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MAP  >  Fruit Research
2023 Volume 3
Article Contents
Abstract
Introduction
Materials and methods
Plant materials, growth conditions, and stress treatment
Vector construction
Chlorophyll and anthocyanin content determination
Physiological analysis
RNA isolation and qRT-PCR analysis
Results
Screening and characterization of lncRNAs
Generation of lncRNA overexpression lines
Effect of lncRNA overexpression on heat tolerance in Arabidopsis
Effect of lncRNA overexpression on the expression quantity of genes related to high-temperature response in Arabidopsis
Effect of lncRNA overexpression on drought tolerance in Arabidopsis
Discussion
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Supplementary information
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References
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ARTICLE   Open Access    

Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers

  • 1.

    College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

  • 2.

    Economic Crop Technology Promotion Station, Gutian Agricultural Bureau, Ningde, Fujian 352200, China

More Information
  • Molecular characterization of 39 late-mature peach (Prunus persica L.) accessions was carried out using ISSR (inter simple sequence repeats) and SRAP (sequence-related amplified polymorphism) markers to assess the value and magnitude of genetic divergence. The ISSR primers revealed 70.09% polymorphism and the SRAP marker generated 73.33% polymorphism. Pooled ISSR and SRAP, along with UPGMA clustering based on similarity coefficients, were estimated to assess the efficiency of the marker system in peaches. Similarity coefficients between accessions regarding the ISSR and SRAP markers ranged from 0.65 to 0.89, indicating a broad genetic peach base. A maximum similarity coefficient of 0.89 was observed between C12 'Weiduanmihong' (Fujian Yikangyuan Farm Co., Ltd., 26.58° N, 118.75° E) and C32 'Weiduanmihong' (WeiduanVillage, 26.58° N, 118.75° E), and a minimum of 0.65 was observed in C6 'Huangjinmitao 5' (Gutian Natural Pantaoyuan Family Farm, 26.58° N, 118.75° E) and the rest of the genotypes. The present study found that a high level of polymorphism indicated their applicability in framing more extensive studies to develop superior progeny, conduct molecular breeding, investigate genetic population diversity, make comparative maps, select parents, etc., in various peach crop improvement programs.

  • Introduction

    Plants are continuously subjected to unpredictable environmental conditions and encounter a multitude of stressors throughout their growth and development, posing a significant challenge to global crop production and food security[1]. Heat and drought are undoubtedly the two most important stresses that have a huge impact on crops. Both elicit a wide array of biochemical, molecular, and physiological alterations and responses, impacting diverse cellular processes and ultimately influencing crop yield and quality[2].

    A primary physiological consequence of both stresses is the diminished photosynthetic capacity, partially resulting from the degradation of chlorophyll due to leaf senescence under stress conditions. Chlorophyll accumulation was diminished in numerous plants subjected to drought or heat stress conditions[3,4]. Various environmental stresses prompt excessive generation of reactive oxygen species (ROS), initiating oxidative damage that compromises lipids, and proteins, and poses a serious threat to cellular functions[2]. To mitigate oxidative stress and minimize damage, plants have developed various protective mechanisms to neutralize ROS. Several antioxidant enzymes, such as SOD, POD, and CAT, are integral to cellular antioxidative defense mechanisms. Additionally, antioxidants such as anthocyanins and proline serve as crucial ROS scavengers[5,6]. The elevation in temperature typically induces the transient synthesis of heat shock proteins (Hsps), which function as molecular chaperones in protecting proteins from denaturation and aggregation, with their activity primarily regulated at the transcriptional level by heat shock factors (Hsfs)[7]. The significance of Hsps and Hsfs in all organisms, including plants, has been assessed in various stress conditions that could disrupt cellular homeostasis and result in protein dysfunction[7]. Drought stress can also trigger the transcription of a suite of marker genes, including RD29A, RD29B, NCED3, AREB1, Rab18, etc., which assist plants in mitigating cellular damage during dehydration and bolstering their resilience to stress[8−10].

    Previous research efforts focusing on the regulatory control of stress-related genes have largely centered around protein-coding genes. In recent years, non-protein-coding transcripts have emerged as important regulatory factors in gene expression. Among them, long non-coding RNAs (lncRNAs) lncRNAs have been identified as implicated in various abiotic stresses[11,12]. LncRNAs are a class of non-coding RNAs (ncRNAs) exceeding 200 nucleotides in length. They possess minimal or no protein-coding potential[13]. In plants, lncRNAs are specifically transcribed by RNA polymerases Pol IV, Pol V, Pol II, and Pol III[14,15]. LncRNAs exhibit low abundance and display strong tissue and cellular expression specificity relative to mRNAs. Moreover, sequence conservation of lncRNAs is was very poor across different plant species[13,16,17]. The widespread adoption of high-throughput RNA sequencing technology has revealed lncRNAs as potential regulators of plant development and environmental responses. In cucumber, RNA-seq analysis has predicted 2,085 lncRNAs to be heat-responsive, with some potentially acting as competitive endogenous RNAs (ceRNAs) to execute their functions[18]. In radish, a strand-specific RNA-seq (ssRNA-seq) technique identified 169 lncRNAs that were differentially expressed following heat treatment[19]. In Arabidopsis, asHSFB2a, the natural antisense transcript of HSFB2a was massively induced upon heat stress and exhibited a counteracted expression trend relative to HSFB2a. Overexpression of asHSFB2a entirely suppressed the expression of HSFB2a and impacted the plant's response to heat stress[20]. For drought stress resistance, 244 lncRNAs were predicted in tomatoes to be drought responsive probably by interacting with miRNAs and mRNAs[21]. Under drought stress and rehydration, 477 and 706 lncRNAs were differentially expressed in drought-tolerant Brassica napus Q2 compared to drought-sensitive B. napus, respectively[22]. In foxtail millet and maize, 19 and 644 lncRNAs, respectively, were identified as drought-responsive[23,24]. Despite the identification of numerous lncRNAs by high-throughput sequencing, which suggests their potential involvement in various abiotic stress processes, only a minority have been experimentally validated for function.

    In our previous study, we characterized 1,229 differentially expressed (DE) lncRNAs in Chinese cabbage as heat-responsive, and subsequent bioinformatics analysis reduced this number to 81, which are more likely associated with heat resistance[25]. lnc000283 and lnc012465 were selected from among them for further functional investigation. The findings indicated that both lnc000283 and lnc012465 could be promptly induced by heat shock (HS). Overexpression of either lnc000283 or lnc012465 in Arabidopsis plants enhanced their capacity to tolerate heat stress. Additionally, both lnc000283 and lnc012465 conferred drought tolerance to transgenic Arabidopsis.

    Materials and methods

    Plant materials, growth conditions, and stress treatment

    The lncRNA sequences examined in this study were from Chiifu-401-42 Chinese cabbage and all Arabidopsis plants were of the Col-0 background. Transgenic plants expressing lnc000283 and lnc012465 were generated using the Agrobacterium tumefaciens-mediated floral dip method[26]. Single-copy and homozygous T3 plants were identified through genetic segregation on an agar medium supplemented with kanamycin. The T3 generation plants, or their homozygous progeny, were utilized in the experiments.

    For phenotypic assessment, Arabidopsis seeds were initially sown on filter paper moistened with ddH2O and placed in a 4 °C freezer for 2 d. Subsequently, they were evenly planted in nutrient-rich soil and transferred to a growth chamber operating a 16-h day/8-h night cycle, with day/night temperatures of 22 °C/18 °C and a light intensity of 250 μmol·m−2·s−1. After 10 d of growth, Arabidopsis plants with uniform growth were transferred to 50-hole plates. Arabidopsis plants grown in Petri dishes were firstly seed-sterilized and then sown on 1/2 MS medium supplemented with 10 g·L−1 sucrose. The seeds were then placed in a 4 °C refrigerator for 2 d in the dark before transferring them to a light incubator. The day/night duration was set to 16 h/8 h, the day/night temperature to 21 °C/18 °C, and the light intensity to 100 μmol·m−2·s−1.

    For heat treatment, 3-week-old seedlings were subjected to 38 °C for 4 d within a light incubator, subsequently transferred to their original growth conditions under the same light/dark cycles. For drought treatment, 3-week-old Arabidopsis seedlings were deprived of water for 10 d, followed by rehydration to facilitate a 2-d recovery period. Plants were photographed and surveyed both before and after treatment.

