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Integrative analysis of transcriptomic and metabolomic profiles reveals new insights into the molecular foundation of fruit quality formation in Citrullus lanatus (Thunb.) Matsum. & Nakai

Integrative analysis of transcriptomic and metabolomic profiles reveals new insights into the... Abstract In this study, an integrated transcriptome and metabolome analysis was used to explore the molecular foundation of fruit quality in two parent lines of Citrullus lanatus with distinct flesh characteristics, including ‘14-1’ (sweet, red, and soft) and ‘W600’ (bitter, light yellow, and firm), as well as the corresponding F1 population (bitter, light yellow, and firm). Numerous differentially expressed genes (DEGs) were identified in the fruit samples: 3766 DEGs for ‘14-1’ vs. ‘W600’, 2767 for ‘14-1’ vs. F1, and 1178 for F1 vs. ‘W600’ at the transition stage; and 4221 for ‘14-1’ vs. ‘W600’, 2447 for ‘14-1’ vs. F1, and 446 for F1 vs. ‘W600’ at the maturity stage. Weighted gene co-expression network analysis (WGCNA) revealed that a gene module including 1111 DEGs was closely associated with flesh taste and color, and another gene module including 1575 DEGs contributed significantly to flesh texture. The metabolomic results showed that there were 447 differential metabolites (DMs) for ‘14-1’ vs. ‘W600’ fruits, 394 for ‘14-1’ vs. F1, and 298 for F1 vs. ‘W600’ at the maturity stage. Combining WGNCA and metabolomic results, several DEGs and DMs were further identified as hub players in fruit quality formation: 6 DEGs with 4 DMs for flesh sweetness; 6 DEGs with 13 DMs for bitterness; 9 DEGs with 10 DMs for flesh color; and 9 DEGs with 4 DMs for flesh texture. Altogether, these observations not only expand our knowledge of the molecular basis of fruit quality in watermelon, but also provide potential targets for future watermelon improvement. Fruit quality, metabolome, molecular foundation, transcriptome, watermelon Introduction Watermelon (Citrullus lanatus (Thunb.) Matsum. and Nakai (2n=2x=22)) is a popular fruit crop in the Cucurbitaceae family that is cultivated all over the world, with annual production being the highest in China, Turkey, and Iran (Guo et al., 2019; Pal et al., 2020). It is native to Africa and can be taxonomically divided into seven different species (Ren et al., 2017; Guo et al., 2019). As an important dietary source, watermelon fruits contain a variety of health-promoting phytochemicals such as sugars, lycopene, and amino acids (Guo et al, 2013). Therefore, its fruit quality has attracted extensive attention from horticultural researchers and breeders, and significant effort has been made in elucidating the molecular foundation of quality-related traits such as taste, color, and texture. Sweetness is closely associated with sugar content in fruits. While sucrose is the most stored sugar in watermelon fruits, oligosaccharides usually serve as the major form of transported sugars from source leaves, along with a small amount of sucrose and hexose followed by a hydrolysis process (Zhang et al., 2012). Previous studies have demonstrated that the transport, hydrolysis, and storage courses of sugars from source leaves to fruits are strictly controlled by a series of genetic loci/genes in watermelon, such as BCE2.1 and BCE8.1 (Liu et al., 2014), ClTST2 (Ren et al., 2017), ClVST1 (Gao et al., 2018), and ClAGA1, ClSWEET3, and ClTST2 (Ren et al., 2021). Using comparative transcriptomic investigation of watermelon fruits with distinct flesh flavors, various differentially expressed genes (DEGs) related to soluble sugar accumulation and metabolism have been identified, providing a genome-wide view of fruit sweetness regulation (Gao et al., 2018). A more recent report demonstrated that ClAGA2, which encodes an alkaline alpha-galactosidase, can serve as a crucial modulator for the hydrolysis process of stachyose and raffinose in the vascular bundle of watermelon, while sugar transport and storage are controlled by ClSWEET3 and ClTST2 in the plasma membrane and vacuoles of fruit cells, respectively (Ren et al., 2021). In contrast to sweet fruits from cultivated watermelons, progenitor watermelon generally produces small-sized fruits with bitter or bland flesh—a flavor trait that is closely associated with the biosynthesis and accumulation of the cucurbitacin group, particularly cucurbitacin E glycosides, which are Cucurbitaceae plant-specific tetracyclic terpenes with biological significance, including anti-pathogenic effects (Yousaf et al., 2018; Kim et al., 2020). Regarding the genetic basis for bitterness, several candidate loci/genes have been detected, such as qbt-c1-1 (Li et al., 2018) and Cla011508 (Gong et al., 2022). Furthermore, two acetyltransferases (ACT1/ACT2) and one uridine diphosphate (UDP)-glucosyltransferase (UGT) have been revealed to be critical players in the production of cucurbitacin derivatives based on enzymatic assay results (Kim et al., 2020). Using a high-density genetic map constructed via re-sequencing of two watermelon parent lines and a recombinant inbred line (RIL) population, Li et al. (2018) successfully mapped one locus (qbt-c1-1) responsible for flesh bitterness on chromosome 1, one locus (qsc-c3-1) responsible for seed coat color on chromosome 3, and one locus (qrc-c8-1) responsible for rind color on chromosome 8 (Li et al., 2018). In addition to sweetness/bitterness, flesh color and firmness are two important sensory quality parameters of watermelon fruits. Previous studies have demonstrated that the differences in the coloration of horticultural products among different species or different accessions can mainly be attributed to the variations in the amounts and categories of carotenoids, which are controlled by different loci/genes related to the biosynthesis and accumulation of carotenoids, such as LCYB4.1, ClPHT4;2, ClPSY1, ClPAP, or ClLCYB (Liu et al., 2014; Zhang et al., 2016, 2020; Fang et al., 2020; Guo et al., 2021; Xu et al., 2021). Based on a transcriptomic investigation of the fruits of five watermelon accessions at different developmental stages, four candidate genes (Cla003760, Cla007686, Cla018406, and Cla021635) were found to play essential roles in the coloration of watermelon fruits (Yuan et al., 2021). For flesh firmness, a close association has been revealed with cellulose and pectin contents, as well as genes involved in their metabolism (Brummell et al., 2004; Qin and Ren, 2007; Sun et al., 2020; Anees et al., 2021; Chen et al., 2021). However, some contradicting observations suggest that fruit firmness exhibits little relationship with cellulose or pectin content in previous studies (Hiwasa et al., 2004; Figueroa et al., 2010). The involvement of phytohormone signaling and long non-coding RNAs (lncRNAs) has been demonstrated in the regulation of fruit softening/firmness (Lindo-García et al., 2019; Chen et al., 2021; Li et al., 2021). In the past several years, integrative profiling of the transcriptome and metabolome has been used to explore the regulatory networks underlying fruit quality formation in watermelon. For example, combining RNA sequencing (RNA-seq) and high-performance liquid chromatography (HPLC)-based metabolomic analysis, Umer et al. (2020) found that sugars and organic acids were highly correlated with three gene networks/modules in watermelon, including 2443 genes, of which 7 were proposed as critical regulators in the metabolism of sucrose, as well as malic and citric acid. Similarly, a series of hub genes and metabolites related to flesh taste have been identified based on transcriptomic and metabolomic profiling of wild and cultivated watermelon fruits at different developmental stages (Gong et al., 2021). Although great improvements in our mechanistic understanding of fruit quality formation have been obtained at both single gene/locus and genome-wide levels, the complexity and large divergence in the regulation of quality-related traits among different plant materials emphasizes the need to further explore their molecular foundation in watermelon. In this study, fruit quality was investigated using an integrated transcriptome and metabolome approach for ‘14-1’, ‘W600’, and F1 hybrid watermelons, which display apparent variations in flesh taste, color, and texture. A large divergence in both transcriptomic and metabolic profiles was identified among the three samples, providing a comprehensive explanation for their phenotypical variations. A series of hub genes and metabolites closely associated with flesh taste, color, and texture were identified and could be used as potential targets for watermelon improvement in the future. Materials and Methods Plant materials Wild and cultivated watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) accessions ‘W600’ and ‘14-1’, which were collected and stored in our laboratory, were used as the male and female parents, respectively, to construct an F1 hybrid population. After sterilization, 100 watermelon seeds of ‘W600’, ‘14-1’, or F1 were grown in plastic pots with nutrient soil and placed in a growth chamber that was set at a 14-h light period with 28 °C air temperature, 10-h dark period with 26 °C air temperature, and approximately 60% relative humidity. At the three-leaf stage, the seedlings were cultivated in the greenhouse of Shandong Agricultural University (Tai'an, China) from March to June in 2021 with a plant spacing of 40 cm×80 cm. Standard field management was carried out during the cultivation period. Two even-sized fruits were collected as one biological repeat at the transition stage (approximately 21 d after pollination (DAP)) and maturity stages (approximately 35 DAP) for phenotypical and multi-omic investigation. Three biological repeats were prepared. Phenotypical investigation Flesh hardness was measured with a digital fruit hardness tester with an 8-mm probe (STEP Systems GmbH, Nuerenberg, Germany). Briefly, the sampled fruit was first longitudinally divided into two halves, and the flesh hardness was then determined by vertically inserting the probe into the central flesh of one divided half until the calibration line. The values in the hardness tester were collected and converted into flesh firmness readings according to the manufacturer’s instructions. The soluble solid content (SSC) of the central flesh was measured with a PAL-1 digital pocket refractometer (ATAGO, Tokyo, Japan). A total of 16 randomly selected points (eight per fruit) in the central flesh was used for the evaluation of flesh hardness and SSC. After removing values with a large variance, the remaining data were averaged as flesh hardness or SSC for each repeat. Fruit sweetness/bitterness was determined by following the human tasting method previously introduced by Zhang et al. (2013). Briefly, participants were randomly selected to taste the fruits from different watermelon inbred lines with a gargling interval between each line, and the tasted fruits were generally classified into bitter and non-bitter groups based on the participants’ responses (Mondal and Saxena, 2016; Guo et al., 2019; Santis and Frangipane, 2020). For flesh color, visual observation was adopted according to the protocol previously introduced by Guo et al. (2019). All flesh firmness and SSC data were statistically analyzed with SPSS software (version 26.0; IBM Corp., Armonk, NY, USA). Nanopore RNA sequencing and data analysis Total RNA extraction from fruit samples, quality evaluation, and library construction were