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Physiological, transcriptomic, and metabolomic analyses of the chilling stress response in two melon (Cucumis melo L.) genotypes

BMC Plant Biology volume 24, Article number: 1074 (2024) Cite this article

Abstract

Background

Chilling stress is a key abiotic stress that severely restricts the growth and quality of melon (Cucumis melo L.). Few studies have investigated the mechanism of response to chilling stress in melon.

Results

We characterized the physiological, transcriptomic, and metabolomic response of melon to chilling stress using two genotypes with different chilling sensitivity (“162” and “13-5A”). “162” showed higher osmotic regulation ability and antioxidant capacity to withstand chilling stress. Transcriptome analysis identified 4395 and 4957 differentially expressed genes (DEGs) in “162” and “13-5A” under chilling stress, respectively. Metabolome analysis identified 615 and 489 differential enriched metabolites (DEMs) were identified in “162” and “13-5A” under chilling stress condition, respectively. Integrated transcriptomic and metabolomic analysis showed enrichment of glutathione metabolism, and arginine (Arg) and proline (Pro) metabolism, with differential expression patterns in the two genotypes. Under chilling stress, glutathione metabolism-related DEGs, 6-phosphogluconate dehydrogenase (G6PDH), glutathione peroxidase (GPX), and glutathione s-transferase (GST) were upregulated in “162,” and GSH conjugates (L-gamma-glutamyl-L-amino acid and L-glutamate) were accumulated. Additionally, “162” showed upregulation of DEGs encoding ornithine decarboxylase, Pro dehydrogenase, aspartate aminotransferase, pyrroline-5-carboxylate reductase, and spermidine synthase and increased Arg, ornithine, and Pro. Furthermore, the transcription factors (TFs), MYB, ERF, MADS-box, and bZIP were significantly upregulated, suggesting their crucial role in chilling tolerance of melon.

Conclusions

These findings elucidate the molecular response mechanism to chilling stress in melon and provide insights for breeding chilling-tolerant melon.

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Background

Chilling stress adversely affects plant growth and development, reduces plant biomass and quality, and limits their geographical distribution [1, 2]. Many tropical and subtropical plant species are susceptible to chilling injury. Chilling stress causes various physiological and biochemical changes and alters gene expression patterns. It alters the membrane structure and fluidity, causes excessive accumulation of reactive oxygen species (ROS), limits water availability, inhibits photosynthetic efficiency, and slows down biochemical reactions [3, 4].

Plants respond and adapt to low temperature through a process known as cold acclimation. Cold-responsive genes play a crucial role in cold acclimation [5]. The inducer of C-REPEAT BINDING FACTOR expression (ICE)-C-repeat binding factor (CBF)-cold-regulated gene (COR) pathway has been extensively studied. As an upstream inducer, ICE induces the expression of CBF and a series of COR genes located downstream, thus enhancing plant chilling tolerance [6, 7]. Transcription factors (TFs) such as HSFC1, CZF1, ZAT12, NPR1, ZF, RAV1, and HY5 induce COR gene expression in a CBF-independent manner under chilling stress [8, 9]. Many studies have reported that improved cold tolerance in melon is correlated with higher expression levels of CmCBF1, CmCBF2, CmCBF3, CmCBF4, and CmEAF7 [10,11,12,13].

More studies are confirming on the impact of chilling stress on the physiology and molecular levels of plants. When plants are under chilling stress, the metabolites, like proline (Pro), sugars, polyamine (PA), glycine betaine, and amino acids accumulate to elevated levels [2]. According to Meng et al. (2020), the accumulation of fasciclin-like arabinogalactan protein in banana under low temperatures could improve chilling tolerance by facilitating cold signal pathways and increasing the biosynthesis of plant cell wall [14]. A study on Argyranthemum frutescens demonstrated that abscisic acid signal transduction and sugar metabolism pathways were involved in the response to cold stress [15]. In Populus euphratica. He et al. (2019) revealed that PeSTZ1 could enhance freezing tolerance through modulation of ROS scavenging by directly regulating PeAPX2 [16]. Nian et al. (2024) reported CpCOR1 plays an important role in enhancing cold resistance and antioxidant activity in papaya fruit [17]. In melon, Zhang et al. (2020) observed that key cold response genes were mainly obtained from starch and sucrose metabolism and glycolysis [18]. Ning et al. (2022) observed Hami melon enhances low-temperature adaptability by slowing down the oxidative degradation of fatty acids and synthesizing new fatty acids [19]. Song et al. (2021) and Liu et al. (2024) reported GSTs and CmPYL6 play significant roles in melon cold stress adaptation [20, 21]. Additionally, Li et al. (2024) found that silencing of CmRR6 or CmPRR3 significantly improved cold tolerance in melon. CmABF1 or CmCBF4 targets CmADC to modulate putrescine levels and enhance chilling tolerance of melon seedlings [22].

Melon, a member of the widespread Cucurbitaceae family, represents an economically important fruit crop. According to FAOSTAT (http://faostat.fao.org, 2020), the global melon production was 27.5 million tons. In China, the largest producer of melons worldwide, the melon planting area and output reached 3.95 × 105 ha and 1.38 × 107 tons, respectively. Melon is sensitive to low temperatures, especially during the germination and seedlings stages. However, melon cultivation in early spring or winter is often affected by the chilling stress, which adversely affects yield and quality [23]. Li et al. (2022) reported that suboptimal low temperatures inhibit melon growth and delay flowering [24].

