Plasma total homocysteine is associated with DNA methylation in patients with schizophrenia

Schizophrenia (SCZ) is a devastating psychiatric disorder with a median lifetime prevalence rate of 0.7–0.8%. Elevated plasma total homocysteine has been suggested as a risk factor for SCZ, and various biological effects of hyperhomocysteinemia have been proposed to be relevant to the pathophysiology of SCZ. As increased attention is paid to aberrant DNA methylation in SCZ, homocysteine is attracting additional interest as a potential key substance. Homocysteine is formed in the methionine cycle, which is involved in one-carbon methyl group-transfer metabolism, and it acts as a methyl donor when it is converted to S-adenosyl-methionine. To date, no studies have examined the relationship between homocysteine and genome-wide DNA methylation in SCZ. We examined the relationship between plasma total homocysteine and DNA methylation patterns in the peripheral leukocytes of patients with SCZ (n = 42) using a quantitative high-resolution DNA methylation array (485,764 CpG sites). Significant homocysteine-related changes in DNA methylation were observed at 1,338 CpG sites that were located across whole gene regions, including promoters, gene bodies and 3′-untranslated regions. Of the 1,338 sites, 758 sites (56.6%) were located in the CpG islands (CGIs) and in the regions flanking CGIs (CGI: 15.8%; CGI shore: 28.2%; CGI shelf: 12.6%), and positive correlations between plasma total homocysteine and DNA methylation were observed predominantly at CpG sites in the CGIs. Our results suggest that homocysteine might play a role in the pathogenesis of SCZ via a molecular mechanism that involves alterations to DNA methylation.     

CpG islands: Algorithms and applications in methylation studies

Methylation occurs frequently at 5’-cytosine of the CpG dinucleotides in vertebrate genomes; however, this epigenetic feature is rarely observed in CpG islands (CGIs) or CpG clusters in the promoter regions of genes. Aberrant methylation of the promoter-associated CGIs might influence gene expression and cause carcinogenesis. Because of the functional importance, multiple algorithms have been available for identifying CGIs in a genome or a sequence. They can be categorized into the traditional algorithms (e.g., Gardiner-Garden and Frommer (1987), Takai and Jones (2002), and CpGPRoD (2002)) or statistical property based algorithms (CpGcluster (2006) and CG cluster (2007)). We reviewed the features of these algorithms and evaluated their performance on identifying functional CGIs using genome-wide methylation data. Moreover, identification of CGIs is an initial step in many recent studies for predicting methylation status as well as in the design of methylation detection platforms. We reviewed the benchmarks and features used in these studies.     

High-resolution mapping of DNA methylation in human genome using oligonucleotide tiling array

DNA methylation is an epigenetic mark crucial in regulation of gene expression. Aberrant DNA methylation causes silencing of tumor suppressor genes and promotes chromosomal instability in human cancers. Most of previous studies for DNA methylation have focused on limited genomic regions, such as selected genes or promoter CpG islands (CGIs) containing recognition sites of methylation-sensitive restriction enzymes. Here, we describe a method for high-resolution analysis of DNA methylation using oligonucleotide tiling arrays. The input material is methylated DNA immunoprecipitated with anti-methylcytosine antibodies. We examined the ENCODE region (∼1% of human genome) in three human colorectal cancer cell lines and identified over 700 candidate methylated sites (CMS), where 24 of 25 CMS selected randomly were subsequently verified by bisulfite sequencing. CMS were enriched in the 5′ regulatory regions and the 3′ regions of genes. We also compared DNA methylation patterns with histone H3 and H4 acetylation patterns in the HOXA cluster region. Our analysis revealed no acetylated histones in the hypermethylated region, demonstrating reciprocal relationship between DNA methylation and histone H3 and H4 acetylation. Our method recognizes DNA methylation with little bias by genomic location and, therefore, is useful for comprehensive high-resolution analysis of DNA methylation providing new findings in the epigenomics. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Identification of CpG islands in DNA sequences using statistically optimal null filters

CpG dinucleotide clusters also referred to as CpG islands (CGIs) are usually located in the promoter regions of genes in a deoxyribonucleic acid (DNA) sequence. CGIs play a crucial role in gene expression and cell differentiation, as such, they are normally used as gene markers. The earlier CGI identification methods used the rich CpG dinucleotide content in CGIs, as a characteristic measure to identify the locations of CGIs. The fact, that the probability of nucleotide G following nucleotide C in a CGI is greater as compared to a non-CGI, is employed by some of the recent methods. These methods use the difference in transition probabilities between subsequent nucleotides to distinguish between a CGI from a non-CGI. These transition probabilities vary with the data being analyzed and several of them have been reported in the literature sometimes leading to contradictory results. In this article, we propose a new and efficient scheme for identification of CGIs using statistically optimal null filters. We formulate a new CGI identification characteristic to reliably and efficiently identify CGIs in a given DNA sequence which is devoid of any ambiguities. Our proposed scheme combines maximum signal-to-noise ratio and least squares optimization criteria to estimate the CGI identification characteristic in the DNA sequence. The proposed scheme is tested on a number of DNA sequences taken from human chromosomes 21 and 22, and proved to be highly reliable as well as efficient in identifying the CGIs.     

