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Article

Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis

1
Hainan Sanya Crop Breeding Experimental Center, Xinjiang Academy of Agricultural Sciences, Sanya 572022, China
2
Zhengzhou Fruit Tree Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China
3
College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
4
Hami Melon Research Center, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1965; https://doi.org/10.3390/agronomy15081965
Submission received: 15 May 2025 / Revised: 4 August 2025 / Accepted: 11 August 2025 / Published: 14 August 2025
(This article belongs to the Section Pest and Disease Management)

Abstract

The root-knot nematode (Meloidogyne incognita) poses a major threat to global agriculture by impairing root function, reducing nutrient uptake, and ultimately limiting seed development and crop productivity. This study investigated the molecular and metabolic defense responses of Cucumis metuliferus (prickly pear) to M. incognita infection. Gene expression and metabolic pathway reprogramming in M. incognita-infected roots were examined using integrated transcriptomics and metabolomics approaches. The identified genes were involved in stress responses and defense activation. Furthermore, metabolite profiling revealed significant shifts in secondary metabolite production, with an upregulation of defense-related compounds like jasmonic acid, salicylic acid, and prostaglandins. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis highlighted critical pathways such as biotin metabolism and nucleotide metabolism, underscoring the adaptive metabolic responses of C. metuliferus plants. GO (Gene Ontology) analysis from the integrated transcriptomics and metabolomics data highlighted significant upregulation of enzymatic pathways, transporter activities, and reorganization of cellular structures. Furthermore, KEGG pathway analysis revealed activation of secondary metabolite biosynthesis, immune-related signaling pathways, and metabolic reprogramming including increased carbon metabolism and nucleotide biosynthesis. This study provides a valuable molecular framework for breeding of M. incognita-resistant cultivars, ultimately supporting more stable seed distribution and agricultural productivity in M. incognita-prone regions.

1. Introduction

Root-knot nematodes (RKNs), belonging to the Meloidogyne genus, are soil-borne pathogens that significantly impact a broad spectrum of crops, causing substantial economic losses globally [1,2]. These nematodes can infect various plants, including tobacco, tomatoes, cucumbers, peppers, and egg plants [3]. Plant-parasitic nematodes are considered the fourth most invasive plant disease, causing annual losses exceeding 157 billion dollars worldwide [4]. In China, the impact of RKN-mediated diseases on tobacco and other soil-borne diseases is responsible for yield reductions of 30 to 50% [4]. More than 100 RKN species have been identified throughout the world [5], with M. incognita, M. hapla, M. arenaria, and M. javanica being common pathogens in China. RKN infections can facilitate the onset of other pathogen infections, such as fusarium wilt, root rot, and bacterial diseases [6,7]. Prickly pear (C. metuliferus) is a member of the Cucurbitaceae family that is widely known as African horned cucumber, jelly melon, or Kiwano in English. C. metuliferus fruits are rich in different phytochemical components that are essential for a daily diet. The exceptional flexibility of C. metuliferus growth makes it a potentially profitable nutritional source for reducing malnutrition as well as for supplying phytochemicals to the food and pharmaceutical sectors [8,9]. C. metuliferus fruits are valuable not only for consumption but also for the phytochemical content in their non-edible parts, which, despite having low economic value, can serve as a renewable source of bioactive compounds with potential added value [10].
C. metuliferus plants are rarely affected by pests or diseases in their natural environment. However, this species is susceptible to several viruses, including cucumber mosaic virus, tobacco ringspot virus, tomato ringspot virus, watermelon mosaic virus 2, and a severe strain of bean yellow mosaic virus. Some varieties are also vulnerable to fusarium wilt (Fusarium oxysporum), powdery mildew (Sphaerotheca fuliginea), powdery mildew (Erysiphe cichoracearum), and the greenhouse whitefly (Trialeurodes vaporariorum). Additionally, C. metuliferus is highly resistant to root-knot nematodes (Meloidogyne spp.), with several accessions showing resistance to powdery mildew, melon aphid (Aphis gossypii), greenhouse whitefly, and fusarium wilt [11]. RKNs create feeding sites within galls by triggering the transformation of host cells into large, multinucleate, and metabolically active cells known as giant cells. Effectors are molecules that are secreted by RKNs to aid infection, either by altering the metabolism of hosts or by hindering plant defense mechanisms. Various transcriptomic, proteomic, and genomic studies have revealed extensive repertoires of effector gene families in plant-parasitic nematodes [12]. The infection rates of root-knot nematodes in nature are affected by various factors, including soil type, climate, and the presence of other host plants [13]. When a plant is attacked by RKNs, their innate immune system activates various local and systemic defense responses [14,15].
Multiple studies have documented physiological and biochemical changes in various plant species during RKN infection [16]. Responses upon RKN infection includes cell wall reinforcement, reactive oxygen species bursts, receptor-like kinases, antioxidant activity, phytohormone signaling, and the production of anti-nematode chemicals. However, the specific gene expression and signaling pathways triggered by RKN infection can vary across different plant species [17,18,19,20,21,22,23]. A study explored the defense mechanisms of Panax ginseng at different developmental stages against RKN infestation and revealed that different age groups activate the phenylpropanoid and flavonoid biosynthesis pathways, leading to the synthesis of compounds like phenolics, flavonoids, lignin, and anthocyanins as part of their defense response [24]. Five genes (encoding germin-like protein, anthranilate synthase subunit, isocitrate lyase, and an uncharacterized protein) were identified as consistently upregulated, which suggests that these genes play an important role in response to M. incognita infection in sweet potato [25]. Several metabolites including choline, ethanolamine, glutamine, glutamate, valine, isoleucine, malate, fructose, alanine, 4-aminobutyrate, succinate, oxaloacetate, and glucose were identified in the nematode resistant roots of watermelon plants [26].
C. metuliferus is a prominent and valuable crop in Xinjiang, with distinctive characteristics and advantages. However, with the rise of consecutive and continuous cropping in the region, the prevalence of M. incognita in facility greenhouses has significantly been increased, which, in turn, escalates the harmful effects of M. incognita. Additionally, under infected conditions, C. metuliferus plants are more susceptible to root rot, which is caused by a mixed infection of bacteria and fungi and ultimately leads to the death of the entire plant [27,28]. By integrating transcriptomics and metabolomics analysis, this study aimed to capture a comprehensive view of host responses at both the gene and metabolite levels, particularly focusing on defense signaling, metabolic reprogramming, and cellular adaptation during M. incognita infection. A deeper understanding of these complex interactions provides critical insight into how resistant cultivars manage to suppress nematode development and maintain physiological function under biotic stress. This study will contribute to the broader goal of improving crop resilience and productivity by identifying molecular markers and pathways that can be exploited to develop M. incognita-resistant cultivars that support the efforts to secure food production in M. incognita-prone regions and reduce the environmental impact of chemical control methods.

2. Materials and Methods

2.1. Plant Materials and Experimental Setup

M. incognita nematodes used in this study were multiplied on tomato plants that were grown in a greenhouse on 23 September 2022. Prior to planting, soil was prepared through mixing coconut coir, sand, and peat soil in a 3:1:1 ratio, sterilized at 120 °C for 2 h, and then filled into seedling trays. Tomato seeds were surface-sterilized using 0.1% mercuric chloride (HgCl2) for 15–20 min, rinsed thoroughly, and sown into the prepared substrate for germination. After emergence, each seedling was fertilized using 0.3 kg of compound fertilizer (N:P:K = 14:14:14) and 0.1 kg of monopotassium phosphate (KH2PO4). The experiment was carried out in Sanya, Hainan, during November and December 2022. Environmental conditions included an average maximum temperature of 27 °C in November and 26 °C in December, with corresponding minimum temperatures of 20 °C and 19 °C. The average relative humidity was around 81%, with an average of 10.1 h of clear or partly cloudy weather per day. After four weeks, tomato seedlings were thinned to three plants per pot. Three days later, each seedling was inoculated with 1000 eggs and newly hatched second-stage juveniles (J2) of M. incognita. After a 50-day growth period, when tumors had developed on the tomato roots, nematode eggs were isolated from the tumor sites. These eggs were then extracted and suspended in an equal volume of water. A standardized dosage of 5000 eggs was injected into each basin.
The experimental setup was covered with water to facilitate the M. incognita infection. Prickly pear cultivar “Huang Mengcui” (Hami melon, Xinjiang Academy of Agricultural Sciences) was grafted onto rootstock “PI1029” (Zhengzhou Institute of Pomology, Chinese Academy of Agricultural Sciences). This study included both control (CK) and infected (CM-SNG) plants. Each sample group included 3 replicates designated as CK-1, CK-2, CK-3, CM-SNG-1, CM-SNG1-2, and CM-SNG1-3, respectively. Root tissues of C. metuliferus were harvested, washed with distilled water, wrapped in aluminum foil, and immediately frozen in liquid nitrogen for subsequent transcriptomics and metabolomics analysis.

