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Article

MicroRNA-Mediated Changes in DNA Methylation Affect the Expression of Genes Involved in the Thickness-of-Pod-Canopy Trait in Brassica napus L.

by
Ledong Jia
1,2,†,
Lu Cao
3,†,
Lijun Zeng
1,
Kun Lu
1,2,4,
Cunmin Qu
1,2,4,
Jiana Li
1,2,4 and
Zhiyou Chen
1,2,*
1
Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
2
Chongqing Engineering Research Center for Rapeseed, Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
3
Neijiang Animal and Plant Disease Control and Agricultural Product Quality Detecting Center, Neijiang 641000, China
4
Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this article.
Agronomy 2025, 15(2), 398; https://doi.org/10.3390/agronomy15020398
Submission received: 28 December 2024 / Revised: 28 January 2025 / Accepted: 31 January 2025 / Published: 2 February 2025
(This article belongs to the Section Crop Breeding and Genetics)
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Abstract

:
Methylation plays an important role in regulating crop development, but little is known about how methylation regulates plant architecture in rapeseed (Brassica napus L.). Here, we examined how methylation affects the thickness-of-pod-canopy (TPC) trait in rapeseed by performing genome-wide methylation analysis of two extreme TPC lines. In flower buds, 26 genes had significantly higher methylation levels in the high-TPC samples compared to the low-TPC samples, resulting in significantly reduced gene expression. By contrast, in the stem apex samples, the promoter regions of 22 genes were hypermethylated in the high- vs. low-TPC samples. The promoters of 19 and 21 genes had significantly reduced methylation levels in the flower bud and stem apex, respectively, of the high- vs. low-TPC samples, resulting in significantly higher expression levels. Some of these differentially expressed genes are associated with TPC-related traits, such as BnaC01g12960D (NRT1.8). In addition, 14 important genes related to growth and development were differentially regulated between the two groups due to miRNA-mediated differences in methylation levels in their promoters. For example, hypermethylation in the promoter region of BnaCnng64040D (Lipase family protein), mediated by miR159, led to significantly reduced gene expression in flower buds of high-TPC vs. low-TPC lines. These results, together with our previously generated RNA-seq and miRNA profiling data, indicate that both methylation and miRNAs are perhaps involved in regulating the expression of genes, thereby affecting the TPC trait in B. napus, providing a reference for uncovering the molecular mechanism regulating this crucial trait.

1. Introduction

Oilseed rape (B. napus, 2n = 38, AACC) is one of the most important oilseed crops worldwide. Yield improvement is an extremely important goal for all rapeseed breeders. Modifying plant type is the most important way to increase rapeseed yields. The thickness of pod canopy (TPC), i.e., the thickness of the plant canopy from the bottom-most effective (seed-bearing) pod to the uppermost effective pod, is a key trait that determines the three-dimensional structure of plants. TPC is directly related to other important traits, such as economic yield (EY, grain weight per plant), plant height (PH), pod terminal height (PTH), stem height (SH), first effective branch height (FEBH), the height of the lowest pod (HLP), the first effective branch number (FEBN), the main inflorescence effective length (MIEL), and the first ineffective branch number (FIBN) [1]. Hence, understanding the molecular mechanism of TPC could shed light on the formation of plant architecture and help breeders further increase production in B. napus.
In recent years, many studies have focused on traits in crops. PH is an important factor affecting rice yield. OsMPH1 improves grain yield by regulating PH in rice [2]. MiR319 expression induces dwarfism to suppress PH in rice [3]. A genome-wide association study (GWAS) of PH and primary branch number in rapeseed revealed eight PH-related quantitative trait loci (QTLs) on chromosomes A03, A05, A07, and C07 and five PB-related QTLs on chromosomes A01, A03, A07, and C07 [4]. A GWAS also identified four, five, and seven SNPs for FEBH, FEBN, and PH, respectively, in B. napus and uncovered many genes associated with these traits [5]. We recently reported that TPC is regulated by miRNAs, and identified many candidate genes for TPC based on RNA-Seq and miRNA profiling analyses [1]. Many genes involved in nitrogen-related responses were dramatically differentially expressed in high- vs. low-TPC lines, such as ASP5, ASP2, ASN3, ATCYSC1, PAL2, APT2, CRTISO, and COX15 [1], suggesting that nitrogen metabolism may play roles in regulating TPC in B. napus.
DNA methylation is a critical epigenetic modification in many plants. The methylation and demethylation of genes are dynamically regulated during plant growth [6]. Plant DNA is methylated in three sequence contexts: symmetrical mCG and mCHG, and asymmetrical mCHH (H = A, T, or C) [7,8]. Methylation is regulated and maintained by independent pathways that can be broadly classified into maintenance of methylation and de novo methylation pathways [6]. Due to their symmetrical nature, CG and CHG sites are substrates for maintenance methyltransferases, which recognize hemimethylated DNA after replication and methylate newly synthesized unmethylated strands [9]. De novo methyltransferases establish DNA methylation across all sequence contexts and are required for maintaining asymmetric (CHH) methylation.
Small RNAs and proteins related to modified histones affect DNA methylation [6]. DNA methylation and demethylation are distinct processes: the latter is regulated by 5-methylcytosine DNA glycosylase enzymes in plants [10]. 5-methylcytosine is removed from DNA by base excision repair, which is essential for the expression of imprinted genes and endosperm development [11].
In plants, DNA methylation plays a key role in the activation of maturation-inducing genes and the inhibition of maturation-inhibiting genes in tomato (Solanum lycopersicum) [12]. DNA methylation increases substantially during citrus (Citrus sp.) fruit development and maturation [13]. DNA methylation is closely related to the development of many grain crops. Genome-wide changes in DNA methylation are related to drought stress tolerance [14] and cadmium stress in plants [15]. Overall DNA methylation levels increase during somatic embryogenesis in soybean (Glycine max) [16]. Cytosine methylation plays a positive role in regulating isoflavone synthase gene expression and isoflavonoid biosynthesis in soybean seeds [17]. Low zinc levels lead to the loss of methylation in maize (Zea mays) roots; conversely, the loss of methylation in root cells leads to zinc deficiency [18]. DNA methylation changes dynamically in response to heat stress in maize seedlings [19]. Lead, cadmium, and zinc toxicity alter DNA methylation levels, thereby enhancing heavy metal tolerance in wheat (Triticum aestivum) [20]. Genome-wide DNA methylation profiling of flower buds revealed the role of DNA methylation in the molecular regulation of genic male sterility in B. napus [21]. Finally, short-term heat-shock treatment of cultured B. napus microspores led to global changes in DNA methylation, indicating that DNA methylation plays a key role in heat-stress responses [22].
Methylation also affects plant architecture. The proper H3K4me3 levels, which are regulated by COMPASS-like complexes, are critical for rice development and can affect flowering and branching [23]. In addition, changes in methylation at the rice (Oryza sativa) Fertilization Independent Endosperm 2 protein play important roles in regulating plant height and yield in rice [24]. Changes in methylation are also related to final plant height in Arabidopsis thaliana [25]. However, our understanding of the mechanisms underlying how methylation regulates plant-type traits in crops remains limited.
Small RNA-directed DNA methylation (RdDM) is a key regulatory pathway that affects numerous plant traits by altering the expression of various genes. The ARGONAUTE 4 (AGO4)-dependent RdDM pathway represses the expression of HOMOLOG OF RPW8 4 and alters the response to submergence in Arabidopsis thaliana [26]. RdDM inhibits the expression of APETALA3 (AP3); transgenic Arabidopsis plants harboring promoter lines showed abnormal stamens and petals [27]. The RdDM pathway is also involved in regulating the activity of the poly (A) polymerase PAPS1 in Arabidopsis, thereby affecting sporophyte and pollen development [28]. RdDM also regulates seed dormancy in plants [29]. This pathway regulates the expression of numerous genes in crops and affects the corresponding traits. ITRAQ (isobaric tags for relative and absolute quantitation) analysis of the leaf proteome indicated that the RdDM pathway plays a key role in defense against geminivirus-associated betasatellite infection in tobacco (Nicitiana tabacum) [30]. The reinforcement of DNA methylation at CHH sites regulated by the RdDM pathway leads to decreased gene expression, thereby influencing somatic embryogenesis in soybean [16]. However, the role of the RdDM pathway in regulating plant-type traits in crops has not been reported.
TPC is an important plant-type trait that is closely associated with various phenotypes. We recently identified many genes related to the formation of TPC and identified miRNAs that limit their expression in B. napus [1]. In the current study, we performed genome-wide methylation analysis of high- and low-TPC B. napus lines. The results of this study, combined with previously obtained RNA- and sRNA-sequencing data, shed light on the molecular mechanism of TPC in this important oilseed crop.

