Skip to main content
Download PDF

Genetic dissection of Meloidogyne incognita resistance genes based on VIGS functional analysis in Cucumis metuliferus

BMC Plant Biology volume 24, Article number: 964 (2024) Cite this article

Abstract

The southern root-knot nematode, Meloidogyne incognita, is a highly serious plant parasitic nematode species that causes significant economic losses in various crops, including cucumber (Cucumis sativus L.). Currently, there are no commercial cultivars available with resistance to M. incognita in cucumber. However, the African horned melon (Cucumis metuliferus Naud.), a semi-wild relative of cucumber, has shown high resistance to M. incognita. In this study, we constructed an ultrahigh-density genetic linkage bin-map using low-coverage sequences from an F2 population generated through the cross between C. metuliferus inbred lines CM3 and CM27. Finally, we identified a QTL (quantitative trait locus, QTL3.1) with a LOD (logarithm of the odds) score of 3.84, explaining 8.4% of the resistance variation. Subsequently, by combining the results of qPCR (quantitative PCR) and VIGS (virus-induced gene silencing), we identified two genes, EVM0025394 and EVM0006042, that are potentially involved in the resistance to M. incognita in CM3. The identification of QTLs and candidate genes in this study serve as a basis for further functional analysis and lay the groundwork for harnessing this resistance trait.

Peer Review reports

Introduction

The southern root-knot nematode (Meloidogyne incognita (Kofoid & White) Chitwood), can cause serious damage to plant growth and yield loss around the world. Achieving complete control or eradication of M. incognita is unattainable [1]. Among management techniques, nematicides have been effective in controlling nematodes in many crops for decades. Many traditional nematicides with a broad-spectrum, including fumigants (soil sterilant), organophosphates and carbamates (neural toxins), have been banned in recent years due to their potential harm to the environment and humans [1,2,3]. Thus, the use of M. incognita-resistant cultivars would be a more economical and effective approach.

Cucumber (Cucumis sativus L., 2n = 14) is an economically important vegetable crop worldwide. It was cultivated on an area of 2,172,193 ha with a total production of 93,528,796 tons in 2021 (http://faostat.fao.org). However, M. incognita can cause severe yield loss of cucumber [4,5,6]. Among cucumber germplasm accessions, no M. incognita-resistant sources were identified and no commercial cucumber cultivars with resistance to the nematode are currently available [7,8,9,10].

The African horned melon (Cucumis metuliferus Naud., 2n = 24) is considered as the wild or semi-wild relative of cucumber and highly resistant to M. incognita [4, 10,11,12,13,14,15]. The resistance in C. metuliferus was shown to be associated with hindrance of larval development, with most second-stage juveniles (J2s) delaying development to later stage juveniles and stimulating toward maleness, resulting in few nematodes developing to the adult female stage [4, 16, 17]. Therefore, compared with cucumber or melon, the susceptibility in C. metuliferus to root knot nematodes is significantly reduced but not completely absent [4, 10]. However, the efforts to transfer the resistance to cultivars by creating viable interspecific hybrids between cucumber and C. metuliferus have encountered serious reproductive barriers, resulting in failure so far [4, 8, 9, 15]. Specifically, no fruit development was observed when C. metuliferus was used as the female parent. In crosses where cucumber was used as the female parent, only seedless fruits or fruits with non-viable seed development were observed [8].

In recent years, many researches have been conducted in transcriptomic studies on the incompatible interaction between C. metuliferus and M. incognita [17,18,19]. Transcriptomic analysis revealed that multiple signaling pathways, such as salicylic acid (SA), jasmonic acid (JA) and Ca2+ signaling, play a positive role in the resistance [19]. In addition, cytoskeleton-related genes may also be involved in resistance [18]. Furthermore, the genes related to phenylpropanoid biosynthesis, plant hormone signal transduction, and plant-pathogen interaction were involved in the resistance in C. metuliferus [17]. However, the M. incognita-resistant genes in C. metuliferus have not been identified yet. In order to harness the resistance, it is important to explore its resistance genes and investigate the underlying mechanisms of resistance to M. incognita in C. metuliferus.

In this study, we report on QTL mapping to explore the M. incognita-resistance in C. metuliferus using an F2 population. The function of candidate genes in QTL regions were identified using qPCR and VIGS. Our results provide valuable information for mining M. incognita-resistance in C. metuliferus. Furthermore, these candidate genes possess a potential application in cucumber breeding programs for conferring resistance to the nematode.

Results

Phenotypic identification and genetic analysis

The gall numbers and Gall Index (GI) were used to measure susceptibility/resistance of CM3 and CM27 to M. incognita (Fig. 1A-C). 40 days after inoculation, the average gall numbers and GI on CM3 were ∼29 and 1.2, respectively. The number of galls and GI on CM27 roots (∼110 and 3.7) were significantly higher than that on CM3 roots (P = 0.0171 < 0.05). The results demonstrated that CM3 is moderately resistant, rather than completely resistant to M. incognita. Further, the gall numbers and GI of CM3 and F1 did not differ significantly (P = 0.8110 > 0.05) (Fig. 1B and C). The results revealed that the F1 population was resistant to M. incognita, indicating a dominant resistance. In the two F2 populations, the histogram of GI indicates the resistance is quantitative (Supplementary Figure S3). Since the CM3 is not completely resistant with a GI between 1 and 2, we divided the F2 population lines into two groups, resistant (GI = 0,1,2) and susceptible (GI = 3,4,5). The ratio of the two groups was in accordance with the expected ratio of 3:1 (resistant : susceptible) for a dominant locus (Supplementary Table S2).