2.2. Transcriptomics Analysis of Control (CM) and Treated (CM_SNG) C. metuliferus Plants

2.2.1. RNA Extraction, Quality Control, and Sequencing from C. metuliferus Roots

C. metuliferus roots were harvested on 13 December 2022. Total RNA was extracted from both the control and M. incognita-infected C. metuliferus root samples using the RNAprep Plant Kit (Tiangen, Beijing, China). The RNA concentration and purity were assessed using a NanoDropTM 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The thresholds value for RNA Integrity Number (RIN) was 10 to ensure reproducibility. Sample integrity was verified by 28S and 18S ribosomal bands on gel electrophoresis.
Following quality control, a cDNA library was constructed and sequenced on Illumina NovaSeq 6000 platform using 100 base-pair end sequencing, resulting in a read depth of 60 M in a single sample. About 47.21 Gb of Clean Data was generated from sequencing. The clean data of each sample reached 7.3 Gb, and the percentage of Q30 bases was higher than 86%. Based on the comparison results, alternative splicing prediction analysis, gene structure optimization analysis, and new gene discovery were carried out.

2.2.2. Annotation and Functional Enrichment Analysis of Differentially Expressed Genes (DEGs)

Gene Set Enrichment Analysis (GSEA) was used to identify overrepresented gene sets that are classified based on their false-discovery-rate (FDR) p-value [29]. DEGs were defined as those with |log2 fold change| > 1 and a p-value of <0.05 [30]. Gene Ontology (GO) enrichment was performed using the Blast2GO (version 5.2.5) and topGOR packages (version 3.18) to identify functional modules for all significant DEGs. Additionally, pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) (accessed on 20 February 2025) [31]. The significance of gene enrichment in each pathway was assessed using the hypergeometric distribution test.

2.2.3. Quantitative Real-Time PCR Validation

Total RNA was extracted from both control and treated C. metuliferus plants using TRIzol™ Plus RNA Purification Kit (Thermo Fisher Scientific, Waltham, MA, USA). Complementary DNA (cDNA) was synthesized using the Advantage® RT-for-PCR Kit (Takara, Naha City, Japan) following the manufacturer’s instructions. Subsequent RT-qPCR analysis was performed using the AceQ® qPCR SYBR® Green Master Mix (Vazyme, Nanjing, China) on a Quant Studio 5 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Relative expression levels of target genes were calculated using the 2−ΔΔCT method, with Cm β-actin gene (ID: c168675_g9) used as the internal reference gene.

2.3. Metabolomics Analysis of Control (CM) and Treated (CM_SNG) C. metuliferus Plants Inoculated with M. incognita

2.3.1. Extraction of Metabolites

Grafting was performed on 22 November 2022, using the top insertion method. Metabolomics analysis was conducted using C. metuliferus plants grafted onto African gourd rootstock following infection by M. incognita. Metabolites were extracted according to the described protocol [32]. To ensure the reliability and reproducibility of the metabolomics data, the samples were evaluated through quality control using blank samples and pooled samples. Blank controls were included to monitor potential contamination introduced during sample preparation, extraction, and instrument analysis. These blanks consisted of methanol, which was used during the extraction process, and were processed in parallel with the biological samples. In addition to blank controls, pooled quality control samples (generated by combining small aliquots from each individual plant sample, resulting in a representative matrix that captured the overall metabolic profile of the sample set) were utilized to monitor instrument stability and analytical performance across the entire study. Root samples (60 mg) were combined with 20 μL of an internal standard solution (L-2-chloro-phenylalanine, 0.3 mg/mL) and 1 mL of methanol. The mixture was homogenized and subjected to ultrasonic extraction for 45 min. After extraction, the samples were centrifuged, and the resulting supernatants were collected for LC–MS analysis.

2.3.2. LC–MS Analysis

LC–MS analysis was conducted using a Waters UPLC I-class system, including a Waters VION IMS Q-TOF Mass Spectrometer equipped with electrospray ionization (Waters, Ocean City, MD, USA), which is capable of operating in both positive and negative ion modes. A 100 mm × 2.1 mm i.d., 1.7 μm Acquity BEH C18 column was used, and the column temperature was maintained at 45 °C. Mobile phases A and B consisted of water and acetonitrile, each containing 0.1% formic acid. The flow rate was set to 0.40 mL/min, and the injection volume was 3.00 μL. The ion source temperature was set to 120 °C, and the desolvation temperature was maintained at 500 °C, with a desolvation gas flow rate of 900 L/h. The system was allowed to reach the set temperatures before the start of the run, and the heating was maintained throughout the entire LC–MS/MS analysis, which lasted approximately 25 min. The mass spectrum was scanned in the range of 50 to 1000 m/z (mass/charge number), with a scan time of 0.1 s and an interval of 0.02 s.

2.3.3. Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

Multivariate statistical analysis, including PCA and OPLS-DA, along with model validation, were performed using SIMCA-P 14.1. The study performed unsupervised and supervised multivariate statistical analysis. The variables are typically metabolite features detected in the LC–MS/MS data including m/z-retention time pairs, identified metabolites, and concentrations. The purpose of the PCA test (unsupervised) was to study metabolite features (intensities), natural clustering, and variance patterns, while the variables (metabolites) best differentiated between the groups were identified through an OPLS-DA (unsupervised) test. For univariate statistical analysis and one-way ANOVA, IBM SPSS Statistics 20.0 (SPSS, Inc., Chicago, IL, USA) was used. One-way ANOVA was used to identify metabolites that exhibited statistically significant differences in abundance across the control and infected groups. For each metabolite, the mean levels were compared between groups, and p-values were calculated to determine the significance of these differences. The analysis was performed with significance thresholds of appropriately (p < 0.05). Metabolic pathway analysis was conducted through the online platform MetaboAnalyst 4.0 (http://www.Metaboanalyst.ca/faces/ModuleView.xhtml) (accessed on 8 January 2025). Heatmaps were generated using the online tool (https://matrix2png.msl.ubc.ca/bin/matrix2png.cgi) (accessed on 12 January 2025), and Venn diagrams were created using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) (accessed on 15 January 2025).

2.3.4. Data Processing and Analysis

The raw data were converted into mzXML format using Proteowizard software (v3.0.8789). Peak identification, filtration, and alignment were carried out with the XCMS package in R (v3.1.3). A data matrix was generated, which included the mass-to-charge ratio (m/z), retention time, and intensity. Subsequently, precursor molecules were identified in both positive and negative ion modes; finally, the data were exported to Excel for further analysis.

3. Results

3.1. Transcritptomics Analysis of M. incognita-Infected and Control C. metuliferus Plants

3.1.1. Overview of Gene Set Enrichment Analysis (GSEA) in M. incognita-Infected and Control C. metuliferus Plants

GSEA identified a total of 20 gene sets significantly enriched in the control and infected groups. The most strongly enriched pathway was KO03010 (Ribosome) with the highest normalized enrichment score (NES) of 2.48 and an enrichment score (ES) of 0.69. Similarly, KO03050 (Proteasome) demonstrated robust enrichment (NES = 2.08, ES = 0.72), suggesting highly upregulated proteolytic activity. Other top-ranked gene sets included KO00010 (Glycolysis/Gluconeogenesis) and KO03030 (DNA Replication). Both pathways were highly significant, indicating elevated metabolic and proliferative activity. KO00190 (Oxidative Phosphorylation) and KO04141 (Cell Cycle) also displayed notable enrichment. Further enriched pathways included KO01230 (Biosynthesis of Amino Acids), KO03060 (Spliceosome), and KO00240 (Pyrimidine Metabolism), which point to transcriptional and translational upregulation alongside nucleotide biosynthesis.
GSEA identified five gene sets significantly downregulated in the infected groups relative to the control groups. The most significantly enriched gene set in the negative direction was KO00196 (Linoleic acid metabolism), with a normalized enrichment score (NES) of −1.84 and enrichment score (ES) of −0.73. This gene set demonstrated strong statistical significance (nominal p = 0.0025), indicating a notable suppression of fatty acid metabolic processes. Other negatively enriched pathways included KO00511 (other glycan degradation) and KO00531 (glycosaminoglycan degradation), both with NES values of −1.53 and −1.40, respectively. Additional gene sets with moderate downregulation included KO00280 (valine, leucine, and isoleucine degradation) and KO00195 (alpha-linolenic acid metabolism), which suggests that downregulated gene sets are related to lipid degradation and glycan catabolism.

