miRNA polymorphisms and risk of premature coronary artery disease

Journal Pre-proof miRNA polymorphisms and risk of premature coronary artery disease Konstantinos Agiannitopoulos, Pinelopi Samara, Miranta Papadopoulou, Astradeni Efthymiadou, Eirini Papadopoulou, Georgios N. Tsaousis, George Mertzanos, Dimitrios Babalis, Klea Lamnissou PII:

S1109-9666(20)30031-2

DOI:

https://doi.org/10.1016/j.hjc.2020.01.005

Reference:

HJC 470

To appear in:

Hellenic Journal of Cardiology

Received Date: 7 October 2019 Revised Date:

19 January 2020

Accepted Date: 22 January 2020

Please cite this article as: Agiannitopoulos K, Samara P, Papadopoulou M, Efthymiadou A, Papadopoulou E, Tsaousis GN, Mertzanos G, Babalis D, Lamnissou K, miRNA polymorphisms and risk of premature coronary artery disease, Hellenic Journal of Cardiology, https://doi.org/10.1016/ j.hjc.2020.01.005. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Hellenic Society of Cardiology. Publishing services by Elsevier B.V. All rights reserved.

• 200 CAD patients

• 200 Healthy Controls

DNA • DNA extraction

• PCR-RLFP • HRM • Sanger Sequencing

Blood

Genotyping Methods

miR196a2 C>T , miR499 A>G and miR146C-miR149CmiR196T-miR499G • Associated with

increased risk for CAD • Could represent useful biomarkers of CAD

miRNA polymorphisms and risk of premature coronary artery disease Konstantinos

Agiannitopoulos1*,

Pinelopi

Samara1,

Miranta

Papadopoulou1,

Astradeni Efthymiadou1, Eirini Papadopoulou2, Georgios N. Tsaousis2, George Mertzanos3, Dimitrios Babalis3 and Klea Lamnissou1 1

Division of Genetics & Biotechnology, Department of Biology, National &

Kapodistrian University of Athens, Athens, Greece 2

3

Genekor M.S.A, Athens, Greece

Department of Cardiology, “KAT” General Hospital, Athens, Greece

*Corresponding author Konstantinos Agiannitopoulos Division of Genetics & Biotechnology, Department of Biology, National & Kapodistrian University of Athens Panepistimiopolis, Illisia , Athens, 15784, Greece Tel: +302107274636, Fax: +302107274318 E-mail:[email protected]

Abstract Background: Several microRNA polymorphisms have been associated with susceptibility to specific health disorders, including cardiovascular diseases. The aim of the present study was to investigate whether four well-studied miRNA polymorphisms in non-Caucasian populations, namely miR146a G>C (rs2910164), miR149 C>T (rs2292832), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444), contribute to the risk for the development of premature Coronary Artery Disease (CAD) in the Greek population. Methods: We used a case-control study to examine these associations in 400 individuals: 200 CAD patients [including a subgroup of myocardial infraction (MI) patients] and 200 healthy controls, all of Greek origin. MiRNA polymorphisms were genotyped using three different assays: Polymerase chain reaction – restriction fragment length polymorphism (PCR-RFLP), High resolution Melting (HRM) and Sanger sequencing. Results: Two of these polymorphisms, miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) were found to be strongly associated with increased risk for CAD (p=0.0388 and p=0.0013, respectively) and for MI (p=0.0281 and p=0.0273, respectively).

Furthermore,

miR146C-miR149C-miR196T-miR499G

allele

combination appeared to be significantly related to CAD (p=0.0185) and MI (p=0.0337) prevalence. Conclusions: Our results suggest that at least two of the studied polymorphisms, miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444), as well as the miR146C-miR149C-miR196T-miR499G allele combination could represent useful biomarkers of CAD and/or MI susceptibility in the Greek population. These special genetic characteristics, in combination with environmental factors and personal habits, might contribute to CAD and/or MI prevalence. Keywords: Coronary Artery Disease (CAD); Myocardial Infarction (MI); microRNAs; polymorphisms; risk factor

1.1.

Introduction Cardiovascular diseases remain the leading cause of global mortality [1].

