
==== Front
Plant Commun
Plant Commun
Plant Communications
2590-3462
Elsevier

S2590-3462(24)00281-5
10.1016/j.xplc.2024.100973
100973
Research Article
Whole-genome resequencing identifies candidate genes and allelic variation in the MdNADP-ME promoter that regulate fruit malate and fructose contents in apple
Fu Weihong 1
Zhao Lin 2
Qiu Wanjun 1
Xu Xu 1
Ding Meng 1
Lan Liming 1
Qu Shenchun 1
Wang Sanhong wsh3xg@njau.edu.cn
1∗
1 College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
2 Xuzhou Institute of Agricultural Sciences in Xuhuai Region of Jiangsu, Xuzhou 221131, China
∗ Corresponding author wsh3xg@njau.edu.cn
14 5 2024
09 9 2024
14 5 2024
5 9 10097314 2 2024
29 3 2024
10 5 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Soluble sugar and organic acids are key determinants of fruit organoleptic quality and directly affect the commodity value and economic returns of fruit crops. We performed whole-genome sequencing of the apple varieties Gala and Xiahongrou, along with their F1 hybrids, to construct a high-density bin map. Our quantitative genetic analysis pinpointed 53 quantitative trait loci (QTLs) related to 11 sugar and acid traits. We identified a candidate gene, MdNADP-ME, responsible for malate degradation, in a stable QTL on linkage group 15. Sequence analysis revealed an A/C SNP in the promoter region (MEp-799) that influences binding of the MdMYB2 transcription factor, thereby affecting MdNADP-ME expression. In our study of various apple genotypes, this SNP has been demonstrated to be linked to malate and fructose levels. We also developed a dCAPS marker associated with fruit fructose content. These results substantiate the role of MdNADP-ME in maintaining the equilibrium between sugar and acid contents in apple fruits.

This study reports the identification of candidate genes associated with sugar- and acid-related traits in apple through whole-genome resequencing of two apple varieties and their F1 hybrids. It demonstrates that allelic variation in the promoter of MdNADP-ME, a gene responsible for malate degradation, affects apple fruit malate and fructose contents. A dCAPS marker has been developed to help assess fruit fructose content, providing a valuable resource for molecular breeding.

Key words

apple
quantitative trait locus
malate degradation
sugar content
promoter
Published: May 14, 2024
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pmcIntroduction

Apple (Malus × domestica Borkh.) stands as a key deciduous fruit tree in temperate regions, valued for its rich mineral and vitamin contents (Mimida et al., 2009). The Chinese apple industry has increasingly focused on enhancing aspects of fruit quality such as taste, flavor, and texture. Quality enhancement is now a significant goal in China’s apple-breeding efforts (Ma et al., 2015). Soluble sugars and organic acids, along with some aroma volatiles, help make up fruit organoleptic quality (Borsani et al., 2009; Khan et al., 2013). Therefore, maintaining an appropriate balance of sugar and acid contents is important for creating desirable apple varieties.

Fructose is the predominant sugar in various apple varieties, making up 40%–60% of the total sugar content (Ma et al., 2015) and thus determining fruit sweetness (Doty, 1976; Wu et al., 2007; Demircan et al., 2022). The acidity of apple cells comes from organic acid accumulation, with malate representing about 85% of the total acid content (Etienne et al., 2013; Ma et al., 2015). Malate plays vital roles in plant processes such as fruit ripening, root growth, stomatal movement, and nutrient absorption (Martinoia et al., 2007; Lee et al., 2008; Centeno et al., 2011). Importantly, it collaborates with other acids and sugars to affect fruit quality. Therefore, levels and ratios of fructose and malate significantly influence the organoleptic quality of apple fruits. Understanding the physiological and molecular mechanisms of these compounds and identifying key genes and markers are essential for improving apple quality and developing unique apple varieties (Bordonaba and Terry, 2010).

The malate concentration in fruits depends on acid biosynthesis, degradation, and transport, with key enzymes including phosphoenolpyruvate carboxylase (PEPC, EC 4.1.1.31), NAD-dependent malate dehydrogenase (NAD-MDH, EC 1.1.1.37), NADP-dependent malic enzyme (NADP-ME, EC 1.1.1.40), and phosphoenolpyruvate carboxykinase (PEPCK, EC 4.1.1.49) (Yao et al., 2011; Etienne et al., 2013). Malate is mainly synthesized in the cytoplasm by PEPC and NAD-MDH (Moing et al., 2000). PEPC catalyzes the β-carboxylation of pyruvate to oxaloacetate acid (OAA) and inorganic phosphate (Rademacher et al., 2002). Jiang et al. (2019) found a correlation between PEPC activity and malate accumulation during plum development. NAD-MDH regulates malate synthesis by converting OAA to malate (Miller et al., 1998). Cytoplasmic MDH can increase transcription levels of malate-associated genes, thereby enhancing malate accumulation (Yu et al., 2021). Upon overexpression of the mitochondrial MDH12 gene, Gao et al. (2022) observed increased malate levels in apple calli. Malate content decreases as fruit ripens owing to degradation by NADP-ME and PEPCK. NADP-MEs in the cytosol and plastids decarboxylate malate to pyruvate, CO2, and NADPH (Franke and Adams, 1995). Cytosolic NADP-ME supports the pentose cycle and lignin synthesis and regulates pH by controlling malate levels (Wheeler et al., 2005; Brown et al., 2010). One study found that decreased fruit malate content was mediated primarily by stimulation of NADP-ME activity rather than by changes in the expression of cytosolic NADP-ME genes (Blanch et al., 2013). However, expression of NADP-ME-related genes has shown a positive correlation with malate content, indicating a possible transcriptional influence on malate metabolism (Kou et al., 2014; Onik et al., 2019; Ali et al., 2023). As fruit ripens, organic acids degrade, providing carbon substrates for synthesis of soluble sugars (Famiani et al., 2012). PEPCK, a cytosolic enzyme, converts OAA to phosphoenolpyruvate (PEP) (Sweetman et al., 2009). In ripening tomatoes, PEPCK activity rises as malate decreases (Bahrami et al., 2001). The released malate may fuel gluconeogenesis, affecting glucose and fructose accumulation in the fruit flesh (Famiani et al., 2009).

Malus sieversii (Ledeb.) Roem. is the ancestral species of the globally grown apple M. × domestica (Duan et al., 2017). After extensive natural selection, M. sieversii apples show significant variation in traits such as mineral elements, sugars, polyphenols, and volatile compounds, making them a valuable resource for trait exploration.

