Abstract
Major depressive disorder (MDD) is one of the most severe psychiatric disorders. The objective of this study was to explore the effects of CREB1 gene polymorphisms on risk of developing MDD and the joint effects of gene-environment interactions. Genotyping was performed by Taqman allelic discrimination assay among 586 patients and 586 healthy controls. A significant impact on rs6740584 genotype distribution was found for childhood trauma (P = 0.015). We did not find an association of CREB1 polymorphisms with MDD susceptibility. However, we found a significantly increased risk associated with the interactions of CREB1 polymorphisms and drinking (OR = 11.67, 95% CI = 2.52-54.18; OR = 11.52, 95% CI = 2.55-51.95 for rs11904814; OR = 4.18, 95% CI = 1.87-9.38; OR = 5.02, 95% CI = 2.27-11.14 for rs6740584; OR = 7.58, 95% CI = 2.05-27.98; OR = 7.59, 95% CI = 2.12-27.14 for rs2553206; OR = 8.37, 95% CI = 3.02-23.23; OR = 7.84, 95% CI = 2.93-20.98 for rs2551941). We also noted that CREB polymorphisms combined with family harmony and childhood trauma conferred increased susceptibility for MDD. In conclusion, polymorphisms in the CREB gene may not be independently associated with MDD risk, but they are likely to confer increased susceptibility by interacting with environmental risk factors in the Chinese population.
Keywords: Polymorphism, CREB1, environmental risk factors, MDD, susceptibility
Introduction
CREB1 (cAMP response element-binding protein) is a transcription factor and plays an important role in neuronal signal transduction [1-3]. CREB1 protein, encoded by the CREB1 gene located on human chromosome 2q34, belongs to the leucine zipper family that serves as DNA-binding proteins [4]. The cAMP signal transduction pathway is induced by the activation of G-protein-coupled receptors and promoted through phosphorylation of the CREB protein [4,5]. Zubenko et al. reported a clear association of the loci in 2q33-35 chromosomal region with mood disorders in women [6] and identified that CREB1 predisposes to major depressive disorder (MDD) in a sex-specific manner [7]. An increasing body of literature confirmed that CREB1 might be involved in suicide [8-10], and antidepressant response in patients suffering from MDD [11-13]. Recently, Zubenko et al. identified a significant contribution of genetic variations in CREB1 to MDD in women [7]. Subsequent studies provided further support for the significant involvement of single nucleotide polymorphisms (SNP) at CREB1 locus in suicidal behaviors, anger expression and MDD, including rs467590, rs7569963 and G(-656)A [14-16].
MDD is a well-known mental disease resulting in cognitive dysfunction [17]. Many groups have demonstrated data on the substantial importance of SNPs in candidate genes in MDD pathogenesis and antidepressant response [18-20]. Results from family, twins and epidemiological studies indicated that about 30% to 40% of MDD incidences result from genetic factors and environmental carcinogens [21,22]. Caspi et al. showed that certain polymorphisms in the SLC6A4 gene regulate the effects of SLEs, including childhood maltreatment associated with MDD [23]. In recent years, a broad range of human genes and their interactions with exogenous variables have been identified to have an impact on the risk of developing MDD, such as FKBP5, CRHR1 and 5-HTTLPR [24-26]. However, few studies have previously focused on the joint effect of CREB1 gene polymorphisms and environmental factors. Herein, we studied the association of CREB1 SNPs with MDD and investigated the effects of common confounding factors in conjunction with these SNPs on the susceptibility to the disease.
