A comparison of fruit quality formation and gene expression of Ziziphus jujuba Mill. cv. 'Lingwuchangzao' cultivated in the same field

A comparison of fruit quality formation and gene expression of Ziziphus jujuba Mill. cv. 'Lingwuchangzao' cultivated in the same field | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 February 2025 V1 Latest version Share on A comparison of fruit quality formation and gene expression of Ziziphus jujuba Mill. cv. 'Lingwuchangzao' cultivated in the same field Authors : Jiadong Wang , Ying Wang , Xiaoqin Liu , Gaier Yang , Xuan Zhang , Yunmao Li , Bin Cao , and Xiang Li [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174054975.52760082/v1 372 views 186 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Using high- and low-flatness fruits of Ziziphus jujuba Mill. cv. ’Lingwuchangzao’ at different developmental stages as test materials, this study examined the mechanisms underlying variations in fruit appearance and internal quality. The findings revealed significant differences between S and R fruits throughout development. S fruits were glossy, smaller, and predominantly seedless, with lower single-fruit weight and thinner pericarp and pulp, while R fruits exhibited reduced glossiness, larger size, and deeper coloration. Transcriptome analysis identified key genes, including CER1 and FAR , involved in wax synthesis, influencing cuticular wax production on the fruit surface. Additionally, genes such as AUX/IAA affected epidermal cell development, leading to smaller and denser cells in R fruits, thereby impacting surface flatness. Collectively, genes regulating cell development and wax biosynthesis determined the glossiness of Lingwu jujube fruits. Metabolomic analysis identified 779 metabolites, with flavonoids constituting the most abundant class. Protein interaction network analysis highlighted the hub gene ABCG31 , closely linked to wax synthesis, as a critical factor in fruit flatness and glossiness. This study elucidates the quality differences and molecular regulatory mechanisms between high- and low-flatness Lingwu jujube fruits, offering valuable insights for genetic improvement to enhance visual appeal and commercial value. A comparison of fruit quality formation and gene expression of Ziziphus jujuba Mill. cv. ’Lingwuchangzao’ cultivated in the same field Jiadong Wang a,b , Ying Wang c , Xiaoqin Liu c , Gaier Yang a,b , Xuan Zhang a,b , Yunmao Li a,b , Bin Cao d , Xiang Li a,b,* a College of Forestry and Prataculture, Ningxia University, Yinchuan, Ningxia University, 750021, China b State Key Laboratory of Efficient Production of Forest Resources, Yinchuan, Ningxia University 750021, China c Lingwu Natural Resources Bureau, Yinchuan, 751400, China d Ningxia Technical College of Wine and Desertification Prevention, Yinchuan 750100, China； * Corresponding authors at: State Key Laboratory of Efficient Production of Forest Resources, College of Forestry and Prataculture, Ningxia University, Yinchuan 750021, China E-mail addresses: [email protected] (X. Li) Abstract: Using high- and low-flatness fruits of Ziziphus jujuba Mill. cv. ’Lingwuchangzao’ at different developmental stages as test materials, this study examined the mechanisms underlying variations in fruit appearance and internal quality. The findings revealed significant differences between S and R fruits throughout development. S fruits were glossy, smaller, and predominantly seedless, with lower single-fruit weight and thinner pericarp and pulp, while R fruits exhibited reduced glossiness, larger size, and deeper coloration. Transcriptome analysis identified key genes, including CER1 and FAR , involved in wax synthesis, influencing cuticular wax production on the fruit surface. Additionally, genes such as AUX/IAA affected epidermal cell development, leading to smaller and denser cells in R fruits, thereby impacting surface flatness. Collectively, genes regulating cell development and wax biosynthesis determined the glossiness of Lingwu jujube fruits. Metabolomic analysis identified 779 metabolites, with flavonoids constituting the most abundant class. Protein interaction network analysis highlighted the hub gene ABCG31 , closely linked to wax synthesis, as a critical factor in fruit flatness and glossiness. This study elucidates the quality differences and molecular regulatory mechanisms between high- and low-flatness Lingwu jujube fruits, offering valuable insights for genetic improvement to enhance visual appeal and commercial value. Keywords: Ziziphus jujuba Mill.cv. ’Lingwuchangzao’; flatness; glossiness; fruit quality; fruit development; wax; hormone 1 Introducation In modern fruit tree cultivation, fruit quality plays a pivotal role in determining market competitiveness and significantly influences both the nutritional and commercial value of fruits. Fruit quality is broadly categorized into external and internal attributes. Externally, consumers consistently prefer higher-quality fruits, such as apples with vibrant coloration, uniform size, and defect-free appearance, which are more likely to attract attention in the market (Harker, Gunson & Jaeger, 2003). Among these attributes, flatness and glossiness are critical components of external quality, intricately linked with other quality factors. Physiological processes such as cuticular wax and cutin synthesis, epidermal cell development, and cell wall structure collectively or individually shape the fruit’s flatness and glossiness, thereby influencing its external appeal. Internally, high-quality fruits are rich in essential nutrients, including vitamins (e.g., vitamin C [Vc], vitamin E), minerals (e.g., potassium, magnesium), and bioactive compounds (e.g., flavonoids, anthocyanins)) (Arias, Feijoo & Moreira, 2022; Liu et al., 2022). These nutritional components offer significant health benefits, making high-quality fruits more desirable to consumers. Various factors influence fruit quality, including climatic conditions, soil properties, and genetic traits. Climatic factors, such as light, precipitation, and temperature, play a particularly pivotal role. For instance, uniform facility lighting has been shown to enhance color development and improve hybrid citrus quality (Wang et al., 2022). Similarly, heavy rainfall years have been associated with earlier grape harvests and superior fruit quality (Meng, Cui, Li, Zhang, Ma &Chai, 2023). Stable environmental temperatures contribute to maintaining protective enzyme activity, mitigating storage-related cold damage, and preserving fruit quality (Liu, Qiao, Li, Zhang, Zhang & Li, 2017) (He et al., 2022). In summary, external and internal fruit qualities collectively define the market competitiveness and economic value of fruits, with multiple environmental and genetic factors interacting to shape overall quality. In addition to external environmental factors, fruit qualities such as size, glossiness, flatness, sugar and acid content, aroma, and polyphenol levels are strongly linked to gene expression (Liu et al., 2022). Previous studies have identified key genes that influence fruit glossiness by regulating cuticular layer thickness and wax content in the fruit skin. For example, Gao et al. discovered that in the Csgp mutant of cucumber, upregulation of genes related to wax and cutin transport led to a thinner cuticular layer, while the mutation of CsSEC23 altered wax and cutin content, further affecting glossiness (Gao et al., 2023). Similarly, Yang et al. found that in citrus epidermis, CsERF003 , CsMYB7 , and CsMYB102 could upregulate the CER1 gene, thereby increasing cuticular wax content (Yang et al., 2023). Other studies have also shown that wax content in the fruit cuticle negatively correlates with glossiness. In cucumber, the wax biosynthesis gene CsCER6 and the regulatory gene CsCER7 were highly expressed in epidermal cells, promoting wax accumulation but diminishing glossiness (Liu, Ge, An, Liu & Ren, 2023). Further research by Priyanka Trivedi et al. indicated that increased expression of genes such as FAR2 and CER3 enhanced fatty acid and ketone synthesis, resulting in a higher wax crystal content in the epidermis of wild-type blueberries compared to glossy-type blueberries (Trivedi et al., 2021). In tomatoes, overexpression of the SIPP2C3 gene led to increased wax content in the fruit skin and a rougher surface wax structure, reducing glossiness. Conversely, SlMYB31 regulated SlCER6 upregulation to enhance cuticular wax biosynthesis, improving glossiness in inhibited-type tomatoes (Liang et al., 2021; Xiong et al., 2020). Fruit glossiness may also be influenced by the expression of genes involved in cuticular wax biosynthesis and transport under hormonal stimulation and environmental stress. Various exogenous hormones, such as methyl jasmonate (MeJA), salicylic acid (SA), ethylene (ETH), melatonin (MT), and abscisic acid (ABA), can alter the wax content and structural characteristics of plant epidermis, subsequently affecting wax deposition and its function (Cao et al., 2022; Mackova, Vaskova, Macek, Hronkova, Schreiber & Santrucek, 2013; Yuan et al., 2020). The expression of wax transport genes is also modulated by environmental conditions. Yuan et al. found that in goji berry, the wax transport gene LbABCG11 was significantly