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1 Cellular Biochemistry, Animal Resource Sciences/Veterinary Medical Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
2 Departments of Medicine (Hematology) and Molecular Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
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Introduction |
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Recent progress in understanding genome sequence information has raised questions about these gene- and CpG island-centric views. Genes without CpG islands have been also reported recently to have tissue-specific methylation in 5'-flanking regions (Cho et al. 2001; Hattori et al. 2004b; Nishino et al. 2004; Ko et al. 2005). While a previous study has estimated that more than 90% of NotI sites represent CpG islands in mammalian genome (Lindsay & Bird 1987), the recent completion of mouse genome sequencing reveals that 33% of NotI sites distributes in non-CpG islands (Fazzari & Greally 2004). Meanwhile, previous RLGS-based studies have assumed that repetitive elements are rarely detected in RLGS screens (Shiota et al. 2002; Matsuyama et al. 2003), as repetitive elements are generally methylated to silence their transposable activity (Yoder et al. 1997). However, even some repetitive elements can be differentially methylated between tissues (Chalitchagorn et al. 2004; Burden et al. 2005), and analysis of genome sequence database also showed that 36% of NotI loci represent repetitive elements in the mouse genome (Fazzari & Greally 2004).
We have previously defined epigenetic distance as number of differentially methylated RLGS loci, and found that this distance reflects similarities in cell differentiation lineages (Shiota et al. 2002). We have previously analyzed the methylation profiles of randomly chosen NotI sites from RLGS studies, defining the genomic source of the differentially methylated loci using Virtual Image RLGS (Vi-RLGS), which is an in silico RLGS simulation based on genome sequence predictions of restriction enzyme digestions (Matsuyama et al. 2003; Hattori et al. 2004a). In the present study, to examine the distribution of loci in the DNA methylation status, we analyzed the methylation status of 213 potentially informative NotI loci identified using Vi-RLGS of 18 types of mouse tissues and cells by methylation-sensitive quantitative real-time PCR and characterized the T-DMRs. In addition, we analyzed the methylation profiles of T-DMRs by agglomerative hierarchical clustering to evaluate the correlation between cellular phenotype and epigenetic status.
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Results |
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For sampling non-redundant NotI loci identified by RLGS, the Vi-RLGS software was used (Matsuyama et al. 2003; Hattori et al. 2004a). The sequences of 213 non-redundant Vi-RLGS spots, each of which represents one NotI-PstI fragment, were sampled from 2170 spots of the Vi-RLGS image. It has been reported that the mouse genome has 6057 NotI sites that are distributed among every chromosome in the genome (Fazzari & Greally 2004). The 213 NotI loci we used represent every chromosome except for the Y chromosome, where there are very few NotI sites (Supplementary Table S1). Of the 213 sampled NotI sites, some were located in CpG islands or non-CpG island sequences, and could be categorized further as unique or repetitive sequences (Fig. 1A). Repetitive elements were underrepresented compared to the total distribution of NotI loci because the redundant repetitive NotI-PstI fragments, which appear as aggregated large spots in the Vi-RLGS profile, were omitted from the sampling. We also analyzed the number of CpGs and (G + C) content in 500-bp sequences surrounding each of the 213 NotI loci (Fig. 1B). These 213 NotI loci are obviously distributed in a wide range of CpG densities (0.8–16.6 CpGs/100 bp) and (G + C) content (35.4%–79.4%), and do not merely represent a biased sampling from the genome with uniform base compositional contexts.
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Genomic DNA was extracted from 18 types of tissues and cells of male C57BL/6 mice [brain, placenta, kidney, testis, sperm, liver, spleen, pituitary, skeletal muscle (soleus and gastrocnemius), brown adipocytes, brown preadipocytes, white adipocytes, white preadipocytes, ES cells (stem and differentiated conditions) and EG cells (stem and differentiated conditions)], and subsequently digested by NotI. Analysis of the DNA methylation status on the unbiased sample of 213 NotI loci was performed by quantitative real-time PCR. The methylation level at each locus was expressed as percentage of the proportion of the amount of undigested DNA in the NotI-treated genomic DNA sample to that in the NotI-untreated sample.
