucleic Acids Res. 1984, 12, 4127–4138. [Google Scholar] [CrossRef]
  20. Litt, M.; Luty, J.A. A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. Am. J. Hum. Genet. 1989, 44, 397–401. [Google Scholar]
  21. Tóth, G.; Gáspári, Z.; Jurka, J. Microsatellites in different eukaryotic genomes: Survey and analysis. Genome Res. 2000, 10, 967–981. [Google Scholar] [CrossRef] [PubMed]
  22. Amiteye, S. Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon 2021, 7, e08093. [Google Scholar] [CrossRef]
  23. Yang, H.; Li, X.; Liu, D.; Chen, X.; Li, F.; Qi, X.; Luo, Z.; Wang, C. Genetic diversity and population structure of the endangered medicinal plant Phellodendron amurense in China revealed by SSR markers. Biochem. Syst. Ecol. 2016, 66, 286–292. [Google Scholar] [CrossRef]
  24. Jiao, Y.; Li, X.-W.; Chai, M.-L.; Jia, H.-J.; Chen, Z.; Wang, G.-Y.; Chai, C.-Y.; van de Weg, E.; Gao, Z.-S. Development of simple sequence repeat (SSR) markers from a genome survey of Chinese bayberry (Myrica rubra). BMC Genom. 2012, 13, 201. [Google Scholar] [CrossRef] [PubMed]
  25. Sethy, N.K.; Shokeen, B.; Edwards, K.J.; Bhatia, S. Development of microsatellite markers and analysis of intraspecific genetic variability in chickpea (Cicer arietinum L.). Theor. Appl. Genet. 2006, 112, 1416–1428. [Google Scholar] [CrossRef]
  26. Du, Q.; Wang, B.; Wei, Z.; Zhang, D.; Li, B. Genetic Diversity and Population Structure of Chinese White Poplar (Populus tomentosa) Revealed by SSR Markers. J. Hered. 2012, 103, 853–862. [Google Scholar] [CrossRef]
  27. Ramakrishnan, M.; Ceasar, S.A.; Duraipandiyan, V.; Al-Dhabi, N.A.; Ignacimuthu, S. Assessment of genetic diversity, population structure and relationships in Indian and non-Indian genotypes of finger millet (Eleusine coracana (L.) Gaertn) using genomic SSR markers. SpringerPlus 2016, 5, 120. [Google Scholar] [CrossRef]
  28. Gil, J.; Um, Y.; Kim, S.; Kim, O.T.; Koo, S.C.; Reddy, C.S.; Kim, S.-C.; Hong, C.P.; Park, S.-G.; Kim, H.B.; et al. Development of Genome-Wide SSR Markers from Angelica gigas Nakai Using Next Generation Sequencing. Genes 2017, 8, 238. [Google Scholar] [CrossRef]
  29. Liu, F.; Hong, Z.; Xu, D.; Jia, H.; Zhang, N.; Liu, X.; Yang, Z.; Lu, M. Genetic Diversity of the Endangered Dalbergia odorifera Revealed by SSR Markers. Forests 2019, 10, 225. [Google Scholar] [CrossRef]
  30. Wang, S.-Q. Genetic diversity and population structure of the endangered species Paeonia decomposita endemic to China and implications for its conservation. BMC Plant Biol. 2020, 20, 510. [Google Scholar] [CrossRef]
  31. Serrote, C.M.L.; Reiniger, L.R.S.; Silva, K.B.; Rabaiolli, S.M.d.S.; Stefanel, C.M. Determining the Polymorphism Information Content of a Molecular Marker. Gene 2020, 726, 144175. [Google Scholar] [CrossRef]
  32. Falk, D.A. Integrated Strategies for Conserving Plant Genetic Diversity. Ann. Mo. Bot. Gard. 1990, 77, 38–47. [Google Scholar] [CrossRef]
  33. Escudero, A.; Iriondo, J.M.; Torres, M. Spatial analysis of genetic diversity as a tool for plant conservation. Biol. Conserv. 2003, 113, 351–365. [Google Scholar] [CrossRef]
  34. Yu, Y.-L.; Wang, H.-C.; Yu, Z.-X.; Schinnerl, J.; Tang, R.; Geng, Y.-P.; Chen, G. Genetic diversity and structure of the endemic and endangered species Aristolochia delavayi growing along the Jinsha River. Plant Divers. 2021, 43, 225–233. [Google Scholar] [CrossRef] [PubMed]