performed according to the standard protocol provided by Oxford Nanopore Technologies. Long-read sequencing was then conducted for the libraries with the Oxford Nanopore PromethION flow cells (FLO-PRO002) in Benagene Company (Wuhan, China). After removal of the low-quality reads using an in-house script, the remaining reads were subjected to identification and classification of full-length transcripts followed by alignment to the watermelon ‘97103’ v2 reference genome (http://cucurbitgenomics.org/organism/21) with the aid of the Pychopper v2.4.0 (-Q 7, -z 50; https://hpc.nih.gov/apps/pychopper.html) and Pinfish v0.1.0 packages (https://www.opensourceagenda.com/projects/pinfish/versions) under default settings, respectively. The abundance of genome-matched transcripts was calculated and normalized as fragments per kilobase of transcript per million mapped reads (FPKM). The DEGs were identified in each comparison of transcriptomic samples, with the cutoff parameters of fold-change>2 and P-value<0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out for the DEGs in R, and the terms with a P-value<0.05 were considered as significantly enriched. For weighted gene co-expression network analysis (WGCNA) analysis, the DEGs were first divided into different modules, and the correlation between DEG FPKMs in each module and flesh quality parameters (SSC, color, and hardness) of the two parents and F1 plants was then evaluated and displayed in a heat map using the online BMKCloud Visualization Toolkit (http://en.biocloud.net/toolset). Sample preparation for metabolome and data analysis For untargeted gas chromatography–mass spectrometry (GC-MS) analysis of the metabolome, 80 mg of fresh flesh was placed into a 1.5-mL Eppendorf tube with 360 μL precooled methanol and 40 μL 0.3 mg/mL 2-chloro-l-phenylalanine; the latter used as an internal standard. After being frozen at −20 °C for 2 min, the mixture was ground well for 2 min with a JXFSTPRP-24/32 tissue grinder (Jingxin, Shanghai, China) set at a frequency of 60 Hz. The ground flesh was subjected to ultrasonic extraction at 4 °C for 30 min followed by the addition of 200 μL chloroform and 400 μL MilliQ H2O, followed by a second round of 30-min ultrasonic extraction at 4 °C. The resulting sample was frozen at −20 °C for 30 min and centrifuged at 13,000 r/min for 10 min at 4 °C. Two hundred microliters of supernatant was transferred to a glass sampling vial for vacuum-drying at room temperature, and the dried sample was resuspended in 80 μL of 15 mg/mL methoxylamine hydrochloride solution followed by 2 min of vigorous vortexing and 90 min of incubation at 37 °C. After the addition of 80 μL of bis(trimethylsilyl) trifluoroacetamide (BSTFA) derivatization reagent (containing 1% (in volume) trimethylchlorosilane (TMCS)) and 20 μL n-hexane, the resuspension was vigorously vortexed for 2 min, derivatized at 70 °C for 60 min, and finally subjected to GC-MS analysis according to a previous protocol (Yan et al., 2020). To perform untargeted liquid chromatography–mass spectrometry (LC-MS) analysis of the metabolome, 80 mg flesh sample was placed into a 1.5-mL Eppendorf tube with 1 mL 70% (in volume) methanol and 20 μL 0.3 mg/mL 2-chloro-l-phenylalanine, the latter used as an internal standard. The mixture was kept at −20 °C for 2 min and then homogenized for 2 min with the abovementioned tissue grinder at 60 Hz. After 30 min ultrasonic extraction at 4 °C, the resulting sample was kept at −20 °C overnight and centrifuged at 13,000 r/min at 4 °C for 10 min. The supernatant (150 μL) was transferred to an LC vial through a 0.22-μm syringe filter for subsequent LC-MS investigation. For targeted metabolomic analysis, 50 mg of freeze-dried and pulverized flesh sample was treated with 500 μL extraction buffer, which was composed of 0.01% (mass concentration) butylated hydroxytoluene (BHT) in a solution of n-hexane, acetone, and ethanol (1:1:1, in volume). Additionally, 10 μL 20 μg/mL 2-chloro-l-phenylalanine was added as an internal control. The resulting mixture was vortexed for 20 min at room temperature and subjected to 5-min centrifugation at 4 °C. The supernatant was transferred to a brown vial, and the pellet was re-extracted by following the above-mentioned protocol. Both supernatants were pooled together, evaporation-dried under nitrogen gas, and resuspended in the mixture of methanol and methyl tert-butyl ether (MTBE) at a volume ratio of 1:1. The resuspension was filtered into an LC vial through a 0.22-μm syringe filter for UPLC-MS investigation. The raw data of the untargeted and targeted metabolomes were analyzed with Progenesis QI v2.3 (https://www.nonlinear.com/) and MultiQuant v3.0.3 software (https://sciex.com/products/), respectively. The overall distribution and reproducibility of the metabolome data from all fruit samples were then evaluated by following the principal component analysis (PCA) protocol as described previously (Worley and Powers, 2013). Differential metabolites (DMs) in each comparison of fruit samples were identified with the cutoff parameters of VIP (variable importance in the projection)>1 and P-value<0.05 for untargeted metabolome data sets, and fold-change>2 and P-value<0.05 for the targeted metabolome data sets. Quantitative real-time polymerase chain reaction (qRT-PCR) verification For experimental verification of the DEGs, total RNAs were extracted from fruit samples following a previously reported method (Yang et al., 2021). Using 1 μg total RNAs as the template, the first-strand cDNAs were synthesized with a RevertAid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s instructions. qRT-PCR analyses for selected DEGs were conducted on an ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using the corresponding primers, which were designed using Primer Premier 5.0 software (https://en.freedownloadmanager.org/users-choice/Primer_5.0_Free_Download.html). For each DEG, three biological repeats together with three technical repeats were prepared, and the relative expression was calculated according to the 2–ΔΔCt method as described by Livak and Schmittgen (2001). All qRT-PCR primers have been provided in Table S1. Statistical analysis The flesh firmness and SSC data were statistically analyzed using one-way analysis of variance (ANOVA) in SPSS 26.0 at a significance level of 0.05 between each sample. The results are displayed as the mean±standard deviation of three biological repeats. High-throughput sequencing data availability All RNA-seq data sets were deposited in the NCBI Sequence Read Archive database (http://www.ncbi.nlm.nih.gov/sra/) with accession No. PRJNA784391. Results and Discussion Phenotypic variations in watermelon fruits from two parent lines and F1 plants To understand the inheritance of fruit quality-related traits, an F1 hybrid population was generated from the crossing of watermelon inbred lines ‘14-1’ and ‘W600’, which were used as the female and male parents, respectively. We focused on flesh taste, color, and texture in the fruits of F1 plants, because these traits were quite distinct in the two parent lines according to our previous field observations. Phenotypic investigation was carried out for the fruits from two parent lines and F1 plants at both the transition and maturity stages. The results showed that, at the transition stage, the fruit flesh of ‘W600’, ‘14-1’, and F1 was white in color and exhibited no significance differences (Figure 1A). At the maturity stage, while the flesh of ‘14-1’ was red in color, only a slight color change was observed from white to light yellow for the flesh of ‘W600’ and F1 (Figure 1A). In terms of flesh texture, significant differences among the three watermelon lines were observed. The fruits of ‘W600’ were the hardest (21.2 kg/cm2 and 16.8 kg/cm2), followed by F1 (12.1 kg/cm2 and 9.1 kg/cm2) and ‘14-1’ (2.4 kg/cm2 and 2.3 kg/cm2) at both investigation stages (Figure 1B). A general decreasing trend was also confirmed for flesh hardness with the development of the watermelon fruits (Figure 1B). Being consistent with bitter taste, SSC levels in the flesh at the transition and maturity stages were only 3 and 3.1 for ‘W600’ and 3.1 and 3.6 for F1 (Figure 1C). By contrast, the SSC level was significantly increased from 6.1 at the transition stage to 13.4 at the maturity stage in the flesh of ‘14-1’, thus leading to the sweet taste of ‘14-1’ fruits. Dramatic accumulation of sugars at the maturity stage has been observed for watermelon inbred lines with sweet fruits in previous studies (Gao et al., 2018; Ren et al., 2021), implying that maturation-related signaling might play a critical role in flesh flavor formation. Figure 1. Open in new tabDownload slide Fruit phenotypes of two watermelon parent lines and F1 plants. (A) Transverse view for watermelon fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Flesh firmness and (C) soluble solid content (SSC) for watermelon fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. Data display as mean±standard deviation of three biological repeats. Different lowercase letters above the bars indicate significant differences at a statistical level of 0.05 (one-way ANOVA test). Transcriptomic and metabolomic variations in watermelon fruits from the two parent lines and F1 plants We initially wondered how these fruit quality-related traits were transcriptionally regulated. To this end, Nanopore sequencing was carried out for RNA samples obtained from ‘14-1’, ‘W600’, and F1 fruits at the transition and maturity stages. Three biological repeats were tested for each sample. In contrast to the previous studies (Giulia et al., 2018; Anees et al., 2021), wherein two inbred lines with distinct fruit traits have typically been used for RNA-seq analysis, we added the F1 population together with the two parent lines for transcriptomic comparison to improve the precision of hub gene identification due to increased phenotypic variations. A similar strategy has been adopted by a recent report (Sun et al., 2020). After discarding the low-quality reads, over 3,900,000 genome-matched reads were obtained for all libraries, which accounted for over 78% of clean sequenced reads (Table S2). A variable number of full-length unique transcripts were further identified in the fruit samples of ‘14-1’ (10,459–13,706 for the transition stage and 8598–9686 for the maturity stage), ‘W600’ (7945–14,049 for the transition stage and 6526–7995 for the maturity stage), and F1 (6692–9685 for the transition stage and 6610–8503 for the maturity stage; Table S2), covering more than 97% of the annotated genes in the watermelon genome. PCA analysis was carried out for all data sets, and the results indicated that the three biological repeats were clustered together for each fruit sample, demonstrating the reproducibility of the Nanopore RNA sequencing libraries. Both high mapping efficiency and good reproducibility evidenced the high quality of the transcriptomic data sets. A comparison of the transcriptomic profiles was next performed among different fruit samples, and a series of DEGs were identified with the cut-off parameters of fold-change>2 and P-value<0.05. For ‘14-1’ vs. ‘W600’, there were 1804 upregulated DEGs and 1962 downregulated DEGs at the transition stage, as well as 1975 upregulated DEGs and 2246 downregulated DEGs at the maturity stage; for ‘14-1’ vs. F1, 1359 and 1003 DEGs were upregulated at the transition and maturity stages, respectively, while 1408 and 1444 DEGs were downregulated at the transition