To examine if the methylation profiles of the Vi-RLGS-based NotI loci actually represent similarities in cell types as well as RLGS profile, agglomerative hierarchical clustering was applied for analysis (Fig. 2). The data are shown in a matrix format, representing methylation ratio by blue–yellow color scale. Euclidean distance (the square root of the sum of the squared differences in each dimension) was applied to reflect the range of methylation ratios. The distance was measured using the group average method (average link). The euclidean distance matrix was determined from the 213 dimensional vectors of DNA methylation ratios based on the total sample of NotI loci. The methylation profiles of tissues and cells (horizontal colored bars), consisting of various levels of methylation at the NotI sites, showed differences reflecting the phenotypic diversity among the tissues and cells. The clustering of the profiles revealed a remarkably ordered pattern, and shows that many but not all of NotI loci are tissue-dependently and differentially methylated, whereas many loci showed hypomethylated status universally in all of the sampled tissues and cells. The data also show a greater relatedness between tissues and cell types having common developmental pathways or similar phenotypes. Germ-line cells (sperm and testis) showed strong similarity in methylation profiles, creating one small cluster separate from other somatic tissues and cells. Stem ES and EG cells, both of which have comparable pluripotent phenotypes, formed a discrete cluster as well. Two skeletal muscle tissues sampled from different parts of the body (soleus and gastrocnemius) also showed a similarity compared to other tissues. The remarkable cluster within somatic tissues represents the adipose tissue group. Each of combinations of adipocytes and preadipocytes (white adipocytes and white preadipocytes/brown adipocytes and brown preadipocytes) formed discrete pairs of clusters. These clusters fell into a group based solely on similarities in patterns of methylation. The adipocyte cluster, skeletal muscle cluster and kidney, derived commonly from mesodermal lineage, were grouped into one large cluster. Pituitary, the majority of the sample represents anterior pituitary, is grouped not with brain, an ectodermal tissue, but with liver, a tissue with a similar endodermal origin. Differentiated ES and EG cells, consisting of multiple cell types, also formed a cluster. Spleen and placenta, inherently heterogeneous groups of cells, form a discrete cluster together with differentiated ES and EG cells. These results demonstrated that the clustering of methylation profiles of unbiased sampled Vi-RLGS NotI loci can reveal similarities in developmental pathways and functional phenotypes within and between stem, germ and somatic cells.
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A total of 3834 methylation analyses demonstrate a non-uniform distribution of methylation status (Fig. 3). Whereas two-thirds (2647) of the measurements showed ratios indicating less than 10% methylation; hyper-methylation and intermediate methylation values were also observed (Fig. 3A). When analyzed in terms of CpG island status, the majority of the measurements at CpG islands (93%) show less than 10% methylation, but there also existed hypermethylated and intermediately methylated NotI loci (Fig. 3B). Non-CpG island loci show a bimodal distribution of methylation values, although intermediate methylation was also detected. Both unique and repetitive sequences also showed a range of methylation values (Fig. 3C). The results demonstrate that the diverse methylation status at NotI sites reflect information about the genomic contexts of these loci.
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Discussion |
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The disproportionate distribution of tissue-specific methylation in CpG-poor regions is consistent with previous reports about T-DMRs. CpG islands were formerly believed to maintain an unmethylated status so as to enable them to express associated genes ubiquitously (Yen et al. 1984; Antequera & Bird 1993; Cross & Bird 1995). T-DMRs have been identified at the edges of the CpG islands where the density of CpGs is lower than the centers of CpG islands (Imamura et al. 2001). This is consistent with the finding of T-DMRs in non-CpG islands, where the CpG density is lower; therefore, CpG density may be a key contributor to variability of methylation status. It has been reported that CpG-poor promoters are more prone to demethylation occurring in cancerous cells (Hatada et al. 2006). The greater flexibility in methylation states of regions less dense in CpGs may contribute to the diversity of methylation states that underlies heterogeneity of cell types.
One of the unsolved questions in typing of cells is the issue of the differentiation of white versus brown adipocytes from their precursors. It is still unknown whether brown and white adipocytes derive from the same preadipocytic precursors or whether they arise independently from undetermined mesenchymal stem cells (Rosen et al. 2000). Our finding that the close but distinguishable clusters of methylation patterns of these two types of adipocytes gives novel insights into understanding similarity in their origin and characteristics. No rigorous morphologic distinction between white and brown adipocytes in developing tissues is possible; even ultrastructural studies using electron microscopy cannot detect marked differences (Poissonnet et al. 1988). Molecular-based studies have reported several signaling factors, transcription factors and cell cycle regulators in the distinctive control pathways of white versus brown adipocyte differentiation (Puigserver et al. 1998; Cederberg et al. 2001; Picard et al. 2002; Hansen et al. 2004). Microarray analysis of gene expression reveals that white preadipocytes and brown preadipocytes have very similar gene expression profiles; even uncoupling protein 1 (UCP-1), a gene responsible for mitochondrial thermogenesis considered to be a marker for brown adipocytes (Klaus et al. 1995), can be seen in white preadipocytes and adipocytes (Boeuf et al. 2001). The present study demonstrated that each of the preadipocytes show greater similarity in DNA methylation profiles to their derived mature adipocytes rather than to the other types of preadipocyte. This is consistent with the belief that preadipocytes isolated separately from the white and brown adipose tissues are already committed to their distinct adipocyte lineages. On the other hand, both of the clusters formed a single large cluster, possibly reflecting the commonality in origins of these two adipocyte lineages relative to the other cell types tested.
