Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets

Abstract

Using a unique microarray platform for cytosine methylation profiling, the DNA methylation landscape of the human genome was monitored at more than 21,000 sites, including 79% of the annotated transcriptional start sites (TSS). Analysis of an oligodendroglioma derived cell line LN-18 revealed more than 4000 methylated TSS. The gene-centric analysis indicated a complex pattern of DNA methylation exists along each autosome, with a trend of increasing density approaching the telomeres. Remarkably, 2% of CpG islands (CGI) were densely methylated, and 17% had significant levels of 5 mC, whether or not they corresponded to a TSS. Substantial independent verification, obtained from 95 loci, suggested that this approach is capable of large scale detection of cytosine methylation with an accuracy approaching 90%. In addition, we detected large genomic domains that are also susceptible to DNA methylation reinforced inactivation, such as the HOX cluster on chromosome 7 (CH7). Extrapolation from the data suggests that more than 2000 genomic loci may be susceptible to methylation and associated inactivation, and most have yet to be identified. Finally, we report six new targets of epigenetic inactivation (IRX3, WNT10A, WNT6, RARalpha, BMP7 and ZGPAT). These targets displayed cell line and tumor specific differential methylation when compared with normal brain samples, suggesting they may have utility as biomarkers. Uniquely, hypermethylation of the CGI within an IRX3 exon was correlated with over-expression of IRX3 in tumor tissues and cell lines relative to normal brain samples.

Full text

Comprehensive DNA methylation profiling in a human cancer genome identifies
novel epigenetic targets
J.M.Ordway, J.A.Bedell, R.W.Citek, A.Nunberg,
A.Garrido, R.Kendall
2,3
, J.R.Stevens
3
, D.Cao
2,3
,
R.W.Doerge
2,3
, Y.Korshunova, H.Holemon,
J.D.McPherson
4
, N.Lakey, J.Leon, R.A.Martienssen
5
and J.A.Jeddeloh
Orion Genomics, St Louis, MO,
2
Department of Agronomy, Lilly Hall of
Lifesciences 915 West State Street, Purdue University, West Lafayette, IN,
3
Department of Statistics, Purdue University, 150 North University Street,
West Lafeyette, IN,
4
Department of Molecular and Human Genetics, Baylor
College of Medicine, Houston, TX and
5
Cold Spring Harbor Laboratory,
Cold Spring Harbor, NY, USA
To whom corresondence should be addressed at Science and Technology,
Orion Genomics, LLC, 4041 Forest Park Avenue St. Louis, MO 63108,
USA. Tel: +1 314.615.6977; Fax: +1 314.615.6975;
Email: jjeddeloh@oriongenomics.com
Using a unique microarray platform for cytosine methy-
lation profiling, the DNA methylation landscape of the
human genome was monitored at more than 21 000 sites,
including 79% of the annotated transcriptional start sites
(TSS). Analysis of an oligodendroglioma derived cell line
LN-18 revealed more than 4000 methylated TSS. The
gene-centric analysis indicated a complex pattern of DNA
methylation exists along each autosome, with a trend of
increasing density approaching the telomeres. Remark-
ably, 2% of CpG islands (CGI) were densely methylated,
and 17% had significant levels of 5 mC, whether or not
they corresponded to a TSS. Substantial independent
verification, obtained from 95 loci, suggested that this
approach is capable of large scale detection of cytosine
methylation with an accuracy approaching 90%. In
addition, we detected large genomic domains that are
also susceptible to DNA methylation reinforced inacti-
vation, such as the HOX cluster on chromosome 7 (CH7).
Extrapolation from the data suggests that more than 2000
genomic loci may be susceptible to methylation and
associated inactivation, and most have yet to be identified.
Finally, we report six new targets of epigenetic inactiva-
tion (IRX3, WNT10A, WNT6, RARa, BMP7 and
ZGPAT). These targets displayed cell line and tumor
specific differential methylation when compared with
normal brain samples, suggesting they may have utility
as biomarkers. Uniquely, hypermethylation of the CGI
within an IRX3 exon was correlated with over-expression
of IRX3 in tumor tissues and cell lines relative to normal
brain samples.
Introduction
Identifying molecular differences that distinguish tumor tissue
from normal tissue is a current topic area of intense interest.
Although, tumor genomes display only a limited number of
primary sequence differences from the nearly isogenic normal
tissues in proximity to them (1), a large number of molecular
differences exist. In particular, the spectrum of sequences that
normal and tumor genomes specify and mark as silent
chromatin have been used as ‘epigenetic signatures’ to
molecularly discriminate these cells (2).
Disruption of normal gene regulation is important for
carcinogenesis (3,4) resulting in loss, or gain of genetic
function. The molecular events that underlie this altered
regulation include point-mutations and macro-mutations, such
as deletion, amplification or genomic rearrangement (e.g.
translocation), that can result in more complex interactions
when regulatory genes are affected. Recently, the importance
of epigenetic perturbation of gene regulation in the form of
changes in chromatin structure has begun to be more fully
appreciated (5). In the context of cancer, inappropriate
chromatin packaging of genes can lead to gene silencing, or
in some cases, ectopic gene expression (6).
Cytosine methylation is a chemically stable mark that may
establish, or follow as a consequence of, the packaging of a
particular region into silent chromatin. Therefore identifica-
tion of aberrant genomic DNA methylation that is associated
with carcinogenesis should identify loci that are important for
disease progression (2).
Mammalian DNA methylation patterns that transmit
cellular silencing signals are mitotically maintained with
96–99% fidelity (7). This lies in stark contrast with the
primary sequence which is maintained with fidelity over
99.9999% (8). The scale of the difference suggests that
epigenetic deregulation may be under appreciated, and recent
studies have highlighted the role of the environment, e.g.
through dietary folate metabolism, in maintaining DNA
methylation and gene silencing, hinting at a mechanism
underlying predisposition for cancer (9,10).
Several cancer therapies targeting the maintenance of cyto-
sine methylation and silencing states are in human clinical
trials (11–13). Currently, the therapies target either the DNA
methylation machinery, or the histone modification machin-
ery. This machinery works synergistically to maintain gene
silencing. While such therapies are very promising, the clinical
success rates achieved thus far have been similar to more
conventional chemotherapies. Therefore, selecting patients for
Abbreviations: CGI, CpG islands; CH7, chromosome 7; Ct¼cycle threshold;
DD, double digest; DM¼densely or uniformly methylated; FDR, false
discovery rate; IM, intermediately methylated; M¼mock digested; MD,
methylation-depleted; MDRE¼methylation dependent restriction enzyme
explanations; MSRE, methylation sensitive restriction enzyme; R¼fraction
refractory to analysis, or Purine nucleotide (IUPAC); T¼treated with McrBC;
TSS, transcriptional start sites; U¼unmethylated or sparsely methylated;
UT¼untreated; W¼analytical window.
Carcinogenesis vol.27 no.12 pp.2409–2423, 2006
doi:10.1093/carcin/bgl161
Advance Access publication September 4, 2006
2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
by guest on July 13, 2015http://carcin.oxfordjournals.org/Downloaded from

such epigenetic therapies or measuring their success may
require an understanding and characterization of the
sequences affected by epigenetic perturbation in particular
diseases.
Finding and characterizing the genomic loci capable of
driving carcinogenesis from the epigenetic perspective, as
well as those capable of serving as clinically meaningful
disease or therapy-specific markers is a pressing need. We
chose to address the need through the adaptation of a
microarray-based DNA methylation profiling approach for
use in the human genome. We report here the results
obtained in the course of characterizing the oligoden-
droglioma derived cell line LN-18 (CRL-2610). Future
applications of this technology will serve not only to directly
identify those sequences which undergo epigenetic alteration
in disease, but also as an epigenetic ‘Ames’ test for
environmental history.
