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Type: Task
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Status: Closed (View Workflow)
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Priority: Major
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Resolution: Done
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Story Points:3
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Sprint:Spring 2 2022 Jan 18 - Jan 28, Spring 3 2022 Jan 31 - Feb 11, Spring 5 2022 Feb 28 - Mar 11
Description
To understand the significance of our results, we need to learn the current state of knowledge in the field.
For this task, assemble and summarize original research articles published in the last two to three years that investigated how methylation affects alternative splicing.
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- 3069.enl
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- Nowlan Freese
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For each gene under evaluation, we calculated the methylation level as the number of differentially methylated cytosines divided by the number of cytosines evaluated in each genomic region of interest, including exons, introns, or the promoter region. Notably, among the genes with at least one differentially used exon and methylation data (1,216 out of 1,290), methylation level in the significant exons was significantly different than the methylation level in the non-significant exons (Kolmogorov-Smirnov test, P-value < 0.001, Fig. 3). Similarly, methylation level in the introns that were contiguous to significant exons was significantly different than the methylation level in the non-contiguous introns (Kolmogorov-Smirnov test, P-value < 0.001, Fig. 3). On the other hand, among genes that had at least one differentially expressed isoform and had methylation data (125 out of 175), there were no differences in methylation levels among exons.
To further visualize the relationship between DNA methylation and alternative splicing, we evaluated differential exon usage and differential DNA methylation at a single-gene level. As an illustration, Fig. 4 shows the exon expression level and DNA methylation status of pre-mRNA-processing factor 40 homolog A (PRPF40A), a gene that is highly involved in mRNA splicing and mRNA processing. Among the 30 exons and 26 introns annotated in the reference annotation, exon E007 was identified as differentially used between maternal diets. Notably, marked differences in methylation proportion were observed between this (significant) exon and the rest of the (non-significant) exons.
RNA splicing and spliceosome assembly in eukaryotes occur mainly during transcription. However, co-transcriptional splicing has not yet been explored in plants. Here, we built transcriptomes of nascent chromatin RNAs in Arabidopsis thaliana and showed that nearly all introns undergo co-transcriptional splicing, which occurs with higher efficiency for introns in protein-coding genes than for those in noncoding RNAs.
This paper does not investigate splicing and epigenetics, but the hypothesis of methylation affecting splicing is currently grounded in the idea that plants undergo co-transcriptional splicing.
Here, we performed integrative methylome and transcriptome analyses of tomato seedlings under Pi-deficient and sufficient conditions. We found Pi caused a slight increase in the overall methylation level, with millions of differentially methylated cytosines (DmCs) and a few hundred differentially methylated regions (DMRs). We also identified thousands of differentially expressed (DE) and differential AS (DAS) genes induced by Pi-, and found that DmCs were more abundant in non-expressed genes than in DE or DAS genes. Moreover, DNA methylation alterations weakly correlated with transcription changes but not with DAS events, and hyper-CHH-DMRs overlapping with transposable elements (TEs) were enriched in a subset of Pi starvation response (PSR) genes.
To assess the influence of Pi- on AS in tomato, we analyzed the RNA-Seq data using rmats (Shen et al., 2014).
To investigate the preferential localizations of methylation changes and their possible relationships with gene expression or AS, we compared the DmC distribution densities at gene body and flanking regions of DE or DAS genes with those of randomly selected genes. Based on this analysis, we found the biased distribution of different types of DmCs at different genes (Figure 5a; Table S3). Generally, the densities of hyper- or hypo-CG-DmCs at DE genes were slightly higher than those of randomly selected genes in either gene body or flanking regions. By contrast, DAS genes in roots and shoots both had significantly fewer CG-DmCs (the patterns of hyper- and hypo-DmC are similar, if not specified thereafter) in the gene bodies as compared with randomly selected genes.
We found that DAS genes with few DmCs tended to be highly expressed, with longer length and more exons, whereas DmC-rich non-expressed genes were mostly short and contained few exons (Figure S7).
To further assess whether and how DMRs relate to alterations of gene expression and AS upon Pi-, we evaluated the enrichment of different types of DMRs around (inside or in the flanking 10-kb regions of) DE and DAS genes using Fisher's exact test (Table S5). We detected preferential localization of CHH-DMRs around DE genes in both roots and shoots, and CNN-DMRs around shoot DE genes. In comparison, many fewer DMRs were seen close to DAS genes.
Does co-transcriptional regulation of alternative splicing mediate plant stress responses?
