REPORT
DNA Methylation Signatures within the Human Brain
Christine Ladd-Acosta, Jonathan Pevsner, Sarven Sabunciyan, Robert H. Yolken, Maree J. Webster,
Tiffany Dinkins, Pauline A. Callinan, Jian-Bing Fan, James B. Potash,* and Andrew P. Feinberg*
DNA methylation is a heritable modification of genomic DNA central to development, imprinting, transcriptional regulation, chromatin structure, and overall genomic stability. Aberrant DNA methylation of individual genes is a hallmark
of cancer and has been shown to play an important role in neurological disorders such as Rett syndrome. Here, we asked
whether normal DNA methylation might distinguish individual brain regions. We determined the quantitative DNA
methylation levels of 1,505 CpG sites representing 807 genes with diverse functions, including proliferation and differentiation, previously shown to be implicated in human cancer. We initially analyzed 76 brain samples representing
cerebral cortex (n p 35), cerebellum (n p 34 ), and pons (n p 7 ), along with liver samples (n p 3 ) from 43 individuals.
Unsupervised hierarchical analysis showed clustering of 33 of 35 cerebra distinct from the clustering of 33 of 34 cerebella,
7 of 7 pons, and all 3 livers. By use of comparative marker selection and permutation testing, 156 loci representing 118
genes showed statistically significant differences—a ⭓17% absolute change in DNA methylation (P ! .004)—among brain
regions. These results were validated for all six genes tested in a replicate set of 57 samples. Our data suggest that DNA
methylation signatures distinguish brain regions and may help account for region-specific functional specialization.
Epigenetics—the study of information heritable during
cell division, other than the DNA sequence itself—might
help to explain the mechanism by which one tissue is
distinguished from another developmentally.1 Although
cells of varying lineage in a single individual share the
same DNA sequence, they remember their tissue of origin
when they divide. DNA methylation is an epigenetic mark
involving a covalent modification of the nucleotide cytosine that occurs in vertebrates at CpG dinucleotides
and is generally associated with gene silencing, with notable exceptions, such as some regulatory elements controlling genomic imprinting.2 Disruption of DNA methylation is a hallmark of cancer, with genomewide and
gene-specific hypomethylation and hypermethylation of
genes and genomic regions.3 DNA methylation is also
likely to be important in normal brain development. Rett
syndrome (MIM 312750), which involves loss of the normal MeCP2 methylcytosine-recognition protein, causes
loss of neurodevelopmental milestones, severe mental retardation, and motor dysfunction.4 Most studies of DNA
methylation in normal and disease-affected tissues have
been focused on individual genes. However, recent technology allows high-throughput methylation analysis of
larger numbers of CpG sites (from hundreds to thousands) across the genome.5
The human brain is a complex organ, and, although a
great deal is now known about variations in gene expression that distinguish brain regions, an epigenetic connec-
tion to brain anatomy has not been explored. Several studies have examined brain region–specific large-scale geneexpression variation in the mouse. Sandberg et al. found
that gene expression in the cerebellum was most distinct
when its profile was compared with that of the cerebral
cortex, the midbrain, and the hippocampus, with 23 genes
expressed uniquely in cerebellum and 28 genes absent
there, although expressed in other regions.6 A second
study found 1,489–3,220 genes (depending on the threshold used) differentially expressed across five brain regions.7 Finally, a third large-scale study of mouse brain
gene expression, The Allen Brain Atlas project, identified
region-specific gene expression across 12 brain regions.8
Similarly, microarray studies in human postmortem brain
samples have revealed substantial gene-expression differences among the caudate nucleus, cerebellum, and cerebral cortex, as well as differences of smaller magnitude
among cerebral cortex regions, including Broca’s area, prefrontal cortex, premotor cortex, primary visual cortex, and
anterior cingulate cortex.9 More recently, Roth and colleagues10 profiled 65 distinct human tissues, including 20
CNS tissues. They demonstrated a robust distinction between CNS tissues as a whole and various non-CNS tissues.
