Common DNA Methylation Alterations in Multiple Brain Regions in Autism

ORIGINAL ARTICLE Common DNA methylation alterations in multiple brain regions in autism

C Ladd-Acosta1,2, KD Hansen2,3, E Briem2,4, MD Fallin1,2, WE Kaufmann5,6 and AP Feinberg2,4

Autism spectrum disorders (ASD) are increasingly common neurodevelopmental disorders defined clinically by a triad of features including impairment in social interaction, impairment in communication in social situations and restricted and repetitive patterns of behavior and interests, with considerable phenotypic heterogeneity among individuals. Although heritability estimates for ASD are high, conventional genetic-based efforts to identify genes involved in ASD have yielded only few reproducible candidate genes that account for only a small proportion of ASDs. There is mounting evidence to suggest environmental and epigenetic factors play a stronger role in the etiology of ASD than previously thought. To begin to understand the contribution of epigenetics to ASD, we have examined DNA methylation (DNAm) in a pilot study of postmortem brain tissue from 19 autism cases and 21 unrelated controls, among three brain regions including dorsolateral prefrontal cortex, temporal cortex and cerebellum. We measured over 485 000 CpG loci across a diverse set of functionally relevant genomic regions using the Infinium HumanMethylation450 BeadChip and identified four genome-wide significant differentially methylated regions (DMRs) using a bump hunting approach and a permutation-based multiple testing correction method. We replicated 3/4 DMRs identified in our genome-wide screen in a different set of samples and across different brain regions. The DMRs identified in this study represent suggestive evidence for commonly altered methylation sites in ASD and provide several promising new candidate genes.

Molecular Psychiatry (2014) 19, 862–871; doi:10.1038/mp.2013.114; published online 3 September 2013 Keywords: autism; brain; DNA methylation; epigenome; 450 k

INTRODUCTION Although epigenetic involvement has been a persistent Autism spectrum disorders (ASD) are common neurodevelop- minority view of ASD etiology, there is mounting evidence to mental disorders defined by three core clinical features: suggest the contribution of epigenetics to ASD is stronger than 15 (1) impairment in social interaction; (2) impairment in commu- previously thought. Direct evidence comes from identification of nication in social situations; and (3) restricted and repetitive ASD-associated chromosomal abnormalities in imprinted regions, patterns of behaviors and interests.1,2 Given the striking recent ASD linkage and association in areas of known imprinting, and increase in ASD prevalence and its associated social and economic evidence of parent-of-origin effects in association and linkage impact,3 there has been considerable interest in understanding signals.16,17 Additional support stems from the observation of the biological basis of ASD;4 however the molecular under- autistic features, sometimes reaching the diagnostic threshold, in pinnings of ASD still remain elusive. individuals affected with known epigenetic disorders such as Familial aggregation, rare chromosomal abnormality and twin Angelman, fragile X and Rett syndromes. Rett syndrome is caused studies suggest there is a strong genetic basis to ASD. Numerous by mutations in MECP2,18 a gene that encodes a protein respon- genetic studies have examined common variants, rare mutations sible for recognizing DNA methylation (DNAm) marks throughout and copy number variants (CNVs) across many thousands of the genome. Angelman syndrome involves alterations of the autistic children but have had limited success. The strongest chromosome 15q11-q13 imprinted gene cluster.19 Finally, fragile X genetic candidates identified to date involve rare mutations. For syndrome is caused by the expansion of a methylated CGG repeat example, rare variants have been identified in only 6/461 ASD in the 50 untranslated region of the FMR1 gene,20 resulting in cases for CACNA1H5,6 and CNTN4 deletions have been identified in inappropriate gene silencing. 7/B2000 cases.7–10 These genes represent two of the five leading Previous genome-wide DNAm assays, applied to ASD21,22 and candidate genes in ASD to date.11 Several promising recent other diseases, have focused on measuring DNAm at CpG sites studies have revealed an association between de novo mutations assumed to be functionally important, that is, CpG islands (CGI) and CNVs with ASD; however de novo events do not directly and promoters. However, we recently demonstrated that most explain the high heritability of ASD.12 A recent review suggests disease and tissue-associated changes in DNAm occur outside of that at most 10–15% of all non-syndromic cases of ASD are CGI and promoter regions, specifically at CGI shores.23,24 Non- associated with genetic alterations,13 and to date, whole-genome island and promoter regions are the predominant sites of aberrant and in-depth exon sequencing studies have suggested ASD is DNAm for many diseases including several types of cancers23,25–29 minimally explained by genetics alone.14 and rheumatoid arthritis.30,31 Previous limited studies of specific

1Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA; 2Center for Epigenetics, Institute for Basic Biomedical Science, Johns Hopkins School of Medicine, Baltimore, MD, USA; 3Department of Biostatistics and Institute for Genetic Medicine, Johns Hopkins School of Public Health, Baltimore, MD, USA; 4Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA and 5Kennedy-Krieger Institute, Baltimore, MD, USA. Correspondence: Dr AP Feinberg, Center for Epigenetics, Institute for Basic Biomedical Science, Johns Hopkins School of Medicine, 855N. Wolfe Street, Rangos 570, Baltimore, MD 21205, USA. E-mail:[email protected] 6Current address: Department of Neurology, Children’s Hospital, Boston, MA, USA. Received 16 March 2013; revised 3 July 2013; accepted 25 July 2013; published online 3 September 2013 Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 863 candidate genes and CGI or promoter arrays have shown MATERIALS AND METHODS epigenetic differences in lymphoblastoid cell lines and brain Human postmortem brain tissue samples 21,22,32–35 samples from individuals with ASD. However, these We acquired 41 postmortem brain tissue samples, including 20 cases and studies were limited either in scope, focused on particular 21 controls, representing three brain regions: temporal cortex (TC, n ¼ 16), regions of the genome, or by sample size, and/or by cell type PFC (n ¼ 12) and cerebellum (CBL, n ¼ 13) from the Autism Tissue Program (lymphoblast cell lines). There are 20 probes on the Infinium and Harvard Brain Tissue Resource Center at McLean Hospital, Belmont, HumanMethylation450 BeadChip (450 k array), the DNAm plat- MA, USA (TC and PFC samples) and the National Institute of Child Health form used in this study, located in regions previously identified by and Human Development Brain and Tissue Bank for Developmental candidate region studies (Supplementary Table 1).32,33 One recent Disorders at the University of Maryland, Baltimore, MD, USA (CBL samples). These 41 samples represent 36 individuals; five individuals have both a TC study that examined trimethylated histone 3 lysine 4 marks in the and PFC sample included in our analyses (Figure 1a). One of the 20 case postmortem prefrontal cortex (PFC) brain tissue identified a slight samples was removed from downstream analyses due to quality control spreading of histone 3 lysine 4 marks, normally restricted to concerns (described in detail in the statistical methods section below). For promoter regions, into gene bodies among 4/16 autistic indivi- each brain region, autistic and control samples were matched as best as duals.36 Nonetheless, to date, no study has examined DNAm on possible for age, sex and postmortem interval; no significant differences a genome scale, not limited to CGIs, in the brain samples of were detected (t-test, Pp0.05 and Supplementary Table 2). Furthermore, ASD cases. we examined the relationship between DNAm and age/PMI for all four Here, we report the first genome-wide examination of DNAm in DMRs identified in our genomic screen; no correlation between the ASD among 40 postmortem brain samples, including 19 cases and covariates and DNAm was observed (Supplementary Figures 1 and 2). All autistic individuals included in our analysis were diagnosed using scores 21 controls, across three brain regions, using the 450 k array. 37 from the Autism Diagnostic Interview and/or the Autism Diagnostic Availability of ASD brain samples is extremely limited, with Observation Schedule. In Supplementary Table 3, we provide a list of the numbers examined in this study being comparable to postmortem brain samples and their associated features. other molecular analyses of ASD postmortem brain samples.36,38 The 450 k array quantitatively measures DNAm at 485 577 Illumina Infinium 450 k array methylation measurements genomic loci at high value content regions including all CGI and promoter regions as well as CpG shores, cancer and tissue- Genomic DNA was extracted from TC and PFC samples using Trizol (Invitrogen, Carlsbad, CA, USA) and from CBL samples using the MasterPure differentially methylated regions (DMRs), non-coding RNAs and DNA purification kit (Epicentre Biotechnologies, Madison, WI, USA) accord- DNase hypersensitive sites.39,40 We applied a methodology 41 ing to the manufacturer’s specifications for each kit. Each DNA sample was termed ‘bump hunting’ to our analysis, which allows effective bisulfite treated, 500 ng of gDNA, using the EZ DNAm kit (Zymo Research, modeling of measurement error and biological variability, Irvine, CA, USA) according to the manufacturer’s specifications, optimized detection of DMRs, and assessment of genome-wide statistical for the 450 k array. All of the samples per brain region were processed on significance (via permutation testing). Identifying regions of the same plate with cases and controls randomized across the wells to differential methylation is advantageous to single site analysis minimize potential plate or batch effects. for multiple reasons including better protection from tech- nical artifacts associated with individual probes and func- Bisulfite pyrosequencing validation experiments tional significance of regional methylation change versus at a We performed validation experiments using 13/16 TC samples that were single CpG. examined on 450 k array. We bisulfite converted 500 ng of gDNA using the

Figure 1. (a) Summary of postmortem brain samples and analytic procedures utilized. (b, c) Modified region-based (‘dry’) Manhattan plots for epigenome-based analysis. Region-specific DNA methylation was calculated from 450 k array data, and P-values were determined by permutation analysis to accommodate the genome-wide screen. (b) Three differentially methylated regions (DMRs) in the temporal cortex brain region are significantly associated with autism spectrum disorder (ASD). (c) One DMR in the cerebellum is significantly associated with ASD. Each red segment represents the adjusted P-value (natural logarithm scale) for a genomic region on the Illumina Infinium 450 k array Methylation BeadChip. The significance threshold was based on a family-wise error rate of 0.1.

& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 862 – 871 Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 864 EZ DNAm kit (Zymo Research) according to the manufacturers specifica- analyses by restricting our analysis in PFC and TC tissues to the 12 sites tions. Primers were designed using MethPrimer42 and nested PCR reactions located within the CBL DMR. For each probe, we computed a probe- were performed under standard conditions, using a 50 1C annealing specific P-value using a permutation test (permuting sample labels) in a temperature. Genome coordinates (HG19) of the measured sites and linear model controlling for sex. Under the null hypothesis of no difference primer sequences are provided in Supplementary Tables 4 and 5, between cases and controls, these probe-specific P-values are uniformly respectively. distributed. Hence, assuming independence, the total number of probes in a DMR with a probe-specific P-value of less than 5% follows a binomial distribution with a size factor equal to the number of probes in the DMR Statistical analysis and a probability parameter of 0.05. We use this fact to combine the Overview and quality control measures. All statistical analyses were probe-specific P-values into an overall DMR-specific P-value for replication performed using R 2.15 and Bioconductor 2.9. Raw intensity files (.idat) purposes. An overall DMR-specific P-value of less than 5% was considered 43 were obtained and processed using minfi package to obtain log ratios of replication. methylation percentage (M-values). We applied several quality control measures to remove any spurious samples or probes. First, we examined 450 k array control probes to assess bisulfite conversion, extension, Assessment of cell composition differences hybridization, staining, specificity, negative control and others; no outlier Brain cell type-specific DNAm data and cell epigenotype-specific samples were detected. Next, we checked for sex discrepancies by patterns46 were acquired using the cell epigenotype-specific package comparing self-reported genders against data-derived sex values. No sex (http://psychiatry.igm.jhmi.edu/kaminsky/software.htm) in R/Bioconductor. discrepancies were identified. We then looked for poorly performing arrays Overlap between the 450 k array probes identified in our brain samples by calculating the total array intensity (sum of the methylated and and the top 10 000 cell epigenotype-specific marks for brain cell types was unmethylated signals across all probes on the array) for each sample; no computed by simply comparing the two lists of 450 k array probe names. substantial differences were detected. Unsupervised clustering of samples Using the cell epigenotype-specific package, we then estimated the identified one sample that was inappropriately clustering with a different proportion of neuronal and glial cells for each postmortem brain tissue brain region (PFC sample clustering with CBL samples); this sample was sample in our study, as specified previously.46 removed from downstream analysis. We removed probes that had an annotated SNP (dbSNP134) at the single-base extension or CpG site, as it is possible that SNP differences in these locations may manifest as RESULTS differential methylation on the 450 k array, leaving a total of 428 526 Figure 1a depicts an overview of our experimental design and the probes. brain samples utilized in our analyses. Briefly, we performed individual methylome analyses for each of the three brain regions: Preprocessing and normalization. The signal in the methylated and dorsolateral PFC (6 cases/5 controls), TC (6 cases/10 controls) and unmethylated channels was first computed without background correc- 41 tion. Each channel was normalized separately, by first quantile normalizing CBL (7 cases/6 controls) using a ‘bump hunting’ approach. Bump the signal for all autosomal probes and second by quantile normalizing the hunting uses a permutation procedure to assess significance in signal for the sex chromosomes for each sex separately. After normal- such a way that (1) reported P-values are adjusted for multiple ization, log ratios of methylation percentages were computed (M-values). testing and (2) only a small subset of the genome, specifically a set Previous work44 has shown M-values to perform better than beta values for of putative interesting regions, is assigned a P-value. analysis of differential methylation for Illumina methylation arrays. Genome-wide screen Identification of DMRs. We adapted the bump hunting technique previously described41 to the 450 k array. Probes were assigned to For each of the three brain regions, we performed a genome-wide clusters so that two neighboring probes in the same cluster are separated screen and identified a total of four genome-wide significant by at most 500 bp, resulting in a total of 181 762 clusters of which 39 654 (adjusted Pp0.1) DMRs, three in the TC and one in the CBL contain three or more probes. For each probe, we estimated the difference (Figures 1b and c and Table 1). Because bump hunting identifies a in average log ratios between cases and controls, controlled for sex, and differentially methylated region (DMR) rather than a differentially smoothed these estimated differences using running medians. Next, the methylated position, the data display is similar but with a smoothed estimated differences were thresholded based on the 97.5% somewhat more austere appearance than a conventional percent quantile of the empirical distribution of the smoothed estimated differences. This yielded 1179, 1347 and 1227 putative DMRs for TC, PFC Manhattan plot. Therefore, we have termed it a ‘dry Manhattan’ and CBL analyses, respectively. Significance was assigned by permutation plot suitable for this type of DNAm analysis. As described in detail testing. For each of 1000 permutations of case–control status, a new list of in the Methods section, empirical P-values reported in our dry putative DMRs was obtained. The genome-wide family-wise error rate for Manhattan plots (Figures 1b and c) were calculated by permuta- each observed DMR was calculated as the proportion of null-derived DMRs tion with a family-wise error rate of 0.1. across the genome with more CpGs and greater difference between cases As shown in Figure 2a, we identified a DMR near the end of the and controls than the observed DMR. Because a given observed DMR is 30 UTR of PRRT1, proline-rich transmembrane protein 1, that compared against findings in the entire genome, the procedure extends a few hundred base pairs upstream, with relative automatically adjusts for multiple testing. The numbers of putative DMRs hypomethylation in the TC tissue of autistic individuals (shown and the number of measured CpGs contained within these putative DMRs are 1179 (5278 CpGs) for TC, 1347 (6058 CpGs) for PFC and 1227 (5277 in green) compared with controls (shown in purple). On average, CpGs) for CBL. Note that most of the genome, roughly 98.7% of analyzed the cases are 7.8% less methylated at this DMR (empirical CpGs, is not inside a DMR, and is therefore not assigned an empirical P ¼ 0.001) than controls (Table 1). It is possible that changes in significance via this approach. Similar to previously reported genome-wide CNVs may manifest as DNAm changes on the 450 k array. For DMR screens,45 the adjusted P-value cutoff (FWER) used for our genome- example, changes in DNAm would be observed if a gained copy wide analyses was 0.1. We decided to use this cutoff, as opposed to 0.05, has a different methylation pattern from the original. While because we wanted to be slightly more inclusive and were willing to test a existing work suggests that CNV does not bias Illumina array larger number of DMRs in our downstream replication analyses. Using measurements of DNAm,47 we still consider it important to Pp0.1 resulted in a total of four DMRs compared with three DMRs with address the possibility of CNV at sites of DNAm differences. We P 0.05. p would expect CNVs to result in different total intensities, that is, the sum of the methylated and unmethylated channels, on the Replication analysis. For the three DMRs identified in our genome-wide screen of TC tissue, we performed replication analyses in PFC and CBL 450 k array. Therefore, to assess potential CNV changes, in the tissues using methylation data obtained using the 450 k array. For second panel of Figure 2a, we plot the difference between the replication purposes, the PFC and CBL data were restricted to include total probe intensity for a given individual and the mean total only the 74 probes that were located within the three significant TC DMRs. intensity across all individuals for a given probe, with purple and Similarly, for the DMR we identified in CBL tissue, we performed replication green points denoting control and ASD, respectively. There are no

Molecular Psychiatry (2014), 862 – 871 & 2014 Macmillan Publishers Limited Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 865 clear differences between the case and control groups with We validated our genome-wide results from TC using bisulfite respect to CNV status; thus, it is likely the DMR does not reflect a pyrosequencing, a quantitative and independent method for CNV. measuring DNAm. Pyrosequencing confirmed our 450 k array We also identified a DMR (empirical P ¼ 0.013) that is on results for all three DMRs tested (Supplementary Figure 3). Similar average 6.6% less methylated in ASD TC tissue, shown in green, to our array-based results, PRRT1 and C11orf21 are relatively compared with controls, shown in purple (Table 1 and Figure 2b). hypomethylated and ZFP57 is relatively hypermethylated in This DMR is located within the promoter regions of tetraspanin 32 autistic individuals compared with controls. (TSPAN32), and chromosome 11 open reading frame 21 (C11orf21), and continues into the gene body of C11orf21 (bottom panel of Figure 2b). We do not observe substantial changes in CNV levels Replication analyses between cases and controls for this DMR (second panel of Finally, we sought to confirm DMRs identified in our genome-wide Figure 2b). screen in an independent set of samples. In total, 74 probes are The third DMR (empirical P ¼ 0.019) in TC tissue is on average located within the three TC DMRs and 12 probes are located 13.9% more methylated in ASD, shown in green, compared with within the one cerebellar DMR. Given the limited availability of controls, shown in purple (Table 1 and Figure 3). The relatively postmortem brain samples from autistic individuals, we consid- hypermethylated DMR is located in an intergenic region; the ered replication of TC DMRs via evidence of association at those nearest gene is ZFP57, located 3.5 kb away (Figure 3a). Although a probes in the PFC and CBL tissue samples, and used the TC and few control individuals appear to have a slight increase in copy PFC sample sets to replicate our CBL DMR finding. As the samples number for a few probes, across the region there is no substantial for each brain region were obtained from different individuals, difference in CNV state between cases and controls (Figure 3a, with the exception of five individuals who are represented in both second panel). the TC and PFC sample sets, this design is suitable to evaluate how Among 13 CBL samples, including seven cases and six controls, well our genome-wide findings replicate in different individuals we identified one significant DMR located within SDHAP3, and across different brain regions. Figure 1 provides a summary of succinate dehydrogenase complex, subunit A, flavoprotein the total number of individuals and probes examined in our pseudogene 3. Cases are, on average, 15.8% more methylated replication analyses. than controls across this region (Table 1), although there is For the PRRT1-associated DMR discovered in TC, we found considerable variation among the ASD group that seems to be, at significant methylation differences between cases and controls in least in part, related to CNV (Figure 4a). The cases with higher PFC tissue for 14 of 33 sites (Figure 2b, blue). For clarity, we use methylation levels also have higher total intensities, representing dashed boxes to highlight methylation values that correspond to potential CNVs. Thus, the methylation change detected in this the probes with significant nominal P-values. Using a binomial region may be influenced by an underlying CNV change. distribution (Materials and Methods), we assigned this a replica- Nonetheless, it still implicates this particular region as important tion P-value of 2.0e À 10. We did not observe any differences in the etiology of ASD. related to copy number for this region in PFC samples (upper

Table 1. Genome-wide signiﬁcant DMRs associated with ASD

Replication (Po0.05)c

Brain region Genomic location Nearest gene Gene distance (bp) DMa Adjusted P-valueb TC PFC CB

TC chr6: 32115964–32117379 PRRT1 0 À 7.8% 0.001 NA Yes Yes TC chr11: 2321770–2323272 C11orf21 0 À 6.6% 0.013 NA Yes No TC chr6: 29648379–29649092 ZFP57 3449 13.9% 0.019 NA No Yesd CBL chr5: 1594021–1595048 SDHAP3 0 15.8% 0.073 No No NA Abbreviations: ASD, autism spectrum disorder; CB, cerebellum; DMR, differentially methylated region; M, DNA methylation; NA, not applicable; PFC, prefrontal cortex; TC, temporal cortex. aMean methylation difference between autism and control groups for the differentially methylated region. Positive values reflect relative hypermethylation and negative values relative hypomethylation in autistic individuals compared with controls. bEmpirical significance value, computed using permutation testing (n ¼ 1000). cReplication is equal to ‘yes’ if any 450 k array probes located within the DMR (identified in the genome-screen) have a nominal P-value less than 0.05 in a different brain tissue; otherwise, it is reported as ‘no’. dSex-specific replication. Significance is not reached when males and females are examined as one set.

Figure 2. Two genome-wide significant differentially methylated regions (DMRs) located at PRRT1 (a–c) and C11orf21/TSPAN32 (d–f) are relatively hypomethylated in the temporal cortex tissue from autistic individuals. (a, d) DMR plots for the temporal cortex brain samples. (b, e) DMR plots for the PFC brain samples. (c, f) DMR plots for the cerebellum brain samples. For (a, d), methylation data are shown in the top panels. Smoothed lines denote mean methylation levels for individuals with (green) and without (purple) autism spectrum disorder (ASD) across the DMR. Each point represents the methylation level of a particular individual, green for those with autism and purple for those without autism, at a specific genomic location. The methylation values range from 0 to 1 with 0 equal to 0% methylation and 1 equal to 100% methylation. The upper middle panels provide information about potential copy number changes. Each point, green for autistic and purple for normal individuals, represents the difference between total probe intensity for a given individual and the mean total intensity across all individuals for a given probe. The black points in the lower middle panel are individual probe-based nominal P-values for differences in methylation between the two groups (autism and normal). The dashed magenta line indicates the significance threshold used (nominal Pp0.05) such that points above the line are significant in replication analyses. The black asterisks denote sites with nominal P-values equal to 0. The bottom panel displays relevant genome annotation information for the DMR itself, not the entire associated gene. For (b, c, e, and f), plots are as described above but blue and orange points represent individuals with and without autism, respectively. Dotted boxes describe the location of methylation values that correspond to the probes with significant nominal P-values.

& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 862 – 871 Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 866 middle, Figure 2b). We also observed two significant CpG sites and less methylation than control individuals at the PRRT1 locus others with suggestive significance among the cerebellar tissue (Figure 2c) and no differences in CNV between cases and controls. (Figure 2c) in an independent sample of seven cases and six Replication analysis of the TC DMR associated with TSPAN32 and controls. Consistent with both our genome-wide screen and PFC C11orf21 revealed 7 of 26 and 1 of 26 significant (Pp0.05, third replication results, the CBL tissue from autistic individuals showed panel of Figures 2e and f) differentially methylated loci in the PFC

a Temporal cortex d Temporal cortex

0.8 0.8

0.5 0.5 Methylation Methylation 0.2 0.2 autism autism empirical p = 0.001 normal empirical p = 0.013 normal

1.5 1.5 Copy Copy 0 Copy 0 number number ) )

p ** p 4.5 4.5 10 10 -log -log 1.5 1.5 (nominal (nominal PRRT1 C11orf21 TSPAN32 Genes Genes < < AltEvents AltEvents Islands Islands DNase HS DNase HS SNPs SNPs 450K probes 450K probes

32115964 32116317 32116653 32116994 32117379 2321770 2322286 2322808 2323272 Location on chromosome 6 Location on chromosome 11 b Prefrontal cortex e Prefrontal cortex

0.8 0.8

0.5 0.5 Methylation 0.2 Methylation 0.2 autism normal autism normal

1.5 1.5

Copy Copy 0

Copy Copy 0 number number ) ) p p 4 4.5 10 jit.x[, ba9.nor] 10 -log 1.5 -log 1.5 (nominal (nominal PRRT1 C11orf21 TSPAN32 Genes < << Genes < < < < < < < < < < < < < < AltEvents AltEvents Islands Islands DNase HS DNase HS SNPs SNPs 450K probes 450K probes

32115964 32116317 32116653 32116994 32117379 2321770 2322286 2322808 2323272 Location on chromosome 6 Location on chromosome 11 c Cerebellum f Cerebellum autism normal 0.8 0.8

0.5 0.5 Methylation Methylation 0.2 0.2 autism normal

1.5 1.5 Copy Copy 0 Copy 0 number number ) ) p p

4.5 10 4.5 10 -log

-log 1.5 1.5 (nominal (nominal

PRRT1 C11orf21 TSPAN32 Genes < << Genes < < < < < < < < < < < < < < AltEvents AltEvents Islands Islands DNase HS DNase HS SNPs SNPs 450K probes 450K probes

32115964 32116317 32116653 32116994 32117379 2321770 2322286 2322808 2323272 Location on chromosome 6 Location on chromosome 11 Figure 2. For caption please refer page 865.

Molecular Psychiatry (2014), 862 – 871 & 2014 Macmillan Publishers Limited Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 867

a Temporal cortex b Temporal cortex c Temporal cortex

0.8 0.8 0.8

0.5 0.5 0.5 Methylation Methylation 0.2 Methylation 0.2 0.2 autism autism autism normal empirical p = 0.019 normal Males normal Females

1.5 1.5 1.5 Copy Copy 0 Copy 0 0 number number number

4.5 4.5 4.5 10 10 10 1.5 -log -log -log 1.5 1.5 (nominal p) (nominal p) (nominal p) Genes Genes Genes AltEvents AltEvents AltEvents Islands Islands Islands DNase HS DNase HS DNase HS SNPs SNPs SNPs 450K probes 450K probes 450K probes

d Prefrontal cortex efPrefrontal cortex Prefrontal cortex

0.8 0.8 0.8

0.5 0.5 0.5 Methylation Methylation Methylation 0.2 0.2 0.2 autism autism autism normal normal Males normal Females

1.5 1.5 1.5 Copy

Copy 0 Copy 0 0 number number number

4.5 4.5 4.5 10 10 10 -log -log -log 1.5 1.5 1.5 (nominal p) (nominal p) (nominal p) Genes Genes Genes AltEvents AltEvents AltEvents Islands Islands Islands DNase HS DNase HS DNase HS SNPs SNPs SNPs 450K probes 450K probes 450K probes

g Cerebellum h Cerebellum i Cerebellum

0.8 0.8 0.8

0.5 0.5 0.5 Methylation Methylation Methylation 0.2 0.2 0.2 autism autism autism normal normal Males normal Females

1.5 1.5 1.5 Copy 0 Copy 0

Copy 0 number number number

4.5 4.5 4.5 10 10 10 -log -log -log 1.5 1.5 1.5 (nominal p) (nominal p) (nominal p)

Genes Genes Genes AltEvents AltEvents AltEvents Islands Islands Islands DNase HS DNase HS DNase HS SNPs SNPs SNPs 450K probes 450K probes 450K probes

29648379 29648525 29648736 29648901 29649092 29648379 29648525 29648736 29648901 29649092 29648379 29648525 29648736 29648901 29649092 Location on chromosome 6 Location on chromosome 6 Location on chromosome 6 Figure 3. A genome-wide signiﬁcant differentially methylated region (DMR) located 3.45 Kb upstream of ZNF57 is relatively hypermethylated in the temporal cortex tissue from autistic individuals. (a) DMR plots for the temporal cortex brain samples. (b) DMR plots for the temporal cortex brain samples from male individuals. (c) DMR plots for the temporal cortex brain samples from female individuals. (d) DMR plots for all the prefrontal cortex brain samples. (e) DMR plots for the prefrontal cortex tissue samples from male individuals. (f) DMR plots for the prefrontal cortex tissue samples from female individuals. (g) DMR plots for all the cerebellum brain samples. (h) DMR plots for the cerebellum samples from male individuals. (i) DMR plots for the cerebellum samples from female individuals. Panels and labels are as described in Figure 2.

and CBL tissue samples, with replication P-values of 2.2e À 4 and wide result in TC tissue. In addition, no CNV changes are observed 74%, respectively (Figures 2e and f). However, as we would expect at the significant loci (second panel, Figures 2e and f). to see one significant difference by chance, we do not report the The third DMR identified from TC, located 3.5 kb upstream of CBL as significant in our replication result (Table 1, Figure 1a). As ZFP57, showed sex-specific replication in the CBL samples shown in the top panels of Figures 2e and f, autistic individuals (Figure 3i). For this DMR, we decided to stratify the methylation (blue points) are less methylated than the controls (orange points) data by sex, within each brain region, because we observed two for both the PFC and CBL tissues, consistent with our genome- clusters of methylation values that were correlated with sex in

& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 862 – 871 Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 868 Cerebellum controls for females in PFC tissue. However, in the one female autism normal (autistic) with PFC data, we observed high methylation levels 0.8 (Figure 3f), consistent with our ﬁndings in the other two regions

0.5 for autistic females (Figures 3c and i). We also observed suggestive evidence that autistic males have more DNAm variability in TC and Methylation 0.2 CBL tissues than control individuals (Figures 3b and h) for this empirical p = 0.073 genomic region. When we examine males and females as one

1.5 sample set, we do not observe statistically significant P-values in our replication data sets for this region. Copy 0 number As five individuals contributed both a TC and PFC sample, we ) p 4.5 also examined DNAm in PFC for the set of non-overlapping 10 individuals, leaving one control and five cases. Although we -log 1.5

(nominal cannot compute statistical significance with a single control SDHAP3 Genes AltEvents sample, we observed distinct differences in DNAm between the Islands DNase HS control sample and all cases for several loci across all three SNPs 450K probes genomic regions examined (Supplementary Figures 4, 5 and 6). The direction of the differential methylation was also consistent 1594021 1594330 1594579 1594808 1595048 Location on chromosome 5 with our initial findings for all three regions. Furthermore, although five of the prefrontal and TC samples were obtained Prefrontal cortex autism from the same individual, they are independent measurements of normal 0.8 DNAm from two distinct biological sources and thus are appropriate for assessing the validity of DMRs identified in our 0.5 genome-wide screen.

Methylation Lastly, although none of the loci located in the CBL DMR in 0.2 SDHAP3 reached statistical signiﬁcance in the replication sets (Figures 4b and c), we observed the same trend in PFC as we did 1.5 in CBL. More speciﬁcally, a few autistic samples in PFC show

Copy 0 number methylation levels near 50% that are potentially related to CNV

) changes (Figure 4b). p 4.5 10

-log 1.5

(nominal Brain tissue heterogeneity SDHAP3 Genes AltEvents We evaluated the possibility that our ASD-associated DMRs may Islands DNase HS reflect underlying cell-type heterogeneity between ASD cases and SNPs 450K probes controls using neuronal and glial cell type-specific methylation 46 1594021 1594330 1594579 1594808 1595048 data. None of the 86 probes located within the ASD DMRs we Location on chromosome 5 identified are among the list of 10 000 probes with neuron-specific 46 Temporal cortex methylation values from a recent report. Furthermore, for each autism normal of our brain samples, we computed the proportion of neuronal 46 0.8 and glial cells using the Guintivano et al. algorithm and found no significant differences between the ASD cases and controls for 0.5 either brain region (Supplementary Table 6). Thus, our findings do Methylation 0.2 not simply reflect a shift in the proportion of neuronal and glial cells between ASD case and control samples.

1.5

Copy 0 number DISCUSSION

) We provide the ﬁrst evidence for signiﬁcant DNAm changes in p 4.5

10 postmortem brain tissue from ASD patients. After adjusting for -log 1.5 multiple testing, we identified a total of four DMRs, three in TC (nominal SDHAP3 tissues and one in CBL. The magnitude of methylation change Genes AltEvents identified here, ranging from 6.6–15.8%, is comparable to those Islands DNase HS 48,49 SNPs observed in other studies of DNAm in psychiatric disease. 450K probes Three of the four DMRs identified via genome-wide screen 1594021 1594330 1594579 1594808 1595048 replicated in independent samples from different brain regions. Location on chromosome 5 Although the fourth DMR did not meet significant criteria for Figure 4. A genome-wide significant differentially methylated replication, it did show the same trend in the PFC samples, as we region (DMR) at SDHAP3 is relatively hypermethylated in the observed in our genome-wide screen using CBL samples. Finally, cerebellum tissue from autistic individuals. (a) DMR plots for the we demonstrated that the DMRs we identified are not related to cerebellum brain samples. (b) DMR plots for the prefrontal cortex underlying brain tissue cell composition differences between ASD brain samples. (c) DMR plots for the temporal cortex brain samples. and control individuals. Panels and labels are as described in Figure 2. The autism-associated DMRs we identified represent biologi- cally diverse and interesting genomic regions. For example, the DMR within the PRRT1 30 UTR overlaps two DNase hypersensitive Figure 3a. Interestingly, for both TC and CBL tissues, upon sites and an alternative transcript finish site. Given the location, it stratification by sex we found that normal females are less is possible that this DMR is an important regulatory site.50,51 methylated than autistic females. As there was only one female, Although little is known about the function or expression patterns we could not assess significant differences between cases and of PRRT1 in humans it has been shown to be specifically expressed

Molecular Psychiatry (2014), 862 – 871 & 2014 Macmillan Publishers Limited Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 869 in the hippocampus of marmoset,52 a nonhuman primate often several limitations that need to be carefully considered. First, from used as an ideal model for a wide-variety of central nervous an epidemiological and statistical perspective we examined a system disorders. The hippocampus brain region has been relatively small number of samples, 40 in total. It is difficult to previously implicated in human studies of ASD demonstrating ascertain a large number of reliably phenotyped postmortem macroscopic and microscopic anatomical differences53–56 and brain tissues from autistic individuals given the scarcity of the altered synaptic function and plasticity57 in autistic individuals. resource.37 This is a problem the field of ASD faces, generally. Mutations in other genes in the PRRT family have been shown to While this study focused on the brain tissue, due to disease cause several neurological disorders in humans, some of them relevance and a lack of DNAm data in previous autism brain developmental in nature, including familial infantile seizures,58 studies, there is also a need for complementary autism paroxysmal kinesigenic dyskinesia,59 hemiplegic migraine,60 epigenomic studies in peripheral tissues, such as blood. Blood- paroxysmal kinesigenic dyskinesia combined with infantile based samples are particularly useful in ASD because large seizures,61,62 and benign familial infantile seizures.63,64 numbers of samples can be collected and because useful The second DMR identified in TC tissue is located in the biomarkers for disease are needed and most likely will come promoter regions of TSPAN32 and C11orf21 and extends into the from this tissue, as opposed to brain tissue, for practical reasons. gene body of C11orf21. While there is no literature to describe the Second, the DMR we identified at SDHAP3 in CBL showed a DNAm function of C11of21, TSPAN32 is important in cellular immunity65 change in several autistic individuals that was potentially also and acts as a structural and cell signaling scaffold protein.66 related to a CNV. The 450 k array is now the primary platform Functional mutations in other tetraspanins have been identified in utilized for large epidemiology studies and this result highlights schizophrenia and bipolar pateints.67 Mutations in several other the need to examine and recognize the potential contribution of scaffolding proteins such as SHANK3,68–70 SHANK271 and NBEA,72 CNVs to methylation signals in these large studies. In addition, this have been identified in autistic individuals. Interestingly, this DMR finding demonstrates the need for new studies to clarify the is also located within an imprinted region of the genome. relationships between CNVs and DNAm. Imprinting73 and parent-of-origin specific effects16,17 have been Despite these limitations, the findings presented in this study previously implicated in ASD. are of significance to the field of ASD for several reasons. To our The third DMR is located about 3.5 kb upstream of ZFP57 and knowledge, this is the first study to examine DNAm in ASD brains overlaps the 50 end of an alternatively spliced EST. ZFP57 is at genome scale in regions outside of CGIs and promoters, instrumental in maintaining imprinting marks during develop- identifying four DMRs associated with the disorder. Second, while ment74,75 by providing a mechanism for targeting DNA traditional genetic studies have identified rare variants in a methyltransferase,76 responsible for transferring methyl groups minority of ASD cases, here, we identified genomic regions that to cytosines, to specific locations in the genome.77 This is directly commonly show differential methylation between autistic and relevant to ASD, as other methylation machinery proteins are control individuals. Finally, the DMRs themselves are useful mutated in neurodevelopmental syndromes associated with ASD, candidate regions for follow up studies. for example, Rett syndrome.18 Finally, in cerebellar tissue, we identified a DMR at an alternative CONFLICT OF INTEREST promoter for SDHAP3, which is associated with a non-coding RNA and a small coding RNA. This DMR falls directly on a CTCF binding The authors declare no conflict of interest. site and an active regulatory element site identified by the ENCODE project.78 Thus, it is likely to be an important regulatory ACKNOWLEDGEMENTS site; however, currently there is no literature describing the We thank Daniel Geschwind and Neelroop Parikshak for sharing the PFC and TC function of this particular gene. SDHAP3 is a member of the samples, obtained from the Autism Tissue Program (ATP) of Autism Speaks, for these succinate dehydrogenase gene family that includes genes that are analyses. In addition, we would also like to thank the NICHD Brain and Tissue Bank for critical components of the metabolic machinery. Mitochondrial Neurodevelopmental Disorders at The University of Maryland for providing brain respiratory chain complex protein dysfunction has been samples from the CBL brain region. This work was supported by the US National previously associated with ASD.79–81 Institutes of Health Centers of Excellence in Genomic Science, 5P50HG003233 to APF For one of the DMRs, near ZPF57, we identified a striking and Department of Defense (CDMRP) AR080125 to APF and WEK. difference in methylation between autistic and control females in the cerebellar tissue. We performed a BLAST search of the DNA REFERENCES sequence for this DMR and did not find alignment to any other 1 Rapin I. Autism. N Engl J Med 1997; 337: 97–104. regions of the genome, including sex chromosomes. While 2 American Psychiatric Association. Diagnostic and Statistical Manual of Mental intriguing, we examined a relatively small number of female Disorders. 4th edn. American Psychiatric Association Press: Washington, DC, 1994. samples in our analyses and the biological significance of sexually 3 Autism: economic impact and implications. Proceedings of the 2012 Autism dimorphic DNAm differences in the brain is unclear at this time. Summit. 31 March 2012; Hong Kong, China. This result suggests future epigenetic studies may benefit from 4 Dawson G. Dramatic increase in autism prevalence parallels explosion of research inclusion of females and additional sex-specific analyses. into its biology and causes. Arch Gen Psychiatry 2012; 70: 1–2. The autism-associated DMRs identified in this study may 5 Splawski I, Yoo DS, Stotz SC, Cherry A, Clapham DE, Keating MT. CACNA1H mutations in autism spectrum disorders. J Biol Chem 2006; 281: 22085–22091. be influenced by genetic variants or environmental exposures. 82 6 Heron SE, Khosravani H, Varela D, Bladen C, Williams TC, Newman MR et al. Davies et al. recently showed some sites of methylation variation Extended spectrum of idiopathic generalized epilepsies associated with across individuals, potentially influenced by genetic or CACNA1H functional variants. Ann Neurol 2007; 62: 560–568. environmental factors, persist across blood and brain tissues. For 7 Glessner JT, Wang K, Cai G, Korvatska O, Kim CE, Wood S et al. Autism genome- mental health research, variation across brain regions is of wide copy number variation reveals ubiquitin and neuronal genes. Nature 2009; particular importance. Given the scope of this study and its 459: 569–573. limited sample size, we cannot sufficiently address how DNAm 8 Cottrell CE, Bir N, Varga E, Alvarez CE, Bouyain S, Zernzach R et al. Contactin 4 as varies between normal individuals and different brain regions. an autism susceptibility locus. Autism Res 2011; 4: 189–199. Defining the landscape of methylation variation, across individuals 9 Roohi J, Montagna C, Tegay DH, Palmer LE, DeVincent C, Pomeroy JC et al. Disruption of contactin 4 in three subjects with autism spectrum disorder. JMed and between tissue types, is a very important question and Genet 2009; 46: 176–182. avenue of future research. 10 Fernandez T, Morgan T, Davis N, Klin A, Morris A, Farhi A et al. Disruption of While this study is the first to identify commonly altered DNAm Contactin 4 (CNTN4) results in developmental delay and other features of 3p in brain tissue from autistic individuals, we recognize there are deletion syndrome. Am J Hum Genet 2008; 82: 1385.

& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 862 – 871 Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 870 11 Banerjee-Basu S, Packer A. SFARI Gene: an evolving database for the autism 37 Carey B. Brain Banks For Autism Face Dearth. The New York Times 2012. research community. Dis Model Mech 2010; 3: 133–135. 38 Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S et al. Transcriptomic 12 Newschaffer CJ, Fallin D, Lee NL. Heritable and nonheritable risk factors for autism analysis of autistic brain reveals convergent molecular pathology. Nature 2011; spectrum disorders. Epidemiol Rev 2002; 24: 137–153. 474: 380–384. 13 Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of psychiatric disorders: 39 Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM et al. High density DNA the emerging picture and its implications. Nat Rev Genet 2012; 13: 537–551. methylation array with single CpG site resolution. Genomics 2011; 98: 288–295. 14 Gratten J, Visscher PM, Mowry BJ, Wray NR. Interpreting the role of de novo 40 Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M et al. Vali- protein-coding mutations in neuropsychiatric disease. Nat Genet 2013; 45: dation of a DNA methylation microarray for 450 000 CpG sites in the human 234–238. genome. Epigenetics 2011; 6: 692–702. 15 Schanen NC. Epigenetics of autism spectrum disorders. Hum Mol Genet 2006; 15: 41 Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP et al. Bump hunting (Spec No. 2) R138–R150. to identify differentially methylated regions in epigenetic epidemiology studies. 16 Arking DE, Cutler DJ, Brune CW, Teslovich TM, West K, Ikeda M et al. A common Int J Epidemiol 2012; 41: 200–209. genetic variant in the neurexin superfamily member CNTNAP2 increases familial 42 Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinfor- risk of autism. Am J Hum Genet 2008; 82: 160–164. matics 2002; 18: 1427–1431. 17 Fradin D, Cheslack-Postava K, Ladd-Acosta C, Newschaffer C, Chakravarti A, Arking 43 Hansen KAM, Irizarry RA. Minfi: analyzing Illumina 450 K methylation arrays 2012. DE et al. Parent-of-origin effects in autism identified through genome-wide 44 Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L et al. Comparison of Beta- linkage analysis of 16000 SNPs. PLoS One 2010; 5:9. value and M-value methods for quantifying methylation levels by microarray 18 Amir RE, Van den Veyver IB, Wan M, Tran CQ, Francke U, Zoghbi HY. Rett analysis. BMC Bioinformatics 2010; 11: 587. syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG- 45 Lee H, Jaffe AE, Feinberg JI, Tryggvadottir R, Brown S, Montano C et al. DNA binding protein 2. Nat Genet 1999; 23: 185–188. methylation shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with 19 Horsthemke B, Wagstaff J. Mechanisms of imprinting of the Prader-Willi/Angel- gestational age at birth. Int J Epidemiol 2012; 41: 188–199. man region. Am J Med Genet A 2008; 146A: 2041–2052. 46 Guintivano J, Aryee M, Kaminsky Z. A cell epigenotype specific model for the 20 Oberle I, Rousseau F, Heitz D, Kretz C, Devys D, Hanauer A et al. Instability of a correction of brain cellular heterogeneity bias and its application to age, brain 550-base pair DNA segment and abnormal methylation in fragile X syndrome. region and major depression. Epigenetics 2013; 8: 290–302. Science 1991; 252: 1097–1102. 47 Houseman EA, Christensen BC, Karagas MR, Wrensch MR, Nelson HH, Wiemels JL 21 Ginsberg MR, Rubin RA, Falcone T, Ting AH, Natowicz MR. Brain transcriptional et al. Copy number variation has little impact on bead-array-based measures of and epigenetic associations with autism. PLoS One 2012; 7: e44736. DNA methylation. Bioinformatics 2009; 25: 1999–2005. 22 Nguyen A, Rauch TA, Pfeifer GP, Hu VW. Global methylation profiling of 48 Mill J, Tang T, Kaminsky Z, Khare T, Yazdanpanah S, Bouchard L et al. Epigenomic lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum profiling reveals DNA-methylation changes associated with major psychosis. Am J disorders and a novel autism candidate gene, RORA, whose protein product is Hum Genet 2008; 82: 696–711. reduced in autistic brain. Faseb J 2010; 24: 3036–3051. 49 Sabunciyan S, Aryee MJ, Irizarry RA, Rongione M, Webster MJ, Kaufman WE et al. 23 Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P et al. The human Genome-wide DNA methylation scan in major depressive disorder. PLoS One colon cancer methylome shows similar hypo- and hypermethylation at conserved 2012; 7: e34451. tissue-specific CpG island shores. Nat Genet 2009; 41: 178–186. 50 Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ et al. Combinatorial 24 Doi A, Park IH, Wen B, Murakami P, Aryee MJ, Irizarry R et al. Differential microRNA target predictions. Nat Genet 2005; 37: 495–500. methylation of tissue- and cancer-specific CpG island shores distinguishes human 51 Lai EC. Micro RNAs are complementary to 30 UTR sequence motifs that mediate induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat Genet negative post-transcriptional regulation. Nat Genet 2002; 30: 363–364. 2009; 41: 1350–1353. 52 Fukuoka T, Sumida K, Yamada T, Higuchi C, Nakagaki K, Nakamura K et al. Gene 25 Dudziec E, Miah S, Choudhry HM, Owen HC, Blizard S, Glover M et al. Hyper- expression profiles in the common marmoset brain determined using a newly methylation of CpG islands and shores around specific microRNAs and mirtrons is developed common marmoset-specific DNA microarray. Neurosci Res 2010; 66: 62–85. associated with the phenotype and presence of bladder cancer. Clin Cancer Res 53 Groen W, Teluij M, Buitelaar J, Tendolkar I. Amygdala and hippocampus enlar- 2011; 17: 1287–1296. gement during adolescence in autism. J Am Acad Child Adolesc Psychiatry 2010; 26 Feber A, Wilson GA, Zhang L, Presneau N, Idowu B, Down TA et al. Comparative 49: 552–560. methylome analysis of benign and malignant peripheral nerve sheath tumors. 54 Hasan KM, Walimuni IS, Frye RE. Global cerebral and regional multimodal neu- Genome Res 2011; 21: 515–524. roimaging markers of the neurobiology of autism: development and cognition. 27 Rao X, Evans J, Chae H, Pilrose J, Kim S, Yan P et al. CpG island shore methylation J Child Neurol 2012; 28: 874–885. regulates caveolin-1 expression in breast cancer. Oncogene 2012; http:// 55 Agostini G, Mancini J, Chabrol B, Villeneuve N, Milh M, George F et al. [Language dx.doi.org/10.1038/onc.2012.474 (e-pub ahead of print). disorders in children with morphologic abnormalities of the hippocampus]. Arch 28 Akalin A, Garrett-Bakelman FE, Kormaksson M, Busuttil J, Zhang L, Khrebtukova I Pediatr 2010; 17: 1008–1016. et al. Base-pair resolution DNA methylation sequencing reveals profoundly 56 Raymond GV, Bauman ML, Kemper TL. Hippocampus in autism: a Golgi analysis. divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet 2012; 8: Acta neuropathologica 1996; 91: 117–119. e1002781. 57 Clement JP, Aceti M, Creson TK, Ozkan ED, Shi Y, Reish NJ et al. Pathogenic 29 Shen J, Wang S, Zhang YJ, Wu HC, Kibriya MG, Jasmine F et al. Exploring genome- SYNGAP1 mutations impair cognitive development by disrupting maturation of wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium dendritic spine synapses. Cell 2012; 151: 709–723. HumanMethylation 450 BeadChips. Epigenetics 2013; 8:34–43. 58 Labate A, Tarantino P, Palamara G, Gagliardi M, Cavalcanti F, Ferlazzo E et al. 30 Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A et al. Mutations in PRRT2 result in familial infantile seizures with heterogeneous Epigenome-wide association data implicate DNA methylation as an intermediary phenotypes including febrile convulsions and probable SUDEP. Epilepsy Res 2013; of genetic risk in rheumatoid arthritis. Nat Biotechnol 2013; 31: 142–147. 104: 280–284. 31 Nakano K, Whitaker JW, Boyle DL, Wang W, Firestein GS. DNA methylome 59 Chen WJ, Lin Y, Xiong ZQ, Wei W, Ni W, Tan GH et al. Exome sequencing identifies signature in rheumatoid arthritis. Ann Rheum Dis 2013; 72: 110–117. truncating mutations in PRRT2 that cause paroxysmal kinesigenic dyskinesia. Nat 32 Jiang YH, Sahoo T, Michaelis RC, Bercovich D, Bressler J, Kashork CD et al. A mixed Genet 2011; 43: 1252–1255. epigenetic/genetic model for oligogenic inheritance of autism with a limited role 60 Riant F, Roze E, Barbance C, Meneret A, Guyant-Marechal L, Lucas C et al. PRRT2 for UBE3A. Am J Med Genet A 2004; 131: 1–10. mutations cause hemiplegic migraine. Neurology 2012; 79: 2122–2124. 33 Gregory SG, Connelly JJ, Towers AJ, Johnson J, Biscocho D, Markunas CA et al. 61 Lee HY, Huang Y, Bruneau N, Roll P, Roberson ED, Hermann M et al. Mutations in Genomic and epigenetic evidence for oxytocin receptor deficiency in autism. BMC the gene PRRT2 cause paroxysmal kinesigenic dyskinesia with infantile convul- Medicine 2009; 7:62. sions. Cell Rep 2012; 1: 2–12. 34 James SJ, Shpyleva S, Melnyk S, Pavliv O, Pogribny IP. Complex epigenetic reg- 62 Heron SE, Grinton BE, Kivity S, Afawi Z, Zuberi SM, Hughes JN et al. PRRT2 ulation of Engrailed-2 (EN-2) homeobox gene in the autism cerebellum. Transl mutations cause benign familial infantile epilepsy and infantile convulsions with Psychiatry 2013; 3: e232. choreoathetosis syndrome. Am J Hum Genet 2012; 90: 152–160. 35 Nagarajan RP, Patzel KA, Martin M, Yasui DH, Swanberg SE, Hertz-Picciotto I et al. 63 De Vries B, Callenbach PM, Kamphorst JT, Weller CM, Koelewijn SC, ten Houten R MECP2 promoter methylation and X chromosome inactivation in autism. Autism et al. PRRT2 mutation causes benign familial infantile convulsions. Neurology Res 2008; 1: 169–178. 2012; 79: 2154–2155. 36 Shulha HP, Cheung I, Whittle C, Wang J, Virgil D, Lin CL et al. Epigenetic signatures 64 Ono S, Yoshiura K, Kinoshita A, Kikuchi T, Nakane Y, Kato N et al. Mutations in of autism: trimethylated H3K4 landscapes in prefrontal neurons. Arch Gen PRRT2 responsible for paroxysmal kinesigenic dyskinesias also cause benign Psychiatry 2012; 69: 314–324. familial infantile convulsions. J Hum Genet 2012; 57: 338–341.

Molecular Psychiatry (2014), 862 – 871 & 2014 Macmillan Publishers Limited Methylation alterations in PRRT1 and C11orf21 in autism brains C Ladd-Acosta et al 871 65 Gartlan KH, Belz GT, Tarrant JM, Minigo G, Katsara M, Sheng KC et al. A com- 74 Li X, Ito M, Zhou F, Youngson N, Zuo X, Leder P et al. A maternal-zygotic effect plementary role for the tetraspanins CD37 and Tssc6 in cellular immunity. gene, Zfp57, maintains both maternal and paternal imprints. Dev Cell 2008; 15: J Immunol 2010; 185: 3158–3166. 547–557. 66 Goschnick MW, Jackson DE. Tetraspanins-structural and signalling scaffolds that 75 Strogantsev R, Ferguson-Smith AC. Proteins involved in establishment and regulate platelet function. Mini Rev Med Chem 2007; 7: 1248–1254. maintenance of imprinted methylation marks. Brief Funct Genomics 2012; 11: 67 Scholz CJ, Jacob CP, Buttenschon HN, Kittel-Schneider S, Boreatti-Hummer A, 227–239. Zimmer M et al. Functional variants of TSPAN8 are associated with bipolar dis- 76 Zuo X, Sheng J, Lau HT, McDonald CM, Andrade M, Cullen DE et al. Zinc finger order and schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2010; 153B: protein ZFP57 requires its co-factor to recruit DNA methyltransferases and 967–972. maintains DNA methylation imprint in embryonic stem cells via its transcriptional 68 Durand CM, Betancur C, Boeckers TM, Bockmann J, Chaste P, Fauchereau F et al. repression domain. J Biol Chem 2012; 287: 2107–2118. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are 77 Liu Y, Zhang X, Blumenthal RM, Cheng X. A common mode of recognition for associated with autism spectrum disorders. Nat Genet 2007; 39: 25–27. methylated CpG. Trends Biochem Sci 2013; 38: 177–183. 69 Moessner R, Marshall CR, Sutcliffe JS, Skaug J, Pinto D, Vincent J et al. Contribution 78 Raney BJ, Cline MS, Rosenbloom KR, Dreszer TR, Learned K, Barber GP et al. of SHANK3 mutations to autism spectrum disorder. Am J Hum Genet 2007; 81: ENCODE whole-genome data in the UCSC genome browser (2011 update). Nucleic 1289–1297. Acids Res 2011; 39(Database issue): D871–D875. 70 Gauthier J, Spiegelman D, Piton A, Lafreniere RG, Laurent S, St-Onge J et al. Novel 79 Rossignol DA, Frye RE. Mitochondrial dysfunction in autism spectrum disorders: a de novo SHANK3 mutation in autistic patients. Am J Med Genet B Neuropsychiatr systematic review and meta-analysis. Mol Psychiatry 2012; 17: 290–314. Genet 2009; 150B: 421–424. 80 Marui T, Funatogawa I, Koishi S, Yamamoto K, Matsumoto H, Hashimoto O 71 Berkel S, Marshall CR, Weiss B, Howe J, Roeth R, Moog U et al. Mutations in the et al. The NADH-ubiquinone oxidoreductase 1 alpha subcomplex 5 (NDUFA5) SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental gene variants are associated with autism. Acta Psychiatr Scand 2011; 123: retardation. Nat Genet 2010; 42: 489–491. 118–124. 72 Volders K, Nuytens K, Creemers JW. The autism candidate gene Neurobeachin 81 Tsao CY, Mendell JR. Autistic disorder in 2 children with mitochondrial disorders. encodes a scaffolding protein implicated in membrane trafficking and signaling. J Child Neurol 2007; 22: 1121–1123. Curr Mol Med 2011; 11: 204–217. 82 Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S et al. Functional 73 Hogart A, Wu D, LaSalle JM, Schanen NC. The comorbidity of autism with the annotation of the human brain methylome identifies tissue-specific epigenetic genomic disorders of chromosome 15q11.2-q13. Neurobiol Dis 2010; 38: 181–191. variation across brain and blood. Genome biology 2012; 13: R43.

Supplementary Information accompanies the paper on the Molecular Psychiatry website (http://www.nature.com/mp)

& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 862 – 871