Single-Nucleotide Polymorphisms of Genes Involved in Repair of Oxidative DNA Damage and the Risk of Recurrent Depressive Disorder

Piotr Czarny; Dominik Kwiatkowski; Monika Toma; Piotr Gałecki; Agata Orzechowska; Kinga Bobińska; Anna Bielecka-Kowalska; Janusz Szemraj; Michael Berk; George Anderson; Tomasz Śliwiński

doi:10.12659/MSM.898091

. 2016 Nov 20;22:4455–4474. doi: 10.12659/MSM.898091

Single-Nucleotide Polymorphisms of Genes Involved in Repair of Oxidative DNA Damage and the Risk of Recurrent Depressive Disorder

Piotr Czarny ^1,^2,^A,^B,^C,^D,^E,^F,^G, Dominik Kwiatkowski ^1,^B,^C,^D, Monika Toma ^1,^B,^C, Piotr Gałecki ^3,^A,^B,^D,^E, Agata Orzechowska ^3,^B,^C, Kinga Bobińska ^3,^B,^C, Anna Bielecka-Kowalska ^4,^C, Janusz Szemraj ^2,^C,^D,^G, Michael Berk ^5,^6,^C,^E, George Anderson ^7,^C,^E, Tomasz Śliwiński ^1,^A,^C,^D,^E,^F,^G,^✉

PMCID: PMC5119689 PMID: 27866211

Abstract

Background

Depressive disorder, including recurrent type (rDD), is accompanied by increased oxidative stress and activation of inflammatory pathways, which may induce DNA damage. This thesis is supported by the presence of increased levels of DNA damage in depressed patients. Such DNA damage is repaired by the base excision repair (BER) pathway. BER efficiency may be influenced by polymorphisms in BER-related genes. Therefore, we genotyped nine single-nucleotide polymorphisms (SNPs) in six genes encoding BER proteins.

Material/Methods

Using TaqMan, we selected and genotyped the following SNPs: c.-441G>A (rs174538) of FEN1, c.2285T>C (rs1136410) of PARP1, c.580C>T (rs1799782) and c.1196A>G (rs25487) of XRCC1, c.*83A>C (rs4796030) and c.*50C>T (rs1052536) of LIG3, c.-7C>T (rs20579) of LIG1, and c.-468T>G (rs1760944) and c.444T>G (rs1130409) of APEX1 in 599 samples (288 rDD patients and 311 controls).

Results

We found a strong correlation between rDD and both SNPs of LIG3, their haplotypes, as well as a weaker association with the c.-468T>G of APEXI which diminished after Nyholt correction. Polymorphisms of LIG3 were also associated with early onset versus late onset depression, whereas the c.-468T>G polymorphism showed the opposite association.

Conclusions

The SNPs of genes involved in the repair of oxidative DNA damage may modulate rDD risk. Since this is an exploratory study, the results should to be treated with caution and further work needs to be done to elucidate the exact involvement of DNA damage and repair mechanisms in the development of this disease.

MeSH Keywords: Depression; DNA Repair; Inflammation; Oxidative Stress; Polymorphism, Single Nucleotide

Background

Inflammation is present in patients with depressive disorders, including recurrent depressive disorder (rDD), and is thought to play an important role in the risk for and pathogenesis of this disease [1,2]. The presence of inflammation is indicated by the elevated levels of pro-inflammatory cytokines such as interleukin-1b (IL-1β) and interleukin-8 (IL-8), and by increased expression of NOD-like receptor family, pyrin domain containing 3 (NLRP3), one of the NOD-like receptors, which is a component of inflammasome which is necessary for the release of IL-1β and IL-18 [3–6]. In depression, inflammation is often accompanied by increased oxidative stress, since increased lipid peroxidation and production of mitochondrial reactive oxygen species (mtROS) are found in depressed patients [6,7]. ROS may damage biomolecules, including genetic material, as indicated by elevated levels of 8-oxoguanine (8-oxoG), a marker of oxidative DNA damage, in patients with clinical depression as well as depression that coexists with other non-mental diseases [8–13]. Depression severity may be a factor as well in milder, non-clinical depression, although 8-oxoG levels do not differ from controls [14]. Our team has confirmed previous study results that showed increased levels of oxidatively modified purines and pyrimidines, as well as DNA strand breaks and alkali labile sites in peripheral blood mononuclear cells (PBMCs) isolated from patients with rDD, using comet assay [15]. Furthermore, a recent report showed that NRLP3 may regulate the DNA damage response, with NRLP3 knockout increasing the expression of proteins involved in base-excision repair (BER) in murine dendritic cells exposed to genotoxic and oxidative stress [16]. In a previous study, we showed that PBMCs of rDD patients’ cells repaired DNA damage induced by hydrogen peroxide (H₂O₂) less efficiently than in control patients’ cells [15]. Finally, our team genotyped single nucleotide polymorphism (SNPs) of glycosylases involved in the first step of BER, finding that their polymorphisms may modulate rDD risk [17].

The aforementioned studies indicate that depression may be associated with impairment of the DNA damage repair mechanism, more particularly the pathways involved in the repair of oxidative DNA damage such as BER. Accordingly, we chose to study the relationship between rDD occurrence and SNPs of genes encoding proteins involved in BER, namely: c.-441G>A (rs174538) of FEN1 (flap structure-specific endonuclease 1); c.2285T>C (rs1136410) of PARP1 (poly [ADP-ribose] polymerase 1); c.580C>T (rs1799782) and c.1196A>G (rs25487) of XRCC1 (X-ray repair cross-complementing protein 1); c.*83A>C (rs4796030) and c.*50C>T (rs1052536) of LIG3 (DNA ligase 3); c.-7C>T (rs20579) of LIG1 (DNA ligase 1); and c.-468T>G (rs1760944) and c.444T>G (rs1130409) of APEX1 (APEX nuclease 1, DNA-[apurinic or apyrimidinic site] lyase).

Material and Methods

Study subjects

The study included 599 participants: patients with rDD (n=288, 140 women and 148 men; mean age 49.3±10.2) and healthy controls (n=311, 153 women and 158 men; mean age 51.2±13.3). All patients were hospitalized at the Department of Adult Psychiatry of the Medical University of Lodz (Poland) and were randomly recruited for this study without replacement sampling, based on the inclusion criteria for a current episode of depression and rDD outlined in ICD-10 (F32.0–7.32.2, F33.0–F33.8) [18] and a written informed consent to participate in the study. The diagnosis of rDD was established according to ICD-10 criteria. In all qualified cases, medical history was obtained using the standardized Composite International Diagnostic Interview (CIDI) [19]. Exclusion criteria included: the presence of axis I and II disorders other than depressive episodes, a diagnosis of severe and chronic somatic diseases or worsening of symptoms, injuries of the central nervous system, and inflammatory or autoimmune disorders. The control group was selected randomly from respondents with a negative history of mental illness. The control group included community volunteers enrolled in the study following the criteria of the psychiatric CIDI interview as well as depressed patients who were not treated for any severe and chronic diseases or worsening of symptoms, injuries of the central nervous system, or inflammatory or autoimmune disorders. An informed, written consent for participation in the study was obtained from each participant, according to the protocol approved by the Bioethics Committee of the Medical University of Lodz (No. RNN/70/14/KE).

Selection of single-nucleotide polymorphisms

We selected the studied polymorphisms from the public domain of the National Center for Biotechnology Information, Single Nucleotide Polymorphisms database (NCBI dbSNP) at http://www.ncbi.nlm.nih.gov/snp (Bethesda, MD, USA). We chose SNPs with a minor allele frequency larger than 0.05 in a European population (submitter population ID: HapMap-CEU), that are frequently studied in the literature and which are either localized in coding regions causing non-synonymous substitution or in regulatory regions. Finally, we selected nine polymorphisms:

c.-441G>A localized near 5′ end of FEN1;
c.2285T>C localized in exon of PARP1 causing valine to alanine substitution in codon 762;
c.580C>T and c.1196A>G localized in exon of XRCC1 causing arginine to tryptophan substitution in codon 194 and glutamine to arginine substitution in codon 399, respectively;
c.*83A>C and c.*50C>T localized in untranscribed region at 3′ end (UTR-3) of LIG3;
c.-7C>T localized in untranscribed region at 5′ end (UTR-5) of LIG1;
c.-468T>G and c.444T>G localized near 5′ end and in exon causing asparagine to glutamic acid in codon 148, respectively, in APEX1.

Extraction of DNA and genotyping

Blood Mini Kit (A&A Biotechnology, Gdynia, Poland) was used to extract genomic DNA from venous blood. Sample purity was obtained by calculating absorbance ratio at 260 nm and 280 nm.

Chosen SNPs were genotyped using TaqMan^® SNP Genotyping Assay and TaqMan Fast Universal PCR Master mix (Life Technologies, Carlsbad, CA, USA) in conditions recommended by the manufacturer. We used Bio-Rad CFX96 Real-Time PCR Detection System to carry out reactions. The analysis was done with CFX Manager Software (both from Bio-Rad Laboratories Inc., Hercules, California, USA).

Statistical analysis

Analysis of gathered data was performed in SigmaPlot 11.0 and Statistica 12 (Systat Software Inc., San Jose, CA, USA and Statsoft, Tulsa, OK, USA, respectively). Agreement with Hardy-Weinberg equilibrium (HWE) of the studied SNPs’ genotype frequencies was checked using the chi-square test. An unconditional multiple logistic regression model was used to evaluate the association between case/control and each polymorphism or the gene–gene interactions by measuring the odds ratio (OR) and its corresponding 95% confidence interval (CI). We used multi-variable logistic regression model (fractional polynomials) to evaluate the independent relationship between the studied variants and the presence of depression disorder adjusting for age covariates to prevent loss of information resulting from age dichotomization. For multiple testing correction, we calculated the effective number of independent tests using Single Nucleotide Polymorphism Spectral Decomposition (SNPSpD) method which is based on the spectral decomposition (SpD) of matrices of pair-wise linkage disequilibrium values between SNPs [20] applying significance threshold (p<0.012) calculated by the method described by Li and Ji [21]. Although the distribution of male and female did not differ between the patients and controls (p=0.951; χ²=0.004), the OR was adjusted for gender, given that women have a doubled risk of depression compared to men [22].

Results

Single-nucleotide polymorphisms of studied genes and the risk of recurrent depressive disorder

The genotyping of the studied SNPs are presented in Table 1. The distribution of all genotypes was in agreement with HWE, with the exception of the c.-441G>A – FEN1. We did not find any statistically significant differences in the distribution of alleles and the genotype of c.-441G>A – FEN1, c.2285T>C – PARP1, c.580C>T – XRCC1, c.1196A>G – XRCC1, c.-7C>T – LIG1, and c.444T>G – APEX1 between cases and controls. However, genotype A/A and allele A of the c.*83A>C – LIG3 was associated with an increased risk of depression occurrence, whilst allele C was associated with decreased risk. Furthermore, genotype C/C and allele C of the c.*50C>T – LIG3 were positively associated with rDD, with allele T being negatively correlated with rDD. Finally, we found that genotype T/G of the c.-468T>G – APEX1 was associated with an increased risk of depression. It must be noted, that the p values for alleles of c.*50C>T – LIG3 and heterozygotes of c.-468T>G – APEX1 were greater that the statistical significance calculated by the Nyholt correction (p=0.012).

Table 1.

Distribution of genotypes and alleles of the studied single-nucleotide polymorphism and the risk of recurrent depression disorder.

Genotype/Allele	Control (n=311)		Depression (n=288)		Crude OR (95% CI)	p	Adjusted OR (95% CI)^*	p
Genotype/Allele	Number	Frequency	Number	Frequency	Crude OR (95% CI)	p	Adjusted OR (95% CI)^*	p
c.-441G>A – FEN1 (rs174538)
A/A	0	–	0	–	–	–	–	–
A/G	151	0.486	119	0.413	0.746 (0.540–1.031)	0.076	0.746 (0.540–1.030)	0.075
G/G	160	0.514	169	0.587	1.340 (0.970–1.852)	0.076	1.341 (0.970–1.852)	0.075
χ²=2.875; p=0.090
A	151	0.243	119	0.207	0.746 (0.540–1.031)	0.076	0.746 (0.540–1.030)	0.075
G	471	0.757	457	0.793	1.340 (0.970–1.852)	0.076	1.341 (0.970–1.852)	0.075
c.2285T>C – PARP1 (rs1136410)
A/A	201	0.646	191	0.663	1.078 (0.769–1.510)	0.664	1.074 (0.767–1.509)	0.670
A/G	103	0.331	82	0.285	0.804 (0.567–1.139)	0.219	0.804 (0.567–1.141)	0.222
G/G	7	0.023	15	0.052	2.384 (0.959–5.939)	0.062	2.385 (0.958–5.936)	0.062
χ²=4.672; p=0.097
A	505	0.812	462	0.806	0.960 (0.720–1.280)	0.780	0.959 (0.718–1.279)	0.774
G	117	0.188	112	0.194	1.042 (0.781–1.389)	0.780	1.043 (0.782–1.392)	0.774
c.580C>T – XRCC1 (rs1799782)
GG	274	0.881	244	0.847	0.749 (0.468–1.198)	0.228	0.749 (0.467–1.201)	0.231
GA	36	0.116	42	0.146	1.304 (0.809–2.102)	0.275	1.303 (0.807–2.104)	0.279
AA	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.161 (0.191–23.970)	0.530
χ²=1.652; p=0.438
G	584	0.939	530	0.920	0.750 (0.480–1.171)	0.206	0.750 (0.479–1.174)	0.208
A	38	0.061	46	0.080	1.334 (0.854–2.084)	0.206	1.33 (0.852–2.087)	0.208
c.1196A>G – XRCC1 (rs25487)
C/C	130	0.418	108	0.375	0.835 (0.602–1.160)	0.283	0.834 (0.600–1.158)	0.279
C/T	138	0.444	145	0.503	1.271 (0.922–1.753)	0.144	1.272 (0.922–1.755)	0.142
T/T	43	0.138	35	0.122	0.862 (0.535–1.391)	0.543	0.863 (0.535–1.392)	0.546
χ²=2.147; p=0.342
C	398	0.640	361	0.627	0.944 (0.745–1.197)	0.634	0.943 (0.744–1.196)	0.629
T	224	0.360	215	0.373	1.059 (0.835–1.343)	0.634	1.060 (0.836–1.345)	0.629
c.^83A>C – LIG3* (rs4796030)
A/A	31	0.100	53	0.184	*2.037 (1.266–3.279)*	*0.003*	*2.042 (1.268–3.288)*	*0.003*
A/C	135	0.434	120	0.417	0.931 (0.673–1.288)	0.667	0.932 (0.674–1.289)	0.670
C/C	145	0.466	115	0.399	0.761 (0.550–1.053)	0.099	0.759 (0.548–1.051)	0.097
χ²=9.236; p=0.010
A	197	0.317	226	0.392	*1.366 (1.083–1.723)*	*0.008*	*1.369 (1.085–1.728)*	*0.008*
C	425	0.683	350	0.608	*0.732 (0.580–0.923)*	*0.008*	*0.730 (0.579–0.921)*	*0.008*
c.^50C>T – LIG3* (rs1052536)
CC	56	0.180	79	0.274	*1.721 (1.168–2.538)*	*0.006*	*1.721 (1.167–2.537)*	*0.006*
CT	166	0.534	136	0.472	0.782 (0.567–1.078)	0.133	0.781 (0.566–1.077)	0.131
TT	89	0.286	73	0.253	0.847 (0.590–1.216)	0.368	0.848 (0.590–1.218)	0.371
χ²=7.607; p=0.022
C	278	0.447	294	0.510	1.295 (1.029–1.630)	0.028	1.295 (1.029–1.629)	0.028
T	344	0.553	282	0.490	0.772 (0.614–0.972)	0.028	0.772 (0.614–0.972)	0.028
c.-7C>T – LIG1 (rs20579)
A/A	3	0.010	7	0.024	2.558 (0.655–9.986)	0.177	2.566 (0.657–10.026)	0.175
A/G	73	0.235	65	0.226	0.950 (0.649–1.391)	0.793	0.950 (0.649–1.390)	0.792
G/G	235	0.756	216	0.750	0.970 (0.669–1.407)	0.873	0.970 (0.669–1.407)	0.874
χ²=1.984; p=0.371
A	79	0.127	79	0.137	1.093 (0.781–1.530)	0.603	1.093 (0.781–1.530)	0.603
G	543	0.873	497	0.863	0.915 (0.654–1.280)	0.603	0.915 (0.654–1.280)	0.603
c.-468T>G – APEX1 (rs1760944)
T/T	56	0.180	48	0.167	0.911 (0.596–1.392)	0.665	0.910 (0.596–1.391)	0.664
T/G	142	0.457	157	0.545	1.445 (1.047–1.994)	0.025	1.444 (1.047–1.993)	0.025
G/G	113	0.363	83	0.288	0.709 (0.503–1.001)	0.051	0.710 (0.503–1.002)	0.051
χ²=5.084; p=0.079
T	254	0.408	253	0.439	1.146 (0.908–1.445)	0.251	1.145 (0.908–1.445)	0.253
G	368	0.592	323	0.561	0.885 (0.702–1.116)	0.302	0.885 (0.702–1.117)	0.304
c.444T>G – APEX1 (rs1130409)
G/G	71	0.228	64	0.222	0.966 (0.658–1.418)	0.859	0.967 (0.658–1.419)	0.862
G/T	164	0.527	150	0.521	0.974 (0.707–1.343)	0.874	0.974 (0.706–1.342)	0.870
T/T	76	0.244	74	0.257	1.069 (0.739–1.548)	0.723	1.069 (0.739–1.548)	0.722
χ²=0.131; p=0.937
G	306	0.492	278	0.483	0.962 (0.762–1.213)	0.741	0.962 (0.762–1.214)	0.743
T	316	0.508	298	0.517	1.040 (0.824–1.312)	0.741	1.040 (0.824–1.312)	0.743

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OR adjusted for sex. p<0.050 along with corresponding ORs are in bold; p<0.012 along with corresponding ORs are in bold and italic.

Single-nucleotide polymorphisms of studied genes and onset age of first episode of recurrent depressive disorder

In order to evaluate if the studied SNPs were associated with the age of the first rDD episode, we used multivariable logistic regression for analyzing fractional polynomials. We found that lower age of onset of depression was associated with the presence of G/G polymorphic variant of c.-441G>A FEN1 polymorphism (χ²=8.47, p=0.042) and G/G variant of c.-7C>T LIG1 polymorphism (χ²=6.45, p=0,045). For the other study SNPs, we did not find statistically significant correlations with age of onset. Moreover, we analyzed the relationship between the presence of polymorphic variants and age of onset by dividing the patients into two groups, with first episode before the age 35 years and first episode after the age of 35 years (optimal cutpoint in a univariate analysis); results are shown in Table 2. Thirty patients for whom age of first episode was unknown were not included in the analysis. As with the population of patients as a whole, no statistically significant differences were found in the distribution of genotypes and alleles of the c.-441G>A – FEN1, c.2285T>C – PARP1, c.580C>T – XRCC1, c.1196A>G – XRCC1, c.-7C>T – LIG1, and c.444T>G – APEX1 between the controls and either early or late onset depression. Genotype A/A of the c.*83A>C – LIG3 was associated with increased risk of both early and late onset depression, whilst allele A was associated with an increased risk of early onset depression only. Furthermore, genotype C/C and allele C of the same SNP was associated with a decreased risk of early onset depression. In the case of the c.*50C>T – LIG3, genotype C/C and allele C were positively correlated with early onset rDD, and allele T negatively correlated. Finally, the T/G genotype of the c.-468T>G – APEX1 increased the risk of early onset depression, while allele T increased the risk of late onset depression, with the G/G genotype and allele G decreasing this risk.

Table 2.

Distribution of genotypes and alleles of the studied single-nucleotide polymorphism and the age of recurrent depression disorder onset.

