calmodulin-dependent protein kinase (CaMK) pathway is associated with antidepressant response in females

Journal of Affective Disorders 136 (2012) 558–566

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Research report

Genetic variation in the calcium/calmodulin-dependent protein kinase (CaMK) pathway is associated with antidepressant response in females Yanyan Shi a, Yonggui Yuan b,⁎, Zhi Xu b, Mengjia Pu b, Congjie Wang c, Yumei Zhang d, Zhening Liu e, Chuanyue Wang f, Lingjiang Li e, Zhijun Zhang b,⁎ a

Department of Neurology, Nanjing First Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China Department of Neuropsychiatry, Affiliated Zhongda Hospital and Neuropsychiatric Research Institute of Southeast University, Nanjing 210009, China c Department of Psychiatry, Huai'an No.3 People's Hospital, Huai'an 223001, China d Department of Psychiatry, Yangzhou Wutaishan Hospital, Yangzhou 225003, China e Mental Health Institute, The Second Xiangya Hospital Affiliated to Central South University, Changsha 410008, China f Department of Psychiatry, Beijing An Ding Hospital Affiliated to Capital Medical University. Beijing 100088, China b

a r t i c l e

i n f o

Article history: Received 7 June 2011 Received in revised form 29 September 2011 Accepted 21 October 2011 Available online 25 November 2011 Keywords: Major depressive disorder Signal transduction pathway Antidepressant Childhood adversity Life events

a b s t r a c t Objective: Antidepressant effects on monoamine neurotransmission may be influenced by genetic variation in intracellular signal transduction pathways, such as the cyclic adenosine monophosphate (cAMP) — protein kinase A (PKA) pathway, Ras-mitogen activated protein kinase (MAPK) pathway and calcium/calmodulin-dependent protein kinase (CaMK) pathway. The aims of this study were to examine the association of polymorphisms in candidate genes of these three signal transduction pathways with response to antidepressant treatment, and to determine the effects of, and interactions with, environment factors. Methods: We recruited 412 patients who met diagnosis criteria for major depressive disorder (MDD) (DSM-IV Axis I). 284 patients completed 8 weeks treatment with selective serotonin reuptake inhibitors (SSRIs) or serotonin and noradrenergic reuptake inhibitors (SNRIs). Severity of depression was measured with the Hamilton Depression Rating Scale (HDRS) before and after 8 weeks antidepressant treatment. 209 patients completed the Childhood Trauma Questionnaire, 28 item Short Form (CTQ-SF) which was used to evaluate childhood adverse events. 218 patients completed the Life Events Scale (LES) which were used to evaluate life stress before onset. 155 SNPs in 66 candidate genes were genotyping by Illumina GoldenGate, including 28 SNPs in 15 genes of cAMP-PKA pathway, 37 SNPs in 17 genes of Ras-MAPK pathway and 90 SNPs in 34 genes of CaMK pathway. The remission criterion was HDRS score equal to or less than 7. Single SNP and haplotype associations were analyzed by UNPHASED 3.3.13. Gene-environment interactions were analyzed by binary logistic regression with SPSS 11.0 software. Results: The rs2230372 SNP in ITPR2, rs2280272 in PRKCZ, rs17109671, and rs17109674 in PLCE1 were significant associated with remission, as were haplotypes in PRKCZ and PLCE1. All these positive associations were found in genes of the CaMK pathway, but not the cAMP-PKA or Ras-MAPK pathways. There were no significant differences in CTQ scores and LES scores between remitters and non-remitters. No significantly interactions between candidate genes and environment effects were observed. Conclusion: The CaMK pathway may be important in determining antidepressant response. But recent adverse life events, childhood adversity, and interactions between candidate genes and environment factors appear not to influence short term antidepressant outcome. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved.

⁎ Corresponding authors. Tel.: + 86 25 8327 2023; fax: + 86 25 8327 2023. E-mail addresses: [email protected] (Y. Yuan), [email protected] (Z. Zhang). 0165-0327/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2011.10.030

