To gain insights into the mechanisms of CMT3-targeting of DNA methylation, full-length genomic CMT3 from A. thaliana (AtCMT3) was expressed in E. salsugineum under the native A. thaliana promoter. In total, we assessed plants derived from six transformation events. Two of these lineages, referred to as AtCMT3-L1 and AtCMT3-L2, were propagated by single seed descent for six generations following transformation and serve as the main focus of this study (Figure 1A; see Figure 1—figure supplement 1 and Supplementary file 1 for a complete description of all plant lines and associated data described in this study). Whole genome bisulfite sequencing was completed on individual plants for each generation (numbered T1-T6) of each line to assess the impact of AtCMT3 expression on DNA methylation. Plants expressing AtCMT3 showed increased levels of CHG methylation in intergenic regions, associated with preexisting methylation, as well as over some gene bodies with no prior methylation (Figure 1B and Figure 1—figure supplement 2).

Differentially methylated regions (DMRs) were identified in each sequence context (CG, CHG, and CHH), comparing each generation of AtCMT3-L1 and AtCMT3-L2, and wild type (Supplementary file 2 and Supplementary file 3). Consistent with AtCMT3 activity, the majority of DMRs identified were CHG DMRs, with 25,096 identified that were characterized by a median size of 407 base pairs (bp) (Supplementary file 2). A smaller number of CG (3,502 DMRs, median size: 189 bp) and CHH (1,763 DMRs, median size: 243 bp) were also identified (Supplementary file 2). The majority of CHG DMRs (>99%, in each lineage) were hypermethylated CHG DMRs in AtCMT3 expressing lineages relative to wild type (Supplementary file 3). Many of the CG and CHH DMRs (between 45–72% CG and 86–98% CHH, depending on the line) were also hypermethylated DMRs in AtCMT3 expressing lines relative to wild type, and overlapped with CHG DMRs, suggesting they result from cross-talk between CMT3-mediated CHG methylation and other pathways (Figure 1—figure supplement 3, Supplementary file 3). There was also a significant overlap of hypermethylated CHG DMRs between all individuals, suggesting that AtCMT3 was not methylating DNA randomly (Figure 1—figure supplement 4).

The levels of CHG methylation varied between individuals expressing AtCMT3 (Figure 1B, Supplementary file 2 and Supplementary file 3). Plants were transformed using the floral dip method (Clough and Bent, 1998), which results in random and potentially multiple transgene insertions that can segregate out over generations. Therefore, we predicted that AtCMT3 transgene expression likely varied between individuals, which could explain differences in CHG methylation levels. To test this, we performed RNA-sequencing in the T3 - T5 generations of AtCMT3-L1, the T3 - T6 generations of AtCMT3-L2, as well as two, T2 generation plants from an additional, independently transformed lineage (AtCMT3-L3) (Figure 1—figure supplement 1). The results demonstrated a significant correlation between the levels of AtCMT3 expression and genome-wide CHG methylation levels (R² = 0.8828, p=1.669×10⁻⁴, Figure 1—figure supplement 5, see Supplementary file 4 for FPKM values). This was especially notable for two T2 generation plants of AtCMT3-L3, which had the highest AtCMT3 expression levels (233.7 and 372.5 FPKM for AtCMT3-L3^T2 and T2b, respectively) and the highest genome-wide percent CHG methylation (27% and 32%, for T2 and T2b, respectively). These results also revealed that expression of the AtCMT3 transgene was progressively lost in the AtCMT3-L2 lineage following the T4 generation (Supplementary file 4). We also examined the relationship between genome-wide CHG methylation and AtCMT3 expression using qRT-PCR, including plants from additional lineages, and found a weaker although significant relationship (R² = 0.5932, p=0.025, Figure 1—figure supplement 1 and Figure 1—figure supplement 5). Taken together, AtCMT3 expression is likely one factor contributing to genome-wide CHG levels in these lines.

Despite lacking CMT3, wild-type E. salsugineum does exhibit residual levels of CHG methylation, likely deposited by other DNA methylation pathways such as RdDM (Bewick et al., 2016) (Figure 1B and Figure 2A). As the maize ortholog of CMT3 has been shown to preferentially act on hemi-methylated CHG cytosines (Stoddard et al., 2019), we predicted that AtCMT3 would be preferentially targeted to regions with pre-existing CHG methylation. To test this, CHG DMRs were ranked based on wild-type CHG methylation levels. Results showed that the majority of CHG DMRs (18,812/25,096 or 75%) were characterized by the presence of low levels of CHG methylation in wild type that increased upon the expression of AtCMT3 (Figure 2A). Characterizing the DMRs based on overlap with genomic features revealed that 19,576 out of 25,096 (78%) total CHG DMRs overlapped with repetitive elements or intergenic regions, which are expected targets of all DNA methylation pathways (Figure 2A, Supplementary file 2). Summarizing CHG methylation over all repetitive elements confirmed that plants expressing AtCMT3 exhibited increased CHG methylation over these regions from ~15% methylation in wild type to ~30–70% methylation in AtCMT3 lines (Figure 2B).

