Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus

  • Authors:
    • Hua Lin
    • Weiguo Sui
    • Qiupei Tan
    • Jiejing Chen
    • Yue Zhang
    • Minglin Ou
    • Wen Xue
    • Fengyan Li
    • Cuihui Cao
    • Yufeng Sun
    • Yong Dai
  • View Affiliations

  • Published online on: November 19, 2015     https://doi.org/10.3892/ijmm.2015.2416
  • Pages: 139-148
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Abstract

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease which affects different organs and systems that, has a complex genetic inheritance, and is affected by both epigenetic and environmental risk factors. Previous studies on SLE have lacked the statistical power and genetic resolution to fully determine the influence of major histocompatibility complex (MHC) on SLE. In this study, in order to determine this influence, a total of 15 patients with SLE and 15 healthy controls were enrolled. MHC region capture technology, hMeDIP-chip, transcribed ultra-conserved region (T-UCR) microarray and bioinformatics analysis were utilized for both groups. The results revealed methylated CpG enrichment at 6 loci in the MHC segment of SLE. We found 4 single-nucleotide polymorphisms (SNPs) in the CpG promoter of human leukocyte antigen-B (HLA-B) and 2 SNPs in chr6:29521110‑29521833. No significant GO term or KEGG pathway enrichment was noted for an immune-correlated process in the SLE patients for the corresponding CpG-methylated genes. In this study, T-UCR was not discovered in the MHC segment. The analysis of SNPs (rs1050683, rs12697943, rs17881210, rs1065378, rs17184255 and rs16895070) and gene expression in peripheral blood lymphocytes indicated that these SNPs were associated with the occurrence of SLE. Further studies are warranted to examine the roles of these SNPs in the pathogenesis of SLE. Integrative analysis technology provided a view of the molecular signaling pathways in SLE.

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.

Table I

Twelve CpG-methylated enrichment sites in the MHC segment.

Table I

Twelve CpG-methylated enrichment sites in the MHC segment.

CpG name (hg19)Length (bp)ControlSLEGene nameLocation
chr6:30042918-300435005821RNF39Promoter
chr6:31323946-313252111,2651HLA-BPromoter
chr6:31695894-316982452,3511DDAH2Promoter
chr6:31695894-316982452,3511LY6G6CPromoter
chr6:31695894-316982452,3511MSH5Promoter
chr6:32935896-329367928961BRD2Promoter
chr6:29521110-295218337231UBDIntergenic
chr6:30538983-305394875041ABCF1Promoter
chr6:30684836-306855036671MDC1Promoter
chr6:30684836-306855036671TUBBPromoter
chr6:31548436-315492778411LST1Promoter
chr6:32975684-329759262421 HLA-DPB2Intragenic

[i] MHC, major histocompatibility complex; Control, healthy control subjects; SLE, systemic lupus erythematosus patients.

Table II

HLA-B promoter region and HLA-DPB2 intragenic region in patients with SLE.

Table II

HLA-B promoter region and HLA-DPB2 intragenic region in patients with SLE.