In this research, we used an F1 population consisting of 255 individuals to construct a high-density bin map based on whole-genome sequencing. We identified a stable QTL with a high logarithm of the odds (LOD) value on linkage group (LG) 15. Integration of QTL mapping, transcriptome profiling, and RT–qPCR analysis identified MdNADP-ME as a key candidate gene for the regulation of fructose and malate levels. Allelic variation in the MdNADP-ME promoter influences binding of MdMYB2 to specific promoter sequences and is linked to MdNADP-ME transcription and fructose and malate content in the parent fruit. These findings underscore the significant role of MdNADP-ME in modulating malate and fructose content and offer valuable insights for molecular breeding.

Results

Genotyping and high-density genetic map construction

Sequence reads were mapped to the reference genome (M. domestica GDDH13 v1.1) using Burrows–Wheeler Aligner tools. The parents generated over 19 Gb of sequencing data (>26.00× coverage). From 255 hybrid offspring, we obtained 1.48 Tb of sequencing data, averaging 5.95 Gb per sample with effective sequencing depths between 7.69× and 8.93× (Supplemental Figure 1A and 1B). After initial filtration, we obtained 3 082 165 high-quality polymorphic single nucleotide polymorphism (SNP) loci (Supplemental Figure 1C–1E). Genotypic markers were subjected to chi-squared testing (p < 0.001), resulting in the retention of 1 780 302 SNP markers (Supplemental Figure 1D). Each bin contained an average of 251 SNPs, with a range of up to 7350 SNPs (Supplemental Figure 1F). Binmarker-v2.3 and additional filtration yielded a total of 5795 bin markers (Supplemental Figure 1G).

SNPs within the same bin were considered to be recombination free. The resulting high-density genetic map spanned a genetic distance of 1426 cM, averaging 0.52 cM/marker (Supplemental Table 1). The total number of SNPs within bin markers per LG ranged from 35 218 to 122 416 (LG04 and LG05, respectively), with an average of 83 091 SNPs (Supplemental Table 1). In summary, the genetic map was delineated into 4852 bin markers and 1 412 541 SNPs distributed across 17 LGs and was designated the GX map (Figure 1A). The pairwise recombination scores for the 17 LGs were conspicuously positioned along the diagonal of the heatmap, indicating a high-quality map (Figure 1B).Figure 1 Construction of a genetic map of apple trait loci associated with fruit sugar content.

(A) Marker distribution on consensus map of the GX map. Blue bars represent the marker hk × hk.

(B) Recombination fractions between each pair of bins. The red color on the diagonal indicates a high recombination rate.

(C) Plots of LOD values for QTL mapping of TSS and fructose content in an F1 population derived from Gala × Xiahongrou computed by R/qtl in the years 2021 and 2022. The black dashed lines in each plot represent the LOD thresholds 2.5 and 3.

Phenotyping of sugar- and acid-related traits

The segregating population resulting from the Gala × Xiahongrou cross exhibited continuous variation in most fruit traits. The distributions of these traits closely resembled a Gaussian distribution, suggesting their quantitative nature and polygenic genetic control (Supplemental Figure 2; Supplemental Table 2). Significant differences in fructose, sorbitol, glucose, and sucrose contents of mature fruits were observed between the parents in both years (Supplemental Table 3).

Quantitative trait locus analysis

Using the high-density genetic map, we identified potential quantitative trait loci (QTLs) for diverse fruit-related traits through composite interval mapping (Do Nascimento et al., 2000) and the Kruskal–Wallis test with the qtl package in R software. To ensure comprehensive data coverage from the 2-year study location, we set an LOD threshold of 2.5 for QTL identification. An LOD ≥ 3 indicated the presence of a primary-effect QTL. We identified a total of 53 QTLs over the 2 years (Supplemental Table 4).

The substantial number of QTLs highlighted the complex and polygenic nature of fruit sugar content. Four QTLs for fructose content were consistently detected across both years. LG13 contained the same region (42.117–43.685 cM) for soluble solid content (SSC) in 2021, with a peak LOD value of 4.97, which explained 32.49% of the phenotypic variance. Five QTLs for glucose content spanned four different LGs, with LG13 showing a stable localization interval over the two consecutive years. Eight QTLs for sucrose content were identified across six LGs, with LG10 displaying a QTL distribution in both years. A region that overlapped with the localization results for fructose and sorbitol content (0–17.673 cM) was pinpointed on LG04 (Supplemental Table 4). Consistent with the findings of Kunihisa et al. (2014), QTLs for SSC were mapped to LG15 and LG16, and QTLs for sucrose content were located on LGs 10 and 15. One major QTL on LG15, the SNP marker np15_44, with a peak LOD of 3.44 at 30.601 cM, made a substantial contribution to fructose content and total soluble sugar (TSS) (Figure 1C).

Candidate genes related to fructose content

The consistent QTL on LG15, co-localizing with fruit TSS and fructose content, is likely a region that controls fruit fructose content. This region contained 841 genes. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (Supplemental Figure 3) revealed that it contained 34 functional genes or transcription factor (TF) genes related to sugar and acid metabolism (Supplemental Table 5). To pinpoint candidate genes, we performed RNA sequencing (RNA-seq) on pulp samples from Gala and Xiahongrou fruit during different developmental stages.

Significant differences in sugar and acid contents were noted between Gala and Xiahongrou fruit across most stages, with the S4 stage exhibiting particularly notable variation (Supplemental Figure 4). The S4 developmental stage showed the highest number of differentially expressed genes (DEGs) between the two varieties (Figure 2A). Weighted gene co-expression network analysis (WGCNA) identified genes in the indianred4 module whose expression was strongly correlated with each sugar-acid fraction in the fruit. Interestingly, genes in this module were markedly upregulated in Gala during the S4 period but increased consistently throughout development in Xiahongrou (Figure 2B and 2C). This result implied a potential close association of these genes with sugars and acids, suggesting that they contributed to observed varietal differences. We next compared DEGs during the S4 period for both varieties with genes within QTL-LG15. By overlapping these gene sets, we identified MdNADP-ME as a candidate gene for fructose content within the QTL region. In Xiahongrou, MdNADP-ME expression steadily increased during fruit development. By contrast, in Gala, MdNADP-ME expression increased by about 37-fold from the S3 to S4 stage, displaying a pattern distinct from that in Xiahongrou (Figure 2D). Thus, MdNADP-ME was considered to be a potential regulator of fruit sugar and acid contents.Figure 2 RNA-seq analysis and RT–qPCR validation of candidate genes during fruit development.