Materials and methods
Participants
586 Chinese Han patients with MDD were recruited from Psychiatry Department of First Affiliated Hospital of Harbin Medical University. The patients were diagnosed by 2 experienced psychiatrists according to Diagnostic and Statistical manual of Disorders Fourth Edition (DMS-IV). All patients got more than 21 points in Hamilton’s Depression Scale Test and received no anti-depression treatment within 2 weeks before participation. We excluded the patients in case of the following conditions, brain organic mental disorders, other mental disease, and family history of genetic disease, mental retardation, dementia or physical disease. We also excluded the patients who provided insufficient information and accepted recent transfusion therapy. 586 unrelated controls were selected among the healthy individuals visiting the same hospital for physical examination and matched with cases in age (± 5 years), education and career. Their families had no substance dependent member, genetic diseases or interracial marriage in three generations. The baseline information is detailed in Table 1. The Research Ethics Committee of China approved the study and all participants signed the informed consent form.
Table 1.
Characteristics of the controls and MDD patients
| Variables | MDD/586 | Controls/586 | P |
|---|---|---|---|
| Age (years) | 44.16 ± 0.56 | 42.93 ± 0.39 | 0.000 |
| Gender | 0.001 | ||
| Female | 421 (71.84%) | 370 (63.14%) | |
| Male | 165 (28.16%) | 216 (36.86%) | |
| Marital status | 0.720 | ||
| Single | 85 (14.51%) | 78 (13.31%) | |
| Stable | 464 (79.18%) | 475 (81.06%) | |
| Separated or widow | 37 (6.31%) | 33 (5.63%) | |
| Smoking | 0.019 | ||
| No | 84 (14.33%) | 114 (19.45%) | |
| Yes | 502 (85.67%) | 472 (80.55%) | |
| Drinking | < 0.001 | ||
| No | 24 (4.10%) | 111 (18.94%) | |
| Yes | 562 (95.90%) | 475 (81.06%) | |
| Family harmony | < 0.001 | ||
| No | 189 (32.25%) | 21 (3.58%) | |
| Yes | 397 (67.74%) | 565 (96.42%) | |
| Working condition | 0.330 | ||
| Stable | 11 (1.88%) | 16 (2.73%) | |
| Unstable | 575 (98.12%) | 570 (97.27%) | |
| Childhood trauma | 0.004 | ||
| No | 528 (90.10%) | 554 (94.54%) | |
| Yes | 58 (9.90%) | 32 (5.46%) |
Measure of stressful life events
Life event scale (LES) proposed by Desen Yang and Yalin Zhang was used to evaluate stressful life events, such as serious illness, relationships, housing and social difficulties, relationship breakdowns, unemployment and financial crisis. This scale assessed four aspects of negative life events: when the life events occurred (absent = 1, 1 year earlier = 2, in a year = 3, chronicity = 4), how the life events were characterized (good = 1, bad = 2), to what extent the respondent was affected (absent = 1, mild = 2, moderate = 3, severe = 4, extreme = 5), and how long the influence lasted (less than 3 months = 1, 3-6 months = 2, 6-12 months = 3, more than 12 months = 4). A 75% percentile (a score of 4) in controls was taken as a cutoff value to group the events into the high or low level categories.
Measure of child maltreatment
Self-reported childhood maltreatment including abuse and neglect was recorded according to childhood trauma questionnaire (CTQ). It comprises 28 items graded on a five-point Likert scale, with higher scores corresponding to higher degree of traumatic experience. The scores were critical evidence to measure the degree of maltreatment: none = 0, low = 1, moderate = 2, severe and above = 3.
DNA isolation and genotyping
Genomic DNA was isolated from blood samples using a MagNA Pure DNA Isolator (Roche, Indianapolis, IN). Extracted DNA was used for genotyping. We selected 4 tag SNPs (rs11904814, rs6740584, rs2551941, rs2253206) from the whole-gene region of CREB1 using Haploview’s program. Analyses of SNPs were performed using Taqman allelic discrimination assay on a 7900 system (Applied Biosystem Inc) according to the manufacturer’s instructions. To ensure the allele discrimination accuracy, all samples were measured in triplicate, and the results yielded a 100% concordance rate.