In recent years, a multi-omics approach has been effectively applied to study the abiotic stress response in plants [25, 26], including cotton [27], wheat [28], maize [29], tomato [30], apple [31], cucumber [32], watermelon [33], and melon [11, 34]. Although the genome of melon has been sequenced [35], few studies have investigated the mechanism of response to chilling stress in melon. In the present study, two melon genotypes with contrasting responses to chilling stress were subjected to physiological, transcriptomic, and metabolomic analyses. The findings are expected to provide novel insights into the molecular mechanism of response to chilling stress in melon.

Results

Morphological and physiological indicators under chilling stress

Growth morphological characteristics differed between the two melon genotypes exposed to chilling stress for 12 h (Fig. 1A). In the 13–5 A genotype, the whole plant wilted after 12 h of chilling stress. In contrast, the degree of chilling stress injury in the genotype 162 was lower than that in 13–5 A, indicating higher chilling resistance. Under the chilling stress treatment, the Chlorophyll fluorescence (Fv/Fm) of the leaves differed between the two melon genotypes. The Fv/Fm of leaves in the genotype 13–5 A was significantly lower in comparison with the control, whereas that in the genotype 162 maintained a suitable state (Fig. 1B). In the present study, chilling stress caused hydrogen peroxide (H2O2 accumulation in the leaves of 13–5 A genotypes. However, under chilling stress, H2O2 content was no significant difference in 162 (Fig. 1C).

In this study, chilling stress increased the compatible solute content in 162 genotypes under chilling stress (Fig. 1D). At 12 h of chilling stress, the soluble sugar and soluble protein content in 162 was significantly higher than that in 13–5 A (Fig. 1D). As shown in Fig. 1D, the leaves of 13–5 A showed significantly higher superoxide radical (O2−) H2O2 and malondialdehyde (MDA) content after 12 h of chilling stress than that observed in all other groups. In the control group (0 h exposure), The superoxide dismutase (SOD) and peroxidase (POD) activity did not differ between the two melon genotypes. However, in the treatment group (12 h of chilling stress), the SOD and POD activity was significantly higher in the genotype 162 than in 13–5 A (Fig. 1E). After chilling stress, the catalase (CAT) and ascorbate peroxidase (APX) activities of the 162 genotypes showed obviously increased, and it was approximately 32.4-fold to 3.2-fold higher than that in the control group (0 h; Fig. 1E).

Significant physiological differences observed between the two melon genotypes at 12 h of chilling stress treatment suggest that it is an important phase in the chilling response. Therefore, seedlings treated at 6 ℃ for 0 h and 12 h were selected for the subsequent transcriptomic and metabolomic analyses.

Fig. 1
figure 1

Morphological and physiological characteristics of melon seedlings (genotypes 162 and 13-5 A) under chilling stress. Melon seedlings exposed to low temperature 6 °C for 12 h. A Morphological traits; B Fv/Fm; C H2O2 staining; D soluble sugar, soluble protein content, MDA, O2− and H2O2 contents; and (E) SOD, POD, CAT and APX activity of the 162 and 13-5 A genotypes in the control group (0 h exposure) and treatment group (12 h exposure). Asterisks indicate significant differences at P < 0.05

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Transcriptome analysis of 162 and 13-5 A under chilling stress

RNA-seq was performed using a total of 12 samples of the genotypes 162 and 13-5 A under control conditions (28 ℃/20 ℃, “0 h”) and chilling stress treatment at 6 ℃ for 12 h (“12 h”). Principal component analysis (PCA) indicated that the samples showed significant differences among the different treatment groups (Fig. 2B). These findings were consistent with the results of the correlation analysis, which showed high reproducibility among the three biological replicates in each group and showed differences between the two genotypes (Fig. 2A and B), indicating that the data were reliable. A total of 4957, 4395, 3320, and 2133 DEGs were identified in the comparisons 5–12 h vs. 5 A–0 h, 162–12 h vs. 162–0 h, 162–0 h vs. 5 A–0 h and 162–12 h vs. 5–12 h, respectively (Fig. 2D). The Venn diagram in Fig. 2C shows that 215 DEGs were common among all four comparisons, whereas 1209 DEGs were unique in the 5–12 h vs. 5 A–0 h comparison, and 896 DEGs were unique in the 162–12 h vs. 162–0 h comparison. The 5–12 h vs. 5 A–0 h comparison showed the highest number of DEGs, suggesting that the induction effect of chilling stress is more obvious for 13–5 A.

For the 5–12 h vs. 5 A–0 h comparison, gene ontology (GO) enrichment analysis indicated that the DEGs were significantly enriched in the cellular component (CC) terms “plasma membrane” (GO: 0005886), “integral component of membrane” (GO: 0016021), and “peroxisome” (GO: 0005777). The DEGs were primarily involved in “circadian rhythm,” “regulation of seed germination,” and “amino acid transport” in the biological process (BP) category and in molecular function (MF) terms such as “DNA-binding transcription factor activity,” “calcium ion binding,” and “UDP-glycosyltransferase activity” (Fig. 2E and S1). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the beta-alanine metabolism (ko00410), plant hormone signal transduction (ko04075), and limonene and pinene degradation (ko00903) pathways were significantly enriched (Fig. 2F).

