Research
Open access
Published: 29 November 2024

Epigenetic regulation of organ-specific functions in Mikania micrantha and Mikania cordata: insights from DNA methylation and siRNA integration

Yatong Sang¹,
Yitong Ma¹,
Ruonan Wang¹,
Zhen Wang¹,
Ting Wang^2,3 &
…
Yingjuan Su^1,2

BMC Plant Biology volume 24, Article number: 1142 (2024) Cite this article

341 Accesses
Metrics details

Abstract

Background

DNA methylation is a crucial epigenetic mechanism that regulates gene expression during plant growth and development. However, the role of DNA methylation in regulating the organ-specific functions of the invasive weed Mikania micrantha remains unknown.

Results

Here, we generated DNA methylation profiles for M. micrantha and a local congeneric species, Mikania cordata, in three vegetative organs (root, stem, and leaf) using whole-genome bisulfite sequencing. The results showed both differences and conservation in methylation levels and patterns between the two species. Combined with transcriptome data, we found that DNA methylation generally inhibited gene expression, with varying effects depending on the genomic region and sequence context (CG, CHG, and CHH). Genes overlapping with differentially methylated regions (DMRs) were more likely to be differentially expressed between organs, and DMR-associated upregulated differentially expressed genes (DEGs) were enriched in organ-specific pathways. A comparison between photosynthetic (leaf) and non-photosynthetic (root) organs of M. micrantha further confirmed the regulatory role of DNA methylation in leaf-specific photosynthesis. Integrating small RNA-Seq data revealed that 24-nt small interfering RNAs (siRNAs) were associated with CHH methylation in gene-rich regions and regulated CHH methylation in the flanking regions of photosynthesis-related genes.

Conclusion

This study provides insights into the complex regulatory role of DNA methylation and siRNAs in organ-specific functions and offers valuable information for exploring the invasive characteristics of M. micrantha from an epigenetic perspective.

Peer Review reports

Introduction

DNA methylation is a common chromatin modification, typically referring to the addition of a methyl group at the 5’ position of cytosine. It regulates the expression of nuclear genes and contribute to genome stability [1, 2]. DNA methylation is usually conserved, with specific states resulting from dynamic regulation involving de novo methylation, maintenance, and demethylation. These processes are catalyzed by various enzymes in different pathways. In animals, DNA methylation mainly occurs in CG sequences, while in plants, it occurs in all cytosine sequences: CG, CHG, and CHH (H represents A, T, or C) [3, 4]. In plants, cytosines are de novo methylated through the RNA-directed DNA methylation pathway (RdDM) and maintained by different DNA methylation-related enzymes [5]. All three types of methylation can be established de novo through the RdDM pathway, typically guided by 24-nucleotide (nt) siRNA [6]. The generation of siRNA depends on RNA Polymerase IV (POL IV), RNA-dependent RNA Polymerase 2 (RDRP2 or RDR2), and Dicer-like protein 3 (DCL3). Subsequently, siRNA guided DNA de novo methylation through a sequence-independent manner involving Argonaute (AGO) proteins (mainly AGO4 and AGO6), Pol V, and domain-rearranged methyltransferase 2 (DRM2) [6,7,8]. CG cytosine methylation is maintained by methyltransferase 1 (MET1), an orthologue of the mammalian DNA (cytosine-5)-methyltransferase 1 (DNMT1) [9]. CHG methylation is mainly maintained by chromo-methyltransferase 3 (CMT3), with a minor contribution from CMT2, while CHH methylation is primarily maintained by DRM2 or CMT2 [10, 11]. Additionally, there is a demethylation process to maintain the balance of genome methylation. In Arabidopsis, this process is primarily catalyzed by repressor of silencing 1 (ROS1), demeter (DME), demeter-like 2 (DML2), and demeter-like 3 (DML3) [12,13,14,15].

DNA methylation exhibits significant species specificity and shows widespread variation among different species. For instance, in Arabidopsis thaliana leaves, the levels of CG, CHG, and CHH methylation are 30.5%, 10.0%, and 3.9%, respectively, while in Beta vulgaris leaves, they are 92.6%, 81.2%, and 18.9%, respectively [16]. DNA methylation levels also differ among organs; for example, nutrient organs have higher DNA methylation levels compared to cotyledons [17]. Research indicates that the DNA methylation levels between different tissues are associated with the activity of demethylase [18]. DNA methylation influences important physiological processes in plants, including growth, development, stress responses, and photosynthesis [5, 17, 19]. For example, during rapid growth, the levels of CG and CHG methylation decrease significantly in Bonia amplexicaulis [20]. Mutation of the rice MET1b gene leads to abnormal seed development [21]. In stress responses, ddm1 mutants exhibit shorter roots and lower survival rates under salt stress [22]. Virus-induced silencing of the MnMET1 gene enhances the expression of resistance genes in mulberry, increasing resistance to Botrytis cinerea [23]. In pineapple, CHH methylation is reduced in white base tissue compared to green tip photosynthetic tissue, affecting CAM photosynthesis [24].

Transposons can influence genomic stability by relocating and inserting new copies within the genome. DNA methylation primarily regulates transposon activity by targeting transposable elements (TEs) to suppress their transcriptional activity [25]. For example, in the maize genome, active genes and inactive transposons are separated by RdDM-dependent CHH islands. Loss of methylated CHH islands results in the transcriptional activation of transposons, accompanied by reduced CG and CHG methylation in adjacent transposons [26]. In rice, decreased DNA methylation directly leads to increased transcription of Tos17, followed by transposition events [27]. Moreover, in the Arabidopsis met1-cmt3 double mutant, transcription of the endogenous CACTA transposon increases, elevating the frequency of CACTA transposition [28].

In plants, an important molecular function of DNA methylation, which affects genome activity and participates in the regulation of many pathways, is to regulate gene expression. DNA methylation occurring in the promoter, adjacent regions, or within the gene body can influence gene expression [29]. Typically, methylation of promoter regions prevents the binding of transcriptional activators, leading to transcriptional inactivation or reduction [5]. For example, methylation in the promoter region of Arabidopsis represses gene expression [30]. In the soybean genome, elevated levels of non-CG methylation may lead to gene silencing. Additionally, some introns within genes may contain TEs or repeat elements that are highly methylated in all cytosine contexts, regulating mRNA processing [31]. Methylation levels near the transcription start site are also typically negatively correlated with gene expression, a pattern observed across multiple species [30,31,32]. However, DNA methylation can also activate gene transcription, although this is less common [5, 33]. For example, CHH methylation in the upstream regions of maize and tea plants is positively correlated with gene expression [34, 35].

Mikania micrantha H.B.K., native to Central and South America, belongs to the Asteraceae family, genus Mikania [36]. Due to its rapid growth and strong adaptability, it can quickly colonize invaded areas, causing devastating impacts on local ecosystems. Consequently, M. micrantha is listed as one of the world’s top 100 most threatening alien invasive species [37]. Recent studies on M. micrantha have shown a significant expansion of gene families involved in both light and dark reactions. Although M. micrantha is a C3 plant, it can utilize different photosynthetic pathways during the day and night to fix carbon dioxide, achieving a net photosynthetic rate comparable to C4 plants [38]. Comparative transcriptome analysis between M. micrantha and two non-invasive congeners revealed upregulation, accelerated evolution, and positive selection of genes related to photosynthesis and energy metabolism, which may play crucial roles in the invasion and adaptation of M. micrantha [39]. These studies have contributed to a better understanding of the rapid growth of M. micrantha. Additionally, the M. micrantha population has maintained a high level of epigenetic variation and TE-based epigenetic variation, which may contribute to its rapid colonization of new environments [40, 41]. Studies have also elucidated the impact of DNA methylation on seed germination in M. micrantha and its response to cold stress from an epigenomic perspective [42]. However, further research is needed to investigate DNA methylation dynamics in the vegetative organs of M. micrantha.

Here, we utilized whole-genome bisulfite sequencing (WGBS) to investigate the high-resolution DNA methylation profiles of the root, stem, and leaf of M. micrantha. Integrating transcriptome and small RNA data, we analyzed the role of DNA methylation variation in different organs of M. micrantha and its impact on organ-specific functions. We also included M. cordata, a native congeneric species of M. micrantha in China that grows slowly and does not harm local species or ecosystems [43]. We performed WGBS and RNA-seq on M. cordata and conducted a comparative analysis with M. micrantha.

Results

Characterization of DNA methylation patterns among different organs in M. micrantha and M. cordata genome

To investigate the DNA methylation patterns between M. micrantha and M. cordata, we conducted whole-genome bisulfite sequencing (WGBS) on the root, stem, and leaf of both species, with each organ having three biological replicates. After trimming adapter sequences and filtering low-quality reads, each sample from M. micrantha and M. cordata generated an average of approximately 370 million and 340 million paired-end reads, respectively, with mapping rates to their reference genomes of 84% and 92% (Table S1). Furthermore, approximately 77% and 91% of the total cytosines were covered by at least four reads (Table S2), indicating high library quality and sufficient sequencing depth for both species.

Based on these sequencing data, we observed high levels of CG and CHG methylation in TE-enriched regions in the three organs of both M. micrantha and M. cordata. Conversely, low levels of CG and CHG methylation were found in gene-eriched regions (Fig. 1A-B). These findings are consistent with previous observations in maize and sugarcane [18, 34]. Furthermore, we categorized the genome into genic and intergenic regions and calculated their respective methylation levels. CG and CHG methylation levels in genic regions were lower than those in intergenic regions across the three organs of both species, consistent with the observations in Fig. 1A-B. However, the CHH methylation levels differed between the two species: in M. micrantha, CHH methylation levels in intergenic regions were higher than those in genic regions, while the reverse was observed in M. cordata (Fig. 1C). Additionally, we calculated the proportion of methylcytosines in genic and intergenic regions across different organs of both species. The proportion of methylcytosines in the CG context was similar between genic and intergenic regions, whereas the CHH context showed a higher proportion of methylcytosines in genic regions compared to intergenic regions (Fig. 1D).

