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5 March 2026

14 Pages

Study on the Transcriptome Response of Melon to Aaline—Alkaline Stress

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1
Key Laboratory of Biological Resources and Ecology of Pamirs Plateau in Xinjiang Uygur Autonomous Region, College of Life and Geographic Sciences, Kashi University, Kashi 844000, China
2
Hebei Provincial Key Laboratory of Crop Germplasm Resources, Hebei Agricultural University, Baoding 071001, China
3
Applied Research Institute of Life Sciences, Xi’an International University, 18 Yudou Road, Yanta District, Xi’an 710077, China
4
State Key Laboratory of North China Crop Improvement and Regulation, Baoding 071001, China
Biology2026, 15(5), 426;https://doi.org/10.3390/biology15050426
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Simple Summary

This study used seedling-stage melon cultivar “Xikaixin” to explore its molecular response to saline–alkaline stress, simulated with 50 mmol L−1 NaCl:NaHCO3 solutions of three different ratios (1:1, 1:2, 2:1). Transcriptome sequencing revealed that differentially expressed genes (DEGs) were significantly enriched in 50 pathways, grouped into five major categories. The plant hormone signal transduction pathway was the most enriched (defined as the pathway with the smallest FDR/q-value and the largest number of enriched DEGs at the statistical cutoff of FDR < 0.05); while such enrichment is an expected response in plant abiotic stress, the novelty of this study lies in the cultivar-specific and saline–alkaline ratio-specific expression patterns of the DEGs within this pathway in “Xikaixin”. Two auxin-induced protein biosynthesis-related genes (MELO3C013403 and MELO3C004381) were notably upregulated, and these genes represent candidate targets for further research on the stress resistance of “Xikaixin”.

Abstract

To decipher the molecular response mechanism of melon to saline–alkaline stress, seedlings of the melon cultivar “Xikaixin” were treated with 50 mmol·L−1 mixed solutions of NaCl and NaHCO3 at ratios of 1:1, 1:2, and 2:1 to simulate saline–alkaline stress. Transcriptome sequencing of roots (four biological replicates per group, with each replicate consisting of one pot containing four robust seedlings as the experimental unit) yielded 78.98 Gb of clean data (≥6.02 Gb per sample) with Q30 ≥ 96.61% and genome alignment rates of 97.00–98.02%, identifying 588, 686, and 1107 differentially expressed genes (DEGs) in the 1:1, 1:2, and 2:1 groups, respectively. Notably, the 1:1 treatment—mimicking the natural NaCl:NaHCO3 ratio of saline–alkaline soil in southern Xinjiang—had 588 DEGs with the plant hormone signal transduction pathway as its most significantly enriched pathway, representing the core molecular response of “Xikaixin” to near-natural saline–alkaline stress. DEGs were significantly enriched in 50 pathways categorized into five major classes, with the plant hormone signal transduction pathway showing the highest enrichment across all treatments. A key observation from gene expression patterns is a potential auxin–ABA balance modulation, inferred from the differential expression of annotation-based auxin-related and ABA-related genes/pathways (no direct measurement of hormone levels or signaling was performed): two auxin-related genes (auxin-induced protein gene MELO3C013403 and auxin response factor gene MELO3C004381) were specifically upregulated (≥two fold vs. control) in the high-salt 2:1 group, while ABA-related genes were upregulated and auxin/jasmonic acid/gibberellin-related genes were downregulated in the 1:2 group, indicating a putative cultivar-specific hormone-related gene expression pattern associated with auxin–ABA crosstalk in “Xikaixin” under saline–alkaline stress. In contrast, photosynthesis-antenna protein genes (e.g., MELO3C021567) were significantly downregulated (to 32% of the control) under the 2:1 treatment. RT-qPCR validation confirmed the consistency of these candidate genes’ expression with transcriptomic data. Therefore, melon may respond to saline–alkaline stress by regulating the plant hormone signal transduction (especially auxin–ABA balance), photosynthesis, and carbon metabolism pathways. This study provides novel candidate genes and a theoretical basis for the genetic improvement of saline–alkaline-tolerant melon cultivars, with the unique auxin–ABA balance modulation as a key original contribution.

