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TOXLET 8768 1–6 Toxicology Letters xxx (2014) xxx–xxx
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Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population Jianhang Gong a,1, Meng Zhu a,1, Minjie Chu a , Chongqi Sun a , Weihong Chen d, Guangfu Jin a , Jing Yuan d, Juncheng Dai a , Meilin Wang e , Yun Pan a , Yuanchao Song d , Xiaojie Ding e , Mulong Du e , Zhengdong Zhang e, Zhibin Hu a,b,c, *, Tangchun Wu d, **, Hongbing Shen a,b,c, *** a Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China b Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, Jiangsu, China c State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, China d Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China e Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
H I G H L I G H T S
We examined the association of genetic variants in SMARC genes with DNA damage levels. Twenty polymorphisms in five SMARC genes were analyzed in 307 healthy Chinese subjects from southern, central and northern China. The genotypes of rs6857360 in SMARCA5, rs6919 and rs2727280 in SMARCD2, and rs17173769 in SMARCD3 were significantly associated with DNA damage levels. A locus-dosage effect was observed between combined genotypes of SMARC genes and DNA damage levels.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 April 2014 Received in revised form 23 June 2014 Accepted 23 June 2014 Available online xxx
The switching defective/sucrose nonfermenting (SWI/SNF) related, matrix associated, actin dependent regulators of chromatin (SMARC) are components of human SWI/SNF like chromatin remodeling protein complexes, which are essential in the process of DNA damage repair. In this study, we hypothesized that genetic variants in SMARC genes may modify the capacity of DNA repair to damage. To test this hypothesis, we genotyped a total of 20 polymorphisms in five key SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) to evaluate their associations with DNA damage levels in 307 subjects. The DNA damage levels were measured with comet assay. The multiple linear regression was used to assess the relationship between each polymorphism and DNA damage levels in additive model. We found that the genotypes of rs6857360 (b = 0.23, 95% CI = 0.06–0.40, P = 0.008) in SMARCA5, rs6919 (b = 0.20, 95% CI = 0.05–0.34, P = 0.008) and rs2727280 (b = 0.18, 95% CI = 0.04–0.33, P = 0.013) in SMARCD2, and rs17173769 (b = 0.27, 95% CI = 0.52 to 0.01, P = 0.045) in SMARCD3 were significantly associated with DNA damage levels. After combining these four polymorphisms, we found that the more unfavorable alleles the subjects carried, the heavier DNA damage they suffered, suggesting a locus-dosage effect
Keywords: Comet assay DNA damage Genetic variants SMARC genes PM2.5 exposure Population-based study
* Corresponding author at: Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, 818 East Tian-Yuan Rd., Nanjing, China. Tel.: +86 25 86868440; fax: +86 25 86868440. ** Corresponding author at: Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hang-Kong Rd., Wuhan, China. Tel.: +86 27 83692347; fax: +86 27 83692560. *** Corresponding author at: Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, 818 East Tian-Yuan Rd., Nanjing, China. Tel.: +86 25 86868439; fax: +86 25 86868439. E-mail address: [email protected] (H. Shen). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.toxlet.2014.06.034 0378-4274/ ã 2014 Published by Elsevier Ireland Ltd.
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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between combined genotypes and DNA damage levels (P for trend = 0.006). These findings suggest that genetic variants in SMARC genes may contribute the individual variations of DNA damage levels in Chinese population. Further larger and functional studies are warranted to confirm our findings. ã 2014 Published by Elsevier Ireland Ltd.
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1. Introduction
2. Materials and methods
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DNA damage can arise either from external sources, such as exposure to ultraviolet radiation (UV), ionizing radiation (IR) and particulate matter with an aerodynamic diameter 2.5 mm (PM2.5), or from endogenous sources, such as reactive oxygen species and errors during DNA replication (Price and D'Andrea, 2013). The accumulation of DNA damage is a hazardous phenomenon which may lead to cell death and development of various pathological processes (Wlodarczyk and Nowicka, 2012). In eukaryotic cells, there are several intricate mechanisms to maintain genomic integrity, collectively called the DNA damage response (DDR). The switching defective/sucrose nonfermenting (SWI/SNF) related, matrix associated, actin dependent regulators of chromatin (SMARC), also called BRG1 associated factors, are components of human SWI/SNF like chromatin remodeling protein complexes, which are critical in the processes of DNA repair (Ring et al., 1998). The SWI/SNF chromatin remodeling complex consists of an ATPase subunit—either BRM (SMARCA2) or BRG1 (SMARCA4)—and plays essential roles in a variety of cellular processes including DNA repair, differentiation and proliferation (Reisman et al., 2009). SWI/ SNF complexes are required for efficient DNA damage repair as well as cell survival after DNA damage (Park et al., 2006). The ATPdependent chromatin remodeling factors help to facilitate access to damaged DNA by altering chromatin structure at sites of DNA damage (Smeenk and van Attikum, 2013). SMARCA5 is not only the subfamily of SMARC genes but also the ATPase subunit of several chromatin remodeling complexes (Yadon and Tsukiyama, 2011). SMARCA5 is recruited to DNA damage sites (Erdel et al., 2010; Fischer et al., 2011) and promotes DNA damage repair (Smeenk et al., 2013). In this study, we hypothesized that genetic variants of key SMARC genes may modify genotoxic effects of DNA damage. To test this hypothesis, we evaluated the relationship between polymorphisms in SMARC genes and DNA damage levels measured by comet assay in healthy subjects. A total of 22 polymorphisms from five SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) were genotyped in 307 Han Chinese subjects.
2.1. Study population
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A total of 328 subjects were recruited from three independent cohorts (119 from Zhuhai, 123 from Wuhan and 86 from Tianjin) in southern, central and northern China of different PM2.5 exposure levels, respectively. All subjects were unrelated ethnic Han Chinese, and were diseases-free with age >40 and resided locally more than 5 years. Each individual was interviewed by the trained interviewers using a structure questionnaire including tobacco smoking, alcohol consuming, and environment exposure history. After the interview, an approximate 5-mL peripheral venous blood sample was collected from each subject for DNA extraction and examination of DNA damage. 8 subjects (3 from Zhuhai and 5 from Wuhan) were excluded from this study as they did not donate blood samples. Meanwhile, personal 24-h PM2.5 exposure levels were measured for each subject. 9 subjects (3 from Zhuhai and 6 from Tianjin) were excluded as their PM2.5 exposure levels were not obtained. In addition, 4 subjects (3 from Zhuhai and 1 from Tianjin) were further removed due to the poor DNA quality for chip analysis. Informed consents were obtained from all participants and the study was approved by the Ethics and Human Subject Committee of Tongji Medical College and Nanjing Medical University. Finally, 110 subjects in Zhuhai, 118 in Wuhan and 79 in Tianjin were included in this study. Demographic and exposure information was summarized in Table 1.
