Whole-genome DNA methylation patterns and complex associations with gene structure and expression...

Whole-genome DNA methylation patterns and complexassociations with gene structure and expression duringflower development in Arabidopsis

Hongxing Yang1,2,†, Fang Chang1,*,†, Chenjiang You1,3, Jie Cui1, Genfeng Zhu1, Lei Wang1,3, Yu Zheng4, Ji Qi1,* and

Hong Ma1,3,*1State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Institute

of Plant Biology, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200433, China,2Shanghai Chenshan Plant Science Research Center, Shanghai Institutes for Biological Sciences, Shanghai Chenshan

Botanical Garden, Chinese Academy of Sciences, Shanghai 201602, China,3Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institutes of Biomedical Sciences,

Fudan University, Shanghai 200032, China, and4New England Biolabs Inc., 240 County Road, Ipswich, MA 01938, USA

Received 24 September 2014; revised 3 November 2014; accepted 6 November 2014.

*For correspondence (e-mails [email protected], [email protected] and [email protected]).†These authors contributed equally to this work.

SUMMARY

Flower development is a complex process requiring proper spatiotemporal expression of numerous genes.

Accumulating evidence indicates that epigenetic mechanisms, including DNA methylation, play essential

roles in modulating gene expression. However, few studies have examined the relationship between DNA

methylation and floral gene expression on a genomic scale. Here we present detailed analyses of DNA meth-

ylomes at single-base resolution for three Arabidopsis floral periods: meristems, early flowers and late flow-

ers. We detected 1.5 million methylcytosines, and estimated the methylation levels for 24 035 genes. We

found that many cytosine sites were methylated de novo from the meristem to the early flower stage, and

many sites were demethylated from early to late flowers. A comparison of the transcriptome data of the

same three periods revealed that the methylation and demethylation processes were correlated with

expression changes of >3000 genes, many of which are important for normal flower development. We also

found different methylation patterns for three sequence contexts (mCG, mCHG and mCHH) and in different

genic regions, potentially with different roles in gene expression.

Keywords: cytosine methylation, Arabidopsis DNA methylome, MspJI, RNA-seq, flower development, gene

expression.

INTRODUCTION

Flowers are angiosperm reproductive structures, and

develop from the floral meristem. The cells derived from the

floral meristem undergo division and differentiation to form

four types of flower organs, including the reproductive

organs, stamens and pistil. Meiosis generates haploid

spores that then develop into pollen grains and embryo sacs

for fertilization and seed production. Flower development

requires the normal function of receptor-like protein kinases

and ligands, transcription factors, enzymes, and other mole-

cules (Ma, 2005; Ge et al., 2010; Chang et al., 2011). Epige-

netic mechanisms play essential roles by modulating the

expression of numerous genes through histone modifica-

tion, chromatin remodeling, microRNA-mediated mRNA

degradation and DNA methylation. Studies over the past

decade have revealed that these epigenetic pathways are

important for normal gene expression, while DNA methyla-

tion is also known to be involved in genome stability (Chan

et al., 2005; Law and Jacobsen, 2010; Gan et al., 2013).

Although conserved across eukaryotes, DNA methyla-

tion in plants has several unique features with regard to

the pattern of methylation, the methylation machinery, and

demethylation enzymes in non-dividing cells (Chan et al.,

2005). For example, mammalian DNA methylation occurs

mostly at CG sites by the DNA methyltransferase DNMT1

and homologs. In contrast, plant DNA methylation occurs

at CG, CHG and CHH sites, where H = A, T or C; in Arabid-

© 2014 The AuthorsThe Plant Journal © 2014 John Wiley & Sons Ltd

1

The Plant Journal (2014) doi: 10.1111/tpj.12726

opsis thaliana, methylation at these three types of sites is

performed by DNA METHYLTRANSFERASE 1 (MET1), the

plant-specific CHROMOMETHYLASE 3 (CMT3), and

DOMAINS REARRANGED METHYLTRANSFERASEs

(DRMs), respectively (Chan et al., 2005). Each of the three

types of DNA methylation is crucial for development and

responses to environmental stresses (Bird, 2002; Chan

et al., 2005; Goll and Bestor, 2005; He et al., 2011; Jullien

et al., 2012; Song et al., 2013).

Consistent with the role of DNA methylation in genome

stability, a substantial proportion of the methylated cyto-

sines in Arabidopsis are found in genomic regions compris-

ing repetitive sequences such as transposable elements

(TEs) (Arabidopsis Genome Initiative, 2000; Martienssen

and Colot, 2001; Lippman et al., 2004; Chan et al., 2005).

Exogenous repetitive sequences such as transgenes may

also be methylated and induce methylation and consequent

transcriptional silencing of homologous sequences in trans-

genic lines (Mette et al., 2000; Soppe et al., 2000; Zilberman

et al., 2004; Chan et al., 2005). Case studies and genome-

wide analysis in Arabidopsis indicated that DNA methyla-

tion in promoter regions is often associated with transcrip-

tional gene silencing (Park et al., 1996; Stam et al., 1998;

Jones et al., 1999; Zhang et al., 2006; Zilberman et al.,

2007). Although genome-wide DNA methylation has been

investigated for vegetative tissues, little is known about the

patterns of DNA methylation and their association with

gene expression at various stages in plant development.

More specifically, a relationship between DNA methylation

and gene expression during flower development has not

been reported.

Whole-genome bisulfite sequencing as a gold standard

method has been applied in many studies of DNA methylo-

mes (Feng et al., 2010; Zemach et al., 2010), including Ara-

bidopsis (Cokus et al., 2008; Lister et al., 2008) and tomato

(Solanum lycopersicum) (Zhong et al., 2013). It requires

deep sequencing coverage for confident DNA methylation

calling, and thus is not cost-efficient. The recently identified

methylation-dependent endonuclease MspJI has both low

specificity in recognition sites and fixed cut distances: it rec-

ognizes 5-methylcytosine or 5-hydroxymethylcytosine in

the context of CNN(G/A), and cleaves both strands at fixed

distances (N12/N16–17) away from the modified cytosine on

the 30 side; these properties not only increase the number

of detectable methylcytosines (mCs), but also enable

identification of mCs at single-base resolution (Zheng et al.,

2010; Cohen-Karni et al., 2011; Horton et al., 2012; Huang

et al., 2013). In Arabidopsis, 48.8% of all cytosines and gua-

nines are part of CNNR/YNNG sites, of which 90.2% of

methylated sites were detected by methylation-dependent

MspJI DNA digestion combined with high-throughput

sequencing (MspJI-seq).

