Increased alpha-defensin expression is associated with risk of coronary heart disease: a feasible predictive inflammatory biomarker of coronary heart disease in hyperlipidemia patients
© The Author(s). 2016
Received: 7 May 2016
Accepted: 30 June 2016
Published: 18 July 2016
Atherosclerosis is a multifactorial disorder of the heart vessels that develops over decades, coupling inflammatory mechanisms and elevated total cholesterol levels under the influence of genetic and environmental factors. Without effective intervention, atherosclerosis consequently causes coronary heart disease (CHD), which leads to increased risk of sudden death.
Polymorphonuclear neutrophils play a pivotal role in inflammation and atherogenesis. Human neutrophil peptides (HNPs) or alpha (α)-defensins are cysteine-rich cation polypeptides that are produced and released from activated polymorphonuclear neutrophil granules during septic inflammation and acute coronary vascular disorders. HNPs cause endothelial cell dysfunction during early atherogenesis. In this cross-sectional study, control, hyperlipidemia and CHD groups were representative as atherosclerosis development and CHD complications. We aimed to assess the correlation between α-defensin expression and the development of CHD, and whether it was a useful predictive indicator for CHD risk.
First, DNA microarray analysis was performed on peripheral blood mononuclear cells (PBMCs) from Thai control, hyperlipidemia and CHD male patients (n = 7). Gene expression profiling revealed eight up-regulated genes common between hyperlipidemia and CHD patients, but not controls. We sought to verify and compare α-defensin expression among the groups using: 1) real-time quantitative RT-PCR (qRT-PCR) to determine α-defensin mRNA expression (n = 10), and 2) enzyme-linked immunosorbent assay to determine plasma HNP 1–3 levels (n = 17). Statistically significant differences and correlations between groups were determined by the Mann–Whitney U test or the Kruskal–Wallis test, and the Rho-Spearman correlation, respectively.
We found that α-defensin mRNA expression increased (mean 2-fold change) in the hyperlipidemia (p = 0.043) and CHD patients (p = 0.05) compared with the controls. CHD development moderately correlated with α-defensin mRNA expression (r = 0.429, p = 0.023) and with plasma HNP 1–3 levels (r = 0.486, p = 0.000).
Increased α-defensin expression is a potential inflammatory marker that may predict the risk of CHD development in Thai hyperlipidemia patients.
Keywordsalpha-defensin HNP 1–3 Neutrophil CHD Coronary heart disease Hyperlipidemia Biomarker
Atherosclerosis is a multifactorial disorder of heart blood vessels that develops over decades, coupling inflammatory mechanisms and dyslipidemia, and resulting in the formation of atherosclerotic plaques. Atherosclerosis develops under the influence of genetic and environmental factors. It is a chronic inflammatory disorder of the vessel wall, where both innate and adaptive immune responses influence disease progression . This involves: impairment of endothelial cell (EC) function; accumulation of cholesterol in subendothelial macrophage-derived foam cells; adherence and recruitment of leukocytes into the arterial wall; proliferation and migration of smooth muscle cells into the intima; activation and aggregation of platelets; T cell activation; and production of inflammatory cytokines . The innate signals within the lesion can arise from various sources and promote atherosclerosis through inflammatory processes. Oxidized low-density lipoprotein (LDL) accumulation, starting in the fatty streaks, promotes the inflammatory response, which most likely continues throughout lesion development. Furthermore, pathogenic infection and endogenous danger signals increase during tissue injury and have been implicated as inducers of lesion inflammation. ECs are activated by accumulation of modified LDL and increase the expression of adhesion molecules, mainly vascular cell adhesion molecule (VCAM-1) . EC activation occurs predominantly at atherosclerosis-prone sites with increased hemodynamic strain, leading to infiltration of monocytes and T cells into the arterial wall [4, 5]. The recruitment of immune cells is a critical early step in atherosclerosis development and is potentiated by local chemokine release. Monocyte chemotactic protein-1 (MCP-1) appears to be particularly essential for attracting monocytes, whereas T helper type 1 (Th1) cells are recruited to the lesion by production of regulated upon activation, normal T cell expressed and secreted (RANTES), interferon inducible protein (IP)-10 and interferon-inducible T cell alpha chemoattractant (I-TAC) [6–9]. The infiltrated cells further increase local inflammation through production of several cytokines that increase recruitment and activation of additional immune cells. Monocytes differentiate into macrophages in the presence of macrophage colony-stimulating factor (M-CSF) produced in lesions by ECs and smooth muscle cells. This is a crucial step in atherogenesis and is accompanied by up-regulation of innate immune receptors required for phagocytosis and induction of inflammatory processes .
Moreover, previous studies have revealed that polymorphonuclear neutrophils (PMNs) play a prominent inflammatory role in atherogenesis in humans , mice  and pigs . An earlier study has found PMNs in human tissues at lesions of plaque rupture and erosion, or in thrombi from acute coronary syndrome patients . Previous studies have hypothesized that the number of PMNs in circulation, and the amount of PMN-produced elastase and myeloperoxidase (MPO) correlate with atherosclerosis [10, 11]. A positive correlation between peripheral PMN density and myocardial infarction has also been consistently observed .
