DIAB-6425; No. of Pages 7 diabetes research and clinical practice xxx (2015) xxx–xxx
Contents available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease Amirhossein Ahmadi a, Mehrdad Behmanesh a,*, Mohammad Ali Boroumand b, Mahmoud Tavallaei c a
Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, P.O. Box 14115-154, Tehran, Iran Department of Pathology, Tehran Heart Center, Tehran University of Medical Sciences, P.O. Box 1411713138, Tehran, Iran c Human Genetics Research Center, Baqiyatallah Medical Sciences University, Tehran, Iran b
article info
abstract
Article history:
Aims: Coronary artery disease (CAD) is a major problem in some patients with type 2
Received 21 February 2015
diabetes mellitus (T2DM). CAD has been suggested to be the main result of reduced efficacy
Received in revised form
of DNA repair systems. Analysis of the DNA repair system in patients with diabetes can
5 May 2015
potentially uncover the molecular basis of their susceptibility to the CAD. The aim of the
Accepted 28 May 2015
present study was to compare the expression levels of some important DNA repair genes,
Available online xxx
including ATM, XRCC1 and MSH2, in CAD+ versus CAD patients with T2DM. Furthermore, the relevance of putative single nucleotide polymorphisms (SNPs) in the promoter regions of
Keywords:
these genes with mRNA expression was evaluated.
Atherosclerosis
Methods: Expression analysis was performed by RT-qPCR on 76 patients with T2DM (41 CAD+
Biomarker
and 35 CAD individuals confirmed by angiography). The genotypes of the patients were
Coronary artery disease
examined by polymerase chain reaction-restriction fragment length polymorphism analysis.
DNA damage
Results: Significant up-regulation of the MSH2 (2.49-fold, P = 0.001), XRCC1 (2.11-fold, P = 0.001) and ATM (2.15-fold, P = 0.003) genes was observed in patients with T2DM and CAD. We could not detect any function for SNPs by comparing gene expression. In a receiver operating characteristic (ROC) curve analysis, the area under the ROC curve for sum of relative expressions of all genes reached 0.81 (95% CI: 0.690–0.936, P = 0.003), which indicates a potential biomarker for identifying patients with T2DM and CAD. Conclusion: These results suggest that expression levels of DNA repair genes may serve as informative biomarkers for identifying patients with T2DM and CAD. # 2015 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder which its prevalence estimated to increase owing
to urbanization, physical inactivity and obesity [1]. Patients with T2DM usually suffer from both microvascular and macrovascular complications. Coronary artery disease (CAD) is a common complication of T2DM in which plaque formation narrows the coronary arteries and thus, increases the risk of heart attack [2].
* Corresponding author. Tel.: +98 21 82884451; fax: +98 21 82884717. E-mail address: [email protected] (M. Behmanesh). http://dx.doi.org/10.1016/j.diabres.2015.05.049 0168-8227/# 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
DIAB-6425; No. of Pages 7
2
diabetes research and clinical practice xxx (2015) xxx–xxx
Although inflammatory processes are reported to be responsible for the progression of CAD, growing evidence has proposed a possible role for DNA damages in the development of atherosclerosis [3]. Hyperglycemia as the main T2DM marker disturbs cell functions through excessive reactive oxygen species (ROSs) generation. In fact, poor glycemic control not only induces excessive generation of ROSs but also diminishes the ROS-scavenging systems [4]. ROSs, in turn, induce DNA lesions and are considered as the main cause of both micro and macrovascular T2DM complications [5]. Several DNA lesions, including DNA strand breaks, mutated nucleotides and microsatellite instabilities (MSI) were detected in endothelial, smooth muscle cells and even peripheral-blood mononuclear cells (PBMCs) of CAD patients [6]. Multiple DNA repair pathways eliminate genome lesions. DNA strand breaks repair, base excision repair (BER) and mismatch repair (MMR) systems actively detect and repair DNA strand breaks and mutated nucleotides such as oxidized ones [7–9]. Patients with genotypes that decrease DNA repair efficacy are more susceptible to CAD [10]. Deficiency of the ataxiatelangiectasia mutated (ATM) protein that senses double strand breaks and X-ray repair cross-complementing 1 (XRCC1) that acts as the scaffold in the BER raise the risk of CAD [10,11]. The MutS protein homolog 2 (MSH2) is another protein of the repair system which its defect leads to MSI, a condition that was observed in atherosclerotic plaques frequently [12]. Despite all evidence, little is known about how the expressions of these genes modulate during CAD development in patients with T2DM. Here, the expressions of ATM, XRCC1 and MSH2 genes as the key players in DNA repair pathways were analyzed in CAD+ versus CAD patients with T2DM. In addition, it has been shown that some SNPs in the promoter region of these genes are associated with development of several disease developments through altering the mRNA expression [11,13,14]. However, their effects on mRNA expression in patients with T2DM and CAD are unknown. Thus, the effects of SNPs on gene expression were also evaluated.