Statistical analysis
Hardy-Weinberg equilibrium was evaluated for each polymorphism using χ2 test in both patients and controls. T-test was used to assess age difference between cases and controls. χ2-test was used to assess the between-group differences in gender, marital status, smoking, drinking, family harmony and working condition. Multi-factor variance analysis was performed to evaluate the association of demographic factors with genotype distribution. Logistic regression was used to test the SNP associations and the joint effects of gene-environment interactions. All analyses were carried out using SPSS 18.0. The values of P < 0.05 were considered statistically significant.
Results
Demographic and clinical data on study subjects
The characteristics of study subjects are listed in Table 1. The mean age was 44.16 in cases (male/female 28.2%/71.8%) and 42.93 in controls (male/female 36.9%/63.1%). A significant difference in gender distribution was detected between the two groups (P = 0.002). Most subjects were smokers (cases 85.7%, controls 80.6%) and alcohol drinkers (cases 95.9%, controls 81.1%). The cases were statistically different from controls both in alcohol consumption (P < 0.001) and tobacco smoking (P = 0.019). No significant difference was found in family harmony, marital status and working condition (P > 0.05).
Effect of CREB1 SNPs on MDD susceptibility
To evaluate the effects of CREB1 SNPs, we performed logistic regression. The results indicated no association between CREB1 polymoprhisms and MDD susceptibility (Table 2). We then explored the effects of demographic factors on genotype distribution (Table 3) and the data showed that childhood trauma was significantly associated with genotype distribution of rs6740584 in MDD patients (P = 0.015).
Table 2.
Association between CREB1 SNPs and MDD susceptibility
| rs number | Genotype | MDD | Control | OR (95% CI) | ORa (95% CI) |
|---|---|---|---|---|---|
| rs11904814 | TT | 83 | 86 | 1 | |
| TG | 300 | 287 | 1.08 (0.77-1.53) | 1.12 (0.76-1.65) | |
| GG | 203 | 213 | 0.99 (0.69-1.42) | 1.04 (0.69-1.55) | |
| rs6740584 | TT | 206 | 220 | 1 | |
| TC | 299 | 282 | 1.13 (0.88-1.45) | 1.15 (0.86-1.53) | |
| CC | 81 | 84 | 1.03 (0.72-1.48) | 1.00 (0.66-1.49) | |
| rs2253206 | GG | 86 | 89 | 1 | |
| GA | 293 | 286 | 1.06 (0.76-1.49) | 1.08 (0.74-1.59) | |
| AA | 207 | 211 | 1.02 (0.71-1.45) | 1.04 (0.70-1.55) | |
| rs2551941 | AA | 105 | 111 | 1 | |
| AT | 293 | 281 | 0.87 (0.63-1.20) | 1.12 (0.79-1.60) | |
| TT | 188 | 194 | 0.76 (0.54-1.07) | 1.04 (0.71-1.52) |
Adjusted by age, gender, marital status, smoking, drinking, family harmony, working condition and childhood trauma.
Table 3.
Association between demographic factors and genotype distribution in MDD patients
| Factor/genotype | rs11904814 | P | rs6740584 | P | rs2253206 | P | rs2551941 | P | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|||||||||
| TT | TG/GG | TT | TC/CC | GG | GA/AA | AA | AT/TT | |||||
| Gender | ||||||||||||
| Female | 62 | 359 | 0.533 | 149 | 272 | 0.847 | 62 | 359 | 0.956 | 76 | 345 | 0.893 |
| Male | 21 | 144 | 57 | 108 | 24 | 141 | 29 | 136 | ||||
| Smoking | ||||||||||||
| No | 11 | 73 | 0.762 | 23 | 61 | 0.107 | 13 | 71 | 0.823 | 13 | 71 | 0.529 |
| Yes | 72 | 430 | 183 | 319 | 73 | 429 | 92 | 410 | ||||
| Drinking | ||||||||||||
| No | 3 | 21 | 0.812 | 11 | 13 | 0.264 | 2 | 22 | 0.371 | 4 | 20 | 0.871 |
| Yes | 80 | 482 | 195 | 367 | 84 | 478 | 101 | 461 | ||||
| Family harmony | ||||||||||||
| No | 28 | 161 | 0.756 | 71 | 118 | 0.400 | 27 | 162 | 0.854 | 35 | 154 | 0.794 |
| Yes | 55 | 342 | 135 | 262 | 59 | 338 | 70 | 327 | ||||
| Childhood trauma | ||||||||||||
| No | 79 | 449 | 0.095 | 194 | 334 | 0.015* | 82 | 446 | 0.078 | 99 | 429 | 0.113 |
| Yes | 4 | 54 | 12 | 46 | 4 | 54 | 6 | 52 | ||||
indicate a significant (P < 0.05) difference.
