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Article

The Effect of DNA Methylation on the Depth of Peel Color in ‘Red Fuji’

by
Yucheng Liu
1,
Jingyi Su
2 and
Wensheng Li
1,*
1
College of Forestry and Landscape Architecture, Xinjiang Agricultural University, Urumqi 830052, China
2
Institute of Forestry and Horticulture, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China
*
Author to whom correspondence should be addressed.
Horticulturae 2026, 12(2), 219; https://doi.org/10.3390/horticulturae12020219
Submission received: 4 January 2026 / Revised: 6 February 2026 / Accepted: 9 February 2026 / Published: 10 February 2026
(This article belongs to the Topic Genetic Breeding and Biotechnology of Garden Plants)
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Abstract

Red pigmentation in apple peel significantly contributes to its commercial value, and bagging treatment contributes to enhancing red coloration in fruits. However, the regulatory mechanisms underlying bagging-induced coloration remain largely unexplored. Through bagging treatment, this study aimed to investigate the role of DNA methylation in anthocyanin biosynthesis in the ‘Nagafu No. 2’ cultivar and its bud mutation variant, which has enhanced red coloration. We compared bagging and unbagging treatments in both the bud mutant (Mt-Bagging and Mt-NoBagging) and the wild type (Control-Bagging and Control-NoBagging). Our results demonstrated that bagging significantly promoted anthocyanin accumulation while reducing chlorophyll content. At 30 days post-bag removal, anthocyanin content was highest in the Mt-Bagging group, followed by the Mt-NoBagging, Control-Bagging, and Control-NoBagging groups, and the highest level of redness (a* values) was detected in the Mt-Bagging group. Genome-wide methylation analysis revealed that differentially methylated regions predominantly targeted structural genes within the anthocyanin biosynthesis pathway, including C4H1, C4H3, C4HL, ANS1, and ANS2. Notably, quantitative PCR analysis confirmed that the upregulation of C4HL, C4H3, and ANS1 in the bagged mutant correlated with its intensified red coloration. These findings offer novel insights into the epigenetic regulation of apple peel pigmentation during bagging cultivation.

1. Introduction

Anthocyanins, a class of ubiquitous plant pigments, impart the red, orange, purple, blue, and black hues observed across diverse plant species [1]. Beyond their role as critical determinants of fruit’s coloration and, therefore, its esthetic and commercial appeal, these pigments function as protective agents against ultraviolet radiation and pathogenic infections, thus fortifying plants’ resilience against environmental stressors [2]. In apples, the red coloration of the fruit peel, a desirable trait for consumers, is predominantly attributed to anthocyanin accumulation [3].
The metabolic pathway for anthocyanin biosynthesis in plants has been extensively characterized [4]. Anthocyanins are classified as flavonoid pigments and are synthesized via the phenylalanine branch of the flavonoid biosynthetic pathway [5]. As the terminal products of the phenylpropanoid pathway, their biosynthesis is orchestrated by a series of enzymatic reactions, beginning with phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), and p-coumaroyl coenzyme A ligase (4CL) [6]. Subsequently, one molecule of 4-coumaroyl-CoA condenses with three malonyl-CoA molecules, catalyzed by chalcone synthase (CHS) and chalcone isomerase (CHI), to yield naringenin chalcone [7]. This intermediate undergoes further diversification through the action of flavanone 3-hydroxylase (F3H), flavanone 3′-hydroxylase (F3′H), or flavanone 3′5′-hydroxylase (F3′5′H), resulting in various dihydroflavonols. These dihydroflavonols are then catalyzed by dihydroflavonol 4-reductase (DFR) to form leucoanthocyanidins. A critical branching point in the pathway occurs at the leucoanthocyanidin stage, where anthocyanidin synthase (ANS/LDOX) and leucoanthocyanidin reductase (LAR) compete for the substrate. The former enzyme drives the formation of colorless anthocyanidins, which subsequently undergo glycosylation by glycosyltransferases (GTs) to generate colored anthocyanins [8]. Glycosylation events can occur at multiple positions, with the 3-O-position representing a primary site of modification. The enzyme UDP-glucose: anthocyanidin 3-O-glucosyltransferase (3-GT), a member of the UDP-glucose: flavonoid 3-O-glucosyltransferase (UFGT) family, plays a pivotal role in this process, as its specificity dictates the glycosylation pattern [9,10]. Further structural diversity arises from the attachment of various sugar moieties at different positions by corresponding glycosyltransferases [11]. The structural genes involved in anthocyanin biosynthesis are relatively conserved among plant species. In apples, PAL, C4H, C4HL, CHS, CHI, and F3H function as upstream structural genes, whereas DFR, ANS, and UFGT are considered downstream structural genes. The latter group typically comprises key genes that significantly influence anthocyanin synthesis, and their expression levels directly affect anthocyanin content in tissues [12,13]. During apple fruit development, the co-expression of CHS, F3H, DFR, ANS, and UFGT is positively correlated with anthocyanin accumulation [14]. However, the expression patterns of these structural genes in apples vary between varieties, developmental stages, and tissues [15].
The biosynthesis of anthocyanin is influenced by both endogenous factors, such as phytohormones [16] and DNA methylation [17], and exogenous factors, including light exposure [18]. DNA methylation involves the transfer of a methyl group from S-adenosylmethionine to the fifth carbon of cytosine residues [19]. While the regulatory mechanisms of DNA methylation in anthocyanin metabolism vary across fruit species, methylation at the promoter regions of key regulatory genes (including MYB1 [20], MYB10 [21], and bHLH74 [22] in apple, as well as MYBA1 in grape [23]) has been associated with reduced anthocyanin accumulation. Notably, bagging treatments have been shown to elevate anthocyanin levels in apples by decreasing promoter methylation of MYB1 [24,25]. Conversely, such treatments may also upregulate DNA demethylase activity, leading to reduced methylation at the PcUFGT locus and enhanced anthocyanin biosynthesis [26]. Despite these insights, the precise role of DNA methylation in regulating anthocyanin metabolism in fruits, particularly in apple, remains to be fully elucidated.
‘Nagafu No. 2’, a colorful ‘Fuji’ apple cultivar introduced to China in 1980, is highly susceptible to mutation, leading to the emergence of diverse phenotypic variants. In 2016, red-mutant branches were identified in the Hongqipo farm orchard in Aksu, Xinjiang. These bud-mutated fruits possessed significant commercial potential due to their enhanced red coloration. Our previous study demonstrated that bagging treatments resulted in deeper red coloration in both ‘Nagafu No. 2’ and its mutation, with the mutation exhibiting the most pronounced effect. To unravel the regulatory mechanisms underlying the enhanced red coloration of ‘Nagafu No. 2’ bud mutants following bagging treatment, we conducted a comparative analysis of bagged and unbagged fruits derived from both mutant and wild-type ‘Nagafu No. 2’ trees. By integrating peel color assessment with DNA methylation profiling, we aimed to elucidate the epigenetic basis of red-color formation in these bud-variant apples.