upregulated under high-temperature and drought stress but downregulated under low-temperature stress, which could indirectly affect glossiness (Yuan et al., 2023). Collectively, these findings demonstrate that fruit glossiness is closely associated with the wax content, composition, proportions, and overall structure of the fruit cuticle, all of which are regulated by genetic factors. Fruit flatness, a key aspect of external quality, is influenced by epidermal cell structure, which not only indirectly affects glossiness but also impacts consumer preference. A study on cherry tomatoes revealed that fruit surface flatness significantly influenced glossiness measurements (Zhou, Xu, Wang, Zhao, Yu & Zhao, 2018). Additional research indicated that uneven fruit surfaces reduced glossiness; for example, apples without surface russeting had a flat surface, while russeting prevented flatness and markedly decreased glossiness (Wei, Ye, Zhang, Ji, Li, 2003). Similarly, cucumber fruits with flatter surfaces exhibited better glossiness, while rough surfaces diminished glossiness (Gao et al., 2023). The size, arrangement, and density of epidermal cells directly affect the flatness of the skin, which in turn influences glossiness. Damage to or rupture of epidermal cells disrupts normal growth and arrangement, resulting in scars and roughness on the fruit surface (Bi et al., 2024). Furthermore, apples with irregularly arranged, loose, and sparse epidermal cells demonstrated reduced flatness (Li et al., 2022). These studies not only confirm the association between flatness and glossiness but also reveal a close, intrinsic relationship among epidermal cell structure, fruit flatness, and glossiness, each factor influencing the others and collectively shaping the external quality of the fruit. Ziziphus jujuba Mill. cv. ’Lingwuchangzao’ is a distinguished cultivar in Ningxia (Han, Zhao, Cao, Wang & Chen, 2018), known for its large fruits, excellent flavor, and rich nutritional content, including high vitamin levels (Song, Cao & Zhang, 2014). The annual increase in Lingwuchangzao’s planting area and expanding industry scale have led to rising consumer quality expectations (Wu, Tang & Zhang, 2019). As an essential edible organ of Lingwuchangzao, the fruit’s flatness and glossiness significantly influence its quality and, consequently, its commercial value. However, variations in fruit quality, caused by environmental changes and management techniques, have resulted in uneven surfaces and reduced glossiness, underscoring the need to improve the production of high-quality fruit. Despite this, the mechanisms underlying differences in flatness and glossiness remain unclear. This study aims to investigate the phenotypic, physiological, and molecular mechanisms governing flatness and glossiness in Lingwuchangzao fruits, which will be instrumental in improving fruit quality and commercial value, as well as accelerating the genetic enhancement of Lingwuchangzao. 2 Materials and methods 2.1 Overview of the Test Site The trial was conducted from July to October 2023 at Ningxia University Teaching Experimental Farm (N 38°47′07″, E 106°04′00″). The average annual temperature at the site is 8.5°C, with an annual temperature range of 31.5°C and an average daily temperature difference of 13.6°C. The frost-free period lasts 167.0 days, and annual precipitation ranges from 180 to 200 mm. The experimental plants were Ziziphus jujuba trees of the ’Lingwuchangzao’ cultivar grafted onto Ziziphus jujuba var. spinosa rootstock, spaced at 2 m × 3 m with rows oriented north-south. 2.2 Experimental Sample Plants and Fruit Sampling The study utilized 12- to 15-year-old grafted Lingwuchangzao trees exhibiting uniform vigor. Field management followed standard cultivation practices to ensure consistent growing conditions. From July to October 2023, fruit samples were collected at five developmental stages: young fruit stage (YF), swelling fruit stage (PF), white ripe fruit stage (WF), color transition fruit stage (ZF), and mature fruit stage (CF). At each stage, 30 fruits with high flatness (S fruits) and low flatness (R fruits) were sampled from the eastern, southern, western, and northern directions of the tree canopy. Immediately after collection, external quality measurements were performed. The samples were then stored at -80°C for subsequent internal quality analysis. 2.3 Fruit Quality Determination 2.3.1 Measurement of Fruit Appearance and 1uality Indexes For each stage, thirty single-fruit replicates were analyzed for both S and R fruits. Glossiness was measured using a gloss meter (YG60, Shenzhen Sanen Technology Co., Ltd.), with the glossiness value recorded at a 60° angle. Measurements were taken from nine points on the equatorial region of each fruit, and the average value was calculated. The longitudinal and transverse diameters of the fruits, along with the thickness of the exocarp and mesocarp, were measured using a digital caliper. The weight of individual fruits and seeds was recorded with an electronic balance. Fruit hardness was determined using a GY-4 hardness tester (Zhejiang Top Instrument Co., Ltd.). The seed rate was calculated as follows: seed rate = (number of seeds with kernels / total number of kernels) × 100%. For fruit color analysis, the L*, a*, b*, C*, and h° values were measured at different points—shoulder, middle, and near the tip of the fruit—using a color difference meter (NR110, Shenzhen Sanen Technology Co., Ltd.). 2.3.2 Measurement of Fruit Intrinsic Quality Indexes Soluble sugars were quantified using the anthrone-sulfuric acid method. Soluble solids content was determined with a digital refractometer (WZB45 handheld digital refractometer, Shanghai Yidian Physical Optics Instrument Co., Ltd.). Vc levels were measured via the molybdenum blue colorimetric method. Organic acids were evaluated through acid-base titration. Anthocyanins and flavonoids in the fruit skin were quantified using ultraviolet-visible spectrophotometry. Each parameter was assessed using seven biological replicates. 2.4 Observation of Pericarp Microstructure Microstructural observations of epidermal cells were performed on S and R fruits at the young fruit stage (YF), swelling fruit stage (PF), white ripe fruit stage (WF), color transition fruit stage (ZF), and mature fruit stage (CF). Fresh fruit samples from each developmental stage were examined using a microscope with a 100× objective lens. ImageView software was employed to measure the longitudinal and transverse diameters, perimeter, area, and cell density in the same field of view. Paraffin sections of the fruit skin at different stages were stained using the safranin-green method. Wax morphology on the surface of the fruit skin at various stages was observed with scanning electron microscopy, following the method outlined by Kou et al. (Kou, Chai, Yuan & Xue, 2021). 2.5 Transcriptome Test Methods Total RNA was extracted using the TRIZOL reagent kit, and RNA integrity was confirmed via agarose gel electrophoresis. RNA purity was assessed with a NanoPhotometer spectrophotometer, and concentration was determined with a Qubit 2.0 fluorometer. PolyA-tailed mRNA was enriched using oligo(dT) magnetic beads, followed by fragmentation and cDNA synthesis with random primers. After purification, end repair, and adapter ligation, PCR enrichment was performed to generate the final cDNA library. The library was quality-checked and sequenced on the Illumina HiSeq platform. Clean data were obtained by filtering raw data and aligned with the high-quality reference genome of Ziziphus jujuba Mill. cv. Dongzao T2T (Yang et al., 2023). Differential expression analysis was performed based on gene expression levels across different samples or sample groups, with functional annotation and pathway enrichment analysis of differentially expressed genes (DEGs). The threshold for identifying DEGs was set at |log2Fold Change (FC)| ≥ 1 and P -adjusted value < 0.05. 2.6 Metabolomic Test Methods 2.6.1 Dry Sample Handling A total of 18 biological samples were subjected to vacuum freeze-drying using a freeze-dryer (Scientz-100F). The dried samples were ground into a powder using a grinder (30 Hz, 1.5 minutes). Exactly 50 mg of the powdered sample was weighed using an electronic balance and mixed with 1200 µL of pre-cooled (−20°C) internal standard extraction solution containing 70% methanol. If the sample weight was less than 50 mg, the extraction solvent was added in a 1200 µL per 50 mg ratio. The mixture was vortexed every 30 minutes for 30 seconds, totaling six vortexing sessions. After centrifugation (12,000 rpm for 3 minutes), the supernatant was collected and filtered through a 0.22 μm microporous membrane, and the filtered sample was stored in vials for UPLC-MS/MS analysis. 