HLA-B promoter region (chr6:31323946-31325211, 1265 bp)
CGAAGTCCCAGGTCCCGGACGGGGCTCTCAGGGTCTCAGGCTCCGAGGGCCGCGTCTGCAATGGGGAGGCGCAG
CGTTGGGGATTCCCCACTCCCCTGAGTTTCACTTCTTCTCCCAACTTGTGTCGGGTCCTTCTTCCAGGATACTCGTG
ACGCGTCCCCACTTCCCACTCCCATTGGGTATTGGATATCTAGAGAAGCCAATCAGCGTCGCCGCGGTCCCAGTTC
TAAAGTCCCCACGCACCCACCCGGACTCAGAGTCTCCTCAGACGCCGAGATGCTGGTCATGGCGCCCCGAACCGT
CCTCCTGCTGCTCTCGGCGGCCCTGGCCCTGACCGAGACCTGGGCCGGTGAGTGCGGGTCGGGAGGGAAATGGC
CTCTGCCGGGAGGAGCGAGGGGACCGCAGGCGGGGGCGCAGGACCTGAGGAGCCGCGCCGGGAGGAGGGTCGG
GCGGGTCTCAGCCCCTCCTCACCCCCAGGCTCCCACTCCATGAGGTATTTCTACACCTCCGTGTCCCGGCCCGGCC
GCGGGGAGCCCCGCTTCATCTCAGTGGGCTACGTGGACGACACCCAGTTCGTGAGGTTCGACAGCGACGCCGCG
AGTCCGAGAGAGGAGCCGCGGGCGCCGTGGATAGAGCAGGAGGGGCCGGAGTATTGGGACCGGAACACACAGA
TCTACAAGGCCCAGGCACAGACTGACCGAGAGAGCCTGCGGAACCTGCGCGGCTACTACAACCAGAGCGAGGCC
GGTGAGTGACCCCGGCCCGGGGCGCAGGTCACGACTCCCCATCCCCCACGTACGGCCCGGGTCGCCCCGAGTCTC
CGGGTCCGAGATCCGCCTCCCTGAGGCCGCGGGACCCGCCCAGACCCTCGACCGGCGAGAGCCCCAGGCGCGTT
TACCCGGTTTCATTTTCAGTTGAGGCCAAAATCCCCGCGGGTTGGTCGGGGCGGGGCGGGGCTCGGGGGACTGGG
CTGACCGCGGGGCCGGGGCCAGGGTCTCACACCCTCCAGAGCATGTACGGCTGCGACGTGGGGCCGGACGGGCG
CCTCCTCCGCGGGCATGACCAGTACGCCTACGACGGCAAGGATTACATCGCCCTGAACGAGGACCTGCGCTCCTG
GACCGCCGCGGACACGGCGGCTCAGATCACCCAGCGCAAGTGGGAGGCGGCCCGTGAGGCGGAGCAGCGGAGA
GCCTACCTGGAGGGCGAGTGCGTGGAGTGGCTCCGCAGATACCTGGAGAACGGGAAGGACAAGCTGGAGCGCGC
HLA-DPB2 intragenic region (chr6:32975684-32975926, 242 bp)
CGAGGCCGTGTGGCGTCTGCCTGAGTTTGGTGACTTTGCCCGCTTTGACCCGCAGGGCGGGCTGGCCGGCATCGC
CGCAATCAAAGCCCATCTGGACATCCTGGTGGAGCGCTCCAACCGCAGCAGAGCCATCAACGGTACCGGCCCTCC
CTCTGCCCACCCAGTCAGGCGGGAAGGTCCAGAGAAACTTCCTCCCAGTTCCTAGGCTCCCATCACTCTGGGGCG
CGCTCTCAGCGCCCGCGC

[i] SLE, systemic lupus erythematosus; HLA, human leukocyte antigen.

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).

Table III

GO term annotations of corresponding CpG-methylated genes in SLE patients.

Table III

GO term annotations of corresponding CpG-methylated genes in SLE patients.