2.
Subjects
2.1.
Samples
All participants in this study were patients who referred to Tehran Heart Center outpatient clinic and then underwent coronary angiography from September 2013 to September 2014. Patients with T2DM were selected based on standard criteria (FBS > 125 mg/dl (6.9 mmol/L)) and (HbA1C > 6.5% (47 mmol/mol)). The severity of CAD was classified into the single-vessel (SVD) or multi-vessels disease (MVD) by the number of significantly stenosed coronary arteries. Two groups of patients were selected for this study. Group one consisted of T2DM patients without CAD (n = 35) and group two was composed of T2DM patients with CAD (n = 41). In addition, two subgroups were selected from CAD+ patients for further analysis. These subgroups were T2DM patients with SVD and T2DM patients with MVD. Written informed consents were obtained from all subjects prior to the blood sampling. The Ethics Committee of Tehran Heart Center and Tarbiat Modares University approved this study. The summary of
clinical characteristics is shown in Table 1. Three milliliters of whole blood were collected from each patient and PBMCs were immediately isolated using Ficoll-PaqueTM (GE Healthcare) according to manufacturer’s instructions.
3.
Material and methods
3.1.
Biochemical analysis
Peripheral venous blood samples were taken after 10 h overnight fasting. Fasting blood glucose (FBS) was measured by glucose hexokinase method (Cobas Integra 400, Roche Diagnostics). HbA1c was measured by an enzymatic method (Diazyme Laboratories, USA). HDL-cholesterol and triglycerides levels were assessed using an auto-analyzer (Cobas Integra 400, Roche Diagnostics) with enzymatic techniques. Friedewald’s formula used to estimate LDL cholesterol. If the serum triglyceride level was >4.52 mmol/l, LDL-cholesterol was not calculated.
3.2.
RNA extraction and cDNA synthesis
Total RNA of PBMCs was extracted using acid guanidiniumphenol-chloroform procedure by RNXTM-Plus reagent
Table 1 – Baseline characteristics of the patients. Characteristics
CAD positive n = 41 (100%)
CAD negative n = 35(100%)
Gender (male) Age (years) Mean Diabetes duration (years) HbA1ca % BMIb, kg/m 2 Triglycerides, mmol/L HDLc, mmol/L d LDL , mmol/L Smokers Hyperlipidemia Hypertension Treatment Metformin Glibenclamide Insulin Statin
21 (52%)
15 (44%)
0.467*
60 9.91 7.0 [3–12]
61 9.76 6.6 [3–11.9]
0.722** 0.701***
8.32 1.66 27.23 4.32 177 [117–213]
7.5 1.28 27.44 3.98 134 [103–197]
0.043** 0.431** 0.113***
41 [35–45] 103.26 36.38 3 (7.3%) 36 (87.8%) 29 (70.5%)
39 [36–49] 98.45 29.5 2 (5.7%) 29 (82.8%) 28 (80%)
0.452*** 0.548** 0.489* 0.781* 0.108*
35(85.36%) 8 (19.51%) 3 (7.31%) 41 (100%)
31(88.57%) 7 (20%) 2 (5.71%) 32 (91.42%)
0.499* 0.908* 0.683* 0.089*
P-value
Data are presented as mean (SD) for variables with normal distribution and median [interquartile range] for those without normal distribution. * Chi-square or Fisher’s exact test is performed to compare variables between T2DM patients with and without CAD. ** Student’s t-test is performed to compare variables between T2DM patients with and without CAD. *** Mann–Whitney U test is were performed to compare variables between T2DM patients with and without CAD. P < 0.05 was considered statistically significant and presented by bold numerals. a Glycated hemoglobin. b Body mass index. c High density lipoprotein. d Low density lipoprotein.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
DIAB-6425; No. of Pages 7 diabetes research and clinical practice xxx (2015) xxx–xxx
(cinnagen, Iran) according to manufacturer’s instruction. RNA integrity and quantity were examined by agarose gel electrophoresis and spectrophotometry, respectively. The extracted RNA was treated with DNaseI (Thermoscientific, USA) at 37 C for 30 min to eliminate DNA contamination. cDNA synthesis was performed using 3 mg of RNA, M-MulV reverse transcriptase (Thermoscientific, USA), Oligo (dT)18 and random hexamer primers (MWG, Germany) in a total volume of 20 mL reaction mixture, according to manufacturer’s instructions.
