variation of dna methylation patterns associated with gene...

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Title of article: Variation of DNA methylation patterns associated with gene expression in

rice (Oryza sativa) exposed to cadmium

Running title: DNA methylation in Cd-exposed rice

Authors: 1Sheng Jun Feng,

1Xue Song Liu,

1Hua Tao,

1Shang Kun Tan,

1Shan Shan Chu,

2Youko Oono,

1Xian Duo Zhang,

3Jian Chen,

1Zhi Min Yang

Institution: 1Department of Biochemistry and Molecular Biology, College of Life Science,

Nanjing Agricultural University, Nanjing 210095, China; 2Agrogenomics Research Center,

National Institute of Agrobiological Sciences, Tsukuba, Ibaraki, Japan; 3Institute of Food

Safety and Quality, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China

Correspondence: Zhi Min Yang

Department of Biochemistry and Molecular Biology, College of Life Sciences, Nanjing

Agricultural University, Nanjing 210095, China

Telephone: 86-25-8395057

E-mail: [email protected]

http://www.oalib.com/search?kw=Youko%20Oono&searchField=authors


ABSTRACT

We report genome-wide single-base-resolution maps of methylated cytosines and

transcriptome change in Cd-exposed rice. Widespread differences were identified in CG and

non-CG methylation marks between Cd-exposed and Cd-free rice genomes. There are 2320

non-redundant differentially-methylated regions detected in the genome. RNA-sequencing

revealed 2092 DNA methylation-modified genes differentially expressed under Cd exposure.

More genes were found hypermethylated than those hypomethylated in CG, CHH and CHG

(where H is A, C or T) contexts in upstream, genebody and downstream regions. Many of the

genes were involved in stress response, metal transport, and transcription factors. Most of the

DNA methylation-modified genes were transcriptionally altered under Cd stress. A subset of

loss of function mutants defective in DNA methylation and histone modification activities

were used to identify transcript abundance of selected genes. Compared to wide-type,

mutation of MET1 and DRM2 resulted in general lower transcript levels of the genes under

Cd stress. Transcripts of OsIRO2, OsPR1b and Os09g02214 in drm2 were significantly

reduced. A commonly used DNA methylation inhibitor 5-azacytidine was employed to

investigate whether DNA demethylation affected physiological consequences. 5-azacytidine

provision decreased general DNA methylation levels of selected genes, but promoted growth

of rice seedlings and Cd accumulation in rice plant.

Highlights

This study identified global differential DNA methylation marks associated with gene expression and

functional consequences in rice exposed to Cd. Widespread differences were identified in CG and non-CG

methylation marks between Cd-exposed and Cd-free rice genomes. A group of genes encoding metal

transporters, Cd-detoxified proteins and metal-related transcription factors that were differentially

methylated were found to regulate rice tolerance to Cd stress. Our data provide an insight into DNA

methylation pattern associated with activation of specific genes responsible for Cd uptake, accumulation

and detoxification.

Key words: Oryza sativa; cadmium; DNA methylation; bisulfite-sequencing; transcriptome;


INTRODUCTION

Cadmium (Cd) is one of the naturally occurring toxic heavy metals in the Earth’s crust and

negatively affects plant growth and development (Alloway and Steinnes, 1999). Cd is readily

absorbed by plant roots and translocated into the above-ground. Overload of Cd in crops,

particularly in edible parts, can threat to the crop production and human health (Clemens et

al., 2013). Substantial progress has been made in the area of the physiological processes of

Cd uptake, translocation, accumulation and detoxification (Verbruggen et al., 2009; Clemens

et al., 2013). Heavy metal (such as Zn, Fe and Mn) uptake by plants is mediated by a group

of metal transporter families such as ZIP (ZRT/ IRT-like Proteins) family for Zn/Fe transport

(Eide et al., 1996), ABC transporters (ATP-Binding Cassette transporters) mainly located on

tonoplast (Holland et al., 2003), NRAMPs (Natural Resistance Associated Macrophage

Proteins) mainly for Mn and Cd transport (Talke et al., 2006; Sasaki et al., 2012), P-type

ATPases responsible for Cd, Pb and Zn transport and detoxification (Axelsen & Palmgren,

2000)), and cation diffusion facilitators (CDF) (Li & Kaplan, 2001). Intriguingly, some of the

transporters, identified originally for Fe or Zn homeostasis, can also serve as a carrier for Cd

and other non-essential metals (Krämer et al., 2007). Modification of these transporter

activities may confer plant tolerance or sensitivity to metal excess (Nakanishi et al., 2006;

Pedas et al., 2008; Cailliatte et al., 2009; Ueno et al., 2010; Uraguchi et al., 2011; Sasaki et

al., 2012). However, the complex regulatory mechanisms for the processes remain largely

unknown (Ogawa et al., 2009; Uraguchi et al., 2011). Plants respond to excessive metals in

environments by adjusting physiological and molecular machinery through global gene

expression. Recent global profiling of transcriptome identified a large number of genes

involved in heavy metal uptake, translocation and regulation (Herbette et al., 2006; Zhou et

al., 2013; Ogo et al., 2014; Oone et al., 2014). Furthermore, the emerging molecular and

genetic mechanisms at pre- or post-transcriptional levels may facilitate our understanding the


regulatory processes and serve as a basis for developing strategies to minimize Cd

accumulation in crops growing on the metal-contaminated soils (Ishikawa et al., 2012;

Clemens et al., 2013; Yang and Chen, 2013).

DNA methylation has been long considered as a genomic defense system to maintain

genome integrity; it is also proposed as one of the predominant epigenetic mechanisms that

modulate pre-transcriptional gene expression in eukaryotic cells (Chan et al., 2005; Zhang et

al., 2006). Growing evidence demonstrates that distinctive DNA methylation regulates

multiple biological processes in plants including of growth, development and environmental

stress responses (Grafi, 2011; Chen and Zhou, 2013). For example, the methylome dynamics

during the tomato fruit development shows DNA methylation modifications associated with

fruit ripening (Zhong et al., 2013). When plants are exposed to varieties of environment, such

as changes in temperature, light intensity, nutrient and water availability, they tend to

memorize the unfavorable events; such a memory can be heritable through DNA methylation,

which makes them more adaptive to new environmental challenges (Kinoshita and Seki,

2014). DNA methylation at cytosine residue of CG, CHG or CHH contexts is stable and

heritable marks throughout the genome (Ou et al., 2012). Addition of methyl groups into

cytosine residues is catalyzed by a subset of enzymes known as DNA methyltransferases

(DMT), that transfer the methyl-groups from the donor S-adenosine methionine (SAM) to

cytosine. To date, much of our understanding about DNA methylation-modified enzymes in

plants relies on the research on Arabidopsis thaliana. DMT1 or MET1 (DNA

METHYLTRANSFERASE 1) and its members act as maintenance methyltransferases

responsible for introducing methyl groups specifically into CG sequences (Finnegan and

Kovac, 2000). The methyltransferase DRM2 (DOMAINS REARRANGED

METHYLTRANSFERASE 2), guided by 21–24 nt small RNAs along with ARGONAUTE4

(AGO4), is a homologue of the mammalian DNA methyltransferase 3 (DNMT3) family, that

http://www.ncbi.nlm.nih.gov/pubmed/?term=Zhong%20S%5BAuthor%5D&cauthor=true&cauthor_uid=23354102


catalyzes asymmetric CHH methylation through persistent de novo methylation (Cao and