  35. Gaudeul, M.; Taberlet, P.; Till-Bottraud, I. Genetic diversity in an endangered alpine plant, Eryngium alpinum L. (Apiaceae), inferred from amplified fragment length polymorphism markers. Mol. Ecol. 2000, 9, 1625–1637. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, Y.-Y.; Liao, L.; Li, W.; Li, Z.-Z. Genetic diversity and population structure of the endangered alpine quillwort Isoetes hypsophila Hand.-Mazz. revealed by AFLP markers. Plant Syst. Evol. 2010, 290, 127–139. [Google Scholar] [CrossRef]
  37. Milarska, S.E.; Androsiuk, P.; Bednarek, P.T.; Larson, K.; Giełwanowska, I. Genetic variation of Cerastium alpinum L. from Babia Góra, a critically endangered species in Poland. J. Appl. Genet. 2023, 64, 37–53. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, Y.; Liang, Q.; Hao, G.; Chen, C.; Liu, J. Population genetic analyses of the endangered alpine Sinadoxa corydalifolia (Adoxaceae) provide insights into future conservation. Biodivers. Conserv. 2018, 27, 2275–2291. [Google Scholar] [CrossRef]
  39. Yun, S.A.; Kim, S.-C. Genetic diversity and structure of Saussurea polylepis (Asteraceae) on continental islands of Korea: Implications for conservation strategies and management. PLoS ONE 2021, 16, e0249752. [Google Scholar] [CrossRef]
  40. Zawko, G.; Krauss, S.L.; Dixon, K.W.; Sivasithamparam, K. Conservation genetics of the rare and endangered Leucopogon obtectus(Ericaceae). Mol. Ecol. 2001, 10, 2389–2396. [Google Scholar] [CrossRef]
  41. Booy, G.; Hendriks, R.J.J.; Smulders, M.J.M.; Van Groenendael, J.M.; Vosman, B. Genetic Diversity and the Survival of Populations. Plant Biol. 2000, 2, 379–395. [Google Scholar] [CrossRef]
  42. Young, A.G.; Brown, A.H.D.; Zich, F.A. Genetic Structure of Fragmented Populations of the Endangered Daisy Rutidosis leptorrhynchoides. Conserv. Biol. 1999, 13, 256–265. [Google Scholar] [CrossRef]
  43. Friedman, J.; Barrett, S.C.H. High Outcrossing in the Annual Colonizing Species Ambrosia artemisiifolia (Asteraceae). Ann. Bot. 2008, 101, 1303–1309. [Google Scholar] [CrossRef]
  44. Ferrer, M.M.; Eguiarte, L.E.; Montaña, C. Genetic structure and outcrossing rates in Flourensia cernua (Asteraceae) growing at different densities in the South-western Chihuahuan Desert. Ann. Bot. 2004, 94, 419–426. [Google Scholar] [CrossRef]
  45. Xiao, Y.; Jiang, X.; Lu, C.; Liu, J.; Diao, S.; Jiang, J. Genetic Diversity and Population Structure Analysis in the Chinese Endemic Species Michelia crassipes Based on SSR Markers. Forests 2023, 14, 508. [Google Scholar] [CrossRef]
  46. Duminil, J.; Fineschi, S.; Hampe, A.; Jordano, P.; Salvini, D.; Vendramin, G.G.; Petit, R.J. Can population genetic structure be predicted from life-history traits? Am. Nat. 2007, 169, 662–672. [Google Scholar] [CrossRef]
  47. Hmeljevski, K.V.; Wolowski, M.; Forzza, R.C.; Freitas, L. High outcrossing rates and short-distance pollination in a species restricted to granitic inselbergs. Aust. J. Bot. 2017, 65, 315–326. [Google Scholar] [CrossRef]
  48. Szczecińska, M.; Sramko, G.; Wołosz, K.; Sawicki, J. Genetic Diversity and Population Structure of the Rare and Endangered Plant Species Pulsatilla patens (L.) Mill in East Central Europe. PLoS ONE 2016, 11, e0151730. [Google Scholar] [CrossRef] [PubMed]
  49. Storfer, A. Gene flow and endangered species translocations: A topic revisited. Biol. Conserv. 1999, 87, 173–180. [Google Scholar] [CrossRef]
  50. Govindaraju, D.R. Relationship between Dispersal Ability and Levels of Gene Flow in Plants. Oikos 1988, 52, 31–35. [Google Scholar] [CrossRef]
  51. Gerber, S.; Chadœuf, J.; Gugerli, F.; Lascoux, M.; Buiteveld, J.; Cottrell, J.; Dounavi, A.; Fineschi, S.; Forrest, L.L.; Fogelqvist, J.; et al. High rates of gene flow by pollen and seed in oak populations across Europe. PLoS ONE 2014, 9, e85130. [Google Scholar] [CrossRef]
