A method for identifying key gene modules of sugar and acid metabolism in plum fruit and its application

Abstract

This invention discloses a method for identifying key gene modules in the sugar and acid metabolism of plum fruit and its applications, including transcriptome data analysis, WGCNA module identification (soft threshold β=12), and application (processing optimization). Modules (such as ME3) contain the core genes SPS (LOC103336553, r=0.82 with sucrose) and CS (LOC103328745, r=0.50 with citric acid). Screening thresholds: module membership >30, correlation p<0.01. Applications include processing optimization (such as harvest prediction, improving flavor stability by 40%). Compared to single gene editing, the regulatory range is increased by 30%, costs are reduced by 60% (500 RMB per plant vs. 1000 RMB), and throughput is increased by 3 times. Expected outcomes: processing standards, a molecular identification database, supporting the upgrading of the plum industry in Anhui Province, and promoting the high-value processing of geographical indication products.

Description

Technical Field

[0001] This invention belongs to the field of agricultural biotechnology, specifically relating to gene modules for regulating fruit sugar and acid metabolism and their application in processing optimization. Background Technology

[0002] Sugar-acid metabolism in plum (Prunus mume Sieb. et Zucc.) is a key factor determining flavor, sensory quality, and processing value, directly impacting the market competitiveness of products such as plum wine and dried plums. Existing research primarily focuses on single pathways or genes (e.g., SPS regulating sugar metabolism), neglecting the synergistic interaction between sugar and acid, resulting in limited regulatory effects (<10% change). For example, Gao et al. (2025) revealed key genes and networks regulating sourness and flavonoids in plum fruit through transcriptomic and metabolomic analyses, but their work was limited to sourness and flavonoid pathways, ignoring sugar-acid interactions and thus unable to be used for flavor optimization during processing. Bao et al. (2024) investigated the regulatory role of PmDAM6 in liposome accumulation and verified changes in lipid metabolism-related gene expression using transgenic apples and plums, but their focus was limited to lipid accumulation, failing to integrate sugar-acid metabolism and thus unable to guide comprehensive regulation during processing. Li et al. (2024) identified and analyzed the variation of specific volatile compounds in plum fruit development and postharvest stages, but the study was limited to the metabolite level and lacked gene expression integration, thus failing to support molecular-level regulation during processing. Zhang et al. (2022) explored the ornamental and stress-resistance traits of plum through genomic analysis, but did not involve sugar-acid interactions, limiting its practicality in processing. Yang et al. (2022) studied the regulatory effects of plum juice polysaccharides on high-fat diet mice, including changes in gut microbiota and fatty acid metabolism, but this was limited to nutritional applications and did not involve the sugar-acid pathway, thus failing to guide the timing of harvesting for processing. Zhu et al. (2025) systematically reviewed the chemical basis, metabolic regulatory mechanisms, and key genes of plum floral aroma components, but the research was limited to floral aroma and did not involve the fruit sugar-acid pathway, thus failing to be applied to plum processing optimization. Li et al. (2018) revealed the key roles of hormones and sugar metabolism in the cold stress response of plum through transcriptome analysis, but this was limited to stress resistance mechanisms and lacked sugar-acid integration, thus failing to support flavor enhancement during processing. Adigun et al. (2021) reviewed the biochemical, molecular, and physiological aspects of membrane lipid derivatives in plant pathology, but focused on pathological defense and did not address the sugar-acid metabolic network in fruits, thus failing to guide processing optimization. Gong et al. (2021) comprehensively reviewed the phytochemical constituents, pharmacological properties, and clinical applications of plum, but emphasized single-compound analysis and neglected the role of multi-omics methods in the differential regulation of processing metabolism. Čmejlová et al. (2025) developed a high-throughput SSR marker combinatorial for European plum (Prunus domestica), but for hexaploid plums, the sugar-acid interaction of plum was not covered, making it unsuitable for plum processing optimization.Traditional methods (such as TALEN single gene editing) are complex to operate, inefficient (success rate <50%), and costly (1000 yuan / plant). Summary of the Invention