Furthermore, within the CNS tissues, they discovered region-specific transcriptional expression and a strikingly
distinct cerebellar profile, as compared with all other CNS
tissues.10 Although much research has elucidated brain region–specific gene-expression differences, no comprehen-
From the Center for Epigenetics (C.L.-A.; S.S.; T.D.; P.A.C.; J.B.P.; A.P.F.), Stanley Division of Developmental Neurovirology (S.S.; R.H.Y.), and Departments
of Medicine (C.L.-A.; T.D.; P.A.C.; A.P.F.), Psychiatry (J.B.P.), and Pediatrics (SS.; R.H.Y.), Johns Hopkins University School of Medicine, and Department
of Neurology, Kennedy-Krieger Institute (J.P.), Baltimore; Stanley Laboratory of Brain Research, Department of Psychiatry, Uniformed Services University
of the Health Sciences (M.J.W.), Bethesda; and Illumina (J.-B.F.), San Diego
Received May 18, 2007; accepted for publication August 13, 2007; electronically published November 1, 2007.
Address for correspondence and reprints: Dr. James B. Potash, Johns Hopkins Hospital, 600 N. Wolfe Street, Meyer 4-119, Baltimore, MD 21287-7419.
E-mail: jpotash@jhmi.edu
* These two authors contributed equally to this work.
Am. J. Hum. Genet. 2007;81:1304–1315. 䉷 2007 by The American Society of Human Genetics. All rights reserved. 0002-9297/2007/8106-0018$15.00
DOI: 10.1086/524110
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The American Journal of Human Genetics
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sive survey of methylation in rodent or human brain has
yet been attempted.
The recent application of high-throughput technology
to the field of epigenetics enables researchers to perform
large-scale studies of DNA methylation at thousands of
CpG dinucleotides across hundreds of genes. Here, we
measured methylation levels at 1,505 CpG sites representing 807 genes, using the Illumina GoldenGate Methylation Cancer Panel I platform,11 to determine whether
methylation profiles vary in brain tissues.
Snap-frozen tissues were acquired from the Harvard
Brain Tissue Resource Center at McLean Hospital, Belmont, MA, and from the National Institute of Child
Health and Human Development (NICHD) Brain and Tissue Bank for Developmental Disorders at the University
of Maryland, Baltimore. A summary of characteristics for
the 76 brain samples representing 43 individuals analyzed
in our study can be found in table 1. Care was taken to
dissect tissue away from white matter and blood vessels.
Genomic DNA was isolated for all samples included in the
study by use of the MasterPure DNA purification kit (Epicentre Biotechnologies) in accordance with the manufacturer’s specifications. Bisulfite conversion of 500 ng of
genomic DNA was achieved through use of the EZ DNA
methylation gold kit (Zymo Research). Bisulfite treatment
of genomic DNA results in unmethylated cytosine nucleotides being changed to thymidine while methylated cytosines remain unchanged. This difference is then detected as a C/T nucleotide polymorphism at each CpG site.
A total of 83 samples (76 brain samples with matched
specimens) were run on the Illumina platform, and a b
value of 0–1.0 was reported for each CpG site, signifying
the percentage of methylation. b values were calculated
by subtracting background with use of negative controls
on the array and taking the ratio of the methylated signal
intensity to the sum of both methylated and unmethylated signals.11 The Illumina GoldenGate methylation assay is reported to be accurate for b value differences ⭓0.17,
which was thus chosen as our threshold for methylation
differences. Sample quality was assessed by computing
mean methylation levels across all samples (0.33), excluding two outliers (four samples with means of 0.22 and
0.40). Array quality was also assessed through linear regression of the correlation of two pairs of replicate samples
showing r2 values of 0.991 and 0.982. Computational analysis was performed using the GenePattern12 comparative
marker–selection module, to define specific loci with the
greatest difference in methylation levels between tissues.
In defining our list of significantly different loci, we applied two constraints: (1) P ! .004, as determined by comparative marker–selection analysis (with use of a two-sided
t test statistic), and (2) mean methylation-level difference
across brain regions ⭓0.17.
