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Shotgun bisulphite sequencing of the
Arabidopsis
genome reveals DNA methylation patterning.
Author: S. J. Cokus
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"LETTERS Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning Shawn J. Cokus 1 *, Suhua Feng 1,2 *, Xiaoyu Zhang 1 {, Zugen Chen 3 , Barry Merriman 3 , Christian D. Haudenschild 4 , Sriharsa Pradhan 5 , Stanley F. Nelson 3 , Matteo Pellegrini 1 & Steven E. Jacobsen 1,2 Cytosine DNA methylation is important in regulating gene expression and in silencing transposons and other repetitive sequences 1,2 . Recent genomic studies in Arabidopsis thaliana have revealedthatmanyendogenousgenesaremethylatedeitherwithin their promoters or within their transcribed regions, and that gene methylation is highly correlated with transcription levels 3?5 . However, plants have different types of methylation controlled by different genetic pathways, and detailed information on the methylationstatusofeachcytosineinanygivengenomeislacking. To this end, we generated a map at single-base-pair resolution of methylated cytosines for Arabidopsis, by combining bisulphite treatment of genomic DNA with ultra-high-throughput sequen- cing using the Illumina 1G Genome Analyser and Solexa sequen- cing technology 6 . This approach, termed BS-Seq, unlike previous microarray-based methods, allows one to sensitively measure cytosine methylation on a genome-wide scale within specific sequence contexts. Here we describe methylation on previously inaccessible components of the genome and analyse the DNA methylation sequence composition and distribution. We also describe the effect of various DNA methylation mutants on genome-wide methylation patterns, and demonstrate that our newlydevelopedlibraryconstructionandcomputationalmethods can be applied to large genomes such as that of mouse. Togenerate aDNAmethylation mapatone-nucleotide resolution across the genome, we adapted the Illumina 1G Genome Analyser using Solexa sequencing technology (Illumina GA) for shotgun sequencing of bisulphite-treated Arabidopsis genomic DNA. Sodium bisulphite converts unmethylated cytosines to uracils, but 5-methylcytosines remain unconverted. Hence, after amplification by polymerase chain reaction (PCR), unmethylated cytosines appear as thymines and methylated cytosines appear as cytosines 7 .We created genomic DNA libraries after bisulphite conversion and pro- duced,3.8billionnucleotidesofhigh-qualitysequencethatsuccess- fully mapped to the genome. We subsequently used several filters to ensure accuracy, including only retaining reads mapping to sequences that are unique in the genome after bisulphite conversion from every possible methylation pattern (see Supplementary MethodsandSupplementaryTable1).Thisresultedinaconservative data set of ,2.6billion nucleotides mapping to unique genomic locations with very high confidence, covering,93% of all cytosines that could theoretically be covered (,92% of the ,43million cytosines in the ,120-megabase (Mb) Arabidopsis genome can be covered uniquely with 31 nucleotide sequences). This represents ,20-foldaveragecoverage,similartotypicalcoverageinatraditional bisulphite-sequencing experiment for a single locus. Methylation in Arabidopsis exists in three sequence contexts: CG, CHG (where H is A, C or T) and asymmetric CHH 1 . We observed overall genome-wide levels of 24% CG, 6.7% CHG and 1.7% CHH methylation (Supplementary Fig. 1a). Most CGs were either unmethylated or highly methylated (80?100%), whereas CHH sites wereeitherunmethylatedormethylatedat,10%.CHGsitesshowed a more uniform distribution of between 20% and 100% (Supple- mentary Fig. 1b?d). These differences underscore the fact that each type