Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus
- Authors:
- Published online on: November 19, 2015 https://doi.org/10.3892/ijmm.2015.2416
- Pages: 139-148
Abstract
Introduction
Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that affects different organs and systems and has a complex genetic inheritance (1). SLE has a complex etiology and is affected by both genetic and environmental factors (2). The major histocompatibility complex (MHC) located on chromosome 6p21 is one of the key factors that contribute to the development of SLE (3). The human leukocyte antigen (HLA) has been shown to be associated with susceptibility to SLE. Genome-wide association studies have demonstrated that variants within the MHC region confer the greatest genetic risk of developing SLE in European and Chinese populations. However, the causal variants remain elusive due to the tight linkage disequilibrium across disease-associated MHC haplo-types, the highly polymorphic nature of several MHC genes, and the heterogeneity of SLE phenotypes. The loci include HLA-DPB1, HLA-G and MSH5, which are independent of each other and HLA-DRB1 alleles. These data highlight the usefulness of mapping disease susceptibility loci using a transancestral approach, particularly in a region as complex as the MHC, and offer a springboard for further fine-mapping, resequencing and transcriptomic analysis (4). Certain studies have suggested an association between MHC class I and II (HLA-A*29, HLA-B*51, HLA-DRB1*15 and HLA-DQB1*06) and susceptibility to SLE in the Saudi population (5). In African-American women, a single-nucleotide polymorphism (SNP) which is closely associated with SLE, rs9271366, was found near the HLA-DRB1 gene (6).
Although genetic variations within the MHC are associated with the development of SLE, its role in the development of the clinical manifestations and autoantibody production has not been well defined. A meta-analysis of 4 independent European SLE case collections was previously performed in an effort to identify associations between SLE sub-phenotypes and MHC SNP genotypes, HLA alleles, and variant HLA amino acids. The results provided strong evidence for a multilevel risk model for HLA-DRB1*03:01 in SLE, wherein the association with anti-Ro and anti-La antibody-positive SLE is much stronger than with SLE without these autoantibodies (7). Despite the research which has been performed to date, a complete picture of these correlations has not yet been painted, and further studies are still needed to shed light on the associations between SLE and its susceptibility genes. In addition, novel methods should be used to investigate this subject.
Using a predictive bioinformatics algorithm, in a previous study, Mantila Roosa et al created a linear model of gene expression and identified 44 transcription factor-binding motifs and 29 miRNA-binding sites that were predicted to regulate gene expression across a time course. In addition to known sites, novel transcription factor-binding motifs and several novel miRNA-binding sites were identified throughout the time course. These time-dependent regulatory mechanisms may be important for controlling the loading-induced bone formation process (8). This integrated bioinformatics analysis method was also used in this study. Although the link between MHC and SLE has been proven, further investigations are likely to reveal the involvement of MHC in simple and complex genetic diseases, such as SLE. Indeed, we are interested in studying MHC, CpG methylation and transcribed ultra-conserved region (T-UCR) as a first step toward a better understanding of the regulation of gene expression in SLE. In the present study, we provide an extensive view of SLE based on an integrated bioinformatics analysis of MHC, CpG methylation and T-UCR datasets.
Materials and methods
Patients and controls
Whole blood samples from 15 patients with SLE (8 females; 7 males; aged 18–50 years, with an average age of 35.64±11.27 years) and 15 normal healthy controls (8 females; 7 males; aged 20–45 years, with an average age of 33.47±9.61 years) were collected from the 181st Hospital of Guilin, China, between January and September 2011. The SLE diagnoses were confirmed based on pathological and clinical evidence according to the American Rheumatism Association classification criteria (9,10). Written informed consent was obtained from all the subjects or their guardians. The use of biopsy material for studies beyond routine diagnosis was approved by the Ethics Committee of the 181st Hospital of Guilin. This study abides by the Helsinki Declaration on Ethical Principles for Medical Research Involving Human Subjects.