2. Materials and Methods

2.1. Plant Materials

Two apple (Malus domestica) cultivars, ‘Nagafu No. 2’ and its bud mutation line (both 15-year-old trees), were selected from the Hongqipo orchard in Aksu Prefecture, Xinjiang Uygur Autonomous Region, China. The fruits from these trees were subjected to bagging or unbagging treatment under identical growth conditions.

2.2. Experimental Design

The bagging experiment was conducted from 12 June to 20 September 2022, and twelve trees were selected: six from the ‘Nagafu No. 2’ bud mutation and six from the ‘Nagafu No. 2’ cultivar. The treatments were assigned as follows: Mt-Bagging (T1) and control 1 (C1: Mt-NoBagging) involved bagging and unbagging of the ‘Nagafu No. 2’ bud mutation, respectively. Treatment 2 (T2: Control-Bagging) and control 2 (C2: Control-NoBagging) consisted of bagging and unbagging the ‘Nagafu No. 2’ cultivar, respectively. Three biological replicates were performed for each treatment.

2.3. Samples Collected

Fruits were harvested from 20 September 2022 (0 day, 0 d) to 20 October 2022 (30 days post-bag removal), following protocols established in previous research [25,27,28]. Fruits at 0 d, 2 d, 4 d, 6 d, 8 d, 10 d, 15 d, 20 d, and 30 d were collected and used for peel color measurement, and at each of these timepoints, the peel tissues of corresponding fruits were collected to measure the anthocyanin and chlorophyll content. Furthermore, peel tissues at 30 days were used for DNA methylation sequencing and analysis. Specifically, from each tree, 3–5 fruits were collected from each cardinal direction (east, west, south, and north) and immediately transported on ice to the laboratory for processing. Peel tissues (approximately 0.1 mm thickness) were excised using a manual peeler, and both peel and flesh samples were flash-frozen in liquid nitrogen before storage at −80 °C. Three trees per treatment were sampled, totaling 12 trees, and multiple peel tissues from each tree were used as one replicate.

2.4. Measurement of Fruit Peel Color

The color of the apple peel was measured using the CIE Lab* color space coordinates, specifically lightness (L*), hue angle (h°), chroma (C*), and the two chromatic components, a* (redness) and b* (yellowness). Measurements were taken using a Color Reader (CR-10; Konica Minolta Sensing Inc., Tokyo, Japan).

2.5. Anthocyanin and Chlorophyll Content Detection

Anthocyanin content was determined using the method of Pirie [29]. Specifically, 0.5 g of peel was ground into a powder with liquid nitrogen and transferred to a 10 mL centrifuge tube. Next, 5 mL of 1% (v/v) HCl-methanol solution (Sigma-Aldrich, St. Louis, MO, USA) was added, and anthocyanins were extracted under dark conditions at 4 °C for 24 h, with the tubes shaken twice during the extraction period. Extraction was considered complete when the peel residue turned white. The supernatant was then centrifuged at 12,000 rpm for 10 min at 4 °C. Absorbance was measured using a UV-Vis spectrophotometer (Shimadzu Corp., Kyoto, Japan) at wavelengths of 530 nm and 657 nm, with 1% HCl-methanol serving as the control.
Chlorophyll content was determined according to a slightly modified version of the method of Lichtenthaler [30]. A total of 0.5 g of peel was ground with 80% pre-cooled acetone (Sigma-Aldrich, St. Louis, MO, USA) and transferred to a 10 mL centrifuge tube. The mortar was washed with 80% acetone, and the final volume was adjusted to 10 mL. Chlorophyll was extracted under dark conditions at 4 °C for 24 h, with the tubes shaken several times during the experiment. The extracts were then centrifuged at 12,000 rpm for 20 min at 4 °C. Absorbance was measured using a spectrophotometer at wavelengths of 470 nm, 645 nm, and 663 nm, with 80% acetone serving as the blank control.

2.6. DNA Methylation Sequencing and Analysis

Total genomic DNA (gDNA) was extracted from peels using the cetyltrimethylammonium bromide (CTAB) method [31]. A total of 100 ng of genomic DNA, spiked with 0.5 ng of lambda DNA, was fragmented by sonication to 200–300 bp using a Covaris S220 (Covaris Inc., Woburn, MA, USA). These DNA fragments were then treated with bisulfite using an EZ DNA Methylation-Gold™ Kit (Zymo Research, Irvine, CA, USA). Library construction was performed by Novogene Corporation (Beijing, China), and subsequently, pair-end sequencing of the samples was conducted on an Illumina platform (Illumina, San Diego, CA, USA). Library quality was assessed using a 5400 Fragment Analyzer System (Agilent Technologies, Santa Clara, CA, USA), and the libraries were sequenced on an Illumina NovaSeq platform. Image analysis and base calling were performed with the Illumina CASAVA pipeline (version 1.8), generating 150 bp paired-end reads. Raw data underwent filtering to remove adapter sequences and low-quality reads. Finally, the apple reference genome (Malus domestica, GCF_002114115.1_ASM211411v1, NCBI, downloaded on 9 November 2022) was used for mapping, and Bismark software (version 0.16.3) [32] was used to align bisulfite-treated reads to a reference genome (-X 700 -dovetail).
Methylated sites were identified with a binomial test using the methylated counts (mC), total counts (mC + umC), and non-conversion rate (r). Sites with FDR-corrected p-values < 0.05 were considered methylated sites. To calculate the methylation level of the sequence, we divided the sequence into multiple bins, with a bin size of 10 kb, and the sums of methylated and unmethylated read counts in each window were calculated. The methylation level (ML) for each window or C site shows the fraction of methylated Cs, and is defined as ML = mC/(mC + umC). Differentially methylated regions (DMRs) were identified using DSS software (version 1.12.0) (p < 0.05), with DSS representing a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-binomial distributions. According to the distribution of DMRs throughout the genome, we defined the genes related to DMRs as those whose gene body region (from the transcription start site (TSS) to the transcription end site (TES)) or promoter region (2 kb upstream of the TSS) overlaps with the DMRs [33]. Gene Ontology (GO) enrichment analysis of genes related to DMRs was implemented using the GOseq R package (version 1.0.0) [34], in which gene length bias was corrected. GO terms with corrected p-value less than 0.05 were considered significantly enriched by DMR-related genes. We used KOBAS software (version 1.0) [35] to test the statistical enrichment of DMR-related genes in KEGG pathways.

