## Abstract

The molecular basis of vulnerability to stress during the adolescent period is largely unknown. To identify potential molecular mediators that may play a role in stress-induced behavioral deficits, we imposed social isolation on a genetically vulnerable mouse model. We report that 3-week (5–8 weeks of age) adolescent stress in combination with disrupted-in-schizophrenia 1 (Disc1) genetic risk elicits alterations in DNA methylation of a specific set of genes, tyrosine hydroxylase, brain-derived neurotrophic factor and FK506 binding protein 5. The epigenetic changes in the mesocortical dopaminergic neurons were prevented when animals were treated with a glucocorticoid receptor (GR) antagonist RU486 during social isolation, which implicates the role for glucocorticoid signaling in this pathological event. We define the critical period of GR intervention as the first 1-week period during the stress regimen, suggesting that this particular week in adolescence may be a specific period of maturation and function of mesocortical dopaminergic neurons and their sensitivity to glucocorticoids. Our study may also imply the clinical significance of early detection and prophylactic intervention against conditions associated with adolescent social stress in individuals with genetic risk.

## Introduction

Adolescence is a sensitive developmental period when environmental stressors can affect dynamic brain maturation, which in turn determines adult behaviors (1). For example, a strong correlation of exposure to adversity during adolescence to adult and midlife psychopathology has been reported at clinical levels (1–3). In animal models, developmental alternations induced by stress exposure during adolescence are associated with physical and behavioral changes that are possibly relevant to adult-onset neuropsychiatric disorders (3,4).

We recently reported that psychosocial stress imposed during adolescence in the presence of a genetic risk led to excess glucocorticoid-mediated neurochemical changes and behavioral deficits in adulthood (5). These phenotypical changes were mediated, in part, by an increase in DNA methylation of the tyrosine hydroxylase (Th) gene promoter that occurred selectively in the mesocortical dopaminergic neurons (5). We also demonstrated that the epigenetic modification was elicited by excess glucocorticoids, as treatment of the animals with the glucocorticoid receptor (GR) antagonist RU486 for 3 weeks (5–8 weeks of age) during the stress regimen ameliorated the pathological changes. Due to the similarities between behavioral deficits and pharmacological response, such animal models may be of great use for studying the underlying biology of stress-associated dopaminergic changes and psychotic depression.

Nonetheless, there are at least two crucial but unanswered questions to the model reported in our previous publication (5). First, it has not been determined whether the Th gene is the exclusive target of epigenetic modifications by glucocorticoids. Secondly, we have not yet identified the precise period during which the GR antagonist can ameliorate the pathological changes. The present study is designed to address these questions to enhance our understanding of stress-associated epigenetic modification of dopaminergic neurons and to explore the translational potential of the model in psychotic depression and other severe mental illnesses. In the present manuscript, we designate the model as disease model (DM).

## Results

### Epigenetic regulation of a specific set of genes in the DM

In order to address the first question, i.e. to determine whether additional genes are epigenetically regulated in a similar fashion as the Th gene, we used bisulfite pyrosequencing to interrogate several hypothalamic–pituitary–adrenal (HPA)-axis/glucocorticoid-related genes in the mesocortical dopaminergic neurons. Using this approach, we first attempted to replicate our colony bisulfite sequencing results on the Th promoter in our previous study (5). We observed relatively high-methylation content in the groups-housed, unstressed control (CTL) samples with significant increase in DNA methylation in the DM, the stressed, genetically susceptible animals. The increase in methylation in the DM ranged from 9.7 to 19.1% (P < 0.01, Fig. 1A). As the promoter of Th harbors a glucocorticoid response element (GRE), we investigated GREs of several additional genes that have been studied in the context of glucocorticoid targeting. We found similar increase in DNA methylation in brain-derived neurotrophic factor (Bdnf) (8.5–23.1%, P < 0.01, Fig. 1B) and a decrease in DNA methylation in FK506 binding protein 5 (Fkbp5) (5.2–16.3%, P < 0.01, Fig. 1C). For Fkbp5, the decrease in methylation was consistent with that previously observed in the hippocampus with excessive glucocorticoid exposure and associated with increased anxiety-like behavior on the elevated plus maze (6).

Figure 1.

Influence of adolescent isolation on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in VTA neurons that project to the frontal cortex between CTL and DM. (A) DNA methylation in the promoter of Th. (B) DNA methylation in the intronic GRE of Bdnf. (C) DNA methylation in the intronic GRE of Fkbp5. CTL, wild-type mice without isolation. DM, DISC1 mutant exposed to 3-week isolation. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using two-way ANOVA with repeated measures (group, F(1,4) = 52.34, P< 0.01 for A; F(1,4) = 444.3, P< 0.01 for B; F(1,4) = 53.24, P< 0.01 for C), followed by Bonferroni post hoc tests (**P< 0.01).

Figure 1.

Influence of adolescent isolation on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in VTA neurons that project to the frontal cortex between CTL and DM. (A) DNA methylation in the promoter of Th. (B) DNA methylation in the intronic GRE of Bdnf. (C) DNA methylation in the intronic GRE of Fkbp5. CTL, wild-type mice without isolation. DM, DISC1 mutant exposed to 3-week isolation. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using two-way ANOVA with repeated measures (group, F(1,4) = 52.34, P< 0.01 for A; F(1,4) = 444.3, P< 0.01 for B; F(1,4) = 53.24, P< 0.01 for C), followed by Bonferroni post hoc tests (**P< 0.01).

Consistent with the epigenetic changes in Figure 1, we observed expression changes in the target genes (Fig. 2A–C). These results suggest that the epigenetic changes in these GREs influence gene expression in the mesocortical dopaminergic neurons. In addition, we measured the expression levels of the GR in mesocortical neurons, as it is the central mediator for the stress response, and found a significant increase in expression in the DM, compared with the CTL group (Fig. 2D).

Figure 2.

Influence of adolescent isolation on mRNA expression of Th, Bdnf, Fkbp5 and GR genes in VTA neurons that project to the frontal cortex between CTL and DM. (A) Expression levels of Th mRNA. (B) Expression levels of Bdnf mRNA. (C) Expression levels of Fkbp5 mRNA. (D) Expression levels of Gr mRNA. Expression levels of β-Actin were used as an internal control. N = 4–8. Each sample was pooled from five males. Values are means ± SEM. Statistical differences between two groups were analyzed with the t-test (**P< 0.01 and *P< 0.05).