In addition to methylated regions, a subset of regions (6,284 out of 25,096 (25%) CHG DMRs) that gained CHG methylation in AtCMT3-expressing lines had no pre-existing CHG methylation, and the majority (4,725/6,284 or 75%) of these regions overlapped annotated genes (Figure 1B, Figure 2A, and Figure 1—figure supplement 2, Supplementary file 2). Examination of percent CHG methylation over all annotated genes revealed increases in CHG methylation over gene bodies, ranging from ~2–10% CHG methylation on average, compared to no CHG methylation in wild-type plants (Figure 2C). The patterns of CHG methylation in several of the generations of the AtCMT3-L2 lineage, which exhibited higher levels of AtCMT3 transgene expression (Supplementary file 4), were reminiscent of that seen for gbM (Bewick and Schmitz, 2017), in that the percent methylation levels were highest towards the center of the gene bodies, decreasing towards the transcription start sites (TSS) and transcription termination sites (TTS) (Figure 2C).

To further characterize the genes that gained CHG methylation, genes that had less than 1% total DNA methylation in any context in wild type and that gained a minimum of 5% CHG methylation were identified in each line (CHG-gain genes) (Figure 2D, Supplementary file 5). The number of CHG-gain genes varied between individuals and lineages. In AtCMT3-L1, the number of CHG-gain genes generally increased over generational time from 99 genes in T1 to 583 genes in T6 (Figure 2D, Supplementary file 5). AtCMT3-L2 individuals were characterized by higher numbers of CHG-gain genes relative to AtCMT3-L1, with the T1 generation showing gains in 718 genes, 2,996 genes in T2, 3,553 genes in T3, and 4,055 genes in T4 (Figure 2D, Supplementary file 5). Following the T4 generation, the T5 and T6 generations of AtCMT3-L2 showed a decline in the number of CHG-gain genes, with 1,937 in T5 and only 88 genes in T6 (Figure 2D, Supplementary file 5). Despite the variation in the number of CHG-gain genes, the overlap of CHG-gain genes between all individuals was significantly higher than expected by chance (Figure 2—figure supplement 1). As CHG-gain genes had no prior CHG methylation, these results demonstrate a de novo methyltransferase activity of CMT3 in vivo.

We hypothesized that the variation in the number of CHG-gain genes between individuals could be related to variation in the expression levels of the AtCMT3 transgene, similar to genome-wide CHG methylation levels. Indeed, based on RNA-seq assessment of AtCMT3 expression, the levels of AtCMT3 expression and number CHG-gain genes were correlated (R² = 0.7951, p=0.001) (Figure 2—figure supplement 2, Supplementary file 4). Again, two T2 generation individuals of the AtCMT3-L3 lineage, which had the highest AtCMT3 expression (Supplementary file 4), also had the highest number of CHG-gain genes (5,566 and 6,346 CHG-gain genes for AtCMT3-L3^T2 and T2b, respectively) (Supplementary file 5). This result was also confirmed utilizing qRT-PCR (R2 = 0.9503, p=3.91×10⁻⁵) (Figure 2—figure supplement 2). Therefore, the decline in the number of CHG-gain genes in the AtCMT3-L2^T5 and T6 generations is correlated with the progressive loss of AtCMT3 transgene expression.

CMT3 can directly bind to H3K9me2 (Du et al., 2012). To determine if increases of CHG methylation detected in plants expressing AtCMT3 were correlated with H3K9me2, we conducted H3K9me2 chromatin immunoprecipitation and sequencing (ChIP-seq) in wild-type E. salsugineum. Consistent with CMT3 binding of H3K9me2, regions enriched for H3K9me2 in wild type showed increased CHG methylation in all lines expressing AtCMT3 (Figure 3A–B and Figure 3—figure supplement 1).

We next sought to determine how gains in CHG methylation affected the distribution of H3K9me2 by conducting H3K9me2 ChIP-seq in the T3 and T5 generation plants for AtCMT3-L1 and AtCMT3-L2. We found plants expressing AtCMT3 also showed enrichment for H3K9me2 across H3K9me2 ChIP peaks identified in wild type, but there were no further increases of H3K9me2 in heterochromatin, despite the increase in CHG methylation (Figure 3A,C–F, and Figure 3—figure supplement 1). In contrast to repeat regions, gains in CHG methylation over gene bodies in AtCMT3-expressing lines were not associated with pre-existing H3K9me2 in wild-type plants (Figure 3A,C–F, and Figure 3—figure supplement 1). Also unexpected, the establishment of CHG methylation following AtCMT3 expression did not result in detectable H3K9me2 across CHG-gain genes (Figure 3A,C–F, and Figure 3—figure supplement 1).

CMT3 can also directly bind to H3K9me1, which distinguishes it from CMT2 (Du et al., 2012; Stroud et al., 2014). Therefore, we also conducted H3K9me1 ChIP-seq in transgenic and non-transgenic lines. Similar to findings in A. thaliana (Jackson et al., 2004), H3K9me1 was enriched over regions characterized by H3K9me2 (Figure 3—figure supplement 2A–B). H3K9me1 was preferentially enriched in heterochromatin relative to CHG-gain genes before or after introduction of the transgene and the H3K9me1 signal over CHG-gain genes was indistinguishable from genes that remained unmethylated (Figure 3—figure supplement 2B–C). Overall, we conclude that gains of CHG methylation at genic loci resulting from expression of AtCMT3 are not associated with stable H3K9 methylation.

