|
|
|
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online 10 October 2007
doi: 10.1242/dev.001131
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Review |
1 University of California, 211 Koshland Hall, Berkeley, CA 94720, USA.
2 Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle,
WA 98109, USA.
3 Howard Hughes Medical Institute, 1100 Fairview Avenue North, Seattle, WA
98109, USA.
e-mail: steveh{at}fhcrc.org
SUMMARY
Cytosine methylation is the most common covalent modification of DNA in eukaryotes. DNA methylation has an important role in many aspects of biology, including development and disease. Methylation can be detected using bisulfite conversion, methylation-sensitive restriction enzymes, methyl-binding proteins and anti-methylcytosine antibodies. Combining these techniques with DNA microarrays and high-throughput sequencing has made the mapping of DNA methylation feasible on a genome-wide scale. Here we discuss recent developments and future directions for identifying and mapping methylation, in an effort to help colleagues to identify the approaches that best serve their research interests.
Introduction
DNA methylation is a unique and noteworthy process because it involves the
covalent modification of a cell's genetic material
(Chan et al., 2005
;
Freitag and Selker, 2005;
Gehring and Henikoff, 2007;
Goll and Bestor, 2005;
Klose and Bird, 2006;
Richards, 2006). At gene
promoters, methylation generally leads to transcriptional silencing. Complex
multicellular eukaryotes (plants and animals) primarily methylate DNA at
cytosines within CG dinucleotides. Following each round of DNA replication, a
DNA methyltransferase [from the Dnmt1 family
(Goll and Bestor, 2005)] fills
in the missing methylation on the newly synthesized strand, allowing faithful
maintenance of DNA methylation patterns through many rounds of cell division
and, at least in plants, through multiple generations
(Chan et al., 2005;
Soppe et al., 2000). For this
reason, differential methylation is a process that most closely approximates
genetic differences between cell types (or organisms) with identical DNA
sequence.
There is abundant evidence that aberrant DNA methylation can preclude
normal development. Knockout mutations of any one of the three mouse genes
that encode DNA methyltransferases (Dnmt1, Dnmt3a and
Dnmt3b) are lethal (Goll and
Bestor, 2005). Loss-of-function of MET1, the
Arabidopsis thaliana ortholog of Dnmt1, leads to
developmental abnormalities such as delayed flowering and reduced fertility,
which become very severe when additional methyltransferase genes
(CMT3 and/or DRM2) are mutated
(Xiao et al., 2006;
Zhang and Jacobsen, 2006).
Depletion of Dnmt1 in zebrafish embryos causes defects in terminal
differentiation of the intestine, exocrine pancreas and retina
(Rai et al., 2006). Either the
loss or gain of methylation at specific genes (FWA, SUP) can lead to
developmental abnormalities in Arabidopsis
(Gehring and Henikoff, 2007;
Jacobsen et al., 2000;
Soppe et al., 2000). In plants
and mammals, DNA methylation has a central role in genomic imprinting, the
monoallelic expression of a gene from either the maternal or the paternal copy
(Gehring and Henikoff, 2007;
Goll and Bestor, 2005).
X-chromosome inactivation in female mammals is also dependent on DNA
methylation (Heard and Disteche,
2006). The high failure rate of cloning by somatic nuclear
transfer has been attributed to improper reprogramming of DNA methylation
patterns in the donor nucleus (Meissner
and Jaenisch, 2006).
Despite the clear importance of DNA methylation, the extent to which
changes in somatic DNA methylation are involved in mammalian gene regulation
is unclear (Goll and Bestor,
2005; Walsh and Bestor,
1999). This is largely owing to our limited knowledge of DNA
methylation patterns. A study published in early 2006 estimated that DNA
methylation of less than 0.1% of the human genome has been analyzed in detail
(Schumacher et al., 2006). A
number of recent reports have considerably expanded our knowledge of
eukaryotic DNA methylation (Bibikova et
al., 2006a; Eckhardt et al.,
2006; Hellman and Chess,
2007; Keshet et al.,
2006; Khulan et al.,
2006; Rollins et al.,
2006; Weber et al.,
2007; Yuan et al.,
2006; Zhang et al.,
2006; Zilberman et al.,
2007). Nonetheless, we are just beginning to unravel genomic
methylation patterns, particularly in the complex genomes of vertebrates.
Fortunately, technological advances in high-density microarray technology and
high-throughput DNA sequencing should allow the comprehensive analysis of DNA
methylation to become a routine technique. Here we focus on the most promising
new methodologies and their suitability for addressing outstanding questions
about the role of DNA methylation in development and disease.
Methodologies for detection of DNA methylation
Many methods of DNA methylation analysis have been developed over the years
and are described in detail in a number of recent reviews
(Brena et al., 2006;
Callinan and Feinberg, 2006;
Laird, 2003;
Lieb et al., 2006;
Ushijima, 2005). All of these
approaches are based on one of three techniques: bisulfite conversion,
digestion with methylation-sensitive restriction enzymes, and affinity
purification of methylated DNA.