Materials and methods
Cell line, normal and tumor brain tissue and blood nucleic acid samples
Cell lines LN-18 (glioblastoma), A-172 (glioblastoma), LN-229 (glioblas-
toma), T-98G [glioblastoma multiforme(GBM)], U-138 MG (astrocytoma)
and U-87 MG (astrocytoma) were obtained from American Type Culture
Collection (Manassas, VA) and cultured under supplier’s recommendations
(http://www.atcc.org/). Histologically normal/non-malignant brain tissue
(0.4 mg ea) from the cerebrum (right temporal lobe) of three different
patients (15084A1, 16920A1 and 8387B1) were acquired from Asterand plc.
(Detroit, MI). Primary tumor samples [two astrocytoma (1FMXAFPB,
JA4CIFNL) and one GBM specimen (CAURPFJD)] were acquired from
Genomics Collaborative Inc (Caimbridge, MA). All the tumor samples
exhibited >90% neoplastic cellularity. The age range of the normal samples
(45,56,87) was controlled relative to the tumors (48,59,59). Genomic DNA
was extracted by the MasterPure DNA Purification kit under manufacturer’s
recommended conditions (Epicentre Biotechnologies, Madison, WI). Male
whole blood genomic DNA, representing a pool of peripheral blood samples
obtained from approximately six individuals, was obtained from Novagen
(Madison, WI).
RNA was purified from the tumor tissues and cell lines using the RNeasy
Kit from Qiagen (Valencia, CA). cDNA from normal brain and astrocytoma
tissues was purchased from Invitrogen (Carlsbad, CA). cDNA from the cell
lines and tumor samples was produced using the oligo dT primers provided
in the Superscript III first strand system from Invitrogen (Carlsbad, CA)
under the manufacturer’s recommended conditions.
Microarray experimental and analytical approach
Methylation profiling was performed as described previously (14,15) but
with adaptations to apply the method to the human genome. The adaptations
included the use of target mass normalization before labeling, the
employment of long oligonucleotide probes (60mer) on the NimbleGen
systems CGH platform, and the use of sequences within the human genome
that are incapable of digestion by McrBC as controls.
Methylation-dependent DNA fractionation
LN-18 DNA was mechanically sheared into a uniform molecular weight
distribution using a GeneMachines HydroShear apparatus. Sheared DNA was
split into four equal portions of 100 mg each. Two portions (treated technical
replicates) were digested with McrBC (NEB) in 300 ml total volume
including 1·NEB2 buffer, 0.1 mg/ml BSA, 2 mM GTP and 200 U McrBC.
The remaining two portions were mock-treated under identical conditions
except that 20 ml water was added instead of McrBC. Treated and mock-
treated reactions were incubated at 37C overnight. All reactions were
treated with 5 ml proteinase K (50 mg/ml) for 1 h at 50C, and precipitated
with EtOH under standard conditions. Pellets were washed twice, dried and
resuspended in 50 mlH
2
O. A total of 50 mg of each fraction was resolved on
a 1% low melting point SeaPlaque GTG Agarose gel (Cambridge Bio
Sciences, Rockland, ME). Untreated and treated replicates were resolved
side-by-side. DNA (1 kb) sizing ladder was run adjacent to each untreated/
treated pair to guide accurate gel slice excision. Gels were visualized with
long-wave ultraviolet (UV), and gel slices including DNA within the modal
size range of the untreated fraction (1–4 kb) were excised with a clean
razor blade. DNA was extracted from gel slices using the GenElute agarose
spin columns (Sigma–Aldrich).
Human array (OGHA v1.0) design
To identify epigenetic changes in gene regulation, our efforts were focused
largely upon the transcription start sites of genes. Since promoter annotation
and TSS identification are generally less reliable than gene annotation, we
tested the ENSEMBL gene predictions using a highly characterized set of
human promoters from the Eukaryotic Promoter Database (EPD, http://www.
epd.isb-sib.ch/). Substantial similarity was observed between ENSEMBL
gene predictions and EPD annotations. Therefore, we decided that the
ENSEMBL annotated genes would be an ideal probe design source. The TSS
information for the 24 847 ENSEMBL annotated human genes (NCBIv34)
was extracted. 60mer probes were designed and uniqueness tested
using custom primer selection scripts (J. D. McPherson, unpublished data).
The average position of the TSS associated 60mer probes relative to +1is
500 bp.
The array design consists of 85 176 probes. Each feature is represented
with four on-board replicates yielding a total of 21 294 unique feature
probes. The features represent 19 595 transcriptional start sites (TSS) (79%
of the identified human genes), 1395 GenBank BAC annotated CG islands
(CGi), 161 probes spanning 165 kb along the MTAPase/CDKN2A/B locus
on CH9, 66 additional probes dedicated to cancer gene promoters, and
77 probes designed as copy number (HERV, LINEs and SINE) and other
controls. We analyzed CGI in two ways. The first was using GenBank BAC
accessions with annotated CGI upon them as a source for probe design. The
second was mapping University of California Santa Cruz (UCSC) annotated
CGI relative to our probe set. Together, the TSS probes and CGi probes
scan more than 9000 UCSC annotated human CG islands. Feature coverage
of the OGHA v1.0 microarray in the human genome is depicted in Figure 2.
The vertical lines extending up from the chromosome represent features
designed to the Watson strand, and the vertical lines down from the
chromosome represent features designed to the Crick strand.
Dye labeling and microarray hybridization
Each fraction was labeled with Cy3 and separately with Cy5 by in vitro
synthesis reactions. A total of 200 ng of fractionated template DNA and
20 ml 2.5·random hexamer cocktail (Invitrogen) were combined in a total
volume of 25.5 ml and incubated in a boiling water bath for 5 min. Reactions
were placed on ice for 3 min. Reaction volumes were adjusted to 50 ml
including 140 mM each dATP, dGTP and dTTP, 60 mM dCTP (Invitrogen),
1.5 ml 25 nmol Cyanine 3-dCTP or Cyanine 5-dCTP (NEN), and 100 U 30–50
exo

Klenow (NEB). Reactions were incubated at 37C for 45 min then
inactivated by addition of 50 ml TE. Labeled DNA was purified using
MinElute spin columns (Qiagen) and eluted in 35 ml EB buffer. Synthesis
yields and Cy-dye specific activities were determined by Nanodrop
spectrophotometry. One-half of each labeled reaction was used per
microarray hybridization. A duplicated dye-swap experimental design was
employed and four microarray hybridizations were performed. Microarray
hybridization, washing and scanning were performed by Nimblegen Systems
of Iceland under their standard CGH operating protocols.
Statistical analysis of microarray data
A two-stage ANOVA model was employed (16). In the first stage the model
fit was: y
gijkl
¼m+T
i
+A
j
+D
k
+e
gijkl
, where y ¼log(intensity), m¼global
mean, T ¼fixed treatment effect, i¼1, 2; A ¼fixed array effect, j¼1, 2, 3,
4; D ¼fixed dye effect, k¼1, 2; g¼1, 2, ..., 21 294 (gene), l¼1, ...,g
l
,
(some genes have replicates for combinations of T, A and D), e
gijkl
¼error,
iid N(0, s
2
).
Let T
1
and T
2
correspond to untreated samples (UT) and treated
methylation-depleted (MD) samples, respectively. In the second stage of the
ANOVA the residual for each gene gfrom the first stage analysis, r
gijkl
, was
used as the response variable in the model: r
gijkl
¼G
g
+(GT)
gi
+(GA)
gj
+
(GD)
gk
+g
gijkl
, where the error terms, g
gijkl,
are assumed to be independent
N(0,s
g
2
).
Normalization to RCG zero features
Subsequent analysis of the LN-18 data utilized those features having no
McrBC half-sites within 1000 bp of genomic sequence spanning the probe
[i.e. RCG/kb frequency values of zero (RCG 0)] as controls. One would not
expect the RCG0 features to be affected by McrBC due to their lack of
McrBC half-sites. As such, any variability observed from these features’
intensities should be solely attributed to the overall treatment, array and
dye effects, plus chance variability. Using the RCG0 features to estimate
these effects allows for a clearer picture of the methylation status of the
other features. For this reason, the first-stage ANOVA model was fit using
only the RCG0 features. The estimates of m,T
i
,A
j
and D
k
were obtained
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and then used in the normalization of the remaining non-control genes.
