Mediators of Inflammation
Volume 2018 (2018), Article ID 1847696, 10 pages
https://doi.org/10.1155/2018/1847696

Research Article

Raet1e Polymorphisms Are Associated with Increased Risk of Developing Premature Coronary Artery Disease and with Some Cardiometabolic Parameters: The GEA Mexican Study

Rosalinda Posadas-Sánchez,¹ Bladimir Roque-Ramírez,² José Manuel Rodríguez-Pérez,³ Nonanzit Pérez-Hernández,³ José Manuel Fragoso,³ Teresa Villarreal-Molina,⁴ Ramón Coral-Vázquez,⁵ Maria Elizabeth Tejero-Barrera,² Carlos Posadas-Romero,¹ and Gilberto Vargas-Alarcón³

¹Departamento de Endocrinología, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, Mexico
²Laboratorio de Nutrigenética y Nutrigenómica, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
³Departamento de Biología Molecular, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, Mexico
⁴Laboratorio de Genómica Cardiovascular, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
⁵Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México, Mexico

Correspondence should be addressed to Gilberto Vargas-Alarcón; gvargas63@yahoo.com

Received 4 July 2018; Revised 28 October 2018; Accepted 12 November 2018; Published 18 December 2018

Academic Editor: Vera L. Petricevich

Copyright © 2018 Rosalinda Posadas-Sánchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

In an animal model, new evidence has been reported supporting the role of raet1e as an atherosclerosis-associated gene. Our objective was to establish if raet1e polymorphisms are associated with the risk of developing premature coronary artery disease (CAD) or with the presence of cardiometabolic parameters. After an informatic analysis, five polymorphisms were chosen and determined in 1158 patients with premature CAD and 1104 controls using 5′ exonuclease TaqMan genotyping assays. Standardized questionnaires were applied to all participants to obtain family medical history, demographic information, history of nutritional habits, physical activity, alcohol consumption, and pharmacological treatment. The functional effect of the rs7756850 polymorphism was analyzed by luciferase assays. Under different models, adjusted by age, gender, body mass index, current smoking, and type 2 diabetes mellitus, the rs6925151 (, ; , ), rs9371533 (, ), rs7756850 (, ; , ), and rs9383921 (, ) polymorphisms were associated with increased risk of premature CAD. When compared to the rs7756850 G allele, the C allele showed a decreased luciferase activity. In premature CAD patients, associations with low levels of adiponectin, with a high presence of hypertension, and with high levels of gamma-glutamyltransferase and total cholesterol were observed. In healthy controls, associations with a decrease in LDL pattern B, aspartate aminotransaminase, and hypo-α-lipoproteinemia were detected. An association of the raet1e polymorphisms with an increased risk of developing premature CAD and with cardiometabolic parameters has been shown for the first time. In addition, the functional effect of the rs7756850 polymorphism was defined.

1. Introduction

Acute coronary artery disease (CAD) is the most common cause of death in industrialized countries, accounting for up to 40% of all deaths [1]. CAD is a multifactorial pathology resulting from an atherosclerotic process that develops and progresses for decades prior to an acute event. Atherosclerosis is considered a low-grade inflammatory state of the intima (inner lining) of medium- and large-sized arteries; this inflammatory state is accelerated by the well-known classical risk factors, such as smoking, diabetes, obesity, high blood pressure, high cholesterol, and genetics [2]. In the last decades, significant advances have been made in identifying chromosomal loci linked to genetic variations that confer susceptibility to CAD; both candidate-gene and genome-wide association studies (GWAS) have been part of the strategies used [3–5]. Important contributions have been made using animal models, which have allowed for the detection of atherogenic loci. Intercrosses and backcrosses between strains varying in complex disease-related traits have been performed, and quantitative trait locus (QTL) mapping has been used to identify the modifier loci [6]. Using this strategy, a region called Ath locus has been identified [7]. This is a complex locus with a 10a proximal region in females containing 21 genes and a 10b distal region in both sexes containing 7 genes (Pde7b, Ahi1, Myb, Hbs1L, Aldh8a1, Sgk1, and Raet1e) [8]. Rodríguez et al., using a sequence analysis of these genes in a subcongenic mouse line, revealed raet1e (encodes Raet1, a major histocompatibility complex class 1-like molecule expressed in lesion aortic endothelial cells and macrophage-rich regions) as a novel atherosclerosis gene [9]. Raet1 is a family of molecules that are ligands of the NKG2D receptor present in NK and in a variety of T cell subsets, including CD8⁺ cytotoxic T cells [10]. The NKG2D system itself in an activated state also releases pro- and anti-inflammatory cytokine transcripts to establish either communication with other cells or self-regulation. The role of raet1 in NKG2D system activation and, in consequence, in the production of pro- and anti-inflammatory cytokines could be the link between the molecule-encoding gene and the development of atherosclerosis. Accordingly, our objective was to analyze the possible association of the raet1e polymorphisms and CAD development in a cohort of Mexican patients. After an informatics analysis, we included 5 polymorphisms in our study, which were selected for their possible functional effects and/or their informative nature (minor allele ).

2. Material and Methods

2.1. Subjects

The present study included 2262 individuals (1158 patients with premature CAD and 1104 healthy controls) belonging to the Genetics of Atherosclerotic Mexican Study (GEA). Premature CAD was defined as history of myocardial infarction, angioplasty, revascularization surgery, or coronary on angiography, diagnosed before age 55 in men and before age 65 in women. Controls were apparently healthy asymptomatic individuals without family history of premature CAD, recruited from blood bank donors and through brochures posted in social service centers. Standardized questionnaires were applied to all participants to obtain family medical history, demographic information, history of nutritional habits, physical activity, alcohol consumption, and pharmacological treatment.

