Skip to main content
NCBI home page
Plants logoLink to Plants
. 2023 Jul 17;12(14):2670. doi: 10.3390/plants12142670

Identification of the Efficacy of Ex Situ Conservation of Ammopiptanthus nanus Based on Its ETS-SSR Markers

Jingdian Liu 1,2,†, Xiyong Wang 1,3,†, Ting Lu 2, Jiancheng Wang 1,3,*, Wei Shi 1,3,*
Editors: Jana Žiarovská, Katarína Ražná
PMCID: PMC10386304  PMID: 37514284

Abstract

Ammopiptanthus possesses ancestral traits and, as a tertiary relict, is one of the surviving remnants of the ancient Mediterranean retreat and climate drought. It is also the only genus of super xerophytic, evergreen, broad-leaved shrubs. Ammopiptanthus nanus, one of the two species in this genus, is predominantly found in extremely arid and frigid environments, and is increasingly threatened with extinction. Study of the species’ genetic diversity is thus beneficial for its survival and the efficacy of ex situ conservation efforts. Based on transcriptome data, 15 pairs of effective EST-SSR were screened to evaluate A. nanus genetic diversity. In all, 87 samples from three populations were evaluated, the results of which show that ex situ conservation in the Wuqia region needs to be supplemented. Conservation and breeding of individual A. nanus offspring should be strengthened in the future to ensure their progeny continue to exhibit high genetic diversity and variation.

Keywords: Ammopiptanthus nanus, ex situ conservation, ETS-SSR

1. Introduction

Ammopiptanthus, an oligotypic legume and Tertiary relict plant of the ancient Mediterranean, is the only super xerophytic, evergreen, broad-leaved shrub endemic to desert areas in central Asia [1]. It includes two species, namely, Ammopiptanthus nanus (M. Pop.) Cheng f. and Ammopiptanthus mongolicus (Maxim. ex Kom.) Cheng f., both of which are listed in the China Plant Red Data Book as a Rare and Endangered Plant, and also listed as critically endangered in the IUCN Red List Category and Criteria [2,3]. The enormous root structure of A. nanus is physiologically and ecologically adapted to difficult environments, allowing it to live on barren mountains and the stony Gobi under significant drought conditions by preventing wind and fixing sand [4]. Temperatures in the distribution area of A. nanus frequently reach over 35 °C in the summer and −20–30 °C or lower in the winter. The process of soil humus accumulation is weak in the region, and the soil has low fertility; therefore, the sand holly has acquired traits such as early resistance, cold resistance, infertility resistance, and a developed root system [5]. According to the experience of herdsmen, running the branches of A. nanus through a camel’s nose can prevent infection [6]. Additionally, the shrub’s broad-leaved, evergreen character in desert areas as well as its thick leaves, golden yellow corolla, and short plant body lend it utility as an ornamental flower in the spring [7]. As the raw material for extracting industrial oil containing various special alkaloids and flavonoids in its stems, branches, leaves, and seeds, A. nanus has great value for research and development in the biological industries [8]. Various quinolizidines including lupanine, sparteine, and their derivatives have been isolated from the leaves of A. nanus [9]. A. nanus is a relatively primitive legume species belonging to the third generation of surviving remnant plants along the coast of the ancient Mediterranean Sea prior to climate-driven drought and retreat [10]. Thus, A. nanus also provides an important scientific basis for studying climatic characteristics, geographic effects of environmental change, plant migration, and even the origin and formation of desert vegetation in central Asia during the Tertiary period [11]. A. nanus grows in a dryer and cooler habitat than that of A. mongolicus. Therefore, the former has a shorter habit, usually with one-foliolate leaves, conspicuous leaf venation, a thicker root cortex, a more complex karyotype, and more vulnerable phyto-communities [12,13]. A. nanus is primarily found in arid and frigid environments with fragile ecologies and generally more challenging environmental conditions [14]. A. nanus has not undergone successful ex situ preservation as an outcome of climate and geographical changes and other ecological characteristics, and its distribution area is quickly diminishing.

For these reasons, it is urgent to take effective measures to protect A. nanus. There are two methods of plant conservation: in situ and ex situ. The latter is a more reasonable method for the wide distribution and objective environment of A. nanus, and may also serve as an effective research condition for move conservation, a practice which has become an important component of global biodiversity conservation initiatives [15,16]. The practice is intended for the protection of wildlife species in areas outside their native community while maintaining the normal growth and reproduction of the species in the protected area [17]. Ex situ conservation is a supplement to in situ conservation. When the original habitat of a species is destroyed due to natural or man-made causes, or the population of the species is very low, ex situ conservation serves as a more effective means to protect the species [18]. Through ex situ conservation, scientists and practitioners can better understand the morphological characteristics, systematic and evolutionary relationships, and growth and development of conservation organisms, thus providing a stronger basis for in situ conservation and monitoring of wild species and laying a foundation for their population reconstruction [19]. Several interventions involving ex situ management were anticipated to provide a >50% likelihood that the species in question would persist; however, these actions were more expensive than in situ alternatives alone. It was predicted that the anticipated benefits of ex situ measures would be negated by the greater uncertainty and randomness associated with establishing and maintaining ex situ populations [20]. Ex situ conservation that is employed naively can produce ineffective management outcomes. Thus, successful ex situ protection requires rigorous planning that precisely analyzes the objectives, management alternatives, and the likelihood of success of the conservation program.

Genetic diversity is the foundation of biological diversity and the basis for maintaining a species’ reproductive vitality, resistance to pests and diseases, and ability to adapt to environmental changes [21]. It is also important for breeding programs seeking to improve desirable traits and create new varieties. Genetic diversity lays a foundation for evolution and adaptation to the environment. The richer a population’s genetic diversity, the stronger its adaptability to environmental changes [22,23]. Genetic diversity is not only closely related to the long-term vitality and reproductive capacity of biological species [24], but is also important for the introduction and domestication of plants and gene preservation [25]. Previous research has found that the traits of A. nanus, like those of other plants, vary across different regions due to environmental factors [13]. Understanding such environmental effects of genotype–environment (GxE) interactions and associated problems and opportunities is critical for efficient breeding program design and genetic material deployment. The various consequences of (GxE) interactions determine trait phenotypes at the level of plant biological organization. The impacts of rainfall and temperature on blackcurrant fruit quality, for example, were revealed by merging metabolomics and sensory investigations and established the groundwork for selecting high-quality blackcurrant cultivars and the constructing suitable production sites [26]. In addition, GxE can be used to screen for appropriate environmental conditions in order to maximize the genetic benefits of forest tree breeding [27]. This shows there is still a lot of space for growth in this field.

SSR consists of short (generally 1–6 bp) tandem replication of nucleotide motifs and is characterized by a high degree of variation, codominant inheritance, and wide distribution. SSR is recognized as one of the best molecular markers [28], and its simple operation is a common means of analyzing molecular genetic diversity [29]. SSR markers are superior to SNP and AFLP markers for detecting polymorphism because SSR is an effective multiple-allele marker, whereas SNP and AFLP are single-allele markers [30]. The features and co-dominance of SSR markers make them the preferred DNA markers for assessing plant genetic diversity [31]. In particular, the highly conserved nature of EST-SSR makes it more accurate than G-SSR at predicting genetic differences and more generalizable across similar species [32,33].

In this study, we utilized EST-SSR markers developed from the transcriptome genes of A. nanus to assess the efficacy of ex situ conservation efforts. By investigating the differences in genetic diversity between the original population in Wuqia and an ex situ population at the molecular level, we were able to identify the impact of ex situ conservation on A. nanus. Thus, the study provides a theoretical foundation for the ex situ conservation, breeding, and inheritance of A. nanus.

2. Results

2.1. Library Quality Management, Data Sequencing and Quality Assurance

Following the preceding series of analyses and quality checks, we obtained 14.57 GB of clean data, and the proportion of Q30 bases in each sample did not fall below 92.77%. Two samples of clean data are presented in Table 1.

Table 1.

Sample sequencing data evaluation statistics of A. nanus.

Samples Read Number Base Number GC Content % ≥ Q30
A. nanus 1 23,712,117 7,072,497,166 44.94% 92.77%
A. nanus 2 25,148,183 7,496,125,488 45.26% 92.86%
Open in a new tab

1,2 Samples: sample name; Read Number: total number of pair-end Reads in clean data; Base Number: total number of bases in clean data; GC Content: clean data GC content, which refers to the ratio of G and C bases to the total number of bases in the clean data; % ≥ Q30: percentage of bases with clean data quality value greater than or equal to 30.