3.1.2. Identification of DEGs

The differential expression of genes is presented in Figure 1A–C through statistical graphs, a volcano plot, and a heat map, respectively. Table 1 represents a summarized list of identified genes. The identified upregulated genes such as XTH1 are involved in cell wall modification, and DOX2 plays a role in the biosynthesis of jasmonic acid, a crucial signaling molecule in plant defense. Additionally, WRKY33, a well-known transcription factor, was also downregulated, underlining its pivotal role in adapting defense responses against biotic stresses. Transport-related genes, including HAK12, are involved in potassium ion transport, and At1g30570, related to auxin transport, was also identified, indicating a potential role in maintaining cellular homeostasis and signaling during infection. Defense-specific genes, such as AHA11, encoding a proton pump that helps in modulating cellular pH and signaling, and PILS2, which regulates auxin distribution, were identified, further reflecting the intricate defense strategy of plants. Other identified genes like ERF4 and ERF061, belonging to the ethylene-responsive factor family, underscore the role of hormonal cross-talk in mitigating M. incognita-induced stress.
Among the upregulated genes, several key regulators of the metabolic process were identified. Genes such as RPL29A and RPL18A, encoding ribosomal proteins, suggest enhanced protein synthesis activity in response to M. incognita infection. Similarly, DOX2, which is involved in the biosynthesis of jasmonic acid and HERK1, a receptor kinase, associated with cell wall signaling, underscores the activation of defense-related metabolic pathways of plants. The upregulation of FAD7A-1 and CUV reflects adjustments in lipid metabolism, potentially to modify membrane composition or produce signaling molecules with critical role during stress responses.
Stress-related genes like SERINC3 and RD19A, known for their roles in abiotic and biotic stress tolerance, were also prominently expressed, indicating the strategy of plants to mitigate nematode-induced cellular damage. Genes linked to energy metabolism and photosynthesis, such as PHOT1 and CURT1B, were upregulated, potentially reflecting the effort of plants to maintain energy production under M. incognita stress.
Key downregulated genes included those coding for Xyloglucan endotransglucosylase (XTH1), a key enzyme involved in cell wall modification, which suggests an adaptive response to M. incognita infection. Other downregulated genes included CYP92C6, involved in the biosynthesis of secondary metabolites, and PHI-1, which plays a role in plant defense mechanisms. Several genes implicated in stress responses, such as TIP1-1 (a tonoplast intrinsic protein) and FAD7A-1 (involved in fatty acid biosynthesis), were also downregulated, indicating a possible role in maintaining cellular integrity and modulating membrane fluidity during nematode invasion. The gene CURT1B, involved in cell division and expansion, was also downregulated, which could be related to cellular remodeling in response to the nematode infection. Additionally, genes like RD19A, an early-response gene to stress, and BHLH72, which plays a role in plant development and stress responses, were also upregulated.

3.1.3. GO Analysis of DEGs

The GO analysis highlighted significant biological, molecular, and cellular adaptations (shown in Figure 2A,B). Within the biological processes category, there was a strong upregulation of genes associated with “metabolic process” (568 upregulated, 301 downregulated) and “cellular process” (609 upregulated, 339 downregulated), indicating heightened metabolic and cellular activity in response to M. incognita stress. Genes involved in “response to stimulus” (228 upregulated, 130 downregulated) and “biological regulation” (229 upregulated, 131 downregulated) were also significantly altered, reflecting the activation of stress-responsive pathways and regulatory mechanisms. The “signaling” (74 upregulated, 51 downregulated) and “immune system process” (22 upregulated, 12 downregulated) terms underscore the coordinated defense responses of plants, including interspecies interactions (24 upregulated, 16 downregulated).
At the molecular function level, “catalytic activity” (431 upregulated, 272 downregulated) and “binding” (497 upregulated, 294 downregulated) were dominant, indicating increased enzymatic activity and molecular interactions under M. incognita stress. Structural adaptations were evident through the upregulation of genes related to “structural molecule activity” (83 upregulated, 1 downregulated). Other significant molecular functions included “transcription regulator activity” (66 upregulated, 43 downregulated) and “transporter activity” (71 upregulated, 45 downregulated), highlighting roles in gene expression and nutrient or ion transport under stress conditions. Notably, antioxidant activity (5 upregulated, 13 downregulated) suggests a dynamic balance in redox regulation to counteract oxidative stress.
For cellular components, genes associated with “cellular anatomical entity” (508 upregulated, 273 downregulated) and “protein-containing complex” (182 upregulated, 61 downregulated) were highly enriched.

3.1.4. Validation of Transcriptomics Data

To validate the findings from the transcriptome analysis, quantitative real-time PCR (qRT-PCR) was employed to assess the expression levels of randomly selected eight genes from relevant metabolic pathways. Gene-specific primers were designed using the PRIMER5 software (version 5.2), as listed in Table 2. Gene expression was quantified relative to the reference gene Cm β-actin. The analyzed genes included ERF (Ethylene Response Factor), MYB73, PAT (phosphinothricin acetyltransferase), PHI-1, PUB19, RLP51, TLP (Thaumatin-like Protein), and WRKY33. The qRT-PCR results aligned with the transcriptomic data, confirming consistent expression trends across the selected genes (Figure 3).

3.2. Metabolomics Analysis

3.2.1. PCA Analysis

PCA was conducted to evaluate the variation between control and treated C. metuliferus plants inoculated with M. incognita (Figure 4A). The results indicated that PC1 and PC2 account for the majority of the variance, effectively distinguishing control and treated groups. Control samples exhibited higher variability along PC1, with values ranging from −8.921 to 37.681, compared to the treated samples, which had negative PC1 values between −27.119 and −2.8935. Similarly, PC2 highlighted differences, with control samples showing wider dispersion (−21.804 to 27.562) compared to treated samples (−14.129 to 8.0679). These results suggest significant shifts in the underlying biological responses between control and treated plants, emphasizing the impact of M. incognita inoculation on their physiological state.

3.2.2. PLSDA Results

PLS-DA was performed to differentiate control and treated C. metuliferus plants inoculated with M. incognita based on their multivariate profiles (Figure 4B). In the PLS-DA analysis, the multivariate profiles consist of the relative abundances of detected metabolites. Each sample was represented by a set of metabolite intensity values derived from LC–MS/MS data, and these were used to construct a predictive model that classified samples based on their treatment status (control vs. M. incognita-infected). The analysis revealed clear separation between the two groups, primarily along Component 1, which accounted for the largest variance. Control samples exhibited negative scores on Component 1 (ranging from −0.41025 to −28.603), whereas treated samples had positive scores (ranging from 6.9635 to 25.561), indicating a distinct shift in their profiles. Component 2 further highlighted differences, with treated samples displaying higher and more variable scores (ranging from −3.0922 to 12.801) compared to the control group (−20.5 to 10.957).

3.2.3. OPLSDA Results

The PLS-DA analysis results summarized the performance of the model in differentiating control and treated C. metuliferus plants inoculated with M. incognita based on the explained variance and predictive accuracy (Figure 4C). The R2X and R2X (cum) values indicate that the first component (p1) explains 25.6% of the total variance in the predictor variables, with a cumulative explained variance of 73.2% across all components. The response variables are well explained, with R2Y and R2Y (cum) values reaching 73.7% and 99.9%, respectively. The Q2 and Q2 (cum) values, which indicated the predictive performance of the model, were 0.449 for the first component and 0.536 cumulatively, showing moderate predictive power.

3.2.4. Fold Change in Volcano Plot Analysis

The metabolite expression profiles of C. metuliferus plants inoculated with M. incognita revealed a substantial reprogramming of the metabolome in treated plants compared to controls (Figure 5). Several metabolites exhibited significant fold changes, suggesting their active role in the response of plants upon M. incognita infection (Table 3). Among the most upregulated metabolites, 8-iso prostaglandin A2 demonstrated the highest expression (log2 (fold change) = 5.0515), highlighting its potential role in signaling or defense mechanisms. Other prostaglandin derivatives, such as thromboxane B2 (log2 (fold change) = 4.1945) and 13 and 14-dihydro-15-keto prostaglandin A2 (log2 (fold change) = 4.2815), were also significantly increased, suggesting a pronounced involvement of lipid-based signaling pathways in M. incognita-induced stress responses.
Metabolites such as FAHFA (16:1/18:3) (log2 (fold change) = 4.7162) and FAHFA (5:0/18:2) (log2 (fold change) = 2.9583) displayed considerable upregulation, emphasizing alterations in fatty acid ester metabolism. Patchouli alcohol (log2 (fold change) = 3.5852) and (±) 18-HEPE (log2 (fold change) = 3.5475) further underline the activation of specialized lipid biosynthesis pathways. Additionally, betaine (log2 (fold change) = 2.9893), known for its role in osmo protection and stress mitigation, was significantly upregulated, suggesting an enhanced protective mechanism against M. incognita stress. Several secondary metabolites, including lappaconitine (log2 (fold change) = 2.3325), quercetin 3-O-sophoroside (log2 (fold change) = 2.228), and naringin dihydrochalcone (log2 (fold change) = 2.2062), showed pronounced increases in treated plants, pointing to an amplified production of bioactive compounds with potential antioxidant and defense properties.

3.2.5. Metabolite Expression Status in C. metuliferus Plants in Response to M. incognita

The analysis of metabolites in C. metuliferus plants inoculated with M. incognita revealed significant differences in metabolite expression between control and infected groups. A group of metabolites demonstrated a dominance in expression under treatment conditions, while others were more prevalent in the control group. Among the metabolites upregulated in treated plants, indole-3-acrylic acid and DL-tryptophan showed significant increases, with mean levels of 2074.39 and 2023.08 compared to 1964.48 and 1919.88 in control plants, respectively. Similarly, 9-HpOTrE, acetophenone, and 9-oxo-ODE were prominently expressed in treated plants with means of 899.62, 603.63, and 704.19, respectively, surpassing their levels in the control. Other metabolites such as 2,3-dinor prostaglandin E1, 12-oxo phytodienoic acid, and D-(+)-proline also showed increased expression in treated plants, highlighting their potential roles in the plant’s response to nematode stress.
Conversely, metabolites such as D-(−)-glutamine, L-pyroglutamic acid, and choline were more abundant in control plants, with mean levels of 1913.28, 1912.91, and 1738.14, respectively, compared to 1893.25, 1892.14, and 1226.48 in treated plants. Significant reductions in the levels of DL-malic acid, citric acid, and L-(−)-malic acid were also observed in treated plants, with the means dropping to 495.44, 122.07, and 371.41, respectively, compared to 1156.28, 991.23, and 987.56 in control plants. Another group of metabolites exhibited dominance in treated plants, suggesting a physiological adaptation or response to nematode infestation. For instance, jasmonic acid, a key phytohormone involved in defense signaling, showed a marked increase in treated plants (21.97 vs. 35.11). Similarly, other defense-associated compounds like salicylic acid (17.09 vs. 26.57) and trans-cinnamic acid (6.29 vs. 28.57) were upregulated, indicating the activation of plant immune pathways.

