A Haplotype of the SMTN Gene Associated with Myocardial Infarction in Japanese Women

Abstract

Objectives: Smoothelin is a specific kind of cytoskeletal protein present in smooth muscle cells. Some researchers have shown the relationship between smoothelin and atherosclerotic plaque. The human SMTN gene encodes smoothelin-A and smoothelin-B. The aim of the present study was to assess the association between the human SMTN gene and myocardial infarction (MI) using a haplotype-based case-control study. Methods: A total of 227 MI patients and 257 supercontrols were genotyped for five single-nucleotide polymorphisms used as genetic markers of the human smoothelin gene. Data were analyzed for three separate groups: total subjects, men, and women. Results: For the women, the frequency of the C-T-T-G haplotype (established by rs5997872, rs56095120, rs9621187, and rs10304) was significantly higher in the MI group than in the control group (p=0.012). Conclusions: We confirmed that the haplotype constructed using rs5997872, rs56095120, rs9621187, and rs10304 is a useful genetic marker of MI in Japanese females.

Introduction

Cardiovascular disease is the most significant cause of morbidity and mortality in developed countries, and it is soon expected to become a pre-eminent health problem in developing countries as well (Murray and Lopez, 1997). This global problem of cardiovascular disease carries with it a heavy financial burden (Fuster et al., 2006). Atherosclerosis constitutes the single most important contributor to this growing problem of cardiovascular disease. Smooth muscle cells (SMCs) are known to play a part in the evolution of atheromatous plaque and also to participate in the establishment of conditions facilitating plaque rupture and/or thrombus formation through several mechanisms, including vasospasm and the synthesis of extracellular matrix components or proteolytic enzymes. Remodeling and deformation of the intima through tensile force generated by SMCs could also be instrumental in the process of plaque rupture, which may result in myocardial infarction (MI) (Ross, 1999; Libby, 2002; Rikitake and Liao, 2005).

Smoothelin is a specific kind of cytoskeletal protein of SMCs. There are two major isoforms in adult humans: smoothelin-A (∼59 kDa) and smoothelin-B (∼110 kDa) (van Eys et al., 1997; Wehrens et al., 1997). Smoothelin-A is abundant in visceral smooth muscle and is essential for functional contractility of intestinal smooth muscle (Niessen et al., 2005). Smoothelin-B is specifically expressed in vascular SMCs, but the level of its expression differs according to the type of blood vessel. The expression of smoothelin-B is particularly high in muscular arteries, while its expression in elastic arteries is modest (van der Loop et al., 1997). The human SMTN gene that encodes smoothelin-A and smoothelin-B is located on chromosome 22q12.2 and consists of 21 exons. Smoothelin-B is encoded by 21 exons, whereas smoothelin-A transcription starts in the middle of exon 10 (Rensen et al., 2002). Some researchers have shown that smoothelin expression changes under different circumstances. Tharp et al. (2006) reported that smoothelin-B expression was significantly reduced in the medial cells of early atherosclerosis in coronary arteries from swine. During the formation of atherosclerotic plaque, the expression of smoothelin-B in caps is downregulated (Hao et al., 2006). However, when the plaque becomes quiescent and no longer expands, smoothelin expression can be detected in SMCs of the cap (van der Loop et al., 1997). These previous studies indicate that the smoothelin-B gene may be related to atherosclerotic plaque expansion and unstable plaque. MI is thought to be a multifactorial disease that is affected by several environmental factors and genetic variants (Ghatrehsamani et al., 2009; Abu-Amero et al., 2010). The aim of the present study was to investigate the relationship between the SMTN gene and MI using single-nucleotide polymorphisms (SNPs) and a haplotype-based case-control study.