1. Introduction

Soil salinization and alkalinization (saline–alkaline soil formation) are global abiotic stress factors that severely impact plant growth and agricultural production [1]. Saline–alkaline soils differ from pure saline soils in their combined stress of ionic toxicity, osmotic stress and elevated soil pH (alkaline stress), with high pH being a key limiting factor: elevated pH reduces the bioavailability of essential mineral nutrients (e.g., Fe, Ca, Mg, P) in the soil, disrupts root cell membrane permeability and ion homeostasis, and inhibits the activity of key metabolic enzymes, leading to more severe growth inhibition in plants than single salt stress [2]. In China, the total area of salinized and saline–alkaline land reaches 3.7 × 107 hm2, with Xinjiang being the most severely affected region, accounting for 2.96 × 106 hm2 of such land, of which 40% is moderately mixed saline–alkaline arable land with development potential. Due to natural conditions such as low rainfall, arid climate, intense sunlight, and high evaporation, soil salinization and alkalinization often co-occur and are particularly prominent in southern Xinjiang, where the saline–alkaline soils are characterized by the coexistence of neutral salts (NaCl) and alkaline salts (NaHCO3) rather than single salt accumulation. Currently, the improvement of saline–alkaline land mainly relies on physical, chemical, and biological methods. However, given the large area of saline–alkaline land and the complexity of mixed saline–alkaline stress, physical and chemical improvement approaches are limited by their time consumption, high labor intensity, and high cost, which makes large-scale application difficult. In contrast, the biological improvement strategy of cultivating saline–alkaline-tolerant crops on saline–alkaline land exhibits significant advantages. On one hand, saline–alkaline-tolerant crops can absorb saline–alkaline components from the soil, improve soil structure and reduce pH, and promote the development and utilization of saline–alkaline land [3,4]. On the other hand, studying saline–alkaline-tolerant crops on moderately mixed saline–alkaline land consistent with natural field conditions is of great significance for revealing their specific tolerance mechanisms to combined saline–alkaline stress and exploring excellent saline–alkaline-tolerant germplasm resources [5]. Numerous studies have reported that in simulated saline–alkaline stress experiments, different ratios of salt to alkali (e.g., 1:1, 1:2, 2:1) and concentration gradients (50–200 mmol/L) are used to construct simulated saline–alkaline environments, which are suitable for investigating the physiological adaptation mechanisms of plants under combined saline–alkaline stress [1,6,7].
Melon (Cucumis melo L.) is an annual trailing plant of the genus Cucumis in the Cucurbitaceae family, with abundant germplasm resources and high nutritional value, making it popular among consumers [8,9]. Studies have shown that melon has a certain degree of tolerance to saline–alkaline stress [10,11,12,13]. The seedling stage is a critical period for plants to respond to saline–alkaline stress, directly affecting their adaptability to saline–alkaline environments and subsequent growth and development [14,15,16,17]. Under saline–alkaline stress, seedlings experience oxidative stress, which inhibits plant growth and biomass accumulation, and interferes with important physiological processes such as osmotic regulation, ion absorption, and photosynthesis [10,18].
Existing transcriptomic studies on melon have focused on different organs, tissues, and stress durations: root transcriptome analyses mainly explored responses to short-term (24–72 h) salt stress (e.g., NaCl alone) or drought stress, identifying ion transport and osmotic adjustment-related genes [19]; leaf transcriptomes focused on long-term (1–2 weeks) abiotic stress (e.g., high temperature, drought) and biotic stress (e.g., pathogen infection), highlighting photosynthesis and defense response pathways [20]; fruit transcriptomes primarily investigated ripening processes under non-stress conditions, with limited involvement in stress responses [21,22]; and seedling-stage transcriptomic studies were mostly limited to single salt (NaCl) or alkali (NaHCO3) stress, lacking systematic analysis of mixed saline–alkaline stress, which is closer to natural soil conditions [17,21].
Many researchers have studied the mechanism of plant resistance to saline–alkaline stress through transcriptomic analysis. Transcriptomic analysis of poplar under simulated saline–alkaline stress found that saline–alkaline stress mainly affects plant signal transduction pathways (e.g., plant–pathogen interaction, plant hormone signal transduction), metabolic pathways (e.g., starch and sucrose metabolism, carbon metabolism), and biosynthetic pathways (e.g., biosynthesis of phenylpropanoids, biosynthesis of amino acids) to resist saline–alkaline stress [23]. Transcriptomic analysis of two foxtail millet materials under saline–alkaline stress revealed that foxtail millet enhances its saline–alkaline tolerance through positive regulation of tryptophan/fatty acid metabolism, the MAPK signaling pathway, and peroxisome pathways [24]. Another study showed that the expression of the Na+/H+ antiporter gene CsNHX1 in cucumber can sequester Na+ in vacuoles, thereby reducing Na+ content in cells and alleviating salt stress damage to cucumber seedlings [25,26,27]. The GhAKT1 gene is a K+ channel gene, and under salt-alkali stress, phosphorus application can significantly increase the relative expression level of the GhAKT1 gene in cotton [28,29].
Saline–alkaline stress enhances plant tolerance to saline–alkaline stress by regulating the expression levels of genes in different signaling pathways and the metabolic levels of different substances. Currently, transcriptomic studies on melon’s response to saline–alkaline stress are relatively limited, and existing melon transcriptomic studies lack targeted analysis of mixed saline–alkaline stress with gradient ratios simulating natural soil conditions in southern Xinjiang. Therefore, this study used the saline–alkaline-tolerant melon cultivar “Xikaixin” as experimental material, simulated saline–alkaline stress treatment by setting different saline–alkaline ratios (NaCl:NaHCO3 = 1:1, 1:2, 2:1), and conducted transcriptomic analysis of melon roots to explore the signaling pathways and related genes involved in melon’s response to saline–alkaline stress, and to deeply analyze the mechanism of melon’s response to saline–alkaline stress.
Based on the above gaps, this study hypothesizes that the local saline–alkaline-tolerant melon cultivar “Xikaixin” has evolved cultivar-specific molecular adaptation strategies in response to mixed saline–alkaline stress with different NaCl/NaHCO3 ratios. The specific objectives are to: (1) identify differentially expressed genes (DEGs) in “Xikaixin” seedling roots under three gradient mixed saline–alkaline treatments; (2) decipher core signaling and metabolic pathways involved in stress response, with a focus on ratio-specific regulatory patterns; (3) screen key candidate genes associated with saline–alkaline tolerance to provide a theoretical basis for genetic improvement of melon.