CMT3-mediated CHG methylation in heterochromatin is associated with transcriptional silencing (Bartee et al., 2001; Lindroth et al., 2001; Stroud et al., 2014). To determine if gains in CHG methylation over gene bodies resulted in transcriptional changes, we compared expression of CHG-gain genes between wild-type and transgenic plants. In each line there was only a small proportion of CHG-gain genes (less than or equal to 10%) that showed a greater than two log₂ fold change and a similar number were both down and up-regulated (Figure 4A and Figure 4—figure supplement 1, Supplementary file 5). There was also no relationship between the levels of CHG methylation gain and expression changes (Figure 4A and Figure 4—figure supplement 1). Additionally, we defined up- and down-regulated genes genome wide in AtCMT3-expressing lines (defined as greater than a two log₂ fold change relative to wild type), and CHG gain genes were not significantly enriched in either up- or down-regulated genes (p>0.05, Fishers exact test) (Supplementary file 6 and Supplementary file 7). We also considered that the changes in gene expression may be indirect effects of AtCMT3 expression and assessed up- and down-regulated genes for significant enrichments in Gene Ontology (GO) biologic processes. We found that many of the enriched terms for up- and down-regulated genes were involved in abiotic stress responses (Supplementary file 8). However, there were no consistently identified GO-term enrichments across lineages suggesting that AtCMT3 expression was unlikely the direct or indirect cause of these changes (Supplementary file 8). Lack of transcriptional changes directly related to CHG methylation is consistent with the lack of stable H3K9me2 at these loci (Figure 3C–F). We conclude that genic CHG methylation in AtCMT3-expressing lines is uncoupled from heterochromatin formation and transcriptional silencing, similar to gbM.

GbM is present on conserved orthologous genes across diverse plant species (Bewick et al., 2017; Niederhuth et al., 2016; Seymour et al., 2014; Takuno and Gaut, 2013; Takuno et al., 2016). To determine if genes that gain CHG methylation in AtCMT3-expressing lineages are genes that would be predicted to have gbM based on orthology, orthologs of CHG-gain genes in E. salsugineum were identified in A. thaliana. Among the generations of AtCMT3-L1 and L2, there was a total of 4,769 CHG-gain genes identified in at least one individual (Supplementary file 5). Among these genes, 4,104 have an orthologous gene encoded in the A. thaliana genome, and, out of those, 1,526 were classified as gbM in the A. thaliana Col-0 accession, which is significantly more than expected by chance (p=2.83×10⁻²²², hypergeometric test) (Supplementary file 9).

Relative to unmethylated genes, gbM genes are generally characterized as being constitutively expressed at moderate levels, they tend to be longer and have more exons, and have a higher frequency of CMT3-preferred CHG sites (Bewick et al., 2016; Bewick et al., 2017; Lister et al., 2008; Niederhuth et al., 2016; Takuno and Gaut, 2012; Takuno and Gaut, 2013; Takuno et al., 2016; Zhang et al., 2006). To determine whether the CHG-gain genes had similar characteristics, we compared the 4,769 CHG-gain genes identified to the remaining unmethylated E. salsugineum genes (UM genes). On average, CHG-gain genes were longer, contained more exons, exhibited a more moderate, but on average higher, range of expression, and had a higher frequency of CHG cytosines relative to gene length compared to unmethylated genes (Figure 4B–E). Therefore, AtCMT3 in E. salsugineum methylates genes that are orthologs and/or characterized by similar properties of gbM loci in A. thaliana.

GbM is defined as strictly CG context methylation (Bewick and Schmitz, 2017), which contrasts with the predominantly CHG methylation observed over gene bodies in the AtCMT3-Lines (Figure 1B, Figure 3A, Figure 1—figure supplement 2, and Figure 3—figure supplement 1). To determine if AtCMT3 expression resulted in non-CHG methylation over gene bodies as well, we focused on the AtCMT3-L2 lineage, which showed higher AtCMT3 expression levels and CHG-gain genes relative to AtCMT3-L1. We analyzed all CHG DMRs that overlapped CHG-gain genes identified in the T4 generation (4,312 CHG DMRs overlapping 4,055 CHG-gain genes) (Supplementary file 2 and Supplementary file 5), and determined the levels of CG, CHG, and CHH methylation over these regions (Figure 5A). Re-focusing the analysis to the level of DMRs over genes, rather than whole genes, revealed some residual methylation present on genes below our original cutoff of 1% gene-wide methylation. This residual methylation was almost exclusively in the CG context and is consistent with CMT3 preferentially localizing to methylated DNA (Figure 5A). Importantly, 2,094 of the 4,312 regions had no detectable CHG methylation in wild type and 597 of the 4,312 had no methylation in any sequence context (see also Figure 5A, Figure 1—figure supplement 2, and Figure 3—figure supplement 1).

As observed previously, the major gains in genic methylation in AtCMT3-Lines occurred in the CHG contexts (Figure 5A). Further dividing CHG methylation into CWG and CCG contexts revealed that the CHG methylation was highly enriched for CWG methylation, consistent with the preferred substrates of CMT3 (Gouil and Baulcombe, 2016; Stoddard et al., 2019) (Figure 5A). Widespread, but lesser gains were also identified for CHH cytosines (Figure 5A). The CHH methylation was almost exclusively in the CWA context, which is indicative of CMT2 activity and not RdDM (Gouil and Baulcombe, 2016), suggesting that CMT3 activity likely leads to the recruitment of CMT2. Gains in genic CG methylation were comparatively lower than the other sequence contexts, but did occur appreciably over most loci, including regions with no prior CG methylation (Figure 5A). Further parsing of the data into all 16 trinucleotide contexts did not reveal any additional trends in the patterns of methylation (Figure 5—figure supplement 1A).