of methylation is under distinct genetic control 1 . Our reads also contained 504-fold average coverage of 99.97% of theoretically cov- erable cytosines in the unmethylated chloroplast genome 3,8 , giving false-positive rates of 0.29% (CG), 0.29% (CHG) and 0.25% (CHH) (Supplementary Figs 1aand 2). TheBS-Seq data were highly consist- ent with traditional bisulphite sequencing data from individual methylated or unmethylated loci 3 (Supplementary Table 2, Supple- mentary Fig. 3, and below). AlthoughCG,CHGandCHHmethylationwerehighlycorrelated, showingenrichmentinrepeat-richpericentromericregions(Fig.1a), a marked deviation was found within gene bodies, which contained almost exclusively CG methylation (Fig. 1b). This is consistent with previous studies 3,4,9 and with a depletion of short interfering RNAs (siRNAs) in the bodies of genes (Fig. 1b). Conversely, genomic regions corresponding to siRNAs were highly correlated with CG, CHG and CHH methylation, consistent with the known molecular nature of RNA-directed DNA methylation (Fig. 1c) 1 . For methyla- tionofalltypestherewasastrongpositivecorrelationwiththelength of the methylated sequence (Fig. 1d). BS-Seqseemstobemoresensitivethanpreviouslyusedmicroarray- based methods 3?5 . For example, we found a cluster of five methylated CG sites in a 34-base-pair region and a lone methylated CG site, both within the FWA locus, that were not detected by previous methods (Supplementary Fig. 4). We also found CG methylation within genes previously classified as unmethylated 3,4 (Supplementary Fig. 5). Finally, in analysing genes for which expression is de-repressed in DNA methyltransferase mutants, BS-Seq was more accurate in identifyinggeneswithpromotermethylationthatwasotherwisevari- ably detected in previous microarray studies (Supplementary Fig. 6). BS-Seqcanbeusedtoanalyserepetitivesequencesthataredifficult to study with microarrays as they may exceed the dynamic detection range or cross-hybridize. For example, we mapped methylation across the highly repetitive Arabidopsis ribosomal DNA loci and found high levels of CG, CHG and CHH methylation, including on the minimal promoter and upstream SAL1 repeats (Supplemen- tary Fig. 7). Further, we detected methylation in telomeric repeat sequences (CCCTAAA) n that have not been previously shown to *These authors contributed equally to this work. 1 Department of Molecular Cell and Developmental Biology, 2 Howard Hughes Medical Institute, 3 Department of Human Genetics, David Geffen School of Medicine, University of CaliforniaatLosAngeles,LosAngeles,California90095,USA. 4 IlluminaInc.,Hayward,California94545,USA. 5 NewEnglandBioLabs,Ipswich,Massachusetts01938,USA.{Present address: Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA. Vol 452|13 March 2008|doi:10.1038/nature06745 215 Nature Publishing Group�2008 16 bp 1.6 kb Inverted repeats 16 bp 3.2 kb Tandem repeats 3.2 kb128 bp Protein-coding genes 1.6 kb64 bp Pseudogenes and transposons Repeats CG methylation CHG methylation CHH methylation siRNA 0 20 40 60 80 100 0 5 10 15 20 25 30 0 5 10 15 20 0 5 10 15 0 5 10 15 20 250 5 10 15 0 20 40 60 80 0 5 10 15 20 25 30 0 5 10 15 0 5 10 15 20 0 5 10 15 0 5 10 15 20 25 Chromosome 1 Chromosome 2 Chromosome 3 Chromosome 4 Chromosome 5 kb Length of r e peats Methylation level (%) Mb Mb 0 20 40 60 80 CG CHG CHH Overlap with siRNA Genome average Methylation level (%) a b c d e Protein-coding genes 0 10 20 30 0 0.03 0.06 0.09 0 50 100 Transcribed region (%) Upstream (kb) Downstream (kb) 1 0.5 0.5 1 0 20 40 60 0 0.4 0.8 1.2 Tandem repeats 0 30 60 90 0 0.1 0.2 0.3 0.4 Pseudogenes and transposons Inverted repeats 0 20 40 60 0 0.4 0.8 1.2 Methylation level (%) A verage number of siRNAs per bp Methylation level (%) 0 30 60 90 