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Article

Metabolome and Transcriptome Analysis Reveals Molecular Mechanisms of Soil Amendment (Volcanic Ash) Alleviating Salt–Alkali Stress in Melons (Cucumis melo L.)

by
Lina Fu
,
Xiaoxin Tian
,
Wei Wang
* and
Chunyan Wu
*
College of Horticulture, Jilin Agricultural University, Changchun 130118, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(11), 2478; https://doi.org/10.3390/agronomy14112478
Submission received: 5 September 2024 / Revised: 18 October 2024 / Accepted: 18 October 2024 / Published: 24 October 2024
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

:
Salt–alkali stress can lead to a decrease in crop quality and yield, therefore, the ability to alleviate crop salt–alkali stress and elucidate its mechanism of action will be of great significance. This study investigated the effects of applying five different proportions (0%, 5%, 15%, 25%, and 35%) of volcanic ash on thin-skinned melons (Cucumis melo L.) under salt–alkali stress. Physiological test results indicated that the application of volcanic ash had a certain alleviating effect on salt–alkali stress on melons, and the effect of 35% volcanic ash was the best. Metabolome and transcriptome analysis was performed on melons grown in three different soils (rural soil, salt–alkali soil, and 35% volcanic ash treated salt–alkali soil). Notably, a total of 71 differentially expressed genes were predominantly enriched in the amino acid biosynthesis pathway. The metabolites involved in differential metabolism exhibited significant enrichment in phenylpropanoids, flavonoids, amino acids, and arginine. Intriguingly, correlation analysis between metabolomics and transcriptomics revealed significant associations among pathways such as cysteine and methionine metabolism, amino acid biosynthesis, arginine biosynthesis, alanine-aspartate-glutamate metabolism, as well as fructose–mannose metabolism. Our research elucidated the molecular mechanism of salt–alkali tolerance in thin-skinned melons, providing new references for improving salt–alkali tolerance and improving salt–alkali soil in the future.

1. Introduction

The melon (Cucumis melo L.) is an important horticultural crop that belongs to the Cucurbitaceae family [1]. The melon is native to Iran (Persia) and its western and eastern neighbors and is widely cultivated in tropical to temperate regions of the world [2]. Due to its rich nutrition, tender, fresh, sweet taste, and juicy flesh, it is deeply loved by widespread consumers [3]. In 2020, the world’s melon cultivation area was 1.07 million hectares, with a yield of 28 million tons; China’s melon production accounted for over 49% of this [4]. As an important economic crop in China, melons are subjected to various types of biotic and abiotic stresses during their growth, with salinization being one of the main abiotic stresses [5].
Salt–alkali stress refers to the osmotic stress and high pH harm that crop root cells are subjected to in high-concentration salt and alkali environments [6,7]. The adverse effects of salt–alkali stress on plants include osmotic stress, ion toxicity, and oxidative stress, etc. [8,9]. The increase in pH under alkaline stress can cause further damage to plants [10,11]. Salt–alkali stress not only affects the photosynthesis and respiration of crops, but also hinders the synthesis of crop proteins, affects the structure of plant cell membranes, leads to nutrient ion imbalance, reduced osmotic regulation ability, inhibition of antioxidant systems, and more severe plant growth inhibition [12]. The formation of salt–alkali stress is related to various factors, including soil types, the application of pesticides and fertilizers, cultivation techniques, and climate conditions [13]. In the arid and semi-arid regions of Northeast and Northwest China, due to low precipitation and high evaporation, salt dissolved in water is prone to accumulation on the surface of the soil, forming saline alkali soil. Therefore, making melon grow well on saline alkali soil has become a practical issue. To cope with saline alkali stress, researchers and agricultural experts are exploring various improvement measures, including methods to improve saline alkali soil from a chemical perspective, as well as attempting to plant adaptable crops in saline alkali soil [14]. These efforts aim to improve soil quality and promote the normal growth of crops in saline alkali land.
Applying soil amendment is an effective method of improving saline alkali soil [15]. Soil amendment can reduce the harm of saline alkali soil to crops by regulating soil pH, reducing soil salinity, increasing soil permeability, improving the stability of loam aggregates, and enhancing soil microbial activity, achieving the goal of alleviating crop salt damage, promoting crop growth, and improving yield and quality [16,17]. Volcanic ash, as a mineral material, has a porous structure and strong adsorption performance. It can adsorb ammonium and other nutrient elements in soil, loosen soil, regulate pH value, improve soil microbial environment, and enhance soil water and fertilizer retention capacity. Volcanic ash has broad application prospects in soil regulation and improvement, and its application in agriculture is also becoming increasingly widespread [16,18].
In response to the practical problems in the production of thin-skinned melon in salt–alkali soil, we studied the alleviating effect of different proportions of volcanic ash on salt–alkali stress on thin-skinned melons, as well as the mechanism of salt alkali stress resistance in thin-skinned melon. This provides a basis for the application of volcanic ash in the thin-skinned melon’s salt–alkali resistant cultivation, and achieves the goals of promoting soil amendment, green production, and improving quality and efficiency. It has important significance for promoting the effective utilization of waste resources.

