Identification of pathways and key genes in male late‑stage carotid atherosclerosis using bioinformatics analysis
- Authors:
- Published online on: May 20, 2022 https://doi.org/10.3892/etm.2022.11387
- Article Number: 460
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Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Atherosclerosis is a progressive chronic inflammatory and metabolic disease with lipid deposition, focal intimal thickening, smooth muscle cell proliferation and plaque formation (1). With the changes in diet, atherosclerosis and its complications have increased and caused elevated morbidity and mortality worldwide (2). Atherosclerosis of the internal carotid artery, leading to narrowing of the vessel lumen by >50% of the original size, affects nearly 10% of the population above the age of 70 years and causes ~15% of ischemic strokes (3). According to the statistics of patients with carotid atherosclerosis, males are more prone to carotid atherosclerosis and the incidence rate of carotid atherosclerosis in males is higher than that in females (4). Elucidating the mechanisms of male carotid atherosclerosis may help to explain the high incidence rate in males and this may prevent male carotid atherosclerosis in the future.
The mechanisms of atherosclerosis have remained to be fully elucidated. However, lipid metabolism disorders, endothelial dysfunction (5), inflammation (6) and oxidative stress are involved in the formation of atherosclerosis. In the Gene Expression Omnibus (GEO) dataset GSE100927, atherosclerosis was assessed in numerous parts of the human artery, including the atherosclerotic femoral artery, infra-popliteal artery and carotid artery (7). The GSE100927 dataset contains 96 samples, including 31 healthy arterial samples and 65 late-stage atherosclerotic arterial samples. For the present study, the gene expression profile of GSE100927 was selected and the data of 10 male normal samples and 21 male late-stage atherosclerosis samples were acquired. To date, the diagnosis rate of atherosclerotic femoral artery and infra-popliteal artery is less than that of carotid artery. So, carotid atherosclerosis rather than atherosclerotic femoral artery and infra-popliteal artery was selected in this study.
In the present study, key genes in carotid atherosclerosis were screened out via bioinformatics, including MMP7, MMP9, IL1β, C-C motif chemokine ligand 4 (CCL4), secreted phosphoprotein 1 (SPP1), CCL3 and interferon regulatory factor 5 (IRF5). Subsequently, the mRNA levels of these genes were measured in human umbilical vein endothelial cells (HUVECs), human aortic vascular smooth muscle cells (HAVSMCs) and Tohoku Hospital Pediatrics-1 (THP-1)-induced macrophages.
Materials and methods
Data sources
The GSE100927 gene expression dataset was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds/), which may be used for genome-wide expression analyses. The gene expression dataset is based on the GPL17077 platform (Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381). The GSE100927 dataset contains 31 healthy artery samples and 65 atherosclerotic artery samples. Male carotid atherosclerosis samples were selected for analysis, including 10 healthy samples (control samples) and 21 late-stage atherosclerosis samples. The raw data were downloaded as MINiML files. The extracted data were normalized. A fold change of at least 2 and P≤0.05 was considered to indicate a statistically significant difference. A total of 32 upregulated and 13 downregulated genes were identified.
Functional annotation of DEGs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis
The cluster Profiler package in R is a tool to implement methods when analyzing and visualizing the functional profiles of genomic coordinates and was used to perform GO and KEGG analysis (8). GO analysis is a useful method, which includes three biological aspects: Biological Process (BP), Molecular Function (MF) and Cellular Component (CC) (9). KEGG is a commonly used bioinformatics database, which analyzes gene functions and enriched genes with their pathways (10,11). The GO and KEGG enrichment analyses were performed for DEGs using the cluster Profiler package. The top 10 terms were selected.
Construction of the protein-protein interaction (PPI) network and Venn diagram
The Search Tool for the Retrieval of Interacting Genes and proteins (STRING) database (https://string-db.org/) may be used for the prediction of PPIs. A total of 45 genes were inputted into STRING. In addition, disconnected nodes in the network were hidden and the minimum required interaction score was 0.700. The line thickness indicated the strength of data support. Genes ranking first in BP, MF and CC were inputted into a Venn diagram and the common genes were acquired.