Figure 2.

Influence of adolescent isolation on mRNA expression of Th, Bdnf, Fkbp5 and GR genes in VTA neurons that project to the frontal cortex between CTL and DM. (A) Expression levels of Th mRNA. (B) Expression levels of Bdnf mRNA. (C) Expression levels of Fkbp5 mRNA. (D) Expression levels of Gr mRNA. Expression levels of β-Actin were used as an internal control. N = 4–8. Each sample was pooled from five males. Values are means ± SEM. Statistical differences between two groups were analyzed with the t-test (**P< 0.01 and *P< 0.05).

To determine whether additional HPA-axis genes were epigenetically altered, we examined putative GREs and/or promoter regions of Fkbp4, corticotropin-releasing hormone (Crh) and Crh receptor-1 (Crhr1). We did not observe any differences in DNA methylation in Fkbp4 and Crh between CTL and DM (Fig. 3A and B). We found increase in DNA methylation in Crhr1, but with only subtle changes at several CpGs (Fig. 3C). We also tested promoter regions of the Leptin receptor (Lepr) and glycogen synthase kinase3β (Gsk3β). The promoter regulatory regions of the Lepr and Gsk3β genes contain putative GREs, as determined by TRANSFAC analysis. Biological roles of these molecules in dopaminergic neurons (not necessarily mesocortical dopaminergic neurons) have been reported (7–9), but it was uncertain whether these functions were regulated by the HPA axis. Thus, we took these two genes as interesting references (possibly negative controls) in contrast to other genes that are reportedly associated with the HPA axis, such as Fkbp4, Crh and Crhr1. However, we failed to observe any epigenetic differences in Lepr and Gsk3β between CTL and DM (Fig. 3D and E). These results suggest that the epigenetic changes in Th, Bdnf and Fkbp5 genes in the mesocortical neurons may be specific to the activation of the HPA axis.

Figure 3.

No influence of adolescent isolation on genes involved in the HPA-axis and other signaling pathways in VTA neurons that project to the frontal cortex between CTL and DM. (A) DNA methylation in the intronic GRE/ERE of Fkbp4. (B) DNA methylation in the promoter of Crh. (C) DNA methylation in the promoter of Crhr1. (D) DNA methylation in the upstream region of Lepr. (E) DNA methylation in the promoter of Gsk3β. N = 3. Each sample was pooled from five males. Values are means ± SEM. No statistical differences were detected using two-way ANOVA with repeated measures (group, F(1,4) = 0.04, P = 0.86 for A; F(1,4) = 1.21, P = 0.33 for B; F(1,4) = 44.73, P < 0.01 for C; F(1,4) = 0.69, P = 0.45 for D; F(1,4) = 0.00, P = 0.96 for E).

Figure 3.

No influence of adolescent isolation on genes involved in the HPA-axis and other signaling pathways in VTA neurons that project to the frontal cortex between CTL and DM. (A) DNA methylation in the intronic GRE/ERE of Fkbp4. (B) DNA methylation in the promoter of Crh. (C) DNA methylation in the promoter of Crhr1. (D) DNA methylation in the upstream region of Lepr. (E) DNA methylation in the promoter of Gsk3β. N = 3. Each sample was pooled from five males. Values are means ± SEM. No statistical differences were detected using two-way ANOVA with repeated measures (group, F(1,4) = 0.04, P = 0.86 for A; F(1,4) = 1.21, P = 0.33 for B; F(1,4) = 44.73, P < 0.01 for C; F(1,4) = 0.69, P = 0.45 for D; F(1,4) = 0.00, P = 0.96 for E).

### Crucial role for glucocorticoids in epigenetic changes in the Th, Bdnf and Fkbp5 genes in the DM

We then asked whether administration of the GR antagonist RU486 might normalize the stress-induced DNA methylation changes in the Th, Bdnf and Fkbp5 genes. Treatment with RU486 for 3 weeks during social isolation, from 5 to 8 weeks of age, prevented 7 out of the 9 CpGs from stress-induced increase in DNA methylation in the Th gene (Fig. 4A). Similar preclusion of epigenetic changes was observed in Bdnf (Fig. 4B). Finally, we also found that RU486 was effective in preventing the loss of DNA methylation in Fkbp5 at three out of four of the CpGs interrogated (Fig. 4C).

Figure 4.

Influence of glucocorticoids from 5 to 8 weeks of age on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in the DM. (A) Effects of RU486 on increased DNA methylation in the promoter of Th. (B) Effects of RU486 on increased DNA methylation in the intronic GRE of Bdnf. (C) Effects of RU486 on decreased DNA methylation in the intronic GRE of Fkbp5. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using three-way ANOVA with repeated measures (group × drug, F(1,8) = 1798.03, P< 0.01 for A; F(1,8) = 730.52, P< 0.01 for B; F(1,8) = 149.29, P< 0.01 for C) and two-way ANOVA (group × drug, F(1,8) = 0.68, P = 0.43 for CpG1 in A; F(1,8) = 685.10, P< 0.01 for CpG2 in A; F(1,8) = 183.80, P < 0.01 for CpG3 in A; F(1,8) = 125.80, P< 0.01 for CpG4 in A; F(1,8) = 15.55, P< 0.01 for CpG5 in A; F(1,8) = 104.00, P< 0.01 for CpG6 in A; F(1,8) = 1307.00, P< 0.01 for CpG7 in A; F(1,8) = 75.72, P< 0.01 for CpG8 in A; F(1,8) = 154.60, P< 0.01 for CpG9 in A; F(1,8) = 89.96, P< 0.01 for CpG1 in B; F(1,8) = 54.55, P< 0.01 for CpG2 in B; F(1,8) = 43.26, P< 0.01 for CpG3 in B; F(1,8) = 5.73, P< 0.05 for CpG4 in B; F(1,8) = 7.60, P< 0.05 for CpG5 in B; F(1,8) = 89.89, P< 0.01 for CpG6 in B; F(1,8) = 85.22, P< 0.01 for CpG7 in B; F(1,8) = 21.23, P< 0.01 for CpG8 in B; F(1,8) = 12.30, P< 0.01 for CpG1 in C; F(1,8) = 306.60, P< 0.01 for CpG2 in C; F(1,8) = 2.77, P = 0.13 for CpG3 in C; F(1,8) = 44.16, P< 0.01 for CpG4 in C), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Figure 4.