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2.2. PM2.5 exposure measurement
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Personal 24-h PM2.5 exposure levels were monitored by a PM2.5 sampler and pump of Gilian 5000 (Sensidyne Company, Florida, USA). The cut point of the PM2.5 sampler is 2.5 mm, which can filter the coarse particles. Therefore, the filters inside will trap the fine particulate matter with diameter less than 2.5 mm. The pump for personal sampling was placed in a small backpack and PM2.5 sampler was placed at the height of the respiratory zone of the participant. Sampling was done on 37-mm Teflon filters (Beijing LianyiXingtong Apparatus & Instrument Co., Ltd., China) at
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Table 1 Characteristics, PM2.5 exposure levels and the percentage of tail DNA (Tail%) of peripheral blood lymphocytes for the subjects among three cohorts. Characteristics
Zhuhai (N = 110)
Wuhan (N = 118)
Tianjin (N = 79)
Age (mean SD) Sex Male Female
53.07 6.84
51.35 6.10
66.61 5.53
37 73
54 64
32 47
Smoking status Ever Never
19 91
48 70
25 54
PM2.5 (mg/m3)a
68.35 (37.17–116.79)
114.96 (86.55–153.20)
Tail%b
1.36 (0.65–2.59)
1.85 (0.77–4.39)
146.60 (88.63–261.41) 2.97 (1.47–6.32)
a b
Median (25–75 percentile). The percentage of tail DNA. Median (25–75 percentile).
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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Table 2 Selected SNPs in five human SMARC genes and relationship with DNA damage levels. Gene
SNPs
Location
Selection strategy
Call rate (%)
MAF
HWEa
bb
95% CIb
Pb
SMARCA5 SMARCA5 SMARCA5 SMARCA5 SMARCA5 SMARCC2 SMARCD1 SMARCD1 SMARCD1 SMARCD1 SMARCD2 SMARCD2 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3
rs11100790 rs1510881 rs1877112 rs28364609 rs6857360 rs7960225 rs2272391 rs376184 rs836177 rs844359 rs2727280 rs6919 rs11973301 rs17173769 rs219231 rs219233 rs219236 rs3789818 rs4236430 rs758249
Nonsense Intron Intron 50 near gene Intron Intron 50 near gene 50 near gene Intron 50 near gene Intron 30 UTR Intron Intron Intron Intron Intron Intron Intron Intron
F F F F T T T F T T T F T T T T T T T T
100.0 99.3 99.7 99.7 100.0 99.3 99.7 99.7 99.3 99.7 99.7 99.7 99.0 99.3 99.0 99.0 99.3 99.7 99.7 99.7
0.37 0.12 0.11 0.17 0.25 0.47 0.24 0.17 0.08 0.16 0.46 0.40 0.15 0.09 0.17 0.14 0.17 0.15 0.16 0.32
0.81 0.78 0.23 0.55 0.76 0.14 1.00 0.10 0.25 0.22 0.30 0.48 1.00 0.49 0.84 1.00 0.30 0.82 0.83 1.00
0.11 0.16 0.22 0.27 0.23 0.08 0.01 0.02 0.008 0.02 0.18 0.20 0.11 0.27 0.0004 0.02 0.02 0.02 0.008 0.02
0.42–0.26 0.37–0.06 0.02–0.46 0.04–0.58 0.06–0.40 0.23–0.06 0.17–0.15 0.20–0.16 0.28–0.29 0.20–0.16 0.04–0.33 0.05–0.34 0.09–0.31 0.52 to 0.01 0.19–0.19 0.18–0.23 0.17–0.20 0.18–0.22 0.19–0.20 0.17–0.14
0.159 0.159 0.071 0.090 0.008 0.265 0.891 0.826 0.956 0.855 0.013 0.008 0.279 0.045 0.997 0.814 0.872 0.846 0.939 0.830
a
89 90 91 92 93 94 Q2 95 96
Hardy–Weinberg equilibrium (HWE) was tested using a goodness-of-fit x2 test. F, potentially functional SNP; T, tagging SNP.
the flow rate of 2.0 L/min. Before and after sampling, the filters were weighted in the laboratory after conditioning for 24 h. A 24-h average exposure concentration was calculated to represent personal PM2.5 exposure levels, details of counting the value of PM2.5 (mg/m3) was shown in the equation below, where C is the mass concentrations of PM2.5 (mg/m3), m1 and m2 is the mass of Teflon filter before and after sampling (mg), V is the flow rate of sampling (L/min), t is the duration of sampling (min). m2 m1 C¼ V t
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2.3. Comet assay
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Human lymphocytes from 5.0-mL peripheral venous blood were isolated and resuspended in 5.0-mL ice-cold PBS (pH 7.4) within 4 h after blood sample collecting. The comet assay was carried out basically according to the method of Singh with minor
99 100 101
modifications (Singh et al., 1988). Three duplicate slides were prepared for each subject. For each slide, 50 randomly selected lymphocytes were analyzed. The observation of cells was made at 400 magnification using a fluorescence microscope (Olympus, BX51). The percentage of tail DNA (Tail%) which served as the indicator of DNA damage levels was calculated by a computerbased image analysis system (version 1.0, IMI comet analysis software, China). The comet assay and measurement of DNA damage levels were conducted in a blinded manner.