Ancestry estimation of premature CAD patients and healthy controls was determined and reported previously [11]. Mean global ancestry was not significantly different between patients and controls (55.8% vs 54.0% Amerindian ancestry, 34.3% vs 35.8% Caucasian, and 9.8% vs 10.1% African mean ancestry for patients and controls, respectively, ), strongly suggesting that population stratification was not a bias or confounding factor in this study.

The GEA study was approved by the Bioethics Committee of the Instituto Nacional de Cardiología Ignacio Chávez (INCICH) and aligned to the 1975 Declaration of Helsinki. All participants provided informed consent.

2.2. Biochemical and Clinical Characteristics

Biochemical, anthropometric, metabolic, and cardiovascular risk factors were evaluated in both premature CAD cases and controls and defined as previously described [12–14]. Briefly, after a 12 h fast, blood samples were collected from the participants. Plasma triglycerides, total and high-density lipoprotein (HDL) cholesterol, and serum aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT) were measured in fresh samples, using standardized enzymatic procedures in a Hitachi 902 analyzed (Hitachi Ltd, Tokyo, Japan). LDL-C was estimated using the DeLong et al. formula [15]. Enzyme-linked immunosorbent assay (ELISA) technique (R&D Systems Quantikine Kit, Minneapolis, Minnesota, USA) was used to measured total serum adiponectin, the intra- and interassay variation coefficients were < 10%.

Height, weight, and waist circumference were measured, and BMI was calculated as weight in kilograms divided by height in meters squared. After subjects rested for at least 10 minutes, systolic and diastolic blood pressures were measured, and the average of the second and third measurements was used as the blood pressure of the subject. Hypercholesterolemia was defined as total cholesterol (TC) . Hypoalphalipoproteinemia was defined as and < 40 mg/dL in women and men, respectively. Type 2 diabetes mellitus was defined by the American Diabetes Association criteria [16] and was also considered when participants reported glucose lowering treatment or a physician diagnosis of diabetes. The current use of antihypertensive medication as well as systolic blood or diastolic blood were used to defined hypertension. LDL particle size was estimated with the LDL-cholesterol/apoB ratio, and LDL pattern B was considered with a LDL-cholesterol/apoB , which corresponded to an LDL diameter of 25.5 nm [17]. Low adiponectin was defined as serum adiponectin percentile (≤8.67 μg/mL in women and ≤5.30 μg/mL in men). Elevated aspartate aminotransaminase (AST) and gamma glutamyltransferase (GGT) were defined as AST activity (25 IU/L in women and 28 IU/L in men) and GGT activity (28 IU/L in women and 34 IU/L in men) ≥75th percentile. These cutoff points were obtained from a GEA study sample of 101 men and 180 women without obesity and with normal values of blood pressure and fasting glucose and lipids.

2.3. Computed Axial Tomography Study

Computed tomography of the chest and abdomen was performed using a 64-channel multidetector helical computed tomography system (Somatom Sensation, Siemens) and interpreted by experienced radiologists. Scans were read to assess and quantify the CAC score using the Agatston method [18]; total, subcutaneous, and visceral abdominal fat areas (TAF, SAF, and VAF) as described by Kvist et al. [19] and the hepatic to splenic attenuation ratio as described by Longo et al. [20].

2.4. Genetic Analysis

The 5 exonuclease TaqMan genotyping assays were used to determine the raet1e (rs6925151, rs9371533, rs7756850, rs2151910, and rs9383921) polymorphisms. The determinations were made with an ABI Prism 7900HT Fast Real-Time PCR system, according to manufacturer’s instructions (Applied Biosystems, Foster City, CA, USA). Previously sequenced samples of the different genotypes were included as positive controls.

2.5. Functional Prediction Analysis

In order to predict the potential effect of the raet1e polymorphisms, we used the following bioinformatics tools: SNP Function Prediction [21], Human-transcriptome Database for Alternative Splicing [22], SplicePort: An Interactive Splice Site Analysis Tool [23], SNPs3D [24], PESX: Putative Exonic Splicing enhancers/Silencers [25], and ESEfinder release 3.0 [26].

2.6. Constructs and Plasmids

A 503 bp DNA fragment of the raet1e regulatory sequence (−460/+43) was amplified by PCR from genomic DNA of homozygous individuals for the rs7756850 variant (Figure 1(a)) and cloned upstream of the luciferase reporter gene into MluI/XhoI sites of the pGL3-Basic vector (Promega) to generate the constructions pRAET1EgLUC (G allele) and pRAET1EcLUC (C allele) (Figure 1(b)). Primers used to amplify this sequence were forward 5-GCGCACGCGTCACACACACAAAACCCATCTG-3 and reverse 5-GCGCCTCGAGGGCACTGCCCAAATTCTTTA-3. Polymerase chain reaction conditions were as follows: 1 cycle at 95°C for 3 minutes; 33 cycles at 95°C for 30 s, 62°C for 30 s, and 72°C for 30 s; and a final extension at 72°C for 5 minutes. Integrity of the clone was confirmed by DNA sequencing (Figure 1(b)).