Genotype/Allele	Control (n=298)	Early onset depression (n=130)	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p	Late onset depression (n=128)	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
Genotype/Allele	N (Freq.)	N (Freq.)	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p	N (Freq.)	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
c.-441G>A – FEN1 (rs174538)
A/A	0 (−)	0 (−)	–	–	–	–	0 (–)	–	–	–	–
A/G	151 (0.49)	59 (0.45)	0.88 (0.58–1.33)	0.544	0.88 (0.58–1.33)	0.549	50 (0.39)	0.68 (0.45–1.03)	0.070	0.68 (0.45–1.04)	0.073
G/G	160 (0.51)	71 (0.55)	1.14 (0.75–1.71)	0.544	1.13 (0.75–1.71)	0.549	78 (0.61)	1.47 (0.97–2.24)	0.070	1.47 (0.96–2.23)	0.073
χ²=0.253; p=0.615							χ²=2.919; p=0.088
A	151 (0.24)	59 (0.23)	0.88 (0.58–1.33)	0.544	0.88 (0.58–1.33)	0.549	50 (0.19)	0.68 (0.45–1.03)	0.070	0.68 (0.45–1.04)	0.073
G	471 (0.76)	201 (0.77)	1.14 (0.75–1.71)	0.544	1.13 (0.75–1.71)	0.549	206 (0.81)	1.47 (0.97–2.24)	0.070	1.47 (0.96–2.23)	0.073
c.2285T>C – PARP1 (rs1136410)
A/A	201 (0.65)	85 (0.65)	1.03 (0.67–1.59)	0.880	1.03 (0.67–1.58)	0.898	86 (0.67)	1.12 (0.72–1.73)	0.609	1.13 (0.73–1.75)	0.590
A/G	103 (0.33)	39 (0.30)	0.86 (0.56–1.35)	0.523	0.87 (0.56–1.36)	0.542	35 (0.27)	0.76 (0.48–1.12)	0.237	0.75 (0.48–1.19)	0.219
G/G	7 (0.02)	6 (0.05)	2.10 (0.69–6.38)	0.190	2.08 (0.68–6.31)	0.197	7 (0.06)	2.51 (0.86–7.31)	0.091	2.60 (0.89–7.60)	0.081
χ²=2.024; p=0.364							χ²=3.997; p=0.136
A	505 (0.81)	209 (0.80)	0.95 (0.65–1.38)	0.776	0.94 (0.65–1.37)	0.766	207 (0.81)	0.98 (0.67–1.42)	0.908	0.98 (0.67–1.43)	0.914
G	117 (0.19	51 (0.20)	1.06 (0.73–1.54)	0.776	1.06 (0.73–1.54)	0.766	49 (0.19)	1.02 (0.70–1.49)	0.908	1.02 (0.70–1.49)	0.914
c.580C>T – XRCC1 (rs1799782)
G/G	274 (0.88)	108 (0.83)	0.66 (0.37–1.18)	0.159	0.67 (0.38–1.19)	0.174	110 (0.86)	0.83 (0.45–1.51)	0.534	0.79 (0.43–1.46)	0.459
G/A	36 (0.12)	21 (0.16)	1.47 (0.82–2.63)	0.193	1.45 (0.81–2.61)	0.210	17 (0.13)	1.17 (063–2.17)	0.618	1.21 (0.65–2.26)	0.547
A/A	1 (0.00)	1 (0.01)	2.40 (0.15–38.7)	0.536	2.41 (0.15–38.9)	0.535	1 (0.01)	2.44 (0.15–39.3)	0.529	2.71 (0.17–43.9)	0.484
χ²=2.159; p=0.334							χ²=0.688; p=0.709
G	584 (0.94)	237 (0.91)	0.67 (0.39–1.15)	0.146	0.68 (0.39–1.17)	0.159	237 (0.93)	0.81 (0.46–1.44)	0.475	0.78 (0.44–1.39)	0.400
A	38 (0.06)	23 (0.09)	1.49 (0.87–2.57)	0.146	1.48 (0.86–2.55)	0.159	19 (0.07)	1.23 (0.70–2.18)	0.475	1.28 (0.72–2.28)	0.400
c.1196A>G – XRCC1 (rs25487)
C/C	130 (0.42)	52 (0.40)	0.93 (0.61–1.41)	0.736	0.92 (0.61–1.40)	0.706	50 (0.39)	0.89 (0.59–1.36)	0.596	0.91 (0.59–1.38)	0.642
C/T	138 (0.44)	65 (0.50)	1.25 (0.83–1.89)	0.208	1.26 (0.84–1.90)	0.268	60 (0.47)	1.11 (0.73–1.67)	0.632	1.10 (0.73–1.66)	0.654
T/T	43 (0.14)	13 (0.10)	0.69 (0.36–1.34)	0.273	0.69 (0.36–1.34)	0.273	18 (0.14)	1.02 (0.56–1.85)	0.948	1.01 (0.55–1.82)	0.987
χ²=1.760; p=0.415							χ²=0.295; p=0.863
C	398 (0.64)	169 (0.65)	1.05 (0.77–1.42)	0.774	1.04 (0.77–1.41)	0.789	160 (0.63)	0.94 (0.70–1.27)	0.681	0.95 (0.70–1.28)	0.734
T	224 (0.36)	91 (0.35)	0.96 (0.71–1.30)	0.774	0.96 (0.71–1.30)	0.789	96 (0.37)	1.06 (0.79–1.43)	0.681	1.05 (0.78–1.42)	0.734
c.^83A>C – LIG3* (rs4796030)
A/A	31 (0.10)	28 (0.21)	*2.48 (1.42–4.34)*	*0.001*	*2.48 (1.42–4.34)*	*0.001*	24 (0.19)	2.08 (1.17–3.72)	0.013	2.06 (1.15–3.67)	0.015
A/C	135 (0.43)	58 (0.45)	1.05 (0.69–1.59)	0.816	1.06 (0.70–1.60)	0.792	46 (0.36)	0.73 (0.48–1.12)	0.149	0.73 (0.47–1.11)	0.141
C/C	145 (0.47)	44 (0.34)	0.59 (0.38–0.90)	0.014	0.58 (0.38–0.89)	0.013	58 (0.45)	0.95 (0.63–1.43)	0.802	0.96 (0.64–1.46)	0.851
χ²=12.697; p=0.002							χ²=6.844; p=0.033
A	197 (0.32)	114 (0.44)	*1.66 (1.23–2.23)*	*<0.001*	*1.67 (1.24–2.24)*	*<0.001*	94 (0.37)	1.23 (0.92–1.66)	0.164	1.22 (0.91–1.65)	0.184
C	425 (0.68)	146 (0.56)	*0.60 (0.45–0.81)*	*<0.001*	*0.60 (0.45–0.81)*	*<0.001*	162 (0.63)	0.81 (0.60–1.09)	0.164	0.82 (0.61–1.10)	0.184
c.^50C>T – LIG3* (rs1052536)
C/C	56 (0.18)	40 (0.31)	*2.02 (1.26–3.24)*	*0.003*	*2.02 (1.26–3.24)*	*0.003*	32 (0.25)	1.52 (0.93–2.49)	0.098	1.53 (0.93–2.50)	0.094
C/T	166 (0.53)	64 (0.49)	0.85 (0.56–1.28)	0.427	0.84 (0.56–1.27)	0.408	58 (0.55)	0.72 (0.48–1.09)	0.125	0.73 (0.48–1.10)	0.133
T/T	89 (0.29)	26 (0.20)	0.62 (0.38–1.02)	0.062	0.63 (0.38–1.03)	0.066	38 (0.30)	1.05 (0.67–1.66)	0.822	1.04 (0.66–1.64)	0.863
χ²=9.773; p=0.008							χ²=3.404; p=0.182
C	278 (0.45)	144 (0.55)	*1.58 (1.16–2.13)*	*0.003*	*1.57 (1.16–2.13)*	*0.003*	122 (0.48)	1.13 (0.84–1.52)	0.417	1.14 (0.85–1.53)	0.392
T	344 (0.55)	116 (0.45)	*0.64 (0.47–0.86)*	*0.003*	*0.64 (0.47–0.86)*	*0.003*	134 (0.52)	0.88 (0.66–1.19)	0.417	0.88 (0.65–1.18)	0.392
c.-7C>T – LIG1 (rs20579)
A/A	3 (0.01)	3 (0.02)	2.43 (0.48–12.2)	0.282	2.43 (0.48–12.2)	0.281	4 (0.03)	3.31 (0.73–15.0)	0.120	3.20 (0.70–14.6)	0.132
A/G	73 (0.23)	25 (0.19)	0.78 (0.47–1.29)	0.329	0.78 (0.47–1.29)	0.328	32 (0.25)	1.09 (0.67–1.75)	0.733	1.09 (0.68–1.76)	0.718
G/G	235 (0.76)	102 (0.79)	1.18 (0.72–1.93)	0.513	1.18 (0.72–1.93)	0.512	92 (0.72)	0.83 (0.52–1.32)	0.421	0.83 (0.52–1.31)	0.417
χ²=2.059; p=0.357							χ²=2.908; p=0.234
A	79 (0.13)	31 (0.12)	0.93 (0.59–1.45)	0.748	0.93 (0.59–1.45)	0.747	40 (0.16)	1.28 (0.84–1.94)	0.246	1.28 (0.84–1.94)	0.249
G	543 (0.87)	229 (0.88)	1.08 (0.69–1.68)	0.748	1.08 (0.69–1.68)	0.747	216 (0.84)	0.78 (0.52–1.19)	0.246	0.78 (0.52–1.19)	0.249
c.-468T>G – APEX1 (rs1760944)
T/T	56 (0.18)	16 (0.12)	0.64 (0.35–1.16)	0.142	0.64 (0.35–1.16)	0.137	29 (0.23)	1.33 (0.81–2.21)	0.263	1.34 (0.81–2.23)	0.254
T/G	142 (0.46)	73 (0.56)	1.52 (1.01–2.30)	0.045	1.52 (1.01–2.29)	0.047	66 (0.52)	1.27 (0.84–1.91)	0.261	1.27 (0.84–1.92)	0.253
G/G	113 (0.36)	41 (0.32)	0.81 (0.52–1.25)	0.336	0.81 (0.53–1.26)	0.353	33 (0.26)	0.61 (0.39–0.96)	0.034	0.60 (0.38–0.95)	0.031
χ²=2.908; p=0.234							χ²=4.716; p=0.095
T	254 (0.41)	105 (0.40)	0.98 (0.73–1.32)	0.900	0.98 (0.73–1.31)	0.872	124 (0.48)	1.35 (1.01–1.80)	0.043	1.36 (1.01–1.82)	0.039
G	368 (0.59)	155 (0.60)	1.02 (0.76–1.37)	0.900	1.03 (0.76–1.38)	0.872	132 (0.52	0.74 (0.56–0.99)	0.043	0.74 (0.55–0.98)	0.039
c.444T>G – APEX1 (rs1130409)
G/G	71 (0.23)	27 (0.21)	0.89 (0.54–1.46)	0.635	0.89 (0.54–1.48)	0.664	33 (0.26)	1.17 (0.73–1.89)	0.509	1.17 (0.73–1.89)	0.517
G/T	164 (0.53)	68 (0.52)	0.98 (0.65–1.48)	0.935	0.98 (0.65–1.47)	0.914	66 (0.51)	0.95 (0.63–1.44)	0.823	0.96 (0.64–1.45)	0.844
T/T	76 (0.24)	35 (0.27)	1.14 (0.72–1.82)	0.584	1.14 (0.71–1.81)	0.590	29 (0.23)	0.91 (0.55–1.48)	0.691	0.90 (0.55–1.47)	0.677
χ²=0.403; p=0.817							χ²=0.477; p=0.788
G	306 (0.49)	122 (0.47)	0.91 (0.67–1.22)	0.527	0.91 (0.68–1.23)	0.547	132 (0.52)	1.11 (0.82–1.49)	0.514	1.11 (0.82–1.49)	0.512
T	316 (0.51)	138 (0.53)	1.10 (0.82–1.48)	0.527	1.10 (0.81–1.48)	0.547	124 (0.48)	0.91 (0.67–1.22)	0.514	0.91 (0.67–1.22)	0.512

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Early onset depression – the first episode occurred at or before 35 years of age. Late onset depression – the first episode occurred after 35 years of age.

OR adjusted for sex. p<0.050 along with corresponding ORs are in bold; p<0.012 along with corresponding ORs are in bold and italic.

Gene-gene interactions and the risk of recurrent depression disorder

Additionally, we evaluated whether the gene-gene combinations can modulate the risk of depression. The results of this analysis are shown in Table 3 which presents only statistically significant results, and Supplementary Table 1 which presents all results. We found several significant associations, some of which were for genes that alone did not modulate rDD risk. Combined genotype G/T-C/C of the c.444T>G – APEX1 and the c.1196A>G – XRCC1 was positively correlated with rDD, while the combined genotype G/T-C/T negatively correlated with rDD. Additionally, the combined genotype A/G-A/G of the c.-7C>T – LIG1 and the c.-441G>A – FEN1 was associated with a decreased risk of depression.

Table 3.

Gene-gene interactions of the studied polymorphisms and the risk of rDD.

Combined genotype	Control (n=311)		Depression (n=288)		Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
Combined genotype	Number	Frequency	Number	Frequency	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
c.444T>G – APEX1 (rs1130409) and c.1196A>G – XRCC1 (rs25487)
G/T-C/C	75	0.241	48	0.167	0.629 (0.420–0.943)	0.025	0.627 (0.418–0.940)	0.024
G/T-C/T	68	0.219	88	0.306	1.572 (1.089–2.271)	0.016	1.573 (1.089–2.272)	0.016
c.-468T>G – APEX1 (rs1760944) and c.^50C>T – LIG3* (rs1052536)
G/G-C/T	66	0.212	37	0.128	*0.547 (0.353–0.849)*	*0.007*	*0.547 (0.353–0.850)*	*0.007*
c.-468T>G – APEX1 (rs1760944) and c.^83A>C – LIG3* (rs4796030)0
G/G-A/C	53	0.170	28	0.097	*0.524 (0.321–0.855)*	*0.010*	*0.524 (0.321–0.856)*	*0.010*
c.-468T>G – APEX1 (rs1760944) and c.580C>T – XRCC1 (rs1799782)
G/G-G/G	99	0.318	69	0.240	0.675 (0.470–0.968)	0.033	0.675 (0.470–0.969)	0.033
c.-468T>G – APEX1 (rs1760944) and c.1196A>G – XRCC1 (rs25487)
T/G-C/T	62	0.199	80	0.278	1.545 (1.057–2.257)	0.025	1.546 (1.058–2.260)	0.024
c.-468T>G – APEX1 (rs1760944) and c.-441G>A – FEN1 (rs174538)
T/G-G/G	75	0.241	93	0.323	1.501 (1.049–2.148)	0.026	1.500 (1.048–2.148)	0.027
G/G-A/G	55	0.177	32	0.111	0.582 (0.364–0.930)	0.024	0.582 (0.364–0.931)	0.024
c.-7C>T – LIG1 (rs20579) and c.^50C>T – LIG3* (rs1052536)
G/G-C/C	44	0.141	59	0.205	1.563 (1.019–2.400)	0.041	1.564 (1.019–2.400)	0.041
c.-7C>T – LIG1 (rs20579) and c.^83A>C – LIG3* (rs4796030)
G/G-A/A	25	0.080	40	0.139	1.845 (1.088–3.128)	0.023	1.851 (1.091–3.139)	0.022
c.-7C>T – LIG1 (rs20579) and c.-441G>A – FEN1 (rs174538)
A/G-A/G	37	0.119	19	0.066	0.523 (0.293–0.933)	0.028	0.523 (0.293–0.932)	0.028
c.^50C>T – LIG3* (rs1052536) and c.580C>T – XRCC1 (rs1799782)
C/C-G/G	47	0.151	63	0.219	1.573 (1.036–2.388)	0.034	1.572 (1.036–2.387)	0.034
c.^50C>T – LIG3* (rs1052536) and c.2285T>C – PARP1 (rs1136410)
C/C-A/A	39	0.125	57	0.198	1.721 (1.105–2.681)	0.016	1.720 (1.104–2.681)	0.017
c.^50C>T – LIG3* (rs1052536) and c.-441G>A – FEN1 (rs174538)
C/C-G/G	27	0.087	48	0.167	*2.104 (1.274–3.475)*	*0.004*	*2.104 (1.274–3.475)*	*0.004*
C/T-A/G	86	0.277	58	0.201	0.660 (0.451–0.965)	0.032	0.658 (0.449–0.963)	0.031
c.^83A>C – LIG3* (rs4796030) and c.580C>T – XRCC1 (rs1799782)
A/A-G/G	25	0.080	43	0.149	*2.008 (1.192–3.383)*	*0.009*	*2.015 (1.195–3.398)*	*0.009*
c.^83A>C – LIG3* (rs4796030) and c.1196A>G – XRCC1 (rs25487)
A/A-C/T	13	0.042	27	0.094	2.371 (1.199–4.691)	0.013	2.374 (1.200–4.698)	0.013
c.^83A>C – LIG3* (rs4796030) and c.2285T>C – PARP1 (rs1136410)
A/A-A/A	19	0.061	37	0.128	*2.265 (1.271–4.039)*	*0.006*	*2.268 (1.272–4.044)*	*0.006*
C/C-A/G	54	0.174	31	0.108	0.574 (0.357–0.922)	0.022	0.574 (0.357–0.922)	0.022
c.^83A>C – LIG3* (rs4796030) and c.-441G>A – FEN1 (rs174538)
A/A-G/G	13	0.042	35	0.122	*3.171 (1.642–6.125)*	*< 0.001*	*3.171 (1.642–6.124)*	*<0.001*
c.580C>T – XRCC1 (rs1799782) and c.-441G>A – FEN1 (rs174538)
G/G-A/G	135	0.434	101	0.351	0.704 (0.506–0.979)	0.037	0.704 (0.506–0.979)	0.037
c.1196A>G – XRCC1 (rs25487) and c.2285T>C – PARP1 (rs1136410)
C/T-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.691 (1.006–13.549)	0.049
c.2285T>C – PARP1 (rs1136410) and c.-441G>A – FEN1 (rs174538)
G/G-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.699 (1.007–13.578)	0.049

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OR adjusted for sex. p<0.05 along with corresponding ORs are in bold; p<0.012 along with corresponding ORs are in bold and italic.

Haplotypes of the studied single-nucleotide polymorphism and the risk of recurrent depression disorder

The distribution of haplotypes of the studied SNPs are presented in Table 4 and Supplementary Table 2. We only found statistically significant results for the haplotypes of c.*50C>T – LIG3 and c.*83A>C – LIG3 – haplotype CA, which were associated with an increased rDD risk, while haplotype TC was associated with a decreased rDD risk.

Table 4.

Distribution of haplotypes of in the studied SNPs.

Haplotypes	Control (n=311)		Depression (n=288)		Crude OR (95% CI)	p
Haplotypes	Number	Frequency	Number	Frequency	Crude OR (95% CI)	p
c.50C>T – LIG3* (rs1052536) and c.83A>C – LIG3* (rs4796030)
CA	264	0.212	350	0.304	1.620 (1.346–1.949)	<0.001
CC	292	0.235	238	0.207	0.849 (0.699–1.030)	0.098
TA	130	0.105	102	0.089	0.832 (0.634–1.093)	0.187
TC	558	0.449	462	0.401	0.818 (0.696–0.963)	0.016

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p<0.05 along with corresponding ORs are in bold.

Discussion

As indicated in the introduction, depression is accompanied by inflammation and increased oxidative stress, both of which may be important in its pathogenesis [2]. Oxidative stress may induce DNA damage, and our team and others have found increased levels of oxidatively modified DNA bases, especially 8-oxoG, DNA breaks, and an alkali labile site in depressed patients [8–13,15]. This kind of DNA damage is mainly repaired by the BER. PBMCs isolated from cells of rDD patients repaired hydrogen peroxide-induced oxidative DNA damage less efficiently than from the cells of controls, which may indicate impairments in this repair pathway [15]. Furthermore, recent studies have shown that some polymorphic variants of the BER genes may negatively impact the repair of oxidative DNA damage, and our team found that glycosylase SNPs can modulate rDD risk [17,23,24]. Therefore, in this study, nine SNPs of the six genes encoding proteins involved in later steps of BER were genotyped; and to our knowledge, none of them have been studied in the context of mental illnesses.