Y. Shi et al. / Journal of Affective Disorders 136 (2012) 558–566

1. Introduction Major depression disorder (MDD) is predicted to be the second leading cause of death and disability by the year 2020 (Murray and Lopez, 1996). Recent research showed 60–70% of MDD patients fail to reach complete remission even when adequately treated (Sackeim, 2001). Factors associated with the efficacy of antidepressants include genetic factors (Laje and McMahon, 2007), the experience of negative life events (Amital et al., 2008) and adverse childhood experiences (Kaplan and Klinetob, 2000). The mechanisms underlying the actions of antidepressant treatment are still under investigation, but the requirement for long-term, chronic antidepressant treatment has lead to the hypothesis that alterations in neuronal plasticity are necessary for a therapeutic response (Duman et al., 1997; Nestler et al., 2002). The transcription factor cAMP-response element binding protein (CREB) is considered to be important for cell survival, neuroplasticity, neurons and neuronal net works adapting their short- and long-term responses to environment stimuli. Diverse stimuli may induce different CREB-mediated responses through the activation of different signaling mechanisms such as the cyclic adenosine monophosphate (cAMP) — protein kinase A (PKA) pathway, the Ras-mitogen-activated protein kinase (MAPK) pathway and the calcium/calmodulin-dependent protein kinases (CaMK) pathway, which all ultimately activate CREB by phosphorylation at Ser 133 (Montminy, 1997). CREB mRNA expression has been found to be decreased in MDD patients (Dowlatshahi et al., 1998), an effect that is reversed with antidepressant treatment by increasing activation of the cAMP-PKA signal transduction pathway, an effect of adenylate cyclase elevation via effects on β-adrenergic and serotonin (5-HT) receptors (Duman et al., 1997). CREB can also mediate antidepressant-induced neural plasticity by

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regulating brain-derived neurotrophic factor (BDNF), maintaining synaptic function and cell survival though activated the Ras-MAPK pathway (Mercier et al., 2004). The RasMAPK cascade can be activated by 5-HT and norepinephrine (NE), via 5-HT and/or α-adrenergic receptors, which may be stimulated by increases in 5-HT or NE activity resulting from antidepressant treatment (Errico et al., 2001). Finally, CREB has also been identified as a calcium-inducible factor. The calcium/CaM-dependent kinases (CaMKs) can directly phosphorylate CREB, upon activation by calcium. Animal studies demonstrate that chronic antidepressant administration can increase the phosphorylation of CREB by activation of CaMK IV (Tiraboschi et al., 2004). Phospholipase C (PLC) is an another important enzyme that activates the phospholipid signaling cascade to catalyzes the hydrolysis of phosphatidylinosito(4,5) bisphosphate (PIP2) to generate inositol triphosphate (IP3) and diacylglycerol (DAG), which direct calcium mobilization and protein kinase C (PKC) activity respectively, in turn inducing active CREB phosphorylation (Nestler et al., 1989). In the present study, we focus on the genetics of these pharmacodynamic aspects of antidepressant action by investigating polymorphic variability in genes from the cAMP-PKA, Ras-MAPK and CaMK pathways. We also assessed stressful life events and adverse childhood experiences in an attempt to better characterize the phenotypic impact of these genes, and to investigate the effect of interactions between gene variants and stress factors on antidepressant efficacy. 2. Methods 2.1. Subjects The rationale, methods, and design of this study have been detailed elsewhere (Xu et al., 2011a). In brief, investigators at

Fig. 1. Flow chart of genotyping and analysis of our sample.