condition was as follows: after initial denaturation at 95 8C for 5 min, there were 40 cycles of initial denaturation at 95 8C for 30 s, followed by annealing at 60 8C for 30 s and extension at 72 8C for 30 s and a final extension step of 5 min at 72 8C. PCR products containing SNPs of ATM, XRCC1 and MSH2 were digested with NotI, SacI and BsrI restriction enzymes respectively. The digested products were analyzed by the 5% polyacrylamide gel for rs189037 & rs3213245 SNPs or 2% agarose gel electrophoresis for rs2303425 SNP.
3.5. 3.3.
Statistical analysis
Real-time PCR
The mRNA expression levels of ATM, XRCC1 and MSH2 genes were evaluated using Real-time PCR and SYBR green master mix (Takara, Japan). Gene-specific primers were designed by PRIMER EXPRESS software (Applied Biosystems, USA). The sequence of primers were as follows: forward: GGACTGCATCTTAGCCCGAGTAGG, reverse: GCCCATGCTAACCCAAATCCATCG for MSH2, forward: AGGGTGGCAGAGCAGAAGGAACA, reverse: GTGCTTGCCCTGGAAGAAATCTGG for XRCC1, forward: CAAACGAAATCTCAGTGATATTGACC, reverse: AGTGCCTfor ATM and forward: TCTTCCACTCCTTTCAG AGCCTTCCTTCCTGGGCATGG, reverse: AGCACTGTGTTGGCGTACAGGTC for ACTB which was used as the internal control. Real-time PCR was carried out using a Rotor-Gene Q instrument (Qiagen, Germany) in a final reaction volume of 20 ml with 20 ng cDNA, 10 ml SYBR green master mix 2X and 200 nM of each forward and reverse primers. The thermal reaction condition was as follows: an initial denaturation at 95 8C for 5 min, followed by 40 cycles of denaturation at 95 8C for 10 s, annealing at 60 8C for 20 s and extension at 72 8C for 20 s. Specificity of PCR products were confirmed by poly acrylamide gel electrophoresis and dissociation curve analysis. All reactions were run at least in duplicate and the normalized expression were used for data analysis. In order to calculate the normalized expression, DCts were calculated by subtracting the cycle threshold (Ct) values of the ACTB as the housekeeping gene from Ct values of target genes. Finally, since DCts were exponential raw data they were converted to the linear form by 2-DDCt formula, for further analysis [15].
3.4.
3
DNA extraction and genotyping
Genomic DNA was extracted from whole blood samples using DNPTM Kit (Cinnagen, Iran). The quantity and quality of extracted DNA were examined spectrophotometrically and 1% agarose gel electrophoresis, respectively. Putative SNPs in promoter regions of ATM (rs189037), XRCC1 (rs3213245) and MSH2 (rs2303425) genes were genotyped by polymerase chain reaction restriction fragment length polymorphism (PCRRFLP) method. PCR products were amplified by following primers: forward: TGGCTAACGGAGAAAAGAAGACGCGG, reverse: GACAGAATTCCAATTCCAGCCGTATG for ATM (514 bp), forward: GCCAACGCAGGGCGCACCGAGC, Reverse: TGAGTCCTCCCAAGGCCTCTACG for XRCC1 (480 bp) and forward: TGGGCCGCGTCTGCTTATGATTGG, reverse: TCGCCGTGCGCCGTATAGAAGTCG for MSH2 (332 bp). In order to create restriction sites, some primers were designed to have mismatch bases that are shown by bold and italic letters. The thermal
Statistical analysis was performed by using Graphpad Prism version 5.0 (GraphPad Prism Software, Inc., San Diego, CA) and SPSS (SPSS, Chicago, IL, USA). Data was analyzed for normal distribution by calculating skewness and kurtosis and performing the Shapiro–Wilk test. Based on the normal distribution Pearson test was conducted to analysis correlations. Student’s t-test and Mann–Whitney U test used to compare parametric and nonparametric continuous variables respectively. Chisquare test or Fisher’s exact test was performed to compare categorical variables. The fold changes of genes expression were analyzed using Relative Expression Software Tool (REST) (Qiagen, Germany, Version 2.0.13), REST allows for a normalization of the target genes with a reference gene. It calculates the fold change of gene expression based on the PCR efficiency and crossing point deviation of the investigated transcripts [16]. In order to evaluate the effects of confounders on expression of genes, logistic regression model was used. All variables with P value C and MSH6-159C > T promoter polymorphisms and the risk of colorectal cancer. Carcinogenesis 2007;28:2575–80. [15] Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2DDCT method. Methods 2001;25:402–8. [16] Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST#) for group-wise comparison and statistical analysis of relative expression results in realtime PCR. Nucleic Acids Res 2002;30:e36.