    Vector construction

    The lncRNA sequences (lnc000283 and lnc012465) were chemically synthesized based on RNA-seq data, with restriction sites for BamH1 and Kpn1 engineered upstream and downstream. The resultant lncRNA constructs were subcloned into the pCambia2301 binary vector, incorporating a cauliflower mosaic virus (CaMV) 35S promoter. The recombinant vectors were transformed into Escherichia coli TOP10 competent cells (Clontech), incubated at 37 °C overnight, after which single clones were selected for PCR verification, and the confirmed positive colonies were submitted for sequencing. Following verification, the correct plasmids were introduced into A. tumefaciens strain GV3101 using the freeze-thaw method and subsequently transformed into Arabidopsis wild-type (Col) plants.

    Chlorophyll and anthocyanin content determination

    To quantify the chlorophyll content, the aerial portions of wild-type and transgenic Arabidopsis plants, grown in Petri dishes were weighed, minced, and then subjected to boiling in 95% ethanol until fully decolorized. Aliquots of 200 μL from the extract were transferred to a 96-well plate and the absorbance at 663 nm and 645 nm was measured via spectrophotometry by a microplate reader (Multiskan GO, Thermo Scientific, Waltham, MA, USA). Three biological replicates were analyzed for WT and each transgenic line. Chlorophyll content was determined according to the formula of the Arnon method[27]: Chlorophyll a = (12.72A663 − 2.59A645) v/w, Chlorophyll b = (22.88A645 − 4.67A663) v/w, Total chlorophyll = (20.29A645 + 8.05A663) v/w.

    The quantification of anthocyanin was performed as follows: aerial parts of wild-type and transgenic Arabidopsis plants, cultivated in Petri dishes, were weighed and ground to powder in liquid nitrogen. Subsequently, the samples were incubated in 600 μL of acidified methanol (containing 1% HCl) at 70 °C for 1 h. Following this, 1 mL of chloroform was added, and the mixture was vigorously shaken to remove chlorophyll. The mixture was then centrifuged at 12,000 rpm for 5 min, after which the absorbance of the aqueous phase was determined at 535 nm using a spectrophotometer (Shimadzu, Kyoto, Japan). Three biological replicates were analyzed for WT and each transgenic line. The relative anthocyanin content was calculated according to anthocyanin concentration and extraction solution volume. One anthocyanin unit is defined as an absorption unit at a wavelength of 535 nm in 1 mL of extract solution. In the end, the quantity was normalized to the fresh weight of each sample.

    Physiological analysis

    Three-week-old transgenic and WT A. thaliana plants, subjected to normal conditions or varying durations of heat or drought stress, were utilized for subsequent physiological assessments. All assays were performed in accordance with the method described by Chen & Zhang[28]. In brief, 0.1 g of fresh leaf tissue was homogenized in 500 μL of 100 mM PBS (pH 7.8) while chilled on ice. The homogenate was then centrifuged at 4 °C, and the resultant supernatant was employed for further analysis. For the determination of MDA content, 100 μL of the supernatant was combined with 500 μL of a 0.25% thiobarbituric acid (TBA) solution (which was prepared by dissolving 0.125 g of TBA in 5 mL of 1 mol·L−1 NaOH before being added to 45 mL of 10% TCA) and boiled for 15 min. Following a 5 min cooling period on ice, the absorbance was measured at 532 nm and 600 nm. The activity of POD was determined as follows: initially, 28 μL of 0.2% guaiacol and 19 μL of 30% H2O2 were sequentially added to 50 mL of 10mM PBS (pH 7.0), after thorough heating and mixing, 1 mL was transferred into a cuvette, then 50 μL of the supernatant was added to the cuvette and the absorbance at 470 nm was monitored every 15 s for 1 min. To determine the proline content, a reaction solution was prepared by mixing 3% sulfosalicylic acid, acetic acid, and 2.5% acidic ninhydrin in a ratio of 1:1:2, then 50 μL of the supernatant was added to 1 mL of the reaction solution, which was then subjected to a boiling water bath for 15 min (the solution turned red after the boiling water bath). Following cooling on ice, the absorbance at 520 nm was recorded. For the quantification of proline, an L-proline standard curve was prepared by dissolving 0, 5, 10, 15, 20, 25, and 30 μg of L-proline in 0.5 mL of ddH2O, followed by the addition of 1 mL of the reaction solution and measuring the absorbance at 520 nm. The proline content in the samples was then determined based on the L-proline standard curve.

    RNA isolation and qRT-PCR analysis

    Total RNA was isolated from the aerial parts of Arabidopsis using the TaKaRa MiniBEST Plant RNA Extraction Kit, followed by purification and reverse transcription using the PrimeScript RT reagent Kit with gDNA Eraser (Takara). The cDNA product was diluted 10 times and real-time PCR was conducted in triplicate for each biological replicate using SYBR PCR Master Mix (Applied Biosystems) on the ABI 7500 system under the following conditions: 98 °C for 3 min, followed by 40 cycles of 98 °C for 2 s and 60 °C for 30 s. The relative expression levels of each gene were normalized against the transcript abundance of the endogenous control UBC30 (At5g56150) and calculated using the 2−ΔCᴛ method. The specific primers employed for qRT-PCR are detailed in Supplemental Table S1.

    Results

    Screening and characterization of lncRNAs

    In our prior investigation, dozens of lncRNAs associated with the heat stress response in Chinese cabbage were identified through informatics analysis. Two lncRNAs (lnc000283 and lnc012465) were chosen for genetic transformation in Arabidopsis to elucidate their functions comprehensively. Transcriptome data analysis indicated that the expression of lnc000283 and lnc012465 in Chinese cabbage were both induced by HS. To verify the accuracy, the expression patterns of lnc000283 and lnc012465 were confirmed through quantitative real-time PCR (qRT-PCR), and the results from qRT-PCR were consistent with those obtained from RNA-seq (Fig. 1a). The corresponding homologous genes in Arabidopsis were identified as CNT2088434 and CNT2088742, exhibiting sequence similarities of 88% and 87%, respectively (Supplemental Fig. S1). Subcellular localization predictions using the lnclocator database (www.csbio.sjtu.edu.cn/bioinf/lncLocator) suggested that both lncRNAs are localized within the nucleus (Supplemental Table S2). Bioinformatics analysis was conducted using the CPC tool (http://cpc.cbi.pku.edu.cn/) indicated that lnc000283 and lnc012465 are noncoding sequences, with coding probabilities of 0.0466805 and 0.0432148, respectively comparable to the well-characterized lncRNAs COLDAIR and Xist, but significantly lower than those of the protein-coding genes UBC10 and ACT2 (Fig. 1b).

    Figure 1.  Characteristics of lnc000283 and lnc012465. (a) Expression level of lnc000283 and lnc012465 in Chinese cabbage leaves treated at 38 °C at different time points, as determined by qRT-PCR and RNA-seq. CK is a representative plant before heating, and T1, T4, T8, and T12 denote plants that were subjected to 38 °C for 1, 4, 8, and 12 h, respectively. The expression levels were normalized to the expression level of Actin. (b) Analysis of coding potential for lnc000283 and lnc012465. The coding potential scores were calculated using the CPC program. UBC10 (At5g53300) and ACT2 (At3g18780) are positive controls that encode proteins. COLDAIR (HG975388) and Xist (L04961) serve as negative controls, exhibiting minimal protein-coding potential.
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    Generation of lncRNA overexpression lines

    To elucidate the role of lnc000283 and lnc012465 in response to abiotic stress, overexpression vectors were constructed for these lncRNAs, driven by the CaMV 35S promoter, and they were introduced into Arabidopsis thaliana (Col-0 ecotype). Through PCR identification and generational antibiotic screening, two homozygous positive lines for lnc012465 and lnc000283 were obtained. The relative expression levels of these lncRNAs were assessed using qRT-PCR (Fig. 2a). When plants were grown in 1/2 MS medium, with the consumption of nutrients, and reduction of water, the leaves of WT began to turn yellow, but the lnc000283 and lnc012465 overexpression lines developed a deep purple color of leaf veins (Fig. 2b). Examination of chlorophyll and anthocyanin contents in the plants revealed that both overexpression lines had higher levels of chlorophyll and anthocyanin compared to the WT, suggesting that the transgenic plants might possess enhanced resistance to nutritional or water stress (Fig. 2c, d).

