Abstract
Background:
The extracellular matrix (ECM) glycoprotein changes are associated with the pathogenesis and complications of atherosclerosis, leading to acute coronary syndrome (ACS). Tenascin-C (TNC), an ECM protein, has been implemented in the pathogenesis, diagnosis, and prognosis of patients with cardiovascular disease.
Aim:
The study aimed to compare the genetic variants of the TNC gene (rs13321, rs2104772, and rs12347433) between South Indians with ACS and healthy participants.
Materials and Methods:
This case−control study recruited 150 ACS patients as cases and 150 healthy participants as controls. TNC genotyping was performed using TaqMan 5′-exonuclease allele discrimination assay. Serum TNC levels were measured by enzyme-linked immunosorbent assay.
Results:
Serum TNC levels were significantly higher in cases compared with controls. No significant difference was observed in allele and genotype frequencies of rs13321, rs2104772, and rs12347433 between cases and controls, which was confirmed by dominant, recessive, codominant, and homozygotic genetic models. The patients with heterozygous genotypes of rs13321, rs2104772, and rs12347433 had significantly lower serum TNC levels than patients with respective homozygous genotypes. Haplotype analyses revealed that the C-T-A haplotype in the block of rs13321-rs12347433-rs2104772 was associated with lower ACS risk (OR = 0.33, 95% CI: 0.15 − 0.75; p = 0.005). Also, the C-T-T and G-T-A haplotypes of the TNC gene were associated with higher and lower serum TNC levels, respectively.
Conclusion:
Our study demonstrated no genetic association between single nucleotide polymorphisms of the TNC gene and ACS risk; however, the C-T-A haplotype of the TNC gene might be associated with reduced ACS risk in South Indians.
Keywords: acute coronary syndrome, extracellular matrix proteins, tenascin-C, single nucleotide polymorphisms
Introduction
Cardiovascular diseases (CVDs) are the prime cause of mortality and morbidity worldwide. It is the leading cause of death in the United States, resulting in more than 9 lac deaths in 2020 (Tsao et al., 2023). In India, around 28% of fatalities are due to CVDs, contributing 14.1% of total disability-adjusted life years in 2016 (Sreeniwas Kumar and Sinha, 2020). CVD consists of coronary artery disease (CAD), cerebrovascular disease, peripheral artery disease, and aortic atherosclerosis. Due to reduced myocardial perfusion causing myocardial infarction (MI) or heart failure, CAD accounts for one-third to one-fourth of all cases of CVDs. Acute coronary syndrome (ACS) is a specific group of CAD consisting of ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (non-STEMI), and unstable angina (UA) (Malakar et al., 2019). The occurrence of CAD in family members and patients at an early age points toward the genetic basis of ACS, besides traditional modifiable and nonmodifiable risk factors.
A genome-wide association study on CAD shows its association with chromosome 9p21.3 and 396 single nucleotide polymorphisms (SNPs) and reported that 30 SNPs are clustered in nine chromosomal regions (Tcheandjieu et al., 2022). However, genes identified until now account only for a fraction of the total genetic risk for ACS (Roberts et al., 2021).
Atherosclerotic plaque develops due to the involvement of lipids, inflammatory cells, smooth muscle cells, cytokines, calcium, and extracellular matrix (ECM) proteins. The significance of ECM proteins is linked with plaque vulnerability and risk stratification in CAD patients (Gialeli et al., 2021). Tenascin-C (TNC) is a hexameric ECM protein with different isoforms and domains. Although TNC appears at various sites during tissue morphogenesis in the embryonic period, its expression is found in wound healing and pathological conditions, such as chronic inflammation, cancer, CVD, and so forth.
The diagnostic, prognostic, or therapeutic significance of TNC is demonstrated in tissue repair, heart diseases, various cancers like breast and pancreas, chronic inflammatory conditions like systemic lupus erythematosus, rheumatoid arthritis, and so on. (Midwood et al., 2016). As TNC is located mainly at pathological sites, the interest in understanding its role in pathogenesis, diagnosis, prognosis, and therapeutics in ACS patients is increasing (Golledge et al., 2011). The protective role of TNC in atherosclerosis is demonstrated by altering the expression of vascular cell adhesion molecule-1 (Wang et al., 2012).
However, various studies have shown the role of TNC in the development and complications of atherosclerosis by increasing inflammation, promoting matrix metalloprotein production, chemotaxis, increasing fibrosis, and so on. (Golledge et al., 2011; Imanaka-Yoshida, 2021). Also, TNC is associated with adverse cardiovascular events in patients with ischemic heart disease (IHD) (Kong Ho et al., 2022) and diabetes mellitus (DM) (Gellen et al., 2020). TNC expression is altered in CAD patients and is involved in endothelial differentiation and CAD development (Gholipour et al., 2022). Thus, beneficial and harmful TNC effects are observed in patients with CVD.
