Abstract
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. DNA methylation can regulate gene expression. Understanding the potential molecular mechanism of COPD is of great importance. The aim of this study was to find differentially methylated/expressed genes in COPD. DNA methylation and gene expression profiles in COPD were downloaded from the dataset, followed by functional analysis of differentially-methylated/expressed genes. The potential diagnostic value of these differentially-methylated/expressed genes was determined by receiver operating characteristic (ROC) analysis. Expression validation of differentially-methylated/expressed genes was performed by in vitro experiment and extra online datasets. Totally, 81 hypermethylated-low expression genes and 121 hypomethylated-high expression genes were found in COPD. Among which, 9 core hypermethylated-low expression genes (CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7 and IL7R) and 8 core hypomethylated-high expression genes (TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3 and LYZ) were identified in the protein-protein interaction (PPI) network. Moreover, these genes had a potential diagnostic utility for COPD. Some signaling pathways were identified in COPD, including T cell receptor signaling pathway, cytokine-cytokine receptor interaction, hematopoietic cell lineage, HTLV-I infection, endocytosis and Jak-STAT signaling pathway. In conclusion, differentially-methylated/expressed genes and involved signaling pathways are likely to be associated with the process of COPD.
Introduction
Chronic obstructive pulmonary disease (COPD), a chronic inflammatory lung disease, causes obstructed airflow from the lungs [Citation1]. COPD is characterized by persistent airway inflammation and airflow limitation. In addition, patients with COPD suffer from other comorbidities/conditions, including weight loss, malnutrition, chronic heart failure, coronary artery disease, interstitial lung disease, pulmonary hypertension, aging, cancer, diabetes, metabolic syndrome, hypertension and hyperlipidemia [Citation2]. COPD is related to chronic inflammation characterized by an increased number of T lymphocytes and innate lymphoid cells recruited from the circulation [Citation3–5]. It is shown that adaptive immunity directed against lung self-antigens plays important roles in perpetuating the immune response [Citation6–9]. Some factors that affect the development of COPD have been identified such as genetic factors, sex, age, lung development, exposure to particles, asthma and airway hyperresponsiveness, socioeconomic status, infections and chronic bronchitis [Citation10–12]. It is noted that cigarette smoke is the most commonly risk factor for COPD [Citation10].
Clinically, the overall 5 year survival for patients with COPD is from 56% to 92%, which depends on the disease severity [Citation13]. Therefore, early diagnosis of COPD is necessary, before the occurrence of disability/irreversible lung structural changes [Citation14]. It is worth mentioning that DNA methylation can regulate gene expression. Characterizing methylation signatures could have important implications as biomarkers for early diagnosis and prognosis of COPD. The hypermethylation of SERPINA1 has been found in COPD, which could represent a potential biomarker for predicting the development of COPD in acute coronary syndrome patients [Citation15]. In addition, some differential methylated sites, closely associated with smoking, have potential significance in COPD susceptibility [Citation16,Citation17]. Many studies have confirmed that DNA methylation is a potential biomarker for diagnosis, prognosis and therapy of COPD [Citation18]. Thus it can be seen that DNA methylation may shed new insights into the pathogenesis of COPD. In the present study, we aimed to find potential differentially expressed genes (DEGs), differentially methylated genes (DMGs) and related signaling pathways in COPD patients by integrating high-throughput transcriptome expression data with DNA methylation data. Our study may be helpful in exploring the pathogenesis of COPD and providing a certain research basis for the prediction and drug development of COPD.
Methods
Filtering of data sets
Keywords of “chronic obstructive pulmonary disease” and “Homo sapiens” [porgn:__txid9606] were searched. Study types of data sets were "Expression profiling by array" and "Methylation Profiling by array". Detailed inclusion criteria of data sets were as follows: (1) the selected data set was genome-wide mRNA transcriptome data and DNA methylation data; (2) these data were obtained from peripheral blood mononuclear cell samples from COPD patients and normal controls; (3) both standardized and raw data sets were considered in this study. After screening, 1 mRNA data set (GSE42057) and 1 methylation data set (GSE118468) were obtained.
