Genetic Variations in MicroRNA Processing Genes Are Associated with Susceptibility in Depression

Abstract

MicroRNAs (miRNAs) are new prominent gene expression regulators that have critical roles in neural development by regulating synaptic functions, and miRNA biogenesis may play an important role in psychiatric disorders. Despite emerging evidences demonstrating that single-nucleotide polymorphisms in the miRNA processing genes were associated with cancer and cardiovascular disorders, evidences about association between variants of the genes and depression are lacking. This study aims to find the association between miRNA processing gene variants and depression. We genotyped three polymorphisms from three miRNA processing genes in a case–control study including 314 patients and 252 matched healthy controls. The high-resolution melting method was used to genotype the three loci. Frequencies of genotypes and alleles showed significant difference between patients with depression and healthy controls in DGCR8 rs3757 and AGO1 rs636832. An allele frequency was significantly higher in rs3757 and lower in rs636832, respectively. Variant allele of DGCR8 rs3757 was associated with increased risk of suicidal tendency and improvement response to antidepressant treatment, whereas the variant of AGO1 rs636832 showed decreased risk of suicidal tendency, suicidal behavior, and recurrence. Besides allele frequency showed significant difference when compared patients with remission to controls, no significant differences were found in GEMIN4 rs7813 between patients and healthy controls. DGCR8 rs3757 and AGO1 rs636832 were found to have significant association with depression, and GEMIN4 rs7813 did not affect susceptibility to depression. These observations suggested that miRNA processing polymorphisms may affect depression risk and treatment.

Introduction

D epression is a common mood disorder in which feelings of sadness, loss, anger, or frustration interfere with everyday life for an extended period of time; its major symptom is suicidal behavior. The lifetime prevalence rate is at least 10% in the general population (O'Donovan et al., 2009). Despite numerous studies involving candidate genetic variants, such as SLC6A4, 5HTR2A, and GSK3β, have proved that heredity plays an important role in depression (Levinson, 2006; Hur and Zhou, 2010), the molecular mechanisms associated with the etiopathology of depression are not clearly understood. Several evidences indicated that depression was associated with altered neuronal and structural plasticity and neurogenesis (Li et al., 2008; Dwivedi, 2011).

MicroRNAs (miRNAs) are classic small noncoding RNA molecules that involved in a diversity of cellular function (Poy et al., 2004; Chen et al., 2006; Schratt et al., 2006; Care et al., 2007; Li et al., 2007), and they are new prominent gene expression regulators that have critical roles in neural development by regulating synaptic functions, particularly protein synthesis in dendritic spines. Contributing roles in neural plasticity and neurogenesis, miRNAs were implicated in psychiatric illnesses, including depression and schizophrenia (Zhou et al., 2010; Dwivedi, 2011; Chen et al., 2012).

MiRNAs negatively affect the expression levels of their target genes through two distinctive mechanisms. One way is matching between miRNAs and their binding sequences within the 3′ untranslated regions (UTRs) of their target mRNAs that induces the RNA-mediated interference; another pathway is recognizing the miRNA–mRNA interaction and cleaving the mRNA through an endonuclease activity by RNA-induced silencing complex (Yang et al., 2008). In general, miRNAs are generated in two-step pathway. MiRNA genes, which are encoded in intergenic or introns, are transcribed to long primary miRNA (pri-miRNA) by RNA polymerase II or III. Then, the pri-miRNAs are processed within the nucleus to form one or series of small hairpin miRNA precursors or precursor miRNAs (pre-miRNAs); Drosha RNase and Digeorge syndrome critical region 8 (DGCR8) are enrolled in this process. Through the assistance of RNA GTPase and Exportin 5 (XP05), pre-miRNAs translocate into cytoplasm, where they are further processed by RNase III enzyme Dicer (DICER), TAR RNA-binding protein (TRBP), AGO1, AGO2, and GEMIN4, and mature miRNAs are produced (Chendrimada et al., 2005; Han et al., 2006).