MHC gene capture, hMeDIP-chip and T-UCR microarray analysis
Genomic DNA was isolated from peripheral blood samples. According to the MHC genomic sequence, a completely complementary probe was designed and fixed on a support and then applied to the genomic DNA after coupling with a probe connector. The unhybridized probe was washed away, and the probe that had hybridized with the DNA was eluted to directly build a library for DNA sequencing (HiSeq 2000 high-throughput sequencing). The MHC region capture technology was based on the NimbleGen SeqCap EZ Choice Library, enabling the deep sequencing coverage of the human MHC region. The data were analyzed using the Chi-squared test with Yates' correction for continuity.
Genomic DNA was extracted using a DNeasy Blood and Tissue kit (Qiagen, Fremont, CA, USA). The sonicated genomic DNA (1 µg) was used for immunoprecipitation with a mouse monoclonal antibody. For DNA labeling, a NimbleGen Dual-Color DNA Labeling kit was used according to the manufacturer's instructions detailed in the NimbleGen hMeDIP-chip protocol (NimbleGen Systems, Inc., Madison, WI, USA). The microarrays were hybridized in Nimblegen hybridization buffer/hybridization component A in a hybridization chamber (Hybridization System-Nimblegen Systems, Inc., Madison, WI, USA). For array hybridization, the NimbleGen Promoter plus CpG Island array (Roche, Basel, Switzerland) was used.
The Arraystar Human T-UCR Microarray profiles the expression of 1,518 long non-coding RNAs (lncRNAs) and 2,261 mRNAs with transcription units (TU) that overlap UCRs in either the sense or antisense orientation. Sample RNA labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology, Santa Clara, CA, USA), with minor modifications. The hybridized arrays were washed, fixed and scanned, using Agilent DNA Microarray Scanner (part no. G2505C). Agilent Feature Extraction software (version 11.0.1.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v12.1 software package (Agilent Technologies).
Bioinformatics analysis
CpG methylation enrichment in the MHC segment and analysis for differential enrichment
The MHC gene capture sequencing segment was chr6:28477797-33448354. To search the enrichment location, we analyzed the CpG peaks in the MHC segment.
T-UCR expression in the MHC segment
To search for the location of transcripts, we analyzed T-UCR expression in the MHC segment.
Effect of the CpG methylation level and T-UCR expression level in immunological processes
We analyzed all methylated CpGs, T-UCR, and their corresponding genes, and then analyzed the related genes with regard to immunological processes. To further examine the functions of these genes, we used the Online Gene Ontology Tool EASE (http://david.abcc.ncifcrf.gov/ease/ease1.htm). The differentially expressed genes were classified with regard to biological processes. Gene Ontology (GO) and KEGG pathway mapping of the genes was performed using the web-accessible DAVID annotation system.
Correlation of MHC mutation with CpG methylation
To identify correlations, we calculated the data of differential CpG methylation and MHC mutation and analyzed the correlation coefficients.
Results
Capturing the number of genes and SNP loci in the MHC region
We obtained 150 genes and 27,066 SNPs by MHC gene capture and high-throughput sequencing in the patients with SLE compared with the normal controls (data not shown).
hMeDIP-chip
The 3,826 genes with CpG islands had significantly different methylation levels in the patients with SLE compared with the normal controls, as was also previously noted (11).
T-UCR microarray analysis
To identify potential differentially expressed T-UCRs, we performed fold change filtering of the SLE patients compared with the normal controls. We found a signature of 8 upregulated T-UCRs and 29 downregulated T-UCRs (data not shown).
CpG peak in the MHC segment
To search for enrichment locations, we analyzed CpG peaks in the MHC segment. The results indicated the enrichment of 12 CpG-methylated sites (Table I), with 6 in the patients with SLE and 6 in the normal controls. One CpG-methylated enrichment site was found to be located in the HLA-B promoter region (chr6:31323946-31325211, 1,265 bp) (Table II) in the patients with SLE; another site was located in the HLA-DPB2 intragenic region (chr6:32975684-32975926, 242 bp) (Table II) in the control group.