Bisulfite conversion
Methylated cytosine has roughly the same base-pairing characteristics as
unmethylated cytosine, and is thus indistinguishable by standard sequencing
approaches. To overcome this, genomic DNA can be treated with sodium bisulfite
(Clark et al., 1994;
Clark et al., 2006). Under
appropriate conditions, this treatment causes deamination of unmethylated
cytosine to uracil, while leaving methylated cytosine intact
(Fig. 1). PCR amplification of
converted DNA replaces the uracil with thymine. Analysis of the PCR product by
Sanger sequencing (Eckhardt et al.,
2006), pyrosequencing (Tost
and Gut, 2006), or mass spectrometry
(Ehrich et al., 2006;
Ehrich et al., 2005;
Schatz et al., 2004;
Schatz et al., 2006;
Tost et al., 2003), can be
used to quantify the extent of methylation at each cytosine. A potential issue
with bisulfite analysis is that it depends on the complete conversion of
unmethylated cytosines. In animal DNA, a sure sign of incomplete conversion is
abundant methylation at cytosines that are not in CG dinucleotides. In plant
DNA, this problem can be more difficult to detect, but will frequently
manifest itself as continuous stretches of heavily methylated cytosines in all
sequence contexts. Spiking the reaction with known unmethylated DNA, such as
yeast genomic DNA, can be used as a control. It is essential to ensure that
bisulfite-treated samples have been completely converted before utilizing them
in high-throughput applications.
|
; Bird and Southern,
1978; Lindsay and Bird,
1987). Most of these are inhibited by methylation of their
recognition site, whereas some, most notably McrBC, specifically digest
methylated DNA. Many variations of restriction enzyme-based methods have been
used in conjunction with genomic analysis
(Khulan et al., 2006;
Lippman et al., 2005;
Rollins et al., 2006;
Schumacher et al., 2006;
Tompa et al., 2002;
Yuan et al., 2006). Here we
briefly describe the general logic behind all such approaches.
Restriction enzyme-based methods either enrich for methylated DNA or
unmethylated DNA (Fig. 2).
Generally, comparisons are made in one of three ways: between a sample treated
with an enzyme or a cocktail of enzymes and an untreated control; between a
sample treated with a methylation-sensitive enzyme compared with a control
treated with a methylation-insensitive isoschizomer (HpaII and
MspI, see below); or between two test samples, such as two tissue
types or mutant and wild-type samples, both treated with the same enzyme. The
ability to enrich unmethylated DNA, by digesting away methylated DNA or by
isolating smaller fragments generated by methylation-inhibited enzymes, is
particularly useful for analyzing large, heavily methylated genomes (as
discussed in more detail below). In the human genome, over 60% of CG sites are
methylated (Goll and Bestor,
2005), so enriching unmethylated DNA significantly reduces the
complexity of the sample. An important limitation is that all restriction
enzyme-based techniques are limited to analysis of methylation within
recognition sites.
The most commonly used restriction enzymes are the isoschizomers
HpaII and MspI, which recognize the sequence CCGG.
HpaII is blocked by methylation of either cytosine, whereas
MspI is blocked only by methylation of the outer C
(Korch and Hagblom, 1986;
Waalwijk and Flavell, 1978).
In mammalian genomes, where methylation is almost exclusively in CG sites
(Goll and Bestor, 2005),
HpaII is inhibited and MspI is not. In plant genomes, where
methylation of cytosines in the CNG context is also common, MspI can
be used to detect CNG methylation. Another useful enzyme employed in genomic
studies is McrBC (Lippman et al.,
2004; Rollins et al.,
2006; Schumacher et al.,
2006). McrBC is an E. coli endonuclease that cleaves
between two methylated cytosines in the context (G/A)metC
(Sutherland et al., 1992). The
two sites can be separated by up to 3 kb, but the optimal separation is 55-100
bp (Gowher et al., 2000;
Zhou et al., 2002). For this
reason, McrBC is an excellent tool for the removal of densely methylated DNA.
Although less of an issue with McrBC, sequence polymorphisms between samples
can mimic methylation differences if they affect the enzyme recognition site.
Therefore, it is safest to use restriction enzymes to compare samples that
have no or little polymorphism, such as different tissues from the same
organism. Alternatively, the MspI/HpaII isoschizomer pair
can be used to control for polymorphic sites
(Khulan et al., 2006).
Affinity purification
The most recent and simplest way to enrich methylated DNA is by affinity
purification (Fig. 3). One
approach is to take advantage of the methyl-binding domain (MBD), which binds
methylated CG sites. A tagged MBD domain expressed in E. coli is
affinity purified and the MBD column is subsequently used to purify methylated
DNA (Cross et al., 1994;
Selker et al., 2003;
Zhang et al., 2006).
Alternatively, a commercially available monoclonal antibody that specifically
recognizes methylated cytosine can be used to immunoprecipitate methylated DNA
(Keshet et al., 2006;
Reynaud et al., 1992;
Weber et al., 2005;
Weber et al., 2007;
Zhang et al., 2006;
Zilberman et al., 2007). For
plant researchers, a potential advantage is that the MBD method purifies only
CG-methylated DNA, whereas the antibody will work against DNA methylated in
any context. However, as almost all the methylated loci that have been
characterized in plants have CG methylation, the results obtained with the two
methods should be broadly similar. For most researchers, the commercial
availability of the antibody combined with wide utilization of
immunoprecipitation will make this the method of choice for enriching
methylated DNA.