TNC is on a substantial gene with 29 exons over 100 kb on the long arm of chromosome 9. A single promotor transcribed the gene, which is regulated by positive and negative elements in the first untranslated exon. The 1167 SNPs surround the gene, of which 67 are in the coding region. However, the functional significance of these SNPs and their association with CVD is yet to be wholly demonstrated (Gherzi et al., 1995; Golledge et al., 2011).
The rs13321 (G > C) is a nonsynonymous, missense variant at 115030304 on exon 24, causing the replacement of glutamate to glutamine at the 2191 position. The rs2104772 (T > A) is a nonsynonymous, missense variant at 115046506 on exon 17, causing the replacement of isoleucine to leucine at the 1860 position (Lulińska-Kuklik et al., 2019). It is observed that rs13321 and rs2104772 influence the TNC's ability to interact with conjugating proteins or alter its molecular elasticity (Orsmark-Pietras et al., 2008). The rs12347433 is a synonymous variant at 115035318, causing a change in the mRNA with no change in the amino acid (arginine to arginine) sequence of the TNC protein. It may alter translation and TNC expression (Minear et al., 2011).
The TNC gene polymorphisms are associated with various diseases, for example, rs13321 and rs2104772, with the risk of Achilles tendon injuries (Saunders et al., 2013), rs2104772 with exercise-induced angiogenesis (Valdivieso et al., 2017), and so on. It is demonstrated that the rs3789875, rs12347433, and rs4552883 within the same linkage disequilibrium (LD) block of the TNC gene were associated with atherosclerotic plaques in the aorta and CAD patients (Minear et al., 2011).
Due to the increased prevalence and complications of ACS, the active role of ECM proteins in atherosclerosis is extensively studied. As TNC expression is altered following ischemic events, its role in diagnosis or prognosis is suggested by various studies, particularly in inflammatory heart diseases (Imanaka-Yoshida et al., 2020). Due to limited information about the TNC gene polymorphisms and ACS risk, the study aimed to elucidate the association between the TNC gene polymorphisms (rs13321, rs12347433, and rs2104772) and ACS risk among South Indians. The study may explain the interplay between the TNC gene polymorphisms, serum TNC levels, and ACS severity.
Materials and Methods
Study population
The present case−control genetic association study was conducted in the Department of Biochemistry and the Department of Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, from August 2021 to July 2023. One hundred and fifty South Indians (over 18 years old) with ACS, including UA, non-STEMI, and STEMI, presented in Casualty, JIPMER, within 24 h of chest pain were recruited as cases. Clinical presentation, electrocardiographic changes, and cardiac enzyme elevation diagnose ACS (Bhatt et al., 2022).
One hundred and fifty age and gender-matched healthy participants (healthy volunteers/patient bystanders other than relatives/patients who were reported to hospitals for minor ailments) were recruited as controls. Patients with connective tissue diseases, inflammatory diseases, bronchial asthma, symptomatic degenerative diseases, cancers, dilated cardiomyopathy, chronic kidney disease with serum creatinine levels of more than 2.5 mg/dL, and symptomatic peripheral vascular diseases were excluded from the cases. Written informed consent was obtained from all the participants or their relatives before participation.
The study protocol followed ICMR Revised National Ethical Guidelines for Biomedical and Health Research involving human participants-2017 and the World Medical Association Declaration of Helsinki's ethical principles for medical research involving human subjects. This study was approved by the Institute Ethics Committee for Human Studies (JIP/IEC/2021/113 dated 09/07/2021).
Data collection
Patient data, including age, gender, present and past medical history, treatment and personal habits (smoking and alcohol intake), were collected. The same observer measured height, weight, and waist circumference. Body mass index was calculated as weight (kg) divided by height (meter) square. Under strict aseptic conditions, blood samples (3 mL in the EDTA tube and 3 mL in the plain tube) were collected upon arrival in the casualty from all participants enrolled in the study. Blood was centrifuged, and serum was separated. Half of the serum was used for biochemical investigations, and half was stored at −40°C for serum TNC analysis. Serum TNC levels were estimated by sandwich enzyme-linked immunosorbent assay (Abbkine, Inc., Wuhan, China) with intra-assay and inter-assay coefficients of variation less than 10%.
Biochemical investigations, which include serum total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatine kinase-MB (CK-MB), creatine kinase-total (CK-total), blood urea nitrogen (BUN), and creatinine, measured on fully automated chemistry analyzers (Beckman AU5800 and Beckman AU 680; Brea, CA) based on principles of spectrophotometry. Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) Risk scores were assessed in ACS patients (Antman et al., 2000).