Review paper 2019
Plants exhibit extensive variation in DNA methylation and gene expression under different developmental and stress conditions (104-108). In eukaryotes, DNA methylation occurs in symmetric CG and CHG (H = A, T or C) and asymmetric CHH contexts (109). However, DNA methylation is largely dependent on the CpG context in plants. In the Arabidopsis genome, 24% of CG sites are methylated, compared with only 6.7% of CHG and 1.7% of CHH sites (110,111). Interestingly, nucleosomal DNA is highly methylated, and exons rather than the introns are marked at the DNA level by high occupancy of nucleosomes. These are preferentially positioned at intron-exon and exon-intron boundaries in both mammals and Arabidopsis (42,77,112,113). Additionally, nucleosome occupancy is also lower in alternatively spliced exons compared with constitutively spliced exons (77-79,114,115). Since DNA is packaged into nucleosomes, pol II elongation rate is inherently subject to frequent pausing at constitutively spliced exons with high GC levels (116,117), and regions of high nucleosome density slow down pol II to facilitate the recruitment of SFs to weaker upstream splice sites (24,28,79,114).
An example of this is found in the honeybee, in which DNA methylation is almost exclusively found in exons with a strong correlation between methylation patterns on alternative exons and splicing patterns of these exons in workers and queens (73). Intriguingly, a reduction in methylation of the dnmt3 gene encoding a methyltransferase via RNAi results in widespread changes in AS in honeybee fat tissues (118). Additionally, a DNA-binding protein, CCCTC-binding factor (CTCF), promotes inclusion of weak upstream exons in the CD45 gene by causing local pol II pausing in mammals. Methylation of exon 5 abolished CTCF binding and resulted in the complete loss of exon 5 from CD45 transcripts (28). Interestingly, a direct link was very recently unveiled between DNA methylation and AS in humans by perturbing DNA methylation patterns of alternatively spliced exons. In this study, the authors used CRISPR-dCas9 proteins (for details, see the 'Engineering splicing variation' section below) and methylating/demethylating enzyme fusions (119). This work clearly demonstrates that changes in the methylation pattern of alternatively spliced exons mediates their inclusion, but has no effect on introns or constitutively spliced exons (119).
Recent work in plants demonstrated abundant DNA methylation and splicing variation under different growth and stress conditions, and during different developmental stages. For example, quantification of AS in wild-type (WT) and OsMet1-2 (CG methyltransferase mutant) rice lines revealed widespread differences in splicing variation (120). Consistent with the metazoan data (120), CG methylation was found to be higher in WT exons compared with adjacent introns, and was not solely dependent on the CG composition of exons and introns (120). Further evidence from cotton showed similar CG methylation levels in constitutive and alternative exons, but variable patterns during different fiber development stages (121). By contrast, CG methylation was higher in alternative introns than constitutive introns. Furthermore, differential CG methylation has a strong influence on nucleosome formation since constitutive exons displayed higher nucleosome occupancy than alternative exons. However, alternative exons exhibited higher nucleosome density than constitutive introns (121). These findings clearly demonstrate that the relationship between DNA methylation and nucleosome occupancy is conserved between animals and plants, and AS is also predominantly regulated at the chromatin level in plants (42,82,92).
The Crosstalk Between Epigenetic Mechanisms and Alternative RNA Processing Regulation
Review paper 2020
DNA methylation, resulting in 5' methylation of cytosine (5mC), is a conserved and heritable DNA modification that affects gene expression in a genome-wide manner (Li and Zhang, 2014). The impact of DNA methylation on gene expression varies depending on its genomic contexts. The role of promoter DNA methylation in gene expression has been well investigated, which is widely believed to cause transcriptional inhibition of downstream genes (Law and Jacobsen, 2010). Interestingly, recent studies in model plant Arabidopsis revealed that two SU(VAR)3-9 homologs, SUVH1 and SUVH3, bind to methylated DNA and recruit the DNAJ proteins to enhance proximal gene expression, thereby counteracting the repressive effects of transposon insertion near genes (Harris et al., 2018; Xiao et al., 2019; Zhao et al., 2019). Compared to DNA methylation in promoter regions, the function of genic DNA methylation remains elusive (Ball et al., 2009). During the last decade, several studies indicate that genic DNA methylation has a positive effect on the expression of associated genes and prevents spurious transcription initiation, and it is present within a number of cancer-related genes and has been regarded as a hallmark of human cancer (Baylin and Jones, 2011; Yang et al., 2014; Neri et al., 2017).