Our unpublished data indicate a high proportion of repetitive sequences in the genomes of M. micrantha (78%) and M. cordata (82%). Previous studies suggested a correlation between high repetitive sequence content and elevated DNA methylation levels [35]. To confirm this, we compared the genomic characteristics and methylation levels of M. micrantha, M. cordata and 11 other representative plant species. We found that the proportion of repetitive sequences in M. micrantha and M. cordata is similar to that in maize [16], with comparable CG and CHG methylation levels. However, the CHH methylation levels are approximately twice that of maize (Fig. 1E). Correlation analysis between genome size, repetitive sequence content, and methylation levels further confirmed that plants with larger genomes or higher repetitive sequence content typically exhibit higher DNA methylation levels (R > 0.5) (Fig. S1). Thus, the high DNA methylation levels in the genomes of M. micrantha and M. cordata are likely associated with the high content of repetitive sequences.

DNA methylation patterns of gene and TE regions.

To investigate differential methylation patterns in the genomic regions of M. micrantha and M. cordata, we generated DNA methylation maps for both species across three organs, focusing on gene and TE regions. The results revealed that methylation patterns in gene regions are consistent with those observed in in other species, showing increased methylation in gene bodies and flanking regions within CG sequence contexts. However, there was a sharp reduction in DNA methylation around the transcription start site (TSS) and transcription termination site (TTS). CG methylation levels were similar between the two species, with higher levels in the gene body region and lower levels in the flanking regions of M. cordata compared to M. micrantha. In both species, CHH methylation was relatively depleted in gene bodies but increased in upstream and downstream regions. Notably, “CHH islands” similar to those identified in maize and tea plants, were present upstream in both species [34, 35], demonstrating a significant elevation in DNA methylation levels from the flanking regions towards the TSS. The most significant variation was observed in the CHG context, with M. cordata showing higher CHG methylation levels than M. micrantha in both gene body and flanking regions (Fig. 2A).

Next, we analyzed average DNA methylation levels in TE regions and found that methylation levels in TE body regions were generally higher than in flanking regions in both CG and CHG contexts, with the exception of CHH methylation, which differed from previous studies in other plants. In the same organ, CG methylation levels were similar between the two species, while M. cordata exhibited higher CHG methylation levels and lower CHH methylation levels compared to M. micrantha (Fig. 2B). Further analysis of methylation levels in different transposon families, including Copia, Gypsy, L1, hAT, and Helitron, revealed that methylation patterns were not entirely consistent in both TE body and flanking regions. For instance, Copia showed higher methylation levels in TE body regions than in flanking regions in the CHH context, contrary to the overall trend. Similarly, in the CHG context for L1, significant variation in CHH methylation levels in L1 body regions was observed across different organs (Fig. S2). Finally, we investigated the relationship between the insertion age of LTR retrotransposons and DNA methylation. Younger transposons tend to be heavily methylated at CG and CHG sites, while CHH methylation was positively correlated with insertion age, a pattern consistent across the three organs of M. micrantha and M. cordata (Figs. S3 and S4).

We also described the methylation profiles of different genomic regions (promoter, 5’UTR, exon, intron, 3′UTR, and repeat) in M. micrantha and M. cordata. In various organs, methylation levels in the 5’UTR and 3’UTR regions of M. micrantha were higher than in M. cordata across all three sequence contexts, whereas intron regions exhibited lower methylation levels in M. micrantha in CHG sequence contexts. CG methylation levels in exon regions varied slightly across organs but were highly similar in CHG and CHH sequence contexts (Fig. 2C). This suggested that CHG methylation differences in gene bodies between the two species primarily originated from intronic regions. Previous studies have attributed increased gene body methylation to TE insertions, particularly in introns [25]. To investigate the impact of TEs on genic methylation, we separated genes with TE insertions in introns (TE genes) from those without TE insertions (non-TE genes) in both species. The average methylation levels in the gene bodies of TE genes in both species were significantly higher than in non-TE genes, with a slight decrease in the flanking regions across all three sequence contexts. However, methylation levels at the TSS and TTS remained consistently low (Fig. S5). Additionally, gene body methylation levels in non-TE genes of M. micrantha and M. cordata remained similar in CHG contexts, suggesting that differences in CHG contexts between the two species were primarily due to TE insertions in introns.

Extensive changes in gene expression among different organs.

To investigate the differences in gene expression among organs in M. micrantha and M. cordata, we performed RNA-seq on leaf, stem, and root samples, each with three biological replicates. Pearson correlation coefficients between different biological replicates confirmed the reliability and usability of our data (Fig. S6). Approximately 79% and 89% of clean reads from the organs of M. micrantha and M. cordata, respectively, aligned to their genomes (Table S3). Using the DESeq2 package [52], we identified 16,137 and 18,515 DEGs across the various organs of M. micrantha and M. cordata, respectively. Additionally, 1,087 and 959 DEGs were common among the three comparative groups in M. micrantha and M. cordata, respectively (Fig. 3A-B). The leaf vs. root comparison revealed the highest number of DEGs, with 7,123 in M. micrantha and 8,887 in M. cordata, followed by leaf vs. stem (4,513 and 5,069) and root vs. stem (4,501 and 4,559) (Fig. S7). Genes with organ-specific expression patterns are generally considered to form the basis for organ-specific functions. To investigate this, we defined organ-specific genes as those with elevated expression levels in one organ compared to the other two, while showing no significant differences between the other two organs within the same species. We identified 2,516 and 2,515 organ-specifically expressed genes in M. micrantha and M. cordata, respectively. The majority were found in the leaf (1,103 and 972 genes), whereas the fewest were found in the stem (488 and 773 genes) (Fig. 3C-D). KEGG enrichment analysis revealed that in M. micrantha, leaf-specific genes were enriched in photosynthesis, root-specific genes in the biosynthesis of secondary metabolites, and stem-specific genes in plant hormone signal transduction. In M. cordata, leaf-specific genes were enriched in metabolic pathways, root-specific genes in secondary metabolite biosynthesis, and stem-specific genes in starch and sucrose metabolism (Fig. S8). These results suggest that organ-specific genes are involved in biological pathways related to organ-specific physiological functions.

Next, we identified 6,839 one-to-one orthologous genes between the two species to characterize gene expression differences. Principal component analysis based on gene expression variation revealed a clear separation between the two species (Fig. 3E). We then performed differential expression analysis of the one-to-one orthologous genes across three organs in both species. In the leaves, stems, and roots of M. micrantha, we identified 886, 824, and 1,141 upregulated genes, respectively (Fig. 3F). Gene Ontology (GO) enrichment analysis of these upregulated genes revealed significant enrichment in processes such as potassium ion transmembrane transport, NAD biosynthesis and metabolism, and carbohydrate metabolism. These processes are associated with energy metabolism, nutrient uptake, and stress responses [53,88,54], offering insights into the invasive adaptability of M. micrantha. (Fig. 3G and Fig. S9).

Association between DNA methylation and gene expression

DNA methylation in genes and their flanking regions is known to play a role in gene expression regulation [5, 56]. However, how DNA methylation regulates gene expression in M. micrantha and M. cordata remains unknown. We first calculated the correlation between methylation and transcriptome data in both species and observed that the Spearman correlation coefficients were higher for the same organs than for different organs. This finding suggests a reliable association between the two datasets (Fig S10). To explore the relationship between gene expression and methylation and identify potential differences in their regulatory patterns, we excluded genes with undetectable expression levels (FPKM = 0). The remaining genes were divided into three equal-sized groups based on their expression levels: low, middle, and high (Fig. 4A). We then examined their methylation levels. Consistent with previous studies [45], genes with moderate expression levels in M. micrantha and M. cordata exhibited the highest CG methylation levels in gene bodies. Additionally, methylation levels in the upstream regions were significantly higher than those in low-expressed genes. Non-CG methylation (CHG and CHH) in gene and flanking regions showed distinct regulatory patterns. Low-expressed genes exhibited significantly higher CHG and CHH methylation in gene body regions, suggesting a suppressive role for non-CG methylation. Furthermore, with the exception of CHG methylation in downstream regions, high-expressed genes had significantly higher non-CG methylation in flanking regions, particularly in the upstream regions, implying a promotion of gene expression by upstream non-CG methylation in both species. The regulatory pattern was opposite for CHG and CHH methylation in the downstream regions (Fig. 4B), similar to findings in tea plants [35].

Next, we categorized the genes of M. micrantha and M. cordata into two groups: expressed (FPKM > 0) and non-expressed (FPKM = 0), and examined their methylation levels in different sequence contexts. Expressed genes exhibited significantly higher methylation levels in the gene body regions compared to non-expressed genes across all three sequence contexts (CG, CHG and CHH). In the flanking regions, except for CHG methylation in the upstream region of M. micrantha, non-expressed genes showed higher methylation levels in the CG and CHG contexts, consistent with the gene body regions. Conversely, in the CHH context, expressed genes had significantly higher methylation levels. Notably, the average CG and CHG methylation levels of non-expressed genes in M. micrantha were lower than those in M. cordata, particularly in the CHG context within the gene body regions (average 0.43 vs. 0.51). Interestingly, the methylation levels of expressed genes were comparable between the two species (Fig. 4C). This suggests that non-expressed genes in M. cordata experience more severe methylation disturbances, which explains the higher CG and CHG methylation levels in both the gene body and flanking regions, especially in CHG methylation within the gene body.