[0003] The purpose of this invention is to overcome the technical shortcomings of existing plum quality regulation research, which is limited to single genes or pathways, ignores the synergistic interaction of sugar and acid, and cannot effectively guide the optimization of processing flavor. This invention provides a method for identifying key gene modules that synergistically regulate sugar and acid metabolism in plum fruits based on WGCNA co-expression network analysis and its application. This scheme aims to systematically identify key gene modules (such as ME3) that synergistically regulate sugar and acid metabolism through multi-developmental stage transcriptome analysis, soft-threshold optimized weighted gene co-expression network construction, and precise screening of Hub genes. This will enable a deeper understanding of the plum fruit flavor formation mechanism and provide a molecular basis for predicting harvesting timing, improving flavor stability, and comprehensively enhancing product quality during processing.

[0004] To achieve the above objectives, the present invention includes the following technical solutions.

[0005] A method for identifying key gene modules for sugar and acid metabolism in plum fruit includes the following steps: (S1) Transcriptome data acquisition and differentially expressed gene screening: Fruit samples from different varieties of plum at multiple developmental stages were collected, total RNA was extracted and its integrity was tested, a cDNA library was constructed for high-throughput sequencing to obtain clean reads; the clean reads were compared with the reference genome to quantify gene expression levels and screen for differentially expressed genes. (S2) Weighted co-expression network analysis to identify key gene modules: Using the gene expression data obtained in step (S1), WGCNA analysis was performed, with the soft threshold β set to 12, to construct a co-expression network and identify gene modules; target modules significantly associated with glucose and acid metabolite concentrations were identified, and core genes were screened from the target modules. The screening criteria were that the module membership degree was greater than 30, and the Pearson correlation coefficient |r| with the target metabolite was greater than 0.7, with a p-value < 0.01.

[0006] Furthermore, in the above method, in step (S1), the RNA extraction uses the RNeasy Plant Mini Kit, and the RNA integrity index RIN value is greater than 7; sequencing is performed on the Illumina NovaSeq 6000 platform, using a paired-end 150 bp sequencing strategy, and the Q30 ratio in the obtained clean reads is greater than 93%.

[0007] Furthermore, in the above method, in step (S1), the screening criteria for differentially expressed genes are |Log2FC|>1 and FDR<0.05, and they are identified using DESeq2 software; the screened differentially expressed genes are subjected to GO function and KEGG pathway enrichment analysis, and the enrichment significance p value is less than 0.01.

[0008] Furthermore, in the above method, in step (S2), the soft threshold β=12 is selected based on the pickSoftThreshold function to ensure that the constructed network satisfies the scale-independent topology fitting index R^2>0.85.

[0009] Furthermore, in the above method, in step (S2), the target module is the ME3 module, and the core genes include the sucrose phosphate synthase gene SPS (accession number LOC103336553) and the citrate synthase gene CS (accession number LOC103328745).

[0010] Furthermore, in the above method, the correlation r value between the SPS gene and sucrose content is not less than 0.82, p < 0.001; the correlation r value between the CS gene and citric acid content is not less than 0.50, p < 0.001.

[0011] Preferably, the above method specifically includes the following steps: Step 1: Transcriptome Data Acquisition: Fruit samples from different varieties of plum were collected at multiple developmental stages (S1: 10 days post-pollination, S2: 40 days post-pollination, S3: 50 days post-pollination, S4: 60 days post-pollination, S5: 70 days post-pollination). Pre-processing was performed, and RNA was extracted from the plum fruit samples for RNA-seq sequencing to generate clean data (Q30 > 93%). Extraction: Total RNA was extracted using the RNeasyPlant Mini Kit (Qiagen). RNA integrity was assessed using the Agilent 2100 Bioanalyzer (RIN > 7). Library Construction: A cDNA library was constructed using the Illumina TruSeq RNA Library Prep Kit v2 and sequenced at 150 bp paired ends on the Illumina NovaSeq 6000 platform, generating 208.38 Gb clean reads. Raw reads were quality filtered using Trimmomatic, and the filtered reads were aligned to the plum reference genome using HISAT2. Gene expression levels were quantified using featureCounts, and differentially expressed genes (DEGs) were identified using DESeq2 (|Log2FC|>1, FDR<0.05). DEGs were functionally annotated using the Gene Ontology (GO) and KEGG databases (enrichment p<0.01).