In the first experiment, we examined 8 cerebral cortex
samples and 16 cerebellar samples from 24 individuals,
analyzing 1,532 loci representing 473 genes from two
sources. The first was a group of 380 CpG sites, including
www.ajhg.org
Table 1. Characteristics of 43 Individuals from
Whom 76 Brain Samples Were Analyzed
Characteristic
Aged (years)
Sex:
Female
Male
Indeterminate
PMId,e (h)
Race:
White
African American
Unknown
Cause of death:
Accident
Asphyxia
Cardiopulmonary
Drowning
Epiglottitis
Pancreatitis
Pneumonia
Thermal burns
Seizures
Suicide
Unknown
Diagnosis:
Normal
Autism
Bipolar
CBLa
(n p 34)
CMb
(n p 35)
PNc
(n p 7)
20.6Ⳳ13.6
28.6Ⳳ22.1
64.4Ⳳ24.3
6
27
1
19.2Ⳳ12.4
7
27
1
18.7Ⳳ10.6
1
6
0
20.5Ⳳ7.4
11
12
11
13
12
10
0
0
7
8
1
6
6
1
1
0
0
1
1
9
5
1
7
7
1
1
1
1
1
1
9
0
0
1
0
0
0
1
0
0
0
5
18
16
0
16
15
4
3
0
4
a
CBL p cerebellum.
CM p cerebrum.
c
PN p pons.
d
Values are given as meanⳲSD.
e
PMI p postmortem interval (time from death to tissue
freezing).
b
those normally found in methylated CpG islands and in
GC-rich sequences and those with methylation changes
in response to trichostasin A (TSA) or 5-aza-deoxycytidine
treatment that we had identified earlier through a systematic genomewide screen.13,14 The second was a group of
1,152 loci from the Illumina Golden Gate Methylation
Cancer Panel I.11 The Illumina panel was employed because it has already been validated on human tissue samples, including colon, lung, ovary, breast, and prostate,
and the set of genes included in the panel are growthand development-related and thus might also influence
brain development.11 Hierarchical clustering analysis revealed a striking separation of gene methylation between
specimens from the two brain regions, with clustering of
7 of 8 cerebral cortex samples and 15 of 16 cerebellar
samples (fig. 1). The greatest number of methylation differences was related to brain region rather than to age,
sex, postmortem interval, race, diagnosis, or cause of
death (fig. 2). Please note that, in the subsequent experiments described below, comparisons were made in the
same individual, thereby negating differences due to these
other factors. The top 20 differentially methylated probes,
with P ! .004 and a minimum mean methylation change
of 17% across the two tissues, are provided in table 2.
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samples were not paired from the same individual and
thus could represent interindividual variation.
In the second experiment, we tested the potential confounding effect of interindividual variation by examining
tissues from 26 individuals, including additional brain
regions of 14 individuals from experiment 1, thus providing perfectly matched cerebral cortex and cerebellum, as
well as matched liver from three individuals. In this experiment, we examined 1,505 CpGs from 807 genes on
the Illumina Golden Gate Methylation Cancer Panel I.11
This set of genes included 621 loci also represented on the
array in experiment 1. In the second experiment, there
was just as dramatic a separation of gene methylation as
that seen in the first experiment, with clustering of 25 of
26 cerebral cortex samples, 26 of 26 cerebellar samples,
Figure 1. Hierarchical clustering of methylation data from cerebral cortex and cerebellum samples analyzed in experiment 1.
Methylation profiles of 1,532 CpG sites from 24 brain samples (16
cerebella and 8 cerebra) from 24 individuals were clustered using
uncentered correlation and pairwise average linkage. Columns represent samples; rows correspond to CpG sites. Two major branches
are defined by our methylation data and correlate with brain region, one containing 7 of 8 cerebra and one containing 15 of 16
cerebella. A heat map showing relative methylation differences
(red indicates more methylated; blue indicates less methylated)
from a handful of analyzed loci is represented in the clustering
dendrogram.
Genes showing significant relative hypomethylation in
the cerebral cortex compared with the cerebellum included engrailed 2 (EN2 [MIM 131310]) (table 2), which
influences cerebellar development15 and may play a role
in autism (MIM 209850),16 and HDAC7A (MIM 606542),
which encodes part of a family of enzymes that regulate
chromatin remodeling in the brain. Among those hypomethylated in cerebellum was HTR2A (MIM 182135),
which is epigenetically regulated17 and encodes a serotonin receptor implicated in many neuropsychiatric phenotypes.18 One limitation of this experiment is that the
1306
Figure 2. Hierarchical clustering of methylation data from cerebral cortex and cerebellum samples analyzed in experiment 1.