Recent studies reveal a strong correlation between DNA methylation and alternative splicing. Yang et al. (2014) showed that gene body DNA demethylation mediated by DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine results in reduced efficiencies of transcription elongation or splicing. In human cells, Shukla et al. (2011) reported that a DNA-binding protein, called CCCTC-binding factor (CTCF), can promote inclusion of weak upstream exons by mediating local RNA polymerase II pausing. In this case, DNA methylation inhibits CTCF binding to CD45 exon 5, which enables Pol II to transcribe more rapidly, giving rise to an exon 5 exclusion (Ong and Corces, 2014). More recently, Nanavaty et al. (2020) further revealed that CTCF is a bifunctional regulator which influences both alternative splicing and alternative polyadenylation. Removal of DNA methylation enables CTCF binding and recruitment of the cohesin complex, which in turn form chromatin loops to promote proximal polyadenylation site usage. These works clearly demonstrate that DNA methylation has an important participation in RNA processing regulation. While, limited information is currently available regarding how DNA binding proteins disturb the elongation of Pol II. It reminded us that there maybe are other factors influencing Pol II elongation in CTCF-mediated AS regulation, like the cohesin complex.
Unlike CTCF protein which binds to unmethylated DNA, a growing number of studies have shown that the methyl cytosine-guanine dinucleotide (CpG) binding protein 2 (MeCP2) binds to methylated regions to influence AS. MeCP2 is the earliest reported multifunctional protein that contains both methyl-CpG-binding domains and transcriptional repressor domains (Nan et al., 1997). Acting as a chromatin adaptor, MeCP2 is attracted to 5mC on alternative exons, triggering its interaction with histone deacetylases (HDACs), which modulate alternative splicing (Maunakea et al., 2013). As we delve deeper into the function of MeCP2, it is becoming clear that MeCP2 recruiting splicing factors to regulate mRNA splicing is also a nearly ubiquitous mechanism in animals (Cheng et al., 2017; Wong et al., 2017).
In plants, the available information regarding whether gene body DNA methylation affects AS and the extent of this mediation is currently limited. The first study of DNA methylation-related functions in splicing was reported in maize (Regulski et al., 2013). More recently, the cytosine methyltransferase OsMET1 was found to affect global AS events in rice, in which a total of 6319 more events were identified with the met1 mutant compared with those associated with the wild-type strain (Wang et al., 2016). However, deeper research combining DNA methylation and AS/APA in plant is lacking. Whether it has the similar regulatory mechanism with mammals needs to be further elucidated.
Unlike animals, plants display a high degree of plasticity during growth and development. In plants, to overcome the constant challenge from a rapidly changing environment, specific adaptation mechanisms have been evolved, among which alternative RNA processing is an important strategy (Chaudhary et al., 2019). Recent work has indicated that the role of epigenetic modifications in regulating AS/APA under stress is emerging (Jabre et al., 2019). Temperature is one of the environmental signals that strongly affects plant development. An recent study indicated that temperature variation is memorized by chromatin via H3K36me3 modification, resulting in a specific splicing pattern, which enables a feasible adaptation to stress conditions (Pajoro et al., 2017). Another study showed that genes which are quickly activated under cold stress and differentially expressed at the splicing level, were found to be modified by H3K27me3 in non-stress conditions (Vyse et al., 2020). These reports suggest a dynamic regulation of temperature stress-responsive genes by alternative RNA processing and histone modification. In Arabidopsis, the Nuclear speckle RNA binding proteins (NSRs) have been known as regulators of AS functioning in auxin-associated developmental processes such as lateral root formation (Bazin et al., 2018). These proteins were shown to interact with specific alternatively spliced mRNA targets and at least with one structured lncRNA named ASCO (Bardou et al., 2014). The specific interaction of NSR with the ASCO is able to modulate AS patterns of a subset of NSR target genes, thereby impacting auxin response (Bazin et al., 2018). In other plants, specific association between epigenetic regulators and RNA processing factors under stress conditions has also been found. A maize SWI3D protein, ZmCHB101, has been found to impact alternative splicing contexts of a subset of osmotic stress-responsive genes on genome-wide level (Yu et al., 2019). In turn, alternative RNA processing of pivotal regulatory genes confers plants quick response to the changing climate conditions through alteration of reversible epigenetic marks. While, most of the current researches only focus on one aspect of how plants respond to changeable environment. That means, alternative RNA processing impacts the transcriptome of responsive genes or environment change leads to dynamic alterations of diverse epigenetic modifications (Rataj and Simpson, 2014; Calixto et al., 2018; Li et al., 2018). The mechanistic insights into the detailed interplay between epigenetic regulation and AS/APA in changing environment remains largely limited. In addition, the complicated regulatory mechanisms controlling mRNA isoform ratios in a tissue- or condition-specific manner still remain unclear.
Epigenetic differences in an identical genetic background modulate alternative splicing in A. thaliana
How stable and temperature-dependent variations in DNA methylation and nucleosome occupancy influence alternative splicing (AS) remains poorly understood in plants. To answer this, we generated transcriptome, whole-genome bisulfite, and MNase sequencing data for an epigenetic Recombinant Inbred Line (epiRIL) of A. thaliana at normal and cold temperature. Our transcriptome data revealed that differential DNA methylation and nucleosome occupancy modulate expression levels of many genes and AS in response to cold. Collectively, DNA methylation and nucleosome levels exhibit characteristic patterns around intron-exon boundaries at normal and cold conditions, and any perturbation in them, in an identical genetic background is sufficient to modulate AS in Arabidopsis.