One of the selected genes was LIG3, which encodes ligase, an essential component of BER, sealing in place the new base in the final step of this pathway [25,26]. It is mainly utilized in short-patch BER, but it can be used as a “back-up” ligase in the long-patch sub-pathway [27]. LIG3 also plays a major role in mitochondrial BER (mtBER), since its depletion by siRNA leads to the reduction of mitochondrial DNA (mtDNA) copy number and elevations in DNA single-strand breaks [28,29]. We examined the c.*50C>T and the c.*83A>C, both localized in UTR-3, where they can alter gene expression by affecting mRNA stability, half-life, and degradation [30]. Indeed, the c.*83A>C affects the binding site of microRNA, modulating the risk of bladder cancer, whilst the c.*50C>T modulates the risk of young-onset lung cancer [31,32]. Here, both SNPs showed a significant association with rDD (Table 1). We found that the AA genotype of the c.*83A>C and the CC genotype of c.*50C>T increased the risk of depression, with these genotypes also increasing cancer risk [31,32]. These SNPs are also associated with rDD onset with the c.*50C>T variant modifying the risk of early onset rDD only, with the c.*83A>C variant also being more associated with early rather than late onset depression (Table 2). Such data indicates that these SNPs are linked to early incidence of rDD, possibly being less related to non-genetic factors, which may have a greater role in late-life depression. Finally, we found that the haplotype C/A of the c.*50C>T and the c.*83A>C were associated with a significantly increased rDD risk, in contrast to the haplotype T/C, which was associated with decreased risk (Table 4).

The next gene studied was APEX1. APEX1 encodes endonuclease, which recognizes the apurinic/apyrimidinic (AP) site arising from the actions of glycosylases after the first BER step, but is also responsible for RNA processing and the regulation of transcription [33,34]. This protein takes part in mtBER, although its role is still the subject of investigation. On the one hand, overexpression of mitochondrial APEX1 in human umbilical vein endothelial cells decreases apoptosis after H₂O₂-induced oxidative stress, probably by enhancing mitochondrial DNA repair. However, on the other hand, expression of exonuclease III (with which APEX1 has significant homology) in the mitochondria of a malignant breast epithelial cell line caused cells to be more sensitive to oxidative stress due to deficient mtDNA repair [35,36]. We genotyped two polymorphisms in this gene: the c.-468T>G and the c.444T>G. The latter, located near the 5′ end of the gene, was found to affect the promoter strength and by this may modulate the risk of lung cancer [37,38]. The former causes amino acid substitution of asparagine to glutamic acid in codon 148 and although it did not alter the in vitro activity of APEX1, it modulated the risk of cancer and was associated with Parkinson’s disease, probably by affecting interactions of the enzyme with other BER proteins [39–42]. Data in our study showed that the heterozygote of the c.-468T>G increased rDD risk (Table 1). This SNP was more associated with late onset rather than with early onset depression, which could mean that the c.-468T>G has lower penetration than the LIG3 polymorphisms and needs other factors to induce rDD later in life (Table 2). Although the heterozygote increased the risk only of early onset depression, the A allele homozygote and the A allele alone decreased late onset rDD risk, while the T allele increased this risk. These results are consistent with the work of Lo and colleagues that showed that the G allele increased luciferase reported gene expression in several adenocarcinoma cell lines [38]. The hypothesis that this polymorphism had less impact on depression occurrence was confirmed by the fact that, only together with heterozygotes of LIG3 SNPs genotype GG, was the risk for the disease decreased (Table 3). In the case of c.444T>G as well as haplotypes of these two APEX1 polymorphisms, we did not find any statistically significant results (Tables 1, 4).

Two SNPs were also genotyped in XRCC1, which encodes a protein that is not only an important component of BER, but also a component of other DNA repair pathways, such as single-strand break repair and non-homologous end joining [43,44]. In BER, XRCC1 is involved in each of these pathway steps due to its function as a scaffold protein, which physically associates with repair enzymes [45]. Its polymorphisms are associated with cancers and neurodegenerative diseases such as Alzheimer’s disease [46–48]. Again two SNPs were genotyped: the c.580C>T, which is localized in a region that coordinates protein integrations, whereas the c.1196A>G is positioned in the breast cancer 1 C terminus (BRCT1) domain, which is responsible for interactions with PARP [48–50]. No significant associations of depression with either genotypes, alleles, nor haplotypes of XRCC1 SNPs were found (Tables 1, 2, and Supplementary Table 1). However, combined genotypes of this gene with genes encoding proteins interacting with XRCC1 provided some statistically significant results. The most interesting being the combination of the c.1196A>G – XRCC1 and the c.444T>G – APEX1, which alone were not rDD associated. In this gene-gene interaction, the latter SNP is more important due to changes of this polymorphism’s genotype causing either a decrease (homozygote CC) or an increase (heterozygote) in rDD risk (Table 3). Moreover, this heterozygote increased the risk of rDD in combination with the TG genotype of the c.-468T>G – APEX1 and AA genotype of the c.*83A>C – LIG3. Such results suggest that this SNP may have little if any impact on the development of depression. Similarly, the two polymorphisms of FEN1 and LIG1, although neither alone influenced rDD risk, the combined genotype consisting of heterozygotes decreased rDD risk (Tables 1, 3). FEN1 encodes structure specific endonuclease that removes the 5′-flap structures arising during long-patch BER and is involved in maturation of Okazaki fragments, as well as releasing stalled replication forks [51–53]. It also participates in maintaining the mitochondrial genome [54]. A polymorphism of this gene, the c.-441G>A, reduced expression of FEN1, elevated levels of DNA damage, and increased risk of lung cancer in individuals carrying the GG genotype [55]. In our study, this SNP was associated with depression in combination with other SNPs, most notably the combination of its GG genotype with the homozygote AA of the c.*83A>C – LIG3 or the homozygote CC of the c.*50C>T – LIG3 significantly increased rDD risk (Table 4). The product of LIG1 is also involved in long-patch BER and maturation of Okazaki fragments [56]. The SNP located in the 5′-UTR of this gene is associated with lung cancer in heterozygote carriers, although its influence on gene expression is unknown [32]. We found that its heterozygote in combination with the homozygote AA of the c.*83A>C – LIG3 or the homozygote CC of the c.*50C>T – LIG3 was correlated with rDD (Table 4). It must be noted that this association was weaker than in the case of the c.-441G>A – FEN1, which could indicate that the latter SNP is more involved in the pathogenesis of rDD than the former. Finally, we genotyped one SNP in PARP1, which encodes protein involved not only in DNA damage, including the BER, but also in the inflammation response, transcription, and apoptosis [57,58]. This SNP was found to reduce enzyme activity by about 40% [59]. However, no significant correlation was found between this polymorphisms and depression (Tables 1, 2).

The results showing a strong association between depression and some polymorphism variants of LIG3, their haplotypes, or their interactions with other SNPs of BER genes are especially interesting in the context of recent work demonstrating impairments in oxidative DNA damage repair in rDD patients [15]. DNA damage repair kinetics in the aforementioned study revealed that the differences between controls and cases were more likely to be present in the later stages of BER, as initial DNA damage caused by the actions of DNA glycosylases in both groups occurred at the same time. As such, it can be speculated that this difference may arise, at least partly, by the more frequent occurrence of a specific variant of LIG3 and others genes involved in the later steps of BER in depressed patients versus controls. Furthermore, as indicated earlier, LIG3 plays an important role in the maintenance of the mitochondrial genome and a growing number of reports indicate the importance of mitochondrial dysfunctions in depression [28,29]. Depressed patients have lower mitochondrial ATP production, lower CoQ10 levels and increased mitochondrial ROS, all of which indicate mitochondrial damage [60–63]. The mitochondrial impairments may be due to increased mtDNA damage, and elevated levels of mtDNA deletions are found in depressed patients’ muscles and PBMCs [61,62]. Such deletions can be triggered by DNA damage, and one of the most frequently occurring deletions, called “common deletion”, is triggered by a single-strand break following incomplete DNA damage repair, when ligase does not seal the nick in the last step of BER [64]. Given the classical decrease in serotonin in depression, and its role as a precursor for melatonin and a significant regulator of mitochondrial functioning as well as an inhibitor of oxidative damage and inflammation, it is not unlikely that variations in melatonin availability will interact with genes regulating the BER [65].

Our work has some limitations. One limitation was a relatively small sample size, although comparable studies concerning depression had similar study group size [66,67]. In addition, our results cannot be generalized to the world population, due to ethnic homogeneity of the studied population.

Conclusions

This study is concordant with the extant literature in showing that SNPs of genes involved in oxidative DNA damage repair may modulate the risk of rDD [17]. Further studies are needed to elucidate the role of nuclear DNA as well as mitochondrial DNA damage and repair in pathogenesis of depression, and the effect on clinical outcome. Aging effects on telomerase, which can be offset by melatonin, would be expected to interact with the genetic susceptibility to rDD, driven partly by BER SNPs.

Supplementary materials

Supplementary Table 1.

Gene-gene interactions of the studied polymorphisms and the risk of rDD.