(A) Comparison of DEGs at different developmental stages between Gala and Xiahongrou.

(B) Correlation analysis between WGCNA modules and sugar and acid contents.

(C) Eigengene expression profiles of the indianred4 module.

(D) Expression patterns of MdNADP-ME in Gala and Xiahongrou fruits at different developmental stages. The values are presented as the mean ± SD of three replicates. The asterisks indicate significant differences at p ≤ 0.05.

Relationship between MdNADP-ME and sugar and acid contents in apple

The open reading frame (ORF) of MdNADP-ME was cloned from Gala and Xiahongrou. Sequence alignment revealed differences in the MdNADP-ME coding sequences between the two varieties (Supplemental Figure 5A). However, the amino acid sequences of MdNADP-ME in Gala, Xiahongrou, and Golden Delicious were identical (Supplemental Figure 5B).

To investigate the role of MdNADP-ME in regulating sugar and acid content, we introduced overexpression, silencing, and negative control vectors into cultured calli derived from Orin apple fruit (Figure 3A). The overexpression lines exhibited a three-fold increase in MdNADP-ME transcript levels and a 24%–27% increase in fructose content (Figure 3B and 3C). Glucose content was unchanged, whereas sucrose content increased by 10%–14% (Supplemental Figure 6). Malate content decreased by approximately 18%, and citric acid content increased by 25% (Figure 3C and Supplemental Figure 6). By contrast, the silenced lines showed significantly lower MdNADP-ME expression compared with the control, resulting in 30% and 16% reductions in fructose and sucrose content, respectively (Figure 3B, 3C and Supplemental Figure 6). Malate content increased by approximately 31%, and citric acid content decreased by approximately 11% (Figure 3C and Supplemental Figure 6). Collectively, our results indicate that MdNADP-ME plays a pivotal role in negatively modulating malate content. We also found that an important gene involved in gluconeogenesis, MdPEPCK, was highly expressed in MdNADP-ME overexpression lines (Figure 3B). This suggests that sugar metabolism may be influenced by gluconeogenesis. Specifically, PEPCK catalyzes the decarboxylation of OAA into PEP, facilitating the entry of malate into the gluconeogenesis process and promoting fructose accumulation in apple. These findings highlight the crucial role of MdNADP-ME in regulating the sugar-acid balance in apple.Figure 3 Functional analysis of MdNADP-ME in transgenic Orin apple calli and Gala fruit.

(A) Overexpression and silencing of MdNADP-ME in Orin apple calli. Scale bar, 1 cm.

(B) Relative expression of MdNADP-ME and MdPEPCK in apple calli.

(C) Fructose and malate contents in transgenic apple calli.

(D) Overexpression (Super1300) and silencing (TRV) of MdNADP-ME in Gala apple fruits.

(E) Expression levels of MdNADP-ME in Gala apple fruits measured by RT–qPCR.

(F) Measurement of fructose and malate contents in transgenic Gala apple fruits. OE-EV, overexpression empty vector Super 1300; Ri-EV/TRV-EV, RNA-interference empty vector pTRV2. Values are presented as the mean ± SD of three replicates. Different letters indicate significant differences at p ≤ 0.05.

To corroborate our hypotheses, we transiently introduced each vector into Gala apple fruits (Figure 3D). The transcript levels of MdNADP-ME were significantly higher in overexpression lines and lower in silenced lines (Figure 3E). Overexpression and suppression of MdNADP-ME resulted in decreased and increased malate content, respectively, compared with the negative control (Figure 3F), confirming that MdNADP-ME is a positive regulator of malate degradation in apples.

Analysis of the MdNADP-ME promoter sequence and TF-binding sites in Gala and Xiahongrou

To determine the cause of the differences in MdNADP-ME expression between Gala and Xiahongrou, we cloned and designated four 1500-bp MdNADP-ME promoter haplotypes, labeled GL-1/2 and XHR-1/2, located upstream of the start codon (ATG). The promoter sequences of Gala and Xiahongrou contained 47 SNPs compared with the Golden Delicious reference genome (Supplemental Figure 7). Prediction of cis-acting elements using New PLACE revealed that the MYB1AT element (WACCA) was present exclusively in the XHR-1 haplotype. In addition, TF binding analysis of these promoter haplotype sequences using Plant TFDB showed a unique MYB transcription factor only in the XHR-1 haplotype (Supplemental Table 6). The SNP responsible for the difference in this binding site is located 799 bp upstream of the ATG of MdNADP-ME. In Gala, the genotype is homozygous CC(GG), whereas in Xiahongrou, it is heterozygous CA(GT); it was therefore designated MEp-799 (A/C).

In Arabidopsis, AtMYB2 serves as a transcriptional activator in ABA signaling (Abe et al., 2003). A role for the MYB2 cis-acting element in ABA-regulated lignan biosynthesis has been verified (Corbin et al., 2013). Rice MYB2 (OsMYB2) upregulates ABA signaling genes through an ABA-dependent pathway (Lim et al., 2022). Upregulation of plant hormone signal transduction pathways, including those of ABA and ethylene, regulates MYB2 expression via a signaling cascade, resulting in an increase in hemicellulose content of cotton (Song et al., 2020). Here, MD17G1279200 was identified as the target MdMYB2 through phylogenetic analysis, RNA-seq, and RT–qPCR validation. MD17G1279200 had higher expression in Xiahongrou fruit than in Gala fruit and is genetically closer to AtMYB2 (Supplemental Figure 8).

The SNP MEp-799 (A/C) in the MdNADP-ME promoter influences the binding of MdMYB2

The cDNA sequences of MdMYB2 from Gala and Xiahongrou were cloned and sequenced, and the predicted amino acid sequences of Gala-MYB2 and XHR-MYB2 were identical (Supplemental Figure 9). In yeast one-hybrid (Y1H) experiments, pAbAi bait vectors containing XHR-1 and XHR-2 haplotype promoters were co-transformed with pGADT7 prey vectors to assess MdMYB2 binding to the MdNADP-ME promoter (Figure 4A). The Y1H assay confirmed MdMYB2 binding to the XHR-1 haplotype and weak binding to the XHR-2 haplotype (Figure 4B). Recombinant vectors of different haplotypes (GL-1/2, XHR-1/2) and MdMYB2 were then constructed and transformed into Orin calli for orthogonal combination experiments. Analysis of β-glucuronidase (GUS) activity revealed no significant differences in promoter activity between Gala and Xiahongrou for any haplotype except XHR-1. Co-transformation of vectors 35S:MdMYB2 and MdNADP-MEpro:GUS into both varieties revealed MdMYB2 binding to the AAACCA sequence in the XHR-1 haplotype, inhibiting transcription of the GUS reporter gene and reducing GUS activity. However, MdMYB2 showed weaker binding to the AAACCC sequence in GL-1, GL-2, and XHR-2 haplotypes, with no significant effect on transcription levels or GUS activity (Figure 4C).Figure 4 Binding of MdMYB2 to MdNADP-ME promoters and transcriptional activation of MdNADP-ME by MdMYB2.

