Next Article in Journal
Heavy Metal-Based Fungicides Alter the Chemical Fractions of Cu, Zn, and Mn in Vineyards in Southern Brazil
Previous Article in Journal
The Effects of Tillage and the Combined Application of Organic and Inorganic Fertilizers on the Antioxidant Enzyme Activity and Yield of Maize Leaves
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 5
10.3390/agronomy14050970
agronomy-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

Genetic Diversity Assessment of Cupressus gigantea W. C. Cheng & L. K. Fu Using Inter-Simple Sequence Repeat Technique

by
Ximei Ji
1,2,†,
Yaxuan Jiang
2,†,
Jianxin Li
2,
Pei Lei
1,* and
Fanjuan Meng
1,*
1
Jilin Provincial Key Laboratory of Tree and Grass Genetics and Breeding, College of Forestry and Grassland Science, Jilin Agricultural University, Changchun 130118, China
2
College of Life Science, Northeast Forestry University, Harbin 150040, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(5), 970; https://doi.org/10.3390/agronomy14050970
Submission received: 5 April 2024 / Revised: 26 April 2024 / Accepted: 1 May 2024 / Published: 5 May 2024
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Cupressus gigantea W. C. Cheng & L. K. Fu is an endemic conifer tree species that is distributed widely along the northern portion of the deep gorge of the Yarlung Tsangbo River on the Tibetan Plateau. However, as a key plant species growing on the Tibetan plateau, C. gigantea has since become an endangered species due to habitat loss and degradation, overexploitation, and other factors. It has been listed as a first-grade national protected wild plant species in China. Accordingly, to conserve this plant species, we should obtain more information on its genetic structure. In this study, the genetic diversity and structure among 67 samples were evaluated by the inter-simple sequence repeat (ISSR) technique. Overall, 78 bands were produced with a molecular length of 200 bp to 3100 bp using 10 ISSR primers. The mean values for the average number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity (H), and Shannon’s information index (I) were 1.529, 1.348, 0.199, and 0.293, respectively. Additionally, the number of polymorphic loci (NPLs) and percentage of polymorphic loci (PPLs) averaged 41.25 and 52.90, respectively. Further, total variation among populations was 14.2%, while that within populations was 85.8%; accordingly, the within-population genetic differentiation was found to be significant (p < 0.001). These results demonstrated that a genetic structure model with K = 3 fitted the data best, which agreed with the unweighted pair group method with arithmetic average (UPGMA) cluster and the principal coordinate analysis (PCoA). These findings are beneficial for ensuring the development and genetic protection of C. gigantea populations in the future.

1. Introduction

Cupressus gigantea W. C. Cheng & L. K. Fu (Tibetan juniper), Cupressaceae, Cupressus, is an endemic plant species [1]. The number of chromosomes in C. gigantea somatic cells is 2n = 22, and it mainly reproduces from seeds [2,3]. C. gigantea is sparsely distributed along the northern part of the deep gorge of the Yarlung Tsangbo River on the Tibetan Plateau, also known as the Yarlung Zangbo River cypress [4]. This region features extremely severe environmental conditions, including high radiation, cold temperature, strong winds, and barren soil [5]. Nevertheless, C. gigantea can survive in this harsh habitat where it plays a pivotal role in soil protection and the prevention of desertification [6,7]. Additionally, this evergreen tree is relatively tall (up to 30–50 m in height) and lives long (from 100 to 1000 years), making C. gigantea one of the long-lived endemic cypresses in China [8,9,10]. Therefore, it is generally respected as being a god tree by local residents [11]. At the same time, it is also an important raw material for making Tibetan incense, so C. gigantea has important cultural value for the local residents [12]. It is also well known for its ornamental and medicinal properties [13]. Notably, C. gigantea has high economic value due to its high wood density, straight grain, and radial symmetry [10]. Up to now, studies on C. gigantea have mainly focused on its total protein extraction, determination of the complete chloroplast genome, comprehensive transcriptome characterization, community characteristics, growth characteristics, propagation, and other related aspects [3,14,15,16,17]. To date, the analysis of the genetic population of C. gigantea is lacking.
However, as a key plant species growing on the Tibetan plateau, C. gigantea has since become an endangered species due to a low seed-setting rate, habitat loss, and degradation, as well as extensive logging for Tibetan incense production and timber production [18,19,20,21,22]. Currently, C. gigantea has been listed as a first-grade national protected wild plant species in China [17,23]. Hence, conservation efforts must be initiated to halt the decline of this tree species and the associated loss of biodiversity. Generally, in this respect, an evaluation of the genetic structure and diversity of C. gigantea is highly important for their future germplasm resource management and the formulation of breeding strategies. Moreover, studying the genetic structure in C. gigantea is fundamental for understanding the internal variation in the species, which is conducive to the identification and preservation of germplasm. While traditional morphological and biochemical characteristics are often used for this purpose. However, their limitations in assessing genetic diversity highlight the need for more precise and accurate markers. In recent years, a series of molecular marker techniques have been used to analyze the genetic structures of various plant species [24].
To date, various molecular marker techniques developed over nearly four decades have led to insights into the biology, genetics, and genome evolution of conifers [25]. Accordingly, to estimate the genetic diversity in C. gigantea populations, some DNA-based marker techniques have been carried out, such as amplified fragment length polymorphism (AFLP), inter-simple sequence repeat (ISSR), randomly amplified polymorphic DNA (RAPD), and polymorphic fluorescent-labeled SSR markers. Among them, in 2008, Zha reported that nine populations of C. gigantean were sampled and analyzed by AFLP markers, indicating high genetic diversity in the populations and significant genetic variation within the various populations [12]. Another group discovered that the ISSR primers produced more polymorphic bands than the RAPD markers. Additionally, there were high levels of genetic differentiation within populations (p < 0.001) [26]. Similarly, the AMOVA of SSR showed that genetic variation mainly existed within populations [27]. Overall, there are still some inadequacies in the aforementioned studies. On the one hand, the collection time is 20 years ago, and the populations of C. gigantea have undergone dramatic changes due to the lack of effective conservation measures. On the other hand, the individual studies collected only five groups, which lacks universality. Therefore, our research on the existing populations of C. gigantea is more conducive to the development of more detailed conservation measures.
However, some molecular markers have shown some shortcomings including the high cost of AFLP and the radioactive labeling of RFLP [28]. Until now, SSR markers for mining SSR motifs in the whole genome greatly reduce the cost but show somewhat lower consistency with dominant marker data, and some plants’ genomic data are not published [29]. ISSR markers have the advantages of simplicity, high reproducibility, reliability, and stability, as well as important applications in genetic diversity research, gene mapping, and genetic fingerprints [30]. The ISSR markers are specific by using longer primers based on SSRs. They also have a higher annealing temperature, which helps generate clearer and more consistent amplification [31]. In addition, this technique can also evaluate the genetic relationship between accessions and enables the construction of genetic linkage maps [32]. Notably, this method is very efficient in terms of time and labor when seeking to compare genetic resources from different collection sites [33,34]. Abundant evidence suggests that ISSR markers have proven to be a powerful tool for detecting genetic differentiation and describing the germplasm of various cultivated and wild plant species [35,36]. These characteristics make ISSR markers particularly suitable for assessing the genetic structure in C. gigantea. Surprisingly, however, this type of molecular marker has not been used in recent years to investigate the genetic variation in C. gigantea.
In this study, we screened 10 ISSR primers to uncover the genetic relationship and genetic diversity of eight C. gigantea populations collected along the bank of the Yarlung Tsangbo River. Using this information, we sought to comprehensively reveal the genetic variation level among and within populations of C. gigantea, and thereby proposing effective conservation strategies for C. gigantea.