Identification of DEGs and DMGs
Data set GSE42057 was downloaded from Gene Expression Omnibus (GEO) database. Probes were corresponding to genes. The mean value of multiple probes corresponding to one gene was taken as the expression level of the gene. Difference analysis of gene was carried out by limMA package of analysis software R-4.0.5. The screening criterion of DEGs was p value < 0.05. CHAMP package was used for differential methylation analysis of genes under the selection criteria of p value < 0.05 and | deltaBeta | > 0.1. Intersection genes between down-regulated genes and hypermethylated genes were regarded as hypermethylated-low expression genes. In addition, intersection genes between up-regulated genes and hypomethylated genes were considered as hypomethylated-high expression genes. The volcano map and the heatmap were drawn using the ggplot2 package in R and the pheatmap package in R, respectively.
Functional analysis of hypermethylated-low expression and hypomethylated-high expression genes
Firstly, to explore the protein interaction between proteins encoded by hypermethylated-low expression/hypomethylated-high expression genes, protein-protein interaction (PPI) network was constructed through online database STRING. The setting in the analysis was combined_score > 0.15 and false discovery rate (FDR) < 0.05. The results obtained in the STRING database were imported Cytoscape software. CytoHubba plug-in was utilized to filter core genes. A total of 4 algorithms were adopted, including Maximal Clique Centrality (MCC), Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC) and Degree. Core genes were screened after the intersection of the first 10 genes of each algorithm. Secondly, in order to study the function of hypermethylated-low expression and hypomethylated-high expression genes, the David database (background set: Homo sapiens) was used for GO and KEGG functional analysis under the screening criteria of p value < 0.05.
Diagnostic analysis of hypermethylated-low expression and hypomethylated-high expression genes
To study whether hypermethylated-low expression and hypomethylated-high expression genes have diagnostic value, the receiver operating characteristic (ROC) curve was used to determine the accuracy of key genes. The area under the curve (AUC), the evaluation index of model performance, under binomial exact confidence interval was calculated. The AUC value ranges 0-1, where 0.7 is acceptable performance and 0.9 is excellent performance. ROC curves were plotted using pROC packages of R language.
Expression validation of hypermethylated-low expression and hypomethylated-high expression genes by RT-PCR
To validate the expression of hypermethylated-low expression and hypomethylated-high expression genes, in vitro RT-PCR was performed. Totally, 14 COPD patients and 16 healthy individuals were enrolled in this study. Detailed inclusion and exclusion criteria of COPD patients were as follows: (1) patients were diagnosed as COPD based on the standard of Global Initiative for Chronic Obstructive Pulmonary Disease (2021 version) and had complete case data; (2) patients aged from 45 to 80 years old; (3) lung function test indicators of forced expiratory volume (FEV1)/forced vital capacity (FVC) < 70%; (4) repeat hospitalized patients were included in the first hospitalization data; (5) patients were conscious and stable with no obvious communication problems. The inclusion criteria for healthy controls were as follows: (1) normal individuals with normal lung function tests; (2) age of healthy individual was matched with COPD patient. The exclusion criteria for healthy controls were as follows: (1) individuals were complicated with other pulmonary diseases including lung cancer, bronchial asthma, pulmonary interstitial disease, active pulmonary tuberculosis, allergic bronchopulmonary mycosis, etc.; (2) individuals had hypertension, diabetes, serious heart, liver, kidney, hematopoietic system and nervous system diseases; (3) individuals had a history of cold, fever, lung or digestive tract infection in the past 1 week; (4) individuals were unable to comply with lung function examination and incomplete medical history. The study was approved by the ethics committee of Qingdao Fuwai Cardiovascular Hospital (2022-QF-005). In addition, all individuals provided the informed consent of the patients and their families.