In the 162–12 h vs. 162–0 h comparison, the DEGs were found to be enriched in 7 MF terms (e.g., “DNA-binding transcription factor activity,” “carboxylic ester hydrolase activity,” and “ligase activity”), 3 CC terms (“Plasma membrane,” “integral component of membrane,” and “peroxisome”), and 10 BP terms (e.g., “cytokinin biosynthetic process,” “circadian rhythm,” “regulation of jasmonic acid-mediated signaling pathway;” Fig. S2). KEGG analysis showed that the plant hormone signal transduction (ko04075), alpha-linolenic acid metabolism (ko00592), and beta-alanine metabolism (ko00410; Fig. S3) pathways were significantly enriched.

In the 162–0 h vs. 5 A–0 h comparison, the DEGs were predominantly enriched in “Ribosome” (ko03010), “Sesquiterpenoid and triterpenoid biosynthesis” (ko00909), and “phenylpropanoid biosynthesis” (ko00940; Fig. S3). In the 162–12 h vs. 5–12 h comparison, most DEGs were enriched in “phenylpropanoid biosynthesis” (ko00940), “phenylalanine metabolism” (ko00360), “sesquiterpenoid and triterpenoid biosynthesis” (ko00909), “glutathione metabolism” (ko00480) and “tyrosine metabolism” (ko00350; Fig. S3). In all comparison groups, the pathways related to phenylpropanoid biosynthesis (ko00940) and ubiquinone and other terpenoid-quinone biosynthesis (ko00130) were significantly enriched, indicating that they are crucial in the chilling stress response.

Fig. 2
figure 2

Transcriptome analysis of melon genotypes 162 and 13-5 A under chilling stress (6 ℃) for 0 h and 12 h. A Correlation analysis (n = 12); B PCA; n = 12; C Venn diagram of the number of DEGs in various comparisons (5–12 h vs. 5 A–0 h, 162–12 h vs. 162–0 h, 162–0 h vs. 5 A–0 h and 162–12 h vs. 5–12 h); D Numbers of DEGs in each comparison; E GO analysis of DEGs shown in (D); F KEGG analysis of DEGs shown in (D)

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Analysis of transcription factors related to chilling stress

In the present study, in comparison with 5 A–0 h, 216 differentially expressed TF genes (TF-DEGs; 119 upregulated and 97 downregulated) were identified in 5–12 h, and 126 TF-DEGs (70 upregulated and 56 downregulated) were identified in 162–0 h. Compared with 162–12 h, 75 TF-DEGs were identified in 5–12 h (49 upregulated and 26 downregulated) and 225 TF-DEGs (126 upregulated and 99 downregulated) were identified in 162–0 h (Fig. 3A). Remarkably, and the number of upregulated TFs was higher than that of downregulated TFs in all comparison groups. In addition, there were 140 overlapping TF-DEGs regulated by cold stress between the comparisons 5–12 h vs. 5 A–0 h and 162–12 h vs. 162–0 h (Fig. 3B). Among the TFs common between the two comparison groups, a total of 25 MYB members, 20 ERF members, 8 WRKY members, and 7 bZIP members were identified (Fig. 3C, Table S2). Members of the MYB, ERF, MADS-box, and bZIP TF families were upregulated under chilling stress treatment, suggesting that these TFs play a vital role in the chilling stress response in melon. The expression levels of ethylene-responsive transcription factor ERF106-like (MELO3C005466.2) were upregulated under cold stress in the genotype 13–5 A but downregulated in the genotype 162 (Fig. 3C, Table S2). Members of the MYB family such as MELO3C002090.2, MELO3C013364.2, MELO3C007330.2, MELO3C013925.2, MELO3C032396.2, and MELO3C010893.2 were upregulated in the two genotypes after chilling stress treatment (Fig. 3C, Table S2). These results indicate that TFs play an important role in the cold stress response of melon.

Moreover, six key genes were selected for qRT-PCR analysis. The RNA sequencing and qRT-PCR results showed similar trends under chilling stress (Fig. S4), indicating that these data were reliable.

Fig. 3
figure 3

Analysis of TFs in two melon genotypes exposed to chilling stress. A Number of TFs with differential expression; B Venn diagram showing the differentially expressed TF genes common between the comparisons 5–12 h vs. 5 A–0 h and 162–12 h vs. 162–0 h; C Expression profiles of the MYB, AP2/ERF, WRKY, TCP, and bZIP genes in the two genotypes

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Metabolomic profiles of two melon genotypes under chilling stress

Metabolomic analysis performed using LC-MS integrated with GC-MS identified a total of 2347 differentially expressed metabolites (DEMs) in 12 samples. The number of DEMs identified using gas chromatography-mass spectrometry (GC-MS) was 362, of which 211 and 151 were downregulated and upregulated, respectively (Table S3, Fig. S5). The DEMs were classified into 11 categories; the top five categories were as follows: lipids and lipid-like molecules, organoheterocyclic compounds, organic oxygen compounds, organic acids and derivatives, and phenylpropanoids and polyketides (Fig. S6). In addition, 1985 DEMs were identified using liquid chromatography–mass spectrometry (LC-MS), including 1158 downregulated and 827 upregulated DEMs (Table S3, Fig. S5). Among them, the top three largest groups of metabolites were organic oxygen compounds (28.48%), organic acids and derivatives (21.84%), lipids and lipid-like molecules (18.04%; Fig. S6). The majority of DEMs belonged to the lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, and phenylpropanoids and polyketides (Table S3).