SNP selections and sample size calculation
The selection of SNPs was made by the Target Gene Approach. Those SNPs with a minor allele frequency of more than 0.2 among the Asian population from the NCBI SNP database were selected for the study. The sample size required to achieve statistically significant associations was calculated using the power calculator for genetic association studies (PGA) and the CaTS Power Calculator, considering minor allele frequencies for each SNP. Parameters with the highest sample size calculation were as follows: The power of 0.90 (90%), α of 0.05, the prevalence of ACS 14%, and the lowest minor allele frequency of rs12347433 (23.5%). Considering these parameters, the final sample size was 150 participants in each group.
DNA extraction and polymorphism genotyping
Two hundred microliters of EDTA whole blood were used for DNA extraction. Genomic DNA was extracted using a kit (the QIAamp DNA Blood Mini Kit, Cat No: 51104; Qiagen, Hilden, Germany) as per the manufacturer's instructions. The extracted DNA samples were checked for purity and quantified using a spectrophotometer (NanoDrop 2000; Thermo Scientific, Waltham, MA), diluted to 50 ng/μL, and stored at – 40°C for further use. The TaqMan 5′-exonuclease allelic discrimination assay (Applied Biosystems) on an Applied Biosystem Quant Studio 5 Real-time system (Applied Biosystems) was used to analyze three loci. Allelic discrimination was carried out using Design and analysis software by Thermo Fisher Scientific v.2.6.0. The reproducibility was assessed by randomly regenotyping 5% of the samples and matching them with the previously detected genotypes.
For a 10 μL reaction, the reaction mixture was prepared using 5 μL of TaqMan genotyping master mixture, 2.25 μL nuclease-free water and 0.25 μL of SNP assay probe mix for each reaction well. From this reaction mix, 7.5 μL was pipetted into each well, followed by 2.5 μL of the DNA sample.
The plate was briefly centrifuged before loading into the instrument. Thermal conditions followed were as follows: pre-read stage for 30 s at 60°C, polymerase activation for 10 min at 95°C, denaturation for 15 s at 95°C and annealing/extension for 1 min at 60°C repeated for 40 cycles, and a post-read stage at 60°C for 30 s.
Statistical analyses
Statistical analyses were performed using SPSS version 20 (SPSS, Inc., Chicago, IL) and GraphPad Prism 6.0 (GraphPad Software, Inc., San Diego, CA). The Kolmogorov–Smirnov test assessed the normality of data. Based on the distribution, a Student's t-test for normally distributed continuous data (expressed as mean ± standard deviation) or Mann–Whitney U test for non-normally distributed continuous data (expressed as median with interquartile ranges) was performed as appropriate. Categorical data were compared using the Chi-square test. Spearman's correlation analyzed the association between serum TNC level and clinical characteristics in cases. Direct gene counting was applied to analyze genotype frequencies in cases and controls. The Chi-square Hardy–Weinberg equilibrium test compared the observed allele frequencies in cases and controls with expected frequencies. The rs13321, rs12347433, and rs2104772 genotypes and allele frequencies were compared between cases and controls using a Chi-square test on 2 × 2 contingency tables. The odds ratio (OR) and 95% confidence interval (95% CI) were computed.
Binary logistic regression analysis determined the association between each genotype and ACS risk in four different genetic models. The statistical comparisons of rs13321, rs12347433, and rs2104772 of TNC genotype with demographic and biochemical characteristics of patients with ACS were made using a multivariate analysis model based on logistic regression. Haplotype analysis for three TNC gene polymorphisms was done using the SHEsis (Shi and He, 2005). All analyses were 2-sided, and values of p < 0.05 were considered significant.
Results
Clinical characteristics of the study participants
The primary and clinical characteristics of 150 patients with ACS as cases and age and gender-matched 150 healthy participants as controls are shown in Table 1. Out of 150 cases, 94 (62.66%) were smokers, 74 (49.33%) were alcoholics, 66 (44%) had a history of DM, 71 (47.33%) had a history of HTN, 35 (23.33%) had a family history of DM, 21 (14%) had a family history of HTN, 28 (18.66%) had a family history of IHD, and 57 (38%) had irregular treatment. In cases, the time between the onset of chest pain and blood sample collection was 8.12 ± 1.51 h. Out of 150 cases, 52 (34.67%) had STEMI, 94 (62.66%) had non-STEMI, 4 (2.67%) had UA, 99 (66%) had single-vessel disease ACS, 47 (31.33%) had double-vessel disease ACS, and 4 (2.66%) had triple-vessel disease ACS.
Table 1.