2.2. Transcriptome Sequencing Library Data Assembling

Qualified transcriptome sequencing libraries are essential for reliable transcriptome data analysis results [34]; the clean data from the two samples were compared with the assembled Transcript or Unigene libraries [35]. The statistical results of this comparison are presented in Table 2.

Table 2.

Comparison table of sequencing data and assembly results of A. nanus.

Samples Clean Reads Mapped Reads Mapped Ratio
A. nanus 1 23,712,117 18,438,968 77.76%
A. nanus 2 25,148,183 19,535,876 77.68%
Open in a new tab

1,2 Samples: sample name; Clean Reads: number of Clean Reads, counted on both ends; Mapped Reads: number of Mapped Reads, counted on both ends; Mapped Ratio: ratio of Mapped Reads to Clean Reads.

2.3. Transcriptome Sequencing Library Quality Assessment

To assure the quality of transcriptome sequencing libraries, these were evaluated from three distinct vantage points. The randomness of mRNA fragmentation and mRNA degradation were assessed by examining the distribution of insert fragments on Unigene. After mRNA fragmentation, the insert fragment size selection can be interpreted as the random extraction of subsequences from mRNA sequences. If the sample size (number of mRNAs) is larger, the interruption method and time control are more suitable, and the probability of each part of the target RNA being extracted is closer. That is, the randomness of mRNA fragmentation is greater, and the reads coverage on mRNA is more uniform. The degree of randomness of mRNA fragmentation was examined by simulating the results of mRNA fragmentation through the distribution of mapped reads in Unigene. Each sample’s positional distribution of mapped reads on mRNA transcripts was obtained (Figure 1).

Figure 1.

Figure 1

Open in a new tab

Positional distribution of mRNA mapped reads of A. nanus.

The horizontal axis in Figure 1 represents the mRNA position, while the vertical axis represents the percentage of reads in mapped reads within the corresponding position interval. Because the lengths of the reference mRNA varied, each mRNA was divided into 100 intervals based on its length, and the number and proportion of mapped reads in each interval was determined. During library preparation, the dispersion of insert lengths reflects the effect of gel cutting or magnetic bead purification (Figure 2). The horizontal axis represents the distance between the starting and ending points of the double-ended reads in the Unigene library, ranging from 0 to 800 bp, and the vertical axis represents the number of double-ended reads or insert fragments at various distances between the starting and ending points of the comparison.

Figure 2.

Figure 2

Open in a new tab

Simulation of insert fragment length distribution of A. nanus.

Finally, the library capacity and the sufficiency of reads (mapped reads) relative to the Unigene library was assessed by plotting the saturation. Sufficient valid data are required for accurate analysis. There is a positive correlation between the number of genes detected by transcriptome sequencing and the amount of sequencing data; the greater the quantity of sequencing data, the greater the number of genes detected. However, the number of genes in a species is limited, and gene transcription is time- and space-specific, so the number of detected genes tends to saturate as the sequencing quantity increases. To determine if the data are sufficient, it is necessary to determine if the number of newly detected genes decreases with increasing quantity of sequencing data and signaling saturation. Using mapped reads for each sample, the saturation of the number of detected genes was simulated and depicted on a graph (Figure 3).

Figure 3.

Figure 3

Open in a new tab

Simulation of transcriptome sequencing data saturation of A. nanus.

Saturation curves were generated by dividing mapped reads into 100 equal portions and progressively increasing the number of genes detected by viewing the data. The horizontal axis represents the number of reads (in 106 units), while the vertical axis represents the number of genes detected (in 103 units). If the slope gradually flattens as the curve extends to the right, it indicates that as the amount of sequencing data increases and tends to saturate with an adequate amount of valid sequencing data, each sample contains fewer and fewer newly detected genes.

2.4. Annotation of Unigene Function

Unigene Orthology results were obtained using the BLAST [36] software to compare Unigene sequences with the NR [37], Swiss-Prot [38], GO [39], COG [40], KOG [41], eggNOG4.5 [42], and KEGG [43] databases, and by obtaining KEGG in KEGG using KOBAS2.0 [44]. After predicting the amino acid sequence of Unigene, the software HMMER [45] was utilized to compare it with the Pfam [46] database in order to obtain Unigene’s annotation information.

NCBI’s NR database is a non-redundant protein database that contains the Swiss-Prot, PIR (Protein Information Resource), PRF (Protein Research Foundation), and PDB (Protein Data Bank) protein databases, as well as translated CDS data from GenBank and RefSeq. The EBI (European Bioinformatics Institute) maintains the Swiss-Prot database, which contains a highly reliable database of annotated protein information with relevant references and revision. The GO (Gene Ontology) database is a standardized international gene function classification system that offers a dynamically updated standard vocabulary for describing the functional properties of genes and gene products in an organism. The Molecular Function, Cellular Component, and Biological Process categories of the Gene Ontology (GO) database each describe the molecular functions that a gene product may execute, as well as the cellular environment and biological processes in which it is involved. The COG (Clusters of Orthologous Groups) database is a homology classification database for gene products and an early database for identifying direct homologous genes, based on extensive comparisons of protein sequences from numerous organisms. The KOG (euKaryotic Orthologous Groups) database is intended for eukaryotes and classifies homologous genes from various species into different orthologous clusters based on direct homology of genes combined with evolutionary relationships; KOG has 4852 classifications at present. Genes from the same orthologous clusters have the same function, allowing functional annotations to be directly inherited by other KOG cluster members. The eggNOG (v4.5) database contains functional descriptions and functional classifications of immediate homologous proteins, combining COG, KOG, and many more proteins; it covers significantly more protein sequences than COG and KOG, and it includes 5228 viral proteins. The KEGG (Kyoto Encyclopedia of Genes and Genomes) database is a database that comprehensively analyzes the metabolic pathways and functions of gene products in cells. It includes metabolic pathways (PATHWAY), pharmaceuticals (DRUG), diseases (DISEASE), gene sequences (GENES), and genomes (GENOME). Use of this database facilitates the investigation of gene and expression data as a holistic network.

The Pfam (Protein family) database is the most comprehensive classification system for annotating protein structural domains by constructing HMM statistical models from the amino acid sequences of each family via protein sequence alignment. Proteins are composed of one or more structural domains, which are functional regions, and exhibit sequence conservation. The functions of proteins can be predicted based on the sequence of structural domains. Selecting a BLAST parameter E-value that was ≤1 × 10−5 and an HMMER parameter E-value that was ≤10−10 yielded a total of 39,437 Unigene sequences with annotation information for this research (Table 3).

Table 3.

Comparison table of sequencing data and assembly results.

Anno Database Annotated Number 300 ≤ Length < 1000 Length ≥ 1000
Nr Annotation 37,530 13,665 16,236
Swissprot Annotation 21,475 7073 11,007
GO Annotation 23,157 7830 10,705
COG Annotation 9093 2285 5664
KOG Annotation 21,174 7081 9841
eggnog Annotation 34,853 12,261 15,588
KEGG Annotation 11,687 3901 5707
Pfam Annotation 21,979 6245 12,883
Nr Annotation 37,530 13,665 16,236
Open in a new tab

2.5. EST-SSR the Select of Primers

A. nanus gene sequences were screened using MISA (Table 4). In a transcriptome genome of approximately 42.52 Mb, there were 11,645 SSR marker repeats with a total length of 169,920 bp. The frequency of SSR markers in the gene sequence was 273.88 SSRs/Mb, with a density of 3996.48 bp/Mb, and the transcriptome genome’s total SSR content was 0.4%.

Table 4.

Overview of SSRs in the A. nanus transcriptome genome.

Items Numbers
Total size of transcriptome genome (Mb) 42.52
Total number of identified SSRs 11,645
Total length of SSRs (bp) 169,920
Frequency (SSRs/Mb) 273.88
Density (bp/Mb) 3996.48
Total content of transcriptome genome SSRs (%) 0.40
Open in a new tab

Figure 4 summarizes the results, classified by length of the SSR loci. All loci ranged in length from 10 to 42 bp, with an average of 14.59 bp. Overall, 10 bp was the most common repeating unit, occurring 1945 times. It was followed by 15 bp, 12 bp, and 18 bp, with respective frequencies of 1634, 1447, and 1391.

Figure 4.

Figure 4

Open in a new tab

Length distribution of SSRs in the A. nanus transcriptome genome.

The statistical analysis of the number of repeats of SSR loci (Figure 5) showed that all repeats fell within the range of 5 to 24. The maximum proportion of duplicates among them was 10, accounting for 18.78% of the total. This was followed by 5, 6, and 11 times, representing 13.39%, 12.52%, and 10.57% of the duplicates, respectively.