Combined genotype	Control (n=311)		Depression (n=288)		Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
Combined genotype	Number	Frequency	Number	Frequency	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
c.444T>G – APEX1 (rs1130409) and c.-7C>T – LIG1 (rs20579)
G/G-A/A	0	-	1	0.003	–	–	–	–
G/G-A/G	20	0.064	18	0.063	0.970 (0.502–1.873)	0.928	0.970 (0.502–1.873)	0.928
G/G-G/G	51	0.164	45	0.156	0.944 (0.610–1.462)	0.797	0.945 (0.610–1.464)	0.800
G/T-A/A	2	0.006	5	0.017	2.730 (0.525–14.181)	0.232	2.771 (0.531–14.459)	0.227
G/T-A/G	44	0.141	33	0.115	0.785 (0.485–1.273)	0.327	0.785 (0.484–1.272)	0.326
G/T-G/G	118	0.379	112	0.389	1.041 (0.749–1.447)	0.812	1.040 (0.748–1.446)	0.817
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
T/T-A/G	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.713 (0.730–4.021)	0.217
T/T-G/G	66	0.212	59	0.205	0.956 (0.645–1.419)	0.825	0.957 (0.645–1.420)	0.827
c.444T>G – APEX1 (rs1130409) and c.^50C>T – LIG3* (rs1052536)
G/G-C/C	13	0.042	16	0.056	1.348 (0.637–2.855)	0.435	1.348 (0.637–2.854)	0.435
G/G-C/T	35	0.113	26	0.090	0.783 (0.458–1.336)	0.369	0.783 (0.459–1.337)	0.370
G/G-T/T	23	0.074	22	0.076	1.036 (0.564–1.902)	0.910	1.037 (0.565–1.906)	0.906
G/T-C/C	30	0.096	40	0.139	1.511 (0.913–2.499)	0.108	1.510 (0.912–2.498)	0.109
G/T-C/T	91	0.293	76	0.264	0.867 (0.606–1.240)	0.434	0.866 (0.605–1.240)	0.433
G/T-T/T	43	0.138	34	0.118	0.834 (0.515–1.350)	0.461	0.834 (0.516–1.350)	0.461
T/T-C/C	13	0.042	23	0.080	1.990 (0.988–4.006)	0.054	1.994 (0.990–4.018)	0.053
T/T-C/T	40	0.129	34	0.118	0.907 (0.557–1.478)	0.695	0.905 (0.555–1.476)	0.690
T/T-T/T	23	0.074	17	0.059	0.785 (0.411–1.502)	0.466	0.786 (0.411–1.505)	0.468
c.444T>G – APEX1 (rs1130409) and c.^83A>C – LIG3* (rs4796030)
G/G-A/A	5	0.016	12	0.042	2.661 (0.926–7.649)	0.069	2.658 (0.924–7.642)	0.070
G/G-A/C	32	0.103	26	0.090	0.865 (0.502–1.491)	0.602	0.866 (0.503–1.494)	0.606
G/G-C/C	34	0.109	26	0.090	0.808 (0.472–1.384)	0.439	0.809 (0.472–1.385)	0.440
G/T-A/A	17	0.055	27	0.094	1.789 (0.953–3.357)	0.070	1.790 (0.954–3.358)	0.070
G/T-A/C	70	0.225	64	0.222	0.984 (0.670–1.445)	0.933	0.984 (0.670–1.447)	0.936
G/T-C/C	77	0.248	59	0.205	0.783 (0.533–1.151)	0.213	0.781 (0.531–1.148)	0.208
T/T-A/A	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.730 (0.735–4.072)	0.210
T/T-A/C	33	0.106	30	0.104	0.980 (0.581–1.652)	0.938	0.979 (0.580–1.651)	0.936
T/T-C/C	34	0.109	30	0.104	0.947 (0.564–1.593)	0.838	0.946 (0.563–1.591)	0.835
c.444T>G – APEX1 (rs1130409) and c.580C>T – XRCC1 (rs1799782)
G/G-G/G	62	0.199	51	0.177	0.864 (0.573–1.304)	0.784	0.865 (0.573–1.306)	0.491
G/G-G/A	9	0.029	13	0.045	1.586 (0.668–3.769)	0.296	1.583 (0.666–3.766)	0.299
G/G-A/A	0	–	0	–	–	–	–	–
G/T-G/G	142	0.457	130	0.451	0.979 (0.710–1.351)	0.898	0.979 (0.710–1.351)	0.899
G/T-G/A	22	0.071	19	0.066	0.928 (0.491–1.752)	0.817	0.925 (0.489–1.749)	0.810
G/T-A/A	0	–	1	0.003	–	–	–	–
T/T-G/G	70	0.225	63	0.219	0.964 (0.655–1.418)	0.852	0.964 (0.656–1.419)	0.854
T/T-G/A	5	0.016	10	0.035	2.201 (0.743–6.520)	0.154	2.198 (0.742–6.512)	0.155
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
c.444T>G – APEX1 (rs1130409) and c.1196A>G – XRCC1 (rs25487)
G/G-C/C	28	0.090	30	0.104	1.175 (0.684–2.021)	0.559	1.177 (0.684–2.024)	0.556
G/G-C/T	36	0.116	27	0.094	0.790 (0.467–1.338)	0.381	0.791 (0.467–1.339)	0.382
G/G-T/T	7	0.023	7	0.024	1.082 (0.375–3.123)	0.884	1.082 (0.375–3.124)	0.884
G/T-C/C	75	0.241	48	0.167	0.629 (0.420–0.943)	0.025	0.627 (0.418–0.940)	0.024
G/T-C/T	68	0.219	88	0.306	1.572 (1.089–2.271)	0.016	1.573 (1.089–2.272)	0.016
G/T-T/T	21	0.068	14	0.049	0.706 (0.352–1.415)	0.326	0.706 (0.352–1.417)	0.328
T/T-C/C	27	0.087	30	0.104	1.223 (0.708–2.113)	0.470	1.222 (0.707–2.111)	0.472
T/T-C/T	34	0.109	30	0.104	0.947 (0.564–1.593)	0.838	0.948 (0.564–1.594	0.840
T/T-T/T	15	0.048	14	0.049	1.008 (0.478–2.127)	0.983	1.009 (0.478–2.129)	0.981
c.444T>G – APEX1 (rs1130409) and c.2285T>C – PARP1 (rs1136410)
G/G-A/A	44	0.141	48	0.167	1.214 (0.778–1.893)	0.393	1.214 (0.778–1.893)	0.393
G/G-A/G	25	0.080	16	0.056	0.673 (0.352–1.288)	0.232	0.674 (0.352–1.291)	0.234
G/G-G/G	2	0.006	0	–	–	–	–	–
G/T-A/A	104	0.334	96	0.33	0.995 (0.708–1.398)	0.978	0.993 (0.706–1.397)	0.967
G/T-A/G	57	0.183	46	0.160	0.847 (0.553–1.298)	0.446	0.848 (0.553–1.301)	0.450
G/T-G/G	3	0.010	8	0.028	2.933 (0.771–11.166)	0.115	2.938 (0.772–11.186)	0.114
T/T-A/A	53	0.170	47	0.163	0.949 (0.617–1.460)	0.813	0.950 (0.618–1.461)	0.816
T/T-A/G	21	0.068	20	0.069	1.031 (0.546–1.944)	0.926	1.029 (0.546–1.942)	0.929
T/T-G/G	2	0.006	7	0.024	3.849 (0.793–18.681)	0.094	3.845 (0.792–18.666)	0.095
c.444T>G – APEX1 (rs1130409) and c.-441G>A – FEN1 (rs174538)
G/G-A/A	0	–	0	-	–	–	–	–
G/G-A/G	38	0.122	24	0.083	0.653 (0.381–1.119)	0.121	0.653 (0.381–1.119)	0.121
G/G-G/G	33	0.106	40	0.139	1.359 (0.831–2.222)	0.222	1.361 (0.832–2.226)	0.220
G/T-A/A	0	–	2	–	–	–	–	–
G/T-A/G	75	0.241	61	0.212	0.846 (0.576–1.241)	0.392	0.845 (0.575–1.240)	0.389
G/T-G/G	89	0.286	89	0.309	1.116 (0.786–1.584)	0.541	1.115 (0.785–1.584)	0.542
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	38	0.122	34	0.118	0.962 (0.587–1.575)	0.877	0.692 (0.587–1.575)	0.877
T/T-G/G	38	0.122	40	0.139	1.159 (0.720–1.865)	0.544	1.159 (0.720–1.865)	0.544
c.-468T>G – APEX1 (rs1760944) and c.-7C>T – LIG1 (rs20579)
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
T/T-A/G	11	0.035	11	0.038	1.083 (0.462–2.538)	0.854	1.086 (0.463–2.546)	0.850
T/T-G/G	44	0.141	36	0.125	0.867 (0.540–1.391)	0.554	0.865 (0.539–1.389)	0.550
T/G-A/A	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.187 (0.397–12.046)	0.369
T/G-A/G	32	0.103	33	0.115	1.128 (0.674–1.888)	0.646	1.128 (0.674–1.888)	0.647
T/G-G/G	108	0.347	120	0.417	1.343 (0.965–1.869)	0.081	0.856 (0.614–1.192)	0.356
G/G-A/A	0	-	2	0.007	–	–	–	–
G/G-A/G	30	0.096	21	0.073	0.737 (0.412–1.319)	0.304	0.735 (0.411–1.317)	0.301
G/G-G/G	83	0.267	60	0.208	0.723 (0.495–1.057)	0.094	0.723 (0.494–1.058)	0.095
c.-468T>G – APEX1 (rs1760944) and c.^50C>T – LIG3* (rs1052536)
T/T-C/C	12	0.039	19	0.066	1.760 (0.839–3.693)	0.135	1.759 (0.838–3.691)	0.136
T/T-C/T	25	0.080	18	0.063	0.763 (0.407–1.430)	0.398	0.760 (0.405–1.426)	0.392
T/T-T/T	19	0.061	11	0.038	0.610 (0.285–1.306)	0.203	0.611 (0.285–1.310)	0.205
T/G-C/C	28	0.090	39	0.125	1.444 (0.857–2.434)	0.168	1.443 (0.855–2.433)	0.169
T/G-C/T	75	0.241	81	0.281	1.231 (0.854–1.774)	0.264	1.231 (0.854–1.774)	0.265
T/G-T/T	39	0.125	40	0.139	1.125 (0.701–1.806)	0.626	1.021 (0.636–1.639)	0.931
G/G-C/C	16	0.051	24	0.083	1.676 (0.872–3.224)	0.122	1.682 (0.874–3.237)	0.120
G/G-C/T	66	0.212	37	0.128	*0.547 (0.353–0.849)*	*0.007*	*0.547 (0.353–0.850)*	*0.007*
G/G-T/T	31	0.100	22	0.076	0.747 (0.422–1.323)	0.317	0.747 (0.422–1.324)	0.318
c.-468T>G – APEX1 (rs1760944) and c.^83A>C – LIG3* (rs4796030)0
T/T-A/A	4	0.013	10	0.035	2.761 (0.856–8.903)	0.089	2.768 (0.858–8.930)	0.088
T/T-A/C	18	0.058	18	0.063	1.085 (0.553–2.129)	0.812	1.083 (0.552–2.126)	0.816
T/T-C/C	34	0.109	20	0.069	0.608 (0.341–1.083)	0.091	0.608 (0.341–1.083)	0.091
T/G-A/A	17	0.055	24	0.083	1.572 (0.826–2.991)	0.168	1.572 (0.826–2.991)	0.168
T/G-A/C	64	0.206	74	0.257	1.335 (0.911–1.954)	0.138	1.335 (0.912–1.955)	0.138
T/G-C/C	61	0.196	59	0.205	1.056 (0.708–1.576)	0.790	1.054 (0.706–1.574)	0.796
G/G-A/A	10	0.032	19	0.066	2.126 (0.971–4.653)	0.059	2.134 (0.975–4.674)	0.058
G/G-A/C	53	0.170	28	0.097	*0.524 (0.321–0.855)*	*0.010*	*0.524 (0.321–0.856)*	*0.010*
G/G-C/C	50	0.161	36	0.125	0.746 (0.470–1.184)	0.213	0.745 (0.469–1.183)	0.212
c.-468T>G – APEX1 (rs1760944) and c.580C>T – XRCC1 (rs1799782)
T/T-G/G	45	0.145	41	0.142	0.981 (0.621–1.550)	0.935	0.982 (0.621–1.551)	0.937
T/T-G/A	10	0.032	7	0.024	0.750 (0.282–1.997)	0.565	0.745 (0.279–1.989)	0.557
T/T-A/A	1	0.003	0	–	–	–	–	–
T/G-G/G	130	0.418	134	0.465	1.211 (0.877–1.674)	0.244	1.211 (0.877–1.673)	0.245
T/G-G/A	12	0.039	21	0.073	1.960 (0.946–4.059)	0.070	1.958 (0.945–4.058)	0.071
T/G-A/A	0	–	2	0.007	–	–	–	–
G/G-G/G	99	0.318	69	0.240	0.675 (0.470–0.968)	0.033	0.675 (0.470–0.969)	0.033
G/G-G/A	14	0.045	14	0.049	1.084 (0.508–2.315)	0.835	1.082 (0.506–2.311)	0.840
G/G-A/A	0	–	0	–	–	–	–	–
c.-468T>G – APEX1 (rs1760944) and c.1196A>G – XRCC1 (rs25487)
T/T-C/C	27	0.087	19	0.066	0.743 (0.404–1.368)	0.340	0.741 (0.402–1.365)	0.336
T/T-C/T	25	0.080	23	0.080	0.993 (0.550–1.792)	0.981	0.993 (0.550–1.792)	0.982
T/T-T/T	4	0.013	6	0.021	1.633 (0.456–5.847)	0.451	1.638 (0.457–5.868)	0.448
T/G-C/C	58	0.186	57	0.198	1.076 (0.717–1.617)	0.723	1.075 (0.715–1.615)	0.728
T/G-C/T	62	0.199	80	0.278	1.545 (1.057–2.257)	0.025	1.546 (1.058–2.260)	0.024
T/G-T/T	22	0.071	20	0.069	0.980 (0.523–1.837)	0.951	0.979 (0.522–1.835)	0.947
G/G-C/C	45	0.145	32	0.111	0.739 (0.455–1.200)	0.221	0.739 (0.455–1.201)	0.222
G/G-C/T	51	0.164	42	0.146	0.870 (0.558–1.357)	0.540	0.871 (0.558–1.357)	0.541
G/G-T/T	17	0.055	9	0.031	0.558 (0.245–1.272)	0.165	0.559 (0.244–1.276)	0.167
c.-468T>G – APEX1 (rs1760944) and c.2285T>C – PARP1 (rs1136410)
T/T-A/A	31	0.100	29	0.101	1.011 (0.593–1.725)	0.967	1.011 (0.593–1.724)	0.969
T/T-A/G	23	0.074	16	0.056	0.737 (0.381–1.424)	0.363	0.736 (0.381–1.423)	0.362
T/T-G/G	2	0.006	3	0.010	1.626 (0.270–9.803)	0.596	1.631 (0.270–9.835)	0.594
T/G-A/A	96	0.309	105	0.365	1.285 (0.915–1.805)	0.148	1.285 (0.913–1.808)	0.150
T/G-A/G	44	0.141	45	0.156	1.124 (0.716–1.763)	0.612	1.128 (0.718–1.772)	0.603
T/G-G/G	2	0.006	7	0.024	3.849 (0.793–18.681)	0.094	3.845 (0.792–18.666)	0.095
G/G-A/A	74	0.238	57	0.198	0.790 (0.535–1.167)	0.237	0.791 (0.535–1.169)	0.239
G/G-A/G	36	0.116	21	0.073	0.601 (0.342–1.056)	0.077	0.601 (0.342–1.057)	0.077
G/G-G/G	3	0.010	5	0.017	1.814 (0.430–7.659)	0.418	1.810 (0.428–7.645)	0.420
c.-468T>G – APEX1 (rs1760944) and c.-441G>A – FEN1 (rs174538)
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	29	0.093	23	0.080	0.844 (0.476–1.496)	0.561	0.838 (0.471–1.491)	0.548
T/T-G/G	27	0.087	25	0.087	1.000 (0.566–1.767)	1.000	1.004 (0.567–1.777)	0.990
T/G-A/A	0	–	0	–	–	–	–	–
T/G-A/G	67	0.215	64	0.222	1.041 (0.706–1.533)	0.841	1.041 (0.706–1.534)	0.840
T/G-G/G	75	0.241	93	0.323	1.501 (1.049–2.148)	0.026	1.500 (1.048–2.148)	0.027
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	55	0.177	32	0.111	0.582 (0.364–0.930)	0.024	0.582 (0.364–0.931)	0.024
G/G-G/G	58	0.186	51	0.177	0.939 (0.619–1.423)	0.765	0.939 (0.619–1.423)	0.766
c.-7C>T – LIG1 (rs20579) and c.^50C>T – LIG3* (rs1052536)
A/A-C/C	0	–	0	–	–	–	–	–
A/A-C/T	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.187 (0.397–12.046)	0.369
A/A-T/T	1	0.003	3	0.010	3.263 (0.338–31.550)	0.307	3.264 (0.338–31.560)	0.307
A/G-C/C	12	0.039	20	0.069	1.859 (0.892–3.876)	0.098	1.858 (0.891–3.873)	0.098
A/G-C/T	42	0.135	31	0.108	0.773 (0.471–1.267)	0.306	0.773 (0.471–1.267)	0.307
A/G-T/T	19	0.061	14	0.049	0.785 (0.386–1.597)	0.504	0.785 (0.386–1.597)	0.504
G/G-C/C	44	0.141	59	0.205	1.563 (1.019–2.400)	0.041	1.564 (1.019–2.400)	0.041
G/G-C/T	122	0.392	101	0.351	0.837 (0.600–1.166)	0.293	0.836 (0.599–1.165)	0.290
G/G-T/T	69	0.222	56	0.194	0.847 (0.570–1.258)	0.410	0.847 (0.570–1.260)	0.413
c.-7C>T – LIG1 (rs20579) and c.^83A>C – LIG3* (rs4796030)
A/A-A/A	0	–	0	–	–	–
A/A-A/C	0	–	2	0.007	–	–
A/A-C/C	3	0.010	5	0.017	1.814 (0.430–7.659)	0.418	1.821 (0.431–7.694)	0.415
A/G-A/A	6	0.019	13	0.045	2.403 (0.901–6.409)	0.080	2.402 (0.901–6.407)	0.080
A/G-A/C	34	0.109	25	0.087	0.774 (0.450–1.333)	0.356	0.775 (0.450–1.335)	0.359
A/G-C/C	33	0.106	27	0.094	0.871 (0.510–1.489)	0.615	0.870 (0.508–1.487)	0.610
G/G-A/A	25	0.080	40	0.139	1.845 (1.088–3.128)	0.023	1.851 (1.091–3.139)	0.022
G/G-A/C	101	0.325	93	0.323	0.992 (0.704–1.397)	0.962	0.992 (0.704–1.397)	0.963
G/G-C/C	109	0.350	83	0.288	0.750 (0.531–1.060)	0.103	0.749 (0.530–1.059)	0.101
c.-7C>T – LIG1 (rs20579) and c.2285T>C – PARP1 (rs1136410)
A/A-A/A	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.151 (0.193–23.970)	0.534
A/A-A/G	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.202 (0.399–12.159)	0.365
A/A-G/G	0	–	1	0.003	–	–	–	–
A/G-A/A	49	0.158	46	0.160	1.016 (0.655–1.576)	0.942	1.016 (0.655–1.575)	0.945
A/G-A/G	24	0.077	17	0.059	0.750 (0.394–1.427)	0.381	0.751 (0.395–1.428)	0.382
A/G-G/G	0	–	2	0.007	–	–	–	–
G/G-A/A	151	0.486	143	0.497	1.045 (0.758–1.440)	0.788	1.044 (0.758–1.439)	0.792
G/G-A/G	77	0.248	61	0.212	0.817 (0.557–1.197)	0.299	0.817 (0.557–1.199)	0.302
G/G-G/G	7	0.023	12	0.042	1.888 (0.733–4.864)	0.188	1.886 (0.732–4.859)	0.189
c.-7C>T – LIG1 (rs20579) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	2	0.006	5	0.017	2.730 (0.525–14.181)	0.232	2.730 (0.525–14.181)	0.232
A/A-G/G	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.161 (0.195–23.970)	0.530
A/G-A/A	0	–	0	–	–	–	–	–
A/G-A/G	37	0.119	19	0.066	0.523 (0.293–0.933)	0.028	0.523 (0.293–0.932)	0.028
A/G-G/G	36	0.116	46	0.160	1.452 (0.908–2.321)	0.119	1.452 (0.908–2.321)	0.119
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	112	0.360	95	0.330	0.875 (0.624–1.226)	0.437	0.874 (0.624–1.225)	0.435
G/G-C/C	123	0.395	121	0.420	1.107 (0.799–1.535)	0.540	1.108 (0.800–1.535)	0.538
c.^50C>T – LIG3* (rs1052536) and c.580C>T – XRCC1 (rs1799782)
C/C-G/G	47	0.151	63	0.219	1.573 (1.036–2.388)	0.034	1.572 (1.036–2.387)	0.034
C/C-G/A	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.714 (0.730–4.023)	0.216
C/C-A/A	0	-	2	0.007	–	–	–	–
C/T-G/G	152	0.489	122	0.424	0.769 (0.557–1.061)	0.110	0.769 (0.557–1.062)	0.110
C/T-G/A	13	0.042	14	0.049	1.171 (0.541–2.536)	0.688	1.167 (0.5358–2.534)	0.696
C/T-A/A	1	0.003	0	–	–	–	–	–
T/T-G/G	75	0.241	59	0.205	0.811 (0.551–1.193)	0.287	0.811 (0.551–1.196)	0.291
T/T-G/A	14	0.045	14	0.049	1.084 (0.508–2.315)	0.835	1.081 (0.505–2.311)	0.841
T/T-A/A	0	–	0	–	–	–	–	–
c.^50C>T – LIG3* (rs1052536) and c.1196A>G – XRCC1 (rs25487)
C/C-C/C	23	0.074	32	0.111	1.565 (0.893–2.745)	0.118	1.565 (0.893–2.745)	0.118
C/C-C/T	25	0.080	37	0.128	1.686 (0.988–2.879)	0.056	1.686 (0.988–2.879)	0.056
C/C-T/T	8	0.026	10	0.035	1.362 (0.530–3.501)	0.521	1.361 (0.530–3.498)	0.522
C/T-C/C	65	0.209	46	0.160	0.719 (0.474–1.092)	0.122	0.715 (0.470–1.087)	0.117
C/T-C/T	74	0.238	72	0.250	1.068 (0.735–1.550)	0.731	1.069 (0.736–1.553)	0.726
C/T-T/T	27	0.087	18	0.063	0.701 (0.378–1.303)	0.261	0.702 (0.378–1.303)	0.262
T/T-C/C	42	0.135	30	0.104	0.745 (0.452–1.226)	0.247	0.745 (0.453–1.228)	0.249
T/T-C/T	39	0.125	36	0.125	0.996 (0.614–1.617)	0.988	0.996 (0.614–1.617)	0.987
T/T-T/T	8	0.026	7	0.024	0.944 (0.338–2.636)	0.912	0.947 (0.339–2.651)	0.918
c.^50C>T – LIG3* (rs1052536) and c.2285T>C – PARP1 (rs1136410)
C/C-A/A	39	0.125	57	0.198	1.721 (1.105–2.681)	0.016	1.720 (1.104–2.681)	0.017
C/C-A/G	17	0.055	17	0.059	1.085 (0.543–2.168)	0.818	1.086 (0.543–2.170)	0.815
C/C-G/G	0	-	5	0.017	–	–	–	–
C/T-A/A	108	0.347	89	0.309	0.841 (0.597–1.183)	0.320	0.839 (0.595–1.181)	0.314
C/T-A/G	52	0.167	40	0.139	0.803 (0.514–1.257)	0.338	0.804 (0.514–1.259)	0.341
C/T-G/G	6	0.019	7	0.024	1.266 (0.421–3.813)	0.675	1.263 (0.419–3.807)	0.678
T/T-A/A	54	0.174	45	0.156	0.881 (0.572–1.359)	0.567	0.882 (0.572–1.360)	0.570
T/T-A/G	34	0.109	25	0.087	0.774 (0.450–1.333)	0.356	0.775 (0.450–1.335)	0.359
T/T-G/G	1	0.003	3	0.010	3.263 (0.338–31.550)	0.307	3.250 (0.336–31.466)	0.309
c.^50C>T – LIG3* (rs1052536) and c.-441G>A – FEN1 (rs174538)
C/C-A/A	0	–	0	–	–	–	–	–
C/C-A/G	29	0.093	31	0.108	1.173 (0.688–2.000)	0.558	1.172 (0.687–1.999)	0.559
C/C-G/G	27	0.087	48	0.167	*2.104 (1.274–3.475)*	*0.004*	*2.104 (1.274–3.475)*	*0.004*
C/T-A/A	0	–	0	–	–	–	–	–
C/T-A/G	86	0.277	58	0.201	0.660 (0.451–0.965)	0.032	0.658 (0.449–0.963)	0.031
C/T-G/G	80	0.257	78	0.271	1.072 (0.746–1.543)	0.706	1.073 (0.746–1.544)	0.704
T/T-A/A	0	-	0	-	–	–	–	–
T/T-A/G	36	0.116	30	0.104	0.888 (0.532–1.484)	0.651	0.890 (0.532–1.489)	0.658
T/T-G/G	53	0.170	43	0.149	0.854 (0.551–1.325)	0.482	0.854 (0.551–1.324)	0.481
c.^83A>C – LIG3* (rs4796030) and c.580C>T – XRCC1 (rs1799782)
A/A-G/G	25	0.080	43	0.149	*2.008 (1.192–3.383)*	*0.009*	*2.015 (1.195–3.398)*	*0.009*
A/A-G/A	6	0.019	9	0.031	1.640 (0.576–4.666)	0.354	1.637 (0.575–4.660)	0.356
A/A-A/A	0	–	1	0.003	–	–	–	–
A/C-G/G	124	0.399	106	0.368	0.878 (0.631–1.222)	0.441	0.879 (0.632–1.223)	0.444
A/C-G/A	10	0.032	13	0.045	1.423 (0.614–3.297)	0.411	1.423 (0.614–3.297)	0.411
A/C-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–14.231)	0.963
C/C-G/G	125	0.402	95	0.330	0.732 (0.524–1.023)	0.068	0.732 (0.524–1.023)	0.068
C/C-G/A	20	0.064	20	0.069	1.086 (0.572–2.063)	0.801	1.082 (0.567–2.061)	0.812
C/C-A/A	0	–	0	–	–	–	–	–
c.^83A>C – LIG3* (rs4796030) and c.1196A>G – XRCC1 (rs25487)
A/A-C/C	13	0.042	22	0.076	1.896 (0.937–3.838)	0.075	1.897 (0.937–3.841)	0.075
A/A-C/T	13	0.042	27	0.094	2.371 (1.199–4.691)	0.013	2.374 (1.200–4.698)	0.013
A/A-T/T	5	0.016	4	0.014	0.862 (0.229–3.242)	0.826	0.865 (0.230–3.259)	0.831
A/C-C/C	51	0.164	39	0.135	0.798 (0.508–1.254)	0.329	0.796 (0.507–1.252)	0.324
A/C-C/T	59	0.190	63	0.219	1.196 (0.803–1.781)	0.378	1.199 (0.805–1.787)	0.372
A/C-T/T	25	0.080	18	0.063	0.763 (0.407–1.430)	0.398	0.764 (0.407–1.432)	0.401
C/C-C/C	66	0.212	47	0.163	0.724 (0.478–1.095)	0.126	0.723 (0.478–1.095)	0.126
C/C-C/T	66	0.212	55	0.191	0.876 (0.587–1.308)	0.518	0.875 (0.586–1.306)	0.513
C/C-T/T	13	0.042	13	0.045	1.084 (0.494–2.378	0.841	1.082 (0.493–2.375)	0.844
c.^83A>C – LIG3* (rs4796030) and c.2285T>C – PARP1 (rs1136410)
A/A-A/A	19	0.061	37	0.128	*2.265 (1.271–4.039)*	*0.006*	*2.268 (1.272–4.044)*	*0.006*
A/A-A/G	12	0.039	14	0.049	1.273 (0.579–2.800)	0.548	1.275 (0.579–2.805)	0.546
A/A-G/G	0	–	2	0.007	–	–	–	–
A/C-A/A	93	0.299	78	0.271	0.871 (0.610–1.242)	0.445	0.870 (0.610–1.242)	0.443
A/C-A/G	37	0.119	37	0.128	1.092 (0.671–1.776)	0.724	1.097 (0.672–1.789)	0.712
A/C-G/G	5	0.016	5	0.017	1.081 (0.310–3.774)	0.902	1.079 (0.309–3.768)	0.905
C/C-A/A	89	0.286	76	0.264	0.894 (0.624–1.281)	0.542	0.893 (0.623–1.279)	0.536
C/C-A/G	54	0.174	31	0.108	0.574 (0.357–0.922)	0.022	0.574 (0.357–0.922)	0.022
C/C-G/G	2	0.006	8	0.028	4.414 (0.930–20.963)	0.062	4.408 (0.928–20.939)	0.062
c.^83A>C – LIG3* (rs4796030) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	18	0.058	18	0.063	1.085 (0.553–2.129)	0.812	1.089 (0.554–2.139)	0.805
A/A-G/G	13	0.042	35	0.122	*3.171 (1.642–6.125)*	*< 0.001*	*3.171 (1.642–6.124)*	*<0.001*
A/C-A/A	0	–	0	–	–	–	–	–
A/C-A/G	71	0.228	49	0.170	0.693 (0.462–1.040)	0.076	0.692 (0.461–1.039)	0.076
A/C-G/G	64	0.206	71	0.247	1.263 (0.860–1.854)	0.234	1.266 (0.862–1.860)	0.229
C/C-A/A	0	–	0	-	–	–	–	–
C/C-A/G	62	0.199	52	0.181	0.885 (0.588–1.332)	0.558	0.884 (0.587–1.331)	0.555
C/C-G/G	83	0.267	63	0.219	0.769 (0.528–1.120)	0.171	0.768 (0.527–1.119)	0.169
c.580C>T – XRCC1 (rs1799782) and c.2285T>C – PARP1 (rs1136410)
G/G-A/A	178	0.572	160	0.556	0.934 (0.676–1.290)	0.679	0.934 (0.676–1.291)	0.679
G/G-A/G	90	0.289	72	0.250	0.819 (0.570–1.176)	0.279	0.819 (0.570–1.178)	0.282
G/G-G/G	6	0.019	12	0.042	2.210 (0.818–5.968)	0.118	2.208 (0.818–5.964)	0.118
G/A-A/A	22	0.071	30	0.104	1.527 (0.859–2.715)	0.149	1.528 (0.857–2.727)	0.151
G/A-A/G	13	0.042	10	0.035	0.825 (0.356–1.911)	0.653	0.825 (0.356–1.912)	0.654
G/A-G/G	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.161 (0.195–23.970)	0.530
A/A-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
A/A-A/G	0	–	0	–	–	–	–	–
A/A-G/G	0	–	1	0.003	–	–	–	–
c.580C>T – XRCC1 (rs1799782) and c.-441G>A – FEN1 (rs174538)
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	135	0.434	101	0.351	0.704 (0.506–0.979)	0.037	0.704 (0.506–0.979)	0.037
G/G-G/G	139	0.447	143	0.497	1.220 (0.885–1.683)	0.225	1.224 (0.887–1.689)	0.219
G/A-A/A	0	–	0	–	–	–	–	–
G/A-A/G	16	0.051	17	0.059	1.157 (0.573–2.335)	0.685	1.156 (0.573–2.334)	0.685
G/A-G/G	20	0.064	25	0.087	1.383 (0.751–2.548)	0.298	1.382 (0.747–2.556)	0.303
A/A-A/A	0	-	0	-	–	–	–	–
A/A-A/G	0	-	1	0.003	–	–	–	–
A/A-G/G	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
c.1196A>G – XRCC1 (rs25487) and c.2285T>C – PARP1 (rs1136410)
C/C-A/A	84	0.270	77	0.267	0.986 (0.687–1.416)	0.940	0.984 (0.685–1.414)	0.930
C/C-A/G	43	0.138	27	0.094	0.645 (0.387–1.074)	0.092	0.645 (0.387–1.075)	0.093
C/C-G/G	3	0.010	4	0.014	1.446 (0.321–6.517)	0.631	1.449 (0.321–6.532)	0.629
C/T-A/A	88	0.283	90	0.313	1.152 (0.811–1.636)	0.429	1.152 (0.811–1.636)	0.429
C/T-A/G	47	0.151	45	0.156	1.040 (0.667–1.622)	0.862	1.042 (0.668–1.625)	0.858
C/T-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.691 (1.006–13.549)	0.049
T/T-A/A	29	0.093	24	0.083	0.884 (0.502–1.557)	0.670	0.884 (0.502–1.557)	0.670
T/T-A/G	13	0.042	10	0.035	0.825 (0.356–1.911)	0.653	0.827 (0.356–1.920)	0.659
T/T-G/G	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
c.1196A>G – XRCC1 (rs25487) and c.-441G>A – FEN1 (rs174538)
C/C-A/A	0	–	0	–	–	–	–	–
C/C-A/G	66	0.212	48	0.167	0.742 (0.492–1.121)	0.157	0.739 (0.489–1.118)	0.152
C/C-G/G	64	0.206	60	0.208	1.016 (0.684–1.508)	0.939	1.016 (0.684–1.509)	0.937
C/T-A/A	0	–	0	–	–	–	–	–
C/T-A/G	59	0.190	57	0.198	1.054 (0.703–1.581)	0.800	1.055 (0.703–1.583)	0.795
C/T-G/G	79	0.254	88	0.306	1.292 (0.903–1.848)	0.160	1.292 (0.904–1.848)	0.160
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	26	0.084	14	0.049	0.560 (0.286–1.095)	0.090	0.561 (0.287–1.097)	0.091
T/T-G/G	17	0.055	21	0.073	1.360 (0.703–2.633)	0.361	1.360 (0.702–2.632)	0.362
c.2285T>C – PARP1 (rs1136410) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	100	0.322	78	0.271	0.784 (0.551–1.115)	0.175	0.782 (0.550–1.113)	0.172
A/A-G/G	101	0.325	113	0.392	1.343 (0.960–1.877)	0.085	1.342 (0.960–1.876)	0.085
A/G-A/A	0	–	0	–	–	–	–	–
A/G-A/G	47	0.151	36	0.125	0.802 (0.503–1.280)	0.356	0.803 (0.503–1.283)	0.359
A/G-G/G	56	0.180	46	0.160	0.866 (0.564–1.328)	0.508	0.866 (0.565–1.329)	0.511
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	4	0.013	5	0.017	1.356 (0.361–5.100)	0.652	1.352 (0.359–5.088)	0.656
G/G-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.699 (1.007–13.578)	0.049