2. Materials and Methods

2.1. Experimental Design

The specific physical and chemical indicators of the volcanic ash used in the experiment were shown in Table S1. The salt–alkali soil used in the experiment was taken from Da’an City, Jilin Province, and its specific chemical composition is shown in Table S2. Volcanic ash and salt–alkali soil were evenly mixed in different proportions for application, as shown in Table S3. The experiment was conducted at the teaching and experimental base of the College of Horticulture, Jilin Agricultural University, using the potting method. The variety of melon was ‘Big Tongue’, which was a thin-skinned melon provided by the Vegetable Research Institute of Jilin Province. The experiment was conducted with 6 treatments, 3 repetitions per treatment, and 15 plants per repetition. The plants were pruned to have two main vines. All the experimental materials were placed in a growth chamber with controlled conditions: a light cycle of 16/8 h, a temperature of 30 °C/25 °C, a relative humidity of 75%, and a light intensity of 300 μmol·m−2·s−1. After 15 days of settlement, the fruits of three uniformly growing plants were randomly selected from each of the three treatments (rural soil: TRZ, salt–alkali soil: TRY, and salt–alkali soil treated with a 35% soil amendment: TRH) for transcriptome and metabolome analysis.

2.2. Analysis of Physiological Changes

The melon yield was calculated by harvesting mature melons per hectare. In the mature stage of the melons, the content of organic acid (malic acid) was determined using the acid-base (NaOH) titration method. The content of soluble sugar was determined using the anthrone colorimetric method, the content of superoxide dismutase (SOD) was detected using the colorimetric method, the content of peroxidase (POD) was detected using the guaiacol colorimetric method, and the content of catalase (CAT) was detected using the ultraviolet absorption method [19].

2.3. Transcriptome Sequencing and Data Analysis

A total of 1.5 μg of RNA per sample was utilized as the input material for RNA sample preparations. The NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (Ipswich, MA, USA) was employed to generate sequencing libraries in accordance with the manufacturer’s recommendations, and index codes were incorporated to assign sequences to each individual sample. Subsequently, the library preparations were subjected to sequencing on an Illumina Novaseq 6000 platform by Beijing Allwegene Technology Company Limited (Beijing, China).
The raw data (raw reads) in fastq format were initially processed using custom Perl scripts. During this step, clean data (clean reads) were obtained by removing reads containing adapters, reads containing poly-N sequences, and low-quality reads from the raw data. The adapter sequences and low-quality sequence reads were eliminated from the datasets. Subsequently, the raw sequences were transformed into clean reads through data processing. These clean reads were then aligned to the reference genome sequence using STAR.
The GATK2 software (version 4.6.0.0) was utilized for SNP calling, and the raw VCF files were filtered using the standard filter method provided by GATK along with additional parameters (clusterWindowSize: 10; MQ0 ≥ 4 and (MQ0/(1.0*DP)) > 0.1; QUAL < 10; QUAL < 30.0 or QD < 5.0 or HRun > 5). Only SNPs with a distance greater than 5 were retained. HTSeq v0.5.4 p3 was employed to quantify the number of reads mapped to each gene. The estimation of gene expression levels was conducted using the fragments per kilobase of transcript per million fragments mapped (FPKM) metric. Differential expression analysis between two conditions or groups was performed utilizing the DESeq R package (version v1.12.0). Genes exhibiting an adjusted p-value < 0.05, as determined by DESeq, were designated as differentially expressed. A volcano map, veen map, and differential expression heatmap were visualized by python (version v3.13.0, https://blog.python.org, accessed on 15 October 2024).
GO (Gene Ontology, http://www.geneontology.org/, accessed on 8 September 2024) is an international standardization of the Gene function classification system. The purpose of this study is to establish a standard language vocabulary for the delineation and description of gene and protein functions, which can be updated with the development of research. The GO enrichment analysis of the differentially expressed genes (DEGs) was performed using the GOseq R packages (version v1.22, Corrected p-Value < 0.05), which are based on the Wallenius non-central hyper-geometric distribution [20]. This approach effectively accounts for gene length bias in DEGs. Additionally, KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp, accessed on 1 October 2024) is a database for systematic analysis of gene function and genome information, which helps researchers to study gene and expression information as a whole network. The KOBAS software (version v2.0, Corrected p-Value < 0.05) was employed to assess the statistical enrichment of differentially expressed genes within KEGG pathways [21]. The KEGG enriched pathway metabolic map was produced at https://www.kegg.jp, accessed on 1 October 2024 website.

2.4. Metabolome Analysis and Data Analysis

The preparation of the extract and metabolite spectrum analysis of 18 melon fruit samples was conducted by Beijing Allwegene Technology Company Limited (Beijing, China) according to standard procedures. In simple terms, each fresh fruit sample (0.6 g) is freeze-dried in a freeze dryer (the model is Scientz 100F, purchased from Shanghai Bilang Instrument Co., Ltd., Shanghai, China), ground into a powder using a grinder, and 50 mg of the powder is dissolved in 1.2 mL of 70% methanol. The mixture is rotated every 30 min for 30 s, repeated 6 times. After centrifugation at 12,000× g for 3 min, the extract was filtered through a microfiltration membrane with a pore size of 0.22 mm and analyzed using the UPLC-ESI-MS/MS system (UPLC: Exion LC™ADSystem; MS: QTRAP4500; both AB Science Pte. Ltd., Singapore). A principal component analysis is conducted on the samples, including quality control samples, to gain a preliminary understanding of the overall metabolic differences between sample groups and the variability within each group. The PCA results indicate a separation trend of metabolic components among different groups, suggesting the presence of differences in the metabolic profiles within each sample group. Image visualization of PCA was performed by python (version v3.13.0, https://blog.python.org, accessed on 15 October 2024). The inherent characteristics of metabolomic data based on LC-OE-MS require us to analyze the data using multivariate statistical analysis methods. The criteria used are as follows: The p-value of the student’s t-test is less than 0.05, the fold change is greater than 1.5 or less than 0.67, and at the same time, the variable importance in projection (VIP) of the first principal component of the OPLS-DA model is greater than 1. Finally, the significantly changed metabolites were annotated using the KEGG compound database (https://www.genome.jp/kegg/annotation/, accessed on 23 September 2024) and then mapped to the KEGG pathway database (http://www.genome.jp/kegg/pathway.html, accessed on 1 October 2024) to determine their metabolic pathways and potential functions in the melon’s response to salt stress.