Fig. 1
figure 1

Phenotyping of Meloidogyne incognita resistance in CM3, CM27 and F1 population. A Roots of CM3 and CM27 at 40 days post-inoculation (dpi). Bar = 2 cm. B and C Average gall numbers (B) and Gall Index (C) of CM3, CM27 and F1 at 40 dpi. Data are presented as means ± standard deviation (SD). ∗P<0.05; ∗∗P<0.01; ns, not significant (one-way ANOVA following Dunnett’s post hoc test)

Full size image

Sequencing and SNP identification

We resequenced both parents with 10x depth and 200 F2 individual plants with 5x depth on an Illumina HiSeq platform and produced clean data for mining SNPs and developing bin markers. In total, 10,071,087 and 13,630,504 clean reads were generated for the CM3 and CM27, respectively. Meanwhile, 7,983,138 clean reads were generated for the 200 F2-1 population individuals, with more than 85% of the bases higher than Q30 (Supplementary Table S3). By aligning clean reads with the reference genome sequence of C. metuliferus (CM27), we obtained 224,978 SNPs in CM3, 12,730 SNPs in CM27, 102,368 SNPs between the parents (Supplementary Table S4). Finally, we obtained 3,126 bin markers with a sliding window of 15 SNPs (Supplementary Table S5).

Construction of a sequence-based C. Metuliferus genetic map

3,126 bin markers were filtered prior to linkage analysis. Then, the remaining 3,028 bin markers were used to construct a genetic linkage map. We constructed an ultra-dense genetic map consisting of 12 linkage groups (LG) with 3,028 bin markers, which covered 1,713.07 cM, with an average distance of 0.57 cM between adjacent bin markers (Table 1; Fig. 2A, Supplementary Table S6). The chromosome-level reference genome of C. metuliferus was used to evaluate the quality of the genetic map [11]. In a comparison with C. metuliferus chromosomes, Spearman’s rank-order correlation coefficients for 12 linkage groups ranged from 0.9373 to 0.9984 (Fig. 2B, Supplementary Figure S2). These results imply that a good syntenic relationship exists between the two genomes.

Table 1 Construction of Cucumis metuliferus genetic map
Full size table
Fig. 2
figure 2

Genetic map construction and syntenic relationship analysis. A The genetic map for the F2-1 population derived from a cross between CM3 and CM27. Bin markers are located according to genetic distance (cM). B Comparison of the syntenic relationships between the linkage groups and chromosomes of C. metuliferus using R software. The dots indicate the bin markers

Full size image

QTL analysis for M. incognita resistance in the F2 population

To identify the candidate genomic region for M. incognita resistance in C. metuliferus, the IM model in MapQTL 6.0 software was used to detect QTLs. We identified a 37Kb region on chromosome 03 (chr 03) with two bin markers (Block7973 and Block7979) that satisfied the LOD threshold of 3.6 (Fig. 3). The LOD peak value was 3.84, with the phenotypic variation of 8.4%. The QTL was named QTL3.1. The physical interval of the QTL locus was from 25,323,564 bp to 25,360,528 bp on chr03. A total of four candidate genes were annotated in the QTL3.1 region based on the C. metuliferus reference genome [11] (Fig. 3).

Fig. 3
figure 3

Mapping of Meloidogyne incognita resistance candidate genes on chromosome 03 (corresponding to LG5 in Fig. 2B). QTL mapping of M. incognita resistance in the F2 population. The blue line represents the LOD score and the red line represents the phenotypic variation. The candidate genes in the QTL region are depicted below the mapping interval

Full size image

The first candidate gene, EVM0025394 at position 25,324,104 − 25,328,367, encodes Lysine histidine transporter 1. An InterPro analysis indicated that EVM0006042 (25,335,818 − 25,338,965) contains a 295-amino acid von Willebrand factor type A domain. EVM0018474 and EVM0028872, located from 25,350,241 to 25,355,854, 25,356,688 to 25,361,129, are predicted to encode COP9 signalosome complex subunit 3 and actin-related protein 2/3 complex subunit 5 A, respectively.

To further screen possible candidate genes underlying the detected QTLs, the CDS sequences of the four candidate genes were cloned and sequenced. We then aligned the CDS sequences of candidate genes between CM3 and CM27. Three nucleotide changes (294th A/T, 1146th G/C and 609th A/G were found at the 294th, 1146th and 609th positions of the CDS respectively in EVM0028872, EVM0018474 and EVM0025394), which did not change their amino acid sequences (Supplementary Figure S4). There is no sequence difference in EVM0006042 between CM3 and CM27.

qPCR analysis

A qPCR analysis was used to assess the expression of these four genes potentially associated with M. incognita resistance (Fig. 4). In total, the expression patterns of these candidate genes were analyzed three times in CM3 and CM27 before and after inoculation with M. incognita. The expression levels of EVM0028872 and EVM0018474 did not change following inoculation in the two materials (Fig. 4E-H). In contrast, EVM0006042 expression was reduced at 3- and 7-dpi, considerably upregulated in CM3 at 14 dpi (Fig. 4C). However, the opposite expression pattern was found in CM27 at 14 dpi (Fig. 4D). In addition, the expression of EVM0025394 in CM3 was significantly upregulated from 3 dpi to 14dpi in replicates 1 and 3, and upregulated between 7dpi and 14dpi in replicate 2 (Fig. 4A). Conversely, the EVM0025394 gene was downregulated in CM27 after inoculation (Fig. 4B). These results indicated that EVM0006042 and EVM0025394 are likely the genes responsible for M. incognita resistance in CM3.

