Skip to main content
Download PDF

QTL associated with Gummy Stem Blight (GSB) resistance in watermelon

BMC Genomics volume 23, Article number: 632 (2022) Cite this article

Abstract

Background

Gummy stem blight (GSB), caused by Didymella bryoniae (syn. Stagonosporopsis cucurbitacearum), produces devastating symptoms on whole plants of watermelon (Citrullus lanatus) and other cucurbits, significantly reducing yield and quality. Identification of genetic determinants and sources of resistance to this devastating GSB disease in watermelon is essential for developing resistant varieties.

Results

In this study, we aimed at identifying quantitative trait loci (QTLs) linked to GSB resistance in melon. We identified the genome-wide single nucleotide polymorphisms (SNPs) by genotyping by sequencing (GBS) of an F2 population developed from C. lanatus lines, ‘PI 279461’ (resistant) ✕ ‘PI 223764’ (susceptible). Inheritance analysis indicated that resistance to GSB is a multi-genic trait in this population. Three QTLs namely, ClGSB1.1, ClGSB10.1, and ClGSB11.1 associated with GSB resistance, explaining approximately 10% of the phenotypic variation, were identified. Among these, the QTL ClGSB1.1 on chromosome 1 is identified as a major QTL harboring five candidate genes associated with GSB resistance including two RLKs (ClC01G014900 and ClC01G015010), two WRKY transcription factors (ClC01G014910 and ClC01G014990), and one AvrRpt-cleavage domain protein (ClC01G015130).

Conclusion

Two high resolution melting (HRM) markers, WmGSB1.1–2 and WmGSB1.1–7 having a high positive correlation with the phenotypic variations, were developed. Five potential candidate genes were predicted to be associated with GSB resistance. These findings will help breeders to develop watermelon cultivars resistant to GSB.

Peer Review reports

Introduction

Watermelon (Citrullus lanatus) is one of the most popular fruits in the Cucurbitaceae family, and is grown throughout tropical to temperate regions of the world where the climate is favorable (FAOstat 2021). Watermelon fruit is rich in water (91%), and important nutritional compounds such as sugars, lycopene, β-carotene and citrulline, which are very beneficial to human health [1].

Watermelon production is frequently hampered by various insects and diseases, of which gummy stem blight (GSB), caused by the soil, airborne, and seed-borne fungal pathogen Didymella bryoniae (syn. Stagonosporopsis cucurbitacearum), is the most devastating disease of watermelon [2,3,4]. GSB symptoms include circular dark tan lesions that blight the leaf, stem cankers, and gummy brown ooze exuding from cankers. Although chemical control methods had been moderately effective in controlling GSB, the repeated use of chemicals has a negative impact on the environment, and may become ineffective due to the rise of resistance to chemicals in certain pathogenic isolates [5, 6]. Therefore, development of resistant cultivars is the most eco-friendly, cost-effective and sustainable method of watermelon production.

Several sources of genetic resistance to GSB have been identified, mostly in the wild relative, Citrullus amarus [7, 8] and later, in both C. amarus and C. lanatus [9, 10]. Genetic studies of these resistant genotypes indicated various patterns of genetic control of resistance to GSB, including monogenic control in PI 189225 [11] and polygenic control with minor effects from individual genes in PI 189225, PI 482283, and PI 526233 [4, 12, 13]. Several studies have mapped the QTLs underlying GSB resistance in watermelon, including one QTL on chromosome 8 of PI 189225 explaining 32% of the phenotypic variations [13]; three QTLs on chromosomes 3, 5 and 7, explaining between 6.4 and 21.1% of the phenotypic variations [14] and another three QTLs on chromosomes 8 and 6 [15]. Besides, QTLs for GSB resistance have also been identified in other cucurbits, such as Cucumber [16, 17] and melon [12, 18].

The release of draft genomes of watermelon and genotyping by sequencing (GBS) has made it possible to discover genome-wide sequence variations including single nucleotide polymorphism (SNP) and Insertion/Deletion (InDel) especially in the causal genes within the QTL regions [19, 20]. This facilitated the development of high throughput molecular markers such as Cleaved amplified polymorphic sequence (CAPS) markers, high-resolution melting (HRM) and PCR amplicon using InDel based markers in Cucurbitaceae. CAPS/HRM markers have been reported for ‘PMR 5’ resistance to powdery mildew race 5 in melon [21]. GBS and ddRAD-seq techniques have been used for mapping genes and development of markers in several plant species such as rapeseed, soybean, maize, strawberry and melon [22,23,24,25]. Among these assays, HRM and Kompetitive allele specific PCR (KASP) assays have relatively simple, fast, and cost-effective approaches. Recently, high throughput KASP markers for resistance to GSB have been reported in watermelon [13,14,15]. However, these markers were developed based on the resistance of C. amarus (PI 482276 and PI 189225) and hence, are often not effective on C. lanatus cultivars. Therefore, identification of QTLs conferring resistance to prevalent pathogenic D. bryoniae strains of different parts of the world and development of effective high-throughput markers based on C. lanatus are essential for any marker-assisted future breeding endeavors. This study describes the identification of QTL and development of HRM markers using C. lanatus accessions against the devastating Korean isolate of D. bryoniae in watermelon.

