Hypermethylation in the ZBTB20 gene is associated with major depressive disorder

Observed methylation changes do not relate to anti-depressant use

The identified DMRs associated with MDD could reflect the consequence rather than the cause of the disorder. One possible consequence is use of anti-depressant medication, which was assessed as a confounder for the UK MZT pairs, for whom a record of drug usage was available. Two additional linear mixed models were calculated. The first included those individuals taking anti-depressant medication as an additional factor, the second eliminated twin pairs where the depressed twin was taking medication. Both produced results relatively consistent with the UK linear mixed model, suggesting that the observed methylation differences are not caused by anti-depressant medication based on data available to us (Tables S1, S2 and S3 in Additional file 1). ZBTB20 is significantly associated with MDD to an unadjusted P-value of 2.99 × 10^-7 if anti-depressant medication is included in the regression model and significant to a P-value of 1.28 × 10^-5 even if all depressed individuals taking medication are removed from the study. The β coefficient of the DMR also remains consistent with the full linear model giving a β value of 1.073. Two other linear models, one where the anti-depressant medication was included as a co-factor and one where the model removed medicated twin pairs completely, produced β coefficients of 1.078 and 1.082, respectively. No association was observed between cell count and calculated methylation levels for the most significant DMRs in the UK linear mixed model (see Materials and methods).

Our MeDIP-seq data indicated that twins with MDD had a significantly increased variance in methylation when compared with their unaffected co-twin. Comparison of variance in global methylation between the depressed twin and their unaffected co-twin revealed a highly significant increased genome-wide variance in twins with MDD in both the UK and Australian cohorts (P < 2.2 × 10^-16 in both datasets). This is in agreement with Byrne et al.[24], who used the much sparser 450 k array but also reported increased variance of methylation in the affected twin in a cohort of 12 MZT pairs, a subsample of the current Australian cohort. Byrne et al. failed to find any genome-wide significant DMRs in the 12 MZT pairs discordant for MDD. This could be the result of a limited sample size and/or the usage of the 450 k array, which has a much lower resolution than MeDIP-seq [25].

Several mouse studies have already demonstrated the importance of ZBTB20 for normal hippocampal function. ZTBT20 targets hippocampal neurons as well as cerebellum granule cells [26], consistent with our observation of a high ZTBT20 expression in the hippocampal, cerebellum and white matter regions of the brain. Conditionally deleting ZBTB20 specifically in mature CA1 pyramidal neurons impairs long-term potentiation and NMDA receptor (NMDAR)-mediated excitatory post-synaptic currents [27]. ZBTB20 is also crucial for the regionalization and volume of the archicortex [28], which plays a role in depression. In mice, mis-expression of ZBTB20 causes the development of a compact homogenous pyramidal cell layer within the hippocampal region, which is linked to behavioural abnormalities [29].

Magnetic resonance imaging scans of MZT pairs discordant for MDD identified volume reduction in the left posterior hippocampal region in the depressed co-twin [30]. In MDD, the dentate gyrus, and pyramidal neuron soma size is significantly decreased [31-34], suggesting that altered neuronal development rather than outright neuronal loss is responsible for the structural abnormalities linked to depression [35]. This is consistent with the pattern of reduced hippocampal volume and impaired regionalization suggested by the mouse model. The DMR of ZBTB20 we identified is hypermethylated in subjects with MDD and occurs within an identified splice region, which may have the effect of creating distinct isoforms based upon the specific methylation profile. ZBTB20 is also functionally related to the only SNP so far associated with MDD to a genome-wide significance in a genome-wide association study (common SNP rs1545843 (minor allele frequency = 0.41)) [36] occurring within the gene SLC6A15, which like ZTBT20 is associated with hippocampal structure. Down-regulation of SLC6A15 causes a reduced hippocampal volume (an effect that was replicated in stress-susceptible mice) and lower SLC6A15 expression in hippocampus reduces neural integrity and excitatory neurotransmission in the brain.

ZBTB20 shows specific gene expression in the hippocampus

Gene expression data of 932 brain samples from 10 different brain regions from 101 unrelated individuals taken from the Edinburgh Brain Bank (see Materials and methods) showed that, in the overall dataset, ZBTB20 is highly expressed in the hippocampal, cerebellum and white matter regions of the brain and lowly expressed in the frontal, occipital and temporal cortex (Figure S1 in Additional file 1). A weighted gene co-expression network analysis using WGCNA [37] generated a hippocampus ZBTB20-cointaining module (with a total N = 216 genes) that was unique to the tissue (Z summary preservation statistic <10), suggesting that ZBTB20 is co-expressed with a unique set of genes in the hippocampus, suggesting it has a key function in its regulation (Figure 4).

RNA-seq data for MDD from NIMH Center for Collaborative Genomic Studies on Mental Disorders

Data were adjusted for the confounding covariates BMI, smoking, age, gender and various medication intake indicators. No association between total gene expression level of ZBTB20 and MDD was observed (P-value >0.5). However, two non-standard exons identified within ZBTB20 transcript variants (exon 33 in ENST00000463890 and exon 45 in ENST00000470556) are associated with unadjusted P-values of 0.041 and 0.04, respectively. For these two exons, we visually inspected the per-base read distribution in cases and controls, and observed a smooth shift of mean expression levels across all bases in each case (Figure S2 in Additional file 1). Both exons 33 and 45 are downstream of the DMR and are located at 114099729-114099787 and 114137901-114138014, respectively. In the mouse homologue gene, Zbtb20, two isoforms of the gene were shown to have distinct roles in the development of the hippocampus [29]. The expression of non-standard exons in ZBTB20 may similarly have an impact on the development and regionalization of the human hippocampus.

Limitations

Several possible limitations to our study should be noted. We used whole blood rather than brain samples of discordant MZT pairs to identify methylation differences associated with MDD, which is sub-optimal but clearly more accessible from living patients. Furthermore, if epigenetic studies are to be of clinical use, they will ultimately have to rely upon peripheral tissue biomarkers such as buccal, gut and white blood cells. However, a previous study comparing the methylation status of pre-mortem blood and post-mortem brain tissue [2] showed that significant variation in the methylation profile of brain tissue can be reflected in blood. Also, recent studies have shown that DMRs associated with both chronic pain [38] and ageing are similar in brain and blood tissue [39]. Although in the UK sample analyses of anti-depressant treatment could not explain the observed associations, other potential factors can not be entirely eliminated. For example, birth weight and chorionicity of the twin pair are possibly correlated with adult methylation levels [40,41], but that information was not available for the MZT pairs included in the study. Another potential confounding factor in the study was that the samples were predominately female. Our RNA-seq validation data set was more evenly distributed than the initial study with 30% of the subjects male (103 in case, 171 in control). It should, however, be noted that the RNA-seq association is only of nominal significance and would not retain significance if adjusted for multiple testing.

Included subjects

The 27 MZT pairs (n = 54) of the UK samples were selected from the TwinsUK Registry. The 54 participants were all females aged 23 to 73 years, of European ancestry and had no other psychiatric condition nor had they been diagnosed with any known neurodegenerative disorder. The study was approved by the St Thomas’ Hospital Research Ethics Committee (REC reference: EC04/015). All participants in the study provided written informed consent in accordance with the St Thomas’ Hospital Local Ethics Committee. UK twin pairs completed the Composite International Diagnostic Interview questionnaire [45]. A diagnosis of MDD was constructed from these questionnaires according to the DSM-IV criteria [46]. Whole blood samples were collected from the twins and stored at -80°C in EDTA tubes. DNA was extracted from 2 × 6 ml EDTA blood using the Nucleon Genomic DNA Extraction Kit BACC3 and stored at -20°C in TE Buffer.

The 23 MZT pairs of the Australian samples (n = 46) were drawn from the Australian Twin Registry. The 46 participants comprised 7 male and 16 female MZ twin pairs aged 25 to 73 years. Seven pairs were discordant for smoking and a partially overlapping set of seven twin pairs was discordant for alcohol dependence. The study was approved by the Human Research Ethics Committee of the Queensland Institute of Medical Research. The assessment of Australian MZT pairs included a diagnostic telephone interview, adapted from the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) [47]. The SSAGA is a comprehensive psychiatric interview that was designed to assess life-time psychiatric disorders in adults according to DSM-III-R but subsequently updated to DSM-IV criteria and modified for use as a telephone survey instrument in Australia (SSAGA-OZ). SSAGA also assesses history of alcohol dependence and tobacco smoking with questions derived from the Composite International Diagnostic Interview [48]. Structured interviews were administered by trained telephone interviewers, closely supervised by a clinical psychologist. DNA was extracted from whole blood using a salt extraction method [49].

Sample preparation for MeDIP-seq

All sample preparation and MeDIP-sequencing was performed by the BGI-Shenzhen, Shenzhen, China. Extracted DNA was fragmented using a Covaris sonication system and sequencing libraries were prepared from 5 μg fragmented genomic DNA. End repair, <A > base addition and adaptor ligation steps were performed using Illumina’s Single-End DNA Sample Prep kit. Adaptor-ligated DNA was immunoprecipitated by anti-5mC using a commercial antibody (Diagenode), and MeDIP products were validated by quantitative PCR. MeDIP DNA was purified with ZYMO DNA Clean & Concentrator-5 columns, and amplified using adaptor-mediated PCR. DNA fragments between 220 and 320 bp in size were gel-excised, and amplification quality and quantity were evaluated by Agilent BioAnalyzer analysis. The libraries were subjected to highly parallel 50 bp single-end sequencing on the Illumina HiSeq platform.

Sequencing quality control and alignment

From the raw fastq files, Illumina quality scores were converted into Sanger Phred quality scores using MAQ [50]. Quality control was performed on the raw sequence data using in-house scripts and FastQC [51]. After stringent quality control, an average of 19 million uniquely mapped 50 bp reads were obtained from each of the 100 samples. Alignment to hg19 was performed using the Burrows-Wheeler algorithm [52]. The MEDIPS package [53] was used to calculate RPM scores by defining bin sizes of 500 bp with an overlap of 250 bp across the genome (Figure 1). The total number of 500 bp bins generated by the MEDIPS packages was 12,145,229. The number of bins was filtered to include only bins where more than 10% of samples had a read coverage greater than 0. For the UK samples, this reduced the number to 11,132,286 bins, for the Australian samples to 10,480,864. For both data sets, the raw FastQ files and calculated RPM scores from the aligned data are available from the Gene Expression Omnibus (GEO) database [54]. The UK samples are Study ID GSE54222 and the Australian samples are Study ID GSM1313979.

Linear mixed effect models

A linear mixed effect model was fitted for the RPM values of each 500 bp bin using the R package lmer [55]. Models were derived separately for the UK and Australian cohorts, reflecting the different fixed and random effects that needed to be incorporated for the two datasets. For the UK dataset, the linear mixed effect model incorporated depression status, age, BMI, smoking and alcohol consumption as fixed effects predictors [40,56-58] and twin pair as random effect (Table S1 in Additional file 1). In order to determine whether the discordant methylation could be a result of anti-depressant medication, two further linear mixed models were calculated. The first included those individuals taking anti-depressant medication as an additional factor, the second eliminated twin pairs where the depressed twin was taking medication from the study (Tables S2 and S3 in Additional file 1). Research has suggested that heterogeneity in whole blood cell counts could confound estimates of DNA methylation levels [59,60]. Cell count data were available for 21 twin pairs in the UK dataset; the top hits from the linear mixed model were analysed for evidence of association with cell counts of lymphocytes, neutrophils, eosinophils, monocyte, and total white blood and blood cell counts. For both data sets, the variance in methylation for cases and controls was compared for each bin.

The linear mixed effect model for the Australian dataset also incorporated depression status, age, tobacco addiction (yes/no) and alcohol dependence (yes/no) as fixed effects and twin pair as a random effect; medication data were not available for these samples. Sex was also incorporated as a fixed effect, but BMI was excluded, as this information was unavailable for four twin pairs. We investigated the effect of BMI as a covariate by fitting a second linear mixed effect model for the subset of 19 Australian twin pairs with available BMI information. Incorporating BMI as a fixed effect had only a minor effect on the P-values observed for the DMRs shown in Table S4 in Additional file 1.

Meta-analysis

As the UK and Australian datasets were drawn from different populations and modeled with different fixed effects, the data were integrated through a meta-analysis. A fixed effect inverse variance meta-analysis was carried out on all approximately 11 M bins of the UK and Australian datasets, using GWAMA [61]. P-values were Bonferoni adjusted to correct for multiple testing. We only present results for DMRs that show no strong evidence for heterogeneity in the meta-analysis as evaluated by the Cochran’s Q statistic (Cochran’s Q P > 0.05) [62] and the I² statistic (I² < 0.75) [63].

Replication in independent case-control cohort of 354 unrelated females

Differentially methylated genes identified in the meta-analysis of UK and Australian MZT pairs were evaluated with the existing scientific literature to select for likely MDD-related DMRs. Four DMRs were located within genes related to the pathology of MDD and selected for replication (see Results). For an independent case-control replication we included blood MeDIP-seq data of 354 unrelated, age-matched females from the EpiTwin project, 118 suffering from MDD and 236 controls. MeDIP-seq data of 118 cases and 236 controls were compared by t-test (RPM values of each 500 bp bin).

Postmortem brain expression data obtained from the UK Brain Expression Consortium

The four DMRs selected for replication were also compared with an independent expression dataset of 932 postmortem brain samples collected from the Edinburgh Brain Bank as part of the UK Brain Expression Consortium [64]. Expression data were generated with the Affymetrix GeneChip Human Exon 1.0 ST Array and the dataset contains 932 brain samples of 10 different brain regions obtained from 101 unrelated individuals (24 male and 77 female) aged from 16 to 83 years. The 10 brain regions are cerebellum, frontal cortex, hippocampus, medulla, occipital cortex, putamen, substantia niagra, temporal cortex, thalamus and intralobular white matter. The WGCNA R package was used to analyze the data for incidence of conserved co-expression gene networks [65].

Case-control MDD RNA-seq data

The cohort is of European ancestry and contains 463 individuals with recurrent MDD and 459 controls. A detailed description of RNA-sequence and phenotype data for this cohort is provided elsewhere [66,67]. RNA-sequencing was performed using whole-blood, with an average yield of 70 million reads per individual (50 or 51 bp, single-ended). Reads were mapped to the NCBI v37 H. sapiens reference genome using TopHat [68].Gene expression data were directly obtained from the previous study on this cohort [66]. Gene-level expression was quantified using HTSeq [69]. Additionally, we used samtools mpileup to quantify reads at each exonic position within the gene [70]. Only uniquely aligned reads with base quality of at least 30 were used for quantification. We then aggregated reads within the start and end points of each distinct exon identified in ENSEMBL Homo sapiens gene annotation, and normalized by the total read depth of each library. Total gene expression and exon expression of ZBTB20 were tested for association with MDD status. Associating testing followed the procedure reported previously for this cohort: a logistic regression likelihood ratio test was used to test for association between expression levels and MDD status while accounting for environmental, demographic, and medication intake covariates [66]. The list of covariates include age, gender, BMI, smoking status, cholesterol and blood pressure medication intake indicators. Genotype, raw RNA-seq, quantified expression, and covariate data are available by application through the NIMH Center for Collaborative Genomic Studies on Mental Disorders. Instructions for requesting access to data can be found at NIMH Repository and Genomics Resource [71] and inquiries should reference the 'Depression Genes and Networks study' (D Levinson, PI) [66].