1st 2nd 3rd 0 2 4 6 8 10 12 1st 2nd 3rd Methylation level (%) WT drm1 drm2 cmt3 Cytosines by position in telomeric repeat unit (CCCTAAA) n 100 0 50 100 Transcribed region (%) Upstream (kb) Downstream (kb) 1 0.5 0.5 1 0 50 100 Repeat region (%) Upstream (kb) Downstream (kb) 1 0.5 0.5 1 0 50 100 Repeat region (%) Upstream (kb) Downstream (kb) 1 0.5 0.5 1 Figure 1 | Methylation of different fractions of the Arabidopsis genome. a, Chromosome-wide distribution of methylation and correlation with repeats in sliding 100-kb windows. b, Methylation levels and siRNA abundance 26 are plotted across different types of repeats and genes. c, High levels of methylation are detected at loci corresponding to siRNAs. d, Relationship between methylation levels and the length of different types of repeats and genes. e, From left to right, methylation levels of the three consecutive cytosines in the (CCCTAAA) n telomeric repeat unit are calculated in wild type (WT) and the drm1drm2cmt3 mutant, respectively. a b 0 1 2 0 1 2 0 1 2 0 1 2 Bits Bits CG CHG CHH 1 2 34567 12 34567 12 34567 Sequence position High methylation Low methylation High methylation Low methylation Figure 2 | Sequence preferences for methylation in CG, CHG and CHH contexts. Logos of sequence contexts that are preferentially methylated at the highest or lowest levels for 7-mer sequences in which the methylated cytosine is in the fifth position. In a, all genomic 7-mers in chromosome 1 were analysed, whereas, in b, sequences were restricted to previously definedmethylatedsequences 3 .The logo graphically displays the sequence enrichmentat aparticular position in the alignment of 7-mers in each class, measured in bits. The maximum sequence conservation per site is 2bits (that is, 1base) when a site is perfectly conserved, and 0 if there is no preference for a nucleotide. LETTERS NATURE|Vol 452|13 March 2008 216 Nature Publishing Group�2008 be methylated (Fig. 1e). Interestingly, most methylation occurred at the cytosine in the third position (Fig. 1e). The single-base resolution of BS-Seq allows determination of the precise boundaries between methylated and unmethylated regions. For example, we found that the boundary between tandem repeats and flanking DNA showed a sharp drop in methylation, but DNA methylation extended from inverted repeats into flanking DNA, showing a more gradual reduction (Fig. 1b). This apparent ?spread- ing?ofmethylationwasnotcorrelatedwithsiRNAspreading,because siRNA-abundance levels drop sharply at the flanks of both tandem and inverted repeats (Fig. 1b). We analysed the relationship between sequence context and pre- ference of methylation. We calculated the percentage methylation of all possible 7-mer sequences in which the methylated cytosine was either in the fifth position (allowing an analysis of four nucleotides upstream of CG, CHG and CHH methylation; Fig. 2 and Supple- mentary Table 3) or in the first position (allowing analysis of six nucleotides following the methylated cytosine; Supplementary Fig. 8 and Supplementary Table 4). To ensure that sequence preferences were not simply 7-mers enriched in particular components of the genome,weanalysedallofchromosome1,onlysequencespreviously defined to be methylated by methyl-DNA immunoprecipitation, or a group of 9,507 body-methylated genes containing mostly CG methylation 3 (Fig. 2 and Supplementary Figs 8 and 9). We observed a surprisingly high level of sequence context specificity. The 7-mers with the highest and lowest levels of methylation showed a 13-fold difference for CG-methylation, an 11-fold difference for CHG methylation, and .900-fold difference for CHH methylation (Sup- plementary Table 3). Sequences with the lowest CG methylation were highly enriched for the