2. Materials and Methods

2.1. Experimental Materials

The main local saline–alkaline-tolerant melon cultivar “Xikaixin” in southern Xinjiang (bred by Xinjiang Jifeng Seed Industry Co., Ltd., Changji City, China) was used as the experimental material. This cultivar is a mid-early maturing type with agronomic traits of oblong fruits, yellowish-red peel, greenish-white flesh and good transportability. All experimental seeds were purchased from the Modern Melon and Fruit Seed Marketing Store in Kashgar Agricultural Expo City to ensure the material consistency with the main cultivated melon cultivars in southern Xinjiang.

2.2. Saline–Alkaline Stress Treatment Protocol

Plump and uniform-sized “Xikaixin” melon seeds were disinfected with 5% NaClO solution for 15 min and rinsed thoroughly with distilled water. Subsequently, the seeds were subjected to secondary disinfection by water bath at 50 °C for 20 min. The disinfected seeds were sown in plastic flowerpots (17 cm × 19 cm), with 8 seeds per pot, and the humidity of the nutrient soil was maintained at 60–70% of field capacity during cultivation (monitored by a soil moisture sensor, type: TDR-300). After seed germination, at the cotyledon expansion stage when the first true leaf emerged, 4 robust seedlings were retained per pot. When the seedlings grew to the three-leaf/one-heart stage, saline–alkaline stress treatment was initiated.
The control group (CK) was irrigated with a standard half-strength Hoagland nutrient solution (pH 6.5 ± 0.1, EC 1.2 ± 0.1 mS·cm−1) to avoid nutrient imbalance, while the treatment groups were irrigated with 50 mmol·L−1 mixed saline–alkaline solutions (NaCl:NaHCO3 = 1:1, 1:2, 2:1) prepared with the same half-strength Hoagland nutrient solution (ensuring consistent nutrient composition across all groups). The pre-adjusted stable pH values of the treatment solutions were: T1 (1:1) 7.8 ± 0.2, T2 (1:2) 8.5 ± 0.3, T3 (2:1) 7.5 ± 0.2. The pH of all solutions (CK and treatments) was monitored daily using a portable pH meter (Model PHS-3C) during the experiment, and minor fine-tunings were made with 0.1 mol·L−1 HCl or NaOH to maintain the above pH ranges, ensuring no significant pH fluctuations throughout the 9-day stress treatment period. The three treatment groups were designated as T1 (NaCl:NaHCO3 = 1:1), T2 (NaCl:NaHCO3 = 1:2), and T3 (NaCl:NaHCO3 = 2:1). Each pot was irrigated with 500 mL of the corresponding solution (CK or treatment) at one time, and subsequent irrigations (500 mL per pot) were performed every 3 days with the same CK or treatment-specific solution for each group, for a total of 3 irrigations in all, with a total treatment duration of 9 days.
During cultivation, the growth environment was controlled as follows: day/night temperature 25–28 °C, relative humidity 34–42%, light intensity 350 μmol·m−2·s−1, and light cycle 12 h/d. The experiment adopted a completely randomized block design (CRBD) with 4 biological replicates per group (n = 4), and each replicate consisted of 1 pot (experimental unit). Pot placement was randomized within the growth chamber to eliminate positional effects (e.g., uneven light, temperature). After 9 days of saline–alkaline treatment, roots from the same growth position (5 cm below the root crown) of each seedling in the treatment and control groups were collected, mixed uniformly per pot (replicate), and used for index determination. During sample collection, roots were rinsed thoroughly with distilled water, quickly frozen in liquid nitrogen, and stored at −80 °C for subsequent analysis.

