Mistakes in the maintenance of CG methylation are a source of heritable epimutations in plants. Multigenerational surveys indicate that the rate of these stochastic events varies substantially across the genome, with some regions harbouring localized ‘epimutation hotspots’. Using Arabidopsis as a model, we show that epimutation hotspots are indexed by a specific set of chromatin states that map to subregions of gene body methylation genes. Although these regions comprise only ~12% of all CGs in the genome, they account for ~63% of all epimutation events per unit time. Molecular profiling revealed that these regions contain unique sequence features, harbour steady-state intermediate methylation levels and act as putative targets of antagonistic DNA methylation pathways. We further demonstrate that experimentally induced shifts in steady-state methylation in these hotspot regions are sufficient to significantly alter local epimutation intensities. Our work lays the foundation for dissecting the molecular mechanisms and evolutionary consequences of epimutation hotspots in plants.
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All code used to identify DMRs can be accessed from the GitHub repository at https://github.com/jlab-code/jDMR.
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We thank B. Williams and M. Gehring for sharing the drdd data with us. F.J., R.R.H. and R.J.S. acknowledge support from the Technical University of Munich Institute for Advanced Study, funded by the German Excellence Initiative and the European Seventh Framework Programme under grant agreement no. 291763. Z.Z. holds a fellowship from the China Scholarship Council (no. CSC202006380020). This work was supported by the National Institutes of Health (grant no. R01GM134682 to R.J.S.).
The authors declare no competing interests.
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(a) Genomic coverage of 36 CS. (b) CG epimutation gain (α) and loss (β) rates of the 36 CS. The estimates represent a weighted average for MA1_1 and MA1_2 lines. Data in the bar plots are represented as CG epimutation rate estimates ± 95% confidence intervals. The highest and lowest rates for α and β are indicated by a rectangle. (c) Correlation of α, β and divergence values between MA1_1 and MA1_2. The plots indicate that the results of MA1_1 and MA1_2 are highly concordant.
(a) Best-prediction model for mCG divergence in pericentromeric regions and chromosome arms. (b) Distribution of observed mCG divergence values of 10Kb regions of MA1_1 and MA1_2 lines at Δt=62. The top 10% mCG divergence values are highlighted in grey.
Extended Data Fig. 3 Example JBrowse screenshot of a typical gbM gene showing location of SPMRs and non-SPMRs.
SPMRs harbour a mixture of methylated and unmethylated CGs yielding intermediate methylation levels (0.2 to 0.8). On the other hand, non-SPMRs are either fully methylated or unmethylated.
mCG divergence over Δt (generations) (left panels) and CG epimutation gain (α) and loss (β) rates in MA1_1 and MA1_2 (right panels) after removal of SPMRs from all red states, SPMRs associated with gbM genes in all red states and hotspot associated SPMRs (CS4, CS5, CS6) as compared to global. Data in the barplots are represented as CG epimutation rates ± 95% confidence intervals. Sample variation can be observed in the fitted lines in the mCG divergence versus Δt plots (left panel).
Heatmaps showing hypo- and hyper- methylated redCS SPMRs in gbM genes of MA3_wt versus MA_suvh4/5/6 (left) and MA_cmt3 (right). All redCS SPMRs were divided into quartiles (redCS4, redCS3, redCS3 and redCS1) and arranged in descending order of methylation level in mutants. The barplots show the α and β rates of the redCS SPMRs in each of the quartiles in the heatmap. Data in the barplots are represented as CG epimutation rates ± 95% confidence intervals.
(a) Schematic representation showing a possible mechanism of interaction between heterochromatin and gbM-SPMRs and how they affect local epimutation rates. (b) CG and non-CG DRDD targets in MA_suvh/4/5/6 (generation 16, lineage 8) and MA_cmt3 (generation 16, lineage 8) are hypomethylated. (c) Venn diagram showing all-contexts DMR overlaps of drdd hyper-DMRs with hypo-DMRs of MA_suvh4/5/6 and MA_cmt3. Asterisks represent significance levels (*** indicates highly significant overlaps (P = 9.9E-05). (d) RNA-Seq analysis in suvh4/5/6 and cmt3 shows a significant downregulation of DML3, and a similar, non-significant trend, for DML2, DME and ROS1 genes. (e) Distribution of total sequence space (base pair) taken up by redCS-SPMRs. The bottom 10% (low sequence space) and top 10% (high sequence space) fraction of all gbM genes are highlighted (top right panel). Violin plots showing gbM frequency of the top 10% and bottom 10% subsets from the histogram (bottom right panel).
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Hazarika, R.R., Serra, M., Zhang, Z. et al. Molecular properties of epimutation hotspots. Nat. Plants 8, 146–156 (2022). https://doi.org/10.1038/s41477-021-01086-7