Jacobsen, 2002; Zilberman et al., 2007). The third type of methyltransferases CMTs

(CHROMOMETHYLASES) is plant specific DNA methyltransferases involved primarily in

the maintenance of symmetrical CHG methylation (Lindroth et al., 2001). Furthermore,

methylation in cytosine residues can be reversed by active demethylation, which is catalyzed

by a group of demethylating enzymes such as ROS1 (REPRESSOR OF SILENCING), DME

(DEMETER), DML2 (DEMETER-LIKE) and DML3 (Penterman et al., 2007). Genomic

DNA methylation concerns many important biological processes, such as transposable

element (TE) activity, genomic imprinting, alternative splicing, and regulation of temporal

and spatial gene expression (Zhang et al., 2006; Zilberman et al., 2007; Saze & Kakutani,

2011). DNA methylation occurring at the promoter CG is associated with transcriptional

repression of genes, while the functional impact of DNA methylation outside gene promoters

is complicated and not so well understood (Henderson and Jacobsen, 2007;

Hernando-Herraez et al., 2015). Gene body methylation is involved in alternative splicing

and also associated with transcriptional activation (Zemach et al., 2010). Methylation of

transposons is associated with suppression of retrotransposition and affects the transcription

of neighboring genes (Walsh et al., 1998). These findings suggest that DNA methylation

patterns are complex and highly dependent on the genomic context (Hernando-Herraez et al.,

2015).

Recent studies have shown that Cd exposure alters genomic DNA methylation marks in

human cells (Wang et al., 2012), and the process is associated with transcriptional regulation

and functional consequences as well (Benbrahim-Tallaa et al., 2007; Fujishiro et al., 2009).

For example, Cd-induced global DNA hypermethylation is related to the malignant

transformation of human embryo lung fibroblast cells (Jiang et al., 2008). Conversely, global

DNA hypomethylation was induced by Cd as a potential facilitator of K562 cell proliferation


(Huang et al., 2008). In plants, specific hypomethylation of DNA was reported in clover and

industrial hemp exposed to cadmium, nickel and chromium (Aina et al., 2004). Recent

studies using the methylation-sensitive amplification polymorphism (MSAP) approach also

demonstrate that global DNA methylation changes occur in Cd-exposed Arabidopsis, rice,

Posidonia oceanica and Gracilaria dura genomes (Greco et al., 2012; Kumar et al., 2012;

Ou et al., 2012). These studies, albeit low-resolution technique of cytosine methylaton, have

provided a foundation for further characterization of DNA methylation marks throughout the

genome of plants. It is suggested that change of DNA methylation patterns is responsible for

plasticity of transcriptional regulation necessary for plant adaptive responses to

environmental change (Vaillant and Paszkowski, 2007). Our recent profiling of rice

transcriptome has shown that expression of genes encoding DNA methylation-modified

enzymes was substantially changed by Cd (Oono et al., 2014; data in this study). In this study,

we adopted the recent advances in high-throughput single-base-resolution

bisulfite-sequencing (BS-Seq) and RNA-sequencing (RNA-Seq) to identify the pattern and

degree of cytosine methylation in Cd-exposed rice seedlings. With detailed analyses of

specific methylation, a large set of genes regarding stress response, metal transporters,

transcription factors and metabolic process were found to be differentially methylated. Most

of the differentially methylated genes (DMGs) under Cd exposure were also transcriptionally

activated. Transcript abundance and detailed methylation of selected genes in a subset of loss

of function mutants defective in DNA methylation/demethylation and histone modification

activities were measured. Furthermore, phenotypic analyses revealed that provision of

azacitidine (a global DNA methylation inhibitor) attenuated root growth inhibition, but

promoted biomass and Cd accumulation under Cd exposure. Thus, the differential expression

of a set of genes with altered DNA methylation can support an epigenetic component of plant

acclimation to Cd excess.


MATERIALS AND METHODS

Plant culture and treatment

Rice (Oryza sativa ssp japonica cv. Nipponbare) seeds were surface-sterilized by 5% NaClO,

rinsed thoroughly with distilled water and germinated under the condition of 30 °C and

darkness for 2 d. After germination, seedlings were transferred to 1.2 L polyetheylene

containers with 1/2- Hoagland nutrient solution in darkness for 2 d and transferred to the

same nutrient solution and grew under the condition of a 14/10 light/dark cycle at 30/25±1 °C

(day/night) and 200 μmol m-2

s-1

light intensity. After growing for 2 d, seedlings were treated

with 0 and 80 μM Cd (as CdSO4) in 1/2-strength nutrient solution (pH 5.8). Each treatment

was prepared in triplicate. Treatment solutions were renewed every day. For construction of

BS-Seq and RNA-sequencing libraries, seedlings were exposed to 80 μM Cd for 4 d. For

long-term experiments, two days-old rice seedlings were hydroponically treated with 0 and 5

μM Cd (as CdSO4) for 30 d. After treatment, seedlings were harvested and immediately

frozen liquid nitrogen for following analysis.

Homozygous mutant Osrdr1 (Hitomebore or His background, accession # H0643, rice

retrotransposon Tos17 insertion lines) was kindly provided by Professor Bao Liu at Institute

of Genetics & Cytology, Northeast Normal University, Changchun 130024, China. jmj706

(Kitaake or KT background, accession # PFG_K-00085.R), Met1 (KT, PFG_K-02237.L),

sdg714 (Dong Jing or DJ background, PFG_2A-30024.L), sdg724 (DJ, PFG_3A-02454.L),

Osdrm2 (DJ, PFG_3A-05515.R) and Osros1 (DJ, PFG_1B-00939.R) were obtained from

http://signal.salk.edu/cgi-bin/RiceGE. The T-DNA insertion mutants and their corresponding

gene expression were analyzed by Identifying PCR (iPCR) and quantitative RT-PCR

(qRT-PCR). Germinating seedlings were growing for 2 d and exposed to Cd (0 and 80 M)

http://signal.salk.edu/cgi-bin/RiceGE


for 4 d. The treated seedlings were harvested for the subsequent analysis.

Generation of genomic methyl cytosine libraries and bisulfite sequencing

The rice genome DNA was extracted from full rice seedlings (n=36 seedlings for each library)

exposed to Cd (-Cd as a control) by QIAamp DNA Mini Kit (Qiagen, USA). The samples

were fragmented by sonication to 100-300 bp. After 3’A addition and adaptor ligation, the

DNA fragments were subject to sodium bisulfite conversion using the ZYMO EZ DNA

Methylation-Gold kit (ZYMO REASEARCH, USA). The sequencing was performed with

Illumina hiseq2500. Clean and high quality reads were generated by filtering out the raw and

low quality reads. As bisulfite conversion of cytosine to thymidine resulted in

noncomplementarity of the two strands of a DNA duplex, cytosines from the sense strand

were changed to thymidine, and guanines from the antisense strand were changed to

adenosine. Reads of sequences were aligned to the rice reference genome

(http://rice.plantbiology.msu.edu/index.shtml) using SOAP 2.21. Only perfect matches were

filtered in for methylation analysis.

Identification of differentially methylated regions

A sliding-window approach was used to screen genomic regions that were differentially

methylated under Cd exposure. Only cytosine sites covered by at least four reads were used.