Clinical and Biochemical Properties of the Study Population
| Parameters | Patients with ACS, cases (n = 150) | Healthy participants, controls (n = 150) | p |
|---|---|---|---|
| Age (years) | 57.06 ± 8.65 | 55.42 ± 7.86 | 0.088 |
| Male/Female (n/n) | 117/33 | 112/38 | 0.689 |
| Smokers N (%) | 94 (62.66) | — | — |
| Alcoholics N (%) | 74 (49.33) | — | — |
| DM N (%) | 66 (44) | — | — |
| Presence of HTN N (%) | 71 (47.33) | — | — |
| Family History of DM N (%) | 35 (23.33) | — | — |
| Family History of HTN N (%) | 21 (14) | — | — |
| Family History of IHD N (%) | 28 (18.66) | — | — |
| Patients on Irregular treatment N (%) | 57 (38) | — | — |
| The time between the onset of chest pain and the collection of blood samples (hours) | 8.12 ± 1.51 | — | — |
| Type of ACS N (%) | |||
| STEMI | 52 (34.67) | — | — |
| Non-STEMI | 94 (62.66) | — | — |
| UA | 4 (2.67) | — | — |
| Type of vessel diseases N (%) | |||
| Single-vessel disease | 99 (66) | — | — |
| Double-vessel disease | 47 (31.33) | — | — |
| Triple-vessel disease | 4 (2.66) | — | — |
| Weight (kg) | 74.32 ± 12.36 | 69.74 ± 11.84 | 0.001 |
| Height (cm) | 160 (157 − 164) | 177 (174 − 181) | <0.001 |
| Waist circumference (inches) | 36 (35 − 38) | 37 (35 − 38) | 0.861 |
| Body mass index (kg/m2) | 26.58 ± 3.73 | 24.98 ± 2.39 | <0.001 |
| Systolic blood pressure (mmHg) | 130 (120 − 140) | 126 (110 − 130) | <0.007 |
| Diastolic blood pressure (mmHg) | 80 (71 − 90) | 78 (70 − 84) | <0.005 |
| Pulse rate (per minute) | 80 (72 − 88) | 76.5 (70 − 80) | <0.001 |
| RBS (mg/dL) | 99 (92 − 211) | 90 (88 − 110) | <0.001 |
| HbA1c (%) | 7.60 ± 1.48 | 4.62 ± 0.42 | <0.001 |
| Total cholesterol (mg/dL) | 165.74 ± 32.85 | 113.65 ± 27.09 | <0.001 |
| Triglycerides (mg/dL) | 182 (123 − 249) | 126.5 (102 − 137) | <0.001 |
| LDL-C (mg/dL) | 110 (92.75 − 129) | 96 (82 − 100) | <0.001 |
| HDL-C (mg/dL) | 33.5 (26 − 37.25) | 36 (35, 38) | <0.001 |
| BUN (mg/dL) | 28 (22 − 36) | 19 (16 − 30) | <0.001 |
| Creatinine (mg/dL) | 0.8 (0.6 − 1.12) | 0.6 (0.2 − 0.9) | <0.001 |
| CK-Total (IU/L) | 403 (252 − 554) | 90 (70 − 94) | <0.001 |
| CK-MB (IU/L) | 83 (71 − 91) | 7 (5 − 9) | <0.001 |
| Troponin-T (pg/mL) | 254 (154 − 548) | 19 (10 − 30) | <0.001 |
| Serum TNC (pg/mL) | 1772 (1463 − 2670) | 1325 (1085 − 1608) | <0.001 |
Bold numbers represent p-values (p < 0.05).
Data are presented as mean ± standard deviation, median (interquartile range) or frequency (percent).
ACS, acute coronary syndrome; DM, diabetes mellitus; HTN, hypertension; IHD, ischemic heart disease; STEMI, ST-segment elevation myocardial infarction; Non-STEMI, Non-ST-segment elevation myocardial infarction; UA, unstable angina, RBS, random blood glucose; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen; CK-total, creatine kinase-total; CK-MB, creatine kinase-MB.
Cases had significantly higher levels of weight, body mass index, pulse rate, systolic and diastolic blood pressure, RBS, HbA1c, serum total cholesterol, triglycerides, LDL-C, BUN, creatinine, CK-total, CK-MB, and troponin-T while a lower level of height, HDL-C compared with controls (p < 0.05). Cases and controls were identical for waist circumference. Cases had significantly higher serum TNC levels than controls (p < 0.05). Among 94 patients with non-STEMI, 35 (37.23%) had low and 59 (62.77%) had intermediate TIMI risk score; however, 61 (64.89%) had low, 28 (29.79%) had intermediate, and 5 (5.32%) had high GRACE risk scores. Among 52 patients with STEMI, 02 (3.85%) had intermediate and 50 (96.15%) had high TIMI risk scores; however, 27 (51.92%) had low, and 25 (48.08%) had intermediate GRACE risk scores (Supplementary Table S1).
Serum TNC levels were positively associated with HbA1c levels (r = 0.343, p = 0.002) and negatively associated with a family history of IHD (r = − 0.413, p = 0.044) and TIMI score (r = −0.179, p = 0.029) among cases. However, serum TNC levels were not significantly associated with weight, height, smoking, alcoholics, waist circumference, body mass index, the time between the onset of the chest pain and sample collection, systolic and diastolic blood pressure, pulse rate, RBS, BUN, serum creatinine, serum total cholesterol, triglycerides, LDL-C, HDL-C, troponin-T, CK-total, CK-MB levels, and family history of DM and IHD (p > 0.05) (Supplementary Table S2).