Materials and Methods

Subjects

The study group consisted of 257 healthy Japanese control subjects (non-MI group) and 227 Japanese patients with MI (MI group). Subjects were evaluated based on answers to a detailed questionnaire that provided information about coronary risk factors, such as smoking habits, and the presence of diabetes mellitus, dyslipidemia, or hypertension. Smokers were defined as current or former smokers, whereas nonsmokers were defined as subjects with no history of previous or current smoking. Diagnosis of diabetes mellitus was based on the World Health Organization (WHO) criteria. For a diagnosis of dyslipidemia, patients had to have a plasma total cholesterol>220 mg/dL, plasma triglycerides>150 mg/dL, and a plasma high-density lipoprotein cholesterol<40 mg/dL or be currently using a lipid-lowering drug in addition to having a confirmed diagnosis of hyperlipidemia. Hypertension was defined as having a sitting systolic blood pressure (SBP)>160 mmHg, diastolic blood pressure (DBP)>100 mmHg, or both on three occasions within 2 months after the first medical examination. Patients were also considered to have hypertension if they were currently using an antihypertensive drug due to a history of arterial hypertension. History of alcohol use was recorded with habitual consumers defined as individuals who had ≥2 alcoholic beverages per day. History of MI was confirmed by fulfillment of at least two of the following three criteria: (1) a history of chest pain indicative of MI; (2) creatine kinase and creatine kinase MB levels greater than three times the upper reference limit during a post-MI follow-up; or (3) observation of characteristic electrocardiographic changes at the time of diagnosis (ST-segment elevation>0.1 mV in at least two leads). The age of patients in the MI group ranged from 31 to 87 years (mean±SD, 61.4±9.8 years). Patients were selected from subjects who were admitted between 1995 and 2005 at our hospital (Nihon Univ. Hospital in Tokyo) or at other neighboring hospitals within the Tokyo area. Age of the non-MI subjects ranged from 66 to 94 years (mean±SD, 77.8±4.2 years). Because the mean age of the non-MI group was older compared with the MI group, the non-MI group was regarded as a supercontrol group (Kaneko et al., 2006). These subjects were all members of the New Elder Citizen Movement in Japan who lived in Tokyo or in the suburbs of Tokyo. While all of the controls had coronary risk factors, none of the subjects had a history of MI, ischemic heart disease, or angina pectoris. Informed consent was obtained from each participant in accordance with the protocol approved by the Human Studies Committee of Nihon University (Nakayama et al., 2003).

Genotyping

There are 301 SNPs for the human SMTN gene listed in the National Center for Biotechnology Information (NCBI) SNP database Build 129 (www.ncbi.nlm.nih.gav/SNP). In this study, we also screened the data for the Tag SNPs on the International HapMap Project website (www.hapmap.org/index.html.jp). We selected five SNPs (rs2074738, rs5997872, rs56095120, rs9621187, and rs10304) that had a minor allele frequency of more than 0.15 as the markers for the genetic association experiment (Fig. 1). This selection was based on the SNP allelic frequency information that had been previously registered with NCBI, Celera Discovery System-Applied Biosystems and the International HapMap project. Among all of these SNPs, rs5997872 was located in the exon region, rs10304 was located in the 3′ untranslated region (UTR), and the other three SNPs were located in the intron regions. Because only three out of the five SNPs (rs2074738, rs5997872, and rs10304) were shown on the International HapMap Project website, we were unaware of the |D′| and r² values between the five SNPs before starting the present study. We designated the five SNPs as SNP1 (rs2074738, C_16164833_10), SNP2 (rs5997872, C_2628872_10), SNP3 (rs56095120, AHT95ID), SNP4 (rs9621187, AHVI30L), and SNP5 (rs10304, AHS07PD). Genotyping for these SNPs was done using a kit from Applied Biosystems (Foster City, CA).

FIG. 1.

Structure of the human SMTN gene. The gene consists of 21 exons (boxes) separated by 20 introns (lines; intergenic regions). Boxes indicate the exons, while arrows indicate the locations of single-nucleotide polymorphisms (SNPs). kbp, kilobase pairs.

Blood samples were collected from all participants, and genomic DNA was extracted from the peripheral blood leukocytes using phenol and chloroform extraction (Nakayama et al., 2001).