Windows with fewer than 5 sequenced cytosine sites were discarded. For each window, the

methylation level at each cytosine site was calculated for each of the two rice samples. A

Kruskal–Wallis test was performed for each window. P-values from Kruskal–Wallis tests

were corrected for multiple tests with the false discovery rate (FDR) (Lister et al. 2009; Li et

al. 2012). Windows with FDR < 0.05 and changes of methylation level after Cd treatment of

at least two-fold were identified as differentially methylated regions (DMRs). Overlapping


DMRs were concatenated.

Validation of BS-Seq by bisulfite-RT-PCR

To validate the quality of BS-sequencing, two genes were randomly selected for bisulfate

PCR assessment. Confirmation study was conducted with rice seedlings treated or

non-treated with Cd described above. DNA (1-500 ng) from each sample was treated with

bisulphate using ZYMO EZ DNA Methylation-Gold kit (ZYMO REASEARCH, USA) and

amplified using specific primers designed by Methyl Primer Express v1.0

(http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi). Bisulfite PCR product was

cloned into pEASY-T1 Simple Cloning Vector (TransGen Biotech, China); 20 positive clones

identified by PCR were sequenced (Ngezahayo et al., 2009). The sequencing results were

analyzed using software BiQ Analyzer (http://biq-analyzer.bioinf.mpi-inf.mpg.de/).

Preparation of total RNA libraries and RNA sequencing

Total RNA from Cd-exposed and Cd free rice tissues (shoots and roots) were separately

isolated using the TRIzol Reagent (Invitrogen, USA) for RNA sequencing (n=36 seedlings

for each library). The isolated RNA was treated with DNaseI (Qiagen, USA) at 25 °C for 30

min. RNA quality was assayed with the absorbance between 1.8 and 2.0 at 260/280 nm. RNA

was enriched and purified with oligo (dT)-rich magnetic beads and broken into short

fragments. The first and second strand cDNAs were synthesized using TruSeq RNA Sample

Prep Kit v2 (Illumina, USA) with random hexamer primers.

The resulting cDNAs were subjected to end-repair and phosphorylation using T4 DNA

polymerase and Klenow DNA polymerase. An ‘A’ base was inserted as an overhang at the 3’

ends of the repaired cDNA fragments. The Illumina paired-end solexa adaptors were ligated

to the cDNA fragments to distinguish the different sequencing samples. To select a size range

http://www.urogene.org/cgi-bin/methprimer/methprimer.cgihttp://biq-analyzer.bioinf.mpi-inf.mpg.de/


of templates for downstream enrichment, products of the ligation reaction were purified and

selected on a 2% agarose gel. The PCR amplification was run to enrich the purified cDNA

template. Finally, four libraries (Shoot+Cd, Shoot-Cd, Root+Cd, Root-Cd) were sequenced

using an Illumina hiseq2500 with paired-end of Solexa RNA.

The original image data generated by the sequence providers were transferred into

nucleotide sequence data by base calling, defined as raw reads and saved as ‘fastq’ files. Raw

reads produced from deep-sequencing contained ‘dirty’ reads from remaining trace adapters,

unknown or low quality bases (with lengths < 35 bp and quality threshold < 20). These reads

were removed from the datasets. The remaining high-quality reads (Table S1) were aligned to

the rice reference genome sequence

(ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudo

molecules/version_7.0/all.dir/) with bowtie2 and eXpress.

A rigorous algorithm was used to identify differentially expressed genes (DEGs)

between the samples (Audic & Claverie, 1997). The expression level for each transcript was

calculated as FPKM (fragments per kilobaseof exon per million fragments mapped)-derived

read counts based on the number of uniquely mapped reads that overlapped with exonic

regions (Trapnell et al., 2010). FDR was used to determine the threshold of p-values in

multiple tests, which corresponded to the differential gene expression test (Lister et al. 2009;

Li et al. 2012). In this study, FDR ≤ 0.001 and the absolute value of Log2Ratio > 1 were used

as a threshold to judge the significant differences of gene expression. The similar datasets

from our recently sequenced transcriptome of rice with Cd exposure were referenced for

validation (Oono et al., 2014).

Gene expression analysis

For quantitative Real-time RT-PCR (qRT-PCR) analysis, total RNA was isolated by TRIzol


reagent (Invitrogen). RNA extracts were pre-treated with DNase. The first-strand cDNA was

synthesized by reverse transcription with TranScript. One-Step gDNA Removal and cDNA

Synthesis SuperMix (Transgen, China) using Oligo dT RTprimer (20 m) and qPCR was

performed by gene special primers (Table S2). OsACTIN were used as an internal control

with primers of OsACTIN-F (10 M) and OsACTIN-R (10 M). The reactions were

pre-incubated at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 10 s,

annealing at 60 °C for 1 min, in the 7500 Real-Time PCR System (Applied Biosystems)

using iTaqTM Universal SYBR Green Supermix (BIO-RAD USA). PCR specificity was

checked by melting curve analysis and data were analyzed using 2–ΔΔ

Ct method (Livak and

Schmittgen, 2001).

For transcript analyses of genes, one μg of total RNA (treated with DNase) was used for

all reverse transcription reactions with TranScript One-Step gDNA Removal and cDNA

Synthesis SuperMix (Transgen China). Twenty L of the reaction mixture including 1 L

Oligo dT primer and 1 L RNA and other components were incubated at 42 °C for 30 min,

followed by 85 °C for 5 min. The PCR reactions with the rTaq DNA polymerase (TAKARA

Japan) were run in an PCR Thermal Cycler Dice (TAKARA Japan) with the following

cycling profile: at 94 °C for 5 min, followed by 26 to 40 cycles of 94 °C for 30 s, 60 °C for

30 s, and 72 °C for 1 min. Ten L of the PCR product was separated in a 1% agarose gel and

stained with ethidium bromide for visualization. A pair of primers specific to rice Ubiquitin

gene were used for RT-PCR (Table S2). The primers with a 400 to 600 bp amplicon were

used for RT-PCR with 26, 28, 30, 32, 34, 36, 38 and 40 cycles, depending on the expression

levels of different genes. All RT-PCR analyses were independently repeated in triplicate.

Analyses of GO and KEGG pathways

To analyze functional cluster of DEGs, the Gene Ontology (GO)


(http://www.geneontology.org/) categories of the DEGs was tested with Benjamini-Hochberg

correction (Benjamini and Yekutieli, 2001). GO terms with corrected p-value < 0.05 were

regarded as significant enrichment for the DEGs compared to the genome background. We

further analyzed the biological pathways of the DEGs by employing Kyoto Encylopedia of

Genes and Genomes (KEGG) databases (http://www.genome.jp/kegg/pathway.html). KEGG

is a database resource for understanding various biological pathways related to diverse gene

functions. The higher order functional information is stored in the PATHWAY database,

which contains graphical representations of cellular processes, such as metabolism,

membrane transport, signal transduction and cell cycle (Kanehisa et al., 2004). With the

criterion, pathways with p-value < 0.05 were considered to be significantly enriched in

DEGs.

Phenotypic analysis and metal quantification in rice

Five day-old rice seedlings were treated with a combination of 80 μM Cd and 20 μM

5-aza-2-deoxycytidine (5-azacytidine, azacitidine or Aza) for 3 d. After that, the roots were

washed with 5 mM CaCl2 solution for three times and separated from the shoots with a razor

(Sasaki et al., 2014). Samples were dried at 70 °C in an air-forced oven and weighted. The

dried samples were digested with nitric acid and hydrogen peroxide (HNO3: HClO4 = 1: 1,

v/v). Samples with Cd were quantified using Inductively Coupled Plasma-Atomic Emission

Spectrometry (ICP-AES) (Optimal 2100DV, Perkin Elmer Instruments, Waltham MA, USA).