The blood samples from the above individuals were collected for RT-PCR. The lysate was mixed with blood sample, shaken continuously for 30 s, and incubated at 15–30 °C for 10 min to completely decompose the ribosomes. The mixture was added with chloroform, shaken vigorously for 15 s, placed at room temperature for 5 min, and centrifuged at 12,000 rpm for 10 min at 4 °C. After removing supernatant, 70% of ethanol and buffer was orderly added and centrifuged at 12,000 rpm for 45 s, followed by adding RNase free water. Reverse transcription of the gene was performed using FastKing cDNA first-strand synthesis kit (TIANGEN). The relative quantitative analysis of the data was performed by 2-△△CT method using ABI 7300 fluorescence quantitative PCR instrument. GAPDH and ACTB were used as internal reference.
Expression validation of hypermethylated-low expression and hypomethylated-high expression genes in GSE94916 and GSE56767 datasets
To further validate the expression of hypermethylated-low expression and hypomethylated-high expression genes, GSE94916 dataset (involving peripheral blood mononuclear cell from 6 COPD cases and 6 normal controls) was used for analysis. The expression of these genes is presented as the box plot.
Results
Identification of hypermethylated-low expression and hypomethylated-high expression genes
Totally, 1592 DEGs were identified in COPD, including 479 up-regulated genes (such as heme binding protein 2, HEBP2) and 1113 down-regulated genes (such as nitric oxide synthase interacting protein, NOSIP) (Supplementary Table 1). In addition, a total of 5840 differentially methylated sites were obtained, involving 2634 DMGs (607 hypermethylated genes and 2027 hypomethylated genes) (Supplementary Table 2). The volcano plot and heat map of all DEGs and DMGs is presented in . Thus it can be seen that there is a significant difference of gene expression between COPD and normal controls. Among which, 81 hypermethylated-low expression genes and 121 hypomethylated-high expression genes were respectively identified between down-regulated genes and hypermethylated genes, and between up-regulated genes and hypomethylated genes ().
Figure 1. The volcano plot and heat map of all DEGs and DMGs. (a) The volcano plot of all DEGs; (b) the heat map of all DEGs; and (c) the volcano plot of all DMGs.
Figure 2. The Venn diagram of 81 hypermethylated-down-regulated genes (a) and 121 hypomethylated-up-regulated genes (b).
Table 1. Identification of 9 core hypermethylated-low expression genes.
Table 2. Identification of 8 core hypomethylated-high expression genes.
PPI network of hypermethylated-low expression and hypomethylated-high expression genes
shows the PPI network of 81 hypermethylated-low expression genes, which consists of 373 interacting gene pairs. A total of 9 core genes were identified after the intersection of the first 10 genes of each algorithm ( and ), including CD247 molecule (CD247) (expression value = −0.23729; p value = 0.007209; | deltaBeta | = 0.143968175), C-C motif chemokine receptor 7 (CCR7) (expression value = −0.42712; p value = 0.000283; | deltaBeta | = 0.123951118), CD5 molecule (CD5) (expression value = −0.17527; p value = 0.017409; | deltaBeta | = 0.125307121), IKAROS family zinc finger 1 (IKZF1) (expression value = −0.10353; p value = 0.006298; | deltaBeta | = 0.128153926), signaling lymphocytic activation molecule family member 1 (SLAMF1) (expression value = −0.18131; p value = 0.049751; | deltaBeta | = 0.132532141), interleukin 2 receptor subunit beta (IL2RB) (expression value = −0.2602; p value = 0.01408; | deltaBeta | = 0.121637175), CD3 epsilon subunit of T-cell receptor complex (CD3E) (expression value = −0.17105; p value = 0.017572; | deltaBeta | = 0.138873971), CD7 molecule (CD7) (expression value = −0.22309; p value = 0.004285; | deltaBeta | = 0.110875454) and interleukin 7 receptor (IL7R) (expression value = −0.22648; p value = 0.013863; | deltaBeta | = 0.155605074). shows the PPI network of 121 hypomethylated-high expression genes, which consist of 576 interacting gene pairs. Totally, 8 core genes were identified after the intersection of the first 10 genes of each algorithm (, ), including triggering receptor expressed on myeloid cells 1 (TREM1) (expression value = 0.385208; p value =; | deltaBeta | = −0.11855486), aquaporin 9 (AQP9) (expression value = 0.441979; p value = 9.69E-05; | deltaBeta | = −0.13560176), CD300 molecule like family member f (CD300LF) (expression value = 0.13993; p value = 0.02407; | deltaBeta | = −0.10395052), C-type lectin domain family 12 member A (CLEC12A) (expression value = 0.307716; p value = 0.01503; | deltaBeta | = −0.10492296), nucleotide binding oligomerization domain containing 2 (NOD2) (expression value = 0.145429; p value = 0.046749; | deltaBeta | = −0.1030296), interleukin 1 receptor associated kinase 3 (IRAK3) (expression value = 0.260752; p value = 0.005461; | deltaBeta | = −0.14495124), NLR family pyrin domain containing 3 (NLRP3) (expression value = 0.203008; p value = 0.047256; | deltaBeta | = −0.13672507) and lysozyme (LYZ) (expression value = 0.157934; p value = 0.02912; | deltaBeta | = −0.13213402).