2. Materials and Methods

2.1. Materials

Sixty-seven C. gigantea samples were collected along the coast of the Yarlung Tsangbo River of Tibet. The detailed information of the collection sites of all samples used in this study is listed in Figure 1. Fresh and young leaves of all the samples were collected in the Spring season (March) of the year 2017 and preserved at the Genetic Department of College of Life Science of Northeast Forestry University, Harbin, China.

2.2. DNA Extraction

Fresh and young leaves were frozen in liquid nitrogen for DNA extraction. Total DNA was extracted from 0.1 g of C. gigantea leaves using the modified CTAB method [37]. DNA samples were estimated qualitatively and quantitatively by a spectrophotometer and 0.8% agarose gel electrophoresis, respectively. Then, a 100 ng∙μL−1 dilution of DNA samples was prepared for further PCR analysis.

2.3. ISSR Analysis

ISSR amplification was carried out according to a previous method, with some modifications described by Xie et al. [38]. Fifty ISSR primers (University of British Columbia, Vancouver, BC, Canada) were tested, and ten ISSR primers (UBC #808, #824, #827, #836, #841, #842, #847, #856, #857, and #873) showed clear polymorphic bands and good repeatability, and these were screened further using genetic analysis. All primers were synthesized in Shanghai Sangon Biological Engineering Technology and Service (Co., Ltd., Shanghai, China). The ISSR reactions were amplified in total mixture volume of 20 μL containing DNA template (80 ng), 1 μL primer (10 mM−1), 2 μL dNTPs; (0.5 mM∙L−1), 2.5 μL 10 × PCR  reaction buffer, 2.5 μL MgCl2 (2.5 mM∙L−1), 0.5 μL Taq DNA polymerase (5 U∙μL−1) (Takara Biotechnology Co., Ltd., Dalian, China), and 9.5 μL ddH2O.
PCR cycling conditions were carried out in an Eppendorf PCR an Amplifier GeneAmp PCR System 9700 (Eppendorf, Hamburg, Germany) with the following protocol: run at 94 °C for 5 min, followed by 40 cycles of 94 °C for 30 s, 55 °C for 45 s, and 72 °C for 2 min, and a final extension at 72 °C for 10 min. To estimate the sizes of DNA fragments, a 2000 bp DNA weight marker (NewEngland BioLabs, Massachusetts, USA) was included. The amplified products were separated in 2.0% agarose gels stained with GelRed (0.5 μL∙mL−1) and imaged under an ultraviolet illuminator and photographed.

2.4. Statistical Analysis

For each ISSR marker, all the assessed bands were distinguished based on the presence (1) or absence (0) of genetic diversity. Based on distinguished results, a binary data matrix was obtained. Accordingly, a dendrogram was constructed according to the unweighted pair group method with arithmetic average (UPGMA) of NTSYS-pc version 2.10e [39]. The genetic parameters including Na, Ne, H, I, NPLs, and PPLs were calculated by the POPGENE (VERSION-1.31) software package [40]. Genetic structure was constructed by STRUCTURE 2.3.4 Microsoft [41]. Genetic differentiation was assessed by the method of molecular variance (AMOVA) version 1.55 [42].