(A) Y1H heterozygous vector construction.

(B) Y1H assay showing binding of MdMYB2 to the MdNADP-ME promoter. Yeast cells were grown on SD medium lacking Leu at different concentrations without (left) or with (right) aureobasidin A (AbA).

(C) GUS assay showing binding of MdMYB2 to MdNADP-ME promoters; haplotypes differ in the extent of MdMYB2 binding. Gala-1/2 and XHR-1/2 are the haplotype sequences from Gala and Xiahongrou, respectively. CaMV35S, empty vector pBI121. Values are presented as the mean ± SD of three replicates. Different letters indicate significant differences at p ≤ 0.05.

(D) EMSA analysis. Probe 1, sequence with AAACCA (MEp-799(A)); probe 2, sequence with AAACCC (MEp-799(C)). Protein MdMYB2 is the purified protein with the Gala amino acid sequence and His-tag fusion protein; 200× and 500× are the relative contents of the competitor probe to the detective probe.

(E) Promoters used in vector construction for the luciferase assay.

(F) Assay using P1, P2, P3, P4, and P5 fragments with A and C genotypes. P1 indicates the 1500 bp upstream from the ATG promoter in the XHR-1 haplotype, with an A at the putative MYB2 TF-binding region, and P2 is 855 bp. P3 is 275 bp with an A around the putative MYB2 TF-binding region, and P4 is the 1500 bp upstream from the ATG promoter in the XHR-2 haplotype. Xiahongrou is heterozygous A/C, and Gala is homozygous C/C. P5 is the 1500 bp upstream of the ATG promoter in the XHR-2 haplotype with C mutated to A at the putative MYB2 TF-binding region. The green region on the right indicates the coding sequence of MdNADP-ME starting from ATG, and the red region indicates the binding region of the promoter. 0800, pGreenII0800-luc; 1300, Super1300; MdMYB2, MdMYB2 from Gala. Values are presented as the mean ± SD of three replicates. Different letters indicate significant differences at p ≤ 0.05.

We next investigated MdMYB2 binding to the two promoter haplotypes using an electrophoretic mobility shift assay (EMSA). MdMYB2 showed significant binding to the AAACCA probe but weaker binding to the AAACCC probe (Figure 4D). To further confirm the binding region of MdMYB2 on the MdNADP-ME promoter, we performed a luciferase (LUC) activation assay (Figure 4E). Dual-luciferase analysis revealed an MdMYB2-binding site approximately 855 bp upstream of the ATG. A point mutation (P5) in the XHR-2 promoter haplotype significantly altered MdMYB2-binding capacity compared with the empty vector. Co-expression of P1, P2, P3, and P5 with Super1300-MdMYB2 resulted in significantly reduced luminescence activity (>three-fold). However, co-expression of P4 and Super1300-MdMYB2 did not produce significant changes (<1.8-fold) in luminescence activity (Figure 4F). Overall, these results confirm that MdMYB2 binds to the MdNADP-ME promoter and represses transcription in the XHR-1 haplotype.

Effect of MdMYB2 overexpression on sugar and acid contents of apple fruits

Agrobacterium tumefaciens carrying the 35S::MdMYB2 vector was injected into apple fruits of Gala and Xiahongrou for transient expression to examine the effect of MdMYB2-overexpression on sugar-acid content in fruit (Figure 5A). In Xiahongrou fruits, MdMYB2 overexpression significantly reduced MdNADP-ME expression and fructose content while increasing malate content (Figure 5C and 5D). In Gala fruits, MdMYB2 overexpression only slightly reduced MdNADP-ME expression and had no significant effects on fructose and malate contents (Figure 5C and 5D).Figure 5 Analysis of MdMYB2 expression level and sugar and acid contents in fruits of Gala and Xiahongrou transiently transformed with MdMYB2.

(A) Overexpression (Super1300) of MdMYB2 in Gala and Xiahongrou apple fruits.

(B) RT–PCR analysis of MdMYB2 expression in apple fruit. The band with a length of 418 bp represents the vector sequence (Super1300) between the multicloning sites, and the band with a length of 1271 bp represents the total length of the sequence successfully inserted with MdMYB2.

(C) Expression levels of MdMYB2 and MdNADP-ME in Gala and Xiahongrou apple fruits measured by RT–qPCR.

(D) Fructose and malate contents in transgenic Gala and Xiahongrou apple fruits. OE-EV, empty vector Super-1300. Values are presented as the mean ± SD of three replicates. Different letters indicate significant differences at p ≤ 0.05.

Association of SNP MEp-799 (A/C) in the MdNADP-ME promoter with fructose and malate contents

To confirm the broad applicability of the SNP locus MEp-799 (A/C) in the MdNADP-ME promoter, individuals were randomly selected from hybrid progenies (C/C, 21 individuals; A/C, 21 individuals) and 20 cultivars (Supplemental Table 8). Sanger sequencing indicated that the majority of cultivars had C/C genotypes at this locus, with 13 out of 20 individuals possessing this genotype, and no A/A genotypes were found. Hybrid progenies with the A/C genotype exhibited lower MdNADP-ME expression and fructose content than those with the C/C genotype and also displayed higher malate content (Supplemental Figure 10A and 10B). Similarly, in cultivars, fruits with the C/C genotype had higher MdNADP-ME transcript levels and fructose contents than those with the A/C genotype (Supplemental Figure 10C and 10D).