Figure 5.

Figure 5

Open in a new tab

Distribution of repeat numbers of SSRs in the A. nanus transcriptome genome.

The repetitive unit classes of SSR loci are summarized in Table 5. The total length of the SSR sequence was 169,920 bp, with an average length of 14.59 bp. The most prevalent nucleotide type is the single-nucleotide repeat (57.8%), with an average length of 13.17 bp and a total length of 88,629 bp. The remaining sequences are trinucleotide repeats (20.4%), dinucleotide repeats (19.16%), tetranucleotide repeats (2.15%), and pentanucleotide repeats (0.28%), with lengths of 39,996 bp, 34,494 bp, 5088 bp, and 858 bp, respectively, and average lengths of 16.84 bp, 15.46 bp, 20.35 bp, 25.91 bp, and 34.32 bp, respectively. The SSR sequences contained a total of 238 nucleotide repeats; Table 6 lists the principal SSR sequence repeat units and the corresponding numbers of repeats.

Table 5.

SSR markers analysis of A. nanus.

Repeat Type Predominant Type Number Proportion (%) Frequency (SSRs/Mb) Total Length (bp) Average Length (bp)
Mono A/T 6731 57.80 158.31 88,629 13.17
Di AG/TC 2231 19.16 52.47 34,494 15.46
Tri GAA/AAT 2375 20.40 55.86 39,996 16.84
Tetra AAAT/ATTT 250 2.15 5.88 5088 20.35
Penta - 33 0.28 0.78 855 25.91
Hexa - 25 0.21 0.59 858 34.32
Total 11,645 100 273.89 169,920 14.59
Open in a new tab

Table 6.

Main motif of A. nanus transcriptome genome SSRs.

The Motif of Repeat Repeat Numbers Total Percentage (%)
5 6 7 8 9 >9
A/T 0 0 0 0 0 6505 6505 55.86
AG/TC 0 236 131 142 117 109 735 6.31
AT/CT 0 163 121 102 103 97 586 5.03
TA/GA 0 168 83 113 103 91 558 4.79
AAT/GAA 127 71 37 2 0 0 237 2.04
AGA/CTT 90 66 37 0 0 0 193 1.66
ATT/TTC 97 56 29 1 0 0 183 1.57
AAG/TCT 78 70 19 0 0 0 167 1.43
ATA/TTA 73 46 23 3 0 0 145 1.25
TAT/GTT 68 44 23 0 0 0 135 1.16
TTG/CAA 72 31 13 0 1 0 117 1.00
ACA/TCA 51 38 14 0 0 0 103 0.88
AAAT/ATTT 18 0 0 0 0 0 18 0.15
AGAA/AGAT 13 1 0 0 0 0 14 0.12
TATT/TCAT 11 1 0 0 0 0 12 0.10
AAGG/ATAA 9 2 0 0 0 0 11 0.09
Total 707 993 530 363 324 6,802 9,719 83.46
Percentage (%) 6.07 8.53 4.55 3.12 2.78 58.41 83.46
Open in a new tab

The loci results for A. nanus are depicted in Figure 6, where the vertical axis represents the intensity of fluorescence, and the horizontal axis represents the size of SSR product fragments.

Figure 6.

Figure 6

Open in a new tab

Partial genotyping peak plots of polymorphic SSR primers for the representative samples of A. nanus.

2.6. Genetic Diversity Analysis of A. nanus

Table 7 shows that the total genetic diversity (Ht) of the 15 primer pairs detected in 87 samples ranged from 0.691 to 0.904, with a mean value of 0.83. The mean observed heterozygosity (Mean Ho) ranged from 0 to 0.55, with a mean value of 0.25. The mean expected heterozygosity (Mean He) ranged from 0.218 to 0.716, with a mean value of 0.47. The ranges of the intra-population inbreeding coefficient (Fis),total population inbreeding coefficient (Fit), and genetic differentiation coefficient (Fst) are −0.021 to 1, 0.326 to 1, and 0.208 to 0.733, respectively. Gene flow (Nm) ranged from 0.091 to 0.951, with a mean value of 0.43. Values of Nm which are <1 indicate that differentiation between populations may occur due to genetic drift, and that there is a low level of gene flow among most populations. As shown in Table 7, F6 is the most efficient primer, followed by F3 and F1. F2 produces fewer effective results.

Table 7.

Genetic diversity of the 15 SSRs primers.

Locus Ht Mean He Mean Ho Fis Fit Fst Nm
F1 0.899 0.698 0.282 0.595 0.686 0.224 0.868
F2 0.854 0.228 0.000 1.000 1.000 0.733 0.091
F3 0.902 0.713 0.265 0.628 0.706 0.210 0.942
F4 0.816 0.447 0.167 0.627 0.796 0.452 0.303
F5 0.815 0.581 0.550 0.055 0.326 0.287 0.622
F6 0.904 0.716 0.379 0.471 0.581 0.208 0.951
F7 0.772 0.315 0.292 0.073 0.621 0.592 0.173
F8 0.806 0.419 0.074 0.823 0.908 0.481 0.270
F9 0.721 0.218 0.222 −0.021 0.692 0.698 0.108
F10 0.691 0.292 0.072 0.752 0.895 0.578 0.182
F11 0.861 0.250 0.218 0.129 0.747 0.710 0.102
F12 0.818 0.481 0.098 0.797 0.881 0.412 0.357
F13 0.832 0.497 0.476 0.042 0.428 0.403 0.370
F14 0.821 0.462 0.203 0.562 0.753 0.437 0.323
F15 0.889 0.670 0.515 0.231 0.420 0.246 0.764
Mean 0.83 0.47 0.25 0.45 0.70 0.44 0.43
Open in a new tab

At the populations’ level, 15 pairs of SSR primers revealed the population genetic diversity of a total of 87 samples from the three populations (Table 8). The number of alleles (Na) ranged from 3.6 to 6.333, containing a total of 14.93, with a mean value of 4.978; the effective number of alleles (Ne) ranged from 2.429 to 2.714, with a mean value of 2.58; the Shannon’s information index (I) ranged from 0.827 to 1.121, with a mean value of 0.969; the observed heterozygosity (Ho) ranged from 0.231 to 0.29, with a mean value of 0.254; expected heterozygosity (He) ranged from 0.409 to 0.535 with a mean value of 0.466; genetic diversity parameters of the populations (PPB) ranged from 0.7333 to 0.9333, with a mean value of 82.22% and the highest, at 93.33%, in the Wuqia region. This indicates that the top priority for conservation of A. nanus genetic diversity is in the Wuqia region.

Table 8.

Genetic diversity parameters of A. nanus populations.

Pop N Na Ne I Ho He uHe F PPB
Wuqia 31.867 6.333 2.598 1.121 0.29 0.535 0.552 0.514 0.9333
Ex situ 16.667 5 2.714 0.957 0.231 0.453 0.476 0.437 0.8
Kyrgyzstan 7.533 3.6 2.429 0.827 0.242 0.409 0.449 0.456 0.7333
Mean 18.689 4.978 2.58 0.969 0.254 0.466 0.492 0.472 0.8222
Total 56.067 14.933 7.741 2.905 0.763 1.397 1.477 1.407 2.4666
Open in a new tab

The genetic distance and genetic differentiation coefficient (FST) are important parameters for evaluating the degree of genetic difference and similarity among A. nanus populations. These were calculated among different populations of A. nanus based on Nei’s genetic distances using the GenAlEx software (Table 9). The genetic distances between populations of A. nanus ranged from 2.659 to 4.9, with the largest genetic distances between the Wuqia and Kyrgyzstan populations and the smallest between the ex situ and Kyrgyzstan populations. The genetic differentiation coefficients between populations ranged from 0.352 to 0.431, with the largest between the Wuqia and Kyrgyzstan populations and the smallest between the Wuqia and Kyrgyzstan populations. The genetic differentiation coefficient between populations ranged from 0.352 to 0.431, with the Wuqia and Kyrgyzstan populations having the largest genetic differentiation coefficient and the Wuqia and ex situ populations having the smallest genetic distance.

Table 9.

Genetic distances and genetic differentiation coefficients of A. nanus populations.