3. Results

3.1. ISSR Analysis

We successfully screened 10 primers to amplify all the collected samples (Table 1). Altogether, these screened ISSR markers produced a total of 78 bands from UBC-836 (4) to UBC-857 (13), with molecular lengths of 200–3100 bp. Among them, 67 polymorphic bands were amplified. The percentage of polymorphism varied from 50% (UBC-836) to 100%, with an average of 85.90%. Three ISSR markers (UBC-824, UBC-827, and UBC-847) exhibited high polymorphism (100%) (Table 1). This polymorphism was an indication of higher prevalence of diversity among the eight C. gigantea genotypes. The results indicated that C. gigantea has a considerable number of polymorphisms and genetic variants among its genotypes. As a representative, the amplified diagram produced by the primer UBC-842 is shown in Figure 2.

3.2. Genetic Relationship among C. gigantea Genotypes

The observed number of alleles (Na) of the eight populations ranged from 1.333 to 1.756, averaging 1.529. The effective number of alleles (Ne) spanned from 1.231 (population 4) to 1.489 (population 11), averaging 1.348. The Nei’s gene diversity (H) was between 0.137 (population 4 and 15) and 0.280 (population 11), with a mean of 0.199. The value of Shannon’s information index (I) ranged more than two-fold, from 0.199 (population 15) to 0.413 (population 11), with a mean of 0.293. Additionally, the average values of the number of polymorphic loci (NPLs) and the percentage of polymorphic loci (PPLs) were 41.25 and 52.90, respectively. Among all the populations, population 11 harbored the most genetic diversity (Ne = 1.489; H = 0.280; I = 0.413), whereas population 4 showed the least genetic diversity (Ne = 1.231; H = 0.137; I = 0.205). The order of genetic diversity among the populations in the descending order is as follows: 11 > 13 > 12 > 8 > 14 > 4 > 10 > 15 (Table 2).
Using the AMOVA method, the genetic variation among 67 samples from different locations was analyzed (Table 3). Total variation among the population was 14.2%, whereas that within the populations was 85.8%. The results indicated that the substantial genetic diversity of C. gigantea mainly came from within the population and genetic variation occurred (p < 0.001).

3.3. Cluster Analysis in C. gigantea

Binary data obtained for the 10 ISSR primers from 67 samples were scored and computed. Accordingly, a UPGMA tree was built according to Jaccard’s coefficient of similarity. At the threshold value of 0.64, 67 individuals were classified into two categories (Figure 3). Here, two different clusters (Cluster I and II) were derived, in which Cluster I further formed two subclusters labeled here as IA and IB. Subcluster IA contained most of the samples from populations 4, 8, 12, 14, and 15, while subcluster IB included most of the samples from population 13. Cluster II contained three samples from population 10 and one sample from population 11.
The clustering pattern can be attributed to two factors, including history and environment. Several genotypes stand out as distinct entities within their respective subclusters. Noticeably, individual samples of population 13 in subcluster IA and population 11 in Cluster II exhibit distinct genetic profiles, emphasizing their possible roles in conservation programs.
Using the ISSR data, the dendrogram was generated and the principal coordinate analysis (PCoA) was performed to group the genotypes from the two main clusters in C. gigantea. The PCoA also clearly displayed three groupings of the samples, consistent with the above results (Figure 4). The geographical distribution of the population (Figure 1) showed that the habitat conditions divided by longitude and the direction of the river affected the genetic structure of C. gigantea.
In order to study the genetic structures of the genotypes of C. gigantea, a non-spatial Bayesian clustering method was used to determine the optimal number of subpopulations (K) (Figure 5). These results demonstrated that the fitted model with K = 3 robustly explained the data. Red, green, and blue vertical bars represented the three main groups. This result was consistent with that obtained from the UPGMA cluster analysis as well as that from the PCoA analysis (Figure 3 and Figure 4). These results indicate that the individuals of the C. gigantea population may be genetically derived from three gene pools.