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2.4. Polymorphisms selection and genotyping
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Single nucleotide polymorphisms (SNPs) in five key SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) were selected by a strategy combining potentially functional SNPs as well as tagging SNPs. We used the public HapMap Project Q3 (phase II + III February 2009, on NCBI B36 assembly, dbSNP b126) to search all common SNPs (minor allele frequency,
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Table 3 The association between four SNPs (rs6857360, rs6919, rs2727280 and rs17173769) and DNA damage levels among three cohorts. SNPs Zhuhai
Wuhan
Tianjin
Pa
N
Tail% (median, 25–75 percentile)
Pa
0.125
72 38 8
1.53 (0.36–3.63) 2.97 (1.10–4.85) 3.40 (0.03–5.93)
0.048 34 2.26 (0.79–5.14) 39 5.06 (1.63–7.08) 6 1.81 (0.55–4.82)
0.104
45 55 18
1.78 (0.70–4.10) 1.80 (0.84–4.58) 2.68 (0.04–6.85)
rs2727280 26 1.04 (0.57–1.73) GG GA 56 1.57 (0.64–2.88) AA 28 1.44 (0.92–3.72)
0.086 37 60 21
rs17173769 93 1.32 (0.68–2.71) AA AG 17 1.42 (0.55–2.22) GG 0
N
Tail% (median, 25–75 percentile)
rs6857360 TT 68 1.20 (0.57–2.48) TC 36 1.45 (0.92–3.17) CC 6 1.84 (1.25–2.67) rs6919 AA 33 1.07 (0.57–1.86) AT 55 1.55 (0.62–2.86) TT 21 1.29 (0.94–3.31)
a b
Pooled Tail% (median, 25–75 percentile)
Pb
0.259 174 113 20
1.59 (0.58–3.21) 2.72 (1.08–5.33) 1.81 (0.73–4.98)
0.008
0.606 30 1.93 (0.59–5.09) 43 4.85 (1.86–7.01) 6 3.28 (1.53–6.71)
0.007 108 153 45
1.55 (0.58–3.56) 2.24 (0.95–4.70) 1.62 (0.94–5.05)
0.008
1.54 (0.24–3.76) 1.85 (0.88–4.76) 2.96 (0.33–6.15)
0.211
20 1.93 (0.61–5.17) 46 4.41 (1.79–6.74) 12 3.08 (1.49–4.79)
0.194
83 162 61
1.52 (0.58–3.32) 2.21 (0.88–4.64) 1.90 (0.94–4.92)
0.013
0.426 100 1.85 (0.84–4.65) 16 1.25 (0.10–4.41) 0
0.217
59 3.13 (1.63–6.65) 19 2.26 (1.28–5.60) 1
0.100
252 1.88 (0.84–4.32) 52 1.62 (0.60–3.86) 1
0.045
N
Tail% (median, 25–75 percentile)
Pa
N
Data were calculated by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration. Pooled results in additive model after meta-analysis.
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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Table 4 Joint analysis of association between four SNPs (rs6857360, rs6919, rs2727280 and rs17173769) and DNA damage levels. Risk allele numbera
Zhuhai N
0–2 3–4 5–8 Ptrendb a b c
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Tail% (median, 25–75 percentile)
23 0.97 (0.55–1.71) 39 1.47 (0.70–3.17) 47 1.55 (0.89–2.70) 0.048
Wuhan
Tianjin
Pooled
N
N
N
Tail% (median, 25–75 percentile)
24 1.47 (0.14–3.50) 57 1.90 (0.74–3.97) 35 2.96 (0.84–6.07) 0.071
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2.5. Statistical analysis
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Hardy–Weinberg equilibrium (HWE) was tested using a goodness-of-fit x2 test. The values of the percentage of tail DNA (Tail%) were normalized by rank-based inverse-normal transformed (INT) (Yang et al., 2012) for regression analysis. The
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
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13 2.24 (0.58–4.78) 35 2.47 (0.65–5.20) 30 5.71 (2.60–7.47) 0.001
Tail% (median, 25–75 percentile)
60 1.35 (0.55–2.26) 131 1.86 (0.70–3.90) 112 2.32 (0.99–5.84) 0.006c
The number of risk allele of rs6857360 (C), rs6919 (T), rs2727280 (A) and rs17173769 (A). The trend effect were calculated by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration. Pooled results after meta-analysis for three cohorts by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration.
MAF > 0.05) located at the gene region and 10 kb upstream of the gene. The functional significance of these SNPs were firstly evaluated using a web tool SNPinfo (Xu and Taylor, 2009). We then selected tagging SNPs using Haploview software 4.2 based on following items: (i) MAF > 0.05; (ii) P > 0.05 for Hardy–Weinberg equilibrium (HWE); (iii) call rate > 90%; (iv) r2 of pairwise linkage disequilibrium (LD) < 0.8 and (v) potentially functional SNPs were forced to include as tagging SNPs. As a result, a total of 22 SNPs were chosen for genotyping. Genomic DNA was extracted from leucocytes of venous blood by the standard method with proteinase K digestion and phenol/ chloroform extraction. All SNPs were genotyped by using Illumina Infinium1 BeadChip (Illumina Inc.,). Genotype calling was performed using the GenTrain version 1.0 clustering algorithm in GenomeStudio V2011.1 (Illumina). Ultimately, 20 SNPs were successfully genotyped in all subjects with call rates >95% and consistent with those expected from the Hardy–Weinberg equilibrium (P > 0.05) (Table 2), whereas two SNPs (rs10263398 and rs219240 in SMARCD3) that failed in probe design, were removed from our analysis.
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Tail% (median, 25–75 percentile)
Fig. 1. Box-and-whisker plots for DNA damage levels among three cohorts, divided into three groups according to the number of risk allele.
multiple linear regression was used to assess the relevance between each genetic polymorphism and DNA damage levels in additive model. The influences of age, gender, smoking status and PM2.5 exposure levels, as potential factors that may modify the DNA damage levels, were adjusted in each cohort. The fixedand random-effect meta-analyses were used to calculate the pooled b across of the three cohorts. If the Q test for heterogeneity among three cohorts was not significant, the fixed-effect model was chosen. Otherwise, the pooled result was estimated using the random-effect model. All analyses were performed using the R (version 2.15.3) and Stata software (version 12.0; StataCorp LP).
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3. Results
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General characteristics of the 307 subjects from three cohorts (Zhuhai, Wuhan and Tianjin) are shown in Table 1. The mean ages were 53.07, 51.35 and 66.61 for Zhuhai, Wuhan and Tianjin subjects, respectively. PM2.5 exposure levels were different among three cities (median value: 68.35 mg/m3, 114.96 mg/m3 and 146.60 mg/m3, respectively). As expected, low DNA damage levels were observed in Zhuhai subjects with a median percentage of tail DNA of 1.36%, while moderate and heavy levels were detected in Wuhan and Tianjin subjects (median value: 1.85 and 2.97, respectively). The associations between genetic variants of SMARC genes and the DNA damage levels in the subjects are summarized in Table 2. The genotypes of rs6857360 (b = 0.23, 95% CI = 0.06–0.40, P = 0.008) in SMARCA5, rs6919 (b = 0.20, 95% CI = 0.05–0.34, P = 0.008) and rs2727280 (b = 0.18, 95% CI = 0.04–0.33, P = 0.013) in SMARCD2, and rs17173769 (b = 0.27, 95% CI = 0.52 to 0.01, P = 0.045) in SMARCD3 was significantly associated with DNA damage levels. Consistent results were observed among three cohorts for the associations of rs6857360, rs6919 and rs2727280 while conflicting direction of association was indicated for rs17173769 (Table 3). Moreover, we did not observe significant interactions between any SNP and PM2.5 exposure levels on DNA damage levels (data not shown). We further assessed the combined effects of these four independent predictors of rs6857360, rs6919, rs2727280 and rs17173769 on DNA damage. As shown in Table 4, the results showed that the more unfavorable alleles the subjects carried, the heavier DNA damage they suffered, suggesting a locus-dosage effect between combined genotypes and DNA damage (P for trend = 0.006). Such a locus-dosage effect of combined genotypes was also consistent among three cohorts (Fig. 1). Besides, we have done the stratification analysis by the mean age of the subjects and the smoking status in the association analysis of genetic variants with DNA damage levels. As shown in the Supplemental Table 1, similar association strengths were observed between subgroups for each SNP (P for heterogeneity >0.05). In this study, we attempted to test whether the genetic variants in SMARC genes
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Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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Fig. 2. The association of four risk variants with host gene expression.
