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The DNA Methylome and Transcriptome of Different
Brain Regions in Schizophrenia and Bipolar Disorder
Yun Xiao
2
, Cynthia Camarillo
3
, Yanyan Ping
2
, Tania Bedard Arana
1
, Hongying Zhao
2
,
Peter M. Thompson
4
, Chaohan Xu
2
, Bin Brenda Su
2
, Huihui Fan
2
, Javier Ordonez
3
, Li Wang
2
,
Chunxiang Mao
5
, Yunpeng Zhang
2
, Dianne Cruz
4
, Michael A. Escamilla
1,3
, Xia Li
2
*,ChunXu
1,2,3
*
1Departments of Psychiatry, Texas Tech University Health Science Center, El Paso, Texas, United States of America, 2College of Bioinformatics Science and Technology,
Harbin Medical University, Harbin, Heilongjiang, China, 3The Center of Excellence in Neuroscience, Texas Tech University Health Science Center, El Paso, Texas, United
States of America, 4Southwest Brain Bank, Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of
America, 5University of Toronto, Toronto, Canada
Abstract
Extensive changes in DNA methylation have been observed in schizophrenia (SC) and bipolar disorder (BP), and may
contribute to the pathogenesis of these disorders. Here, we performed genome-scale DNA methylation profiling using
methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) on two brain regions (including frontal cortex
and anterior cingulate) in 5 SC, 7 BP and 6 normal subjects. Comparing with normal controls, we identified substantial
differentially methylated regions (DMRs) in these two brain regions of SC and BP. To our surprise, different brain regions
show completely distinct distributions of DMRs across the genomes. In frontal cortex of both SC and BP subjects, we
observed widespread hypomethylation as compared to normal controls, preferentially targeting the terminal ends of the
chromosomes. In contrast, in anterior cingulate, both SC and BP subjects displayed extensive gain of methylation. Notably,
in these two brain regions of SC and BP, only a few DMRs overlapped with promoters, whereas a greater proportion occurs
in introns and intergenic regions. Functional enrichment analysis indicated that important psychiatric disorder-related
biological processes such as neuron development, differentiation and projection may be altered by epigenetic changes
located in the intronic regions. Transcriptome analysis revealed consistent dysfunctional processes with those determined
by DMRs. Furthermore, DMRs in the same brain regions from SC and BP could successfully distinguish BP and/or SC from
normal controls while differentially expressed genes could not. Overall, our results support a major role for brain-region-
dependent aberrant DNA methylation in the pathogenesis of these two disorders.
Citation: Xiao Y, Camarillo C, Ping Y, Arana TB, Zhao H, et al. (2014) The DNA Methylome and Transcriptome of Different Brain Regions in Schizophrenia and
Bipolar Disorder. PLoS ONE 9(4): e95875. doi:10.1371/journal.pone.0095875
Editor: Chunyu Liu, University of Illinois at Chicago, United States of America
Received October 28, 2013; Accepted April 1, 2014; Published April 28, 2014
Copyright: ß2014 Xiao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 91129710, 31200997, and 61170154), the National
Science Foundation of Heilongjiang Province (Grant Nos. C201207) and the Center of Excellence in Neuroscience of the Paul L. Foster School of Medicine. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: chun.xu@ttuhsc.edu (CX); lixia@hrbmu.edu.cn (XL)
Introduction
Psychiatric disorders characterized by long-lasting behavioral
abnormalities constitute a considerable public health burden [1].
Two major psychiatric disorders including schizophrenia (SC) and
bipolar disorder (BP) have received considerable attention in
molecular biological studies; nevertheless their etiology remains
largely enigmatic. Despite the completion of numerous large-scale
genome-wide association studies and the recent application of
exon sequencing to identify risk loci and structural genomic
variants (e.g. copy number variation) associated with these
psychiatric disorders, it is becoming clear that the few number
of risk genes/loci and extremely rare structural variants are
insufficient to account for the risk of psychiatric disorders [2]. This
is because most psychiatric disorders are associated with molecular
abnormalities in multiple genes and signals that control their
expression, rather than mere genetic variants in a few genes.
Increasing evidence suggests that epigenetic modification plays
important roles in normal biology (e.g. development) and disease
(e.g. psychiatric disorders) by influencing gene expression. As one
type of epigenetic events, DNA methylation has been extensively
explored in different cellular conditions [3–6], whose abnormal-
ities at specific regions can induce expression changes mostly
through alterations of chromosomal accessibility or local chroma-
tin structure. There is mounting evidence that DNA methylation is
involved in the pathogenesis of SC and BP. Initial studies focused
on DNA methylation alterations in candidate genes, such as RELN
[7,8], COMT [9] and GAD67 [10]. The first epigenome-wide study
performed by Mill et al. [11] comprehensively characterized DNA
methylation in the prefrontal cortex of patients with major
psychosis by investigating ,27,000 CpG dinucleotides using
microarray. They identified significant epigenetic changes associ-
ated with SC and BP. Subsequently, Dempster et al. [12]
performed genome-wide analysis of DNA methylation of blood
samples from 22 twin pairs discordant for SC and BP using
microarray and further demonstrated important DNA methyla-
tion changes in the molecular mechanisms associated with SC and
BP.