Figure 3. PPI networks of 81 hypermethylated-low expression genes and 121 hypomethylated-high expression genes. (a) PPI network of 81 hypermethylated-low expression genes; (b) PPI network of 9 hypermethylated-low expression genes; (c) PPI network of 121 hypomethylated-high expression genes; (d) PPI network of 8 hypomethylated-high expression genes.
Functional analysis of hypermethylated-low expression and hypomethylated-high expression genes
According to GO analysis of 81 hypermethylated-low expression genes, type I interferon signaling pathway, membrane and protein binding was the most significantly enriched biological process, cytological component and molecular function, respectively (). In the KEGG analysis, CD247 was involved in T cell receptor signaling pathway, CCR7 was involved in cytokine-cytokine receptor interaction, CD5, CD3E, CD7 and IL7R were involved in hematopoietic cell lineage, IL2RB was involved in HTLV-I infection, endocytosis and Jak-STAT signaling pathway (). Based on GO analysis of 121 hypomethylated-high expression genes, blood coagulation, plasma membrane and catalytic activity was the most significantly enriched biological process, cytological component and molecular function, respectively (). It is a pity that no enriched signaling pathways were found in these hypomethylated-high expression genes.
Figure 4. Functional analysis of 81 hypermethylated-low expression genes. (a) GO analysis; (b) KEGG analysis.
Figure 5. GO analysis of 121 hypomethylated-high expression genes.
Diagnostic analysis of hypermethylated-low expression and hypomethylated-high expression genes
ROC curve analysis was carried out to assess the possible diagnostic utility of CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7, IL7R, TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3, LYZ, HEBP2 and NOSIP (). The AUC values of these genes were all greater than 0.6, which indicates that they have a potential diagnostic utility for COPD.
Figure 6. The ROC curves of CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7, IL7R, TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3, LYZ, HEBP2 and NOSIP in COPD.
Expression validation of hypermethylated-low expression and hypomethylated-high expression genes by RT-PCR
To validate the expression of randomly selected CD247, CCR7, IKZF1, CD7, AQP9, NOD2 and IRAK3, in vitro RT-PCR was performed in blood samples from 14 COPD patients and 16 healthy individuals (). The primer sequences of these genes are listed in Table 3. The clinical information of these individuals is listed in Supplementary Table 3. AQP9, NOD2 and IRAK3 were significantly up-regulated, CD247, CCR7 and CD7 were remarkably down-regulated. IKZF1 was down-regulated without statistical significance. In addition, further expression validation of CD247, CCR7, CD5, IKZF1, SLAMF1, CD3E, IL7R, AQP9, NOD2, IRAK3, and NOSIP was performed in GSE94916 dataset (). The result showed that AQP9, NOD2 and IRAK3 were up-regulated, CD247, CCR7, CD5, IKZF1, SLAMF1, CD3E, IL7R and NOSIP were down-regulated, which was consistent with bioinformatics analysis.
Figure 7. Expression validations of CD247, CCR7, IKZF1, CD7, AQP9, NOD2 and IRAK3 by RT-PCR. *p value < 0.05; **p value < 0.01; ***p value < 0.001.