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modify the process of PM2.5 causing DNA damage. As shown in Supplemental Table 2, we performed interaction analysis between the four identified SNPs and PM2.5 exposure levels and did not observe significant joint effects on DNA damage levels.
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4. Discussion
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In the present study, we investigated the associations of genetic variants of SMARC genes with DNA damage levels in three independent cohorts of Chinese population. We found that the genotypes of rs6857360 in SMARCA5, rs6919 and rs2727280 in SMARCD2 and rs17173769 in SMARCD3 were significantly associated with DNA damage levels. To our knowledge, this is the first report to show that genetic variants of SMARC genes were associated with DNA damage levels, providing further evidence supporting the importance of SMARC genes in DNA repair. SMARCA5 (hSNF2H) is a member of SWI/SNF family, which contains helicase and ATPase activities. As part of a chromatin remodeling complex, it facilitates transcription initiation and ATPdependent nucleosome remodeling (LeRoy et al., 2000). Recently, SMARCA5 was found to be recruited to DNA damage sites (Erdel et al., 2010; Fischer et al., 2011) and promotes DNA double-strand breaks (DSB) repair by NHEJ (non-homologous end-joining) and HR (homologous recombination) (Smeenk et al., 2013). Besides, the downregulation of SMARCA5, a direct target of miR-99 family, mediated radiation sensitivity through its role in facilitating DNA repair. Furthermore, miR-99 reduces the rate of DNA repair through downregulation of SMARCA5 (Mueller et al., 2013). Taken together, these findings suggest a critical role of SMARCA5 in the process of DNA damage repair. In this study, we found that the SNP rs6857360 in the intron of SMARCA5 was significantly associated with DNA damage levels, which further reflected an important role of SMARCA5 in population level. However, since this variant is just a proxy of genetic variants of SMARCA5, it is largely unknown in terms of the functional relevance of genetic variants with SMARCA5. SMARCD2 (BAF60b) and SMARCD3 (BAF60c) are two variants of the BAF60 subunits. It was reported that SMARCD2 was upregulated in well and poorly differentiated esophageal squamous cell
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carcinoma (ESCC) cell types at both irradiation doses (Bo et al., 2004). SMARCD3 was related to nucleic acid metabolism, manifested as degraded DNA duplication and transcription, and increased DNA damage repair (Ze-Min et al., 2013). Furthermore, chromatin immunoprecipitation (ChIP)-on-chip analysis demonstrated that SMARCD3 knockdown impairs the myogenic determination factor (MyoD) binding to target genes (Puri and Mercola, 2012) and MyoD was inhibited by DNA damage (Puri et al., 2002). Besides, SMARCD3 is essential for chromatin remodeling complexes in heart development (Lickert et al., 2004) and possible candidate for being novel prognostic markers for neuroblastoma (NB) (Takita et al., 2004). Two SNPs (rs2727280 and rs6919) located in the intron and 30 UTR (untranslated region) of SMARCD2, respectively, were identified to be associated with DNA damage levels. We analyzed the correlation between genotypes of the SNPs and the expression levels of their host genes in Chinese population using public available data from GENEVAR (Yang et al., 2010). As shown in Fig. 2, we found that the genotypes of rs2727280 and rs6919 in SMARCD2 gene were significantly associated with SMARCD2 expression level, in which the lower SMARCD2 expression levels were observed in subjects with risk alleles of DNA damage (rs2727280-A: P = 0.026; rs6919-T: P = 0.044). The identified SNP rs17173769 is located in the intron of SMARCD3 and there are not known functional evidence for the association of this variant. In addition, we did not found significant associations for selected functional or tagging SNPs in other SMARC genes with DNA damage levels. These SMARC genes have been previously reported to be associated with DNA damage. For example, after damage to DNA, BRIT1 increases its interaction with SWI/SNF through ATM (ataxia-telangiectasia mutated) and/or ATR (ATM and Rad3-related), two central kinases in the DNA-damage response network, dependent phosphorylation on the SMARCC2 (Peng et al., 2009). Besides, SMARCD1 would almost completely block androgen receptor (AR)-driven expression of TMPRSS2 (van de Wijngaart et al., 2009), while faulty DNA damage repair has been implicated in the formation of TMPRSS2–ERG fusions during prostate carcinogenesis (Jaamaa and Laiho, 2012). One explanation is that the selected SNPs may have no effect on the biological
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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function of these SMARC genes. For another, our study may have no enough power to detect the true associations and lead to false positive results. In summary, our study provides evidence that genetic variants in SMARCA5, SMARCD2 and SMARCD3 may contribute to the DNA damage levels in Chinese population. Larger well-designed studies and functional studies are warranted to confirm our findings. Conflict of interest The authors declare no competing financial interest.
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Transparency document
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The Transparency document associated with this article can be found in the online version.
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Funding sources
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This work was funded by the National Key Basic Research Program Grant (2011CB503805), the National Science and Technology Support Program (2011BAI09B02), the Major Program of the National Natural Science Foundation of China (81390543), the State Key Program of National Natural Science of China (81230067), Jiangsu Province Clinical Science and Technology Projects (BL2012008) and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine) and Collaborative Innovation Center For Cancer Personalized Medicine in Jiangsu Province. The authors wish to thank all the study participants, research staff and students who participated in this work.
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Appendix A. Supplementary data
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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2014.06.034.