These results were consistent across two additional organs of M. micrantha and M. cordata, indicating the conserved regulation of DNA methylation in gene expression processes across these species (Fig. S11 and S12). To validate our findings, Spearman correlation analysis between gene expression and methylation levels in three genomic regions (upstream, gene body, and downstream regions) was performed across different organs of both species. Consistent with previous results, DNA methylation in gene body regions showed a significant negative correlation with gene expression across all three sequence contexts (CG, CHG, and CHH). In upstream regions, M. micrantha exhibited positive correlations between non-CG methylation and gene expression, while M. cordata showed positive correlations only in the CHH context. In downstream regions, both species showed positive correlations between CHH methylation and gene expression (Fig. 4D). These results indicate that DNA methylation can influence gene expression and, in most cases, suppress it. DNA methylation in different genomic regions and sequence contexts plays distinct roles in gene expression within the same species, but these roles varying across species. Moreover, the 3’UTR region of M. micrantha had significantly higher methylation levels than M. cordata across all sequence contexts. Therefore, we investigated whether the methylation levels of the 3’UTR regions were associated with gene expression. However, correlation analysis between 3’UTR methylation levels and gene expression in M. micrantha showed no significant correlation (Fig. 4E and Fig. S13).

Identification of differentially methylated regions (DMRs) among different organs

To investigate DNA methylation differences among various organs of M. micrantha, we calculated the average DNA methylation levels for each sequence context using a 200 bp window. Subsequently, we identified DMRs between different organs of M. micrantha. A similar analysis was conducted for M. cordata. A total of 16,007 CG-DMRs, 14,715 CHG-DMRs, and 421,194 CHH-DMRs were identified among different organs of M. micrantha, while in M. cordata, we identified 14,886 CG-DMRs, 15,415 CHG-DMRs, and 390,409 CHH-DMRs (Fig. 5A and Table S4). Regardless of species and methylation type, the comparison between leaf and root revealed the highest number of DMRs, whereas the comparison between stem and root identified the fewest DMRs. Furthermore, CHH-DMRs were consistently the most abundant type across organs in both species, with hyper-DMRs predominating. We also observed a significant enrichment of hyper- CHH-DMRs in the root, followed by stem and leaf, consistent with the increase in CHH methylation levels from leaf to root (Fig. 5A and Table S4).

We then examined the distribution of DMRs across different genomic features, such as TEs, intergenic regions, promoters, exons, introns, 5’UTRs, and 3’UTRs. We found that CG-DMRs were primarily enriched in intergenic regions, while non-CG DMRs predominantly occurred in TE regions. This pattern was consistent across both species and aligned with previous research in sugarcane [18]. In genic regions (promoters, exons, introns, 5’UTRs, and 3’UTRs), CG-DMRs had the highest proportion, followed by CHG-DMRs and CHH-DMRs. Additionally, for the same sequence context and comparison group, the proportion of genic region DMRs in M. micrantha was consistently higher than in M. cordata, especially in the exon regions (Fig. 5B). This suggests that gene regions in different organs of M. micrantha are subject to more differentiated methylation regulation. Next, we focused on the transposon classification in the overlapping regions of DMRs between the two species. We found that DMRs primarily overlapped with Copia, Gypsy, and hAT transposons in all three sequence contexts. Interestingly, in M. micrantha, the CHH-DMRs exhibited the highest overlap with hAT transposons, whereas in M. cordata, the highest overlap was with Gypsy transposons (Fig. S14).

Characterization of DMR-associated differential expression genes

Considering the relationship between DNA methylation and gene expression, DMRs may influence the expression of adjacent genes. We defined DMR-associated genes as those overlapping with DMRs within the gene body and its upstream and downstream 2,000 bp regions. In all comparison groups across organs in M. micrantha and M. cordata, over 51% of DEGs were associated with DMRs. The leaf and root comparison groups in both species identified the highest number of DMR-associated DEGs, with 6,535 and 5,375, respectively, accounting for 74% and 75% of the total DEGs. Conversely, the root and stem comparison groups in both species identified the fewest DMR-associated DEGs, with the lowest proportion relative to the total DEGs (Fig. 5C). Furthermore, the random sampling distribution further confirmed the association between DMRs and DEGs (Fig.S15). To understand the key roles of DMR-associated DEGs in different organs of M. micrantha and M. cordata, we conducted KEGG enrichment analysis for upregulated DMR-associated DEGs in each organ compared to the other organs. The results indicated that M. micrantha and M. cordata shared similar functional categories in the same organs. In all organ comparisons of both species, there was significant enrichment in the biosynthesis of secondary metabolites. Additionally, in leaves, upregulated DMR-associated DEGs showed significant enrichment in processes such as photosynthesis, photosynthesis-antenna proteins, and plant-pathogen interactions. In stems compared to roots, upregulated DEGs were significantly enriched in photosynthesis and photosynthesis-antenna proteins, while upregulated DMR-associated DEGs in stems compared to leaves showed significant enrichment in plant hormone signal transduction. Upregulated DMR-associated DEGs in roots were enriched in plant hormone signal transduction, phenylpropanoid biosynthesis, and ABC transporters (Fig. 5D and Fig. S16). Therefore, DMR-associated DEGs in different organs of different species participate in fundamental biological pathways with significant organ specificity.

The expression of photosynthesis-related genes is regulated by DNA methylation

We further investigated whether the impact of inter-organ DMRs on DEGs was consistent with the patterns observed within individual organs across all three sequence contexts. For comparison, we focused on the two organs with the greatest differences in M. micrantha: the leaf and root. We extracted the upregulated genes in the leaf and root separately and conducted statistical analyses based on the types (hyper- and hypo-), locations (upstream, body, and downstream), and sequence contexts (CG, CHG and CHH) of the identified DMRs. We found that CG-hypo-DMRs were significantly enriched in the gene bodies and flanking regions of upregulated genes, particularly in the gene body regions, which contained numerous hypo-DMRs. Similar results were observed in the CHG context, consistent with the relationship between gene expression and methylation within individual organs. However, the situation in the CHH context was markedly different. Hypo-DMRs were significantly enriched in the three positional regions of upregulated genes in leaf, while in root, hyper-DMRs were significantly enriched in these regions (Fig. 6A). This discrepancy might be due to the higher CHH methylation levels in the root. Nevertheless, the number of hypo-DMRs in the gene body regions of both leaf and root remained higher than that in the flanking regions, indicating that the influence of CHH methylation on DEGs between organs, at least between leaf and root organs, was limited.

We then focused on the upregulated genes in the leaf associated with CG-DMRs in the gene body regions and conducted GO analysis on these genes. We found significant enrichment of genes involved in the photosynthetic system (Fig. 6B). We extracted 30 genes enriched in this GO term and identified genes involved in photosystem I, such as evm. model.Contig210_pilon.63 (psaD), evm.model.Contig294_pilon.111(psaL), and evm. model.Contig31_pilon.158 (psaN), as well as genes involved in photosystem II, such as evm. model.Contig32_pilon.310 (psbW). Additionally, we identified genes encoding chlorophyll a/b-binding proteins involved in light harvesting, such as evm.model.Contig32_pilon.85(CAB3C) and evm. model.Contig350_pilon.45(CAB6A). More than half of these genes (18/30) were highly expressed in the leaf (FPKM > 100), and the vast majority (25/30) were also upregulated compared to the stem (Fig. 6C and Table S5). The expression levels of half of these genes were further validated by RT-qPCR (Fig. S17). Except for evm.model.Contig219_pilon.16 (psbA), the gene body regions of all other genes were associated with hypo-DMRs in the CG sequences context. Meanwhile, the upstream regions of five genes and the downstream regions of seven genes overlapped with hypo-DMRs (Fig. 6D and Table S5). This result indicates the importance of CG hypo-methylation, particularly in gene body regions, for the expression of photosynthesis-related genes.

We further investigated the relationship between non-CG-DMRs and these genes. Exactly half of the genes (15/30) were associated with CHG-DMRs. Among them, the gene body regions of 14 genes overlapped with hypo-DMRs, while only one gene was associated with hyper-DMRs. Only three genes were found to overlap with CHG-DMRs in the flanking regions (Fig. 6D and Table S5). Additionally, the majority of these genes (28/30) were also associated with CHH-DMRs, with numerous CHH-DMRs identified within these genes. To avoid the influence of the high CHH methylation levels in the root, we focused on genes associated with flanking regions. We found that the upstream regions of 18 genes overlapped with CHH-DMRs, of which only two were associated with hypo-DMRs. Similarly, the downstream regions of 16 genes overlapped with CHH-DMRs, with only three associated with hypo-DMRs (Fig. 6D and Table S5). This high proportion of CHH hyper-DMRs distribution was apparently different from the statistical results shown in Fig. 6A, indicating that CHH methylation in the flanking regions might also be involved in the regulation of gene expression. Therefore, the expression of these genes might result from the combined action of one or all three types of methylation, as exemplified by genes such as evm.model.Contig60_pilon.265 and evm.model.Contig31_pilon.158, etc. These genes were associated with all three types of DMRs, with numerous different types of DMRs identified in their upstream, gene body, and downstream regions. It is worth noting that the methylation levels of all three types in the gene body regions of the stem are lower than those in the leaf but higher than those in the root, while the CHH methylation levels in the flanking regions are exactly the opposite. This is consistent with previous findings and also aligns with the fact that the stem of M. micrantha possesses some photosynthetic capability (Fig. 6E and Fig. S18).