sequence ACGT (Fig. 2 and Supplementary Fig. 9). Poorly methylated CHG sites were depleted of upstream cytosines but tended to contain cytosine after the methylated cytosine. This trend is consistent with a nearest-neighbour analysis of wheat germ DNA thatfoundCAGandCTGsitesmethylatedatahigherlevelthanCCG sites 10 . Highly methylated CHH sequences had a very specific con- figuration, with a tendency for cytosines and CG dinucleotides to be present upstream (Supplementary Table 3) and the sequence TA following the methylated cytosine. In contrast, poorly methylated CHH sequences always contained a cytosine after the methylated cytosine, and frequently contained a cytosine but always lacked an adenine two nucleotides downstream (Fig. 2 and Supplemen- tary Fig. 8). These results are consistent with data from individual plant genes showing that cytosines preceding a cytosine are under- methylated whereas those following a cytosine are more heavily methylated 11?13 , and with asymmetric methylation in mammalian genomes thatis foundatCTand CAsequences more frequently than CC sequences 14 . It is also of interest that Arabidopsis telomere sequences(CCCTAAA) n arecomposedofnearlyoptimalasymmetric target units, possibly explaining the high methylation of the third cytosines (Fig. 1e). Although the molecular basis for these trends is unknown, the results suggest that DNA methyltransferases show strong sequence preferences beyond the CG, CHG and CHH con- texts. Finally, we found that regions with higher concentrations of CG dinucleotides were more heavily methylated at CG sites (Supple- mentary Fig. 10). Interestingly, this is different from observations in mammalian genomes, which show the opposite trend: CGs are depleted in methylated regions and at a higher density in unmethy- lated CpG islands. We used autocorrelation analysis to examine the correlation between methylation in different sequence contexts and methylation at adjacent residues. We observed significant correlation between methylated cytosines for distances up to 5,000 nucleotides or more?probablyareflectionofregionalfociofmethylationthrough- out the genome and of large blocks of pericentromeric heterochro- matin (Supplementary Fig. 11 and Supplementary Table 5). We also found a high correlation of CHG and CHH methylation within several nucleotides downstream of methylated CG sites, and a tend- encyforCHHmethylation fournucleotidesdownstreamofmethyla- tionatCHGsites(SupplementaryFig.12andSupplementaryTable5). CHH versus CHH Corr elation a Distance (nucleotides) CHH versus CHH Power b Cycles per 100 bases Corr elation CHG versus CHG Distance (nucleotides) c 0.16 0.20 0.24 0.28 0.32 0.36 0.40 110203040506070 0.65 0.70 0.75 0.80 0.85 1 50 100 150 200 250 300 350 400 450 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 3 4 5 6 7 8 9 10111213141516171819202122 Figure 3 | Methylation shows periodic patterns. a, c, Correlation of the methylation status of cytosines in a CHH (a) and CHG (c) context. The x axisindicatesthedistancebetweenthetwocytosines.Theyaxisindicatesthe levelofautocorrelationinmethylation.Theredlineshowsarunningaverage of windows that are 62 bases around a single base. b, Fourier transform analysisofCHHmethylationcorrelation.Thexaxisindicatesthenumberof cycles per 100 bases. The y axis is the amplitude of the corresponding frequency. The peak at position 10 represents a periodicity of ten nucleotides,withaP-valuesmallerthan10 2108 forobservingthisperiodicity value by chance in random permutations of the genome. In a?c, Monte Carlosamplingofthreedatasets,eachconsistingofhalfthedata,wasusedto compute the mean and standard deviations of the autocorrelations and Fourier transforms. Mean values are shown, and error bars (a and b) represent