Materials and methods

Plant materials and population development

The F2 segregating population of 128 plants were generated from the cross of the resistant watermelon (C. lanatus) genotype, PI 279461 (♀) and the susceptible genotype, PI 223764 (♂). In addition, another seven PI accessions were obtained from the U.S. National Plant Germplasm System (https://npgsweb.ars-grin.gov/gringlobal/search), U.S. Department of Agriculture (USDA), USA, and one accession, SNWK108 was obtained from Sunchon National University, Korea. All watermelon plants were grown in plant culture room at 24–28 °C and 60% relative humidity under long-day conditions (14-h light/10-h dark cycles).

Pathogen culture and inoculation

The fungal isolate Didymella bryoniae, 13–020, was obtained from the National Institute of Horticultural and Herbal Science (NIHHS) in Korea [12]. The fungus was grown on potato dextrose agar (PDA) medium for 2 weeks at 24 ± 2 °C until the formation of pycnidia [12]. The inoculum was prepared at a final concentration of 5 × 105 spores/mL in deionized water using a hemocytometer. Inoculation was done using a hand-spray bottle when the second true leaves of the plants were fully open. The inoculated plants were then incubated in a plastic tent to maintain high relative humidity [12, 26]. Re-inoculation was performed after 3 days of first inoculation to achieve successful inoculation of all plants and to eliminate false positives.

Disease assessment

Each inoculated plant was scored at 2 weeks after inoculation (WAI) using a scale of 1 to 5: 1, < 1% affected leaf area; 2, 1–10% affected leaf area; 3, 11–30% affected leaf area; 4, 31–50% affected leaf area; and 5, 51–100% affected leaf area and death of plant (Fig. 1). The percentage of inoculated area was measured by the ratio of inoculated to total leaf area, multiplied by 100. The percent disease index (PDI) was calculated to reliably identify phenotypic data using the following equation:

$$\mathrm{PDI}=100\times \sum \frac{\mathrm{Sum}\ \mathrm{of}\ \mathrm{numerical}\ \mathrm{disease}\ \mathrm{rating}}{\ \mathrm{Number}\ \mathrm{of}\ \mathrm{plants}\ \mathrm{evaluated}\times \mathrm{maximum}\ \mathrm{disease}\ \mathrm{rating}\ \mathrm{score}\ }$$
Fig. 1
figure 1

Disease scores used to estimate the severity of gummy stem blight caused by Didymella bryoniae at 2 weeks after inoculation (WAI). Leaves were rated according to the following scales: 1 = 0–1% affected leaf area, 2 = 1–10%, 3 = 11–30%, 4 = 31–50%, and 5 = 51–100% and death of plant

Full size image

Individulas with a PDI of ≤ 30 (when leaves of the inoculated plant have few symptoms) and > 30 were considered as resistant and susceptible, respectively.

Extraction of genomic DNA and HRM assay

Genomic DNA (gDNA) was extracted from the young leaves using the DNeasy Plant Mini Kit (QIAGEN, Hilden, Germany) as per the manufacturer’s instructions. The concentration of DNA was quantified by a ND-100 Micro- spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). To detect SNPs, HRM curve analysis was combined with a 3-blocked and unlabeled oligonucleotide probe (HybProbe) specific to the SNP site [21]. SYTO 9 green fluorescent nucleic acid stain (Invitrogen, Thermo Fisher Scientific, USA) was used in PCR to generate melting curves specific to the probe genotype. Primer and probe were synthesized by Macrogen Co., Seoul, Korea. The HRM PCR mixture contains 1 μl of genomic DNA at 5 ng/μl, 0.1 μl forward and 0.5 μl reverse primers (10 pmol), 0.5 μl probe (10 pmol), 0.3 μl SYTO 9 fluorescent dye, 5 μl HS prime LP premix (GENETBIO, Daejeon, Korea), and 2.6 μl DDW in a total volume of 10 μl. Primers are listed in Table S1. The PCR was performed as: a 5 minute initial pre-incubation at 95 °C followed by 45 cycles of 95 °C for 20 seconds, annealing at 64 °C and 56 °C for 20 s under touchdown command, and 72 °C for 20 s. At the final step, four readings per °C were taken after 60 seconds at 95 °C, 120 seconds at 40 °C, and 1 s at 97 °C. HRM curve analysis was performed using LightCycler 96 software version 1.1 (Roche, Mannheim, Germany) at 100% discrimination for both delta Tm and curve shape with a 0.02 positive/negative threshold level.

Genotyping by sequencing and QTL mapping

For genotyping by sequencing (GBS), samples of a total of 96 plants (two plants of each of the parents; two plants of the F1 generation; 45 resistant and 45 susceptible plants from the F2 generation) were prepared [27, 28]. QIAquick PCR Purification Kit (QIAGEN, Hilden, Germany) was used to purify the samples according to the manufacturer’s instructions. Genomic DNA sequencing was carried out at the Macrogen Co., Seoul, Korea, through the HiseqX instrument (Illumina, San Diego, USA). GBS was performed for the genomic DNA sequence of PI 223764, PI 279461, F1 generation and F2 generation with a custom designed method [29] by DNACARE, Seoul, Korea (Table S2). Using Burrow-Wheeler Aligner (BWA), pair end reads of each parent were aligned to the reference genome (97,103 watermelon genome version 2) [30, 31]. GATK Hablotypecaller-GVCF genotyper pipeline was used to SNP call variants which was then filtered by VCF tools (MQ < 40 and FS > 60) [32]. The SNPs were filtered and selected with the following characteristics: (i) homozygous SNPs in parent lines and heterozygous SNPs in F1 population, (ii) SNPs with A/G, A/C, T/G, and T/C combinations, excluding A/T and G/C, (iii) a criterion of a genotype missing < 40%, (iv) maintaining 100 kb of spacing between markers, and (v) segregation distortion at P < 0.001.