2. Materials and Methods

2.1. Plant Materials and Bisulfite Sequencing

We performed Whole Genome Bisulfite Sequencing (WGBS) analysis of plants cultivated at the Chongqing Rapeseed Engineering Research Center, Southwest University, Chongqing, China (106.40° E, 29.80° N). Seeds were sown on the seedbed in September every year, and seedings were transplanted in the field in October, with plants bolting and flowering in February and March of the following year, to be harvested in May. The field water and fertilizer management was carried out according to normal cultivation techniques. B. napus lines YC4 (SWU71, from Chongqing province of China) and YC33 (10-1047, from Hunan province of China) are two inbred lines, which showed relatively high TPC values (total average 102.1 cm) based on three years (2015/2016, 2016/2017, 2017/2018) of continuous observation [1], while YC11 (Zhongshuang11, from Hubei province of China) and YC15 (Zhongyou821, from Hubei province of China) showed relatively low TPC values (total average 69.4 cm) [1], and they are two widely planted rapeseed cultivars bred from the varieties (Zhongshuang 9/2F10)//26102 and Suzao 3× (Baiyou 1, Yunyou 7, Ganyou 1, Ganyou 3, 71-5), respectively, we therefore used these lines for comparative analyses. When the plants produced tiny flower buds before bolting (February 2018), tissue from the apical meristem and the surrounding young floral buds were collected as stem apex (S) and flower bud (F) samples: this represents the key stage for the formation of TPC (after this stage, the stem begins to elongate and pollen begins to develop). We collected at least five sets of samples per line, each set from a different individual. Samples were collected at approximately the same time, and all samples from each line were mixed into one pool.
Total DNA was isolated from the samples using a Rapid Plant Genomic DNA Isolation Kit (Sangon, Shanghai, China) according to the manufacturer’s protocols. Quality evaluations of the genomic DNA were monitored using gel electrophoresis. The genomic DNA spiked with lambda DNA was fragmented by sonication into 200–300 bp, followed by end repair and adenylation. Then, cytosine-methylated barcodes were ligated to sonicated DNA, and the DNA fragments were treated twice using bisulfite. After that, the resulting single-strand DNA fragments were amplified as a bisulfite DNA library. Preparation of the bisulfite DNA library and sequencing were carried out by Novogene Bioinformatics Institute (Beijing, China), and bisulfite sequencing was carried out on an Illumina HiSeq 4000 platform, generating 125 bp paired-end reads.

2.2. Quality Control

We preprocessed the reads (in FASTQ format) produced by Illumina sequencing using Trimmomatic (version 0.33) with the default parameters. The steps were as follows: first, reads containing adapter sequences were filtered out; second, reads with N (unknown bases) >10% were deleted; third, reads containing >50% low-quality bases (PHRED score ≤20) were removed. The Q20, Q30, and GC contents of the data were calculated at the same time. The remaining reads that passed all the filtering steps were counted as clean reads and used for subsequent analysis.

2.3. Aligning the Reads to the Reference Genome

Bismark software (version 0.12.5) was used to compare the bisulfite-treated reads to the reference genome using default values. The reference genome of ‘Darmor-bzh’ V5 (http://brassicadb.cn/#/Download/, accessed on 30 October 2020) was converted to a bisulfite-converted version (C-T and G-A conversion) and indexed by Bowtie2 (version 2.2.5). The reads were also converted into bisulfite-converted versions (C-T and G-A conversion) and directly compared with the converted reference genome. The reads were then compared with the normal genomic sequence to infer the methylation status of each cytosine position in the reads. Reads pairs with the same coordinates in the genome were treated as duplicates and deleted before the methylation status was determined to avoid potential calculation bias of the methylation level. The non-conversion rate of bisulfite was calculated as the percentage of sequenced cytosine to the reference cytosine in the lambda genome.

2.4. Analysis of Differentially Methylated Regions

Differentially methylated regions (DMRs) were identified using swDMR 1.0.7 software (https://sourceforge.net/projects/swdmr/, accessed on 25 February 2019) with the conditions read coverage ≥5, methylation level difference ≥0.1 or fold change ≥2, and corrected p-value < 0.01 using the sliding window method. The window size was set to 1000 bp and the step size to 100 bp. Fisher’s exact test was used to detect DMRs. Genes whose functional regions overlapped with DMRs by at least 1 bp were defined as DMR-associated genes (DMGs).

2.5. GO Analysis of DMGs

The DMGs, which were corrected for gene length deviations, were subjected to gene ontology (GO) analysis using the GOseq 1.30.0 R package. GO terms with corrected p-values < 0.05 were considered to be significantly enriched by the DMGs. All DMGs were annotated with BGI Web Gene Ontology Annotation plot (https://wego.genomics.cn/, accessed on 5 March 2019).

2.6. Data Verification Using Traditional Bisulfite Sequencing PCR

Traditional bisulfite sequencing PCR was used to verify the WGBS data. A total of 1 µg genomic DNA of each sample was subjected to sodium bisulfite using an EpiTect Bisulfite kit according to the manufacturer’s instructions (Imported from Qiagen, Hilden, Germany). The processed DNA was purified with a Qiagen PCR purification kit (Cat. No. 28106) and used as a template for bisulfate sequencing PCR (BS-PCR). The primers were designed using MethPrimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi, accessed on 10 June 2019). 2 × Taq Master Mix (Vazyme Biotech, Nanjing, China) was used for BS-PCR. The amplicon was cloned into pMD18-T (Takara Biotechnology, Dalian, China), and 10–14 positive clones per PCR product were sequenced by Biotechnology (Shanghai) Co., Ltd., Shanghai, China. The sequencing results were processed using the Seqman 14.2 program of the DNASTAR Lasergene 14 software package to remove the vector and primer sequences and analyzed on the QUMA website (http://quma.cdb.riken.jp/, accessed on 16 July 2019) [31]. BnaIND.a-A3 was used to determine the conversion efficiency of bisulfite [32].

3. Results

3.1. Analysis of Clean Reads

We trimmed off the sequencing adapters and low-quality fragments from the raw sequencing data using Trimmomatic-0.33 software (http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.33.zip, accessed on 20 January 2019), and the results are shown in Table S1. A comparison of the clean reads with the reference genome showed that the number of unique reads accounted for ~50% of the total, and the ratio of repetitive sequences in the reads to total reads was ~10% (Table S2).
To fully detect the methylation of a genome, we used a sequencing depth of 30× to perform methylation sequencing of all samples and to calculate the coverage of each single-base site in the genome (that is, the number of reads that support that site). The coverage statistics and distribution diagrams are shown in Table S3 and Figure S1, respectively.
The coverage level of the C site is an important indicator of the sequencing depth in the methylation test. We calculated the coverage of the C site separately, as well as the coverage of the C site under each context (CpG, CHH, CHG) (that is, the number of reads that support that context). The results and cumulative distribution diagram are shown in Table S4 and Figure S2, respectively. Among the three methylation environments (CG, CHG, and CHH), CG had the highest coverage (47.99–51.59%), followed by CHG (18.36–24.75%) and CHH (2.34–4.59%).

3.2. Types and Distribution of Methylation in the High- and Low-TPC Lines

We performed statistical analysis of the methylated C sites in three sequence environments (CG, CHH, CHG, where H stands for A, C, or T) in the two plant parts (young buds and stem apex) for the two sets of samples: high-TPC (YC4, YC33) and low-TPC (YC11, YC15) lines (Table 1 and Figure S3). The percentage of methylated C sites in the three sequence regions of all samples represents the percentage of the total number of C sites in that region. The percentages of CG sites were the highest (33.32–37.32%), followed by CHG (21.05–26.69%) and CHH (2.88–6.52%). The overall average level of methylation of the three sequences in all samples (from high to low) was CG (43.99%), CHG (29.09%), and CHH (26.92%).
The average methylation levels of mC (10.96%), mCpG (35.37%), mCHG (23.68%), and mCHH (3.34%) sites in young buds were higher in the high-TCP lines than in the low-TPC lines (10.42%, 34.65%, 21.30% and 3.21%, respectively) (Table 1). In the stem apex, although the methylation level at the mCHG sites was higher in the high-TPC lines (24.99%) than in the low-TPC lines (23.62%), the methylation levels at the mC, mCpG, and mCHH sites were lower in the high-TPC lines (12.94%, 35.54%, and 5.82%, respectively) than in the low-TPC lines (12.96%, 36.48%, and 5.94%, respectively) (Table 1). A comparison of the average methylation levels of the different plant parts in all samples showed that the methylation levels at mC, mCpG, mCHG, and mCHH sites were lower in young buds (10.69%, 35.01%, 22.49%, and 3.28%, respectively) than in the stem apex (12.95%, 36.01%, 24.30%, and 5.88%, respectively) (Table 1). An analysis of the methylation level of each chromosome in each sample showed that the ratios of methylated C sites to total methylated C sites on the chromosome in different sequence environments were consistent across all materials: CG (with red color) had the highest ratio, followed by CHG (with green color) and CHH (with blue color) (Figure 1).