2.5. Integrated Transcriptomic and Metabolomic Analysis

To integrate transcriptome and metabolome data, bidirectional orthogonal PLS (O2PLS) analysis was performed [22]. The method decomposes the variation in the two data matrices into three parts: joint variation between the two data sets, orthogonal variation unique to each data set, and noise. The model assumes that some latent variables are responsible for the variation in the joint and orthogonal parts. The O2PLS model was calculated using the OmicsPLS package (Version 2.0.2) of r. To determine the optimal number of components, the proposed alternative cross-validation procedure was used [23]. The best model was used for the integration analysis. When conducting KEGG annotation, differentially abundant metabolites (DAMs) and DEGs are simultaneously annotated to multiple metabolic pathways. Relevant metabolic pathways are then selected by applying a significance threshold of p-value < 0.05 for both gene pathways and metabolic pathways. In order to integrate the metabolomic and transcriptomic analyses, the data were log2 transformed. Screening was performed using the PCC (Pearson correlation coefficient) and corresponding p-values, with a threshold of PCC > 0.80 and a p-value < 0.05. Cytoscape software (version v3.7.1) was used to make a network diagram to describe the correlation between metabolites and genes, and differentially enriched metabolites were selected for visualization based on DEGs and each pathway (PCC > 0.80, p < 0.05).

3. Results

3.1. The Effect of Volcanic Ash on the Morphology and Physiological Indexes of Melons Under Salt–Alkali Stress

To study the effects of salt–alkali stress on physiological indices of melons and the mitigation effect of pozzolanic acid, melon yield and soluble sugar, organic acid, superoxide dismutase (SOD), the activities of peroxidase (POD) and catalase (CAT) contents were measured in rural soil (CK), salt–alkali soil (TH1) and different concentrations of volcanic ash (TH2-TH5) (Figure 1 and Figure S1). The results showed that the yield of CK exceeded 40,000 kg/hm2, while that of other treatment groups did not reach 40,000 kg/hm2; and TH5 had the highest yield compared with other treatment groups (Figure 1A). The soluble sugar content of CK was more than 8%, which was lower in the other treatment groups than in the control, while the soluble sugar content in the TH5 was higher than that in the other treatment groups (Figure 1B). The organic acid content of CK exceeded 30%, whereas that of TH5 was close to that of the control (Figure 1C). The activities of SOD and POD in melons gradually decreased with the increase in the concentration of volcanic ash (Figure S1). The activities of SOD and POD in TH5 were similar to or lower than those in CK, while the activity of CAT increased first and then decreased with the increase in the concentration of volcanic ash (Figure S1). The results showed that the yield, soluble sugar content, and organic acid content of melons in salt–alkali soil were increased after volcanic ash treatment, while the activities of SOD and POD were decreased. The 35% volcanic ash treatment had the best effect. In conclusion, soil salinization would reduce melon yield and quality, and volcanic ash application could alleviate this negative effect.