Open in a new tab

OR adjusted for sex. p<0.05 along with corresponding ORs are in bold; p<0.012 along with corresponding ORs are in bold and italic.

Footnotes

Statement

The authors declare no conflict of interest.

Source of support: This study was supported with funding from scientific research grants from the Polish National Science Centre (no. DEC-2014/13/N/NZ7/00232) and Medical University of Lodz (no. 503/5-139-01/503-51-008). MB is supported by a NHMRC Senior Principal Research Fellowship (1059660)

References

1.Pasco JA, Nicholson GC, Williams LJ, et al. Association of high-sensitivity C-reactive protein with de novo major depression. Br J Psychiatry. 2010;197:372–77. doi: 10.1192/bjp.bp.109.076430. [DOI] [PubMed] [Google Scholar]
2.Gardner A, Boles RG. Beyond the serotonin hypothesis: Mitochondria, inflammation and neurodegeneration in major depression and affective spectrum disorders. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35:730–43. doi: 10.1016/j.pnpbp.2010.07.030. [DOI] [PubMed] [Google Scholar]
3.Maes M, Bosmans E, Meltzer HY, et al. Interleukin-1 beta: A putative mediator of HPA axis hyperactivity in major depression? Am J Psychiatry. 1993;150:1189–93. doi: 10.1176/ajp.150.8.1189. [DOI] [PubMed] [Google Scholar]
4.Rawdin BJ, Mellon SH, Dhabhar FS, et al. Dysregulated relationship of inflammation and oxidative stress in major depression. Brain Behav Immun. 2013;31:143–52. doi: 10.1016/j.bbi.2012.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Leemans JC, Cassel SL, Sutterwala FS. Sensing damage by the NLRP3 inflammasome. Immunol Rev. 2011;243:152–62. doi: 10.1111/j.1600-065X.2011.01043.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Alcocer-Gómez E, de Miguel M, Casas-Barquero N, et al. NLRP3 inflammasome is activated in mononuclear blood cells from patients with major depressive disorder. Brain Behav Immun. 2014;36:111–17. doi: 10.1016/j.bbi.2013.10.017. [DOI] [PubMed] [Google Scholar]
7.Anderson G, Maes M. Oxidative/nitrosative stress and immuno-inflammatory pathways in depression: treatment implications. Curr Pharm Des. 2014;20:3812–47. doi: 10.2174/13816128113196660738. [DOI] [PubMed] [Google Scholar]
8.Irie M, Asami S, Nagata S, et al. Psychosocial factors as a potential trigger of oxidative DNA damage in human leukocytes. Jpn J Cancer Res. 2001;92:367–76. doi: 10.1111/j.1349-7006.2001.tb01104.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Forlenza MJ, Miller GE. Increased serum levels of 8-hydroxy-2′-deoxyguanosine in clinical depression. Psychosom Med. 2006;68:1–7. doi: 10.1097/01.psy.0000195780.37277.2a. [DOI] [PubMed] [Google Scholar]
10.Irie M, Asami S, Ikeda M, Kasai H. Depressive state relates to female oxidative DNA damage via neutrophil activation. Biochem Biophys Res Commun. 2003;311:1014–18. doi: 10.1016/j.bbrc.2003.10.105. [DOI] [PubMed] [Google Scholar]
11.Maes M, Mihaylova I, Kubera M, et al. Increased 8-hydroxy-deoxyguanosine, a marker of oxidative damage to DNA, in major depression and myalgic encephalomyelitis/chronic fatigue syndrome. Neuro Endocrinol Lett. 2009;30:715–22. [PubMed] [Google Scholar]
12.Wei YC, Zhou FL, He DL, et al. Oxidative stress in depressive patients with gastric adenocarcinoma. Int J Neuropsychopharmacol. 2009;12:1089–96. doi: 10.1017/S1461145709000091. [DOI] [PubMed] [Google Scholar]
13.Kupper N, Gidron Y, Winter J, Denollet J. Association between type D personality, depression, and oxidative stress in patients with chronic heart failure. Psychosom Med. 2009;71:973–80. doi: 10.1097/PSY.0b013e3181bee6dc. [DOI] [PubMed] [Google Scholar]
14.Yi S, Nanri A, Matsushita Y, et al. Depressive symptoms and oxidative DNA damage in Japanese municipal employees. Psychiatry Res. 2012;200:318–22. doi: 10.1016/j.psychres.2012.05.035. [DOI] [PubMed] [Google Scholar]
15.Czarny P, Kwiatkowski D, Kacperska D, et al. Elevated level of DNA damage and impaired repair of oxidative DNA damage in patients with recurrent depressive disorder. Med Sci Monit. 2015;21:412–18. doi: 10.12659/MSM.892317. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Licandro G, Ling Khor H, Beretta O, et al. The NLRP3 inflammasome affects DNA damage responses after oxidative and genotoxic stress in dendritic cells. Eur J Immunol. 2013;43:2126–37. doi: 10.1002/eji.201242918. [DOI] [PubMed] [Google Scholar]
17.Czarny P, Kwiatkowski D, Galecki P, et al. Association between single nucleotide polymorphisms of MUTYH, hOGG1 and NEIL1 genes, and depression. J Affect Disord. 2015;184:90–96. doi: 10.1016/j.jad.2015.05.044. [DOI] [PubMed] [Google Scholar]
18.World Health Organization. ICD-10 Classification of Mental and Behavioural Disorders. Geneva: 1992. [Google Scholar]
19.Patten SB. Performance of the Composite International Diagnostic Interview Short Form for major depression in community and clinical samples. Chronic Dis Can. 1997;18:109–12. [PubMed] [Google Scholar]
20.Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74:765–69. doi: 10.1086/383251. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 2005;95:221–27. doi: 10.1038/sj.hdy.6800717. [DOI] [PubMed] [Google Scholar]
22.Kessler RC. Epidemiology of women and depression. J Affect Disord. 2003;74:5–13. doi: 10.1016/s0165-0327(02)00426-3. [DOI] [PubMed] [Google Scholar]
23.Erčulj N, Zadel M, Dolžan V. Genetic polymorphisms in base excision repair in healthy slovenian population and their influence on DNA damage. Acta Chim Slov. 2010;57:182–88. [PubMed] [Google Scholar]
24.Zielinska A, Davies OT, Meldrum RA, Hodges NJ. Direct visualization of repair of oxidative damage by OGG1 in the nuclei of live cells. J Biochem Mol Toxicol. 2011;25:1–7. doi: 10.1002/jbt.20346. [DOI] [PubMed] [Google Scholar]
25.Chen J, Tomkinson AE, Ramos W, et al. Mammalian DNA ligase III: Molecular cloning, chromosomal localization, and expression in spermatocytes undergoing meiotic recombination. Mol Cell Biol. 1995;15:5412–22. doi: 10.1128/mcb.15.10.5412. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Dianov GL, O’Neill P, Goodhead DT. Securing genome stability by orchestrating DNA repair: removal of radiation-induced clustered lesions in DNA. Bioessays. 2001;23:745–49. doi: 10.1002/bies.1104. [DOI] [PubMed] [Google Scholar]
27.Petermann E, Keil C, Oei SL. Roles of DNA ligase III and XRCC1 in regulating the switch between short patch and long patch BER. DNA Repair (Amst) 2006;5:544–55. doi: 10.1016/j.dnarep.2005.12.008. [DOI] [PubMed] [Google Scholar]
28.Lakshmipathy U, Campbell C. Antisense-mediated decrease in DNA ligase III expression results in reduced mitochondrial DNA integrity. Nucleic Acids Res. 2001;29:668–76. doi: 10.1093/nar/29.3.668. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Simsek D, Furda A, Gao Y, et al. Crucial role for DNA ligase III in mitochondria but not in Xrcc1-dependent repair. Nature. 2011;471:245–48. doi: 10.1038/nature09794. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Langaee T, Shin J. The genetics basis of pharmacogenomics. In: Zdanowicz MM, editor. Concepts in Pharmacogenomics. Bethesda: American Society of Health-System Pharmacists; 2010. p. 29. [Google Scholar]
31.Teo MT, Landi D, Taylor CF, et al. The role of microRNA-binding site polymorphisms in DNA repair genes as risk factors for bladder cancer and breast cancer and their impact on radiotherapy outcomes. Carcinogenesis. 2012;33:581–86. doi: 10.1093/carcin/bgr300. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Landi S, Gemignani F, Canzian F, et al. DNA repair and cell cycle control genes and the risk of young-onset lung cancer. Cancer Res. 2006;66:11062–69. doi: 10.1158/0008-5472.CAN-06-1039. [DOI] [PubMed] [Google Scholar]
33.Krokan HE, Nilsen H, Skorpen F, et al. Base excision repair of DNA in mammalian cells. FEBS Lett. 2000;476:73–77. doi: 10.1016/s0014-5793(00)01674-4. [DOI] [PubMed] [Google Scholar]
34.Li M, Wilson DM., III Human apurinic/apyrimidinic endonuclease 1. Antioxid Redox Signal. 2014;20:678–707. doi: 10.1089/ars.2013.5492. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Li MX, Wang D, Zhong ZY, et al. Targeting truncated APE1 in mitochondria enhances cell survival after oxidative stress. Free Radic Biol Med. 2008;45:592–601. doi: 10.1016/j.freeradbiomed.2008.05.007. [DOI] [PubMed] [Google Scholar]
36.Shokolenko IN, Alexeyev MF, Robertson FM, et al. The expression of Exonuclease III from E. coli in mitochondria of breast cancer cells diminishes mitochondrial DNA repair capacity and cell survival after oxidative stress. DNA Repair (Amst) 2003;2:471–82. doi: 10.1016/s1568-7864(03)00019-3. [DOI] [PubMed] [Google Scholar]
37.Lu J, Zhang S, Chen D, et al. Functional characterization of a promoter polymorphism in APE1/Ref-1 that contributes to reduced lung cancer susceptibility. FASEB J. 2009;23:3459–69. doi: 10.1096/fj.09-136549. [DOI] [PubMed] [Google Scholar]
38.Lo YL, Jou YS, Hsiao CF, et al. A polymorphism in the APE1 gene promoter is associated with lung cancer risk. Cancer Epidemiol Biomarkers Prev. 2009;18:223–29. doi: 10.1158/1055-9965.EPI-08-0749. [DOI] [PubMed] [Google Scholar]
39.Hadi MZ, Coleman MA, Fidelis K, et al. Functional characterization of Ape1 variants identified in the human population. Nucleic Acids Res. 2000;28:3871–79. doi: 10.1093/nar/28.20.3871. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Jin F, Qian C, Qing Y, et al. Genetic polymorphism of APE1 rs1130409 can contribute to the risk of lung cancer. Tumour Biol. 2014;35:6665–71. doi: 10.1007/s13277-014-1829-9. [DOI] [PubMed] [Google Scholar]
41.Agaçhan B, Küçükhüseyin O, Aksoy P, et al. Apurinic/apyrimidinic endonuclease (APE1) gene polymorphisms and lung cancer risk in relation to tobacco smoking. Anticancer Res. 2009;29:2417–20. [PubMed] [Google Scholar]
42.Parildar-Karpuzoğlu H, Doğru-Abbasoğlu S, Hanagasi HA, et al. Single nucleotide polymorphisms in base-excision repair genes hOGG1, APE1 and XRCC1 do not alter risk of Alzheimer’s disease. Neurosci Lett. 2008;442:287–91. doi: 10.1016/j.neulet.2008.07.047. [DOI] [PubMed] [Google Scholar]
43.Brem R, Hall J. XRCC1 is required for DNA single-strand break repair in human cells. Nucleic Acids Res. 2005;33:2512–20. doi: 10.1093/nar/gki543. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Han L, Mao W, Yu K. X-ray repair cross-complementing protein 1 (XRCC1) deficiency enhances class switch recombination and is permissive for alternative end joining. Proc Natl Acad Sci USA. 2012;109:4604–8. doi: 10.1073/pnas.1120743109. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Campalans A, Marsin S, Nakabeppu Y, et al. XRCC1 interactions with multiple DNA glycosylases: A model for its recruitment to base excision repair. DNA Repair (Amst) 2005;4:826–35. doi: 10.1016/j.dnarep.2005.04.014. [DOI] [PubMed] [Google Scholar]
46.Yin G, Morita M, Ohnaka K, et al. Genetic polymorphisms of XRCC1, alcohol consumption, and the risk of colorectal cancer in Japan. J Epidemiol. 2012;22:64–71. doi: 10.2188/jea.JE20110059. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Yang M, Guo H, Wu C, et al. Functional FEN1 polymorphisms are associated with DNA damage levels and lung cancer risk. Hum Mutat. 2009;30:1320–28. doi: 10.1002/humu.21060. [DOI] [PubMed] [Google Scholar]
48.Kwiatkowski D, Czarny P, Galecki P, et al. Variants of base excision repair genes MUTYH, PARP1 and XRCC1 in Alzheimer’s disease risk. Neuropsychobiology. 2015;71:176–86. doi: 10.1159/000381985. [DOI] [PubMed] [Google Scholar]
49.Srivastava A, Srivastava K, Pandey SN, et al. Single-nucleotide polymorphisms of DNA repair genes OGG1 and XRCC1: Association with gallbladder cancer in North Indian population. Ann Surg Oncol. 2009;16:1695–703. doi: 10.1245/s10434-009-0354-3. [DOI] [PubMed] [Google Scholar]
50.Li Y, Liu F, Tan SQ, et al. X-ray repair cross-complementing group 1 (XRCC1) genetic polymorphisms and cervical cancer risk: a huge systematic review and meta-analysis. PLoS One. 2012;7:e44441. doi: 10.1371/journal.pone.0044441. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Shen B, Singh P, Liu R, et al. Multiple but dissectible functions of FEN-1 nucleases in nucleic acid processing, genome stability and diseases. Bioessays. 2005;27:717–29. doi: 10.1002/bies.20255. [DOI] [PubMed] [Google Scholar]
52.Hegde ML, Hazra TK, Mitra S. Early steps in the DNA base excision/single-strand interruption repair pathway in mammalian cells. Cell Res. 2008;18:27–47. doi: 10.1038/cr.2008.8. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Qian L, Yuan F, Rodriguez-Tello P, et al. Human Fanconi anemia complementation group a protein stimulates the 5′ flap endonuclease activity of FEN1. PLoS One. 2013;8:e82666. doi: 10.1371/journal.pone.0082666. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Kalifa L, Beutner G, Phadnis N, et al. Evidence for a role of FEN1 in maintaining mitochondrial DNA integrity. DNA Repair (Amst) 2009;8:1242–49. doi: 10.1016/j.dnarep.2009.07.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Yang CH, Lin YD, Yen CY, et al. A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS. 2015;19:238–47. doi: 10.1089/omi.2014.0121. [DOI] [PubMed] [Google Scholar]
56.Levin DS, McKenna AE, Motycka TA, et al. Interaction between PCNA and DNA ligase I is critical for joining of Okazaki fragments and long-patch base-excision repair. Curr Biol. 2000;10:919–22. doi: 10.1016/s0960-9822(00)00619-9. [DOI] [PubMed] [Google Scholar]
57.Beneke S, Bürkle A. Poly(ADP-ribosyl)ation in mammalian ageing. Nucleic Acids Res. 2007;35:7456–65. doi: 10.1093/nar/gkm735. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Kim MY, Zhang T, Kraus WL. Poly(ADP-ribosyl)ation by PARP-1: ‘PAR-laying’ NAD+ into a nuclear signal. Genes Dev. 2005;19:1951–67. doi: 10.1101/gad.1331805. [DOI] [PubMed] [Google Scholar]
59.Wang XG, Wang ZQ, Tong WM, Shen Y. PARP1 Val762Ala polymorphism reduces enzymatic activity. Biochem Biophys Res Commun. 2007;354:122–26. doi: 10.1016/j.bbrc.2006.12.162. [DOI] [PubMed] [Google Scholar]
60.Rezin GT, Cardoso MR, Gonçalves CL, et al. Inhibition of mitochondrial respiratory chain in brain of rats subjected to an experimental model of depression. Neurochem Int. 2008;53:395–400. doi: 10.1016/j.neuint.2008.09.012. [DOI] [PubMed] [Google Scholar]
61.Gardner A, Johansson A, Wibom R, et al. Alterations of mitochondrial function and correlations with personality traits in selected major depressive disorder patients. J Affect Disord. 2003;76:55–68. doi: 10.1016/s0165-0327(02)00067-8. [DOI] [PubMed] [Google Scholar]
62.Moreno-Fernández AM, Cordero MD, Garrido-Maraver J, et al. Oral treatment with amitriptyline induces coenzyme Q deficiency and oxidative stress in psychiatric patients. J Psychiatr Res. 2012;46:341–45. doi: 10.1016/j.jpsychires.2011.11.002. [DOI] [PubMed] [Google Scholar]
63.Maes M, Mihaylova I, Kubera M, et al. Lower plasma Coenzyme Q10 in depression: a marker for treatment resistance and chronic fatigue in depression and a risk factor to cardiovascular disorder in that illness. Neuro Endocrinol Lett. 2009;30:462–69. [PubMed] [Google Scholar]
64.Shoffner JM, Lott MT, Voljavec AS, et al. Spontaneous Kearns-Sayre/chronic external ophthalmoplegia plus syndrome associated with a mitochondrial DNA deletion: A slip-replication model and metabolic therapy. Proc Natl Acad Sci USA. 1989;86:7952–56. doi: 10.1073/pnas.86.20.7952. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Anderson G, Berk M, Dean O, et al. Role of immune-inflammatory and oxidative and nitrosative stress pathways in the etiology of depression: therapeutic implications. CNS Drugs. 2014;28:1–10. doi: 10.1007/s40263-013-0119-1. [DOI] [PubMed] [Google Scholar]
66.Szczepankiewicz A, Leszczyńska-Rodziewicz A, Pawlak J, et al. FKBP5 polymorphism is associated with major depression but not with bipolar disorder. J Affect Disord. 2014;164:33–37. doi: 10.1016/j.jad.2014.04.002. [DOI] [PubMed] [Google Scholar]
67.van West D, Van Den Eede F, et al. Glucocorticoid receptor gene-based SNP analysis in patients with recurrent major depression. Neuropsychopharmacology. 2006;31:620–27. doi: 10.1038/sj.npp.1300898. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1.