GO termGene countP-valueFDR
GO:0030182 - neuron differentiation1131.39E-132.58E-10
GO:0051252 - regulation of RNA metabolic process3192.37E-104.41E-07
GO:0006355 - regulation of transcription, DNA-dependent3132.50E-104.66E-07
GO:0045449 - regulation of transcription4298.22E-101.53E-06
GO:0000904 - cell morphogenesis involved in differentiation663.46E-096.45E-06
GO:0048666 - neuron development821.08E-082.01E-05
GO:0007409 - axonogenesis551.18E-082.19E-05
GO:0006350 - transcription3492.66E-084.96E-05
GO:0048667 - cell morphogenesis involved in neuron differentiation573.36E-086.26E-05
GO:0000902 - cell morphogenesis835.01E-089.34E-05
GO:0007389 - pattern specification process661.46E-072.73E-04
GO:0048812 - neuron projection morphogenesis561.71E-073.18E-04
GO:0031175 - neuron projection development633.43E-076.39E-04
GO:0048858 - cell projection morphogenesis613.52E-076.56E-04
GO:0032989 - cellular component morphogenesis874.22E-077.86E-04
GO:0032990 - cell part morphogenesis627.71E-070.001437153
GO:0006357 - regulation of transcription from RNA polymerase II promoter1381.20E-060.002241345
GO:0030030 - cell projection organization801.79E-060.003344582
GO:0003002 - regionalization502.55E-060.004748657
GO:0021953 - central nervous system neuron differentiation182.92E-060.005449045
GO:0030900 - forebrain development414.82E-060.008984544
GO:0048663 - neuron fate commitment186.52E-060.012150091
GO:0048598 - embryonic morphogenesis671.19E-050.022252245
GO:0021954 - central nervous system neuron development151.47E-050.02743134
GO:0045944 - positive regulation of transcription from RNA polymerase II promoter771.68E-050.031223255
GO:0007411 - axon guidance311.93E-050.035915973
GO:0045165 - cell fate commitment372.06E-050.038453597
GO:0021872 - generation of neurons in the forebrain102.17E-050.040470383
GO:0045893 - positive regulation of transcription, DNA-dependent932.77E-050.051580919
GO:0051254 - positive regulation of RNA metabolic process933.85E-050.071644433
GO:0006928 - cell motion923.98E-050.074087062
GO:0007423 - sensory organ development524.33E-050.08062919
GO:0007169 - transmembrane receptor protein tyrosine kinase signaling pathway514.81E-050.089665676
GO:0021879 - forebrain neuron differentiation94.99E-050.092920613
GO:0045935 - positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process1146.21E-050.115713935
GO:0051173 - positive regulation of nitrogen compound metabolic process1176.28E-050.116921469
GO:0060284 - regulation of cell development478.31E-050.154746845
GO:0045941 - positive regulation of transcription1049.33E-050.173822702
GO:0050767 - regulation of neurogenesis401.00E-040.186897834
GO:0045664 - regulation of neuron differentiation341.11E-040.206978981
GO:0010628 - positive regulation of gene expression1061.20E-040.223869384
GO:0010557 - positive regulation of macromolecule biosynthetic process1171.23E-040.228227922
GO:0016192 - vesicle-mediated transport1051.36E-040.253359896
GO:0051960 - regulation of nervous system development441.46E-040.271915907
GO:0031328 - positive regulation of cellular biosynthetic process1211.62E-040.302155781
GO:0009891 - positive regulation of biosynthetic process1221.98E-040.368213146
GO:0007167 - enzyme linked receptor protein signaling pathway682.08E-040.386237807
GO:0045892 - negative regulation of transcription, DNA-dependent702.33E-040.432663991