An important point regarding all affinity-based techniques is that they
measure the density of methylation in a given region. Therefore, a methylated
stretch of DNA where methylation target sites (CG sites in animals) are sparse
might be difficult to differentiate from an unmethylated region. This is
particularly an issue with mammalian genomes, where CG density is generally
low and CG-dense sequences are typically unmethylated
(Weber et al., 2007).
A potential twist on the affinity-based approach is to enrich for unmethylated DNA by isolating the unbound fraction from either affinity method. The ratio of antibody (or MBD domain) to DNA would have to be carefully optimized to ensure that essentially all methylated DNA is removed. Alternatively, unmethylated DNA prepared by McrBC digestion could be further enriched by the removal of residual methylated DNA by affinity reagents. This approach would be especially suitable for analyses of mammalian and other heavily methylated genomes because it would substantially reduce sample complexity and would overcome the limitation imposed by restriction enzyme recognition sites.
|
A number of approaches exist that enable the large-scale analysis of DNA
methylation. The Human Epigenome Project
(www.epigenome.org)
has used standard sequencing approaches to sequence a massive amount of
bisulfite-converted DNA from human tissues and primary cells, and has
identified a substantial number of tissue-specific differentially methylated
regions (DMRs) (Eckhardt et al.,
2006). Another study used restriction enzymes and standard cloning
and sequencing to analyze almost 14 Mb of unmethylated human DNA and over 8 Mb
of methylated DNA (Rollins et al.,
2006). These approaches, although highly informative, are
expensive and labor-intensive ventures that are beyond the capabilities of
most laboratories. Here, we discuss two approaches that, either currently or
in the near future, can be used by any laboratory to perform genome-wide DNA
methylation analyses: DNA microarrays and high-throughput DNA sequencing.
|
;
Tompa et al., 2002).
Fortunately, high-quality commercial oligonucleotide arrays - bead arrays made
by Illumina, lithographic arrays made by Affymetrix, adaptive lithographic
arrays made by NimbleGen, and inkjet arrays manufactured by Agilent - are
widely available. Both the design and the technology behind an array influence
their utility in methylation analysis. For example, the Illumina arrays are
designed to analyze bisulfite-converted DNA, whereas the other arrays are well
suited for restriction enzyme- and affinity-based assays
(Fig. 4).
Bead arrays (Illumina)
The bead array-based analysis of DNA methylation developed by Illumina is
an outgrowth of their genotyping method
(Bibikova et al., 2006b;
Fan et al., 2006), which is
designed to provide single-base resolution, although two or more closely
spaced cytosines may have to be analyzed together. Bisulfite-converted DNA is
assayed with two primers, each labeled with a different fluorescent dye. One
primer is designed to hybridize if the cytosine is methylated (and
unconverted), whereas the other will only hybridize to a converted sequence.
The two primers are used in a PCR reaction with a locus-specific
methylation-insensitive primer. The ratio of the PCR products is ascertained
using Illumina's Sentrix Array Matrix bead array platform, which can assay up
to 1536 sites in 96 samples in one experiment. This approach provides less
coverage than other array-based methods, and necessitates the development and
evaluation of a large set of selective primers, thus limiting its utility for
de novo genome analysis. The strength of the technique is that it provides
quantitative evaluation of specific cytosines and can process many samples in
parallel. Therefore, this method is well suited to compare a set of known
methylated loci among a large number of cell lines or individuals to ascertain
methylation polymorphisms (Fig.
4). Using this approach, a set of methylation markers that could
distinguish lung carcinoma samples from normal tissue was identified
(Bibikova et al., 2006b). A
subsequent study identified diagnostic differences between human embryonic
stem (ES) cell lines and differentiated cells
(Bibikova et al., 2006a).
Short oligonucleotide arrays (Affymetrix)
Affymetrix GeneChip arrays are produced using photolithographic technology
to achieve very high feature density, with millions of probes per chip
(Dalma-Weiszhausz et al.,
2006). Each feature consists of 25-mer oligonucleotides. These
short probes provide good specificity, but suffer from decreased sensitivity
and increased random signal variation (noise) compared with longer probes
(Kreil et al., 2006). Each
chip is designed for `single channel' hybridization - they are hybridized with
one sample at a time. To compare samples, such as two cell lines, each sample
is hybridized to a separate array and the resulting signals are compared.
Generally, each sample is hybridized at least three times to allow statistical
treatment of the data to identify significant differences. For methylation
analysis, a tiling design is most useful, with equidistantly spaced probes
across portions of a genome or an entire genome. Tiling arrays are available
for the human, mouse and Arabidopsis genomes, as well as for human
and mouse promoters (Drosophila, C. elegans, S. cerevisiae and S.
pombe arrays are also available, but these organisms lack DNA
methylation). Restriction enzyme-enriched unmethylated DNA from human brain
tissue has been analyzed using Affymetrix tiling arrays covering chromosomes
21 and 22 (Schumacher et al.,
2006). This study found that most of the unmethylated sites were
close to the 5' end of genes, consistent with the need to keep promoters
free of methylation. The Arabidopsis array has been successfully used
to profile methylated DNA enriched by MBD and antibody affinity purification
to yield a high-resolution methylation map of the entire Arabidopsis
genome (Zhang et al., 2006).