3.3. Methylation Density on Chromosomes and Distribution of Methylation Levels in Genes

B. napus is a heterotetraploid with two sets of chromosomes (A and C). Circos diagrams are commonly used to show the distribution of methylation density on chromosomes [33,34]. There were significant differences in the distribution of methylation density on C vs. A chromosomes in all samples. Even though A chromosomes contain more genes than C chromosomes, the methylation density of C chromosomes was generally higher than that of A chromosomes (Figure S4).
We analyzed the average methylation levels of C sites in the CG, CHG, and CHH contexts in various functional genomic regions (such as promoter, exon, intron, 5 UTR, 3 UTR, and so on). Here, the 2 kb region upstream of the transcription start site (TSS) was considered to be the promoter region. The distribution of the average methylation levels in the functional elements of the genes is shown in Figure 2. In both the high- and low-TPC lines, the results were consistent in all contexts. The promoter regions had the highest methylation levels, followed by introns. We also analyzed the methylation levels in the upstream and downstream regions of each gene in each sample. Specifically, we calculated the average methylation level of the C site in each gene body, 2 kb upstream of the TSS, and 2 kb downstream of the transcription termination site in each context; the results are shown in Figure S5. The average methylation levels were higher in the regions 2 kb upstream and downstream of the TSS than in the gene body regions in all samples.

3.4. Comparison of the Overall Methylation Levels of High- and Low-TPC Lines

We constructed a Circos diagram to display differences in the methylation levels in different samples from the high- vs. low-TPC lines [33,35]. We detected significant differences in the overall methylation levels of multiple chromosomes between the two groups of lines (Figure S6A–C). An analysis of the differences in methylation levels in different gene functional regions in the high- and low-TPC lines revealed significant differences in the average methylation levels of C sites under all three contexts (CG, CHG, and CHH) between groups, especially in the promoter region (2 kb upstream of the TSS) (Figure S6D–F). We compared the methylation levels of the 2 kb upstream and downstream regions of genes between the high- and low-TPC lines at C sites under all three contexts and detected significant differences in both stems and flower buds (Figure S6G–I).
We also examined the differences in overall methylation levels between stem and flower buds in both groups. The C methylation level of CG showed the greatest difference between stems and young buds, followed by CHG. By contrast, for all samples, there was no significant difference in the level of C methylation in the CHH context between stems and young buds (Figure 3).

3.5. DMR Analysis of High- vs. Low-TPC Lines

We used DSS-Single software of R Package to identify DMRs between the high- and low-TPC lines [36,37,38]. We generated a Circos diagram to visualize the distribution of DMRs in the genome and the results of mapping analysis. We detected numerous DMRs in all three sequence contexts (CG, CHG, and CHH) between the two sets of lines in both the stem apex and flower buds (Figure S7). All comparisons generated consistent results. The most significant DMRs included both hypermethylated and hypomethylated regions in the CG sequence context (Figure S7).
We performed statistical mapping of the DMR anchoring areas (such as the promoter, exon, intron, CGI, CGI shore, repeat, TSS, and TES regions) to distinguish hypermethylated vs. hypomethylated DMRs (Figure 4). We detected both hypermethylated and hypomethylated DMRs in all functional elements in both the high- and low-TPC lines in the stem apex and flower bud. Notably, there were many DMRs in the promoter regions of all samples, including the CG, CHG, and CHH contexts.

3.6. DMR Analysis of the Promoters of High- vs. Low-TPC Lines

Methylation of the promoters of plant genes plays an important role in regulating gene expression, thus affecting plant traits [39]. Therefore, we identified genes with significantly different methylation levels in their promoter regions. In flower buds, 367 genes were hypermethylated and 531 genes were hypomethylated in the promoter regions of the high-TPC lines (Figure 5A,B). In the stem apex, 427 genes were hypermethylated and 547 genes were hypomethylated in the promoter regions in the high-TPC lines compared to the low-TPC lines (Figure 5C,D). Compared to the low-TPC lines, 275 genes were hypermethylated and 376 genes were hypomethylated in the promoter regions in both the stem apex and flower buds (Figure 5E). In addition, for a few genes, the promoter regions were significantly hypermethylated or hypomethylated (Figure 5E).

3.7. DMRs in Promoters Underlie Differences in Gene Expression Between High- and Low-TPC Lines

Increased methylation in the promoter region of a gene inhibits gene expression, while demethylation in the promoter region of a gene promotes gene expression [39]. We combined the current results with our previously reported transcriptome data [1] and identified 26 genes in the high-TPC materials that were significantly hypermethylated in their promoter regions compared to the low-TPC materials in B. napus flower buds, resulting in significant decreases in their expression (Figure 5F and Table S5). We also identified 22 genes that were hypermethylated in their promoter regions in the high- vs. low-TPC materials in the stem apex, resulting in significant decreases in their expression (Figure 5F and Table S5). Finally, the methylation levels of the promoters of 19 and 21 genes were significantly reduced in the flower bud and stem apex, respectively, in high- vs. low-TPC materials, which led to significant increases in their expression (Figure 5G and Table S5).
Therefore, the promoters of many genes are affected by hypermethylation or demethylation in the high- vs. low-TPC materials, leading to significant differences in gene expression. Some of these genes are related to the TPC trait. For example, in both the stem apex and flower buds of the high-TPC lines, the promoter regions of BnaC03g53050D (UBC32), BnaA05g26660D (CYSB), and BnaA10g07880D (TCP1) had low methylation levels in the high-TPC lines, and they were expressed at significantly higher levels in the high-TPC vs. low TPC-lines. The promoters of BnaC03g70780D (auxin associated family protein), BnaC05g36710D (myb family transcription factor), BnaAnng09670D (SMP1), BnaA09g02000D (SDH2-2), and BnaC01g12960D (NRT1.8) were hypermethylated in the high- vs. low-TPC lines, leading to significantly lower expression levels in both buds and the stem apex. The promoter region of BnaC09g30490D (TAF15b) was hypomethylated in the flower buds of the high- vs. low-TPC lines, leading to higher expression in this tissue.

3.8. GO and KEGG Analysis of Differentially Methylated Genes

To investigate the functions of genes whose promoter regions were differentially regulated by methylation and were differentially expressed between the high- and low-TPC lines, we performed GO and KEGG analysis of these 52 genes (Figure 6). The significantly enriched GO terms are involved in nitrogen metabolism, biosynthesis regulation, cytoskeleton composition, and so on (Figure 6A). KEGG analysis showed that the significantly enriched pathways mainly involve energy-related processes such as carbon metabolism (Figure 6B).

3.9. MiRNAs and Methylation Jointly Regulate the TPC Trait

To examine whether miRNAs and methylation are both involved in regulating the expression of genes involved in the TPC trait, a joint analysis of transcriptome, methylation, and miRNA data was performed for the high- vs. low-TPC samples. The promoter regions of 14 important genes related to growth and development were differentially methylated and their expression regulated by miRNAs, resulting in significant differences in gene expression between the two groups (Table 2). For example, the expression of BnaCnng64040D (Lipase family protein) is regulated by miR159, and hypermethylation in its promoter region led to a significant decrease in gene expression in the flower buds of high- vs. low-TPC samples. The expression of BnaC09g30490D (TAF15b) and BnaC03g09180D is regulated by miR167 and miR827, respectively, and the loss of methylation in their promoter regions resulted in a significant increase in their expression in the flower buds of the high-TPC lines. Both BnaC02g22120D (Spc97 family of spindle pole body component) and BnaA07g07800D are regulated by miR319. Increased promoter methylation led to a significant decrease in the expression of these genes in the stem apex of the high-TPC lines. BnaA04g14930D (S-locus lectin protein kinase family protein) and BnaCnng56050D (Pyridoxal phosphate-dependent Transferases superfamily protein) are regulated by miR159, miR319, miR122, and so on. The loss of methylation in the promoters of these genes led to a significant increase in gene expression in the stem apex and flower buds of the high-TPC lines.