GO:0030817 - regulation of cAMP biosynthetic process273.08E-040.572491129
GO:0002009 - morphogenesis of an epithelium273.08E-040.572491129
GO:0030902 - hindbrain development193.48E-040.647196595
GO:0021537 - telencephalon development203.49E-040.648170377
GO:0021761 - limbic system development133.51E-040.6529104
GO:0051253 - negative regulation of RNA metabolic process703.83E-040.7106764
GO:0045934 - negative regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process933.88E-040.7213528
GO:0010604 - positive regulation of macromolecule metabolic process1443.97E-040.737772867
GO:0030814 - regulation of cAMP metabolic process274.29E-040.795879925
GO:0009792 - embryonic development ending in birth or egg hatching655.31E-040.984899092
GO:0008285 - negative regulation of cell proliferation695.90E-041.094286156
GO:0051172 - negative regulation of nitrogen compound metabolic process936.13E-041.136642775
GO:0009890 - negative regulation of biosynthetic process1016.30E-041.168027793
GO:0010558 - negative regulation of macromolecule biosynthetic process976.65E-041.231652838
GO:0009952 - anterior/posterior pattern formation336.76E-041.252734624
GO:0031327 - negative regulation of cellular biosynthetic process996.87E-041.273119736
GO:0043583 - ear development256.92E-041.282237143
GO:0051339 - regulation of lyase activity267.49E-041.385942497
GO:0048732 - gland development327.50E-041.387778635
GO:0045761 - regulation of adenylate cyclase activity258.13E-041.504562886
GO:0022037 - metencephalon development139.00E-041.663443567
GO:0048568 - embryonic organ development389.18E-041.697487359
GO:0021766 - hippocampus development109.31E-041.721218707
GO:0016481 - negative regulation of transcription839.44E-041.744715102
GO:0030808 - regulation of nucleotide biosynthetic process270.0012372.280679952
GO:0030802 - regulation of cyclic nucleotide biosynthetic process270.0012372.280679952
GO:0010629 - negative regulation of gene expression890.0012882.373367738
GO:0031279 - regulation of cyclase activity250.0012922.380392647
GO:0035107 - appendage morphogenesis250.0012922.380392647
GO:0035108 - limb morphogenesis250.0012922.380392647
GO:0021543 - pallium development150.001322.432197518
GO:0014031 - mesenchymal cell development160.0013272.444625723
GO:0048762 - mesenchymal cell differentiation160.0013272.444625723
GO:0060562 - epithelial tube morphogenesis190.0014682.701431115
GO:0010941 - regulation of cell death1340.0015182.791424456
GO:0035295 - tube development450.0015622.870764354
GO:0048705 - skeletal system morphogenesis270.0016322.999117627
GO:0060485 - mesenchyme development160.0016483.027543666
GO:0030799 - regulation of cyclic nucleotide metabolic process270.0018683.424511611
GO:0001709 - cell fate determination120.0018983.478414435
GO:0043009 - chordate embryonic development620.0019043.49042025
GO:0016331 - morphogenesis of embryonic epithelium170.0019593.588514741
GO:0042127 - regulation of cell proliferation1290.0021013.843962583
GO:0048736 - appendage development250.0022934.187971146
GO:0060173 - limb development250.0022934.187971146
GO:0051349 - positive regulation of lyase activity170.0023744.334185806
GO:0035239 - tube morphogenesis290.0025044.564895517
GO:0043067 - regulation of programmed cell death1320.0025084.573381853
GO:0017145 - stem cell division60.0027424.98879198