Combined effects of SNPs and drinking, family harmony and childhood trauma
Considering that the SNPs of CREB1 alone did not show any impact on MDD susceptibility, we analyzed the interactions between SNPs and drinking, family harmony and childhood trauma (Table 4). We found significantly increased risk of MDD associated with rs11904814 genotypes among drinkers (OR = 11.67, 95% CI = 2.52-54.18; OR = 11.52, 95% CI = 2.55-51.95). A similar trend was indicated for rs6740584, rs2253206 and rs2551941. These results demonstrated that drinking served as an important risk factor for MDD. We also found a significant role of family harmony in the risk of MDD. As shown in Table 5, lack of family harmony significantly promoted the development of MDD. In addition, we identified a 2.19-fold increased risk in relation to TC/CC genotypes of rs6740584 in conjunction with childhood trauma (OR = 2.19, 95% CI = 1.16-4.15, Table 6).
Table 4.
Interaction between drinking and CREB1 genetic polymorphisms
| Variable | Case | Control | OR (95% CI) | ORa (95% CI) |
|---|---|---|---|---|
| rs11904814 | ||||
| TT and no drinking | 3 | 23 | 1 | 1 |
| TG or GG and no drinking | 21 | 88 | 1.83 (0.50-6.67) | 1.91 (0.41-8.97) |
| TT and drinking | 80 | 63 | 9.74 (2.80-33.90) | 11.67 (2.52-54.18) |
| TG or GG and drinking | 482 | 412 | 8.97 (2.67-30.09) | 11.52 (2.55-51.95) |
| rs6740584 | ||||
| TT and no drinking | 11 | 34 | 1 | 1 |
| TC or CC and no drinking | 13 | 77 | 0.52 (0.21-1.28) | 0.61 (0.24-1.59) |
| TT and drinking | 195 | 186 | 3.24 (1.60-6.58) | 4.18 (1.87-9.38) |
| TC or CC and drinking | 367 | 289 | 3.93 (1.96-7.88) | 5.02 (2.27-11.14) |
| rs2553206 | ||||
| GG and no drinking | 2 | 24 | 1 | 1 |
| GA or AA and no drinking | 22 | 87 | 3.034 (0.666-13.825) | 1.22 (0.32-4.59) |
| GG and drinking | 84 | 65 | 15.51 (3.54-68.01) | 7.58 (2.05-27.98) |
| GA or AA and drinking | 478 | 410 | 13.99 (3.29-59.55) | 7.59 (2.12-27.14) |
| rs2551941 | ||||
| AA and no drinking | 4 | 29 | 1 | 1 |
| AT or TT and no drinking | 20 | 82 | 1.77 (0.56-5.61) | 1.33 (0.45-3.89) |
| AA and drinking | 101 | 82 | 8.93 (3.02-26.43) | 8.37 (3.02-23.23) |
| AT or TT and drinking | 461 | 393 | 8.50 (2.96-24.40) | 7.84 (2.93-20.98) |
Table 5.