3.10. Verification of the Methylation Sequencing Results

The correlation of methylation levels between samples is an important indicator of the reliability of an experiment and whether the sample selection is reasonable. The closer the correlation coefficient is to 1, the higher the similarity of the methylation patterns between samples. We used the 2 kb/bin sub-sequence environment to calculate the methylation level in each bin and performed Pearson correlation analysis of the data [40]. The correlation coefficients of the CG, CHG, and CHH methylation levels between the stem apex and flower buds in the four high- and low-TPC lines were very high (Figure S8). This finding indicates that the overall differences in methylation between the two plants parts were not significant and that the sequencing results are reliable.
To further verify the reliability of the methylation sequencing results, we used a DNA Bisulfite Conversion Kit (Tiangen, Beijing, China) to process the same samples from the WGBS experiment (we randomly selected YC4F and YC11F). We amplified segments of the differentially methylated regions of 20 gene promoters by PCR, followed by sequencing after TA cloning. We compared the sequencing results with the previously generated high-throughput sequencing data. The promoter regions of all 20 genes showed the same methylation patterns in both sets of data (Table 3). Therefore, the results were reliable and could be used for further analysis.

4. Discussion

Cytosine DNA methylation is a chemical modification that produces 5-mC, also known as the fifth base of DNA. In plants, DNA methylation occurs in three different sequence environments: symmetric CG and CHG and asymmetric CHH, where H stands for C, A, or T. These DNA methylation patterns are stably inherited through cell division. Changes in DNA methylation can occur spontaneously or be induced by genetic factors and environmental stimuli. High-throughput DNA sequencing is used for single-base resolution transcriptome and epigenome analyses, as well as genome resequencing [41]. The integration of these omics-based data has shed light on the biological roles of the epigenome [42,43]. There is considerable intra- and inter-species variation in DNA methylation patterns. The analysis of this type of data is not limited to model plant species with compact genomes, but instead extends to important agronomic crops with large, complex genomes [34,44,45,46,47]. Natural genomic variations, such as single-nucleotide mutations and structural variations, have been used at tools in plant breeding [34,47].
In the current study, in order to minimize the impact of environmental factors on the plant/pod morphology and gene expression as much as possible, four high- and low-TPC B. napus lines were cultivated for three consecutive years at same place. Meanwhile, the planting environment, planting and field water management, and fertilizer management methods were the same. On the other hand, the sampling methods were ensured to be consistent as much as possible, for instance, with regard to the sampling period, sampling tissues and sampling method. We determined that the average methylation levels of all B. napus samples (from high to low) were mCG (43.99%), mCHG (29.09%), and mCHH (26.92%). A recent study indicated that a B. napus male sterile line (7365A) and a restorer line (7365B) showed completely different methylation patterns [21]. There was no significant difference in the methylation levels of CG, CHG, and CHH between the sterile line and the restorer line, but for both lines, the overall methylation level was mCHH > mCG > mCHG. This finding indicates that changes in methylation patterns are strongly related to the fertility of B. napus.
In the current study, all of the B. napus samples used for sequencing showed the same trend: the methylation levels of all three sequence contexts were significantly higher on C chromosomes than that on A chromosomes. This result is consistent with previous studies [21,48]. The A and C chromosomes of B. napus come from Chinese cabbage (Brassica rapa) and Brassica oleracea, respectively [48,49,50]. Based on separate studies, the overall methylation level of the B. oleracea genome is significantly higher than that of B. rapa [51,52]. Together, these findings further support the notion that B. napus evolved from the hybridization of B. rapa and B. oleracea and that the methylation patterns of parental species B. rapa and B. oleracea were stably inherited by their offspring, B. napus.
Through joint analysis of methylation and transcriptome data, we determined that the promoter regions of many important genes are hypermethylated or demethylated, which affects the differential expression of the genes between the high- and low-TPC materials. Due to hypermethylation in the promoter region of SDH2-2, the expression of this gene was reduced in both the stem apex and buds of the high-TPC lines. Indeed, increased methylation in the promoter region affects the expression of SDH2-2 in maize seeds, thereby affecting the glyoxylic acid cycle and seed germination [53]. Therefore, the differential methylation of the SDH2-2 promoter region causes significant differences in gene expression between the high- and low-TPC materials, thereby affecting the glyoxylic acid cycle, leading to differences in the characteristics of the two groups of materials.
NRT1.8 is hypermethylated in both the stem apex and buds of the high TPC lines, leading to significantly reduced gene expression. NRT1.8 affects nitrogen use efficiency in B. napus, and nitrogen has major effects on TPC-related traits [54,55]. The loss of methylation in the promoter regions of CYSB and UBC32 cause these genes to be expressed at high levels in the high-TPC lines. CYSB is involved in the nitrogen stress response in spinach [56]. The Arabidopsis ubiquitin-conjugating enzyme UBC32 is an ERAD element that plays an important role in brassinolide-mediated plant growth and salt stress tolerance [57]. In the flower buds of the high-TPC materials, the TAF15b promoter was hypomethylated, leading to significantly higher gene expression compared to the low-TPC lines. TAF15b participates in the autonomous pathway of flowers, inhibits the transcription of FLOWERING LOCUS C, and regulates the development of floral organs [58,59]. Floral organ development plays an important role in silique development, which in turn is important for determining the TPC trait.
RdDM is an important epigenetic pathway that affects many traits in plants [60,61]. MicroRNA-mediated methylation is widely involved in regulating gene function in both animals and plants. In humans, miR-29 targets DNA demethylase genes, and its activity is mediated by members of the ten eleven translocation (TET) family, which may cause the dysregulation of genes involved in key cell functions, leading to lung cancer [62]. MiR-140-5p regulates T cell differentiation and reduces autoimmune encephalomyelitis by affecting CD4+ T cell metabolism and DNA methylation [63]. miR2936 and miR398-mediated DNA methylation affect respiratory energy metabolism in Arabidopsis by regulating the expression of AGO1 and AGO4 [26]. Floral organ development plays a vital role in plant reproduction and the TPC trait. The RdDM pathway mediated by microRNAs regulates the poly (A) polymerase gene PAPS1 in Arabidopsis, thus affecting both sporophyte and pollen development [28]. Therefore, miRNA-mediated DNA methylation plays an important role in the normal growth and development of animals and plants.
Here, we compared genome-wide methylation, transcriptome, and miRNA data and found that many gene promoter regions in the high- and low-TPC materials were jointly regulated by miRNAs and methylation, leading to significant differential gene expression. These genes are closely related to the TPC trait. For example, the BnaCnng64040D (Lipase family protein) promoter is regulated by miR159b and is hypermethylated in flower buds of the high-TPC materials, leading to significantly reduced expression. Lipase family protein plays an important role in regulating lipid metabolism and oil content of B. napus [64]. The promoter region of BnaC09g30490D (TAF15b), which affects organ development, is regulated by miR167c and undergoes demethylation in the high-TPC materials, leading to significantly higher expression in flower buds compared to the low-TPC materials. The promoter region of BnaC04g55380D (MAP65-4) is regulated by miR6029/miR9049 and is hypermethylated, which may affect the formation of the cytoskeleton in B. napus [65]. In fact, we found that together with differences in methylation, many miRNAs that are differentially expressed between high- and low-TPC lines are closely related to the formation of TPC, such as miR159, miR827, miR319, and so on [1].
Finally, GO and KEGG analysis of genes that are regulated by methylation in their promoter regions and are differentially expressed in high- vs. low-TPC materials primarily function in metabolic processes such as carbon and nitrogen metabolism. We previously reported that miRNAs are involved in the differential expression of genes between high- and low-TPC lines. GO and KEGG analyses revealed that these genes are also mainly involved in carbon and nitrogen metabolism [1]. Therefore, the results obtained in the current study are consistent with previous results. These findings indicate that the TPC trait is simultaneously regulated by miRNAs and methylation and may be related to carbon and nitrogen metabolism processes.
DNA methylation is an epigenetic modification that affects gene expression and transposable element activity. Because cytosine DNA methylation patterns are inherited through mitotic and meiotic cell division, differences in these patterns may lead to phenotypic variation. Advances in high-throughput sequencing technology have led to the generation of abundant DNA sequence data. The comprehensive analysis of genome-wide gene expression and DNA methylation patterns has revealed the underlying mechanisms and functions of DNA methylation. In addition, various associations between DNA methylation and agronomic traits have been uncovered [66]. The information obtained from this study could be used for crop breeding based on natural epigenomic variations in the future. In addition, this study will provide important genetic resources for future B. napus breeding programs, and artificial epigenome editing based on the results of this study could be used to produce new crop varieties with improved agronomic traits.