Earlier studies have reported that human neutrophil peptides (HNPs) play a role in endothelial cell dysfunction during early atherosclerotic development. HNPs are cysteine-rich positively charged polypeptides that are produced and released from activated PMN granules. The α-defensin genes, DEFA1 and DEFA3, encode HNP-1, 2 and 3 [13, 14]. During inflammation, large amounts of intracellular proteins are released from activated PMNs into the extracellular milieu as a consequence of PMN degranulation, leakage during phagosome formation, and cell death. The highly homologous HNP-1, -2 and -3 make up more than half of the total protein contents within PMN azurophilic granules . Moreover, HNP 1–3 levels are markedly increased in inflammation, including sepsis and acute coronary vascular disorders .
The present study was a cross-sectional design, with three male groups, including healthy control subjects, patients with hyperlipidemia and patients with CHD. These groups were representative of atherosclerosis development and CHD complications. Our data demonstrated an association between α-defensin expression levels and CHD development. We suggest that this knowledge may be applied to develop a key inflammatory marker for CHD risk in Thai hyperlipidemia patients.
In this study we used D-PBS (Wisent Inc., Quebec, Canada), TRIzol® Reagent (Invitrogen™, Carlsbad, CA, USA), IsoPrep (Robbins Scientific Corporation, Sunnyvale, CA, USA), RNeasy total RNA kit (Qiagen, Hilden, Germany), and the Affymetrix GeneChip® Human Gene 1.0 ST Arrays (Affymetrix, Santa Clara, CA, USA). For qRT-PCR, we designed primers based on previous studies and Genbank, and had primers synthesized by Pacific Science Co., Ltd. (Bangkok, Thailand). Human HNP 1–3 enzyme-linked immunosorbent assay (ELISA) reagents were purchased from Hycult® Biotech (Uden, The Netherlands). All other reagents were from Sigma-Aldrich (St. Louis, MO, USA).
Study design and patient population
All volunteers were unrelated males and born to Thai parents. All patients were diagnosed, classified and treated by a specialist (KP) at Pramongkutklao Hospital. Here, we focused our study on two groups of patients based on their clinical outcomes according to the criteria of the American Association. The 45 patients examined included: 1) 24 hyperlipidemia patients with high cholesterol levels [total cholesterol (TC), LDL and high-density lipoprotein (HDL)], but with no evidence of vital organ dysfunction; and 2) 21 CHD patients who had been diagnosed and had coronary bypass grafting under KP supervision. An additional 20 healthy controls were non-infected, and had no underlying disease or cardiovascular risk factors. The age distribution did not significantly differ between the groups. Before inclusion, all healthy volunteers and hyperlipidemia patients had not received any cholesterol or blood pressure-lowering medication. CHD patients were enrolled before coronary bypass grafting by KP.
Blood sample collection
Heparinized blood samples (5 mL) were collected once from healthy donors and from all patients before hyperlipidemia treatment or coronary bypass grafting. Plasma (2 mL) was immediately collected by centrifugation of whole blood, and an aliquot for lipid measurement was kept at −70 °C for detection of HNP 1–3 by ELISA (Hycult Biotech). Packed blood cells were resuspended in D-PBS (Wisent Inc.) and used to isolate mononuclear cells. Approximately 2 million peripheral blood mononuclear cells (PBMCs) in TRIzol (Invitrogen™) were kept at −70 °C for gene expression profiling by DNA microarray analysis using Affymetrix GeneChip® Human Gene 1.0 ST. α-defensin expression was validated by qRT-PCR.
Lipid markers including TC, TG, HDL cholesterol (HDL-c) and LDL cholesterol (LDL-c) were analyzed enzymatically using kits (Randox Laboratories limited, Crumlin, UK) and a biochemistry analyzer (Architect CI 16200, Abbott Laboratories, Abbott Park, IL, USA).
Mononuclear cell isolation
PBMCs were separated by Isoprep (Robbins Scientific Corporation) gradient centrifugation according to the manufacturer’s recommendation.
PBMC gene expression profiling using DNA microarray analysis
Total RNA was extracted from PBMCs of hyperlipidemia patients and CHD patients who had undergone bypass grafting, and of control volunteers (n = 3) using RNA isolation kits (Qiagen). Total RNA was quantified using a NanoDrop ND-1000 spectrophotometer with ND-1000 3.3 software, and RNA integrity (RIN) was examined using an Agilent Bioanalyzer (Santa Clara, CA, USA). Affymetrix GeneChip® Human Gene 1.0 ST was performed using 5 μg of total RNA with RIN ≥ 8.0, according to the manufacturer’s protocol (Affymetrix Inc.). The data were analyzed by Agilent GeneSpring GX Software version 12.0. Differentially expressed genes correlating with inflammation were identified using the criteria of a > 2.0-fold increase/decrease expression in the two patient groups compared with the control group . Up-regulated genes common between hyperlipidemia and CHD patients, but not with controls were selected to further assess the feasibility of using them as inflammatory markers of CHD development .
Validation of α-defensin DEFA1/DEFA3 expression
Investigation of α-defensin mRNA expression by qRT-PCR
Total RNA was isolated from PBMCs (2 × 106) using Trizol (Invitrogen). cDNA was synthesized using 1 μg of total RNA with SuperScript III First-Strand Synthesis System for RT-PCR (Invitrogen), according to the manufacturer’s protocol. We designed primers based on DEFA1/DEFA3 genes (GenBank accession numbers NM_005217.3). qRT-PCR was performed in duplicate. Each 20-μl PCR reaction contained 10 μl of LightCyCler 480 SYBR Green I Master mix (Roche Diagnostic, Mannheim, Germany) mixed with 100 ng of cDNA and 0.5 μM of each primer (forward: 5′-TCCTTGCTGCCATTCTCCTG-3′ and reverse: 5′-TGCACGCTGGTATTCTGCAA-3′). Amplification was conducted in a LightCycler® Real-Time PCR system (Roche Applied Science, Indianapolis, IN, USA). PCR reactions were subjected to 95 °C for 5 min, followed by 45 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s, and melting curve analysis at 65 °C for 1 min. The expected PCR product size was 204 bp. β-Actin (ACTB) primers (forward: 5′-TCACCCACACTGTGCCCATCTACGA −3′ and reverse: 5′ -CAGCGGAACCGCTCATTGCCAATGG-3′) were used to normalize the relative expression level of α-defensin . The 2−(ΔΔCt) method was used to quantify relative expression levels.