[i] A P-value <0.005 was considered to indicate a statistically significant difference. The false discovery rate (FDR) of a set of predictions is the expected percentage of false predictions in the set of predictions; an FDR <5% may be quite meaningful. SLE, systemic lupus erythematosus.

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).

Table IV

KEGG pathway annotation of corresponding CpG-methylated genes in patients with SLE.

Table IV

KEGG pathway annotation of corresponding CpG-methylated genes in patients with SLE.

PathwaysGene countP-valueFDR
hsa05200: pathways in cancer605.25E-040.6471034
hsa04916: melanogenesis230.0023872.9086362
hsa05217: basal cell carcinoma150.0039914.8195711

[i] A P-value <0.005 was considered to indicate a statistically significant difference. The false discovery rate (FDR) of a set of predictions is the expected percentage of false predictions in the set of predictions; an FDR <5% may be quite meaningful. SLE, systemic lupus erythematosus.

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.

Table V

GO term annotation of T-UCR corresponding genes in SLE patients.

Table V

GO term annotation of T-UCR corresponding genes in SLE patients.

GO termGene countP-valueFDR
GO:0008380 - RNA splicing351.24E-112.15E-08
GO:0006397 - mRNA processing372.04E-113.55E-08
GO:0016071 - mRNA metabolic process383.00E-105.22E-07
GO:0006396 - RNA processing476.45E-101.12E-06
GO:0006357 - regulation of transcription from RNA polymerase II promoter551.75E-093.04E-06
GO:0045449 - regulation of transcription1291.15E-081.99E-05
GO:0051252 - regulation of RNA metabolic process975.09E-088.85E-05
GO:0045935 - positive regulation of nucleobase, nucleoside,447.25E-070.001261
nucleotide and nucleic acid metabolic process
GO:0051254 - positive regulation of RNA metabolic process379.25E-070.00161
GO:0006355 - regulation of transcription, DNA-dependent911.01E-060.00175
GO:0010558 - negative regulation of macromolecule biosynthetic process401.07E-060.001865
GO:0000398 - nuclear mRNA splicing, via spliceosome191.20E-060.002082
GO:0000377 - RNA splicing, via transesterification reactions with bulged adenosine as nucleophile191.20E-060.002082
GO:0000375 - RNA splicing, via transesterification reactions191.20E-060.002082
GO:0045944 - positive regulation of transcription from RNA polymerase II promoter311.60E-060.002782
GO:0051173 - positive regulation of nitrogen compound metabolic process441.68E-060.002914
GO:0031327 - negative regulation of cellular biosynthetic process401.97E-060.003434
GO:0045893 - positive regulation of transcription, DNA-dependent362.06E-060.003575
GO:0045941 - positive regulation of transcription402.26E-060.00393
GO:0009890 - negative regulation of biosynthetic process403.34E-060.005812
GO:0048598 - embryonic morphogenesis273.46E-060.006015
GO:0045934 - negative regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process373.86E-060.00672
GO:0006350 - transcription1014.00E-060.006958
GO:0010628 - positive regulation of gene expression404.62E-060.008041
GO:0051172 - negative regulation of nitrogen compound metabolic process375.24E-060.009119
GO:0031328 - positive regulation of cellular biosynthetic process448.06E-060.014026
GO:0051253 - negative regulation of RNA metabolic process298.35E-060.014517
GO:0010605 - negative regulation of macromolecule metabolic process468.87E-060.015419
GO:0010604 - positive regulation of macromolecule metabolic process511.09E-050.018888
GO:0009891 - positive regulation of biosynthetic process441.15E-050.020058
GO:0016481 - negative regulation of transcription331.61E-050.027966
GO:0045892 - negative regulation of transcription, DNA-dependent281.72E-050.029862
GO:0010557 - positive regulation of macromolecule biosynthetic process413.08E-050.053493
GO:0010629 - negative regulation of gene expression344.16E-050.072327
GO:0000122 - negative regulation of transcription from RNA polymerase II promoter212.37E-040.412261
GO:0048568 - embryonic organ development162.97E-040.515968
GO:0016055 - Wnt receptor signaling pathway138.69E-041.501407
GO:0030900 - forebrain development148.89E-041.535269
GO:0043009 - chordate embryonic development220.0015442.651627
GO:0009792 - embryonic development ending in birth or egg hatching220.0017182.946609
GO:0046907 - intracellular transport350.0023554.018647
GO:0015931 - nucleobase, nucleoside, nucleotide and nucleic acid transport110.0026484.507588
GO:0048562 - embryonic organ morphogenesis120.0028214.795051

[i] A P-value <0.005 was considered significant. The false discovery rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions; an FDR <5% may be quite meaningful. SLE, systemic lupus erythematosus; T-UCR, transcribed ultra-conserved regions.

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).

Table VI

Six CpG-methylated SNPs of the MHC segment in patients with SLE.

Table VI

Six CpG-methylated SNPs of the MHC segment in patients with SLE.

Chromosome segmentSNPGeneLocation
chr6:31324019rs1050683HLA-BExonic
chr6:31324057rs12697943HLA-BExonic
chr6:31324448rs17881210HLA-BIntronic
chr6:31324633rs1065378HLA-BExonic
chr6:29521289rs17184255Intergenic
chr6:29521557rs16895070Intergenic

[i] MHC, major histocompatibility complex; SLE, systemic lupus erythematosus; SNP, single nucleotidepolymorphism.

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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January-2016
Volume 37 Issue 1

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Spandidos Publications style
Lin H, Sui W, Tan Q, Chen J, Zhang Y, Ou M, Xue W, Li F, Cao C, Sun Y, Sun Y, et al: Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus. Int J Mol Med 37: 139-148, 2016
APA
Lin, H., Sui, W., Tan, Q., Chen, J., Zhang, Y., Ou, M. ... Dai, Y. (2016). Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus. International Journal of Molecular Medicine, 37, 139-148. https://doi.org/10.3892/ijmm.2015.2416
MLA
Lin, H., Sui, W., Tan, Q., Chen, J., Zhang, Y., Ou, M., Xue, W., Li, F., Cao, C., Sun, Y., Dai, Y."Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus". International Journal of Molecular Medicine 37.1 (2016): 139-148.
Chicago
Lin, H., Sui, W., Tan, Q., Chen, J., Zhang, Y., Ou, M., Xue, W., Li, F., Cao, C., Sun, Y., Dai, Y."Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus". International Journal of Molecular Medicine 37, no. 1 (2016): 139-148. https://doi.org/10.3892/ijmm.2015.2416