Malate accumulation and transcriptome patterns during fruit development in sweet cherry (Prunus avium L.)

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Congli Liu, Lei Chen, Xiliang Qi, Lulu Song, Manqing Wang, Shu Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7060936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in BMC Plant Biology → Version 1 posted 14 You are reading this latest preprint version Abstract Background Fruit acidity serves as a primary determinant of organoleptic quality in fleshy fruits. Malate predominates and significantly contributes to the fruit flavor profile and palatability in sweet cherry. However, the molecular mechanisms regulating malate accumulation in fruit cells of this species remain poorly understood. Results In this study, we performed quantitative profiling of TA in 97 sweet cherry cultivars at maturity, establishing a phenotyping framework for acidity classification. Temporal metabolomic analyses identified malate as the dominant organic acid throughout fruit development, exhibiting a biphasic accumulation pattern. Integrated transcriptomic profiling of high-acid and low-acid fruits across developmental stages revealed 3,643 differentially expressed genes, with functional annotation highlighting six structural genes ( PavPEPC3 , PavMDH1 , PavME1 , PavPHA5 , PavALMT1 , and PavALMT6 ) whose expression strongly correlated with malate content dynamics. Transcriptional regulatory network analysis further identified four candidate transcription factors, among which PavWRKY33 and PavbHLH149 were co-localized with a chromosome 6 quantitative trait locus (QTL)‌ associated with TA variation. Conclusion Our findings establish a comprehensive phenotyping framework for systematic acidity classification in sweet cherry, while elucidating the core genetic regulatory network governing malate accumulation. These mechanistic insights provide a robust scientific foundation for precision breeding strategies aimed at optimizing fruit quality through targeted modulation of acidity profiles. Sweet cherry fruit acidity malate metabolism transcriptome sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Enhancing fruit quality is a key objective in modern‌ breeding programs of fleshy crops. Organic acids are important components influencing flavor profiles and consumer preference‌, while serving as substrates in respiratory metabolism processes including cytoplasmic glycolysis, the mitochondrial tricarboxylic acid (TCA) cycle, and glyoxylic acid cycle, which generate energy for plants[1, 2].Malate is the predominant organic acid controlling fruit flavor quality in apple ( Malus domestica ) [3], plum ( Prunus salicina )[4, 5],apricot ( Prunus armeniaca )[6, 7],jujube ( Ziziphus jujuba )[8] and loquat ( Eriobotrya japonica )[9]. In sweet cherry, malate constitutes > 90% of the organic acids in ripe fruits[10–13]. Elucidating the molecular regulation of malate biosynthetic pathways and vacuolar transport mechanisms in sweet cherry is critical for advancing precision breeding strategies aimed at optimizing fruit acidity quality. Malate accumulation in fruit is a complex process involving synthesis, degradation, and transport. Malate is synthesised in the cytosol and is subsequently transported into vacuoles for storage[ 14 ]. NAD-dependent malate dehydrogenase (NAD-cytMDH) and NADP-dependent cytosolic malic enzyme (NADP-cytME) are key enzymes regulating malate metabolism, catalyzing the reversible interconversion of oxaloacetate (OAA) to malate and malate to pyruvate, respectively[ 2 , 14 , 15 ]. Studies have demonstrated that malate accumulation is primarily driven by vacuolar storage, with transmembrane transport requiring coordinated activity of proton pumps and transporters[ 14 , 16 , 17 ]. Two major malate transporters—aluminum-activated malate transporter (ALMT) and tonoplast dicarboxylate transporter (tDT)—and three proton pumps, including vacuolar H + -ATPase (V-ATPase), vacuolar H + -pyrophosphatase (V-PPase) and plasma membrane P-type ATPase (P-ATPase), have been shown to play essential roles in vacuolar acidification and malate accumulation[ 8 , 18 – 24 ]. Emerging evidence substantiates that transcription factors (TFs), including ‌MYB‌, ‌bHLH‌, and ‌WRKY‌ family proteins, regulate the transcriptional activation of malate transporter and vacuolar proton pump genes. In apple, MdMYB1 , MdMYB21 , MdMYB44 , and MdMYB73 transcriptionally regulate malate transporter and proton pump genes, thereby coordinating malate accumulation and vacuolar acidification[25–28]. In tomato ( Solanum lycopersicum ), SlWRKY42 negatively regulates fruit malate content by directly regulating SlALMT9 expression[24]. Conversely, MdWRKY126 enhances malate accumulation in apple through transcriptional activation of MdMDH5 [29], whereas ‌ ZjWRKY7‌ promotes malate accumulation in jujube ( Ziziphus jujuba ) by upregulating ZjALMT4 expression[8]. Similarly, PpWRKY44 regulates malate storage in pear via PpALMT9 modulation[30]. Beyond MYB and WRKY families, ‌bHLH‌ TFs also regulate malate metabolism. For instance, ‌MdbHLH3 ‌ directly activates MdMDH1 , encoding a cytosolic malate dehydrogenase critical for apple fruit acidity[31]. Collectively, these studies reveal a multifaceted transcriptional network governing malate transmembrane transport and compartmentalization in horticultural species. Sweet cherry is an economically important diploid fruit crop widely cultivated in warm-temperate regions. Reference genomes of three cultivars have been published: ‘Big Star’ (322 Mb)[ 32 ], ‘Satonishiki’ (352.9 Mb)[ 33 ] and ‘Tieton’ (341.38 Mb)[ 34 ]. Although malate is recognized as the predominant organic acid in sweet cherry fruits[ 11 , 12 , 35 ], comprehensive analyses of organic acid composition across developmental stages and cultivars remain limited. A major quantitative trait locus (QTL) controlling titratable acidity (TA), ‌qP-TA6.1 m ‌, has been mapped to ‌linkage group 6 (LG6)‌ in sweet cherry[3 6 , 37].However, the molecular mechanisms underlying ‌temporal acid dynamics‌ and ‌genome-wide regulatory networks‌ governing malate accumulation are poorly characterized in this species. In this study, TA contents were evaluated in ripe fruits of ninety-seven sweet cherry cultivars. Organic acid accumulation profiles were systematically examined across key developmental stages in six cultivars exhibiting contrasting malic acid accumulation capacities. Using transcriptome data from 18 developing fruit samples (high- and low-acid groups), we investigated gene upregulation, downregulation, and functional transitions during fruit development. Coexpression networks of genes and transcription factors associated with malate metabolism and transport were inferred. Our results provide insights into the dynamic changes of organic acid accumulation and elucidate mechanisms underlying malate metabolism regulation in sweet cherry. These data serve as valuable resources for future investigations of the genetic basis of fruit acidity in this species. 2. Materials and methods 2.1. Plant materials Sweet cherry fruits from ninety-seven accessions were harvested during three consecutive growing seasons (2019–2021) at optimal commercial maturity stage from the National Horticulture Germplasm Repository at Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences (34.70° N, 113.70° E). The experimental germplasm panel comprised ‌42 Chinese breeding lines‌ and ‌55 international cultivars originating from 10 countries (Table S1)‌. All measurements were conducted on 10-year-old CAB-11E rootstock-grafted trees managed under standardized horticultural protocols (irrigation, pruning, pest control). For organic acid profiling, fruits of ‌medium-early‌ cultivars ('Hongdeng', 'Ruixiang', 'Longguan') and ‌medium-late‌ cultivars ('Rainier', 'Baxing', 'Van') were sampled at ‌six developmental phases‌ (14, 21, 28, 35, 42, 49 days after full flowering; DAF) and ‌seven phases‌ (14–56 DAF at 7-day intervals), respectively. Harvested fruits were ‌snap-frozen in liquid nitrogen‌ and stored at ‌-80°C‌ until analysis. 2.2. Determination of titratable acidity (TA) Fruit juice was extracted from 15 ripe fruits per cultivar (n = 97 cultivars). TA was quantified ‌via acid-base titration‌ using the following protocol: 3 mL of fruit juice was diluted with 50 mL deionized water, and the mixture was titrated with ‌0.1 M NaOH‌ until reaching a pH endpoint of 8.1 (calibrated pH meter). Results were calculated based on malic acid equivalents and expressed as ‌grams of malic acid per 100 grams of fresh weight (g/100 g FW)‌. 2.3. Quantification of organic acids by HPLC Individual organic acids (malic acid, citric acid, and oxalic acid) were analyzed via ‌high-performance liquid chromatography (HPLC)‌ following the method of Cao et al. (2015)[38] with modifications. Briefly, 5 g of frozen pulp was homogenized to a fine powder in liquid nitrogen and suspended in ‌20 mL of ultrapure water‌ in a 50-mL centrifuge tube. The mixture was sonicated for 20 min, followed by centrifugation at 9000 rpm for 15 min at 4℃. The supernatant was collected, and the residual pellet was re-extracted with 10 mL of ultrapure water‌. Combined supernatants were ‌adjusted to 50 mL‌ with ultrapure water and ‌filtered through a 0.22-µm millipore filter prior to HPLC analysis. Chromatographic analysis was performed using a ‌Waters 1525 binary HPLC system‌ equipped with an ‌RI 2414 refractive index detector‌ and a ‌2998 photodiode array (PDA) detector‌ (Waters Corp., Milford, MA, USA). Malic acid, citric acid, and oxalic acid were used as certified standards to identify and quantify target compounds. Separation was achieved on an ‌Ultimate ® AQ-C18 (4.6 × 250 mm, 5 µm, Welch Science & Technology Co., Ltd, Shanghai, China) maintained at 30℃, using 0.02 mol/L (NH4) 2 HPO 4 buffer (pH 2.4) as mobile phase at a flow rate of 1.0 mL/min. Detection was performed at a wavelength of 210 nm. 2.4. RNA extraction Total RNA was extracted from frozen fruits using RNA Easy FAST Plant Tissue Kit (Tiangen, Beijing, China) following the manufacturer’s instructions. First-strand cDNA was synthesized using Fast Quant RT Kit (Tiangen, Beijing, China). 2.5. Transcriptome analysis Fruits from the low-acid variety 'Ruixiang' (designated 'RX') and high-acid variety 'Longguan'(designated 'LG') were collected at three developmental stages: 21 DAF, 42 DAF and 49 DAF. Three biological replicates per time point were analyzed by ‌RNA sequencing. Sequencing libraries were constructed and sequenced on an Illumina novaseq X plus platform‌ (Novogene Co., Ltd., Beijing, China). Clean reads were ‌aligned to the 'Satonishiki' reference genome (PAV_r1.0)‌ using ‌Hisat2 v2.0.5‌ with default parameters. Gene expression levels were quantified as ‌fragments per kilobase of transcript per million mapped reads (FPKM)‌. Differential gene expression analysis between 'RX' and 'LG' was performed using the DESeq2 package[39]. Differentially expressed genes (DEGs) were identified with thresholds of ‌adjusted P -value < 0.05 and |log 2 (fold change)|≥2. Expression patterns of DEGs were visualized using the ‌HeatMap module in TBtools v2.210‌[40]. 