Influence of glucocorticoids from 5 to 8 weeks of age on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in the DM. (A) Effects of RU486 on increased DNA methylation in the promoter of Th. (B) Effects of RU486 on increased DNA methylation in the intronic GRE of Bdnf. (C) Effects of RU486 on decreased DNA methylation in the intronic GRE of Fkbp5. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using three-way ANOVA with repeated measures (group × drug, F(1,8) = 1798.03, P< 0.01 for A; F(1,8) = 730.52, P< 0.01 for B; F(1,8) = 149.29, P< 0.01 for C) and two-way ANOVA (group × drug, F(1,8) = 0.68, P = 0.43 for CpG1 in A; F(1,8) = 685.10, P< 0.01 for CpG2 in A; F(1,8) = 183.80, P < 0.01 for CpG3 in A; F(1,8) = 125.80, P< 0.01 for CpG4 in A; F(1,8) = 15.55, P< 0.01 for CpG5 in A; F(1,8) = 104.00, P< 0.01 for CpG6 in A; F(1,8) = 1307.00, P< 0.01 for CpG7 in A; F(1,8) = 75.72, P< 0.01 for CpG8 in A; F(1,8) = 154.60, P< 0.01 for CpG9 in A; F(1,8) = 89.96, P< 0.01 for CpG1 in B; F(1,8) = 54.55, P< 0.01 for CpG2 in B; F(1,8) = 43.26, P< 0.01 for CpG3 in B; F(1,8) = 5.73, P< 0.05 for CpG4 in B; F(1,8) = 7.60, P< 0.05 for CpG5 in B; F(1,8) = 89.89, P< 0.01 for CpG6 in B; F(1,8) = 85.22, P< 0.01 for CpG7 in B; F(1,8) = 21.23, P< 0.01 for CpG8 in B; F(1,8) = 12.30, P< 0.01 for CpG1 in C; F(1,8) = 306.60, P< 0.01 for CpG2 in C; F(1,8) = 2.77, P = 0.13 for CpG3 in C; F(1,8) = 44.16, P< 0.01 for CpG4 in C), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

### Critical period during adolescence for influence of glucocorticoids

We then proceeded to address the second question, i.e. to identify the precise period when RU486 can ameliorate the pathological changes in the DM. We administered RU486 at different periods during adolescence and evaluated neurochemical and behavioral changes at 8 weeks of age.

The DM animals were tested for behavioral deficits on the prepulse inhibition and forced swim tests, as utilized in a previous study (5). Increasing evidence suggests that the behaviors in the prepulse inhibition and forced swim tests are related to, at least in part, the function of the prefrontal cortex [in particular, the medial prefrontal cortex (mPFC)] that is regulated by the mesocortical dopaminergic projection. Swerdlow et al. (10) indicated a pivotal role of mPFC in prepulse inhibition. Warden et al. (11) demonstrated that the pyramidal neuron activity is crucial for the phenotype of the forced swim test by using an optogenetic approach. Furthermore, perturbation of disrupted-in-schizophrenia 1 (DISC1) in the developing cortex reportedly led to selective abnormalities in postnatal mesocortical dopaminergic maturation and deficits in prepulse inhibition (12).

RU486 administration only for the first 2-week period of the stress regimen, from 5 to 7 weeks, exhibited prophylactic effects on the prepulse inhibition and forced swim tests and normalized the decreased levels of extracellular dopamine in the frontal cortex of the DM (Fig. 5). Similar positive results were obtained by administration of the antagonist only during the first 1-week period during the stress regimen, from 5 to 6 weeks (Fig. 6), suggesting that early adolescence is the time that is particularly sensitive to glucocorticoids in this pathological model.

Figure 5.

Influence of glucocorticoids from 5 to 7 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 5 to 7 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. Veh, treated with vehicle; RU, treated with RU486. N = 11–13 for (B and C), N = 6 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,43) = 3.29, P= 0.08 for B), and two-way ANOVA (group × drug, F(1,43) = 0.42, P = 0.52 for prepulse 74 in B; F(1,43) = 2.19, P= 0.15 for prepulse 78 in B; F(1,43) = 4.08, P< 0.05 for prepulse 86 in B; F(1,43) = 8.37, P< 0.01 for C; F(1,20) = 5.88, P< 0.05 for D), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Figure 5.

Influence of glucocorticoids from 5 to 7 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 5 to 7 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. Veh, treated with vehicle; RU, treated with RU486. N = 11–13 for (B and C), N = 6 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,43) = 3.29, P= 0.08 for B), and two-way ANOVA (group × drug, F(1,43) = 0.42, P = 0.52 for prepulse 74 in B; F(1,43) = 2.19, P= 0.15 for prepulse 78 in B; F(1,43) = 4.08, P< 0.05 for prepulse 86 in B; F(1,43) = 8.37, P< 0.01 for C; F(1,20) = 5.88, P< 0.05 for D), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Figure 6.

Influence of glucocorticoids from 5 to 6 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 5 to 6 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. N = 11 for (B and C), N = 6–10 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,40) = 2.80, P= 0.10 for B), and two-way ANOVA (group × drug, F(1,40) = 0.48, P = 0.49 for prepulse 74 in B; F(1,40) = 0.91, P= 0.35 for prepulse 78 in B; F(1,40) = 4.25, P< 0.05 for prepulse 86 in B; F(1,40) = 6.70, P< 0.05 for C; F(1,24) = 6.14, P< 0.05 for D), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Figure 6.