A genetic linkage map was constructed using JoinMap software version 4.1 (Kyazma, Wageningen, Netherlands) using the Kosambi mapping function, and they were plotted on the 11 linkage groups based on their physical and genetic distance. QTL mapping was used to identify the position of QTL for GSB resistance traits using inclusive composite interval mapping conducted in QTL ICIMapping software version 4.2 [33]. Significant thresholds with 1000 permutations were found in stepwise regressions with p < 0.001. The location of the QTL was described using the LOD (logarithm of odds) value. The contribution rate (PVE) was calculated by dividing the percentage of variance explained by each QTL by the total phenotypic variance. The additive effect was estimated to determine whether there was a positive or negative effect on the target trait. Putative candidate genes within significant QTLs were identified from the 97,103 genome v2 . Syntenic regions associated with GSB resistance in other cucurbits were identified using the Search Synteny Blocks of the CuGenDB (http://cucurbitgenomics.org) [34].

RNA extraction and qRT-PCR analysis

Total RNAs were extracted from 100 mg of powdered watermelon leaves inoculated for 0 hours (control), 12 hours, 24 hours, and 72 hours using a MiniBEST Plant RNA Extraction Kit (TaKaRa, Seoul, Korea) according to the manufacturer’s instructions. Total RNA concentration and quality were measured using a ND-100 Micro-spectrophotometer. A first-strand synthesis kit (Enzynomics, Daejeon, Korea) with oligo (dT) primers was used for cDNA synthesis from 1 μg of total RNA. The cDNA was then used for real time quantitative PCR with a LightCycler 96 instrument using qPCRBIO SyGreen Mix (PCR Biosystems, London, UK). The qRT-PCR conditions were as follows: 95 °C for 5 min; 3-step amplification at 95 °C for 15 second, 58 °C for 15 s and 72 °C for 20 s for 45 cycles. Primers are listed in Supplementary Table 3. The relative expression level of each gene was calculated by 2- ΔΔct method [35], using Actin (ClC02G026960) as an internal control [36].

Statistical data analysis

Using the XLSTAT software, a chi-square (2) test for goodness-of-fit was used to determine deviations of observed data from expected segregation ratios. The data is presented as the mean ± standard error of the mean. The student’s t-test or one-way ANOVA were used to assess statistical differences. A p-value of 0.05 was used to determine the significance of differences between means. PRISM 6 software was used for all analyses (ver. 6.01, GraphPad Inc., San Diego, USA).

Results

Inheritance of GSB resistance in watermelon

The average (of 10 individuals) percent disease index (PDI) of the resistant parent, PI 279461 (24%) and the F1 plants (26%) were significantly lower compared to that of the susceptible parent, PI 223764 (85%) (Fig. 2A). Among the 128 F2 genotypes, 85 were resistant while 43 were susceptible (Table S2 and Fig. S1). Chi-square (χ2) test revealed that resistance to GSB doesn’t follow a 3:1 (resistant: susceptible) segregation ratio (Chi-square value of 5.04 at p < 0.05). In addition, the frequency distribution of the PDI of F2 population doesn’t show a normal distribution either (Fig. 2B). These suggest the presence of a quantitative mode of inheritance of resistance to GSB in the F2 population indicating that the resistance trait is most likely to be controlled by multiple genes.

Fig. 2
figure 2

A Phenotypes of the resistant (PI 2794461), susceptible (PI 223764), and their F1 generation two-weeks after inoculation with D. bryoniae. B Frequency distribution of disease severity in F2 population. P1, P2 and F1 indicate PI 279461, PI 223764 and their hybrids, respectively. Error bars indicate S.E.M.

Full size image

Genetic map construction and QTL mapping

Genotyping by sequencing (GBS) analysis of parental, F1 and F2 populations identified a total of 593,024 SNPs and InDels polymorphisms between resistant vs susceptible genotypes (Table S4). A genetic map was constructed using 90 F2 plants segregating for GSB resistance, and 92 filtered markers (Fig. 3). The total length of the genetic linkage map was 1434.2 cM, with an average length of 130.4 cM and an average of 9.6 markers per linkage group. The average interval between markers was 18.11 cM (Fig. 3 and Table S5). A total of three QTLs for resistance to GSB namely, ClGSB1.1 on chromosome 1 (89.5–117.5 cM), ClGSB10.1 on chromosome 10 (98.5–115.5 cM) and ClGSB11.1 one chromosome 11 (36.5–52.5 cM) with maximum LOD scores of 6.66, 6.05 and 6.55, respectively, were identified via composite interval mapping (Fig. 3 and Table 1). The genes within each of the three QTLs (1209 within ClGSB1.1, 1225 within ClGSB10.1 and 348 within ClGSB11.1) were identified as shown in Table S6.