The length of fresh rice roots was measured using a ruler (Shen et al., 2011).

Prediction of potential miRNA targets

The target genes of miRNAs were predicted based on their extensive and conserved

complementarity to the miRNAs. By searching for the SGN unigene database using the

http://www.geneontology.org/http://www.genome.jp/kegg/pathway.htmlhttp://www.ncbi.nlm.nih.gov/pubmed/?term=Kanehisa%20M%5BAuthor%5D&cauthor=true&cauthor_uid=10592173


psRNATarget web server (http://plantgrn.noble.org/psRNATarget/) (Zhang et al., 2011),

potential target genes were identified and matched to perfectly or near-perfectly complement

rice miRNAs.

Statistical analysis

Each result shown in the figures was the mean of three replicated treatments and each

treatment contained at least 8-16 seedlings. The significant differences between treatments

were statistically evaluated by standard deviation and analysis of variance (ANOVA). The

data between differently treated groups were compared statistically by ANOVA followed by

the least significant difference (LSD) test if the ANOVA result is significant at p


intergenic regions was over 90% and 88%, respectively (Tables S5, S6), indicating that the

methylation status was determined with a high percentage of genomic CG and non-CG sites

by at least one read (Fig. S1C). To confirm the quality of the BS-Seq, two loci

(LOC_Os05g37060 and LOC_Os01g28450) were randomly selected and subjected to the

methylation-specific PCR (bisulfate-PCR or BS-PCR) analysis. As shown in Fig. S1D, the

DNA methylation patterns of the two loci from BS-Seq were well confirmed by BS-PCR.

To clarify the possible difference of genomic DNA methylation between roots and

shoots under Cd stress, a BS-PCR was conducted with more loci. Our analysis showed no

differences of DNA methylation levels of all 11 loci between roots and shoots (Fig. S2),

consistent with the previous reports (Zemach et al., 2010; Widman et al., 2014). According to

the recent report (Greco et al., 2012), treatment with 80 M Cd for 4 d is sufficient to alter

DNA methylation in plants. To ensure the stability of DNA methylation, a long-term Cd

exposure experiment was carried out. Rice seedlings were hydroponically treated with 5 M

Cd for 30 d based on the method described previously (Sasaki et al., 2012). Eight loci were

tested. The methylation patterns of the loci were identical no matter what the short-term (80

M for 4 d) or long-term (5 M Cd for 30 d) treatment was involved (Fig. S3), indicating that

Cd-induced change in DNA methylation was stable.

Differential landscapes of DNA methylation marks in Cd-free and Cd-exposed rice

We identified 24,749,280 and 24,238,924 mCs from all map reads of Cd-free and Cd-exposed

rice, respectively. Of these, approximate 51% mCs occurred at CG sites and 49% at non-CG

sites (17% at CHG and 32% at CHH; H = A, T, or C) (Fig. S1E), which is very similar to the

recent report of rice (Li et al., 2012). Compared to the control, Cd exposure led to < 1%

variation at the corresponding context. There was no difference of the total cytosine

methylation levels (mC) across the two samples (Fig. S1F). However, the overall genomic


methylation degree at mCHHs was slightly higher under Cd stress, whereas the level at

mCGs and mCHGs was generally lower as compared to the control, suggesting that DNA

methylation at the three contexts was unevenly affected by Cd.

We further overviewed DNA methylation levels throughout the 12 chromosomes (Fig.

S4). Coordinating smoothed lines were displayed in a semblable fashion with mCG and

mCHG methylation at a 10 kilobase (kb) resolution, which has complementary distributional

characteristics with mCHH sites. The local changes in DNA methylation levels were detected

in the context. An example came from chromosome 5, where the methylation patterns at mCG,

mCHG and mCHH between –Cd and +Cd treated rice were clearly distinguished (Fig. 1A, B).

Analyses of entire chromosomal methylcytosine levels also showed changes in mCG (Fig.

1C). Under Cd stress, the methylcytosine levels were further inspected in different regions of

3’-UTR, 5’-UTR, CDS, intron, mRNA and genome. To demonstrate the distribution of DNA

methylation of CG (for CHG and CHH, see Fig. S5), a 200 bp sliding window (x axis) against

methylation levels (%) between –Cd and +Cd samples was profiled (Fig. 1D, E). n represents

the number of methylcytosines. The DNA methylation levels in all specific regions were

lower in Cd-treated rice than in the control (-Cd).

Cd alters global DNA methylation patterns of genes in rice

To investigate specific DNA methylation in Cd-exposed rice, the genomic regions associated

with CG, CHG and CHH hypermethylation or hypomethylation were profiled. In total, 2393

non-redundant Cd-responsive differentially methylated regions were examined (p < 0.05). All

regions were mapped to the chromosomes (Table S7). Because DNA methylation is

associated with gene expression at certain developmental stages or in response to

environmental stresses (Zhang et al., 2006; Yan et al., 2010; Karan et al., 2012), the

DMRs-associated genes in upstream, genebody and downstream regions were identified. A


total of 2320 DMRs-associated genes with 2 fold-changes (p < 0.05) were identified. There

were more genes hypermethylated than genes hypomethylated in the three contexts. Most of

the genes identified were hyper- or hypo-methylated in the upstream context, and the minor

genes were modified by methylation in genebody (Fig. 2A), an observation consistent with

the previous studies where the regulatory region of genes is the major target of

methylation/demethylation (Cao and Jacobsen, 2002).

Using BGI WEGO (Web Gene Ontology Annotation Plotting), the DMR-associated

genes was functionally categorized. A strong enrichment in different biological processes was

observed (Fig. 2B). Based on their specific processes, these genes could be divided into three

major groups including biological process, cellular component and molecular function. In

biological process, a high cluster pointed to cellular and metabolic processes and response to

stimulus. We also found some transporter genes that could be methylated. There are 3%

(30/993) up-methylated genes and 3.2% (44/1374) down-methylated genes.

Interconnection of DNA methylome and RNA transcriptome

Although a number of genes indicated above were modified by DNA methylation under Cd

exposure, whether they were transcriptionally affected by Cd was unknown. To identify the

genes that were differentially methylated and expressed under Cd stress, we performed a

genome-wide analysis of transcripts using the high throughput RNA-Seq technology. The

transcript abundance was assessed from shoot and root tissues prepared from Cd-treated and

Cd-free rice seedlings. A total of 2092 differentially expressed genes ( 1.5 fold change, p <

0.05) under Cd stress were identified. Expression pattern of the Cd-responsive genes was

well confirmed by another transcriptome dataset of Cd-exposed rice (Oono et al., 2014). Cd

induced overall changes in gene expression (Fig. 3A). Compared to control, more genes were

repressed in shoots and roots with Cd, as it showed massive dots (genes) shift downwards


(Fig. 3B-D). Gene Ontology (GO) analysis revealed enrichment of functions associated with

biological process, cellular component and molecular function (Fig. S6A, B), which was

consistent with the previous report in rice (Oono et al., 2014) and Medicago truncatula (Zhou

et al., 2013). We further mapped 1664 (shoot) and 687 (root) annotated genes to the

canonical reference pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG)

(Kanehisa et al., 2004), from which, 73 (shoot) and 30 (root) DEGs were assigned to the 20

pathways (Fig. 3E). Eight genes with different abundance were randomly selected for

qRT-PCR validation. The transcriptional pattern of the genes in shoots and roots was well

confirmed (Fig. 3F, G).