Determination of plasma HNP 1–3 by ELISA
Plasma was centrifuged at 2500 × g for 5 min and HNP 1–3 concentrations were measured by ELISA (Hycult Biotechnology), according to the manufacturer’s instructions. Briefly, 100 μl of plasma from the three groups (n = 17) were diluted 20-fold in sample dilution buffer, and standards at concentrations of 10,000 to 136 pg/ml were transferred to microtiter wells coated with the specific antibody and incubated for 60 min at room temperature (RT). Each well was then washed to remove unbound material and biotinylated tracer antibody (100 μl) was added, followed by incubation for 60 min at RT. After removing unbound materials, streptavidin-peroxidase conjugate was added (100 μl/well) and incubated for 60 min at RT. Bound enzyme was detected by adding 100 μl of tetramethylbenzidine substrate to each well, and reactions were stopped by adding 100 μl of stop solution. Optical density was determined at 450 nm using Tecan Sunrise OEM Remote Microplate Absorbance Reader (Tecan, Grödig, Austria). HNP 1–3 concentrations were calculated from the HNP 1–3 standard curves.
Clinical data are reported as median (upper and lower range limits). The amount of α-defensin mRNA is represented as fold change relative to healthy controls. HNP 1–3 levels were expressed as median ± SEM. The significance of the differences between two groups was determined by the Mann–Whitney U test, and those among three groups were determined by the Kruskal–Wallis test. Correlations between CHD development and α-defensin mRNA expression or plasma HNP 1–3 levels were analyzed by the Rho-Spearman correlation analysis. The α level was set at < 0.05 at a 95 % confidence interval. All statistical analyses were performed using SPSS version 11.5 (SPSS, Chicago, IL, USA).
General description and clinical manifestations of the study population
Normal (N) n = 17
Hyperlipidemia (H) n = 17
Coronary Heart Disease (CHD) n = 17
N vs. H
H vs. CHD
N vs. CHD
Profiling of hyperlipidemia and CHD patients reveals common up-regulated genes
Profiles of intersected up-regulated genes in hyperlipidemia and CHD patients
hemoglobin alpha2/hemoglobin alpha1
IL - 1-beta
superoxide dismutase 2, mitochondrial
pro-platelet basic protein (chemokine C-X-C motif ligand 7)
DEFA3 | DEFA1 | DEFA1B
defensin alpha 3, neutrophil-specific | defensin alpha 1 | defensin alpha 1B
chemokine (C-C motif) ligand 3, macrophage inflammatory protein 1 alpha
chemokine (C-C motif) ligand 4, macrophage inflammatory protein 1 beta
Increased expression of α-defensin mRNA in hyperlipidemia and CHD patients
Increased levels of plasma HNP 1–3 in hyperlipidemia and CHD patients
There was a trend of increased plasma HNP 1–3 in hyperlipidemia (p = 0.373) and CHD patients (p = 0.004) compared with the levels in the healthy volunteers. Furthermore, CHD patients had higher HNP 1–3 levels than hyperlipidemia patients (p = 0.036; Fig. 3b).
Correlations between clinical manifestation, expression of DEFA1/DEFA3, HNP 1–3 levels and CHD development
Our cross-sectional study revealed that CHD development is associated with an increase in DEFA1/DEFA3 mRNA expression and plasma HNP 1–3 levels. Based on these results, we suggest that increased α-defensin expression has potential as an inflammatory marker for predicting CHD in hyperlipidemia patients. However, α-defensin expression as a predictive marker should be confirmed on a larger patient cohort in a future multicenter study, to validate these observations.
Previous cross-sectional studies covered a shorter observation time and were more appropriate for understanding the long-term development from initiation to complete pathogenesis [18–22]. Similarly, we profiled the expression of control, hyperlipidemia and CHD groups that are representative of the development of atherosclerosis to CHD (Fig 1).
In this preliminary study, we chose only male volunteers to control the influence of the sex-hormone factor. Estrogens are primary examples of female sex steroids. Epidemiological studies in animal models, e.g., rabbits, mice, and monkeys, have shown that estrogen has protective effects in cardiovascular disease. Previous evidence has also suggested that estrogen protects women against CHD pre-menopause (reviewed in ). We expect that the results of this study will help further studies find appropriate biomarkers to predict CHD in both male and female Thai hyperlipidemia patients.
Blood is an accessible source for diagnosing disease processes in disorders such as autoimmune disease, asthma and breast cancer . Blood is a particularly fitting surrogate for atherosclerotic tissue, because it contains inflammatory cells, which play an important role in atherogenesis . Consistent with this knowledge, previous studies used PBMC expression profiling to study the pathogenesis, diagnosis and pharmacokinetics in human atherosclerosis, stroke and other vascular diseases [24–26]. These approaches are consistent with our study, which used PBMCs from healthy, hyperlipidemia and CHD patients to investigate expression differences associated with atherosclerosis and CHD complications.