(a)

(b)

(c)

(d)

Figure 1: rs7756850 affects transcriptional activity of raet1e promoter sequence. (a) Schematic diagram of the raet1e gene and location of allelic variants on promoter sequence (−426 bp, G/C). (b) Construction of plasmid. The promoter sequence with each variant was cloned in the pGL3 vector into MluI/XhoI sites to direct the expression of the luciferase gene. Both constructions (G allele and C allele) were confirmed by sequencing. (c) Dual-luciferase assay in HEK293 cells. The presence of the C allele reduces the promoter activity of the cloned sequence (−503 bp) in ≈85% as compared with the promoter activity of the sequence harboring the G allele (). Lines represent the median of four independent experiments performed in triplicate (). was determined by the Mann-Whitney test. (d) In silico analysis suggests that variant rs7756850 modifies the consensus site for p53 and Nkx2.5 transcription factors. Due to the raet1e gene orientation into chromosome 6, the G allele is considered the ancestral allele in the luciferase assays.

2.7. Cell Culture and Transfection

HEK293 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) (Invitrogen Life Technologies Inc., Carlsbad, CA, USA) supplemented with 10% fetal bovine serum and 1% antibiotics-antimycotics (Invitrogen) at 37°C with 5% CO₂. A total of 600,000 cells were seeded in six-well plates in DMEM supplemented with 4% fetal bovine serum (Invitrogen). Twenty-four hours later, the cells were transiently transfected using Lipofectamine 3000 (Invitrogen) with 2 μg of each promoter construct. The Plos pGL3-basic vector without insert was used as a negative control. For all assays, 300 ng of pRL/CMV Renilla reniformis luciferase vector (Promega) was cotransfected for normalization.

2.8. Dual-Luciferase Assay

HEK293 cells were harvested forty-eight hours post-transfection. Luciferase activity was measured using the Dual Luciferase Reporter Assay System (Promega) according to the manufacturer’s instructions with a TD-20/20 luminometer (Turner BioSystems, Sunnyvale, CA).

2.9. Statistical Analysis

The analysis was made using the SPSS version 15.0 statistical package (SPSS, Chicago, Il). Means, medians, interquartile ranges, and frequencies were calculated as the case may be. Continuous and categorical variables were analyzed by Student’s test, Mann-Whitney test, Kruskal-Wallis, and chi-square or Fisher test as appropriate. We analyzed the polymorphism associations with premature CAD and other variables using logistic regression under the following inheritance models: additive, codominant 1, codominant 2, dominant, heterozygote, and recessive. The models were adjusted by age, gender, body mass index, current smoking, and type 2 diabetes mellitus. Statistical power to detect the association of the polymorphisms with pCAD was estimated with QUANTO software (http://biostats.usc.edu/Quanto.html). For rs6925151, polymorphism was 75.2% (heterozygote model) and 80.3% (codominant 1 model), for rs9371533 it was 76.9% (heterozygote model), for rs7756850 it was 81.9 (heterozygote model) and 86% (codominant 1 model), and for rs9383921 it was 69.7% (heterozygote model). Logistic regression analyses were performed to assess the associations of raet1e polymorphisms with metabolic parameters and cardiovascular risk factors under different inheritance models and adjusting for age, gender, and BMI, as appropriate. Genotype frequencies did not deviate from the Hardy-Weinberg equilibrium in any case (HWE, ).

3. Results

3.1. Biochemical, Clinical, Demographic, Lifestyle, and Tomographic Characteristics

The GEA study originally included 1500 healthy controls; yet, after the computed tomography, the CAC score of 396 subjects was greater than zero. They were thus considered as individuals with subclinical atherosclerosis and were not included in the analysis. The final control group included 1104 individuals with a CAC score of zero. The CAC was not measured in pCAD patients because the presence of stents and previous coronary surgery results in artifacts that do not allow the correct interpretation of tomographic images; thus, the CAC score of pCAD patients was not reported.

Several biochemical, clinical, demographic, lifestyle, and tomography differences were observed between premature CAD patients and healthy controls (Table 1). As expected, some characteristics such as total cholesterol (mg/dL), low-density lipoprotein-cholesterol (mg/dL), total , and smoking habit were higher in the control group than in premature CAD patients. This result is due to the patients who are diagnosed receive statin treatment in addition to their lifestyle change. As can be seen in the table, systolic and diastolic blood pressure in patients shows statistical difference compared to the control group, but the range of this values is within normal values; nevertheless, 68% of the CAD group are presented as hypertensive. The reason for this discrepancy is that some patients with hypertension are under treatment and in consequence their pressure levels were within the normal range.

Table 1: Biochemical, clinical, demographic, lifestyle, and tomography characteristics of the study population.