3.2. Transcriptome Characterization of Melons Under Salt–Alkali Stress

The square of the Pearson correlation coefficient (R2) needs to be greater than 0.92 to indicate the repeatability of the sample and the reliability of the results. In this study, the square of the R2 is greater than 0.92, indicating the reliable biological repeatability of the sample (Figure S2). A total of 2950 DEGs were identified through TRH vs. TRZ, of which 1254 were upregulated and 1696 were downregulated (Figure S3A); and through TRY vs. TRZ, 3035 DEGs were identified, of which 1372 were upregulated and 1663 were downregulated (Figure S3B); moreover, there were 1300 DEGs co-expressed between two comparative groups (Figure S3C). The above results indicated that the genes expressed in melons will change with the degree of soil salinization, and these genes may play a key role in the response of the melon to salt–alkali stress.
A GO enrichment analysis was performed on all DEGs in melons under different degrees of salt–alkali stress (Figure 2). The results showed that there were significant differences in the functional classification of DEGs annotated between the TRH vs. TRZ group and the TRY vs. TRZ group in terms of biological process, cellular component and molecular function. Among them, the TRH vs. TRZ group was significantly enriched in pathways such as the regulation of cellular process, RNA metabolic process, organic cyclic compound biogenetic process, and RNA biogenetic process in terms of biological processes (Figure 2A), indicating that salt–alkali stress caused changes in nucleotide and sugar-related pathways in melons, affecting cell synthesis, signal transduction, and energy supply. The significantly enriched cell components in the TRH vs. TRZ group were found to be the internal organelle, membrane-bound organelle, intracellular membrane bound-organelle, and nucleus (Figure 2A), indicating that under salt–alkali stress, histones and chromosomal related components are affected, and changes in galactose metabolism and energy supply may occur in response to salt–alkali stress. In terms of molecular function, transfer activity, transcription–regulator activity, DNA binding transcription factor activity, and transferase activity, transferring glycosyl groups were significantly enriched (Figure 2A), indicating that under salt–alkali stress, enzyme activity and transcription-related factors related to substrate transfer were regulated and altered, and enzyme activity during gene transcription and protein synthesis or degradation processes in cells was affected.
In addition, the TRY vs. TRZ group was significantly enriched in the response to stimulus pathway in terms of biological process, indicating that the melon had undergone a stress response under salt–alkali stress to maintain the stability of the intracellular environment (Figure 2B). The cellular components were mainly enriched in chromatin, protein-DNA complex, nucleosome, and DNA packaging complex (Figure 2B). In terms of molecular function, pathways such as transferase activity, transferase activity, transferring glycosyl groups, and transferase activity, transferring one-carbon groups were significantly enriched (Figure 2B). The above results indicated that TRH vs. TRZ and TRY vs. TRZ groups are similar in the transferase activity pathway, with changes in intracellular transfer-related enzymes affecting cell activity in response to salt–alkali stress. The majority of the enriched pathways were different, indicating that the melon exhibits different mechanisms of salt–alkali tolerance under different salt–alkali stressors.
KEGG enrichment analysis was performed on all DEGs of melons under different degrees of salt–alkali stress (Figure 3). The results showed that in the TRH vs. TRZ group, DEGs were mainly enriched in pathways such as endocytosis, ribosome biogenesis in eukaryotes, cysteine and methionine metabolism, biosynthesis of amino acids, amino sugar and nucleotide sugar metabolism, and plant-pathogen interaction, which were related to cellular processes, genetic information processing, metabolism, and organizational systems (Figure 3A). In the TRY vs. TRZ group, DEGs were mainly enriched in pathways such as ribosome biogenesis in eucaryotes, purine metabolism, cysteine and methionine metabolism, biosynthesis of amino acids, amino sugar and nucleoside sugar metabolism, which were associated with genetic information processing and metabolism (Figure 3B). Comparing the TRY vs. TRZ group with the TRH vs. TRZ group, different enrichments were observed in the plant pathway interaction and circadian rhythm plant pathway. The above results indicated that when the melon was subjected to salt–alkali stress, their cells mainly produced related metabolites such as sugars and amino acids to alleviate the impact on their growth. Especially, the comparison groups TRH vs. TRZ and TRY vs. TRZ were mainly enriched in the biosynthesis of amino acids pathway.
As shown in Figure S4, there were 71 DEGs in the biosynthesis of the amino acids pathway, indicating that the genes in this pathway were crucial in the mechanism of salt–alkali stress tolerance in melons. As shown in Figure 4, compared with TRZ, in TRY and TRH, the genes gene-LOC103493015, gene-LOC103484914, gene-LOC103487549, gene-LOC103490133, gene-LOC103494637, gene-LOC103483500, gene-LOC103485701, gene-LOC103484619, gene-LOC103490146, gene-LOC103491257, gene-LOC103486846, gene-LOC103492249, gene-LOC103495826, gene-LOC103499829, gene-LOC103496846, gene-LOC103498090, gene-LOC103504567, gene-LOC103486577, gene-LOC103504001, gene-LOC103489982, gene-LOC103496957, gene-LOC103502870, gene-LOC103485166, and gene-LOC103503487 were upregulated, indicating that the above genes positively regulate salt–alkali stress in melons; In TRZ, the expression level of gene-LOC127151373, gene-LOC103495735, gene-LOC103499979, gene-LOC103484671, gene-LOC103488335, gene-LOC103489339, gene-LOC103499973, gene-LOC103489934, gene-LOC103500643, gene-LOC103493750, gene-LOC103501911, gene-LOC103502185, gene-LOC127150640, gene-LOC103501468, gene-LOC103501500, gene-LOC103499198, gene-LOC103488412, gene-LOC103494833, gene-LOC103501785, gene-LOC103484309, and gene-LOC103491108 were significantly higher than TRY and TRH, indicating that these 21 genes may play a negative regulatory role in salt–alkali stress in melons; Compared to TRZ and TRY, in TRH, gene-LOC103493015, gene-LOC103484914, gene-LOC103487549, gene-LOC103490133, gene-LOC103494637, gene-LOC103483500, gene-LOC103485701, gene-LOC103484619, gene-LOC103490146, gene-LOC103491257, gene-LOC103486846, gene-LOC103492249, gene-LOC103495826, gene-LOC103499829, gene-LOC103496846, gene-LOC103498090, gene-LOC103504567 have the highest expression levels, indicating that high expression of these 17 genes is beneficial for the growth of melons in salt–alkali soil with volcanic ash treatment.