Cell lines
Cells were cultured in 5% CO2 at 36.5±0.5˚C and under 95% relative humidity. HAVSMCs were purchased from Shenzhen Kuyuan Biotechnology Co., Ltd. HUVECs (CL-0122) and THP-1 (CL-0233) cells were purchased from Procell Life Science & Technology Co., Ltd. THP-1 cells were cultured in RPMI-1640 (Gibco; Thermo Fisher Scientific, Inc.) +10% FBS + 0.05 mM β-mercaptoethanol (PB180633) + 1% antibiotics. HAVSMCs and HUVECs were cultured in 10% FBS + 89% DMEM (Gibco; Thermo Fisher Scientific, Inc.) + 1% antibiotics. HAVSMCs, HUVECs and THP-1 cells were immortalized cell lines.
Reagents
RNAiso Plus was acquired from Takara Biotechnology Co., Ltd. SYBR®-Green Premix qPCR, an Evo M-MLV RT-PCR kit and RNase-free water (cat. nos. AG11701, AG11602 and AG11012, respectively) were obtained from Accurate Biotechnology Co., Ltd. 2',7'-dichloro-dihydrofluorescein diacetate (DCFH-DA; cat. no. D6883) and a Cell Counting Kit-8 (CCK-8; cat. no. 96992) were acquired from MilliporeSigma. Hoechst 33342 (cat. no. M5112) was obtained from Guangzhou Juyan Biological Co., Ltd. Palmitic acid and solvent (SYSJ-KJ0040) were acquired from Xi'an Quantum Technology Development Co., Ltd. The Annexin V APC Apoptosis Detection Kit I (cat. no. 62700-80) was purchased from Guangzhou Squirrel Biological Co., Ltd.
Cell viability and cytotoxicity assays
The viability of cells was determined using a CCK-8 assay. First, all cells were seeded into 96-well plates at a density of 6x103 cells/well and incubated for 24 h. To assess the effect of palmitic acid, the cells were then incubated with palmitic acid at various concentrations (0, 25, 50, 75, 100, 125, 150, 175 or 200 µM) for 6, 12, 24 or 36 h, and then subjected to the CCK-8 assay at 37˚C for 1 h. The absorbance at 450 nm was measured using a microplate reader (BioTek Instruments, Inc.).
Experimental grouping
The groups were as follows: Control group, solvent control group (equal volume of solvent) and palmitic acid group (200 µM palmitic acid). Following co-cultivation with palmitic acid for 24 h, cells were used in a series of experiments.
Intracellular reactive oxygen species (ROS) measurement
Cells (1x106) in a 6-well plate or collected in an Eppendorf tube were incubated at 37˚C for 20 min in PBS containing 20 µM DCFH-DA. After the DCFH-DA was removed, cells were washed three times with PBS. Subsequently, intracellular ROS production was measured using an inverted fluorescence microscope (Axio Vert.A1; Carl Zeiss, Inc.) or a flow cytometer (CytExpert 2.3; Beckman Coulter, Inc.).
Cell apoptosis detection via flow cytometry
Annexin V allophycocyanin (APC) and PI were used to evaluate the apoptotic rates of cells in different groups. Cells were collected with trypsin (Gibco; Thermo Fisher Scientific, Inc.) and washed with PBS. Subsequently, 1x106 cells were placed in binding buffer and double-stained with Annexin V-allophycocyanin and propidium iodide in the dark for 15 min at 4˚C. The proportion of apoptotic cells was then analyzed on a flow cytometer (CytExpert 2.3; Beckman Coulter, Inc.) to determine the apoptotic rate.
Hoechst 33258 staining
Cells were incubated for 20 min with 5 µl Hoechst 33258 in 0.995 ml PBS at 37˚C. After washing twice with PBS, the fluorescence images were captured using an inverted fluorescence microscope (Axio Vert.A1; Carl Zeiss, Inc.), and Image-Pro Plus 6.0 (Media Cybernetics, Inc.) was used for analysis to measure the fluorescence intensity.
Reverse transcription-quantitative PCR (RT-qPCR)
According to the manufacturer's protocol, total RNA from cells was isolated using RNAiso Plus. Subsequently, cDNA was synthesized based on the instructions of the RT-PCR kit (catalog no. AG11602). Subsequently, a Bio-Rad CFX96 Real-Time PCR System (Bio-Rad Laboratories, Inc.) was used to perform qPCR. The amplification parameters were as follows: 95˚C for 30 sec, followed by 40 cycles of 95˚C for 5 sec and 60˚C for 34 sec, 95˚C for 15 sec, 60˚C for 60 sec and 95˚C for 15 sec. Relative mRNA expression was calculated using the 2-ΔΔCq method after normalization to β-actin (12,13). For this procedure, SYBR®-Green Premix qPCR and primers (Table I) were used.