2.7. RNA Isolation and qPCR Verification

For the gene expression validation experiments, total RNA was extracted using a Plant RNA Kit (TIANGEN Biotech Co., Ltd., Beijing, China), and a Fast King RT Kit (TIANGEN Biotech Co., Ltd., Beijing, China) was used to synthesize the first cDNA strand from the RNA. Actin was used as the internal reference gene for RT-qPCR verification, and primers for the screened genes were designed using Primer software (version 5.0). The primer sequences are listed in Table 1. Specificity and efficiency analyses of the primers used for qPCR were performed according to previously reported methods [36]. Specifically, cDNA from all stages was pooled equally as a template for semi-quantitative PCR. Subsequently, 1% agarose gel electrophoresis was carried out to confirm that each product was well-amplified with no primer dimers (Figure 1). The pooled cDNA was diluted into five concentration gradients: 100, 10−1, 10−2, 10−3, and 10−4. This diluted cDNA was then used to perform qPCR and draw a standard curve. The results indicated that expression efficiency ranged from 90% to 110%, with R2 > 0.99. qPCR analysis was performed on a Rotor-Gene Q (QIAGEN, Duesseldorf, Germany). Each 20 μL PCR reaction system contained 2 μL of first-strand cDNA, 200 nM of primers, and a BlasTaq™ 2× qPCR MasterMix (Amyjet Scientific Co., Ltd., Wuhan, China). The amplification conditions were as follows: initial 3 min denaturation at 95 °C, followed by 40 cycles of 30 s at 95 °C, 1 min at 56 °C, and 1 min at 72 °C, with a final extension for 10 min at 72 °C. Three replicates were performed for each sample, and the relative quantification values were calculated using the 2−ΔΔCT method. In this study, the expression level at 0 d was typically set to 1.

2.8. Statistical Analyses

The results presented in the figures and tables represent the means of three biological replicates. Statistical analyses were performed using SPSS software (version 12.0, IBM Corp., Armonk, NY, USA), and the data were analyzed using Tukey’s multiple-range test (p < 0.05) with triplicates. Welch’s t-test was performed using R software (version 4.4.1, R Core Team, Vienna, Austria) with a base function t-test() (default: var.equal = FALSE) in the stats package. Raw p-values from multiple comparisons were adjusted for FDR via the p-adjust() function with the method = “BH” parameter. Statistical significance was defined as a p-adjusted value < 0.05.

3. Results

3.1. Comparative Analyses of Peel Color Phenotypic Parameters After Bag Removal Across Treatment Groups

To elucidate the dynamic changes in peel color following the termination of bagging treatments, we quantitatively assessed the peel’s chromatic attributes (redness (a*), yellowness (b*), lightness (L*), chroma (C*), and hue angle (h°)) using a Color Reader. Across all four treatments, redness values consistently increased after bag removal. At the initial measurement point on September 20th (designated as 0 days after bag removal, 0 d), both control groups—the unbagged ‘Nagafu No. 2’ bud mutation (Mt-NoBagging) and the unbagged ‘Nagafu No. 2’ (Control-NoBagging)—exhibited significantly higher redness values compared to their respective bagged counterparts (Mt-Bagging and Control-Bagging). Notably, the redness value for the Mt-Bagging group was significantly higher than that for the other three treatment groups from 6 d to 30 d. The redness value of the Mt-NoBagging group was significantly lower than that of the Mt-Bagging group but significantly higher than that of the Control-Bagging and Control-NoBagging groups from 4 d to 20 d. Overall, the redness values displayed a trend of Mt-Bagging > Control-Bagging > Mt-NoBagging > Control-NoBagging (Figure 2 and Figure 3).
A distinct decreasing trend in yellowness values was observed across all four treatments during the 30-day period following bag removal. Notably, the Mt-Bagging group (‘Nagafu No. 2’ bud mutation with bagging treatment) exhibited significantly lower yellowness values compared to the other treatment groups (Mt-NoBagging, Control-Bagging, and Control-NoBagging) from 4 d to 30 d. The Control-NoBagging group consistently maintained significantly higher yellowness values than the other three treatment groups, except at 4 d, 6 d, and 10 d (Figure 2 and Figure 3).
A divergent pattern emerged in color saturation dynamics between the control and bagging treatment groups. Both control groups (Mt-NoBagging and Control-NoBagging) exhibited a gradual decrease in chroma values from 0 d to 30 d. Conversely, bagging treatments displayed a trend of increasing chroma values after bag removal. Notably, the Mt-Bagging group showed significantly higher chroma values compared to all other groups from 8 d to 30 d. The Mt-Bagging group achieved the highest chroma values by 30 d, suggesting enhanced color development in response to re-exposure to ambient light conditions. Consistent with the trend observed for yellowness values, hue angle values decreased across all treatment groups. The Mt-Bagging group showed significantly lower hue angle values than the other treatment groups from 15 d to 30 d, indicating a more pronounced shift toward red-orange hues. All groups exhibited decreasing lightness values from 0 d to 30 d, suggesting a general darkening of the peel surface over time. Notably, both bagging treatments maintained significantly higher lightness values than the controls throughout the observation period (Figure 2 and Figure 3).