We identified 2320 DNA methylation regions (DMRs)-associated genes or differentially

methylated genes (DMGs)(≥ 2 fold change, p < 0.05) and 2092 DEGs ( 1.5 fold change, p <

0.05) under Cd stress (Fig. 4A). A cross analysis identified 1454 genes from both DMGs and

DEGs. Among these, 49.2% genes (715 out of 1454) were up-regulated and 50.8% (739 out

of 1454) were down-regulated under Cd stress. The proportion of DMGs and non-DMGs was

further analyzed within the 2092 genes. Among the DEGs (≥ 1.5 fold change, p≤0.05), 69.5%

(65.6% genes with ≥ 1.5 fold change and 3.9% with ≥ 2 fold change) of the genes could be

differentially methylated, whereas only 30.5% were not, suggesting that most of

Cd-responsive DEGs tended to be modified by methylation (Fig. 4B).

We then upgraded the standard of DEGs to a 2-fold change of gene expression and

found only 82 genes could be filtered in (≥ 2 fold change, p < 0.05) (Fig. 4B, Table S8). In

the 82 DMGs/DEGs, 21/12/11 genes were hypermethylated and 15/9/14 were

hypomethylated in their upstream, genebody and downstream regions, respectively. These

DMGs could be divided into five groups based on their biological functions including stress

response (10), transporter (5), transcription factor (6), metabolic process (19) and other

biological processes (or unknown, 42) (Fig. 4C). As an example, we profiled the DNA


methylation marks throughout the upstream, genebody and downstream of four loci including

LOC_Os11g14040 (glutathione S-transferase 2, GSH2), LOC_Os10g22310 (glutathione

S-transferase U35, GSHU35), LOC_Os08g39840 (lipoxygenase, LOX), and

LOC_Os06g40080 (heme oxygenase-1, HO1)(Fig. 5A). While both GSH2 and GSHU35

upstream regions were hypermethylated under Cd stress, Cd treatment led to

hypermethylation of LOX downstream and hypomethylation of HO1 downstream (Fig. 5B-E).

RNA-seq data showed that the four genes in shoots and roots were differentially induced by

Cd (Fig. 5F). qRT-PCR validated the expression of all tested genes except GSHU35 in shoots

(Fig. 5G-J). In similar, another set of genes including OsIRO2 (Iron-related transcription

factor 2), OsZIP1 (Q94DG6), LOC_Os02g37280.1 (heavy metal transport/detoxification

protein with only a HMA metal binding domain, thereafter referred as HMT) and

LOC_Os06g19210.1 (cadmium tolerance factor, CTF)(Q2MJU2) were inspected (Fig. S7A).

The OsIRO2 downstream was hypermethylated under Cd stress, whereas Cd exposure led to

OsZIP1 hypomethylation in genebody and HMT and CTF in downstream regions,

respectively (Fig. S7B-E). Transcript levels of the genes were also found to be affected by Cd

(Fig. S7F-J). In addition, two other genes encoding LOC_Os09g02214 (encoding a citrate

transporter)(A5PGV3) and OsSPL1 (SQUAMOSA promoter-binding-like) were also found to

be hypermethylated in upstream and significantly up-regulated at the transcriptional level

(Fig. S8). These data indicated that both DNA methylation and transcription of the genes

including GSH2, GSH35, LOX, HO1, OsIRO2, OsZIP1, HMT, CTF, LOC_Os09g02214 and

OsSPL1 were affected by Cd.

Because DNA hypermethylation, especially occurring in the regions of transposable

elements, could be interpreted as enhancement of genomic stability (Saze and Kakutani,

2011), the DNA methylation regions linked to the transposable elements were identified.

BS-seq analysis revealed that 108 transposons and 254 retrotransposons were differentially


methylated (≥ 2 fold change, p < 0.05) under Cd stress (Fig. S9A). Meanwhile, transcriptome

analyses identified 49 transposons and 92 retrotransposons that were differentially expressed

under Cd stress (≥ 2 fold change, p < 0.05) (Fig. S9B). Combinational analyses with the

BS-Seq and RNA-Seq datasets resulted in identification of 30 genes located nearby the

transposons/retrotransposons (Fig. S9C). Using the datasets, their enrichment and pathways

were further analyzed. The top three enrichments pointed to cellular process, metabolic

process and response to environmental stimuli (Fig. S9D).

Cd exposure alters expression of genes relevant to DNA methylation

Rice contains 10 DNA methyltransferase family members that can be phylogenetically

classified into CMT, MET (DNMT1-like), DNMT2-like and DRM (DNMT3-like) clades (Chen

and Zhou, 2013; Ahmad et al., 2014; Qian et al., 2014; Fig. 6). DMT702 (OsMET1-1) and

DMT707 (OsMET1-2) belong to the MET family. They play a basic role in maintaining DNA

methylation marks (Teerawanichpan et al., 2004; Yamauchi et al., 2008, 2009). These genes

could be induced by Cd exposure (Fig. 6A). DMT701 (Os03g12570), DMT703 (Os05g13790)

and DMT704 (Os10g01570) encode chromomethylases (CMT) family methyltransferases

responsible for CHG methylation (Lindroth et al., 2001; Cao and Jacobsen, 2002). While

DMT701 and DMT704 were repressed in expression, DMT703 was enhanced in Cd-treated

rice. The last part of DNA methyltransferase subfamily is the domains rearranged

methyltransferases responsible for DNA CHH-specific methylation, including DMT705

(Os01g42630), DMT706 (Os03g02010), DMT708 (Os12g01800), DMT709 (Os11g01810)

and DMT710 (Os05g04330)(Cao et al., 2000; Moritoh et al., 2012). However, most of the

genes (DMT705, DMT708 and DMT709) appeared not to be affected by Cd in rice. Only

DMT706 and DMT710 in shoots were weakly induced by Cd. Notably, ROS1 (REPRESSOR

OF SILENCING 1) was a recently identified demethylase-coding gene responsible for DNA


demethylation (Agius et al., 2006). In this study, the rice orthorlog gene OsROS1 was

drastically induced by Cd (Fig. 6A). We further inspected several RNA-dependent RNA

polymerase (RDR) protein genes, which are playing a role in generating the 24 nt small

RNAs (sRNAs)(Zhang and Zhu, 2011). RDR1-4 and RDR6 are required for RNA-directed

DNA methylation (RdDM) (Wang et al., 2014). Interestingly, all five genes including

RDR1-4 and RDR6 identified here were up-regulated by Cd exposure (Fig. 6A).