Although RNA polymerase, Drosha RNase, DGCR8, DICER, AGO1, AGO2, and GEMIN4 play critical roles in the miRNA processing, and single-nucleotide polymorphisms (SNPs) in the processing genes may affect the neurogenesis through alterative expression of miRNAs in breast cancer (Sung et al., 2011) and endometrial cancer (Torres et al., 2011), miRNA processing gene studies are lacking in depression patients. Our previous study has proved that SNPs in miRNA processing genes, such as DGCR8, AGO1, and GEMIN4, are associated with susceptibility in schizophrenia. The present study aims to find evidences for association between miRNA processing gene variants and depression.

Materials and Methods

Study population

This study population was drawn from consenting patients from inpatient unit of West China Hospital, an affiliated hospital of the Sichuan University in Chengdu, China. Three hundred and fourteen patients with depression and 252 healthy control subjects were enrolled in the current study, from May 2010 to March 2011. Diagnosis and severity of depression were made according to the Diagnostic and Statistical Manual of Mental Disorders (First et al., 1997), and International Statistical Classification of Diseases and Related Health Problems (ICD-10), and a 17-item Hamilton Depression Rating Scale (HAM-D17) score of ≥17 (Hamilton, 1960; Williams, 1988), which was established after the administration of the semistructured interview Schedules for Clinical Assessment in Neuropsychiatry. The depression symptoms were administered by experienced research assistants who had received special training.

Clinical characteristics of 314 Chinese patients with depression were shown in Table 1. About 252 samples of the healthy controls were collected from physical examination population. Individuals with a history of any severe medical illness, psychotic symptoms, substance abuse, or organic brain disease were excluded. This study was approved by the ethical committee of West China Hospital, Sichuan University, and signed informed consent forms were obtained from all the subjects.

Table 1.

Clinical Data of Patients

Variables	Male (n=98), n (%)	Female (n=216), n (%)	t or χ² value	p-Value
Age (years)	42.34±18.72	47.93±17.15	2.37	0.02
Onset years	39.00±17.82	43.91±16.66	2.64	0.01
Pathogenesis (years)
≤1	30 (30.61)	55 (25.46)
1∼5	43 (43.88)	70 (32.41)	8.16	0.02
≥5	25 (25.51)	91 (42.13)
Education (years)
<6	14 (14.29)	40 (18.52)
9–12	58 (59.18)	132 (61.11)	1.89	0.39
>12	26 (26.53)	44 (20.37)
Marriage	59 (60.20)	147 (68.06)	1.84	0.17
Sleep quality
Normal	13 (13.26)	32 (14.82)
Insomnia	82 (83.67)	180 (83.33)	0.56	0.75
Drowsiness	3 (3.06)	4 (1.85)
Medications taken
No medication	23 (23.47)	43 (19.91)	0.52	0.47
Medications taken	75 (76.53)	173 (80.09)
HAM-D17
>24	26 (26.53)	62 (28.70)	0.16	0.69
Family history	16 (16.33)	28 (12.96)	0.63	0.43
Suicidal tendency	32 (32.65)	72 (33.33)	0.01	0.91
Suicidal behavior	12 (12.24)	37 (17.13)	1.22	0.27

Genomic DNA extraction, polymerase chain reaction, and high-resolution melting method

Genomic DNA was extracted from peripheral vein blood sample using a QIAamp^® DNA Blood mini kit (Qiagen) and diluted to 10 ng/μL using the AE buffer provided by the manufacturer. Controls of the three SNPs were from the 314 samples after sequencing. Polymorphisms of the three SNPs were analyzed by high-resolution melting (HRM) method. PCR amplification for rs3757:G/A, rs636832:G/A, and rs7813:C/T variants was performed under the same condition in a 96-well plate in the LightCycler^® 480 Real-Time PCR System (Roche Diagnostics). A volume of 20 μL PCR mixture comprising 10 ng genomic DNA, 5 pmol of forward and reverse primers, 2.5 mM of each dNTP (Promega), 25 mM MgCl₂ (TaKaRa), 2×buffer (TaKaRa), 1×Eva green^® dye (Biotium), and 5 U of Hotstar Taq^® plus DNA polymerase (TaKaRa) was used. The thermal cycling profile was initial denaturation at 95°C for 15 min, and then amplification for 50 cycles by denaturing at 95°C for 15 s, annealing at 60°C for 20 s, and extension at 72°C for 30 s. After amplification, PCR products of samples as well as that of controls were denatured at 95°C for 1 min and cooled to 40°C for 1 min to form double-strand DNA. Then, the HRM analysis was performed by gradually increasing the temperature from 65°C to 95°C at a rate of 0.01°C/s. After the melting segment, the instrument was cooled down to 40°C.