Genetic analysis
The rs13321, rs1234733, and rs2104772 of the TNC gene were in Hardy–Weinberg equilibrium in cases and controls (p > 0.05). The study showed no significant difference in the allele and genotype frequencies distribution of rs13321, rs1234733, and rs2104772 between cases and controls (p > 0.05), as shown in Table 2. The correlation between selected SNPs of the TNC gene and ACS risk has been analyzed in four kinds of genetic models. The dominant, recessive, codominant, and homozygotic genetic models suggested that rs13321, rs1234733, and rs2104772 genotypes were not associated with ACS risk, as shown in Table 2. We discovered that patients with the CG genotype of rs13321, AT genotype of rs2104772, and CT genotype of rs1234733 had significantly lower serum TNC levels compared with patients with the GG genotype of rs13321, TT genotype of rs2104772, and TT genotype of rs1234733, respectively, as shown in Figure 1.
Table 2.
Genotype and Allele Frequencies of the Single Nucleotide Polymorphisms in Acute Coronary Syndrome Patients as Cases, and Healthy Participants as Controls
| SNP | Genotype/allele | Cases, (n = 150) | Controls, (n = 150) | P | OR (95% CI) |
|---|---|---|---|---|---|
| rs13321 | GG | 60 (40) | 54 (36) | 1.00 (Reference) | |
| CG | 75 (50) | 70 (46.66) | 0.884 | 1.04 (0.63 − 1.69) | |
| CC | 15 (10) | 26 (17.33) | 0.070 | 0.78 (0.25 − 2.42) | |
| G | 195 (65) | 178 (59.33) | 1.00 (Reference) | ||
| C | 105 (35) | 122 (40.66) | 0.152 | 1.27 (0.91–1.77) | |
| Dominant model | |||||
| CG+CC | 90 (60) | 96 (64) | 1.00 (Reference) | ||
| GG | 60 (40) | 54 (36) | 0.478 | 0.84 (0.52 − 1.34) | |
| Recessive model | |||||
| CG+GG | 135 (90) | 124 (82.67) | 1.00 (Reference) | ||
| CC | 15 (10) | 26 (17.33) | 0.067 | 1.88 (0.95 − 3.72) | |
| Codominant model | |||||
| CG | 75 (50) | 70 (46.67) | 1.00 (Reference) | ||
| CC+GG | 75 (50) | 80 (53.33) | 0.566 | 0.87 (0.55 − 1.37) | |
| Homozygotic model | |||||
| GG | 60 (40) | 54 (36) | 1.00 (Reference) | ||
| CC | 15 (10) | 26 (17.33) | 0.082 | 0.78 (0.56 − 1.09) | |
| rs1234733 | TT | 98 (65.33) | 99 (66) | 1.00 (Reference) | |
| CT | 47 (31.33) | 47 (31.33) | 0.967 | 0.99 (0.61 − 1.62) | |
| CC | 5 (3.33) | 4 (2.66) | 0.733 | 0.79 (0.21 − 3.04) | |
| T | 243 (81) | 245 (81.66) | 1.00 (Reference) | ||
| C | 57 (19) | 55 (18.33) | 0.834 | 1.05 (0.69 − 1.57) | |
| Dominant model | |||||
| CT+CC | 52 (34.67) | 51 (34) | 1.00 (Reference) | ||
| TT | 98 (65.33) | 99 (66) | 0.903 | 1.03 (0.63–1.65) | |
| Recessive model | |||||
| CT+TT | 145 (96.67) | 146 (97.33) | 1.00 (Reference) | ||
| CC | 5 (3.33) | 4 (2.67) | 0.735 | 0.79 (0.20–3.01) | |
| Codominant model | |||||
| CT | 47 (31.33) | 47 (31.33) | 1.00 (Reference) | ||
| CC+TT | 103 (68.67) | 103 (68.67) | 1.000 | 1.00 (0.61–1.62) | |
| Homozygotic model | |||||
| TT | 98 (65.33) | 99 (66) | 1.00 (Reference) | ||
| CC | 5 (3.33) | 4 (2.67) | 0.749 | 1.04 (0.69 − 1.57) | |
| rs2104772 | TT | 65 (43.33) | 59 (39.33) | 1.00 (Reference) | |
| AT | 73 (48.66) | 71 (47.33) | 0.157 | 0.69 (0.42 − 1.15) | |
| AA | 12 (8) | 20 (13.33) | 0.132 | 1.84 (0.83 − 4.08) | |
| T | 203 (67.66) | 189 (63) | 1.00 (Reference) | ||
| A | 97 (32.33) | 111 (37) | 0.230 | 0.82 (0.58 − 1.14) | |
| Dominant model | |||||
| AT+AA | 85 (56.67) | 91 (60.67) | 1.00 (Reference) | ||
| TT | 65 (43.33) | 59 (39.33) | 0.481 | 0.84 (0.53 − 1.34) | |
| Recessive model | |||||
| AT+TT | 138 (92) | 130 (86.67) | 1.00 (Reference) | ||
| AA | 12 (8) | 20 (13.33) | 0.427 | 1.37 (0.62 − 3.01) | |
| Codominant model | |||||
| AT | 73 (48.67) | 71 (47.33) | 1.00 (Reference) | ||
| AA+TT | 77 (51.33) | 79 (52.67) | 0.063 | 0.64 (0.4 − 1.02) | |
| Homozygotic model | |||||
| TT | 65 (43.33) | 59 (39.33) | 1.00 (Reference) | ||
| AA | 12 (8) | 20 (13.33) | 0.139 | 1.13 (0.80 − 1.59) | |
Data are presented as frequency (percent).