T-UCR expression in the MHC segment
In the present study, we analyzed the expression of all T-UCRs; however, UCR-overlapping and UCR-proximal genes were not discovered in the MHC segment.
Effect of the CpG methylation level on immunological processes
We annotated corresponding CpG-methylated genes in the patients with SLE with GO schemes using the DAVID gene annotation tool. The genes produced a total of 97 GO terms in the patients with SLE (Table III). However, no significant enrichment was found for immune-correlated process GO terms, such as GO:0006955 - immune response (48 genes: TNFAIP8 L2, ITGAL, GALNT2, YWHAZ, LST1, TOLLIP, IFITM2, IFITM3, SUSD2, TLR2, NLRX1, VTN, TLR5, CX3CL1, PYDC1, FTH1, IGF1R, TUBB, IL2RG, CFD, SPON2, APLN, SPN, DNAJA3, POLL, TRPM4, DBNL, IL18R1, SMAD6, EOMES, CNPY3, STXBP2, POLR3A, HLA-B, VAV1, WAS, CD1D, LAT, CYBA, PRELID1, GPI, SARM1, ULBP1, TGFBR3, ADAM17, TCF12, ICOSLG, TNFAIP1, P=0.999999, FDR >0.05).
In addition, we obtained 3 KEGG pathways for genes in patients with SLE (Table IV), although no significant enrichment was found for immune-correlated process KEGG pathways, such as hsa04660:T cell receptor signaling pathway (Fig. 1; 15 genes: PIK3CG, HRAS, VAV1, LOC407835, MAPK1, LAT, MAPK12, NCK1, PAK4, JUN, NFAT5, PPP3CB, CHP, PIK3R3, NFATC1, P=0.406339, FDR >0.05).
Effect of T-UCR expression levels on immunological processes
We also annotated T-UCR-corresponding genes with GO schemes using the DAVID gene annotation tool. The genes produced total 43 GO terms in patients with SLE (Table V); however, no significant enrichment was found for immune-correlated process GO terms. In addition, we did not obtain immune-correlated process KEGG pathways for the genes in patients with SLE, i.e., there was no significant enrichment.
Correlation between MHC mutation and CpG methylation
In this study, we found 4 SNPs in the CpG promoter (chr6:31323946-31325211) of HLA-B and 2 SNPs in chr6:29521110-29521833 in the control patients (Table VI).
Discussion
The first genetic factors to be identified as important in the pathogenesis of SLE were those of the MHC on chromosome 6. It is now widely accepted that MHC genes constitute a part of the genetic susceptibility to SLE (12). However, previous studies on SLE have lacked statistical power and the genetic resolution to fully define the influences of the MHC (13,14). In this study, we attempted to identify MHC, CpG methylation and T-UCR to reveal the potential mechanisms responsible for the development of SLE using a novel and combinatorial approach involving MHC gene capture technology, hMeDIP-chip, T-UCR microarray and bioinformatics analysis. A total of 27,066 SNPs were detected and thus these may be involved in SLE. Moreover, we integrated the datasets and identified 6 of the most important SNPs in SLE. Our next step is to perform research on the function of these SNPs.