Collected data were analyzed by the LightCycler 480 Gene Scanning software v1.2 (Roche Diagnostics). Three known controls of three genotypes were run in all experiments. PCR products (controls) were purified using Shrimp Alkaline Phosphatase. Nucleotide sequencing was performed using BigDye Terminator v3.1 Cycle Sequencing Kit and ABI 3130 genetic analyzer (Applied Biosystems). According to the controls, genotypes of subjects were defined.

Statistical analysis

All genotype frequencies were performed with Hardy–Weinberg equilibrium (HWE) in the two groups. Genotype and allele frequencies among the two groups were compared by Pearson's chi-square (χ ²) test. Odds ratio (OR) and 95% confidence interval (CI) were calculated using the Risk option of Crosstabs. In addition, Comparisons of clinical characteristics between patients with depression and healthy controls were done with χ ² tests for categorical data and Wilcoxon's rank-sum test for continuous data. Significant differences were considered when p<0.05. For these analyses, SPSS software was used (version 15.0; SPSS, Inc.).

Results

Baseline characteristics of the studied cases and healthy controls

We analyzed the polymorphisms of three SNP loci, DGCR8 (rs3757 G>A), AGO1 (rs636832 G>A), and GEMIN4 (rs7813 C>T), in 314 depression patients (98 men and 216 women; age: 46.2±17.8 years) and 252 healthy controls (82 men and 170 women; age: 43.7±15.5 years) from Chinese Han ethnic population. There were no significant differences in age and gender among depression patients and healthy controls (t=0.12, p>0.05, χ ²=0.11, p>0.05). Demographic and other baseline characteristics of depression patients were summarized in Table 1.

Determination of DGCR8, AGO1, and GEMIN4 gene SNP genotypes

With the specific primers, PCR amplification generated a 120 bp product for DGCR8, an 86 bp product for AGO1, and an 88 bp product for GEMIN4. After increasing the temperature, the melting phenomenon of the PCR products was monitored by plotting the changes in fluorescence that occurred by gradually temperature-dependent releasing of a saturating double-strand DNA binding dye. Heterozygous DNA samples formed heteroduplexes, resulting in a different shape of the melting curve compared with a homozygous sample. Different genotypes of homozygous sample, in contrast, were detected by a melting temperature (T_m ) shift rather than an altered curve shape. Three genotype melting profiles could be distinguished from the normalized melting curves and difference plots. PCR products were randomly selected for DNA sequencing for confirmation and genotypes. The genotypes of the samples were identified according to the control samples in the same run.

DGCR8, AGO1, and GEMIN4 gene polymorphisms of depression patients and healthy controls

Genotype distributions of the three SNPs in depression patients and healthy controls were in concordance with the HWE (rs3757: patients and controls, χ ²=2.441 and χ ²=2.310; p>0.05; rs636832: patients and controls, χ ²=0.036 and χ ²=1.362; p>0.05; rs7813: patients and controls, χ ²=0.620 and χ ²=2.402; p>0.05). The genotype distribution and allele frequency of the three SNPs are shown in Table 2.

Table 2.

Genotype Distribution and Allele Frequency of DGCR8, AGO1, and GEMIN4 Genes Among Patients with Depression and Healthy Controls