3.2.6. Identification of Key Metabolites with High Importance and Rank Frequency

The identified metabolites, which are involved in defense, signaling, nucleotide metabolism, and phenolic compound biosynthesis, appear to be significantly altered. Among the most prominent metabolites, stachydrine showed the highest rank frequency (0.6) and was identified with high importance (9.47 × 10−6), suggesting a potentially crucial role in plant responses to nematode stress. Similarly, GKK ranked highly (0.52), though its p-value (0.061363) indicated borderline significance. Thymol, a compound with known antimicrobial properties, also appeared with moderate rank frequency (0.46) and a p-value of 0.022184, highlighting its potential involvement in defensive pathways against M. incognita invasion. Metabolites such as 1, 2-dihydroxyheptadec-16-yn-4-yl acetate and 1-(2, 4-difluorobenzoyl)-4-piperidinecarboxylic acid demonstrated high importance (8.54E-06 and 8.18E-06, respectively) with p-values (0.010101 and 0.0085256, respectively), supporting their relevance in the host response. Compounds like SPK and 2-(1H-indol-3-yl)-3-[4-(trifluoromethyl) phenyl] acrylonitrile were also significant for their low p-values (0.0044901 and 0.0072642), suggesting their biological importance in host response.
Figure 6 presents the 15 most important metabolites as determined by the support vector machine (SVM) analysis. These metabolites were identified based on their ability to contribute to group separation and are, therefore, considered significantly different between the two groups. Metabolites with higher importance scores have a greater influence on distinguishing between the M. incognita-infected and control groups.

3.2.7. KEGG Pathway Enrichment of Metabolites

Among the pathways, nucleotide metabolism (ko01232) demonstrated the most significant enrichment, with a raw p-value of 0.00017111 and an FDR of 0.028233, highlighting its pivotal role in responding to M. incognita stress. This pathway showed a high fold enrichment, reflecting increased metabolic activity associated with nucleotides. Pyrimidine metabolism (ko00240) (p: 0.0069) and purine metabolism (ko00230) (p: 0.012) were also significantly enriched, respectively (Figure 7A).
Specialized metabolic pathways, such as biotin metabolism (ko00780) (Figure 7B), limonene degradation (ko00903), and monoterpenoid biosynthesis (ko00902), were highlighted with fold enrichments. These pathways underline the significance of terpenoid and biotin-related compounds in C. metuliferus plant–M. incognita interactions, potentially contributing to plant defense responses and metabolic adaptations (Figure 8A). Pathways related to lipid metabolism, such as fatty acid biosynthesis (ko00061) and fatty acid metabolism (ko01212), were moderately enriched. A sample compound reaction network of biotin metabolism is presented in Figure 8B.

3.3. Combined Analysis of Transcriptomics and Metabolomics

3.3.1. GO Analysis

In the biological processes (BP) category, a large proportion of the terms (such as metabolic processes, organic substance metabolism, and nitrogen compound metabolism) indicated a strong shift in metabolic activity in response to M. incognita infection (Figure 9).

3.3.2. KOG (Eukaryotic Orthologous Groups) Analysis

The combined transcriptomics and metabolomics analysis of C. metuliferus plants following M. incognita infection provides a comprehensive overview of the molecular and biochemical responses triggered by the M. incognita infection. KOG classification categorizes genes and metabolites into functional classes based on their predicted roles in cellular processes (Figure 10A). The most significant category accounts for 10,353 genes.
Other important categories included intracellular trafficking, secretion, and vesicular transport, purine and pyrimidine metabolism, lipid metabolism, secondary metabolite biosynthesis, transcription-related processes, amino acid transport and metabolism, energy production and conversion, and nucleotide metabolism, respectively.

3.3.3. KEGG Pathway Analysis

Figure 10B presents the KEGG pathways analysis in response to M. incognita infection. In the metabolism category, several key pathways are significantly enriched, pointing to substantial metabolic adjustments in the plant. Notably, metabolic pathways (10.43%) and biosynthesis of secondary metabolites (5.42%) were prominently represented. Carbon metabolism (1.63%) and glycolysis/gluconeogenesis (0.75%) were also enriched, reflecting a potential shift in energy production to meet the increased demands of the plant’s immune and stress response systems. The increased activity in amino acid biosynthesis (1.50%) and purine metabolism (1.07%) indicates a reorganization of nitrogen and nucleotide metabolism, which is essential for protein synthesis, cell growth, and the activation of defense mechanisms.
The organismal systems category also revealed key pathways involved in plant–pathogen interactions. The plant–pathogen interaction pathway (0.74%) is particularly significant, highlighting the activation of immune signaling mechanisms to recognize and respond to the M. incognita. Within the genetic information processing category, the enrichment of pathways related to ribosome biogenesis (1.49%) and protein processing in the endoplasmic reticulum (1.10%) suggests that the plant is synthesizing and folding proteins at an increased rate in response to the stress imposed by M. incognita infection. The environmental information processing category revealed several signaling pathways, including MAPK (mitogen-activated protein kinase) signaling (0.95%) and plant hormone signal transduction (0.94%).

4. Discussion

This study highlighted a complex transcriptional and metabolic reprogramming in C. metuliferus roots in response to M. incognita infection, exhibiting coordinated changes in genes related to defense, metabolism, hormonal signaling, and cellular structure. The significant upregulation of stress-responsive transcription factors (ERF4, NAC075, and MYB73) and receptor-like kinases (such as HERK1) aligned with findings in tomato and rice, where comparable regulatory components were induced during nematode infection to mediate downstream defense signaling [18,33]. Key defense genes such as DOX2, involved in jasmonic acid biosynthesis, and AHA11, linked to pH regulation, were also upregulated, reinforcing the role of hormone signaling and cellular ion balance in primary defense against M. incognita infection, which is consistent with prior work on Arabidopsis and eggplant under RKN-mediated stress [34]. In contrast, the downregulation of WRKY33, a central regulator of biotic stress responses, may reflect a nematode-driven suppression of host defenses, as previously observed in susceptible host plants [35]. Interestingly, genes associated with photosynthesis (such as PSBO and petM) and energy metabolism (FAD6 and RCA1) were downregulated, indicating a shift in energy allocation toward stress responses a phenomenon widely documented in RKN infections [36]. Moreover, upregulated ribosomal protein genes like RPL29A and RPS26C pointed to an increase in translational activity, supporting the enhanced synthesis of stress and defense proteins. Similar response was reported in nematode-infected soybean and rice cultivars [37].
GO analysis supports the transcriptomics data, revealing pronounced enrichment of genes involved in metabolic processes, signal transduction, and transcriptional regulation. These findings aligned with studies reporting large-scale induction of regulatory pathways in resistant cultivars under nematode infection [38]. The differential expression of genes associated with cell wall remodeling (XTH1 and CURT1B) and membrane integrity (TIP1-1 and FAD7A-1) underscores the structural adaptations employed by plants to impede nematode penetration and feeding site establishment [15]. Despite strong defense activation, the observed downregulation of secondary metabolites biosynthesis genes (such as CYP92C6) and some antioxidant-related genes suggests a trade-off or partial suppression of specific defense pathways. This complex balance between growth and defense is highlighted by altered expression in growth-related genes like GSO1 and NAC081. Similar findings were reported in rice and tomato, where nematodes interfered with hormonal and developmental cues to facilitate feeding site establishment [18,33].
The metabolomics analysis of C. metuliferus under M. incognita infection reveals substantial shifts in primary and secondary metabolic pathways, indicating a robust reprogramming of metabolic activity to mitigate biotic stress. Key defense-related metabolites, including jasmonic acid, salicylic acid, and trans-cinnamic acid, were significantly upregulated, which is consistent with their established roles in activating plant immune responses [39]. These phytohormones are known to mediate complex cross-talk in defense signaling, especially under nematode and pathogen attacks. The elevated levels of indole-3-acrylic acid and DL-tryptophan aligned with previous findings in Arabidopsis and tomato, where tryptophan-derived compounds contribute to the biosynthesis of auxins and defense-related indole alkaloids [40]. Similarly, the elevated levels of 12-oxo-phytodienoic acid and γ-aminobutyric acid (GABA) support their roles in stress signaling and redox homeostasis, consistent with previous findings in rice and soybean under nematode infestation [37,41].
Pathway enrichment analysis further highlighted the activation of nucleotide metabolism (for example, purine and pyrimidine pathways), indicating increased nucleic acid turnover and energy demand under stress. This is in line with previous transcriptomics studies, which reported upregulation of genes involved in DNA repair and synthesis during nematode infection [38]. Enrichment in phenylpropanoid and terpenoid pathways underscores the production of phenolics and aromatic compounds, which are known to fortify plant cell walls and scavenge reactive oxygen species. Expression changes in metabolites like catechin, gallate, and ferulic acid O-hexoside suggests that antioxidant activity plays a central role in maintaining cellular redox homeostasis during M. incognita infection. These findings are consistent with reports that nematode stress induces oxidative bursts, necessitating enhanced antioxidant responses [15].
The integrative analysis of transcriptomics and metabolomics data in C. metuliferus reveals a multifaceted molecular response to M. incognita infection, highlighting dynamic changes in gene expression, metabolic reprogramming, and cellular adaptation. GO enrichment underscores the upregulation of biological processes related to metabolic regulation, stimulus response, and macromolecule metabolism, suggesting a concerted cellular effort to counter M. incognita-induced stress. Similar GO term enrichments have been reported in resistant cultivars of tomato and rice during early nematode interaction, pointing to conserved defense strategies across plant species [36,37]. Cellular component shifts, including changes in nucleus and cytoplasmic organelle-related gene expression, suggest stress-induced compartmental reorganization, consistent with previous observations of endoplasmic reticulum and vesicle trafficking involvement during pathogen attack [42]. Enrichment in carbohydrate and amino acid metabolism reflects an energy shift to support defense rather than growth, a typical response in stressed plants [39]. KEGG pathway enrichment further supports the activation of metabolic and immune responses. Pathways such as biosynthesis of secondary metabolites, carbon metabolism, and purine metabolism were significantly enriched, reinforcing the metabolomic data that showed increased synthesis of defense-related compounds like jasmonic acid and phenolics. Similar patterns have been noted in cucurbit and legume in responses to nematode stress, where enhanced secondary metabolism confers resistance [15,38]. In resistant tomato plants, the accumulation of SA leads to the inhibition of catalase activity, promoting a hypersensitive response characterized by localized cell death and enhanced resistance. Similarly, in C. metuliferus, transcriptomics analysis has revealed upregulation of genes associated with various plant hormones, including SA, jasmonic acid (JA), and abscisic acid (ABA), suggesting a coordinated hormonal response to M. incognita attack. These hormonal changes are indicative of a robust defense mechanism aiming at limiting nematode proliferation [7,43,44]. Biotin metabolism pathways are fundamental for maintaining cellular energy balance and responding to biotic stresses. The interaction between M. incognita and its host plants involves complex molecular mechanisms where the nematode secretes effectors to modulate host immune responses. One such effector, MiCTL1a, targets plant catalases, enzymes involved in hydrogen peroxide (H2O2) homeostasis. By interacting with catalases, MiCTL1a reduces their activity, thereby manipulating reactive oxygen species (ROS) levels to favor nematode parasitism. This disruption of ROS homeostasis is a common strategy employed by nematodes to suppress plant defense responses [38]. In addition, immune-related pathways including MAPK signaling, plant hormone transduction, and plant–pathogen interaction were significantly represented, underscoring the role of signaling cascades in activating defense mechanisms. These pathways are essential for PAMP-triggered and effector-triggered immunity, as previously demonstrated in nematode-infected Arabidopsis and rice [18,33].
While this study provided a comprehensive overview of transcriptional and metabolic reprogramming in C. metuliferus roots upon M. incognita infection, it is limited by the analysis of a single time point and whole-root sampling, which may mask temporal and tissue-specific dynamics. Future research should include time-course studies and spatial profiling (such as galls vs. surrounding tissue) to better resolve defense kinetics. Integrative proteomics and functional validation of key candidate genes and metabolites are recommended to confirm their roles in M. incognita resistance. Comparative analysis with susceptible lines will further clarify resistance mechanisms and identify biomarkers for breeding.