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Table 1 List of genes screened. Hypothesis and Gene name gene symbol cAMP-PKA signaling transduction pathway ADCY2 Adenylate cyclase 2 (brain) ADCY3 Adenylate cyclase 3 ADCY7 Adenylate cyclase 7 ADCY8 Adenylate cyclase 8 (brain) ADCY9 Adenylate cyclase 9 ADCY10 Adenylate cyclase 10 (soluble) AKT1 v-akt murine thymoma viral oncogene homolog 1 AKT2 v-akt murine thymoma viral oncogene homolog 2 BCL2 B-cell CLL/lymphoma 2 BDNF Brain-derived neurotrophic factor CREB1 cAMP responsive element binding protein 1 FRK Fyn-related kinase GSK3B Glycogen synthase kinase 3 beta SERTAD1 SERTA domain containing 1 TSPAN1 Tetraspanin 1 Ras-mitogen-activated protein kinase (MAPK) pathway MAPK1 Mitogen-activated protein kinase 1 MAPK4 Mitogen-activated protein kinase 4 MAPK6 Mitogen-activated protein kinase 6 MAPK15 Mitogen-activated protein kinase 15 MAP2K1 Mitogen-activated protein kinase kinase 1 MAP2K3 Mitogen-activated protein kinase kinase 3 MAP2K6 Mitogen-activated protein kinase kinase 6 MFAP4 Microfibrillar-associated protein 4 RPS6KA2 Ribosomal protein s6 kinase, 90 kDa, polypeptide 2 RPS6KA3 Ribosomal protein s6 kinase, 90 kDa, polypeptide 3 RPS6KA1 Ribosomal protein s6 kinase, 90 kDa, polypeptide 1 RPS6KA6 Ribosomal protein S6 kinase, 90 kDa, polypeptide 6 RPS6KC1 Ribosomal protein S6 kinase, 52 kDa, polypeptide 1 RPS6KL1 Ribosomal protein S6 kinase-like 1 RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog Calcium/calmodulin (CaM)-dependent protein kinases (CaM kinases) pathway CAMK1D Calcium/calmodulin-dependent protein kinase ID CAMK1G Calcium/calmodulin-dependent protein kinase IG CAMK2A Calcium/calmodulin-dependent protein kinase II alpha CAMK2B Calcium/calmodulin-dependent protein kinase II beta CAMK2D Calcium/calmodulin-dependent protein kinase II delta CAMK2G Calcium/calmodulin-dependent protein kinase II gamma CAMK4 Calcium/calmodulin-dependent protein kinase IV DAG1 Dystroglycan 1 (dystrophin-associated glycoprotein 1) ITPR2 Inositol 1,4,5-triphosphate receptor, type 2 ITPR3 Inositol 1,4,5-triphosphate receptor, type 3 PIK3C2A Phosphoinositide-3-kinase, class 2, alpha polypeptide PIK3C2B Phosphoinositide-3-kinase, class 2, beta polypeptide PIK3R5 Phosphoinositide-3-kinase, regulatory subunit 5 PRKCA Protein kinase C, alpha PRKCB Protein kinase C, beta PRKCD Protein kinase C, delta PRKCE Protein kinase C,epsilon PRKCG Protein kinase C, gamma PRKCH Protein kinase C, eta PRKCQ Protein kinase C, theta PRKCZ Protein kinase C, zeta PLCB1 Phospholipase C, beta 1 (phosphoinositide-specific) PLCB2 Phospholipase C, beta 2 PLCB3 Phospholipase C, beta 3 PLCB4 Phospholipase C, beta 4 PLCD1 Phospholipase C, delta 1 PLCD3 Phospholipase C, delta 3 PLCD4 Phospholipase C, delta 4 PLCE1 Phospholipase C, epsilon 1 PLCG2 Phospholipase C, gamma 2 (phosphatidylinositol-specific)

Table 1 (continued) Hypothesis and Gene name gene symbol PLCH1 PLCH2 PNCK STARD3

Phospholipase C, eta 1 Phospholipase C, eta 2 Pregnancy up-regulated non-ubiquitously expressed CaM kinase StAR-related lipid transfer (START) domain containing 3

5 regional centers across the China implemented a standard study protocol. A total of 281 MDD patients entered the study and were treated with single antidepressant drugs for 8 weeks according to local clinical practice (SSRI: n = 161, SNRI: n = 120) (Fig. 1). All subjects were new or recently relapsed patients, drug-free for over two weeks and had a baseline score of 18 or over on the 17-item Hamilton Depression Rating Scale (HAMD-17) (Hamilton, 1960; Tang, 1984), having presented depressive symptoms for at least 2 weeks before entry. The remission criterion was HAMD-17 scores equal to or less than 7 by the end of 8 weeks of treatment (Horstmann et al., 2010; Rush et al., 2006). Two evaluation tools, the Childhood Trauma Questionnaire (28 item Short Form, CTQ-SF) (Bemstein and Fink, 1998) and the Life Events Scale (LES) (Zhang and Yang, 1999), were used to evaluate the occurrence of stressful life events that took place before the age of 16 years or during the previous year, respectively.

2.2. Gene selection and genotyping methods 66 Candidate genes were selected based on their involvement in the signaling pathways in the mechanism of action of antidepressant drugs, including cAMP-PKA, Ras-MAPK and CaMK pathways. 155 SNPs were identified based on search of the dbSNP and HapMap project, selecting only exonic SNPs, all with higher population diversities in Chinese Han people. Details are provided in supplementary Table 1. The genomic DNA of 281 patients was genotyped by Berkeley Biotech Inc using Illumina GoldenGate assays (Illumina Inc., San Diego, CA). All the SNPs selected for the custom oligo pooled assays had Illumina design scores >0.6. All our samples had Illumina 10% GenCall scores above 0.4 and call rates above 90%. Genotype data on SNPs were generated by BEADSTUDIO 3.0.