2.6. Enrichment pathway analysis of DEGs Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to categorize the biological functions of differentially expressed genes (DEGs), with a significance threshold of ‌adjusted P -value < 0.05‌. GO enrichment analysis was conducted using the ‌GOseq R package[41]‌. For metabolic pathway interpretation, KEGG enrichment analysis was implemented through the ‌KEGG database[42]‌, and statistical significance of pathway enrichment was evaluated using the ‌KOBAS 3.0 software[43]‌. 2.7. Quantitative real-time RT-PCR (qRT-PCR) analysis Real-time qPCR was performed using an ABI 7500 Real-Time PCR System‌ (Applied Biosystems, USA). The sweet cherry Histone2 ( Pav_sc0000671.1_g260.1.mk ) gene was used as the internal control. Relative gene expression was calculated using the 2 −△△Ct method. Three biological replicates and three technical replicates were analyzed. All primer sequences are listed in Table S2. 2.8. Statistical analysis All data were expressed as mean ± SD of triplicate determinations. The statistical analysis was performed using Origin version 2019b. Duncan's multiple range test performed the significant analysis at a 5% significance level. 3. Results 3.1 Variations in TA content of mature sweet cherry fruits TA was quantified in ripe fruits of 97 sweet cherry accessions, comprising 77 ‌novel accessions ‌unreported in previous studies. TA values ranged from 0.49–1.36% (w/w, malic acid equivalent), with extremal values observed in 'RX' ( ‌ min‌: 0.49%) and '15 − 3' (‌max‌: 1.36%), yielding a population mean of 0.89% (Fig. 1 A and Supplementary Table S1). Normality of TA distribution was statistically validated‌ ( P > 0.05, Fig. 1 B). Frequency-based hierarchical clustering delineated three phenotypic classes: low-acid (≤ 0.60%), medium-acid (0.61%-1.04%), and high-acid (≥ 1.05%). Notably, 'LG', 'Bing', 'Van', and 'Black Tartarian' clustered within the high-acid group, while 'RX', 'Baxing' and '13–33' displayed low-acid phenotypes. 3.2 Variations in organic acid profiles during sweet cherry fruit development HPLC analysis was performed to assess the dynamic changes in organic acid concentrations across low-acid 'RX', 'Baxing', medium-acid 'Hongdeng', 'Rainier' and high-acid 'Van', 'LG' (Fig. 3 ). In mature fruits, total organic acid content‌ ranged from 6.44 mg/g FW ('RX') to 12.59 mg/g FW ('LG'), with a population mean of ‌10.44 mg/g FW. Malic acid constituted ‌98%‌ of total acidity (range: 6.22–12.06 mg/g FW), followed by oxalic acid (mean: 0.44 mg/g FW) and trace citric acid (mean: 0.01 mg/g FW) (Fig. 2 B)‌. Notably, 'LG' exhibited the ‌highest malic acid concentration‌ (12.06 mg/g FW), contrasting with 'RX' showing ‌minimal accumulation‌ (6.22 mg/g FW) (Fig. 2 C,D). Developmental trajectories revealed progressive declines in oxalic and citric acids during maturation, while malic acid demonstrated sustained accumulation until one week prior to ripening. High-acid cultivars maintained consistently higher malic acid levels than medium- and low-acid cultivars throughout fruit development. At 14 DAF, mean malic acid content ranged from 1.4 ('RX') to 4.62 mg/g FW ('LG'), representing ‌63–73%‌ of total organic acids. No significant differences were observed between: low-acid cultivars 'RX' and 'Baxing', mid-acid cultivars 'Hongdeng' and 'Rainier’, and high-acid cultivars 'Van' and 'LG'. One week pre-ripening‌, malic acid concentrations peaked at: 8.51–9.42 mg/g FW‌ in low-acid cultivars, ‌12.07–12.56 mg/g FW‌ in medium-acid cultivars, 14.51–15.42 mg/g FW‌ in high-acid cultivars. At full maturity, all cultivars exhibited ‌12–27% reductions‌ in malic acid content (Fig. 2 B). These results demonstrate that malic acid concentration and its proportional contribution to total organic acids increase during early development, reach maximal levels pre-ripening, then decline steadily through final maturation. 3.3 Identification of differentially expressed genes associated with malic acid accumulation through RNA-seq transcriptome analysis To elucidate molecular mechanisms underlying malic acid accumulation, RNA sequencing was performed on fruit samples of 'RX' (low-acid) and 'LG' (high-acid) cultivars at three developmental stages: ‌21‌, ‌42‌, and ‌49 days after flowering (DAF)‌. Total RNA was extracted from ‌three biological replicates per time point‌, generating ‌18 cDNA libraries‌ for sequencing. High-quality reads (mean: ‌42.4 million per sample‌) were obtained, with ‌94.72%‌ mapping efficiency to the Prunus avium reference genome (‌PAV_r1.0; Table S3‌). Principal component analysis (PCA) revealed tight clustering of biological replicates, confirming high reproducibility of transcriptome data (Fig. S1). Differential gene expression analysis was conducted using the ‌DESeq R package‌ (v1.40.2) with thresholds of ‌|log 2 (fold change)| ≥ 2‌ and ‌ P < 0.05‌ in at least one developmental stage. Comparative analysis between 'LG' and 'RX' identified ‌3,643 differentially expressed genes (DEGs)‌. Stage-specific comparisons showed dynamic regulation patterns: 21 DAF‌: 983 up-regulated vs. 1013 down-regulated genes; 42 DAF‌: 1417 up-regulated vs. 902 down-regulated genes; 49 DAF‌: 1766 up-regulated vs. 1751 down-regulated genes (Fig. 3 A, B). A Venn diagram identified ‌670 conserved DEGs‌ across all three developmental stages (Fig. 3 C). Notably, DEG numbers progressively increased during fruit development, reaching maximal levels (‌3517 DEGs‌) at the ripening stage. 3.5 Enrichment Analysis of DEGs GO enrichment analysis of ‌3643 DEGs‌ revealed functional annotations across three categories: ‌molecular function‌ (1429 DEGs), ‌biological process‌ (1036 DEGs), and ‌cellular component‌ (283 DEGs). Key enriched terms included: ‌Molecular function‌: Calcium ion binding (GO:0005509), transcription regulator activity (GO:0140110), ADP binding (GO:0032549), transmembrane transporter activity (GO:0022857), and transferase activity (GO:0016758). ‌Biological process‌: organic acid metabolic process (GO:0006082), Cell communication (GO:0007154), signal transduction (GO:0007165), ion transport (GO:0006811), and cellular component biogenesis (GO:0044085). ‌Cellular component‌: Cell periphery (GO:0071944), extracellular region (GO:0005576), cell wall (GO:0005618), and apoplast (GO:0048046)(Fig. 4 A). Based on these findings, we hypothesize that DEGs associated with ‌ transcription regulator activity‌, ‌organic acid metabolism‌, and transmembrane transporter activity are critical for sweet cherry fruit acidity regulation. KEGG pathway enrichment analysis identified significant enrichment in flavonoid biosynthesis (pper00941), photosynthesis-antenna proteins (pper00196), plant hormone signal transduction (pper04075), and phenylpropanoid biosynthesis (pper00940)(Fig. 4 B). Integrated GO-KEGG analysis suggests that ‌acidity regulation‌ in sweet cherry fruits is mechanistically linked to ‌transcriptional control‌, ‌hormone signaling pathways‌, and ‌organic acid metabolic homeostasis‌. 3.6 Transcriptional regulation of malate metabolism genes Genome-wide annotation identified multiple gene families associated with malate dynamics in the Prunus avium transcriptome: 8 PavPEPCs , 15 PavMDHs , 5 PavMEs , 20 PavVHAs , 3 PavVHPs , 7 PavPHAs , 4 PavALMTs , and 2 PavtDTs homologs (Fig. 5 ). Transcriptional coordination with malic acid accumulation was observed for six key genes: PavPEPC3 , PavMDH1 , PavME1 , PavPHA5 , PavALMT1 , and PavALMT6 . Expression dynamics revealed‌ that the transcript levels of PavPEPC3 and PavMDH1 increased at early development stages but decreased later (Fig. 5 A). The transcript levels of PavME1 , PavPHA5 and PavALMT1 increased over the course of sweet cherry fruit development (Fig. 5 A, B, C). Expression of PavALMT6 decreased slightly in 'LG', whereas in 'RX', the transcript level initially decreased and subsequently increased later (Fig. 5 C). ‌ Developmental stage-dependent transcriptional variation‌ in malate metabolism genes was quantitatively validated between cultivars ('LG' vs. 'RX'). When compared with 'LG', PavPEPC3 maintained 1.5 ± 0.3-fold lower transcripts in 'RX' cross developmental stages. PavMDH1 exhibited no inter-cultivar variation at 21 DAF, however, displayed 1.1-fold lower transcripts in 'RX' at 42 DAF and 49 DAF, respectively. PavME1 , PavPHA5 and PavALMT6 exhibited 1.13–2.47 fold higher transcripts in 'RX' during development. PavALMT1 showed no inter-cultivar variation at 21 DAF, however, displayed 1.17-fold lower transcripts in 'RX' at 42 DAF, 1.26-fold higher transcripts in 'RX' at 49 DAF, respectively (Table S4). 3.7 Transcriptional regulators of malate dynamics Transcription factors (TFs), including MYB, basic helix-loop-helix (bHLH), and WRKY family proteins, serve as critical regulators of malate accumulation and vacuolar acidification by directly regulating the expression of malate transporters and proton pumps. We identified 189 TFs displaying differential expression patterns during developmental stages of 'RX' and 'LG'. These TFs were classified into 32 distinct subfamilies, including AP2/ERF, MYB, NAC, WRKY, and bHLH (Fig. 6 A). Notably, the expression profiles of PavMYB10.1 , PavMYB306, PavWRKY33 , and PavbHLH149 were consistent with malate accumulation across three developmental stages of 'RX' and 'LG'. In 'LG', expression levels of PavMYB10.1 and PavMYB306 exhibited progressive upregulation during fruit development, with significantly higher transcript abundance in 'LG' compared to 'RX'. Conversely, 'RX' displayed cultivar-specific dynamics: both genes were transiently upregulated during early developmental stages but diverged during maturation, with PavMYB10.1 stabilizing and PavMYB306 declining progressively (Fig. 6 C). PavWRKY33 showed a biphasic expression profile, characterized by initial downregulation in early development followed by pronounced upregulation in later stages. At terminal maturation, PavWRKY33 transcript levels in 'RX' were 4-fold higher than in 'LG' (Fig. 6 E). In contrast, PavbHLH149 expression transiently increased during early development but declined sharply in later phases, with 'RX' maintaining significantly higher transcript levels than 'LG' throughout fruit development (Fig. 6 F). To validate the transcriptome dataset, Quantitative real-time PCR (qRT-PCR) was conducted, and expression patterns were directly compared with RNA-seq profiles. We observed clear positive correlations between the qPCR and RNA-seq data across both cultivars during fruit development (Fig. S2). 