TNC, tenascin-C, OR, odds ratio, CI, confidence interval; SNP, single nucleotide polymorphisms.
FIG. 1.
Effect of rs13221 (A), rs2104772 (B), and rs12347433 (C) genotypes on serum tenascin-C (TNC) levels in patients with acute coronary syndrome. (ns, nonsignificant, *p < 0.05).
LD and haplotype analysis
The relationship among three SNPs was demonstrated by an LD plot, and the degree of LD between every two SNPs was expressed as a D’ value, as shown in Figure 2. The D’ value closer to one or the deeper color of the rhombus indicates strong LD between two SNPs. It revealed a D’ value of 0.48 between rs13321 and rs1234733, 0.56 between rs1234733 and rs2104772, and 0.49 between rs13321 and rs2104772. The result of the haplotype analysis is shown in Table 3. Four haplotypes were combined by each allele of rs13321, rs1234733, and rs2104772. The C-T-A haplotype in the block of rs13321-rs12347433-rs2104772 protected significantly against ACS risk in our study population (OR = 0.33, 95% CI: 0.15 − 0.75, p = 0.005). No association was found between other haplotypes and ACS risk. Furthermore, the association between various haplotypes and serum TNC levels showed that the C-T-T haplotype was associated with a significant increase in serum TNC levels (β = 353.27, p = 0.010) and the G-T-A haplotype was significantly associated with a decrease in serum TNC levels (β = − 325.90, p = 0.037) (Table 4).
FIG. 2.
Linkage disequilibrium plot for the rs13321, rs12347433, and rs2104772 single nucleotide polymorphisms of the tenascin-C gene.
Table 3.
Haplotype Frequencies of the Single Nucleotide Polymorphisms in Acute Coronary Syndrome Patients as Cases, and Healthy Participants as Controls
| Haplotype | Cases, n (%) | Control, n (%) | χ2 | Fisher's p | Pearson's p | OR (95% CI) |
|---|---|---|---|---|---|---|
| C T T | 101 (33.6) | 87 (29) | 1.518 | 0.252 | 0.217 | 1.24 (0.88 − 1.76) |
| G T T | 85 (28.3) | 87 (29) | 0.032 | 0.928 | 0.856 | 0.97 (0.68 − 1.38) |
| G T A | 49 (16.3) | 48 (16) | 0.012 | 0.999 | 0.911 | 1.02 (0.66 − 1.58) |
| G C A | 36 (12) | 30 (10) | 0.612 | 0.514 | 0.433 | 1.23 (0.73 − 2.05) |
| G C T | 14 (4.6) | 13 (4.3) | 0.038 | 0.999 | 0.843 | 1.08 (0.50 − 2.34) |
| C T A | 8 (2.6) | 23 (7.6) | 7.653 | 0.008 | 0.005 | 0.33 (0.15 − 0.75) |
Bold numbers represent p-values (p < 0.05).
Table 4.
Association Between Haplotypes and Serum Tenascin-C Levels in Acute Coronary Syndrome Patients
| SN | Haplotype | n (%) | Beta | p |
|---|---|---|---|---|
| Hap 1 | C T T | 101 (33.6) | 353.27 | 0.010 |
| Hap 2 | G T T | 85 (28.3) | −184.07 | 0.160 |
| Hap 3 | G T A | 49 (16.3) | −325.90 | 0.037 |
| Hap 4 | G C A | 36 (12) | −88.242 | 0.641 |
| Hap 5 | G C T | 14 (4.6) | 80.298 | 0.791 |
| Hap 6 | C T A | 8 (2.6) | 336.507 | 0.358 |
Bold numbers represent p-values (p < 0.05).