3.3. Metabolomics Profiles of Melons Under Salt–Alkali Stress

Figure S5 showed the total sample PCA of the metabolome, both including QC (Quality Control) and excluding QC. The results showed that TRH and TRY samples were not significantly distinguishable, while TRZ samples were able to be clearly distinguished from TRH and TRY samples, indicating that the treatment of salt–alkali soil and salt–alkali soil with volcanic ash resulted in little difference in metabolic products in melons, while compared with the untreated field soil, there was a significant change in the metabolism in melons. The TRH vs. TRZ group and the TRY vs. TRZ group obtained 540 (Figure S6A) and 506 DAMs (Figure S6B), respectively. There were 341 DAMs that overlapped between the TRH vs. TRZ group and the TRY vs. TRZ group (Figure S6C).
In the TRH vs. TRZ group, the 20 most enriched metabolic pathways were mainly enriched in biosynthesis pathways of secondary metabolites and amino acids such as flavonoid biosynthesis, aminoacyl-tRNA biosynthesis, phenolpropanoid biosynthesis, biosynthesis of amino acids, and arginine biosynthesis (Figure 5A). And in the TRY vs. TRZ group, the 20 most enriched metabolic pathways were mainly enriched in pathways such as ABC transporters, biosynthesis of amino acids, flavonoid biosynthesis, purine metabolism, nucleotide metabolism, arginine biosynthesis, and phenylpropanoid biosynthesis (Figure 5B). The pathways that were significantly enriched together include arginine biosynthesis, phenolpropanoid biosynthesis, flavonoid biosynthesis, and biosynthesis of amino acids, indicating that melons may mainly respond to salt–alkali stress by mediating the metabolism of some amino acids and flavonoids. The different enriched pathways in TRH vs. TRZ and TRY vs. TRZ, such as aminoacyl-tRNA biosynthesis, ABC transporters, purine metabolism, and nucleotide metabolism, may be important pathways for melon to cope with different salt–alkali stressors of growth. The pathways of amino acid synthesis and metabolism were significantly enriched in KEGG of transcriptome and metabolome, indicating that genes and metabolites in this pathway play an indispensable role in the salt–alkali tolerance mechanism of melons.

3.4. Joint Analysis of Transcriptome and Metabolome in Melons Under Salt–Alkali Stress

In order to better understand the relationship between genes and metabolites, joint analysis was conducted on the DAMs and DEGs of TRH vs. TRZ and TRY vs. TRZ, to obtain the KEGG enrichment pathway enriched by both transcriptome and metabolome. The results showed that the TRH vs. TRZ group and the TRY vs. TRZ group significantly enriched the synthesis and metabolic pathways of various amino acids belonging to the primary metabolic pathway, such as cysteine and methionine metabolism, biosynthesis of amino acids, arginine biosynthesis, alanine, and aspartate and glutamate metabolism, fructose and mannose metabolism were commonly enriched in carbohydrate related metabolic pathways, and amino sugar and nucleotide sugar metabolism, and other metabolic pathways were co enriched in pyrimidine metabolism, purine metabolism, isoquinoline alkaloid biosynthesis, ABC transporters, and 2-oxocarboxylic acid metabolism (Figure 6A,B), indicating that the synthesis and metabolism processes related to various amino acids and sugars in the melon undergo important adjustments in response to salt–alkali stress. In addition, pathways enriched in the TRH vs. TRZ group but not in the TRY vs. TRZ group, such as tryptophan metabolism, sphingolipid metabolism, phosphatidylinositol signaling system, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, inositol phosphate metabolism, glycerophospholipid metabolism, glycerolipid metabolism, and glutathione metabolism, may be important pathways for melons to adapt to the growth of saline alkali soil after volcanic ash treatment.
Joint analysis revealed that metabolic pathways related to amino acids and sugars were significantly enriched in the TRH vs. TRZ group and the TRY vs. TRZ group. Subsequently, a network graph of correlation coefficients between amino acid and carbohydrate genes and metabolites was constructed, and it was found that the amino acid metabolite fumarate was significantly positively correlated with most of the differentially expressed genes in the TRH vs. TRZ group, while glutamate, citrulline, L-aspartate, and N6-acetyllysine were significantly negatively correlated with most of the differentially expressed genes in the TRH vs. TRZ group (Figure 7A). In the TRY vs. TRZ group, the metabolites of fumarate and 3-isopropylmalic acid were significantly positively correlated with most of their differentially expressed genes, while glutamate and homoserine were significantly negatively correlated with most of their differentially expressed genes (Figure 7C), indicating that fumarate positively regulates salt–alkali stress in melons through the amino acid pathway, while glutamate may negatively regulate salt–alkali stress in melons.
In the TRH vs. TRZ group, the carbohydrate metabolites manose 6-phosphate and fructose 6-phosphate were significantly positively correlated with most of their differentially expressed genes, playing a positive regulatory role in melon response to salt–alkali stress, and UDP-beta-L-rhamnose was significantly negatively correlated with its differentially expressed genes (Figure 7B). In the TRY vs. TRZ group, the metabolite allose was significantly positively correlated with most of their differentially expressed genes, indicating that allose may positively regulate the function of salt–alkali stress; glucose, UDP-beta-L-rhamnose, and N-Acetyl-D-glucose were significantly negatively correlated with most of their differentially expressed genes (Figure 7D). The above results indicated that the UDP-beta-L-rhamnose in the carbohydrate metabolism pathway co-enriched by the TRH vs. TRZ group and the TRY vs. TRZ group negatively regulates salt–alkali stress in melons; under salt–alkali stress, melons reduce the intake of UDP-beta-L-rhamnose, which was beneficial for their growth.