Coronary artery disease (CAD) is one of the most severe forms of cardiovascular diseases, as it is characterized by plaque formation in the coronary arteries, resulting in decreased blood supply of the heart. Controllable (smoking, high blood pressure and cholesterol, physical inactivity, obesity) and uncontrollable (age, gender and family history) factors lead to the pathogenesis and progression of CAD [2,3]. In fact, epidemiological studies show that genetic factors account for 40-60% of susceptibility to CAD [4,5]. MicroRNAs (miRNAs) are small, highly conserved non-coding RNAs involved in the regulation of gene expression. Their biological role is essential, as they control diverse cellular and metabolic pathways, by binding to their target mRNAs and altering the protein expression of their candidate targets [6]. Singlenucleotide polymorphisms (SNPs) in miRNA sequence or their binding sites may affect mRNA-miRNA target site interaction and, thus, result in the deregulation of target gene expression [7]. Hence, SNPs in miRNAs have been associated with the pathogenesis of several pathologies, including several types of cancer, and cardiovascular diseases [8-10]. The so far well-studied miRNA SNPs are rs2910164, rs2292832, rs11614913 and rs3746444. Several studies have evaluated the correlation between miRNA polymorphisms and the risk for cardiovascular diseases, yet leading to conflicting results [11]. The vast majority of these studies have been conducted in Asian populations with all that this entails in the racial differences and particularities. Therefore, studies about the association of SNPs and cardiovascular diseases in the Caucasian/European populations are quite limited. The aim of this study was to determine whether the four miRNA SNPs miR146a G>C (rs2910164), miR149 C>T (rs2292832), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) are related to the risk for premature CAD or myocardial infarction (MI) in the Caucasian Greek population.

1.2. Materials and Methods 1.2.1. Study population The study included 400 unrelated participants: 200 CAD patients and 200 healthy controls. All individuals enrolled in this study were Greek-Caucasians. The patient group consisted of subjects aged less than 58 years presenting symptomatic CAD, as documented by coronary angiography performed in the Department of Cardiology of “KAT” General Hospital, Athens, Greece. CAD was defined with at least one main coronary vessel >50% luminal narrowing or with a history of acute MI. Moreover, the same polymorphisms were examined in a subgroup of 80 CAD patients, who had previously suffered from MI. The diagnosis of MI was made according to the criteria of the World Health Organization [12]. The control group consisted of individuals visiting the hospital for routine checkup. They had normal electrocardiogram tests with no evidence of cardiovascular disease (absence of history of angina pectoris or MI, and absence of symptoms). Subjects were defined as hypertensive if their blood pressure was > 140/90 mm Hg and diabetic when they had fasting glucose > 126 mg/dl. Hypercholesterolemia was defined as having total cholesterol > 220 mg/dl. Family history was considered positive for CAD if at least one first-degree relative was diagnosed with CAD or MI by the age of 65 years. Finally, subjects were characterized as smokers if they were current or past smokers. The study was carried out in accordance with the Helsinki Declaration and the ethical

standards

of

the

responsible

Institutional

Committee

for

human

experimentation. All participants provided their informed consent. 1.2.2. DNA isolation and genotyping Genomic DNA was extracted from peripheral blood leukocytes using a DNA isolation kit (Macherey-Nagel GmbH &Co.Kg, Germany), according to the manufacturer’s instructions. Genotyping of the microRNA polymorphisms was carried out using three different assays: 1) Polymerase Chain Reaction-Restriction Fragment Length Polymerase (PCR-RFLP), 2) High- resolution melting (HRM) analysis and 3) Sanger Sequencing. MiR146a G>C (rs2910164), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) polymorphisms were analyzed with all three genotyping methods, while miR149 C>T (rs2292832) was analyzed only using