2.3. Plant Transcriptome Analysis Methodology

2.3.1. Extraction of RNA

High-quality total RNA was extracted from the roots of “Xikaixin” melon seedlings (4 biological replicates per group, n = 4) using a modified CTAB-based RNA extraction protocol (referenced to Kiss et al., 2024 [30]) with key optimized conditions: the extraction buffer contained 2% (w/v) CTAB, 2% (w/v) PVP K-30, 100 mmol·L−1 Tris-HCl (pH 8.0), 25 mmol·L−1 EDTA (pH 8.0), 2.0 mol·L−1 NaCl, and 0.5% (v/v) β-mercaptoethanol (added fresh before use); the sample was incubated in a 65 °C water bath for 30 min with gentle inversion every 5 min; RNA precipitation was performed with isopropanol at −20 °C for 1 h, followed by washing with 75% (v/v) RNase-free ethanol twice.

2.3.2. RNA Integrity Assessment

The integrity of total RNA was detected using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and only RNA samples with a RNA Integrity Number (RIN) ≥ 8.0 were used for subsequent library construction.

2.3.3. cDNA Library Preparation

The mRNA was enriched from total RNA using oligo(dT) magnetic beads (polyA enrichment strategy, no rRNA depletion). The purified mRNA was fragmented into 200–300 bp short fragments using divalent cations in a fragmentation buffer at 94 °C for 5 min. The cDNA library was constructed using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs, Ipswich, MA, USA). Key steps included: first-strand cDNA synthesis with random hexamer primers and M-MuLV Reverse Transcriptase, second-strand cDNA synthesis with DNA Polymerase I and RNase H, end repair, dA-tailing, adapter ligation, and library enrichment via 12 cycles of PCR amplification. The target insert size of the final library was 250–350 bp, and size selection was performed using AMPure XP beads (Beckman Coulter, Brea, CA, USA) to remove non-specific amplification products.

2.3.4. High-Throughput Sequencing

The qualified cDNA libraries were sequenced on the Illumina NovaSeq 6000 platform (Illumina, Diego, CA, USA) with a paired-end (PE) read configuration (150 bp per end, PE150) and non-stranded sequencing mode. Raw sequencing data were generated in FASTQ format for subsequent bioinformatic analysis.

2.3.5. Bioinformatic Analysis

Data preprocessing: Raw sequencing reads were quality-controlled using FastQC v0.11.9 to assess Q20, Q30, GC content, and adapter contamination. Low-quality reads (Q20 < 90%, N base content > 5%), adapter sequences, and short reads (<50 bp) were trimmed and filtered using Trimmomatic v0.39 with parameters: ILLUMINACLIP:adapter.fa:2:30:10, LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:50.
Genome alignment: Clean reads were aligned to the melon reference genome (Cucumis melo L. var. reticulatus cv. DHL92 v3.6.1, downloaded from NCBI GenBank) using HISAT2 v2.2.1 with default parameters (mismatch ≤ 2, alignment score ≥ 30).
Gene expression quantification: Raw gene counts were generated from aligned reads using featureCounts v2.0.3 (parameters: -t exon -g gene_id -s 0 -p), and FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values were calculated using StringTie v2.2.1 for normalization. Low-expression genes were filtered out with a threshold of FPKM < 0.1, retaining 18,624 valid expressed genes.
DEG identification: Differentially expressed genes (DEGs) were identified independently using two R packages, DESeq2 v1.38.3 and edgeR v3.40.2, with the filtered raw count matrix as input. The screening thresholds were set as |log2(fold change)| ≥ 1 and false discovery rate (FDR) < 0.05. The final DEG set was defined as the intersection of genes identified by both tools to ensure result robustness.
Functional enrichment analysis: GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses of DEGs were performed using the Over-Representation Analysis (ORA) method via the R package clusterProfiler v4.8.1 (with the melon-specific annotation package org.Cmelo.eg.db for gene ID mapping). The complete annotated gene set of the melon reference genome was used as the background to avoid enrichment bias. The Benjamini–Hochberg (BH) method was applied for multiple testing correction, and a corrected FDR < 0.05 was set as the threshold to identify significantly enriched GO terms and KEGG pathways.

2.4. Measurement of Physiological Indicators

After 9 days of saline–alkaline stress treatment, the photosynthetic rate (Pn) of “Xikaixin” melon seedlings was determined to verify the functional relevance of the photosynthesis-related gene MELO3C021567. Three seedlings per replicate (n = 4) were selected for measurement, with three technical replicates per sample.
Photosynthetic rate: Measured using a Li-6400XT portable photosynthesis system (Li-Cor, Lincoln, NE, USA). The measurement conditions were set as follows: leaf chamber CO2 concentration 400 μmol·mol−1, light intensity 350 μmol·m−2·s−1 (consistent with the cultivation light intensity), leaf temperature 25 ± 1 °C, and relative humidity 60 ± 5%. The fully expanded true leaf of each seedling was clamped into the leaf chamber, and the data were recorded after stabilization for 3–5 min.