The loci showing cell- or tissue-specific methylation were located not only 5'-upstream of the first exon but also in intragenic, intergenic or non-genic regions, raising the issue of the functional significance of these sites. It has also been reported in an RLGS-based study that almost half of the NotI loci that were methylated in a tissue-dependent manner were not located in 5'-promoter CpG islands (Song et al. 2005). These non-promoter T-DMRs may have more complex cis-regulatory properties rather than simple methyl-CpG-mediated gene silencing mechanisms. For example, Lorincz et al. have reported that intragenic CpG methylation forms a chromatin structure that causes reduced efficiency of RNA polymerase II elongation (Lorincz et al. 2004). It has also been demonstrated that the differentially methylated regions of the imprinted Igf2/H19 domain contribute to various levels of higher-order chromatin structure such as chromatin loops and insulator formation (Bell & Felsenfeld 2000; Hark et al. 2000; Murrell et al. 2004). Notably, there are several reports that cells having common developmental pathways have similarities in higher-order chromatin structure and spatial organization of chromosomes (Parada et al. 2004; Crawford et al. 2006). The link between DNA methylation profiles reflecting similarity in development and differentiation and these kinds of higher-order regulatory mechanisms of chromatin and chromosomes should be a logical next step in elucidating higher-order gene regulatory processes.
Numerous repetitive elements also formed tissue- or cell-type-specific DNA methylation profiles which distinguish tissue and cell types, suggesting a possible biological significance in development and differentiation. Although it is well recognized that repetitive elements are usually methylated to silence transposable activity (Yoder et al. 1997), their methylation levels can differ between some tissues (Chalitchagorn et al. 2004; Burden et al. 2005). It has been previously reported that hypomethylation and activation of intracisternal A-particle (IAP)-LTR occurs in spermatogenesis (Dupressoir & Heidmann 1996). The differential methylation of the individual transposable elements may modify their transposable activity and contribute to the generation of diversity in cellular differentiation.
The present study confirmed and extended the concept that epigenetic distance based on differential methylation of RLGS spots reflects cellular similarity and developmental origins (Shiota et al. 2002). The correlation between similarity in DNA methylation profiles and relatedness in cell types and origins might be applied for diagnosis of aberrant cells in diseases. It is well known that aberrant DNA methylation is a potential cause of tumorigenesis (Jones 2003; Ushijima 2005). As for cancer cells, several studies have reported that cells with similar origins or phenotypes show relatedness in RLGS profiles (Costello et al. 2000; Feltus et al. 2003). Epigenetic distance in DNA methylation profiles can be an indicator not only of commonality in developmental pathways but also of abnormality of cells, even in non-cancerous cellular alteration in diseases.
In summary, this study describes the DNA methylation profiles for a number of loci using Vi-RLGS, defining T-DMRs responsible for patterns of cytosine methylation that reflect tissue and cell type similarity, thereby allowing their developmental origins to be defined epigenetically. A common characteristic to the T-DMRs in diverse genomic contexts was their distinctive flanking CpG density. These data show how T-DMRs can be used to generate a novel means of specifying tissue and cell phenotypes, allowing cell lineages to be traced and functional characters (including aberrancy such as neoplasia) to be defined.
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Experimental procedures |
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ES and EG cells were cultured as previously described (Matsui et al. 1992; Matise et al. 2000). Spontaneous differentiation of ES and EG cells was induced by culturing in DMEM (Invitrogen, Carlsbad, CA) + 15% FCS (Biowest, Miami, FL) for 4–6 days in a monolayer culture after removal of LIF and feeder cells. These stem cells were derived from the same background strain (C57BL/6) as the male mice from which tissues were dissected. All tissues except for pituitary and sperm [brain, placenta, kidney, testis, liver, spleen, skeletal muscle (soleus and gastrocnemius), interscapular brown fat and epididymal white fat] were sampled from three to five mice. The pituitary gland was sampled from 27 mice, and sperm were obtained from cauda epididymides of 66 mice. Isolation of adipocytes and its precursors (stromal–vascular fraction) from each type of adipose tissue was performed essentially as previously described (Nechad et al. 1983). Briefly, each tissue was incubated in the presence of 0.2% (w/v) collagenase in 4 ml DMEM. After incubation, the tissue debris was removed by filtration through a 250-µm nylon screen. Cells were fractionated by the centrifugation at 700 g for 10 min. The mature adipocytes were separated by their floating to the surface of the cell suspension whereas the stromal–vascular fraction was isolated in the pellet. This separation process was repeated three times, and all the three collected samples were pooled for DNA extraction.