[17] Andreassi MG. Coronary atherosclerosis and somatic mutations: an overview of the contributive factors for oxidative DNA damage. Mutat Res 2003;543:67–86. [18] Yu E, Calvert PA, Mercer JR, Harrison J, Baker L, Figg NL, et al. Mitochondrial DNA damage can promote atherosclerosis independently of reactive oxygen species through effects on smooth muscle cells and monocytes and correlates with higher-risk plaques in humans. Circulation 2013;128:702–12. [19] Cervelli T, Borghini A, Galli A, Andreassi MG. DNA damage and repair in atherosclerosis: current insights and future perspectives. Int J Mol Sci 2012;13:16929–44. [20] Koncˇarevic´ S, Lo¨ßner C, Kuhn K, Prinz T, Pike IZucht H-D. In-depth profiling of the peripheral blood mononuclear cells proteome for clinical blood proteomics. Int J Proteomics 2014;2014:9–17. [21] Botto N, Rizza A, Colombo M, Mazzone A, Manfredi S, Masetti S, et al. Evidence for DNA damage in patients with coronary artery disease. Mutat Res 2001;493:23–30. [22] Botto N, Masetti S, Petrozzi L, Vassalle C, Manfredi S, Biagini A. Elevated levels of oxidative DNA damage in patients with coronary artery disease. Coron Artery Dis 2002;13:269–74. [23] Blasiak J, Arabski M, Krupa R, Wozniak K, Zadrozny M, Kasznicki J. DNA damage and repair in type 2 diabetes mellitus. Mutat Res 2004;554:297–304. [24] Al-Aubaidy HA, Jelinek HF. Oxidative DNA damage and obesity in type 2 diabetes mellitus. Eur J Endocrinol 2011;164:899–904. [25] de Bandeira SM, da Fonseca LJS, da Guedes GS, Rabelo LA, Goulart MO, Vasconcelos SML. Oxidative stress as an underlying contributor in the development of chronic complications in diabetes mellitus. Int J Mol sciences 2013;14:3265–84. [26] Hinokio Y, Suzuki S, Hirai M, Chiba M, Hirai A, Toyota T. Oxidative DNA damage in diabetes mellitus: its association with diabetic complications. Diabetologia 1999;42:995–8. [27] Frosina G. Overexpression of enzymes that repair endogenous damage to DNA. Eur J Biochem 2000;267:2135–49. [28] Martinet W, Knaapen MW, De Meyer GR, Herman AG, Kockx MM. Elevated levels of oxidative DNA damage and DNA repair enzymes in human atherosclerotic plaques. Circulation 2002;106:927–32. [29] Pang J, Xi C, Dai Y, Gong H, Zhang T. Altered expression of base excision repair genes in response to high glucoseinduced oxidative stress in HepG2 hepatocytes. Med Sci Monit 2012;18:BR281–5. ˚ , Enroth SB, Gyllensten U. Strong [30] Enroth S, Johansson A effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat Commun 2014;5. [31] Wu LL, Chiou C-C, Chang P-Y, Wu JT. Urinary 8-OHdG: a marker of oxidative stress to DNA and a risk factor for cancer, atherosclerosis and diabetics. Clin Chim Acta 2004;339:1–9. [32] Haycock PC, Heydon EE, Kaptoge S, Butterworth AS, Thompson A, Willeit P. Leucocyte telomere length and risk of cardiovascular disease: systematic review and metaanalysis. BMJ 2014;349. g4227. [33] Schafmayer C, Buch S, Egberts JH, Franke A, Brosch M, El Sharawy A, et al. Genetic investigation of DNA repair pathway genes PMS2, MLH1, MSH2, MSH6, MUTYH, OGG1 and MTH1 in sporadic colon cancer. Int J Cancer 2007;121:555–8. [34] Brem R, Cox DG, Chapot B, Moullan N, Romestaing P, Gerard J-P, et al. The XRCC1-77T ! C variant: haplotypes, breast cancer risk, response to radiotherapy and the cellular response to DNA damage. Carcinogenesis 2006;27:2469–74.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
7
Contents available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease Amirhossein Ahmadi a, Mehrdad Behmanesh a,*, Mohammad Ali Boroumand b, Mahmoud Tavallaei c a
Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, P.O. Box 14115-154, Tehran, Iran Department of Pathology, Tehran Heart Center, Tehran University of Medical Sciences, P.O. Box 1411713138, Tehran, Iran c Human Genetics Research Center, Baqiyatallah Medical Sciences University, Tehran, Iran b
article info
abstract
Article history:
Aims: Coronary artery disease (CAD) is a major problem in some patients with type 2
Received 21 February 2015
diabetes mellitus (T2DM). CAD has been suggested to be the main result of reduced efficacy
Received in revised form
of DNA repair systems. Analysis of the DNA repair system in patients with diabetes can
5 May 2015
potentially uncover the molecular basis of their susceptibility to the CAD. The aim of the
Accepted 28 May 2015
present study was to compare the expression levels of some important DNA repair genes,
Available online xxx
including ATM, XRCC1 and MSH2, in CAD+ versus CAD patients with T2DM. Furthermore, the relevance of putative single nucleotide polymorphisms (SNPs) in the promoter regions of
Keywords:
these genes with mRNA expression was evaluated.