Recent evidence showed interplay between DNA methylation and histone modification;

histone modification is responsible for DNA methylation, which is not restored when histone

modification is lost (Stroud et al., 2013). KRYPTONITE H3K9 histone methyltransferase

(HMTase) is required for CHG and CHH DNA methylation in Arabidopsis (Jackson et al.,

2002). SDG714 and SDG728 are histone H3K9 methyltransferases (Ding et al., 2007; Qin et

al., 2010). While transcriptional expression of SDG714 was induced by Cd, SDG728 was

repressed under the same condition (Fig. 6B). SDG724 and SDG725 were also well identified

as histone H3K36 methyltransferases (Sui et al., 2012; Sun et al., 2012; Chen and Zhou,

2013). Although both SDG724 and SDG725 are involved in regulation of rice development,

they were also induced by Cd. Two genes JMJ703 and JMJ706 encoding H3K4 and H3K9

demethylases involved in rice development (Chen and Zhou, 2013), were also found to be

up-regulated in roots by Cd (Fig. 6B). In shoots, JMJ703 were up-regulated by Cd, whereas

expression JMJ706 was suppressed. In addition, several other genes involved in chromatin

remodeling, histone deacetylase and polycomb repressive complex 2 proteins were also found

to be differentially regulated under Cd stress (Fig. 6B).

Genetic validation reveals that Cd-responsive gene expression can be modified by DNA

methylation

We used a subset of loss of function mutants defective in activities of DNA


methylation/demethylation, histone modification and small RNA generation to assess

transcript abundance of selected genes. The DNA structures, T-DNA insertion and null

expression of the mutants were characterized (Fig. 7). Since the mutants in the Nipponbare

background are unavailable, several mutants from other well-known rice genotypes including

DJ, KT, and Hit were identified. Compared to Nipponbare, the genotypes DJ, KT, and Hit

showed a similar response to Cd exposure (Fig. S10). Four representative genes were selected

for transcript analysis in the mutants by qRT-PCR, including (a) LOC_Os09g02214

(encoding putative transporter); (b) OsSPL1 (belongs to OsSPL16-like class of long SPLs)

transcription factor is a member of SQUAMOSA(SQUA) promoter-binding-like (SPL) genes

family involved in governing plant developmental processes (Xie et al., 2006) and

environmental signal response (Kropat et al., 2005); (c) OsPR1b involved in biotic stress

response (Ogo et al., 2007); and (d) OsIRO2, a transcription factor regulating iron and zinc

homeostasis in rice plants (Ogo et al., 2007). We found that under Cd exposure the transcript

levels of the genes were significantly lower in sdg714 and sdg724 than in wide-type (Fig.

8A-D). Compared to WT, Osdrm2 showed a significantly lower level of transcripts of

OsIRO2, OsPR1b and LOC_Os09g02214 under Cd stress, but no difference of transcripts for

OsSPL1 was observed. Transcripts of OsIRO2 and OsPR1b in met1 were significantly

reduced compared to wild-type, but no significant difference of OsSPL1 and

LOC_Os09g02214 transcripts was observed under Cd stress.

In Arabidopsis, REPRESSOR OF SILENCEING 1 (ROS1) is a major DNA demethylase

responsible for erasing DNA methylation for dynamic transcriptional regulation; thus, ros1-4

mutants have higher levels of DNA methylation in all sequence contexts (Lei et al., 2015). In

this study, expression of the four genes was suppressed in Osros1 as to WT (Fig. 8A-D). In

rice, the JMJ706 mutation led to increased di- and trimethylations of H3K9 (Sun and Zhou,

2008). While jmj706 mutants had a higher transcript level of OsIRO2 and LOC_Os09g02214

javascript:void(0);javascript:void(0);http://www.ncbi.nlm.nih.gov/pubmed/?term=Sun%20Q%5BAuthor%5D&cauthor=true&cauthor_uid=18765808http://www.ncbi.nlm.nih.gov/pubmed/?term=Sun%20Q%5BAuthor%5D&cauthor=true&cauthor_uid=18765808


over the wide-type, a relatively lower transcript level of OsSPL1 and OsPR1b was observed

in jmj706, suggesting that these genes can be differentially regulated by methylation under

Cd stress. Finally, expression of the genes was tested in the rice mutant Osrdr1. Mutation of

OsRDR1 globally changed the expression of rice genes (including small RNAs) and DNA

methylation marks (Wang et al., 2014). We showed that both OsIRO2 and OsPR1b

expression was drastically down-regulated in Osrdr1, whereas OsSPL1 expression was

increased compared to WT under Cd stress. For LOC_Os09g02214, no significant difference

was observed between WT and Osrdr1 under Cd stress.

To demonstrate the association between DNA methylation and transcriptional expression,

we exemplified the methylation level of OsPR1b in the WT and mutants under Cd or non-Cd

stress. OsPR1b was cloned by BS-PCR. Under non-Cd condition, the methylation levels of

OsPR1b were generally lower in the mutants than the control (except for Osrdr1); under Cd

stress, the methylation levels of OsPR1b was increased in sdg714, sdg724, Osdrm2, Osros1,

and Osrdr1, but no change of OsPR1b was found only in jmj706 and met1 (Fig. S11). These

results suggest that mutation of some specific DNA methylation activities should be

responsible for the changed expression of OsPR1 (Fig. 8C).

DNA methylation inhibitor promotes the growth and Cd accumulation of rice

To investigate whether DNA methylation affected downstream phenotypes of rice plants, a

commonly used DNA methylation inhibitor 5-azacytidine was applied to this study (Goffin et

al., 2002; Zhong et al., 2013). Rice seedlings were treated with 80 μM CdSO4 and/or 20 μM

Aza for 3 d. The growth response to Cd exposure was assessed by measuring root elongation

and seedling dry weight. Aza provision attenuated root growth inhibition and enhanced

biomass under Cd exposure (Fig. 9 A-C). Cd accumulation in seedlings with Aza was found

to be higher than that of the control (Cd treated alone) (Fig. 9D). To confirm the effect of Aza


on DNA methylation, eleven genes described above (Fig. S2) were tested by BS-PCR. As

expected, supply of Aza led to a generally lower degree of DNA methylation of the genes, but

failed to change the DNA methylation patterns shaped by Cd (Fig. 9E). Their transcript levels

of the genes were further inspected by qRT-PCR. Compared to Cd treatment alone, Aza in the

presence of Cd (Aza+Cd) enhanced transcription of most of the genes including HMT,

OsZIP1, CTF, GSH2, GSHU35, LOX and HO1, suggesting that the altered expression pattern

of the genes could be attributed to the lower levels of DNA methylation as a result of Aza

application. However, compared to Cd treatment alone, expression of IRO2 with Aza+Cd was

repressed. Furthermore, HMA2, HMA3 and NRAMP5 showed no responses under the

treatments of either Cd or Aza or both (Fig. 9F).

Several loci generating microRNAs were differentially methylated under Cd stress

MicroRNAs (miRNAs) are a class of non-protein-coding small RNAs with 21-24

nucleotide-long and usually transcribed by RNA polymerase II (Lee et al., 2004). Recently, a

set of miRNAs were identified in response to heavy metals (Zhou et al., 2012a, b). To

investigate DNA methylation status of miRNA loci under Cd stress, we mapped all rice

miRNA precursor sequences from the publicly available database

(http://www.mirbase.org/index.shtml) to our DNA bisulfite-sequenced datasets. A total of 15

miRNA-generating loci were found to be differentially methylated (Table 1). Of these, 13

passed through two-fold change (p < 0.05) filter criterion. Using the Cd-treated rice miRNA

datasets (Tang et al., 2014), seven miRNAs including miR164a, miR439a, miR812c/i/k/l/m

and miR5810 in shoots and/or roots were differentially expressed under Cd stress. The rest of

them showed no significant response to Cd. Because most of the miRNA targets are

unavailable, we attempted to predict the targets for the 15 miRNAs. miR439a and miR2924

were predicted to target putative transposons LOC_Os12g11340.1 and LOC_Os08g27740.1,

http://en.wikipedia.org/wiki/RNA_polymerase_IIhttp://plantgrn.noble.org/psRNATarget/getseq.do?sessionid=1430128514883143&source=target&seqID=LOC_Os12g11340.1http://plantgrn.noble.org/psRNATarget/getseq.do?sessionid=1430132167121994&source=target&seqID=LOC_Os08g27740.1


respectively; and miR5498 were predicted to target a putative retrotransposon

LOC_Os08g11860.1 (Table 1). miR812c/i/k/l/m and miR1872 were predicted to target a

putative heavy metal transport/detoxification protein and unknown transporter family protein,

respective. Several other genes such zinc finger domain-containing transcription factors were

predicted as the targets of miRNAs (miR1872 and miR5831).