Fig. 3
figure 3

Genetic map of watermelon constructed by using the identified SNPs via GBS analysis. The numbers on the left side represent the g enetic distance from the top of each chromosome in centi Morgan (cM). Names of the right side indicate SNP marker name. QTLs, ClGSB1.1, ClGSB10.1 and ClGSB11.1 obtained by composite interval mapping (CIM) analysis are shown in black, red and blue boxes on chromosome 1, 10 and 11, respectively

Full size image
Table 1 Details of the QTLs identified for resistance to GSB in the F2 populations derived from the cross of PI 279461 (♀) and PI 223764 (♂)
Full size table

Development of HRM markers using identified SNPs in QTLs

To develop high throughput HRM (high-resolution melting) markers, 60, 65, and 11 SNPs/InDels were identified in the QTLs of chromosomes 1, 10, and 11, respectively. Among these, 9, 3, and 4 SNP markers on chromosomes 1, 10, and 11, respectively, were selected based on the high agreement of GBS data based genotype and phenotype in P1, P2, F1 and F2 (Table 2 and Table S7). Five SNPs: WmGSB1.1–3, WmGSB1.1–5, WmGSB1.1–6, WmGSB1.1–7 and WmGSB10.1–2 were found in intragenic regions and the others in intergenic regions (Table 1). These markers were first validated in parental and F1 generation (Fig. S2). WmGSB1.1–2, WmGSB1.1–3, WmGSB1.1–4, WmGSB1.1–5, WmGSB1.1–6, WmGSB1.1–7, WmGSB1.1–9, WmGSB10.1–1, and WmGSB10.1–2 showed polymorphic melting curves between resistant and susceptible parents, as well as a heterogeneous melting curve in the F1 generation (Fig. S2). The marker WmGSB1.1–8 identified polymorphism in the parental lines, but failed to show the heterogeneity in the F1 generation.

Table 2 Genomic location of SNPs/InDel and their nucleotide variation with the watermelon reference genome
Full size table

Validation of marker performance

The nine markers, seven within the QTL, ClGSB1.1 and two within ClGSB10.1 that clearly distinguished the resistant and susceptible parents and their heterozygous hybrids were further validated using the eight watermelon accessions and F2 populations (Table 3). Among these, two markers, WmGSB1.1–2 (Chr01_28,572,939) and WmGSB1.1–7 (Chr01_28,931,313), were found to be highly associated with GSB resistance in watermelon accessions (both match 6 out of 8 accessions), with a highly significant correlation between marker genotyping and F2 population PDI scores (R2 value of 9.48 and 7.92%, respectively at P < 0.05) (Figs. 4 and S2). However, the genotyping of markers in ClGSB10.1 didn’t show a strong correlation with PDI scores for resistance to GSB. For WmGSB1.1–2 and WmGSB1.1–7 assays, PDI scores were significantly lower for individuals homozygous for the resistant allele (R/R) than that of individuals homozygous for the susceptible allele (S/S) (Fig. 4), whereas other markers didn’t show such significant correlation (data not shown). Genotyping by marker WmGSB1.1–2 (Chr01_28,572,939) in F2 population showed a significant association with PDI scores (RR = 32.8, SS = 44.5, P < 0.05) and had an R2 value of 9.48%. Marker WmGSB1.1–7 (Chr01_28,931,313) showed a significant difference (P < 0.05) in PDI between RR allele (PDI = 33.64) and SS allele (PDI = 42.8) in F2 population (R2 = 7.92%).

Table 3 Genotyping efficiency of the selected nine HRM markers in distinguishing GSB resistant vs susceptible individual from diverse genotypes
Full size table
Fig. 4
figure 4

Box plots showing the efficacy of the HRM markers ClGSB1.1–2 and ClGSB1.1–7 in distinguishing the resistant and susceptible genotypes in parents, F1 and F2 population. The whiskers represent the lowest or highest data point within the 1.5 interquartile range of the lower or upper quartile. The thick horizontal lines in the boxes represent the median (P < 0.05; one-way ANOVA). R/R genotype represents the resistant (PI 279461) allele, H genotype represents the heterozygote and S/S genotype represents the susceptible (PI 223764) allele. Dots within the box plots indicate means of lines harboring respective alleles

Full size image

Candidate genes identification

The flanking markers, WmGSB1.1–2 and WmGSB1.1–7 of the QTL ClGSB1.1, had the highest phenotypic variance and had a significant difference in PDI between the resistant allele (R/R) and the susceptible allele (S/S) (Fig. 4). The distance between these two flanking markers on chromosome 1 is approximately 0.36 Mbp, a region with 40 genes in CuGenDB (http://cucurbitgenomics.org/; accessed on 20 Dec 2021) (Table S6). Among these, the key genes include the necrotrophic fungal disease-related genes, two receptor-like kinase (RLKs) domain-containing genes- ClC01G014900 and ClC01G015010, two WRKY transcription factor genes- ClC01G014910 and ClC01G014990 [37,38,39] and one pathogenic type III effector avirulence factor Avr cleavage site-containing protein- ClC01G015130 [40,41,42]. Syntenic analysis revealed conserved synteny between two flanking markers in ClGSB1.1 and a locus in Cucumis sativus (cucumber) chromosome 6 associated with GSB resistance (Fig. 5) [16].

Fig. 5
figure 5

Syntenic analysis of cucumber gummy stem blight-linked quantitative trait locus (QTL) areas in chromosome 6 with the genome of watermelon chromosome 1. Orange and blue represent Cucumis sativus (Chinese Long v2) and Citrullus lanatus (97,103 v2) genome, respectively

Full size image

Previously, we reported an expression analysis of forty-four NBS-encoding R genes related to GSB resistance [12]. To validation the expression pattern of these NBS genes in PI 279461 and PI 223764, qRT-PCR was used to confirm. An expression analysis of forty-four NBS genes revealed up-regulation of forty-two genes and down-regulation of two genes, at least at one time point after pathogen inoculation in the resistant line compared to that of the susceptible line (Fig. S3). Among the up-regulated genes, ClC01G015700 was identified to be within the QTL ClGSB1.1. On the other hand, five genes, ClC10G198730, ClC10G198620, ClC10G199670, ClC10G199720, and ClC10G198140 were located within ClGSB10.1 and two genes, ClC11G216720 and ClC11G216940 were within ClGSB11.1 QTL regions (Table S3). ClC01G015700, encoded NBS-LRR and RPW8 domain and located 0.52 Mbp downstream of the developed marker WmGSB1.1–7 within the QTL ClGSB1.1 (29,450,021 – 29,452,887), was also highly expressed in resistant line and potential candidate gene in our previous study [12]. The disease resistance related domain containing genes within the identified QTL regions including the genes that showed higher expression upon pathogen inoculation may be potential candidate genes for GSB resistance. However, additional research will be required to fine map the region to identify the causal gene(s) upon functional varification.