Genotyping was performed using the TaqMan^® SNP Genotyping Assay (Applied Biosystems) by the Taq amplification method (Sano et al., 2005). In the first step of the 5′ nuclease assay, allele-specific fluorogenic probes are hybridized to the template. Subsequently, the 5′ nuclease activity of the Taq polymerase makes discrimination possible during the polymerase chain reaction (PCR). The probes contain a 3′ minor groove-binding group that hybridizes to single-stranded targets that have a greater sequence specificity than the ordinary DNA probes. This reduces nonspecific probe hybridization, which results in low background fluorescence in the 5′ nuclease PCR assay. Cleavage results in increased emission of a reporter dye. Each 5′ nuclease assay requires two unlabeled PCR primers and two allele-specific probes. Each probe is labeled with two reporter dyes at the 5′ end. In the present study, VIC and FAM were used as the reporter dyes. The primers and probes used in the TaqMan^® SNP Genotyping Assays were chosen based on the information available on the ABI website (http://myscience.appliedbiosystems.com).

PCR amplification was performed using 2.5 μL of TaqMan^® Universal Master Mix, No AmpErase^® UNG (2×) (Applied Biosystems) in a 5-μL final reaction volume, along with 2 ng DNA, 2.375 μL ultrapure water, 0.079 μL Tris-EDTA buffer (1×), 0.046 μL TaqMan^® SNP Genotyping Assay Mix (40×) containing a 331.2-nM final concentration of primers, along with a 73.6-nM final concentration of the probes. The thermal cycling conditions were as follows: 50°C for 2 min; 95°C for 10 min; 50 cycles of 95°C for 15 s; and 60°C for 1 min. Thermal cycling was performed using the GeneAmp 9700™ system.

Each 96-well plate contained 80 DNA samples of an unknown genotype and four reaction mixtures containing reagents, but no DNA (control). The control samples without DNA are a necessary part of the Sequence Detection System (SDS) 7700™ signal processing, as outlined in the TaqMan Allelic Discrimination Guide (Applied Biosystems). The plates were read on the SDS 7700 instrument with the end-point analysis mode of the SDS version 1.6.3 software package (Applied Biosystems). The genotypes were determined visually based on the dye-component fluorescent emission data depicted in the X-Y scatter-plot of the SDS software. The genotypes were also determined automatically by the signal-processing algorithms of the software. The results of each scoring method were saved in two separate output files for later comparison (Livak et al., 1995).

Biochemical analysis

We measured the plasma concentration of total cholesterol and the serum concentration of creatinine using standard methods employed by the Clinical Laboratory Department of Nihon University Hospital.

Based on our initial estimated sample size, the number of patients enrolled was considered to be sufficient for a gene polymorphism study. During our analyses, we obtained 90% power for the detection of the disequilibrium at the 5% level of significance. Results of a previously published study also confirmed that our sample sizes were appropriate for this type of case-control study (Olson and Wijsman, 1994).

Statistical analysis

All continuous variables were expressed as mean±SD. Differences in continuous variables between the MI patients and control subjects were analyzed using the Mann-Whitney U test. Differences in categorical variables were analyzed using the Fisher's exact test. Differences in distributions of genotypes and alleles between MI patients and control subjects were analyzed using the Fisher's exact test. Based on the genotype data of the genetic variations, we performed linkage disequilibrium (LD) and haplotype-based case-control analyses using the expectation maximization algorithm (Dempster et al., 1977) and the software SNPAlyze version 3.2 (Dynacom Co., Ltd., Yokohama, Japan). The pairwise LD analysis was performed using three SNP pairs. We used |D′| values of >0.5 to assign SNP locations to one haplotype block. SNPs with an r² value of<0.5 were selected as tagged. In the haplotype-based case-control analysis, haplotypes with a frequency of<0.02 were excluded. The frequency distribution of the haplotypes was calculated by performing a permutation test using the bootstrap method. Statistical significance was established at p<0.05. Statistical analyses were performed using SPSS software for Windows, version 12 (SPSS, Inc., Chicago, IL).

Results

Table 1 shows the clinical characteristics of the study participants. Our analyses examined the total, men's, and women's groups and compared the MI subjects with the controls in each of the groups. For total subjects, men, and women, the following values were significantly higher for the MI group as compared to the control group: pulse rate, incidence of diabetes, and smoking. For total subjects, men, and women, the following values were significantly lower for the MI patients as compared to the control subjects: age, and the plasma concentration of total cholesterol. For total subjects and men, the following value was significantly higher for the MI patients as compared to the control subjects: body mass index. For total subjects, the following values were significantly higher for the MI patients as compared to the control subjects: DBP, the plasma concentration of creatinine and incidence of drinking. There were no significant differences found for the following variable between the MI patients and the control subjects: SBP.