To further validate our results, we also conducted GO analysis on upregulated DEGs in the leaf compared to the root, including genes overlapping with hypo-CG-DMRs in the upstream and downstream regions, genes overlapping with hypo-CHG-DMRs in gene body and downstream regions, and genes overlapping with hyper-CHH-DMRs in the upstream and downstream regions. We found significant enrichment of genes involved in photosynthesis (Fig. S19). These results suggest that DNA methylation may be an important regulatory factor in the photosynthetic function of M. micrantha leaves.

The CHH methylation is positively correlated with 24-nt siRNA

Previous studies have shown that small RNAs can guide de novo DNA methylation at their target sites through the RdDM pathway [57]. Therefore, we performed small RNA sequencing in the root, stem, and leaf of the same M. micrantha plant, with three biological replicates for each sample. After filtering the raw data, we found that 24-nt siRNAs were the most abundant type in all three organs, followed by 21-nt and 22-nt siRNAs (Fig. 7A), consistent with previous studies in mulberry [23]. Given the unclear roles of different siRNAs in CHH methylation in M. micrantha, we analyzed the effects of the nucleotide composition of 21-nt, 22-nt, and 24-nt siRNAs, as well as their immediate 10 bp flanking regions, on the methylation of both the sense and antisense DNA strands. Generally, the distribution pattern of cytosines in small RNA sequences correlates with methylation of cytosines on the sense strand, whereas guanines correlate with methylation of cytosines on the antisense strand. We observed that changes in cytosine methylation on both strands were closely associated with cytosines and guanines in the 24-nt siRNA sequences, showing the highest enrichment compared to 21-nt and 22-nt siRNAs (Fig. 7B). Similar results were observed in the root and stem (Fig. S20). This suggests a close relationship between DNA methylation and 24-nt siRNAs.

Next, we analyzed the differences in CHH methylation levels between mapping and no mapping regions for 21-nt, 22-nt, and 24-nt siRNAs. We found that it was the 24-nt siRNAs, rather than the 21-nt or 22-nt siRNAs, that exhibited higher methylation levels in mapping regions compared to no mapping regions (Fig. 7C). To determine whether the increase in CHH methylation in the mapping regions of 24-nt siRNAs was due to activation of the RdDM pathway, we identified eight genes potentially involved in the RdDM pathway by homology alignment in the M. micrantha genome. We examined their expression levels in different organs. The expression levels of four genes, MmNRPD, MmRDR2, and two MmDRM2, showed a progressively increasing pattern in the leaf, stem, and root (Fig. S21), consistent with the CHH methylation patterns in the three organs. However, the other four genes did not exhibit a similar pattern. This suggests that the differential CHH methylation among the different organs may be partially related to the RdDM pathway.

We then investigated whether CHH methylation in the gene-enriched region was associated with siRNAs. We generated a distribution density map of 21-nt, 22-nt, and 24-nt siRNA in the three organs, and a significant enrichment of 24-nt siRNAs in gene-rich regions was observed (Fig. 7D). We calculated their correlation coefficients and found that in all three organs, the correlation coefficient between 24-nt siRNAs (R = 0.48, leaf) and gene density is greater than that between 21- nt siRNAs (R = 0.38, leaf) and 22-nt siRNAs (R = 0.1) (Fig. S22). Furthermore, a significant decrease in 24-nt siRNA enrichment was observed in the TE-enriched regions (Fig. 7D). We further analyzed the abundance of 24-nt siRNAs in the gene body and its upstream and downstream regions. The results showed that the abundance of 24-nt siRNAs in the gene body region was higher than in the flanking regions (Fig. 7E), which differs from studies in apple [58] and castor bean [57], indicating that siRNAs regulation varies across species. Specifically, except in the root, the abundance of 24-nt siRNAs in the upstream region was higher than in the downstream region (Fig. 7E), consistent with the higher upstream methylation levels compared to downstream in M. micrantha in the CHH sequence contexts. Interestingly, the abundance of siRNAs was higher in the flanking regions than in TE regions (Fig. 7F), which contrasts with the distribution pattern observed in gene regions. Together with the relationship between 24-nt siRNAs and CHH methylation, these results further indicate that 24-nt siRNAs are involved in mediating CHH methylation in gene regions of M. micrantha.

Finally, we sought to determine whether the hyper-CHH methylation in the flanking regions of photosynthetic genes is associated with 24-nt siRNAs. We discovered regions enriched with 24-nt siRNAs in the flanking regions of photosynthetic genes in the leaf, which exhibit significantly higher CHH methylation levels compared to the root, where these regions have little to no distribution of 24-nt siRNAs (Fig. 7G and Fig. S23). This result suggests that 24-nt siRNAs modulate the CHH methylation levels in the flanking regions of organ-specific functional genes, thereby affecting the expression levels of these genes in different organs.

Identification of differentially methylated variants (DMVs) in M. micrantha

DMVs are genomic regions that are either lowly methylated or unmethylated, widely present across various species, and believed to be associated with seed development and tissue-specific gene regulation [59]. Using the method described by Chen et al. [60], we identified 48,420, 47,485, and 50,080 DMVs in the stem, root, and leaf of M. micrantha, respectively (Fig. 8A). Most of these DMVs exhibited methylation levels of less than 2% across all cytosine contexts, indicating a state of low methylation (Fig. S24). Additionally, combined DMVs account for 4.2% of the total genome length (66.1 Mb in leaf), with minimal variation observed between different organs. This ratio is higher than in sugarcane [18] but lower than in castor bean [59], suggesting species-specific distribution of DMVs. In all three organs, we observed a DMV region with a length of 16.8 kb, which contains one protein-coding gene, evm.model.Contig1410_pilon.24 (RPS4) (Fig. 8B).

We then analyzed the conservation of DMVs and found that over 65% of DMVs identified in each organ were shared with other organs, indicating a high level of conservation (Fig. 8C). Approximately 50% of DMVs overlapped with gene bodies, similar to findings in castor bean [59]. Additionally, about 67% of DMVs overlapped with gene body and flanking regions, while the remaining 33% were located in intergenic regions (Fig. 8D). Previous research has shown that highly expressed genes tend to have lower DNA methylation levels [5]. Therefore, we calculated the proportions of expressed and non-expressed genes among all genes and DMV genes. However, we did not observe a significantly higher proportion of expressed genes among DMV genes; in fact, the proportion of expressed genes in leaf was slightly decreased (Fig. 8E). This result may be due to fewer DMV genes in M. micrantha or because gene expression requires appropriate regulation of DNA methylation rather than complete hypo-methylation.

Discussion

In this study, we employed whole-genome bisulfite sequencing to generate high-resolution DNA methylation maps for the roots, stems, and leaves of M. micrantha and M. cordata. Previous research indicates that transposons not only drive genome expansion but are also associated with high levels of genome methylation [35, 61]. The transposon content in M. micrantha (78%) and M. cordata (82%) is similar to that of tea plants (Camellia sinensis var. sinensis) (87%) and maize (84%), with comparable CG and CHG methylation levels. However, their CHH methylation levels are higher than in maize but lower than those in tea. A previous study found a positive correlation between repeat number and mCG and mCHG, but not with mCHH [16], possibly due to greater variation in mCHH between species. We also observed that methylation levels in M. micrantha root, stem, and leaf are higher than those previously found in seed [42]. This is consistent with studies in other species such as Silene latifolia and Arabidopsis, which suggests an increase in DNA methylation levels in vegetative organs compared to seedling cotyledons [62, 63]. Additionally, M. micrantha and M. cordata exhibit similar levels of methylation across all three sequence contexts (CG, CHG, and CHH), except in the 3’UTR regions. The 3’UTR is where microRNAs bind to regulate gene activity [64, 65]. Studies in humans and mice have identified a positive correlation between the 3’UTR and gene expression, with hypermethylation of the 3’UTR as a feature of transcribed genes [66, 67]. However, such a strong positive correlation was not observed in M. micrantha. Therefore, the high methylation of the 3’UTR, as a characteristic of M. micrantha compared to M. cordata, ensures further investigation into its role in the genome.

DNA methylation is known to participate in various biological processes by regulating gene expression, generally repressing it [5]. However, in our study, DNA methylation in M. micrantha and M. cordata does not always negatively regulate gene expression. For example, although CG methylation in gene bodies and flanking regions is negatively correlated with gene expression, moderately expressed genes tend to exhibit higher levels of DNA methylation in gene body regions, similar to findings in tea plants [45]. In some species, CG methylation in gene bodies is positively correlated with gene expression, although highly expressed genes do not necessarily show the highest levels of CG methylation. This phenomenon has been observed in species such as sugarcane [18], cassava [68], and pineapple [24]. Differences in CG methylation patterns may be related to genome size and TE content. For example, the TE content of M. micrantha, M. cordata, and Camellia sinensis is around 80%, whereas the TE content of sugarcane and pineapple is 58% and 44%, respectively [69, 70]. Non-CG methylations (CHG and CHH) in gene bodies and flanking regions differentially regulate gene expression. In M. micrantha, upstream methylation of expressed genes is positively correlated with gene expression, while gene body methylation is negatively correlated. Differential regulatory patterns are observed in downstream regions for CHG and CHH methylation; for example, CHG methylation is negatively correlated with gene expression, whereas CHH methylation is positively correlated. This pattern was conserved in M. micrantha and M. cordata, and was consistent with findings in tea plants [35]. One explanation for the positive regulation of gene expression by CHH methylation in gene-flanking regions is that mCHH islands present in upstream and downstream regions prevent the transcription of nearby transposons [26, 34], although this association is not universal across species [16]. More research is needed to elucidate the underlying mechanisms of this differential regulation.