PCR-RFLP due to technical reasons (difficulties in standardizing the other two methods). 1.2.2.1. PCR-RFLP The primers and the Tm temperature used for the generation of the PCR products of the four polymorphisms are outlined in Table S1. PCR conditions are as follows: initial denaturation step at 94oC for 2 min; amplification for 35 cycles at 94oC for 30s, Anneling temperature (oC) for 40s, and 72oC for 40s, followed by a final extension step at 72oC for 7 min using a thermocycler (Biometra, Germany). The studied polymorphisms, rs2910164, rs2292832, rs11614913 and rs3746444 were detected following incubation of the PCR products with restriction endonucleases (New England BioLabs) for 16 hours. The reaction products were run on 2% agarose gel, stained with ethidium bromide and visualized under ultraviolet illumination (Table S2). 1.2.2.2. HRM MiR146a G>C, miR196a2 C>T and miR499 A>G polymorphisms were also studied using HRM analysis. PCR cycling and HRM analysis were performed on the Rotor-Gene 6000™ (Corbett Research, Mortlake, Australia), while the intercalating dye used was SYTO 9 (Invitrogen Life Technologies, Carlsbad, CA, USA). In brief, PCR assays were performed in a 25-µL reaction volume containing 50 ng genomic DNA, 5 µmol/l SYTO 9 (Invitrogen Life Technologies), 1X PCR buffer (Qiagen Inc., Valencia, CA, USA), 2.5 mmol/l MgCl2 (Qiagen Inc.), 200 nmol/l of each primer (Invitrogen Life Technologies), 200 µmol/l of each deoxynucleotide (New England Biolabs, Inc., Ipswich, MA, USA), 1.25 Units HotStarTaq (5 U/µL; Qiagen Inc.) and PCR grade water (Invitrogen Life Technologies). Primers used in HRM are shown in Table S3. PCR conditions were as follows: initial denaturation at 95°C for 15 min, followed by 40 cycles of 15s at 95°C, 10s at Anneling temperature (oC) and 30s at 72°C. For the HRM melting profile, samples were denatured with an initial hold at 95°C for 1s and a melting profile from 65–88°C rising by 0.2°C every 1s for miR146a G>C (rs2910164) and miR196a2 C>T (rs11614913) polymorphisms, and from 70– 85°C rising by 0.2°C every 1s for miR499 A>G (rs3746444). After the normalization of the melting curves, different genotypes could be easily distinguished. As expected, the normalized melting peaks showed that homozygous samples had an evidently

defined single peak for each miRNA SNP (GG or CC peaks for miR146a G>C; CC or TT peaks for miR196a2 C>T and AA or GG peaks for miR499 A>G, and heterozygous samples had both of the abovementioned peaks for each microRNA SNP. 1.2.2.3. Sequencing analysis For Sanger sequencing, a NucleoFast® 96 PCR Clean-up kit (Macherey-Nagel GmbH and Co., Düren, Germany) was used in order to purify the PCR amplification products, according to the manufacturer's instructions. Briefly, 4 µL of purified product was used for each sequencing reaction, which was performed using the BigDye® Terminator v1.1 Cycle Sequencing kit (Applied Biosystems, Foster City, CA, USA). Subsequently, the sequencing reaction products were purified prior to electrophoresis using the Montage™ SEQ96 Sequencing Reaction kit (EMD Millipore Corp., Billerica, MA, USA). Finally, sequencing analysis was achieved using an Applied Biosystems 3130 Genetic Analyzer (Applied Biosystems). Approximately 10% of the samples in each study group were randomly tested for miR146a G>C (rs2910164), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) miRNA polymorphisms with all the three different assays, presenting 100% concordance. 1.2.3. Statistical analysis Statistical analysis was carried out using GraphPadPrim 5 (GraphPad Software, Inc., San Diego, CA). Data are presented as total numbers, % percent or means. Chi-square test was used for defining independent relationships between categorical variables, such as genotype distribution. Differences in allele frequencies were tested for statistical significance at the 95% confidence interval, using Fisher’s exact test. Differences between groups were considered to be significant at a p value of <0.05. The odds ratio (OR) was used to quantify the strength of the association between

allele

frequencies

and

CAD

or

MI.

HaploView

program

(https://www.broadinstitute.org/haploview/haploview) was used to estimate the frequencies of the allele combinations for the polymorphisms and their potential effect on disease progression.