The normalized methylation levels y
~
gijkl ¼ygijkl ðm
^þT
^
iþA
^
jþD
^
kÞwere
obtained for all non-control features and the two-stage model fit.
Testing for methylation
Once the two-stage analysis was performed, the following hypotheses were
tested to detect methylated features (i.e. log scale): H
0
:MD¼UT versus H
1
:
MD 6¼ UT. Equivalently, for each gene gthe hypotheses H
0
:GT
g2
¼GT
g1
versus H
1
:GT
g2
6¼ GT
g1
was tested. The test statistic for testing expression
of untreated versus methylation-depleted samples, V
*
g
¼GT
g1
GT
g2
,is
easily interpreted as UT-MD (or U-T), the untreated treatment effect minus
the methylation-depletion treatment effect for gene g.
Because of the number of statistical tests made (21 294) two methods were
used to control for multiple testing errors. Holm’s sequential adjustment
provides strong control of the family-wise error rate (FWER) below
significance level a, with greater power than the standard Bonferroni method
(17). The false discovery rate (FDR) controlling method of Benjamini and
Hochberg (18) provides weak control of the FWER, and controls the FDR
below level a. The FDR is defined as the expected proportion of falsely rejected
null hypotheses relative to the total number of rejections. The FDR was
controlled at 0.05. Matlab (Mathworks) and SAS statistical packages (SAS
Institute) were used for the analyses and generation of figures.
Bisulphite sequencing
A total of 2 mg LN-18 genomic DNA was bisulphite converted with an EZ
DNA Methylation Kit (Zymo Research) using the manufacturer’s recom-
mended protocol. Primers and loci selected for validation, along with PCR
conditions are indicated in the Supplementary Data. Sequencing and analysis
was performed as described previously (19).
Quantitative PCR-based validation of DNA methylation predictions
(MethylScreen)
For each analyzed genomic DNA sample, 4 mg of genomic DNA was
digested with McrBC (NEB) in 100 ml total volume including 1·NEB2
buffer, 0.1 mg/ml BSA, 2 mM GTP and 50 U McrBC overnight at 37C. In
parallel, 4 mg of each genomic DNA sample was mock-treated under
identical conditions with the exception that water was substituted for McrBC.
Following digestion, enzyme activity was heat inactivated at 65C for
20 min. A total of 40 ng McrBC-digested and 40 ng mock-treated DNA were
analyzed by quantitative real-time PCR. In all cases, digested and mock-
treated templates were analyzed in adjacent wells. Reactions included 1·
FailSafe Buffer G (EpiCentre), 5 mM each primer (see Supplementary Data),
and 1 U Taq polymerase (Invitrogen) in 25 ml total volume. Standard
quantitative PCR cycling conditions were used with a ‘hot’ plate read of
72C for 2 min. The melt curve of each amplicon was calculated within a
temperature gradient from 60 to 95Cat1
C increments with a 10 s hold
time for each read. The cycle number at which the McrBC-digested sample
crossed the threshold was subtracted from the cycle number at which the
mock-treated sample crossed the threshold to determine the DCt of each
locus. Since McrBC digests only DNA including purine-5mC, thereby
decreasing the amplifiable copies of loci containing DNA methylation and
increasing the Ct relative to the mock-treated sample, increasing DCt values
reflect increasing levels of local DNA methylation. All average DCt values
(UT-McrBC) less than 1 were set to 1. Four condition MethylScreen
assays employed the two treatments described above [mock and methylation
dependant restriction enzyme (MDRE)], as well as a methylation sensitive
restriction enzyme treatment [(MSRE) e.g. HhaI (NEB (Beverly, MA))] and
a double digest (DD) treatment (e.g. HhaI +McrBC).
DNA methylation occupancy calculations
The amount of DNA remaining after each treatment can be calculated using
a comparative threshold method, wherein the relative amount of a population
of interest is determined by comparing it to a reference value. In this case,
the reference value is internal and can be either the mock treated reaction or
the DD reaction. Because it is based on the same locus, the same DNA
sample, and the same primers there is no amplification efficiency bias
difference to consider. Using this approach, if the efficiency of amplification
with a particular primer pair is 100%, the quantity of DNA molecules in the
variously treated populations of interest is proportional to the equation:
PðT1jT2Þ¼2ðCt2Ct1Þ
;ð1Þ
where P is the proportional amount of template DNA remaining in Treatment
1 (T1) compared to Treatment 2 (T2), Ct1 is the cycle-threshold for T1 and
Ct2 is the cycle-threshold for T2.
For example, given an MSRE treatment Ct of 27 and a mock treatment Ct
of 25, the proportion of template DNA in the MSRE reaction is 2 (25–27) ¼
1
⁄
4
¼25% of the mock treatment. The proportion of template remaining after
the MSRE treatment is an indication of the amount of densely methylated
DNA (all MSRE sites must be blocked; i.e. 25% in the example above). The
proportion of template remaining after MDRE treatment is an indication of
the amount of unmethylated or sparsely methylated DNA (methylated
cytosines are not prevalent enough to allow for digestion between the
primers). DNA that is not accounted for by either of these treatments must be
in an intermediate state of methylation.
The following equations describe some features of the quantitative PCR:
Tc /2Ct
;ð2Þ
where Tc is the total copies of DNA (absolute number can be calculated by
comparisons to known dilutions of standards) and Ct is cycle-threshold.
W¼CtDD CtM‚ ð3Þ
where W is the analytical window, CtDD is the cycle-threshold for the DD
and CtM is the cycle-threshold for the Mock (untreated) sample
R¼2W‚ ð4Þ
where R is the proportion of fragments that are refractory to treatment.
The window (W, in Equation 3) establishes the number of molecules that
participate in the restriction reactions. Windows of 2 cycles or less indicate
that >25% of the molecules are refractory (R, in Equation 4) to enzyme
digestion and amplification. As such, samples with analytical windows less
then 2 cycles are typically considered to be analytical failures.
DM ¼2ðCtMSRECtMÞ‚ð5Þ
U¼2ðCtMDRECtMÞ‚ð6Þ
where DM is the proportion of densely methylated template DNA, U is the
proportion of Unmethylated DNA, CtMSRE is the Cycle-threshold of MSRE
sample and CtMDRE is the Cycle-threshold of the MDRE sample.
If CtMSRE CtM is less than 1.0, then it is more accurate to calculate
DM using the CtMDRE and calculate DM as the remainder after subtracting
off the proportion of U and R (i.e. DM ¼1UR). Similarly, if
CtMDRE CtM is less than 1.0, then it is more accurate to calculate U
using the CtMSRE and calculate U as the remainder after subtracting off the
proportion of DM and R (i.e. U ¼1DM R). One can verify the
accuracy of these calculations by considering the difference between CtDD
and CtMSRE/MDRE. If the methylated gene copies are in fact present
following MSRE restriction one would expect them to be destroyed in a DD.
Conversely, if the unmethylated gene copies are in fact present following
MDRE restriction, one would expect them to be destroyed by the DD.
Confidence in the DM and U calls made in this way is assessed by the size of
Ct difference between DD and the MSRE/MDRE. Large Ct differences elicit
higher confidence. Measurements with a DCt less than 0.5 cannot be resolved
apart from machine error.
IM ¼1DM U‚ ð7Þ
where IM is the proportion of Intermediately methylated (IM) DNA.
Some molecules can be methylated such that both enzymes are capable of
cutting them. That is, they contain 5 mC but are not completely methylated.
They exist as part of each of the above calculations. Typically the fraction of
IM molecules are identified and calculated only if the DCt of the UT-MSRE
and UT-MDRE are each above or near 1.0 (e.g. >0.7). An important note
about this method is that the IM copy calculation contains both the error
functions associated with the MD and U calculations; typically adjusting this
calculation for the amount refractory will not affect the calculation.
All DCt calculations yield a unit-less parameter reflecting relative
proportion of template DNA between various treatments. The fold treatment
effects can be applied to the total calculation so that each of the molecular
proportions (DM, U and IM) can be converted back into a unit of copies.