Nei Genetic Distance Wuqia Ex Situ Kyrgyzstan FST Values Wuqia Ex Situ Kyrgyzstan
Wuqia 0.000 Wuqia 0.000
Ex situ 4.299 0.000 Ex situ 0.352 0.000
Kyrgyzstan 4.909 2.659 0.000 Kyrgyzstan 0.405 0.431 0.000
Open in a new tab

Molecular ANOVA of the genetic variation within and between populations of A. nanus, as shown in Table 10, indicates that 29% of the genetic variation in the 87 A. nanus germplasms collected from the three populations originated between populations, while nearly 71% of the genetic variation originated within populations (Figure 7). The population genetic structure was analyzed using Structure Harvester, and the mean value corresponding to each K value was calculated (Figure 8). The maximal inflection point occurred when K = 3, indicating that the population genetic structure had a theoretical population size of 3.

Table 10.

Analysis of molecular variance (ANOVA) of genetic variation within and among groups of A. nanus.

Source DF SS MS Est. Var. Proportion
Among Pops 2 211.259 105.629 1.956 29%
Among Indiv 84 707.678 8.425 3.583 53%
Within Indiv 87 109.5 1.259 1.259 18%
Total 173 1028.437 6.797 1
Open in a new tab

Figure 7.

Figure 7

Open in a new tab

The percentage of molecular variance in genetic variation within and between A. nanus populations.

Figure 8.

Figure 8

Open in a new tab

Genetic structure analysis of A. nanus based on Bayesian model.

Visual coordinates and primary coordinate analysis were utilized to depict data similarity or dissimilarity for study sample populations. The data from 87 samples were displayed and processed to create PCoA scatter plots (Figure 9).

Figure 9.

Figure 9

Open in a new tab

Principal coordinate analysis of three groups of A. nanus based on SSR genetic distance. The different colors and shapes represent different study populations: blue diamonds for the Wuqia population, red squares for the ex situ population, and green triangles for the Kyrgyzstan population. The axis labels refer to the percentage of explained variance for each principal coordinate. The different numbers for each sample are listed in Table 11.

The results show that the three populations of A. nanus still belong to the same species but are extremely differentiated due to different natural conditions. There is substantial genetic variation both within and between populations, and within-population variation is greater. The high degree of genetic divergence across populations may be the result of genetic drift or the combined influence of natural selection and genetic differentiation induced by gene flow between places.

3. Discussion

Ex situ conservation conserves a species germplasm through artificial breeding in order to increase population sizes and prevent extinction in the wild [47]. There has been extensive work conducted in this area through methods like artificial seeding and propagation in botanical gardens and natural environments. However, these efforts can be expensive, and their efficacy has not been confirmed [48]. Inbreeding can lead to decreased fitness and thus endanger the survival of small populations [49] through what is known as inbreeding depression [50]. Genetically, this phenomenon may occur because favorable alleles at one locus are typically dominant, allowing rare and recessive deleterious alleles at the same locus to persist in a population [51]. The increased purity caused by inbreeding increases the likelihood of deleterious alleles occurring as pure heterozygotes. When this occurs, the effects of deleterious alleles cannot be concealed by dominant favorable alleles, resulting in inbreeding depression [52]. It appears that dominance is the most prevalent cause of inbreeding depression [53]. This has the potential to accelerate the extinction of A. nanus [54].

Due to the present scarcity of A. nanus populations, it is crucial to avoid inbreeding. Purifying deleterious alleles is one effective method for avoiding inbreeding depression [55]. Inbreeding increases the purity of alleles and, consequently, the likelihood that the associated deleterious traits will manifest. This may result in the elimination of deleterious alleles from congenic populations [56]. However, in extremely tiny populations, the effect of removal is also quite limited [57]. There are few direct indications from natural populations that purification can eradicate inbreeding depression [58]. Studies have shown that deleterious alleles are eliminated over time [59]. In the past, it has been proposed to eliminate deleterious alleles from small populations through artificial inbreeding. Nevertheless, many biologists believe the associated hazards outweigh the potential benefits [60]. SSR markers are valuable instruments for analyzing genetic diversity and genetic structure due to their high information density, homology, and multiple alleles [61]. Our predecessors conducted a genomic survey and SSR analysis of A. nanus homologs of A. mongolicus [62]. Several molecular investigations of A. nanus have also been conducted, including chloroplast genome comparisons, phylogenetic analyses [63], and RAPD analyses of genetic diversity [64]. In addition, it is possible to derive numerous specific markers associated with specific resistance traits, which provides a clearer indication of the level of kinship and genetic diversity among sampled materials [65]. SSR not only makes it possible to identify and evaluate the efficacy of ex situ conservation of A. nanus, but it also provides a valuable tool for future population genetics research and conservation initiatives.

In the Wuqia region, there are only a few populations and individuals of A. nanus, but their descendants exhibit a high level of genetic diversity and progeny variation. From the results of this study, they can thus be divided into two conventional protected areas, with the Kyrgyz subspecies in its own protected area. This study indicates that A. nanus has a high level of individual genetic diversity, so both in situ and ex situ conservation should be implemented for this population along with abundant genetic variation and rare alleles. A. nanus must also be conserved in situ to assure genetic diversity, as is the case with species that are widely distributed but rapidly declining [66]. The results of this investigation indicate that the ex situ conservation of Wuqia germplasms requires strengthening. Significant investments in protected areas such as the Kyrgyz area are required to reduce the risk of extinction. However, A. nanus has a very limited range, as evidenced by its likelihood of genetic isolation, genetic drift, and absence of genetic mutations [67]. Additionally, it is common for plants to decline due to hard inbreeding, particularly for such narrowly distributed species, so the potential for genetic diversity loss is high [68]. Inbreeding may be another major threat.

SSR analysis for A. nanus alone, on the other hand, has clearly identified the distinct communities of A. nanus generated by the environment. GxE has long been regarded as a critical component in plant breeding [69]. With the advent of genomics technology, several good predictors for the genotypic dimension of GxE interactions have been presented [70,71]. The ex situ conservation did not occupy all the germplasm resources, especially lacking the genotypes in Kyz. However, we possess the fundamental theory and methodology to improve the conservational efficiency of future work using more clarified evidence on GxE of A. nanus. Due to habitat devastation in the wild, ex situ conservation is an extremely effective conservation measure for species that are critically endangered. Establishing botanical gardens is also among the most effective methods for ex situ conservation of endangered species such as A. nanus in TEBG [72]. Nonetheless, the genetic diversity of ex situ plants in botanical gardens has been largely ignored or inadequately evaluated [73].

Given the increasing threats to genetic diversity posed by inbreeding and environmental change, genetic characterization is necessary to prevent the decline of these new populations and maximize their evolutionary potential [74]. While population genetic data are rarely incorporated into species assessments and management plans, this does not mean that conservationists are unwilling to incorporate genetics into their practices [75]. This suggests that, while researchers are most likely to conduct conservation genetics research, they are often unable to link policy and conservation outcomes in practice [76,77]. With more frequent collaboration between researchers and governments, there may be a larger role for genetics in future conservation programs. In this research, 87 samples of A. nanus from three populations were analyzed using ETS-SSR molecular markers to evaluate the efficacy of ex situ conservation and support further conservation and artificial breeding of this germplasm resource.

A. nanus remains on the verge of extinction. For comprehensive conservation of this germplasm resource, it is necessary to focus on the genetic diversity of ex situ plant offspring as well as the quantitative conservation of individuals. Doing so will ensure the reproductive renewal of this extremely small population. Additionally, it is important to strengthen government collaboration with botanical gardens by incorporating the results of research into resource-management policy. Effective cooperation will have a positive influence on the conservation and utilization of the unique genes of A. nanus. Future research and investment need to be increased to demonstrate the value of A. nanus for adapting to extreme environments.

Given the distributional characteristics of A. nanus, we make the following recommendations for its conservation in the future:

  1. The government should intervene to increase investment in in situ conservation and broadcast relevant policies. Seeds of different populations in A. nanus with high genetic diversity should be collected for propagation in order to obtain the maximum available genetic diversity.

  2. Plants from different populations should be introduced for appropriate crosses to increase the level of biodiversity of A. nanus. Further research on the reproductive biology of A. nanus is required to ensure the effective conservation of the species.

4. Materials and Methods

4.1. RNA Sample Detection and RNA Library Development

At the beginning of the study, one sample of A. nanus was collected from the Turpan Eremophytes Botanic Garden (CAS) (TEBG, 42.3° N 89.2° E) for transcriptome sequencing. The Nanodrop [78], Qubit 2.0 [79], and Aglient 2100 [80] methods were used to verify the purity, concentration, and integrity of the RNA samples to ensure that sufficiently high-quality samples were used for transcriptome sequencing.