Establishment of methylation in non-CHG sequence contexts over CHG-gain genes is consistent with CMT3 activity recruiting additional methyltransferase pathways. The natural transgene silencing that occurred in the AtCMT3-L2 lineage following the T4 generation provided an opportunity to test this hypothesis and determine whether non-CHG methylation remained following loss of AtCMT3 expression. To do so, we analyzed AtCMT3-L2 individuals for which we had both RNA-seq and whole genome bisulfite sequencing data (wild type, AtCMT3-L2 T3-T6) relative to the T4 generation, which showed the highest number of CHG-gain genes. The number of newly methylated cytosines in each sequence context in the CHG DMRs overlapping the 4,055 CHG-gain genes identified in AtCMT3-L2^T4 were identified for each individual (same regions as Figure 5A; cytosines analyzed were corrected for coverage in all lines). Then the ratio of methylated cytosines was calculated relative to the T4 generation.

Results showed that the proportional CHG methylation levels on genes mirrored AtCMT3 expression levels, with a slight increase in proportional methylation from 92% in AtCMT3-L2^T3 to 100% (by definition) in AtCMT3-L2^T4 (Figure 5B). Following the T4 generation, and consistent with progressive silencing of the AtCMT3 transgene, the number of CHG cytosines methylated showed a marked decrease, with only 62% of T4 levels remaining methylated in the T5 generation and 4% remaining methylated in the T6 generation where AtCMT3 expression was greatly reduced (Figure 5B). CHH context cytosines showed a similar trend with 97% cytosines methylated in the T3 relative to the T4 generation, followed by a steep decline, with only 24% and 5% methylated remaining in the T5 and T6 generations, respectively, relative to the T4 (Figure 5B). Further dividing CHG and CHH cytosines into CWG vs. CCG or CWA vs. other CHH contexts did not reveal any differences in these trends (Figure 5—figure supplement 1B–C).

CG context cytosines showed a more substantial increase from the T3 to T4 generation, with CG cytosines in the T3 generation at 54% of the levels measured in T4 (Figure 5B). Also contrasting with CHG and CHH methylation, following the T4 generation, the proportion of methylated CG sites remaining following the progressive AtCMT3 silencing was relatively high and much more stable, with 68% in T5% and 51% in T6 of the CG cytosines remaining methylated, relative to T4 (Figure 5B).

The results of the analysis of genic methylation for AtCMT3-L2 contrast with the identical analysis conducted on AtCMT3-L1, which did not show evidence of silencing of the AtCMT3 transgene. In this case, the relative methylation levels were maintained in all sequence contexts across generations (Figure 5—figure supplement 1D). Furthermore, to verify that the remaining genic CG methylation in AtCMT3-L2^T6 did not result from residual AtCMT3 activity, we also completed an alternative approach where we crossed the AtCMT3-L1^T5 transgene expressing line to wild type (non-transgenic) to segregate out the transgene. We analyzed two F2 progeny from this cross in which the transgene was segregated out and one F2 progeny that still contained the transgene (Figure 1—figure supplement 1). In the F2 progeny that did not encode the transgene, the ectopic genic CHG and CHH methylation that was present in the in the transgenic parent was lost but the genic CG methylation was maintained, similar to the result from AtCMT3-L2 when the transgene was silenced (Figure 5—figure supplement 1E). In contrast, the F2 progeny that still encoded the transgene maintained similar levels of genic methylation in all contexts as the transgenic parent (Figure 5—figure supplement 1E).

Importantly, the levels of newly methylated CG cytosines on CHG-gain genes in both the lines that lost AtCMT3 through silencing or crossing out were higher than background due to bisulfite non-conversion, estimated by comparing them to randomly sampled, un-methylated genes that did not gain CHG methylation (Figure 5—figure supplement 2A–B). The genic CG methylation is also unlikely to have occurred independently of AtCMT3, as the gains in genic CG methylation at these loci are also higher that those found by comparing an additional, non-transgenic E. salsugineum accession (Yukon) (Figure 5—figure supplement 2A–B). Thus, genic CG methylation showed a lesser dependency on AtCMT3 expression and was preferentially maintained following loss of transgene expression, either through natural silencing or crossing out the transgene, consistent with maintenance of CG methylation by other methyltransferases recruited by initial AtCMT3 activity.

The majority CHG DMRs resulting from AtCMT3 expression were annotated as repeats or intergenic regions with prior methylation in wild type (~75% compared to ~25% genic loci with no prior methylation) (Figure 2A). Furthermore, the levels of CHG methylation in AtCMT3 expressing lines were much higher on repeats relative to genes (30–70% CHG methylation on repeats compared to 2–10% on genes) (Figure 2B–C). Therefore, we next sought to determine whether this difference was a result of AtCMT3 preferentially methylating heterochromatic loci relative to genic loci. We reasoned that favorable activity of AtCMT3 on heterochromatin could be revealed in lines where AtCMT3 transgene expression was reduced, as they would be expected to show a greater proportional loss of CHG methylation over genic regions relative to heterochromatic loci when compared to lines with high AtCMT3 expression.

