Balkan Journal of Medical Genetics

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

MATERIALS AND METHODS

We have examined the methylation status of Bulgarian patients with schizophrenia compared to sex - and age-matched healthy controls. We analyzed methylation profiles of DNAs in six pools, consisting of: 1) general pool of 220 schizophrenia patients [110 males with a mean age of 42 years, standard deviation (SD) = 11, and 110 females, aged 45, SD = 11 years]; 2) general pool of 220 healthy controls (110 males aged 50 years, SD = 14 and 110 females aged 51 years. SD = 14); 3) a pool of 110 female cases (the same female patients from the general pool); 4) a pool of 110 healthy females (the same female controls from the general pool); 5) a pool of 110 male cases (the same male patients from the general pool); 6) a pool of 110 healthy males (the same male controls from the general pool). We also investigated methylation status of 20 individual schizophrenia patient DNA samples (eight females and 12 males). Informed consent was obtained from all investigated subjects and the relevant Ethics Committees of the hospitals where subjects were recruited gave approval for the use of these samples in genetic studies. The diagnosis of schizophrenia was made by experienced psychiatrists, according to Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria on the basis of extensive clinical interviews [15]. Genomic DNA was extracted from peripheral blood by the phenol-chloroform extraction method. Concentration and purity were determined on all DNA samples (NanoDrop 2000C; Thermo Scientific, Wilmington, DE, USA). All samples were tested electrophoretically to verify the integrity of DNA. Six DNA pools were constructed using equal amount of DNA (at 100 ng/μL) from each patient/ control samples and placing them in a single tube-pool [17]. We based our DNA methylation profiling strategy on a recently developed technique, methylated DNA immunoprecipitation (MeDIP), which utilizes a monoclonal antibody against 5-methylcytosine to enrich the methylated fraction of a genomic DNA sample [18,19]. Genome-wide DNA methylation was assessed using the Agilent Human DNA Methylation Microarray (Agilent Technologies, Santa Clara, CA, USA) platform. We used oligonucleotide microarrays (1 × 244K, density 237,227 sequences covering 27,627 CpG). All included arrays passed standard quality control metrics. Agilent methylation microarrays were scanned, using Agilent High-Resolution Microarray Scanner G2505 with a resolution of 2 μm. Scans were performed with 532 nm wavelength of green laser and 635 nm for red laser. The resulting .tif images were processed with the Agilent Feature Extraction 11.0.1.1 and Agilent Workbench 6.5.0.18 software, according to the manufacturer’s instructions. These software products gave the position of the CpG island in the gene structure: promoter, intragenic, downstream, divergent promoter. Since such studies are still new there are no universally accepted algorithms for analysis of results. According to the latest data, the most suitable algorithm for the methylation analysis of immunoprecipitated DNA is the Bayesian tool for methylation analysis (BATMAN) [20]. BATMAN enabled the estimation of absolute methylation levels from immunoprecipitation-based DNA methylation profiles. This parameter can have the following values: –1 (hypomethylation), 1 (hypermethylation) or 0 (uninterpretable) [20]. For further analysis, we developed a software program to interpret the obtained methylation profiles data. It was designed to estimate the methylation status of one CpG island at a time and to compare island methylation status across arrays in search for differently methylated regions. The methylation status is based on the percentage of methylated probes in the island. The differently methylated islands list is generated in a separate table. Genes with uninterpretable results were excluded from the analysis. According to the literature, when over 60.0% of a CpG island is methylated, it is defined as “methylated” and if <40.0% of a CpG island is methylated, it is considered as “unmethylated.” CpG islands with a methylation status in the range 40.0-60.0% are considered as “intermediate” and were excluded from further analysis [20]. For further analysis of all genes with DMRs revealed from pool analysis (726) we used online data mining service (Biograph; http:// biograph.be/). It was very helpful in identification of susceptibility genes, because it used different databases and analyses functional relations in order to rank the genes according to their relevance in disease etiopathogenesis [21]. There are literature data for some of the genes about association with the disease so they are defined as “known.” For other genes, the relation to the disease was not proved, so they were defined as “inferred.”

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