    Figure 2.  Arabidopsis plants overexpressing lnc000283 and lnc012465 had higher anthocyanins and chlorophyll content. (a) The relative expression level of lnc000283 and lnc012465 in WT and different transgenic lines. UBC10 (At5g53300) was used as an internal control. Each value is mean ± sd (n = 3). (b) The phenotype of WT and Arabidopsis overexpressing lnc000283 or lnc012465 grown on 1/2 MS medium 50 d after sowing. The (c) anthocyanin and (d) chlorophyll content of WT and transgenic Arabidopsis overexpressing lnc000283 or lnc012465. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01).
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    Effect of lncRNA overexpression on heat tolerance in Arabidopsis

    Given that lnc000283 and lnc012465 were highly induced by heat, the thermotolerance of the overexpressing (OE) plants were compared to that of the wild type. Arabidopsis plants were initially exposed to a an HS treatment at 38 °C for 4 d, followed by recovery at room temperature. The death caused by HS was processive. Post-severe HS challenge for 4 d, OE plants initially appeared similar to WT, but upon recovery, their leaves started to fold or curl, followed by a transition to yellow, white, and eventually drying out (Fig. 3a). OE lnc000283 and OE lnc012465 plants exhibited enhanced thermotolerance compared to WT, with lnc012465 showing particularly strong tolerance (Fig. 3a; Supplemental Fig. S2a). After 5 d of recovery, leaf coloration indicated that transgenic plants maintained a significantly higher percentage of green leaves and a lower percentage of bleached leaves compared to WT (Fig. 3b; Supplemental Fig. S2b). Under non-heat-stress conditions, WT and OE plants possessed comparable water content. However, following heat stress, the fresh-to-dry weight ratio of OE lnc000283 and lnc012465 lines was significantly greater than that of WT (Fig. 3c; Supplemental Fig. S2c). Abiotic stresses frequently trigger the production of excessive reactive oxygen species (ROS), which are believed to cause lipid peroxidation of membrane lipids, leading to damage to macromolecules. Leaf MDA content is commonly used as an indicator of lipid peroxidation under stress conditions; therefore, the MDA content in both transgenic and WT plants was assessed. Figure 3d shows that the MDA content in WT plants progressively increased after heat treatment, whereas in the two lines overexpressing lnc012465, the MDA content increased only slightly and remained significantly lower than that in WT at all time points. In plants overexpressing lnc000283, the MDA content did not significantly differ from that of WT before heat stress. However, after 4 d of heat treatment, the MDA content was significantly lower compared to WT (Supplemental Fig. S2d). The results suggested that the expression of both lnc012465 and lnc000283 can mitigate injury caused by membrane lipid peroxidation under heat-stress conditions. Peroxidase (POD) is a crucial antioxidant enzyme involved in ROS scavenging. Figure 3e and Supplemental Fig. S2e demonstrate that POD activity increased in both transgenic and WT plants after heat treatment. However, the increase in WT plants was modest, whereas OE lnc000283 and OE lnc012465 plants exhibited consistently higher POD activity. As anticipated, proline levels were induced in response to stress in all studied plants (Fig. 3f; Supplemental Fig. S2f). However, under normal conditions and 2 d post-heat stress treatment, the proline content in OE lnc000283 and OE lnc012465 plants did not exhibit significant changes compared to WT (Fig. 3f; Supplemental Fig. S2f). Moreover, after 4 d of heat stress, the proline content in OE lnc012465 lines was significantly lower than in WT, and the OE lnc000283 transgenic line 12-6 also showed a marked decrease in proline content compared to WT (Fig. 3f; Supplemental Fig. S2f). The results indicated that the thermotolerance of plants overexpressing either lnc000283 or lnc012465 was independent of proline accumulation.

    Figure 3.  Overexpressing lnc012465 lines are more tolerant to heat stress. (a) Phenotypes of WT and OE lnc012465 plants were assessed before and after exposure to heat stress. The heat treatment was applied to 25-day-old Arabidopsis plants. (b) The percentage of leaves with different colors in Arabidopsis after heat treatment and recovery for 5 d. (c) The fresh-to-dry weight ratio of Arabidopsis leaves was measured before and after 38 °C heat treatment. (d)−(f) depict the MDA content, POD activity, and proline content in Arabidopsis leaves at varying durations of heat stress. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Effect of lncRNA overexpression on the expression quantity of genes related to high-temperature response in Arabidopsis

    To elucidate the molecular mechanisms by which lncRNAs enhance thermotolerance in Arabidopsis, the expression of the Hsf gene HsfA7a and three Hsps (Hsp25.3, Hsa32, and Hsp18.1-CI) in OE lnc000283, OE lnc012465, and WT Arabidopsis plants were investigated at various time points following heat treatment. As shown in Fig. 4 and Supplemental Fig. S3, both Hsf and Hsps exhibited a rapid response to heat stress with strong induction. Notably, the transcripts of HsfA7a and Hsp25.3 were significantly upregulated at 1 h after heat exposure, then experienced a sharp decrease. Hsa32 and Hsp18.1-CI were highly induced at 1 h and, unlike the other proteins, sustained high expression levels at 3 h (Fig. 4; Supplemental Fig. S3). At 1 h post-heat treatment, the transcript levels of Hsa32 and HsfA7a in OE lnc000283 did not significantly differ from those in WT. However, by 3 h, Hsa32 expression was roughly 50% of the WT level, while HsfA7a expression was approximately double that of WT (Supplemental Fig. S3). The overexpression of lnc000283 did not significantly affect the transcript level of Hsp25.3 at any of the tested time points. Notably, Hsp18.1-CI expression in both lines overexpressing lnc000283 was significantly induced at all three detection points post-heat treatment, reaching approximately 4-9-fold higher levels than in the WT (Supplemental Fig. S3). In Arabidopsis plants with elevated expression of lnc012465, the expression patterns of all Hsp and Hsf genes were similar to those in plants overexpressing lnc000283, with the notable exception of Hsa32. Unlike the WT, Hsa32 did not show a trend of down-regulation at 3 h post-heat treatment (Fig. 4). The findings suggest that the substantial induction of Hsp18.1-CI may play a role in enhancing the thermotolerance of Arabidopsis plants overexpressing lnc000283 and lnc012465.

    Figure 4.  The expression of HSF and HSP genes in lnc012465 overexpressing lines before and after different heat treatment times. Gene expression levels were quantified using RT-qPCR and normalized to UBC10 (At5g53300). Each value represents the mean ± standard deviation (n = 3). The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Effect of lncRNA overexpression on drought tolerance in Arabidopsis

    Prior research has implicated a significant proportion of genes in conferring resistance to various abiotic stresses. To elucidate the functions of lnc000283 and lnc012465 more thoroughly, WT and transgenic plants were subjected to drought stress by depriving them of water for 9 d. It was noted that the majority of leaves in WT plants withered and dried, whereas the OE lnc000283 and OE lnc012465 plants exhibited reduced withering, with only a minority displaying dryness (Fig. 5a; Supplemental Fig. S4a). Eight days post-rewatering, a negligible fraction of WT seedlings exhibited recovery, whereas the overwhelming majority of transgenic plants regained vigorous growth (Fig. 5a; Supplemental Fig. S4a). The transgenic plants demonstrated a significantly higher survival rate compared to the WT plants. Following 9 d of water deficit treatment, less than 40% of the WT plants survived, whereas the OE 012465 lines 8-7 and 9-1 exhibited survival rates of 100% and 95%, respectively, and the OE 000283 lines 11-10 and 12-6 had survival rates of 87% each. (Fig. 5b; Supplemental Fig. S4b). Water loss serves as a critical metric for assessing plant drought tolerance, hence the fresh-to-dry weight ratio of excised leaves was assessed via desiccation analysis. Following 4 d of drought treatment, the fresh-to-dry weight ratio for WT plants was reduced to 43%, whereas for OE lnc000283 lines 11-10 and 12-6, it was reduced to 73% and 75%, respectively. For OE 012465 lines 8-7 and 9-1, the ratios were reduced to 67% and 62%, respectively (Fig. 5c; Supplemental Fig. S4c). The findings indicated that lnc000283 and lnc012465 endow the transgenic plants with drought tolerance.