DISCUSSION

DNA methylation has been emerging as a new modulator of plant growth, development and

stress responses (Zhang et al., 2010; Grafi, 2011). To follow up the hypothesis that DNA

methylation may play a central role in regulating plant response to heavy metals (Aina et al.,

2004; Greco et al., 2012; Kumar et al., 2012), we profiled DNA methylomes with single-base

resolution for rice seedlings exposed to Cd. Although the Cd-exposed and Cd-free rice plants

had a similar genomic cytosine methylation levels, local variations were observed for the

specific DNA methylation in the contexts of CG and CHG, where Cd tended to reduce the

overall DNA methylation levels. This result is consistent with the recent reports that Cd and

other environmental challenges are able to reduce the global DNA methylation (Alina et al.,

2004; Wang et al., 2011; Ou et al., 2009, 2012; Marconi et al., 2013). The differentially

methylated regions were identified between the Cd-exposed and control rice genomes. We

found more DMRs where DNA methylation levels were increased in Cd-exposed rice than

those where DNA methylation levels were decreased (Fig. 2A), indicating that specific DNA

methylation sites can be modified by Cd exposure.

Profiling of both methylome and transcriptome allowed comprehensive analysis of DNA

methylation patterns and gene expression. We found more genes hypermethylated than those

hypomethylated in upstream, genebody and downstream regions under Cd stress (Fig. 2A).

http://plantgrn.noble.org/psRNATarget/getseq.do?sessionid=1430132473164860&source=target&seqID=LOC_Os08g11860.1


The reason for the biased DNA methylation of genes remains to be investigated, but the

variation of methylation seemed to be associated with the change in transcript levels of

methylation-related genes, of which 715 genes were up-regulated and 739 genes were

down-regulated under Cd stress. We analyzed all genes involved in DNA methylation and

demethylation in RNA-seq datasets including different types of DNA methyltransferase

family genes, histone methyltransferase family genes, RNA-dependent RNA polymerase

(RDR) protein genes, and demethylase-coding genes like REPRESSOR OF SILENCING 1

(ROS1). Some of the DNA methylation-related genes were substantially induced under Cd

stress ( 2 fold change, p0.05)(Fig. 6), suggesting that variation of the epigenetic modified

protein genes would be responsible for the specific DNA methylation patterns of other genes

under Cd stress.

A group of genes (82) (2 fold change, p0.05) in DNA methylation were identified

with a strong preference of differential expression in Cd-exposed rice plants (Fig. 4). Many of

them were found to involve stress response, metal transport, transcription factors and

metabolic process. When rice is exposed to Cd, a large number of genes are transcriptionally

altered (Ogo et al., 2014). Ogawa et al. (2009) reported that a short-term (3 h) exposure of

rice to 10 μM Cd strongly induced a bunch of genes such as zinc-finger domain-containing

protein genes, heat shock protein genes, and WRKY transcription factor-coding genes. These

protein-coding genes and their expression patterns could also come up in this study. For

example, genes encoding MYB-family transcription factor, OsWAKY38, and hydrolase were

differentially regulated under Cd stress. Importantly, these genes identified here could be

differentially methylated under Cd stress. We further specified methylation levels at upstream,

genebody and downstream of several genes. OsIRO2 is a basic helix-loop-helix (bHLH)

transcription factor gene that plays an important role in rice responding to Fe deficiency (Ogo

et al., 2007). It is synchronously expressed with other genes concerning Fe uptake and


transport in rice (Maria et al., 2012). The hypermethylation of OsIRO2 downstream region

may suggest a causal relationship with enhanced expression of OsIRO2 under Cd exosure.

Furthermore, analysis of mutants of DNA methylation showed that OsIRO2 expression levels

in most of the mutants were repressed (except jmj706), suggesting that DNA-methylated

modification was most likely involved in transcriptional regulation of OsIRO2. Ishimaru et

al., (2008) reported that OsIRO2 was induced by excess zinc (Zn). In accord with it, the

levels of high-affinity transition metal chelators such as nicotianamine (NA) and

deoxymugineic acid (DMA) increased, suggesting that induced OsIRO2 could confer plant

tolerance to Zn excess through the metal chellators (Ishimaru et al., 2008). Plants with Cd

toxicity usually exhibit leaf chlorosis, a typical symptom of Fe deficiency (Clemens, 2006).

Cd-triggered Fe deficiency could be attributed to the competition of metal-binding molecules

with Fe (Connolly et al., 2002). In this regard, Cd-induced low Fe availability provokes Fe

transporters to load Cd into plants (Lombi et al., 2002). In Arabidopsis (strategy I plants),

bHLH transcription factors FIT, AtbHLH38 and AtbHLH39 are induced by Fe-deficiency

(Wu et al., 2012). Co-overexpression of FIT with AtbHLH38 or AtbHLH39 constitutively

activates the expression of Heavy Metal Associated3 (HMA3), Iron Regulated Transporter2

(IRT2), and Iron Regulated Gene2 (IREG2), which are involved in Cd uptake and

detoxification (Wu et al., 2012). In rice, OsIRO2 (homologous to AtbHLH38/AtbHLH39),

together with IDEF1 and IDEF2 are among the three major transcription factors and also

demonstrated to regulate Fe deficiency response (Kobayashi et al., 2007; Ogo et al., 2007),

where a range of iron chelators and transporters were activated (Kobayshi et al., 2014). These

results suggest that Cd-induced Fe deficiency may also induce Fe transporters through IRO2,

which eventually took up Cd into rice plants.

Of particular interest, DNA methylation and transcription of several metal transporter

genes OsZIP1 and HMT and CTF were identified in this study. In addition, several well


known transporter genes such as OsNRAMP5 (Sasaki et al., 2012), OsHMA2

(E7EC32)(Satoh-Nagasawa et al., 2012; Takahashi et al., 2012; Yamaji et al., 2013), and

OsHMA3 (Q8H384)(Miyadate et al., 2010; Ueno et al., 2010) were inspected. Unexpectedly,

none of them were found to be differentially modified by DNA methylation under Cd stress

(Fig. S3). Furthermore, transcription of the known transporters was not induced by Cd

treatment (Fig. S3), although they were showed to actively mediate Cd uptake and

translocation in rice (Miyadate et al., 2010; Satoh-Nagasawa et al., 2012; Sasaki et al., 2012).