4 Discussion 4.1 Composition and dynamic regulation of organic acids in sweet cherry fruit TA of sweet cherry, quantified as malic acid equivalents (%), serves as a robust indicator of organic acid content. In this study, TA values across 97 global accessions (0.49–1.36%) enabled stratification into three phenotypic clusters: low-acid (≤ 0.60%), medium-acid (0.61%-1.04%), and high-acid (≥ 1.05%). This classification aligns with prior observations of TA variability (0.4–1.5%) in diverse varieties‌[ 10 , 11 , 35], while refining thresholds through population-scale analysis. Malic acid is the predominant organic acid in sweet cherry fruits, with well-documented inter-cultivar variability in its concentration[ 12 , 35 ]. In this study, malic acid concentrations ranged from ‌6.22 to 12.06 mg/g FW ‌ , accounting for ‌ >98% of total organic acids ‌ in ripe fruits. These findings align with prior reports documenting malic acid concentrations of ‌ 3.22–12.77 mg/g FW ‌ across diverse cultivars[ 13 , 35]. Notably, wild progenitors and landraces exhibited significantly higher malic acid accumulation (‌10.78–36.56 mg/g FW) compared to modern commercial varieties[1 2 ], which consistent with domestication-driven selection for reduced acidity observed in apples[44] and jujubes[8]. Malic acid concentrations exhibit significant inter-cultivar variability during fruit development[ 45 – 47 ]. In this study, both malic acid concentration and its proportional contribution to total organic acids increased during early fruit development but declined in the maturation phase. These findings align with Zhao et al. (2013), who documented similar biphasic patterns in TA contents of 'Chelan', 'Bing', and 'Selah' cultivars[ 47 ]. By contrast, other varieties including 'Prime Giant', 'Cristalina', and 'Marvin Niram' demonstrate continuous malic acid accumulation throughout development without maturation-associated decline[ 45 , 46 ]. This cultivar-specific divergence in malate dynamics suggests that genetic factors primarily regulate terminal acid content in mature sweet cherries. Furthermore, the accumulation of malic acid in fruit cells is systemically regulated by interacting agro-environmental variables, such as water supply, mineral nutrition, and temperature[ 14 ]. 4.2 Candidate genes for fruit malic acid accumulation and its related mechanism in sweet cherry Malic acid accumulation in plant cells is governed by coordinated malate metabolism and vacuolar storage mechanisms[ 2 , 14 , 17 ]. Cytosolic malate metabolism involves enzymatic regulation through PEPC, NAD-cytMDH and NADP-cytME. Notably, promoter insertion in malate dehydrogenase genes MdMa7 ( MDH1 ) in apple, have been shown to regulate fruit malic acid accumulation[ 48 ]. Functional studies demonstrate that overexpression of NAD-cytMDH elevates malate levels in transgenic apple calli[ 15 , 29 ]. In this study, comparative transcriptomic profiling of high-acid 'LG' and low-acid 'RX' cultivars throughout fruit development identified 3643 DEGs. Notably, PavMDH1 exhibited significantly higher expression levels in 'LG' than in 'RX', whereas PavME1 displayed reciprocal expression patterns. This transcriptional divergence underlies the observed metabolic differences: low-acid 'RX' fruits accumulated 58% less malate than the high-acid 'LG', attributable to enhanced degradation capacity and reduced synthesis efficiency. Although metabolic processes have been shown to alter malate accumulation in fleshy fruits[ 2 , 15 , 48 ], vacuolar storage has been demonstrated to play a predominant role in determining fruit malate content. The transport of malate across the tonoplast into the vacuole is mediated by transporters and proton pumps, including tDT, ALMT, V-ATPase, P-ATPases, and V-PPase[ 14 , 16 , 21 , 24 ]. Among these components, the ALMT family genes, which encode aluminum-activated malate transporters, function as central regulators of malate accumulation. In apple, the ALMT9 homolog Ma1 critically controls fruit malate content. A premature stop codon mutation ‌in‌ Ma1 , truncating the C-terminus by 84 amino acids, is genetically linked to low-acid phenotypes[1 8 – 20 , 49].In tomato, SlALMT9 determines fruit malate accumulation, with a 3-bp indel in its promoter region driving genotypic variation in fruit acidity[24]. In jujube, the vacuolar malate transporter ZjALMT4 governs fruit malate accumulation, where cis-regulatory polymorphisms in its promoter region drive divergent malate concentrations between domesticated cultivars and wild sour jujube[8]. Strikingly, Ma1 , SlALMT9 , and ZjALMT4 share high amino acid sequence identity, suggesting evolutionary conservation of their functional roles in malate metabolism[8, 16].In our study, PavALMT1 and PavALMT6 exhibited significantly higher expression levels in the low-acid sweet cherry cultivar compared to the high-acid cultivar during late fruit development. The strong negative correlations between their expression profiles and malic acid content suggest that vacuolar malate trafficking mediated by these transporters may be a key determinant of acidity variation in sweet cherry. Proton pumps, including V-ATPase, P-ATPases, and V-PPase, are essential for proton transportation and the generation of proton-electrochemical gradient, which provides the driving force for malate transport into the vacuole[ 14 , 50 ]. These proton pumps have been functionally characterized in several fruit crops, such as pear[ 51 ], grape[ 52 ], peach[ 53 ], and apple[ 21 , 26 , 54 ]. In apple, three P-ATPase proton pump genes— Ma10 , MdPH1 and MdPH5 —have been demonstrated to regulate vacuolar acidification and malate accumulation[ 21 , 26 ]. Notably, Ma10 , encoding a tonoplast P 3A -type proton pump, directly interacts with the malate transporter MdMa1 to coordinate malic acid storage[ 21 ]. In our study, the P-type vacuolar proton pump PavPHA5 were highly expressed in the low-acid variety compared to high-acid variety during fruit development, suggests that vacuolar proton pump activity may drive malate compartmentalization in sweet cherry fruits. 4.3 Candidate TFs regulating malic acid accumulation in sweet cherry ‌ Emerging evidence‌ has established the critical regulatory function of TFs in modulating the expression of proton pump genes and malate transporters. In apple, multiple MYB family members MdMYB1 , MdMYB21 , MdMYB44 , MdMYB73 , and MdMYB123 coordinately regulate the transcriptional activity of malate transporter MdMa1 and proton pump MdPH5 , thereby controlling vacuolar acidification and malate accumulation[ 25 – 28 , 55]. These MYB TFs form MBW (MYB-bHLH-WD40) complexes with WD40 and bHLH partners to synergistically acidify vacuoles[25–27]. Notably, allelic variation in the MdMYB44 promoter region has been genetically associated with divergent malate levels in apple fruits[27].‌ In addition to MYB families, the apple bHLH transcription factor MdbHLH3 regulates fruit malate accumulation by directly binding to the promoter of MdcyMDH [ 31 ]. In tomato, a 15-bp indel within the SlALMT9 promoter disrupts a WRKY-binding W-box motif, thereby preventing‌ the binding capacity of the transcriptional repressor SlWRKY42 and ‌consequently derepressing‌ SlALMT9 expression to elevate fruit malate levels[24]. Similarly, in sour jujube, ZjWRKY7 activates ZjALMT4 through direct binding to the W-box elements in its promoter, resulting in‌ enhanced malate accumulation[8]. Parallel regulatory mechanisms‌ have been characterized in apple, where MdWRKY126 directly binds to the promoter of the malate dehydrogenase gene MdMDH5 , activating its expression and increasing apple fruit acidity[29]. ‌Through comparative transcriptome profiling‌, ‌ 189 differentially expressed TFs ‌ were identified during fruit development of ‌sweet cherry cultivars 'RX' and 'LG' ‌ . ‌ Among these candidates‌, ‌ PavMYB10.1‌ , ‌ PavMYB306 ‌, ‌ PavWRKY33 ‌, and ‌ PavbHLH149 ‌ ‌ exhibited significant correlations with temporal malic acid accumulation patterns‌. Notably, both PavWRKY33 and PavbHLH149 ‌ are mapped to the qP-TA6.1 m ‌ locus, a QTL on chromosome 6 that ‌ genetically controls ‌ fruit acidity in sweet cherry[36, 37]. ‌ This positional association and expression evidence provides compelling support for ‌ their ‌ functional role in regulating ‌ vacuolar malate storage. 5 Conclusion ‌Integrating metabolic phenotypes with transcriptomic data provides a robust framework ‌ for identifying gene regulatory networks and ‌ prioritizing candidate genes ‌ driving metabolic variation. ‌ In this study ‌ , we developed a fruit acidity classification system‌ by quantifying TA levels across ‌97 geographically diverse sweet cherry accessions‌. Multi-omics analysis revealed developmental stage-resolved gene expression patterns that covaried with malate accumulation dynamics during fruit maturation (Fig. 7 ). Notably, our study identified four transcription factors and six structural genes functionally implicated in malate biosynthesis, vacuolar transport, and proton homeostasis. The colocalization‌ of PavWRKY33 and PavbHLH149 within ‌the major-effect QTL qP-TA6.1 m ‌ , establishing these regulators as central hubs‌ controling acidity variation in sweet cherry fruit. Declarations Acknowledgements Authors want to express their gratitude to all people and institutions that helped and unconditioned support in the elaboration of this work. Authors’ contributions C.L.: Writing – original draft, Validation, Investigation, Data curation. L.C., X.Q., L.S., M.W. and S.H.: Methodology, Investigation. M. L.: Project administration, Supervision, Validation, Writing – review & editing. Funding This research was supported by the National Natural Science Foundation of China [No.3210180675] and the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2025-ZFRI). Availability of data and materials All data supporting the findings of this study are available within the paper and its supplementary data. The raw transcriptome sequencing data have been deposited in the National Genomics DataCenter (NGDC), Beijing Institute of Genomics, Chinese Academy of Sciences, under BioProject accession number PRJCA043157. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Fernie AR, Carrari F, Sweetlove LJ. Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport. Curr Opin Plant Biol. 2004; 7(3):254-261. https://doi.org/10.1016/j.pbi.2004.03.007. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7060936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487161578,"identity":"50967303-e9a4-4559-a98f-e52fef78d1f9","order_by":0,"name":"Congli Liu","email":"","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Congli","middleName":"","lastName":"Liu","suffix":""},{"id":487161579,"identity":"20b27f03-6be5-4be8-904c-1bb53c88e5d5","order_by":1,"name":"Lei Chen","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chen","suffix":""},{"id":487161580,"identity":"c4e28caf-62fa-4239-a44a-d35dee5b30f0","order_by":2,"name":"Xiliang Qi","email":"","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiliang","middleName":"","lastName":"Qi","suffix":""},{"id":487161581,"identity":"5b0fa472-3b4a-4ad5-b570-5622758b29f1","order_by":3,"name":"Lulu Song","email":"","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Song","suffix":""},{"id":487161582,"identity":"4d347d49-8440-41b8-a0c1-b22e4e6fce54","order_by":4,"name":"Manqing Wang","email":"","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Manqing","middleName":"","lastName":"Wang","suffix":""},{"id":487161583,"identity":"4b99083d-4c32-44cc-b379-fde14baa69d9","order_by":5,"name":"Shu Han","email":"","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Han","suffix":""},{"id":487161584,"identity":"882651ee-fcd9-42ac-9ae6-28c0ff4f23c6","order_by":6,"name":"Ming Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACPmYGhgMMDBJy/ERrYYNosTGWbCBaC4RKSzQ4QLQWdu7EwwW/DicYHz/88OMPBrs8IhzGu+HwzL7DeWZn0oyleRiSi4nTwttzuNjsBoOBNNBfiQ3EakncPIP9888fRGvh+ZGWuEGCx0yCh3hbGmyMJc7klFnzGCQT1sLPf3bzZ54/wKhsP7755o8KO8JawICxDcYyIEo9CPwhWuUoGAWjYBSMRAAA2k46rGdLy9YAAAAASUVORK5CYII=","orcid":"","institution":"National Key Laboratory for Germplasm Innovation \u0026 Utilization of Horticultural Crops, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-07 03:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7060936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7060936/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07608-1","type":"published","date":"2025-11-12T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87215950,"identity":"61c1154a-d230-498b-b882-2b854e35ee1f","added_by":"auto","created_at":"2025-07-21 15:25:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2810299,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution (A) and biological variation (B) of TA in mature fruits from 97 geographically diverse sweet cherry accessions. TA levels were quantified across three consecutive harvesting seasons (2019-2021) and expressed as percentage of malic acid equivalents per 100 g fresh weight. Data points represent mean values ± standard deviation (SD) of three biologically independent replicates.