To test this, we performed the same analysis reported in Figure 5B, except instead of genic loci, we focused on regions defined as hyper-CHG DMRs in AtCMT3-L2^T4 that did not overlap genes (i.e. repeats and intergenic loci). Results showed that the relative number of methylated cytosines was much more robustly maintained in the AtCMT3-L2 T5 and T6 generations at these loci relative to genic loci following loss of AtCMT3 transgene expression (compare Figure 5B and C). This was especially evident in the T6 generation that showed a −4.6 log₂ fold change in AtCMT3 expression relative to AtCMT3-L2^T4 (Figure 5C). Despite substantial loss of transgene expression, 55% and 57% of CHG and CHH cytosines, respectively, remained methylated in the T6 generation relative to T4 across repeats and intergenic loci (Figure 5C). In contrast, only 4% of CHG and 5% of CHH cytosines remaining methylated across genes (Figure 5B). Methylated CG cytosines were also slightly more robust to loss of AtCMT3 transgene expression, dropping to 66% of T4 levels over repeats compared to 51% over genes in the T6 generation (Figure 5B–C).

An alternative explanation for these results is that methylation induced by AtCMT3 expression is more readily maintained by other methylation pathways in heterochromatin relative to genes, as other methyltransferases preferentially target these regions. To consider this possibility, we also analyzed the relative maintenance of DNA methylation in heterochromatin in lines in which the transgene was removed via crossing to wild type. In the F2 progeny of the AtCMT3-L1T5 X wild type cross that no longer encoded the transgene, both CG and CHH methylation were maintained at relatively similar levels as the transgenic parent, in contrast to CHG methylation that was only maintained at ~25% of the levels of the transgenic parent (Figure 5—figure supplement 1F). This is consistent with the possibility that AtCMT3-induced CG and CHH methylation, and to a lesser extent CHG methylation, can be perpetuated following loss of AtCMT3 in heterochromatin in preference to genes by other methylation pathways. However, the relative levels of CHG methylation in particular are lower than those detected over heterochromatin for lines in which the transgene was silenced, consistent with residual AtCMT3 preferentially targeting heterochromatin in these lines (Figure 5C). We conclude that AtCMT3 preferentially targets heterochromatin and does not readily methylate genic loci until expressed at high levels.

Further information and requests for resources and reagents should be directed to and will be fulfilled by Robert J. Schmitz (schmitz@uga.edu).

Eutrema salsugineum Shandong ecotype was grown on soil at 21°C in long day conditions (16 hr light, 8 hr dark). Plant transgenesis was conducted using the floral dip method (Clough and Bent, 1998). The pEarleyGate 302 vector containing genomic sequence of Arabidopsis thaliana CMT3, including the native promoter, published by Du et al. (2012) was transformed into Agrobacterium tumefaciens strain C58C1. Bacteria were grown for 2 days at 30°C in 200 ml cultures containing gentamicin (25 µg/mL), kanamycin (50 µg/mL), and rifampicin (50 µg/mL) and pelleted by centrifugation at 4°C. Bacterial pellets were resuspended in 5% sucrose and 0.03% Silwet L-77 (Phyto Technology Laboratories) and used to dip open E. salsugineum inflorescences. Transgenic plants were selected for using Finale (BASTA, Bayer).

Whole genome bisulfite sequencing libraries were generated based on methods described in Urich et al. (2015). DNA was extracted from cauline leaves of individual plants flash-frozen in liquid nitrogen using the Qiagen DNeasy Kit according to the manufacturer’s instructions. DNA was fragmented via sonication to a peak size of ~200 bp and further size selected using AMPure beads (Beckman Coulter) to between 150 bp and 500 bp. Fragment end repair was performed using End-It from Lucigen incubated at room temperature for 45 min, followed by purification using AMPure beads. Next, A-tailing was conducted using Klenow Fragment from NEB in NEBNext dA-tailing reaction buffer at 37°C for 30 min, followed by purification with AMPure beads and Illumina indexed adapter (NEXTFLEX Bisulfite-Seq Barcodes) ligation using T4 DNA ligase from NEB. Ligation was conducted at 16°C for 16 hr. Ligation products were purified twice using AMPure beads and bisulfite converted using the EZ DNA Methylation-Gold Kit from Zymogen. Bisulfite converted DNA was then amplified using KAPA HiFi Uracil + and universal primers with the following parameters: 95°C for 2 min, 98°C for 30 s, 8 cycles of 98°C for 15 s, 60°C for 30 s, 72°C for 4 min, and a final extension time of 72°C for 10 min. PCR products were purified using AMPure beads and sequenced with an Illumina NextSeq500 instrument by the Georgia Genomics and Bioinformatics Core. Adapters and primers used were those provided in the NEXTFLEX Bisulfite Sequencing Library Prep Kit (Bioo Scientific).

Total RNA was extracted from cauline leaves of individual plants using the Direct-Zol RNA Mini-prep plus kit from Zymogen according to the manufacturer’s instructions. Sequencing libraries were prepared from 1.3 μg input RNA with the Illumina TruSeq mRNA Stranded Library Kit according to the manufacturer’s instructions, except all volumes were reduced to 1/3 of the recommended quantity. Sequencing was completed using an Illumina NextSeq500 instrument by the Georgia Genomics and Bioinformatics Core.