Apparently, these metal transporters are not involved in epigenetic regulation of plant

response to Cd. Metal uptake and transport is a quantitative trait subjected to multi gene

control. In this regard, regulation of Cd transport in plants needs multiple pathways. In

addition to the Cd transporters, some others may also exist for Cd transport. HMT, CTF and

OsZIP1 were differentially methylated under Cd stress (2 fold change, p0.05). While

OsZIP1 was hypomethylated in genebody, HMT and CTF were hypomethylated in

downstream region. Interestingly, these genes were induced by Cd (2 fold change, p0.05).

OsZIP1 was identified as a Zinc transporter (Ramesh et al., 2003). Transcriptional expression

of OsZIP1 was induced by Zn deficiency and Cd excess. Yeast cells (ZHY3) expressing

OsZIP1 showed sensitivity to Cd at 10 M Cd, suggesting that OsZIP1 may transport Cd.

HMT and CTF have not been functionally characterized for Cd transport. DNA methylation,

gene expression and physiological consequences of HMT and CTF under Cd stress remain to

be investigated.

We employed a global DNA methylation inhibitor 5-azacytidine to validate the effect of

DNA methylation status on gene expression and physiological responses. Rice seedlings with

Aza showed improved root growth and biomass but more Cd accumulation under Cd

exposure, suggesting that demethylation by Aza could be beneficial to plant detoxification or

tolerance to Cd. In line with it, several Cd-detoxified genes such as GSH2, GSHU35, LOX


and HO1 were transcriptionally induced by Cd exposure. Interestingly, their expression was

more pronounced in the presence of Aza under Cd stress (Fig. 9F). GSH2 and GSHU35

encode glutathione S-transferases (EC 2.5.1.18), catalyzing conjugation of glutathione to

electrophilic and cytotoxic substrates by transfer of these conjugated substrates to cellular

compartments such as vacuoles and apoplasts. Thus, they are believed as a part of

detoxification system of xenobiotics (e.g. toxic heavy metals) in plants (Schroder, 2006).

HO1 (or HY1) encoding heme oxygenase-1 (EC 1.14.99.3) is the first rate-limiting enzyme

that yields biliverdin IX, carbon monoxide (CO) and iron. Recent studies show that HO1

positively regulates plant response to heavy metal such as Cd and Hg and improves plant

growth under metal stress (Shen et al., 2011; Han et al., 2014). LOX encoding a lipoxygenase

(EC 1.13.11.1) catalyzes oxygenation of fatty acids to their hydroperoxy derivatives and is

one of the key enzymes in octadecanoid pathway upstream controlling jasmonic acid (JA)

biosynthesis (Gardner, 1991). LOXs-activated pathways involve JA and abscisic acid

signaling, thereby priming the rice plants for enhanced survival under abiotic or

biotic stress conditions (Jisha et al., 2015). In this study, GSH2, GSHU35, LOX and HO1

were found to be differentially methylated, but accompanied with increased transcripts with

Aza under Cd stress (Fig. 9E), suggesting that hypomethylation in the specific context of the

genes as a result of Aza application might contribute to the activation of GSH2, GSHU35,

LOX and HO1 under Cd stress. Demethylation or hypomethylation is thought of a common

feature associated with adaptive response to various stresses (Agius et al., 2006; Jiang et al.,

2008; Lei et al., 2015). Under the condition, the increased transcripts of GSH2, GSHU35,

LOX and HO1 could be partially responsible for the improvement of the growth of rice

seedlings exposed to Cd (Fig. 9A, B). However, under Cd stress, as both DNA methylation

and active demethylation were likely involved, the outcome of the gene expression and

physiological phenotypes could be a coordinated response to DNA methylation and

https://en.wikipedia.org/wiki/Enzyme_Commission_numberhttp://enzyme.expasy.org/EC/1.13.11.-http://www.ncbi.nlm.nih.gov/pubmed/?term=Gardner%20HW%5BAuthor%5D&cauthor=true&cauthor_uid=1909580


demethylation.

miRNAs regulate plant growth and development by silencing gene expression at

post-transcriptional level. Recent studies have demonstrated that miRNAs are also involved

in the plant response to metal toxicity (Ding et al., 2011; Zhou et al., 2012; Chen and Yang,

2013; Zhang et al., 2013). However, whether miRNA gene expression under Cd stress is

meditated by DNA methylation in plants is unknown. This study identified 15 miRNA loci

that were found to be differentially methylated under Cd exposure. Based on the predicted

targets, these miRNAs are involved in several biological functions such as metal

transporter/detoxification, transcription factor and transposon/retrotransposon. For example,

some miR812 species target a putative heavy metal transport/detoxification protein.

Interestingly, miR812q is a 24-nucleotide miRNAs and its precursor is possibly processed in

alternative AGO complexes (Wu et al., 2009). Thus, it has been proposed that these

24-nucleotide miRNAs are mostly recruited by AGO4 to induce DNA methylation of their

target genes (Wu et al., 2010). Whether the miR812 species are able to mediate plant

response to heavy metals remains to be investigated.

Acknowledgements

This research was supported by the Priority Academic Program Development of Jiangsu

Higher Education Institution (200910). We gratefully acknowledge Professor Bao Liu

(Institute of Genetics & Cytology, Northeast Normal University, Changchun 130024, China)

for his critical review of the manuscript and the technique assistance of Annoroad Gene

Technology Co., Ltd, Beijing, China.


SUPPORTING INFORMATION

Supplementary Table S1. Output data of RNA-seq from four rice libraries exposed –Cd and

+Cd.

Supplementary Table S2. Primer sequences used for this study

Supplementary Table S3. Output data of bisulfite sequencing (BS-Seq) of rice seedlings

exposed Cd (+Cd) and Cd-free (-Cd)

Supplementary Table S4. Data description of BS-Seq reads for the two rice samples (-Cd

and +Cd)

Supplementary Table S5. Table S3. Effective coverage of chromosomes

Supplementary Table S6. Effective coverage of intergenic region

Supplementary Table S7. Distribution of DMRs in chromosomes

Supplementary Table S8. Genes were modified by DNA methylation and differentially

expressed under Cd stress.

Supplementary Figure S1. Sequencing and mapping summary. (A and B): The

methylcytosine cumulative distribution of effective sequencing depth.

Supplementary Figure S2. Analysis of DNA methylation levels of selected genes in roots

and shoots of rice plants exposed to Cd.

Supplementary Figure S3. Comparison of DNA methylation patterns and corresponding

transcriptional expression of genes between two Cd-treated rice groups.

Supplementary Figure S4. The density profile of methylcytosines in chromosomes of rice.

Supplementary Figure S5. The heatmap of CHG and CHH methylation changes in the

specific regions (3-UTR, 5-UTR, CDS, intron, mRNA and genome) under Cd exposure.

Supplementary Figure S6. Analysis of transcript abundance of Cd-free (CK, control) and

Cd-exposed rice.


Supplementary Figure S7. Profiling of DNA methylation and expression of representative

genes coding for OsIRO2, heavy metal transporter (HMT), metal cation transporter (OsZIP1)

and cadmium tolerance factor (CTF).

Supplementary Figure S8. DNA methylation and expression in genes coding for putative

transporter (A5PGV3) and OsSPL1

Supplementary Figure S9. DNA methylation and expression in transposons and

retrotransposon.

Supplementary Figure S10. Reponses of different rice genotypes to Cd stress.

Supplementary Figure S11. Effects of null mutation of sdg714, sdg724, Osdrm2, Osros1,

jmj706, Osmet1 and Osrdr1 on DNA methylation of OsPR1b downstream locus in rice.

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