Fig. 4
figure 4

Analysis of candidate gene expression levels in roots of CM3 and CM27 before and after inoculation with Meloidogyne incognita. A-H The expression levels of EVM0025394(A-B), EVM0006042(C-D), EVM0028872(E-F), and EVM0018474(G-H) in CM3 and CM27. Relative gene expression was calculated according to the 2−ΔΔCt method. The relative gene expression levels were initially normalized to the levels of the beta-actin gene of Cucumis metuliferus, followed by further normalization to the samples at 0 dpi. Data are presented as means ± SD. Asterisks indicate statistically significant differences compared with the control (#) (one-way ANOVA following Dunnett’s post hoc test significance, ∗P < 0.05; ∗∗P < 0.01; ns, not significant)

Full size image

Functional verification analysis via VIGs

To further investigate the function of the four candidate genes, the expression of EVM0025394, EVM0006042, EVM0018474, and EVM0028872 were silenced in CM3 through VIGS (Fig. 5A). The efficiency of the four target genes silencing was established by qPCR. Compared with pV190 control plants, the expression of EVM0025394, EVM0006042, EVM0018474, and EVM0028872 decreased by approximately 30–80% in the silenced plants (Fig. 5B–D). The number of M. incognita-induced galls and root fresh weight were recorded at 40 dpi (Supplementary Figure S5). Then, the number of galls per gram of roots were calculated (Fig. 5E–G). Overall, the number of galls per gram on EVM0025394-silenced plants in the three trials were significantly increased compared with the control (P < 0.01). Similarly, the roots of the EVM0006042-silenced plants contained significantly more galls compared with the control (P < 0.05). However, the number of galls per gram in EVM0018474- and EVM0028872-silenced lines showed no significant difference compared with that on pV190 plants. Therefore, inhibiting the expression of EVM0025394 and EVM0006042 could increase susceptibility to M. incognita in CM3.

Fig. 5
figure 5

Silencing of the four candidate genes in CM3. A Schematic overview of virus-induced gene silencing of the four candidate genes. B-D Relative expression levels of EVM0025394, EVM0006042, EVM0018474, and EVM0028872 in silenced and control (pV190) plants. Relative gene expression was calculated according to the 2−ΔΔCt method. Gene expressions were normalized to the beta-actin gene of Cucumis metuliferus, and subsequently normalized to pV190 plants. Data are presented as means ± SD (n = 3/4). *, P < 0.05; **, P < 0.01 (Student’s t-test). E–G Number of galls per gram of virus-induced gene-silenced plants 40 days post-inoculation. Data are presented as means ± SD. *, P < 0.05; **, P < 0.01; ns, not significant (one-way ANOVA followed by Dunnett’s multiple comparisons test)

Full size image

Discussion

M. incognita has a detrimental impact on the growth and development of cucumber, leading to a decrease in yield [4,5,6, 20]. Several studies have demonstrated that C. metuliferus, an important wild or semi-wild relative of cucumber, exhibits high resistance to M. incognita [4, 10,11,12,13,14,15]. In C. metuliferus, it has been observed that only a small number of second-stage juveniles (J2) progress into later stage juveniles (J3/J4) between 10 dpi and 15 dpi [16]. This suggests that hindering larval development is one potential resistance mechanism [4, 16]. Although several transcriptome-based studies have explored the resistant mechanisms, the identification of resistance genes has not yet been undertaken. Consequently, the resistance genes associated with M. incognita resistance in C. metuliferus remain unreported [17].

In this investigation, we report that M. incognita resistance in C. metuliferus CM3 may be controlled by a major dominant locus. Moreover, we successfully identified a QTL (QTL3.1) that contains four candidate genes on chr 03. Further qPCR assays on parents determined that the expression levels of EVM0025394 and EVM0006042 were upregulated in the roots of the resistant parental CM3 after inoculation, suggesting that these two genes are likely responsible for the resistance to M. incognita. To validate this hypothesis, we performed VIGS silencing of the four candidate genes, which resulted in increased susceptibility of the roots in EVM0025394- and EVM0006042-silenced plants to nematodes compared to roots transformed with an empty vector. The VIGS analysis further supported the involvement of these two candidate genes in conferring M. incognita resistance at this specific locus.

