Screening of underlying genetic biomarkers for ankylosing spondylitis
- Authors:
- Published online on: April 24, 2019 https://doi.org/10.3892/mmr.2019.10188
- Pages: 5263-5274
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Copyright: © Fan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Ankylosing spondylitis (AS) is a common inflammatory rheumatic disease, with an estimated prevalence (per 10,000) of 23.8 in Europe, 16.7 in Asia, 31.9 in North America, 10.2 in Latin America and 7.4 in Africa (1). AS mainly affects the spine and sacroiliac joints in the pelvis to cause low back pain, stiffness and functional disability, which seriously influence the quality of life of patients and impose a heavy economic burden on both family and society (2). Therefore, there is a need for the timely diagnosis and effective treatment of AS.
Although the pathogenesis remains not clearly defined, accumulating evidence has suggested that AS is highly heritable. Human leukocyte antigen (HLA)-B27, a class I surface antigen encoded by B locus in the major histocompatibility complex (MHC) on the short (p) arm of chromosome 6, is one of the convincing genetic factors associated with AS (3). HLA-B27 was reported to be present in 94.3% of patients with AS, but only 9.34% in organ donors (4). The expression of HLA-B27 was found to be significantly higher in patients with AS than that in healthy subjects (5). Meta-analyses indicated that HLA-B27 genetic polymorphism B2704 and B2702 may be risk factors, while B2703, B2706, B2707, B2727, B2729 and B2747 may be protective factors for AS (6,7). HLA-B27-positive patients had a significantly younger age at symptom onset, more uveitis, and a higher frequency of peripheral and hip joint involvement than HLA-B27-negative patients (7,8). Thus, HLA-B27 has been the most commonly used biomarker for the diagnosis of AS (9). However, twin and family studies suggest that HLA-B27 only can explain less than 30% of the overall risk for AS (10,11), meaning there are other genes related with the genetic disorder of AS. Recently, scholars have also aimed to investigate other inflammatory biomarkers for AS, including interleukin (IL)-8 (12), tumor necrosis factor (TNF)-α (13), C-reactive protein (hsCRP) (14) and C-C motif chemokine 11 (CCL11) (15), but studies that have focused on the genetic biomarkers are limited (16,17).
The aim of the present study was to integrate the microarray data of mRNA and the single nucleotide polymorphism (SNP) expression profile in whole blood of AS patients and healthy controls to screen for differentially expressed genes (DEGs), and those that also possess differential SNP loci, which has not been previously performed. These SNP-related DEGs may be crucial genetic biomarkers for AS.
Materials and methods
Microarray data
Three microarray datasets under accession nos. GSE73754 (18), GSE25101 (19) and GSE39428 (20,21) were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). GSE73754 (platform: GPL10558; Illumina HumanHT-12 V4.0 expression BeadChip) detected the gene expression profile in whole blood samples from 52 AS and 20 healthy controls; GSE25101 (platform: GPL6947; Illumina HumanHT-12 V3.0 expression BeadChip) compared the gene expression profile in whole blood samples between 16 AS and 20 healthy controls; and GSE39428 (GPL15779; Illumina custom human SNP VeraCode microarray) analyzed the SNPs in 384 genes of 51 AS and 163 healthy controls.
Data normalization
For the two expression data from the Illumina platform, the TXT. data were downloaded and preprocessed using the Linear Models for Microarray data (LIMMA) method (22) (version 3.34.0; http://www.bioconductor.org/packages/release/bioc/html/limma.html) in the Bioconductor R package (version 3.4.1; http://www.R-project.org/), including base-2 logarithmic (log2) transformation and quantile normalization. The SNP signal spectrum in the GSE39428 dataset was preprocessed using hidden Markov model (HMM)-based program PennCNV (23) (version 1.0.4; http://penncnv.openbioinformatics.org/en/latest/), including the following steps: i) the signal intensity of the A and B alleles in each SNP were extracted and quantile normalized using the quantile method; ii) the normalize_affy_geno_cluster.pl procedure in the PennCNV package was used to calculate the Log R ratio (LRR) and B allele frequency (BAF) in each SNP, resulting in the generation of baf. files; the kcolumn.pl procedure in the PennCNV package was utilized to split the baf. files to signal intensity of single sample; the copy number variation (CNV) was detected using the detect_cnv.pl procedure in the PennCNV package.