RT-PCR expression analysis
A five point (neat, 1:5, 1:25, 1:125: 1:625) 5-fold dilution curve of cDNA
into water was used as template for limited cycle end-point PCR. No-
template (water) and genomic DNA (positive) controls were run in parallel.
Primers designed to exonic sequences which span the 30most intron in either
the IRX3 gene or the GAPDH gene were used to monitor expression. The
IRX3 primers (Supplementary Table) would amplify a 463 bp product if
genomic DNA were the template, while cDNA should yield a product of
293 bp. Similarly, the GAPDH control primers (Supplementary Table)
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should yield a product of 380 bp if genomic DNA were the template, while
cDNA should yield a product of 273 bp. The PCR was performed using
the Epicenter (Madison, WI) FailSafe 2·buffer G in a 12 ml reaction. The
following cycling parameters were used for both loci: 1 repetition of 95C
for 5 min, 30 repetitions of 95C for 30 s, 65C for 15 s, 72C for 15 s,
1 repetition of 72C for 10 min. Amplification products were visualized
using ethidium-stained agarose gels, and were quantified using an Agilent
Bioanalyzer 3100 (Palo Alto, CA) and the DNA 12000 kit.
RESULTS
Methylation profiling was performed using the methylation-
dependent restriction enzyme McrBC to separate methylated
and unmethylated DNA fragments for labeling and hybridi-
zation to microarrays (14). The profiling scheme is depicted
in Figure 1. First, purified genomic DNA from a sample of
interest is randomly sheared into a uniform size range. The
sheared DNA is then split into four portions: two mock
treated and two McrBC-digested. In order to restrict DNA,
McrBC requires that two methylated half sites (R
m
C, where
R¼A or G) lie within 40–3000 bp of each other (optimal
spacing 55–103 bp); cutting has been demonstrated to occur
between the two half sites in the proximity of one half-site
(20–27). Each matched treatment pair was then
size fractionated by electrophoresis and the high-molecular
weight DNA that was resistant to treatment was purified from
excised agarose gel slices. We have employed independent
digests so that any variance during digestion can be assessed
across technical replicates. The purified DNA represents the
whole genome in the mock-treated sample and the unmethy-
lated fraction of the genome in the McrBC-treated sample.
Next, the same mass of the whole genome sample
(untreated, UT) and the methylation-depleted genome sample
(treated, T) was independently labeled with different
fluorophores. The ratio of mock-treated or ‘untreated’
genome (UT) to methylation-depleted or ‘treated’ genome
(T) dye-signal is obtained for each feature (UT/T). Digestion
with McrBC removed more than half of the DNA from the
T sample, but because the same amount of T and UT DNA
was used for each labeling, unmethylated sequences will
be enriched in the T sample. This mass normalization step
increases the signal from unmethylated sequences, and makes
these measurements more reproducible, but requires careful
subsequent normalization to internal control probes. The
ideal set of control probes are sequences which are devoid of
McrBC half-sites for a large stretch of sequence (i.e. >1000
or >2000 bp). The resultant two color sample datasets yield
hybridization ratios for each feature, and these are assigned a
color: blue (low UT/T ratio ¼unmethylated), yellow
(moderate UT/T ratio) or red (high UT/T ratio ¼methylated)
in Figure 1. Sample analysis utilized a duplicated dye swap
on pairs of independently treated and UTs for each genome,
requiring four arrays and allowing a balanced Latin-square
analysis design (See Materials and methods).
Methylated sequences will have a ratio UT/T that
approaches infinity (because of the depletion of methylated
DNA in the T sample) while unmethylated sequences will
have a low UT/T ratio that should be less than 1 because of
the enrichment caused by the mass normalization of the UT
and T targets. In practice, because the genomic DNA is
sheared to a finite size, probes that are adjacent to methylated
sequences are also capable of detecting that methylation
(feature Y, Figure 1; yellow and orange probes, Figure 2).
The distance at which probes can detect adjacent methylation
is related to the degree of shearing, and was termed
‘wingspan’.
DNA methylation profile of LN-18
The stage IV oligodendroglioma derived cell line LN-18
was chosen as the subject genome for this experiment
because it was previously demonstrated to be homozygous
for a deletion of the p16(CDKN2A) locus on CH9, which
acted as a control region for ploidy assessment (see below).
Features representing the CDKN2A region were included on
the microarray in addition to the CpG islands (CGI) and TSS
throughout the annotated genome (above and Materials and
methods). The features in Figure 2 are colored according to
the representative methylation profile data [Robust Multi-
array Average (28) (performed by NG Systems, WI), linear
interpretation] obtained from a duplicated dye swap analysis
of LN-18. The color scheme is depicted as a gradient from
blue (no methylation) to red (highest-methylation). Yellow
features detect intermediate levels of methylation or methy-
lation in regions adjacent to the probe due to wingspan.
A substantial number of methylated regions (red) are evident,
as well as a large number of unmethylated regions (blue).
Globally, each autosome displayed a trend of increasing
methylation from pericentromeric regions to telomeres
(Figures 2 and 5).
The results from the statistical analysis of the LN-18
genome’s cytosine methylation profile are depicted in
Figure 3. The signal at each feature was first corrected for
technical and biological variation using ANOVA
(see Materials and methods), and then normalized to features
that represent 1 kb segments of the human genome that
contain no McrBC half sites (RCG ¼0/kb, denoted hereafter
as RCG0). The normalized data were then tested using a
Fig. 1. MethylScope array analysis procedure and theoretical results A
schematic of three array probes (X, Y and Z) arranged along a chromosome
is shown. If the DNA near feature Z is heavily methylated (vertical bars)
approximately half of those methylated CG dinucleotides will be half-sites
for McrBC (R
m
CG). Sheared and size-selected genomic DNA was labeled
with Cy5 (red), while McrBC treated DNA was labeled with Cy3 (blue).
There are 13 red fragments and 13 blue fragments as a consequence of
the mass normalization prior to target synthesis. Fragments which contain
two R
m
CG sites have been depleted from the Cy3-labeled population due
to the action of McrBC, while unmethylated blue fragments are enriched
by mass normalization. The two color array hybridization results are
depicted as blue circles for unmethylated, yellow for intermediate or
adjacent methylation, or red for densely methylated. The relative signal
from the two colors is indicated as a log
2
ratio.
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null-hypothesis in which whole genome (UT) signal should
be the same as the methyl-depleted (T) signal if the
sequences surrounding a given feature were unmethylated.
Panel A depicts a graph of the LN-18 methylation ratio
(y-axis) by feature name (x-axis). The methylation data are
depicted as the ANOVA corrected signal ratio [log
2
(UT/T)].
Each gene was allowed to have its own variance. In this
analysis, methylated sequences are expected to have a
positive log ratio, while unmethylated sequences are
expected to have a log ratio of zero (corresponding to a
UT/T ratio of 1.0). The data points are colored according to
the multiple comparison corrected P-values based upon an
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
X
Y
Un
0 50000000 100000000 150000000 200000000
Base pair
Chromosome
Fig. 2. Methylation profile of LN-18. (A) Each of 21 294 60mer probes were mapped onto the human genome (NCBIv35) by BLAST and is depicted as a
vertical line from the Watson (above the line) or Crick (below the line) DNA strand. Areas devoid of probes represent the centromeres, NOR or 5S gene
clusters. DNA methylation from the LN-18 genome is measured by the UT/T ratio (see text for details) depicted as blue (unmethylated), yellow (IM)
to red (densely methylated).
Fig. 3. ANOVA results of LN-18 DNA methylation profile DNA methylation values are plotted for each feature, grouped by category [CGI (black bar),
CDKN2A/B (black arrow) and TSS (red bar)]. Methylated sequences have a large positive log ratio while unmethylated sequences have a zero or negative
log ratio. Each probe is represented by a color which reflects the statistical significance of the methylation measurement. Blue probes pass the Holm
multiple testing correction and have a P-value <0.001. Pink probes pass the FDR multiple testing correction and have a P-value <0.05. Features indicated
by gray probes are not significantly different from the RCG0 controls (and are likely unmethylated). (B) depicts the ANOVA corrected signal data for
each probe divided by its standard error (i.e. t-test values) on the y-axis. The x-axis is the same as (A).