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/74e9f3c38f6eeb3ecd75f810.png"},{"id":87215952,"identity":"37cc3684-7f3a-4ded-af7a-13df5195478e","added_by":"auto","created_at":"2025-07-21 15:25:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1041901,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of physiological and morphological dynamics across six sweet cherry cultivars during fruit maturation. \u0026nbsp;‌(A)\u003cstrong\u003e‌ \u003c/strong\u003eTemporal patterns of individual fruit fresh weight (g) at key developmental phases (21, 42, 49 DAF). \u003cstrong\u003e‌\u003c/strong\u003e(B)\u003cstrong\u003e‌\u003c/strong\u003e Metabolic dynamics of malate, citrate, oxalate, and total organic acids. ‌(C)\u003cstrong\u003e‌ \u003c/strong\u003eMorphological progression of representative high-acid 'LG' and low-acid 'RX' fruits (Scale bar: 1 cm). \u003cstrong\u003e‌\u003c/strong\u003e(D)\u003cstrong\u003e‌\u003c/strong\u003e Differential accumulation of malic acid in 'LG' versus 'RX' during fruit development.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/55fcab26b75a7d8a690cbbd0.png"},{"id":87215953,"identity":"0fe9cd9a-37be-442b-8e41-f34c7d001413","added_by":"auto","created_at":"2025-07-21 15:25:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2946739,"visible":true,"origin":"","legend":"\u003cp\u003e‌Transcriptome dynamics of DEGs between high-acid 'LG' and low-acid 'RX' sweet cherry cultivars during fruit maturation. (A) Comparative transcriptomic profiles visualized by volcano plots (LG1/RX1, LG2/RX2, LG3/RX3), with a significance threshold of \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.05 and |log\u003csub\u003e2\u003c/sub\u003e(fold change)|≥2.\u003cem\u003e \u003c/em\u003e(B) Stage-specific DEG distribution: Light red indicates upregulated genes, while light yellow denotes downregulated genes across three maturation stages. (C) Venn analysis identifies 670 consensus DEGs (highlighted in grayish blue) co-regulated in malic acid accumulation pathways.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/ac960ff9220a55302d1e59ca.png"},{"id":87215954,"identity":"8d2c5b2a-6081-4da7-99f9-7d27ae519e59","added_by":"auto","created_at":"2025-07-21 15:25:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4173256,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DEGs in GO and KEGG pathways. (A) GO enrichment analysis of the 3643 DEGs classified into three categories, i.e., cellular component (CC), molecular function (MF), and biological processes (BP). (B) KEGG enrichment analysis bubble chart, where color and size of the points indicate the \u003cem\u003ep\u003c/em\u003e value and number of differentially enriched genes, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/91a8da4e1644380095350bae.png"},{"id":87215957,"identity":"7d4259e4-f4b5-4605-99a8-66588f8ba3c0","added_by":"auto","created_at":"2025-07-21 15:25:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3818348,"visible":true,"origin":"","legend":"\u003cp\u003e‌Expression dynamics of malate metabolism and transport genes in ‘LG’ and ‘RX’ cultivars. (A) Heat map of malate synthesis/degradation genes, (B) proton pump genes, and (C) malate transporter genes across three fruit developmental stages in high-acid ‘LG’ and low-acid ‘RX’. The color blocks represent the relative expression levels of genes, with high and low expression shown in red and blue color, respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/841ee9657e606bcd48aa122b.png"},{"id":87215966,"identity":"193610e2-78b0-485f-a98e-c501a8c0f30b","added_by":"auto","created_at":"2025-07-21 15:25:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11222530,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome-wide profiling of differentially expressed TFs. (A) Family classification of 189 common differentially expressed TFs in three comparison groups. (B-F) Heat maps of FPKM-normalized expression patterns for \u003cem\u003ePavERFs\u003c/em\u003e (B), \u003cem\u003ePavMYBs\u003c/em\u003e (C), \u003cem\u003ePavNACs\u003c/em\u003e (D), \u003cem\u003ePavWRKYs\u003c/em\u003e (E), and \u003cem\u003ePavbHLHs\u003c/em\u003e (F).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/3ea237fc235ad9151d39a620.png"},{"id":87216600,"identity":"4912e640-4cda-4586-9f50-0a8d955147f8","added_by":"auto","created_at":"2025-07-21 15:33:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2591826,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of malate metabolism and accumulation dynamics across three developmental stages in sweet cherry fruit. Putative enzymatic components were annotated using the sweet cherry genome database. Expression patterns of key genes are visualized via heatmaps for cultivars ‘LG’ and ‘RX’, with detailed transcriptomic profiles provided in Supplementary Table S4. ‌Metabolic pathway abbreviations: PEP: Phosphoenolpyruvate; PEPC: Phosphoenolpyruvate carboxylase; PEPCK: Phosphoenolpyruvate carboxykinase; OAA: Oxaloacetate; PK: Pyruvate kinase; PPDK: Pyruvate orthophosphate dikinase; NAD-MDH: NAD-dependent malate dehydrogenase; NAD-ME: NAD-malic enzyme; ALMT: Aluminium-activated malate transporter; tDT: Tonoplast dicarboxylate transporter; V-PPase: Vacuolar H\u003csup\u003e+\u003c/sup\u003e-pyrophosphatase; V-ATPase: Vacuolar H\u003csup\u003e+\u003c/sup\u003e-ATPase; P-ATPase: Plasma membrane H\u003csup\u003e+\u003c/sup\u003e-ATPase.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/1f43a07bea6c0882e6b6c663.png"},{"id":96105171,"identity":"bc96bf5a-84c0-4cf1-912e-d76ba40417b5","added_by":"auto","created_at":"2025-11-17 16:09:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29682531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/a618b845-3d18-4c5d-8f2e-f570d741f943.pdf"},{"id":87215949,"identity":"96a6e7c0-0c5e-42cf-b003-e38a2793dd9e","added_by":"auto","created_at":"2025-07-21 15:25:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":385021,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureandTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7060936/v1/af9076d5c1f086c844303dc8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMalate accumulation and transcriptome patterns during fruit development in sweet cherry (\u003cem\u003ePrunus avium L\u003c/em\u003e.)\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEnhancing fruit quality is a key objective in modern\u0026zwnj; breeding programs of fleshy crops. Organic acids are important components influencing flavor profiles and consumer preference\u0026zwnj;, while serving as substrates in respiratory metabolism processes including cytoplasmic glycolysis, the mitochondrial tricarboxylic acid (TCA) cycle, and glyoxylic acid cycle, which generate energy for plants[1, 2].Malate is the predominant organic acid controlling fruit flavor quality in apple (\u003cem\u003eMalus domestica\u003c/em\u003e) [3], plum (\u003cem\u003ePrunus salicina\u003c/em\u003e)[4, 5],apricot (\u003cem\u003ePrunus armeniaca\u003c/em\u003e)[6, 7],jujube (\u003cem\u003eZiziphus jujuba\u003c/em\u003e)[8] and loquat (\u003cem\u003eEriobotrya japonica\u003c/em\u003e)[9]. In sweet cherry, malate constitutes\u0026thinsp;\u0026gt;\u0026thinsp;90% of the organic acids in ripe fruits[10\u0026ndash;13]. Elucidating the molecular regulation of malate biosynthetic pathways and vacuolar transport mechanisms in sweet cherry is critical for advancing precision breeding strategies aimed at optimizing fruit acidity quality.\u003c/p\u003e\u003cp\u003eMalate accumulation in fruit is a complex process involving synthesis, degradation, and transport. Malate is synthesised in the cytosol and is subsequently transported into vacuoles for storage[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. NAD-dependent malate dehydrogenase (NAD-cytMDH) and NADP-dependent cytosolic malic enzyme (NADP-cytME) are key enzymes regulating malate metabolism, catalyzing the reversible interconversion of oxaloacetate (OAA) to malate and malate to pyruvate, respectively[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have demonstrated that malate accumulation is primarily driven by vacuolar storage, with transmembrane transport requiring coordinated activity of proton pumps and transporters[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Two major malate transporters\u0026mdash;aluminum-activated malate transporter (ALMT) and tonoplast dicarboxylate transporter (tDT)\u0026mdash;and three proton pumps, including vacuolar H\u003csup\u003e+\u003c/sup\u003e-ATPase (V-ATPase), vacuolar H\u003csup\u003e+\u003c/sup\u003e-pyrophosphatase (V-PPase) and plasma membrane P-type ATPase (P-ATPase), have been shown to play essential roles in vacuolar acidification and malate accumulation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging evidence substantiates that transcription factors (TFs), including \u0026zwnj;MYB\u0026zwnj;, \u0026zwnj;bHLH\u0026zwnj;, and \u0026zwnj;WRKY\u0026zwnj; family proteins, regulate the transcriptional activation of malate transporter and vacuolar proton pump genes. In apple, \u003cem\u003eMdMYB1\u003c/em\u003e, \u003cem\u003eMdMYB21\u003c/em\u003e, \u003cem\u003eMdMYB44\u003c/em\u003e, and \u003cem\u003eMdMYB73\u003c/em\u003e transcriptionally regulate malate transporter and proton pump genes, thereby coordinating malate accumulation and vacuolar acidification[25\u0026ndash;28]. In tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e), \u003cem\u003eSlWRKY42\u003c/em\u003e negatively regulates fruit malate content by directly regulating \u003cem\u003eSlALMT9\u003c/em\u003e expression[24]. Conversely, \u003cem\u003eMdWRKY126\u003c/em\u003e enhances malate accumulation in apple through transcriptional activation of \u003cem\u003eMdMDH5\u003c/em\u003e[29], whereas \u0026zwnj;\u003cem\u003eZjWRKY7\u0026zwnj;\u003c/em\u003e promotes malate accumulation in jujube (\u003cem\u003eZiziphus jujuba\u003c/em\u003e) by upregulating \u003cem\u003eZjALMT4\u003c/em\u003e expression[8]. Similarly, \u003cem\u003ePpWRKY44\u003c/em\u003e regulates malate storage in pear via \u003cem\u003ePpALMT9\u003c/em\u003e modulation[30]. Beyond MYB and WRKY families, \u0026zwnj;bHLH\u0026zwnj; TFs also regulate malate metabolism. For instance, \u003cem\u003e\u0026zwnj;MdbHLH3\u003c/em\u003e\u0026zwnj; directly activates \u003cem\u003eMdMDH1\u003c/em\u003e, encoding a cytosolic malate dehydrogenase critical for apple fruit acidity[31]. Collectively, these studies reveal a multifaceted transcriptional network governing malate transmembrane transport and compartmentalization in horticultural species.