and maturity stages, respectively. For F1 vs. ‘W600’, the number of upregulated DEGs was 675 for the transition stage and 435 for the maturity stage, while the number of downregulated DEGs was 503 for the transition stage and 281 for the maturity stage (Figure 2A; Table S3). We randomly selected 12 DEGs for qRT-PCR verification using the same RNA samples as for the Nanopore sequencing, and the experimental results were consistent with their FPKM variations in the different fruit samples, further supporting the reliability of our transcriptomic results. Figure 2. Open in new tabDownload slide Transcriptomic variations in the fruits of two watermelon parent lines and F1 plants. (A) The number of differentially expressed genes (DEGs) in different comparisons of fruit samples. (B) A dendrogram showing gene modules from the weighted gene co-expression network analysis (WGCNA) of DEGs. (C) An optimized dendrogram showing gene modules from the WGCNA analysis of DEGs. (D) A heat map displaying the correlation between DEG modules and three flesh traits including firmness, color and taste for ‘W600’, ‘14-1’, and F1 watermelons. TW600, ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. We assessed how these DEGs contributed to phenotypic variations in the fruits of the three watermelon lines. Accordingly, WGCNA—an algorithm used for discovering high correlations between gene clusters/modules and phenotypes of interest (Umer et al., 2020)—was adopted to investigate the relationship of the identified DEGs with fruit quality-related parameters, including flesh sweetness/bitterness, color, and hardness. All DEGs that were identified in different sample comparisons were first divided into 12 distinct gene modules of different colors based on their expression patterns, and the correlation efficiency between each gene module and quality parameter was then calculated. The results showed that the dark green module composed of 1111 DEGs displayed a significant correlation with both flesh sweetness (R2=0.97) and color (R2=0.95), and the dark red module that was composed of 1575 DEGs was significantly associated with flesh bitterness and hardness (R2=0.85; Figure 2; Table S4). KEGG analysis was performed for DEGs in the dark green and dark red modules. A total of five KEGG terms, including ‘Carotenoid biosynthesis’, ‘Fatty acid metabolism’, and ‘Pyruvate metabolism’, were enriched for DEGs in the dark green module, and 18 terms such as ‘Pentose phosphate pathway’, ‘Plant hormone signal transduction’, and ‘Lysine biosynthesis’ were observed for the DEGs in the dark red module following the cut-off parameter of P-value<0.05 (Table S5). Metabolomic variations in watermelon fruits from the two parent lines and F1 plants Recently, metabolomics has been used to monitor the variations in metabolites at the global level in crop plants upon developmental clues or stressful conditions (Qiu et al., 2020), providing a metabolic explanation for their quality formation or acclimation to unfavorable environments. To explore the global variations in metabolite profiles, both untargeted and targeted metabolomic analyses were carried out for the fruit samples of ‘14-1’, ‘W600’, and F1 at the maturity stage, when apparent differences were observed for quality-related traits including flesh taste, color, and texture among the three watermelon lines. The PCA results showed that three biological repeats for each metabolomic preparation were reproducible (Figure S2), demonstrating that high-quality metabolome data sets were obtained. We further performed metabolite comparison in the different fruit samples, and the DMs were identified using the cut-off parameters of VIP>1 and P-value<0.05 for both LC-MS- and GC-MS-based untargeted metabolomes. In the comparison of ‘14-1’ vs. ‘W600’, the contents of 214 and 199 metabolites were significantly enhanced and reduced, respectively; in the comparison of ‘14-1’ vs. F1, 204 metabolites displayed dramatically increased contents, while 146 metabolites displayed dramatically decreased contents; and in the comparison of F1 vs. ‘W600’, the number of significantly increased metabolites was 124, while the number of significantly decreased metabolites was 148 (Figures 3A and 3B; Table S6). Figure 3. Open in new tabDownload slide Metabolomic variations in the fruits of two watermelon parent lines and F1 plants. Heat maps showing the identified differential metabolites (DMs) in the fruits of ‘MW600’, ‘M14-1’ and MF1 at the maturity stage by (A) gas chromatography–mass spectrometry (GC-MS), (B) liquid chromatography–mass spectrometer (LC-MS), and (C) ultraperformance liquid chromatography (UPLC). MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh coloration largely depends on watermelon cultivars and carotenoid categories and contents (Guo et al., 2019). To explore the molecular mechanisms underlying different flesh colors in the three watermelon samples, targeted metabolomics analysis was employed to monitor metabolite changes in the carotenoid metabolic pathway in ‘W600’, ‘14-1’, and F1 mature fruits. The differential carotenoids were identified using the cut-off parameters of fold-change>2 and P-value<0.05. The number of enhanced carotenoids was 11 for ‘14-1’ vs. ‘W600’, 13 for ‘14-1’ vs. F1, and 12 for F1 vs. ‘W600’, while the contents of 23, 21, and 16 carotenoids were significantly reduced in the comparisons of ‘14-1’ vs. ‘W600’, ‘14-1’ vs. F1, and F1 vs. ‘W600’, respectively (Figure 3C). Apparent variations in metabolite profiles have been observed in different cultivars of horticultural crops such as tomato (Bai et al., 2021) and melon (Biais et al., 2010). Molecular insights into flesh taste, color, and texture in watermelon To obtain a comprehensive understanding of fruit quality formation, we analyzed the combined contributions of transcriptomic and metabolomic variations to the phenotypic differences in flesh taste, color, and texture of ‘14-1’, ‘W600’, and F1 fruits. Previous studies have demonstrated that the major form of soluble sugars in watermelon fruits is sucrose (Figure 4E), which serves as the determinant for flesh sweetness (Zhu et al., 2017; Zamuz et al., 2021). Sucrose content in fruits can be influenced by various genes encoding metabolism-related enzymes such as SuSy (Liu et al.,2013; Zhu et al., 2017) and transporters such as TST (Huang et al., 2020). In our former WGCNA analysis, a close association of the dark green gene module, which was composed of 1111 DEGs, was revealed with flesh sweetness/bitterness. As the SSC of ‘14-1’ was significantly higher than that of W600 and F1, and the SSC of W600 and F1 was constantly at a comparatively low level, we speculated that the major contributors to flesh sweetness might be ‘14-1’-specific DEGs in the dark green gene module (Figure 4A). We identified a total of 323 DEGs of this type, among which six genes (Cla97C08G153160, Cla97C05G101500, Cla97C10G204040, Cla97C07G131650, Cla97C01G001950, and Cla97C04G070460) have been demonstrated to be involved in the sucrose biosynthesis pathway in previous studies (Figure 4B; Yativ et al., 2010; Dai et al., 2011). Figure 4. Open in new tabDownload slide Correlation analysis of sweetness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) Venn diagrams showing the distribution of sweetness-related DEGs among ‘W600’, ‘14-1’ and F1 fruits. Top panel: upregulated DEGs; bottom panel: downregulated DEGs. (B) A heat map displaying the abundance of sweetness-related DEGs in the fruits of ‘W600’, ‘14-1’ and F1 at the transition and maturity stages. (C) Sugar accumulation in sucrose metabolic pathways in the fruits of ‘W600’, ‘14-1’ and F1 at the maturity stage. (D) Correlation of the sweetness-related hub DEGs and DMs in watermelon fruits. (E) Tandem mass spectrometry (MS/MS) diagram for sucrose in watermelon fruits. SPS, sucrose phosphate synthase; SS, sucrose synthase; AMY, alpha-amylase; BGLU, beta-glucosidase; UXS1, UDP-glucuronic acid decarboxylase 1; UXS5, UDP-glucuronic acid decarboxylase 5; TRE, trehalase; ATSIP, alkaline alpha-galactosidase seed imbibition protein; TW600: ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage; UDP, uridine diphosphate; FPKM, fragments per kilobase of transcript per million mapped reads. Interestingly, a recent publication demonstrated that ClAGA2 (Cla97C04G070460), which encodes an alkaline alpha-galactosidase, can serve as a crucial modulator for the hydrolysis process of stachyose and raffinose in the vascular bundle of watermelon, while sugar transport and storage are controlled by ClSWEET3 and ClTST2 in the plasma membrane and vacuoles of fruit cells, respectively (Ren et al., 2021). This evidence points to the functional conservation of Cla97C04G070460 in sucrose metabolism, which could be used as a potential target locus for the flesh taste improvement of watermelon fruits. The metabolites in the sucrose biosynthesis pathway were further investigated, and the contents of sucrose, d-fructose-6-phosphate, glucose-1-phosphate, and inulobiose were observed to be overrepresented in ‘14-1’ flesh in comparison to that of ‘W600’ and F1 (Figure 4B). These observations largely corroborated the report of Liu et al. (2013), wherein the sucrose content in sweet watermelon was significantly higher than that in non-sweet watermelon. In addition, sucrose content in watermelon fruits can also be influenced by developmental stages and cultivation conditions (Meng et al., 2009; Liu et al., 2013). To analyze the association between DEGs and sucrose metabolism, a correlation analysis was carried out between the six DEGs and four metabolites, and the highest correlation was observed for Cla97C08G153160 and inulobiose (R2=0.9999), followed by Cla97C01G001950 and glucose-1-phosphate (R2=0.9992) and Cla97C07G131650 and sucrose (R2=0.9973; Figure 4C). In contrast to the sweet flesh of ‘14-1’, the fruits of ‘W600’ and F1 possessed an apparent bitter taste, which is mainly caused by the accumulation of cucurbitacin E glycosides (Guo et al., 2019). Using a high-density genetic map constructed via re-sequencing of two watermelon parent lines and a RIL population, Li et al. (2018) successfully mapped one locus (qbt-c1-1) responsible for flesh bitterness on chromosome 1 (Li et al., 2018). To explore the potential genes closely associated with cucurbitacin metabolism, we focused on the dark red gene module and found that 406 DEGs displayed a ‘W600’ and F1-specific distribution (Figure 5A). Based on the annotation information, six genes (Cla97C09G173590, Cla97C11G210690, Cla97C05G101010, Cla97C01G003790, Cla97C05G100930, and Cla97C05G100990) of the 406 DEGs were proposed to be directly involved in flesh bitterness regulation in watermelon (Figure 5B). Cucurbitacin biosynthesis mainly includes terpenoid skeleton formation and oxidative modifications, and the modification reactions are commonly controlled by ACTs (acetyltransferases), CYP450s (cytochrome P450s), and OSCs (oxidosqualene cyclases; Zhou et al., 2016; Kim et al., 2020). The OSC gene family is highly conserved in the plant kingdom, and the critical roles of different OSCs have been revealed in the catalyzation of terpenoid skeletons to generate specific cucurbitacin (Thimmappa et al., 2014). By contrast, we observed that four CYP450 genes (Cla97C05G101010, Cla97C01G003790, Cla97C05G100930, and Cla97C05G100990), the expression of which was apparently higher in ‘W600’ than in ‘14-1’, were closely related to cucurbitacin E biosynthesis (Zhou et al., 2016), perhaps reflecting