5. Conclusions

Significant differences were detected in overall methylation levels between the high- and low-TPC lines in the CG, CHG, and CHH contexts in the promoters of genes in the stem apex and flower buds. In addition, 14 important genes related to growth and development were differentially regulated between the two groups due to miRNA-mediated differences in methylation levels in their promoters. These results, together with our previously generated RNA-seq and miRNA profiling data, indicate that both methylation and miRNAs are perhaps involved in regulating the expression of genes affecting the TPC trait in B. napus.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15020398/s1: Figure S1: Genomic coverage map of all samples; Figure S2: Partial cumulative distribution map of C-site coverage; Figure S3: Proportional distribution map of methylation C site; Figure S4: Circos diagram of chromosome methylation density; Figure S5: The distribution of sample methylation levels 2K upstream and downstream of the genebody; Figure S6: Comparison of the overall level of methylation between the two groups of materials; Figure S7: The overall display of the three sequence environment (CG/CHG/CHH) DMR; Figure S8: Correlation analysis of three methylation sequence contexts of each sample; Table S1: Raw data quality control statistics; Table S2: Comparison list of reads and reference genome; Table S3: Statistics list of genomic coverage; Table S4: Statistics on methylation status of C site; Table S5: DEGs with hypermethylation or hypomethylation in promoter region.

Author Contributions

Conceptualization, Project administration and Funding acquisition, J.L. and Z.C.; Visualization, L.J. and K.L.; Data curation, L.C. and L.Z.; Formal analysis, L.J., L.C. and K.L.; Writing—original draft, L.J. and Z.C.; Investigation, L.Z.; Methodology and Supervision, C.Q.; Writing—review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Horizontal Scientific Research Project (M2024025), National Key Research and Development Plan (2018YFD0100504), the National Natural Science Foundation of China (31830067 and U1302266), the 973 Project (2015CB150201), and the 111 Project (B12006).