    Figure 5.  Overexpressing lnc012465 lines are more tolerant to drought stress. (a) Phenotype of WT and OE lnc012465 plants before and after subjecting to drought stress. Drought treatment was carried out on 20-day-old Arabidopsis plants. (b) The percentage of leaves with different colors in Arabidopsis after heat treatment and recovery for 5 d. (c) The fresh weight to dry weight ratio of Arabidopsis leaves before and after undergoing 38 °C heat treatment. (d)−(f) MDA content, POD activity, and proline content in Arabidopsis leaves under different times of heat stress. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001)
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    MDA content in leaves is a standard biomarker for assessing the extent of drought stress-induced damage. Prior to drought stress exposure, MDA levels in WT and transgenic plants were comparable. However, following 7 and 9 d of water deficit, the MDA content in the transgenic plants was markedly reduced compared to the WT, suggesting a less severe degree of membrane lipid peroxidation in the transgenic plants (Fig. 5d; Supplemental Fig. S4d). Oxidative stress frequently coincides with drought stress, hence the activity of POD was assessed to evaluate the ROS scavenging ability. The findings indicated that as the duration of drought treatment increased, POD activity progressively rose. Before drought exposure, the POD activity in lines 11-10 and 12-6 of OE 000283 was 2.4-fold and 2.2-fold higher than that of the WT, respectively (refer to Supplemental Fig. S4e). Following drought treatment, the POD activity in the transgenic lines remained significantly elevated compared to the wild type, although the enhancement was less pronounced than before the treatment (Supplemental Fig. S4e). In the OE 012465 plants, the POD activity in lines 8-7 and 9-1 significantly surpassed that of the wild type, with the discrepancy being more pronounced during drought stress (Fig. 5e). The proline content in WT and OE 000283 plants exhibited no significant differences before and after 7 d of treatment. However, after 9 d of drought, the proline content in OE 000283 plants was significantly lower compared to that in the WT (Supplemental Fig. S4f). OE 000465 plants showed no significant difference from the wild type before and after drought treatment (Fig. 5f). The findings were consistent with those under heat stress, indicating that the enhanced stress resistance due to the overexpression of lnc000283 and lnc012465 in Arabidopsis is not reliant on proline accumulation.

    Following drought stress treatment, the expression levels of drought-related genes such as RD29A, RD29B, NCED3, AREB1, and Rab18 were significantly elevated in plants overexpressing lnc000283 and lnc012465 compared to WT plants. These findings suggest that lnc000283 and lnc012465 modulate Arabidopsis drought tolerance by regulating the expression of genes associated with the drought stress response (Fig. 6; Supplemental Fig. S5).

    Figure 6.  The expression of drought-responsive genes in lnc012465 overexpressing lines before and after different drought treatment time. Gene expression levels were determined by qRT-PCR normalized against UBC10 (At5g53300). Each value is mean ± sd (n = 3). The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Discussion

    The integrity of global food security is under threat due to the confluence of rapid population expansion and profound climatic shifts[29]. Amidst the shifting climatic landscape, heat and drought stress have emerged as primary limitations to crop yield and global food security. Understanding how plants detect stress cues and acclimate to challenging conditions is a pivotal biological inquiry. Moreover, enhancing plant resilience to stress is essential for maintaining agricultural productivity and fostering environmental sustainability[2]. Concurrently, the advancement of next-generation sequencing (NGS) technology has led to the identification of a substantial number of lncRNAs that participate in diverse stress responses, with functional analyses having been conducted on several of these molecules.[30] For instance, in the case of potatoes, the lncRNA StFLORE has been identified to modulate water loss through its interaction with the homologous gene StCDF1[31]. LncRNA TCONS_00021861 can activate the IAA biosynthetic pathway, thereby endowing rice with resistance to drought stress[32]. In wheat, the expression of TalnRNA27 and TalnRNA5 was upregulated in response to heat stress[33]. Our prior investigation identified a total of 81 lncRNAs in Chinese cabbage that engage in intricate interactions with their respective mRNA targets across various phases of heat treatment[25]. Two lncRNAs, lnc000283 and lnc012465, were chosen for subsequent functional analysis. Findings confirmed that these lncRNAs endow transgenic Arabidopsis plants with enhanced tolerance to both heat and drought, thereby offering novel resources for enhancing stress resistance through genetic engineering.

    Abiotic stresses frequently trigger the synthesis of anthocyanins, serving as natural antioxidants that mitigate oxidative damage by neutralizing surplus reactive oxygen species (ROS), thereby protecting plants from growth inhibition and cell death, allowing plants to adapt to abiotic stresses[34,35]. For instance, during chilling stress, the accumulation of anthocyanins within leaves can mitigate oxidative damage, thereby enhancing the photosynthetic rate[36]. Consequently, the level of abiotic stress tolerance can be inferred from the concentration of anthocyanins. The reduction of photosynthetic ability is one of the key physiological phenomena of stresses, which is partly due to the degradation of chlorophyll caused by leaf senescence during stress. The reduced accumulation of chlorophyll in the plants was seen in many plants when exposed to drought or heat stress conditions. The current investigation revealed that lncRNA-overexpressing plants cultivated in Petri dishes exhibited increased accumulation of both chlorophyll and anthocyanins in advanced growth phases, indicating that these transgenic plants, overexpressing lnc000283 and lnc012465, demonstrated enhanced stress tolerance and superior growth performance relative to WT (Fig. 2c, d).

    Upon exposure to heat stress, there is a marked induction of transcription for numerous genes that encode molecular chaperones in plants, with the vast majority of these genes contributing to the prevention of protein denaturation-related damage and the augmentation of thermotolerance[37−39]. The present investigation identified multiple heat-inducible genes in plants overexpressing lnc000283 and lnc012465, as well as in WT (Fig. 4; Supplemental Fig. S3). The findings indicated that of the four HSP or HSF genes examined, Hsp18.1-CI exhibited a significantly greater abundance in both OE lnc000283 and OE lnc012465 plants compared to the WT following heat treatment for several days. Hsp18.1-CI, formerly referred to as Hsp18.2 has been the subject of investigation since 1989.[40] Following the fusion of the 5' region of Hsp18.2 in frame with the uidA gene of Escherichia coli, the activity of GUS, serving as the driver gene was observed to increase upon exposure to HS[40]. The Arabidopsis hsfA2 mutant exhibited diminished thermotolerance after heat acclimation, with the transcript levels of Hsp18.1-CI being substantially reduced compared to those in wild-type plants following a 4-h recovery period[41]. The findings revealed that the upregulation of Hsp18.1-CI protein is a critical mechanism by which plants achieve enhanced protection against heat stress in adverse environmental conditions, thereby bolstering their thermotolerance.

    Plants cultivated in natural settings are often subjected to concurrent multiple abiotic stresses, which can exacerbate threats to their routine physiological functions, growth, and developmental processes[42,43]. Elucidating the molecular mechanisms underlying plant responses to abiotic stress is crucial for the development of new crop varieties with enhanced tolerance to multiple abiotic stresses. Previous research has indicated that the overexpression of certain protein-coding genes can endow plants with resistance to a variety of abiotic stresses. For instance, tomatoes with robust expression of ShCML44 demonstrated significantly enhanced tolerance to drought, cold, and salinity stresses[44]. Overexpression of PeCBF4a in poplar plants confers enhanced tolerance to a range of abiotic stresses[45]. With respect to lncRNAs, transgenic Arabidopsis plants that overexpress lncRNA-DRIR displayed marked increased tolerance to salt and drought stresses compared to the wild-type[46]. In the present study, both overexpression lines of lnc000283 and lnc012465 exhibited resistance to heat and drought stresses, thereby contributing to the enhancement of plant resilience against multiple stresses (Figs 3, 5; Supplemental Figs S2, S4).