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Here, we performed a genome-wide DNA methylation analysis
in two brain regions, Brodmann area 9 (BA9, part of the frontal
cortex) and Brodmann area 24 (BA24, part of the anterior
cingulate) from patients with SC or BP and normal controls using
methylated DNA immunoprecipitation and sequencing (MeDIP-
seq) [13], which provided more comprehensive DNA methylation
interrogation than microarray-based technologies, and then
comprehensively characterized DNA methylation alterations in
different brain regions of SC and BP. Furthermore, we detected
transcriptome-wide gene expression changes between patients and
controls using RNA-seq. Finally, we combined DNA methylome
and transcriptome to explore their possible links in different brain
regions of SC and BP.
Materials and Methods
Ethics statement
The study was approved by the institutional review board of
Texas Tech University Health Science Center, Texas, United
States. All patients provided written informed consent.
Patient samples
Five SC, seven BP and six normal samples were included in this
study. These samples were collected from the Southwest Brain
Bank with consent from the next-of-kin (NOK) (see Methods S1
for details). The NOK agreed to provide the donation and they
read a State approved form. We called the NOK and recorded
their agreement. The NOK interview (psychological autopsy)
about the donor was performed by trained clinicians. All of the
patients in this study have met best estimate consensus diagnoses of
SC or BP as defined by the DSM-IV-TR criteria, as previously
reported [14]. These studies have been approved by the
Institutional Review Board of the University of Texas Health
Science Center at San Antonio. The quality of the postmortem
brain tissue was determined by a neuro-pathologist through both
gross and microscopic neuropathological examinations. All sub-
jects in this study were free of confounding neuropathology. For
tissue identification of BA9 and BA24 taken from the same
hemisphere, we used the criteria described by Rajkowska and
Goldman-Rakic [15].
MeDIP-seq
The genomic DNA was extracted from two brain regions (BA9
and BA24) in samples as detailed in the Methods S1. Solexa
libraries were subsequently prepared as follows: at least 5 mg
genome DNA was fragmented to a mean size of approximately
250 bp by sonication, followed by the blunt-ending and dA
addition to the 39-end. Adapters were then ligated to the end of
DNA fragments. Double-stranded DNA was denatured and the
DNA fragments were immunoprecipitated by 5 mC antibodies.
Real-time PCR was used to validate the quality of immunopre-
cipitation. After PCR amplification, the material was sequenced
using the genome-wide massively parallel paired-end sequencing
platform Illumina HiSeq2000 (read length of 2650 bp).
Identification of differentially methylated regions
After removing low-quality reads (reads containing Ns.5) using
a custom script, we aligned reads to the standard hg19 build of the
human reference genome using SOAP (version 2.20) allowing up
to two mismatches [16]. Only uniquely mapped reads were used
for further analysis. Whole genome peak scanning was performed
by MACS (with default parameters, version 1.4.2) that models the
number of reads from a genomic region as a Poisson distribution
[17]. A genomic region with a p value,10e-5 was defined as a
significant peak. For each peak on autosomes, using a custom
script, we calculated reads per kilobase per million mapped reads
(RPKM) for each significant peak in a specific sample as its DNA
methylation level
RPKM~
n
N|l|109
where nrepresents read counts within a peak, Nrepresents total
read counts and lrepresents the length of the peak. The two-sided
t-test was used to identify significantly differentially methylated
peaks with p value,0.01. Then, two significant peaks were
merged if their spacing was less than 50 bp. The merged
significant peaks were regarded as differentially methylated regions
(DMRs).
In order to identify DMR-related genes, we obtained known
gene location information from UCSC known gene track (hg19).
The genes with at least one element (including promoter, 59UTR,
exon, intron and 39UTR) overlapping with DMRs were selected
for functional characterization. Regions from 22.5 kb upstream to
+0.5 kb downstream of transcriptional start sites were defined as
promoters of genes.
RNA-seq
A detailed description of RNA extraction methods as well as the
entire experimental setup has been published previously [18].
Briefly, the tissue samples were homogenized in TRIzol solvent
(Invitrogen, Carlsbad, CA, USA), and the total RNA was isolated
with RNeasy Lipid Tissue Mini Kit (Qiagen #74804) and QIAzol
Lysis Reagent (Qiagen #79306). Five hundred nanogram RNA
was reverse-transcribed to cDNA which was used as template in
TaqMan Gene Expression Assays (Applied Biosystems). The
quality of postmortem brain tissue was determined by toxicology
studies, brain tissue pH [19,20] and RNA integrity number [21].