In this study, we designed the DEFA1/DEFA3 qRT-PCR primers based on DEFA1/DEFA3 genes (GenBank accession numbers NM_005217.3) and examined primers and target using primer blast from the NCBI website. The primer blast analysis showed that the DEFA1/DEFA3 primers in our study completely matched (100 %) the DEFA1/DEFA3 genes (NM_005217.3) and other variant defensin alpha genes in the NCBI database: 1. Homo sapiens defensin, alpha 3, neutrophil-specific (DEFA3), transcript variant X1, mRNA (XM_011534741.1), 2. Homo sapiens defensin, alpha 1 (DEFA1), transcript variant X3, mRNA (XM_011534740.1), 3. Homo sapiens defensin alpha 1B (DEFA1B), transcript variant 1, mRNA (NM_001302265.1), 4. Homo sapiens defensin alpha 1 (DEFA1), mRNA (NM_004084.3), and 5. Homo sapiens defensin alpha 1B (DEFA1B), transcript variant 2, mRNA (NM_001042500.1)
The PCR product size of this gene was 204 bp, as calculated by this program. Our DEFA1/DEFA3 primers were optimized and the size of target product determined by conventional PCR method and gel electrophoresis, respectively. We found a single band of PCR product of about 200 bp. In addition, all PCR products from qRT-PCR, which amplified with the DEFA1/DEFA3 qRT-PCR primers, were confirmed by gel electrophoresis. Those PCR products showed the same size as expected. The DEFA1/DEFA3 qRT-PCR primers were predicted to hit homo sapiens DEAH-box helicase 8 (DHX8) gene. However, both forward and reverse primers did not completely match the DHX8 sequence, and the PCR product was clearly different from the expected target product (660 bp). Therefore, our DEFA1/DEFA3 gene primer is specific enough and appropriate for qRT-PCR in this study.
For beta actin (ACTB) primers, we followed the primer sequences from previous studies [17, 27]. Using primer blast from the NCBI website, this ACTB primer set was predicted to hybridize the same region with both human ACTB gene and a part of the POTE gene family. Although the nucleotide sequence of the actin part of the POTE gene family and human beta actin are similar, the POTE gene family is expressed in many cancers, but restricted to a few in normal reproductive tissues (prostate, testes, ovaries, and placenta) [28–30]. In this study, we determined gene expression from the PBMC of hyperlipidemia, CHD patients, and normal volunteers with no underlying disease. Therefore, no target for the POTE gene family could interfere in the present study. Similarly, some earlier studies used the ACTB primer from a similar region to our study. These primer sets were also predicted to amplify the POTE gene family [31, 32]. In addition our study using ACTB as a house keeping gene was in agreement with previous studies related to α − defensin gene expressions in other diseases [33–35] and other gene expression . We suggest these ACTB primers were appropriate for normalizing the relative expression level of the DEFA1/DEFA3 gene.
DNA microarray analysis revealed that DEFA1/DEFA3 are expressed in both hyperlipidemia and CHD patients (Fig. 2). Our results showed that DEFA1/DEFA3 mRNA expression was not significantly different between these groups (p > 0.05; Fig. 3a), i.e., DEFA1/DEFA3 expression was similar between hyperlipidemia and CHD patients. Therefore, we suggest that DEFA1/DEFA3 may serve as a biomarker for CHD development in hyperlipidemia patients.
The highest HNP 1–3 concentrations were found in CHD patients (Fig. 3b), and differences were observed in HNP 1–3 levels between CHD and hyperlipidemia patients (p = 0.036), and healthy controls (p = 0.004). These elevated HNP 1–3 levels may be the consequence of the degranulation of neutrophils recruited from atherosclerotic plaques .
Our findings revealed a correlation between HNP 1–3 levels and TC (r = 0.530, p = 0.024) and LDL (r = 0.525 p =0.030) levels, but not TG (r = 0.525 p =0.030) or HDL (r = −0.870, p = 0.714; Fig. 4) levels. These observations are in line with a previous study on human patients  and an in vitro study . Moreover, an earlier murine model, lacking HNP expression, suggests that HNP and LDL play equally important roles in the pathogenesis of vascular disease . Collectively, these findings are in agreement with our results. CHD patients underwent coronary bypass grafting. Therefore, the TC, LDL and TG levels of these patients were significantly lower than those of hyperlipidemia patients, and similar to those of control patients. Interestingly, we observed that the TC and LDL levels decreased to normal ranges following administration of the appropriate lipid-lowering medication, while the HNP 1–3 levels remained elevated compared with the control and hyperlipidemia patients (Fig. 3b). We suggest that chronic neutrophil recruitment to atherosclerotic plaques in the vasculature of CHD patients was not inhibited by lipid-lowering drugs. Therefore, the highest HNP levels were found in the CHD group. Moreover, our findings on HNP expression provide new insight into CHD pathogenesis, which may affect future applications.
In contrast to a murine model , α-defensin in a porcine model reduced EC-dependent vasorelaxation. This effect is associated with increased superoxide radical production and decreased endothelial nitric oxide synthase (eNOS) expression in porcine coronary arteries .