5. Conclusions

M. incognita infections in C. metuliferus plants induced significant metabolic and transcriptomic alterations, as revealed through a combined analysis of transcriptomics and metabolomics. The downregulation of genes involved in pathogen resistance and structural integrity, alongside the upregulation of stress-related pathways, reflects the struggle of plants to balance growth and immunity under nematode attack. Key findings included the activation of detoxification mechanisms, increased metabolic activity for energy production, and structural reinforcements at the cellular level to limit nematode penetration. Additionally, alterations in signaling pathways and molecular functions, such as antioxidant activity and protein synthesis, emphasize the integrated defense strategies of plants. The identification of defense-related genes and signaling pathways provides promising targets for developing nematode-resistant melon rootstocks. These rootstocks could be used directly in grafting practices in the fields, offering a sustainable alternative to nematicides. Farmers cultivating C. metuliferus could benefit from rootstocks that inherently resist M. incognita penetration and feeding, particularly in nematode-endemic regions. Deploying such genotypes can lead to higher yields, reduced input costs, and lower environmental impact.

Author Contributions

H.Z.: writing—original draft, methodology, investigation, and data curation. Q.L.: writing—review and editing and validation. J.C.: software and conceptualization. J.W.: methodology, investigation, and data curation. Y.H.: software and methodology. B.L.: methodology and writing—original draft. X.Z.: investigation and methodology. B.Z.: supervision, funding acquisition, conceptualization, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2023D01B38); Key Laboratory of Agro-products Quality and Safety of Xinjiang, Urumqi 830091, China (xjnkywdzc-2025002-09); Project of Renovation Capacity Building for the Young Sci-Tech Talents Sponsored by Xinjiang Academy of Agricultural Sciences (xjnkq-2021011); Hainan Province Major Science and Technology Plan Project (ZDYF2025XDNY089).