Data Availability Statement

The whole-genome bisulfite sequencing (WGBS) and the transcriptomes of different tissues used in this study were deposited in the NCBI database under BioProject ID PRJNA507497.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, Z.; Huo, Q.; Yang, H.; Jian, H.; Qu, C.; Lu, K.; Li, J. Joint RNA-Seq and miRNA Profiling Analyses to Reveal Molecular Mechanisms in Regulating Thickness of Pod Canopy in Brassica napus. Genes 2019, 10, 591. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, Y.; Yu, C.; Lin, J.; Liu, J.; Liu, B.; Wang, J.; Huang, A.; Li, H.; Zhao, T. OsMPH1 regulates plant height and improves grain yield in rice. PLoS ONE 2017, 12, e0180825. [Google Scholar] [CrossRef] [PubMed]
  3. Liu, W.-T.; Chen, P.-W.; Chen, L.-C.; Yang, C.-C.; Chen, S.-Y.; Huang, G.; Lin, T.C.; Ku, H.-M.; Chen, J.J.W. Suppressive effect of microRNA319 expression on rice plant height. Theor. Appl. Genet. 2017, 130, 1507–1518. [Google Scholar] [CrossRef]
  4. Li, F.; Chen, B.; Xu, K.; Gao, G.; Yan, G.; Qiao, J.; Li, J.; Li, H.; Li, L.; Xiao, X.; et al. A genome-wide association study of plant height and primary branch number in rapeseed (Brassica napus). Plant Sci. 2016, 242, 169–177. [Google Scholar] [CrossRef] [PubMed]
  5. Zheng, M.; Peng, C.; Liu, H.; Tang, M.; Yang, H.; Li, X.; Liu, J.; Sun, X.; Wang, X.; Xu, J.; et al. Genome-Wide Association Study Reveals Candidate Genes for Control of Plant Height, Branch Initiation Height and Branch Number in Rapeseed (Brassica napus L.). Front. Plant Sci. 2017, 8, 1246. [Google Scholar] [CrossRef]
  6. Satyaki, P.R.V.; Gehring, M. DNA methylation and imprinting in plants: Machinery and mechanisms. Crit. Rev. Biochem. Mol. Biol. 2017, 52, 163–175. [Google Scholar] [CrossRef] [PubMed]
  7. Gruenbaum, Y.; Naveh-Many, T.; Cedar, H.; Razin, A. Sequence specificity of methylation in higher plant DNA. Nature 1981, 292, 860–862. [Google Scholar] [CrossRef]
  8. Meyer, P.; Niedenhof, I.; Lohuis, M.T. Evidence for cytosine methylation of non-symmetrical sequences in transgenic Petunia hybrida. EMBO J. 1994, 13, 2084–2088. [Google Scholar] [CrossRef] [PubMed]
  9. Law, J.A.; Jacobsen, S.E. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat. Rev. Genet. 2010, 11, 204–220. [Google Scholar] [CrossRef]
  10. Gehring, M.; Huh, J.H.; Hsieh, T.-F.; Penterman, J.; Choi, Y.; Harada, J.J.; Goldberg, R.B.; Fischer, R.L. DEMETER DNA Glycosylase Establishes MEDEA Polycomb Gene Self-Imprinting by Allele-Specific Demethylation. Cell 2006, 124, 495–506. [Google Scholar] [CrossRef]
  11. Zemach, A.; Kim, M.Y.; Silva, P.; Rodrigues, J.A.; Dotson, B.; Brooks, M.D.; Zilberman, D. Local DNA hypomethylation activates genes in rice endosperm. Proc. Natl. Acad. Sci. USA 2010, 107, 18729–18734. [Google Scholar] [CrossRef]
  12. Lang, Z.; Wang, Y.; Tang, K.; Tang, D.; Datsenka, T.; Cheng, J.; Zhang, Y.; Handa, A.K.; Zhu, J.-K. Critical roles of DNA demethylation in the activation of ripening-induced genes and inhibition of ripening-repressed genes in tomato fruit. Proc. Natl. Acad. Sci. USA 2017, 114, E4511–E4519. [Google Scholar] [CrossRef]
  13. Huang, H.; Liu, R.; Niu, Q.; Tang, K.; Zhang, B.; Zhang, H.; Chen, K.; Zhu, J.-K.; Lang, Z. Global increase in DNA methylation during orange fruit development and ripening. Proc. Natl. Acad. Sci. USA 2019, 116, 1430–1436. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, W.; Qin, Q.; Sun, F.; Wang, Y.; Xu, D.; Li, Z.; Fu, B. Genome-Wide Differences in DNA Methylation Changes in Two Contrasting Rice Genotypes in Response to Drought Conditions. Front. Plant Sci. 2016, 7, 1675. [Google Scholar] [CrossRef] [PubMed]
  15. Feng, S.J.; Liu, X.S.; Tao, H.; Tan, S.K.; Chu, S.S.; Oono, Y.; Zhang, X.D.; Chen, J.; Yang, Z.M. Variation of DNA methylation patterns associated with gene expression in rice (Oryza sativa) exposed to cadmium. Plant Cell Environ. 2016, 39, 2629–2649. [Google Scholar] [CrossRef] [PubMed]
  16. Ji, L.; Mathioni, S.M.; Johnson, S.; Tucker, D.; Bewick, A.J.; Do Kim, K.; Daron, J.; Slotkin, R.K.; Jackson, S.A.; Parrott, W.A.; et al. Genome-Wide Reinforcement of DNA Methylation Occurs during Somatic Embryogenesis in Soybean. Plant Cell 2019, 31, 2315–2331. [Google Scholar] [CrossRef] [PubMed]
  17. Gupta, O.P.; Dahuja, A.; Sachdev, A.; Jain, P.K.; Kumari, S.; Vinutha, T.; Praveen, S. Cytosine Methylation of Isoflavone Synthase Gene in the Genic Region Positively Regulates Its Expression and Isoflavone Biosynthesis in Soybean Seeds. DNA Cell Biol. 2019, 38, 510–520. [Google Scholar] [CrossRef] [PubMed]
  18. Mager, S.; Schönberger, B.; Ludewig, U. The transcriptome of zinc deficient maize roots and its relationship to DNA methylation loss. BMC Plant Biol. 2018, 18, 372. [Google Scholar] [CrossRef]
  19. Qian, Y.; Hu, W.; Liao, J.; Zhang, J.; Ren, Q. The Dynamics of DNA methylation in the maize (Zea mays L.) inbred line B73 response to heat stress at the seedling stage. Biochem. Biophys. Res. Commun. 2019, 512, 742–749. [Google Scholar] [CrossRef]
  20. Shafiq, S.; Zeb, Q.; Ali, A.; Sajjad, Y.; Nazir, R.; Widemann, E.; Liu, L. Lead, Cadmium and Zinc Phytotoxicity Alter DNA Methylation Levels to Confer Heavy Metal Tolerance in Wheat. Int. J. Mol. Sci. 2019, 20, 4676. [Google Scholar] [CrossRef] [PubMed]
  21. Wang, Z.; Wu, X.; Wu, Z.; An, H.; Yi, B.; Wen, J.; Ma, C.; Shen, J.; Fu, T.; Tu, J. Genome-Wide DNA Methylation Comparison between Brassica napus Genic Male Sterile Line and Restorer Line. Int. J. Mol. Sci. 2018, 19, 2689. [Google Scholar] [CrossRef] [PubMed]
  22. Li, J.; Huang, Q.; Sun, M.; Zhang, T.; Li, H.; Chen, B.; Xu, K.; Gao, G.; Li, F.; Yan, G.; et al. Global DNA methylation variations after short-term heat shock treatment in cultured microspores of Brassica napus cv. Topas. Sci. Rep. 2016, 6, 38401. [Google Scholar] [CrossRef]
  23. Jiang, P.; Wang, S.; Jiang, H.; Cheng, B.; Wu, K.; Ding, Y. The COMPASS-Like Complex Promotes Flowering and Panicle Branching in Rice. Plant Physiol. 2018, 176, 2761–2771. [Google Scholar] [CrossRef]
  24. Liu, X.; Wei, X.; Sheng, Z.; Jiao, G.; Tang, S.; Luo, J.; Hu, P. Polycomb Protein OsFIE2 Affects Plant Height and Grain Yield in Rice. PLoS ONE 2016, 11, e0164748. [Google Scholar] [CrossRef] [PubMed]
  25. Hu, Y.; Morota, G.; Rosa, G.J.M.; Gianola, D. Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data. Genetics 2015, 201, 779–793. [Google Scholar] [CrossRef] [PubMed]
  26. Loreti, E.; Betti, F.; Ladera-Carmona, M.J.; Fontana, F.; Novi, G.; Valeri, M.C.; Perata, P.P. ARGONAUTE1 and ARGONAUTE4 Regulate Gene Expression and Hypoxia Tolerance. Plant Physiol. 2020, 182, 287–300. [Google Scholar] [CrossRef] [PubMed]
  27. Wang, B.; Liu, J.; Chu, L.; Jing, X.; Wang, H.; Guo, J.; Yi, B. Exogenous Promoter Triggers APETALA3 Silencing through RNA-Directed DNA Methylation Pathway in Arabidopsis. Int. J. Mol. Sci. 2019, 20, 4478. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Ramming, A.; Heinke, L.; Altschmied, L.; Slotkin, R.K.; Becker, J.D.; Kappel, C.; Lenhard, M. The poly(A) polymerase PAPS1 interacts with the RNA-directed DNA-methylation pathway in sporophyte and pollen development. Plant J. 2019, 99, 655–672. [Google Scholar] [CrossRef] [PubMed]
  29. Iwasaki, M.; Hyvärinen, L.; Piskurewicz, U.; Lopez-Molina, L. Non-canonical RNA-directed DNA methylation participates in maternal and environmental control of seed dormancy. eLife 2019, 8, e37434. [Google Scholar] [CrossRef] [PubMed]
  30. Zhong, X.; Wang, Z.Q.; Xiao, R.; Wang, Y.; Xie, Y.; Zhou, X. iTRAQ analysis of the tobacco leaf proteome reveals that RNA-directed DNA methylation (RdDM) has important roles in defense against geminivirus-betasatellite infection. J. Proteom. 2017, 152, 88–101. [Google Scholar] [CrossRef] [PubMed]
  31. Kumaki, Y.; Oda, M.; Okano, M. QUMA: Quantification tool for methylation analysis. Nucleic Acids Res. 2008, 36, W170–W175. [Google Scholar] [CrossRef] [PubMed]
  32. Wang, J.; Wang, C.; Long, Y.; Hopkins, C.; Kurup, S.; Liu, K.; King, G.J.; Meng, J. Universal endogenous gene controls for bisulphite conversion in analysis of plant DNA methylation. Plant Methods 2011, 7, 39. [Google Scholar] [CrossRef]
  33. Krzywinski, M.; Schein, J.; Birol, İ.; Connors, J.; Gascoyne, R.; Horsman, D.; Jones, S.J.; Marra, M.A. Circos: An information aesthetic for comparative genomics. Genome Res. 2009, 19, 1639–1645. [Google Scholar] [CrossRef]