    The number of genes implicated in plant drought resistance is regulated by both ABA-dependent and ABA-independent pathways[47,48]. It is well established that the expression of RD29A exhibits a high level of responsiveness to drought stress, operating through both ABA-dependent and ABA-independent mechanisms[49]. RD29B, AREB1, and RAB18 are governed by an ABA-dependent regulatory pathway[10,49,50]. NCED3 is involved in ABA biosynthesis[51]. In the present study, the transcript levels of RD29A, RD29B, NCED3, AREB1, and RAB18 were significantly elevated in OE lnc000283 and OE lnc012465 plants compared to those in the WT plants (Fig. 6; Supplemental Fig. S5). The findings indicated that the drought tolerance imparted by OE lnc000283 and OE lnc012465 plants is contingent upon an ABA-dependent mechanism.

    Prior research has indicated that certain long non-coding RNAs (lncRNAs) can assume analogous roles across diverse biological contexts. For example, the lncRNA bra-miR156HG has been shown to modulate leaf morphology and flowering time in both B. campestris and Arabidopsis[52]. Heterogeneous expression of MSL-lncRNAs in Arabidopsis has been associated with the promotion of maleness, and similarly, it is implicated in the sexual lability observed in female poplars[53]. In the present study, lnc000283 and lnc012465 were induced by heat in Chinese cabbage, and their heterologous expression was found to confer heat tolerance in Arabidopsis. Additionally, sequences homologous to lnc000283 and lnc012465 were identified in Arabidopsis (Supplemental Fig. S1). The data suggest that these sequences may share a comparable function to that of heat-inducible sequences, potentially accounting for the conservation of lnc000283 and lnc012465'os functionality across various species.

    Conclusions

    In conclusion, the functions of two heat-inducible lncRNAs, lnc000283 and lnc012465 have been elucidated. Transgenic Arabidopsis lines overexpressing these lncRNAs accumulated higher levels of anthocyanins and chlorophyll at a later stage of growth compared to the WT when grown on Petri dishes. Furthermore, under heat and drought stress conditions, these OE plants exhibited enhanced stress tolerance, with several genes related to the stress resistance pathway being significantly upregulated. Collectively, these findings offer novel insights for the development of new varieties with tolerance to multiple stresses.

    Author contributions

    The authors confirm contribution to the paper as follows: study conception and supervision: Li N, Song X; experiment performing: Wang Y, Sun S; manuscript preparation and revision: Wang Y, Feng X, Li N. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    All data generated or analyzed during this study are included in this published article and its supplementary information files.

    Acknowledgments

    This work was supported by the National Natural Science Foundation of China (32172583), the Natural Science Foundation of Hebei (C2021209019), the Natural Science Foundation for Distinguished Young Scholars of Hebei (C2022209010), and the Basic Research Program of Tangshan (22130231H).

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Name and sources of sampled plants and tree age of the leaf materials.
    Supplemental Table S2 ISSR primer sequence.
    Supplemental Table S3 SRAP primer sequence.
    Supplemental Table S4 Polymorphism detected using ISSR and SRAP primers in 39 late-mature germplasm accessions.
    Supplemental Fig. S1 Electrophoresis results of 39 late-mature peach germplasm resources amplified via ISSR primer UBC817.
    Supplemental Fig. S2 Electrophoresis results of 39 late-mature peach germplasm resources amplified via ISSR primer UBC855.
    Supplemental Fig. S3 Electrophoresis results of 39 late-mature peach germplasm resources amplified via SRAP primer me5/em3.
    Supplemental Fig. S4 Electrophoresis results of 39 late-mature peach germplasm resources amplified via SRAP primer me5/em6.
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  • Cite this article

    Li W, Ma Y, Kou Y, Zeng Z, Qiu D, et al. 2023. Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers. Fruit Research 3:36 doi: 10.48130/FruRes-2023-0036
    Li W, Ma Y, Kou Y, Zeng Z, Qiu D, et al. 2023. Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers. Fruit Research 3:36 doi: 10.48130/FruRes-2023-0036
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Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers

  • 1.

    College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

  • 2.

    Economic Crop Technology Promotion Station, Gutian Agricultural Bureau, Ningde, Fujian 352200, China

  • Received: 06 May 2023
  • Accepted: 04 September 2023
  • Published online: 15 December 2023
Fruit Research  3,  Article number: 36  (2023)  |  Cite this article

Abstract: Molecular characterization of 39 late-mature peach (Prunus persica L.) accessions was carried out using ISSR (inter simple sequence repeats) and SRAP (sequence-related amplified polymorphism) markers to assess the value and magnitude of genetic divergence. The ISSR primers revealed 70.09% polymorphism and the SRAP marker generated 73.33% polymorphism. Pooled ISSR and SRAP, along with UPGMA clustering based on similarity coefficients, were estimated to assess the efficiency of the marker system in peaches. Similarity coefficients between accessions regarding the ISSR and SRAP markers ranged from 0.65 to 0.89, indicating a broad genetic peach base. A maximum similarity coefficient of 0.89 was observed between C12 'Weiduanmihong' (Fujian Yikangyuan Farm Co., Ltd., 26.58° N, 118.75° E) and C32 'Weiduanmihong' (WeiduanVillage, 26.58° N, 118.75° E), and a minimum of 0.65 was observed in C6 'Huangjinmitao 5' (Gutian Natural Pantaoyuan Family Farm, 26.58° N, 118.75° E) and the rest of the genotypes. The present study found that a high level of polymorphism indicated their applicability in framing more extensive studies to develop superior progeny, conduct molecular breeding, investigate genetic population diversity, make comparative maps, select parents, etc., in various peach crop improvement programs.

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    Introduction

    • Peach (Prunus persica L.) is a typical plant belonging to Rosacea, which is indigenous to China. Given its economic importance and health-promoting properties, research on peach has begun to receive wide attention. The yield of Chinese peaches reached 15.02 million tons in 2020 and ranks first in the world (www.fao.org/faostat/zh/#data/QC). Peaches are the main type of deciduous fruit trees in Fujian Province, but peach ripening occurs only from late May to July, which often leads to low prices, seriously restricting improvements to the economic benefits of Fujian's peach industry. Late-mature peach varieties (those that ripen from late July to August) bring advantages to Fujian, extending the supply period for the fresh market, reducing the pressure of centralized listings, and improving the local peach industry's economic benefits. However, it is hard to tell different peach varieties apart based on plant and leaf morphology. This causes a lot of inconvenience in the accurate utilization and preservation of resources. Thus, studies on genetic diversity using various molecular marker systems can be useful in characterizing and protecting the genetic resources of late-mature peach varieties.

      Molecular markers are genetic markers based on nucleotide sequence variations in genetic material between individuals that can directly reflect genetic polymorphism on a DNA level. This is a powerful tool for estimating the characteristics of genetic diversity and distinguishing individuals from different sources[1]. For example, a high-resolution genetic linkage map of litchi was constructed using random amplified polymorphic DNA (RAPD), sequence-related amplified polymorphism (SRAP), and amplified fragment length polymorphism (AFLP); the distribution uniformity of SRAP markers in the genetic map is much better than that of AFLP[2]. A previous study showed that RAPD markers and simple sequence repeat (SSR) markers cannot distinguish between the 'Shatangju' variety and its bud sport 'Wuzishatangju', and specific bands were only obtained via the inter simple sequence repeat (ISSR) marker and the SRAP marker[3]. The results showed that ISSR and SRAP markers make relationship identification feasible. ISSR marker systems can detect polymorphisms in inter-microsatellite DNA regions without any prior sequence knowledge (which consist of repeating units ranging from one to six base pairs), targeting regions between two simple sequence repeat (SSR) sequences[4]. The SRAP marker is a PCR-based molecular marker[5] that uses a unique dual-primer design to amplify specific regions of open reading frames (ORFs). The ISSR and SRAP markers have proven their effectiveness for genetic diversity studies, variety identification, genetic map construction, localization, and cloning fruit tree genes. One study determined the relationships and genetic structures of 48 jujube cultivars derived from seven geographical regions of northern China using ISSR markers, and it was believed that there was a correlation between the genetic relationships between cultivars and their origins[6]. The high level of variation between Turkish apples can also be shown using the ISSR marker[7]. SRAP was used to evaluate the genetic diversity of wild Chinese persimmon species and foreign cultivars, indicating that different persimmon genera have broad genetic backgrounds and various origins[8]. The ISSR and SRAP markers have been used for cultivar identification in peaches. Sharma & Sharma[9] utilized the ISSR marker to analyze 45 peach cultivars and assess the value and magnitude of genetic divergence, confirming that it had validity in assessing genetic diversity in peach germplasms. The SRAP marker was employed to assess the biological and botanical characteristics of the 'Kawanakajima' peach and its bud variant line[10]. ISSR and SRAP stably amplified specific bands in the genome of the 'Piqiu' peach (white flesh) and its natural mutant (yellow flesh), proving that there was a small difference at the DNA level and that the yellow-flesh mutant budded from this peach variety[11]. Our previous results showed that the SRAP molecular marker can identify genetic relationships in late-mature peach varieties more effectively than the ISSR marker, showing that the 'Weiduanmihong' variety was a bud mutation of 'Yihong'[12].