4. Discussion

The melon cultivation period is relatively short, requiring simple cultivation techniques; it has a large market demand and offers high economic benefits, making it an efficient and cost-effective fruit crop; however, melon planting soil is often affected by salinization and alkalization [2]. In this study, the growth of melons was significantly affected by salt–alkali stress. After the application of a volcanic ash soil amendment to salt–alkali soil, the yield, soluble sugar content, and organic acid content of melon increased, indicating that as the salt–alkali stress decreased, the negative impact on melon growth gradually decreased. Soil amendment can mitigate the impact of saline soil on crops by regulating soil pH, reducing soil salinity, enhancing soil permeability, improving soil aggregation stability, and optimizing soil microbial activity; this may contribute to ameliorating crop salinity stress, fostering crop growth, and augmenting crop yield [16]. Plants have evolved multiple protective mechanisms to cope with osmotic stress caused by salt–alkali stress; the synthesis and accumulation of low molecular weight metabolites are common mechanisms for plant osmotic regulation [24]. The accumulation and augmentation of amino acids induce alterations in plant stress tolerance while also fulfilling diverse functions in plant growth and development [25]. The research on seaweed has revealed that the upregulation of the MhASP3 protein, which plays a crucial role in the synthesis of aspartic acid and glutamic acid, triggers an amino acid defense response to mitigate salt-induced stress [26,27]. Metabolomics analysis of soybeans indicates that soybeans enhance their salt tolerance by increasing amino acids and proteins [28,29]. The transcriptome analysis results of this study showed that under salt–alkali stress, the regulated DEGs in melons, such as gene-LOC103493015 and gene-LOC103484914 (Figure 3B), were involved in the synthesis and degradation of various amino acid metabolic pathways, and the changes in these genes may enhance the tolerance of melons to salt–alkali stress. Metabonomic analysis of this study revealed that the pathways involved in phenolylpropanoid biosynthesis, flavonoid biosynthesis, the biosynthesis of amino acids, and arginine biosynthesis were related to the salt–alkali stress response in melons (Figure 5A,B), suggesting that the primary mechanisms for salt–alkali tolerance are likely similar. The findings of this study significantly enhance our understanding of melon amino acid metabolic pathways, highlighting the potential of salt–alkali stress in mitigating plant damage through the regulation of amino acid metabolism. Moreover, it underscores the intricate relationship between amino acid metabolism and salt–alkali stress.
Flavonoids have long been acknowledged for their physiological significance in the human body, encompassing antioxidant, antibacterial, and anticancer attributes [30,31]. Moreover, they are widely recognized as primary phytochemical antioxidants [32]. In this study, under salt–alkali stress, the activities of oxidative-related enzymes (SOD, CAT, and POD) in melons increased significantly compared to the control group. Among all experimental groups, the activity of SOD, CAT, and POD was lowest when treated with a 35% volcanic ash amendment (Figure S1), indicating that salt–alkali soil amendment with 35% volcanic ash is suitable for the growth of melons. After treatment with a 35% volcanic ash amendment and under untreated salt–alkali stress, the DAMs (phenylpropanoid biosynthesis and flavonoid biosynthesis) of melons were enriched in pathways related to flavonoid synthesis, indicating that seedlings produce similar flavonoid-related metabolites under different salt–alkali stressors. This promotes the expression of oxidative-related enzyme activity to eliminate harmful ROS in melons, and the mechanisms involved in different salt–alkali stressors were roughly similar. Salt–alkali stress has been demonstrated to enhance the biosynthesis of plant terpenoid compounds [33], some of which contribute to plants’ resilience against biotic and abiotic stress [34]. The current KEGG enrichment analysis of DEGs indicated that the tetracyclic triterpenoid metabolism in volcanic ash-treated group under salt–alkali stress has significantly changed. Both comparison groups are rich in genes related to tropane, piperidine, and pyridine alkaloid biosynthesis, which may play a role in alleviating the pressure on melons under salt–alkali stress. The above findings suggested that the identification of DEGs and DEMs involved in flavonoid and terpenoid metabolic pathways could serve as a foundation for the exploration of salt–alkali-tolerant genes and metabolites in melons.
Carbohydrate metabolism is a key response pathway for plants to cope with abiotic stress. Sugars, such as sucrose, glucose, and fructose, can serve as crucial molecules and substrates to mediate various metabolic reactions [35], as well as participate in signaling processes during stress responses [36]. Numerous studies have substantiated the involvement of carbohydrate metabolism in the response to salt–alkali stress in various crops, including oats [37], ryegrass [38], and rapeseed [39]. Furthermore, it serves as a crucial osmoregulatory substance for mitigating abiotic stress-induced damage. Sucrose plays a pivotal role as a signaling molecule in maintaining osmotic balance and scavenging reactive oxygen species during salt–alkali stress. It directly modulates the accumulation of D-phenylalanine, tryptophan, and alkaloids, as well as the expression of proteins related to aspartate and glutamate [26]. In this study, the KEGG enrichment analysis of transcriptome and metabolome combined analysis showed that a higher number of DEGs and DAMs were enriched in pathways related to carbohydrate metabolism, such as fructose and mannose metabolism and amino sugar and nucleotide sugar metabolism. These sugar-related metabolic products may primarily regulate the accumulation of amino acids and terpenoids after salt–alkali stress, protecting the melon from salt–alkali induced damage.
In conclusion, it is of great significance to explore the mechanism of salt–alkali tolerance of melon by means of transcriptomics and metabolomics, and to further understand the key pathways of salt-tolerance and gene regulation of melons, so as to solve the problem of a low yield of melons caused by salt–alkali stress. Additionally, adopting soil amendments can rectify severely saline-alkaline soil environments, thereby addressing the problem of low yield in melons from a macroscopic perspective.