DNA methylation regulates gene expression, and DMR-related DEGs are enriched in many important biological pathways. For example, genes involved in tanshinone biosynthesis are upregulated during the rapid accumulation period and are associated with CHH methylation in the promoter and downstream regions [71]. During orange fruit development and ripening, DNA methylation levels gradually increase, with upregulated genes involved in fruit ripening-related processes, such as response to abscisic acid and abscisic acid-activated signaling pathways [72]. In our studies on M. micrantha and M. cordata, DMR-associated DEGs were enriched in organ-specific functional pathways. For instance, DMR-associated genes upregulated in leaves were significantly enriched in the photosynthetic pathway. Genes upregulated in stems were enriched in metabolism and plant hormone signal transduction pathways, while those upregulated in roots were enriched in pathways related to the biosynthesis of secondary metabolites. Similar findings were observed in sugarcane [18]. Furthermore, GO enrichment analysis of genes upregulated in M. micrantha leaf compared to roots, which overlapped with CG hypo-DMRs in gene bodies and flanking regions, as well as CHG hypo-DMRs in gene bodies and downstream regions, and CHH hyper-DMRs in flanking regions, revealed significant enrichment in the photosynthetic pathway. These genes were predominantly associated with hypo-methylation in gene body regions, indicating the control of DNA methylation over photosynthesis. The relationship between DNA methylation and photosynthesis has also been observed in other species. For example, in apple seedlings, genes associated with photosynthesis were upregulated and tended to be hypo-methylated [73]. Tissue comparisons in Catharanthus roseus revealed that genes associated with photosynthesis were overexpressed and strongly correlated with hypo-methylation, suggesting that the establishment of photosynthesis in green tissues is likely triggered by the strong hypo-methylation of photosynthesis-related genes [74]. Additionally, DNA methylation mediated epigenetic regulation of photosynthesis-related genes has been observed in the suspension cells of Chinese white poplar [75] and sycamore [76]. Previous studies have suggested that selective methylation of DNA is a potential mechanism for suppressing the transcription of nuclear genes for photosynthesis in non-photosynthetic plant cells [76]. Photosynthetic genes in leaf might be selectively hypo-methylated to ensure high expression, while roots, as non-photosynthetic organ, were hyper-methylated to suppress the expression of photosynthetic genes. Therefore, it is likely that in the M. micrantha genome, the expression of photosynthetic genes in leaf is regulated through the maintenance or demethylation of specific DNA methylation sites. The leaves of M. micrantha exhibit photosynthetic rates similar to those of C4 plants, and its stems also demonstrate higher photosynthetic capacity than those of some other species, contributing to its rapid growth [38, 77]. The regulation of the photosynthetic function in M. micrantha leaf by DNA methylation suggests a potential role in its high photosynthetic rates. However, the extent to which DNA methylation contributes to the higher photosynthetic rates of M. micrantha compared to other species remains to be further investigated.

In the RdDM pathway, 24-nt siRNAs guide DRM2 to methylate CHH sites [4, 78]. In our research, 24-nt siRNAs were the most abundant, guiding CHH methylation levels in mapping regions to be higher than in no-mapping regions, as opposed to 21-nt and 22-nt siRNAs. This suggests that the RdDM pathway is likely activated in M. micrantha, consistent with studies in apple [58], mulberry [23], and castor bean [57]. Additionally, we noticed that although CHH methylation levels were highest in roots, the highest abundance of 24-nt siRNAs was not observed. Among several genes associated with the production of 24-nt siRNAs in the RdDM pathway, MmNRPD2 and MmRDR2 were expressed at the highest levels in roots, while MmDCL3 was expressed at the lowest level. Therefore, the down-regulation of MmDCL3 may be the main reason for the reduced abundance of 24-nt siRNA in roots. In the subsequent 24-nt siRNA-guided DNA methylation process, two MmDRM2 genes were expressed at the highest levels in roots, followed by stems and leaves, while MmNRPE and two MmAGO4 genes did not show this expression pattern. This suggests that the gradual increase in CHH methylation in leaves, stems, and roots is not solely established and maintained by the RdDM pathway; other factors or pathways may also be involved in mediating CHH methylation in M. micrantha. Additionally, the distribution of 24-nt siRNAs is positively correlated with gene density and is highly enriched in gene bodies, which may represent a unique siRNA distribution pattern in M. micrantha, indicating significant species specificity. Previous studies have suggested that mCHH is significantly positively correlated with the expression of siRNAs in gene flanking regions and affects gene expression in switchgrass by regulating DNA methylation [50]. In M. micrantha, siRNAs in gene flanking regions play a crucial role in regulating the expression of organ-specific functional genes, offering valuable insights for future research.

DNA methylation valleys (DMVs), or unmethylated regions are typically enriched with transcription factors and developmental regulators and are associated with histone modifications [59, 79, 80]. For instance, significant histone modification reorganization occurs in seed-specific genes during castor bean seed development [59]. Research on DMVs in plants remains limited, with identification and studies conducted in only a few species, such as soybean [79, 80], Arabidopsis [79, 80], maize [81], castor bean [59], and sugarcane [18]. DMVs are highly conserved during development and across different species and are associated with tissue-specific genes [59]. In our study of M. micrantha, the identified DMVs accounted for only a small part of the genome. In each organ, only a few dozen DMV genes were identified. This may be related to the larger TE content and higher DNA methylation levels of M. micrantha. Additionally, most of the identified DMVs were conserved in the three organs and were mainly located in gene regions, suggesting the potential role of DMVs in regulating gene expression.

Conclusions

DNA methylation is associated with plant growth, development, and stress responses. In this study, we present the DNA methylation patterns in the roots, stems, and leaves of the invasive species M. micrantha and the native species M. cordata, as well as the relationship between DNA methylation, gene expression, and siRNA. Our results indicate that the expression of organ-specific, function-related genes is regulated by DNA methylation and 24-nt siRNAs. These findings provide crucial epigenetic information for understanding the role of DNA methylation in the growth and development of M. micrantha. In future research, identifying methylation patterns or methylation markers associated with the invasion of M. micrantha could enable the development of specific control strategies.

Materials and methods

Plant materials and sample collection

The seedlings of Mikania micrantha H.B.K. and Mikania cordata (Burm. f.) B.L. Rob. at the same growth stage were cultivated in the greenhouse of Sun Yat-sen University. After six months, three vigorously growing plants of each species were selected for sampling. Mature roots, stems, and leaves were collected and flash-frozen in liquid nitrogen. Genomic DNA and total RNA were extracted using a modified cetyltrimethylammonium bromide (CTAB) method and the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA), respectively. DNA concentration and integrity were measured using a NanoPhotometer^® spectrophotometer (IMPLEN, CA, USA), and RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Qualified DNA/RNA samples were utilized for subsequent experiments.

Whole-genome bisulfite sequencing and analysis

The genomic DNA was fragmented into 100–300 bp fragments using Sonication (Covaris, Massachusetts, USA) and purified using the MiniElute PCR Purification Kit (QIAGEN, MD, USA). The fragmented DNA was subjected to end repair, followed by the addition of a single “A” base at the 3’ end, and then ligated to sequencing adapters. The DNA fragments with adapters were bisulfite-treated using the ZYMO EZ DNA Methylation-Gold Kit (ZYMO, CA, USA), which converts unmethylated cytosines to uracils. Finally, the converted DNA fragments were PCR amplified and sequenced using Illumina HiSeq™ 2500 by Gene Denovo Biotechnology Co. (Guangzhou, China). After removing adapter sequences and low-quality reads, the filtered data were aligned to the M. micrantha and M. cordata reference genomes using BSMAP [82] (version: 2.90), respectively. Unique sequences aligned to the reference genomes were selected for subsequent analysis.

Transcriptome sequencing and data analysis

After extracting total RNA, mRNA from eukaryotic cells was enriched using magnetic beads containing Oligo(dT). RNA-seq libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB#7530, New England Biolabs, Ipswich, MA, USA) and sequenced on an Illumina NovaSeq 6000 platform. Initial read quality control was performed using FASTP [83]. Bowtie2 [84] (version 2.2.8) was employed to map reads to the ribosomal RNA (rRNA) database, followed by the removal of reads mapped to rRNA. The remaining clean reads were further utilized for assembly and gene abundance calculation. HISAT2 [85] (version 2.4) was used to map the clean reads of M. micrantha and M. cordata to their respective reference genome sequences. StringTie v1.3.1 [86, 87] was then employed to assemble the mapped reads for each sample. For each transcript region, the RSEM [88] software was used to calculate FPKM (fragments per kilobase of transcript per million mapped reads) values, quantifying expression abundance and variation.

Differential expression analysis of RNA between different groups was conducted using DESeq2 [52] software. Genes with false discovery rate (FDR) < 0.05 and a 2-fold change were considered differentially expressed genes. The DEGs between the comparison groups of different organs for M. micrantha and M. cordata are presented in Table S6 and Table S7, respectively. Organ-specific genes were defined as those genes significantly upregulated in one organ compared to the other two organs, while remaining nonsignificant in the other two organs.

Small RNA sequencing and data analysis

Small RNA sequencing was performed on RNA samples used in the transcriptome analysis of M. micrantha. Initially, RNA molecules in the size range of 18–30 nt were enriched using polyacrylamide gel electrophoresis (PAGE). Subsequently, 3’ and 5’ adapters were ligated separately. Reverse transcription ligated products were amplified by PCR, and PCR products of 140–160 bp were enriched to generate cDNA libraries, which were sequenced using the Illumina HiSeq Xten. Following this, the raw data underwent quality control and filtering. To obtain siRNAs, non-coding small RNAs such as rRNA, scRNA, snoRNA, snRNA, and tRNA were first filtered out by aligning to databases. Next, mRNA degradation fragments originating from exons of coding genes were filtered by aligning to the genome. Finally, miRNAs identified in the samples were removed, leaving primarily siRNAs among the remaining small RNAs.