Discussion

Identifying the genetic determinants of resistance to GSB is important for developing suitable varieties against the devastating disease. Watermelon accessions ‘PI 189225’ and ‘PI 271778’ [7, 43] and very recently, the accessions ‘PI 279461,’ ‘PI 526233,’ and ‘PI 482283’ [9] have been reported to carry sources of GSB resistance. Resistance to GSB in cucurbits has been identified to be monogenic dominant, monogenic recessive and polygenic previously [14, 15]. For example, GSB resistance in ‘PI 189225’ was regulated by a single gene, db [11]. However, new study revealed the involvement of numerous genes with environmental variables for GSB resistance [4]. We identified the inheritance of this trait to be controlled by polygenes as evident from the continuous distribution of the resistance scores and the segregation ratio of resistance and susceptible F2 genotypes upon infection with the inoculum of the causal pathogen (Fig. 2B and Table S4). This is in agreement with the findings of several recent studies as well [4, 12,13,14, 16, 17, 44].

Previous mapping studies have identified several QTLs that regulated resistance to major diseases such as bacterial fruit blotch (BFB), fusarium wilt, and papaya ringspot in watermelon [45,46,47,48,49]. QTLs associated with GSB resistance have been identified in the seedlings [16, 17], and stems [44] of cucumber. Syntenic regions of the known GSB QTL of cucumber were explored on chromosome 9 of melon to explore the potential genes associated with GSB resistance in melon [26]. In watermelon, several QTLs for resistance to GSB have been reported very recently [13,14,15]. Using a single-nucleotide polymorphism [SNP]-index identified by the bulk sergeant analysis of 211 inter-specific (cultivated C. lanatus breeding line K3 ✕ wild C. amarus accession PI 189225) F2:3 plants upon inoculation with a single isolate of S. cucurbitacearum, a major QTL, Qgsb8.1 spanning a region of 5.7 Mb on chromosome 8 was identified for GSB resistance in watermelon genotype, PI 189225 [13]. Gimode et al., 2021 identified three QTLs, ClGSB3.1, ClGSB5.1 and ClGSB7.1, based on the SNPs detected by GBS analysis of an F2:3 interspecific population derived from Crimson Sweet (C. lanatus) ✕ PI 482276 (C. amarus) against Stagonosporopsis citrulli 12178A in the greenhouse grown seedlings of watermelon. Lee et al., 2021 constructed a linkage map using 113 polymorphic SNPs using Fluidigm® SNP Type™ assays and identified three QTLs, qSB6.1, qLL8.1, and qSB8.1 from an F2 population of 178 genotypes from a cross of susceptible line ‘920533’ (C. lanatus) ✕ resistant line ‘PI 189225’ (C. amarus). These studies identified QTLs for GSB resistance on chromosomes 3, 5, 6, 7, and 8, which have a common donor species, C. amarus [14, 15]. Contrastingly, we identified three QTLs, ClGSB1.1, ClGSB10.1, and ClGSB11.1, located on chromosomes 1, 10, and 11, respectively, using parents both belonging to C. lanatus. Besides the differences in the location of the QTLs, the phenotypic variation explained by the identified QTLs is also variable, as our QTLs explained only around 10% of phenotypic differences, whereas the previously identified QTLs, Qgsb8.1 and ClGSB7.1, explained > 30 and > 20% of phenotypic variation, respectively [14, 15]. The phenotypic variation explained by the identified QTLs, qLL8.1, and qSB8.1, however, explained similar percentages (9.7–10.5%) of phenotypic variations. These differences in the location of QTLs and the percentages of phenotypic variations explained may be due to the use of different species as sources of resistance, geographically different and differentially aggressive pathogenic isolates, different methods of inoculation and disease assessment, and differential marker density in QTL mapping.

In this study, one major QTL, ClGSB1.1, was found on watermelon chromosome 1 at 8.89 Mbp within the flanking markers, WmGSB1.1–2 and WmGSB1.1–7, spanning 0.36 Mbp region (Table 2). Within this QTL, a total of 40 genes were identified in ClGSB1.1 region, of which five genes including two RLKs (ClC01G014900 and ClC01G015010), two WRKY transcription factors (ClC01G014910 and ClC01G014990), and one AvrRpt-cleavage domain protein (ClC01G015130) are predicted to be associated with GSB resistance. Many RLK protein have been shown to belong to a candidate resistance gene for necrotrophic pathogen [12, 50]. Besides, WRKY transcription factor 75 was found to positively modulate jasmonate-mediated plant defenses against necrotic fungal pathogens [38, 39, 51]. The C-terminal domains of WRKY, AvrRpt-cleavage site, and protein kinase genes belong to a family of proteins that play critical roles in plant defense signaling [52,53,54]. The majority of R genes in plants encode proteins with a nucleotide-binding site (NBS) and leucine-rich repeats (LRRs), both of which are essential for plant–pathogen recognition. Forty-four NBS-encoding R genes were previously identified in watermelon based on genome version 1 of accession 97,103 [12, 19]. We also identified the equivalent number of NBS genes in 97,103 genome version 2 [31] and analyzed their expressions upon pathogen inoculation. Up-regulation of the genes within the QTL regions such as ClC10G198730, ClC10G198620, ClC10G199670, ClC10G199720, and ClC10G198140 within ClGSB10.1; ClC11G216720 and ClC11G216940 within ClGSB11.1; and especially, the highly expressed gene ClC01G015700 within the QTL ClGSB1.1 (located only 0.52 Mbp downstream of the developed marker WmGSB1.1–7) are putative candidates. All these genes thus could be the key genes playing important roles in conferring resistance to GSB.