Table 2 Demographic features and baseline HDRS between remitter and non-remitter.

Age (years) Sex (Male/Female) Education (years) Duration of illness (years) Episodes Family history (n.%) Treatment (SSRIs/SNRIs) Baseline HDRS

Remitter (N = 134)

Non-remitter (N = 147)

t/χ2

P

37.83 ± 14.14 64/70 11.78 ± 3.96 50.72 ± 72.51

38.46 ± 12.03 49/98 11.33 ± 3.49 46.48 ± 75.48

− 0.403 6.084 1.012 0.479

0.687 0.015 0.313 0.632

2.50 ± 2.51 26(19.4%) 85/49

2.14 ± 2.49 23(15.6%) 77/70

1.195 0.687 3.507

0.233 0.434 0.061

26.97 ± 5.70

27.93 ± 5.34

− 1.452

0.148

Y. Shi et al. / Journal of Affective Disorders 136 (2012) 558–566

between remitters and non-remitters (t = 6.084, P = 0.015) (Table 2). However, there was no significant difference in age, time in education, duration of illness, number of episodes, family history, types of antidepressant and baseline HAMD-17 scores between remitters and non-remitters (All P > 0.05).

Table 3 General characteristic of SNPs in ITPR2, PRKCZ and PLCE1 genes.

ITPR2

PRKCZ PLCE1

SNP

HWpval

%Gene

MAF

Alleles

rs2230376 rs2230375 rs2230377 rs2230372 rs2230380 rs2291264 rs1900941 rs12184 rs2280272 rs17109671 rs17109674 rs17417407 rs2274224 rs3765524 rs2274223

0.7605 1 0.8316 0.6956 1 0.2754 0.2482 0.5947 0.1483 0.7748 0.3945 0.7122 0.5989 0.6642 0.8324

98.2 100 99.6 100 99.6 100 100 99.3 100 100 99.6 100 99.3 92.9 99.6

0.123 0.103 0.071 0.304 0.123 0.388 0.194 0.036 0.093 0.484 0.475 0.151 0.421 0.213 0.209

C:T A:G C:T C:T C:T G:A A:G C:T G:C G:A G:A C:A G:C G:A A:G

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3.2. Allelic association results

Note: HWpval: the Hardy–Weinberg equilibrium P value; %gene: the percentage of nonmissing for this marker; MAF: the minor allele frequency for this marker; Alleles: the major and minor alleles for this marker.

2.3. Statistical analysis Differences in clinical variables between responder and non-responder groups were evaluated by Student's t-test or Pearson's χ 2 Tests using SPSS 13.0 (SSPS Inc., Chicago, IL, USA). Associations of alleles, genotypes and haplotypes with treatment response were analyzed using UNPHASED 3.0.13 (Dudbridge, 2007). All the correct P values were performed by one thousand permutations for multiple testing in allelic, genotypic and haplotype association analysis, for each block analyzed. Interaction of gene and environment was analyzed by logistic regression using SPSS 13.0 (SSPS Inc., Chicago, IL, USA) statistical software package. Details are provided in Xu et al. (2011a). All the P values from genetic analyses were corrected by one thousand permutations for multiple testing in allelic, genotypic and haplotype association analysis. Bonferroni correction was applied to the statistical significance of P b 0.05. Corrected P values are listed in the text. 3. Results 3.1. Descriptive statistics data All samples were grouped into 134 remitters and 147 nonremitters. The sex distributions were significant differences

