Abstract
Transgenerationally heritable epialleles are defined by the stable propagation of alternative transcriptional states through mitotic and meiotic cell cycles. Given that the propagation of DNA methylation at CpG sites, mediated in Arabidopsis by MET1, plays a central role in epigenetic inheritance, we examined genome-wide DNA methylation in partial and complete loss-of-function met1 mutants. We interpreted the data in relation to transgenerational epiallelic stability and provide evidence that DNA sequence features such as density of CpGs and genomic repetitiveness can be used to predict susceptibility to epiallelic switching. The importance of these rules was confirmed by analyses of common epialleles in natural Arabidopsis accessions and verified in rice.
Introduction
Transgenerational epigenetic inheritance have been well documented in plants, however the primary competence that leads to the formation of alternative epialleles at only certain loci is not well understood. It has been reported that maintenance of mCpG (methylated CpG) patterns by MET1 and the chromatin remodeler DDM1 is central for transgenerational epigenetic inheritance (Reinders et al. 2009; Teixeira et al. 2009). Importantly, inactivation of MET1 or DDM1 results in the loss of mCpGs, which is not easily corrected after re-introduction of MET1 and DDM1, although remethylation occurs at certain loci (Reinders et al. 2009; Teixeira et al. 2009). Thus, chromosomal targets of CpG methylation were divided into two broad categories: (a) those that can form two distinct epigenetic states (epialleles) that are maintained over generations in the presence of all epigenetic activities of the wild type, and (b) those that revert to only one dominant epigenetic state (reversible) and are thus not able to form heritable epialleles (Reinders et al. 2009; Teixeira et al. 2009). Although epiallelic reversion was associated with RNA directed DNA methylation (RdDM)(Teixeira et al. 2009), the primary determinants underlying differences between the chromosomal targets of epigenetic regulation in susceptibility to epiallelic switching remained unknown. Here we compare the whole-genome distribution of DNA methylation in Arabidopsis plants carrying either a weak or strong allele of MET1. Although the partial loss-of-function allele met1-1 reduces CpG methylation levels to approximately 25% of wild type (Kankel et al. 2003), this causes only minor developmental defects. In contrast, the null allele met1-3 causes an almost complete loss of mCpGs and is semi-lethal (Mathieu et al. 2007). The methylation remaining in metl-1 thus identifies a particularly important subset of mCpG sites, offering a unique opportunity to understand the function of mCpG methylation. We find that loci forming stable epialleles are similarly affected in the two mutants, while epigenetically reversible loci are affected differently in metl-1 and metl-3. These observations allow inferences about the molecular mechanisms of epigenetic transgenerational inheritance at these distinct classes of loci and the formulation of genetic and epigenetic rules predicting the capacity of chromosomal targets to form stable epialleles.
Results and Discussion
While the null allele metl-3 causes an almost complete loss of mCpGs (Lister et al. 2008; Tariq et al. 2003) and is semi-lethal (Mathieu et al. 2007), the partial loss-of-function allele met1-1, which reduces CpG methylation levels to approximately 25% of wild type (Kankel et al. 2003), causes only minor developmental defects. This suggests that the 25% mCpGs remaining in met1-1 (Figure S1) are important for a large fraction of epigenetic information. Therefore, we compared whole-genome transcriptomes and methylomes between wild-type Col-0 plants, met1-1 and met1-3, and also the F2 progeny of a hybrid derived from a cross between metl-3 and wild type. The F2 plants were genotyped and only individuals with homozygous wild-type alleles of MET1 were analysed. These MET1+ F2 (hereafter MET1+) siblings had inherited half of their genomes from a metl-3 grandparent, except for the region on chromosome 5 around MET1. For simplicity, we excluded chromosome 5 from subsequent analyses. The MET1+ segregants had on average 57% of wild-type mCpGs that indicated remethylation of the sequences inherited from the metl grandparent (Figure S1C, Table S1).