Among the detected M. incognita-resistance candidate genes, EVM0025394 encodes a Lysine histidine transporter 1 (LHT1), which is involved in growth and development in plants and plant pathogens [21]. In Arabidopsis, AtLHT1 is involved in leaf mesophyll import as well as root uptake of acidic and neutral amino acids (AAs), both at naturally occurring concentrations and from agricultural soil [22, 23]. Up to now, the role of AtLHT1 during pathogen infection has been identified in several studies. The expression of AtLHT1 was induced when the host was infected with various pathogens. However, the hypothesis that AtLHT1 regulates plant resistance mechanisms is divergent [21]. AtLHT1 was highly expressed in syncytia and roots when the host was infected with biotrophic nematode Heterodera schachtii [24]. Additionally, one of the important substrates of AtLHT1, Glutamine (Gln), was observed that its level increased in syncytia when compared to uninfected roots [25]. Based on the above evidences, AtLHT1 is associated with nematode infection in host. However, lht1-1 mutants did not exhibit increased resistance to H. schachtii [24]. Combining expression and functional verification results, the mechanism of AtLHT1 regulating nematode resistance is uncertain.

Our findings demonstrate that the expression of LHT1 is upregulated in CM3 after inoculation and the resistance of CM3 is compromised when the expression is partially suppressed. Our results indicate that LHT1 may act as a positive regulator in defense against M. incognita. A notable example of amino acid transporters acting as nematode resistance genes is Glyma.18G022400, a gene located at the Rhg1 locus that encodes the Rhg1-GmAAT in soybean. The amino acid transporter is one of three genes which are responsible for the resistance to soybean cyst nematode in soybean [26]. This result partially supports our findings.

Another important candidate gene, EVM0006042, encodes a protein of unknown function that contains a 295-amino acid von Willebrand factor type A domain. Limited functional studies on this gene family have been conducted in plants. In fact, Von Willebrand factor (VWF) is a large, multimeric glycoprotein that is found in blood plasma, platelet α-granules, and subendothelial connective tissue [27]. The OVATE gene, which is homology with human von Willebrand factor genes, is involved in making pear-shaped fruits and was discovered in tomato “Yellow Pear” [28]. It is evident that this function is unrelated to resistance.

In general, the default pathway for M. incognita J2s is to enter the root and develop into adult females, which can produce about a thousand eggs through mitotic parthenogenesis [29]. However, certain conditions, such as host stress or nutrient deprivation, can trigger the development of adult males [30, 31]. These males will then abandon their feeding sites and exit the root as a mechanism to maintain an optimal parasitic load for the population’s survival [31, 32]. The presence of resistance genes in the host has been identified as one of the masculinizing influences [33]. Similarly, resistance in C. metuliferus was associated with hindrance of larval development beyond the second stage, delayed development of larvae to adults and stimulation toward maleness [4, 17,18,19]. Here, differential expression of EVM0006042 in CM3 and CM27 suggests its involvement in defense against nematode infestation. Further validation through VIGS supports this hypothesis. Notably, unlike gene EVM0025394, the induction of EVM0006042 expression occurs 14 days after nematode inoculation. At 14 days, about 40% M. incognita were developed into females in C. melo, while the proportion of females was less than 10% in C. metuliferus [34]. Similarly, most of M. incognita have developed from J2s into J3/J4 stages in cucumber at 14 dpi. Contrastingly, in C. metuliferus, the majority of J2s remained in the J2 stage at the same time point [18]. Therefore, 14 dpi is a pivotal stage in nematode development. Taken together with these previous studies, we speculate that the identified EVM0006042 is probably not associated with early defense mechanisms, but it might be implicated in nematode development.

In conclusion, our findings would serve as a foundation for deciphering the resistance mechanisms of C. metuliferus to M. incognita. Current conclusions suggested that the resistance of C. metuliferus may be not only controlled by typical resistance genes. However, further research and confirmation of the functions of these two candidate genes are needed, utilizing techniques such as transgenic/genome editing technologies.

Conclusions

In this study, we constructed an F2 population using a susceptible C. metuliferus inbred line (CM27) and a resistant inbred line (CM3). Genetic analysis revealed that the resistance in C. metuliferus CM3 inherited dominantly. A QTL on chr 03 was identified. Subsequently, by combining the results of qPCR and VIGS, we identified two genes, EVM0025394 (encoding Lysine histidine transporter 1) and EVM0006042 (containing a 295-amino acid von Willebrand factor type A domain), which are potentially involved in the resistance to M. incognita in CM3. our findings would serve as a foundation for deciphering the resistance mechanisms of C. metuliferus to M. incognita. Current conclusions suggest that the resistance of C. metuliferus may be not only controlled by typical resistance genes. However, further research and confirmation of the functions of these two candidate genes are needed, utilizing techniques such as transgenic/genome editing technologies.

Materials and methods

Plant materials and nematode resistance screening

C. metuliferus accessions CM3 (accession no. PI 202681) and CM27 (accession no. PI 482460) from the US National Plant Germplasm System (NPGS) were used in this study [13]. After multiple generations of self-crossing, two inbred lines were obtained. An F1 population and two F2 populations (F2-1 and F2-2) developed from a cross between CM3 and CM27 were used. The original seed source of CM3, CM27, F1 population and F2 populations were provided by Prof. Xingfang Gu of the Department of Cucurbits Genetics and Breeding, Institute of Vegetable and Flowers, Chinese Academy of Agricultural Science. Preparation of nematodes was performed as described in previous studies [35, 36].