Each SNP was tested for association with treatment remission in the 281 samples. In 66 genes, only 3 genes met or exceeded the nominal significance level after controlling for gender, including ITPR2, PRKCZ and PLCE1, which were all from CaMK pathway. Table 3 summarizes the general characteristics of SNPs in the 3 genes. The one SNP of ITPR2, rs2230372, the frequencies of CC genotype were significantly higher in remitters than non-remitters after controlled for gender (χ 2 = 9.320, P = 0.009). In PRKCZ, CG genotype and C allele of rs2280272 were found more frequently in remitters than non-remitters after controlled for gender (χ2 = 9.583, P = 0.001; χ2 = 8.612, P = 0.003). 6 SNPs from PLCE1 were tested. Frequencies of rs17109671 GG genotype and G allele (χ2 = 10.750, P = 0.004; χ2 = 10.300, P = 0.001), rs17109674 AA genotype and A allele (χ2 = 9.78, P = 0.007; χ2 = 8.346, P = 0.003) were significantly less in remitters than nonremitters after controlled for gender (Table 4). None of the other 151 SNPs demonstrated associations with antidepressant remission. After gender stratification analysis, we found the results of ITPR2 and PLCE1 genes in women patients were consistent with the total samples (Table 5). But the remaining 152 SNPs showed no significant differences between remitters and non-remitters in women patients. No SNP demonstrated significant associations within the male patient subgroup. 3.3. Haplotype analysis We performed moving-window haplotype analysis on all SNPs examined. In the 66 genes, only PRKCZ and PLCE1 had haplotypes which showed significant differences between remitters and non-remitters. The LD was weak between rs12184 and rs2280272 in PRKCZ (r 2 = 0.008). In PLCE1, the LD was strongest between rs17109671 and rs17109674, and between rs3765524 and rs2274223 (r 2 = 0.827 and 0.932, respectively) (Fig. 2). The results showed that the C–C haplotype of PRKCZ was associated with antidepressant remission (P = 0.0004, OR =

Table 4 Distributions of genotypes and alleles for remitters and non-remitters. Gene

SNP

Genotypes and alleles

Remitters

Non-remitters

P (genotype)⁎

P (allele)⁎

ITPR2

rs2230372

0.002

0.003

PLCE1

rs17109671

71/70/6 72.1 17/130 5.7 47/75/25 57.5 31/75/40 46.9

0.144

rs2280272

63/53/18 66.8 35/99 13.1 26/69/39 45.2 42/73/19 58.6

0.009

PRKCZ

CC/CT/TT C allele (%) CG/GG C allele (%) GG/GA/AA G allele (%) GG/GA/AA G allele (%)

0.004

0.001

0.007

0.003

rs17109674

The P values have withstood the correction of permutation test for 1000 times, by using the UNPHASED program, controlling for gender. ⁎ P (genotype) and P (allele) denote the P-values for genotype and allele, respectively.

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Table 5 Genotype and allele comparisons for genetic variations of the gene between remitters and non-remitters after separated by gender. Gene

SNP

Gender

Genotypes and alleles

Remitters

Non-remitters

P (genotype)⁎

P (allele)⁎

ITPR2

rs2230372

Female

CC/CT/TT C allele (%) CC/CT/TT C allele (%) GG/GA/AA G allele (%) GG/GA/AA G allele (%) GG/GA/AA G allele (%) GG/GA/AA G allele (%)

30/28/12 62.9 33/25/6 71.1 5/40/25 35.7 21/29/14 55.5 27/38/5 65.7 15/35/14 50.8

49/45/4 72.9 22/25/2 70.4 32/50/16 58.2 15/25/9 56.1 17/53/27 44.8 14/22/13 51.1

0.017

0.048

0.324

0.911

0.9 × 10− 4

0.5 × 10− 4

0.818

0.922

Male PLCE1

rs17109671

Female Male

rs17109674

Female Male

3.4 × 10 0.586

−4

1.4 × 10− 4 0.972

The P values have withstood the correction of permutation test for 1000 times, by using the UNPHASED program. ⁎ P (genotype) and P (allele) denote the P-values for genotype and allele, respectively.

2.349, 95%CI: 1.281–4.307, after corrected by permutation test for 1000 times). Frequencies of haplotypes A–C and G–C of PLCE1 gene were significantly higher in remitters than nonremitters (P = 0.0077, OR = 0.403, 95%CI: 0.124–1.926; P = 0.0077, OR = 2.404, 95%CI: 1.201–4.814, after corrected by permutation test for 1000 times) (Table 6). No other haplotypes found associations with antidepressant remission. 3.4. Gene-environment interactions In the present study, there were 218 patients that completed the LES and 209 patients completed the CTQ-SF. The total and factor scores of LES and CTQ-SF showed no significant differences between remitters and non-remitters (All P > 0.05) (Table 7). A main effect on remission by logistic regression analysis was apparent for gender (P = 0.026). No significantly effects were found for age, education, duration of illness, episodes, family history, and baseline HDRS scores.

Fig. 2. Location SNPs in PLCE1 and linkage disequilibrium of them. The figure shows the relative location of the 6 SNPs in PLCE1 using Haploview program. D′ values shows in the LD structure.