The postmortem interval was limited to 36 h (ranges between 13–
36 h). Beads with oligo(dT) were used to isolate poly(A) mRNA
after total RNA was collected from all samples. Fragmentation
buffer was added to interrupt mRNA to fragments. Taking these
200,300 bp fragments as templates, random hexamer-primer
was used to synthesize the first-strand cDNA. The second-strand
cDNA was synthesized using buffer, dNTPs, RNase H and DNA
polymerase I. Fragments were purified with QiaQuick PCR
extraction kit and resolved with EB buffer for end reparation and
adding poly(A). The fragments were then connected with
sequencing adaptors. Amplification with PCR was done by
selecting suitable fragments as templates based on the results of
agarose gel electrophoresis. The library was then sequenced as
paired-end 90 bp sequence reads using Illumina HiSeq 2000.
Reads after low quality filtering were mapped to reference genome
(hg19) using SOAP. Mismatches of no more than 2 bases were
allowed. The RPKM method [22] was then used to calculate gene
expression. Differentially expressed genes were identified with fold
changes greater than 1.5. We also applied Cuffdiff (version 2.1.1,
with default parameters) [23] to RNA-seq data aligned by TopHat
(version 2.0.8, with default parameters) [24] for identification of
differentially expressed genes (FDR,0.05).
Functional enrichments
The hypergeometric distribution test was used to identify
biological processes (BP) from Gene Ontology [25] significantly
enriched in a specified gene set with FDR,0.05, which was
implemented in the Bioconductor software GOstats [26] package
together with GO.db package [27]. Because standard hypergeo-
metric test can introduce bias when applied to RPKM
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transformed RNA-seq data, a bias corrected tool GOseq (a
Bioconductor package) were also used to re-perform the function
enrichment analysis with the same threshold FDR,0.05 [28].
Motif discovery in DMRs
The 8–20 nt sequence motifs significantly enriched in DMRs
were detected by the software suite HOMER (Hypergeometric
Optimization of Motif EnRichment, http://biowhat.ucsd.edu/
homer/) [29]. The findMotifsGenome.pl command was run in
HOMER using hg19 as the reference genome with default
parameters.
We have deposited our dataset (all MeDIP-seq and RNA-seq
data) in NIH Short Read Archive (ID: SRP035524).
Results
Identification of differentially methylated regions
We detected DNA methylation levels for two human brain
regions (BA9 and BA24) from 18 individuals composed of five SC,
seven BP and six control subjects (Table S1) by MeDIP-seq that
uses antibody-based immunoprecipitation of 5-methylcytosine
followed by sequencing the immunoprecipitated fractions. No
significant difference was detected between the age of the patients
and normal individuals. On average, 73,469,388 paired-end reads
were generated for each sample, 87% of which were uniquely
aligned to the human genome. We analyzed the distributions of
reads around CpG islands (CGI), and observed higher DNA
methylation levels at upstream and downstream of CGI (Figure
S1A). We further analyzed the distributions of reads around gene
body, and found depletion of DNA methylation around
transcription start sites (Figure S1B). Subsequently, we divided
the genome into 10 kb windows and calculated read per million
values for each window. Notably, in many chromosomes, some
local methylation changes in the disorders relative to normal
controls were found in the BA9 brain region and interestingly,
these local changes tended to occur at the terminal ends of the
autosomes (Figure S1C and S1D, and Figure S2). In order to
characterize the similarities of DNA methylation for each intra-
class category, we calculated the methylation levels within 10 kb
windows scanned across genome and their pairwise Pearson
correlation coefficients. Our results showed consistent methylation
patterns (Pearson correlation coefficients from 0.89 to 0.98, with
an average of 0.95, Figure S3A). Moreover, we completed the
principal component analysis of methylation levels of 10 kb
windows for both case and control samples (36 samples in total)
and did not found obvious outliers (Figure S4A).
Whole genome peak scanning was then carried out using
MACS [30,31]. Using the RPKM method, we calculated DNA
methylation levels for peaks and then identified differentially
methylated regions (DMRs) between disease (SC or BP) and
normal subjects. We identified 4985 and 13925 DMRs in the BA9
region of SC and BP samples, respectively, and 3867 and 2672
DMRs in the BA24 regions of SC and BP samples, respectively, in
comparison to the corresponding normal samples (Table S2).
DMRs in the two brain regions of SC and BP have similar lengths,
with an average length of 1.2 kb. A much higher number of
DMRs in the BA9 regions of SC and BP was observed than those
in the BA24 regions. The highest number of DMRs was observed
in the BA9 region of BP samples. To our surprise, both SC and BP
showed different DNA methylation alternation patterns between
the two brain regions. In the BA9 regions of SC and BP, most
autosomes showed obvious asymmetrical distributions of hyper-
and hypomethylations, that was, a predominance of hypomethyla-
tion present in these autosomes, with a few dispersed hypermethy-
lated DMRs (Figure 1A and 1B). In contrast, profound
hypermethylation distributed across all autosomes was identified
in the BA24 regions of SC and BP subjects (Figure 1C and 1D).