Control ()
Premature CAD ()
value

Demographic

    Age (years)
51 ± 9
54 ± 8
<0.001

    Gender (% male)
41.1
81.0
<0.001

Biochemical and clinical characteristics

    Body mass index (kg/m²)
27.9 [25.4-30.8]
28.3 [25.9-31.1]
0.004

    Systolic blood pressure (mmHg)
112 [104-122]
116 [106-127]
<0.001

    Diastolic blood pressure (mmHg)
71 [65-76]
71 [66-78]
0.011

    Total cholesterol (mg/dL)
189 [166-210]
160 [132-193]
<0.001

    High density lipoprotein cholesterol (mg/dL)
45 [36-55]
37 [32-44]
<0.001

    Low density lipoprotein cholesterol (mg/dL)
115 [95-134]
91 [69-116]
<0.001

    Triglycerides (mg/dL)
145 [107-201]
162 [119-219]
<0.001

    Aspartate aminotransaminase (IU/L)
25 [21-30]
26 [22-31]
0.001

    Gamma-glutamyltransferase (IU/L)
26 [18-42]
33 [22-49]
<0.001

    Adiponectin (μg/mL)
8.1 [5.0-12.8]
5.2 [3.2-8.1]
<0.001

    Total (%)
36.6
30.2
<0.001

    Hypo-α-lipoproteinemia (%)
52.0
67.2
<0.001

    LDL pattern B (%)
47.0
62.0
<0.001

    Low adiponectin (%)
42.8
57.5
<0.001

    Hypertension (%)
18.9
68.0
<0.001

    Type 2 diabetes mellitus (%)
9.8
35.6
<0.001

    Increased aspartate aminotransaminase activity (%)
36.3
38.9
0.207

    Increased gamma-glutamyltransferase activity (%)
41.3
49.9
<0.001

Tomography

    Total abdominal fat (cm²)
432 [347-534]
425 [339-523]
0.254

    Visceral abdominal tissue (cm²)
139 [104-180]
168 [129-215]
<0.001

    Subcutaneous abdominal fat (cm²)
286 [218-366]
245 [193-313]
<0.001

Lifestyle

    Current smoking (%)
22.8
11.7
<0.001

Data are shown as deviation, median [interquartile range], or percentage. Student test, Mann-Whitney test or chi-square test. CAD = coronary artery disease; HDL = high-density lipoprotein; LDL = low-density lipoprotein.

3.2. Association of the raet1e Polymorphisms with Premature CAD

The association of the raet1e polymorphisms with CAD is shown in Table 2. Under different models adjusted by age, gender, body mass index, current smoking, and type 2 diabetes mellitus (T2DM), rs6925151 (, 95% CI: 1.027-1.521, ; , 95% CI: 1.018-1.581, ), rs9371533 (, 95% CI: 1.031-1.527, ), rs7756850 (, 95% CI: 1.047-1.550, ; , 95% CI: 1.024-1.635, ), and rs9383921 (, 95% CI: 1.012-1.499, ) were all associated with an increased risk of developing CAD.

Table 2: Association of Raet1e gene polymorphisms with premature coronary artery disease.

Polymorphism
(genotype frequency)
MAF
Model
OR [95% CI]

rs6925151
C > T

CC
CT
TT

Control ()
392 (0.355)
510 (0.462)
202 (0.183)
0.414
Heterozygote
1.250 [1.027-1.521]
0.026

CAD ()
373 (0.322)
569 (0.491)
216 (0.187)
0.432
Codominant 1
1.268 [1.018-1.581]
0.034

rs9371533
A > G

AA
AG
GG

Control ()
293 (0.265)
523 (0.474)
288 (0.261)
0.498
Heterozygote
1.255 [1.031-1.527]
0.024

CAD ()
314 (0.271)
585 (0.505)
259 (0.224)
0.476

rs7756850
G > C

GG
GC
CC

Control ()
316 (0.285)
523 (0.474)
265 (0.240)
0.477
Heterozygote
1.274 [1.047-1.550]
0.016

CAD ()
299 (0.258)
584 (0.504)
275 (0.237)
0.490
Codominant 1
1.294 [1.024-1.635]
0.031

rs9383921
C > T

CC
CT
TT

Control ()
318 (0.288)
525 (0.476)
261 (0.236)
0.474
Heterozygote
1.232 [1.012-1.499]
0.037

CAD ()
318 (0.261)
584 (0.504)
272 (0.235)
0.487

Data are expressed as odds ratio (OR) and 95% confidence interval (CI) as assessed by multivariate logistic regression analysis. Each model was adjusted for age, gender, body mass index, current smoking, and type 2 diabetes mellitus. MAF = minor allele frequency.

3.3. Association of the raet1e Polymorphisms with Metabolic and Clinical Parameters

Raet1e polymorphism associations with clinical and metabolic parameters were analyzed separately in premature CAD patients and healthy controls (Tables 3 and 4, respectively). The associations found in premature CAD patients under different models adjusted by age, gender, and BMI were as follows (Table 3). rs9371533 was associated with a raised risk of hypertension (, 95% CI: 1.054-1.515, ; , 95% CI: 1.092-2.063, ; , 95% CI: 1.125-2.358, ). An association of rs9371533 (, 95% CI: 1.071-1.926, ) and rs2151910 (, 95% CI: 1.133-2.027, ; , 95% CI: 1.108-2.053, ) with an augmented risk of having total cholesterol was found. Both rs7756850 (, 95% CI: 1.080-1.909, ) and rs9383921 (, 95% CI: 1.078-1.903, ) showed a significant association with raised levels of GGT, whereas rs7756850 (, 95% CI: 1.042-1.467, ; , 95% CI: 1.119-1.991, ; , 95% CI: 1.096-2.185, ) and rs9383921 (, 95% CI: 1.059-1.490, ; , 95% CI: 1.1129-2.016, ; , 95% CI: 1.132-2.260, ) were all associated with low levels of adiponectin.

Table 3: Association of raet1e gene polymorphisms with clinical and metabolic parameters in the coronary patient group.