Atherosclerosis
Methods: Expression analysis was performed by RT-qPCR on 76 patients with T2DM (41 CAD+
Biomarker
and 35 CAD individuals confirmed by angiography). The genotypes of the patients were
Coronary artery disease
examined by polymerase chain reaction-restriction fragment length polymorphism analysis.
DNA damage
Results: Significant up-regulation of the MSH2 (2.49-fold, P = 0.001), XRCC1 (2.11-fold, P = 0.001) and ATM (2.15-fold, P = 0.003) genes was observed in patients with T2DM and CAD. We could not detect any function for SNPs by comparing gene expression. In a receiver operating characteristic (ROC) curve analysis, the area under the ROC curve for sum of relative expressions of all genes reached 0.81 (95% CI: 0.690–0.936, P = 0.003), which indicates a potential biomarker for identifying patients with T2DM and CAD. Conclusion: These results suggest that expression levels of DNA repair genes may serve as informative biomarkers for identifying patients with T2DM and CAD. # 2015 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder which its prevalence estimated to increase owing
to urbanization, physical inactivity and obesity [1]. Patients with T2DM usually suffer from both microvascular and macrovascular complications. Coronary artery disease (CAD) is a common complication of T2DM in which plaque formation narrows the coronary arteries and thus, increases the risk of heart attack [2].
* Corresponding author. Tel.: +98 21 82884451; fax: +98 21 82884717. E-mail address: [email protected] (M. Behmanesh). http://dx.doi.org/10.1016/j.diabres.2015.05.049 0168-8227/# 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
DIAB-6425; No. of Pages 7
2
diabetes research and clinical practice xxx (2015) xxx–xxx
Although inflammatory processes are reported to be responsible for the progression of CAD, growing evidence has proposed a possible role for DNA damages in the development of atherosclerosis [3]. Hyperglycemia as the main T2DM marker disturbs cell functions through excessive reactive oxygen species (ROSs) generation. In fact, poor glycemic control not only induces excessive generation of ROSs but also diminishes the ROS-scavenging systems [4]. ROSs, in turn, induce DNA lesions and are considered as the main cause of both micro and macrovascular T2DM complications [5]. Several DNA lesions, including DNA strand breaks, mutated nucleotides and microsatellite instabilities (MSI) were detected in endothelial, smooth muscle cells and even peripheral-blood mononuclear cells (PBMCs) of CAD patients [6]. Multiple DNA repair pathways eliminate genome lesions. DNA strand breaks repair, base excision repair (BER) and mismatch repair (MMR) systems actively detect and repair DNA strand breaks and mutated nucleotides such as oxidized ones [7–9]. Patients with genotypes that decrease DNA repair efficacy are more susceptible to CAD [10]. Deficiency of the ataxiatelangiectasia mutated (ATM) protein that senses double strand breaks and X-ray repair cross-complementing 1 (XRCC1) that acts as the scaffold in the BER raise the risk of CAD [10,11]. The MutS protein homolog 2 (MSH2) is another protein of the repair system which its defect leads to MSI, a condition that was observed in atherosclerotic plaques frequently [12]. Despite all evidence, little is known about how the expressions of these genes modulate during CAD development in patients with T2DM. Here, the expressions of ATM, XRCC1 and MSH2 genes as the key players in DNA repair pathways were analyzed in CAD+ versus CAD patients with T2DM. In addition, it has been shown that some SNPs in the promoter region of these genes are associated with development of several disease developments through altering the mRNA expression [11,13,14]. However, their effects on mRNA expression in patients with T2DM and CAD are unknown. Thus, the effects of SNPs on gene expression were also evaluated.