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alpha level of 0.05. Blue data points passed the Holm
multiple testing correction and have a P-value <0.001. Pink
data points passed the FDR correction and have a P-value
<0.05. Ratios that are not significantly different from the
control RCG0 features are depicted in gray. Panel B depicts
the same data using the respective t-test statistic values
(y-axis) with the same x-axis. The t-test data representation
allows a visual assessment of the variation on a per feature
basis since the t-test values are the log
2
(UT/T) signal divided
by the standard error for that probe; it corresponds directly
with the significance results of panel A.
Table I summarizes the ANOVA results. Approximately
60 loci were used as RCG0 controls for normalization
(Table I). Over half of the 21 047 measurements detected
statistically significant DNA methylation levels relative to
the controls, with 4152 significant methylated regions
(log ratio >0) and 6994 significantly unmethylated regions
(log ratio <0) (Table I). The existence of unmethylated
regions suggests that the controls could in fact detect
some methylation, probably because of ‘wingspan’ from
surrounding, RCG containing features.
CpG Islands (CGI) have been characterized as CG rich
genomic sequences that do not show CG depletion; they
are often in association with genes [(29), for a review see
(30)]. CGI are generally thought to be devoid of 5mC,
which accounts for the absence of CG depletion which is
driven by the mutagenesis of 5mC over evolutionary time,
(31–33). Aberrant cytosine methylation of CGI has been
associated with inactivation of tumor suppressor genes in
human cancers, consequently the DNA methylation status
of CGI is of immense interest (5). There are 9787 CGI
probes in our design, which reflects roughly 1/3 of the total
CGI content in the human genome. The BAC based CGI
designed features performed similarly to the UCSC
identified CGI; both were present in the same frequency
in both the significant methylated and unmethylated loci
(Table I).
As expected, very few (1–2%) of the 9786 CGI features
reported dense methylation, whether or not they corre-
sponded to known TSS. However, almost 1 in 3 CG islands
had low levels of methylation. In these cases, methylation
may be confined to sequences near, but not within the CGI.
Features in the region would still detect signal because of
‘wingspan’. Bisulphite sequencing and other validation
methods were used to test this idea, as described below.
Independent verification suggests high accuracy
with a quantitative capacity
Independent verification of DNA methylation levels was
performed using two methods. The first was clone-based
bisulphite sequencing. 13 loci were selected from a range of
the microarray data, and 1 locus devoid of RCG motifs was
selected. More than 10 clones were analyzed per locus (19).
The locus devoid of RCG motifs was substantially methy-
lated, and because no McrBC half-sties were in proximity of
the probe it yielded a negative log ratio (Supplementary
Table). Of the 13 remaining loci, 11 yielded statistically
significant measurements: 6 were methylated and 5 were
unmethylated according to the microarray experiments. In 9
of the 11 cases, the methylation occupancy information
determined by bisulphite sequencing agreed with the array
prediction (Figure 5A) (Supplementary Table). Interestingly,
the data from the 9 concordant suggested that there may be a
relationship between the array’s output ratio value and the
average regional 5 mC occupancy (Figure 4A–C). In the two
discordant cases, the data were consistent with a wingspan
effect upon the probes (Figure 4A, two points off line). Panel
B contains bar-charts depicting the average methylation
density at each locus. For each locale the y-axis depicts the
average methylation occupancy at the CGs within the
analyzed interval. The CGs are spaced along the x-axis by
their relative position.
The second verification technique employed a more high-
throughput quantitative PCR approach recently developed by
Table I. ANOVA results and CpG Island methylation analysis from LN-18
P<0.001
Holm
P<0.05
FDR
Significant 5 mC
Total % Sig V >0.75
Dense
0.05 <V<0.75
Sparse
V<0
Unmethylated
Not Sig
CGI 9786 1387 6461 66% 3326
Within probe 2964 662 2232 75% 71 797 1361 732
Adjacent to probe 6822 725 4229 62% 32 1321 2976 2594
2% 33% 67%
CGI TSS 8403 971 5510 66% 2893
Within probe 1761 288 1312 75% 19 353 940 449
Adjacent to probe 6642 683 4198 63% 30 1261 2907 2444
1% 29% 70%
Total features 21294
Features considered 21047 2117 11146 53% 9901
TSS 19595 1648 9946 51% 95 3425 6426 9649
BAC annotated CGI 1395 449 1069 77% 75 573 421 326
Other 304 20 131 43% 1 32 98 173
1% 19% 33% 47%
Not considered 247
FDR¼false discovery rate, Holm ¼Holm error correction. The significance level for both tests was set at 0.05. CGI ¼UCSC annotated CpG Island.
Adjacent to probe means annotated CGI was located by position ± 500 bp of probe. Dense, Sparse and Unmethylated refer to the array ratio range and the
implied 5 mC content based upon the MethylScreen calibration (Figure 5C). TSS ¼Transcription start site. The methylation values are represented by V and
correspond to the corrected signal (RCG normalized log
2
(UT/T)), cut-offs set based upon results from Figure 5C. Dense ¼methylated probe and region,
Sparse ¼either IM probe and region or unmethylated probe adjacent to methylated DNA, Unmethylated¼substantially devoid of 5 mC. Features not
considered include the 62 used for normalization, and 185 that mapped to multiple loci in the genome.
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A
B
C
Norm log2 (UT/T)
Norm log2 (UT/T)
Norm log2(UT/T)
Fig. 4. Independent accuracy analysis suggests quantitative capacity within array analysis. (A) A scatter plot of the average 5 mC density determined
by bisulphite sequencing obtained from the 11 statistically significant loci are plotted versus the normalized log
2
(UT/T) for each feature.
The R
2
value obtained reflects the result of a linear correlation analysis. Two of the 11 were considered to be discordant with the regression line.
(B) Bar charts depicting high resolution cytosine methylation analysis from loci including the points i, ii and iii. Each of the charts
indicate the position of each CG analyzed within each window on the x-axis, and the average relative 5 mC occupancy for greater than 10 clones at each
site within the locus on the y-axis. The ANOVA corrected signal log ratio for each locus is indicated, along with the multiple testing correction P-value
intervals the measurements fell within, are the inset. The position of the array probe within the interval is denoted by the black bar in each window.
(C) A scatter-plot of methylation values for 84 genomic loci is shown. Signal ratios from MethylScope are plotted against the change in cycle threshold
(DCt) measured by MethylScreen quantitative PCR (34). The data are categorized into four quadrants: In clockwise order, false negatives, methylated
predictions, false positives and unmethylated predictions. Methylated and unmethylated features are those for which both assays agree. A cut-off of
0.5 cycles is used for the MethylScreen data, as explained in the text (34). The data points are colored according to their significance level: FDR¼open circle,
Holm¼filled circle, grey points are not significant.
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our group [MethylScreen, see Materials and methods and
(34)]. We randomly selected 50 loci without regard to the
magnitude of the microarray measurement, and selected an
additional 34 loci representing the range of measurements.
We performed MethylScreen upon these 84 loci, 4 of which
were included in the bisulphite sequencing study. Figure 4C
depicts the log ratio of UT/T (x-axis) plotted against the DCt
obtained by MethylScreen (y-axis). Like Figure 3A, the data
points are colored by their statistical significance level,
gray data were not significant. The limit of detection of
methylation by MethylScreen was set to a DCt of 0.5 cycles.
This was because under ideal circumstances, quantitative
PCR has a platform error of ±0.5 Ct. Using these criteria, the
upper left quadrant represents false negatives in which
significantly hypermethylated regions were not detected by
the array (6 total). The upper right quadrant represents
methylated features (25 total) as determined by both assays.
The lower right quadrant represents false positives (3 total).