4. Discussion

The identification of genetic diversity is important for understanding plant evolution and adaptation [43]. In general, those plant species with genetic variability have the potential to adapt to extreme and unique environmental conditions [44]. Consequently, it is important to assess the genetic diversity levels of tree species for tree management and conservation strategies, especially for rare tree species [26]. Ample evidence on the pattern and distribution of genetic variation in conifers indicates that they have higher levels of genetic variation than other plant species, with little differentiation among populations [45,46].
Herein, a high level of genetic diversity occurred within populations, which is consistent with a previous report [12]. Our results revealed that ISSR markers were efficient molecular markers for evaluating the genetic polymorphism among different C. gigantea populations because three out of the ten ISSR markers that we used displayed 100% polymorphism (Table 1). Noteworthily, a high level of among-population genetic diversity was also reported with AFLP markers in C. gigantea [26]. Similar results were found in other research studies on conifer trees. For example, both the RAPD and ISSR methods indicated that rich genetic diversity and most variation came from within populations in Larix gmelinii [47]. Subsequently, by using six RAPD and three ISSR markers for four subpopulations of Abies cephalonica, Papageorgiou et al. demonstrated that all subpopulations exhibit high genetic diversity, with relatively low levels of differentiation among them [48]. More recently, another group discovered that the 129 individuals revealed a high level of genetic diversity and 85% of genetic variation within populations in Tetraclinis articulate [49]. This may be because C. gigantea has an outcrossing breeding system with high potential for long-distance gene flow.
Therefore, ISSR markers are highly sensitive and reproducible methods to detect the population structure of C. gigantea. For other plant species, ISSR primers were found to be more effective than other types of molecular markers [50,51]. For instance, Linos et al. proposed that the average number of alleles per locus was 5.8 for SSR, while ISSR and RAPD showed lower values (2 and 2, respectively), indicating somewhat lower congruence with the dominant marker data [52]. Another group also demonstrated that the polymorphism of ten ISSR primers (100%) is higher than SSR (80.4%), and significant differences were found in three distinct groups among the genotypes in chrysanthemum [53]. However, combining and contrasting the findings of the ISSR technique with those derived from other molecular methodologies, such as AFLP, SNP, or next-generation sequencing, can enrich the understanding of the genetic diversity and configuration of C. gigantea populations [54]. In summary, when assessing large numbers of samples, it is important to combine multiple and reliable methods to evaluate the genetic diversity of different plant species.
The genetic diversity differed greatly between all studied populations (Table 2). Accordingly, this result could be helpful to analyze the current status of different C. gigantea populations, leading to sound conservation strategies. For example, because populations 4, 10, and 15 had lower genetic diversity (Table 2), they should be declared as critical populations, as they are at risk of extinction. Habitat fragmentation reduces the gene flow among populations, resulting in a loss of genetic diversity. Therefore, conservation strategies should be immediately implemented such as ex situ collections (such as the collection of germplasm and the establishment of seed banks) and the introduction of new germplasm and the establishment of breeding systems (including cutting, grafting propagation, and tissue culture). In addition, populations 11 and 13 exhibited high levels of genetic diversity (Table 2). These populations represent the core populations with strong environmental adaptability and evolutionary potential. Hence, regulations and management strategies must be established to protect the natural habitat of C. gigantea. Notably, national-level core germplasm banks of C. gigantea should be established to maintain the genetic variation and develop new genetic breeding materials.
Furthermore, the results of AMOVA found that the average genetic variation within population was higher than among populations, indicating high gene flow between populations (Table 3). This result has been attributed primarily to the long-distance dispersal of pollen and seeds, the longevity, and the outcrossing of C. gigantean with other conifers. In general, the establishment of in situ protected areas is an ideal way to protect wild plants. However, the complex terrain and diverse climate of the Tibetan plateau make it unsuitable for the conservation of endangered plant species. Strikingly, in situ conservation also requires local authorities to establish appropriate policies [55]. Correspondingly, considering these factors, an ex situ conversation based on a seed bank is a feasible option to protect the germplasm resources of C. gigantean. At the same time, promoting the cultivation and domestication of this wild plant is of great significance for meeting the market demand and protecting endangered plant species.
Some factors may contribute to the reduction in the high genetic diversity of C. gigantea, including natural selection and human impact. The first factor can be the response of Tibet’s Yarlung Tsangbo River to global climate change; conversely, the generation of complex landscapes under climate fluctuations may partly account for the high genetic diversity of C. gigantea [56,57]. The second factor can be logging and other forms of human damage to trees [58]. Here, the AMOVA results showed a higher magnitude of genetic differentiation within the populations of C. gigantea. This result probably reflects the frequent gene flow occurring among C. gigantea populations because seeds and pollen of C. gigantea can be dispersed to long distances. Therefore, marked geographic barriers of and climate changes in the Yarlung Tsangbo River are not factors limiting the gene flow of C. gigantea. Furthermore, the suitable habitat of C. gigantea faces various threats; therefore, an assessment of C. gigantea’s in situ genetic diversity would be beneficial for its development and genetic protection in future. The molecular marker technique is an efficient tool for distinguishing C. gigantea populations and for robustly identifying the populations most at risk.

5. Conclusions

Herein, we used ISSR to analyze the genetic structure of C. gigantea populations collected along the north of the deep gorge of the Yarlung Tsangbo River. The results show that a large proportion of genetic diversity is present at the within-population level in C. gigantea. Additionally, UPGMA clustering together with PCoA analysis revealed that all accessions can be divided into two main groups. Furthermore, marked geographic barriers of and climate changes in the Yarlung Tsangbo River were not factors limiting gene flow of C. gigantea. Accordingly, a gene bank of C. gigantea should be founded by collecting more germplasm around the Yarlung Tsangbo River.

Author Contributions

F.M., P.L., X.J. and Y.J. conducted the experiments. X.J., Y.J. and J.L. wrote the manuscript. F.M., X.J., Y.J. and J.L. edited the manuscript. F.M., P.L. and X.J. critically revised the draft and updated the manuscript for publication. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities (2572018CG05).

Data Availability Statement

Data are contained within the article.