2.
Subjects
2.1.
Samples
All participants in this study were patients who referred to Tehran Heart Center outpatient clinic and then underwent coronary angiography from September 2013 to September 2014. Patients with T2DM were selected based on standard criteria (FBS > 125 mg/dl (6.9 mmol/L)) and (HbA1C > 6.5% (47 mmol/mol)). The severity of CAD was classified into the single-vessel (SVD) or multi-vessels disease (MVD) by the number of significantly stenosed coronary arteries. Two groups of patients were selected for this study. Group one consisted of T2DM patients without CAD (n = 35) and group two was composed of T2DM patients with CAD (n = 41). In addition, two subgroups were selected from CAD+ patients for further analysis. These subgroups were T2DM patients with SVD and T2DM patients with MVD. Written informed consents were obtained from all subjects prior to the blood sampling. The Ethics Committee of Tehran Heart Center and Tarbiat Modares University approved this study. The summary of
clinical characteristics is shown in Table 1. Three milliliters of whole blood were collected from each patient and PBMCs were immediately isolated using Ficoll-PaqueTM (GE Healthcare) according to manufacturer’s instructions.
3.
Material and methods
3.1.
Biochemical analysis
Peripheral venous blood samples were taken after 10 h overnight fasting. Fasting blood glucose (FBS) was measured by glucose hexokinase method (Cobas Integra 400, Roche Diagnostics). HbA1c was measured by an enzymatic method (Diazyme Laboratories, USA). HDL-cholesterol and triglycerides levels were assessed using an auto-analyzer (Cobas Integra 400, Roche Diagnostics) with enzymatic techniques. Friedewald’s formula used to estimate LDL cholesterol. If the serum triglyceride level was >4.52 mmol/l, LDL-cholesterol was not calculated.
3.2.
RNA extraction and cDNA synthesis
Total RNA of PBMCs was extracted using acid guanidiniumphenol-chloroform procedure by RNXTM-Plus reagent
Table 1 – Baseline characteristics of the patients. Characteristics
CAD positive n = 41 (100%)
CAD negative n = 35(100%)
Gender (male) Age (years) Mean Diabetes duration (years) HbA1ca % BMIb, kg/m 2 Triglycerides, mmol/L HDLc, mmol/L d LDL , mmol/L Smokers Hyperlipidemia Hypertension Treatment Metformin Glibenclamide Insulin Statin
21 (52%)
15 (44%)
0.467*
60 9.91 7.0 [3–12]
61 9.76 6.6 [3–11.9]
0.722** 0.701***
8.32 1.66 27.23 4.32 177 [117–213]
7.5 1.28 27.44 3.98 134 [103–197]
0.043** 0.431** 0.113***
41 [35–45] 103.26 36.38 3 (7.3%) 36 (87.8%) 29 (70.5%)
39 [36–49] 98.45 29.5 2 (5.7%) 29 (82.8%) 28 (80%)
0.452*** 0.548** 0.489* 0.781* 0.108*
35(85.36%) 8 (19.51%) 3 (7.31%) 41 (100%)
31(88.57%) 7 (20%) 2 (5.71%) 32 (91.42%)
0.499* 0.908* 0.683* 0.089*
P-value
Data are presented as mean (SD) for variables with normal distribution and median [interquartile range] for those without normal distribution. * Chi-square or Fisher’s exact test is performed to compare variables between T2DM patients with and without CAD. ** Student’s t-test is performed to compare variables between T2DM patients with and without CAD. *** Mann–Whitney U test is were performed to compare variables between T2DM patients with and without CAD. P < 0.05 was considered statistically significant and presented by bold numerals. a Glycated hemoglobin. b Body mass index. c High density lipoprotein. d Low density lipoprotein.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
DIAB-6425; No. of Pages 7 diabetes research and clinical practice xxx (2015) xxx–xxx
(cinnagen, Iran) according to manufacturer’s instruction. RNA integrity and quantity were examined by agarose gel electrophoresis and spectrophotometry, respectively. The extracted RNA was treated with DNaseI (Thermoscientific, USA) at 37 C for 30 min to eliminate DNA contamination. cDNA synthesis was performed using 3 mg of RNA, M-MulV reverse transcriptase (Thermoscientific, USA), Oligo (dT)18 and random hexamer primers (MWG, Germany) in a total volume of 20 mL reaction mixture, according to manufacturer’s instructions.
condition was as follows: after initial denaturation at 95 8C for 5 min, there were 40 cycles of initial denaturation at 95 8C for 30 s, followed by annealing at 60 8C for 30 s and extension at 72 8C for 30 s and a final extension step of 5 min at 72 8C. PCR products containing SNPs of ATM, XRCC1 and MSH2 were digested with NotI, SacI and BsrI restriction enzymes respectively. The digested products were analyzed by the 5% polyacrylamide gel for rs189037 & rs3213245 SNPs or 2% agarose gel electrophoresis for rs2303425 SNP.