More hypomethylated DMRs in the BA9 regions of SC and BP
samples, and more hypermethylated DMRs in the BA24 regions
were observed (Figure 2A). Furthermore, we also used limma
[32,33] to detect DMRs between case and control adjusting for
gender and age. With a threshold of FDR 0.05, we did not find
any significant DMRs. Instead, using p value of 0.05, we identified
17 hyper- and 621 hypomethylated DMRs of SC and 213 hyper-
and 1253 hypomethylated ones of BP in the BA9 regions, and 620
hyper- and 29 hypomethylated DMRs of SC and 136 hyper- and
77 hypomethylated DMRs of BP in the BA24 regions, which
showed similar DMR patterns as previous ones. Our findings
suggest that DNA methylation alterations in these two disorders
may be heavily dependent on distinct brain regions.
Through examination of the methylation alterations around
CGI (CGI and CGI shores) in SC and BP samples, we found that
more DMRs in the BA9 and BA24 overlap with CGI shores
relative to CGI (odd ratio = 0.16, p value = 4.79e-141 for BP in
BA9; odd ratio = 0.16, p value = 3.12e-40 for SC in BA9; odd
ratio = 0.13, p value = 7.60e-22 for BP in BA24; odd ratio = 0.19,
p value = 7.71e-27 for SC in BA24; Fisher’s exact test), consistent
with previous findings in other diseases [34], whereas the majority
of DMRs were located outside the CGIs and their shores
(Figure 2B). Moreover, the DNA methylation alteration patterns
were consistent across different brain regions of SC and BP
subjects, suggesting that the preference of DNA methylation
alternations at CGI shores may be an inherent nature irrespective
of brain regions and disease types. We also examined the
distribution of DMRs across different gene elements including
promoter, 59UTR, exon, intron, 39UTR and intergenic region
(Figure 2B). There were only parts of DMRs overlapping with
promoters, which was in line with recent observations in many
genome-wide DNA methylation studies [35]. To determine
significant difference in the distribution of DMRs in various
elements, we performed a permutation analysis. In details, a set of
random regions was randomly generated from genomes, keeping
the same distribution of chromosome, length and size as real
DMRs. The set of random regions was defined as a pseudo-
random DMR set. This process was then repeated 1000 times to
generate 1000 pseudo-random DMR sets in each comparison
between patients (BP or SC) and normal subjects. For each type of
elements, we computed the percentage of pseudo-random DMR
sets that showed more overlap with the elements than the real
DMR set as the p value for the statistical significance of the
enrichment of DMRs in the elements. We found that DMRs in the
BA9 region of BP were significantly enriched in various gene
elements including promoter, 39UTR, intron, exon and 59UTR
(p value = 0.001). In the same brain region, DMRs in SC were
significantly enriched in intron and exon (p value =0.001 and p
value =0.049, respectively). However, in the BA24 brain region,
only the intergenic regions were found to be significantly enriched
by DMRs in SC (p value =0.001). Interestingly, the proportions of
DMRs overlapping with introns in the BA9 region of SC and BP
samples were substantially higher than those in the BA24 region
(odd ratio = 1.45, p value = 9.89e-18 for SC and odd ratio = 1.37,
p value = 1.13e-13 for BP, Fisher’s exact test). However, the
proportions of DMRs overlapping with intergenic regions in the
BA9 were obviously lower than those in the BA24 region (odd
ratio = 0.66, p value = 7.25e-22 for SC and odd ratio = 0.67, p
value =1.47e-20 for BP, Fisher’s exact test).
Also, we investigated DMR-related genes identified in SC and
BP. Numerous known SC and BP genes, including RELN,
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PPP3CC,DNMT1,DTNBP1,NOS1,HTR1E,GRM5,PRIMA1,
HTR2A and HTR2A, were found. RELN is one of the most
abnormal markers in the context of SC and BP [36]. MRNA and
protein expression levels of RELN have been observed to be
severely reduced in various cortical structures of postmortem brain
from SC and BP [37,38] with its promoter hypermethylated [7].
In addition, the mRNA encoding the DNA methyltransferase
enzyme, DNMT1, is up-regulated in the neurons accompanied
with reduced expression of RELN [39]. Also, we compared our
findings with gene lists identified in the study of (Mill et al., 2008)
and found 57 common genes. One of these common genes, the
dystrobrevin binding protein 1 (DTNBP1), has been found to
harbor a potential susceptibility locus for SC. A recent study also
demonstrated that DTNBP1 encoding a susceptibility protein in
SC was important for AMPAR-mediated synaptic transmission
and plasticity in the developing hippocampus [40].