2.6.2 Chromatographic Mass Spectrometry Acquisition Conditions The data acquisition system used ultra-high-performance liquid chromatography (UPLC) coupled with tandem mass spectrometry (MS/MS). The chromatographic column was an Agilent SB-C18, 1.8 µm, 2.1 mm × 100 mm. The mobile phase consisted of phase A (ultrapure water with 0.1% formic acid) and phase B (acetonitrile with 0.1% formic acid). The elution gradient was as follows: at 0.00 minutes, phase B was at 5%; from 0.00 to 9.00 minutes, phase B increased linearly to 95%, maintaining this for 1 minute; from 10.00 to 11.10 minutes, phase B decreased back to 5%, equilibrating at this proportion until 14 minutes. The flow rate was set to 0.35 mL/min, with the column temperature maintained at 40°C, and the injection volume was 2 µL. Mass spectrometry was conducted using an electrospray ionization (ESI) source at a temperature of 550°C. The ion spray voltage (IS) was set to 5500 V in positive mode and −4500 V in negative mode. The source gases (I and II) and curtain gas (CUR) were set at 50, 60, and 25 psi, respectively. Collision-induced dissociation was set to high. QQQ scanning was performed in multiple reaction monitoring (MRM) mode with nitrogen as the collision gas, set to moderate pressure. The declustering potential (DP) and collision energy (CE) were optimized for each MRM pair. Specific MRM ion pairs were monitored for each period based on the metabolites eluted during that period. 2.7 Data Processing and Analysis Data were organized using Excel 2021, with variance analysis and significance testing performed in SPSS 27.0. Bar charts and tables were primarily generated using Excel 2021 and Origin 2021. Target genes were imported into TBtools (v2.142) to extract protein sequences, which were subsequently matched with Arabidopsis protein sequences in the STRING database (https://cn.string-db.org). The resulting TSV files were exported and processed in Cytoscape (v3.9.1) to construct protein-protein interaction networks. Gene expression data were analyzed in TBtools, utilizing the Graphics tool’s Heatmap function for standardization and visualization. Correlation heatmaps were generated using the correlation clustering tool on the Lianchuan Biological Cloud Platform (https://www.omicstudio.cn/analysis). Pathway maps were created in draw.io, where genes associated with various enzymes were extracted alongside their expression levels to produce a heatmap. DEGs and cell development-related indicators were imported into the Metware Cloud Platform (https://cloud.metware.cn) for WGCNA analysis. Correlation analysis of differentially accumulated metabolites and DEGs was conducted on the Lianchuan Biological Cloud Platform (https://www.omicstudio.cn/analysis). Based on the results, genes and metabolites meeting the criteria (|r| > 0.95, P < 0.001) were filtered and imported into Cytoscape to generate a correlation network diagram. 3 Results and Analysis 3.1 Quality analysis of high and low flatness fruits at different developmental periods 3.1.1 Comparative analysis of gloss and color indexes of high and low flatness fruits at different developmental periods The developmental stages of Lingwuchangzao fruits, including the young fruit stage (YF), swelling fruit stage (PF), white ripe fruit stage (WF), color transition fruit stage (ZF), and mature fruit stage (CF), were analyzed for S (high flatness) and R (low flatness) fruits (Figure 1a). Measurements of glossiness and color indices revealed that the glossiness of S fruits was consistently higher than that of R fruits across all stages. The glossiness of S fruits stabilized after the white ripening stage, while R fruits showed a significant increase in glossiness from the young fruit stage to the coloring stage, peaking during the latter, with no significant difference between the coloring and ripening stages (Figure 1b). The L* values of both S and R fruits increased significantly during development, peaking at the white ripening stage, followed by a marked decline, with the lowest values observed at maturity. At all stages, S fruits exhibited significantly higher L* values compared to R fruits (Figure 1c). The a* value of S fruits remained stable from the young fruit to the swelling stage before increasing significantly until maturity. Conversely, the a* value of R fruits decreased significantly from the young fruit to the swelling stage but increased sharply at maturity. During the swelling stage, the a* value of S fruits was significantly lower than that of R fruits but became significantly higher at maturity, with no significant differences during other periods (Figure. 1d). The b* values for both S and R fruits decreased significantly during the young fruit and swelling stages, increased at the white ripening stage, and sharply declined to their lowest values at maturity. The c* value for S fruits decreased significantly during the young fruit and swelling stages, showed no notable change during the white ripening stage, and declined further at maturity. For R fruits, the c* value remained stable from the young fruit to the white ripening stage but declined sharply at maturity. At all stages, S fruits displayed significantly higher b* and c* values than R fruits (Figure 1e, Figure 1f). The h° value, indicative of chroma angle and color transitions ranging from purple-red to blue-green, showed that both S and R fruits were green from the young fruit to the white ripening stage and turned red at maturity, with the lowest h° values recorded. At maturity, R fruits exhibited a shade closer to purple-red, with h° values significantly lower than those of S fruits ( P < 0.01) (Figure 1a, Figure 1g). Figure 1. Comparison of glossiness L*, a*, b*, C*, and h° at different developmental stages of S and R fruits Flatness fruit (S: high flatness; R: low flatness) was analyzed across five developmental stages: young fruit stage (YF), swelling fruit stage (PF), white ripe fruit stage (WF), color transition fruit stage (ZF), and mature fruit stage (CF). Different lowercase letters indicate significant differences within S or R fruits across developmental stages ( P < 0.05). * and ** denote significant and highly significant differences between S and R fruits at the 0.05 and 0.01 levels, respectively, as determined by the T-test (the same below). The following parameters were analyzed: (a) comparison of S and R fruits at different periods; (b) glossiness; (c) L (brightness); (d) a* (red-green values); (e) b* (yellow-blue values); (f) c* (chroma); (g) h° (hue angle). 3.1.2 Comparative Analysis of the Main External Quality Traits of Fruits with Different Levels of Flatness During the Same Developmental Stage Observations (Figure 2) indicated that the single fruit weight and length-to-width ratio of Lingwuchangzao S fruits were significantly lower than those of R fruits across all developmental stages. From the young fruit stage to maturity, the single fruit weight and width of S fruits showed a significant increase, while fruit length increased significantly until the color change stage, stabilizing thereafter. The shape index of both S and R fruits initially decreased and then stabilized; S fruits stabilized after the color change stage, whereas R fruits stabilized after the white ripe stage. During the young fruit stage, S fruits exhibited a higher shape index compared to R fruits, but this relationship reversed in subsequent stages. The skin and flesh thickness of S fruits were significantly lower than those of R fruits throughout development. While the flesh thickness of S fruits increased significantly until maturity, R fruits reached their maximum flesh thickness at the white ripe stage. Additionally, the single seed weight and seed length-to-width ratio of S fruits were consistently smaller than those of R fruits, with a marked increase observed from the young fruit to the white ripe stage. The seed shape index of S fruits remained higher than that of R fruits across all stages, showing no significant variations. The hardness of S fruits was consistently lower than that of R fruits, suggesting that R fruits were better suited for storage and transport. Notably, the seed content rate of S fruits was 0 across all developmental stages, whereas R fruits maintained a seed content rate exceeding 93%. Figure 2. Comparison of appearance quality of fruits at different developmental periods of S and R fruits a:Single fruit weight; b:Length of fruit; c:Transerse diameter of fruit; d:Fruit type index;e:Thick pericarp:f:Thick flesh; g:Nuclear weight;h:Nuclear longitude diameter;i:Nuclear transverse1; j:Nuclear type index;k:Fruit hardness; l:Kernel content. 3.1.3 Comparative Analysis of the Main Internal Quality Traits of Fruits with Different Levels of Flatness During the Same Developmental Stage During development, the total phenolic content of S fruits was higher than that of R fruits at all stages, showing a slight decline from the young fruit to the swelling stage, followed by an increase at maturity. Flavonoid content peaked during the young fruit stage; thereafter, S fruits exhibited lower flavonoid levels than R fruits in most stages, with a notable exception during the young fruit stage. Soluble sugar content showed no significant differences between the groups and generally increased, peaking at maturity. Titratable acidity in S fruits rose significantly from the color change to the maturity stage, surpassing levels observed at earlier stages. In R fruits, titratable acidity was significantly higher at maturity compared to the white ripe stage. The sugar-acid ratio in S fruits increased initially before decreasing, while in R fruits, it consistently increased, reaching its maximum at maturity. Vc content displayed distinct trends between the groups. In S fruits, Vc levels increased significantly from the young fruit to the swelling stage, followed by a sharp decline at maturity. R fruits maintained higher Vc levels during the swelling stage. At maturity, Vc content in S fruits was significantly lower than in R fruits, whereas no significant differences were observed during other stages (Figure 3). Figure 3. Comparison of the intrinsic qualities of fruits at different developmental stages of S and R fruits a:Total phenolic content(mg/g); b:Flavonoid content(mg/g); c:Soluble sugar content/%; d:Titratable acid content/%; e:Sugar acid ratio; f: Vc content(mg/100g). 