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

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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