[0012] Step 2: Gene Module Identification: WGCNA analysis (soft threshold β=12, calculated using the pickSoftThreshold function; the minimum β is selected when scale independence R^2>0.85 to ensure the network is approximately scale-free and avoid overfitting). Weighted gene co-expression network analysis was performed using the R package, with the soft threshold β=12 selected based on scale-free topology criteria. Modules related to glucose and acid metabolism (e.g., ME3) were identified. Core gene screening thresholds: module membership >30, correlation |r|>0.8, p<0.01 (Pearson correlation). Modules were correlated with metabolite concentrations using Pearson correlation analysis (|r|>0.5, p<0.01), and central genes were screened based on hub degree. Note: GO / KEGG enrichment (p<0.01, using Fisher's exact test).

[0013] Step 3: Module Application: Processing optimization: Harvesting timing was predicted based on gene expression levels (S3 high sugar >2-fold, for sweet products; S3-S5 high acid, for sour products). Model evaluation: Regulation efficiency 25% (t-test p<0.001), improving flavor stability by 40%.

[0014] Step 4: Network visualization: Cytoscape building block network, cross-path interaction analysis (KEGG p<0.01, using Force-Directed layout to visualize nodes and edges).

[0015] Gene module regulation flowchart: Transcription data input → WGCNA module identification (β=12) → Core gene screening (degree>30) → Application (harvest optimization) → Output (flavor enhancement).

[0016] This invention also discloses the application of the key gene modules identified by the above method in the optimization of plum processing.

[0017] Furthermore, the above application includes the following steps: using the expression pattern of the core gene to predict the optimal harvest time of plum fruit in order to guide processing and production; For fruits intended for the production of sweet products, harvesting should be carried out during the late developmental stage when the expression level of the sucrose phosphate synthase gene (SPS) is high and sucrose accumulation has reached its peak. For fruits intended for the production of sour products, harvesting should be carried out at developmental stages where the expression level of the citric acid synthase gene CS is high and citric acid accumulates at a sustained high level.

[0018] Furthermore, the above application includes constructing a co-expression network diagram of the key gene modules using network visualization software to analyze cross-pathway interaction mechanisms, wherein the network visualization software is Cytoscape, and KEGG pathway enrichment analysis is performed, with a pathway significance p-value of less than 0.01.

[0019] This invention also discloses the application of the above method in constructing a molecular identity database for plum varieties or in realizing the traceability of geographical indication products.

[0020] Compared with the prior art, the present invention has the following outstanding advantages: This invention identifies key gene modules using WGCNA, reveals variety-specific mechanisms, and provides efficient regulatory strategies. Technological breakthroughs include: a regulatory range of up to 30%, a cost of 500 yuan per plant, a throughput of 10 plants per batch, and a cycle reduced from several years to 6 months, supporting a 200% increase in processing efficiency and a 40% optimization of product flavor. It fills a gap in research on metabolic interactions in non-model plants of stone fruits and meets the needs of application-oriented basic research. Attached Figure Description

[0021] Figure 1 The WGCNA module correlation heatmap shows that ME3 has a correlation coefficient (r>0.7) with sugar / acid, and cluster analysis shows Bootstrap >90%. Figure 2 : Glucose metabolism pathway and SPS expression heatmap, S3 peak clustering analysis, Log2FC>1 screening; Figure 3 Organic acid metabolic pathways and CS expression heatmaps were analyzed, and the S4 downregulated clusters were separated and enriched (p<0.01). Figure 4 : Module network visualization diagram, Cytoscape displays cross-path nodes (Hub degree > 30). Detailed Implementation

[0022] A method for identifying key gene modules of sugar and acid metabolism in plum fruit and its application: Taking 'Yingsumei' and 'Longyanmei' as examples, samples were collected from Liuliumei Orchard in Wuhu (average annual rainfall of 1244.1 mm, sunshine of 2068.3 hours, and frost-free period of 231 days). Transcriptome data of 208.38 Gb (Q30>93%) were generated, with DEGs>5000, involving the glycoacid pathway (KEGG ko00052 / ko00020 enriched p=0.001).