Here, we present detailed analyses of DNA methylomes

during Arabidopsis flower development, using MspJI-seq.

We analyzed three periods during flower development -

the meristem, early flower development (organogenesis),

and late flower development (maturation) - and detected

many more methylated cytosines in the second period

than either the first or third periods, suggesting de novo

methylation at many sites during organogenesis, followed

by demethylation at many sites. These developmental

stage-dependent methylation and demethylation activities

are correlated with changes in the expression levels of

over 3000 genes, including many genes that are important

for flower development. Moreover, the methylation pat-

terns and the potential influences on transcription vary sig-

nificantly across sequence contexts and genic regions. Our

study provides valuable insights into the possible func-

tions of DNA methylation during floral development.

RESULTS

The DNA methylation landscape during Arabidopsis floral

development

To survey the DNA methylation and gene expression dur-

ing flower development, we sampled wild-type early flow-

ers of stages 1–9 (E), wild-type late flowers of stages 10–12

(L), and meristems (M) from ap1 cal double mutant plants,

as this mutant is arrested at the inflorescence meristem

stage (Bowman et al., 1993; Ferrandiz et al., 2000), and has

been used previously as a source of meristems for tran-

scriptomic analyses (Gomez-Mena et al., 2005; Wellmer

et al., 2006; Kaufmann et al., 2010). Using high-throughput

sequencing of DNA fragments obtained by MspJI digestion

(Table S1), we identified 1 565 127 cytosines that were

potentially methylated during flower development. We ran-

domly selected nine regions of eight genes that contained

identified mC sites to perform real-time PCR-based valida-

tion, and the results confirmed the methylation status

detected by MspJI-seq (Figure S1 and Table S2). To further

validate the technical reproducibility of our MspJI-seq data,

we obtained methylation data for 44 randomly selected

genes, including the same eight genes mentioned above,

and 39 randomly selected genomic regions, by performing

bisulfite sequencing experiments on the same tissues; we

observed good consistency between MspJI-seq and bisul-

fite sequencing results (Figure S2).

Among the mC sites we identified, 453 066 were in the

CG dinucleotide context, including 207 115 that were previ-

ously detected in Arabidopsis seedlings (Cokus et al.,

2008). In addition, we detected 425 428 mCHG and 685 100mCHH sites (Figure 1a); these mC sites accounted for

17.5%, 13.7% and 5.2%, respectively, of the three types of

cytosine sites (in the CNNR context) that are potentially

digested and detected by MspJI-seq. Among detected

methylation sites, approximately 73% (1 141 758) were in

exons, 3% (46 208) were within introns, 8% (125 325) were

in putative promoter regions (1 kb region upstream of tran-

© 2014 The AuthorsThe Plant Journal © 2014 John Wiley & Sons Ltd, The Plant Journal, (2014), doi: 10.1111/tpj.12726

2 Hongxing Yang et al.

scription start sites), and 16% (250 303) were in intergenic

regions. Interestingly, we found a slightly higher percent-

age of mCHH sites (17%) in intergenic regions, compared

with mCG (15.2%) and mCHG (15.0%) sites (P < 1e-60, v2

test) (Figure 1b), indicating that symmetric and non-sym-

metric methylation sites were not evenly distributed

between genic and intergenic genomic sequences.

To obtain an overview of the detected DNA methylation,

we compared the methylation levels of each 100 kb win-

dow throughout the genome in various sequence contexts

(mCG, mCHG, mCHH) and genic regions (exon, intron, pro-

moter or intergenic). First we normalized the methylation

level as reads per kilobase of cytosines of CNNR sites

(each site counts as 1 bp) per million mapped reads

(RKCM) (see Experimental procedures). We found that het-

erochromatic regions (centromeres and peri-centromeric

zones) had relatively high methylation levels for all three

the sequence contexts (Figure 1c), and the methylation

status was highly stable between the three floral develop-

mental periods, consistent with the high frequency of TEs

in these regions (Figure 1d and Figure S3). Hence,

methylation at the heterochromatic regions is least corre-

lated with development, reminiscent of the discovery that

DNA methylation around centromere and peri-centromeric

regions varies least among Arabidopsis populations (Sch-

mitz et al., 2013). In contrast, methylation levels in euchro-

matin were relatively low but more variable between the

various types of mC site: methylation at mCG sites was

higher than at mCHG and mCHH sites, consistent with pre-

vious findings (Cokus et al., 2008; Lister et al., 2008) and

lower percentages of mCG sites were stage-specific thanmCHG and mCHH sites; furthermore, exons were most

highly methylated, particularly for mCG sites.

Difference in methylated sites between developmental

stages

The number of mC sites increased by 8% from meristems

(1 000 123) to early flowers (1 080 179), then decreased

slightly in late flowers (1 074 245). This trend was true for

all sequence contexts (mCG, 6.4%; mCHG 7.2%; 9.8% formCHH). The number of mCs detected in early flowers but

not in meristems was 96 708 for mCG sites, 95 240 for

(a)

(c)

(b) (e)

(f)

(d) (g)

Figure 1. The DNA methylome of Arabidopsis

flowers.

(a) Percentage of identified mCs for each

sequence context. Numbers on the top of each

bar indicate the number of methylcytosines.

(b) Percentages of mCs found in each genomic

region.

(c) Log2-transformed methylation levels (RKCM

values; scale shown on top right) for 100 kb

sliding windows (step size 50 kb) for each

sequence context, floral stage and genomic

region. M, meristem; E, early flower; L, late

flower. Genomic coordinates on concatenated

chromosomes are indicated at the bottom of

the heatmaps.

(d) Proportions of floral stage-specific as well

as non-specific mCs across the genome (200 kb

sliding window; step size, 100 kb). ME, ML, EL,

methylcytosines shared between two tissues;

MEL, methylcytosines common to three tissues.

(e–g) Comparison of mCs between developmen-

tal stages for mCG, mCHG and mCHH sites,

respectively.