Methylation profiles of 1,532 CpG sites from 24 brain samples (16
cerebella and 8 cerebra) from different individuals were clustered
using uncentered correlation and pairwise average linkage. Columns represent samples; rows are color bars that correspond to
sample characteristics. As shown by the color bars, the two major
dendrogram branches defined by our methylation data correlate
most strongly with brain region, as opposed to age, sex, postmortem interval (PMI), cause of death (COD), or race.
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Table 2. Loci Demonstrating Significant Differential Methylation
(P ! .004; b 1 0.17) between Cerebellum and Cerebral Cortex from
Unmatched Individuals
Feature IDa
SFTPA1_E340_R
RBP1_P426_R
TIMP2_S1512_Fc
EN2_B_S1503_Fc
PRSS8_E134_R
HTR2A_P1387_R
HDAC7A_P344_F
IL16_P93_R
CDH3_P779_Fc
PDGFRB_P273_F
ZP3_P220_F
SLC5A5_E60_F
ASB4_P391_F
GSTM2_P453_R
KCNK4_P257_Fc
BLK_E202_Fc
PTPN6_P126_Rc
APOA1_P261_F
HLA_DPB1_S746_Fc
SERPINA5_P156_F
a
b
c
Cerebral
Cortex
Meanb
SD
Meanb
SD
Differenceb
P
FalseDiscovery
Rate
.16
.80
.58
.52
.37
.27
.75
.79
.15
.50
.24
.78
.40
.76
.22
.08
.04
.84
.17
.53
.15
.08
.12
.15
.09
.07
.03
.07
.05
.22
.09
.15
.05
.13
.08
.05
.02
.04
.13
.08
.73
.31
.18
.16
.72
.60
.43
.46
.46
.20
.53
.50
.68
.48
.49
.35
.31
.58
.42
.28
.36
.29
.21
.24
.22
.20
.24
.23
.25
.20
.21
.23
.18
.22
.19
.22
.18
.21
.17
.14
.57
.48
.41
.35
.35
.33
.33
.32
.31
.30
.30
.28
.28
.27
.27
.27
.26
.25
.25
.25
.002
.002
.002
.002
.002
.002
.002
.002
.002
.004
.002
.002
.002
.002
.002
.002
.002
.002
.004
.002
.02
.02
.02
.02
.02
.02
.02
.02
.02
.03
.02
.02
.02
.02
.02
.02
.02
.02
.03
.02
Cerebellum
Gene symbols are contained within the Feature ID before the first underscore.
Mean b value (fractional methylation from 0 to 1).
Features not present on the Illumina 1505 Cancer Methylation Panel I array.
and all 3 liver samples (fig. 3). Furthermore, analysis of
variation (ANOVA) showed that the methylation pattern
correlated much more strongly within a brain region
across individuals (728 of 1,505 CpG with correlation at
P ! .05) than within an individual across brain regions
(151 loci of 1,505 CpG with correlation at P ! .05). Table
3 shows the 20 most differentially methylated probes,
whereas table 4 contains a complete list of the 131 that
differed significantly. Of the 46 brain-region methylation
markers discovered in experiment 1, 32 were present on
the second array, and 26 of these were rediscovered as
significantly different in experiment 2. Among the genes
hypomethylated in cerebral cortex was SLC22A3 (MIM
604842), which encodes an extraneuronal monoamine
transporter that inactivates catecholamine neurotransmitters and is thus a candidate gene for neuropsychiatric
disease.19 It has been shown to be imprinted in a tissuespecific and temporally restricted fashion.20
In the third experiment, we again paired brain regions
from the same individuals, using seven from whom we
had matched cerebral cortex and pons regions, on the
same array as that in experiment 2. Again, there was striking separation of gene methylation, with clustering of
seven of seven cerebral cortex samples and seven of seven
pons (fig. 4). Similarly, ANOVA showed that the methylation pattern correlated much more strongly within
a brain region across individuals (292 of 1,505 CpG with
correlation at P ! .05) than within an individual across
brain regions (116 of 1,505 CpG with correlation at P !