Earlier studies have found discrepancies in HNP levels in sera [39–41], plasma  or biological fluids  of normal and various diseases including infectious diseases [41, 43, 44], metabolic diseases (e.g., diabetes) [45, 46], cardiovascular disease [2, 47], cancers of urinary bladder , colorectal cancer , colorectal adenoma  and colon carcinoma . It has been reported that normal plasma levels of HNPs range from undetectable to 50–100 ng/ml. Similarly, our findings showed that normal HNPs’ levels ranged from undetectable to 10 ng/ml (Fig. 3b). Earlier studies have found that at the onset of bacterial infection and during nonbacterial infection, the mean HNP levels were 2- to 4-fold of those in healthy volunteers . In bacterial meningitis, plasma HNP levels in sepsis patients ranged from 900 to 170,000 ng/ml compared with a mean of 42–53 ng/ml in the plasma of healthy controls .
We found that the increased levels of HNP 1–3 were moderately associated with DEFA1/DEFA3 expression (r = 0.536, p = 0.010). Previous studies have reported increased levels of HNPs in inflammatory diseases, indicating that HNPs play a critical role in the leukocyte-dominant proinflammatory responses that may contribute to cardiovascular disorders [49, 50] and bacterial meningitis . Moreover, it has been reported that HNP deposits in skin biopsies are a strong independent predictor of coronary artery disease . This suggested a link between PMN activation and progression of atherosclerosis, and provided a novel approach to assess the risk of coronary artery disease.
There is a correlation between HNP concentration and the number of blood PMNs in patients with inflammatory diseases . Previous studies have reported a positive correlation between the peripheral PMN count and myocardial infarction . However, we lacked data on the number of circulating neutrophils and the association between DEFA1/DEFA3 gene copy numbers and HNP 1–3 levels. Nevertheless, we assumed that the increased HNP levels, which are the main proteins in PMN granules, may correlate with PMN levels. In contrast, Nemeth et al. have reported that the copy number did not correlate with gene expression .
Quinn et al. have suggested a possible mechanism of action by which HNPs mediate cardiovascular disease. HNP 1–3 modulate the development of atherosclerosis by increasing the binding of LDL to the EC surface, promoting accumulation of LDL in the vasculature and inhibiting fibrinolytic activity on the surface of vascular cells accumulating in atherosclerotic plaques . Moreover, HNPs form stable, multivalent complexes with LDL , both in solution and on the cell surface . They also stimulate the binding of 125I-labeled LDL to human umbilical vein endothelial cells (HUVECs), smooth muscle cells and fibroblasts in a dose-dependent and saturable manner . Additionally, it has been proposed that HNP-LDL complexes bind to heparin sulfate-containing proteoglycans (HSPG) . The LDL receptor-related protein (LRP)/-2 macroglobulin receptor is a membrane protein of the LDL receptor (LDLR) superfamily  involved in atherogenesis [52, 53]. Increased expression of LRP has been demonstrated in vascular smooth muscle cells isolated from human atherosclerotic lesions . LRP1 gene expression is also increased in blood mononuclear cells from patients with myocardial infarction . An earlier study has demonstrated that HNPs directly bind LRP both in solution and on the surface of smooth muscle cells . The structure of HNPs, with a hydrophobic and cationic site, is similar to many apolipoproteins that bind to LDLR family members and proteoglycans . Hence, the ability of HNPs to modulate the catabolism of LDL may occur through similar mechanisms . However, because many molecules (e.g., ApoE, thrombospondin, protein C, tPA, thrombin) are also ligands of LRP, the biological consequences of HNP and LRP binding remain to be investigated in the pathogenesis of atherosclerosis .
A previous study  used an ApoE −/− mouse model, which is important for studying the mechanisms by which LDL, as a sole mediator, induces atherosclerosis. Murine PMNs lack HNPs, which raises the question of the importance of HNPs in the atherosclerotic pathology observed in ApoE −/− mice. The study has demonstrated that the HNP-mediated inflammatory responses and the direct effects of LDL on the pathogenesis of cardiovascular diseases are equally important. PMN infiltration in chronic arterial inflammation, and the pivotal role of PMN contributing to atherogenesis was supported by a decrease in the atherosclerotic burden when PMNs were depleted in ApoE −/− mice .
Our cross-sectional findings demonstrated increased expression of α-defensin during CHD development. Therefore, this could be indirectly interpreted as an important role for neutrophils in the development of atherosclerosis and consequently CHD. In contrast, our study indicated that DEFA1/DEFA3 mRNA expression and plasma HNP levels were consistent, hence they should be considered for further cross-sectional or larger cohort studies, including more randomized population groups, to elucidate the feasibility of predictive CHD biomarkers. We found a moderate correlation between HNP expression and disease development. This suggests that there may be more appropriate predictive markers that could lead to more reliable prediction of CHD. Additional studies are needed to validate whether additional genes (Table 2) are appropriate as inflammatory predictive markers for the risk of CHD development in Thai populations.
This study has some limitations. First, our sample size was small due to a limited budget. Statistical bias might occur. Further studies with a larger sample size and alternative simple techniques to detect markers are needed to confirm the current hypothesis. In addition, longitudinal studies are clearly needed to better define the importance of α-defensin expression. However, the hyperlipidemia and CHD groups significantly differ in age; thus, a longer observation period is needed for a cohort study. In this study we did not examine the α-defensin mRNA expression and plasma HNP 1–3 levels in medical treatment hyperlipidemia and CHD patients, which would provide more evidences to support our hypothesis. The effect of drugs on decreasing cholesterol levels is a very interesting factor, but was considered excessively complex for inclusion in the present study. Various lipid-lowering drugs are used, with different modes of action. Further studies with larger sample sizes would be more appropriate to assess the effects of these drugs on the development of CHD. Furthermore, the absence of neutrophil numbers and DEFA1/DEFA3 copy number is another limitation.