1.3. Results 1.3.1. CAD The clinical characteristics of patient and control groups are shown in Table 1. The mean age of patient group is lower than that of the control group. An elderly healthy group is regarded an ideal CAD-free population. The patient group, had higher risk factors such as family history of CAD, hyperlipidemia, hypertension, smoking and diabetes, compared with control subjects. All patients and controls were examined for all four microRNA polymorphisms. Genotype frequencies of rs2910164, rs2292832, rs11614913 and rs3746444 polymorphisms in patient and control groups were in complete Hardy-Weinberg equilibrium. The distribution of the studied polymorphisms both in CAD patients and healthy controls are summarized in Table 2. As shown, the differences of genotype and allele frequencies between the two groups were statistically significant for miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) polymorphisms (p=0.0388 and p=0.0013, respectively). Concerning polymorphisms miR146a G>C (rs2910164) and miR149 C>T (rs2292832), statistically significant differences in genotype and allele frequencies were not observed (p>0.05). Specifically, for miR196a2 C>T, the frequencies of mutant allele T were 0.355 for the patient and 0.2725 for the control group, respectively. The statistical analysis of genotype and allele frequencies indicated significant differences in the distribution of this particular miRNA polymorphism between CAD and control group (p=0.0388 for genotype and OR=1.469, 95% CI:1.088-1.1985, p= 0.0147 for allele comparison, respectively). Similar results were found in the recessive model [OR=2.109, 95% CI:1.114-3.995, p=0.0289], and more prominent results were recorded for miR499 A>G. Precisely, the genotype frequency of homozygous for the mutant allele G was 0.4675 for the CAD and 0.3400 for the control group, respectively. Statistical analysis indicated association for this specific miRNA variant and occurrence of CAD following comparison of genotype and allele frequencies both in dominant and recessive models (p<0.05). On the other hand, our results for miR146a G>C and miR149 C>T showed that genotype and allele frequencies did not differ significantly among patient and control groups (p> 0.05 for both models) (Table 5).

We analyzed combined allele frequencies to evaluate potential synergistic effect of the miR146a G>C, miR149 C>T, miR196a2 C>T and miR499 A>G polymorphisms (Table 3). The C-C-T-G haplotype (miR-146a/miR-149/miR196a2/miR-499) was observed more frequently in CAD patients than in control subjects (p=0.0487) and may represent a risk factor for CAD. On the contrary, the GC-C-A allele combination occurred more often in control subjects than in CAD patients (p=0.0185), possibly acting as a protective factor, which decreases the risk for CAD. These results suggest that various haplotypes of the miRNA polymorphic alleles can alter the risk for CAD. 1.3.2. MI Within the CAD group, we examined the four microRNA polymorphisms in a subgroup of 80 patients who had previously suffered from MI. The genotype distribution of the polymorphisms among MI patients is shown in Table 4. As presented, the differences of genotype and allele frequencies between the two groups were statistically significant for miR196a2 C>T and miR499 A>G polymorphisms [p=0.0281 and p=0.0273 for genotype, and, OR=1.689 95% CI:1.147-2.487, p=0.0084 and OR=1.713 95% CI:1.179-2.488 p=0.0051 for allele frequencies, respectively]. For polymorphisms miR146a G>C and miR149 C>T, genotype and allele frequencies did not differ significantly (p>0.05). C-C-T-G haplotype (miR146a/miR-149/miR-196a2/miR-499) was observed more frequently in MI patients than in control subjects (p=0.0337) and could, therefore, be a risk factor for the disease. In contrast, G-C-C-A allele combination was recorded more regularly in the control subjects compared to MI patients (p=0.0102) and could act as a protective factor, possibly decreasing the risk for MI for those who carry it (Table 5).

1.4. Discussion MiRNAs have attracted the attention of scientific community due to their central role in various biological processes. Dysregulation of miRNAs is involved in the

