GENOME-WIDE METHYLATION PROFILING
OF SCHIZOPHRENIA
Rukova B1, Staneva R1, Hadjidekova S1, Stamenov G2, Milanova V3, Toncheva D1,
*Corresponding Author: Professor Draga Toncheva, Department of Medical Genetics, Medical University of
Sofia, 1431 2 Zdrave Str., Sofia, Bulgaria. Tel./Fax: +35929520357. Email: dragatoncheva@ gmail.com
page: 15
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RESULTS
The threshold between hypo - or hypermethylated
islands was 60.0% of the oligonucleotide sequences
with BATMAN, call –1 or 1, respectively.
By comparing data from the pool analysis of patients
and controls (220 each) we obtained 394 DMRs. We
found 170 DMRs in the pooled analysis of male patients
and controls (110 each) and 162 DMRs in the
pooled analysis of female patients and controls (110
each). The results of DMRs pool analysis between
patients and controls are presented in Table 1.
Comparing the 394 DMRs of the general pool
with the 170 DMRs in the male pool we found a coincidence
of 36 DMRs. Twentytwo of them a were
methylated in the same direction, while the other 14
were methylated in different directions. Ten of the
22 DMRs were situated in the gene promoter region,
while the other 12 were intragenic. The comparison
of the general (394 DMRs) to the female pool (162
DMRs) showed 25 common DMRs. Eight of them
were methylated in the same direction (two in the
promoters and six intragenic) and 17 had a different
methylation status (Figure 1).
There are multiple reports in the literature confirming
the negative effect of hypermethylation of
CpG islands in the promoter regions on gene expression
level. So far, the effect of methylation of CpG
islands inside the gene or within other regulatory
regions has not been considered and should be interpreted
on a case-by-case basis. According to some
authors this should be considered as a suppressing
signal, while others propose that it is an activating
function. We found that in the three groups, location
of most of the DMRs were defined as intragenic.
Very interestingly, in the female pool, the number
of hypermethylated CpG islands was considerably greater than that of hypomethylated CpG islands
(96.3 vs. 3.7%).
Using the Biograph software, we prioritized the
genes that could be related to schizophrenia. Table
2 shows the top 10 genes with different methylation
profiles in the general pool.
Our data showed that most of the CpG islands
were situated in the gene promoter region (seven out
of 10 CpG islands). Five of the islands in promoter
region were hypermethylated, while two were hypomethylated.
It is believed that hypermethylation in
the promoter region leads to expression inhibition,
whereas hypomethylation leads to expression activation
[9]. Thus, it appears that most of the top hit
genes were repressed. Only three gene CpG islands
were located intragenically. Two of them were hypermethylated,
while one was hypomethylated. The real
function of methylation in areas other than promoters
is uncertain [9,10]. So we can only speculate about its
effect on gene expression. In the general pool, only two
genes (HRH1 and FGFR1) from 394 with DMRs are
of known relation with schizophrenia according to data
in the Biograph software and the others are inferred.
The GABRA2 gene plays a role in inhibitory neurotransmission
but is defined as inferred in regards to
schizophrenia according to the Biograph software [22].
It is known that there are gender differences in
methylation [23,24]. So we decided to study DMRs
in sex-separated patient and control pools. Table 3
shows the top 10 DMRs, chosen by Biograph software,
from 170 genes with a different methylation
profile in pool analysis of male patients vs. male controls
and Table 4 shows the top 10 genes with a different
methylation profile in schizophrenia females. Four of the genes show differential methylation
in the promoter CpG islands. Three of them were
hypermethylated and one was hypomethylated. The
other six gene CpG islands were intragenic. Three of
the top hits in the male pool were identical with the
genes in the general pool and also hypermethylated:
GABRA2, LIN7B, CASP3.
Five of all CpG islands were located in the promoter
areas, four were intragenic and one was downstream
of the promoter. Most of the CpG islands in the
female pool were hypermethylated (nine of 10). Two
of those were identical with the genes from the general
pool: CASP3, MACF1. However, the MACF1 gene
had a DMR that was hypermethylated in females and
hypomethylated in the general pool and was therefore
not a consistently implicated one. In the male patient
pool, this gene did not show differently methylated
regions in comparison with the male control pool.
In the female pool, the GABRD gene was of known
relevance to schizophrenia [25]. The CASP3 gene was
found to be in the top 10 genes from the three pools.
For confirmation of data from gender-specific
pools, we performed individual analyses on eight
female and 12 male schizophrenia samples. The patient
samples were compared to the control pool of
the corresponding gender.
In the individual analysis of seven female samples,
we found the entire top 10 DMRs from the female pool analysis differentially methylated in the
same direction in between one and seven patients (Table
3). The most frequent XIAP promoter DMR was
found in seven of the eight patients. Another three
DMRs in GABRD, OXT and KRT7 genes are found
in five of the eight analyzed patients. Therefore, we
propose that these genes are strong candidates for
schizophrenia biomarkers. These results confirm the
data received from pool analysis and enabled the usage
of pools as a tool for epigenetic analysis.
In the 12 male patients, we detected all top 10
DMRs specific for the male pool, present in between
two and eight patients (Table 4). The CpG islands had
the same localization and methylation status. One of
the top 10 genes (MAP2K2) was found in eight of 12
patients. Three of these genes with different methylated
regions, DHX37, GIPC1 and FNDC4, occurred
in seven of the 12 patients (7). Another two genes,
MIR181C and LIN7B, were found in six of the 12
analyzed patients [6].
These data confirm our pool results in a subset
of individual patients included in the pool. We
therefore propose that gender-specific pools are more
informative than general pools and that these can be
used for determination of new biomarkers.
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