Identification of differentially methylated regions (DMRs) and DMR-associated genes

For the differential DNA methylation analysis, the methylkit [89] (V1.4.1) software was employed. Initially, sites with low sequencing depth were filtered out, with a minimum sequencing depth of ≥ 4. A 200 bp window was used to scan the entire genome, calculating the average DNA methylation rate for each window across different samples. Differences in methylation levels within each window across samples were compared. For differential methylation in CG context, a minimum of 5 GC sites within the window was required, with an absolute value of the difference in methylation ratio ≥ 0.25 and Q-value ≤ 0.05. For CHG, number in a window ≥ 5, with an absolute value of the difference in methylation ratio ≥ 0.25 and a Q-value ≤ 0.05. For CHH, number in a window ≥ 15, with an absolute value of the difference in methylation ratio ≥ 0.15 and a Q-value ≤ 0.05. Genes with DMR overlap within the gene body and adjacent 2 kb (upstream or downstream) regions were defined as DMR-associated genes. The DMR information for the different organ comparison groups of M. micrantha and M. cordata in different sequence contexts is presented in Table S8 and Table S9, respectively.

Identification of DNA methylation valley (DMV) in M. micrantha

The identification of differentially methylated valley (DMV) regions followed the previously described method [60]. Firstly, the genome of M. micrantha was divided into 1 kb bins, and then the methylation levels within each bin were calculated across all three sequence contexts (CG, CHG, and CHH). Windows were defined as DMVs where the methylation levels were less than 5% across all three sequence contexts in each sample. Subsequently, overlapping DMVs were merged into contiguous DMV regions for further analysis. Genes and their flanking 1 kb regions entirely contained within DMVs were defined as DMV-associated genes.

qRT-PCR analysis

Fifteen genes related to the photosynthesis pathways were selected for qRT-PCR to validate their expression levels. Total RNA extraction from M. micrantha root, stem, and leaf was performed using the HiPure HP Plant RNA Mini Kit (Magen, Guangzhou, China) according to the manufacturer’s instructions. Subsequently, 1 µg of total RNA from each sample was reverse transcribed to cDNA in a 20 µl reaction volume using FastKing gDNA Dispelling RT SuperMix (TIANGEN, Beijing, China). qPCR was performed on a Roche LightCycler 480 II system (Basel, Switzerland) using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) with the program: 95 °C for 30 s; 40 cycles of 95 °C for 10 s and 60 °C for 30 s. For each condition, we performed three technical replicates and three independent biological replicates. Mmactin was used as a reference for normalization. The PCR primers are listed in Table S10.

GO and KEGG enrichment analysis

GO and KEGG analysis were performed using the OmicShare (www.omicshare.com/tools) online tools. Only terms with Q-value less than 0.05 were used for further analysis.

Data availability

The raw data of RNA sequencing, small RNA sequencing, and DNA bisulfite libraries for this study have been submitted to the NCBI with accession number PRJNA1126335.

Abbreviations

DMRs:: Differentially methylated regions
DEGs:: Differential expression genes
RdDM:: RNA-directed DNA methylation
GO:: Gene ontology
KEGG:: Kyoto Encyclopedia of Genes and Genomes
WGBS:: Whole genome bisulfite sequencing
FPKM:: Fragments per kilobase per million mapped reads
DMVs:: DNA methylation valleys
TE:: Transposable element