occurrence

of

several

pathologies

including

cardiovascular

diseases,

predominantly CAD and MI [13]. Polymorphisms in these genes are recognized to affect multiple pathways, resulting in alterations in the levels of many target genes [14,15]. A case of such a remarkable change is the susceptibility to cardiovascular occurrences. The most well studied microRNAs - up to date- are miR-146a,-149,-196a2 and -499. Several previous studies have revealed that these miRNAs can regulate the expression of key-molecules like TNF-α, MTHFR, Annexin A1 (ANXA1) and CRP, all of which are well-known risk factors for atherosclerotic disease [16-18]. Analytically, TNF-α has been related with increased plasminogen activator inhibitor-1 protein levels, while MTHFR dysfunction with plasma homocysteine accumulation. ANXA1 is linked with decreased TNF-α levels, while CRP can raise blood pressure, body mass index, insulin resistance and thyroglobulin levels. In addition, CRP and TNF-α are simultaneously stimulated during stress conditions. All these data suggest that TNF-α, ANXA1 and CRP are closely connected [19]. Some subsequent studies deepened more and analyzed miRNA polymorphisms. A noteworthy number of studies evaluated the potential association of miRNA polymorphisms with the risk for developing cardiovascular diseases. Specifically, their majority investigated four particular miRNA polymorphisms, namely miR146a G>C (rs2910164), miR149 C>T (rs2292832), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444). Concerning the studies in the level of polymorphism in CAD incidents, Xiong et al. [20] showed that individuals belonging in Chinese Han population and carrying C allele of rs2910164 have increased susceptibility to CAD. Additionally, this allele goes along with higher expression levels of mature miR-146a. The modification from G to C allele in precursor miRNA tends to result in increased expression of mature miR146a in peripheral blood mononuclear cells (PBMCs) of CAD patients. This finding is in agreement with another study carried out in young South African Indians, where miR-146a expression is increased in PBMCs of CAD patients carrying CC genotype of miR-146a (G to C). Authors assumed that increased expression of mature

miR-146a results in increased function of Th1 cells, leading to augmented CAD risk. Ramkaran et al. [21], proposed association between miR146a rs2910164 and its influence on the levels of miR-146a and its main targets IRAK-1 and TRAF-6, involved in TLR and IL-1 receptor signaling in CAD patients. Data associated with the function of the other three polymorphisms are quite limited. Precisely, it was displayed that miR‑149T>C and miR‑ 499A>G were located only in the pre-miRNA structure and not in the mature form, being significantly affected by miRNA biogenesis. Moreover, miR‑499AA was associated with decreased plasma CRP concentrations and miR-196a2T allele with decreased mature miRNA levels [22]. In this study, we selected to further examine the most prominent polymorphisms so far for such cases. In fact, the vast majority of these studies concerns populations originating from Asia (China and South Korea) with their results being controversial, while few studies have been conducted in European populations. Some studies have included countries like Iran, South Africa, Egypt, as well as Poland and Germany [23]. As the genetic background greatly affects the expression of all these factors, the study of these polymorphisms in another Caucasian population, such as the Greek, was imperative in order to clarify their role. Based on the literature, we decided, for the first time to our knowledge, to examine these polymorphisms in Greek CAD patients and a subset of it, using three different methods in parallel: PCR-RFLP, HRM and Sanger sequencing. Our results are in accordance with some of the previous findings. Actually, two out of four polymorphisms, miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444), were strongly associated with increased CAD risk. Moreover, the additional analysis of miR146C-miR149C-miR196T-miR499G allele combination revealed significant relation to CAD prevalence. Only one study so far, in a Chinese population, has also presented that rs11614913 is associated with CAD, using Taqman as a genotype method, while the correlation of rs3746444 with CAD has been confirmed by five studies until now. Our results about rs2910164 and rs2292832 polymorphisms are also in agreement with other studies, that support not statistically significant differences between CAD and control group [23]. Of course, there are some limitations in the present study. Healthy controls had less incidence of hypertension, diabetes, hyperlipidemia, smoking habits and

family history. Yet, the mean age of CAD patients is lower compared to the control group, in order to avoid including subjects with alterations in the vessels just because of their advanced age. Thus, as CAD is a multi-factor disease, the observed associations may not be specific to the disease, but may also reflect relations between the polymorphisms and risk factors of atherosclerosis. Moreover, comparing the three different methodologies we applied about the identification of the polymorphisms, all of them are used in the clinical genetic setting for many years, presenting both advantages and disadvantages. PCR-RFLP retains the main advantage of minimal requirements in terms of investment in instrumentation, being considered a simple, low-cost and accurate method for genotyping, which can be easily completed by visualization of the restriction fragments by gel electrophoresis, without requiring specific software. However, perhaps, its most essential disadvantage is that it is a quite time-consuming process, containing several sequential and mostly not automated steps. HRM analysis is undoubtedly the cheapest technique compared to the other two, but needs primers which will produce small PCR amplicons (not always possible, as in our case of miR-149C>T). Sanger sequencing is still considered the “gold standard” in determining sequence variations, principally for single nucleotide polymorphisms, as it is a robust and reproductive approach. Nevertheless, the high cost of the technique, the experienced personnel that requires and the software for the analysis act as limitations in its wide application. Concluding, multiple methodologies could be suitable for genotyping miRNA polymorphisms, offering results fully compatible. The selection of which method will be used each time will highly depend on the: 1) laboratory equipment, 2) timeframe for result reporting and 3) existing budget. 1.5. Conclusions MiR196a2 C>T and miR499 A>G polymorphisms seem to be associated with increased CAD risk, as well as MI, in the Greek population irrespective of their determination method. Indeed, the present study is the first report about potential association of miRNA polymorphisms (miR146a G>C, miR149 C>T, miR196a2 C>T and miR499 A>G) between CAD and MI in Greeks. Certainly, supplementary studies in other European populations and ethnic minorities about the biological functions of