a distinct regulatory network for bitterness formation in ‘W600’ fruits. It should be noted that Cla97C09G173590, which encodes a geranylgeranyl pyrophosphate synthase and functions as a rate-limiting factor in the synthesis pathway of cucurbitacins (Chen et al., 2005), was classified as a ‘14-1’-specfic DEG in the dark red gene module, but its abundance was constantly higher in the flesh of ‘W600’ and F1 in comparison to that of ‘14-1’ (Table S6), providing a reasonable explanation for the differences in flesh flavor among the three watermelon lines. Variable proportions and categories of cucurbitacins have been reported in different watermelon varieties (Lavie et al., 1964), leading to distinct flesh flavors. We further explored the metabolites in the cucurbitacin biosynthesis pathway, and the content of cucurbitacin E (Figure 5E) was overaccumulated in ‘W600’ and F1 flesh in comparison to that of ‘14-1’ (Figure 5C). The contributions of cucurbitacin-related DEGs to DMs were evaluated, and a high correlation was observed for Cla97C09G173590 and absinthin (R2=0.9989), cucurbitacin I 2-glucoside (R2=0.9989), and cucurbitacin F (R2=0.9971), while Cla97C05G100990 displayed the lowest correlation with l-tyrosine (Figure 5C). The manner in which Cla97C09G173590 is involved in the regulation of flesh taste deserves further exploration in the future. Figure 5. Open in new tabDownload slide Correlation analysis of bitterness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) Venn diagrams showing the distribution of bitterness-related DEGs among ‘W600’, ‘14-1’, and F1 fruits. Top panel: upregulated DEGs; bottom panel: downregulated DEGs. (B) A heat map displaying the abundance of bitterness-related DEGs in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (C) Contents of bitterness-related DMs in related metabolic pathway in the fruits of ‘W600’, ‘14-1’, and F1 at the maturity stages. (D) Correlation of the bitterness-related hub DEGs and DMs in watermelon fruits. (E) Mass spectrometry (MS) diagram for cucurbitacin E in watermelon fruits. GGPPS, geranylgeranyl pyrophosphate synthase; CYP, cytochrome P450; IDI, isopentenyl diphoshate; GPS, geranylgeranyl pyrophosphate synthase; FPKM, fragments per kilobase of transcript per million mapped reads; FPS, farnesyl pyrophosphate synthase; SQS, squalene synthase; SQE, squalene; OSC, oxidosqualene cyclase; P450, cytochrome P450. TW600: ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh color is largely dependent on the composition and contents of carotenoids, and can thus be regulated by genes involved in carotenoid metabolism (Zhao et al., 2013). Based on the WGCNA analysis, we found that DEGs in the dark green gene module were significantly related to flesh color (Figure 6A). Based on the observation of a striking color change from white to red for ‘14-1’ flesh, but only a slight color change for ‘W600’ and F1 flesh, at the two investigation stages, it might be proposed that the DEGs closely associated with flesh coloration might be ‘14-1’-specific. We identified a total of 323 ‘14-1’-specfic DEGs, which included various key genes in the carotenoid metabolic pathway, such as PSY and NCED, in the dark green gene module (Figure 2D). Using an F2:3 population, Branham et al. (2017) mapped a major quantitative trait locus (QTL) related to watermelon flesh color and further discovered that Cla97C01G008760 encoding a PSY is the candidate gene, the expression of which was positively correlated with the accumulation of lycopene. Lycopene is the main form of carotenoid that is responsible for the red flesh of a major proportion of cultivated watermelons (Figure 6D; Liu et al., 2012). By contrast, yellow flesh is mainly attributed to the accumulation of violanthin and lutein in watermelon, while the depletion of carotenoids leads to white flesh, a typical trait for fruits of wild watermelon (Hermanns et al., 2020). Using targeted metabolomic analysis, a total of 36 metabolites were identified, of which 10 (γ-carotene, β-carotene, lycopene, (E/Z)-phytoene, α-cryptoxanthin, β-cryptoxanthin, lutein, neoxanthin, violaxanthin, and zeaxanthin) were directly derived from the carotenoid metabolic pathway and displayed differential accumulation in the fruit flesh between ‘14-1’ and the other two lines (Figure 6C), providing a metabolic explanation for the large divergence in flesh color among the three watermelon lines. A correlation analysis was further carried out between the carotenoid-related DEGs and metabolites. The correlation coefficients between the seven DMs (γ-carotene, β-carotene, lycopene, (E/Z)-phytoene, α-cryptoxanthin, β-cryptoxanthin, and zeaxanthin) and DEGs were all greater than 0.9, while a weak correlation was observed between the other three DMs (violaxanthin, neoxanthin, and lutein) and the DEGs (Figure 6B). This observation was consistent with the previous claim that flesh color might be genetically controlled by a complex regulatory network (Zhao et al., 2013; Wang et al., 2019; Hermanns et al., 2020). Figure 6. Open in new tabDownload slide Correlation analysis of coloration-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) A heat map displaying the expression of carotenoid-related genes in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Contents of coloration-related DMs in carotenoid biosynthetic pathway in the fruits of ‘W600’, ‘14-1’, and F1 at the maturity stages. (C) Correlation of the coloration-related hub DEGs and DMs in watermelon fruits. (D) Total ion chromatography diagrams for lycopene in the fruits of ‘W600’, ‘14-1’, and F1. The peak time of lycopene was 8.23 min. CYP, cytochrome P450; FPKM, fragments per kilobase of transcript per million mapped reads; GGPP, geranylgeranyl diphosphate; AIR12, cytochrome b561 and domon domain-containing protein; PSY, phytoene synthase; PDS, phytoene desaturase; Z-ISO, ζ-carotene isomerase; ZDS, ζ-carotene desaturase; LCY, lycopene β-cyclase; ZEP, zeaxanthin epoxidase; VED, violaxanthin de-epoxidase; NXS, neoxanthin synthase; NCED, 9-cis-epoxycarotenoid dioxygenase; CrtISO, carotenoid isomerase; BCH, β-carotene hydroxylase; CYP97A, cytochrome P450 carotene β-hydroxylase; CYP97C, cytochrome P450 carotene ε-hydroxylase; TW600, ‘W600’ fruits at the transition stage; T14-1, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh hardness, a quality trait genetically controlled by QTLs, has been demonstrated to be determined to a great extent by the contents of cell-wall components such as cellulose and pectin (Sun et al., 2020). Bulked segregant analysis (BSA) has demonstrated that Cla016033 and Cla012507 can influence flesh hardness by regulating cell wall composition and fruit maturity (Sun et al., 2020). In a study carried out by Sehgal et al. (2021), Cla012351 encoding a cellulose synthase and Cla004251 encoding a pectinesterase were observed to be key players in the metabolism of cellulose and pectin due to their high correlation with the contents of cellulose and pectin in watermelon fruits. Our phenotypic results showed that ‘14-1’ yielded fruits with soft flesh, and the degree of its flesh firmness was almost constant at both the transition and maturity stages; while for ‘W600’ and F1, both firm flesh and significantly varied hardness were observed at the two investigation points, indicating that hardness-related DEGs might be ‘W600’/F1-specific. We identified a total of 406 ‘W600’/F1-specific DEGs in the dark red gene module, which displayed a close association with flesh hardness in the WGCNA analysis. Interestingly, nine genes (Cla97C09G163490, Cla97C03G065030, Cla97C06G110120, Cla97C05G098130, Cla97C05G108640, Cla97C04G070440, Cla97C08G146150, Cla97C01G019430, and Cla97C06G118820) of the ‘W600’/F1-specific DEGs might function as critical players in cellulose and pectin degradation according to their annotation information (Figures 7A and 7C). Liao et al. (2020) reported a high correlation between ethylene responsive transcription factor 4 (ERF4) and the firmness of watermelon peel, partially supporting that Cla97C06G118820 encoding an ERF might be profoundly involved in the regulation of flesh firmness. To provide functional evidence for these DEGs, the contents of cellulose and pectin-related metabolites were further explored in an untargeted metabolomic analysis, and four DMs were identified, including galactonic acid, pectin, ethyl cellulose, and d-galactose (Figure 7B; Table S3). As a main component of the cell wall, pectin can be divided into protopectin, soluble pectin, and pectin acid, and the degradation of protopectin to soluble pectin leads to fruit softening (Figueroa et al., 2010). Correlation investigation was further carried out between the firmness-related DEGs and DMs, and the highest correlation with pectin was observed for Cla97C09G163490 (R2=0.9821) followed by Cla97C04G070440 (R2=0.9773; Figure 7), while the poorest correlation was revealed between Cla97C05G108640 and ethyl cellulose (Figure 7B). Figure 7. Open in new tabDownload slide Correlation analysis of hardness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) A heat map displaying the expression of hardness-related DEGs in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Correlation of the hardness-related hub DEGs and DMs in watermelon fruits. (C) Tandem mass spectrometry (MS/MS) diagram for pectin in watermelon fruits. PME inhibitor, pectin methylesterase inhibitor; CESA1, cellulose synthase 1; CESA2, cellulose synthase 2; CESA3, cellulose synthase; CSLC5, cellulose synthase-like C5; BGAL3, beta-galactosidase 3; ERF041, ethylene-responsive transcription factor 1; FPKM, fragments per kilobase of transcript per million mapped reads; PME2, pectinesterase 2; TW600: ‘W600’ fruits at the transition stage; T14-1, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Conclusions In the present study, an integrated transcriptome and metabolome approach was adopted to investigate the molecular foundation of flesh taste, color, and texture in the fruits of ‘14-1’, ‘W600’, and F1 hybrid lines. Significant divergence was revealed in both the transcriptomic and metabolomic profiles of the fruits from the three watermelon lines, providing a comprehensive explanation for their phenotypic variations. A series of hub genes and metabolites closely associated with flesh taste, color, and texture were identified and could be used as potential targets for watermelon improvement in the future. Author Contributions Qinghua Shi and Xiaoyu Yang conceived the research; Shunpeng Chu and Mengmeng Yin performed the experiments; Shunpeng Chu, Shuoshuo Wang, Qinghua Shi and Xiaoyu Yang analyzed data; Qinghua Shi, Xiaoyu Yang, Shunpeng Chu, Shuoshuo Wang wrote the manuscript; Ruimin Zhang provided technical assistance during manuscript preparation. All authors read and approved the final manuscript. Funding This work was supported by the Agricultural Variety Improvement Project of Shandong Province (2019LZGC005) and the Shandong Vegetable Research System (SDAIT-05), China. 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For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of Zhejiang University Press. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Food Quality and Safety Oxford University Press

Integrative analysis of transcriptomic and metabolomic profiles reveals new insights into the molecular foundation of fruit quality formation in Citrullus lanatus (Thunb.) Matsum. & Nakai

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Oxford University Press