      In this study, the ISSR and SRAP markers were applied to elucidate genetic diversity and genetic relationship information from late-mature peach varieties in Fujian Province, China. Our objectives were as follows: (1) to access the genetic diversity and relationships between late-mature peach varieties to provide appropriate germplasm management and clonal identification at field breeding stations and (2) to evaluate the usefulness of the ISSR and SRAP markers in identifying the closest-related late-mature peach cultivars.

    Materials and methods

      Plant materials

    • Thirty-nine samples were collected from eight sites in Fujian Province and Hunan Province during the spring and summer of 2019 (Supplemental Table S1). To ensure the reliability of sampling, 30 young leaves were randomly collected from the peach trees. The samples were carefully placed in a ziplock bag (280 mm × 400 mm, 0.03 mm thick, Heyuan Evergreen Plastic MFG. Co., Ltd., China), immediately placed in a foam box containing an ice pack, returned to the laboratory on the same day, and conserved at −20 °C for later use.

      DNA extraction

    • Total genomic DNA was extracted following the CTAB method by Sun et al.[13]. The purity of the DNA was determined using a BioPhotometer and a nucleic acid protein analyzer. DNA with a ratio of absorbance (OD) of 260 nm/280 nm between 1.8 and 2.0, determined using an ultraviolet spectrophotometer (Eppendorf International Trade Co., Ltd.), was used for ISSR and SRAP amplification reactions. DNA samples were stored at −20 °C, and the quality was verified via electrophoresis on ethidium bromide stained with 1% agarose gel.

      ISSR-PCR amplification

    • The optimum ISSR-PCR reaction system (20 μL) includes 10 μL of 2×easy taq PCR Super Mix (+ dye) (TransGen Biotech, Beijing, China), 0.3 μmol/L ISSR primers (Fuzhou Shangya Biotechnology Co., Ltd., Fuzhou, China), 50 ng/μL genomic DNA, adding up to 20 μL with ddH2O.

      The PCR reaction was as follows: an initial denaturation step at 94 °C for 3 min, followed by 35 cycles of 94 °C for 30 s, annealing at 52 °C for 45 s (different ISSR primers may have different annealing temperatures), and 72 °C for 90 s; the final extension at 72 °C was held for 7 min. All amplified products were resolved on 1% agarose electrophoresis in 1× TAE buffer and then stained. The images were acquired using a JS-3000 automatic gel imaging analyzer (Peiqing Technology Co., Ltd., Shanghai, China).

      SRAP-PCR amplification

    • The optimum SRAP-PCR reaction system (25 μL) included 12.5 μL of 2×easy taq PCR Super Mix (+ dye) (TransGen Biotech, Beijing, China), 0.5 μmol/L primers (Fuzhou Shangya Biotechnology Co., Ltd., Fuzhou, China), 80 ng/μL genomic DNA, adding up to 25 μL with ddH2O.

      The PCR reaction was as follows: initial denaturation at 94 °C for 5 min followed by 5 cycles of denaturation at 94 °C for 1 min; annealing at 35 °C for 1 min and extension at 72 °C for 1 min; for the next 35 cycles, denaturation at 94 °C for 1 min, annealing at 50 °C for 1 min, and extension at 72 °C for 1 min; and a final extension step at 72 °C for 10 min. All amplified products were resolved on 1.5% agarose electrophoresis in 1× TAE buffer and then stained. The images were acquired using a JS-3000 automatic gel imaging analyzer (Peiqing Technology Co., Ltd., Shanghai, China).

      Data analysis

    • The band patterns obtained with each ISSR and SRAP primer were scored as absent (0) or present (1). Only clear, reproducible bands were scored. The values were recorded using Microsoft Excel 2010 (Microsoft Co, Washington, USA). Genetic similarity between accessions was evaluated by calculating the Dice similarity coefficient, and cluster analysis was performed using the UPGMA (Unweighted Pair Group Method of Arithmetic Means) algorithm. A dendrogram was then produced based on the Dice similarity matrices for each marker type to investigate relationships between genotypes using the NTSYS-PC software package[13].

    Results

      Primer screening

    • A total of 55 ISSR primers (Supplemental Table S2) were screened initially in three representative samples from the 39 accessions, which were designed by the University of British Columbia (UBC set No. 9) in Canada[14−18]. Figure 1 showed the ISSR-PCR electrophoretogram for the 55 primers.

      Figure 1. 

      ISSR-PCR electrophoretogram with 55 ISSR primers of the 'Okubao' peach. Note: M: DNA Marker; 1-55: UBC807, UBC808, UBC809, UBC810, UBC811, UBC812, UBC815, UBC816, UBC817, UBC818, UBC820, UBC823, UBC824, UBC825, UBC826, UBC827, UBC829, UBC830, UBC834, UBC835, UBC836, UBC837, UBC840, UBC841, UBC842, UBC843, UBC844, UBC845, UBC846, UBC847, UBC848, UBC849, UBC850, UBC851, UBC853, UBC854, UBC855, UBC856, UBC857, UBC861, UBC862, UBC864, UBC865, UBC866, UBC867, UBC873, UBC874, UBC876, UBC880, UBC881, UBC888, UBC889, UBC890, UBC891, UBC895.

      In total, 12 forward primers combined with 14 reverse-primer crossover trials were used to screen the primer pair[5, 19−20]; 168 pairs of SRAP-labeled PCR amplification primers were randomly combined using a forward primer and reverse primer (Supplemental Table S3). Figure 2 showed the SRAP-PCR electrophoretogram for the 168 primers.

      Figure 2. 

      PCR electrophoretogram with different SRAP primers of the 'Okubao' peach. Note: M: DNA Marker; 1-87: me1/em1, me1/em2, me1/em3, me1/em4, me1/em5, me1/em6, me1/em7, me1/em8, me1/em9, me1/em10, me1/em11, me1/em17, me1/em18, me1/em19, me2/em1, me2/em2, me2/em3, me2/em4, me2/em5, me2/em6, me2/em7, me2/em8, me2/em9, me2/em10, me2/em11, me2/em17, me2/em18, me2/em19, me3/em1, me3/em2, me3/em3, me3/em4, me3/em5, me3/em6, me3/em7, me3/em8, me3/em9, me3/em10, me3/em11, me3/em17, me3/em18, me3/em19, me4/em1, me4/em2, me4/em3, me4/em4, me4/em5, me4/em6, me4/em7, me4/em8, me4/em9, me4/em10, me4/em11, me4/em17, me4/em18, me4/em19, me5/em1, me5/em2, me5/em3, me5/em4, me5/em5, me5/em6, me5/em7, me5/em8, me5/em9, me5/em10, me5/em11, me5/em17, me5/em18, me5/em19, me6/em1, me6/em2, me6/em3, me6/em4, me6/em5, me6/em6, me6/em7, me6/em8, me6/em9, me6/em10, me6/em11, me6/em17, me6/em18, me6/em19, me7/em1, me7/em2, me7/em3, me7/em4, me7/em5, me7/em6, me7/em7, me7/em8, me7/em9, me7/em10, me7/em11, me7/em17, me7/em18, me7/em19, me8/em1, me8/em2, me8/em3, me8/em4, me8/em5, me8/em6, me8/em7, me8/em8, me8/em9, me8/em10, me8/em11, me8/em17, me8/em18, me8/em19, me9/em1, me9/em2, me9/em3, me9/em4, me9/em5, me9/em6, me9/em7, me9/em8, me9/em9, me9/em10, me9/em11, me9/em17, me9/em18, me9/em19, me10/em1, me10/em2, me10/em3, me10/em4, me10/em5, me10/em6, me10/em7, me10/em8, me10/em9, me10/em10, me10/em11, me10/em17, me10/em18, me10/em19, te1/em1, te1/em2, te1/em3, te1/em4, te1/em5, te1/em6, te1/em7, te1/em8, te1/em9, te1/em10, te1/em11, te1/em17, te1/em18, te1/em19, mo1/em1, mo1/em2, mo1/em3, mo1/em4, mo1/em5, mo1/em6, mo1/em7, mo1/em8, mo1/em9, mo1/em10, mo1/em11, mo1/em17, mo1/em18.