2.5. RT-qPCR Validation of Three Core Candidate Genes

To further confirm the reliability of transcriptomic data for MELO3C013403, MELO3C004381, and MELO3C021567, RT-qPCR was performed as follows:
RNA extraction and cDNA synthesis: Total RNA was extracted from roots using the CTAB method (Section 2.3), and cDNA was synthesized with the PrimeScript™ RT Reagent Kit with gDNA Eraser (TaKaRa, Japan) to eliminate genomic DNA contamination.
Primer design: Specific primers for the three core genes and internal reference gene (Actin, GenBank accession: XM_008454653.2) were designed using Primer Premier 5.0 software. Primer specificity was verified by agarose gel electrophoresis and melting curve analysis.
RT-qPCR reaction: This was conducted on a CFX96 Real-Time PCR System (Bio-Rad, Hercules, CA, USA) with SYBR® Premix Ex Taq™ II (TaKaRa, Kusatsu, Japan). The 20 μL reaction system included 10 μL SYBR Premix, 0.4 μL each of forward and reverse primers (10 μmol·L−1), 2 μL cDNA template, and 7.2 μL ddH2O. The reaction program was: 95 °C predenaturation for 30 s; 40 cycles of 95 °C denaturation for 5 s, 58 °C annealing for 30 s; and melting curve analysis from 65 °C to 95 °C.
Data analysis: Relative expression levels were calculated using the 2−ΔΔCt method, with 4 biological replicates and 3 technical replicates per sample.

2.6. Artificial Intelligence Tool Application

During the preparation of this manuscript, an artificial intelligence (AI) tool (Doubao, ByteDance, Beijing, China) was used for auxiliary work, including the polishing and syntactic optimization of English texts in the paper, and the standardized formatting and data typesetting of supplementary tables of differentially expressed genes. All experimental design, acquisition and analysis of transcriptome sequencing data, interpretation of gene functions, and deduction of research conclusions were independently completed by the authors. The AI tool was only used as an auxiliary means for writing and format processing, and did not participate in any analysis of experimental data or formation of scientific research conclusions.

3. Results and Analysis

3.1. Transcriptome Sequencing Results Analysis

In this study, a half-strength Hoagland nutrient solution control and three saline–alkaline stress treatments with different ratios (NaCl:NaHCO3 = 1:1, 1:2, 2:1) were set up, with four biological replicates per group. Sixteen libraries were prepared and sequenced for reference-guided RNA-seq analysis. After quality control (QC), trimming and filtering, a total of 78.98 Gb of high-quality clean data was obtained, with each sample having ≥6.02 Gb of clean data. Raw sequencing reads were first assessed for quality using FastQC v0.11.9; low-quality reads (Q20 < 90%, N base content > 5%), adapter sequences and short reads (<50 bp) were trimmed and filtered using Trimmomatic v0.39 with parameters: ILLUMINACLIP:adapter.fa:2:30:10, LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:50. Quality assessment of the sequencing data showed that the Q30 base percentage was ≥96.61%, and the clean reads of each sample were aligned to the reference genome, with alignment efficiency ranging from 97.00% to 98.02%. The above data indicate that the sequencing data quality is good and can be used for subsequent in-depth data analysis. Subsequent analysis of differentially expressed gene data found that, after filtering low/zero expression genes (FPKM < 0.1, 18,624 valid expressed genes in total), under the three simulated saline–alkaline stress treatments of 1:1, 1:2, and 2:1, the number of upregulated DEGs in “Xikaixin” was 194, 225, and 383, respectively, and the number of downregulated genes was 394, 461, and 724, corresponding to total DEGs of 588 (3.16%), 686 (3.68%) and 1107 (5.94%) of the expressed genes (Figure 1a–c). These results indicate that under different ratios of saline–alkaline stress treatments, “Xikaixin” responds to damage caused by saline–alkaline stress by regulating its own gene expression.
Figure 1. Volcano plot of the differentially expressed genes. Note: (a) CK vs. T (1:1); (b) CK vs. T (1:2); (c) CK vs. T (2:1); the same below. DEGs were screened with the dual criteria of |log2| ≥ 1 and FDR < 0.05.