The two-leaf stage seedlings were inoculated with approximately 800 M. incognita second-stage larvae (J2s). All seedlings were grown with the seedling substrate (peat : vermiculite = 1:1 volume ratio) in a greenhouse with day/night temperatures 28/24°C. Approximately 40 days after inoculation, individual F2 plants, CM3, CM27 and F1 were scored for their phenotype using a 0–5 galling index (GI) scale. 0 = no infection; 1 = 1–20% of roots galled; 2 = 21–40% of roots galled; 3 = 41–60% of roots galled; 4 = 61–80% of roots galled and 5 = 81–100% of roots galled (Supplementary Figure S1) [37]. GI was calculated based on the proposition of roots with galls. In addition to GI, the gall numbers of CM3, CM27 and F1 were also counted.

Sequencing library construction and high-throughput sequencing

Genomic DNA from young leaves of CM3, CM27 and 200 F2-1 population plants were extracted via the CTAB method [38]. The concentration and quality of DNA were determined by using NanoDrop2000 (Thermo Fisher Scientific). DNA libraries (350 bp) for Illumina sequencing were constructed according to the manufacturer’s specifications. Then, sequencing was performed on an Illumina HiSeq NovaSeq platform by a commercial service (Biomarker Technologies, Beijing, China), with 150-bp read lengths.

Genotyping and genetic map construction

Raw reads were filtered and mapped to the C. metuliferus genome using the Burrows-Wheeler Aligner software [11, 39]. Duplicates were marked with Samtools, and GATK was used for local realignment and base recalibration [11, 39]. A SNP set was generated by combining GATK and Samtools SNP calling analyses [40]. Polymorphic SNPs between the parents were selected. The SNPs with less than 4x coverage in either parent were removed. The obtained SNPs were scanned by sliding on the genome with 15 SNPs as a window and one SNP as a step. The windows were genotyped as ‘aa’ or ‘bb’ when the number of aa-type or bb-type in the window was > 12; otherwise, it was genotyped as ‘ab’. SNPs without recombination were consolidated into one bin marker. Subsequently, the bin markers exhibiting abnormal patterns, low coverage, or obvious segregation distortions were filtered out. Only markers exceeding 10 Kb in length were retained, and bin markers with abnormal base and segregation distortion (P < 0.01) were removed.

The selected bin markers were used to construct the genetic linkage map as described previously [41]. Bins were used as markers to construct a high-density genetic map. Marker loci were assigned to linkage groups (LGs) based on their genomic positions. Modified logarithm of odds (MLOD) scores were calculated to validate marker reliability. HighMap strategy was employed for marker ordering and error correction [42]. The collinearity between genetic map and physical map was analyzed via R software and AllMaps [43]. The default parameters were selected when using the above software.

QTL detection

Map-based QTL analysis was performed using interval mapping (IM) implemented by MapQTL6.0 software with IM methods [44]. The permutation test option (1000 permutations) within MapQTL was applied to determine the significance threshold for the LOD. The genome-wide threshold for determining QTL significance in terms of LOD was 3.6 at 95% confidence interval in F2-1 population.

RNA isolation, cDNA preparation, and quantitative real-time PCR

Leaves or roots were sampled and frozen in liquid nitrogen. Total RNA was isolated using a MiniBEST Plant RNA Extraction kit (Takara, Dalian, China). The extracted RNA was subjected to cDNA synthesis and RT-qPCR analysis employing a PrimeScript RT reagent kit with gDNA Eraser and TB Green Premix Ex Taq II (Takara). RT-qPCR amplifications were performed as previously reported [36]. The RT-qPCR results were analyzed using the 2−ΔΔCt method [45]. The beta-actin gene of C. metuliferus was used as an internal control [18]. The experiment was carried out with three biological replicates, each with three or four technical replicates.

Virus-induced gene silencing (VIGS)

The function of the candidate genes was further analyzed using VIGS. The VIGS vector, pV190, has been used for functional verification in several Cucurbitaceae crops successfully [46,47,48,49,50]. Briefly, 300-bp coding sequence fragments of the candidate genes were separately cloned into the pV190 vector. Then, the constructs were introduced into A. tumefaciens strain GV3101 as described previously [47, 48]. Two cotyledons of one/two-leaf-stage C. metuliferus seedlings were infiltrated with one of the A. tumefaciens strains using a syringe. The gene expression level in VIGS plants was detected via qPCR. Plants with significantly downregulated expression were next used to inoculate with nematodes.

Nematode assays in silenced seedlings

The nematode assays were performed as previously described with slight modifications [35]. The silenced seedlings were grown in plastic pots filled with peat and vermiculite (peat : vermiculite = 1:1 volume ratio). Three evenly distributed 5-cm deep holes were created around the roots of the seedlings. Subsequently, 200 J2s were added to each hole using a pipetting gun, resulting in a final inoculation of 600 J2s per seedling. After 40 days, the number of galls and root fresh weight were measured and recorded, the plants transformed with empty pV190 vector acted as control. The VIGS validation of three independent trials were performed, and each trial contained 11– 44 plants.

Primers

Primer design was performed using the online software Primer3Plus (Primer3Plus - Pick Primers). All primers used in this study were listed in Supplementary Table S1.

Statistical analysis

All data were recorded using Microsoft Excel. Statistical analyses were performed using GraphPad Prism and the statistical package IBM SPSS Statistics software.