Association between Glucocorticoid Receptor Methylation and Hippocampal Subfields in Major Depressive Disorder

Background

DNA methylation in the promoter region of the glucocorticoid receptor gene (NR3C1) is closely associated with childhood adversity and suicide. However, few studies have examined NR3C1 methylation in relation to major depressive disorder (MDD) and hippocampal subfield volumes. We investigated the possible association between NR3C1 methylation and structural brain alterations in MDD in comparison with healthy controls.

Methods

We compared the degree of NR3C1 promoter methylation in the peripheral blood of non-psychotic outpatients with MDD and that of healthy controls. Correlations among NR3C1 promoter methylation, structural abnormalities in hippocampal subfield volumes and whole-brain cortical thickness, and clinical variables were also analyzed.

Results

In total, 117 participants (45 with MDD and 72 healthy controls) were recruited. Patients with MDD had significantly lower methylation than healthy controls at 2 CpG sites. In MDD, methylations had positive correlations with the bilateral cornu ammonis (CA) 2–3 and CA4-dentate gyrus (DG) subfields. However, in healthy controls, methylations had positive correlation with the subiculum and presubiculum. There were no differences in total and subfield volumes of the hippocampus between patients with MDD and healthy controls. Compared with healthy controls, patients with MDD had a significantly thinner cortex in the left rostromiddle frontal, right lateral orbitofrontal, and right pars triangularis areas.

Conclusions

Lower methylation in the NR3C1 promoter, which might have compensatory effects relating to CA2-3 and CA4-DG, is a distinct epigenetic characteristic in non-psychotic outpatients with MDD. Future studies with a longitudinal design and a comprehensive neurobiological approach are warranted in order to elucidate the effects of NR3C1 methylation.     

CpGIF: an algorithm for the identification of CpG islands

CpG islands (CGIs) play a fundamental role in genome analysis and annotation, and contribute to improving the accuracy of promoter prediction. Besides, CGIs in promoter regions are abnormally methylated in cancer cells and thus can be used as tumor markers. However, current methods for identifying CGIs suffer from various drawbacks. We present a new algorithm for detecting CGIs, called CpG Island Finder (CpGIF), which combines the best features in the most commonly used algorithms and avoids their disadvantages as much as possible. Five public tools for CpG island searching are used to compare with CpGIF for the assessment of accuracy and computational efficiency. The results reveal that CpGIF has higher performance coefficient and correlation coefficient than these previous methods, which indicates that CpGIF is able to provide high sensitivity and specificity at the same time. CpGIF is also faster than those methods with comparable prediction accuracy.     

Unmethylated state of 5′ upstream CpG islands may be necessary but not sufficient for the testis-enriched expression of ZNF230/Znf230

The testis-enriched genes ZNF230/Znf230 are located on human chromosome 11p15/mouse chromosome 7 near conserved imprinting control regions. Typical CpG islands (CGIs) extend from the promoter to the first exon in each of these genes. To investigate the correlation between the methylation status of the above CGIs and the expression patterns of the two genes, we performed bisulfite genomic sequencing of genomic DNA from human and mouse tissues and cells. The results showed that the CGIs of ZNF230/Znf230 were completely unmethylated in all selected tissues and cells, regardless of the expression levels of the two genes. Further experiments using Znf230-second-exon-knockout mice to investigate the imprinting status of Znf230 showed that its expression was not affected by genomic imprinting. However, an in vitro methylation assay illustrated that the methylation of these CpG sites could repress the expression of the luciferase reporter gene. Furthermore, chromatin immunoprecipitation with anti-Specificity protein 1 (Sp1) antibody showed that Sp1 could bind to the CGIs in the ZNF230/Znf230 gene promoter. Thus, we propose that the unmethylated state of ZNF230/Znf230 CGIs may be a prerequisite for their expression but not sufficient for their abundant expression in the testis, and that Sp1 binding may be one factor involved in preserving the methylation-free state of ZNF230/Znf230 CGIs.     

Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic  = 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.