Arabidopsis floral DNA methylomes and transcriptomes 3

mCHG sites, and 178 087 for mCHH sites, significantly out-

numbering mCs found in floral meristems but not in early

flowers (77 052 for mCG sites, 74 861 for mCHG sites, and

138 066 for mCHH sites) (Figure 1e–g), indicating extensive

new methylation during early flower development. The

newly methylated sites in early flowers are associated with

a large number of genes, of which 6570 genes containedmCG sites, 4570 contained mCHG sites, and 5602 containedmCHH sites (results supported by five reads or more). From

early to late flowers, only the number of mCG sites was

slightly increased (by 2.1%), whereas the numbers of mCHG

and mCHH sites were both slightly decreased by 1.7% (Fig-

ure 1e–g).

The proportion of tissue-specific mC sites also appeared

to vary across sequence contexts. In particular, 144 299mCG sites (31.8%), 147 328 mCHG sites (34.6%), and

301 267 mCHH sites (44.0%) were specific to one of the

three floral tissues. On the other hand, 32.7% of mCHH sites

were methylated in all three tissues, compared with 43.4%

and 46.7% of mCHG and mCG sites, respectively, observed

for all tissues. Therefore, we found more between-tissue

variations in number of mCHH sites than the number ofmCHG sites, which in turn was slightly more variable than

the number of mCG sites (Figure 1e–g).

Distinct gene methylation patterns during floral

development

To investigate the relationship between gene expression

and DNA methylation during floral development, we deter-

mined the positions of mC sites relative to individual genes.

In total, we found 24 035 genes with at least one mC site

supported by five reads or more (Table S3). With regard to

the three sequence contexts, we found 20 569, 17 409 and

18 746 genes containing at least one mCG, mCHG and mCHH

site, respectively. The overall methylation at gene level in

Arabidopsis flowers was compared with recently published

DNA methylome data (Schmitz et al., 2013) from mixed

stages of Arabidopsis flowers, for which >20 000 genes

were found to have at least one mC site. Over 83% of these

genes were also found in our list of methylated genes, rep-

resenting a high level of consistency even though different

ecotypes were used in these two studies (Col-0 for Schmitz

et al. and Landsberg erecta for this analysis).

We then divided genes into classes according to their

coding potential, namely protein-coding, microRNA (miR-

NA), other non-coding RNA, pseudogenes and TE genes,

and observed distinct methylation patterns for the various

classes of genes. Approximately 70% of annotated protein-

coding genes (19 182 of 27 416) were methylated in one or

more of the floral tissues, forming the largest group of

methylated genes (79.81%) (Figure S4). Only 15% of these

protein-coding genes were methylated specifically in one

of the three tissues (meristem, 1518, approximately 7.9%;

early flower, 667, approximately 3.5%; late flower, 715,

approximately 3.7%) (Figure 2a,b). Furthermore, protein-

coding genes were more likely to be methylated at mCG

sites than at mCHG and mCHH sites (mCG: 16 187, approxi-

mately 59%, mCHG, 13 060, approximately 48%, mCHH,

14 438, approximately 53%; P < 1e-100, v2 test).Consistent with the role of DNA methylation in silencing

TEs (Zhang et al., 2006; Cokus et al., 2008; Law and Jacob-

sen, 2010), almost 92% of annotated TE genes (3588 of

3903) were methylated in one or more floral tissues, with

only approximately 2.4% (93) being specific to one of the

tissues (Figure 2a,b). We found no significant differences

in methylation at the different mC sequence contexts for TE

genes (P > 0.1 for all comparisons, v2 test). In addition to

TE genes, the Arabidopsis genome is annotated as pos-

sessing 31 118 TEs, most of which do not contain genes,

but may impair genome integrity after activation by trans-

posases or reverse transposases (Law and Jacobsen,

2010). We thus examined methylation of these TEs in floral

tissues. In contrast to the broad methylation of TE genes,

we found that only 24% of TEs (7516 of 31 188) were

methylated in meristems. Previous studies revealed strong

associations between TE methylation and actions of siR-

NAs (Lister et al., 2008; Ahmed et al., 2011). To check how

often siRNAs participated in the methylation of TEs in flo-

ral tissues, we obtained siRNA sequencing data for Arabid-

opsis seedlings (Chodavarapu et al., 2010), and found that

approximately 40% TEs were potential targets of siRNAs

(Figure S5). This relatively low hit ratio may be ascribed to

tissue-specific expression of some siRNA species, the pos-

sibility that a substantial proportion of TEs was not methy-

lated via siRNAs, or the possibility that silencing of TEs

may be primarily achieved via silencing of TE genes.

Genes of different classes also showed differences in the

patterns of methylation variation during floral develop-

ment. Significantly higher proportions of mCG-containing

protein-coding genes (42.1%) than mCHG-containing pro-

tein-coding genes (28.7%) or mCHH-containing protein-cod-

ing genes (23.2%) were differentially methylated between

meristems and early flowers. Similar patterns were

observed for pseudogenes, miRNA and non-coding RNA

genes, although to a lesser extent (data not shown), sug-

gesting similar DNA methylation-related regulatory mecha-

nisms that may reflect a common evolutionary origin and/

or functional constraints.

In contrast, more mCHH-containing TE genes showed

methylation variation than mCG or mCHG TE genes. A closer

look showed that, for TE genes, methylations at mCHH sites

primarily decreased during flower development, while

those at mCG sites were mainly increased (Figure 2c). Con-

cordantly, TEs mainly showed increased methylation,

which primarily occurred at mCG sites and mCHH sites,

although only 2124 TEs (6.8%) were differentially methylat-

ed between meristem and early/late flower development

stages (Figure S5). Taken together, TEs tended to become



hypermethylated as floral development proceeded, possi-

bly because of a greater need to protect the reproductive

cells against the mutagenic activities of TEs (Slotkin et al.,

2009; Yang et al., 2011). Furthermore, CG methylation may

play critical roles in the increased methylation of TEs as well

as TE genes. TEs may be classified into various families that

may have different methylation patterns during floral devel-

opment. Indeed, the LTR/Gypsy family of retrotransposons,

comprising 13.4% of all TEs in Arabidopsis, accounted for

36.0% of methylated TEs, whereas the RC/Helitron type of

DNA transposons, representing 41.5% of all TEs, consti-

tuted only 11.7% of methylated TEs in the meristem (Figure

S5), indicating that retrotransposons are more tightly con-

trolled by methylation than DNA transposons. Furthermore,

some TE families were more likely to be differentially

methylated than others. In brief, differentially methylated

retrotransposons mainly fall into the families of LTR/Copia,

LTR/Gypsy and Line/L1, with little preference observed with

respect to mC context class; differentially methylated DNA

transposons primarily came from the families of hAT and

DNA/others (Figure S6).