	All depression patients vs. all controls		Female depression patients vs. female controls
Variable polymorphism	Depression patients (n=314)	Controls (n=252)	Depression patients (n=216)	Controls (n=170)
rs3757 G>A
Genotype n (%)
GG	202 (64.33)	176 (69.84)	137 (63.43)	126 (74.12)
GA	94 (29.94)	73 (28.97)	72 (33.33)	44 (25.88)
AA	18 (5.73)	3 (1.19)	7 (3.24)	1 (0.59)
χ ² (p-value)	8.45 (0.01)^a	NA	6.58 (0.04)^a	NA
Allele n (%)
G	498 (79.30)	425 (84.33)	346 (80.09)	294 (86.47)
A	130 (20.70)	79 (15.67)	86 (19.91)	46 (13.53)
χ ² (p-value)	4.69 (0.03)^a	NA	5.46 (0.02)^a	NA
OR (95% CI)	0.71 (0.52–0.97)^a	NA	0.63 (0.43–0.93)^a	NA
rs636832 G>A
Genotype n (%)
GG	34 (10.83)	22 (8.73)	23 (10.65)	14 (8.23)
GA	141 (44.90)	92 (36.51)	97 (44.91)	65 (38.24)
AA	139 (44.27)	138 (54.76)	96 (44.44)	91 (53.53)
χ ² (p-value)	6.16 (0.046)^a	NA	3.21 (0.20)	NA
Allele n (%)
G	209 (33.28)	136 (26.98)	143 (33.10)	93 (27.35)
A	419 (66.72)	368 (73.02)	289 (66.90)	247 (72.65)
χ ² (p-value)	5.23 (0.02)^a	NA	2.96 (0.08)	NA
OR (95% CI)	1.35 (1.04–1.75)^a	NA	1.31 (0.96–1.80)	NA
rs7813 C>T
Genotype n (%)
CC	41 (13.06)	24 (9.52)	31 (14.35)	17 (10.0)
CT	136 (43.31)	110 (43.65)	95 (43.98)	78 (45.88)
TT	137 (43.63)	118 (46.83)	90 (41.68)	75 (44.12)
χ ² (p-value)	1.84 (0.40)	NA	1.66 (0.44)	NA
Allele n (%)
C	218 (34.71)	158 (31.35)	157 (36.34)	112 (32.94)
T	410 (65.29)	346 (68.65)	275 (63.66)	228 (67.06)
χ ² (p-value)	1.43 (0.23)	NA	0.97 (0.32)	NA
OR (95% CI)	1.16 (0.91–1.50)	NA	1.16 (0.86–1.57)	NA

p-Value is <0.05 or the 95% CI does not include 1.

NA, not applicable; OR, odds ratio; CI, confidence interval; DGCR8, Digeorge syndrome critical region 8.

The distribution of three genotypes of DGCR8 rs3757 was significantly different between patients and controls. Among depression patients, the frequency of AA was 5.73%, which was higher than that among controls (1.19%) (p=0.01). The frequencies of allele G and A were significantly different among depression patients (A=20.70%) and controls (A=15.67%) (p=0.03). Analysis indicated that variant genotype in the DGCR8 gene (rs3757) was associated with a significantly increased depression risk (OR=0.71; 95% CI=0.52–0.97).

Significant distribution of genotypes of AGO1 rs636832 was found in depression patients. The frequencies of genotypes GG, GA, and AA were 10.83%, 44.90%, and 44.27%, respectively, in cases, and the frequencies of genotypes GG, GA, and AA were 8.73%, 36.51%, and 54.76% in controls (p<0.05). Analysis indicated that variant genotype in the AGO1 gene (rs636832) was associated with a significantly increased depression risk (OR=1.35, 95% CI=1.04–1.75, p=0.02).

In GEMIN4 rs7813, there was no significant difference in frequency of genotypes and alleles between patients and controls (p>0.05).

Further, we compared genotype distribution between female depression patients and female controls for that female weighed large part in depression patients. Similar results were observed in rs3757 and rs7813 genotypes (χ ²=6.58, p=0.04 and χ ²=1.66, p=0.44, respectively), and also in allele frequency (χ ²=5.46, p=0.02 and χ ²=0.97, p=0.32, respectively). However, the results were inconsistent in genotype and allele distribution in rs636832 among depression patients and controls (χ ²=3.21, p=0.20 and χ ²=2.96, p=0.88, respectively).

DGCR8, AGO1, and GEMIN4 gene polymorphisms in patients with various depression symptoms

Because psychiatric disorders were associated with hereditary factor, and many genes were related with etiology and clinical symptoms, we analyzed the relationship between the three SNPs and typical clinical symptoms, including family history, HAM-D17 score ≥24, suicidal tendency, suicidal behavior, response to antidepressant treatment, and recurrence of depression.