Further, we compared DMRs from different brain regions of
the same disease. Strikingly, only a few overlapping DMRs
between different brain regions were found in the same disease
(Figure 2C). There were only 25 hyper- and 20 hypomethylated
DMRs in the BA9 region of BP subjects overlapping with hyper-
and hypomethylated DMRs in the BA24 region of BP, respec-
tively. Three genes including COL1A2,LMO1 and IGDCC4 located
in the common hyper-DMRs in BP across the two brain regions,
without significantly differential expression. Only one gene (hsa-
mir-4266) was found to be located in the common hyper-DMRs in
SC. By comparison, more overlapping DMRs between these two
disorders from the same brain regions were found. We found 220
hyper- and 1123 hypomethylated DMRs in the BA9 region of BP
Figure 1. The distribution of hyper- and hypomethylated DMRs. Autosome ideogram representing differential methylation in the BA9 brain
regions of SC vs. normal (A), BP vs. normal (B), and in the BA24 brain regions of SC vs. normal (C) and BP vs. normal (D). Red points represent
hypermethylation and green ones represent hypomethylation relative to normal subjects.
doi:10.1371/journal.pone.0095875.g001
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samples overlapping with DMRs in the same brain region of SC
samples, but without statistical significance (hypergeometric test).
These are six genes for hyper-DMRs and 86 genes for hypo-
DMRs between BP and SC in the BA9 region. Among the 86
genes, DNMT1 has already been reported to be associated with SC
[39], 15 genes were confirmed by a previous study of Weber et al.
[13], and 5 genes showed different expression of FC larger than
1.5. Our results suggest that different brain regions exhibit
completely different DNA methylation alternations even within
the same disease, whereas some shared dysfunctions of DNA
methylation occur in the same brain regions of these two disorders.
DMR-related functions
Through function enrichment analyses based on DMR-related
genes (960, 4497, 1268 and 1955 in the BA9 and BA24 regions of
BP and SC respectively), we found the over-representation of
many brain-related biological processes (Figure 3A and Table S3),
such as neuron development and axon guidance, consistent with
previous reports [11]. Notably, many common biological processes
were identified between different comparison groups (Figure 4).
For example, two common biological processes between BA9 and
BA24 brain regions of SC were identified, although they showed a
few overlapping DMRs. In particular, axon guidance and
signaling were observed in all comparisons except for the BA24
region of BP. Multicellular organismal development was observed
in all comparisons of SC. Interestingly, nervous system develop-
ment was found to be only present in the BA9 region of BP, and
only one biological process ‘cell adhesion’ was significantly
enriched by DMR-related genes in the BA24 region of BP. Our
results suggest that DNA methylation changes can induce
dysfunction of neuron development and projection and in turn
contribute to the pathogenesis of psychiatric disorders, and
different brain regions exhibit different DNA methylation changes
but show similar DMR-related biological processes.
In addition, we further identified biological processes signifi-
cantly enriched by genes with their different elements overlapping
with DMRs (Figure S5 and Table S4). Notably, genes with their
promoters overlapping with DMRs were not significantly involved
Figure 2. Features of DMRs. (A) DMRs in distinct brain regions of SC and BP. (B) DNA methylation alteration patterns across CGIs and gene
elements. (C) Overlapping of DMRs between different brain regions of SC and BP. Red color represents hypermethylation and green color represents
hypomethylation. Gray color represents that DMRs in one comparison do not overlap with hyper- or hypomethylated DMRs in the other comparison.
doi:10.1371/journal.pone.0095875.g002
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in any biological processes in all comparisons. Surprisingly, many
brain-related biological processes, such as neuron development,
axon guidance and synaptic transmission, were found to be
enriched in genes with their introns overlapping with DMRs
rather than promoters, suggesting that DNA methylation alter-
ations in introns may exert important roles in the pathogenesis of
these two disorders.
In addition, we detected motifs enriched in DMRs by HOMER
(Hypergeometric Optimization of Motif EnRichment) with default
parameters [29]. Seven and three known motifs were found to be
enriched in the DMRs of BP (BA9) and SC (BA9), respectively
(Figure S6). And we also found two known motifs enriched in the
DMRs of SC (BA24), including TP53 and VDR. Consistently,
TP53 has been demonstrated to be associated with SC in previous
studies [41], suggesting that DNA methylation alteration on
regulatory elements can influence the binding affinity of regulators
and in turn induce the development of disease.
Transcriptome of SC and BP
Also, we detected the transcriptional profiles of the correspond-
ing brain regions from SC, BP and control samples using RNA-
seq. On average, each sample generated more than 10 million
high-quality paired-end reads, with more than 85% reads uniquely
mapped to the reference genome. Gene expression was calculated
using the RPKM method. To determine intra-class correlation
using RNA-seq data, we calculated Pearson correlation coefficients
between gene expression profiles for each intra-class category.
Results showed high intra-class correlations (Pearson correlation
Figure 3. Functional enrichment analyses using DMR-related genes and differentially expressed genes. (A) The top 20 biological
processes determined by functional enrichment analyses of DMR-related genes. (B) The top 20 biological processes determined by functional
enrichment analyses of significantly differentially expressed genes.
doi:10.1371/journal.pone.0095875.g003
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coefficients from 0.68 to 0.97, with an average of 0.91, Figure
S3B). By principal component analysis of gene expression, we did
not find obvious outliers (Figure S4B). Differentially expressed
genes in SC and BP were identified with fold changes greater than
1.5. A total of 1077 and 3639 differential genes were found in the
BA9 and BA24 regions of SC, respectively, and 2085 and 1643
were identified in the BA9 and BA24 regions of BP, respectively
(Table S2 and Table S5).
