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Article

Transcriptome and Proteome Reveal Heat Shock Promotes Haploid Induction Rate via Activating ABA Signal Transduction in Watermelon

by
Shiqi Gong
1,
Bingqian Tang
1,
Yujuan Dai
1,
Xiangyu Sun
1,
Huijuan Song
1,
Cheng Xiong
1,
Tian Zou
2,
Longjun Sun
3,
Guang Liu
4,
Hongbo Yang
1,
Shengxiu Zhu
5,
Sihui Dai
1,* and
Xiaowu Sun
1,*
1
College of Horticulture, Hunan Agricultural University, Changsha 410128, China
2
College of Plant Technology, Hunan Biological and Electromechanical Polytechnic, Changsha 410127, China
3
Hunan Xuefeng Seeds Co., Ltd., Shaoyang 422001, China
4
Institute of Vegetable Research, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
5
Xinjiang Western Oasis Ecological Development Co., Ltd., Karamay 834000, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1063; https://doi.org/10.3390/agronomy15051063
Submission received: 25 March 2025 / Revised: 18 April 2025 / Accepted: 23 April 2025 / Published: 27 April 2025
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Versions Notes

Abstract

:
Haploid breeding technology has advantages in terms of saving time and reducing labor intensity and costs. However, the low induction rate limits the application of this technology. Previous researchers found that heat shock can increase the rate of Embryo-like structures (ELSs) induction. However, molecular mechanisms underlying heat-shocked haploid induction remain poorly understood. In the current study, unfertilized ovules of watermelon were subjected to heat shock for 0–5 days and conducted transcriptomics sequencing and DIA-based proteomics sequencing. Results indicated that, in contrast to the non-heat-shock condition, the expression level of protein phosphatase 2C (PP2C), a negative regulator in abscisic acid (ABA) signal transduction pathway, was repressed, and the expression level of Sucrose-non-fermenting 1-related protein kinases (SnRK2) was activated. The activated SnRK2s are enabled to promote the accumulation of storage substances in ovules. Through analysis, the expression of many genes involved in the biosynthesis of unsaturated fatty acids and amino acids has indeed been upregulated. In conclusion, our findings demonstrate that heat shock promotes the accumulation of storage substances in unfertilized ovules by activating the signal transduction process of ABA, which correspondingly increases ELSs induction rate.

1. Introduction

Watermelon (Citrullus lanatus) belongs to the Cucurbitaceae family and is one of the most widely cultivated cash crops. The production of watermelon was estimated to be 63.96 million tons in 2023 in China (https://www.fao.org (accessed on 13 April 2025)). The east Asian cultured accession ‘97103’ and the North America cultured accession ‘Charleston Gray’ have been de novo sequenced and assembled with the rapid development of sequence technology [1,2]. Then the free-gap genomes of ‘G42’ and other wild watermelon accessions have greatly expanded the previously published reference genome, significantly enhancing our understanding of watermelon genome diversity [3]. A recent study showed that mutating the mi396 microRNA target site in ClGRF4 and GIF1, combined with clustered, regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) genome editing tools, achieved highly efficient gene editing in watermelon [4]. Doubled haploid technology is broadly used to rapidly produce homozygous plants to accelerate crop breeding. A recently developed, novel technology that combines gene editing and haploid induction, directly employing haploid-inducer lines for gene editing, is likely to be the most efficient approach for rapidly generating new homozygous accessions [5]. And it is less known how to establish a highly efficient haploid induction in watermelon.
Compared to traditional breeding, haploid breeding technology has advantages in terms of saving time and reducing labor intensity and costs and is widely applied in the modern maize breeding process [6]. Doubled haploids are of great significance for crop improvement and genetic analysis, and an ideal type of material for genome sequencing, especially for highly heterozygous polyploid horticultural species [7]. There are two approaches to induce a haploid—in vitro haploid induction technology and in vivo haploid induction technology. In vivo techniques encompass distant hybridization, parthenogenesis, pollen treatment, and utilization of haploid-inducer lines. Also, with the cloning of key genes, such as MTL/ZmPLA1/NLD, ZmDMP and their homolog genes in other species, researchers found a novel way to produce haploid plants by knocking down these genes [8,9,10].
In vitro methods include embryo sac/endosperm culture and microspore/anther culture. In Cucurbitaceae, haploid induction is mainly carried out by using unfertilized ovules and ovaries. In the donor materials genotype, medium, hormone and pretreatment are the main factors affecting the induction efficiency of haploids in cucurbits. Cold shock and heat shock are the main methods of pretreatment, but the effects of pre-cold treatment vary among different species [11,12]. Therefore, heat shock is the main pretreatment in many cucurbit species for haploid induction. Zou et al. [13] achieved up to 15.14% Embryo-like structures (ELSs) induction rate using unfertilized ovules from watermelon and aimed at using heat shock as an essential role in the process. Shalaby [14] showed that heat shock may promote the induction rate of ELSs. However, molecular mechanisms underlying heat-shocked haploid induction remain poorly understood.
In recent years, a variety of omics technologies, including transcriptomics, proteomics and metabolomics, have been developing at a rapid pace. The combined analysis of multiple omics to decipher the molecular mechanisms of important biological processes in plants has emerged as a new approach. Zou et al. [15] conducted whole-transcriptome sequencing on the ovaries of pumpkins after heat shock treatment, constructed a ceRNA (competing endogenous RNA) network using the differentially expressed RNAs, and identified the gene MSTRG.28850.1, which is associated with embryogenesis and regulated by the miRNA Nov-m0336-5p. It is speculated that Nov-m0336-5p may affect the embryogenesis in the culture of unfertilized pumpkin ovaries by regulating this gene. After conducting transcriptome sequencing and analysis on the unfertilized ovaries of watermelons that had been heat-shocked for 0 to 24 h, Zhu et al. [16] found that heat shock could promote the biosynthesis of amino acids. Among them, the metabolic pathways of glycine, serine, and threonine changed significantly, indicating that heat shock affects the synthesis and transformation of amino acids during the enlargement of ovules. In proteomics, data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics [17]. Wang et al. combined transcriptomic and proteomic analyses to reveal pigmentation pathways in loquat, while Zhang et al. identified co-expressed genes involved in red jute leaf protein synthesis through multi-omics approaches [18,19]. In the current study, unfertilized watermelon ovules were subjected to heat-shock treatment. Subsequently, transcriptomics sequencing and DIA-based proteomics sequencing were conducted. Results indicated that heat shock can exert an influence on the process of haploid induction in watermelons by interfering with the biosynthesis, content and signal transduction pathway of ABA. Additionally, numerous genes involved in unsaturated fatty acid and amino acid biosynthesis were upregulated. In summary, our findings suggested ABA may play an essential role in haploid induction after heat shock.