Differential analysis of mRNAs and SNPs
The DEGs between control and AS in the GSE73754 and GSE25101 datasets were identified using the LIMMA method (22) based on the t-test where statistical significance was set to |logFC(fold change)| >0.263 and Benjamini and Hochberg adjusted (24) false discovery rate (FDR) <0.05. Hierarchical clustering heatmap illustrating the expression intensity and direction of the common DEGs in two mRNA datasets was constructed using the pheatmap R package (version 1.0.8; http://cran.r-project.org/web/packages/pheatmap) based on Euclidean distance. The differential SNPs were screened by comparing the LRR between AS and controls by using the Student's t-test. The genotype and allele frequencies of SNPs in DEGs between AS and controls were also compared using the Chi-square test (or Fisher's exact test), with P-value <0.05 set as the threshold value.
PPI (protein-protein interaction) network construction
The interaction pairs of the common DEGs were retrieved from the STRING 10.0 (Search Tool for the Retrieval of Interacting Genes; http://string db.org/) database (25) and then the PPI network was visualized using the Cytoscape software (version 3.6.1; www.cytoscape.org/) (26). Four topological characteristics of the genes in the PPI network, including degree [the number of edges (interactions) of a node (protein)], betweenness centrality (BC, the number of shortest paths that run through a node), closeness centrality (CC, the average length of the shortest paths between one node and any other node in the network) and average path length (APL, the average of distances between all pairs of nodes), were calculated using the CytoNCA plugin in Cytoscape software (http://apps.cytoscape.org/apps/cytonca) (27), the overlapped genes of the top 35 in four parameters were suggested as crucial genes.
To identify functionally related and highly interconnected clusters from the PPI network, module analysis was carried out by using the Molecular Complex Detection (MCODE) plugin of Cytoscape software under the followed parameters: Degree cutoff =2, Node score cutoff =0.2 and K-core =2 (ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE) (28).
Function enrichment analysis
The underlying functions of common DEGs between two mRNA datasets, genes in the PPI and modules enrichment analyses were predicted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). P<0.05 was chosen as the threshold to determine the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms which were visualized using R language.
Results
Identification of DEGs
Based on the threshold (FDR <0.05 and |logFC| >0.263), a total of 1,056 and 1,073 DEGs were identified between AS and controls for GSE73754 and GSE25101 datasets, respectively. After comparison analysis, 105 upregulated and 129 downregulated DEGs were found to be shared in both two datasets. The hierarchical clustering heatmap suggested that these 234 common DEGs could well distinguish AS from control samples (Fig. 1).
Function enrichment analysis for the common DEGs
DAVID database was used to predict the underlying functions of the common DEGs. The results showed that 8 significant KEGG pathways (Fig. 2) were enriched, such as hsa05130:Pathogenic Escherichia coli infection (TLR4, toll like receptor 4) and hsa04145:Phagosome (TLR4) (Table I). In addition, 23 significant GO biological process (BP) terms including GO:0006418~tRNA aminoacylation for protein translation (EEF1E1, eukaryotic translation elongation factor 1 epsilon 1; YARS, tyrosyl-tRNA synthetase), GO:0051092~positive regulation of NF-κB transcription factor activity (TLR4), GO:0050776~regulation of immune response (KLRD1, killer cell lectin like receptor D1), and GO:0032715~negative regulation of interleukin-6 production (TLR4); 6 significant GO molecular function (MF) terms, consisting of GO:0005515~protein binding (SERPINA1, serpin family A member 1; TLR4); and 6 significant GO molecular function (MF), such as GO:0005515~protein binding (SERPINA1, EEF1E1); 26 GO cell component (CC) terms, including GO:0070062~extracellular exosome (SERPINA1, EEF1E1), GO:0005737~cytoplasm (EEF1E1) and GO:0005829~cytosol (EEF1E1); were enriched (Fig. 3 and Table I).