Influence of glucocorticoids from 5 to 6 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 5 to 6 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. N = 11 for (B and C), N = 6–10 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,40) = 2.80, P= 0.10 for B), and two-way ANOVA (group × drug, F(1,40) = 0.48, P = 0.49 for prepulse 74 in B; F(1,40) = 0.91, P= 0.35 for prepulse 78 in B; F(1,40) = 4.25, P< 0.05 for prepulse 86 in B; F(1,40) = 6.70, P< 0.05 for C; F(1,24) = 6.14, P< 0.05 for D), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Although the previous experiments showed improvements in altered adult behaviors by inhibition of glucocorticoid signaling during adolescence (5), it is not clear whether the improvements can be observed after neurochemical and behavioral deficits have already become established. To address this question, we treated the stressed animals with RU486 from 8 to 10 weeks, and examined behavioral and neurochemical changes immediately following treatment. In this paradigm, we failed to observe any beneficial reversal of deficits in the prepulse inhibition and forced swim tests and in the decreased levels of extracellular dopamine in the frontal cortex (Fig. 7).

Figure 7.

No influence of glucocorticoids from 8 to 10 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 8 to 10 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. N = 12–14 for (B and C), N = 6 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,48) = 0.56, P= 0.46 for B), and two-way ANOVA (group × drug, F(1,48) = 0.00, P = 0.99 for prepulse 74 in B; F(1,48) = 0.82, P= 0.37 for prepulse 78 in B; F(1,48) = 0.44, P= 0.51 for prepulse 86 in B; F(1,48) = 2.06, P= 0.16 for C; F(1,20) = 0.81, P= 0.38 for D).

Figure 7.

No influence of glucocorticoids from 8 to 10 weeks on behavioral and neurochemical abnormalities in the DM. (A) CTL and DM were treated with RU486 from 8 to 10 weeks of age. (B) Effects of RU486 on performance of prepulse inhibition. (C) Effects of RU486 on performance of the forced swim test. (D) Effects of RU486 on basal levels of extracellular dopamine in the frontal cortex. N = 12–14 for (B and C), N = 6 for (D). Values are means ± SEM. Statistical differences were determined using three-way ANOVA with repeated measures (group × drug, F(1,48) = 0.56, P= 0.46 for B), and two-way ANOVA (group × drug, F(1,48) = 0.00, P = 0.99 for prepulse 74 in B; F(1,48) = 0.82, P= 0.37 for prepulse 78 in B; F(1,48) = 0.44, P= 0.51 for prepulse 86 in B; F(1,48) = 2.06, P= 0.16 for C; F(1,20) = 0.81, P= 0.38 for D).

We then tested whether the observed epigenetic alterations might occur during the 1-week susceptible period. We observed that treatment with RU486 during the critical period (the first-week period of the stress regimen) normalized epigenetic changes in the Th, Bdnf, and Fkbp5 genes in the DM animals (Fig. 8).

Figure 8.

Influence of glucocorticoids from 5 to 6 weeks of age on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in the DM. (A) Effects of RU486 on increased DNA methylation in the promoter of Th. (B) Effects of RU486 on increased DNA methylation in the intronic GRE of Bdnf. (C) Effects of RU486 on decreased DNA methylation in the intronic GRE of Fkbp5. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using three-way ANOVA with repeated measures (group × drug, F(1,8) = 358.35, P< 0.01 for A; F(1,8) = 106.99, P< 0.01 for B; F(1,8) = 374.77, P< 0.01 for C), and two-way ANOVA (group × drug, F(1,8) = 0.10, P = 0.76 for CpG1 in A; F(1,8) = 6.61, P< 0.05 for CpG2 in A; F(1,8) = 157.80, P< 0.01 for CpG3 in A; F(1,8) = 17.29, P< 0.01 for CpG4 in A; F(1,8) = 21.96, P< 0.01 for CpG5 in A; F(1,8) = 61.17, P< 0.01 for CpG6 in A; F(1,8) = 67.61, P< 0.01 for CpG7 in A; F(1,8) = 53.16, P< 0.01 for CpG8 in A; F(1,8) = 8.15, P< 0.05 for CpG9 in A; F(1,8) = 21.69, P< 0.01 for CpG1 in B; F(1,8) = 15.53, P< 0.01 for CpG2 in B; F(1,8) = 91.51, P< 0.01 for CpG3 in B; F(1,8) = 6.69, P< 0.05 for CpG4 in B; F(1,8) = 19.85, P< 0.01 for CpG5 in B; F(1,8) = 106.90, P< 0.01 for CpG6 in B; F(1,8) = 56.31, P< 0.01 for CpG7 in B; F(1,8) = 22.46, P< 0.01 for CpG8 in B; F(1,8) = 82.82, P< 0.01 for CpG1 in C; F(1,8) = 83.44, P< 0.01 for CpG2 in C; F(1,8) = 4.40, P = 0.07 for CpG3 in C; F(1,8) = 77.72, P< 0.01 for CpG4 in C), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

Figure 8.

Influence of glucocorticoids from 5 to 6 weeks of age on epigenetic modifications of the Th, Bdnf and Fkbp5 genes in the DM. (A) Effects of RU486 on increased DNA methylation in the promoter of Th. (B) Effects of RU486 on increased DNA methylation in the intronic GRE of Bdnf. (C) Effects of RU486 on decreased DNA methylation in the intronic GRE of Fkbp5. N = 3. Each sample was pooled from five males. Values are means ± SEM. Statistical differences were detected using three-way ANOVA with repeated measures (group × drug, F(1,8) = 358.35, P< 0.01 for A; F(1,8) = 106.99, P< 0.01 for B; F(1,8) = 374.77, P< 0.01 for C), and two-way ANOVA (group × drug, F(1,8) = 0.10, P = 0.76 for CpG1 in A; F(1,8) = 6.61, P< 0.05 for CpG2 in A; F(1,8) = 157.80, P< 0.01 for CpG3 in A; F(1,8) = 17.29, P< 0.01 for CpG4 in A; F(1,8) = 21.96, P< 0.01 for CpG5 in A; F(1,8) = 61.17, P< 0.01 for CpG6 in A; F(1,8) = 67.61, P< 0.01 for CpG7 in A; F(1,8) = 53.16, P< 0.01 for CpG8 in A; F(1,8) = 8.15, P< 0.05 for CpG9 in A; F(1,8) = 21.69, P< 0.01 for CpG1 in B; F(1,8) = 15.53, P< 0.01 for CpG2 in B; F(1,8) = 91.51, P< 0.01 for CpG3 in B; F(1,8) = 6.69, P< 0.05 for CpG4 in B; F(1,8) = 19.85, P< 0.01 for CpG5 in B; F(1,8) = 106.90, P< 0.01 for CpG6 in B; F(1,8) = 56.31, P< 0.01 for CpG7 in B; F(1,8) = 22.46, P< 0.01 for CpG8 in B; F(1,8) = 82.82, P< 0.01 for CpG1 in C; F(1,8) = 83.44, P< 0.01 for CpG2 in C; F(1,8) = 4.40, P = 0.07 for CpG3 in C; F(1,8) = 77.72, P< 0.01 for CpG4 in C), followed by Bonferroni post hoc tests (**P< 0.01 and *P< 0.05).