Next, we screened for differentially methylated regions (DMRs) in pairwise comparisons with metl-3, metl-1 and MET1+ plants using wild type as the common denominator. DMRs in metl-3 included almost all DMRs of both metl-1 and MET1+ plants (Figures 1A and S2). Interestingly, DMRs of metl-1and MET1+ segregants overlapped in more than 85′000 commonly methylated cytosines, representing 57% and 48% of metl-1 and MET1+ DMRs, respectively (Figure 1B). There was a significant correlation (Spearman R2=0.48, Pearson R2=0.63) of methylation distribution between metl-l and METl+ (Figure 1C), which was further confirmed by hierarchical clustering of mCpGs in 200-bp non-overlapping genomic windows (tiles) (Figure 1D). These results suggested that certain methylation patterns in met1-1 and MET1+ are associated with particular loci, possibly due to intrinsic characteristics of DNA sequences of the affected loci themselves. This hypothesis was tested in subsequent experiments.
It has been proposed that targets of DNA methylation in plants are of two distinct types (Saze and Kakutani 2011): (1) gene body methylation (hereafter referred to as Gene Body Like or GEL), present mostly in coding regions of expressed genes and consisting exclusively of mCpGs, and (2) transposable element methylation (hereafter referred to as Transposable Element Like or TEL), found predominantly at transposons and chromosomal repeats and affecting CpGs but also non-CpGs. Here, GELs were defined as gene models in which averaged methylation of cytosines in CpG context was above 5%, but less than 5% in non-CpG contexts and TELs as loci with methylation of both mCpGs and non-CpGs over 5% (Table S2). This produced totals of 11,746 GELs and 4,743 TELs. As expected, 98% of GELs were annotated as genes, while 73% of TELs were annotated as transposable elements (Figure S3) and 22% as genes (Figure S3), indicating that transposon-like methylation is also associated with a subset of protein coding genes. We also annotated this way 200-bp genomic tiles containing at least 5% of averaged CpG methylation. We then examined GEL and TEL methylation in met1-3, met1-1 and MET1+. In met1-3, the two types of methylation were equally erased (Figure S4). In contrast, although in met1-1 methylation at GELs was uniformly lost, methylation losses were not uniform across TELs, with many predominantly losing and others predominantly maintaining DNA methylation (Figures 2A and S5). Interestingly, MET1+ segregants displayed a methylation pattern very similar to met1-1. Analyses of GELs in the MET1+ segregants revealed an average methylation of 50%, suggesting ubiquitous maintenance of the mid-parental methylation levels (Figures 2A, S4 and S5) and TELs in the MET1+ segregants revealed an average methylation of 70% relative to wild type, implying substantial remethylation above the mid-parental level (Figures 2A, S4 and S5). Notably, the patterns of methylation at particular TELs in met1-1 and MET1+ were overlapping (Figures 2A and S5), indicating that TELs have similar remethylation capacities in the two genotypes or, in the case of metl-1, similar resistance to methylation loss. The tiling arrays transcriptome mapping of metl-1, metl-3 and wild type revealed that residual methylation in metl-1 was also reflected by transcriptional silencing of these TELs (Figure S6).
Using 200-bp tiles, we further classified CpG-methylated regions according to their association with methylation of non-CpGs and performed hierarchical clustering that defined three main clusters (Figure 2B). The cluster 1 contained almost exclusively GELs, displaying the greatest methylation deficiency in MET1+ segregants relative to other clusters, while cluster 2 and cluster 3 identified TELs with different degree of methylation in MET1+ and metl-1 (Figures 2B and S7). Further comparison of the global CpG methylation levels of GELs and TELs in met1-3, met1-1 and MET1+ in relation to wild type indicated that in met1-3, GELs and TELs both lacked mCpGs, while in met1-1 and MET1+TELs had retained or regained significant levels of CpG methylation (Figure 2B and S4). These methylome and clustering analyses suggested that TELs are heterogeneous, consisting of at least two classes (Figure 2B).
To define genetic and epigenetic features of TELs that result in differential methylation losses in met1-1, correlated with methylation levels observed in MET1+, we sought DNA sequence properties of TELs that coincide with persistence or loss of CpG methylation in met1-1. We discovered that increase in the number of CpGs is associated with the propensity of TELs to lose CpG methylation (Figure 3A), suggesting a link between DNA sequence properties at methylated TELs and the formation of stably demethylated epialleles. We also found that presence of tandem repeats longer than 100 bp and increasing levels of their repetitiveness jointly correlate with maintenance of methylation in met1-1 or thus remethylation in MET1+ (Figure 3B). Therefore, intrinsic features of TEL DNA sequence are associated with the distribution of their methylation in met1-1 and also with the formation of transgenerationally stable methylation patterns (epialleles) in MET1+ segregants.