Acknowledgments

This study was supported by the Fundamental Research Funds for the Central Universities (2572018CG05).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, L. Complete genome sequence of Bacillus velezensis LM2303, a biocontrol strain isolated from the dung of wild yak inhabited Qinghai-Tibet plateau. J. Biotechnol. 2017, 251, 124–127. [Google Scholar] [CrossRef] [PubMed]
  2. Lei, P.; Liu, Z.; Li, J.; Jin, G.; Xu, L.; Ji, X.; Zhao, X.; Tao, L.; Meng, F. Integration of the Physiology, Transcriptome and Proteome Reveals the Molecular Mechanism of Drought Tolerance in Cupressus gigantea. Forests 2022, 13, 401. [Google Scholar] [CrossRef]
  3. Yin, Z.; Wang, Y.; Wang, Y.; Sang, L.; Luo, Q.; Meng, F. Improvement on the method of the extraction of total proteins on Cupressus gigantea leaves for two-dimensional gel electrophoresis. J. Cent. South Univ. For. Technol. 2019, 39, 143–146. [Google Scholar]
  4. Zhou, S.S.; Xing, Z.; Liu, H.; Hu, X.G.; Gao, Q.; Xu, J.; Jiao, S.Q.; Jia, K.H.; Jin, Y.Q.; Zhao, W.; et al. In-depth transcriptome characterization uncovers distinct gene family expansions for Cupressus gigantea important to this long-lived species’ adaptability to environmental cues. BMC Genom. 2019, 20, 213. [Google Scholar] [CrossRef] [PubMed]
  5. Cheng, G.D.; Wu, T.H. Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau. J. Geophys. Res.-Earth Surf. 2007, 112. [Google Scholar] [CrossRef]
  6. Lan, X.; Liao, Z.; Wang, J. The diurnal course of photosynthesis of the endangered species Tibetan Cupressus gigantea in Tibet Plateau. Acta Ecol. Sin. 2005, 25, 3172–3175. [Google Scholar]
  7. Luo, J.; Wang, J.; Daqing, L.; Pan, G.; Zheng, W. Study on the Characteristics of Cupressus gigantea Communtiy in Tibet. For. Res. 2006, 19, 295–300. [Google Scholar]
  8. Zheng, W.; Xue, H.; Luo, D.; Wang, J.; Luo, J. Eco-geographic Distribution and Coenology Characteristics of Cupressus gigantea. Sci. Silvae Sin. 2007, 43, 8–15. [Google Scholar]
  9. Yang, B.; Kang, X.; Braeuning, A.; Liu, J.; Qin, C.; Liu, J. A 622-year regional temperature history of southeast Tibet derived from tree rings. Holocene 2010, 20, 181–190. [Google Scholar] [CrossRef]
  10. Ma, Y.; Wang, J.; Hu, Q.; Li, J.; Sun, Y.; Zhang, L.; Abbott, R.J.; Liu, J.; Mao, K. Ancient introgression drives adaptation to cooler and drier mountain habitats in a cypress species complex. Commun. Biol. 2019, 2, 213. [Google Scholar] [CrossRef]
  11. Yin, J.; Zhao, G. Research history and prospect of Cupressus gigantean in Tibetan Plateau. For. Environ. Sci. 2019, 2, 116–122. [Google Scholar]
  12. Tsering, T. Studies on Genetic Diversity and Variation in Essential Oil of Tibetan Giant Cypress, Cupressus gigantea, and Implications for Its Conservation Biology. Ph.D. Thesis, Fudan University, Shanghai, China, 2008. [Google Scholar]
  13. Lu, X.; Xu, H.; Li, Z.; Shang, H.; Adams, R.P.; Mao, K. Genetic Diversity and Conservation Implications of Four Cupressus Species in China as Revealed by Microsatellite Markers. Biochem. Genet. 2014, 52, 181–202. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, H.; Wei, Y.; Yue, J.; Wang, Z.; Zou, H.; Ji, X.; Zhang, S.; Liu, Z. Prediction of Potential Suitable Areas and Priority Protection for Cupressus gigantea on the Tibetan Plateau. Plants 2024, 13, 896. [Google Scholar] [CrossRef] [PubMed]
  15. Liu, C.S.; Li, T.; Zhang, R.W.; Wang, C.; Qu, X.; Luo, D.Q. Growth characteristics and biomass model of Cupressus gigantea sapling. Trees-Struct. Funct. 2024, 38, 27–35. [Google Scholar]
  16. Xin, F.; Wang, Y.; Li, S. DanzengluobuPubuciren. Effects of different temperatures on photosynthesis and rooting of Cupressus gigantea seedlings. J. Zhejiang Univ. Agric. Life Sci. 2019, 45, 102–108. [Google Scholar]
  17. Li, H.; Guo, Q.; Zheng, W. The complete chloroplast genome of Cupressus gigantea, an endemic conifer species to Qinghai-Tibetan Plateau. Mitochondrial DNA Part A 2016, 27, 3743–3744. [Google Scholar] [CrossRef] [PubMed]
  18. Wang, J.; Zheng, W.; Pan, G. Relation between Being Endangered and Seed Vigor about Cupressus gigantea in Tibet. Sci. Silvae Sin. 2005, 41, 37–41. [Google Scholar]
  19. Zhang, L.; Liang, Z.; Zhang, L.; Xing, Z.; Zhang, Y. Effects of tourist disturbance on giant cypress forest in Linzhi, Tibet. Ecol. Environ. Sci. 2009, 18, 1413–1421. [Google Scholar]
  20. Luo, D.; Guo, Q.; Wang, Z.; Zhao, K. Physiological Response of Three Cypress Species to Drought Stress in Semi-arid Region of Tibet. Acta Bot. Boreali-Occident. Sin. 2011, 31, 1611–1617. [Google Scholar]