References

Slotkin RK, Martienssen R. Transposable elements and the epigenetic regulation of the genome. Nat Rev Genet. 2007;8(4):272–85.
Article CAS PubMed Google Scholar
Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6(8):597–610.
Article CAS PubMed Google Scholar
Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SW, Chen H, Henderson IR, Shinn P, Pellegrini M, Jacobsen SE, et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell. 2006;126(6):1189–201.
Article CAS PubMed Google Scholar
Law JA, Jacobsen SE. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet. 2010;11(3):204–20.
Article CAS PubMed PubMed Central Google Scholar
Zhang H, Lang Z, Zhu JK. Dynamics and function of DNA methylation in plants. Nat Rev Mol Cell Biol. 2018;19(8):489–506.
Article CAS PubMed Google Scholar
Matzke MA, Mosher RA. RNA-directed DNA methylation: an epigenetic pathway of increasing complexity. Nat Rev Genet. 2014;15(6):394–408.
Article CAS PubMed Google Scholar
Zhong X, Du J, Hale CJ, Gallego-Bartolome J, Feng S, Vashisht AA, Chory J, Wohlschlegel JA, Patel DJ, Jacobsen SE. Molecular mechanism of action of plant DRM de novo DNA methyltransferases. Cell. 2014;157(5):1050–60.
Article CAS PubMed PubMed Central Google Scholar
Matzke MA, Kanno T, Matzke AJ. RNA-Directed DNA methylation: the evolution of a Complex Epigenetic Pathway in Flowering plants. Annu Rev Plant Biol. 2015;66:243–67.
Article CAS PubMed Google Scholar
He XJ, Chen T, Zhu JK. Regulation and function of DNA methylation in plants and animals. Cell Res. 2011;21(3):442–65.
Article CAS PubMed PubMed Central Google Scholar
Stroud H, Do T, Du J, Zhong X, Feng S, Johnson L, Patel DJ, Jacobsen SE. Non-CG methylation patterns shape the epigenetic landscape in Arabidopsis. Nat Struct Mol Biol. 2014;21(1):64–72.
Article CAS PubMed Google Scholar
Arora H, Singh RK, Sharma S, Sharma N, Panchal A, Das T, Prasad A, Prasad M. DNA methylation dynamics in response to abiotic and pathogen stress in plants. Plant Cell Rep. 2022;41(10):1931–44.
Article CAS PubMed Google Scholar
Choi Y, Gehring M, Johnson L, Hannon M, Harada JJ, Goldberg RB, Jacobsen SE, Fischer RL. DEMETER, a DNA glycosylase domain protein, is required for endosperm gene imprinting and seed viability in arabidopsis. Cell. 2002;110(1):33–42.
Article CAS PubMed Google Scholar
Gong Z, Morales-Ruiz T, Ariza RR, Roldan-Arjona T, David L, Zhu JK. ROS1, a repressor of transcriptional gene silencing in Arabidopsis, encodes a DNA glycosylase/lyase. Cell. 2002;111(6):803–14.
Article CAS PubMed Google Scholar
Morales-Ruiz T, Ortega-Galisteo AP, Ponferrada-Marin MI, Martinez-Macias MI, Ariza RR, Roldan-Arjona T. Demeter and Repressor of silencing 1 encode 5-methylcytosine DNA glycosylases. Proc Natl Acad Sci U S A. 2006;103(18):6853–8.
Article CAS PubMed PubMed Central Google Scholar
Ortega-Galisteo AP, Morales-Ruiz T, Ariza RR, Roldan-Arjona T. Arabidopsis DEMETER-LIKE proteins DML2 and DML3 are required for appropriate distribution of DNA methylation marks. Plant Mol Biol. 2008;67(6):671–81.
Article CAS PubMed Google Scholar
Niederhuth CE, Bewick AJ, Ji L, Alabady MS, Kim KD, Li Q, Rohr NA, Rambani A, Burke JM, Udall JA, et al. Widespread natural variation of DNA methylation within angiosperms. Genome Biol. 2016;17(1):194.
Article PubMed PubMed Central Google Scholar
Bartels A, Han Q, Nair P, Stacey L, Gaynier H, Mosley M, Huang QQ, Pearson JK, Hsieh TF, An YC et al. Dynamic DNA methylation in Plant Growth and Development. Int J Mol Sci 2018, 19(7).
Xue Y, Zou C, Zhang C, Yu H, Chen B, Wang H. Dynamic DNA methylation changes reveal tissue-specific gene expression in sugarcane. Front Plant Sci. 2022;13:1036764.
Article PubMed PubMed Central Google Scholar
Chang YN, Zhu C, Jiang J, Zhang H, Zhu JK, Duan CG. Epigenetic regulation in plant abiotic stress responses. J Integr Plant Biol. 2020;62(5):563–80.
Article CAS PubMed Google Scholar
Niu LZ, Xu W, Ma PF, Guo ZH, Li DZ. Single-base methylome analysis reveals dynamic changes of genome-wide DNA methylation associated with rapid stem growth of woody bamboos. Planta. 2022;256(3):53.
Article CAS PubMed Google Scholar
Yamauchi T, Johzuka-Hisatomi Y, Terada R, Nakamura I, Iida S. The MET1b gene encoding a maintenance DNA methyltransferase is indispensable for normal development in rice. Plant Mol Biol. 2014;85(3):219–32.
Article CAS PubMed Google Scholar
Yao Y, Bilichak A, Golubov A, Kovalchuk I. ddm1 plants are sensitive to methyl methane sulfonate and NaCl stresses and are deficient in DNA repair. Plant Cell Rep. 2012;31(9):1549–61.
Article PubMed Google Scholar
Xin Y, Ma B, Zeng Q, He W, Qin M, He N. Dynamic changes in transposable element and gene methylation in mulberry (Morus notabilis) in response to Botrytis Cinerea. Hortic Res. 2021;8(1):154.
Article CAS PubMed PubMed Central Google Scholar
Shi Y, Zhang X, Chang X, Yan M, Zhao H, Qin Y, Wang H. Integrated analysis of DNA methylome and transcriptome reveals epigenetic regulation of CAM photosynthesis in pineapple. BMC Plant Biol. 2021;21(1):19.
Article CAS PubMed PubMed Central Google Scholar
Niu SH, Li J, Bo WH, Yang WF, Zuccolo A, Giacomello S, Chen X, Han FX, Yang JH, Song YT, et al. The Chinese pine genome and methylome unveil key features of conifer evolution. Cell. 2022;185(1):204–.
Article CAS PubMed Google Scholar
Li Q, Gent JI, Zynda G, Song JW, Makarevitch I, Hirsch CD, Hirsch CN, Dawe RK, Madzima TF, McGinnis KM, et al. RNA-directed DNA methylation enforces boundaries between heterochromatin and euchromatin in the maize genome. P Natl Acad Sci USA. 2015;112(47):14728–33.
Article CAS Google Scholar
Cheng C, Daigen M, Hirochika H. Epigenetic regulation of the rice retrotransposon. Mol Genet Genomics. 2006;276(4):378–90.
Article CAS PubMed Google Scholar
Kato M, Miura A, Bender J, Jacobsen SE, Kakutani T. Role of CG and non-CG methylation in immobilization of transposons in arabidopsis. Curr Biol. 2003;13(5):421–6.
Article CAS PubMed Google Scholar
Kumar S, Mohapatra T. Dynamics of DNA methylation and its functions in Plant Growth and Development. Front Plant Sci 2021, 12.
Zilberman D, Gehring M, Tran RK, Ballinger T, Henikoff S. Genome-wide analysis of DNA methylation uncovers an interdependence between methylation and transcription. Nat Genet. 2007;39(1):61–9.
Article CAS PubMed Google Scholar
Song QX, Lu X, Li QT, Chen H, Hu XY, Ma B, Zhang WK, Chen SY, Zhang JS. Genome-wide analysis of DNA methylation in soybean. Mol Plant. 2013;6(6):1961–74.
Article CAS PubMed Google Scholar
Xu JD, Zhou SS, Gong XQ, Song Y, van Nocker S, Ma FW, Guan QM. Single-base methylome analysis reveals dynamic epigenomic differences associated with water deficit in apple. Plant Biotechnol J. 2018;16(2):672–87.
Article CAS PubMed Google Scholar
Lucibelli F, Valoroso MC, Aceto S. Plant DNA methylation: an epigenetic Mark in Development, Environmental interactions, and evolution. Int J Mol Sci 2022, 23(15).
Gent JI, Ellis NA, Guo L, Harkess AE, Yao Y, Zhang X, Dawe RK. CHH islands: de novo DNA methylation in near-gene chromatin regulation in maize. Genome Res. 2013;23(4):628–37.
Article CAS PubMed PubMed Central Google Scholar
Tong W, Li R, Huang J, Zhao H, Ge R, Wu Q, Mallano AI, Wang Y, Li F, Deng W, et al. Divergent DNA methylation contributes to duplicated gene evolution and chilling response in tea plants. Plant J. 2021;106(5):1312–27.
Article CAS PubMed Google Scholar
Day MD, Clements DR, Gile C, Senaratne WK, Shen S, Weston LA, Zhang F. Biology and impacts of Pacific Islands invasive species. 13. Mikania micrantha Kunth (Asteraceae) 1. Pac Sci. 2016;70(3):257–85.
Article Google Scholar
100 of the World’s Worst Invasive Alien Species: A Selection From The Global Invasive Species Database. In: Encyclopedia of Biological Invasions. Edited by Daniel S, Marcel R. Berkeley: University of California Press. 2011: 715–716.
Liu B, Yan J, Li WH, Yin LJ, Li P, Yu HX, Xing LS, Cai ML, Wang HC, Zhao MX et al. Mikania micrantha genome provides insights into the molecular mechanism of rapid growth. Nat Commun 2020, 11(1).
Guo W, Liu Y, Ng WL, Liao PC, Huang BH, Li W, Li C, Shi X, Huang Y. Comparative transcriptome analysis of the invasive weed Mikania micrantha with its native congeners provides insights into genetic basis underlying successful invasion. BMC Genomics. 2018;19(1):392.
Article PubMed PubMed Central Google Scholar
Su YJ, Huang QQ, Wang Z, Wang T. High genetic and epigenetic variation of transposable elements: potential drivers to rapid adaptive evolution for the noxious invasive weed. Ecol Evol. 2021;11(19):13501–17.
Article PubMed PubMed Central Google Scholar
Shen J, Wang Z, Su YJ, Wang T. Associations between population epigenetic differentiation and environmental factors in the exotic weed mile-a-minute (Mikania micrantha). Weed Sci. 2021;69(3):307–32.
Article Google Scholar
Cui C, Wang Z, Su Y, Wang T. Antioxidant regulation and DNA methylation Dynamics during Mikania micrantha seed germination under cold stress. Front Plant Sci. 2022;13:856527.
Article PubMed PubMed Central Google Scholar
Lu J, Yanwu Z, Qiang G, Ying L, Chunmei LI. Cytological study on Mikania cordata(Asteraceae),a native plant in China. Guihaia.
Wang L, Xie JH, Hu JT, Lan BY, You CJ, Li FL, Wang ZJ, Wang HF. Comparative epigenomics reveals evolution of duplicated genes in potato and tomato. Plant J. 2018;93(3):460–71.
Article CAS PubMed Google Scholar
Wang L, Shi Y, Chang XJ, Jing SL, Zhang QJ, You CJ, Yuan HY, Wang HF. DNA methylome analysis provides evidence that the expansion of the tea genome is linked to TE bursts. Plant Biotechnol J. 2019;17(4):826–35.
Article CAS PubMed Google Scholar
Cokus SJ, Feng SH, Zhang XY, Chen ZG, Merriman B, Haudenschild CD, Pradhan S, Nelson SF, Pellegrini M, Jacobsen SE. Shotgun bisulphite sequencing of the genome reveals DNA methylation patterning. Nature. 2008;452(7184):215–9.
Article CAS PubMed PubMed Central Google Scholar
Liang D, Zhang ZJ, Wu HL, Huang CY, Shuai P, Ye CY, Tang S, Wang YJ, Yang L, Wang J et al. Single-base-resolution methylomes of reveal the association between DNA methylation and drought stress. Bmc Genet 2014, 15.
Liang XL, Hou X, Li JY, Han YQ, Zhang YX, Feng NJ, Du JD, Zhang WH, Zheng DF, Fang SM. High-resolution DNA methylome reveals that demethylation enhances adaptability to continuous cropping comprehensive stress in soybean. BMC Plant Biol 2019, 19.
Li X, Zhu JD, Hu FY, Ge S, Ye MZ, Xiang H, Zhang GJ, Zheng XM, Zhang HY, Zhang SL et al. Single-base resolution maps of cultivated and wild rice methylomes and regulatory roles of DNA methylation in plant gene expression. BMC Genomics 2012, 13.
Yan HD, Bombarely A, Xu B, Frazier TP, Wang CR, Chen PL, Chen J, Hasing T, Cui CM, Zhang XQ et al. siRNAs regulate DNA methylation and interfere with gene and lncRNA expression in the heterozygous polyploid switchgrass. Biotechnol Biofuels 2018, 11.
Ausin I, Feng SH, Yu CW, Liu WL, Kuo HY, Jacobsen EL, Zhai JX, Gallego-Bartolome J, Wang L, Egertsdotter U, et al. DNA methylome of the 20-gigabase Norway spruce genome. P Natl Acad Sci USA. 2016;113(50):E8106–13.
Article CAS Google Scholar
Love MI, Huber W, Anders S. Moderated estimation of Fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
Article PubMed PubMed Central Google Scholar
Trouvelot S, Héloir MC, Poinssot B, Gauthier A, Paris F, Guillier C, Combier M, Trdá L, Daire X, Adrian M. Carbohydrates in plant immunity and plant protection: roles and potential application as foliar sprays. Front Plant Sci 2014, 5.
Fernie A, Hashida SN, Yoshimura K, Gakière B, Mou Z, Pétriacq P. Editorial: NAD metabolism and signaling in plants. Front Plant Sci. 2020;11:146.
Lian WL, Geng AJ, Wang YH, Liu MH, Zhang Y, Wang X, et al. The molecular mechanism of potassium absorption, transport, and utilization in rice. Int J Mol Sci. 2023;24(23).
Fan S, Gao X, Gao C, Yang Y, Zhu X, Feng W, Li R, Mobeen Tahir M, Zhang D, Han M, et al. Dynamic cytosine DNA methylation patterns Associated with mRNA and siRNA expression profiles in alternate Bearing Apple Trees. J Agric Food Chem. 2019;67(18):5250–64.
Article CAS PubMed Google Scholar
Han B, Wu D, Zhang Y, Li DZ, Xu W, Liu A. Epigenetic regulation of seed-specific gene expression by DNA methylation valleys in castor bean. BMC Biol. 2022;20(1):57.
Article CAS PubMed PubMed Central Google Scholar
Chen M, Lin JY, Hur J, Pelletier JM, Baden R, Pellegrini M, Harada JJ, Goldberg RB. Seed genome hypomethylated regions are enriched in transcription factor genes. Proc Natl Acad Sci U S A. 2018;115(35):E8315–22.
Article CAS PubMed PubMed Central Google Scholar
Xia EH, Zhang HB, Sheng J, Li K, Zhang QJ, Kim C, Zhang Y, Liu Y, Zhu T, Li W, et al. The tea Tree Genome provides insights into tea flavor and independent evolution of Caffeine Biosynthesis. Mol Plant. 2017;10(6):866–77.
Article CAS PubMed Google Scholar
Ruiz-Garcia L, Cervera MT, Martinez-Zapater JM. DNA methylation increases throughout Arabidopsis development. Planta. 2005;222(2):301–6.
Article CAS PubMed Google Scholar
Zluvova J, Janousek B, Vyskot B. Immunohistochemical study of DNA methylation dynamics during plant development. J Exp Bot. 2001;52(365):2265–73.
Article CAS PubMed Google Scholar
Maussion G, Yang J, Suderman M, Diallo A, Nagy C, Arnovitz M, Mechawar N, Turecki G. Functional DNA methylation in a transcript specific 3’UTR region of TrkB associates with suicide. Epigenetics. 2014;9(8):1061–70.
Article PubMed PubMed Central Google Scholar
Maussion G, Yang J, Yerko V, Barker P, Mechawar N, Ernst C, Turecki G. Regulation of a truncated form of tropomyosin-related kinase B (TrkB) by hsa-miR-185* in frontal cortex of suicide completers. PLoS ONE. 2012;7(6):e39301.
Article CAS PubMed PubMed Central Google Scholar
Luo RS, Bai CL, Yang L, Zheng Z, Su GH, Gao GQ, Wei ZY, Zuo YC, Li GP. DNA methylation subpatterns at distinct regulatory regions in human early embryos. Open Biol 2018, 8(10).
McGuire MH, Herbrich SM, Dasari SK, Wu SY, Wang Y, Rupaimoole R, Lopez-Berestein G, Baggerly KA, Sood AK. Pan-cancer genomic analysis links 3’UTR DNA methylation with increased gene expression in T cells. EBioMedicine. 2019;43:127–37.
Article PubMed PubMed Central Google Scholar
Wang H, Beyene G, Zhai J, Feng S, Fahlgren N, Taylor NJ, Bart R, Carrington JC, Jacobsen SE, Ausin I. CG gene body DNA methylation changes and evolution of duplicated genes in cassava. Proc Natl Acad Sci U S A. 2015;112(44):13729–34.
Article CAS PubMed PubMed Central Google Scholar
Ming R, VanBuren R, Wai CM, Tang H, Schatz MC, Bowers JE, Lyons E, Wang ML, Chen J, Biggers E, et al. The pineapple genome and the evolution of CAM photosynthesis. Nat Genet. 2015;47(12):1435–42.
Article CAS PubMed PubMed Central Google Scholar
Zhang J, Zhang X, Tang H, Zhang Q, Hua X, Ma X, Zhu F, Jones T, Zhu X, Bowers J, et al. Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L. Nat Genet. 2018;50(11):1565–73.
Article CAS PubMed Google Scholar
Li J, Li C, Deng Y, Wei H, Lu S. Characteristics of Salvia miltiorrhiza methylome and the regulatory mechanism of DNA methylation in tanshinone biosynthesis. Hortic Res. 2023;10(7):uhad114.
Article CAS PubMed PubMed Central Google Scholar
Huang H, Liu R, Niu Q, Tang K, Zhang B, Zhang H, Chen K, Zhu JK, Lang Z. Global increase in DNA methylation during orange fruit development and ripening. Proc Natl Acad Sci U S A. 2019;116(4):1430–6.
Article CAS PubMed PubMed Central Google Scholar
Perrin A, Daccord N, Roquis D, Celton JM, Vergne E, Bucher E. Divergent DNA methylation signatures of Juvenile seedlings, grafts and Adult Apple Trees. Epigenomes 2020, 4(1).
Duge de Bernonville T, Maury S, Delaunay A, Daviaud C, Chaparro C, Tost J, O’Connor SE, Courdavault V. Developmental Methylome of the Medicinal Plant Catharanthus roseus unravels the tissue-specific control of the Monoterpene Indole Alkaloid Pathway by DNA methylation. Int J Mol Sci 2020, 21(17).
Ma K, Song Y, Jiang X, Zhang Z, Li B, Zhang D. Photosynthetic response to genome methylation affects the growth of Chinese white poplar. Tree Genet Genomes. 2012;8(6):1407–21.
Article Google Scholar
Ngernprasirtsiri J, Kobayashi H, Akazawa T. Transcriptional regulation and DNA methylation of nuclear genes for photosynthesis in nongreen plant cells. Proc Natl Acad Sci U S A. 1989;86(20):7919–23.
Article CAS PubMed PubMed Central Google Scholar
Day MD, Clements DR, Gile C, Senaratne WKAD, Shen SC, Weston LA, Zhang FD. Biology and impacts of Pacific Islands Invasive Species. 13. Mikania micrantha Kunth (Asteraceae). Pac Sci. 2016;70(3):257–85.
Article Google Scholar
Mosher RA, Melnyk CW. siRNAs and DNA methylation: seedy epigenetics. Trends Plant Sci. 2010;15(4):204–10.
Article CAS PubMed Google Scholar
Chen M, Lin JY, Hur J, Pelletier JM, Baden R, Pellegrini M, Harada JJ, Goldberg RB. Seed genome hypomethylated regions are enriched in transcription factor genes. P Natl Acad Sci USA. 2018;115(35):Eb315–22.
Article Google Scholar
Lin JY, Le BH, Chen M, Henry KF, Goldberg RB. Similarity between soybean and Arabidopsis seed methylomes and loss of non-CG methylation does not affect seed development. Proceedings of the National Academy of Sciences 2017, 114(45):201716758.
Crisp PA, Marand AP, Noshay JM, Zhou P, Lu ZF, Schmitz RJ, Springer NM. Stable unmethylated DNA demarcates expressed genes and their cis-regulatory space in plant genomes. P Natl Acad Sci USA. 2020;117(38):23991–4000.
Article CAS Google Scholar
Xi Y, Li W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics. 2009;10:232.
Article PubMed PubMed Central Google Scholar
Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.
Article PubMed PubMed Central Google Scholar
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9.
Article CAS PubMed PubMed Central Google Scholar
Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–60.
Article CAS PubMed PubMed Central Google Scholar
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–5.
Article CAS PubMed PubMed Central Google Scholar
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11(9):1650–67.
Article CAS PubMed PubMed Central Google Scholar
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
Article CAS PubMed PubMed Central Google Scholar
Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, Mason CE. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13(10):R87.
Article PubMed PubMed Central Google Scholar
Cai S, Shen Q, Huang Y, Han Z, Wu D, Chen ZH, Nevo E, Zhang G. Multi-omics Analysis reveals the mechanism underlying the Edaphic Adaptation in Wild Barley at Evolution Slope (Tabigha). Adv Sci (Weinh). 2021;8(20):e2101374.
Article PubMed Google Scholar
Xu W, Yang T, Dong X, Li DZ, Liu A. Genomic DNA methylation analyses reveal the distinct profiles in Castor Bean seeds with Persistent endosperms. Plant Physiol. 2016;171(2):1242–58.
CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank Prof. Xia Huang (School of Life Sciences, Sun Yat-sen University) for kindly providing greenhouse.