ChIP was conducted based on the protocol described in Schubert et al. (2006). Cauline leaves were harvested from individual plants and submerged in cross-linking buffer (10 mM Tris-HCl pH 8, 1 mM EDTA, 0.4 M sucrose, 100 mM PMSF, 1% formaldehyde). Tissues were vacuum infiltrated for 5 min at 85 kPa, followed by release of vacuum and 10 additional minutes at 85 kPa. Crosslinking was quenched with the addition of glycine to a concentration of 100 mM followed by 5 min under vacuum at 85 kPa. Tissues were then washed five times in water, flash frozen in liquid nitrogen, and ground to a fine powder with mortar and pestle. Powder was suspended in 10 mls extraction buffer 1 (0.4 M sucrose, 10 mM Tris-HCl pH 8, 10 mM MgCl₂, 5 mM BME, 0,1 mM PMSF, 1 mM EDTA, one tab/10 ml Pierce Protease Inhibitors (ThermoFisher)). The suspension was filtered through 2 layers of Miracloth to enrich nuclei and pelleted by centrifugation for 20 min at 4000 rpm at 4°C. Pellets were resuspended in 1 ml extraction buffer 2 (0.25 M sucrose, 10 mM Tris-HCl pH 8, 10 mM MgCl₂, 1% Triton X-100, 1 mM EDTA, 5 mM BME, 0.1 mM PMSF, one tab/10 ml protease inhibitors) and centrifuged for 10 min at 12,000 g at 4°C. Pellets were then suspended in 300 μl extraction buffer 3 (1.7 M sucrose, 10 mM Tris-HCl pH 8, 0.15% Triton X-100, 2 mM MgCl₂, 1 mM EDTA, 5 mM BME, 0.1 mM PMSF, one tab/10 ml protease inhibitors) and layered on top of 300 μl extraction buffer 3. Samples were then centrifuged for 1 hr at 16,000 g at 4°C, supernatant was removed, and chromatin pellets were resuspended in 100 μl nuclei lysis buffer (50 mM Tris-HCl pH 8, 10 mM EDTA, 1% SDS, 0.1 mM PMSF, one tab/10 ml protease inhibitors). Chromatin was fragmented via sonication to a fragment size of ~200 base pairs and debris were removed by centrifugation at 16,000 g for 5 min at 4°C. 10 μl of the supernatant was removed for input controls and the remaining supernatant was diluted 1:10 in ChIP dilution buffer (1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl pH 8, 167 mM NaCl, 0.1 M PMSF, one tab/10 ml protease inhibitors).

Protein A Dynabeads (Invitrogen) were prepared by washing 25 μl beads three times with 1 ml ChIP dilution buffer (1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl pH 8, 167 mM NaCl). Beads were then resuspended in 100 μl ChIP dilution buffer with 5 ug anti-H3K9me2 (Cell Signaling Technology, Cat. # 9753 s) or anti-H3K9me1 (Abcam # 8896) added. Antibodies were bound to beads at 4°C with end over end rotation for 3 hr. Beads were then washed three times with ChIP dilution buffer (1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl pH 8, 167 mM NaCl, 0.1 M PMSF, one tab/10 ml protease inhibitors) and resuspended in the diluted chromatin samples from above. Samples were incubated with rotation over night at 4°C.

Following incubation, beads were washed twice with 1 ml each of low salt wash buffer (150 mM NaCl, 0.1% SDS, 1% TritonX-100, 2 mM EDTA, 20 mM Tris-HCl pH 8), high salt wash buffer (500 mM NaCl, 0.1% SDS, 1% TritonX-100, 2 mM EDTA, 20 mM Tris-HCl pH 8), and LiCl wash buffer (0.25 LiCl, 1% NP40, 1% sodium deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8). Beads were then washed one time with 1 ml TE buffer (10 mM Tris-HCl pH 8, 1 mM EDTA) and resuspended in 250 μl elution buffer (1% SDS, 0.1 M NaHCO₃). Samples were eluted with incubation at 65°C for 15 min with gentle agitation. The supernatant was removed and saved and the elution was repeated with an additional 250 μl elution buffer. Supernatants were then combined and 20 μl 5 M NaCl was added. 500 μl elution buffer and 20 μl 5 M NaCl were also added to the input controls. Crosslinks were reversed over night at 65°C. Following crosslink reversal, 10 μl of 0.5 M EDTA, 20 μl 1 M Tris-HCl (pH 6.5), and 2 μl of 10 mg/ml proteinase K (ChIP grade, Thermo Fisher Scientific) was added to each sample and incubated at 45°C for 1 hr. DNA was extracted with phenol/chloroform/isoamyl alcohol (25:24:1) and resuspended in water.

ChIP sequencing libraries were prepared by conducting end repair, A-tailing, and adaptor ligation steps identical to those described for bisulfite sequencing library preparation, except that with the substitution of Illumina TruSeq adaptors and indexed primers. Libraries were amplified with Phusion DNA Polymerase (NEB) with the following parameters: 95°C for 2 min, 98°C for 30 s, 15 cycles of 98°C for 15 s, 60°C for 30 s, 72°C for 4 min, and a final extension step of 72°C for 10 min. Libraries were sequenced on an Illumina NextSeq500 instrument by the Georgia Genomics and Bioinformatics Core.

RNA was extracted from cauline leaves of individual plants using the Direct-Zol RNA Mini-prep plus kit from Zymogen according to the manufacturer’s instructions. Synthesis of cDNA was completed using SuperScript III with random hexamers (Invitrogen) according to the manufacturer’s instructions. Real time qRT-PCR was conducted using LightCycler 480 SYBR green master mix in a Light Cycler 480 instrument (Roche). Primers used include: AtCMT3 qRT-PCR FP: TGGTTTGAACCTCGTCACTAAA; AtCMT3 qRT-PCR RP: CGTTTGTCTCTGGGTGGTTAT; EsTUB4 qRT-PCR FP: CCTCCATATCCAAGGCGGTC; EsTUB4 qRT-PCR RP: GTACTGGCCGGTGTGATCAA.