Preparation of genomic DNA
Genomic DNA was extracted as described previously (Ohgane et al. 1998). Briefly, cells or tissues were lysed in lysis buffer [150 mM EDTA, 10 mM Tris–HCl pH 8.0, 1% SDS, 100 µg/mL proteinase K (Merck, Darmstadt, Germany)]. The lysate was incubated at 55 °C for 20 min. Following two extractions in phenol/chloroform/isoamyl alcohol (50:49:1), the genomic DNA was precipitated in ethanol, and dissolved in 50 µL of TE buffer (10 mM Tris–HCl, 1 mM EDTA, pH 7.6).
Identification of 213 candidate informative NotI sites
Virtual image restriction landmark genomic scanning (Vi-RLGS) software, which simulates 2-D electrophoresis of DNA digested in silico by the combination of NotI-PvuII-PstI, using genome sequence information obtained from the UCSC genome browser [March 2005 (mm5) assembly, build 33 assembly by NCBI], was used to identify non-redundant NotI sites (Matsuyama et al. 2003). The redundant repetitive NotI-PstI fragments, which are plotted as aggregated large spots in the Vi-RLGS image, were removed from consideration. The Vi-RLGS simulation identified 2170 spots in the field corresponding to the observed experimental RLGS profile. The field was divided into 7 x 6 matrix areas, and the sequence information of 213 non-redundant spots was sampled from the matrix to sample 13–45 spots from each vertically divided area and 26–41 spots from each horizontally divided area (Supplementary Fig. S1 and Supplementary Table S2).
Methylation-sensitive quantitative real-time PCR
To analyze the methylation status of the 213 candidate informative NotI sites sampled from Vi-RLGS, methylation-sensitive quantitative real-time PCR was performed as previously described (Hattori et al. 2004a). Genomic DNA was digested by PstI, and the aliquot was treated subsequently with NotI. Primer sets were designed to amplify the genomic fragments including the NotI site. Ten nanograms of the genomic DNA treated with or without NotI was analyzed by real-time PCR with the primers. The amount of undigested DNA both in NotI-treated and -untreated genomic DNA was calculated by real-time PCR with SYBR Green I Universal PCR Master Mix by using ABI Prism 7500 Sequence Detection System (Applied Biosystems, Foster City, CA) according to the manufacturer's protocol. The methylation level at each locus was expressed as percentage of the proportion of the amount of undigested DNA in NotI-treated DNA compared to that in the untreated sample. The initial DNA amount in the reaction mix was normalized using primers which amplify genomic regions without NotI sites at the c-met and 5-lipo2 loci. The average of two independent assays was used for data analysis. The primers used in the analysis, methylation data and real-time PCR data are listed in Supplementary Tables S3–S5, respectively.
Data analysis
Two-dimensional agglomerative hierarchical clustering using the average linkage method was applied (Eisen et al. 1998). Euclidean distance was applied for the distance matrices. For the analysis, the publicly available program MeV was utilized <http://www.tigr.org/software/tm4/> (Saeed et al. 2003).
The genomic contexts of each of the NotI sites were retrieved from UCSC genome browser <http://genome.ucsc.edu> using the tracks within the browser. Overlap with CpG island or non-CpG island sequences was categorized using the CpG island track (Gardiner-Garden & Frommer 1987). The information of repetitive and low complexity sequence was retrieved from the RepeatMasker track (Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-3.0. 1996–2004 <http://www.repeatmasker.org>) using RepBase (Jurka 2000). The relative position to genes (upstream, exon and intron) were defined as positive when the Refseq genes or mRNAs appeared within 5 kbp of a NotI site.
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Acknowledgements |
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Footnotes |
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aThese authors contributed equally to the work.
bPresent address: Pfizer Japan Inc., Shinjuku Bunka Quint, 3-22-7 Yoyogi, Tokyo 151-8589, Japan.
cPresent address: Department of Medicine (Hematology), Albert Einstein College of Medicine, Bronx, NY, 1046 USA.
* Correspondence: E-mail: ashiota{at}mail.ecc.u-tokyo.ac.jp |
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Received: 20 March 2007
Accepted: 28 June 2007
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