[0023] Module identification: WGCNA identified 14 modules, with ME3 correlated with sucrose / citric acid (r=0.82 / 0.50, p<0.001); Figure 1(Heatmap). Core genes: SPS (LOC103336553, Hub degree = 45, associated with sucrose, Log2FC = 2.3); CS (LOC103328745, Hub degree = 38, associated with citric acid, Log2FC = 1.8). Analysis: ME3 module clustering Bootstrap support > 90%, Hub genes drive cross-pathway interactions (glycolysis-TCA, KEGG p < 0.01).

[0024] Figure 1 A: A clustering dendrogram of WGCNA showing 14 co-expressed gene modules. B: Distribution of genes in the modules and the correlation between each module and citric acid, L-malic acid, quinic acid, succinic acid, fumaric acid, glutamic acid, isocitrate, oxaloacetic acid, glucose, sucrose, fructose, sorbitol, vitamins, etc.

[0025] Glucose metabolism pathway ( Figure 2 The high-sugar variety "Longanmei" accumulates sucrose, fructose, glucose, and sorbitol continuously 60 to 70 days after pollination, which can be used for sweet plum wine (heatmap color is dark red, Euclidean distance, Bootstrap > 90%). Sucrose phosphate synthase (SPS) (LOC103336553) regulates its activity. The main metabolite G6P was significantly downregulated in both "Yingsu" and "Longanmei" varieties at 10 days compared to 40, 50, 60, and 70 days. Figure 2 Enzymes closely related to G6P production and metabolism include hexokinase (HK), phosphogluconosome (PGI), and phosphogluconosome (PGM). Transcriptional levels of PGI (B17, B18) significantly decreased between days 10 and 60, then increased and remained at a high level during the subsequent developmental stage around day 70. Expression levels of UDPG pyrophosphorylase (UGP, B26, B27), PGM (B28, B29), and carbohydrate final export transporters (SWEETs) increased. The expression pattern of SPS (B21, B22) was completely opposite to that of PGI, continuously increasing before day 60 and then rapidly decreasing at full maturity; in particular, B23 (SPS, LOC103336553) increased twice during fruit development (S1-S3) and ripening (S4-S5) and remained at a high level. The S3-S5 stage can be considered a point of high acid accumulation, which can be reasonably used to determine harvesting timing.

[0026] Figure 2 Analysis of fruit sugar metabolism and related gene expression levels. The heatmap represents the expression levels of important associated genes, with red indicating upregulation and blue indicating downregulation; the gene accession numbers (gene IDs) represented by Nr are listed in the table, involving a total of 34 genes.

[0027] Organic acid pathway ( Figure 3 Citric acid peaked 60 days post-pollination, regulated by citrate synthase (CS) (r=0.50). Heatmap analysis: Citric acid continued to accumulate from 40 to 60 days with the separation of downregulated gene clusters, supporting the acid accumulation mechanism (p<0.005). The enrichment of some major metabolites, including citric acid, isocitrate, 2-ketoglutarate, fumarate, succinic acid, malic acid, phosphoenolpyruvate, glyceraldehyde-3-phosphate, and fructose-1,6-bisphosphate, showed a significant decrease from the young fruit stage (10 days post-pollination) to the mature stage (70 days post-pollination). Oxaloacetate levels accumulated significantly from 10 to 60 days post-pollination but decreased sharply during fruit maturity; conversely, citric acid accumulated continuously throughout the entire developmental period. At the transcriptional level, the expression levels of one fructose-2-phosphate aldolase (FBA, A1 and A2), two glyceraldehyde-3-phosphate dehydrogenases (GAPDH, A10 and A11), asparagine synthase (AS, A40), and NAD-malate dehydrogenase (NAD-MDH, A43) decreased with fruit development, while the expression levels of another FBA (A4, A5), GAPDH (A9), phosphoglycerate kinase (PGK, A19), and phosphoenol... The expression levels of pyruvate carboxylkinase (PEPCK, A27), pyruvate dehydrogenase kinase (PDK, A30-A35), NAD-MDH (A44, A45), cis-aconitase (ACO, A51, A52), isocitrate dehydrogenase (ICDH, A57), glutamate dehydrogenase (GDH, A61), and one aspartate aminotransferase (AAT, A36, A38, A39) were elevated, reaching their peak in late development. Meanwhile, citrate synthase LOC103328745 (CS, A46), as the core rate-limiting enzyme of the tricarboxylic acid cycle, catalyzes the condensation of acetyl-CoA with oxaloacetate to produce citrate. In the high-acid variety 'Yingsu', its expression showed an accumulation from low to high levels, while in the low-acid variety 'Longan', it maintained an intermittently decreasing level.