HLA antigens and genes have long been reported to be associated with SLE susceptibility in a number of populations (15). With advances in technologies, such as genome-wide association studies, a number of newly discovered SLE-associated SNPs have been reported in recent years. These include HLA-DRB1/HLA-DQA1 rs9271366 and HLA-DQB1/HLA-DQA2 rs9275328 (15). Previously, a meta-analysis of the MHC region in patients with SLE was performed to determine associations with both SNPs and classical HLA alleles. The results of a conditional analysis and model choice with the use of the Bayesian information criterion indicated that the best model for SLE association includes both classical loci (HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQA1*01:02) and 2 SNPs, rs8192591 (in class III and upstream of NOTCH4) and rs2246618 (MICB in class I) (16). Single-marker analyses have revealed strong signals for SNPs within several MHC regions, as well as for HLA-DRB1. The most strongly associated DRB1 alleles are *0301, *1401 and *1501, and the MHC region SNP demonstrating the strongest evidence of an association with SLE is rs3117103 (3). These results delineate with high resolution several MHC regions which contribute independently to the risk of developing SLE. In the present study, we integrated the MHC and CpG-methylated datasets, and the results indicated CpG methylation enrichment at 6 sites: RNF39, HLA-B, DDAH2, LY6G6C, MSH5 and BRD2 in the MHC regions of SLE. These genes play important roles in various immune diseases, including SLE. SNPs in the region of the RNF39 gene have been found to be associated with the disease course of HIV-1 (17,18). Behcet's disease is a chronic inflammatory autoimmune disease that is strongly associated with HLA-B51 and -A26. It has previously been suggested that RNF39 is involved in the etiology of Behcet's disease (19). The MHC region is suspected to host susceptibility loci for HIV-related Kaposi's sarcoma, involving the rs1065356 (LY6G6C) and rs3749953 (MSH5-SAPCD1) (20). MSH5 has been found to be mutated in patients with common variable immunodeficiency (21). In a previous study, a significant increase in the frequency of HLA-A*01, A*03, A*11, A*23, A*26 A*69, HLA-B*27, B*40, B*49, B*51, B*52, B*53, B*54, B*95, HLA-DRBI*01, DRBI*03, DRBI*11 and DRBI*14 was observed in SLE patients, indicating a positive association of these alleles with SLE. By contrast, HLA-A*24, A*29, A*31, A*34, A*68, A*92, HLA-B*18 and HLA-DRB1*12 were found to be decreased in the patient group compared to the controls, indicating a negative association of these alleles with SLE. Thus, it was concluded that SLE is associated with certain MHC alleles, such as HLA-B, in the Pakistani population (12). As the results of the present study indicated the enrichment of one CpG methylation site located in the HLA-B promoter region, HLA-B may indeed play an important role in the pathogenesis of SLE. Moreover, we found 4 SNPs (rs1050683, rs12697943, rs17881210 and rs1065378) in the CpG region of the HLA-B promoter and 2 SNPs (rs17184255 and rs16895070) in MHC regions. These SNPs were significantly associated with an increased risk of developing SLE.
SLE is an autoimmune disease with known genetic, epigenetic, and environmental risk factors. Epigenetic events play a central role in the priming, differentiation and subset determination of T lymphocytes. CpG-DNA methylation and post-translational modifications to histone tails are the two most well-accepted epigenetic mechanisms. Furthermore, the involvement of epigenetic mechanisms in the pathogenesis of SLE has been suggested by the development of lupus-like symptoms in individuals who are treated with procainamide or hydralazine, resulting in a reduction in CpG-DNA methylation (22). In SLE, global CpG-DNA hypomethylation correlates with disease activity. A number of cytokine genes are overexpressed in CD4+ T lymphocytes from patients with SLE in a chromatin-dependent manner, including IL-6 (23). Region-specific histone acetylation in certain tissues is associated with increased disease activity, whereas histone acetylation in other regions has protective effects. In SLE, acetylation of the TNF promoter in monocytes is associated with increased monocyte maturation and cytokine expression (24). Thus, a better understanding of the molecular events that contribute to epigenetic alterations and subsequent immune imbalance is essential for the establishment of disease biomarkers and the identification of potential therapeutic targets (22). To assess the role of DNA methylation in SLE, researchers collected CD4+ T-cells, CD19+ B-cells, and CD14+ monocytes, and performed a genome-wide DNA methylation analysis with the use of IlluminaMethylation 450 microarrays. Interferon hypersensitivity was apparent in memory, naïve and regulatory T-cells, suggesting that this epigenetic state in lupus patients is established in progenitor cell populations. These cell type-specific effects are consistent with the disease-specific changes in the composition of the CD4+ population and suggest that shifts in the proportion of CD4+ subtypes can be monitored at CpGs with subtype-specific DNA methylation patterns (25). In the present study, we annotated the corresponding CpG-methylated genes using the DAVID gene annotation tool. However, immune-correlated process GO terms, such as GO:0006955-immune response, and KEGG pathways, such as hsa04660-T-cell receptor (TCR) signaling pathway exhibited no significant enrichment. The GO analysis did reveal that the 'theme' immune response (GO:0006955), which is known to be affected by anti-TNF treatment in the inflammatory tissue of rheumatoid arthritis patients, was significantly over-represented (26). Regardless, it is relevant to note in our context that our GO analysis identified immune functions as potentially relevant mechanisms. The activation of T lymphocytes is a key event for an efficient response of the immune system (hsa04660-TCR signaling pathway) and requires the involvement of the TCR as well as costimulatory molecules, such as CD28. The engagement of these receptors through interaction with a foreign antigen is associated with MHC molecules (27), and our findings may thus facilitate the selection of better target molecules for further studies. The findings of the present study may also aid future research by providing details of new pathways to be studied using a more focused approach, confirmation at the protein level and emphasis of the clinical significance.