KEGG enrichment analysis revealed that in the 5–12 h vs. 5 A–0 h comparison, the most significantly enriched KEGG terms were pentose phosphate pathway, galactose metabolism, TCA cycle, C5-branched dibasic acid metabolism, carbon fixation in photosynthetic organisms. In the 162–12 h vs. 162–0 h comparison, the top five significantly enriched pathways were the aminoacyl-tRNA biosynthesis; ABC transporters; alanine, aspartate and glutamate metabolism; arginine biosynthesis; and pentose phosphate pathway (Fig. S6).

The Venn diagram in Fig. 4 shows 235 common DEMs between the 5–12 h vs. 5 A–0 h and 162–12 h vs. 162–0 h comparisons. The melon leaves exposed to chilling stress showed accumulated a large amount of lipids, organic acids, phenylpropanoids, and polyketides (Fig. S6). Among the significantly differentially expressed lipids, the top upregulated lipids were beta-glycerophosphoric acid, 7Z,9E-dodecadienoic acid, chrysosplenol C 6,4’-diglucoside, and ganoderic acid H,1-(2-methoxy-13-methyl-6Z-tetradecenyl)-sn-glycero-3-phosphoethanolamine. The top downregulated lipids were cannabidiol, quercetin 3-glucuronide-7-glucoside, (25 S)-spirostane-3b,5b,6a-triol 3-[4’’-rhamnosylglucoside], PGP(a-13:0/18:2(9Z,11Z)), and 1-nonanol. Among the significantly differentially expressed organic acids, allantoic acid, carbonate, S-propyl-L-cysteine, and L-methionine were most significantly upregulated (Table S4). Comparative analysis indicated that most flavonoids, steroids, and steroid derivatives were downregulated in 162 and 13-5 A subjected to chilling stress (Table S4).

Fig. 4
figure 4

Analysis of DEMs in melon leaves under chilling stress. A The number of DEMs in 5–12 h vs. 5 A–0 h, 162–12 h vs. 162–0 h, 162–0 h vs. 5 A–0 h, and 162–12 h vs. 5–12 h. B Venn diagram showing the overlap of DEMs in 162–12 h vs. 162–0 h and 13–5 A-12 h vs. 13–5 A-0 h

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Integrated transcriptomic and metabolomic analysis

The KEGG analysis showed that in the 162–0 h vs. 5 A–0 h comparison, the coenriched pathways of DEGs and DEMs included phenylpropanoid biosynthesis and glycine, serine and threonine metabolism. Similarly, glutathione metabolism and glycine, serine and threonine metabolism were coenriched in the 162 –12 h vs. 5–12 h comparison. There were more coenriched pathways identified in the 5–12 h vs. 5 A–0 h comparison, including glyoxylate and dicarboxylate metabolism; starch and sucrose metabolism; alanine, aspartate, and glutamate metabolism; ascorbate and aldarate metabolism; ABC transporters; taurine and hypotaurine metabolism; glycine, serine, and threonine metabolism; and arginine biosynthesis. Notably, there were fewer enriched pathways in 162–12 h vs. 162–0 h comparison, including ABC transporters, glutathione metabolism, glycerolipid metabolism, and arginine and proline metabolism (Fig. 5; Table 1). Thus, during chilling stress response, the ABC transporters pathway was coenriched in both 162 and 13-5 A. In addition, glycine, serine and threonine metabolism (ko00260) and glutathione metabolism (ko00480) were the most common coenriched pathways, suggesting that these pathways play key roles in response to cold stress.

Fig. 5
figure 5

KEGG analysis of DEGs and DEMs in the 5–12 h vs. 5–12 h and 162–12 h vs. 162–12 h comparisons

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Table 1 Coenriched pathways of DEGs and DEMs in two melon genotypes under chilling stress
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Transcriptomic and metabolic changes in glutathione metabolism under chilling stress

Glutathione metabolism was identified as a significantly enriched pathway in the 162–12 h vs. 162–0 h and 162–12 h vs. 13–5 A-12 h comparisons (Table 1), indicating that this pathway was significantly affected in melon leaves undergoing chilling stress. In this pathway, three metabolites and twenty-six genes were found to be involved in 162 leaves under chilling stress (Fig. 6). 5-oxoproline, L-gamma-glutamyl-L-amino acid, L-glutamate, and L-ornithine were found to be significantly accumulated. In addition, the transcription of most related genes was upregulated. For example, two 6-phosphogluconate dehydrogenase (G6PDH) genes, glutathione peroxidase (GPX), and 15 glutathione s-transferase (GST) genes were upregulated, whereas two GST genes were downregulated. Enhanced GPX and dehydroascorbate reductase (DHAR) expression likely activates the GSSG-GSH cycle. Although the content of GSH did not change substantially, the content of GSH conjugates, L-gamma-glutamyl-L-amino acid and L-glutamate was significantly higher (Fig. 6).