The statistical comparison of rs13321, rs1234733, and rs2104772 genotypes with patients' characteristics was made using multinomial logistic regression. The result showed that the TNC rs13321 polymorphism was significantly associated with serum total cholesterol levels (Supplementary Table S3), the TNC rs1234733 polymorphism was significantly associated with gender, presence of DM, serum triglyceride levels, GRACE and TIMI scores (Supplementary Table S4), and the TNC rs2104772 polymorphism was significantly associated with gender, presence of DM and STEMI, and serum HDL-C levels (Supplementary Table S5).
Discussion
As an important component of ECM, TNC plays an essential role in the pathogenesis and complications of atherosclerosis. Therefore, we aimed to determine the association between serum TNC levels, TNC gene polymorphisms (rs13321, rs1234733, and rs2104772), and ACS risk in the South Indians. The present study concluded that TNC gene polymorphisms may not be associated with ACS risk in the study population. However, the C-T-A haplotype in the block of rs13321-rs12347433-rs2104772 may be related to reduced ACS risk.
Patients with ACS as cases and healthy participants as controls were identical in age and gender. Risk factors, such as alcohol intake, smoking, obesity, hyperlipidemia, DM, HTN, and family history of DM, HTN, and IHD were found in significant percentages in cases that may be involved in the pathogenesis and complications of atherosclerosis, resulting in ischemic myocardial insult. The effects of these risk factors are also demonstrated on ECM proteins. For example, the expression of various genes of ECM proteins was altered by alcohol in the heart in animal models (Steiner et al., 2015). Smoking is associated with CVDs through oxidative stress, inflammation, changing vasomotor and platelet function, fibrinolysis, and so on. (Gallucci et al., 2020). Nicotine exposure during the perinatal period was associated with cardiac dysfunction by excessive collagen deposition in cardiac tissue (Chuang et al., 2020). Obesity is a strong risk factor for CVD. Adipose tissue releases various proinflammatory cytokines, which impair cardiac function and participate in atherosclerosis (Carbone et al., 2019).
As different genetic variants occur in a family, a family history of DM, HTN, and IHD was a significant risk factor in cases (Tirdea et al., 2022). The current study demonstrated that the percentage of non-STEMI was higher than STEMI and UA in cases. Also, in cases, the percentage of single-vessel disease ACS was higher than double- and triple-vessel ACS. However, the predominance of STEMI over non-STEMI, as the ACS presentation was demonstrated from the tertiary care center in Bangalore (Sidhu et al., 2020) that may be due to the early referral of STEMI patients to a tertiary center for urgent revascularization. It indicates the difference in etiology and clinical presentations among patients from various regions in India and the significance of biochemical markers in diagnosing non-STEMI ACS cases. The present study observed a higher percentage of STEMI patients with severe TIMI risk scores than intermediate or low TIMI risk scores. Also, non-STEMI patients had low or intermediate TIMI and GRACE risk scores.
The difference in TIMI and GRACE risk scores in STEMI patients is because the TIMI score is a more sensitive score at 30 days of heart attack for predicting adverse events, and the GRACE score is less sensitive and predicts risk of adverse events at 6 months of a heart attack in STEMI patients (de Araújo Gonçalves et al., 2005; Morrow et al., 2000).
Atherosclerosis is initiated due to the deposition of plasma lipoproteins and lipid-laden macrophages in the intima of the vessel. Later, the interplay of inflammatory cells, cytokines, calcium, ECM proteins, smooth muscle cells, endothelial cells, and so on, progresses and finally leads to clinical complications. Alterations of ECM proteins are associated with the pathogenesis of atherosclerosis. Matrix proteins interact with vascular smooth muscles, macrophages, and metalloproteins and alter ECM remodeling, which is implicated in developing atherosclerotic complications, such as aneurysms, stenosis, rupture, and calcifications (Trinh et al., 2022). One of the obligatory ECM proteins, that is, TNC, maintains ECM integrity and alters cell proliferation, migration, and adhesion by binding with cell surface receptors and structural proteins and upregulating the expression of type I collagen (Imanaka-Yoshida et al., 2020; Matsumoto and Aoki, 2020). The present study observed higher serum TNC levels in ACS patients than healthy participants.
It is justified by its more expression, cell-to-cell or cell-to-protein interaction, inflammation, fibrosis in the atherosclerotic plaque, subsequent precipitating artery occlusion, and ischemic events. Various studies in the literature have shown similar results. For example, TNC was involved in post-MI left ventricular dilation and influenced ECM remodeling in the TNC knockout mouse model of MI (Santer et al., 2020). Patients with hypertrophic dilated cardiomyopathy observed higher heart failure events if they had higher serum TNC levels than those with low TNC levels (Kitaoka et al., 2012). It implies that TNC is associated with inflammation, tissue repair, and myocardial regeneration and deteriorates myocardial remodeling through its proinflammatory and profibrotic effects.
The present study showed a positive association between serum TNC levels and HbA1c; it indicates that the harmful effects of TNC are aggravated by hyperglycemia. It was reported that serum TNC level was associated with the occurrence and severity of CVD in patients with T2DM (Li et al., 2022). Previously, we demonstrated higher serum TNC levels in 42 T2DM patients with ACS than 42 T2DM patients without ACS (Vasanthi et al., 2020).
