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RNA interference-independent reprogramming of DNA methylation in Arabidopsis

Nature Plants volume 6, pages 1455–1467 (2020)Cite this article

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Abstract

DNA methylation is important for silencing transposable elements (TEs) in diverse eukaryotes, including plants. In plant genomes, TEs are silenced by methylation of histone H3 lysine 9 (H3K9) and cytosines in both CG and non-CG contexts. The role of RNA interference (RNAi) in establishing TE-specific silent marks has been extensively studied, but the importance of RNAi-independent pathways remains largely unexplored. Here, we directly investigated transgenerational de novo DNA methylation of TEs after the loss of silent marks. Our analyses uncovered potent and precise RNAi-independent pathways for recovering non-CG methylation and H3K9 methylation in most TE genes (that is, coding regions within TEs). Characterization of a subset of TE genes without the recovery revealed the effects of H3K9 demethylation, replacement of histone H2A variants and their interaction with CG methylation, together with feedback from transcription. These chromatin components are conserved among eukaryotes and may contribute to chromatin reprogramming in a conserved manner.

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Fig. 1: CH methylation in TE genes recovers efficiently.
Fig. 2: CH methylation recovery in TE genes does not depend on RdDM.
Fig. 3: CH methylation recovery is less efficient in TE genes targeted by H3K9 demethylase, IBM1.
Fig. 4: TE genes without efficient CH methylation recovery show reduced CG methylation.
Fig. 5: Dynamics of H2A variants in GLTs are distinct from those in the other TE genes.
Fig. 6: Crosstalk among CG methylation, H2A.Z and transcription.
Fig. 7: Model of the modification dynamics of TE genes during the loss and gain of heterochromatin machinery.

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Data availability

WGBS, ChIP–seq and RNA-seq reads in this study were deposited in the GEO with the accession number GSE148753. All other reasonable requests for data and research materials are available via contacting the corresponding author. Source data are provided with this paper.

Code availability

The code used in this study are available via contacting the corresponding author.

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Acknowledgements

We thank K. Kato, M. Takahashi, K. Takashima and A. Terui for technical assistance; V. Colot, E. Dennis, Y. Hiromi and L. Quadrana for comments on the manuscript and J. Bender, R. Fischer, E. Richards and D. Zilberman for sharing mutant strains. Computations were partially performed on the NIG supercomputer at NIG, Japan. This work was supported by grants from the Mitsubishi Foundation (to T.K.), Japanese Ministry of Education, Culture, Sports, Science and Technology (nos. 26221105, 15H05963 and 19H00995 to T.K.; nos. 19H05740 and 17K15059 to T.K.T.), CREST grant, Japan (no. JPMJCR15O1 to T.K.), Systems Functional Genetics Project of the Transdisciplinary Research Integration Center, ROIS, Japan (no. to Y.T., A.T., A.F. and T.K.), PREST grant from Japan Science and Technology Agency (JPMJPR17Q1 to S.I.), Austrian Academy of Sciences (to F.B.) and FWF stand-alone programs P32054 and P33380 (to F.B.).

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  1. These authors contributed equally: Taiko Kim To, Yuichiro Nishizawa, Soichi Inagaki, Yoshiaki Tarutani.

Authors and Affiliations

  1. Department of Biological Sciences, The University of Tokyo, Tokyo, Japan

    Taiko Kim To, Yuichiro Nishizawa, Soichi Inagaki, Sayaka Tominaga & Tetsuji Kakutani

  2. Department of Integrated Genetics, National Institute of Genetics (NIG), Mishima, Shizuoka, Japan

    Soichi Inagaki, Yoshiaki Tarutani & Tetsuji Kakutani

  3. PREST, Japan Science and Technology Agency, Kawaguchi, Japan

    Soichi Inagaki

  4. Center for Genetic Resource Information, National Institute of Genetics, Mishima, Shizuoka, Japan

    Atsushi Toyoda & Asao Fujiyama

  5. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna, Austria

    Frédéric Berger

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Contributions

T.K.T., Y.N., S.I. Y.T., F.B. and T.K. designed the study. T.K.T., Y.N., S.I. Y.T., S.T., A.T., A.F. and T.K. performed the experiments. T.K.T., Y.N., S.I. and Y.T. analysed the data. T.K.T. and T.K. wrote the paper incorporating comments from the other authors.