www.ajhg.org
.05). Thus, methylation consistently distinguished brain
regions in a given individual. Table 5 shows the most
differentially methylated probes between cerebral cortex
and pons. Among the genes hypomethylated in cerebral
cortex in this experiment was insulin-like growth factor
1 (IGF1 [MIM 147440]), previously shown to have distinct developmental patterns of expression in differing
brain regions.21 Hypomethylated genes in pons included
FGF1 (MIM 131220) and FGFR2 (MIM 176943), fibroblast
growth-factor system genes that are part of a signaling
pathway that plays a role in brain development and
differentiation.22
We performed analyses of reliability and of potential
confounding, as well as validation experiments, to assess
the robustness of our findings. We tested the reproducibility of methylation measurements of the arrays, examining seven samples at 1,505 CpG sites. Linear-regression analysis was performed, and correlation coefficients
ranged from 0.94 to 0.99. To control for any effects that
might be attributable to disease state, we assessed for differences in DNA methylation between normal and disease-affected samples. We found no correlation, using all
1,505 CpG loci, comparing unaffected individuals (n p
16) with those with bipolar disorder (n p 4) and autism
(n p 13) in cerebral cortex (fig. 5A), cerebellum (fig. 5B),
or pons (fig. 5C).
Finally, we sought to validate the observed methylation
data by an independent method, bisulfite pyrosequencing, which measures methylation variation at 190% pre-
The American Journal of Human Genetics
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Figure 3. Hierarchical clustering of methylation data from cerebral cortex, cerebellum, and liver samples analyzed in experiment 2.
Methylation profiles of 1,505 CpG sites from 55 samples (26 cerebra, 26 cerebella, and 3 livers) from the same individuals were clustered
using uncentered correlation and pairwise average linkage. Columns represent samples; rows correspond to CpG sites. Clustering of 26
of 26 cerebella, 25 of 26 cerebra, and all 3 livers is shown by the dendrogram. A heat map showing relative methylation differences
(red indicates more methylated; blue indicates less methylated) from a handful of loci analyzed is represented below the clustering
dendrogram. The heat map shows genes with greatest difference between the groups (complete list in table 4).
cision.23 Bisulfite-pyrosequencing validation was performed by bisulfite treatment of genomic DNA (Qiagen
Epitect kit), PCR amplification, and pyrosequencing of
CpG sites. We obtained sequences for all Illumina probes
and designed flanking primers (methylation-unbiased
nested PCR and sequencing primers) to the CpG site for
which a b value was reported by Illumina (available from
the authors on request). Pyrosequencing was performed
on a Biotage PSQ HS96 Pyrosequencer. Bisulfite conversion controls and quantitative levels of methylation for
each CpG dinucleotide were evaluated with Pyro Q-CpG
software.
We chose genes for validation that showed a range of
variation between tissues on the arrays, including 12%,
24%, and 36% differences. Six genes in 2–20 samples were
analyzed for quantitative methylation by bisulfite pyrosequencing. All the loci tested showed substantial differences in DNA methylation across brain regions in the
same direction and magnitude as we had found using Illumina arrays (fig. 6). For example, the Illumina data had
revealed hypermethylation of HDAC7A in cerebellum
1308
compared with cerebrum by a magnitude of 0.33 in experiment 1 and 0.36 in experiment 2; in pyrosequencing,
we also saw hypermethylation of cerebellum relative to
cerebrum by a magnitude of 0.46. Linear regression was
performed, comparing the percentage of methylation reported by pyrosequencing and Illumina b values, and correlation coefficients equaled 0.99 (EN2), 0.89 (HTR2A),
0.96 (GABRB3), 0.72 (MT1A), 0.74 (RASSF1), and 0.76
(HDAC7A).
To assess the reproducibility of our methylation results
in an independent set of individuals, we obtained snapfrozen brain tissue (donated by The Stanley Medical Research Institute’s brain collection, courtesy of Drs. Michael
B. Knable, E. Fuller Torrey, Maree J. Webster, and Robert
H. Yolken) and performed bisulfite pyrosequencing of six
genes (RASSF1, HDAC7A, HTR2A, GABRB3, EN2, and
MT1A) in 52, 46, 55, 33, 30, and 57 paired cerebral cortex
and cerebellum samples, respectively. All six of the loci
confirmed our initial brain-region methylation findings
in this independent set of individuals (fig. 7).