Both purification methods produced comparable results. Most academic
microarray facilities are set up to handle Affymetrix arrays, making them a
convenient resource for researchers. However, the lithographic mask technology
makes custom arrays prohibitively expensive, so that most researchers are
effectively limited to the standard array designs.
|
).
Tiling arrays for chromatin immunoprecipitation and methylation analysis
consist of 
380,000 (soon to be expanded to over 2 million) 60-mer
oligonucleotide probes. Agilent manufactures microarrays consisting of
240,000 60-mers using inkjet technology
(Wolber et al., 2006). Both
arrays are dual channel - two samples are labeled with different fluorescent
dyes, such as immunoprecipitated test DNA and control DNA, and are hybridized
on a single chip. Hybridizing the test and control samples on the same array
controls for between-array variation and thus reduces the need for replicates.
The major disadvantage of these arrays versus the Affymetrix array is reduced
oligonucleotide probe density. However, the longer probes provide a better
balance between specificity, sensitivity and noise than the 25-mers on the
Affymetrix array (Kreil et al.,
2006). This translates into array data that require less
statistical manipulation. We routinely use raw data from NimbleGen arrays with
good results (Mito et al.,
2005; Mito et al.,
2007; Penterman et al.,
2007; Zilberman et al.,
2007). Both NimbleGen and Agilent manufacturing methods also allow
the production of custom arrays. This allows for flexibility in experimental
design, as well as in the analysis of DNA methylation in organisms other than
mammals and Arabidopsis.
A restriction enzyme-based comparison of mouse spermatogenic and brain
cells on custom-designed NimbleGen arrays identified over 200 DMRs in 6.2
Mb of the mouse genome (Khulan et al.,
2006). Analysis of immunoprecipitated DNA using customized
NimbleGen tiling arrays produced genome-wide Arabidopsis DNA
methylation mapping data that were broadly similar to the genome-wide DNA
methylation profile generated using the Affymetrix platform
(Zilberman et al., 2007). In
another application of this approach with NimbleGen arrays, the whole-genome
DNA methylation profile of wild-type Arabidopsis was compared with
that of plants with loss-of-function mutations in the DNA demethylase genes
ROS1, DML2 and DML3. This approach accurately revealed
nearly 200 small methylation differences
(Penterman et al., 2007). The
profiling of immunoprecipitated methylated DNA from human fibroblasts and
sperm on NimbleGen promoter arrays has also identified a number of promoters
that are methylated specifically in fibroblasts, including many
germline-specific promoters (Weber et al.,
2007). NimbleGen also offers dye-labeling and array hybridization
as a service, which could be useful to researchers who do not have access to a
microarray facility.
Single-nucleotide polymorphism arrays
Single-nucleotide polymorphism (SNP) arrays have probes that selectively
bind to specific polymorphic sequences, thus providing genotype information
based on relative hybridization to the polymorphic probes. Using SNP arrays
for DNA methylation analysis allows the genotyping of methylated DNA that has
been isolated from polymorphic individuals
(Hellman and Chess, 2007;
Yuan et al., 2006). A recent
study used Affymetrix SNP arrays to distinguish methylation of the active and
inactive X chromosomes, and found that transcribed regions of genes were
preferentially methylated on the active X
(Hellman and Chess, 2007).
This approach should be generally useful for analyzing DNA methylation that is
associated with allele-specific processes, such as genomic imprinting and X
inactivation.
High-throughput sequencing
The newest and most promising methodology for genome-scale analysis of DNA
methylation is high-throughput sequencing. A number of high-throughput
sequencing technologies exist, most of which are still in development
(Bentley, 2006;
Braslavsky et al., 2003;
Levene et al., 2003;
Margulies et al., 2005;
Meyers et al., 2004;
Shendure et al., 2005;
Vercoutere et al., 2003). The
aim of each approach is to produce a very large amount of sequence
information, more rapidly and at a lower cost than conventional Sanger
sequencing, and without the need for cloning.
High-throughput sequencing can be employed as an alternative to analyzing
DNA methylation with oligonucleotide arrays. Instead of labeling and
hybridizing the test and control samples, as in array experiments, they can be
sequenced directly. The frequency of a given sequence will be represented by
its abundance in the sample. With enough sequences, information density
comparable to microarray data can be achieved. Direct sequencing offers a
number of advantages. Counts of sequence reads provide a quantitative measure
of methylation abundance, rather than the relative measure that array-based
methods provide. The sample does not need to be amplified, except as part of
the sequencing strategy. Single-molecule sequencing methods, although still in
development, would eliminate the need for amplification entirely
(Braslavsky et al., 2003;
Levene et al., 2003;
Vercoutere et al., 2003).