WGBS data were processed using ‘single-end-pipeline’ function from Methylpy as described in Schultz et al. (2015). Briefly, quality-filtering and adapter-trimming were performed using cutadapt v1.9.dev1 (Martin, 2011). Qualified Reads were aligned to the E. salsugineum v1.0 reference genome (Yang et al., 2013) (downloaded from: https://phytozome.jgi.doe.gov) using bowtie 2.2.4 (Langmead and Salzberg, 2012). Only uniquely aligned and non-clonal reads were retained. Chloroplast DNA (which is fully unmethylated) was used as a control to calculate the sodium bisulfite reaction non-conversion rate of unmodified cytosines. A binomial test was used to determine the methylation status of cytosines with a minimum coverage of three reads.

Identification of DMRs (Differential Methylated Regions) was performed using ‘DMRfind’ function from Methylpy pipeline as described in Schultz et al. (2015). Default parameters were adopted and only DMRs with at least 5 DMSs (Differential Methylated Sites) were reported and used for subsequent analysis.

To produce metaplots, 1 kb regions upstream and downstream features of interest were divided into 20 bins each. Features of interest were also divided into total of 20 bins. Weighted methylation levels were computed as the number of methylated reads divided by the total reads for each bin.

To generate the heatmap shown in Figure 2A, weighted percent CHG methylation was calculated for all significant CHG DMRs with a minimum of 5 cytosines with three read coverage each in all lines (listed in Supplementary file 2). DMRs were then ranked by weighted %CHG methylation levels in wild type (vertical axis) and arranged by lineage (horizontal axis). To identify called DMRs reported in Supplementary file 3, all significant DMRs with minimum coverage requirements were filtered by cutoffs of a minimum of 10% change, relative to wild type, to be considered a hypo- or hyper- CHH or CHG DMR, and a minimum of 20% change, relative to WT, to be considered a hypo- or hyper- CG DMR. Overlaps of hyper-CG, CHG, and CHH DMRs and generation of upset plots, shown in Figure 1—figure supplement 3, were calculated using Intervene v.0.6.1 (Khan and Mathelier, 2017). To calculate the overlap of hyper-CHG DMRs between all individuals of the AtCMT3-L1 and AtCMT3-L2 lineages reported in Figure 1—figure supplement 4, coordinates of the called hyper-CHG DMRs reported in Supplementary file 3 were input to the bedtools v2.27.1 fisher command (Quinlan and Hall, 2010). P values reported were calculated from Fisher’s Exact Test with significance set at p<0.0004 based on the Bonferroni correction for multiple testing.

To identify CHG-gain genes, genes were first filtered for those that had less than 1% cytosine methylation in any sequence context in wild-type. Genes were then filtered for coverage and only those with at least 10 informative CHG cytosines (min. five read coverage) in each line were assessed. Coverage corrections were separately done for: 1. wild type and all individuals of the AtCMT3-L1 and AtCMT3-L2 lineages described in Figure 1A; 2. wild type and all other individuals besides AtCMT3-L3T2c and the individuals of the AtCMT3-L1 and AtCMT3-L2 described in Figure 1A; and 3. wild type and AtCMT3-L3T2c. Genes were then called as CHG-gain genes if they showed a minimum of 5% increase in CHG methylation in AtCMT3 expressing lines relative to wild-type. Percent CHG methylation was calculated as the number of methylated reads mapping to CHG sites at the gene of interest divided by the total number of reads mapping to CHG sites. CHG-gain genes are listed in Supplementary file 5. The significance of the overlap of CHG-gain genes between individuals shown in Figure 2—figure supplement 1 was calculated with a hypergeometric test with significance set at p<0.0004 based on the Bonferroni correction for multiple testing.

To generate the heatmap shown in Figure 5A and Figure 5—figure supplement 1A, all CHG DMRs identified in Supplementary file 2 that overlapped the CHG-gain genes AtCMT3-L2^T4 were analyzed. Weighted methylation levels of trinucleotide sub-contexts (CNN, N = A/T/C/G) were calculated for each qualified DMR in wild type and AtCMT3-L2, T1-T6 derived from WGBS. The methylation levels of sub-contexts in wild type was used to determine their hierarchical clustering relationships. Corresponding methylation levels in ATCMT3-L2, T1-T6 were plotted in the same order.

To determine the relative number of methylated cytosines over CHG-gain genes in individuals of the AtCMT3-L2 lineage relative to AtCMT3-L2^T4 (shown in Figure 5B, Figure 5—figure supplement 1B–C), the analysis was limited to cytosines located within the regions analyzed in Figure 5A that had a minimum of 3 read coverage in each line and were not methylated in wild type. Methylation status was determined with a binomial test. Number of methylated cytosines in each sequence context were reported as a ratio, relative to the number of methylated cytosines in AtCMT3-L2^T4. In Figure 5C, the same analysis was performed as described in Figure 5B, except cytosines analyzed were those found in hyper-CHG DMRs defined in AtCMT3-L2^T4 that did not overlap genes and were thus annotated as repeats or intergenic regions. In Figure 5—figure supplement 1D, the same analysis was conducted except the regions analyzed were the hyper-CHG DMRs identified in AtCMT3-L1^T4 that overlapped AtCMT3-L1^T4 CHG-gain genes and the ratios of methylated cytosines are relative to AtCMT3-L1^T4. The same analysis was also conducted to produce Figure 5—figure supplement 1E–F, except the regions analyzed were the hyper CHG DMRs identified in AtCMT3-L1^T5 that overlapped AtCMT3-L1^T5 CHG gain genes (Figure 5—figure supplement 1E) or did not overlap genes (Figure 5—figure supplement 1F) and the ratios of methylated cytosines are relative to AtCMT3-L1^T5.