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Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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TOXLET 8768 1–6 Toxicology Letters xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
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Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population Jianhang Gong a,1, Meng Zhu a,1, Minjie Chu a , Chongqi Sun a , Weihong Chen d, Guangfu Jin a , Jing Yuan d, Juncheng Dai a , Meilin Wang e , Yun Pan a , Yuanchao Song d , Xiaojie Ding e , Mulong Du e , Zhengdong Zhang e, Zhibin Hu a,b,c, *, Tangchun Wu d, **, Hongbing Shen a,b,c, *** a Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China b Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, Jiangsu, China c State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, China d Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China e Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
H I G H L I G H T S
We examined the association of genetic variants in SMARC genes with DNA damage levels. Twenty polymorphisms in five SMARC genes were analyzed in 307 healthy Chinese subjects from southern, central and northern China. The genotypes of rs6857360 in SMARCA5, rs6919 and rs2727280 in SMARCD2, and rs17173769 in SMARCD3 were significantly associated with DNA damage levels. A locus-dosage effect was observed between combined genotypes of SMARC genes and DNA damage levels.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 April 2014 Received in revised form 23 June 2014 Accepted 23 June 2014 Available online xxx
The switching defective/sucrose nonfermenting (SWI/SNF) related, matrix associated, actin dependent regulators of chromatin (SMARC) are components of human SWI/SNF like chromatin remodeling protein complexes, which are essential in the process of DNA damage repair. In this study, we hypothesized that genetic variants in SMARC genes may modify the capacity of DNA repair to damage. To test this hypothesis, we genotyped a total of 20 polymorphisms in five key SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) to evaluate their associations with DNA damage levels in 307 subjects. The DNA damage levels were measured with comet assay. The multiple linear regression was used to assess the relationship between each polymorphism and DNA damage levels in additive model. We found that the genotypes of rs6857360 (b = 0.23, 95% CI = 0.06–0.40, P = 0.008) in SMARCA5, rs6919 (b = 0.20, 95% CI = 0.05–0.34, P = 0.008) and rs2727280 (b = 0.18, 95% CI = 0.04–0.33, P = 0.013) in SMARCD2, and rs17173769 (b = 0.27, 95% CI = 0.52 to 0.01, P = 0.045) in SMARCD3 were significantly associated with DNA damage levels. After combining these four polymorphisms, we found that the more unfavorable alleles the subjects carried, the heavier DNA damage they suffered, suggesting a locus-dosage effect
Keywords: Comet assay DNA damage Genetic variants SMARC genes PM2.5 exposure Population-based study
* Corresponding author at: Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, 818 East Tian-Yuan Rd., Nanjing, China. Tel.: +86 25 86868440; fax: +86 25 86868440. ** Corresponding author at: Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hang-Kong Rd., Wuhan, China. Tel.: +86 27 83692347; fax: +86 27 83692560. *** Corresponding author at: Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, 818 East Tian-Yuan Rd., Nanjing, China. Tel.: +86 25 86868439; fax: +86 25 86868439. E-mail address: [email protected] (H. Shen). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.toxlet.2014.06.034 0378-4274/ ã 2014 Published by Elsevier Ireland Ltd.
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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between combined genotypes and DNA damage levels (P for trend = 0.006). These findings suggest that genetic variants in SMARC genes may contribute the individual variations of DNA damage levels in Chinese population. Further larger and functional studies are warranted to confirm our findings. ã 2014 Published by Elsevier Ireland Ltd.
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1. Introduction
2. Materials and methods
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DNA damage can arise either from external sources, such as exposure to ultraviolet radiation (UV), ionizing radiation (IR) and particulate matter with an aerodynamic diameter 2.5 mm (PM2.5), or from endogenous sources, such as reactive oxygen species and errors during DNA replication (Price and D'Andrea, 2013). The accumulation of DNA damage is a hazardous phenomenon which may lead to cell death and development of various pathological processes (Wlodarczyk and Nowicka, 2012). In eukaryotic cells, there are several intricate mechanisms to maintain genomic integrity, collectively called the DNA damage response (DDR). The switching defective/sucrose nonfermenting (SWI/SNF) related, matrix associated, actin dependent regulators of chromatin (SMARC), also called BRG1 associated factors, are components of human SWI/SNF like chromatin remodeling protein complexes, which are critical in the processes of DNA repair (Ring et al., 1998). The SWI/SNF chromatin remodeling complex consists of an ATPase subunit—either BRM (SMARCA2) or BRG1 (SMARCA4)—and plays essential roles in a variety of cellular processes including DNA repair, differentiation and proliferation (Reisman et al., 2009). SWI/ SNF complexes are required for efficient DNA damage repair as well as cell survival after DNA damage (Park et al., 2006). The ATPdependent chromatin remodeling factors help to facilitate access to damaged DNA by altering chromatin structure at sites of DNA damage (Smeenk and van Attikum, 2013). SMARCA5 is not only the subfamily of SMARC genes but also the ATPase subunit of several chromatin remodeling complexes (Yadon and Tsukiyama, 2011). SMARCA5 is recruited to DNA damage sites (Erdel et al., 2010; Fischer et al., 2011) and promotes DNA damage repair (Smeenk et al., 2013). In this study, we hypothesized that genetic variants of key SMARC genes may modify genotoxic effects of DNA damage. To test this hypothesis, we evaluated the relationship between polymorphisms in SMARC genes and DNA damage levels measured by comet assay in healthy subjects. A total of 22 polymorphisms from five SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) were genotyped in 307 Han Chinese subjects.
2.1. Study population
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A total of 328 subjects were recruited from three independent cohorts (119 from Zhuhai, 123 from Wuhan and 86 from Tianjin) in southern, central and northern China of different PM2.5 exposure levels, respectively. All subjects were unrelated ethnic Han Chinese, and were diseases-free with age >40 and resided locally more than 5 years. Each individual was interviewed by the trained interviewers using a structure questionnaire including tobacco smoking, alcohol consuming, and environment exposure history. After the interview, an approximate 5-mL peripheral venous blood sample was collected from each subject for DNA extraction and examination of DNA damage. 8 subjects (3 from Zhuhai and 5 from Wuhan) were excluded from this study as they did not donate blood samples. Meanwhile, personal 24-h PM2.5 exposure levels were measured for each subject. 9 subjects (3 from Zhuhai and 6 from Tianjin) were excluded as their PM2.5 exposure levels were not obtained. In addition, 4 subjects (3 from Zhuhai and 1 from Tianjin) were further removed due to the poor DNA quality for chip analysis. Informed consents were obtained from all participants and the study was approved by the Ethics and Human Subject Committee of Tongji Medical College and Nanjing Medical University. Finally, 110 subjects in Zhuhai, 118 in Wuhan and 79 in Tianjin were included in this study. Demographic and exposure information was summarized in Table 1.