In the interaction model of SNPs with negative life events, main effects of rs2280272 in PRKCZ, rs17109671 and rs17109674 in PLCE1 were found before (P=0.003, 0.007 and 0.027, respectively) and after adjusting for gender (P=0.004, 0.005 and 0.023, respectively), but no significant interactions between the SNPs and negative life events were found (P>0.05). Similar results were found in the interaction model of SNPs and CTQ total scores (Table 8). Also, no interactions were found between the other 152 SNPs and negative life events or CTQ total scores.

4. Discussion The present study was conducted to explore genetic variations in signal transduction pathways and their interactions with stressful life events or childhood adversity on the outcome of antidepressant treatment. The results suggested that ITPR2 (rs2230372 CC genotype), PRKCZ (rs2280272 CG genotype and C allele) and PLCE1 (rs17109671 GG genotype and G allele, rs17109674 AA genotype and A allele) were associated with antidepressant remission. The SNPs of ITPR2 and PLCE1 were found especially associated with depression in women patients. 3 common and significant haplotypes (C–C of PRKCZ, A–C and G–C of PLCE1) in two of these genes also showed a relationship with antidepressant remission, all with a frequency above 5% in remitter and non-remitter groups. All these SNPs came from genes contributing to the CaMK pathway, but not the cAMPPKA or Ras-MAPK pathways. However, we failed to find any association between negative life events, childhood adversity or their interaction with genes and antidepressant remission in the subjects. This is first report on antidepressant pharmacogenetics of major depressive disorder that focuses on the main potential signaling pathways likely to be involved in antidepressant action. ITPR2 gene encoded Inositol 1,4,5-trisphosphate receptor (InsP3) type 2 (IP3R2), a receptor for both IP3 and a calcium channel (Yamamoto-Hino et al., 1994). It could increase IP3′ sensitivity, which is induced by PLC and activated the Ca 2 + signals to elevate intracellular Ca2 + release (Park et al., 2008). In addition, IP3R2 has been identified as a new target for cAMP, providing a connection between the two signaling pathways (Tovey et al., 2008). Our results may provide genetic

Y. Shi et al. / Journal of Affective Disorders 136 (2012) 558–566

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Table 6 Haplotype analysis in PRKCZ and PLCE1 genes. Haplotype

Control

Ca-Freq

Co-Freq

Odds-R

95%Lo

95%Hi

PKCz rs12184–rs2280272 35 C–Ca 227 C–Gb

Case

Chisq

P-value

17 259

0.130 0.847

0.058 0.893

2.349 1

1.281 1

4.307 1

12.43 4.233

0.0004 0.0390

PLCE1 rs17109671–rs17417407 14.58 A–Ab A–Ca 132.4 G–A 19.42 G–C 101.6

6.403 118.6 44.6 124.4

0.054 0.494 0.072 0.379

0.021 0.403 0.151 0.423

1 0.4905 0.1914 0.3587

1 0.124 0.038 0.099

1 1.926 0.943 1.297

1.383 7.087 6.083 2.376

0.2396 0.0077 0.0136 0.1232

PLCE1 rs17109674–rs17417407 A–Ab 18.7 A–C 92.3 G–A 15.3 G–Ca 141.7

39.97 115 11.03 126

0.069 0.344 0.057 0.528

0.136 0.394 0.037 0.431

1 1.715 2.964 2.404

1 0.775 0.700 1.201

1 3.796 12.55 4.814

5.339 2.417 0.283 7.099

0.0208 0.1200 0.5946 0.0077

P value has withstood the correction of permutation test for 1000 times. a The significant haplotype. b The reference haplotype.

evidence for IP3R as a potential target for novel antidepressant drugs. Protein kinase C zeta (PRKCZ) is a member of the PKC family, which has been implicated in a variety of cellular processes regulated by phosphatidylinositol (PI)-3,4,5-trisphosphate (PIP3) (Hirai and Chida, 2003). A previous study has reported depressed patients have decreased PKC protein compared to healthy controls (Akin et al., 2005). This may influence regulation of expression of BDNF, the BDNF receptor trk-b and the glucocorticoid receptors (GR) (Barrett and Vedeckis, 1996; Deogracias et al., 2004). PLCE1 gene encodes an isoform of PLC (PLCε), which is not only the enzyme that activate the phospholipid signaling cascade, but is also an indirect mediator of MAPK signal transduction through Ras, which may participate in the mechanism of depression and may thus be a target

Table 7 LES and CTQ-SF scores between remitters and non-remitters. LES Positive life events Negative life events Total life events CTQ-SF Emotional Abuse Scale Physical Abuse Scale Sexual Abuse Scale Emotional Neglect Physical Neglect Total scores