2. Materials and Methods

2.1. Unfertilized Ovary Collection and Ovule Culture

The unfertilized ovary culture of the watermelon variety “Xue-long No. 3” were collected at the first day of the flowering stage and cultured on the Murashige and Skoog (MS) medium with 2 mg/L 6-Benzylaminopurine (6-BA) and 1 mg/L Naphthaleneacetic acid (NAA) (pH = 5.8). The ovary sections were treated in an incubator at 33 °C in darkness. The detailed methods were described by Zou [13]. The ovules were isolated from the ovary sections and obtained, respectively, before heat shock and 1 day, 2 days, 3 days, 4 days and 5 days after heat shock, which were named A, B, C, D, E and F. The samples were rapidly frozen in liquid nitrogen and stored at −80 °C. Each treatment had 3 biological replicates and the mass of each sample was at least 1.0 g. A total of 18 samples were taken.

2.2. Library Construction, Transcriptome Sequencing and Expression Analysis

After grinding each frozen sample into powder in liquid nitrogen, total RNA was extracted using FastPure® Universal Plant Total RNA Isolation Kit (RC411) (Vazyme, Nanjing, China) and strictly quality controlled by Agilent 2100 bioanalyzer to accurately detect RNA integrity. Then, according to the recommendations of the NEB Next® Ultra™ RNA Library Prep Kit for Illumina® (NEB, Ipswich, MA, USA, Catalog #E7770) library preparation kit, a library was constructed with 1 µg RNA as the starting sample. Subsequently, the library concentration and insert size were detected to ensure the quality of the library. We obtained sequence information by performing 150 bp double-end sequencing on the Illumina platform in the qualified library. Fastp software (v0.23.1) was used to process the raw data in Fastq format, remove reads containing adapters and poly-N and low-quality ones, and obtain high-quality clean reads [20]. The Hisat2 v2.0.5 was used to build a genome index and align clean reads to the 97103_V2 genome [2,21]. The aligned reads of each sample were assembled by StringTie (v1.3.3b), and the full-length transcripts were quantified using a novel network flow algorithm and de novo assembly steps [22]. Feature Counts v1.5.0-p3 was used to calculate the number of gene reads and calculate Fragments Per Kilobase of transcript per Million mapped reads (FPKM) based on gene length [23]. The edgeR R package (3.22.5) was used for differential expression analysis (p-value < 0.05 and|Log2FoldChange| > 1.0) [24]. The Mfuzz R package was used to conduct a temporal trend analysis of gene expression and divide the clustering groups [25]. To gain a comprehensive understanding of the overall distribution and variations within the dataset, PCA was applied to the transcriptomic and proteomic data [26].