lncRNAs are transcripts longer than ~200 nucleotides with little or no protein-coding capacity (28). Research has shown that lncRNAs play important roles in disease development and are associated with a number of human diseases, such as cancer, Alzheimer's disease and heart disease (29). T-UCR transcripts are a novel class of lncRNAs transcribed from UCRs, a class of 481 non-coding sequences located in both intra- and intergenic regions of the genome. UCRs are absolutely conserved (100%) between the orthologous regions of the human, rat, and mouse genomes and are actively transcribed (30,31). It has recently been proven in cancer systems that differentially expressed T-UCRs alter the functional characteristics of malignant cells. Indeed, recent data suggest that T-UCRs are altered at the transcriptional level in human tumorigenesis and that the aberrant T-UCR expression profiles can be used to differentiate human cancer types (31,32). Researchers observed that DNA hypomethylation induces T-UCR silencing in cancer cells, and the analysis of a large set of primary human tumors demonstrated that the hypermethylation of the described T-UCR CpG islands is a common event in the various tumor types (33). In the present study, we integrated the MHC and T-UCR datasets and examined the expression levels of T-UCR in the MHC segment by T-UCR microarray. We annotated the T-UCR corresponding genes using the DAVID gene annotation tool. However, no significant enrichment was found for immune-correlated process GO terms and KEGG pathways. Thus, T-UCR expression levels did not correlate with the commonly used clinicopathological features of the patients with SLE.
Taken together, in the present study, we identified 6 of the most important SNPs (rs1050683, rs12697943, rs17881210, rs1065378, rs17184255 and rs16895070) in patients with SLE. The present study indicates that SNPs in the MHC segment are potential biomarkers and are likely factors which are involved in the pathogenesis of SLE. However, further studies are required to investigate the mechanisms through which polymorphisms in this region lead to the development of SLE. A major advantage of combining multiple levels of measurement is the ability to dissect mechanisms not apparent in a single dimension. The integration of MHC, CpG methylation, and T-UCR datasets is a powerful strategy for understanding SLE biology. Our findings provide insight into the potential contribution of anomalously regulated SNPs to the abnormalities in SLE and may aid in the structuring of antenatal diagnostic biomarkers of SLE, as well as in obtaining novel therapeutic targets which can be used in the treatment of patients with SLE. Moreover, our study of SNPs may aid in the development of novel methods which may prove to be useful for treating and preventing other diseases.
Acknowledgments
The authors of this study would like to thank the patients with SLE and the healthy volunteers who participated in this study. Bioinformatics analysis was performed by Shanghai Biotree Biotech Co., Ltd., Shanghai, China. The present study was supported financially by the Key Project of Guangxi Natural Science Foundation (no. 2012GXNSFDA053017), the Construction Project Planning Assignment of Guangxi Key Laboratory (no. 13-051-31) and the Scientific Problem Tackling of Guilin Science and Technology Program (no. 20130120-20), China.
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