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2.2. PM2.5 exposure measurement
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Personal 24-h PM2.5 exposure levels were monitored by a PM2.5 sampler and pump of Gilian 5000 (Sensidyne Company, Florida, USA). The cut point of the PM2.5 sampler is 2.5 mm, which can filter the coarse particles. Therefore, the filters inside will trap the fine particulate matter with diameter less than 2.5 mm. The pump for personal sampling was placed in a small backpack and PM2.5 sampler was placed at the height of the respiratory zone of the participant. Sampling was done on 37-mm Teflon filters (Beijing LianyiXingtong Apparatus & Instrument Co., Ltd., China) at
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18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
Table 1 Characteristics, PM2.5 exposure levels and the percentage of tail DNA (Tail%) of peripheral blood lymphocytes for the subjects among three cohorts. Characteristics
Zhuhai (N = 110)
Wuhan (N = 118)
Tianjin (N = 79)
Age (mean SD) Sex Male Female
53.07 6.84
51.35 6.10
66.61 5.53
37 73
54 64
32 47
Smoking status Ever Never
19 91
48 70
25 54
PM2.5 (mg/m3)a
68.35 (37.17–116.79)
114.96 (86.55–153.20)
Tail%b
1.36 (0.65–2.59)
1.85 (0.77–4.39)
146.60 (88.63–261.41) 2.97 (1.47–6.32)
a b
Median (25–75 percentile). The percentage of tail DNA. Median (25–75 percentile).
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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Table 2 Selected SNPs in five human SMARC genes and relationship with DNA damage levels. Gene
SNPs
Location
Selection strategy
Call rate (%)
MAF
HWEa
bb
95% CIb
Pb
SMARCA5 SMARCA5 SMARCA5 SMARCA5 SMARCA5 SMARCC2 SMARCD1 SMARCD1 SMARCD1 SMARCD1 SMARCD2 SMARCD2 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3 SMARCD3
rs11100790 rs1510881 rs1877112 rs28364609 rs6857360 rs7960225 rs2272391 rs376184 rs836177 rs844359 rs2727280 rs6919 rs11973301 rs17173769 rs219231 rs219233 rs219236 rs3789818 rs4236430 rs758249
Nonsense Intron Intron 50 near gene Intron Intron 50 near gene 50 near gene Intron 50 near gene Intron 30 UTR Intron Intron Intron Intron Intron Intron Intron Intron
F F F F T T T F T T T F T T T T T T T T
100.0 99.3 99.7 99.7 100.0 99.3 99.7 99.7 99.3 99.7 99.7 99.7 99.0 99.3 99.0 99.0 99.3 99.7 99.7 99.7
0.37 0.12 0.11 0.17 0.25 0.47 0.24 0.17 0.08 0.16 0.46 0.40 0.15 0.09 0.17 0.14 0.17 0.15 0.16 0.32
0.81 0.78 0.23 0.55 0.76 0.14 1.00 0.10 0.25 0.22 0.30 0.48 1.00 0.49 0.84 1.00 0.30 0.82 0.83 1.00
0.11 0.16 0.22 0.27 0.23 0.08 0.01 0.02 0.008 0.02 0.18 0.20 0.11 0.27 0.0004 0.02 0.02 0.02 0.008 0.02
0.42–0.26 0.37–0.06 0.02–0.46 0.04–0.58 0.06–0.40 0.23–0.06 0.17–0.15 0.20–0.16 0.28–0.29 0.20–0.16 0.04–0.33 0.05–0.34 0.09–0.31 0.52 to 0.01 0.19–0.19 0.18–0.23 0.17–0.20 0.18–0.22 0.19–0.20 0.17–0.14
0.159 0.159 0.071 0.090 0.008 0.265 0.891 0.826 0.956 0.855 0.013 0.008 0.279 0.045 0.997 0.814 0.872 0.846 0.939 0.830
a
89 90 91 92 93 94 Q2 95 96
Hardy–Weinberg equilibrium (HWE) was tested using a goodness-of-fit x2 test. F, potentially functional SNP; T, tagging SNP.
the flow rate of 2.0 L/min. Before and after sampling, the filters were weighted in the laboratory after conditioning for 24 h. A 24-h average exposure concentration was calculated to represent personal PM2.5 exposure levels, details of counting the value of PM2.5 (mg/m3) was shown in the equation below, where C is the mass concentrations of PM2.5 (mg/m3), m1 and m2 is the mass of Teflon filter before and after sampling (mg), V is the flow rate of sampling (L/min), t is the duration of sampling (min). m2 m1 C¼ V t
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2.3. Comet assay
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Human lymphocytes from 5.0-mL peripheral venous blood were isolated and resuspended in 5.0-mL ice-cold PBS (pH 7.4) within 4 h after blood sample collecting. The comet assay was carried out basically according to the method of Singh with minor
99 100 101
modifications (Singh et al., 1988). Three duplicate slides were prepared for each subject. For each slide, 50 randomly selected lymphocytes were analyzed. The observation of cells was made at 400 magnification using a fluorescence microscope (Olympus, BX51). The percentage of tail DNA (Tail%) which served as the indicator of DNA damage levels was calculated by a computerbased image analysis system (version 1.0, IMI comet analysis software, China). The comet assay and measurement of DNA damage levels were conducted in a blinded manner.