To further characterize the epiallelic behaviour of TELs, we rank ordered TELs of cluster 2 and 3 (Figure 2B) according to their levels of methylation in met1-1 and selected two contrasting subsets of TELs for further study (Figures 3C and S8). The first subset retained less in met1-1 than 5% of wild-type CpG methylation. As this subset resembled GELs in the capacity to form stable epialleles, we refer to them as Epiallelic-TELs or E-TELs. More than 80% of CpG methylation was retained in met1-1in the second subset, correlating with regain of CpG methylation in MET1+ and, thus, rapid reversal of the levels towards wild type. This subset of TELs was named Reversible-TELs or R-TELs. It is important to bear in mind that GELs, E-TELs and R-TELs are equally depleted of CpG methylation in metl-3 (Figure S4).
To directly compare methylation levels at all cytosines of E-TELs and R-TELs in wild-type, met1-3, met1-1 and MET1+ plants, we aligned their annotated sequences to construct plots for CpG, CpHpG and CpHpH methylation (Figure 3D). E-TELs displayed a complete loss of CpG methylation in both metl mutants and showed midparent levels of 50% of wild type in MET1+ segregants, suggesting a lack of or minimal remethylation activity at these sequences by MET1, which is present already in met1-3/MET1+ F1 hybrids and in MET1+ segregants. In contrast, although R-TELs completely lost CpG methylation in metl-3, they reached approximately 80% of wild-type mCpGs in MET1+ segregants (Figure 3D), supporting their active remethylation in the presence of MET1. Interestingly, although E-TELs and R-TELs do not show a relevant difference in their non-CpG methylation levels in wild-type plants (Figure S9), both CpHpG and CpHpH methylation were significantly reduced in both metl alleles at E-TELs but not at R-TELs (Figure 3D). Moreover, in MET1+ plants, only E-TELs displayed a significant reduction in CpHpG methylation compared to the wild type (Figure 3D). Thus, non-CG methylation seems to persist at R-TELs in both metl mutant alleles but is depleted at E-TELs.
Methylation at CpHpGs is a part of the self-reinforcing regulatory loop with histone 3 dimethylation in lysine 9 (H3K9me2), and methylation at CpHpHs is maintained primarily by the RdDM pathway directed by small RNAs (Law and Jacobsen 2010). Using published genomic data for H3K9me2 and sRNA distribution data available for wild type and metl-3 (Deleris et al. 2012; Lister et al. 2008), we surveyed their distributions over R-TELs and E-TELs (Figure 3E, 3F). In wild-type plants, the levels of both H3K9me2 and the different classes of small RNAs were similar at R-TELs and E-TELs; however, in the metl-3 mutant only R-TELs retained near wild-type levels of both H3K9me2 and sRNAs, while E-TELs lose both (Figures 3E, F and S10). These results are consistent with previously observed association of sRNAs and non-CpG methylation with transgenerationally “remethylable” loci (Teixeira et al. 2009).
Next, we examined whether E-TELs and/or R-TELs form larger epigenetically co-regulated domains or their local DNA sequences determine susceptibility to epigenetic switching. We found multiple examples of neighbourhoods of R-TELs and E-TELs (Figure S11), consistent with the hypothesis that local features of DNA sequences can be used for the prediction of epigenetic reversibility (R-TELs) or ability to form stable epialleles (E-TELs) (Figure 3A and 3B). Certain transposon superfamilies are overrepresented among E-TELs or R-TELs (Figure S12), the most striking of which are Helitrons, which have the lowest CpG content and are present exclusively in R-TELs (Figure S12). Moreover, although most TE superfamilies were represented in E-TELs and R-TELs, certain TE families were enriched differentially among E-TELs and R-TELs (Tables S3, S4 and Figure S13A), indicating that in addition to the sequence characteristics of a particular family, further sequence features, as delineated here, may explain the epigenetic properties of TEs. Certain transposon families could be clearly separated, consistent with E-TEL or R-TEL characteristics (Figure S13B), thus further reinforcing the hypothesis that DNA sequence composition, in combination with repetitiveness, can be used in defining likelihood of epiallelic properties of loci. Interestingly, one TE family (ATENSPM5) was represented in both E-TELs and R-TELs (Figure S13A). This unusual feature of ATENSPM5 appeared to exhibit a surprising duality of epigenetic regulation within this transposons by which one open reading frame (ORF) behaves like an E-TEL and the other as an R-TEL (Figure S14), again supporting the importance of very local DNA sequence determinants in differential epigenetic regulation.