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Correspondence to Taiko Kim To or Tetsuji Kakutani.

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Extended data

Extended Data Fig. 1 siRNA is associated with recovery of CG methylation in TE genes.

a, Genetic scheme to examine recovery of CG methylation. b–e, CG methylation levels of cellular genes (black dots) and TE genes (red dots) are compared between wild-type plant (x-axis) and corresponding plant (y-axis). Mutations in the maintenance methyltransferase MET1 (b), as well as the triple mutations in the redundant genes, VIM1, VIM2 and VIM3 (c), induced a loss of CG methylation, and the recovery of CG methylation was examined in the F1 (d) and F2 (e) progenies. The F2 plant in panel E was a self-pollinated progeny from the F1, with a genotype of MET1/MET1 VIM1/VIM1 VIM2/VIM2 vim3/vim3. f, Comparison of two individual F1 plants. The re-methylation efficiency for each of the TE genes was similar in the two F1 plants. R represents the pearson’s correlation coefficient between two methylation values in the TE genes. g, Comparison of remethylation in an F1 plant and its F2 progeny. TE genes remethylated in the F1 plant tended to be further remethylated in the F2 progeny. h, CG remethylation in TE genes was associated with the presence of 24-nt siRNAs in an met1 mutant. siRNA-Seq data were obtained from GEO (GSE1096763 and TE genes were classified according to the matching siRNA levels with high (left, RPKM>0.1, 3283 TE genes) or low (right, RPKM<0.1, 372 TE genes) in met1-3 (see Methods for details). The box plot indicates the median (line in the box), the lower and upper quartiles (box), the largest and smallest data points within the interval of 1.5 times the interquartile range from the box (whiskers). i, j, Correlation of CG remethylation in TE genes and the levels of 24-nt siRNAs in met1 mutant. In Fig. 1h–j, TE genes with low CG methylation (mCG <0.1) were excluded and the remaining TE genes (n=3655) were analyzed to avoid division by values near zero.

Extended Data Fig. 2 DNA methylation profiles of TE genes and normal protein coding genes.

a, Metaplots of DNA methylation around TE genes in the wild-type plants, cc, sss, and their F1 progeny. In both mutants, CH methylation is lost but CG methylation is largely unaffected. b, Metaplots of DNA methylation around normal protein coding genes in the wild-type plants, cc, sss, and their F1 progeny. c, Metaplots of DNA methylation around GLTs (that is TE genes without CH methylation recovery) in the wild-type plants, cc, sss, and their F1 progeny. Note that the methylation pattern in the F1 resembles that of normal protein coding genes in wild-type.

Extended Data Fig. 3 RdDM is dispensable for the recovery of H3K9me2 and CH methylation.

a, Recovery of CHG methylation in F9 progeny from a cross between ddm1 and wild-type (x-axis; reanalyzed from data66), and F1 (cc x sss) (y-axis). Left, TE genes of ddm1-haplotype (that is TE genes localized on the ddm1-derived chromosome segments for both alleles in the line epiRIL98) were analyzed (n=1178) after exclusion of TE genes with small CH methylation difference in the mutants (CHG<0.1 and CHH<0.05). Middle, TE genes with low levels of matching 24-nt siRNA (RPKM < 2) in ddm1 (reanalyzed from data64, n=461). Right, TE genes with low levels of matching 24-nt siRNA (RPKM < 2) in sss mutants (reanalyzed from data8, n=144). b, Recovery of CHH methylation as shown in a. c, d, The CH methylation recovery from the ddm1-epiallele was associated with CG methylation recovery. Recovery of CH methylation were compared to CG methylation level for CHG sites (c) and CHH sites (d). R represents the pearson’s correlation coefficient between two methylation recovery in the TE genes. e, Comparison of H3K9me2 levels in WT, cc and sss mutants, and their F1 progeny in DRM2 and drm2 backgrounds, based on western blot analysis. Black bars show the positions of molecular weight marker of the size 17kDa. Source data are provided as a Source Data file. f, CHG remethylation in F1 (cc x sss) in wild-type, drm2 and rdr1 rdr2 rdr6 (rdr126) mutant background estimated by digestion with Pst I, an enzyme sensitive to methylation at CHG sites, followed by quantitative-polymerase chain reaction analysis. The experimental details are described in the Methods section. Even in the drm2 or rdr126 background, CHG methylation lost in cc or sss mutations recovers in their F1 progeny to levels comparable to that in the control wild-type plant.