Fig. 6
figure 6

Changes in DEGs and DEMs involved in glutathione metabolism in the melon genotypes 162 and 13-5 A under chilling stress conditions. The color of the rectangles represents significance

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Transcriptomic and metabolic changes in arginine and proline metabolism under chilling stress

In the genotype 162 under chilling stress, 5 metabolites and 21 genes were found to be involved in arginine and proline metabolism (Fig. 7). Genes associated with arginine and Pro metabolism were significantly upregulated in the genotype 162 in response to chilling stress; these genes included ornithine decarboxylase (MELO3C011335.2), proline dehydrogenase (MELO3C022076.2), aspartate aminotransferase (MELO3C011284.2), pyrroline-5-carboxylate reductase (MELO3C019039.2), and spermidine synthase (MELO3C008477.2). Upregulation of these genes resulted in elevated levels of arginine, ornithine, and proline in 162 subjected to chilling stress, leading to better adaptability to the chilling stress conditions.

Fig. 7
figure 7

Changes in DEGs and DEMs in Arg and Pro metabolism

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Discussion

Melon is a crop cultivated worldwide but is sensitive to cold stress. Chilling stress restricts the cultivation and production of melon in winter and early spring. In the present study, we conducted physiological, transcriptomic, and metabolomic analyses of two melon genotypes (13–5 A, chilling sensitive; 162, chilling tolerant) under chilling stress.

Chilling stress leads to morphological and physiological changes of various plant species [36, 37]. In the present study, in the chilling-sensitive melon genotype 13–5 A, the plant growth was severely inhibited (Fig. 1A); Fv/Fm of 13–5 A decreased under chilling stress (Fig. 1B). However, the chilling-tolerant genotype 162 maintained normal growth after 12 h of treatment and was less affected by chilling stress. Furthermore, the accumulation of H2O2, O2− and MDA under chilling stress was lower in 162 than in 13–5 A (Fig. 1C-D), and the activity of SOD, POD, CAT, and APX was higher in 162 (Fig. 1E), indicating that the genotype 162 had a stronger antioxidant defense response than 13–5 A. which is consistent with a previous report that cold-tolerant papaya expressed higher SOD, GPX, APX, and GR [38]. After exposure to chilling stress, the soluble sugar and soluble protein levels were higher in the genotype 162 than in 13–5 A (Fig. 1D), suggesting that 162 is more adaptable to chilling stress than 13–5 A owing to the higher levels of osmotic regulatory substances and higher antioxidant enzyme activity. Similarly, studies on Solanum melongena, Zanthoxylum bungeanumand and Dendrobium spp. observed that differential osmotic regulators levels caused the differences between cold-sensitive and cold-tolerant plants [39,40,41].

Furthermore, we analyzed the transcriptome and metabolome of melon leaves exposed to chilling stress to explore the relationship between the expression of cold-responsive genes and metabolite accumulation. Several DEGs identified in the 162–12 h vs. 5–12 h comparison was found to be involved in pathways such as phenylpropanoid biosynthesis, phenylalanine metabolism, sesquiterpenoid and triterpenoid biosynthesis, glutathione metabolism, and tyrosine metabolism (Fig. 2). KEGG analysis revealed that most DEMs identified in the present study were associated with amino acid metabolism and sugar metabolism (Fig. 4). Previous studies indicate that lipid, amino acid, and sugar metabolism are highly correlated with stress responses [42,43,44,45,46]. In the present study, the top upregulated lipids were beta-glycerophosphoric Acid, 7Z,9E-dodecadienoic acid, chrysosplenol C 6,4’-diglucoside, and ganoderic acid H,1-(2-methoxy-13-methyl-6Z-tetradecenyl)-sn-glycero-3-phosphoethanolamine. In contrast, the metabolism of most amino acids (such as D-glutamine, argininic acid, D-proline, L-glutamine, ornithine) was downregulated. Glutathione plays a vital role in plants exposed to various environmental stresses by alleviating oxidative stress. The glutathione metabolism pathway is crucial in plant response to abiotic stress [47,48,49]. Glutathione exists in both oxidized and reduced forms (GSSG and GSH, respectively) [50]. Glutathione reductase is an essential enzyme that catalyzes the reduction of GSSG to GSH via an NADPH-dependent mechanism [51]. As the substrate of GPX and GST, GSH participates in the defense against ROS [52]. In the present study, the increase in the levels of 5-oxoproline, L-gamma-glutamyl-L-amino acid, L-glutamate, and L-ornithine was approximately 1.16-, 1.79-, 0.28-, and 2.08-fold higher in 162 after chilling stress treatment than in 13–5 A, respectively. Furthermore, in comparison with control group (0 h exposure), the expression of most phenylpropanoid biosynthetic genes was significantly upregulated. G6PDH, GPX, and GST genes were upregulated in 162 under chilling stress, and the increase in the expression levels of these genes in 162 was significantly higher than that in 13–5 A. A previous study indicated that GST and GPX overexpression promoted tobacco seedling growth under stressful and non-stressful conditions [53]. Moreover, as a hub, glutamate is converted to Pro. Enhanced L-glutamate accumulation may increase the content of proline and its derivatives (Fig. 7). Research by Liu et al. (2021) had demonstrated that blue light remarkably changed the transcription signal transduction and metabolism of glutathione metabolism in maize seedling leaf [54]. Cui et al. (2020) indicated that hydrogen gas (H2) regulated the expression of genes relevant to sulfur and glutathione metabolism and enhanced glutathione metabolism which resulted in Cd tolerance [55]. Fang et al. (2023) conducted KEGG analysis on salt-tolerant and salt-sensitive rice cultivars subjected to salt stress, revealing glutathione metabolism pathways play vital roles in salt stress tolerance [56]. Wang et al. (2024) also reported that the glutathione metabolic pathway was identified as crucial pathways in S. nigrum roots [57]. Thus, these results indicate that the genes together with these metabolites involved in glutathione metabolism are important for resistance to chilling stress in melon.