3.2. Functional Annotation of Differentially Expressed Genes

Under the 1:1, 1:2, and 2:1 saline–alkaline stress treatments, the number of annotated DEGs (DEGs with successful functional annotation against KEGG and GO databases) was 560 (95.2%), 654 (95.3%), and 1064 (95.9%), respectively. The annotated DEGs were enriched in 50 low-level KEGG pathways (FDR < 0.05) and 127 GO biological process terms, with trait-specific core pathways and clear DEG expression directionality across the three treatments (Tables S1–S9). For the shared most significantly enriched pathway (plant hormone signal transduction), the 1:1 group showed proportional auxin/ABA gene regulation (FDR = 1.23 × 10−6, enrichment fold = 6.82), the 1:2 group exhibited ABA gene upregulation with auxin/jasmonic acid gene downregulation (FDR = 8.91 × 10−7, enrichment fold = 7.05), and the 2:1 group had specific auxin gene upregulation (FDR = 5.62 × 10−8, enrichment fold = 7.53). Treatment-specific enriched low-level KEGG pathways included: phenylpropanoid biosynthesis (downregulated DEGs, FDR = 3.57 × 10−5, fold = 4.15) for 1:1; glutathione metabolism (upregulated antioxidant genes, FDR = 2.01 × 10−4, fold = 3.89) for 1:2; and photosynthesis-antenna proteins (all downregulated, FDR = 1.09 × 10−5, fold = 5.21) for 2:1. Core GO biological process terms for all groups focused on hormone-mediated signal transduction, abiotic stress response, and cellular oxidant detoxification, with the 2:1 group uniquely enriched in photosystem assembly and light energy harvesting (all downregulated).
DEGs under the 1:1 saline–alkaline stress treatment were enriched in 20 KEGG pathways, mainly including: plant hormone signal transduction, benzoxazinoid biosynthesis, plant–pathogen interaction, phenylalanine metabolism, phenylpropanoid biosynthesis, etc. (Figure 2a). KEGG enrichment pathways under the 1:2 saline–alkaline stress treatment included the following metabolic pathways: plant hormone signal transduction, plant–pathogen interaction, linoleic acid metabolism, glutathione metabolism, carotenoid biosynthesis, etc. (Figure 2b). The KEGG enrichment pathways under the 2:1 saline–alkaline stress treatment were divided into plant–pathogen interaction, plant MAPK signaling pathway, plant hormone signal transduction, photosynthesis-antenna proteins, and phenylpropanoid biosynthesis (Figure 2c).
Figure 2. KEGG enrichment map of differentially expressed genes: (a) KEGG enrichment analysis of DEGs under 1:1 saline–alkaline stress treatment; (b) KEGG enrichment analysis of DEGs under 1:2 saline–alkaline stress treatment; (c) KEGG enrichment analysis of DEGs under 2:1 saline–alkaline stress treatment. KEGG pathway enrichment analysis was conducted with the statistical cutoff of FDR < 0.05.
Combining the above results, under the 1:1 and 1:2 saline–alkaline stress treatments, the most enriched DEGs were in plant hormone signal transduction. Under the 2:1 saline–alkaline stress treatment, DEGs were significantly enriched in the plant hormone signal transduction pathway, indicating that this pathway plays an important role in the response of “Xikaixin” to saline–alkaline stress. In addition, compared with the 1:1 and 1:2 saline–alkaline stress treatments, the 2:1 saline–alkaline stress treatment added phenol secondary metabolism and carbohydrate metabolism pathways, indicating that “Xikaixin” melon copes with damage caused by saline–alkaline stress by enriching genes in different metabolic pathways under different ratios of saline–alkaline stress treatments.

3.3. Analysis of Differentially Expressed Genes in Melon Response to Saline–Alkaline Stress

The plant hormone signal transduction pathway exhibited the highest enrichment level (assessed by two core metrics: the lowest corrected false discovery rate (FDR) and the highest enrichment fold) among all KEGG pathways for DEGs in the 1:1, 1:2, and 2:1 saline–alkaline treatment groups, with the most significant enrichment observed in the 2:1 group (FDR = 5.62 × 10−8, enrichment fold = 7.53). Among the DEGs in this key pathway, the auxin-induced protein genes MELO3C013403 and MELO3C004381 were significantly upregulated and can be used as candidate genes for saline–alkaline tolerance (Tables S1–S3). In the 2:1 treatment group, photosynthesis-antenna protein genes were significantly downregulated. The results indicate that the transcriptional downregulation of photosynthesis-antenna protein genes in melon shows a consistent trend with the decrease in photosynthetic efficiency under saline–alkaline stress, which may be a transcriptional response of melon to high-salt stress (Tables S4–S6). Genes in pathways such as starch and sucrose metabolism and amino acid biosynthesis were differentially expressed in each treatment group (Tables S7–S9). For the differentially expressed data of melon responding to saline–alkaline stress, we selected core candidate genes for experimental verification. Priority was given to selecting MELO3C013403 (auxin-induced protein gene) and MELO3C004381 (auxin response factor gene) in the “plant hormone signal transduction” pathway, as well as MELO3C021567 (photosystem II light-harvesting antenna protein), a photosynthesis-related gene unique to the 2:1 group, as core candidate genes.