Taken together, our findings suggest that hyperlipidemia patients with elevated TC and/or LDL levels in combination with increased α-defensin mRNA expression and/or elevated plasma HNP levels may be at risk of developing CHD.
CHD, coronary heart disease; ELISA, enzyme-linked immunosorbent assay; HDL, high-density lipoprotein; HNP 1–3, human neutrophil peptides 1–3; LDL, low-density lipoprotein; PBMC, peripheral blood mononuclear cells; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; TC, total cholesterol; TG, triglyceride; α-defensin, alpha-defensin.
This study was supported by the Faculty of Tropical Medicine, Mahidol University, the Mahidol University Research Fund of the National Research Council of Thailand (2011–2012), and Pramongkutklao Hospital. We wish to thank the volunteers and patients who donated their blood and the staff at Pramongkutklao Hospital for the blood collection. the Edanz group (www.edanzediting.com) provided editorial assistance. We thank Mr. Paul R Adams for English proofreading. The funding was granted to YM.
KP selected healthy control, hyperlipidemia and CHD patients, and conducted their coronary bypass grafting. YM and WD were responsible for laboratory work including plasma collection and PBMC preparation. YM, UC and WD performed DNA microarray analysis and ELISA assays. SB carried out the qRT-PCR assays and analyses. YM was responsible for data analysis and statistical calculations. YM conceived the study and wrote the manuscript. All authors interpreted the results, and read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352:1685–95.View ArticlePubMedGoogle Scholar
- Quinn K, Henriques M, Parker T, Slutsky AS, Zhang H. Human neutrophil peptides: a novel potential mediator of inflammatory cardiovascular diseases. Am J Physiol Heart Circ Physiol. 2008;295:H1817–1824.View ArticlePubMedPubMed CentralGoogle Scholar
- Cybulsky MI, Iiyama K, Li H, Zhu S, Chen M, Iiyama M, et al. A major role for VCAM-1, but not ICAM-1, in early atherosclerosis. J Clin Invest. 2001;107:1255–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Dai G, Kaazempur-Mofrad MR, Natarajan S, Zhang Y, Vaughn S, Blackman BR, et al. Distinct endothelial phenotypes evoked by arterial waveforms derived from atherosclerosis-susceptible and -resistant regions of human vasculature. Proc Natl Acad Sci U S A. 2004;101:14871–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Nakashima Y, Raines EW, Plump AS, Breslow JL, Ross R. Upregulation of VCAM-1 and ICAM-1 at atherosclerosis-prone sites on the endothelium in the ApoE-deficient mouse. Arterioscler Thromb Vasc Biol. 1998;18:842–51.View ArticlePubMedGoogle Scholar
- Smith JD, Trogan E, Ginsberg M, Grigaux C, Tian J, Miyata M. Decreased atherosclerosis in mice deficient in both macrophage colony-stimulating factor (op) and apolipoprotein E. Proc Natl Acad Sci U S A. 1995;92:8264–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Dorweiler B, Torzewski M, Dahm M, Kirkpatrick CJ, Lackner KJ, Vahl CF. Subendothelial infiltration of neutrophil granulocytes and liberation of matrix-destabilizing enzymes in an experimental model of human neo-intima. Thromb Haemost. 2008;99:373–81.PubMedGoogle Scholar
- van Leeuwen M, Gijbels MJ, Duijvestijn A, Smook M, van de Gaar MJ, Heeringa P, et al. Accumulation of myeloperoxidase-positive neutrophils in atherosclerotic lesions in LDLR−/− mice. Arterioscler Thromb Vasc Biol. 2008;28:84–9.View ArticlePubMedGoogle Scholar
- Kougias P, Chai H, Lin PH, Yao Q, Lumsden AB, Chen C. Neutrophil antimicrobial peptide alpha-defensin causes endothelial dysfunction in porcine coronary arteries. J Vasc Surg. 2006;43:357–63.View ArticlePubMedGoogle Scholar
- Naruko T, Ueda M, Haze K, van der Wal AC, van der Loos CM, Itoh A, et al. Neutrophil infiltration of culprit lesions in acute coronary syndromes. Circulation. 2002;106:2894–900.View ArticlePubMedGoogle Scholar
- Ovbiagele B, Lynn MJ, Saver JL, Chimowitz MI, Group WS. Leukocyte count and vascular risk in symptomatic intracranial atherosclerosis. Cerebrovasc Dis. 2007;24:283–8.View ArticlePubMedGoogle Scholar
- Kawaguchi H, Mori T, Kawano T, Kono S, Sasaki J, Arakawa K. Band neutrophil count and the presence and severity of coronary atherosclerosis. Am Heart J. 1996;132:9–12.View ArticlePubMedGoogle Scholar
- Aldred PM, Hollox EJ, Armour JA. Copy number polymorphism and expression level variation of the human alpha-defensin genes DEFA1 and DEFA3. Hum Mol Genet. 2005;14:2045–52.View ArticlePubMedGoogle Scholar