After testing and qualifying the samples, the library was constructed according to the following procedures. First, eukaryotic mRNA was enriched with oligo (dT)-coated magnetic particles [81]. Fragmentation buffer was then added to interrupt the mRNA in a random manner. The first cDNA strand was then synthesized using mRNA as a template and six-base random hexamers, followed by the addition of buffer, dNTPs, RNase H, and DNA polymerase I to synthesize the second cDNA strand [82]. cDNA was purified with the aid of AMPure XP particles. Then, end repair, A-tail addition, and sequencing junction ligation were performed. The fragment size was then selected using AMPure XP beads [83]. Finally, the cDNA library was enriched via PCR.

4.2. Library Quality Management, Data Sequencing and Quality Assurance

After library construction, the library concentration and insert size were analyzed using Qubit 2.0 and Agilent 2100, respectively, and the effective library concentration was quantified using the Q-PCR method [84]. After ensuring qualification, Illumina Hiseq high-throughput sequencing was performed [82].

Based on sequencing-by-synthesis (SBS) technology, the cDNA libraries were sequenced using the Illumina HiSeq high-throughput sequencing platform, which can generate a large number of high-quality reads [85]. These reads or bases generated by the sequencing platform are referred to as raw data, and the majority of their base quality scores can meet or exceed Q30 (99.9% identification accuracy).

Before continuing with the subsequent analysis, it was necessary to ensure that the reads used were of sufficient quality to ensure the accuracy of sequence assembly and the subsequent analysis. In addition, there were very few reads containing sequencing primers, connectors, and other artificial sequences in the raw data, so it was necessary to truncate the sequencing connectors and primer sequences in the reads and filter the low-quality data to ensure data quality [86].

4.3. Transcriptome Sequencing Data Compilation

After obtaining high-quality sequencing data, the sequence was assembled. Trinity [87] is a transcriptome sequencing assembly software designed for high-throughput sequencing. The sequencing depth of a transcript is influenced by the quantity of sequencing data and other factors, as well as the abundance of the transcript’s expression. The sequencing depth has a direct impact on the quality of the assembly [88].

In the beginning, the k-mer library was created by breaking the sequencing reads into shorter k-mers using the Trinity software and removing any k-mers that contained errors. Next, the k-mer with the highest frequency was chosen as the seed for greedy extension at both ends, and the process was repeated until the k-mer library was exhausted [89]. Next, the contigs were clustered to produce components, the De Bruijn graph was constructed for each component, and the nodes and edges were merged and trimmed to simplify the De Bruijn graph [90]. Finally, the De Bruijn graph [91] was deconstructed using the actual read in order to identify the transcribed sequences in each fragment collection independently.

4.4. Plant Materials in Populations

The ETS-SSR markers in plants were used to assess the efficacy of ex situ A. nanus conservation efforts. Plant samples were collected from Wuqia (50) and an ex situ reserve (24), as well as Kyrgyzstan (13), to identify genetic diversity in different regions and protect germplasm resources (Table 11).

Table 11.

Population information of A. nanus.

Pop Number Latitude Longitude Elevation
Wuqia 1~50 39.7° N 75.01° E 2181 m
Ex situ 51~74 39.72° N 75.27° N 2178 m
Kyrgyzstan 75~87 41.71° N 74.34° E 1698 m
Open in a new tab

4.5. DNA Extraction

Sampled plant materials were stored in liquid nitrogen following field collection. After transport to the laboratory, about 0.25 g fresh leaves was ground into a powder in liquid nitrogen. Next, the CTAB method was used to extract their DNA following standard procedure. The concentration was measured by spectrophotometry, diluted to about 0.10–0.25 mg/µL, and refrigerated at −20 °C for later use [90].

4.6. PCR Amplification and EST-SSR Primers Selection

The primers and DNA template described above were used for PCR reaction. The PCR reaction system was 25 µL, as follows: ddH2O 9.5 µL, Mix12.5 µL, primers at both ends 1 µL, and DNA1 µL. The PCR reaction process was as follows: denaturing at 94 °C for 2 min; 30 cycles at 94 °C for 1 min, annealing temperature 53–56 °C for 1 min and 72 °C for 2 min; and 72 °C for 5 min. The PCR products were subjected to 1% agarose gel electrophoresis using an electrophoresis system running the buffer solution of 1 × Tris EDTA (TE), with a constant voltage of 130 V and an electrophoresis time of 15 min. Successful amplifications (with the clear band) were sent to Shanghai SBS, Biotech Ltd. for analysis. Then, 15 pairs (Table 12) of 2–5 base SSR sequences with 3–4 repeats (about 22 bases) were selected for primer synthesis by a bio-company (Sangon, Shanghai, China) with 5′ fringe HEX fluorescence labeling.

Table 12.

EST-SSR primers sequence information of A. nanus.

SSR Primer Pair SSR Primer Sequence (5′-3′) Fluorescence Tag
c11315 F:ACTGCACCTGTCCAGATTCC 5′HEX
R:TTGATTCGCTGTCTCCCTCT
c23139 F:AGCATGCATGTGCCTTTTTA 5′HEX
R:ATCGGGAGAGCAACAGTACG
c23483 F:AACGCGTCCCATTCTCTCT 5′HEX
R:TCATCTTTCAAAAGGGGCAC
c23540 F:GATCAGCTTCTCACCCGAAG 5′HEX
R:TCTGGGTCTCTTGGCCTCTA
c24361 F:AATGCATGATTGATTGTCACTG 5′HEX
R:TTGCGGACTGCAACAATTAG
c24982 F:CCGTCTTGCAGGTTTCAAGT 5′HEX
R:TTACCGGCCATAGATTGAGG
c32449 F:TCAATCTTCGTAGCTTTCCCA 5′HEX
R:GCTCCAAAGTCCACCACATT
c34776 F:CCACTTTGGCTCATTGCTTT 5′HEX
R:CTGAAGGGAGTGTGGCATTT
c34960 F:GACCCATCAAAACATGACCC 5′HEX
R:GGACCTCAGGATGGTCAAGA
c35322 F:TTGTTACAAAAGCCGATCCC 5′HEX
R:TAGGAATGACTCCCACTGCC
c44083 F:TGAAGAGACGCAATGGTGAG 5′HEX
R:ATGCCCAACTTGGATAGTGC
c44298 F:AGCTGTCTTCCCAGTACCGA 5′HEX
R:ATGGTGAGCACAACCATGAA
c44674 F:TAATTGACCTCGAACCTCCG 5′HEX
R:TGGAGGAGAAGGGGGTTACT
c52855 F:GAAAGGTTGGGGATTTTGGT 5′HEX
R:AGTGGGTGAGGGTGAAACTG
c52997 F:CAATATCCAAAGGTGGGGTG 5′HEX
R:GCTACGTCTCGTGCTAGCCT
Open in a new tab

4.7. Statistical Analyses

MISA [92] is a Perl-based script that identifies SSRs from FASTA sequences and has become the tool of choice for most EST-SSR researchers due to its ease of use, lack of networking, minimal hardware requirements, and compatibility with multiple tools.

GeneMarker [93] was used to analyze the original data obtained using high-performance capillary electrophoresis (HPCE). GenAlEx [94] was used to calculate the population genetic parameters of each primer pair, including the number of alleles per locus (Na), observed heterozygosity (Ho), expected heterozygosity (He), effective number of alleles (Ne), and information index and percentage of polymorphic sites (PPB) values. Principal component analysis (PCoA) was also performed in GenAlEx. Additionally, an analysis of molecular variance (AMOVA) function was adhibited in GenAlEx to measure ex situ conservational efficacy for A. nanus within and among populations, and within and among groups.

In total, 999 random permutations were implemented in GenAlEx for each sample. The genetic structures of three (Wuqia, Kyrgyzstan, and ex situ reserve) and two (Wuqia, Kyrgyzstan, no ex situ reserve) natural populations of A. nanus were analyzed based on Bayesian analysis using the STRUCTURE software (version 2.3.4) [95]. Ten independent runs were performed for each K value. Then, results were submitted to the online software Structure Harvester to analyze the optimal K value, which was determined by the method described by the burn-in period iterations and Markov chain Monte Carlo repetitions. Finally, Core Finder was used to calculate the size of individual protected germplasm resources.

Acknowledgments

We thank Xinyu Wu (Xinjiang Academy of Agricultural Sciences) for provided some of the experimental materials.

Author Contributions

Formal analysis, T.L.; Investigation, X.W.; Resources, J.W.; Writing—original draft, J.L.; Writing—review & editing, W.S. All authors edited and pro-vided a critical review of the manuscript. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research was funded by Strategic Biological Resources Capacity Building Project (KFJ-BRP-017-72) and The Second Comprehensive Scientific Study of the Tibetan Plateau: Survey Evaluation of Saussurea involucrata and Ammopiptanthus nanus (2019QZKK05020207).