Gene-gene interactions of the studied polymorphisms and the risk of rDD.

Combined genotype	Control (n=311)		Depression (n=288)		Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
Combined genotype	Number	Frequency	Number	Frequency	Crude OR (95% CI)	p	Adjusted OR^* (95% CI)	p
c.444T>G – APEX1 (rs1130409) and c.-7C>T – LIG1 (rs20579)
G/G-A/A	0	-	1	0.003	–	–	–	–
G/G-A/G	20	0.064	18	0.063	0.970 (0.502–1.873)	0.928	0.970 (0.502–1.873)	0.928
G/G-G/G	51	0.164	45	0.156	0.944 (0.610–1.462)	0.797	0.945 (0.610–1.464)	0.800
G/T-A/A	2	0.006	5	0.017	2.730 (0.525–14.181)	0.232	2.771 (0.531–14.459)	0.227
G/T-A/G	44	0.141	33	0.115	0.785 (0.485–1.273)	0.327	0.785 (0.484–1.272)	0.326
G/T-G/G	118	0.379	112	0.389	1.041 (0.749–1.447)	0.812	1.040 (0.748–1.446)	0.817
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
T/T-A/G	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.713 (0.730–4.021)	0.217
T/T-G/G	66	0.212	59	0.205	0.956 (0.645–1.419)	0.825	0.957 (0.645–1.420)	0.827
c.444T>G – APEX1 (rs1130409) and c.^50C>T – LIG3* (rs1052536)
G/G-C/C	13	0.042	16	0.056	1.348 (0.637–2.855)	0.435	1.348 (0.637–2.854)	0.435
G/G-C/T	35	0.113	26	0.090	0.783 (0.458–1.336)	0.369	0.783 (0.459–1.337)	0.370
G/G-T/T	23	0.074	22	0.076	1.036 (0.564–1.902)	0.910	1.037 (0.565–1.906)	0.906
G/T-C/C	30	0.096	40	0.139	1.511 (0.913–2.499)	0.108	1.510 (0.912–2.498)	0.109
G/T-C/T	91	0.293	76	0.264	0.867 (0.606–1.240)	0.434	0.866 (0.605–1.240)	0.433
G/T-T/T	43	0.138	34	0.118	0.834 (0.515–1.350)	0.461	0.834 (0.516–1.350)	0.461
T/T-C/C	13	0.042	23	0.080	1.990 (0.988–4.006)	0.054	1.994 (0.990–4.018)	0.053
T/T-C/T	40	0.129	34	0.118	0.907 (0.557–1.478)	0.695	0.905 (0.555–1.476)	0.690
T/T-T/T	23	0.074	17	0.059	0.785 (0.411–1.502)	0.466	0.786 (0.411–1.505)	0.468
c.444T>G – APEX1 (rs1130409) and c.^83A>C – LIG3* (rs4796030)
G/G-A/A	5	0.016	12	0.042	2.661 (0.926–7.649)	0.069	2.658 (0.924–7.642)	0.070
G/G-A/C	32	0.103	26	0.090	0.865 (0.502–1.491)	0.602	0.866 (0.503–1.494)	0.606
G/G-C/C	34	0.109	26	0.090	0.808 (0.472–1.384)	0.439	0.809 (0.472–1.385)	0.440
G/T-A/A	17	0.055	27	0.094	1.789 (0.953–3.357)	0.070	1.790 (0.954–3.358)	0.070
G/T-A/C	70	0.225	64	0.222	0.984 (0.670–1.445)	0.933	0.984 (0.670–1.447)	0.936
G/T-C/C	77	0.248	59	0.205	0.783 (0.533–1.151)	0.213	0.781 (0.531–1.148)	0.208
T/T-A/A	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.730 (0.735–4.072)	0.210
T/T-A/C	33	0.106	30	0.104	0.980 (0.581–1.652)	0.938	0.979 (0.580–1.651)	0.936
T/T-C/C	34	0.109	30	0.104	0.947 (0.564–1.593)	0.838	0.946 (0.563–1.591)	0.835
c.444T>G – APEX1 (rs1130409) and c.580C>T – XRCC1 (rs1799782)
G/G-G/G	62	0.199	51	0.177	0.864 (0.573–1.304)	0.784	0.865 (0.573–1.306)	0.491
G/G-G/A	9	0.029	13	0.045	1.586 (0.668–3.769)	0.296	1.583 (0.666–3.766)	0.299
G/G-A/A	0	–	0	–	–	–	–	–
G/T-G/G	142	0.457	130	0.451	0.979 (0.710–1.351)	0.898	0.979 (0.710–1.351)	0.899
G/T-G/A	22	0.071	19	0.066	0.928 (0.491–1.752)	0.817	0.925 (0.489–1.749)	0.810
G/T-A/A	0	–	1	0.003	–	–	–	–
T/T-G/G	70	0.225	63	0.219	0.964 (0.655–1.418)	0.852	0.964 (0.656–1.419)	0.854
T/T-G/A	5	0.016	10	0.035	2.201 (0.743–6.520)	0.154	2.198 (0.742–6.512)	0.155
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
c.444T>G – APEX1 (rs1130409) and c.1196A>G – XRCC1 (rs25487)
G/G-C/C	28	0.090	30	0.104	1.175 (0.684–2.021)	0.559	1.177 (0.684–2.024)	0.556
G/G-C/T	36	0.116	27	0.094	0.790 (0.467–1.338)	0.381	0.791 (0.467–1.339)	0.382
G/G-T/T	7	0.023	7	0.024	1.082 (0.375–3.123)	0.884	1.082 (0.375–3.124)	0.884
G/T-C/C	75	0.241	48	0.167	0.629 (0.420–0.943)	0.025	0.627 (0.418–0.940)	0.024
G/T-C/T	68	0.219	88	0.306	1.572 (1.089–2.271)	0.016	1.573 (1.089–2.272)	0.016
G/T-T/T	21	0.068	14	0.049	0.706 (0.352–1.415)	0.326	0.706 (0.352–1.417)	0.328
T/T-C/C	27	0.087	30	0.104	1.223 (0.708–2.113)	0.470	1.222 (0.707–2.111)	0.472
T/T-C/T	34	0.109	30	0.104	0.947 (0.564–1.593)	0.838	0.948 (0.564–1.594	0.840
T/T-T/T	15	0.048	14	0.049	1.008 (0.478–2.127)	0.983	1.009 (0.478–2.129)	0.981
c.444T>G – APEX1 (rs1130409) and c.2285T>C – PARP1 (rs1136410)
G/G-A/A	44	0.141	48	0.167	1.214 (0.778–1.893)	0.393	1.214 (0.778–1.893)	0.393
G/G-A/G	25	0.080	16	0.056	0.673 (0.352–1.288)	0.232	0.674 (0.352–1.291)	0.234
G/G-G/G	2	0.006	0	–	–	–	–	–
G/T-A/A	104	0.334	96	0.33	0.995 (0.708–1.398)	0.978	0.993 (0.706–1.397)	0.967
G/T-A/G	57	0.183	46	0.160	0.847 (0.553–1.298)	0.446	0.848 (0.553–1.301)	0.450
G/T-G/G	3	0.010	8	0.028	2.933 (0.771–11.166)	0.115	2.938 (0.772–11.186)	0.114
T/T-A/A	53	0.170	47	0.163	0.949 (0.617–1.460)	0.813	0.950 (0.618–1.461)	0.816
T/T-A/G	21	0.068	20	0.069	1.031 (0.546–1.944)	0.926	1.029 (0.546–1.942)	0.929
T/T-G/G	2	0.006	7	0.024	3.849 (0.793–18.681)	0.094	3.845 (0.792–18.666)	0.095
c.444T>G – APEX1 (rs1130409) and c.-441G>A – FEN1 (rs174538)
G/G-A/A	0	–	0	-	–	–	–	–
G/G-A/G	38	0.122	24	0.083	0.653 (0.381–1.119)	0.121	0.653 (0.381–1.119)	0.121
G/G-G/G	33	0.106	40	0.139	1.359 (0.831–2.222)	0.222	1.361 (0.832–2.226)	0.220
G/T-A/A	0	–	2	–	–	–	–	–
G/T-A/G	75	0.241	61	0.212	0.846 (0.576–1.241)	0.392	0.845 (0.575–1.240)	0.389
G/T-G/G	89	0.286	89	0.309	1.116 (0.786–1.584)	0.541	1.115 (0.785–1.584)	0.542
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	38	0.122	34	0.118	0.962 (0.587–1.575)	0.877	0.692 (0.587–1.575)	0.877
T/T-G/G	38	0.122	40	0.139	1.159 (0.720–1.865)	0.544	1.159 (0.720–1.865)	0.544
c.-468T>G – APEX1 (rs1760944) and c.-7C>T – LIG1 (rs20579)
T/T-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
T/T-A/G	11	0.035	11	0.038	1.083 (0.462–2.538)	0.854	1.086 (0.463–2.546)	0.850
T/T-G/G	44	0.141	36	0.125	0.867 (0.540–1.391)	0.554	0.865 (0.539–1.389)	0.550
T/G-A/A	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.187 (0.397–12.046)	0.369
T/G-A/G	32	0.103	33	0.115	1.128 (0.674–1.888)	0.646	1.128 (0.674–1.888)	0.647
T/G-G/G	108	0.347	120	0.417	1.343 (0.965–1.869)	0.081	0.856 (0.614–1.192)	0.356
G/G-A/A	0	-	2	0.007	–	–	–	–
G/G-A/G	30	0.096	21	0.073	0.737 (0.412–1.319)	0.304	0.735 (0.411–1.317)	0.301
G/G-G/G	83	0.267	60	0.208	0.723 (0.495–1.057)	0.094	0.723 (0.494–1.058)	0.095
c.-468T>G – APEX1 (rs1760944) and c.^50C>T – LIG3* (rs1052536)
T/T-C/C	12	0.039	19	0.066	1.760 (0.839–3.693)	0.135	1.759 (0.838–3.691)	0.136
T/T-C/T	25	0.080	18	0.063	0.763 (0.407–1.430)	0.398	0.760 (0.405–1.426)	0.392
T/T-T/T	19	0.061	11	0.038	0.610 (0.285–1.306)	0.203	0.611 (0.285–1.310)	0.205
T/G-C/C	28	0.090	39	0.125	1.444 (0.857–2.434)	0.168	1.443 (0.855–2.433)	0.169
T/G-C/T	75	0.241	81	0.281	1.231 (0.854–1.774)	0.264	1.231 (0.854–1.774)	0.265
T/G-T/T	39	0.125	40	0.139	1.125 (0.701–1.806)	0.626	1.021 (0.636–1.639)	0.931
G/G-C/C	16	0.051	24	0.083	1.676 (0.872–3.224)	0.122	1.682 (0.874–3.237)	0.120
G/G-C/T	66	0.212	37	0.128	*0.547 (0.353–0.849)*	*0.007*	*0.547 (0.353–0.850)*	*0.007*
G/G-T/T	31	0.100	22	0.076	0.747 (0.422–1.323)	0.317	0.747 (0.422–1.324)	0.318
c.-468T>G – APEX1 (rs1760944) and c.^83A>C – LIG3* (rs4796030)0
T/T-A/A	4	0.013	10	0.035	2.761 (0.856–8.903)	0.089	2.768 (0.858–8.930)	0.088
T/T-A/C	18	0.058	18	0.063	1.085 (0.553–2.129)	0.812	1.083 (0.552–2.126)	0.816
T/T-C/C	34	0.109	20	0.069	0.608 (0.341–1.083)	0.091	0.608 (0.341–1.083)	0.091
T/G-A/A	17	0.055	24	0.083	1.572 (0.826–2.991)	0.168	1.572 (0.826–2.991)	0.168
T/G-A/C	64	0.206	74	0.257	1.335 (0.911–1.954)	0.138	1.335 (0.912–1.955)	0.138
T/G-C/C	61	0.196	59	0.205	1.056 (0.708–1.576)	0.790	1.054 (0.706–1.574)	0.796
G/G-A/A	10	0.032	19	0.066	2.126 (0.971–4.653)	0.059	2.134 (0.975–4.674)	0.058
G/G-A/C	53	0.170	28	0.097	*0.524 (0.321–0.855)*	*0.010*	*0.524 (0.321–0.856)*	*0.010*
G/G-C/C	50	0.161	36	0.125	0.746 (0.470–1.184)	0.213	0.745 (0.469–1.183)	0.212
c.-468T>G – APEX1 (rs1760944) and c.580C>T – XRCC1 (rs1799782)
T/T-G/G	45	0.145	41	0.142	0.981 (0.621–1.550)	0.935	0.982 (0.621–1.551)	0.937
T/T-G/A	10	0.032	7	0.024	0.750 (0.282–1.997)	0.565	0.745 (0.279–1.989)	0.557
T/T-A/A	1	0.003	0	–	–	–	–	–
T/G-G/G	130	0.418	134	0.465	1.211 (0.877–1.674)	0.244	1.211 (0.877–1.673)	0.245
T/G-G/A	12	0.039	21	0.073	1.960 (0.946–4.059)	0.070	1.958 (0.945–4.058)	0.071
T/G-A/A	0	–	2	0.007	–	–	–	–
G/G-G/G	99	0.318	69	0.240	0.675 (0.470–0.968)	0.033	0.675 (0.470–0.969)	0.033
G/G-G/A	14	0.045	14	0.049	1.084 (0.508–2.315)	0.835	1.082 (0.506–2.311)	0.840
G/G-A/A	0	–	0	–	–	–	–	–
c.-468T>G – APEX1 (rs1760944) and c.1196A>G – XRCC1 (rs25487)
T/T-C/C	27	0.087	19	0.066	0.743 (0.404–1.368)	0.340	0.741 (0.402–1.365)	0.336
T/T-C/T	25	0.080	23	0.080	0.993 (0.550–1.792)	0.981	0.993 (0.550–1.792)	0.982
T/T-T/T	4	0.013	6	0.021	1.633 (0.456–5.847)	0.451	1.638 (0.457–5.868)	0.448
T/G-C/C	58	0.186	57	0.198	1.076 (0.717–1.617)	0.723	1.075 (0.715–1.615)	0.728
T/G-C/T	62	0.199	80	0.278	1.545 (1.057–2.257)	0.025	1.546 (1.058–2.260)	0.024
T/G-T/T	22	0.071	20	0.069	0.980 (0.523–1.837)	0.951	0.979 (0.522–1.835)	0.947
G/G-C/C	45	0.145	32	0.111	0.739 (0.455–1.200)	0.221	0.739 (0.455–1.201)	0.222
G/G-C/T	51	0.164	42	0.146	0.870 (0.558–1.357)	0.540	0.871 (0.558–1.357)	0.541
G/G-T/T	17	0.055	9	0.031	0.558 (0.245–1.272)	0.165	0.559 (0.244–1.276)	0.167
c.-468T>G – APEX1 (rs1760944) and c.2285T>C – PARP1 (rs1136410)
T/T-A/A	31	0.100	29	0.101	1.011 (0.593–1.725)	0.967	1.011 (0.593–1.724)	0.969
T/T-A/G	23	0.074	16	0.056	0.737 (0.381–1.424)	0.363	0.736 (0.381–1.423)	0.362
T/T-G/G	2	0.006	3	0.010	1.626 (0.270–9.803)	0.596	1.631 (0.270–9.835)	0.594
T/G-A/A	96	0.309	105	0.365	1.285 (0.915–1.805)	0.148	1.285 (0.913–1.808)	0.150
T/G-A/G	44	0.141	45	0.156	1.124 (0.716–1.763)	0.612	1.128 (0.718–1.772)	0.603
T/G-G/G	2	0.006	7	0.024	3.849 (0.793–18.681)	0.094	3.845 (0.792–18.666)	0.095
G/G-A/A	74	0.238	57	0.198	0.790 (0.535–1.167)	0.237	0.791 (0.535–1.169)	0.239
G/G-A/G	36	0.116	21	0.073	0.601 (0.342–1.056)	0.077	0.601 (0.342–1.057)	0.077
G/G-G/G	3	0.010	5	0.017	1.814 (0.430–7.659)	0.418	1.810 (0.428–7.645)	0.420
c.-468T>G – APEX1 (rs1760944) and c.-441G>A – FEN1 (rs174538)
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	29	0.093	23	0.080	0.844 (0.476–1.496)	0.561	0.838 (0.471–1.491)	0.548
T/T-G/G	27	0.087	25	0.087	1.000 (0.566–1.767)	1.000	1.004 (0.567–1.777)	0.990
T/G-A/A	0	–	0	–	–	–	–	–
T/G-A/G	67	0.215	64	0.222	1.041 (0.706–1.533)	0.841	1.041 (0.706–1.534)	0.840
T/G-G/G	75	0.241	93	0.323	1.501 (1.049–2.148)	0.026	1.500 (1.048–2.148)	0.027
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	55	0.177	32	0.111	0.582 (0.364–0.930)	0.024	0.582 (0.364–0.931)	0.024
G/G-G/G	58	0.186	51	0.177	0.939 (0.619–1.423)	0.765	0.939 (0.619–1.423)	0.766
c.-7C>T – LIG1 (rs20579) and c.^50C>T – LIG3* (rs1052536)
A/A-C/C	0	–	0	–	–	–	–	–
A/A-C/T	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.187 (0.397–12.046)	0.369
A/A-T/T	1	0.003	3	0.010	3.263 (0.338–31.550)	0.307	3.264 (0.338–31.560)	0.307
A/G-C/C	12	0.039	20	0.069	1.859 (0.892–3.876)	0.098	1.858 (0.891–3.873)	0.098
A/G-C/T	42	0.135	31	0.108	0.773 (0.471–1.267)	0.306	0.773 (0.471–1.267)	0.307
A/G-T/T	19	0.061	14	0.049	0.785 (0.386–1.597)	0.504	0.785 (0.386–1.597)	0.504
G/G-C/C	44	0.141	59	0.205	1.563 (1.019–2.400)	0.041	1.564 (1.019–2.400)	0.041
G/G-C/T	122	0.392	101	0.351	0.837 (0.600–1.166)	0.293	0.836 (0.599–1.165)	0.290