Statistical analysis
Values are expressed as the mean ± standard deviation. The experiments were repeated three times. GraphPad Prism 8 (GraphPad Software, Inc.) was used to perform statistical analysis. The data were analyzed by one-way ANOVA. Bonferroni's test was used as the post-hoc test after ANOVA. P<0.05 was considered to indicate a statistically significant difference.
Results
Genes detected in DEG analysis and interaction diagram of proteins
DEG analysis was performed on the GSE100927 dataset (Fig. 1A). The extracted data were processed by log2 transformation, and P≤0.05 was deemed significant. A total of 32 upregulated and 13 downregulated genes were identified (Fig. 1B). The expression matrices of the identified genes were selected from the GSE100927 dataset and the clustered heat map was constructed using the heatmap function (Fig. 1C). A total of 45 genes were inputted into STRING and the PPI relationship was determined (Fig. 1D).
Functional enrichment analysis of DEGs
The top 10 significant terms in the GO annotation (Fig. 2A-C) and 6 most significant terms in the pathway enrichment analysis (Fig. 1E; Table II) were identified. The results of the pathway enrichment analysis revealed that the ‘Toll-like receptor signaling pathway’ ranked first and this was selected for experimental verification (Fig. 1E). As for the GO annotation, the terms ranking first in BP (Table III), CC (Table IV) and MF (Table V) were selected. The Venn diagram indicated two genes (MMP7 and MMP9) that were involved in these three categories of terms. Subsequently, the expression levels of IL1β, CCL4, SPP1, CCL3, IRF5, MMP7 and MMP9 were examined. The results indicated that IL1β, CCL4, SPP1, CCL3, IRF5, MMP7 and MMP9 were highly expressed in the atherosclerosis group (Fig. 2E-K).
Palmitic acid affects cell viability
To examine the cytotoxicity of palmitic acid, HUVECs, HAVSMCs and THP-1-induced macrophages were incubated with different doses of palmitic acid in the culture medium containing 10% FBS for 6, 12, 24 or 36 h. Cytotoxicity/growth inhibition was determined by a CCK-8 assay. Palmitic acid (50, 75, 100, 125, 150, 175 or 200 µM) significantly lowered the viability of HUVECs and HAVSMCs at different time-points (P<0.05; Fig. 3A and B), with a maximum response at 200 µM, while 100, 125, 150, 175 and 200 µM palmitic acid increased the viability of THP-1-induced macrophages at different time-points (P<0.05) with a maximum response at 200 µM (Fig. 3C). Since incubation with 200 µM palmitic acid for 36 h markedly decreased cell viability and was associated with increased cell death, incubation with 200 µM palmitic acid for 24 h was considered more suitable.
Palmitic acid induces ROS production of cells
To determine whether palmitic acid has an effect on intracellular oxidative stress, it was investigated whether palmitic acid had effects on the production of ROS in cells. Intracellular ROS levels in the different groups of cells were examined after incubation with palmitic acid for 24 h via fluorescence microscopy and flow cytometry. As indicated in Fig. 4Aa-d, incubation with palmitic acid increased intracellular ROS levels of HUVECs (P<0.05), while the solvent alone did not. As presented in Fig. 4Ba-d, incubation with palmitic acid increased intracellular ROS levels of HAVSMCs (P<0.05), while the solvent alone had no such effect. Of note, palmitic acid and solvent incubation did not change the ROS levels of THP-1-induced macrophages (Fig. 4Ca-b).
Effects of palmitic acid on cell apoptosis
Certain studies have reported the use of palmitic acid to represent atherosclerosis (14,15). To determine the damaging effects of palmitic acid, it was investigated whether palmitic acid had effects on cell apoptosis and cell nuclear changes. In the present study, cell apoptosis was examined after incubation with palmitic acid for 24 h via fluorescence microscopy and nuclear changes were observed via Hoechst 33258 staining. As presented in Fig. 5A and B, palmitic acid increased the proportion of apoptotic HAVSMCs (P<0.05), while the solvent alone did not. Palmitic acid also increased the apoptosis rate of HUVECs (P<0.05), while the solvent did not (Fig. 5C and D). However, palmitic acid and solvent did not affect the nuclei of THP-1-induced macrophages (Fig. 5E and F), as the fluorescence intensity did not vary in the three groups.
mRNA levels of IL1β, CCL4, SPP1, CCL3, IRF5, MMP7 and MMP9
The mRNA expression levels of IL1β, CCL4, SPP1, CCL3, IRF5, MMP7 and MMP9 were detected in HAVSMCs, HUVECs and THP-1-induced macrophages (Fig. 6). Palmitic acid increased the levels of IL1β, CCL4, SPP1, CCL3, IRF5, MMP7 and MMP9 both in THP-1-induced macrophages and HUVECs (P<0.05). In addition, palmitic acid increased the levels of CCL4 and MMP9 (P<0.05), while the expression levels of IRF5 were only slightly altered.