Pro plays a crucial role in plant development and stress [57, 58]; it triggers or participates in stress defense [59, 60]. The accumulation of proline has been reported to increase under chilling stress in several plant species such as Elymus nutans, Arabidopsis, and mango [61,62,63]. Arg is a basic amino acid with the highest nitrogen-to-carbon ratio. Arginine serves as the precursor to synthesize many biologically active metabolites, including nitro oxide (NO), Pro, and PAs [64]. Pro biosynthesis in plants typically occurs through either the glutamate pathway or the ornithine pathway [65]. The glutamate synthesis pathway uses glutamic acid as a substrate, and the ornithine synthesis pathway uses ornithine as a substrate, and the level of Pro depends on the balance between its synthesis and degradation. Arg produces ornithine through a reaction catalyzed by arginase, which is converted to Pro through the ornithine pathway. Arg can be converted to putrescine (Put) under the catalysis of ornithine decarboxylase. Put is in turn converted to spermidine and spermine-two common PAs-by spermidine synthase and spermine synthase. Arg can also generate NO under the catalysis of NO synthase. Thus, Pro, Arg, and PAs in plants can be interconverted, which is crucial in plant stress adaptation [66]. In the present study, Arg, ornithine, urea, and Pro content was increased in 162 plants (Fig. 7). Moreover, most genes involved in Arg and Pro metabolism, such as ornithine decarboxylase, aspartate aminotransferase, and spermidine synthase, were upregulated, which was consistent with the increased accumulation of Arg, ornithine, and proline in leaves of 162 subjected to cold stress. The present findings suggest that chilling stress accelerated the conversion of Glu and ornithine to Pro synchronously and improve the chilling tolerance of melon. Previous studies suggest that low temperatures induce Pro accumulation in plants by regulating the corresponding genes [11, 58, 67, 68]. Zhang et al. (2023) observed that the drought tolerance of I. lactea var. chinensis might depend on Pro accumulation [69]. Cheng et al. (2023) also confirmed arginine improve the cold resistance of tea plants by activating the PA synthesis pathway and CBF-COR regulation pathway [70]. Therefore, it was proposed that the Arg and Pro Metabolism might be crucial contributors during the adaptation of melon to chilling stress.

TFs play a vital role in plant growth, development, and stress response [71, 72]. In higher plants, TFs such as AP2/ERF, NAC, WRKY, MYB, and bHLH participate in the response to chilling stress by regulating downstream stress-responsive genes [73, 74]. In the present study, it was found that the TFs MYB, ERF, MADS-box, and bZIP were induced by chilling stress. In addition, we found that MYB108 (MELO3C002090.2), MYB308 (MELO3C013364.2), MYB34 (MELO3C007330.2), and MYB44 (MELO3C010893.2) were upregulated in the two genotypes after chilling stress treatment, with 162 being more affected than 13–5 A. Similarly, Dong et al. (2021) reported that RmMYB108 was positively involved in the cold, salt, or drought tolerance responses in Rosa multiflora [75]. Furthermore, Li et al. (2019) found that ZmMYB31 overexpression in maize enhanced plant resistance to chilling stress by reducing ion extravasation, ROS content, and low-temperature photoinhibition [76]. Future studies need to investigate the regulatory network of MYB to provide a basis for the development of melon with enhanced cold tolerance.

Conclusion

Physiological, transcriptional, and metabolomic analyses of two melon genotypes under chilling stress demonstrated that the genotype 162 has higher chilling tolerance than 13–5 A. A comprehensive analysis of transcriptomic and metabolomic datasets highlighted the importance of glutathione metabolism and arginine and proline metabolism in melon leaves exposed to chilling stress. TFs such as MYB, ERF, MAD-box, and bZIP are crucial for enhancing the cold tolerance of melon. Thus, the present findings improve the understanding of the molecular regulatory network associated with the chilling stress response in melon.

Materials and methods

Plant materials

We used the genotypes “162” (chilling tolerant) and “13-5A” (chilling sensitive) as the experimental materials. Melon seeds were harvested from our laboratory at the Shanghai Academy of Agricultural Science (Shanghai, China). The seeds were rinsed thoroughly with distilled water, germinated in an incubator at 30 °C, and then transferred into 12 × 12 cm plastic trays containing soil matrix (Tianfeng gardening corporation, Taiwan province, Pingdong, China) and cultivated in a growth chamber (MGC-400 H, Shanghai Bluepard Instruments Co., Ltd., Shanghai, China) at 28 °C/20°C (day/night), 80% relative humidity, and 400 µmol m−2 s−1 irradiance at Shanghai Academy of Agricultural Sciences. Melon seedlings were exposed to chilling stress (6 °C) when they reached the five true-leaf stage. The third fully expanded leaves from 50 uniform seedlings of the two genotypes were sampled at 0 h and 12 h of chilling stress and stored at -80 °C for subsequent analysis. Three replicates were used for physiological and transcriptomic analysis, and six replicates were used for metabolomic analysis.