Developing high-throughput molecular markers that can be used for mass screening is essential for success in modern breeding programs as it can reduce the labor-intensive and time-consuming phases of the breeding process and can offer precise genotyping based selection. In this study, we developed and validated two HRM markers, WmGSB1.1–2 and WmGSB1.1–7, linked to QTL ClGSB1.1 on chromosome 1, which showed high correlation with the phenotypic difference in PDI in F2 population. Besides, other markers, WmGSB1.1–3, WmGSB1.1–4, WmGSB1.1–5, WmGSB1.1–6, and WmGSB1.1–9, linked to the same QTL also showed reasonable correlation with the phenotypic variation in F2. These HRM markers are advantageous from other recently developed KASP markers as the HRM markers are robust to PCR inhibition and thereby, results in fewer miscalled alleles and increased accuracy of results. Besides, those KASP markers were designed based on the QTLS found in different populations with resistance derived mainly from C. amarus against isolates of various origins [13, 14]. Whereas, the source of resistance in our population was derived from C. lanatus. Yet, our HRM markers were found to be effective in screening resistance alleles of C. amarus as well (Fig. 3). So, the developed HRM markers can be used in developing varieties with high resistance to GSB. Future studies should be focused on fine mapping and functional analyses of key R-genes that may offer durable resistance against diverse pathogenic strains, which can be used to further improve the resistance to GSB in watermelon.

Conclusion

In this study, we identified three QTLs, ClGSB1.1, ClGSB10.1, and ClGSB11.1 on chromosomes 1, 10 and 11, respectively, using an F2 population derived from two C. lanatus cultivars, ‘PI 279461’ and ‘PI 223764’, and several key candidate R-genes. Two HRM markers linked to GSB resistance in ClGSB1.1 were also developed and validated using diverse watermelon lines and F2 population. This will be helpful in breeding programs aimed at improving GSB resistance in watermelon.

Availability of data and materials

The raw sequence data from this study have been deposited in the publicly accessible National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) database as PRJNA804585. The datasets supporting the conclusions of this article are included within the article and its additional files.

References

  1. Soteriou G, Kyriacou M, Siomos A, Gerasopoulos D. Evolution of watermelon fruit physicochemical and phytochemical composition during ripening as affected by grafting. Food Chem. 2014;165:282–9.

    Article  CAS  PubMed  Google Scholar 

  2. Keinath A, Farnham M, Zitter T Morphological, pathological, and genetic differentiation of Didymella bryoniae and Phoma spp. isolated from cucurbits. 1995.

  3. Keinath AP. Fungicide timing for optimum management of gummy stem blight epidemics on watermelon. Plant Disease. 1995;79(4):354-58.

  4. Gusmini G, Rivera-Burgos LA, Wehner TC. Inheritance of resistance to gummy stem blight in watermelon. HortScience. 2017;52(11):1477–82.

    Article  Google Scholar 

  5. Keinath AP, Zitter TA. Resistance to benomyl and thiophanate-methyl in Didymella bryoniae from South Carolina and New York. Plant Dis. 1998;82(5):479–84.

    Article  CAS  PubMed  Google Scholar 

  6. Wolukau JN, Zhou X-H, Li Y, Zhang Y-B, Chen J-F. Resistance to gummy stem blight in melon (Cucumis melo L.) germplasm and inheritance of resistance from plant introductions 157076, 420145, and 323498. HortScience. 2007;42(2):215–21.

    Article  Google Scholar 

  7. Sowell G Jr. Additional source of resistance to gummy stem blight in watermelon. Plant Disease Rep. 1975; 59: 413-15.

  8. Levi A, Jarret R, Kousik S, Wechter WP, Nimmakayala P, Reddy UK. Genetic resources of watermelon. In: Genetics and Genomics of Cucurbitaceae. Springer; 2017: 87–110.

  9. Gusmini G, Song R, Wehner TC. New sources of resistance to gummy stem blight in watermelon. Crop Sci. 2005;45(2):582–8.

    Article  Google Scholar 

  10. Grumet R, Katzir N, Garcia-Mas J. Genetics and genomics of Cucurbitaceae: Springer; 2017. p 87-110.

  11. NORTON JD, JD N. Inheritance of resistance to gummy stem blight in watermelon. Horti Sci. 1979;14(5):630-2.

  12. Hassan MZ, Rahim MA, Jung H-J, Park J-I, Kim H-T, Nou I-S. Genome-wide characterization of NBS-encoding genes in watermelon and their potential association with gummy stem blight resistance. Int J Mol Sci. 2019;20(4):902.