Remitters (N = 101)

Non-remitter (N = 117)

t

P

9.65 ± 19.95

12.82 ± 30.05

− 0.901

0.368

44.21 ± 51.56

47.97 ± 57.75

− 0.504

0.614

54.55 ± 63.08

62.34 ± 74.17

− 0.826

0.410

Remitters (N = 98)

Non-remitter (N = 111)

t

P

7.64 ± 3.67

7.22 ± 2.84

0.944

0.346

6.20 ± 2.15

6.02 ± 2.15

0.623

0.534

6.00 ± 2.56

5.90 ± 2.27

0.296

0.768

12.32 ± 4.29

12.03 ± 4.38

0.481

0.631

9.43 ± 3.26 41.59 ± 11.40

9.58 ± 3.06 40.74 ± 10.62

− 0.338 0.560

0.736 0.576

Note: CTQ-SF: the Childhood Trauma Questionnaire (28 item Short Form); LES: the Life Events Scale.

for antidepressant action (Qi et al., 2008). On the other hand, PLC could activate the phospholipid signaling cascades to regulate expression of the serotonin 1A receptor (5-HT1A) (Berg and Clarke, 2001), a particularly important antidepressant target (Blier and de Montigny, 1999). Our results suggest that PRKCZ and PLCE1 polymorphisms are both associated with remission of symptoms after 8 weeks antidepressant treatment. Several studies have shown that placing individual SNPs into the context of a haplotype increases biological information (Balciuniene et al., 2002; Van Eerdewegh et al., 2002). The major contribution from the haplotype analysis to antidepressant response was mainly due to rs2280272 of PRKCZ (G/C), rs17109671 (G/A) and rs1710974 (G/A) of PLCE1. It is unclear whether these SNPs have a combined functional effect or whether they are in strong LD with other functional polymorphisms. The influence of stress factors, such as early or recently life adverse events, has been reported to interact with genetic vulnerability in the pathogenesis of depression (Caspi and Moffitt, 2006). Few published studies have examined the association between early life adversity and response to antidepressants in MDD. Nemeroff et al. (2003) found that patients with chronic depression and a history of early adversity would respond better to psychotherapy or psychotherapy combined with pharmacotherapy than to pharmacotherapy alone. Klein et al. (2009) reported that childhood adversity was associated with chronic depression and less responsive to antidepressants. Indeed, stressful life experience is another important environment factor for MDD. Several studies focus on the influence of stressful life events on antidepressant pharmacotherapy, but results have been inconsistent. A retrospective study (Amital et al., 2008) suggested the treatmentresistant depression patients had more negative life events than responders. But Mandelli et al. (2009) failed to find a relationship between stressful life stress and response after 6 weeks antidepressant treatment. The present results showed no association between early life stress or stressful life events and response to 4 weeks antidepressant treatment or remission to 8 weeks antidepressant treatment. Our results suggest that

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Table 8 Interactions between SNPs and negative life events or total CTQ scores in remitters and non-remitters. β

S.E

OR 95%CI

P

Corrected P

Negative life events (NLE) rs2280272 (G) NLE (E) rs2280272 × NLE (G × E) Constant

− 1.276 0.964 − 0.402 2.723

0.447 1.571 0.832 0.927

0.279(0.116–0.670) 2.622(0.121–56.949) 0.669(0.131–3.418) 16.014

0.003 0.618 0.693 0.008

0.004 0.539 0.629

rs17109671(G) NLE (E) rs17109671 × NLE (G × E) Constant

0.717 0.461 − 0.351 0.000

0.255 0.528 0.429 0.519

2.048(1.243–3.373) 1.586(0.564–4.462) 0.704(0.304–1.632) 0.438

0.007 0.415 0.380 0.005

0.005 0.382 0.413

rs17109674(G) NLE (E) rs17109674 × NLE (G × E) Constant

0.558 0.007 0.084 0.034

0.246 0.569 0.446 0.529

1.747(1.079–2.828) 1.007(0.331–3.071) 1.087(0.454–2.606) 1.035

0.027 0.964 0.887 0.018

0.023 0.990 0.851

CTQ-SF total scores rs2280272 (G) total scores (E) rs2280272 × total scores (G × E) Constant