2.3. Peptide Preparation

Each sample was individually ground into powder using a liquid nitrogen mortar. Proteins were extracted following a published protocol and then subjected to trypsin digestion [27]. The tryptic peptides were fractionated using an ZORBAX 300 Extend C18 column (Agilent, Santa Clara, CA, USA) and a high-pH reverse-phase HPLC system.

2.4. Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) Analysis and DIA Mode

For transition library construction, shotgun proteomics analyses were performed using an EASY-nLCT 1200 UHPLC system (Thermo Fisher, Bremen, Germany) coupled with an Orbitrap Q ExactiveTM HF-X mass spectrometer (Thermo Fisher, Bremen, Germany) operating in the data-independent acquisition (DlA) mode.

2.5. The Identification and Quantitation of Protein

The resulting spectra were searched separately against the Cucurbitaceae database (http://cucurbitgenomics.org/ (accessed on 23 September 2024)) by the search engine Spectronaut-Pulsar (Biognosys, Cambridge, MA, USA). DIA data were used to generate a data-dependent acquisition (DDA) spectral library in Spectronaut, extract ion pairs and calculate peak areas for peptide qualification and quantification. Index Retention Time (iRT) was used to correct retention times, and the Q value of precursor ions was truncated to 0.01. Significantly different proteins were screened through a t-test analysis (p-value < 0.05 and|Log2 FoldChange | > 1.0).

2.6. Functional Analysis of Differentially Expressed Proteins and Genes

Gene Ontology (GO) functional analyses were performed using the InterProScan program against the non-redundant protein database (including Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER) [28]. Additionally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to analyze protein families and pathways. Differentially expressed proteins (DEPs) were subjected to volcano plot analysis, cluster heatmap analysis, and enrichment analysis of GO and KEGG [29]. The visualization of some results was performed using TBtools (v2.097) [30].

2.7. Differentially Expressed Genes Analysis by Quantitative Real-Time PCR (qRT-PCR)

To verify the reliability of the RNA-Seq analyses, 19 candidate genes related to ABA biosynthesis, signal transduction and the biosynthesis of unsaturated fatty acids and amino acids were selected for qRT-PCR analysis. All gene ID and primer sequences are shown in Table 1. The total of RNA was extracted with a FastPure® Universal Plant Total RNA Isolation Kit (RC411) (Vazyme, Nanjing, China) and reverse transcribed to cDNA with a HiScript III RT SuperMix for qPCR (+gDNA wiper) (R223-01) (Vazyme, China). The templates were performed with LightCycler® 96 Instruments (ROCHE, Basel, Switzerland) by using ChamQ Universal SYBR qPCR Master Mix (Q711-02) (Vazyme, China). The methods and procedures are detailed in the manual. All primers were designed using Primer3 and their specificity was tested with Primer-Check. The primers used were synthesized by Beijing Tsingke Biotech Co., Ltd (Beijing, China). The relative DEGs expression was calculated by using the 2−ΔΔct method [31].

2.8. Abscisic Acid Content Detection

The unfertilized ovules that were subjected to heat shock and not subjected to heat shock for 4 days, respectively, were ground into powder in liquid nitrogen. The endogenous ABA in the samples was extracted using the 90% acetonitrile solution. After removing impurities using the QuEChERS method [32] and concentrating the sample by nitrogen blow-down, separation and purification were performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Then, the characteristic fragment ions of the target analytes were scanned and detected in multiple reaction monitoring (MRM) mode for structural identification and quantitative analysis. Finally, the target analytes were quantified based on the standard curve.

3. Results

3.1. Phenotype Differences, Transcriptome and Proteome Profiles of Unfertilized Ovules

On the first day after heat shock, the color of the ovary sections changed significantly from yellow to transparent. Simultaneously, the color of unfertilized ovules turned yellow. As the time the ovules were exposed to heat shock increased, the size of the unfertilized ovules gradually enlarged (Figure 1).

3.2. Transcriptome and Proteome Profiles of Unfertilized Ovules

In total, 130.29 Gb of raw data from 18 libraries were collected for the transcriptome analysis, and 119.09 Gb of clean data were procured. Each library yielded 42,449,150–57,662,538 quality reads, with 94.10–96.45% of them mapped to the 97103_V2 genome. The output summary of all samples is listed in Table 2. Moreover, 78,182 peptides were obtained and 10,123 proteins were identified using the data-independent acquisition (DIA) mode.