standard deviations. In a and b, methylation from the whole genomewasanalysed,whereas,inc,theanalysiswasrestrictedtopreviously defined methylated sequences 3 (see Supplementary Fig. 15 for details). NATURE|Vol 452|13 March 2008 LETTERS 217 Nature Publishing Group�2008 Thesedatasuggestcomplexinteractionsbetweenthedifferenttypesof methylation. We analysed the propensity for full methylation of the strand- symmetrical CG and partially symmetrical CHG sequences. As expected, CG methylation on one strand was highly correlated with CGmethylationontheopposingstrand.Wealsosawahighcorrelation for CHG methylation of the two strands, showing that, as for CG methylation,CHGsitesshowastrongtendencyforsymmetricalmethy- lation (Supplementary Fig. 12). Unexpectedly, we observed a correla- tion between CHH methylation on one strand, and methylation at the cytosine three nucleotides downstream and on the opposite strand (Supplementary Fig. 12 and Supplementary Table 5). Because the sequence of suchsites is CHHG, this shows that ?asymmetric?methyla- tionshowsapropensityforsymmetricalmethylationatthesesites,even thoughmethylationonCHHGsitesisnotparticularlyprominentinthe genome (Supplementary Fig. 8 and Supplementary Table 4). Autocorrelation analysis also revealed a marked periodicity of ten nucleotides (the length of one helical DNA turn) for CHH methyla- tion(Fig. 3a,b).Weconfirmedthisperiod usingdatafrom thewhole genome and from regions previously defined to be methylated, and confirmed that the periodicity was not caused by our computational filteringofthedata(SupplementaryFig.13).Weobservedthisperiod both when looking at the average methylation of cytosines in the genome (Fig. 3a, b and Supplementary Fig. 13) and when individual reads are examined directly (Supplementary Fig. 14). Mammalian DNA methyltransferase 3a (Dnmt3a) was recently shown to act as a tetramer with DNA methyltransferase 3-like protein (Dnmt3L), and two active sites methylate two CG sequences spaced ,8?10nucleo- tides apart 15 . Because DOMAINS REARRANGED METHYLASE 2 (DRM2) is the main enzyme controlling asymmetric methylation in Arabidopsis and is a homologue of Dnmt3 16 , these data suggest that themechanismofactionofthese enzymesmaybeconservedbetween plants and mammals. Autocorrelation also showed a period of 167 nucleotides (Fig. 3c and Supplementary Fig. 15), which is similar to, but slightly shorter than, estimates of the average spacing of nucleosomes in plant chro- matin 17?19 . One explanation for this period is that nucleosomes or particular histone modifications might dictate access to the DNA by methyltransferase proteins. Furthermore, the slightly shorter length of 167 nucleotides relative to most estimates of plant nucleosome repeat length (175?185 nucleotides) 17?19 suggests that DNA- methylated chromatin may be more compact because of shorter linker regions or depletion in linker histones 20 . We used BS-Seq to study the genome-wide effects of a variety of ArabidopsismethyltransferasemutantsonDNAmethylation(Fig.4). The MET1, CMT3 and DRM1/DRM2 DNA methyltransferase enzymesaremostlyresponsibleforCG,CHGandCHHmethylation, respectively, although at many loci CHG and CHH methylation is redundantly controlled by CMT3 and DRM1/DRM2 (refs 1 and 12). We sequenced and mapped ,90million nucleotides of BS-Seq data from each of several combinations of DNA methyltransferase mutants (Supplementary Table 1) including met1 single mutants, cmt3 single mutants, drm1drm2 double mutants, met1cmt3 double mutants, met1drm1drm2 triple mutants and drm1drm2cmt3 triple mutants 21 . We then analysed the effect of these mutants on global methylation, on methylation in genes and chromosomes, and on methylation in rDNA and telomeres (Supplementary