Finally, we examined relative methylation levels (RKCM)

across transcribed regions and the surrounding genomic

regions. We found similar profiles to those previously

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2. Distinct methylation patterns for

genes in Arabidopsis flowers.

(a) Percentage of methylated genes for each tis-

sue. The colored squares at the bottom of each

vertical bar represent mC contexts. TE, trans-

posable element; ncRNA, non-coding RNA;

miRNA, microRNA.

(b) Between-tissue comparison of methylated

genes for each gene class. The colored bar at

the bottom of each vertical bar indicate mC con-

text, as defined in (a).

(c) Percentage of differentially methylated

genes for each class (ncRNA and miRNA com-

bined) and sequence context. The plus and

minus symbols indicate increased/decreased

methylation, respectively, between meristems

and early flowers.

(d–f) Normalized methylation levels at various

stages (M, E, and L) for protein-coding genes

(shades of green), pseudogenes (purple, red

and orange) and transposable element genes

(shades of blue). TSS, transcription start site;

TES, transcription termination site.



reported (Cokus et al., 2008; Feng et al., 2010; Zemach et

al., 2010), and these were also quite similar between floral

stages. For example, the methylation of protein-coding

genes mainly occurred at mCG sites, and was confined

within genic regions. The methylation of TE genes showed

similar patterns across sequence contexts, i.e. primarily

occurred within genic regions, dramatically decreased at

the transcription start site (TSS) and after the transcription

end site (TES), and increased again at adjacent regions fur-

ther upstream of the TSS and downstream of the TES (Fig-

ure 2d–f). Methylations in pseudogenes showed similar

patterns to those for TE genes, with levels intermediate

between TE genes and protein-coding genes, consistent

with previous observations (Cokus et al., 2008). These

results suggested a primary role of MET1 (for mCG) in the

methylation maintenance of protein-coding genes, and

comparable contributions of MET1, DRM1/2 and CMT2/3

(for mCHG and mCHH) to methylation of pseudogenes and

TE genes (Chan et al., 2005; Stroud et al., 2014).

DNA methylation at different genic regions differentially

correlates with gene expression

DNA methylation in promoter regions is often associated

with transcriptional silencing (Zhang et al., 2006), but

recent studies have revealed distinct relationships between

gene expression and methylation in different genic regions

(Brenet et al., 2011). To assess this relationship, we mea-

sured gene expression profiles by RNA sequencing using

the same three tissues used for DNA methylation studies.

Transcripts of 26 764 genes were detected in at least one

floral tissue, with 5768 of them significantly differentially

expressed between the three tissues (see Experimental

procedures). To examine the relationships between expres-

sion and methylation, genes were sorted into three equal-

sized groups according to expression levels or methylation

intensities at a specific genic region (exon, intron, 1 kb

upstream/downstream of the TSS). We found a general

trend of negative associations between expression levels

and methylation levels for all mC contexts and gene

regions, especially gene body regions (Figure 3a–c).

We then compared the low- and high-expression genes

with respect to normalized methylation levels in different

genic regions, with exons and introns classified according

to their positions relative to TSS and TES sites. Consistent

with previous findings (Zhang et al., 2006; Zemach et al.,

2010), genes in the high-expression group were relatively

hypomethylated at all mC contexts in the 1 kb upstream and

downstream regions (Figure 3d–f), and first exons of genes

in the high-expression group were methylated at lower lev-

els than first exons of genes in the low-expression group,

for all mC contexts (Brenet et al., 2011; Chuang et al., 2012).

Notably, the methylation level in the first intron showed

even more significant negative correlation with expression

levels. Hence, regions near TSS may closely participate in

methylation-dependent transcriptional silencing. Further-

more, internal exons of genes with high expression tended

to be significantly highly methylated at mCG sites, but

methylated to low levels at mCHG and mCHH sites; internal

introns showed little or no association between expression

and methylation levels (Figure 3d–f). These observations

suggest that the influences of methylation on gene expres-

sion vary depending on genic region and sequence context.

DNA methylation in different sequence contexts is

differentially associated with gene expression levels

during floral development

By comparing raw read numbers and RKCM values for

each gene in each of the three sequence contexts (Figure

S7 and Table S3), we identified 11 880 genes with statisti-

cally significant methylation variations between meristem

and the early flower, indicating that DNA methylation in

the gene body (body methylation) is broadly regulated

across the genome (Figure 4a and Table S4). In addition,

2503 genes showed significant methylation variations in

their putative promoter regions during the same develop-

mental period, consistent with the relatively poor methyla-

tion of promoter regions (promoter methylation) (Zhang

et al., 2006). Further comparison revealed that only 1235

genes were simultaneously differentially methylated in

both body and promoter regions during early flower devel-

opment (Figure 4a), suggesting that body methylation and

promoter methylation are largely regulated separately,

possibly by independent mechanisms.

We identified 3067 genes that showed significant varia-

tions in both methylation and gene expression during floral

development (hereafter termed co-differential genes), with

only 10% (317 of 3067) being differentially methylated

across all the three sequence contexts (Figure 4b), suggest-

ing sequence context-dependent effects of methylation on

transcription. Moreover, among the 3986 genes differen-

tially expressed between meristems and early flowers, 2117

contained mCG sites, and 1048 (49.5%) of them were co-dif-

ferential at mCG sites, compared with 601 of 1744 mCHG-

containing genes (34.5%) and 509 of 1894 mCHH-containing

genes (26.9%) (Figure 4c and Table S5). We also observed

that a significantly higher proportion of transcriptionally

up-regulated genes were co-differential between meristem

and early or late flowers in comparison with genes that

were down-regulated at the same periods; this was consis-

tent across all mC sequence contexts (Figure 4c). Hence,

variation in DNA methylation levels may function as a sig-

nal affecting transcription during early flower development.