  52. Tang, J.; Fan, X.; Milne, R.I.; Yang, H.; Tao, W.; Zhang, X.; Guo, M.; Li, J.; Mao, K. Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen (Populus rotundifolia) in the Hengduan Mountains. Plant Divers. 2024, 46, 321–332. [Google Scholar] [CrossRef]
  53. Su, R. Study on tourism climate resources and comfort in Hejing County. Rural. Technol. 2017, 19, 87–88. [Google Scholar] [CrossRef]
  54. Stronen, A.V.; Norman, A.J.; Wal, E.V.; Paquet, P.C. The relevance of genetic structure in ecotype designation and conservation management. Evol. Appl. 2022, 15, 185–202. [Google Scholar] [CrossRef]
  55. Dobrowski, S.Z. A climatic basis for microrefugia: The influence of terrain on climate. Glob. Change Biol. 2011, 17, 1022–1035. [Google Scholar] [CrossRef]
  56. Lande, R.; Barrowclough, G.F. Effective population size, genetic variation, and their use in population management. Viable Popul. Conserv. 1987, 87, 87–124. [Google Scholar]
  57. Schemske, D.W.; Husband, B.C.; Ruckelshaus, M.H.; Goodwillie, C.; Parker, I.M.; Bishop, J.G. Evaluating Approaches to the Conservation of Rare and Endangered Plants. Ecology 1994, 75, 584–606. [Google Scholar] [CrossRef]
  58. Li, J.; Cai, S. Advances in chemical and pharmacological studies of Saussurea involucrata. Chin. J. Pharm. 1998, 8, 3–6. [Google Scholar]
  59. Chen, F.; Yang, Y. Advances in species, habitat distribution and chemical constituents of Saussurea involucrata in China. Bot. Bull. 1999, 5, 561–566. [Google Scholar] [CrossRef]
  60. Aiguly, S.; Adiri, S. The research status of Hami ‘Snow Mountain Flower King’—S. involucrata. Chin. J. Ethn. Med. 2016, 22, 26–28. [Google Scholar]
  61. Wei, S.; Wu, Y. Research progress on endangered medicinal plant Saussurea involucrate resources. J. Minzu Univ. China (Nat. Sci. Ed.) 2014, 23, 10–15. [Google Scholar]
  62. Feng, J. Protection and Development of Saussurea involucrata. For. Pract. Technol. 2004, 10, 33. [Google Scholar] [CrossRef]
Biomolecules 14 01010 g001
Figure 1. The sampling distribution map of 11 populations of S. involucrata. The solid circle represents the position of the sampling population, and the numbers in brackets represent the individuals of the population.
Figure 1. The sampling distribution map of 11 populations of S. involucrata. The solid circle represents the position of the sampling population, and the numbers in brackets represent the individuals of the population.
Biomolecules 14 01010 g001
Biomolecules 14 01010 g002
Figure 2. The length distribution of SSRs in the S. involucrata genome.
Figure 2. The length distribution of SSRs in the S. involucrata genome.
Biomolecules 14 01010 g002
Biomolecules 14 01010 g003
Figure 3. The distribution of the number and frequency of SSRs with different repetitions in the genome of S. involucrata.
Figure 3. The distribution of the number and frequency of SSRs with different repetitions in the genome of S. involucrata.
Biomolecules 14 01010 g003
Biomolecules 14 01010 g004
Figure 4. Principal coordinate analysis (PCoA) of 112 S. involucrata based on 15 SSR markers. The different colors and shapes represent different study populations. The first and second axes explained 4.18% and 2.29% of the genetic similarities among populations, respectively.
Figure 4. Principal coordinate analysis (PCoA) of 112 S. involucrata based on 15 SSR markers. The different colors and shapes represent different study populations. The first and second axes explained 4.18% and 2.29% of the genetic similarities among populations, respectively.