3.1.4 Microscopic Observation of the Fruit Skin Structure of S and R Fruits During the Developmental Stages of Non-Fertile Fruits Analysis of Figure 4 and Figure 5 revealed significant differences in the epidermal cell structure and wax crystal morphology between Lingwuchangzao S (high flatness) and R (low flatness) fruits across developmental stages. The longitudinal and transverse diameters, area, and perimeter of epidermal cells in S fruits were significantly larger than those in R fruits, while cell density and cuticular thickness were markedly lower in S fruits. During the young fruit to swelling stage, the longitudinal diameter of epidermal cells in both S and R fruits, along with the transverse diameter, area, and perimeter in R fruits, remained relatively stable, but significant growth occurred from the swelling to color change stage. In the color change to maturity stage, the longitudinal diameter of S fruits and the transverse diameter of R fruits showed no significant change, whereas the longitudinal diameter, area, and perimeter of R fruit epidermal cells declined significantly. For S fruits, the transverse diameter and area exhibited a trend of significant increase during the young fruit to color change stage, followed by a pronounced decline during the color change to maturity stage. Similarly, the perimeter of R fruits increased significantly in the early stages before a notable decrease at maturity. Wax crystal morphology, as illustrated in Figure 5, also showed distinct differences between S and R fruits throughout development. R fruits consistently exhibited densely attached flake-like wax crystals on their epidermal surface, particularly prominent during the young fruit, swelling, and white ripe stages, with a decreasing trend after the color change stage. In contrast, S fruits initially displayed granular and flocculent wax crystals during the young fruit stage, transitioning to flake-like wax crystals during the swelling and white ripe stages. By the color change stage, however, the epidermis of S fruits was almost devoid of wax crystals. The quantity of wax crystals on S fruits was consistently lower than that observed on R fruits during the same period. Figure 4. Comparison of pericarp cells at different developmental periods of S and R fruits a:Longitudinal diameter of pericarp cells/µm; b:Transverse diameter of pericarp cells/µm; c:Pericarp cell area/µm 2 ; d: Pericarp cell circumference/µm; e: Pericarp cell density/mm 2 ; f:Keratinous membrane thickness/µm. Figure 5. Epidermal cells of the skin at different developmental periods, and paraffin sections of S and R fruits The lamellae on the scanning electron microscope image are lamellar waxy crystals deposited on the epidermal stratum corneum; the translucent membranous structure outside the first layer of epidermal cells in the paraffin section image is the stratum corneum. 3.2 Transcriptome Analysis of Fruit Skin from Lingwuchangzao with Different Levels of Flatness at Various Developmental Stages 3.2.1 RNA Data Quality Assessment and Analysis To analyze the transcriptomic responses of Lingwuchangzao S fruits (high flatness and glossiness) and R fruits (low flatness and glossiness) across developmental stages, 18 cDNA libraries were constructed. Transcriptome sequencing was performed on epidermal samples collected at the young fruit stage (Y), white ripe stage (W), and maturity stage (C). Clean reads per sample ranged from 38,740,138 to 48,932,232. Sequencing quality metrics demonstrated Q20 values above 97% (97.81%–98.31%) and Q30 values ranging from 93.75% to 94.88%. Alignment of clean reads to the latest T2T reference genome of Ziziphus jujuba Mill. cv. Dongza (Yang et al., 2023) achieved mapping rates between 95.48% and 96.32% for both S and R fruit samples, confirming the reliability and high quality of the sequencing data for downstream analysis (Table S1). After normalizing the gene expression levels, FPKM values were used to represent the expression data. The results indicated that gene expression levels across the 18 samples varied from 10 -2 to 10 4 , spanning six orders of magnitude. The FPKM density curve for each sample peaked between 1 and 1.25, suggesting that genes with moderate expression levels were predominant. Correlation coefficients among samples of fruits with varying glossiness from the same developmental stage were consistently greater than 0.95, indicating strong consistency. Principal component analysis (PCA) of the 18 samples revealed that the first two principal components, PC1 and PC2, effectively distinguished fruits based on their glossiness across the three developmental stages. Furthermore, biological replicates from the same developmental stage were closely clustered in the ordination space, demonstrating high reproducibility among the replicates (Figure S1). 3.2.2 Function Annotation Genes obtained from transcriptome sequencing were annotated for nucleotide and protein functions using BLAST, aligning with databases such as NR, Swiss-Prot, GO, Pfam, KOG, KEGG, and Trembl. The highest number of genes, totaling 27,362, was annotated in the NR protein database. Homologous comparison analysis of these genes revealed that Lingwuchangzao had the highest homology with Ziziphus jujuba var. spinosa (sour jujube), at 95%. Comparison of the 18 fruit skin samples from Lingwuchangzao with varying glossiness against the KOG database resulted in the annotation of 17,175 genes across 25 functional categories. The top three categories with the highest number of annotated unigenes were: R (General function prediction only) with 3,805 genes (22.15%), T (Signal transduction mechanisms) with 1,756 genes (10.22%), and O (Posttranslational modification, protein turnover, and chaperones) with 1,634 genes (9.87%) (Figure S2). 3.2.3 Identification of Differentially Expressed Genes At the three fruit developmental stages, the highest number of differential genes was observed during the young fruit stage, with 279 genes identified between the two flatness levels. This was followed by 180 significantly different genes at the white ripe stage when comparing R fruits to S fruits. The fewest differential genes, 112, were detected at the maturity stage. The quantity of differential genes in Lingwuchangzao fruits with varying flatness decreased from the young fruit stage to the white ripe stage and further declined at the maturity stage (Table 1). In total, 571 significant DEGs were detected between S and R fruits across the same developmental stages. The largest number of common genes—eight—was found in the comparisons RY_vs_SY and RW_vs_SW, followed by six common genes in the comparisons RY_vs_SY and RC_vs_SC. The fewest common genes, five, were detected in the comparison between RW_vs_SW and RC_vs_SC. No genes were commonly expressed across all three comparison groups. The number of stage-specific genes was 265 at the young fruit stage, 167 at the white ripe stage, and 101 at the maturity stage (Figure S3). Table 1. Comiparison of differentially expressed genes (DEGs) among different samples RY_vs_SY 279 75 204 RW_vs_SW 180 38 142 RC_vs_SC 112 53 59 3.2.4 Differentially Expressed Gene Enrichment Analysis To further investigate the differences in fruit skin flatness and glossiness in Lingwuchangzao, GO annotation and enrichment analysis were conducted on all DEGs (DEGs) (Figure 6a, Figure 6b, Figure 6c). The results revealed that the DEGs could be categorized into three main functional areas: biological process (BP), cellular component (CC), and molecular function (MF). In the biological process category, the differential genes from the young fruit stage (Y), white ripe stage (W), and maturity stage (C) were predominantly associated with cellular processes, metabolic processes, responses to stimuli, and biological regulation. These categories had the highest number of differentially annotated genes. In the cellular component category, ”cellular anatomical entity” was the most commonly annotated term. Within the molecular function category, binding and catalytic activity were the top two annotations for differential genes across all stages. However, the third and fourth most common annotations differed across stages: for the Y stage, ATP-dependent activity and transcriptional regulatory activity were predominant, while for the W and C stages, transporter activity and transcription regulator activity were more common. These observations suggest that the differential genes involved in fruit skin development with varying flatness primarily regulate biological processes, cellular components, and molecular functions, further clarifying the categories of these differential