Discussion
Atherosclerosis may occur throughout the arterial vascular system and lead to various diseases. The present study provided evidence that inflammation markers serve a vital role in male late-stage carotid atherosclerosis. The major findings were as follows: i) Carotid atherosclerosis is closely related to arterial inflammation, including pathways such as ‘Toll-like receptor signaling pathway’, ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’; ii) macrophages and vascular endothelial cells are involved in vascular inflammation; iii) palmitic acid causes apoptosis of HUVECs and HAVSMCs, indicating that hyperlipidemia may cause blood vessel damage, while it does not affect THP-induced macrophages; and iv) palmitic acid increased the levels of oxidative stress in HUVECs and HAVSMCs, while it did not increase the levels of oxidative stress in THP-induced macrophages.
Steenman et al (7) performed a canonical pathway analysis, revealing the important involvement of immune system processes in atherosclerotic and healthy carotid arteries. In the original study providing the GSE100927 dataset, it was demonstrated that the immune system served a key role in atherosclerosis. MMP7 was the top differentially expressed gene between the three arterial territories in both atherosclerotic and healthy arteries and carotid atherosclerosis displayed higher expression levels of MMP7, MMP9 and MMP12 than those in the atherosclerotic femoral artery and infra-popliteal artery (7). In the present study, MMP7 and MMP9 were indicated to be involved in biological aspects of carotid atherosclerosis and were higher expressed after palmitic acid treatment, which was consistent with the results of GSE100927.
The pathogenesis of atherosclerosis is closely related to hyperlipidemia. Free fatty acids may cause endothelial damage and lipid deposition. The effect of palmitic acid on endothelial cells, smooth muscle cells and macrophages is able to induce the high-fat model in vitro (14,15). Palmitic acid may lead to inflammation and apoptosis of vascular endothelial cells by mediating the PI3K/Akt/endothelial nitric oxide synthase signaling pathway (16). Palmitic acid induced endothelial lipotoxicity and lectin-like oxidized LDL receptor-1 upregulation by reducing endoplasmic reticulum stress in HUVECs. High glucose- and palmitic acid-induced apoptosis, oxidative stress and inflammatory response in HUVECs (17). Nearly all studies included inflammation, and oxidative stress was involved as well. Thus, inhibiting inflammation requires further investigation in future experiments.
Oxidative stress serves a vital role in the process of atherosclerotic plaque formation (18). Oxidative stress is associated with systemic inflammation, endothelial cell proliferation and apoptosis, as well as vasoconstriction, which contribute to endothelial dysfunction, leading to atherosclerosis (19). Toualbi et al (20) suggested that cardiovascular diseases, mainly atherosclerosis, may be diagnosed indirectly by measuring oxidative stress markers. In addition, oxidative stress and inflammation are two major proatherogenic factors, responsible for the modification of vascular wall integrity (21). The present study revealed that palmitic acid may induce increased levels of oxidative stress in HUVECs and HAVSMCs, which is consistent with previous research. Furthermore, attenuating oxidative stress may potentially decelerate the progression of atherosclerotic plaque formation (22). Ji et al (23) revealed that propolis ameliorated restenosis in hypercholesterolemia rabbits with carotid balloon injury by inhibiting lipid accumulation, oxidative stress and the Toll-like receptor 4/NF-κB signaling pathway. Zhang et al (24) reported that quercetin is a potential therapeutic agent ameliorating atherosclerotic pathophysiology in rat carotid arteries by inhibiting oxidative stress and inflammatory response, the mechanism of which involved modulating the AMP-activated protein kinase/sirtuin 1/NF-κB signaling pathway. Overproduction of ROS was reported to cause vascular endothelial damage (11,13,25). Endothelial dysfunction-induced lipid retention is an early feature of atherosclerotic lesion formation (26). Apoptosis of vascular smooth muscle cells is one of the major modulating factors of atherogenesis, which accelerates atherosclerosis progression by causing plaque destabilization and rupture (27). The present study revealed that palmitic acid induces apoptosis of HUVECs and HAVSMCs, which may induce arterial lipid accumulation and exacerbation of atherosclerosis.