Polymorphism
(genotype frequency)
MAF
Model
OR [95% CI]

Coronary patients

    Hypertension

        rs9371533
AA
AG
GG

Additive
1.264 [1.054-1.515]
0.012

            No ()
113 (0.305)
188 (0.508)
69 (0.186)
0.441
Recessive
1.501 [1.092-2.063]
0.012

            Yes ()
201 (0.255)
397 (0.504)
190 (0.241)
0.493
Codominant 2
1.629 [1.125-2.358]
0.010

    Total

        rs9371533
AA
AG
GG

            No ()
261 (0.282)
448 (0.485)
215 (0.233)
0.475
Heterozygote
1.437 [1.071-1.926]
0.016

            Yes ()
53 (0.226)
137 (0.585)
44 (0.188)
0.481

        rs2151910
TT
TA
AA

            No ()
426 (0.461)
391 (0.423)
107 (0.116)
0.327
Heterozygote
1.515 [1.133-2.027]
0.005

            Yes ()
88 (0.376)
125 (0.534)
21 (0.090)
0.357
Codominant 1
1.508 [1.108-2.053]
0.009

    GGT > p75

        rs7756850
CC
CG
GG

            No ()
166 (0.287)
273 (0.471)
140 (0.242)
0.478
Codominant 1
1.436 [1.080-1.909]
0.013

            Yes ()
131 (0.227)
312 (0.538)
136 (0.234)
0.504

        rs9383921
CC
CT
TT

            No ()
168 (0.290)
273 (0.471)
138 (0.238)
0.474
Codominant 1
1.432 [1.078-1.903]
0.013

            Yes ()
133 (0.229)
311 (0.538)
135 (0.233)
0.502

    Low adiponectin

        rs7756850
CC
CG
GG

Additive
1.237 [1.042-1.467]
0.015

            No ()
137 (0.278)
257 (0.523)
98 (0.200)
0.460
Recessive
1.493 [1.119-1.991]
0.006

            Yes ()
163 (0.244)
324 (0.487)
179 (0.269)
0.512
Codominant 2
1.538 [1.096-2.185]
0.013

        rs9383921
CC
CT
TT

Additive
1.257 [1.059-1.490]
0.009

            No ()
140 (0.284)
256 (0.521)
96 (0.195)
0.455
Recessive
1.509 [1.129-2.016]
0.005

            Yes ()
161 (0.242)
326 (0.490)
179 (0.268)
0.514
Codominant 2
1.600 [1.132-2.260]
0.008

Data are expressed as odds ratio (OR) and 95% confidence interval (CI) as assessed by multivariate logistic regression analyses. All models were adjusted by age, gender, and body mass index. MAF = minor allele frequency; GGT = gamma-glutamyltransferase.

Table 4: Association of raet1e gene polymorphisms with clinical and metabolic parameters in the control group.

Polymorphism
(genotype frequency)
MAF
Model
OR [95% CI]

Controls

    Hipo-α-lipoproteinemia

        rs9371533
AA
AG
GG

            No ()
125 (0.236)
277 (0.522)
128 (0.242)
0.503
Heterozygote
0.694 [0.545-0.885]
0.003

            Yes ()
168 (0.293)
246 (0.429)
160 (0.279)
0.493
Codominant 1
0.658 [0.490-0.884]
0.005

    LDL pattern B

        rs566850
CC
CG
GG

            No ()
153 (0.262)
277 (0.473)
154 (0.265)
0.502
Additive
0.815 [0.690-0.964]
0.017

            Yes ()
164 (0.316)
245 (0.473)
110 (0.211)
0.448

        rs9383921
CC
CT
TT

            No ()
158 (0.270)
273 (0.466)
154 (0.263)
0.496
Recessive
0.710 [0.533-0.946]
0.019

            Yes ()
161 (0.310)
251 (0.484)
107 (0.206)
0.409

    AST > p75

        rs7756850
CC
CG
GG

            No ()
184 (0.261)
343 (0.486)
178 (0.253)
0.496
Dominant
0.720 [0.549-0.944]
0.018

            Yes ()
133 (0.333)
179 (0.446)
88 (0.221)
0.387

        rs9383921
CC
CT
TT

            No ()
183 (0.260)
345 (0.489)
177 (0.251)
0.496
Additive
0.800 [0.673-0.951]
0.011

            Yes ()
136 (0.341)
178 (0.446)
85 (80.213)
0.436
Dominant
0.695 [0.695-0.911]
0.008

Codominant 2
0.651 [0.461-0.919]
0.015

Data are expressed as odds ratio (OR) and 95% confidence interval (CI) as assessed by multivariate logistic regression analyses. All models were adjusted by age, gender, and body mass index. MAF = minor allele frequency; AST = aspartate aminotransaminase.

In healthy controls (Table 4), moreover, the following associations were found under different models adjusted by age, gender, and BMI. An association of rs9371533 (, 95% CI: 0.545-0.885, ; , 95% CI: 0.490-0.884, ) with a decreased risk of hypo-α-lipoproteinemia was found. rs566850 (, 95% CI: 0.690-0.964, ) and rs9383921 (, 95% CI: 0.533-0.946, ) were nominally associated with a decreased risk of having LDL pattern B. Finally, both rs7756850 (, 95% CI: 0.549-0.944, ) and rs93833921 (, 95% CI: 0.673-0.951, ; , 95% CI: 0.695-0.911, ; , 95% CI: 0.461-0.919, ) were associated with a diminished risk of .

3.4. Luciferase Assays

Because the in silico analysis suggests that rs7756850 modifies important binding sites for transcription factors, we decided to examine the effect of either allele G or C on the promoter activity. To this end, we transfected HEK293 cells with a luciferase reporter gene construct harboring 503 bp of the raet1e regulatory sequence with the G or C allele of the rs7756850 polymorphism. Interestingly, cells transfected with the construct pRAET1EcLUC (C allele) showed an ≈85% reduction in luciferase activity () compared with those transfected with pRAET1EgLUC (G allele) (Figure 1(c)).