Footnotes

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.

References

  • 1.Fu L. The Red Book of Chinese Plants: Rare and Endangered Plants. Volume 1. Science Press; Beijing, China: 1992. p. 256. (In Chinese) [Google Scholar]
  • 2.Shi W., Su Z., Pan B., Wu S. Using DNA data to determine the taxonomic status of Ammopiptanthus kamelinii in Kyrgyzstan (Thermopsideae, Leguminosae) Phytotaxa. 2018;360:103–113. doi: 10.11646/phytotaxa.360.2.2. [DOI] [Google Scholar]
  • 3.Hao W., Zhou X., Wang H., Liu Y. Research progress on rare and endangered plant Ammopiptanthus nanus. Plant Sci. J. 2019;37:109–115. [Google Scholar]
  • 4.Zhou X., Yuan L. A preliminary investigation on the cultivation technology and garden application of Ammopiptanthus nanus introduction. J. Tarim Univ. 2016;28:88–94. (In Chinese with English Abstract) [Google Scholar]
  • 5.Liu M., Wu S., Pan B., Wang D. Geographical distribution and habitat characteristics of Ammopiptanthus. in China. Geogr. Arid Reg. 2017;40:380–387. [Google Scholar]
  • 6.Li C., Yu Q., Li A. Progress and Prospects of Research on the Solanaceous Plant Ammopiptanthus. Chin. Wild Plant Resour. 2004;5:21–23. (In Chinese with English Abstract) [Google Scholar]
  • 7.Yin L.K., Wang Y. Biological Characterization of Flowering in Ammopiptanthus. Chin. Bull. Bot. 1993;2:54–56. (In Chinese with English Abstract) [Google Scholar]
  • 8.Liu Y. Ph.D. Thesis. Huazhong Agricultural University; Wuhan, China: 2020. Transcriptomics-based response to drought stress in Ammopiptanthus nanus seedling and functional studies of AnEXPA1 and AnEXPA2. (In Chinese with English Abstract) [Google Scholar]
  • 9.Feng W. Master’s Thesis. Xinjiang Normal University; Ürümqi, China: 2011. Studies on the chemical composition of Ammopiptanthus. (In Chinese with English Abstract) [Google Scholar]
  • 10.Wang Y., He Q., Wang X., Xu M., Mishima S., Wang X., Yu L., Feng X., Ando Y., Huang S., et al. Research on the growth and renewal status of natural Ammopiptanthus and prospects for its utilization. Ningxia Agric. For. Sci. Technol. 2004;3:28–31. (In Chinese with English Abstract) [Google Scholar]
  • 11.Si S., Li X., Long L. Plant Diversity and its Conservation Countermeasures in the Desertified Areas of Northern China. J. Desert Res. 1999;19:272–277. [Google Scholar]
  • 12.Shi W., Pan B., Zhang Q. Comparison of the content of 15 inorganic elements in leaves of Saxifraga saxifraga in Xinjiang and Saxifraga saxifraga in Mongolia and their habitat soils. J. Appl. Environ. Biol. 2009;15:660–665. [Google Scholar]
  • 13.Shi W., Su Z., Liu P., Pan B., Zhao Y., Wang J. Molecular, Karyotypic, and Morphological Evidence for Ammopiptanthus (Fabaceae) Taxonomy1. Ann. Mo. Bot. Gard. 2017;102:559–573. doi: 10.3417/2016029. [DOI] [Google Scholar]
  • 14.Wang Z., Wang J. Research progress on the causes of endangered dwarf sand holly and conservation strategies. Mod. Agric. Sci. Technol. 2016;675:187–190. (In Chinese with English Abstract) [Google Scholar]
  • 15.Witzenberger K.A., Hochkirch A. Ex-situ conservation genetics: A review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 2011;20:1843–1861. doi: 10.1007/s10531-011-0074-4. [DOI] [Google Scholar]
  • 16.Swarts N.D., Dixon K.W. Terrestrial orchid conservation in the age of extinction. Ann. Bot. 2009;104:543–556. doi: 10.1093/aob/mcp025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Maunder M., Byers O. The IUCH Technical Guidelines on the Management of Ex Situ Populations for Conservation: Reflecting major changes in the application of ex situ conservation. Oryx. 2005;39:95–98. doi: 10.1017/S0030605305000177. [DOI] [Google Scholar]
  • 18.Pritchard D.J., Fa J.E., Oldfield S., Harrop S.R. Bring the captive closer to the wild: Redefining the role of ex situ conservation. Oryx. 2012;46:18–23. doi: 10.1017/S0030605310001766. [DOI] [Google Scholar]
  • 19.Wei X., Jiang M. Meta-analysis of genetic representativeness of plant populations under ex situ conservation in contrast to wild source populations. Conserv. Biol. 2021;35:12–23. doi: 10.1111/cobi.13617. [DOI] [PubMed] [Google Scholar]
  • 20.Canessa S., Converse S.J., West M., Clemann N., Gillespie G., McFadden M., Silla A.J., Parris K.M., McCarthy M.A. Planning for ex situ conservation in the face of uncertainty. Conserv. Biol. 2016;30:599–609. doi: 10.1111/cobi.12613. [DOI] [PubMed] [Google Scholar]
  • 21.Xu Y., Zang R. Theoretical and practical research on conservation of Wild Plants with Extremely Small Populations in China. Biodiversity. 2022;30:84–105. doi: 10.17520/biods.2022505. [DOI] [Google Scholar]
  • 22.Shen H., Liu D. Overview of genetic diversity. Biol. 2001;3:5–7+4. (In Chinese with English Abstract) [Google Scholar]
  • 23.Zhang D.Y., Jiang X. Evolution of plant mating systems, resource allocation responses and genetic diversity. Plant Ecol. 2001;2:130–143. (In Chinese with English Abstract) [Google Scholar]
  • 24.Huang H.W., Liao J. On China’s national botanical gardens: Building a comprehensive system of ex situ conservation of national botanical gardens with task oriented disciplines. Biodiversity. 2022;30:197–213. [Google Scholar]
  • 25.Pott D.M., Duran-Soria S., Allwood J.W., Pont S., Gordon S.L., Jennings N., Austin C., Stewart D., Brennan R.M., Masny A., et al. Dissecting the impact of environment, season and genotype on blackcurrant fruit quality traits. Food Chem. 2023;1:402. doi: 10.1016/j.foodchem.2022.134360. [DOI] [PubMed] [Google Scholar]
  • 26.Li Y., Suontama M., Burdon R.D., Dungey H.S. Genotype by environment interactions in forest tree breeding: Review of methodology and perspectives on research and application. Tree Genet. Genomes. 2017;13:3. doi: 10.1007/s11295-017-1144-x. [DOI] [Google Scholar]
  • 27.Hou B., Tian M., Luo J., Ji Y., Xue Q., Ding X. Genetic diversity assessment and ex-situ conservation strategy of the endangered Dendrobium officinale (Orchidaceae) using new trinucleotide microsatellite markers. Plant Syst. Evol. 2012;298:1483–1491. doi: 10.1007/s00606-012-0651-3. [DOI] [Google Scholar]
  • 28.Sajib A.M., Hossain M.M., Mosnaz A.T.M.J., Hossain H., Islam M.M., Ali M.S., Prodhan S.H. SSR marker-based molecular characterization and genetic diversity analysis of aromatic landreces of rice (Oryza sativa L.) BioSci. Biotechnol. 2012;1:3251–3265. [Google Scholar]
  • 29.Boccacci P., Aramini M., Ordidge M., van Hintum T.J.L., Marinoni D.T., Valentini N., Sarraquigne J.P., Solar A., Rovira M., Bacchetta L. Comparison of selection methods for the establishment of a core collection using SSR markers for hazelnut (Corylus avellana L.) accessions from European germ plasm repositories. Tree Genet. Genomes. 2021;17:6. doi: 10.1007/s11295-021-01526-7. [DOI] [Google Scholar]
  • 30.Yin J., Zhang J. Several Common Genetic Markers Methods Applied in the Research of Genetic Evolution. Anhui Agric. Sci. 2007;35:10965–10968. [Google Scholar]
  • 31.Gao F., Li H., Xiao Z., Wei C., Feng J., Zhou Y. De novo transcriptome analysis of Ammopiptanthus nanus and its comparative analysis with A. mongolicus. Trees-Struct. Funct. 2018;32:287–300. doi: 10.1007/s00468-017-1631-6. [DOI] [Google Scholar]
  • 32.Garcia A.A.F., Benchimol L.L., Barbosa A.M.M., Geraldi I.O., Souza Jr C.L., Souza A.P.D. Comparison of RAPD, RFLP, AFLP and SSR markers for diversity studies in tropical maize inbred lines. Genet. Mol. Biol. 2004;27:579–588. doi: 10.1590/S1415-47572004000400019. [DOI] [Google Scholar]