\u003c/p\u003e\u003cp\u003eSweet cherry is an economically important diploid fruit crop widely cultivated in warm-temperate regions. Reference genomes of three cultivars have been published: \u0026lsquo;Big Star\u0026rsquo; (322 Mb)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], \u0026lsquo;Satonishiki\u0026rsquo; (352.9 Mb)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and \u0026lsquo;Tieton\u0026rsquo; (341.38 Mb)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although malate is recognized as the predominant organic acid in sweet cherry fruits[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], comprehensive analyses of organic acid composition across developmental stages and cultivars remain limited. A major quantitative trait locus (QTL) controlling titratable acidity (TA), \u003cem\u003e\u0026zwnj;qP-TA6.1\u003c/em\u003e\u003csup\u003e\u003cem\u003em\u003c/em\u003e\u003c/sup\u003e\u0026zwnj;, has been mapped to \u0026zwnj;linkage group 6 (LG6)\u0026zwnj; in sweet cherry[3\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, 37].However, the molecular mechanisms underlying \u0026zwnj;temporal acid dynamics\u0026zwnj; and \u0026zwnj;genome-wide regulatory networks\u0026zwnj; governing malate accumulation are poorly characterized in this species.\u003c/p\u003e\u003cp\u003eIn this study, TA contents were evaluated in ripe fruits of ninety-seven sweet cherry cultivars. Organic acid accumulation profiles were systematically examined across key developmental stages in six cultivars exhibiting contrasting malic acid accumulation capacities. Using transcriptome data from 18 developing fruit samples (high- and low-acid groups), we investigated gene upregulation, downregulation, and functional transitions during fruit development. Coexpression networks of genes and transcription factors associated with malate metabolism and transport were inferred. Our results provide insights into the dynamic changes of organic acid accumulation and elucidate mechanisms underlying malate metabolism regulation in sweet cherry. These data serve as valuable resources for future investigations of the genetic basis of fruit acidity in this species.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Plant materials\u003c/h2\u003e\n \u003cp\u003eSweet cherry fruits from ninety-seven accessions were harvested during three consecutive growing seasons (2019\u0026ndash;2021) at optimal commercial maturity stage from the National Horticulture Germplasm Repository at Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences (34.70\u0026deg; N, 113.70\u0026deg; E). The experimental germplasm panel comprised \u0026zwnj;42 Chinese breeding lines\u0026zwnj; and \u0026zwnj;55 international cultivars originating from 10 countries (Table S1)\u0026zwnj;. All measurements were conducted on 10-year-old CAB-11E rootstock-grafted trees managed under standardized horticultural protocols (irrigation, pruning, pest control). For organic acid profiling, fruits of \u0026zwnj;medium-early\u0026zwnj; cultivars (\u0026apos;Hongdeng\u0026apos;, \u0026apos;Ruixiang\u0026apos;, \u0026apos;Longguan\u0026apos;) and \u0026zwnj;medium-late\u0026zwnj; cultivars (\u0026apos;Rainier\u0026apos;, \u0026apos;Baxing\u0026apos;, \u0026apos;Van\u0026apos;) were sampled at \u0026zwnj;six developmental phases\u0026zwnj; (14, 21, 28, 35, 42, 49 days after full flowering; DAF) and \u0026zwnj;seven phases\u0026zwnj; (14\u0026ndash;56 DAF at 7-day intervals), respectively. Harvested fruits were \u0026zwnj;snap-frozen in liquid nitrogen\u0026zwnj; and stored at \u0026zwnj;-80\u0026deg;C\u0026zwnj; until analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Determination of titratable acidity (TA)\u003c/h2\u003e\n \u003cp\u003eFruit juice was extracted from 15 ripe fruits per cultivar (n\u0026thinsp;=\u0026thinsp;97 cultivars). TA was quantified \u0026zwnj;via acid-base titration\u0026zwnj; using the following protocol: 3 mL of fruit juice was diluted with 50 mL deionized water, and the mixture was titrated with \u0026zwnj;0.1 M NaOH\u0026zwnj; until reaching a pH endpoint of 8.1 (calibrated pH meter). Results were calculated based on malic acid equivalents and expressed as \u0026zwnj;grams of malic acid per 100 grams of fresh weight (g/100 g FW)\u0026zwnj;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Quantification of organic acids by HPLC\u003c/h2\u003e\n \u003cp\u003eIndividual organic acids (malic acid, citric acid, and oxalic acid) were analyzed via \u0026zwnj;high-performance liquid chromatography (HPLC)\u0026zwnj; following the method of Cao et al. (2015)[38] with modifications. Briefly, 5 g of frozen pulp was homogenized to a fine powder in liquid nitrogen and suspended in \u0026zwnj;20 mL of ultrapure water\u0026zwnj; in a 50-mL centrifuge tube. The mixture was sonicated for 20 min, followed by centrifugation at 9000 rpm for 15 min at 4℃. The supernatant was collected, and the residual pellet was re-extracted with 10 mL of ultrapure water\u0026zwnj;. Combined supernatants were \u0026zwnj;adjusted to 50 mL\u0026zwnj; with ultrapure water and \u0026zwnj;filtered through a 0.22-\u0026micro;m millipore filter prior to HPLC analysis.\u003c/p\u003e\n \u003cp\u003eChromatographic analysis was performed using a \u0026zwnj;Waters 1525 binary HPLC system\u0026zwnj; equipped with an \u0026zwnj;RI 2414 refractive index detector\u0026zwnj; and a \u0026zwnj;2998 photodiode array (PDA) detector\u0026zwnj; (Waters Corp., Milford, MA, USA). Malic acid, citric acid, and oxalic acid were used as certified standards to identify and quantify target compounds. Separation was achieved on an \u0026zwnj;Ultimate\u003csup\u003e\u0026reg;\u003c/sup\u003e AQ-C18 (4.6 \u0026times; 250 mm, 5 \u0026micro;m, Welch Science \u0026amp; Technology Co., Ltd, Shanghai, China) maintained at 30℃, using 0.02 mol/L (NH4)\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e buffer (pH 2.4) as mobile phase at a flow rate of 1.0 mL/min. Detection was performed at a wavelength of 210 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. RNA extraction\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from frozen fruits using RNA Easy FAST Plant Tissue Kit (Tiangen, Beijing, China) following the manufacturer\u0026rsquo;s instructions. First-strand cDNA was synthesized using Fast Quant RT Kit (Tiangen, Beijing, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Transcriptome analysis\u003c/h2\u003e\n \u003cp\u003eFruits from the low-acid variety \u0026apos;Ruixiang\u0026apos; (designated \u0026apos;RX\u0026apos;) and high-acid variety \u0026apos;Longguan\u0026apos;(designated \u0026apos;LG\u0026apos;) were collected at three developmental stages: 21 DAF, 42 DAF and 49 DAF. Three biological replicates per time point were analyzed by \u0026zwnj;RNA sequencing. Sequencing libraries were constructed and sequenced on an Illumina novaseq X plus platform\u0026zwnj; (Novogene Co., Ltd., Beijing, China). Clean reads were \u0026zwnj;aligned to the \u0026apos;Satonishiki\u0026apos; reference genome (PAV_r1.0)\u0026zwnj; using \u0026zwnj;Hisat2 v2.0.5\u0026zwnj; with default parameters. Gene expression levels were quantified as \u0026zwnj;fragments per kilobase of transcript per million mapped reads (FPKM)\u0026zwnj;. Differential gene expression analysis between \u0026apos;RX\u0026apos; and \u0026apos;LG\u0026apos; was performed using the DESeq2 package[39]. Differentially expressed genes (DEGs) were identified with thresholds of \u0026zwnj;adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e(fold change)|\u0026ge;2. Expression patterns of DEGs were visualized using the \u0026zwnj;HeatMap module in TBtools v2.210\u0026zwnj;[40].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Enrichment pathway analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to categorize the biological functions of differentially expressed genes (DEGs), with a significance threshold of \u0026zwnj;adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026zwnj;. GO enrichment analysis was conducted using the \u0026zwnj;GOseq R package[41]\u0026zwnj;. For metabolic pathway interpretation, KEGG enrichment analysis was implemented through the \u0026zwnj;KEGG database[42]\u0026zwnj;, and statistical significance of pathway enrichment was evaluated using the \u0026zwnj;KOBAS 3.0 software[43]\u0026zwnj;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Quantitative real-time RT-PCR (qRT-PCR) analysis\u003c/h2\u003e\n \u003cp\u003eReal-time qPCR was performed using an ABI 7500 Real-Time PCR System\u0026zwnj; (Applied Biosystems, USA). The sweet cherry Histone2 (\u003cem\u003ePav_sc0000671.1_g260.1.mk\u003c/em\u003e) gene was used as the internal control. Relative gene expression was calculated using the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e method. Three biological replicates and three technical replicates were analyzed. All primer sequences are listed in Table S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eAll data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of triplicate determinations. The statistical analysis was performed using Origin version 2019b. Duncan\u0026apos;s multiple range test performed the significant analysis at a 5% significance level.