Extended Data Fig. 4 DNA methylation levels at single-nucleotide resolution for TE genes and TEs without coding region.

a, Sigle-nucleotide resolution of CHG and CHH methylation levels within TE genes in the cc and sss mutants and their F1 hybrids, compared to that in wild-type plants. The results shown in panel a are the same as those shown in Fig. 1r–y. b, Results for plants with a drm2 background, shown in the same format used for panel a. c, d, Results for non-coding TEs (TEs without annotated coding region) shown in the same format used for panels a and b.

Extended Data Fig. 5 RdDM controls the recovery of CH methylation in noncoding regions of TEs.

a, Non-coding regions showed the properties of RdDM targets. The levels of CH methylation and matching 24-nt sRNAs were analyzed in TEs with zero, one, or multiple coding regions. The shaded areas represent non-coding regions. The white areas between the shaded areas represent coding regions (center and right panels). For TEs with multiple coding regions (right panel), the values of non-coding regions in the center represent the average of one or more intergenic non-coding regions in each TE, and the values for two terminal coding regions are shown in between the terminal and intergenic non-coding regions. The results of CH methylation and siRNA are from reanalysis of data from previous report on ddm1 and rdr2 mutants9 or siRNAs65, respectively. The results are consistent with a previous interpretation that short TEs and the edges of long TEs are targets for RdDM9, with a modification that intergenic non-coding regions also showed the properties of RdDM targets (right panels). b, Recovery of CHH methylation in coding and non-coding regions. Recovery in the non-coding regions, including intergenic non-coding regions, depends on DRM2, consistent with the results shown in Extended Data Fig. 4c,d.

Extended Data Fig. 6 Properties of GLTs.

a, TE genes without efficient CHG remethylation did not show efficient CHH remethylation. The red dots represent GLTs, that is, TE genes without efficient recovery of CHG methylation (n=73, also shown as red dots in Figs. 3, 4a, and Extended Data Fig. 6b). Other TE genes (n=3381) are shown as black dots. Each dot represents the CHH methylation level of each TE gene. b, TE genes without efficient CHG remethylation also showed inefficient H3K9me2 remethylation. Each dot represents the ChIP-seq reads of H3K9me2 in each TE gene, normalized with that of input DNA, and further normalized by spike-in of yeast chromatin. The red dots represent GLTs, that is, TE genes without efficient recovery of CHG methylation (also shown as red dots in Figs. 3, 4a, and Extended Data Fig. 6a). c, GLTs are overrepresented in COPIA (yellow) and LINE (blue) TEs, although other TEs are also included. Further information for the GLTs (expression and DNA methylation) is shown in Supplementary Table. d, The length of GLTs. Type I GLTs (n=34), Type IL GLTs (n=31), All TE genes (n=3903). The box plot indicates the median (line in the box), the lower and upper quartiles (box), the largest and smallest data points within the interval of 1.5 times the interquartile range from the box (whiskers). e, GLTs, which are concentrated on top, have CHG methylation in wild-type plants (left), lose CHG methylation in the cc or sss mutant (center), and do not recover CHG methylation in the F1 progeny (right). The results shown in the right panel are the same as those shown in the left panel in Fig. 4b. f, Three contexts of DNA methylation in GLTs shown for the wild-type, cc, sss, and the F1 plants. The format is the same as that shown in Fig. 1n,p, which show a typical TE gene with efficient recovery of CHG and CHH methylation.