Body methylation variations may serve as important

transcriptional regulatory signals during floral

development

Our dataset provides clues to understanding how the varia-

tion in methylation level affects development. Among the



co-differential genes are several genes encoding important

floral developmental regulators, including SEP1, LEUNIG

and SEEDSTICK (Figure 5a–c). SEP1 encodes a MADS box

protein that is important for determining floral organ iden-

tity (Pelaz et al., 2000), and was found to have dramatically

increased methylation and transcription levels during early

flower development. LEUNIG encodes an important repres-

sor of AG, whose product is required for the identity of sta-

mens and carpels, and for meristem determinacy

(Mizukami and Ma, 1992; Conner and Liu, 2000; Sridhar

et al., 2004). We also identified co-differential TE genes,

including AT1G64270, which encodes the transposase for

Mutator-like DNA transposons. We found no methylation

in the putative promoter of AT1G64270, and its transcribed

region was dramatically demethylated during the early

flower development, which may have resulted in its tran-

scriptional suppression (Figure 5d). Hence, gene region

demethylation may also function to silence TEs whose

mutagenic activities may destabilize the genome during

reproduction.

Interestingly, we found that the increase in methylation

at the 30 exons and demethylation at the 50 part of the gene

DEMETER-LIKE 1 (DML1) correlated with its transcriptional

reduction (RNA-seq RPKM values: M, 46.3; E, 21.8; L, 21.2)

(Figure 5e). DML1 may function as a transcriptional repres-

sor and demethylase, consistent with the genome-wide

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3. Methylation at various genic regions

differentially associated with gene expression.

(a–c) Comparison of gene expression and meth-

ylation levels for mCG, mCHG and mCHH sites,

and for each genic region: upstream 1 kb

regions (Up1k), exons, introns, and down-

stream 1 kb regions (Down1k). Low and high

methylation represent the one-third of genes

with the lowest and highest methylation levels

at each genic region, respectively. L and H at

the bottom of the bars indicate the third of

genes with the lowest or highest expression

levels. Gene percentages were calculated sepa-

rately for each L or H expression group.

(d–f) Comparison of normalized methylation

levels (RKCM) between low- and high-expres-

sion genes for mCG, mCHG or mCHH sites in

each genic region. Asterisks indicate the statisti-

cal significance of the indicated differences

(*P > 0.05; **P > 0.001; ***P < 0.001; Mann–Whitney U test).



up-regulation of important floral development regulators

and the massive de novo methylation during early flower

development (Gong et al., 2002; Agius et al., 2006).

Methylated genes exhibited distinct patterns among

sequence contexts/stages, and were enriched for diverse

biological processes

To discover any shared methylation patterns among the

3067 methylated genes, we performed consensus cluster-

ing on the normalized methylation levels for the three mC

sequence contexts in the three tissues (Figure 6a), result-

ing 11 clusters of distinct methylation patterns. Genes in

different clusters showed significantly different mean

methylation levels and patterns across floral development

stages (Figure 6b). For example, genes in cluster I were

highly methylated at mCG, mCHG and mCHH sites, with

higher levels in the early and late flower tissues than in the

flower meristem, but those in cluster II showed relatively

high methylation for mCG, intermediate methylation formCHH, and low methylation for mCHG, at all developmental

stages (Figure 6b). Clusters III–VI showed similar methyla-

tion levels for two of the three sequence contexts. Cluster

VII had higher methylation levels in the meristems than

the other two tissues, for all three sequence contexts,

whereas cluster VIII showed a similar developmental pat-

tern for mCG sites only. Clusters IX–XI were similar in that

the methylation levels increase from the meristem stage to

early and late flower development, but differed in

sequence contexts: cluster IX showed similar patterns for

all three sequence contexts, whereas clusters X and XI,

respectively, showed increased methylation levels for mCG

and mCHH sites only (Figure 6b). These results support the

hypothesis that demethylation and de novo methylation by

different methyltransferases during floral development lar-

gely occur independently of each other.

We next performed gene ontology (GO) enrichment

analysis to explore the associations between methylation

variation and gene functions. The enriched GO categories

for methylated genes suggested possible involvement in

diverse biological processes, including meristem develop-

ment, floral organ development, mitosis and meiosis, as

well as plant body pattern specification (Figure 6c and

Tables S6 and S7). Several genes known to be involved in

floral organ development and reproduction processes were

found among these genes, including MSH7, VIM3, CHR42

ans NUA (Figure 5f and Figure S8). Therefore, DNA meth-

ylation reprogramming may contribute significantly to the

regulation of floral development in Arabidopsis.

DISCUSSION

We used MspJI-seq to survey the genome-wide DNA meth-

ylation during Arabidopsis flower development, and

uncovered a large number of potential de novo methyla-

tion sites in early flower development and demethylation

sites in late flower development, supporting the idea that

extensive de novo methylation as well as demethylation

occur during flower development. Different methylation

patterns for the three sequence contexts (mCG, mCHG andmCHH) and in different genic regions potentially have dif-

ferent effects on gene expression. The whole-genome DNA

methylation and gene expression patterns and derived

hypotheses of their interactions reveal more complex rela-

tionships than expected. Future functional studies are

required to elucidate the mechanisms of control of DNA

methylation and gene expression, and to understand the

biological functions and mechanisms for regulating floral

genes.

The ap1 cal double mutant has been widely used to

obtain a relatively large amount of meristems (Wellmer

et al., 2006) (Kaufmann et al., 2010; Wuest et al., 2012),

(a) (b)

(c)

Figure 4. Genes with DNA methylation variations during Arabidopsis floral

development.

(a) Comparison of genes differentially methylated at one or more sequence

contexts and differentially expressed between meristem and early flower.

‘Gene Body’ and ‘Promoter’ represent the transcribed region and the 1 kb

upstream region of genes, respectively; ‘Transcription’ represents genes

that are differentially expressed.

(b) Comparison of differentially expressed genes according to methylation

variations among sequence contexts.

In (a) and (b), the numbers outside each circle indicate the total gene counts

in the respective group.

(c) Percentage of differentially methylated genes among each group of dif-

ferentially expressed genes during floral development. ME, meristem to

early flower; ML, meristem to late flower; EL, early to late flower. Plus sym-

bol, up-regulation; minus symbol, down-regulation. The gray lines divide

each bar into a bottom part representing genes with increased methylation,

and a top part representing genes with decreased methylation. Asterisk

indicate statistically significant differences (*P < 0.05; **P < 1e-3; v2 test).



because the mutant is arrested at the inflorescence meri-

stem stage (Bowman et al., 1993; Ferrandiz et al., 2000).