  34. Zhong, S.; Fei, Z.; Chen, Y.-R.; Zheng, Y.; Huang, M.; Vrebalov, J.; McQuinn, R.; Gapper, N.; Liu, B.; Xiang, J.; et al. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nat. Biotechnol. 2013, 31, 154–159. [Google Scholar] [CrossRef] [PubMed]
  35. Kulis, M.; Heath, S.; Bibikova, M.; Queirós, A.C.; Navarro, A.; Clot, G.; Martínez-Trillos, A.; Castellano, G.; Brun-Heath, I.; Pinyol, M.; et al. Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nat. Genet. 2012, 44, 1236–1242. [Google Scholar] [CrossRef] [PubMed]
  36. Feng, H.; Conneely, K.N.; Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 2014, 42, e69. [Google Scholar] [CrossRef] [PubMed]
  37. Wu, H.; Xu, T.; Feng, H.; Chen, L.; Li, B.; Yao, B.; Qin, Z.; Jin, P.; Conneely, K.N. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Res. 2015, 43, e141. [Google Scholar] [CrossRef]
  38. Park, Y.; Wu, H. Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 2016, 32, 1446–1453. [Google Scholar] [CrossRef]
  39. Bartels, A.; Han, Q.; Nair, P.; Stacey, L.; Gaynier, H.; Mosley, M.; Huang, Q.; Pearson, J.; Hsieh, T.-F.; An, Y.-Q.; et al. Dynamic DNA Methylation in Plant Growth and Development. Int. J. Mol. Sci. 2018, 19, 2144. [Google Scholar] [CrossRef]
  40. Smallwood, S.A.; Lee, H.J.; Angermueller, C.; Krueger, F.; Saadeh, H.; Peat, J.; Andrews, S.R.; Stegle, O.; Reik, W.; Kelsey, G. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 2014, 11, 817–820. [Google Scholar] [CrossRef]
  41. Lister, R.; O’Malley, R.C.; Tonti-Filippini, J.; Gregory, B.D.; Berry, C.C.; Millar, A.H.; Ecker, J.R. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell 2008, 133, 523–536. [Google Scholar] [CrossRef] [PubMed]
  42. Kawakatsu, T.; Carol Huang, S.-S.; Jupe, F.; Sasaki, E.; Schmitz, R.J.; Urich, M.A.; Castanon, R.; Nery, J.R.; Barragan, C.; He, Y.; et al. Epigenomic diversity in a global collection of Arabidopsis thaliana accessions. Cell 2016, 166, 492–505. [Google Scholar] [CrossRef] [PubMed]
  43. Niederhuth, C.E.; Bewick, A.J.; Ji, L.; Alabady, M.S.; Kim, K.D.; Li, Q.; Rohr, N.A.; Rambani, A.; Burke, J.M.; Udall, J.A.; et al. Widespread natural variation of DNA methylation within angiosperms. Genome Biol. 2016, 17, 194. [Google Scholar] [CrossRef] [PubMed]
  44. Regulski, M.; Lu, Z.; Kendall, J.; Donoghue, M.T.A.; Reinders, J.; Llaca, V.; Deschamps, S.; Smith, A.; Levy, D.; McCombie, W.R.; et al. The maize methylome influences mRNA splice sites and reveals widespread paramutation-like switches guided by small RNA. Genome Res. 2013, 23, 1651–1662. [Google Scholar] [CrossRef]
  45. Schmitz, R.J.; He, Y.; Valdés-López, O.; Khan, S.M.; Joshi, T.; Urich, M.A.; Nery, J.R.; Diers, B.; Xu, D.; Stacey, G.; et al. Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population. Genome Res. 2013, 23, 1663–1674. [Google Scholar] [CrossRef] [PubMed]
  46. Daccord, N.; Celton, J.-M.; Linsmith, G.; Becker, C.; Choisne, N.; Schijlen, E.; van de Geest, H.; Bianco, L.; Micheletti, D.; Velasco, R.; et al. High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nat. Genet. 2017, 49, 1099–1106. [Google Scholar] [CrossRef] [PubMed]
  47. Turco, G.M.; Kajala, K.; Kunde-Ramamoorthy, G.; Ngan, C.Y.; Olson, A.; Deshphande, S.; Tolkunov, D.; Waring, B.; Stelpflug, S.; Klein, P.; et al. DNA methylation and gene expression regulation associated with vascularization in Sorghum bicolor. New Phytol. 2017, 214, 1213–1229. [Google Scholar] [CrossRef]
  48. Chalhoub, B.; Denoeud, F.; Liu, S.; Parkin, I.A.P.; Tang, H.; Wang, X.; Chiquet, J.; Belcram, H.; Tong, C.; Samans, B.; et al. Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science 2014, 345, 950–953. [Google Scholar] [CrossRef] [PubMed]
  49. Nagaharu, U. Genome analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilisation. Jpn. J. Bot. 1935, 7, 389–452. [Google Scholar]
  50. Allender, C.J.; King, G.J. Origins of the amphiploid species Brassica napus L. investigated by chloroplast and nuclear molecular markers. BMC Plant Biol. 2010, 10, 54. [Google Scholar] [CrossRef]
  51. Parkin, I.A.; Koh, C.; Tang, H.; Robinson, S.J.; Kagale, S.; Clarke, W.E.; Town, C.D.; Nixon, J.; Krishnakumar, V.; Bidwell, S.L.; et al. Transcriptome and methylome profiling reveals relics of genome dominance in the mesopolyploid Brassica oleracea. Genome Biol. 2014, 15, R77. [Google Scholar] [CrossRef]
  52. Liu, Y.; Xu, C.; Tang, X.; Pei, S.; Jin, D.; Guo, M.; Yang, M.; Zhang, Y. Genomic methylation and transcriptomic profiling provides insights into heading depression in inbred Brassica rapa L. ssp. pekinensis. Gene 2018, 665, 119–126. [Google Scholar] [CrossRef] [PubMed]
  53. Eprintsev, A.T.; Fedorin, D.N.; Karabutova, L.A.; Igamberdiev, A.U. Expression of genes encoding subunits A and B of succinate dehydrogenase in germinating maize seeds is regulated by methylation of their promoters. J. Plant Physiol. 2016, 205, 33–40. [Google Scholar] [CrossRef] [PubMed]
  54. Han, Y.-L.; Song, H.-X.; Liao, Q.; Yu, Y.; Jian, S.-F.; Lepo, J.E.; Liu, Q.; Rong, X.-M.; Tian, C.; Zeng, J.; et al. Nitrogen Use Efficiency Is Mediated by Vacuolar Nitrate Sequestration Capacity in Roots of Brassica napus. Plant Physiol. 2016, 170, 1684–1698. [Google Scholar] [CrossRef] [PubMed]
  55. Khan, S.; Anwar, S.; Kuai, J.; Ullah, S.; Fahad, S.; Zhou, G. Optimization of Nitrogen Rate and Planting Density for Improving Yield, Nitrogen Use Efficiency, and Lodging Resistance in Oilseed Rape. Front. Plant Sci. 2017, 8, 532. [Google Scholar] [CrossRef] [PubMed]
  56. Takahashi, H.; Saito, K. Subcellular localization of spinach cysteine synthase isoforms and regulation of their gene expression by nitrogen and sulfur. Plant Physiol. 1996, 112, 273–280. [Google Scholar] [CrossRef]
  57. Cui, F.; Liu, L.; Zhao, Q.; Zhang, Z.; Li, Q.; Lin, B.; Wu, Y.; Tang, S.; Xie, Q. Arabidopsis Ubiquitin Conjugase UBC32 Is an ERAD Component That Functions in Brassinosteroid-Mediated Salt Stress Tolerance. Plant Cell 2012, 24, 233–244. [Google Scholar] [CrossRef] [PubMed]
  58. Eom, H.; Park, S.J.; Kim, M.K.; Kim, H.; Kang, H.; Lee, I. TAF15b, involved in the autonomous pathway for flowering, represses transcription of FLOWERING LOCUS C. Plant J. 2017, 93, 79–91. [Google Scholar] [CrossRef]
  59. Eom, H.; Lee, I. Role of TAF15b in transcriptional regulation of autonomous pathway for flowering. Plant Signal. Behav. 2018, 13, e1471300. [Google Scholar] [CrossRef]
  60. Matzke, M.A.; Mosher, R.A. RNA-directed DNA methylation: An epigenetic pathway of increasing complexity. Nat. Rev. Genet. 2014, 15, 394–408. [Google Scholar] [CrossRef]
  61. Cuerda-Gil, D.; Slotkin, R.K. Non-canonical RNA-directed DNA methylation. Nat. Plants 2016, 2, 16163. [Google Scholar] [CrossRef] [PubMed]
  62. Humphries, B.; Wang, Z.; Yang, C. MicroRNA Regulation of Epigenetic Modifiers in Breast Cancer. Cancers 2019, 11, 897. [Google Scholar] [CrossRef] [PubMed]
  63. Zhu, S.; Zhang, X.; Guan, H.; Huang, F.; Wu, L.; Hou, D.; Zheng, Z.; Yu, M.; Huang, L.; Ge, L. miR-140-5p regulates T cell differentiation and attenuates experimental autoimmune encephalomyelitis by affecting CD4+T cell metabolism and DNA methylation. Int. Immunopharmacol. 2019, 75, 105778. [Google Scholar] [CrossRef]
  64. Kelly, A.A.; Shaw, E.; Powers, S.J.; Kurup, S.; Eastmond, P.J. Suppression of the SUGAR-DEPENDENT1 triacylglycerol lipase family during seed development enhances oil yield in oilseed rape (Brassica napus L.). Plant Biotechnol. J. 2012, 11, 355–361. [Google Scholar] [CrossRef] [PubMed]
  65. Li, H.; Sun, B.; Sasabe, M.; Deng, X.; Machida, Y.; Lin, H.; Julie Lee, Y.R.; Liu, B. Arabidopsis MAP65-4 plays a role in phragmoplast microtubule organization and marks the cortical cell division site. New Phytol. 2017, 215, 187–201. [Google Scholar] [CrossRef]
  66. Kawakatsu, T.; Ecker, J.R. Diversity and dynamics of DNA methylation: Epigenomic resources and tools for crop breeding. Breed. Sci. 2019, 69, 191–204. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Percentage of C-site methylation for each sequence environment at the chromosome level. Different colors represent methylation C sites under different contexts, as follows: red color (CG), green color (CHG), and blue color (CHH). The length of each column represents the percentage of the sequence methylation.