Finally, the lower left quadrant represents unmethylated
features (25 total). A total of 25 of the randomly selected loci
were no more or less methylated than the controls and so met
the null hypothesis (gray). Of these, only 5 were substantially
methylated according to MethylScreen, indicating that only a
small number of features in this class failed to be detected by
the array hybridization, with the remainder being unmethy-
lated. Altogether, the qPCR (MethylScreen) and microarray
(MethylScope) approaches agreed 85% of the time. These
validation experiments, along with the bisulphite sequencing
data, indicate that the MethylScreen and MethylScope techno-
logies are highly reproducible, and support the view that
the MethylScope array hybridization approach generates an
accurate characterization of genome-wide cytosine methyla-
tion. In the few cases where they disagree, the level of
methylation was almost always very low, and in only three
cases (one false negative and two false positives) did the
MethylScope and MethylScreen techniques differ markedly.
We cannot exclude the possibility that both ‘false positives’
were detecting methylation adjacent to the probe.
Simultaneous methylation profile and copy
number assessment
LN-18 is homozygous for a deletion of the cell cycle
regulatory gene CDKN2A (35), and so we included 161
probes from this region on the array as controls. Close
inspection of Figure 3A appeared to indicate there was a lack
of hybridization of either the UT or T fractions to the probe
elements (black arrow) alphabetically between the CGI
features (red line) and the promoter features (black line).
The 161 probes dedicated to CDKN2A/B fall into this region.
In an attempt to confirm that the array could detect the
absence of the CDKN2A locus, we sorted the t-values
(signal/error) such that the features were depicted in
chromosome order. CH7, CH8 and CH9 are illustrated in
Figure 5. Another region of the genome with a similar feature
density to that present within the CDKN2A/B region is the
HOX gene cluster on CH7, represented by 41 features. The
boxes highlight each region in Figure 5. The HOX cluster is
hypermethylated while the CDKN2A region on CH9 has no
significant signal. The mean of the t-values corresponding to
the 161 features from the CDKN2A region is 1.8 with a
standard deviation of 1.3, indicating that for the vast majority
of the probes the signal (UT/T) was of the same order as that
of its standard error, and therefore not above background
(Supplementary Data). We estimated the size of the deletion
to be <12 Mb; it likely lies between base pair positions CH9:
19219777 and 31243697.
LN-18 displays HOX cluster hypermethylation
The HOX interval is 165 kb, and our array contains
approximately 1 feature per 4 kb. The values from our
analysis are indicated as a blue line in Figure 6. Methylation
profiling data from the same region was previously obtained
from colon cancer and fibroblast cell lines, by using immuno-
precipitation with antibodies raised against 5 mC and
overlapping BAC clones on the array (36), and these data
are indicated as a green line in Figure 6. In this particular
region, the BACs were larger than average, with more than
80 kb between the midpoints of the BACS. The TSS for the
genes in the interval are depicted as arrows pointed in the
direction of their transcription (not to scale). Thus, while
both analyses indicate methylation, locally unmethylated
regions could also be detected using our higher resolution
approach, afforded by a higher density and smaller size of
probes used. Unmethylated regions included the TSS from
only 3 of the HoxA genes surveyed (see Discussion).
Indications of biological relevance
In an effort to discern whether or not the methylation
events detected were biologically meaningful, we ran the 25
quantitative PCR assays that detected regional methylation
against a panel of genomic DNAs from six brain cancer
derived cell lines, as well as pooled male and female
peripheral blood which were collected independently from
the tumor origin. We employed the CGI in the TH2B gene
as a positive control for single copy gene associated 5 mC
detection (37). Among these loci, 8 were differentially
methylated in the majority of cell lines relative to
peripheral blood (Figure 7). Two of these 8 loci were
previously demonstrated to be subject to DNA methylation
mediated gene silencing; RASSF1A (38) and E-Cadherin
(CDH1) (39). The remaining six genes that displayed
/
Norm T value (log2(UT/T)/SE)
Fig. 5. LN-18 methylation profile reveals ploidy monitoring capacity
Methylation data from chromosomes 7, 8 and 9 are represented by the
normalized test statistic value t for each feature, aligned by genome
co-ordinates on the x-axis. Large positive t-test values reflect high-density
methylation with small variance, while ratios near 1.0 represent low
signals with high variance (background signal). The boxes highlight
the HOX cluster on CH7, and the CDKN2A/B locus on CH9. LN-18 is
homozygous for a deletion of CDKN2A, while the HOX cluster is heavily
methylated. The estimated chromosomal positions of the deletion are
indicated on CH9.
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hypermethylation relative to blood, and normal brain tissue
have not been reported to be subject to epigenetic silencing.
These newly discovered methylated loci were associated
with the IRX3, BMP7, WNT10A, WNT6, RARaand
ZGPAT genes. The same panel of loci was differentially
methylated in a comparison of primary brain tissue from
three apparently normal patients in comparison to primary
tumor tissues derived from two astrocytomas and one
glioma (GBM).
IRX3 CGI methylation is correlated with IRX3 overexpression
Interestingly, the methylated CGI detected at the IRX3 locus
corresponds to a an exon rather than the gene’s promoter
(Figure 8). A high resolution analysis of the annotation and
DNA methylation data obtained from the MethylScope
analysis of the LN-18 genome is depicted in Figure 8A.
The promoter of IRX3 was significantly unmethylated, while
the CGI in exon 2 was methylated. Bisulphite genomic
sequence was obtained from the IRX3 exonic CGI
(Figure 8B). The graph displays the average methylation
occupancy per CG in the interval when considering the 38
clones sequenced, the X and Y axes are the same as those in
Figure 4B. MethylScreen and bisulphite sequencing analyses
demonstrated that this region is unmethylated in normal
blood and normal brain DNA (Figure 7 and data not shown).
The position of the array feature is denoted by the gray bar in
Figure 8B. There are six HhaI restriction sites in the analyzed
region, five of which appear to be nearly always occupied.
The MethylScreen amplicon designed for the qPCR analysis
presented in Figure 7 surveys all six of these HhaI sites. This
amplicon was then employed in a MethylScreen assay
(Figure 9). The kinetic profiles obtained from each of the
four conditions of the assay obtained from each genomic
sample are displayed. The data depict results from cell lines
(upper row), normal brain tissue (middle row) and three
brain tumors (lower row); each genome’s results are color
coded by their treatment: mock restriction (red), HhaI
restriction (green), McrBC restriction (blue) and a HhaI +
Fig. 6. High resolution methylation profile of the CH7:HOX cluster in LN-18 cells. Methylation in the CH7 HoxA gene cluster. Genes are represented as
black arrows (not to scale). The arrows reflect the direction of transcription for the TSS features, their tail is aligned to the end of the feature. Methylation data is
from LN-18 cells reported here (blue dashed line) and from immuno-analysis of SW48 cells (green line) reported previously (36) plotted against CH7 (NCBIv35).
The average signal ratio from SW48 was normalized by the average normal fibroblast control, scaled for comparison and the midpoint of the BAC insert
was assigned the normalized array signal ratio (yellow x-box). The standard error of the LN-18 measurements is indicated by error bars. Features passing
the Holm correction are indicated by blue (P<0.001), those passing the FDR correction (P<0.05) are pink. Those whose methylation level was not
significantly different from unmethylatable (RCG0) controls are indicated in gray.
Fig. 7. Discovered differentially methylated loci molecularly classify brain tissues and cell lines. Results from two independent qPCR methylation assays
at 10 genomic loci are depicted. Red cells indicate substantial DNA methylation at the genomic locus (DCt >>2). Green cells indicate little detectable 5 mC
at the locus (DCt <1). Yellow cells indicate an intermediate amount of methylation (1 <DCt <2).