## Discussion

This exploratory study was aimed to address two unanswered questions in a model that represents, at least in part, the phenotypes associated with psychotic depression (designated DM as described above) (5), with an idea that the efforts would provide important insights in biological and translational psychiatry.

First, we wished to know whether the Th gene was the exclusive target of epigenetic modifications under the influence of glucocorticoids in the DM. Our candidate gene approach identified a specific set of HPA-axis related genes that became epigenetically modified under the direct influence of glucocorticoids (e.g. Th, Bdnf and Fkbp5). FKBP5 regulates HPA-axis function by altering the negative feedback of intracellular glucocorticoid signaling (13). Both Bdnf and Th genes are genomic targets of GR: the former is a neurotrophic factor underlying neuronal survival and plasticity, whereas the latter is crucial for neurotransmission. We speculate that prolonged exposure to stress and glucocorticoids affect more than one molecular/cellular pathway in mesocortical neurons, through maladaptive alterations in such genes that collectively contribute to the behavioral changes. This exploratory candidate gene approach provides the rationale to address this question in the next step through more comprehensive ‘epigenome-wide’ screens.

Secondly, we aimed to determine the precise period when the GR antagonist can ameliorate the pathological changes. We observed pronounced prophylactic effects of RU486 in epigenetic, neurochemical and behavioral changes in the DM, when it was administered during the first week or the first 2 weeks of the stress regimen. In contrast, post-stress administration of RU486 did not have any therapeutic effects on the pathological changes. These results provide at least one basic and two translational implications. Beneficial influence achieved by the blockade of aberrant GR signaling from 5 to 6 weeks of age during the first week of isolation stress implies that this particular week may reflect a specific period of maturation and function of mesocortical dopaminergic neurons and their sensitivity to glucocorticoids. In addition to the implication to basic neurobiology, we state that early detection of adverse experiences (e.g. social isolation in combination with a genetic risk in this case) and early intervention, particularly at the critical period, may be important in translational applications. Early identification of genetic risks that affect stress vulnerability is also an issue to be considered. Although early detection and intervention might constitute an ideal scenario, therapeutic strategies that involve late detection and amelioration of debilitating consequences of stress are also highly desirable in social and clinical settings. In that regard, our results do not support a significant role for the GR antagonist in post-stress reversal of behavioral deficits. Therefore, the second implication of our data to translational application is the development of new compounds that can reverse DNA methylation marks even after their establishment.

## Materials and Methods

### Animal model

DISC1 dominant-negative transgenic mice under control of the prion protein promoter (DISC1-DN-Tg-PrP) were generated at the Transgenic Core Laboratory of the Johns Hopkins University (5). Heterozygous transgenic line 51 mice and wild-type littermates established by mating with C57BL6 mice maintaining the purity of the genetic background were compared in present experiments.

DISC1-DN-Tg-PrP is a model to perturb several pathways involving DISC1 and its protein network that play pivotal roles in the pathobiology of mental illnesses (14). A question may arise on the construct validity for the relationship between the expression of the putative dominant-negative DISC1 in our animal model and the chromosomal abnormality present in the original Scottish pedigree (15–17). Although we used the prion protein promoter for our model, the use of the endogenous DISC1 gene promoter would be valuable in future studies. For instance, there is a model in which a dominant-negative truncated mutant DISC1 is expressed under the endogenous promoter in a BAC construct (18). In the present study, we addressed novel and important biological questions by using a model whose face validity has already been established (5).

For the CTL group, wild-type littermate mice were group-housed in wire-topped clear plastic cages (21 × 32 × 13 cm) until sampling after behavioral tests at 8 weeks of age, under a controlled environment (23 ± 1°C; 50 ± 5% humidity; light and dark cycles started at 8 am and 8 pm, respectively) with free access to food and water. For the DM group, DISC1-DN-Tg-PrP mice were isolated from 5 to 8 weeks of age in individual wire-topped clear plastic cages (21 × 32 × 13 cm) at 5 weeks of age, and maintained until sampling after behavioral tests. All animal procedures were in accordance with guidelines for the care and use of laboratory animals issued by the National Institutes of Health and Johns Hopkins University.

We reported similar changes in the phenotypes between males and females that were observed in a previously published work (5). Furthermore, we did not observe any significant differences among the unstressed wild-type, isolated wild-type and unstressed DISC1 mutant mice in behavioral tests, neurochemical analysis and assessment of corticosterone levels in our previous study (5). Therefore, we used only male mice, and defined the CTL and CTL + Veh groups as ‘Control’ in the present study.

Many publications have validated the effects of transgenes by demonstrating consistent phenotypic changes from more than one line of the same transgenic models (19–21). We previously demonstrated that two independent transgenic lines 5 and 51 of the model displayed consistent changes in the basal levels of extracellular dopamine in the frontal cortex (5), and in the prepulse inhibition and forced swim tests (data not shown). We have followed established methods to validate transgenic lines (19–21), and used line 51 as the representative model. In the present study, we performed epigenetic, neurochemical and behavioral experiments that we already established in our previous work (5).