Data availability

Raw Illumina sequencing reads have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2024), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA019402) that are publicly accessible at https://bigd.big.ac.cn/gsa/browse/CRA019402. All relevant data can be found within the paper and its supporting materials.

Abbreviations

QTL:

Quantitative trait locus

LOD:

Logarithm of the odds

qPCR:

Quantitative PCR

VIGS:

Virus-induced gene silencing

SA:

Salicylic acid

JA:

Jasmonic acid

GI:

Gall Index

Dpi:

Days post-inoculation

LG:

Linkage groups

LHT1:

Lysine histidine transporter 1

AAs:

Acidic and neutral amino acids

VWF:

Von Willebrand factor

IM:

Interval mapping

MLOD:

Modified logarithm of odds

References

  1. Shrestha J, Thapa B, Subedi S. Root-knot nematode (Meloidogyne incognita) and its management: a review. J Agric Nat Resour. 2020;3(2):21–31.

    Article  Google Scholar 

  2. Desaeger J, Wram C, Zasada I. New reduced-risk agricultural nematicides - rationale and review. J Nematol 2020, 52.

  3. Zasada IA, Halbrendt JM, Kokalis-Burelle N, LaMondia J, McKenry MV, Noling JW. Managing nematodes without methyl bromide. Annu Rev Phytopathol. 2010;48:311–28.

    Article  PubMed  CAS  Google Scholar 

  4. Fassuliotis G. Resistance of Cucumis spp. to the root-knot nematode, Meloidogyne incognita acrita. J Nematology. 1970;2(2):174.

    CAS  Google Scholar 

  5. Kayani MZ, Mukhtar T, Growth and yield responses of fifteen cucumber cultivars to root-knot nematode. (Meloidogyne incognita). Acta Scientiarum Polonorum Hortorum Cultus. 2019;18(3):45–52.

    Google Scholar 

  6. Kayani MZ, Mukhtar T, Hussain MA. Effects of southern root knot nematode population densities and plant age on growth and yield parameters of cucumber. Crop Prot. 2017;92:207–12.

    Article  Google Scholar 

  7. Mukhtar T, Kayani MZ, Hussain MA. Response of selected cucumber cultivars to Meloidogyne incognita. Crop Prot. 2013;44:13–7.

    Article  Google Scholar 

  8. Walters SA. TC Wehner Incompatibility in diploid and tetraploid crosses of Cucumis sativus and Cucumis metuliferus. Euphytica 2002;128:371–4.

  9. Walters SA, Wehner TC, Barkel KR. Root-knot nematode resistance in cucumber and horned cucumber. HortScience. 1993;28(2):151–4.

    Article  Google Scholar 

  10. Wehner TC, Walters SA, Barker KR. Resistance to root-knot nematodes in cucumber and horned cucumber. J Nematology. 1991;23(4S):611.

  11. Ling J, Xie X, Gu X, Zhao J, Ping X, Li Y, Yang Y, Mao Z, Xie B. High-quality chromosome-level genomes of Cucumis metuliferus and Cucumis melo provide insight into Cucumis genome evolution. Plant J. 2021;107(1):136–48.

    Article  PubMed  CAS  Google Scholar 

  12. Benzioni A, Mendlinger S, Ventura M, Huyskens S. Effect of sowing dates, temperatures on Germination, Flowering, and yield of Cucumis metuliferus. HortScience HortSci. 1991;26(8):1051–3.

    Article  Google Scholar 

  13. Weng Y. Genetic diversity among Cucumis metuliferus populations revealed by Cucumber microsatellites. HortScience Horts. 2010;45(2):214–9.

    Article  Google Scholar 

  14. Dalmasso A, de Vaulx RD, Pitrat M. Response of some Cucurbita and Cucumis accessions to three Meloidogyne species. Cucurbit Genet Coop 1981:27.

  15. Nugent PE, Dukes P. Root-knot nematode resistance in Cucumis species. HortScience. 1997;32(5):880–1.

    Article  Google Scholar 

  16. Walters SA, Wehner TC, Daykin ME, Barker KR. Penetration rates of root-knot nematodes into Cucumis sativus and C. Metuliferus roots and subsequent histological changes. Nematropica. 2006;36(2):231–42.

    Google Scholar 

  17. Ye DY, Qi YH, Cao SF, Wei BQ, Zhang HS. Histopathology combined with transcriptome analyses reveals the mechanism of resistance to Meloidogyne incognita in Cucumis metuliferus. J Plant Physiol. 2017;212:115–24.

    Article  PubMed  CAS  Google Scholar 

  18. Ling J, Mao Z, Zhai M, Zeng F, Yang Y, Xie B. Transcriptome profiling of Cucumis metuliferus infected by Meloidogyne incognita provides new insights into putative defense regulatory network in Cucurbitaceae. Sci Rep. 2017;7(1):3544.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shao H, Fu Y, Zhang P, You C, Li C, Peng H. Transcriptome analysis of resistant and susceptible mulberry responses to Meloidogyne enterolobii infection. BMC Plant Biol. 2021;21(1):338.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Fassuliotis G. Plant resistance to root-knot nematodes. Nematology South Region United States Ark Agricultural Exp Stn Fayettev AR USA. 1982;Southern Cooperative Series Bulletin, 276:31–49.