4. Discussion

Reat1e is a new gene recently associated with the development of atherosclerosis in animal models [9]. The role of this gene product as a ligand of the NKG2D receptor (present in several T cell subsets) and its subsequent effect in the release of pro- and anti-inflammatory cytokines support this association. In the present work, we analyzed the distribution of 5 polymorphisms of the raet1e gene in both CAD patients and healthy controls; our goal was to establish the raet1e gene’s role in the susceptibility of developing this disease and its association with cardiometabolic parameters. While four out of five polymorphisms were associated with an increased risk of developing premature CAD, some of them were associated with several metabolic and clinical parameters in both patients and controls. In premature CAD patients, associations with low levels of adiponectin, high levels of GGT and TC, and a high presence of hypertension were observed. In healthy controls, on the other hand, associations with a decrease in LDL pattern B, AST, and hypo-α-lipoproteinemia were detected. As can be seen, the associations with metabolic and clinical parameters were different in patients and healthy controls.

Of note, there was a lack of previous studies on the raet1e polymorphisms with which to compare our results. Nevertheless, in the same cohort of patients and healthy controls, other polymorphisms in candidate genes have been associated with both premature CAD and cardiometabolic parameters [11, 27–29]. The five polymorphisms included in the present study are functional and informative (frequency of the minor ) according to an informatics analysis. The rs7756850 polymorphism is located in the promoter region and produces a binding site for the Nkx-2 cardiac transcription factor, which regulates tissue-specific gene expression involved in early heart formation and development. Mutations in the NKx-2-encoding gene can cause different forms of congenital heart defects, including ventricular and atrial septal defect, atrial ventricular block, and Tetralogy of Fallot [30]. In a recent study, Kontaraki et al. [31] analyzed the expression levels of some early cardiac marker genes, including Nkx-2, in peripheral blood mononuclear cells of patients with CAD and healthy individuals. When compared to healthy controls, they demonstrated a higher expression of these markers in CAD patients; in the same way, they found higher transcript levels in patients with a more severe disease. Herein, the functional effect of rs7756850 was demonstrated using a luciferase assay. Luciferase activity assay shows that the rs7756850 variant modifies the transcriptional activity of the raet1e promoter in ≈85% in HEK293 cells (Figure 1(c)). The change in luciferase activity may be due to the multiple binding sites that are affected by this variant (Figure 1(d)). The G allele preserves the binding site to transcription factor p53 (NGRCWTGYYY); this transcription factor is of ubiquitous expression and positively modulates multiple genes [32]. On the other hand, the C allele eliminates the binding site p53 and consequently suggests a negative modulation of the raet1e promoter (Figure 1(c)). In addition, the C allele generates a new binding site to transcription factor Nkx2.5 (YBYCACTTSM) that could favor raet1e gene expression only in positive cells for Nkx2.5 protein (Figure 1(d)). It would be necessary to conduct further experiments to corroborate this hypothesis.

On the other hand, rs6925151, rs9371533, rs2151910, and rs9383921 polymorphisms are located in coding regions and all of them produce amino acid changes. These changes could produce modifications in the raet1e protein, which may then have an effect in its linkage with the NKG2D receptor. In the mouse model, the lack of aortic expression of the raet1e gene results in increased atherosclerosis [9]. In humans, the participation of this molecule in the development of atherosclerosis has yet to be established; however, its participation in the immune process supports its role in this pathology. While in normal cells, raet1 proteins are either not expressed or expressed at low levels, these molecules are overexpressed under a stimulus, such as heat shock, DNA damage, or oxidative stress [33]. Oxidative stress is considered an important mechanism associated with the development of cardiovascular diseases [34, 35]. Several studies in human samples and animal models suggest that atherosclerotic plaques contain accumulated DNA damage and activated DNA damage response elements. In fact, it has been reported that the accumulation of DNA damage in atherosclerotic plaques is mediated, at least in part, by oxidative stress [36].

The activation of the NKG2D receptor in NK cells and some T cell subsets through raet1e stimulates the production of certain cytokines, such as interleukin 12, interferon-γ, and tumor necrosis factor-α (TNF-α). The important role of interferon-γ and TNF-α in the pathogenesis of atherosclerosis is well known [37]. It has been reported that IL-12, known as the Th1 response master controller, stimulates T and NK cells to produce IFN-γ, which induces multiple proatherogenic processes in the atherosclerotic lesion [2]. An important expression of both mRNA and protein of the IL-12 has been demonstrated in human atherosclerotic plaques [38], suggesting that plaque progression is supported by IL-12 production [39]. The possible role of the raet1e polymorphisms in either the protein expression levels or in the structural changes could in part regulate the raet1e molecule and thus modulate the different effects on all the immune phenomena related to the NKG2D receptor. Taken together, these facts could partially explain the association of these polymorphisms with CAD and with cardiovascular risk factors.

To the best of our knowledge, no studies have focused on the association of the raet1e polymorphisms with any disease. Interestingly, the gene is located in the q24.2–q25.3 region in chromosome 6 near the major histocompatibility complex (MHC) region. Genome-wide association (GWA) studies have revealed the correlation of MHC genes or intergenic regions in the three MHC classes of genes with cardiovascular diseases [40]. This region is one of the most polymorphic in humans and is well known for its role in the immunologic process.