Two independent methods, namely the vectorette method and whole-genome sequencing, have used to identify the insertion site of the transgene. First, the vectorette method (22) was employed, where the DNA extracted from the tail of transgenic animals was digested with an enzyme and ligated with vectorette adapters that are complementary to each enzyme: EcoRI, AflII, XbaI, BamHI and AscI. Vectorette-ligated fragments were polymerase chain reaction (PCR) amplified using a vectorette primer and a primer from either the 5′-end or the 3′-end of the PrP-DISC1 vector, with the goal of amplifying across the transgene, a portion of the insertion region, and the vectorette sequence. PCR reactions were resolved on an agarose gel and bands were excised for DNA extraction, topoisomerase-mediated PCR cloning and subsequent Sanger sequencing. Results show that the 3′ sequence of the PrP vector, which is composed of the endogenous Prn (prion) sequence, is highly repetitive and did not provide useful sequences that can be uniquely mapped. Similarly, repetitive elements were identified from the 5′end of the transgene, which strongly suggested the potential insertion of the transgene into a repetitive element or a highly repetitive region. Secondly, libraries were constructed for whole-genome sequencing of the entire transgenic mouse genome in order to obtain an independent verification of the vectorette results and to identify additional regions of insertion. To demonstrate the validity of the deep-sequencing technique and the difficulty of identifying the prion transgene, DNA derived from another independent DN-DISC1-Tg-CaMKII line (23) was simultaneously sequenced. Sequencing of both transgenic animals yielded >200 M reads, of which >97% aligned to the genome. As expected, the prion transgene could not be mapped to a unique genomic locus due to repetitive sequences, which were present in the insertion site and the Prn transgene. On the other hand, the CaMKII-DISC1 transgene mapped to Chr2, at 98 666 765 and Chr12 at 30 977 100 in the University of California Santa Cruz genome browser mm10 build. Until a better approach can be adopted, we assert that the two independent transgenic lines with consistent phenotypes diminish the possibility that an endogenous gene has been disrupted by the transgene.

### Drug treatment

A GR antagonist, RU486 [mifepristone: 17-hydroxy-11-(4-dimethylamino-phenyl)-17-(prop-1-ynyl)-estra-4,9-dien-3-one; Sigma-Aldrich, St Louis, MO, USA] (24,25), was dissolved in saline with 2% ethanol. To determine a critical period during adolescence for glucocorticoid-induced epigenetic modulation of mesocortical dopaminergic neurons in DISC1-DN-Tg-PrP mice with isolation stress (DM), mice were administered RU486 (20 mg/kg, s.c., once per day) from 5 to 7, 5 to 6 or 8 to 10 weeks of age.

### Retrograde tracing and fluorescence-activated cell sorting

Retrograde tracing was performed as described previously (5,26) with minor modifications. In retrograde tracer analysis, male DISC1-DN-Tg-PrP and wild-type littermates at 5 weeks of age were used. The location of the injection site in the frontal cortex (15° angle from anteroposterior (AP): +1.7 mm, mediolateral (ML): ±1.0 mm from Bregma, dorsoventral (DV): −2.0 mm from the dura) was standardized among animals by using stereotaxic coordinates (27). Mice were bilaterally injected with 100 nl of the green retrograde beads (Lumafluor, Durham, NC, USA) into the frontal cortex of mice. For sufficient labeling, a survival period for retrograde tracer transport was determined as 3 weeks after the injection of the beads. According to the mouse brain atlas (27), the ventral tegmental area (VTA) regions (AP: −2.92 to −3.88 mm; ML: ±0.75 to ±0.25 mm from Bregma; DV: +4.1 to +4.6 mm from the dura) labeled with green retrograde beads were dissected from coronal sections with tweezers by using fluorescent protein flashlight (Night Sea, Bedford, MA, USA) (28), which includes blue high intensity light-emitting diodes to enable screening of green-labeled cells. Samples from five mice in each group were pooled for FACS. After obtaining single-cell suspensions from dissected cells, only cells with these green latex beads were isolated by FACSAria (Becton Dickinson, Franklin Lakes, NJ, USA) at the flow cytometry core facility of the Johns Hopkins University by a standard protocol (29).

### DNA extraction and bisulfite conversion

Genomic DNA (gDNA) from FAC-sorted (green beads) cells of the VTA was extracted with the Masterpure DNA Purification Kit, according to manufacturer's instructions (Epicentre Biotechnologies, Madison, WI, USA). Concentration of the gDNA was determined using a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Rockford, IL, USA), and 250 ng of the DNA was used for bisulfite conversion according to manufacturer's protocol (EZ DNA Methylation Gold Kit; Zymo Research, Irvine, CA, USA).

### Bisulfite polymerase chain reaction and pyrosequencing

We measured DNA methylation by pyrosequencing of genomic targets that were considered bioinformatically and experimentally annotated regulatory regions of genes that are important for mediating HPA-axis function (Fkbp4, Fkbp5, Crh and Crhr1), are targets of glucocorticoid signaling (Bdnf and Th), and serve as negative controls (Lepr and Gsk3β). List of PCR and pyrosequencing primers are provided in Table 1. Briefly, PCR amplification is performed using two outside primers and 3.5 µl (∼80 ng) of bisulfite-treated DNA. An additional nested PCR was performed with 2 µl of the outside PCR reaction and one biotinylated primer (other primer being unmodified). Amplification for both PCR steps consisted of 40 cycles (94°C for 1 min, 53°C for 30 s and 72°C for 1 min). PCR products were confirmed on agarose gels. Pyro Gold reagents were used to prepare samples for pyrosequencing according to manufacturer's instructions (Qiagen, Germantown, MD, USA). The percentage of methylation at each CpG as determined by pyrosequencing was compared between DNA from group-housed wild-type versus isolated DISC1 mutant mice.

Table 1.