Finally, we examined the pattern of gene expression of
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Table 3. Loci Demonstrating Greatest Differential
Methylation (P ! .004; b 1 0.17) between Matched
Cerebral Cortex and Cerebellum Samples from the Same
Individual
Feature IDa
SFTPA1_E340_R
RBP1_P426_R
HPN_P823_F
MAPK9_P1175_F
JAK3_P1075_R
BCR_P422_F
MYCL2_P19_F
ACVR1_P983_F
MAPK4_E273_R
IL1RN_P93_R
IL8_E118_R
SLC22A3_P528_F
PIK3R1_P307_F
FGF3_P171_R
PLA2G2A_P528_F
CD9_P585_R
SERPINE1_P519_F
TRIP6_P1090_F
LIMK1_P709_R
BLK_P14_F
Cerebellum
Meanb
Cerebral Cortex
Meanb
Differenceb
.18
.98
.12
.31
.15
.14
.83
.12
.13
.13
.08
.90
.18
.56
.39
.11
.07
.10
.11
.10
.96
.30
.79
.94
.77
.76
.22
.71
.72
.67
.62
.37
.70
.04
.90
.59
.55
.57
.58
.56
.78
.68
.67
.63
.63
.62
.61
.59
.59
.55
.54
.53
.52
.52
.50
.48
.48
.47
.47
.46
a
Gene symbols are contained within the Feature ID before the first
underscore.
b
Mean b value (fractional methylation from 0 to 1).
five genes, RASSF1, HDAC7A, GABRB3, EN2, and HTR2A.
We chose these genes because they represent a diverse set
of cellular functions, including regulation of cell proliferation, chromatin structure modification, development,
and neurotransmission, and they represent three genes
(RASSF1, HDAC7A, and GABRB3) in which the analyzed
methylation sites were within the promoter and two genes
(EN2 and HTR2A) in which the sites were 11 kb from the
promoter. Two of these genes, one in which the CpG locus
analyzed was within the promoter (HDAC7A) and one in
which it was distal to the promoter (EN2), were not in the
commercial Illumina GoldenGate Methylation Cancer
Panel I but were added to a custom array on the basis of
our identification of genes with altered expression in response to TSA, a chromatin-modifying drug, or 5-azadeoxycytidine, a drug known to decrease DNA methylation.14 In all three cases in which the methylated sites were
within the promoter, the difference in expression was as
expected—that is, the more-methylated tissue showed the
lower mean expression, although, in one of these cases,
the results did not achieve statistical significance (table 6).
Interestingly, in both cases in which the methylated sites
Table 4. Loci Demonstrating Significant
Differential Methylation (P ! .004;
b 1 0.17) between Cerebellum and Cerebral
Cortex from the Same Individual
The table is available in its entirety in the online
edition of The American Journal of Human Genetics.
www.ajhg.org
Figure 4. Hierarchical clustering of methylation data from cerebral cortex and pons samples analyzed in experiment 3. Methylation profiles of 1,505 CpG sites from 14 brain samples (7 cerebra
and 7 pons) from the same individual were clustered using uncentered correlation and pairwise average linkage. Columns represent samples; rows correspond to CpG sites. Two major branches
are defined by our methylation data and correlate with brain region: one containing seven of seven cerebra and one containing
seven of seven pons. A heat map showing relative methylation
differences (red indicates more methylated; blue indicates less
methylated) from a handful of loci analyzed is represented below
the clustering dendrogram.
lay outside the promoter, the more-methylated tissue also
showed the greater expression. Similar to our findings that
EN2 expression is decreased in brain tissue with less methylation, Gius et al.14 previously discovered that expression
of EN2 is down-regulated 1.7-fold in response to TSA, a
chromatin-modifying drug that normally results in increased gene expression.
In summary, we have found a DNA methylation signature that distinguishes three human brain regions.
These brain methylation differences correlated much
more strongly within a brain region across individuals
than within an individual across brain regions. The result
is surprising, since the genes analyzed were not preselected
for known brain function. They came from a panel of
genes previously known to show altered DNA methylation
or a functional role in tumor development or progression.