G/G-T/T	69	0.222	56	0.194	0.847 (0.570–1.258)	0.410	0.847 (0.570–1.260)	0.413
c.-7C>T – LIG1 (rs20579) and c.^83A>C – LIG3* (rs4796030)
A/A-A/A	0	–	0	–	–	–
A/A-A/C	0	–	2	0.007	–	–
A/A-C/C	3	0.010	5	0.017	1.814 (0.430–7.659)	0.418	1.821 (0.431–7.694)	0.415
A/G-A/A	6	0.019	13	0.045	2.403 (0.901–6.409)	0.080	2.402 (0.901–6.407)	0.080
A/G-A/C	34	0.109	25	0.087	0.774 (0.450–1.333)	0.356	0.775 (0.450–1.335)	0.359
A/G-C/C	33	0.106	27	0.094	0.871 (0.510–1.489)	0.615	0.870 (0.508–1.487)	0.610
G/G-A/A	25	0.080	40	0.139	1.845 (1.088–3.128)	0.023	1.851 (1.091–3.139)	0.022
G/G-A/C	101	0.325	93	0.323	0.992 (0.704–1.397)	0.962	0.992 (0.704–1.397)	0.963
G/G-C/C	109	0.350	83	0.288	0.750 (0.531–1.060)	0.103	0.749 (0.530–1.059)	0.101
c.-7C>T – LIG1 (rs20579) and c.2285T>C – PARP1 (rs1136410)
A/A-A/A	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.151 (0.193–23.970)	0.534
A/A-A/G	2	0.006	4	0.014	2.176 (0.396–11.971)	0.371	2.202 (0.399–12.159)	0.365
A/A-G/G	0	–	1	0.003	–	–	–	–
A/G-A/A	49	0.158	46	0.160	1.016 (0.655–1.576)	0.942	1.016 (0.655–1.575)	0.945
A/G-A/G	24	0.077	17	0.059	0.750 (0.394–1.427)	0.381	0.751 (0.395–1.428)	0.382
A/G-G/G	0	–	2	0.007	–	–	–	–
G/G-A/A	151	0.486	143	0.497	1.045 (0.758–1.440)	0.788	1.044 (0.758–1.439)	0.792
G/G-A/G	77	0.248	61	0.212	0.817 (0.557–1.197)	0.299	0.817 (0.557–1.199)	0.302
G/G-G/G	7	0.023	12	0.042	1.888 (0.733–4.864)	0.188	1.886 (0.732–4.859)	0.189
c.-7C>T – LIG1 (rs20579) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	2	0.006	5	0.017	2.730 (0.525–14.181)	0.232	2.730 (0.525–14.181)	0.232
A/A-G/G	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.161 (0.195–23.970)	0.530
A/G-A/A	0	–	0	–	–	–	–	–
A/G-A/G	37	0.119	19	0.066	0.523 (0.293–0.933)	0.028	0.523 (0.293–0.932)	0.028
A/G-G/G	36	0.116	46	0.160	1.452 (0.908–2.321)	0.119	1.452 (0.908–2.321)	0.119
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	112	0.360	95	0.330	0.875 (0.624–1.226)	0.437	0.874 (0.624–1.225)	0.435
G/G-C/C	123	0.395	121	0.420	1.107 (0.799–1.535)	0.540	1.108 (0.800–1.535)	0.538
c.^50C>T – LIG3* (rs1052536) and c.580C>T – XRCC1 (rs1799782)
C/C-G/G	47	0.151	63	0.219	1.573 (1.036–2.388)	0.034	1.572 (1.036–2.387)	0.034
C/C-G/A	9	0.029	14	0.049	1.715 (0.730–4.024)	0.216	1.714 (0.730–4.023)	0.216
C/C-A/A	0	-	2	0.007	–	–	–	–
C/T-G/G	152	0.489	122	0.424	0.769 (0.557–1.061)	0.110	0.769 (0.557–1.062)	0.110
C/T-G/A	13	0.042	14	0.049	1.171 (0.541–2.536)	0.688	1.167 (0.5358–2.534)	0.696
C/T-A/A	1	0.003	0	–	–	–	–	–
T/T-G/G	75	0.241	59	0.205	0.811 (0.551–1.193)	0.287	0.811 (0.551–1.196)	0.291
T/T-G/A	14	0.045	14	0.049	1.084 (0.508–2.315)	0.835	1.081 (0.505–2.311)	0.841
T/T-A/A	0	–	0	–	–	–	–	–
c.^50C>T – LIG3* (rs1052536) and c.1196A>G – XRCC1 (rs25487)
C/C-C/C	23	0.074	32	0.111	1.565 (0.893–2.745)	0.118	1.565 (0.893–2.745)	0.118
C/C-C/T	25	0.080	37	0.128	1.686 (0.988–2.879)	0.056	1.686 (0.988–2.879)	0.056
C/C-T/T	8	0.026	10	0.035	1.362 (0.530–3.501)	0.521	1.361 (0.530–3.498)	0.522
C/T-C/C	65	0.209	46	0.160	0.719 (0.474–1.092)	0.122	0.715 (0.470–1.087)	0.117
C/T-C/T	74	0.238	72	0.250	1.068 (0.735–1.550)	0.731	1.069 (0.736–1.553)	0.726
C/T-T/T	27	0.087	18	0.063	0.701 (0.378–1.303)	0.261	0.702 (0.378–1.303)	0.262
T/T-C/C	42	0.135	30	0.104	0.745 (0.452–1.226)	0.247	0.745 (0.453–1.228)	0.249
T/T-C/T	39	0.125	36	0.125	0.996 (0.614–1.617)	0.988	0.996 (0.614–1.617)	0.987
T/T-T/T	8	0.026	7	0.024	0.944 (0.338–2.636)	0.912	0.947 (0.339–2.651)	0.918
c.^50C>T – LIG3* (rs1052536) and c.2285T>C – PARP1 (rs1136410)
C/C-A/A	39	0.125	57	0.198	1.721 (1.105–2.681)	0.016	1.720 (1.104–2.681)	0.017
C/C-A/G	17	0.055	17	0.059	1.085 (0.543–2.168)	0.818	1.086 (0.543–2.170)	0.815
C/C-G/G	0	-	5	0.017	–	–	–	–
C/T-A/A	108	0.347	89	0.309	0.841 (0.597–1.183)	0.320	0.839 (0.595–1.181)	0.314
C/T-A/G	52	0.167	40	0.139	0.803 (0.514–1.257)	0.338	0.804 (0.514–1.259)	0.341
C/T-G/G	6	0.019	7	0.024	1.266 (0.421–3.813)	0.675	1.263 (0.419–3.807)	0.678
T/T-A/A	54	0.174	45	0.156	0.881 (0.572–1.359)	0.567	0.882 (0.572–1.360)	0.570
T/T-A/G	34	0.109	25	0.087	0.774 (0.450–1.333)	0.356	0.775 (0.450–1.335)	0.359
T/T-G/G	1	0.003	3	0.010	3.263 (0.338–31.550)	0.307	3.250 (0.336–31.466)	0.309
c.^50C>T – LIG3* (rs1052536) and c.-441G>A – FEN1 (rs174538)
C/C-A/A	0	–	0	–	–	–	–	–
C/C-A/G	29	0.093	31	0.108	1.173 (0.688–2.000)	0.558	1.172 (0.687–1.999)	0.559
C/C-G/G	27	0.087	48	0.167	*2.104 (1.274–3.475)*	*0.004*	*2.104 (1.274–3.475)*	*0.004*
C/T-A/A	0	–	0	–	–	–	–	–
C/T-A/G	86	0.277	58	0.201	0.660 (0.451–0.965)	0.032	0.658 (0.449–0.963)	0.031
C/T-G/G	80	0.257	78	0.271	1.072 (0.746–1.543)	0.706	1.073 (0.746–1.544)	0.704
T/T-A/A	0	-	0	-	–	–	–	–
T/T-A/G	36	0.116	30	0.104	0.888 (0.532–1.484)	0.651	0.890 (0.532–1.489)	0.658
T/T-G/G	53	0.170	43	0.149	0.854 (0.551–1.325)	0.482	0.854 (0.551–1.324)	0.481
c.^83A>C – LIG3* (rs4796030) and c.580C>T – XRCC1 (rs1799782)
A/A-G/G	25	0.080	43	0.149	*2.008 (1.192–3.383)*	*0.009*	*2.015 (1.195–3.398)*	*0.009*
A/A-G/A	6	0.019	9	0.031	1.640 (0.576–4.666)	0.354	1.637 (0.575–4.660)	0.356
A/A-A/A	0	–	1	0.003	–	–	–	–
A/C-G/G	124	0.399	106	0.368	0.878 (0.631–1.222)	0.441	0.879 (0.632–1.223)	0.444
A/C-G/A	10	0.032	13	0.045	1.423 (0.614–3.297)	0.411	1.423 (0.614–3.297)	0.411
A/C-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–14.231)	0.963
C/C-G/G	125	0.402	95	0.330	0.732 (0.524–1.023)	0.068	0.732 (0.524–1.023)	0.068
C/C-G/A	20	0.064	20	0.069	1.086 (0.572–2.063)	0.801	1.082 (0.567–2.061)	0.812
C/C-A/A	0	–	0	–	–	–	–	–
c.^83A>C – LIG3* (rs4796030) and c.1196A>G – XRCC1 (rs25487)
A/A-C/C	13	0.042	22	0.076	1.896 (0.937–3.838)	0.075	1.897 (0.937–3.841)	0.075
A/A-C/T	13	0.042	27	0.094	2.371 (1.199–4.691)	0.013	2.374 (1.200–4.698)	0.013
A/A-T/T	5	0.016	4	0.014	0.862 (0.229–3.242)	0.826	0.865 (0.230–3.259)	0.831
A/C-C/C	51	0.164	39	0.135	0.798 (0.508–1.254)	0.329	0.796 (0.507–1.252)	0.324
A/C-C/T	59	0.190	63	0.219	1.196 (0.803–1.781)	0.378	1.199 (0.805–1.787)	0.372
A/C-T/T	25	0.080	18	0.063	0.763 (0.407–1.430)	0.398	0.764 (0.407–1.432)	0.401
C/C-C/C	66	0.212	47	0.163	0.724 (0.478–1.095)	0.126	0.723 (0.478–1.095)	0.126
C/C-C/T	66	0.212	55	0.191	0.876 (0.587–1.308)	0.518	0.875 (0.586–1.306)	0.513
C/C-T/T	13	0.042	13	0.045	1.084 (0.494–2.378	0.841	1.082 (0.493–2.375)	0.844
c.^83A>C – LIG3* (rs4796030) and c.2285T>C – PARP1 (rs1136410)
A/A-A/A	19	0.061	37	0.128	*2.265 (1.271–4.039)*	*0.006*	*2.268 (1.272–4.044)*	*0.006*
A/A-A/G	12	0.039	14	0.049	1.273 (0.579–2.800)	0.548	1.275 (0.579–2.805)	0.546
A/A-G/G	0	–	2	0.007	–	–	–	–
A/C-A/A	93	0.299	78	0.271	0.871 (0.610–1.242)	0.445	0.870 (0.610–1.242)	0.443
A/C-A/G	37	0.119	37	0.128	1.092 (0.671–1.776)	0.724	1.097 (0.672–1.789)	0.712
A/C-G/G	5	0.016	5	0.017	1.081 (0.310–3.774)	0.902	1.079 (0.309–3.768)	0.905
C/C-A/A	89	0.286	76	0.264	0.894 (0.624–1.281)	0.542	0.893 (0.623–1.279)	0.536
C/C-A/G	54	0.174	31	0.108	0.574 (0.357–0.922)	0.022	0.574 (0.357–0.922)	0.022
C/C-G/G	2	0.006	8	0.028	4.414 (0.930–20.963)	0.062	4.408 (0.928–20.939)	0.062
c.^83A>C – LIG3* (rs4796030) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	18	0.058	18	0.063	1.085 (0.553–2.129)	0.812	1.089 (0.554–2.139)	0.805
A/A-G/G	13	0.042	35	0.122	*3.171 (1.642–6.125)*	*< 0.001*	*3.171 (1.642–6.124)*	*<0.001*
A/C-A/A	0	–	0	–	–	–	–	–
A/C-A/G	71	0.228	49	0.170	0.693 (0.462–1.040)	0.076	0.692 (0.461–1.039)	0.076
A/C-G/G	64	0.206	71	0.247	1.263 (0.860–1.854)	0.234	1.266 (0.862–1.860)	0.229
C/C-A/A	0	–	0	-	–	–	–	–
C/C-A/G	62	0.199	52	0.181	0.885 (0.588–1.332)	0.558	0.884 (0.587–1.331)	0.555
C/C-G/G	83	0.267	63	0.219	0.769 (0.528–1.120)	0.171	0.768 (0.527–1.119)	0.169
c.580C>T – XRCC1 (rs1799782) and c.2285T>C – PARP1 (rs1136410)
G/G-A/A	178	0.572	160	0.556	0.934 (0.676–1.290)	0.679	0.934 (0.676–1.291)	0.679
G/G-A/G	90	0.289	72	0.250	0.819 (0.570–1.176)	0.279	0.819 (0.570–1.178)	0.282
G/G-G/G	6	0.019	12	0.042	2.210 (0.818–5.968)	0.118	2.208 (0.818–5.964)	0.118
G/A-A/A	22	0.071	30	0.104	1.527 (0.859–2.715)	0.149	1.528 (0.857–2.727)	0.151
G/A-A/G	13	0.042	10	0.035	0.825 (0.356–1.911)	0.653	0.825 (0.356–1.912)	0.654
G/A-G/G	1	0.003	2	0.007	2.168 (0.196–24.036)	0.528	2.161 (0.195–23.970)	0.530
A/A-A/A	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
A/A-A/G	0	–	0	–	–	–	–	–
A/A-G/G	0	–	1	0.003	–	–	–	–
c.580C>T – XRCC1 (rs1799782) and c.-441G>A – FEN1 (rs174538)
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	135	0.434	101	0.351	0.704 (0.506–0.979)	0.037	0.704 (0.506–0.979)	0.037
G/G-G/G	139	0.447	143	0.497	1.220 (0.885–1.683)	0.225	1.224 (0.887–1.689)	0.219
G/A-A/A	0	–	0	–	–	–	–	–
G/A-A/G	16	0.051	17	0.059	1.157 (0.573–2.335)	0.685	1.156 (0.573–2.334)	0.685
G/A-G/G	20	0.064	25	0.087	1.383 (0.751–2.548)	0.298	1.382 (0.747–2.556)	0.303
A/A-A/A	0	-	0	-	–	–	–	–
A/A-A/G	0	-	1	0.003	–	–	–	–
A/A-G/G	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.080 (0.067–17.354)	0.956
c.1196A>G – XRCC1 (rs25487) and c.2285T>C – PARP1 (rs1136410)
C/C-A/A	84	0.270	77	0.267	0.986 (0.687–1.416)	0.940	0.984 (0.685–1.414)	0.930
C/C-A/G	43	0.138	27	0.094	0.645 (0.387–1.074)	0.092	0.645 (0.387–1.075)	0.093
C/C-G/G	3	0.010	4	0.014	1.446 (0.321–6.517)	0.631	1.449 (0.321–6.532)	0.629
C/T-A/A	88	0.283	90	0.313	1.152 (0.811–1.636)	0.429	1.152 (0.811–1.636)	0.429
C/T-A/G	47	0.151	45	0.156	1.040 (0.667–1.622)	0.862	1.042 (0.668–1.625)	0.858
C/T-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.691 (1.006–13.549)	0.049
T/T-A/A	29	0.093	24	0.083	0.884 (0.502–1.557)	0.670	0.884 (0.502–1.557)	0.670
T/T-A/G	13	0.042	10	0.035	0.825 (0.356–1.911)	0.653	0.827 (0.356–1.920)	0.659
T/T-G/G	1	0.003	1	0.003	1.080 (0.067–17.349)	0.957	1.068 (0.066–17.231)	0.963
c.1196A>G – XRCC1 (rs25487) and c.-441G>A – FEN1 (rs174538)
C/C-A/A	0	–	0	–	–	–	–	–
C/C-A/G	66	0.212	48	0.167	0.742 (0.492–1.121)	0.157	0.739 (0.489–1.118)	0.152
C/C-G/G	64	0.206	60	0.208	1.016 (0.684–1.508)	0.939	1.016 (0.684–1.509)	0.937
C/T-A/A	0	–	0	–	–	–	–	–
C/T-A/G	59	0.190	57	0.198	1.054 (0.703–1.581)	0.800	1.055 (0.703–1.583)	0.795
C/T-G/G	79	0.254	88	0.306	1.292 (0.903–1.848)	0.160	1.292 (0.904–1.848)	0.160
T/T-A/A	0	–	0	–	–	–	–	–
T/T-A/G	26	0.084	14	0.049	0.560 (0.286–1.095)	0.090	0.561 (0.287–1.097)	0.091
T/T-G/G	17	0.055	21	0.073	1.360 (0.703–2.633)	0.361	1.360 (0.702–2.632)	0.362
c.2285T>C – PARP1 (rs1136410) and c.-441G>A – FEN1 (rs174538)
A/A-A/A	0	–	0	–	–	–	–	–
A/A-A/G	100	0.322	78	0.271	0.784 (0.551–1.115)	0.175	0.782 (0.550–1.113)	0.172
A/A-G/G	101	0.325	113	0.392	1.343 (0.960–1.877)	0.085	1.342 (0.960–1.876)	0.085
A/G-A/A	0	–	0	–	–	–	–	–
A/G-A/G	47	0.151	36	0.125	0.802 (0.503–1.280)	0.356	0.803 (0.503–1.283)	0.359
A/G-G/G	56	0.180	46	0.160	0.866 (0.564–1.328)	0.508	0.866 (0.565–1.329)	0.511
G/G-A/A	0	–	0	–	–	–	–	–
G/G-A/G	4	0.013	5	0.017	1.356 (0.361–5.100)	0.652	1.352 (0.359–5.088)	0.656
G/G-G/G	3	0.010	10	0.035	3.693 (1.006–13.556)	0.049	3.699 (1.007–13.578)	0.049

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OR adjusted for sex. p<0.05 along with corresponding ORs are in bold; p<0.012 along with corresponding ORs are in bold and italic.