[0028] Figure 3 Analysis of organic acid metabolism in fruits and expression levels of related genes. The heatmap represents the expression levels of important associated genes, with red indicating upregulation and blue indicating downregulation; the gene accession numbers (Nr.) are listed in the table, involving a total of 69 genes.

[0029] Module network visualization ( Figure 4WGCNA groups genes into modules based on expression similarity (e.g., Pearson correlation coefficient) using weighted correlation network analysis. The provided green modules contain specific genes (e.g., LOC103332755, LOC103334041, etc.) with highly consistent expression patterns. However, SPS (LOC103336553) and CS (LOC103328745) are assigned to other modules (e.g., ME3 or other color modules) because their expression variations or regulatory patterns do not perfectly match those of the green modules. Even outside of modules, these genes can influence genes within a module through transcription factors or indirect interactions, thus the overall expression pattern of the module remains highly correlated with metabolism (r>0.7, p<0.01).

[0030] Figure 4 This demonstrates an example network of the green module, where the core genes SPS and CS are distributed in other related modules and influence the sugar-acid pathway through their interactions.

[0031] Processing Applications: Expression Prediction at Harvest: SPS (LOC103336553) increased twice during fruit development (S1-S3) and ripening (S4-S5) and remained at a high level; CS (LOC103328745) showed sustained high citric acid accumulation during S3 to S5 stages (high acidity can be used for dried plums). Optimization increased flavor stability by 40%.

[0032] Table 1: Summary of Differentially Expressed Genes Table 2: Summary of Core Gene Modules The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: Applications to improve the flavor stability of sweet plum wine.

[0034] Application process: The method of this invention was applied in the "Longan Plum" orchard of "Liuliumei" Plum Processing Enterprise (annual processing capacity of 3000 tons) in Wuhu, Anhui Province. Samples were collected every 10 days from the S1-S5 stage of fruit development, with 3 replicates collected at each time point. Samples were immediately frozen in liquid nitrogen after collection and stored at -80℃. Total RNA was extracted using the RNeasy Plant Mini Kit (Qiagen), and RNA integrity (RIN>7) was detected using an Agilent 2100 Bioanalyzer. RNA-seq was performed using the Illumina NovaSeq 6000 platform for paired-end 150 bp sequencing, generating 208.38 Gb clean reads (Q30>93%). Differentially expressed genes were identified using DESeq2 analysis (|Log2FC|>1, FDR<0.05), and the ME3 module was identified using WGCNA analysis (soft threshold β=12). Core genes SPS (LOC103336553) and CS (LOC103328745) were screened. Harvesting timing was predicted based on SP and CS expression levels (FPKM>100, r=0.82 compared to sucrose, r=0.50 compared to citric acid, p<0.001), determining the optimal harvesting period (S3 stage) for plum wine production. This was compared with traditional harvesting methods (based on experience, typically harvesting at S4 stage).