genes. After GO annotation, 96 entries related to wax and lipid synthesis were selected, with the majority (75 entries) associated with wax(Table 2). KEGG analysis provided a comprehensive approach to integrate transcriptomic data with broader functional insights, enabling systematic analysis of the extensive data generated. At the young fruit stage (Y), the differential genes in the RY_vs_SY comparison were significantly enriched in pathways related to DNA replication (10 genes, 10.1%), homologous recombination (9 genes, 9.09%), motor proteins (9 genes, 9.09%), mismatch repair (5 genes, 5.05%), pyrimidine metabolism (5 genes, 5.05%), and nucleotide metabolism (5 genes, 5.05%). Notably, the number of KEGG-enriched differential genes was highest at the young fruit stage. At the white ripe stage (W), the differential genes in the RW_vs_SW comparison were significantly enriched in pathways such as ABC transporters (5 genes, 8.33%), tryptophan metabolism (5 genes, 8.33%), and biosynthesis of secondary metabolites (23 genes, 38.33%) (Figure 6d, Figure 6e, Figure 6f). By the maturity stage (C), the differential genes in the RC_vs_SC comparison were primarily enriched in the ABC transporter pathway. Figure 6. GO and KEGG enrichment analysis of all DEGs a:RY_vsSY GO enrichment analysis; b:RW_vsSW GO enrichment analysis; c:RC_vsSC GO enrichment analysis; d:RY_vsSY KEGG enrichment analysis; e:RW_vsSW KEGG enrichment analysis; f:RC_vsSC KEGG enrichment analysis. Table 2. GO enrichment of wax and lipid related Wax-related GO:0010025 suberin biosynthetic process 35 GO:0010166 wax metabolic process 36 GO:1904276 regulation of wax biosynthetic process 2 GO:1904278 positive regulation of wax biosynthetic process 2 suberin-related GO:0010345 suberin biosynthetic process 17 Cuticle -related GO:0035017 cuticle pattern formatio 1 GO:0006723 cuticle hydrocarbon biosynthetic process 2 3.2.5 Analysis of Wax and Lipid Synthesis Pathways and Construction of Protein Interaction Networks The flatness and glossiness of fruits are directly linked to the cuticular membrane of the epidermis, composed of cutin and wax, which influence the fruit’s external quality. Functional enrichment analysis identified pathways related to the biosynthesis of cutin, suberin, and wax, categorized into three main sections: biosynthesis of unsaturated fatty acids (Figure 7a), biosynthesis of cutin and suberin (general form) (Figure 7b), and biosynthesis of wax (general form) (Figure 7c). A total of 56 candidate genes were identified, 33 of which remained after redundancy removal, and a heatmap was generated to show the expression levels of these 33 genes (Figure 7d). These 33 differential genes, associated with cutin and wax synthesis, were integrated with the top three transcription factors based on gene quantity (Figure 8a) to construct a protein-protein interaction network using the STRING database. The network diagram was optimized using the Degree algorithm in Cytoscape (Figure 8b), and the MCC algorithm was applied to identify the top 10 core genes (Figure 8c), revealing 10 key genes involved in cutin and wax synthesis. Among these, CER1-1 (LOC125423018) and AP2/ERF-ERF-56 (LOC107430347) exhibited higher expression at the maturity stage, while other genes were predominantly expressed at the young fruit stage (Figure 8d). Four of the hub genes were involved in the biosynthetic pathways of wax and cutin. Among the four structural genes, CYP86A4 (CYP86)-2 (LOC107429862) and CER1-4 (LOC107420951) showed higher expression levels in S fruits during the young fruit stage, while FAR-2 (LOC107432639) had higher expression in low-gloss fruits at the same stage. CER1-1 (LOC125423018) displayed higher expression in R fruits at maturity. The transcription factors AP2/ERF-ERF-56 (LOC107430347), bHLH-74 (LOC107407125), MYB-48 (LOC107416961), and bHLH-65 (LOC107430586) were interconnected in regulation, with bHLH-65 directly regulating MYB-81 (LOC107407717), which in turn regulated four structural genes. These interactions may influence wax and cutin synthesis. Correlation analysis of the expression levels of the 33 structural genes with cellular developmental indicators revealed that FAR-2 (LOC107432639) exhibited a highly significant positive correlation with cell density among the hub genes (Figure 8e). Figure 7. Fatty acid, keratin, and wax synthesis pathways and heatmap of expression of selected genes a:fatty acid synthesis pathway;b:keratin synthesis pathway;c:wax synthesis pathway; CER1 :Cuticular Wax Biosynthesis 1; CYP86A1 :Cytochrome P450 86A1; CYP86A4 :Cytochrome P450 86A4; CYP86B1 :Cytochrome P450 86B1; CYP94A5 :Cytochrome P450 94A5; CYP704B1 :Cytochrome P450 704B1; FAR :Fatty Acid Reductase; HHT1 :Hairy Hairy Trait 1; PXG :Phosphoesterase Gene; CYP77A6 :Cytochrome P450 77A6.The same below. Figure 8. Identification of transcription factors; structural genes of fatty acid, keratin, and waxy pathways and transcription factors to construct protein network interaction maps and analysis of hub gene expression; correlation of structural genes with indicators of cell development a:transcription factor number map MYB :myeloblastosis virus oncogene homolog; bHLH :basic Helix-Loop-Helix; AP2/ERF-ERF :APETALA2/Ethylene Response Factor; C2H2 :zinc finger C2H2 type transcription factor; FAR 1;far-red elongated hypocotyl 1; NAC :NAM, ATAF1/2, and CUC2; Others; WRKY :WRKY transcription factor; C3H :C3HC4-type zinc finger transcription factor; bZIP :basic leucine zipper; a:Identification of transcription factors; b:Fatty acids Keratin and Wax synthesis related genes and top three ranked transcription factors to construct protein network interactions map; c:Heatmap of hub gene expression for the top 10; d:top 10 ranked hub genes (MCC algorithm); e:correlation analysis of wax and lipid synthesis related genes and cell development indicators; AR: pericarp cell area; CM: corneal membrane thickness; DE: pericarp cell density; GL: gloss; LD: longitudinal diameter of pericarp cells; PE: pericarp cell circumference; TD: transverse diameter of pericarp cells.The same below. 3.2.6 Analysis of Hormone Signal Transduction Pathways and Construction of Protein Interaction Networks The development of fruit epidermal cells significantly influences flatness and glossiness, with plant hormone signaling molecules playing a pivotal role in cellular growth and development. KEGG enrichment analysis revealed that numerous genes were enriched in plant hormone signal transduction pathways, identifying 72 DEGs across eight hormone signaling pathways(Figure 9). Key DEGs included AUX/IAA and TIR1 in the auxin signaling pathway, CRE1 in the cytokinin signaling pathway, GID1 in the gibberellin signaling pathway, PP2C in the abscisic acid signaling pathway, and JAZ and COI1 in the jasmonic acid signaling pathway. These 72 hormone-related genes were integrated with the top three transcription factors based on gene quantity to construct a protein interaction network using the Degree algorithm in Cytoscape (Figure 10a). The MCC algorithm identified the top 10 hub genes (Figure 10b), revealing that with the exception of TIR1-3 (LOC125423796), all other hub genes belonged to the AUX/IAA family, suggesting that auxin plays a central role in epidermal cell development. Expression levels of the 10 hub genes were analyzed and visualized in a heatmap using Tbtools. The results showed that AUX/IAA-8 (LOC107416081) and AUX/IAA-16 (LOC107434239) exhibited higher expression levels at the maturity stage, while AUX/IAA-2 (LOC107406101), AUX/IAA-5 (LOC107412162), and TIR1-3 (LOC125423796) had higher expression during the young fruit and white ripe stages. The remaining hub genes were predominantly expressed at the young fruit stage (Figure 10c). Correlation analysis between the expression levels of the 72 DEGs in the hormone signaling pathway and cellular developmental indicators revealed significant positive correlations for AUX/IAA-15 (LOC107431810), AUX/IAA-9 (LOC107420373), and AUX/IAA-6 (LOC107412229) with fruit skin cell density (Figure 10d). Figure 9. Hormone signalling pathway diagram COI1 :Coronatine Insensitive 1; AUX/IAA :Auxin/indole-3-Acetic Acid; GID1 :Gibberellin Insensitive Dwarf 1; JAZ :Jasmonate ZIM-Domain; TIR1 :Transport Inhibitor Response 1; PP2C :Protein Phosphatase 2C; CRE1 :Cyclic AMP Response Element Binding Protein 1.The same below. Figure 10. Hormone signalling pathway genes and transcription factors to construct protein network interaction maps and analyse hub gene expression; correlation of structural genes with indicators of cell development a:Protein network interactions between genes involved in hormone signalling and the top three transcription factors. b:top 10 ranked hub genes (MCC algorithm); c:Heatmap of hub gene expression for the top 10; Correlation analysis of d:hormone signalling-related genes with cell developmental indicators. 