Besides, differentially expressed genes in SC and BP were
identified using Cuffdiff with FDR less than 0.05. A total of 204
and 1503 differential genes were found in the BA9 and BA24
regions of SC, respectively, and 0 and 70 were identified in the
BA9 and BA24 regions of BP, respectively. Comparing with BP,
fewer differentially expressed genes were observed between SC
and controls in both BA9 and BA24 regions, suggesting an
important role of subtle dysregulation of genes in schizophrenia
parents.
Through functional enrichments of differentially expressed
genes, we identified many brain-related biological processes, such
as neuron development, axonogenesis and synaptic transmission
(Figure 3B and Table S6). The roles of these biological processes
have been demonstrated in a number of neuropsychiatric
disorders, including BP and SC [42]. Like functional analyses of
DMRs, we also found common biological processes associated
with SC and BP in both the BA9 and BA24 regions (Figure 4).
Synaptic transmission, nervous system development and axon
guidance were observed to be shared among almost all of the
comparisons. Importantly, numerous abnormal biological process-
es inferred from DNA methylome were consistent with those
inferred from transcriptome (Figure 4). For instance, multicellular
organismal development was identified through both DNA
methylation changes and transcriptional changes in the BA24 of
SC. Both DMR-related and differentially expressed genes were
found to be significantly enriched in synaptic transmission and
axon guidance. Our results support that DNA methylation
alterations play an important role in the pathogenesis of these
two severe psychiatric disorders.
Additionally, considering the bias of standard hypergeometric
test for function enrichment analysis when applied to genome-wide
RNA-seq data, even using the RPKM transformed data, we also
used the GOseq, an R package of bias corrected method [28], to
re-perform the function enrichment analysis. We found many
consistent biological functions between the results of the standard
hypergeometric test and the bias corrected method (Table S7),
such as nervous system development and synaptic transmission.
Common molecular mechanisms between SC and BP
Previous studies have demonstrated that SC and BP shared
genetic variation [43,44]. Consistently, we observed similar
abnormal functions between SC and BP when compared to
controls. To further characterize their common molecular
mechanisms, we combined SC and BP samples to identify
common DNA methylation alterations in different brain regions.
Our results showed that 2650 DMRs and 3506 DMRs in the BA9
and BA24 regions were identified, respectively. Also, we compared
the differences between SC and BP, and found relatively small
numbers of DMRs (398 and 443) in the BA9 and BA24,
respectively.
In addition, we attempted to directly compare the differences of
differentially expressed genes between SC and BP in the BA9 and
BA24 regions using Cuffdiff algorithm (FDR,0.05) with default
options. Interestingly, a total of 0 and 34 differentially expressed
genes were found between SC and BP in the BA9 and BA24
regions, respectively. These subtle expression differences between
SC and BP are consistent with similar gene expression pattern
between SC and BP [45].
Brain region-specific DNA methylation linking SC with BP
We extracted the top 50 methylation sites and the top 50
expressed genes that were most variable between case (BP or SC)
and control, and generated clustering figures using these top 50
methylation sites and genes. We found that the top 50 methylation
sites could explicitly distinguish disease samples from controls
when compared to the top 50 expressed genes (Figure S7). Then,
we sought to examine whether DMRs identified in a specific brain
region of SC or BP could be used to distinguish disease patients
(SC or BP) from controls in the same or distinct brain regions. We
used DMRs identified in different brain regions of SC (or BP) to
cluster SC vs. normal in BA9 (that is, distinguishing SC patients
from controls in the BA9), SC vs. normal in BA24, BP vs. normal
in BA9, and BP vs. normal in BA24. Using DMRs identified in the
BA9 region of SC, we calculated normalized DNA methylation
levels in the same region of BP and normal samples and performed
clustering analysis. Interestingly, we found that these DMRs can
successfully distinguish BP patients from normal samples
(Figure 5A). However, using the DMRs identified in the BA9
region of SC could not distinguish patients (BP and SC) from
normal samples based their DNA methylation levels in the BA24
region. Likewise, the DMRs identified in the BA9 region of BP can
also distinguish SC from normal samples in the same region but
not in the BA24 region. Similarly, DMRs in the BA24 region can
distinguish both the disorders from normal subjects in the BA24
region rather than the BA9 region. Similarly, we examined
whether gene expression profiles show similar tendencies. We
obtained differentially expressed genes with FC.1.5 in a specific
brain region of SC or BP subjects and re-calculated expression
levels in case and control in two brain regions. We found that
differentially expressed genes identified in a specific brain region of
SC or BP subjects could not distinguish disease from normal
samples, even in the same brain region (Figure 5B). In addition, we
also identified differentially expressed genes using Cuffdiff with
FDR,0.05. Clustering analysis exhibited consistent results (Figure
S8). Taken together, our results showed that DNA methylation
alterations were more stable than gene expression changes,
suggesting brain region-specific DMRs might be effectively used
for disease diagnosis and treatment of SC and BP.