3.2. Comparison of Anthocyanin and Chlorophyll Content Under Different Treatments

The dynamic profiles of chlorophyll and anthocyanin concentrations in fruit peels subjected to different treatments exhibited a general ascending trend, reaching maximum levels at 30 days post-bag removal. Notably, anthocyanin content displayed the following rank order at 30 d: Mt-Bagging > Mt-NoBagging > Control-Bagging > Control-NoBagging. Minimal anthocyanin levels were detected in Mt-Bagging and Control-Bagging peels at 0 d; a significant, discernible accumulation of anthocyanins commenced in Control-NoBagging and Control-Bagging peels from 20 d onwards, whereas the mutation groups (Mt-NoBagging and Mt-Bagging) demonstrated pronounced escalation starting as early as 8 d. Specifically, Mt-NoBagging peels exhibited remarkably higher anthocyanin concentrations than all other treatments from 0 d to 10 d. From 4 d to 6 d, Mt-Bagging and Control-NoBagging peels showed comparable levels, both significantly surpassing those of Control-Bagging peels. Between 8 d and 10 d, Mt-Bagging peels maintained significantly elevated anthocyanins relative to the normal groups (Control-NoBagging and Control-Bagging), while no significant difference was observed between Control-Bagging and Control-NoBagging peels. In the period from 15 to 30 days, the Mt-Bagging group consistently outperformed the other groups (p < 0.05), followed by the Mt-NoBagging, Control-NoBagging, and Control-Bagging groups, with the Control-NoBagging and Control-Bagging groups showing no significant disparity (Figure 4a).
The chlorophyll content exhibited distinct trends across treatments. In the unbagging treatment groups (Mt-NoBagging and Control-NoBagging), chlorophyll content peaked at 6 days post-bag removal, then remained relatively stable until 30 days. Conversely, in the bagging treatment groups (Mt-Bagging and Control-Bagging), chlorophyll content demonstrated a continuous increase from 0 d to 30 d. Notably, chlorophyll content in the unbagging treatment groups (Mt-NoBagging and Control-NoBagging) was significantly higher than that in the bagging treatment groups (Mt-Bagging and Control-Bagging) throughout the entire observation period.
Within the bagging treatment groups, chlorophyll content in the Control-Bagging group was significantly greater than that in the Mt-Bagging group at 0 d, 2 d, and 15 d. However, no significant difference was observed between the Mt-Bagging and Control-Bagging groups on the remaining sampling dates. In the unbagging treatment groups, there was no significant difference in chlorophyll content between the Mt-NoBagging and Control-NoBagging groups from 0 d to 30 d, except at 4 d, 20 d, and 30 d (Figure 4b).

3.3. Repeated Measures Analysis of Phenotypes

Repeated-measures analysis of variance (RM-ANOVA) revealed that the main effects of group, time, and their interaction (group × time) were highly significant across all measured parameters, including anthocyanin content, chlorophyll content, redness, yellowness, chroma, lightness, and hue angle (all F values corresponding to p < 0.01).
The group factor (different treatments) exerted a highly significant influence on anthocyanin content (F = 815.99, p < 0.01), chlorophyll content (F = 613.02, p < 0.01), redness (F = 779.31, p < 0.01), yellowness (F = 227.02, p < 0.01), chroma (F = 94.49, p < 0.01), lightness (F = 342.74, p < 0.01), and hue angle (F = 480.73, p < 0.01). Similarly, time had a highly significant effect on all of the above parameters (anthocyanin content: F = 359.11, p < 0.01; chlorophyll content: F = 30.94, p < 0.01; redness: F = 390.19, p < 0.01; yellowness: F = 299.00, p < 0.01; chroma: F = 48.09, p < 0.01; lightness: F = 174.33, p < 0.01; hue angle: F = 714.92, p < 0.01). The interaction between group and time also differed significantly for each parameter (anthocyanin content: F = 32.18, p < 0.01; chlorophyll content: F = 13.67, p < 0.01; redness: F = 34.51, p < 0.01; yellowness: F = 11.09, p < 0.01; chroma: F = 21.59, p < 0.01; lightness: F = 10.45, p < 0.01; hue angle: F = 36.95, p < 0.01). This indicates that the temporal change patterns of each parameter varied significantly between groups (Table 2).

3.4. Whole-Genome DNA Methylation Sequencing and Analysis

To elucidate the mechanisms potentially responsible for the enhanced coloration observed in the bud mutation and bagging treatment groups, whole-genome bisulfite sequencing (WGBS) was performed. A total of 998.40 million raw reads were generated, and after the removal of low-quality sequences, 959.58 million clean reads were retained, with each sample yielding over 18 G of clean reads. The alignment rate against the apple reference genome exceeded 70%, accompanied by a sequencing depth of 15×. The bisulfite conversion efficiency for each sample surpassed 99%, underscoring the high accuracy and reliability of the data for subsequent DNA methylation analysis (Table 3).
The average methylation levels of cytosine (C) sites across the entire genome were calculated for all treatments. No significant differences were observed between all pairs of groups within the same context. A consistent trend was observed across all four treatments for C, CG, CHG, and CHH contexts: the highest methylation values were found in the Control-Nobagging group, followed by Mt-Nobagging, Mt-Bagging, and Control-Bagging (Figure 5).

3.5. Comparative Examination of DNA Methylation Patterns Across Treatment Groups

A comprehensive comparative analysis was conducted to assess the average DNA methylation levels between treatment groups within both the intergenic and genic regions. In the intergenic region, the analysis of DNA methylation levels revealed that methylation loci were predominantly located in repeat regions. Notably, significant variation in DNA methylation levels was observed within promoter regions across the four comparison groups: Mt-Bagging versus Mt-NoBagging, Control-Bagging versus Control-NoBagging, Mt-NoBagging versus Control-NoBagging, and Mt-Bagging versus Control-Bagging (Figure 6). Within genic regions, detailed analysis of DNA methylation levels was carried out across various sequence segments, including the upstream 2 kb region, transcription start site (TSS), gene body, transcription end site (TES), and downstream 2 kb region, under different treatment conditions. The results indicated a significant difference in DNA methylation levels within the upstream 2 kb region across all four comparison groups. Specifically, when comparing the Mt-Bagging group to the Mt-NoBagging or Control-Bagging group, a hyper-methylation phenomenon was observed in the upstream 2 kb region across the CHH, CHG, and CG contexts. Conversely, in the Control-Bagging versus Control-NoBagging comparison, the Control-Bagging group exhibited hypomethylation in the upstream 2 kb region across the CHH, CHG, and CG contexts. Furthermore, in the Mt-NoBagging versus Control-NoBagging comparison, hyper-methylated loci were identified only within the CHH methylation type in the upstream 2 kb region. Similar trends in DNA methylation levels were observed in the gene body and downstream 2 kb regions across the same comparison groups (Figure 7). Additionally, an analysis of differentially methylated regions (DMRs) was performed for both genes and promoters. The results demonstrated that DMRs in both genes and promoters exhibited a comparable pattern, with the highest number of DMRs observed in the CHH context (Figure 8).