List of bisulfite PCR and pyrosequencing primers

Bisulfite PCR primers Pyrosequencing primers
Bdnf intronic GRE
5′-TAAGATTATTTTAATGAAYGAAAGTATT-3′ 5′-TGGATTAGATGATTGTTTATAGGTTGT-3′
5′-CCAATTCTACRTTCAACCCTACAAATCC-3′ 5′-TTATATAAAGGGAGYGTAGGGTAG-3′
5′-ATTTTGYGTAGGTGGAAATGATGAATTTA-3′ 5′-AGYGAATGGGGTATAGATAGTT-3′
5′-ACAACACCATCTAAAAAAAATAAATAAC-3′
Crh promoter
5′-GTATTGTTGTTTTTGTAGAGGGAT-3′ 5′-AGAAAAGGGAAAGTAGG-3′
5′-AACAAATCATAAAAAACCCTTCCA-3′ 5′-TAGTTTTTGTTATTGTTGTATG-3′
5′-ATATTAGAGTTTGGAGTGAGATT-3′
5′-TCTATCCCTCRCTCCTTAACAA-3′
Crh receptor, type 1 CpG island
5′-GGTAGGAGAGAGGTAGTTGTGGGT-3′ 5′-GGGTAATTAGAGGYGTTT-3′
5′-AATCCCATCCAAAACCTTAA-3′ 5′-GATTTTGGTATTTAGGA-3′
5′-GGGTTTAGTTGAGTGGGGAGAAGG-3′ 5′-GTGAAGGTAAGAGAGGAT-3′
5′-TTTTCCACCAAAATTATCCCTCTA-3′
Fkbp4 intronic GRE
5′-AAAGTTAGGAGTATTTTTGGATTAA-3′ 5′-ATGTAATGTAGGTTAGGATTTTA-3′
5′-TACATACCRAAACCTATAATTCCTC-3′ 5′-GGATTGTTGGTAGAATTTGTTTTGAA-3′
5′-GGTTTTTTAAGGTTTTTGGTTTGTATAAGT-3′ 5′-GGGTTATTTGTAGTTAGG-3′
5′-AATTCCTCCCCTACCCCTCCTAAATCTTTC-3′
Fkbp5 intronic GRE
5′-GATGATTAGTTTTTTTTAGTAGTGATGT-3′ 5′-AGAAAAGGGAAAGTAGG-3′
5′-CTTATTATTCTCTTACTACCCTAA-3′ 5′-TAGTTTTTGTTATTGTTGTATG-3′
5′-TAGTTTTTGGGGAAGAGTGTAGAGTTAT-3′
5′-ATTTTAAAAAACACAAAACACCCTATT-3′
Gsk3
β
promoter
5′-GGAGATAGTATTTTAATAGAAAGAGATGAT-3′ 5′-GTGTTTATTTTAATTTATTTTAG-3′
5′-CCCTTTACAACAAACRCACAAAACCA-3′ 5′-GGATGGATTAAGGTAATGG-3′
5′-GGGATTATAGTTAGGATTGTTAGTGT-3′ 5′-GTAGTAGTGTGTTGATTATTTAAT-3′
5′-ACAAAACCAAACCAAACCTTCTA-3′
Lepr upstream promoter
5′-GTAAATTTATATGTTTAGATGTG-3′ 5′-GAATAGATATAGTGGGTGGT-3′
5′-ACCTCTATCCACCACTTTACCACCAA-3′ 5′-ATTAGGGAAAGAGAGTAGTTA-3′
5′-GGTTTAGTGTGGGTAGGGAATAGA-3′ 5′-AGGTGTTAGTATAATTGTTT-3′
5′-CTTACCACCCCAACAACCTA-3′ 5′-ATAATTGTTTACGGTTGTGAGT-3′
5′-TAAGTTTYGTTGATTGATTGTTT-3′
Th promoter
5′-TTTTGGTTTGATTAGAGAGTTTTAGATG-3′ 5′-AGTGGATGTAATTAGATTTAATGG-3′
5′-AATTCTATCTCCACAACCCTTACCA-3′ 5′-GTTTTTTTTAGGTATAGTAGG-3′
5′-GAGGGTGATTTAGAGGTAGGTGTTG-3′ 5′-GAGGATGCGTAGGAGGTAGGAGGT-3′
5′-TCTATCTCCACAACCCTTACCAAAC-3′
Bisulfite PCR primers Pyrosequencing primers
Bdnf intronic GRE
5′-TAAGATTATTTTAATGAAYGAAAGTATT-3′ 5′-TGGATTAGATGATTGTTTATAGGTTGT-3′
5′-CCAATTCTACRTTCAACCCTACAAATCC-3′ 5′-TTATATAAAGGGAGYGTAGGGTAG-3′
5′-ATTTTGYGTAGGTGGAAATGATGAATTTA-3′ 5′-AGYGAATGGGGTATAGATAGTT-3′
5′-ACAACACCATCTAAAAAAAATAAATAAC-3′
Crh promoter
5′-GTATTGTTGTTTTTGTAGAGGGAT-3′ 5′-AGAAAAGGGAAAGTAGG-3′
5′-AACAAATCATAAAAAACCCTTCCA-3′ 5′-TAGTTTTTGTTATTGTTGTATG-3′
5′-ATATTAGAGTTTGGAGTGAGATT-3′
5′-TCTATCCCTCRCTCCTTAACAA-3′
Crh receptor, type 1 CpG island
5′-GGTAGGAGAGAGGTAGTTGTGGGT-3′ 5′-GGGTAATTAGAGGYGTTT-3′
5′-AATCCCATCCAAAACCTTAA-3′ 5′-GATTTTGGTATTTAGGA-3′
5′-GGGTTTAGTTGAGTGGGGAGAAGG-3′ 5′-GTGAAGGTAAGAGAGGAT-3′
5′-TTTTCCACCAAAATTATCCCTCTA-3′
Fkbp4 intronic GRE
5′-AAAGTTAGGAGTATTTTTGGATTAA-3′ 5′-ATGTAATGTAGGTTAGGATTTTA-3′
5′-TACATACCRAAACCTATAATTCCTC-3′ 5′-GGATTGTTGGTAGAATTTGTTTTGAA-3′
5′-GGTTTTTTAAGGTTTTTGGTTTGTATAAGT-3′ 5′-GGGTTATTTGTAGTTAGG-3′
5′-AATTCCTCCCCTACCCCTCCTAAATCTTTC-3′
Fkbp5 intronic GRE
5′-GATGATTAGTTTTTTTTAGTAGTGATGT-3′ 5′-AGAAAAGGGAAAGTAGG-3′
5′-CTTATTATTCTCTTACTACCCTAA-3′ 5′-TAGTTTTTGTTATTGTTGTATG-3′
5′-TAGTTTTTGGGGAAGAGTGTAGAGTTAT-3′
5′-ATTTTAAAAAACACAAAACACCCTATT-3′
Gsk3
β
promoter
5′-GGAGATAGTATTTTAATAGAAAGAGATGAT-3′ 5′-GTGTTTATTTTAATTTATTTTAG-3′
5′-CCCTTTACAACAAACRCACAAAACCA-3′ 5′-GGATGGATTAAGGTAATGG-3′
5′-GGGATTATAGTTAGGATTGTTAGTGT-3′ 5′-GTAGTAGTGTGTTGATTATTTAAT-3′
5′-ACAAAACCAAACCAAACCTTCTA-3′
Lepr upstream promoter
5′-GTAAATTTATATGTTTAGATGTG-3′ 5′-GAATAGATATAGTGGGTGGT-3′
5′-ACCTCTATCCACCACTTTACCACCAA-3′ 5′-ATTAGGGAAAGAGAGTAGTTA-3′
5′-GGTTTAGTGTGGGTAGGGAATAGA-3′ 5′-AGGTGTTAGTATAATTGTTT-3′
5′-CTTACCACCCCAACAACCTA-3′ 5′-ATAATTGTTTACGGTTGTGAGT-3′
5′-TAAGTTTYGTTGATTGATTGTTT-3′
Th promoter
5′-TTTTGGTTTGATTAGAGAGTTTTAGATG-3′ 5′-AGTGGATGTAATTAGATTTAATGG-3′
5′-AATTCTATCTCCACAACCCTTACCA-3′ 5′-GTTTTTTTTAGGTATAGTAGG-3′
5′-GAGGGTGATTTAGAGGTAGGTGTTG-3′ 5′-GAGGATGCGTAGGAGGTAGGAGGT-3′
5′-TCTATCTCCACAACCCTTACCAAAC-3′