Biases that affect hybridization, such as sequence composition, are generally
not an issue in this approach. There is also no need to represent the entire
genome on an array to identify the methylated fraction. As with SNP arrays,
sequencing provides allele-specific information in polymorphic individuals,
but the SNPs do not need to be known in advance - a potentially major
advantage. Finally, any microarray is only as good as the quality of the
genome sequence. Similarly, because short reads produced by high-throughput
sequencing are very challenging to assemble de novo, a high-quality reference
sequence is required. But one advantage of high-throughput sequencing is that
the data can be re-analyzed following improvements in the reference
sequence.
The methylation detection technique best suited to high-throughput
sequencing depends on the genome to be analyzed. For a smaller, less
repetitive genome like Arabidopsis, the direct sequencing of
bisulfite-converted DNA is a good option. However, the analysis of data
generated by this approach presents a special challenge because of C to T
conversion. The benefit of this approach is that it provides the best possible
resolution: quantitative information about the methylation status of every
cytosine. Restriction enzyme- and affinity-based methods are also suitable for
the analysis of smaller genomes (see Fig.
4). Such data are easier to analyze, but do not provide the
resolution of bisulfite analysis. For larger genomes with a high repeat
content, such as the human and mouse genomes, direct bisulfite sequencing
would be more challenging, but has already been performed on a small scale
(Meissner et al., 2005).
Affinity-based purification of methylated DNA would also be challenging,
because most of the genome is methylated. The best approach might be to enrich
unmethylated DNA, either by affinity purification or by utilizing restriction
enzymes (Fig. 4).
Two high-throughput sequencing platforms are currently commercially
available: a high-throughput pyrosequencing approach developed by 454 Life
Sciences (Margulies et al.,
2005), and a fluorescent nucleotide-based system developed by
Solexa (now Illumina) (Bentley,
2006). The 454 system can produce 400,000 reads of over 100 bases
per run. The Solexa system can produce 40 million reads of about 25-35 bases.
For DNA methylation analysis, the number of reads is more important than the
length, because most 30-mers can be unambiguously aligned to a reference
genome sequence (Bentley,
2006). Therefore, the Solexa system is the best currently
available for this type of analysis, and has already been successfully used to
analyze the distribution of a number of post-translational histone
modifications in the human genome (Barski
et al., 2007).
Conclusions
After decades of work, DNA methylation research is entering a new phase. The ability to analyze methylation patterns of whole genomes will enable us, for the first time, to obtain the most basic type of information about this modification - its location within the genome. This, in turn, should enable the elucidation of how DNA methylation influences chromatin function, and the role it plays in development and disease. The choice of which approach is best to analyze DNA methylation ultimately depends on one's biological question, model organism, budget, and to some extent adventurousness (see Fig. 4, Table 1). Microarray technology is transitioning from an esoteric tool used primarily to measure mRNA levels to a general approach that might soon be as common as Southern blotting and PCR. Exciting developments in high-throughput sequencing might make this the next technology of choice, replacing microarrays within a few years. Further down the road, single-molecule sequencing technologies promise to make a reality the sensitive analysis of tiny quantities of a sample that is free of amplification artifacts. The transition of genomics from the province of highly specialized laboratories and consortia to a standard tool of molecular biology promises to revolutionize every field in which, as in DNA methylation research, genomic information is of use.
|
Barski, A., Cuddapah, S., Cui, K., Roh, T. Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I. and Zhao, K. (2007). High-resolution profiling of histone methylations in the human genome. Cell 129,823 -837.[CrossRef][Medline]
Bentley, D. R. (2006). Whole-genome re-sequencing. Curr. Opin. Genet. Dev. 16,545 -552.[CrossRef][Medline]
Bibikova, M., Chudin, E., Wu, B., Zhou, L., Garcia, E. W., Liu,
Y., Shin, S., Plaia, T. W., Auerbach, J. M., Arking, D. E. et al.
(2006a). Human embryonic stem cells have a unique epigenetic
signature. Genome Res.
16,1075
-1083.
Bibikova, M., Lin, Z., Zhou, L., Chudin, E., Garcia, E. W., Wu,
B., Doucet, D., Thomas, N. J., Wang, Y., Vollmer, E. et al.
(2006b). High-throughput DNA methylation profiling using
universal bead arrays. Genome Res.
16,383
-393.
Bird, A. P. and Southern, E. M. (1978). Use of restriction enzymes to study eukaryotic DNA methylation: I. The methylation pattern in ribosomal DNA from Xenopus laevis. J. Mol. Biol. 118,27 -47.[CrossRef][Medline]
Bird, A., Taggart, M., Frommer, M., Miller, O. J. and Macleod, D. (1985). A fraction of the mouse genome that is derived from islands of nonmethylated, CpG-rich DNA. Cell 40, 91-99.[CrossRef][Medline]
Braslavsky, I., Hebert, B., Kartalov, E. and Quake, S. R.
(2003). Sequence information can be obtained from single DNA
molecules. Proc. Natl. Acad. Sci. USA
100,3960
-3964.