To directly determine transgenerational epigenetic properties of GELs, E-TELs and R-TELs, and especially the remethylation timing of R-TELs, we backcrossed met1-3, which is in the Col-0 accession, to the wild-type Landsberg erecta(Ler) accession. This allowed the use of DNA sequence polymorphism to discriminate between alleles of the two parents in F1 hybrids and, thus, to separately examine their methylation levels. Parental methylation levels of GELs and E-TELs were maintained in F1 plants, displaying clear epi-heterozygosity (Figure 4A). In contrast, met1-3-derived R-TELs underwent efficient remethylation, suggesting that de-novo DNA methylation occurred as soon as functional MET1 became available (Figure 4B). Therefore R-TELs are clearly different from “remethylable” loci where remethylation does not occur in the F1(Teixeira et al. 2009). In addition, we examined in reciprocal backcrosses expression at R-TELs and E-TELs. The expression of E-TELs observed in met1-1 was maintained in F1 plants, R-TEL expression was efficiently silenced to the initial wild-type level, independent of the crossing direction (Figure 4C, D). This was true for loci annotated as transposons as well as for protein-coding genes with R-TELs or E-TELs in their promoters (Figure 4C, D).
Importantly, a backcross of met1-1 to wild type brings hypomethylated and wild-type epialleles together in the F1, which opens the possibility of trans interactions between epialleles (Greaves et al. 2012; Rigal et al. 2016). To avoid for the first time such confounding epiallelic trans-interactions, we introduced a MET1 transgene into met1-1 plants and examined R-TELs and E-TELs in two complemented transgenic lines with wild type-like MET1 protein levels (Figure S15 and S16). Similar to what was observed in met1/MET1 F1 hybrids and in MET1+ plants, R-TELs and E-TELs displayed their previously documented transcriptional attributes in both complemented transgenic lines (Figure 4E). Finally, we examined genome wide DNA methylation profiles of the two MET1complemented lines (Table S1 and Figure S1) and observed that transgenic MET1-dependent DNA remethylation occurs exclusively at R-TELs, and not at E-TELs or GELs (Figure 4F and S16A, B). Likewise, epigenetic proprieties of certain TEs recorded in MET plants (Figure S14) are consistent with those observed in the transgenic lines (Figure S16C). Taken together, these results confirmed that distinct DNA sequence properties rather than trans-epiallelic interactions determine DNA epigenetic stability or switching.
Finally, we wanted to know whether our predictive rules of DNA sequence properties in defining susceptibility to epigenetic switching have broader applicability. For that, we identified GELs, E-TELs and R-TELs in data from published experiments. For example, it has been demonstrated that progeny of heterozygous metl-3 plants experience CpG methylation loss in the absence of MET1 during post-meiotic divisions of the haploid gametophytes (Saze et al. 2003). Therefore, it can be predicted that inbreeding of metl-3 heterozygous plants will result in gradual methylation losses. However, since MET1 would be present at each generation during somatic development, R-TELs would be subjected to remethylation, while GELs and E-TELs would remain hypomethylated. To test this prediction, we scored methylation levels of GELs, E-TELs and R-TELs in previously reported methylation profiles of plants propagated as heterozygous metl-3(metl-3+/-) and wild-type plants (metl-3 +/+) segregating from these lines (Stroud et al. 2013). Consistent with our predictions, complete loss of CpG methylation at GELs and strong reduction in E-TELs were found in these datasets. In contrast, levels of methylation at R-TELs were similar to wild type (Figure 5A).
Next, we tested distribution of DMRs in R-TELs, GELs and E-TELs in the results of inbreeding experiments of Arabidopsis for 30 generations (Becker et al. 2011) and in natural Arabidopsis accessions (Schmitz et al. 2013). In both populations, we registered the occurrence of DMRs at GELs and E-TELs at a high frequency and at R-TELs at a very low frequency (Figure 5B). Thus, the revealed DNA sequence properties can be used to predict transgenerational epigenetic inheritance in nature.