3.5. 3.3.
Statistical analysis
Real-time PCR
The mRNA expression levels of ATM, XRCC1 and MSH2 genes were evaluated using Real-time PCR and SYBR green master mix (Takara, Japan). Gene-specific primers were designed by PRIMER EXPRESS software (Applied Biosystems, USA). The sequence of primers were as follows: forward: GGACTGCATCTTAGCCCGAGTAGG, reverse: GCCCATGCTAACCCAAATCCATCG for MSH2, forward: AGGGTGGCAGAGCAGAAGGAACA, reverse: GTGCTTGCCCTGGAAGAAATCTGG for XRCC1, forward: CAAACGAAATCTCAGTGATATTGACC, reverse: AGTGCCTfor ATM and forward: TCTTCCACTCCTTTCAG AGCCTTCCTTCCTGGGCATGG, reverse: AGCACTGTGTTGGCGTACAGGTC for ACTB which was used as the internal control. Real-time PCR was carried out using a Rotor-Gene Q instrument (Qiagen, Germany) in a final reaction volume of 20 ml with 20 ng cDNA, 10 ml SYBR green master mix 2X and 200 nM of each forward and reverse primers. The thermal reaction condition was as follows: an initial denaturation at 95 8C for 5 min, followed by 40 cycles of denaturation at 95 8C for 10 s, annealing at 60 8C for 20 s and extension at 72 8C for 20 s. Specificity of PCR products were confirmed by poly acrylamide gel electrophoresis and dissociation curve analysis. All reactions were run at least in duplicate and the normalized expression were used for data analysis. In order to calculate the normalized expression, DCts were calculated by subtracting the cycle threshold (Ct) values of the ACTB as the housekeeping gene from Ct values of target genes. Finally, since DCts were exponential raw data they were converted to the linear form by 2-DDCt formula, for further analysis [15].
3.4.
3
DNA extraction and genotyping
Genomic DNA was extracted from whole blood samples using DNPTM Kit (Cinnagen, Iran). The quantity and quality of extracted DNA were examined spectrophotometrically and 1% agarose gel electrophoresis, respectively. Putative SNPs in promoter regions of ATM (rs189037), XRCC1 (rs3213245) and MSH2 (rs2303425) genes were genotyped by polymerase chain reaction restriction fragment length polymorphism (PCRRFLP) method. PCR products were amplified by following primers: forward: TGGCTAACGGAGAAAAGAAGACGCGG, reverse: GACAGAATTCCAATTCCAGCCGTATG for ATM (514 bp), forward: GCCAACGCAGGGCGCACCGAGC, Reverse: TGAGTCCTCCCAAGGCCTCTACG for XRCC1 (480 bp) and forward: TGGGCCGCGTCTGCTTATGATTGG, reverse: TCGCCGTGCGCCGTATAGAAGTCG for MSH2 (332 bp). In order to create restriction sites, some primers were designed to have mismatch bases that are shown by bold and italic letters. The thermal
Statistical analysis was performed by using Graphpad Prism version 5.0 (GraphPad Prism Software, Inc., San Diego, CA) and SPSS (SPSS, Chicago, IL, USA). Data was analyzed for normal distribution by calculating skewness and kurtosis and performing the Shapiro–Wilk test. Based on the normal distribution Pearson test was conducted to analysis correlations. Student’s t-test and Mann–Whitney U test used to compare parametric and nonparametric continuous variables respectively. Chisquare test or Fisher’s exact test was performed to compare categorical variables. The fold changes of genes expression were analyzed using Relative Expression Software Tool (REST) (Qiagen, Germany, Version 2.0.13), REST allows for a normalization of the target genes with a reference gene. It calculates the fold change of gene expression based on the PCR efficiency and crossing point deviation of the investigated transcripts [16]. In order to evaluate the effects of confounders on expression of genes, logistic regression model was used. All variables with P value C and MSH6-159C > T promoter polymorphisms and the risk of colorectal cancer. Carcinogenesis 2007;28:2575–80. [15] Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2DDCT method. Methods 2001;25:402–8. [16] Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST#) for group-wise comparison and statistical analysis of relative expression results in realtime PCR. Nucleic Acids Res 2002;30:e36.