Funding

This work was supported by the National Natural Science Foundation of China [31872670 and 32071781], Guangdong Basic and Applied Basic Research Foundation [2021A1515010911], Science and Technology Projects in Guangzhou [202206010107], Project of Department of Science and Technology of Shenzhen City, Guangdong, China [JCYJ20210324141000001, and JCYJ20230807110359040], and Research Project of the Reform about Teaching Method and Skills from Sun Yat-Sen University and Guangdong Province.

Author information

Authors and Affiliations

School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China
Yatong Sang, Yitong Ma, Ruonan Wang, Zhen Wang & Yingjuan Su
Research Institute of Sun Yat-Sen University in Shenzhen, Shenzhen, 518057, China
Ting Wang & Yingjuan Su
College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
Ting Wang

Authors

Yatong Sang
View author publications
You can also search for this author in PubMed Google Scholar
Yitong Ma
View author publications
You can also search for this author in PubMed Google Scholar
Ruonan Wang
View author publications
You can also search for this author in PubMed Google Scholar
Zhen Wang
View author publications
You can also search for this author in PubMed Google Scholar
Ting Wang
View author publications
You can also search for this author in PubMed Google Scholar
Yingjuan Su
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

T.W. and Y.Su. conceived and designed the research. Y.Sang. wrote the manuscript. Y.Sang., Y.M. and R.W. performed bioinformatics. Y.Sang. and Y.M. performed experiments. Z.W. checked the grammar. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Ting Wang or Yingjuan Su.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Supplementary Material 6

Supplementary Material 7

Supplementary Material 8

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Sang, Y., Ma, Y., Wang, R. et al. Epigenetic regulation of organ-specific functions in Mikania micrantha and Mikania cordata: insights from DNA methylation and siRNA integration. BMC Plant Biol 24, 1142 (2024). https://doi.org/10.1186/s12870-024-05858-z

Download citation

Received: 02 August 2024
Accepted: 21 November 2024
Published: 29 November 2024
DOI: https://doi.org/10.1186/s12870-024-05858-z

Epigenetic regulation of organ-specific functions in Mikania micrantha and Mikania cordata: insights from DNA methylation and siRNA integration

Abstract

Background

Results

Conclusion

Introduction

Results

Characterization of DNA methylation patterns among different organs in M. micrantha and M. cordata genome

Association between DNA methylation and gene expression

Identification of differentially methylated regions (DMRs) among different organs

Characterization of DMR-associated differential expression genes

The expression of photosynthesis-related genes is regulated by DNA methylation

The CHH methylation is positively correlated with 24-nt siRNA

Identification of differentially methylated variants (DMVs) in M. micrantha

Discussion

Conclusions

Materials and methods

Plant materials and sample collection

Whole-genome bisulfite sequencing and analysis

Transcriptome sequencing and data analysis

Small RNA sequencing and data analysis

Identification of differentially methylated regions (DMRs) and DMR-associated genes

Identification of DNA methylation valley (DMV) in M. micrantha

qRT-PCR analysis

GO and KEGG enrichment analysis

Data availability

Abbreviations

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher’s note

Electronic supplementary material

Rights and permissions

About this article

Cite this article

Share this article

Keywords

BMC Plant Biology

Contact us