However, we cannot rule out possible effects of the muta-

tions on the DNA methylation and gene expression pat-

terns, and caution is required when interpreting the

differences in results between the ap1 cal meristem and

the early and late flowers. Efficient methods for separating

and collecting wild-type meristems are required for further

studies to confirm the differences relating to the meris-

tems.

Different methylation patterns between mC sequence

contexts and genic regions

The results here allow comprehensive analyses of many

aspects of DNA methylation patterns. The approximately

1.5 million methylation sites from three flower tissues

included 453 066 mCG sites, 425 428 mCHG sites and

685 100 mCHH sites, representing a dramatic increase of

37% for detection of mCHH sites compared with approxi-

mately 500 000 mCHH sites in young flowers as previously

reported (Lister et al., 2008). Previously CHH methylation

has been found to play important roles in various plant

developmental processes in Arabidopsis endosperm,

maize (Zea mays) and cotton fibers (Gossypium hirsutum)

(Hsieh et al., 2009; Gent et al., 2013; Jin et al., 2013). The

relatively high proportion of newly identified mCHH sites

reported here (Figure 1d) and the correlation of >1000genes with variation in CHH methylation (Figure 4b) sug-

gest that CHH methylation may be important for floral

gene expression.

In addition, our observations that methylation patterns

and developmental changes in methylation vary depend-

ing on sequence contexts and gene classes suggest com-

plex relationships between sequences/genes, methylation

and developmental gene functions. For example, the num-

ber of mCs increased from the meristems to early flowers

for all sequence contexts, but only mCG sites increased in

number from early to late flowers. The period from meri-

stem to the early flower involves mainly organ identity

specification and organogenesis, whereas the subsequent

period includes much of the floral organ growth, as well as

gametophyte development. Therefore, our results suggest

that gene expression associated with mCG sites may play a

greater role in floral organ growth. The finding that

(a)

(c)

(e)

(b)

(d)

(f)

Figure 5. Representative Arabidopsis genes

with variations in DNA methylation and floral

expression.

Tracks of MspJI-seq and RNA-seq reads are

shown for each gene, encompassing the tran-

scribed region as well as the upstream and

downstream 1 kb regions. Gene structures are

shown at the bottom of each graph, with blue

boxes representing exons and arrows indicat-

ing introns and the transcription direction of

the respective gene. In some cases, parts of the

exon–intron structure for adjacent genes are

shown; they do not span the central regions

and are not connected with the centrally located

gene models. M, meristem; E, early flower; L,

late flower.



protein-coding genes showed preferential enrichment ofmCG sites and depletion of mCHG and mCHH sites in the

transcribed regions is consistent with previous results

(Zhang et al., 2006; Cokus et al., 2008; Feng et al., 2010; Ze-

mach et al., 2010). In contrast, TE genes and pseudogenes

showed similar mC frequencies in the various sequence

contexts across transcribed and nearby genomic regions,

with mCs being enriched in transcribed regions and

depleted near the transcription start sites (Figure 2d–f).

Therefore, protein-coding genes and other types of genes

are probably affected by DNA methylation in different

ways. Moreover, only a few genes show simultaneously

changes in methylation levels and expression levels at all

sequence contexts, suggesting that influences on expres-

sion by mCs of different sequence contexts tended to be

unrelated to each other (Figure 4b).

Our findings also support the idea that the relationships

between DNA methylation and gene expression levels are

more complicated than previously thought (Zhang et al.,

2006; Suzuki and Bird, 2008; Ball et al., 2009). Promoter

methylation is often linked with gene expression suppres-

sion, whereas the role of gene body methylation is far

more uncertain, with both positive and negative relation-

ships reported (Zhang et al., 2006; Zilberman et al., 2007;

Li et al., 2008; Zemach et al., 2010). These contradictory

findings may be explained when considering the observa-

tions that DNA methylation interacts with other factors,

including histone modifications and siRNAs, to determine

transcriptional status (Li et al., 2008; Stroud et al., 2014). In

addition, the effect of DNA methylation on expression may

also depend on genic regions or sequence contexts. In

humans, methylation of the first exon was more signifi-

cantly associated with gene silencing than methylation of

the nearby promoter (Brenet et al., 2011). Similarly, our

observation in Arabidopsis of strong negative relationships

between DNA methylation at all sequence contexts in the

first exon and expression (Figure 3) suggests that the rele-

vant mechanisms may be conserved between animals and

plants. The even stronger negative effects of methylation

the first intron are consistent with the fact that the first in-

trons of eukaryotic genes often carry regulatory elements

for transcription (Majewski and Ott, 2002; Bradnam and

Korf, 2008; Bieberstein et al., 2012). We also found that

methylation at mCG sites of internal exons tended to be

positively correlated with gene expression, unlike methyla-

tion at the mCHG and mCHH sites of internal exons and all

sequence contexts of internal introns (Figure 3). Therefore,

our separate analyses regarding sequence contexts and

genic regions revealed that the effects of DNA methylation

on gene expression are not only position-dependent (i.e.

(a) (b) (c) Figure 6. Methylation pattern and functional

implications for differentially methylated and

expressed genes.

(a) Clustering of genes with concurrent varia-

tions in expression and methylation on the

basis of similar methylation patterns during flo-

ral development. The color gradient represents

the log2-transformed RKCM values, as indicated

by the color bar at the top.

(b) Summarized methylation patterns for each

cluster. M, meristem; E, early flower; L, late

flower.

(c) Enriched biological processes among each

gene cluster. Gene ontology (GO) terms were

grouped into more general biological pro-

cesses, as shown on the right (see also Table

S6). Colors represent the number of genes for

each GO term in each gene cluster, as indicated

by the color bar at the top.



with respect to genic region), but also sequence context-

dependent, providing further insight into the relationships

between methylation and gene expression.