First, we compared differences in subgroups and healthy controls. The comparison of DGCR8, AGO1, and GEMIN4 gene polymorphisms among patients with different clinical features and healthy controls was shown in Table 3. In rs3757, genotype distribution was associated with suicidal tendency and recurrence when compared with healthy controls (χ ²=10.06, p=0.01 and χ ²=6.71, p=0.04, respectively). Although variant genotype was associated with a significantly increased suicidal tendency and remission to antidepressant treatment risk in depression (OR=1.77, 95% CI=1.19–2.63, p=0.01; OR=1.86, 95% CI=1.20–2.87, p=0.00, respectively), no association was found in genotypes between other symptoms and healthy controls, such as family history, HAM-D17 score, and suicidal behavior. For genotype and allele frequency of rs636832, the A allele was significantly lower in depression with HAM-D17 score ≥24, suicidal tendency, suicidal behavior, and recurrence of depression versus healthy controls, but associations were detected just between depression with suicidal tendency, suicidal behavior, and controls in genotype distribution. There was no significant increased risk found in GEMIN4 rs7813 polymorphisms, besides the frequency of C allele was lower in depression (59.21%) than that in controls (66.32%).

Table 3.

Comparison of DGCR8, AGO1, and GEMIN4 Gene Polymorphisms Among Patients with Different Clinical Features and Healthy Controls

	Patients n (%)
					Antidepressant treatment
Variable polymorphism	Family history (n=44)	HAM-D17 score ≥24 (n=88)	Suicidal tendency (n=103)	Suicidal behavior (n=49)	Remission (n=76)	Nonremission (n=48)	Recurrence (n=190)	Healthy controls (n=252)
Genotype n (%)
GG	30 (68.18)	62 (70.46)	58 (56.31)	33 (67.35)	44 (57.89)	36 (75.00)	122 (64.21)	176 (69.84)
GA	13 (29.55)	24 (27.27)	39 (37.86)	14 (28.57)	25 (32.86)	11 (22.92)	58 (30.53)	73 (28.97)
AA	1 (2.27)	2 (2.27)	6 (5.83)	2 (4.08)	7 (9.21)	1 (2.08)	10 (5.26)	3 (1.19)
χ ² (p-value)	0.34 (0.84)	0.59 (0.75)	10.06 (0.01)^a	2.10 (0.35)	0.92 (0.63)	0.92 (0.63)	6.71 (0.04)^a	NA
Allele n (%)
G	73 (82.96)	148 (84.09)	155 (75.24)	80 (81.63)	113 (74.34)	83 (86.46)	302 (79.47)	425 (84.33)
A	15 (17.04)	28 (15.92)	51 (24.76)	18 (18.37)	39 (25.66)	14 (14.58)	78 (20.53)	79 (15.67)
χ ² (p-value)	0.11 (0.74)	0.01 (0.94)	8.06 (0.01)^a	0.44 (0.51)	7.89 (0.00)^a	0.10 (0.76)	3.49 (0.06)	NA
OR (95% CI)	1.11 (0.60–2.03)	1.02 (0.64–1.63)	1.77 (1.19–2.63)^a	1.21(0.69–2.13)	1.86 (1.20–2.87)^a	0.91 (0.49–1.68)	1.39 (0.98–1.96)	NA
rs636832 G>A
Genotype n (%)
GG	5 (11.36)	11 (12.50)	15 (14.56)	6 (12.25)	9 (11.84)	5 (10.42)	20 (10.53)	22 (8.73)
GA	21 (47.73)	42 (47.73)	46 (44.66)	26 (53.06)	31 (40.79)	23 (47.92)	87 (45.79)	92 (36.51)
AA	18 (40.91)	35 (39.77)	42 (40.78)	17 (34.69)	36 (47.37)	20 (41.67)	83 (43.68)	138 (54.76)
χ ² (p-value)	2.88 (0.24)	5.92 (0.05)	6.46 (0.04)^a	6.62 (0.04)^a	2.81 (0.25)	2.81 (0.25)	5.33 (0.07)	NA
Allele n (%)
G	31 (35.23)	64 (36.36)	76 (36.89)	38 (38.78)	49 (32.24)	33 (34.38)	127 (33.42)	136 (26.98)
A	57 (64.77)	112 (63.64)	130 (63.11)	60 (61.22)	103 (67.76)	63 (65.63)	253 (66.58)	368 (73.02)
χ ² (p-value)	2.51 (0.11)	5.53 (0.02)	6.86 (0.00)	5.55 (0.02)	1.59 (0.21)	2.18 (0.14)	4.30 (0.04)^a	NA
OR (95% CI)	0.68 (0.42–1.10)	0.65 (0.45–0.93)^a	0.63 (0.45–0.89)^a	0.58 (0.37–0.92)^a	0.78 (0.52–1.15)	0.71 (0.44–1.12)	0.74 (0.55–0.98)^a	NA
rs7813 C>T
Genotype n (%)
CC	5 (11.36)	9 (10.23)	8 (7.77)	5 (10.20)	9 (11.84)	6 (12.50)	26 (13.68)	24 (9.52)
CT	15 (34.09)	29 (32.96)	51 (49.52)	22 (44.90)	44 (57.90)	16 (33.33)	76 (40.00)	110 (43.65)
TT	24 (27.27)	50 (56.82)	44 (42.72)	22 (44.90)	23 (30.26)	26 (54.17)	88 (46.32)	118 (46.83)
χ ² (p-value)	0.62 (0.73)	1.11 (0.57)	3.83 (0.15)	0.97 (0.62)	1.09 (0.58)	1.09 (0.58)	3.76 (0.15)	NA
Allele n (%)
C	25 (28.41)	47 (26.71)	67 (32.52)	32 (32.65)	62 (40.79)	28 (29.17)	128 (33.68)	158 (31.35)
T	63 (71.59)	129 (73.29)	139 (67.48)	66 (67.35)	90 (59.21)	68 (70.83)	252 (66.32)	346 (68.65)
χ ² (p-value)	0.30 (0.58)	1.34 (0.25)	0.09 (0.76)	0.06 (0.80)	4.67 (0.03)^a	0.18 (0.67)	0.54 (0.46)	NA
OR (95% CI)	1.15 (0.70–1.90)	1.25 (0.85–1.84)	0.95 (0.45–0.89)	0.94 (0.59–1.50)	0.66 (0.46–0.96)^a	1.11 (0.69–1.79)	0.90 (0.67–1.19)	NA