Figure 8. Expression validations of CD247, CCR7, CD5, IKZF1, SLAMF1, CD3E, IL7R, AQP9, NOD2, IRAK3, and NOSIP in GSE94916 dataset. *p value < 0.05; **p value < 0.01; ns: not significant.
Discussion
CD247, involved in immune function, are associated with asthma, allergies and systemic sclerosis with pulmonary fibrosis [Citation19,Citation20]. In COPD patients, the expression of CD247 is down-regulated, which results in the immunosuppressive state and defective effector cell function [Citation21,Citation22]. The down-regulation of CD247 may account for the predisposition to recurrent airway infections in COPD patients [Citation23]. CCR7 is significantly down-regulated in pulmonary tuberculosis and COPD patients [Citation24–26]. CD5, a T cell receptor signaling gene, is down-regulated in bronchopulmonary dysplasia and COPD [Citation27,Citation28]. IKZF1, associated with autoimmune and allergic diseases (such as asthma/allergic rhinitis/chronic rhinosinusitis), is down-regulated in bronchopulmonary dysplasia [Citation27,Citation29,Citation30]. A susceptible genotype (rs17634369) near the IKZF1 gene is found in a general Japanese population of COPD [Citation31]. SLAMF1 can be found on activated B, T, and dendritic cells and monocytes [Citation32]. Altered activity of SLAMF1 is found in the dustmite-associated allergic asthma patients [Citation33].
IL2RB, a gene causing a predisposition to asthma, plays roles in the regulation of the epithelial barrier function and the adaptive and innate immune responses [Citation34]. The expression levels of IL2RB are decreased in peripheral blood from patients with COPD [Citation35]. CD3E is involved in T-cell receptor signaling pathway in end stage chronic respiratory diseases [Citation36]. CD7, belongs to the members of immunoglobulin super-family, is expressed in T cells and natural killer cells and plays an important role in the interaction between T cells [Citation37]. It is found that CD7 is down-regulated in peripheral blood mononuclear cells of COPD patients [Citation35]. IL7R, adaptive immunity related gene, is down-regulated in the asthmatic and COPD patients [Citation33,Citation35]. Single nucleotide polymorphism in IL7R is related to lung cancer risk in women with COPD [Citation38]. In this study, we found that CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7 and IL7R were hypermethylated and down-regulated in COPD, which was in line with previous reports. Moreover, they had the potential diagnostic value for COPD. These genes may be involved in the immune regulation of COPD, which can be regarded as potential immune diagnostic target molecules for COPD.
TREM1 is up-regulated in chronic pulmonary aspergillosis patients and children with nonallergic asthma [Citation39,Citation40]. In fungal asthma, TREM1 plays roles in modulating the immune response directed against A. fumigates [Citation41]. AQP9 mainly expressed and play active roles in neutrophil volume and migration [Citation42]. Increased expression levels of AQP9 have been found in peripheral blood in patients with acute exacerbation of COPD [Citation35]. CD300LF, a gene codes for the inhibitory receptor of the Ig super-family of myeloid cells, is up-regulated in acute respiratory distress syndrome [Citation43]. It is noted that CD300LF is considered as a novel gene for COPD [Citation44]. CLEC12A is related to evaluation of COPD longitudinally [Citation45]. NOD2, expressed in airway epithelial cells, is involved in the clearance of bacterial pathogens [Citation46]. The relationship between NOD2 gene polymorphism results in the advancement of COPD [Citation47,Citation48]. The increases in the prevalence of the NOD2 rs1077861 single nucleotide polymorphism have been found in COPD patients [Citation48]. IRAK3 is involved in viral acute respiratory infections [Citation49]. Expressions of IRAK3 are elevated in patients with frequent exacerbations of COPD [Citation50]. In COPD patients, NLRP3 is over-expressed in the lung, which is associated with airflow obstruction [Citation51]. It is suggested that the activation of NLRP3 results in the occurrence of COPD airway inflammation [Citation52]. In addition, many different types of air pollutants can enhance the risk of COPD via regulating the NLRP3 inflammasome [Citation53]. Therefore, blockade of NLRP3 may be the possible therapeutic strategy for COPD [Citation54]. LYZ, an immune-related gene, has an important antibacterial activity in the airway epithelium. The expression of LYZ is found in allergic asthma [Citation55]. Additionally, LYZ is involved in the inflammatory response biological pathway in COPD [Citation56]. Herein, we found that TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3 and LYZ were hypomethylated and up-regulated in COPD. It is noted that these genes had the potential diagnostic value for COPD patients. Expression dysregulation of these genes may be associated with immune response and airway inflammation in COPD.