miRNAs are necessary to fully understand their role in CAD risk and their future application in its prevention and monitoring.

Tables Table 1. Demographic and clinical characteristics of the study populations. CAD patients

Controls

(n=200)

(n=200)

Age (years)

53.2 (35-57)

68.6 (62-85)

<0.05

Male, n (%)

191 (83.0)

160 (80.0)

NS

Hypertension, n (%)

108 (46.9)

32 (16.0)

<0.05

Diabetes, n (%)

57 (24.8)

9 (4.5)

<0.05

Hypercholesterolemia, n (%)

126 (54.8)

33 (16.5)

<0.05

Smoking, n (%)

122 (53.0)

66 (33.0)

<0.05

Family History, n (%)

74 (32.2)

23 (11.5)

<0.05

Characteristics

p value

Table 2. Genotypes and allele frequencies of the miR polymorphisms in CAD patients and control subjects. Polymorphism

CAD patients (n=200)

Control subjects (n=200)

p value

OD (95%CI)

miR146aG>C (rs2910164)

Genotype

GG

91 (0.455)

101 (0.505)

GC

95 (0.475)

84 (0.420)

CC

14 (0.070)

15 (0.075)

G

0.6925

0.715

Allele C

0.3075

0.285

GG

91 (0.455)

101 (0.505)

Dominant model GC+CC

109 (0.545)

99 (0.495)

GG+GC

186 (0.930)

185 (0.925)

Recessive model CC

14 (0.070)

15 (0.075)

0.5403

0.5375

1.114 (0.8222-1.509)

0.3678

1.222 (0.8250-1.810)

1.0000

0.9283 (0.4357-1.978)

HWE-P

0.1029

0.6657

CC

100 (0.500)

89 (0.445)

CT

76 (0.380)

85 (0.425)

TT

24 (0.120)

26 (0.130)

C

0.690

0.658

miR149C>T (rs2292832)

Genotype

Allele T

0.310

0.342

CC

100 (0.500)

89 (0.445)

Dominant model CT+TT

100 (0.500)

111 (0.555)

CC+CT

176 (0.880)

174 (0.870)

Recessive model TT HWE-P

24 (0.120)

26 (0.130)

0.1140

0.4235

0.5425

0.3655

0.8625 (0.6415-1.160)

0.3166

0.8018 (0.5411-1.188)

0.8800

0.9126 (0.5043-1.652)

miR196a2C>T (rs11614913)

Genotype

CC

89 (0.445)

107 (0.535)

CT

80 (0.400)

77 (0.385)

TT

31 (0.155)

16 (0.080)

C

0.645

0.7275

Allele T

0.355

0.2725

CC

89 (0.455)

107 (0.535)

Dominant model CT+TT

111 (0.555)

93 (0.465)

CC+CT

169 (0.855)

184 (0.920)

Recessive model TT HWE-P

31 (0.155)

16 (0.080)

0.0735

0.6819

0.0388

0.0147

1.469 (1.088-1.985)

0.0889

1.435 (0.9679-2.127)

0.0289

2.109 (1.114-3.995)

miR499A>G (rs3746444)

Genotype

AA

58 (0.290)

92 (0.460)

AG

97 (0.485)

80 (0.400)

GG

45 (0.225)

28 (0.140)