3.6. GO and KEGG Enrichment

To gain deeper insight into the biological functions of differentially methylated regions (DMRs), Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on hyper- and hypo-DMRs, respectively (p < 0.01) (Figure 9, Figure 10, Figure 11 and Figure 12). GO enrichment analyses showed that the bagging effect showed distinct enrichment patterns between genotypes in hyper-DMRs. In the Mt-Bagging versus Mt-NoBagging comparison group, hyper-DMRs were mainly enriched in biological processes, including galactose metabolic process, hexose metabolic process, monosaccharide metabolic process, and carbohydrate metabolic process (Figure 9a). In the Control-Bagging versus Control-NoBagging comparison group (Figure 9b), hyper DMRs were primarily associated with biological processes. Key terms included steroid dehydrogenase activity, steroid biosynthetic process, steroid metabolic process, glycerophospholipid biosynthetic process, and lipid biosynthetic process. The genotype effect also exhibited clear differences. The hyper-DMRs between two cultivars under bagging treatment (Mt-Bagging versus Control-Bagging) were enriched in molecular functions such as UDP-glycosyltransferase activity, catalytic activity, and amino acid binding, as well as cellular components including mitochondrial outer membrane and organelle outer membrane (Figure 9c). On the other hand, the hyper-DMRs between two cultivars (Mt-NoBagging versus Control-NoBagging) were enriched in biological processes, including organonitrogen compound biosynthetic process, translation, and peptide biosynthetic process, alongside the cellular component term ribonucleoprotein complex and the molecular function term hydroxymethylbilane synthase activity (Figure 9d).
For hypo-DMRs, the bagging effect was different between the two genotypes. In Mt-Bagging versus Mt-NoBagging comparison (Figure 10a), hypo-DMRs were enriched in molecular functions including carboxyl- or carbamoyltransferase activity and sialyltransferase activity, biological processes including trehalose biosynthetic process and trehalose metabolic process, and the cellular component myosin complex. Meanwhile, in the Control-Bagging versus Control-NoBagging comparison groups, hypo-DMRs were primarily enriched in molecular functions, including amino acid, carboxylic acid, UDP-glucose-hexose-1-phosphate uridylyltransferase activity, and organic acid. The only biological process term was the galactose metabolic process (Figure 10b). The genotype effect remained relatively stable across treatments. In bagging treatment comparison (Mt-Bagging versus Control-Bagging), hypo-DMRs were enriched in molecular functions with ion binding (transition metal ion binding, zinc ion binding) and proteolysis (peptidase activity, peptidase activity acting on L-amino acid peptides), as well as hydroxymethylbilane synthase activity (Figure 10c). Under non-bagging comparison (Mt-NoBagging versus Control-NoBagging), hypo-DMRs were primarily enriched in molecular functions including peroxidase activity, oxidoreductase activity acting on peroxide as acceptor, antioxidant activity) and ATP phosphoribosyltransferase activity (Figure 10d).
KEGG enrichment analysis showed that bagging treatment resulted in the hyper-DMRs becoming enriched in KEGG pathways such as protein export, plant–pathogen interaction, and selenocompound metabolism in the ‘Nagafu No. 2’ bud mutation (Mt-Bagging versus Mt-NoBagging) (Figure 11a). In the ‘Nagafu No. 2’ cultivar (Control-Bagging versus Control-NoBagging), bagging treatment led to hyper-DMRs becoming enriched in KEGG pathways such as other types of O-glycan biosynthesis, butanoate metabolism, and sesquiterpenoid and triterpenoid biosynthesis (Figure 11b). Between two cultivars (Mt-Bagging versus Control-Bagging), bagging treatment led to hyper-DMRs becoming enriched in KEGG pathways such as fructose and mannose metabolism, degradation of other glycan, and mismatch repair (Figure 11c). However, hyper-DMRs between two cultivars without bagging treatment (Mt-NoBagging versus Control-NoBagging) became enriched in ascorbate and aldarate metabolism, phenylpropanoid biosynthesis, cyanoamino acid metabolism, and other KEGG pathways (Figure 11d).
KEGG enrichment of hypo-DMRs showed that bagging treatment resulted in hypo-DMRs becoming enriched in anthocyanin-related pathways within one cultivar. In both the Mt-Bagging versus Mt-NoBagging comparison group and the Control-Bagging versus Control-NoBagging comparison group, hypo-DMRs were enriched in phenylpropanoid biosynthesis and biosynthesis of secondary metabolites (Figure 12a,b). Similarly, bagging treatment resulted in hypo-DMRs in the Mt-Bagging versus Control-Bagging comparison group becoming enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis, and biosynthesis of secondary metabolites (Figure 12c). In addition, hypo-DMRs in the Mt-NoBagging versus Control-NoBagging comparison group were enriched in DNA replication, limonene and pinene degradation, zeatin biosynthesis, and other KEGG pathways (Figure 12d).