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Copyright © 2022 Zhejiang University Press
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2399-1399
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2399-1402
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10.1093/fqsafe/fyac015
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Abstract

Abstract In this study, an integrated transcriptome and metabolome analysis was used to explore the molecular foundation of fruit quality in two parent lines of Citrullus lanatus with distinct flesh characteristics, including ‘14-1’ (sweet, red, and soft) and ‘W600’ (bitter, light yellow, and firm), as well as the corresponding F1 population (bitter, light yellow, and firm). Numerous differentially expressed genes (DEGs) were identified in the fruit samples: 3766 DEGs for ‘14-1’ vs. ‘W600’, 2767 for ‘14-1’ vs. F1, and 1178 for F1 vs. ‘W600’ at the transition stage; and 4221 for ‘14-1’ vs. ‘W600’, 2447 for ‘14-1’ vs. F1, and 446 for F1 vs. ‘W600’ at the maturity stage. Weighted gene co-expression network analysis (WGCNA) revealed that a gene module including 1111 DEGs was closely associated with flesh taste and color, and another gene module including 1575 DEGs contributed significantly to flesh texture. The metabolomic results showed that there were 447 differential metabolites (DMs) for ‘14-1’ vs. ‘W600’ fruits, 394 for ‘14-1’ vs. F1, and 298 for F1 vs. ‘W600’ at the maturity stage. Combining WGNCA and metabolomic results, several DEGs and DMs were further identified as hub players in fruit quality formation: 6 DEGs with 4 DMs for flesh sweetness; 6 DEGs with 13 DMs for bitterness; 9 DEGs with 10 DMs for flesh color; and 9 DEGs with 4 DMs for flesh texture. Altogether, these observations not only expand our knowledge of the molecular basis of fruit quality in watermelon, but also provide potential targets for future watermelon improvement. Fruit quality, metabolome, molecular foundation, transcriptome, watermelon Introduction Watermelon (Citrullus lanatus (Thunb.) Matsum. and Nakai (2n=2x=22)) is a popular fruit crop in the Cucurbitaceae family that is cultivated all over the world, with annual production being the highest in China, Turkey, and Iran (Guo et al., 2019; Pal et al., 2020). It is native to Africa and can be taxonomically divided into seven different species (Ren et al., 2017; Guo et al., 2019). As an important dietary source, watermelon fruits contain a variety of health-promoting phytochemicals such as sugars, lycopene, and amino acids (Guo et al, 2013). Therefore, its fruit quality has attracted extensive attention from horticultural researchers and breeders, and significant effort has been made in elucidating the molecular foundation of quality-related traits such as taste, color, and texture. Sweetness is closely associated with sugar content in fruits. While sucrose is the most stored sugar in watermelon fruits, oligosaccharides usually serve as the major form of transported sugars from source leaves, along with a small amount of sucrose and hexose followed by a hydrolysis process (Zhang et al., 2012). Previous studies have demonstrated that the transport, hydrolysis, and storage courses of sugars from source leaves to fruits are strictly controlled by a series of genetic loci/genes in watermelon, such as BCE2.1 and BCE8.1 (Liu et al., 2014), ClTST2 (Ren et al., 2017), ClVST1 (Gao et al., 2018), and ClAGA1, ClSWEET3, and ClTST2 (Ren et al., 2021). Using comparative transcriptomic investigation of watermelon fruits with distinct flesh flavors, various differentially expressed genes (DEGs) related to soluble sugar accumulation and metabolism have been identified, providing a genome-wide view of fruit sweetness regulation (Gao et al., 2018). A more recent report demonstrated that ClAGA2, which encodes an alkaline alpha-galactosidase, can serve as a crucial modulator for the hydrolysis process of stachyose and raffinose in the vascular bundle of watermelon, while sugar transport and storage are controlled by ClSWEET3 and ClTST2 in the plasma membrane and vacuoles of fruit cells, respectively (Ren et al., 2021). In contrast to sweet fruits from cultivated watermelons, progenitor watermelon generally produces small-sized fruits with bitter or bland flesh—a flavor trait that is closely associated with the biosynthesis and accumulation of the cucurbitacin group, particularly cucurbitacin E glycosides, which are Cucurbitaceae plant-specific tetracyclic terpenes with biological significance, including anti-pathogenic effects (Yousaf et al., 2018; Kim et al., 2020). Regarding the genetic basis for bitterness, several candidate loci/genes have been detected, such as qbt-c1-1 (Li et al., 2018) and Cla011508 (Gong et al., 2022). Furthermore, two acetyltransferases (ACT1/ACT2) and one uridine diphosphate (UDP)-glucosyltransferase (UGT) have been revealed to be critical players in the production of cucurbitacin derivatives based on enzymatic assay results (Kim et al., 2020). Using a high-density genetic map constructed via re-sequencing of two watermelon parent lines and a recombinant inbred line (RIL) population, Li et al. (2018) successfully mapped one locus (qbt-c1-1) responsible for flesh bitterness on chromosome 1, one locus (qsc-c3-1) responsible for seed coat color on chromosome 3, and one locus (qrc-c8-1) responsible for rind color on chromosome 8 (Li et al., 2018). In addition to sweetness/bitterness, flesh color and firmness are two important sensory quality parameters of watermelon fruits. Previous studies have demonstrated that the differences in the coloration of horticultural products among different species or different accessions can mainly be attributed to the variations in the amounts and categories of carotenoids, which are controlled by different loci/genes related to the biosynthesis and accumulation of carotenoids, such as LCYB4.1, ClPHT4;2, ClPSY1, ClPAP, or ClLCYB (Liu et al., 2014; Zhang et al., 2016, 2020; Fang et al., 2020; Guo et al., 2021; Xu et al., 2021). Based on a transcriptomic investigation of the fruits of five watermelon accessions at different developmental stages, four candidate genes (Cla003760, Cla007686, Cla018406, and Cla021635) were found to play essential roles in the coloration of watermelon fruits (Yuan et al., 2021). For flesh firmness, a close association has been revealed with cellulose and pectin contents, as well as genes involved in their metabolism (Brummell et al., 2004; Qin and Ren, 2007; Sun et al., 2020; Anees et al., 2021; Chen et al., 2021). However, some contradicting observations suggest that fruit firmness exhibits little relationship with cellulose or pectin content in previous studies (Hiwasa et al., 2004; Figueroa et al., 2010). The involvement of phytohormone signaling and long non-coding RNAs (lncRNAs) has been demonstrated in the regulation of fruit softening/firmness (Lindo-García et al., 2019; Chen et al., 2021; Li et al., 2021). In the past several years, integrative profiling of the transcriptome and metabolome has been used to explore the regulatory networks underlying fruit quality formation in watermelon. For example, combining RNA sequencing (RNA-seq) and high-performance liquid chromatography (HPLC)-based metabolomic analysis, Umer et al. (2020) found that sugars and organic acids were highly correlated with three gene networks/modules in watermelon, including 2443 genes, of which 7 were proposed as critical regulators in the metabolism of sucrose, as well as malic and citric acid. Similarly, a series of hub genes and metabolites related to flesh taste have been identified based on transcriptomic and metabolomic profiling of wild and cultivated watermelon fruits at different developmental stages (Gong et al., 2021). Although great improvements in our mechanistic understanding of fruit quality formation have been obtained at both single gene/locus and genome-wide levels, the complexity and large divergence in the regulation of quality-related traits among different plant materials emphasizes the need to further explore their molecular foundation in watermelon. In this study, fruit quality was investigated using an integrated transcriptome and metabolome approach for ‘14-1’, ‘W600’, and F1 hybrid watermelons, which display apparent variations in flesh taste, color, and texture. A large divergence in both transcriptomic and metabolic profiles was identified among the three samples, providing a comprehensive explanation for their phenotypical variations. A series of hub genes and metabolites closely associated with flesh taste, color, and texture were identified and could be used as potential targets for watermelon improvement in the future. Materials and Methods Plant materials Wild and cultivated watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) accessions ‘W600’ and ‘14-1’, which were collected and stored in our laboratory, were used as the male and female parents, respectively, to construct an F1 hybrid population. After sterilization, 100 watermelon seeds of ‘W600’, ‘14-1’, or F1 were grown in plastic pots with nutrient soil and placed in a growth chamber that was set at a 14-h light period with 28 °C air temperature, 10-h dark period with 26 °C air temperature, and approximately 60% relative humidity. At the three-leaf stage, the seedlings were cultivated in the greenhouse of Shandong Agricultural University (Tai'an, China) from March to June in 2021 with a plant spacing of 40 cm×80 cm. Standard field management was carried out during the cultivation period. Two even-sized fruits were collected as one biological repeat at the transition stage (approximately 21 d after pollination (DAP)) and maturity stages (approximately 35 DAP) for phenotypical and multi-omic investigation. Three biological repeats were prepared. Phenotypical investigation Flesh hardness was measured with a digital fruit hardness tester with an 8-mm probe (STEP Systems GmbH, Nuerenberg, Germany). Briefly, the sampled fruit was first longitudinally divided into two halves, and the flesh hardness was then determined by vertically inserting the probe into the central flesh of one divided half until the calibration line. The values in the hardness tester were collected and converted into flesh firmness readings according to the manufacturer’s instructions. The soluble solid content (SSC) of the central flesh was measured with a PAL-1 digital pocket refractometer (ATAGO, Tokyo, Japan). A total of 16 randomly selected points (eight per fruit) in the central flesh was used for the evaluation of flesh hardness and SSC. After removing values with a large variance, the remaining data were averaged as flesh hardness or SSC for each repeat. Fruit sweetness/bitterness was determined by following the human tasting method previously introduced by Zhang et al. (2013). Briefly, participants were randomly selected to taste the fruits from different watermelon inbred lines with a gargling interval between each line, and the tasted fruits were generally classified into bitter and non-bitter groups based on the participants’ responses (Mondal and Saxena, 2016; Guo et al., 2019; Santis and Frangipane, 2020). For flesh color, visual observation was adopted according to the protocol previously introduced by Guo et al. (2019). All flesh firmness and SSC data were statistically analyzed with SPSS software (version 26.0; IBM Corp., Armonk, NY, USA). Nanopore RNA sequencing and data analysis Total RNA extraction from fruit samples, quality evaluation, and library