    Article  CAS  PubMed Central  Google Scholar 

  13. Ren R, Xu J, Zhang M, Liu G, Yao X, Zhu L, et al. Identification and molecular mapping of a gummy stem blight resistance gene in wild watermelon (Citrullus amarus) germplasm PI 189225. Plant Dis. 2020;104(1):16–24.

    Article  CAS  PubMed  Google Scholar 

  14. Gimode W, Bao K, Fei Z, McGregor C. QTL associated with gummy stem blight resistance in watermelon. Theoretical Appl Genet. 2021;134(2):573–84.

    Article  CAS  Google Scholar 

  15. Lee ES, Kim D-S, Kim SG, Huh Y-C, Back C-G, Lee Y-R, et al. QTL mapping for gummy stem blight resistance in watermelon (Citrullus spp.). Plants. 2021;10(3):500.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lou L, Wang H, Qian C, Liu J, Bai Y, Chen J. Genetic mapping of gummy stem blight (Didymella bryoniae) resistance genes in Cucumis sativus-hystrix introgression lines. Euphytica. 2013;192(3):359–69.

    Article  CAS  Google Scholar 

  17. Liu S, Shi Y, Miao H, Wang M, Li B, Gu X, et al. Genetic analysis and QTL mapping of resistance to gummy stem blight in Cucumis sativus seedling stage. Plant Dis. 2017;101(7):1145–52.

    Article  CAS  PubMed  Google Scholar 

  18. Hu Z, Deng G, Mou H, Xu Y, Chen L, Yang J, et al. A re-sequencing-based ultra-dense genetic map reveals a gummy stem blight resistance-associated gene in Cucumis melo. DNA Res. 2018;25(1):1–10.

    Article  PubMed  CAS  Google Scholar 

  19. Guo S, Zhang J, Sun H, Salse J, Lucas WJ, Zhang H, et al. The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions. Nat Genet. 2013;45(1):51–8.

    Article  CAS  PubMed  Google Scholar 

  20. He J, Zhao X, Laroche A, Lu Z-X, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci. 2014;5:484.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Howlader J, Hong Y, Natarajan S, Sumi KR, Kim H-T, Park J-I, Nou I-S. Development of powdery mildew race 5-specific SNP markers in Cucumis melo L. using whole-genome resequencing. 2020.

  22. Bus A, Hecht J, Huettel B, Reinhardt R, Stich B. High-throughput polymorphism detection and genotyping in Brassica napus using next-generation RAD sequencing. BMC Genomics. 2012;13(1):1–11.

    Article  CAS  Google Scholar 

  23. Sonah H, Bastien M, Iquira E, Tardivel A, Légaré G, Boyle B, et al. An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS One. 2013;8(1):e54603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang H, Yi H, Wu M, Zhang Y, Zhang X, Li M, et al. Mapping the flavor contributing traits on" Fengwei melon"(Cucumis melo L.) chromosomes using parent resequencing and super bulked-segregant analysis. PLoS One. 2016;11(2):e0148150.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Natarajan S, Hossain MR, Kim H-T, Denison MIJ, Ferdous MJ, Jung H-J, et al. ddRAD-seq derived genome-wide SNPs, high density linkage map and QTLs for fruit quality traits in strawberry (Fragaria x ananassa). 3. Biotech. 2020;10(8):1–18.

    Google Scholar 

  26. Hassan MZ, Rahim MA, Natarajan S, Robin AHK, Kim H-T, Park J-I, et al. Gummy stem blight resistance in melon: inheritance pattern and development of molecular markers. Int J Mol Sci. 2018;19(10):2914.

    Article  PubMed Central  CAS  Google Scholar 

  27. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5):e19379.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jang HA, Oh S-K. Development of an efficient genotyping-by-sequencing (GBS) library construction method for genomic analysis of grapevine. Korean J Agricult Sci. 2017;44(4):495–503.

    CAS  Google Scholar 

  29. Bhatia D, Wing RA, Yu Y, Chougule K, Kudrna D, Lee S, et al. Genotyping by sequencing of rice interspecific backcross inbred lines identifies QTLs for grain weight and grain length. Euphytica. 2018;214(2):1–16.

    Article  Google Scholar 

  30. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Guo S, Zhao S, Sun H, Wang X, Wu S, Lin T, et al. Resequencing of 414 cultivated and wild watermelon accessions identifies selection for fruit quality traits. Nat Genet. 2019;51(11):1616–23.

    Article  CAS  PubMed  Google Scholar 

  32. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Meng L, Li H, Zhang L, Wang J. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J. 2015;3(3):269–83.

    Article  Google Scholar 

  34. Zheng Y, Wu S, Bai Y, Sun H, Jiao C, Guo S, et al. Cucurbit genomics database (CuGenDB): a central portal for comparative and functional genomics of cucurbit crops. Nucleic Acids Res. 2019;47(D1):D1128–36.

    Article  PubMed  Google Scholar 

  35. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25(4):402–8.

    Article  CAS  PubMed  Google Scholar 

  36. Lv P, Li N, Liu H, Gu H, Zhao W-E. Changes in carotenoid profiles and in the expression pattern of the genes in carotenoid metabolisms during fruit development and ripening in four watermelon cultivars. Food Chem. 2015;174:52–9.

    Article  CAS  PubMed  Google Scholar 

  37. Zheng Z, Qamar SA, Chen Z, Mengiste T. Arabidopsis WRKY33 transcription factor is required for resistance to necrotrophic fungal pathogens. Plant J. 2006;48(4):592–605.