  • 33.Song Y., Jiang X., Zhang M., Wang Z., Bo W., An X., Zhang D., Zhang Z. Differences of EST-SSR and genomic-SSR markers in assessing genetic diversity in poplar. For. Stud. China. 2012;14:1–7. doi: 10.1007/s11632-012-0106-5. [DOI] [Google Scholar]
  • 34.Croucher N.J., Fookes M.C., Perkins T.T., Turner D.J., Marguerat S.B., Keane T., Quail M.A., He M., Assefa S., Bahler J., et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic Acids Res. 2009;37:e148. doi: 10.1093/nar/gkp811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Casel P., Moreews F., Lagarrigue S., Klopp C. sigReannot: An oligo-set re-annotation pipeline based on similarities with the Ensembl transcripts and Unigene clusters. BMC Proc. 2009;3:S3. doi: 10.1186/1753-6561-3-S4-S3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI BLAST. A New Generation of Protein Database Search Programs. Nucleic Acids Res. Ital. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Deng Y.Y., Li J.Q., Wu S.F., Zhu Y.P., Chen Y.W., He F.C. Integrated nr Database in Protein Annotation System and Its Localization. Comput. Eng. Ital. 2006;32:71–74. [Google Scholar]
  • 38.Apweiler R., Bairoch A., Wu C.H., Barker W.C., Boeckmann B., Ferro S., Gasteiger E., Huang H., Lopez R., Magrane M., et al. UniProt: The Universal Protein knowledgebase. Nucleic Acids Res. Ital. 2004;32:D115–D119. doi: 10.1093/nar/gkh131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J., Davis A., Dolinski K., Dwight S., Eppig J., et al. Gene ontology: Tool for the unification of biology. Nat. Genet. Ital. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Tatusov R.L., Galperin M.Y., Natale D.A. The COG database: A tool for genome scale analysis of protein functions and evolution. Nucleic Acids Res. Ital. 2000;28:33–36. doi: 10.1093/nar/28.1.33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Koonin E.V., Fedorova N.D., Jackson J.D., Jacobs A., Krylov D., Makarova K., Mazumder R., Mekhedov S., Nikolskaya A., Rao B., et al. A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes. Genome Biol. Ital. 2004;5:R7. doi: 10.1186/gb-2004-5-2-r7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Huerta-Cepas J., Szklarczyk D., Forslund K., Cook H., Heller D., Walter M.C., Rattei T., Mende D.R., Sunagawa S., Kuhn M., et al. eggNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–D293. doi: 10.1093/nar/gkv1248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Kanehisa M., Goto S., Kawashima S., Okuno Y., Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. Ital. 2004;32:D277–D280. doi: 10.1093/nar/gkh063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Xie C., Mao X., Huang J., Ding Y., Wu J., Dong S., Kong L., Gao G., Li C., Wei L. KOBAS 2.0. A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:W316–W322. doi: 10.1093/nar/gkr483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Eddy S.R. Profile hidden Markov models. Bioinform. Ital. 1998;14:755–763. doi: 10.1093/bioinformatics/14.9.755. [DOI] [PubMed] [Google Scholar]
  • 46.Finn R.D., Bateman A., Clements J., Coggill P., Eberhardt R.Y., Eddy S.R., Heger A., Hetherington K., Holm L., Mistry J., et al. Pfam: The protein families database. Nucleic Acids Res. Ital. 2014;42:D222–D230. doi: 10.1093/nar/gkt1223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Que L., Yang G., Miao J.H., Wang H.Y., Chen M., Zang C.X. Current status and prospects of traditional Chinese medicine resource ex-situ conservation. China J. Chin. Mater. Medica. 2016;41:3703–3708. doi: 10.4268/cjcmm20162001. [DOI] [PubMed] [Google Scholar]
  • 48.Cavender N., Cavender N., Westwood M., Bechtoldt C., Donnelly G., Oldfield S., Gardner M., Rae D., McNamara W. Strengthening the conservation value of ex situ tree collections. Oryx. 2015;49:416–424. doi: 10.1017/S0030605314000866. [DOI] [Google Scholar]
  • 49.Wang J., Hill W.G., Charlesworth D., Charlesworth B. Dynamics of inbreeding depression due to deleterious mutations in small populations: Mutation parameters and inbreeding rate. Genet. Res. 1999;74:165–178. doi: 10.1017/S0016672399003900. [DOI] [PubMed] [Google Scholar]
  • 50.Charlesworth D., Willis J.H. The genetics of inbreeding depression. Nat. Rev. Genet. 2009;10:783–796. doi: 10.1038/nrg2664. [DOI] [PubMed] [Google Scholar]
  • 51.Charlesworth D., Charlesworth B. Inbreeding depression and its evolutionary consequences. Annu. Rev. Ecol. Syst. 1987;18:237–268. doi: 10.1146/annurev.es.18.110187.001321. [DOI] [Google Scholar]
  • 52.Hedrick P.W., Kalinowski S.T. Inbreeding depression in conservation biology. Annu. Rev. Ecol. Syst. 2000;31:139–162. [Google Scholar]
  • 53.Charlesworth B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 2009;10:195–205. doi: 10.1038/nrg2526. [DOI] [PubMed] [Google Scholar]
  • 54.Shi W., Liu P.L., Duan L., Pan B., Su Z. Evolutionary response to the Qinghai-Tibetan Plateau uplift: Phylogeny and biogeography of Ammopiptanthus and tribe Thermopsideae (Fabaceae) PeerJ. 2017;5:e3607. doi: 10.7717/peerj.3607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Crnokrak P., Roff D.A. Inbreeding depression in the wild. Heredity. 1999;83:260–270. doi: 10.1038/sj.hdy.6885530. [DOI] [PubMed] [Google Scholar]
  • 56.Weiser E.L., Grueber C.E., Kennedy E.S., Jamieson I.G. Unexpected positive and negative effects of continuing inbreeding in one of the world’s most inbred wild animals. Evolution. 2016;70:154–166. doi: 10.1111/evo.12840. [DOI] [PubMed] [Google Scholar]
  • 57.Harmon L.J., Braude S. Conservation of small populations: Effective population sizes, inbreeding, and the 50/500 rule. Introd. Methods Models Ecol. Evol. Conserv. Biol. 2010;1:125–138. [Google Scholar]
  • 58.Lande R., Schemske D.W. The evolution of self-fertilization and inbreeding depression in plants. I. Genetic models. Evolution. 1985;39:24–40. doi: 10.1111/j.1558-5646.1985.tb04077.x. [DOI] [PubMed] [Google Scholar]
  • 59.Cheptou P.O., Donohue K. Environment-dependent inbreeding depression: Its ecological and evolutionary significance. New Phytol. 2011;189:395–407. doi: 10.1111/j.1469-8137.2010.03541.x. [DOI] [PubMed] [Google Scholar]
  • 60.Leberg P.L., Firmin B.D. Role of inbreeding depression and purging in captive breeding and restoration programmes. Mol. Ecol. 2008;17:334–343. doi: 10.1111/j.1365-294X.2007.03433.x. [DOI] [PubMed] [Google Scholar]
  • 61.Zhou J.Y., Wang X., Gao F., Zhou Y. Genome survey and SSR analysis of Ammopiptanthus mongolicus. Genom. Appl. Biol. 2017;36:4334–4338. [Google Scholar]
  • 62.Duan Y., Zhang K. Comparative Analysis and Phylogenetic Evolution of the Complete Chloroplast Genome of Ammopiptanthus. Acta Bot. Boreali-Occident. Sin. 2020;40:1323–1332. [Google Scholar]
  • 63.Hu J., Wang L., Li J. Comparison of genomic SSR and EST-SSR markers for estimating genetic diversity in cucumber. Biol. Plant. 2011;55:577–580. doi: 10.1007/s10535-011-0129-0. [DOI] [Google Scholar]
  • 64.Zhao P., Yong X., Hu G., Lv T., Jiao P. RAPD analysis of the genetic diversity of the endangered plant Ammopiptanthus nanus. Arid. Zone Resour. Environ. 2016;30:74–79. [Google Scholar]