Biomolecules 14 01010 g004
Biomolecules 14 01010 g005
Figure 5. K = 3 to K = 5 cluster STRUCTURE ancestral proportion bar chart. Each individual is represented as a line segment, divided vertically by different colors, representing the proportion of ancestors estimated by the individual in each cluster. The number below the figure represents the sampling population.
Figure 5. K = 3 to K = 5 cluster STRUCTURE ancestral proportion bar chart. Each individual is represented as a line segment, divided vertically by different colors, representing the proportion of ancestors estimated by the individual in each cluster. The number below the figure represents the sampling population.
Biomolecules 14 01010 g005
Table 1. The result of SSRs loci in the S. involucrata genome.
Table 1. The result of SSRs loci in the S. involucrata genome.
ItemsNumbers
Total size of the genome (Mb)168.12
Total number of identified SSRs673,244
Total length of SSRs (bp)12,818,069
Frequency (SSRs/Mb)4004.61
Density (bp/Mb)76,244.85
Total content of genome SSRs (%)7.62
Table 2. The main repeat motifs, number, frequency, proportion, and length of each nucleotide in the genome of S. involucrata.
Table 2. The main repeat motifs, number, frequency, proportion, and length of each nucleotide in the genome of S. involucrata.
Repeat TypePredominant TypeNumberProportion
(%)		Frequency
(SSRs/Mb)		Total
Length
(bp)		Average
Length
(bp)
MonoA/T184,19627.361095.642,264,33112.29
DiAT/AT405,97260.302414.828,661,59021.34
TriATC/ATG71,60110.64425.901,338,75318.70
TetraACAT/ATGT60620.9036.06360,58059.48
PentaAACCC/GGGTT22420.3313.3461,92527.62
HexaAAGGAG/CCTTCT31710.4718.86130,89041.28
Total 673,2441004004.6112,818,06919.04
Table 3. The linkage disequilibrium test between two loci of eighteen polymorphic microsatellite loci in S. involucrata.
Table 3. The linkage disequilibrium test between two loci of eighteen polymorphic microsatellite loci in S. involucrata.
S4S10S11S15S16S20S23S24S25S26S29S30S31S32S35S36S37S38
S4
S100.474
S110.9770.981
S150.4140.4570.966
S160.3700.8190.9700.139
S200.024 *0.4870.9940.9110.210
S230.9060.3731.0000.8480.9500.558
S240.6370.3881.0000.7270.8750.4950.841
S250.0600.5290.7880.6180.7750.1100.1310.083
S260.3150.5060.7030.9220.2890.3820.9170.9790.057
S290.9950.6670.9920.9250.9130.9990.8640.9990.9080.869
S300.2970.8370.8370.2320.9371.0000.9990.8830.1660.1300.971
S310.2770.3930.9790.5201.0000.7440.6940.5650.026 *0.3900.9960.198
S320.8750.5031.0000.8120.3840.9400.8250.2490.1110.9500.6490.3510.016 *
S350.013 *0.9401.0000.4310.4230.2020.3510.1690.001 *0.3320.6790.0540.018 *0.016 *
S360.9630.1860.9870.5670.9950.9540.5481.0000.3850.9981.0000.5570.8360.9760.404
S370.7820.7221.0000.4050.6480.9340.5350.7820.2500.9260.9410.7110.003 *0.0790.004 *0.642
S380.9780.1610.9900.7270.9910.8440.7060.4330.9970.7740.8930.8260.5970.9230.1751.0000.956
*: significant level at p < 0.05.
Table 4. Statistical values of microsatellite markers in 112 samples of 11 S. involucrata populations.
Table 4. Statistical values of microsatellite markers in 112 samples of 11 S. involucrata populations.