Discussion
Previous evidence has shown distinct DNA methylation levels in
different regions of normal brain [46,47]. Interestingly, our results
demonstrated that DNA methylation alternations in SC and BP
relative to normal subjects depend strongly on distinct brain
regions. In the BA9 region, both SC and BP subjects showed more
hypomethylated DMRs. In contrast, in the BA24 region, more
hypermethylated DMRs were found. One possible explanation for
the opposite patterns of DNA methylation alternations is cellular
heterogeneity among different brain regions [48]. Previous studies
have determined different morphologies in these two brain regions
associated with SC and BP. In the BA9 region, decreased neuronal
Figure 4. Comparisons of biological processes between different brain regions of SC and BP. The significant biological processes were
determined based on DNA methylation alternation (left) and transcriptional changes (right).
doi:10.1371/journal.pone.0095875.g004
DNA Methylome and Transcriptome in Major Psychoses
PLOS ONE | www.plosone.org 8 April 2014 | Volume 9 | Issue 4 | e95875
and glial density was associated with BP and elevated neuronal
density was found to be associated with SC [49]. In the BA24,
O
¨ngu¨ret al. [50] found a reduction of glia in BP subjects. It should
also be noted that only a few DMRs commonly occur in the two
brain regions of either SC or BP. However, relatively more
overlapping DMRs between SC and BP within the same brain
region were observed. The findings suggest that these common
epigenetic abnormalities between SC and BP may contribute to
the similar cognitive and neurobiological deficits associated with
these disorders [11].
In parallel, transcriptome analyses also found a number of genes
showing unique differential expression for a specific brain region
of SC or BP patients, consistent with previous findings that
substantial gene expression differences were observed among
different regions of healthy human and mouse brains [51,52]. In
the BA9 region, we observed more up-regulated genes related with
SC, yet more down-regulated genes in BP subjects. In the BA24
region, both SC and BP harbor similar numbers of up- and down-
regulated genes. We further investigated the correlation (i.e.
Pearson correlation coefficients using the ‘cor.test’ function in R)
between DNA methylation changes of DMRs and expression
changes of genes categorized by different elements (i.e. promoter,
exon, intron, 59UTR and 39UTR) overlapping with DMRs
(Figure S9). We found that expression changes of genes in which
introns overlap with DMRs showed a weak but significantly
positive correlation with DNA methylation changes of corre-
sponding DMRs in the BA9 and BA24 of SC (Pearson correlation
coefficient =0.056 with p value =0.033 and Pearson correlation
coefficient = 0.073 with p value = 0.033, respectively). An inverse
correlation in promoter was observed in the BA24 of BP (Pearson
correlation coefficient = 20.28 with p value = 0.033), whereas a
positive correlation in promoter was shown in the BA9 of BP
(Pearson correlation coefficient = 0.22 with p value = 0.0497). Our
findings were partially consistent with previous reports, suggesting
complex relations between DNA methylation and gene expression.
Subsequently, we found that 31.9%, 27.7%, 23.7% and 26.6% of
DMRs were located in promoter or gene body in the BA9 and
BA24 regions of SC and BP, respectively. Among them, 214, 467,
103 and 269 DMRs were located near genes with at least 1.5-fold
change between case and control, and 14, 0, 137 and 1 DMRs
were located near genes that are differentially expressed using
Cuffdiff. Such complex relations between DNA methylation and
gene expression have been observed in many studies [53,54], and
the molecular mechanisms underlying the complex relations are
still poorly understood. One possible reason is that DNA
methylation alternations at different genomic regions (such as
introns) also contribute to control of gene expression, not just
promoters [35]. Only a few DMRs overlapping with promoters
were observed, however, a large number of DMRs located at the
introns and intergenic regions were identified, supporting previous
findings that the majority of methylated CpGs were located in
intragenic and intergenic regions by generation of a map of DNA
methylation from human brain [55]. A recent study further
demonstrated that intragenic methylation exert functions in
regulating alternative promoters, which are generally used in
different contexts or tissues [56]. Another possible reason is that
both DNA methylation and other epigenetic modification marks
(e.g. histone modification and nucleosome locations) are required
to cooperatively control gene expression [57]. Extensive cross-talk
between DNA methylation and histone modification has been
recently characterized [30]. DNA methylation changes may be
insufficient to lead to expression changes of downstream genes.
Interestingly, DNA methylation changes (hypo- or hyper-
methylation) in ten genes identified in the brain of SC and BP
were also confirmed in peripheral blood samples in our previous
study (under review in the Translational Psychiatry), including
1q32 [58] and 22q11.22 [59] which were considered as ‘‘hot
spots’’ for SC and BP (Table S8). Because brain tissue availability
is limited and DNA methylation changes are not limited to the
brain [60], global DNA methylation abnormality in blood
provides an important opportunity to develop diagnostic and
therapeutic biomarkers for mental diseases [61]. In summary, this
study reinforces important roles of DNA methylation and brain-
region specific DNA methylation alternations in SC and BP, and
highlights complex relations between DNA methylation and gene
expression in the disorders.