To test whether these DNA sequence rules are universal and thus applicable to other plant species, we turned to methylation data from recently characterized metl mutants of rice (Hu et al. 2014). In this mutant, CpG methylation is reduced by 76%, closely resembling levels in Arabidopsis metl-l(Figure S17A and Table S1). CpG methylation at rice GELs was lost efficiently and TELs were characterized by heterogeneous methylation losses (Figure 6A), which mirrored Arabidopsis results (Figure 2A, S4). We then compared the DNA sequences of Arabidopsis and rice E-TELs and R-TELs (Figure 3C, S8). Since Arabidopsis and rice genomes have different sizes, CG contents and number of repeats, we normalized their sequences according to the general properties of each genome. The characteristics of DNA sequences (specific CpG frequencies) of rice E-TELs and R-TELs and degrees of sequence repetitiveness were found to be similar to those of Arabidopsis (Figure 6B and C and S17B and C).
It has often been discussed whether and, if so, to what extent DNA sequence itself impacts on epigenetic regulation (Schubeler 2015). Recent transgenic experiments, with site-directed insertion of various DNA fragments into a predefined chromosomal position of mammalian cells, provided evidence that the base composition of DNA sequence may attract or prevent DNA methylation according to CpG content and the occurrence of transcription factor binding sites (Krebs et al. 2014). However, molecular determinants that distinguish between chromosomal loci that rapidly revert to one dominant epigenetic state and loci that form alternative transgenerationally heritable epiallelic states (epialleles) remained largely unknown. Our results demonstrate that the properties of DNA sequence can be used to predict the epiallelic behaviour of certain classes of plant chromosomal loci. Repetitiveness and relative scarcity of CpGs are associated with rapid reversion to one dominant epigenetic state and low copy number and high CpG content are linked to the formation and support of the transgenerational stability of alternative epiallelic states.
Methods
Plant growth and material
Plants used in this work were derived from Arabidopsis thaliana Columbia-0 lines, 13th and 2nd generation homozygous metl-l (Kankel et al. 2003) and metl-3 (Saze et al. 2003) have been used, respectively. The met2,3,4 triple mutant was generated by crossing the lines SALK_010893, SALK_099592 and SALK_098878, genotyping and selecting the segregating homozygous mutants through PCR. Seeds were stratified at 4°C for 4 days and plants grown in 1/2 MS 1.5% agar vertical plates or in soil, depending on the analysis. Plants were grown under long-day conditions (21°C, 16 h light, 8 h dark).
Genomic data
Histone methylation data and small RNA data for Col-0 and metl-3 were GSE370775 and GSE10967, respectively (Deleris et al. 2012; Lister et al. 2008). DMRs recovered in the long-term inbreeding experiments and DMRs found in Arabidopsis accessions were retrieved from previously published work(Schmitz et al. 2013; Becker et al. 2011).
Nucleic Acid Extraction
DNA and RNA were extracted from pools of 2-week-old seedlings (25-30 plants per pool) grown on plates of wild type, met1-1 and met1-3. Due to epiallelic segregation in MET1+ plants, two MET1+pools (8 plants each) were generated starting from genotyped MET1 wild-type leaves derived from the segregating progeny of two F1 plants, obtained by backcrossing metl-3 with Col-0. For each genotype, genomic DNA was extracted using the Qiagen Plant DNeasy kit (Qiagen), starting from nuclei purified as described in (Becker et al. 2011). Total RNA was extracted using the Trizol (Invitrogen) method according to the manufacturer’s instructions.
Library Preparation
Bisulfite-converted DNA libraries of met1-1,wmet1-3, two MET1+ replicates, two transgenic complemented lines (T-MET1a and T-MET1b) and wild type Col-0 control for genomic sequencing were performed starting from 0.5 - 1 µg of genomic DNA using the NEBNext DNA Sample PrepReagentSet1 (New England Biolabs), following the Illumina Genomic Sample Prep Guide (Illumina), as described in (Becker et al. 2011). Libraries for RNA expression analysis were prepared in triplicate from 3 µg of total RNA, processed with the GeneChip Whole Transcript Amplified Double-Stranded Target Assay (Affymetrix), according to the manufacturer’s protocol, to generate labelled cDNA for tiling microarray hybridization.
Analysis of Gene Expression
The labelled cDNA was hybridised to the GeneChip Arabidopsis Tiling 1.0R array (Affymetrix) and scanned following the manufacturer’s instructions. Tiling array hybridisation data were processed with the R statistical software (r-project.org) and BioConductor (www.bioconductor.org) applying the chip definition file (CDF), kindly provided by Naouar et al (2009), as previously described (Yokthongwattana et al. 2010).