  21. Tunnermann L, Colou J, Nasholm T, Gratz R. To have or not to have: expression of amino acid transporters during pathogen infection. Plant Mol Biol. 2022;109(4–5):413–25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Ganeteg U, Ahmad I, Jamtgard S, Aguetoni-Cambui C, Inselsbacher E, Svennerstam H, Schmidt S, Nasholm T. Amino acid transporter mutants of Arabidopsis provides evidence that a non-mycorrhizal plant acquires organic nitrogen from agricultural soil. Plant Cell Environ. 2017;40(3):413–23.

    Article  PubMed  CAS  Google Scholar 

  23. Svennerstam H, Jamtgard S, Ahmad I, Huss-Danell K, Nasholm T, Ganeteg U. Transporters in Arabidopsis roots mediating uptake of amino acids at naturally occurring concentrations. New Phytol. 2011;191(2):459–67.

    Article  PubMed  CAS  Google Scholar 

  24. Elashry A, Okumoto S, Siddique S, Koch W, Kreil DP, Bohlmann H. The AAP gene family for amino acid permeases contributes to development of the cyst nematode Heterodera schachtii in roots of Arabidopsis. Plant Physiol Biochem. 2013;70:379–86.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Hofmann J, El Ashry AEN, Anwar S, Erban A, Kopka J, Grundler F. Metabolic profiling reveals local and systemic responses of host plants to nematode parasitism. Plant J. 2010;62(6):1058–71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, Wang J, Hughes TJ, Willis DK, Clemente TE. Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean. science 2012;338(6111):1206–1209.

  27. Sadler JE. Biochemistry and genetics of Von Willebrand factor. Annu Rev Biochem. 1998;67(1):395–424.

    Article  PubMed  CAS  Google Scholar 

  28. Liu J, Van Eck J, Cong B, Tanksley SD. A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proc Natl Acad Sci. 2002;99(20):13302–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Marella HH, Nielsen E, Schachtman DP, Taylor CG. The amino acid Permeases AAP3 and AAP6 are involved in Root-Knot Nematode Parasitism of Arabidopsis. Mol Plant-Microbe Interactions®. 2013;26(1):44–54.

    Article  CAS  Google Scholar 

  30. Davide R, Triantaphyllou A. Influence of the Environment On Development and Sex differentiation of Root-Knot nematodes. Nematologica. 1967;13(1):111–7.

    Article  Google Scholar 

  31. Snyder DW, Opperman CH, Bird DM. A method for generating Meloidogyne incognita males. J Nematology. 2006;38(2):192.

    Google Scholar 

  32. Eisenback J. Detailed morphology and anatomy of second-stage juveniles, males, and females of the genus Meloidogyne (root-knot nematodes). Adv Treatise Meloidogyne. 1985;1:47–77.

    Google Scholar 

  33. Moura R, Davis E, Luzzi B, Boerma H, Hussey R. Post-infectional development of Meloidogyne incognita on susceptible and resistant soybean genotypes. Nematropica 1993:7–13.

  34. Faske T. Penetration, post-penetration development, and reproduction of Meloidogyne incognita on Cucumis melo var. Texanus. J Nematology. 2013;45(1):58.

    CAS  Google Scholar 

  35. Xie X, Ling J, Mao Z, Li Y, Zhao J, Yang Y, Li Y, Liu M, Gu X, Xie B. Negative regulation of root-knot nematode parasitic behavior by root-derived volatiles of wild relatives of Cucumis metuliferus CM3. Hortic Res. 2022;9:uhac051.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhao J, Sun Q, Quentin M, Ling J, Abad P, Zhang X, Li Y, Yang Y, Favery B, Mao Z, et al. A Meloidogyne incognita C-type lectin effector targets plant catalases to promote parasitism. New Phytol. 2021;232(5):2124–37.

    Article  PubMed  CAS  Google Scholar 

  37. Oka Y, Offenbach R, Pivonia S. Pepper Rootstock Graft compatibility and response to Meloidogyne Javanica and M. Incognita. J Nematology. 2004;36(2):137–41.

    Google Scholar 

  38. Wang HJ, Yue WU, Wei GU, Sun XD, Qin ZW. Extraction of DNA from Cucumber by Improved CTAB Method. Heilongjiang Agricultural Sci 2006.

  39. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. Genome Project Data Processing S: the sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Weng Q, Huang T, et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009;19(6):1068–76.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Liu D, Ma C, Hong W, Huang L, Liu M, Liu H, Zeng H, Deng D, Xin H, Song J, et al. Construction and analysis of high-density linkage map using high-throughput sequencing data. PLoS ONE. 2014;9(6):e98855.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Tang H, Zhang X, Miao C, Zhang J, Ming R, Schnable JC, Schnable PS, Lyons E, Lu J. ALLMAPS: robust scaffold ordering based on multiple maps. Genome Biol. 2015;16(1):3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. van Ooijen JW, Boer MP, Jansen RC, Maliepaard C. MapQTL 6.0, Software for the calculation of QTL positions on genetic maps. Plant Research International, Wageningen, The Netherlands 2009.