3.7. qPCR Analysis of Candidate Genes Involved in Flavonoid Biosynthesis

Based on the results of KEGG pathway analysis, five genes associated with hypo-DMRs enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis, and secondary-metabolite biosynthesis from three comparison groups (Mt-Bagging versus Mt-NoBagging, Control-Bagging versus Control-NoBagging, and Mt-Bagging versus Control-Bagging) and involved in anthocyanin biosynthesis were selected for quantitative polymerase chain reaction (qPCR) analysis. These five genes were identified as cinnamoyl-CoA 4-hydroxylase 1 (C4H1) (LOC103418731), C4H3 (LOC103418775), cinnamoyl-CoA 4-hydroxylase-like (C4HL) (LOC103447265), anthocyanidin synthase 1 (ANS1) (LOC103437327), and ANS2 (LOC103437326). The relative expression levels of these genes were analyzed at 0 d, 4 d, 8 d, 15 d, 20 d, and 30 d, with the expression level at 0 d serving as the reference.
In the early stage post-bag removal (from 4 d to 8 d), a prominent genotype effect was evident in the Mt-NoBagging group, which had significantly higher C4H3, C4HL, and C4H1 expression at 8 d than all other groups. Meanwhile, bagging induced marked upregulation of C4H3 and ANS1 in the Mt-Bagging group relative to the two Control groups, demonstrating the initial regulatory role of the bagging effect in the mutant genotype background.
During the middle stage of post-bag removal (from 15 d to 20 d), the bagging effect was observed in both genotypes, with the Mt-Bagging group exhibiting a sequential peak gene expression in C4HL at 15 d, ANS1 and ANS2 at 15 d and 20 d. The Control-Bagging group also showed a secondary high level of ANS1 and ANS2, reflecting the universal promotion of bagging on anthocyanin gene expression while being modulated by genotype.
In the late stage post-bag removal (30 d), the bagging effect presented a stable regulatory trend, with bagging treatments (Mt-Bagging and Control-Bagging) consistently displaying higher ANS1 expression than non-bagging groups (Mt-NoBagging and Control-NoBagging) (Figure 13).

3.8. Pearson Correlation Analysis of log2-Fold Changes in Gene Expression and Corresponding Methylation Differences

To investigate the association between gene expression and DNA methylation, the Pearson correlation in Table 4 was calculated between the log2-fold change (log2FC) of gene expression and the average methylation level difference in the promoter regions for the same set of genes and identical comparison groups. For example, the fold change (FC) of ANS1 was determined by comparing its gene expression levels across different experimental groups. Similarly, the differential methylation value of ANS1 (mANS1) represented the average difference in methylation levels from the same comparison groups, specifically within the CHH context of the promoter region. Pearson correlation analysis showed that there was a significant negative correlation across all gene tests conducted (r < −0.95, p < 0.05) (Table 4).

4. Discussion

4.1. Enhancing the Phenotype of Apple Skin Color via Bagging Treatment

Approximately 73.6% of apple cultivars cultivated in China are derived from ‘Fuji’ bud sport clones, exhibiting novel traits such as spur-type growth habits, red peel color, and early maturation [37], with red peel color particularly favored by consumers [26]. To further enhance the red coloration of apple skin, a bud mutation exhibiting a significantly deeper red hue, along with its original cultivar, was selected for bagging treatment. Previous studies have demonstrated that bag removal intensifies fruit peel coloration, particularly in apples [38] and pears [26]. Consistent with prior research [24,38], our study revealed that bagging treatment notably increased redness values, resulting in a deeper red hue. Elevated redness values are generally attributed to higher anthocyanin content [24,39]. After bag removal, apples were re-exposed to sunlight, stimulating red pigmentation and deepening skin coloration, thereby underscoring the correlation between anthocyanin presence and red apple peel coloration [38,40]. Interestingly, despite significantly higher redness values in the Control-Bagging group compared to the Mt-NoBagging group, anthocyanin content was significantly lower in the Control-Bagging group. The literature indicates that fruit and flower coloration results from various pigments, including anthocyanins, chlorophyll, and carotenoids [41,42], and similar findings have been reported in other plant species. For instance, in Osmanthus fragrans ‘Ziyan Gongzhu’, chlorophyll content demonstrated a highly significant negative correlation with redness value. Notably, a higher chlorophyll content was associated with a lower redness value, even when anthocyanin levels were comparable. Furthermore, tissue with high anthocyanin and high chlorophyll exhibited a significantly lower redness value than tissue with lower anthocyanin and lower chlorophyll [43]. This suggests that elevated chlorophyll content may decrease the redness value, a phenomenon also observed in the present study. Consequently, the significantly higher chlorophyll content in the Mt-NoBagging group compared to the Control-Bagging group could explain the lower redness value observed in the Mt-NoBagging group. Chlorophyll accumulation is light-inducible [44], and the Mt-NoBagging and Control-NoBagging groups received prolonged sunlight exposure relative to the Mt-Bagging and Control-Bagging groups.
Notably, the observed phenotypic variation in apple skin color—influenced by both genetic background (bud mutation versus original cultivar) and environmental factors (bagging versus non-bagging, i.e., light exposure)—exemplifies a broader pattern of genotype × environment (G × E) interactions shaping phenotypic traits across crop species. Similar findings have been reported in saffron (Crocus sativus L.), with ecotype and specific cultivation conditions found to significantly impact vegetative characteristics [45]. In our study, the pronounced differences in redness and pigment content between the bud mutation (Mt) and the control (original cultivar) under both bagged and non-bagged conditions further corroborate that genetic background establishes the baseline of phenotypic expression, while environmental factors such as light modulate the extent of trait manifestation. Moreover, epigenetic mechanisms, including DNA methylation, have been proposed as a potential pathway linking environmental cues to gene regulation and phenotypic outcomes [46].