Implications for functional roles of DNA methylation in

flower development

Changes in genome-wide DNA methylation levels have

been associated with plant development, including global

demethylation in the endosperm of the developing seed

compared with the embryo (Gehring et al., 2009; Hsieh

et al., 2009) and lower methylation in the central cell of the

female gametophyte than in somatic cells (Jullien and Ber-

ger, 2010). Previous studies also suggested that the DNA

methylation level tends to increase during development

from seedling through vegetative stages to floral stages

(Ruiz-Garcia et al., 2005). Our analysis of differential meth-

ylation between three floral tissues revealed that more

sites are methylated de novo than demethylated from mer-

istem to early flowers, whereas similar numbers of sites

were methylated and demethylated from early to late

flower development (Figure 1e–g). These findings suggest

that plant DNA methylation is under critical control during

development, and proper re-patterning of DNA methyla-

tion is important for the developmental program.

The importance of DNA methylation in development has

been demonstrated by genetic studies of methyltransfer-

ase genes or genes encoding chromatin remodelers,

including MET1 and DDM1, with effects on embryogenesis,

meristem identity and flowering time (Finnegan et al.,

1996; Kakutani et al., 1996; Ronemus et al., 1996; Xiao

et al., 2006). The effects of DNA methylation occur at least

in part via modulating transcription of developmental reg-

ulators (Xiao et al., 2006; Li et al., 2008; Gehring et al.,

2009; Hsieh et al., 2009). In addition, DNA methylation reg-

ulates genes that play critical roles during flower develop-

ment; for example, hypermethylation of SUPERMAN and

AG phenocopies the corresponding mutants (Jacobsen

et al., 2000). Our results showing that DNA methylation is

associated with changes in the expression of over 3000

genes suggest that methylation affects genes with diverse

roles during flower development, such as regulation of

early flower development (33 genes) and pollen develop-

ment (21 genes) (Table S6). In addition, the expression of

regulatory genes is probably affected by methylation,

including genes for transcriptional control (201), chromatin

organization (29) and signal transduction (56).

In particular, changes in DNA methylation were linked to

expression changes for several key regulators. For

instance, the expression of floral regulators SEP1 and

SEEDSTICK was affected by DNA methylation (Figure 5a,

c). An effect of DNA methylation on the C function gene

AG was previously reported: hypermethylated epi-alleles

of AG were found in plants with reduced global methyla-

tion (Jacobsen et al., 2000). Our data suggest that DNA

methylation may indirectly influence the expression of AG

in wild-type plants, similar to the case for the FLC gene

(Finnegan et al., 2005). Decreased methylation in early and

late flowers may result in down-regulation of LEUNIG (Fig-

ure 5b), a negative regulator of AG (Sridhar et al., 2004). In

addition, methylation may also function to activate BLH9

and/or suppress PERIANTHIA (Figure S8a), both genes

encoding transcription factors that are required for proper

floral expression of AG (Bao et al., 2004; Das et al., 2009).

On the other hand, the methylcytosine-binding proteins

VIM1, VIM2 and VIM3, which are involved in hypermethy-

lation of the flowering-time gene FWA and its subsequent

suppression (Woo et al., 2008), were observed to have

high gene expression levels that may also be affected by

methylation (Figure S8b), suggesting deep involvement of

DNA methylation in flowering transition.

Our results also suggest that DNA methylation may

affect other cellular and developmental processes during

reproduction, such as pollen tube growth, mitosis and mei-

osis. For example, GO enrichment analysis revealed over-

representation of 21 genes associated with pollen develop-

ment in cluster VI of methylated genes (Figure 6 and Table

S7), and correlated changes in methylation and expression

between the three floral tissues/stages for genes participat-

ing in meiosis, including AtMSH7, AtMSH4, AtDMC1 and

AtSMC3 (Figure 5f and Table S4), suggesting that DNA

methylation may be involved in the floral development

program as well as embryogenesis and other aspects of

plant development. Our analyses provide insights into the

possible roles of DNA methylation in gene expression, and

important resources for further investigation of the genetic

pathway that regulates flower development.

EXPERIMENTAL PROCEDURES

Plant growth, tissues collection, and DNA isolation

Plants of the Arabidopsis thaliana Landsberg ecotype that werehomozygous for the erecta mutation (Ler) and ap1 cal mutantplants(also in the Ler background) were grown in soil in a plantgrowth room at 22°C under 16 h light/8 h dark cycles. The meris-tems of the ap1 cal mutant plants, wild-type early flowers (stages1–9) and late flowers (stages 10–12) were collected separately (Fig-ure S9). The materials collected for each sample were taken frommany individuals and in large amounts (2 g) to control the effect ofbiological variations. Genomic DNA was extracted using a DNeasyplant mini kit (Qiagen, http://www.qiagen.com), precipitated usingtwo volumes of EtOH with a one-tenth volume of 3 M NaAc (pH5.2), and resuspended in Tris/EDTA buffer (pH 8.0) to a final con-centration of 1 lg ll�1.

MspJI digestion and DNA recovery

A 5 lg aliquot of each DNA sample was added to 90 ll of well-mixed NE Buffer 4 (New England Biolabs, https://www.neb.com),BSA and nuclease-free digestion mix before addition of 20 unitsof MspJI enzyme (New England Biolabs, https://www.neb.com) foreffective digestion without obvious DNA degradation (FigureS10a), and incubated at 37°C for 16 h.



http://www.qiagen.com

https://www.neb.com

https://www.neb.com

After digestion, the DNAs were separated in a 20% polyacryl-amide gel (arc/bis: 29:1; 50 mA, 2.5 h). The polyacrylamide gelpieces containing DNA of approximately 32 bp (Figure S9a) wereexcised from the polyacrylamide gel and crushed and transferredinto sterile microfuge tubes containing 300 ll of buffer comprising0.3 M sodium acetate, pH 7.5, with 0.1 mM EDTA), and shakenovernight at 37°C. Then the gel was pelleted by centrifugation for2 min at 14 000 g at room temperature, and the supernatant wascollected. DNA was precipitated by adding 2 ll glycogen and twovolumes of 100% ethanol to each tube, keeping the tubes at�80°C for 30 min, followed by centrifugation for 30 min at14 000 g at 4°C. The DNA pellets were washed with 70% ethanoltwice (for 2 min at 14 000 g at room temperature) before resuspen-sion in Tris/EDTA buffer (pH 8.0).