  21. Li, S.; Qian, Z.; Fu, Y.; Zheng, W.; Li, H. Isolation and characterization of polymorphic microsatellites in the Tibetan cypress Cupressus gigantea using paired-end Illumina shotgun sequencing. Conserv. Genet. Resour. 2014, 6, 795–797. [Google Scholar] [CrossRef]
  22. Li, Y.; Yang, X.; Zhao, K.; Yao, Q.; Zhao, Y. Research on the growth characteristics and model of Cupressus gigantea in southeast Tibet. J. Plateau Agric. 2020, 4, 269–276. [Google Scholar]
  23. Guo, Q.; Chi, X.; Wang, S.; Ren, Y.; Zheng, W. Types and species diversity of Cupressus communities in Southeast Tibet, China. Ying Yong Sheng Tai Xue Bao = J. Appl. Ecol. 2019, 30, 735–742. [Google Scholar]
  24. Hamdan, Y.A.S. Genetic diversity analysis of Palestinian safflower (Carthamus tinctorius L.) using inter simple sequence repeat (ISSR) markers. Genet. Resour. Crop Evol. 2024. [Google Scholar] [CrossRef]
  25. Agarwal, M.; Shrivastava, N.; Padh, H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 2008, 27, 617–631. [Google Scholar] [CrossRef] [PubMed]
  26. Xia, T.; Meng, L.; Mao, K.; Tian, B.; Miehe, G.; Liu, J. Genetic variation in the Qinghai-Tibetan Plateau endemic and endangered conifer Cupressus gigantea, detected using RAPD and ISSR markers. Silvae Genet. 2008, 57, 85–92. [Google Scholar] [CrossRef]
  27. Fu, Y.; Li, S.; Guo, Q.; Zheng, W.; Yang, R.; Li, H. Genetic diversity and population structure of two endemic Cupressus (Cupressaceae) species on the Qinghai-Tibetan plateau. J. Genet. 2019, 98, 14. [Google Scholar] [CrossRef]
  28. Pecina-Quintero, V.; Anaya-López, J.L.; Núñez-Colín, C.A.; Zamarripa-Colmenero, A.; Montes-García, N.; Solís-Bonilla, J.L.; Aguilar-Rangel, M.R. Assessing the genetic diversity of castor bean from Chiapas, México using SSR and AFLP markers. Ind. Crops Prod. 2013, 41, 134–143. [Google Scholar] [CrossRef]
  29. Liu, S.; Yin, X.G.; Lu, J.N.; Liu, C.; Bi, C.; Zhu, H.B.; Shi, Y.Z.; Zhang, D.; Wen, D.Y.; Zheng, J.; et al. The first genetic linkage map of Ricinus communis L. based on genome-SSR markers. Ind. Crops Prod. 2016, 89, 103–108. [Google Scholar] [CrossRef]
  30. Zangeneh, M.; Salehi, H. Genetic Diversity as Revealed by Intersimple Sequence Repeat Polymorphism in Narcissus Accessions to Identify the Tolerant Genotypes for Deficit Irrigation. J. Am. Soc. Hortic. Sci. 2019, 144, 92–106. [Google Scholar] [CrossRef]
  31. Mohamed, H.M.; El-Assal, S.E.-D.S.M.; Gamal El-Din, A.-K.Y.; El-Khishin, D.A. Analysis of genetic diversity and population structure in some Egyptian Berseem (Trifolium alexandrinum) accessions based on ISSR, SCoT and SRAP markers. Genet. Resour. Crop Evol. 2024, 8, 127–132. [Google Scholar] [CrossRef]
  32. Singh, S.K.; Chhajer, S.; Pathak, R.; Bhatt, R.K.; Kalia, R.K. Genetic diversity of Indian jujube cultivars using SCoT, ISSR, and rDNA markers. Tree Genet. Genom. 2017, 13, 12. [Google Scholar] [CrossRef]
  33. Al-Turki, T.A.; Basahi, M.A. Assessment of ISSR based molecular genetic diversity of Hassawi rice in Saudi Arabia. Saudi J. Biol. Sci. 2015, 22, 591–599. [Google Scholar] [CrossRef] [PubMed]
  34. Ballesta, P.; Mora, F.; Contreras-Soto, R.I.; Ruiz, E.; Perret, S. Analysis of the genetic diversity of Eucalyptus cladocalyx (sugar gum) using ISSR markers. Acta Sci.-Agron. 2015, 37, 133–140. [Google Scholar] [CrossRef]
  35. Bryan, G.J.; McNicoll, J.; Ramsay, G.; Meyer, R.C.; Jong, W.S. Polymorphic simple sequence repeat markers in chloroplast genomes of Solanaceous plants. TAG Theor. Appl. Genet. 1999, 99, 859–867. [Google Scholar] [CrossRef]
  36. Wolfe, A.; Xiang, Q.; Kephart, S. Assessing hybridization in natural populations of Penstemon (Scrophulariaceae) using hypervariable intersimple sequence repeat (ISSR) bands. Mol. Ecol. 1998, 7, 1107–1125. [Google Scholar] [CrossRef] [PubMed]
  37. Doyle, J.J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 1987, 19, 11–15. [Google Scholar]
  38. Xie, G.; Zhang, J.; Zheng, Y.; Peng, X. DNA extraction and optimization of ISSR reaction system for rare and endangered plant Monimopetalum chinense. Guihaia 2007, 27, 817–820. [Google Scholar]
  39. Sneath, P.H.; Sokal, R.R. Numerical taxonomy. Nature 1962, 193, 855–860. [Google Scholar] [CrossRef]
  40. Sneath, P.H.A. Numerical Taxonom: The Principles and Practice of Numerical Classification. Classification. 1973.
  41. Earl, D.A.; Vonholdt, B.M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  42. Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 1992, 131, 479–491. [Google Scholar] [CrossRef]
  43. Singh, K.M.; Kumar, B. Molecular Markers: It’s Application in Crop Improvement. J. Crop Sci. Biotechnol. 2009, 12, 168–178. [Google Scholar]