p-Value is <0.05 or the 95% CI does not include 1.

Moreover, we have compared genotype and allele distribution of the three gene polymorphisms in patients with and without various depression symptoms. Detailed results were shown in Tables 4, 5, and 6. In rs3757, frequency of genotype showed significant difference between female patients with depression and male patients with depression, but there was no significant difference in distribution of genotype and allele between depression patients with and without family history, suicidal tendency and suicidal behavior (Table 4). As shown in Table 5, regard to rs636832, variant genotype was associated with a significantly increased suicidal behavior risk in depression patients (OR=2.30, 95% CI=1.53–3.46, p=0.00). In rs7813, variant genotype was not associated with a significant increased suicidal tendency and suicidal behavior risk (Table 6).

Table 4.

Comparison of DGCR8 Gene Polymorphisms Among Patients With and Without Different Clinical Features

	Genotype n				Allele n
	GG	AG	AA		G	A
Variables	n=202	n=94	n=18	χ² (p-value)	n=498	n=130	χ² (p-value)	OR (95% CI)
Gender (M/F)	65/137	22/72	11/7	10.25 (0.01)^a	152/346	44/86	0.53 (0.46)	0.86 (0.57–1.29)
Onset age (years)	43.42±17.37	40.61±16.06	38.17±15.01	NA (0.27)	NA	NA	NA	NA
Family history (P/N)	30/172	13/82	1/17	1.20 (0.55)	73/425	15/115	0.83 (0.36)	1.32 (0.73–2.38)
HAM-D17 (score ≥24/<24)	50/152	34/60	4/14	4.46 (0.11)	134/364	42/88	1.49 (0.22)	0.17 (0.12–0.27)
Suicidal tendency (P/N)	58/144	39/55	6/12	4.75 (0.09)	155/343	51/79	3.07 (0.08)	0.29 (0.19–0.44)
Suicidal behavior (P/N)	33/169	14/80	2/16	0.39 (0.82)	80/418	18/112	0.39 (0.53)	1.19 (0.69–2.07)
Relapse (P/N)	122/80	58/36	10/8	0.24 (0.89)	302/196	78/52	0.02 (0.89)	1.03 (0.69–1.52)

p-Value is <0.05 or the 95% CI does not include 1.