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 Variations in TA content of mature sweet cherry fruits\u003c/h2\u003e\n \u003cp\u003eTA was quantified in ripe fruits of 97 sweet cherry accessions, comprising 77 \u0026zwnj;novel accessions \u0026zwnj;unreported in previous studies. TA values ranged from 0.49\u0026ndash;1.36% (w/w, malic acid equivalent), with extremal values observed in \u0026apos;RX\u0026apos; (\u003cstrong\u003e\u0026zwnj;\u003c/strong\u003emin\u0026zwnj;: 0.49%) and \u0026apos;15\u0026thinsp;\u0026minus;\u0026thinsp;3\u0026apos; (\u0026zwnj;max\u0026zwnj;: 1.36%), yielding a population mean of 0.89% (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eA and Supplementary Table S1). Normality of TA distribution was statistically validated\u0026zwnj; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eB). Frequency-based hierarchical clustering delineated three phenotypic classes: low-acid (\u0026le;\u0026thinsp;0.60%), medium-acid (0.61%-1.04%), and high-acid (\u0026ge;\u0026thinsp;1.05%). Notably, \u0026apos;LG\u0026apos;, \u0026apos;Bing\u0026apos;, \u0026apos;Van\u0026apos;, and \u0026apos;Black Tartarian\u0026apos; clustered within the high-acid group, while \u0026apos;RX\u0026apos;, \u0026apos;Baxing\u0026apos; and \u0026apos;13\u0026ndash;33\u0026apos; displayed low-acid phenotypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 Variations in organic acid profiles during sweet cherry fruit development\u003c/h2\u003e\n \u003cp\u003eHPLC analysis was performed to assess the dynamic changes in organic acid concentrations across low-acid \u0026apos;RX\u0026apos;, \u0026apos;Baxing\u0026apos;, medium-acid \u0026apos;Hongdeng\u0026apos;, \u0026apos;Rainier\u0026apos; and high-acid \u0026apos;Van\u0026apos;, \u0026apos;LG\u0026apos; (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). In mature fruits, total organic acid content\u0026zwnj; ranged from 6.44 mg/g FW (\u0026apos;RX\u0026apos;) to 12.59 mg/g FW (\u0026apos;LG\u0026apos;), with a population mean of \u0026zwnj;10.44 mg/g FW. Malic acid constituted \u0026zwnj;98%\u0026zwnj; of total acidity (range: 6.22\u0026ndash;12.06 mg/g FW), followed by oxalic acid (mean: 0.44 mg/g FW) and trace citric acid (mean: 0.01 mg/g FW) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB)\u0026zwnj;. Notably, \u0026apos;LG\u0026apos; exhibited the \u0026zwnj;highest malic acid concentration\u0026zwnj; (12.06 mg/g FW), contrasting with \u0026apos;RX\u0026apos; showing \u0026zwnj;minimal accumulation\u0026zwnj; (6.22 mg/g FW) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eC,D).\u003c/p\u003e\n \u003cp\u003eDevelopmental trajectories revealed progressive declines in oxalic and citric acids during maturation, while malic acid demonstrated sustained accumulation until one week prior to ripening. High-acid cultivars maintained consistently higher malic acid levels than medium- and low-acid cultivars throughout fruit development. At 14 DAF, mean malic acid content ranged from 1.4 (\u0026apos;RX\u0026apos;) to 4.62 mg/g FW (\u0026apos;LG\u0026apos;), representing \u0026zwnj;63\u0026ndash;73%\u0026zwnj; of total organic acids. No significant differences were observed between: low-acid cultivars \u0026apos;RX\u0026apos; and \u0026apos;Baxing\u0026apos;, mid-acid cultivars \u0026apos;Hongdeng\u0026apos; and \u0026apos;Rainier\u0026rsquo;, and high-acid cultivars \u0026apos;Van\u0026apos; and \u0026apos;LG\u0026apos;. One week pre-ripening\u0026zwnj;, malic acid concentrations peaked at: 8.51\u0026ndash;9.42 mg/g FW\u0026zwnj; in low-acid cultivars, \u0026zwnj;12.07\u0026ndash;12.56 mg/g FW\u0026zwnj; in medium-acid cultivars, 14.51\u0026ndash;15.42 mg/g FW\u0026zwnj; in high-acid cultivars. At full maturity, all cultivars exhibited \u0026zwnj;12\u0026ndash;27% reductions\u0026zwnj; in malic acid content (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB). These results demonstrate that malic acid concentration and its proportional contribution to total organic acids increase during early development, reach maximal levels pre-ripening, then decline steadily through final maturation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 Identification of differentially expressed genes associated with malic acid accumulation through RNA-seq transcriptome analysis\u003c/h2\u003e\n \u003cp\u003eTo elucidate molecular mechanisms underlying malic acid accumulation, RNA sequencing was performed on fruit samples of \u0026apos;RX\u0026apos; (low-acid) and \u0026apos;LG\u0026apos; (high-acid) cultivars at three developmental stages: \u0026zwnj;21\u0026zwnj;, \u0026zwnj;42\u0026zwnj;, and \u0026zwnj;49 days after flowering (DAF)\u0026zwnj;. Total RNA was extracted from \u0026zwnj;three biological replicates per time point\u0026zwnj;, generating \u0026zwnj;18 cDNA libraries\u0026zwnj; for sequencing. High-quality reads (mean: \u0026zwnj;42.4 million per sample\u0026zwnj;) were obtained, with \u0026zwnj;94.72%\u0026zwnj; mapping efficiency to the \u003cem\u003ePrunus avium\u003c/em\u003e reference genome (\u0026zwnj;PAV_r1.0; Table S3\u0026zwnj;). Principal component analysis (PCA) revealed tight clustering of biological replicates, confirming high reproducibility of transcriptome data (Fig. S1).\u003c/p\u003e\n \u003cdiv\u003eDifferential gene expression analysis was conducted using the \u0026zwnj;DESeq R package\u0026zwnj; (v1.40.2) with thresholds of \u0026zwnj;|log\u003csub\u003e2\u003c/sub\u003e(fold change)| \u0026ge; 2\u0026zwnj; and \u0026zwnj;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026zwnj; in at least one developmental stage. Comparative analysis between \u0026apos;LG\u0026apos; and \u0026apos;RX\u0026apos; identified \u0026zwnj;3,643 differentially expressed genes (DEGs)\u0026zwnj;. Stage-specific comparisons showed dynamic regulation patterns: 21 DAF\u0026zwnj;: 983 up-regulated vs. 1013 down-regulated genes; 42 DAF\u0026zwnj;: 1417 up-regulated vs. 902 down-regulated genes; 49 DAF\u0026zwnj;: 1766 up-regulated vs. 1751 down-regulated genes (Fig. \u003cspan\u003e3\u003c/span\u003eA, B). A Venn diagram identified \u0026zwnj;670 conserved DEGs\u0026zwnj; across all three developmental stages (Fig. \u003cspan\u003e3\u003c/span\u003eC). Notably, DEG numbers progressively increased during fruit development, reaching maximal levels (\u0026zwnj;3517 DEGs\u0026zwnj;) at the ripening stage.\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.5 Enrichment Analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eGO enrichment analysis of \u0026zwnj;3643 DEGs\u0026zwnj; revealed functional annotations across three categories: \u0026zwnj;molecular function\u0026zwnj; (1429 DEGs), \u0026zwnj;biological process\u0026zwnj; (1036 DEGs), and \u0026zwnj;cellular component\u0026zwnj; (283 DEGs). Key enriched terms included: \u0026zwnj;Molecular function\u0026zwnj;: Calcium ion binding (GO:0005509), transcription regulator activity (GO:0140110), ADP binding (GO:0032549), transmembrane transporter activity (GO:0022857), and transferase activity (GO:0016758). \u0026zwnj;Biological process\u0026zwnj;: organic acid metabolic process (GO:0006082), Cell communication (GO:0007154), signal transduction (GO:0007165), ion transport (GO:0006811), and cellular component biogenesis (GO:0044085). \u0026zwnj;Cellular component\u0026zwnj;: Cell periphery (GO:0071944), extracellular region (GO:0005576), cell wall (GO:0005618), and apoplast (GO:0048046)(Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). Based on these findings, we hypothesize that DEGs associated with \u0026zwnj; transcription regulator activity\u0026zwnj;, \u0026zwnj;organic acid metabolism\u0026zwnj;, and transmembrane transporter activity are critical for sweet cherry fruit acidity regulation.\u003c/p\u003e\n \u003cp\u003eKEGG pathway enrichment analysis identified significant enrichment in flavonoid biosynthesis (pper00941), photosynthesis-antenna proteins (pper00196), plant hormone signal transduction (pper04075), and phenylpropanoid biosynthesis (pper00940)(Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB). Integrated GO-KEGG analysis suggests that \u0026zwnj;acidity regulation\u0026zwnj; in sweet cherry fruits is mechanistically linked to \u0026zwnj;transcriptional control\u0026zwnj;, \u0026zwnj;hormone signaling pathways\u0026zwnj;, and \u0026zwnj;organic acid metabolic homeostasis\u0026zwnj;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.6 Transcriptional regulation of malate metabolism genes\u003c/h2\u003e\n \u003cp\u003eGenome-wide annotation identified multiple gene families associated with malate dynamics in the \u003cem\u003ePrunus avium\u003c/em\u003e transcriptome: 8 \u003cem\u003ePavPEPCs\u003c/em\u003e, 15 \u003cem\u003ePavMDHs\u003c/em\u003e, 5 \u003cem\u003ePavMEs\u003c/em\u003e, 20 \u003cem\u003ePavVHAs\u003c/em\u003e, 3 \u003cem\u003ePavVHPs\u003c/em\u003e, 7 \u003cem\u003ePavPHAs\u003c/em\u003e, 4 \u003cem\u003ePavALMTs\u003c/em\u003e, and 2 \u003cem\u003ePavtDTs\u003c/em\u003e homologs (Fig. \u003cspan\u003e5\u003c/span\u003e). Transcriptional coordination with malic acid accumulation was observed for six key genes: \u003cem\u003ePavPEPC3\u003c/em\u003e, \u003cem\u003ePavMDH1\u003c/em\u003e, \u003cem\u003ePavME1\u003c/em\u003e, \u003cem\u003ePavPHA5\u003c/em\u003e, \u003cem\u003ePavALMT1\u003c/em\u003e, and \u003cem\u003ePavALMT6\u003c/em\u003e. Expression dynamics revealed\u0026zwnj; that the transcript levels of \u003cem\u003ePavPEPC3\u003c/em\u003e and \u003cem\u003ePavMDH1\u003c/em\u003e increased at early development stages but decreased later (Fig. \u003cspan\u003e5\u003c/span\u003eA). The transcript levels of \u003cem\u003ePavME1\u003c/em\u003e, \u003cem\u003ePavPHA5\u003c/em\u003e and \u003cem\u003ePavALMT1\u003c/em\u003e increased over the course of sweet cherry fruit development (Fig. \u003cspan\u003e5\u003c/span\u003eA, B, C). Expression of \u003cem\u003ePavALMT6\u003c/em\u003e decreased slightly in \u0026apos;LG\u0026apos;, whereas in \u0026apos;RX\u0026apos;, the transcript level initially decreased and subsequently increased later (Fig. \u003cspan\u003e5\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003e\u0026zwnj; Developmental stage-dependent transcriptional variation\u0026zwnj; in malate metabolism genes was quantitatively validated between cultivars (\u0026apos;LG\u0026apos; vs. \u0026apos;RX\u0026apos;). When compared with \u0026apos;LG\u0026apos;, \u003cem\u003ePavPEPC3\u003c/em\u003e maintained 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3-fold lower transcripts in \u0026apos;RX\u0026apos; cross developmental stages. \u003cem\u003ePavMDH1\u003c/em\u003e exhibited no inter-cultivar variation at 21 DAF, however, displayed 1.1-fold lower transcripts in \u0026apos;RX\u0026apos; at 42 DAF and 49 DAF, respectively. \u003cem\u003ePavME1\u003c/em\u003e, \u003cem\u003ePavPHA5\u003c/em\u003e and \u003cem\u003ePavALMT6\u003c/em\u003e exhibited 1.13\u0026ndash;2.47 fold higher transcripts in \u0026apos;RX\u0026apos; during development. \u003cem\u003ePavALMT1\u003c/em\u003e showed no inter-cultivar variation at 21 DAF, however, displayed 1.17-fold lower transcripts in \u0026apos;RX\u0026apos; at 42 DAF, 1.26-fold higher transcripts in \u0026apos;RX\u0026apos; at 49 DAF, respectively (Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.7 Transcriptional regulators of malate dynamics\u003c/h2\u003e\n \u003cp\u003eTranscription factors (TFs), including MYB, basic helix-loop-helix (bHLH), and WRKY family proteins, serve as critical regulators of malate accumulation and vacuolar acidification by directly regulating the expression of malate transporters and proton pumps.