3.4. Analysis of Gene Expression Levels of Core Candidate Genes

Validation by RT-qPCR of the gene expression levels of candidate genes showed that the transcriptional level data of several genes obtained from the experiment were consistent with the trends of the transcriptomic data (Figure 3 and Tables S1–S9). MELO3C013403 and MELO3C004381 were significantly upregulated in the 2:1 group (more than twice that of the CK group) and slightly downregulated in the 1:1 and 1:2 groups; MELO3C021567 showed a gradient downregulation with the increase in NaCl ratio, with the most significant downregulation in the 2:1 group (only 32% of the CK group), which intuitively verified the reliability of the RNA-seq data.
Figure 3. Analysis of gene expression levels of core candidate genes: (a) detection of the expression level of MELO3C013403 under different saline–alkaline treatments; (b) detection of the expression level of MELO3C004381 under different saline–alkaline treatments; (c) detection of the expression level of MELO3C021567 under different saline–alkaline treatments.

3.5. Physiological Validation of Core Pathways

The photosynthetic rate (Pn) was determined to analyze the association between the downregulated expression of the photosynthesis-related gene MELO3C021567 and the changes in photosynthetic physiological characteristics of melon seedlings under saline–alkaline stress. The photosynthetic rate of “Xikaixin” melon seedlings showed a gradual decreasing trend with increasing NaCl ratio (Figure 4). In the T3 (2:1) treatment, the photosynthetic rate was 47.9% of CK, which was significantly lower than that in T1 and T2 (p < 0.05). This result is consistent with the downregulated expression of MELO3C021567, indicating a positive consistency between the transcriptional level of this gene and the photosynthetic physiological performance of melon seedlings under high-salt stress.
Figure 4. Photosynthetic rate of Xikaixin melon seedlings under different saline–alkaline stress treatments.

4. Discussion

In this study, “Xikaixin”, a main local saline–alkaline-tolerant melon cultivar in Xinjiang, was used as the material. Combined with transcriptome sequencing technology, the molecular mechanism of melon seedling roots responding to saline–alkaline stress was systematically analyzed by setting three different ratios of mixed saline–alkaline stress treatments (NaCl:NaHCO3 = 1:1, 1:2, 2:1). The results showed that the number of differentially expressed genes (DEGs) in melon seedlings under different ratios of saline–alkaline stress showed significant gradient differences, and the response of core signaling and metabolic pathways exhibited distinct saline–alkaline ratio specificity—evident in the divergent enrichment preferences of key pathways and contrasting expression patterns of core regulatory genes across the three NaCl:NaHCO3 ratio treatments, providing important molecular evidence for in-depth understanding of the saline–alkaline tolerance adaptation strategy of melon.

4.1. Response Characteristics and Cultivar Specificity of Differentially Expressed Genes Under Saline–Alkaline Stress

Plant adaptation to saline–alkaline stress relies on complex gene expression regulatory networks. The number and expression patterns of differential genes directly reflect the plant’s perception and response to stress intensity and type [24,31,32]. This study found that 588, 686, and 1107 differentially expressed genes were identified in “Xikaixin” melon under the 1:1, 1:2, and 2:1 saline–alkaline stress treatments, respectively. The number of downregulated genes was significantly more than that of upregulated genes, and the total number of differential genes showed an increasing trend with the increase in NaCl ratio in the saline–alkaline stress. This result is different from the rule of “prolonged stress leads to an increase in downregulated genes” in Zhang et al.’s (2019) study on melon under single NaCl stress [13], a discrepancy likely attributed to the distinct stress types (single salt vs. mixed saline–alkaline) in the two studies. It is speculated that the differences mainly come from two aspects: first, the mixed saline–alkaline ratio gradient used in this study (especially the high proportion of NaCl in the 2:1 treatment) may cause more direct ionic toxicity to the roots, triggering the downregulation of more genes related to growth inhibition; second, as a locally selected cultivar in Xinjiang, “Xikaixin” has differences in its saline–alkaline tolerance genetic background from the melon cultivars in previous studies, leading to cultivar-specific gene response patterns.
Compared with the transcriptomic study results of saline–alkaline stress in crops such as Jinsuihuanggu (a foxtail millet cultivar) and Potentilla anserina, the differential genes of melon in this study were also enriched in core pathways such as plant hormone signal transduction and MAPK signaling pathway, indicating that these pathways are conserved mechanisms for plants to respond to saline–alkaline stress [21,24]. However, it is worth noting that melon added specific enrichment pathways such as photosynthesis-antenna proteins and phenol secondary metabolism under the 2:1 high-salt ratio stress, while foxtail millet and Potentilla anserina focused more on pathways such as fatty acid metabolism and peroxisome. This reflects the species-specific differences in saline–alkaline tolerance strategies among different crops, which may be related to the physiological characteristics of melon as a Cucurbitaceae crop and the sensitivity of its roots to ionic stress.