For real-time qRT-PCR analysis, total RNA (2 µg) was treated with RQ1 DNase (Promega) and reverse transcribed with the SuperScript VILO cDNA Synthesis Kit (Invitrogen) according to the manufacturer’s instructions. PCR reactions were carried out in triplicate using 10 ng of template cDNA, 200 nM target specific primers (Table S5), and LightCycler 480 SYBR Green I Master (Roche) in the LightCycler 480 II detection system (Roche) in a volume of 10 µl.
Sequencing and processing
Bisulfite-converted libraries were sequenced with 2 × 101-bp paired-end reads on an Illumina GAIIx or Illumina NextSeq 500 instrument. For image analysis and base calling, we used the Illumina OLB software version 1.8. The raw reads were trimmed using Trimmomatic (Bolger et al. 2014) in order to remove adapter sequences. Reads with an average quality value of at least 15 in a window of 4 nucleotides were trimmed from both ends. After trimming, reads shorter than 16 bases were discarded. The remaining sequences (on average 77% of raw reads) were aligned against the Arabidopsis thaliana genome TAIR10 version using Bismark (Krueger and Andrews 2011). Duplicated reads were collapsed into one read. Chloroplast sequences were used to estimate the bisulfite conversion (on average above 99%) (Table S1). To account for non-converted DNA, we applied a correction according to (Lister et al. 2013). The number of methylated reads were decreased as: m*=max(0, m - nc) (where m* is the corrected number of methylated reads, m is the raw number of methylated reads, n is the total number of reads and c is the conversion rate). DMRs (differentially methylated regions) were defined comparing methylation in wild type Col-0 with the other conditions analysed using the R package “DMRcaller” (Zabet and Tsang 2015). We used “noise filter” method to compute CpG and CpHpG DMRs. Briefly, the “noise filter” method uses a triangular kernel to smooth the total number of reads and the total number of methylated reads. Note that the “noise filter” method uses the assumptions of BSmooth package (Hansen et al. 2012), namely, that adjacent cytosines display correlated methylation. In particular, we used a window size of 172 nt, for CpG methylation, and 160 nt, for CpHpG methylation, to smooth the data and then we performed a Score test a each position to determine the positions that display a statistically significant differences in methylation levels between the two conditions (note that using the Score test leads to the same results as Fisher’s exact test, but was much faster to compute). At each position we computed the p-value and adjusted for multiple testing using the Benjamini and Hochberg’s method (Benjamini and Hochberg 1995) to control the false discovery and we discarded positions with FDR higher than 0.05. We further discarded the positions with less than 4 reads and the positions with differences in methylation levels lower than 0.4, in the case of CpG, and 0.2 in the case of CpHpG. Adjacent positions within 200 nt of each other were joined only if the resulting DMR displayed an minimal average number of reads per cytosine of 4 and statistically significant difference in methylation level (FDR lower than 0.05) which is at least 0.4 in the case of CpG and 0.2 in the case of CpHpG. For CpHpH DMRs, we used the “neighbouring” method, which performs the same algorithm as the “noise filter” method except that it does not perform data smoothing. In the case of CpHpHp DMRs, we considered only regions that display a minimal difference in the methylation level of 0.1 and a minimum size of 50 bp. Rice metl mutant and wild type control sequencing data were obtained from (Hu et al. 2014) and re-analysed with the parameters described for Arabidopsis.
Sequence Methylation Analysis
Annotated DNA sequences or genomic 200-bp tiles (with 150 bp overlap) were classified according to the methylation information available in wild type Col-0 for Arabidopsis (provided in this work) and Nipponbare for rice (Hu et al. 2014). Methylation values were averaged among all cytosines in the same context in each TAIR10 annotated sequence or 200 bp tile, with a minimum coverage of five reads. A sequence was considered GEL if more that 5% methylation was present in CpG context, and less than 5% of methylation was present in each of the non-CpG contexts (CpHpG and CpHpH). Sequences were considered TEL if more that 5% of methylation was present in each cytosine context. Sequences were considered not methylated if less than 5% of methylation was present in each context (Table S2). Fisher exact test or Wilcoxon test were used according to the data distribution. Shapiro and Bartlett tests were used for normal distribution or variance similarity, respectively.