\u003c/p\u003e\n \u003cp\u003eWe identified 189 TFs displaying differential expression patterns during developmental stages of \u0026apos;RX\u0026apos; and \u0026apos;LG\u0026apos;. These TFs were classified into 32 distinct subfamilies, including AP2/ERF, MYB, NAC, WRKY, and bHLH (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eA). Notably, the expression profiles of \u003cem\u003ePavMYB10.1\u003c/em\u003e, \u003cem\u003ePavMYB306, PavWRKY33\u003c/em\u003e, and \u003cem\u003ePavbHLH149\u003c/em\u003e were consistent with malate accumulation across three developmental stages of \u0026apos;RX\u0026apos; and \u0026apos;LG\u0026apos;. In \u0026apos;LG\u0026apos;, expression levels of \u003cem\u003ePavMYB10.1\u003c/em\u003e and \u003cem\u003ePavMYB306\u003c/em\u003e exhibited progressive upregulation during fruit development, with significantly higher transcript abundance in \u0026apos;LG\u0026apos; compared to \u0026apos;RX\u0026apos;. Conversely, \u0026apos;RX\u0026apos; displayed cultivar-specific dynamics: both genes were transiently upregulated during early developmental stages but diverged during maturation, with \u003cem\u003ePavMYB10.1\u003c/em\u003e stabilizing and \u003cem\u003ePavMYB306\u003c/em\u003e declining progressively (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eC). \u003cem\u003ePavWRKY33\u003c/em\u003e showed a biphasic expression profile, characterized by initial downregulation in early development followed by pronounced upregulation in later stages. At terminal maturation, \u003cem\u003ePavWRKY33\u003c/em\u003e transcript levels in \u0026apos;RX\u0026apos; were 4-fold higher than in \u0026apos;LG\u0026apos; (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eE). In contrast, \u003cem\u003ePavbHLH149\u003c/em\u003e expression transiently increased during early development but declined sharply in later phases, with \u0026apos;RX\u0026apos; maintaining significantly higher transcript levels than \u0026apos;LG\u0026apos; throughout fruit development (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eTo validate the transcriptome dataset, Quantitative real-time PCR (qRT-PCR) was conducted, and expression patterns were directly compared with RNA-seq profiles. We observed clear positive correlations between the qPCR and RNA-seq data across both cultivars during fruit development (Fig. S2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Composition and dynamic regulation of organic acids in sweet cherry fruit\u003c/h2\u003e\u003cp\u003eTA of sweet cherry, quantified as malic acid equivalents (%), serves as a robust indicator of organic acid content. In this study, TA values across 97 global accessions (0.49\u0026ndash;1.36%) enabled stratification into three phenotypic clusters: low-acid (\u0026le;\u0026thinsp;0.60%), medium-acid (0.61%-1.04%), and high-acid (\u0026ge;\u0026thinsp;1.05%). This classification aligns with prior observations of TA variability (0.4\u0026ndash;1.5%) in diverse varieties\u0026zwnj;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, 35], while refining thresholds through population-scale analysis.\u003c/p\u003e\u003cp\u003eMalic acid is the predominant organic acid in sweet cherry fruits, with well-documented inter-cultivar variability in its concentration[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, malic acid concentrations ranged from \u0026zwnj;6.22 to 12.06 mg/g FW\u003cb\u003e\u0026zwnj;\u003c/b\u003e, accounting for\u003cb\u003e\u0026zwnj;\u003c/b\u003e\u0026gt;98% of total organic acids\u003cb\u003e\u0026zwnj;\u003c/b\u003e in ripe fruits. These findings align with prior reports documenting malic acid concentrations of \u003cb\u003e\u0026zwnj;\u003c/b\u003e3.22\u0026ndash;12.77 mg/g FW\u003cb\u003e\u0026zwnj;\u003c/b\u003e across diverse cultivars[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, 35]. Notably, wild progenitors and landraces exhibited significantly higher malic acid accumulation (\u0026zwnj;10.78\u0026ndash;36.56 mg/g FW) compared to modern commercial varieties[1\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which consistent with domestication-driven selection for reduced acidity observed in apples[44] and jujubes[8].\u003c/p\u003e\u003cp\u003eMalic acid concentrations exhibit significant inter-cultivar variability during fruit development[\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In this study, both malic acid concentration and its proportional contribution to total organic acids increased during early fruit development but declined in the maturation phase. These findings align with Zhao et al. (2013), who documented similar biphasic patterns in TA contents of 'Chelan', 'Bing', and 'Selah' cultivars[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. By contrast, other varieties including 'Prime Giant', 'Cristalina', and 'Marvin Niram' demonstrate continuous malic acid accumulation throughout development without maturation-associated decline[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This cultivar-specific divergence in malate dynamics suggests that genetic factors primarily regulate terminal acid content in mature sweet cherries. Furthermore, the accumulation of malic acid in fruit cells is systemically regulated by interacting agro-environmental variables, such as water supply, mineral nutrition, and temperature[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Candidate genes for fruit malic acid accumulation and its related mechanism in sweet cherry\u003c/h2\u003e\u003cp\u003eMalic acid accumulation in plant cells is governed by coordinated malate metabolism and vacuolar storage mechanisms[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cytosolic malate metabolism involves enzymatic regulation through PEPC, NAD-cytMDH and NADP-cytME. Notably, promoter insertion in malate dehydrogenase genes \u003cem\u003eMdMa7\u003c/em\u003e (\u003cem\u003eMDH1\u003c/em\u003e) in apple, have been shown to regulate fruit malic acid accumulation[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Functional studies demonstrate that overexpression of \u003cem\u003eNAD-cytMDH\u003c/em\u003e elevates malate levels in transgenic apple calli[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, comparative transcriptomic profiling of high-acid 'LG' and low-acid 'RX' cultivars throughout fruit development identified 3643 DEGs. Notably, \u003cem\u003ePavMDH1\u003c/em\u003e exhibited significantly higher expression levels in 'LG' than in 'RX', whereas \u003cem\u003ePavME1\u003c/em\u003e displayed reciprocal expression patterns. This transcriptional divergence underlies the observed metabolic differences: low-acid 'RX' fruits accumulated 58% less malate than the high-acid 'LG', attributable to enhanced degradation capacity and reduced synthesis efficiency.\u003c/p\u003e\u003cp\u003eAlthough metabolic processes have been shown to alter malate accumulation in fleshy fruits[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], vacuolar storage has been demonstrated to play a predominant role in determining fruit malate content. The transport of malate across the tonoplast into the vacuole is mediated by transporters and proton pumps, including tDT, ALMT, V-ATPase, P-ATPases, and V-PPase[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Among these components, the \u003cem\u003eALMT\u003c/em\u003e family genes, which encode aluminum-activated malate transporters, function as central regulators of malate accumulation. In apple, the \u003cem\u003eALMT9\u003c/em\u003e homolog \u003cem\u003eMa1\u003c/em\u003e critically controls fruit malate content. A premature stop codon mutation \u0026zwnj;in\u0026zwnj; \u003cem\u003eMa1\u003c/em\u003e, truncating the C-terminus by 84 amino acids, is genetically linked to low-acid phenotypes[1\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, 49].In tomato, \u003cem\u003eSlALMT9\u003c/em\u003e determines fruit malate accumulation, with a 3-bp indel in its promoter region driving genotypic variation in fruit acidity[24]. In jujube, the vacuolar malate transporter \u003cem\u003eZjALMT4\u003c/em\u003e governs fruit malate accumulation, where cis-regulatory polymorphisms in its promoter region drive divergent malate concentrations between domesticated cultivars and wild sour jujube[8]. Strikingly, \u003cem\u003eMa1\u003c/em\u003e, \u003cem\u003eSlALMT9\u003c/em\u003e, and \u003cem\u003eZjALMT4\u003c/em\u003e share high amino acid sequence identity, suggesting evolutionary conservation of their functional roles in malate metabolism[8, 16].In our study, \u003cem\u003ePavALMT1\u003c/em\u003e and \u003cem\u003ePavALMT6\u003c/em\u003e exhibited significantly higher expression levels in the low-acid sweet cherry cultivar compared to the high-acid cultivar during late fruit development. The strong negative correlations between their expression profiles and malic acid content suggest that vacuolar malate trafficking mediated by these transporters may be a key determinant of acidity variation in sweet cherry.\u003c/p\u003e\u003cp\u003eProton pumps, including V-ATPase, P-ATPases, and V-PPase, are essential for proton transportation and the generation of proton-electrochemical gradient, which provides the driving force for malate transport into the vacuole[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These proton pumps have been functionally characterized in several fruit crops, such as pear[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], grape[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], peach[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and apple[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In apple, three P-ATPase proton pump genes\u0026mdash;\u003cem\u003eMa10\u003c/em\u003e, \u003cem\u003eMdPH1\u003c/em\u003e and \u003cem\u003eMdPH5\u003c/em\u003e\u0026mdash;have been demonstrated to regulate vacuolar acidification and malate accumulation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, \u003cem\u003eMa10\u003c/em\u003e, encoding a tonoplast P\u003csub\u003e3A\u003c/sub\u003e-type proton pump, directly interacts with the malate transporter \u003cem\u003eMdMa1\u003c/em\u003e to coordinate malic acid storage[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, the P-type vacuolar proton pump \u003cem\u003ePavPHA5\u003c/em\u003e were highly expressed in the low-acid variety compared to high-acid variety during fruit development, suggests that vacuolar proton pump activity may drive malate compartmentalization in sweet cherry fruits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Candidate TFs regulating malic acid accumulation in sweet cherry\u003c/h2\u003e\u003cp\u003e\u0026zwnj; Emerging evidence\u0026zwnj; has established the critical regulatory function of TFs in modulating the expression of proton pump genes and malate transporters. In apple, multiple MYB family members \u003cem\u003eMdMYB1\u003c/em\u003e, \u003cem\u003eMdMYB21\u003c/em\u003e, \u003cem\u003eMdMYB44\u003c/em\u003e, \u003cem\u003eMdMYB73\u003c/em\u003e, and \u003cem\u003eMdMYB123\u003c/em\u003e coordinately regulate the transcriptional activity of malate transporter \u003cem\u003eMdMa1\u003c/em\u003e and proton pump \u003cem\u003eMdPH5\u003c/em\u003e, thereby controlling vacuolar acidification and malate accumulation[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, 55]. These MYB TFs form MBW (MYB-bHLH-WD40) complexes with WD40 and bHLH partners to synergistically acidify vacuoles[25\u0026ndash;27]. Notably, allelic variation in the \u003cem\u003eMdMYB44\u003c/em\u003e promoter region has been genetically associated with divergent malate levels in apple fruits[27].