Extended Data Fig. 7 Distribution of H2A variants in GLTs.

a, ChIP-seq analysis to examine H2A.W7 distribution for type I GLTs (n=34), type II GLTs (n=31), and other TE genes (n=3381). The distribution pattern was similar to that found with H2A.W6 (Fig. 5b). b, ChIP-seq results for a biological replicate for those shown in Fig. 5 and Extended Data Fig. 7a.

Extended Data Fig. 8 Crosstalk among DNA methylation, H2A.Z, and transcription.

a, Type I GLTs (n=27), which show transcriptional depression in cc, contains two population of TE genes, with and without silencing by additional zzz mutations. Beanplot shows GLT-expression levels in wild-type and mutants. Each horizontal black line represents the median. For each genotype, RNA-expression levels were examined in two independent replicates. The values are in log2 RPKM. b, Original CG methylation results for Fig. 6c with the control wild-type results. c, Original CG methylation levels for each of TE genes shown by beeswarm plots.

Extended Data Fig. 9 GLTs showed properties different from those of TE genes affected by RNAi.

a, TE genes not recovering CHG methylation in the F1 between cc and sss mutants were compared among backgrounds of WT, drm2, and rdr1 rdr2 rdr6 (rdr126). The criteria used are the same as those used to define GLTs. While most of GLTs (that is TE genes not recovering CHG methylation in WT F1) did not recover CHG methylation in F1 of drm2 or rdr126 background, a subset of TE genes did not recover CHG methylation specifically in drm2 or rdr126 background (highlighted by blue edge). We designated such TE genes as RDTs (that is RNAi-Dependent TE genes: n=134). b, The distributions of GLTs and RDTs in five chromosomes of Arabidopsis genome. GLTs and RDTs are shown in bottom part, and above them are the numbers of TE genes and normal protein coding genes counted and plotted in 100 kb bins. Both GLTs and RDTs are distributed in the gene-rich chromosomal arm regions. c, The numbers of TE genes and normal protein coding genes around GLTs (n=73), RDTs (n=134), and the other TE genes (n=3247) (for 50 kb each side, 100 kb in total). Both GLTs and RDTs are located in gene-rich regions, rather than TE-rich regions. d, ChIP-seq results for H2A variants in wild-type plants for GLTs (n=73), RDTs (n=134), and the other TE genes (n=3247). Both GLTs and RDTs show low level of H2A.W and high level H2A.Z, compared to the other TE genes. e, The gene length of GLTs (n=73), RDTs (n=134), and the other TE genes (n=3247). While RDTs tend to be short, GLTs tend to be long, compared to the other TE genes. f, The H3K9me levels in wild-type plants for GLTs (n=73), RDTs (n=134), and the other TE genes (n=3247) as well as normal protein coding genes. While RDTs tend to have low level of H3K9me2, GLTs tend to have high level of H3K9me2, compared to the other TE genes. g, CG methylation in wild-type, cc, sss, and their F1. GLTs (left, n=73) showed reduction of CG methylation in the mutants and F1. In contrast, both RDTs (center, n=134) and other TE genes (n=3247) did not show comparable changes in CG methylation in the mutants and F1, even in the background of drm2 or rdr126, where the CHG methylation recovery was compromised. h, ChIP-seq results for H2A variants (H2A.W6, H2A.W7, and H2A.Z) in wild-type, cc, sss, and their F1. GLTs (n=73), RDTs (n=134), and the other TE genes (n=3247) are compared in each panel. GLTs showed replacement of H2A.W to H2A.Z in the mutants and F1. In contrast, RDTs did not show comparable changes in H2A variants in the mutants and F1. The box plot indicates the median (line in the box), the lower and upper quartiles (box), the largest and smallest data points within the interval of 1.5 times the interquartile range from the box (whiskers).

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplemental Table

The list of GLTs and RDTs.

Source data

Source Data Fig. 1

Unprocessed western blots for Fig. 2h.

Source Data Fig. 2

Unprocessed western blots for Extended Data Fig. 3e.

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To, T.K., Nishizawa, Y., Inagaki, S. et al. RNA interference-independent reprogramming of DNA methylation in Arabidopsis. Nat. Plants 6, 1455–1467 (2020). https://doi.org/10.1038/s41477-020-00810-z

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