4.2. Altering DNA Methylation-Mediated Expression of Anthocyanin Biosynthesis Genes Through Bagging Treatment

Emerging research underscores the critical role of DNA methylation in regulating various fruit traits. Studies have implicated DNA methylation in the pigmentation of apples [38] and pears [26], as well as in apple flowering [47]. Furthermore, bagging treatment has proven effective in promoting fruit peel [48] and flesh [49] pigmentation in diverse species. In the present study, samples were collected for DNA methylation sequencing and subsequent analysis one month following the cessation of bagging treatment, a time point associated with optimal apple skin pigmentation [38]. Significant differences in DNA methylation were detected across four pairwise comparisons: Mt-Bagging versus Control-Bagging, Control-Bagging versus Control-NoBagging, Mt-Bagging versus Mt-NoBagging, and Mt-NoBagging versus Control-NoBagging. Integrated analysis of differentially methylated regions (DMRs) and KEGG pathway enrichment revealed five genes central to anthocyanin biosynthesis, with DMRs situated within their promoter regions. These genes—specifically C4H1, C4H3, and C4HL (upstream), as well as ANS1 and ANS2 (downstream) [50]—are consequently considered candidate genes responsible for the discrepancies in anthocyanin content and pigmentation observed among the experimental groups.
At 0 days and 4 days post-bag removal, the Mt-Bagging and Control-Bagging groups exhibited significantly lower anthocyanin levels than the Mt-NoBagging and Control-NoBagging groups. This correlated with higher C4H3 and ANS2 expression in the Mt-NoBagging and Control-NoBagging groups, a response attributed to the continuous sunlight exposure during unbagging treatments [51]. By 8 days post-bag removal, anthocyanin content in the Mt-Bagging and Control-Bagging groups increased substantially, though through distinct mechanisms. In the Mt-Bagging group, elevated expression of C4H3, C4HL, ANS1, and ANS2 drove accumulation. In contrast, the increased anthocyanin in the Control-Bagging group was primarily mediated by the induction of C4HL and ANS2. From 15 to 30 days, sustained high ANS1 expression in the Mt-Bagging group led to deeper coloration, whereas the Control-Bagging group relied on both ANS1 and C4HL. In the Mt-NoBagging group, steady C4H3 expression from 0 to 20 days, coupled with ANS1 upregulation, maintained higher anthocyanin levels. Differential gene expression, modulated by DNA methylation, likely represents the primary determinant of the observed pigmentation differences.
Interestingly, prior studies report similar outcomes, with bagging treatment enhancing anthocyanin accumulation and red coloration in apple [38] and pear [28] skins. Previous reports identified differential methylation of anthocyanin biosynthetic genes (e.g., UPGT, PAL, ANS) as key drivers after bagging treatment termination [26]. This is because post-bag removal, hypomethylated regions in these genes caused increased relative expression, effectively explaining the significant negative correlation between relative gene expression and methylation levels found in this study. Our findings align with these results, emphasizing the direct impact of structural gene methylation on pigmentation mediated by bagging treatment.

5. Conclusions

This study reveals pronounced DNA methylation variations between bagging treatment groups and cultivars, with differentially methylated regions (DMRs) occurring in the promoter regions of anthocyanin biosynthesis genes (C4H1, C4H3, C4HL, ANS1, and ANS2), suggesting epigenetic regulation of pigmentation. qPCR analysis showed elevated ANS1, C4HL, and C4H3 expression in the Mt-Bagging treatment group post-bag removal, linking methylation patterns to anthocyanin accumulation. These findings demonstrate that methylation divergence could directly impact functional genes in anthocyanin pathways. This research advances our understanding of epigenetic mechanisms in apple pigmentation post-bag removal and provides a framework for studying environmental cues, methylation dynamics, and metabolic flux in plants.

Author Contributions

Conceptualization, Y.L. and W.L.; methodology, Y.L. and J.S.; software, Y.L.; validation, Y.L., J.S., and W.L.; formal analysis, Y.L.; investigation, Y.L. and J.S.; resources, W.L.; data curation, J.S.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L. and W.L.; visualization, Y.L.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project “Breeding of Drought-resistant Apple Rootstocks and New Cultivars” (Major Science and Technology Project of Xinjiang Uygur Autonomous Region), Grant No. 2022A02033-1.