      In this study, 18 ISSR primers and 18 SRAP primers were selected, which produced clear and repeatable fragments for variety identification and genetic relationship analysis (Supplemental Table S4).

      ISSR and SRAP polymorphisms

    • As noted, 18 ISSR primers were selected for cultivar identification and genetic relationship analysis in this study. Supplemental Table S4 showed a total of 123 bands with an average of 6.83 were screened out from the 39 late-mature peach cultivars, among which, 86 were polymorphic, yielding a polymorphism rate of 70.09%. The number of polymorphic bands varied from 1 (UBC835) to 8 (UBC880). The amplification results of primer UBC812 were shown in Fig. 3. The results showed that the genetic diversity of late-mature peach resources in Fujian Province was rich, and the genetic differences between individuals were large. The ISSR marker was suitable for determining the genetic diversity of late-mature peach resources.

      Figure 3. 

      Electrophoretic profiles of genomic DNA amplification products using ISSR primer UBC812. The numbers in the figure correspond to the numbers listed in Supplemental Table S1.

      Eighteen SRAP primers that generate high polymorphic bands were chosen for genetic diversity analysis, and a total of 90 bands were generated by these 18 primers, ranging from 3 (me1/em6, me5/em6, me9/em8, and me9/em11) to 9 (me2/em11). Of the 90 bands produced, 66 (73.33%) were polymorphic. The above results were showed in Supplemental Table S4. The amplification results of primer me2/em11 were shown in Fig. 4. Although the total number of bands in SRAP was smaller than in the ISSR marker, the polymorphism of SRAP marker bands was higher than those of the ISSR marker. These results suggested that the SRAP marker was also suitable for determining the genetic diversity of late-mature peach resources.

      Figure 4. 

      Electrophoretic profiles of genomic DNA amplification products using SRAP marker me2/em11. The numbers in the figure correspond to the numbers listed in Supplemental Table S1.

      Figures 3 & 4, Supplemental Figs S1−S4 showed that there was no single ISSR or SRAP primer to distinguish all late-mature peach cultivars independently. Furthermore, the results showed that ISSR and SRAP markers could effectively reveal polymorphisms between late-mature peach materials, which indicated the presence of high genetic diversity between late-mature peach germplasms.

      Analysis of ISSR marker

    • The ISSR marker (Fig. 5) had a similarity coefficient ranging from 0.61 to 0.90, indicating substantial diversity in the germplasm. A maximum similarity coefficient of 0.90 was observed between C12 'Weiduanmihong' and C32 'Weiduanmihong' and a minimum of 0.61 was observed in C37 'Linkui 1' and the rest of the genotypes. A cluster tree analysis obtained after pooled ISSR analysis showed that germplasm C37 'Linkui 1' was far removed from other germplasms.

      Figure 5. 

      Dendrogram obtained after pooled ISSR analysis of late-mature peach germplasms. The numbers in the figure correspond to the numbers listed in Supplemental Table S1.

      The pairwise similarity coefficient heatmap obtained using the combination of both ISSR markers ranged from 0.5408 to 0.8953, with an average of 0.6971 (Fig. 6). A maximum similarity coefficient of 0.8953 was observed between C12 'Weiduanmihong' and C32 'Weiduanmihong', and a minimum of 0.5408 was observed in C10 'Zhongtao 5' and C37 'Linkui 1', which indicated that C12 'Weiduanmihong' from YKY had a close relationship with C32 'Weiduanmihong' from WD, but its genetic relationship with C10 'Zhongtao 5' and C37 'Linkui 1' was the furthest. Thus, significant genetic variation has occurred between C10 'Zhongtao 5' and C37 'Linkui 1'.

      Figure 6. 

      Genetic similarity coefficient heatmap of 39 late-mature peach germplasm based on ISSR markers. The numbers in the figure represent different late-mature peach germplasms in Supplemental Table S1.

      Analysis of SRAP marker

    • The SRAP marker (Fig. 7) showed that the similarity coefficient ranged from 0.63 to 0.91, indicating substantial diversity present in the germplasm. A maximum similarity coefficient of 0.91 was observed between C31 'Weiduanmihong' and C32 'Weiduanmihong', and a minimum of 0.63 was observed in C35 'Qinhuang 2', C36 'Xianghuang', C39 'Yonglian No.1', and the rest of the genotypes. The cluster tree analysis obtained after the pooled SRAP analysis showed that germplasms C35, C36, and C39 were far removed from the other germplasms.

      Figure 7. 

      Dendrogram obtained after pooled SRAP analysis of late-mature peach germplasms. The numbers in the figure correspond to the numbers listed in Supplemental Table S1.

      The pairwise similarity coefficient heatmap obtained using the combination of both ISSR markers ranged from 0.5352 to 0.9063, with an average of 0.6945 (Fig. 8). A maximum similarity coefficient was observed between C31 'Weiduanmihong' and C32 'Weiduanmihong', and a minimum was observed in C2 'Baili' and C36 'Xianghuang', which indicated that C31 from WD had a close relationship with C32 from WD, and its genetic relationship with C2 and C36 was the furthest. Thus, significant genetic variation had occurred between C2 and C36.

      Figure 8. 

      Genetic similarity coefficient heatmap of 39 late-mature peach germplasms based on SRAP markers. The numbers in the figure represent different late-mature peach germplasms in Supplemental Table S1.

      Pooled Analysis of ISSR and SRAP Studies

    • The pooled ISSR and SRAP studies (Fig. 9) showed that the similarity coefficient ranged from 0.65 to 0.89, indicating substantial diversity present in the germplasms. A maximum similarity coefficient of 0.89 was observed between C12 'Weiduanmihong' and C32 'Weiduanmihong', and a minimum of 0.65 was observed in C6 'Huangjin-mitao 5' and the rest of the genotypes. The cluster tree analysis obtained after the pooled ISSR and SRAP analysis showed that the 39 peach genotypes could be grouped into two major clusters, one comprising 38 genotypes, and the other comprising C6 'Huangjin-mitao 5' (Fig. 9). Germplasm C6 'Huangjin-mitao 5' was far removed from the other germplasms.

      Figure 9. 

      Dendrogram obtained after pooled ISSR and SRAP analysis of late-mature peach germplasms. The numbers in the figure correspond to the numbers listed in Supplemental Table S1.

      The pairwise similarity coefficient heatmap obtained using the combination of both ISSR and SRAP markers ranged from 0.5767 to 0.8926, with an average of 0.6957 (Fig. 10). A maximum similarity coefficient of 0.8926 was observed between C12 'Weiduanmihong' and C32 'Weiduanmihong', and a minimum of 0.5767 was observed in C6 'Huangjinmitao 5' and C26 'Tiantao', which indicated that C12 'Weiduanmihong' from YKY had a close relationship with C32 'Weiduanmihong' from WD, and its genetic relationship with C6 'Huangjinmitao 5' and C26 'Tiantao' was the furthest. Thus, significant genetic variation had occurred between C6 'Huangjinmitao 5' and C26 'Tiantao'.

      Figure 10. 

      Genetic similarity coefficient heatmap of 39 late-mature peach germplasms based on ISSR and SRAP markers. The numbers in the figure represent different late-mature peach germplasms in Supplemental Table S1.