Interaction between family harmony and CREB1 genetic polymorphisms
| Variable | Case | Control | OR (95% CI) | ORa (95% CI) |
|---|---|---|---|---|
| rs11904814 | ||||
| TT and harmony | 55 | 85 | 1 | 1 |
| TT and no harmony | 28 | 1 | 43.27 (5.72-327.28) | 40.91 (5.40-309.81) |
| TG or GG and harmony | 342 | 480 | 1.10 (0.76-1.58) | 1.07 (0.74-1.55) |
| TG or GG and no harmony | 161 | 20 | 12.41 (7.00-22.12) | 12.71 (7.07-22.85) |
| rs6740584 | ||||
| TT and harmony | 135 | 213 | 1 | 1 |
| TT and no harmony | 71 | 7 | 16.00 (7.15-35.82) | 16.77 (7.47-37.66) |
| TC or CC and harmony | 262 | 352 | 1.17 (0.90-1.54) | 1.24 (0.94-1.62) |
| TC or CC and no harmony | 118 | 14 | 13.30 (7.34-24.10) | 13.78 (7.59-25.01) |
| rs2253206 | ||||
| GG and harmony | 59 | 86 | 1 | 1 |
| GG and no harmony | 27 | 3 | 13.12 (3.80-45.24) | 38.13 (5.04-288.68) |
| GA or AA and harmony | 338 | 479 | 1.03 (0.72-1.47) | 1.03 (0.72-1.47) |
| GA or AA and no harmony | 162 | 18 | 13.12 (7.28-23.64) | 11.69 (6.59-20.72) |
| rs2551941 | ||||
| AA and harmony | 70 | 105 | 1 | 1 |
| AA and no harmony | 35 | 6 | 8.75 (3.50-21.90) | 14.55 (4.96-42.67) |
| AT or TT and harmony | 327 | 460 | 1.07 (0.76-1.49) | 1.13 (0.80-1.58) |
| AT or TT and no harmony | 154 | 15 | 15.40 (8.36-28.35) | 13.86 (7.69-24.97) |
Table 6.
Interaction between childhood trauma and CREB1 genetic polymorphisms
| Variable | Case | Control | OR (95% CI) | ORa (95% CI) |
|---|---|---|---|---|
| rs11904814 | ||||
| TT and no trauma | 79 | 84 | 1 | 1 |
| TG or GG and no trauma | 449 | 470 | 1.02 (0.72-1.42) | 1.05 (0.75-1.48) |
| TT and trauma | 4 | 2 | 2.13 (0.38-11.94) | 2.37 (0.41-13.61) |
| TG or GG and trauma | 54 | 30 | 1.91 (1.11-3.29) | 1.43 (0.80-2.54) |
| rs6740584 | ||||
| TT and no trauma | 194 | 210 | 1 | 1 |
| TC or CC and no trauma | 334 | 344 | 0.69 (0.82-1.35) | 1.05 (0.82-1.35) |
| TT and trauma | 12 | 10 | 1.30 (0.55-3.08) | 0.76 (0.34-1.70) |
| TC or CC and trauma | 46 | 22 | 2.26 (1.31-3.90) | 2.19 (1.16-4.15) |
| rs2253206 | ||||
| GG and no trauma | 82 | 83 | 1 | 1 |
| GA or AA and no trauma | 446 | 471 | 0.96 (0.69-1.34) | 1.02 (0.73-1.42) |
| GG and trauma | 4 | 6 | 0.68 (0.18-2.48) | 2.19 (0.39-12.50) |
| GA or AA and trauma | 54 | 26 | 2.10 (1.20-3.68) | 1.37 (0.78-2.41) |
| rs2551941 | ||||
| AA and no trauma | 99 | 105 | 1 | 1 |
| AT or TT and no trauma | 429 | 449 | 1.01 (0.75-1.37) | 1.04 (0.76-1.41) |
| AA and trauma | 6 | 6 | 1.06 (0.33-3.40) | 1.02 (0.32-3.28) |
| AT or TT and trauma | 52 | 26 | 2.12 (1.23-3.66) | 1.48 (0.83-2.65) |
Discussion
As genetic polymorphisms are usually impossible to work alone in predisposing human diseases, analysis of gene-environment interactions is frequently used to test the combined effects conferred by genetic polymorphsims and environment variables. It is reported that environmental factors such as emotional abuse, emotional and physical neglect are important components in the pathogenesis of depression [27,28]. Either high- or low-predisposing genes always function in combination with many exogenous substances [29]. Rice et al. found that candidate genes exert strong effects on depressive symptoms, stronger in males than in females [30]. Grabe et al. also reported that interaction of TAT-haolotype of CRHR1 and childhood physical neglect has a role in MDD pathogenesis [31]. Actually, various environmental factors are involved in the etiology of MDD, including gender, age and marital status as substantiated by Benitez et al. [32], neighborhood environment (OR = 2.2, 95% CI = 1.2-3.9) as suggested by Kessler et al. [33], and low self-esteem family depression, childhood abuse, traumatic experiences education state as indicated by Blanco et al. [34]. These data highlight the important role of exogenous substances in the development of MDD and identification of the risk factors may contribute to an increased understanding of the mechanisms that underlie this disease.