Development of dCAPS markers for SNP MEp-799 (A/C)

Using the predicted genotypes from GATK, three SNP markers were found to be associated with fruit sugar and acid content. Marker 1 (A/C) and marker 2 (G/A) were identified using a validation panel of F1 individuals with extremely high and low fruit malate contents (Supplemental Figure 11A). Marker 3 (T/G) corresponded to the MEp-799 (A/C) site, which served as the main SNP for derived cleaved amplified polymorphic sequences (dCAPS) marker development. Alleles of the three markers segregated in the F1 population. Marker 3 showed a significant correlation with fruit fructose content; individuals with T/G(A/C) had a lower fructose content, and those with G/G(C/C) had a higher fructose content (Supplemental Figure 11B). These findings indicate a strong correlation between fruit malate and fructose contents and the identified markers.

The dCAPS markers were developed on the basis of the MEp-799 (A/C) site (Figure 6A). The PCR-amplified products had a size of 217 bp, and specific bands were obtained after digestion with FspB I restriction endonuclease (cleavage site C/TAG). For Gala (G/G) and F1 individuals with a homozygous genotype at this locus, two bands of 192 and 25 bp were visible (Figure 6B). By contrast, Xiahongrou (T/G) and F1 individuals heterozygous at this locus showed three bands (Figure 6B). The dCAPS marker was similarly validated in cultivars (Figure 6C). These results indicate that marker 3 can be used to distinguish between homozygous and heterozygous genotypes at this locus by agarose gel electrophoresis.Figure 6 The two apple genotypes were distinguished by dCAPS markers.

(A) Schematic diagram of dCAPS markers.

(B) Specific bands obtained by restriction endonuclease digestion in F1 individuals.

(C) Specific bands obtained by restriction endonuclease digestion in cultivars. Red lines in (B) and (C) indicate images from two different gels, placed together for easy comparison.

Discussion

Construction of high-density genetic maps for precise QTL identification

The unique challenges presented by apple, a large woody perennial with an extended juvenile phase, make traditional analyses reliant on high-throughput assessments of complex traits impractical. This necessitates the development of new genomic resources for marker-assisted selection to facilitate more efficient apple breeding. High-density genetic mapping is a valuable tool for QTL mapping and map-based cloning in perennial fruit trees (Fukuda et al., 2019; Shi et al., 2020). With next-generation sequencing techniques, we were able to genotype an F1 population in a genome-wide manner using thousands to millions of high-quality DNA markers. However, these high-throughput technologies also lead to an increase in co-segregating SNPs, creating computational challenges and making the construction of genetic linkage maps time consuming. To tackle these issues, we used a bin-mapping strategy to combine consistent SNPs into recombinant bins and generated an ultra-dense genetic linkage map (Ma et al., 2016b; Hu et al., 2018). In this approach, we merged 100% identical genotypes within a sliding window to build a high-density genetic linkage map composed of 4852 bins and 17 LGs. The linkage groups exhibit strong collinearity with the reference genome, with only 1.03% of markers diverging from the corresponding chromosome, and 8.35% newly assembled markers. Some of the LGs, such as LG06, LG07, LG08, and LG17, exhibit large marker spacing at the polar regions of the chromosomes, consistent with the genetic map construction results of Di Pierro et al. (2016). These regions are commonly known as recombination hotspots and show a positive correlation with SNP density, gene density, GC content, and specific gene regulation. DNA sequences enriched with GC bases, for instance, may increase the likelihood of recombination in the region (Lawrence et al., 2017).

Prior studies on malate have mainly targeted LG08 and LG16 (Maliepaard et al., 1998; Ma et al., 2016a), whereas our research identified QTLs for malate on LG02, LG05, and LG07 (Supplemental Table 4). These findings highlight the complex genetic regulation and polygenic traits involved. Variations in QTL localization may also be attributed to the diverse genetic backgrounds of the parental genotypes, as fewer loci may be identified if parental genotypes are not readily separable. Such variations in QTL localization suggest challenges in marker-assisted selection owing to different parental genotypes, methodological variations in phenotypic analysis, and the complex nature of quality traits. Nonetheless, even minor-effect QTLs are valuable for genomic selection and predictive breeding to improve fruit quality (Muranty et al., 2015; Roth et al., 2020). The QTLs identified here warrant further validation and comparison across different populations to pinpoint stable QTLs. Notably, we did observe two overlapping QTLs (QTL-LG15) for TSS and fructose content (Figure 1C), leading us to designate QTL-LG15 as the region controlling sugar content.

MdNADP-ME plays an important role in sugar and acid coordination

After performing GO enrichment and KEGG pathway analysis, we identified 34 genes related to sugars and acids among the 841 genes on QTL-LG15 (Supplemental Table 5). To delve deeper, we also analyzed transcriptomic data from four developmental stages of the two parent varieties. Our findings suggested that MdNADP-ME, characterized by its high expression level and pattern, was a potential candidate gene for the control of sugar and acid contents in apples. Plant NADP-ME is one of the key enzymes of malate metabolism. NADP-ME is involved in carbon fixation in young fruit and in malate decarboxylation during ripening (Chen et al., 2019). NADP-ME has also been suggested to be associated with many biological processes, such as cytosolic pH stabilization and plant stress resistance (Doubnerová and Ryslavá, 2011). In the current study, transcript levels of MdNADP-ME in apple fruit were low during early development, increased slightly during the pre-climacteric stage, then increased substantially in the mature stage (Figure 2D). This increase coincided with a decrease in malate content (Supplemental Figure 4), supporting the role of MdNADP-ME in malate degradation.

Li et al. (2012) reported that at least 80% of the total carbon flux in apple fruit is directed toward soluble sugars. Organic acids and soluble sugars are often not separated from each other. Famiani et al. (2016) observed that increased malate release from the vacuole led to gluconeogenesis in peaches, causing a temporary increase in the conversion of malate to sugar. Manipulation of genes encoding malate metabolism enzymes can produce significant changes in malate and sugar content. For example, transgenic apple calli expressing the cytoplasmic malate dehydrogenase MdcyMDH1 showed a considerable increase in malate levels after genetic manipulation (Yao et al., 2011). In addition, the transcription of MdcyMDH is activated by MdbHLH3, which stimulates the synthesis and metabolism of malate after soluble sugar accumulation (Yu et al., 2021). Here, we found that overexpression of MdNADP-ME in apple calli and fruits resulted in a significant decrease in malate content and an increase in fructose content (Figure 3C and 3F). This suggests that MdNADP-ME plays a role in malate degradation and the conversion of malate to sugars. The altered expression levels of MdPEPCK in MdNADP-ME-OE/Ri calli lines also suggest that MdNADP-ME is involved in regulating gluconeogenesis (Figure 3B). Two pathways for conversion of malate to sugars are recognized in plants, one involving PEPCK and the other involving pyruvate orthophosphate dikinase (Famiani et al., 2015). The PEPCK pathway predominantly facilitates gluconeogenesis from malate, with PEPCK deemed the pivotal enzyme in this conversion (Walker et al., 2015). In a study of 42 F1 offspring of a cross between Gala and Xiahongrou and 20 apple cultivars, MdNADP-ME expression was correlated with malate and fructose levels in mature fruit (Supplemental Figure 10). Overall, our findings suggest that apple MdNADP-ME likely coordinates malate and sugar metabolism by catalyzing malate degradation in the cytoplasm. This is supported by malate’s feedback regulation of PEPCK-mediated gluconeogenesis, with positive feedback in MdNADP-ME-OE lines and negative feedback in MdNADP-ME-Ri lines.