  44. Batista, D.; Roberto, M.; Bastos, R.F.; Bernard, K.L. Altitudinal distribution of two sibling species of the Drosophila tripunctata group in a preserved tropical forest and their male sterility thermal thresholds. J. Therm. Biol. 2018, 71, 69–73. [Google Scholar] [CrossRef] [PubMed]
  45. Petit, R.J.; Bialozyt, R.; Garnier-Géré, P.; Hampe, A. Ecology and genetics of tree invasions: From recent introductions to Quaternary migrations. For. Ecol. Manag. 2004, 197, 117–137. [Google Scholar] [CrossRef]
  46. Petit, R.; Duminil, J.; Fineschi, S.; Giuseppe, V.G. Comparative organization of chloroplast, mitochondrial and nuclear diversity in plant populations. Mol. Ecol. 2005, 14, 689–701. [Google Scholar] [CrossRef] [PubMed]
  47. Zhang, L.; Zhang, H.G.; Li, X.F. Analysis of genetic diversity in Larix gmelinii (Pinaceae) with RAPD and ISSR markers. Gen. Mol. Res. 2013, 12, 196–207. [Google Scholar] [CrossRef] [PubMed]
  48. Papageorgiou, A.C.; Kostoudi, C.; Sorotos, I.; Varsamis, G.; Korakis, G.; Drouzas, A.D. Diversity in needle morphology and genetic markers in a marginal Abies cephalonica (Pinaceae) population. Ann. For. Res. 2015, 58, 217–234. [Google Scholar] [CrossRef]
  49. Makkaoui, M.; Abbas, Y.; El Antry-Tazi, S.; Medraoui, L.; Alami, M.; Rabani, S. Filali-Maltouf, A. Genetic diversity of ten Moroccan populations of Tetraclinis articulata as revealed by Inter Simple Sequence Repeat (ISSR) markers. Bois For. Trop. 2020, 345, 13–23. [Google Scholar] [CrossRef]
  50. Basheer-Salimia, R.; Mujahed, A. Genetic diversity of grapevine (Vitis vinifera L.) as revealed by ISSR markers. J. Plant Biotechnol. 2019, 46, 1–8. [Google Scholar]
  51. Kim, H.; Lei, P.; Wang, A.; Liu, S.; Zhao, Y.; Huang, F.; Yu, Z.; Zhu, G.; He, Z.; Tan, D.; et al. Genetic Diversity of Castor Bean (Ricinus communis L.) Revealed by ISSR and RAPD Markers. Agronomy 2021, 11, 457. [Google Scholar] [CrossRef]
  52. Linos, A.; Nikoloudakis, N.; Katsiotis, A.; Hagidimitriou, M. Genetic structure of the Greek olive germplasm revealed by RAPD, ISSR and SSR markers. Sci. Hortic. 2014, 175, 33–43. [Google Scholar] [CrossRef]
  53. Asha, K.M.; Sane, A.; Kumar, R.; Biradar, M.S. Molecular characterization of chrysanthemum (Dendranthema grandiflora) genotypes using ISSR and SSR markers. Indian J. Agric. Sci. 2017, 87, 115–121. [Google Scholar]
  54. Abdollah, K.K.; Aboozar, S. Comprehensive genetic discrimination of Leonurus cardiaca populations by AFLP, ISSR, RAPD and IRAP molecular markers. Mol. Biol. Rep. 2014, 41, 4007–4016. [Google Scholar]
  55. Xing, C.; Tian, Y.; Guan, F.; Meng, F. Evaluation of genetic diversity in Amygdalus mira (Koehne) Ricker using SSR and ISSR markers. Plant Syst. Evol. 2015, 301, 1055–1064. [Google Scholar] [CrossRef]
  56. Luo, D.Q.; Qu, X.; Wang, H.Y. Analysis of the Characteristics of Vertical Microclimate in the Ecological Distribution area of Cupressus gigantea. J. Plateau Agric. 2017, 1, 22–26. [Google Scholar]
  57. Bian, X.; Yang, X.; Li, Y.; Bian, B.; Yin, J. Analysis of effect factors on the root architecture of Cupressus Gigantea (Southeast Tibet). J. Sichuan Univ. Nat. Sci. Ed. 2018, 55, 848–852. [Google Scholar]
  58. Chen, D. Research status and prospect of Cupressus gigantean in Tibet. Tibet. Sci. Technol. 1995, 2, 7–11. [Google Scholar]
Agronomy 14 00970 g001
Figure 1. Collection sites of eight populations from the Yarlung Tsangbo River of Tibet: the numbers represents the collection sites of sample; the red line represents China’s Yaya highway.
Figure 1. Collection sites of eight populations from the Yarlung Tsangbo River of Tibet: the numbers represents the collection sites of sample; the red line represents China’s Yaya highway.
Agronomy 14 00970 g001
Agronomy 14 00970 g002
Figure 2. Bands produced in all samples using the ISSR marker UBC-842.
Figure 2. Bands produced in all samples using the ISSR marker UBC-842.
Agronomy 14 00970 g002
Agronomy 14 00970 g003
Figure 3. UPGMA dendrogram of 67 samples based on Jaccard’s coefficient: Δ-samples from population 4; O-samples from population 8; □-samples from population 10; ★-samples from population 11; ▲-samples from population 12; ●-samples from population 13; ■-samples from population 14; and ☆-samples from population 15.
Figure 3. UPGMA dendrogram of 67 samples based on Jaccard’s coefficient: Δ-samples from population 4; O-samples from population 8; □-samples from population 10; ★-samples from population 11; ▲-samples from population 12; ●-samples from population 13; ■-samples from population 14; and ☆-samples from population 15.
Agronomy 14 00970 g003
Agronomy 14 00970 g004