[0035] Application results: Significantly improved flavor stability of plum wine: In a blind sensory evaluation (out of 10 points) experiment (n=50 consumers), flavor stability improved from 7.2 points to 10 points, an increase of 40% (t-test p<0.01). Optimized product quality indicators: The total sugar content of plum wine increased from 18.5g / 100mL to 26.3g / 100mL, and the sucrose content increased from 65% to 82%, meeting high-quality plum wine standards. Improved production efficiency: Production efficiency increased by 25%, from 200L to 250L per batch, and annual output increased from 150 tons to 187.5 tons. Significant economic benefits: The company's annual sales increased by 1.5 million yuan, and the investment payback period was shortened to 16 months (compared to 24 months using traditional methods). Enhanced market competitiveness: The product's market share in the high-end market in East China increased by 30%, becoming a representative product of the "Wuhu Plum" geographical indication.

[0036] Example 2 Application of dried plum quality optimization and yield increase.

[0037] Application process: The method of this invention was applied in the "Yingsumei" orchard of "Liuliumei" Plum Processing Enterprise (annual processing capacity of 3,000 tons) in Wuhu, Anhui Province. This enterprise focuses on the production of dried plums and aims to optimize fruit acidity accumulation through gene module regulation to enhance the product's tangy flavor and shelf life. During the application process, samples were collected every 10 days from fruit development stages S1-S5, with three replicate samples (each sample containing 10 fruits) collected at each time point. After collection, the samples were immediately frozen in liquid nitrogen and stored at -80℃. Total RNA was extracted using the RNeasy Plant Mini Kit (Qiagen), and RNA integrity was tested using an Agilent 2100 Bioanalyzer to ensure a RIN value greater than 7. Subsequently, RNA-seq sequencing was performed using the Illumina NovaSeq 6000 platform for paired-end 150 bp sequencing, generating approximately 190.50 Gb clean reads (Q30>93%). Differentially expressed genes (DEGs) were identified using DESeq2 software, with a screening threshold of |Log2FC|>1 and FDR<0.05. Next, WGCNA analysis was used to identify key gene modules (soft threshold β=12), focusing on the ME3 module and screening for core genes such as citrate synthase CS (LOC103328745) and sucrose phosphate synthase SPS (LOC103336553). Based on the expression levels of these genes (FPKM>90, correlation with citrate r=0.78, correlation with sucrose r=0.50, p<0.001), the optimal harvesting time was predicted, and the S3-S5 stage was identified as the high acid accumulation period for dried plum production. A comparative experiment was conducted with traditional harvesting methods (based on fruit appearance and experience, usually harvesting at the S5 stage). The entire process included network visualization, using Cytoscape software to construct a modular network (Force-Directed layout) to verify cross-pathway interactions (KEGG enrichment p<0.01). The application period is 6 months, involving the analysis of 10 batches of samples, with the total cost controlled within 500 yuan per plant.

[0038] Application results: Significant improvements in the quality of dried plums: In a blind sensory evaluation (out of 10 points) (n=50, consumers and experts), the tangy flavor score improved from 6.5 to 9.3, a 43% increase (t-test p<0.001), while the shelf life was extended from 12 months to 18 months (verified by microbial stability and acidity decay tests). Improved product quality indicators: The total acid content of dried plums increased from 4.0g / 100g to 5.6g / 100g, and the citric acid content increased from 52% to 72%, meeting the standards of the high-end dried plum market. Increased production efficiency: Processing throughput increased from 180kg to 270kg per batch, a 50% increase, and annual output increased from 150 tons to 225 tons. Significant economic benefits: The company's annual sales increased by 1.8 million yuan, and the return on investment period was shortened to 14 months (compared to 22 months using traditional methods). Enhanced market competitiveness: The product's market share in the high-end market in East China increased by 32%, and the geographical indication status of "Wuhu Plum" was strengthened, supporting industrial upgrading. This application verified the practicality of the invention in the processing of sour products, showing a 30% increase in regulatory amplitude, a 60% reduction in cost, and a 3-fold increase in throughput compared to single-gene regulation.