One of the strengths of this study was the fact that our control group included only individuals without evidence of subclinical atherosclerosis defined using the coronary calcium score. In addition, population stratification was ruled out as a potential confounding factor, because the proportions of Caucasian, Native American, and African ancestries were similar in patients and healthy controls. Another strength of this study was that tomographic, clinical, and biochemical data were collected in both groups, which allowed us to adjust the analyses for a large number of potential confounding factors. Similarly, we demonstrated the functional effect of the rs7756850 polymorphisms using luciferase assays. Despite this, the limitations of this study should be considered. First, in this work, we have only included five polymorphisms of the raet1e gene, which seem to be functional and/or informative based on the analysis of the prediction software results. Second, conclusions on causality cannot be made because of the transversal character of the study. Third, since the selection of participants was not random, the findings may not be applicable to the general population; however, considering that the participants have no knowledge of their genotype, its distribution would be expected to be similar to a randomly selected sample. Finally, since the correlation of the raet1e polymorphisms with premature CAD and cardiovascular parameters is proved here for the first time, further studies in an independent group of patients are mandatory to validate the results.

5. Conclusions

In conclusion, this is the first study to report an association of the raet1e polymorphisms with an increased risk of developing premature CAD and with some cardiometabolic parameters. The functional effect of the rs7756850 was corroborated in luciferase assays.

Data Availability

No data were used to support this study.

Disclosure

Part of the present study was presented as an abstract in the 85th European Atherosclerosis Society (EAS) Congress and published in Atherosclerosis journal in 2017.

Conflicts of Interest

The authors declare that there are no conflicts of interests to declare regarding the publication of this article.

Acknowledgments

This work was supported in part by grants from the Consejo Nacional de Ciencia y Tecnología (Fronteras de la Ciencia No. 1958 and SALUD-2014-1-233727). The authors are grateful to the study participants and to María del Carmen González-Salazar, Jairo Rodríguez-Neris, and Leticia Juárez-Gómez for technical assistance.

References

A. D. Lopez, C. D. Mathers, M. Ezzati, D. T. Jamison, and C. J. L. Murray, “Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data,” The Lancet, vol. 367, no. 9524, pp. 1747–1757, 2006.

G. K. Hansson, “Inflammation, atherosclerosis, and coronary artery disease,” New England Journal of Medicine, vol. 352, no. 16, pp. 1685–1695, 2005.

R. Pranavchand and B. M. Reddy, “Genomics era and complex disorders: implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers,” Journal of Postgraduate Medicine, vol. 62, no. 3, pp. 188–198, 2016.

T. Kessler, B. Vilne, and H. Schunkert, “The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease,” EMBO Molecular Medicine, vol. 8, no. 7, pp. 688–701, 2016.

R. McPherson and A. Tybjaerg-Hansen, “Genetics of coronary artery disease,” Circulation Research, vol. 118, no. 4, pp. 564–578, 2016.

K. W. Hunter and N. P. S. Crawford, “The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies,” Annual Review of Genetics, vol. 42, no. 1, pp. 131–141, 2008.

J. T. Eppig, J. A. Blake, C. J. Bult, J. A. Kadin, J. E. Richardson, and the Mouse Genome Database Group, “The mouse genome database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse,” Nucleic Acids Research, vol. 40, no. D1, pp. D881–D886, 2011.

S. Wolfrum, J.́. M. Rodríguez, M. Tan, K. Y. Chen, D. Teupser, and J. L. Breslow, “The mouse atherosclerosis locus at chromosome 10 (Ath11) acts early in lesion formation with subcongenic strains delineating 2 narrowed regions,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 30, no. 8, pp. 1583–1590, 2010.

J. M. Rodríguez, S. Wolfrum, M. Robblee et al., “Altered expression of Raet1e, a major histocompatibility complex class 1-like molecule, underlies the atherosclerosis modifier locus Ath11 10b,” Circulation Research, vol. 113, no. 9, pp. 1054–1064, 2013.

D. H. Raulet, “Roles of the NKG2D immunoreceptor and its ligands,” Nature Reviews Immunology, vol. 3, no. 10, pp. 781–790, 2003.

R. Posadas-Sánchez, N. Pérez-Hernández, J. M. Rodríguez-Pérez et al., “Interleukin-27 polymorphisms are associated with premature coronary artery disease and metabolic parameters in the Mexican population: the genetics of atherosclerotic disease (GEA) Mexican study,” Oncotarget, vol. 8, no. 38, pp. 64459–64470, 2017.

T. Villarreal-Molina, C. Posadas-Romero, S. Romero-Hidalgo et al., “The ABCA1 gene R230C variant is associated with decreased risk of premature coronary artery disease: the Genetics of Atherosclerotic Disease (GEA) study,” PLoS One, vol. 7, no. 11, p. e49285, 2012.

R. Posadas-Sánchez, Á. R. López-Uribe, C. Posadas-Romero et al., “Association of the I148M/PNPLA3 (rs738409) polymorphism with premature coronary artery disease, fatty liver, and insulin resistance in type 2 diabetic patients and healthy controls. The GEA study,” Immunobiology, vol. 222, no. 10, pp. 960–966, 2017.

A. Medina-Urrutia, C. Posadas-Romero, R. Posadas-Sánchez et al., “Role of adiponectin and free fatty acids on the association between abdominal visceral fat and insulin resistance,” Cardiovascular Diabetology, vol. 14, no. 1, p. 20, 2015.

D. M. DeLong, E. DeLong, P. D. Wood, K. Lippel, and B. M. Rifkind, “A comparison of methods for the estimation of plasma low- and very low-density lipoprotein cholesterol. The Lipid Research Clinics Prevalence Study,” JAMA, vol. 256, no. 17, pp. 2372–2377, 1986.