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Jeger, M.; Bragard, C.; Caffier, D.; Candresse, T.; Chatzivassiliou, E.; Dehnen-Schmutz, K.; Gilioli, G.; Grégoire, J.-C.; Miret, J.A.J.; MacLeod, A.; et al. Pest categorisation of Nacobbus aberrans. EFSA J. 2018, 16, e05249. [Google Scholar] [CrossRef]
  2. Hemmati, S.; Saeedizadeh, A. Root-knot nematode, Meloidogyne javanica, in response to soil fertilization. Braz. J. Biol. 2020, 80, 621–630. [Google Scholar] [CrossRef]
  3. Cao, Y.; Lu, N.; Yang, D.; Mo, M.; Zhang, K.Q.; Li, C.; Shang, S. Root-knot nematode infections and soil characteristics significantly affected microbial community composition and assembly of tobacco soil microbiota: A large-scale comparison in tobacco-growing areas. Front. Microbiol. 2023, 14, 1282609. [Google Scholar] [CrossRef]
  4. Huang, K.; Jiang, Q.; Liu, L.; Zhang, S.; Liu, C.; Chen, H.; Ding, W.; Zhang, Y. Exploring the key microbial changes in the rhizosphere that affect the occurrence of tobacco root-knot nematodes. AMB Express 2020, 10, 72. [Google Scholar] [CrossRef]
  5. Xu, C.; Han, X.; Staehelin, C.; Zhang, J. First report of Meloidogyne arenaria on roots of Grona triflora in Guangdong Province, China. Plant Dis. 2021, 105, 3763. [Google Scholar] [CrossRef]
  6. Khan, M.R.; Ahamad, F. Incidence of root-knot nematode (Meloidogyne graminicola) and resulting crop losses in paddy rice in northern India. Plant Dis. 2020, 104, 186–193. [Google Scholar] [CrossRef]
  7. Leonetti, P.; Molinari, S. Epigenetic and metabolic changes in root-knot nematode-plant interactions. Int. J. Mol. Sci. 2020, 21, 7759. [Google Scholar] [CrossRef]
  8. Šeregelj, V.; Šovljanski, O.; Tumbas Šaponjac, V.; Vulić, J.; Ćetković, G.; Markov, S.; Čanadanović-Brunet, J. Horned melon (Cucumis metuliferus E. Meyer Ex. Naudin)—Current knowledge on its phytochemicals, biological benefits, and potential applications. Processes 2022, 10, 94. [Google Scholar] [CrossRef]
  9. Ferrara, L. A fruit to discover: Cucumis metuliferus E. Mey Ex Naudin (Kiwano). Clin. Nutr. Metab. 2018, 5, 1–2. [Google Scholar] [CrossRef]
  10. Usman, J.G.; Sodipo, O.A.; Kwaghe, A.V.; Sandabe, U.K. Uses of Cucumis metuliferus: A review. Cancer Biol. 2015, 5, 24–34. [Google Scholar]
  11. Vieira, P.; Gleason, C. Plant-parasitic nematode effectors—Insights into their diversity and new tools for their identification. Curr. Opin. Plant Biol. 2019, 50, 37–43. [Google Scholar] [CrossRef] [PubMed]
  12. Thakur, S.; Rana, A.; Sharma, A.; Yangchan, J.; Choudhary, K.; Kumar, R.; Sharma, D. Plant nematode interaction and omics: A focus on Meloidogyne incognita. J. Crop Health 2024, 76, 1281–1291. [Google Scholar] [CrossRef]
  13. Habteweld, A.; Kantor, M.; Kantor, C.; Handoo, Z. Understanding the dynamic interactions of root-knot nematodes and their host: Role of plant growth promoting bacteria and abiotic factors. Front. Plant Sci. 2024, 15, 1377453. [Google Scholar] [CrossRef] [PubMed]
  14. Dhankher, O.P.; Foyer, C.H. Climate resilient crops for improving global food security and safety. Plant Cell Environ. 2018, 41, 877–884. [Google Scholar] [CrossRef]
  15. Holbein, J.; Grundler, F.M.W.; Siddique, S. Plant basal resistance to nematodes: An update. J. Exp. Bot. 2016, 67, 2049–2061. [Google Scholar] [CrossRef]
  16. Postnikova, O.A.; Hult, M.; Shao, J.; Skantar, A.; Nemchinov, L.G. Transcriptome analysis of resistant and susceptible alfalfa cultivars infected with root-knot nematode Meloidogyne incognita. PLoS ONE 2015, 10, e0118269. [Google Scholar] [CrossRef]
  17. Xing, X.; Li, X.; Zhang, M.; Wang, Y.; Liu, B.; Xi, Q.; Zhao, K.; Wu, Y.; Yang, T. Transcriptome analysis of resistant and susceptible tobacco (Nicotiana tabacum) in response to root-knot nematode Meloidogyne incognita infection. Biochem. Biophys. Res. Commun. 2017, 482, 1114–1121. [Google Scholar] [CrossRef]
  18. Shukla, N.; Yadav, R.; Kaur, P.; Rasmussen, S.; Goel, S.; Agarwal, M.; Jagannath, A.; Gupta, R.; Kumar, A. Transcriptome analysis of root-knot nematode (Meloidogyne incognita)-infected tomato (Solanum lycopersicum) roots reveals complex gene expression profiles and metabolic networks of both host and nematode during susceptible and resistance responses. Mol. Plant Pathol. 2018, 19, 615–633. [Google Scholar] [CrossRef]
  19. Macharia, T.N.; Bellieny-Rabelo, D.; Moleleki, L.N. Transcriptome profiling of potato (Solanum tuberosum) responses to root-knot nematode (Meloidogyne javanica) infestation during a compatible interaction. Microorganisms 2020, 8, 1443. [Google Scholar] [CrossRef]
  20. Petitot, A.S.; Dereeper, A.; Silva, C.D.; Guy, J.; Fernandez, D. Analyses of the root-knot nematode (Meloidogyne graminicola) transcriptome during host infection highlight specific gene expression profiling in resistant rice plants. Pathogens 2020, 9, 644. [Google Scholar] [CrossRef]
  21. Zhou, Y.; Zhao, D.; Shuang, L.; Xiao, D.; Xuan, Y.; Duan, Y.; Chen, L.; Wang, Y.; Liu, X.; Fan, H.; et al. Transcriptome analysis of rice roots in response to root-knot nematode infection. Int. J. Mol. Sci. 2020, 21, 848. [Google Scholar] [CrossRef]
  22. Zhang, M.; Zhang, H.; Tan, J.; Huang, S.; Chen, X.; Jiang, D.; Xiao, X. Transcriptome analysis of eggplant root in response to root-knot nematode infection. Pathogens 2021, 10, 470. [Google Scholar] [CrossRef]
  23. Wang, Z.; Wang, W.; Wu, W.; Wang, H.; Zhang, S.; Ye, C.; Guo, L.; Wei, Z.; Huang, H.; Liu, Y.; et al. Integrated analysis of transcriptome, metabolome, and histochemistry reveals the response mechanisms of different ages P. notoginseng to root-knot nematode infection. Front. Plant Sci. 2023, 14, 1258316. [Google Scholar] [CrossRef] [PubMed]
  24. Sung, Y.W.; Kim, J.; Yang, J.W.; Shim, D.; Kim, Y.H. Transcriptome-based comparative expression profiling of sweet potato during a compatible response with root-knot nematode Meloidogyne incognita infection. Genes 2023, 14, 2074. [Google Scholar] [CrossRef]
  25. Kantor, M.; Levi, A.; Thies, J.; Guner, N.; Kantor, C.; Parnham, S.; Boroujerdi, A. NMR analysis reveals a wealth of metabolites in root-knot nematode resistant roots of watermelon plants. J. Nematol. 2018, 50, 303–316. [Google Scholar] [CrossRef] [PubMed]
  26. Howe, G.T.; Horvath, D.P.; Dharmawardhana, P.; Priest, H.D.; Mockler, T.C.; Strauss, S.H. Extensive transcriptome changes during the natural onset and release of vegetative bud dormancy in Populus. Front. Plant Sci. 2015, 6, 989. [Google Scholar] [CrossRef]
  27. Seo, Y.; Kim, Y.H. Pathological interrelations of soil-borne diseases in cucurbits caused by Fusarium species and Meloidogyne incognita. Plant Pathol. J. 2017, 33, 410–423. [Google Scholar] [CrossRef]
  28. Miao, G.P.; Han, J.; Zhang, K.G.; Wang, S.C.; Wang, C.R. Protection of melon against Fusarium wilt-root knot nematode complex by endophytic fungi Penicillium brefeldianum HS-1. Symbiosis 2019, 77, 83–89. [Google Scholar] [CrossRef]
  29. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  30. Conesa, A.; Götz, S.; García-Gómez, J.; Terol, J.; Talon, M.; Robles, M. BLAST2GO: A universal tool for annotation, visualization, and analysis in functional genomics research. Bioinformatics 2005, 21, 3674–3676. [Google Scholar] [CrossRef]
  31. Kanehisa, M.; Araki, M.; Goto, S.; Hattori, M.; Hirakawa, M.; Itoh, M.; Katayama, T.; Kawashima, S.; Okuda, S.; Tokimatsu, T.; et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36, 480–484. [Google Scholar] [CrossRef]
  32. Zhang, S.; Nie, L.; Zhao, W.; Cui, Q.; Wang, J.; Duan, Y.; Ge, C. Metabolomic analysis of the occurrence of bitter fruits on grafted oriental melon plants. PLoS ONE 2019, 14, e0223707. [Google Scholar] [CrossRef] [PubMed]
  33. Kumari, C.; Dutta, T.K.; Banakar, P.; Rao, U. Comparing the defence-related gene expression changes upon root-knot nematode attack in susceptible versus resistant cultivars of rice. Sci. Rep. 2016, 6, 22846. [Google Scholar] [CrossRef] [PubMed]
  34. Sato, K.; Uehara, T.; Holbein, J.; Sasaki-Sekimoto, Y.; Gan, P.; Bino, T.; Shirasu, K. Transcriptomic Analysis of Resistant and Susceptible Responses in a New Model Root-Knot Nematode Infection System Using Solanum torvum and Meloidogyne arenaria. Front. Plant Sci. 2021, 12, 680151. [Google Scholar] [CrossRef]
  35. Ali, M.A.; Abbas, A.; Sajjad, M.; Khan, M.A. WRKY transcription factors and plant defense responses: Current perspectives. Int. J. Agri. Biol. 2015, 17, 617–626. [Google Scholar] [CrossRef]
  36. Balestrini, R.; Rosso, L.C.; Veronico, P.; Melillo, M.T.; De Luca, F.; Fanelli, E.; Pentimone, I. Transcriptomic responses to water deficit and nematode infection in mycorrhizal tomato roots. Front. Microbiol. 2019, 10, 1807. [Google Scholar] [CrossRef]
  37. Kyndt, T.; Denil, S.; Haegeman, A.; Trooskens, G.; De Meyer, T.; Van Criekinge, W.; Gheysen, G. Transcriptional reprogramming by root knot and migratory nematode infection in rice. New Phytol. 2012, 196, 887–900. [Google Scholar] [CrossRef]
  38. 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, 3544. [Google Scholar] [CrossRef]
  39. Erb, M.; Kliebenstein, D.J. Plant secondary metabolites as defenses, regulators, and primary metabolites: The blurred functional trichotomy. Plant Physiol. 2020, 184, 39–52. [Google Scholar] [CrossRef]