− 1.500 0.197 − 0.068 2.550

0.480 1.482 0.798 0.899

0.223(0.087–0.571) 1.218(0.067–22.238) 0.934(0.195–4.463) 12.801

0.002 0.894 0.932 0.005

0.002 0.851 0.879 0.002

rs17109671(G) total scores (E) rs17109671 × total scores (G × E) Constant

0.658 0.444 − 0.345 − 0.809

0.269 0.551 0.445 0.316

1.931(1.139–3.273) 1.559(0.529–4.589) 0.709(0.296–1.695) 0.445

0.014 0.421 0.439 0.010

0.009 0.514 0.511 0.963

rs17109674(G) total CTQ scores (E) rs17109674 × total scores (G × E) Constant

0.603 0.365 − 0.247 − 0.808

0.263 0.599 0.459 0.330

1.828(1.092–3.062) 1.441(0.446–4.658) 0.781(0.318–1.920) 0.446

0.022 0.542 0.591 0.014

0.017 0.616 0.646 0.928

Corrected P for gender.

early life stress or stressful life events may not be important factors for response or remission to antidepressant in Chinese MDD patients. However, it is unclear whether gene-environment interactions can influence antidepressant action. El Khoury et al. (2006) showed that antidepressant administration can ameliorate the abnormal behavior caused by early gene-environment interaction in the Flinders Sensitive Line (FSL) rats. Musazzi et al. (2009) found this interaction causes life-long synaptic changes through MAP kinases Erk1/2. Nevertheless, there are few clinical studies of gene-environment interaction on antidepressant response. Most research has focused on the interaction between serotonin transporter gene promoter polymorphism (HTTLPR) and stressful life events in MDD, with few addressing antidepressant pharmacotherapy. Previous investigators have demonstrated affective disorder patients with HTTLPR S-allele and adverse life-events preceding the onset showed a poor outcome after 6 weeks antidepressant treatment (Mandelli et al., 2009) and in geriatric major depression (Murphy et al., 2004). In our previous paper, we demonstrated the genes in serotonin system (HTR1B, TPH2) (Xu et al., 2011a) and catecholamine neurotransmitter systems (SLC6A2) (Xu et al., 2011b) could interact with childhood trauma or recent events to influence antidepressant response. However, a large sample study (Bukh et al., 2010) reported no interaction between stressful life events and several polymorphisms, as well as in the study of Keers et al. (2011). In the current study, we found no significant interaction between genes and negative life events or child adversity, but only main effects of significant SNPs in PRKCZ or PLCE1 on antidepressant

remission. In other words, environmental factors did not significant modify the effects of these SNPs on antidepressant remission. However, this result should be further explored in a large sample. MDD is 2 to 3 times more common in women than men, with differential effects of antidepressant treatment (Weissman et al., 1993). Several studies find women show a more robust response to antidepressant than men, with either SSRIs or SNRIs (Bano et al., 2004; Kornstein et al., 2006). These results are consistent with our finding. On other hand, we found the association of ITPR2 and PLCE1 SNPs with remission was apparent only in women patients. These genetic factors may indicate the potential for genetic predictors of response to antidepressant drug treatment. There were several limitations to this study, as follows. First, we select only the exonic SNPs in the candidate genes, and inevitably these could not fully capture the overall variations within the genes. Secondly, in the absence of a placebo or control group, we cannot definitively determine whether the observed associations were directly attributable to antidepressant treatment. Moreover, the definition of remission may affect the findings. Finally, these findings may not be generalizable to other populations, and large ethnically-matched studies are necessary in order to assess this. 5. Conclusion In conclusion, according to our results, we provide support that the CaMK pathway may be associated with response to antidepressant treatment, involving polymorphic variability

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in ITPR2, PRKCZ and PLCE1 genes. However these genetic factors do not interact with recent adverse life events or childhood adversity, which also showed no influence, in this study, on short term antidepressant outcome. Role of funding source Funding for this study was provided by National Basic Research Program of China (973 Program) (No. 2007CB512308 and 2009CB918303), National Hi-Tech Research and Development Program of China (863 Program) (No. 2008AA02Z413), and National Natural Science Foundation of China (No. 30770779, 30825014 and 30830046). All the programs had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. Conflict of interest No conflict declared. Acknowledgment This study was partly supported by the National Basic Research Program of China (973 Program) (Nos. 2007CB512308 and 2009CB918303), National HiTech Research and Development Program of China (863 Program) (No. 2008AA02Z413), and National Natural Science Foundation of China (Nos. 30770779, 30825014, 30830046, 30970814 and 81071101).

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