Natural SNPs in the MdNADP-ME promoter cause changes in MYB2 binding and fructose content

Identification of functional SNPs or indels in genes and analysis of their phenotypic effects has become an effective approach for understanding gene function and genetic improvement (Han et al., 2018; Xiao et al., 2023). Gala, Xiahongrou, and their progeny had different levels of MdNADP-ME expression and fructose content, particularly at the mature fruit stage. Although the amino acid sequence of MdNADP-ME was the same in the two genotypes, its promoter sequence differed. This led to the hypothesis that promoter variation affected transcriptional regulation and, consequently, traits. The C-to-A variant at the SNP site MEp-799 in the MdNADP-ME promoter of Xiahongrou introduced the MYB-binding element MYB1AT (AAACCA), which was absent in the Gala promoter (Supplemental Figure 7; Supplemental Table 6). Given that MD17G1279200 shared the highest sequence identity with AtMYB2, we named it MdMYB2. In Medicago truncatula, MtMYB2 acts as a negative regulator of anthocyanin biosynthesis, but it is not clear whether MtMYB2 binds to the promoters of target genes (Jun et al., 2015). Functional characterization through transient luciferase expression assays, Y1H assays, and EMSAs showed that MdMYB2 bound more efficiently to AAACCA than to AAACCC (Figure 4B, 4D and 4F). More importantly, MdMYB2 suppressed MdNADP-ME expression in Xiahongrou and in hybrid fruit with the A/C genotype at MEp-799, consistent with the genotype-dependent differences in malate and fructose contents (Figure 5C and 5D). On the basis of this SNP site, we developed a dCAPS marker associated with fruit fructose content, which can be used in the breeding of apple cultivars with higher fruit sugar content (Figure 6). The overall conclusion was that MdMYB2 strongly interacted with the MdNADP-ME promoter via the MYB1AT (AAACCA) element, reducing MdNADP-ME transcription in MEp-799 A/C genotypes compared with C/C genotypes and thus causing differences in fructose content at maturity (Figure 7). Multidimensional networks of transcriptional activation and repression are known to underlie responses to stress or hormones in plants (Kazan, 2006; Voss et al., 2014). Multiple hormone signals are integrated to control plant development and growth; in particular, hormonal crosstalk with sugar metabolic or signaling pathways has been widely reported (Thalmann et al., 2016; Ma et al., 2017; La et al., 2019). In this respect, it remains to be determined whether the transcriptional regulation of MdMYB2 or the post-transcriptional regulation of its activity or stability are mediated by hormones and how hormone signals influence acid metabolism and sugar accumulation.Figure 7 Proposed model for how SNP variation in the MdNADP-ME promoter affects transcriptional regulation of MdNADP-ME by MYB2, as well as malate and fructose contents.

A SNP variation (A/C) in the MdNADP-ME promoter region (SNP site MEp-799) affects the binding affinity of the TF MdMYB2, which regulates MdNADP-ME expression, causing differences in the regulation of malate content. MdNADP-ME may also promote the conversion of malate to fructose through gluconeogenesis by regulating the expression of MdPEPCK.

Methods

Plant materials

The F1 population used to construct genetic linkage maps was derived from a cross between Gala (M. domestica Borkh.) and Xiahongrou (M. sieversii), whose fruit differ significantly in sugar and acid contents. The trees were planted at a spacing of 1 × 2 m at the Modern Agricultural Science and Technology test and demonstration base of Xuzhou Academy of Agricultural Sciences (34.2824°N, 117.3059°E) in 2017 and received uniform horticultural management as well as fungicide and pesticide applications. Three to five young leaves (not fully expanded) were randomly collected from each individual for DNA extraction and whole-genome resequencing. Fruits were obtained from 92 individuals in 2021 and 152 individuals in 2022. Five to seven fruits were randomly collected from the perimeter of the tree crown at physiological maturity for trait measurement. Fruit maturity was judged from external and internal indicators to ensure that the fruit on a tree had reached physiological maturity; this included checking the smoothness, gloss, and smell of the skin; tasting the flavor of the pulp; and observing the extent of sepal spread and the color of the pulp. Fruits from Gala and Xiahongrou were collected at 15-day intervals from 15 to 120 days after bloom, with three biological replicates. Fruits collected at 30, 60, 90, and 120 days after bloom (Gala/XHR_S1/S2/S3/S4) were used for RNA-seq on the Illumina NovaSeq 6000 platform (150-bp paired-end reads). Orin apple calli were cultured in Murashige and Skoog (MS) solid medium with 4.43 g l−1 MS, 15 g l−1 sucrose, 8 g l−1 agar powder, 1.5 mg l−1 2,4-dichlorophenoxyacetic acid (2,4-D), and 0.4 mg l−1 6-benzylaminopurine and grown in a culture room at 25°C under dark conditions. Tobacco plants (Nicotiana benthamiana) were grown at 25°C under a 16 h/8 h light/dark photoperiod in a culture room.

Trait phenotyping

Pulp from five to seven fruits was mixed and divided into three replicates for trait evaluation. SSC was measured with a pocket refractometer (PAL-1, ATAGO). The TSS content was determined by the anthrone colorimetric method. The titratable acid content was determined by the NaOH titration method. Apple juice (5 ml) for each treatment was mixed with 15 ml of distilled water, and the mixture was titrated with 0.1% NaOH using 1% phenolphthalein as an indicator (Obi et al., 2018). The sugar (fructose, glucose, sucrose, and sorbitol) contents were analyzed with an ACQUITY UPLC H-Class system (Waters, Milford, USA), and organic acid (malate, quinate, shikimate, and citrate) contents were analyzed with the UltiMate 3000 system (Thermo Fisher, Massachusetts, USA). Soluble sugars were separated using a UPLC ACQUITY BEH Amide Column (2.1 × 100 mm, 1.7 μm) operated at 48°C. The mobile phase was double-distilled water, and the flow rate was 0.2 ml/min. The total run time was 15 min, and an ELSD detector was used to monitor soluble sugars as described by Liu et al. (2016) with minor changes. Organic acids were analyzed by HPLC using an ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm) associated with a PDA detector set to 214 nm. The column temperature was set to 30°C. The elution solvent was 50 mM disodium hydrogen phosphate at a flow rate of 0.25 ml/min. The duration of the analysis was 7 min, as described by Liu et al. (2013) with minor changes. Sugar and organic acid contents were expressed as mg·g−1 fruit weight (FW).