Table 6, Figs 1e and 4, and Supplementary Figs 7 and 16). The met1 single mutant, or any mutant combination containing met1, essentially eliminated CG methylation throughout the genome. For instance, gene-body methylation, which is almost exclusively CG, was elimi- nated in all met1-containing strains (Fig. 4a). Surprisingly, in the met1drm1drm2triplemutant,weobservedamarkedhypermethyla- tion of CHG sites in the bodies of genes (Fig. 4a). This methylation was skewed towards the 39end and in this way assumed a pattern of methylation similar to the missing CG methylation. Although pre- vious studies have suggested that the drm1drm2cmt3 triple mutant eliminatesCHGandCHHmethylation 12 ,BS-Seqdatashowsresidual methylation (Supplementary Table 6), particularly in pericentro- meric heterochromatin (Fig. 4b), suggesting that another enzyme is involved 22 . Furthermore, the met1cmt3 double mutant was equally effective in reducing CHH methylation, as was drm1drm2cmt3 (SupplementaryTable6),suggestingthatCHHmethylationdepends in part on the presence of CG and CHG methylation. These com- pensating behaviours suggest that the different DNA methyltrans- ferases act redundantly, and help to explain the viability of these mutant combinations, whereas the met1cmt3drm1drm2 quadruple mutant causes embryonic lethality 21 . TheBS-Seqproceduredescribedhereshouldbegenerallyusefulin other organisms. For example, we applied BS-Seq to quantify the 0 5 10 15 20 25 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 0 10 20 30 40 0 2 4 6 8 Number of methylated CG sites per one million sequenced nucleotides Methylation level (%) Methylation level (%) 8,000 4,000 6,000 2,000 0 WT Uhrf1 ?/? a b c CHG CHHCG CHGCG CHH WT met1 drm1 drm2 cmt3 met1 drm1 drm2 met1 cmt3 drm1 drm2 cmt3 1 Mb1 Mb1 Mb Upstream DownstreamTranscribed region Upstream DownstreamTranscribed region Upstream DownstreamTranscribed region Figure 4 | BS-Seq profiling of methylation mutants in Arabidopsis and mouse. a,BS-Seqdatamappingtoprotein-codinggeneswasplottedin500- nucleotide sliding windows. Two vertical blue lines mark the boundaries between upstream regions and gene bodies (left) and between gene bodies and downstream regions (right). b, Distribution of methylation along chromosome 4 in 25-nucleotide sliding windows. In a and b, a horizontal blue line indicates zero per cent methylation. c, Comparison of the amount of CG methylation in wild type and mouse Uhrf1 ?/? embryonic stem cells, represented as the average number of CGs appearing per million sequenced nucleotides. LETTERS NATURE|Vol 452|13 March 2008 218 Nature Publishing Group�2008 overall genomic methylation difference between wild-type mouse embryonic stem cells and cells carrying a mutation in the Uhrf1 gene recently shown to control maintenance of CG methylation 23,24 .By analysing,60million nucleotides of shotgun sequencing data from each, we found that Uhrf1 ?/? cells contained only 25% of the CpG methylation level of the wild type (Fig. 4c). Furthermore, to demon- strate that the complete analysis pipeline used for Arabidopsis is applicable to larger genomes, we produced a library from mouse germ-cell tissue and generated,46million nucleotides of high qua- lity mapped BS-Seq data. Approximately 66% of the reads mapped uniquely?a level only slightly lower than that of Arabidopsis (Supplementary Table 1), suggesting that it is practical to apply BS-Seq to entire mammalian genomes. In summary, BS-Seq analysis of wild type and methyltrans- ferase mutants has allowed a more detailed characterization of the Arabidopsis methylome. In addition, the computational approaches developed in this study should be generally useful for other short- read sequencing genomics approaches. An installation of the