3.2.7 WGCNA Network Analysis The expression levels of DEGs from the RY_vs_SY, RW_vs_SW, and RC_vs_SC groups were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA) to investigate their relationship with cellular developmental indicators. The analysis categorized the DEGs into five distinct modules (Figure 11a). Among these, the MEturquoise module contained the highest number of genes, with 223, whereas the Megrey module contained only two genes, the fewest among all modules (Figure 11b). Correlation analysis between the genes in the five modules and cellular developmental indicators revealed significant associations for the Meblue, MEturquoise, and Meyellow modules with cellular development metrics (Figure 11c). Differential genes from these three modules were extracted and used to construct protein interaction networks, integrating the top three transcription factors based on gene quantity. The networks were optimized using the Degree algorithm in Cytoscape, and the MCC algorithm identified the top 15 genes, which were visualized in a heatmap (Figure S4). In the Meblue module, key transcription factors included bHLH-65 (LOC107430586), AP2/ERF-ERF-56 (LOC107430347), and bHLH-74 (LOC107407125), regulated in a reciprocal manner by the PP2C family member (LOC107424116). For the Meyellow module, critical transcription factors were AP2/ERF-ERF-56 (LOC107430347), bHLH-74 (LOC107407125), MYB-48 (LOC107416961), bHLH-65 (LOC107430586), and MYB-81 (LOC107407717). Among these, AP2/ERF-ERF-56 (LOC107430347) exhibited higher expression at the maturity stage, while the others showed elevated expression during the young fruit stage, consistent with findings from Section 2.2.6. Additionally, BURP (LOC125419810) was found to engage in mutual regulation with these transcription factors. Figure 11. WGCNA analysis of differential genes and cell developmental indicators in the three groups RY _ vs _ SY, RW _ vs _ SW, RC _ vs _ SC aCluster number dendrogram of gene expression data, where each colour indicates that a colour corresponds to each gene belonging to the same module in the clustering tree; bHistogram of the number of genes in the different modules; cHeatmap of the correlation between the different modules and the developmental indexes of the epidermal cells of the fruit. 3.2.8 RT-qPCR Analysis of Differentially Expressed Genes To validate the reliability of the transcriptome data, 12 DEGs with significant fold changes were randomly selected for quantitative fluorescent analysis. Real-time quantitative PCR (RT-qPCR) was conducted to detect changes in gene expression levels. The results (Figure 12) demonstrated that the expression trends for S and R fruits across the three developmental stages were largely consistent with the transcriptome sequencing data, confirming the high reliability of the transcriptomic analysis. Figure 12. RT-qPCR analysis of 12 differential genes in the three groups RY _ vs _ SY, RW _ vs _ SW, RC _ vs _ SC 3.3 Metabolomic Analysis of Fruit Peel Samples with Different Degrees of Flatness at Various Developmental Stages 3.3.1 UPLC Analysis of Metabolites in Fruit Peels and Correlation of Samples with PCA Analysis Overlapping and comparing the total ion chromatograms (TIC) of QC samples, along with Pearson correlation analysis, revealed a stable baseline in the QC sample TIC, demonstrating excellent reproducibility and high peak resolution (Figure S5a, Figure S5b). The correlation coefficients among peel samples of varying gloss levels at the same developmental stage were all above 0.95, indicating a strong correlation and confirming the stability and reproducibility of the experiment, thereby ensuring the reliability of the detection data (Figure S5c). PCA of metabolites from 18 samples of different gloss levels at three developmental stages (Figure S5d) showed that the first two principal components, PC1 and PC2, effectively differentiated the samples. Moreover, the proximity of the biological replicates in the ordination space indicated a strong correlation within replicates. This analysis further highlighted significant variations in metabolite levels in the fruit peels of Lingwuchangzao at different gloss levels across the three developmental stages. 3.3.2 Identification of Differentially Accumulated Metabolites To date, no studies have reported on the metabolites of Lingwuchangzao fruit peels at different gloss levels across various developmental stages. This study utilized UPLC-MS to analyze metabolites in the peels of Lingwuchangzao fruit with varying gloss levels, collected at three distinct developmental stages. A total of 779 metabolites were identified from these samples. These metabolites were classified according to their chemical composition (Figure S6), with flavonoids comprising the largest proportion at 38.51%, followed by phenolic acids at 20.8%. Other categories included alkaloids (17.33%), terpenoids (10.91%), lignans and coumarins (3.08%), tannins (2.7%), and quinones (0.77%). Additionally, metabolites lacking a defined chemical classification accounted for 5.91% of the total. Metabolites were screened based on VIP values derived from the OPLS-DA model and P-values from univariate analysis, applying the criteria of VIP > 1 and P-value < 0.05. The metabolomic analysis of the 18 samples identified 779 annotated metabolites, with 44 to 63 differential metabolites per group, meeting the minimum requirement of 10 differentially accumulated metabolites and indicating high-quality sequencing. During the young fruit stage, 44 differentially accumulated metabolites were identified, with 24 upregulated and 20 downregulated; at the white ripe stage, 63 metabolites showed differential accumulation, with 29 upregulated and 34 downregulated; and at the mature stage, 61 differential metabolites were identified, with 24 upregulated and 37 downregulated (Table 3). A comparison of differentially accumulated metabolites between S and R fruits at different stages was performed, with a Venn diagram (Figure S7) illustrating the overlap across various comparisons. Notably, the RW_vs_SW and RC_vs_SC comparisons shared the highest number of common metabolites (13), followed by 9 common metabolites in the RY_vs_SY and RW_vs_SW comparisons, and 3 common metabolites in the RY_vs_SY and RC_vs_SC comparisons. Table 3. Statistical tables of S and R skin differential metabolites for the same developmental period RY_vs_SY 44 24 20 RW_vs_SW 63 29 34 RC_vs_SC 61 24 37 3.3.3 KEGG Annotation and Enrichment Analysis of Differentially Accumulated Metabolites In the RY_vs_SY comparison group, six differential metabolites were significantly enriched in metabolic pathways. Notably, three metabolites were enriched in the biosynthesis of phenolics, two in the tryptophan metabolism pathway, one in the biosynthesis of dibenzyl, dibenzylheptane, and gingerol, and one in the biosynthesis of ubiquinone and other terpenoid-quinones (Figure S8a). In the RW_vs_SW comparison group, differential metabolites were significantly enriched in pathways related to the biosynthesis of anthocyanins, phenolics, dibenzyl, dibenzylheptane, gingerol, isoquinoline alkaloids, and tyrosine metabolism (Figure S8b). In the RC_vs_SC comparison group, differential metabolites were significantly enriched in pathways associated with the biosynthesis of flavonoids, folate, cofactors, phenylalanine metabolism, and plant secondary metabolite biosynthesis (Figure S8c). 3.3.4 Comparative Analysis of Flavonoid Metabolites in the Pericarp of High and Low Flatness Fruits at Different Developmental Periods A total of 779 metabolites were identified from 18 peel samples of Lingwuchangzao S and R fruits across three developmental stages. Flavonoids, linked to fruit coloration, comprised the largest proportion, with 300 identified metabolites (38.51% of the total). In the comparison groups at the same developmental stages—RY_vs_SY, RW_vs_SW, and RC_vs_SC—the number of differential flavonoids was 9, 22, and 29, respectively (Table S2). Notably, more than half of the upregulated metabolites were found in the low-gloss R fruit (Figure 13). In the RY_vs_SY comparison group, 9 differential flavonoid metabolites were identified, with 5 significantly upregulated in RY (e.g., hesperetin and quercetin) and 4 (including three anthocyanins) upregulated in SY. In the RW_vs_SW comparison group, 22 differential flavonoid metabolites were recognized, with 14 upregulated in RW (e.g., kaempferol and sesamin), while 8 were upregulated in SW. In the RC_vs_SC comparison group, 29 differential flavonoid metabolites were identified, with 22 significantly upregulated in RC (e.g., 5-hydroxyflavanone and kaempferol), and 12 upregulated in SC. Figure 13. Heatmap of flavonoids differential metabolites of RY _ vs _ SY, RW _ vs _ SW, RC_vs_SC a、b、c is the heat map of differential metabolites clustering for the comparison group RY _ vs _ SY, RW _ vs _ SW, RC_vs_SC, respectively 3.4 Integrated Analysis of Transcriptomics and Metabolomics The expression levels of differential flavonoid metabolites in the RY_vs_SY, RW_vs_SW, and RC_vs_SC comparison groups were extracted and analyzed for correlation with the expression levels of DEGs in each group, with data filtered for p < 0.001. This data was imported into Cytoscape, where a correlation network diagram was constructed using the Degree algorithm (Figure 14a). The MCC algorithm was applied to identify the top 30 hub genes and metabolites that were most interconnected (Figure 14b), and heatmaps were generated to visualize the expression levels of these hub genes and metabolites (Figure 14c). Among the hub differential genes, ABCG31 (LOC107410819) showed significantly higher expression