Determination of physicochemical indexes of melon leaves

After dark adaptation for 30 min, chlorophyll fluorescence (Fv/Fm) was measured using a plant efficiency analyzer (Hanstech, HandyPEA, UK). Soluble sugar, soluble protein, and MDA content and SOD, POD, CAT, and APX activity were determined using assay kits (Comin Biotechnology, Suzhou, China). H2O2 content was determined using the method described by Patterson et al. (1984) [77]. The O2− production rate was determined as described by Elstner and Heupel (1976) [78]. In situ localization of H2O2 was performed by staining the leaves with 3,3-diaminobenzidine (DAB) according to the method described by Xu et al. (2012) [79].

Transcriptomic analysis

Leaves from the two melon genotypes in the control and treatment groups (subjected to chilling stress for 0 and 12 h, respectively) were used for transcriptomic analysis. Total RNA was extracted from three biological replicates of melon leaves (0.1 g) using the TRIzol reagent (Invitrogen). The RNA quality was assessed using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The RNA libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit.

The libraries were sequenced on the Illumina Novaseq 6000 platform, and 150 bp paired-end reads were generated. Raw sequencing reads were processed to obtain clean reads by filtering out low-quality reads. The clean reads were mapped to the melon reference genome (melonet-db-v1.41P) using HISAT2. Transcript abundance of each gene was estimated (in FPKM) [80], and the read count of each gene was obtained using HTSeq-count [81]. Q value < 0.05 and foldchange > 2 or foldchange < 0.5 were set as the threshold for significantly differential expression. PCA analysis was performed using R (v 3.2.0) to evaluate the biological duplication of samples. GO analysis and KEGG enrichment analysis were performed using R (v 3.2.0).

Metabolomic analysis

LC-MS and GC-MS analysis was performed by Shanghai Lu-Ming Biotech Co., Ltd. (Shanghai, China).

Sample preparation

60 mg accurately weighed sample was transferred to a 1.5 mL Eppendorf tube. Two small steel balls were added to the tube. 4 µg/mL of L-2-chlorophenylalanine dissolved in methanol as internal standard and 600 µL mixture of methanol and water (7/3, vol/vol) were added to each sample, samples were placed at -40 °C for 2 min. Then grinded at 60 HZ for 2 min, and the whole samples were extracted by ultrasonic for 30 min in ice-water bath, then placed at -20 °C for 20 min. Samples were centrifuged at 4 °C (13,000 rpm) for 10 min prior to decanting of 150 µL supernatants from each tube were collected using crystal syringes, filtered through 0.22 μm microfilters and transferred to LC vials. The vials were stored at -80 °C until LC-MS analysis. QC were prepared by mixing aliquot of the all samples to be a pooled sample.

An additional 150 µL of supernatant was collected, and the sample was dried in a freeze concentration centrifugal dryer. 80 µL of 15 mg/mL methoxylamine hydrochloride in pyridine was subsequently added. The resultant mixtures were incubated at 37 °C for 60 min. 50 µL of BSTFA (with 1% TMCS) and 20 µL nhexane was added into the mixture, which was vortexed vigorously for 2 min and then derivatized at 70 °C for 60 min. The samples were placed at ambient temperature for 30 min before GC-MS analysis.

LC-MS analyses and data processing

A Dionex Ultimate 3000 RS UHPLC fitted with Q-Exactive plus quadrupole-Orbitrap mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. An ACQUITY UPLC HSS T3 column (1.8 μm, 2.1 × 100 mm) were employed in both positive and negative modes. The binary gradient elution system consisted of (A) water (containing 0.1% formic acid, v/v) and (B) acetonitrile and separation was achieved using the following gradient: 0 min, 5% B; 2 min, 5% B; 4 min, 25% B; 8 min, 50% B; 10 min, 80% B; 14 min, 100% B; 15 min, 100% B; 15.1 min, 5% and 16 min, 5%B. The flow rate was 0.35 mL/min, and column temperature was 45 ℃. All the samples were kept at 4℃ during the analysis. The injection volume was 2 µL.

Data preprocessing

The original LC-MS data were processed by software Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK). Main parameters of 5 ppm precursor tolerance, 10 ppm product tolerance, and 5% product ion threshold were applied. Compound identification were based on precise mass-to-charge ratio (M/z), secondary fragments, and isotopic distribution.

The extracted data were then further processed by removing any peaks with a missing value (ion intensity = 0) in more than 50% in groups, by replacing zero value by half of the minimum value, and by screening according to the qualitative results of the compound. Compounds with resulting scores below 36 (out of 60) points were also deemed to be inaccurate and removed. A data matrix was combined from the positive and negative ion data.

GC-MS analyses and data processing

The derivatived samples were analyzed on an Agilent 7890B gas chromatography system coupled to an Agilent 5977 A MSD system (Agilent Technologies Inc., CA, USA). A DB-5MS fused-silica capillary column (30 m × 0.25 mm × 0.25 μm, Agilent J & W Scientific, Folsom, CA, USA) was utilized to separate the derivatives. Helium (> 99.999%) was used as the carrier gas at a constant flow rate of 1 mL / min through the column. The injector temperature was maintained at 260 °C. Injection volume was 1 µL by splitess mode. The initial oven temperature was 60 °C held at 60 °C for 0.5 min, ramped to 125 °C at a rate of 8 °C/min, to 210 °C at a rate of 4 °C/min, to 270 °C at a rate of 5 °C/min, to 305 °C at a rate of 10 °C/min, and finally held at 305 °C for 3 min. The temperature of MS quadrupole, and ion source (electron impact) was set to 150, and 230 °C, respectively. The collision energy was 70 eV. Mass data was acquired in a full -scan mode (m/z 50–500), and the solvent delay time was set to 5 min. The QCs were injected at regular intervals (every 24 samples) throughout the analytical run to provide a set of data from which repeatability can be assessed.