Table 1.

Characteristics of Study Participants

	Controls	Total MI	p-Value	Controls	Men MI	p-Value	Controls	Women MI	p-Value
Number of subjects	257	227		125	183		132	44
Age (years)	77.8±4.2	61.4±9.8	<0.0001^a	78.0±4.8	60.5±8.9	<0.0001^a	77.5±3.6	64.9±12.	<0.0001^a
BMI (kg/m²)	22.7±2.9	23.7±3.2	<0.0001^a	22.9±2.8	23.9±3.0	0.003^a	22.5±3.0	22.9±4.0	0.585
SBP (mmHg)	135.0±16.8	134.7±22.1	0.665	134.6±15.8	134.9±21.1	0.890	135.4±17.8	134.0±26.5	0.670
DBP (mmHg)	77.8±10.1	81.1±14.9	0.029^a	78.3±10.1	81.7±14.7	0.102	77.3±10.1	78.7±15.8	0.717
Pulse (beats/min)	69.9±11.0	75.1±12.1	<0.0001^a	68.9±12.0	74.6±11.3	<0.0001^a	70.8±10.0	77.5±15.1	0.011^a
Creatinine (mg/dL)	0.9±0.2	1.0±0.6	0.01^a	0.9±0.2	1.0±0.5	0.715	0.8±0.2	1.1±1.1	0.752
Total cholesterol (mg/dL)	218.1±44.6	195.5±50.3	<0.0001^a	204.3±32.1	195.4±45.9	0.006^a	231.21±50.593	195.6±67.6	0.007^a
Hyperlipidemia (%)	46.7	27.8	<0.0001^a	30.6	24.0	0.231	61.8	44.7	0.065
Diabetes (%)	1.6	26.9	<0.0001^a	3.2	26.2	<0.0001^a	0.0	29.5	<0.0001^a
Drinking (%)	39.4	56.0	0.035^a	48.7	63.4	0.126	28.1	26.1	1.000
Smoking (%)	30.0	71.1	<0.0001^a	40.5	75.4	<0.0001^a	18.2	51.7	0.007^a

Continuous variables were expressed as means±standard deviation. Categorical variables were expressed as percentages. P values of continuous variables were calculated by the Mann-Whitney U test.

The p-values of categorical variables were calculated by the Fisher's exact test. ^ap<0.05

MI, myocardial infarction; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Table 2 shows the distribution of the genotypes and alleles of the five SNPs. The genotype distribution of SNP1 was not in good agreement with the Hardy-Weinberg equilibrium (data not shown). The SNP2-SNP5 selected for the present study showed no differences in regard to genotype distributions, or the dominant and recessive model distributions for the total subjects, men, and women. Dominance and recessiveness of the models were defined by their frequency among the total subjects.

Table 2.

Genotype and Allele Distributions in Normotensives and Patients with Myocardial Infarction