3.3. Differentially Expressed Genes from Unfertilized Ovary After Heat Shock

In the transcriptome, the contribution rates of Principal Component 1 (PC1) and Principal Component 2 (PC2) were elucidated to be 49.93% and 15.17%, respectively (Figure 2A). Subsequently, the DEGs across six groups were systematically enumerated. It was found that as the duration of the heat shock was extended, the number of DEGs continuously escalated. Specifically, in the comparison between Group B and Group A, 3901 DEGs exhibited differential expression; between Group C and Group A, 4405; between Group D and Group A, 4571; between Group E and Group A, 4743; and between Group F and Group A, 4863 (Figure 2). The results of PCA in the proteome depicted that the contribution rates of PC1 and PC2 were 55.83% and 17.27%, respectively (Figure 3A). Compared with the transcriptome, the protein expression profile of Group B in the proteome presented a transitional state (Figure 3A). A total of 4503 differentially expressed proteins were precisely identified in the proteome. Among these, in the comparison between Group B and Group A, 931 proteins were differentially expressed; between Group C and Group A, 2921; between Group D and Group A, 2314; between Group E and Group A, 2354; and between Group F and Group A, 2800 (Figure 3). In addition, both the differentially expressed genes and proteins gradually decrease as the heat shock time increases (such as B vs. C, C vs. D, etc.) (Figures S1 and S2). These results indicate that heat shock can alter the gene expression patterns in unfertilized ovaries, and the response in the transcriptome precedes that in the proteome.

3.4. Differentially Expressed Genes Enrichment in Unfertilized Ovules After Heat Shock

All different expression genes and proteins were used for gene ontology annotation and enrichment in the five groups. The GO classes of cellular component organization or biogenesis and cell wall organization or biogenesis were commonly enriched at both the transcriptome and proteome (Figure 4A). The volume of unfertilized ovules increased after heat shock also confirms this point. Moreover, different enrichment of GO classes was also detected in the transcriptome and proteome. The metabolism and signal transduction of plant hormones, metabolism of amino acids and fatty acid binding were enriched in the transcriptome. For proteins, the GO classes enriched nutrient reservoir activity and amino acid transmembrane transport (Figure S3 and Figure 4). Notably, both of these categories are closely associated with the accumulation of storage substances. These results provided a multidimensional profile of metabolic and regulation characteristics at both the transcript and protein levels for heat-shocked unfertilized ovules. At the same time, the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the proteome and transcriptome are also basically consistent, and they are in line with the GO enrichment results. The important substance transmembrane transport pathway in plants-ABC transporter and the important intracellular signal transduction pathway-MAPK signal pathway both appeared in the enrichment results of the transcriptome and proteome. In addition, unsaturated fatty acid metabolisms (α-linolenic acid and linoleic acid), essential amino acids metabolisms (phenylalanine and methionine), plant hormone signal transduction and the biosynthesis of secondary metabolites related to stress resistance (flavonoids and phenylpropanoids) were all enriched (Figure 4B). These results provided a multidimensional profile of metabolic and regulation characteristics at both the transcript and protein levels for heat-shocked unfertilized ovules.

3.5. Expression of Genes Involved in Biosynthesis and Signal Transduction of ABA

In general, ABA is not added to the culture medium in tissue culture. However, the genes that control the biosynthesis of ABA and signal transduction were shown to have different expression levels and were enriched in the proteome and transcriptome, which implied that ABA may influence the development of ovules during heat shock. Then, we observed that the 9-cis-epoxy carotenoid dioxygenase (NCED, Gene ID: Cla97C07G137260), the core gene of ABA biosynthesis, had an increased expression level after heat shock. Additionally, Pyrabactin Resistance-Like Proteins (PYLs), Protein Phosphatase 2Cs (PP2Cs) and Sucrose-non-fermenting 1-related protein kinases (SnRKs), the core components of ABA signal transduction, showed different expression levels in the transcriptome and proteome, as the number of heat-shock days increased (Figure 5A). And many gene expression levels in transcriptome and proteome have similar patterns (Figure 5B), such as Cla97C03G052090, Cla97C08G152820, Cla97C01G017970, Cla97C02G038540 and Cla97C10G186750. However, strikingly, in contrast to the heat-shock condition, Cl97C07g137260 exhibited upregulation, and the ABA content of ovules exhibits a notably higher level under non-heat-shock conditions (Figure 5D,E). Furthermore, many members of PP2Cs, a negative regulator, exhibited lower expression levels, and the members of SnRK2s exhibited higher expression levels under heat-shock conditions (Figure 5C). The expression levels of members in PYLs and SnRK2s are shown in Figure S5. These results suggest that heat-shock-influenced ABA biosynthesis and ABA signal transduction may play an essential role in haploid induction.