102
2.4. Polymorphisms selection and genotyping
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Single nucleotide polymorphisms (SNPs) in five key SMARC genes (SMARCA5, SMARCC2, SMARCD1, SMARCD2, SMARCD3) were selected by a strategy combining potentially functional SNPs as well as tagging SNPs. We used the public HapMap Project Q3 (phase II + III February 2009, on NCBI B36 assembly, dbSNP b126) to search all common SNPs (minor allele frequency,
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Table 3 The association between four SNPs (rs6857360, rs6919, rs2727280 and rs17173769) and DNA damage levels among three cohorts. SNPs Zhuhai
Wuhan
Tianjin
Pa
N
Tail% (median, 25–75 percentile)
Pa
0.125
72 38 8
1.53 (0.36–3.63) 2.97 (1.10–4.85) 3.40 (0.03–5.93)
0.048 34 2.26 (0.79–5.14) 39 5.06 (1.63–7.08) 6 1.81 (0.55–4.82)
0.104
45 55 18
1.78 (0.70–4.10) 1.80 (0.84–4.58) 2.68 (0.04–6.85)
rs2727280 26 1.04 (0.57–1.73) GG GA 56 1.57 (0.64–2.88) AA 28 1.44 (0.92–3.72)
0.086 37 60 21
rs17173769 93 1.32 (0.68–2.71) AA AG 17 1.42 (0.55–2.22) GG 0
N
Tail% (median, 25–75 percentile)
rs6857360 TT 68 1.20 (0.57–2.48) TC 36 1.45 (0.92–3.17) CC 6 1.84 (1.25–2.67) rs6919 AA 33 1.07 (0.57–1.86) AT 55 1.55 (0.62–2.86) TT 21 1.29 (0.94–3.31)
a b
Pooled Tail% (median, 25–75 percentile)
Pb
0.259 174 113 20
1.59 (0.58–3.21) 2.72 (1.08–5.33) 1.81 (0.73–4.98)
0.008
0.606 30 1.93 (0.59–5.09) 43 4.85 (1.86–7.01) 6 3.28 (1.53–6.71)
0.007 108 153 45
1.55 (0.58–3.56) 2.24 (0.95–4.70) 1.62 (0.94–5.05)
0.008
1.54 (0.24–3.76) 1.85 (0.88–4.76) 2.96 (0.33–6.15)
0.211
20 1.93 (0.61–5.17) 46 4.41 (1.79–6.74) 12 3.08 (1.49–4.79)
0.194
83 162 61
1.52 (0.58–3.32) 2.21 (0.88–4.64) 1.90 (0.94–4.92)
0.013
0.426 100 1.85 (0.84–4.65) 16 1.25 (0.10–4.41) 0
0.217
59 3.13 (1.63–6.65) 19 2.26 (1.28–5.60) 1
0.100
252 1.88 (0.84–4.32) 52 1.62 (0.60–3.86) 1
0.045
N
Tail% (median, 25–75 percentile)
Pa
N
Data were calculated by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration. Pooled results in additive model after meta-analysis.
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
103 104 105 106 107 108 109 110
113 114 115 116 117
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Table 4 Joint analysis of association between four SNPs (rs6857360, rs6919, rs2727280 and rs17173769) and DNA damage levels. Risk allele numbera
Zhuhai N
0–2 3–4 5–8 Ptrendb a b c
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Tail% (median, 25–75 percentile)
23 0.97 (0.55–1.71) 39 1.47 (0.70–3.17) 47 1.55 (0.89–2.70) 0.048
Wuhan
Tianjin
Pooled
N
N
N
Tail% (median, 25–75 percentile)
24 1.47 (0.14–3.50) 57 1.90 (0.74–3.97) 35 2.96 (0.84–6.07) 0.071
137 138
2.5. Statistical analysis
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Hardy–Weinberg equilibrium (HWE) was tested using a goodness-of-fit x2 test. The values of the percentage of tail DNA (Tail%) were normalized by rank-based inverse-normal transformed (INT) (Yang et al., 2012) for regression analysis. The
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
140 141 142
13 2.24 (0.58–4.78) 35 2.47 (0.65–5.20) 30 5.71 (2.60–7.47) 0.001
Tail% (median, 25–75 percentile)
60 1.35 (0.55–2.26) 131 1.86 (0.70–3.90) 112 2.32 (0.99–5.84) 0.006c
The number of risk allele of rs6857360 (C), rs6919 (T), rs2727280 (A) and rs17173769 (A). The trend effect were calculated by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration. Pooled results after meta-analysis for three cohorts by multiple linear regression with adjustment for age, sex, smoking status and PM2.5 exposure concentration.
MAF > 0.05) located at the gene region and 10 kb upstream of the gene. The functional significance of these SNPs were firstly evaluated using a web tool SNPinfo (Xu and Taylor, 2009). We then selected tagging SNPs using Haploview software 4.2 based on following items: (i) MAF > 0.05; (ii) P > 0.05 for Hardy–Weinberg equilibrium (HWE); (iii) call rate > 90%; (iv) r2 of pairwise linkage disequilibrium (LD) < 0.8 and (v) potentially functional SNPs were forced to include as tagging SNPs. As a result, a total of 22 SNPs were chosen for genotyping. Genomic DNA was extracted from leucocytes of venous blood by the standard method with proteinase K digestion and phenol/ chloroform extraction. All SNPs were genotyped by using Illumina Infinium1 BeadChip (Illumina Inc.,). Genotype calling was performed using the GenTrain version 1.0 clustering algorithm in GenomeStudio V2011.1 (Illumina). Ultimately, 20 SNPs were successfully genotyped in all subjects with call rates >95% and consistent with those expected from the Hardy–Weinberg equilibrium (P > 0.05) (Table 2), whereas two SNPs (rs10263398 and rs219240 in SMARCD3) that failed in probe design, were removed from our analysis.
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Tail% (median, 25–75 percentile)
Fig. 1. Box-and-whisker plots for DNA damage levels among three cohorts, divided into three groups according to the number of risk allele.
multiple linear regression was used to assess the relevance between each genetic polymorphism and DNA damage levels in additive model. The influences of age, gender, smoking status and PM2.5 exposure levels, as potential factors that may modify the DNA damage levels, were adjusted in each cohort. The fixedand random-effect meta-analyses were used to calculate the pooled b across of the three cohorts. If the Q test for heterogeneity among three cohorts was not significant, the fixed-effect model was chosen. Otherwise, the pooled result was estimated using the random-effect model. All analyses were performed using the R (version 2.15.3) and Stata software (version 12.0; StataCorp LP).
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3. Results
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General characteristics of the 307 subjects from three cohorts (Zhuhai, Wuhan and Tianjin) are shown in Table 1. The mean ages were 53.07, 51.35 and 66.61 for Zhuhai, Wuhan and Tianjin subjects, respectively. PM2.5 exposure levels were different among three cities (median value: 68.35 mg/m3, 114.96 mg/m3 and 146.60 mg/m3, respectively). As expected, low DNA damage levels were observed in Zhuhai subjects with a median percentage of tail DNA of 1.36%, while moderate and heavy levels were detected in Wuhan and Tianjin subjects (median value: 1.85 and 2.97, respectively). The associations between genetic variants of SMARC genes and the DNA damage levels in the subjects are summarized in Table 2. The genotypes of rs6857360 (b = 0.23, 95% CI = 0.06–0.40, P = 0.008) in SMARCA5, rs6919 (b = 0.20, 95% CI = 0.05–0.34, P = 0.008) and rs2727280 (b = 0.18, 95% CI = 0.04–0.33, P = 0.013) in SMARCD2, and rs17173769 (b = 0.27, 95% CI = 0.52 to 0.01, P = 0.045) in SMARCD3 was significantly associated with DNA damage levels. Consistent results were observed among three cohorts for the associations of rs6857360, rs6919 and rs2727280 while conflicting direction of association was indicated for rs17173769 (Table 3). Moreover, we did not observe significant interactions between any SNP and PM2.5 exposure levels on DNA damage levels (data not shown). We further assessed the combined effects of these four independent predictors of rs6857360, rs6919, rs2727280 and rs17173769 on DNA damage. As shown in Table 4, the results showed that the more unfavorable alleles the subjects carried, the heavier DNA damage they suffered, suggesting a locus-dosage effect between combined genotypes and DNA damage (P for trend = 0.006). Such a locus-dosage effect of combined genotypes was also consistent among three cohorts (Fig. 1). Besides, we have done the stratification analysis by the mean age of the subjects and the smoking status in the association analysis of genetic variants with DNA damage levels. As shown in the Supplemental Table 1, similar association strengths were observed between subgroups for each SNP (P for heterogeneity >0.05). In this study, we attempted to test whether the genetic variants in SMARC genes
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Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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Fig. 2. The association of four risk variants with host gene expression.