[b1-medscimonit-22-4455] 1.Pasco JA, Nicholson GC, Williams LJ, et al. Association of high-sensitivity C-reactive protein with de novo major depression. Br J Psychiatry. 2010;197:372–77. doi: 10.1192/bjp.bp.109.076430. [DOI] [PubMed] [Google Scholar]

[b2-medscimonit-22-4455] 2.Gardner A, Boles RG. Beyond the serotonin hypothesis: Mitochondria, inflammation and neurodegeneration in major depression and affective spectrum disorders. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35:730–43. doi: 10.1016/j.pnpbp.2010.07.030. [DOI] [PubMed] [Google Scholar]

[b3-medscimonit-22-4455] 3.Maes M, Bosmans E, Meltzer HY, et al. Interleukin-1 beta: A putative mediator of HPA axis hyperactivity in major depression? Am J Psychiatry. 1993;150:1189–93. doi: 10.1176/ajp.150.8.1189. [DOI] [PubMed] [Google Scholar]

[b4-medscimonit-22-4455] 4.Rawdin BJ, Mellon SH, Dhabhar FS, et al. Dysregulated relationship of inflammation and oxidative stress in major depression. Brain Behav Immun. 2013;31:143–52. doi: 10.1016/j.bbi.2012.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b5-medscimonit-22-4455] 5.Leemans JC, Cassel SL, Sutterwala FS. Sensing damage by the NLRP3 inflammasome. Immunol Rev. 2011;243:152–62. doi: 10.1111/j.1600-065X.2011.01043.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b6-medscimonit-22-4455] 6.Alcocer-Gómez E, de Miguel M, Casas-Barquero N, et al. NLRP3 inflammasome is activated in mononuclear blood cells from patients with major depressive disorder. Brain Behav Immun. 2014;36:111–17. doi: 10.1016/j.bbi.2013.10.017. [DOI] [PubMed] [Google Scholar]

[b7-medscimonit-22-4455] 7.Anderson G, Maes M. Oxidative/nitrosative stress and immuno-inflammatory pathways in depression: treatment implications. Curr Pharm Des. 2014;20:3812–47. doi: 10.2174/13816128113196660738. [DOI] [PubMed] [Google Scholar]

[b8-medscimonit-22-4455] 8.Irie M, Asami S, Nagata S, et al. Psychosocial factors as a potential trigger of oxidative DNA damage in human leukocytes. Jpn J Cancer Res. 2001;92:367–76. doi: 10.1111/j.1349-7006.2001.tb01104.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b9-medscimonit-22-4455] 9.Forlenza MJ, Miller GE. Increased serum levels of 8-hydroxy-2′-deoxyguanosine in clinical depression. Psychosom Med. 2006;68:1–7. doi: 10.1097/01.psy.0000195780.37277.2a. [DOI] [PubMed] [Google Scholar]

[b10-medscimonit-22-4455] 10.Irie M, Asami S, Ikeda M, Kasai H. Depressive state relates to female oxidative DNA damage via neutrophil activation. Biochem Biophys Res Commun. 2003;311:1014–18. doi: 10.1016/j.bbrc.2003.10.105. [DOI] [PubMed] [Google Scholar]

[b11-medscimonit-22-4455] 11.Maes M, Mihaylova I, Kubera M, et al. Increased 8-hydroxy-deoxyguanosine, a marker of oxidative damage to DNA, in major depression and myalgic encephalomyelitis/chronic fatigue syndrome. Neuro Endocrinol Lett. 2009;30:715–22. [PubMed] [Google Scholar]

[b12-medscimonit-22-4455] 12.Wei YC, Zhou FL, He DL, et al. Oxidative stress in depressive patients with gastric adenocarcinoma. Int J Neuropsychopharmacol. 2009;12:1089–96. doi: 10.1017/S1461145709000091. [DOI] [PubMed] [Google Scholar]

[b13-medscimonit-22-4455] 13.Kupper N, Gidron Y, Winter J, Denollet J. Association between type D personality, depression, and oxidative stress in patients with chronic heart failure. Psychosom Med. 2009;71:973–80. doi: 10.1097/PSY.0b013e3181bee6dc. [DOI] [PubMed] [Google Scholar]

[b14-medscimonit-22-4455] 14.Yi S, Nanri A, Matsushita Y, et al. Depressive symptoms and oxidative DNA damage in Japanese municipal employees. Psychiatry Res. 2012;200:318–22. doi: 10.1016/j.psychres.2012.05.035. [DOI] [PubMed] [Google Scholar]

[b15-medscimonit-22-4455] 15.Czarny P, Kwiatkowski D, Kacperska D, et al. Elevated level of DNA damage and impaired repair of oxidative DNA damage in patients with recurrent depressive disorder. Med Sci Monit. 2015;21:412–18. doi: 10.12659/MSM.892317. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b16-medscimonit-22-4455] 16.Licandro G, Ling Khor H, Beretta O, et al. The NLRP3 inflammasome affects DNA damage responses after oxidative and genotoxic stress in dendritic cells. Eur J Immunol. 2013;43:2126–37. doi: 10.1002/eji.201242918. [DOI] [PubMed] [Google Scholar]

[b17-medscimonit-22-4455] 17.Czarny P, Kwiatkowski D, Galecki P, et al. Association between single nucleotide polymorphisms of MUTYH, hOGG1 and NEIL1 genes, and depression. J Affect Disord. 2015;184:90–96. doi: 10.1016/j.jad.2015.05.044. [DOI] [PubMed] [Google Scholar]

[b18-medscimonit-22-4455] 18.World Health Organization. ICD-10 Classification of Mental and Behavioural Disorders. Geneva: 1992. [Google Scholar]

[b19-medscimonit-22-4455] 19.Patten SB. Performance of the Composite International Diagnostic Interview Short Form for major depression in community and clinical samples. Chronic Dis Can. 1997;18:109–12. [PubMed] [Google Scholar]

[b20-medscimonit-22-4455] 20.Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74:765–69. doi: 10.1086/383251. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b21-medscimonit-22-4455] 21.Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 2005;95:221–27. doi: 10.1038/sj.hdy.6800717. [DOI] [PubMed] [Google Scholar]

[b22-medscimonit-22-4455] 22.Kessler RC. Epidemiology of women and depression. J Affect Disord. 2003;74:5–13. doi: 10.1016/s0165-0327(02)00426-3. [DOI] [PubMed] [Google Scholar]

[b23-medscimonit-22-4455] 23.Erčulj N, Zadel M, Dolžan V. Genetic polymorphisms in base excision repair in healthy slovenian population and their influence on DNA damage. Acta Chim Slov. 2010;57:182–88. [PubMed] [Google Scholar]

[b24-medscimonit-22-4455] 24.Zielinska A, Davies OT, Meldrum RA, Hodges NJ. Direct visualization of repair of oxidative damage by OGG1 in the nuclei of live cells. J Biochem Mol Toxicol. 2011;25:1–7. doi: 10.1002/jbt.20346. [DOI] [PubMed] [Google Scholar]

[b25-medscimonit-22-4455] 25.Chen J, Tomkinson AE, Ramos W, et al. Mammalian DNA ligase III: Molecular cloning, chromosomal localization, and expression in spermatocytes undergoing meiotic recombination. Mol Cell Biol. 1995;15:5412–22. doi: 10.1128/mcb.15.10.5412. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b26-medscimonit-22-4455] 26.Dianov GL, O’Neill P, Goodhead DT. Securing genome stability by orchestrating DNA repair: removal of radiation-induced clustered lesions in DNA. Bioessays. 2001;23:745–49. doi: 10.1002/bies.1104. [DOI] [PubMed] [Google Scholar]

[b27-medscimonit-22-4455] 27.Petermann E, Keil C, Oei SL. Roles of DNA ligase III and XRCC1 in regulating the switch between short patch and long patch BER. DNA Repair (Amst) 2006;5:544–55. doi: 10.1016/j.dnarep.2005.12.008. [DOI] [PubMed] [Google Scholar]

[b28-medscimonit-22-4455] 28.Lakshmipathy U, Campbell C. Antisense-mediated decrease in DNA ligase III expression results in reduced mitochondrial DNA integrity. Nucleic Acids Res. 2001;29:668–76. doi: 10.1093/nar/29.3.668. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b29-medscimonit-22-4455] 29.Simsek D, Furda A, Gao Y, et al. Crucial role for DNA ligase III in mitochondria but not in Xrcc1-dependent repair. Nature. 2011;471:245–48. doi: 10.1038/nature09794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b30-medscimonit-22-4455] 30.Langaee T, Shin J. The genetics basis of pharmacogenomics. In: Zdanowicz MM, editor. Concepts in Pharmacogenomics. Bethesda: American Society of Health-System Pharmacists; 2010. p. 29. [Google Scholar]

[b31-medscimonit-22-4455] 31.Teo MT, Landi D, Taylor CF, et al. The role of microRNA-binding site polymorphisms in DNA repair genes as risk factors for bladder cancer and breast cancer and their impact on radiotherapy outcomes. Carcinogenesis. 2012;33:581–86. doi: 10.1093/carcin/bgr300. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b32-medscimonit-22-4455] 32.Landi S, Gemignani F, Canzian F, et al. DNA repair and cell cycle control genes and the risk of young-onset lung cancer. Cancer Res. 2006;66:11062–69. doi: 10.1158/0008-5472.CAN-06-1039. [DOI] [PubMed] [Google Scholar]

[b33-medscimonit-22-4455] 33.Krokan HE, Nilsen H, Skorpen F, et al. Base excision repair of DNA in mammalian cells. FEBS Lett. 2000;476:73–77. doi: 10.1016/s0014-5793(00)01674-4. [DOI] [PubMed] [Google Scholar]

[b34-medscimonit-22-4455] 34.Li M, Wilson DM., III Human apurinic/apyrimidinic endonuclease 1. Antioxid Redox Signal. 2014;20:678–707. doi: 10.1089/ars.2013.5492. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b35-medscimonit-22-4455] 35.Li MX, Wang D, Zhong ZY, et al. Targeting truncated APE1 in mitochondria enhances cell survival after oxidative stress. Free Radic Biol Med. 2008;45:592–601. doi: 10.1016/j.freeradbiomed.2008.05.007. [DOI] [PubMed] [Google Scholar]

[b36-medscimonit-22-4455] 36.Shokolenko IN, Alexeyev MF, Robertson FM, et al. The expression of Exonuclease III from E. coli in mitochondria of breast cancer cells diminishes mitochondrial DNA repair capacity and cell survival after oxidative stress. DNA Repair (Amst) 2003;2:471–82. doi: 10.1016/s1568-7864(03)00019-3. [DOI] [PubMed] [Google Scholar]

[b37-medscimonit-22-4455] 37.Lu J, Zhang S, Chen D, et al. Functional characterization of a promoter polymorphism in APE1/Ref-1 that contributes to reduced lung cancer susceptibility. FASEB J. 2009;23:3459–69. doi: 10.1096/fj.09-136549. [DOI] [PubMed] [Google Scholar]

[b38-medscimonit-22-4455] 38.Lo YL, Jou YS, Hsiao CF, et al. A polymorphism in the APE1 gene promoter is associated with lung cancer risk. Cancer Epidemiol Biomarkers Prev. 2009;18:223–29. doi: 10.1158/1055-9965.EPI-08-0749. [DOI] [PubMed] [Google Scholar]

[b39-medscimonit-22-4455] 39.Hadi MZ, Coleman MA, Fidelis K, et al. Functional characterization of Ape1 variants identified in the human population. Nucleic Acids Res. 2000;28:3871–79. doi: 10.1093/nar/28.20.3871. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b40-medscimonit-22-4455] 40.Jin F, Qian C, Qing Y, et al. Genetic polymorphism of APE1 rs1130409 can contribute to the risk of lung cancer. Tumour Biol. 2014;35:6665–71. doi: 10.1007/s13277-014-1829-9. [DOI] [PubMed] [Google Scholar]

[b41-medscimonit-22-4455] 41.Agaçhan B, Küçükhüseyin O, Aksoy P, et al. Apurinic/apyrimidinic endonuclease (APE1) gene polymorphisms and lung cancer risk in relation to tobacco smoking. Anticancer Res. 2009;29:2417–20. [PubMed] [Google Scholar]

[b42-medscimonit-22-4455] 42.Parildar-Karpuzoğlu H, Doğru-Abbasoğlu S, Hanagasi HA, et al. Single nucleotide polymorphisms in base-excision repair genes hOGG1, APE1 and XRCC1 do not alter risk of Alzheimer’s disease. Neurosci Lett. 2008;442:287–91. doi: 10.1016/j.neulet.2008.07.047. [DOI] [PubMed] [Google Scholar]

[b43-medscimonit-22-4455] 43.Brem R, Hall J. XRCC1 is required for DNA single-strand break repair in human cells. Nucleic Acids Res. 2005;33:2512–20. doi: 10.1093/nar/gki543. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b44-medscimonit-22-4455] 44.Han L, Mao W, Yu K. X-ray repair cross-complementing protein 1 (XRCC1) deficiency enhances class switch recombination and is permissive for alternative end joining. Proc Natl Acad Sci USA. 2012;109:4604–8. doi: 10.1073/pnas.1120743109. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b45-medscimonit-22-4455] 45.Campalans A, Marsin S, Nakabeppu Y, et al. XRCC1 interactions with multiple DNA glycosylases: A model for its recruitment to base excision repair. DNA Repair (Amst) 2005;4:826–35. doi: 10.1016/j.dnarep.2005.04.014. [DOI] [PubMed] [Google Scholar]

[b46-medscimonit-22-4455] 46.Yin G, Morita M, Ohnaka K, et al. Genetic polymorphisms of XRCC1, alcohol consumption, and the risk of colorectal cancer in Japan. J Epidemiol. 2012;22:64–71. doi: 10.2188/jea.JE20110059. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b47-medscimonit-22-4455] 47.Yang M, Guo H, Wu C, et al. Functional FEN1 polymorphisms are associated with DNA damage levels and lung cancer risk. Hum Mutat. 2009;30:1320–28. doi: 10.1002/humu.21060. [DOI] [PubMed] [Google Scholar]

[b48-medscimonit-22-4455] 48.Kwiatkowski D, Czarny P, Galecki P, et al. Variants of base excision repair genes MUTYH, PARP1 and XRCC1 in Alzheimer’s disease risk. Neuropsychobiology. 2015;71:176–86. doi: 10.1159/000381985. [DOI] [PubMed] [Google Scholar]

[b49-medscimonit-22-4455] 49.Srivastava A, Srivastava K, Pandey SN, et al. Single-nucleotide polymorphisms of DNA repair genes OGG1 and XRCC1: Association with gallbladder cancer in North Indian population. Ann Surg Oncol. 2009;16:1695–703. doi: 10.1245/s10434-009-0354-3. [DOI] [PubMed] [Google Scholar]

[b50-medscimonit-22-4455] 50.Li Y, Liu F, Tan SQ, et al. X-ray repair cross-complementing group 1 (XRCC1) genetic polymorphisms and cervical cancer risk: a huge systematic review and meta-analysis. PLoS One. 2012;7:e44441. doi: 10.1371/journal.pone.0044441. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b51-medscimonit-22-4455] 51.Shen B, Singh P, Liu R, et al. Multiple but dissectible functions of FEN-1 nucleases in nucleic acid processing, genome stability and diseases. Bioessays. 2005;27:717–29. doi: 10.1002/bies.20255. [DOI] [PubMed] [Google Scholar]

[b52-medscimonit-22-4455] 52.Hegde ML, Hazra TK, Mitra S. Early steps in the DNA base excision/single-strand interruption repair pathway in mammalian cells. Cell Res. 2008;18:27–47. doi: 10.1038/cr.2008.8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b53-medscimonit-22-4455] 53.Qian L, Yuan F, Rodriguez-Tello P, et al. Human Fanconi anemia complementation group a protein stimulates the 5′ flap endonuclease activity of FEN1. PLoS One. 2013;8:e82666. doi: 10.1371/journal.pone.0082666. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b54-medscimonit-22-4455] 54.Kalifa L, Beutner G, Phadnis N, et al. Evidence for a role of FEN1 in maintaining mitochondrial DNA integrity. DNA Repair (Amst) 2009;8:1242–49. doi: 10.1016/j.dnarep.2009.07.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b55-medscimonit-22-4455] 55.Yang CH, Lin YD, Yen CY, et al. A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS. 2015;19:238–47. doi: 10.1089/omi.2014.0121. [DOI] [PubMed] [Google Scholar]

[b56-medscimonit-22-4455] 56.Levin DS, McKenna AE, Motycka TA, et al. Interaction between PCNA and DNA ligase I is critical for joining of Okazaki fragments and long-patch base-excision repair. Curr Biol. 2000;10:919–22. doi: 10.1016/s0960-9822(00)00619-9. [DOI] [PubMed] [Google Scholar]

[b57-medscimonit-22-4455] 57.Beneke S, Bürkle A. Poly(ADP-ribosyl)ation in mammalian ageing. Nucleic Acids Res. 2007;35:7456–65. doi: 10.1093/nar/gkm735. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b58-medscimonit-22-4455] 58.Kim MY, Zhang T, Kraus WL. Poly(ADP-ribosyl)ation by PARP-1: ‘PAR-laying’ NAD+ into a nuclear signal. Genes Dev. 2005;19:1951–67. doi: 10.1101/gad.1331805. [DOI] [PubMed] [Google Scholar]

[b59-medscimonit-22-4455] 59.Wang XG, Wang ZQ, Tong WM, Shen Y. PARP1 Val762Ala polymorphism reduces enzymatic activity. Biochem Biophys Res Commun. 2007;354:122–26. doi: 10.1016/j.bbrc.2006.12.162. [DOI] [PubMed] [Google Scholar]

[b60-medscimonit-22-4455] 60.Rezin GT, Cardoso MR, Gonçalves CL, et al. Inhibition of mitochondrial respiratory chain in brain of rats subjected to an experimental model of depression. Neurochem Int. 2008;53:395–400. doi: 10.1016/j.neuint.2008.09.012. [DOI] [PubMed] [Google Scholar]

[b61-medscimonit-22-4455] 61.Gardner A, Johansson A, Wibom R, et al. Alterations of mitochondrial function and correlations with personality traits in selected major depressive disorder patients. J Affect Disord. 2003;76:55–68. doi: 10.1016/s0165-0327(02)00067-8. [DOI] [PubMed] [Google Scholar]

[b62-medscimonit-22-4455] 62.Moreno-Fernández AM, Cordero MD, Garrido-Maraver J, et al. Oral treatment with amitriptyline induces coenzyme Q deficiency and oxidative stress in psychiatric patients. J Psychiatr Res. 2012;46:341–45. doi: 10.1016/j.jpsychires.2011.11.002. [DOI] [PubMed] [Google Scholar]

[b63-medscimonit-22-4455] 63.Maes M, Mihaylova I, Kubera M, et al. Lower plasma Coenzyme Q10 in depression: a marker for treatment resistance and chronic fatigue in depression and a risk factor to cardiovascular disorder in that illness. Neuro Endocrinol Lett. 2009;30:462–69. [PubMed] [Google Scholar]

[b64-medscimonit-22-4455] 64.Shoffner JM, Lott MT, Voljavec AS, et al. Spontaneous Kearns-Sayre/chronic external ophthalmoplegia plus syndrome associated with a mitochondrial DNA deletion: A slip-replication model and metabolic therapy. Proc Natl Acad Sci USA. 1989;86:7952–56. doi: 10.1073/pnas.86.20.7952. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b65-medscimonit-22-4455] 65.Anderson G, Berk M, Dean O, et al. Role of immune-inflammatory and oxidative and nitrosative stress pathways in the etiology of depression: therapeutic implications. CNS Drugs. 2014;28:1–10. doi: 10.1007/s40263-013-0119-1. [DOI] [PubMed] [Google Scholar]

[b66-medscimonit-22-4455] 66.Szczepankiewicz A, Leszczyńska-Rodziewicz A, Pawlak J, et al. FKBP5 polymorphism is associated with major depression but not with bipolar disorder. J Affect Disord. 2014;164:33–37. doi: 10.1016/j.jad.2014.04.002. [DOI] [PubMed] [Google Scholar]

[b67-medscimonit-22-4455] 67.van West D, Van Den Eede F, et al. Glucocorticoid receptor gene-based SNP analysis in patients with recurrent major depression. Neuropsychopharmacology. 2006;31:620–27. doi: 10.1038/sj.npp.1300898. [DOI] [PubMed] [Google Scholar]

PERMALINK

Single-Nucleotide Polymorphisms of Genes Involved in Repair of Oxidative DNA Damage and the Risk of Recurrent Depressive Disorder

Piotr Czarny

Dominik Kwiatkowski

Monika Toma

Piotr Gałecki

Agata Orzechowska

Kinga Bobińska

Anna Bielecka-Kowalska

Janusz Szemraj

Michael Berk

George Anderson

Tomasz Śliwiński

Abstract

Background

Material/Methods

Results

Conclusions

Background

Material and Methods

Study subjects

Selection of single-nucleotide polymorphisms

Extraction of DNA and genotyping

Statistical analysis

Results

Single-nucleotide polymorphisms of studied genes and the risk of recurrent depressive disorder

Table 1.

Single-nucleotide polymorphisms of studied genes and onset age of first episode of recurrent depressive disorder

Table 2.

Gene-gene interactions and the risk of recurrent depression disorder

Table 3.

Haplotypes of the studied single-nucleotide polymorphism and the risk of recurrent depression disorder

Table 4.

Discussion

Conclusions

Supplementary materials

Supplementary Table 1.

Footnotes

References

Associated Data

Supplementary Materials

Supplementary Table 1.

ACTIONS

PERMALINK

RESOURCES

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