3.6. Expression of Genes Involved in Biosynthesis of Amino Acids and Unsaturated Fatty Acids

In this study, we identified a total of 4503 differentially expressed proteins in proteomics. And we analyzed their expression patterns in the transcriptome using the Mfuzz R package (Figure S6). We found that cluster 4 and cluster 6 exhibited similar expression patterns, with both showing a sudden increase in expression after heat shock (Figure 6A,B). In total, the 1539 genes from cluster 4 and cluster 6 were used for KEGG analysis. The enrichment results showed that many genes were involved in the synthesis of amino acids and unsaturated fatty acids following heat shock (Figure 6C). The Cla97C04g071820, Cla97C05G081950, Cla97C05G084200, Cla97C07G136380 and Cla97C10G200350 that are involved in the biosynthesis of unsaturated fatty acids were shown to be strongly correlated in the transcriptome and proteome (Figure 6D). In addition, the Cla97C01G006270, Cla97C05G191110 and Cla97C04G076580 that are involved in the biosynthesis of amino acids were shown to be strongly correlated (Figure 6E). Additionally, when we examined the expression of these correlated genes under non-heat-shock conditions, their expression levels were downregulated compared to those under heat shock (Figure S7). These findings indicated that heat shock could enhance the synthesis of amino acids and unsaturated fatty acids in unfertilized ovules and promote the development of the unfertilized ovules.

4. Discussion

At present, traditional crossbreeding is the main method used in watermelon breeding programs. Previously, breeders mainly applied watermelon haploid induction technology to shorten the breeding cycle. The new research has demonstrated the application potential of haploid technology in distant hybridization [33]. The haploid induction technology can be used to create bridge parents in the interspecific hybrids of the genus Citrullus, break the reproductive isolation, and introduce resistance genes or other genes related to the regulation of nutrient accumulation from other species within the genus Citrullus to improve the resistance and quality of existing cultivated watermelon varieties. In comparison, androgenesis gynogenesis is practiced relatively less [34]. During this procedure, cells are required to experience dedifferentiation, which enables the differentiated cells to re-acquire totipotency, thereby obtaining the potential to develop into an entire plant. Such a complex process is subject to the influence of hormones, environmental stress and the genetic background of the donor [14,35]. The majority of experiments have verified that indole-3-acetic acid (IAA) and cytokinin play crucial roles in this process. As an important hormone, ABA is involved in many phases of embryo development during the alternation of generations, including stomatal closure, cuticular wax accumulation, leaf senescence, bud dormancy, seed germination, osmotic regulation, and growth inhibition among many others [36]. However, it is less known that ABA influenced the ovule development. We harnessed transcriptome sequencing and proteome sequencing techniques to sequence the in vitro unfertilized ovules subjected to heat shock. The differential expression of core genes within the ABA biosynthesis and signal transduction pathways, including NCED, PYLs, PP2Cs and SnRKs, were detected among both the differentially expressed genes and the differentially expressed proteins. Moreover, the members of PP2C exhibited a downregulated expression level under heat shock. This evidence suggests that biosynthesis and signal transduction of ABA may influence the unfertilized ovule development during heat shock.
PP2Cs, a negative regulator, can activate the activity of SnRK2s, if its expression level is decreased [37]. The activated SnRK2s can phosphorylate some downstream transcription factors, enabling them to bind to the promoter regions of genes related to the synthesis of storage substances, thus activating gene expression and promoting the accumulation of storage substances [36]. Lipids and proteins are important storage substances and can not only provide energy but also supply nitrogen and carbon sources for the biosynthesis of substances that maintain subsequent growth and development. However, during the process of haploid induction, fertilization does not occur, and it is unable to initiate embryo development normally; therefore, it cannot accumulate storage substances. In the absence of sufficient storage substances, the ovules cannot germinate normally in the subsequent stages. Unsaturated fatty acids are the most important lipid substances in seeds and also important components of biological membranes and can be integrated into phospholipid molecules to form biological membranes with specific fluidity and functions, thus ensuring the normal physiological functions and material exchange of cells [38]. Amino acids are the precursors of protein biosynthesis. They are mainly used for the synthesis of seed storage proteins, but can also serve as precursors for the biosynthesis of secondary metabolites and as an energy source [39]. Zhu et al. [16] carried out RNA sequencing and analysis on the unfertilized ovaries of watermelons after they were subjected to heat shock for 0 to 24 h and found that heat shock could promote the biosynthesis of amino acids. Among them, the metabolic pathways of glycine, serine and threonine changed significantly. These discoveries bear a resemblance to the outcomes of our research. In our study, we found that after heat-shock treatment, the biosynthetic pathways of unsaturated fatty acids (such as linoleic acid) and the biosynthetic pathway of amino acids were activated. Compared with the situation without heat shock, the expression levels of the related genes were significantly upregulated. This further confirms that heat shock can activate the ABA signal transduction pathway and promote the accumulation of storage substances such as amino acids and unsaturated fatty acids in the unfertilized ovules.