Construction of a DNA methylation fragment library for

sequencing

The recovered DNA samples were used to construct sequencinglibraries according to the fragment library preparation protocoldescribed in the SOLiDTM system library preparation guide (LifeTechnologies, http://www.lifetechnologies.com/) with some mod-ifications. Both ends of recovered DNA were repaired using ENDpolishing enzymes 1 and 2 from the SOLiDTM fragment libraryconstruction kit, and purified using a QIAquick nucleotideremoval kit (Qiagen) and spin columns from a MinElute� reac-tion clean-up kit (Qiagen). After purification, the P1 and P2 adap-tors from the SOLiDTM fragment library construction kit wereligated to the DNA fragments, followed by another purificationstep using the QIAquick nucleotide removal kit. Recovered DNAlibraries were nick-translated and amplified by PCR for tencycles, then purified using a QIAquick nucleotide removal kit.Purified DNA libraries were subjected to 4% agarose gel electro-phoresis, and bands of approximately 100 bp (Figure S10b) wererecovered using a QIAquick gel extraction kit (Qiagen). Theresulting libraries were analyzed using an Agilent bioanalyzer(http://www.agilent.com), and 0.5 pmol of each DNA library wereused to perform emulsion PCR reactions according to the tem-plated bead preparation guide from the SOLiDTM system. The35 bp sequence reads were obtained using the SOLiDTM 3.0 sys-tem, and were subsequently aligned against the Arabidopsis ref-erence genome sequences (TAIR10, http://www.arabidopsis.org)using BIOSCOPE software version 1.3 (Life Technologies) (seeMethods S1). The overall mapping rates were approximately61% (Table S1).

DNA methylome analysis

We developed an in-house program to identify SOLiD reads sup-porting methylcytosines. In brief, six cases of paired sequencepatterns were inferred based on the MspJI digestion properties,with each case resulting in DNA fragments that could be recov-ered (Figure S11 and Methods S2). Aligned reads that may beclassified into one of the six patterns were identified as methylreads, each supporting two potential mCs. MspJI cut double-stranded DNA at fixed distances downstream of the recognizedsequence (mCNNR), with the cleavage on the reverse strand wob-bling by one nucleotide (16 or 17) (Zheng et al., 2010). Methylreads were counted separately for each of the two cleavage pat-terns (N12/N16 or N12/N17), resulting in an N16/N17 ratio of approxi-mately 3:1 (Table S8). The number of reads supportingmethylcytosines was calculated for each genomic feature (genes,exons, etc.) and sequence context separately, and normalized asRKCM values. Fisher’s exact test was performed on the readcounts of a pair of tissues for each gene, and a gene was defined

as differentially methylated when the false discovery rate (calcu-lated by the Benjamini-Hochberg method) was < 0.001 and theratio of RKCM values was >2.

Gene ontology enrichment analysis was performed using the R

package topGO (Alexa and Rahnenfuhrer, 2010). The graphs inFigure 5 displaying the DNA methylation and gene transcriptionprofiles were produced using the R package Gviz (Hahne et al.,2013). Consensus clustering on the log2-transformed RKCM valuesof differentially methylated genes was performed using the R

package clusterCons (Simpson et al., 2010) with the algorithm ofpartitioning (clustering) of the data into ‘k’ clusters ‘around med-oids’ (PAM).

Total RNA isolation and transcriptome analysis

All three tissues were frozen in liquid nitrogen immediately aftercollection. Total RNAs were extracted using a Plant RNase mini kit(Qiagen). More than 2 lg of total RNA with an absorbance at 260/280 nm between 1.8 and 2.0 from each sample were used to cre-ate libraries that were deep-sequenced using the IlluminaTM Hi-seq2000 system (Illumina, http://www.illumina.com), to obtain 100 bppaired-end reads. All reads with fewer than two mismatches weremapped to the Arabidopsis genome (TAIR10) by TopHat (Trapnellet al., 2009). Calculation of expression values and differentialexpression analysis were performed using GFOLD (Feng et al.,2012) with default parameters.

ACKNOWLEDGEMENTS

We thank Zhiyi Sun from New England Biolabs for discus-

sion regarding computational identification of MspJI-

digested methylation sites. We appreciate the data kindly

provided by Steven Jacobsen and Matteo Pellegrini from

University of California, Los Angeles. This work was sup-

ported by the Ministry of Science and Technology of the

People’s Republic of China (MOST 2012CB910503), the

National Natural Science Foundation of China (31130006

and 31371330), and start-up funds from Fudan University

to F.C.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online ver-sion of this article.Figure S1. Validation of selected methylcytosine sites by PCRexperiments.

Figure S2. Methylation profiles determined by MspJI-seq andbisulfite sequencing were consistent for most of the randomlyselected genes and genomic regions.

Figure S3. Density of genes and TEs across Arabidopsis genome.

Figure S4. Percentages of genes of each class among all methylat-ed genes.

Figure S5. Methylation of TEs during Arabidopsis floral develop-ment.

Figure S6. Enrichment of TEs of various families in different mCsequence contexts.

Figure S7. Distribution of normalized methylation levels for eachmC context for genes of various classes.

Figure S8. Examples of genes with correlated variations in methyl-ation and expression levels.

Figure S9. Phenotypes of the three Arabidopsis floral stages usedfor experiments.



http://www.lifetechnologies.com/

http://www.agilent.com

http://www.arabidopsis.org

http://www.illumina.com

Figure S10. MspJI digestion and DNA library recovery.

Figure S11. Identification of mCs based on MspJI-seq.

Table S1. Summary of SOLiD reads sequenced and mappedagainst the Arabidopsis reference genome and reads that wereidentified as arising from MspJI digestion for each possible recog-nition site pattern.

Table S2. Primers used in PCR experiments for selected generegions digested by MspJI.

Table S3. DNA methylation and expression levels for genes in Ara-bidopsis flowers.

Table S4. Arabidopsis genes that were differentially methylatedand differentially expressed during floral development.

Table S5. Statistics for genes that were differentially expressedand methylated between flower meristems and early flowers.

Table S6. Significantly enriched biological processes for eachgene cluster in Figure 6.

Table S7. Number of genes for enriched GO terms for each genecluster in Figure 6.

Table S8. Relative frequencies of the wobbling cut positions ofMspJI.

Methods S1. Mapping of SOLiD short sequencing reads.

Methods S2. Identification of methylcytosines based on MspJI-seq.

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