A

0.5325

0.66

Allele G

0.4675

0.34

AA

58 (0.290)

92 (0.460)

Dominant model AG+GG

142 (0.710)

108 (0.540)

AA+AG

155 (0.775)

172 (0.860)

Recessive model GG HWE-P

45 (0.225) 0.7143

28 (0.140) 0.1241

0.0013

0.0003

1.704 (1.281-2.267)

0.0006

2.086 (1.380-3.152)

0.0378

1.783 (1,061-2,998)

Table 3. Frequencies of the allele combinations of miRNA gene polymorphisms in CAD patients and control subjects. Haplotype miR-146/miR-149/miR-196a2/miR-499 G-C-C-A G-C-C-G G-C-T-A C-C-C-A G-T-C-G G-T-C-A C-T-C-A G-C-T-G C-C-C-G G-T-T-A C-C-T-G C-T-T-A C-C-T-A C-T-C-G

Frequencies 0.195 0.119 0.100 0.085 0.078 0.078 0.056 0.068 0.072 0.044 0.020 0.013 0.026 0.016

Case, Control Ratios 0.162,0.228 0.128,0.110 0.105,0.096 0.068,0.101 0.078,0.079 0.068,0.087 0.044,0.068 0.085,0.050 0.090,0.053 0.043,0.045 0.030,0.010 0.014,0.012 0.029,0.023 0.017,0.015

X2

p

5.550 0.639 0.186 2.794 0.005 1.065 2.204 3.787 3.991 0.015 3.885 0.043 0.284 0.037

0.0185 0.4241 0.6662 0.0946 0.9414 0.3022 0.1377 0.0516 0.0557 0.9023 0.0487 0.836 0.594 0.8474

Table 4. Genotypes and allele frequencies of the miR polymorphisms in MI patients and control subjects. MI patients (n=80)

Polymorphism

Controls subjects (n=200)

p value

OD (95%CI)

miR146aG>C (rs2910164)

Genotype

GG

41 (0.513)

101 (0.505)

GC

31 (0.387)

84 (0.420)

CC

8 (0.100)

15 (0.075)

G

0.706

0.715

Allele C

0.294

0.285

GG

41 (0.513)

101 (0.505)

Dominant model

Recessive model

GC+CC

39 (0.487)

99 (0.495)

GG+GC

72 (0.900)

185 (0.925)

0.7452

0.8369

1.0430 (0.6969-1.562)

1.0000

0.9704 (0.5776-1.630)

0.4784

1.370 (0.5569-3.372)

CC

8 (0.100)

15 (0.075)

0.5544

0.6657

CC

35 (0.437)

89 (0.445)

CT

33 (0.413)

85 (0.425)

TT

12 (0.150)

26 (0.130)

C

0.644

0.658

HWE-P miR149C>T (rs2292832)

Genotype

Allele T

0.356

0.342

CC

35 (0.437)

89 (0.445)

Dominant model CT+TT

45 (0.563)

111 (0.555)

CC+CT

68 (0.850)

174 (0.870)

Recessive model TT

12 (0.150)

26 (0.130)

0.9063

0.7687

1.062 (0.7237-1.560)

1.0000

1.031 (0.6113-1.738)

0.7003

1.181 (0.5638-2.474)

HWE-P

0.3679

0.4235

CC

32 (0.400)

107 (0.535)

CT

34 (0.425)

77 (0.385)

TT

14 (0.175)

16 (0.080)

C

0.6125

0.7275

miR196a2C>T (rs11614913)

Genotype

Allele T

0.3875

0.2725

CC

32 (0.400)

107 (0.535)

Dominant model CT+TT

48 (0.600)

93 (0.465)

CC+CT

66 (0.825)

184 (0.920)

Recessive model TT HWE-P

14 (0.175)

16 (0.080)

0.3491

0.6819

0.0281

0.0084

1.689 (1.147-2.487)

0.0475

1.726 (1.019-2.922)

0.0307

2.439 (1.129-5.272)

miR499A>G (rs3746444)

Genotype

AA

25 (0.312)

92 (0.460)

AG

35 (0.438)

80 (0.400)

GG

20 (0.250)

28 (0.140)

A

0.53

0.66

Allele G

0.47

0.34

AA

25 (0.312)