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McrBC DD (orange). The pie chart insets display the result
of each assay where red ¼fraction of molecules with all
HhaI sites occupied, green ¼fraction of molecules devoid
of RmC methylation between the primers, and the yellow ¼
fraction of molecules susceptible to both treatments
(see Materials and methods for calculations). The exon
appeared to be hypermethylated in all the tumors and
cell lines relative to the three independent normal brain
samples, reminiscent of the differentially methylated regions
(DMR) in imprinted genes like IGF2 and H19. We therefore
A
B
Fig. 8. High resolution analysis of IRX3 hypermethyaltion reveals an exonic rather than promoter location. (A) A gbrowse view of the NCBIv35 genome
build with the ENSEMBL_36 annotation is depicted along with the LN-18 microarray measurements of local DNA methylation. The arrow scale at the top
denotes the base pair position along the chromosome. The first data track contains a bar representing the IRX3 locus, the arrow depicts
its direction of transcription. The second track is the splicing model of the transcript as determined by ENSEMBL_36. The third data track (RCG) depicts
the relative abundance of methylateable McrBC recognition sites within a defined 1 kb genomic window. The fourth track (OGHA) denotes the positions of
the two array features (60mer probes). The fifth track depicts a scaled black bar chart of the ANOVA corrected array measurements for the two features
Log
2
(UT/T); the actual measurements along with their P-values are indicated below each feature. Positive log ratios indicate 5 mC and negative log ratios
indicate its relative absence. (B) Bisulphite sequencing results from analysis of the LN-18 methylation pattern surrounding the array feature are depicted
as a scatter plot. The x-axis depicts the relative position of each base pair within the interval. The average methylation occupancy at each CG is represented
as an open circle. The area under the dashed line between points represents the methylation density. Black arrows denote the position of HhaI restriction
sites in the interval (one was outside the sequenced region). The black circles represent methylation occupancies of CGs at HhaI restriction sites within
the interval. The gray bar is the position of the array probe.
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hypothesized that IRX3 may be over-expressed in tumors
and cell lines (due to its intragenic position) rather
than silenced. Since the MethylScreen results from normal
brain tissues are not consistent with hemizygous methylation,
we do not suspect IRX3 as being imprinted (Figure 9,
middle row).
The expression of IRX3 in tumor and normal brain
samples was assessed using semi-quantitative PCR ampli-
fication of dilutions of oligo-dT primed cDNA libraries.
IRX3 expression was measured relative to GAPDH expres-
sion as a positive control (Figure 10A). The MethylScreen
IRX3 DNA methylation result from each sample is depicted
in Figure 10B. The expression amplicon selected for each
target spanned an intron of 100 bp or more to eliminate
genomic DNA contamination of the cDNA libraries. In all
cases, the level of DNA contamination was negligible (data
not shown). Comparison of amplification from each dilution
indicated that all three of the tumors expressed between
5- and 8-fold more IRX3 than the normal tissue library
(with a SD of 3-fold). Commercially acquired astrocytoma
cDNA libraries behaved similarly (data not shown), although
matched DNA samples were not available. LN-18 produced
more than 20-fold more IRX3 than normal brain, and had the
most methylated DNA (Figures 10A and B, 9). The U87MG
cell line expressed 4-fold less IRX3 than LN-18, but
5-fold more than the normal tissue, making its expression
level most similar to the tumor libraries (data not shown).
Interestingly, it was also the sample with a methylation
pattern most similar to the tumors (Figure 9).
DISCUSSION
Precise, accurate and quantitative whole genome
DNA methylation profiles
DNA methylation is a stable epigenetic signal associated
with gene silencing (2), and quantitative measurement of
DNA methylation during disease has immense appeal as a
diagnostic tool, especially in cancer. Several DNA methyla-
tion profiling approaches have been developed (14,36,40–50).
Few have demonstrated the ability to identify both methy-
lated and unmethylated sequences with statistical confidence,
and fewer yet, have the capacity to monitor the entire
genome.
Biologically meaningful changes in DNA methylation
typically occur over regions with multiple CG dinucleotides,
Fig. 9. The IRX3 exonic CGI is hypermethylated in brain cancer cell lines and tumors but not normal brain samples Representative SYBR green quantitative
PCR kinetic reaction profiles are depicted for nine templates following four MethylScreen treatments. The top row is brain cancer cell lines. The middle
row is normal brain samples. The bottom row consists of two primary astrocytoma and one GBM sample. Within each profile the four treatments of MethylScreen
are indicated by colors: Red ¼mock treatment, Green¼HhaI treatment, Blue¼McrBC treatment and Orange¼DD (HhaI +McrBC). The inset pie-charts
reflect the interpretation of the methylation status of the molecular population (See Materials and methods) based upon the average profile data. Red ¼proportion
with uniform HhaI methylation, Green ¼proportion with low to no methylation, Yellow¼proportion IM. All the tumor samples had a McrBC conditioned
change in Ct >2 cycles; indicating the majority of molecules present contain aberrant methylation relative to the normal samples.
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which are methylated in concert. Thus, regions of methylated
sequence rather than single bases may be the most important
unit of information in the epigenome. The unique enzymatic
activity of McrBC is well suited for identifying these regions,
as it recognizes at least half of the potentially methylated
CG-dinucleotides in the human genome (RCG half sites); no
other enzymatic system has such a broad capacity. Further,
the methods described here monitor methylation of repeated
sequence elements as well as unique sequences. Because
each array feature has the capacity to detect methylation
adjacent to it, unique features may be employed to monitor
adjacent repeat methylation (Figure 1). The first reported
epigenetic alteration in cancer genomes was a loss of
methylation from repeated elements (51), and such changes
in methylation may have profound consequences for the
genome as a whole (52). Our approach is also unique in its
ability to report the relative density of 5 mC around each
feature (Figure 4) as well as to identify changes in regional
genomic copy number (Figure 5); although a relatively high
probe density was required to observe this effect.
The purpose of this study was to demonstrate that
methylated and unmethylated sequences could be detected
within the human genome, and to understand the precision
and accuracy of the cytosine methylation profiling proce-
dure. Because a duplicated dye-swap experiment was
performed within each DNA sample, and each feature was
replicated on the array, a stringent measure of precision
can be gauged though a correlation analysis between the
independent technical replicates. The average correlation
(R
2
) was 0.77 for LN-18 and ranged between 0.72 and
0.83. Recent experiments involving nearly 50 arrays have
displayed R
2
averages of 0.87 and a range of 0.81 to 0.93
(J. M. Ordway, R. M. Kendall and J. A. Jeddeloh,
unpublished data). Using two independent validation
approaches, we have demonstrated that the MethylScope
array hybridization method is robust and accurate. In total,
between the two verification approaches, 95 loci were tested,
70 of which were identified as either significantly methylated
or unmethylated according to the microarray. The array
measurements agreed with the verification measurements
59 times so that the accuracy of the array approach is
at least 84% (59/70), assuming that bisulphite sequencing
and MethylScreen are almost always accurate. Importantly,
the verification of data obtained from measurements which
were not statistically significant yielded appropriate results;
i.e. these features were predominantly unmethylated as
predicted by our analytical hypothesis (20/25). However,
future analyses would not be able to identify the true status
of these measurements without a second analytical method
(i.e. not array only).
These values should be considered a conservative estimate
of accuracy because of wingspan effects (Figure 1). The
MethylScreen quantitative PCR approach is only sensitive to
DNA methylation between the primers while the array itself
is capable of reporting methylation of a larger region.
LN-18 epigenetic landscape
Our analysis of the LN-18 genome revealed more than 4000
methylated loci, by design the majority of which were
associated with the transcription start sites of human
genes (Table I). CpG islands (CGI) were disproportionately
unmethylated in agreement with previous reports [for review
a see (30)]; however far more were hypermethylated than
anticipated if both sparse and dense methylation are
considered (Table I). A conservative estimate of the CGI
hypermethylation is revealed by the CGI features reporting
dense methylation, which were 2% of the total CGI features.
However, since approximately half of the features with
intermediate array ratios were also methylated within the
feature, upwards of 19% (2 +17%) of the CGI in this
genome are methylated (Table I). Perhaps this unexpected
finding reflects the more extreme epigenomic representation
displayed by cancer derived cell lines.
Importantly, the CGI associated with TSS were most
often associated with unmethylated and intermediate array
ratios. This observation highlights the importance of their
unmethylated status, since half of these intermediate
measurements will likely represent unmethylated features
with adjacent methylated DNA. Further, it suggests that
many more TSS may be susceptible to the spread of silencing
Fig. 10. IRX3 CGI hypermethylation correlates with over-expression (A) depicts Bioanalyzer results obtained following quantification of RT-PCR
products in reactions from cDNA libraries prepared from normal brain, tumor and cell line controls. The expression of IRX3 was monitored relative
to GAPDH as a benchmark. The normal library was purchased from Invitrogen. Neat, 1:5 and 1:25 reflect the cDNA template dilution factor
(into water). (B) depicts the IRX3 exonic CGI MethylScreen results from Figure 9.