  • 65.Ukoskit K., Posudsavang G., Pongsiripat N., Chatwachirawong P., Klomsa-Ard P., Poomipant P., Tragoonrung S. Detection and validation of EST-SSR markers associated with sugar-related traits in sugarcane using linkage and association mapping. Genomics. 2019;111:1–9. doi: 10.1016/j.ygeno.2018.03.019. [DOI] [PubMed] [Google Scholar]
  • 66.Niu Y.L., Bhatt A., Peng Y., Chen W., Gao Y., Zhan X., Zhang Z., Hu W., Song M., Yu Z. Genetic diversity and population structure analysis of Emmenopterys henryi Oliv., an endangered relic species endemic to China. Genet. Resour. Crop Evol. 2021;68:1135–1148. [Google Scholar]
  • 67.Waser N.M. the Natural History of Inbreeding and Outbreeding: The-Oretical and Empirical Perspectives. Volume 1. University of Chicago Press; Chicago, IL, USA: 1993. Population structure, optimal outbreeding and assortative mating; pp. 173–199. [Google Scholar]
  • 68.Perrino E.V., Wagensommer R.P. Crop Wild Relatives (CWR) Priority in Italy: Distribution, Ecology, In Situ and Ex Situ Conservation and Expected Actions. Sustainability. 2021;13:1682. [Google Scholar]
  • 69.Cooper M., Tang T., Gho C., Hart T., Hammer G., Messina C. Integrating genetic gain and gap analysis to predict improvements in crop productivity. Crop Sci. 2020;60:582–604. doi: 10.1002/csc2.20109. [DOI] [Google Scholar]
  • 70.Cooper M., Messina C.D. Can we harness “Enviromics” to accelerate crop improvement by integrating breeding and agronomy? Front. Plant Sci. 2021;12:735143. doi: 10.3389/fpls.2021.735143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Tong H., Nikoloski Z. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Plant Physiol. 2021;257:153354. doi: 10.1016/j.jplph.2020.153354. [DOI] [PubMed] [Google Scholar]
  • 72.Kang M., Ye Q., Huang H. Genetic risk in plant ex-situ conservation. Genetics. 2005;01:160–166. [PubMed] [Google Scholar]
  • 73.Wang L., Zhan Q., Liao J., Huang H. Vascular plant diversity of National Key Protected Wild Plants, threatened species, and endemic species ex situ conserved in botanic gardens of China. Biodiversity. 2023;31:52–63. doi: 10.17520/biods.2022495. [DOI] [Google Scholar]
  • 74.Wu Y., Feng Z. Rare and Endangered Plants and National Key Protected Plants for ex situ Conservation in South China Agricultural University Arboretum. J. South China Agric. Univ. Nat. Sci. Ed. 2006;27:118–121. (In Chinese with English Abstract) [Google Scholar]
  • 75.Taylor H.R., Dussex N., van Heezik Y. Bridging the conservation genetics gap by identifying barriers to implementation for conservation practitioners. Glob. Ecol. Conserv. 2017;10:231–242. doi: 10.1016/j.gecco.2017.04.001. [DOI] [Google Scholar]
  • 76.Freeland J.R. Molecular Ecology. Volume 1. John Wiley & Sons; Hoboken, NJ, USA: 2020. p. 416. [Google Scholar]
  • 77.Ellstrand N.C., Elam D.R. Population genetic consequences of small population size: Implications for plant conservation. Annu. Rev. Ecol. Syst. 1993;24:217–242. doi: 10.1146/annurev.es.24.110193.001245. [DOI] [Google Scholar]
  • 78.Desjardins P., Conklin D. NanoDrop microvolume quantitation of nucleic acids. J. Vis. Exp. 2010;45:e2565. doi: 10.3791/2565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Ponti G., Maccaferri M., Manfredini M., Kaleci S., Mandrioli M., Pellacani G., Ozben T., Depenni R., Bianchi G., Pirola G.M. The value of fluorimetry (Qubit) and spectrophotometry (Nano Drop) in the quantification of cell-free DNA (cfDNA) in malignant melanoma and prostate cancer patients. Clin. Chim. Acta. 2018;479:14–19. doi: 10.1016/j.cca.2018.01.007. [DOI] [PubMed] [Google Scholar]
  • 80.Panaro N.J., Yuen P.K.I., Sakazume T., Fortina P., Kricka L.J., Wilding P. Evaluation of DNA fragment sizing and quantification by the agilent 2100 bioanalyzer. Clin. Chem. 2000;46:1851–1853. doi: 10.1093/clinchem/46.11.1851. [DOI] [PubMed] [Google Scholar]
  • 81.Jakobsen K.S., Breivold E., Hornes E. Purification of mRNA directly from crude plant tissues in 15 minutes using magnetic oligo dT microspheres. Nucleic Acids Res. 1990;18:3669. doi: 10.1093/nar/18.12.3669. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Zhong S., Joung J.G., Zheng Y., Chen Y., Liu B., Shao Y., Xiang J.Z., Fei Z., Giovannoni J.J. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harb. Protoc. 2011;8:prot5652. doi: 10.1101/pdb.prot5652. [DOI] [PubMed] [Google Scholar]
  • 83.Nagalakshmi U., Waern K., Snyder M. RNA-Seq: A method for comprehensive transcriptome analysis. Curr. Protoc. Mol. Biol. 2010;89:4–11. doi: 10.1002/0471142727.mb0411s89. [DOI] [PubMed] [Google Scholar]
  • 84.Smith C.J., Osborn A.M. Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology. FEMS Microbiol. Ecol. 2009;67:6–20. doi: 10.1111/j.1574-6941.2008.00629.x. [DOI] [PubMed] [Google Scholar]
  • 85.Ju J., Kim D.H., Bi L., Meng Q., Bai X., Li Z., Li X., Marma M.S., Shi S., Wu J., et al. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. Proc. Natl. Acad. Sci. USA. 2006;103:19635–19640. doi: 10.1073/pnas.0609513103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Martin J.A., Wang Z. Next-generation transcriptome assembly. Nat. Rev. Genet. 2011;12:671–682. doi: 10.1038/nrg3068. [DOI] [PubMed] [Google Scholar]
  • 87.Patterson J., Carpenter E.J., Zhu Z., An D., Liang X., Geng C., Drmanac R., Wong G.K. Impact of sequencing depth and technology on de novo RNA-Seq assembly. BMC Genom. 2019;20:604. doi: 10.1186/s12864-019-5965-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Grabherr M.G., Haas B.J., Yassour M., Levin J.Z., Thompson D.A., Amit I., Adiconis X., Fan L., Raychowdhury R., Zeng Q., et al. Full-length transcriptome assembly from RNA Seq data without a reference genome. Nat. Biotechnol. Ital. 2011;29:644–652. doi: 10.1038/nbt.1883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Kim C.S., Winn M.D., Sachdeva V., Jordan K.E. K-mer clustering algorithm using a MapReduce framework: Application to the parallelization of the Inchworm module of Trinity. BMC Bioinform. 2017;18:467. doi: 10.1186/s12859-017-1881-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Compeau P.E.C., Pevzner P.A., Tesler G. How to apply de Bruijn graphs to genome assembly. Nat. Biotechnol. 2011;29:987–991. doi: 10.1038/nbt.2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Iqbal A., Sadaqat H.A., Khan A.S., Amjad M. Identification of sunflower (Helianthus annuus, Asteraceae) hybrids using simple-sequence repeat markers. Genet. Mol. Res. 2011;10:102–106. doi: 10.4238/vol10-1gmr918. [DOI] [PubMed] [Google Scholar]
  • 92.Beier S., Thiel T., Münch T., Scholz U., Mascher M. MISA-web: A web server for microsatellite prediction. Bioinformatics. 2017;33:2583–2585. doi: 10.1093/bioinformatics/btx198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Hulce D., Li X., Snyder-Leiby T., Liu C.J. GeneMarker® genotyping software: Tools to increase the statistical power of DNA fragment analysis. Biomol. Tech. JBT. 2011;22:S35. [Google Scholar]
  • 94.Peakall R.O.D., Smouse P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes. 2006;6:288–295. doi: 10.1093/bioinformatics/bts460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Berger J.O., Moreno E., Pericchi L.R., Bayarri M.J., Bernardo M.J., Cano J.A., Horra J.D., Martín J., Ríos-Insúa D., Betrò B., et al. An overview of robust Bayesian analysis. Test. 1994;3:5–124. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Articles from Plants are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES