Introduction
Post-traumatic stress disorder (PTSD) is a prevalent and debilitating mental health disorder that may arise following exposure to a potentially traumatic event (APA, 2013). While the lifetime prevalence of traumatic exposure is 50–90% (Kessler et al. Reference Kessler, Sonnega, Bromet, Hughes and Nelson1995), PTSD in the general US population is estimated to be only 6.8% (Kessler & Wang, Reference Kessler and Wang2008). Although the majority of persons exposed to trauma display resiliency (Kessler et al. Reference Kessler, Sonnega, Bromet, Hughes and Nelson1995; Breslau et al. Reference Breslau, Kessler, Chilcoat, Schultz, Davis and Andreski1998; Acierno et al. Reference Acierno, Ruggiero, Galea, Resnick, Koenen, Roitzsch, de Arellano, Boyle and Kilpatrick2007; Kessler & Wang, Reference Kessler and Wang2008), the molecular underpinnings of risk remain poorly characterized. The identification of risk markers, and particularly biomarkers, that distinguish between persons at high and low risk of developing PTSD following trauma exposure has been identified as a priority research goal by the Institute of Medicine (2012), Department of Defense (CDMRP, Department of Defense, 2010) and the National Institute of Mental Health (2008). Ideally, the ability to identify persons at high risk of developing PTSD would enable providers to target evidence-based interventions to high-risk groups (Andrews & Neises, Reference Andrews and Neises2012). The identification of robust predictive biomarkers may also improve our understanding of the pathophysiology of PTSD and lead to more effective pharmacological interventions.
Although much work has been done to identify social and environmental factors that contribute to PTSD risk (e.g. Kulka et al. Reference Kulka, Schlenger, Fairbank, Hough, Jordan, Marmar, Weiss and Grady1990; Breslau et al. Reference Breslau, Davis, Andreski and Peterson1991, Reference Breslau, Wilcox, Storr, Lucia and Anthony2004; Brewin et al. Reference Brewin, Andrews and Valentine2000; Koenen et al. Reference Koenen, Stellman, Stellman and Sommer2003; DiGrande et al. Reference DiGrande, Perrin, Thorpe, Thalji, Murphy, Wu, Farfel and Brackbill2008; Galea et al. Reference Galea, Ahern, Tracy, Hubbard, Cerda, Goldmann and Vlahov2008; Kun et al. Reference Kun, Han, Chen and Yao2009), the biological undergirding of differential PTSD risk and resiliency remains to be more fully elucidated. Twin studies have demonstrated heritability and genetic contribution to PTSD risk (True et al. Reference True, Rice, Eisen, Heath, Goldberg, Lyons and Nowak1993; Koenen et al. Reference Koenen, Harley, Lyons, Wolfe, Simpson, Goldberg, Eisen and Tsuang2002; Stein et al. Reference Stein, Jang, Taylor, Vernon and Livesley2002) and targeted gene and Genome-wide Association Study (GWAS) approaches have identified both genetic risk loci (Lu et al. Reference Lu, Ogdie, Jarvelin, Moilanen, Loo, McCracken, McGough, Yang, Peltonen, Nelson, Cantor and Smalley2008; Ressler et al. Reference Ressler, Mercer, Bradley, Jovanovic, Mahan, Kerley, Norrholm, Kilaru, Smith, Myers, Ramirez, Engel, Hammack, Toufexis, Braas, Binder and May2011; Chang et al. Reference Chang, Koenen, Galea, Aiello, Soliven, Wildman and Uddin2012; Logue et al. Reference Logue, Baldwin, Guffanti, Melista, Wolf, Reardon, Uddin, Wildman, Galea, Koenen and Miller2013) and important gene × environment interactions (Binder et al. Reference Binder, Bradley, Liu, Epstein, Deveau, Mercer, Tang, Gillespie, Heim, Nemeroff, Schwartz, Cubells and Ressler2008; Xie et al. Reference Xie, Kranzler, Poling, Stein, Anton, Farrer and Gelernter2010; Uddin et al. Reference Uddin, Chang, Zhang, Ressler, Mercer, Galea, Keyes, McLaughlin, Wildman, Aiello and Koenen2013) that contribute to risk for the disorder; nevertheless, a substantial proportion of biologically mediated variance in PTSD risk has yet to be explained.
Epigenetic variability is considered a plausible and increasingly empirically supported contributor to the etiology of phenotypes with marked genetic and environmental influences (Meaney, Reference Meaney2010), including certain psychopathologies (Toyokawa et al. Reference Toyokawa, Uddin, Koenen and Galea2012). Indeed, recent advances have revealed that PTSD risk and resiliency are associated with differential epigenetic variation (El-Sayed et al. Reference El-Sayed, Haloossim, Galea and Koenen2012). Epigenetic mechanisms – including histone modifications, non-protein coding RNAs, and, most notably, DNA methylation (DNAm) – affect gene expression and cellular phenotype without altering the underlying DNA sequence (Feinberg, Reference Feinberg2008; Meaney, Reference Meaney2010). DNAm is stably heritable across mitotic replications, but is modifiable throughout the life course in response to lived experiences and environmental exposures (Bird, Reference Bird2002). In primordial mammalian germ cells, global DNAm is removed (with the exception of imprinted loci) (Reik et al. Reference Reik, Dean and Walter2001), with new patterns established by de novo DNA methyltransferases DNMT3A, DNMT3B and DNMT3L following fertilization (Bourc'his et al. Reference Bourc'his, Xu, Lin, Bollman and Bestor2001; Bourc'his & Bestor, Reference Bourc'his and Bestor2004; Kaneda et al. Reference Kaneda, Okano, Hata, Sado, Tsujimoto, Li and Sasaki2004; Kato et al. Reference Kato, Kaneda, Hata, Kumaki, Hisano, Kohara, Okano, Li, Nozaki and Sasaki2007; Ooi et al. Reference Ooi, Qiu, Bernstein, Li, Jia, Yang, Erdjument-Bromage, Tempst, Lin, Allis, Cheng and Bestor2007). These reprogrammed DNAm patterns are largely maintained throughout mitotic DNA replication by the action of the maintenance methyltransferase, DNMT1 (Li et al. Reference Li, Bestor and Jaenisch1992; Seisenberger et al. Reference Seisenberger, Peat, Hore, Santos, Dean and Reik2013).
Although influenced by other variables, global DNAm patterns are largely established and maintained by the activity of the DNA methyltransferases DNMT1, DNMT3A, DNMT3B and DNMT3L (Feng & Fan, Reference Feng and Fan2009). Gene expression evidence suggests that these DNMTs may be active throughout the life course (Robertson et al. Reference Robertson, Uzvolgyi, Liang, Talmadge, Sumegi, Gonzales and Jones1999; Feng et al. Reference Feng, Chang, Li and Fan2005; Siegmund et al. Reference Siegmund, Connor, Campan, Long, Weisenberger, Biniszkiewicz, Jaenisch, Laird and Akbarian2007), including in brain tissue (Goto et al. Reference Goto, Numata, Komura, Ono, Bestor and Kondo1994; Veldic et al. Reference Veldic, Caruncho, Liu, Davis, Satta, Grayson, Guidotti and Costa2004; Feng et al. Reference Feng, Chang, Li and Fan2005) and in association with mental disorders (Veldic et al. Reference Veldic, Caruncho, Liu, Davis, Satta, Grayson, Guidotti and Costa2004, Reference Veldic, Guidotti, Maloku, Davis and Costa2005). In addition, protein-level expression of DNMT1 (Inano et al. Reference Inano, Suetake, Ueda, Miyake, Nakamura, Okada and Tajima2000; Veldic et al. Reference Veldic, Guidotti, Maloku, Davis and Costa2005) and DNMT3A (Feng et al. Reference Feng, Chang, Li and Fan2005) has been demonstrated in the mouse and human brain. With respect to PTSD, recent work confirms that DNMT activity plays a role in mediating risk for PTSD-related phenotypes, including fear conditioning and memory consolidation (Miller & Sweatt, Reference Miller and Sweatt2007; Feng et al. Reference Feng, Zhou, Campbell, Le, Li, Sweatt, Silva and Fan2010). Together, these findings suggest that DNAm and DNMTs represent promising targets for the identification of epigenetic underpinnings of differential PTSD risk and resiliency.
Studies of epigenetic variation have provided important insights into PTSD risk, but have been largely limited by cross-sectional analyses of post-trauma samples. Most notably, epidemiological cohorts from Detroit (Uddin et al. Reference Uddin, Aiello, Wildman, Koenen, Pawelec, de Los Santos, Goldmann and Galea2010) and Atlanta (Smith et al. Reference Smith, Conneely, Kilaru, Mercer, Weiss, Bradley, Tang, Gillespie, Cubells and Ressler2011) have been the basis of research that has demonstrated cross-sectional differential DNAm that distinguishes between trauma-exposed individuals with versus without PTSD. DNMT3B and DNMT3L were among the differentially methylated loci identified in the Detroit study (Uddin et al. Reference Uddin, Aiello, Wildman, Koenen, Pawelec, de Los Santos, Goldmann and Galea2010). More recently, longitudinal DNAm data among PTSD cases and controls have been reported, including studies using samples from a cohort of US military personnel deployed to Iraq and Afghanistan (Rusiecki et al. Reference Rusiecki, Chen, Srikantan, Zhang, Yan, Polin and Baccarelli2012, Reference Rusiecki, Byrne, Galdzicki, Srikantan, Chen, Poulin, Yan and Baccarelli2013). To further elucidate whether differential DNAm between trauma-exposed controls and PTSD cases represent pre-existing susceptibility/resiliency factors or downstream biomarkers of PTSD, additional longitudinal analyses are required. Finally, while the identification of epigenetic variation associated with mental health outcomes is important, work must begin to test the putative functionality of mental health-associated differential DNAm. For example, the identification of transcription factor binding sites (TFBS) that overlap with differentially methylated CpG sites and to which transcription factor binding may be disrupted offer one possibility of supporting DNAm functionality (Weaver et al. Reference Weaver, Cervoni, Champagne, D'Alessio, Sharma, Seckl, Dymov, Szyf and Meaney2004).
Here, we analysed DNAm from individuals pre- and post-trauma to identify differences that characterize individuals who are susceptible versus resilient to PTSD following trauma. To assess potential functional consequences of examined DNAm differences, we then performed a bioinformatic search for the presence of putative TFBS (Weaver et al. Reference Weaver, Cervoni, Champagne, D'Alessio, Sharma, Seckl, Dymov, Szyf and Meaney2004). Results from this work suggest that PTSD-relevant DNAm differences in DNMT loci may exist both prior to and following trauma, with implications for future targeted interventions.
Method
Subjects
Samples are from a subset of participants from the Detroit Neighborhood Health Study (DNHS), a longitudinal, community-representative cohort of adult residents in Detroit, MI, USA. The current study draws on peripheral blood samples and survey data obtained at two time points from 60 DNHS participants; 46 were female and 14 male; 46 were African-American and 12 were Caucasian, and two were Hispanic. The average age was 55.1 years. PTSD diagnosis was assessed via structured interview administered via telephone (Breslau et al. Reference Breslau, Kessler, Chilcoat, Schultz, Davis and Andreski1998). PTSD symptoms were assessed in reference to both the traumatic event that the participant regarded as their worst and one randomly selected traumatic event from the remaining traumas that the participant experienced. Lifetime PTSD cases met all six Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria (APA, 1994) in reference to either the worst or random traumatic event. The diagnostic interview showed good validity against the Clinician Administered PTSD Scale (Blake et al. Reference Blake, Weathers, Nagy, Kaloupek, Gusman, Charney and Keane1995) as described elsewhere (Uddin et al. Reference Uddin, Aiello, Wildman, Koenen, Pawelec, de Los Santos, Goldmann and Galea2010). The Institutional Review Board of the University of Michigan reviewed and approved the study protocol. Incident cases (n = 30) of PTSD were identified in either waves 2, 3 or 4 of DNHS data collection among individuals for whom blood samples were available at both the wave of first PTSD diagnosis and the immediately previous, pre-incident trauma wave. Non-PTSD controls (n = 30) were matched to cases on the basis of age, sex and number of traumatic event types. DNA samples were isolated from both pre- and post-trauma time points for both cases and controls. The time between pre- and post-trauma time points was approximately 1 year. Cases and controls had no history of PTSD prior to the post-trauma wave.
Methylation quantification by targeted bisulfite pyrosequencing
DNA isolation
DNA was isolated from whole blood acquired via venepuncture when available from DNHS participants selected for inclusion in this study. Blood spots were used as an alternative source of whole blood-derived DNA when venepuncture samples were unavailable. The exact tissue type was shared between matched case–control pairs in all instances. Venepuncture- and bloodspot-derived whole blood represents the same tissue and therefore should not differ with respect to DNAm, as confirmed by numerous studies to date (Wong et al. Reference Wong, Morley, Saffery and Craig2008; Aberg et al. Reference Aberg, Xie, Nerella, Copeland, Costello and van den Oord2013; Hollegaard et al. Reference Hollegaard, Grauholm, Norgaard-Pedersen and Hougaard2013).
Whole blood
DNA was isolated from whole blood using the QIAamp DNA Blood Mini Kit (Qiagen, USA) and the QuickGene DNA Whole blood Kit S (Lifesciences, FujiFilm, Japan) using the manufacturers' recommended protocols.
Blood spots
DNA was isolated using the QIAamp DNA Micro kit (Qiagen, USA) using the manufacturer's recommended protocol. For each sample, one 6 mm punch was taken from dried blood spots using a disposable, sterile biopsy punch (Miltex, USA) within a sterile field and placed immediately into a sterile 1.7 ml microcentrifuge tube. New gloves, biopsy punches and sterile fields were utilized for each sample. Negative controls in the form of blank extractions were included with all DNA isolations.
Bisulfite conversion
For each sample, about 750 ng of DNA was bisulfite converted using the EpiTect Bisulfite Kit (Qiagen, USA) using the manufacturer's recommended protocol. Negative controls in the form of bisulfite conversion of water were included with each bisulfite conversion.
Pyrosequencing
Assays to assess the methylation levels of CpG sites found in the DNMT1, DNMT3A, and DNMT3L and DNMT3B (see below for assay-specific details) were custom designed using the Pyromark Q24 Assay Design Software 2.0 (Qiagen, USA). Targeted CpG sites were selected based on prior evidence (Uddin et al. Reference Uddin, Aiello, Wildman, Koenen, Pawelec, de Los Santos, Goldmann and Galea2010) of involvement in epigenetic regulation of PTSD risk (DNMT3B, DNMT3L) and to investigate whether longitudinal, PTSD-associated DNAm differences exist across DNMT genes more broadly (DNMT1, DNMT3A, DNMT3B and DNMT3L). Because the DNMT3B target CpG is located in a CpG island, our designed assay captures DNAm at 12 CpG sites in an approximately 70 base pair region of exon 1 (see DNMT3B assay section below for details). Single CpG sites were assessed at DNMT1, DNMT3A and DNMT3L loci (see individual assay section below for details); these CpG sites did not fall into CpG islands. DNMT1, DNMT3A and DNMT3L CpG sites and 2 DNMT3B CpG sites assessed are also found on the HM27 and HM450 K methylation bead chips from Illumina (see below for actual HG19 nucleotide location). The capacity for each assay to capture DNAm levels ranging from 0 to 100% was validated using commercially available demethylated and highly methylated DNA at dilutions of 1:0 (unmethylated), 3:1, 1:1, 1:3 and 0:1 (highly methylated). Polymerase chain reaction (PCR) amplification of target sequences was performed on 20 ng of bisulfite-converted DNA samples using the PyroMark PCR kit (Qiagen, USA). Bisulfite-converted, PCR-amplified DNA was pyrosequenced on the Pyromark Q24 Pyrosequencer (Qiagen, USA) using the manufacturer's recommended protocol and default settings. All methylation analyses were conducted in triplicate with appropriate negative controls included at each of the following steps: DNA isolation, bisulfite conversion, PCR amplification and pyrosequencing reaction.
Details of each custom assay are listed below.
DNMT1
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PCR forward primer: TTTTTTTAGGTGTGATGGGGATAAAG
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PCR reverse primer (biotinylated): CAAAAACTCTCACAAACCCTTAAA
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PCR program (50 cycles):
Initial 15 min at 95°C
Denaturation 30 s at 94°C
Annealing 30 s at 58°C
Extension 30 s at 72°C
Final 10 min at 72°C
Hold 4°C
Sequencing primer: GTGATGGGGATAAAGT
Target sequence: AGCGAGAAGCCCCCAAGGGTTTGTGAGA (CpG target in bold; hg19: chr19:10,305,909-10,305,936)
DNMT3A:
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PCR forward primer: GGTGGGAGGTTGAATGAAATGA
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PCR reverse primer (biotinylated): AATACCCAACCCCAAATCCTAC
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PCR program (50 cycles):
Initial 15 min at 95°C
Denaturation 30 s at 94°C
Annealing 30 s at 58°C
Extension 30 s at 72°C
Final 10 min at 72°C
Hold 4°C
Sequencing primer: AGTTGGAAGATTTTGTG
Target sequence: TGTGCCTACACACCGCCCTCACCCCTTCACYGTGGGGGCTGTTCTCCTTCCCCATGGAGYGCTCAGGGCTCTAGGTTCCTGACTTGGGGCACCTCTGTCTAATTCCACCAGCACAGCCACTCACTATGTGCTCATCTCACTCCTCCAGCAGCYGCTGTAGGACTTGGGGCTGGGCACC (CpG target in bold; hg19: chr2:25,565,782-25,565,959)
DNMT3B:
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PCR forward primer: GGGGTTAAGTGGTTTAAGTAAAT
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PCR reverse primer (biotinylated): CCTCCAAAAATCCCTAAAAAAAATCT CTCC
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PCR program (45 cycles):
Initial 15 min at 95°C
Denaturation 30 s at 94°C
Annealing 30 s at 52°C
Extension 30 s at 72°C
Final 10 min at 72°C
Hold 4°C
Sequencing primer: GTTAAGTGGTTTAAGTAAATTTAG
Target sequence: CTCGGCGATCGGCGCCGGAGATTCGCGAGCCCAGCGCCCTGCACGGCCGCCAGCCGGCCTCCCGCCAGCCAGCCCCGACCCGCGGCTCCGCCGCCCAGCCGCGCCCCAGCCAGCCCTGCGGCAGGTGAGCGCCCCGGGGCCC (CpG targets in bold; hg19: chr20:31,350,382-31,350,523)
DNMT3L:
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PCR forward primer: AGTTTTTTTTATTGGGGTAGTTAGG
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PCR reverse primer (biotinylated): CTTAAAACCAAAAAACCACATTTTAT TCA
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PCR program (45 cycles):
Initial 15 min at 95°C
Denaturation 30 s at 94°C
Annealing 30 s at 50°C
Extension 30 s at 72°C
Final 10 min at 72°C
Hold 4°C
Sequencing primer: GATTTAGGGATAGAGAGGG
Target sequence: GCGGTAGGGAGTGGGAAATCTGAATAA (CpG target in bold; hg19: chr21:45,683,527-45,683,553)
To demonstrate the ability of our assays to resolve DNAm differences as small as reported, we computed intraclass correlation coefficients (ICCs) between triplicate replicates for each assay. Average within-sample coefficient of variation was computed using a two-way mixed model, using an absolute agreement definition (Shrout & Fleiss, Reference Shrout and Fleiss1979), as implemented in SPSS (IBM, USA). ICCs for the 15 total CpG sites assayed ranged from 0.703 to 0.937, with a mean ICC of 0.855 (s.d. = 0.066). This strongly supports the conclusion that these assays are capable of consistently resolving small DNAm differences.
TFBS prediction
Putative TFBS were identified that overlap target CpG sites using the MatInspector (Cartharius et al. Reference Cartharius, Frech, Grote, Klocke, Haltmeier, Klingenhoff, Frisch, Bayerlein and Werner2005) tool from Genomatix, with default parameters. Input sequence included 200 bp up- and downstream of the CpG site. Only putative TFBS that directly overlapped CpG sites of interest were retained.
Statistical analyses
Statistical testing was performed using SPSS Statistics for Windows, version 21.0 (IBM Corp., USA). DNAm at DNMT3B CpG sites was treated on a regional and an individual CpG site basis, similar to previous work (Rusiecki et al. Reference Rusiecki, Byrne, Galdzicki, Srikantan, Chen, Poulin, Yan and Baccarelli2013). Regional values were calculated as the mean of 12 CpG sites. Paired-sample t tests were used to test for differences in pre-trauma DNAm between cases and controls and to test for differences between pre- and post-trauma time points within cases and controls. Linear regression was used to test whether pre-trauma DNAm levels are predictive of post-trauma symptom severity (PTSS) changes. PTSS change was calculated as the difference between PTSS and pre-trauma symptom severity. Analyses included severity scores of individual symptom criteria (hyperarousal, avoidance or intrusion symptoms) as well as a total severity score that is inclusive of each symptom subdomain. Regression models were adjusted for age, gender and pre-trauma symptom severity. The contribution of pre-trauma DNAm to PTSS change was tested via the change in R 2 values comparing full with reduced models. We present primary results uncorrected for multiple testing as is consistent with the current state of the science of DNAm variation in association with psychiatric endpoints (Perroud et al. Reference Perroud, Paoloni-Giacobino, Prada, Olie, Salzmann, Nicastro, Guillaume, Mouthon, Stouder, Dieben, Huguelet, Courtet and Malafosse2011, Reference Perroud, Salzmann, Prada, Nicastro, Hoeppli, Furrer, Ardu, Krejci, Karege and Malafosse2013; Unternaehrer et al. Reference Unternaehrer, Luers, Mill, Dempster, Meyer, Staehli, Lieb, Hellhammer and Meinlschmidt2012; Rusiecki et al. Reference Rusiecki, Byrne, Galdzicki, Srikantan, Chen, Poulin, Yan and Baccarelli2013). In addition, to assess the extent to which our results may be attenuated by multiple hypothesis testing correction, we calculated stringent Bonferroni-corrected significance values (Dunn, Reference Dunn1961) as well as false discovery rate (FDR) Q values (Benjamini & Hochberg, Reference Benjamini and Hochberg1995). FDR has recently been utilized to correct multiple hypothesis testing in studies utilizing DNAm data, with user-defined Q values ranging from 0.05 to 0.2 (Provencal et al. Reference Provencal, Suderman, Caramaschi, Wang, Hallett, Vitaro, Tremblay and Szyf2013; Zhao et al. Reference Zhao, Goldberg, Bremner and Vaccarino2013).
Results
PTSD cases and controls did not differ in age, gender, ethnicity or pre-trauma symptom severity, including individual symptoms of intrusion, avoidance and hyperarousal (Table 1).
Table 1. Demographic and pre-trauma characteristics of 30 PTSD case–control pairs
Data are given as mean (standard deviation) unless otherwise indicated.
PTSD, Post-traumatic stress disorder; df, degrees of freedom; n.a., not applicable.
Pre-trauma DNAm variation is associated with PTSD
PTSD-associated DNAm variation may both exist before trauma and be associated with post-trauma PTSD outcome. To test for pre-existing protective/risk factors, pre-trauma DNAm at DNMT1, DNMT3A, DNMT3B and DNMT3L loci was compared between trauma-exposed individuals with versus without PTSD. Pre-trauma DNAm was higher in controls compared with cases at a single DNMT3B CpG site (CpG 9) [Fig. 1; t = 2.250, degrees of freedom (df) = 29, p = 0.032]; no difference in pre-trauma DNMT3B regional DNAm mean was observed (t = 1.538, df = 29, p = 0.135). We observed no pre-trauma differences between cases and controls at DNMT1 (t = 0.582, df = 29, p = 0.565), DNMT3A (t = 0.579, df = 29, p = 0.567) and DNMT3L (t = 1.386, df = 29, p = 0.176) loci.
Fig. 1. Pre-trauma DNA methyltransferase 3B (DNMT3B) DNA methylation (DNAm) is significantly higher in trauma-exposed controls compared with post-traumatic stress disorder (PTSD) cases at CpG 9. Pre-trauma DNAm did not differ between cases and controls at the other 11 DNMT3B CpG sites assessed. Values are means, with standard errors represented by vertical bars. Differences between controls and cases were tested by paired-sample t tests (n = 60; 30 cases and 30 matched controls). * Mean values were significantly different (p < 0.05).
Pre-trauma DNAm variation predicts post-trauma changes in trauma symptom severity
To explore whether this PTSD-associated pre-trauma DNAm is predictive of trauma response, we performed linear regression analyses with pre-trauma DNAm of DNMT3B at CpG 9 and PTSS change as predictor and outcome variables, respectively. Controlling for age, gender and pre-trauma symptom severity, pre-trauma DNAm of CpG 9 (Fig. 2; unstandardized B = –2.318, s.e. = 1.25, p = 0.034) predicted PTSS change. In this model, only pre-trauma symptom severity and pre-trauma DNAm were significant predictor variables. DNMT3B CpG 9 DNAm explained approximately 6.8% of the variance in PTSS change, as revealed by a comparison of the full and reduced models. The full model that included DNMT3B CpG 9 DNAm, age, gender and pre-trauma symptom severity explained approximately 24% of the variance in post-trauma PTSS change (adjusted R 2 = 0.242, p = 0.005).
Fig. 2. Linear regression model of post-trauma symptom severity (PTSS) change and pre-trauma DNA methyltransferase 3B (DNMT3B) CpG 9 DNA methylation (DNAm), adjusting for age, gender and pre-trauma symptom severity (n = 60). Only pre-trauma symptom severity and DNAm were significant variables in this model. Values in error bar plots are means, with 95% confidence intervals represented by vertical bars. Differences between post-traumatic stress disorder (PTSD cases and trauma-exposed controls were tested by paired-sample t tests (n = 60; 30 PTSD cases and 30 matched controls). * Mean values were significantly different (p < 0.05).
Because the relationship between pre-trauma DNAm and post-trauma changes in PTSS may be driven by distinct symptom subdomains (hyperarousal, avoidance and intrusion), we regressed separately each subdomain symptom severity change onto pre-trauma DNAm, controlling for age, gender and pre-trauma symptom severity of the relevant subdomain. Pre-trauma DNAm of DNMT3B CpG 9 (hyperarousal: p = 0.249; avoidance: p = 0.137; intrusion: p = 0.071) did not predict change in subdomain symptom severity.
Trauma induces PTSD-associated DNAm modifications
DNAm differences may arise following trauma and be associated with PTSD development. To test this, we compared pre-trauma DNAm with post-trauma DNAm within PTSD cases and within trauma-exposed, healthy controls. Both PTSD-associated and PTSD-independent changes in DNAm following trauma were observed at DNMT loci (Fig. 3). DNMT1 DNAm increased (Fig. 3 a; t = 3.887, df = 29, p = 0.001) following trauma in the PTSD group, but not in the control group (t = 1.903, df = 29, p = 0.067). At DNMT3A (Fig. 3 b) and DNMT3B (Fig. 3 c) loci, DNAm increased following trauma in both PTSD case (DNMT3A: t = 2.806, df = 29, p = 0.009; DNMT3B: t = 4.286, df = 29, p < 0.001) and control (DNMT3A: t = 3.421, df = 29, p = 0.002; DNMT3B: t = 3.938, df = 29, p < 0.001) groups. No change was observed in DNMT3L (Fig. 3 d) DNAm in either cases (t = 1.551, df = 29, p = 0.132) or controls (t = 1.146, df = 29, p = 0.261). Table 2 presents a summary including uncorrected p values, Bonferroni-corrected p values and FDR values, as well as accompanying effect sizes, of our results described above.
Fig. 3. Longitudinal DNA methylation modifications of DNA methyltransferase (DNMT) loci in response to trauma in post-traumatic stress disorder (PTSD) cases and trauma-exposed controls. DNMT3B (region) represents the mean of 12 CpG sites. Values are means, with standard errors represented by vertical bars. Differences between PTSD cases and trauma-exposed controls were tested by paired-sample t tests (n = 60; 30 PTSD cases and 30 matched controls). Mean values were significantly different: * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2. Observed and corrected significance values of tests a
s.e., Standard error; FDR, false discovery rate; DNMT, DNA methyltransferase.
a Corrected significance thresholds at p < 0.05 are listed using two controlling procedures: Bonferroni and FDR using the procedure of Benjamini & Hochberg (Reference Benjamini and Hochberg1995).
b In percent DNA methylation. For regression analyses, ‘B’ represents unstandardized β values.
c The list of observed p values is sorted from smallest to largest (indicated by the rank column).
* Values meet significance at p < 0.05 for the various correction procedures.
TFBS prediction
DNAm is associated with gene expression. One mechanism by which increased DNAm can lead to decreased gene expression is by affecting the binding of trans-activating factors to cis-regulatory elements. To contextualize our DNAm findings, we used bioinformatic methods to identify putative TFBS that overlap CpG sites showing PTSD-associated DNAm differences. In total, we identified 24 putative TFBS, including 2, 3, 14 and 5 that overlap DNMT1, DNMT3A, DNMT3B and DNMT3L CpG target sites, respectively (Table 3). Notable among these 24 TFBS are those that overlap with CpG sites at which we identified PTSD-associated differential methylation (two overlap the DNMT1 CpG; three overlap DNMT3B CpG 9). Binding sites for heat shock factor 1 and E2F-4/DP-2 heterodimeric complex were identified to overlap with the DNMT1 CpG site at which an increase in DNAm was observed in PTSD cases, but not controls. Overlapping with DNMT3B CpG site 9, at which decreased pre-trauma DNAm was associated with PTSD development and predictive of worsening of PTSS, we identified binding sites for human motif ten element, ZF5 POZ domain zinc finger, and the insulator protein CTCF.
Table 3. Putative transcription factor binding sites overlap DNMT CpG sites of interest a
DNMT, DNA methyltransferase.
a V$ matrix families indicate Genomatix-annotated transcription factor binding site matrix families. DNMT3B CpG sites are described in the Method section.
Discussion
Our data represent preliminary findings suggesting that pre-trauma DNAm states and post-trauma DNAm modifications differ between those who develop PTSD following trauma and those who display resiliency. While baseline symptom severity did not differ between cases and controls, baseline DNAm at a DNMT3B CpG site was higher in resilient individuals compared with those who eventually developed PTSD. Additionally, longitudinal change in DNAm at a DNMT1 CpG site was associated with PTSD, with an increase in DNAm being observed in those with PTSD but not controls. Finally, increases in DNAm were observed following trauma at DNMT3A and DNMT3B loci that were independent of PTSD outcome, being observed in both PTSD cases and trauma-exposed controls. Although some of these results were attenuated following correction for multiple hypothesis testing, our findings suggest that epigenetic variation plays a complex regulatory role in PTSD risk and etiology.
One way in which DNAm may regulate gene transcription is by altering the strength and occupancy of transcription factor binding (Weaver et al. Reference Weaver, Cervoni, Champagne, D'Alessio, Sharma, Seckl, Dymov, Szyf and Meaney2004). To provide insight into potential functional consequences of the observed PTSD-associated differences, we conducted a secondary analysis of TFBS overlapping the distinguishing CpG sites. Among the sites identified was a binding site for CTCF, a transcription factor known to be involved in chromatin remodeling (Barkess & West, Reference Barkess and West2012). We identified this binding site overlapping with DNMT3B CpG site 9, at which higher DNAm was identified as a protective/risk factor for PTSD and symptom severity change following trauma exposure. Differential methylation at this site is particularly compelling as a determinant of PTSD risk, given that DNAm at CTCF binding sites has been shown to significantly affect CTCF occupancy (Wang et al. Reference Wang, Maurano, Qu, Varley, Gertz, Pauli, Lee, Canfield, Weaver, Sandstrom, Thurman, Kaul, Myers and Stamatoyannopoulos2012) and downstream levels of gene transcription (Renaud et al. Reference Renaud, Loukinov, Abdullaev, Guilleret, Bosman, Lobanenkov and Benhattar2007). Due to the nature of our samples, we are unable to test directly whether DNAm at these identified TFBS influences gene expression. Where available, we have utilized Encyclopedia of DNA Elements (ENCODE) Project data (ENCODE Project Consortium, 2011) to provide evidence for or against transcription factor binding at the PTSD-associated sites in blood-derived cell types. Among the TFBS identified that overlap PTSD-associated CpG sites (DNMT1 and DNMT3B CpG 9), ENCODE data include binding of CTCF and E2F4. ENCODE data support the binding of CTCF to DNMT3B in blood tissue (specifically B-lymphocyte cell lines: GM12864 and GM12874), but do not support the binding of E2F4 to DNMT1. This supports the potential functionality of observed DNAm differences at DNMT3B CpG 9 in pre-trauma samples in cases versus controls.
DNMTs have been previously implicated in PTSD, anxiety and fear conditioning. In suicide completers relative to controls, DNMT3B was up-regulated in the frontopolar cortex, hypothalamus and dorsal vagal complex, and down-regulated, along with DNMT1, in the hippocampus (Poulter et al. Reference Poulter, Du, Weaver, Palkovits, Faludi, Merali, Szyf and Anisman2008). Additionally, de novo methyltransferases have been shown to be up-regulated during contextual fear conditioning, also in the hippocampus (Miller & Sweatt, Reference Miller and Sweatt2007); DNMTs are required for fear conditioning and memory consolidation as demonstrated, respectively, by administration of DNMT inhibitors (Miller & Sweatt, Reference Miller and Sweatt2007) and the creation of mice with the combined knockout of DNMT1 and DNMT3A (Feng et al. Reference Feng, Zhou, Campbell, Le, Li, Sweatt, Silva and Fan2010). Our results thus add to the growing evidence implicating DNMTs in phenotypes of relevance to PTSD, and of psychiatric phenotypes more broadly.
The expression of DNMTs at the mRNA (Goto et al. Reference Goto, Numata, Komura, Ono, Bestor and Kondo1994; Veldic et al. Reference Veldic, Caruncho, Liu, Davis, Satta, Grayson, Guidotti and Costa2004; Kang et al. Reference Kang, Kawasawa, Cheng, Zhu, Xu, Li, Sousa, Pletikos, Meyer, Sedmak, Guennel, Shin, Johnson, Krsnik, Mayer, Fertuzinhos, Umlauf, Lisgo, Vortmeyer, Weinberger, Mane, Hyde, Huttner, Reimers, Kleinman and Sestan2011; Sterner et al. Reference Sterner, Weckle, Chugani, Tarca, Sherwood, Hof, Kuzawa, Boddy, Abbas, Raaum, Gregoire, Lipovich, Grossman, Uddin, Goodman and Wildman2012) and protein (Inano et al. Reference Inano, Suetake, Ueda, Miyake, Nakamura, Okada and Tajima2000; Feng et al. Reference Feng, Chang, Li and Fan2005; Veldic et al. Reference Veldic, Guidotti, Maloku, Davis and Costa2005) levels in post-mitotic neurons of the central nervous system suggests that they are involved in methyltransferase activity that persists into adulthood and that is unrelated to DNA replication (Goto et al. Reference Goto, Numata, Komura, Ono, Bestor and Kondo1994). Indeed, previous work has identified DNMT1 protein expression in multiple brain regions in rodents (e.g. cortex, cerebellum; Inano et al. Reference Inano, Suetake, Ueda, Miyake, Nakamura, Okada and Tajima2000), as well as in specific cortical regions in adult humans (e.g. Brodmann area 9; Veldic et al. Reference Veldic, Guidotti, Maloku, Davis and Costa2005). Furthermore, recent work suggests that our epigenetic findings in peripheral blood may be relevant to brain tissue: environmental exposures such as trauma have been shown to induce parallel epigenetic modifications in peripheral blood and the brain (McGowan et al. Reference McGowan, Suderman, Sasaki, Huang, Hallett, Meaney and Szyf2011; Klengel et al. Reference Klengel, Mehta, Anacker, Rex-Haffner, Pruessner, Pariante, Pace, Mercer, Mayberg, Bradley, Nemeroff, Holsboer, Heim, Ressler, Rein and Binder2013). Although the current study, based on living participants drawn from a population-based cohort, precludes such work, future research is needed to address whether the epigenetic determinants of risk observed here in peripheral blood-derived DNA are also found in brain-derived DNA.
Importantly, this study adds to emerging work utilizing a longitudinal study design capable of measuring biological markers prior to disease onset as well as change between pre-disease and post-disease time points (Rusiecki et al. Reference Rusiecki, Chen, Srikantan, Zhang, Yan, Polin and Baccarelli2012, Reference Rusiecki, Byrne, Galdzicki, Srikantan, Chen, Poulin, Yan and Baccarelli2013; Perroud et al. Reference Perroud, Salzmann, Prada, Nicastro, Hoeppli, Furrer, Ardu, Krejci, Karege and Malafosse2013; Nieratschker et al. Reference Nieratschker, Grosshans, Frank, Strohmaier, von der Goltz, El-Maarri, Witt, Cichon, Nöthen, Kiefer and Rietschel2014). Existing longitudinal studies have documented the importance of DNAm to mental health disorder risk, including differential change in DNAm of BDNF among individuals with versus without borderline personality disorder (Perroud et al. Reference Perroud, Salzmann, Prada, Nicastro, Hoeppli, Furrer, Ardu, Krejci, Karege and Malafosse2013), increased DAT (SLC6A3) DNAm with age that may be driven by alcohol dependence (Nieratschker et al. Reference Nieratschker, Grosshans, Frank, Strohmaier, von der Goltz, El-Maarri, Witt, Cichon, Nöthen, Kiefer and Rietschel2014), and increasing SERT DNAm associated with bullying (Ouellet-Morin et al. Reference Ouellet-Morin, Wong, Danese, Pariante, Papadopoulos, Mill and Arseneault2013). Most relevant to the present study is work by Rusiecki et al. (Reference Rusiecki, Chen, Srikantan, Zhang, Yan, Polin and Baccarelli2012) which provides evidence for increased global DNAm in controls, but not cases, following trauma exposure, suggesting that resiliency is associated with increased global DNAm, potentially mediated by increased activity and expression of DNMTs. Indeed, our data presented here are consistent with this scenario, as DNAm of DNMT1 was observed to increase following trauma in cases, but not controls. In contrast, however, we observed an increase in DNMT3B DNAm following trauma in both cases and controls, and a pre-trauma association between higher DNAm pre-trauma and resiliency post-trauma. The presence of a CTCF binding site opens the possibility that increased DNAm at this locus is associated with increased gene expression because CTCF can act as either a transcriptional activator or repressor (Phillips & Corces, Reference Phillips and Corces2009), with strength of DNA binding inversely correlated with local DNAm (Barkess & West, Reference Barkess and West2012). If binding of CTCF to the DNMT3B locus results in transcriptional repression, then increased DNAm, and concurrent decreased CTCF binding, would be associated with increased, not decreased, gene expression. If true, this would put these findings in line with the previously published, longitudinal, trauma-associated epigenetic data: decreased DNAm in pre-trauma PTSD cases would result in tighter CTCF binding and reduced DNMT3B transcription and lower global DNAm levels, as reported by Ruisecki et al. (Reference Rusiecki, Chen, Srikantan, Zhang, Yan, Polin and Baccarelli2012). Although DNMT1 is typically thought to maintain DNAm in adult tissues, evidence suggests that DNMT1 and DNMT3B cooperatively maintain DNAm, with one or the other, but not both, required for global DNAm (Rhee et al. Reference Rhee, Bachman, Park, Jair, Yen, Schuebel, Cui, Feinberg, Lengauer, Kinzler, Baylin and Vogelstein2002). More broadly, our data add to the emerging evidence that longitudinal DNAm changes may contribute to the etiology of mental illness and can be taken as a proof of principle that locus-specific epigenetic variability both pre-exists and arises following disease onset in biologically meaningful ways.