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from these proximally methylated elements than originally
anticipated (5,30). The use of ultra-high density microarrays
with much higher feature densities will permit the more
accurate mapping of methylation changes in the future.
Among the independently verified hypermethylated loci
that the array discovered were E-cadherin and RASSF1A.
These loci were important for two reasons. First, they served
as an internal positive control since their ability to become
hypermethylated had been shown previously (38,39). Second,
the methylation pattern present at the RASSF1A promoter
region and the high density feature placement suggested
wingspan: As predicted, features some distance from the
verified center of methylation, detected methylation proximal
to them, though with lower confidence (data not shown).
Wingspan was confirmed elsewhere by bisulfite sequencing
(Figure 4).
Because a given feature seemed to be capable of detecting
adjacent methylation, the quantitative value obtained from
any probe represents a complex output. Signal from a feature
provides not only data regarding the density of methylation
surrounding the feature, but also information about its
chromosomal context. Future studies are aimed at analytical
methods to de-convolute the contribution of both signals.
Comprehensive nature of analysis supports
epigenetic mapping
Recently, the need for large-scale epigenetic mapping of the
human genome (The Human Epigenome Project) has been
realized in the context of cancer, neurological and other
diseases (53). The American Association for Cancer
Research, National Human Genome Research Institute, as
well as National Cancer Institute have recently called for a
Human Epigenome Project to determine genome-wide
patterns of DNA and histone modifications. Such a coordi-
nated effort would run in parallel with the Cancer Genome
Anatomy Project which will identify genetic changes in
cancer cells. Epigenetic profiles are also important in testing
stem cell lines for their suitability for therapeutic cloning
(54,55). Preliminary efforts to generate such epigenomic
maps have included the large scale bisulphite sequencing of
several human chromosomal regions (56,57); the analysis
of modified histones associated with chromosomes 21 and 22
by chromatin immunoprecipitation (ChIP) chip (58), as well
as array-based methylation profiling using plasmid and BAC
subclones from the human genome (36,59).
The method we describe here, in addition to detecting
biomarkers differentially methylated in disease, has potential
to rapidly map quantitative methylation differences through-
out the genome at very high resolution, and could make a
substantial contribution to epigenomic maps of this sort. The
analysis presented here demonstrates that any whole genome
effort will need better than 4 kb resolution in order to fully
capture the profile of the epigenetic landscape and resolve the
wingspan effects. Higher probe density would mitigate any
negative effects of this possibility and are readily achievable.
Targets of epigenetic modification identified
Several targets of DNA hypermethylation were identified in
the course of this characterization. We have independently
confirmed that the testis specific histone 2B (TH2B) locus is
methylated in at least 7 non-testes tissues (brain, cervix,
ovary, lung, colon, breast and lymphocytes), suggesting
conservation of epigenetic regulation from rodents to
humans (Figure 7 and data not shown) (37). This finding
makes the locus useful for a positive control. Ten of 13 HOX
cluster transcriptional starts sites were in methylated regions
in the chromosome 7 HOX cluster (Figures 5 and
6). It appeared that only three TSS in the region were subs-
tantially unmethylated, Q96MZ23, HOXA3 and HOXA11
(Figure 6 and Supplementary Table). Members of this HOX
cluster have previously been found to be concomitantly
hypermethylated in cell lines and tumors (60). These findings
suggest that domains larger than individual genes may also
be targets of epigenetic alteration. HOX hypermethylation
may be general to cell lines other than LN-18, since the
SW48 (colon tumor cell line) profile reported by Weber et al.
(36) has similar characteristics (Figure 6). However, the local
highs and lows in the HOX region (Figure 6) are only
apparent from our analysis. Interestingly, inappropriate
CDKN2A and HOX gene regulation in brain cancer, and
altered DNA methyltransferase functions have been mecha-
nistically connected through interactions with the polycomb
repressive complex member BMI1 (61).
Finding that 6 of the 8 differentially methylated loci
identified were novel hypermethylation targets suggests that
the full number of genomic loci susceptible to epigenetic
modification is large, and that the majority have yet to be
revealed. Four of these genes have been implicated in normal
brain development [IRX3 (62), BMP7 (63), WNT6 (64) and
WNT10A (65)]. Finally, RARais a member of a gene family
that includes other genes subject to hypermethylation in
cancer (66).
If this fraction of differentially methylated sequences (8 of
24) is representative of the whole dataset, we would predict
that approximately 1500 loci in the human genome might
behave similarly (35% of the 4152 methylated loci). This
value is in close agreement with an estimate put forth by
Baylin and colleagues (43). However, neither estimate
considers the likely existence of thousands of loci with the
opposite molecular phenotype (unmethylated in tumors).
This study identified a hypermethylated region in exon 2
of the IRX3 gene and coincident upregulation of IRX3
transcription in glial-derived primary tumors and tumor cell
lines, relative to normal brain. Given the reported functions
of IRX (iroquois) family proteins, this finding may have
implications for the development of gliomas. IRX family
genes encode homeobox transcription factors conserved
from nematodes to humans (67). In vertebrates, these factors
participate in regulation of proneural genes during early
neurulation (68) as well as in antero-posterior and dorso-
ventral subdivision of the neural plate [reviewed in (69)]. In
these contexts, regulation of IRX3 activity has been shown to
be directly responsive to signaling from both the Wnt and
sonic hedgehog (Shh) pathways (70,71). Traditionally, high-
grade gliomas (including glioblastoma and astrocytoma) have
been presumed to arise from glial cells. However, recent
studies support a model in which human glioma arises from
neural stem-like cells (72–75). Given that Shh functions in
maintaining neural stem cell characteristics (76), and ectopic
expression of Irx3 in chick embryos is sufficient to induce
Shh responsiveness (77), it is tempting to speculate that
overexpression of IRX3 may be functionally involved in glial
tumorigenesis. Furthermore, the interesting finding of coor-
dinate differential methylation of HOXA10, WNT10A,
WNT6 and IRX3 genes in primary glial tumors and tumor
cell lines raises the possibility of an underlying epigenetic
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signature that decouples these developmental factors from
their normal regulatory mechanisms.
The power of genomic survey to detect biomarkers is best
illustrated by the exonic CGI within the IRX3 gene. Current
views of the importance of epigenetic regulation in cancer
are based upon the belief that the regulatory regions of tumor
suppressor genes become hypermethylated and/or that the
regulatory regions of oncogenes may become unmethylated,
and that these alterations lead to changes in expression.
While certainly true, IRX3 serves as a reminder of the danger
implicit in strictly interpreting rules before the landscape of
epigenetic alteration has been fully characterized.
Clearly, comparing methylation profiles of normal brain
tissues and primary tumors with this data set would be the
optimal means to discover and characterize the number and
types of novel hypermethylation and hypomethylation
targets. Such studies using our array assay are presently
underway.
Supplementary data
Supplementary data are available at Carcinogenesis online
Acknowledgements
The authors thank Z. Lippman, V. Colot, R.Wilson, W.R. McCombie,
E.J. Richards, M. and J. Finney, S. Smith, R. Green and Nimblegen
Biosciences for their contributions to technology development. S. Peterson,
M. Smith, J. Fries, J. McMenamy, L. Trianni and T. Rohlfing provided
technical and other support. C. Tatham, D. Robbins and A. Garrido provided
network and data-base support. E.J. Richards provided critical analysis.
Conflict of Interest Statement: Orion Genomics, L.L.C. markets the described
MethylScopeTm (array based) and MethylScreenTm (qPCR based) tech-
nologies. R.A. Martienssen, J.D. McPherson, R.W. Doerge, and the authors
from Orion Genomics recognize a financial interest in the company.
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Received January 25, 2006; revised August 23, 2006;
accepted August 25, 2006
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