    Discussion

    • Genetic diversity refers to the sum of genetic variation between populations or individuals within a population, reflecting the genetic background, genetic differentiation, and breeding potential of a species[21]. Molecular marker technology is a robust approach to studying the genetic diversity of crop germplasm resources[22]. Studies have shown that the ISSR molecular marker could overcome the shortcomings of the RAPD and SSR markers[3, 23], and that SRAP combines the advantages of RAPD and AFLP markers[24]. ISSR and SRAP markers have the advantages of simplicity, rapidity, high stability, good repeatability, relatively low cost, and rich polymorphism, making them suitable for genetic diversity analyses[25]. In a previous study, ISSR and SRAP molecular markers have been used for cultivar identification in peaches. Based on 20 SSR and 49 SRAP polymorphic fragments, the relationship among the 38 peach and nectarine cultivars was represented by the UPGMA dendrogram, with the nectarine cultivars being interspersed among the peach cultivars[20]. Additionally, SSR and SRAP molecular markers have been used for the grouping and identification of peach and nectarine cultivars, with SRAP protocols revealing more polymorphisms in peach cultivars than SSR marker[20]. Of the cultivars analyzed, more than 85% had a unique SRAP fingerprint, confirming which confirmed the high efficiency of this marker system for identifying genetic diversity in peach and nectarine cultivars[20]. A total of 132 useful markers were generated from 10 ISSR primers for 16 ornamental peach taxa, and using UPGMA, two different categories of peach taxa could be clustered at the coefficient level of 0.775[26]. These studies proved that the ISSR marker was represents a useful technique to reveal groups (different pedigrees and different growth habits) and genetic relationship among ornamental peach varieties. The pedigree prunus davidiana has been involved in the breeding and selection of ornamental peach cultivars, and these cultivars apparently have genetic distance from pure prunus persica cultivars[26]. Techniques using ISSR and SRAP molecular markers can also be applied to the identification and analysis of peach bud variation materials to provide a foundation for genetic analysis for the future breeding of new peach varieties[11, 12]. SSR primers were able to produce one or two discrete fragments in 'Beijing 28' and its bud-variant material, and 39 ISSR primers were able to amplify 314 bands, with a polymorphic percentage of 11.46%. ISSR showed higher polymorphism than SSR[14]. SRAP markers have been applied to analyze the genetic relationships among Taiwan precocious peach and its bud sport germplasm, 'May Red' peach. It was determined that 'May Red' showed polymorphic bands that were different from those of Taiwan precocious peach and were mutated at the DNA genetic level, indicating that this germplasm might belong to a new, especially early-mature germplasm resource[27]. ISSR and SRAP markers were applied to analyze 'Piqiu' peach and its mutant strains, 'Yihong' peach and 'Weiduanmihong', respectively. The primers stably amplified specific bands in the genome of 'Piqiu' peach and its mutant strain as well as in 'Yihong' peach and its mutant strain 'Weiduanmihong'[11,12]. The above proves the existence of DNA-level differences between the control group and the mutant strains[11,12]. In our study, 123 bands were amplified using 18 ISSR primers, with 6.83 bands per primer on average, and 90 bands were amplified using 18 SRAP primers with 5 bands per primer on average. The ISSR marker generated more bands than the SRAP marker. This difference may be because ISSR amplified the whole plant genome sequence, while SRAP amplified only the ORFs[4,5, 28]. ISSR and SRAP also had a higher percentage of polymorphic bands (PPB) in the 39 late-mature peach germplasms samples (ISSR = 70.09%; SRAP = 73.33%), indicating that both markers effectively revealed polymorphisms among the late-mature peaches and that the SRAP marker was more efficient in distinguishing differences between them. This was supported by Yan et al.[1] and Huang et al.[3], who found that genetic diversity revealed using the SRAP and ISSR markers is highly consistent.

      The combination selection of different markers can cover the genome to the greatest extent, which is conducive to increasing the density and quality of the map[29]. The relationships based on the ISSR and SRAP molecular markers analysis had many similarities, such as clustering in 'Weiduanmihong' samples (C12, C31, and C32) from different places, clustering in 'Yihong' samples (C13 and C18) from different places, clustering C33 'Wanbaifeng' and C34 'Zhonghuashoutao' samples together, clustering C5 'Jinxiu' and C7 'Jinyuan' samples together, all of which indicates that the markers were effective. These results agreed with the views of Li et al.[30] and Mao et al.[31]. Li et al.[30] indicated that combinations of high-efficiency and -capacity ISSR and SRAP marker systems could be useful in discriminating apricot cultivars. In addition, the genetic diversity of various late-mature peach genotypes was investigated using ISSR and SRAP marker technologies, along with dendrogram construction. Our results showed that the similarity coefficient of the 39 late-mature peaches was as low as 0.65 and as high as 0.89, and the pairwise similarity coefficients ranged between 0.5767 and 0.8926, indicating that the collected peach samples had high genetic diversity. The genetic relationships between the 39 late-mature peach cultivars were analyzed by combining the ISSR and SRAP markers to avoid the limitations of a single marker. The UPGMA analysis showed that C6 'Huangjinmitao 5' was clustered in a single group, indicating that it had the furthest-removed genetic relationship from other cultivars. Ma et al.[28], Wang et al.[32], and Parthiban et al.[33] reported that their UPGMA analyses showed slightly different clustering patterns based on SRAP and ISSR, which might be related to a different portion of genomes amplified with different marker systems. Therefore, the combination of ISSR and SRAP markers could result in more comprehensive and accurate analyses of genetic relationships between accessions[34,35]. The similarity coefficient between C12 'Weiduanmihong' (Fujian Yikangyuan Farm Co., Ltd., 26.58° N, 118.75° E), C31 'Weiduanmihong' (WeiduanVillage, 26.58° N, 118.75° E), and C32 'Weiduanmihong' (WeiduanVillage, 26.58° N, 118.75° E) was largest of all (0.8560−0.8926), meaning there was a close relationship between the three; the similarity coefficient between C13 Zhijiang 'Yihong' (Hunan Province Kangruinong Ecological Agriculture Co., Ltd., 27.45° N, 109.68° E) and C18 Zhijiang 'Yihong' (Fujian Yikangyuan Farm Co., Ltd., 26.58° N, 118.75° E) was larger (0.8924) than the one between the other resources, which meant that C12 and C32 are the same peach germplasm, and C13 and C18 should be the same clone. Similar results were reported by Sun et al.[15].

    Conclusions

    • ISSR and SRAP markers revealed low genetic diversity between different late-mature peach accessions from different regions of Fujian Province, indicating that there were rich late-mature peach germplasm resources in Fujian. Analyses of genetic diversity and genetic relationships between late-mature peach germplasms will help gather and select good-quality peach lines in the future. Our results showed that ISSR and SRAP were useful methods in detecting high genetic diversity in late-mature peach germplasms. The results will also provide technical support for the further construction of late-mature peach germplasm resources and provide a theoretical basis for their protection, breeding, and development.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Ma C, Qiu D; data collection: Li W, Ma Y, Kou Y, Zeng Z; analysis and interpretation of results: Li W, Ma C, Qiu D; draft manuscript preparation: Li W, Ma C, Qiu D. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • The datasets generated during the current study are available from the corresponding author on reasonable request.

      Acknowledgments

      • This work was financially supported by the Fujian Provincial Natural Science Foundation Project (2022J01590), the National College Student Innovation Training Program (202210389036), and the Major Project of Industry–University Cooperation for the University in Fujian Province (2018N5004). The authors thank the teachers and students at the laboratory for their help and support during the experiment and article-writing process. We are also extremely grateful to the Gutian Mengxiangyuan Fruit Professional Cooperative, Gutian Natural Pantaoyuan Family Farm, Fujian Yikangyuan Farm Co., Ltd., Hunan Province Kangruinong Ecological Agriculture Co., Ltd., Fujian Feihu Agricultural Development Co., Ltd., and Nianqing Family Farm, who kindly provided large quantities of different peach varieties.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

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      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Cite this article
    Li W, Ma Y, Kou Y, Zeng Z, Qiu D, et al. 2023. Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers. Fruit Research 3:36 doi: 10.48130/FruRes-2023-0036
    Li W, Ma Y, Kou Y, Zeng Z, Qiu D, et al. 2023. Analysis of genetic diversity and relationships between late-mature peach (Prunus persica L.) varieties assessed with ISSR and SRAP markers. Fruit Research 3:36 doi: 10.48130/FruRes-2023-0036
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  • Introduction
  • Materials and methods

  • Plant materials

  • DNA extraction

  • ISSR-PCR amplification

  • SRAP-PCR amplification

  • Data analysis

  • Results

  • Primer screening

  • ISSR and SRAP polymorphisms

  • Analysis of ISSR marker

  • Analysis of SRAP marker

  • Pooled Analysis of ISSR and SRAP Studies

  • Discussion
  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
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