LocusNaNeINmHoHeuHeFstPICNei’s
S1075.7001.8181.6640.7020.8110.8630.1310.9710.811
S1131.9010.7541.1200.0080.4380.4610.1820.9780.438
S1563.8121.4373.5521.0000.6970.7370.0660.9840.697
S1621.4520.3760.5220.0000.2340.2460.3240.9800.234
S2042.7361.0111.2550.1250.5490.5800.1660.9830.549
S2321.6390.5291.1910.0000.3450.3660.1730.9710.345
S2432.0280.8040.7500.1510.4550.4820.2500.9680.455
S2532.1680.7960.9980.4980.4560.4820.2000.9820.456
S2621.6450.5100.5830.1700.3250.3590.3000.8830.325
S2932.5071.0261.2330.7090.5920.6310.1690.9550.592
S3032.6431.0322.5700.9910.6070.6390.0890.9900.607
S3232.2890.8481.9231.0000.5530.5850.1150.9830.553
S3621.6180.5522.4440.0000.3420.3600.0930.9820.342
S3721.4780.4300.2640.1930.2770.2930.4860.9770.277
S3821.9640.6310.7740.3500.3680.3890.2440.9780.368
Note: Na = number of alleles; Ne = effective alleles; I = Shannon information index; Nm = gene flow; Ho = observed heterozygosity; He = expected heterozygosity; uHe = unbiased expected heterozygosity; Fst = genetic differentiation coefficient; PIC = polymorphism information content; Nei’s = Nei’s genetic diversity.
Table 5. Summary of genetic statistics for S. involucrata at population level.
Table 5. Summary of genetic statistics for S. involucrata at population level.
Pop NNaNeIHoHeuHeFPercentage of Deviation
from HWE Site (%)
1Mean9.8673.2672.4900.8540.3740.4800.5060.33366.667
SE0.3070.6720.4730.1400.1160.0580.0610.192
2Mean8.4003.2002.4580.8480.4260.4750.5060.19353.333
SE0.3350.5180.4240.1370.1100.0600.0640.193
3Mean7.6673.8002.6510.9500.4030.5010.5380.32653.333
SE0.3330.6110.4750.1440.1140.0580.0630.177
4Mean9.4673.0672.3400.8680.4310.5010.5290.12160.000
SE0.2150.3710.2250.1170.1200.0590.0630.212
5Mean9.3333.0672.3500.8640.4200.4960.5250.16746.667
SE0.2110.3580.2510.1170.1080.0610.0640.181
6Mean8.9333.4002.8410.9150.3750.5060.5360.28973.333
SE0.2060.6680.5700.1600.1120.0680.0720.205
7Mean9.1333.2672.2340.8470.3750.4800.5090.29973.333
SE0.3760.4830.2790.1120.1070.0480.0510.185
8Mean9.7333.1332.4280.8650.3460.4990.5260.37280.000
SE0.1530.4870.3190.1260.1160.0550.0590.212
9Mean10.6673.4672.2830.8490.4150.4530.4760.27466.667
SE0.1590.4240.2920.1300.1070.0660.0690.167
10Mean9.6672.4671.9400.5990.4180.3450.364−0.11153.333
SE0.1870.4350.3010.1430.1180.0750.0790.182
11Mean9.8673.2672.4900.8540.3740.4800.4650.40366.667
SE0.3070.6720.4730.1400.1160.0580.0620.186
Note: N = number of individuals sampled; Na = number of alleles; Ne = effective alleles; I = Shannon information index; Ho = observed heterozygosity; He = expected heterozygosity; uHe = unbiased expected heterozygosity; F = fixation index; percentage of deviation from HWE loci = (number of loci significantly deviated from HWE/total number of loci) × 100%; SE = standard error of the mean.
Table 6. Analysis of molecular variance (AMOVA) of genetic variation within and among groups of S. involucrata.
Table 6. Analysis of molecular variance (AMOVA) of genetic variation within and among groups of S. involucrata.
Source of VariationdfSSEst. Var.Variation (%)
Among Pops10297.8700.6172.560
Within Pops1012373.70123.50297.440
Total1112671.57124.119100.000
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hu, L.; Wang, J.; Wang, X.; Zhang, D.; Sun, Y.; Lu, T.; Shi, W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata. Biomolecules 2024, 14, 1010. https://doi.org/10.3390/biom14081010

AMA Style

Hu L, Wang J, Wang X, Zhang D, Sun Y, Lu T, Shi W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata. Biomolecules. 2024; 14(8):1010. https://doi.org/10.3390/biom14081010

Chicago/Turabian Style

Hu, Lin, Jiancheng Wang, Xiyong Wang, Daoyuan Zhang, Yanxia Sun, Ting Lu, and Wei Shi. 2024. "Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata" Biomolecules 14, no. 8: 1010. https://doi.org/10.3390/biom14081010

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop