© 2014 John Wiley & Sons A/S Published by John Wiley & Sons Ltd.

Bipolar Disorders 2015: 17: 150–159

BIPOLAR DISORDERS

Original Article

DNA methylation and expression of KCNQ3 in bipolar disorder Kaminsky Z, Jones I, Verma R, Saleh L, Trivedi H, Guintivano J, Akman R, Zandi P, Lee RS, Potash JB. DNA methylation and expression of KCNQ3 in bipolar disorder. Bipolar Disord 2015: 17: 150–159. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. Objectives: Accumulating evidence implicates the potassium voltagegated channel, KQT-like subfamily, member 2 and 3 (KCNQ2 and KCNQ3) genes in the etiology of bipolar disorder (BPD). Reduced KCNQ2 or KCNQ3 gene expression might lead to a loss of inhibitory Mcurrent and an increase in neuronal hyperexcitability in disease. The goal of the present study was to evaluate epigenetic and gene expression associations of the KCNQ2 and KCNQ3 genes with BPD. Methods: DNA methylation and gene expression levels of alternative transcripts of KCNQ2 and KCNQ3 capable of binding the ankyrin G (ANK3) gene were evaluated using bisulfite pyrosequencing and the quantitative real-time polymerase chain reaction in the postmortem prefrontal cortex of subjects with BPD and matched controls from the McLean Hospital. Replication analyses of DNA methylation findings were performed using prefrontal cortical DNA obtained from the Stanley Medical Research Institute. Results: Significantly lower expression was observed in KCNQ3, but not KCNQ2. DNA methylation analysis of CpGs within an alternative exonic region of KCNQ3 exon 11 demonstrated significantly lower methylation in BPD, and correlated significantly with KCNQ3 mRNA levels. Lower KCNQ3 exon 11 DNA methylation was observed in the Stanley Medical Research Institute replication cohort, although only after correcting for mood stabilizer status. Mood stabilizer treatment in rats resulted in a slight DNA methylation increase at the syntenic KCNQ3 exon 11 region, which subsequent analyses suggested could be the result of alterations in neuronal proportion. Conclusion: The results of the present study suggest that epigenetic alterations in the KCNQ3 gene may be important in the etiopathogenesis of BPD and highlight the importance of controlling for medication and cellular composition-induced heterogeneity in psychiatric studies of the brain.

Numerous lines of evidence suggest that dysregulated ion channel function leading to neuronal hyperexcitability might represent an underlying pathophysiological feature of bipolar disorder (BPD). Anticonvulsant drugs such as valproic acid (VPA), lamotrigine, and carbamazepine mediate neuronal hyperexcitability and have long been known for their ability to treat BPD symptoms (1). Bioinformatic analysis of BPD genome-wide

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Zachary Kaminskya, Ilenna Jonesa, Ranjana Vermab, Lena Saleha, Hersh Trivedia, Jerry Guintivanoa, Ryan Akmana, Peter Zandia,c, Richard S Leea and James B Potashd a Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, b Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, cDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, d Department of Psychiatry, University of Iowa, Iowa City, IA, USA

doi: 10.1111/bdi.12230 Key words: bipolar disorder – DNA methylation – epigenetics – glia – KCNQ3 – neurons – pyrosequencing Received 28 January 2014, revised and accepted for publication 29 April 2014 Corresponding author: Zachary Kaminsky, Ph.D. Department of Psychiatry and Behavioral Sciences Johns Hopkins University 720 Rutland Avenue Ross Research Building 1070 Baltimore, MD 21205 USA Fax: 410-502-0065 E-mail: [email protected]

association study (GWAS) data has identified evidence for the involvement of voltage-gated ion channels in BPD risk (2, 3). One of the most widely associated and replicated genes from BPD GWAS is the ankyrin G (ANK3) gene (4–7), a member of the ankyrin family of proteins. Ankyrin G is required for the proper cellular localization of the Kv7 family of voltage-gated potassium (K+) channels, called M-channels, at neuronal axon initial

Epigenetics of KCNQ3 in bipolar disorder splice variants, in which an open reading frame shift created by alternative exon inclusion results in disruption of the ankyrin G-interaction motif. Accumulating evidence implicates exonic DNA methylation density in directing co-transcriptional splicing events (24, 25). Epigenetic dysregulation at key exonic positions may influence alternative splicing decisions and the ratios of ankyrin Ginteracting versus non-interacting splice variants. In the present study, we tested the hypothesis that alternatively spliced transcriptional variants of the KCNQ2 and KCNQ3 genes are associated with bipolar disorder in a sample of postmortem prefrontal cortex tissue. We investigated whether any identified differences were associated with DNA methylation changes at gene regulatory regions and exonic locations, and whether these, in turn, were associated with disease. We also investigated the association of common medications for BPD with the observed epigenetic changes, and the ratio of neurons to non-neurons estimated using genome-wide DNA methylation cell type proxies.

segments and nodes of Ranvier (8–11). M-channels are protein heteromers comprising the Kv7.2 and Kv7.3 proteins encoded by the potassium voltagegated channel, KQT-like subfamily, member 2 and 3 (KCNQ2 and KCNQ3) genes, respectively (9). Dysregulated neuronal hyperexcitability in BPD might be mediated by altered M-channel function (12–14). Mutations in the KCNQ2 and KCNQ3 genes result in rare forms of epilepsy known as benign neonatal epilepsy (15–18) as well as other forms of epileptic neuronal hyperexcitability (19). The inhibitory ion current, or M-current, generated by M-channels determines neuronal subthreshold excitability and thus the neuron’s ability to respond to synaptic stimulus (20–22). Reduced M-channel function has been associated with spontaneous neuronal depolarization, burst firing, repetitive discharges, and a shift from phasic to tonic neuronal firing (20). Normal placement and function of the M-channel proteins require both normal steady-state mRNA levels and proper interaction of the proteins with ankyrin G. Gene expression of KCNQ2 and KCNQ3 are mediated by an specificity protein 1 (SP1) transcription factor binding site in the promoter region and a RE1-silencing transcription factor (REST) transcriptional silencing site (21). KCNQ2- and KCNQ3-encoded proteins Kv7.2 and Kv7.3 interact with ANK3 via an 80-amino-acid binding motif in the C-terminal amino acid domain (8–11), which may also be required for protein heteromer formation (17). However, both KCNQ2 and KCNQ3 have multiple alternative transcripts that result from altered mRNA splicing, only some of which contain the consensus sequence capable of interacting with ankyrin G. Alternative splice products of KCNQ2-containing truncated C-terminal sequences have been significantly associated with BPD (23). KCNQ3 contains three alternative

Materials and methods Samples

Postmortem brain tissue of prefrontal cortex tissue was provided by the Harvard Brain Tissue Resource Center at McLean Hospital (McL) from which genomic DNA was extracted [12 BPD samples and 10 control (CON) samples]. At the time of this study, remaining tissue available for mRNA extraction was limited to nine BPD samples and nine CON samples. Prefrontal cortical DNA was obtained as a replication sample from the Stanley Medical Research Institute (SMRI) (34 BPD samples and 35 CON samples). Finally, prefrontal cortical neuronal and glial DNA was obtained from six CON samples from the National Institute of

Table 1. Sample demographics Cohort

Diagnosis

DNA methylation analyses McL BPD CON SMRI BPD CON NICHD CON Gene expression analyses McL BPD CON

N

Age (years)

12 10 34 35 8

60.50 60.90 45.40 44.20 30.88

9 9








1.70 1.80 0.46 0.25 1.47

63.67
2.40 62.62
2.10

Gender (M:F)

PMI (h)

7:5 8:2 16:18 26:9 4:4

21.07 22.44 37.90 29.40 13.75

5:4 6:2








0.88 0.39 0.82 0.53 0.77

20.98
1.30 22.27
0.53

BPD = bipolar disorder; CON = controls; F = female; McL = prefrontal cortex samples from the Harvard Brain Tissue Bank at McLean Hospital; M = male; NICHD = prefrontal cortical samples from the National Institutes of Child Health and Development Brain Bank of Developmental disorders; PMI = postmortem interval (hours); SMRI = prefrontal cortex samples from the Stanley Medical Research Institute.

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Kaminsky et al. Child Health and Development (NICHD) Brain and Tissue Bank for Developmental Disorders from the University of Maryland. A summary of the demographic information for samples used is shown in Table 1. Diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). For the purposes of analysis, mood stabilizer status consisted of those individuals reportedly using lithium or VPA. No details on lithium or VPA dose were available. Animals

We administered lithium (n = 12) and VPA (n = 12) to Norway Brown rats for 30 days. Caloric restriction was imposed on controls (n = 12) to match the decreased weight of treated rats. Tail blood (~500 lL) was collected weekly to ensure clinically relevant levels of the drugs in the animals. Levels of lithium and VPA were 1.0
0.07 mM and 37.9
5.5 lg/mL at Week 4, respectively. After 30 days of treatment, the animals were sacrificed and relevant tissues, including prefrontal cortex, were dissected using a brain matrix and frozen prior to genomic DNA extraction. All procedures were approved by the Institutional Animal Care and Use Committee at Johns Hopkins University School of Medicine and were performed in accordance with guidelines established in the National Research Council’s Guide for the Care and Use of Laboratory Animals.

Cell culture

Human SH-Sy5y neuroblastoma cell lines were cultured using Dulbecco’s Modified Eagle Medium (DMEM) (Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (Hyclone, Logan, UT, USA) and 1% penicillin/streptomycin/neomycin (Invitrogen) under standard conditions (5% CO2, 37°C). For alternative splicing experiments, SH-Sy5y cells were treated with 10 lM of 5-aza-cytidine (Sigma-Aldrich, St. Louis, MO, USA), replenished daily, and harvested at four, five, six, seven, and eight days. For mood stabilizer experiments, cells were treated with 10 lM of 5-aza-cytidine for five days, with media being replenished daily, before being trypsinized and replated in six-well plates. Cells were then treated with culture media devoid of 5-aza-cytidine and containing either 5 mM lithium chloride (SigmaAldrich) or 1 mM VPA (Sigma-Aldrich) for 28 days. Cells were split and media replenished every two days to maintain them in the log phase of growth. After 28 days, cells were harvested for genomic DNA and mRNA. Bisulfite pyrosequencing

One region spanning the published SP1 transcription factor binding site and two regions flanking the alternative exon 10 were selected, including four CpGs located at the 50 end of intron 10, and the entirety of exon 11 (Fig. 1). Genomic DNA from all samples was extracted using the Master-

Fig. 1. KCNQ3 promoter and alternative splice variants. The potassium voltage-gated channel, KQT-like subfamily, member 3 (KCNQ3) gene is depicted from 50 to 30 with arrows representing the direction of transcription. Expanded depictions of regions assayed for DNA methylation change including the specificity protein 1 (SP1) transcription factor binding region within the gene promoter as well as the region between exons 10 and 11 (transcripts A and B) where intron inclusion results in alternative transcript C. Red and blue lollypops represent CpG dinucleotides interrogated and not interrogated by sodium bisulfite pyrosequencing, respectively. Pyrosequencing assay locations are depicted to scale below genomic coordinates investigated. Column graphs representing the mean control (gray) and bipolar disorder (black) DNA methylation levels per region are depicted.

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Epigenetics of KCNQ3 in bipolar disorder Pure DNA Purification kit (Epicentre Biotechnologies, Madison, WI, USA). Sodium bisulfite modification was performed on 500 ng of genomic DNA from all samples using Zymo Easy DNA methylation GOLD kits (Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. Nested polymerase chain reaction (PCR) amplification was performed prior to pyrosequencing analysis using primers described in Supplementary Table 1. After agarose gel electrophoresis to ensure successful amplification and specificity, PCR amplicons were processed for pyrosequencing analysis according to the manufacturer’s standard protocol (Qiagen, Germantown, MD, USA) and run on a PyroMark 96 pyrosequencing machine. Quantitative real-time PCR

mRNA was extracted from brain tissue using the RNeasy Lipid and Tissue kit (Qiagen) and from tissue culture using the RNeasy Mini kit (Qiagen) according to the manufacturer’s protocols, followed by RNA quality assessment through visualization following electrophoresis. Reverse transcription was carried out using a combination of oligo DT and random primers using the Quantitect Reverse Transcription kit (Qiagen), according to the manufacturer’s protocol. Quantitative realtime PCR was performed on an ABI 7900HT Fast Real-Time PCR system to assess steady state mRNA levels. For KCNQ2 (Hs01548339_m1) and KCNQ3-AB (Hs01120404_m1) transcripts, ABI Taqman probes (Life Technologies, Carlsbad, CA, USA) were obtained from the manufacturer’s website (http://www.idtdna.com/scitools/Applications/ RealTimePCR). For the KCNQ3-C transcript, we used the online design tool located on the IDT website (http://www.idtdna.com/scitools/Applications/RealTimePCR) and submitted the output primer sequences for synthesis at Life Technologies. To avoid amplification of genomic DNA, the PCR amplicon spanned an intron gap between exons 11 and 12. Primer sequences for the KCNQ3-C variant were as follows: forward primer 50 -AGCCTTTTCCAGGTCTGTG-30 ; reverse primer 50 -GCTCAATCACATCCTTCACATC-30 ; Taqman probe 50 -ATTCCCATAGCCCCTGTCTT CCG-30 . The specificity of this assay for the KCNQ3-C variant was determined through visualization following electrophoresis. Assays were run in triplicate alongside a reference gene, b-actin (Hs03023943_g1), and average relative gene expression of gene-specific transcripts selected was quantified using the delta CT method. To determine relative expression values, the DDCt method (Life

Technologies) was used, where triplicate Ct values for each sample were averaged and subtracted from those derived from the housekeeping gene ACTB. The Ct difference for a calibrator sample was subtracted from those of the test samples, and the resulting DDCt values were raised to the power of 2 to determine normalized relative expression. CHARM-based profiling

genome-wide

DNA

methylation

The CHARM assay was performed as described previously (26). Briefly, DNA was extracted from tissues using the MasterPure DNA Purification kit (Epicentre Biotechnologies) and 10 lg of DNA was sheared in 100 lL using a Hydroshear device (Digilab, Holliston, MA, USA) into 1.6–3 kb fragments. Sheared DNA was then divided into two fractions. One fraction was digested overnight at 37°C with the methyl-sensitive enzyme McrBC (NEB, Ipswich, MA, USA). Following digestion, cut and uncut fractions from the same sample were electrophoresed in adjacent wells of a 1% agarose gel. Areas corresponding to the 1.6–3 kb regions were excised and purified using Qiagen Spin Gel Purification columns (Qiagen). The gel-purified DNA was quantified on a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Rockford, IL, USA) and 30 ng of DNA from each fraction was amplified using a GenomePlex Whole Genome Amplification Kit (Sigma-Aldrich). The amplified DNA was then isolated with a Qiagen PCR Purification column, then quantified on NanoDrop. The untreated, total DNA fraction was labeled with Cy3 and the methyl-depleted DNA fraction was labeled with Cy5 and hybridized onto the custom NimbleGen 2.1M feature CHARM microarray (Roche, Madison, WI, USA). Data analysis

Normality for the tested distributions was evaluated using Anderson–Darling tests. Where data tested displayed non-Gaussian distributions, we evaluated the significance of group-wise differences and correlations with non-parametric Wilcoxon Rank Sum tests and Spearman’s correlations, respectively. The effects of gender, age, postmortem interval, and brain pH were tested for association to DNA methylation or gene expression outcomes for the human samples and no associations were observed (See Supplementary Table 2). Means and standard errors of the mean are reported in the text. Values of p ≤ 0.05 were deemed significant. Quantification of neuronal proportion was performed using the CETS package

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Kaminsky et al. in R using 438 CETS markers located within 100 bp from an originally published CETS probe and present in both SMRI and McL DNA methylation datasets. For the SMRI dataset, normalized HpaII-generated BED files were downloaded for available individuals processed by Khare et al. (27) from the Gene Expression Omnibus (http://www. ncbi.nlm.nih.gov/geo/) (GEO) accession GSE40 166. Where indicated, adjustment of DNA methylation on neuronal proportion was performed by taking the residuals of a linear regression modeling DNA methylation as a function of CETS estimated neuronal proportion. For CHARM analysis, background correction and normalization of raw microarray data were performed using the CHARM Package in R (http://www.bioconductor. org/packages/release/bioc/html/charm.html). Data are deposited in under GEO accession GSE32528.

DNA methylation differences in KCNQ3

Results Gene expression differences at KCNQ2 and KCNQ3 ankyrin G-interacting transcripts

We evaluated gene expression at KCNQ2 and KCNQ3 in the prefrontal cortex samples from the McL cohort and tested for association to BPD relative to matched CON using Taqman probes specific to mRNA transcripts containing the ankyrin G binding motif. These splice variants include A through D for KCNQ2 and A and B for KCNQ3 according to AceView notation. In the case of KCNQ2, variants E through M represent truncated mRNA transcripts, while in KCNQ3 variant C an alternative open reading frame shift abolishes A

formation of the ankyrin G-interacting amino acid sequence. We observed a significant decrease of KCNQ3 ankyrin G-interacting mRNA (KCNQ3-AB) in BPD as compared to CON (CON = 6.89
0.38, BPD = 2.82
0.35, p = 0.012) (Fig. 2A). KCNQ2 ankyrin G-interacting variants were not significantly different (CON = 4.89
0.39, BPD = 2.5
0.26; p = 0.11). We next attempted to quantify the relative gene expression levels of the KCNQ3 ankyrin G non-interacting variant C (KCNQ3-C). The levels of KCNQ3-C variants were 4.4
0.84-fold lower than those of KCNQ3-AB variants (KCNQ3-AB = 4.5
0.2, KCNQ3-C = 0.037
0.0018, p = 8.8 9 1010). We observed a significant decrease in relative expression in BPD cases compared to CON for the KCNQ3-C variant (CON = 0.053
0.0035, BPD = 0.019
0.0014; p = 0.012) (Fig. 2B).

B

C

We sought to investigate disease-specific DNA methylation variation at the SP1 and REST1 binding motifs implicated in KCNQ3 gene expression regulation; however, the REST1 binding motif of KCNQ3 was not evaluated as it was located in a CpG sparse region and did not contain CpGs within the published binding region. For SP1, we evaluated a total of seven CpGs covering the published SP1 binding site. No CpGs demonstrated an association with disease diagnosis in the McL sample (percent methylation across SP1 CpGs: CON = 5.6
0.5, BPD = 5.8
0.2; p = 0.97) (Fig. 2C). We next targeted CpGs within the beginning of alternative exon 10 (KCNQ3-C) and exon 11 D

E

Fig. 2. Harvard Brain Tissue Resource Center at McLean Hospital (McL) potassium voltage-gated channel, KQT-like subfamily, member 3 (KCNQ3) gene expression and DNA methylation. The figure shows KCNQ3-AB (A) and C (B) variant gene expression as a function of diagnosis in the McL sample. It also shows DNA methylation levels as a function of diagnosis in the McL sample for the mean specificity protein 1 (SP1) transcription factor (C), exon 10 (D), and exon 11 (E) regions. BPD = bipolar disorder; CON = controls. *p ≤ 0.05.

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Epigenetics of KCNQ3 in bipolar disorder (KCNQ3-AB) and evaluated them for DNA methylation association to disease (Fig. 1). CpG methylation within the exon 10 region was not associated with BPD (percent methylation: CON = 91.0
2.4, BPD = 91.2
2.4; p = 0.98) (Fig. 2D). The mean DNA methylation across five CpGs within exon 11 was significantly reduced in BPD (CON = 93.2
0.3, BPD = 89.5
0.3; p = 0.0071) (Fig. 2E). The largest DNA methylation difference (9.4%) occurred at exon 11 CpG 2 (CON = 83.0
0.8, BPD = 73.6
0.7; p = 0.0056) located directly after the splice acceptor site for this exon. KCNQ3 exon 11 region DNA methylation was not significantly different in posterior cingulate cortical samples (Brodmann area 23) from BPD and matched CON brains from the McL sample (CON = 95.7
0.04, BPD = 96.0
0.1; p = 0.33), suggesting that the prefrontal cortical finding may be brain region specific. KCNQ3 SP1 and exon 11 DNA methylation correlates with KCNQ3 expression

KCNQ3 exon 11 DNA methylation was significantly correlated with gene expression levels of KCNQ3-AB (rho = 0.6, p = 0.02) (See Supplementary Fig. 1A), but not KCNQ3-C (rho = 0.4, p = 0.12). Only CpG 6 (SP1Motif CpG6) within the previously analyzed SP1 region correlated significantly with the KCNQ3-AB (rho = 0.6, p = 0.027) (See Supplementary Fig. 1A) and KCNQ3-C gene expression (rho = 0.5, p = 0.05). Subsequently, KCNQ3-AB variant gene expression was modeled against DNA methylation in both the SP1 and exon 11 regions. It displayed significant associations in an additive linear model (SP1Motif CpG6: b = 0.71
0.26, p = 0.019; exon 11: b = 0.59
0.2, p = 0.015), while the overall model generated an adjusted R2 of 0.47 (p = 0.012). Neither the exon 11 DNA methylation (b = 0.005
0.0023, p = 0.053) nor the SP1Motif CpG6 (b = 0.004
0.0028, p = 0.18) showed significant association when modeling the KCNQ3-C variant. Analysis in vitro replicated the above association, as KCNQ3 exon 11 DNA methylation was significantly associated with KCNQ3-AB variant expression in human SH-Sy5y neuroblastoma cells using the same additive linear model used in the in vivo analysis (b = 0.12
0.033, p = 0.021) (See Supplementary Fig. 1B).

the SMRI. Only DNA was available from these samples, which precluded gene expression analysis but enabled the analysis of the DNA methylation difference identified at the KCNQ3 exon 11 region. We found no differences in this cohort when comparing the average DNA methylation across the five CpGs in exon 11 between BPD and matched CON (CON = 94.0
0.1, BPD = 93.1
0.1; p = 0.97). Recent reports demonstrate the ability of mood stabilizers, primarily lithium, to alter DNA methylation (28) in neuronal cell lines. Incorporation of mood stabilizer status as a covariate generated a significant additive linear model (R2 = 0.16, p = 0.03) and identified significant associations for BPD diagnosis (b = 0.99
0.41, p = 0.02) and mood stabilizer status [individuals taking either lithium or VPA (n = 14), b = 1.93
0.94, p = 0.045]. The model results show that after controlling for mood stabilizer status, the BPD group in the SMRI sample exhibits significantly lower DNA methylation, and suggest that lithium or VPA treatment may increase DNA methylation in a subset of individuals in the BPD group. Mood stabilizers increase KCNQ3 exon 11 DNA methylation in vivo but not in vitro

We attempted to verify experimentally the lithium and VPA-induced increase in DNA methylation observed in the SMRI cohort using both in vitro and in vivo systems. However, following four weeks of mood stabilizer treatment to SH-Sy5y cells, we observed no significant mean exon 11 DNA methylation changes in VPA-treated, lithium-treated, or combined mood stabilizer-treated cells (Table 2). We next evaluated prefrontal cortex DNA methylation of brown Norway rats across four CpGs

Table 2. DNA methylation with mood stabilizer treatment Treatment

Treated

Untreated

p-value

0.25 0.59 0.16

86.45
0.38 86.45
0.38 86.45
0.38

0.63 0.84 0.68

0.23 0.48 0.16

73.40
0.24 73.40
0.24 73.40
0.24

0.035 0.13 0.025

1.00 0.39 0.56

59.60
0.27 59.60
0.27 59.60
0.27

0.043 0.46 0.65

Replication of DNA methylation differences in the SMRI cohort

SH-Sy5y DNA methylation Lithium 87.08
VPA 86.81
Mood stabilizer 86.98
Rat DNA methylation Lithium 75.9
VPA 75.9
Mood stabilizer 75.9
SMRI neuronal proportion Lithium 55.05
VPA 61.79
Mood stabilizer 58.76


We attempted to replicate our above findings in an alternative cohort of postmortem brain tissue from

SMRI = Stanley Medical Research Institute; VPA = valproic acid.

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Kaminsky et al. within the KCNQ3 exon 10 region and two CpGs within exon 11 syntenic to the human sequence. Rats were treated for four weeks with lithium (n = 12) or VPA (n = 12) and compared to untreated controls (n = 12). In exon 10, no significant differences with lithium or VPA were identified; however, a small, but significant elevation of mean exon 11 DNA methylation was observed after treatment with lithium (Table 2, Fig. 3A). The VPA treatment alone demonstrated a moderate increase in DNA methylation levels compared to controls that did not reach statistical significance, while being treated with either lithium or VPA also significantly increased exon 11 DNA methylation (Table 2, Fig. 3A). Given that in vivo analysis demonstrated elevated DNA methylation levels while the in vitro models did not, we reasoned that the in vivo changes in DNA methylation could be a result of medication-induced alterations in neuron to glial ratios. KCNQ3 exon 11 DNA methylation is significantly different between neurons and glia

We performed fluorescence activated cell sorting (FACs) to separate neuronal and glial nuclei from eight control postmortem prefrontal cortical samples obtained from the NICHD Brain Bank of Developmental Disorders and evaluated KCNQ3 SP1 and exon 11 DNA methylation. No significant differences were observed at the SP1 CpGs. Mean KCNQ3 exon 11 DNA methylation was slightly, and significantly, lower in neurons as compared to glia (paired t-test, neurons = 92.1
0.062, A

B

glia = 93.9
0.066; p = 0.00067), suggesting that alterations in neuronal proportion due to medication status may influence the observed disease associations. Mood stabilizer use decreases the proportion of neurons in the SMRI cohort and masks the association of KCNQ3 with BPD

We quantified the proportion of neurons to glia by DNA methylation proxy using the CETS algorithm (29) on genome-wide DNA methylation in the McL and SMRI cohorts. In the SMRI cohort, exposure to lithium significantly decreased neuronal relative to non-neuronal proportions; however, no significant effects were observed for VPA or the combination of lithium and VPA (Table 2, Fig. 3B). Similarly, BPD diagnosis was significantly associated with lower neuronal proportion relative to CON (Wilcoxon Rank Sum: CON = 0.62
0.0028, BPD = 0.57
0.0039, p = 0.028) (Fig. 3C). No differences in neuronal proportion were observed with BPD diagnosis in the McL sample (Wilcoxon Rank Sum: CON = 0.39
0.0093, BPD = 0.39
0.010; p = 0.63) (Fig. 3D). Because all individuals with BPD in this cohort were taking mood stabilizers, no comparison of mood stabilizer status with neuronal proportion could be made. Cumulatively, the data suggest that mood stabilizer status may account for differences in neuronal proportion that cause discrepancies in DNA methylation measurements across cohorts. Importantly, adjusting for neuronal proportion in the McL sample did not C

D

Fig. 3. Mood stabilizers on potassium voltage-gated channel, KQT-like subfamily, member 3 (KCNQ3) DNA methylation and neuronal proportion. (A) KCNQ3 exon 11 DNA methylation in the prefrontal cortex (PFC) of rats treated for one month with lithium (Li), valproic acid (VPA), either Li or VPA (Moods), and controls (CON). (B) The proportion of neurons to glia as determined by the CETS analysis of microarray-based DNA methylation proxies for individuals in the Stanley Medical Research Institute (SMRI) cohort medicated with Li, Moods, and VPA. (C) The proportion of neurons to glia in the SMRI sample as a function of bipolar disorder (BPD) and CON status. (D) The proportion of neurons to glia in the Harvard Brain Tissue Resource Center at McLean Hospital (McL) sample as a function of BPD and CON status. *p ≤ 0.05.

156

Epigenetics of KCNQ3 in bipolar disorder affect the previously observed association between KCNQ3 exon 11 DNA methylation and BPD status (Wilcoxon Rank Sum: CON = 2.0
0.24, BPD = 1.703
0.29; p = 0.0056). Discussion

We identified a significantly lower level of DNA methylation in the KCNQ3 exon 11 region in postmortem BPD brains, which does not appear to be the result of treatment. Epigenetic variation in this region correlated significantly with expression levels for this gene. Based on a growing literature showing that epigenetic marks are capable of directing alternative splicing decisions (24, 30–32), we were initially drawn to investigate this region to test the hypothesis that DNA methylation might influence a disease-specific shift in the ratios of KCNQ3-AB and KCNQ3-C, alternative splice variants coding for proteins respectively capable and incapable of interacting with ankyrin G. Despite this hypothesis, we observed significantly reduced expression of both the KCNQ3-AB and KCNQ3-C variants, with DNA methylation in the region correlating with expression of both variants. The observed ~4-fold lower levels of KCNQ3-C as compared to AB variants suggest that the AB variant may be the primary variant expressed in the brain. One possible explanation for the observed correlations between DNA methylation and expression is that we have identified an as yet undiscovered enhancer sequence. This region exhibited a cell type-specific difference, which has been shown to be enriched in enhancers differing between neurons and non-neurons (29, 33). Additional studies will be necessary to explore this further. While significantly associated with BPD, the ~9% magnitude of the maximum epigenetic change associated with BPD at exon 11 is not large. A majority of psychiatric DNA methylation studies to date have identified disease associations of small effect, often not larger than a 10–15% difference (34, 35). While it is possible that such small differences are functionally irrelevant, it is also possible that the observed differences may reflect larger effects occurring in specific populations of cells. Glial cells outnumber neurons approximately four to one in the cortex (29, 36). In our data, if the observed association were specific to neurons, it would have the potential to be roughly four times larger after accounting for the signal-diluting effects of disease-irrelevant cell types. Alternatively, the identified effect may be an indication of larger expression-correlated epigenetic changes located in a proximal, but as yet unidentified region. Unfortunately, the majority of the KCNQ3

gene sequence was not represented by probes on the CHARM array or the microarray utilized by Khare et al., which precluded using microarray data to search for such a possibility. One discrepancy in our findings above is that, in the McL cohort, we identified an epigenetic change at KCNQ3 exon 11 without accounting for mood stabilizing medication, while in the SMRI cohort, mood stabilizer status was required in our analytical model to identify a similar change. Our further analysis suggested that the mood stabilizer-mediating effect may be the result of altering neuron to non-neuron ratios in the brain. Altered cell type ratios would, in turn, alter the observed levels of DNA methylation at KCNQ3 exon 11 as the assayed region was confirmed to exhibit cell typespecific epigenetic differences. This interpretation was supported by the observation that treatment of rats with mood stabilizers altered the DNA methylation levels in a cell epigenotype-specific location, while treatment of a cell culture representing a homogeneous cell type did not. In the McL cohort, no differences in neuronal proportion were observed between cases and controls, which likely explains why accounting for mood stabilizer status was not necessary to unmask the association of DNA methylation with BPD in this cohort. Differences in medication history between these two samples, such as the proportion of individuals taking lithium, could have resulted in this discrepancy. Additional factors beyond mood stabilizer status are likely to affect neuronal proportion such as age, which has been previously associated with frontal cortical changes in neuronal proportion (29). The significantly higher age of individuals in the McL cohort relative to the SMRI cohort (25) may have influenced the observed disease- and mood stabilizer status-specific proportions of neurons. One caveat for these analyses is that only a limited subsample of the SMRI samples were present in the microarray dataset generated by Khare et al. (27) used to predict neuronal proportion, so the interpretation of the findings is limited by the smaller subsample of the originally analyzed cohort. A second potential caveat is that the neuronal proportion estimates were derived from genome-wide DNA methylation microarray experiments performed on different array platforms. Despite using the same set of cell type-specific DNA methylation proxies for neuronal proportion quantification, the predicted neuronal proportion was higher in the SMRI sample relative to the McL sample. It is possible that differences in enrichment of the hypomethylated fraction as well as subsequent DNA analysis steps between the CHARM package and those steps utilized by Khare et al. resulted in these differences across

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Kaminsky et al. experimental cohorts. Importantly, the predicted neuronal proportion within experiments would be expected to be relative and comparable and should therefore not affect the interpretation of lithiumbased alterations in neuronal proportion in the SMRI cohort. The observation that lithium decreases neuronal proportion in the SMRI cohort appears to contradict the known effects of lithium in the cortex. Lithium is anti-inflammatory, reduces astrogliagenesis, and promotes survival of neuronal progenitor cells (37, 38). However, treatment of rat neuronal progenitor cells with lithium resulted in a reduction in differentiation, and in markers of neuron maturation such as neurite outgrowth (39). Importantly, both the FACS-based separation and quantification of neuronal proportions identify only terminally differentiated neurons expressing nuclear antigen, NeuN. The observed reduction of the NeuN-positive signal with lithium exposure may reflect a higher proportion of neural progenitor cells being detected in the non-neuronal fraction and could thus be a reflection of lithiuminduced reductions in the rate of neuronal precursor turnover. Alternatively, as the differences we observed were fairly small, we cannot rule out the possibility that they were merely chance findings. In summary, epigenetic variation in KCNQ3 may result in dysregulated ion channel function and ultimately in channelopathy, thus potentially contributing to the pathophysiology of BPD. The analyses performed in the present study provide suggestive evidence as to the pathways leading to this dysregulation and highlight the importance of accounting for psychiatric medication exposure and cellular heterogeneity in psychiatric epigenetic studies of the brain. Disclosures The authors of this paper do not have any commercial associations that might pose a conflict of interest in connection with this manuscript.

Acknowledgements We would like to thank the Harvard Brain Tissue Resource Center and the NICHD Maryland Brain Bank of Developmental Disorders for providing tissue samples and the Stanley Medical Research Institute for providing replication DNA samples. We further thank Gilbert Lamphere and Project Match for generous support of this study.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Figure S1. DNA methylation at the SP1Motif [specificity protein 1 (SP1)] CpG6 and exon 11 positions versus steady-state mRNA levels of the potassium voltage-gated channel, KQTlike subfamily, member 3 (KCNQ3)-AB transcript are depicted for the Harvard Brain Tissue Resource Center at McLean Hospital (McL) brain tissue (A) and for the neuronal model (B). As SP1 transcription factor and RE1-silencing transcription factor (REST) transcriptional corepressor binding is reported to modulate KCNQ3 expression, we evaluated the relationship of DNA methylation in the context of the gene expression levels of these two factors in the McL cohort. Gene expression data generated on the Affymetrix HGU133A gene expression microarray platform (Gene Expression Omnibus Platform: GPL96) was downloaded for the McL cohort from the National Brain Databank (http://national_databank.mclean. harvard.edu/brainbank/Main) and the gene expression values for the SP1 and REST genes were incorporated into the additive model. All factors displayed significant association in the additive model (SP1 expression b = 21.27
7.19, p = 0.018; REST expression b = 12.44
4.61, p = 0.027; SP1Motif CpG 6 methylation b = 0.52
0.21, p = 0.035; exon 11 methylation b = 1
0.2, p = 0.0009), while the adjusted R2 increased to 0.77 (p = 0.0026). This analysis corroborates the known effect of SP1 and REST transcriptional control on KCNQ3 and highlights that epigenetic modulation at SP1Motif CpG6 and exon 11 remain important when relevant transcription factor expression is controlled for. Table S1. Pyrosequencing primers. Table S2. KCNQ3 exon 11 DNA methylation versus covariates.

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