			Controls	Total MI		Controls	Men MI		Controls	Women MI
Number of participants			257	227	p-Value	125	183	p-Value	132	44	p-Value
Variants
rs2074738 (SNP1)	Genotype	GG	97 (0.377)	64 (0.282)		40 (0.32)	50 (0.273)		57 (0.432)	14 (0.3182)
		CG	95 (0.37)	107 (0.471)		50 (0.4)	89 (0.486)		45 (0.341)	18 (0.4091)
		CC	65 (0.253)	56 (0.247)	0.043^a	35 (0.28)	44 (0.24)	0.327	30 (0.227)	12 (0.2727)	0.413
	Dominant model	CC+CG	65 (0.253)	56 (0.247)		35 (0.28)	44 (0.24)		30 (0.227)	12 (0.2727)
		CC+CG	192 (0.747)	171 (0.753)	0.916	90 (0.72)	139 (0.76)	0.507	102 (0.773)	32 (0.7273)	0.545
	Recessive model	GG+CG	97 (0.377)	64 (0.282)		40 (0.32)	50 (0.273)		57 (0.432)	14 (0.3182)
		GG+CG	160 (0.623)	163 (0.718)	0.027^a	85 (0.68)	133 (0.727)	0.376	75 (0.568)	30 (0.6818)	0.216
	Allele	G	289 (0.562)	235 (0.518)		130 (0.52)	189 (0.516)		159 (0.602)	46 (0.5227)
		C	225 (0.438)	219 (0.482)	0.175	120 (0.48)	177 (0.484)	0.935	105 (0.398)	42 (0.4773)	0.213
rs5997872 (SNP2)	Genotype	CC	218 (0.848)	192 (0.846)		105 (0.84)	151 (0.825)		113 (0.856)	41 (0.9318)
		CT	37 (0.144)	34 (0.15)		19 (0.152)	31 (0.169)		18 (0.136)	3 (0.0682)
		TT	2 (0.008)	1 (0.004)	0.882	1 (0.008)	1 (0.005)	0.891	1 (0.008)	0 (0)	0.399
	Dominant model	TT	2 (0.008)	1 (0.004)		1 (0.008)	1 (0.005)		1 (0.008)	0 (0)
		CC+CT	255 (0.992)	226 (0.996)	1.000	124 (0.992)	182 (0.995)	1.000	131 (0.992)	44 (1)	1.000
	Recessive model	CC	218 (0.848)	192 (0.846)		105 (0.84)	151 (0.825)		113 (0.856)	41 (0.9318)
		TT+CT	39 (0.152)	35 (0.154)	1.000	20 (0.16)	32 (0.175)	0.759	19 (0.144)	3 (0.0682)	0.292
	Allele	C	473 (0.92)	418 (0.921)		229 (0.916)	333 (0.91)		244 (0.924)	85 (0.9659)
		T	41 (0.08)	36 (0.079)	1.000	21 (0.084)	33 (0.09)	0.885	20 (0.076)	3 (0.0341)	0.217
rs56095120 (SNP3)	Genotype	TT	241 (0.938)	215 (0.947)		118 (0.944)	172 (0.94)		123 (0.932)	43 (0.9773)
		AT	15 (0.058)	12 (0.053)		7 (0.056)	11 (0.06)		8 (0.061)	1 (0.0227)
		AA	1 (0.004)	0 (0)	0.619	0 (0)	0 (0)	1.000	1 (0.008)	0 (0)	0.514
	Dominant model	AA+AT	1 (0.004)	0 (0)		0 (0)	0 (0)		1 (0.008)	0 (0)
		AA+AT	256 (0.996)	227 (1)	1.000	125 (1)	183 (1)	—	131 (0.992)	44 (1)	1.000
	Recessive model	TT+AT	241 (0.938)	215 (0.947)		118 (0.944)	172 (0.94)		123 (0.932)	43 (0.9773)
		TT+AT	16 (0.062)	12 (0.053)	0.700	7 (0.056)	11 (0.06)	1.000	9 (0.068)	1 (0.0227)	0.455
	Allele	T	497 (0.967)	442 (0.974)		243 (0.972)	355 (0.97)		254 (0.962)	87 (0.9886)
		A	17 (0.033)	12 (0.026)	0.577	7 (0.028)	11 (0.03)	1.000	10 (0.038)	1 (0.0114)	0.304
rs9621187 (SNP4)	Genotype	TT	249 (0.969)	222 (0.978)		121 (0.968)	179 (0.978)		128 (0.97)	43 (0.9773)
		TC	8 (0.031)	5 (0.022)		4 (0.032)	4 (0.022)		4 (0.03)	1 (0.0227)
		CC	0 (0)	0 (0)	0.586	0 (0)	0 (0)	0.719	0 (0)	0 (0)	1.000
	Dominant model	CC	0 (0)	0 (0)		0 (0)	0 (0)		0 (0)	0 (0)
		TT+TC	257 (1)	227 (1)	—	125 (1)	183 (1)	—	132 (1)	44 (1)	—
	Recessive model	TT	249 (0.969)	222 (0.978)		121 (0.968)	179 (0.978)		128 (0.97)	43 (0.9773)
		CC+TC	8 (0.031)	5 (0.022)	0.583	4 (0.032)	4 (0.022)	0.719	4 (0.03)	1 (0.0227)	1.000
	Allele	T	506 (0.984)	449 (0.989)		246 (0.984)	362 (0.989)		260 (0.985)	87 (0.9886)
		C	8 (0.016)	5 (0.011)	0.588	4 (0.016)	4 (0.011)	0.721	4 (0.015)	1 (0.0114)	1.000
rs10304 (SNP5)	Genotype	GG	89 (0.346)	79 (0.348)		46 (0.368)	61 (0.333)		43 (0.326)	18 (0.4091)
		AG	122 (0.475)	114 (0.502)		66 (0.528)	95 (0.519)		56 (0.424)	19 (0.4318)
		AA	46 (0.179)	34 (0.15)	0.667	13 (0.104)	27 (0.148)	0.509	33 (0.25)	7 (0.1591)	0.392
	Dominant model	AA	46 (0.179)	34 (0.15)		13 (0.104)	27 (0.148)		33 (0.25)	7 (0.1591)
		AA+AG	211 (0.821)	193 (0.85)	0.394	112 (0.896)	156 (0.852)	0.303	99 (0.75)	37 (0.8409)	0.229
	Recessive model	GG	89 (0.346)	79 (0.348)		46 (0.368)	61 (0.333)		43 (0.326)	18 (0.4091)
		GG+AG	168 (0.654)	148 (0.652)	1.000	79 (0.632)	122 (0.667)	0.544	89 (0.674)	26 (0.5909)	0.362
	Allele	G	300 (0.584)	272 (0.599)		158 (0.632)	217 (0.593)		142 (0.538)	55 (0.625)
		A	214 (0.416)	182 (0.401)	0.647	92 (0.368)	149 (0.407)	0.355	122 (0.462)	33 (0.375)	0.173