Of course, these same genes are themselves implicated
generally in normal development and differentiation, and
80% of all genes are expressed in the normal brain.8 A
substantial body of evidence shows brain region–specific
differences in gene expression,9,10 and the region-specific
patterns in DNA methylation shown here may help to
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Table 5. Loci Demonstrating Significant Differential Methylation
(P ! .004; b 1 0.17) between Cerebral Cortex and Pons from the Same
Individual
Cerebral
Cortex
Feature IDa
HTR2A_E10_R
MATK_P190_R
WRN_P969_F
BCR_P346_F
IGF1_E394_F
DKFZP564O0823_P386_F
TNFSF10_E53_F
SERPINE1_P519_F
FGFR2_P460_R
AXIN1_P995_R
CD40_E58_R
SPARC_P195_F
PRKCDBP_P352_R
TJP2_P518_F
ZMYND10_P329_F
CASP10_P186_F
TNF_P158_F
CDK2_P330_R
FGF1_E5_F
CASP10_P334_F
MPO_P883_R
RAB32_P493_R
IL8_E118_R
STAT5A_E42_F
MMP9_P189_F
a
b
Meanb
SD
Meanb
SD
Differenceb
P
FalseDiscovery
Rate
.62
.35
.31
.20
.42
.39
.54
.62
.52
.53
.48
.39
.72
.29
.19
.72
.65
.20
.52
.71
.70
.53
.60
.66
.88
.06
.08
.05
.05
.09
.04
.13
.13
.12
.16
.06
.09
.07
.11
.08
.06
.07
.08
.11
.09
.05
.08
.06
.06
.05
.84
.55
.50
.38
.60
.57
.20
.32
.23
.82
.20
.13
.48
.07
.39
.52
.45
.01
.32
.52
.51
.35
.42
.48
.71
.04
.09
.10
.09
.04
.07
.05
.05
.06
.06
.07
.05
.07
.03
.11
.06
.20
.01
.05
.05
.08
.06
.07
.04
.04
.22
.20
.19
.19
.18
.18
.34
.30
.30
.29
.28
.26
.24
.22
.21
.20
.20
.20
.20
.19
.18
.18
.18
.18
.17
.002
.002
.002
.002
.002
.002
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
.05
Gene symbols are contained within the Feature ID before the first underscore.
Mean b value (fractional methylation from 0 to 1).
explain these functional differences. Furthermore, because
methylation variation in tissues is acquired developmentally, the differences in brain methylation may help to
determine or stabilize normal brain differentiation. This
idea is consistent with the fact that a failure to recognize
DNA methylation marks caused by absence of the MECP2
protein in Rett syndrome causes progressive loss of neurodevelopmental milestones, as well as poorly regulated
gene expression in affected brain tissue.24
These data highlight several areas for further study. First,
the degree of complexity of the brain is greater than that
of any other organ. In addition to the gross brain-region
distinctions, such as cerebral cortex and cerebellum, the
cerebral cortex itself is broadly divided into four lobes and
is more finely divided into 47 Brodmann areas. Furthermore, in the classic studies by Mountcastle, the cortex is
functionally organized into countless vertical columns
∼300–600 mm wide.25 In addition, cellular complexity involves both differing compositions of neurons and glia,
as well as potential differences among subtypes of those
classes, such as oligodendrocytes and astrocytes. This cellular complexity may also be a major contributor to differences among brain regions. Thus, it will be important
to analyze DNA methylation across a great many brain
1310
Pons
subregions and cell types. However, such studies will likely
require whole genome–based approaches, to discover the
genes and potentially intergenic regions that epigenetically distinguish individual brain regions. Such technologies are emerging and should be generally available
within the next few years. A second issue for further study
is the degree to which brain epigenetic signatures might
be altered in disease. Although we found no evidence of
variation in bipolar disorder (also known as MAFD1 [MIM
125480]) or autism in these brain regions, our study was
not designed to detect such variation. Both our sample
size and our gene set were small and were not targeted to
functional candidates. A third issue is the developmental
role of methylation variation in the brain. Differential
methylation may affect an early event in brain development, having an impact even in the absence of adult brain
expression of the relevant genes. Sorting this out will require animal models involving both genetics and epigenetics. For example, one might knock out a brain region–
specific methylation mark to determine its functional
effect on the normal development of that region. A fourth
issue is the relationship of brain methylation to expression. Although, in three instances, increased methylation
in our samples correlated with gene silencing, in two oth-
The American Journal of Human Genetics
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December 2007
www.ajhg.org
Figure 5. Hierarchical clustering of methylation data from cerebral cortex, cerebellum, and pons samples analyzed in experiments 2
and 3. Columns represent samples, rows correspond to CpG sites, and heat maps showing relative methylation differences (red indicates
more methylated; blue indicates less methylated) from a handful of loci analyzed are represented below the clustering dendrograms.