Data preprocessing

The obtained GC/MS raw data in. D format were transferred to .abf format via software Analysis Base File Converter for quick retrieval of data. Then, data were imported into software MS-DIAL, which performs peak detection, peak identification, MS2Dec deconvolution, characterization, peak alignment, wave filtering, and missing value interpolation. Metabolite characterization is based on LUG database. A data matrix was derived. The three-dimensional matrix includes: sample information, the name of the peak of each substance, retention time, retention index, mass-to-charge ratio, and signal intensity. In each sample, all peak signal intensities were segmented and normalized according to the internal standards with RSD greater than 0.3 after screening. After the data was normalized, redundancy removal and peak merging were conducted to obtain the data matrix.

Multivariate statistical analyses

The matrix was imported in R to carry out Principle Component Analysis (PCA) to observe the overall distribution among the samples and the stability of the whole analysis process. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were utilized to distinguish the metabolites that differ between groups. To prevent overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) were used to evaluate the quality of the model.

Variable Importance of Projection (VIP) values obtained from the OPLS-DA model were used to rank the overall contribution of each variable to group discrimination. A two-tailed Student’s T-test was further used to verify whether the metabolites of difference between groups were significant. Differential metabolites were selected with VIP values greater than 1.0 and p-values less than 0.05.

Statistical analysis

The data were analyzed using one-way analysis of variance (ANOVA) and Duncan’s multiple range test at the 0.05 level of significance using SPSS 22.0 software package. The figures were prepared using Origin 8.0.

Data availability

The datasets generated and analysed during the current study are available in the NCBI Gene Expression Omnibus (GEO) repository, under the accession number GSE225921 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE2259 21）.

Abbreviations

APX:

Ascorbate peroxidase

Arg:

Arginine

BP:

Biological process

CAT:

Catalase

CBF:

C-repeat binding factor

CC:

Cellular component

COR:

Cold-regulated gene

DAB:

Diaminobenzidine

DEGs:

Differentially expressed genes

DEMs:

Differential enriched metabolites

DHAR:

Dehydroascorbate reductase

Fv/Fm:

Chlorophyll fluorescence

GC-MS:

Gas chromatography-mass spectrometry

GO:

Gene ontology

G6PDH:

6-phosphogluconate dehydrogenase

GPX:

Glutathione peroxidase

GST:

Glutathione s-transferase

H2 :

Hydrogen gas

H2O2 :

Hydrogen peroxide

ICE:

Inducer of C-REPEAT BINDING FACTOR

KEGG:

Kyoto encyclopedia of genes and genomes

LC-MS:

Liquid chromatography–mass spectrometry

MDA:

Malondialdehyde

MF:

Molecular function

NO:

Nitro oxide

O2 - :

Superoxide radical

PAs:

Polyamines

PCA:

Principal component analysis

POD:

Peroxidase

Pro:

Proline

Put:

Putrescine

ROS:

Reactive oxygen species

SOD:

Superoxide dismutase

TFs:

Transcription factors

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Acknowledgements

The authors would like to express their gratitude to ELIXIGEN (http://www.elixigen.com/) for the expert linguistic services provided.

Funding

The study was funded by the Shanghai Melon and Fruit Industry Technology System [Shanghai Agricultural Science (2024) No.1]. Excellent Team of Shanghai Academy of Agricultural Sciences, watermelon and Melon Innovation Team (2022),020.

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Author notes

  1. Qiannan Diao and Shoubo Tian contributed equally to this work.

Authors and Affiliations

  1. Horticultural Research Institute, Shanghai Key Lab of Protected Horticultural Technology, Shanghai Academy of Agricultural Sciences, 1018, Jinqi Road, Shanghai, 201403, China

    Qiannan Diao, Shoubo Tian, Yanyan Cao, Dongwei Yao & Yongping Zhang

  2. Shanghai Agriculture Technology Extension and Service Center, Shanghai, 201103, China

    Hongwei Fan & Wenxian Zhang

  3. Shanghai Jinshan Agriculture Technology Extension and Service Center, Shanghai, 201599, China

    Xuejun Jiang

Authors
  1. Qiannan Diao
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  2. Shoubo Tian
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  3. Yanyan Cao
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  4. Dongwei Yao
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  5. Hongwei Fan
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  6. Xuejun Jiang
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  7. Wenxian Zhang
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  8. Yongping Zhang
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Contributions

Q.D and S.T designed the research. Q.D., S.T., Y.C. and D.Y., performed the experiments. Q.D., S.T and Y.Z analyzed the data. X.J., H.F. and W.Z collected the plant materials, helped in the experiment and made suggestions. Q.D and S.T wrote the manuscript. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Wenxian Zhang or Yongping Zhang.

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Diao, Q., Tian, S., Cao, Y. et al. Physiological, transcriptomic, and metabolomic analyses of the chilling stress response in two melon (Cucumis melo L.) genotypes. BMC Plant Biol 24, 1074 (2024). https://doi.org/10.1186/s12870-024-05773-3

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Keywords

BMC Plant Biology

ISSN: 1471-2229

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