 | Table I.Function enrichment for the differentially expressed genes between patients with ankylosing spondylitis and controls. |
PPI network
After mapping the DEGs to the STRING database, 356 interaction pairs were obtained which were used for constructing the PPI network where 154 nodes (64 upregulated and 88 downregulated) were included (Fig. 4). By calculating degree, BC, CC and APL, and comparing genes ranked as the top 30, HDAC1 (histone deacetylase 1), YARS, EPRS (glutamyl-prolyl-tRNA synthetase), APEX1 (apurinic/apyrimidinic endodeoxyribonuclease 1), ACTG1 (actin γ 1), MDH2 (malate dehydrogenase 2), TNF (tumor necrosis factor), CCT3 (chaperonin containing TCP1 subunit 3), TLR4 (Toll-like receptor 4), TUBB (tubulin β class I), FCGR2A (Fc fragment of IgG receptor IIa), KLRD1 (killer cell lectin-like receptor D1) and FASN (fatty acid synthase) were found to be shared by these 4 topological characteristics, suggesting they were hub genes for AS (Tables II and III).
| Table III.Overlapping DEGs according to topological features (degree, closeness centrality, betweenness centrality and average path length). |
Subsequently, four functionally related and highly interconnected modules were screened (Fig. 5). The genes in module 1 were associated with aminoacyl-tRNA biosynthesis (YARS) (Fig. 5A); the genes in module 2 were related with natural killer cell mediated cytotoxicity (KLRD1) and immune response (KLRD1) (Fig. 5B); the genes in module 3 were relevant with metabolic pathways (EPRS) (Fig. 5C); and the genes in module 4 were enriched in GO terms of platelet degranulation (SERPINA1) (Fig. 5D) (Table IV).
Integration of SNP microarray and expression profile data
The LRR of each SNP for 384 genes in AS and control samples was computed. The LRR in most samples were lower than 1, indicating the presence of copy number deletions. Subsequently, the statistical difference in LRR of each SNP between AS and control samples were determined by Student's t-test, with 122 differential SNP identified. After overlapping the genes having differential SNP with the DEGs, two common genes (EEF1E1 and SERPINA1) were obtained. SERPINA1 was upregulated in AS (Fig. 6A) and the average expression LRR of the rs6575424 polymorphism in AS samples was significantly higher than that in the controls (0.05 vs. −0.14, P=6.57E-07) (Fig. 6B); EEF1E1 was also upregulated in AS (Fig. 6A) and the average expression LRRs of rs7763907 (−4.88 vs. −5.91, P=0.048), rs9328453 (0.07 vs. −0.12, P=3.69E-05) (Fig. 6B), rs7751386 (−0.85 vs. −1.49, P=2.52E-04), and rs12660697 (0.08 vs. −0.02, P=0.02) polymorphisms in AS samples were significantly higher than that in controls.
Furthermore, the genotype and allele frequencies of SNPs in EEF1E1 and SERPINA1 between AS and controls were compared using the Chi-square (or Fisher's exact) test. The results showed there were significant differences in the genotype and allele frequencies of rs7763907 between AS and control samples. The genotype frequency of rs7751386 between AS and control samples was also significantly differential. These findings suggest that these two polymorphic sites of the EEF1E1 gene may be associated with the susceptibility to acquire AS (Table V).
Discussion
In the present study, two crucial genes (EEF1E1 and SERPINA1) were identified for the diagnosis of ankylosing spondylitis (AS) by analyzing two mRNA expression profile datasets and one single nucleotide polymorphism (SNP) dataset. Their expression levels were significantly upregulated and the average expression LRRs of SNP sites in these genes were significantly higher in AS patients that those in the controls. EEF1E1 was involved in AS by influencing aminoacyl-tRNA biosynthesis, while SERPINA1 may be associated with AS by participating in platelet degranulation.