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

    Article  PubMed  CAS  Google Scholar 

  46. Bi X, Guo H, Li X, Zheng L, An M, Xia Z, Wu Y. A novel strategy for improving watermelon resistance to cucumber green mottle mosaic virus by exogenous boron application. Mol Plant Pathol. 2022;23(9):1361–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Liu M, Liang Z, Aranda MA, Hong N, Liu L, Kang B, Gu Q. A cucumber green mottle mosaic virus vector for virus-induced gene silencing in cucurbit plants. Plant Methods. 2020;16:9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Xu J, Zhang N, Wang K, Xian Q, Dong J, Qi X, Chen X. Chitinase Chi 2 positively regulates Cucumber Resistance against Fusarium oxysporum f. sp. cucumerinum. Genes (Basel) 2021, 13(1).

  49. Zhang Y, Wang Y, Wen W, Shi Z, Gu Q, Ahammed GJ, Cao K, Shah Jahan M, Shu S, Wang J, et al. Hydrogen peroxide mediates spermidine-induced autophagy to alleviate salt stress in cucumber. Autophagy. 2021;17(10):2876–90.

    Article  PubMed  CAS  Google Scholar 

  50. Zhu H, He M, Jahan MS, Wu J, Gu Q, Shu S, Sun J, Guo S. CsCDPK6, a CsSAMS1-Interacting protein, affects Polyamine/Ethylene Biosynthesis in Cucumber and enhances salt tolerance by overexpression in Tobacco. Int J Mol Sci 2021, 22(20).

Download references

Acknowledgements

The authors wish to thank Prof. Qinsheng Gu (Chinese Academy of Agricultural Sciences, China) for providing the cucumber green mottle mosaic virus vector pV190. We also thank Prof. Xingfang Gu (Chinese Academy of Agricultural Sciences, China) for providing C. metuliferus seeds. ChatGPT 3.5 was used for polishing the English text of the introduction and discussion of this manuscript.

Funding

This work was supported by the National Key R & D Program of China (NO. 2023YFD1400400), the National Natural Science Foundation of China (31571996, 32372508), National Agriculture Science and Technology Major Program (NK20220904).

Author information

Author notes

  1. Xiaoxiao Xie and Jian Ling contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Xiaoxiao Xie, Jian Ling, Junru Lu, Zhenchuan Mao, Jianlong Zhao, Shijie Zheng, Qihong Yang, Yan Li & Bingyan Xie

  2. Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, 6708 PB, The Netherlands

    Xiaoxiao Xie, Richard G. F. Visser & Yuling Bai

  3. Graduate School Experimental Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands

    Xiaoxiao Xie

  4. Shanghai Key Laboratory of Protected Horticulture Technology, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China

    Xiaoxiao Xie

  5. College of Life Sciences, Beijing Normal University, Beijing, 100875, China

    Junru Lu

Authors
  1. Xiaoxiao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junru Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenchuan Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianlong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shijie Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qihong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Richard G. F. Visser
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yuling Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Bingyan Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.X., J. Ling. and X.X. designed the experiments. X.X. carried out sampling, DNA extraction and data analysis; J.Lu. and J. Ling. participated in data analysis; Z.M., J.Z., S.Z., Q.Y. and Y.L. helped in the morphological analysis experiments; Y.B., B.X. and R.G.F.V. were responsible for the development and guidance of the project; X.X. wrote the manuscript, and Y.B., B.X. and R.G.F.V. revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bingyan Xie.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

: Supplementary figure S1. The six rating levels for symptoms evaluation. The scoring scale of 0 to 5 (0 no symptoms and 5 heavily infected) in roots. Scale bars, 2 cm.

Supplementary Material 2

: Supplementary figure S2. The co-linearity between the genetic map constructed from the F2 population in this study and a published chromosome-level genomes of Cucumis metuliferus.

Supplementary Material 3

: Supplementary figure S3. The phenotypic distribution of the two F2 populations.

Supplementary Material 4

: Supplementary figure S4. The gene structure and SNPs in the four candidate genes. They were generated via The Gene Structure Display Server 2.0 (GSDS) (http://gsds.gao-lab.org/). The UTR and CDS are shown in blue and red, respectively. The bases in red and black are in CM3 and CM27 CDS regions.

Supplementary Material 5

: Supplementary figure S5. The root fresh weight and gall numbers per root of virus-induced gene-silenced plants 40 days post-inoculation. Data are presented as means ± SD. *, P < 0.05; **, P < 0.01; ns, not significant; #, control (one-way ANOVA followed by Dunnett’s multiple comparisons test).

Supplementary Material 6

: Supplementary table S1. The primers used in this study.

Supplementary Material 7

: Supplementary table S2. Inheritance of Meloidogyne incognita resistance in Cucumis metuliferus.

Supplementary Material 8

: Supplementary table S3. The clean reads were generated for parental lines and 200 F2-1 population individuals.

Supplementary Material 9

: Supplementary table S4. The SNPs in parental lines and 200 F2-1 population individuals.

Supplementary Material 10

: Supplementary table S5. The bin markers with a sliding window of 15 SNPs.

Supplementary Material 11

: Supplementary table S6. The bin markers in the genetic map.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, X., Ling, J., Lu, J. et al. Genetic dissection of Meloidogyne incognita resistance genes based on VIGS functional analysis in Cucumis metuliferus. BMC Plant Biol 24, 964 (2024). https://doi.org/10.1186/s12870-024-05681-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-024-05681-6

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us