5. Conclusions

In this study, we conducted a comprehensive analysis of the physiology, transcriptome, and metabolome of melons under volcanic ash amendment. Our research findings indicate that the adaptability of plants to salt is related to maintaining appropriate levels of soluble sugars, organic acid content, SOD, CAT, and POD activity concentrations. Through KEGG pathway enrichment analysis, it was found that the DEGs in volcanic ash amendment treatment groups were mainly enriched in the biosynthesis of amino acids pathway. Within this pathway, 71 DEGs play a crucial role in the melon’s response to salt stress. In addition, our differential metabolite analysis also revealed significant enrichment in pathways related to amino acid metabolism as well as in pathways related to flavonoid biosynthesis. It is worth noting that amino acid metabolic pathways and sugar metabolic pathways have a significant importance in the correlation analysis between metabolomics and transcriptomics, and this highlights their critical role in the salt stress response. In conclusion, this study contributes to a deeper understanding of the molecular mechanisms of melon adaptation to salt–alkali stress under different salt–alkali concentrations under volcanic ash treatment, thus providing valuable insights for future salt-tolerance breeding and salt–alkali soil improvement.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14112478/s1, Figure S1: The effect of different treatments on SOD, CAT, and POD content in melon under salt–alkali stress. Note: Data are presented as mean ± S.E (n = 3). Containing the same letter indicates no significant difference at the p < 0.05 level based on Tukey’s HSD test. Figure S2: Repeatability of the melon samples. Note: TH3-/TH2-/TH1-: samples treated with 35% volcanic ash; Y_3-/Y_2-/Y_1-: samples treated with salt–alkali soil; Z_3-/Z_2-/Z_1-: samples of rural contrast soil treatment.; Figure S3: Variations in the number of DEGS in melon under different salt–alkali stress. Note: A: differential gene volcano plot for the TRH and TRZ groups; B: differential gene volcano plot for the TRY and TRZ groups; C: venn diagram of DEGs between the TRH vs. TRZ and the TRY vs. TRZ group. Figure S4: KEGG pathway of biosynthesis of amino acids. Note: KEGG enriched pathway metabolic map was produced at https://www.kegg.jp website. Figure S5: A: metabolomic PCA including QC; B: metabolomic PCA excluding QC; the full name of PCA is principal components analysis; the full name of QC is quality control. Figure S6: Metabolic changes in sweet melon seedlings under different salt–alkali stress. Note: A: differences in metabolic volcano plots between TRH and TRZ; B: differences in metabolic volcano plots between TRY and TRZ; C: venn diagram of metabolic differences between two comparison groups. Table S1: Physical and chemical properties of volcanic ash. Table S2: Chemical analysis table for salt–alkali soil. Note: the full name of Cl− is chloride ion, the full name of SO42− is Sulfate, the full name of HCO3− is carbonic acid hydrogen radical, the full name of CO32− is carbonate, the full name of Ca2+ is calcium ion, the full name of Mg2+ is magnesium ion, the full name of Na+ is natrium ion, the full name of K+ is Kalium ion. Table S3: Application ratio of soil amendment.

Author Contributions

Conceptualization, W.W.; methodology, C.W.; software, C.W.; validation, X.T.; formal analysis, L.F.; investigation, L.F.; resources, W.W.; data curation, C.W.; writing—original draft preparation, L.F.; writing—review and editing, W.W.; visualization, C.W.; supervision, W.W.; project administration, C.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