Brena, R. M., Huang, T. H. and Plass, C. (2006). Quantitative assessment of DNA methylation: potential applications for disease diagnosis, classification, and prognosis in clinical settings. J. Mol. Med. 84,365 -377.[CrossRef][Medline]
Callinan, P. A. and Feinberg, A. P. (2006). The
emerging science of epigenomics. Hum. Mol. Genet.
15 Spec No 1,R95
-R101.
Chan, S. W., Henderson, I. R. and Jacobsen, S. E. (2005). Gardening the genome: DNA methylation in Arabidopsis thaliana. Nat. Rev. Genet. 6, 351-360.[CrossRef][Medline]
Clark, S. J., Harrison, J., Paul, C. L. and Frommer, M.
(1994). High sensitivity mapping of methylated cytosines.
Nucleic Acids Res. 22,2990
-2997.
Clark, S. J., Statham, A., Stirzaker, C., Molloy, P. L. and Frommer, M. (2006). DNA methylation: bisulphite modification and analysis. Nat. Protoc. 1,2353 -2364.[CrossRef][Medline]
Cross, S. H., Charlton, J. A., Nan, X. and Bird, A. P. (1994). Purification of CpG islands using a methylated DNA binding column. Nat. Genet. 6, 236-244.[CrossRef][Medline]
Dalma-Weiszhausz, D. D., Warrington, J., Tanimoto, E. Y. and Miyada, C. G. (2006). The affymetrix GeneChip platform: an overview. Methods Enzymol. 410, 3-28.[Medline]
Eckhardt, F., Lewin, J., Cortese, R., Rakyan, V. K., Attwood, J., Burger, M., Burton, J., Cox, T. V., Davies, R., Down, T. A. et al. (2006). DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38,1378 -1385.[CrossRef][Medline]
Ehrich, M., Nelson, M. R., Stanssens, P., Zabeau, M., Liloglou,
T., Xinarianos, G., Cantor, C. R., Field, J. K. and van den Boom, D.
(2005). Quantitative high-throughput analysis of DNA methylation
patterns by base-specific cleavage and mass spectrometry. Proc.
Natl. Acad. Sci. USA 102,15785
-15790.
Ehrich, M., Field, J. K., Liloglou, T., Xinarianos, G., Oeth,
P., Nelson, M. R., Cantor, C. R. and van den Boom, D. (2006).
Cytosine methylation profiles as a molecular marker in non-small cell lung
cancer. Cancer Res. 66,10911
-10918.
Fan, J. B., Gunderson, K. L., Bibikova, M., Yeakley, J. M., Chen, J., Wickham Garcia, E., Lebruska, L. L., Laurent, M., Shen, R. and Barker, D. (2006). Illumina universal bead arrays. Methods Enzymol. 410,57 -73.[Medline]
Freitag, M. and Selker, E. U. (2005). Controlling DNA methylation: many roads to one modification. Curr. Opin. Genet. Dev. 15,191 -199.[CrossRef][Medline]
Gehring, M. and Henikoff, S. (2007). DNA methylation dynamics in plant genomes. Biochim. Biophys. Acta 1769,276 -286.[Medline]
Goll, M. G. and Bestor, T. H. (2005). Eukaryotic cytosine methyltransferases. Annu. Rev. Biochem. 74,481 -514.[CrossRef][Medline]
Gowher, H., Leismann, O. and Jeltsch, A. (2000). DNA of Drosophila melanogaster contains 5-methylcytosine. EMBO J. 19,6918 -6923.[CrossRef][Medline]
Heard, E. and Disteche, C. M. (2006). Dosage
compensation in mammals: fine-tuning the expression of the X chromosome.
Genes Dev. 20,1848
-1867.
Hellman, A. and Chess, A. (2007). Gene
body-specific methylation on the active X chromosome.
Science 315,1141
-1143.
Jacobsen, S. E., Sakai, H., Finnegan, E. J., Cao, X. and Meyerowitz, E. M. (2000). Ectopic hypermethylation of flower-specific genes in Arabidopsis. Curr. Biol. 10,179 -186.[CrossRef][Medline]
Keshet, I., Schlesinger, Y., Farkash, S., Rand, E., Hecht, M., Segal, E., Pikarski, E., Young, R. A., Niveleau, A., Cedar, H. et al. (2006). Evidence for an instructive mechanism of de novo methylation in cancer cells. Nat. Genet. 38,149 -153.[CrossRef][Medline]
Khulan, B., Thompson, R. F., Ye, K., Fazzari, M. J., Suzuki, M.,
Stasiek, E., Figueroa, M. E., Glass, J. L., Chen, Q., Montagna, C. et al.
(2006). Comparative isoschizomer profiling of cytosine
methylation: the HELP assay. Genome Res.
16,1046
-1055.
Klose, R. J. and Bird, A. P. (2006). Genomic DNA methylation: the mark and its mediators. Trends Biochem. Sci. 31,89 -97.[CrossRef][Medline]
Korch, C. and Hagblom, P. (1986). In-vivo-modified gonococcal plasmid pJD1. A model system for analysis of restriction enzyme sensitivity to DNA modifications. Eur. J. Biochem. 161,519 -524.[Medline]
Kreil, D. P., Russell, R. R. and Russell, S. (2006). Microarray oligonucleotide probes. Methods Enzymol. 410,73 -98.[Medline]
Laird, P. W. (2003). The power and the promise of DNA methylation markers. Nat. Rev. Cancer 3, 253-266.[CrossRef][Medline]
Levene, M. J., Korlach, J., Turner, S. W., Foquet, M.,
Craighead, H. G. and Webb, W. W. (2003). Zero-mode waveguides
for single-molecule analysis at high concentrations.