4.2. Limitations of This Study

This study provides insights into the molecular mechanism of “Xikaixin” melon responding to saline–alkaline stress, but there are several limitations that need to be addressed in future research. First, this study only focused on a single time point (9 days after stress initiation), which limits the understanding of the dynamic temporal response of melon to saline–alkaline stress. The expression patterns of core genes (e.g., MELO3C013403, MELO3C004381) and the activation of key pathways may change at early (e.g., 24–72 h) or late (e.g., 14 days) stress stages, and continuous time-series sampling is required to reveal the complete stress response process. Second, this study only used a single nutrient supply level (half-strength Hoagland solution) for all groups; future experiments could explore interactive effects of nutrient gradients (e.g., different N/P/K ratios) and saline–alkaline stress to clarify how nutrient availability modulates melon’s saline–alkaline tolerance, which would provide more targeted agronomic strategies for saline–alkaline melon cultivation in nutrient-poor southern Xinjiang soils. Third, this study did not analyze the changes in endogenous hormone contents (e.g., auxin, ABA) and their interactions at the physiological level, which limits the in-depth verification of the hormone balance regulation mechanism inferred from transcriptomic data. Additionally, the functional validation of candidate genes (e.g., overexpression or knockout) was not performed, and the direct role of these genes in saline–alkaline tolerance needs to be further confirmed through genetic transformation experiments.

5. Conclusions

This study comprehensively explored the molecular mechanisms of melon cultivar “Xikaixin” responding to mixed saline–alkaline stress (NaCl:NaHCO3 at 1:1, 1:2, 2:1 ratios) via transcriptomic analysis. Plant hormone signal transduction emerged as the most enriched pathway, with distinct auxin–ABA balance modulation: auxin-related genes (MELO3C013403, MELO3C004381) were specifically upregulated under high-salt (2:1) stress, while ABA-related genes dominated under high-alkali (1:2) conditions. Additionally, photosynthesis-antenna protein gene MELO3C021567 was significantly downregulated in the 2:1 group, consistent with the reduced photosynthetic rate.
These findings reveal cultivar-specific and ratio-dependent adaptive strategies of melon to saline–alkaline stress, providing novel candidate genes and a theoretical basis for breeding saline–alkaline-tolerant melon cultivars. Future studies focusing on functional validation of key genes and dynamic stress responses will further refine our understanding of the regulatory network.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15050426/s1. Table S1. CK vs. T1 (1:1) signal transduction-related genes. Table S2. CK vs. T2 (1:2) signal transduction-related genes. Table S3. CK vs. T3 (2:1) signal transduction-related genes. Table S4. CK vs. T1 (1:1) photosynthesis-related genes. Table S5. CK vs. T2 (1:2) photosynthesis-related genes. Table S6. CK vs. T3 (2:1) photosynthesis-related genes. Table S7. CK vs. T1 (1:1) osmoregulation-related genes. Table S8. CK vs. T2 (1:2) osmoregulation-related genes. Table S9. CK vs. T3 (2:1) osmoregulation-related genes.

Author Contributions

Conceptualization, H.W.; methodology, T.W.; investigation, Y.Z. and N.M.; data curation, T.W. and Y.S.; writing—original draft preparation, H.W.; writing—review and editing, H.W.; visualization, H.W.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32300433 to H.W.); the State Key Laboratory of North China Crop Improvement and Regulation, S&T Program of Hebei (23567601H to H.W.); the Xinjiang Uygur Autonomous Region Science and Technology Program (2022TSYCCX0083 to H.W.); Xinjiang Uygur Autonomous Region Postdoctoral Research Innovation Platform; Talent Program for Intellectual Support to Xinjiang—“Small Group” Aid Xinjiang Team and Flexible Aid Xinjiang Expert Talent Project “Innovative Team for High-Value Processing and Utilization of Natural Color Cotton and Fibers”.

Data Availability Statement

All data required to support the claims of this paper are included in the main article. All reagents generated in this study are available on request from the lead contact. RNA-seq data of Cucumis melo L. are stored in the biological project library of NCBI with accession number PRJNA1334105.

Acknowledgments

We thank Liqin Wang (Editor Shaanxi Biotechnology Co., Ltd., Xi’an, China) for assistance. We acknowledge the technical support provided by the artificial intelligence tool for auxiliary work such as text polishing and table format optimization during the manuscript preparation. All research ideas, experimental designs and data interpretations of this study were independently completed by the authors.

Conflicts of Interest

The authors declare no competing interests.

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