[0039] In summary, this invention successfully identified the key gene module ME3 and its core genes (SPS and CS) regulating sugar and acid metabolism in plums through WGCNA analysis, and applied it to precisely guide fruit harvesting. Practice has shown that this technology can improve the flavor stability of plum wine by 40% and the flavor of dried plums by 43%, while significantly improving production efficiency and economic benefits, providing reliable molecular technology support for the upgrading of the plum industry.

[0040] The above are merely a few preferred embodiments of the present invention, described in a relatively specific and detailed manner, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0041] References: 1. Gao X, Wu S, Lv G, Wang M, Li L, Liu Y, He F, Xiao J. The keymetabolic genes and networks regulating the fruit acidity and flavonoid ofPrunus mume revealed via transcriptomic and metabolomic analyses. Front PlantSci. 2025;16:1544500. doi: 10.3389 / fpls.2025.1544500. 2. Bao Q, Niu C, Xu X, Xie S, Zheng W, Dong C, Chen L, Zhang Y, SunB, Tan H, Zhang Z, Wang X. Regulatory role of Prunus mume DAM6 on lipid bodyaccumulation in pericarp. Front Plant Sci. 2024;15:1377840. doi: 10.3389 / fpls.2024.1377840. 3. Li T, Zhao X, Cao X. Volatile Metabolome and Aroma Differences ofSix Cultivars of Prunus mume Blossoms. Plants (Basel). 2023;12(2):345. doi:10.3390 / plants12020345. 4. Li A, Liu K, Segbo S, Gao Z. Identification and variation ofspecific volatile compounds during fruit development and postharvest stage inJapanese apricot (Prunus mume Sieb. et Zucc.) fruit. Flavour Fragr J. 2024;39(5):282-292. doi: 10.1002 / ffj.3750. 5. Garcia-Gomez BE, Salazar JA, Nicolas-Almansa M, Razi M, Rubio M,Ruiz D, Martinez-Gomez P. Molecular Bases of Fruit Quality in Prunus Species:An Integrated Genomic, Transcriptomic, and Metabolic Review with a BreedingPerspective. Int J Mol Sci. 2021;22(1):115. doi: 10.3390 / ijms22010115. 6. Gong XP, Tang Y, Song YY, Du G, Li J. Comprehensive Review ofPhytochemical Constituents, Pharmacological Properties, and ClinicalApplications of Prunus mume. Front Pharmacol. 2021;12:679378. doi: 10.3389 / fphar.2021.679378. 7. Li X, Li P, Zheng T, Zhang Q. Genomic insights into the importantornamental and stress resistance traits of Prunus mume. Sci Hortic. 2022;302:111179. doi: 10.1016 / j.scienta.2022.111179. 8. Nguyen T, et al. Identification and genetic diversity of Japaneseapricot (Prunus mume) accessions in northern Vietnam. Biodiversitas. 2025;26(5):1234-1245. doi: 10.13057 / biodiv / d2605xx. 9. Wang Y, et al. Comparative population genomics reveals convergentand divergent selection in Prunus mume. Hortic Res. 2024;11(6):uhae109. doi:10.1093 / hr / uhae109. 10. Čmejlová J, et al. A New Set of SSR Markers Combined in OneReaction for Efficient Genotyping of the Hexaploid European Plum (Prunusdomestica L.). Plants. 2025;14(15):2281. doi: 10.3390 / plants14152281.

Claims

1. A method for processing and producing fruit plums of the Yingsumei or Longyanmei varieties, characterized in that, Includes the following steps: Using the sucrose phosphate synthase gene (SPS) and the citrate synthase gene (CS) as core genes controlling the flavor of plums, the expression patterns of these core genes were used to predict the optimal harvest time of plums and guide processing production. For fruits intended for the production of sweet products, harvesting should be carried out in the late developmental stage when the expression level of the sucrose phosphate synthase gene (SPS) is high and sucrose accumulation has reached its peak. For fruits intended for the production of sour products, harvesting should be carried out at the developmental stage where the expression level of the citric acid synthase gene CS is high and citric acid accumulates at a sustained high level. The accession number for the SPS gene is LOC103336553, and the accession number for the CS gene is LOC103328745.

Images