Beside above core genes in PPI network, we also found that HEBP2 and NOSIP were respectively one of top 10 up-regulated and down-regulated genes in COPD. HEBP2 is up-regulated in mouse lung telocytes as compared with mesenchymal stem cells and fibroblasts [Citation57]. NOSIP is differentially expressed in bronchopulmonary dysplasia [Citation58]. Decreased expression of NOSIP has been found in COPD [Citation28]. HEBP2 and NOSIP play important roles in COPD. In the KEGG analysis, we found that CD247 was involved in T cell receptor signaling pathway, CCR7 was involved in cytokine-cytokine receptor interaction, CD5, CD3E, CD7 and IL7R were involved in hematopoietic cell lineage, IL2RB was involved in HTLV-I infection, endocytosis and Jak-STAT signaling pathway. It is reported that COPD-specific down-regulated genes are involved in T cell receptor signaling pathway [Citation35]. In addition, down-regulation of T cell receptor results in the inadequate response to infection, causing increased susceptibility to COPD complications [Citation22]. Cytokine-cytokine receptor interaction is associated with fibrotic or pulmonary diseases and idiopathic pulmonary fibrosis [Citation59]. In peripheral blood mononuclear cells of COPD patients, cytokine-cytokine receptor interaction is a significantly enrichment pathway of dysregulated genes [Citation60]. In the lung tissue of COPD smokers, hematopoietic cell lineage is related to activation of metabolic pathways [Citation61]. HTLV-I infected lung epithelial cells produce cytokines and cell adhesion molecules, contributing to the clinical features of HTLV-I-related pulmonary diseases [Citation62]. Interestingly, HTLV-I infection is related to chronic thromboembolic pulmonary hypertension [Citation63]. Cigarette smoke can increase caveolin-mediated endocytosis, which could contribute to lung infection in smokers [Citation64]. The relationship between endocytosis and COPD has been found [Citation65]. In animal models, targeting Jak-STAT signaling pathway reduces airway hyperresponsiveness, leading to efforts to develop inhaled therapeutics targeting the Jak-STAT pathway for COPD [Citation66]. It is indicated that CD247, CCR7, CD5, CD3E, CD7, IL7R and IL2RB were involved in the development of COPD by participating above signaling pathways.
Conclusions
Some core differentially-methylated/expressed genes (CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7, IL7R, TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3 and LYZ) and involved signaling pathways were identified in COPD. Significantly, the above genes had a potential diagnostic value for COPD. Our study may increase the understanding of the DNA methylation molecular mechanism of COPD. However, there are some limitations to our study. Firstly, the sample and validated gene number in the RT-PCR are small. Larger numbers of samples and more genes are further needed. Secondly, the expression of differentially methylated genes should be validated by bisulfite PCR. Thirdly, the potential molecular mechanism study lacked, and in vitro experiments are further needed.
Author contributions
Conception and design: Zongling Wang.
Administrative support: Zongling Wang.
Supply of materials and samples: Shuyuan An, Lina Dai and Shuo Xu.
Data collection and collation: Dan Liu and Lizhi Wang.
Data analysis and interpretation: Ruixue Zhang and Fengliang Wang.
Article writing: Wen Pan and Zongling Wang.
All authors approve and revise the manuscript.
Consent for publication
Not applicable.
Ethics approval and consent to participate
This study was performed in line with the principles of the Declaration of Helsinki and approved by the Medical Ethics Committee of Qingdao Fuwai Cardiovascular Hospital.
Patient consent for publication
Not applicable.
Supplemental Material
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Availability of data and materials
Data are available in the article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The author(s) reported there is no funding associated with the work featured in this article.
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