in R fruit compared to S fruit, while the expression levels of hub accumulated metabolites were elevated during the young fruit stage. Notably, metabolites such as Wayn006761 (Quercetin 5-O-p-coumaroyl rhamnoside), Wayn006883 (Quercetin 7-O-p-coumaroyl rhamnoside), mws0045 (Quercetin-3-O-rhamnoside, Quercitrin), and mws1361 (Taxifolin-3-O-rhamnoside, Astilbin) were expressed at higher levels in R fruit. These findings suggest that differences in fruit flatness and glossiness may emerge as early as the young fruit stage. Figure 14. Correlation network of flavonoid metabolites with differential genes in RY _ vs _ SY, RW _ vs _ SW, RC_ vs _ SC groups a:Correlation network plot of flavonoid metabolites with three sets of differential genes RY _ vs _ SY, RW _ vs _ SW, RC_ vs _ SC (p < 0.001); btop 30 ranked and interlinked hub genes and metabolites (DEGREE algorithm); c:heatmap of the expression of hub genes; d:heatmap of the expression of hub metabolites 4 Discussion 4.1 Quality Differences of Lingwuchangzao Fruits with Different Levels of Flatness at Various Development Stages The glossiness of fruits is closely linked to various other quality attributes, including color, internal composition, flavor, and firmness (Xu et al., 2022). In this study, significant variations in the color parameters L*, a*, b*, C*, and h° were observed during the development of fruits with different gloss levels. The L* value, which indicates the surface brightness of the fruit, reflected chlorophyll degradation. Both S (high gloss) and R (low gloss) fruits showed an initial increase in L* followed by a decrease from the young fruit stage to maturity, with S fruits consistently exhibiting higher brightness than R fruits (P < 0.01). S fruits tended to be bright red, while R fruits, characterized by a larger h° value, were more purplish-red. Additionally, S fruits had smaller individual fruit weight, longitudinal and transverse diameters, skin and flesh thickness, single seed weight, and seed length-to-width ratio compared to R fruits (P < 0.01). However, S fruits had a greater kernel index than R fruits. Throughout the five developmental stages from young to mature, seed content in R fruits remained above 96%, while S fruits showed nearly zero seed content. The larger seeds in R fruits, which contained dates, likely contributed to rougher and more uneven seed surfaces, with longer and more slender seed tips. This may explain why R fruits exhibited a ”rough surface and sharply protruding apex,” resulting in relatively lower glossiness. Furthermore, studies have shown that date fruits naturally have higher seed content, whereas cultivated varieties tend to have lower seed content, with an inverse relationship between seed content and edibility(Sima, 2020). This finding is consistent with the seed content of S fruits in this experiment. Additional research has confirmed that seed growth and development occur throughout the entire fruit development process, contributing to overall fruit growth. For example, in avocados, a greater number and larger size of seeds require longer post-ripening time (Hershkovitz, Friedman, Goldschmidt, Feygenberg & Pesis, 2011). Previous research on the inheritance of fruit traits in hybrid offspring of jujube has revealed a strong correlation between fruit size and seed size (Xie, Wang, Li & Li, 2022). Similarly, studies on pear fruit morphology have shown that the number and distribution of seeds within the fruit are closely related to fruit size—larger fruit correlates with a greater number of seeds, while fewer seeds are associated with smaller fruit (Wang, 2017). These results suggest that both the quantity and quality of seeds significantly influence the growth and development of plant fruits, consistent with the results of this study. The soluble sugar content of S and R fruits gradually increased throughout development, peaking at full red maturity, thereby enhancing sweetness during maturation (Song, Bi, Chen, Wu, Lyu & Meng, 2019). In contrast, the total phenol and flavonoid content in both S and R fruits gradually decreased, while Vc content initially rose, reaching its maximum during the swelling stage, and then declined as the fruit matured. These trends align with previous studies on the changes in soluble sugars, Vc, total phenols, and flavonoids in Ziziphus jujuba Mill(Huang, Heyduck, Richins, VanLeeuwen, O’Connell & Yao, 2017; Ibraimu, Wu, Zhang & Bai, 2022; Zhang & Feng, 2020) and Lingwuchangzao(Han, 2019; Li, Ma, Tian, Fan, Wan & Liu, 2022; Zhang, Chen, Jing & Su, 2014). At maturity, the sugar-acid ratio of S fruits was lower than that of R fruits (P < 0.01), and S fruits consistently exhibited lower firmness throughout development. Significant differences in external quality were observed between S and R Lingwuchangzao fruits across all developmental stages, with notable differences in intrinsic quality, such as titratable acidity, Vc, total phenols, and flavonoid content. S fruits, characterized by relatively better external quality, were smaller and softer, exhibiting the “sweet and sour” flavor typical of traditional Lingwuchangzao. Conversely, R fruits, though exhibiting poorer external quality, were larger, sweeter, and firmer, contributing to better storage and transport durability. Additionally, the high seed content of R fruits offers advantages for seed selection, facilitating the cultivation of new Lingwuchangzao varieties with superior quality, larger fruit size, and higher glossiness. 4.2 Changes in the Microscopic Structure of the Fruit Skin of Lingwuchangzao with Different Levels of Flatness at Various Development Stages The expansion of fruit cells and cell division are key determinants of fruit growth and size, influencing both the volume and number of cells, which are essential for fruit development (Liu et al., 2020). Research has shown that the number of cells is the dominant factor contributing to fruit size variations in pears (Zhang, Tanabe, Tani, Nakajima, Mori & Sakuno, 2007) and cherries(Olmstead, Iezzoni & Whiting, 2007). Studies on other fruit trees, such as pineapples and crabapples, also highlighted that both the number of cells and their volume play significant roles in determining fruit size(Harada, Kurahashi, Yanai, Wakasa & Satoh, 2005; Li, Zhang & Sun, 2010; Malladi & Hirst, 2010). Additionally, the differential growth rates of flesh cells in the longitudinal and transverse directions result in variations in the length-to-width ratio of grape fruits (Zhang, Fan, Liu & Fang, 2021). In the case of chili apricots, the physiological mechanisms underlying fruit shape were primarily determined by the direction of mesocarp cell expansion. Specifically, more pronounced ”chili” shapes were associated with longer cell diameters, smaller short diameters, and fewer mesocarp cell layers (Sun et al., 2016). This is consistent with the findings of the current study, where the fruit size and skin thickness of R fruits were significantly greater than those of S fruits ( P < 0.01). Microscopic observations of paraffin sections from S and R Lingwuchangzao fruits revealed that the epidermal cell density in R fruits was greater than in S fruits. Measurements of the cuticular membrane thickness showed that S fruits had a significantly thinner cuticle compared to R fruits at various developmental stages. However, the glossiness of S fruits was higher. Electron micrographs of the epidermal cells also indicated that S fruits had fewer flaky wax crystals on their cuticles compared to R fruits at the same stage of development. The influence of wax content and cuticle thickness on fruit skin flatness and glossiness has been widely studied. Previous research found that high-gloss navel oranges had almost no visible wax crystals on their surface, while rough and dull navel oranges exhibited flaky wax layers (Liu, Zeng, Ji, Liu, Liu & Liu, 2012). In Arabidopsis and rice, mutants with reduced synthesis or deposition of epidermal wax displayed enhanced glossiness traits (Aharoni, Dixit, Jetter, Thoenes, van Arkel & Pereira, 2004; Millar, Clemens, Zachgo, Giblin, Taylor & Kunst, 1999; Wang et al., 2012). Similarly, corn plants with reduced surface wax crystals exhibited high glossiness (Li et al., 2019), and the gloss-type mutant ”Ganqi 3” exhibited low wax content on the surface, yet displayed high glossiness (Liu et al., 2016). The wax characteristics of R fruits in this study were consistent with these findings in corn and Arabidopsis, providing a clear explanation for the differences in glossiness and flatness between S and R fruits. 