Local DNA Methylation Analysis
Approximately 1 µg of DNA extracted using a Mini plant DNA kit (Qiagen) was used for bisulfite conversion with the EpiTect Bisulfite kit (Qiagen) according to the manufacturer’s instructions. One microliter of converted DNA was PCR-amplified with degenerate primers designed to match the tested target. The PCR products were cloned into pGEM-T Easy (Promega) and independent clones were sequenced for each condition analysed. KISMETH (Gruntman et al. 2008), a plant-specific tool for bisulfite sequencing analysis, was used to calculate the percentage of methylation and average different clones. We estimated a bisulfite conversion rate of 98%, based on the methylation level at the non-methylated PHAVOLUTA (At1g30490) locus (Bao et al. 2004).
MET1 Antibody Production
The BAH_DMC domain of MET1 of 175 amino acids (695aa-869aa) was cloned into pDEST-15-GST (Glutathione S-Transferase tagged; Gateway, Invitrogen) using the primers met1_BAH1_attB1 and met1_BAH1_attB2 (Table S5); the transformed BL21 E.coli strain as used to produce the recombinant protein. Purified GST::BAH protein was injected into two rabbits (AGRO-BIO, La Ferte Saint Aubin, France). Although both rabbit sera produced non-specific signals with Arabidopsis protein extract, one serum (anti-MET-84/J77) yielded the signal expected for MET1 protein in wild type (172.4 kDa).
Transgenicmet1-1Complementation
The genomic version of MET1 and the 2,677-bp upstream promoter sequence were independently amplified from genomic DNA using the primer combinations met1_adapF/met1_FLAGR, and Met1PR_adap_F/Met1PR_adap_R, respectively (Table S5). The MET1 gene was assembled into a pGPTVII-bar-MCS (multi-cloning-site) plasmid using the XhoI/XmaI sites and cloned into the barII_pUBQ10_MCS binary vector. The ubiquitin promoter was then removed and substituted with the MET1 promoter using the HindIII/XhoI sites. The Agrobacterium tumefaciens pGV3101 strain was used to transform A. thaliana met1-1 using the standard floral-dip method. Transgenic lines were selected in vivo for resistance to BASTA (dl-phosphinothricin, Duchefa). Two T2 lines homozygous for transgenic MET1 gene (T-MET1a and T-MET1b) were used for preparing bisulfite-converted DNA libraries and sequenced as described for the other genotypes.
Protein Extraction and Analysis
Arabidopsis 15-day-old seedlings grown on plates were used for protein analysis. Around 10 seedlings were pooled in 2-ml tubes and frozen in liquid nitrogen. The samples were homogenized in the TissueLyser II (Qiagen) and proteins extracted with 200 µl of 2X Laemmli Sample Buffer (Sigma). Samples were incubated for 3 min at 95°C and centrifuged; aliquots of15 µl were loaded onto Mini-PROTEAN precast polyacrylamide gels (Biorad) for electrophoresis and SDS-PAGE separation. The separated proteins were transferred to PVDF membrane and immunodetection performed using 1:2500 primary antibody (anti-MET-84/J77) dilution follow by 1:25000 dilution of anti-rabbit-HRP (Sigma). Signal intensities were measured with Odyssey Fc (Li-COR Biosciences).
Author Contributions
Conception and design of the experiments: MC and JP. Nucleic acid library preparations: MC, MD, MLL, C Becker and C Bayon. Genome-wide sequencing: C Becker. Local DNA methylation profiles: JG and MC. Performance of all other experiments: MC. Analysis of the data: MC, NRZ and C Becker. Contribution of reagents/materials/analysis tools: JP and DW. Writen the paper: MC and JP.
Acknowledgments
This work was supported by EVOBREED ERC grant 322621, Gatsby Fellowship AT3273/GLE, AENEAS and Max Planck Society.
Footnotes
Contact Information Marco Catoni: marco.catoni{at}slcu.cam.ac.uk, Jayne Griffiths: jayne.griffiths{at}slcu.cam.ac.uk, Claude Becker: claude.becker{at}tuebingen.mpg.de, Nicolae Radu Zabet: radu.zabet{at}slcu.cam.ac.uk, Carlos Bayon: carlos.bayon{at}rothamsted.ac.uk, Mélanie Dapp: melanie.dapp{at}gmail.com, Michal Lieberman-Lazarovich: liebermic{at}gmail.com, Detlef Weigel: weigel{at}tue.mpg.de, Jerzy Paszkowski: jerzy.paszkowski{at}slcu.cam.ac.uk