  40. Ayyanath, M.M.; Shukla, M.R.; Sriskantharajah, K.; Hezema, Y.S.; Saxena, P.K. Stable indoleamines attenuate stress—A novel paradigm in tryptophan metabolism in plants. J. Pineal Res. 2024, 76, e12938. [Google Scholar] [CrossRef]
  41. Arraes, F.B.; Vasquez, D.D.; Tahir, M.; Pinheiro, D.H.; Faheem, M.; Freitas-Alves, N.S.; Grossi-de-Sa, M.F. Integrated omic approaches reveal molecular mechanisms of tolerance during soybean and Meloidogyne incognita interactions. Plants 2022, 11, 2744. [Google Scholar] [CrossRef]
  42. Nadarajah, K.K. ROS homeostasis in abiotic stress tolerance in plants. Int. J. Mol. Sci. 2020, 21, 5208. [Google Scholar] [CrossRef]
  43. Zhao, J.; Sun, Q.; Quentin, M.; Ling, J.; Abad, P.; Zhang, X.; Xie, B. A Meloidogyne incognita C-type lectin effector targets plant catalases to promote parasitism. New Phytol. 2021, 232, 2124–2137. [Google Scholar] [CrossRef]
  44. Zhang, X.; Song, M.; Gao, L.; Tian, Y. Metabolic variations in root tissues and rhizosphere soils of weak host plants potently lead to distinct host status and chemotaxis regulation of Meloidogyne incognita in intercropping. Mol. Plant Pathol. 2024, 25, e13396. [Google Scholar] [CrossRef]
Figure 1. (A) Statistical representation of DEGs; (B) volcano plot of control and treated C. metuliferus plants; (C) heat map showing the gene expression differences from transcriptomics analysis. Vertical dashed lines represent the fold change cut-off, with genes outside these boundaries (|log2FC| > threshold) considered biologically meaningful. The horizontal dashed line denotes the statistical significance threshold (−log10 p-value), with genes positioned above this line classified as statistically significant. (B) Black Parts represent the background data points, which are genes that show no significant difference in expression between the experimental and control conditions. These points typically fall within the neutral zone of the plot, indicating genes that are neither upregulated nor downregulated in the analysis.
Figure 1. (A) Statistical representation of DEGs; (B) volcano plot of control and treated C. metuliferus plants; (C) heat map showing the gene expression differences from transcriptomics analysis. Vertical dashed lines represent the fold change cut-off, with genes outside these boundaries (|log2FC| > threshold) considered biologically meaningful. The horizontal dashed line denotes the statistical significance threshold (−log10 p-value), with genes positioned above this line classified as statistically significant. (B) Black Parts represent the background data points, which are genes that show no significant difference in expression between the experimental and control conditions. These points typically fall within the neutral zone of the plot, indicating genes that are neither upregulated nor downregulated in the analysis.
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Figure 2. (A) Circular diagram of GO analysis of DEGs identified from transcriptomics analysis; (B). Bar plot of top 20 GO enrichment analysis of DEGs identified from transcriptomics analysis.
Figure 2. (A) Circular diagram of GO analysis of DEGs identified from transcriptomics analysis; (B). Bar plot of top 20 GO enrichment analysis of DEGs identified from transcriptomics analysis.
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Figure 3. Validation of selected genes by quantitative real-time PCR in control and treated C. metuliferus plants inoculated with M. incognita.
Figure 3. Validation of selected genes by quantitative real-time PCR in control and treated C. metuliferus plants inoculated with M. incognita.
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Figure 4. (A) Principal Component Analysis (PCA) of control and treated C. metuliferus plants inoculated with M. incognita; (B) partial least squares discriminant analysis (PLS-DA) of control and treated C. metuliferus plants; (C) orthogonal partial least squares discriminant analysis (OPLS-DA) analysis of control and treated C. metuliferus plants. The blue and red shaded areas represent the 95% confidence ellipses for the control and treatment groups, respectively.
Figure 4. (A) Principal Component Analysis (PCA) of control and treated C. metuliferus plants inoculated with M. incognita; (B) partial least squares discriminant analysis (PLS-DA) of control and treated C. metuliferus plants; (C) orthogonal partial least squares discriminant analysis (OPLS-DA) analysis of control and treated C. metuliferus plants. The blue and red shaded areas represent the 95% confidence ellipses for the control and treatment groups, respectively.
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Figure 5. Volcano plot showing changes in metabolites in control and infected C. metuliferus plants. Black circles represent data points that are not significantly different or do not meet the threshold for upregulation or downregulation, indicating no major change in gene expression. Green circles indicate genes that are significantly different, highlighting genes with changed expression in the experimental condition compared to the control. The vertical dotted lines indicate the fold change (FC) threshold (|log2FC| = 1), which separates metabolites with biologically meaningful changes from those with minimal variation. The horizontal dotted line corresponds to the p-value threshold (−log10 (p) = 1.301, equivalent to p = 0.05), distinguishing statistically significant metabolites from non-significant ones.
Figure 5. Volcano plot showing changes in metabolites in control and infected C. metuliferus plants. Black circles represent data points that are not significantly different or do not meet the threshold for upregulation or downregulation, indicating no major change in gene expression. Green circles indicate genes that are significantly different, highlighting genes with changed expression in the experimental condition compared to the control. The vertical dotted lines indicate the fold change (FC) threshold (|log2FC| = 1), which separates metabolites with biologically meaningful changes from those with minimal variation. The horizontal dotted line corresponds to the p-value threshold (−log10 (p) = 1.301, equivalent to p = 0.05), distinguishing statistically significant metabolites from non-significant ones.
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Figure 6. The 15 most important metabolites identified by support vector machine (SVM) analysis that distinguish between M. incognita-infected and control groups.
Figure 6. The 15 most important metabolites identified by support vector machine (SVM) analysis that distinguish between M. incognita-infected and control groups.
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Figure 7. (A) KEGG pathway enrichment of identified metabolites; (B) biotin metabolism pathway.
Figure 7. (A) KEGG pathway enrichment of identified metabolites; (B) biotin metabolism pathway.
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Figure 8. (A) ORA (over-representation analysis) enrichment analysis and topological analysis of metabolic pathways; (B) Compound reaction network of biotin metabolism; each arrow points from substrate to product. Metabolites in red are metabolites that are significantly different between groups.
Figure 8. (A) ORA (over-representation analysis) enrichment analysis and topological analysis of metabolic pathways; (B) Compound reaction network of biotin metabolism; each arrow points from substrate to product. Metabolites in red are metabolites that are significantly different between groups.
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Figure 9. GO analysis of combined transcriptomics and metabolomics analysis.
Figure 9. GO analysis of combined transcriptomics and metabolomics analysis.
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Figure 10. (A) KOG analysis of the combined transcriptomics and metabolomics analysis of C. metuliferus plants following RKN inoculation; (B) the KEGG pathways analysis of combined transcriptome and metabolomics profiling of C. metuliferus plants.
Figure 10. (A) KOG analysis of the combined transcriptomics and metabolomics analysis of C. metuliferus plants following RKN inoculation; (B) the KEGG pathways analysis of combined transcriptome and metabolomics profiling of C. metuliferus plants.
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Table 1. List of identified genes and their expression level.
Table 1. List of identified genes and their expression level.
Gene IDlog2 Fold Changep-ValueSymbolDescription
PI0020800.12.1910985881.68 × 10−11At2g19810PREDICTED: zinc finger CCCH-domain-containing protein 20-like [Cucumis melo]
PI0027185.1−2.1494172592.50 × 10−11BHLH25PREDICTED: transcription factor bHLH18-like [Cucumis melo]
PI0013166.11.3885742446.07 × 10−11CYCU2-1PREDICTED: cyclin-U2-1 [Cucumis melo]
PI0023141.1−2.0717051575.06 × 10−9BHLH19PREDICTED: transcription factor bHLH18-like [Cucumis melo]
PI0009242.13.2425089341.75 × 10−8PATROL1PREDICTED: uncharacterized protein LOC103490971 [Cucumis melo]
PI0021520.11.9960705458.48 × 10−7PER18PREDICTED: peroxidase 18 [Cucumis melo]