p-value of the genotype calculated by the Fisher's exact test. ^ap<0.05.

SNP, single-nucleotide polymorphism.

Table 3 shows the patterns of LD in the SMTN along with their |D′| and r² values. Since most of the values for |D′| between each of the SNP pairs were beyond 0.5, all four SNPs were considered to be within a single haplotype block. Therefore, the haplotype-based case-control study was performed using four SNPs (SNP2-SNP3-SNP4-SNP5).

Table 3.

Pairewise Linkage Disequilibrium for the Five Single-Nucleotide Polymorphisms

|D′| values are shown above and r² values are shown below the diagonal line.

The shadowed portion indicates |D′|>0.5 and r²>0.5.

In the haplotype-based case-control analysis, haplotypes were established through the use of different combinations of the SNPs (Table 4). For the women, the overall distribution of the haplotypes established by SNP3-SNP5, SNP2-SNP3-SNP5, SNP3-SNP4-SNP5, and SNP2-SNP3-SNP4-SNP5 were significantly different between the MI patients and the control subjects. For the women, the frequencies of the T-G haplotype established by SNP3-SNP5 were significantly higher for the MI patients compared to the control subjects (p=0.039), and there was a significant difference in the frequencies of the C-T-G haplotype, T-T-G haplotype, and C-T-T-G haplotype established by SNP2-SNP3-SNP5, SNP3-SNP4-SNP5, and SNP2-SNP3-SNP4-SNP5 between the MI patients and the control subjects (p=0.015, 0.032, 0.012, respectively). For the total subjects and men, there were no significant differences noted for the distribution of the haplotypes established by SNP2-SNP3, SNP2-SNP4, SNP3-SNP4, SNP2-SNP5, SNP3-SNP5, SNP4-SNP5, SNP2-SNP3-SNP4, SNP2-SNP3-SNP5, SNP2-SNP4-SNP5, SNP3-SNP4-SNP5, and SNP2-SNP3-SNP4-SNP5 between the MI patients and the control subjects.

Table 4.