- Linzmeier RM, Ganz T. Human defensin gene copy number polymorphisms: comprehensive analysis of independent variation in alpha- and beta-defensin regions at 8p22-p23. Genomics. 2005;86:423–30.View ArticlePubMedGoogle Scholar
- Ganz T, Lehrer RI. Defensins. Curr Opin Immunol. 1994;6:584–9.View ArticlePubMedGoogle Scholar
- Hansen KF, Sakamoto K, Pelz C, Impey S, Obrietan K. Profiling status epilepticus-induced changes in hippocampal RNA expression using high-throughput RNA sequencing. Sci Rep. 2014;4:6930.View ArticlePubMedPubMed CentralGoogle Scholar
- Kotepui M, Thawornkuno C, Chavalitshewinkoon-Petmitr P, Punyarit P, Petmitr S. Quantitative real-time RT-PCR of ITGA7, SVEP1, TNS1, LPHN3, SEMA3G, KLB and MMP13 mRNA expression in breast cancer. Asian Pac J Cancer Prev. 2012;13:5879–82.View ArticlePubMedGoogle Scholar
- Assar O, Nejatizadeh A, Dehghan F, Kargar M, Zolghadri N. Association of Chlamydia pneumoniae Infection with atherosclerotic plaque formation. Glob J Health Sci. 2015;8:48916.Google Scholar
- Kim TW, Kim YH, Kim KH, Chang WH. White matter hyperintensities and cognitive dysfunction in patients with infratentorial stroke. Ann Rehabil Med. 2014;38:620–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Ren L, Cai J, Liang J, Li W, Sun Z. Impact of cardiovascular risk factors on carotid intima-media thickness and degree of severity: a cross-sectional study. PLoS One. 2015;10:e0144182.View ArticlePubMedPubMed CentralGoogle Scholar
- Sacre A, Barthelemy P, Korenbaum C, Burgy M, Wolter P, Dumez H, et al. Prognostic factors in second-line targeted therapy for metastatic clear-cell renal cell carcinoma after progression on an anti-vascular endothelial growth factor receptor tyrosine kinase inhibitor. Acta Oncol. 2016;55:324–40.View ArticleGoogle Scholar
- van Breukelen-van der Stoep DF, van Zeben D, Klop B, van de Geijn GJ, Janssen HJ, Hazes MJ, et al. Association of cardiovascular risk factors with carotid intima media thickness in patients with rheumatoid arthritis with low disease activity compared to controls: a cross-sectional study. PLoS One. 2015;10:e0140844.View ArticleGoogle Scholar
- Arnal JF, Scarabin PY, Tremollieres F, Laurell H, Gourdy P. Estrogens in vascular biology and disease: where do we stand today? Curr Opin Lipidol. 2007;18:554–60.View ArticlePubMedGoogle Scholar
- Kang JG, Patino WD, Matoba S, Hwang PM. Genomic analysis of circulating cells: a window into atherosclerosis. Trends Cardiovasc Med. 2006;16:163–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore DF, Li H, Jeffries N, Wright V, Cooper Jr RA, Elkahloun A, et al. Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation. 2005;111:212–21.View ArticlePubMedGoogle Scholar
- Sharp FR, Xu H, Lit L, Walker W, Pinter J, Apperson M, et al. Genomic profiles of stroke in blood. Stroke. 2007;38:691–3.View ArticlePubMedGoogle Scholar
- Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res. 1996;6:986–94.View ArticlePubMedGoogle Scholar
- Bera TK, Saint Fleur A, Lee Y, Kydd A, Hahn Y, Popescu NC, et al. POTE paralogs are induced and differentially expressed in many cancers. Cancer Res. 2006;66:52–6.View ArticlePubMedGoogle Scholar
- Bera TK, Zimonjic DB, Popescu NC, Sathyanarayana BK, Kumar V, Lee B, et al. POTE, a highly homologous gene family located on numerous chromosomes and expressed in prostate, ovary, testis, placenta, and prostate cancer. Proc Natl Acad Sci U S A. 2002;99:16975–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee Y, Ise T, Ha D, Saint Fleur A, Hahn Y, Liu XF, et al. Evolution and expression of chimeric POTE-actin genes in the human genome. Proc Natl Acad Sci U S A. 2006;103:17885–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–50.View ArticlePubMedGoogle Scholar
- Jitoboam K, Phaonakrop N, Libsittikul S, Thepparit C, Roytrakul S, Smith DR. Actin interacts with dengue virus 2 and 4 envelope proteins. PLoS One. 2016;11:e0151951.View ArticlePubMedPubMed CentralGoogle Scholar
- Etienne G, Dupouy M, Costaglioli P, Chollet C, Lagarde V, Pasquet JM, et al. alpha-defensin 1–3 and alpha-defensin 4 as predictive markers of imatinib resistance and relapse in CML patients. Dis Markers. 2011;30:221–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Jespersgaard C, Fode P, Dybdahl M, Vind I, Nielsen OH, Csillag C, et al. Alpha-defensin DEFA1A3 gene copy number elevation in Danish Crohn's disease patients. Dig Dis Sci. 2011;56:3517–24.View ArticlePubMedGoogle Scholar
- Muller CA, Markovic-Lipkovski J, Klatt T, Gamper J, Schwarz G, Beck H, et al. Human alpha-defensins HNPs-1, −2, and −3 in renal cell carcinoma: influences on tumor cell proliferation. Am J Pathol. 2002;160:1311–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Li YX, Lin CQ, Shi DY, Zeng SY, Li WS. Upregulated expression of human alpha-defensins 1, 2 and 3 in hypercholesteremia and its relationship with serum lipid levels. Hum Immunol. 2014;75:1104–9.View ArticlePubMedGoogle Scholar