PI0002150.1−2.5908121971.66 × 10−6At3g43660PREDICTED: vacuolar iron transporter homolog 2-like [Cucumis melo]
PI0002806.1−4.39414691.68 × 10−6DTX42PREDICTED: protein DETOXIFICATION 43-like [Cucumis melo]
PI0018265.1−1.8850215922.17 × 10−6QWRF2PREDICTED: QWRF motif-containing protein 2 [Cucumis melo]
PI0025339.12.6223561193.10 × 10−6OMT3Trans-resveratrol di-O-methyltransferase-like [Cucumis melo var. makuwa] [Cucumis melo]
PI0014793.13.0316989793.18 × 10−6P4H3PREDICTED: probable prolyl 4-hydroxylase 3 isoform X2 [Cucumis melo]
PI0013781.11.7638199244.87 × 10−6At3g27220PREDICTED: kelch repeat-containing protein At3g27220-like [Cucumis melo]
PI0017889.12.7250581484.93 × 10−6AAOPREDICTED: L-ascorbate oxidase-like [Cucumis melo]
PI0006726.12.0664437375.27 × 10−6TPPJPREDICTED: probable trehalose-phosphate phosphatase J [Cucumis melo]
PI0025578.11.7966612626.51 × 10−6S-ACP-DES6Stearoyl-(acyl-carrier-protein) 9-desaturase 6 [Cucumis melo var. makuwa] [Cucumis melo]
PI0006807.1−1.1236641447.96 × 10−6BRH1Zinc finger protein [Cucumis melo var. makuwa] [Cucumis melo]
PI0022271.21.4648583791.11 × 10−5TIR1PREDICTED: protein TRANSPORT INHIBITOR RESPONSE 1 [Cucumis melo]
PI0019118.32.3198233641.30 × 10−5At1g08570PREDICTED: thioredoxin-like 1-2, chloroplastic [Cucumis melo]
PI0016220.11.564816841.94 × 10−5ANSPREDICTED: 1-aminocyclopropane-1-carboxylate oxidase 5 isoform X1 [Cucumis melo]
PI0024504.1−0.862095352.45 × 10−5At5g22810PREDICTED: GDSL esterase/lipase At5g22810 [Cucumis melo]
PI0007042.13.2170556173.11 × 10−5ZHD11PREDICTED: zinc-finger homeodomain protein 9 [Cucumis melo]
PI0027455.12.1274805293.41 × 10−5AUX22DPREDICTED: auxin-induced protein 22D-like [Cucumis melo]
PI0001538.1−1.102427283.95 × 10−5SERINC3PREDICTED: serine incorporator 3 [Cucumis melo]
PI0016478.11.239386674.27 × 10−5At5g24760PREDICTED: alcohol dehydrogenase-like 6 [Cucumis melo]
PI0027502.12.7356414134.50 × 10−5At2g24130PREDICTED: putative leucine-rich repeat receptor-like serine/threonine-protein kinase At2g24130 [Cucumis melo]
PI0024420.11.5392402365.38 × 10−5qtrt1PREDICTED: queuine tRNA-ribosyltransferase-like isoform X1 [Cucumis melo]
PI0007226.11.3481129517.13 × 10−5EXPA1PREDICTED: expansin-A1 [Cucumis melo]
PI0004383.11.3268788997.27 × 10−5IQM3PREDICTED: IQ domain-containing protein IQM3 [Cucumis melo]
PI0006849.13.0649554428.55 × 10−5ADS3PREDICTED: palmitoyl-monogalactosyldiacylglycerol delta-7 desaturase, chloroplastic-like [Cucumis melo]
PI0013752.11.5533299230.000104TCP20PREDICTED: transcription factor TCP20-like [Cucumis melo]
PI0023307.12.261745370.000109ADH1alcohol dehydrogenase 1 [Cucumis melo var. makuwa] [Cucumis melo]
PI0021014.12.1490252080.000111Reg-2Haloacid dehalogenase-like hydrolase domain-containing protein 3 [Cucumis melo var. makuwa] [Cucumis melo]
PI0025553.11.9100888790.000115SAUR72PREDICTED: auxin-responsive protein SAUR64 [Cucumis melo]
PI0000301.12.0565231640.000118ATJ8PREDICTED: chaperone protein dnaJ 8, chloroplastic [Cucumis melo]
PI0021996.11.5119611460.000123PERK1PREDICTED: probable LRR receptor-like serine/threonine-protein kinase At5g10290 [Cucumis melo]
PI0001566.1−3.4385102330.000135EIX1PREDICTED: probable LRR receptor-like serine/threonine-protein kinase At4g36180 [Cucumis melo]
PI0016064.11.7858787490.000149PUB27PREDICTED: U-box domain-containing protein 27 [Cucumis melo]
PI0008364.11.8955307330.000177VUP1PREDICTED: uncharacterized protein LOC103495948 [Cucumis melo]
PI0019164.1−2.6254326170.000198IPT5PREDICTED: adenylate isopentenyltransferase 5, chloroplastic-like [Cucumis melo]
Table 2. Primer ID and corresponding sequences used in qRT-PCR validation analysis.
Table 2. Primer ID and corresponding sequences used in qRT-PCR validation analysis.
Primer IDPrimer Sequence (5′ to 3′)
ERF008-FTTCCTTGTTGTTCTTCTTGTT
ERF008-RAGTCGCCATCTGAATCTT
MYB73-FCGAAGTGAGGAAGTACAT
MYB73-RCCATACGCTTAACAACAG
PAT-FCTTCCAACGCATATAGACAA
PAT-RTTCATCAGACAGCACAGA
PHI-1-FTTCTTCTTCTTCTTCTTCTT
PHI-1-RTATGTGACGATTGGTTAG
PUB19-FAATCCTTCTCGCATTCTT
PUB19-RTCCTCCAACAGATACAGA
RLP51-FGGAGTGATGAGAATGATG
RLP51-RCTTGGATAAGAGAACAGAA
TLP-FCCAGTGATTATACGAAGTT
TLP-RCCAGTAGAAGGACACATA
WRKY33-FATGATTATGAGGAGGTTGAC
WRKY33-RTGGAAGAAGAGGACTGAA
Cm (ID: c168675_g9)-FATCCACGAAACTACTTACAACTCC
Cm (ID: c168675_g9)-RATAGACCCTCCAATCCAGACAC
Table 3. List of identified metabolites.
Table 3. List of identified metabolites.
MetabolitesControl MeanInfected MeanDominancelog2(FC)p-Value
All trans retinal0.1015354310.311489633treatment1.61720.007029
Lappaconitine0.0182937720.092140971treatment2.33250.007117
Ganoderic acid C60.1589042360.047436385control−1.74410.007313
3-phenyl-5-[3-(trifluoromethyl)-1H-pyrazol-1-yl]-1,2,4-thiadiazole1.1455799560.257296513control−2.15460.007686
Alprazolam-d50.2592557851.427059707treatment2.46060.00811
Carbaprostacyclin0.9753456120.096032635control−3.34430.008338
1-(2,4-difluorobenzoyl)-4-piperidinecarboxylic acid1.6671326875.529450855treatment1.72980.008526
Pomolic acid beta-D-glucopyranosyl ester0.2896772971.022428149treatment1.81950.009681
Thymine2.8385367247.464759358treatment1.39490.012748
2′-Deoxyinosine0.2118927361.565708163treatment2.88540.0137
Stachydrine1.8549103664.959460711treatment1.41880.01381
Aleuritic acid0.1123533570.302231114treatment1.42760.014773
Biocytin0.2339941020.706542651treatment1.59430.016332
Trillin0.0902551460.489521353treatment2.43930.0184
Cortisol1.7015060524.799858874treatment1.49620.019333
MAG (18:3)3.2218481828.325305253treatment1.36960.019676
16-Heptadecyne-1,2,4-triol0.4745324082.358917926treatment2.31350.020412
Thymol6.318631328.369650429treatment0.405560.022184
Furanodiene0.4902626622.020872956treatment2.04340.023776
Deoxycytidine0.0666724630.44656114treatment2.74370.023786
N-(9-oxodecyl)acetamide0.4060448052.457205609treatment2.59730.024876
N’-[6-(tert-butyl)thieno [3,2-d]pyrimidin-4-yl]-4-methylbenzohydrazide0.4018503172.668319076treatment2.73120.024881
trans-Cinnamic acid6.28725418528.57467445treatment2.18420.026732
2-hydroxy-3,6-diphenylcyclohexyl acetate0.2226395630.952058363treatment2.09630.027331
Quillaic acid1.2042693955.036769436treatment2.06430.030163
Echinocystic acid0.6384959972.319019362treatment1.86080.030859
Denin2.6056010450.100645612control−4.69430.030874
Peonidin chloride0.2235739490.885680407treatment1.9860.031339
Yamogenin0.0629865290.266231264treatment2.07960.032182
Madecassic acid0.0550871740.220479365treatment2.00090.032955
Geranylgeraniol0.2919811120.062638856control−2.22070.033176
2′-Deoxyadenosine7.1881473828.88530241treatment2.00660.033775
1-acetyl-N-(6-chloro-1,3-benzothiazol-2-yl)-4-piperidinecarboxamide0.4805688310.127523038control−1.9140.034298
N4-Acetylsulfamethoxazole2.8610437640.368191965control−2.9580.034881
Umbelliferone1.2359718920.311934508control−1.98630.035234
Propionylcarnitine0.3984814562.614906537treatment2.71420.035475
Ginsenoside-Ro0.045992960.015483668control−1.57070.036032
Bornyl acetate3.65475469111.58644708treatment1.66460.036208
6-Phosphogluconic acid10.978585662.952990938control−1.89440.036683
Thymidine3.44231990414.61293082treatment2.08580.037049
Pectolinarin0.3128248610.058819418control−2.4110.03862
Indole-3-pyruvate0.0059844390.029613179treatment2.3070.038944
Phytolaccagenin0.1579400880.64478966treatment2.02950.039018
Isopimpinellin3.9391644430.119320866control−5.0450.040097
Xanthine6.45478492626.49281664treatment2.03720.040905
AKBA0.1830285230.740192089treatment2.01580.042246
Schisantherin E27.367281871.54371722control−4.1480.046453
Hypoxanthine19.53201248134.6580964treatment2.78540.047824
8-iso Prostaglandin A20.41122262813.63770527treatment5.05150.049548
N2-(6-ethoxy-1,3-benzothiazol-2-yl)-5-nitro-2-furamide2.6357736240.681659989control−1.95110.050529
Flavin adenine dinucleotide0.5395267860.107283214control−2.33030.051897
Wulignan A10.9995466870.295952156control−1.75590.052689
Avocadyne 1-acetate1.0289699867.377756165treatment2.8420.053599
Bevirimat2.0469553958.851003111treatment2.11240.054015
Carnosic acid0.1747781680.589385684treatment1.75370.055867
TPH1.4455222330.226635062control−2.67310.057537
4-{3-[(3,4-dihydroxyphenyl)methyl]-2-methylbutyl}benzene-1,2-diol1.1866418050.136122491control−3.12390.059361
KKK0.0611268850.23307464treatment1.93090.059737
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Zhang, H.; Liang, Q.; Chen, J.; Wang, J.; Huang, Y.; Liu, B.; Zhang, X.; Zhou, B. Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis. Agronomy 2025, 15, 1965. https://doi.org/10.3390/agronomy15081965

AMA Style

Zhang H, Liang Q, Chen J, Wang J, Huang Y, Liu B, Zhang X, Zhou B. Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis. Agronomy. 2025; 15(8):1965. https://doi.org/10.3390/agronomy15081965

Chicago/Turabian Style

Zhang, Hao, Qigan Liang, Jihao Chen, Jiming Wang, Yuan Huang, Bin Liu, Xuejun Zhang, and Bo Zhou. 2025. "Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis" Agronomy 15, no. 8: 1965. https://doi.org/10.3390/agronomy15081965

APA Style

Zhang, H., Liang, Q., Chen, J., Wang, J., Huang, Y., Liu, B., Zhang, X., & Zhou, B. (2025). Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis. Agronomy, 15(8), 1965. https://doi.org/10.3390/agronomy15081965

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