All clustering analyses were performed using uncentered correlation and pairwise average linkage. A, Methylation profiles of 1,505 CpG
sites from 33 cerebra samples (16 from normal individuals, 13 from individuals given diagnoses of autism, and 4 from individuals with
bipolar disorder) were clustered. Clustering does not reveal disease-specific branches. B, Methylation profiles of 1,505 CpG sites from
26 cerebella samples (13 from normal individuals and 13 from individuals given diagnoses of autism) were clustered. Clustering does
not reveal an autism-specific branch. C, Methylation profiles of 1,505 CpG sites from seven pons samples (three from normal individuals
and four from individuals with bipolar disorder) were clustered. Clustering does not reveal a bipolar-specific branch.
ers that were outside promoters, methylation sites likely
represent regulatory regions in which methylation is associated with gene expression. Similarly, Gius et al.14
found that half of genes showing altered expression after
demethylation become silenced rather than activated.
www.ajhg.org
Finally, the identification of brain region–specific methylation differences shows that there are stable marks heritable during cell division that distinguish one brain region from another and are consistent in these differences
from one individual to another. These differences in meth-
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Figure 6. Box plots of methylation data from bisulfite-pyrosequencing analysis. A, RASSF1. B, HDAC7A. C, HTR2A. D, GABRB3. E, EN2.
F, MT1A. Mean methylation levels across all Illumina experiments are denoted by blue lines. n is the number of samples analyzed by
pyrosequencing.
Figure 7. Box plots of methylation data from bisulfite-pyrosequencing analysis. A, RASSF1. B, HDAC7A. C, HTR2A. D, GABRB3. E, EN2.
F, MT1A. n is the number of samples analyzed by pyrosequencing.
Table 6. Quantitative Real-Time PCR Results of Five Genes
Assayed in Paired Cerebellum and Cerebral Cortex Samples
from 15 Individuals
Measure
a
Methylation
Distanceb
Fold changec
P
Expressiond
RASSF1
HDAC7A
GABRB3
EN2
HTR2A
CM 1 CBL
200
⫺2.0
.011
CBL 1 CM
CBL 1 CM
200
1.5
.007
CM 1 CBL
CBL 1 CM
440
1.4
.210
CM 1 CBL
CBL 1 CM
6500
⫺100
!.001
CBL 1 CM
CM 1 CBL
1400
40.4
!.001
CM 1 CBL
NOTE.—CBL p cerebellum; CM p cerebral cortex.
a
Methylation level from greatest to least, based on b value.
b
Distance (in bp) to transcriptional start site from the locus showing
differential methylation across brain regions.
c
Fold change p 2⫺DDCT; DDCT p (CT cerebrum target gene⫺CT phosphglycerate kinase 1)⫺
(CT cerebellum target gene⫺CT phosphglycerate kinase 1).
d
Quantitative real-time PCR result. For each gene, brain-region expression is ranked from greatest to least.
ylation appear to be widespread at the gene level, perhaps
reflecting global regulation rather than discrete effects at
a small number of genes. Although these results are in a
relatively early stage, they do raise the intriguing possibility that epigenetic signatures in part determine the
functional programs that have been traditionally associated with neuroanatomical distinctions.
Acknowledgments
Author contributions: C.L.-A. performed most of the experiments
and data analysis; J.P., S.S., and R.Y. provided samples, expertise,
and technical assistance; T.D. and P.A.C. assisted with experiments; J.-B.F. performed the initial Illumina hybridization; and
J.B.P. was the clinical and A.P.F. the molecular senior investigator.
We thank Rafael Irizarry for advice regarding statistical analysis.
This work was supported by National Institutes of Health (NIH)
grant HG003233. Some tissues were provided by the Harvard
Brain Tissue Resource Center, which is supported in part by NIH
grant MH68855, and by the University of Maryland Brain Bank,
which is supported in part by NICHD contract NO1-HD-8-3283.
Web Resource
The URL for data presented herein is as follows:
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi
.nlm.nih.gov/Omim/ (for Rett syndrome, EN2, autism, HDAC7A,
HTR2A, SLC22A3, IGF1, FGF1, FGFR2, and MAFD1)
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