Data Availability Statement

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) of the National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA025457). They are publicly accessible at https://ngdc.cncb.ac.cn/gsa (accessed on 9 May 2025).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Specificity analysis of five gene primers.
Figure 1. Specificity analysis of five gene primers.
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Figure 2. Peel color change in different treatment groups after bag removal.
Figure 2. Peel color change in different treatment groups after bag removal.
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Figure 3. Comparison of peel color phenotype parameters under different treatments. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at a 5% significance level are indicated by lowercase letters for comparisons within the same period between the control and treatment groups. All experiments were performed in triplicate. Means sharing the same lowercase letter do not differ significantly.
Figure 3. Comparison of peel color phenotype parameters under different treatments. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at a 5% significance level are indicated by lowercase letters for comparisons within the same period between the control and treatment groups. All experiments were performed in triplicate. Means sharing the same lowercase letter do not differ significantly.
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Figure 4. Comparison of anthocyanin (a) and chlorophyll (b) content under different treatments after bag removal. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at 5% significance are indicated by lowercase letters for comparisons in the same period between the control and treatment groups. All experiments were performed in triplicate. The data are presented as mean ± SEM. Means followed by the same letter do not differ significantly.
Figure 4. Comparison of anthocyanin (a) and chlorophyll (b) content under different treatments after bag removal. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at 5% significance are indicated by lowercase letters for comparisons in the same period between the control and treatment groups. All experiments were performed in triplicate. The data are presented as mean ± SEM. Means followed by the same letter do not differ significantly.
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Figure 5. Methylation level of the whole genome. (a) The proportion of C sites under different treatments; (b) the proportion of methylated C sites in the CHG context; (c) the proportion of methylated C sites in the CG context; (d) the proportion of methylated C sites in the CHH context. A t-test was performed between two treatments within the same index.
Figure 5. Methylation level of the whole genome. (a) The proportion of C sites under different treatments; (b) the proportion of methylated C sites in the CHG context; (c) the proportion of methylated C sites in the CG context; (d) the proportion of methylated C sites in the CHH context. A t-test was performed between two treatments within the same index.
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Figure 6. Comparative analysis of DNA methylation levels in the genomic region. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 6. Comparative analysis of DNA methylation levels in the genomic region. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
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Figure 7. Comparative analysis of DNA methylation levels in the genic region. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 7. Comparative analysis of DNA methylation levels in the genic region. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
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Figure 8. Gene numbers of DMRs and promoter numbers of DMRs. (a) Gene numbers of DMRs that are overlapped or unique in different comparison groups. (b) Promoter numbers of DMRs that are overlapped or unique in different comparison groups.
Figure 8. Gene numbers of DMRs and promoter numbers of DMRs. (a) Gene numbers of DMRs that are overlapped or unique in different comparison groups. (b) Promoter numbers of DMRs that are overlapped or unique in different comparison groups.
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Figure 9. GO enrichment analysis of hyper-DMR-related genes of four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 9. GO enrichment analysis of hyper-DMR-related genes of four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
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Figure 10. GO enrichment analysis of hypo-DMR-related genes of four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 10. GO enrichment analysis of hypo-DMR-related genes of four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Horticulturae 12 00219 g010
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Figure 11. KEGG enrichment analysis of hyper-DMR-related genes in four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 11. KEGG enrichment analysis of hyper-DMR-related genes in four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
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Horticulturae 12 00219 g012
Figure 12. KEGG enrichment analysis of hypo-DMR-related genes in four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
Figure 12. KEGG enrichment analysis of hypo-DMR-related genes in four comparison groups. (a) Mt-Bagging versus Mt-NoBagging, (b) Control-Bagging versus Control-NoBagging, (c) Mt-Bagging versus Control-Bagging, (d) Mt-NoBagging versus Control-NoBagging.
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Figure 13. Relative expression of candidate genes in four treatments. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at 5% significance are indicated by lowercase letters for comparisons in the same period between the control and treatment groups. All experiments were performed in triplicate. Data are presented as mean ± SEM. Means followed by the same letter do not differ significantly.
Figure 13. Relative expression of candidate genes in four treatments. One-way ANOVA and Tukey’s multiple-range test were performed on the same date for the four treatments, and the least significant range analysis results at 5% significance are indicated by lowercase letters for comparisons in the same period between the control and treatment groups. All experiments were performed in triplicate. Data are presented as mean ± SEM. Means followed by the same letter do not differ significantly.
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Table 1. Primers used for qPCR analysis.
Table 1. Primers used for qPCR analysis.
PrimerSequence (5′-3′)
ANS2 FCTGGTAAGATTCAAGGCTATGGA
ANS2 RAGTCCACCAACTTCTTTCTCCA
ANS1 FCTCTGACGAGCTCATGGACAAG
ANS1 RGTAATCAGCAGGTGTTTGAGGC
C4H1 FGAAACGTCGTCTTTGATATCTTCAC
C4H1 RGGATAAAATCGCCGTAGTTGTAGT
C4H3 FCGTCGTCGTTGATATCCTCAAC
C4H3 RCGATCGAACATGATTCTGTATACG
C4HL FACCCGAAACGTCGTCTTTG
C4HL RCGGTCGAACATGATTCTGTACAT
Actin FGGATTTGCTGGTGATGATGCT
Actin RAGTTGCTCACTATGCCGTGC
Table 2. Repeated-measures analysis of fruit peel phenotypes.
Table 2. Repeated-measures analysis of fruit peel phenotypes.
Factor AnthocyaninChlorophyllRednessYellownessChromaLightnessHue Angle
GroupWilk’s lambda<0.01<0.01<0.01<0.01<0.01<0.01<0.01
F815.996132.00779.31227.0294.49342.74480.73
P<0.01<0.01<0.01<0.01<0.01<0.01<0.01
TimeWilk’s lambda<0.01<0.01<0.01<0.010.01<0.01<0.01
F359.1130.94390.20299.00148.09174.34714.92
P<0.01<0.01<0.01<0.01<0.01<0.01<0.01
group × timeWilk’s lambda<0.01<0.01<0.01<0.01<0.01<0.01<0.01
F32.1813.6634.5111.4121.6010.4536.96
P<0.01<0.010.01<0.01<0.01<0.01<0.01
Table 3. Results of read alignment with the reference genome.
Table 3. Results of read alignment with the reference genome.
SamplesRaw ReadsClean ReadsMapped ReadsMapping Rate (%)Duplication Rate (%)Q30%Sequencing DepthBS Conversion Rate (%)
Mt-Bagging-171,308,95068,045,01748,081,55970.668.3089.4213.0699.64
Mt-Bagging-282,690,87580,140,38858,211,19372.648.7390.5715.1199.63
Mt-Bagging-395,439,38692,125,84467,331,68073.098.5489.9517.2799.68
Control-Bagging-172,742,12270,625,62953,674,88376.009.3990.4114.3999.64
Control-Bagging-271,461,47069,150,79453,104,65276.808.9089.8113.5699.67
Control-Bagging-375,519,11672,248,48553,210,90973.658.9789.0814.1699.65
Mt-NoBagging-173,295,95969,978,50149,594,99970.878.9688.8313.0099.70
Mt-NoBagging-284,720,43382,701,06859,958,15772.509.4791.0615.6499.67
Mt-NoBagging-391,389,62489,107,29264,912,10972.859.3290.8516.8199.61
Control-NoBagging-175,023,84073,831,66454,524,87373.859.3891.5713.4499.71
Control-NoBagging-273,247,07872,084,50052,666,24873.069.2091.6913.5299.69
Control-NoBagging-3121,565,818119,543,35988,370,47873.929.1891.2420.4299.70
Table 4. Pearson correlation analysis of log2FC in gene expression and methylation differences in corresponding genes. *: p < 0.05; **: p < 0.01.
Table 4. Pearson correlation analysis of log2FC in gene expression and methylation differences in corresponding genes. *: p < 0.05; **: p < 0.01.
mC4H3mANS1mC4HLmC4H1mANS2
qC4H3−0.988 *−0.161−0.365−0.982 *0.536
0.0120.8390.6350.0180.464
qANS1−0.172−0.971 *0.9250.0490.85
0.8280.0290.0750.9510.15
qC4HL0.0870.955 *−0.954 *−0.133−0.802
0.9130.0450.0460.8670.198
qC4H1−0.995 **−0.214−0.313−0.977 *0.582
0.0050.7860.6870.0230.418
qANS20.6520.909−0.60.461−0.998 **
0.3480.0910.40.5390.002
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Liu, Y.; Su, J.; Li, W. The Effect of DNA Methylation on the Depth of Peel Color in ‘Red Fuji’. Horticulturae 2026, 12, 219. https://doi.org/10.3390/horticulturae12020219

AMA Style

Liu Y, Su J, Li W. The Effect of DNA Methylation on the Depth of Peel Color in ‘Red Fuji’. Horticulturae. 2026; 12(2):219. https://doi.org/10.3390/horticulturae12020219

Chicago/Turabian Style

Liu, Yucheng, Jingyi Su, and Wensheng Li. 2026. "The Effect of DNA Methylation on the Depth of Peel Color in ‘Red Fuji’" Horticulturae 12, no. 2: 219. https://doi.org/10.3390/horticulturae12020219

APA Style

Liu, Y., Su, J., & Li, W. (2026). The Effect of DNA Methylation on the Depth of Peel Color in ‘Red Fuji’. Horticulturae, 12(2), 219. https://doi.org/10.3390/horticulturae12020219

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