To identify the background levels of CG methylation shown in Figure 5—figure supplement 2, an equal number of genes as the number of CHG-gain genes identified in AtCMT3-L2^T4 (for Figure 5—figure supplement 2A) or AtCMT3-L1^T5 (for Figure 5—figure supplement 2B) was randomly selected from all genes that were classified as unmethylated in wild type and did not gain CHG methylation in AtCMT3-expressing lineages using the R command sample. Then an equal amount of sequence as analyzed in Figure 5B (for Figure 5—figure supplement 2A) or Figure 5—figure supplement 1E (for Figure 5—figure supplement 2B) was extracted from the randomly chosen genes, with the total number of nucleotides distributed evenly across each gene. All CG cytosines with less than three read coverage in each line assessed and cytosines found to be methylated in wild type (Shandong accession) were removed from the analysis. Coverage filtering was completed individually for the lineages assessed in each panel of Figure 5—figure supplement 2. Methylated CG cytosines were then identified in each lineage using a binomial test and percent CG methylation was calculated as the number of methylated CG cytosines divided by the total number of CG cytosines. This was completed for five randomly chosen sets of unmethylated genes and compared to the identical analysis completed on the CHG gain gene regions assessed in Figure 5B (for Figure 5—figure supplement 2A) or Figure 5—figure supplement 1E (for Figure 5—figure supplement 2B). Bisulfite sequencing data for E. salsugineum Yukon accession were from Bewick et al. (2016).

Quality-filtering and adapter-trimming were performed using Trimmomatic v0.33 with default parameters (Bolger et al., 2014). Qualified reads were aligned to the E. salsugineum v1.0 reference genome using HISAT2 v2.0.5 (Kim et al., 2015). Gene expression (calculated as fragments per kilobase million; FPKM) values were computed using StringTie v1.3.3b (Pertea et al., 2016). To compare expression between wild type and AtCMT3-expressing lines, Log₂ fold change values were calculated as Log₂ (FPKM AtCMT3 line/FPKM wild-type). Genes with zero FPKM values were removed from expression analyses. A cutoff of ±2 Log₂ fold change was used to identify genes undergoing substantial changes in expression.

To conduct Gene Ontology enrichment analyses of up- and down-regulated genes, A. thaliana orthologs of E. salsugineum genes identified in Niederhuth et al. (2016) were utilized to extract GO annotations from TAIR (www.arabidopsis.org). These annotations were then used to identify significantly enriched GO terms with an elim Fisher’s exact test (p-value<0.01) using the R package topGO (https://bioconductor.org/packages/release/bioc/html/topGO.html).

Quality-filtering and adapter-trimming were performed using Trimmomatic v0.33 (Bolger et al., 2014) with default parameters. The remaining reads were aligned to the E. salsugineum v1.0 reference genome using Bowtie v1.1.1 (Langmead et al., 2009) with the following parameters: ‘bowtie -m 1 v 2 --best --strata --chunkmbs 1024 S’. Aligned reads were sorted using SAMtools v1.2 and duplicated reads were removed using SAMtools v0.1.19 (Li et al., 2009). ChIP-peaks were defined in wild type relative to input using HOMER 4.10 (Heinz et al., 2010) with the following parameters: ‘-region -tagThreshold 10 -size 1000 -minDist 2500 -tbp 0’. Identified peaks that were directly connected together were merged into a single region. Peaks were then further filtered by read density. The density for each merged region was defined as follows: aligned reads divided by region length. Only merged regions with density greater than 0.05 were outputted as peaks and used for subsequent analysis.

We used these ChIP-peak regions and the coordinates of CHG-gain genes to create metaplots to compare enrichment between samples. In the metaplots, mapped reads were normalized to total mapped reads for each locus of interest, and were averaged over 4 bins representing 2 kb upstream, 2 kb from the transcription start site into the gene, 2 kb from the transcription stop site into the gene, and 2 kb downstream. Finally, the average bin values were normalized to account for the number of loci.

Relative expression of the AtCMT3 transgene to TUB4 (Thhalv10003210m) was calculated using the double delta threshold cycle (Ct) method as 2 ^ -((Average AtCMT3 Ct) – (Average TUB4 Ct)) (Livak and Schmittgen, 2001). Average Ct values were calculated from three technical replicates.

Gene length and exon number for CHG-gain genes and UM genes were derived from E. salsugineum reference annotation files. CHG site number was calculated by scanning the whole gene sequence with a three base window and step size of 1 base. Both positive strand and negative strand were considered. The CHG frequency was calculated by normalizing CHG site number to gene length. Significance for differences in characteristics between CHG-gain and UM genes was calculated with a Wilcoxon rank-sum in the stats package of R v3.44.

Orthologs of CHG-gain genes in A. thaliana and their gbM status reported in Supplementary file 9 were those identified in Niederhuth et al. (2016). To determine if gbM-gain genes had more A. thaliana orthologs that were classified as gbM than expected by chance, a hypergeometric test was conducted using the values of 20,211 total A. thaliana genes with an E. salsugineum ortholog, with 4532 of those being classified as gbM in A. thaliana by Niederhuth et al. (2016).

All sequencing data generated have been deposited into NCBI Gene Expression Omnibus under accession number: GSE128687.