Similar results in patients with ACS as cases and healthy participants as controls with a larger sample size in the current study confirm our previous findings.
Higher serum TNC levels may be due to excessive transcription by various regulatory factors, mutations of the TNC gene or posttranscriptional and posttranslational modifications, etc. The TNC gene is a sizable intron-rich gene on chromosome 9q33 and contains 29 exons. Various SNPs on promotor, coding, and noncoding regions are associated with clinical diseases, for example, rs13321 and rs2104772 with the risk of Achilles tendinopathy in South African and Australian populations (Saunders et al., 2013), rs2104772 with exercise-induced angiogenesis (Valdivieso et al., 2017), SNPs on coding region with adult asthma (Matsuda et al., 2005), SNPs on coding and noncoding region with allergic diseases (Orsmark-Pietras et al., 2008), and so on. The present study analyzed three SNPs on the coding region of the TNC gene, that is, rs13321, rs12347433, and rs2104772, out of which rs13321 and rs2104772 are nonsynonymous and rs12347433 is synonymous. The current study demonstrated that none of the allele and genotype frequencies of TNC rs13321, rs12347433, and rs2104772 polymorphisms was associated with ACS risk.
However, the present study showed lower serum TNC levels in patients with heterozygous variants of rs13321, rs12347433, and rs2104772 than those with homozygous variants. It is justified by the effects of selected SNPs on TNC's ability to interact with conjugating proteins or on its molecular elasticity. Haplotype analysis revealed that the C-T-A haplotype of the TNC gene was associated with reduced ACS risk in the study population. Also, the effect of various haplotypes on serum TNC levels showed that the C-T-T haplotype was associated with a significant increase in serum TNC levels, and the G-T-A haplotype was significantly associated with a decrease in serum TNC levels. It is justified due to the cumulative effects of many SNPs on phenotypes (Morrow et al., 2000). Similarly, the rs3789875, rs12347433, and rs4552883 within the same LD block of the TNC gene were associated with atherosclerotic plaques in aorta and CAD patients in CATHeterization GENetics (CATHGEN) and the Genetics of Early Onset Cardiovascular Disease (GENECARD) datasets (Minear et al., 2011).
The present study indicated that the TNC rs13321, rs12347433, and rs2104772 polymorphisms might not modulate the susceptibility to ACS in South Indians. The study's results may be justified because ACS risk is modulated by various genes encoding proteins involved in the pathogenesis and complications of atherosclerosis (Roberts et al., 2021; Tirdea et al., 2022). Also, the expression of the TNC gene is influenced by mechanical stress and cytokines involved in inflammation and tissue repair (Chiovaro et al., 2015). The present study showed that the TNC gene polymorphisms were associated with the patient's gender, lipid status (serum total cholesterol, triglycerides, and HDL-C levels), the presence of DM and HTN, and ACS risk scores. As these traditional risk factors have prognostic value in ACS patients and are associated with selected SNPs of the TNC gene, the present study demonstrated their use in the prognosis in ACS patients.
There are a few limitations in the study. As it is a cross-sectional study, we do not have information regarding the occurrence of ACS later in healthy participants. The genetic data are limited to South Indians, which could not be compared with different regions and ethnicities in India. We have analyzed limited SNPs in the coding region of the TNC gene. Due to financial constraints, confirmation of SNP genotypes by sequencing for random samples could not be done. Regulation of the TNC gene expression has not been considered in the study.
Conclusion
The study shows higher serum TNC levels may be associated with ACS risk in South Indians. The TNC gene polymorphisms (rs13321, rs12347433, and rs2104772) may not be associated with ACS risk in the study population, which is confirmed by four genetic models. It may be due to the involvement of various genes modulating ACS risks or differential regulation of the TNC gene expression by multiple factors. However, the C-T-A haplotype of the TNC gene may be associated with reduced ACS risk in South Indians. A larger sample size research on different ethnic groups might verify the significance of TNC gene variants in the pathogenesis of ACS.
Supplementary Material
Acknowledgments
The authors thank all the participants in this study. The authors thank Ms. Sushmita Bora, Ph.D. scholar, for her assistance during the analysis and interpretation of data.
Authors' Contributions
P.S.A. and K.V.V. contributed to the concept and design of the study. S.A. and K.V.V. contributed to the recruitment of cases and controls. S.A., P.S.A., and K.V.V. contributed to the analysis and interpretation of the data. S.A. drafted the article. P.S.A. and K.V.V. critically revised the article. All authors gave final approval to this article.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
This study was supported by the JIPMER Intramural Research Committee, Pondicherry (JIP/Res/Intramural/phs 2/2021-22 dated 17/07/2021).
Supplementary Material
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