UCSC browserallowingcommunityaccesstodetailed methylationpatterns of individual genes and a source code distribution of the computa- tional methods are available at http://epigenomics.mcdb.ucla.edu/ BS-Seq/. METHODS SUMMARY Construction and sequencing of DNA libraries. Bisulphite treatment of DNA wasperformedasdescribedpreviously 25 ,exceptthatadaptorsequencesandPCR conditions were modified and optimized for this study. Library generation and ultra-high-throughput sequencing were carried out according to manufacturer instructions (Illumina). Processingofsequencedataandmappingofreads.RawdatafromIlluminaGA were processed using the initial stages of the Solexa software pipeline (Illumina) into short reads, except that per-lane per-cycle multidimensional gaussian mix- ture models (GMMs) were developed to optimize base call A-versus-C-versus- G-versus-Tprobabilitydistributionaccuraciesateachsequencedbasecompared to the Solexa software pipeline?s ?_prb? files. Sequenced reads were mapped to reference genomes fully using per-base probabilities from the GMMs using highly optimized novel C11 tools. Sequences that mapped to more than one position with similar scores (within 1% of the maximum likelihood mapping) were removed to retain only reads that mapped uniquely. To eliminate uncon- verted bisulphite reads, a filter discarded reads with three or more consecutive methylated cytosines when each of these was in a CHH context, resulting in a loss of,0.23% of reads. This filter was effective and gave only minimal loss of true CHH methylation (Supplementary Table 1 and Supplementary Figs 13, 17 and 18). Validation of BS-Seq results. Traditional bisulphite sequencing was used to validate BS-Seq results at select loci (Supplementary Table 2 and Supplemen- tary Figs 4, 6 and 17). The PCR primers used in the validation are listed in Supplementary Table 7. Received 28 November 2007; accepted 30 January 2008. Published online 17 February 2008. 1. Henderson, I. R. & Jacobsen, S. E. Epigenetic inheritance in plants. Nature 447, 418?424 (2007). 2. Goll, M. G. & Bestor, T. H. Eukaryotic cytosine methyltransferases. Annu. Rev. Biochem. 74, 481?514 (2005). 3. Zhang, X. et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126, 1189?1201 (2006). 4. Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nature Genet. 39, 61?69 (2007). 5. Vaughn, M.W. et al.Epigenetic naturalvariation in Arabidopsis thaliana. PLoS Biol. 5, e174 (2007). 6. Bentley, D. R. Whole-genome re-sequencing. Curr. Opin. Genet. Dev. 16, 545?552 (2006). 7. Frommer,M.etal.Agenomicsequencingprotocolthatyieldsapositivedisplayof 5-methylcytosineresiduesinindividualDNAstrands.Proc.NatlAcad.Sci.USA89, 1827?1831 (1992). 8. Ngernprasirtsiri, J., Kobayashi, H. & Akazawa, T. DNA methylation as a mechanism of transcriptional regulation in nonphotosynthetic plastids in plant cells. Proc. Natl Acad. Sci. USA 85, 4750?4754 (1988). 9. Tran, R. K. et al. DNA methylation profiling identifies CG methylation clusters in Arabidopsis genes. Curr. Biol. 15, 154?159 (2005). 10. Gruenbaum, Y., Naveh-Many, T., Cedar, H. & Razin, A. Sequence specificity of methylation in higher plant DNA. Nature 292, 860?862 (1981). 11. Meyer,P.,Niedenhof,I.&tenLohuis,M.Evidenceforcytosinemethylationofnon- symmetrical sequences in transgenic Petunia hybrida. EMBO J. 13, 2084?2088 (1994). 12. Cao, X. & Jacobsen, S. E. Locus-specific control of asymmetric and CpNpG methylationbytheDRMandCMT3methyltransferasegenes.Proc.NatlAcad.Sci. USA 99 (Suppl 4), 16491?16498 (2002). 13. Dieguez, M. J., Vaucheret, H., Paszkowski, J. & Mittelsten Scheid, O. Cytosine methylation at CG and CNG sites is not a prerequisite for the initiation of transcriptionalgenesilencinginplants,butitisrequiredforitsmaintenance. Mol. Gen. Genet. 