For each genomic region, outside bisulfite PCR primer pairs are listed in the first and second rows and the nested bisulfite PCR primers are listed in the third and fourth rows. Primers in the fourth row are biotinylated to enable subsequent pyrosequencing reactions.

### Quantitative real-time PCR

Total RNA was isolated from FAC-sorted (green beads) cells of the VTA by using the ARCTURUS PicoPure RNA Isolation Kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and converted into cDNA with the SuperScript™ III First-Strand System for RT-PCR Kit (Thermo Fisher Scientific, Inc.). All reactions were carried out in duplicate using the 1 × Taqman master mix (Thermo Fisher Scientific, Inc.), 1 × Taqman probes for each gene (Th, Bdnf, Fkbp5, Gr and β-actin) and 35 ng of cDNA template in a total volume of 20 µl. The β-actin Taqman probe was used as the internal control. Real-time reactions were performed on an Applied Biosystems 7900HT Fast Real-Time PCR System (Thermo Fisher Scientific, Inc.) with standard PCR conditions (50°C for 2 min; 95°C for 10 min and 95°C for 15 s and 60°C for 1 min for 40 cycles). The expression levels were calculated as described previously (12).

### In vivo microdialysis

Microdialysis was carried out as previously described (5,12), with a minor modification. A guide cannula (AG-6, Eicom, San Diego, CA, USA) was implanted into frontal cortex (15° angle from AP: +1.7 mm, ML: −1.0 mm from Bregma, DV: −2.0 mm from the dura) of male DISC1-DN-Tg-PrP and wild-type littermates according to the mouse atlas (27). Artificial cerebrospinal fluid (147 mm NaCl, 2.8 mm KCl, 1.2 mm CaCl2 and 1.2 mm MgCl2) was perfused at a flow rate of 1.0 µl/min. The dialysates were collected every 10 min and analyzed by an HPLC system (Eicom).

### Behavioral analyses

Animals were subjected to prepulse inhibition test followed by the forced swim test. The prepulse inhibition and forced swim tests were carried out as described previously (5,12) with minor modifications. During the 2-day behavioral analysis for the prepulse inhibition and forced swim tests, daily administration of RU486 (20 mg/kg, s.c.) was made continuously to avoid washout periods. To standardize the conditions of both behavioral studies and in vivo micro dialysis, such administration included one injection 24 h prior to these experiments.

#### Prepulse inhibition test

Male mice were subjected to startle trials consisting of three conditions: (i) a 120 dB noise burst presented alone; (ii) a pre-pulse that was 4, 8 or 16 dB above background noise (i.e. 74, 78 or 86 dB) followed 120 dB noise burst or (iii) no stimulus (background noise alone), which was used to measure baseline movement in the startle chamber (SR-Laboratory Systems, San Diego Instruments). Percent prepulse inhibition of a startle response was calculated as:

$100−[(startleresponseonprepulse+startlepulse)/(startleresponseonstartlepulse)]×100.$

#### Forced swim test

Each male mouse was placed in a transparent glass cylinder (8 cm in diameter × 20 cm high), containing water at 22–23°C to a depth of 15 cm, and forced to swim for 10 min. Immobility time was calculated as follows: 600 s—swimming time (seconds) = immobility time (seconds).

### Statistical analysis

All data are expressed as the mean ± SEM. Statistical analyses were performed using the commercial software (GraphPad Prism 6, GraphPad Software, Inc., and IBM SPSS statistics 23, IBM). Statistical differences between two groups were calculated with Student's t-test (Fig. 2). Statistical differences among three groups/factors or more (Figs 1, 3–8) were determined using a two-way analysis of variance (ANOVA), two-way ANOVA with repeated measures, three-way ANOVA with repeated measures, followed by Bonferroni post hoc tests. Corrections for multiple comparisons were made when appropriate. Statistically significance: **P < 0.01 and *P < 0.05.

## Funding

This work was supported by National Institutes of Health [MH-084018, MH-094268 Silvo O. Conte Center, MH-069853, MH-085226, MH-088753 and MH-092443 (A.S.), DA-040127 (A.S. and M.N.) and K99MH-094408 (M.N.)], Brain & Behavior Research Foundation (A.S. and M.N.), Stanley, RUSK, S-R Foundations and Maryland Stem Cell Research Fund (A.S.), Japan Society for the Promotion of Science and Japan Science and Technology Agency Precursory Research for Embryonic Science and Technology (M.N.).

Conflict of Interest statement. None declared.

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