92 (0.460)

Dominant model AG+GG AA+AG

55 (0.688) 60 (0.750)

HWE-P

0.0051

1.713 (1.179-2.488)

0.0315

1.874 (1.083-3.244)

0.0349

2.048 (1.074-3.902)

108 (0.540) 172 (0.860)

Recessive model GG

0.0273

20 (0.250)

28 (0.140)

0.2768

0.1241

Table 5. Frequencies of allele combinations of miRNA gene polymorphisms in MI patients and control subjects. Haplotype miR-146/miR-149/miR-196a2/miR-499 G-C-C-A G-C-C-G G-C-T-A C-C-C-A G-T-C-G G-T-C-A C-T-C-A G-C-T-G C-C-C-G G-T-T-A C-C-T-G C-T-T-A C-C-T-A C-T-C-G

Frequencies 0.196 0.123 0.104 0.095 0.090 0.079 0.066 0.057 0.049 0.049 0.022 0.018 0.017 0.016

Case, Control Ratios 0.128,0.223 0.144,0.144 0.114,0.101 0.065,0.108 0.100,0.086 0.073,0.081 0.058,0.069 0.075,0.049 0.055,0.047 0.054,0.047 0.043,0.014 0.027,0.015 0.018,0.017 0.018,0.015

X2

p

6.601 0.949 0.211 2.446 0.291 0.091 0.217 1.475 0.162 0.131 4.510 0.894 0.005 0.007

0.0102 0.33 0.6461 0.1178 0.5896 0.7631 0.6417 0.2245 0.6869 0.7169 0.0337 0.3445 0.9443 0.7813

Supplementary Tables Table S1. Primers and PCR conditions used in PCR-RFLP method. Anneling Polymorphism

Primers (5’→3’)

temperature (oC)

miR146a G>C

F: CAT-GGG-TTG-TGT-CAG-TGT-CAG-AGC-T

(rs2910164)

R: TGC-CTT-CTG-TCT-CCA-GTC-TTC-CAA

miR149 C>T

F: TGT-CTT-CAC-TCC-CGT-GCT-TGT-CC

(rs2292832)

R: TGA-GGC-CCG-AAA-CAC-CCG-TA

miR196a2 C>T

F: CCC-CTT-CCC-TTC-TCC-TCC-AGA-TA

(rs11614913)

R: CGA-AAA-CCG-ACT-GAT-GTA-ACT-CCG

miR499 A>G

F: ATT-GGA-TGC-CGC-AGT-GGT

(rs3746444)

R: TGT-TTA-ACT-CCT-CTC-CAC-GTG-ATC

Amplicon Size (bp)

65

147

62

254

65

149

52

263

Table S2. Details of PCR-RFLP following digestion by restriction endonucleases. Polymorphism

Restriction endonucleases

Incubation (oC)

Genotype (bp) GG:147

miR146a G>C (rs2910164)

Sac I

37

GC:147, 122, 25 CC:122, 25 CC: 254

miR149 C>T (rs2292832)

PvuII

37

CT:254, 194, 60 TT: 194, 60 CC:125,24

miR196a2 C>T (rs11614913)

MspI

37

CT:149,125,24 TT:149 AA:239,24

miR499 A>G (rs3746444)

BclI

50

AG: 263,239,24 GG:263

Table S3. Details for HRM analysis.

Polymorphis m

Anneling Primers (5’→3’)

o

( C)

miR146a

F: CAT-GGG-TTG-TGT-CAG-TGT-CAG-

G>C

AGC-T

(rs2910164)

R: TGC-CTT-CTG-TCT-CCA-GTC-TTC-CAA

miR196a2

F: TGA-ACT-CGG-CAA-CAA-GAA-AC

C>T (rs11614913) miR499 A>G (rs3746444)

temperature

R: GGT-AGG-AGT-GGG-AGA-GGT

Amplicon Size (bp)

HRM Temperature Range (oC)

63

147

65-88

59

81

65-88

57

72

70-85

F: CAG-CGT-AGG-GAC-GGG-AA R: CTT-GCA-GTG-ATG-TTT-AAC-TCC-TC

Acknowledgments The Authors thank the Hellenic Cardiological Society and Genekor M.S.A for their support. Declarations of interest: None

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