As stated in the declaration.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The yield (A), soluble sugar content (B), and organic acid content (C) of melon grown in rural soil (CK), salt–alkali soil (TH1), and salt–alkali soil with the application of different concentrations of volcanic ash (TH2-TH5). Data are presented as mean ± S.E (n = 3). Containing the same letter indicates no significant difference at the p < 0.05 level based on Tukey’s HSD test.
Figure 1. The yield (A), soluble sugar content (B), and organic acid content (C) of melon grown in rural soil (CK), salt–alkali soil (TH1), and salt–alkali soil with the application of different concentrations of volcanic ash (TH2-TH5). Data are presented as mean ± S.E (n = 3). Containing the same letter indicates no significant difference at the p < 0.05 level based on Tukey’s HSD test.
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Figure 2. GO enrichment analysis of DEGs in melons under different degrees of salt–alkali stress. (A): The 20 GO pathways with the most enrichment in TRH vs. TRZ; (B): The 20 GO pathways with most enrichment in TRY vs. TRZ. GOseq (version v1.22) [20] was used for GO enrichment analysis.
Figure 2. GO enrichment analysis of DEGs in melons under different degrees of salt–alkali stress. (A): The 20 GO pathways with the most enrichment in TRH vs. TRZ; (B): The 20 GO pathways with most enrichment in TRY vs. TRZ. GOseq (version v1.22) [20] was used for GO enrichment analysis.
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Figure 3. KEGG enrichment analysis of DEGs in melons under different degrees of salt–alkali stress. (A): The 20 KEGG pathways with the most enrichment in TRH vs. TRZ; (B): The 20 KEGG pathways with most enrichment in TRY vs. TRZ. The KOBAS software (version v2.0) [21] was used for KEGG enrichment analysis.
Figure 3. KEGG enrichment analysis of DEGs in melons under different degrees of salt–alkali stress. (A): The 20 KEGG pathways with the most enrichment in TRH vs. TRZ; (B): The 20 KEGG pathways with most enrichment in TRY vs. TRZ. The KOBAS software (version v2.0) [21] was used for KEGG enrichment analysis.
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Figure 4. Heat map of biosynthesis of amino acids related gene expression patterns; The differential gene heatmap was completed by python (version v3.13.0, https://blog.python.org, accessed on 15 October 2024).
Figure 4. Heat map of biosynthesis of amino acids related gene expression patterns; The differential gene heatmap was completed by python (version v3.13.0, https://blog.python.org, accessed on 15 October 2024).
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Figure 5. KEGG analysis of differential metabolites in melons under salt–alkali stress. (A): The 20 most enriched metabolic pathways of TRH vs. TRZ; (B): The 20 most enriched metabolic pathways in TRY vs. TRZ.
Figure 5. KEGG analysis of differential metabolites in melons under salt–alkali stress. (A): The 20 most enriched metabolic pathways of TRH vs. TRZ; (B): The 20 most enriched metabolic pathways in TRY vs. TRZ.
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Figure 6. Joint analysis of transcriptome and metabolome. (A): The transcriptome and metabolome KEGG co-enrichment pathway in TRH vs. TRZ; (B): The transcriptome and metabolome KEGG co-enrichment pathway in TRY vs. TRZ; Metabolites are marked with blue triangles, and genes are marked with orange circles.
Figure 6. Joint analysis of transcriptome and metabolome. (A): The transcriptome and metabolome KEGG co-enrichment pathway in TRH vs. TRZ; (B): The transcriptome and metabolome KEGG co-enrichment pathway in TRY vs. TRZ; Metabolites are marked with blue triangles, and genes are marked with orange circles.
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Figure 7. Protein interaction analysis of DEGs and DAMs. (A): The cytoscape network of DEGs and DAMs in amino acid synthesis and metabolism pathways in TRH vs. TRZ. (B): The Cytoscape Network of DEGs and DAMs in carbohydrate synthesis and metabolic pathways of TRH vs. TRZ; (C): The Cytoscape Network of DEGs and DAMs in amino acid synthesis and metabolism pathways in TRY vs. TRZ. (D): The Cytoscape Network of DEGs and DAMs in carbohydrate synthesis and metabolic pathways of TRY vs. TRZ. Metabolites are marked with blue triangles, and genes are marked with orange circles. The solid line represents positive correlation, and the dashed line represents negative correlation. Cytoscape software (version v3.7.1) was used to make a network diagram to describe the correlation between metabolites and genes.
Figure 7. Protein interaction analysis of DEGs and DAMs. (A): The cytoscape network of DEGs and DAMs in amino acid synthesis and metabolism pathways in TRH vs. TRZ. (B): The Cytoscape Network of DEGs and DAMs in carbohydrate synthesis and metabolic pathways of TRH vs. TRZ; (C): The Cytoscape Network of DEGs and DAMs in amino acid synthesis and metabolism pathways in TRY vs. TRZ. (D): The Cytoscape Network of DEGs and DAMs in carbohydrate synthesis and metabolic pathways of TRY vs. TRZ. Metabolites are marked with blue triangles, and genes are marked with orange circles. The solid line represents positive correlation, and the dashed line represents negative correlation. Cytoscape software (version v3.7.1) was used to make a network diagram to describe the correlation between metabolites and genes.
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Fu, L.; Tian, X.; Wang, W.; Wu, C. Metabolome and Transcriptome Analysis Reveals Molecular Mechanisms of Soil Amendment (Volcanic Ash) Alleviating Salt–Alkali Stress in Melons (Cucumis melo L.). Agronomy 2024, 14, 2478. https://doi.org/10.3390/agronomy14112478

AMA Style

Fu L, Tian X, Wang W, Wu C. Metabolome and Transcriptome Analysis Reveals Molecular Mechanisms of Soil Amendment (Volcanic Ash) Alleviating Salt–Alkali Stress in Melons (Cucumis melo L.). Agronomy. 2024; 14(11):2478. https://doi.org/10.3390/agronomy14112478

Chicago/Turabian Style

Fu, Lina, Xiaoxin Tian, Wei Wang, and Chunyan Wu. 2024. "Metabolome and Transcriptome Analysis Reveals Molecular Mechanisms of Soil Amendment (Volcanic Ash) Alleviating Salt–Alkali Stress in Melons (Cucumis melo L.)" Agronomy 14, no. 11: 2478. https://doi.org/10.3390/agronomy14112478

APA Style

Fu, L., Tian, X., Wang, W., & Wu, C. (2024). Metabolome and Transcriptome Analysis Reveals Molecular Mechanisms of Soil Amendment (Volcanic Ash) Alleviating Salt–Alkali Stress in Melons (Cucumis melo L.). Agronomy, 14(11), 2478. https://doi.org/10.3390/agronomy14112478

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