Science 299,682
-686.
Lieb, J. D., Beck, S., Bulyk, M. L., Farnham, P., Hattori, N., Henikoff, S., Liu, X. S., Okumura, K., Shiota, K., Ushijima, T. et al. (2006). Applying whole-genome studies of epigenetic regulation to study human disease. CytoGenet. Genome Res. 114, 1-15.[CrossRef][Medline]
Lindsay, S. and Bird, A. P. (1987). Use of restriction enzymes to detect potential gene sequences in mammalian DNA. Nature 327,336 -338.[CrossRef][Medline]
Lippman, Z., Gendrel, A. V., Black, M., Vaughn, M. W., Dedhia, N., McCombie, W. R., Lavine, K., Mittal, V., May, B., Kasschau, K. D. et al. (2004). Role of transposable elements in heterochromatin and epigenetic control. Nature 430,471 -476.[CrossRef][Medline]
Lippman, Z., Gendrel, A. V., Colot, V. and Martienssen, R. (2005). Profiling DNA methylation patterns using genomic tiling microarrays. Nat. Methods 2, 219-224.[CrossRef][Medline]
Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y. J., Chen, Z. et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437,376 -380.[Medline]
Meissner, A. and Jaenisch, R. (2006). Mammalian nuclear transfer. Dev. Dyn. 235,2460 -2469.[CrossRef][Medline]
Meissner, A., Gnirke, A., Bell, G. W., Ramsahoye, B., Lander, E.
S. and Jaenisch, R. (2005). Reduced representation bisulfite
sequencing for comparative high-resolution DNA methylation analysis.
Nucleic Acids Res. 33,5868
-5877.
Meyers, B. C., Vu, T. H., Tej, S. S., Ghazal, H., Matvienko, M., Agrawal, V., Ning, J. and Haudenschild, C. D. (2004). Analysis of the transcriptional complexity of Arabidopsis thaliana by massively parallel signature sequencing. Nat. Biotechnol. 22,1006 -1011.[CrossRef][Medline]
Mito, Y., Henikoff, J. G. and Henikoff, S. (2005). Genome-scale profiling of histone H3.3 replacement patterns. Nat. Genet. 37,1090 -1097.[CrossRef][Medline]
Mito, Y., Henikoff, J. G. and Henikoff, S.
(2007). Histone replacement marks the boundaries of
cis-regulatory domains. Science
315,1408
-1411.
Nuwaysir, E. F., Huang, W., Albert, T. J., Singh, J., Nuwaysir,
K., Pitas, A., Richmond, T., Gorski, T., Berg, J. P., Ballin, J. et al.
(2002). Gene expression analysis using oligonucleotide arrays
produced by maskless photolithography. Genome Res.
12,1749
-1755.
Penterman, J., Zilberman, D., Huh, J. H., Ballinger, T.,
Henikoff, S. and Fischer, R. L. (2007). DNA demethylation in
the Arabidopsis genome. Proc. Natl. Acad. Sci. USA,10.1073/pnas.0701861104
.
Rai, K., Nadauld, L. D., Chidester, S., Manos, E. J., James, S.
R., Karpf, A. R., Cairns, B. R. and Jones, D. A. (2006).
Zebra fish Dnmt1 and Suv39h1 regulate organ-specific terminal differentiation
during development. Mol. Cell. Biol.
26,7077
-7085.
Reynaud, C., Bruno, C., Boullanger, P., Grange, J., Barbesti, S. and Niveleau, A. (1992). Monitoring of urinary excretion of modified nucleosides in cancer patients using a set of six monoclonal antibodies. Cancer Lett. 61,255 -262.[CrossRef][Medline]
Richards, E. J. (2006). Inherited epigenetic variation - revisiting soft inheritance. Nat. Rev. Genet. 7,395 -401.[CrossRef][Medline]
Rollins, R. A., Haghighi, F., Edwards, J. R., Das, R., Zhang, M.
Q., Ju, J. and Bestor, T. H. (2006). Large-scale structure of
genomic methylation patterns. Genome Res.
16,157
-163.
Schatz, P., Dietrich, D. and Schuster, M.
(2004). Rapid analysis of CpG methylation patterns using RNase T1
cleavage and MALDI-TOF. Nucleic Acids Res.
32, e167.
Schatz, P., Distler, J., Berlin, K. and Schuster, M.
(2006). Novel method for high throughput DNA methylation marker
evaluation using PNA-probe library hybridization and MALDI-TOF detection.
Nucleic Acids Res. 34,e59
.
Schumacher, A., Kapranov, P., Kaminsky, Z., Flanagan, J.,
Assadzadeh, A., Yau, P., Virtanen, C., Winegarden, N., Cheng, J., Gingeras, T.
et al. (2006). Microarray-based DNA methylation profiling:
technology and applications. Nucleic Acids Res.