5. Conclusions

The unfertilized ovules of watermelon were subjected to transcriptome and proteome sequencing. Through GO and KEGG analyses of the differentially expressed genes (DEGs), we found the many genes associated with ABA showed different expression levels after heat shock in watermelons. The transcription and translation of NCED, PP2Cs and SnRKs were influenced, which may promote the accumulation of storage substances and increase the rate of ELSs induction. This study has broadened our understanding of the mechanism and significance of the role of heat shock in haploid induction and provided new experimental directions for in vitro gynogenesis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15051063/s1, Figure S1: Volcano plots from B_VS_C; B_VS_D; B_VS_E; B_VS_F; C_VS_D; C_VS_E; C_VS_F; D_VS_E; D_VS_F and E_VS_F of transcriptome. Figure S2: Volcano plots from B_VS_C; B_VS_D; B_VS_E; B_VS_F; C_VS_D; C_VS_E; C_VS_F; D_VS_E; D_VS_F and E_VS_F of proteome. Figure S3: Gene ontology (GO) enrichment for differentially expressed proteins (DEPs) in five comparison groups in protemo. A: B_VS_A; B: C_VS_A; C: D_VS_A; D: E_VS_A; E: F_VS_A; Figure S4: Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for differentially expressed proteins (DEPs) in five comparison groups, A: B_VS_A; B: C_VS_A; C: D_VS_A; D: E_VS_A; E: F_VS_A; Figure S5: The expression levels of members in PYLs and SnRK2s under heat shock and non-heat shock; Figure S6: The expression patterns of 4503 differentially expressed proteins (DEPs) in proteomics; Figure S7: qRT-PCR analysis of genes involved in biosynthesis of amino acids and unsaturated fatty acids under non-heat-shock and heat shock.

Author Contributions

Conceptualization: X.S. (Xiaowu Sun) and S.D.; methodology: S.G., B.T., Y.D., S.D., and X.S. (Xiaowu Sun); validation: S.G., B.T., and Y.D.; formal analysis: S.G. and B.T.; investigation: S.G., B.T., Y.D., X.S. (Xiangyu Sun), H.S., C.X., T.Z., G.L., and H.Y.; resources provision: S.G., B.T., Y.D., X.S. (Xiangyu Sun), H.S., C.X., T.Z., L.S., G.L., and S.Z.; data curation: S.G. and B.T.; writing—original draft preparation: S.G., B.T., Y.D., X.S. (Xiangyu Sun), H.S., C.X., X.S. (Xiaowu Sun), and S.D.; writing—review and editing: S.G., B.T., X.S. (Xiangyu Sun), T.Z., L.S., G.L., S.Z., and H.Y.; visualization: S.G., B.T., L.S., T.Z., and Y.D.; supervision: X.S. (Xiaowu Sun) and S.D.; project administration: X.S. (Xiaowu Sun) and S.D.; funding acquisition, S.D. and S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Agriculture Research System, grant number CARS-25, and Germplasm Innovation of Cucurbit Vegetables and Demonstration and Promotion of New Varieties, grant number 2022SNGGT019.

Data Availability Statement

All data analyzed during this study are included in this article and Supplementary Materials.

Acknowledgments

The authors are grateful for the financial support provided.