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modify the process of PM2.5 causing DNA damage. As shown in Supplemental Table 2, we performed interaction analysis between the four identified SNPs and PM2.5 exposure levels and did not observe significant joint effects on DNA damage levels.
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4. Discussion
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In the present study, we investigated the associations of genetic variants of SMARC genes with DNA damage levels in three independent cohorts of Chinese population. We found that the genotypes of rs6857360 in SMARCA5, rs6919 and rs2727280 in SMARCD2 and rs17173769 in SMARCD3 were significantly associated with DNA damage levels. To our knowledge, this is the first report to show that genetic variants of SMARC genes were associated with DNA damage levels, providing further evidence supporting the importance of SMARC genes in DNA repair. SMARCA5 (hSNF2H) is a member of SWI/SNF family, which contains helicase and ATPase activities. As part of a chromatin remodeling complex, it facilitates transcription initiation and ATPdependent nucleosome remodeling (LeRoy et al., 2000). Recently, SMARCA5 was found to be recruited to DNA damage sites (Erdel et al., 2010; Fischer et al., 2011) and promotes DNA double-strand breaks (DSB) repair by NHEJ (non-homologous end-joining) and HR (homologous recombination) (Smeenk et al., 2013). Besides, the downregulation of SMARCA5, a direct target of miR-99 family, mediated radiation sensitivity through its role in facilitating DNA repair. Furthermore, miR-99 reduces the rate of DNA repair through downregulation of SMARCA5 (Mueller et al., 2013). Taken together, these findings suggest a critical role of SMARCA5 in the process of DNA damage repair. In this study, we found that the SNP rs6857360 in the intron of SMARCA5 was significantly associated with DNA damage levels, which further reflected an important role of SMARCA5 in population level. However, since this variant is just a proxy of genetic variants of SMARCA5, it is largely unknown in terms of the functional relevance of genetic variants with SMARCA5. SMARCD2 (BAF60b) and SMARCD3 (BAF60c) are two variants of the BAF60 subunits. It was reported that SMARCD2 was upregulated in well and poorly differentiated esophageal squamous cell
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carcinoma (ESCC) cell types at both irradiation doses (Bo et al., 2004). SMARCD3 was related to nucleic acid metabolism, manifested as degraded DNA duplication and transcription, and increased DNA damage repair (Ze-Min et al., 2013). Furthermore, chromatin immunoprecipitation (ChIP)-on-chip analysis demonstrated that SMARCD3 knockdown impairs the myogenic determination factor (MyoD) binding to target genes (Puri and Mercola, 2012) and MyoD was inhibited by DNA damage (Puri et al., 2002). Besides, SMARCD3 is essential for chromatin remodeling complexes in heart development (Lickert et al., 2004) and possible candidate for being novel prognostic markers for neuroblastoma (NB) (Takita et al., 2004). Two SNPs (rs2727280 and rs6919) located in the intron and 30 UTR (untranslated region) of SMARCD2, respectively, were identified to be associated with DNA damage levels. We analyzed the correlation between genotypes of the SNPs and the expression levels of their host genes in Chinese population using public available data from GENEVAR (Yang et al., 2010). As shown in Fig. 2, we found that the genotypes of rs2727280 and rs6919 in SMARCD2 gene were significantly associated with SMARCD2 expression level, in which the lower SMARCD2 expression levels were observed in subjects with risk alleles of DNA damage (rs2727280-A: P = 0.026; rs6919-T: P = 0.044). The identified SNP rs17173769 is located in the intron of SMARCD3 and there are not known functional evidence for the association of this variant. In addition, we did not found significant associations for selected functional or tagging SNPs in other SMARC genes with DNA damage levels. These SMARC genes have been previously reported to be associated with DNA damage. For example, after damage to DNA, BRIT1 increases its interaction with SWI/SNF through ATM (ataxia-telangiectasia mutated) and/or ATR (ATM and Rad3-related), two central kinases in the DNA-damage response network, dependent phosphorylation on the SMARCC2 (Peng et al., 2009). Besides, SMARCD1 would almost completely block androgen receptor (AR)-driven expression of TMPRSS2 (van de Wijngaart et al., 2009), while faulty DNA damage repair has been implicated in the formation of TMPRSS2–ERG fusions during prostate carcinogenesis (Jaamaa and Laiho, 2012). One explanation is that the selected SNPs may have no effect on the biological
Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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function of these SMARC genes. For another, our study may have no enough power to detect the true associations and lead to false positive results. In summary, our study provides evidence that genetic variants in SMARCA5, SMARCD2 and SMARCD3 may contribute to the DNA damage levels in Chinese population. Larger well-designed studies and functional studies are warranted to confirm our findings. Conflict of interest The authors declare no competing financial interest.
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Transparency document
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The Transparency document associated with this article can be found in the online version.
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Funding sources
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This work was funded by the National Key Basic Research Program Grant (2011CB503805), the National Science and Technology Support Program (2011BAI09B02), the Major Program of the National Natural Science Foundation of China (81390543), the State Key Program of National Natural Science of China (81230067), Jiangsu Province Clinical Science and Technology Projects (BL2012008) and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine) and Collaborative Innovation Center For Cancer Personalized Medicine in Jiangsu Province. The authors wish to thank all the study participants, research staff and students who participated in this work.
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Appendix A. Supplementary data
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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2014.06.034.
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Please cite this article in press as: Gong, J., et al., Genetic variants in SMARC genes are associated with DNA damage levels in Chinese population, Toxicol. Lett. (2014), http://dx.doi.org/10.1016/j.toxlet.2014.06.034
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