Haplotype Analysis in Patients with Myocardial Infarction and Control Subjects

					Overall p-value			Frequency in total subjects			Frequency in men			Frequency in women
Haplotype	No1	No2	No3	No4	Total	Men	Women	MI patients	Control subjects	p-Value	MI patients	Control subjects	p-Value	MI patients	Control subjects	p-Value
	SNP2	SNP3			0.831	0.953	0.069
H1	C	T						0.894	0.887	0.724	0.883	0.880	0.755	0.965	0.886	0.030^a
H2	T	T						0.393	0.378	0.623	0.090	0.084	0.791	0.035	0.076	0.184
H3	C	A						0.075	0.078	0.876	0.030	0.028	0.882	0.000	0.038	0.067
	SNP2		SNP4		0.933	0.773	0.166
H1	C		T					0.921	0.920	0.933	0.911	0.917	0.773	0.965	0.922	0.166
H3	T		T					0.079	0.080	0.933	0.089	0.083	0.773	0.035	0.078	0.166
	SNP2			SNP5	0.924	0.541	0.163
H1	C			G				0.520	0.514	0.850	0.503	0.548	0.269	0.591	0.481	0.074
H2	T			G				0.079	0.075	0.781	0.090	0.084	0.791	0.034	0.065	0.276
H3	C			A				0.401	0.412	0.731	0.407	0.368	0.329	0.375	0.454	0.197
		SNP3	SNP4		0.528	0.899	0.066
H1		T	T					0.973	0.966	0.528	0.969	0.971	0.899	1.000	0.961	0.066
H2		A	T					0.027	0.034	0.528	0.031	0.029	0.899	0.000	0.039	0.066
		SNP3		SNP5	0.702	0.596	0.040^a
H1		T		G				0.573	0.551	0.489	0.563	0.604	0.310	0.628	0.500	0.039^a
H2		A		G				0.026	0.033	0.545	0.030	0.028	0.882	0.000	0.038	0.067
H3		T		A				0.401	0.416	0.625	0.407	0.368	0.329	0.372	0.462	0.144
			SNP4	SNP5	0.607	0.341	0.134
H1			T	G				0.597	0.580	0.607	0.589	0.628	0.341	0.628	0.535	0.134
H2			T	A				0.403	0.420	0.607	0.411	0.372	0.341	0.372	0.465	0.134
	SNP2	SNP3	SNP4		0.814	0.950	0.066
H1	C	T	T					0.894	0.886	0.674	0.880	0.888	0.749	0.964	0.883	0.029^a
H2	T	T	T					0.078	0.080	0.933	0.089	0.083	0.779	0.036	0.078	0.179
H3	C	A	T					0.027	0.034	0.528	0.031	0.029	0.899	0.000	0.039	0.066
	SNP2	SNP3		SNP5	0.895	0.719	0.038^a
H1	C	T		G				0.493	0.480	0.687	0.473	0.520	0.249	0.593	0.442	0.015^a
H2	T	T		G				0.079	0.075	0.781	0.090	0.084	0.791	0.035	0.065	0.293
H3	C	A		G				0.026	0.033	0.531	0.030	0.028	0.882	0.000	0.039	0.065
H4	C	T		A				0.401	0.412	0.731	0.407	0.368	0.329	0.372	0.454	0.185
	SNP2		SNP4	SNP5	0.925	0.549	0.140
H1	C		T	G				0.518	0.510	0.809	0.500	0.545	0.274	0.593	0.476	0.061
H2	T		T	G				0.079	0.075	0.821	0.089	0.083	0.773	0.035	0.068	0.269
H4	C		C	A				0.403	0.415	0.713	0.411	0.372	0.341	0.372	0.456	0.174
		SNP3	SNP4	SNP5	0.678	0.614	0.034^a
H1		T	T	G				0.570	0.546	0.466	0.559	0.599	0.325	0.631	0.496	0.032^a
H2		A	T	G				0.027	0.034	0.528	0.031	0.029	0.899	0.000	0.039	0.066
H3		T	T	A				0.403	0.420	0.607	0.411	0.372	0.341	0.369	0.465	0.125
	SNP2	SNP3	SNP4	SNP5	0.884	0.729	0.032^a
H1	C	T	T	G				0.491	0.476	0.640	0.469	0.517	0.256	0.595	0.437	0.012^a
H2	T	T	T	G				0.079	0.075	0.821	0.089	0.083	0.773	0.036	0.068	0.287
H3	C	A	T	G				0.027	0.034	0.514	0.030	0.029	0.899	0.000	0.040	0.064
H4	C	T	T	A				0.403	0.415	0.713	0.411	0.372	0.341	0.369	0.456	0.162

p-values were calculated by the permutation test using the bootstrap method.

p<0.05. Haplotypes with a frequency >0.02 were estimated using SNPAlyze software.