34,528
-542.
Selker, E. U., Tountas, N. A., Cross, S. H., Margolin, B. S., Murphy, J. G., Bird, A. P. and Freitag, M. (2003). The methylated component of the Neurospora crassa genome. Nature 422,893 -897.[CrossRef][Medline]
Shendure, J., Porreca, G. J., Reppas, N. B., Lin, X.,
McCutcheon, J. P., Rosenbaum, A. M., Wang, M. D., Zhang, K., Mitra, R. D. and
Church, G. M. (2005). Accurate multiplex polony sequencing of
an evolved bacterial genome. Science
309,1728
-1732.
Soppe, W. J., Jacobsen, S. E., Alonso-Blanco, C., Jackson, J. P., Kakutani, T., Koornneef, M. and Peeters, A. J. (2000). The late flowering phenotype of fwa mutants is caused by gain-of-function epigenetic alleles of a homeodomain gene. Mol. Cell 6, 791-802.[CrossRef][Medline]
Sutherland, E., Coe, L. and Raleigh, E. A. (1992). McrBC: a multisubunit GTP-dependent restriction endonuclease. J. Mol. Biol. 225,327 -348.[CrossRef][Medline]
Tompa, R., McCallum, C. M., Delrow, J., Henikoff, J. G., van Steensel, B. and Henikoff, S. (2002). Genome-wide profiling of DNA methylation reveals transposon targets of CHROMOMETHYLASE3. Curr. Biol. 12,65 -68.[CrossRef][Medline]
Tost, J. and Gut, I. G. (2006). Analysis of gene-specific DNA methylation patterns by pyrosequencing(r) technology. Methods Mol. Biol. 373,89 -102.
Tost, J., Schatz, P., Schuster, M., Berlin, K. and Gut, I.
G. (2003). Analysis and accurate quantification of CpG
methylation by MALDI mass spectrometry. Nucleic Acids
Res. 31,e50
.
Ushijima, T. (2005). Detection and interpretation of altered methylation patterns in cancer cells. Nat. Rev. Cancer 5,223 -231.[CrossRef][Medline]
Vercoutere, W. A., Winters-Hilt, S., DeGuzman, V. S., Deamer,
D., Ridino, S. E., Rodgers, J. T., Olsen, H. E., Marziali, A. and Akeson,
M. (2003). Discrimination among individual Watson-Crick base
pairs at the termini of single DNA hairpin molecules. Nucleic Acids
Res. 31,1311
-1318.
Waalwijk, C. and Flavell, R. A. (1978). MspI,
an isoschizomer of HpaII which cleaves both unmethylated and methylated hpaII
sites. Nucleic Acids Res.
5,3231
-3236.
Walsh, C. P. and Bestor, T. H. (1999). Cytosine
methylation and mammalian development. Genes Dev.
13, 26-34.
Weber, M., Davies, J. J., Wittig, D., Oakeley, E. J., Haase, M., Lam, W. L. and Schubeler, D. (2005). Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37,853 -862.[CrossRef][Medline]
Weber, M., Hellmann, I., Stadler, M. B., Ramos, L., Pääbo, S., Rebhan, M. and Schübeler, D. (2007). Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39,457 -466.[CrossRef][Medline]
Wolber, P. K., Collins, P. J., Lucas, A. B., De Witte, A. and Shannon, K. W. (2006). The Agilent in situ-synthesized microarray platform. Methods Enzymol. 410, 28-57.[Medline]
Xiao, W., Custard, K. D., Brown, R. C., Lemmon, B. E., Harada,
J. J., Goldberg, R. B. and Fischer, R. L. (2006). DNA
methylation is critical for Arabidopsis embryogenesis and seed viability.
Plant Cell 18,805
-814.
Yuan, E., Haghighi, F., White, S., Costa, R., McMinn, J., Chun,
K., Minden, M. and Tycko, B. (2006). A single nucleotide
polymorphism chip-based method for combined genetic and epigenetic profiling:
validation in decitabine therapy and tumor/normal comparisons.
Cancer Res. 66,3443
-3451.
Zhang, X. and Jacobsen, S. E. (2006). Genetic analyses of DNA methyltransferases in Arabidopsis thaliana. Cold Spring Harb. Symp. Quant. Biol. 71,439 -447.[CrossRef][Medline]
Zhang, X., Yazaki, J., Sundaresan, A., Cokus, S., Chan, S. W., Chen, H., Henderson, I. R., Shinn, P., Pellegrini, M., Jacobsen, S. E. et al. (2006). Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126,1189 -1201.[CrossRef][Medline]
Zhou, Y., Bui, T., Auckland, L. D. and Williams, C. G. (2002). Undermethylated DNA as a source of microsatellites from a conifer genome. Genome 45, 91-99.[Medline]
Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. and Henikoff, S. (2007). Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat. Genet. 39, 61-69.[CrossRef][Medline]
This article has been cited by other articles:
|
|
 |
|
  X. Zhang The Epigenetic Landscape of Plants Science, April 25, 2008; 320(5875): 489 - 492. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