American Diabetes Association, “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 32, Supplement_1, pp. S62–S67, 2008.

T. Hirano, Y. Ito, and G. Yoshino, “Measurement of small dense low-density lipoprotein particles,” Journal of Atherosclerosis and Thrombosis, vol. 12, no. 2, pp. 67–72, 2005.

G. C. Mautner, S. L. Mautner, J. Froehlich et al., “Coronary artery calcification: assessment with electron beam CT and histomorphometric correlation,” Radiology, vol. 192, no. 3, pp. 619–623, 1994.

H. Kvist, B. Chowdhury, U. Grangård, U. Tylén, and L. Sjöström, “Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations,” The American Journal of Clinical Nutrition, vol. 48, no. 6, pp. 1351–1361, 1988.

R. Longo, C. Ricci, F. Masutti et al., “Fatty infiltration of the liver. Quantification by 1H localized magnetic resonance spectroscopy and comparison with computed tomography,” Investigative Radiology, vol. 28, no. 4, pp. 297–302, 1993.

“SNP function prediction,” http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.html.

“Human-transcriptome database for alternative splicing,” http://www.h-invitational.jp/h-dbas/.

“SplicePort: an interactive splice site analysis tool,” http://spliceport.cbcb.umd.edu/SplicingAnalyser.html.

“SNPs3D,” http://www.snps3d.org/.

“PESX: Putative Exonic Splicing enhancers/Silencers,” https://omictools.com/putative-exonic-splicing-enhancers-silencers-tool.

“ESEfinder release 3.0,” http://rulai.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi.

G. Vargas-Alarcón, J. Angeles-Martínez, T. Villarreal-Molina et al., “Interleukin-17A gene haplotypes are associated with risk of premature coronary artery disease in Mexican patients from the genetics of atherosclerotic disease (GEA) study,” PLoS One, vol. 10, no. 1, article e0114943, 2015.

R. Posadas-Sánchez, J. Ángeles-Martínez, N. Pérez-Hernández et al., “Receptor-interacting protein 2 (RIP2) gene polymorphisms are associated with increased risk of subclinical atherosclerosis and clinical and metabolic parameters. The Genetics of Atherosclerotic Disease (GEA) Mexican study,” Experimental and Molecular Pathology, vol. 102, no. 1, pp. 1–6, 2017.

R. Posadas-Sánchez, N. Pérez-Hernández, J. Angeles-Martínez et al., “Interleukin 35 polymorphisms are associated with decreased risk of premature coronary artery disease, metabolic parameters, and IL-35 levels: the genetics of atherosclerotic disease (GEA) study,” Mediators of Inflammation, vol. 2017, no. 2017, 10 pages, 2017.

M. G. Posch, A. Perrot, F. Berger, and C. Özcelik, “Molecular genetics of congenital atrial septal defects,” Clinical Research in Cardiology, vol. 99, no. 3, pp. 137–147, 2010.

J. E. Kontaraki, G. E. Kochiadakis, M. E. Marketou et al., “Early cardiac gene transcript levels in peripheral blood mononuclear cells reflect severity in stable coronary artery disease,” Hellenic Journal of Cardiology, vol. 55, no. 2, pp. 119–125, 2014.

Y. Yang, L. Jiang, Y. She et al., “Olaquindox induces DNA damage via the lysosomal and mitochondrial pathway involving ROS production and p53 activation in HEK293 cells,” Environmental Toxicology and Pharmacology, vol. 40, no. 3, pp. 792–799, 2015.

A. Samarakoon, H. Chu, and S. Malarkannan, “Murine NKG2D ligands: “double, double toil and trouble”,” Molecular Immunology, vol. 46, no. 6, pp. 1011–1019, 2009.

M. Haidara, H. Yassin, M. Rateb, H. Ammar, and M. Zorkani, “Role of oxidative stress in development of cardiovascular complications in diabetes mellitus,” Current Vascular Pharmacology, vol. 4, no. 3, pp. 215–227, 2006.

U. Singh and I. Jialal, “Oxidative stress and atherosclerosis,” Pathophysiology, vol. 13, no. 3, pp. 129–142, 2006.

W. Martinet, M. W. M. Knaapen, G. R. Y. de Meyer, A. G. Herman, and M. M. Kockx, “Elevated levels of oxidative DNA damage and DNA repair enzymes in human atherosclerotic plaques,” Circulation, vol. 106, no. 8, pp. 927–932, 2002.

J. Frostegård, A. K. Ulfgren, P. Nyberg et al., “Cytokine expression in advanced human atherosclerotic plaques: dominance of pro-inflammatory (Th1) and macrophage-stimulating cytokines,” Atherosclerosis, vol. 145, no. 1, pp. 33–43, 1999.

K. Uyemura, L. L. Demer, S. C. Castle et al., “Cross-regulatory roles of interleukin (IL)-12 and IL-10 in atherosclerosis,” Journal of Clinical Investigation, vol. 97, no. 9, pp. 2130–2138, 1996.

T. S. Lee, H. C. Yen, C. C. Pan, and L. Y. Chau, “The role of interleukin 12 in the development of atherosclerosis in ApoE-deficient mice,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 19, no. 3, pp. 734–742, 1999.

J. Sinisalo, E. Vlachopoulou, M. Marchesani et al., “Novel 6p21.3 risk haplotype predisposes to acute coronary syndrome,” Circulation Cardiovascular Genetics, vol. 9, no. 1, pp. 55–63, 2016.