Library construction and whole-genome resequencing

DNA extraction was performed using the Tiangen DNAsecure Plant Mini Kit (DP320, Tiangen, Beijing, China). The DNA sequencing libraries were constructed and sequenced on the BGISEQ-500 platform (BGI-Tianjin) following the manufacturer’s instructions. Paired-end 150-bp reads were generated from the whole genomes of parents and the segregating progeny.

Mapping and variant calling

Adapter sequences were removed from the raw 150-bp paired-end sequence data generated by the BGI platform. Low-quality reads with too many Ns or low base quality were discarded. The clean reads were aligned to the apple (M. domestica Borkh.) reference genome GDDH13 v1.1 (Daccord et al., 2017) using BWA–MEM (version 0.7.17-r1188) (Li et al., 2010). The alignment information was stored in BAM files, which were then sorted with SAMtools (version 1.9, http://samtools.sourceforge.net/) and filtered on the basis of mapping quality, and duplicate reads were labeled. SNP calling was performed using GATK (version 3.2, https://www.broadinstitute.org/gatk) (McKenna et al., 2010). The identified SNPs were filtered using VCFtools (v0.1.16) (Danecek et al., 2011). Any genotypes with genotyping ratio <30% or heterozygosity >10% were filtered out for subsequent analysis. We tested the observed segregation pattern of all SNP sites against the expected Mendelian segregation in the F1 population using the chi-squared test and discarded those sites with distorted segregation (p < 0.001).

Construction of a genetic linkage map

The genetic map was constructed using the pseudotestcross mapping strategy (Grattapaglia, 1997). Three segregation types (lm × ll, nn × np, and hk × hk) were identified for further analysis. SNP markers were independently loaded into Binmarker-v2.3 (https://github.com/lileiting/Binmarkers-v2) (Qin et al., 2022) to create bins by merging 100% identical markers within a given genomic window. The OrderMarkers2 module in Lep-Map3 software (Rastas, 2017) was used to analyze the linear arrangement of markers within the chained clusters obtained and to estimate the genetic distance between adjacent markers. Pairwise recombination fractions were checked for all bins and visualized using R/qtl software (Broman et al., 2003).

QTL mapping

QTL mapping was performed using the phenotype data from 2021 and 2022 separately with R/qtl software. In R/qtl, analysis was performed by the composite interval mapping method with the Kosambi map function, 0.01 error probability, and other settings at default values. The confidence interval was calculated with the function lodint (Dupuis and Siegmund, 1999), and the drop value was set to 1.5. After a permutation test with 1000 replicates, an LOD threshold of 2.5 was used to identify QTLs at the 95% confidence level. All bin markers located in QTL intervals in the GX map were used for candidate gene identification by locating their physical positions on the Golden Delicious apple genome.

Candidate gene selection and gene expression analysis

Functional annotations of genes in the QTL regions were obtained from the Rosaceae Genome Database (GDR) (Malus × domestica GDDH13 v1.1). GO and pathway annotation was performed using the InterProScan gene annotation tool (https://github.com/ebi-pf-team/interproscan), and the KASS online tool (http://www.genome.jp/tools/kaas/) was used for KEGG pathway analysis. Screening of SNPs in specific regions was performed using bcftools (Danecek and McCarthy, 2017).

RNA-seq

Library construction and RNA-seq were performed by Novogene Beijing Technology (Beijing, China). HISAT2 (2.0.5) was used to map the clean RNA-seq reads to the reference genome of Golden Delicious. Quantitative gene analyses were performed using the featureCounts tool in subread software (1.5.0-p3) (Yang and Gordon, 2014). DEGs were identified using read counts in the R package DESeq2 (Love et al., 2014) with a threshold of |log2(fold change)|≥1 and adjusted p value (false discovery rate) ≤0.05. The weighted gene co-expression network was constructed with the R package WGCNA (Langfelder and Horvath, 2008).

Phylogenetic analysis, sequence alignment, and TF prediction

MYB gene sequences were identified from GDR and NCBI (http://www.ncbi.nlm.nih.gov). Amino acid sequences were aligned using ClustalW in MEGA X software (Kumar et al., 2018) and analyzed by the neighbor-joining method with 1000 bootstrap replicates. Sequence alignment was carried out with MAFFT (https://www.ebi.ac.uk/Tools/msa/mafft/). Promoter cis-acting element predictions were obtained using the PlantTFDB website (http://planttfdb.gao-lab.org/), and New PLACE (https://www.dna.affrc.go.jp/PLACE/?action=newplace) was used to analyze the TF-binding sites.

Plasmid construction and genetic transformation

The complete coding sequences (CDSs) of MdNADP-ME and MdMYB2 were inserted into the pCAMBIA Super1300 expression vector activated by the CaMV35S promoter to produce the overexpression vectors Super1300-MdNADP-ME and Super1300-MdMYB2. A 290-bp coding sequence of MdNADP-ME was cloned into the pTRV2 vector to produce the pTRV2-MdNADP-ME recombinant plasmid. Super1300-MdNADP-ME and pTRV2- MdNADP-ME were transformed into Orin apple calli and Gala fruit and Super1300-MdMYB2 was transformed into Gala and Xiahongrou fruits via Agrobacterium-mediated transient transformation (Sun et al., 2006). The primer sequences used are shown in Supplemental Table 7. The treated apple fruits were placed in darkness for 3 days at 25°C. The area surrounding the fruit injection site was collected and analyzed for gene expression and sugar and acid contents. One fruit was used as a biological replicate, and at least three biological replicates were performed.