\u0026zwnj;\u003c/p\u003e\u003cp\u003eIn addition to MYB families, the apple bHLH transcription factor \u003cem\u003eMdbHLH3\u003c/em\u003e regulates fruit malate accumulation by directly binding to the promoter of \u003cem\u003eMdcyMDH\u003c/em\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In tomato, a 15-bp indel within the \u003cem\u003eSlALMT9\u003c/em\u003e promoter disrupts a WRKY-binding W-box motif, thereby preventing\u0026zwnj; the binding capacity of the transcriptional repressor \u003cem\u003eSlWRKY42\u003c/em\u003e and \u0026zwnj;consequently derepressing\u0026zwnj; \u003cem\u003eSlALMT9\u003c/em\u003e expression to elevate fruit malate levels[24]. Similarly, in sour jujube, \u003cem\u003eZjWRKY7\u003c/em\u003e activates \u003cem\u003eZjALMT4\u003c/em\u003e through direct binding to the W-box elements in its promoter, resulting in\u0026zwnj; enhanced malate accumulation[8]. Parallel regulatory mechanisms\u0026zwnj; have been characterized in apple, where \u003cem\u003eMdWRKY126\u003c/em\u003e directly binds to the promoter of the malate dehydrogenase gene \u003cem\u003eMdMDH5\u003c/em\u003e, activating its expression and increasing apple fruit acidity[29].\u003c/p\u003e\u003cp\u003e\u0026zwnj;Through comparative transcriptome profiling\u0026zwnj;, \u003cb\u003e\u0026zwnj;\u003c/b\u003e189 differentially expressed TFs\u003cb\u003e\u0026zwnj;\u003c/b\u003e were identified during fruit development of \u0026zwnj;sweet cherry cultivars 'RX' and 'LG'\u003cb\u003e\u0026zwnj;\u003c/b\u003e.\u003cb\u003e\u0026zwnj;\u003c/b\u003eAmong these candidates\u0026zwnj;,\u003cb\u003e\u0026zwnj;\u003c/b\u003e\u003cem\u003ePavMYB10.1\u0026zwnj;\u003c/em\u003e, \u0026zwnj;\u003cem\u003ePavMYB306\u003c/em\u003e\u0026zwnj;, \u003cb\u003e\u0026zwnj;\u003c/b\u003e\u003cem\u003ePavWRKY33\u003c/em\u003e\u0026zwnj;, and\u003cb\u003e\u0026zwnj;\u003c/b\u003e\u003cem\u003ePavbHLH149\u003c/em\u003e\u003cb\u003e\u0026zwnj; \u0026zwnj;\u003c/b\u003eexhibited significant correlations with temporal malic acid accumulation patterns\u0026zwnj;. Notably, both \u003cem\u003ePavWRKY33\u003c/em\u003e and \u003cem\u003ePavbHLH149\u003c/em\u003e\u0026zwnj; are mapped to the \u003cem\u003eqP-TA6.1\u003c/em\u003e\u003csup\u003e\u003cem\u003em\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026zwnj;\u003c/em\u003e locus, a QTL on chromosome 6 that \u003cb\u003e\u0026zwnj;\u003c/b\u003egenetically controls\u003cb\u003e\u0026zwnj;\u003c/b\u003e fruit acidity in sweet cherry[36, 37]. \u003cb\u003e\u0026zwnj;\u003c/b\u003eThis positional association and expression evidence provides compelling support for\u003cb\u003e\u0026zwnj;\u003c/b\u003e their\u003cb\u003e\u0026zwnj;\u003c/b\u003efunctional role in regulating\u003cb\u003e\u0026zwnj;\u003c/b\u003e vacuolar malate storage.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e\u0026zwnj;Integrating metabolic phenotypes with transcriptomic data provides a robust framework\u003cb\u003e\u0026zwnj;\u003c/b\u003e for identifying gene regulatory networks and \u003cb\u003e\u0026zwnj;\u003c/b\u003eprioritizing candidate genes\u003cb\u003e\u0026zwnj;\u003c/b\u003e driving metabolic variation. \u003cb\u003e\u0026zwnj;\u003c/b\u003eIn this study\u003cb\u003e\u0026zwnj;\u003c/b\u003e, we developed a fruit acidity classification system\u0026zwnj; by quantifying TA levels across \u0026zwnj;97 geographically diverse sweet cherry accessions\u0026zwnj;. Multi-omics analysis revealed developmental stage-resolved gene expression patterns that covaried with malate accumulation dynamics during fruit maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, our study identified four transcription factors and six structural genes functionally implicated in malate biosynthesis, vacuolar transport, and proton homeostasis. The colocalization\u0026zwnj; of \u003cem\u003ePavWRKY33\u003c/em\u003e and \u003cem\u003ePavbHLH149\u003c/em\u003e within \u0026zwnj;the major-effect QTL \u003cem\u003eqP-TA6.1\u003c/em\u003e\u003csup\u003e\u003cem\u003em\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026zwnj;\u003c/em\u003e, establishing these regulators as central hubs\u0026zwnj; controling acidity variation in sweet cherry fruit.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors want to express their gratitude to all people and institutions that helped and unconditioned support in the elaboration of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.L.: Writing \u0026ndash; original draft, Validation, Investigation, Data curation. L.C., X.Q., L.S., M.W. and S.H.: Methodology, Investigation. M. L.: Project administration, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China [No.3210180675] and the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2025-ZFRI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its supplementary data. The raw transcriptome sequencing data have been deposited in the National Genomics DataCenter (NGDC), Beijing Institute of Genomics, Chinese Academy of Sciences, under BioProject accession number PRJCA043157.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFernie AR, Carrari F, Sweetlove LJ. Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport. 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Plant Physiol. 2024. https://doi.org/10.1093/plphys/kiae303.\u003c/li\u003e\n\u003cli\u003eKhan SA, Beekwilder J, Schaart JG, Mumm R, Soriano JM, Jacobsen E,\u003cem\u003e et al\u003c/em\u003e. Differences in acidity of apples are probably mainly caused by a malic acid transporter gene on LG16. Tree Genet Genomes. 2012; 9(2):475-487. https://doi.org/10.1007/s11295-012-0571-y.\u003c/li\u003e\n\u003cli\u003eMaxson ME, Grinstein S. The vacuolar-type H\u003csup\u003e+\u003c/sup\u003e-ATPase at a glance \u0026ndash; more than a proton pump. J Cell Sci. 2014; 127(23):4987-4993. https://doi.org/10.1111/j.1365-313X.2011.04587.x.\u003c/li\u003e\n\u003cli\u003eSuzuki Y, Shiratake K, Yamaki S. Seasonal changes in the activities of vacuolar H\u003csup\u003e+\u003c/sup\u003e-pumps and their gene expression in the developing Japanese pear fruit. J Jan Soc Hortic Sci. 2000; 69(1):15-21. https://doi.org/10.2503/jjshs.69.15.\u003c/li\u003e\n\u003cli\u003eTerrier N, Sauvage Fo-X, Ageorges As, Romieu C. Changes in acidity and in proton transport at the tonoplast of grape berries during development. Planta. 2001; 213(1):20-28. https://doi.org/10.1007/s004250000472.\u003c/li\u003e\n\u003cli\u003eLobit P, Genard M, Soing P, Habib R. Modelling malic acid accumulation in fruits: relationships with organic acids, potassium, and temperature. J Exp Bot. 2006; 57(6):1471-1483. https://doi.org/10.1093/jxb/erj128.\u003c/li\u003e\n\u003cli\u003eHu DG, Sun CH, Sun MH, Hao YJ. MdSOS2L1 phosphorylates MdVHA-B1 to modulate malate accumulation in response to salinity in apple. Plant Cell Rep. 2016; 35(3):705-718. https://doi.org/10.1007/s00299-015-1914-6.\u003c/li\u003e\n\u003cli\u003eZheng L, Liao L, Duan C, Ma W, Peng Y, Yuan Y,\u003cem\u003e et al\u003c/em\u003e. Allelic variation of \u003cem\u003eMdMYB123\u003c/em\u003e controls malic acid content by regulating \u003cem\u003eMdMa1\u003c/em\u003e and \u003cem\u003eMdMa11\u003c/em\u003e expression in apple. Plant Physiol. 2023; 192(3):1877-1891. https://doi.org/10.1093/plphys/kiad111.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sweet cherry, fruit acidity, malate metabolism, transcriptome sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7060936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7060936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFruit acidity serves as a primary determinant of organoleptic quality in fleshy fruits. Malate predominates and significantly contributes to the fruit flavor profile and palatability in sweet cherry. However, the molecular mechanisms regulating malate accumulation in fruit cells of this species remain poorly understood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, we performed quantitative profiling of TA in 97 sweet cherry cultivars at maturity, establishing a phenotyping framework for acidity classification. Temporal metabolomic analyses identified malate as the dominant organic acid throughout fruit development, exhibiting a biphasic accumulation pattern. Integrated transcriptomic profiling of high-acid and low-acid fruits across developmental stages revealed 3,643 differentially expressed genes, with functional annotation highlighting six structural genes (\u003cem\u003ePavPEPC3\u003c/em\u003e, \u003cem\u003ePavMDH1\u003c/em\u003e, \u003cem\u003ePavME1\u003c/em\u003e, \u003cem\u003ePavPHA5\u003c/em\u003e, \u003cem\u003ePavALMT1\u003c/em\u003e, and \u003cem\u003ePavALMT6\u003c/em\u003e) whose expression strongly correlated with malate content dynamics. Transcriptional regulatory network analysis further identified four candidate transcription factors, among which \u003cem\u003ePavWRKY33\u003c/em\u003e and \u003cem\u003ePavbHLH149\u003c/em\u003e were co-localized with a chromosome 6 quantitative trait locus (QTL)\u0026zwnj; associated with TA variation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings establish a comprehensive phenotyping framework for systematic acidity classification in sweet cherry, while elucidating the core genetic regulatory network governing malate accumulation. These mechanistic insights provide a robust scientific foundation for precision breeding strategies aimed at optimizing fruit quality through targeted modulation of acidity profiles.\u003c/p\u003e","manuscriptTitle":"Malate accumulation and transcriptome patterns during fruit development in sweet cherry (Prunus avium L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 15:25:30","doi":"10.21203/rs.3.rs-7060936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-04T04:34:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T15:50:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T03:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194569192773314698705736089971865389877","date":"2025-08-20T23:03:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165425005187965383079642530445644874104","date":"2025-08-20T01:06:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41199903643862318014893983175412853540","date":"2025-08-19T13:53:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T17:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142884938562836963802460798552113828355","date":"2025-07-18T02:28:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146034557220151622471593941394455396558","date":"2025-07-17T12:04:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T08:06:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T08:02:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T06:51:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T03:57:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-15T03:51:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"679ee906-5253-4780-81e3-883db4c4a89e","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:04:02+00:00","versionOfRecord":{"articleIdentity":"rs-7060936","link":"https://doi.org/10.1186/s12870-025-07608-1","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-11-12 15:57:26","publishedOnDateReadable":"November 12th, 2025"},"versionCreatedAt":"2025-07-21 15:25:30","video":"","vorDoi":"10.1186/s12870-025-07608-1","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07608-1","workflowStages":[]},"version":"v1","identity":"rs-7060936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7060936","identity":"rs-7060936","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}