construction were performed according to the standard protocol provided by Oxford Nanopore Technologies. Long-read sequencing was then conducted for the libraries with the Oxford Nanopore PromethION flow cells (FLO-PRO002) in Benagene Company (Wuhan, China). After removal of the low-quality reads using an in-house script, the remaining reads were subjected to identification and classification of full-length transcripts followed by alignment to the watermelon ‘97103’ v2 reference genome (http://cucurbitgenomics.org/organism/21) with the aid of the Pychopper v2.4.0 (-Q 7, -z 50; https://hpc.nih.gov/apps/pychopper.html) and Pinfish v0.1.0 packages (https://www.opensourceagenda.com/projects/pinfish/versions) under default settings, respectively. The abundance of genome-matched transcripts was calculated and normalized as fragments per kilobase of transcript per million mapped reads (FPKM). The DEGs were identified in each comparison of transcriptomic samples, with the cutoff parameters of fold-change>2 and P-value<0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out for the DEGs in R, and the terms with a P-value<0.05 were considered as significantly enriched. For weighted gene co-expression network analysis (WGCNA) analysis, the DEGs were first divided into different modules, and the correlation between DEG FPKMs in each module and flesh quality parameters (SSC, color, and hardness) of the two parents and F1 plants was then evaluated and displayed in a heat map using the online BMKCloud Visualization Toolkit (http://en.biocloud.net/toolset). Sample preparation for metabolome and data analysis For untargeted gas chromatography–mass spectrometry (GC-MS) analysis of the metabolome, 80 mg of fresh flesh was placed into a 1.5-mL Eppendorf tube with 360 μL precooled methanol and 40 μL 0.3 mg/mL 2-chloro-l-phenylalanine; the latter used as an internal standard. After being frozen at −20 °C for 2 min, the mixture was ground well for 2 min with a JXFSTPRP-24/32 tissue grinder (Jingxin, Shanghai, China) set at a frequency of 60 Hz. The ground flesh was subjected to ultrasonic extraction at 4 °C for 30 min followed by the addition of 200 μL chloroform and 400 μL MilliQ H2O, followed by a second round of 30-min ultrasonic extraction at 4 °C. The resulting sample was frozen at −20 °C for 30 min and centrifuged at 13,000 r/min for 10 min at 4 °C. Two hundred microliters of supernatant was transferred to a glass sampling vial for vacuum-drying at room temperature, and the dried sample was resuspended in 80 μL of 15 mg/mL methoxylamine hydrochloride solution followed by 2 min of vigorous vortexing and 90 min of incubation at 37 °C. After the addition of 80 μL of bis(trimethylsilyl) trifluoroacetamide (BSTFA) derivatization reagent (containing 1% (in volume) trimethylchlorosilane (TMCS)) and 20 μL n-hexane, the resuspension was vigorously vortexed for 2 min, derivatized at 70 °C for 60 min, and finally subjected to GC-MS analysis according to a previous protocol (Yan et al., 2020). To perform untargeted liquid chromatography–mass spectrometry (LC-MS) analysis of the metabolome, 80 mg flesh sample was placed into a 1.5-mL Eppendorf tube with 1 mL 70% (in volume) methanol and 20 μL 0.3 mg/mL 2-chloro-l-phenylalanine, the latter used as an internal standard. The mixture was kept at −20 °C for 2 min and then homogenized for 2 min with the abovementioned tissue grinder at 60 Hz. After 30 min ultrasonic extraction at 4 °C, the resulting sample was kept at −20 °C overnight and centrifuged at 13,000 r/min at 4 °C for 10 min. The supernatant (150 μL) was transferred to an LC vial through a 0.22-μm syringe filter for subsequent LC-MS investigation. For targeted metabolomic analysis, 50 mg of freeze-dried and pulverized flesh sample was treated with 500 μL extraction buffer, which was composed of 0.01% (mass concentration) butylated hydroxytoluene (BHT) in a solution of n-hexane, acetone, and ethanol (1:1:1, in volume). Additionally, 10 μL 20 μg/mL 2-chloro-l-phenylalanine was added as an internal control. The resulting mixture was vortexed for 20 min at room temperature and subjected to 5-min centrifugation at 4 °C. The supernatant was transferred to a brown vial, and the pellet was re-extracted by following the above-mentioned protocol. Both supernatants were pooled together, evaporation-dried under nitrogen gas, and resuspended in the mixture of methanol and methyl tert-butyl ether (MTBE) at a volume ratio of 1:1. The resuspension was filtered into an LC vial through a 0.22-μm syringe filter for UPLC-MS investigation. The raw data of the untargeted and targeted metabolomes were analyzed with Progenesis QI v2.3 (https://www.nonlinear.com/) and MultiQuant v3.0.3 software (https://sciex.com/products/), respectively. The overall distribution and reproducibility of the metabolome data from all fruit samples were then evaluated by following the principal component analysis (PCA) protocol as described previously (Worley and Powers, 2013). Differential metabolites (DMs) in each comparison of fruit samples were identified with the cutoff parameters of VIP (variable importance in the projection)>1 and P-value<0.05 for untargeted metabolome data sets, and fold-change>2 and P-value<0.05 for the targeted metabolome data sets. Quantitative real-time polymerase chain reaction (qRT-PCR) verification For experimental verification of the DEGs, total RNAs were extracted from fruit samples following a previously reported method (Yang et al., 2021). Using 1 μg total RNAs as the template, the first-strand cDNAs were synthesized with a RevertAid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s instructions. qRT-PCR analyses for selected DEGs were conducted on an ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using the corresponding primers, which were designed using Primer Premier 5.0 software (https://en.freedownloadmanager.org/users-choice/Primer_5.0_Free_Download.html). For each DEG, three biological repeats together with three technical repeats were prepared, and the relative expression was calculated according to the 2–ΔΔCt method as described by Livak and Schmittgen (2001). All qRT-PCR primers have been provided in Table S1. Statistical analysis The flesh firmness and SSC data were statistically analyzed using one-way analysis of variance (ANOVA) in SPSS 26.0 at a significance level of 0.05 between each sample. The results are displayed as the mean±standard deviation of three biological repeats. High-throughput sequencing data availability All RNA-seq data sets were deposited in the NCBI Sequence Read Archive database (http://www.ncbi.nlm.nih.gov/sra/) with accession No. PRJNA784391. Results and Discussion Phenotypic variations in watermelon fruits from two parent lines and F1 plants To understand the inheritance of fruit quality-related traits, an F1 hybrid population was generated from the crossing of watermelon inbred lines ‘14-1’ and ‘W600’, which were used as the female and male parents, respectively. We focused on flesh taste, color, and texture in the fruits of F1 plants, because these traits were quite distinct in the two parent lines according to our previous field observations. Phenotypic investigation was carried out for the fruits from two parent lines and F1 plants at both the transition and maturity stages. The results showed that, at the transition stage, the fruit flesh of ‘W600’, ‘14-1’, and F1 was white in color and exhibited no significance differences (Figure 1A). At the maturity stage, while the flesh of ‘14-1’ was red in color, only a slight color change was observed from white to light yellow for the flesh of ‘W600’ and F1 (Figure 1A). In terms of flesh texture, significant differences among the three watermelon lines were observed. The fruits of ‘W600’ were the hardest (21.2 kg/cm2 and 16.8 kg/cm2), followed by F1 (12.1 kg/cm2 and 9.1 kg/cm2) and ‘14-1’ (2.4 kg/cm2 and 2.3 kg/cm2) at both investigation stages (Figure 1B). A general decreasing trend was also confirmed for flesh hardness with the development of the watermelon fruits (Figure 1B). Being consistent with bitter taste, SSC levels in the flesh at the transition and maturity stages were only 3 and 3.1 for ‘W600’ and 3.1 and 3.6 for F1 (Figure 1C). By contrast, the SSC level was significantly increased from 6.1 at the transition stage to 13.4 at the maturity stage in the flesh of ‘14-1’, thus leading to the sweet taste of ‘14-1’ fruits. Dramatic accumulation of sugars at the maturity stage has been observed for watermelon inbred lines with sweet fruits in previous studies (Gao et al., 2018; Ren et al., 2021), implying that maturation-related signaling might play a critical role in flesh flavor formation. Figure 1. Open in new tabDownload slide Fruit phenotypes of two watermelon parent lines and F1 plants. (A) Transverse view for watermelon fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Flesh firmness and (C) soluble solid content (SSC) for watermelon fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. Data display as mean±standard deviation of three biological repeats. Different lowercase letters above the bars indicate significant differences at a statistical level of 0.05 (one-way ANOVA test). Transcriptomic and metabolomic variations in watermelon fruits from the two parent lines and F1 plants We initially wondered how these fruit quality-related traits were transcriptionally regulated. To this end, Nanopore sequencing was carried out for RNA samples obtained from ‘14-1’, ‘W600’, and F1 fruits at the transition and maturity stages. Three biological repeats were tested for each sample. In contrast to the previous studies (Giulia et al., 2018; Anees et al., 2021), wherein two inbred lines with distinct fruit traits have typically been used for RNA-seq analysis, we added the F1 population together with the two parent lines for transcriptomic comparison to improve the precision of hub gene identification due to increased phenotypic variations. A similar strategy has been adopted by a recent report (Sun et al., 2020). After discarding the low-quality reads, over 3,900,000 genome-matched reads were obtained for all libraries, which accounted for over 78% of clean sequenced reads (Table S2). A variable number of full-length unique transcripts were further identified in the fruit samples of ‘14-1’ (10,459–13,706 for the transition stage and 8598–9686 for the maturity stage), ‘W600’ (7945–14,049 for the transition stage and 6526–7995 for the maturity stage), and F1 (6692–9685 for the transition stage and 6610–8503 for the maturity stage; Table S2), covering more than 97% of the annotated genes in the watermelon genome. PCA analysis was carried out for all data sets, and the results indicated that the three biological repeats were clustered together for each fruit sample, demonstrating the reproducibility of the Nanopore RNA sequencing libraries. Both high mapping efficiency and good reproducibility evidenced the high quality of the transcriptomic data sets. A comparison of the transcriptomic profiles was next performed among different fruit samples, and a series of DEGs were identified with the cut-off parameters of fold-change>2 and P-value<0.05. For ‘14-1’ vs. ‘W600’, there were 1804 upregulated DEGs and 1962 downregulated DEGs at the transition stage, as well as 1975 upregulated DEGs and 2246 downregulated DEGs at the maturity stage; for ‘14-1’ vs. F1, 1359 and 1003 DEGs were upregulated at the transition and maturity stages, respectively, while 1408 and 1444 DEGs were downregulated at