P/N, positive/negative, patients with and without different clinical features.

Table 5.

Comparison of AGO1 Gene Polymorphisms Among Patients With and Without Different Clinical Features

	Genotype n (%)				Allele n (%)
	GG	AG	AA		G	A
Variables	n=34	n=141	n=139	χ² (p-value)	n=209	n=419	χ² (p-value)	OR (95% CI)
Gender (M/F)	11/23	44/97	43/96	0.03 (0.99)	66/143	130/289	0.02 (0.89)	1.03 (0.72-1.47)
Onset age (years)	40.71±16.67	41.29±16.68	43.65±17.18	NA (0.37)	NA	NA	NA	NA
Family history (P/N)	5/29	21/120	18/121	0.23 (0.89)	31/178	57/362	0.17 (0.68)	1.11 (0.69–1.78)
HAM-D17 (score ≥24/<24)	8/26	43/98	37/102	0.90 (0.64)	59/150	117/302	0.01 (0.94)	1.02 (0.70–1.47)
Suicidal tendency (P/N)	15/19	46/95	42/97	2.40 (0.30)	76/133	130/289	1.80 (0.18)	1.27 (0.90–1.80)
Suicidal behavior (P/N)	6/28	26/115	17/122	2.17 (0.34)	58/151	60/359	16.49 (0.00)^a	2.30 (1.53–3.46)
Relapse (P/N)	20/14	87/54	83/56	0.16 (0.92)	127/82	253/166	0.01 (0.93)	1.02 (0.72–143)

p-Value is <0.05 or the 95% CI does not include 1.

Table 6.

Comparison of GEMIN4 Gene Polymorphisms Among Patients With and Without Different Clinical Features

	Genotype n (%)				Allele n (%)
	CC	CT	TT		G	A
Variables	n=41	n=136	n=137	χ² (p-value)	n=218	n=410	χ² (p-value)	OR (95% CI)
Gender (M/F)	10/31	41/95	47/90	1.57 (0.46)	61/157	135/275	1.62 (0.20)	0.79 (0.55-1.14)
Onset age (years)	39.88±16.68	43.13±17.16	42.14±16.74	NA (0.59)	NA	NA	NA	NA
Family history (P/N)	5/36	15/121	24/113	2.51 (0.28)	25/193	63/347	1.80 (0.18)	0.71 (0.43–1.17)
HAM-D17 (score ≥24/<24)	7/34	39/97	42/95	2.94 (0.23)	53/165	123/287	2.28 (0.13)	0.75 (0.52–1.09)
Suicidal tendency (P/N)	8/33	51/85	44/93	4.68 (0.10)	67/151	139/271	0.65 (0.42)	0.87 (0.61–1.23)
Suicidal behavior (P/N)	5/36	22/114	22/115	0.42 (0.81)	32/186	66/344	0.22 (0.64)	0.90 (0.57–1.42)
Relapse (P/N)	26/15	76/61	88/48	2.60 (0.27)	128/90	252/158	0.45 (0.50)	0.89 (0.64–1.25)

Discussion

Depression is a common mental disorder, which is prevalent to affect more and more people's life. Although the molecular and cellular mechanisms are still not clear for many years, hereditary factor has been implicated in etiology. Nemours evidences point to altered synaptic, structural plasticity and neurogenesis in major depression (Frodl et al., 2006; Rajkowska and Miguel-Hidalgo, 2007). In fact, it has repeatedly been demonstrated that depression was associated with alterations in expression of many genes, which were involved in regulation of neural and structural plasticity. Such as decreased expression of brain-derived neurotrophic factor (Dwivedi et al., 2009), tyrosine kinases receptor (Trk), cyclic adenosine monophosphate response element binding protein (CREB) (Gass and Riva, 2007), and vascular endothelial growth factor (Malberg and Monteggia, 2008) was found in postmortem brain and peripheral blood. From these studies, it was suggested that expression of genes involved in neural plasticity via intracellular signaling cascades may play major role in the pathophysiology of depression.