Inflammation is one of the major proatherogenic factors, destroying the structure of blood vessels (21). IL1 is a critical factor in the process of atherosclerosis. Mice with knockout of apolipoprotein E (ApoE) and IL1β have markedly smaller sizes of atherosclerotic lesions in the aortic sinus and ratios of atherosclerotic areas of the aorta compared with single ApoE-knockout mice (28). Furthermore, artificial IL1β expression on one side of the coronary artery led to increases in coronary stenosis and aggravation of vascular diseases (29). The present study demonstrated that the expression levels of IL1β in the control group were higher than those in other groups in HUVECs and THP-1 induced macrophages, which was consistent with the previous conclusions.
The circulating levels of C-C chemokine ligand (CCL) are increased in atherosclerotic patients (30). CCL4 may be detected in T-cells, smooth muscle cells and macrophages in atherosclerotic plaques (31), and is further upregulated in vulnerable plaques (32). In the present study, the levels of CCL4 in HAVSMCs, HUVECs and THP-1-induced macrophages were determined. Chang et al (31) considered that direct inhibition of CCL4 stabilized atheroma and reduced endothelial and macrophage activation. Komissarov et al (33) reported that T-cell migration into human atherosclerotic plaques may predominantly occur via C-C motif chemokine receptor 5-CCL3 and C-X3-C motif chemokine receptor 1-C-X3-C motif chemokine ligand 1 interactions. Munjal and Khandia (34) suggested that the chemokines (family of small cytokines) involved in atherosclerotic plaque formation are CCL3, chemokine (C-X-C motif) ligand 4 and macrophage migration-inhibitory factor. Bai et al (35) indicated that the serum levels of SPP1, CD36, ATPase H+ transporting V0 subunit d2, chitinase 3 like 1, myosin heavy chain 11 and brain-derived neurotrophic factor in patients with coronary heart disease differed from those in healthy subjects. The present study revealed that the levels of CCL3 and SPP1 in HUVECs and THP-1-induced macrophages were higher than those in the control group, which was consistent with these reports.
IRF5 serves a central role in inflammation, mediating the production of proinflammatory cytokines, such as IL6, IL12, IL23 and TNF-α (36). Seneviratne et al (37) and Posadas-Sanchez et al (38) reported that IRF5 was detrimental in atherosclerosis, promoting the maintenance of proinflammatory CD11c+ macrophages. Gaubatz et al (39) suggested a positive association between MMP-7 and the calcification of the carotid arteries. Polonskaya et al (40) considered that the relative risk of coronary artery calcification was associated with MMP-9 and the MMP-7 levels were markedly higher in patients with coronary heart disease and verified coronary artery atherosclerosis than in the control group. The present results were consistent with these reports.
There are certain deficiencies in the present study. It was not possible to obtain male primary endothelial cells, VSMCs and macrophages to perform validation in vitro; thus, HVSMC, HUVEC and THP-1 cell lines were used. These cell lines are more widely used in cardiovascular disease, although these three cell types do not optimally represent the carotid artery. It may be attempted to generate stable male cell cultures in the future. Furthermore, pre-term and mid-term atherosclerosis was not investigated in the present study. These dynamic processes should be included in future research.
In conclusion, inflammation is closely related to atherosclerosis, as demonstrated using bioinformatics and experimental verification. IL1β, CCL3, CCL4, SPP1, IRF5, MMP7 and MMP9 are markers of carotid atherosclerosis.
Acknowledgements
The authors would like to thank Miss Yixuan Li (College of Traditional Chinese Medicine, Jinan University, Guangzhou, China) for her valuable comments on English language revision.
Funding
Funding: The National Natural Science Foundation of China (grant no. 81874404), Guangdong Medical Research Fund project (grant no. B2021324), TCM Research Project of Guangdong Provincial Bureau of TCM (grant no. 20222171) and the Guangzhou TCM and Integrated Traditional Chinese and Western Medicine Science and Technology Project (grant no. 202185151002) supported this study.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The gene expression datasets generated and/or analyzed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100927.
Authors' contributions
DZ and GZ designed the experiments and wrote this manuscript. GQ was involved in the experimental design and manuscript revision. DZ, BJ and XL completed the experiments. HS, SC, ZC, YZ and YP provided help with the English language revision and were involved in data analysis. DZ, XL, BJ, GQ and GZ confirmed the authenticity of the data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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