the transition and maturity stages, respectively. For F1 vs. ‘W600’, the number of upregulated DEGs was 675 for the transition stage and 435 for the maturity stage, while the number of downregulated DEGs was 503 for the transition stage and 281 for the maturity stage (Figure 2A; Table S3). We randomly selected 12 DEGs for qRT-PCR verification using the same RNA samples as for the Nanopore sequencing, and the experimental results were consistent with their FPKM variations in the different fruit samples, further supporting the reliability of our transcriptomic results. Figure 2. Open in new tabDownload slide Transcriptomic variations in the fruits of two watermelon parent lines and F1 plants. (A) The number of differentially expressed genes (DEGs) in different comparisons of fruit samples. (B) A dendrogram showing gene modules from the weighted gene co-expression network analysis (WGCNA) of DEGs. (C) An optimized dendrogram showing gene modules from the WGCNA analysis of DEGs. (D) A heat map displaying the correlation between DEG modules and three flesh traits including firmness, color and taste for ‘W600’, ‘14-1’, and F1 watermelons. TW600, ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. We assessed how these DEGs contributed to phenotypic variations in the fruits of the three watermelon lines. Accordingly, WGCNA—an algorithm used for discovering high correlations between gene clusters/modules and phenotypes of interest (Umer et al., 2020)—was adopted to investigate the relationship of the identified DEGs with fruit quality-related parameters, including flesh sweetness/bitterness, color, and hardness. All DEGs that were identified in different sample comparisons were first divided into 12 distinct gene modules of different colors based on their expression patterns, and the correlation efficiency between each gene module and quality parameter was then calculated. The results showed that the dark green module composed of 1111 DEGs displayed a significant correlation with both flesh sweetness (R2=0.97) and color (R2=0.95), and the dark red module that was composed of 1575 DEGs was significantly associated with flesh bitterness and hardness (R2=0.85; Figure 2; Table S4). KEGG analysis was performed for DEGs in the dark green and dark red modules. A total of five KEGG terms, including ‘Carotenoid biosynthesis’, ‘Fatty acid metabolism’, and ‘Pyruvate metabolism’, were enriched for DEGs in the dark green module, and 18 terms such as ‘Pentose phosphate pathway’, ‘Plant hormone signal transduction’, and ‘Lysine biosynthesis’ were observed for the DEGs in the dark red module following the cut-off parameter of P-value<0.05 (Table S5). Metabolomic variations in watermelon fruits from the two parent lines and F1 plants Recently, metabolomics has been used to monitor the variations in metabolites at the global level in crop plants upon developmental clues or stressful conditions (Qiu et al., 2020), providing a metabolic explanation for their quality formation or acclimation to unfavorable environments. To explore the global variations in metabolite profiles, both untargeted and targeted metabolomic analyses were carried out for the fruit samples of ‘14-1’, ‘W600’, and F1 at the maturity stage, when apparent differences were observed for quality-related traits including flesh taste, color, and texture among the three watermelon lines. The PCA results showed that three biological repeats for each metabolomic preparation were reproducible (Figure S2), demonstrating that high-quality metabolome data sets were obtained. We further performed metabolite comparison in the different fruit samples, and the DMs were identified using the cut-off parameters of VIP>1 and P-value<0.05 for both LC-MS- and GC-MS-based untargeted metabolomes. In the comparison of ‘14-1’ vs. ‘W600’, the contents of 214 and 199 metabolites were significantly enhanced and reduced, respectively; in the comparison of ‘14-1’ vs. F1, 204 metabolites displayed dramatically increased contents, while 146 metabolites displayed dramatically decreased contents; and in the comparison of F1 vs. ‘W600’, the number of significantly increased metabolites was 124, while the number of significantly decreased metabolites was 148 (Figures 3A and 3B; Table S6). Figure 3. Open in new tabDownload slide Metabolomic variations in the fruits of two watermelon parent lines and F1 plants. Heat maps showing the identified differential metabolites (DMs) in the fruits of ‘MW600’, ‘M14-1’ and MF1 at the maturity stage by (A) gas chromatography–mass spectrometry (GC-MS), (B) liquid chromatography–mass spectrometer (LC-MS), and (C) ultraperformance liquid chromatography (UPLC). MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh coloration largely depends on watermelon cultivars and carotenoid categories and contents (Guo et al., 2019). To explore the molecular mechanisms underlying different flesh colors in the three watermelon samples, targeted metabolomics analysis was employed to monitor metabolite changes in the carotenoid metabolic pathway in ‘W600’, ‘14-1’, and F1 mature fruits. The differential carotenoids were identified using the cut-off parameters of fold-change>2 and P-value<0.05. The number of enhanced carotenoids was 11 for ‘14-1’ vs. ‘W600’, 13 for ‘14-1’ vs. F1, and 12 for F1 vs. ‘W600’, while the contents of 23, 21, and 16 carotenoids were significantly reduced in the comparisons of ‘14-1’ vs. ‘W600’, ‘14-1’ vs. F1, and F1 vs. ‘W600’, respectively (Figure 3C). Apparent variations in metabolite profiles have been observed in different cultivars of horticultural crops such as tomato (Bai et al., 2021) and melon (Biais et al., 2010). Molecular insights into flesh taste, color, and texture in watermelon To obtain a comprehensive understanding of fruit quality formation, we analyzed the combined contributions of transcriptomic and metabolomic variations to the phenotypic differences in flesh taste, color, and texture of ‘14-1’, ‘W600’, and F1 fruits. Previous studies have demonstrated that the major form of soluble sugars in watermelon fruits is sucrose (Figure 4E), which serves as the determinant for flesh sweetness (Zhu et al., 2017; Zamuz et al., 2021). Sucrose content in fruits can be influenced by various genes encoding metabolism-related enzymes such as SuSy (Liu et al.,2013; Zhu et al., 2017) and transporters such as TST (Huang et al., 2020). In our former WGCNA analysis, a close association of the dark green gene module, which was composed of 1111 DEGs, was revealed with flesh sweetness/bitterness. As the SSC of ‘14-1’ was significantly higher than that of W600 and F1, and the SSC of W600 and F1 was constantly at a comparatively low level, we speculated that the major contributors to flesh sweetness might be ‘14-1’-specific DEGs in the dark green gene module (Figure 4A). We identified a total of 323 DEGs of this type, among which six genes (Cla97C08G153160, Cla97C05G101500, Cla97C10G204040, Cla97C07G131650, Cla97C01G001950, and Cla97C04G070460) have been demonstrated to be involved in the sucrose biosynthesis pathway in previous studies (Figure 4B; Yativ et al., 2010; Dai et al., 2011). Figure 4. Open in new tabDownload slide Correlation analysis of sweetness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) Venn diagrams showing the distribution of sweetness-related DEGs among ‘W600’, ‘14-1’ and F1 fruits. Top panel: upregulated DEGs; bottom panel: downregulated DEGs. (B) A heat map displaying the abundance of sweetness-related DEGs in the fruits of ‘W600’, ‘14-1’ and F1 at the transition and maturity stages. (C) Sugar accumulation in sucrose metabolic pathways in the fruits of ‘W600’, ‘14-1’ and F1 at the maturity stage. (D) Correlation of the sweetness-related hub DEGs and DMs in watermelon fruits. (E) Tandem mass spectrometry (MS/MS) diagram for sucrose in watermelon fruits. SPS, sucrose phosphate synthase; SS, sucrose synthase; AMY, alpha-amylase; BGLU, beta-glucosidase; UXS1, UDP-glucuronic acid decarboxylase 1; UXS5, UDP-glucuronic acid decarboxylase 5; TRE, trehalase; ATSIP, alkaline alpha-galactosidase seed imbibition protein; TW600: ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage; UDP, uridine diphosphate; FPKM, fragments per kilobase of transcript per million mapped reads. Interestingly, a recent publication demonstrated that ClAGA2 (Cla97C04G070460), which encodes an alkaline alpha-galactosidase, can serve as a crucial modulator for the hydrolysis process of stachyose and raffinose in the vascular bundle of watermelon, while sugar transport and storage are controlled by ClSWEET3 and ClTST2 in the plasma membrane and vacuoles of fruit cells, respectively (Ren et al., 2021). This evidence points to the functional conservation of Cla97C04G070460 in sucrose metabolism, which could be used as a potential target locus for the flesh taste improvement of watermelon fruits. The metabolites in the sucrose biosynthesis pathway were further investigated, and the contents of sucrose, d-fructose-6-phosphate, glucose-1-phosphate, and inulobiose were observed to be overrepresented in ‘14-1’ flesh in comparison to that of ‘W600’ and F1 (Figure 4B). These observations largely corroborated the report of Liu et al. (2013), wherein the sucrose content in sweet watermelon was significantly higher than that in non-sweet watermelon. In addition, sucrose content in watermelon fruits can also be influenced by developmental stages and cultivation conditions (Meng et al., 2009; Liu et al., 2013). To analyze the association between DEGs and sucrose metabolism, a correlation analysis was carried out between the six DEGs and four metabolites, and the highest correlation was observed for Cla97C08G153160 and inulobiose (R2=0.9999), followed by Cla97C01G001950 and glucose-1-phosphate (R2=0.9992) and Cla97C07G131650 and sucrose (R2=0.9973; Figure 4C). In contrast to the sweet flesh of ‘14-1’, the fruits of ‘W600’ and F1 possessed an apparent bitter taste, which is mainly caused by the accumulation of cucurbitacin E glycosides (Guo et al., 2019). Using a high-density genetic map constructed via re-sequencing of two watermelon parent lines and a RIL population, Li et al. (2018) successfully mapped one locus (qbt-c1-1) responsible for flesh bitterness on chromosome 1 (Li et al., 2018). To explore the potential genes closely associated with cucurbitacin metabolism, we focused on the dark red gene module and found that 406 DEGs displayed a ‘W600’ and F1-specific distribution (Figure 5A). Based on the annotation information, six genes (Cla97C09G173590, Cla97C11G210690, Cla97C05G101010, Cla97C01G003790, Cla97C05G100930, and Cla97C05G100990) of the 406 DEGs were proposed to be directly involved in flesh bitterness regulation in watermelon (Figure 5B). Cucurbitacin biosynthesis mainly includes terpenoid skeleton formation and oxidative modifications, and the modification reactions are commonly controlled by ACTs (acetyltransferases), CYP450s (cytochrome P450s), and OSCs (oxidosqualene cyclases; Zhou et al., 2016; Kim et al., 2020). The OSC gene family is highly conserved in the plant kingdom, and the critical roles of different OSCs have been revealed in the catalyzation of terpenoid skeletons to generate specific cucurbitacin (Thimmappa et al., 2014). By contrast, we observed that four CYP450 genes (Cla97C05G101010, Cla97C01G003790, Cla97C05G100930, and Cla97C05G100990), the expression of which was apparently higher in ‘W600’ than in ‘14-1’, were closely related to cucurbitacin E biosynthesis (Zhou et al., 2016), perhaps