Conflicts of Interest

Author Longjun Sun was employed by the company Hunan Xuefeng Seeds Co., Ltd., Author Shengxiu Zhu was employed by the company Xinjiang Western Oasis Ecological Development Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. The phenotype of an unfertilized ovary and ovule after heat shock for 0–5 days.
Figure 1. The phenotype of an unfertilized ovary and ovule after heat shock for 0–5 days.
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Figure 2. (A) PCA in the transcriptome. (B) DEGs in the comparison between Group B and Group A. (C) DEGs in the comparison between Group C and Group A. (D) DEGs in the comparison between Group (D) and Group A. (E) DEGs in the comparison between Group E and Group A and (F) DEGs in the comparison between Group F and Group A. Significantly DEGs were treated with red dots (up-regulated) or green dots (down-regulated) and others indicated with blue dots.
Figure 2. (A) PCA in the transcriptome. (B) DEGs in the comparison between Group B and Group A. (C) DEGs in the comparison between Group C and Group A. (D) DEGs in the comparison between Group (D) and Group A. (E) DEGs in the comparison between Group E and Group A and (F) DEGs in the comparison between Group F and Group A. Significantly DEGs were treated with red dots (up-regulated) or green dots (down-regulated) and others indicated with blue dots.
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Figure 3. (A) PCA in protemo. (B) DEGs in the comparison between Group B and Group A. (C) DEGs in the comparison between Group C and Group A. (D) DEGs in the comparison between Group D and Group A. (E) DEGs in the comparison between Group E and Group A and (F) DEGs in the comparison between Group F and Group A. Significantly DEPs were treated with red dots (up-regulated) or green dots (down-regulated), others indicated with black dots.
Figure 3. (A) PCA in protemo. (B) DEGs in the comparison between Group B and Group A. (C) DEGs in the comparison between Group C and Group A. (D) DEGs in the comparison between Group D and Group A. (E) DEGs in the comparison between Group E and Group A and (F) DEGs in the comparison between Group F and Group A. Significantly DEPs were treated with red dots (up-regulated) or green dots (down-regulated), others indicated with black dots.
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Figure 4. Gene ontology (GO) (A) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (B) enrichment for differentially expressed genes (DEGs) in five comparison groups.
Figure 4. Gene ontology (GO) (A) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (B) enrichment for differentially expressed genes (DEGs) in five comparison groups.
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Figure 5. Expression of genes related to biosynthesis and signal transduction of ABA. (A) Expression trends of genes in cluster 4 after heat shock. (B) Expression trends of genes in cluster 6. (C) Specific significantly enriched KEGG pathways for DEGs in cluster 4 and cluster 6. (D) Expression trends and correlations of genes (proteins) related to the biosynthesis of amino acids in the transcriptome and proteome. (E) Expression trends and correlations of genes (proteins) related to unsaturated biosynthesis of fatty acids in the transcriptome and proteome. Transcriptome: blue; Protemo: yellow. **: p < 0.01.
Figure 5. Expression of genes related to biosynthesis and signal transduction of ABA. (A) Expression trends of genes in cluster 4 after heat shock. (B) Expression trends of genes in cluster 6. (C) Specific significantly enriched KEGG pathways for DEGs in cluster 4 and cluster 6. (D) Expression trends and correlations of genes (proteins) related to the biosynthesis of amino acids in the transcriptome and proteome. (E) Expression trends and correlations of genes (proteins) related to unsaturated biosynthesis of fatty acids in the transcriptome and proteome. Transcriptome: blue; Protemo: yellow. **: p < 0.01.
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Figure 6. Expression of genes related to the biosynthesis of amino acids and unsaturated fatty acids. (A) Expression trends of signal transduction genes in the transcriptome after heat shock. (B) Correlation of their expression trends in proteome and transcriptome. (C) qRT-PCR analysis of PP2Cs genes under heat-shock and non-heat-shock conditions for 1, 3, and 5 days. (D) qRT-PCR analysis of NCED under heat-shock and non-heat-shock for 1, 3, and 5 days. (E) ABA content in unfertilized ovules on the 4th day under heat-shock and non-heat-shock conditions. Non-heat-shock: blue; heat-shock: yellow.
Figure 6. Expression of genes related to the biosynthesis of amino acids and unsaturated fatty acids. (A) Expression trends of signal transduction genes in the transcriptome after heat shock. (B) Correlation of their expression trends in proteome and transcriptome. (C) qRT-PCR analysis of PP2Cs genes under heat-shock and non-heat-shock conditions for 1, 3, and 5 days. (D) qRT-PCR analysis of NCED under heat-shock and non-heat-shock for 1, 3, and 5 days. (E) ABA content in unfertilized ovules on the 4th day under heat-shock and non-heat-shock conditions. Non-heat-shock: blue; heat-shock: yellow.
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Table 1. Gene ID and primer sequences used for qRT-PCR analysis.
Table 1. Gene ID and primer sequences used for qRT-PCR analysis.
PathwayGene IDPrimer
Biosynthesis of ABACla97C07G137260CTTACCGGTGATTGGGAAAG
CAGAGAATTCCACAGCGTGA
ABA signal transduction (PYL/PYR)Cla97C05G099080CAGCCTTGCAGGGATTAGAA
CATCGATGCGTTGGGTAATA
Cla97C10G186260GGAGATGACGGAGCACCA
GCTACCAAGTCCTTCAACTCG
Cla97C04G073350AATCCCCCTAAATCCTCTGC
CAACAGCGTGGTTGTGGTAA
Cla97C05G090960AGCTCCGTCCTAATCAAGCA
ATTCCCCTGCACTACACACC
ABA signal transduction (PP2C)Cla97C03G052090ATGGCGGAGATTTGCTGTA
CTCCGGCGACAAATTTACAC
Cla97C08G152820GGTTGTGGTGCCATTTAGGT
ACAGAATCAGATAACAAACCAGCA
Cla97C07G140660TCTCCTGCTGTTTCTTTGACC
GGGGACGATGCAGAGAATAA
ABA signal transduction (SnRK2)Cla97C01G017970TTGGTGTTGCCAAACTTGTG
TTGGGATGCTTCAAAGACCT
Cla97C10G186750AGGACCTTGGCTCTGGAAAT
TCCCTCTGAACTTTCTCATCG
Biosynthesis of unsaturated acidCla97C04G071820GCCACGCTTCCGTTAGAC
TGGAAGCGTAGTTGATGACG
Cla97C08G149220TCGGGTCGATATGATGATGA
ATATGAACGTGGCTCGCTCT
Cla97C05G081950CTTGGCCAATTGAGATACGG
GTGCAAGGCTTGATCATCTG
Cla97C05G084200TCCAAAATGCTCAGAACTGC
TGCAGCCTTCCTGTGAGAC
Cla97C07G136380TTAAGATGGGCGTCCAGTTC
TCATAGCAAAACAGCCAGGA
Cla97C10G200350TGGAGGTCGAATGTCCTCTC
GAACGTTGAAAGCAATGTGG
Biosynthesis of amino acidCla97C01G006270ACGTCTTTCTGTTCCGATCC
CAAGCCAATGGATGCTAACC
Cla97C09G174910TGTTCGGGGAAAAGTTGTCT
CAATCTGTAGTCCCACGACCT
Cla97C04G076580ACCAAGATGACTGGGAGCAC
CCTTGAGAAGAAGGGCATTG
Table 2. Summary of the Illumina platform sequencing output for all samples.
Table 2. Summary of the Illumina platform sequencing output for all samples.
SampleRaw_ReadsRaw_RasesClean_ReadsClean_BasesMapped ReadsMapping RateQ20Q30
A144,778,1166.72 G43,909,1366.59 G42,129,43195.95%96.8491.72
A245,116,2646.77 G44,273,2926.64 G42,477,37695.94%97.2992.68
A343,285,3806.49 G42,572,2466.39 G40,958,80396.21%96.8991.74
B145,404,2886.81 G44,454,8966.67 G42,724,58796.11%97.2392.57
B247,347,8547.1 G46,330,3506.95 G44,550,65196.16%97.3592.72
B351,724,2067.76 G50,458,7787.57 G48,450,09996.02%96.991.86
C144,974,7426.75 G43,807,3646.57 G42,096,25696.09%97.2792.6
C245,738,2846.86 G44,775,6526.72 G43,009,04896.05%96.9591.92
C343,450,5326.52 G42,449,1506.37 G40,536,72395.49%96.6291.24
D146,381,6306.96 G45,147,0206.77 G43,544,92296.45%97.2792.55
D250,617,1167.59 G49,620,3887.44 G46,992,45094.70%97.292.5
D358,917,7408.84 G57,662,5388.65 G54,607,52494.70%97.0592.19
E147,949,0307.19 G47,160,7387.07 G45,251,32795.95%97.0992.22
E250,399,0367.56 G48,975,5487.35 G46,655,74495.26%96.9992.04
E348,461,7247.27 G47,519,5967.13 G45,538,95595.83%97.2192.49
F156,076,3548.41 G54,869,1208.23 G52,307,67495.33%97.0892.2
F247,838,4507.18 G46,586,6626.99 G44,277,42295.04%96.9291.88
F350,067,9687.51 G48,707,7047.31 G45,836,37094.10%96.8591.73
Unfertilized ovaries heat shocked for 0 days (A), 1 day (B), 2 days (C), 3 days (D), 4 days (E), and 5 days (F), respectively, at 33 °C on the first day of the flowering stage. Each treatment was repeated 3 times.
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Gong, S.; Tang, B.; Dai, Y.; Sun, X.; Song, H.; Xiong, C.; Zou, T.; Sun, L.; Liu, G.; Yang, H.; et al. Transcriptome and Proteome Reveal Heat Shock Promotes Haploid Induction Rate via Activating ABA Signal Transduction in Watermelon. Agronomy 2025, 15, 1063. https://doi.org/10.3390/agronomy15051063