GUS analysis

The 1500 bp upstream from the ATG start codon of MdNADP-ME from two allelic sequences of both Gala and Xiahongrou (GL-1/2 and XHR-1/2) were cloned into the pESI-Blunt simple vector for sequencing. They were then cloned into the pBI121 vector (with the CaMV35S::GUS reporter gene) to replace the CaMV35S promoter, thus constructing the pBI12-PMdNADP-ME::GUS reporter plasmid. The MdMYB2 ORF was introduced into Super1300 to produce the Super1300-MdMYB2 recombinant plasmid. For the GUS assay, the recombinant plasmids (pBI121-PMdNADP-ME and Super1300-MdMYB2) were co-transformed into Orin calli by Agrobacterium-mediated transient transformation. For histochemical staining, the samples were immersed in GUS staining buffer (phosphate buffer [pH 7.2], 0.4% Triton X-100, 10 mmol l−1 Na2EDTA, 0.5 mmol l−1 ferrocyanide trihydrate, 0.5 mmol l−1 potassium ferrocyanide trihydrate, 1 mg ml−1 X-Gluc, and 10% methanol) at 37°C overnight, and GUS activity was assayed using a GUS gene quantitative detection kit (Coolaber, Beijing, China).

Y1H assay

The Y1H assay was performed using the Y1H system Matchmaker Gold Kit (Clontech) following the manufacturer’s instructions. The MdNADP-ME promoters of two allelic sequences from Xiahongrou (XHR-1/2) were ligated into the pAbAi vector (bait vector). The full-length ORF of MdMYB2 was fused in frame with the GAL4 activation domain (AD) in the pGADT7 vector (prey plasmid). The pGADT7-MdMYB2 and pGADT7 empty vectors were transformed into Y1H yeast cells using a linearized recombinant pAbAi-MdNADP-ME-Pro vector. Yeast strains were grown in a medium containing 250 ng ml−1 Aureobasidin A (AbA) (Yeasen, Shanghai, China).

EMSA

The full-length CDS of MdMYB2 was cloned into the pCold-TF vector. The MdMYB2-His fusion protein was expressed in Escherichia coli BL21 (DE3). The protein was purified using His-agarose affinity chromatography. The 30-bp probe sequences of the Xiahongrou and Gala promoters were synthesized with/without a biotin marker (General Biol, Chuzhou, China) (Supplemental Table 7). EMSA was performed using the Chemiluminescent Nucleic Acid Detection Module (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions.

Promoter dual-luciferase assay

In brief, promoter fragments of MdNADP-ME (1500, 855, and 275 bp, as P1, P2, and P3) from the Xiahongrou haplotype (XHR-1, with the AAACCA sequence) were inserted upstream of the LUC reporter gene in the pGreenII0800-LUC vector to produce pro-LUC reporter vectors, and the empty vector (pGreenII0800-LUC) was used as the negative control. P1 and P2 each contained the SNP MEp-799 (the putative MYB2 TF-binding site), and P3 was 275 bp of XHR-1 around the SNP MEp-799. P4 was a fragment of the MdNADP-ME promoter (1500 bp) from XHR-2. P5 was the XHR-2 fragment with a C-to-A mutation at SNP MEp-799. The complete MdMYB2 ORF was cloned into the pCAMBIA Super 1300 overexpression vector to produce an effector vector (Super1300-MdMYB2). The reporter and effector constructs were co-transformed into A. tumefaciens GV3101. Tobacco (N. benthamiana) transient expression and dual-luciferase assays were performed using the Dual Luciferase Reporter Gene Assay Kit (Yeasen, Shanghai, China). Activity was expressed as the ratio of LUC to REN and normalized to the ratio of the negative control (set to 1).

RNA extraction and RT–qPCR

Total RNA was extracted using the Trelief RNAprep Pure Plant Plus Kit (Tsingke, Beijing, China). cDNA was synthesized using the PrimeScript RT Reagent Kit with gDNA Eraser (Takara, Dalian, China). Gene-specific primers (Supplemental Table 7) were generated based on the sequences available at the NCBI website (https://www.ncbi.nlm.nih.gov/). RT–qPCR was performed using SYBR Green Pro Taq HS Premix (Accurate, Changsha, China). Three biological replicates (each containing three technical replicates) were tested. MdTublin (MD07G1270800) was used as an internal control to calculate relative gene expression by the 2−ΔΔCt method.

Development of dCAPS markers

For candidate SNP markers potentially linked to fruit fructose content, primers flanking the SNPs were designed using the dCAPS Finder 2.0 (Neff et al., 2002) website (http://helix.wustl.edu/dcaps/dcaps.html) (Supplemental Table 7). PCR amplification was performed using 2×Hieff Canace Plus PCR Master Mix (Yisheng, Shanghai, China). PCR products were purified and then identified by digestion with FspB I endonuclease (Thermo Fisher Scientific, Waltham, MA, USA).

Statistical analysis

All samples were analyzed in triplicate, and all data are presented as the mean ± SE. Data analysis and visualization were performed with GraphPad Prism software (version 9.0). Statistically significant differences between samples were determined using Student’s t test (p ≤ 0.05). Multiple comparisons were tested using Tukey’s test, and significant differences at the p ≤ 0.05 level are indicated by different letters.

Data and code availability

The sequence data have been submitted to the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA1067512.

Funding

This research was supported by the 10.13039/501100012166 National Key Research and Development Program of China , “Physiological basis and regulation of fruit tree quality and high yield” (2019YFD1000103 ), a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (10.13039/501100012246 PAPD ).

Supplemental information

Document S1. Supplemental Figures S1‒S12 and Supplemental Tables S1–S8

Document S2. Article plus supplemental information

Acknowledgments

We thank Y.H.M. (Central Laboratory of College of Horticulture, Nanjing Agricultural University) for assistance in using the Cytation 3 cell-imaging multimode reader (BioTek, California, USA). We also thank S.Y.L. (State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization) for assistance in using the ACQUITY UPLC H-Class system (Waters, Milford, USA) and the UltiMate 3000 system (Thermo Fishers, Massachusetts, USA). No conflict of interest is declared.

Author contributions

All authors contributed to the study conception and design. S.H.W., S.C.Q., and L.Z. conceived and designed the experiments. W.H.F., W.J.Q., and M.D. performed the experiments. W.H.F., L.M.L., and X.X. analyzed the data. W.H.F. wrote the article, and all authors revised and approved the final manuscript.

Published by the Plant Communications Shanghai Editorial Office in association with Cell Press, an imprint of Elsevier Inc., on behalf of CSPB and CEMPS, CAS.

Supplemental information is available at Plant Communications Online.
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