Discussion

Although it has been more than 10 years since smoothelin was discovered in the chicken stomach, we still have little knowledge about its function as a SMC protein (van Eys et al., 2007). Some studies have shown the expression of smoothelin during atherosclerotic plaque formation. In atherosclerotic lesions, smoothelin expression in the media below the affected area decreases in human and pig models (Verhamme et al., 2002; Tharp et al., 2006). During plaque formation, the neointima, which caps the necrotic core, contains little smoothelin (Hao et al., 2006; Tharp et al., 2006). Moreover, when the plaque no longer expands, smoothelin expression can be detected in SMCs of the cap (van der Loop et al., 1997). All of these studies indicate a correlation between smoothelin and atherosclerotic plaque. However, until now, little research has been done to illustrate in detail the function of smoothelin in atherosclerotic plaques. Many researchers have elucidated that atherosclerotic plaque rupture is a major cause of MI (Friedman and van den Bovenkamp, 1966; Falk, 1992; Davies, 1996). In the present study, we genotyped five SNPs of the SMTN gene in Japanese subjects and assessed the association between this gene and MI. Unfortunately, in this association study, we found no significant differences in overall distribution of genotypic and allelic frequencies of these SNPs between the MI and control groups. Genetic risk factors for complex diseases need to be assessed on a large scale (Ioannidis et al., 2003). However, analyses based on haplotypes are more advantageous than those based on individual SNPs in the presence of multiple susceptibility alleles (Morris and Kaplan, 2002). Based on such findings, we hypothesized that a haplotype and LD analysis would be useful in assessing the association between haplotypes and MI. Thus, we designed the current study as a way to establish haplotypes of SMTN based on the SNPs. We succeeded in identifying susceptibility haplotypes (C-T-T-G, SNP rs5997872-rs56095120-rs9621187-rs10304) in Japanese females.

In the present study, we used a supercontrol group consistent with aged and unaffected subjects. Healthy elderly subjects are more suitable than young or middle-aged subjects for the determination of phenotypes of cardiovascular disease related to aging, as many of these diseases primarily occur late in life (Morita et al. 2006).

Some case-control studies have identified gene variants associated with female susceptibility to MI. Yamada et al. (2002) have reported that the CD14 polymorphism was significantly (p<0.05) associated with MI in females, but not in males. Yang et al. (2006) demonstrated that common genetic variants in the ACE2 gene might have an impact on MI in females. The present study indicated that the C-T-T-G haplotype may be a genetic marker for MI in females; however, the reason for this positive gender-specific association remains unclear. Arbustini et al. (1999) examined more than 800 cases of sudden coronary death at autopsy and found that the major etiologies of this disease were plaque rupture (55%-60%), plaque erosion (30%-35%), and calcified nodules (2%-7%). Approximately 37% of the women with MI had plaque erosion, whereas erosion was present in only 18% of the men (p=0.0004) (Arbustini et al., 1999). Therefore, plaque erosion appears to be the primary cause of acute coronary thrombi in women under the age of 50 years who present with sudden coronary death (Farb et al., 1996), and is also the dominant mechanism in postmenopausal women over 50 years of age (Burke et al., 2001). These observations suggest that the etiology of MI in women may be modulated by sex hormones, especially considering that SMTN is associated with the risk of MI in female patients.

This is the first time that correlations between the human SMTN gene and MI have been examined in the Japanese female population. The limitations of our present study should be considered. In addition, we cannot exclude the possibility of false positives due to the small sample size of our female population (132 controls and 44 patients with MI). Further research with a larger sample size is required.

Footnotes

Acknowledgments

We would like to thank Ms. K. Sugama for her excellent technical assistance. This work was supported by a grant from the Toray Co., Ltd. and the Ministry of Education, Culture, Sports, Science and Technology in Japan (MEXT)-supported Program for the Strategic Research Foundation at Private Universities, 2008-2012.

Author Disclosure Statement

No competing financial interests exist.

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