AMA Style

Gong S, Tang B, Dai Y, Sun X, Song H, Xiong C, Zou T, Sun L, Liu G, Yang H, et al. Transcriptome and Proteome Reveal Heat Shock Promotes Haploid Induction Rate via Activating ABA Signal Transduction in Watermelon. Agronomy. 2025; 15(5):1063. https://doi.org/10.3390/agronomy15051063

Chicago/Turabian Style

Gong, Shiqi, Bingqian Tang, Yujuan Dai, Xiangyu Sun, Huijuan Song, Cheng Xiong, Tian Zou, Longjun Sun, Guang Liu, Hongbo Yang, and et al. 2025. "Transcriptome and Proteome Reveal Heat Shock Promotes Haploid Induction Rate via Activating ABA Signal Transduction in Watermelon" Agronomy 15, no. 5: 1063. https://doi.org/10.3390/agronomy15051063

APA Style

Gong, S., Tang, B., Dai, Y., Sun, X., Song, H., Xiong, C., Zou, T., Sun, L., Liu, G., Yang, H., Zhu, S., Dai, S., & Sun, X. (2025). Transcriptome and Proteome Reveal Heat Shock Promotes Haploid Induction Rate via Activating ABA Signal Transduction in Watermelon. Agronomy, 15(5), 1063. https://doi.org/10.3390/agronomy15051063

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