- Higazi AA, Nassar T, Ganz T, Rader DJ, Udassin R, Bdeir K, et al. The alpha-defensins stimulate proteoglycan-dependent catabolism of low-density lipoprotein by vascular cells: a new class of inflammatory apolipoprotein and a possible contributor to atherogenesis. Blood. 2000;96:1393–8.PubMedGoogle Scholar
- Eisenhauer PB, Harwig SS, Lehrer RI. Cryptdins: antimicrobial defensins of the murine small intestine. Infect Immun. 1992;60:3556–65.PubMedPubMed CentralGoogle Scholar
- Albrethsen J, Bogebo R, Gammeltoft S, Olsen J, Winther B, Raskov H. Upregulated expression of human neutrophil peptides 1, 2 and 3 (HNP 1–3) in colon cancer serum and tumours: a biomarker study. BMC Cancer. 2005;5:8.View ArticlePubMedPubMed CentralGoogle Scholar
- Kemik O, Kemik AS, Sumer A, Begenik H, Purisa S, Tuzun S. Human neutrophil peptides 1, 2 and 3 (HNP 1–3): elevated serum levels in colorectal cancer and novel marker of lymphatic and hepatic metastasis. Hum Exp Toxicol. 2013;32:167–71.View ArticlePubMedGoogle Scholar
- Zhao Y, Zhang X, Xue X, Li Z, Chen F, Li S, et al. High throughput detection of human neutrophil peptides from serum, saliva, and tear by anthrax lethal factor-modified nanoparticles. ACS Appl Mater Interfaces. 2013;5:8267–72.View ArticlePubMedGoogle Scholar
- Gunes M, Gecit I, Pirincci N, Kemik AS, Purisa S, Ceylan K, et al. Plasma human neutrophil proteins-1, −2, and −3 levels in patients with bladder cancer. J Cancer Res Clin Oncol. 2013;139:195–9.View ArticlePubMedGoogle Scholar
- Ihi T, Nakazato M, Mukae H, Matsukura S. Elevated concentrations of human neutrophil peptides in plasma, blood, and body fluids from patients with infections. Clin Infect Dis. 1997;25:1134–40.View ArticlePubMedGoogle Scholar
- Panyutich AV, Panyutich EA, Krapivin VA, Baturevich EA, Ganz T. Plasma defensin concentrations are elevated in patients with septicemia or bacterial meningitis. J Lab Clin Med. 1993;122:202–7.PubMedGoogle Scholar
- Nemeth BC, Varkonyi T, Somogyvari F, Lengyel C, Fehertemplomi K, Nyiraty S, et al. Relevance of alpha-defensins (HNP1-3) and defensin beta-1 in diabetes. World J Gastroenterol. 2014;20:9128–37.PubMedPubMed CentralGoogle Scholar
- Saraheimo M, Forsblom C, Pettersson-Fernholm K, Flyvbjerg A, Groop PH, Frystyk J, et al. Increased levels of alpha-defensin (−1, −2 and −3) in type 1 diabetic patients with nephropathy. Nephrol Dial Transplant. 2008;23:914–8.View ArticlePubMedGoogle Scholar
- Joseph G, Tarnow L, Astrup AS, Hansen TK, Parving HH, Flyvbjerg A, et al. Plasma alpha-defensin is associated with cardiovascular morbidity and mortality in type 1 diabetic patients. J Clin Endocrinol Metab. 2008;93:1470–5.View ArticlePubMedGoogle Scholar
- Mothes H, Melle C, Ernst G, Kaufmann R, von Eggeling F, Settmacher U. Human Neutrophil Peptides 1-3--early markers in development of colorectal adenomas and carcinomas. Dis Markers. 2008;25:123–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Barnathan ES, Raghunath PN, Tomaszewski JE, Ganz T, Cines DB, Higazi A-R. Immunohistochemical localization of defensin in human coronary vessels. Am J Pathol. 1997;150:1009–20.PubMedPubMed CentralGoogle Scholar
- Nassar H, Lavi E, Akkawi S, Bdeir K, Heyman SN, Raghunath PN, et al. alpha-Defensin: link between inflammation and atherosclerosis. Atherosclerosis. 2007;194:452–7.View ArticlePubMedGoogle Scholar
- Schulz S, Birkenmeier G, Schagdarsurengin U, Wenzel K, Muller-Werdan U, Rehfeld D, et al. Role of LDL receptor-related protein (LRP) in coronary atherosclerosis. Int J Cardiol. 2003;92:137–44.View ArticlePubMedGoogle Scholar
- Huang JS. Alpha-2-macroglobulin--a modulator for growth factors? Am J Respir Cell Mol Biol. 1989;1:169–70.View ArticlePubMedGoogle Scholar
- Strickland DK, Gonias SL, Argraves WS. Diverse roles for the LDL receptor family. Trends Endocrinol Metab. 2002;13:66–74.View ArticlePubMedGoogle Scholar
- Llorente-Cortes V, Badimon L. LDL receptor-related protein and the vascular wall: implications for atherothrombosis. Arterioscler Thromb Vasc Biol. 2005;25:497–504.View ArticlePubMedGoogle Scholar
- Nassar T, Akkawi S, Bar-Shavit R, Haj-Yehia A, Bdeir K, Al-Mehdi AB, et al. Human alpha-defensin regulates smooth muscle cell contraction: a role for low-density lipoprotein receptor-related protein/alpha 2-macroglobulin receptor. Blood. 2002;100:4026–32.View ArticlePubMedGoogle Scholar