Furthermore, the AS analysis showed that epigenetic differences between Col-0 and epiRIL-368 induced fewer but contrasting changes under similar temperature conditions (22 C and 4 C) (one-way ANOVA p-value = 1.7492e-09 for DAS genes). For instance, the number of identified DAS genes between Col-0 versus epiRIL-368 was 305 and 311 at 22 C and 4 C, respectively (Fig. 1A a,b).
Intrestingly, there is no overlap between DEGs and DAS genes between Col-0 and epiRIL-368 at 22 C (hypergeometric test p-value =0.198) and 4 C (hypergeometric test p-value = 2.520e-05) (Fig. 1B a-b). Whereas, this number significantly increases (Col-0 at 22 C versus epiRIL-368 at 4 C; 7.1%; hypergeometric test p-value 0.011) when epigenetic variations and cold stress adds on together (Fig. 1B c).
Further, we performed gene functional enrichment analysis for DEGs and DAS genes (Supplementary File S1) for all three gene ontology (GO) terms i.e. Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) (Fig. S2). Among DEGs, the most significant (FDR <0.05) terms involved transcription regulation, Pol II processivity, cold and other such as response to abscisic acids, are highly enriched (Fig. S2 A) in different contrasting groups.
Therefore, we reasoned those variations at the methylation and nucleosome (epigenetic) levels may affect AS because of epigenetic differences between Col-0 and epiRIL-368 ecotypes under different temperatures. To further investigate these variations, we performed WGBS for epiRIL-368 plants grown at 22 C and 4 C. We identified high confidence differentially methylated regions (hc_DMRs) (Fisher's exact test, p-value <0.01) in comparison to 54 Columbia (Col) lines of Arabidopsis using the hc_DMR caller pipeline developed by the Jacobsen group at the University of California [60].
The high number of hypomethylated regions suggests relatively lower methylation levels in epiRIL-368 compared to Col ecotype (Fig. 2A). Among all hcDMRs, 22,968 were hypomethylated regions in the epiRIL-368 line as compared to Col lines (Fig. 2A).
We first calculated AS event inclusion levels (PSI) for a total of 43,953 AS events identified in the reference annotation file of Arabidopsis, followed by the differences in their inclusion (PSI) among different contrast groups. Differential AS events analysis suggests that epiRIL-368 display significant (p-value <0.05) differences in 474 and 516 AS events compared to Col-0 at 22 C and 4 C, respectively (Fig. 2C; Supplementary File S4).
Different types of AS events detected in our analysis show an overall similar distribution observed previously in Arabidopsis [9,11] where intron retention (IR) events are the most prevalent, followed by usage of the alternative acceptor (A3'SS) and alternative donor (A5'SS) sites, and exon skipping (ES) (Fig. 2C; Supplementary File S4).
The CpG, CHG, and CHH methylation levels generated using methylpy [65] were plotted around the donor sites (exon-intron; 5'SS), the acceptor sites (intron-exon; 3'SS), and the exons for epiRIL-368 at 22 C and 4 C (Fig. 3A; Fig. S3). We observed a sharp drop in CpG methylation at both splice sites (5'SS and 3'SS; Fig. 3A a-b) suggesting its role in transcription and splicing dynamics by affecting Pol II processing around 5'SS and 3'SS as compared to flanking regions. DNA methylation around exons always shows a higher methylation level and can be differentiated from their flanking regions (introns, especially splice sites) (Fig. 3A; Fig. S3A). Regardless of temperature treatment, we also found the level of methylated CpG dinucleotides (mCpG) is higher in exons as compared to flanking regions including introns and splice sites (Fig. 3A).
Next, we looked at the function of the genes with high confidence differentially methylated regions (hcDMRs), differential nucleosome positioning (DNPs), and differential splice junctions (DSJs) in our dataset. We first identified the genes with hcDMRs, and DNPs in addition to genes with DSJs for the contrasting groups Col-0 at 22 C versus epiRIL-368 at 22 C, and Col-0 at 22 C versus epiRIL-368 at 4 C. We divided genes into three groups including genes with hcDMRs (hcDMR genes), genes with significant DNPs (DNP genes) and genes with significant DSJs (DSJ genes). Finally, significantly overlapping (Fig. 4A) genes between hcDMR, DNP, and DSJ genes for the contrasting groups Col-0 at 22 C versus epiRIL-368 at 22 C, and Col-0 at 22 C versus epiRIL-368 at 4 C were selected for gene ontology (GO) functional enrichment analysis.