259, 207?215 (1998). 14. Ramsahoye,B.H.etal.Non-CpGmethylationisprevalentinembryonicstemcells and may bemediated byDNA methyltransferase 3a. Proc. Natl Acad. Sci. USA 97, 5237?5242 (2000). 15. Jia, D., Jurkowska, R. Z., Zhang, X., Jeltsch, A. & Cheng, X. Structure of Dnmt3a bound to Dnmt3L suggests a model for de novo DNA methylation. Nature 449, 248?251 (2007). 16. Cao, X. et al. Conserved plant genes with similarity to mammalian de novo DNA methyltransferases. Proc. Natl Acad. Sci. USA 97, 4979?4984 (2000). 17. Bers, E. P., Singh, N. P., Pardonen, V. A., Lutova, L. A. & Zalensky, A. O. Nucleosomal structure and histone H1 subfractional composition of pea (Pisum sativum) root nodules, radicles and callus chromatin. Plant Mol. Biol. 20, 1089?1096 (1992). 18. Vershinin,A.V.&Heslop-Harrison,J.S.Comparativeanalysisofthenucleosomal structure of rye, wheat and their relatives. Plant Mol. Biol. 36, 149?161 (1998). 19. Fulnecek,J.,Matyasek,R.,Kovarik,A.&Bezdek,M.Mappingof5-methylcytosine residues in Nicotiana tabacum 5S rRNA genes by genomic sequencing. Mol. Gen. Genet. 259, 133?141 (1998). 20. Fan, Y. et al. Histone H1 depletion in mammals alters global chromatin structure but causes specific changes in gene regulation. Cell 123, 1199?1212 (2005). 21. Zhang, X. & Jacobsen, S. E. Genetic analyses of DNA methyltransferases in Arabidopsis thaliana. Cold Spring Harb. Symp. Quant. Biol. 71, 439?447 (2006). 22. Henderson,I.R.etal.DissectingArabidopsisthalianaDICERfunctioninsmallRNA processing, gene silencing and DNA methylation patterning. Nature Genet. 38, 721?725 (2006). 23. Bostick, M. et al. UHRF1 plays a role in maintaining DNA methylation in mammalian cells. Science 317, 1760?1764 (2007). 24. Sharif, J. et al. The SRA protein Np95 mediates epigenetic inheritance by recruiting Dnmt1 to methylated DNA. Nature 450, 908?912 (2007). 25. Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868?5877 (2005). 26. Rajagopalan,R.,Vaucheret,H.,Trejo,J.&Bartel,D.P.Adiverseandevolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 20, 3407?3425 (2006). Supplementary Information is linked to the online version of the paper at www.nature.com/nature. Acknowledgements We thank Y. Bernatavichute for nuclear DNA isolation protocols, A. Clarke for providing embryonic stem cell DNA, A. Girard and G. Hannon for providing mouse germ cell DNA, J. Hetzel for technical assistance, and C. F. Li for assistance with rDNA annotation. S.F. is a Howard Hughes Medical Institute Fellow of the Life Science Research Foundation. X.Z. was supported by a fellowship from the Jonsson Cancer Center Foundation. S.E.J. is an investigator of the Howard Hughes Medical Institute. This work was supported in part by grants from the NSF Plant Genome Research Program and the NIH, and some aspects of the work were performed in the UCLA DNA Microarray Facility. Author Contributions S.J.C. developed computational methods for mapping and base-calling. S.F. designed and created DNA libraries and performed all molecular biologyexperiments.S.F.,Z.C.,B.M.andS.F.N.sequencedthelibraries.M.P.,S.J.C., S.F. and S.E.J. analysed data. S.E.J. and M.P. designed and directed the study. X.Z., C.D.H. and S.P. assisted in the design of experiments. S.F. and S.J.C. wrote the manuscript. Author Information The authors declare competing financial interests: details accompany the full-text HTML version of the paperat www.nature.com/nature. Reprints and permissions information is available at www.nature.com/reprints. Correspondence and requests for materials should be addressed to S.E.J. (jacobsen@ucla.edu) or M.P. (matteop@mcdb.ucla.edu). NATURE|Vol 452|13 March 2008 LETTERS 219 Nature Publishing Group�2008 "
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