    Article  CAS  PubMed  Google Scholar 

  38. Adachi H, Ishihama N, Nakano T, Yoshioka M, Yoshioka H. Nicotiana benthamiana MAPK-WRKY pathway confers resistance to a necrotrophic pathogen Botrytis cinerea. Plant Signal Behav. 2016;11(6):e1183085.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Chen L, Zhang L, Xiang S, Chen Y, Zhang H, Yu D. The transcription factor WRKY75 positively regulates jasmonate-mediated plant defense to necrotrophic fungal pathogens. J Experiment Botany. 2021;72(4):1473–89.

    Article  CAS  Google Scholar 

  40. Day B, Dahlbeck D, Staskawicz BJ. NDR1 interaction with RIN4 mediates the differential activation of multiple disease resistance pathways in Arabidopsis. Plant Cell. 2006;18(10):2782–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Liu J, Elmore JM, Lin Z-JD, Coaker G. A receptor-like cytoplasmic kinase phosphorylates the host target RIN4, leading to the activation of a plant innate immune receptor. Cell Host Microbe. 2011;9(2):137–146.

  42. Narayanan SP, Lung S-C, Liao P, Lo C, Chye M-L. The overexpression of OsACBP5 protects transgenic rice against necrotrophic, hemibiotrophic and biotrophic pathogens. Sci Reports. 2020;10(1):1–19.

    Google Scholar 

  43. Sowell G, Pointer GR. Gummy stem blight resistance of introduced watermelons; 1962.

    Google Scholar 

  44. Zhang S, Liu S, Miao H, Shi Y, Wang M, Wang Y, et al. Inheritance and QTL mapping of resistance to gummy stem blight in cucumber stem. Mol Breed. 2017;37(4):49.

    Article  Google Scholar 

  45. Branham SE, Wechter WP, Lambel S, Massey L, Ma M, Fauve J, et al. QTL-seq and marker development for resistance to Fusarium oxysporum f. sp. niveum race 1 in cultivated watermelon. Mol Breed. 2018;38(11):1–9.

    Article  CAS  Google Scholar 

  46. Fall LA, Clevenger J, McGregor C. Assay development and marker validation for marker assisted selection of Fusarium oxysporum f. sp. niveum race 1 in watermelon. Mol Breed. 2018;38(11):1–10.

    Article  CAS  Google Scholar 

  47. Branham SE, Levi A, Katawczik ML, Wechter WP. QTL mapping of resistance to bacterial fruit blotch in Citrullus amarus. Theoretical Appl Genet. 2019;132(5):1463–71.

    Article  CAS  Google Scholar 

  48. Jang YJ, Seo M, Hersh CP, Rhee S-J, Kim Y, Lee GP. An evolutionarily conserved non-synonymous SNP in a leucine-rich repeat domain determines anthracnose resistance in watermelon. Theoretical Appl Genet. 2019;132(2):473–88.

    Article  CAS  Google Scholar 

  49. Branham SE, Wechter WP, Ling K-S, Chanda B, Massey L, Zhao G, et al. QTL mapping of resistance to Fusarium oxysporum f. sp. niveum race 2 and papaya ringspot virus in Citrullus amarus. Theoretical Appl Genet. 2020;133(2):677–87.

    Article  CAS  Google Scholar 

  50. Goff KE, Ramonell KM. The role and regulation of receptor-like kinases in plant defense. Gene Regulation Syst Biol. 2007;1:117762500700100015.

    Google Scholar 

  51. Rushton PJ, Somssich IE, Ringler P, Shen QJ. WRKY transcription factors. Trends Plant Sci. 2010;15(5):247–58.

    Article  CAS  PubMed  Google Scholar 

  52. Pandey SP, Somssich IE. The role of WRKY transcription factors in plant immunity. Plant Physiol. 2009;150(4):1648–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Deslandes L, Rivas S. Catch me if you can: bacterial effectors and plant targets. Trends Plant Sci. 2012;17(11):644–55.

    Article  CAS  PubMed  Google Scholar 

  54. Lin W, Ma X, Shan L, He P. Big roles of small kinases: the complex functions of receptor-like cytoplasmic kinases in plant immunity and development. J Integrative Plant Biol. 2013;55(12):1188–97.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Statement

All our experiments complied with local and national regulations.

Funding

This work was supported by (in part) the Golden Seed Project (Center for Horticultural Seed Development) of the Ministry of Agriculture, Food and Rural Affairs in the Republic of Korea (MAFRA) (Gant no. 213007–05-5-CG100) and Sunchon National University Research Fund in 2020 (Grant no. 2020–0192).

Author information

Authors and Affiliations

  1. Department of Horticulture, Sunchon National University, 255 Jungang-ro, Suncheon, Jeonnam, 57922, Korea

    Jeong-Eui Hong, Hee-Jeong Jung & Ill-Sup Nou

  2. Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh

    Mohammad Rashed Hossain

Authors
  1. Jeong-Eui Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Rashed Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hee-Jeong Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ill-Sup Nou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ISN and HJJ conceptualized the work, JEH conducted all experiments and JEH & MRH prepared the data, interpreted the results and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ill-Sup Nou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hong, JE., Hossain, M.R., Jung, HJ. et al. QTL associated with Gummy Stem Blight (GSB) resistance in watermelon. BMC Genomics 23, 632 (2022). https://doi.org/10.1186/s12864-022-08849-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-022-08849-2

Keywords

  • Watermelon
  • QTL
  • Gummy stem blight

BMC Genomics

ISSN: 1471-2164

Contact us