In the present study, we explored the effects of SNPs in CREB1 gene and gene-environment interactions on MDD susceptibility, and showed evidence of no association between MDD and CREB1 SNPs, a finding that contradicts Zubenko et al., indicating that CREB1 is a potential susceptibility locus for MDD [7], and Guo et al. reporting that rs6740584 of CREB1 has an effect on selective attention and retrieval [35]. As expected, we found a significantly increased risk when both polymorphisms and exogenous variables were considered. This joint effect is supported by Perlis et al., who found a strong, gender-specific association between variation at CREB1 locus and anger expression in MDD [15]. Although we cannot exclude the possibility that the lack of an association between CREB1 polymorphisms and MDD susceptibility is a false negative finding, it seems likely that the polymorphisms at CREB1 do not function alone in predisposing MDD, as suggested by Hettema et al. and Crisafulli et al. [36].
In addition, we evaluated the impact of demographic factors on the genotype distribution in MDD patients and only childhood trauma was identified as a regulatory factor of rs6740584 genotypes (P = 0.015). To obtain a more comprehensive understanding of MDD etiology, we assessed the association of MDD risk with gender, smoking, drinking, family harmony and childhood trauma (Table 1). A significant interaction was detected for drinking, family harmony and childhood trauma, which were found to increase genetic susceptibility to MDD by interplaying with various CREB1 SNPs. Appel et al. uncovered interactions between physical abuse and rs1360780 in the FKBP5 gene and confirmed its role in depression susceptibility [24]. Moreover, significant interactions between stressful life events and 5-HTTPR and BDNF genotypes were observed in patients with depression [26]. A recent study carried out by Wong et al. provided further evidence supporting the impact of exogenous factors (smoking, marriage status, age, alcohol abuse and gender) on susceptibility to major depression [37]. The previous studies along with the present study suggest that MDD is a heterogeneous disease and both genetic variations and a large repertoire of exogenous variables may have modulating effects on the susceptibility.
In conclusion, presence of CREB1 gene polymorphisms combined with exogenous factors including drinking, family harmony and childhood trauma may increase the risk of developing MDD. Further large-scale studies are warranted to identify the role of environment-genetic risk factors in the etiology of MDD, thus facilitating better understandings of MDD pathogenesis and subsequent preventive measures and treatment.
Acknowledgements
This research was supported by the National Natural Science Foundation of China (31271093, 81473054) to Prof. Yanjie Yang and the National Natural Science Foundation of China (81202213) to Xiuxian Yang.
Disclosure of conflict of interest
None.
References
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