[17] Andreassi MG. Coronary atherosclerosis and somatic mutations: an overview of the contributive factors for oxidative DNA damage. Mutat Res 2003;543:67–86. [18] Yu E, Calvert PA, Mercer JR, Harrison J, Baker L, Figg NL, et al. Mitochondrial DNA damage can promote atherosclerosis independently of reactive oxygen species through effects on smooth muscle cells and monocytes and correlates with higher-risk plaques in humans. Circulation 2013;128:702–12. [19] Cervelli T, Borghini A, Galli A, Andreassi MG. DNA damage and repair in atherosclerosis: current insights and future perspectives. Int J Mol Sci 2012;13:16929–44. [20] Koncˇarevic´ S, Lo¨ßner C, Kuhn K, Prinz T, Pike IZucht H-D. In-depth profiling of the peripheral blood mononuclear cells proteome for clinical blood proteomics. Int J Proteomics 2014;2014:9–17. [21] Botto N, Rizza A, Colombo M, Mazzone A, Manfredi S, Masetti S, et al. Evidence for DNA damage in patients with coronary artery disease. Mutat Res 2001;493:23–30. [22] Botto N, Masetti S, Petrozzi L, Vassalle C, Manfredi S, Biagini A. Elevated levels of oxidative DNA damage in patients with coronary artery disease. Coron Artery Dis 2002;13:269–74. [23] Blasiak J, Arabski M, Krupa R, Wozniak K, Zadrozny M, Kasznicki J. DNA damage and repair in type 2 diabetes mellitus. Mutat Res 2004;554:297–304. [24] Al-Aubaidy HA, Jelinek HF. Oxidative DNA damage and obesity in type 2 diabetes mellitus. Eur J Endocrinol 2011;164:899–904. [25] de Bandeira SM, da Fonseca LJS, da Guedes GS, Rabelo LA, Goulart MO, Vasconcelos SML. Oxidative stress as an underlying contributor in the development of chronic complications in diabetes mellitus. Int J Mol sciences 2013;14:3265–84. [26] Hinokio Y, Suzuki S, Hirai M, Chiba M, Hirai A, Toyota T. Oxidative DNA damage in diabetes mellitus: its association with diabetic complications. Diabetologia 1999;42:995–8. [27] Frosina G. Overexpression of enzymes that repair endogenous damage to DNA. Eur J Biochem 2000;267:2135–49. [28] Martinet W, Knaapen MW, De Meyer GR, Herman AG, Kockx MM. Elevated levels of oxidative DNA damage and DNA repair enzymes in human atherosclerotic plaques. Circulation 2002;106:927–32. [29] Pang J, Xi C, Dai Y, Gong H, Zhang T. Altered expression of base excision repair genes in response to high glucoseinduced oxidative stress in HepG2 hepatocytes. Med Sci Monit 2012;18:BR281–5. ˚ , Enroth SB, Gyllensten U. Strong [30] Enroth S, Johansson A effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat Commun 2014;5. [31] Wu LL, Chiou C-C, Chang P-Y, Wu JT. Urinary 8-OHdG: a marker of oxidative stress to DNA and a risk factor for cancer, atherosclerosis and diabetics. Clin Chim Acta 2004;339:1–9. [32] Haycock PC, Heydon EE, Kaptoge S, Butterworth AS, Thompson A, Willeit P. Leucocyte telomere length and risk of cardiovascular disease: systematic review and metaanalysis. BMJ 2014;349. g4227. [33] Schafmayer C, Buch S, Egberts JH, Franke A, Brosch M, El Sharawy A, et al. Genetic investigation of DNA repair pathway genes PMS2, MLH1, MSH2, MSH6, MUTYH, OGG1 and MTH1 in sporadic colon cancer. Int J Cancer 2007;121:555–8. [34] Brem R, Cox DG, Chapot B, Moullan N, Romestaing P, Gerard J-P, et al. The XRCC1-77T ! C variant: haplotypes, breast cancer risk, response to radiotherapy and the cellular response to DNA damage. Carcinogenesis 2006;27:2469–74.
Please cite this article in press as: Ahmadi A, et al. Up-regulation of MSH2, XRCC1 and ATM genes in patients with type 2 diabetes and coronary artery disease. Diabetes Res Clin Pract (2015), http://dx.doi.org/10.1016/j.diabres.2015.05.049
7