EEF1E1, also known as aminoacyl-tRNA synthetase-interacting multifunctional protein 3 (AIMP3/p18), was initially found to encode an auxiliary component of the macromolecular aminoacyl-tRNA synthase complex that catalyzes the ligation of a specific amino acid to its compatible cognate tRNA to form an aminoacyl-tRNA to initiate protein translation (29,30). Thus, EEF1E1 may be upregulated to promote the development of various types of cancer (31). However, recent studies indicate that EEF1E1 may also function as a tumor-suppressor (32,33) by upregulating the growth factor- or Ras-dependent induction of p53 (34,35). Cells with loss of EEF1E1 were found to exhibit impaired p53 transactivity and genomic instability and thus were found to became susceptible to cell malignant transformation (34,36), while overexpression of EEF1E1 induced cellular senescence phenotypes (37). It was also demonstrated that the p53 level was significantly higher in the peripheral blood supernatant of a rheumatoid arthritis (RA) group than the level in control groups and there was a positive correlation between p53 levels and the disease activity score in the RA group (38). In addition, in RA synovial tissues, 80% of p53-positive cells were found to be TUNEL-positive (39). These results indicate that upregulation of the p53 gene may result in chronic inflammation and apoptosis in RA patients. In addition, other members of the AIMP families, such as AIMP1, were also found to promote the expression of pro-inflammatory genes in monocytes/macrophages and dendritic cells (40) and induce cytokine (i.e. TNF-α)-dependent apoptosis (41). The antibody atliximab was reported to neutralize the expression of AIMP1 and then block the AIMP1-mediated production of inflammatory cytokines, ultimately attenuating collagen-induced arthritis (42). Accordingly, we speculate that EEF1E1 may also be involved in inflammation of AS by upregulating p53 and pro-inflammatory cytokines. In line with this hypothesis, our results showed that EEF1E1 was upregulated in the whole blood of AS patients compared with the control. Upregulation of EEF1E1 may be attributed to genetic mutations (rs7763907 and rs7751386) since the LRR of AS was significantly higher than that of controls and the genotype and allele frequencies were significantly different. However, further experimental validation is needed as studies investigating the SNPs of EEF1E1 are limited apart from the study of Liu et al that showed the number of risk alleles of rs12199241 in AIMP3 to be significantly associated with high DNA damage level (43).
SERPINA1 is a gene that encodes alpha-1-antitrypsin (AAT). It was found that the AAT concentration was higher in AS patients under active phase than the patients with remission/partial remission (44). In addition, the carboxyl terminal fragment of AAT was demonstrated to significantly induce the production of pro-inflammatory molecules (gelatinase B, monocyte chemoattractant protein-1 and IL-6) in human monocytes by interactions with the CD36 scavenger receptor and low density lipoprotein (LDL) receptor (45). These findings suggest that SERPINA1 may be a potential biomarker for the diagnosis of AS and evaluation of the efficacy of treatment by influencing inflammation. In line with these studies, we also found that SERPINA1 was upregulated in AS patients and it participated in GO terms of platelet degranulation. Platelet-specific degranulation gene Munc13-4 knockout mice were shown to display a reduction in airway hyper-responsiveness and eosinophilic inflammation, indirectly confirming the pro-inflammatory roles of SERPINA1 in AS (46). Importantly, a study was conducted to use TaqMan method to genotype tag SNPs (rs2753934, rs2749531 and rs6575424) in SERPINA1 of 56 AS cases and 160 healthy controls. The results revealed an increased expression of AAT in synovial membranes of AS compared with control samples, but no significant association was observed between the AAT polymorphism and AS (47). This also seems to be in accordance with our results and indicates that SERPINA1 may not be a genetically related biomarker for AS.
However, there were some limitations to the present study. First, this study was only performed to preliminarily screen the potential genetic biomarkers for AS. Further experiments are necessary, including clinical confirmation of the association between the polymorphism of EEF1E1 and SERPINA1 and the risk of AS and patient prognosis; clinical validation of the expression of EEF1E1 and SERPINA1; clinical (correlation analysis), in vitro (site-directed mutagenesis to construct the expression vector with different alleles, transfection of monocytes or osteoblasts followed by detection of cell proliferation, inflammatory factor release or mineralization) and in vivo (mutation knockout in animal models followed by assessment of histology and bone joint) verification of the association between gene polymorphisms and their expressions as well as corresponding phenotypic changes. Second, the SNP microarray used in this study only analyzed the SNPs in specific 384 genes, but not all the genes. Additional SNP discovery by deep sequencing with a larger sample size is essential to obtain more genetic biomarkers.
In conclusion, our findings preliminarily suggest that EEF1E1 may be an underlying novel, important genetic biomarker for the diagnosis of AS. Its rs7763907 and rs7751386 polymorphisms may lead to its upregulated expression and then promote the transcription of p53 and pro-inflammatory cytokines, leading to the development of AS.
Acknowledgements
Not applicable.
Funding
No funding was received.
Availability of data and materials
The microarray data GSE73754 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73754), GSE25101 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25101) and GSE39428 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39428) were downloaded from the GEO database in NCBI.
Authors' contributions
XF was involved in the conception and design, analysis and interpretation of data and drafted the initial manuscript. BQ collected the data. LM and FM contributed to the interpretation of the data. BQ, LM and FM revised the manuscript critically for important intellectual content. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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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