Figure 5. Cross cluster analyses. In a specific brain region of a given disorder, the DMRs (A) and differentially expressed genes (B) were used to
distinguish patients (from the other disease or the other brain region) from normal subjects based on hierarchical clustering. Each hierarchical
clustering tree described whether disease-specific DMRs (or differentially expressed genes) identified in a specific brain region, such as DMRs
identified in SC vs. normal in BA9, can be used to distinguish patients (SC or BP) from normal samples in the same or distinct brain regions.
doi:10.1371/journal.pone.0095875.g005
DNA Methylome and Transcriptome in Major Psychoses
PLOS ONE | www.plosone.org 9 April 2014 | Volume 9 | Issue 4 | e95875
Supporting Information
Figure S1 Distribution of reads around CGI and gene body.
The upstream and downstream 2 kb regions of CGI (A) and gene
body (B) were divided into 20 equal regions. CGI and gene body
were divided into 40 equal regions respectively. For each region,
the normalized number of reads was calculated. DNA methylation
levels across the whole chromosome 17 (C) and 19 (D).
(TIF)
Figure S2 DNA methylation levels across different chromo-
somes. Remarkable hypo-methylation occur in the extreme ends
in the BA9 regions of SC and BP relative to normal samples.
(TIF)
Figure S3 The correlation of global DNA methylation and gene
expression for each intra-class category. We used log2-transformed
normalized DNA methylation levels in 10 kb windows (A) and
log2-transformed gene expressions (B) to calculate the Pearson
correlation coefficients between different samples from each group
(case or normal individuals), separately. The numbers in the lower
triangular matrixes represent Pearson correlation coefficients.
(TIF)
Figure S4 Principle component analysis. The principle compo-
nent analysis of (A) methylation levels of 10 kb windows and (B)
gene expression levels for both case and control samples. The x-
axis and y-axis represent the first principal component and the
second principal component. The colors of red, blue and green
show the BP, SC and normal samples, respectively. The asterisk
and diamond represent the BA9 and BA24, respectively.
(TIF)
Figure S5 Functional enrichment analyses of DMR-related
genes. The top 20 biological processes determined by functional
enrichment analyses of genes with different regions overlapping
with DMRs.
(TIF)
Figure S6 Significant motifs enriched in DMRs of BP and SC by
HOMER.
(TIF)
Figure S7 Cluster analysis. The cluster analysis of (A) methyl-
ation levels of top 50 variable DMRs and (B) expression levels of
top 50 variable differentially expressed genes between case (BP or
SC) and control samples.
(TIF)
Figure S8 Cross-clustering analyses of differentially expressed
genes identified by Cuffdiff.
(TIF)
Figure S9 The correlations between DNA methylation and gene
expression changes in various gene elements. We identified DMR-
related genes in various gene elements (promoter, exon, intron, 59
UTR and 39UTR) and calculated Pearson correlation coefficients
and their corresponding statistical p values (using the ‘cor.test’
function in R) between changes of DNA methylation of DMRs
(fold change) and expression of DMR-related genes (fold change)
in different brain regions of BP and SC.
(TIF)
Table S1 Clinical information of five SC, seven BP and six
normal subjects detected. (f = female, m = male; NA = not avail-
able; PMI = postmortem interval).
(XLS)
Table S2 Differentially methylated and differential expressed
genes. The numbers of differentially methylated regions and their
associated genes and the numbers of differentially expressed genes
between the cases (SC or BP) and normal subjects.
(DOC)
Table S3 The functional enrichment analysis of DMR-related
genes of BP and SC. Significances were determined using
hypergeometric test with FDR,0.05. The red color represents
the brain-related functions.
(XLS)
Table S4 Genes with DMRs located in their different regions.
(XLS)
Table S5 Differentially expressed genes in different compari-
sons.
(XLS)
Table S6 The functional enrichment analysis of differentially
expressed genes. Differentially expressed genes of BP (BA9 and
BA24) and SC (BA9 and BA24) identified by Cuffdiff and Fold
Change method, using hypergeometric test with FDR,0.05. The
red color represents the brain-related functions.
(XLS)
Table S7 The functional enrichment analysis of differential
expressed genes of BP and SC using GOseq. The red color
represents the brain-related functions.
(XLS)
Table S8 Ten DMR-related genes confirmed in peripheral
blood samples.
(XLS)
Methods S1 Samples of BP, SC and controls used for MeDIP-
seq and RNA-seq.
(DOC)
Author Contributions
Conceived and designed the experiments: YX XL Chun Xu. Performed
the experiments: YX YP HZ HF LW CM YZ DC XL. Analyzed the data:
YX CC TBA PMT Chaohan Xu BBS JO MAE. Contributed reagents/
materials/analysis tools: CC TBA PMT BBS JO CM DC MAE Chun Xu.
Wrote the paper: YX YP HZ Chaohan Xu HF LW YZ XL Chun Xu.
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