reflecting a distinct regulatory network for bitterness formation in ‘W600’ fruits. It should be noted that Cla97C09G173590, which encodes a geranylgeranyl pyrophosphate synthase and functions as a rate-limiting factor in the synthesis pathway of cucurbitacins (Chen et al., 2005), was classified as a ‘14-1’-specfic DEG in the dark red gene module, but its abundance was constantly higher in the flesh of ‘W600’ and F1 in comparison to that of ‘14-1’ (Table S6), providing a reasonable explanation for the differences in flesh flavor among the three watermelon lines. Variable proportions and categories of cucurbitacins have been reported in different watermelon varieties (Lavie et al., 1964), leading to distinct flesh flavors. We further explored the metabolites in the cucurbitacin biosynthesis pathway, and the content of cucurbitacin E (Figure 5E) was overaccumulated in ‘W600’ and F1 flesh in comparison to that of ‘14-1’ (Figure 5C). The contributions of cucurbitacin-related DEGs to DMs were evaluated, and a high correlation was observed for Cla97C09G173590 and absinthin (R2=0.9989), cucurbitacin I 2-glucoside (R2=0.9989), and cucurbitacin F (R2=0.9971), while Cla97C05G100990 displayed the lowest correlation with l-tyrosine (Figure 5C). The manner in which Cla97C09G173590 is involved in the regulation of flesh taste deserves further exploration in the future. Figure 5. Open in new tabDownload slide Correlation analysis of bitterness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) Venn diagrams showing the distribution of bitterness-related DEGs among ‘W600’, ‘14-1’, and F1 fruits. Top panel: upregulated DEGs; bottom panel: downregulated DEGs. (B) A heat map displaying the abundance of bitterness-related DEGs in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (C) Contents of bitterness-related DMs in related metabolic pathway in the fruits of ‘W600’, ‘14-1’, and F1 at the maturity stages. (D) Correlation of the bitterness-related hub DEGs and DMs in watermelon fruits. (E) Mass spectrometry (MS) diagram for cucurbitacin E in watermelon fruits. GGPPS, geranylgeranyl pyrophosphate synthase; CYP, cytochrome P450; IDI, isopentenyl diphoshate; GPS, geranylgeranyl pyrophosphate synthase; FPKM, fragments per kilobase of transcript per million mapped reads; FPS, farnesyl pyrophosphate synthase; SQS, squalene synthase; SQE, squalene; OSC, oxidosqualene cyclase; P450, cytochrome P450. TW600: ‘W600’ fruits at the transition stage; ‘T14-1’, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh color is largely dependent on the composition and contents of carotenoids, and can thus be regulated by genes involved in carotenoid metabolism (Zhao et al., 2013). Based on the WGCNA analysis, we found that DEGs in the dark green gene module were significantly related to flesh color (Figure 6A). Based on the observation of a striking color change from white to red for ‘14-1’ flesh, but only a slight color change for ‘W600’ and F1 flesh, at the two investigation stages, it might be proposed that the DEGs closely associated with flesh coloration might be ‘14-1’-specific. We identified a total of 323 ‘14-1’-specfic DEGs, which included various key genes in the carotenoid metabolic pathway, such as PSY and NCED, in the dark green gene module (Figure 2D). Using an F2:3 population, Branham et al. (2017) mapped a major quantitative trait locus (QTL) related to watermelon flesh color and further discovered that Cla97C01G008760 encoding a PSY is the candidate gene, the expression of which was positively correlated with the accumulation of lycopene. Lycopene is the main form of carotenoid that is responsible for the red flesh of a major proportion of cultivated watermelons (Figure 6D; Liu et al., 2012). By contrast, yellow flesh is mainly attributed to the accumulation of violanthin and lutein in watermelon, while the depletion of carotenoids leads to white flesh, a typical trait for fruits of wild watermelon (Hermanns et al., 2020). Using targeted metabolomic analysis, a total of 36 metabolites were identified, of which 10 (γ-carotene, β-carotene, lycopene, (E/Z)-phytoene, α-cryptoxanthin, β-cryptoxanthin, lutein, neoxanthin, violaxanthin, and zeaxanthin) were directly derived from the carotenoid metabolic pathway and displayed differential accumulation in the fruit flesh between ‘14-1’ and the other two lines (Figure 6C), providing a metabolic explanation for the large divergence in flesh color among the three watermelon lines. A correlation analysis was further carried out between the carotenoid-related DEGs and metabolites. The correlation coefficients between the seven DMs (γ-carotene, β-carotene, lycopene, (E/Z)-phytoene, α-cryptoxanthin, β-cryptoxanthin, and zeaxanthin) and DEGs were all greater than 0.9, while a weak correlation was observed between the other three DMs (violaxanthin, neoxanthin, and lutein) and the DEGs (Figure 6B). This observation was consistent with the previous claim that flesh color might be genetically controlled by a complex regulatory network (Zhao et al., 2013; Wang et al., 2019; Hermanns et al., 2020). Figure 6. Open in new tabDownload slide Correlation analysis of coloration-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) A heat map displaying the expression of carotenoid-related genes in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Contents of coloration-related DMs in carotenoid biosynthetic pathway in the fruits of ‘W600’, ‘14-1’, and F1 at the maturity stages. (C) Correlation of the coloration-related hub DEGs and DMs in watermelon fruits. (D) Total ion chromatography diagrams for lycopene in the fruits of ‘W600’, ‘14-1’, and F1. The peak time of lycopene was 8.23 min. CYP, cytochrome P450; FPKM, fragments per kilobase of transcript per million mapped reads; GGPP, geranylgeranyl diphosphate; AIR12, cytochrome b561 and domon domain-containing protein; PSY, phytoene synthase; PDS, phytoene desaturase; Z-ISO, ζ-carotene isomerase; ZDS, ζ-carotene desaturase; LCY, lycopene β-cyclase; ZEP, zeaxanthin epoxidase; VED, violaxanthin de-epoxidase; NXS, neoxanthin synthase; NCED, 9-cis-epoxycarotenoid dioxygenase; CrtISO, carotenoid isomerase; BCH, β-carotene hydroxylase; CYP97A, cytochrome P450 carotene β-hydroxylase; CYP97C, cytochrome P450 carotene ε-hydroxylase; TW600, ‘W600’ fruits at the transition stage; T14-1, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Flesh hardness, a quality trait genetically controlled by QTLs, has been demonstrated to be determined to a great extent by the contents of cell-wall components such as cellulose and pectin (Sun et al., 2020). Bulked segregant analysis (BSA) has demonstrated that Cla016033 and Cla012507 can influence flesh hardness by regulating cell wall composition and fruit maturity (Sun et al., 2020). In a study carried out by Sehgal et al. (2021), Cla012351 encoding a cellulose synthase and Cla004251 encoding a pectinesterase were observed to be key players in the metabolism of cellulose and pectin due to their high correlation with the contents of cellulose and pectin in watermelon fruits. Our phenotypic results showed that ‘14-1’ yielded fruits with soft flesh, and the degree of its flesh firmness was almost constant at both the transition and maturity stages; while for ‘W600’ and F1, both firm flesh and significantly varied hardness were observed at the two investigation points, indicating that hardness-related DEGs might be ‘W600’/F1-specific. We identified a total of 406 ‘W600’/F1-specific DEGs in the dark red gene module, which displayed a close association with flesh hardness in the WGCNA analysis. Interestingly, nine genes (Cla97C09G163490, Cla97C03G065030, Cla97C06G110120, Cla97C05G098130, Cla97C05G108640, Cla97C04G070440, Cla97C08G146150, Cla97C01G019430, and Cla97C06G118820) of the ‘W600’/F1-specific DEGs might function as critical players in cellulose and pectin degradation according to their annotation information (Figures 7A and 7C). Liao et al. (2020) reported a high correlation between ethylene responsive transcription factor 4 (ERF4) and the firmness of watermelon peel, partially supporting that Cla97C06G118820 encoding an ERF might be profoundly involved in the regulation of flesh firmness. To provide functional evidence for these DEGs, the contents of cellulose and pectin-related metabolites were further explored in an untargeted metabolomic analysis, and four DMs were identified, including galactonic acid, pectin, ethyl cellulose, and d-galactose (Figure 7B; Table S3). As a main component of the cell wall, pectin can be divided into protopectin, soluble pectin, and pectin acid, and the degradation of protopectin to soluble pectin leads to fruit softening (Figueroa et al., 2010). Correlation investigation was further carried out between the firmness-related DEGs and DMs, and the highest correlation with pectin was observed for Cla97C09G163490 (R2=0.9821) followed by Cla97C04G070440 (R2=0.9773; Figure 7), while the poorest correlation was revealed between Cla97C05G108640 and ethyl cellulose (Figure 7B). Figure 7. Open in new tabDownload slide Correlation analysis of hardness-related differentially expressed genes (DEGs) and differential metabolites (DMs) in the fruits of two watermelon parent lines and F1 plants. (A) A heat map displaying the expression of hardness-related DEGs in the fruits of ‘W600’, ‘14-1’, and F1 at the transition and maturity stages. (B) Correlation of the hardness-related hub DEGs and DMs in watermelon fruits. (C) Tandem mass spectrometry (MS/MS) diagram for pectin in watermelon fruits. PME inhibitor, pectin methylesterase inhibitor; CESA1, cellulose synthase 1; CESA2, cellulose synthase 2; CESA3, cellulose synthase; CSLC5, cellulose synthase-like C5; BGAL3, beta-galactosidase 3; ERF041, ethylene-responsive transcription factor 1; FPKM, fragments per kilobase of transcript per million mapped reads; PME2, pectinesterase 2; TW600: ‘W600’ fruits at the transition stage; T14-1, ‘14-1’ fruits at the transition stage; TF1, F1 fruits at the transition stage; MW600, ‘W600’ fruit at the maturity stage; M14-1, ‘14-1’ fruit at the maturity stage; MF1, F1 fruits at the maturity stage. Conclusions In the present study, an integrated transcriptome and metabolome approach was adopted to investigate the molecular foundation of flesh taste, color, and texture in the fruits of ‘14-1’, ‘W600’, and F1 hybrid lines. Significant divergence was revealed in both the transcriptomic and metabolomic profiles of the fruits from the three watermelon lines, providing a comprehensive explanation for their phenotypic variations. A series of hub genes and metabolites closely associated with flesh taste, color, and texture were identified and could be used as potential targets for watermelon improvement in the future. Author Contributions Qinghua Shi and Xiaoyu Yang conceived the research; Shunpeng Chu and Mengmeng Yin performed the experiments; Shunpeng Chu, Shuoshuo Wang, Qinghua Shi and Xiaoyu Yang analyzed data; Qinghua Shi, Xiaoyu Yang, Shunpeng Chu, Shuoshuo Wang wrote the manuscript; Ruimin Zhang provided technical assistance during manuscript preparation. All authors read and approved the final manuscript. Funding This work was supported by the Agricultural Variety Improvement Project of Shandong Province (2019LZGC005) and the Shandong Vegetable Research System (SDAIT-05), China. 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Food Quality and SafetyOxford University Press

Published: Jan 1, 2022

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