Figure 1. Percentage of C-site methylation for each sequence environment at the chromosome level. Different colors represent methylation C sites under different contexts, as follows: red color (CG), green color (CHG), and blue color (CHH). The length of each column represents the percentage of the sequence methylation.
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Figure 2. Distribution of sample methylation levels on different genomic elements. The abscissa represents different genomic elements and the ordinate represents the level of methylation.
Figure 2. Distribution of sample methylation levels on different genomic elements. The abscissa represents different genomic elements and the ordinate represents the level of methylation.
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Figure 3. The methylation level between stem apex (S) and flower buds (F). From the outside to the inside, the circle indicates the methylation level of the flower buds, the difference in methylation level between the sample groups, and the methylation level of the stem apex.
Figure 3. The methylation level between stem apex (S) and flower buds (F). From the outside to the inside, the circle indicates the methylation level of the flower buds, the difference in methylation level between the sample groups, and the methylation level of the stem apex.
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Figure 4. DMR anchoring area display of three sequence environment (CG, CHG, CHH). The abscissa represents the respective area categories, and the ordinate represents the number of DMRs of the hyper/hypo DMR in each area.
Figure 4. DMR anchoring area display of three sequence environment (CG, CHG, CHH). The abscissa represents the respective area categories, and the ordinate represents the number of DMRs of the hyper/hypo DMR in each area.
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Figure 5. Venn diagram illustrating the overlap of DMR-associated genes at high- and low-TPC lines and in two distinct tissues. (A,B) Genes with hypermethylation and hypomethylation at flower bud. (C,D) Genes with hypermethylation and hypomethylation at stem apex. (E) Common genes with hypermethylation and hypomethylation at flower bud and stem apex. (F) Genes with hypermethylation and down-regulated DEGs at flower bud and stem apex. (G) Genes with hypomethylation and up-regulated DEGs at flower bud and stem apex.
Figure 5. Venn diagram illustrating the overlap of DMR-associated genes at high- and low-TPC lines and in two distinct tissues. (A,B) Genes with hypermethylation and hypomethylation at flower bud. (C,D) Genes with hypermethylation and hypomethylation at stem apex. (E) Common genes with hypermethylation and hypomethylation at flower bud and stem apex. (F) Genes with hypermethylation and down-regulated DEGs at flower bud and stem apex. (G) Genes with hypomethylation and up-regulated DEGs at flower bud and stem apex.
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Figure 6. Analysis of GO (A) and KEGG (B) of genes regulated by promoter methylation in two sets of TPC materials.
Figure 6. Analysis of GO (A) and KEGG (B) of genes regulated by promoter methylation in two sets of TPC materials.
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Table 1. The percentage of different context in whole genome methylation.
Table 1. The percentage of different context in whole genome methylation.
SamplesTPC TraitmC (%)mCpG (%)mCHG (%)mCHH (%)
YC4FHigh11.31%37.15%23.92%3.42%
YC33FHigh10.61%33.59%23.43%3.26%
YC11FLow9.96%33.32%21.05%2.88%
YC15FLow10.88%35.98%21.55%3.54%
YC4SHigh12.1%34.88%23.29%5.12%
YC33SHigh13.78%36.2%26.69%6.52%
YC11SLow12.8%35.64%24.51%5.71%
YC15SLow13.11%37.32%22.72%6.17%
Notes: F: flower buds; S: stem apex.
Table 2. MiRNAs and methylation are involved in the regulation of gene expression TPC trait.
Table 2. MiRNAs and methylation are involved in the regulation of gene expression TPC trait.
Gene_IDMiRNAsMethylationMiRNAs_
Expression		Gene_
Expression		SitesComparison with Arabidopsis Genes
BnaAnng30250DmiR319hypermethylationupdowncommon-
BnaC04g55380DmiR9409/miR6029hypermethylationupdowncommonmicrotubule-associated protein 65-4 (MAP65-4)
BnaC08g03460DmiR5726/miR9409hypermethylationupdowncommonFUNCTIONS IN: sequence-specific DNA binding transcription factor activity
BnaCnng50740DmiR9563hypermethylationupdowncommonRNA-directed DNA polymerase (reverse transcriptase)-related family protein
BnaA09g11170DmiR9409hypermethylationupdownflower budpentatricopeptide (PPR) repeat-containing protein
BnaCnng64040DmiR159hypermethylationupdowncommonLipase family protein
BnaA07g07800DmiR319hypermethylationupdownstem apex-
BnaC02g22110DmiR9409hypermethylationupdownstem apex-
BnaC02g22120DmiR319hypermethylationupdowncommonSpc97 family of spindle pole body (SBP) component
BnaA04g14930DmiR159/miR319/miR5726hypomethylationdownupcommonS-locus lectin protein kinase family protein
BnaCnng56050DmiR122/miR5726/miR9410hypomethylationdownupcommonPyridoxal phosphate (PLP)-dependent transferases superfamily protein
BnaA06g09210DmiR395/miR827hypomethylationdownupflower budIQ-domain 28 (IQD28)
BnaC03g09180DmiR827hypomethylationdownupflower bud-
BnaC09g30490DmiR167hypomethylationdownupflower budTBP-associated factor 15B (TAF15b)
Note: In the “Sites” column, “common” indicates significant differential expression in stem apex and flower buds. “flower bud” indicates significant differential expression in flower buds, and “stem apex” indicates significant differential expression in stem apex.
Table 3. Genes used for methylation validation.
Table 3. Genes used for methylation validation.
ChromosomeStartEndLengthContextGene_IDRegionMethylationMaterials
chrC0428,932,48428,933,278795CGBnaC04g27680DpromoterhypermethylationYC4F/YC11F
chrC0535,959,73535,961,4831749CGBnaC05g36710DpromoterhypermethylationYC4F/YC11F
chrC063,994,7513,995,435685CGBnaC06g03310DpromoterhypermethylationYC4F/YC11F
chrA078,074,8198,075,354536CHGBnaA07g07800DpromoterhypermethylationYC4F/YC11F
chrC083,215,2093,217,0651857CGBnaC08g03460DpromoterhypermethylationYC4F/YC11F
chrCnn_random63,802,91563,803,399485CGBnaCnng64040DpromoterhypermethylationYC4F/YC11F
chrC0360,388,54260,389,391850CGBnaC03g70780DpromoterhypermethylationYC4F/YC11F
chrA0516,370,91816,372,1041187CHGBnaA05g21120DpromoterhypermethylationYC4F/YC11F
chrC0354,554,40854,555,203796CGBnaC03g65070DpromoterhypermethylationYC4F/YC11F
chrA0920,870,97320,871,783811CGBnaA09g27840DpromoterhypermethylationYC4F/YC11F
chrC0314,444,57414,445,9251352CGBnaC03g25740DpromoterhypermethylationYC4F/YC11F
chrA095,783,7755,784,177403CHGBnaA09g11170DpromoterhypermethylationYC4F/YC11F
chrC0429,430,76929,431,7831015CGBnaC04g28000DpromoterhypermethylationYC4F/YC11F
chrC0447,080,05947,080,491433CHGBnaC04g48480DpromoterhypomethylationYC4F/YC11F
chrC0359,224,10759,224,481375CHGBnaC03g69480DpromoterhypomethylationYC4F/YC11F
chrA0412,508,63412,509,001368CHGBnaA04g14930DpromoterhypomethylationYC4F/YC11F
chrA0519,453,84919,454,171323CHGBnaA05g26660DpromoterhypomethylationYC4F/YC11F
chrA106,400,4436,401,340898CGBnaA10g07880DpromoterhypomethylationYC4F/YC11F
chrC0933,390,64133,391,416776CGBnaC09g30490DpromoterhypomethylationYC4F/YC11F
chrC034,369,7744,371,0391266CGBnaC03g09180DpromoterhypomethylationYC4F/YC11F
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Jia, L.; Cao, L.; Zeng, L.; Lu, K.; Qu, C.; Li, J.; Chen, Z. MicroRNA-Mediated Changes in DNA Methylation Affect the Expression of Genes Involved in the Thickness-of-Pod-Canopy Trait in Brassica napus L. Agronomy 2025, 15, 398. https://doi.org/10.3390/agronomy15020398

AMA Style

Jia L, Cao L, Zeng L, Lu K, Qu C, Li J, Chen Z. MicroRNA-Mediated Changes in DNA Methylation Affect the Expression of Genes Involved in the Thickness-of-Pod-Canopy Trait in Brassica napus L. Agronomy. 2025; 15(2):398. https://doi.org/10.3390/agronomy15020398

Chicago/Turabian Style

Jia, Ledong, Lu Cao, Lijun Zeng, Kun Lu, Cunmin Qu, Jiana Li, and Zhiyou Chen. 2025. "MicroRNA-Mediated Changes in DNA Methylation Affect the Expression of Genes Involved in the Thickness-of-Pod-Canopy Trait in Brassica napus L." Agronomy 15, no. 2: 398. https://doi.org/10.3390/agronomy15020398

APA Style

Jia, L., Cao, L., Zeng, L., Lu, K., Qu, C., Li, J., & Chen, Z. (2025). MicroRNA-Mediated Changes in DNA Methylation Affect the Expression of Genes Involved in the Thickness-of-Pod-Canopy Trait in Brassica napus L. Agronomy, 15(2), 398. https://doi.org/10.3390/agronomy15020398

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