While our study is one of the first of its kind to compare pre- and post-trauma DNAm levels with regard to the development of PTSD, there is a minimum of four study limitations that should be kept in mind when interpreting our results. First, it is important to recognize that the epidemiological nature of our cohort precludes sample collection with a well-controlled experimental time course; times between pre-trauma data collection, trauma exposure and post-trauma data collection differed between each test subject. As such, we are unable to resolve whether observed PTSD-associated post-trauma DNAm changes precede PTSD development (i.e. occurred within the first 4 weeks following trauma). As DNMTs are involved in the global regulation of DNAm, it is tempting to conclude from our data that observed changes in DNMT DNAm are an upstream process of PTSD development, thereby having the potential to help explain differences in DNAm epigenome-wide reported elsewhere (Uddin et al. Reference Uddin, Aiello, Wildman, Koenen, Pawelec, de Los Santos, Goldmann and Galea2010; Smith et al. Reference Smith, Conneely, Kilaru, Mercer, Weiss, Bradley, Tang, Gillespie, Cubells and Ressler2011; Rusiecki et al. Reference Rusiecki, Chen, Srikantan, Zhang, Yan, Polin and Baccarelli2012). However, it is also possible that the observed PTSD-associated DNAm changes are downstream effects of PTSD development, with no or little involvement in epigenetic modifications across the epigenome. Second, the nature of the epidemiological samples collected precluded the assessment of pre- and post-trauma gene expression differences and changes; furthermore, we did not collect data on blood cell composition. Third, the DNAm differences and effect sizes reported here are small; however, they are consistent with published work showing functional effects of DNAm variation (Tyrka et al. Reference Tyrka, Price, Marsit, Walters and Carpenter2012). High ICCs between experimental replicates for each of our assays increase confidence of the validity of observed DNAm differences. Indeed, our sample size and observed effect sizes are consistent with published work in the field (Perroud et al. Reference Perroud, Paoloni-Giacobino, Prada, Olie, Salzmann, Nicastro, Guillaume, Mouthon, Stouder, Dieben, Huguelet, Courtet and Malafosse2011; Byrne et al. Reference Byrne, Carrillo-Roa, Henders, Bowdler, McRae, Heath, Martin, Montgomery, Krause and Wray2013). Fourth, our primary results presented in this work are not corrected for multiple testing. Although this is consistent with the current state of the science of DNAm variation in association with psychiatric endpoints (Perroud et al. Reference Perroud, Paoloni-Giacobino, Prada, Olie, Salzmann, Nicastro, Guillaume, Mouthon, Stouder, Dieben, Huguelet, Courtet and Malafosse2011; Unternaehrer et al. Reference Unternaehrer, Luers, Mill, Dempster, Meyer, Staehli, Lieb, Hellhammer and Meinlschmidt2012; Perroud et al. Reference Perroud, Salzmann, Prada, Nicastro, Hoeppli, Furrer, Ardu, Krejci, Karege and Malafosse2013; Rusiecki et al. Reference Rusiecki, Byrne, Galdzicki, Srikantan, Chen, Poulin, Yan and Baccarelli2013), we do report corrected results (Table 2) to assess the degree to which our findings might be attenuated by multiple hypothesis test correction. Accepting a stringent FDR of 0.05 requires that we reject several findings reported as significant in our study, notably pre-trauma DNAm differences between cases and controls at DNMT3B CpG 9. However, it also means that a significant association between DNAm and PTSD emerges as a result of correction, as a significant change in DNAm at DNMT3A following trauma is only seen in controls at this stringent FDR cut-off and would therefore be suggestive of a resiliency-associated change in DNAm (Table 2). While we have chosen to utilize a stringent FDR cut-off of 0.05, other DNAm analyses have accepted a cut-off as high as 0.20 (Provencal et al. Reference Provencal, Suderman, Caramaschi, Wang, Hallett, Vitaro, Tremblay and Szyf2013). Overall, we stress the preliminary nature of these findings – both uncorrected and corrected for multiple hypothesis testing – and the importance of replication in an independent cohort.
Individuals exposed to trauma differ in their risk for subsequent PTSD. Our data suggest that variation in pre-trauma DNAm and post-trauma DNAm change may be part of the molecular underpinnings of PTSD risk and resiliency. Future research is needed to determine if the DNAm variation observed here is associated with functional changes that affect the long-term biology of individuals exposed to trauma. The identification of risk markers, including epigenetic markers, is an important step to understanding the biological underpinnings of PTSD risk and may lead to the development of tools to identify those individuals most at risk of developing PTSD as well as to develop evidence-based interventions.
Acknowledgements
We thank Dr Chet Sherwood and Amy Bauernfeind from The George Washington University for contributions to this work. We thank the many Detroit residents who chose to participate in the DNHS.
This research was funded by National Institutes of Health grants (3R01DA022720-05, 3R01DA022720 and 1RC1MH088283-01) and a National Science Foundation grant (BCS-0827546). L.S. was funded by a Graduate Research Assistantship from the Wayne State University Office of the Vice President for Research, and a Grant-in-Aid of Research from Sigma Xi. K.C.K. is funded by R01MH093612, RC4MH092707, P51RR000165, U01OH010416 and U01OH010407.
Declaration of Interest
S.G. is funded in part by a grant from Merck Pharmaceuticals for work unrelated to this project. D.E.W. is funded in part from Lung, LLC for work unrelated to this project and also reports receiving honoraria from Elsevier, Inc. A.E.A. consults for SCA Tork for work unrelated to this study.