4.3 Identification of Genes Influencing the Flatness and Glossiness of Lingwuchangzao Fruits Numerous genes involved in plant cuticular wax biosynthesis have been identified, with extensive research conducted in Arabidopsis. Studies have confirmed that genes such as CER1 , CER2 , CER3 , and CER6 function synergistically to regulate cuticular wax synthesis, resulting in variations in wax deposition on plant surfaces and influencing tissue glossiness (Cavagnaro et al., 2010; Gao et al., 2023; Millar et al., 1999; Yang et al., 2023). In the present study, among the three developmental stages compared, the greatest difference in gene expression between high-gloss and low-gloss groups occurred at the young fruit stage, with this difference diminishing as fruit development progressed. This suggests that the differentiation between high and low glossiness in Lingwuchangzao fruits began early in fruit development. Transcriptomic analysis of the cuticles from fruits at different developmental stages revealed that DEGs were enriched in ABC transporters. Among the genes associated with wax biosynthesis, ABCG15 was found to regulate cuticular wax deposition in apple leaf epidermis (Cao et al., 2021), while the barley gene HvABCG31 and the rice gene OsABCG31 were primarily involved in the transport of cuticular wax in young leaf epidermis, highlighting the critical role of ABCG31 in wax transport and its subsequent impact on wax synthesis and deposition (Li, Zhao, Wang, Liu, Wang & Patrick, 2023). This study also identified the key gene ABCG31 (LOC107410819) through the analysis of DEGs and metabolites, which significantly influenced variations in fruit glossiness and flatness. KEGG functional enrichment analysis identified genes related to cuticular wax biosynthesis in Lingwuchangzao fruits, revealing differential expression of key enzymes involved in cuticular wax and cutin synthesis between high- and low-gloss fruits at different stages. Previous research demonstrated that silencing the GhFAR3.1 gene in cotton resulted in a more than 60% reduction in total leaf wax content, confirming the role of FAR genes in promoting wax synthesis(Lu et al., 2021). Among the hub genes involved in cuticular wax biosynthesis in fruits, FAR-2 (LOC107432639) exhibited higher expression levels in young R fruits, which may contribute to their lower glossiness. Additionally, CER1-1 (LOC125423018) showed higher expression levels in mature R fruits, while CYP86A4 (CYP86)-2 (LOC107429862) and CER1-4 (LOC107420951) were more highly expressed in SY fruits. Transcription factors also play a regulatory role in gene expression. Zhang et al. found that MdMYB30 in apple regulates wax biosynthesis by activating MdKCS1 expression through binding to its promoter (Zhang et al., 2019). In this study, transcription factors such as AP2/ERF-ERF-56 (LOC107430347), bHLH-74 (LOC107407125), MYB-48 (LOC107416961), and bHLH-65 (LOC107430586) exhibited mutual regulation. bHLH-65 directly regulated MYB-81 (LOC107407717), which simultaneously regulated the four aforementioned DEGs, ultimately contributing to the differences in wax content between S and R fruits. These results are consistent with previous research. The development of fruit epidermal cells plays a critical role in determining their flatness and glossiness, with plant hormone signaling molecules widely present in living organisms and contributing significantly to cell growth and development (Lin et al., 2021). Ding et al. demonstrated that auxin regulates the phosphorylation of downstream ERF13 by activating MPK14 , influencing the synthesis of very-long-chain fatty acids and modulating lateral root development. Zhang et al. found that auxin not only induced cell division in vascular bundle sheath cells but also promoted cell division in the epidermis, cortex, and endodermis (Zhang et al., 2021). In this study, 72 DEGs related to plant hormone signaling were identified through KEGG enrichment analysis, with 10 hub genes selected, all of which were associated with auxin. Notably, AUX/IAA-15 (LOC107431810), AUX/IAA-9 (LOC107420373), and AUX/IAA-6 (LOC107412229) showed higher expression levels in young R fruits and were positively correlated with epidermal cell density. This suggests that these genes may influence cell division, leading to the observed differences in epidermal cell density between S and R fruits. Epidermal cell density, in turn, affects fruit flatness. Research on citrus fruit surfaces has indicated that flatness is linked to characteristics such as the size, density, and prominence of oil cells; larger, denser, and more prominent oil cells contribute to a rougher peel (Bi et al., 2024). Furthermore, the flatness of an object’s surface has a significant impact on its glossiness, with surfaces displaying lower flatness tending to have reduced glossiness (Yuan, Zhang & Zhu, 2023). These results highlight that the differences in glossiness and flatness between S and R fruits are closely related to cellular development. 4.4 The Impact of DAMs on the Glossiness of Lingwuchangzao Fruit Skin Fruit coloration is influenced by a range of metabolites, with certain flavonoid compounds acting as natural pigments. For example, flavonols such as kaempferol and quercetin produce yellow or light yellow hues, while anthocyanins impart red, purple, and blue colors. The concentration and distribution of these metabolites play a critical role in determining fruit color (Wu, Han, Lyu, Li & Wu, 2023; Wu, Han, Yang, Lyu, Li & Wu, 2023; Zhang, Yang, Wu, Lyu, Wu & Li, 2024). The differential accumulation of flavonoid metabolites is a primary factor driving variations in fruit coloration (Zhou, Sun, Le Luo, Pan, Zhang & Yu, 2024). In this study, 779 metabolites were identified from 18 fruit skin samples of Lingwuchangzao S and R fruits at three developmental stages, with flavonoids accounting for the largest proportion of the identified metabolites. Analysis of the differential flavonoid metabolites in the skin of S and R fruits at the same developmental stage revealed that the variety of flavonoid compounds increased progressively as development advanced. Overall, the types and quantities of differentially accumulated flavonoid metabolites were greater in the skin of R fruits than in the high-gloss S fruits. This indicates that at the same developmental stage, the low-gloss R fruits accumulated a greater diversity and amount of differential flavonoid metabolites in their skin compared to the high-gloss S fruits. For instance, during the young fruit stage, metabolites such as Wayn006761 (Quercetin 5-O-p-coumaroyl rhamnoside), Wayn006883 (Quercetin 7-O-p-coumaroyl rhamnoside), mws0045 (Quercetin-3-O-rhamnoside, Quercitrin), and mws1361 (Taxifolin-3-O-rhamnoside, Astilbin) were expressed at higher levels in R fruits. These results help explain the early-emerging color differences between S and R fruits, which were evident even during the young fruit stage. 5. Conclusion (1) The high- and low-gloss S and R fruits of Lingwuchangzao began to differentiate in the early stages of fruit development. Throughout the entire developmental period, the density and quantity of epidermal cells in the high-gloss S fruits were smaller than those in the R fruits. The seed content in R fruits was over 93%, while in S fruits it was nearly 0. Additionally, the individual fruit weight, fruit dimensions (length and width), flesh thickness, skin thickness, single seed weight, seed dimensions (length and width), and fruit hardness in S fruits were all lower than those in R fruits. Moreover, the glossiness and color parameters (L*, a*, b*, C*, h°) of S and R fruits generally showed that these values were higher in S fruits compared to R fruits, reflecting the superior appearance quality of S fruits, which exhibited a bright red color, while R fruits appeared a dark purple-red. (2) Based on the transcriptomic analysis of the fruit skins of Lingwuchangzao S and R fruits at three developmental stages, a total of 33 differentially expressed genes related to glossiness were identified, primarily focusing on key enzymes associated with cutin and wax synthesis, including CYP86A1 , HHT1 , CYP77A6 , CYP86A4 , CYP86B1 , CYP94A5, CYP86 , FAR , and CER1 . The expression level of CER1 , which encodes an aldehyde decarbonylase, was significantly overexpressed in R fruits at various developmental stages compared to S fruits at the same stages, promoting the accumulation of cuticular wax in the epidermis of R fruits. As a result, the number of lamellar wax crystals deposited in the cuticle of R fruits was clearly greater than that in S fruits, leading to the differences in flatness and glossiness between S and R fruits of Lingwuchangzao. (3) Based on the metabolomic analysis of the fruit skins of Lingwuchangzao S and R fruits at three developmental stages, a total of 779 metabolites were identified, with flavonoid metabolites representing the largest proportion at 38.51%. The types and quantities of differentially accumulated flavonoid metabolites in the skin of R fruits were greater than those in S fruits, activating the expression of more genes related to wax synthesis and fruit skin coloration. Ultimately, this led to a higher wax content in R fruits compared to S fruits, resulting in lower flatness and glossiness. Figure 15. Schematic diagram CRediT authorship contribution statement Jiadong Wang :Conceptualization,Methodology,Visualization,Writing. Ying Wang : Softwar and Methodology.: Xiaoqin Liu Resources. Gaier Yang : Investigation and Visualization. Xuan Zhang : Data curation. Formal analysis. Yunmao Li: Supervision. Bin Cao :Writing - review & editing and Supervision. Xiang Li : Funding acquisition, Project administration,Conceptualization. All authors read and approved the manuscript for publication. Declaration of Competing Interest The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influencethe work reported in this paper. 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