Figure 4. Principal coordinate analysis for the ISSR data: Δ-samples from population 4; O-samples from population 8; □-samples from population 10; ★-samples from population 11; ▲-samples from population 12; ●-samples from population 13; ■-samples from population 14; and ☆-samples from population 15; red border represents Cluster IA; blue border represents Cluster IB; green border represents Cluster II.
Figure 4. Principal coordinate analysis for the ISSR data: Δ-samples from population 4; O-samples from population 8; □-samples from population 10; ★-samples from population 11; ▲-samples from population 12; ●-samples from population 13; ■-samples from population 14; and ☆-samples from population 15; red border represents Cluster IA; blue border represents Cluster IB; green border represents Cluster II.
Agronomy 14 00970 g004
Agronomy 14 00970 g005
Figure 5. Population structure for K = 3 based on the ISSR data: (A)-mean of likelihood value L (K) of each K; (B)-optimum group numbers of tested apricot samples inferred by ΔK; and (C)-genetic structure of 67 individuals analyzed with STRUCTURE (K = 3). Red, green, and blue color vertical bars represent the genotypes from different populations.
Figure 5. Population structure for K = 3 based on the ISSR data: (A)-mean of likelihood value L (K) of each K; (B)-optimum group numbers of tested apricot samples inferred by ΔK; and (C)-genetic structure of 67 individuals analyzed with STRUCTURE (K = 3). Red, green, and blue color vertical bars represent the genotypes from different populations.
Agronomy 14 00970 g005
Table 1. Information on ISSR primers among eight populations.
Table 1. Information on ISSR primers among eight populations.
Primer NamePrimer Sequence (5′-3′)TNB (n)NPB (n)PPB (%)Range of the Band Size (bp)
UBC-808(AG)8-C6583.33550~2300
UBC-824(TC)8-G88100.00700~3100
UBC-827(AC)8-G88100.00750~2400
UBC-836(AG)8-YA4250.00500~1500
UBC-841(GA)8-YC6583.33200~1200
UBC-842(GA)8-YG6466.66200~1800
UBC-847(CA)8-RC88100.00800~2400
UBC-856(AC)8-YA10990.00500~2400
UBC-857(AC)8-YG131184.62300~2600
UBC-873(GACA)49777.77300~2400
Total─78.0067.00──
Mean─7.806.7085.90─
Note: TNB, total number of bands; NPB, number of polymorphic bands; PPB (%), percentage of polymorphic bands; and R = (A,G), Y = (C,T).
Table 2. Genetic parameters revealed through ISSR for eight populations of C. gigantean.
Table 2. Genetic parameters revealed through ISSR for eight populations of C. gigantean.
Population NameNLatitude (N°)Longitude (E°)Altitude (m)NaNeHINPLsPPLs (%)
Population 4493°26′50.0″29°06′34.5″29891.385 ± 0.4901.231 ± 0.3430.137 ± 0.1880.205 ± 0.2733038.46
Population 8493°07′33.8″29°08′00.0″30671.551 ± 0.5011.381 ± 0.4100.214 ± 0.2130.315 ± 0.3024355.13
Population 10393°02′25.3″29°03′00.0″30811.372 ± 0.4861.253 ± 0.3690.145 ± 0.1990.214 ± 0.2872937.18
Population 111793°14′46.6″28°59′50.0″31051.756 ± 0.4321.489 ± 0.3810.280 ± 0.1960.413 ± 0.2735975.64
Population 121393°09′45.0″29°00′50.0″30481.615 ± 0.4901.387 ± 0.3910.222 ± 0.2070.330 ± 0.2934861.54
Population 131593°05′06.8″29°05′48.3″30381.705 ± 0.4591.443 ± 0.3840.255 ± 0.1990.379 ± 0.2805570.51
Population 14793°09′10.0″29°05′50.0″30121.513 ± 0.5031.353 ± 0.3990.199 ± 0.2130.292 ± 0.3034050.28
Population 15493°10′00.0″29°02′50.0″30241.333 ± 0.4751.250 ± 0.3900.137 ± 0.2050.199 ± 0.2912633.33
Mean 1.5291.3480.1990.29341.2552.90
N: number of samples per population, Na: observed number of alleles, Ne: effective number of alleles, H: Nei’s gene diversity, I: Shannon’s information index, NPLs: the number of polymorphic loci, PPLs: the percentage of polymorphic loci.
Table 3. AMOVA of genetic variance within and among populations based on ISSR data.
Table 3. AMOVA of genetic variance within and among populations based on ISSR data.
SourcedfMSVariance ComponentPercentage of Variation (%)Fixation Index
Among populations722.0071.58014.20.22480
Within populations599.5449.54485.8p < 0.001
Total6631.55111.124100
df, degrees of freedom.
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

Ji, X.; Jiang, Y.; Li, J.; Lei, P.; Meng, F. Genetic Diversity Assessment of Cupressus gigantea W. C. Cheng & L. K. Fu Using Inter-Simple Sequence Repeat Technique. Agronomy 2024, 14, 970. https://doi.org/10.3390/agronomy14050970

AMA Style

Ji X, Jiang Y, Li J, Lei P, Meng F. Genetic Diversity Assessment of Cupressus gigantea W. C. Cheng & L. K. Fu Using Inter-Simple Sequence Repeat Technique. Agronomy. 2024; 14(5):970. https://doi.org/10.3390/agronomy14050970

Chicago/Turabian Style

Ji, Ximei, Yaxuan Jiang, Jianxin Li, Pei Lei, and Fanjuan Meng. 2024. "Genetic Diversity Assessment of Cupressus gigantea W. C. Cheng & L. K. Fu Using Inter-Simple Sequence Repeat Technique" Agronomy 14, no. 5: 970. https://doi.org/10.3390/agronomy14050970

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