In the past few years, it has been proved that gene expression was regulated by many levels, and that miRNA has been considered as a new regulator. In recent years, many studies focus on neurodevelopmental and brain disorders, and directly or indirectly indicate that miRNAs have potential part to participate in major way to contribute depression. RNA polymerase and many proteins influence the processing of maturation of miRNAs, and their gene polymorphisms may directly regulate the expression of miRNAs. SNPs in these miRNA processing genes, such as DICER, AGO1, and GEMIN3, were enrolled in cancer and cardiovascular disorders (Bauersachs and Thum, 2011; Sung et al., 2011; Torres et al., 2011), but there was no study relating SNPs and depression. We hypothesized that these SNPs in miRNA processing genes are associated with susceptibility in depression, and could increase clinical symptom risk. So, in this study we assessed the effect of three SNPs in DGCR8, AGO1, and GEMIN4 genes, and studied the association of polymorphisms and depression.

DGCR8 protein as a cofactor for Drocha activation plays an important role in converting pri-miRNA into pre-miRNA. Rs3757, which is an untranslated polymorphism that is located in the 3′UTR, could indirectly cause variants to disturb the match between miRNA and their binding sequences. Fenelon et al. (2011) demonstrated that deficiency of DGCR8 resulted in abnormal processing of specific brain miRNAs and working memory deficits. Other evidences point its potential role in regulating miRNA processing in schizophrenia and Huntington's disease (Lee et al., 2011; Schofield et al., 2011). AGO1 protein is one of the component of protein complex leading to the production of mature miRNAs; rs636832 is an untranslated polymorphism located in intron8, so it could be indirectly causing variants to change the three-dimensional structure of DNA and its transcription, splicing, and then influences AGO1 expression. Previous studies have suggested that variants of AGO1 gene are associated with renal cell carcinoma and lung cancer (Kim et al., 2010; Lin et al., 2010).

Fortunately, significant differences are detected in our study in genotype and allele distribution among patients with depression and controls whether DGCR8 rs3757 or AGO1 rs636832; variant genotypes in the two SNP loci were associated with a significantly increased depression risk, respectively. Moreover, we subgroup depression patients with typical clinical feature, and analyze the association of frequencies of genotypes, allele, and different clinical symptoms. In AGO1 rs636832, variant was found associated with HAM-D17 score, suicidal tendency, suicidal behavior, and recurrence, and similar associations are detected with suicidal tendency in DGCR8 rs3757.

GEMIN4 protein is core component of a larger macromolecular complex that interacts with neuron protein and plays an essential role in pre-miRNA splicing and ribonucleoprotein (Shpargel and Matera, 2005). Despite variant of GEMIN4 rs7813 was found as a risk in ovarian cancer and renal cell cancer (Liang et al., 2010; Lin et al. 2010), no significant genotype and allele distributions are found between patients with depression and healthy controls in our study. However, T allele frequency among patients who show improved response to antidepressant treatment is lower than that among patients who are not.

In conclusion, our study suggests that variants of DGCR8 rs3757 and AGO1 rs636832 increased depression risk; variant in rs3757 was associated with suicidal tendency and response to antidepressant treatments, and variant in rs636832 was associated with HAM-D17 score, suicidal tendency, suicidal behavior, and recurrence. However, the number of subjects was small in subgroups when we compared the genotype and allele distribution between typical clinical features. Therefore, we need to enroll more patients to get enough number of subgroup subjects, thus to obtain more powerful statistical results. In future, the association of SNPs and miRNA expression, and miRNA expression and clinical symptoms should be studied to verify the effects of miRNA processing gene variation in depression pathogenesis; moreover, many depression risks, including education, stress, environments, and others, should be estimated.

Footnotes

Disclosure Statement

No competing financial interests exist.

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